Chapter One: Background - Environment and Health · Chapter One: Background - Environment and...

Chapter One: Background - Environment and Health

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1.1 Environment and Health in Ecosystem Theory

Ecosystems have many desirable or beneficial attributes to humanity and their general

importance is often measured in relation to the overall services they offer. The

benefits humans derive from them may be direct or indirect depending on the nature

of the ecosystem services. The services include food, fibre, wood fuel, medicines, and

the intangibles such as water purification and other provisioning as well as regulatory

functions. The dependence of humanity on ecosystem services for survival imposes

external stresses or disturbances. For this reason, most of the pressures on ecosystems

largely come as anthropogenic activities which influence biogeochemical,

hydrological, and ecological processes from local to global scales [1]. These

disturbances are not necessarily intended to be detrimental until they become so in

magnitudes that can affect the structure and function of ecosystems adversely in an

irreversible manner. Under such conditions, the pressures impair the innate ability of

ecosystems to perform their natural self-recovery functions (ecosystem resilience),

which if prolonged, may change the states and form of ecosystems irreversibly. Like

all complex adaptive systems, the self-recovery ability of ecosystems may ultimately

be compromised depending upon the magnitude, importance and duration of the

external force of change (driver). Here the concept of ecosystem resilience emerges

because different ecosystems are more adapted differently to different stressors [1-3].

If the change is such that ecosystems can no longer sustain their roles with respect to

provisioning, regulation, support and cultural services, then the ecosystem function is

said to be compromised. At that point, the ecosystem health would be adversely

affected and thus jeopardize human well-being in all four dimensions (i.e. basic

material for good life, health, security and good social relations). For example,


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changes in ecosystem can significantly alter the amounts of food and fibres, fuel

wood, genetic resources, biochemical and natural medicine, ornamental resources and

fresh water, with significant impacts on food security and human survival.

1.2 Ecosystems and Human Wellbeing

Ecosystem health has both direct and indirect influences on human health through the

delivery of a variety of services for human basic needs and livelihood capabilities [4-

6]. The Millennium Ecosystem Assessment (MA) framework recognizes that through

their provisioning services, ecosystems affect human health in just a few predictable

casual-chains and in yet several unpredictable pathways by supplying food,

freshwater (for many metabolic, physiological, and biochemical processes) fuel wood

for energy, fiber and many bio-chemicals for medicines [3, 6]. In addition, through

their regulatory services, ecosystems affect human health indirectly through their

moderation and regulatory effects on climate, disease, water, air and waste cleansing

[3].

These services support and promote human health functions by guaranteeing the

ability for adequate nourishment, ability to be free from avoidable diseases, ability to

have adequate and clean drinking water, ability to have clean air, the ability to have

energy to keep warm and cool as well as the ability to maintain clean physical

surroundings in homes [3, 7-10]. Deterioration in ecosystem function or health would

lead to a reduction in per capita amounts of their services, which may bring about a

decline in the overall human health status [3, 8, 11]. It is important to note that,

ecosystem damage almost always hits those living in poverty the hardest. The

overwhelming majority of those who die each year from air pollution are believed to

be poor people in developing countries [3, 11, 12].


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1.3 Ecosystem Change

Ecosystems broadly defined as adaptive systems of both biotic and abiotic

components of the natural environment represent an outcome of several years of a

series of evolutionary processes of natural successions. While it is recognized that

ecosystems are continuously undergoing natural change, much of the ecological

change observed today is a product of the interaction between natural systems and

humans [13].

Humans belong to a subset of the biotic component of the ecosystem and like all other

biotic components; they depend on a variety of ecosystem services for survival needs,

which aggregately translate into human well-being.

The interaction between humans and ecosystems continue to shape the ultimate

integrity of ecosystem function in a variety of both predictable and unpredictable

ways [1, 13]. The world’s present million or so species are the modern-day survivors

of an estimated several billions species that have ever existed [13]. While ecosystem

change and specie extinctions have occurred periodically in the geological time scale,

the present rate of loss of the world’s biological diversity is at its greatest due to

human influences since life began [8, 13]. This is the cause of the increasing global

concerns about whether the ecosystem may still be able to support life on earth at the

current scale of their exploitation.

Ample evidence exists to show that up to 25 percent of the earth’s total diversity is at

serious risk of extinction during the next 20-30 years [8, 9, 13, 14].

While the past extinctions had been blamed on natural causes, today humans are

largely responsible for the current ecological change and extinctions [8, 11, 13, 15].

Factors that cause changes in ecosystems and their services are called drivers. A

driver therefore is any natural or human-induced factor that directly or indirectly


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causes a change in ecosystem integrity [11, 15]. The MA conceptual framework

recognizes two types of drivers: direct drivers, (drivers that unequivocally influence

ecosystem processes and can be identified and measured to differing degrees of

certainty) and indirect drivers (drivers that operate more diffusely, often by altering

one or more direct drivers and their influences are established by understanding their

effects on the direct drivers) [3, 16].

Different drivers have different effects on ecosystems both in temporal and spatial

scales [1, 3, 9, 17]. These effects vary in importance, magnitude and strength [1, 3].

While some of the effects may be minor, others may be considerably profound or

while some may have short-term stress, others tend to have long-term stress on

ecosystem function. In many other cases while some of the effects of drivers on

ecosystem function may be insignificant so as to be reparable, and/or reversible, the

effects on ecosystem function of most known drivers are irreparable and irreversible

[3, 18, 19].

Moreover, changes in ecosystem integrity can directly alter the abundance of human

pathogens, example cholera and insect vectors such as mosquitoes, black fly and tse-

tse-fly. The change in ecosystem state may also significantly alter the quality of air,

which may directly alter the epidemiology of respiratory and coronary diseases in

populations. Thus, the nature of the “wedlock” between ecosystems change and

human health is dependent upon a complex interplay of a host of drivers that operate

at different scales in space and time.


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1.4 Scales of ecosystem change

‘Scale’ refers to the spatial extent, temporal duration or institutional level of the unit

of analysis. It is expressed in units of length (or area), time, or social organization

(e.g. individuals, households, communities etc.) [3].

Ecological change phenomena and processes have varying characteristics largely

determined by the location of their occurrence, the driving forces, and their influences

on other natural processes. They occur and operate at a wide variety of scales,

geographically, temporally and institutionally [16, 20, 21]. These can range from very

small change phenomena as a tree fall in a large rainforest, variation of waste

disposition in urban spaces, etc., to very large ones like the replacement of a whole

forest by human settlements [22]. The change processes may also vary in time-scales

from very short period such as the temporary migration of birds to very long time-

intervals as the process of desertification [16, 20, 23]. The characteristic spatial scales

of ecological systems are largely set by factors such as the home range of individual

mobile organisms, or the range of influence of sessile organisms in the case of living

agents, the inducing agents and the area over which a disturbance occurs [24]. The

scale of an ecosystem change may also be influenced by the distance over which a

material bounded by the change is transported within its resident life as in the case of

carbon dioxide which can be transported in its multi-year effective lifetime. On the

contrary, the same wind fields can only transport tropospheric ozone a few hundred

kilometres after which it is consumed by atmospheric reactions; thus its characteristic

scale is regional.

In other instances, the temporal characteristic of the scale of an ecological change

may be set by the lifespan of organisms, the turnover rate of material pools, and the

average period between successive disturbances at a location [18, 19, 23]. An


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important distinction, particularly for determining system resilience, lies in how ‘fast’

or ‘slow' a change process progresses to a threshold of irreversibility typically relating

to the quantum of the external driving force.

1.4.1 Global Scale

The spatial scale characteristics of any ecological change event are governed by the

geographic spread of the drivers operating to cause the change and the nature of the

ecosystem components involved in the process [1, 3, 18]. Often, ecological change

phenomena are local which means they are location specific and operate about and

within a given location [1, 18, 23]. The ecological importance of these changes lies in

the magnitude of their impact on the ecosystem structure and function as well as on

human health [24], although many of the change processes may have synergistic or

cumulative effects, which may be experienced in a wider global scale. For example,

the aggregate effect of local level carbon dioxide emissions in several locations

around the globe is the large-scale ozone effect which causes global warming and

climate change with concomitant heat related mortalities [16, 24-26]. Depending upon

the magnitude of the change, some of the processes may be reversible and more often;

many may not be reversible [1, 27]. While the effects of local change processes (i.e.

carbon dioxide emissions) may be insignificant at the local scale and in the short-

term, their long-term effects on humans at the global scale may be phenomenal [26].

1.4.2 Sub-global Scale

The cause, action and effects of an ecological change process may be confined to a

given geographic boundary such as the clearing of land for farming, the construction

of a football park, the sinking of a well, a volcanic eruption etc. Such change


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processes are regarded as sub-global ecosystem change phenomena and are well

recognised to affect local and regional populations in many different ways [1, 3, 27].

For example, the characteristic scale of an individual household in a freehold tenure

system may be the area of land which it owns; for a community it may be a village or

municipal boundary, and for a country it is the area included in the national borders

and the exclusive economic zone over the ocean. Interactions between humans and

ecosystems are most directly observable at local, micro or lower scales [3]. Examples

of such direct human-ecosystem interactions include agriculture, forestry or land-use

change, sanitation and access to clean drinking water and wastewater discharges [1].

The characteristics of some change processes may exist in more multiple scales. For

instance, whereas climate change processes occur at both global and sub-global

levels, such processes could be local in action where the actual changes implied are

mainly determined by local changes in climate and the ecosystem’s response to it [1,

23]. Examples of such local scale or microclimate change phenomena include “heat

islands”, waste accumulation and particulate plumes at traffic intersections in urban

centers.

1.5 Ecosystem Change and health implications

Ecosystem change affects human health through a complex web of proximate causal

chains, but the most direct and obvious human health and ecosystem change nexuses

are water, air and the soil. Water is an important medium for human survival and

through its lack and/or contamination or its pollution; severe ill-health consequences

on humans can ensue [28-33]. Similarly, the contamination and pollution of the air

through various dangerous gaseous emissions may also provoke profound ill-health

concerns [34-36]. Equally imperative is the deposition of residuals and wastes (both


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solid and liquid wastes) in the soil, which changes the microbial profile of the soil - a

condition that may impact adversely on human health [37-39].

Regrettably, ecosystem damage almost always hits hardest, those living in poverty

and who might not have been responsible for the damage. Evidence exists to show

that the overwhelming majority of those who die each year from air and water

pollution are poor people in developing countries [6, 40-44].

The major contaminants in water include toxic chemicals and minerals such as

pesticides, heavy metals, and bacteria from human excrement, organic pollutants and

suspended solids among others [45, 46]. The result of water contamination is water

related and water based diseases such as diarrhoea, dysentery, intestinal worms,

guinea worm and various debilitating skin conditions. Diarrhoea and dysentery

account for an estimated 20 percent of the total burden of disease in developing

countries and every year, polluted water causes nearly 2 billion cases of diarrhoea,

resulting in the death of some 5 million people (including 3 million children) in these

countries [12, 21, 44, 47-49]. Air pollution from industrial emissions, car exhaust and

burning of wood fuels (e.g. coal, gasoline and wood fuel) at home kills more than 2.7

million people every year – mainly from respiratory damage, some cancers, and heart

and lung disease [12, 21, 47, 48, 50-58]. Some ill-health conditions directly relate to

sanitation infrastructure in both developed and developing countries [30, 59-63].

Domestic solid waste continues to increase worldwide in both absolute and per capita

terms as incomes of people and consumption of goods and services increase [38, 46,

64, 65]. In cities of developing countries, an estimated 20-50 percent of domestic

solid waste generated remains uncollected and much higher proportion occurs in

urban slums where garbage lifting is limited because of lack of access routes for

tipping-trucks [12, 39]. Poorly managed domestic solid waste is a major cause of


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many mortalities and high morbidity in many large cities in developing countries [39,

66]. In many urban communities in developing countries where sanitation services

are inadequate, waste heaps become mixed with excreta, contributing to outbreaks of

and spread of sanitation related infectious diseases [60, 67-69]. Children in such

communities are reported to be five times more likely to fall ill than their counterparts

in high-income residences [39, 70, 71].

More generally, uncollected domestic waste is the most common cause of blocked

urban drainage channels in Asian and African cities, thus increasing the risk of

flooding and water-borne diseases, which affect the poorer populations living near

domestic waste dumps [39, 71-76].

1.6 Vector ecology and infectious disease transmission

Changes in the ecosystem directly affect vector ecology and indirectly affect vector-

borne disease transmission [77-80]. While some of the changes may favour sustained

vector breeding, other change phenomena may tend to be less favourable for vector

breeding and growth. In instances where the change processes favour vector breeding

on sustainable basis; holoendemic disease transmission develops over time [81, 82].

On the contrary, if the change process does not favour vector survival, then vector

resistance or adaptation strategies may begin to develop in those areas [83-85].

Understanding the ecological change processes and vector adaptation strategies is

very crucial for the development of disease control measures through environmental

management [12, 78, 86, 87]. For instance, malaria transmission is strongly associated

with location [88]. Malaria is known to be highly prevalent around specific mosquito

breeding sites and can normally be transmitted only within certain distances from the

breeding sites. The range of dispersal is typically between a few hundred meters and a

kilometre, but rarely exceeds 2-3 kilometers [88, 89]. Clustering of malaria is widely


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reported and persons with clinical symptoms tend to cluster within small geographic

ranges, usually at household and community levels [54]. In areas or clusters of low

endemicity, the level of malaria risk or case incidence could vary widely between

households because the specific characteristics of houses and their locations affect the

contact dynamics between humans and vectors [88-90]. The exact nature of the

influence of ecological change events on the patterns and dynamics of infections is

highly dependent on human/vector interactions and area characteristics [77, 91-95].

For example, where endemicity is high, the effects of differences in human/vector

contact rates on malaria case incidences in different households may be less

pronounced [88-90, 96, 97]. This may be a consequence of the blurring of the

proportional relationship between inoculation rates and case incidences by super-

infection and exposure-acquired immunity [88, 98].

1.7 Urban Ecosystems

Urban systems form the matrix of urban ecology, and in terms of ecosystem services,

they are primarily sites of consumption [27, 99-102]. They contrasts with the other

systems such as cultivated systems (engineered or artificial ecosystems), dry-lands

and coastal systems (natural ecosystems), which are primarily sites of production and

harvesting of ecosystem services [3, 100, 103]. Understanding the ecosystems in

urban areas will not only help with ecosystem management, environmental risk

reduction, disease control and direction of planning resources, but also may help to

deepen our understanding of how urban systems function more broadly to influence

urban health [104]. Urban systems are not only characterized by a varied landscape,

comprising a range of ecosystems and habitats, but are also generally viewed as a

whole that comprises several mosaics of distinct community areas. Thus urban change


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processes may be seen as the spatial heterogeneity of the different community

(cluster) types or neighbourhood types, which may exert different degree of

influences on urban health. Urban environments may also be conceived along

demographic perspective which means a change process could be perceived in terms

of population numbers as often observed with the transition from rural to urban areas.

Typical rural-urban gradients include not only increasing human population density,

and increasing shares of impermeable land cover, but also decreasing population

density for many non-human species [31, 101, 105-109] as well as changing levels of

species diversity. The dynamics of ecosystem change in and around urban centres are

also influenced by a number of features characteristic of how urban landscapes

change, such as a high rate of introduction of alien species (exotic insect vectors and

rodents), high habitat diversity and fragmentation, and a high rate of (human-induced)

habitat disturbance [110, 111].

Changes at very different scales often combine to create new challenges for humans.

Urban development and trade, for example, enabled the epidemics that devastated

Europe in the middle ages, and introduced large parts of the world to infectious

diseases that were never encountered [112]. Indeed, urban researchers have long

viewed urban systems not as individual settlements but as networks of urban centres

connected by flows of capital, people, information and commodities regionally [113]

making a careful study of the association between the spatially varied urban structure

and urban health outcomes appropriately relevant.

1.7.1 Physico-chemical Component

The physico-chemical component of urban ecosystem includes the atmosphere and

the chemicals contained therein. This implicitly is the surrounding air and its


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constituent chemicals as well as particulate matter. Since the industrial revolution

many countries have experienced sustained high levels of air pollution in their major

cities and industrial prefectures resulting in the contamination of urban air by noxious

gases and minute particles of both solid and liquid matter in concentrations that

endanger health [58, 114-118]. The major sources of air pollution are transportation

engines, power and heat generation, industrial processes, and the burning of fossil fuel

and solid waste [42, 48, 57, 58, 119]. While in industrialized nations, a rapid rise of

particulate and chemical concentrations in urban air are largely contributed by

industrial activities, in developing countries, major contributors to increased

deterioration in urban air quality are less of industrial activities, but more of increased

use of fossil fuel and old and/or improperly maintained vehicular fleet [120, 121]. A

study of transportation and its impact on environmental quality in Senegal, found that

the health costs associated with vehicle emissions were among the factors costing that

country the equivalent of five per cent of its Gross Domestic Product (GDP) [122]. In

Northern Africa, cities in which refineries and coal power stations are sited tend to

experience sulphur dioxide concentrations double that recommended by the World

Health Organization [123]. In general, the number of motor vehicles over 30years has

nearly doubled in the past 10 to 15 years in East and West Africa. In Uganda for

example, the number of registered vehicles has more than quadrupled since 1971

[122-125]. Older cars emit up to 20 times more gaseous pollutants than newer ones. In

less than three decades, urban centers in Sub-Sahara Africa have doubled their

gaseous emission loads which are reportedly responsible for the elevation in the

prevalence of cardiovascular diseases in the region [122, 123]. A study conducted by

the Clean Air Initiative in Cotonou in Benin to establish urban air concentration levels

reported overwhelmingly high emission loads with carbon monoxide (CO)


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concentration of 18 mg/Nm3 at traffic intersections. The concentration levels for

hydrocarbons (HCs) were far above 18 mg/Nm3 [122]. A similar study conducted by

the Ghana environmental protection agency (Ghana-EPA) and Ghana Health Service

(GHS) to establish the baseline ambient air concentration for a policy switch from the

use of leaded fuel to unleaded fuel showed unacceptably high levels of lead (Pb) and

cadmium (Cd) in the blood of high exposure population group in Accra [122, 126].

Lead levels in soils, and air were abnormally above the WHO recommended limits

with its concentration in the blood of 396 exposed subjects from 8 institutions [126].

1.7.2 Biological Component

Urbanism is implicitly modernism in many respects. Urbanisation, which therefore

means a large-scale process of modernisation, results in considerable habitat

destruction leading to a wanton replacement of original species by human populations

and their activities. Urbanisation is a disruption of a natural ecosystem (reduction in

specie diversity), which establishes a new couple-system between humans and their

activities on the one hand and the indigenous species on the other hand. Although it is

widely agreed that urbanisation leads to reduction in specie diversity, many studies

that have been conducted on urban microbiology have reported high microbial

diversity in urban settings. Studies conducted in many large cities with poor sanitation

infrastructure have documented evidence of prolific diversification of microbial

phylogeny and communities [127-129]. Uncollected garbage and solid waste dumps

constitute breeding grounds for insect vectors and the development of many microbial

communities [10].


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1.7.3 Infrastructure and Built Environment

The built environment (BE) affects ecosystem functions, ecosystem services and the

overall urban landscape. The artificially built structures affect airflows and thus exert

some influence on urban air quality and human health. Urban areas have many

attractive and beneficial influences to human well-being that may be attributable to

the BE. Nevertheless, many conditions which are the products of the BE but injurious

to humans also exist in the urban areas [130]. One important feature of BE is its

ability to create micro-climate conditions in urban areas that differ markedly from the

overall urban climate (e.g. urban heat islands) and the climate of peripheral rural

areas. The network of urban roads has direct influence on urban vehicular volume,

daily fuel consumption and emission levels [131].

Transportation, especially when based on weak transport policy could be a major

cause of air pollution [122]. Air pollution has many adverse health effects, including

obstructive pulmonary and cardiovascular diseases [132]. Transport systems based

largely on the use of private cars rather than mass transits are a major contributor to

local warming and to urban air quality degradation [33, 133]. Other adverse effects

from car-based transport systems include the degradation of local ecosystems and

urban landscape due to dust in the case of un-tarred urban roads, traffic accidents, and

the stress and loss of productive time spent in traffic [28, 86, 125, 134]. Poorly

designed urban areas aggravate many forms of human and environmental stresses in

urban spaces. Urban road surfaces and crowded buildings are able to retain heat,

which together with the loss of vegetation contribute to “heat islands” and exacerbate

the adverse effect of particulate matter in urban air [36, 101, 134].


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1.7.4 Housing

Housing type is a major determinant of the ultimate health status of residents in

human settlements [36, 96]. This is because the nature of housing structure may create

localised microclimate far different from the general neighbourhood climate [36,

135]. Buildings act as windshields and have great influence on airflow within

settlements [36, 96, 136, 137]. They also trap solar energy in cities and may thus

cause the elevation of local area temperatures [138]. Depending on the type of the

local climate of an area, the architecture-induced microclimate may influence the

course and distribution of health outcomes which completely differ from that of a

scenario with a different architecture [139-141]. Once built, housing quality tends to

determine the social class and composition of the residents who reside in the given

neighbourhood [142, 143]. Large single-family homes will tend to perpetuate

occupancy by the upper socioeconomic groups [139, 144-147]. Large high-rise

apartment buildings with small dwelling units and modest amenities tend to

perpetuate occupancy by lower income families [55, 136, 141, 146, 148, 149]. In

many metropolitan areas, where building codes are strongly enforced, there is a

tendency to construct housing stock that influences the composition of the occupants

[144]. Therefore spatial segregation of urban populations to a large extent is

determined by the interaction of a range of physical, structural and social factors

which include socioeconomic status, race/ethnicity, configuration and quality of

housing [150]. In a study that was conducted to determine the settlement and social

adjustment patterns among female migrants in Dhaka, it was reported that migrants

initially experienced extreme difficulties in locating affordable housing and shelter

[151, 152]. The women did not have much choice and settled in highly congested


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areas and that, relatives and friends provided a few months of start-up assistance in

terms of shelter and food upon arrival [151].

1.7.5 Socioeconomic

Economic conditions are not quite obvious features of an urban center as its

population size, and are less easy to define and measure [55, 141, 153-155]. However,

urban populations tend to cluster in a manner that reveals distinctive social and

economic structure in the urban landscape [136, 156, 157]. Urban residents of

approximately the same income brackets tend to cluster together naturally [158-160].

For instance, while rich urban residents are able to afford well planned housing in

relatively unpolluted urban areas, those who are poor are spatially segregated to

highly polluted, congested and low-cost housing [159, 161]. Many studies have

classified urban areas and the distribution of urban population using the World Bank’s

classification of low, lower middle, upper middle, and high income quintiles [149,

162-164] and methods used in measuring socioeconomic status of residents have been

based on household assets [159, 165]. Socioeconomic variables of urban population

tend to affect individuals’ health seeking behaviour, which in turn affects their overall

health status [160, 166, 167]. In such circumstances, poor individuals are unable to

visit health centers due to financial difficulties and inability to pay treatment bills

[168].

1.7.6 Social-cultural

Over the last five decades, urban areas have experienced an increased ethnic, cultural

and social diversity as more ethnic groups have moved from the rural areas to urban

centers [153, 169-172]. Urbanisation has played a major role in intensifying both

ethnic and cultural heterogeneity in urban areas thus making both features of urban


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populations very important determinants of urban health [173-176]. Urban

populations are ordered by the way residents from different social and ethnic groups

settle and neighbourhoods appear to be homogenous in terms of the level of income,

education and occupation of residents [149, 162, 163]. In urban areas in America,

whether from enforced segregation or association by choice, black populations,

Hispanics and other distinctive ethnic minorities tend to concentrate in different

neighbourhoods from of those of native-born Americans [150]. Such ethnic and

cultural diversification is a feature of all urban areas and a key determinant of urban

health inequality [170, 177-180].

1.8 Health Consequence of Changing Urban Structure

The physical, economic, social and cultural characteristics of urban life all have great,

but varying levels of influence on urban population health [134, 152, 181-183]. Urban

ecology affects urban health through such processes as population clustering, changes

in architectural structure and the physical environment as well as changes in social

organization [184]. Additionally, health is affected by a mix of both biotic and abiotic

factors such as the climate, air quality, population density, housing stock, the nature

of economic and industrial activities, income distribution, transport systems and

opportunity for leisure and recreation [141, 185-187]. Therefore, a structural change

in anyone or more of these aspects of urban life has considerable implications for

urban health [55, 70, 144, 145, 147, 188-190]. This is because the structure of the

built environment (i.e. how closely dense or how sparse) tends to produce a particular

urban microclimate, which may have significant influence on the diversity of urban

microbial populations [77, 191-193]. This in turn may influence the trends of

infectious and communicable disease transmission in urban settings [146].


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Urban structural change may significantly influence the distribution of

particulate matter as well as the emission composition in urban air [34, 42, 194, 195].

This can have profound implications in respect of the risk of cardiovascular diseases

in urban settings [137, 145, 146, 188]. A longitudinal study of a migrant Kenyan low-

blood pressure (BP) population living in an urban environment had significantly

higher BPs than a cohort of matched, non-migrant controls [196]. While preliminary

conclusions attributed these observations to selective migration, the BPs of 90 males

studied prior to migration were almost identical to those found in the age-specific

rural controls in the low-BP community from which they came (120.9/59.0 mm Hg vs

120.5/60.1 mm Hg) [196]. Indeed, on the basis of subsequent analysis of pre-

migration data supported by other evidence from the Kenyan Luo Migrant Study, it

was concluded that the higher BP levels of the Luo migrants were not due to selective

migration, but rather, a consequence of environmental changes, including changes in

electrolyte intake, which occurred rapidly after migration [55].

1.8.1 Income and employment

In all urban areas, residents may be grouped under one of two distinctive sub-

categories – the formal and the informal sector urban economies, based on their

general employment and income levels [197]. The overall urban political economy is

an expression of the arithmetic sum of these two sub-categories built properly on

classical economic theories that unite microeconomic and macroeconomic functions

[198]. However, the urban informal sector remains a dominant sub-sector of the urban

economy as urban population fast outpaces the formal sector job openings [197, 199,

200]. Many studies suggest that employment and income levels have large influence

on resident’s health and the overall urban health outcomes [186, 201, 202]. The


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nutritional status of residents is a function of household food holding capacity, which

in turn is determined by household income status [203-207]. Recently conducted

research works on urban nutrition have reported that malnutrition is widespread

among low-income groups in urban centers in Africa [198, 203-209]. There is a

growing number of low-income groups who are excluded from attending health care

facilities due to financial barriers and the inability to afford treatment cost out-of-

pocket in Ghana [178, 210-212].

1.9 Conclusion

Urbanisation is an urban ecological change and has been dramatic in low income

countries during the last three decades. Much of this is due in part to high

reproductive rates among urban adolescents, and partly because of rural population

influx to the urban areas [197, 213-217]. Approximately, 3 billion of the world’s

people now live in urban centers and population projections suggest that well over

70% of the global population will be found in urban areas by the year 2025 [33, 218].

These populations will be surrounded by poor urban environmental conditions which

may cause health problems, especially in the low-income communities [39, 219]. In

poor countries, these environmental problems would relate to inadequate provision of

basic services and the lack of sanitation facilities including, inadequate potable water

as well as poor housing conditions and living arrangements.

Often, large-scale migration from rural to urban areas drives deterioration of

biophysical and socio-environmental conditions with complex public health

consequences in the urban centers. Sheer population numbers far outstrip the existing

public services and the physical infrastructure in the urban centres. However, because

of inadequate municipal budgets, the city authorities are usually unable to provide the


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basic services equally across urban areas leading to inequalities in environmental

health. Large inequalities in urban environmental sanitation tend to be a feature of

urban areas experiencing rapid urbanization.

Additionally, competing demands for urban lands for transportation, housing,

industry, agriculture and sports increase the value of potential lands for housing and

for residential purposes. As a result, low-income groups are compelled to build in

areas with deplorable environmental conditions [70, 144, 147, 187, 190, 197, 210-

212, 220-229]. In Ghana, despite massive evidence that rapid urbanization is

associated with deterioration in urban environmental conditions, urban centers

continue to expand rapidly without regard to planning and environmental regulations

[39, 66]. The result is the proliferation of substandard structures which lack access

routes for garbage removal leading to waste accumulation in residential areas. This

has significant influence on vector breeding and disease transmission. There is

evidence that the poor among urban residents suffer the worst from the effects of poor

environmental conditions [39, 187, 203, 209, 216, 230]. However, no studies have

been conducted to determine the level of association between malaria/diarrhoea

mortality and the urban environmental quality conditions as well as how much worse

off the poor are affected compared to their richer counterparts [22, 39, 140, 189, 231-

233].

1.10 Problem Statement and Research Questions

There is a growing consensus that environmental change affects human health in

many ways and contributes to a wide variety of diseases and premature deaths [6, 16,

54, 114, 139, 140, 189, 214, 233-239]. The cause of death data and information are


21

hard to find in Africa and where they exist, they are hardly used in epidemiological

analysis to inform health policy decisions. Although almost all the countries in Africa

have national Vital Registration Systems (VRS) (i.e. Births and Deaths Registries)

which record and report annual births and deaths, the reporting systems are still weak.

Nevertheless, despite the obvious structural weakness, the data generated from the

VRS when combined with urban environmental variables could generate very useful

policy relevant information on environmental determinants of urban health in African

megacities.

The environmental health of urban settlements in Africa remains one of the least

understood both biophysically and socio-environmentally [39, 51, 180, 231, 233, 240-

242]. By their transient nature, urban settlements in countries south of the Sahara have

a reputation of high instability with unpredictable environmental changes which may

fuel complicated human health outcomes. In Ghana, urban sanitation infrastructure is

inadequate, largely open-drain type and mainly narrow drainage channels [39, 50,

121, 201, 231, 243-249]. In the urban centers, the limited sanitation services are not

evenly distributed across urban space providing for wide environmental health

inequalities, e.g. certain areas have more intense waste accumulation than others.

Areas close to lagoons and other large surface water bodies are more prone to

flooding and offer cheaper lands for residential purpose than those areas far away

from these water bodies. The flood prone areas tend to attract low income groups and

therefore tend to be home to residents with low socioeconomic status. Additionally,

rapid waste accumulation in these areas offer good opportunities for insect vector

breeding and high infectious disease transmission compared to areas where waste

lifting is far more frequent. In general, the result of the combination of differing levels

of sanitation services, waste collection, different housing arrangements and the effects


22

of water bodies provide for high heterogeneity in infectious disease (especially

malaria and diarrhoea) transmission in the urban complexes. While high

heterogeneity in transmission had been reported for malaria and diarrhoea in urban

complexes, a key question in this study was, “could the observed urban malaria and

diarrhoea mortalities show the same level of heterogeneity in the urban complexes in

Ghana”? In addition, whereas neighbourhood urban environmental conditions have

been reported to influence urban health, no studies have been conducted to describe

how much each of the different environmental conditions and neighbourhood

characteristics was contributing to the observed urban malaria and diarrhoea

mortalities in Ghana. While there is a clear understanding of the relationship between

the state of wastes disposal, pathogen load in storm water and outbreak of enteric

diseases such as diarrhoea and cholera in urban areas surrounded by garbage fields,

there are no reported studies which show the relationship between neighbourhood

urban environmental conditions and the observed infectious disease (e.g. malaria and

diarrhoea) mortalities in the urban complexes [250-252]. To this end, an important

unresolved general question was to what extent was each urban environmental

condition/neighbourhood characteristic currently contributing to the pattern of the

observed urban malaria/diarrhoea mortality in Accra and how could this be

reasonably determined given the limited availability of mortality data? Finally, how

were the urban neighbourhood environmental quality conditions associated with the

existing urban socioeconomic status (SES) (i.e. what is the precise nature of the

association between the different area-based SES variables and the urban

environmental conditions)? Solutions to the general questions would offer great

opportunities for national disease control authorities to strengthen malaria and


23

diarrhoea control programs and intervention strategies in urban centers in rapidly

urbanization areas in low income settings.

More specific research questions in this study included the following:

a) what were the age- and sex-specific malaria and diarrhoea mortality patterns

in urban Accra and how are these associated (if any at all) with of each of the

environmental variables?

b) was there a difference in the way age-group specific malaria/diarrhoea

mortality was associated with the environmental variables, e.g. which age-

class was more susceptible and by how much?

c) what was the relationship between urban environmental conditions and the

observed urban malaria and diarrhoea mortalities and what fraction of the

observed mortalities were contributed by each environmental variable?

d) how were malaria and diarrhoea mortalities distributed in space across the city

complex, given the wide inter-cluster variation in the environmental

conditions and neighbourhood characteristics?

e) what was the precise nature of the association between the different area-based

measures of SES and the urban environmental conditions?

f) what was the precise nature of the association between the different area-based

measures of SES and the observed urban malaria and diarrhoea mortalities?

g) was there a difference in the levels of environmental health inequalities across

urban socioeconomic landscape?

h) what amount of variability in urban neighbourhood environmental conditions

could be explained by area-based socioeconomic factors?

and


24

i) did lagoons/large water bodies within urban spaces contribute to excess

malaria/diarrhoea?

1.11 Aim of the study

The general goal of this study was to investigate, and describe the spatial

characteristics of malaria/diarrhoea mortality in Accra in the hope of deepening our

understanding of how urban environmental conditions/neighbourhood characteristics

(census variables) contributed to the observed malaria/diarrhoea mortality in a large

city in Africa.

1.12 Multiple Objectives of the Study

The ultimate objective of this study was to deepen our understanding of and inform

policy on how urban structure was influencing the observed urban malaria and

diarrhoea mortality in cities with poor water supply & sanitation, poor hygiene

conditions and complex living arrangements. In order to answer the research

questions raised, the study undertook the following tasks:

i. reviewed existing body of literature on urban health and mortalities determine

what was already known and what gaps there were,

ii. pulled out and collected into database, routinely reported death events in

Accra over 1998-2002 period and allocated the death events to the census

clusters,

iii. obtained urban environmental data for Accra from the 2000 population census

database,

iv. produced a digital map of Accra showing the 70 ‘Census Clusters’ or

‘Localities’ used in the 2000 Population and Housing Census,


25

v. mapped the pattern of spatial distribution of malaria and diarrhoea mortality,

vi. compared the risk of malaria/diarrhoea mortality in different census clusters,

vii. determined the kind of association between area-based SES conditions and the

quality of neighbourhood urban environmental conditions,

viii. determined the amount of variability in urban neighbourhood environmental

conditions that could be explained by area-based socioeconomic factors,

ix. assessed the levels of environmental health inequalities across urban

socioeconomic landscape,

x. studied the spatial patterns of the malaria and diarrhoea mortalities in an

urbanizing area with declining environmental quality and social services in a

low income setting,

xi. compared the spatial patterns of the observed urban malaria and diarrhoea

mortalities, and

xii. found out if there were differences in the quality of the neighbourhood urban

environmental conditions across the different wealth quintiles.

1.13 Theoretical Framework

The focus of research in environmental health has always been on the debilitating

effects of various environmental exposures such as toxic chemicals and dangerous

radiation, emissions and biological as well as physical contaminants in the natural

environment [16, 21, 57, 58, 114, 115, 117, 140, 148, 253-256]. It is now almost a

tradition that every conceived research design on environmental determinants of

health from onset almost always ascribes or attributes poor health to a complex of

environmental agents [255, 256]. However, an important fact often remains that, some

environmental exposures may have positive health outcomes [256]. Frumkin (2001)


26

in his article “beyond toxicity – human health and the natural environment”, found

evidence from four aspects of the natural world: animals, plants, landscape and

wilderness – to support this hypothesis. Through research, it is now well known that

air pollution can cause pulmonary and respiratory diseases, that heavy metal

contamination may cause neurotoxicity and that global climate change would likely

fuel increased transmission of some infectious diseases [34, 131, 169, 223, 257-264].

For instance, small drains, swamps, puddles, etc may be associated with increase

larval density and therefore high malaria transmission [265-267], while large surface

water environments in general tend to be associated with low larval density and

therefore low malaria transmission. It has been demonstrated that a high heterogeneity

of transmission intensity was an important characteristic of urban malaria in a study

conducted in Dar-es-Salaam, Tanzania [265]. Other studies have reported that high

frequencies of flooding were also surrounded by environmental media which offered

good breeding opportunities for microbial agents and pathogens [29, 268-270].

Incidentally, those areas were reportedly associated with high frequency of diarrhoeal

diseases [62, 63, 271-273]. In the past, malaria was widely reported to be exclusively

a rural illness, but it is now generally accepted that urbanization is associated with

increasing breeding sites for both malaria vectors and diarrhoea pathogens [265].

Many recent studies have demonstrated that rapid urbanization in developing

countries was contributing to development inequalities and differential provision of

sanitation services which in turn provided for high heterogeneity of mosquito vector

and diarrhoea pathogen transmission intensity in urban complexes [266, 267].

Conceptually, this study assumed that, a typical urban environment was broadly a

spatially heterogeneous whole, consisting of many different neighbourhoods or

clusters at differing levels of environmental quality conditions (e.g. sanitation,


27

hygiene, water contamination, etc), exhibiting high spatial heterogeneity for

malaria/diarrhoea transmission. The proposed model, while it did not assume that

every urban ecosystem configuration would necessarily convey negative health

effects, it recognized a health function gradient from a state of positive health

outcomes (e.g. good health or absence of diseases) coinciding with a healthy

environmental state (improved hygiene, sanitation, greenery, etc conditions)

composed of positive health determining factors. This as opposed to a negative health

state (e.g. worse environmental performance – poor hygiene, sanitation and

brownfields conditions) that coincided with an environmental state composed largely

of negative health determinants, see box5 in Figure 1.1.

The simplified model in Figure 1.1 assumed that urbanization was a driver of many

human pressures (box 1 in Figure 1.1) which interacted in a variety of complex ways

with environmental media (box 2 in Figure 1.1) and human systems to either produce

positive health outcomes or negative ones or both shown as box 3 in Figure 3.1. Some

of the environmental media and human pressures included, land use cover change

such as agriculture, horticulture, landscaping, mining, industry, commerce,

transportation, housing, solid and liquid wastes generation, environmental protection

and biodiversity conservation, disease control programmes, healthcare and health

interventions, distribution of surface water bodies and surface water quality, potable

water supply or its contamination among others. The model assumed that the

environmental interactions produced outcomes that conferred beneficial attributes on

human health and therefore drove health functions toward the positive state i.e. good

health along the green arrows in Figure 1.1. On the contrary, the complex interactions

could also produce outcomes that were inimical to human survival and therefore

hypothesized to drive the human health functions towards the negative health state


28

along the red arrows, thus manifesting complex episodes of disease conditions whose

terminal outcome was mortality or death. However, in reality, these interactions did

not generate outcomes in this clear dichotomy, but rather, most of them tended to

generate a mix of both beneficial and detrimental effects and the ultimate human

health outcome depended on how the intervening variables such socio-economic

factors interacted with the physical urban environmental conditions. For example,

urban environmental protection initiatives such as an upgrade of slums would result in

improvement of urban health status and move the health function along the green

arrow from box3 to box4 (i.e. the absence of illness/disease).


29

8. Mortality Malaria deaths Diarrhoea deaths

4. Good Health Disease absence

2. Environmental Media • Ambient air medium • Soil/land – medium, incl. piped water • Drains/lagoons/swamps, etc. • Environmental interventions

1. Human Pressures • Pop. growth & urbanisation • Urban agriculture • Housing & construction • Transportation • Commerce/markets • Health interventions

Hum

an-E

nviro

nmen

t In

tera

ctio

ns

7. Health Intervention • Disease mgt • Treatment • Bednet use • Microbicide use

6. Morbidity Disease presence • Malaria • Diarrhoea

3. Effects of Human-Environment Interactions • Surface water conditions – lagoons/swamps • Environmental quality condition

o Water pollution/sanitation conditions o Cluster hygiene o Waste accumulation o Cluster housing/living arrangements, etc.

-ve Health state +ve Health state

5. Vector & microbial transmission • Mosquito breeding

o Culex sp, Aedes sp (non-malaria sp). o Anopheles, sp

gambiae/funestus arabeniensis/merus culicifacies/stephensi, etc

• Microbial/pathogen contamination o Salmonella sp, Yersinia entero o E. coli, Shigella,

Campylobecter o Staphylococcus, Bacilli, etc

9. Purpose of study: Association between boxes 3 and 8.

Box 3 Box 8through boxes 5 & 6 to

Figure 1.1: Health and Ill-Health Continuum: A theoretical model


30

On the contrary, environmental deterioration, e.g. poor hygiene, poor sanitation,

overcrowding and poorly constructed structures; poor neighbourhoods environmental

conditions would drive the health function along the red arrow to ill-health (box6) and

death (box8) from box3 through box5. Many studies have already demonstrated high

level of association between box3 and box6 in both rural and urban settings. However,

only limited information which shows the association between box3 and box6 in rural

setting exists while there is no such information showing the nature of the link between

box3 (environmental conditions) and box8 (mortality) in urban setting in Ghana.

Finally, the model assumed that between box6 and box8 laid an intervening condition,

e.g. health interventions (disease management, therapy, etc – box7) with a potential to

push the health function back to good health along the green arrow. This condition could

act as a confounding factor or effect modifier in this study (see box9) depending on the

interpretation of the socio-economic factors often tied to health intervention variables and

existing health policy. In areas where the health policy required free treatment for malaria

and diarrhoea as in the case for Ghana, this condition was considered an effect modifier

as income levels tended to highly correlate with environmental quality conditions.

1.14 Major Hypothesis

The main hypothesis of this work was that, the burden of malarial and diarrhoeal

mortalities disproportionately affected urban residents living in neighbourhoods with low

provision of sanitation services and poor neighbourhood urban environmental conditions

in greater intensity than residents in urban settings with better neighbourhood

environmental quality conditions.


31

1.14.1 Subsidiary Hypothesis The subsidiary hypotheses were:

I. that neighbourhood urban environmental conditions contributed to excess malaria

and diarrhoea mortalities.

II. that malaria/diarrhoea mortality varied with cluster distances from lagoons within

urban areas.

III. that urban living arrangements, housing structure and type of construction

materials were likely to be associated with high malaria and diarrhoea mortalities.

Chapter Two: Literature Review

32


2.1 Introduction

The effect of ecosystem change on human wellbeing has been studied for several

decades [2, 3, 6, 16, 44, 53, 57, 114, 140, 234, 274]. This has strengthened scientific

consensus that ecosystem change affects human well-being in several ways and at

multiple scales. For instance, the process of urbanization (urban ecological change) alters

the mix of natural and artificial elements in the urban landscape and thus changes many

attributes that affect urban environmental quality (e.g., air quality, water quality, air

temperatures) and ecosystem function (e.g., nutrient cycling, soil properties, water

purification), both of which affect urban health. More specifically, urbanization or urban

change both in time and in space tends to alter many components of urban environments

as follows:

solid/liquid wastes accumulation due to poor waste collection services and lack of

sanitation facilities

sealing of urban land surfaces leading to reduction in infiltration and increased

overland flows which increase the frequency of urban flood disasters

poor urban water supply infrastructure, leading to intermittent interruptions in

supply and widespread contamination of potable water

urban air pollution (both indoor and outdoor air quality deterioration) through

increased motorization and wood fuel consumption

structural changes in urban economies produce socioeconomic inequalities and

inequitable access to health services across different income groups which in turn

produce urban health inequalities and finally


33

uneven distribution of urban environmental sanitation services across urban space

produces different urban neighbourhood conditions of different environmental

quality.

While strong scientific evidence exists to show that the causal chain between ecological

change and human health is both complex and multi-dimensional, most past efforts to

understand the linkage between urban change and human health have focused on single-

variable to single-effect analysis [111, 187, 275].

At global level, the millennium ecosystem assessment, which aimed to provide deeper

understanding of this subject, attempted to evaluate and quantify the various dimensions

of environmental change consequence on human wellbeing in an integrated fashion [2,

10, 26, 27, 49, 259, 276-278].

At the national level, a few such assessments included the study on intra-urban morbidity

differentials [213, 214, 231, 243] and the Accra study on household environmental

problems in the Greater Accra Metropolitan Area (GAMA) [213]. The Accra study was

part of a larger tri-city project in the south, covering Accra in Ghana, Jakarta in Indonesia

and Sao Paulo in Brazil. Although this study touched in great detail, on nearly all aspects

of urban environmental problems, the analysis of data and interpretation of results were

done on a piecemeal basis [213, 231, 243, 279]. This unit-by-unit type of interpretation,

failed to bring out the effects of synergistic interaction of the environmental variables and

the contribution by each variable to the expressed urban morbidity. In the current study, it

was envisaged that a more useful and holistic approach to more informative interpretation

required the use of some physical or concrete tool, which combined quantitative

measures of environmental variables and health outcomes.


34

In order to identify gaps in the subject area within the regional context, a literature review

was conducted relying upon the following databases and resources: CabDirect, PubMed,

African healthline, ELDIS, government reports from Ghana, Library catalogue and

similar documents. A search in all the databases was conducted using search terms such

as “malaria/diarrhoea and environment”, “urbanization”, “mortality”, “death”, “urban*”,

“town*”, “city”, “cities”, “Africa”, “West Africa”, “Ghana”, “ecosystem change”, etc, in

different combinations.

The search effort yielded 2814 and 2885 published materials for malaria and diarrhoea

respectively from different parts of the world. When the search terms were further

restricted successively from Africa through to Ghana, 32 and 13 published materials were

found for malaria and diarrhoea respectively, see Table 2.1 below.

Table 2.1: Summary statistics of published research on the subject Number of Published Materials Region

Malaria Diarrhoea Global 2814 2885 Africa 1110 375 West Africa 112 30 Ghana 32 13 Therefore given the paucity of published materials on the subject in Ghana, other

resources, e.g. catalogued library texts, government reports, Ghana Ministry of Health

annual reports were included. After a scrutiny of the 32 published materials on malaria

and 13 on diarrhoea, a selection based on 27 most relevant ones to this study (18 on

malaria and 9 on diarrhoea) was made and summarized as follows.


35

Table 2.2: Summary of Published Studies on Malaria and Diarrhoea in Ghana

Summary of relevant studies on Malaria and Diarrhoea in Ghana Malaria studies

Author Year Title of study Study objectives and methods Main findings/conclusion 1. Kreuels, et al 2008 Spatial variation of

malaria incidence in young children from a geographically homogeneous area with high endemicity

The spatial variation of malaria incidences and socioeconomic factors were assessed over 21 months, from January 2003 to September 2005, in 535 children from 9 villages of a small rural area with high Plasmodium falciparum transmission in Ghana. Household positions were mapped by use of a global positioning system, and the spatial effects on malaria rates were assessed by means of ecological analyses and bivariate Poisson regression controlling for possible confounding factors.

Malaria incidence was surprisingly heterogeneous between villages, and ecological analyses showed strong correlations with village area (R (2) = 0.74; P = .003) and population size (R (2) = 0.68; P = .006). Malaria risk was affected by a number of socioeconomic factors. Poisson regression showed an independent linear rate reduction with increasing distance between children's households and the fringe of the forest. The exact location of households in villages is an independent and important factor for the variation of malaria incidence in children from high-transmission areas.

2. Ahorlu, et al 2006 Socio-cultural determinants of treatment delay for childhood malaria in southern Ghana.

Socio-cultural determinants of timely appropriate treatment seeking for children under 5 years suspected of having a perceived malaria-related illness were studied to assess the determinants of delays in malaria treatment. Caretakers of children with suspected malaria were interviewed about illness-related experiences, meanings and behaviour in two endemic villages in southern Ghana.

Only 11% of children suspected of having a perceived malaria-related illness received timely appropriate treatment consistent with the Abuja target of treating malaria within 24 h of illness onset; 33% of children received appropriate treatment within 48 h. Reported perceived causes of phlegm predicted timely, appropriate treatment within 24 h of illness onset (P = 0.04) in a multivariate logistic regression model; playing on the ground (P < 0.01) predicted such treatment within 48 h. Two categories of distress, paleness or shortage of blood (P = 0.05) and sweating profusely (P = 0.03), also predicted timely, appropriate treatment within 24 h in a multivariate logistic regression model. Knowing that mosquitoes transmit malaria was not associated with timely, appropriate help seeking for the children, even though such knowledge may promote personal protective measures, especially use of bednets. Patterns of distress and PC were related to timely, appropriate help seeking, but not as expected. Effects on health seeking of illness-related experience and meaning are complex, and explaining their role may strengthen interventions for childhood malaria.

3. Ronald, et al 2006 Malaria and anaemia among

A cross-sectional house-to-house survey of P. falciparum parasitaemia, clinical malaria, anaemia,

In total, 296 children were tested from 184 households. Prevalences of P. falciparum, clinical malaria, anaemia, and


36

children in two communities of Kumasi, Ghana: a cross-sectional survey

anthropometric indices, and intestinal helminths was conducted in April-May 2005. Data collection included child and household demographics, mosquito avoidance practices, distance to nearest health facility, child's travel history, symptoms, and anti-malarial use. Risk factors for P. falciparum and anaemia (Hb < 11 g/dl) were identified using generalized linear mixed models.

stunting were significantly higher in Moshie Zongo (37.8%, 16.9%, 66.2% and 21.1%, respectively) compared to Manhyia (12.8%, 3.4%, 34.5% and 7.4%). Of 197 children tested for helminths, four were positive for Dicrocoelium dendriticum. Population attributable risks (PAR%) of anaemia were 16.5% (P. falciparum) and 7.6% (malnutrition). Risk factors for P. falciparum infection were older age, rural travel, and lower socioeconomic status. Risk factors for anaemia were P. falciparum infection, Moshie Zongo residence, male sex, and younger age. Heterogeneities in malariometric indices between neighbouring Kumasi communities are consistent over time.

4. Sama, et al. 2006 Age and seasonal variation in the transition rates and detectability of Plasmodium falciparum malaria

Objective: To assess the effect of acquired immunity on the duration of Plasmodium falciparum infections in order to understand the models of malaria transmission. Method: Use of a dynamic model for infection incidence, clearance and detection of multiple genotype P. falciparum infections and fitted model to a panel dataset from a longitudinal study in Northern Ghana

Models indicate that there is seasonal variation in the infection rate, and age dependence in detectability. Best fitting models had no age dependence in infection or clearance rates, suggesting that acquired immunity mainly affects detectability.

5. Rindsjo, et al. 2006 Presence of IgE cells in human placenta is independent of malaria infection or chorioamnionitis, in Sweden and Ghana

Objective: To investigate the role of IgE cells in human placenta in malaria infection foetal deaths. Method: Use of immunohistochemical staining pattern for IgE to assess placental IgE distribution and malaria infection/deaths in foetuses

No difference in the amount or distribution of IgE(+) cells between malaria-infected and non-infected placentas, nor between different degrees of chorioamnionitis. The IgE score in the placenta did not correlate with the levels of IgE in maternal serum or plasma. However, the IgE score was significantly higher in second- compared to third-trimester placentas (P = 0.03). Opinion held that a maturation time-point in the fetus and in the intrauterine environment during the second trimester, or it might be associated with the increased number of intrauterine fetal deaths in the second trimester.

6. Kobbe, et al. 2006 Seasonal variation and high multiplicity of first Plasmodium falciparum infections in children from a holoendemic area in Ghana, West

Objective: To assess the prevalence and multiplicity of Plasmodium falciparum infections in Ghanaian infants. Method: Typing of the genes encoding the merozoite surface proteins 1 and 2 (msp-1, msp-2) in 1069 three month-old infants over a recruitment period of one year in an area holoendemic for malaria in Ghana

The occurrence of early infections was dependent on the season (month-stratified prevalence 6.4-29.0%). Diversity of msp-alleles was extensive and significantly higher in the dry than in the rainy season. The level of infection prevalence and the high multiplicity of infections (median 4, maximum 14 strains per isolate) in the first months of life suggested early contacts with parasites exhibiting a wide repertoire of antigens and, most likely, multiple infections per single mosquito bite.


37

Africa 7. Klinkenberg, et al.

2006 Urban malaria and anaemia in children: a cross-sectional survey in two cities of Ghana

Objective: To describe the epidemiology of urban malaria, an emerging problem in sub-Saharan Africa. Method: Cross-sectional surveys of communities in Accra and Kumasi, Ghana, determining risk factors for malaria infection and anaemia in children aged 6-60 months.

Malaria prevalence rates ranged from 2% to 33% between urban communities. 47.1% of children were anaemic (Hb<11.0 g/dl). Factors associated with malaria prevalence were low socio-economic status, age and anaemia. The attributable risks of anaemia and severe anaemia (Hb<8.0 g/dl) caused by malaria were 5% and 23% respectively. Malaria in urban areas displayed a heterogeneity and complexity that differed from the rural environment.

8. Ehrhardt, et al. 2006 Malaria, anaemia, and malnutrition in African children--defining intervention priorities

Objectives: To investigate the interactions among malaria, anaemia, and malnutrition in childhood morbidity in sub-Saharan Africa. Method: Evaluation of plasmodial infection, anaemia, and nutritional indices in 2 representative surveys comprising >4000 children in northern Ghana in 2002.

Infection with Plasmodium species was observed in 82% and 75% of children in the rainy and dry season, respectively. The fraction of fever attributable to malaria was 77% in the rainy season and 48% in the dry season and peaked in children of rural residence. Anaemia (hemoglobin level, <11 g/dL) was seen in 64% of children and was, in multivariate analysis, associated with young age, season, residence, parasitemia, P. malariae coinfection, and malnutrition (odds ratio [OR], 1.68 [95% confidence interval [CI], 1.38-2.04]). In addition, malnutrition was independently associated with fever (axillary temperature, > or = 37.5 degrees C; OR, 1.59 [95% CI, 1.13-2.23]) and clinical malaria (OR, 1.67 [95% CI, 1.10-2.50]). Malnutrition was found to be a fundamental factor contributing to malaria-associated morbidity and anaemia, even if the latter exhibits multifactorial patterns.

9. Tetteh, et al. 2004 An analysis of the environmental health impact of the Barekese Dam in Kumasi, Ghana

Objective: To examine the impact of the Barekese Dam in Ghana on the health status of three riparian communities downstream against a control. Method: Baseline analysis of environmental health status of communities with reference to traditional endemic communicable water-related diseases in the catchment area (e.g. malaria, urinary schistosomiasis, infectious hepatitis, diarrhoeal diseases and scabies). Case-control study was then conducted in the three phases of the dam (pre-construction, at the end of the construction and in the late operational phases) to analyse the health status of the communities as a function of the phases of the dam.

The control community consistently had a much better health status than two of the riparian communities, which were closer to the dam in all the three phases. However, it had a better health status than the third riparian community, which was farthest downstream, only in the first two phases. The study concluded that there was a strong association between the presence of the dam and poorer health status of the downstream communities in close proximity to it.


38

10. Appawu, et al. 2004 Malaria transmission dynamics at a site in northern Ghana proposed for testing malaria vaccines

Objective: To study malaria transmission dynamics in Kassena Nankana district (KND), a site in northern Ghana proposed for testing malaria vaccines. Method: Mosquito sampling for 1 year using human landing catches in three micro-ecological sites (irrigated, lowland and rocky highland) yielded 18 228 mosquitoes.

Transmission was highly seasonal, and the heaviest transmission occurred from June to October. The intensity of transmission was higher for people in the irrigated communities than the non-irrigated ones. An overall annual entomological inoculation rate (EIR) of 418 infective bites was estimated in KND. There were micro-ecological variations in the EIRs, with values of 228 infective bites in the rocky highlands, 360 in the lowlands and 630 in the irrigated area. Approximately 60% of malaria transmission in KND occurred indoors during the second half of the night, peaking at daybreak between 04.00 and 06.00 hours.

11. Afrane, et al. 2004 Does irrigated urban agriculture influence the transmission of malaria in the city of Kumasi, Ghana?

Objective: To verify the possible impact of irrigated urban agriculture on malaria transmission in cities, we studied entomological parameters, self-reported malaria episodes, and household-level data in the city of Kumasi, Ghana. Method: A comparison was made of larvae abundance/distribution of Anopheles spp between city locations without irrigated agriculture, city locations with irrigated urban vegetable production, and peri-urban (PU) locations with rain-fed agriculture.

Polymerase chain reaction (PCR) analysis of Anopheles gambiae sensu lato revealed that all specimens processed were A. gambiae sensu stricto. The pattern observed in the night catches was consistent with household interviews because significantly more episodes of malaria and subsequent days lost due to illness were reported in peri-urban and urban agricultural locations than in non-agricultural urban locations.

12. Koram, et al. 2003 Seasonal profiles of malaria infection, anaemia, and bednet use among age groups and communities in northern Ghana

Objective: To profile the severity and seasonality of malaria and anaemia in Kassena-Nankana District of northern Ghana. Method: Random cross-sectional surveys, timed to coincide with the end of low (May 2001) and high (November 2001) malaria transmission seasons and to yield information as to the potential value of haemoglobin (Hb) levels and parasitaemia as markers of malaria morbidity and/or malaria vaccine effect.

Parasitaemia was found in 22% (515 of 2286) screened in May (dry-low transmission), and in 61% of the general population (1026 of 1676) screened in November (wet-high transmission). Malaria prevalence in May ranged from 4% (infants <6 months and adults 50-60 years) to 54% (children 5-10 years). A significantly lower risk of parasitaemia among infants [odds ratio (OR) 6-8] and young children (OR 3-4) living in the central, more urbanized sector of the study area.

13. Baird, et al. 2002 Seasonal malaria attack rates in infants and young children in northern Ghana

Objective: To study the incidence density of infection and disease caused by Plasmodium falciparum in children aged six to 24 months living in the holoendemic Sahel of northern Ghana was measured during the wet and dry seasons of 1996 and 1997. Method: A cohort study of 259 and 277 randomly selected children received supervised curative therapy with quinine and Fansidar and primaquine for those

The incidence density of parasitemia after radical cure was 4.7 infections/person-year during the dry season and 7.1 during the wet season (relative risk = 1.51, 95% confidence interval [CI] = 1.25-1.81; P = 0.00001). Although the mean parasitemia count at time of reinfection in the dry season (3,310/microl) roughly equaled that in the wet season (3,056/microl; P = 0.737), the risk ratio for parasitemia > 20,000/microl during the wet season was 1.71 (95% CI = 1.2-2.4; P = 0.0025). No seasonal differences in


39

with normal glucose-6-phosphate dehydrogenase activity and 20 weeks of post-therapy follow-up consisted of three home visits weekly and examination of Giemsa-stained blood films once every two weeks.

anaemia was observed to exist in this region, probably because the longitudinal cohort design using first parasitemia as an end point prevented the subjects from developing the repeated or chronic infections required for anaemia induction.

14. Ofosu-Okyere, et al.

2001 Novel Plasmodium falciparum clones and rising clone multiplicities are associated with the increase in malaria morbidity in Ghanaian children during the transition into the high transmission season

Objective: To study the effect of the onset of the malaria transmission season on disease incidence. Method: Blood samples were collected from 40 children living in the rural town of Dodowa, between February and August 1998. P. falciparum parasite densities were calculated and PCR genotyping was carried out using the polymorphic MSP-1 and MSP-2 genes as target loci for the estimation of the number of parasite clones in each sample.

The study showed that the probability of a Ghanaian child having a symptomatic malaria episode is positively associated with both increasing numbers and novel types of P. falciparum clones.

15. McGuinness, et al.

1998 Clinical case definitions for malaria: clinical malaria associated with very low parasite densities in African infants

Objective: To determine case definitions for the diagnosis of clinical malaria, age- and season-specific estimates of the fraction of fevers attributable to malaria. Method: A cohort study of 154 children recruited at birth and monitored for fever and malaria infection until 2 years of age.

Estimates of AF varied with age and season. For infants, AF was 51% during the wet season and 22% during the dry season; for children over one year of age, AF was 89% during the wet season and 36% during the dry season.

16. Binka, et al. 1994 Patterns of malaria morbidity and mortality in children in northern Ghana

Objective: To study patterns of malaria morbidity and mortality in children in northern Ghana Method: A malaria prevalence survey was carried out in young children in northern Ghana between October 1990 and September 1991, in an area with continuous mortality and morbidity surveillance.

There was marked seasonal variation in malaria deaths, reported fevers, parasite rates and mean parasite densities, with parasite rates reaching 85-94% in the wet season. The monthly numbers of malaria deaths were highly correlated with rainfall in the previous 2 months (r = 0.90, P < 0.001). Parasite rates were highest in the oldest children (5-7 years), but parasite densities and rates of febrile illness were highest in those 6-11 months old.

17. Afari, et al. 1993 Seasonal characteristics of malaria infection in under-five children of a rural community in

Objective: To assess the seasonal characteristics of malaria infection in under-five children of a rural community Method: Blood samples analysis in the under-five children conducted at Gomoa Onyadze/Otsew Jukwa, from December, 1986 to September, 1987

Crude parasite rates ranged from 19.6 to 33.5 per cent in the dry season (December and March) and 33.0 to 44.0 per cent in the wet season (June and September). P. falciparum was the predominant parasite species by parasite formula analysis with higher rates in the rainy season (94.2 to 95.8 per cent) compared to that of the dry season (51.4 to 78.8 per cent). P. malariae


40

southern Ghana (20.4 per cent), P. ovale (2.7 per cent) and mixed infection rates were higher in the dry season (December and March).

18. Chinery, W. A. 1984 Effects of ecological changes on the malaria vectors Anopheles funestus and the Anopheles gambiae complex of mosquitoes in Accra, Ghana

Objective: To study the effects of ecological changes on the malaria vectors Anopheles funestus. Method: A review of studies has been conducted on mosquito breeding, indoor resting density and some parasitological and vectorial indices in Accra since 1911.

An. gambiae s.l. has adapted to breeding appreciably in water-filled domestic containers in recent times (viz. 21.14 +/- 4.4% of all breeding), compared with a low frequency of breeding in such domestic containers in the earlier years 1911-1930 (viz. 1.97 +/- 1.67% of all breeding). Its breeding has also increased (viz. 5.3% to 25.4% of all breeding) in the numerous polluted water habitats created as a result of urbanization. Diminished malaria parasite rates between 1912 and 1964 were attributed to the almost complete elimination of An. funestus and decrease in breeding intensity of An. gambiae s.l.

Diarrhoea studies 19. Osei and Duker 2008 Spatial dependency

of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modeling.

In this study, exploitation of the importance of two main spatial measures of sanitation in cholera transmission to determine the relationship between refuse dumps and cholera transmission in an urban city, Kumasi. These are proximity and density of refuse dumps within a community.

A spatial statistical modelling carried out to determine the spatial dependency of cholera prevalence on refuse dumps show that, there is a direct spatial relationship between cholera prevalence and density of refuse dumps, and an inverse spatial relationship between cholera prevalence and distance to refuse dumps. A spatial scan statistics also identified four significant spatial clusters of cholera; a primary cluster with greater than expected cholera prevalence, and three secondary clusters with lower than expected cholera prevalence. A GIS based buffer analysis also showed that the minimum distance within which refuse dumps should not be sited within community centres is 500 m. Authors concluded that proximity and density of open space refuse dumps play a contributory role in cholera infection in Kumasi.

20. Reither el al. 2007 Acute childhood diarrhoea in northern Ghana: epidemiological, clinical and microbiological characteristics

Objective: To examine the microbiological causes and clinico-epidemiological aspects were examined during the dry season 2005/6 in Tamale, urban northern Ghana [280]. Methods: Stool specimens of 243 children with acute diarrhoea and of 124 control children were collected. Patients were clinically examined, and malaria and anaemia were assessed. Rota-, astro-, noro- and adenoviruses were identified by (RT-) PCR assays. Intestinal parasites were diagnosed by microscopy,

Watery stools, fever, weakness, and sunken eyes were the most common symptoms in patients (mean age, 10 months). Malaria occurred in 15% and anaemia in 91%; underweight (22%) and wasting (19%) were frequent. Intestinal micro-organisms were isolated from 77% of patients and 53% of controls (P < 0.0001). The most common pathogens in patients were rotavirus (55%), adenovirus (28%) and norovirus (10%); intestinal parasites (5%) and bacteria (5%) were rare. Rotavirus was the only pathogen found significantly more frequently in patients than in controls (odds ratio 7.7; 95%CI, 4.2-14.2), and was associated with


41

stool antigen assays and PCR, and bacteria by culturing methods.

young age, fever and watery stools. Patients without an identified cause of diarrhoea more frequently had symptomatic malaria (25%) than those with diagnosed intestinal pathogens (12%, P = 0.02). Rotavirus-infection was the predominant cause of acute childhood diarrhoea in urban northern Ghana. The abundance of putative enteropathogens among controls indicated prolonged excretion or limited pathogenicity. In this population with a high burden of diarrhoeal and other diseases, sanitation, health education, and rotavirus-vaccination was shown to have expected substantial impact on childhood morbidity.

20. Obiri-Danso, et al.

2005 Aspects of health-related microbiology of the Subin, an urban river in Kumasi, Ghana

Objective: To assess the influence of urban waste, sewage and other human centred activities on the microbiological quality of the river Subin, which flows through the metropolis of Kumasi, Ghana, and serves as drinking water for communities downstream. Method: Three sites, Racecourse, Asafo and Asago, on the Subin were monitored over a year for total coliforms, faecal coliforms, enterococci and biochemical oxygen demand.

Bacterial indicator numbers (geometric mean 100 ml(-1)) varied from 1.61 x 10(9) to 4.06 x 10(13) for total coliforms, 9.75 x 10(8) to 8.98 x 10(12) for faecal coliforms and 1.01 x 10(2) to 6.57 x 10(6) for enterococci. There was a consistent increase in bacterial loading as the river flows from the source (Racecourse) through Kumasi. Bacterial numbers were significantly (p < or = 0.05) higher during the rainy season compared with the dry (harmattan) season. The biochemical oxygen demand ranged from 8 mg l(-1) at the source of the river to 419 mg l(-1) at Asago; none of the sites achieved internationally accepted standards for water quality.

21. Boadi & Kuitunen

2005 Environmental and health impacts of household solid waste handling and disposal practices in third world cities: the case of the Accra Metropolitan Area, Ghana

Objective: To examine the impacts on environment and health of household-level waste management and disposal practices in the Accra Metropolitan Area, Ghana. Method: A simple questionnaire survey.

Only 13.5 percent of respondents are served with door-to-door collection of solid waste, while the rest dispose of their waste at communal collection points, in open spaces, and in waterways. The majority of households store their waste in open containers and plastic bags in the home. Waste storage in the home is associated with the presence of houseflies in the kitchen (r = .17, p < .0001). The presence of houseflies in the kitchen during cooking is correlated with the incidence of childhood diarrhoea (r = .36, p < .0001). Inadequate solid waste facilities result in indiscriminate burning and burying of solid waste. There is an association between waste burning and the incidence of respiratory health symptoms among adults (r = .25, p < .0001) and children (r = .22, p < .05).

22. Curtis, V. 2003 Talking dirty: how to save a million

Objective: To promote public-private hand-washing programmes in Ghana and India.

The most effective way of stopping infection is to stop faecal material getting into the child's environment by safe disposal of


42

lives Method: Implementation of behaviour change communications programmes

faeces and washing hands with soap once faecal material has contaminated them in the home. A review of the literature on handwashing puts it top in a list of possible interventions to prevent diarrhoea. Handwashing with soap has been calculated to save a million lives.

23. Mensah, et al 2002 Street foods in Accra, Ghana: how safe are they?

Objective: To investigate the microbial quality of foods sold on streets of Accra and factors predisposing to their contamination. Method: Use of structured questionnaires to collect data from 117 street vendors on their vital statistics, personal hygiene, food hygiene and knowledge of food borne illness. Standard methods were used for the enumeration, isolation, and identification of bacteria.

Most vendors were educated and exhibited good hygiene behaviour. Diarrhoea was defined as the passage of > or =3 stools per day) by 110 vendors (94.0%), but none associated diarrhoea with bloody stools; only 21 (17.9%) associated diarrhoea with germs. The surroundings of the vending sites were clean, but four sites (3.4%) were classified as very dirty. The cooking of food well in advance of consumption, exposure of food to flies, and working with food at ground level and by hand were likely risk factors for contamination. Examinations were made of 511 menu items, classified as breakfast/snack foods, main dishes, soups and sauces, and cold dishes. Mesophilic bacteria were detected in 356 foods (69.7%): 28 contained Bacillus cereus (5.5%), 163 contained Staphylococcus aureus (31.9%) and 172 contained Enterobacteriaceae (33.7%). Concluded that street foods can be sources of enteropathogens.

24. Shier, et al. 1996 Drinking water sources, mortality and diarrhoea morbidity among young children in northern Ghana

Objective: To describe seasonal and geographical variations in drinking water sources; to look for other predictors of water source use; and to establish whether the drinking water source was associated with the risk of child death or the period prevalence of diarrhoea among young children. Method: Part of the Ghana Vitamin A Supplementation Trials' Survival Study which was carried out in one of the districts of the Upper East Region between January 1989 and December 1991

Boreholes were used as the main source of drinking water by about 60-70% of respondents. They were used slightly more frequently in the dry season. In the rainy season, the use increased of more traditional sources such as rainwater or holes dug in stream beds. The use of boreholes was greatest in the northern zone of the study area and was more common in those who had had some formal education and were of higher socioeconomic status. Some association was found between reported drinking water source and diarrhoeal morbidity, although this association appeared to be seasonal. No significant association was found between drinking water source and child mortality.

25. Armah, et al. 1994 Seasonality of rotavirus infection in Ghana

Objective: To study human rotavirus (HRV) infection and its seasonal distribution was studied over a 12-month period in Ghana. Method: Collection and analysis of a total of 561 stool samples, 447 diarrhoea stools and 114 non-diarrhoea

Rotavirus was detected during 10 of the 12 months and showed a seasonal trend. It was high during the relatively cool dry months and low during the wet season. Peaks of infection were in February (26.2%) and September (24.5%). HRV was detected in 67 of 447 of the diarrhoea stools (15.0%) and in eight of 114


43

stools (controls), obtained from children attending three polyclinics in Accra.

controls (7.0%). The HRV isolation rate was highest (20.2%) in the under-18-months age group. The RNA electropherotype of the HRV isolates was predominantly (83.6%) of the long type. Non-group A HRV was detected in 14.9% of the HRV-positive samples.

26. De Sherbinin, A.

1993 Spotlight: Ghana Objective: To initiate discussion on water supply economies in Africa Method: Review

The underdeveloped water supply systems make water-borne diseases, such as diarrhoea and bilharzia, serious health threats. Insect-borne onchocerciasis is also a problem.

27. Agbodaze & Owusu

1989 Cockroaches (Periplaneta americana) as carriers of agents of bacterial diarrhoea in Accra, Ghana

Objective: To demonstrate that cockroaches could play an important role in the transmission of pathogenic organisms (diarrhoeal pathogens), especially in our environment. Method: Characterisation of cultures of enteric bacterial pathogens from bodies and intestinal contents of 208 cockroaches (Periplaneta americana), collected from kitchens in Accra and some surrounding villages.

Six of them harboured three different serogroups of Salmonella, one had Shigella dysenteriae, 64 had Coliforms, 13 had Proteus species, two had Pseudomonas species and the rest (122) carried none of the bacterial species mentioned above. The presence of Salmonella species Shigella dysenteriae and Coliforms in these insects, which were collected from kitchens where foods are kept, points to the facts that these insects could play an important role in the transmission of these pathogenic organisms, especially in our environment. Permanent solution to these bacterial diarrhoea disease problems could only be solved when food, animals and the environments are free of these microbes.


44

2.2 Burden of Morbidity and Mortality

Assessment of data from health facilities and annual reports showed evidence that the

Ghanaian population was at the cross-roads of an epidemiological transition [281]. Many

studies have reported that while the burden of infectious diseases was still huge, that of

non-communicable diseases had increased considerably over last two decades [282, 283].

While the major causes of child mortality reportedly include malaria, diarrhoea,

respiratory infection, and neonatal conditions, those for adult deaths were predominantly;

HIV infection, hypertension, diabetes mellitus and road traffic [281]. Although a broad

range of risk factors had been reported to be responsible for the observed burden; low

level of literacy, poor sanitation, under-nutrition, alcohol abuse, sedentary life styles and

unhealthy diets were the major determinants of ill-health contributing to the observed

high morbidity and mortality rates in the Ghanaian population [39, 60, 68, 154, 160, 281,

284].

2.3 The Burden of Diarrhoea Specific Morbidity and Mortality

The cause and persistence of diarrhoeal morbidity and mortality in the populations in

Ghana has remained a major concern to health authorities. For instance, the biology of

the agents of diarrhoeal causation has been abundantly documented in literature [2, 10,

28, 29, 91, 97, 111, 150, 235, 285-287]. Yet, diarrhoea has remained a huge challenge to

public health, especially in poor urban neighbourhoods where infectious diseases still

constituted a huge threat to the quality of life. Diarrhoea is one of the leading cause of

morbidity and mortality among children today and it is estimated that well over 12

million people die from diarrhoeal related causes [288]. Despite report of moderate


45

reduction in annual diarrhoeal deaths at the beginning of this millennium; probably due to

the implementation of more or less potentially powerful interventions in the preceding

decades, the disease has remained a leading cause of child mortality globally [280, 288-

292]. Evidence from literature suggests that such global trends appear to be declining due

to remarkable progress in diagnosis and the development of effective control strategies

[63, 272, 293-296].

Additionally, despite the global reduction in diarrhoeal morbidity, country-specific

situations continue to vary widely [296-298]. In Ghana, diarrhoea still exerts a significant

pressure on health facilities and is reported as the fourth most common illness in

Outpatient Department (OPD) [45, 250, 252, 272, 299, 300]. Although the analyses of

clinical and OPD records have shown that diarrhoea had been declining during the last

decade, its contribution to outpatient visitation is still substantial, making a little over 40

percent of the reason for outpatient visitations in Ghana in 2007 (Fig 1.1). While the

proportion of hospitalization due to diarrhoea has been declining since 2000; its

contribution to the overall outpatient attendance gives cause for worry and paints a

gloomy picture about Ghana’s capacity to attain the Millennium Development Goals

(MDGs). It is not exactly clear from routine clinical records what combinations of factors

account for its sustained transmission which places diarhea among the top-five causes of

infant deaths in Ghana [45, 250-252, 289, 297, 299, 301].


46

Figure 1: P roportion of annual outpatient vis itations due to diarrhoea in A cc ra - 2000-2007

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

2000 2001 2002 2003 2004 2005 2006 2007

Ye a r

Prop

ortio

n of

vis

itatio

ns

Diarrhoea

2.4 The burden of malaria morbidity and mortality

Malaria is a debilitating outcome of the parasitic association between a host (usually the

human host) and an infective agent called Plasmodium sp [37, 92, 302-309]. Malaria is

extremely devastating, remaining widespread throughout the tropics, but also occurring in

some temperate regions [97, 302, 304, 308, 310]. It exerts a heavy burden of ill health

and death; especially amongst children and pregnant women, in high transmission areas

[97, 308, 311, 312]. It poses a dangerous risk to travellers and immigrants with imported

cases increasing in non-endemic areas [97, 309].

Around 90% of the malarial deaths occur in Africa and mostly in young children [302,

308, 313, 314]. Malaria is Africa’s leading cause of under-five mortality (20%) and

constitutes 10% of the continent’s overall disease burden [89, 97]. It accounts for 40% of

public health expenditure, 30-50% of inpatient admissions, and up to 50% of outpatient

visits in areas with high malaria transmission rate [97, 314].

Fig. 2.1: The proportion of annual outpatient visitation due to diarrhoea in Accra, 2000-07


47

Each year, an estimated 0.7-2.7 million people who die from malaria, an overwhelming

75% of them reportedly occurs in African children [97] due to poor case management and

other factors working together. In particular, Ghana national malaria control programs

continue to rely on effective case management while attempting the large-scale

deployment of insecticide-treated bed nets and awaiting the discovery of an effective and

affordable vaccine [82, 315-318]. While awaiting vaccine discovery, an important control

strategy has focused on manipulation of both environmental and social factors to be able

to strengthen control programs [90, 317, 319-321]. Previous studies demonstrate a lack of

complete understanding of how urban environmental conditions and neighbourhood

characteristics are currently influencing the pattern of urban malaria mortality in Ghana

[90]. For malaria control strategies to be effective, they should concentrate on targeting

measures at ecological factors which influence malaria mortality, the groups at highest

risk of disease and those who are prone to death from malarial attacks [37, 83, 92, 98,

288, 303, 322]. However, this cannot be done effectively unless there is a clear or

unambiguous understanding of how these urban environmental factors are associated

with urban malaria mortality.

It is evident from many studies that the persistence of the disease is a consequence of a

combination of social, economic and behavioural factors operating across a broad

spectrum of ecological/environmental factors [318, 320, 323]. Efforts to eradicate malaria

have increased and some success has been achieved in reducing its case fatality rate in

many areas [90, 183, 283]. However, analysis of routine outpatient data in Ghana shows

that the disease has consistently remained the topmost cause of ill health and death in

both rural and urban areas (Fig 2.2).


48

Figure 2: P roportion of annual outpatient vis itations due to m alaria in A cc ra: 2000-2007

0.34

0.35

0.36

0.37

0.38

0.39

0.4

0.41

0.42

0.43

2000 2001 2002 2003 2004 2005 2006 2007

Ye a r

Pro

port

ion

of v

isita

tions

M alaria

Despite all the efforts by the national malaria control program to bring the disease under

control and the intensive mass campaigns for its prevention, the burden of the disease

continues to grow contributing nearly 50 percent of the reason for outpatient visitation

among related infectious diseases in Ghana in 2007 (see Fig 2.2). Although there seems

to be a consensus that urban environmental/ecological conditions and neighbourhood

characteristics appear to have a very strong influence on malaria morbidity in Ghana, it is

unclear how these factors contribute to the current patterns of the observed urban malaria

mortality [89, 306]

2.5 Conclusion This review found many studies on many aspects of urban health and in a wide variety of

settings [90, 181, 183, 324-326]. The range of variables observed by the review was wide

Fig. 2.2: The proportion of annual outpatient visitation due to malaria in Accra, 2000-07


49

and included the influence on human health outcomes of different aspects of urban

ecosystem (e.g. physico-chemical, biophysical and socio-economic characteristics) [90,

168, 181, 183, 265, 324, 327]. The studies reported in the review have varied in the

degree of sophistication and while including different measures of health such as

morbidity, disability, immunity, and parasite resistance to drugs, none included mortality

as the health outcome [134, 185, 248, 256, 328]. The causal chains linking disease and

biophysical states of urban ecology as well as many intermediate pathways have been

studied, and while some of the variables have been more thoroughly examined than

others, none has investigated the association between mortality and environmental

conditions in low income settings [90, 101, 241, 255, 289, 303, 306, 329-331]. For

example, the influence of urban environmental factors on morbidity has been more

widely and thoroughly investigated than the effects of these factors on mortality.

Additionally, the differences between the status of rural and urban population health

(applies only morbidity and not mortality) has been thoroughly investigated than

disaggregated analyses of urban sub-population groups.

In Ghana, researches describing the effects of urban ecological change on human health

were scanty and covered only few environmental variables [92, 97, 113, 255, 304, 330,

332]. Moreover, while only a few studies have investigated the link between

malaria/diarrhoea morbidity and environmental/climatic factors (with greater focus on

climatic than biophysical environmental factors), none has explored such relationships

between urban/environmental conditions/neighbourhood characteristics and

malaria/diarrhoea deaths [89, 90, 92, 252, 303-305, 308, 309, 314, 322, 329, 333, 334].

The few studies that investigated the relationships between ecological factors and health


50

(e.g. morbidity) were more widely conducted in rural than in urban settings in Ghana [84,

92, 252, 303, 304, 307, 312, 319, 322, 329, 333, 335]. A few other studies have observed

a strong association between environmental/climatic factors and malaria/diarrhoea

diseases [45, 250-252, 289, 297, 299, 301, 304, 310, 313, 314, 329, 332, 336, 337].

However, none of the studies has assessed and described the nature and level of the

association between the environmental factors (e.g. water supply & sanitation, hygiene,

housing/living arrangements, open drains, influence of large water bodies such as lagoons

within settlements, etc.) and malaria/diarrhoea mortality [45, 81, 83, 92, 98, 250-252,

289, 297, 299, 301, 302, 304, 305, 307, 310, 312-314, 319, 329, 332, 333, 336, 337]. Of

all the studies reviewed, only Shier and others, 1996 attempted to assess the level of

association between rural water supply and diarrhoeal deaths. This study reported that

while there was a strong positive association between rural water supply sources and

diarrhoeal illnesses, no significant association was found between drinking water sources

and diarrhoeal deaths in rural Ghana [301]. This finding was theoretically consistent

because, while the ingestion of polluted/contaminated water could potentially increase

the incidence of diarrhoeal disease, the interaction with intervening factors such as early

detection/diagnosis and treatment, efficient case management, etc., could affect the

course of transgression from illness to death [45, 98, 251, 297, 301, 302, 305, 311, 313,

314, 322, 329, 330, 336, 338]. The effects of the intervening factors on the course of

transgression from disease to death could be different in urban areas where deteriorating

urban environmental conditions and urban poverty could fuel widespread transmission of

infectious diseases [28, 198, 329, 331, 339-341]. There was massive evidence that the

causal chain linking environmental factors and mortality were influenced by


51

socioeconomic factors such as income, employment and education [89, 90, 301, 302, 304,

305, 310, 330, 337]. However, it is not completely understood how the observed urban

mortality is contributed by each of the environmental factors if socio-economic factors

were held constant. For instance, in rural areas the relationship between environmental

conditions and health could be less pronounced because the effects of the environmental

factors on health outcome and poverty were less strongly associated compared to the case

in urban centers [28, 39, 45, 59, 73, 83, 92, 101, 103, 108, 111, 141, 151, 200, 241, 255,

279, 285, 329, 334, 341-344]. Urbanization has been widely implicated as the major

cause of deterioration in urban environmental quality (e.g. urban water supply, sanitation,

hygiene, etc) and indirectly influences the transmission of infectious diseases and urban

mortalities due to malaria and diarrhoea in particular [41, 57, 90, 97, 104, 113, 130, 136,

138, 184, 206, 211, 232, 234, 299, 311, 330, 331, 341, 345-348]. It could be possible that

the interaction among deteriorated environmental quality, intensified urban poverty and

low education could influence the intermediate processes and thus alter the sequence of

events in the pathway from disease to death in a much more complex way in urban areas

than in rural areas. While some entomological studies have reported that Anopheles

mosquito breeds well only in clear standing water such as in puddles, small containers

and other discarded receptacles, there are conflicting opinions suggesting that waste

water in open drains could support mosquito breeding and therefore promote malaria

transmission [81, 89, 97, 104, 111, 285, 303, 307, 312-314, 322, 329, 344, 349]. Sattler

and others have demonstrated that the larvae of A. gambiae were found in habitats

organically polluted by rotten vegetation, human faeces or oil in Dar es Saalam and thus

contributed to excess malaria morbidity. However, it is not clear if such habitats could


52

contribute to excess malaria mortality as well. Finally, no research work has reported to

have assessed whether or not large water bodies such as lagoons in urban spaces

supported Anopheles mosquito breeding, and whether such large water bodies may thus

contribute to more localised excess malaria mortality in nearby residential areas [28, 45,

73, 83, 89, 92, 98, 101, 103, 108, 111, 250, 255, 285, 289, 334, 336, 344]. Most

researchers agree that research findings from studies conducted on vector ecology in rural

settings might not be applicable to urban settings and for this reason, it is reasonable to

explore urban specific understanding of malaria and diarrhoea mortalities.

Chapter Three: Research Conceptualization and Study Design

53


3.1 Methodology

This analysis explored a large number of environmental and socioeconomic variables

(explanatory variables) with the response variables (malaria/diarrhoea mortality) using a

mix of models to test the stated hypotheses. An ecological study design which used the

city-level urban environmental and socioeconomic data from the 2000 Census and

mortality data, aimed to deepen our understanding of the inter-relationship among urban

environmental conditions, urban socioeconomic conditions and health outcomes

(malaria/diarrhoea mortality) in a rapidly urbanizing setting in a low income economy.

The analysis was complex and therefore called for a complex research design which

employed a combination of several models. Some of the models used included Principal

Component Analysis (PCA), bivariate and multi-variate linear regression analysis and

geostatistical approaches. The study had a strong Geographic Information System (GIS)

component utilizing a digital map which allowed for spatial regression of mortality data

on environmental and socioeconomic variables and the assessment of excess mortalities.

The study adopted the boundary classification into census clusters of the study setting

(the city of Accra) which had already been used during previous national population

censuses and the Ghana Demographic and Health Surveys. The unit of analysis was the

census clusters, also referred to as “localities or supervision areas” by the Ghana

Statistical Service (GSS). The assessment of the influence of rivers, lagoons and other

water bodies was one of the interests in this study and therefore the polygons of these

features were combined with those of the census clusters. The geostatistical approaches


54

used were local and global autocorrelation analyses as well as Geographic Weighted

Regression (GWR).

3.2 Study Area and Sample Population This research was conducted in Accra, the capital city of Ghana which occupies a

total land area of 238, 537 square kilometres and had a total population of 18.9 million as

of 2000 census [7, 225, 228, 350-353]. For administrative convenience, the city has been

divided into six (6) submetro districts which are made up of 70 census clusters and 1700

Enumeration Areas (EAs) for census enumeration purposes.

Greater Accra Region, where Accra is located is the smallest of the ten political regions

in Ghana. However, it is the largest of the ten leading urban centers. The population of

Accra was 1.7 million in 1990 and 2.7 million in 2000. The city harbours over 30% and

nearly 15% of the urban and of the total population of Ghana respectively. Regarding

sanitation and urban environmental conditions, the generation and annual rate of increase

of solid waste is high in Ghana, especially in the urban centers. For instance, the per

capita production of refuse in Accra was 0.40 kg/day [354, 355] in 2000 and nearly 70%

by weight was degradable organic materials; representing 0.3 million metric tons of total

wastes generated annually [38, 355, 356]. In terms of city-wide sanitation, over 50

percent of the solid waste generated remained uncollected in 2000 [355]. The general

topography of the study area is a flat low-lying terrain, underlain with clayish and

impervious soils and characterised by inadequate and undersized drains (Fig. 3.1).


55

The city is drained by two rivers namely; the Odaw River and the Korle River and their

tributaries. Dotted at several points by lagoons, swamps, large drains and other water

bodies which are largely polluted by both solid and liquid wastes (Fig. 3.1), the city

forms several neighbourhoods and community areas with differing levels of urban


Figure 3.1: Slum conditions in Accra, Ghana

Source: Adapted from manuscript

This provides for high habitat diversity and high heterogeneity in vector breeding and

pathogen transmission across the city. The high habitat diversity together with hot and

humid climate; provide excellent breeding opportunities for Anopheles mosquitoes. The

different levels of pollution of the water bodies present high uncertainty for their

suitability to support Anopheles sp larval growth and development, although there is no

doubt about their suitability to support diarrhoea pathogen growth and transmission

[289]. For example, the amount of total Kjeldahl nitrogen (TKN), the chemical oxygen

demand (COD) and the total organic carbon (TOC) will dramatically affect the stability


56

of wastes strewn lagoon as a medium of support for mosquito larval growth [63, 69, 270,

294, 296]. The TKN values for lagoon water less than 50 mg/litre or greater than 500

mg/litre (4.2 lbs/1000gal) do not support early stages of mosquito larvae [265, 301]. TOC

values that fall outside the range of 100 to 1000 mg/litre are also effective, as are COD

values of less than 400 mg/litre and greater than 2000 mg/litre. Total nitrogen appears to

be the most important characteristic of lagoon water regarding the control of mosquitoes.

High TKN values alone will dramatically reduce larval survival even if TOC and COD

values were ideal for mosquito breeding [252, 265, 266, 301]. All these factors combine

to offer a unique opportunity for assessing the influence of a fast degrading urban

complex on malaria and diarrhoea mortality.

3.3 Study Population The study population was the urban sub-population of Ghana. The urban population

of Accra was used as a generalizable sample of the urban population of Ghana.

3.4 Study Design and Data Sources (Study variables) The study used multiple datasets, which called for a mix of data collection strategies.

In the study, geographically contiguous EAs of fairly similar population and area

characteristics were grouped into census clusters or localities within urban Accra. This

was based upon the 2000 Population and Housing Census sampling frame for

enumeration. The census 2000 database held several cluster level measures of

socioeconomic status including educational attainment, literacy rate, school enrolment,

religion, ethnicity, marital status, employment status, type of employment, economic


57

activity status (e.g. whether employable or not) and environmental variables such as: per

capita waste generation, total waste generation, amount of solid wastes uncollected

(waste deposition), volume of liquid wastes disposed through the sewer system, volume

of liquid wastes by non-sewer disposal, number of households with pit-latrines, number

of households with toilet/bath facility in different house, number of households with pan-

latrines, etc which were obtained by a written permission of the Government Statistician.

We extracted the relevant socioeconomic and environmental variables from the database

for each of the 70 clusters in Accra.

In all, we extracted a total of 118 socioeconomic and environmental variables. For all

the 70 clusters in Accra, we computed appropriate summary measures of the

environmental and socioeconomic variables at aggregate cluster level. The 118 variables

were sub-grouped into eleven sub-categories; namely, waste generation, sanitation and

water supply, housing type and construction materials, hygiene conditions, education

level, occupation type, place of work, household energy use, economic activity, marital

status and ethnicity. In order to deal with multicollinearity and to include only variables

with significant associations in the dataset, we carried out a stepwise removal multiple

regression modelling and correlation analyzes (using STATA 9, College Station, TX)

thus excluding several redundant variables on the basis of the magnitude of their

variation inflation factors (VIFs). We excluded variables on household energy use

because no evidence exists in literature which demonstrates a link between them and the

outcome measure (i.e. malaria and diarrhoea mortality). Finally, variables on marital

status and ethnicity were excluded in the analysis by dint of their cultural sensitivity.


58

3.5 Mortality Data Collection Death registers were obtained from the Ghana Births and Deaths Registry (i.e. the Ghana

Vital Registration System - VRS) for the years 1998 to 2002 inclusive. The information

on death events e.g. place address or normal place of residence of the deceased, age,

nationality, cause of death, month of death, etc contained in these registers was entered

into a database. To do this, a template of the death registration form was created using

Microsoft Access forms wizard (Fig. 3.2). Using this electronic form, relevant

information on all deaths as recorded on death certificates were captured and stored in

Microsoft Access database. The total number of deaths recorded and coded according to

ICD-9 in the registers over this period in urban Accra was 24,716 out of which 1,292 and

1,001 were deaths attributed to malaria and diarrhoea respectively. The codes for the 9th

edition of the International Classification of Disease (ICD-9) were defined as follows:

a) Diarrhoea: broadly included all diarrhoea, cholera and gastroenteritis of

presumed infectious origin which were defined by codes A00 and A09 according

to ICD-9, e.g.

i) cholera

ii) catarrh, enteric or intestinal

(1) colitis NOS (not otherwise specified)

(2) enteritis haemorrhagic

(3) gastroenteritis septic

(4) diarrhoea

(a) NOS

(b) dysentery

(c) infectious diarrhoeal diseases


59

b) Malaria: classified broadly in the range of codes B50 – B54 according to ICD-9.

This included all other parasitologically confirmed malaria.

Once the death records were transferred into an electronic database, the database

was cleaned, validated and the death records were allocated to the 70 clusters in Accra

according to the house address of normal place of residence prior to death. The death

records were then linked to the clusters via cluster codes in a Geographic Information

System (GIS) (ArcMap 9.3.1).

Figure 3.2: Electronic Template of Death Registration Form

3.6 Physical Urban Environmental Data

The Ghana Statistical Service (GSS) classified urban environmental conditions in

the census 2000 as follows:

• Population and demographics


60

o e.g. age group, by sex, etc

• Cluster hygiene conditions

o number of households with Water Closet (WC) per cluster

o number of households with pit-latrine per cluster

o number of households with Kumasi Ventilated Improved Pit (KVIP)

latrine per cluster

o number of households with bucket/pan latrine per cluster

o number of households using public toilets per cluster

o number of households without toilets (bush/field defecation) per cluster

o number of households with baths for exclusive use by members of

households per cluster

o number of households using public baths per cluster

o number of households using open spaces around house for bath per cluster

• Cluster water supply & sanitation

o total amount solid wastes generated per cluster

o percentage solid wastes collected per cluster

o percentage solid wastes uncollected per cluster

o quantity of solid wastes properly managed at public dump sites per cluster

o number of households with waste dumps inside homes per cluster

o total amount of liquid wastes generated per cluster

o quantity of liquid waste managed through the sewer system per cluster

o quantity discarded in the open drains/gutters per cluster


61

o quantity discarded in open spaces in the compound of households per

cluster

o percentage households with pipe-borne water (potable water) per cluster

o percentage of households using public standpipes per cluster

o percentage of households using well/hand dugout pond water per cluster

o percentage of households using borehole water per cluster

o cluster distance from water bodies/lagoons

• Cluster housing & living arrangements

o number of households with mud/mud brick/earth as construction materials

per cluster

o number of households with wood as construction materials per cluster

o number of households per cluster, with metal sheet/slate/asbestos as

construction materials

o number of households per cluster, with cement blocks/concrete as

construction materials

o average number of persons per housing unit per cluster

o number of persons per square meter of cluster (cluster population density).

A considerable amount of evidence exists in literature demonstrating the effect of the

physical environment on human health. However, no consensus exists on the standard for

definition environmental quality classifications that allow for cross-study comparisons. In

literature, different studies have different environmental classifications, thus making

comparison of study outcomes between and among different studies rather impossible. In

this analysis, the environmental variables were obtained from the census database already


62

classified into 1) “population and waste generation”, 2) “water supply and sanitation”, 3)

“hygiene conditions” and 4) “built structure, form, construction material type and living

arrangements”. In all, a total of 65 environmental variables were extracted from the

census database under the four distinct sub-categories and summary measures (e.g. the

proportions of cluster level conditions) appropriately computed. In order to detect and

deal with multicollinearity as well as include only variables with significant associations

in the analysis, we conducted correlation analysis and evaluated the correlation matrix for

strongly associated covariates. We also conducted a stepwise backward removal multiple

regression modelling and assessed the variance inflation factor (VIF) prior to determining

the final model for the analysis. After this process, a total of 60 variables were

maintained for use in the next stage of analysis. As a first step, we explored the large

number of environmental variables under each sub-category to determine the direction of

their eigen vectors using PCA. In a data reduction strategy, we again used PCA to

decompose the variables under each sub-category into a manageable uni-directional

variable which was employed to develop distinctly ordinal urban environmental zones of

differing levels of urban environmental quality conditions [165]. The resulting

environmental zones nominally defined as “extremely deteriorated zone”, “moderately

deteriorated zone” and “least deteriorated zone” represented worst, somewhat worse and

good environmental conditions respectively.

3.7 Socioeconomic Data

The influence of socioeconomic factors on health outcomes has long been recognized and

past research effort has focused on the relationship between socioeconomic status (SES)


63

and health inequalities among different subpopulation groups [159]. Socioeconomic

status (SES) is frequently implicated as a contributor to the disparate health observed

among racial/ethnic minorities, women and elderly populations [155, 174, 357, 358].

There is scientific consensus that several factors (both SES and the physical environment,

Fig. 3.3) interact to influence health and health status among populations and, as a

consequence, health disparities [159, 174, 357, 359-361]. In the U.S., socioeconomic

status (SES) is among the factors most frequently implicated as a contributor to the

disparities in health observed among populations [155, 159]. Other factors include

lifestyle, the cultural and physical environment, living and working conditions and social

and community networks [362, 363]. Adler et al. present three pathways through which

SES impacts health, which include its association with healthcare, environmental

exposure, and health behaviour and lifestyle [364]. In Figure 3.3, a simplified theoretical

model of SES, environment and health interaction is presented.

Although analysis of data on socioeconomic status is nearly always included in

epidemiologic research, its specific use is often dependent on data availability [155, 159,

362, 365]. While it is often concluded that differences in SES are the cause of differences

in health outcomes among population groups, there is often little, if any, discussion of the

specific manner in which SES might have exerts its influence within the context of the

study outcomes [366-368]. This then leaves gaps in the causal path with respect to the

chain of causal events leading from the multiple pressures from neighbourhood

socioeconomic conditions driving changes in neighbourhood environmental conditions

which in turn directly influence health outcomes, see Fig. 3.3. In epidemiological studies,

experimental designs are often targeted at finding out whether observed differences in


64

health outcomes among study subjects or groups are indeed real differences or may

merely be due to chance [181, 368-370]. However in more complex study settings such

as ecological designs, the existence of confounders and effect modifiers (intervening

physical environmental media) do not lend easy interpretation of findings from studies

which aim to look at the influence of SES variables on health outcomes. In other words,

evaluation of the influence of SES on health disparities is difficult to achieve realistically

without first understanding the influence of these variables on the physical environmental

conditions.

Area-based SES

Physical Environment

Health Outcomes

• Mosquito breeding o Culex spp., Aedes spp. (non-malaria sp). o Anopheles, spp.

• A. gambiae/ A. funestus • A. arabeniensis/ A. merus • A. culicifacies/ A. stephensi, etc

• Microbial/pathogen contamination o Salmonella spp., Yersinia enterocolitica o E. coli, Shigella, Campylobacter o Staphylococcus, Bacilli, etc.

• Malaria • Diarrhea

• Employment situation • Income levels • Occupation type, etc.

• Garbage generation & accumulation

• Water supply & sewerage • Sanitation & hygiene

facilities • Housing structure, etc.

Figure 3.3: Interaction among Area-base SES, Environmental Quality and Health

(Source: Fobil et al., 2010, IJERPH, pp125-145) Secondly, the precise role of SES variables in determining the observed health outcomes

in populations is not clearly defined i.e. whether these factors themselves alone directly

influence health outcomes (e.g. issues of economic barriers to healthcare) or they do so

through an intermediate (e.g. intervening physical environmental media) [46, 155, 360].


65

Another question to ask is: are SES variables confounders or mere effect modifiers? The

answer to this question is “it depends on several considerations” both on the perspective

of SES variables or on the perspective of health outcomes. As provided in the conceptual

model in Fig. 3.3, if it can be established that any given SES variable influences a

physical environmental variable which in turn influences a particular health outcome,

then the SES variable is an effect modifier. On the other hand, if it can be demonstrated

that a given SES variable has no direct association with any physical environmental

variable, but has direct association with a given health outcome, then the SES variable is

a confounding variable in environment and health analysis.

Consequently, given the amount of spurious effects SES variables cast upon environment

and health analysis, it becomes a worthwhile undertaking to investigate the kind of

effects the different SES variables exert on environmental variables in urban settings, i.e.

what is the precise nature of the association between the different contextual or area-

based SES variables and the different environmental variables?

Although it must be acknowledged that no standard measures of the concept of SES exist

and there is only very little agreement in the literature on its definition and its exact

measurement, construction of proxies for the SES variables is possible and already

widely applied in SES and health inequality research [159, 366, 371]. For instance, in the

absence of individual level data on social backgrounds, area-based measures of

socioeconomic status constructed based on social and economic aspects of the area in

which study subjects reside can be used. In Australia where this technique has already

been widely applied, the units of measurement have been based on postcodes, Statistical

Local Areas, Local Government Areas and Census Collection Districts. For the purposes


66

of construction of area based SES measures, Census Collection Districts or census

clusters were adopted as the units of analysis.

The census 2000 database held several cluster level measures of socioeconomic status

including educational attainment, literacy rate, school enrolment, religion, ethnicity,

marital status, employment status, type of employment, economic activity status (e.g.

weather employable or not, etc. There were 53 of these socioeconomic variables in total

and were grouped under 4 main categories as:

(a) economic activity status

(b) educational attainment

(c) occupation

(d) place of work

(e) marital status, and

(f) ethnicity.

In the study, marital status and ethnicity were excluded because they were regarded as

politically and culturally sensitive. We explored the remaining variables using PCA to

determine their relationships with each other and to be able to develop a uni-dimensional

measure of SES, e.g. socioeconomic zones (quintiles) for the study area. The variables

used in constructing area-based socioeconomic measures were computed as a proportion

of individuals with a given socioeconomic characteristic among the total number of

individuals in a cluster and these were stratified by sex (i.e. the SES measures were

constructed separately for males and females). These area-based measures were used as

proxies for cluster level socioeconomic conditions in lieu of the traditional or


67

conventional measures of SES using household incomes, asset-based measures,

consumption or expenditure measures, etc., because they can be more reliably measured

compared to their traditional counterparts. For instance, while most people feel reluctant

to talk about incomes and earnings and may not be reporting correct income levels, it is

fairly easy to accurately count the number of unemployed or those in employment or

economically active and economically inactive people in a survey. For this reason, the

measures of economic status adopted in this study seem superior to the conventional

ones.

We demonstrate here how these measures were constructed and give explanation of the

rationale for their application in this analysis. The economically active and inactive

cluster level population was simply computed as the as a proportion of the cluster level

total population. The factors measure how active or inactive a given cluster population is

in terms of self-sustaining itself through economic activities. This is a measure of the

ability of the population to take advantage of economic opportunities available to all the

resident population given the same level of skills. Similarly, employment and

unemployment rates were computed as proportions of cluster level total populations.

Conceptually the employment and unemployment status of a given population measures

the ability of a given area to generate wealth to strengthen the community level social

capital. The social standing of any community depends on whether its residents are

working or not.

The economically active people are those who are fit and legally permitted to engage in

any form of employment without cause to worry about any harm to their wellbeing. In

Ghana, the legal age for first employment is 18years at the minimum. The legally


68

accepted retiring age in Ghana is 60years in public service. The legal definition of

economically active age rage in Ghana is 18 to 60years [351, 372]. Strictly speaking

however, the social definition of economically active population relates primarily to

human capabilities and wellbeing i.e. to be able to perform a task without recourse to

inimical effects on one’s health and wellbeing. But whichever way economically active

or economically inactive population is defined, in terms of socioeconomic status of

communities, the proportion of economically active population gives an index of how

many members of communities there are potentially employable and to contribute to the

pool of income or wealth of the communities. The greater the number of community

members in this group, the larger this proportion and the brighter the chance to generate

wealth in the communities. This means that the greater the proportion of economically

active population in a given community, the higher its socioeconomic status and the

smaller the proportion, the lower the socioeconomic status of the community.

Conversely, the economically inactive population is potentially not employable, therefore

the larger the proportion of economically inactive population of a given community, the

lower the socioeconomic standing of the community.

In terms of employment and unemployment, members of a community in employment

are the ones that earn income and therefore contribute to the pool of wealth in the

community. Community members who are unemployed do not earn regular income and

therefore do not contribute to the pool of wealth of the community. For this reason, the

higher the proportion of the employed cluster population the higher the socioeconomic

status of the given cluster. However, if the proportion of unemployed population in a


69

given community or cluster is high, then the socioeconomic status of the cluster or

community is low.

3.8 Geographic Information System and Digital Map Production

The Accra metropolis consists of 1700 Enumeration Areas (EAs) drawn up for the

2000 National Population and Housing Census survey. For purposes of this study, not

only geographically contiguous EAs, but also EAs with similar population characteristics

were merged to produce census clusters (the units of the analyses) according to the

sampling frame used by Ghana’s Statistical Service in the 2000 national population

census. The boundaries of these clusters were digitised to produce polygons of the census

clusters, which were merged together to produce a complete digital map of urban Accra.

In the digitization process, we made use of hardcopy toposheets, some of which were

drawn to scale while others were not. The toposheets that were drawn to scale were in the

scale of 1:2500 resolutions and were in forms ready for direct digitisation. This was done

by plotting and registering four (4) points or codes (geo-codes) of four (4) physical

landmarks (arranged in a rectangle) identified on the hardcopy maps into a computer

which was connected to a digitiser in MapInfo environment. Polygons of the census

clusters produced were transferred into ArcView for further processing and refinement

into the final digital map of Accra. Two approaches were used in creating the polygons of

census clusters as follows:

Procedure (1): Procedure (1) involved the identification of key features or landmarks on

the maps based on the census description of the enumeration areas. Coordinates (geo-

codes) [x-y coordinates] of the physical landmarks were then taken using a geographic


70

positioning system (GPS). These were plotted and registered into MapInfo which was

installed on a computer connected to a digitiser. The rectangles were used as reference

points for various boundary lines to be traced by a digitiser.

Procedure (2): Procedure (2) was much simpler. The procedure involved the

identification of fours points (forming a rectangle) on a map already drawn to scale, but

which were also found on the maps that were not drawn to scale. The scaled-maps were

used as a base-maps or standards from which geo-referenced points were projected onto

the non-scaled maps (i.e. maps not drawn to scale). The projected geo-referenced points

now formed rectangles on the non-scaled maps. The rectangles were plotted and

registered into a computer connected to a digitiser, which enabled all cluster boundary

lines to be traced as with procedure (1).

The maps produced from the separate approaches were superimposed and used to

validate the digitization process. A perfect overlap was evidence of consistency and proof

of validity of the two processes.

3.9 Framework for Data Analysis (Estimation of Mortality Summary Measure)

The main hypothesis of this work was that:

• a disproportionately greater burden of malarial and diarrhoeal mortalities lay on

residents living in neighbourhoods and communities characterized by worse urban

environmental conditions with low provision and coverage of sanitation services

compared to their counterparts living in neighbourhoods and communities with

better coverage of these services, and


71

• a subsidiary hypotheses being that, excess malaria/diarrhoea mortality was

associated with worse indices of urban environmental and socioeconomic

conditions.

Key Considerations in the Analysis:

Units of Analysis: Census clusters – were defined by the statistical system of Ghana as a

group of geographically contiguous census enumeration areas of fairly homogeneous

populations based upon defined characteristics such as accessibility of population to

enumerators, socioeconomic and cultural factors [351, 373-375]. For this reason and for

the purpose of this study, a cluster with fairly homogeneous population was regarded as

one in which all deaths had an equal probability of being enumerated regardless of cause.

Exposure: The exposure condition was defined as all physical and non-physical urban

environmental and socioeconomic conditions e.g. type of urban water supply, housing

characteristics, solid/liquid wastes, lagoons, type of construction materials for housing,

cluster population density, etc., all of which were obtained from the 2000 Ghana census

database.

Exposed Population: - all individuals, born alive and had spent at least 24 hours of life

after birth, and those who lived up to the 100th Birthday. By the definition of the exposed

population, the numerator excluded all foetal deaths, e.g. still births (expulsion of a

product of pregnancy without any signs of life: neo- and peri-natal deaths, abortion and

miscarriages). The reason for excluding all foetal deaths was that, such deaths had

occurred before ever directly experiencing the exposure under consideration. Individuals

dying after 100 years were taken as dying from natural causes, since immune decline


72

begins to be experienced at about age 60 and above. After 100 years therefore, immunity

may have decreased substantially for senescence to be completely responsible for cause

of death.

Sample Size Calculation: This was an ecological study which used already collected

data from a National Population Census database and routinely available mortality data

from the vital registration system. Therefore sample size calculation was neither relevant

nor required. However, it was necessary to calculate and/or estimate the statistical power

of the study if the probabilities (risks) of dying from malaria and diarrhoea in each of the

70 clusters were known. We assumed that PMR in each cluster constituted the probability

(risk) of dying from a specific cause in the given clusters. The statistical power was then

computed as a range using the smallest and the largest risk difference for each specific

cause (malaria and diarrhoea) and the corresponding cluster population. The calculation

of the statistical power for detecting a difference between two samples (with sample sizes

s1 and s2) and known proportions or risks (i.e. p1 and p2) at 5 percent significance level

was achieved using a computer program studysize2.0. This was done iteratively by

inputting values for proportions p1 and p2 as well as population sizes s1 and s2 into the

computer program. In this particular case, the smallest and the largest risk differences

were used to estimate the least and largest power obtainable for malaria and diarrhoea

mortality (see Table 3.1).

These were extreme theoretical values and despite the case that malaria PMRs in some

clusters might have fallen within low power range (Table 3.1), the overall statistical

power was still reasonable since such extreme cases were not considered in the analysis.


73

Table 3.1: Power calculation of study

PMR Pop. PMR Pop. Power range PMR Pop. PMR Pop. Malaria 0.008 7313 0.017 2128 32.1-99.9% … 0.115 22140 0.121 8929 Diarrhoea 0.011 9517 0.018 17369 74.3-99.6% … 0.092 17092 0.182 8053 Explanatory variables (see section 3.6):

cluster hygiene conditions

cluster water supply & sanitation

cluster socioeconomic conditions

cluster housing & living arrangements.

Response (outcome) variable

cluster level malaria/diarrhoea mortalities (the fraction of deaths due to a specific

cause i.e. malaria and diarrhoea).

Potential biases:

Potential confounders:

o age and sex (was addressed by calculating age- and sex specific

proportional mortality ratios)

levels of cluster registration completeness, misclassification/ascertainment biases

and

chance/random errors (discussed under data quality section).

Potential effect modifiers:

socioeconomics e.g. income status of cluster residents, level of education, etc.

Smallest: Increasing order of PMR: Largest


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3.9.1 Data Quality Issues and Methods for a Valid Summary Measure Derivation

It was recognized that the use of routine data had several advantages including the fact

that they were cheaply available as compared to survey data, which usually took several

months and lots of money to obtain. However, one major disadvantage that was

contemplated was that, the use of routinely generated data also presented many quality

problems. Although it was conceded that survey data too had their own quality issues, the

mortality data from Ghana VRS were suspected to be burdened with the following

potential biases and quality issues:

different levels of death registration completeness by cluster,

death record losses from recording points (registration centres) to storage point

(Births and Deaths Registry),

data input errors during data entry from death certificates onto computer,

misclassification or inaccurate diagnoses of diseases which may have led to death

ascertainment biases.

3.9.2 Different levels of death registration completeness by cluster

Leakage in death registration e.g. illegal burials, non-reporting on some deaths, etc

tended to result in incompleteness of death registration which could differ by age, sex and

cluster. This could have affected the overall mortality reporting by the Ghana VRS data.

Table 3.2 presents the annual death registration coverage for both rural and urban areas in

Ghana for years 2000 to 2006 inclusive. Table 3.3 shows that for urban Accra from 2000

to 2004. The variability in registration completeness by age, sex, cluster, etc could affect

the results, interpretation and conclusions of studies which made use of these data. In


75

other words, mortality rates (crude rates) calculated directly from these routine data

without any controlling procedures could produce misleading results. To be able to deal

with this meant the use of sophisticated epidemiologic procedures to compute mortality

summary measures valid for comparative purposes. In the case of death registration

incompleteness, standardization procedures (e.g. either direct or indirect standardization)

were deemed sufficient to handle such data quality issues.

Table 3.2: Annual Deaths Reporting Coverage in Ghana (2000-06) Region Year Population Expected Deaths Registered Deaths % Coverage

2000 18,912,079 226945 45402 20 2001 19,422,705 233072 51639 22 2002 19,947,118 239635 49630 21 2003 20,485,690 204857 47492 23 2004 21,038,804 210388 50625 24 2005 21,606,852 216069 - - A

ll R

egio

ns

2006 22,190,237 221902 - - Source: GSS/Ghana Births and Registry, 2006

Table 3.3: Annual Deaths Reporting Coverage in Urban Accra (2000-04)

Variable/Year 2000 2001 2002 2003 2004 Expected 13916 10309 13947 13633 15160 Reported 7078 6468 6514 6318 7304

Coverage (%) 50.86 62.74 46.71 46.34 48.18 Source: GSS/Ghana Births and Registry, 2006

3.9.3 Death record losses or data leakage

Death record losses were observed to be a consequence of lifting, transfer and

transportation from one point to the other, storage, etc of death record files. Such

losses could usually be detected directly and much less problematic than data leakage

through non-reporting and/or illegal burials. Our interviews with staff at the VRS of

Ghana revealed that records were first transmitted as monthly statistical summaries

and then subsequently as annual death registers. This provided a feasible avenue for

comparing tallies from the two data transmission processes in order to detect, account


76

and correct for all such losses. Data losses led to registration incompleteness and its

data quality implications were handled by the same procedures used for addressing

death registration incompleteness.

3.9.4 Data entry errors

Data entry errors occurred in the form of typological errors. These did not

affect the data quality in a manner that the analysis could have led to invalid

conclusion or deceptive outcomes. The data entry errors were generally random in

nature and did not affect one cluster more than other. Errors of this nature were also

random errors and their effects were distributed equally by cause and by cluster.

These errors were easily detected and corrected simply by running field-code checks

in Microsoft Excel 2003 and Stata 9.0.

3.9.5 Misclassification or inaccurate diagnoses (cause of death ascertainment bias)

Misclassification or inaccurate diagnoses were classified under common errors

in practice and largely led to cause of death ascertainment biases. Misclassification

routinely arose from the following causes:

a) physician’s lack of appreciation of the clinical symptoms for an illness,

b) physician’s prejudice about neighbourhood characteristics of the area an

individual lived prior to death,

c) physician’s prejudice about socioeconomic status of the deceased,

d) physician’s prejudice about season (either wet or dry) i.e. physicians may

over-diagnose malaria in wet season than in dry season,


77

e) imprecise predictive ability (variable predictive values) of diagnostic test

or instrument.

We recognized two (2) types of misclassification (or ascertainment) biases; viz

differential and non-differential misclassifications. Non-differential misclassification

occurred when the amount of diagnostic error in certifying a death event as from

malaria/diarrhoea or otherwise was the same in all death events presenting to a

pathologist or physician. Thus, non-differential misclassification occurred when the

degree of measurement or medical cause of death certification error was the same in

death events occurring in different clusters. Conditions more likely to give rise to non-

differential misclassification were lack of appreciation of clinical symptoms and low

prediction of diagnostic test or instrument as in (a) and (e) above. Non-differential

misclassification is a random error, equally distributed among all deaths in different age-

groups, sexes and clusters. This meant that in different age-groups, sexes, and census

clusters there would be an equal chance of assigning fever cases as malaria in different

sub-population groups. In this case, if census clusters differed in the structure of their

populations, the cause of death ascertainment in the different age-groups, sexes and

clusters did not differ because different sub-population groups had equal likelihood of

ascertainment within each sub-group (for each cause of death),.

Differential misclassification (ascertainment bias) on the other hand occurred

when the degree of measurement or ascertainment error was different in different death

events within different subpopulation groups (e.g. age-groups and sexes) or in different

clusters. Therefore if the different groups were not evenly distributed among all sub-

population groups in different clusters that could lead to variation in cause of death


78

ascertainment in different age-groups, sexes and clusters. McGuinness and others in a

study to determine case definitions for diagnosis of clinical malaria, age- and sex-specific

diagnosis of the fraction of fevers attributable to malaria (AF) varied with age and season.

For infants, the AF was 51% during the wet season and 22% during the dry season. For

children over one year of age, AF was 89% during the wet season and 36% during the dry

season. Although this fraction differed between age-groups and between seasons, it was

found to be constant within age-groups and within seasons.

Differential misclassification (ascertainment bias), a systematic form of error

would gave rise to different levels in ascertainment by age, sex and by cluster. Causes of

this form of ascertainment bias had been largely due to human stereotypes which include

prejudices about neighbourhood characteristics and socioeconomic conditions in different

clusters. As an illustration, consider that two people died of an illness which manifested

as discharge of 2-3 watery stools per day (being definition of diarrhoea). Also consider

that the two people died in two different geographic locations i.e. a neighbourhood with a

low income and poor environmental quality – cluster “A” and the other neighbourhood

with a high income population and clean environmental conditions – cluster “B”. In

assigning medical cause of death presumptively without laboratory confirmation, the

certifying physicians could simply conclude that diarrhoeal causes were more probable

explanation of loose stools in neighbourhood with poor environmental conditions than in

neighbourhoods with clean environment conditions. Under the influence of such

stereotypes, physicians could inaccurately assign the cause of 2-3 loose/watery stools per

day (underlying cause of death) to diarrhoea more likely so for persons coming from

cluster “A” compared to those coming from cluster “B”. This meant that such biases


79

could lead to diarrhoea over-diagnoses in cluster “A” compared to cluster “B”. Such

prejudices about socioeconomic status would lead to similar over-diagnoses in low

income residential areas compared to high income neighbourhoods. Fortunately,

certifying physicians look beyond these stereotypes during cause of death ascertainment

in Ghana.

3.9.6 Methods for Addressing the Biases – Strengths and Weaknesses

This section has been devoted to a discussion of some of the analytical tools or

procedures available for handling the data quality issues described. In the particular case

of the death registration, the quality issues which were most likely to affect the results of

this study in a significant way were:

different levels of death registration completeness,

misclassification or inaccurate diagnoses of diseases which may lead to cause of

death ascertainment biases, and

misclassification due to seasonality – seasonal misdiagnoses.

The section presents a discussion on the strengths and weaknesses of the methods

available for use in handling the quality issues. The discussion of the strengths and the

weaknesses then informed the choice of the method used in deriving a more valid

malaria/diarrhoea mortality summary measure for this analysis. There was a wide range

of techniques available for use in estimating mortality from deficient data.

However, the techniques varied in the levels of their sophistication, robustness and the

extent of their adequacy to fully address all the potential quality problems. Although

direct and indirect standardisation procedures, Preston-Coale and Brass methods could

address the issue of variation in the levels of registration completeness, they did not


80

address misclassification (ascertainment bias) and the mortality data used in this study

did not even meet the data requirement of those methods [376-378]. Furthermore, indirect

standardisation was shown to be of limited application in this study because standard

rates (available from the Ghana Demographic and Health Surveys - GDHS) were only

available for under-5s and infant mortalities. In the specific case of child mortalities,

these rates were not even available. Detailed description of these traditional procedures is

available in literature and a discussion on them was not considered. Instead, the ensuing

sections will focus attention on discussion of the novel procedures developed specifically

for this study.

3.9.6.1 Wet Season Mortality to Dry Season Mortality Ratio

Misclassification associated with seasonal influences (i.e. vary from season for both

malaria and diarrhoea) are very common biases. For example, it is well known that

mosquitoes breed better in wet seasons than in dry seasons due to overabundance of

poodles and standing pools of water in wet season compared to dry season. For this

reason, physicians will most likely over diagnose both malaria and diarrhoea in wet

season than in dry season. In the diagram (Fig 3.4), there are four (4) clusters (i.e. C1, C2,

C3 and C4) shown in sky-blue colour. There are two sets of double-ended arrows, a set of

four in green colour and another set of four in deep blue colour. The deep blue arrows

with double arrows represent “within cluster variation” (wcv) in mortality and the green

arrows show “between cluster variation” (bcv) in mortality as a consequence of seasonal

biases in diagnoses.


81

Figure 3.4: Seasonal Biases in Disease Diagnoses In order to address seasonality biases, the task was to get a mortality outcome measure

which was independent on the “between cluster variation”. The ratio of wet season

mortality to dry season mortality would completely address the issue of both seasonality

bias and misclassification because the two absolute parameters for mortality, from which

the ratio was computed, came from same the cluster. The ratio did not depend on other

clusters because its derivation did not involve mortality figures from the other clusters.

This meant that the associated seasonality bias would cancel out in the resultant summary

measure since its effect was the same in both the numerator and the denominator. Table

3.4 shows how to compute wet and dry season mortality ratio. The ratio addresses the

problem of seasonal variation in diagnosis in that it is entirely unique to a given cluster

and independent of any other cluster.

Table 3.4: Ratio of wet season mortality to dry season mortality Cluster Wet-mot Dry-mot Ratio = [(wet-mot)/(Dry-mot)] C1 4/1000 2/1000 2 C2 8/1000 2/1000 4 Cnth 16/1000 2/1000 8

Characteristics of the ratio [(wet-mot)/(Dry-mot)].

it is a relative value,

it measures the magnitude of mortality in wet season in relation to that in dry

season,

C1 C3

C2 C4

wcv

wcv

wcv

wcv bc

v

bcv

bcv

bcv


82

such a measure considers two different values within one single cluster,

the ratio in one cluster does not depend on another ratio in a different cluster,

the ratio being a relative measure, it is not affected by misdiagnosis and

ascertainment biases inherent in the two absolute values from which it is derived.

Limitation of the ratio [(wet-mot)/(Dry-mot)] The ratio combines mortality figures from both wet and dry seasons in its derivation and

cannot therefore be used to compare seasonal effects on the mortality outcome. A key

assumption was that seasonality did not vary within cluster and therefore, the summary

measure would be rendered invalid if the assumption was violated, i.e. if seasonal

intensity varied within cluster.

3.9.6.2 Instantaneous Death Rate or Force of Mortality (µ)

In deriving instantaneous death rate or force of mortality (µ), a fairly homogeneous

population conditions was assumed as in Preston-Coale and Brass [376, 379] but with

slight amendment to the assumptions therein. The slightly modified assumption was that

“the completeness of death reporting although not 100 percent complete will be the same

for same ages”. This meant that probability of recording death events among individuals

within same age group would be the same. On the basis of this assumption the total

mortality in any given cluster was decomposed into the following components:

(a) [(AC)] = “all-cause of death”

(b) [(SfC)] = “specific-cause of death”

(c) [(AOC)]1 = “all-other-cause of death”

1 [(AC)] - [(SfC)] = [(AOC)] {e.g. “all-cause of death” - “specific cause of death” = “all-other cause of death”}


83

A relationship was then established among the three (3) components of mortality, in a

view to derive a summary measure on mortality that would be unaffected by all the five

(5) data quality issues associated with mortality reporting in Ghana. In order to do this, an

assumption was invoked and some logical inferences drawn from the assumption as

follows:

Main Assumption: There is no strong reason why mortality data incompleteness (i.e.

caused by under-registration of deaths, under-reporting, record losses, etc) should

vary by cause (e.g. malaria or diarrhoea or hypertension, etc), “all-cause of death”

(all deaths, which includes the given “specific cause” of death) and all-other cause

(all death excluding a given specific cause of death) (see footnote for the

mathematical relationship among the three mortality components) within age groups

in a given cluster. In other words, in any one cluster, the level of mortality registration

factor “k1” (pronounced k-prime) and registration incompleteness factor “k” will be

the same within age groups or within a cohort of the same birthday. The premise of

this assumption is that, it is the “fact-of-death” rather than its cause (“cause-of-

death”) that is important in determining whether a given death event is reported or

not.

Key Inference from the assumption above: If this assumption holds true, then in any

given cluster, say C, the “reported specific cause of death” [R(SfC)] = “true specific

cause of death” [T(SfC)] multiplied by “reporting incompleteness factor” (k) and that

the “reported all-cause of death” [R(AC)] = “true all-cause of death” [T(AC)] multiplied

by incompleteness factor (k), see Table 3.5.


84

Table 3.5: Relationship between specific cause, all-cause and reporting incompleteness of mortality Specific Cause (SfC) All Cause of Death (AC) Cluster (C) Reporting

Incompleteness Factor (k)

True Specific Cause [T(SfC)]

Reported Specific Cause

[R(SfC)]

True All Cause

[T(AC)]

Reported All Cause

[R(AC)]

C1 k1 [T(SfC)]1 [R(SfC)]1 [T(AC)]1 [R(AC)]1 C2 k2 [T(SfC)]2 [R(SfC)]2 [T(AC)]2 [R(AC)]2 C3 k3 [T(SfC)]3 [R(SfC)]3 [T(AC)]3 [R(AC)]3 C4 k4 [T(SfC)]4 [R(SfC)]4 [T(AC)]4 [R(AC)]4 : : :

: : :

: : :

: : :

: : :

: : :

Cn kn [T(SfC)]n [R(SfC)]n [T(AC)]n [R(AC)]n n= total number clusters in urban Accra i.e. 70.

Mathematical formulation of inference above:

Expressing the above inference using cluster (C1) values in Table 3.5, the following

models were constructed:

[R(SfC)]1 = [T(SfC)]1 * k1……………………..……………………….……………. (1)

and

[R(AC)]1 = [T(AC)]1 * k1………………………………..…………………………… (2)

Solving for k1, equations (1) and (2) can be re-written as:

i.e. equation (1) becomes

k1 = 1(SfC)

1(SfC)

][T ][R .…………………………...……………………...……………………(3)

and

equation (2) becomes

k1 = 1(AC)

1(AC)

][T ][R ………………………….…………….……….……………………… (4)

But k1 (death registration incompleteness and/or misclassification factor in cluster C1) in

equations (3) and (4) is the same, and so (3) = (4)


85

∴1(SfC)

1(SfC)

][T ][R

= 1(AC)

1(AC)

][T ][R = β ………………………………………………………….. (5)

Now, “if the quotients (β) of the terms on the left hand side of the equation and that on

the right hand side of the equation are the same, then the quotients (β1) of the numerator

terms and that of the denominator terms are also the same”.

∴ ][R ][R

1(AC)

1(SfC) =

1(AC)

1(SfC)

][T ][T = β = β1…...………………………………………………… (6)

Equation (6) gives a cross-product ratio (β) which can be computed (i.e. [R(SfC)]1 and

[R(AC)]1 are known) from already collected mortality data. The summary measure β, gives

an estimate of the fraction of any specific cause of death (e.g. malaria, diarrhoea,

hypertension, etc) in any one cluster with a given schedule of all-cause mortality.

Additionally, (β) measures the force (stress) of mortality due to any specific cause

relative to all-cause of death at any given time and it is the fraction of specific cause

mortality in relation to all-cause mortality.

This means that if β1 in cluster C1 is not affected by k1, in the same cluster, then β2 in

cluster C2 will not be affected by k2 in cluster C2, and that holds for all other clusters. It

was then concluded that, (β) was an unbiased estimate of cause specific mortality in

relation to all-cause mortality in different clusters and could be used to make valid

comparison of specific cause mortality across different clusters.

A final consideration was the possibility to obtain (β) from “reported specific cause and

“reported all-other cause” (i.e. all-cause mortality {[T(AC)]} minus specific cause

mortality {[T(SfC)]}; [T(AC)] - [T(SfC)] = [R(AOC)]). Using the figures in cluster C1, as usual,

this was achieved by imagining that the “reported specific cause of death” [T(SfC)]1,

multiplied by k1 would give a certain value (mi) = {[R(SfC)]1} and that the “reported all-


86

other cause of death” {[T(AOC)]1} multiplied by k1 would also give another value (do) =

[R(AOC)]1. Therefore, using figures in cluster C1, in Table 1 as usual, we obtained the

following mathematical expressions:

[R(SfC)]1* k1 = mi ……………………………………………………………….… (7)

[R(AOC)]1* k1 = do ………………………………………………………...………. (8)

Now, dividing equation (7) by equation (8) as before, we obtained the following

expression:

1(AOC)

1(SfC)

][R ][R

= domi = βo ……………………………………………………………….. (9)

The quotient (βo) in equation (9) is an estimate of the fraction mortality of specific cause

relative to all-other cause mortality in a cluster with a given mortality schedule. This

estimate is also unbiased with respect to (k) at cluster level. A point to note here is that

(βo), just like (β) is not affected by both misclassification and incompleteness biases for

similar reasons.

Dividing equation (9) by equation (6), we obtained the relationship between (βo) and

(β) as follows:

ββo =

o

i

dm

÷1d

mi = od

d1 ………………………..……………………………………. (10)

Also, from equation (9), βo = 1(AOC)

1(SfC)

][R ][R and β in equation (6) =

][R ][R

1(AC)

1(SfC)

∴ββo =

1(AOC)

1(SfC)

][R ][R ÷

][R ][R

1(AC)

1(SfC) = µ (cross product ratio). The effects of [R(SfC)]1 also cancels

out, but it does contribute to generating the final outcome estimate (µ) and therefore

gives an indirect estimation of the contribution (fraction of deaths due to a specific


87

cause) of [R(SfC)] to all-cause mortality deaths in a given area with a defined schedule of

mortality.

⇒ ββo =

1(AOC)

1(AC)

][R][R = µ …..…………………….………..……………………….. (11)

The quantity (µ) in equation (11); a cross product ratio, is an estimate of the fraction of

deaths to a specific cause. It measures the force or stress of mortality due to a given

specific cause (e.g. malaria, diarrhoea, hypertension, etc.) in a given cluster with a

defined mortality schedule. Since (µ) was derived from β and βo which were unaffected

by the incompleteness and misclassification factors, it was also unaffected by these

factors. In reality, µ measures the inverse of the proportion of specific cause of death or

the inverse of the force (stress) of mortality (defined as instantaneous death rate and

represented as the ratio of probability function to distribution function) [377].

Attributes of (µ) (a) (µ) varies from the value 1 to infinity (specific cause = all-cause) depending on

the magnitude of the cause-specific mortality at any one point in time and its

trajectory is described by a curve,

(b) the integral of the curve (i.e. area under the curve) gives the absolute value of a

given specific cause,

(c) the derivative of the curve (i.e. instantaneous change) of the curve at any one

point in time gives (µ), the fraction of deaths due to a specific cause,

(d) (µ) is a ratio and unaffected by the magnitudes of the numerator and denominator,

and finally

(e) (µ) is not affected by the levels of uncertified cause of deaths (unknown cause of

death) and death records with unknown place of residence addresses (see Table


88

3.5). This is because individuals without home addresses were regarded as

homeless people and were excluded from the target population. Since there was

no strong reason why there will be ascertainment bias by cause, records with

unknown cause of death did not affect (µ).

Table 3.6 summarises the main quality issues and gives logical explanation of how they

can be surmounted.

Table 3.6: Highlights of key quality issues and proposed solutions No Bias Solution 1 Registration incompleteness:

Illegal burials Relocation of aged or some old people Data losses

Illegal burials, relocation of aged people and data losses all ultimately result in incompleteness of death registration. But the incompleteness factor; denoted by (k), of death registration is a function of the statistical/registration system and cluster population characteristics, which cancels out in computing the ratio µ. So when µ is computed for specific age groups in each cluster, the factor (k) which is the same in a given should cross out.

2 Misclassification : Physician’s personal attribute Poor precision of diagnostic test

Malaria/diarrhoea diagnoses or ascertainment may vary from physician to physician. But this should not vary in a given cluster because factors pre-empting misdiagnoses are constant within each cluster. This means misclassification will remain constant throughout each and therefore even out when computing for µ. The diagnostic test or equipment may give variable results, but this will also be constant for a given cluster. Finally, because physicians are not assigned as per cluster basis, this error is random and should not cause ascertainment levels to differ by cluster.

3 Ascertainment bias based upon area characteristics Given that misclassification due to physician’s personal attributes does not cause ascertainment to vary by cluster, in other circumstances, the levels in the ascertainment may still vary all the same by cluster as a consequence of variation in area characteristics, socioeconomic statuses, etc., of different clusters. But this will be expected to be the same in a given cluster (i.e. area characteristics and socioeconomic conditions within each cluster are fairly homogeneous). So in deriving µ, the factors present in both the numerator and denominator will reasonably cross out.

4 Ascertainment bias based upon seasonality Both malaria and diarrhoea diagnoses may be affected by seasons (wet & dry seasons) e.g. there might be over-diagnoses in wet season compared to dry season. But the seasonality effects on ascertainment will not affect this analysis because the study considers exclusively variation in spatial components, other than temporal components. Moreover, the two seasons are experienced by all clusters equally (e.g. severity and duration) and the same levels of over-diagnosis will occur during wet seasons in all clusters. There should be no strong reason why seasonality should cause ascertainment levels to vary by cluster.

5 Accuracy of allocation of deaths by place and time Deaths are registered in Ghana based on normal place of residence prior to death, place of death, date of death and date of registration for 99.7 percent of the events recorded for the period. These were all recorded and therefore there was no confusion about death misallocation by place and time.


89

3.10 Proportional Mortality Ratio and Fraction of due to Specific Cause

Although there was enough proof that µ was unaffected by all the data quality

issues, its derivation was fraught with a lot of assumptions thus making its use less

preferable to such more direct and widely applied estimates of mortality, such as

Proportional Mortality Ratios (PMRs).

To surmount these data quality issues fairly robustly and more directly, a computation of

proportional mortality ratio (PMR) was adopted using the routinely collected mortality

data. The fraction of cluster level deaths due to specific cause was estimated from the

cluster level PMRs and Table 3.7 displays the components which make up the formula

for calculating the PMR.

Table 3.7: Computation of PMR and the Fraction of Deaths due to a Specific Cause Cluster Level Reported Mortality Cluster Level True Mortality Age Group Cluster Level

Reported Specific Cause

Cluster Level Reported All

Cause

Cluster Level Expected Specific Cause

Cluster Level Expected All

Cause

0-4 s1 a1 S1 A1 5-9 s2 a2 S2 A2

10-14 s3 a3 S3 A3 15-19 s4 a4 S4 A4

: : : : : : : : : : : : : : :

95-99 s19 a19 S19 A19 100+ s20 a20 S20 A20

In this study, the concept of PMR was used in the context of cluster level and all-cause

city level all-cause mortalities. On the basis of this, a PMR was defined as the proportion

of deaths in a specific cluster that were due to a specific cause-of-death (malaria or

diarrhoea) divided by the proportion of deaths in all clusters (city level) that were due to

that specific cause of death (malaria or diarrhoea). The Proportional Mortality Ratio is


90

ideally used where information on the population at risk is not reliable or unavailable at

all. Although information currently exists on population at risk at cluster level, because of

high mobility among urban dwellers, data on cluster level population was not used. The

PMR was not based on mortality rates; it compared the deaths due to a particular cause of

death in a cluster with that which would be expected on the basis of the distribution of all

deaths by cause in some standard population (the citywide population in the case of this

study). A PMR greater than 100 indicates that members of that cluster were more likely

than average to die of that cause i.e. malaria or diarrhoea, while a PMR of less than 100

indicates that they were less likely than average to die of that cause. This gave a caution

that PMRs should be interpreted with care because the proportion of deaths from the

cause of interest (i.e. malaria and diarrhoea) could be affected by the relative frequency

of other causes. If mortality from all causes was low in a given cluster, a high PMR from

a given specific cause (in this case malaria/diarrhoea) could be expected, even if the

malaria/diarrhoea rate in that cluster was lower than the national rate. As a result, an

observed excess could represent a true difference, but could also simply represent a

deficit of deaths from other causes.

From Table 3.7, cluster level reported mortality included all deaths reported by the

existing vital registration system (VRS) and cluster level expected mortality included the

total number of deaths that would be expected if the VRS were perfect. But we know that

the VRS in Ghana is imperfect and therefore the reported number of deaths should be less

than the actual number of deaths expected if the registration system were complete. From

the data available, the fraction of cluster level deaths due to a specific cause was

computed from the cluster level PMR as follows:


91

Let s = reported cause specific deaths in the imperfect VRS and S = expected cause

specific deaths if reporting were complete, then it follows that:

s = Is*S, [ ]10 ≤< sI ……………………. (1),

where Is is a certain incompleteness (constant) factor imposed by the strength and

characteristics of the existing VRS.

Also, let a = reported all-cause deaths and A = expected all-cause deaths if the VRS were

perfect, then again, it follows that:

a = Ia*A, [ ]10 ≤< aI ….………..……… (2),

where Ia has the same connotation as in equation (1) above. Note that Is and Ia represent

the same registration parameter associated with the strength and characteristics of the

VRS for cause-specific and all-cause deaths respectively. So that when both Is and Ia take

the value 1, we have a perfect VRS. In reality however, Is and Ia are strictly less than 1

because the VRS is imperfect.

Dividing (1) and (2) we obtain,

as =

A*S*

a

s

II , …………………….…….. (3).

Now, assuming there were no selective death registration or if the factors that contribute

to biases in death registration were independent of cause of death so that reporting

incompleteness would be the same for both specific-cause and all-cause (unbiased

registration by cause), then Is = Ia

as II ≅ and as =

AS = City _ Specific _Cause

City _ All −Cause … (4).


92

This then means I is constant and cancels out. Note however that 0<IsIa < ∞ . Hence

IsIa

may be viewed as an adjustment factor for reported health statistics (e.g. morbidity,

mortality, etc.) from an inefficient VRS. Note also that IsIa represents an under- or over-

estimating factor such that if Is < Ia then the VRS under-reports specific health events

(selective reporting bias against specific cause) compared to all-cause morbidity, then the

procedure over-estimates the specific cause morbidity. Also, if Is > Ia then the system

over-reports specific cause morbidity (selective reporting bias in favour of specific cause)

compared to all-cause morbidity, the procedure would under-estimate the specific cause

events.

Equation (4) estimates that the proportion of reported cause-specific deaths or the fraction

of deaths due to a specific cause PMRfd = (as ).

The PMRfd takes account of incompleteness in death reporting as (I) cancels out in

equation (4) and could be used to estimate both age- and sex-specific mortality as in

Table 3.7.

3.10 Conclusion

Of the three techniques considered (wet/dry seasons mortality ratio, instantaneous death

rate and the fraction of deaths due to specific cause), two (instantaneous death rate and

the fraction of deaths due to specific cause) appear to fully address all the quality issues

alone. Although the ratio of wet season mortality to dry season mortality although could

address most of the data issues fairly robustly; it was unable to handle seasonality biases.

The instantaneous death rate (IDR) and PMRfd both provided adequate response to death


93

registration incompleteness and misclassification problems, i.e. the factors responsible for

variation in death registration completeness across cluster (illegal burials, relocation of

aged or some old people and data losses) and all forms of misclassification would be

constant in a given cluster and within each age-class. It would therefore be reasonable to

assume that they would cancel out in the computation of the two summary measures.

However, the PMRfd would be preferred over IDR because the computation of PMRfd

was more direct and devoid of the many assumptions associated with the computation of

IDR. Both techniques assumed that there was no selective registration bias by cause.

Using the PMRfd, all-age, sex-specific and age-specific mortalities were calculated

separately for each cluster and which were then employed in multiple regression analyses

and other geo-statistical models to determine the nature and levels of the association

between environmental and socioeconomic variables separately for malaria and diarrhoea

mortality across clusters. The PMRfd was employed to provide spatially explicit excess

mortality risk analysis. The equation describing the general linear regression model is

given by:

Where Y = response variable (e.g. malaria and diarrhoea mortality ratio) in equation (2)

above X(1-n) = the different explanatory (environmental or census variables) or exposure variables 1 to n.

β0 = the value of mortality ratio when the value of all the environmental variables assume the value of zero

e = error term n assumes the value 1, 2, 3, … 70 (number of census clusters in urban Accra)

Y = β0 + β1X1 + β2X2 +… βnXn + en


94

Other analyses involved the use of the PMRfd to study the spatial association between the

neighbourhood urban environmental and socioeconomic conditions on the one hand and

malaria and diarrhoea mortalities on the other hand.

3.11 Consent and Ethical Consideration The data (e.g. urban environmental data) were already in public domain and there

were no ethical issues surrounding their use, except a written permission of

Government’s Statistician. However ethical issues associated with the mortality data

(usually kept confidential) were identified and clearance sought from the Institutional

Review Board (IRB) of the Noguchi Memorial Institute for Medical Research.

Chapter Four: Results and Summary of Findings

95


4.1 Demographics and health

It is well established that health outcomes are not evenly distributed across different

populations and that different individuals in a given population differ in susceptibility to

different risk factors [380-382]. A growing body of literature exists which seeks to

explain the phenomenon of social and spatial disparities in health [82, 318, 323, 380, 381,

383, 384] and a range of structural, material, and socio-cultural factors have been

implicated [283, 323, 380, 385-387]. On account of changes in different levels of

physiological and immune statuses across sexes and with aging, inequalities in health

outcomes develop among different age groups [318, 323, 381, 383]. In literature, age and

sex have been widely reported to be strongly associated with some cause-specific

mortalities which have been reported to differ by age and sex [323, 381, 384].

4.1.1 Cluster population structure and distribution

Figure 4.1 shows cluster level age-specific population distribution in Accra which

provided an opportunity age specific distributions among the different census clusters in

this analysis. Age and sex have important influence on some health outcomes such that in

comparing those health events across countries or regions, the summary measure of the

given health outcome under consideration has to account for sex and age differences in

the different comparison groups. For instance, a given health event could be associated

with age such that if the age-structure differed for the different groups, then age could

play a confounding role and thus bias the results of the investigation. This is also true for

sex differences in the health outcome in the different comparison groups. For this reason,


96

to compare malaria and diarrhoea mortality among different clusters, a useful approach

was to explore the influence of both age and sex on the cause-specific mortality

outcomes.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

0 to Less

than 1 1 to

45 to

9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84 85+

Age Group

Num

ber o

f Res

iden

ts

Figure 4.1: Cluster population age distribution in Accra

By inspection, the population distributions in the different census clusters in Accra were

fairly uniform and approximately the same in the different clusters. This perhaps meant

that the all-age mortality parameter computed for each cluster was unaffected by age

differences (Figure 4.1).


97

4.1.2. Age, and sex Specific malaria and diarrhoea mortalities

Figure 4.2 shows the proportion of malaria deaths in different age groups. The overall

mean fraction of deaths due to malaria was higher than the age-specific fraction of deaths

due to malaria in many of the older age-groups. 0

.1.2

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0<1 1-4 5-9

10-14

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30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84 85

+

Age Group

Figure 4.2: Age-specific Malaria Mortality

Age group 1-4years had the highest fraction of deaths due to malaria followed by that for

age-group 5-9 years, with age-group 15-19 years tying with the overall all-age mean.

Logistic regression analysis of malaria mortality variability across age showed significant

differences across age groups. The results showed that malaria mortality among 1-4-year

olds was approximately four times higher than that among the under-1-year olds (OR:3.5;

95%CI: 2.75 4.55; p < 0.01). Although the proportion of malaria mortality was lower

among members of 5-9-year olds compared to that among the 1-4-year olds, it was still

more than double that among the under-1 year olds (OR:2.5; 95%CI:1.85-3.26; p < 0.01).


98

However, there was no significant difference in the fraction of deaths due to malaria

between the under-1 year olds and that for the 10-14-year olds (OR: 0.18; 95%CI: 0.75-

1.48, p = 0.73). 0

.1.2

.3Fr

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0<1 1-4 5-9

10-14

15-19

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35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84 85

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Age Group

Female Male

Figure 4.3: Sex and Age-specific Malaria Mortality

Figure 4.3 shows sex stratified malaria mortality across age groups. There was no

evidence of a difference in malaria mortality among males and females across age (p =

0.79; 95%CI: -0.059 0.045). This perhaps meant that a malaria mortality measure

calculated without adjusting for differences in sex could be valid for comparison across

different subpopulation groups.

On the basis of the results from the sex- and age-specific malaria mortality, a more

appropriate malaria mortality classification based on age was regrouped into seven major

categories as: 0<1, 1-4, 5-9, 10-14, 15-19, 20-29 and 30+ (Fig. 4.4). Malaria mortality


99

differed only among the lower age groups and tended to remain fairly the same at the age

of 30 years and above.

0.1

.2.3

Frac

tion

of D

eath

s du

e to

Mal

aria

0<1 1-4 5-9

10-14

15-19

20-29 30

+

Age Group

Figure 4.4: Age Stratified Urban Malaria Mortality in Accra

Figure 4.4 shows urban malaria mortality classification on the basis of differences due to

age. Differences in malaria mortality among the different groups could not be ignored for

the lower ages i.e. 29 years and below as shown in Fig. 4.4.

.02

.04

.06

.08

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0<1 1-4 5-9

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20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84 85

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Age Group

Figure 4.5: Age-specific Diarrhoea Mortality


100

Figure 4.5 shows variation of diarrhoea mortality across age. There was a gradual decline

of diarrhoea mortality with the highest mortality among the under-1 year group and

dropped sharply, reaching 6.7 percent of deaths attributable to diarrhoea among the 5-9-

year age group. Diarrhoea mortality rose sharply by nearly 20 percent of the deaths

among the 5-9-year group to 8.0 percent of all-cause deaths among members of 10-14

year-olds. Analysis showed no evidence of a real difference between the 0<1-year olds

and 1-4-year olds (OR: 0.80; 95%CI: 0.59 1.07; p = 0.13). Nevertheless, a strong

evidence of a difference was observed between groups 0<1-year olds and 5-9 year olds

(OR: 0.59; 95%CI: 041 086; p = 0.005). Although there was only moderate evidence of a

difference in diarrhoea mortality between age groups 0<1 and 10-14 (OR: 0.69; 95%CI:

0.47 1.005; p < 0.05), a strong evidence of a difference in diarrhoea mortality was

observed between the members of higher age groups i.e. diarrhoea mortality among

members of 0<1 age-group was approximately half of that among members of the 15-19-

year group (OR: 0.51; 95%CI: 0.34 0.75; p = 001). The pattern of variability in diarrhoea

mortality across age made convenient re-grouping quite the same way as that for malaria

mortality.

Figure 4.6 shows sex stratified age-specific diarrhoea mortality in urban Accra. Diarrhoea

mortality varied non-uniformly across the different age-groups for both males and

females. Although it appeared there was a difference in diarrhoea mortality between

males and females especially among the lower age-groups, the evidence of this difference

was not shown to be significant (p = 0.37; 95%CI: -0.010 0.26). Additionally, there were

differences in age-specific malaria and diarrhoea mortalities (Figs. 4.3 & 4.6). This very

much provided evidence that although diarrhoea mortality summary measure did not


101

account for differences in sex, it could still be valid for comparison among different

groups.

.02

.04

.06

.08

.1.1

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oea

0<1 1-4 5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84 85

+

Age Group

Female Male

Figure 4.6: Sex and Age-specific Diarrhoea Mortality

However, age-specific diarrhoea mortality showed slightly different pattern of variability

compared to that for malaria mortality. The point at which diarrhoea mortality variability

showed no significant differences among the age groups was observed to be much earlier

in life for diarrhoea mortality than for malaria mortality. But for purposes of uniformity

in age-grouping, seven categories were produced for diarrhoea as in Fig. 4.7.


102

.02

.04

.06

.08

.1.1

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Dea

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iarrh

oea

0<1 1-4 5-9

10-14

15-19

20-29 30

+

Age Group

Figure 4.7: Age Stratified Urban Diarrhoea Mortality in Accra

As could be shown in Fig. 4.7, the seven age-groups were based on strong evidence of

differences that existed for age-specific diarrhoea mortality, especially among the lower

age-groups.

4.2 Urban socioeconomic conditions and urban environmental conditions

Generally, health inequalities exist among rural and urban dwellers, different incomes

groups, different gender and age-groups. The dependence on cash-for-service policies in

many low-income economies has increased inequalities in access to affordable health

care which tend to produce disparate health outcomes among different social groups.

Wide inequalities in income levels also mean uneven access to environmental services

which drive environmental health inequalities across these social groups. In literature,

many studies exist which highlight health problems of the urban population in the cities

of Africa, Asia and Latin America [38, 39, 358, 364]. Intra-urban differentials in social,

environmental and health conditions between groups in cities are now broadly understood

[174, 357] and depending on the region, between 35 and 55 percent of the populations


103

have incomes or consumption levels below the standard poverty line [93, 155, 362, 388].

While urban poverty is rapidly exacerbating, a marginally small but numerically

consequential proportion of urban residents have lifestyles and living conditions which

mirror those of the very affluent countries [77, 153, 154, 358]. Several review articles

have reported widening intra-urban differentials in environmental quality conditions in

the poor countries [77, 88, 153-155, 174, 325, 358-360, 362, 363, 365, 389, 390]. In

Ghana, such reviews and assessments reported pervasive intra-urban environmental

quality differentials in the fast growing urban centers including Accra, Kumasi, Tamale,

Cape Coast and Takoradi, where deprived areas exist alongside privileged areas,

distinguished only by the overall area-based socioeconomic conditions [38, 71, 215, 216,

391]. In Accra, up to 46 percent of people live in the most deprived zones [71, 215, 391].

These areas are hosts to people with the lowest educational standards, the lowest incomes

and the poorest facilities in terms of water, sanitation and housing [71, 215, 391].

4.2.1 Structure of urban socioeconomic status (SES) (quintiles)

Despite increased number of interventions aimed at poverty reduction, urban

poverty is deepening in Ghana with 33 percent of the poor now living in the urban areas.

From 1988 to 1993, while informal urban employment grew from 66 to 83 percent of

total national employment, wages fell significantly in absolute terms over the same

period [392]. As a result, urban poverty has reportedly increased rapidly, with some poor

groups now threatened by food insecurity and malnutrition. During the same period, the

number of severely malnourished children has more than doubled. The rapid decline in

income levels of urban residents had direct impact on access to health, water supply, and

sanitation services. The consequence of a weak urban economy has been that the urban


104

residents constantly face a reality of double tragedy [393]. While 14 percent of the

population without potable water supply now lives in urban areas, 40 percent of the

population without sanitation coverage also resides in these urban areas. The combination

of unequal access to the basic services and income inequalities directly drive health

inequalities in the urban areas in Ghana.

4.2.2 Differences in environmental quality across socioeconomic quintiles

Table 4.1 shows the variation in neighbourhood urban environmental quality

conditions across socioeconomic classes in a typical urban setting in a low-income

economy. In general, while there was very strong evidence of differences in the levels of

environmental quality with respect to total waste generation (p < 0.001), waste collection

rate (p < 0.001), sewer disposal rate (p < 0.001), non-sewer disposal (p = 0.003) and the

proportion of households using public toilets (p = 0.005), only moderate evidence of a

difference in the environmental quality was observed for per capita waste generation rate

(p < 0.015) and the proportion of households with toilet/bath facilities outside own

household (p = 0.02) across the socioeconomic classes.

In the specific case of inter-quintile variability, whereas there was no evidence of

differences between the poorest class and the lower middle class for total waste generated

(p = 0.064), per capita waste generated (p = 0.103) and the proportion of waste collected

(p = 0.403), there was very strong evidence of a difference across the higher wealth

quintiles.

Also, a strong evidence of differences in neighbourhood urban environmental quality

conditions was observed across the wealth quintiles; i.e. the lower middle class and


105

middle class (p = 0.002), middle class and the upper middle class (p < 0.001), the upper

middle class and the high class (p = 0.004) for the amount of waste generated at cluster

level. For per capita waste generation, the weight of the evidence of differences was

equally very strong i.e. the lower middle class and middle (p = 0.001), middle class and

the upper middle class (p = 0.014), the upper middle class and the high class (p = 0.010).

Similar trend was observed for waste collection rate at cluster levels. There was even

much stronger evidence of a difference across the wealth quintiles for uncollected waste

(deposition rate), sewer disposal rate, non-sewer disposal rate and the proportion of

households relying upon facilities outside households and public toilets (Table 4.1).

Although, there were differences in the levels of inter-quintile variability of the different

urban environmental quality conditions, the weight of the evidence; except for the

proportion of households with pit and bucket/pan latrines, was generally strong (Table

4.1), suggesting a strong link between area-based SES and urban neighbourhood



106

Table 4.1: Socioeconomic classes and environmental health inequality Environmental Variable SES Quintile Mean Coef. Std. Err. p-value 95%CI

Poorest 2970 5170 2742 0.064 -307 10647 Lower Middle Class 8140 9156 2787 0.002 3588 14723 Middle Class 12126 13748 2787 0.000 8180 19315 Upper Middle Class 16718 8439 2838 0.004 2769 14108

Total waste generated (kg)

Richest 11409 - - - - -

Poorest 0.340 0.067 0.040 0.103 -0.014 0.147 Lower Middle Class 0.407 0.139 0.041 0.001 0.057 0.220 Middle Class 0.478 0.104 0.041 0.014 0.022 0.186 Upper Middle Class 0.444 0.110 0.042 0.010 0.027 0.194

Per cap waste generation (kg/person/day)

Richest 0.450 - - - - -

Poorest 0.073 -0.111 0.044 0.403 -0.057 0.139 Lower Middle Class 0.069 -0.217 0.045 0.044 0.003 0.201 Middle Class 0.089 -0.238 0.045 0.016 0.023 0.222 Upper Middle Class 0.195 -0.233 0.046 0.023 0.017 0.219

Proportion of waste collected (%)

Richest 0.306 - - - - -

Poorest 0.427 0.041 0.049 0.015 -0.110 - 0.023 Lower Middle Class 0.432 0.102 0.041 0.000 -0.308 -0.127 Middle Class 0.411 0.123 0.050 0.000 -0.328 -0.148 Upper Middle Class 0.350 0.118 0.051 0.000 -0.325 -0.142

Proportion of waste uncollected (waste deposition) (%)

Richest 0.309 - - - - -

Poorest 0.047 -0.193 0.039 0.000 -0.271 -0.115 Proportion households using sewer disposal (%) Lower Middle Class 0.041 -0.227 0.040 0.000 -0.307 -0.148

Middle Class 0.067 -0.253 0.040 0.000 -0.333 -0.174 Upper Middle Class 0.101 -0.246 0.041 0.000 -0.327 -0.166

Richest 0.294 - - - - -

Poorest 0.453 0.099 0.036 0.008 0.027 0.171 Lower Middle Class 0.459 0.112 0.037 0.003 0.038 0.185 Middle Class 0.433 0.137 0.037 0.000 0.064 0.211 Upper Middle Class 0.421 0.131 0.038 0.001 0.056 0.206

Proportion of households using non-sewer disposal (%)

Richest 0.322 - - - - -

Poorest 0.032 -0.008 0.011 0.454 -0.029 0.013 Lower Middle Class 0.024 -0.012 0.011 0.273 -0.033 0.010 Middle Class 0.020 0.013 0.011 0.231 -0.008 0.034 Upper Middle Class 0.045 -0.001 0.011 0.950 -0.022 0.021

Proportion of households using pit latrine services (%)

Richest 0.031 - - - - -

Poorest 0.043 0.010 0.018 0.573 -0.025 0.045 Lower Middle Class 0.053 0.020 0.018 0.278 -0.016 0.055 Middle Class 0.063 0.028 0.018 0.127 -0.008 0.063 Upper Middle Class 0.071 0.001 0.018 0.949 -0.035 0.038

Proportion of household using bucket/pan latrine services (%)

Richest 0.044 - - - - -

Poorest 0.071 -0.021 0.009 0.021 -0.039 -0.003 Lower Middle Class 0.050 -0.028 0.009 0.003 -0.046 -0.010 Middle Class 0.043 -0.025 0.009 0.007 -0.043 -0.007 Upper Middle Class 0.046 -0.026 0.009 0.005 -0.045 -0.008

Proportion of households using facility in different house (%)

Richest 0.044 - - - - -

Poorest 0.206 0.101 0.040 0.013 0.022 0.180 Lower Middle Class 0.149 0.133 0.040 0.002 0.052 0.213 Middle Class 0.186 0.096 0.116 0.020 0.015 0.176 Upper Middle Class 0.155 0.152 0.134 0.000 0.071 0.234

Proportion of households using public toilet services (%)

Richest 0.054 - - - - -


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4.2.3 Association between socioeconomic conditions and urban environmental quality

In the next stage of the analysis, a key interest was in how multiple socioeconomic

factors influenced the overall neighbourhood environmental quality. The desire was to

assess the relationship between area-based SES and neighbourhood urban environmental

quality conditions. For example, per capita solid waste generation was regarded as an

important urban environmental quality measure since it was the basis for calculating the

total amount of solid waste a given population generated per unit time and often the basis

of waste management planning programs (e.g. size of sanitary landfills to construct, type

of tipping-trucks to import, financial capital required for solid waste transport, etc.).

Authors used bivariate and multiple regression techniques to assess such relationships.

There was a positive (i.e. a unit increase in population economic inactivity resulted in

an increase in per capita solid waste generation rate) association between the proportion

of economically inactive cluster population (economic inactivity) and per capita solid

waste generation (regression coefficient = 0.276) and the amount of variation explained

by economic inactivity was 3.5 percent (R2 = 0.0346). Economic inactivity was defined

as the number of economically inactive residents within a given self-sustaining resident

urban population who were technically dependent on economically active residents for

social support and this measure was computed separately for males and females.

Despite this marginal increase, there was no evidence of association between

economic inactivity and per capita solid waste generation (p = 0.13; 95%CI: -0.079

0.631). Additionally, a sex-stratified analysis of the economic inactivity or any of the

remaining SES [i.e. for male (p = 0.50), and for female (p = 0.40)] found no evidence of

association with the neighbourhood urban environmental conditions. The amount of


108

variation in neighbourhood urban environmental quality conditions explained by

variation in each of the two SES measures separately was less than 3 percent.

However, there was an inverse association (i.e. unit increase in economic activity led

to a decrease in per capita solid waste generation) between economic activity and per

capita solid waste generation (regression coefficient = -0.276) and the amount of

variation explained by economic activity was 3.5 percent (R2 = 0.0346) and essentially

the same as the amount of variation explained by economic inactivity.

Further analysis showed a moderate positive (a unit increase in urban employment

rate led to a slight increase per capita solid waste generation rate) association between

urban employment rate and per capita solid waste generation rate (regression coefficient

= 0.566) and the amount of variation in per capita solid waste generation rate that was

explained by urban employment was 4.2 percent (R2 = 0.042). There was no evidence of

association between urban unemployment and per capita solid waste generation rate (p =

0.09; 95%CI: -0.093 1.224).

Additionally, a positive (regression coefficient = 0.884) association was observed

between urban employment and urban solid waste collection rate. The amount of

variation explained by urban employment was 6.2 percent (R2 = 0.062). A moderate

evidence of association was observed between urban employment and urban solid waste

collection rate (p = 0.039; 95%CI: 0.046 1.721).

Figure 4.8 depicts the relationship between urban employment rate and urban solid

waste deposition rate. An inverse (regression coefficient = -1.007) was demonstrated and

the amount of variation in solid waste deposition rate that was explained by urban

employment was 9.5 percent (R2 = 0.095). As shown, a unit increase in the proportion of


109

urban employment resulted in a significant decrease in urban solid waste deposition rate.

A very strong evidence of association was observed between urban solid waste deposition

rate and the proportion of urban employment (p = 0.01; 95%CI: -1.764 -0.250).

The relationship between urban employment and the proportion of households

connected to the central sewer system (sewer disposal rate) showed a positive (regression

coefficient = 0.841) association. The amount of variation in the proportion of households

connected to the central sewer system explained by the proportion of urban employment

was 6.4 percent (R2 = 0.064). This meant that a unit increase in the proportion of

employed cluster population resulted in a corresponding increase in the proportion of

cluster households connected to the central sewer system in the Accra metropolis.

Moderate evidence of association was observed between the proportion of households

connected to central sewer system and the urban employment (p = 0.036; 95%CI: 0.058

1.624).


110

0.2

.4.6

.8P

ropo

rtion

of S

olid

Was

te U

ncol

lect

ed

.7 .75 .8 .85 .9 .95Employment Rate

Fitted values 95% CIFitted values Solid Waste Deposition Rate

Figure 4.8: Variation of employment rate with proportion of solid waste deposition rate

However, an inverse (regression coefficient = -1.084) relationship was observed

between urban employment and the proportion of households engaged in non-sewer

(improper) liquid waste disposal (Fig. 4.9). The amount of variation in non-sewer liquid

waste disposal explained by the urban employment was 18 percent (R2 = 0.181). A very

strong evidence of association was observed between non-sewer liquid waste disposal

and urban employment (p < 0.001; 95%CI: -1.646–-0.521).


111

.2.3

.4.5

.6.7

Pro

porti

on o

f Non

-sew

er D

ispo

sal

.7 .75 .8 .85 .9 .95Employment Rate

Fitted values 95% CIFitted values Non-sewer Disposal

Figure 3: Variation of employment rate with non-sewer disposal rate

Figure 4.9: Variation of employment rate with non-sewer disposal rate

In contrast to the strong association observed between the proportion of urban

households connected to the central sewer system and urban employment, no such

evidence of association was observed between urban employment and such cluster

hygiene conditions which included the proportion of households with water closets (WC),

proportion of households with pit-latrines, proportion of households with Kumasi

Ventilated Improved Pits (KVIPs) i.e. a locally constructed improvised community toilet,

proportion of households with pan-latrines and proportion of households using public

toilets at bivariate level. This was in contrast to what was observed at community level

when the area-based socioeconomic factors were aggregated and categorized into wealth

quintiles. Although the area-based socioeconomic factors exhibited no evidence of

association with the neighbourhood urban environmental quality conditions at the

household level, a strong evidence of association was observed between the area-based


112

socioeconomic factors and urban environmental conditions across wealth quintiles at the

community level.

In further multilevel analysis, we examined the characteristics of the area-based SE-

conditions in relation to their ability to drive changes in the quality of the neighbourhood

urban environmental conditions. Multiple regression analysis showed no evidence of

association between total waste generated and the area-based socioeconomic variables,

except residents’ occupation.

In other words, educational attainment and residents’ place of work did not appear to

be important factors in driving the underlying difference in the amount of wastes

generated in the residential communities. Nevertheless, a few elements from residents’

occupation category showed very strong evidence of association with the amount of

wastes generated in the communities i.e. administrative and managerial occupations (p =

0.004), clerical and related occupations (p < 0.001), service occupations (p = 0.014)

agriculture/husbandry/forestry/fishing/hunting occupation (p = 0.008),

production/transport and equipment operators and labourers (p = 0.028), and professional

technical and related workers (p = 0.023). In addition, the area-based SES did not show

evidence of association with the amount of waste generated per person per day (per capita

waste generation rate). While educational attainment and residents’ place of work showed

no evidence of association, some variables which together represented residents’

occupation category showed substantial evidence of association with waste collection rate

e.g. administrative and managerial occupations (p = 0.004), clerical and related

occupations (p < 0.001), agriculture/husbandry/forestry/fishing/hunting occupation (p =

0.021), production/transport and equipment operators and labourers (p = 0.010), and


113

professional technical and related workers (p = 0.044). Although education level did not

show evidence of association with total waste generated, per capita generation rate and

waste collection rate, residents’ educational attainment showed a very strong evidence of

association between waste deposition rate (proportion of wastes left uncollected) [i.e. no

education (p = 0.005), pre-school education (p = 0.001), middle/JSS education (p <

0.001), secondary/SSS education (p < 0.001), vocational/technical/commercial education

(p = 0.014) and residents with tertiary education (p < 0.001)]. Similarly, whereas both

educational attainment and residents’ place of work showed strong evidence of

association with wastes deposition rate (proportion of wastes left uncollected), residents’

occupation did not. Additionally, all but one of the 16 elements representing the

residents’ occupation category showed strong evidence of association with waste

deposition in the communities.

On the contrary, while educational attainment and residents’ occupation only showed

moderate evidence of association with the proportion of households engaged in sewer

disposal, all the elements representing residents’ place of work showed very strong

evidence of association with sewer disposal rate. Both residents’ place of work and

residents’ education attainment showed a very strong evidence of association with

households engaged in non-sewer disposal. While the proportion of households using pit-

latrine services did not show evidence of association with the area-base socioeconomic

variables, two of the area-based SES; namely, residents’ education attainment and

residents’ occupation showed very strong evidence of association with the proportion of

households using bucket/pan latrine services. Finally, whereas only a moderate evidence

of association was observed between the proportion of households using sanitation


114

facilities in a different house and residents’ educational attainment as well as residents’

occupation and residents’ place of work showed a very strong evidence of association

with the proportion of households using facilities in a different house.

4.3 Area-based urban SES and health (urban mortalities)

In epidemiological studies, two discrete measures of SES; i.e. area-based and individual

level measures, exist which have been widely applied in different levels of health

analysis. Whereas the individual level measures include education, occupation, income,

ownership of household durable commodities, etc., the area-based indicators are largely

indices based on an array of social characteristics of residential areas usually drawn from

census data, aggregate income, employment rate, etc. The association between

socioeconomic status and mortality, morbidity and access to health services has been well

investigated at both individual and area-based levels. However, the specific manner in

which individual and area-based measures of SES influence health inequalities can differ

according to the specific health conditions largely on the basis of their effects on disease

aetiology and the disease management process. In this section, the results of the analysis

of the association between malaria and diarrhoea mortalities on the one hand and area-

based measures of SES on the other hand are presented.

4.3.1 Differentiation of urban SES

In Ghana, several demographic and economic analyses undertaken over the last ten years

tended to agree that urban unemployment and poverty have been, in part, consequences

of urbanization [350, 351, 353, 373-375, 394 and 1984 #1200, 395, 396]. Development


115

experts generally agree that the dependent economic development, poor urban

management, and high population growth rates of low income economies have made

them more susceptible to urbanization-induced unemployment and poverty compared to

their middle and high income counterparts [397-400].

The Ghanaian national economy is largely dualistic in nature, i.e. a couple-

existence of a modern, urban, capitalist sector geared toward capital-intensive, large-scale

production and a traditional, rural, subsistence sector geared toward labour-intensive,

small-scale production [399, 400]. As would be expected, the urban economies are also

dualistic in character and are often decomposed into a formal and an informal sector

economies [282, 399-402]. In the informal sector, the self-employed are engaged in a

remarkable array of activities, ranging from hawking, street vending, letter writing, knife

sharpening, and junk collecting to selling fireworks, engaging in prostitution, drug

peddling, and snake charming [399, 400, 403, 404]. Other groups include mechanics,

carpenters, small-scale artisans, barbers, apprentices, and personal servants [327, 399,

400, 404]. This has meant that the informal sector residents have highly unstable and

variable incomes [402, 405]. The low and often irregular incomes have strong

implications on household expenditures on healthcare which need state intervention to

remove systemic financial barriers to accessing healthcare, especially by the poorest of

urban residents [402, 404-407].

4.3.2 Mortality outcomes across socioeconomic quintiles

The relationship between area-based SES and all-age urban malaria as well as diarrhoea

mortality in Accra has been presented. Table 4.2 gives the mean of the fraction of cluster


116

level mortality due to a specific cause mortality and relative mortality (RM) for all-age

malaria and diarrhoea across SE-quintiles. While there was strong evidence of an overall

difference in the all-age fraction of deaths due to malaria across the socioeconomic

quintiles (p < 0.001), no such evidence of an overall difference in all-age fraction of

deaths due to diarrhoea was observed across the SE-quintiles (p = 0.288). Additionally,

inter-quintile analyses revealed that while there was no evidence of a difference in all-age

malaria mortality between the middle class and the lower middle class (Mean = 0.0508,

95%CI: 0.035 0.067, p = 0.087), the upper middle class and the high class (Mean =

0.026, 95%CI: 0.013 0.039 p = 0.73), a very strong evidence of a difference was

observed between the middle class and the upper middle class (Mean = 0.059, 95%CI:

0.049 0.069, p = 0.006) and somewhat strong evidence of a difference between the

poorest class and the lower middle class (Mean = 0.060, 95%CI: 0.043 0.077, p =

0.018). On the contrary, no evidence of a difference at all in diarrhoea mortality was

observed between any of the socioeconomic quintiles i.e. the poorest class and the lower

middle class (Mean = 0.041, 95%CI: 0.028 0.054, p = 0.122), the lower middle class and

middle class (Mean = 0.037, 95%CI: 0.024 0.050, p = 0.262), the middle class and the

upper middle class (Mean = 0.041, 95%CI: 0.033 0.050, p = 0.060) and the upper middle

class and the highest class (Mean = 0.063, 95%CI: 0.015 0.112, p = 0.156) (Table 4.2).

Generally speaking on the strength of inter-quintile RMs and the inter-quintile mortality

differences for urban malaria, the first three lower quintiles did not differ very much and

could conveniently be grouped together as one distinct category and the upper middle

class and high class merging as another distinct category (Table 4.2, column 5).


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Table 4.2: Socioeconomics and malaria/diarrhoea mortality inequality Health Variable (Mortality)

SES Quintiles Mean PMRfd 95%CI RM p-value

Richest Class 0.0298 0.0104 0.0492 1.000 - Upper Middle Class 0.0261 0.0133 0.0389 0.876 0.725 Middle Class 0.0592 0.0492 0.0692 1.987 0.006 Lower Middle Class 0.0508 0.0347 0.0669 1.705 0.087

Malaria

Poorest Class 0.0599 0.0431 0.0766 2.010 0.018

Richest Class 0.0271 0.0138 0.0404 1.000 - Upper Middle Class 0.0634 0.0152 0.1116 2.339 0.156 Middle Class 0.0411 0.0328 0.0494 1.517 0.060 Lower Middle Class 0.0370 0.0239 0.0501 1.365 0.262

Diarrhoea

Poorest Class 0.0409 0.0277 0.0541 1.509 0.122 However, while the risk of dying from malaria among members of the poorest class was

approximately 2 times (RM = 2.01) that of members of the high class, the risk of dying

from diarrhoea was approximately 1.5 times (RM = 1.51) in the poorest class compared

to the high class. Another striking observation was that while members of the upper

middle class stood a lower risk of dying from malaria (RM = 0.88) compared to their

counterparts in the high class, they were approximately 2 times more likely to die from

diarrhoea compared to their high class counterparts (RM = 2.34).

4.3.3 Association between mortalities and the area-based measures of SES

As the interest in this analysis was not only to assess whether there was a difference in

malaria/diarrhoea mortality levels across the socioeconomic classes, but to investigate the

relationship between area-based socioeconomic measures and urban malaria/diarrhoea

mortality, we conducted further bi-variate and multi-variate analyses.

The general observation in the case for malaria mortality was that, while only two (the

proportion of residents with vocational, technical and commercial education and the

proportion of electricity, gas and water supply sector workforce) of the area-based

measures of SES showed evidence of association at the bi-variate level, four (the

proportion of residents with vocational, technical and commercial education, the


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proportion of electricity, gas and water supply sector workforce, the proportion of

education sector workforce and the proportion of residents with tertiary education)

variables showed strong evidence of association at multi-variate level, controlling for all

other measures of SES (Table 4.3).

Table 4.3 Association between area-base SES and the fraction of deaths due to malaria and diarrhoea Variable SES Variable Coefficienta 95% CIb p-value

Basis (intercept)c 0.10 0.06 0.13 <0.001 Proportion of residents with vocational, technical and commercial education

-0.49 -0.81 -0.18 0.003

Proportion of electricity, gas and water supply sector workforce

-2.27 -3.68 -0.86 0.002

Proportion of education sector workforce 0.95 0.47 1.43 <0.001

Fraction of deaths due to malaria

Proportion of residents with tertiary education

-0.42 -0.10 -0.24 <0.001

Fraction of deaths due to diarrhoead

Basis (intercept)e 0.04 0.03 0.05 < 0.001

a, Unit increase of the disease-specific fraction of deaths per unit increase of the SES variable b, Confidence interval c, Basic fraction of deaths due to malaria d, None of the area-based SES variables showed evidence of association with the fraction of deaths due to diarrhoea e, Basic fraction of deaths due to diarrhoea Table 4.3 shows the association between the area-based socioeconomic measures and the

cluster level fraction of deaths due to malaria and diarrhoea as obtained from multiple

linear regression analyses. In this analysis, we conducted a stepwise reverse removal of

variables in order to detect and deal with variables that showed strong collinearity (by

evaluating the VIF) in the regression model. As shown in Table 4.3, of the 39 area-based

socioeconomic variables, only 4 variables emerged to be strongly associated with the

cluster level fraction of deaths due to malaria. In a sharp contrast, there was no evidence

of association between the area-based measures of SES and the cluster level fraction of

deaths due to diarrhoea at both bi-variate and multi-variate levels.


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Figure 4.10: Association between the proportion of residents with vocational, technical and commercial education and malaria mortality

Fig. 4.10 is a graphical illustration of the relationship between the proportion of residents

with vocational, technical and commercial education and the cluster level fraction of

deaths due to malaria, which showed a strong evidence of association. It was noted that a

unit increase in the proportion of residents at the level of vocational, technical and

commercial education led to a significant decrease (coefficient = -0.45; 95%CI: -0.75 -

0.15, p = 0.004) in the cluster level fraction of deaths due to malaria with 14 % of the

variation (R2 = 0.14) explained by the proportion of residents at the level of vocational,

technical and commercial education at bivariate level. The strength of association

observed between the proportion of residents at the level of vocational, technical and

commercial education and the fraction of deaths due malaria was even much stronger

(coefficient = -0.49: 95%CI: -0.81 -0.18, p = 0.003) at multivariate level. On the contrary,

while no evidence of association (coefficient. = 0.07; 95%CI: -1.90 1.91, p = 0.994) was

observed between the fraction of deaths due to malaria and the proportion of electricity,


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gas and water supply sector workforce at bivariate level (Fig. 4.11), at multivariate level

however, a very strong evidence of association (coefficient = -2.27; 95%CI: -3.68 -0.86,

p = 0.002) was observed between the two variables.

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Figure 4.11: Variation of the proportion of electricity, gas and water supply

sector workforce with malaria mortality


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Figure 4.12: Variation of the proportion of education sector workforce

with malaria mortality

Furthermore, whereas in bivariate analysis, no evidence of association (coefficient = -

0.06; 95%CI: -0.21 0.09, p = 0.423) was observed between the fraction of deaths due to

malaria and the proportion of education sector workforce (Fig. 4.12), a very strong

evidence of association was observed between the two variables at multivariate level,

with a unit increase in the proportion of education sector workforce leading to a

corresponding increase in the fraction of deaths due to malaria at cluster level

(coefficient. = 0.95; 95%CI: 0.47 1.43; p < 0.001).

Lastly, an inverse relationship was observed between the fraction of cluster level deaths

due to malaria and the proportion of urban population with tertiary education. Although a

very strong evidence of association was observed between the proportion of urban

population with tertiary education and urban malaria mortality (coefficient. = -0.42;

95%CI: -0.60 -0.24; p < 0.001) at multivariate level, there was no evidence of association


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(coefficient = -0.05; 95%CI: -0.11 0.006, p = 0.076) between the two parameters at

bivariate level (Fig. 4.13).

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Figure 4.13: Association between the proportion of residents with tertiary education and malaria mortality

4.4 Neighbourhood urban environmental conditions and health (urban mortalities)

The mere lack of sanitation, poor infrastructure and deplorable living conditions in

rapidly urbanizing areas are believed to increase diarrhoea risk [38, 63, 320, 326, 408-

410]. The effectiveness of a city-wide sanitation intervention on diarrhoea in large urban

centres in low income countries has recently been demonstrated [408-410]. This section

presents the results of the analysis of the influence of neighbourhood environmental

conditions on malaria and diarrhoea mortalities.

4.4.1 Mortality differentials across urban environmental zones

Life within urban spaces is a characteristic feature of modern human ecology as the

global number of cities has increased exponentially and expanded rapidly over the past

century [38, 134, 320, 408]. While cities are seen as sources of creativity and technology,


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often conceived as the engines for economic growth, they are also in many cases

especially in low income economies centers of pervasive poverty, inequality, and health

hazards from environmental agents [38, 134, 320]. In an analysis to assess the influence

of neighbourhood urban environmental quality on the fraction of deaths due to malaria

and diarrhea, we present the means of the fraction of deaths (mean fraction) and their

respective relative mortalities (RMs) in scenarios of differing urban environmental

quality conditions in a rapidly urbanizing area in a low income economy.

Table 4.4: Malaria mortality in different urban environmental zones Urban Environmental Variable Zonation RM Mean

fraction p-value 95%CI

Extremely Deteriorated 1.755 0.050 0.061 0.031 0.070 Moderately Deteriorated 1.769 0.051 0.018 0.041 0.059

Total waste generation in relation to cluster population

Least Deteriorated 1.000 0.029 - 0.011 0.045


Water supply and sanitation facilities



Hygiene facilities


Extreme Slum 2.349 0.066 0.004 0.052 0.080 Moderate Slum 1.794 0.050 0.112 0.040 0.061

Housing structure & form, construction material type and arrangement Non Slum (Well built) 1.000 0.028 - 0.019 0.037

Table 4.4 compares the fraction of cluster level deaths due to malaria across

environmental zones defined on basis of four sub-components of environmental quality,

namely a) total waste generated in relation to population (per capita generation), b) water

supply and sanitation, c) hygiene facilities/conditions, and d) housing structure, form and

construction material type. On the basis of the “population and waste generation” sub-

component, there was strong evidence of a difference (p = 0.018) in the risk of urban

malaria mortality between the least deteriorated zone (mean fraction = 0.029, 95%CI:

0.011 - 0.045) and the moderately deteriorated zone (mean fraction = 0.051, 95%CI: 0.04

- 0.059) with a relative mortality [RM] = 1.77 between the two zones. However, there


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was no evidence of a difference (p = 0.061) in the risk of malaria mortality between

moderately deteriorated zone and extremely deteriorated (mean fraction = 0.50, 95%CI:

0.031 - 0.070) zone. Regarding “water supply and sanitation”, a strong evidence of a

difference in the risk of malaria mortality was observed both between the least

deteriorated zone (p = 0.004; mean fraction = 0.024; 95%CI: 0.012 - 0.036) and the

moderately deteriorated zone (mean fraction = 0.049, 95%CI: 0.040 - 0.058) on the one

hand and between the extremely deteriorated zone (mean fraction = 0.054, p = 0.007,

95% CI: 0.035 - 0.072) and the moderately deteriorated zone on the other hand. The

relative risk death due to malaria in the moderately deteriorated (RM = 2.041) and

extremely deteriorated (RM = 2.221) zones compared to the least deteriorated zone was

approximately the same. Whereas a strong evidence of a difference (p = 0.012) in malaria

mortality was observed between the extremely deteriorated (mean fraction = 0.024,

95%CI: 0.011 - 0.038) and the moderately deteriorated (mean fraction = 0.048, 95%CI:

0.039 - 0.058) zones for “hygiene facilities/conditions”, no evidence of a difference (p =

0.690) was observed between the moderately deteriorated and the least deteriorated zones

(mean fraction = 0.052, 95%CI: 0.040 - 0.062). The risk of death due to malaria in the

zone of moderately deteriorated hygiene conditions (RM = 0.931) and in the zone of

extremely deteriorated hygiene conditions (RM = 0.470) was remarkably different

although lower in both situations compared to that in the zone of the least deteriorated

hygiene conditions. The “housing structure and form, construction materials and living

arrangement” component of urban environment was classified into three categories,

namely, 1) “extreme slum”, 2) “moderate slum” and 3) “non slum” conditions. While we

observed a strong evidence of malaria mortality (p = 0.004) between the zone of


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moderate slum conditions (mean fraction = 0.050, 95%CI: 0.040 - 0.061) and the zone of

extreme slum conditions (mean fraction = 0.066, 95%CI: 0.052 - 0.080), no evidence of

mortality difference (p = 0.112) was observed between the zone of least slum conditions

(mean fraction = 0.028, 95%CI: 0.019 - 0.037) and the zone of moderate slum conditions

(Table 4.4).

Table 4.5: Diarrhea mortality in different urban environmental zones Urban Environmental Variable Zonation RM Mean

fraction p-value 95%CI


Population, water, housing and waste generation



Water supply and sanitation facilities



Hygiene facilities


Extreme Slum 1.349 0.060 0.035 0.027 0.094 Moderate Slum 0.700 0.031 0.035 0.024 0.039

Housing construction material type and arrangement

Least Slum (Well built) 1.000 0.045 - 0.038 0.052

Table 4.5 summarizes urban diarrhea mortality burden in the different zones. In contrast

to the mortality pattern observed for malaria, the evidence of diarrhea mortality

difference across the different spatially distinct environmental zones was not as striking.

While there was moderate evidence of diarrhea mortality difference (p = 0.035) across

the 3 slum grades, there was no evidence of diarrhea mortality differential in the other

components of environmental quality except for a moderate evidence of difference (p =

0.036) between moderately deteriorated (mean fraction = 0.035, 95%CI: 0.029 - 0.042)

and extremely deteriorated (mean fraction = 0.067, 95%CI: 0.016 - 0.119) zones of

hygiene conditions (Table 4.5).


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4.4.2 Association between urban environmental conditions and health

The environmental quality and health analysis focuses on the relationship between

malaria and diarrhoea mortality on the one hand and the neighbourhood urban

environmental quality conditions on the other hand using bi-variate regression analyses. 0

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0 2 4 6 8Residential Water Supply

Figure 4.14: Variation of malaria mortality with the number of residential water supply sources

Figure 4.14 shows the association between the number of water supply sources per house

and malaria mortality. There was a very strong evidence (p = 0.007; 95%CI: 0.0020

0.0129) of association between malaria mortality and the number of water supply sources

depended upon by homes.


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Figure 4.15: Variation of malaria mortality with total waste generation

In an assessment of the relationship between solid waste generation and malaria

mortality, it was observed that a unit increase in solid generation resulted in an increase

in the fraction of deaths due to malaria (Fig. 4.15), with the amount of solid waste

generated accounting for approximately 10 percent of the observed variation in malaria

mortality (R2 = 0.1095). There was strong evidence of a strong association (p = 0.005;

95%CI: 3.60e-07 1.96e-06) between solid waste generation and malaria mortality.

However, the relationship between malaria mortality and per capita waste generation (a

quantity that measures the amount of solid generated per person per day and the basis for

determination of total waste generation) showed only weak evidence of association (p =

0.050; 95%CI: 0.0001 0.1227).

Fig. 4.16 shows the relationship between solid waste collection rate (proportion of the

amount of collected out of the total amount generated) and malaria mortality. A unit

increase in waste collection rate resulted in a unit decrease in malaria mortality.


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Figure 4.16: Variation of malaria mortality with the

proportion of solid waste collected

There was a very strong evidence of association (p = 0.002; 95%CI: -0.1203 -0.0294)

between waste collection rate and the fraction of cluster level deaths attributed to malaria.

The amount of variation in urban malaria mortality which was explained by the variation

in waste collection rate was 14 percent (R2 = 0.1370).

Similarly, an increase in the proportion of households connected to the central sewer

system led to a decrease in urban malaria mortality. The amount of variation in urban

malaria mortality that was explained by the variation in the proportion of households

connected to the central sewer system was 15 percent (R2 = 0.1465). A very strong

evidence of association (p = 0.001; 95%CI: -0.1311 -0.0344) was observed between the

proportion of households connected to the sewer system and urban malaria mortality.

Fig 4.17 shows the relationship between the proportion of households connected to the

Water Closet (WC) and urban malaria mortality. It was observed that an increase in the

proportion of households connected to the WC resulted in a decrease in urban malaria


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mortality and the amount of variation in urban malaria mortality explained by the

variation in the proportion of households connected to the WC was approximately 20

percent (R2 = 0.1966).

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Fitted values 95% CIFitted values Malaria Mortality

Figure 4.17: Variation of malaria mortality with the proportion of

households connected to the WC

There was a very strong evidence of association (p < 0.001; 95%CI: -0.1290 -0.0443)

between urban malaria mortality and the proportion of households connected to WC. This

perhaps meant that WC which was a component of environmental sanitation service was

also a strong predictor of urban malaria mortality.

It was also desired to assess the influence of hygiene facilities such as public toilets,

shared-bath facility, own-bath facility, public bath, shared cubicle, whether hygiene

facility was in household or outside household, etc., on urban malaria mortality. Fig. 4.18

shows the relationship between the proportion of households using public toilets and

urban malaria mortality.


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Figure 4.18: Variation of malaria mortality with the proportion of households

using public toilets

Although the use of public toilets has not been previously reported in literature to

influence malaria transmission, it was observed that an increase in the proportion of

households using public toilets resulted in an increase in urban malaria mortality in this

study. The amount of variation in urban malaria mortality that was explained by a unit

increase in the proportion of households depending on public toilets as hygiene facilities

was approximately 15 percent (R2 = 0.1498). A very strong evidence of association was

observed between the proportion of urban households using public toilets and urban

malaria mortality (p = 0.001; 95%CI: 0.0422 0.1571).

Similarly, when an assessment of the relationship between the proportion of households

without hygiene facilities and urban malaria mortality was conducted, it was established

that an increase in the proportion of households without hygiene resulted in a

commensurate increase in urban malaria mortality. The amount of variation in urban

malaria mortality attributable to a unit increase in the proportion of households without


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hygiene facilities was approximately 8 percent (R2 = 0.0817) with a strong evidence of

association (p = 0.016; 95%CI: -0.3498 -0.0365) between urban malaria mortality and the

proportion of households without hygiene facilities. In both bi-variate and multi-variate

analyses, the response of urban malaria mortality to the different hygiene variables varied

widely in direction, strength, degree and levels of association. For example, while a unit

increase in the proportion of households with own-bath facilities and those with shared-

bath facilities resulted in a decrease in urban malaria mortality, the degree of urban

mortality variability explained was approximately 13 percent (R2 = 0.1249) for

households with own-bath facilities and 7 percent (R2 = 0.0701) for households with

shared-bath facilities. Moreover, while the strength of the evidence of association

between urban malaria mortality and the proportion of households with own-bath

facilities was very strong (p = 0.003; 95%CI: -0.0807 -0.0177), only moderate evidence

of association (p = 0.027; 95%CI: -0.1325 -0.0084) was observed between urban

malaria mortality and the proportion of households with shared-bath facilities.

Fig. 4.19 and Fig. 4.20 compare the differences in the levels and strengths of association

between urban malaria mortality and the proportion of households using public bath

facilities on one hand and the association between the proportion of households using

shared-cubicle baths and urban malaria mortality on the other hand.


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households using public baths

In both instances, a unit increase in the explanatory variable (i.e. the proportion of

households using public baths and the proportion of households using shared cubicle-

baths) resulted in a corresponding increase in urban malaria mortality. However, while

the amount variation in urban malaria mortality explained by the proportion of

households using public bath facilities was 8 percent (R2 = 0.0817), the amount of

variability in urban malaria mortality that was explained by the proportion of households

using shared-cubicle baths was 12 percent (R2 = 0.1200). There was a very strong

evidence of association (p = 0.003; 95%CI: 0.0561 0.2694) between urban malaria

mortality and the proportion of households using shared-cubicle baths. Although the

association between urban malaria mortality and the proportion of households using

public baths was equally strong (p = 0.016; 95%CI: 0.0327 0.3139), it was slightly

weaker than that between urban malaria mortality and the proportion of households using

shared-cubicle baths.


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Figure 4.20: Variation of malaria mortality with the proportion of households using shared-cubicle baths

Another aspect of neighbourhood urban environmental quality that was assessed to

determine its influence on urban malaria mortality was housing structure, housing

characteristics and living arrangements. The city of Accra has been poorly planned;

composed largely of light density buildings thrown in a sprawling fashion. The form and

structure of built-up areas represent a diversity of environmental archetypes of different

outlook and varying levels of neighbourhood environmental quality conditions. Indeed,

the influence of different building and structure types on infectious disease transmission,

especially urban malaria and diarrhoea mortalities in a rapidly urbanizing low-income

economy has not been previously established. In this analysis therefore, our interest did

not only focus on how the specific neighbourhood environmental quality issues

influenced infectious disease mortalities, but also how the different structural designs and

structure types were linked to urban mortalities. Classified broadly under the heading


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“housing construction material type and living arrangements” both bi-variate and

multiple linear regression procedures were conducted on several variables related to the

structure and form of buildings, construction material type, living arrangements i.e.

number of persons per unit, etc. Under this category, of a total of fifteen variables (e.g.

proportion of standalone structures, proportion of semi-detached structures, proportion

flats/apartment structures, proportion of compound structures, proportion of huts,

proportion of mud-brick structures, proportion concrete-brick structures, proportion of

bamboo structures, etc.) were used in both bi-variate and multiple regression analyses.

Out of the several variables, only three showed significant association with urban malaria

mortality. The three variables that showed significant (p ≤ 0.05) association with urban

malaria mortality were: 1) the proportion of standalone structures, 2) the proportion of

flats/apartment buildings and 3) the proportion of compound structures which housed

multiple households.

Fig. 4.21 shows the relationship between the proportion of standalone structures and

urban malaria mortality. In this investigation, it was observed that a unit increase in the

proportion of standalone structures led to a large decline in the fraction of deaths due to

urban malaria, with the amount of variation in urban mortality that was explained by a

unit increase in the proportion of standalone structures being as large as 21 percent (R2 =

0.2051). There was a very strong evidence of association between the proportion of

standalone building structures and urban malaria mortality (p < 0.001; 95%CI:-0.1742 -

0.0618).


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standalone households

In a penultimate assessment, a unit increase in the proportion of flats/apartment structures

resulted in a decrease in the fraction of deaths due to urban malaria. Approximately 7

percent (R2 = 0.0730) of the variation in the fraction of deaths due to urban malaria was

explained by a unit increase in the proportion of flats/apartment building structures.

Nonetheless, there was a moderate evidence of association (p = 0.024; 95%CI:-0.1833 -

0.0136) between the proportion of flats/apartment structures and urban malaria mortality

compared to the very strong evidence of association observed between urban malaria

mortality and the proportion of standalone building structures.


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Figure 4.22: Variation of malaria mortality with the proportion of compound households

In a more or less extended analysis, the relationship between the proportion of compound

structures and urban malaria mortality was assessed. Fig. 4.22 shows the relationship

between the proportion of compound building structures and urban malaria mortality and

it was observed that a unit increase in the proportion of compound structures resulted in a

corresponding increase in urban malaria mortality. A little over 21 (R2 = 0.2126) percent

of the variation in urban malaria mortality was explained by a unit increase in the

proportion of compound building structures. The observed evidence of association

between the proportion of compound building structures and urban malaria mortality was

very strong (p < 0.001; 95%CI: 0.0381 0.1045).

4.5 Mapping urban malaria/diarrhoea mortality

Using the PMRfd, we explored spatial relationships and developed mortality distribution

maps separately for malaria and diarrhoeal deaths in Accra.


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Figure 4.23: The patterns of the observed urban malaria and diarrhoea deaths in Accra

Figure 4.23 shows the spatial distribution of the observed malaria and diarrhoeal deaths

in Accra. The spatial patterns displayed represent cluster level fraction of deaths due to

malaria and diarrhoea. The fraction of deaths due to each cause ranged from 0 to 0.12 for

urban malaria and from 0 to 0.33 for urban diarrhoea mortality. Whereas the distribution

of diarrhoea mortality was heterogeneous with high mortality ratios widely scattered

around the Korle Lagoon, malaria mortalities tended to be more homogenous with only a

few hot spots in Accra West, distributed roughly radial to the lagoon. By visual

inspection, the observed patterns of mortality distribution showed no strong clustering for

either urban malaria or diarrhoea mortality (Fig. 4.23). On account of the fact that no

evidence of clustering was detected by visual analysis subsequent autocorrelation

analyses were conducted, but first on a global scale using global Moran’s I and then on a

local scale using the Local Indicator Spatial Autocorrelation (LISA) analysis.

4.5.1 Spatial autocorrelation – Global Moran’s I

The preliminary spatial analyses on the basis of pictorial distributions showed no

evidence of spatial clustering for both malaria and diarrhoea mortalities. For that reason,

we conducted further spatial analysis, applying different neighbourhood weights (Inverse


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Distance, Inverse Distance squared, Queen and Rook Contiguity and k Nearest

Neighbours) to ensure a coverage of all possible and plausible neighbourhood

relationships.

Table 4.6 shows the results of spatial autocorrelation analysis of malaria and diarrhoea

mortalities, allowing for different simulations of spatial relationships to be conducted

among the clusters at global (city-wide) level (i.e. at inverse distance, inverse distance

squared and polygon contiguity). Both malaria and diarrhoea deaths did not show any

evidence of clustering at the spatial relationships considered (Table 4.6) although both

diseases showed high level of randomness (Moran’s I = 0.0123, p = 0.68 for malaria and

Moran’s I = -0.0647, p = 0.33 for diarrhoea) when inverse distance was considered.

Similar pattern of random distribution was observed when inverse distance squared was

considered (i.e. Moran’s I = -0.0432, p = 0.85 for the distribution of malaria deaths and

Moran’s I = -0.0605, p = 0.69 for the distribution of diarrhoea deaths).

Table 4.6: Results of Global spatial autocorrelation analysis Spatial Relation Global Moran’s I Statistic Malaria Diarrhoea

Moran's Index 0.0123 -0.0647 Expected Index -0.0145 -0.0145 Variance 0.0042 0.0027 Z Score 0.4126 -0.9695

Inverse distance

p-value 0.68 0.33 Moran's Index -0.0432 -0.0605 Expected Index -0.0145 -0.0145 Variance 0.0216 0.0135 Z Score -0.1952 -0.3951

Inverse distance squared

p-value 0.85 0.69 Moran's Index 0.0320 -0.0276 Expected Index -0.0145 -0.0145 Variance 0.0053 0.0032 Z Score 0.6404 -0.2309

Polygon contiguity first order

p-value 0.52 0.82


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We considered the polygon contiguity first order as a third and final simulation of spatial

relationships among the clusters. The results from the nearest neighbour analyses also

showed strong evidence of randomness in the distribution of both malaria (Moran’s I =

0.032, p = 0.52) and diarrhoea (Moran’s I = -0.0276, p = 0.82) deaths (Table 4.6). In

consideration of nearest neighbour analysis, no evidence of clustering was observed

except for six and seven nearest neighbours (6NN and 7NN) ordering where the closest to

clustering was a 5-10% likelihood that the clustered pattern was by chance. While eight

and ten nearest neighbours (8NN and 10NN) ordering were somewhat clustered, the

pattern was flagged to be due to random chance (i.e. Moran’s I = 0.0505, p = 0.22 for

8NN and Moran’s I = 0.0500, p = 0.16). Other nearest neighbour ordering i.e. 4NN

(Moran’s I = 0.0288, p = 0.58), 5NN (Moran’s I = 0.0543, p = 0.31) and 6NN (Moran’s I

= 0.0898, p = 0.095) that were considered did not show even subtle evidence of

clustering. No evidence of significant (p ≥ 0.05) global clustering was detected for both

malaria and diarrhoea mortalities in any of the neighbourhood scenarios considered at the

global (city-wide) level.

4.5.2 Spatial autocorrelation – Local Indicators of Spatial Association (LISA)

This section presents the results of further spatial analyses using univariate Local

Indicators of Spatial Association (LISA) which were conducted to assess the local scale

of clustering for both malaria and diarrhoea mortalities. In each case, a first order queen

contiguity was utilized as neighbourhood condition. In this approach the mean Local

Moran value was 0.042 for malaria and -0.041 for diarrhoea respectively. The range of

the Local Moran values was from -1.27 to 1.27 for malaria with a standard deviation of


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0.39 and from -2.59 to 0.60 for diarrhoea with a standard deviation of 0.35. In order to

increase the power of cluster detection, spatial sampling in the order of 9,999

permutations was conducted.

The choropleths occupying the left half of both Figures 4.24 and 4.25 display those

locations with a significant Local Moran statistic classified by type of spatial correlation.

HH identifies those clusters with high malaria mortality lying near clusters with high

malaria mortality, HL identifies those clusters with high malaria mortality lying near

clusters with low malaria mortality and LL identifies those clusters with low malaria

mortality lying near clusters with low malaria mortality. Thus, HH and LL clusters

indicated a clustering of similar values, whereas the HL and LH clusters gave an

indication of spatial outliers. Areas with plain white backgrounds were locations that

showed no significant Local Moran statistic (p ≥ 0.05). The choropleths occupying the

right half of both figures show the level of statistical significance (i.e. p-values or the

probability of the distribution). The locations with a significant Local Moran statistic

were shown in different coloration, depending on the significance level. In this study,

four significance levels were shown as p = 0.05, p = 0.01, p = 0.001, p = 0.0001 with

dark green shade being the strongest extreme of p = 0.0001 and red colouration being the

lowest extreme of p = 0.05.


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Figure 4.24: Map of Accra showing spatial clustering of malaria mortality

Figure 4.24, shows a spatial choropleth for malaria mortality distribution displaying those

locations with a significant Local Moran statistic classified by type of spatial correlation:

bright red for the high-high (HH) association, bright blue for low-low (LL), white for no

clustering, and brick red for high-low (HL). One location with strong evidence (p <

0.0001) of clustering i.e. one hot spot (high-high location) near the Korle Lagoon, three

cold spots (low-low locations), two of which were located in south-central Accra and one

in the north-eastern quadrant of Accra were observed. In addition, there were two outliers

in south-central Accra. A total of nine significant (p ≤ 0.05) clusters were observed in the

study area for malaria mortality. While the four HH clusters were located adjacent to one

another and bordering the eastern shoreline of the Korle Lagoon (p-range from < 0.001 to

0.05), two of the three LL clusters were observed in southern central Accra (0.01 < p <

0.05), one adjacent to the coast, the other one slightly north-west of the Korle Lagoon.

The third LL cluster (0.01 < p < 0.025) was located at the north eastern outskirt of the

city. Furthermore, two HL clusters (0.001 < p < 0.01) were observed in south central

Accra and in close proximity of the coastline.

The choropleth for diarrhoea mortality distribution (Fig. 4.25) displays four cold spots

(low-low locations) showing slightly lower evidence (p = 0.05) of clustering compared to


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malaria mortality and two outliers lying opposite sides of one of the cold spots. Although

the choropleths for both malaria and diarrhoea distributions showed clustering at the local

level, that for malaria was slightly stronger compared to for the distribution of diarrhoea

mortality. Unlike the case for malaria mortality, LISA analysis of the fraction of deaths

due to diarrhoea showed evidence of six significant (p ≤ 0.05) but weakly clustered

distribution within the study area. Two of the four LL clusters which showed very strong

evidence (0.01 < p < 0.05) of cold-spots were detected in the eastern margins of the city

while the other two which were much smaller in size were observed in the west. There

were also two HL clusters (0.025 < p < 0.05) lying to the mid-eastern stretches of the

city, sandwiching one of the large LL clusters (Fig. 4.25).

Figure 4.25: Map of Accra showing spatial clustering of diarrhoea mortality

4.6 Vulnerability and Excess Mortality assessment

The results of the stepwise removal multiple regression modelling showed no significant

association between diarrhoea mortality and the neighbourhood urban environmental and

socioeconomic and therefore excess mortality maps were created for malaria mortality

only. On the excess mortality maps, while values less than one (greenish colours)

indicated locations with fewer than expected events, values larger than one (reddish


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colours) indicated locations with more than the expected events. The deep red and deep

green colourations represented the upper and lower extreme values respectively. Census

clusters showing excess rates larger than one had a higher risk of mortality than the

average risk in the whole study area for the particular explanatory variable used.

Figure 4.26: Excess malaria mortality in relation to occupation of residents

The two choropleths in Fig. 4.26 show excess malaria mortality applying the occupation

of residents as potential risk factor in Accra. The choropleth to the right shows retail

sector workforce as a potential risk factor for excess malaria mortality while that to the

left presents administrative sector workforce as a potential risk factor for the same

condition. As shown by the intensities of reddish colouration of the choropleth to the left,

administrative sector workforce tended to contribute more to the observed excess malaria

mortality than retail sector workforce i.e. locations with excess malaria mortality tended

to occur in the western part of Accra with relatively higher proportion of administrative

sector workforce (Fig. 4.26). In contrast, the choropleth to the right showed that lower

than expected number of malaria deaths tended to occur in the western part of Accra,

coinciding with locations which had lower proportion of retail sector workforce.

Proportion of retail sector workforce

Proportion of administration sector workforce


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Figure 4.27: Excess malaria mortality in relation to education level of residents

Fig. 4.27 displays the excess malaria mortality with educational status as a potential risk

factor. As can be gleaned from the choropleth, only one small area close to the Korle

Lagoon had excess mortality far above what was expected. The western part of Accra

was observed to have elevated mortality although less pronounced compared to the case

for which administrative sector workforce was considered as a risk factor and much the

same pattern compared to the case for which retail sector workforce was considered as a

risk factor.

Figure 4.28: Excess malaria mortality in relation to hygiene, water and sanitation

Proportion of residents with vocation/technical

Proportion of households with pipe outside

Proportion of households with Water Closet (WC)


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The choropleths in Fig. 4.28 show excess malaria mortality considering the proportions

of households with pipe-borne water supply source outside and those connected to WC as

the underlying risk factors. Locations with excess malaria mortality were consigned to

the western part of Accra when proportion of households connected to water closet (WC)

was imposed as the potential risk factor (Fig. 4.28, right half). On the other hand, when

we imposed the proportion of household with pipe outside as the potential risk factor, we

observed fewer locations with excess malaria mortality than in the case for which the

proportion of households with WC was imposed as the potential risk factor. In both cases

however, locations to the eastern parts of the city recorded fewer than expected malaria

mortality whiled several clusters in the western part of the city exhibited more than

expected number of malaria deaths.

Figure 4.29: Excess malaria mortality in relation to housing type

Figure 4.29 shows an excess mortality map considering housing type as the underlying

risk factor driving malaria mortality in Accra. Housing type was defined as the type of

built units, i.e. compound units, single units, construction material types, etc. The


146

choropleth represents the proportion of separate or stand-alone structure as the potential

risk factor for malaria mortality. Areas with more than expected malaria mortality and

those with less than expected mortality appeared to occur randomly, although clusters

with more than expected malaria mortality tended to be more common in the western part

of the city while those with lower than expected malaria mortality were more ubiquitous

in the eastern half of the city.

Chapter Five: Discussions

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The discussion section has been structured into several parts to reflect the different

aspects of the analysis conducted. In conformity with the analyses section, a discussion

on cluster demographics and health is presented first and which is followed by a

discussion on socioeconomics and environment analysis. A discussion on

socioeconomics and health was presented after which we present a discussion on

environment and health analysis. Finally, a discussion on cluster analysis and

geographically weighted regression concludes the section on discussion.

5.1 Cluster population, demographics and health inequalities

The study observed that malaria mortality in Accra was lowest among the under-1 year

olds and highest among members of group 1-4 years. This observation was consistent

with several studies reported in literature. Maternal environment/factors provide

protection against malaria until after 1year when children would presumably begin to lose

the immunity conferred by maternal factors [80, 411-413]. The consistency of the

findings in this study with those reported in literature is a confirmation that the summary

measure of mortality computed for the study from the routine data was a valid measure of

mortality. In the study, malaria mortality was shown to be low among under-1 year olds,

but increased to about 30 percent of all deaths among members of 1-4 years. This

observation was attributed to immune protection conferred by maternal environment.

Despite ample evidence in literature establishing that pregnant women were at increased

risk of malaria infection, newborns and infants were also reported to inherit some

maternal immunity against malaria during the first few months of life [80, 318, 380, 411].

This epidemiological paradox reflected the fact that the underlying interactions between


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the Plasmodium falciparum parasite, the mother, and the foetus were complex,

incompletely understood and still needed further scientific inquiry [412]. In the case of

pregnancy related vulnerability to malaria infection, the mechanisms that explain the

increased susceptibility were reportedly not yet fully understood. However, new insights

suggested that an initial accumulation of parasitized erythrocytes in the blood spaces of

the placenta was a key feature of maternal infection with P. falciparum [414]. It has been

reported that placental parasites expressed surface ligands and antigens that differed from

those of other P. falciparum variants, facilitating evasion of existing immunity, and

mediate adhesion to specific molecules, such as chondroitin sulfate A, in the placenta

[412-414]. The polymorphic and clonally variant P. falciparum erythrocyte membrane

protein 1, encoded by var genes, which reportedly binds to placental receptors in vitro,

was suspected to be the target of protective antibodies [385, 414-416]. A suggested end-

stage mechanistic path then appeared to be an intense infiltration of immune cells,

including macrophages, into the placental intervillous spaces, and the production of pro-

inflammatory cytokines often occurring in response to infection, and were reportedly

associated with low birth weight and maternal anaemia [283, 414, 417]. Expression of

alpha and beta chemokines have been ultimately reported to be responsible for the

initiation or facilitation of this cellular infiltration during placental malaria [385, 414,

415]. A characteristic feature of malaria infection in pregnancy has been reported to be its

strong association with maternal and foetal morbidity and mortality [411]. There have

been reports that the severity of the disease during pregnancy depends on the level of pre-

pregnancy acquired immunity against malaria, and the consequences of infection were

more severe in non-immune women [80, 411, 413]. For instance, in highly endemic


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areas, the frequency and severity of the infection have been reported to be higher in

primigravida and decreases with increasing parity [80, 412, 418]. However, in non-

immune women, the risk has been reportedly similar across the parity gradient and

malaria has been suggested to be an important direct cause of maternal mortality [80].

The second observation was that urban malaria mortality began to decline after age four,

suggesting an increased level of protection against malaria mortality with increasing age.

This perhaps meant that immunity against malaria probably starts to develop after age 4

years with malaria mortality beginning to decline sharply until age 24 (Figure 4.2). This

then follows prolonged age-dependent malaria mortality stabilization after 25th year on

until age 65 when age-dependent mortality levels began to increase probably due to

senescence or old-age. This observation has been consistent with findings from studies in

other regions in Africa [323, 381, 382, 419]. For example, in a study to examine malaria

distribution in 543 villages in central Ethiopia over a 4-year period, a decrease in malaria

incidence which fit a quadratic model was observed with increasing age. In addition, a

significant difference in the malaria incidence density ratio (IDRs) was detected in males

but not in females [380].

There is overwhelming evidence of health inequalities, especially inequalities/differences

in malaria cause-specific morbidity and mortality across age, space, time, seasons and

sub-population groups. Pregnant women and infants are reportedly particularly

susceptible to malaria cause-specific morbidity and mortality [80, 323, 380]. The risks of

morbidity and mortality associated with malaria have been reportedly characterized by

spatial and temporal variation across the known transmission zones [82, 380, 381].

However, no evidence of association was found between malaria mortality and sex. The


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observed malaria mortality did not differ between males and females. Although, previous

studies present conflicting findings on the differences in malaria susceptibility in males

and females, a general consensus has been that the case fatality rate remains fairly same

in both sexes [420, 421]. This is consistent with the findings from the present study

which observed no difference in malaria mortality between males and females.

On the other hand, the observed urban mortality was highest among the under-1 year olds

(Fig. 4.5). Unlike the pattern shown by malaria mortality, maternal factors did not appear

to confer any kind of protection against urban diarrhoea mortality for any age group. This

was evident from the high levels in urban diarrhoea mortality among members of under-1

year olds with a progressive decline across the age groups as individuals begin to develop

immunity with age. This observation has been consistent with findings from other studies

which established that maternal environments did not confer any protection against infant

diarrhoea [298, 422, 423]. There was no evidence of association between diarrhoea

mortality and sex since the observed diarrhoea mortality did not differ significantly

between males and females.

5.2 Urban socioeconomic conditions and urban environmental conditions

In this analysis, the association between area-based socioeconomic conditions and

neighbourhood urban environmental quality conditions was assessed. Often, studies

which sought to evaluate the influence of socioeconomic status on health inequalities

have neglected such important intermediate variables as the physical environmental

conditions (environmental media), which have direct influences on health outcomes. Poor

environmental quality provided condition for insect vector breeding and ultimately

influences vector-borne disease transmission (e.g. mosquito, an important agent for


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malaria transmission, common housefly as a mechanical vector for many microbial

diseases, including diarrhoea, enterohaemorrhagic fever, etc.).

Environmental burden (e.g. local sanitation) has been generally understood to be

heavier in poor communities and declined as communities became wealthier [78]. In

urban areas where consumption of goods and services per person was usually high,

residual deposition (e.g. waste production) was also high in those areas. In rural

communities, consumption of goods and services and waste production were generally

understood to be much lower per unit compared to urban areas. However, the high

consumption and high residual deposition (waste production) in the urban areas have not

been backed by equitable distribution of wealth thus leaving some of the urban

communities financially weak to be able to manage the waste produced. In this study, the

observed varied levels of influence of the area-based SES on spatial changes in the

quality of the neighbourhood urban environmental conditions were suggestive that the

area-based SES did not exert the same degree of influence on the quality of the

neighbourhood urban environmental conditions. While some of the area-based

socioeconomic variables were important in driving changes in the quality of some

neighbourhood urban environmental conditions, they did not show any perceived

influence on some other components of the neighbourhood urban environmental quality

conditions. For example, whereas education level did not show evidence of association

with total waste generated, per capita generation rate and waste collection rate, waste

deposition rate (proportion of wastes collected) was observed to be strongly associated

with residents’ educational attainment.


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However urban employment, urban unemployment, educational attainment, residents’

place of work and residents’ occupation have demonstrated high reliability as measures

of area-based SES. The nature of the associations observed between neighbourhood

urban environmental conditions on the one hand and urban employment and urban

unemployment on the other hand was consistent with previously reported studies [38,

355].

Although a unit increase in urban unemployment resulted in a marginal decrease in

per capita solid waste generation (regression coefficient = -0.566), there was no evidence

of association between urban unemployment and per capita solid waste generation (p =

0.09; 95%CI: -1.224 0.093). Nonetheless, a positive relationship was observed between

urban employment and per capita solid waste generation rate. In this case, once in both

instances no evidence of association was observed between the two area-based SES

measures and per capita solid waste generation rate, urban unemployment and urban

employment were probably not good predictors of waste generation. However, some

studies have observed association between per capita waste generation rate and income

levels (employment provides opportunities for earning incomes) [38, 354, 355, 424].

Additionally, whereas there was moderate evidence of association between urban

employment and the proportion of households connected to the central sewer system, a

substantially stronger evidence of association was observed between urban

unemployment and proportion of households engaged in non-standard practices of liquid

waste disposal. This probably meant that the implementation of Ghana’s poverty

reduction strategies (GPRS) without consideration to bridge urban unemployment gaps

could exacerbate the widening urban health inequalities [153, 154, 174, 181].


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5.3 Area-based versus individual level SES

Socioeconomic status measures the social circumstances in which an individual lives and

experiences. Such social circumstances at individual level have been measured by a wide

range of proxy indicators, namely income levels, expenditures, ownership of durable

personal assets, etc. These individual level measures of SES differ markedly from area-

based measures ones which by definition connote the social conditions of the area in

which an individual resides. Much as the individual level and area-based measures of

SES differ conceptually, the outcome of their use in assessing health inequalities among

different social groups also differ significantly. In a study to quantify the agreement

between area-based SES measures and SES assessed at the individual level, Demissie and

others observed a substantial discrepancy between area-based SES measures and SES

assessed at the individual level [425].

5.4 Area-based urban SES and health (urban mortalities)

In literature, studies which have explored the relationship between socioeconomic

conditions and health have varied widely in approach in relation to the nature and type of

the socioeconomic variables and the type of health outcomes investigated [98, 312, 426-

431]. While most documented studies have examined the role of individual level

socioeconomic conditions in determining the status of human health in a variety of

settings and across several health outcome measures, only few studies have explored the

relationship between area-based measures of socioeconomic conditions and these health

outcomes [61, 270, 432-438]. In particular, whereas the influence of social conditions on

chronic diseases and their mortalities and much lesser extent for infectious diseases and

their mortalities has been more widely studied, not very much has been investigated on


154

the influence of these condition on malaria and diarrhea mortalities [370, 439-444].

Although infectious diseases have been reported to exhibit significant spatial clustering;

especially malaria and diarrhoea, of the few studies reported in literature none has

explored the association between area-based measures of SES and malaria and diarrhoea

mortalities [62, 63, 438, 445-448]. Moreover, a number of living standard surveys

conducted in Ghana have reported a much worsened urban poverty as well as widened

income and socioeconomic inequalities particularly in urban areas during the period

between 1990 and 2004 [350, 375] and many of the studies have linked the increased

health inequalities to widening socioeconomic inequalities [152, 268, 326, 449-452].

5.4.1 Mortality outcomes across socioeconomic quintiles

In this study, an exploration of the relationship between several area-based measures of

SES and the fraction of deaths due to malaria and diarrhoea was conducted in Accra

using linear multiple regression analyses and other models. A remarkable observation

was an overall reduction in the inter-quintile mortality risk differentials for both malaria

and diarrhoea from the 1993 study which reported five times risk of dying from both

malaria and diarrhoea in the poorest class compared to that in the high class [30]. The

observation provided empirical evidence in demonstration of a substantial reduction in

malaria and diarrhoea mortality differentials across the socioeconomic quintiles from a

baseline study in the early 1990s. Despite the deepened socioeconomic inequality gaps

across the social classes in Accra, mortality inequality gaps had narrowed for both

malaria and diarrhoea, but more so for diarrhoea than for malaria mortality. A plausible

explanation for this observation was attributed to the implementation of a mix of health

policy over the period considered could perhaps have been effective in addressing any


155

associated mortality inequalities due to exacerbated urban poverty and wider gaps of

urban socioeconomic inequalities. The inter-quintile mortality differentials were smaller

in the case of diarrhoea mortality than in the case of malaria mortality, suggesting that

there was greater equalization in diarrhoea mortality inequality across quintiles than that

for malaria mortality compared to the situation in the early 1990s. This could be

explained by the fact that the health policy mix might have been more effective in

addressing inequality gaps for diarrhoea mortality compared to malaria mortality.

Alternatively, this could have also meant that the area-based measures of SES had no

conceivable linear relationship with the urban diarrhoea mortality at aggregate levels.

Additionally, it was observed that while many of the area-based measures of SES did not

show evidence of association with urban malaria at bivariate level, a reasonable number

showed very strong evidence of association with urban malaria mortality at multivariate

level, very much starkly in contrast to similar studies reported in literature [61, 436, 453].

This observation probably meant that some of the area-based measures of SES had

blurring effects on others for urban malaria mortality, which was suggestive that those

area-based measures of SES could have important implications for urban malaria

mortality at household level, but less likely so at community level. The observations also

suggested that the interaction of different environmental factors could be both synergistic

(i.e. reinforce one another) and antagonistic (i.e. blur the effect of each other) at

aggregate level.

Moreover, there were remarkable non-uniformities in the risk of mortality for both

malaria and diarrhoea among the different socioeconomic quintiles. For instance, the

lower middle class and the middle class had lower risk of urban malaria mortality


156

compared to the high class. However, the risk of urban diarrhoea mortality in the same

group was nearly four times higher than that in the high class. The highly irregular

pattern of inter-quintile mortality differentials for urban malaria and diarrhoea mortality

could be explained by the fact that perhaps socioeconomic factors alone were not

exclusively responsible for the inter-quintile mortality gaps. An opinion put forward in

relation to this observation was that other factors, such as lifestyle, differences in health

seeking behaviour, traditional beliefs, taboos, etc., might have accounted for the inter-

quintiles mortality anomalies observed for the middle class.

5.4.2 Association between urban mortalities area-based measures of SES

Of the large number of area-based measures of socioeconomic conditions explored, only

four showed strong evidence of association with urban malaria mortality. For instance

increase in the proportion of individuals with vocational, technical, commercial and

tertiary education led to a decrease risk of urban malaria mortality in the given area. This

could be due to the fact that urban areas with individuals in these categories were

probably more likely to take malaria preventive and treatment measures more seriously

compared to areas with high proportion of education sector workforce. This observation

was perhaps empirical evidence in support of the inclusion of these measures of SES as

potential risk factors for urban malaria mortality in future case-control studies aimed to

determine city-wide distribution of risks for urban malaria mortality at both household

and community levels in rapidly urbanizing settings. Finally, urban areas with high

proportion of electricity, gas and water supply sector workforce tended to be associated

with higher urban malaria mortality and perhaps served as evidence that those areas could

constitute important hotspots and zones of vulnerability to urban malaria mortality. This


157

in turn provides evidence in support of appropriate targeting of scarce resources in those

areas if urban malaria control programs were to be cost-effective.

5.5 Neighbourhood urban environmental conditions and health (urban mortalities)

Both solid and liquid wastes have been widely known to constitute important breeding

media for insect vectors including opportunity to create stagnant pools of water for

mosquito breeding. As a consequence, increased waste collection decreases habitat space

and capacity for the larvae of the mosquito which is the main vector of malaria parasites -

Plasmodium falciparum, P. vivax, P. ovale and P. malariae. This probably leads to a

decrease in malaria transmission and therefore a reduction in mortality due to malaria

infections. Moreover, non disposal of liquid waste could generally create poodles and

standing water conditions which would favour the breeding of mosquito larvae in open

drains systems. As a consequence, residential areas with low sewer connection rate may

be noted for high mosquito breeding (high malaria transmission) while areas with high

sewer connection rate (i.e. the proportion of households connected to the sewer system is

high) low mosquito breeding (low malaria transmission). This probably accounts for the

reduction in urban malaria mortality with increase in the proportion of households

connected to the sewer system. This observation was consistent with findings reported in

many previous studies in which the association between mosquito vector breeding and

waste material deposition has been documented [192, 454-457]. Like both solid waste

collection rate and the proportion of households connected to the sewer system, the

higher proportion of households connected to the WC, the smaller the chance of liquid

wastes left to collect as standing pools of water. This perhaps reduced the size of habitats

for mosquito breeding and thus lowered urban malaria transmission. This could also be


158

responsible for the decrease in urban malaria mortality with an increase in the proportion

of households connected to the WC. Although many studies have reported increased

malaria transmission rate and intensity with deteriorating environmental quality [317,

458-464], no reports of such relationship between environmental conditions and malaria

mortality have been documented. In a study to investigate risk factors for malaria,

children living in houses with mud roofs had significantly higher risk of getting P.

falciparum infection compared to those living in iron-sheet roofed houses (Odds Ratio

2.6; 95% CI: 1.4-4.7) [465]. The lack of evidence of association between the fraction of

cluster level deaths due to diarrhoea and environmental conditions was perhaps a

consequence of blurring of study outcomes by intermediate socioeconomic conditions

more than a lack of association. There assertion was corroborated by evidence in

literature which reported a strong association of diarrhoea morbidity with poor hygiene

and sanitation [63, 293, 408-410]. In a study to explore how a city-wide sanitation

intervention altered the magnitude of relative and attributable risks of diarrhoea

determinants and the pathways by which those factors affected diarrhoea risk, the authors

reported that the intervention reduced diarrhoea and also changed attributable and relative

risks of diarrhoea determinants by altering the pathways of mediation [410]. In addition,

the study reported that socioeconomic status was a major distal diarrhoea determinant

(attributable risk: 24%) with 90% of risk mediated by other factors, mostly by lack of

sanitation and poor infrastructure (53%) and only accounted for 13% of risk with only

42% mediated by other factors (18% by lack of sanitation and poor infrastructure) [410].


159

5.6 Mapping, vulnerability and excess urban mortality assessment

The main goal of this component of the analysis was not only to map and describe the

patterns of the observed urban malaria and diarrhoea mortalities in a rapidly urbanizing

area in a low income country, but also to identify the foci of excess malaria and diarrhoea

mortalities in an urban area with high heterogeneity of neighbourhood environmental

conditions in order to allow for comparison and efficient resource allocation.

Infectious diseases, especially malaria and diarrhoea have been widely reported to exhibit

spatial clustering, generally viewed as a characteristic in regions of high infectious

transmission [88, 128, 183, 311, 466-468]. In the high transmission areas, infectious

diseases could either cluster with respect to socioeconomic factors or cluster based on

differing levels in environmental conditions [88, 300, 327, 466, 469, 470]. Several

published studies have reported excess urban diarrhoea cases in urban residential areas

without access to potable water, but with poor hygiene conditions and low income

neighbourhoods [128, 183, 300, 324, 466, 467, 471, 472]. Malaria has been reported to

exhibit elevated incidences in areas with more breeding opportunities for the mosquito

vector [183, 324, 473, 474]. In the current study, a strong evidence of clustering (i.e.

hotspot of high malaria mortality) was observed in the neighbourhood of an open lagoon

and two cold spots far removed from the lagoonal influences, thus suggesting that large

water bodies in urban spaces could exert some amount of influence on malaria mortality,

an observation which was quite consistent with findings from other studies [324, 466,

474, 475]. This observation has been viewed to have important implication for urban

health, which meant that urban health policy could be strengthened if strategic objectives

and program scope were broadened to encompass the larger urban structure, including


160

surface water bodies and other social matrices and not just the conventional approach of

focusing only on hygiene, water supply and sanitation. On the contrary, the observed

pattern of diarrhoea mortality was rather random, only showing strong evidence of

clustering for low-low values (i.e. only cold spots) in the neighbourhood of a closed

freshwater lagoon in the eastern part of Accra, probably suggesting that closed freshwater

lagoons could perhaps more likely protect against diarrhoea mortality than their open

saline counterparts. However, currently no evidence exists in the literature that

demonstrates that closed freshwater in urban environments protect against urban malaria

or diarrhoea mortality, although the striking clustering of malaria and diarrhoea mortality

(i.e. malaria mortality hotspots near an open saline lagoon and diarrhoea mortality cold

spots near a closed freshwater lagoon) could not be ignored either. Thus, the observation

provides leads for further investigation into influences of urban lagoons and wetlands on

infectious disease mortality [471, 476-480]. Such further investigations would provide

additional evidence for informed decision-making on urban health policy change in

rapidly urbanizing areas in countries with limited health sector budgets. Despite the

obvious limitation and the failure of the study to provide conclusive explanation for the

observed patterns of excess urban malaria mortality, a careful scrutiny of the study results

together with the knowledge of the biology and behaviour of mosquito vector as well as

lifestyle and human behaviour should aid in defining hypotheses of future research on

this subject. We therefore attempt to explain the distribution of the observed urban

mortalities in Accra in the context of the vector and pathogen biology which underlie

their abundance and distribution in the different set of urban environmental media and

socioeconomic conditions. The discussion of our findings will also draw on previous


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studies relating to social and anthropogenic factors which drive changes in urban ecology

and how the changes ultimately influence urban mortalities. For instance, the ideal

temperature range for malaria-carrying mosquitoes is 15 - 30°C [427, 481]. Temperature

exerts varied effects on survival and reproduction rate of mosquitoes. If initial

temperature is high, then an increase in average temperature, associated with global

warming, can decrease the survival and reproduction rate of mosquitoes [481].

Mosquitoes are also highly sensitive to changes in precipitation and humidity. Increased

precipitation can increase mosquito population indirectly by expanding larval habitat and

food supply. Mosquitoes are, however, highly dependent on humidity, surviving only

within a limited humidity range of 55-80% [481-484].

Infectious disease; particularly malaria and diarrhoea, often accompanies extreme

weather events, such as floods, earthquakes and drought [483, 485]. Outbreak of certain

infectious diseases also follows certain environmental change events such as rapid

urbanization and concomitant deterioration of environmental quality [484, 486, 487].

These event-dependent local epidemics occur due to loss of infrastructure, such as

hospitals and sanitation services, but also because of changes in local ecology and

environment. For example, malaria outbreaks have been strongly associated with the El

Nino cycles of a number of countries (e.g. India and Venezuela) [488-490]. In the face of

global warming, there has been a marked trend towards more variable and anomalous

weather leaving no doubt about an imminent transition in infectious disease transmission

[491-493]. The trend towards more variability and fluctuation in local climate conditions

in endemic areas is perhaps more important, in terms of its impact on human health, than


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that of a gradual and long-term trend towards higher average temperature which may tend

to produce more defined and predictable disease transmission patterns [494-496].

Most mosquitoes mate shortly after emergence from pupa and after mating, sperm passed

by male into the female enters the spermatheca [78, 427, 481, 497]. The sperm in the

spermatheca usually serves to fertilize all eggs laid during the life time of the female;

thus only one mating and insemination per female is required [481, 498]. With a few

exceptions, a female mosquito must bite a host and take a blood meal to obtain the

necessary nutrients for the development of the eggs in the ovaries. After a blood-meal the

abdomen of the mosquito is dilated and bright red in colour, but some hours later the

abdomen becomes darker red [481, 482, 499]. A gravid female mosquito searches for

suitable larval habitats in which to lay eggs after full development of eggs upon

completion of blood digestion [481]. Depending on the species, adult female mosquitoes

lay 50-200 eggs per oviposition [481, 482, 500]. The eggs are brown or blackish, quite

small (~0.5 x 0.2 mm), and most Culicinae, are elongated or approximately ovoid in

shape [481, 485]. Eggs are laid singly and directly on water in Anopheles and Culex

species. A unique feature of the eggs is the presence of floats on either side [485, 501].

The eggs are not resistant to drying and cannot survive desiccation, hatch within 2–3

days, although hatching may take up to 2–3 weeks in colder climates [482, 487].

In respect of larval recognition, mosquito larvae can be distinguished from those of the

other aquatic insects by being legless and having a bulbous thorax that is wider than both

the head and the abdomen [502, 503]. They have a well-developed head with mouth

brushes used for feeding, a large thorax and a nine segmented abdomen. All mosquito

larvae require water in which to develop; no mosquito has larvae that can withstand


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desiccation, although they may be able to survive over limited periods in partially aquatic

environments such as in wet mud [427, 502]. Mosquito larvae have well developed head,

bearing a pair of antennae and a pair of compound eyes with prominent mouthbrushes

which are present in most species and serve to sweep water containing minute food

particles into the mouth [503]. The larvae have structures that allow survival in the water

and permit efficient respiration and in most species, they breathe through spiracles

located on the 8th abdominal segment and therefore must come to the surface frequently

[481, 482]. However, Anopheles larvae lack a respiratory siphon and for this reason they

position themselves so that their body is parallel to the surface of the water [481]. The

larvae spend most of their time feeding on algae, bacteria, yeasts and numerous other

microorganisms in the surface microlayer. The larvae of other mosquito species feed on

decaying plant and animal material found in the water. While many browse over the

bottom of habitats, some, including Anopheles species are surface feeders sieving out

food particles from surface microlayer [500]. They dive below the surface only when

disturbed and swim either by jerky movements of the entire body or through propulsion

with the mouth brushes [481, 482].

The larvae develop through multiple stages, or four instars, after which they

metamorphose into pupae through molting, shedding their exoskeleton, or skin, to allow

for further growth at the end of each instar [481, 498]. The process from egg laying to

emergence of the adult is temperature driven with average minimum time of 7 days [78,

504].

The larvae occur in a wide range of habitats but most species prefer clean, unpolluted

water [500]. The habitats vary from large and usually permanent collections of water,


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such as freshwater swamps, marshes, rice-fields and borrow pits, to smaller collections of

temporary water such as pools, puddles, water-filled car tracks, ditches, drains and

gulleys [481, 500]. A variety of “natural container-habitats” also provide breeding places,

such as water-filled tree-holes, rock-pools, water-filled bamboo stumps, bromeliads,

pitcher plants, leaf axils in banana, pineapple and other plants, water-filled coconut husks

and snail shells [481, 505]. Larvae also occur in wells and man-made “container-

habitats”, such as clay pots, water storage jars, tin cans, discarded kitchen utensils and

motor vehicle tyres [481, 485, 505]. Whereas some species prefer shaded larval habitats

and others like sunlit habitats, many species even cannot survive in water polluted with

organic debris with others breeding prolifically in water contaminated with excreta or

rotten vegetation [481, 500, 506]. A few mosquitoes breed almost exclusively in brackish

or salt waters, such as saltwater marshes and mangrove swamps, and are consequently

restricted to mostly coastal areas. Some species are less specific in their habitat

requirements and can tolerate a wide range of different types of breeding place [481,

504]. For this reason, the larvae of Anopheles mosquitoes have been found in fresh- or

salt-water marshes, mangrove swamps, rice fields, grassy ditches, the edges of streams

and rivers, and small, temporary rain pools in both rural and urbanized environments

[481]. Mosquito larvae habitat requirement is so wide to the extent that some breed in

open, sun-lit pools, while others are found only in shaded breeding sites in forests.

Almost any collection or temporary water can constitute a mosquito larval habitat, but

larvae are normally absent from large expanses of uninterrupted water such as lakes,

especially if they contain large numbers of fish and other predators [481, 497]. Larvae are


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also absent from large rivers and fast-flowing waters, except those occurring in marshy

areas and isolated pools and puddles formed at the edges of flowing water [503].

Regarding pupal morphology, mosquito pupae are exclusively aquatic and comma-

shaped when viewed from the side. The head and thorax are fused into a cephalothorax

which carries a pair of respiratory trumpets dorsally and an abdomen which curves

around underneath [481]. As with the larvae, pupae must come to the surface frequently

to breathe, which they do through a pair of respiratory trumpets on the cephalothorax

[502]. The pupae do not feed, spend most of pupating time taking in air and after a few

days as a pupa, the dorsal surface of the cephalothorax splits and the adult mosquito or

imago emerges [503].

The average time for development from egg to adult varies considerably among species

and strongly influenced by ambient temperature. Mosquitoes can develop from egg to

adult in as little as 5 days but usually take 10–14 days in the topics [481, 502, 503]. Like

all mosquitoes, adult Anopheles mosquitoes have slender bodies with 3 sections: head,

thorax and abdomen. The head is specialized for acquiring sensory information and for

feeding. The head contains the eyes and a pair of long, many-segmented antennae. The

antennae are important for detecting host odours as well as odours of breeding sites

where females lay eggs [481, 497]. The head also has an elongated, forward-projecting

proboscis used for feeding, and two sensory palps [481]. The thorax of an adult mosquito

is specialized for locomotion. There are three pairs of legs and a pair of wings which are

attached to the thorax with only one pair employed during flight [481, 503, 507]. The

abdomen is segmented and specialized for food digestion and egg development. The

segmented abdomen expands considerably when a female takes a blood meal. The blood


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takes a long time to digest and serves as a source of protein for the production of eggs,

which gradually fill the abdomen at complete digestion [481, 485]. Anopheles

mosquitoes can be distinguished from other mosquitoes by their possession of palps,

which are as long as the proboscis, and by the presence of discrete blocks of black and

white scales on the wings [427, 481, 498]. Another typical feature for adult Anopheles

identification is their unique resting position: i.e. males and females rest with their

abdomens sticking up in the air rather than parallel to the surface on which they are

resting [481]. Adult mosquitoes will typically mate within a few days after emerging

from the pupal stage. In most cases, the males form large swarms, usually around dusk,

and the females fly into the swarms to mate after which the males live for about a week,

feeding on nectar and other sources of sugar before dying [481, 498, 508]. Usually,

females also feed on sugar sources for energy but require a blood meal for the

development of eggs. After obtaining a full blood meal, the female will rest for a few

days while the blood is digested and eggs are developed. This process is temperature

dependent and usually takes 2–3 days in tropical conditions [78, 481, 483]. Once the eggs

are fully developed, the female lays them and resumes host seeking for another session of

blood meal taking. The cycle repeats itself until the female dies. While the lifespan of

adult mosquito depends on temperature, humidity, and also their ability to successfully

obtain a blood meal while avoiding host defenses, most do not live longer than 1–2 weeks

in nature, albeit females can live longer than a month in captivity [481]. From the

standpoint of both the biology and ecology of the mosquito vector, changing

environmental and climatic conditions may significantly affect either larval or pupal

survival (i.e. vector breeding regime) and thus affect malaria transmission in such


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environments. Once the disease transmission rhythm changes, other social and

behavioural factors including availability of health services, access to healthcare,

financial barriers and health seeking behaviour of individual all interact to change the

course of progression from disease to mortality as supported by the strong association

observed between urban malaria mortality and the environmental and socioeconomic

conditions in Accra, Ghana. Given that our study did not include variables on the biology

of the mosquito vector, an obvious limitation is presented regarding the link between

mosquito vector breeding, malaria transmission and malaria mortality.

Consequently, it is imperative to discuss the results in the context of similar studies that

might provide insights to guide the hypothesis that would develop from this current

analysis. A Brazilian study reported that multiple players such as social policy, migration,

urbanization, city water supply, garbage disposal and housing conditions, as well as

community level understanding of the disease and related practices affect vector ecology

[509]. The descriptive study used a multi-disciplinary approach to compare the effects on

dengue fever transmission of irregularity in water supply in households from both under-

privileged and privileged areas. It was observed that households located in more

privileged blocks where irregularity in water supply was not an issue, dengue

transmission was lower than in households located in underprivileged blocks where water

supply was highly irregular. The study pointed out that due to irregularity in water supply

in the under-privileged blocks households stored water in barrels, open tanks and pots

and thus provided improved breeding opportunities and the chances of Aedes aegypti

survival for increased dengue transmission.


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To examine the relationship between terrestrial change and mosquito composition, a

Peruvian study used remote sensing techniques to define spatially explicit land use

categories along a gradient with low (rural), medium (peri-urban), and high (urban)

anthropogenic influence in the Peruvian Amazon and reported significant differences in

mosquito diversity among the land use categories [510]. The results from the study

provided baseline data linking mosquito distribution to land use in the Peruvian Amazon,

thus creating standardized methods to measure the impact of human influence on the

environment and to predicting disease risk in rapidly changing environments. Although

our study was only able to establish associations between spatial change in urban

environmental conditions and mortalities rather causal relationships, both studies

demonstrate that anthropogenic environmental disturbances are perhaps important factors

driving mosquito community composition and malaria transmission. Malaria is especially

susceptible to changes in the environment as both the pathogen (Plasmodium) and its

vector (mosquitoes) lack the mechanisms necessary to regulate internal temperature and

fluid levels [427, 503, 511]. Thus, there is a limited range of ecological and climatic

conditions within which the parasite and vector can survive, reproduce and infect hosts

and vector-borne diseases, such as malaria, have distinctive characteristics that determine

its pathogenicity [502, 512]. More generally, the survival and reproduction rate of the

vector, the level of vector activity (i.e. the biting or feeding rate), the development and

reproduction rate of the parasite within the vector or host all depend on availability of

suitable habitats and habitat conditions such as temperature, precipitation and humidity

[78, 485, 503, 504, 513].


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In a study to assess the potential effects of different construction features of homes on the

indoor abundance of Culicine mosquitoes in Trinidad & Tobago (TT) and the Dominican

Republic (DR), a survey using xenomonitoring was conducted in different homes in both

countries alongside concurrent indoor resting mosquito collection to determine which

features of the homes were correlated with a greater mosquito abundance [512]. The

study was conducted between June 2002 and April 2003 and data were collected from

104 and 121 homes in TT and DR respectively. While in TT, 61 (58.65%) percent of the

homes were located in urban areas with 43 (41.35%) in rural areas, in the DR 81

(66.94%) of the homes were located in the urban areas with 40 (33.06%) in rural areas.

Out of a total of 1,630 mosquitoes collected in TT, 77% were Culex quinquefasciatus,

while 46% of the 459 mosquitoes collected from homes in the DR were Cx.

quinquefasciatus. However, the mean number of Cx. quinquefasciatus mosquitoes in both

countries was greater in cement homes than in either wood or other poorer quality homes

(TT cement 17.43, others 14.43; DR cement 4.24, others 3.41). In the case of TT homes

that had painted interiors were significantly more likely to have a high abundance of

mosquitoes resting indoors compared to homes without painted interiors (OR 2.90, CI

1.09-8.72). Interestingly, having a painted interior or exterior was found to be a predictor

of a high abundance of indoor resting mosquitoes in the DR (interior OR 3.13, CI 1.41-

6.92; exterior OR 1.97, CI .91-4.26). Reduced adult abundance in TT was reported to

correlate with homes built on stilts, with more than four people sleeping per home with a

painted interior. In the DR however, reductions reportedly correlated with homes where

residents slept under a bed net and rurality. Changes in construction patterns in the


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Caribbean region could help prevent human-mosquito contact potentially reducing the

transmission of certain vector-borne diseases in the population.

A study that assessed the effects of combined sewage overflows (CSOs) from urban

streams in Atlanta, GA, on oviposition site selection by Culex quinquefasciatus under

seminatural field conditions reported that CSO water quality, especially when enriched,

was a more attractive oviposition substrate than nonenriched water [500]. The conclusion

of the study which was consistent with the strong association observed between malaria

mortality and sewer as well as non-sewer disposal practices in Accra was that

environmentally sound management of municipal waste water systems had the potential

to reduce the risk of Culex-borne diseases in urban areas. Many other studies have

reported that ecosystem changes caused by anthropogenic activities have modified the

environment in ways that have promoted the emergence of vector-borne diseases [500,

502, 510, 512]. A health facility-based survey and health care system evaluation carried

out in a peripheral municipality of Abidjan (Yopougon) during the rainy season of 2002

reported that malaria infection rates in fever cases for different age groups were 22.1%

(under one year-olds), 42.8% (one to five years-olds), 42.0% (> five to 15 years-olds) and

26.8% (over 15 years-olds), while those in the control group were 13.0%. 26.7%, 21.8%

and 14.6%, respectively [513]. While the fractions of malaria-attributable fever were

0.12, 0.22, 0.27 and 0.13 in the respective age groups, parasitaemia was homogenously

detected in different areas of the city, thus concluding that rapid urbanization had

changed malaria epidemiology in more urbanized areas compared to peripheral

Yopougon where endemicity was found to be moderate [513]. The conclusion of this

study is consistent with the strong association observed between urban malaria mortality


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and the change in socioeconomic and environmental conditions due urbanization in

Accra.

By and large, whereas several reports exist in literature showing the relationship between

environmental change and vector ecology, not too many studies have described the

relationship between environmental modification and malaria mortality. A study that

monitored oviposition activity Aedes aegypti as a function of demographic and

environmental variables reported that the proportion of weeks of vector infestation and

the total number of eggs showed strong spatial continuity and were higher in areas that

had higher densities of houses and relatively closer to industrial sites compared to areas

with higher human populations or higher densities of flats and far from industrial sites

[485]. While many attempts have been made to quantify the burden of malaria in Africa,

none has addressed how urbanization will affect disease transmission and outcome, and

therefore mortality and morbidity estimates. Nevertheless, the results of a series of

entomological, parasitological and behavioural meta-analyses of the few studies that have

investigated the effect of urbanization on malaria in Africa showed there were 1,068,505

malaria deaths in Africa in 2000 revealing just a modest 6.7% reduction over previous

iterations [484].

A bionomics study of malaria vectors carried out in riverine and non-riverine areas in the

Indian city of Delhi on account of tremendous ecological changes in its topography

showed that day-time resting preferences of the vector species in human dwellings and

cattlesheds did not differ significantly [497]. However, the preferred larval habitats of An.

culicifacies in riverine area had shifted to large lakes, channels and ponds and that whilst

An. culicifacies played a role only in the northern part of the riverine area where water


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pollution was at minimal level, An. stephensi was the main vector of malaria transmission

in both areas [497]. This confirms that changes in habitat preference of the different

vector species have profound implications for malaria transmission and may be the

underlying reason for the observed anomalous distribution of excess malaria mortalities

in the environs of surface water bodies in Accra.

Gauging from the findings of both the present and previous studies, especially relating to

the ecology and biology of the mosquito vector, malaria control efforts might become

more effective only if such efforts were all encompassing by way of bundling the life

cycle of the vector, the parasite and the human host interactions under single intervention.

The Anopheles mosquito is the only species known to transmit malaria. For this reason,

understanding the biology and behavior of Anopheles mosquitoes will not only help in

the discovery of evolving malaria transmission mechanisms, but should ultimately aid in

designing appropriate control strategies. The basis for which ecological and

environmental change processes affect malaria transmission relates to ability of the

processes to alter habitat and survival factors such as breeding, feeding and resting

conditions of the mosquito vector. In the particular case of urbanization, the resulting

environmental change conditions tend to promote the survival conditions of the mosquito

vector, thus favouring a sustained transmission of malaria in rapidly urbanizing endemic

areas. Factors that affect the ability of the mosquito to transmit malaria include its innate

susceptibility to Plasmodium parasite, its host choice and its longevity. Therefore in

designing control programs, the susceptibility and resistance of malaria vectors to

insecticide as well as the preferred feeding and resting location of adult mosquitoes

should be key factors to take into consideration. Another key factor to consider in


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designing malaria control programs is the resistance of the Plasmodium parasite itself to

drugs. Parasite resistance to drugs has been widely reported in many malaria

transmission areas and represents a major obstacle to control efforts. However,

innovative strategies aimed to overcome the problem of drug resistance are being

developed and deployed along discovery of new powerful drugs and natural plant extracts

that can withstand parasite resistance and/or inhibit parasite growth and development. For

instance, a recently published study reported that the hemolytic C-type lectin CEL-III

from Cucumaria echinata, a sea cucumber found in the Bay of Bengal impaired the

development of the malaria parasite when produced by transgenic A. stephensi [482,

514]. This could potentially be used one day to control malaria by spreading genetically

modified mosquitoes refractory to the parasites, although there are reportedly numerous

scientific and ethical issues to be overcome before such a control strategy could be

implemented.

The survey of similar studies on the biology of the mosquito vector and a scrutiny of

the excess malaria mortality maps provided clues that, the urban socioeconomic and

environmental conditions were key drivers of urban malaria mortalities and that their

deterioration could reinforce elevation in the risk of excess mortalities in urban spaces.

The strong evidence of association observed between excess mortality and the urban

environmental and socioeconomic conditions were consistent with findings from several

published studies [183, 466, 467, 473, 475] and strengthened the case that disease control

programs in urban centres would benefit tremendously from urban environmental

management in fast urbanizing areas in low income countries.


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Finally, the strong evidences of local level spatial autocorrelation and the association

between excess mortality maps on the one hand and the socioeconomic and

environmental quality conditions on the other hand suggest that both the socioeconomic

and the environmental conditions could be important risk factors for urban malaria and

diarrhoea mortalities in rapidly urbanizing areas.

5.7 Limitation of study

In general, the proportions of economically active and economically inactive

populations were not shown to be valid measures of the area-based socioeconomic

conditions. For instance, the positive (i.e. a unit increase in population economic

inactivity resulted in an increase in per capita solid waste generation rate) association

between economically inactive population and per capita solid waste generation

(regression coefficient = 0.276) was obtuse. High values of the proportion of

economically inactive population represented low socioeconomic status and high values

of the proportion economically active cluster populations represented high

socioeconomic status. However, with the understanding that the per capita waste

generation rates in high socioeconomic areas have been theoretically reported to be

higher than those from low socioeconomic areas [38, 355], the observed association

between neighbourhood urban environmental quality conditions and the proportion of

economically inactive and/or active populations somewhat did not make sense. On

account of this, both the proportion of economically active and/or inactive cluster

population were regarded as probably unreliable measures/proxies of area-based SES.

For instance, the fact that a resident was economically active did not mean that the

individual was employable and could contribute to the community’s pool of wealth. In a


175

similar argument, the fact that an individual was economically inactive did not mean that

such individual could not generate income and/or contribute to the community’s wealth.

Therefore, economically active or inactive factor did not predict community income or

wealth and probably invalid proxy measure of SES. Data attributes that might affect their

validity and reliability include; data completeness and coverage, misclassification and

reporting biases. The Ghana Census covers the entire population and approximately 100

percent complete. In addition, Ghana’s population is fairly well defined and the variables

enumerated were also fairly discretely defined without overlaps. Therefore both data

completeness and misclassification did not present any perceived data limitation and

therefore presented no perceived validity threats to the Ghana census data. However, it

was possible that respondents to census questionnaire did not provide correct answers to

census questions or might not have responded accurately to questions on the variables

collected during the census. This meant that the Ghana census data might be prone to

reporting bias which might have affected the results and conclusions of this study.

Finally, the PMRfds were computed using all-cause mortality as the denominator and so if

the risk of mortality for one of the components of all-cause mortality varied by cluster,

then this could affect the summary/outcome measure PMRfd and therefore bias the

analysis. A way to deal with this problem was to exclude such components, but

unfortunately, we did not have the liberty of telling apriori if any of the components of

all-cause mortality exhibited differential mortality risk by cluster and therefore this bias

could not be controlled for. Another way to deal with this bias would have been to use a

specific cause of death known to have a uniform risk level across all clusters as the

denominator for both the observed proportion and the expected proportion. But then


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again, this also required knowledge of risk distribution by cause of death in the study area

and which unfortunately was not available for the study area.

Chapter Six: Conclusions and Recommendations

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6.1 Conclusions

Environmental change is a slow process and its ultimate effects on human health can as

well either be gradual or immediate, depending on the nature of the processes and the

chain of links involved. Examples of environmental change events with potential adverse

impacts on human health include urbanization, climate change, stratospheric ozone

depletion, loss of biodiversity, changes in hydrological systems and the supplies of

freshwater, land degradation and stresses on food-producing systems.

Despite emerging realities that environmental change is on-going with concomitant

adverse impacts on human health, research tools required to demonstrate the association

between the change events and human health are still being developed. Perhaps, this has

to do with the difficulty in conceptualizing environmental change to reflect all its diverse

characteristics. Unlike in the laboratory setting where most reaction conditions can be

controlled, in environmental change experiments, it is hard to define and control the

range of conditions forming the universe of the physical and social environment and how

the conditions interact to determine the overall impacts on human health at aggregate

level. Appreciation of the type, magnitude and scale of human health impacts of

environmental change requires a new perspective which focuses on ecosystems and on

the recognition that the foundations of long-term good health in populations rely in great

part on the continued stability and functioning of the biosphere's life-supporting systems.

It also brings an appreciation of the complexity of the systems upon which human health

depends and how changes in the systems can produce adverse health effects on humans.


178

While it is well understood that large scale ecological changes affect human health, most

of the phenomenal ecological changes have been largely human-induced. Human

activities increasingly affect the structure and function of ecosystems. In turn, these

changes also influence the entire chain of factors involved in the infectious disease

transmission cycle: pathogens, vectors, reservoir species and human populations. Strictly

unintended human activities can thus have serious consequences on the transmission of

both communicable and non-communicable diseases. The scientific community

recognizes the growing need to better understand the multi-faceted and complex linkages

between environmental change (including urbanization, climate change, land and sea use

change, biodiversity loss and change, socio-economic change) and human health. As the

analysis evaluated the relationship between urban mortalities and several aspects of the

urban environment in a spatially explicit manner, we logically present multiple

conclusions to reflect the different foci of the analysis, namely demographic and health

analysis, socioeconomic and environment analysis, socioeconomic and health analysis,

environment and health aspect and finally disease clustering and excess mortality

analysis.

In the demographic and health analysis, a key observation was that while maternal factors

appeared to protect <1 year olds against malaria mortality, such a protection was absent

in diarrhoea mortality. This led to a conclusion that intervention programs designed to

control infant malaria and diarrhoea mortality would certainly have different strategic

foci given the different levels of acquired or innate immune protection against the

different disease outcomes.


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In the analysis of the relationship between socioeconomic conditions and urban

environmental conditions however, we observed several levels of association and

remarkable findings which provided new insights for deeper understanding of how

socioeconomic inequalities underpin disparate health outcomes in rapidly urbanizing

areas in a low income economy.

While some of the area-based socioeconomic measures alone were not valid proxies of

SES, others were valid at aggregate levels. On the whole, aggregating the area-based

socioeconomic measures into a uni-dimensional attribute and generating wealth quintiles

from the uni-dimensional attribute was observed to more robustly predict SES and

therefore a more valid measure at community level. Strong evidence of differences in

neighbourhood urban environmental quality existed across the wealth quintiles. This

observation suggested that socioeconomic conditions were important drivers of change in

neighbourhood urban environmental quality conditions thus providing clues that urban

environmental interventions aimed at infectious disease prevention would benefit

considerably from simultaneous implementation with social interventions if they were to

be effective. We then concluded that widening socioeconomic inequalities (e.g., urban

unemployment, urban employment, etc.,) at household level could worsen the existing

urban environmental health inequalities at community level. Therefore, for urban health

policy change on infectious disease control, it would make sense if urban environmental

interventions aimed at infectious disease prevention and control, were implemented

simultaneously with complementary social interventions in order to be effective.

In addition, the analysis of the relationship between area-based measures of SES and

urban malaria and diarrhoea mortalities revealed a number interesting and varying levels


180

of associations across different social contexts. Synthesis of the findings provided

evidence for a conclusion that the health policy reforms implemented by the Ghana’s

Ministry of Health to reduce urban health inequalities were perhaps more responsive to or

effective in reducing urban diarrhoea than urban malaria mortality. A penultimate point is

that the proportion of residents with vocational, technical and commercial, the proportion

electricity, gas and water supply sector workforce, proportion of education sector

workforce, proportion of residents with tertiary education are perhaps potentially

important factors to consider among a range of risk factors for urban malaria mortality in

future case-control studies to explore city-wide distribution of mortality risks in rapidly

urbanizing low income settings. Moreover, the observed non-uniformities in the inter-

quintile relative mortality differentials were perhaps more likely a consequence of

lifestyle factors and inter-quintile differences in health seeking behaviour than the

influence of the health policy mix.

The environment and urban mortalities component of the study showed that urban

malaria mortality was more sensitive to changes in environmental conditions than

diarrhoea mortality. Therefore, the conclusion drawn from the observation was that

environmental management initiatives intended for infectious disease control might

substantially reduce and/or lower the neighbourhood urban environmental-quality-

attributable fraction of deaths due to urban malaria more than that due to urban diarrhoea

in rapidly urbanising areas in a low income setting.

Finally, the geo-statistical analysis showed that the excess malaria mortalities were

strongly associated with the urban socioeconomic and environmental conditions.


181

However, there was none at all or only weak association between urban diarrhoea

mortality and the urban socioeconomic and environmental conditions.

We concluded therefore that, both the socioeconomic and the environmental conditions

could be important risk factors for urban diarrhoea mortalities, and especially for urban

malaria mortality. Our opinion was that salinity could also be an important factor in how

surface water bodies influenced infectious disease mortality. To be able to make firm

conclusion on this assertion we recommend further case-control studies to establish the

range of urban mortality risk factors including those factors relating to surface water

bodies within urban spaces in low income settings. This would help to direct resources

appropriately at high risk areas and the most vulnerable groups as well as to provide more

convincing evidence for urban health policy reforms in rapidly urbanizing areas in low

income settings. Based on these findings, we support the use of small scale mapping to

detect zones of vulnerability for an improved targeting of resources aimed at urban

malaria and diarrhoea control in rapidly urbanizing areas in low income settings.

6.2 Eradication of malaria and diarrhoea – challenges and future direction

Undoubtedly, the cost of preventing malaria is much less than treating the disease in the

long run. Whereas mosquito control is an important component of malaria control

strategy, eliminating malaria in an area does not require the elimination of all Anopheles

mosquitoes. Moreover, eradication of mosquito is not an easy task, which in itself would

lead to a reduction in ecological diversity. For effective prevention of malaria, some

parameters should be met such as conducive conditions in the community or a given

country, improved data on the disease, targeted technical approach to the problem,

behavioural change, very active and committed leadership, government’s total support,


182

monetary free hand, community involvement, skilled technicians from different fields as

well as a well thought-out implementation strategy which incorporates the interests and

views of community members. A wide range of strategies exist for achieving malaria

eradication that start from simple steps to complicated strategies which may not be

possible to enforce with the current tools and knowledge. Additionally, an important step

towards malaria eradication will be to draw lessons from regions that have successfully

eliminated malaria, regarding the strategies adopted, program delivery and

implementation schedules. A combination of socio-economic improvements (e.g. houses

with screened windows, air conditioning and smoothened interior walls) with vector

reduction efforts and effective treatment may lead to the eradication of malaria without

the complete elimination of the vector. Some important measures in mosquito control that

might lead to complete eradication of malaria without eliminating the Anopheles

mosquito include efforts to discourage egg laying, prevent development of eggs into

larvae and adults, kill the adult mosquitoes, do not allow adult mosquitoes into places of

human dwelling, prevent mosquitoes from biting human beings and deny blood meal. On

the basis of the findings from this study, we advocate for the intensification of research

on more targeted interventions to break the vector breeding cycle and human behavioural

change, although many studies have suggested that the sterile mosquitoes might be the

answer to malaria elimination. Another option might be to manipulate or modify the

genetic constitution of the female Anopheles mosquito so that its internal physiological

environment becomes unsuitable for the growth and development of the malaria parasite.

Insecticide-based control measures (e.g. indoor spraying with insecticides, use of

repellents and insecticide treated bednets) are the principal vector-based global control


183

strategies deployed to kill mosquitoes that bite indoors. However, after prolonged

exposure to insecticides over several generations, mosquitoes, like other insects, have

begun to develop counter-strategies against insecticide use. Mosquitoes are increasingly

developing genetic modifications allowing them to develop resistance, a capacity to

survive contact with an insecticide. Since mosquitoes have relatively short life-cycle and

can have many generations per year, it therefore follows that high levels of resistance

may arise very quickly over a relatively short period. It is therefore not surprising that

just within a few years after the insecticides were introduced as a global control strategy,

resistance of mosquitoes to some insecticides has been reported. Now, there are over 125

mosquito species with reported resistance to one or more insecticides. The development

of resistance to insecticides used for indoor residual spraying is seen as a major

impediment to the Global Malaria Eradication Campaign. In Ghana, mosquitoes have

developed resistance to DDT. Also, chloroquine has already been removed from the list

of first line drugs for malaria treatment and combination therapy has been widely

adopted. There are increasing global concerns about the challenges of vector resistance,

with the World Health Organization (WHO) leading a campaign for judicious use of

insecticides so as to limit the development and spread of resistance. This includes

controlled use of insecticides in other sectors such agriculture and fishing. While the use

of insecticides in agriculture has often been reported as contributing to resistance in

mosquito populations, it is possible to detect development of resistance. Thus, control

programs would be well advised to conduct surveillances for this potential problem. Such

surveillances would inform actions that underlie the strategic direction of malaria control

efforts in endemic areas which are susceptible to developing resistance.


184

Unlike malaria, diarrhoea control has been successful by a larger measure in Ghana. The

two main advances in managing diarrhoeal disease – formulated oral re-hydration salts

(ORS) containing lower concentrations of glucose and salt, and success in using zinc

supplementation remain the most effective global strategies for the control of diarrhoea

recommended by the WHO. ORS in particular has been fully deployed for the

management of diarrhoeal diseases in Ghana over the last decade and has proven very

effective. Other strategies used in addition to prevention and treatment of dehydration

with appropriate fluids include adoption of exclusive breastfeeding, continued feeding

and selective use of antibiotics. The observed weak association between diarrhoea

mortality and the changing urban socioeconomic and environmental conditions was

probably a consequence of the tremendous success in diarrhoea treatment and

management in Ghana. On the basis of fact the that several instances of strong

associations were observed among the environmental, socioeconomic and health

variables, our first line of recommendation was therefore that, this study be repeated

using the census 2010 data to confirm these associations and to evaluate how the

introduction of the Ghana National Health Insurance Scheme (GNHIS) had influenced

malaria/diarrhoea morbidity and mortality over the period of implementation.

Ultimately, we would recommend that case-control studies be conducted on the variables

that were observed to exhibit strong associations in order to establish causal chains of

events linking, socioeconomic conditions, neighbourhood urban environmental quality

conditions and infectious disease mortality.

Appendices

185

Appendices

Appendix 1: Exploration of SES using Principal component analysis

SES Variable Mean Std dev Factor score Residents’ economic activity status Economically inactive 0.610 0.076 0.500 Employed 0.139 0.041 0.500 Residents’ educational attainment No education 0.163 0.062 0.095 Pre-school education 0.044 0.008 0.448 Primary education 0.165 0.027 0.520 Middle/JSS education 0.165 0.027 0.520 Secondary/SSS education 0.155 0.032 0.132 Vocational/technical/commercial education 0.076 0.018 0.160 Post secondary education 0.029 0.009 -0.053 Residents with tertiary education 0.076 0.096 -0.451 Residents’ occupation Administrative and managerial occupations 0.147 0.064 0.419 Clerical and related occupations 0.014 0.016 0.459 Sales occupations 0.135 0.029 0.104 Service occupations 0.233 0.075 -0.490 Agriculture/husbandry/forestry/fishing/hunting occupation 0.122 0.067 0.356 Production/transport and equipment operators and labourers 0.042 0.047 -0.143 Proportion of other labourers not elsewhere classified 0.070 0.019 -0.415 Professional technical and related workers 0.237 0.047 -0.207 Residents’ place of work Residents working in agriculture hunting and forestry 0.042 0.014 -0.027 Residents working in fishing 0.029 0.041 -0.065 Residents working in mining and quarrying 0.018 0.009 0.020 Residents working in manufacturing 0.169 0.031 -0.415 Residents working in electricity gas and water supply 0.008 0.004 0.036 Residents working in construction 0.083 0.041 0.015 Residents working in wholesale/retail trade/vehicle repairers 0.264 0.081 -0.483 Residents working in hotels and restaurants 0.024 0.009 -0.071 Residents working in transport storage and communications 0.093 0.026 -0.320 Residents working in banking & finance 0.019 0.009 0.164 Residents working in real estate renting and business activities 0.041 0.016 0.217 Residents working in public administration/defence/social security 0.074 0.087 0.357 Residents working education sector 0.036 0.036 0.231 Residents working in health and social services 0.019 0.032 0.245 Appendix 1, Cont. Residents working in other community social and personal services 0.048 0.009 -0.059 Residents working in private households 0.026 0.027 0.401 Proportion of new workers seeking employment 0.007 0.008 0.024 Residents’ marital status

Appendices

186

Married residents 0.394 0.054 0.446 Residents living together but not married 0.043 0.025 0.446 Residents separated 0.018 0.008 0.269 Residents divorced 0.027 0.017 0.427 Residents widowed 0.016 0.008 0.410 Singles 0.502 0.076 -0.424 Residents’ ethnicity Akan group 0.439 0.106 0.109 Ga Dangme group 0.267 0.164 -0.417 Ewe group 0.153 0.076 0.249 Guan group 0.031 0.013 0.396 Gurma group 0.011 0.025 0.182 Mole-Dagbani group 0.056 0.034 0.408 Grusi group 0.024 0.012 0.413 Mande group 0.008 0.009 0.369 All other ethic groups 0.013 0.021 0.298

Appendix 2: Results of multi-variable SES included in the final PCA model.

SES Variable Mean Std dev Factor score Economically active 0.611 0.077 0.201 Employed 0.861 0.041 -0.131 Pre-school education 0.044 0.008 0.219 Primary education 0.165 0.027 0.305 Middle/JSS education 0.165 0.027 0.305 Residents with tertiary education 0.076 0.096 -0.319 Administrative and managerial occupations 0.014 0.016 -0.317 Clerical and related occupations 0.135 0.029 0.068 Service occupations 0.122 0.067 -0.256 Agriculture/husbandry/forestry/fishing/hunting occupation 0.042 0.047 0.113 Proportion of other labourers not elsewhere classified 0.237 0.047 0.184 Residents working in manufacturing 0.169 0.031 0.285 Residents working in wholesale/retail trade/vehicle repairers 0.264 0.081 0.296 Residents working in transport storage and communications 0.093 0.026 0.266 Residents working in public administration/defence/social security 0.074 0.087 -0.228 Residents working in private households 0.026 0.027 -0.312

Appendices

187

Appendix 3: PCA output showing components produced

Component Eigenvalue Difference Proportion Cumulative Comp1 5.42693 2.63348 0.3392 0.3392 Comp2 2.79345 0.71620 0.1746 0.5138 Comp3 2.07725 0.35214 0.1298 0.6436 Comp4 1.72511 0.55671 0.1078 0.7514 Comp5 1.16841 0.40396 0.0730 0.8244 Comp6 0.76444 0.10618 0.0478 0.8722 Comp7 0.65827 0.10866 0.0411 0.9134 Comp8 0.54960 0.26207 0.0344 0.9477 Comp9 0.28753 0.08384 0.0180 0.9657

Comp10 0.20369 0.08221 0.0127 0.9784 Comp11 0.12149 0.02328 0.0076 0.9860 Comp12 0.09820 0.03019 0.0061 0.9921 Comp13 0.06801 0.03279 0.0043 0.9964 Comp14 0.03522 0.01284 0.0022 0.9986 Comp15 0.02238 0.02238 0.0014 1.0000 Comp16 1.110e-16 0.00000 0.0000 1.0000

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