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2 Defining the current situation - epidemiology 3

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Paul R Hunter, Helen Risebro

INTRODUCTION The first step in any economic appraisal or evaluation is to understand the underlying problem being addressed (see Chapter 1). Clearly such analysis of drinking-water interventions will have a strong public health element. This chapter introduces the reader to the role that epidemiology has in determining the burden of disease in a community that may be attributable to lack of access to safe drinking-water and/or adequate sanitation. In order to determine how important such an issue may be one has to ask three questions: What is the burden of disease in the target group? Burden of disease is a function of the incidence and severity of the disease. What proportion of the burden of disease is due to deficiencies in access to drinking-water to be remedied by the intervention? Are there any spin-off livelihood responses that would result from the outcomes of the intervention?

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This chapter primarily concerns itself with answering the first two questions. Specific data challenges related to livelihood analysis will be raised in the next chapter. The rest of this chapter is primarily concerned with assisting decision makers in gathering evidence to enable them to make an informed decision whether or not there is a public health need for an intervention, as a prerequisite to undertaking a full economic assessment. The chapter introduces the reader to some of the methods of epidemiology to better understand epidemiological papers and reports. The chapter then goes on to describe how existing analyses may be used to estimate disease burden

MEASURES OF DISEASE OCCURRENCE The two predominant measures of disease occurrence are prevalence and

incidence.

Prevalence Prevalence measures the amount of disease in a population at a given time

point and can be expressed as a percentage or shown as cases/population:

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Number of existing cases in defined population at a given point in time Number of people in the defined population at the same point in time

The point prevalence is a single assessment of a fixed point in time whereas

the period prevalence is the percentage of a population that are cases at any time within a stated period. Period prevalence is preferred in infectious disease epidemiology as it can be used when there are repeated or continual assessments of the same individuals over a period of time (such as, multiple episodes of diarrhoea). Longitudinal prevalence can be calculated using the following formula (Morris, 1996):

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Number of days with diarrhoea Number of days under observation

Incidence Incidence measures the number of new cases of disease in a population over a

specific time period. When the population is constant the incidence risk is measured as:

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Number of people who develop disease over a defined period of time Number of disease-free people in that population at the start of the time period

When the population is not constant, for example, through deaths, migration,

births, or through additional participant recruitment, the incidence rate should be calculated:

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Number of new events in a defined population over a defined period of time Total person-time at risk during the defined period of time

When studying illness that last a short time (days or a few weeks), such as

acute diarrhoea, then incidence would usually be the most appropriate measure. For more protracted diseases, such as the health effects of arsenic poisoning, prevalence would be the more appropriate measure.

ESTIMATING DISEASE OCCURRENCE There are different approaches to estimating disease occurrence in a population. The choice of approach will depend on many different factors such as the amount of resources available and the accuracy of result required. Whatever approach is used, one of the most important starting points is to develop a case definition.

Case definition The case definition is essential for both the epidemiological studies and any subsequent cost-benefit analyses. The case definition will enable the researcher to know whether or not a particular health event should be included in the analysis and will enable the cost-benefit analyst to determine the cost of the disease outcome. A case definition may be based on symptoms such as the presence of diarrhoea or clinical features of arsenic poisoning or the results of laboratory investigations such as whether or not a stool sample is positive for Cryptosporidium. For example, WHO defines diarrhoea as three or more loose or fluid stools (which take the shape of a container) in a 24 hour period (WHO 1993). Case definitions may also include age ranges, geographical location or dates of onset.

Whatever case definition is used, it should be clear and standardised to minimise disease misclassification bias. Standardising case definitions becomes especially important when using more than one field researcher/interviewer/clinician or more than one community; this is because definitions of diarrhoea can be culture- and person-specific. For example, a

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study conducted in a rural municipality in Nicaragua in Central America identified a classification encompassing nine different types of diarrhoea (Davey-Smith et al. 1993). The classifications used in Nicaragua were influenced by the place/person consulted for treatment. The source of any existing health care utilisation data should therefore always be carefully considered.

Primary surveys Where prior information is not available or suspected to be unreliable it may be most appropriate to collect data directly from the population concerned. Such primary data is especially valuable for estimating the burden of disease for illnesses that are unlikely to cause people to visit their local health care provider. As such they are particularly valuable for self-reported diarrhoea. They can also be especially valuable in poor or remote communities, with limited access to health care. In these circumstances even people with severe and chronic disease may not come to the attention of the health services. On the other hand such population surveys are poor at identifying uncommon illnesses. These surveys usually involve a questionnaire and this may solely be concerned with determining whether the respondent reports various symptoms to enable a diagnosis on whether or not they satisfy the case definition. Alternately, some form of physical examination, or even laboratory or radiological examination may be included. For example, stool samples may be collected in a study of gastrointestinal disease. Examination of the teeth and radiological examination of the skeleton may be necessary for exposure to fluoride at toxic levels. There are two forms of the population survey; the cross-sectional survey and the cohort study.

Cross-sectional studies are a relatively quick way of getting an estimate of disease incidence or prevalence in a community. Cross-sectional studies look at the disease status of all or a sample of a population at a particular moment in time. In general each individual would be contacted only once. For diseases with seasonal variation in their incidence the results of the survey would clearly depend on what time of year the study was carried out. Conducting repeat studies or lengthening the duration of the data collection period may ameliorate the results. Cross-sectional studies can be conducted using various ways of contacting the participants. The choice of approach will depend on resources available, costs and existing communications. Researchers may contact people by visiting them in their homes, or by post or telephone. Response rates are generally poorest for postal surveys (typically around 50%), slightly better for telephone surveys and best for direct visits to the home. Clearly, if examination needs to be undertaken, the face to face contact is essential.

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A cohort study follows a group of individuals over a period of time. During this period, researchers monitor for the appearance of the disease outcome of interest. Usually the initial contact includes an expanded baseline questionnaire. There are several ways of recording the presence or absence of illness. Probably the simplest method is the repeat visit of the researcher who may contact the participant by phone, post or visit every one or more weeks and ask whether or not there has been any illness since the previous visit. Increasingly email or phone text messages are also being used. Sometimes people are asked to keep a daily diary of symptoms which is then collected by the researcher. An example of a pictorial diary used for recording frequency and consistency of stool is shown in figure 1 (Wright et al. 2006). Some of the advantages and disadvantages of cross-sectional and cohort studies are listed in table 1. In general, cross-sectional surveys are quicker and less costly but they can suffer from recall bias in that people may over-report very recent or current diarrhoea (Boerma et al. 1991). Cohort studies are generally more expensive and take longer, but they are better at detecting time relationships to possible risk factors. Also cohort studies seem to suffer from a fall-off in enthusiasm for reporting (respondent fatigue) which could lead to an under-estimate of actual disease burden (Strickland et al. 1996; Verbrugge 1980).

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Figure 1. Example of a symptom diary (Wright et al., 2006). 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175

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Sampling In the studies described so far it is unlikely that the analysts will have sufficient resources to survey the entire population. What they will have to do is sample a proportion of the actual population. Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a

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population of concern, especially for the purposes of 181 statistical inference. One of the problems with sampling is that it can lead to bias. For example, if only people from the wealthier communities are sampled, disease incidence may be underestimated. In order to avoid systematic sampling errors, it is advisable to select a random sample where each sampling unit (individual, household, family etc) should have an equal probability of selection. As shown in table 2, there are various approaches to random sampling.

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Table 1: Advantages and disadvantages of the cross-sectional and cohort design for estimating disease occurrence (from Bowling and Ebrahim 2005).

Study Design Advantages Disadvantages

Cro

ss-s

ectio

nal

• Quick and relatively easy as no follow-up is required

• Can be used to investigate many exposures and outcomes

• Useful for measuring the true burden of disease in a population

• Problem with direction of causality (reverse causality)

• In aetiological studies, survival bias a potential problem as the sample is based on prevalent rather than incident cases

• Not efficient for rare diseases • Recall bias • Not suitable for diseases of

short duration • Prevalence estimates are

potentially biased by low response rates

• Migration in and out of population influences prevalence

Coh

ort

• Direct measurement of incidence of disease in exposed and unexposed groups

• Time relationships between exposure and disease known

• Reduced bias in exposure measurement

• Multiple outcomes can be studied

• Allows direct calculation of attributable risk

• Effects of rare exposures can be evaluated by careful selection of cohorts

• Natural history of disease can be evaluated

• Can be expensive and time-consuming

• Not useful for rare diseases • Historical cohorts are very

dependent on quality of records • Losses of follow-up can bias

findings (selection bias) • Outcome assessment can be

influenced by knowledge of exposure (information bias)

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The choice of sampling method will depend upon a number of factors, including time, resources and study design. A combination of sampling strategies may be employed. For example, in a study conducted in the Republic of Congo, Mock et al. (1993) selected an equal number of villages from four provinces (stratified cluster sampling). Subsequently, a systematic sample of women with children <30 months was selected from each village. In Zimbabwe,

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Waterkeyn and Cairncross (2005) took a representative systematic sample of 25 health clubs from each of two districts. Three hosts (the 10

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th, 20th and 30th member) and 4 neighbours of each host belonging to the health club were selected from each health club register (cluster sample), resulting in 15 members from each health club.

The sample chosen for analysis should be representative of the population under study and large enough to make statistical inference. Where prevalence of a condition is low, a larger sample is required. If a sample is too small, there will be a lack of precision in results. Specific statistical methods exist for calculating required sample size; an array of text books and journal articles cover this in more detail (see: Woodward, 2005; Kirkwood and Sterne, 2006). If possible, it is advisable to contact a statistician before selecting a sample and designing the survey or investigation.

Utilizing existing local health data A less costly and usually more rapid way of determining disease occurrence is to use existing data collected by local health services. Ethical issues still usually apply to collecting such data, especially when it is possible to identify which individual relates to each report. The big problem with these datasets is that they are based only on people who are ill enough or have sufficient resources to present themselves to the health services. Surveillance may not even capture all people who present to health care. For example if surveillance is based on laboratory positive results then patients who do not have samples taken will not be included in the analysis. This is the so-called reporting pyramid (Wheeler et al. 1999; O’Brien 2007).

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222 Table 2. Approaches to sample selection Sampling Method

Description

Simple random sampling

Each individual or unit is numbered and a sample of the required size is selected using a random numbers table or a computer generated list of random numbers.

Systematic random sampling

A system is in place to select a smaller sample from a larger population. Sampling may begin at a random point but all individuals or units thereafter follow sequence. If the list is ordered in a particular way, this can lead to serious bias. If the list is ordered in a random way, this is called Quasi-random sampling.

Stratified random sampling

The inventory of people is divided into specific subgroups (strata) of, for example, age and sex categories, and a random sample is drawn from each. This sampling method may be practiced when a specific subgroup is over-represented and an even distribution of people in strata is required.

Cluster random sampling

The population is divided into sub-populations (clusters) and a random sample of these clusters is selected (this differs from stratified sampling where all subgroups are selected). For example, to minimise travel costs and time, it may be appropriate to randomly select a number of villages from within a rural area.

Multistage random sampling

A random sample of subgroups is drawn from the target population; further random samples can be drawn from these subgroups.

Sampling for telephone interviews

A list of numbers in the study area is obtained or generated (non-residential numbers should be excluded from the sample) and a random is sample is subsequently selected either manually or by means of random digit dialling. A less resource intensive method is the use of computer generated lists

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Examples of the types of health care data that may be available include:

• National morbidity and mortality data • Consultation data

o Hospital episode and admissions data o Emergency department visits (e.g. Heffernan et al. 2004) o General practice or other health post/clinic consultation data

(Boussard et al. 1996)

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o Telephone helplines (Rodman et al. 1998; Cooper et al. 2003)).

• Specialist communicable/infectious disease surveillance centres: o International (e.g. WHO, Enter-net) o National (e.g. CDSC in the UK, CDC in the US, KTL in

Finland, RIVM in the Netherlands etc.) o Regional or local centres

• Proxy indicators of EID and EID outbreaks in the community: o Anti-diarrhoeal pharmaceutical sales or prescriptions (Sacks et

al., 1986; Beaudeau et al. 1999; Edge et al. 2004) o School/work absenteeism records.

Extrapolating from previous studies It may well be the case that researchers have already conducted studies in the community of interest. Alternately, it is likely that others have conducted studies that have estimated disease occurrence rates in similar communities to the one of interest. If these studies have been published in the peer-reviewed literature they will be listed on one of the on-line searchable databases such as PubMed, MEDLINE and ISI Web of Science. PubMed is available free of charge.

When using existing literature it is important to critically assess the quality of the study and the extent to which can be extrapolated at least to the community of interest. Recommendations on how to assess the quality of such studies are published elsewhere (Blettner et al. 2001; Khan et al. 2001; Rushton, 2000; Downs & Black, 1998).

A particularly valuable source of information on diarrhoeal disease incidence in developing countries is provided by the Measure DHS (Demographic and Health Surveys) Project http://www.measuredhs.com/. This is a USAID funded project which has been collecting and analysing data from developing nations since 1984. For many countries there may be reports already available that give estimates of diarrhoeal incidence, albeit only in children under five years old.

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Using existing global estimates Probably the most common illness linked to inadequate water and sanitation is diarrhoeal disease. There have been various attempts to estimate diarrhoeal disease burden globally (Lopez et al. 2001; Bern 2004; Prüss and Havelaar 2001). Diarrhoeal disease is one of the most significant contributors to preventable disease burden. It is estimated that diarrhoeal diseases are the forth most common cause of death and second most important contributor to disease burden globally (Prüss and Havelaar 2001). There are an estimated 3 million deaths and almost 100 million DALYs lost (explained in Chapter 9) from diarrhoeal disease per year. The burden of disease due to diarrhoeal illness falls most heavily on the youngest and especially children under five years old.

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Diarrhoeal disease also predominantly affects the poorest countries and the poorest communities within those countries. Diarrhoea does not even feature in the top 10 causes of disease burden in developed countries (Prüss and Havelaar 2001). Appendix 1 gives a table with estimated mortality and morbidity from diarrhoeal disease.

ESTIMATING DISEASE ATTRIBUTABLE TO A SPECIFIC ENVIRONMENTAL RISK Once studies as described above have shown that a particular disease is an important public health problem in the community, the next step is to identify the important environmental risk factors that are driving the occurrence of that disease. There are three approaches to estimating the contribution of a risk factor; some form of quantitative risk assessment, one or more epidemiological investigations, or the use of pre-existing global estimates. Furthermore, the DHS data sources referred to above can be used to estimate disease burden attributable to poor water and sanitation.

Risk assessment Quantitative Microbial Risk Assessment (QMRA) has become a popular tool in recent years especially in North America. QMRA uses existing data about the infectivity and distribution of pathogenic microorganisms or indicator bacteria to estimate risk to human health. The four stages of a QMRA are:

1. Hazard Identification 2. Exposure Assessment 3. Dose-Response Analysis 4. Risk Characterisation An accessible review of QMRA methodology is given elsewhere (Hunter et

al. 2003). Howard et al. (2006) give a good example of its use in developing country small rural settings.

QMRA has advantages over epidemiological studies where the disease under investigation is uncommon; where the costs of an epidemiological study would be too great or where serious time constraints apply. On the other hand the input variables and especially the exposure values may not be known with any degree of accuracy and so there may be large uncertainty around the results.

It is highly unlikely that the analyst will be able to generate dose-response data de novo, as there will be plenty of studies already reported that have this information. For some chemical agents such as arsenic there are reasonably well defined relationships between concentration of arsenic in drinking water and risk of disease (WHO, 2001). The principle problem with arsenic is that there are different disease outcomes, such as skin lesions and cardiovascular disease each of which have different dose-response curves. For microbiological dose

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response curves these have had to be extrapolated, often from minimal data and so carry with them significant uncertainty. Another issue with microbiological dose-response curves is that they are usually pathogen-specific and so require data on or estimations of the concentrations of multiple pathogens likely to be present in the drinking-water options under investigation.

Contrary to dose-response data, exposure data will often be specific to the community under investigation. However, national or local authorities such as health agencies, administrative bodies or water and sewerage utilities often undertake routine analyses of water quality in their jurisdictions. Even if routine datasets are not available there may well be previous studies that have collected water samples for analysis in the community of interest or in other similar communities.

Primary data collection would include collection of data on basic microbial or chemical analyses such as E. coli counts or arsenic concentrations. Primary data collection for risk assessment would also include basic sanitary surveys.

It is unlikely that sampling for specific pathogens such as Cryptosporidium or noroviruses would be available. The analyst is referred to the publications by (Hunter et al. 2003) and Howard et al. (2006) for further information and particularly for information on how to conduct the risk characterisation.

Epidemiological approaches There are several epidemiological methods available to estimate the contributions of specific risk factors or transmission pathways to disease burden. These include ecological studies, case-control studies, cohort studies and prospective studies. In regard to waterborne disease, the advantages and disadvantages of these different methods have been discussed in detail elsewhere (Hunter et al. 2002). In addition to the book by Hunter et al. (2002) there are many introductory text books on epidemiology and consequently this discussion will not concentrate on describing such methods.

One of the easiest epidemiological approaches is the ecological study. In ecological studies, the unit of observation and analysis is at the group, population or community level rather than at the level of the individual. Frequently the data used for observation and analysis is derived from existing data sources (secondary data). The Geographical Information Systems (GIS) described in box 1 can be very useful in compiling and analysing this type of data. One of the simplest ecological approaches is to estimate disease incidence in areas with high and low exposure to the potential risk factor such as poor quality water. However, such studies may be susceptible to significant confounding. For example, relatively wealthy populations often have access to high quality water. In the case of a poor community with no sanitation, answers to the question how much of the excess illness compared to a wealthy

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community with well managed sanitation is due to sanitation and how much to other factors linked to wealth will be full of confounding issues. Box 1

A Geographic Information System (GIS) is a computer based graphics software program which enables the user to capture, store, manipulate, analyse and display spatially referenced data from a number of sources. Separate data sources based on geographically referenced information can be connected via relational databases and used to display any number of data attributes in the form of maps. The integrated information can be used in local and regional resource and environmental planning initiatives as well as spatial studies of infectious disease. For example, in Germany GIS has been applied to spatial patterns of diarrhoeal illness in relation to groundwater and surface water supplies (Dangendorf et al., 2002). In Nigeria, GIS was used to evaluate the health impact (diarrhoeal illnesses) of 39 separate water sources displayed in terms of layers, such as, hydrology, geology, and environmental pollution (Njemanze et al., 1999). Examples of GIS software include EDINA Digimap (for example, through which the UK Ordnance Survey Data Collection can be accessed) and ESRI ArcGIS 9.1 (http://www.esri.com). The University of Edinburgh hosts a site with a comprehensive index of GIS resources: http://www.geo.ed.ac.uk/home/giswww.html. It should be noted that when any type of data is gathered at diverse geographical areas it may be difficult to compile at the small area level without complex analysis and this can lead to problems of interpretation.

Cross-sectional studies have been discussed above for the collection of disease incidence data. Such studies can also be useful for collecting data on potential risk factors.

In case-control studies people with an illness are interviewed about past exposure to possible risk factors. The same questions are also asked of controls (people without the disease). Assumptions about the importance of particular risk factors are then made, based on statistical analyses of the proportion of cases and controls reporting exposure. Case control studies are valuable for investigating multiple risk factors. However, they are usually around a single disease and it can be difficult to use the output from these studies for estimating disease burden attributable to a single transmission pathway By comparison, cohort studies follow people with different levels of exposure (say those with and without access to improved sanitation) and observe how much disease develops in each group. The big advantage is that many different disease outcomes can be observed. However, cohort studies are more costly than case-control studies as people are followed over time.

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An intervention may constitute new sanitation facilities, improved access to water, a change in the water treatment process, or the introduction of educational or behavioural programs. Interventions can be introduced in a variety of study designs including cross-sectional, cohort and randomised controlled trial (RCT). Intervention studies can be conducted under natural conditions (i.e. accidental trials, such as, outbreaks), under uncontrolled conditions (i.e. public measures, such as, the introduction of a new water treatment plant), or under controlled conditions (i.e. clinical trials or field studies) (Payment and Hunter 2003).

A particular form of intervention study is the Randomised Controlled Trial. The RCT is one of the most robust epidemiological study designs and permits simultaneous comparison of outcomes in a group of individuals. Study participants are randomly assigned to one or more intervention groups expected to influence disease status, or the control group which receives either the status quo or a placebo (sham) intervention. A comprehensive review of randomised and quasi-randomised controlled trials assessing diarrhoeal disease according to water quality intervention can be found in Clasen et al. (2006).

Existing global and regional estimates In the absence of local data, or data from similar situations, use can be made of global estimates by determining what water and sanitation scenarios communities fit into. This will then give rough estimates of the disease risk attributable to inadequate water, sanitation and hygiene. There have been a number of attempts to estimate the global burden of disease that may be attributable to unimproved drinking water, sanitation and hygiene (Prüss et al. 2002). The authors estimated that globally these factors are responsible for 4.0% of all deaths and 5.7% of disease burden. The authors then went on to define different scenarios of water and sanitation provision and estimated deaths and attributable disease burden for each (Prüss et al. 2002). These scenarios are presented in table 3. Table 3: Exposure scenariosa

Scenario Description Environmental faecal-oral pathogen load

Minimum and realistic relative risk of diarrhoeal disease relative to scenario 1

VI Population not served with improved water supply and no improved sanitation in countries which are not extensively cover by those services (less than 98% coverage), and where water supply is not likely to be routinely controlled

Very high 6.1 / 11.0

Vb* Population having access to improved water Very high 4.9 / 8.7

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supply but not served with improved sanitation in countries which are not extensively covered by those services (less than 98% coverage), and where water supply is not likely to be routinely controlled

Va* Population having access to improved sanitation but no improved water supply in countries where less than 98% of the population is served by water supply and sanitation services, and where water supply is likely not to be routinely controlled.

High 3.8 / 6.9

IV Population having access to improved water supply and improved sanitation in countries where less than 98% of the population is served by water supply and sanitation services and where water supply is likely not to be routinely controlled.

High 3.8 / 6.9

III Piped water in-house and improved sanitation services in countries where less than 98% of the population is served by water supply and sanitation services, and where water supply is likely not to be routinely controlled.

High 2.5 / 4.5

II Population having access to piped water in-house where more than 98% of the population is served by those services; generally corresponds to regulated water supply and full sanitation coverage, with partial treatment of sewage and is typical in developed countries.

Medium to low

2.5 / 2.5

I Ideal situation, corresponding to the absence of transmission of diarrhoeal disease through WSH

Very low 1 / 1

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a Based on Prüss et al. 2002; Prüss-Üstün et al. 2004 * Transitions between exposure levels Va and Vb do not generally occur Scenario I represents the minimum theoretical risk, namely no disease transmission through unsafe WSH; scenario II is the situation typically encountered in developed countries. These scenarios have very low to medium loads of faecal-oral pathogens in the environment, characterized by more than 98% coverage of improved water supply and sanitation. Scenarios III-VI are based in a high faecal-oral pathogen environment, typical for developing countries with less advanced water and sanitation provision. Scenario III represents piped water in-house and improved sanitation but does not occur widely.

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NON-COMMUNICABLE DISEASE 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453

In contrast to disease of microbiological origin, burden of disease estimates due to chemical contamination at a global level are less well developed. A wide range of chemical and radiological contaminants of drinking water have been implicated in human disease (Hunter 1997). Although toxicological assessments do not have to deal with the problems of prior immunity, there are considerable difficulties in assessing disease burden. There is still some considerable debate over whether or not many chemicals in water actually cause disease such as aluminium and dementia (Hunter 1997; Flaten 2001). Even where the association is accepted the balance of acute disease and latent or chronic disease may be far from clear. Recent considerations of burdens of disease due to chemical contaminants of water have included nitrate (Fewtrell 2004), arsenic (Fewtrell et al. 2005) and fluoride (Fewtrell et al. 2006).

Musculoskeletal disease makes a significant contribution to global disease burden that causes substantial disability in both the developed and developing world (Brooks 2006). How much of that burden of disease is due to carrying water is unclear. There is uncertainty over the total disease burden and the proportion due to carrying water. It is likely that the major health effect of carrying water is low back pain and global estimates of disease burden due to back pain are lacking (Brooks 2006). Also the authors are not aware of any good studies directly linking the carrying of water and back pain. When such information is lacking then the assessor may have to extrapolate from studies of similar exposures. For example, the finding that children that carry backpacks to school that are too heavy may suffer from increased musculoskeletal symptoms (Moore et al. 2007). In the absence of any such study the assessor may have to fall back on a process of soliciting expert opinion.

Non-communicable disease poses a number of challenges for conducting an economic analysis. These include:

• As already discussed, existing epidemiological evidence is often relatively poor compared to microbiological data.

• More so than for infectious diseases the actual disease burden can vary, substantially, even between very similar communities situated relatively close to each other due to marked differences in contaminant concentrations. (Rahman et al. 2005)

• For some contaminants, drinking-water (either through consumption or through cooking) is the only or predominant source of exposure (e.g. arsenic, Fewtrell et al. 2005). For others, water is one on several possible routes of exposure (e.g. lead, Romieu et al. 1994).

In these cases it may be difficult to determine the disease burden attributable to water exposure. Reliance on national or regional estimates is also likely to be problematic. These problems with assessing non-communicable disease burden

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are probably responsible for their general exclusion from earlier economic analyses. However, given that many of these illnesses would be chronic and may be common in effected communities their contribution to disease burden is likely to be substantial. It is likely that the exclusion of non-communicable disease from analysis would, in certain communities, heavily understate the benefits of water supply improvement.

This chapter has considered different approaches to estimating disease burden within a community and then estimating what proportion of that disease may be attributed to a specific environmental risk factor. Some of the approaches would take considerable resources to implement, though usually the cost-benefit analyst should be able to find previous studies that would sufficient for his/her purposes. Global and regional estimates, though probably lacking precision for any single country may still be sufficient for most purposes, unless there is evidence of chronic disease of a non-communicable nature.

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Appendix 1. Estimated mortality and morbidity from diarrhoeal disease.

Country

Estimated total deaths ('000), by cause

Estimated deaths per 100,000 population

Age-standardized death rates per 100,000 by cause

Estimated total DALYs ('000), by cause

Estimated DALYs per 100,000 population

Age-standardized DALYs per 100,000

Population total ('000)

Total Rural population ('000)

Number rural with unimproved water supply ('000)

Number rural with no tap ('000)

Afghanistan 41.16 179.51 135.69 1343.11 5857.41 3157.16 28574 21716 14984 21716 Albania 0.01 0.17 0.23 1.15 36.72 36.33 3112 1743 105 924 Algeria 8.14 26.05 24.26 295.41 944.82 803.00 32358 13267 2653 5572 Andorra 0.00 0.89 0.41 0.02 26.47 31.42 67 6 0 Angola 48.77 369.90 233.97 1608.10 12197.30 5832.30 15490 9914 5948 9814 Antigua and Barbuda 0.00 0.60 0.57 0.10 131.56 124.71 81 50 6 11 Argentina 0.43 1.13 1.03 49.46 130.23 126.38 38372 3837 767 2110 Armenia 0.06 1.90 3.51 3.52 114.48 167.91 3026 1089 218 370 Australia 0.03 0.17 0.11 5.18 26.53 29.88 19942 1595 0 Austria 0.01 0.06 0.02 1.88 23.22 29.27 8171 2778 0 0 Azerbaijan 0.92 11.08 14.03 36.54 440.47 461.75 8355 4178 1713 3384 Bahamas 0.00 1.23 1.49 0.40 130.09 119.78 319 32 4 6 Bahrain 0.01 1.47 5.64 0.61 85.77 88.56 716 72 Bangladesh 68.19 47.41 41.25 2297.81 1597.83 1095.60 139215 104411 29235 104411 Barbados 0.00 1.33 0.95 0.26 97.43 110.04 269 129 0 Belarus 0.02 0.24 0.41 3.09 31.10 45.95 9811 2845 0 2134 Belgium 0.09 0.88 0.36 2.73 26.50 30.79 10400 312 Belize 0.03 11.41 8.70 1.27 505.77 368.41 264 137 25 51 Benin 7.34 111.95 84.24 241.10 3676.15 2016.08 8177 4497 1934 4407

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Bhutan 1.32 60.39 43.47 43.99 2008.46 1220.03 2116 1926 770 Bolivia 4.17 48.21 38.34 142.28 1645.74 1105.12 9009 3243 1038 1816 Bosnia and Herzegovina 0.01 0.17 0.25 1.29 31.30 40.95 3909 2150 86 494 Botswana 0.36 20.51 21.64 13.30 751.31 522.96 1769 849 85 611 Brazil 17.33 9.83 9.89 735.40 417.23 393.97 183913 29426 12653 24424 Brunei Darussalam 0.00 0.45 0.38 0.32 91.79 79.13 366 84 Bulgaria 0.03 0.33 0.69 2.65 33.30 54.47 7780 2334 70 654 Burkina Faso 21.79 172.64 122.04 717.31 5681.93 2801.47 12822 10514 4836 10514 Burundi 9.29 140.76 95.40 305.80 4632.19 2497.33 7282 6554 1507 6488 Cambodia 11.84 85.74 71.54 386.82 2801.10 1726.07 13798 11176 7265 10953 Cameroon 14.38 91.41 66.46 480.20 3052.94 1864.37 16038 7698 4311 7544 Canada 0.26 0.83 0.46 8.68 27.77 31.77 31958 6072 61 3765 Cape Verde 0.10 22.46 18.13 3.77 830.90 586.62 495 213 57 204 Central African Republic 4.69 122.75 87.61 151.19 3958.76 2310.77 3986 2272 886 2272 Chad 11.38 136.37 96.10 375.06 4493.03 2256.30 9448 7086 4039 6944 Chile 0.25 1.62 1.71 19.10 122.36 117.80 16124 2096 880 1300 China 108.40 8.32 10.06 5055.36 388.19 441.57 1307989 784793 258982 337461 Colombia 2.66 6.10 5.59 133.79 307.37 259.73 44915 10330 2996 5062 Comoros 0.39 51.98 43.31 13.26 1776.08 1047.90 777 497 90 477 Congo 1.57 43.20 39.24 52.06 1433.11 800.35 3883 1786 1304 1715 Cook Islands 0.00 5.45 4.63 0.05 249.06 195.69 18 5 1 Costa Rica 0.12 2.92 3.63 5.79 141.44 130.00 4253 1659 133 315 Côte d'Ivoire 16.67 101.84 87.84 539.23 3295.03 2065.51 4540 1861 0 651 Croatia 0.01 0.20 0.17 1.16 26.14 32.12 11245 2699 594 1376 Cuba 0.23 2.03 1.66 12.61 111.88 123.41 826 256 0 0 Cyprus 0.00 0.53 0.72 0.62 78.41 92.41 10229 2660 0 239 Czech Republic 0.01 0.06 0.04 2.36 23.08 29.33 17872 9830 2556 9338 Democratic People's Republic of Korea 3.21 14.23 16.47 129.33 573.74 602.45 22384 8730 0 2532 Democratic Republic of the Congo 112.21 219.15 148.16 3669.63 7167.11 3557.20 55853 37980 26966 37600 Denmark 0.07 1.39 0.61 1.58 29.61 32.38 5414 812 0 0 Djibouti 0.84 120.66 93.21 27.87 4023.51 2252.51 779 125 51 118

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Dominica 0.00 1.78 1.64 0.11 146.00 137.45 79 22 2 9 Dominican Republic 1.25 14.50 13.20 49.23 571.36 473.51 8768 3507 316 1333 Ecuador 1.97 15.39 13.48 77.46 604.67 493.75 13040 4955 545 2725 Egypt 13.13 18.63 16.62 464.12 658.26 503.96 72642 42132 1264 10954 El Salvador 0.96 14.92 12.16 37.93 591.31 446.31 6762 2705 811 1677 Equatorial Guinea 0.46 94.88 64.10 14.87 3089.71 1686.11 492 251 146 251 Eritrea 2.58 64.75 59.15 87.03 2180.72 1216.58 4232 3386 1456 3386 Estonia 0.00 0.04 0.08 0.34 25.75 35.69 1335 401 4 108 Ethiopia 63.16 91.58 66.07 2099.77 3044.86 1634.76 75600 63504 56519 63504 Fiji 0.06 6.65 10.72 1.67 200.65 181.35 841 404 198 375 Finland 0.03 0.59 0.29 1.35 26.03 30.78 5235 2042 0 163 France 0.55 0.93 0.46 18.10 30.24 33.91 60257 14462 0 0 Gabon 0.41 31.70 23.30 14.72 1126.65 732.57 1362 204 108 188 Gambia 0.94 67.72 54.65 31.11 2240.50 1350.47 1478 1094 252 1061 Georgia 0.04 0.68 0.65 1.95 37.75 47.14 4518 2169 716 1561 Germany 0.27 0.33 0.17 20.38 24.73 30.99 82645 9917 0 298 Ghana 9.69 47.32 41.25 329.34 1608.77 1062.71 21664 11699 4211 11231 Greece 0.00 0.00 0.00 2.50 22.80 29.18 11098 4328 Grenada 0.00 5.09 5.07 0.15 184.50 174.39 102 60 4 15 Guatemala 3.39 28.14 20.79 122.89 1021.04 620.09 12295 6516 521 2281 Guinea 9.48 113.42 90.44 312.12 3733.96 2054.15 9202 5889 3828 5830 Guinea-Bissau 1.63 112.24 82.35 52.91 3652.10 1824.93 1540 1001 511 1001 Guyana 0.31 40.12 43.76 8.82 1155.11 1020.94 750 465 79 256 Haiti 5.56 67.63 51.40 185.46 2256.80 1529.70 8407 5212 2293 5056 Honduras 1.67 24.69 19.16 61.58 908.17 597.36 7048 3806 723 1446 Hungary 0.02 0.19 0.15 2.70 27.26 35.20 10124 3442 69 310 Iceland 0.00 0.39 0.21 0.08 27.55 29.45 292 20 0 0 India 456.36 43.48 38.89 15253.84 1453.37 1152.88 1087124 782729 133064 720111 Indonesia 35.50 16.35 16.68 1264.05 582.16 522.81 220077 116641 36159 109642 Iran (Islamic Republic of) 8.68 12.75 14.79 321.72 472.63 473.40 68803 22705 3633 Iraq 14.06 57.35 47.22 469.50 1915.53 1156.59 28057 9259 4629 6203 Ireland 0.01 0.14 0.08 1.05 26.85 29.24 4080 1632 65 Israel 0.06 0.88 0.66 2.25 35.62 33.07 6601 528 0 11

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Italy 0.04 0.07 0.03 13.07 22.74 29.34 58033 19151 766 Jamaica 0.13 4.91 4.60 7.04 268.10 240.42 2639 1267 152 684 Japan 1.30 1.02 0.52 36.65 28.75 35.11 127923 43494 0 3914 Jordan 0.69 13.02 10.86 26.47 496.70 344.02 5561 1168 105 222 Kazakhstan 0.35 2.29 2.58 15.62 101.00 114.85 14839 6529 1763 4766 Kenya 24.62 78.06 62.21 817.60 2592.22 1661.56 33467 19746 10663 17376 Kiribati 0.03 33.26 28.90 0.97 1120.97 827.41 97 49 23 39 Kuwait 0.01 0.31 0.41 2.07 84.91 78.83 2606 104 Kyrgyzstan 0.66 13.04 11.62 25.94 511.97 440.93 5204 3435 1168 2507 Lao People's Democratic Republic 5.44 98.48 73.21 175.89 3181.17 1913.67 5792 4576 2608 4301 Latvia 0.01 0.35 0.36 0.68 29.07 41.12 2318 788 32 323 Lebanon 0.27 7.56 7.95 11.17 310.65 295.46 3540 425 0 64 Lesotho 1.51 83.87 67.65 49.24 2735.46 1864.97 1798 1474 354 1356 Liberia 4.87 150.38 97.18 159.04 4909.56 2532.50 3241 1718 825 1718 Libyan Arab Jamahiriya 0.44 8.17 7.57 18.18 333.86 281.98 5740 746 Lithuania 0.00 0.12 0.07 0.91 26.28 32.92 3443 1136 500 Luxembourg 0.00 0.84 0.54 0.14 32.16 35.32 459 37 0 1 Madagascar 18.90 111.75 76.11 621.55 3674.25 1993.93 18113 13222 8595 12958 Malawi 19.51 164.32 105.07 631.65 5320.89 2696.91 12608 10465 3349 10255 Malaysia 0.31 1.29 1.31 27.93 116.53 98.23 24894 8962 358 1165 Maldives 0.05 16.57 14.96 1.90 615.41 379.79 321 228 55 228 Mali 22.74 180.19 126.88 750.48 5945.43 2962.38 13124 8793 5628 8617 Malta 0.00 0.00 0.00 0.10 24.80 29.14 400 32 0 1 Marshall Islands 0.01 21.02 18.20 0.39 738.85 548.15 60 20 1 Mauritania 3.61 128.48 86.82 119.66 4262.68 2330.64 2980 1103 617 959 Mauritius 0.01 1.13 1.53 1.44 118.75 120.66 1233 690 0 0 Mexico 5.57 5.46 6.08 253.51 248.63 214.56 105699 25368 3298 7103 Micronesia (Federated States of) 0.04 33.84 28.29 1.23 1139.92 788.02 110 77 5 Monaco 0.00 1.35 0.82 0.01 37.36 42.77 35 0 Mongolia 0.86 33.77 32.75 30.56 1194.13 1047.87 2614 1124 787 1113 Morocco 6.59 21.92 20.84 231.99 771.46 644.88 31020 13028 5732 10814

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Mozambique 30.13 162.54 121.84 979.93 5286.27 2978.92 19424 12237 9055 11992 Myanmar 24.93 51.02 47.60 816.83 1672.03 1376.36 50004 35003 8051 34303 Namibia 0.85 43.19 32.40 29.19 1488.53 903.48 2009 1346 256 902 Nauru 0.00 7.83 6.80 0.04 324.52 245.32 13 0 Nepal 16.73 67.97 54.04 551.57 2241.36 1405.76 26591 22602 2486 20116 Netherlands 0.03 0.20 0.10 4.13 25.68 29.64 16226 5517 0 0 New Zealand 0.01 0.27 0.22 1.15 29.94 32.69 3989 558 Nicaragua 1.40 26.25 20.51 51.22 960.01 604.87 5376 2258 835 1648 Niger 25.32 219.31 160.02 832.39 7210.52 3381.54 13499 10394 6652 10394 Nigeria 134.46 111.21 84.72 4381.77 3623.95 2035.29 128709 66929 46181 64921 Niue 0.00 0.00 0.00 0.00 79.39 67.03 1 0 0 0 Norway 0.09 1.98 0.77 1.44 31.83 33.27 4598 920 0 0 Oman 0.17 6.23 5.55 7.96 287.51 197.99 2534 557 Pakistan 118.40 78.98 57.66 3904.13 2604.30 1570.41 154794 102164 11238 86839 Palau 0.00 4.98 4.33 0.05 235.82 180.45 20 6 0 5 Panama 0.21 6.71 5.89 10.02 327.05 275.27 3175 1365 287 382 Papua New Guinea 2.40 42.94 40.91 80.28 1437.04 909.15 5772 5022 3415 4821 Paraguay 0.86 15.00 11.82 34.53 601.64 415.00 6017 2527 809 1895 Peru 4.40 16.45 14.19 170.76 637.95 511.09 27562 7166 2508 4371 Philippines 12.23 15.56 16.77 411.04 523.08 422.48 81617 31014 5583 23881 Poland 0.03 0.09 0.08 10.37 26.84 33.97 38559 14652 586 Portugal 0.01 0.12 0.10 2.63 26.14 31.70 10441 4698 Qatar 0.01 1.00 1.38 0.63 105.22 99.35 777 62 0 Republic of Korea 0.26 0.54 0.71 62.75 132.29 130.75 47645 9053 2625 5522 Republic of Moldova 0.03 0.76 1.14 2.20 51.57 70.52 4218 2278 273 2073 Romania 0.15 0.66 0.95 10.41 46.51 67.91 21790 9806 8237 8531 Russian Federation 0.87 0.60 0.76 55.26 38.35 54.77 143899 38853 4662 18649 Rwanda 13.27 160.39 121.00 435.51 5264.60 2781.61 8882 7106 2203 7035 Saint Kitts and Nevis 0.00 8.65 9.03 0.11 263.73 262.62 42 29 0 8 Saint Lucia 0.01 4.31 5.15 0.30 202.09 196.63 159 110 2 27 Saint Vincent and the Grenadines 0.00 0.74 0.78 0.16 135.03 122.32 118 48 3 13 Samoa 0.02 9.48 7.39 0.68 382.94 261.73 184 144 19 69

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San Marino 0.00 0.00 0.00 0.01 22.44 29.19 28 3 Sao Tome and Principe 0.06 36.02 25.74 1.98 1265.66 778.23 153 95 26 77 Saudi Arabia 1.68 7.16 6.10 74.49 316.70 211.06 23950 2874 Senegal 7.35 74.56 64.52 247.92 2515.77 1516.13 11386 5693 2277 4725 Serbia and Montenegro 0.15 1.40 2.01 7.54 71.53 97.04 10510 5045 706 1816 Seychelles 0.00 2.00 2.24 0.10 118.61 114.74 80 40 10 10 Sierra Leone 12.90 270.82 170.53 417.93 8771.91 4599.53 5336 3202 1729 3170 Singapore 0.02 0.49 0.54 2.61 62.41 71.00 4273 0 Slovakia 0.01 0.11 0.11 1.48 27.40 34.21 5401 2268 23 159 Slovenia 0.01 0.26 0.15 0.47 23.78 29.66 1967 964 Solomon Islands 0.22 47.68 42.76 7.31 1577.19 947.46 466 387 135 383 Somalia 15.56 164.18 112.61 509.59 5375.36 2565.43 7964 5177 3779 5177 South Africa 13.59 30.37 29.07 478.58 1069.23 898.38 47208 20299 5481 13804 Spain 0.28 0.68 0.32 10.60 25.86 31.23 42646 9809 0 98 Sri Lanka 0.68 3.59 4.18 32.59 172.34 185.02 20570 16250 4225 15600 Sudan 19.45 59.16 46.57 655.02 1992.25 1259.44 35523 21314 7673 18543 Suriname 0.04 9.43 8.57 1.78 412.25 345.98 446 103 28 53 Swaziland 0.61 57.36 44.72 20.72 1937.46 1183.83 1034 786 361 676 Sweden 0.05 0.60 0.21 2.22 24.99 30.20 9008 1531 0 0 Switzerland 0.03 0.37 0.16 1.75 24.40 30.03 7240 2317 0 23 Syrian Arab Republic 2.05 11.81 10.63 78.34 450.70 324.35 18582 9291 1208 2601 Tajikistan 2.03 32.80 28.99 68.33 1103.02 871.72 6430 4887 2541 3909 Thailand 4.64 7.47 8.42 184.97 297.42 307.40 63694 43312 0 36382 The former Yugoslav Republic of Macedonia 0.03 1.71 2.21 1.62 79.29 93.32 2030 812 Timor-Leste 0.14 19.08 18.58 4.55 616.21 518.70 887 816 359 726 Togo 2.96 61.72 50.39 96.75 2015.21 1189.41 5988 3832 2453 3832 Tonga 0.01 8.78 6.82 0.36 355.74 258.82 102 68 0 16 Trinidad and Tobago 0.02 1.28 1.43 1.44 110.67 116.37 1301 312 37 103 Tunisia 0.66 6.75 7.58 27.49 282.59 294.04 9995 3598 648 2231 Turkey 6.94 9.87 10.86 235.89 335.47 296.89 72220 23833 1668 4052

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Turkmenistan 1.10 22.88 20.97 38.91 811.64 708.23 4766 2574 1184 1827 Tuvalu 0.00 28.66 24.37 0.10 971.75 744.16 10 4 0 Uganda 30.22 120.86 86.24 988.55 3953.64 1907.56 27821 24482 10772 24482 Ukraine 0.16 0.32 0.51 16.16 33.04 48.14 46989 15506 1396 8063 United Arab Emirates 0.02 0.75 1.09 2.78 94.65 95.13 4284 643 0 193 United Kingdom 0.64 1.08 0.52 17.55 29.72 34.21 59479 6543 0 131 United Republic of Tanzania 31.89 87.91 74.31 1058.77 2918.63 1652.12 37627 24081 12281 23359 United States of America 1.49 0.51 0.30 84.23 28.94 30.94 295410 59082 0 0 Uruguay 0.09 2.80 1.96 4.49 132.48 134.58 3439 241 0 39 Uzbekistan 0.54 2.12 2.01 37.74 146.80 126.64 26209 16774 4193 12580 Vanuatu 0.03 13.77 11.02 1.06 513.71 336.92 207 159 77 115 Venezuela (Bolivarian Republic of) 1.58 6.28 6.99 71.44 283.19 239.56 26282 3154 946 1230 Viet Nam 10.75 13.39 13.82 395.50 492.66 461.44 83123 61511 12302 57820 Yemen 19.06 98.69 73.18 623.05 3225.67 1634.92 20329 15043 5265 13539 Zambia 15.50 144.88 110.58 501.27 4685.65 2522.23 11479 7347 4408 7200 Zimbabwe 6.18 48.17 41.99 204.68 1594.65 1039.85 12936 8408 2354 7988

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