Life and Death in African Slums
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Transcript of Life and Death in African Slums
Kenneth HillLIFE AND DEATH IN AFRICAN SLUMS
Seminar 4th February 2013: Center on Population Dynamics, McGill University
Günter Fink, Harvard School of Public Health Isabel Günther, ETH ZurichKenneth Hill, Harvard School of Public Health
This presentation focuses on sub-Saharan Africa, and is part of a larger project focused on the developing world as a whole; Günter and Isabel have no responsibility for what I present.
RESEARCH TEAM
Up to the 20 th century, urban areas had large mortality (and presumably health more generally) penalties
With the application of broad public health measures, the urban advantage in now-developed countries disappeared and eventually reversed in only a few decades
Rapid urbanization in the developing world from about 1950 has not apparently been associated with emerging urban disadvantages in health and mortality indicators
DHS data show a consistent pattern of urban advantage in child mortality
SOME STYLIZED FACTS
RATIOS OF URBAN TO RURAL MORTALITY BY AGE RANGE IN DHS SURVEYS
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SSA NA/ME C. Asia S. Asia E & SE Asia LA & C
Neonatal Post-NeonatalAges 1 to 5
Source: DHS Statcompiler
In general, urban advantage averages about 25%No dramatic differences in advantage by world region
The one outlier is neonatal mortality for Central Asia, unstable because of low fertility
In all regions, the greatest average advantage is for the post-neonatal period But differences are typically small
So our question is: Do the average urban advantages mask large intra-urban differentials? In particular, do “slums” do especially badly?And if so, can we identify any mediating factors ?
BROAD URBAN-RURAL PATTERNS
SLUMS ARE NOT PRETTY …
Focus on child health and mortality rather than adultExpected to be more sensitive to living conditions
Better measured from DHS-type surveys More events given developing country age distributions
Use Demographic and Health Surveys Over 200 surveys covering a high proportion of the
population of the developing world Sampling methodology selects clusters of households Information is collected on child mortality (full birth history)
and child health status (anthropometry and recent disease episodes) among other things
Socio-economic data to identify “slums”
BUT ARE THEY UNHEALTHY? ANALYTIC STRATEGY
DEFINITIONS AND DATA
The UN Habitat definition of a “slum” household is any household lacking any one of: Improved water Improved sanitation Durable structure Sufficient living space Security of tenure
We find this definition too broad. In our view, a “slum” is a neighbourhood concept, an area
of concentrated poverty in a large urban conglomerationOur preferred definition is any household in a sample
cluster in which at least 75% of households lack at least two of the first four above
We have no reliable data on the fifth criterion
WHAT IS A “SLUM”?
Limit to sub-Saharan Africa for this sub-analysisCountries with
at least one city of 1+ million in 2010 (as estimated by the UN Population Division)
DHS surveys with information on housing characteristics
Leaves 91 surveys from 36 countries
INCLUSION CRITERIA
DISTRIBUTION OF SURVEYS (91) AND COUNTRIES (36)
Slum: all households in an urban DHS cluster in which 75% lack 2 or more of Clean water (piped, borehole or protected well) Good excreta disposal (other than defecation in the open or
unimproved pit latrine) Adequate space (3 or fewer people per habitable room) Solid construction (floor of material other than earth, dung,
sand or wood)Distinguish between “cities” and “towns”
“City” we define as an urban area with a population of 1 million or more, “towns” are all other areas classified as urban
In surveys of 25 countries, this can be done using the “province” variable
But in 11 countries, for example those with several large cities, this was ambiguous
DEFINITIONS
DISTRIBUTION OF CHILDREN BY SLUM/NON-SLUM HOUSEHOLD
DEFINITIONSlum Definition
Non-Slum Slum Non-Slum Slum% % % %
U.N. Habitat (1 Indicator) 18.65 81.35 17.01 82.991 Indicator in 50% of Cluster Households 11.07 88.93 9.52 90.481 Indicator in 75% of Cluster Households 26.82 73.18 23.56 76.442 Indicators 57.16 42.84 48.33 51.672 Indicators in 50% of Cluster Households 54.29 45.71 43.85 56.152 Indicators in 75% of Cluster Households 77.38 22.62 67.21 32.79
31.17% 68.83%Large City Other City and Town
Unweighted data; N = 165,285
Additional impact (over SES effect)of living in a slum we expect to be environmental
Environmental conditions expected to have different effects on different age ranges Neonatal (< 1 month) Postneonatal (1 to 11 months) Child (1 to 3 years)
Limited to exposure in 3 years before survey to reduce effects of population mobility
We use episode of diarrhoea in 2 weeks before interview and stunting (< 2 SD’s below mean height for age) as outcomes for surviving children only
CHILD MORTALITY AND HEALTH OUTCOMES
DESCRIPTIVE STATISTICS
Indicator Non-Slum Slum Non-Slum Slum
Neonatal death 0.0367 0.0312 0.0345 0.0287 0.0318Post-neonatal death 0.0457 0.0340 0.0455 0.0271 0.0336Child death 0.0455 0.0333 0.0469 0.0251 0.0352Child stunted 0.4602 0.3089 0.3945 0.2740 0.3467
Number of children born by mother 4.3937 3.6198 4.0916 3.4324 3.8989Education of mother (years) 2.5939 5.2300 3.6056 6.0760 4.5795Household lacks improved sanitation 0.9267 0.6341 0.9334 0.6338 0.9233Household lacks improved water 0.6095 0.1524 0.5030 0.1100 0.3949Household lacks improved floor 0.7888 0.1914 0.5899 0.1216 0.4011Household is overcrowded 0.6199 0.5767 0.6954 0.5955 0.7752
Health infrastructureReceived DPT3 0.5017 0.6592 0.5559 0.5559 0.6251Problem - distance to health facility 0.5510 0.2023 0.2972 0.2972 0.2580Problem - money to treat disease 0.6400 0.4233 0.5422 0.5422 0.5990
Town City
Health Outcomes
Child and household characteristics
Rural
Unweighted data; N = 611,459
EMPIRICAL MODEL
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To explore differentials in more detail, we fit the following logistic model and sequentially add controls
where pijk is the probability of death of child i in cluster j and survey k, Rj are variables for residence, and Sk are survey fixed effects
We also subsequently control for a set of mother characteristics not directly associated with those used to define a slum household
RESULTS
UNCONDITIONAL ASSOCIATIONS
Note: Standard errors are clustered at the survey-cluster level.• Urban areas (slum and non-slum) have advantage over rural areas
on all indicators• Non-slum indicators are always better than slum indicators• City slum indicators are generally better than town non-slum
indicators
Indicator Non-Slum Slum Non-Slum Slum
Neonatal death Ref. 0.865*** 0.899** 0.749*** 0.821**Post-neonatal death Ref. 0.766*** 0.934* 0.563*** 0.645***Child death Ref. 0.737*** 0.973 0.494*** 0.678***Diarrhea last 14 days Ref. 0.768*** 0.965 0.794*** 0.945Child stunted Ref. 0.541*** 0.763*** 0.447*** 0.632***
Odds Ratios
RuralTown City
Health Outcomes
p values: ***<.001; **<0.01;*<0.05
IS THE EFFECT MEDIATED BY MOTHER’S EDUCATION?
Note: Standard errors are clustered at the survey-cluster level.• Effects are uniformly smaller• Slum advantage for diarrhoea disappears• On other outcomes city slums still do much better than rural
areas• Mother’s education is strongly protective for all outcomes
Mother'sIndicator Non-Slum Slum Non-Slum Slum Education
(Years)Neonatal death Ref. 0.908*** 0.920* 0.797*** 0.851** 0.982***Post-neonatal death Ref. 0.851*** 0.985 0.648*** 0.703*** 0.960***Child death Ref. 0.834*** 1.036 0.582*** 0.748** 0.952***Diarrhea last 14 days Ref. 0.834*** 1.005 0.888*** 1.011 0.970***Child stunted Ref. 0.635*** 0.823*** 0.550*** 0.718*** 0.941***
Odds Ratios
RuralTown City
Health Outcomes
p values: ***<.001; **<0.01;*<0.05
OR BY ACCESS TO HEALTH SERVICES?
Note: Standard errors are clustered at the survey-cluster level.• Controlling for whether mothers report access to health
services to be a problem wipes out any town slum advantage (except for stunting) but increases the city slum and non-slum advantage
Indicator Non-Slum Slum Non-Slum Slum Distance Money
Neonatal death Ref. 0.863*** 0.967 0.721*** 0.730** 1.025 1.004Post-neonatal death Ref. 0.764*** 0.972 0.557*** 0.597*** 1.031 1.066*Child death Ref. 0.710*** 0.974 0.464*** 0.652* 1.005 1.104**Diarrhea last 14 days Ref. 0.807*** 1.024 0.835*** 0.914 1.014 1.177***Child stunted Ref. 0.574*** 0.797*** 0.469*** 0.701*** 1.044** 1.150***
p values: ***<.001; **<0.01;*<0.05
Access Problem:Odds Ratios
RuralTown City
Health Outcomes
OR BY A COMBINATION OF BOTH?
Note: Standard errors are clustered at the survey-cluster level.• Town slums now do no better than rural areas except for stunting,
but city slums still do better on most outcomes
Mother'sIndicator Non-Slum Slum Non-Slum Slum Education Distance Money
(Years)Neonatal death Ref. 0.893** 0.983 0.751*** 0.762* 0.987*** 1.021 0.994Post-neonatal death Ref. 0.843*** 1.025 0.642*** 0.667** 0.960*** 1.020 1.036Child death Ref. 0.799*** 1.040 0.549*** 0.750 0.952*** 1.005 1.104**Diarrhea last 14 days Ref. 0.876*** 1.069 0.940 0.999 0.967*** 1.014 1.146***Child stunted Ref. 0.665*** 0.857*** 0.569*** 0.802** 0.943*** 1.025 1.100***
p values: ***<.001; **<0.01;*<0.05
Odds Ratios
RuralTown City Access Problem:
Health Outcomes
How should we define a “slum”?Do DHS clusters reflect neighbourhoods?
Generally based on census enumeration areas, so probably yes
Slums might be expected to do better than rural areas because of better access to health services Is there a better way to capture this than mother’s reports?
Are we missing important mediating or confounding factors? Limited choices because of variables included in the “slum”
definition
DISCUSSION
Children in city slums have better health outcomes than rural children And generally better than children in non-slum areas of
townsChildren in town slums have worse outcomes than
children in city slums, but generally better than those in rural areas
These advantages are partly explained by: the better educational profile of slum mothers Fewer reported problems with access to health services in
town slumsFor one outcome – stunting – urban children whether
in slums or not have much better outcomes than rural children
The mortality advantage is generally largest for children aged 1 to 3 years
CONCLUSIONS
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