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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