Transcript "Life and Death in African Slums"

LIFE AND DEATH IN
AFRICAN SLUMS
Seminar 4th February 2013: Center on Population Dynamics,
McGill University
Kenneth Hill
RESEARCH TEAM
 Günter Fink, Harvard School of Public Health
 Isabel Günther, ETH Zurich
 Kenneth 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.
SOME ST YLIZED FACTS
 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
RATIOS OF URBAN TO RURAL MORTALIT Y
BY AGE RANGE IN DHS SURVEYS
2
1.75
1.5
1.25
1
.75
.5
.25
SSA
NA/ME
C. Asia
Neonatal
Ages 1 to 5
Source: DHS Statcompiler
S. Asia
E & SE Asia
Post-Neonatal
LA & C
BROAD URBAN-RURAL PATTERNS
 In general, urban advantage averages about 25%
 No dramatic dif ferences 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 ?
SLUMS ARE NOT PRETT Y …
BUT ARE THEY UNHEALTHY?
ANALY TIC STRATEGY
 Focus on child health and mortality rather than adult
 Expected 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”
DEFINITIONS AND DATA
WHAT IS A “SLUM”?
 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 conglomeration
 Our 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
INCLUSION CRITERIA
 Limit to sub-Saharan Africa for this sub -analysis
 Countries 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
DISTRIBUTION OF SURVEYS (91) AND
COUNTRIES (36)
DEFINITIONS
 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
DISTRIBUTION OF CHILDREN BY SLUM/NON SLUM HOUSEHOLD DEFINITION
Slum Definition
Large City
31.17%
Non-Slum Slum
%
%
U.N. Habitat (1
Indicator)
1 Indicator in 50% of
Cluster Households
1 Indicator in 75% of
Cluster Households
2 Indicators
2 Indicators in 50% of
Cluster Households
2 Indicators in 75% of
Cluster Households
Unweighted data; N = 165,285
Other City and Town
68.83%
Non-Slum Slum
%
%
18.65
81.35
17.01
82.99
11.07
88.93
9.52
90.48
26.82
57.16
73.18
42.84
23.56
48.33
76.44
51.67
54.29
45.71
43.85
56.15
77.38
22.62
67.21
32.79
CHILD MORTALIT Y AND HEALTH
OUTCOMES
 Additional impact (over SES ef fect)of living in a slum we
expect to be environmental
 Environmental conditions expected to have dif ferent ef fects
on dif ferent 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 ef fects
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
DESCRIPTIVE STATISTICS
Indicator
Health Outcomes
Rural
Town
Non-Slum Slum
City
Non-Slum Slum
Neonatal death
Post-neonatal death
Child death
Child stunted
0.0367
0.0457
0.0455
0.4602
0.0312
0.0340
0.0333
0.3089
0.0345
0.0455
0.0469
0.3945
0.0287
0.0271
0.0251
0.2740
0.0318
0.0336
0.0352
0.3467
Child and household characteristics
Number of children born by mother
Education of mother (years)
Household lacks improved sanitation
Household lacks improved water
Household lacks improved floor
Household is overcrowded
4.3937
2.5939
0.9267
0.6095
0.7888
0.6199
3.6198
5.2300
0.6341
0.1524
0.1914
0.5767
4.0916
3.6056
0.9334
0.5030
0.5899
0.6954
3.4324
6.0760
0.6338
0.1100
0.1216
0.5955
3.8989
4.5795
0.9233
0.3949
0.4011
0.7752
Health infrastructure
Received DPT3
Problem - distance to health facility
Problem - money to treat disease
0.5017
0.5510
0.6400
0.6592
0.2023
0.4233
0.5559
0.2972
0.5422
0.5559
0.2972
0.5422
0.6251
0.2580
0.5990
Unweighted data; N = 611,459
EMPIRICAL MODEL
To explore differentials in more detail, we fit the
following logistic model and sequentially add controls
4
71
pick
ln(
)     R j  j  Sk k   ick ,
1  pick
j 1
k 1
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
Indicator
Health Outcomes
Neonatal death
Post-neonatal death
Child death
Diarrhea last 14 days
Child stunted
Rural
Ref.
Ref.
Ref.
Ref.
Ref.
Odds Ratios
Town
City
Non-Slum Slum Non-Slum Slum
0.865***
0.766***
0.737***
0.768***
0.541***
0.899**
0.934*
0.973
0.965
0.763***
0.749***
0.563***
0.494***
0.794***
0.447***
0.821**
0.645***
0.678***
0.945
0.632***
p values: ***<.001; **<0.01;*<0.05
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
IS THE EFFECT MEDIATED BY MOTHER’S
EDUCATION?
Odds Ratios
Indicator
Health Outcomes
Neonatal death
Post-neonatal death
Child death
Diarrhea last 14 days
Child stunted
Rural
Town
Non-Slum Slum
Ref.
Ref.
Ref.
Ref.
Ref.
0.908***
0.851***
0.834***
0.834***
0.635***
City
Non-Slum Slum
Mother's
Education
(Years)
0.920*
0.797*** 0.851** 0.982***
0.985
0.648*** 0.703*** 0.960***
1.036
0.582*** 0.748** 0.952***
1.005
0.888*** 1.011
0.970***
0.823*** 0.550*** 0.718*** 0.941***
p values: ***<.001; **<0.01;*<0.05
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
OR BY ACCESS TO HEALTH SERVICES?
Odds Ratios
Indicator
Health Outcomes
Neonatal death
Post-neonatal death
Child death
Diarrhea last 14 days
Child stunted
Rural
Town
Non-Slum Slum
City
Non-Slum Slum
Access Problem:
Distance Money
Ref.
Ref.
Ref.
Ref.
Ref.
0.863***
0.764***
0.710***
0.807***
0.574***
0.721***
0.557***
0.464***
0.835***
0.469***
1.025
1.031
1.005
1.014
1.044**
0.967
0.972
0.974
1.024
0.797***
0.730**
0.597***
0.652*
0.914
0.701***
1.004
1.066*
1.104**
1.177***
1.150***
p values: ***<.001; **<0.01;*<0.05
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
OR BY A COMBINATION OF BOTH?
Indicator
Health Outcomes
Neonatal death
Post-neonatal death
Child death
Diarrhea last 14 days
Child stunted
Rural
Ref.
Ref.
Ref.
Ref.
Ref.
Odds Ratios
Town
City
Mother's Access Problem:
Non-Slum Slum Non-Slum Slum Education Distance Money
(Years)
0.893** 0.983
0.751*** 0.762* 0.987*** 1.021
0.994
0.843*** 1.025
0.642*** 0.667** 0.960*** 1.020
1.036
0.799*** 1.040
0.549*** 0.750
0.952*** 1.005
1.104**
0.876*** 1.069
0.940
0.999
0.967*** 1.014
1.146***
0.665*** 0.857*** 0.569*** 0.802** 0.943*** 1.025
1.100***
p values: ***<.001; **<0.01;*<0.05
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
DISCUSSION
 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
CONCLUSIONS
 Children in city slums have better health outcomes than rural
children
 And generally better than children in non-slum areas of towns
 Children 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
slums
 For 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
THANK YOU