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Paper presented at the 8th International Conference on UrbanHealth, International Society for Urban Health, Nairobi, Kenya, 18th-24thOctober, 2009
Citation preview
Socioeconomic Inequalities in Child
Malnutrition in Urban India: A
Systematic Slum Bias?
Sumit Mazumdar (CSSSC, Kolkata) & Papiya G. Mazumdar (FHS, IIHMR, Kolkata)
8th International Conference on Urban Health
KICC, Nairobi, Kenya
October 21st-24th , 2009
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Background & Introduction
• India: The ‘Undernutrition’ Burden
• Not a rural phenomenon alone: Growing spread in Urban India
• Increased Urbanization
• Urban India --Livelihood challenges: Growing population pressure
(‘Metro Migration: The City Lights Syndrome’) leading to Slums &
Squatter Colonies with poor civic infrastructure
• Are the Slum population largely responsible for pushing up child
undernutrition levels in Urban India: Looking for a SLUM BIAS
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Research Questions
• Is the burden of chronic child undernutrition disproportionately on
the population living in the slums of major cities in urban India?
• What is the extent of socioeconomic inequality in chronic child
undernutrition in major cities in India, and how does it relates with
the slum/non-slum differentials in average child undernutrition?
• Is there a definite gradient of socioeconomic inequality in child
undernutrition in the major cities, and is such a gradient more
visible in slums?
• Are the children living in the slums of the major cities in India more
prone to suffer from undernutrition than their counterparts in non-
slum areas?
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Data & Methods
Cities Slum Non-Slum Total
Delhi City 270 (42.1%) 371 (57.9%) 641
Meerut 471 (52.9%) 420 (47.1%) 891
Kolkata 296 (61.5%) 185 (38.5%) 481
Indore 356 (53.3%) 312 (46.7%) 668
Mumbai 231 (60%) 154 (40%) 385
Nagpur 312 (50.4%) 307 (49.6%) 619
Hyderabad 374 (49.5%) 382 (50.5%) 756
Chennai 271 (54.1%) 230 (45.9%) 501
Total 2581 (52.2%) 2361 (47.8%) 4,942
•National Family Health Survey –III
(NFHS-3): Data for eight major cities
(Delhi City, Meerut, Kolkata, Indore,
Mumbai, Nagpur, Hyderabad &
Chennai) in India, with separate
samples of population living in
slums (as defined in Census 2001).
•Anthropometric data: standardized
z-scores for height-for-age (stunting)
primarily used as the major variable
of interest
•SE inequality measured by
Concentration Index. Logistic
regressions and Poisson regressions
used for addressing the research
questions
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Issue 1: Relative burden of Undernutrition in Major Indian
Cities & extent of SE Inequality
-0.300
-0.250
-0.200
-0.150
-0.100
-0.050
0.000
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Na
gp
ur
N. D
elh
i
Ko
lka
ta
All C
ities
Ind
ore
Mu
mb
ai
Me
eru
t
Ch
en
na
i
Hy
de
rab
ad
Slums Non-Slums Combined Concentration Index
�Chronic child undernutrition is
significantly higher in the slums
of all the cities
�Significant Slum/Non-slum
differentials in Nagpur, N.
Delhi, Kolkata & Indore; More
equal in southern cities
�Considerable variation in SE
inequality in chronic
undernutrition in the cities;
Average inequality in the cities
considered substantial (-0.167)
�Very high levels of SE
inequality in Meerut (-0.243) &
Chennai (-0.243), less
pronounced in Mumbai (-0.131)
& Nagpur (-0.142)
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Issue 2: How does SE inequality in chronic undernutrition
associate with intra-city spatial differentials
� No definite association
pattern between extent of
intra-city SE inequality and
slum/non-slum differentials in
chronic undernutrition
(Correlation p = 0.42)
�Apparently SE inequality is
greatest in cities with low
slum/non-slum differentials
, at both ends of the
distribution (Chennai &
Meerut): spatial inequality
overridden by SE inequality
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Issue 3: Examining the Gradient of SE Inequality in
Undernutrition– A Slum Bias?
Along expected lines, a higher
proportion of households in slums
belong to poor SES classes, averaged
over all the major cities.
However, there is no explicit evidence of a
higher burden of chronic undernutrition
among the poor SES from slums (except in
Meerut & Chennai), as seen from a
comparison of inter-SES class differentials
between slum & non-slum households
0
10
20
30
40
50
Slum/Non-slum comparison of chronic
undernutrition by interquintile range for SES
Slum Non-slum
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44.1
26.8
37.3
30.8
18.6
42.5
Slum
Non-
Slum
Poor Middle Rich
Issue 3: Examining the Gradient of SE Inequality in
Undernutrition– A Slum Bias?
�Significantly lower likelihood of
Suffering from chronic
undernutrition in higher SES
classes; Intensification of SES
gradient on controlling for other
model covariates
�No clear evidence of a pro-slum
bias: The poor SES in the slums do
not have (statistically significant)
higher risks of suffering
undernutrition
�No significant differences in the
predicted probabilities of suffering
from chronic undernutrition
between poor SES in slums or non-
slum areas
�The SES gradient in
undernutrition is evident, without
a discernible pro-slum bias
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47.4%
36.0%
25.7%
46.9%
31.6%22.6%
Poor Middle Rich
Predicted probabilities of likelihood of chronic
child undernutrition according to SES, Slum &
Non-Slum areas
Slum Non-slum
Cities Model 1 Model 2 Full Model
Middle SES -0.49** -0.47** -0.27**
Rich SES -0.98** -0.99** -0.37**
Middle SES*Non-Slum - -0.19 -0.14
Rich SES* Non-Slum - -0.18 -0.08
Non-Slum -0.005 -0.003
Odds ratios from Logistic Regression showing relative likelihood of
chronic undernutrition according to SES & place of residence
Issue 4: Do staying in slums per se increase the risks of
being chronically undernourished?
Relative Risk Ratios justify higher risks of being chronically undernourished for children residing in
slums, which, however withers away on gradual introduction of potential confounders with a visible reduction
in risks alongwith loss of statistical significance. Notably, the SES gradient persists even controlling for all
model covariates indicating its overarching influence in shaping risks towards chronic undernutrition in Indian
cities.
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Model Description
Slum-Non Slum
Differences
SES Differentials
(Poor-Middle)
SES Differentials
(Poor-Rich)
With only slum/non-slum parameter 0.733**
With SES confounder 0.898* 0.821** 0.545**
With confounders for SES, mother's
education, mothers BMI, safe water
availability in hh (community proxy) 0.935 0.870** 0.757**
With full model (confounders for
demographic, socioeconomic,
health care seeking behaviour and
community characteristics) 0.966 0.856** 0.834**
Incremental Relative Risk Ratios (IRR) from Poisson Regression showing relative likelihood of
chronic undernutrition according to SES & place of residence (Slum/Non-Slum)
Conclusion & Policy Implications
• A visible spatial (slum/non-slum) differential coupled with significant SE inequality
in child malnutrition marks the scenario for chronic child undernutrition in major
cities of India
• Absence of any clear-cut evidence suggesting higher burden of chronic
undernutrition in slums, or among the poorer SES households in slums compared to
the non-slum counterparts, suggesting SE inequality in child undernutrition to be a
pan-urban phenomenon in Indian cities, without an identifiable slum bias
• The apparent disproportionate burden of chronic undernutrition in slums, is not
singularly due to slum residence, or even due to non-linear impact of SES in slums
and non-slum localities, but most possibly due to other potential factors like access
to improved health & sanitation facilities, education among mothers, and mother’s
own nutritional status
• Future research needs to specifically focus on typical frameworks to further
understand the dynamics of SES and locality-effect on chronic undernutrition in
major cities across the developing world.
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Thanks for a patient hearing
Correspondence:
Sumit Mazumdar, Ph.D.
Research Consultant,
Centre for Studies in Social Sciences, Calcutta
R-1, Baishnabghata-Patuli Township, Kolkata
PIN-700094
WEB: www.cssscal.org
email: [email protected]
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