Is Economic Inequality in Infant Mortality Higher in Urban Than in Rural India?

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Maternal and Child Health Journal ISSN 1092-7875 Matern Child Health JDOI 10.1007/s10995-014-1452-9

Is Economic Inequality in Infant MortalityHigher in Urban Than in Rural India?

Abhishek Kumar & Abhishek Singh

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Is Economic Inequality in Infant Mortality Higher in Urban Thanin Rural India?

Abhishek Kumar • Abhishek Singh

� Springer Science+Business Media New York 2014

Abstract This paper examines the trends in economic

inequality in infant mortality across urban–rural residence

in India over last 14 years. We analysed data from the three

successive rounds of the National Family Health Survey

conducted in India during 1992–1993, 1998–1999, and

2005–2006. Asset-based household wealth index was used

as the economic indicator for the study. Concentration

index and pooled logistic regression analysis were applied

to measure the extent of economic inequality in infant

mortality in urban and rural India. Infant mortality rate

differs considerably by urban–rural residence: infant mor-

tality in rural India being substantially higher than that in

urban India. The findings suggest that economic inequali-

ties are higher in urban than in rural India in each of the

three survey rounds. Pooled logistic regression results

suggest that, in urban areas, infant mortality has declined

by 22 % in poorest and 43 % in richest. In comparison, the

decline is 29 and 32 % respectively in rural India. Eco-

nomic inequality in infant mortality has widened more in

urban than in rural India in the last two decades.

Keywords Infant mortality � Economic inequality �Concentration index � Pooled logistic regression � Urban–

rural residence � India

Introduction

Despite the several efforts at reducing infant mortality rates

(IMRs) in India, the high levels of infant mortality continue

to pose a major challenge to the public health system. Of

the five million children of the developing countries that

die before reaching their first birthday annually [1], India

alone contributes about one-third [2]. Not only is the bur-

den of infant deaths high in India, but also level of

infant mortality differs starkly across states and urban–

rural residence [3]. For example, IMR in 2009 ranged

between a low of 12 infant deaths per 1,000 live births in

Kerala to a high of 67 in Madhya Pradesh. The IMR was 55

per 1,000 live births in rural India compared to only 34 in

urban India [3].

Acknowledging the higher health needs of the rural

masses, the Government of India launched its most ambi-

tious health programme in 2005, commonly known as

National Rural Health Mission (2005–2012). The main goal

of this programme is to ensure provision of high-quality

health services to the rural masses, in particular to the poor

and marginalized. In order to achieve this goal, the pro-

gramme is meant to bring architectural corrections in the

public healthcare system in rural areas. The architectural

corrections (as distinct from what was called health sector

reform in the earlier period) include decentralization and

organizational reforms in the health sector, inter-sectoral

convergence, public–private partnership in health sector, and

the induction of management and financial personnel into

the healthcare management and delivery system. It also aims

at mainstreaming Indian system of medicines, strengthening

community healthcare through community-level activists,

involving Panchyati Raj Institutions and Community Action,

exploring new health financing mechanisms, district plan-

ning, medical education, and technical support [4]. Through

A. Kumar (&)

International Institute for Population Sciences (IIPS), Room No.

19, Old Hostel, Govandi Station Road, Deonar, Mumbai 400088,

India

e-mail: [email protected]

A. Singh

Department of Public Health and Mortality, International

Institute for Population Sciences (IIPS), Govandi Station Road,

Deonar, Mumbai 400088, India

e-mail: [email protected]

123

Matern Child Health J

DOI 10.1007/s10995-014-1452-9

Author's personal copy

this programme, the Government of India has substantially

increased its investments in Reproductive and Child Health

program.

Unfortunately, the Government of India has completely

overlooked the heath needs of the urban population under

the impression that urban populations enjoy better health

than their rural counterparts, presumably because urban

centres are characterised by well-equipped modern health-

care systems coupled with better accessibility to, and

availability of, nutritious food, housing, and employment

opportunities, better education and higher income, improved

water and sanitation facilities [5–9]. In addition, the igno-

rance of urban health status could also have been due to lack

of systematic evidence on the socioeconomic inequalities in

maternal and child health within urban India.

Several studies conducted in recent years have docu-

mented the inferior maternal and child health of the urban

poor in developing countries [10–13]. In developing coun-

tries, though the average health status is better in urban

areas, economic inequality is higher in urban than rural areas

[14]. Using the Demographic and Health Survey (DHS) data

from ten developing countries, a study has shown that the

socioeconomic gradient in childhood undernutrition is

higher in urban areas than in rural areas [15]. Another study,

based on Sub-Saharan African countries, has found that

socioeconomic inequalities in childhood undernutrition are,

to a large extent, higher in cities than in rural areas [5]. A

few recent studies from India have documented socioeco-

nomic disparities in the utilization of maternal healthcare

services and childhood nutritional status in urban areas of

the country [16–18]. These studies clearly show that the

health status and utilization of healthcare services is much

lower among the urban poor than the rest of the urban

population and the gap has widened over time. These studies

raise important concerns about the availability and accessi-

bility of health services to the urban poor.

Literature search yielded only one study that exclusively

investigated economic inequalities in utilization of mater-

nal and child healthcare services by urban–rural residence

in India [17]. Furthermore, we could not come across any

published study that examined economic inequality in

infant mortality separately by urban and rural residence in

India, despite the fact that infant mortality is one of the

most important indicators of socioeconomic development

of a country and that millennium development goals-4

(MDG4) exclusively talks about reducing IMRs. Studies on

economic inequality in infant mortality by urban–rural

residence are particularly important in a country like India

where IMRs differ considerably by urban–rural residence.

Indeed, India is undergoing rapid urbanization, with a

significant proportion of India’s population now living in

urban areas [19]. Notably, lower levels of infant mortality

in urban India compared to rural India may not necessarily

result into lower economic inequality in urban India than in

rural India. The present study, therefore, aims to investigate

the economic inequality in infant mortality by urban–rural

residence in India using the three rounds of the National

Family Health Survey (NFHS) conducted in India during

1992–1993, 1998–1999, and 2005–2006.

Data and Methods

Ethics Statement

The multi-rounds of the NFHS was conducted under the

supervision of the International Institute for Population

Sciences (IIPS), Mumbai, India—a regional centre of

teaching, training, and research in population studies. The

ORC Macro institutional review board approved the data

collection procedures. A formal written consent was

obtained before interviewing the respondent in the survey.

Moreover, this study is based on anonymous public use

datasets with no identifiable information on the survey

participants. Survey data are available upon the request on

the official website of the institute.

Data

We used data from the three successive rounds of the

NFHS conducted in India during 1992–1993, 1998–1999,

and 2005–2006. The NFHS is similar to the DHS of other

countries. The NFHS is a large scale and multi-round

survey conducted on representative sample of households

spanning across the states and union territories of India.

The main purpose of the NFHS is to provide reliable

estimates on fertility, infant and childhood mortality,

family planning, utilization of maternal and child health-

care services, and childhood nutritional status at the

country and state levels. The NFHS also provides these

estimates by urban and rural residence.

The NFHS adopted similar sampling design in each of the

three survey rounds. A two-stage sampling design was

adopted in rural areas—villages were selected at the first stage

using probability proportional to size (PPS) sampling scheme

followed by selection of households at the second stage using

systematic sampling scheme. The sample in urban areas was

selected in three stages. The first stage comprised of selection

of urban wards using PPS sampling scheme. Census enu-

meration blocks (CEB) containing approximately 150–200

households were selected at the second stage. Households

were selected at the third stage using systematic sampling

scheme. The similarity in the sampling design of the three

survey rounds allows for a comparison of the estimates

obtained from the three consecutive rounds [20, 21]. The


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details of sampling design are given in the reports of the

various rounds of NFHS [22–24].

Information in the three rounds of NFHS was collected

through face-to-face interview, using similar interview

schedules. In NFHS 1992–1993 and 1998–1999, the infor-

mation was collected using community, household, and

eligible women schedules. In NFHS 2005–2006, informa-

tion was collected using household, eligible women, and

eligible men schedules. The contents of the interview

schedules remained similar in all the three rounds. The

household response rate was 96 % in the first round and

98 % each in the second and third rounds of the NFHS. The

individual response rates were 96 % each in the first and

second rounds, while it was 94 % in the third round.

Outcome Variable

The outcome variable in this study is infant mortality. IMR

is defined as the probability of death before reaching 1 year

of age. A unique feature of NFHS is that the survey col-

lected detailed information on all births to women (in the

age-group 15–49) interviewed in the respective samples.

This detailed birth history provided us an opportunity to

conduct the statistical analysis presented in the results

section. We first collected information on births that took

place in 10 years preceding the respective survey round.

Then we excluded the births taken place in the last 1 year

preceding the respective survey dates to remove the cen-

sored cases. The outcome variable is a binary variable (1 if

died during infancy; 0 if survived during infancy).

Exposure Variables

Economic status of the household is used as the main

exposure variable in the analysis. Previous studies have

shown a direct relationship between economic status of the

household and infant mortality [25–28]. Like other DHS,

the NFHS does not provide direct data on income or con-

sumption in India. However, the NFHS provides informa-

tion on a set of economic proxies such as housing quality,

household amenities, consumer durables, and size of land

holding. Studies in the past have used these proxy infor-

mation to assess the economic status of the households (in

terms of wealth quintiles) and to capture the economic

differentials in the population and health outcomes [29–

34]. Following the DHS approach, we used principal

component analysis to estimate wealth index separately for

urban and rural areas in each of the three NFHS rounds.

Wealth index was estimated separately for urban and rural

areas to account for the economic diversity between urban

and rural areas [35, 36]. The wealth index was estimated in

such a way that it was comparable over the three NFHS

rounds. The wealth index was subsequently divided into

five quintiles—poorest, poorer, middle, richer, and rich-

est—for conducting pooled logistic regression analysis.

A number of other socio-economic and demographic

variables have also been shown to have significant effect on

infant mortality in developing countries. Accordingly, we

controlled for a set of theoretically pertinent socio-economic

and demographic variables in the analysis. The variables that

were controlled in the pooled logistic regression analysis

are—sex of the newborn, birth order, and preceding birth

interval (first birth order; higher birth order and birth interval

B24 months; higher birth order and birth interval

[24 months), size of the newborn at birth (larger than aver-

age; average; smaller than average), mother’s age at birth of

the newborn (B19, 20–29, and C30 years), mother’s school-

ing (no schooling; 1–5 years of schooling; 6–12 years of

schooling; [12 years of schooling), father’s schooling (no

schooling; 1–5 years of schooling; 6–12 years of schooling;

[12 years of schooling), religion (Hindu; Muslim; Others),

skilled attendance at birth (no; yes), mother’s exposure to

media (no; yes), working status of mother (no; yes), and

geographic region of residence (north; east; central; northeast;

west; south). All the three rounds of NFHS collected infor-

mation on women’s exposure to radio, television, and news-

paper. The information on exposure to the three media sources

was used to compute mother’s exposure to media. Those

mothers who were exposed to at least one media source were

coded as ‘having exposure’ to media. Rest were coded as ‘not

having exposure’ to media.

Statistical Analysis

Infant mortality rates (with 95 % confidence intervals) by

urban–rural residence and by wealth quintiles separately

for urban and rural areas in each round of NFHS were

estimated using the life table technique available in STA-

TA 10.0 [29]. The IMRs were estimated simply to give the

levels and trends in infant mortality by urban–rural resi-

dence and by wealth quintiles in urban and rural areas.

Concentration index (CI) was used to examine the extent

of economic inequality in infant mortality by urban–rural

residence. The CI is widely used to examine the extent of

socio-economic inequality in any health outcome. It is

defined as twice the area between the concentration curve

and the line of equality [29, 37–39]. The value of CI varies

between -1 and ?1. A negative value implies that con-

sidered health variable is concentrated among the poor

while a positive value indicates that it is concentrated

among the rich. A value of ‘0’ implies that health outcome

is equally distributed between the economic groups. We

used factor score of household wealth, obtained from the

principal component analysis, to estimate CI.

A previous study [40] has shown that the lower and

upper bounds for CI can depend on the mean values in case


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of dichotomous outcome variables (as in our case). This

implies that the extent of inequality as measured by the CI

can get affected considerably if the mean of the outcome

variable changes from one survey round to the other. One

solution to address this problem is to normalize the CI or to

divide it by the reciprocal of the mean. We addressed this

issue in the present analysis by normalizing the CI values.

We used binary logistic regression analysis to examine

the economic inequality in infant mortality after adjusting

for important socio-economic and demographic variables.

In the regression analysis, we pooled the data from the

three rounds of NFHS to examine the interaction effect of

time and household wealth in urban and rural India sepa-

rately. We present the pooled logistic regression results as

predicted probabilities for better interpretation. The expo-

sure variables were tested for possible multi-collinearity

before putting them into the regression analysis using the

variation inflation factor (VIF) post estimation command.

We used appropriate weights in the analysis to make the

estimates representative and comparable across the three

survey rounds, and to account for the multi-stage sampling

design adopted in the three rounds of NFHS. The details of

the sampling weights are given in NFHS reports of the

various rounds [22–24]. The analyses presented in the

subsequent sections were carried out in STATA 10.0.

Results

The urban sample comprised 31,373, 27,794, and 37,303

live births in the NFHS 1992–1993, NFHS 1998–1999, and

NFHS 2005–2006 respectively. Likewise, the rural sample

comprised 83,296, 80,208, and 61,266 live births in the

NFHS 1992–1993, NFHS 1998–1999 and NFHS 2005–2006

respectively. In urban areas, 1,793 (6.0 % of total live

births), 1,341 (4.9 % of total live births), and 1,691 (4.9 %

of total live births) infant deaths were observed in NFHS

1992–1993, NFHS 1998–1999, and NFHS 2005–2006

respectively. In rural areas, the number was 7,201 (9.5 % of

total live births), 6,182 (8.0 % of total live births), and 3,952

(7.1 % of total live births) in NFHS 1992–1993, NFHS

1998–1999, and NFHS 2005–2006 respectively. The details

of live births and infant deaths across the household wealth

and urban–rural residence are listed in Table 1.

Trends in Infant Mortality in Urban and Rural India

Infant mortality rate in India has declined considerably

both in urban and rural India as shown in Fig. 1. The IMR

in urban India has declined from 57 per 1,000 live births in

Table 1 Number of live births,

number and (%) of infant deaths

in 10 years preceding the

respective survey dates across

the categories of household

wealth by urban–rural residence

in India, 1992–2006

Number given in parenthesis

indicates the percentage of

infant deaths

Number of births and infant

deaths are based on unweighted

sample

1992–1993 1998–1999 2005–2006

Number of

births

Number of

infant deaths

Number of

births

Number of

infant deaths

Number of

births

Number of

infant deaths

Urban

Poorest 6,218 565 (9.3) 5,571 412 (7.6) 7,922 506 (6.7)

Poor 6,487 422 (6.6) 5,555 296 (5.1) 7,481 385 (6.0)

Middle 6,109 348 (5.7) 5,469 251 (4.1) 7,372 387 (5.7)

Rich 6,146 263 (4.1) 5,517 242 (4.6) 7,079 241 (3.3)

Richest 6,413 195 (3.5) 5,682 140 (2.6) 7,449 172 (2.3)

Total 31,373 1,793 (6.0) 27,794 1,341 (4.9) 37,303 1,691 (4.9)

Rural

Poorest 15,302 1,576 (10.8) 15,396 1,348 (8.8) 11,165 866 (7.6)

Poor 14,966 1,594 (11.0) 15,080 1,464 (9.5) 11,247 897 (8.5)

Middle 15,996 1,487 (9.9) 15,539 1,282 (8.5) 11,900 880 (7.7)

Rich 17,509 1,442 (9.2) 16,543 1,189 (7.5) 12,997 763 (6.6)

Richest 19,523 1,102 (6.2) 17,650 899 (5.3) 13,957 546 (4.8)

Total 83,296 7,201 (9.5) 80,208 6,182 (8.0) 61,266 3,952 (7.1)

57

87

48

77

45

65

0

20

40

60

80

100

RuralUrban

Infa

nt m

orta

lity

rate

per

1000

live

bir

ths

1992-93 1998-99 2005-06

Fig. 1 Estimated IMR (per 1,000 live births) by urban–rural

residence in India, 1992–2006. Source Authors’ calculation from

NFHS data


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1992–1993 to 45 in 2005–2006. Likewise, IMR in rural

India has declined from 87 in 1992–1993 to 65 in

2005–2006. Although the urban–rural gap in IMR has

declined over the three NFHS rounds, a significant gap still

persists.

Trends in Economic Inequality in Infant Mortality

in Urban and Rural India

Figure 2 presents the trends in IMR across the household

wealth quintiles according to the place of the residence in

India during 1992–2006. We present the results only for the

poorest and the richest quintile to make the presentation

simpler. The results, disaggregated by wealth quintiles,

suggest that the IMR among the poorest quintile in urban

areas was 64 per 1,000 live births in NFHS 2005–2006.

This figure compares with an IMR of only 23 in the richest

quintile in the same period. In rural areas the corresponding

IMRs were 78 and 39 respectively. The pace of decline in

infant mortality was not the same in various economic

groups.

The CI values according to urban–rural residence are

presented in Table 2. Results clearly suggest significantly

higher economic inequality in infant mortality in urban

areas compared to that in rural areas in each of the three

NFHS rounds. The CI value for urban area was –0.194 in

2005–2006. The corresponding figure was only –0.081 in

the rural areas. The CIs for urban areas have only mar-

ginally declined from –0.199 in 1992–1993 to –0.194 in

2005–2006. The corresponding decline in rural areas was

from –0.087 in 1992–1993 to –0.081 in 2005–2006.

Regression Analysis

Earlier studies on infant mortality have shown that

important socioeconomic and demographic variables have

significant effect on infant mortality. In order to examine

the effect of household wealth on infant mortality over the

period 1992–2006, we estimated binary logistic regression

having adjusted for important socioeconomic and demo-

graphic characteristics. Only those variables that were

significantly associated with infant mortality in the bivar-

iate analysis (‘‘Appendix’’) were included in the final

logistic regression model, the results of which are shown in

Table 3. The results, adjusted for other important socio-

economic and demographic characteristics, clearly suggest

significantly higher risk of infant deaths among the poorest

quintile compared to that among the richest quintile both in

urban and rural India. In urban India the adjusted proba-

bility of infant death was 0.051 among the poorest quintile

in 2005–2006. In comparison, the probability was only

0.016 among the richest quintile. Likewise, in rural India,

the probability of infant death among the poorest and the

richest quintiles in 2005–2006 was 0.060 and 0.030

respectively. The pattern remained similar in each of the

three survey rounds.

The changes in predicted probabilities, presented in

Table 4, suggest that, in urban areas, for poorest quintile,

the probability of infant death declined by about 22 %

points during 1992–2006. Over the same period, the

probability of infant deaths among the richest quintile

declined by about 43 % points. The corresponding declines

in poorest and richest quintiles in rural areas were 29 and

32 % respectively. These findings suggest that the eco-

nomic inequality in infant mortality has widened in both

urban and rural areas during 1992–2006. However, the

economic inequality has widened more in urban than in

rural India.

Discussion and Conclusion

Our study examined the trends in economic inequality in

infant mortality according to urban–rural residence in India

using the three rounds of the NFHS conducted during

1992–2006. Clearly, the IMR has declined considerably in

both urban and rural India during late 1990s and early

2000s. This decline in IMR might be the result of interplay

of numerous factors such as the rapid economic growth

(with the introduction of the New Economic Policy in the

early 1990’s), improvement in agriculture, medicine, and

information technology. In addition, the maternal and child

health interventions such as Child Survival and Safe-

motherhood Program (CSSM 1992) and Reproductive and

Child Health Program (RCH 1997) might have also played

a significant role in lowering the infant mortality in urban

and rural India.

Like the aggregate level, the IMR has declined among

all the wealth groups in both urban and rural India.

91

7464

103

8878

3025 23

5651

39

0

20

40

60

80

100

120

1992-93 1998-99 2005-06 1992-93 1998-99 2005-06

RuralUrban

Infa

nt m

orta

lity

rate

per

Poorest quintile Richest quintile

1000

live

bir

ths

Fig. 2 Trends in IMR (per 1,000 live births) across household wealth

by urban–rural residence in India, 1992–2006. Source Authors’

calculation from NFHS data


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However, the better-off families have experienced greater

decline in infant mortality compared to households

belonging to lowest wealth quintile in both urban and rural

areas. Interestingly, economic inequality has widened in

urban as well as rural areas during the study period. When

viewed in a context of the economic reforms which took

place in India since 1990s, our findings clearly indicate that

fruits of economic development are not being shared

equally by the different sections of the Indian society. In

both urban and rural India, the richest families have ben-

efitted from the economic and social development to a

greater extent than the poorest families. In addition, the

faster decline in infant mortality among the rich might also

be associated with their increasing access to technologies

to reduce neonatal mortality to which neither urban poor

nor the rural poor have access to [41].

Our findings indicate that the differences in IMR across

household wealth were comparatively higher in urban than

in rural India. In addition, the extent of economic

inequality was considerably higher in urban India com-

pared with rural India. This result was consistent in each of

the three NFHS surveys. Regression analysis further sug-

gests that the economic inequality in infant mortality has

increased over the last two decades in India; the increase

was particularly more pronounced in the urban areas as

compared to rural India. Our finding is consistent with the

findings of previous studies that have also highlighted the

vulnerability of urban poor when it comes to infant mor-

tality [10, 42]. Recent studies from India noted that eco-

nomic inequality in use of maternity services is higher in

urban than rural area of the country [17]. This could be a

possible reason for higher and widening economic

inequality in infant mortality in urban India as utilization of

maternal healthcare services has a direct and significant

influence on survival during infancy [43–45]. In addition,

the higher economic inequality in infant mortality in urban

areas might be a reflection of the greater economic

Table 2 Concentration index

showing the economic

inequality in infant mortality by

urban–rural residence in India,

1992–2006

*** p \ 0.01

Urban Rural

Concentration

index

95 % confidence

interval

Concentration

index

95 % confidence

interval

1992–1993 -0.199*** (-0.233, -0.165) -0.087*** (-0.101, -0.073)

1998–1999 -0.182*** (-0.291, -0.145) -0.088*** (-0.103, -0.073)

2005–2006 -0.194*** (-0.230, -0.159) -0.081*** (-0.100, -0.062)

Table 3 Predicted probabilities (95 % confidence interval) showing

the interaction effect of time with household wealth on infant deaths

by urban–rural residence in India, 1992–2006

Urban Rural

PP� 95 % of CI PP� 95 % of CI

1992–1993

Poorest 0.065 (0.049, 0.083) 0.085 (0.0.78, 0.095)

Poor 0.049 (0.035, 0.062) 0.086 (0.074, 0.096)

Middle 0.045 (0.036, 0.056) 0.076 (0.059, 0.091)

Rich 0.041 (0.034, 0.049) 0.069 (0.053, 0.086)

Richest 0.028 (0.016, 0.038) 0.044 (0.034, 0.054)

1998–1999

Poorest 0.055 (0.044, 0.069) 0.061 (0.053, 0.071)

Poor 0.048 (0.037, 0.060) 0.081 (0.069, 0.094)

Middle 0.035 (0.023, 0.049) 0.059 (0.047, 0.072)

Rich 0.038 (0.029, 0.046) 0.057 (0.048, 0.068)

Richest 0.023 (0.015, 0.33) 0.038 (0.027, 0.050)

2005–2006

Poorest 0.051 (0.038, 0.065) 0.060 (0.051, 0.068)

Poor 0.038 (0.025, 0.054) 0.067 (0.054, 0.082)

Middle 0.051 (0.042, 0.062) 0.064 (0.056, 0.075)

Rich 0.034 (0.026, 0.043) 0.053 (0.040, 0.065)

Richest 0.016 (0.006, 0.027) 0.030 (0.014, 0.046)

The models have been adjusted for sex of the newborn, birth order

and preceding birth interval, size of the newborn at birth, mother’s

age at birth of the newborn, mother’s schooling, father’s schooling,

religion, skilled attendance at birth, mother’s exposure to media,

working status of mother, and geographic region of residence

PP predicted probabilities, CI confidence interval� All the predicted probabilities were significant at p \ 0.05

Table 4 Percentage change in predicted probability of infant deaths

across the categories of household wealth by urban–rural residence in

India, 1992–2006

1992–1998 1998–2006 1992–2006

Urban

Poorest 15.4 7.3 21.5

Poor 2.0 20.8 22.4

Middle 22.2 -45.7 -13.3

Rich 7.3 10.5 17.1

Richest 17.9 30.4 42.9

Rural

Poorest 28.2 1.6 29.4

Poor 5.8 17.3 22.1

Middle 22.4 -8.5 15.8

Rich 17.4 7.0 23.2

Richest 13.6 21.1 31.8


123


inequality that tends to prevail in urban settings [46].

Income constraints and price barriers deprive the urban

poor from access to health care despite their close prox-

imity of health care facilities. Financial barriers may also

limit the advantage that the poor can reap from the better

food supply in urban areas, while the rural poor can benefit

from their own food production and support networks [42].

The fact that the urban rich can benefit from the health care

advantages available in urban areas, while the rural rich

and poor in both rural and urban areas cannot, might

explain the greater economic inequality in child health in

urban areas. The urban poor also have the disadvantage of

poor living and sanitary conditions. Most urban poor are

found to live in extreme unsanitary and congested condi-

tion, leading to tremendous burden of ill-health and mor-

tality, especially among children [47].

Our findings show that, although the IMR is lower in

urban areas compared to rural areas, the economic

inequality in infant mortality in urban India is much higher

than in rural India. These findings clearly indicate that

within urban areas, a considerable proportion of the pop-

ulation bear a disproportionate burden of infant mortality,

and require special attention. Instead of using simple

averages, policy makers and programme managers must

use indicators disaggregated by socio-economic status to

formulate policies and programmes in India. It is probably

because of the better averages in urban areas that the

special needs of the urban poor have been overlooked.

Interestingly, the disparity between the urban poor and

the rich is much more than the disparity between rural poor

and rich. This finding implies a need for programs that

target the urban poor. This is becoming more necessary as

the size of the urban population is increasing in India.

Policies that aim to improve the health of urban poor might

be different than those of their rural counterparts. In urban

areas greater attention needs to be given to the generation

of employment, improving living conditions, education

levels, and economic well-being among the disadvantaged,

providing safe drinking water and public hygiene in slum

dwellings, and securing access to healthcare for the chil-

dren of informal sector workers [48, 49]. With the world

population set to becoming overwhelmingly urban, the

salient findings of the study will help to build a suitable

case for multifaceted policies targeted at improving the

health of the urban poor.

Appendix

See Table 5.

Table 5 Unadjusted� odds

ratios for infant deaths across

selected socio-economic and

demographic characteristics by

urban–rural residence in India,

1992–2006

Urban Rural

1992–1993 1998–1999 2005–2006 1992–1993 1998–1999 2005–2006

Sex of the newborn

Male (Ref)

Female 0.91b 0.87b 0.90b 0.96 0.96b 0.94b

Birth order and preceding birth interval

First birth order (Ref)

Higher birth order and

interval B 24 months

1.54a 1.63a 1.91a 1.46a 1.51a 1.53a

Higher birth order and

interval [ 24 months

0.76a 0.85b 0.80b 0.68a 0.71a 0.60a

Size of the newborn at birth

Larger than average

(Ref)

Average 0.97b 0.84 0.11 1.01 0.97 1.08

Smaller than average 2.63a 1.92b 2.46a 2.46a 1.77a 1.79a

Mother’s age at birth of

the newborn

B 19 years (Ref)

20–29 years 0.76a 0.62a 0.69a 0.68a 0.77a 0.66a

C 30 years 0.72a 0.77b 0.73a 0.79a 0.78a 0.63a

Household wealth

Poorest (Ref)

Poor 0.86b 0.81b 0.81b 0.99b 0.97b 0.93b

Middle 0.68b 0.62b 0.74a 0.86a 0.81a 0.76a


123


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Table 5 continued

Ref reference category� Unadjusted odds ratio is

obtained from binary logistic

regression analysisa p \ 0.01; bp \ 0.05;cp \ 0.10

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Is Economic Inequality in Infant Mortality Higher in Urban Than in Rural India?

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