Socioeconomic and Geographical Disparities in Under-Fiveand Neonatal Mortality in Uttar Pradesh, India

Zoe Dettrick • Eliana Jimenez-Soto •

Andrew Hodge

� Springer Science+Business Media New York 2013

Abstract As a part of the Millennium Development

Goals, India seeks to substantially reduce its burden of

childhood mortality. The success or failure of this goal may

depend on outcomes within India’s most populous state,

Uttar Pradesh. This study examines the level of disparities

in under-five and neonatal mortality across a range of

equity markers within the state. Estimates of under-five and

neonatal mortality rates were computed using five datasets,

from three available sources: sample registration system,

summary birth histories in surveys, and complete birth

histories. Disparities were evaluated via comparisons of

mortality rates by rural–urban location, ethnicity, wealth,

and districts. While Uttar Pradesh has experienced declines

in both rates of under-five (162–108 per 1,000 live births)

and neonatal (76–49 per 1,000 live births) mortality, the

rate of decline has been slow (averaging 2 % per annum).

Mortality trends in rural and urban areas are showing signs

of convergence, largely due to the much slower rate of

change in urban areas. While the gap between rich and

poor households has decreased in both urban and rural

areas, trends suggest that differences in mortality will

remain. Caste-related disparities remain high and show no

signs of diminishing. Of concern are also the signs of

stagnation in mortality amongst groups with greater ability

to access services, such as the urban middle class. Not-

withstanding the slow but steady reduction of absolute

levels of childhood mortality within Uttar Pradesh, the

distribution of the mortality by sub-state populations

remains unequal. Future progress may require significant

investment in quality of care provided to all sections of the

community.

Keywords Childhood mortality � Under-five mortality �Health disparities � Uttar Pradesh � India

Introduction

In 2007 an estimated 1.84 million deaths under the age of

five occurred in India, with over a quarter of these deaths

occurring within the country’s most populous state, Uttar

Pradesh [1]. With one of the highest levels of under-five

mortality in the country [2], Uttar Pradesh is a member of

the Empowered Action Group (EAG) of states that have

been targeted for additional attention under the Indian

Government’s National Rural Health Mission (NRHM) in

order to improve health outcomes.

One of the challenges facing such programs is the high

levels of health disparities in India as a whole [3, 4]. As a

high priority state, Uttar Pradesh has been subject to close

examination in relation to issues of inequality. Much of the

focus has remained on socioeconomic factors, with cov-

erage of essential services, such as immunisation and safe

delivery care, found to be much higher among richer

groups than their poor counterparts [5–7]. Caste is also an

important factor, with members of Scheduled Castes (SC)

reporting significant barriers to health provision [8, 9].

Rural inhabitants in the state consistently report being at a

disadvantage in terms of service delivery [10–12] and

outcomes vary considerably between individual districts

[13].

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10995-013-1324-8) contains supplementarymaterial, which is available to authorized users.

Z. Dettrick (&) � E. Jimenez-Soto � A. Hodge

School of Population Health, The University of Queensland,

Public Health Building, Herston Road, Herston, Brisbane,

QLD 4006, Australia

e-mail: z.dettrick@uq.edu.au

123

Matern Child Health J

DOI 10.1007/s10995-013-1324-8

Despite the identification of clear associations between

the above mentioned characteristics and health disparities,

little attention has been paid to how the relationship

between these factors and health outcomes has changed

over time. Existing studies rely upon the comparison of two

or more discrete time periods rather than examining lon-

gitudinal trends which may mask more complex patterns;

and variation in the methods of data collection lead to some

confusion over absolute levels of mortality differences.

We have attempted to fill this knowledge gap by uti-

lising multiple sources of data to present estimated levels

and trends in under-five and neonatal mortality rates across

several different equity markers in Uttar Pradesh: namely,

rural–urban location, caste group, wealth, and districts.

Data and Methods

We identified three sources of data for possible inclusion in

the study, each comprising of multiple year data. In total

seven potential sets of data were attained (see Table 1). Six

of the seven datasets contained individual child records,

while the remaining dataset comprised of aggregated vital

statistics based on sample registrations of births and deaths.

Due to changing state boundaries, only five of the seven

datasets could be utilised in the final analysis.

The first data source was the series of Indian National

Family Health Surveys (NFHS)—conducted in 1992–1993,

1998–1999, and 2005–2006. Similar to other Demographic

and Health Surveys (DHS), they provide consistent and

reliable estimates of mortality and fertility, family plan-

ning, the utilisation of maternal and child health care ser-

vices, other related health indicators, and socioeconomic

measures. The sampling design was a systematic, stratified

random sample of households, with two stages in rural

areas and three stages in urban areas [14–16].

The second data source was the District Level House-

hold and Facility Surveys (DLHS) series undertaken in

1998–1999, 2002–2004, and 2007–2008. The DLHS is a

collection of nationally representative household surveys,

primarily conducted to monitor and assess the implemen-

tation and operation of the Reproductive and Child Health

program across the districts of India. The DLHS were also

undertaken using a systematic, multi-stage stratified sam-

pling design [17–19].

The final data source was the Sample Registration

System (SRS), which is a sample of birth and death reg-

istrations under the Office of the Registrar General of India.

SRS provides annual estimates of the population, birth

rates, fertility, mortality, live births, maternal mortality, life

expectancy, death rate, and other indicators at the national

and state level and separately for rural and urban place of

residence. Generally, the sample design adopted for the

SRS is a single-stage stratified random sample [2]. Data for

the years 1971–2008 were available for Uttar Pradesh as a

single dataset.

In 2000, the state of Uttarakhand was formed via the

partitioning of 13 north-western districts of Uttar Pradesh.

As a result, the 1992–1993 and 1998–1999 NFHS were not

usable since the NFHS is only representative at the (for-

mer) state level. Fortunately, given that the DLHS were

representative at the district-level, we were able to map the

data to fit into the structure of the newly formed states.

Similarly, the SRS data were available on a yearly basis,

and thus, we were able to account for the changes in the

state boundaries.

We cleaned the datasets by deleting duplicates and

dropping children that had birth and death dates outside of

Table 1 Overview of available datasets obtained from surveys in India for Uttar Pradesh, 1990-2008

Data source Years Data type Sample size Used for equity marker Comment

Women CEB S U/R E W D

DLHS-I 1998–1999 SBH 59,305 215,180 x x x NMR indirectly estimated

DLHS-II 2002–2004 CBH 56,186 213,928 x x x x

DLHS-III 2007–2008 SBH 86,016 309,249 x x x x NMR indirectly estimated

DHS-I 1992–1993 CBH Not used*

DHS-II 1998–1999 CBH Not used*

DHS-III 2005–2006 CBH 8,451 32,768 x x x x Representative at state level only

SRS 1971–2008 Crude death rates x x Data available: 1999–2008

Estimation method Sum. Sum. Sum. D I

DLHS district level health survey, DHS demographic health survey, SRS sample registration system, SBH summary birth history, CBH complete

birth history, CEB children ever born, U5MR under-five mortality rates, NMR neonatal mortality rates

Equity markers S state, U/R urban/rural, E, ethnicity, W wealth, D district, Sum. summary estimation, D direct estimation, I indirect estimation

* Data is only representative at pre-Uttarakhand state level

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123

allowable ranges (e.g. child reported to die after the

interview date). The use of five datasets provided a sample

period from 1990 to 2007. As previously mentioned, esti-

mates were produced at the state level and across four

equity markers: urban–rural location, ethnicity, wealth, and

districts. The choice of equity markers was informed by

previous studies and availability of the data required to

represent the diversity within the state [16, 18, 20–22].

Data on the equity markers are available in all datasets,

with the exception of SRS, which only includes measures

at the state level and for rural/urban location. We utilised

questions on household assets and housing characteristics

to construct a wealth index using principal components

analysis [23]. Acknowledging that the type of assets owned

by rural households (e.g. tractors and agricultural land) is

likely to differ from the type of assets owned by house-

holds in urban areas, the wealth index is derived for both

rural and urban areas separately.

Mortality Estimates

Under-five and neonatal mortality rates were estimated

using available methods suitable to the various data sour-

ces, after which the corresponding mortality estimates were

synthesised into a summary measure These methods have

been described elsewhere [24, 25] and full details of their

application in our study are discussed in the web appendix.

Briefly, three types of estimates were generated. First, in

cases where complete birth histories (CBH) are available,

we pooled all such surveys and restructured the data into

child observations, quantified in months. Under-five mor-

tality rates (U5MR) and neonatal mortality rates (NMR)

were obtained directly by combining the survival rates

from the relevant age groups and subtracting from one.

Second, when CBH were not available, under-five mor-

tality rates were indirectly estimated from summary birth

histories (SBH) using cohort-derived and period-derived

techniques, which were incorporated into a combined

estimate by applying Loess regression [25]. Indirect esti-

mates of U5MR were then converted into NMR using a

hierarchical model with random intercepts and random

slopes to explore the relationships between U5MR and

NMR [24]. To gauge the validity of these modelled neo-

natal mortality rates in our context, we compare direct

estimates available for 1990–2007 with the modelled rates

and find similar trends and point estimates (results avail-

able from authors upon request). Third, U5MR were

derived from crude death rates converted using the tech-

nique outlined by Preston, Heuveline [26].

When applicable, a single summary measure was pro-

duced by averaging all the various estimates of mortality

rates into one estimator via a modified version of Loess

regression [24, 27, 28]. A few modifications to the methods

of Murray and colleagues were employed, relating to the

model specification, the weighting used in Loess regression

procedures and the measures of uncertainty. For predicting

U5MR and NMR beyond the sample period, we used the

same method as in Murray, Laakso [24], which relies on

the Loess regression and the last set of parameter estimates

to project mortality rates towards 2015. Since these pro-

jections are based on extrapolations of recent time trends,

they represent the expected mortality rates if these trends

continue and therefore do not attempt to capture the effects

of recent policy changes.

Finally, several issues should be noted. First, as noted

above, indirect estimates of neonatal mortality are con-

verted from indirect estimates of U5MR. In the case of

wealth quintiles, data limitations imply that such rates are

computed with an excessive degree of uncertainty. Con-

sequently, we only estimated direct estimates across wealth

quintiles, which are associated with a lower but still high

degree of uncertainty. Second, only the DLHS datasets are

representative at the district level and the district estimates

are produced using the most recent wave. Thirdly, in the

absence of complete vital registration systems, we have to

rely on survey based measures, whose limitations are well

known [25]. We attempt to minimize potential biases

associated with individual surveys and techniques, by

pooling the estimates whenever feasible.

All statistical analyses described were carried out using

two statistical packages, Stata and R. The datasets used in

this study were anonymous, with no identifiable informa-

tion on the survey participants, and were obtained through

publicly available online resources. As such a full review

of this study from an institutional review board was not

sought.

Results

At a state level Uttar Pradesh has experienced a reduction

in U5MR from 163 deaths per 1,000 live births in 1990 to

105 in 2007, with an average annual rate of change of

2.33 %, although the rate of change has declined some-

what after 1995 (see Fig. 1). A similar trend can be seen

in regards to NMR. Progress in the latter, however, has

been slightly slower with a decline from 76 deaths per

1,000 live births in 1990 to 49 in 2007. A summary of the

mortality estimates for selected years are reported in

Tables 2 and 3.

In rural areas, in which approximately 78 % of the

state’s population lives, the trend unsurprisingly echoes the

state-wide trend (see Fig. 2). While U5MR is still higher in

rural areas at 110 deaths per 1,000 live births, the rate of

reduction has been much greater than that in the urban

population, where U5MR stands at 82. This slower

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123

reduction in urban mortality rates has led to a considerable

narrowing of the mortality gap to approximately half of the

1990 level. This pattern is even more pronounced in terms

of NMR, where the average annual rate of reduction in

rural areas was almost four times that in urban areas. These

trends have reduced the urban–rural difference in mortality

by approximately three quarters since 1990. It should be

noted, however, that the rate of reduction in rural areas has

slowed somewhat in recent periods, suggesting that con-

vergence is unlikely to occur in the near future.

Disparities in caste-specific trends have been persistent;

with Scheduled Tribes (ST) and SC consistently experi-

encing higher mortality than the rest of the population (see

Fig. 3). While the ST appear to have experienced a decline

in mortality, they make up only 0.1 % of the population in

Uttar Pradesh. These trends are thus subject to a high level

of uncertainty and should be treated with some caution.

The SC have experienced a steady decline, with U5MR

reduced by over a third between 1990 and 2007, although

this reduction too shows signs of having slowed since

Fig. 1 Estimates of under-five

and neonatal mortality rates (per

1,000 live births) from 1990 and

2007 and projections towards

2015 in Uttar Pradesh. Notes

The solid lines represent the

mortality estimates, while the

shaded area signifies 95 %

confidence intervals. Projections

are indicated by the dotted-

lines. The average annual

change (A.C.) in mortality is

reported

Table 2 Estimated under-five mortality rates (per 1,000 live births), with 95 % confidence interval, for selected years

Equity marker 1990 1995 2000 2005/2007* Annual rate of

reduction (%)U5MR 95 % C.I. U5MR 95 % C.I. U5MR 95 % C.I. U5MR 95 % C.I.

Uttar Pradesh 163 (152–172) 132 (126– 140) 117 (109–125) 105 (86–127) 2.33

Urban/Rural

Rural 178 (168–189) 141 (132– 148) 123 (114–132) 110 (90–131) 2.57

Urban 116 (101–129) 104 (93– 116) 93 (78–110) 82 (59–110) 1.82

Ethnicity

Scheduled Caste 201 (183–221) 163 (152– 175) 140 (126–155) 123 (98–156) 2.75

Scheduled Tribe 206 (149–276) 187 (148– 236) 149 (117–187) 148 (102–215) 1.93

Other 150 (142–159) 127 (119– 136) 112 (103–122) 93 (77–112) 2.55

Wealth

Rural

Low Income 221 (200–242) 167 (153– 183) 137 (125–149) 113 (98–129) 4.14

Middle Income 178 (155–204) 143 (125– 162) 117 (100–136) 96 (78–119) 3.91

High Income 128 (110–147) 105 (92– 119) 89 (75–104) 75 (61–92) 3.40

Urban

Low Income 155 (125–192) 131 (108– 159) 117 (91–149) 103 (74–79) 2.48

Middle Income 102 (75–137) 90 (71–117) 84 (64–110) 79 (56–110) 1.56

High Income 75 (51–110) 58 (39–87) 54 (30–92) 52 (26–101) 2.29

U5MR under-five mortality rates, C.I. confidence interval

* The estimates from the most recent year are represented in the final column. For wealth groups the most recent year is 2005, for all other equity

markers the year is 2007

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123

2000. In contrast, U5MR for the ‘‘Other’’ group has con-

tinued to decline steadily over the entire period. As a result,

caste-based disparities in U5MR may be expected to

increase if trends continue.

Trends are slightly different with regards to NMR where

neonatal mortality among the SC has continually fallen. This

decline has not been enough to close the gap with the rest of

the population, which after a period of limited progress has

Table 3 Estimated neonatal mortality rates per 1,000 live births), with 95 % confidence interval, for selected years

Equity marker 1990 1995 2000 2005/2007 Annual rate of

reduction (%)NMR 95 % C.I. NMR 95 % C.I. NMR 95 % C.I. NMR 95 % C.I.

Uttar Pradesh 76 (70–83) 64 (59–71) 57 (50–65) 49 (36–68) 2.23

Urban/rural

Rural 84 (77–92) 69 (63–76) 60 (51–70) 52 (38–68) 2.48

Urban 49 (39–60) 50 (40–62) 47 (35–62) 44 (22–81) 0.64

Ethnicity

Scheduled Caste 91 (75–108) 76 (66–89) 65 (52–82) 54 (32–84) 2.96

Scheduled Tribe 94 (41–213) 92 (50–166) 65 (33–127) 66 (18–241) 2.59

Other 70 (63–77) 61 (55–67) 56 (47–66) 46 (32–62) 1.93

Wealth

Rural

Low income 103 (87–122) 79 (66–93) 64 (53–77) 52 (42–65) 4.26

Middle income 86 (69–107) 69 (56–83) 57 (46–71) 48 (37–61) 3.72

High income 67 (57–77) 55 (48–64) 47 (39–55) 39 (32–49) 3.33

Urban

Low income 69 (52–91) 60 (46–78) 55 (41–75) 51 (35–73) 1.90

Middle income 49 (33–70) 44 (32–62) 41 (30–59) 38 (25–59) 1.52

High income 36 (18–70) 30 (16–55) 31 (15–62) 32 (14–73) 0.40

NMR neonatal mortality rates, C.I. confidence interval

* The estimates from the most recent year are represented in the final column. For wealth groups the most recent year is 2005, for all other equity

markers the year is 2007

Fig. 2 Rural and Urban trends

in under-five and neonatal

mortality rates (per 1,000 live

births) between 1990 and 2007

and projections towards 2015.

Notes The solid lines represent

the mortality estimates, while

the shaded area signifies 95 %

confidence intervals. Projections

are indicated by the dotted-

lines. The average annual

change (A.C.) in mortality is

reported for urban (rural) areas

Matern Child Health J

123

begun to decline at similar rates. Based on current trends, a

convergence between these groups is unlikely.

Within the rural population, wealth-based disparities

(Fig. 4) in mortality trends have reduced over time. The

large differences in mortality seen between the high

income group and the low and middle income groups in

both U5MR and NMR in 1990 have narrowed considerably

due to much higher rates of reduction in the low income

and middle income groups compared to the high income

group. The progress seen in NMR among the low income

group has been particularly strong, and if observed trends

continue, the gap between the low and middle income

groups is likely to diminish even further. In terms of

U5MR, the rates of reduction in the low and middle income

groups are more similar, and the disparity between these

groups is likely to remain for some time.

Wealth-based disparities among the urban population

demonstrate a very different pattern. While the low income

group has shown considerable progress in both U5MR and

NMR, the rates of change have been much lower than their

rural counterparts, and rate of reduction for NMR is

slowing. At the same time, the middle income group has

experienced steady, but much lower, annual reductions.

However, trends among the high income group are perhaps

the most surprising. Although the annual rate of reduction

for this group over the entire period was 2.48 %, the large

majority of the decline occurred prior to 1995 and NMR

has experienced an annual rate of reduction of only

approximately 0.4 %. In these circumstances it appears that

disparities in neonatal mortality have reduced not due to

particularly good progress in disadvantaged groups but

instead by poor progress among the rest of the population.

The performance of individual districts has been mixed.

In 1990 the worst performing district had a U5MR of 256

deaths per 1,000 live births compared to 109 in the best. By

2000 these values had shifted to 184 and 84 deaths per

1,000, and in 2007 the worst and best performers had rates

of 160 and 72 respectively. Despite this generalised

reduction in mortality, individual districts’ performance

was highly variable, with some districts more than halving

their mortality while others saw increases in recent years.

Discussion

This study is the first to utilise district desegregation to

examine mortality trends in Uttar Pradesh, excluding trends

within the region that was to become Uttarakhand. The

state has demonstrated considerable reductions in under-

five and neonatal mortality since 1990, but progress is

slowing. Large, early declines in rural mortality have been

offset by limited progress in urban regions and difficulties

in maintaining the rate of change. A period of economic

liberalisation during the 1990s that led to general

improvements in living standards [29, 30] and the expan-

sion of outreach programs providing immunisation and

family planning services, especially in rural areas, may

help to explain the rapid reductions seen initially [5, 31].

Fig. 3 Caste-specific under-five

and neonatal mortality rates (per

1,000 live births) between 1990

and 2007 and projections

towards 2015. Notes The solid

lines represent the mortality

estimates. Projections are

indicated by the hollow symbols.

The average annual change

(A.C.) in mortality is reported

for Other (SC) [ST] ethnic

groups. S. Cast, Scheduled

Caste; S. Tribe, Scheduled Tribe

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123

However the slowing in recent years suggests that the

success of these vertical programs may have reached a point

where further mortality reductions are increasingly unlikely.

The stagnation in NMR seen in Uttar Pradesh echoes a global

trend in which neonatal mortality has proved to be more

resistant to change than childhood mortality [32] This lim-

ited progress is largely due to the strong dependence of

neonatal health outcomes on the ability of the health system

to provide and the population to utilise a more complex range

of services, such as skilled birth attendance and emergency

obstetric care [10, 33]. As these services cannot be scaled-up

through easily implemented programs, such as immuniza-

tion campaigns, which target causes of post-neonatal mor-

tality, future gains in child health will be dependent upon the

harder task of strengthening health systems [32, 34].

The dependence of neonatal health on improving the

health system is particularly relevant in Uttar Pradesh

where even as access to services has increased, studies

have consistently found that maternal and health services,

particularly in rural areas, are highly dysfunctional and

offer poor quality of care [35, 36]. Local administration of

health services varies considerably, as evidenced by the

large variation in district level outcomes, and consequently

access to functioning services outside major cities remains

limited [35, 37]. This fragmentation of the health system

may help to explain the higher levels of mortality among

even the richest groups in rural areas compared to their

urban counterparts. At the same time observed pro-poor

urban economic growth in recent years [30], may have

improved the financial ability of the urban poor to access

health services and achieve greater declines in mortality

than were observed the high and middle income groups.

Although very recent, incentive programmes aimed par-

ticularly at the poor as part of the NHRM have led to

increases in the coverage of antenatal care and skilled birth

attendance [34]—however coverage is still lower than

other parts of the country [6, 38]. Despite this increase in

service coverage, overall maternal mortality has remained

high, and it has been suggested that poor quality of care

may be preventing additional mortality reductions [39], in

line with the trends observed for NMR and U5MR.

Issues of quality may also be exacerbating the unequal

distribution of mortality within the state. Use of repro-

ductive and child health services in Uttar Pradesh is known

to be much higher among the rich [5]; however it is pos-

sible that the quality of the services that different groups

receive may also vary. Although government services are

expected to provide good quality care to disadvantaged

groups several studies have reported differences in the

clinical management and advice given depending on the

social status of the patient [12, 40, 41], and threatening

behaviour, harassment for additional fees, and service

denial have been reported in regards to lower income and

lower caste women [8, 42, 43].

Fig. 4 Trends in under-five and neonatal mortality rates (per 1,000

live births) between 1990 and 2005 and projections towards 2015 by

urban- and rural-specific wealth groups. Notes The solid lines

represent the mortality estimates. Projections are indicated by the

hollow symbols. The average annual change (A.C.) in mortality is

reported for low (middle) [high] income groups

Matern Child Health J

123

In both rural and urban areas low income women have

reported preference for, and high use of, private providers

due to a perception of greater quality [44, 45]. However

issues of cost mean that the most frequent providers of care

for these groups tend to be unqualified allopathic practi-

tioners, who are not subject to regulation and provide

potentially low or incomplete levels of care [44, 46, 47].

For example, in two urban slums in Aligarh over 60 % of

women had received a tetanus vaccination, but many

reported receiving this immunisation without having

attended any antenatal care [45]. Yet the costs of more

complex services, such as emergency obstetric care or

hospitalisation for childhood pneumonia, continue to limit

access to these services for the poor [46, 48]. As long as

these impediments to the use of health services remain in

place the downward trends in mortality observed in the

poorest groups are unlikely to continue into the future.

Another important consideration in understanding

wealth-based trends is the rising level of caste-based dis-

parities, particularly as 21 % of the population of Uttar

Pradesh belong to the SC. Members of these groups are

over-represented among the poor [49] and previous studies

have found that pervasive forms of social exclusion limit

the ability and willingness of SC to access many forms of

health services in Uttar Pradesh [50]. The increasingly

disproportionate levels of mortality experienced by these

groups compared to the rest of the population is a cause for

concern, as unless these non-financial barriers are addres-

sed decreases in inequality are unlikely, in spite of pro-

poor policies. On the other hand, the large burden of

mortality represented here suggests that the targeting of

these groups, particularly the SC, for intervention may

yield a significant impact on mortality reduction for Uttar

Pradesh as a whole.

While the trends identified in Uttar Pradesh are not

likely to apply to such a vast country as India, similar

equity patterns might plausibly be found in other EAG

states (i.e. Bihar, Chhattisgarh, Jharkhand, Madhya Pra-

desh, Orissa, Rajasthan and Uttarakhand) with large rural

populations, higher than country-average levels of mor-

tality and insufficient public health expenditure [3, 10].

This analysis has several important limitations. Firstly,

while we have attempted to reduce the impact of recall bias

and under-reporting on the estimates of deaths from direct

estimation by pooling multiple datasets and not producing

estimates for periods with less than 5,000 person-month

observations, we cannot rule out the possibility that these

factors may influence our results. Similarly, while mini-

mised through the use of local regression methods, indi-

rectly derived estimates of mortality are subject to

overreliance on generalised patterns in the timing of births

and deaths generated from more complete surveys. Thirdly,

some caution is required in regards to the interpretation of

trends where a limited number of observations are avail-

able due to the potential for large sampling errors. Addi-

tionally that two of the data sources used for this analysis

(DLHS-2 and DLHS-3) do not demonstrate evidence of a

strong gender bias, despite the well documented imbalance

in the sex ratio both at birth and up to 4 years [8, 51, 52],

indicating the presence of at least some inherent bias within

the datasets used. Finally, our projections are based on

extrapolations of recent time trends, and consequently, are

unable to demonstrate the effect of recent strategies in

targeting particular sub-populations.

Notwithstanding the slow but steady reduction of

absolute levels of child mortality within Uttar Pradesh,

disparities between different sub-populations defined by

geography, ethnicity, and wealth remain high, and some

reductions do not appear to be sustainable. Future progress

may require significant investment in the quality and

inclusiveness of the health system in order to not only

reach disadvantaged groups, but also to ensure the popu-

lation as a whole improves.

Acknowledgments This work was supported by The Australian

Agency for International Development (AusAID) [47734] and The

Bill & Melinda Gates Foundation [52125]. The funders had no role in

study design, data collection and analysis, decision to publish, or

preparation of the manuscript.

Conflict of interest The authors declare no competing interests.

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