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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: [email protected]
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
Matern Child Health J
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
Matern Child Health J
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
Matern Child Health J
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
Matern Child Health J
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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