Neonatal, Postneonatal and Childhood Mortality in India

Mortality in children under five years is a critical population health indicator in Low- and Middle-Income Countries including India. Over the years, India has implemented several policies to target maternal and child mortality to promote safe motherhood and to improve nutritional outcomes for pregnant women, lactating mothers, and children. Using the disaggregated data from the fourth National Family Health Survey (NFHS 2015-16) and the estimated annual births from the WorldPop Project, we calculated the probability and headcount of neonatal, postneonatal, and child mortality for 640 districts in India. The geo-visualisations as following are presented with a motivation to help various stakeholders prioritise indicators and districts for interventions. 

Geographic Visualization

Key Figures

Neonatal Mortality

Neonatal Mortality

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Postneonatal Mortality

Postneonatal Mortality

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Childhood Mortality

Childhood Mortality

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Explore Data

The spreadsheet below provides users to customize data views using filters. To enable filtering click on the filter icon as shown in the image below:

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Citation: Akhil Kumar, Weiyu Wang, Julie Kim, Weixing Zhang, Rockli Kim, S V Subramanian. Neonatal, Postneonatal, and Childhood Mortality in India. Apr 2021. Geographic Insights Lab at the Harvard Center for Population and Development Studies; Center for Geographic Analysis at Harvard University, Cambridge, MA.

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Prevalence-Headcount Metric

Blank areas represent unavailable data. We estimated the burden for each mortality indicator in children under five years by two dimensions of Prevalence (P) and Headcount (H), and combined them to derive a Prevalence-Headcount Metric (PHM) which the legend and rank both show.


We used the predicted estimates in probability of death multiplied by 1000 which gives us the number of deaths per 1000 live births. For details on the estimation of the district specific probability of neonatal, post-neonatal and child mortality, see Kim et al. (2020).

The P metric quantifies the risk of child mortality in a district. For example, in the Sitapur district of Uttar Pradesh, the prevalence of neonatal mortality is 5.7%. Thus, the P metric helps identify districts where the probability of a child dying within the first 28 days since birth remains high and should be reduced.

However, the P metric does not contain any information on the absolute number of new births who are at risk because it does not take into account the total population of new births in a district. For example, consider the districts of Uttar Bastar Kanker (5.24%) in Chhattisgarh and Mahrajganj (5.26%) in Uttar Pradesh; both have similar P metric but differ with regards to the estimated number of new births of 15,261 and 68,410, respectively, translating to different levels of absolute burden.


The metric H is given as the product of P and the estimates of annual births by district (from the WorldPop Project) N for each district. 

Hj = Pj × Nj

Returning to the above example, the number (H) of Neonatal mortality is substantially large in Maharajganj (3598) than Uttar Bastar Kanker (800) despite both districts having almost the same prevalence because the total population of new births burden by Neonatal mortality in Maharajganj is substantially larger.

The headcount estimates are the expected number of deaths by a particular age if all births are exposed to the estimated mortality probabilities in that age in a given year. Using the annual death count, we also estimated the average number of death count per day by dividing annual death count by 365. 

These estimates for death counts follow a hypothetical-cohort approach. They estimate what would happen to live-births in 2015 if they experienced the mortality probabilities observed in NFHS-4. This is the same approach taken in calculating period life tables and period life expectancies. As with period life expectancies, the limitation of this approach is that as these children age, mortality probabilities may change. The death counts calculated here assume that the mortality probabilities will remain the same till this cohort of new-borns in 2015 turns 5.

Prevalence-Headcount Metric

We developed a combined Prevalence-Headcount metric (PHM) that takes into account the features of both the risk (P) as well as the headcount (H) to provide a comprehensive picture of the burden of child mortality in a district. Step-by-step instructions can be found in the "PHM Methodology" section.

Types of Under-5 Child Mortality


Age Group


Neonatal Mortality (NM)

0-28 days

The probability of dying within the first 28 days since birth

Postneonatal Mortality (PNM)

28 days-12 months

The probability of dying between the 28 days since birth and the first birthday

Childhood Mortality (CM)

12-48 months

The probability of dying between the first and fifth birthdays


Kim R, Liou L, Xu Y, et al. Precision-weighted estimates of neonatal, postneonatal and child mortality for 640 districts in India, National Family Health Survey 2016. Journal of Global Health. 2020;10 (2).Abstract

Background The conventional indicators of infant and under-five mortality are aggregate deaths occurring in the first year and the first five years, respectively. Monitoring deaths by <1 month (neonatal), 1-11 months (post-neonatal), and 12- 59 months (child) can be more informative given various etiological causes that may require different interventions across these three mutually exclusive periods. For optimal resource allocation, it is also necessary to track progress in robust estimates of child survival at a smaller geographic and administrative level.

Methods Data on 259627 children came from the 2015-2016 Indian National Family Health Survey. We used a random effects model to account for the complex survey design and sampling variability, and predicted district-specific probabilities of neonatal, post-neonatal, and child mortality. The resulting precision-weighted estimates are more reliable as they pool information and borrow strength from other districts that share the same state membership. The Pearson correlation and Spearman’s rank correlation were assessed for the three mortality estimates, and the Moran’s I measure was used to detect spatial clustering of high burden districts for each outcome.

Results The majority of under-five deaths was disproportionately concentrated in the neonatal period. Across all districts, the predicted probability of neonatal, post-neonatal, and child mortality varied from 6.0 to 63.9 deaths, 3.8 to 47.6 deaths, and 1.7 to 11.8 deaths per 1000 live births, respectively. The overall correlation between district-wide probabilities of mortality for the three mutually exclusive periods was moderate (Pearson correlation=0.47-0.58, Spearman’s rank correlation=0.58-0.64). For each outcome, a relatively strong spatial clustering was detected across districts that transcended state boundaries (Moran’s I=0.61-0.76).

Conclusions Sufficiently breaking down the under-five mortality to distinct age groups and using the precision-weighted estimations to monitor performances at smaller geographic and administrative units can inform more targeted interventions and foster accountability to improve child survival

Kim R, Swaminathan A, Swaminathan G, et al. Parliamentary Constituency Factsheet for Indicators of Nutrition, Health and Development in India. Harvard Center for Population and Development Studies. 2019;18 (4).Abstract

In India, data on key developmental indicators that formulate policies and interventions are routinely available for the administrative units of districts but not for the political units of Parliamentary Constituencies (PC). Members of Parliament (MPs) in the Lok Sabha, each representing 543 PCs as per the 2014 India map, are the representatives with the most direct interaction with their constituents. The MPs are responsible for articulating the vision and the implementation of public policies at the national level and for their respective constituencies. In order for MPs to efficiently and effectively serve their people, and also for the constituents to understand the performance of their MPs, it is critical to produce the most accurate and up-to-date evidence on the state of health and well-being at the PC-level. However, absence of PC identifiers in nationally representative surveys or the Census has eluded an assessment of how a PC is doing with regards to key indicators of nutrition, health and development.

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