Background
Life expectancy is a summary indicator of mortality at every age that enables us to compare mortality/longevity between regions (and times) that may have highly different demographics [
1‐
4]. Although there are other ways to calculate life expectancy, the usual approach is to construct a life table that requires robust and extensive data requirements and usually takes a significant amount of personal and computational time [
5‐
9]. Life expectancy at age x
\(({e_x})\)is a popular summary indicator of the mortality and health of a population. It reflects the overall health of a population and is frequently known as an early predictor of a societal issue [10]. As a result life expectancy research is crucial for measuring overall health and assessing the efficacy of health policies [
3,
11,
12].
\({e_x}\) at birth and adult ages has long been used as an indicator of health status and the level of mortality experienced by a population. It has been acknowledged that its primary advantage over alternate methods of assessing mortality is that it does not take into consideration the effects of the age distribution of an actual population and does not call for the adoption of a standard population for comparing mortality levels among various populations [
13].
The rise in life expectancy in contemporary societies in recent years has sparked debates concerning the extent to which individuals value the potential increase in their lifespan. The desire for prolonged life and life extension is a relatively recent and evolving concern in most modern cultures. Limited knowledge exists regarding the factors that motivate individuals of different age groups to aspire to have long lives despite the potential challenges associated with old age [
14]. Life expectancy at birth
\(({e_0})\) is one of the foremost common measurements utilized for summarizing mortality and the well-being status of populations. It communicates the average years an infant is expected to live given the age-specific death rates at a point in time [
15‐
18]. Efforts to compute mortality rates based on limited counts and deaths frequently yield unpredictable patterns that pose significant challenges in terms of interpretation. The statistical significance of these calculations can be enhanced by consolidating data across time periods or age cohorts, assuming there is sufficient vital registration. Unfortunately, this approach is not viable in situations where records are insufficient or unavailable. The examination of mortality and the development of methodologies to estimate life expectancy in regions with small populations are crucial components in generating demographic projections of high quality.
The need for mortality indicators for smaller (subnational, subregional) areas has risen over the past few years either for the purpose of examining geographic disparities in mortality, tracking the impact of public health policies, informing local strategies or developing long-term subnational population projections [
19‐
22]. Calculating life expectancies for small geographic areas is frequently difficult or time-consuming due to the aforementioned data and time requirements and the peculiarities of small population data (ages or age bands with zero deaths, reduced population counts, increasing volatility of death rates, oldest-old mortality rates, etc.). In order to examine the spatial health disparities within a country, reliable subnational mortality estimates are needed as indicators of general health and well-being [
23,
24]. Researchers may be better able to understand how communities and places of residence may influence health status through both compositional and contextual factors with the use of accurate mortality data for regional populations [
25]. The estimates of life expectancy at age x
\(({e_x})\)are routinely available for India and its 22 states for the formulation of policies and programmes but not for the administrative units of the districts. India lacks data and statistical estimates at the district level and other lower geographical levels such as city, village, town, wards and blocks. In the lack of
\({e_x}\) estimates at the district level, state-level Indicators are taken as a proxy for the formulation of the policies and programmes at the district level [
22,
26]. This study recognizes the importance of mortality estimates at a lower level of geography. The Estimation of
\({e_0}\) at the district level is of utmost importance for understanding the disparity and inequality across the micro-regions. This study aims to provide an estimated
\({e_0}\) at the district level and highlights the regional variation in mortality at the district level which would help the policy formulations and programs. The present study aims to examine the subnational (district-level) variations in life expectancy at birth
\(({e_0})\) from the survey data NFHS (2015-16) and NFHS (2019-21) for all 640 districts in NFHS − 4 and 707 districts in NFHS-5 [
27,
28].
Discussion
The present study explored the state and district-level variations in life expectancy at birth \(({e_0})\) from NFHS (2015-16) and NFHS (2019-21). Results observed the district-level differentials in life expectancy at birth for the total, male and female population across the 640 districts in NFHS-4 and 707 districts in NFHS-5 in India. At first this study calculated the age-specific mortality rates across all 36 states from NFHS-4 and NFHS-5 and constructed abridged life tables for these states. We have estimated each state’s specific model parameters from the abridged life tables in 2015-16 and 2019-21 respectively. In the second step we calculated the Infant mortality rates (IMR) across all 36 states and their respective districts for the total, male and female populations. In some of the districts IMR were absent or zero, in these districts we have kept the state IMR as the district IMR. In the third step we have linked each state-specific parameters to the districts of that particular state for the estimation of life expectancy at birth \(({e_0})\). In a country like India where district-level age patterns of mortality data are absent and age-specific death data are unavailable, indirect estimation and small area estimation (SAE) are the only techniques to provide mortality estimates at the subnational level. We used the indirect method of linking IMR to life expectancy at birth \(({e_0})\) and calculated state and districts \({e_0}\) for the 36 states and their respective districts in 2015-16 and 2019-21. State-level results observed the similarities and variations in estimated versus calculated \({e_0}\) in the three population groups males, females and persons for the many states. On the success of state-level estimations of \({e_0}\) through our proposed model, we have proceeded with the estimations of \({e_0}\) at the district level. Results at the state level showed that there were similarities between the estimated and calculated \({e_0}\) in most of the states. Our Findings observed that the highest \({e_0}\) in the ranges of 70 to 90 years among the districts of the southern region. \({e_0}\) falls below 70 years among most of the central and eastern region districts. In the northern region districts, \({e_0}\) lies in the range of 70 years to 75 years. The estimates of life expectancy at birth \(({e_0})\) shows the noticeable variations at the state and district levels for the person, male, and female populations from the NFHS (2015-16) and NFHS (2019-21). In the absence of mortality data at the district level in India we have used the indirect estimation method of relating state parameters with the IMR of each district and estimated \({e_0}\) across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21). Our results have similarities with the state-level estimations of \({e_0}\) from sources of SRS and NFHS data and found the highest \({e_0}\) in the southern region and lowest in the eastern and central region districts.
The results at the state level demonstrated that out of the 36 states examined in this study
\({e_0}\) has decreased for 22 states overall with significant reductions in life expectancy observed in 23 states for men and 21 states for women during NFHS-4 to NFHS-5. These findings were also observed at the district level as life expectancy decreased in some districts from NFHS-4 to NFHS-5. Recent studies from India have observed that
\({e_0}\) has declined during the COVID-19 pandemic for both men and women from 69.5 to 72.0 years in 2019 to 67.5 and 69.8 years respectively in 2020 [
9,
43,
44]. The
\({e_0}\) shows a drop of approximately 2.0 years in 2020 when compared to 2019. Similarly, another study has found that at
\({e_0}\) has declined for the 22 states in total, 23 states in men and 22 states in women in the pandemic year 2019-21 among the Indian population [
16]. The research from South Asian countries aims to determine how urbanization and income inequality affect life expectancy for males and females. The findings demonstrate that urbanization and income inequality reduce life expectancy, but health expenditures have a positive influence. Furthermore, health expenditures lessen the negative impact of urbanization on life expectancy [
45]. The study by Thakuria et al. (2017) found that for males (females) 18 (11) % of the district has the
\({e_0}\) below 60 years, 20 (27) % of the districts have
\({e_0}\) 60–65 years and 30 (62) % of the districts have shown above 65 years of
\({e_0}\) [
46]. The present study findings from NFHS-4 data observed similar results for males (females) 6(4) % of the district have the
\({e_0}\) below 60 years, 21(14) % of the districts have
\({e_0}\) between 60 and 65 years and 73(82) % of the districts have shown
\({e_0}\) of 66 years and above. A Bayesian hierarchical model is proposed to project mortality using common factors and sparse or missing data. The model is applied to mortality data for China and the United States, providing good estimates and reasonable forecasts at both country and provincial levels. The model predicts that in 2030, China will have similar national life expectancy at age 60 and similar heterogeneity in subnational life expectancy as the United States [
47]. Choudhury and Sarma (2014) found that 81% of districts of the southern zone are having
\({e_0}\) in the range of 60–70 years followed by the northern zone (71%), western zone (69.4%), eastern zone (66%), northeastern zone (42.1%) and central zone (34.4%). In the western zone 29% of the districts have
\({e_0}\) above 70 years followed by the southern zone (14.9%) and northern zone (7.5%). No districts of other zones have
\({e_0}\) above the 70 years category [
48]. This study results also found that districts from the southern zone have the highest
\({e_0}\) from both NFHS-4 and NFHS-5 data sources among the total, male and female population. A research from Bangladesh presents findings for different areas and genders using a specific life table model. It examines how changes in the growth rate affect the estimated life expectancy at birth for the districts. The paper compares these estimates with others and finds them generally consistent, except for those from the Bangladesh Bureau of Statistics, which may have accuracy problems. The paper also recognizes the past use of stable population models during periods of constant fertility and mortality rates [
49]. Ranjana (2015) calculated the district level
\({e_0}\) for the census 2001 year among the districts of major states of India and found that
\({e_0}\) is the highest (70.2) years for the Udupi district of Karnataka state followed by Pune (69.7 years) district of Maharashtra. Pune and Sangli districts of Maharashtra show the highest
\({e_0}\) (69 years) and the female’s highest
\({e_0}\) (71.2 years) is found in Udupi district of Karnataka [
22]. This Study found from NFHS-4 data that Rewa district from Madhya Pradesh has the lowest
\({e_0}\) of 53.6 years and Upper Siang district from Arunachal Pradesh showed the highest
\({e_0}\) of 93.5 years. Similarly, our findings from NFHS-5 data found that Dakshina Kannada district from Karnataka observed the maximum
\({e_0}\) of 86.4 years and Ambala district from Haryana observed the minimum
\({e_0}\) of 56.9 years across 707 districts in India. This study obtained significant sex differentials in
\({e_0}\) at the district level and found that there are mortality differentials across districts and between states in India.
A study conducted in England examined mortality and longevity patterns from 2002 to 2019 in 6791 communities, revealing a decline in life expectancy in certain communities, particularly among women, and increasing disparities in life expectancy across geographic regions, emphasizing the importance of fair economic and social policies, as well as increased investment in public health and healthcare, to address these trends and prevent further decline in life expectancy [
50]. The research carried out in Germany places great emphasis on the significance of small-area estimates in identifying marginalized regions and planning appropriate health services, effectively demonstrating the greater influence of district-level socioeconomic indicators on life expectancy prediction compared to population density or the number of primary-care physicians per 100,000 residents, thereby significantly contributing to our understanding of factors impacting life expectancy patterns and providing valuable insights for discussions on promoting equitable living conditions and developing healthcare plans in Germany [
51]. A study conducted in Germany aimed to estimate district-level life expectancy between 1997 and 2016 and examined mortality rate convergence. The findings showed a decrease in life expectancy differences between districts, primarily due to improvements in mortality rates in eastern German districts, although there was variation within federal states. The study suggests that achieving equitable health outcomes is possible through targeted investments in specific places and individuals [
52]. A similar study aimed to use spatio-temporal analysis to calculate the life expectancy at the district level in Korea, employing spatio-temporal models to estimate mortality rates for different age groups in 250 districts from 2004 to 2017, and suggesting the use of life expectancy based on these models for consistent yearly estimations at the district level, thereby making a valuable contribution to the field by providing life expectancy estimates at the district level and showcasing the utility of spatio-temporal modeling in examining health-related indicators [
53]. A study conducted in the United States of America (USA) aimed to estimate the overall life expectancy and the degree of variation within each congressional district. This estimation was achieved by analyzing age-specific life expectancies at the census tract level for the years 2010–2015. The research findings revealed that in congressional districts where overall life expectancy was higher among younger individuals, there were smaller standard deviations observed. However, this observed pattern was found to be reversed among older age groups [
54]. Another study examines the distribution and spatial arrangement of life expectancy in Buenos Aires, Argentina and its connection to socioeconomic characteristics. Women had a higher life expectancy at birth than men, and there was a significant disparity in life expectancy between areas with the highest and lowest values. Enhancing socioeconomic attributes were linked to higher life expectancy, emphasizing the significance of location-based policies to tackle spatial disparities in life expectancy [
55].
A similar research conducted in India proposes a procedure for estimating demographic indicators of Assam state and its districts using indirect techniques of estimation. It focuses on estimating life expectancy at birth, infant mortality rates, under five mortality rates, and life expectancy at birth at the district level of Assam. The paper utilizes a five-parameter polynomial regression model to estimate life expectancy at birth based on child survivorship probabilities obtained from indirect techniques of estimating infant and child mortality rates [
56]. Another research paper presents an empirical investigation of linear relationships between life expectancies of different age groups in Assam state of India, using linear regressions. It also proposes two second-degree polynomial regression models for estimating life expectancies at birth
\(({e_0})\)in India and major Indian states. The estimated values of
\({e_0}\) are compared with values obtained from other methods and SRS tables. Additionally, the paper estimates
\({e_0}\) values for all districts of Assam using polynomial regressions and indirect estimation techniques [
57]. A similar study employs Silicocks and Chiang’s revised methodology to estimate life expectancy at birth in smaller states, specifically examining Kohima and Dimapur districts in Nagaland, India and finds that both methods yield similar life expectancy estimates but Silicocks method has a lower standard error and the simulated results are consistent with both methodologies [
58].
The present study has brought out the extent of variation across districts within and between states in India from NFHS-4 and NFHS-5. From a policy perspective, life expectancy data is constantly required to assess progress in key indicators and prioritize actions. Even with India’s decentralization efforts, obtaining a precise assessment at the district level remains extremely difficult. It is necessary to depend on the census’s decennial data, which uses an indirect method to identify the district indicators. Because indirect estimation necessitates some degree of assumption, therefore enhancing and standardizing the administrative data system for small areas is necessary. Therefore, the current article recommends using the National Family Health Survey (NFHS) data to estimate district-level life expectancy. When reporting is extremely low or subpar, the state must simultaneously make significant steps to strengthen the Civil Registration System (CRS). The government should consider augmenting current central programmes with state-specific health policies or establishing new ones altogether.
Monitoring subnational healthcare quality is critical for identifying and addressing geographic inequities in service provision. Yet, demographic and health surveys are rarely powered to support the generation of estimates at more local levels. With this study, we developed an indirect estimation analytical approach to generate estimates of life expectancy at birth \(({e_0})\) at the district level in India. Using this approach, healthcare programme administrators and decision-makers may be able to gain insights into healthcare quality indicators over time and space together. When recent population census data are unavailable, our approach uses state-specific model parameters to link the state-specific abridged life tables to their respective districts to produce subnational (district-level) cross-sectional/period life expectancy at birth \(({e_0})\) estimates. This method offers a replicable approach for generating subnational and temporal estimates for mortality indicators. Furthermore, our approach can be used to critically assess the health status at the subnational level in India.
The study reveals the extent of variation within and between Indian states in terms of life expectancy, highlighting the challenge of obtaining precise district-level data. To overcome this challenge, the article suggests using the National Family Health Survey (NFHS) data to estimate life expectancy at the district level and recommends strengthening the Civil Registration System (CRS) in states with low reporting. Additionally, the government should consider implementing state-specific health policies or enhancing existing central programs. The study-utilized data from NFHS-4 (2015-16) and NFHS-5 (2019-21) to estimate life expectancy at the district level using an indirect estimation model based on abridged life tables. Identifying districts with low life expectancy is crucial for resource allocation within the health system, but estimating life expectancy for smaller regions has statistical uncertainties. The findings suggest that policies targeting the less fortunate population can reduce disparities in life expectancy, and presenting life expectancy at the district level can serve as an indicator for the effectiveness of health policies. The methodology used in this study can be applied in future studies to produce health-related indicators at the district level in India. Both the Government of India and state governments monitor the implementation progress of most developmental activities at the district level. Consequently, mortality measures at the sub-state level are valuable in evaluating social and health progress, determining the effectiveness of governmental initiatives, identifying high-risk demographics, and even understanding the influence of health-related behaviors.
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