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<p>This study aims to identify the most accurate pair model for predicting the life expectancy at birth (e<sub>0</sub>) at national and subnational levels, including men and women, by rural and urban areas, and which model provides the closest estimates of observed e<sub>0</sub> in the Sample Registration System (SRS) and the National Family Health Survey (NFHS). Furthermore, the decomposition method is applied to e<sub>0</sub> differences to identify mortality pattern differentials between SRS and NFHS in same reference period. The life table were constructed from the Chiang method using SRS statistical report 2015, 2020; NFHS-4 (2015-16); and NFHS-5 (2019-21), then after pair model such as, Brass logit, Splicing, and Modified logit were used to calculate age pattern of mortality and e<sub>0</sub> using under-five mortality rate and adult mortality rate for 2015 and 2020 at national and subnational levels. The results reveal that the Splicing model’s age pattern of mortality and e<sub>0</sub> is the closest to the Chiang method compared to the Brass logit and Modified logit models at both national and subnational levels, among men and women in the SRS and NFHS. The differences in <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{e}_{0}\)</EquationSource> </InlineEquation> values between the Splicing and the Chiang models were one or fewer than a year among men and women in both SRS and NFHS. Decomposition analysis reveals that the infant, child, and 5-14-year age groups made a significant contribution to Δe0 between SRS and NFHS in 2015 and 2020. The Splicing model yields the closest e₀ estimates, particularly with SRS data, compared to NFHS, underscoring its importance for subnational mortality assessment in contexts where mortality data are incomplete and unavailable.</p>

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A pair model to describe the life expectancy at birth in India

  • 期刊论文

  • 发表日期：

    2026年1月30日

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  • Chandan Kumar,
  • Suryakant Yadav

摘要

This study aims to identify the most accurate pair model for predicting the life expectancy at birth (e0) at national and subnational levels, including men and women, by rural and urban areas, and which model provides the closest estimates of observed e0 in the Sample Registration System (SRS) and the National Family Health Survey (NFHS). Furthermore, the decomposition method is applied to e0 differences to identify mortality pattern differentials between SRS and NFHS in same reference period. The life table were constructed from the Chiang method using SRS statistical report 2015, 2020; NFHS-4 (2015-16); and NFHS-5 (2019-21), then after pair model such as, Brass logit, Splicing, and Modified logit were used to calculate age pattern of mortality and e0 using under-five mortality rate and adult mortality rate for 2015 and 2020 at national and subnational levels. The results reveal that the Splicing model’s age pattern of mortality and e0 is the closest to the Chiang method compared to the Brass logit and Modified logit models at both national and subnational levels, among men and women in the SRS and NFHS. The differences in \(\:{e}_{0}\) values between the Splicing and the Chiang models were one or fewer than a year among men and women in both SRS and NFHS. Decomposition analysis reveals that the infant, child, and 5-14-year age groups made a significant contribution to Δe0 between SRS and NFHS in 2015 and 2020. The Splicing model yields the closest e₀ estimates, particularly with SRS data, compared to NFHS, underscoring its importance for subnational mortality assessment in contexts where mortality data are incomplete and unavailable.