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印度出生时预期寿命的国家以下级别估计数:来自 NFHS 和 SRS 数据的证据。

Subnational estimates of life expectancy at birth in India: evidence from NFHS and SRS data.

机构信息

Department of Bio-Statistics and Epidemiology, International Institute for Population Sciences (IIPS), Mumbai, 400088, India.

Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University, Gangtok, Sikkim, 737102, India.

出版信息

BMC Public Health. 2024 Apr 16;24(1):1058. doi: 10.1186/s12889-024-18278-3.


DOI:10.1186/s12889-024-18278-3
PMID:38627658
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11021017/
Abstract

BACKGROUND: Mortality estimates at the subnational level are of urgent need in India for the formulation of policies and programmes at the district level. This is the first-ever study which used survey data for the estimation of life expectancy at birth ([Formula: see text]) for the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21) for the total, male and female population in India. METHODS: This study calculated annual age-specific mortality rates from NFHS-4 and NFHS-5 for India and all 36 states for the total, male and female population. This paper constructed the abridged life tables and estimated life expectancy at birth [Formula: see text] and further estimated the model parameters for all 36 states. This study linked state-specific parameters to the respective districts for the estimation of life expectancy at birth [Formula: see text]for 640 districts from NFHS-4 and 707 districts from NFHS-5 for the total, male and female population in India. RESULTS: Findings at the state level showed that there were similarities between the estimated and calculated [Formula: see text] in most of the states. The results of this article observed that the highest [Formula: see text] varies in the ranges of 70 to 90 years among the districts of the southern region. [Formula: see text] falls below 70 years among most of the central and eastern region districts. In the northern region districts [Formula: see text] lies in the range of 70 years to 75 years. The estimates of life expectancy at birth [Formula: see text] 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 age-specific mortality data at the district level in India, this study used the indirect estimation method of relating state-specific model parameters with the IMR of their respective districts and estimated [Formula: see text] across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21). The findings of this study have similarities with the state-level estimations of [Formula: see text] from both data sources of SRS and NFHS and found the highest [Formula: see text] in the southern region and the lowest [Formula: see text] in the eastern and central region districts. CONCLUSIONS: In the lack of [Formula: see text] estimates at the district level in India, this study could be beneficial in providing timely life expectancy estimates from the survey data. The findings clearly shows variations in the district level [Formula: see text]. The districts from the southern region show the highest [Formula: see text] and districts from the central and eastern region has lower [Formula: see text]. Females have higher [Formula: see text] as compared to the male population in most of the districts in India.

摘要

背景:在印度,需要在地区层面制定政策和计划,因此迫切需要对国家级以下的死亡率进行估计。这是首次使用调查数据对印度 NFHS-4(2015-16 年)的 640 个地区和 NFHS-5(2019-21 年)的 707 个地区的出生时预期寿命([Formula: see text])进行估计的研究。

方法:本研究从 NFHS-4 和 NFHS-5 计算了印度和所有 36 个邦的总、男性和女性人口的年度特定死亡率。本文构建了简化生命表,并估计了出生时的预期寿命[Formula: see text],并进一步估计了所有 36 个邦的模型参数。本研究将州特定参数与各自的地区联系起来,以估计 NFHS-4 的 640 个地区和 NFHS-5 的 707 个地区的出生时预期寿命[Formula: see text],总、男性和女性人口。

结果:在州一级的研究结果表明,在大多数州,估计和计算的[Formula: see text]之间存在相似之处。本文的结果表明,南部地区的最高[Formula: see text]在 70 至 90 岁之间变化。中央和东部地区的大多数地区的[Formula: see text]低于 70 岁。在北部地区,[Formula: see text]在 70 至 75 岁之间。出生时预期寿命的估计[Formula: see text]在 NFHS(2015-16 年)和 NFHS(2019-21 年)的个人、男性和女性人口中,在州和地区一级表现出显著的差异。由于印度在地区一级缺乏特定年龄的死亡率数据,本研究使用间接估计方法,将州特定的模型参数与各自地区的 IMR 联系起来,并从 NFHS-4(2015-16 年)的 640 个地区和 NFHS-5(2019-21 年)的 707 个地区估计[Formula: see text]。本研究的研究结果与 SRS 和 NFHS 这两个数据来源的州一级[Formula: see text]估计结果相似,发现南部地区的[Formula: see text]最高,东部和中部地区的[Formula: see text]最低。

结论:在印度地区一级缺乏[Formula: see text]估计的情况下,本研究可以从调查数据中提供及时的预期寿命估计。研究结果清楚地显示了地区一级[Formula: see text]的变化。南部地区的地区显示出最高的[Formula: see text],而中央和东部地区的地区则显示出较低的[Formula: see text]。与印度大多数地区的男性人口相比,女性人口的[Formula: see text]更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/9e0ab4b67676/12889_2024_18278_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/01a08afe0878/12889_2024_18278_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/a3f04a847df4/12889_2024_18278_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/2a06760bda0a/12889_2024_18278_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/4de66e8e0b55/12889_2024_18278_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/06479c22ecf6/12889_2024_18278_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/9e0ab4b67676/12889_2024_18278_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/01a08afe0878/12889_2024_18278_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/a3f04a847df4/12889_2024_18278_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/2a06760bda0a/12889_2024_18278_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/4de66e8e0b55/12889_2024_18278_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/06479c22ecf6/12889_2024_18278_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b462/11021017/9e0ab4b67676/12889_2024_18278_Fig6_HTML.jpg

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[2]
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[3]
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[4]
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[5]
Machine Learning Algorithms for understanding the determinants of under-five Mortality.

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[6]
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Soc Sci Med. 2022-4

[7]
Impact of COVID-19 on life expectancy at birth in India: a decomposition analysis.

BMC Public Health. 2021-10-21

[8]
Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data.

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[9]
Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries.

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[10]
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