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Progress of Inequality in Age at Death in India: Role of Adult Mortality

July 2021
European Journal of Population 37(6)

July 2021
37(6)

Authors:

International Institute for Population Sciences

India has seen a reduction in infant and child mortality rates for both the sexes since the early 1980s. However, a decline in mortality at adult ages is marked by significant differences in the subgroups of sex and regions. This study assesses the progress of inequality in age at death with the advances in mortality transition during 36 years period between 1981–1985 and 2012–2016 in India, using the Gini coefficients at the age of zero (G0). The Gini coefficients show that in the mid-2000s, women outpaced men in G0. The reduction in inequality in age at death is a manifestation of the process of homogeneity in mortality. The low G0 is concomitant of high life expectancy at birth (e0) in India. The results show the dominance of adult mortality over child mortality in the medium-mortality and low-mortality regimes. Varying adult mortality in the subgroups of sex and variance in the mortality levels of regions are the predominant factors for the variation in inequality in age at death. By lowering of the mortality rates in the age group of 15–29 years, India can achieve a high e0 that appears at high demographic development and the narrow sex differentials in e0 and G0 in a short time. Men in the age group of 15–29 years are the most vulnerable subgroup with respect to mortality. There is an immediate need for health policies in India to prioritise the aversion of premature deaths in men aged 15–29 years.

Sensitivity of the Gini coefficients at the age of zero (G0) based on Eqs. (1) and (2) for India by sex, 1981–2016. Source: Own calculations using Eqs. (1) and (2). Notes: 3MAV: Three-year Moving Average of ASDR; 5MAV: Five-year Moving Average of ASDR

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Trends in the Gini coefficients at the age of zero (G0) for India and its 15 largest states, 1981–2016, by sex and category of demographically advanced (DA) and backward (DB) states. Source: Own calculations using Eq. (1). Notes: a and b show the graphs for demographically advanced (DA) states, and c and d show the graphs for demographically backward (DB) states, The names of Indian states are arranged in an alphabetical order by the category of demographically advanced and backward states

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Scatter plot of the Gini coefficients at the age of zero (G0) and Life expectancy at birth (e0) for India and its 15 largest states, 1981–2016, by sex and category of demographically advanced (DA) and backward (DB) states. Source: Own calculations using Eq. (1). Notes: a, b show the graph for demographically advanced (DA) states, and c, d show the graph for demographically backward (DB) states, The names of Indian states are arranged in an alphabetical order by the category of demographically advanced and backward states

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Area graph showing trends in the contributions of 0–1, 1–4, 5–14, 15–59, and 60+ years age groups in per cent to the Gini coefficients at the age of zero (G0), 1981–2016, by sex in India. Source: Own calculations using Eq. (1). Notes: The Y values in per cent above zero represent equalising effect on G0. The Y values in per cent below zero represent disequalising effect on G0, The embedded age groups represent the respective areas in the graph, The areas in the graph show the age-specific contributions to G0 in per cent between the base year (1981–1985) and the reference period (say 2012–2016), The period 1982–1986 shows the age-specific contributions to G0 in per cent between the periods 1981–1985 and 1982–1986

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Contributions of age groups of 15–19 years and above to the Gini coefficients at the age of zero (G0), 1981–2016, by sex in India. Source: Own calculations using Eq. (1). Notes: The graph shows the age-specific contributions to G0 in absolute values between the base year (1981–1985) and the reference period (say 2012–2016), The negative Y-axis values are the equalising effect on G0, and positive Y-axis values are the disequalising effect on G0

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Vol.:(0123456789)

European Journal of Population (2021) 37:523–550

https://doi.org/10.1007/s10680-021-09577-1

1 3

Progress ofInequality inAge atDeath inIndia: Role

ofAdult Mortality

SuryakantYadav1

Received: 19 March 2018 / Accepted: 4 January 2021 / Published online: 23 February 2021

Abstract

India has seen a reduction in infant and child mortality rates for both the sexes since

the early 1980s. However, a decline in mortality at adult ages is marked by signiﬁ-

cant diﬀerences in the subgroups of sex and regions. This study assesses the pro-

gress of inequality in age at death with the advances in mortality transition during

36years period between 1981–1985 and 2012–2016 in India, using the Gini coef-

ﬁcients at the age of zero (G0). The Gini coeﬃcients show that in the mid-2000s,

women outpaced men in G0. The reduction in inequality in age at death is a mani-

festation of the process of homogeneity in mortality. The low G0 is concomitant of

high life expectancy at birth (e0) in India. The results show the dominance of adult

mortality over child mortality in the medium-mortality and low-mortality regimes.

Varying adult mortality in the subgroups of sex and variance in the mortality lev-

els of regions are the predominant factors for the variation in inequality in age at

death. By lowering of the mortality rates in the age group of 15–29years, India can

achieve a high e0 that appears at high demographic development and the narrow sex

diﬀerentials in e0 and G0 in a short time. Men in the age group of 15–29years are

the most vulnerable subgroup with respect to mortality. There is an immediate need

for health policies in India to prioritise the aversion of premature deaths in men aged

15–29years.

Keywords Inequality in age at death· Gini coeﬃcient· Decomposition· Adult

mortality· Homogeneity in mortality· Life expectancy

* Suryakant Yadav

suryakant11@gmail.com; suryakant_yadav@iips.net; suryakantyadav@iipsindia.ac.in

1 International Institute forPopulation Sciences, IIPS, Room no. 28, Academic Building, Govandi

Station Road, Deonar, Mumbai400088, India

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

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

Article

Full-text available

Oct 2021
BMC PUBLIC HEALTH

Background Quantifying excess deaths and their impact on life expectancy at birth (e0) provide a more comprehensive understanding of the burden of coronavirus disease of 2019 (COVID-19) on mortality. The study aims to comprehend the repercussions of the burden of COVID-19 disease on the life expectancy at birth and inequality in age at death in India. Methods The mortality schedule of COVID-19 disease in the pandemic year 2020 was considered one of the causes of death in the category of other infectious diseases in addition to other 21 causes of death in the non-pandemic year 2019 in the Global Burden of Disease (GBD) data. The measures e0 and Gini coefficient at age zero (G0) and then sex differences in e0 and G0 over time were analysed by assessing the age-specific contributions based on the application of decomposition analyses in the entire period of 2010–2020. Results The e0 for men and women decline from 69.5 and 72.0 years in 2019 to 67.5 and 69.8 years, respectively, in 2020. The e0 shows a drop of approximately 2.0 years in 2020 when compared to 2019. The sex differences in e0 and G0 are negatively skewed towards men. The trends in e0 and G0 value reveal that its value in 2020 is comparable to that in the early 2010s. The age group of 35–79 years showed a remarkable negative contribution to Δe0 and ΔG0. By causes of death, the COVID-19 disease has contributed − 1.5 and − 9.5%, respectively, whereas cardiovascular diseases contributed the largest value of was 44.6 and 45.9%, respectively, to sex differences in e0 and G0 in 2020. The outcomes reveal a significant impact of excess deaths caused by the COVID-19 disease on mortality patterns. Conclusions The COVID-19 pandemic has negative repercussions on e0 and G0 in the pandemic year 2020. It has severely affected the distribution of age at death in India, resulting in widening the sex differences in e0 and G0. The COVID-19 disease demonstrates its potential to cancel the gains of six to eight years in e0 and five years in G0 and has slowed the mortality transition in India.

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

Article

Apr 2024
BMC PUBLIC HEALTH

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 (e0) 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 (e0) 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 (e0)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 e0 in most of the states. The results of this article observed that the highest e0 varies in the ranges of 70 to 90 years among the districts of the southern region. e0 falls below 70 years among most of the central and eastern region districts. In the northern region districts e0 lies in the range of 70 years to 75 years. The estimates of life expectancy at birth (e0) 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 e0 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 e0 from both data sources of SRS and NFHS and found the highest e0 in the southern region and the lowest e0 in the eastern and central region districts. Conclusions: In the lack of e0 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 e0. The districts from the southern region show the highest e0 and districts from the central and eastern region has lower e0. Females have higher e0 as compared to the male population in most of the districts in India. Keywords: Subnational mortality, Age Pattern, Age at death, Life expectancy at birth, District

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

Article

Full-text available

Apr 2024
BMC PUBLIC HEALTH

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 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{e}}_{0}$$\end{document}) 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({e_0})$$\end{document} 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({e_0})$$\end{document}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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} in most of the states. The results of this article observed that the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} varies in the ranges of 70 to 90 years among the districts of the southern region. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} falls below 70 years among most of the central and eastern region districts. In the northern region districts \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} lies in the range of 70 years to 75 years. The estimates of life expectancy at birth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({e_0})$$\end{document} 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} from both data sources of SRS and NFHS and found the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} in the southern region and the lowest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} in the eastern and central region districts. Conclusions In the lack of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document}. The districts from the southern region show the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} and districts from the central and eastern region has lower \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document}. Females have higher \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} as compared to the male population in most of the districts in India.

Impact of COVID-19 on subnational variations in life expectancy and life disparity at birth in India: evidence from NFHS and SRS data

Article

Sep 2023

Background: Measuring life expectancy and life disparity can assist in comprehending how the COVID-19 pandemic has affected the mortality estimates in the Indian population. The present study aims to study the life expectancy and life disparity at birth at the national and subnational levels before and during the COVID-19 pandemic using the NFHS and SRS data. Methods: The measures Life expectancy at birth (e_0) and Life disparity at birth (e_0^†) were computed for the non-pandemic and pandemic years from NFHS (2015-16), SRS (2015) and NFHS (2019-21), SRS (2020) respectively at the national and Subnational level in India. Using NFHS data for the 36 states and SRS data for the 22 states, the study calculates e_0 and e_0^† by total, male and female population. Results: The e_0 for male and female decline from 64.3 years and 69.2 years in 2015-16 to 62.9 years and 68.9 years in 2019-21. The e_0 shows a drop of approximately 1.4 years for males and 0.3 years for females in the pandemic year 2019-21 when compared to the non-pandemic year 2015-16. At the subnational level e_0 shows a decline for 22 states in person, 23 states in males and 21 states in females in the pandemic. year 2019-21 as compared to the non-pandemic years 2015-16. The e_0^† shows a decline for 21 states in person, 24 states in females and 17 states in males in the pandemic year than non-pandemic year The findings shows a significant losses in e_0 and gains in e_0^† for males than females in the pandemic year as compared to the non-pandemic year at the subnational level in India. Conclusions: COVID-19 pandemic has decreased e_0 and increased e_0^† in the pandemic year 2019-21 at the national and subnational level in India. COVID-19 had a significant impact on the age pattern of mortality for many states and male, female population and delayed the mortality transition in India.

Mortality and Epidemiological Transition: India and Its Concerns

Chapter

Sep 2023

Introduction Mortality is the first key component among three demographic processes – that is, mortality, fertility, and migration – that contribute to the population change of a country. The worldwide decline in death rates recorded in the 1960s is labelled as ‘mortality transition’ by demographers to signify the path from high and fluctuating mortality to low and stable mortality. The transition in mortality implies enhancements in the quality of life through developments in healthcare, and, accordingly, a lot of importance has been given to it. Moreover, understanding the shift in mortality, age-specific morbidity, and age–cause-specific mortality is essential for drafting changes in public healthcare policy, allocation of healthcare finances, and human resources, thereby averting premature deaths (Anderson and Silver, 1997; Mathers et al., 2005; Désesquelles et al., 2012; Jha, 2012; Dandona et al., 2017; Nkengasong et al., 2020). Globally, non-communicable diseases (NCDs) account for 7 out of 10 deaths, making them the leading cause of ill health and mortality (World Health Organization [WHO], 2018). In absolute numbers, NCDs cause higher premature mortality in low- and middle-income countries (LMICs) compared to their high-income counterparts. In 2016, 31.5 million NCD deaths occurred in the LMICs – that is, almost three-quarters of global NCD deaths. Since the 1990s, mortality from communicable diseases (CDs) has declined relative to mortality due to NCDs (WHO, 2018). Nevertheless, communicable, maternal, perinatal, and nutritional conditions still account for 38 per cent of all deaths in the LMICs from 2000 to 2015 (WHO, 2016). Thus, most LMICs, including India, are experiencing a dual burden of diseases with higher morbidity and mortality from NCDs and also complementarily a high prevalence of different infectious and parasitic diseases (Agyei-Mensah and Aikins, 2010; Barquera, Pedroza-Tobias, and Medina, 2016). The inadequate health-management system and the lack of parallel socio-economic changes across all population groups are mainly attributed to this high burden of infectious and parasitic diseases among child and young populations (Defo, 2014). Meanwhile, the lifestyle and behavioural transitions since the 1990s in the LMICs have triggered the rapid increase in NCD-induced morbidity and mortality (Boutayeb and Boutayeb, 2005; Habib and Saha, 2010; Morfeld and Erren, 2019; Malta et al., 2020; Coates et al., 2020). Achieving Sustainable Development Goal (SDG)-3 in the LMICs prerequisites reducing avoidable mortality due to both NCDs and CDs (Norheim et al., 2015; Bennett et al., 2018; Fullman et al., 2017).

The role of age inequalities in cause of death in the slow pace of epidemiological transition in India

Article

Full-text available

Nov 2022

In developed countries, low disparity in lifespan contributed by the reduction in the burden of noncommunicable diseases (NCDs) is the key to advances in epidemiological transition. Contrarily, India passing through a phase of the dual burden of CDs and NCDs shows a heavy burden of NCDs responsible for the high disparity in lifespan. The Gini coefficient was decomposed for examining the contribution of 22 causes of death and their repercussions for inequality in age at death for 30 years between 1990–1994 and 2015–2019, using Global Burden of Disease data. The outcomes of the study reveal that India’s epidemiological transition has been just modest on account of high inequality in mortality by NCDs emplaced in the middle through old age despite a consistent mortality decline at infant through old age for communicable diseases (CDs). The structural changes in causes of death structure is shaped by CDs rather than NCDs, but overall bolstered by the adult mortality decline, especially in women. However, the process is restrained by the small contribution of the middle age group and a benign contribution of old mortality decline owing to the low threshold age. India needs to target health interventions in seeking significant mortality decline in the middle age group of 50–69 years that is warranted for epidemiological transition apace as evident in the developed nations.

Mortality Compression and Variability in Age at Death in India

Article

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Oct 2020

The global rise of life expectancy at birth has attracted worldwide interest , especially in understanding the pace of mortality transition in developing countries. In this study, we assess the progress of mortality transition in India during four decades between 1970 and 2013. We estimate measures of mortality compression and variability in age at death to assess the trends and patterns in mortality compression for India as a whole and its twelve biggest states. The results reveal an unequivocal convergence pattern in mortality compression across the states underpinned by the reduction in premature mortality and emerging homogeneity in mortality. Results by gender show that women are more homogenous in their mortality across the country because of an explicit reduction in the Gini coeffi cients at age 10 by the age group of 15-29 years. Mortality compression has changed in recent decades because of the increased survival of women in their reproductive ages, which marked a distinct phase of mortality transition in India. The pace of mortality transition , however, varies; adult mortality decline was greater than senescent mortality decline. These results show that India has passed the middle stage of mortality transition and has entered an early phase of low mortality.

Dynamics of life expectancy and life span equality

Article

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Feb 2020

Significance Why life expectancy and life span equality have increased together is a question of scientific interest. Both measures are calculated for a calendar year and might not describe a cohort’s actual life course. Nonetheless, life expectancy provides a useful measure of average life spans, and life span equality gives insights into uncertainty about age at death. We show how patterns of change in life expectancy and life span equality are described by trajectories of mortality improvements over age and time. The strength of the relationship between life expectancy and life span equality is not coincidental but rather a result of progress in saving lives at specific ages: the more lives saved at the youngest ages, the stronger the relationship is.

Global trends in lifespan inequality: 1950-2015

Article

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May 2019
PLOS ONE

Using data from the UN World Population Prospects, we document global trends in lifespan inequality from 1950 until 2015. Our findings indicate that (i) there has been a sustained decline in overall lifespan inequality, (ii) adult lifespan variability has also declined, but some plateaus and trend reversals have been identified, (iii) lifespan inequality among the elderly has increased virtually everywhere, and (iv) most of the world variability in age-at-death can be attributed to within-country variability. Such changes have occurred against a backdrop of generalized longevity increases. Our analyses suggest that the world is facing a new challenge: the emergence of diverging trends in longevity and age-at-death inequality among the elderly around the globe—particularly in high-income areas. As larger fractions of the world population survive to more advanced ages, it will be necessary for national and international health planners to recognize the growing heterogeneity that characterizes older populations.

Sensitivity Analysis: Matrix Methods in Demography and Ecology

Book

Full-text available

Apr 2019

Hal Caswell

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

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Lifespan Dispersion in Times of Life Expectancy Fluctuation: The Case of Central and Eastern Europe

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Central and Eastern Europe (CEE) have experienced considerable instability in mortality since the 1960s. Long periods of stagnating life expectancy were followed by rapid increases in life expectancy and, in some cases, even more rapid declines, before more recent periods of improvement. These trends have been well documented, but to date, no study has comprehensively explored trends in lifespan variation. We improved such analyses by incorporating life disparity as a health indicator alongside life expectancy, examining trends since the 1960s for 12 countries from the region. Generally, life disparity was high and fluctuated strongly over the period. For nearly 30 of these years, life expectancy and life disparity varied independently of each other, largely because mortality trends ran in opposite directions over different ages. Furthermore, we quantified the impact of large classes of diseases on life disparity trends since 1994 using a newly harmonized cause-of-death time series for eight countries in the region. Mortality patterns in CEE countries were heterogeneous and ran counter to the common patterns observed in most developed countries. They contribute to the discussion about life expectancy disparity by showing that expansion/compression levels do not necessarily mean lower/higher life expectancy or mortality deterioration/improvements. Electronic supplementary material The online version of this article (10.1007/s13524-018-0729-9) contains supplementary material, which is available to authorized users.

Surface Plots of Age-Specific Contributions to the Increase in Life Expectancy

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Rates of mortality improvement, as presented in the previous two chapters, are an excellent tool to illustrate mortality dynamics. They can not be directly translated to contributions to changes in life expectancy. Using Arriaga’s decomposition approach, we plot maps for selected countries from the Human Mortality Database, showing which ages contributed most to the respective change in life expectanc over time.

Nations within a nation: Variations in epidemiological transition across the states of India, 1990-2016 in the Global Burden of Disease Study

Article

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Dec 2017

Background 18% of the world’s population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016. Methods Using all available data sources, the India State-level Disease Burden Initiative estimated burden (metrics were deaths, disability-adjusted life-years [DALYs], prevalence, incidence, and life expectancy) from 333 disease conditions and injuries and 84 risk factors for each state of India from 1990 to 2016 as part of GBD 2016. We divided the states of India into four epidemiological transition level (ETL) groups on the basis of the ratio of DALYs from communicable, maternal, neonatal, and nutritional diseases (CMNNDs) to those from non-communicable diseases (NCDs) and injuries combined in 2016. We assessed variations in the burden of diseases and risk factors between ETL state groups and between states to inform a more specific health-system response in the states and for India as a whole. Findings DALYs due to NCDs and injuries exceeded those due to CMNNDs in 2003 for India, but this transition had a range of 24 years for the four ETL state groups. The age-standardised DALY rate dropped by 36·2% in India from 1990 to 2016. The numbers of DALYs and DALY rates dropped substantially for most CMNNDs between 1990 and 2016 across all ETL groups, but rates of reduction for CMNNDs were slowest in the low ETL state group. By contrast, numbers of DALYs increased substantially for NCDs in all ETL state groups, and increased significantly for injuries in all ETL state groups except the highest. The all-age prevalence of most leading NCDs increased substantially in India from 1990 to 2016, and a modest decrease was recorded in the age-standardised NCD DALY rates. The major risk factors for NCDs, including high systolic blood pressure, high fasting plasma glucose, high total cholesterol, and high body-mass index, increased from 1990 to 2016, with generally higher levels in higher ETL states; ambient air pollution also increased and was highest in the low ETL group. The incidence rate of the leading causes of injuries also increased from 1990 to 2016. The five leading individual causes of DALYs in India in 2016 were ischaemic heart disease, chronic obstructive pulmonary disease, diarrhoeal diseases, lower respiratory infections, and cerebrovascular disease; and the five leading risk factors for DALYs in 2016 were child and maternal malnutrition, air pollution, dietary risks, high systolic blood pressure, and high fasting plasma glucose. Behind these broad trends many variations existed between the ETL state groups and between states within the ETL groups. Of the ten leading causes of disease burden in India in 2016, five causes had at least a five-times difference between the highest and lowest state-specific DALY rates for individual causes. Interpretation Per capita disease burden measured as DALY rate has dropped by about a third in India over the past 26 years. However, the magnitude and causes of disease burden and the risk factors vary greatly between the states. The change to dominance of NCDs and injuries over CMNNDs occurred about a quarter century apart in the four ETL state groups. Nevertheless, the burden of some of the leading CMNNDs continues to be very high, especially in the lowest ETL states. This comprehensive mapping of inequalities in disease burden and its causes across the states of India can be a crucial input for more specific health planning for each state as is envisioned by the Government of India’s premier think tank, the National Institution for Transforming India, and the National Health Policy 2017.

Life expectancy versus lifespan inequality: A smudge or a clear relationship?

Article

Full-text available

Sep 2017
PLOS ONE

Laszlo Nemeth

Interest in inequality, including lifespan inequality, is growing. Several studies, using various measures of variation in the length of life, reveal that as life expectancy increases, lifespan inequality tends to decrease, albeit with considerable variation across populations and over time. The aim of this article is to understand why the strength of the relationship between life expectancy and lifespan inequality varies across publications. Results differ in large part because they are based on different data sources. In addition, some measures show more smudginess than others. All the analyses presented here support the basic finding of a strong relationship between life expectancy and lifespan inequality.

Sensitivity Analysis of Longevity and Life Disparity

Chapter

Apr 2019

Hal Caswell

A similarly basic outcome of population, at the individual or cohort level, is longevity: the length of individual life. The most commonly encountered description of longevity is its expectation, the life expectancy.

Progress of Inequality in Age at Death in India: Role of Adult Mortality

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