Pr Premat ature Mortality y and and Mortal ality y transi - - PDF document

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Pr Premat ature Mortality y and and Mortal ality y transi ansition n in n Indi dia

Authors: Suryakant Yadav And Perianayagam Arokiasamy International Institute for Population Sciences Poster ID: 5759 at the 2017 International Population Conference

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Abstract:

The global rise of e0 has attracted worldwide interest in understanding the pace of mortality transition in developing countries. In this study, we assessed the progress of mortality transition in India during the last four decades as indicated by mortality compression and changes in variance by examining inequalities in age-at-death for India and its bigger states. We estimated mortality compression measures C50 and the Gini (G10). Both measures showed steep decline with more pronounced decline for females than males. The distribution

• f age death also showed much lower inequality for females than males. Both trends testified

to the progress of mortality compression for India. Results revealed stronger mortality compression for India and increasing homogeneity; both of which further confirmed the process of mortality transition in India. The decomposition of the Gini revealed that contribution to the pace of mortality transition was greater from adult than senescent mortality decline. The narrowing sex differentials in e0 and G10 and survival of motherhood together outpaced the life expectancy of females over males. This analysis has established that India has exited the middle stage of mortality transition and has entered into a new phase

• f low mortality.

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• 1. Introduction:

The global rise in life expectancy at birth (e0) is attributed to improvements in mortality at all ages contributing to rising human longevity. The phenomenon has been established with relevant theoretical formulations and empirical evidence over the course of the mortality transition in developed nations (Vaupel 2010). Improvements in mortality result in a transformation in the age pattern of mortality. This transformation is a continuous process primarily because of the phenomenal mortality compression, a process in which deaths are concentrated in a narrower age-interval. Mortality compression is a necessary precursor for progress in the mortality transition. While India has experienced rapid transition from high- medium-low mortality in less than four decades (Yadav & Arokiasamy 2014); a key concern arises as to how quickly and persuasively the phenomenon of mortality compression, spurred by reduction in inequality in age-at-death, has formalized the mortality transition in India. India’s transformation in the age pattern of mortality began in the 1970s. The Infant Mortality Rate (IMR) has been cut by two-thirds from 129 in 1970, to 80 in 1990, to 37 per 1000 live births in 2015 (RGI 2016a, RGI 2016b). Mortality rates have also declined at higher ages. Numerous studies have documented how mortality decline among infants and children initially affects the level of e0; then together with mortality decline in the adult and old ages, the age pattern of mortality reshapes itself (Heligman et al. 1980; Pool and Wong 2006; Rau et al. 2008). The declining mortality trends in conjunction with the transformations in the age pattern of mortality led to the process of mortality compression, advancing the mortality transition to later stage in less time compared to the developed nations. The developed nations are in the last phase of demographic transition where IMR is almost negligible, and currently the advances in mortality transition are because of mortality improvement in adult and old ages. At the later stage of mortality transition researchers have acknowledged shrinking variance in age-at-death as a fundamental demographic process (Smits and Monden 2009; Wilmoth and Horiuchi 1999). The reduction in premature (15–64) deaths contemporaneous with decline in IMR have underpinned the convergence in e0 among the developed nations (Clark 2011; Vaupel, Zhang, and Raalte 2011). Today, the low level of disparity in life spans among the developed nations is attributable to aversion of premature deaths in the adult ages (Shkolnikov, Andreev, and Begun 2003; Shkolnikov et al. 2001). The developing country India has experienced rapid transformation in the age pattern of mortality for females compared to males as the IMR and U5MR have declined swiftly since the early 1980s (NIMS, ICMR and UNICEF 2012: 29) and maternal mortality rate have declined since late 1990s (Memoire 2007). The adult mortality for women and men in India fell from 358 and 330 respectively in 1970 to 145 and 228 respectively in 2010 (Rajaratnam et al. 2010), which is higher than East Asia and Southeast Asia and the developed nations. A higher adult mortality rate indicates a greater spread of deaths over the adult ages and hence signals a greater variation in disparity in life spans in India. In the past two decades, India has experienced a marked rise in ex at adult and older ages. At the national level, between 1970-75 and 2009-13, e50 and e60 increased by 5 and 4 years respectively to 25.7 and 17.9 years respectively. It indicates that the current phase of mortality transition is modulated through improved survival among adults and older ages contributing to the advances in mortality transition. Exploring the mortality data of developed nations, Edwards and Tuljapurkar (2005) acknowledged that higher order moments of distribution of age-at-death (ex; x>10) better demonstrate variability and differentials than the first order moment of distribution of age-at-death (e0), and therefore the advances in mortality

3 transition can be better established on that basis. The estimation of higher moments allows examination of mortality compression and change in inequality in age-at-death, which are pivotal to understanding the progression in mortality transition, especially at the later stage. The varying e0 across the Indian states is a manifestation of existing disparity in life spans. A demographically advanced state Kerala has the highest e0 value 78.1 years for rural females in 2012. Other demographically advanced states such as Maharashtra, Punjab and Tamil Nadu has e0 value 72.4 years, 72.4 years, and 71.3 years respectively for rural females in

• 2012. The less demographically advanced states Uttar Pradesh, Assam and Odisha have e0

value 64.7, 64.6 and 66.5 years respectively for rural females in 2012 (RGI 2016b). The leader and laggard states while the demographic transition partially explain the inequality in e0; nonetheless, the less advanced states recorded greater temporal increase in e0 as compared to the advanced states in the last two decades. For example, the less advanced states gained about 10 years in e0, whereas, the advanced states gained 5–6 years in e0 between the years 1990 and 2012 (RGI 2016a). Furthermore, in most of the developed nations, it is notable that modal age-at-deaths are quite different even though e0 are similar (Thatcher et al. 2010). The developed nations such as Canada, Denmark, France, and United States showed divergence in adult mortality despite overall convergence in mortality leading to differences in the evolution of life spans amongst them (Edwards and Tuljapurkar 2005). The historical lowering of mortality rates is more pronounced and possible in the young ages than in old

• ages. These processes vary across the developed nations and is responsible for inequality in

age-at-death within the countries (Wilmoth and Horiuchi 1999). The phenomenon of mortality compression has been an integral part of demographic and epidemiological transition (Robine 2001, Cheung et al. 2005) as witnessed in the developed

• nations. For India, this study for the first time examines the process of and advances in

mortality compression and inequality in age-at-death. Studies on the association of age pattern of mortality and its linkages with mortality transition are limited. Recognizing such theoretical gaps, this study aims to investigate the progress of mortality transition by testing the phenomena of mortality compression and inequality in age-at-death. The specific objectives of the study are to: (1) test the hypothesis of mortality compression (2) examine the changes in inequality in age-at-death and (3) to examine the age specific- contributions to the G10. The study examined the progression in mortality transition through advances in mortality compression and changes in variance by examining inequality in age- at-death for India and its bigger states during 1970-2013.

• 2. Background:

Worldwide, human longevity has been increasing and its repercussions are wide-ranging. The more developed nations such as Americas and Europe are ahead of Africa and South-East Asia in mortality transition; on average e0 is higher by almost 8-10 years in developed nations than in less developed nations (WHO 2015). During the last stage of the mortality transition, the age pattern of mortality is changed. At this stage, the contours of mortality and morbidity are characterized by low mortality and heavier burden of non-communicable diseases (NCDs). India has higher mortality rates than the developed nations. Until the mid 20th century, endemic diseases including small pox, cholera, plague and malaria made important contributions to the higher mortality rates in India. However, since the 1950s, India experienced a steep fall in mortality rates but with the rise of a dual burden of diseases (Banthia and Dyson 1999, 2000).

4 By the late twentieth century India had made headway in the mortality and epidemiological transition with the control of deaths due to endemic diseases, through better sanitation and housing, vaccination and immunization programmes and disease specific interventions by the mid 1990s (Visaria 2004). This led to decline in mortality rates followed by decline in fertility rates. Further, expansion of education and health care facilities significantly contributed to the continued improvement (Desai 2010, Bhandari 2014), in the mortality conditions of India. By and large, the process of mortality compression is believed to be a phenomenon of senescent ages. Most researchers have focused on old age mortality to test mortality compression hypothesis and related curves (Kannisto 2000, Horiuchi and Wilmoth 1998, Lynch & Brown 2001, Cheung et al. 2005, Thatcher et al. 2010). However, Zhen and Vaupel (2009) asserted that compression or decompression of mortality may depend on the threshold age, and thus is not restricted to old age only. According to them, the threshold age is such that reductions in mortality before and after the threshold age determine the mortality compression or decompression. Also, Cheung et al. (2005) examined mortality compression at adult ages for Switzerland from 1920-2005 that was spurred by decrease in heterogeneity among adult population. The heterogeneity hypothesis states that the deceleration in mortality rates is basically due to attrition of mortality rates of selected individuals. Frailer persons tend to die at younger ages; as a result, survivors to older ages are more homogeneous than younger ones. The multiplying homogeneity among the sub-populations over adult and old ages expedites the process of mortality compression (Vaupel 1979, Myers and Manton 1984, Vaupel et al. 1998). The rate of acceleration of mortality increase at old ages has been declining (Horiuchi and Wilmoth 1998) and therefore, deaths are delayed because of selective survival of healthier individuals at old ages. In parallel to this, the adult (15-59) mortality rate has been

• declining. For instance, the adult mortality (per 1000 persons) for India declined from 266 in

1990 to 203 in 2012 (United Nations 2014). The greater reduction in premature adult (15-45) mortality has led to postponement of deaths at old ages. Taken together, the deaths have been accumulating at old ages. Subsequently, increasing homogeneity in older ages accelerates the process of mortality compression over the mortality transition. The inequality in age-at-death is altogether affected by inter-individual differences and groups differences and through existing health and income inequalities in a country. Choice

• f measures is available to compute inequality in age-at-death which are strongly correlated

and may be taken as surrogates to other measures(Vaupel, Zhang, and Raalte 2011); however, each measure has its own reasoning and limitations. The measures such as fixed/moving rectangle, fastest decline (FD), and Keyfitz’s H are mean-based measures which are based on survival curve. The mean-based measures have a strong positive association with e0 (Wilmoth and Horiuchi 1999). Hence, the mean-based estimates are consistent with the e0 rather than be sensitive to changes in adult age and old age mortality. The rate of change in adult mortality is more varied than the rate of change in infant mortality and under-five mortality in general which is not well examined by the mean-based measures. Other measures such as Inter Quartile Range (IQR), the Gini Coefficient (G), Standard Deviations (STD), VarLog, Theil index (T) are variance-based measures which are based on the distribution of age-at-death. These variance-based measures are preferable as the estimate

• f inequality measures is sensitive to different ages of life spans at different levels of
• development. Edwards and Tuljapurkar (2005) points out the merits of using variance-based

measure than mean-based measures exemplified by the seven industrialized countries. The

5 higher-order moments of the distribution of age-at-death vary for the seven industrialized

• countries. The choice of inequality measure also depends on its sensitivity to temporal

changes in mortality. Shkolnikov, Andreev, and Begun (2003) tested the variance-based measures over a long period of time and across the developed countries. The measures namely VarLog, Standard Deviations (STD), Inter-quartile range (IQR), Theil index, and the Gini index shows remarkable similarity for USA for the period 1950 to 2000. However, the same measures behave differently when tested for Russia between the years 1965 to 1975. The reason for such dissimilarity is the varied sensitivity to the pace of decline in adult mortality rates. The measure ‘Gini’ compared to other measures shows a balanced sensitivity across age and time as well as across regions. The conclusion draws that the Gini is a preferred measure over other inequality measures because the age-specific contributions to the Gini coefficients in age-at-death are not extremely sensitive to the pace of temporal decline in infant and child mortality and reflect the changes in adult ages sufficiently in addition to satisfying the three basic properties of an inequality measure.

• 3. Data and Methods:

In this study, we used multiple data sources and methods to accomplish the analytical

• utcomes. First, we constructed new life tables for the period of 1970-2013. The main input

is the age specific death rates (ASDR) provided by India’s Sample Registration System (SRS) for the entire reference period of 1970-2013 (RGI 1970-2013). SRS provides abridged life tables (RGI 1984-2015); however, it does not provide ex data beyond age 70+. To note, ASDR is provided up to age 70+ for the period 1970-1994 and up to age 85+ for year 1995

• nwards. Therefore, for analytical purposes and to overcome such limitations, the

construction of life table was essential. The abridged life table was further expanded into a single year life table using King-Karup method (Siegel and Swanson 2004). The 5dx column

• f the abridged life table was disaggregated into single years between age 10 and 110, which

yielded the single year truncated distribution of age-at-death. Second, we tested mortality compression hypothesis and inequality in age-at-death. We adopted and computed the following mortality compression measures: modal age-at-death (M*), standard deviation above age 10 (SD(10+)), below age M* (SD(<M*)) and above (SD(M*+)), the Gini coefficient at age 10 (G10) and the best known measure C50 (C-family) (Kannisto 2000, Kannisto 2001, Shlolnikov et al. 2003, Canudas-Romo 2008, Edwards and Tuljapurkar 2005, Thatcher et al. 2010). Third, we fitted GM model over ASDR provided by SRS to obtain the probability of dying in

• ld ages up to age 110 (Carey et al. 1992; Vaupel et al. 1998). Non-linear regression method

was used to obtain the estimated parameters of GM model using LM algorithm (Ibrahim 2008). It may be noted that there has been some contention among Indian demographers over the applicability of GM model and other mortality models over human populations (Saikia et

• al. 2012). However, the GM model fitted well over Indian mortality data, satisfying the

conditions of parameters of the model. The fit of GM model and observed ASDR from age 25 is significant at the one percent level of significance (Figure A1).

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Figure A1: Gompertz-Makeham fit for selected years, female and male, India. Source: Author’s calculation using ASDR provided by SRS (1970-2013). Note: Each year on the X-axis represents the period of five years; 2011 represents 2009-13. 2) Each year on the X-axis represents the period of five years; 2011 represents 2009-13.

Fourth, the Gini coefficients measure inequality in length of life or degree of inter-individual variability in age-at-death (Shkolnikov et al. 2003). The Gini coefficients at age ‘ten’ (G10) is a function of distribution of age-at-death (dt) at time ‘t’ as shown in equation (1). We adjusted 1â0 and nâx (Borgois-Pichat (1951) as cited in Shkolnikov et al. (2003)) for each population. 𝐻" = 1 − 𝐺

'() − 𝐺 ' *+, '-"

𝜚'() + 𝜚' … … … … (1) 𝑥ℎ𝑓𝑠𝑓, 𝜚' = 𝑒: ∗ 𝑢

' :-"

𝑒: ∗ 𝑢

* :-"

𝐺

' =

𝑒:

' :-"

𝑒:

* :-"

where, 𝑒:: distribution of age-at-death at time ‘t’; 𝑢: interval of time;

1975 1995 2011 1985 Sample Registration System (SRS) 1975 1995 2005 2011 Author's Fit (non-linear regression) .2 .4 .6 .8 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Female Male

Force of mortality Age Group

7 n: age-interval w: upper limit of age x.

=â' = ,+?

@ ABC ( ADC (E + AFC + G H ADC)

E+

AFC

where,

=𝑑' = =a' − K

?

and

,â" =

KLM(, + KFM @ N M.P@K ∗ KQM ? N KBM )

In this paper, we applied the above-mentioned methodology for India and its twelve bigger states by sex. We have categorized the selected states as the demographically advanced states and the demographically less advanced states as per the mortality levels in 2000s. The demographically advanced states are Kerala, Karnataka, Maharashtra, Gujarat, Andhra Pradesh, Tamil Nadu, and Punjab and demographically less advanced states are Uttar Pradesh, Odisha, Madhya Pradesh, Haryana, and Assam. The computation for G10 is based on up to age 110. For any level of presentation, the last age group is 85+.

• 4. Results:

4.1. Testing mortality compression

Figure 1 depicts the trends in C50 by residence and sex for India for the period of 1970-2013. First, the C50 values for India declined for all the categories of population. For urban females and males, respectively, the C50 values declined from 23.4 and 24 in 1970-1974, to 17.3 and 19.7 in 2009-2013. Urban females had the lowest values of C50 throughout the period 1970-

• 2013. Urban than rural population had low C50 values throughout 1970-2013. The declining

C50 values revealed that deaths have increasingly been concentrated in a narrower age interval during transition from high to medium/low mortality conditions. Second, the C50 value declined at a greater pace during the 1970s and 1980s and the decline was greater for the rural population. During the 1970s, deaths were dispersed in a wider age interval of age-at-death for rural population, but swiftly tended to concentrate in a narrower age interval of age-at-death by the end of 1980s. The C50 value declined apace for rural females outstripping rural males by the early 1980s. The C50 values for rural females and males narrowed by 8 and 6 years respectively in the 1970s and 1980s, to 22 and 23 years respectively by the end of 1980s. During this rapid transition, the pace of decline in C50 values for rural population caught up with the urban population. For urban population, the interval of age-at-death was wider in the 1970s and 1980s, though lesser than that of rural

• population. By the early 1990s, both the rural and urban population had similar pace of

decline in C50. Urban females compared to males had consistently had a narrower age interval over which deaths were concentrated. The lower C50 values for females were attributable to greater reduction in premature adult (15-45) mortality mainly comprised of maternal mortality in the reproductive ages during the 1970s and 1980s (Navaneetham 1993; Croll 2000, Arokiasamy 2004, Cohen 2000, Sen 1992, Coale 1991, Clark 1987). This remained as prominent reason for increased momentum among females in mortality

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• compression. Also, this advantage for females in the recent period has resulted in a wider

urban female-male gap in C50 values.

Figure 1: Trends in C50 by sex and residence for India, 1970-2013

Source: Author’s calculation from SRS

Third, the C50 values for all the population categories continued to decline contemporaneously, nevertheless at a comparable pace during 1990 to 2013. The C50 value for urban females and rural males was 18.8 years and 22.6 years respectively in 1988-1992, which further declined to17.3 years and 20.2 years respectively in 2009-13; the C50 narrowed by 1.5 years and 2.4 years since 1990. The C50 values were commensurate across the population categories since the early 1990s. By the early 1990s, the mortality compression proceeded to an advanced phase. A greater fall in IMR by late 1980s reduced the dominance of childhood mortality to a progressive phase dominated by adult and old age mortality by early 1990s. The transformation in the distribution of age-at-death emerged as a distinguished phenomenon underscoring the role of higher order moments of the distribution

• f age-at-death. The declining adult mortality and postponement of death turned out as

reinforcing factors of the progress in mortality compression. Furthermore, Yadav et al. (2012) showed a declining pattern in the Life Table Aging Rate (LAR); the force of mortality had been declining rather than increasing at older ages during 1993-2006. Therefore, a deceleration of mortality increase in old ages for India delayed the deaths at old ages. Subsequently, those individuals who survived at old ages were homogeneous enough to propel the mortality compression. A linear increase in the modal age-at-death, delay in deaths and more homogeneous individuals at old ages marked this phase of mortality compression stronger than ever before. On the whole, the results confirm major shifts in the concentration of deaths to a narrower age interval of age-at-death for all the population categories. The phenomenon of mortality compression is in progress unequivocally at the national level and this is true for all categories of population. The process of mortality compression has clearly advanced during the last four decades of transition from high mortality to medium-low mortality conditions. The earlier two decades of 1970s and 80s showed modest mortality compression while

Rural female Rural male Urban female Urban male 5 10 15 20 25 30

C50 - Length in years

1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013

Year

9 stronger advances marked the latter two decades of 1990s and 2000s. By categories of population, urban females experienced the strongest mortality compression during the four decades of mortality transition.

4.2 Trajectories of G10

Figure 2: Trends in G10 by sex and residence for India, 1970-2013

Source: Author’s calculation from SRS

Another imperative measure used to comprehend reduction of inequality in age-at-death is the declining trends in the Gini at age 10 (G10) over the time. Figure 2 displays the trends in the G10 of age-at-death for both females and males of India computed from the abridged life table for ages 10 and above. The Gini coefficients declined steadily for both females and

• males. The pace of decline in the Gini coefficients had been more rapid for females than

males; the Gini coefficients declined at the rate of -0.73 percent and -0.31 percent per year for females and males respectively. A faster decline in the Gini coefficients for the females than males confirmed that the process of homogeneity among females had been intensive and began earlier compared to males. By categories of population, urban females had lowest G10 since late 1970s. Also, the trend in the G10 had been on apace for the urban females. The urban females achieved low inequality of 0.10 in 2009-13 from a higher inequality of 0.14 in early 1970s, whereas, rural males achieved a low inequality of 0.13 in 2009-13 from 0.15 in early 1970s. The decline in the G10 was concomitant with the decline in premature adult (15- 44) mortality (r2 for rural males and urban females was 97.9% and 97.7% respectively). Therefore, the fall in premature adult mortality was a crucial factor for augmenting homogeneity in the population in the later stage of mortality transition. The decline in G10 has in general complied with the advances in mortality transition. The demographically advanced state Kerala had the lowest G10 throughout the transition period. The less demographically less advanced state Uttar Pradesh had the highest G10 in the 1970s and 1980s and has been lower in recent years of the transition period (Figure 3).

Rural female Rural male Urban male Urban female .11 .12 .13 .14 .15 .16 .17 .18 .19 .10 .20 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013

Gini Coefficient at age 10 Year

10

Figure 3: Trends in G10 by sex and residence for India and twelve states, 1970-2013

Source: Author’s calculation from SRS

4.3. Premature mortality

Figure 4: Premature deaths by gender for India, 1975 and 2005

5 10 15 20 25 30 35 40 5 10 15 20 25 30 35 40 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013

Rural, Female Rural, Male Urban, Female Urban, Male

Assam Gujarat Haryana Karnataka Kerala Madhya Pradesh Maharashtra Odisha Punjab Tamil Nadu Uttar Pradesh India Andhra Pradesh

C50 - Length in years Year

Author's Calculation using SRS 1970-2007 Each year on the X-axis represents the period of five years; 2005 represents 2003-07

Distribution of age-at-death

Modal value

Modal age-at-death Mirror image of distribution beyong modal value 500 1000 1500 2000 2500 3000 500 1000 1500 2000 2500 3000 10 20 30 40 50 60 70 80 90 100 110 10 20 30 40 50 60 70 80 90 100 110

Male (1975) Female (1975) Male (2005) Female (2005)

Life table deaths, single year Single Year Age

Author's Calculation using SRS 1970-2007; Year of 2005 represents period of 2003-07

11 In this section, we examine the evidence of declining premature adult mortality. The 30q15 (probability of dying between age 15 and age 45) for females and males declined by half and about a third during 1970-2013. The value of 30q15 declined at the rate of -2.1 percent per year for females compared to -0.9 percent per year for males. Compared to 30q15, 45q15 declined at the rate of -1.8 percent and -1.0 percent for females and males respectively. The 30q15 than

45q15 declined swiftly especially for the rural female population attesting a sharp decline in

premature adult (15-44) mortality for rural females than males. The trends in the 30q15 revealed that rural-urban females outpaced rural-urban males in the reduction of premature adult (15-44) mortality in the late 1980s. The progress of survivorship among females in the reproductive ages is a remarkable contributing factor to an impressive improvement in survivorship of motherhood as a result of almost two-thirds decline in Maternal Mortality Rate (MMR) between 1997-98 and 2011-13 (Figure 4, RGI 2016b). Clearly, the survivorship

• f females during the period of childbearing has sizeable effect in reducing excess female

mortality adult ages during this period.

Conclusion:

In this paper, we assessed the progress of mortality transition in India and its bigger states during the last four decades of 1970-2013 using measures of mortality compression and inequality in age-at-death. First, results revealed that in India the rise of ex has been accompanied with notable advances in mortality compression testifying the swift progress in mortality transition and a sizeable reduction in inequality in age-at-death during 1970-2013. Indeed, India and its major states have progressed in mortality compression in a relatively shorter span of time compared with developed nations. For example, the demographically advanced state of Kerala exhibited C50 value of 14.2 (6.5) years in 2009-13 that corresponds with the C50 value of 14.4 (6.7) years of Netherland in 1990-95. Mortality compression is strongest among urban females of India with the C50 values declining from 23.4 years in 1970-74, to 17.3 years in 2009-13. The G10 for females and males declined from 0.15 and 0.14 respectively in 1972, to 0.19 respectively in 2005, to 0.11 and 0.14 respectively in 2011. The variation in the G10 by gender and states is conspicuous over the time. Females in the most advanced state Kerala had the low G10 throughout the transition period. The trends in the G10 by gender across the states reveals that in general females in advanced states outpaced their counterparts in the 1980s, whereas, females in less advanced states outpaced their counterparts in the mid–

• 2000s. Females transcends males in the temporal decline in the G10 primarily because of

increasing contribution by the adult (15–44) age groups to the fall in G10 for women as compared to men. Overall, this study has established that mortality transition in India has entered a new stage of low mortality regime. This transition is contributed by notable decline in adult rather than senescent mortality. India is a testimony to the progress of mortality transition juxtaposing a different matrix of rapid rise in longevity compared to developed nations.

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