Mortality Compression and Variability in Age at Death in India Mortality Compression and Variability in Age at Death in India Suryakant Yadav, Arokiasamy Perianayagam Abstract: The global rise of life expectancy at birth has attracted worldwide inter- est, especially in understanding the pace of mortality transition in developing coun- tries. 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 com- pression for India as a whole and its twelve biggest states. The results reveal an unequivocal convergence pattern in mortality compression across the states under- pinned by the reduction in premature mortality and emerging homogeneity in mor- tality. 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 dec- ades 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 transi- tion, 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. Keywords: Mortality compression · Variability in age at death · Gini coeffi cient · Adult mortality · Premature mortality · Mortality transition 1 Introduction 1.1 The stages of mortality transition The global rise of life expectancy at birth (e0) is attributed to improvements in mor- tality at all ages, which generally contribute to rising human longevity (Dowd et al. 2010; Vaupel 1986; Vaupel et al. 1998; Vaupel 2010). The mortality decline among infants and children initially affects the level of e0 (Clark 2011; Moser et al. 2005; Wang et al. 2016). Adult mortality, the probability of dying between age 15 and 60, Comparative Population Studies Vol. 45 (2020): 319-358 (Date of release: 27.10.2020) Federal Institute for Population Research 2020 URL: www.comparativepopulationstudies.de DOI: https://doi.org/10.12765/CPoS-2020-20 URN: urn:nbn:de:bib-cpos-2020-20en8 • Suryakant Yadav, Arokiasamy Perianayagam320 together with infant and child mortality decline are reshaping the age pattern of mortality (Cheung/Robine 2007; Heligman/Pollard 1980; Rau et al. 2008; Tuljapurkar et al. 2000). The transformations in the age pattern of mortality in conjunction with mortality decline results in mortality compression, a process in which deaths are concentrated in a narrow age interval of age at death (Cheung et al. 2005; Kan- nisto 2000, 2001; Myers/Manton 1984; Thatcher et al. 2010). The phenomenon of mortality compression and associated changes in other related phenomena such as variability in age at death or degree of inter-individual variability in age at death (Shkolnikov et al. 2003: 306) and rectangularization of the survival curve (Wilmoth/ Horiuchi 1999: 475) are signifi cant determinants of the advances in mortality transi- tion (Cheung et al. 2009; Kannisto 2000; Lynch/Brown 2001; Myers/Manton 1984; Nusselder/Mackenbach 1996; Paccaud et al. 1998; Wilmoth/Horiuchi 1999). Over the historical trends of demographic transition of developed nations, there is consen- sus among researchers that mortality compression is a fundamental demographic process for comprehending the progress of mortality transition (Bohk-Ewald et al. 2017; Kannisto 2000; Robine 2001; Smits/Monden 2009; Thatcher et al. 2010). While developed nations have been passing through a low mortality regime since the mid- dle of the 20th century and have been experiencing strong mortality compression (Kannisto 2000; Thatcher et al. 2010), India, as a developing South Asian country, experienced a rapid mortality decline in 44 years between 1970 and 2013 (Aroki- asamy/Yadav 2014; Bhat et al. 1995; Bhat/Navaneetham 1991; Chaurasia 2010; Na- vaneetham 1993; Visaria 2004). A key concern arises about how quickly and per- suasively the mortality transition in India has characterized the process of mortality compression, spurred by a reduction in variability in age at death (Yadav 2013). 1.2 Mortality transition in India During the last stage of mortality transition, the contours of mortality and morbidity are characterized by a low mortality and a heavier burden of chronic noncommu- nicable diseases (NCDs) (Omran 2005; Robine 2001). Nevertheless, with structural changes in disease patterns (Kumar 1993; Subramanian et al. 2006; Visaria 2004), India endured a dual burden of NCDs and communicable diseases (CDs). Specifi cal- ly, the burden of Infectious and Parasitic Diseases for urban men and urban women declined from 27 percent and 25 percent, respectively, of total deaths in 1975 (ORG 1979: 78) to 11 percent and 10 percent, respectively, of total deaths in 2017 (ORG 2019: 72). The burden of Diseases of the Circulatory System in urban men and urban women increased from 10.7 percent and 13 percent, respectively, of total deaths in 1975 (ORG 1979: 78) to 33.7 percent and 34.3 percent, respectively, of total deaths in 2017 (ORG 2019: 26), with a signifi cant concentration of deaths in adult and old age groups. Despite this dual burden, the rapid mortality decline in India is signify- ing an epidemiological transition similar to that of developed nations (Joshi et al. 2006; Yadav/Arokiasamy 2014). On the whole, the burden of deaths attributed to NCDs and CDs was as high as 63.5 percent and 26.7 percent, respectively, in 2017 (Global Burden of Disease Collaborative Network 2018). Between 1970 and 2018, the Infant Mortality Rate (IMR) was cut by two-thirds from 129 in 1970, to 80 in 1990, to Mortality Compression and Variability in Age at Death in India • 321 32 per 1000 live births in 2018 (ORG 2020; RGI 1972-2013), and adult mortality for men and women showed a decline from 358 and 330 per 1000 persons, respec- tively, in 1970 to 145 and 228 per 1000 persons, respectively, in 2010 (Rajaratnam et al. 2010: 1709). Improvements in these mortality indicators as well as structural changes in disease pattern indicate that the current phase of mortality transition is signifi cantly modulated through improved survival at adult and old (60 and above) ages (Horiuchi/Wilmoth 1998), despite the fact that IMR decline played a dominant role in the past in India (Bhat/Navaneetham 1991; Navaneetham 1993). Edwards/Tuljapurkar (2005: 647) acknowledge that higher-order moments (Vari- ance, Skewness, and Kurtosis) of distribution of age at death better demonstrate dif- ferentials in mortality and variability in age at death that contribute to the process of mortality compression than fi rst-order moments (e0 or Mean) do (Aburto/van Raalte 2018; Canudas-Romo 2008; Chaurasia 2010; Cheung et al. 2009; Missov et al. 2015; Németh 2017; Shkolnikov et al. 2003; Wilmoth/Horiuchi 1999). Developed nations display a low disparity in the lifespan (e†), which is average remaining life expec- tancy at the ages when death occurs, and a higher threshold age, which separates early deaths from late deaths, though not restricted to old age (Zhang/Vaupel 2009: 726), as the characteristics of increasing homogeneity in old ages (Cheung/Robine 2007; Nusselder/Mackenbach 1996; Thatcher et al. 2010). Also, Cheung et al. (2009: 584) show that a decline in adult heterogeneity spurred mortality compression in Switzerland between 1920 and 2005. Both indicators (lifespan, threshold age) are higher in developed nations compared to developing nations (Aburto/van Raalte 2018; Bohk-Ewald et al. 2017; Vaupel et al. 2011). Indian women show an analogue threshold age value of 72.8 years compared to women in Japan, France, Switzer- land, and Italy but a higher e† value of 18.2 years (Vaupel et al. 2011: 2, Table 1). With declining or negligible IMR, a convergence in age at death is incontrovertible as the model age at death (M*) converges (Canudas-Romo 2008: 1198; Vallin/Meslé 2004) and the variance of mortality distribution at age 0 (S0) and age 10 (S10) follows a similar trajectory (Engelman et al. 2010: 521, Fig. 1a). This development attests to multiplying homogeneity among the sub-populations over adult and old ages that expedite the progression in variability in age at death and mortality compression (Myers/Manton 1984; Vaupel et al. 1979; Vaupel et al. 1998) and thus marks the outset of an advanced or later stage of mortality transition. In this context, India has shown an impressive decline in IMR and adult mortality over time. Hence, transfor- mation in the distribution of age at death is exemplifi ed by its contribution to adult- age and old-age mortality, which plays a crucial role in the pace of variability in age at death and mortality compression (Canudas-Romo 2008). Studies on the association of the age pattern of mortality and its linkages with mortality transition are limited in India (Arokiasamy/Yadav 2014; Chaurasia 2010; Ya- dav/Arokiasamy 2014). Previous mortality studies in India dealt predominantly with changes in life expectancy and sex differentials in mortality (Bhat/Navaneetham 1991; Canudas-Romo et al. 2016; Kumar 1993; Navaneetham 1993; Singh/Ladusin- gh 2016; Subramanian et al. 2006; Yadav et al. 2012). It is important to comprehend the interlinkages between the transformation in the age pattern of mortality and the process of mortality transition by exploring the fundamental phenomena of mortal- • Suryakant Yadav, Arokiasamy Perianayagam322 ity compression and variability in age at death. Recognizing this theoretical gap, we investigated the progress of mortality compression and variance of age at death including variability in age at death during 44 years from 1970 to 2013 for India as a whole and its twelve biggest states for the fi rst time. We explored (a) the level of mortality compression and variability in age at death in a high mortality regime ver- sus a medium or a low mortality regime, and (b) the contribution of adult-age mor- tality and old-age mortality to mortality compression and variability in age at death in a high mortality regime versus a medium or a low mortality regime. We tested the hypothesis whether the mortality compression has become stronger with the reduction in variability in age at death over the course of the mortality transition. The specifi c objectives of the study are (1) to measure the concentration of deaths in a narrow interval of age at death, (2) to assess the contribution of variability in age at death, and (3) to investigate the convergence or divergence in mortality compres- sion of different Indian states during mortality transition. Overall, the study exam- ines the progress of mortality transition through advances in mortality compression and variability in age at death. 2 Data and Methods In this study, we used multiple data sources and methods to assess the progress of mortality transition in India and its twelve biggest states, primarily by examining the process of mortality compression and variance of age at death including variability in age at death. 2.1 Sample Registration System The Offi ce of the Registrar General of India (ORGI) introduced the Sample Registra- tion System (SRS) as a pilot scheme in the period 1964-65, and it was implemented as a full-scale system in the period 1969-70. The SRS in India is a dual record sys- tem. The baseline survey of households in sample units provides the usual resident population. Resident part-time enumerators record all births and deaths of usual residents in the sample of villages/urban blocks, and a full-time supervisor records births and deaths in the sample unit that occurred during the six-monthly retrospec- tive survey. The unmatched and partially matched events are re-verifi ed in the fi eld to obtain the count of true events. This procedure leads to a quantitative assessment of the distortions in the two sets of records, and hence, it is a self-evaluating tech- nique, which is a major advantage of SRS (ORG & CC 2019). SRS allows comparison of estimates, as the methodology and defi nition of the statistics are consistent over time. Bhat (2002: 130, Table 6) demonstrates that completeness of death registra- tion for all ages in the period 1971-80 was 94 percent and 93 percent for males and females, respectively. The completeness of death registration for females deterio- rated in the period 1981-1991, which was attributed to the Indian states of Punjab, Haryana, Uttar Pradesh, and Maharashtra. Saikia et al. (2011: 76, Table 1) showed that IMR estimates from the National Family Household Survey (NFHS) were in ac- Mortality Compression and Variability in Age at Death in India • 323 cord with SRS for the periods 1991-1995, 1996-2000, and 2002-2005. Yadav/Ram (2015: 114) observed birth undercount by 2-3 percent by SRS between 1991 and 2010. SRS data in India is considered most reliable regarding the death statistics. 2.2 Construction of life tables We constructed new life tables for the entire period from 1970 to 2013. The main input is annual age-specifi c death rates (ASDR) from India’s SRS for the entire refer- ence period (RGI 2016; RGI 1972-2013). The fi ve-year moving average of ASDR is used for the construction of period life tables for 1970-2013. A life table for a fi ve- year period represents estimates for the mid-year of that interval. SRS provides abridged life tables (ORG 1986-2015) by quinquennial age groups; however, it does not provide life expectancy (ex) data for age groups over 70 years for the period 1970-1994. Notably, ASDR are provided up to age 70+ for the period 1970-1994 and up to age 85+ from 1995 onwards. Therefore, the construction of life tables is essential for the comparability of ex and other estimates based on age at death for analytical purposes. New life tables were constructed based on the methodol- ogy recommended by the United Nations (United Nations 1982; UNPD 2013). Using the last six ASDR, the Gompertz-Makeham (GM) model (Gompertz 1825; Juckett/ Rosenberg 1993; Makeham 1867) was applied to estimate the force of mortality (μx) at higher ages up to the age of 110 years. Thus, the probability of dying between age x and x+n (nqx) was estimated up to age 110 for the construction of the life tables. The newly constructed life tables for the fi ve-year rolling periods of 1970- 2013 were based on this methodology. These new life tables provided life table deaths between age x and x+n (ndx) up to age 110, which was the base parameter for examining the process of mortality compression and variability in age at death (Canudas-Romo 2008; Kannisto 2000). 2.3 Single-year distribution of age at death The distribution of age at death for India was bimodal during the 1970s. One of the two peaks was in the 0-1 age group, and another was in the 70-79 age group. In less than 20 years, the IMR declined swiftly and, hence, by the mid-1980s the age at death was unimodal: the peak only exists in old age groups (Arokiasamy/Yadav 2014: 11, Fig. 3). In such conditions, researchers prefer to eliminate the effect of in- fant and adolescent mortality in order to test mortality compression (Kannisto 2000; Nusselder/Mackenbach 1996; Robine 2001). The life table deaths for age groups 10 and above (5dx : x ≥ 10) were disaggregated into single year life table deaths using the Karup-King third-difference formula. This interpolation method assumes that the distribution pattern of grouped data is a valid indication of the distribution pattern within groups, maintaining the original data and the group totals at the cost of less smoothness (Popoff/Judson 2004: 696, 702). Applying this method, we de- rived single-year distribution of age at death between 10 and 110 years which was smoothened using the cubic spline method (Cheung et al. 2009; Kostaki/Panousis 2001). Henceforth, the single-year, smoothened distribution of age at death between • Suryakant Yadav, Arokiasamy Perianayagam324 10 and 110 years is referred to as the distribution of age at death or age at death which was used to examine mortality compression and variability in age at death. 2.4 Methodology for mortality compression and variability in age at death We adopted and computed the following mortality compression measures: modal age at death (M*), standard deviation (SD) for age 10 and older (SD(10+)), between the age 10 and below M* (SD( 0,b > 1, c ≥ -a (Makeham 1867) takes into account the non-zero extrinsic mortality or as- sumes hazard c at all ages x in addition to a and b which are death rate at initial age and slope of mortality increase, respectively. However, it does not account for the heterogeneity in the population. In that case, the GM model may lead to biases of model parameters (Missov/Németh 2015: 117, 118). The gamma GM model ac- counts for deceleration in mortality rates at older ages and shows a better fi t com- pared to the logistic model (Bongaarts 2005: 25, Fig. 1; Missov/Németh 2015: 118, Fig. 2). However, despite these constraints, the mortality measures like life expec- tancy, life disparity, entropy, and the Gini coeffi cient are only slightly sensitive to model misspecifi cation. Therefore, Gompertz family is most suitable and benefi cial for studying human mortality data. Modal age at death is a measure that is deemed to be sensitive to model misclassifi cation (Missov/Németh 2015: 130, Fig. 5). We applied the GM model for India because mortality data are aggregated for the population and, thus, heterogeneity (γ) in the population is unobserved. It may be noted that there has been some contention among Indian demographers over the success of applying the Gompertz family to the Indian populations. Saikia et al. (2011: 78, Fig. 2) show that the Gompertz model did not fi t well for the periods 1981-1985 and 2000-2004, and hence mortality estimates in old ages for India were called into doubt. However, we confi rm that the GM model fi tted well over ASDR for India for the entire period of 1970-2013, satisfying the conditions of the parameters of the model. Therefore, the mortality estimates in old ages are robust enough (Lee 2003). The fi t of the GM model over the ASDR from age 35 is signifi cant at the one percent level (Table 1, 2, and Fig. 1). The proportion of the variance explained by the GM model averages out at 0.9769 and 0.9678 for women and men, respectively, for the period 1970-2013. The slope b of the GM model for men and women remained nearly constant from 1970 through to 2013 (Table 1, 2 (last column)) and therefore depicts shifting of mortality rates in old ages (Horiuchi/Wilmoth 1998) (Fig. 1) similar to those of men and women in developed nations (Bongaarts 2005: 27, Fig. 2, 28, Mortality Compression and Variability in Age at Death in India • 327 Tab. 1: Parameters of the Gompertz-Makeham model, Indian women, 1970- 2013 Year a c b 1970-1974 0.0179497 0.0002530 1.1129957 1971-1975 0.0181967 0.0002552 1.1136880 1972-1976 0.0177643 0.0002380 1.1151444 1973-1977 0.0161678 0.0002488 1.1139609 1974-1978 0.0137552 0.0003159 1.1096285 1975-1979 0.0128564 0.0003301 1.1083128 1976-1980 0.0124846 0.0002966 1.1091272 1977-1981 0.0118703 0.0003035 1.1083241 1978-1982 0.0119633 0.0002858 1.1081265 1979-1983 0.0118245 0.0002577 1.1093404 1980-1984 0.0113718 0.0002480 1.1101719 1981-1985 0.0100052 0.0002965 1.1069923 1982-1986 0.0108660 0.0002380 1.1106573 1983-1987 0.0099030 0.0002444 1.1102394 1984-1988 0.0092907 0.0002413 1.1103814 1985-1989 0.0091276 0.0002172 1.1116056 1986-1990 0.0094232 0.0001662 1.1160647 1987-1991 0.0092275 0.0001597 1.1164004 1988-1992 0.0102529 0.0001335 1.1195324 1989-1993 0.0098395 0.0001362 1.1187246 1990-1994 0.0094743 0.0001533 1.1162522 1991-1995 0.0088546 0.0001803 1.1128260 1992-1996 0.0072246 0.0002445 1.1071916 1993-1997 0.0050294 0.0003167 1.1026522 1994-1998 0.0047142 0.0003328 1.1018568 1995-1999 0.0030074 0.0003922 1.0992254 1996-2000 0.0016532 0.0004423 1.0972138 1997-2001 0.0004362 0.0005011 1.0948280 1998-2002 0.0013839 0.0005513 1.0931025 1999-2003 0.0021177 0.0005653 1.0921804 2000-2004 0.0003725 0.0004370 1.0956443 2001-2005 0.0005823 0.0004084 1.0965212 2002-2006 0.0001576 0.0003382 1.0993023 2003-2007 0.0013817 0.0002708 1.1025866 2004-2008 0.0018518 0.0002299 1.1050549 2005-2009 0.0000082 0.0002573 1.1038210 2006-2010 0.0002451 0.0002570 1.1037615 2007-2011 0.0005430 0.0002391 1.1046662 2008-2012 0.0004830 0.0002419 1.1042331 2009-2013 0.0010027 0.0002338 1.1043491 Source: Own calculations using ASDR (RGI 1972-2013); μx = c + abx, x is age; the param- eter a, c, and b are all signifi cant at the one percent level for each year. • Suryakant Yadav, Arokiasamy Perianayagam328 Tab. 2: Parameters of the Gompertz-Makeham model, Indian men, 1970-2013 Year a c b 1970-1974 0.00169 0.00133 1.08872 1971-1975 0.00464 0.00109 1.09209 1972-1976 0.00196 0.00132 1.08946 1973-1977 0.00127 0.00146 1.08734 1974-1978 0.00016 0.00153 1.08668 1975-1979 0.00168 0.00169 1.08442 1976-1980 0.00016 0.00135 1.08805 1977-1981 0.00350 0.00100 1.09239 1978-1982 0.00391 0.00083 1.09494 1979-1983 0.00618 0.00065 1.09859 1980-1984 0.00679 0.00056 1.10126 1981-1985 0.00290 0.00085 1.09392 1982-1986 0.00205 0.00089 1.09282 1983-1987 0.00170 0.00092 1.09234 1984-1988 0.00192 0.00088 1.09290 1985-1989 0.00258 0.00080 1.09425 1986-1990 0.00467 0.00064 1.09767 1987-1991 0.00421 0.00062 1.09793 1988-1992 0.00508 0.00057 1.09937 1989-1993 0.00364 0.00064 1.09713 1990-1994 0.00329 0.00068 1.09576 1991-1995 0.00343 0.00067 1.09557 1992-1996 0.00435 0.00064 1.09614 1993-1997 0.00126 0.00085 1.09144 1994-1998 0.00021 0.00092 1.09021 1995-1999 0.00026 0.00097 1.08890 1996-2000 0.00203 0.00116 1.08605 1997-2001 0.00329 0.00128 1.08428 1998-2002 0.00336 0.00129 1.08376 1999-2003 0.00249 0.00125 1.08377 2000-2004 0.00102 0.00103 1.08656 2001-2005 0.00104 0.00083 1.08961 2002-2006 0.00259 0.00069 1.09214 2003-2007 0.00469 0.00054 1.09589 2004-2008 0.00659 0.00045 1.09872 2005-2009 0.00534 0.00049 1.09766 2006-2010 0.00476 0.00053 1.09649 2007-2011 0.00433 0.00055 1.09574 2008-2012 0.00269 0.00066 1.09279 2009-2013 0.00470 0.00053 1.09583 Source: Own calculations using ASDR (RGI 1972-2013); μx = c + abx, x is age; the param- eter a, c, and b are all signifi cant at the one percent level for each year. Mortality Compression and Variability in Age at Death in India • 329 Fig 3, panel b). The differences in life expectancy at age 50 (e50) and life expectancy at age 60 (e60) between SRS and our own calculations are minimal, which is shown in Table 3 for the fi ve-year intercensal periods. Lastly, we conducted analyses for India as a whole and its twelve biggest states by sex and residence for the rolling fi ve-year periods between 1970 and 2013, the critical period that represented India’s transition from a high to a medium and to a low mortality regime. The twelve biggest states included are Andhra Pradesh, Gujarat, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu (pioneer Indian states), Assam, Haryana, Madhya Pradesh, Odisha, and Uttar Pradesh (backward Indian states) (see Table 4). Fig. 1: Gompertz-Makeham (GM) fi t for selected years for India, by sex -7 -6 -5 -4 -3 -2 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group 1973-1977 1983-1987 1993-1997 2003-2007 2009-2013 (a) Women, SRS LN of Force of Mortality (nMx) (c) Men, SRS LN of Force of Mortality (nMx) (b) Women, GM LN of Force of Mortality (nMx) (d) Men, GM LN of Force of Mortality (nMx) -7 -6 -5 -4 -3 -2 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group -7 -6 -5 -4 -3 -2 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group -7 -6 -5 -4 -3 -2 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group Note: Mortality rate is shown per person. Source: Own calculations using ASDR (RGI 1972-2013) • Suryakant Yadav, Arokiasamy Perianayagam330 3 Results 3.1 Age pattern of mortality and premature mortality Figure 2 illustrates the transformation in age patterns of mortality for women and men by residence between 1970 and 2013, signifying a signifi cant period of mortal- ity transition in India. A fl attening age pattern of mortality – across all ages – is evi- dent, and it is most prominent for rural women compared to other population cate- gories. Only in the infant, child, and adolescent ages do rural and urban women now have a higher nqx ; otherwise, women aged 25 and older have a lower nqx compared to men. The 30q15 for rural women (Fig. 2a) and rural men (Fig. 2c) declined by a rate (CAGR) of -2.2 and -1.1 percentage points per year, respectively, in the transitional period. The swift decline of 30q15 for women refl ects a sharp decline in mortality in childbearing ages. Overall, reshaping of the age pattern of mortality in India took a relatively short time, i.e. the last two decades of the 1990s and 2000s. The fi rst of the two components that contributed to this transformation is the decline in the IMR since the 1970s, and the second is the decline in young adult mortality (30q15) since the 1990s. Therefore, infant mortality together with young adult mortality decline indicate an improvement in the chances of survival in India. In principle, the comparison of mortality rates contains the same information as the comparison of mortality distribution (Edwards/Tuljapurkar 2005: 670). The structural changes in the pattern of causes of death immensely contributed to the transformation in age at death leading to the concentration of death in old ages (Yadav/Arokiasamy 2014: Fig. 2 and 3). The shift in the concentration of deaths at- tributable to Certain Infectious and Parasitic Diseases for urban women in the 0-4 age group and 65 plus age group was from 38 percent and 5.6 percent, respectively, in 1975 (ORG 1979: 78) to 8.9 percent and 23.6 percent, respectively, in 2013 (ORG 2015). The toll of deaths attributable to Certain Infectious and Parasitic Diseases Tab. 3: Life expectancy (ex) at age 50 and 60 for selected years, by sex, 1970- 2013 Year Women Men e50 e60 e50 e60 SRS Estimate SRS Estimate SRS Estimate SRS Estimate 1970-1975 21.3 21.3 14.3 14.2 19.8 19.9 13.4 13.5 1976-1980 23.0 22.0 15.9 14.7 20.5 20.1 14.1 13.7 1981-1985 23.8 23.0 16.4 15.6 21.4 21.1 14.6 14.2 1986-1990 23.7 23.5 16.1 15.8 21.7 21.6 14.7 14.6 1991-1995 24.8 24.3 17.1 16.5 22.3 22.2 15.3 15.1 1996-2000 25.5 25.2 17.8 17.5 22.8 22.7 15.8 15.7 2001-2005 26.7 26.5 18.7 18.4 23.7 23.7 16.4 16.4 2002-2006 26.9 26.6 18.9 18.5 24.0 23.9 16.7 16.5 2009-2013 27.2 26.7 19.0 18.9 24.2 24.0 16.9 16.8 Source: Own calculations using ASDR (RGI 1972-2013) Mortality Compression and Variability in Age at Death in India • 331 turned upside down from child to old age groups during 39 years between 1975 and 2013. Since the mid-1990s the country has experienced enormous rise in the burden of chronic NCDs which intruded in the later years of life. The high concentration of deaths of 21 percent and 63 percent, attributable to the Diseases of the Circulatory system, among urban men in the 25-44 and 45-64 age groups, respectively, in 1975 gradually shifted to older age groups, with 36.3 percent and 45.6 percent, respec- tively, in the 45-64 and 65 plus age groups, in 2013 (ORG 1979; ORG 2015). A similar shift in concentration of deaths was evident for other population categories (Yadav/ Tab. 4: Annual estimates of Birth rate, Death rate, IMR, and TFR for selected States, India, by residence, 1971 and 2013 States Region Category 1971 2013 Birth Death IMR TFR Birth Death IMR TFR Rate Rate Rate Rate Rural India – – 38.9 16.4 138 5.4 22.9 7.5 44 2.5 Andhra Pradesh South P 35.6 15.7 115 4.8 17.7 8.3 44 1.9 Assam Northeast B 39.3 18.7 144 5.8 23.5 8.2 54 2.3 Gujarat West P 41.5 17.6 155 5.9 22.2 7.2 43 2.5 Haryana North B 44.2 10.4 74 7.3 22.4 6.7 44 2.3 Karnataka South P 34.6 14.0 105 4.8 19.1 8.0 34 2.0 Kerala South P 31.3 9.1 60 4.2 15.0 7.0 13 1.9 Madhya Pradesh Central B 40.0 16.6 144 6.1 28.2 8.5 57 3.1 Maharashtra West P 33.7 13.5 111 4.9 17.2 7.1 29 1.9 Odisha East B 34.7 15.9 131 4.8 20.5 8.7 53 2.2 Punjab North P 35.0 10.9 109 5.5 16.3 7.5 28 1.7 Tamil Nadu South P 32.9 16.5 127 4.2 15.7 8.1 24 1.7 Uttar Pradesh Central B 46.3 21.1 173 6.9 28.1 8.1 53 3.3 Urban India – – 30.1 9.7 82 4.1 17.3 5.6 27 1.8 Andhra Pradesh South B 31.3 9.1 65 3.8 16.7 5.0 29 1.7 Assam Northeast P 31.0 9.5 73 4.3 15.4 5.6 32 1.5 Gujarat West B 35.8 13.0 110 4.7 18.5 5.5 22 2.0 Haryana North P 43.4 7.3 58 4.6 19.0 5.3 32 2.0 Karnataka South P 25.3 7.2 54 3.4 16.7 5.2 24 1.6 Kerala South B 29.6 8.4 48 3.8 14.0 6.6 9 1.8 Madhya Pradesh Central P 34.5 9.8 79 4.7 19.6 6.1 37 2.0 Maharashtra West B 29.0 9.7 88 3.9 15.4 5.0 16 1.6 Odisha East P 33.0 10.0 84 4.3 14.4 6.3 38 1.5 Punjab North P 31.4 8.7 76 4.4 14.7 5.4 23 1.6 Tamil Nadu South B 27.8 9.3 77 3.3 15.5 6.3 17 1.7 Uttar Pradesh Central P 34.7 13.1 119 4.9 23.3 5.9 38 2.5 Note: The pioneer (P) and backward (B) states are categorized on the basis of death rate, birth rate, and IMR in the period 1993-1997 Source: RGI (2016: Table 1) • Suryakant Yadav, Arokiasamy Perianayagam332 Arokiasamy 2014: Fig. 4 and 5). The high concentration of deaths at adult and old ages was attributable to the prodigious rise of NCDs as well as the structural shift of CDs. The distribution of age at death characterized by an increasing concentration of deaths about M* shifting to older ages demonstrated a greater reduction in prema- ture mortality between the age of 10 and below M* (Kannisto 2001) in the transi- tional period from 1970 to 2013, and was especially conspicuous for women (Fig. 3A and 3B: c and d) compared to men (Fig. 3A and 3B: a and b). The upsurge in the bell- shaped distribution of age at death about M* points to increasing homogeneity in older ages. The M* for urban women and rural women increased respectively from 75 and 73 years to 78 and 81 years, remaining higher than that of men throughout Fig. 2: Probability of dying for selected years for India, by population category -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0- 1 1- 4 5- 9 10 -1 4 15 -1 9 20 -2 4 25 -2 9 30 -3 4 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group 1973-1977 1983-1987 1993-1997 2003-2007 2009-2013 (a) Rural women Ln of probability of dying (c) Rural Men Ln of probability of dying (b) Urban Women Ln of probability of dying (d) Urban Men Ln of probability of dying 0- 1 1- 4 5- 9 10 -1 4 15 -1 9 20 -2 4 25 -2 9 30 -3 4 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0- 1 1- 4 5- 9 10 -1 4 15 -1 9 20 -2 4 25 -2 9 30 -3 4 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0- 1 1- 4 5- 9 10 -1 4 15 -1 9 20 -2 4 25 -2 9 30 -3 4 35 -3 9 40 -4 4 45 -4 9 50 -5 4 55 -5 9 60 -6 4 65 -6 9 70 -7 4 75 -7 9 80 -8 4 Age group 1975 2011 Note: The years 1975 and 2011 in the above graph represents the periods 1973-1977 and 2009-2013, respectively. Source: Own calculations using ASDR (RGI 1972-2013) Mortality Compression and Variability in Age at Death in India • 333 the transitional period (Fig. 4). Developed nations have shown convergence in M* (Canudas-Romo 2008; Thatcher et al. 2010) whereas the developing country India showed a linear increase in M* which lags by 10 years for women and 8 years for men (Yadav/Arokiasamy 2014: Fig. 8). Although India lags behind the developed na- tions in mortality transition, the distribution of age at death has been reshaped into a more left-skewed distribution (Fig. 5) similar to that of developed nations (Cheung/ Robine 2007: 88, Fig. 2; Kannisto 2000: Fig. 6). This development has been faster since the 1990s, basically because young adult mortality exhibited a rapid decline, leading to a period that can be termed as a phenomenal phase of mortality transi- tion in India. 3.2 Mortality compression in India Figure 6 depicts the trends in C50 by residence and sex for India between 1970 and 2013. First, the C50 values for India declined in all population categories. Sorted Fig. 3A: Premature mortality for Rural India, 1973-1977 and 2003-2007, by sex 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age (a) Rural men (1973-1977) Life table deaths, single year (c) Rural women (1973-1977) Life table deaths, single year (b) Rural men (2003-2007) Life table deaths, single year (d) Rural women (2003-2007) Life table deaths, single year 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age Distribution of age at death Mirror image of distribution of age at death beyond modal value 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age Modal value Modal age at death Normality around the modal value Premature deaths Source: Own calculations • Suryakant Yadav, Arokiasamy Perianayagam334 by population category, women living in urban areas had the narrowest C50 val- ues throughout the transitional period. The C50 values for urban women and men declined from 23.4 and 24.0 years in the period 1970-1974 to 17.3 and 19.7 years in the period 2009-2013, respectively. Urban population had narrower C50 values throughout the transitional period than their rural counterparts. The declining C50 values reveal that deaths have been increasingly concentrated in a narrower age interval of age at death during the transition from high to low mortality conditions. Second, the C50 values declined at a faster pace in the 1970s and 1980s, with the fastest pace for the rural population when sorted by population category. Women living in rural areas showed six years decline in C50 from 28 years in early 1970s to 22 years in the late 1980s, outstripping men living in rural areas in the early 1980s. Concerning the rural population, the deaths were dispersed over a wider age interval of age at death during the 1970s but swiftly concentrated in a narrower age interval of age at death by the end of the 1980s. Here, rural women showed a narrower age interval of age at death which was slightly wider than that of urban Fig. 3B: Premature mortality for Urban India, 1973-1977 and 2003-2007, by sex (a) Urban men (1973-1977) Life table deaths, single year (c) Urban women (1973-1977) Life table deaths, single year (b) Urban men (2003-2007) Life table deaths, single year (d) Urban women (2003-2007) Life table deaths, single year Distribution of age at death 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age 0 500 1,000 1,500 2,000 2,500 3,000 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age Modal age at death Distribution of age at death Source: Own calculations Mortality Compression and Variability in Age at Death in India • 335 women. Compared to the rural population, the urban population showed a consist- ent decline in C50 values during the transitional period, with narrower C50 values for urban women compared to urban men. Following a rapid decline in the 1980s, the C50 values stagnated during the 1990s. Women and men living in rural areas had similar C50 values, with a plateau apparent until the late 1990s. Nevertheless, by the early 2000s, rural women had narrower C50 values compared to their male counterparts. Also, urban men showed a modest decline in C50 values, exhibiting a plateau in the 1990s and 2000s. The trends and gender differentials of C50 in rural and urban populations have been the same since the mid-1980s: rural and urban women showed narrower C50 values than rural and urban men. The advantageous pace for rural and urban women in the later years of the transitional period resulted in a wider gap in C50 values between women and men in the rural and urban areas, similar to the pattern seen in developed nations (Kannisto 2000: Table 2). Third, the trends in C50 indicated a coherent decline, leading to a convergence among the population categories. The convergence in C50 was faster among wom- en compared to men. A stronger convergence in C50 among population categories manifest a greater concentration of deaths about M* and hence, greater homogene- ity in mortality, although India lags signifi cantly behind developed nations in mortal- ity transition. Thatcher et al. (2010: Table 3) demonstrate a rapid reduction in vari- Fig. 4: Trends in modal age at death for India, 1970-2013, by sex and residence 65 70 75 80 85 1 9 6 7 -1 9 7 1 1 9 7 1 -1 9 7 5 1 9 7 5 -1 9 7 9 1 9 7 9 -1 9 8 3 1 9 8 3 -1 9 8 7 1 9 8 7 -1 9 9 1 1 9 9 1 -1 9 9 5 1 9 9 5 -1 9 9 9 1 9 9 9 -2 0 0 3 2 0 0 3 -2 0 0 7 2 0 0 7 -2 0 1 1 2 0 1 1 -2 0 1 5 year Rural women Rural men Urban women Urban men Modal age at death Source: Own calculations • Suryakant Yadav, Arokiasamy Perianayagam336 ability in age at death and increase in M* for Sweden, Switzerland, England & Wales, France, and Italy in the period from 1900-1904 to 2000-2004, attesting to stronger mortality compression. The convergence in M* is evident by sex in developed na- tions (Horiuchi et al. 2013: 45, Fig. 3; Missov et al. 2015: 1042, Fig. 4). Therefore, a stronger convergence in C50 among women indicates greater homogeneity in mortality than among their male counterparts. Overall, the results of mortality compression measures confi rm that the pro- cess of mortality compression has been developing at the national level since the 1990s. The two decades between 1970 and 1990 showed a modest progression in mortality compression while the latter two decades, from 1990 to 2013, marked a Fig. 5: Distribution of age at death for intercensal periods for India, 1971-2013, by sex and residence -500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age 2009-2013 2007-2009 2003-2007 1999-2003 1995-1999 1991-1995 1987-1991 1983-1987 1979-1083 1975-1979 1971-1975 (a) Rural women Life table deaths, single year (c) Rural men Life table deaths, single year (b) Urban women Life table deaths, single year (d) Urban men Life table deaths, single year -500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age -500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age -500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 10 20 30 40 50 60 70 80 90 10 0 11 0 Single year age 1973 1989 2011 1997 Note: The years 1973, 1989, 1997 and 2011 in the above graph represent the periods 1971- 1975, 1987-1991, 1995-1999, and 2009-2013, respectively. Source: Own calculations Mortality Compression and Variability in Age at Death in India • 337 stronger progression in mortality compression. Among the population categories, urban women experienced the strongest mortality compression during the transi- tional period. 3.3 Variability in age at death Figure 7 displays the trends in SD(10+), SD(M*+), and SD(