mortality rate in india essay

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The collective term deaths per 1000 encompasses all mortalities (early and late neonatal periods, deaths per 1000 live births ; postneonatal, deaths per 1000 children aged at least 29 days; and child, deaths per 1000 children aged at least 1 year ). NCT Delhi indicates National Capital Territory of Delhi.

The collective term deaths per 1000 encompasses all mortalities (early and late neonatal periods, deaths per 1000 live births ; postneonatal, deaths per 1000 children aged at least 29 days; and child, deaths per 1000 children aged at least 1 year ). NCT Delhi indicates National Capital Territory of Delhi. Red cells indicate an increase in the mortality rate for that specific time period.

The collective term deaths per 1000 encompasses all mortalities (early and late neonatal periods, deaths per 1000 live births ; postneonatal, deaths per 1000 children aged at least 29 days; and child, deaths per 1000 children aged at least 1 year ). The horizontal bar inside the box indicates the median, the lower and upper ends of the boxes are the 25th and 75th percentile respectively and represent the interquartile range (IQR). The whiskers indicate data 1.5 times the IQR and the circles indicate outliers.

NCT Delhi indicates National Capital Territory of Delhi.

eTable 1 . Early-Neonatal Mortality Rate and 95% Confidence Intervals for States/Union Territories of India, 1993-2021

eTable 2 . Late-Neonatal Mortality Rate and 95% Confidence Intervals for States/Union Territories of India, 1993-2021

eTable 3 . Postneonatal Mortality Rate and 95% Confidence Intervals for States/Union Territories of India, 1993-2021

eTable 4. Child Mortality Rate and 95% Confidence Intervals for States/Union Territories of India, 1993-2021

eTable 5. Pearson Correlation Coefficient between Early-Neonatal, Late-Neonatal, Postneonatal and Child Mortality Rates Across States/Union Territories, 1993 and 2021

eTable 6. Standard Deviation (SD) and Interquartile Range (IQR) of Early-Neonatal, Late-Neonatal, Postneonatal and Child Mortality Rates of States/Union Territories of India, 1993-2021

eTable 7. Percentage Share of the Burden of Early-Neonatal, Late-Neonatal, Postneonatal and Child Mortality to Under 5 Mortality Across States/Union Territories of India, 1993-2021

eTable 8. Distribution of the Observed Sample of Live Births, Early-Neonatal, Late-Neonatal, Postneonatal, and Child Deaths Across All States/Union Territories, 1993

eTable 9. Distribution of the Observed Sample of Live Births, Early-Neonatal, Late-Neonatal, Postneonatal, and Child Deaths Across All States/Union Territories, 1999

eTable 10. Distribution of the Observed Sample of Live Births, Early-Neonatal, Late-Neonatal, Postneonatal, and Child Deaths Across All States/Union Territories, 2006

eTable 11. Distribution of the Observed Sample of Live Births, Early-Neonatal, Late-Neonatal, Postneonatal, and Child Deaths Across All States/Union Territories, 2016

eTable 12. Distribution of the Observed Sample of Live Births, Early-Neonatal, Late-Neonatal, Postneonatal, and Child Deaths Across All States/Union Territories, 2021

eFigure 1. Relationship Between Standardized Absolute Change (1993-2021) and Baseline Early-Neonatal, Late-Neonatal, Postneonatal and Child Mortality Rates (1993) Across States/Union Territories of India

eFigure 2. Interactive Dashboard Showing Geographic Distribution of Early-Neonatal, Late-Neonatal, Postneonatal and Child Mortality Rates Across the States and Union Territories of India, 1993-2021

eMethods 1 . Systematic Search of Prior Literature

eReferences

eMethods 2. National Policies That May Directly or Indirectly Influence Childhood Mortality in India

eMethods 3. Stata Codes to Estimate Early-Neonatal, Late-Neonatal, Postneonatal and Child Mortality Rates Across States/Union Territories of India, 1993 to 2021

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Subramanian SV , Kumar A , Pullum TW , Ambade M , Rajpal S , Kim R. Early-Neonatal, Late-Neonatal, Postneonatal, and Child Mortality Rates Across India, 1993-2021. JAMA Netw Open. 2024;7(5):e2410046. doi:10.1001/jamanetworkopen.2024.10046

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Early-Neonatal, Late-Neonatal, Postneonatal, and Child Mortality Rates Across India, 1993-2021

• 1 Harvard Center for Population and Development Studies, Boston, Massachusetts
• 2 Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
• 3 Faculty of Arts and Sciences, University of Toronto, Toronto, Ontario, Canada
• 4 The Demographic and Health Surveys Program, ICF
• 5 Department of Sociology, University of Texas, Austin
• 6 Indian Institute of Technology, Mandi, Himachal Pradesh, India
• 7 Department of Economics, FLAME University, Pune, India
• 8 Division of Health Policy and Management, College of Health Science, Korea University, Seoul, South Korea
• 9 Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea

Question   How have the early-neonatal, late-neonatal, postneonatal, and child mortality rates in the 36 states and union territories of India changed over the past 30 years?

Findings   In this repeated cross-sectional study of 232 772 children who died before their fifth birthday in the past 5 years of each survey from each of the rounds of the National Family Health Survey, the lowest mortality rates were observed for the late-neonatal and child periods; the early-neonatal period was the highest, followed by the postneonatal period. Assessing change in absolute terms, child mortality decreased the most; the burden of mortality at early ages is increasingly concentrated in the early-neonatal and postneonatal phase.

Meaning   The findings of this study suggest that interventions and resources need to be prioritized according to the disaggregated mortality risk in a given area.

Importance   The global success of the child survival agenda depends on how rapidly mortality at early ages after birth declines in India, and changes need to be monitored to evaluate the status.

Objective   To understand the disaggregated patterns of decrease in early-life mortality across states and union territories (UTs) of India.

Design, Setting, and Participants   Repeated cross-sectional data from the 5 rounds of the National Family Health Survey conducted in 1992-1993, 1998-1999, 2005-2006, 2015-2016, and 2019-2021 were used in a representative population-based study. The study was based on data of children born in the past 5 years with complete information on date of birth and age at death. The analysis was conducted in February 2024.

Exposure   Time and geographic units.

Main Outcomes and Measures   Mortality rates were computed for 4 early-life periods: early-neonatal (first 7 days), late-neonatal (8-28 days), postneonatal (29 days to 11 months), and child (12-59 months). For early and late neonatal periods, the rates are expressed as deaths per 1000 live births , for postneonatal, as deaths per 1000 children aged at least 29 days and for child, deaths per 1000 children aged at least 1 year . These are collectively mentioned as deaths per 1000 for all mortalities. The relative burden of each of the age-specific mortalities to total mortality in children younger than 5 years was also computed.

Results   The final analytical sample included 33 667 (1993), 29 549 (1999), 23 020 (2006), 82 294 (2016), and 64 242 (2021) children who died before their fifth birthday in the past 5 years of each survey. Mortality rates were lowest for the late-neonatal and child periods; early-neonatal was the highest in 2021. Child mortality experienced the most substantial decrease between 1993 and 2021, from 33.5 to 6.9 deaths per 1000, accompanied by a substantial reduction in interstate inequalities. While early-neonatal (from 33.5 to 20.3 deaths per 1000), late-neonatal (from 14.1 to 4.1 deaths per 1000), and postneonatal (from 31.0 to 10.8 deaths per 1000) mortality also decreased, interstate inequalities remained notable. The mortality burden shifted over time and is now concentrated during the early-neonatal (48.3% of total deaths in children younger than 5 years) and postneonatal (25.6%) periods. A stagnation or worsening for certain states and UTs was observed from 2016 to 2021 for early-neonatal, late-neonatal, and postneonatal mortality. If this pattern continues, these states and UTs will not meet the United Nations Sustainable Development Goal targets related to child survival.

Conclusions and Relevance   In this repeated cross-sectional study of 5 time periods, the decrease in mortality during early-neonatal and postneonatal phases of mortality was relatively slower, with notable variations across states and UTs. The findings suggest that policies pertaining to early-neonatal and postneonatal mortalities need to be prioritized and targeting of policies and interventions needs to be context-specific.

The Sustainable Development Goals (SDGs) of the United Nations include reducing mortality in the first 5 years to 25 deaths per 1000 live births and the first 28 days to 12 deaths per 1000 live births by 2030. 1 , 2 Nearly 5 million children throughout the world died before their fifth birthday in 2021, with 27% living in South Asia. 3 , 4 India, with a rate of 42 children younger than 5 years (under-5) deaths per 1000 live births, 5 accounts for 14% of the global burden of under-5 mortality. 3 , 6 Meeting the global child mortality SDG target is therefore intrinsically tied to India’s success.

Targeting reduction in mortality risk for children younger than 5 years requires a disaggregation by age. However, deaths during the first 7 days (early neonatal) and 8 to 28 days (late neonatal) are usually combined. Similarly, deaths occurring between the ages of 1 to 11 months (postneonatal) and 12 to 59 months (child) are also often conflated. This is problematic as the causes of death during different ages are distinct, necessitating different interventions at each stage of life. 7 , 8 During the early-neonatal period, most deaths occur due to preterm birth complications or intrapartum-related events, 7 , 8 whereas malnutrition and infections are the major causes of death during late-neonatal, postneonatal, or childhood periods. 7 , 9 , 10 The mortality risk also varies across ages during early years. 11

To our knowledge, systematic assessments of how the patterns of early-neonatal, late-neonatal, postneonatal, and child mortality have changed over time in India and at subnational levels, especially accounting for the changing geographic boundaries of Indian states have not been conducted (eMethods 1 in Supplement 1 ). For India to develop a successful child survival policy framework, a detailed assessment of mortality patterns over time is essential. We present a disaggregated and up-to-date assessment of changes in mortality risk and percentage share of burden for each age period to total deaths in the under-5 population in India and across its 36 states and union territories (UTs) from 1993 to 2021. We also assess the progress states and UTs are making toward achieving SDG targets for early-neonatal, late-neonatal, postneonatal, and child mortality.

This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cross-sectional studies. We used 5 waves of the National Family Health Surveys (NFHS), conducted in 1992-1993, 12 1998-1999, 13 2005-2006, 14 2015-2016, 15 and 2019-2021 5 ; hereafter, identified with the end year of each survey, although the reference date for the estimated 5-year rates is approximately 2½ years before the mean date of the interview for each survey. The mortality data are representative nationally, as well as at the level of states and UTs. The NFHS follows the protocol and procedures of the global Demographic and Health Surveys (DHS) Program currently active in more than 90 countries. 16 , 17 We restricted our analysis to the NFHS as the data source to maintain comparability in the method of data collection for mortality and because the NFHS provides micro data with geographic identifiers below the level of states. This was necessary as the geometry and number of states and UTs changed over the survey years, with the latest configuration being 28 states and 8 UTs. To make states and UTs comparable over time, we adopted a published method that entailed reassigning district-level information from older surveys to states according to the most recent geometry. 18 The NFHS data were collected using informed consent and the protocol for the survey, including the content of all the questionnaires, was approved by the International Institute for Population Studies Institutional Review Board (IRB) and the ICF IRB. For the analysis presented in this study using the NFHS data, the Harvard Longwood Campus IRB allows researchers to self-determine whether their research meets the requirements of IRB oversight using the IRB Decision Tool. These activities did not meet the regulatory definition of human participant research, and our study was determined to be exempt from a full institutional review.

The study population was children who were born in the 5 years preceding each of the surveys. Detailed information on each birth was collected from the mother or primary caretaker. Observations for which the date of birth or age at death of the child were missing or unknown were imputed following DHS imputation procedures. 19

The surveys recorded the month and year the child was born, whether the child was still alive, and if not alive, the age of death in days if less than age 1 month, in months if less than 2 years, and in years if older than 2 years. The underlying sample data are provided in eTables 8-12 in Supplement 1 .

Using these data, we computed the following mortality rates: early-neonatal mortality rate (ENMR) (the number of deaths occurring in the first 7 completed days after the child is born per 1000 live births, late-neonatal mortality rate (LNMR) (the number of deaths occurring on days 8 to 28 completed days after the child is born per 1000 live births, postneonatal mortality rate (PNMR) (the number of deaths occurring on days 29 to 11 completed months per 1000 children who are at least 29 days old, and childhood mortality rate (CMR) (the number of deaths occurring in months 12 to 59 completed months per 1000 children who are at least aged 1 year) (eMethods 1 in Supplement 1 ). 20 Hereafter, all age-specific mortalities are referred to as deaths per 1000 .

The proportion of children who died (probability of death) from the cohort of children included at the starting age of the age bracket was then computed. We used real birth cohorts for the ENMR, LNMR, and overall neonatal mortality rates and the synthetic cohort lifetable approach to calculate the infant and child mortality rates for each of the age groups, as implemented by the DHS. 21 Postneonatal mortality is calculated as infant mortality (calculated via synthetic cohort) minus neonatal mortality (calculated via real cohort). Mortality information collected for a year includes age-specific deaths from more than 1 cohort for a given time. 22 The synthetic cohort approach combines the period spent by children of all cohorts in each survey. The probability of death was then calculated separately for the following age intervals: less than 1 month, 1 to 2 months, 3 to 5 months, 6 to 11 months, 12 to 23 months, 24 to 35 months, 36 to 47 months, and 48 to 59 months. The age-specific probabilities of dying in the specified period were combined to derive early-neonatal, late-neonatal, postneonatal, and child mortality rates and computed for each survey year for all-India and 36 states and UTs. The Stata codes to estimate the mortality rates are provided in eMethods 3 in Supplement 1 . We visualized the changing geographic distribution of ENMR, LNMR, PNMR, and CMR using choropleth maps and also created an online interactive dashboard (eFigure 2 in Supplement 1 ).

The number of years between the surveys was not the same. For instance, there was a 10-year interval between the 2006 and 2016 surveys, but a 5-year interval between the 2016 and 2021 surveys. We compared the absolute change in mortality rates between different time periods using standardized absolute change (SAC) defined as:

SAC = ([ P t − P t–n ]/ n ),where P t is the mortality rate for the time t , P t − n is mortality rate n years before t , and n represents the number of years between any 2 surveys (taking the latest survey year of multiyear surveys). A negative SAC value indicates a decrease in mortality, whereas a positive SAC value indicates an increase. We computed the Pearson correlation coefficient between each of the 4 mortality rates for 1993 and 2021 (eTable 5 in Supplement 1 ). 23

Since each mortality rate is exclusive to the 4 age groups with no overlaps, we added the 4 mortality rates to obtain an adjusted under-5 mortality rate. Each of the age-specific rates was then divided by the calculated under-5 mortality rate and multiplied by 100 to obtain the percentage share of early-neonatal, late-neonatal, postneonatal, and child deaths to total under-5 deaths.

We used a published method to estimate progress toward the SDG targets for each state and UT. 24 This method assumes that the SAC between 2016 and 2021 is maintained until 2030 and classifies each state and UT into 1 of 4 categories for each outcome: achieved-I (goal already met in 2021 and will continue to be met in 2030), achieved-II (goal already met in 2021 but will no longer maintain status by 2030), on-target (goal not met in 2021 but will be met by 2030), and off-target (goal not met in 2021 and will not be met by 2030). The SDG only sets targets for NMR (12 deaths per 1000 live births) and under-5 mortality rate (25 deaths per 1000 live births). 2 The targets used in this study for the different age-periods are, therefore, approximations and meant to provide a general sense of the progress for a particular geographic unit. Given the neonatal and under-5 mortality targets, the target for postneonatal and child mortality rates combined is approximately 13 deaths per 1000. Consequently, we chose the postneonatal mortality target as 8 deaths per 1000, the child mortality target as 5 deaths per 1000, the early-neonatal target as 7 deaths per 1000 live births, and the late-neonatal target as 5 deaths per 1000. Analyses were conducted used Stata, version 17 (StataCorp LLC). 23

The final analytical sample included 33 667 (1993), 29 549 (1999), 23 020 (2006), 82 294 (2016), and 64 242 (2021) children who died before their fifth birthday in the past 5 years of each survey (eTables 8-12 in Supplement 1 ). Specifically, the sample included 9840 (1993), 9741 (1999), 8814 (2006), 37 461 (2016), and 31 124 (2021) early-neonatal deaths, 4804 (1993), 4008 (1999), 2893 (2006), 8739 (2016), and 6489 (2021) late-neonatal deaths, 10 201 (1993), 7980 (1999), 6041 (2006), 19 914 (2016), and 15 597 (2021) postneonatal deaths, and 8822 (1993), 7820 (1999), 5272 (2006), 16 180 (2016), and 11 032 (2021) child deaths (eTables 8-12 in Supplement 1 ).

In 2021, mortality risk was highest for ENMR followed by PNMR and CMR, with the lowest risk being the late-neonatal period (eTables 1-4 in Supplement 1 ). However, in 1993, the mortality risk was the highest for both the ENMR and CMR at 33.5 deaths per 1000 followed by PNMR and LNMR (eTables 1-4 in Supplement 1 ). Between 1993 and 2021, India observed a substantial decrease in all 4 mortality rates ( Figure 1 ; eTables 1-4 in Supplement 1 ). The largest decrease (in absolute percentage points) was observed for CMR followed by PNMR, ENMR, and LNMR. The CMR decreased from 33.5 (95% CI, 31.5-35.5) to 6.9 (95% CI, 6.5-7.4), and the PNMR decreased from 31.0 (95% CI, 31.0-31.0) to 10.8 (95% CI, 10.8-10.8). Meanwhile, the LNMR decreased from 14.1 (95% CI, 13.8-14.5) to 4.1 (95% CI, 3.7-4.4), and the ENMR decreased from 33.5 (95% CI, 32.9-34.1) to 20.3 (95% CI, 19.6-21.1).

There were considerable differences in the mortality decrease patterns across the different time periods and age groups. For the ENMR, the period of the greatest average annual decrease was 2016-2021 (SAC = −0.78); the greatest average annual decrease was 1993-1999 (SAC = −0.57) for the LNMR, 1993-1999 (SAC = −0.95) for the PNMR, and 1999-2006 (SAC = −1.55) for the CMR ( Figure 2 ). The least reductions in average annual decrease for the ENMR were observed between 1999 and 2006; for the LNMR, PNMR, and CMR it was 2016-2021.

Except for Nagaland, Mizoram, Uttarakhand, and Manipur, all 4 mortality rates decreased in the rest of the states and UTs between 1993 and 2021 ( Figures 1 and 2 ). During this period, Nagaland experienced a worsening of ENMR (SAC = 0.06), PNMR (SAC = 0.21), and CMR (SAC = 0.22), whereas Mizoram saw a worsening of ENMR (SAC = 0.14) and PNMR (SAC = 0.12). Uttarakhand saw a worsening for ENMR (SAC = 0.15), whereas Manipur saw worsening of LNMR (SAC = 0.02). There was considerable variation in the amount of reduction experienced across states and UTs ( Figure 2 ).

While most of the states and UTs have seen some decrease in all 4 mortality rates, some concerning patterns in the most recent time periods were observed. Between 2016 and 2021, early-neonatal mortality increased in 9 states and UTs, late-neonatal mortality increased in 13 states and UTs, postneonatal mortality increased in 12 states and UTs, and child mortality increased in 8 states and UTs. Meanwhile, from 2006 to 2016, 7 states and UTs experienced an increase in early-neonatal mortality, and 3 states and UTs for late-neonatal mortality, postneonatal mortality, and child mortality.

The interstate inequalities in all 4 mortality rates were reduced ( Figure 3 ), with CMR experiencing the largest reduction (summary distribution, 13.2-2.7), followed by PNMR (summary distribution, 10.6-4.0), LNMR (summary distribution, 5.9-1.9), and ENMR (summary distribution, 9.9-7.7) (eTable 6 in Supplement 1 ). On average, states with higher baseline mortality rates in 1993 experienced the largest decrease between 1993 and 2021 (eFigure 1, eTable 6 in Supplement 1 ). While there was some degree of variation, overall, there was a moderate to strong correlation between the 4 mortality rates across states (eTable 5 in Supplement 1 ).

The share of early-neonatal deaths to total under-5 deaths increased from 29.9% in 1993 to 48.3% in 2021, while the share of late-neonatal deaths decreased from 12.6% in 1993 to 9.7% in 2021, postneonatal deaths from 27.7% in 1993 to 25.6% in 2021, and child deaths from 29.8% in 1993 to 16.4% in 2021 ( Figure 4 ). Except for Kerala, Goa, and Nagaland, the remaining states and UTs with available data for both time periods experienced an increase in the share of early-neonatal deaths. Substantial shifting of the share was observed between postneonatal and child deaths between 1993 and 2021 (eTable 7 in Supplement 1 ). In 2016, nearly 50% of all deaths in the under-5 population in India occurred within 7 days after a child's birth. This has remained unchanged in 2021.

Based on the most recent rate of change, from 2016 to 2021, India will not meet the SDG targets for early-neonatal and postneonatal mortality by 2030 ( Figure 5 ). Twenty-one states and UTs for ENMR, 9 for LNMR, 17 for PNMR, and 10 for CMR will fail to meet their SDG targets by 2030. Fourteen states and UTs for PNMR, 25 for LNMR, 13 for CMR, and 7 for ENMR have met their SDG targets and will continue to meet the targets in 2030.

Our study has 4 salient findings. First, mortality rates in the late-neonatal and child phases are the lowest and have decreased markedly over the years, along with a substantial reduction in interstate inequalities. Second, the mortality rate is highest within the first week (early-neonatal phase), followed by the postneonatal phase, and interstate inequalities also remain notable. Third, the share of mortality burden is now concentrated during the early-neonatal and postneonatal periods, underscoring the importance of the first week and first year of life, past the late-neonatal period. Fourth, India overall, along with a considerable number of states and UTs, will not meet the SDG targets related to child survival during early-neonatal and postneonatal periods. We elaborate on these findings with an emphasis on the early-neonatal and postneonatal phases since mortality risk and burden during these 2 periods remain high.

Child survival in the first week of life is mostly centered around quality of child delivery settings, newborn care, and conditions related to the child and the mother. 8 While India has made substantial advances in increasing the percentage of institutional deliveries (from 26% in 1993 to 89% in 2021 5 , 12 ), translating these increases to a reduction in early-neonatal mortality has not occurred at the same pace. It is critical that, as India develops a strategy for further reduction in ENMRs, the focus is shifted from the quantity toward the quality of the institutions where delivery and newborn care occur. 25 Risk factors of early-neonatal mortality, such as a higher prevalence of low birth weight, 5 and congenital malformations, are also highly prevalent in India. 26 With identification of high-risk pregnancies in a timely manner still being less than optimal, 27 maternal mortality during delivery also remains high in India (at 99 per 100 000 live births in 2020 28 ), which is likely to also increase the early-neonatal mortality rate. Given these conditions, there is an immediate need to focus on the quality aspect of institutional deliveries, including assessing and building neonatal intensive care units to meet the SDG targets related to early-life mortality.

It is noteworthy that the mortality rate during the late-neonatal phase (deaths in 8-28 days) significantly diminished in comparison with the early-neonatal phase, and then increased during the postneonatal phase (deaths in 29 days to 11 completed months). Even though the underlying biological risk is expected to decrease with age systematically for children younger than 5 years, the increase during the postneonatal phase, with the rate being more than twice that of late-neonatal phase, merits further research.

Existing research on the high prevalence of food deprivation among young children in India suggests that the mortality burden during the postneonatal period could be substantial. 18 For instance, about 30% of children aged 6 to 12 months (ie, the latter half of the postneonatal period) were reported to have not consumed any food for the 24 hours before the survey. 18 Exclusive breastfeeding (which is only recommended for the first 6 months after birth) remains highly prevalent in India even after 6 months, thus depriving the child of the crucial nutritional requirements. 29 Even with substantial progress in expansion of recommended vaccination coverage among young infants as well as overall improvements in environmental conditions, such as sanitation, and thereby reducing susceptibility to environmental infections, the decrease in postneonatal mortality rates over time has not been as substantial as observed for child mortality rates. While there have been marked improvements in expanding vaccination coverage across India, a substantial number of children do not receive any routine vaccinations. 30 Furthermore, preliminary evidence suggests that the impact of COVID-19 may also have adversely affected the continued expansion of the vaccination. 31 Going forward, India needs to closely examine its existing policies that are directly or indirectly related to child survival (eMethods 2 in Supplement 1 ) and develop a specific strategy focused on the postneonatal period, including further examination on what periods during this time matter the most, along with a concerted effort to eliminate any form of food deprivation. The government of India has approved a resolution to provide free food grains to more than 813 million people in India through the Pradhan Mantri Garib Kalyan Anna Yojana program starting January 1, 2024, for a period of 5 years, 32 which if implemented effectively can help with reducing the mortality burden during the postneonatal period.

Our findings should be interpreted alongside the following data-related considerations. First, our estimates rely on maternal reports of birth dates and age at death, thus introducing potential recall bias. While bias is noted for death reporting beyond 5 years, the 0- to 4-year recall period used in our study largely remains unbiased. 33 Second, although NFHS coverage improved, data for several UTs were only available for 2016 and 2021. These include Lakshadweep, Puducherry, Chandigarh, Andaman and Nicobar, Dadra and Nagar Haveli, and Daman and Diu. However, these areas constitute less than 1% of India’s population. 34 Third, due to 2006 survey limitations, we assume that the estimates of new state divisions are the same as their parent state (Ladakh and Telangana). Finally, given the arbitrariness of the definition of the WHO age-specific periods, there is a need for further research to more precisely understand the association between age and mortality risk occurring during childhood years, especially during the first 2 years. While these data considerations need acknowledgment, they do not affect the overall robustness of the findings related to understanding the evolution of mortality among neonates, postneonates, and children in India.

In this repeated cross-sectional study, we provided an up-to-date assessment of age-specific mortality during the first 5 years of a child’s life and how this has evolved in India over the past 30 years. Even as mortality rates have seen a remarkable decrease over the last 30 years, there is crucial need to focus on the early-neonatal and postneonatal periods. Our findings also reveal persistent inequalities across states and UTs, especially for early-neonatal and postneonatal mortality rates, with recent years showing a stagnation or worsening in certain states and UTs. It is critical for policy makers to focus on the specific states and UTs that are of concern and develop context-specific interventions. This is critical to ensure that India attains the SDG targets related to mortality during early life years and, in doing so, positively contribute to the global progress on child survival.

Accepted for Publication: March 6, 2024.

Published: May 10, 2024. doi:10.1001/jamanetworkopen.2024.10046

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Subramanian SV et al. JAMA Network Open .

Corresponding Author: S. V. Subramanian, PhD, Harvard Center for Population and Development Studies, 9 Bow St, Cambridge, MA 02138 ( [email protected] ).

Author Contributions: Mr Kumar and Dr Pullum had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Subramanian.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Subramanian, Kumar, Ambade.

Critical review of the manuscript for important intellectual content: Subramanian, Pullum, Rajpal, Kim.

Statistical analysis: Kumar, Pullum, Rajpal, Kim.

Administrative, technical, or material support: Kumar.

Supervision: Subramanian.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by grant INV-002992 from the Bill & Melinda Gates Foundation.

Role of the Funder/Sponsor: The Bill & Melinda Gates Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank the Demographic and Health Surveys program for making the National Family Health Survey data freely accessible. We also acknowledge Bharat Maps for the making available the latest state/union territory map of India.

Additional Information: The Stata code files to compute estimates for early-neonatal, late-neonatal, postneonatal, and child mortality rates (and 95% CIs) using the cohort approach can be found in eMethods 3 in Supplement 1 . The interactive dashboard created is available at https://geographicinsights.iq.harvard.edu/State-Child-Mortality

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Article contents

Demographic transition in india: insights into population growth, composition, and its major drivers.

• Usha Ram Usha Ram International Institute for Population Sciences, Department of Public Health and Mortality Studies
•  and  Faujdar Ram Faujdar Ram Population Council of India and International Institute for Population Sciences
• https://doi.org/10.1093/acrefore/9780190632366.013.223
• Published online: 26 April 2021

Globally, countries have followed demographic transition theory and transitioned from high levels of fertility and mortality to lower levels. These changes have resulted in the improved health and well-being of people in the form of extended longevity and considerable improvements in survival at all ages, specifically among children and through lower fertility, which empowers women. India, the second most populous country after China, covers 2.4% of the global surface area and holds 18% of the world’s population. The United Nations 2019 medium variant population estimates revealed that India would surpass China in the year 2030 and would maintain the first rank after 2030. The population of India would peak at 1.65 billion in 2061 and would begin to decline thereafter and reach 1.44 billion in the year 2100. Thus, India’s experience will pose significant challenges for the global community, which has expressed its concern about India’s rising population size and persistent higher fertility and mortality levels. India is a country of wide socioeconomic and demographic diversity across its states. The four large states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan accounted for 37% of the country’s total population in 2011 and continue to exhibit above replacement fertility (that is, the total fertility rate, TFR, of greater than 2.1 children per woman) and higher mortality levels and thus have great potential for future population growth. For example, nationally, the life expectancy at birth in India is below 70 years (lagging by more than 3 years when compared to the world average), but the states of Uttar Pradesh and Rajasthan have an average life expectancy of around 65–66 years.

The spatial distribution of India’s population would have a more significant influence on its future political and economic scenario. The population growth rate in Kerala may turn negative around 2036, in Andhra Pradesh (including the newly created state of Telangana) around 2041, and in Karnataka and Tamil Nadu around 2046. Conversely, Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan would have 764 million people in 2061 (45% of the national total) by the time India’s population reaches around 1.65 billion. Nationally, the total fertility rate declined from about 6.5 in early 1960 to 2.3 children per woman in 2016, a result of the massive efforts to improve comprehensive maternal and child health programs and nationwide implementation of the national health mission with a greater focus on social determinants of health. However, childhood mortality rates continue to be unacceptably high in Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh (for every 1,000 live births, 43 to 55 children die in these states before celebrating their 5th birthday). Intertwined programmatic interventions that focus on female education and child survival are essential to yield desired fertility and mortality in several states that have experienced higher levels. These changes would be crucial for India to stabilize its population before reaching 1.65 billion. India’s demographic journey through the path of the classical demographic transition suggests that India is very close to achieving replacement fertility.

• demographic transition
• contraception
• family planning
• life expectancy
• child mortality

India is one of the oldest civilizations and has a vibrant cultural heritage coupled with remarkable diversity. The Mughals ruled the country from 1526 to 1761 , and were mainly located north of Vindhyanchal. India was a British colony from 1612 until 1947 , when the country attained its independence and became a sovereign nation. The British occupied all of present-day India after defeating Tipu Sultan in Mysuru and Marathas in Maharashtra. The British East India Company governed India and controlled trade throughout the region, except for Goa, which the Portuguese controlled in 1510–1961 , and Pondicherry, which the French controlled in 1673–1693 and again in 1699–1962 .

India has conducted a regular decadal census since 1881 that measures population size and composition as well as decadal growth at the national and subnational levels (including states, districts, and tehsils). At the dawn of Indian independence, there were about 345 million Indians. The year 1951 witnessed the first census of an independent India, recording a total population of 361 million and a moderate annual exponential growth rate of 1.25% during 1941–1951 . From a population growth perspective, the year 1951 became a turning point because it indicated a population explosion since it multiplied threefold by 2001 .

According to a United Nations (UN, 2019 ) report, India constituted 17.7% of the total world population, and was second only to China, whose share was 18.5%. The same estimates revealed that India would not only surpass China in the year 2030 with its share of 17.6% (and China’s would decrease to 17.1%) but it would also maintain the first rank after 2030 . The report further indicated that Africa’s share would rise to 25.6% in 2050 and 39.4% in 2100 . In contrast, the percentage share of Asia would decline from 59.5% in 2020 to 43.4% in 2100 . By 2100 , India would attain the first rank as far as the share of a single country is concerned. Nonetheless, its relative share would decline to 16.8% in 2050 and 13.3% in 2100 . It is thus essential to examine the dynamics of population growth, its potential, and future drivers of population growth of India.

The rapid population growth caused by a comparatively quick decline in mortality and persisting higher fertility levels has been the cause of concern in most developing counties, including India. The 1961 census of India revealed an annual exponential national growth rate close to 2% during 1951–1961 . The concerns were raised about the population growth and its rising size, both nationally and globally. The demographics of India—population size, growth rate, fertility, mortality, and so on—continue to occupy significant space discussions concerning its impact on various global health and developmental indicators. Alarmed at burgeoning numbers, and a view to accelerating a rapid decline in fertility levels, many developing countries, especially in Southeast Asia, launched official family planning programs in the mid-1960s. In the 1970s and 1980s, most witnessed a strong commitment by leaders to reduce fertility levels. As a result, they experienced one of the fastest transitions in levels of fertility (Pathak & Ram, 1981 ; Srinivasan & Pathak, 1981 ). Although India launched an official family planning program in the early 1950s, the real inputs for the program were recorded from the 1960s, when the program became method-mixed and target oriented. Post-independence, upon the advice of several researchers (Chitre, 1964 ; Gopalaswamy, 1962 ; Laxmi, 1964 ), the Indian government implemented its official family planning program in 1952 that promoted sterilization on a large scale. This was considered as the most cost-effective and impactful approach by the government given resource constraints. However, Agarwala ( 1964 ) disagreed with this and criticized the program. Recently, Srinivasan ( 2006 ) also opined that the continuous focus on sterilization (female) has dominated the Indian national family planning program. In the mid-1960s, government expanded the basket of methods for the clients and included IUD into the program. This, nonetheless failed due to several side effects on the users (Pujari et al., 1967 ).

The well-known linguistic, economic, and social-cultural diversity of India and its century-old demographic diversity across geographies have expanded, especially since independence. Several states in India, including Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu in the southern region, have moved much faster in achieving the national goal of the replacement fertility. The onset of fertility transition in these southern states occurred when the social and development indicators such as female literacy rates, per capita income, mortality and so on were rather poorer. At the same time, Hindi-speaking states in the northern region, including Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh, continue to experience high levels of fertility as well as mortality. Nationally, fertility levels in India have fallen, and by 2000 Indian women were having an average of about 3.3 children. A significant portion of this decline came from the states in the southern region, where female literacy rates were higher, and women enjoyed greater autonomy than the women in the rest of India. While the southern states of Kerala and Tamil Nadu attained replacement-level fertility long ago, the giant northern states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan continue to reproduce at a prodigious rate (Krishnamoorthy, 1997 ; Rajan, 1994 ; Seal & Talwar, 1994 ). It is important to note that the prevailing social and economic conditions in the southern states at the time of onset of fertility transitions varied considerably. The doctrine of demographic-transition theory advocates indicates that a rise in per capita income, industrialization, and urbanization subsequently leads to reduced levels of fertility and mortality in populations. However, this did not happen in Kerala. Fertility and mortality levels in Kerala were not accompanied by the concurrent improvements in the levels of per capita income, industrialization, and urbanization (Zachariah, 1983 ).

Until the end of the 20th century , family welfare programs and policies in India focused on lowering fertility rates because the authorities visualized that the persisting higher fertility rates would further add to the built-in growth momentum of its population age composition. The UN’s ( 1987 ) population projections revealed that the population momentum alone would add substantially to growing numbers in India. Visaria and Visaria ( 1994 ) warned that the ultimate population size of India would be enormous if the country failed to put a brake on the fertility rate and achieved the replacement levels before 2016 . It would thus be useful to elaborate on the demographic transition in India and identify gaps to provide future directions for the program to enable positive changes in matters of population growth, thereby improving the lives and well-being of its people. The national scenario masks the diversity across states. Thus, achieving the goals may be less feasible without any understanding of the issues at the subnational level. This article documents the demographic transition of India at the national and subnational levels and examines various drivers of the transition.

The data for the present research come from several sources. The world population for the past and future years comes from the UN’s ( 2019 ) World Population Prospects . The time-series data for India on population size, growth rates, and age distribution at the national and state levels come from Indian government censuses conducted between 1881 and 2011 . The Government of India’s National Commission on Population (NCP, 2019 ) projections provides the numbers for the period 2021–2036 . The indicators of fertility (total fertility rates) and mortality (infant mortality rate, under-5 mortality rate, and life expectancy at birth come) are from various rounds of the Indian government’s Sample Registration System (SRS). The data for multiple years is available in the annual statistical reports published by the Registrar General of India ( 2020 ). The information on contraceptive use and marriage comes from the National Family Health Surveys (International Institute for Population Sciences [IIPS], 1993 ; IIPS & ICF, 2017 ; IIPS & ORC-Macro, 2000 , 2007 ). Figures and tables presented throughout the article give detailed data from these sources.

Demographic Transition Theory: A Brief Description

The demographers Warren Thompson ( 1929 ) and Adolphe Landry circa 1934 (Landry, 1987 ), described the classical demography/population transition. However, Frank W. Notestein ( 1945 ), an American demographer proposed a precise framework and presented a systematic formulation of the theory in its real sense According to the demographic transition theory, most countries will go through a process of population change from the stage of high birth and death rates (pretransition stage 1) to the last stage of lowest birth and death rates (stage 4). In other words, countries move from the lowest pretransition stage 1 (sometimes negative growth rate) to the highest growth rate (stages 2 and 3) before reaching stage 4, when the growth rate is extremely low (occasionally negative) and the country has attained below-replacement fertility. According to the theory, the demographic transition of a nation can be described with the help of the growth rates if the country has regular censuses over a reasonably long period. In his critical exploration of the demographic transition, Kirk ( 1996 ) stated that

the timing of the decline in countries with Non-European tradition conformed to the forecast by the original authors of the theory, without exception, fall in mortality preceded the decline in the levels of fertility . . . In general, the transition period was shorter in Non-European countries than the countries inhabited by Europeans. (p. 383)

Further, the non-European countries are transitioning with a lower level of socioeconomic development (Cleland & Wilson, 1987 ).

Several researchers (Kaa, 1987 , 2002 ; Lesthaeghe, 2011 , 2014 ; Lesthaeghe & Surkyn, 2004 ) have referred to a second demographic transition (SDT). The SDT is a period of continued fertility decline much below-replacement fertility. The most critical factors related to this continued decline are increase in nonmarriage, individual autonomy, self-actualization, rising symmetry in sex roles, advancing female education, and economic independence of women (for details, see Lesthaeghe, 2014 ). Nevertheless, the postulate of SDT based on the experiences of European countries may not hold in developing countries (Cleland, 2001 ; Dyson, 2010 ). The SDT, nevertheless, is much more challenging than the original demographic transition because the countries face declining population sizes, shrinking working population, and graying population. To an extent, replacement migration could help these nations overcome these emerging challenges. Coleman ( 2006 ), using the emergence of migrants as the dominant community in some geographies compared to the natives, advocated the concept of the third demographic transition (TDT), which emphasizes the drastic change in population composition. However, the idea of TDT could be a reflection of the adjustment for the shrinking labor force that arises out of SDT, and it does not fit into the purview of demographic transition theory per se.

This section discusses changes in population size, growth and its age-sex composition over time to understand India’s population transition. This is followed by a detailed exploration of the crucial factors that led to population transition. For this, we have considered four major drivers of population change that include fertility, mortality, family planning and changes in marriage pattern. Changes in fertility levels have been studied using total fertility rate. The changes in mortality have been studied using three indicators of infant mortality, under-5 mortality and expectation of life at birth. The changes in contraceptive use is examined with the help of contraceptive prevalence rate. Finally, changes in marriage pattern is examined with the help of percentage of women aged 20–24 years who were married before reaching age 18 years and women aged 30–34 years who remained single.

Population Size, Growth, and Age Structure

The UN ( 2019 ) estimated a total of 7,795 million people globally in 2020 . They suggested that this number would surpass 10 billion by the turn of the 21st century (Table 1 ). In 2020 , about 60% of the people live in Asia and a little over 17% live in Africa. By 2100 , Asia would be home to 43% of the global people and Africa to 39%. The share of European countries is estimated to reduce from 9.6% in 2020 to less than 6% in 2100 . While a similar pattern is predicted for the countries in Latin America and the Caribbean and the North American regions, the share of Oceania remains unchanged. China’s population, was about 19% of the global population in 2020 , would reduce to less than 10% by 2100 . In India the share would decrease from less than 18% in 2020 to slightly over 13% in 2100 .

Table 1. Population Size and Share of the Population of World Regions, China, and India, 2020–2100

Source: UN ( 2019 ).

The indirect estimates of crude birth and death rates for India are for the period 1901–1961 . After 1971 , the SRS, which was established in the late 1960s, started to provide the crude birth rate (CBR) and crude death rate (CDR) for India and bigger states annually. The most recent SRS estimates are available for the year 2017 . At the beginning of the 20th century , India had very high levels of crude birth and death rates (48 births/deaths per 1,000 persons; Figure 1 ), which persisted until 2021 . The death rates started to decline around 1930 and reached 16 deaths per 1,000 persons in 1971 . The CBR, too, began to fall at a much slower pace. While the CBR was 36 births per 1,000 persons in the early 1970s, the CDR was 16 deaths per 1,000 persons. This declining trend continues, and the gap between the two rates is narrowing over time. The CBR was 20 per 1,000 persons in 2017 as compared to the CDR of 6 per 1,000 persons.

Figure 1. Crude birth rate (CBR) and crude death rate (CDR) for India, 1901–2017.

At the beginning of the 20th century , India had 238 million people. The results of the first census of the new millennium revealed that India had crossed the one billion mark by the end of the 20th century as the 2001 census enumerated a total of 1,029 million Indians (Table 2 ). The country annually added 16.1 million people in the 1980s and 18.2 million in the 1990s. While the world population increased threefold (from 2 to 6 billion) during the last century, it grew five times in India. The 15th census of India conducted in 2011 enumerated a total of 1,210 million Indians. The population of India grew with a decadal growth rate of about 17.5% during 2001–2011 , resulting in an annual exponential growth rate of 1.62% (a decline from 1.96% observed during 1991–2001 ). Despite a substantial reduction in the growth rate during 2001–2011 , India added nearly 181 million people. The UN’s 2019 projections indicated a similar addition during 2011–2021 , before the country experienced a drastic decline in the subsequent decades.

Figure 2. Estimated and observed exponential annual population growth rate (%) during 1901–2011 and 2021–2101, respectively, for India.

Indian annual population growth peaked at 2.22% during 1961–1971 (Table 1 and Figure 2 ) and stayed around 2% for the next four decades until 2001 . This period may be referred to as the second stage (population explosion stage) of demographic transition for India, during which the country added approximately 590 million people. Between 2001 and 2011 India experienced a substantial decline in its population growth rate (from 1.95% in 1991–2001 to 1.62% in 2001–2011 ). The UN’s 2019 assessment suggested that as far as the population size as concerned, India would surpass China in the next 7–8 years and would continue to increase until the year 2061 when its population size would reach 1,650 million. India may experience a decline in its total population after 2061 and count 1,444 million people in the year 2101 . Thus, India would add another 440 million people to its 2011 population size before achieving stabilization. In other words, India is likely to enter the fourth stage (near-zero growth rate) in the next 50 years or so. For India, the third stage of the demographic transition may fall between 2011 and 2051 . The momentum inbuilt in the age structure of the population would mostly lead to its growth.

Table 2. Population Size, Intercensal Change (Absolute and Percentage), and Exponential Annual Growth Rate, India, 1901–2001

Source : Registrar General of India ( n.d.-a ); Population estimated from UN ( 2019 ).

Examination of the current growth rate in specific states of India, especially for the larger Indian states (in terms of population size), helps to locate growth potentials. Table 3 gives population size for 2001 and 2011 , the two recent censuses of India, absolute change and state share in the total national change during 2001–2011 , and the exponential population growth rate observed during 2001–2011 for 20 large states of India. The four states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan deserve particular attention. With a population increase of 33.6 million, Uttar Pradesh contributed the most significant growth to the total national change of 182.2 million during 2001–2011 , followed by Bihar at 21.1 million and Maharashtra at 15.5 million. Kerala recorded the lowest annual exponential growth rate of 0.48%, followed by Andhra Pradesh (1.04%), Punjab (1.30%), and Odisha (1.31%). Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh together added 446 million (43%) of the total national addition and each state had an annual growth rate of 2% or more. These states are likely to make significant contributions to Indian population growth in the future because the fertility and mortality rates in these states are comparatively high and the decline in these rates has been much slower than that of other states. The most recent projections of the Government of India (NCP, 2019 ) indicated that by the year 2036 there would be a total of 596 million Indians, and half of them would come from these four states.

Table 3. Population Size, Intercensal Change (Absolute and Percentage), and Exponential Annual Growth Rate for Selected States of India, 2001–2011

a Sum of states may not match to India due to rounding of the numbers.

b Undivided including Telangana.

Table 4 gives a future population scenario in the 13 large states of India subdivided into three groups based on the attainment of the replacement level of fertility. These 13 states together cover nearly 80% of the national total. Group 1 consists of four states—Rajasthan, Uttar Pradesh, Bihar, and Madhya Pradesh—that have yet to attain replacement fertility. Group 2 and Group 3 consist of the states that have recently reached replacement fertility and a long time ago, respectively. The four large states in Group 1 have enormous potential for growth, and during 2026–2036 their combined growth rate is projected to be close to 1% (0.83%). Bihar is an outlier even within this group, with a growth rate of 1.16% annually. Group 2 states would have a growth rate of around 0.37% and Group 3 of about 0.20%. These findings indicate that a major part of India’s population growth potential lies in the four states of Group 1.

Table 4. Population Size and Year of the Attainment of Replacement Fertility in 13 Large States of India Stratified by Level of Total Fertility Rate, 2011–2036

a 2011 population data from the census of India.

b Projected population for the period 2016–2036 is from NCP ( 2019 ).

c Undivided including Uttarakhand.

d Undivided including Jharkhand.

e Undivided including Chattisgarh.

Source : NCP ( 2019 ).

Population Age-Sex Composition

The population age-sex composition of a country narrates historical experiences, including wars, epidemics, famines, and so on. Population age distribution and the female to male ratio are indicative of fertility and mortality levels and the social status of the women in the populations. Along with the demographic transition in India described earlier, there has been an inevitable change in the age-sex structure—that is, the decline in mortality followed by fertility has resulted in changes to the population’s age structure. Several studies have debated and discussed the role of these changes in economic growth. Sex composition (population sex ratio overall and, more important, at birth) reflects the status of women in the society. Globally, the population sex ratio (males per 1,000 females) is favorable to the female gender. An overall sex ratio of 1,030–1,050 females per 1,000 males is standard under the natural conditions. The situation is slightly different in India.

Table 5 gives the sex ratio overall and for children younger than 5 years of age for India for a period of 120 years ( 1881–2011 ) along with the absolute change in them. For India, the overall sex ratio was close to normal until around 1931 . It started to rise gradually in favor of males after that. The 1991 census of India revealed a higher overall sex ratio nationally: 1,078 males per 1,000 females. However, the scenario is different for the child sex ratio. Female children marginally outnumbered male children until 1941 as the sex ratio was in favor of the female children (960–995 male children per 1,000 female children below age 5). However, the scenario reversed when the 1951 census results were declared as the child sex ratio turned in favor of male children (1,008 male children per 1,000 female children) and has deepened over the years with the widening female-male children gap. The child sex ratio in India increased from 1,022 in 1981 to 1,047 in 1991 and further to 1,071 in 2001 and 1,082 in 2011 male children per 1,000 female children. Nationally, during the periods 1981–1991 and 1991–2001 , the child sex ratio increased astonishingly by 25 and 24 units, respectively. The distorted child sex ratio in India as well as in neighboring countries in the region has been a matter of concern and point of debate and investigations among policy makers and researchers. Many have cited widespread gender-based discrimination (neglect) in the form of son preference, lower autonomy to the women, and so on as the leading cause of this distortion. These practices result in sex-selective abortions and gender-specific mortality differentials (Bongaarts, 2013 ; Bongaarts & Guilmoto, 2015 ; Guilmoto et al., 2018 ; Jha et al., 2011 ; Kashyap, 2019 ; Ram & Ram, 2018 ).

Table 5. Sex Ratio (Males per 1,000 Females) of the Total Population and Children Younger Than 5 Years of Age, India, 1881–2011

Notes: The sex ratio for the years 1881 and 1891 was calculated using data from Mukherji ( 1976 ). The sex ratio for children younger than 5 years of age was calculated using data from a C-series in the respective census of India.

Source : Registrar General of India ( n.d.-b ).

A few studies have estimated a decrease in girls due to the practice of sex-selective abortions in India and found that these practices are not universal across geographies. Instead, they vary considerably in subregions of India (Jha et al., 2011 ; Ram & Ram, 2018 ). Table 6 presents the sex ratio for selected states in India for the period 1991–2011 and the change in it. Regardless of the year, Kerala is the only state that has an overall sex ratio lower than 1,000 (i.e., females exceeding the male population). In addition, the male-female gap has widened over the past two decades by almost 43 units. Punjab and Haryana have the most skewed overall sex ratio, varying between 1,117 and 1,162 males per 1,000 females. The overall sex ratio has been in favor of males in the remaining states. However, the gaps in sex ratio seemingly have bridged over time. While the decline was sharp in the states of Uttar Pradesh, West Bengal, and Assam, it has remained mostly similar in Madhya Pradesh and Maharashtra. Similar to the overall sex ratio, Haryana and Punjab had a highly skewed child sex ratio, varying between 1,128 and 1,144, respectively, in 1991 and 1,190 and 1,169 in 2011 . In 2011 , Gujarat (1,110), Rajasthan (1,120), and Maharashtra (1,117) also showed a child sex ratio skewed in favor of male children. Other states also showed a considerable deficit of female children. Haryana topped the list as the child sex ratio increased by 62 units in favor of males during 1991–2011 . The corresponding increase was by 59 units in Maharashtra, 50 units in Rajasthan, 44 units in Gujarat, 42 units in Madhya Pradesh, and 30–39 units in Andhra Pradesh, Bihar, Odisha, and Uttar Pradesh. Kerala was the only state where the child sex ratio improved in favor of female children by 16 units between the 1991 and 2011 censuses.

Table 6. Sex Ratio (Males per 1,000 Females) of the Total Population and Children Younger Than 5 Years of Age for India and Selected States, 1991–2011

Note: Sex ratio from respective censuses of India (Table C-6 of 1991 and C-14 of 2001 and 2011).

a Undivided including Telangana.

Almost half of the districts in the country in 2011 had a deficit of girl children. The practice of neglect of the female child resulting in sex-selective abortion and excess female mortality is universal (Guilmoto et al., 2018 ; Ram & Ram, 2018 ). A more recent analysis for India by Kashyap ( 2019 ) indicated the dominance of prenatal factors (sex-selective abortion) compared to excess female mortality (postnatal factor). Table 7 presents the sex ratio at birth (SRB) for India and selected states. The data suggest that the SRB is favorable to male children for India nationally and subnationally. Punjab and Haryana, followed by Rajasthan, Uttar Pradesh, Gujarat, and Bihar, had a highly disturbing SRB in 1999 . For every 100 female births, Punjab and Haryana recorded 125 to 126 male births each, the other states recorded 112 to 118 male births. The male-female imbalance at birth has continued over time, although with a sign toward bridging the gaps. At the national level, the SRB has mostly remained unchanged at 112 male children for every 100 female children. Nonetheless, the imbalance has widened in Andhra Pradesh, Assam, and Haryana, suggesting that the efforts to address this have failed to yield desirable results. The study by Jha et al. ( 2011 ) demonstrated that the practices are more prevalent among affluent and educated people.

Table 7. Sex Ratio at Birth (Male Births Per 1,000 Female Births) and Absolute Change in Sex Ratio at Birth in India and Selected States, 1999–2016

a Undivided including Telangana for the years 1999, 2004, 2009, and 2013.

b Undivided including Jharkhand for the year 1999.

c Undivided including Chhattisgarh for the year 1999.

d Undivided including Uttarakhand for the years 1999, 2004, and 2009.

Source: Sex ratio from the annual statistical report of the Sample Registration System of India.

Table 8 presents age distribution by sex and dependency ratios (child, old age, and overall) for the period 1981–2011 (census of India) and 2036 for India (NCP, 2019 ). Figures 3A and 3B present age-sex population pyramids. The results in Table 8 suggest a visible change in the age structure over the decades. Nationally, the share of children below age 15 in the total population declined to from about 40% in 1981 to 31% in 2011 . The NCP ( 2019 ) projections indicated that the share would decrease to 20% by 2036 . The percentage of people aged 60 years and older increased to 9% in 2011 and is estimated to reach 15% in 2036 (over 227 million). The changes in the dependency ratios for children and older people also confirm a transition in the age structure. While the child dependency ratio in India declined from 73% in 1981 and to 51% in 2011 , the dependency ratio for older people increased marginally from 12% to 14%. The official population projections suggest that in 2036 the child dependency ratio would further decline to 30% and the dependency ratio for older people would increase to 23% nationally. In 2001 , India had about 587 million people in the working ages, between 15 and 59 years. Those aged 15–34 years accounted for nearly 60% (349 million). The number of people in the working ages of 15–59 years and 15–34 years increased to 733 million and 425 million, respectively, in the year 2011 . Population projections suggest that in 2036 , while the number of people of working age would increase to almost 989 million, young labor would reach 464 million. Such changes would impact future economic development and would call on the government to initiate innovative strategies to take care of the older population. Besides, a sharp rise in the labor force demands generation of more employment.

Table 8. Share of the Male and Female Population Out of the Total Population by Age Groups and Dependency Ratios (for Children, Older People, and Overall), India, 1981–2011 and 2036

a Population is taken from the censuses of India 1981, 1991, 2001, and 2011.

b Projected population for 2036 is from NCP ( 2019 ).

c Dependency ratio from author calculations. The child dependency ratio is defined as the number of children below 15 years of age per 100 persons in the working ages of 15–59 years. The old-age dependency ratio is defined as the number of persons aged 60 years or older per 100 persons in the working ages of 15–59 years. The overall dependency ratio is defined as the number of children below 15 years of age and persons aged 60 years or older per 100 persons in the working ages of 15–59 years.

Figure 3A. Age-sex population pyramids of India, 1991.

Figure 3B. Age-sex population pyramids of India, 2036.

Major Drivers of Population Growth

Three drivers impact the population growth rate and are responsible for demographic transition: fertility, mortality, and international migration. Generally speaking, international migration has a limited role, as its volume is small. Thus, it is mainly the changes in fertility and mortality levels in a population that lead to demographic transition. This section discusses fertility and mortality transition in India and specific programmatic interventions responsible for the change in the fertility and mortality levels. India lacks good quality civil registration data on births and deaths (Ram et al., 2020 ; Yadav & Ram, 2019 ). Until the early 1970s, the estimated fertility and mortality for India and its states came from indirect methods that used census data stratified by age and sex. In the early 1970s, the Registrar General of India launched an annual nationwide system of collecting data on fertility and mortality (known as the sample registration system; SRS), which provides invaluable data for India and its states, especially for the bigger states. For the most part, the present research used fertility and mortality data from the SRS.

Figure 4 presents the total fertility rate (TFR) for India spanning over nearly 150 years (Ram et al., 1995 ). The TFR gives the number of children a woman would have at the end of the reproductive period, assuming that she experiences the prevailing age patterns of fertility. The data suggests that the TFR in India virtually remained unchanged at around 6.3 children per woman from 1871–1881 until 1951–1961 (standard deviation = 0.27). There has been little fluctuation in the TFR, which is mainly attributed to the variations in the quality of age-sex data in different censuses (Mukherji, 1976 ). Coale’s ( 1986 ) proposition of survival strategy postulates that a TFR of less than six for the expectation of life at birth (e o o ) of 20–25 years could lead to a zero or negative population growth. Thus, under a high mortality regime, maintaining a TFR of 6 and above was an excellent strategy to ensure moderate positive population growth. The decline in the TFR during the period 1896–1901 might have been the result of the famines of 1896–1997 and 1899–1901 , which were among the worst ever experienced in history and affected substantial sections of the population (Dyson, 1991 ).

The fertility transition in India most likely began during the late 1960s. Since the inception of fertility transition, the TFR in India declined by 19% to about 1.1 fewer children per woman during the first decade ( 1966–1971 to 1976–1981 ). The 1960s witnessed a substantial change in the family planning program in India, which became target-oriented and included the introduction of intrauterine devices to the official program in 1965 . The initial inherent demand for family planning and a persistently higher level of fertility may have been the reason for a relatively faster fertility decline during the first decade following the onset of the demographic transition. In the next decade ( 1976–1981 to 1986–1991 ), although the decrease in fertility continued, its pace slowed down. The decline in TFR slowed down notably in the subsequent decade of 1976–1981 to 1986–1991 when the reduction was only about 15%. The coercive approach adopted during the emergency period ( 1975–1977 ) was mainly responsible for this reduction in several states, more specifically in the larger Hindi-speaking states of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh. This in turn accelerated the decline in TFR. Between 1986–1991 and 1996–2001 , the TFR declined by 19% (from about 4 children to 3.2 children per woman). During 1996–2001 , the TFR in India declined by about 14%. The mid-1990s saw a paradigm shift in the national family planning program as the country revamped the program from a target-oriented to target-free regime. This paradigm shift resulted in an initial decline/stagnation in the family planning performance in the country.

Figure 4. Total fertility rate, India, 1871–2018.

Nationally, the TFR almost halved in the 30 years between 1986 and 2016 from 4.2 to 2.3 children per woman (Table 9 ). Many states in India showed a similar trend. Rural India also experienced a decline in the TFR from 4.5 in 1986 to 2.5 in 2016 . However, urban India had already achieved replacement fertility in 2006 . Of the states included in this analysis, eight states have already attained replacement or below-replacement fertility. The lagging states are Bihar Madhya Pradesh, Rajasthan, and Uttar Pradesh, where TFR continues to be close to 3 children per woman. As noted, these are the states that are or could be center for India population growth in the coming years. The urban areas in several states attained replacement or below-replacement fertility in 2016 : the urban areas had a TFR of as low as 1.3 children per woman in West Bengal, 1.4 in Odisha, 1.5 in Andhra Pradesh, and 1.6 in Karnataka and Tamil Nadu. Further, the rural areas of Andhra Pradesh, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, and West Bengal had a TFR that varied between 1.7 and 1.9 children per woman in 2016 .

Table 9. Total Fertility Rate for Combined, Rural, and Urban Areas and the Ratio of Rural to Urban Rate for India and Selected States, 1986–2016

a Undivided including Telangana for the years 1986, 1996, and 2006.

b Undivided including Jharkhand for the years 1986 and 1996.

c Undivided including Chhattisgarh for the years 1986 and 1996.

d Undivided including Uttarakhand for the years 1986, 1996, and 2006.

Source: Total fertility rate from the annual statistical report of the Sample Registration System of India.

Improved child survival and concurrent expansion of female education have led to fertility decline in developing countries like India (Davis, 1963 ; Dyson, 2010 ). We have already discussed geographic diversity in the TFR and transition. In Table 10 , we present the levels of TFR by education for India and selected states. In 1992–1993 , the TFR for India was 4.3 per woman for women who had completed fewer than 5 years of schooling (including nonliterate) compared to 3.3 for those who had 10 or more years of schooling; a difference of one child. By 2015–2016 , the TFR declined to 2.9 per woman and 1.8 for the respective groups. Over time there is no convergence in the level of fertility in lower and higher education groups as TFR declined by 45.5% among those who had 10 or more years of schooling compared to 32.6% among those who had fewer than 5 years of schooling. Nationally, around 22% of women aged 15–49 had completed 10 or more years of schooling in 1992–1993 . The share of these women increased to about 60% in 2015–2016 . Although TFR is higher for less educated people in India, their share in total women aged 15–49 has been reducing rapidly due to the expansion of education. The rise in education has a significant impact on delay in age at marriage.

A similar trend is observed at the state level as well. In 2015–2016 , with the exception of Bihar (TFR = 2.3), women who had 10 or more years of schooling had reached the replacement level of fertility. The lowest being in Punjab (TFR = 1.4) and the highest in Uttar Pradesh (TFR = 2.0). Women with 5–9 years of schooling in many states except Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh either reached replacement or below-replacement level fertility or are very close to achieving it. The four larger states (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) have lower child survival and limited outreach of female education. In 2015–2016 , Kerala had 95% of women aged 15–49 with 10 or more years of schooling, which was 44% in Bihar (including Jharkhand), 46% in Rajasthan, 52% in Madhya Pradesh (including Chhattisgarh), and 53% in Uttar Pradesh (including Uttarakhand).

Table 10. Total Fertility Rate by the Educational Status of the Women, India and Selected States, 1992–2016

a Undivided including Telangana (1992–1993, 1998–1999, and 2005–2006).

b Undivided including Jharkhand (1992–1993 and 1998–1999).

c Undivided including Chhattisgarh (1992–1993 and 1998–1999).

d Undivided including Uttarakhand (1992–1003 and 1998–1999).

Source : International Institute for Population Sciences ( 1993 ); International Institute for Population Sciences & ICF ( 2017 ); International Institute for Population Sciences & ORC-Macro ( 2000 ); International Institute for Population Sciences & ORC-Macro ( 2007 ).

The mortality data has information on three key indicators: infant mortality rate (IMR), under-5 mortality rate (U5MR), and expectation of life at birth (LEB; e o o ). The data comes from the SRS for India and covers about 25 years ( 1990–2016 ). The year 1990 is chosen as a base since it benchmarks the Millennium Development Goals (MDG) base year, and the year 2016 benchmarks the base year of the recently declared Sustainable Development Goals (SDGs). The MDG goal for U5MR for India was to attain a U5MR of 42 deaths of children aged below 5 years per 1,000 live births by the year 2015 . The corresponding goal for the IMR was 37 infant deaths per 1,000 live births. Under the SDG, the goals are 21 and 15, respectively, for the year 2030 .

At the beginning of the 20th century , in India, a newborn baby had an average life expectancy of 21–23 years (Davis, 1951 ; Mukherji, 1976 ). The SRS life table available for the period 2013–2017 revealed that a newborn baby in India would live an average of more than 69 years, which is considerably lower than in other countries globally and in the South Asian region. Nonetheless, this is a significant improvement from just about 20 years to close to 70 years, and an essential aspect of this improvement relates to IMR. At the national level, the IMR was 80 infant deaths per 1,000 live births in 1990 , which declined to 68 in 2000 (12 points in 10 years; see Table 11 ). The first decade of the 21st century unfolded a significant decline in the IMR for India— 47 infant deaths per 1,000 live births in 2010 and 34 per 1,000 in 2016 . Mortality decline in India and its states may have been due to improvements in access to health services and also an incremental increase in access to improved drinking water and sanitation. Similar to the global evidence (Fink et al., 2011 ), the National Family Health Survey (NFHS) data for 1992–1993 and 2015–2016 revealed a quantum jump in access to sanitation facilities (IIPS, 1993 ; IIPS & ICF, 2017 ).

The acceleration, especially after 2005 , may be due to the Janani Suraksha Yojana program implemented under the National Health Mission (erstwhile known as the National Rural Health Mission). The program provided a cash incentive of Rs. 1400 to women who delivered their babies in a health facility (Stephen et al., 2010 ). However, compliance varies considerably across India’s states. In the year 1990 , Kerala had the lowest IMR (17 infant deaths per 1,000 live births), whereas it was higher in Odisha (122), followed by Madhya Pradesh (111) and Uttar Pradesh (99). By 2016 , IMR declined significantly in all states. While Kerala continued to occupy the first place with the lowest IMR, Madhya Pradesh replaced Odisha with an IMR of 47 deaths per 1,000 live births. The states, on the whole, have succeeded in reducing the IMR; however, the usual lagging states of Assam, Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh, and Odisha continue to have higher IMRs.

Table 11. Infant Mortality Rate and Percentage Change in the Rate in India and Selected States, 1990–2016

a Undivided including Telangana for the years 1990, 1995, 2005, and 2010

b Undivided including Jharkhand for the years 1990 and 1995.

c Undivided including Chhattisgarh for the years 1990 and 1995.

d Undivided including Uttarakhand for the years 1990 and 1995.

Source: Infant mortality rates from the annual statistical report of the Sample Registration System of India.

Table 12 presents the gender-specific U5MRs for India and states for 1990–2016 . An average of 114 children per 1,000 live births died in India in 1990 before celebrating their 5th birthday, which declined to 39 in 2016 ; a two-thirds decline in 26 years. During the same period, the U5MR declined from 119 to 37 for male children and from 132 to 41 for female children. Similar to IMR, the U5MR fell relatively faster in the last 16 years in India ( 2000–2016 ) when compared with the corresponding change during 1990–2000 . Once again, there are vast differences across states of India in U5MR as well; the lagging states continue to have significantly higher levels of childhood mortality. In 2016 Kerala had the lowest U5MR (11), and the Madhya Pradesh had the highest (55), followed by Assam (52) and Odisha (50). The improvement in child survival in India brought a sense of security for the families to go for smaller families and contributed to the lowering of the TFR. An important point to note here is that regardless of the period studied, the U5MR in India has exceeded for female children compared to the male children. Surprisingly, most states have revealed a gender gap in childhood mortality. A study by Ram et al. ( 2013 , 2014 ) documented wide disparities in the levels of under-5 mortalities in districts of India.

Table 12. Gender-Specific Under-5 Mortality Rate and Percentage Change, India and Selected States, 1990–2016

a Undivided including Telangana for the years 1990, 1995, 2005, and 2010.

b Undivided including Jharkhand for the years 1990, 1995, 2005, and 2010.

c Undivided including Chhattisgarh for the years 1990, 1995, 2005, and 2010.

d Undivided including Uttarakhand for the years 1990, 1995, 2005, and 2010.

Source: Author calculations based on data from SRS Based Life Tables for 1988–1992, 1993–1997, 1998–2002, and 2003–2007. Data for 2015 and 2016 from the annual statistical report of the Sample Registration System of India for the respective years.

We now examine levels of life expectancy at birth (LEB). Table 13 presents the relevant data for India and its states for both sexes combined as well as separately. The LEB for India was nearly 49 years during 1970–1975 , which increased to about 58 years in 1986–1990 , an increase of 9 years in 16 years resulting in an annual improvement of approximately 0.6 years. By 1996–2000 , the LEB in India increased to 62 years and further to 69 years in 2013–2017 . Up until the 1980s, nationally, Indian males lived longer than the Indian females (Ram & Ram, 1997 ). Data on gender-specific LEB since 1993 indicates that in India, females now live longer than males, and the gap was by 2 years in 2013–2017 . The gender gap indeed widened in the mid-1990s when male LEB was at 60.4 years and females at 61.8 years. But at the same time, gender gaps in mortality have also widened for adolescents (to the female disadvantage), an anomaly indicating the downside of using only LEB for exploring gender disparity.

Table 13. Gender-Specific Life Expectancy at Birth and Changes in the Life Expectancy, India and Selected States, 1986–2017

$$ authors calculation using SRS gender-specific life tables.

b Undivided including Jharkhand for 1986–1990 and 1996–2000.

c Undivided including Chhattisgarh for 1986–1990 and 1996–2000.

d Undivided including Uttarakhand for 1986–1990, 1996–2000, and 2006–2010

Source: From Life tables of the Sample Registration System (SRS) of India.

Family Planning and Unmet Need

India acquired the status of being the first nation globally to launch an official family planning program in 1952 . However, the real push to the program came through in the 1960s when the program adopted a target-specific approach. The federal authorities in India assigned targets to the states, which were allocated to districts and further to the individual health workers at the lowest level of service provision. These targets became extremely volatile over the years, and the authorities announced disincentives and incentives to the users and the service providers based on performance (Pachauri, 2014 ). This period was accompanied by the emergency period ( 1975–1977 ) in India, when the program became extremely coercive. This act of the government damaged the program to a great extent and impacted the northern Hindi-speaking belt where fertility levels were higher. Although the success in fertility reduction in India is not comparable to that of other Asian countries, its achievements are by no means modest. In the initial phase, the program success was mostly monitored and evaluated using service statistics with the help of the number of acceptors and births averted as a result of family planning acceptance. Family planning surveys conducted in the 1970s and 1980s (ORG, 1972 , 1982 , 1990 ) complemented monitoring and evaluating efforts. After 1990 , India launched nationwide surveys (see IIPS, 1993 ; IIPS & ICF, 2017 ; IIPS & ORC-Macro 2000 , 2007 ). Tables 14 , 15 , and 16 give selected family planning indicators for India.

There has been a continuous rise in the percentage of married women using modern contraception in India. For example, just over 10% of married Indian women in 1970 used modern contraception (ORG, 1972 ). This percentage increased to 42.8% in 1998–1999 and to 48.5% in 2005–2006 (Table 14 ). India’s contraceptive prevalence rates (CPRs) are presented for the period between 1992–1993 to 2015–2016 in Table 13 . At the national level, overall CPR has increased from a little over 36% in the early 1990s to close to 48% in 2015–2016 , which translates to an increase of 12 units over the 23 years (an annual increase of 1.4%). The 2017 NFHS indicated that modern method CPR had marginally decreased from 48.5% in 2005–2006 to 47.8% in 2015–2016 (IIPS & ICF, 2017 ; IIPS & ORC-Macro, 2007 ). The decline in CPR of the modern method is substantial in many states, including Bihar, Gujarat, Karnataka, Kerala, Madhya Pradesh, and Tamil Nadu. This has raised debates among policy makers and researchers because these states have concurrently exhibited a significant decline in TFR levels. There is some research evidence that has indicated doubt about the estimated CPR for the period 2015–2016 . A study by Jayachandran and Stover ( 2018 ) expressed concern over the quality of contraceptive data collected in the 2017 NFHS. The modern limiting method CPR showed an increase of five units (from 31% to a little over 36%) and there was a twofold rise in the modern spacing method CPR (from about 6% to over 11%) during the same period. Interestingly, CPR for traditional methods also increased, from 4% to almost 6% (IIPS & ICF, 2017 ).

The levels of CPR, as well as the pace of change in it, varied considerably across Indian states included in the analysis. Generally, the states in the southern and western regions revealed higher levels of CPR compared to those in the northern and eastern regions of India. While the CPR rose over time, Gujarat and Kerala had a marginal decline in the overall CPR. Assam, Odisha, and West Bengal (all three in the eastern region) and Uttar Pradesh in the northern part had higher CPR of the traditional method (abstinence and withdrawal/rhythm) compared to the remaining states. While the CPR for traditional methods declined in Assam and West Bengal, it increased from 1%–2% in 1992–1993 to over 12%–14% in 2015–2016 in Odisha and Uttar Pradesh. The use of traditional methods is higher among women who live in urban areas, who were more educated and resided in economically better-off households. The patterns of CPR are somewhat similar for the modern limiting and spacing methods across states, as seen for all methods combined. Nonetheless, a few states, such as Assam, Haryana, Odisha, Uttar Pradesh, and West Bengal, have shown a tremendous rise in the CPR for modern spacing methods.

Table 14. Contraceptive Prevalence Rate for Modem Limiting, Modern Spacing Methods and Traditional Methods of Family Planning and Percentage Change in Them, India and Selected States, 1992–2016

a Undivided including Telangana (1992–1993).

c Undivided including Chhattisgarh (1992–1993).

d Undivided including Uttarakhand (1992–1993 and 1998–1999).

Tables 15 and 16 provide data on the future demand for family planning as assessed using the information on unmet need for family planning over 25 years. Nationally, the unmet need for family planning declined by nearly 37% in two and a half decades; the unmet need of almost 20% in 1992–1993 to about 13% in 2015–2016 (Table 15 ). The unmet need for modern spacing methods had halved in the country from nearly 12% to 6% during the same period. However, the unmet need for family planning seemingly has remained unchanged since 2010 , as the decline was by only one percentage point (from 14% to 13% for all methods and from 6.1% to 5.6% for spacing methods). Gujarat and Kerala were the only states where the total unmet need for family planning increased over time. In the remaining states, the change in the total unmet need has followed the national pattern. While the total unmet need remained nearly unchanged in Haryana, Karnataka, Madhya Pradesh, Maharashtra, and Tamil Nadu, it increased only marginally in Andhra Pradesh, Assam, and Wes Bengal. The unmet need doubled in Gujarat and increased substantially in Kerala.

In contrast, the unmet need declined in Bihar, Odisha, Rajasthan, and Uttar Pradesh during the same period. In case of unmet need for spacing methods, the data indicated substantial decline over the period for all states except Kerala, where unmet need for spacing methods rose from 6% to 8% in the last decade. A on-going investigation of NFHS data by Ram et al. ( in press ) showed that unmet need increased mainly due to the rise in the unmet need among the nonusers.

Table 15. Total Unmet Need for Family Planning, Unmet Need for Spacing, and Percentage Change, India and Selected States, 1992–2016

There are 46 million married women aged 15–49 in India who have expressed an unmet need for modern contraception, of whom 14 million prefer limiting methods and 18 million prefer spacing methods. The remaining 14 million couples, who used traditional methods, are considered to have an unmet need for modern methods of contraception in the NFHS for 2015–2016 (IIPS & ICF, 2017 ). It is important to note that all of the nonusers having unmet need will not convert into the users for various reasons as unmet need is highly unlikely to attain a zero value. The current unmet need of 18.7% may best reduce to 4%–5%, as observed in some states (as well as other countries in the neighborhood). In other words, 35 million couples actually can be converted to users. Nonuse of contraception could be due to sterility (primary and secondary), which varies considerably across India’s states, especially after age 30 (Ram, 2010 ). In other words, the potential pool of available users will include fewer people, around 28–30 million. Table 16 presents the share of current users and couples with unmet needs in the states of India in the national totals. The 14 states included consist of 88% of all users in India, and nearly 84% of the couples with unmet need belonged to these 14 states. Almost 47% of the couples with unmet need come from Bihar (13%), Madhya Pradesh (5%), Rajasthan (7%), and Uttar Pradesh (21%). This share is likely to rise because the demand for contraception in other states has almost reached a saturation point. The geographic allocation of unmet need creates a challenging situation because program strength and social development in these states are inadequate and of poor quality.

Table 16. State Share of the Users of Modern Methods of Family Planning and State Share of Couples Having Total Unmet Need for Family Planning (Limiting and Spacing Combined) in the National Totals, 1992–2016

A very dark side of Indian culture has been the practice of child marriage, which was rampant in the 20th century . The Hindu scripture advocated marriage for a girl before puberty (onset of menstruation). However, girls who married early remained in the parental home until “Gauna” (Kapadia, 1966 ), which was generally performed at the age when the girl attains physical maturity (onset of menstruation). The Sarda Act enacted in 1929 , followed by the Child Marriage Restraint Act of 1978 in India, defined the minimum legal age for marriage as 18 years for girls and 21 years for boys. Early marriage has a multidimensional effect on the lives of the females in India throughout their life course, from deprivation of education, skill development, health care access, and so on. At the macro level, the marriage pattern of a population has a significant effect on fertility and mortality (especially child mortality) levels. Marriage is one of the proximate determinants of fertility besides family planning use. The female age at marriage in India is rising, but rather slowly. The singulate mean age at marriage in India was 15.9 years in 1961 , which increased to 18.3 years in 1981 and 20.8 years in 2011 , an increase of about five years in five decades. In the 1990s, nearly half of the women aged 20–24 in India were married before age 18 years. This percentage reduced to about 45% in 2005–2006 .

The institution of marriage in India almost remained universal. Close to 97% of the Indian women aged 30–34 years in 2011 were married (Table 17 ). The percentage of these women varied marginally across states. Only two states (Kerala and Odisha) had 5% of the women aged 30–34 years who were single. The percentage of single women aged 30–34 years was 4% in Karnataka and West Bengal. Data from the 2015–2016 survey indicated that about one-quarter of women aged 20–24 years were married before they were 18 years (in absolute terms, 14.5 million women married below age 18). There is a great deal of variation across the states. Around 42% of women aged 20–24 years were married before age 18 in West Bengal, followed by 40% in Bihar, 31–33% in Rajasthan, Madhya Pradesh and Andhra Pradesh, and 23–26% in Gujarat, Karnataka, and Maharashtra.

Table 17. Percentage of Women Ages 20–24 Married Before Age 18 and Percentage of Single Women Ages 30–34, India and Selected States, 2015–2016

Source: Authors’ calculation based on data from NCP ( 2019 ) and IIPS and ICF ( 2017 ). Percent of single women data from Census of India, 2011.

Concluding Remarks

Although India holds a national treasure in its decadal censuses that have been continuously reported since 1881 , the country has failed to develop and strengthen its civil registration system for births and deaths. A significant constraint faced by Indian policy makers is a lack of data with regard to its socioeconomic and demographic scenario, including fertility and mortality. This shortcoming became apparent in several policies and programs that lacked evidence-based decisions to improve the health and well-being of the population. These experiences motivated the authorities in India, and nearly two decades after the country attained independence, the Government of India initiated the sample registration system SRS in an effort to replace the civil registration system and fill the data void. In the early 1990s, the government’s focus on health and well-being led to the publication of the first National Family Health Survey in 2017 . The data from these surveys has helped policy makers and researchers to gain insight into the demographic changes in India, nationally and subnationally.

India is the second-most populous country in the world. The international community has expressed concerns about the rising population size and high growth rate in India, which has received unprecedented attention in almost all platforms. Between 1961 and 2001 , India’s population grew at an average rate of about 2%, and the size of the population in absolute terms exceeded one billion in 2001 . During 2001–2011 , the population growth slowed down substantially. However, India continued to add an average of 18 million people annually to its already large base, leading to a total national population of 1.21 billion in 2011 . An assessment by the UN ( 2019 ) indicated that India’s population would peak at 1.65 billion in 2061 and would begin to decline after that and reach 1.44 billion in the year 2100 . The four large states in India (Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan) continue to reveal high levels of fertility and mortality (especially during early childhood), and have great potential for future population growth. The spatial distribution of India’s population will have a significant influence on its future political and economic scenario. Kerala state may experience a negative population growth rate around 2036 . The undivided Andhra Pradesh (including the newly created state of Telangana) may experience the same around 2041 and Karnataka and Tamil Nadu around 2046 . Four states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan would have 764 million people in 2061 (45% of the national total) by the time India’s population reaches around 1.65 billion (Verma, 2018 ).

Changes in fertility and mortality are the two most important demographic factors contributing to population growth in India. The total fertility rate (TFR) in India declined from about 6.5 children per woman in the early 1960s to 2.3 children per woman in 2016 (a reduction of 4.2 children per woman in fewer than six decades). India is concerned about relatively high TFR in Bihar (3.3 children per woman), Uttar Pradesh (3.1 children per woman), Madhya Pradesh (2.8 children per woman), and Rajasthan (2.7 children per woman). The states have exhibited a higher unmet need for contraception and a weak public health-care delivery system. Childhood mortality in India has declined substantially, especially after the 1990s (114 in 1990 to 39 children deaths per 1,000 live births in 2016 ). This remarkable improvement is the result of massive efforts to improve comprehensive maternal and child health programs and nationwide implementation of the national health mission. The latter focused attention on improving the maternal and child health indicators in the country. Despite this, childhood mortality continues to be unacceptably high in Uttar Pradesh (47 children deaths per 1,000 live births), Bihar (43 children deaths per 1,000 live births), Rajasthan (45 children deaths per 1,000 live births), and Madhya Pradesh (55 children deaths per 1,000 live births). Besides, more considerable attention to improving access to public health-care services would promote contraception use immensely by way of reducing unmet needs and, in turn, reduce child mortality.

Figure 5. Future prospects of the demographic transition for India, 1950–2100.

A great deal of scientific evidence suggests that the intertwined programmatic interventions focusing on female education and child survival are essential. Such efforts, notably in the four large states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan, would go a long way to reduce unmet need for contraception and enhance contraception use giving a big push to reducing fertility in the future. This would be crucial for India to stabilize its population before reaching 1.65 billion. India’s demographic journey through the path of the classical demographic transition suggests that the country is very close to achieving replacement fertility. Figure 5 outlines the future path of India’s transition according to the UN’s ( 2019 ) assessment. Although India may achieve replacement level fertility very soon (around 2023 ), the population will continue to grow until 2060 due to population momentum. Only after this, India may experience a negative growth rate; that is, the crude death rate will exceed the crude birth rate.

Further Reading

• Caldwell, J. (1980). Mass education as a determinant of the timing of fertility decline. Population and Development Review , 6 (2), 225–255.
• Cleland, J. , & Rodriguez, G. (1988). The effect of parental education on marital fertility in developing countries. Population Studies , 42 , 419–442.
• Cochrane, S. H. (1979). Fertility and education: What do we really know? Johns Hopkins University Press.
• Dreze, J. , & Murthi, M. (2001). Fertility, education, and development: Evidence from India. Population and Development Review , 27 (1), 33–63.
• Lutz, W. , & Kebede, E. (2016). Education and health: Redrawing the Preston curve. Population and Development Review , 44 (2), 343–361.
• Pamuk, E. R. , Fuchs, R. , & Lutz, W. (2011). Comparing relative effects of education and economic resources on infant mortality in developing countries. Population and Development Review , 37 (4), 637–664.
• Preston, S. H. (1975). The changing relation between mortality and level of economic development . Population Studies , 29 (2), 231–248.
• Preston, S. H. (1980). Causes and consequences of mortality declines in less developed countries during the twentieth century. In R. A. Easterlin (Ed.), Population and economic change in developing countries (pp. 289–360). University of Chicago Press.
• Samir, K. C. , Wurzer, M. , Speringer, M. , & Lutz, W. (2018). Future population and human capital in heterogeneous India . Proceedings of the National Academy of Sciences , 115 (33), 8328–8333.
• Agarwala, S. N. (1964). Sterilisation as a population control device and its economics. The Economic Weekly , 1091–1094.
• Bongaarts, J. (2013). The implementation of preferences for male offspring. Population and Development Review , 39 (2), 185–208.
• Bongaarts, J. , & Guilmoto, C. Z. (2015). How many more missing women? Excess female mortality and prenatal sex selection, 1970–2050. Population and Development Review , 41 (2), 241–269.
• Chitre, K. T. (1964). Sterilization as a method of family limitation and its implementation in the family planning programme. Maharashtra Medical Journal , 11 (2), 288.
• Cleland, J. (2001). The effects of improved survival on fertility: A reassessment. Population and Development Review , 27 (Suppl.), 60–92.
• Cleland, J. , & Wilson, C. (1987). Demand theories of fertility decline: An iconoclastic view. Population Studies , 41 (1), 5–30.
• Coale, A. J. (1986). The decline of fertility in Europe since the 18th century as a chapter in demographic history. In A. J. Coale & S. C. Watkins (Eds.), The decline of fertility in Europe (pp. 1–31). Princeton University Press.
• Coleman, D. (2006). Immigration and ethnic change in low fertility countries: A third demographic transition. Population and Development Review , 32 (3), 401–446.
• Davis, K. (1951). The population of India and Pakistan . Princeton University Press.
• Davis, K. (1963). The theory of change and response in modern demographic history. Population Index , 29 (4), 345–366.
• Dyson, T. (1991). On the demography of South Asian famines, Part 1. Population Studies , 45 (1), 5–25.
• Dyson, T. (2010). Population and development: The demographic transition . Zed Books.
• Fink, G. , Gunther, I. , & Hill, K. (2011). The effect of water and sanitation on child health: Evidence from the demographic and health surveys 1986–2007. International Journal of Epidemiology , 40 , 1196–1204.
• Gopalaswamy, R. A. (1962). Family planning: Outlook for government action in India. In Cycle V. Kise (Ed.), Research in family planning (p. 78). Princeton University Press.
• Guilmoto, C. Z. , Saikia, N. , Tamrakar, V. , & Bora, J. K. (2018). Excess under-5 female mortality across India: A spatial analysis using 2011 census data. Lancet Global Health , 6 , e650–e658.
• International Institute for Population Sciences . (1993). National family health survey (NFHS-1), 1992–93: India . International Institute for Population Sciences.
• International Institute for Population Sciences & ICF . (2017). National family health survey (NFHS-4), 2015–16: India . International Institute for Population Sciences.
• International Institute for Population Sciences & ORC-Macro . (2000). National family health survey (NFHS-2), 1998–1999: India . International Institute for Population Sciences.
• International Institute for Population Sciences & ORC-Macro. (2007). National family health survey (NFHS-3), 2005–2006: India . International Institute for Population Sciences.
• Jayachandran, A. A. , & Stover, J. (2018). What factors explain the fertility transition in India? Health Finance and Governance Project.
• Jha, P. , Kesler, M. A. , Kumar, R. , Ram, F. , Ram, U. , Aleksandrowicz, L. , Bassani, D. G. , Chandra, S. , & Banthia, J. K. (2011). Trends in selective abortions of girls in India: Analysis of nationally representative birth histories from 1990 to 2005 and census data from 1991 to 2011. Lancet , 377 , 1921–1928.
• Kaa, D. J., van de . (1987). Europe’s second demographic transition. Population Bulletin , 42 (1), 1–59.
• Kaa, D. J., van de . (2002, January). The idea of a second demographic transitions in industrialized countries [Paper presentation]. Sixth Welfare Policy Seminar of the National Institute of Population and Social Security, Tokyo, Japan.
• Kapadia, K. M. (1966). Marriage and family in India . Oxford University Press.
• Kashyap, R. (2019). Is prenatal sex selection associated with lower female child mortality? Population Studies , 73 (1), 57–78.
• Kirk, D. (1996). Demographic transition theory. Population Studies , 50 (3), 361–387.
• Krishnamoorthy, S. (1997). India towards population and development goals, UNFPA for United Nations system in India . Oxford University Press.
• Landry, A. (1987). Adolphe Landry on the demographic transition revolution . Population and Development Review , 13 (4), 731–740.
• Laxmi, P. (1964). Family planning programme in madras state: The role of sterilisation as a method of family planning. Maharashtra Medical Journal , 11 (2), 271–272.
• Lesthaeghe, R. J. (2011). The “second demographic transition”: A conceptual map for the understanding of late modern demographic developments in fertility and family formation . Historical Social Research , 36 (2), 179–218.
• Lesthaeghe, R. J. (2014). The second demographic transition: A concise overview of its development . Royal Flemish Academy of Arts and Sciences.
• Lesthaeghe, R. J. , & Surkyn, J. (2004). When history moves on: The foundations and diffusion of a second demographic transition . Free University of Brussels.
• Mukherji, S. B. (1976). The age distribution of the Indian population: A reconstruction for the states and territories, 1881–1961 . East West Population Institute.
• National Commission on Population . (2019). Population projections for India and states 2011–2036 . Ministry of Health and Family Welfare, Technical Group on Population Projections.
• Notestein, F. W. (1945). Population: The long view. In T. Schultz (Ed.), Food for the world (pp. 36–57). University of Chicago Press.
• Operation Research Group . (1972). Family planning practices in India: First all India survey . Operation Research Group.
• Operation Research Group . (1982). Family planning practices in India: Second all India survey . Operation Research Group.
• Operation Research Group . (1990). Family planning practices in India: Third all India survey . Operation Research Group.
• Pachauri, S. (2014). Priority strategies for India’s family planning programme . Indian Journal of Medical Research , 140 (Suppl. 1), S137–S146.
• Pathak, K. B. , & Ram, F. (1981). Recent trends in the fertility of Asian Countries: A comparative analysis. In K. Srinivasan & S. Mukerji (Eds.), Dynamics of population and family welfare (pp. 118–151). Himalaya Publishing House.
• Pujari, S. B. , Joshi, S. K. , Bhatt, R. V. , & Patel, B. C. (1967, November 26–28). Complication following use of LIPPEs-loop [Paper presentation]. 14th All-India Obstetric and Gynaecological Congress, Nagpur, India.
• Rajan, S. L. (1994). Heading towards a billion. Economic and Political Weekly , 17 (24), 3201–3205.
• Ram, F. , Namboodiri, K. , & Ram, U. (1995). Fertility transition in India . International Institute for Population Sciences.
• Ram, F. , & Ram, U. (1997). Fertility transition in Uttar Pradesh and its neighboring states. In K. Gupta & A. Pandey (Eds.), Population and development in Uttar Pradesh (pp. 199–216). BR Publishing Corporation.
• Ram, F. , Verma, A. , Khan, N. , & Acharya, R. (2020). Assessment of death registration in India and selected states, 2000–16 [Unpublished report]. Population Council.
• Ram, F. , Kumar, A. , Saggurti, N. , & Jain, A. (in press). New estimates on demand for contraception in India and its states . Population Council.
• Ram, U. (2010). Levels and patterns of permanent childlessness in India and its states. Demography India , 39 (1), 81–101.
• Ram, U. , Jha, P. , Ram, F. , & Kumar, R. (2014). Absolute and relative declines in child mortality in India’s districts during 2001–12. Lancet Global Health , 2 , e21.
• Ram, U. , Jha, P. , Ram, F. , Kumar, K. , Awasthi, S. , Shet, A. , Pader, J. , Nansukusa, S. , & Kumar, R. (2013). Neonatal, 1–59 month, and under-5 mortality in 597 Indian districts, 2001 to 2012 . Lancet Global Health , 2 , e21.
• Ram, U. , & Ram, F. (2018). To be born and to be alive: The struggle of girl children in subregions of India. Lancet Global Health , 6 , e594–e595.
• Registrar General of India (n.d.-a). Provisional population totals, size, growth rate and distribution of population, census of India 2011 .
• Registrar General of India (n.d.-b). Provisional Population Totals, Gender Composition of the Population, Census of India 2011 .
• Registrar General of India (2020). Sample registration system statistical report for various years . Government of India.
• Seal, K. C. , & Talwar, P. P. (1994). The billion-plus population: Another dimension. Economic and Political Weekly , 29 (36), 2344–2347.
• Srinivasan, K. (2006). Population policies and family planning in India: A review and recommendations. Social Change , 37 (1), 125–136.
• Srinivasan, K. , & Pathak, K. B. (1981, December). The nature of stable high fertility and the determinants of its destabilization: Process in selected countries of Asia . International Population Conference of the IUSSP, Manila.
• Stephen, S. L. , Dandona, L. , Hoisington, J. A. , James, S. L. , Hogan, M. C. , & Gakidou, E. (2010). India’s Janani Suraksha Yojana, a conditional cash transfer programme to increase births in health facilities: An impact evaluation . Lancet , 375 (9730), 2009–2023.
• Thompson, W. S. (1929). Population . American Journal of Sociology , 34 (6), 959–975.
• United Nations . (1987, September). Global trends and prospects of the age structure of population: Different paths to aging . Proceedings of the UN International Symposium on Population, Structure and Development, Tokyo.
• United Nations . (2019). World population prospectsI . United Nations Department of Economic and Social Affairs.
• Verma, R. K. (2018). Convergence to population stability in India: A state-wise assessment of demographic potential and demographic dividend (Unpublished doctoral dissertation, International Institute for Population Sciences (IIPS)).
• Visaria, P. , & Visaria, L. (1994). Demographic transition, accelerating fertility decline in 1980 . Economic and Political Weekly , 29 (51–52), 3281–3292.
• Yadav, A. K. , & Ram, F. (2019). There is a glaring gender bias in death registrations in India. Economic and Political Weekly , 54 (51), 2349–8846.
• Zachariah, K. C. (1983). Anomaly of fertility decline in Kerala (Report No. 1, RPO 677–670). World Bank.

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• Premature adult mortality in India: what is the size of the matter?
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• http://orcid.org/0000-0002-9554-0581 Chalapati Rao 1 ,
• Aashish Gupta 2 ,
• Mamta Gupta 3 ,
• Ajit Kumar Yadav 4
• 1 Research School of Population Health , Australian National University College of Medicine Biology and Environment , Canberra , Australian Capital Territory , Australia
• 2 Demography and Sociology , University of Pennsylvania , Philadelphia , Pennsylvania , USA
• 3 Alchemist Research and Data Analysis , Chandigarh , India
• 4 Gender research project , International Institute for Population Sciences , Mumbai , Maharashtra , India
• Correspondence to Dr Chalapati Rao; chalapati.rao{at}anu.edu.au

Background Reducing adult mortality by 2030 is a key component of the United Nations Sustainable Development Goals (UNSDGs). Monitoring progress towards these goals requires timely and reliable information on deaths by age, sex and cause. To estimate baseline measures for UNSDGs, this study aimed to use several different data sources to estimate subnational measures of premature adult mortality (between 30 and 70 years) for India in 2017.

Methods Age-specific population and mortality data were accessed for India and its 21 larger states from the Civil Registration System and Sample Registration System for 2017, and the most recent National Family and Health Survey. Similar data on population and deaths were also procured from the Global Burden of Disease Study 2016 and the National Burden of Disease Estimates Study for 2017. Life table methods were used to estimate life expectancy and age-specific mortality at national and state level from each source. An additional set of life tables were estimated using an international two-parameter model life table system. Three indicators of premature adult mortality were derived by sex for each location and from each data source, for comparative analysis

Results Marked variations in mortality estimates from different sources were noted for each state. Assuming the highest mortality level from all sources as the potentially true value, premature adult mortality was estimated to cause a national total of 2.6 million male and 1.8 million female deaths in 2017, with Bihar, Maharashtra, Tamil Nadu, Uttar Pradesh and West Bengal accounting for half of these deaths. There was marked heterogeneity in risk of premature adult mortality, ranging from 351 per 1000 in Kerala to 558 per 1000 in Chhattisgarh among men, and from 198 per 1000 in Himachal Pradesh to 409 per 1000 in Assam among women.

Conclusions Available data and estimates for mortality measurement in India are riddled with uncertainty. While the findings from this analysis may be useful for initial subnational health policy to address UNSDGs, more reliable empirical data is required for monitoring and evaluation. For this, strengthening death registration, improving methods for cause of death ascertainment and establishment of robust mortality statistics programs are a priority.

• health policy
• public health

Data availability statement

Estimates of population and deaths by age and sex in 2016 for India and states from the Indian State-level Disease Burden Initiative were obtained through direct request to the authors of reference 19 in the manuscript. All other data are available in public open access repositories as listed in relevant references.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjgh-2020-004451

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Key questions

What is already known.

Reliable measures of mortality at adult ages are required for evidence-based health policy, monitoring and evaluation of progress towards health-related United Nations Sustainable Development Goals (UNSDGs).

In the absence of reliable data from Civil Registration and Vital Statistics systems in many countries including India, these measures are largely derived from alternate data sources, data synthesis or modelling methods.

What are the new findings?

This article presents a comparative analysis of measures of premature adult mortality from several data sources for India and its 21 larger states, examining their reliability and correspondence.

Following a conservative approach, the article proposes the maximum estimate of mortality between the ages of 30 and 70 years by sex for each location from any source as the potential baseline level of premature adult mortality around 2016–2017.

What do the new findings imply?

Although all the six data sources or estimation methods demonstrated some weaknesses, the adequate quality of data from the Civil Registration System (CRS) in several states suggests that through the implementation of strategic interventions, the CRS could be developed into a reliable data source for tracking progress towards the UNSDGs.

The methods and findings from our analyses would be relevant for other countries facing the need to reconcile local mortality data with modelled mortality estimates from international sources.

Introduction

Over the past three decades, there have been declines in child mortality across the world, as a result of global and local actions under the United Nations Millennium Development Goals campaign. 1 2 Concomitantly, mortality at adult ages has been recognised as a growing component of population level disease burden. 3 4 For this reason, the health-related United Nations Sustainable Development Goals (UNSDGs) for 2030 include specific targets to reduce adult mortality from major non-communicable diseases (NCDs), tuberculosis, HIV/AIDS, road traffic accidents, suicides, and occupational and environmental exposures, among other health conditions and risk factors. 5 Monitoring progress towards these UNSDG targets requires routine and reliable measures of population level cause-specific mortality at adult ages, for which Civil Registration and Vital Statistics (CRVS) systems with medical certification of cause of death are the optimal data source. 6 Unfortunately, reliable CRVS systems are not yet in place in many parts of the world, which limits the monitoring of adult mortality trends 7 8

Good-quality CRVS data have been directly used to analyse national adult mortality trends in high-income countries. 9 For several Latin American countries and South Africa, CRVS data were first adjusted for incomplete death registration, prior to similar trend analyses. 10 11 The findings from these studies enabled an improved understanding of underlying diseases and risk factors for adult mortality in these countries. In India, although death registration systems have existed for over 150 years, not all deaths are currently registered in the national Civil Registration System (CRS). 12 Levels of death registration completeness vary by gender, age and location across the country, and these data gaps have limited the reliability of direct adult mortality measures. 13

The Sample Registration System (SRS) and National Family Health Survey (NFHS) programmes were established in 1970 and 1992, respectively, as alternate sources of mortality data, and had been specifically designed to enable reliable measurement of indicators of infant and under-five mortality. 14 Although the SRS and NFHS programmes also compile information on adult deaths, the samples are not adequately powered for precise adult mortality measurement, especially at subnational levels. 13 14 Despite these limitations, there have been several attempts in the past decade to estimate national and subnational adult mortality rates using SRS, NFHS and CRS data, but with varying calculation methods, indicators and adult age categories, which limits the comparability of results across methods or over time. 13 15 16 As a result, patterns and determinants of adult mortality are less well understood.

More recently, there have been two initiatives that employed data synthesis methods using various data sources to derive national and sub national estimates of population and deaths by age and sex. The state-level results from the ‘National Burden Estimates’ (NBE) Study for 2017 were based on national level estimates of population and deaths derived for the United Nations (UN) World Population Project (WPP). 17 18 Similar estimates of populations and deaths for each state are also available from the Indian State-level Burden of Disease initiative, which is a component of the Global Burden of Disease (GBD) Study for 2016, carried out by a collaboration between the Indian Council of Medical Research, the Public Health Foundation of India and the Institute of Health Metrics and Evaluation (IHME), University of Washington, USA. 19 20 The NBE and GBD synthetic estimates add to the available empirical data sources, to help understand levels of mortality at adult ages.

To understand and address biological, environmental, behavioural, socioeconomic or health system factors that potentially influence patterns of disease burden and adult mortality across the country, there is an urgent need for reliable and timely gender specific subnational measures of adult mortality. The aim of this study was to conduct a comparative analysis of premature adult mortality from different data sources at national and state level around the period 2013–2017, to improve overall understanding of subnational levels and differentials in adult mortality patterns. We defined the interval between ages 30 and 70 years as the life span of premature adult mortality, and used three indicators to compare the magnitude of mortality at these ages across the different data sources and analytical approaches. This age interval corresponds to the indicator definition for NCD mortality under the UNSDGs. 21 We propose that our findings on the maximum plausible levels of total mortality from all causes in this age interval across all data sources could be used as a suitable proxy baseline measure for monitoring progress in India and its states towards the UNSDG NCD targets, till such time that detailed information on causes of death is available. The findings are also used to make recommendations for improving adult mortality monitoring levels in India, during and beyond the UNSDG era.

Data sources

This analysis is based on life table derived summary mortality measures by sex for India and 21 large states, during the period 2013–2017. Table 1 presents the general characteristics of data on age-specific population and deaths from the CRS, SRS, NFHS, GBD and NBE data sources 13 17 19 22 23 that were used for life table analysis. Relevant additional details are also available from the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement for this study (see online supplemental appendix 1 ). The SRS life tables were based on observed population and deaths in sample sites during 2017. For the NFHS life tables, the household survey data were used to create lifelines to estimate deaths and person-years lived in the exposure period. For the CRS life tables, recorded deaths in 2017 and population projections for 2017 developed by the National Commission on Population (NCP) were used. 24 The NBE and GBD studies reported estimates of deaths by age and sex for each state derived from the GBD and NBE internal modelling processes. 17 19 25 These studies also reported state specific populations by age and sex, derived from the United Nations World Population Prospects and the IHME international population models, respectively. 18 20 The NBE data did not include separate estimates for states of Andhra Pradesh, Himachal Pradesh, Telangana and Uttarakhand. We used these available GBD and NBE population and death estimates in our life table calculations to derive the GBD and NBE based life tables for each state. The SRS proportionate age distributions of deaths were used to interpolate the coarser age categories of deaths from CRS and NBE to the standard age categories for our life tables, which were as follows: 0–1 year, 1–4 years, 5–9 years ….80–84 years and 85+years.

Supplemental material

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Characteristics of inputs used from different data sources to estimate life tables for India and states, 2013–2017

Since life table quantities are not affected by the age structure of a population, period life tables are standard demographic tools which enable mortality comparisons across space and time. Period life expectancy (LE) at birth, for instance, is the average lifespan of a hypothetical cohort if they experienced the current set of age-specific mortality rates throughout their lifetime. Population and death data from each of the five sources for each location were used as inputs to construct life tables, using a standard spreadsheet programme. 26 From each data source-location-sex specific life table, the following summary mortality risks were extracted for comparison:

LE at birth.

Risk of dying between birth and age 5 years ( 5 q 0 ).

Risk of dying between 15 and 60 years ( 45 q 15 ), conditional on survival up to age 15.

Risk of dying between 30 and 70 years ( 40 q 30 ), (risk of premature adult mortality) conditional on survival up to age 30.

Uncertainty intervals were estimated for each of the above parameters applying bootstrap methods using a publicly available programmed spreadsheet specifically designed for such analysis 27 (see online supplemental appendix 2 ).

Model life table estimation

In addition, we estimated a sixth set of life tables by sex for each location, using the WHO Modified Logit Life Table System, implemented through its customised software tool named ‘MODMATCH’. 28 29 This system uses input values of 5   q  0 and 45  q  15 in a statistical model, to predict a complete schedule of age-specific mortality risks for the location of interest. Mortality patterns in the MODMATCH model life tables approach are predicted from relationships between observed levels of child and adult mortality and their associated age-specific mortality patterns across all ages. For MODMATCH, the regression equations for these relationships were derived using a historical series of reliable life tables. 28 For the current analysis, selected input values of 5   q  0 and 45  q  15 for each state were chosen from the three empirical data sources (CRS, SRS and NFHS). We generally selected the higher value for each variable as available from the CRS and SRS, with a few exceptions. From each MODMATCH derived life table, the LE at birth and risk of premature adult mortality were derived for comparative analysis. The same input parameters were also used in the two-dimensional Logarithmic Quadratic (Log-Quad) Model Life Table System developed by Wilmoth et al for a sensitivity analysis of MODMATCH outputs. 30 Given the little difference between these estimates, we used the MODMATCH results for further analysis.The estimated MODMATCH age-specific mortality risks were applied to respective age-specific populations from the NCP projections, to derive MODMATCH estimated numbers of deaths at each age.

Data quality evaluation

The quality of mortality data from each source was evaluated by comparing the trajectory and slope of graphs of age-specific risks of dying between 30 and 70 years by sex, across all sources. The natural log of mortality rates from ages 30 to 70 were plotted to evaluate if mortality rates from each source followed the Gompertz law of linear increase in log-mortality. 31 32 Model plausibility was also assessed by confirming that the age-specific trajectories of MODMATCH derived mortality rates for each state reflected their input values of 45  q  15 from the CRS, SRS or NFHS, respectively. The MODMATCH graphs were also compared with similar graphs by sex for each state that were derived from the Log-Quad Model, to evaluate model consistency.

Premature adult mortality burden

The absolute numbers of observed or estimated deaths between 30 and 70 years in each state by sex from each source in 2017 were compared, as cross-sectional measures of premature adult mortality burden. In addition, the 40  q  30 risks from each source were applied to a cohort of individuals aged 30 years in 2017, to estimate the potential numbers of deaths that would occur in this cohort over a 40-year period till 2057. The cohort comprised one-fifth of the state population aged 30–34 years in 2017 from the NCP projections, and the estimated deaths are based on the assumption that mortality levels will remain constant at the 2017 level over the ensuing four decades.

Patients and public involvement

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

Evaluation of model inputs

The plausibility of risks of 5  q  0 (child mortality) and 45  q  15 (adult mortality) from different sources were evaluated for each location, to select the most viable inputs for our model life table analysis. The general approach was to select the highest plausible value of each input parameter from the three empirical sources, to reflect the potential maximal levels of mortality for the study reference time period. Table 2 shows that the CRS risks of 5  q  0 are implausibly low, due to known under-registration of infant deaths in all states. 12 13 The NFHS records higher levels of 5 q 0 than the SRS in most states, except for males in Kerala, and for females in Andhra Pradesh, Gujarat, Haryana, Kerala, and Rajasthan. However, the NFHS rates relate to a period of 3 years prior to the survey (2013–2016), in addition to being subject to sampling error and recall bias. In comparison, the SRS has a much larger sample size, and is a continuous recording system with half yearly check surveys, therefore less subject to recall bias. 33 34 For these reasons, the SRS 5   q  0 values were chosen as inputs for MODMATCH in all states except Bihar, Jharkhand and Uttarakhand, for which the NFHS values were chosen, since the SRS values are implausibly low, as observed from other studies. 35 The modelled GBD and NBE 5   q  0 risks demonstrate a relative difference >±5% when compared with the SRS values in most states. Since the NBE and GBD mortality risks are essentially outputs derived from other modelling processes, they were not considered as inputs for our model life table analysis.

Estimated risks of dying before age 5 years and between ages 15 and 60 years from various sources for India and states during 2014–2017

The CRS 45  q  15 values are the highest of the three empirical sources for six states in males, and for two states in females, and were chosen as the most plausible inputs for MODMATCH for these states. For most of the other states, the SRS 45  q  15 estimates were considered more plausible than those from the NFHS, due to its larger sample size, and for the compatibility of SRS measures with the time period of other data sources, for comparison. For Bihar and Jharkhand, the NFHS 45  q  15 measures were deemed more plausible, given the recognised under-reporting of SRS deaths in these states. 35 Similar to the comparisons of under-5 mortality risks, the GBD and NBE modelled values of 45  q  15 differed from SRS values by >±5% in three-fourths of all states,

Model evaluation

figure 1 shows that MODMATCH derived mortality risks show a smooth exponential rise in mortality risks after age 40 years for Tamil Nadu, Punjab and Bihar, and correlate with respective 45  q  15 inputs from the CRS, SRS and NFHS respectively. This is less obvious for Bihar, potentially due to the relative instability of NFHS mortality risk trends by age, as a result of low sample size. These graphs generally support the plausibility of MODMATCH derived age-specific trends as compared with the SRS, GBD and NBE trend lines, which do not appear to conform to the Gompertz law of smooth exponential increase in age-specific mortality (linear increase on the log-scale). Similar aberrations were observed in the graphs for all states (see online supplemental appendix 3 ). Using the same input parameters, the Log-Quad model outputs of LE at birth and of age-specific mortality trends by sex for each state were very closely correlated to those from MODMATCH (see online supplemental appendix 4 ).

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Mortality risk (30–70 years) from different sources for selected states, 2017. CRS, Civil Registration System; GBD, Global Burden of Disease; NBE, National Burden Estimates; NFHS, National Family Health Survey; SRS, Sample Registration System.

Comparison of population exposures

Table 3 shows the estimated total populations and deaths as well as life expectancies at birth by sex for each location from the different sources. National-level and state-level differences in population exposures between the NBE derivations from WPP estimates, the GBD population estimates, and the NCP projections (used for the MODMATCH estimates) were evaluated to assess the likely impact of these denominators on the magnitude of deaths from each source. At the national level, the GBD population estimates are relatively higher than those from the NCP projections by 5.2%. Across states, these relative differences for males range from 1% in Kerala to 13% in Odisha, and in females from 2% in Kerala to 8% in Telangana. Similarly, the NBE populations also exceed the NCP projections by 2% at national level, with concomitantly lower orders of state level differentials, except for Jammu and Kashmir (12% for males, and 8% for females). These population differentials, although mostly in single digit percentages, actually translate into large population counts in most states, and strongly influence the comparisons of estimated deaths across data sources for each state.

Estimated population, total deaths and life expectancy at birth by sex from various sources for India and states during 2014–2017

Overall mortality

CRS mortality reports were excluded from the comparisons of overall mortality shown in Table 3, in view of the known under-registration of deaths in several states. Using the highest plausible empirically observed levels of 5  q  0 and 45  q  15 as model inputs, the MODMATCH LEs at birth were found to be lowest out of all the sources at national level. They were also the lowest in 17 states for males, and in 13 states for females. The MODMATCH age-specific mortality predictions are based on historical empirical mortality schedules from countries with complete mortality data, derived from modelled relationships between 5  q  0 , 45  q  15 and overall mortality. Their plausibility is also supported by the findings from figure 1 and online supplemental appendix 3 , and suggest that LEs at birth could be potentially lower than what is known from the SRS, which is the national standard source for LE estimates for India. 36 Even if the MODMATCH LE estimates are not considered, there is a range of 1 year or more in LEs from the NBE, GBD and SRS for males in 13 states, and for females in 11 out of 21 states. These considerable differences in LE at birth across the various sources of mortality estimates create uncertainty as to the true mortality levels for each state in 2017.

At the national level, MODMATCH estimated the highest number of total male deaths, while the GBD estimated the highest numbers of total female deaths. At state level, there were substantial differences in estimated deaths from each source, arising from variations in age-specific death rates as well as in estimated population exposures. The net effect of these variations was assessed in terms of the dispersion of estimated total deaths across sources. The dispersion was >10 000 deaths in 18 out of 21 states for males, and 15 out of 21 states in females. Very high dispersions (>40 000 deaths) were observed for males in six states, and for females in four states. Such variations in total estimated deaths from different sources for each state also create uncertainty about the likely true levels of overall mortality at sub national level.

Premature adult mortality risk

For interpreting the findings on premature adult mortality across different sources, a conservative approach was used in nominating the highest estimate for each state as the most likely value. figure 2 shows considerable heterogeneity in the maximum values of 40  q  30 mortality risks across the states. For males, these values range from 351 per 1000 in Kerala to 558 per 1000 in Chhattisgarh, and for females from 198 per 1000 in Himachal Pradesh to 409 per 1000 in Assam.

Estimated risk of dying between 30 and 70 years from different sources for India and states, 2017. CRS, Civil Registration System; GBD, Global Burden of Disease; NBE, National Burden Estimates; SRS, Sample Registration System.

On comparing estimated risks across data sources, figure 2 shows that despite a general perception of incomplete death registration, CRS values of the 40  q  30 mortality risks were the highest out of all sources for males in eight states (representing 28% of the national male population), and for females in five states (13% of national female population). The MODMATCH values were the highest values for males in eight states (42%), and for females in four states (11%). The GBD modelled 40  q  30 estimates were the highest values for males in three states (10%), and females in seven states. (37%)

Another important comparison is between the CRS and SRS values. The observed CRS values of 40  q  30 for males were higher than the SRS values in 13 states, and for females in 7 states, indicating the likelihood of bias in SRS data, from sampling error and/or from problems with data quality. The graphs in figure 2 show limited convergence in risks of premature adult mortality across data sources in several states for both males and females, even if the low CRS values for some states are not considered, due to incomplete death registration.

A cross-sectional perspective of the magnitude of premature adult mortality is provided in the left panel of table 4 , in terms of the maximum estimate of deaths between 30 and 70 years in each state during 2017, colour coded to its data source. At the national level, a total of 2.6 million male and 1.8 million female deaths were expected to have occurred in this age group during 2017. Bihar, Maharashtra, Tamil Nadu, Uttar Pradesh and West Bengal account for approximately half of all these deaths (see online supplemental appendix 5A for detailed estimates).

Estimated deaths for India and states at ages 30–70 years in 2017, and expected cohort deaths during 2017 to 2057

There is no single data source that consistently estimates the maximum number of deaths in all states. Although the GBD estimates the maximum deaths for females in two-thirds of all states, and for males in seven states, these are largely a result of the higher GBD population exposures. Similarly, NBE derived deaths are the highest at national level and five other states for males, also driven by higher population bases. On the other hand, the directly observed CRS deaths are the maximum for males in six states and for females in three states, and MODMATCH death estimates are maximum for males and females in three states each. The CRS and MODMATCH deaths are derived using the NCP predicted populations, which are the lowest of all three sources of population data.

The potential future burden from premature adult mortality till 2057 was calculated by applying the 40  q  30 risks to a cohort of individuals from each state who were aged 30 years in 2017. On eliminating the differences from varying baseline population exposures, it was found that the maximum cohort estimates were mostly based on the MODATCH and CRS risks, particularly for males (see online supplemental appendix 5B for detailed estimates). While these estimates of expected deaths offer some value in understanding the broad targets for mortality reductions in each state, the broad ranges of these estimates (approximately 20 000 deaths or more) across different sources for some states are a matter of concern, and will hamper the exact quantification of mortality reductions achieved, in the future, over the 40-year period till 2057.

The principal findings from this analysis are the measures of premature adult mortality between ages 30 and 70 years for India and states from various data sources and methods around the commencement of the UNSDG era in 2017, as shown in figure 2 and table 4 . Based on the assumption that the highest values of estimated mortality by sex for each state from any source represents this baseline measure for UNSDGs, a total of 2.6 million male and 1.8 million female deaths were expected to have occurred in India from this age group, during 2017. Bihar, Maharashtra, Tamil Nadu, Uttar Pradesh and West Bengal account for approximately half of all these deaths. In terms of mortality risks, there was considerable heterogeneity across states, with estimated values for males ranging from 351 per 1000 in Kerala to 558 per 1000 in Chhattisgarh, and for females from 198 per 1000 in Himachal Pradesh to 409 per 1000 in Assam.

Our findings show that at the national level, premature adult mortality accounts for nearly half of all deaths in males, and about a third of all female deaths, and these proportions are consistent across all the sources and for all states (details in online supplemental appendix 5A ). These large proportions actually translate into an annual burden of considerable numbers of deaths in each state each year, as shown in table 4 . These deaths are likely to be mostly from NCDs, injuries, tuberculosis and infectious hepatitis, which can be addressed through strengthening of ongoing programmes for disease and injury prevention and control. 37–40 Also, our proposed estimates of deaths at these ages could be used as an outer bound of deaths to which cause-specific proportional mortality distributions from reliable epidemiological data sources could be fitted, to estimate the population level mortality burden from specific diseases and conditions.

Although the results have mostly focused on the comparisons and variations of estimates from different sources, our overall aim was to estimate the potentially true magnitude of premature adult mortality around 2016–2017. This is of critical importance, since these are required as baseline levels for monitoring the effectiveness of strategies to address health-related UNSDGs that target this specific age category. Since each data source has limitations which vary in scope and effect across locations, it is not possible to nominate any single source as the optimal source for all states in 2017. Hence, we have chosen to report the maximum mortality estimate at these ages for each state, under the principle that health policies should be based on ‘worst case’ scenarios, for pragmatic health sector priority setting and resource allocation.

In this regard, we believe that the incorporation of mortality data from the CRS in 2017 in our estimation process has had an important influence on deriving our maximal mortality estimates. This is evidenced from the direct use of CRS derived values of 45  q  15 risks as inputs for our model life tables for some states, as well as in applying the observed CRS 40  q  30 risks in certain states to estimate the magnitude of premature adult deaths. Data from the CRS were not used for mortality estimation in the GBD and NBE analyses.

Three indicators were used to evaluate premature adult mortality, namely the risk of dying between the 30 and 70 years, the cross-sectional annual number of deaths at a baseline point in time, and a prediction of potential deaths in a cohort over a period of 40 years. Evaluating the risks of dying helps understand the underlying factors driving mortality, while cross-sectional estimates of deaths guide resource allocation for health services and clinical management to prevent death in the diseased, and predicted deaths serve as targets for disease control and mortality reduction strategies. Planning for disease prevention and control to reduce premature adult mortality is critical, since in addition to enabling individuals to fulfil their personal life goals, mortality prevention at these ages has important positive ramifications for their families, as well as society in general.

It is anticipated that the heterogeneity in mortality patterns is likely to be associated with varying disease profiles and causes of death patterns across the states. Hence, there would be a need for customised health programmes for each state, and related indicators and targets to monitor and evaluate their impact. While the GBD and NBE studies have published state level epidemiological profiles, they differ in their respective cause-specific mortality patterns for individual states. 17 19 Moreover, our analysis proves that even at a gross level, the GBD and NBE total estimated numbers of deaths for each state are not the same, and these are influenced by differing patterns of modelled age-specific mortality risks, and differing population exposures, resulting in disparate premature adult mortality risks (see online supplemental appendix 6 ) These myriad explanations for differences in the GBD and NBE deaths for each state, and the absence of specific evidence to assess and justify the reliability of one source over the other, limits the utility of either of these sources, for establishing baseline levels of premature adult mortality to monitor progress towards the UNSDGs.

We have highlighted the lowest LEs at birth and the highest estimates of premature adult mortality by sex from all sources, as the most appropriate values to describe mortality levels for each state. The mortality measures from the CRS are known to be affected by bias due to under-registrtation, but our analyses did not include any measurement of registration completeness, and related adjustments. Therefore we chose not use life expectancies from the CRS for this comparisons in Table 3. The MODMATCH LEs at birth for most states were observed to be lower than what was previously understood from the SRS and GBD reports. 19 36 These findings have implications for the computation and comparative interpretation of the Human Development Index across the states of India, since LE at birth is a key component of this index.

Our evaluation of plausibility of patterns of age-specific mortality risks ( figure 2 ) show that the GBD, NBE and SRS age-specific mortality curves do not adhere to the Gompertz law that describes an exponential rise in mortality beyond 40 years, and such violations are generally considered to indicate problems with underlying data. 32 In contrast, the MODMATCH outputs of age-specific risks are compliant with the Gompertz law. Further, the results from MODMATCH were validated using the Log Quad Model Life Table System. We chose to use the MODMATCH results for our comparative analysis, since the details on state-specific inputs used in MODMATCH, and references to the software tool and relevant documentation are available in the public domain, to replicate and verify our results for each state. Nevertheless, although the MODMATCH 40  q  30 risks are generally higher than the SRS values for most states (see figure 2 ), they are still below the same risks derived from observed deaths in the CRS, for both males and females in some states. These MODMATCH predicted mortality patterns are based on the expectation that current mortality in the study population reflects international historical trends, and this is not the case for these states with higher 40  q  30 risks from empirical CRS data. Hence, the MODMATCH too potentially underestimates premature adult mortality, and the degree of such under-estimation cannot be ascertained for several states with incomplete CRS death registration, particularly for females.

Another limitation of this analysis was our inability to establish the potentially minimum value of 40  q  30 risk in Indian populations, due to all the uncertainty underlying our estimates. A reliable minimum risk could have been used as a counterfactual level, to estimate the potentially avoidable burden from premature adult mortality in each state. 41 For this estimation, the counterfactual minimum risk would be applied to each cohort of 30 years in 2017, to yield the minimum expected deaths for each state over the 40-year period till 2057. Subtracting these minimum expected deaths from the cohort mortality estimates presented in table 4 would yield the potentially avoidable burden from premature adult mortality in each state, if the state were to experience the same population health status and health system attributes as the population with the counterfactual risk. While it is possible to use an estimate of 40  q  30 from a reliable international source as the counterfactual risk, we did not choose to do so, since it may not be epidemiologically coherent with Indian health experience.

From an overall perspective therefore, none of the available data sources or analytical methods appears to serve as the single appropriate source for understanding the true levels of premature adult mortality in India. Our approach to compare and report the maximum estimate for each state as the likely value is only borne out of expediency, to focus attention of health policy analysts and agencies on the potential magnitude of this component of disease burden around 2017, as baseline measures for the UNSDGs. In highlighting the gaps in empirical data, along with the variations in estimates across sources, we hope to draw the attention of public health bureaucrats to the urgent action required to strengthen the CRS as the optimal source for subnational mortality statistics in India. As described in detail elsewhere, the CRS is based on a sound legal framework and administrative structure, with adequate infrastructure and operations for basic registration of deaths by age and sex across the country, which have led to gradual improvements in data quality till date. 12 13 42 The CRS is also the optimal source since its statistical compilations report data for all the smaller states and territories of India. Further interventions are required to improve completeness of death registration in some locations, and attribution of causes of death more generally, based on an incremental sampling approach supported by adequate capacity building, over the next decade. 13 43 44 Also, CRS statistical reports should provide district level data with more detailed age-groups, to directly estimate abridged life tables and other age-related mortality measures. There is also a need to coordinate population projection exercises and methods across different groups, so that there is a unified set of consensus-based population exposures for evaluating mortality risks. The methods and findings from these analyses should be taken into account when using data from the CRS to evaluate and monitor the impact of the COVID 19 pandemic in India during 2020-2021, and into the future. 45 This analytical approach would also be relevant for other countries facing the need to reconcile local mortality data with modelled mortality estimates from international sources.

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• UN Inter-agency Group for Child Mortality Estimation (UN-IGME)
• Global Health Observatory (GHO) Data
• Murray CJL ,
• United Nations Statistics Divsion
• Abouzahr C ,
• Verguet S ,
• Calazans JA ,
• Pillay-van Wyk V ,
• Msemburi W ,
• Laubscher R , et al
• Lakshmi PVM , et al
• Gerland P , et al
• Sharma P , et al
• United Nations Population Division
• India State-Level Disease Burden Initiative Collaborators
• Callender C ,
• Kulikoff XR , et al
• World Health Organization
• Office of the Registrar General and Census Commissioner of India
• International Institute for Population Sciences (IIPS) and ICF
• National Comission on Population
• Dandona L ,
• Dandona R ,
• Kumar GA , et al
• Andreev EM ,
• Shkolnikov VM
• Ferguson BD ,
• Lopez AD , et al
• Wilmoth J ,
• Zureick S ,
• Canudas-Romo V , et al
• Benjamin Gompertz. X
• Heuveline P ,
• Mahapatra P
• National Health Mission
• Trauma and Burns Division Program Managament Unit
• Gavurova B ,
• Mahapatra P ,
• Chalapati Rao PV
• Kelly M , et al

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
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Handling editor Soumitra S Bhuyan

Twitter @aashishg_

Contributors CR conceptualised the research, led the analysis and drafted the initial version of the manuscript. AG collaborated on the analysis and design of figures and tables and contributed to writing the manuscript. MG and AKY provided inputs for the review of data quality, interpretation of results and documentation of implications of the study findings in the discussion. All authors contributed to the preparation of the final version of the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Key facts as India surpasses China as the world’s most populous country

India is poised to become the world’s most populous country this year – surpassing China, which has held the distinction since at least 1950 , when the United Nations population records begin. The UN expects that India will overtake China in April , though it may have already reached this milestone since the UN estimates are projections.

Here are key facts about India’s population and its projected changes in the coming decades, based on Pew Research Center analyses of data from the UN and other sources.

This Pew Research Center analysis is primarily based on the World Population Prospects 2022 report by the United Nations. The estimates produced by the UN are based on “all available sources of data on population size and levels of fertility, mortality and international migration.”

Population sizes over time come from India’s decennial census. The census has collected detailed information on India’s inhabitants, including on religion, since 1881. Data on fertility and how it is related to factors like education levels and place of residence is from India’s National Family Health Survey (NFHS) . The NFHS is a large, nationally representative household survey with more extensive information about childbearing than the census. Data on migration is primarily from the United Nations Population Division .

Because future levels of fertility and mortality are inherently uncertain, the UN uses probabilistic methods to account for both the past experiences of a given country and the past experiences of other countries under similar conditions. The “medium scenario” projection is the median of many thousands of simulations. The “low” and “high” scenarios make different assumptions about fertility: In the high scenario, total fertility is 0.5 births above the total fertility in the medium scenario; in the low scenario, it is 0.5 births below the medium scenario.

Other sources of information for this analysis are available through the links included in the text.

India’s population has grown by more than 1 billion people since 1950, the year the UN population data begins. The exact size of the country’s population is not easily known, given that India has not conducted a census since 2011 , but it is estimated to have more than 1.4 billion people – greater than the entire population of Europe (744 million) or the Americas (1.04 billion). China, too, has more than 1.4 billion people, but while China’s population is declining , India’s continues to grow. Under the UN’s “ medium variant ” projection, a middle-of-the-road estimate, India’s population will surpass 1.5 billion people by the end of this decade and will continue to slowly increase until 2064, when it will peak at 1.7 billion people. In the UN’s “high variant” scenario – in which the total fertility rate in India is projected to be 0.5 births per woman above that of the medium variant scenario – the country’s population would surpass 2 billion people by 2068. The UN’s “low variant” scenario – in which the total fertility rate is projected to be 0.5 births below that of the medium variant scenario – forecasts that India’s population will decline beginning in 2047 and fall to 1 billion people by 2100.

People under the age of 25 account for more than 40% of India’s population. In fact, there are so many Indians in this age group that roughly one-in-five people globally who are under the age of 25 live in India. Looking at India’s age distribution another way, the country’s median age is 28. By comparison, the median age is 38 in the United States and 39 in China.

The other two most populous countries in the world, China and the U.S. , have rapidly aging populations – unlike India. Adults ages 65 and older comprise only 7% of India’s population as of this year, compared with 14% in China and 18% in the U.S., according to the UN. The share of Indians who are 65 and older is likely to remain under 20% until 2063 and will not approach 30% until 2100, under the UN’s medium variant projections.

The fertility rate in India is higher than in China and the U.S., but it has declined rapidly in recent decades . Today, the average Indian woman is expected to have 2.0 children in her lifetime, a fertility rate that is higher than China’s (1.2) or the United States’ (1.6), but much lower than India’s in 1992 (3.4) or 1950 (5.9). Every religious group in the country has seen its fertility rate fall, including the majority Hindu population and the Muslim, Christian, Sikh, Buddhist and Jain minority groups. Among Indian Muslims, for example, the total fertility rate has declined dramatically from 4.4 children per woman in 1992 to 2.4 children in 2019, the most recent year for which data is available from India’s National Family Health Survey (NFHS). Muslims still have the highest fertility rate among India’s major religious groups, but the gaps in childbearing among India’s religious groups are generally much smaller than they used to be.

Fertility rates vary widely by community type and state in India. On average, women in rural areas have 2.1 children in their lifetimes, while women in urban areas have 1.6 children, according to the 2019-21 NFHS . Both numbers are lower than they were 20 years ago, when rural and urban women had an average of 3.7 and 2.7 children, respectively.

Total fertility rates also vary greatly by state in India , from as high as 2.98 in Bihar and 2.91 in Meghalaya to as low as 1.05 in Sikkim and 1.3 in Goa. Likewise, population growth varies across states. The populations of Meghalaya and Arunachal Pradesh both increased by 25% or more between 2001 and 2011, when the last Indian census was conducted. By comparison, the populations of Goa and Kerala increased by less than 10% during that span, while the population in Nagaland shrank by 0.6%. These differences may be linked to uneven economic opportunities and quality of life .

On average, Indian women in urban areas have their first child 1.5 years later than women in rural areas. Among Indian women ages 25 to 49 who live in urban areas, the median age at first birth is 22.3. Among similarly aged women in rural areas, it is 20.8, according to the 2019 NFHS.

Women with more education and more wealth also generally have children at later ages. The median age at first birth is 24.9 among Indian women with 12 or more years of schooling, compared with 19.9 among women with no schooling. Similarly, the median age at first birth is 23.2 for Indian women in the highest wealth quintile, compared with 20.3 among women in the lowest quintile.

Among India’s major religious groups, the median age of first birth is highest among Jains at 24.9 and lowest among Muslims at 20.8.

India’s artificially wide ratio of baby boys to baby girls – which arose in the 1970s from the use of prenatal diagnostic technology to facilitate sex-selective abortions – is narrowing. From a large imbalance of about 111 boys per 100 girls in India’s 2011 census, the sex ratio at birth appears to have normalized slightly over the last decade. It narrowed to about 109 boys per 100 girls in the 2015-16 NFHS and to 108 boys per 100 girls in the 2019-21 NFHS.

To put this recent decline into perspective, the average annual number of baby girls “missing” in India fell from about 480,000 in 2010 to 410,000 in 2019, according to a Pew Research Center study published in 2022 . (Read more about how this “missing” population share is defined and calculated in the “How did we count ‘missing’ girls?” box of the report.) And while India’s major religious groups once varied widely in their sex ratios at birth, today there are indications that these differences are shrinking.

Infant mortality in India has decreased 70% in the past three decades but remains high by regional and international standards. There were 89 deaths per 1,000 live births in 1990, a figure that fell to 27 deaths per 1,000 live births in 2020. Since 1960, when the UN Interagency Group for Child Mortality Estimation began compiling this data, the rate of infant deaths in India has dropped between 0.1% and 0.5% each year.

Still, India’s infant mortality rate is higher than those of neighboring Bangladesh (24 deaths per 1,000 live births), Nepal (24), Bhutan (23) and Sri Lanka (6) – and much higher than those of its closest peers in population size, China (6) and the U.S. (5).

Typically, more people migrate out of India each year than into it, resulting in negative net migration. India lost about 300,000 people due to migration in 2021, according to the UN Population Division . The UN’s medium variant projections suggest India will continue to experience net negative migration through at least 2100.

But India’s net migration has not always been negative. As recently as 2016, India gained an estimated 68,000 people due to migration (likely to be a result of an increase in asylum-seeking Rohingya fleeing Myanmar). India also recorded increases in net migration on several occasions in the second half of the 20th century.

• Birth Rate & Fertility

Laura Silver is an associate director focusing on global attitudes at Pew Research Center .

Christine Huang is a research associate focusing on global attitudes at Pew Research Center .

Laura Clancy is a research analyst focusing on global attitudes research at Pew Research Center .

Few East Asian adults believe women have an obligation to society to have children

A growing share of americans say they’ve had fertility treatments or know someone who has, key facts about china’s declining population, global population skews male, but un projects parity between sexes by 2050, india’s sex ratio at birth begins to normalize, most popular.

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Cardiovascular disease in India: A 360 degree overview

A. sreeniwas kumar.

a Director (Cardiology & Clinical Research), Apollo Hospitals, Apollo Health City, Hyderabad, India

Nakul Sinha

b Cardiologist, Sahara Hospital, Lucknow, India

Introduction

The noncommunicable diseases commonly include cardiovascular disease (CVD), various cancers, chronic respiratory illnesses, diabetes, and so on which are estimated to account for around 60% of all deaths. CVDs such as ischaemic heart disease and cerebrovascular such as stroke account for 17.7 million deaths and are the leading cause. 1 In accordance with the World Health Organization, India accounts for one-fifth of these deaths worldwide especially in younger population. The results of Global Burden of Disease study state age-standardized CVD death rate of 272 per 100000 population in India which is much higher than that of global average of 235. CVDs strike Indians a decade earlier than the western population. 2 For us Indians, particular causes of concern in CVD are early age of onset, rapid progression and high mortality rate. Indians are known to have the highest coronary artery disease (CAD) rates, and the conventional risk factors fail to explain this increased risk. There are no structured data collection methods regarding the cardiac mortality and morbidity for Indian subcontinent, and also majority of deaths happen at home without knowing the exact cause of death. Hospital-based CV morbidity and mortality data may not be representative of overall CV disease burden. In India in 2016, CVDs contributed to 28·1% of total deaths and 14·1% of total disability-adjusted life years (DALYs) compared with 15·2% and 6·9%, respectively in 1990. 3 Within India, the rates of CVD vary markedly with highest in states of Kerala, Punjab and Tamil Nadu. Moreover, these states also have the highest prevalence of raised cholesterol levels and blood pressure. At present, India has the highest burden of acute coronary syndrome and ST-elevation myocardial infarction (MI). Another significant problem in India, among other CVD's, is that of hypertensive heart disease, with 261,694 deaths in 2013 (an increase of 138% in comparison with 1990). Rheumatic heart disease remains in epidemic proportions in India with an estimated prevalence of 1.5-2 per 1000 individuals. Migrant Asian Indians have a 3-time higher prevalence of CAD than the native population. Indians are liable to get hospitalized 2–4 times more frequently for complications of CAD, in comparison with other ethnic groups, and admission rates are 5–10 times higher for populations younger than 40 years. The prevalence of CAD in Indians living in India is 21.4% for diabetics and 11% for nondiabetics. The prevalence of CAD in rural parts of country is nearly half than that in urban population.

Risk factors

The conventional risk factors such as hypertension, diabetes mellitus dyslipidaemia, smoking, obesity are believed to be associated with increased prevalence of CAD in Indians. In INTERHEART study, nine common risk factors (which also included physical inactivity, low fruits and vegetables intake and psychosocial stress) explained more than 90% of acute myocardial infarctions (AMIs) in South Asians. However, all these risk factors cannot still fully explain the increased prevalence or the younger age of onset of CAD in Indians. The overall burden of the conventional risk factors is in a rapid increase phase in the Indian population.

Until 2016, after China, India was the second largest consumer of tobacco. However, as per the report of Global Adult Tobacco Survey-2 in June 2017, there was a 6% decline in the prevalence of tobacco use among adults (>15 years) in India. 3 The smoking rates among men were constantly on decline since 1995–1996 till 2016–2017 and among women from 2.9% to 2% for the corresponding period. 3 The prevalence of current tobacco smoking in males (23.6%) is higher than the global prevalence (22%). Tobacco use is the single largest modifiable and reversible risk factor attributable to CVD.

In Indians, one of every ten persons aged 18 years has an increased blood glucose level. There were more than 73 million cases of diabetes in India in 2017 which is the highest in any country across the globe. Diabetes has become a challenge in India with a prevalence of 8.8% in the age group of 20 and 70 years. 4 The rising prevalence of diabetes and other noncommunicable diseases has been linked to rapid urbanization, globalization coupled with increasing sedentary lifestyles, unhealthy diets, overweight and obesity, tobacco use and increasing life expectancy. Diabetes burden can be significantly reduced by bringing behavioural changes which favour healthy balanced diet and regular physical activity.

Hypertension

One in every four individuals older than 18 years in India has an increased blood pressure level. Hypertension is attributable to 10.8% of all deaths in India. Its prevalence has been on a steep rise over the past three decades both in urban and rural areas. This burden is expected to rise two times from 118 million in 2000 to 213.5 million by 2025. 5 It was estimated that 16% of CAD, 21% of PVD, 24% of AMI and 29% of strokes are attributable to hypertension.

Prevalence of obesity is increasing alarmingly especially in urban areas. The worldwide prevalence of obesity nearly tripled between 1975 and 2016. Overweight or obesity is seen in 30–65% of the adult urban population. Body mass index (BMI) of urban Indians is higher (approximately 24–25) as compared with that of rural population (BMI of about 20). More than raised BMI, it is the abdominal obesity which is a cause for concern. Waist-to-hip ratio in urban setting in men is 0.99 vs 0.95 in rural males. Abdominal obesity is also more prevalent than generalised obesity. 6 Asian adult BMIs of >21 kg/m2 were associated with the development of type II diabetes, ischaemic heart disease, stroke, hypertension, osteoarthritis and cancers. Asians are less likely to have regular physical activity and more sedentary habits compared with the Caucasian population.

Dyslipidaemia

Asian Indians have a unique pattern of atherogenic dyslipidaemia with low high density lipoproteins (HDL), high triglycerides and high small dense low-density lipoprotein (LDL) particles. An Indian Council of Medical Research (ICMR) study in 2014 brought out that more than three-fourth (79%) of the general population had abnormalities in at least one of the lipid parameters, and there was no urban rural variation. Various studies show prevalence of hypercholesterolaemia, hypertriglyceridemia, low HDL-C and high LDL-C is 13.9%, 29.5%, 72.3% and 11.8%, respectively with representative samples from all regions and at all ages. Nearly 25% of Indians and other South Asians have raised levels of Lp(a) (≥50 mg/dl), making it as a important risk factor. Factors strongly associated with dyslipidaemia are female gender, obesity, sedentary lifestyle, diabetes, dysglycaemia and hypertension. 7

Dietary habits and exercise

About half of the Indian population is vegetarian and yet diabetes and CVD risks are comparable with or higher than nonvegetarians as seen in the western population. Indians consume high carbohydrate diets with uneven dietary patterns. Average Indian diets contain more amounts of carbohydrates, high fat dairy, butter, ghee and cheese in their everyday meals. In Kerala, the culture and practice of using coconut oil in cooking has predisposed them to the highest rates of CAD in India. 8 Reusing oil for cooking in Indian culture is common, and it increases transfatty acids. Indians consume less amounts of fresh fruits and vegetables compared with that of rest of the world. The prevalence of malnutrition in Indian subcontinent is unique with high prevalence of malnutrition and low birth weights on one side and rapid increase in obesity with associated morbidities on the other side. Poor living conditions along with low education levels were also associated with higher CAD mortality. Poor people in rich countries and rich people in poor countries suffer more CAD due to various metabolic, social, and cultural maladjustments. Other causes may include rapid lifestyle changes due to urbanization and nutritional transitions that accompany such economic developments. As per Indian Council of Medical Research–India Diabetes (ICMR–INDIAB) study, every second individual is physically inactive, and less than 10% of the studied population was involved in doing regular physical activity.

Genetic risk factors

Coronary artery disease has high familial incidence indicating possible genetic association. Numerous studies suggest the presence of specific ‘candidate genes’ which are associated with pathways leading to coronary heart disease. Studies have revealed large numbers of genes which have predilection for CAD. However, these findings are inconsistent. Coronary Artery Disease Genome-wide Replication and Meta-analysis 9 study and other genome-wide association studies found that 109 loci are associated with CAD and can explain the role of hereditary factors. It has been postulated that the interaction of genes with environmental factors such as smoking increases the risk, and the combined effect may be greater than the sum of either factors alone. There are multiple genes which regulate CAD which is multifactorial, and it will be difficult to pin point one culprit genetic loci.

Emerging risk factors

Other risk factors that are thought to be correlated with high prevalence of CAD are high homocysteine levels, ambient air pollution, variations in outdoor temperatures, psychosocial factors, and mental health and higher high sensitivity C-reactive protein (hsCRP) levels indicating chronic infection and inflammation.

Treatment gaps

Data from Prospective Urban Rural Epidemiology (PURE) study suggests that up to three-fourth of patients with CAD are not on guideline-recommended basic therapy drugs, and this perhaps is one of the major reasons for higher morbidity and mortality.

Strategies for prevention

Promoting health education and awareness about the pathogenesis of CAD, discouraging smoking and tobacco use and adapting a healthy diet and exercise routine will promote better cardiovascular health. Reducing high fat dairy, carbohydrates, saturated fats and increasing daily intake of fruit and vegetables will also improve overall health. Aggressive screening tests beginning at an early age will be beneficial for early detection and treatment. Promoting healthy group exercise activities such as walking, yoga and meditation to be practised regularly will certainly aid in preventing the rising epidemic of CAD.

Conclusions

The deaths due to CVDs have reduced in several developed countries, whereas the same has increased greatly in low- and middle-income countries. These countries bear about 80% of the global burden. Mortality associated with CAD in Asian Indians is 20–50% higher than any other population. Hence, all efforts are required to be proactively taken to clearly understand the role of risk factors in the emerging epidemic and for their effective control. General screening for conventional risk factors right from younger age may increase awareness; help in promoting lifestyle changes which can prevent or slow atherogenesis. Finally, a healthy lifestyle, balanced diet and regular physical exercise should be instilled right from the beginning in childhood to check this epidemic.

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Trends in Mortality Rates :

As anywhere else in the less developed parts of the world, mortality transition in India is a phenomenon of the twentieth century. However, India has the distinction of having experienced one of the earliest declines in mortality among the less developed countries. The less developed countries, in general, began experi­encing decline in mortality levels only by the middle of the twentieth century. As against this, in India, the decline is found to have set in as early as in the 1920s. Prior to that, death rates were usually very high, often exceeding birth rates in the wake of famines and epidemics.

This excess of deaths over births occasionally resulted in the shrink in the population size of the country. The decade 1911-21, for instance, had witnessed a negative growth in population due to heavy loss of life in the wake of influenza epidemic that struck several areas in north India. It is, therefore, rightly remarked that the history of population growth in the country prior to 1921 is the history of a great fight against death.

It may be recalled that accurate estimates on trends in mortality in the country for much of the past, when death rates were very high and fluctuating, are not available. As noted earlier, although civil registration system has a longer history, its estimates are not reliable. Sample registration system, which is based on the principle of dual report system and which provides more reliable estimates, was introduced in the country only in the late 1960s.

Scholars have, however, derived estimated on trends in mortality in the country using some indirect measures. The present account of trends in mortality rates in the country is largely based on those estimates. Table 9.3 presents average annual death rate in India during the last one hundred years.

The table reveals that, prior to 1921 crude death rates in India were very high. Crude death rate, which remained around 45 per thousand persons during the early years of the twentieth century, is found to have come down to below 10 per thousand persons now. This decline in death rates was fraught with great amount of interruptions up to 1921. The decade 1911-21 is marked with a drastic increase in the death rate as compared to the previous decade due to the disastrous influenza epidemic of the year 1918, which claimed the lives of 15 million persons.

The death rate was indeed the highest ever known in the history of India’s population. Thereafter, the rates have declined continuously over the period. The first major decline occurred during the decade 1921-31 when the annual death rate came down to 36 per thousand persons. Decline in death rates, once begun, continued during each of the subsequent decades. From 1911-21 to 1971-81, i.e., over a period of nearly 60 years, death rate in the country is found to have declined by nearly 34 points. By the period 1996-98, it had reached a level that was less than 10 per thousand persons.

The latest estimate for the year 2000 indicates a death rate of 8.5. In other words, over a period of nearly 80 years, death rate in the country declined by a margin that had earlier taken more than 170 years in the developed nations. Obviously, the decline in the death rates in India, as also in other less developed countries, has been much faster than that in the developed countries. Since birth rates responded only with a time lag, and since the pace of decline in it was not as rapid as that in the death rates, India’s population began growing at increasing pace after 1921.

The rates of growth were conspicuously higher during the post-independence period. The pace in growth of population is, however, found to have slowed down since 1980s, thanks to a significant dent in fertility levels.

One of the main reasons of high mortality rates in the past has been a significantly higher incidence of deaths among infants and children in the country. Although, firm data on trends on mortality among infants and children are not available, it is under­stood that infant mortality rates in India during the early parts of the twentieth century were abnormally high. According to one estimate, infant mortality rates during the decade 1901-11 were as high as 290 for males and 284.6 for females.

Though, infant mortality has undergone impressive decline during the last century, percentage decline in it has been smaller than that in the general mortality rates. The current level of infant mortality, which stands at 68 per thousand live births, is still very high in the world. This means that while general mortality rate has responded to community health measures such as control of infectious and parasitic diseases, much is still desired for the improvement in mortality conditions among infants in the country.

As a result of very high mortality rates in the past, particu­larly among infants and children, average life expectancy at birth in India at the turn of the last century was one of the lowest in the recorded history of mankind. During much of the first half the century, average life expectancy in India remained below 30 years. In fact, during the first two decades of the century, it was only marginally above 20 years. Nevertheless, with improvements in general mortality conditions, life expectancy has recorded an increase in each successive decade.

Although, mortality conditions in the country have undergone impressive progress, death rate still remains higher than many of the developing countries of the world. Crude death rate of a little over 8 per thousand persons is still more than those in some other less developed countries in Asia like Sri Lanka, Maldives, Indonesia, Malaysia and Philippines.

The gap becomes wider if one compares the estimates on infant mortality rate. As noted already, infant mortality rate in the country still stands at 68 per thousand live births as against only 13 in Sri Lanka, 11 in Malaysia, 17 in Maldives and 26 in Philippines. Similar is the case of the differences in life expectancy. It can be argued that though mortality condi­tions in the country are far better now than that in the past, there is still a lot of scope for further improvement to ensure a speedy social and economic progress.

The rural areas, as will be seen later, report 48 per cent higher crude death rate than that in the urban areas. Similarly, population in the rural areas exhibits 70 per cent higher infant mortality rate than its counterpart in the urban areas. Remember that more than 70 per cent of the country’s population still resides in the villages. Thus, the health care measures have to be extended further to the rural areas, particularly among the weaker sections of the society, where mortality conditions are far worse despite significant strides made at the aggregate national level.

Mortality conditions in the country are marked with a great amount of differentials from one group to another. According to the findings of the NFHS-2, children born to women belonging to scheduled castes and scheduled tribes have significantly higher probability of dying during infancy and childhood (Table 9.5). Infant mortality and child mortality rates among households having low standard of living are almost twice as high as those of the high standard of living.

More striking difference is found to exist in the case of post-neonatal mortality rate. Households with low standard of living have three times as high post-neonatal mortality as those among households with high standard of living. Interestingly, similar pattern of the differentials exists in rural and urban areas separately. Obviously, child survival programmes need to be further intensified among specific social and economic groups in order to achieve further improvement in mortality conditions. This will go a long way in bringing down fertility rates in the country.

Spatial Patterns :

Table 9.6 presents the estimates on mortality rates in India for the year 2000. The table provides estimates for rural and urban separately. As is expected, there exists a sharp difference in the mortality rates between rural and urban areas. The difference is of a much greater magnitude in the case of infant mortality rate.

Crude death rate varies from a high of 10.5 in Orissa to a low of 3.9 in Chandigarh. If the range were viewed in absolute terms, it would appear that regional variation in crude death rates is of a smaller magnitude than that in crude birth rate. However, this conclusion turns out to be erroneous when we look at the coeffi­cient of variation in birth and death rates among the states and union territories. Contrary to the suggestion of some scholars, the magnitude of regional variation in death rates is greater, albeit marginally, than that in the birth rates. The variation becomes even sharper in the case of infant mortality rate.

Among the major states, such as Orissa, Uttar Pradesh, Madhya Pradesh, Assam, Rajasthan and Bihar, crude death rates are higher than the nation’s average. It can be recalled that the states of Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh are also marked with very high birth rates. Thus, any improvement in mortality conditions in future in these states would mean further rise in the natural rate of growth in population. These states taken together account for a little less than one-fourth of the country’s population in 2001.

The future prospect of India’s demographic transition will continue to depend on the performance of vital transition in these states. It is interesting to note that Orissa, which appears at par with some of the most developed states in India in terms of fertility levels, reports the highest crude death rate in the country. Orissa is perhaps the only instance of such a mismatch between fertility and mortality transition. A high death rate in the state can largely be attributed to a very high incidence of mortality among infants and children.

The state reports the highest infant mortality rate also in the country. According to the findings of the NFHS-2 (1998-99), despite an overall improvement in infant and child mortality in the state, approximately one in every 12 children born during the preceding five years of survey died within five years of life, and more than one in every 10 children died before attaining the age of five.

Among the smaller states, Chhattisgarh, Jharkhand and Meghalaya report a higher crude death rate than the nation’s average. The first two of them were formerly parts of undivided Madhya Pradesh and Bihar respectively. A high crude birth rate in these states is, therefore, understandable. But, what is more striking is a relatively higher death rate in Meghalaya.

Among the northeastern states, Meghalaya appears conspicuous with a very high death rate. Strikingly, in terms of crude birth rate also, the state stands quite distinct from its neighbouring states. The southern states, in general, exhibit a lower death rate – the lowest being in the case of Kerala among the major states. Goa, a smaller state in the southwest, also shows a lower death rate than the nation’s average.

It is interesting to note that all the union territories reveal lower crude death rates than the nation’s average. Chandigarh, with a crude death rate of less than 4 per thousand persons, occupies the lowest position in the country. Among the union territories, the highest crude death rate is noticed in Dadar & Nagar Haveli, followed by Daman & Diu and Pondicherry.

One comes across more or less similar pattern with regard to the levels of infant mortality rates also. The states of Assam, Orissa, Madhya Pradesh, Rajasthan and Uttar Pradesh have a higher infant mortality rate than the nation’s average. Among the smaller states again, Jharkhand and Chhattisgarh report a higher infant mortality rates. On the other hand, Kerala, Goa, north­eastern states like Manipur and Mizoram, and the union territories of Pondicherry, Chandigarh and A&N Islands report one of the lowest infant mortality rates in the country. Interestingly, all the union territories have a lower infant mortality rate than that at the aggregate level in the country.

It is generally argued that factors that cause high mortality rates in a population also contribute to a wide differential among different groups and between rural and urban areas. With rural-urban break up of mortality rates available for the states and union territories, it is worthwhile to examine the relationship between rural-urban differentials, on the one hand, and mortality levels, on the other.

In conformity with this generalization, there exists a positive and significant association between the two. The values of correlation coefficient work out to be 0.612, in the case of crude death rate, and 0.604 in the case of infant mortality rate. Thus, areas marked with high mortality levels are also marked with a wider differential between rural and urban mortality rates.

In the foregoing discussion we have examined the regional dimension of mortality conditions in the country using state level figures. India is a vast country with a great amount of variation in the determinants of mortality levels from one region to another. More often than not, this regional variation turns out to be as great within states as between them. State level patterns, thus, often mask regional variation at local levels. This necessitates a district-level analysis of mortality levels. However, district-level estimates of vital rates, as mentioned already, are not generally available.

The office of the Registrar General of India and also some independent authors, have attempted to derive district-level estimates on mortality rates. Rajan and Mohanachandran (1998) have recently worked out estimates on infant and child mortality rates in the districts of India using 1991 census data.

Although, mortality levels have undergone significant change since then, one can argue that its spatial patterns continue to be same. It is, therefore, worthwhile to examine spatial patterns in mortality levels in the country using this estimate. In the following paragraphs, a brief account of the spatial patterns in infant mortality rate is presented.

An examination of Figure 9.4 reveals that infant mortality rate is strikingly higher in the central parts of the country, lying mainly in the state of Madhya Pradesh. Of the total 24 districts reporting ‘very high’ infant mortality rates (i.e., 115 and above) as many as 15 districts are located in Madhya Pradesh (undivided) alone.

Moreover, 13 of them form a geographically contiguous pocket. To the north of this lies another pocket of ‘very high’ infant mortality rate in the central part of the upper Ganga plain in Uttar Pradesh (undivided). In the eastern part of the country, in Orissa, distinct pockets of ‘very high’ infant mortality can be seen both along the coast and in the upland plateaus. In addition, small patches of ‘very high’ infant mortality can also be observed in Arunachal Pradesh in the northeast.

These pockets of ‘very high’ infant mortality rates are surrounded by districts with ‘high’ infant mortality levels (i.e., between 90 and 114 infant deaths per thousand live births). On an average, the entire stretch from the southwestern Rajasthan in the west to Orissa in the east is marked with ‘high’ to ‘very high’ levels of infant mortality. The southern limit of this vast pocket is demarcated roughly by the northern boundaries of the states of Maharashtra and Andhra Pradesh.

Likewise, in the northwest, the spread is limited by a generally ‘low’ to ‘moderate’ levels of infant mortality rates (i.e., below 90 infant deaths per thousand live births) in the northern parts of Rajasthan and Punjab and Haryana plains. Remarkably, the eastern parts of Uttar Pradesh reveal somewhat better infant mortality conditions than their counterpart in the west. In Bihar (undivided), too, infant mortality rates are lower, and some districts, both in the plains and in Chotanagpur plateaus, report ‘moderate’ to ‘low’ level of infant mortality rates.

In the states of Maharashtra, Andhra Pradesh, Karnataka, Kerala and Tamil Nadu, infant mortality rates are found to vary from ‘low’ to ‘very low’ categories (less than 70 per thousand live births). However, in this peninsular region, the coastal districts, on an average, show a lower level of infant mortality than the interior districts. Thus, in the Karnataka plateaus and its adjacent western districts of Andhra Pradesh, infant mortality rates are somewhat higher. In Maharashtra, too, the Vidarbha region in the east is conspicuous with a higher infant mortality rate than its counterpart in the west.

Sex Differentials in Mortality :

So far we have examined various facets of mortality levels in India for the two sexes taken together. It is also important to look into the sex differentials in mortality rates in the country. Table 9.7 presents crude death and infant death rates by sex in India and its major states. The estimates pertain to the year 1998. Barring a few countries in the less developed parts, mainly in south and Southeast Asia, mortality rates among males exceed that of females throughout the world. Sex differentials in mortality in any population are the result of differential impact of a set of environ­mental and socio-economic factors.

A generally lower mortality rate among females in most of the populations may be attributed to their physiological superiority as well as a relatively lower extent of exposure to hazardous factors than males. Evidences indicate that women live longer than males throughout world. In India, the situation remained contrary to this general rule till recent past. In the country, more females died than males at each age until much of 1980s. The average life expectancy of females, thus, remained lower than that of males till late 1980s. During the early decades of the twentieth century, however, when death rates in the country were very high, female life expectancy was higher than that of males.

It appears that mortality transition in the initial stages were highly male selective. Life expectancy for males improved faster and remained higher than that of females for nearly six decades. With improvement in general mortality condi­tions of females, average life expectancy for females surpassed that of males for the first time after many decades sometime in the late 1980s. However, adverse sex differentials in mortality rates continue to exist in some age brackets mainly during the early stages of life and during childhood.

As revealed in Table 9.7, male death rate exceeds female death rate in all the major states of the country excepting Bihar and Uttar Pradesh. In other words, women continue to suffer from adverse mortality conditions in these two states. Further, average mortality conditions of females are only marginally better than that of males in the states like Gujarat, Haryana, Madhya Pradesh and Orissa. One could attribute this to a somewhat late start in mortality transition in these states.

The states of Kerala, Tamil Nadu, Andhra Pradesh and Maharashtra where mortality transition had an early start, on the other hand, report a markedly lower death rate among females than males. Among these states, Kerala has the largest gap between male and female death rates. The state has been known for a significantly higher status of women for quite some time. A greater prevalence of literacy among females and a better status of women are often cited as the main factors for demographic transition in the state.

Sex differentials in infant mortality, however, present a different picture. As noted above, more female babies continue to die before reaching age one, both in urban and rural areas. According to the SRS estimates, female infant mortality rate in the rural areas in 1998 was 79 as against 76 for males.

The corre­sponding figures for the urban areas were 42 and 49. It is, then, interesting to note that sex differential in infant mortality rate in the urban areas is of a significantly greater magnitude than that in the rural areas. While in the rural areas, female infant mortality rate was only 4 per cent as high as that of male infant mortality rate, the same is nearly 17 per cent higher in the urban areas.

The explanation for this can be sought in the differential impact of better social and economic conditions in the urban areas. It appears that better living conditions and availability of health care facilities in the urban areas have had sex selective impact leading to a greater reduction in death of male infants, who are otherwise more vulnerable to diseases and death in the early stages of life.

At the state level, one comes across a mixed picture relating to sex differentials in infant mortality rates. While some major states do report a higher incidence of death among female infants – in line with the pattern observed at the aggregate national level – others like Assam, Bihar, Karnataka, Kerala, Madhya Pradesh, Orissa and West Bengal show an opposite pattern.

Remarkably, the states reporting higher male infant death rates vary a great deal in terms of social and economic attributes as well as pace of transition in the levels of overall infant mortality rates. Further investigation based on primary level data at lower level will explain this unique sex differential in infant mortality rates in different parts of the country.

However, so far as the case of Kerala is concerned, it can be argued that with infant mortality rate at a very low level, and absence of any sex discrimination, the biological superiority of female infants gets fully reflected in the sex differential of mortality rate at this age.

Related Articles:

• 3 Different Ways to Measure the Mortality Rate
• Public Health: Infant Mortality Rate and Maternal Mortality Ratio

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India achieves significant landmarks in reduction of Child Mortality

Following a steady downward trend, imr, u5mr and nmr further decline india poised to meet sdg 2030 targets of child mortality under leadership of hon. prime minister with focused interventions, strong centre-state partnership and dedication of health workers: dr. mansukh mandaviya.

In a significant milestone, India has achieved landmark achievement in further reduction of child mortality rates. As per the Sample Registration System (SRS) Statistical Report 2020 released on 22nd September 2022 by Registrar General of India (RGI), the country has been witnessing a progressive reduction in IMR, U5MR and NMR since 2014 towards achieving the Sustainable Development Goals (SDG) targets by 2030.

Union Health and Family Welfare Minister, Dr Mansukh Mandaviya congratulated the nation on this achievement and thanked all health workers, caregivers and community members for relentlessly working towards reducing child mortality. “There has been sustained decline since 2014, as revealed by SRS 2020. India is poised to meet 2030 SDG targets of child mortality under leadership of Hon. Prime Minister Shri Narendra Modi ji with focused interventions, strong Centre-State partnership and dedication of all health workers”, he stated.

Following a steady downward trend, IMR, U5MR and NMR have further declined:

· Under 5 Mortality Rate (U5MR) for the country has shown significant decline of 3 points (Annual Decline Rate: 8.6%) from 2019 (32 per 1000 live births in 2020 against 35 per 1000 live births in 2019). It varies from 36 in rural areas to 21 in urban areas.

• U5MR for Female is higher (33) than male (31). There has been a decline of 4 points in male U5MR and 3 points in female U5MR during the corresponding period.
• Highest decline of U5MR is observed in the State of Uttar Pradesh (5 points) and Karnataka (5 points)

• The Rural-Urban difference has narrowed to 12 points (Urban 19, Rural-31).
• No gender differential has observed in 2020 (Male -28, Female - 28).

• Neonatal Mortality Rate has also declined by 2 points from 22 per 1000 live births in 2019 to 20 per 1000 live births in 2020 (Annual Decline Rate: 9.1 %). It ranges from 12 in urban areas to 23 in rural areas.

As per SRS 2020 Report,

• Six (6) States/ UT have already attained SDG target of NMR (<=12 by 2030):  Kerala (4), Delhi (9), Tamil Nadu (9), Maharashtra (11), Jammu & Kashmir (12) and Punjab (12).
• Eleven (11) States/UT have already attained SDGs target of U5MR (<=25 by 2030): Kerala (8), Tamil Nadu (13), Delhi (14), Maharashtra (18), J&K (17), Karnataka (21), Punjab (22), West Bengal (22), Telangana (23), Gujarat (24), and Himachal Pradesh (24).

HFW/SRS 2020-Child Mortality rates/23 Sept 2022/4

Morbidity in India

Geography Notes

Essay on population in india.

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Read this comprehensive essay to learn about the 1. Definition of Population, 2. Aspects of Population in India, 3. Age and Sex Structure, 4. Sex Ratio in India and Its Determinants, 5. Growth Rate of Population in India, 6. Factors Contributing to the High Growth Rate of Population, 7. Population Projection in India (2001-2026), 8. Population Projection in India by 2050.

India like most countries of the world, has evolved from conditions of high mortality due to famines, accidents, illness, infections, and war and from the time when high levels of fertility was essential for survival of offspring. Over the years, enhancement in areas of diseases prevention, cure and vagaries of nature, and better care for women and infants, it has witnessed significant increase in life expectancy along with a steep fall in mortality.

Essay # 1. Definition of Population:

Population is defined as the total number of individuals of a species in a specific geographical area; can interbreed under natural conditions to produce fertile offsprings and functions as a unit of biotic community.

Similar populations of a species occupying different geographical areas are called sister populations of a species e.g., all the frogs (Rana tigrina), water hyacinth (Eichhornia—an aquatic weed) plants found in a pond and individuals of the common grass, Cyanodon dactylon, in a given area form the populations of frog, water hyacinth and common grass respectively of that pond.

The frogs (Rana tigrina) found in different ponds form the local populations and are sister populations of one another. A local population may be occupying a very-small sized area e.g., a temporary pool of water. Other examples of populations are all the cormorants in a wetland, rats in an abandoned dwelling, teak wood trees in a forest tract, Paramecia in a culture tube, mosquito fish in a pond, etc.

In a geographical area, the population is further divisible into sub-groups called demes. The individuals of a population are capable of interbreeding among themselves. The chances of this sexual communication are more between the members of same deme than between the members of different demes of that population which are further reduced between the members of sister-populations. Due to this mating ability, there is free flow of genes in a species.

Essay # 2. Aspects of Population in India:

Size and Growth:

The current population of India is 1,342,528,871 (1.34 billion) people and it is the second most populous country in the world, while China is on the top with over 1,415,489,506 (1.41 billion) people. Out of the world’s 7 billion people, India represents almost 17.85% of the world’s population. It is predicted that India will beat China to become the highest populous country by 2030. With the population growth rate at 1.2%, India is predicted to have more than 1.53 billion people by the end of 2030.

More than 50% of India’s current population is below the age of 25 and over 65% below the age of 35. About 72.2% of the population lives in some 638,000 villages and the rest 27.8% in about 5,480 towns and urban agglomerations. The birth rate (child births per 1,000 people per year) is 22.22 births/1,000 population while death rate (deaths per 1000 individuals per year) is 6.4 deaths/1,000 population. Fertility rate is 2.72 children born/woman and infant mortality rate is 30.15 deaths/1,000 live births.

India has the largest illiterate population in the world. The literacy rate of India as per 2011 Population Census is 74.04%, with male literacy rate at 82.14% and female at 65.46%. Kerala has the highest literacy rate at 93.9%, Lakshadweep (92.3%) is on the second position, and Mizoram (91.6%) is on third. The population of a state like Uttar Pradesh is almost equal to the population of Brazil. It has, as per 2001 Population Census of India, 190 million people and the growth rate is 16.16%. The population of the second most populous state Maharashtra, which has a growth rate of 9.42%, is equal to that of Mexico’s population.

Bihar, with 8.07%, is the third most populous state in India and its population is more than Germany’s. West Bengal with 7.79% growth rate, Andhra Pradesh (7.41%), and Tamil Nadu (6.07%) are at fourth, fifth, and sixth positions respectively. The sex ratio of India stands at 940. Kerala with 1058 females per 1000 males is the state with the highest female sex ratio. Pondicherry (1001) is second, while Chhattisgarh (990) and Tamil Nadu (986) are at third and fourth places respectively. Haryana with 861 has the lowest female sex ratio.

Determinants of Population Change:

The main causes which are generally identified for the high population in India are listed here:

(a) The Birth Rate is still Higher than the Death Rate:

India has been successful in declining the death rate. On the other hand, it has not been able to control the high birth rates. The fertility rate due to the population policies and other measures has been falling, still it is much higher compared to other countries. Various social causes are at the root of overpopulation in India.

(b) Early Marriage and Universal Marriage System:

Though legally the marriageable age of a girl is 18 years, the concept of early marriage still prevails and getting married at a young age prolongs the child bearing age. Also, in India, marriage and child bearing are sacrosanct obligations and a universal practice, and almost every woman is married at the reproductive age.

(c) Poverty and Illiteracy:

Underprivileged families have a presumption that more the number of members in the family, more will be the hands to earn income. Some feel that more children are needed to look after them in their old age. Also, malnutrition can be the cause of death of their children and hence the need for more children. Many parts of India still lag behind the use of contraceptives and birth control methods. Many of them are not willing to discuss or are totally unaware about them.

(d) Age Old Cultural Norm:

Sons are believed to be the bread earners, the carriers of lineage, and the source of salvation for their parents. Many families give birth to multiple children in the hope of a male child.

(e) Illegal Migration:

Finally, the fact that illegal migration is continuously taking place from lesser developed neighbouring countries is leading to increased population density.

Implications of the Size and Growth of Population:

The impact of overpopulation is varied and has far reaching consequences in many areas of life.

Ecological Consequences:

Overpopulation causes massive ecological damage by the wasteful, unnecessary, and unbalanced consumption and exploitation of nature. The review on “Promotion of Sustainable Development- Challenges for Environmental Policies” in the Economic Survey 1998-99 had covered in detail the major environmental problems and policy options for improvement.

According to this review, in many developing countries, continued population growth has resulted in pressure on land, fragmentation of land holding, collapsing fisheries, shrinking forests, rising temperatures, and loss of plant and animal species. Global warming due to increasing use of fossil fuels (mainly by the developed countries) could have serious effects on the populous coastal regions in developing countries, their food production, and essential water supplies.

The Intergovernmental Panel on Climate Change has projected that, if current greenhouse gas emission trends continue, the mean global surface temperature will rise from 1 to 3.5 degrees Celsius in the next century. The panel’s best estimate scenario projects a sea- level rise of 15 to 95 cms by 2100. The ecological impact of rising oceans would include increased flooding, coastal erosion, salination of aquifers, and coastal crop land and displacement of millions of people living near the coast. Patterns of precipitation are also likely to change, which combined with increased average temperatures, could substantially alter the relative agricultural productivity of different regions. Greenhouse gas emissions are closely linked to both population growth and development. Slower population growth in developing countries and ecologically sustainable lifestyles in developed countries would make reduction in greenhouse gas emission easier to achieve and provide more time and options for adaptation to climate change. Rapid population growth, developmental activities either to meet the growing population or the growing needs of the population, as well as changing lifestyles and consumption patterns pose major challenge to preservation and promotion of ecological balance in India.

Some of the major ecological adverse effects reported in India include:

1. Severe pressure on the forests, due to both the rate and the nature of resources used. The per capita forest biomass in the country is only about 6 tons as against the global average of 82 tons.

2. Adverse effect on species diversity.

3. Conversion of habitat to land use such as agriculture, urban development, and forestry operation. Some 70-80% of fresh water marshes and lakes in the Gangetic flood plains have been lost in the last 50 years.

4. Tropical deforestation and destruction of mangroves for commercial needs and fuel wood. The country’s mangrove areas have reduced from 700,000 ha to 453,000 ha in the last 50 years.

5. Intense grazing by domestic livestock.

6. Poaching and illegal harvesting of wildlife.

7. Increase in agricultural area, high use of chemical fertilizers pesticides and weedicides, water stagnation, soil erosion, soil salinity, and low productivity.

8. High level of biomass burning causing large-scale indoor pollution.

9. Encroachment on habitat for rail and road construction, thereby fragmenting the habitat.

10. Increase in commercial activities such as mining and unsustainable resource extraction.

11. Degradation of coastal and other aquatic ecosystems from domestic sewage, pesticides, fertilizers, and industrial effluents.

12. Over fishing in water bodies and introduction of weeds and exotic species.

13. Diversion of water for domestic, industrial, and agricultural uses leading to increased river pollution and decrease in self-cleaning properties of rivers.

14. Increasing water requirement leading to tapping deeper aquifers which have high content of arsenic or fluoride resulting in health problems.

15. Disturbance from increased recreational activity and tourism causing pollution of natural ecosystems with wastes left behind by people.

Urbanisation:

The proportion of people in developing countries who live in cities has almost doubled since 1960 (from less than 22% to more than 40%), while in more developed regions the urban share has grown from 61% to 76%. Urbanisation is projected to continue well into the next century. By 2030, it is expected that nearly 5 billion (61%) of the world’s 8.1 billion people will live in cities. India is also a part of this global trend.

India’s urban population has doubled from 109 million to 218 million during the last two decades. As a consequence, cities are facing the problem of expanding urban slums. Cities and towns have become the location of social change and rapid economic development. Urbanisation is associated with improved access to education, employment, and health care; these result in increase in age at marriage, reduction in family size, and improvement in health indices.

As people have moved towards and into cities, information has flowed outward. Better communication and transportation now link urban and rural areas both economically and socially creating an urban-rural continuum of communities with improvement in some aspects of lifestyle of both. The ever increasing reach of mass media has made information readily available. This phenomenon has affected health care, including reproductive health, in many ways.

For instance, radio and television programmes that discuss gender equity, family size preference, and family planning options are now reaching formerly isolated rural populations. This can create awareness for services for mothers and children, higher contraceptive use, fewer unwanted pregnancies, smaller healthier families, and lead to more rapid population stabilisation.

However, the rapid growth of urban population also poses some serious challenges. Urban population growth has outpaced the development of basic minimum services— housing, water supply, sewerage, and solid waste disposal; increasing waste generation at home, offices, and industries, coupled with poor waste disposal facilities result in rapid environmental deterioration. Increasing automobiles add to air pollution. All these have adverse effect on ecology and health. Poverty persists in urban and peri-urban areas; awareness about the glaring inequities in close urban setting may lead to social unrest.

Rural Population and Their Development:

Over 70% of India’s population still lives in rural areas. There are substantial differences between the states in the proportion of rural and urban population (varying from almost 90% in Assam and Bihar to 61% in Maharashtra). Agriculture is the largest and one of the most important sectors of the rural economy and contributes both to economic growth and employment.

Its contribution to the Gross Domestic Product has declined over the last five decades but agriculture still remains the source of livelihood for over 70% of the country’s population. A large proportion of the rural workforce is small and consists of marginal farmers and landless agricultural labourers. There is substantial under employment among these people; both wages and productivity are low. These in turn result in poverty; it is estimated that 320 million people are still living below the poverty line in rural India.

Though poverty has declined over the last three decades, the number of rural poor has in fact increased due to the population growth. Poor tend to have larger families which puts enormous burden on their meagre resources, and prevent them from breaking out of the shackles of poverty. In States like Tamil Nadu where replacement level of fertility has been attained, population growth rates are much lower than in many other States; but the population density is high and so there is a pressure on land.

In States like Rajasthan, Uttar Pradesh, Bihar, and Madhya Pradesh, population is growing rapidly, resulting in increasing pressure on land and resulting in land fragmentation. Low productivity of small land holders leads to poverty, low energy intake, and under nutrition, and this, in turn, prevents the development, thus, creating a vicious circle. In most of the states, non-farm employment in rural areas has not grown very much and cannot absorb the growing labour force. Those who are getting educated specially beyond the primary level, may not wish to do manual agricultural work.

They would like better opportunities and more remunerative employment. In this context, it is imperative that programmes for skill development, vocational training, and technical education are taken up on a large scale in order to generate productive employment in rural areas. The entire gamut of existing poverty alleviation and employment generation programmes may have to be restructured to meet the newly emerging types of demand for employment.

Rural poor have inadequate access to basic minimum services, because of poor connectivity, lack of awareness, and inadequate and poorly functional infrastructure. There are ongoing efforts to improve these, but with the growing aspirations of the younger, educated population, these efforts may prove to be inadequate to meet the increasing needs both in terms of type and quality of services.

Greater education, awareness, and better standard of living among the growing younger age group population would create the required consciousness among them that smaller families are desirable; if all the felt needs for health and family welfare services are fully met, it will be possible to enable them to attain their reproductive goals, achieve substantial decline in the family size, and improve quality of life.

Water Supply:

In many parts of developed and developing world, water demand substantially exceeds sustainable water supply. It is estimated that currently 430 millions (8% of the global population) are living in countries affected by water stress; by 2020, about one-fourth of the global population may be facing chronic and recurring shortage of fresh water.

In India, water withdrawal is estimated to be twice the rate of aquifer recharge; as a result water tables are falling by one to three meters every year; tapping deeper aquifers have resulted in larger population groups being exposed to newer health hazards such as high fluoride or arsenic content in drinking water. At the other end of the spectrum, excessive use of water has led to water logging and increasing salinity in some parts of the country.

Eventually, both lack of water and water logging could have adverse impact on India’s food production. There is very little arable agricultural land which remains unexploited and in many areas, agricultural technology improvement may not be able to ensure further increase in yield per hectare. It is, therefore, imperative that research in biotechnology for improving development of food grain strains that would tolerate salinity and those which would require less water gets high priority.

Simultaneously, a movement towards making water harvesting, storage, and its need based use part of every citizens life should be taken up.

Food Security:

Technological innovations in agriculture and increase in area under cultivation have ensured that so far, food production has kept pace with the population growth. Evolution of global and national food security systems has improved access to food. It is estimated that the global population will grow to 9 billion by 2050 and the food production will double; improvement in purchasing power and changing dietary habits (shift to animal products) may further add to the requirement of food grains.

Thus, in the next five decades, the food and nutrition security could become critical in many parts of the world, especially in the developing countries and pockets of poverty in the developed countries.

Levels and Trends of Fertility in India:

Recent data suggest a clear decline in fertility throughout the country, including in the large north Indian states (Bihar, Madhya Pradesh, Uttar Pradesh, and Rajasthan), where since 1971, TFR has declined by 27-28%. Elsewhere, fertility decline has been faster. Compared to rural fertility, urban fertility has declined at a faster pace. The urban TFR has dropped to 2.1 or to a replacement level or less in urban areas of Kerala, Tamil Nadu, Andhra Pradesh, Assam, Himachal Pradesh, Karnataka, and West Bengal.

However, we need to be concerned not just with the level of fertility but with the total size of the population or its annual growth. Therefore, we can take little comfort from the observed decline in the TFR, and must recognize the fact that the annual increase in the total population of the country is likely to exceed about 18 million, higher than in China and equal to the total population of several countries.

However, if the success of the family planning programme is neutralised by the success of the health policies, it is certainly not fair to label the former as a failure. The results of knowledge, attitudes, and practice (KAP) surveys indicating a widespread desire to regulate the size of the family induced an excessive faith in what the supply of services by female health workers or the auxiliary nurse midwives (ANMs) might achieve.

Levels and Trends of Mortality in India:

The infant mortality rate (IMR) of around 200-225 per 1000 live-births at the time of India’s independence in 1947 has declined to about 40 per 1000 births today. Admittedly, even this figure far exceeds the IMR in China, which has now declined to around 30. Within India, only Kerala, with about 93% of births occurring in institutions and another 6% attended by trained birth attendants, has achieved an even lower IMR of 17.

Elsewhere, the IMR ranges between low 50s in Punjab, Tamil Nadu, and Maharashtra, and high values between 85 and 98 in Uttar Pradesh, Madhya Pradesh, and Orissa. Obviously, there is substantial scope and need for a further decline in the present high IMR.

The interstate differentials are evident in life expectancy as well, which in India has risen from about 32 years in the 1940s to nearly 66, 21 years during 2012. The figure for Kerala exceeded 73 years, and Punjab was second with 67 years, whereas Assam and Madhya Pradesh reported nearly 18 years lower than Kerala’s life expectancy.

The slow mortality decline may partly be attributed to the fact that the universal programme of immunisation was initiated only in the mid-1970s. It now covers the entire country but even during 1995-96, 33% of the rural children aged 0-4 had not received BCG and 56 and 45% of the rural children had not received oral polio vaccine and the DPT doses.

There has been some controversy in India that the programme has led to a certain imbalance in the allocation of funds. Critics argue that as a result, the much-needed effort to eliminate malnutrition and to minimise the number and proportion of low birth-weight babies has not received the requisite attention.

Implications of the Levels of Mortality:

There is no doubt that a reduction in the level of infant, child, and maternal mortality and an improvement in the availability of prenatal, natal, and postnatal care would help to lower the ‘high wanted fertility’ or the number of living children desired by couples. Unfortunately, the rural infrastructure is so weak that even today only about 30% of all villages had an all-weather approach road.

The possible efforts of pregnant women to access the health care system to meet crisis situations are frustrated by the inadequacies of road transport and communication, which also discourages the teachers of rural schools to attend to their duties. According to the 1991 Census, 65% of Indian villages had a population of less than 1000 persons and 42 had less than 500 persons each.

The average population of a village in Kerala and Tamil Nadu, the two states with a below replacement level of fertility, was 15476 and 2325, much higher than the national average of 1061. The size class of population of a village is an excellent indicator of the size of the rural market, the extent of diversification of economic activities of the population, and also the level of development. The road network integrates villages into the mainstream of the economy and increases the options to access social and economic opportunities and services in the rest of the country.

According to the broad experience of the fertility transition that has occurred in developed countries as well as in the newly industrialised economies of Southeast and East Asia, it is modernisation or westernisation that helps to lower the traditionally high levels of fertility. The process includes high levels (exceeding 75%) of literacy, urbanisation, and industrialisation, and a rise in the status of women. Some recent reviews of the subject have added to these variables the spread of communications and transport as key factors influencing fertility decline.

Implications of the Levels of Fertility:

In an analysis of change in the level of fertility between 1970-72 and 1989-91, the various socio-economic variables (female literacy, urbanisation, infant mortality, percentage of male workers engaged in non-farm activities) in the 16 major states showed no statistically significant association, except for female literacy. However, the values for Kerala seem to contribute a great deal to the association.

Otherwise, one essentially observes two clusters of states. One of the clusters includes the four large North Indian states (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) with both a high TFR and low female literacy and the second cluster having moderate levels of both TFR and female literacy.

The sharp decline in the level of fertility in Tamil Nadu without anything like the high level of literacy and low levels of infant and child mortality observed in Kerala, attests to the difficulty of identifying preconditions for fertility decline. Fertility has declined by more than 50% and reached almost three-fourths of the way towards a replacement level of fertility in at least three districts of Gujarat state where the IMR continues to be high and female literacy rates are much lower than even in Tamil Nadu.

These findings do not imply that universal literacy and low infant and child mortality are not worthwhile goals for a society to pursue or that societies can divert resources from the pursuit of these objectives to other issues. They do confirm, however, that it is difficult or impossible to specify the threshold levels of progress in social goals or modernisation that would usher in a sharp fertility decline.

In several discussions, Kerala’s experience is cited as a model to suggest that universal female literacy, low infant mortality, and a high status of women, summarised as social development, would help to accelerate fertility transition. However, the important role of international migration to the Gulf countries as a means of escaping the poverty trap and the associated rise in the aspirations of living desired for the family and the children is often underestimated.

Likewise, the history of matriarchal tradition in Kerala is often cited as indicating the high status enjoyed by Kerala women. However, the evidence on the subject is by no means clear. The key word at the International Conference on Population and Development (ICPD) at Cairo was empowerment of women. However, the concept of empowerment is difficult to translate. The NPP 2000 has stressed the need for ending discrimination against girls during childhood and early adolescence and against women during the childbearing period in order to improve their health and nutrition. Legal action is certainly not enough. Many laws enacted by our progressive legislatures continue to be violated with impunity in large parts of India.

Determinants of Declining Mortality:

The main reasons responsible for the decline in mortality rate are as under:

1. Decline in Epidemics:

In India, systematic efforts are being made to reduce the incidence of epidemics like plague, malaria, etc.

2. Urbanisation of Population:

Majority of population has migrated to towns. In 2011 Census, about 31% of the total population was in towns as compared to 28% in 1991. Moreover, development of medical and sanitary conditions has also reduced the death rate.

3. Late Marriages:

The late marriages have been encouraged in the country. Laws regarding marriages have been vigorously enforced.

4. More Medical Facilities:

Medical facilities in the country are going on to develop rapidly.

5. Spread of Education:

The literacy ratio in the country has been increasing at an equal interval. People have more resources and better facilities to nourish their children.

6. Change in Habits:

Habits of the people are also changing. Now they have more care for their health which has led to a decline in death rate.

7. Decline in Social Evils:

In India, various social evils like caste system, superstition, etc. are steadily being rooted out. This has also led to the decline in the death rate.

8. Balanced Diet:

People are getting better and balanced diet.

Essay # 3. Age and Sex Structure:

Currently, nearly half of the global population is below 25 years of age and one sixth is in the age group 15-24. In developed countries the reproductive age group population is relatively small; their fertility is low and the longevity at birth is high. Population profiles of these countries resemble a cylinder and not a pyramid. These countries have the advantages of having achieved a stable population but have to face the problems of having a relatively small productive workforce to support the large aged population with substantial non-communicable disease burden.

Some of the developing countries have undergone a very rapid decline in the birth rates within a short period. This enabled them to quickly achieve population stabilisation but they do face the problems of rapid changes in the age structure and workforce which may be inadequate to meet their manpower requirements. In contrast, the population in most of the developing countries, including India, consist of a very large proportion of children and persons in the reproductive age.

Because of the large reproductive age group (Population momentum) the population will continue to grow even when replacement level of fertility is reached (couples having only two children). Age statistics form an important component of population analysis, as most of the analysis is based on age-sex structure of the population.

The usefulness of age data is more noticeable when it is cross classified by variables like marital status, literacy, educational attainment, and economic activity which vary with age in different patterns. Apart from purely demographic concerns, the age-sex data structure is required for age specific analysis of data for planning, scientific, technical, and commercial purposes.

The dependency ratio, which is the ratio of economically active to economically inactive persons, is dependent on age composition. India has one of the largest proportions of population in the younger age groups in the world. 31.2% of the population of the country has been in the age group 0-14 years. Census 2001 data on marital status of persons show that out of over a billion population of the country, 513 million (49.8%) have reported as ‘Never married’, mainly due to high proportion of young people. The ‘Married’ constitute about 45.6% of the total population.

Essay # 4. Sex Ratio in India and Its Determinants:

The sex ratio of India has shown improvement during last two decades. Sex ratio, as per the recent Census is 940 which is largely comparable to the best performance (941 in 1961) in last fifty years. Several steps, including gender equality awareness campaigns were taken by the government to arrest the trend of declining sex ratio.

State Wise Comparison with All India Averages:

The lowest sex ratio among the States has been recorded in Haryana (877), Jammu & Kashmir (883), and Sikkim (889). Among the UTs, the lowest sex ratio has been returned in Daman & Diu (618), Dadra & Nagar Haveli (775), and Chandigarh (818). Among the major States, Bihar, Jammu & Kashmir, and Gujarat have experienced a fall in the sex ratio. The decline ranged from 2 points in Gujarat to 9 points in Jammu & Kashmir.

Other smaller Union Territories showing steep decline are Dadra & Nagar Haveli (37 points) and Daman and Diu (92 points). Perceptible increase has been observed in the major states such as Uttar Pradesh. It is interesting to note that states having historically low sex ratio such as Punjab, Haryana, Delhi, and Chandigarh have shown appreciable increase in the sex ratio in Census 2011 with Chandigarh and Delhi showing an improvement of more than 40 points compared to 2001.

Majority of the states identified as gender critical for special attention and intervention as part of the Census 2011 have shown increasing trend in the sex ratio as per the provisional results.

Essay # 5. Growth Rate of Population in India:

1. Growth during 1891 to 1921 :

The growth of population in India can be properly studied in three distinct phases. During the first phase of 30 years, i.e., from 1891 to 1921, the size of population in India increased from 23.6 crore to 25.1 crore, i.e., by 1.5 crore, showing the annual compound growth rate of only 0.19 per cent per annum. But the average annual growth rate of population gradually increased from 0.30 per cent in 1901 to 0.50 percent in 1911 and then attained a negative growth rate of -0.03 per cent in 1921.

2. Growth during 1921-51 :

During the second phase of 30 years, i.e., from 1921 to 1951, India’s population increased from 25.1 crore to 36.1 crore, i.e., by 11 crore and the annual compound growth rate during this second phase was 1.22 per cent. But the annual average growth rate of population in India gradually increased from 1.06 per cent in 1931 to 1.34 per cent in 1941 and then slightly declined to 1.26 per cent in 1951.

3. Growth during 1951-81:

During the third phase of 30 years, i.e., from 1951-1981, the size of population in India increased from 36.1 crore in 1951 to 68.3 crore in 1981, i.e., by 32.4 crore and the annual compound growth rate during the period was 2.15 per cent. Besides, the annual average growth rate of population in India increased from 1.98 per cent in 1961 to 2.20 per cent in 1971 and then to 2.25 per cent in 1981.

4. Growth during 1981-2011 :

Again as per 1991 census report, the total size of population in India increased to 84.4 crore in 1991 showing an annual average growth rate of 2.11 per cent which is slightly less than the previous decade. The decadal growth rate of population which was 24.7 per cent in during 1971-81 and then finally declined slightly to 24.8 per cent during 1981-91.

As per provisional census figure of 2001, the total population of India as on 1st March, 2001 stood at 102.70 crore. The decadal growth rate of population which was 23,8 per cent during 1981-91, gradually declined to 21.34 per cent in 1991-2001, showing a decadal increase of population to the extent of 18.3 crore. The annual average growth rate of population in India during 1991-2001 stood at 1.93 per cent.

As per provisional population totals of census 2011 the total population of India as on 1st March 2011 Stood at 121.07 crore. The decadal growth rate of population which was 21.34 per cent during 1991-2001, gradually declined to 17.70 per cent during 2001-2011, showing a decadal increase of population to the extent of 18.19 crore.

Thus as it was expected, that the rate of growth of population in India would decline significantly in response to country’s family planning programme. But it has not come true. At present India is passing through the second stage of demographic transition and thus facing a serious ‘population explosion’.

This population explosion itself reflects the cause and consequences of underdevelopment character of the economy. Thus although India experienced a sharp fall in the death rate due to its substantial expansion of hospital and medical facilities but the rate of growth of population in the country remained still high mainly due to its high birth rate.

Table 6.1 reveals that in 1891, total population of India was 23.6 crore and then it subsequently increased to 25.1 crore in 1921, 36.1 crore in 1951, 54.8 crore in 1971, 68.3 crore in 1981 and then to 84.4 crore in 1991. The size of population on 1st March 2001 was 102.7 crore and then it further increased to 121.07 crore in 2011.

Essay # 6. Factors Contributing to the High Growth Rate of Population:

Biological Factors:

1. Sharp Fall in Death Rate:

In India the death rate has fallen sharply during the first half of the twentieth century, i.e., from 42.6 per thousand in 1901-11 to 12.8 per thousand in 1951-61. Various factors are responsible for this sharp fall in death rate. Kingsley Davis mentioned that, “The causes of decline in Indian mortality are harder to establish than the fact itself.”

However, the factors which have largely contributed to this sharp fall in the death rate include removal of famines leading to eradication of starvation death, control of epidemics arising through cholera and small pox, decline in the incidence of malaria and tuberculosis and some other factors like improvement of public health measures like drinking water supply, improved hygienic and sanitation facilities and the improvement of medical and hospital facilities.

Thus all these factors had led to sudden and phenomenal fall in the death rate in recent years, i.e., to 7.0 per thousand in 2013 and this is considered as the most important factor for this high rate of growth of population in India.

2. No Substantial Fall in the Birth Rate:

During the first half of the present century, the birth rate in India did not fall substantially. The birth rate in India declined marginally for 49.2 per thousand in 1901-11 to 41.7 in 1951-61 and then to 21.8 per thousand in 2011.

Due to this maintenance of birth rate to a very high level, the rate of growth of population in India remained all along high. Moreover, due to tropical climate, puberty of women in India starts at an early age leading to a large number of births.

3. Accelerating Natural Growth Rate:

The most important factor which is responsible for the high rate of growth of population is its accelerating natural growth rate. This has resulted from the wide gap between the birth rate and death rate of population in India. The factor which is again responsible for this wide gap is the sudden and phenomenal fall in the death rate no substantial fall in the birth rate.

Due to remarkable advance in medical sciences along with the improvement and expansion of public health and medical facilities, the death rate in India has come down from 27.4 per thousand in 1951 to above 9.0 per thousand in 1996.

But the birth rate of Indian population still remained as high as 27.4 per thousand in 1996. All these had led to a severe increase in the natural growth rate of population from 12.5 per thousand in 1951 to 25.3 per thousand in 1971 and then slightly declined to 14.7 per thousand in 2011.

Social Factors:

1. Universality of Marriage:

Marriage is almost universal in India as it is a religious and social necessity of the country. Parents feel that it is their social obligation to arrange marriages for the daughters. Thus presently in India, about 76 per cent of women of their reproductive age are married and by attaining the age of 50 only 5 out of 1,000 Indian women remain unmarried. Hence, this has resulted a very high birth rate.

2. Practice of Early Marriage:

Practice of early marriage is very much common in various parts of the country and the average age of marriage is still around 18 years. Between the ages of 15 to 20 years, more than 8 out of every 10 girls got married in India. Thus the practice of empty marriage raises the span of reproductively. Some reduction of fertility would be possible if the average age of marriage of Indian women can be raised to 25 or more.

3. Illiteracy:

In India, illiteracy is widespread as a significant portion of Indian population and women in particular are still illiterate. The literacy rate among the women in India is only 65.4 per cent as against 82.1 per cent among men and the incidence of female illiteracy is comparatively much higher in backward states.

It has been observed by most of the economists that spread of education alone can change the attitudes of the people towards marriages, family, birth of a child etc. and help the people to shed irrational ideas and religious superstitions.

There is an inverse correlation between the spread of education and fertility. The findings of the Operations Research Group Survey show that birth rates in general are lower and adoption of family planning norms become more popular in those states where education is more widespread.

Further, due to lack of education, the response of rural population in respect of adoption of family planning norms and use of contraceptives are not at all encouraging.

4. Religious and Social Attitudes:

Religious and social attitudes of the Indian people induce to prefer large families. The idea to have sons and daughters for performing religious rites and to earn religious merit is still very much common in Indian society.

As Mamdani observed, “Marriage vows and blessing put emphasis on the good fortune of having many children………………. Sanctions against childless women further underline the necessity of children.” Moreover, social attitudes towards unmarried men and women and childless couple are not very encouraging. Further, the existence of joint family system induces thoughtlessness about the number of children.

5. Ignorance and Lack of Conscious Family Planning:

People of India are very much ignorant about the biology of reproduction, need for birth control and devices of birth control. In India, there is also lack of conscious family planning along-with lack of birth control devices, more particularly in the rural areas. That is why the Family Planning Programme in India could not do much headway in reducing the birth rate.

6. Other Factors:

Various other factors, viz., tropical climate, existence of polygamy, higher widow remarriages etc. are responsible for this high rate of growth of population in India. Moreover, growing immigration of population from the neighbouring countries like Bangladesh, Nepal etc. is also raising the growth rate of population in India to a considerable extent.

This problem of immigration is very much acute in Assam and north-eastern states, West Bengal and Bihar. This has been creating the problem of influx of population within the country besides raising a threat towards national security.

Essay # 7. Population Projection for India (2001-2026):

Population projection is a scientific attempt to peep into the future population scenario, conditioned by making certain assumptions, using data relating to the past available at that point of time. Assumptions used and their probability of adhering in future, forms a critical input in this mathematical effort.

Predicting the future course of human fertility and mortality is not easy, especially when looking beyond in time as medical and health intervention strategies, food production and its equitable availability, climatic variability, sociocultural setting, politico economic conditions, and a host of other factors influence population dynamics, making it difficult to predict the growth with certainty. Therefore, caution must be exercised while making or using the population projections in the context of various conditions imposed.

The Component Method is the universally accepted method of making population projections because growth of population is determined by fertility, mortality, and migration rates. Twenty-one States have been considered and applied the Component method. They are Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttaranchal, Uttar Pradesh, and West Bengal.

pThe projection of the seven north-eastern states (excluding Assam) has also been carried out as a whole. For the State of Goa and six Union territories, Mathematical Method has been applied. The data used are 2001 Census and Sample Registration System (SRS). SRS provides time series data of fertility and mortality, which has been used for predicting their future levels.

Critical Demographic Issues:

The salient features of the population projections at the national level, and some of the underlying assumptions in this regard, are as under:

1. The population of India is expected to increase from 1029 million to 1400 million during the period 2001-2026—an increase of 36% in twenty- five years at the rate of 1.2% annually. As a consequence, the density of population will increase from 313 to 426 persons per square kilometer.

2. The crude birth rate will decline from 23.2 during 2001-05 to 16.0 during 2021-25 because of falling level of total fertility. In contrast, the crude death rate is expected to fall marginally due to changing age structure of the population with the rising median age as a result of continuing decline in fertility and increase in the expectation of life at birth. It will drop from 7.5 during 2001-05 to 7.2 during 2021-25.

3. The infant mortality rate of the country, which is reported to be 63 in 2002, is expected to go down to 40 by the end of the period 2021-25.

4. Between 2001 and 2026, because of the declining fertility, the proportion of population aged under 15 years is projected to decline from 35.4 to 23.4%; the proportion of the middle (15-59 years) and the older ages (60 years and above) are set to increase considerably.

With the declining fertility, along with the increases in life expectancy, the number of older persons in the population is expected to increase by more than double from 71 million in 2001 to 173 million in 2026—an increase in their share to the total population from 6.9 to 12.4%. The proportion of population in the working age group 15-59 years is expected to rise from 57.7% in 2001 to 64.3% in 2026.

5. Another important consequence of the declining fertility will be that, at the national level, the population in the school-going age of 5-14 years is expected to decline from 243 million in 2001 to 222 million in 2026. The share of the population aged 5-14 years to total population of all ages is expected to decrease by 5% from 24% in 2001 to 19% in 2011 and by 3% between 2011 to 2026 (19 to 16%).

6. The youth population in the age group 15-24 years is expected to increase from 195 million in 2001 to 224 million in 2026. Its proportion to total population is expected to fall from 19% in 2001 to 16% in 2026.

7. The average Indian will be expected to be of 31 years old in 2026 compared to 23 years old in 2001.

8. Out of the total population increase of 371 million between 2001 and 2026, the share of the workers in the age group 15-59 years in this total increase is 83%. This has implication in the productivity of labour in future.

9. The sex ratio of the total population (females per 1000 males) is expected to decrease (i.e., become less feminine) from 933 in 2001 to 930 during 2026.

10. The Total Fertility Rate (TFR) is expected to decline from 2.9 during 2001-2005 to 2.0 during 2021-25. The assumption is that the Total Fertility Rate (TFR) would decline steadily and would touch the floor value of 1.8 in some states. With this, the weighted TFR is projected to reach the replacement level of 2.1 by the period 2021.

11. The urban population in the country, which is 28% in 2001, is expected to increase to 38% by 2026. The urban growth would account for over two-thirds (67%) of total population increase by 2026. Out of the total population increase of 371 million during 2001-2026 in the country, the share of increase in urban population is expected to be 249 million.

12. The demographic projections suggest that by 2026, the population of India will reach 1,384 million.

State Level Demographic Projections:

Considerable variation in the demographic growth amongst the States has been estimated.

The salient features of the projections at the state level are as under:

1. The State, which is expected to have least growth in the quarter century (2001-2026) is Tamil Nadu (15%), followed by Kerala (17%). In contrast, Delhi will have the highest projected growth of 102% during 2001-2026. States, which will have projected growths in the range of 20-30% are Himachal Pradesh, Punjab, West Bengal, Orissa, Andhra Pradesh, and Karnataka.

The population in the states of Haryana, Rajasthan, Uttar Pradesh, and Madhya Pradesh is projected to increase by 40-50% during 2001-2026, which is above the national average of 36%. The population of Uttar Pradesh is expected to be highest among all the states of the country at almost 249 million in 2026.

2. Of the projected increase in population of 371 million in India during 2001-26,187 million is likely to occur in the seven States of Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh, and Uttaranchal (termed as BIMARU states, since it was so before division). Thus, nearly 50% of India’s demographic growth during this period of twenty five years, is projected to take place in these seven states. 22 % of the total population increase in India of 371 million during 2001-26 is anticipated to occur in Uttar Pradesh alone.

The population in these seven states together is expected to grow at 1.5% per annum during 2001-26. In contrast, the contribution of the four southern states, namely Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu, to the total increase in population size of the country during 2001-2026 is expected to be 47 million—13% of total demographic growth of the country. The population in these four states together is expected to grow at 0.8% per annum during 2001-26.

3. Continuing decline in fertility and increase in the expectation of life at birth is expected to make a difference to the proportion of older population (60 years and above) between states. The State of Kerala, where lower fertility and mortality rates have been achieved earlier than the other states, the proportion of older persons aged 60 years and above is expected to increase from 11% in 2001 to 18% in 2026.

Thus, almost every sixth individual in Kerala is expected to be a senior citizen by 2026. In contrast, Uttar Pradesh is expected to have an increase of the proportion of old age population from 6% in 2001 to 10% in 2026, implying that the population of Uttar Pradesh will be expected to be relatively younger compared to that of Kerala. The median age of population in Kerala is expected to go up from 28 years in 2001 to 38 years in 2026. In contrast, the median age in Uttar Pradesh is expected to go up from 19 years to 27 years.

4. Because of declining fertility level in all the states, the crude birth rates (CBR) will also be declining. By 2021-25, except Uttar Pradesh, no state is expected to have a crude birth rate of 20 and above. The highest CBR of 20.5 per thousand is expected to be in Uttar Pradesh followed by Madhya Pradesh (18.0) during 2021-25.

Assam, Chhattisgarh, Bihar, Jharkhand, Rajasthan, and Uttaranchal are expected to have CBRs in the range of 16.5-17.6, close to the projected national level of 16.0. In most of the other states, the CBRs will be in the range 12-15. Kerala will be expected to have the least CBR of 12.3 followed by Tamil Nadu (12.5) during 2021-25.

5. In contrast to the CBRs, the situation is expected to be different in case of crude death rates (CDR). Because of increase in the expected proportion of ageing, in some of the states namely, Himachal Pradesh, Punjab, Delhi, West Bengal, Maharashtra, Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, and North Eastern Region, the crude death rates are likely to increase during 2021-25.

6. The infant mortality rate (IMR) is expected to decline in all the states during 2001-25. The IMR, which was highest in Orissa in 2002 at 87 is expected to come down to 52 in 2021-25, followed by Madhya Pradesh (51). Other states, where IMRs are expected to be in the range of 40-50 during 2021-25 are Jammu & Kashmir, Haryana, Rajasthan, Uttar Pradesh, Assam, and Andhra Pradesh. The lowest IMR is expected to be in Kerala, from 12 in 2001-05 to 8 during 2021-25. It will be followed by Delhi with IMR declining from 25 in 2001-05 to 18 during 2021-25.

7. In so far, as the projected sex ratio is concerned, it is observed that in some of the northern states, the population is expected to be more masculine, that is, the ratio will decrease in 2026. Lowest sex ratio of 789 is expected to be in Delhi in 2026, followed by 839 and 840 in Haryana and Punjab respectively. In the southern and eastern states except Kerala, the situation would be reverse. In Kerala, where there are excess females than males the trend would remain the same in 2026. Tamil Nadu is the other state, where the number of females is expected to be equal to the number of males in 2026.

Essay # 8. Population Projection in India by 2050 :

United Nation Population Fund (UNFPA) has projected the size of population of India and other countries by 2050 and the figures are released in its report ‘State of World Population 2008’. The report reveals that India whose population is growing by 1.5 per cent, will have 165.8 crore people against China’s 140.8 crore by 2050.

Accordingly, India will become the most populous country overtaking China by 2050.

The total fertility rate in India is 2.78 per cent which it is 1.73 in China where the population of growing by 0.6 per cent. The population of Pakistan will also increase from the current figure of 16.7 crore to 29.2 crore by 2050. The population of Bangladesh will increase from 16.1 crore to 25.4 crore by 2050. However, the population of Sri Lanka would witness negative growth as its present population will decline from 1.94 crore to 1.87 crore by 2050.

Some other Asian countries who are projected to be having negative growth include Japan and Korea. The population and U.S.A will increase from 30.8 crore at present to 40.2 crore by 2050. However, the World population will increase from 647 crore at present to 919 crore by 2050. The major chunk of the population growth will be recorded in less developed countries.

Related Articles:

• Population Problems of South Asia
• Essay on Groundwater Resources in India (with map)
• Essay on Hydropower Generation in India (with statistics)
• Essay on India: An Outstanding Essay Written on India

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Maternal Mortality Rate in India

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• Dhaanu's untimely demise due to an undiagnosed congenital heart defect highlights the gaps in maternal healthcare in rural Tamil Nadu.
• Lack of diagnostics and skilled obstetricians at the primary health care center contributed to the preventable tragedy.

ICMR-Funded Study:

• The Indian Council of Medical Research (ICMR) is funding a study to analyze maternal deaths caused by heart diseases.
• The study aims to develop a treatment protocol to prevent future mortality and enhance maternal healthcare services.

FINDINGS AND OTHER DETAILS

Emerging Risk Factor:

Changing Dynamics of Maternal Mortality:

• Maternal mortality rate (MMR) is a crucial indicator of women's health and childcare, reflecting a country's public health preparedness.
• While traditional risk factors like infections and excessive bleeding have been managed well, heart disease is emerging as a significant risk factor.

Trends in Maternal Mortality:

Improvement in MMR:

• Over the last two decades, India has witnessed a remarkable decline in MMR, showcasing progress in maternal healthcare.
• According to government data from 2018 to 2020, the maternal mortality rate stands at 97 deaths per lakh live births, reflecting ongoing efforts to reduce maternal mortality.

Understanding the Causes of Heart Disease Among Mothers :

Metabolic Changes during Pregnancy:

• Pregnancy induces significant metabolic changes in the body, increasing the risk of cardiovascular events.
• Cardiovascular alterations begin within the first eight weeks of pregnancy, with the risk of heart failure steadily rising by 24 weeks, plateauing at 30 weeks, and peaking around delivery.

Prevalent Heart Conditions:

• Valvular Heart Diseases:
• Studies indicate that valvular heart diseases, characterized by abnormal functioning of heart valves, are the most common cause of maternal deaths in India, comprising approximately two-thirds of cases.
• Congenital Heart Diseases:
• Congenital heart diseases, present from birth and affecting the heart's structure, account for 33 percent of maternal deaths.

Late Diagnosis and Stigma:

• Late Discovery of Underlying Conditions:
• Many pregnant women, particularly from lower economic backgrounds, may have undiagnosed heart conditions that surface during pregnancy.
• Stigma and Lack of Awareness:
• Stigma surrounding heart diseases may prevent family members from disclosing the condition to affected women.
• Some women remain unaware of their heart condition until pregnancy due to familial concealment or lack of diagnosis.

Impact on Maternal Health:

• Challenges Faced by Affected Women:
• Women with undisclosed or late-diagnosed heart diseases face heightened risks and complications during pregnancy.
• Lack of optimization of their condition before pregnancy contributes to poor maternal health outcomes.

Implementing a Treatment Protocol:

Collaboration between Specialties:

• Collaboration between cardiologists and obstetricians is crucial for identifying and managing complications of heart disease during pregnancy.
• Cardio-obstetrics teams in hospitals can facilitate interdisciplinary care, potentially reducing maternal deaths due to heart disease.

Role of Specialized Teams:

• Addressing Ignored Cases:
• In the absence of specialized teams, cases of heart disease during pregnancy may be overlooked or ignored.
• Dedicated cardio-obstetrics teams ensure comprehensive management of such cases, improving patient outcomes.

Current Status of Maternal Mortality:

Leading Causes of Maternal Deaths:

• Haemorrhage: 47%
• Pregnancy-related Infections: 12%
• Hypertensive Disorders of Pregnancy: 7% (Between 1997-2020)

States Achieving SDG Targets:

• Maharashtra: 33
• Telangana: 43
• Andhra Pradesh: 45
• Tamil Nadu: 54
• Jharkhand: 56
• Gujarat: 57
• Karnataka: 69

The proposed treatment protocol, coupled with nationwide collaborative efforts and specialized healthcare teams, holds promise for reducing maternal mortality rates due to heart disease and advancing maternal healthcare in India.

MUST READ ARTICLE: https://www.iasgyan.in/daily-current-affairs/maternal-mortality-rate

SOURCE: INDIAN EXPRESS

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• Biology Difference Between

Difference Between Morbidity And Mortality

The terms morbidity and mortality are often related but not identical. Morbidity is the state of being unhealthy for a particular disease or situation, whereas mortality is the number of deaths that occur in a population. Read on to explore the difference between morbidity and mortality in detail.

Morbidity vs Mortality

What is morbidity.

Morbidity refers to the state of being unhealthy. It applies to all the people affected by a disease in a particular region. The morbidity rate refers to the number of people affected by a particular disease. This helps health officials to make risk management and adopt national health systems according to the needs of the population.

What is Mortality?

Mortality shows the number of deaths in a particular population. It is expressed as the number of deaths per 100,000 people per year. It is measured with the help of systems such as SAPS II and III, APACHE-II, Glasgow, Coma scale, etc.

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