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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.

Article

Measuring the impact of a public health crisis in terms of mortality might seem a straightforward method to quantify its effect on the population because deaths are much more easily registered compared to other health outcomes. However, despite the intuitive appeal of this path, it is far from obvious how to best operationalize it, and all the most used methods have drawbacks that should be kept in mind. Especially during the COVID-19 pandemic, the major routes that have been considered are cause-specific death counts (and related measures such as case fatality rates), excess deaths estimates, and life expectancy decline. All the considered approaches have limitations: Cause-specific deaths are often subject to undercount or overcount issues with significant differences both between and within countries, excess deaths estimates may strongly depend on the baseline (there are several methods to estimate it), and life expectancy drop estimates (or estimates of years of life lost) also depend on the reference level used, which can vary substantially across countries. More generally, the issues of available data quality and standardization of age structure should be taken into proper account. Thus, the choice of which approach is worth using depends on the characteristics of the crisis that need to be evaluated and the type and quality of data available. Interestingly, the three approaches can also be combined so that some of their limitations can be mitigated.