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.
Usha Ram and Faujdar Ram
Si Ying Tan and Jeremy Fung Yen Lim
Digital health technology has been adopted rapidly by countries as tools to promote good public health outcomes over the last decade. The COVID-19 pandemic that occurred since November 2019 has further accelerated the salience and relevance of digital health technology in tackling public health issues as countries start to implement movement restriction policies that pose a challenge to the physical delivery of healthcare services. Unarguably, the pandemic has elevated the significances of digital solutions to public health issues, which include improving access to an increased range of health services and the potential of cost-saving, maximizing population-wide health impacts through behavioral modifications, and controlling and managing public health emergencies. In general, digital technology in public health has three major applications—monitoring, decision support, and education. Monitoring is especially relevant in the context of effective disease screening and pandemic surveillance, decision support applies to the promotion of behavior modifications and resource optimization, while education serves to improve population-level health awareness and knowledge. Despite the promises of digital solutions to address various public health issues, there are unintended consequences that could arise consequent to their widespread applications, resulting in governance challenges and ethical issues in their applications, such as data privacy and erosion of trust, safety, cybersecurity, algorithmic bias, liability, autonomy, and social justice. To reap tangible benefits and positive impacts from large-scale deployment of various digital health solutions, countries need to anchor their national digital health policies or strategies by considering not only their benefits and applications, but also various governance challenges and ethical issues that could ensue during their implementations.
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.
Amy L. Ai, Hoa B. Appel, and Sabrina L. Dickey
Cardiovascular disease (CVD) is the leading cause of death in the United States, but the burden of CVD falls disproportionately on racial and ethnic minority populations. Blacks are especially impacted by CVD. Since the 2010s, mortality from CVD has declined and life expectancy disparity between White and Black males has decreased. However, the mortality rate in Blacks remains the highest among all racial and ethnic groups. For example, concerning survival differences between White and Black patients with acute myocardial infarction, 5-year mortality for Black patients is significantly higher than that for White patients. Also, hypertension or high blood pressure and stroke, two of the most disabling diseases, burden Blacks much more than other groups. Furthermore, several major CVD comorbidities or risk factors are linked with disparity in Blacks, especially diabetes, obesity, and chronic kidney diseases. Physical inactivity is a major risk factor. Blacks and Hispanics, as well as Asian American women, all have higher rates of physical inactivity compared with Whites. The literature indicates the remarkable psychosocial and environmental issues that underlie CVD disparities in Black populations. Specifically, the social determinants of health (SDOH) have been shown to be significant indicators of CVD morbidity and mortality causing a disproportionate impact on racial and ethnic minorities and low socioeconomic status populations. These SDOH involving economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context provide a framework for a multifactorial approach to understand the impact of CVD on the Black community. The Black community has a history of trauma from racism and discrimination, which is still evident in the existence of structural racism. Trust in the health care system within the Black community remains an ongoing issue and stems from the unethical Tuskegee Study. The lack of trust in the U.S. health care system by the Black community is evident in the limited number of Black participants in research and the excess of health disparities within the Black community. Utilizing SDOH provides a context for understanding the complexity of addressing health disparities among historically marginalized groups. A unifactorial approach will not suffice when there are a number of physical, psychosocial, economic, and environment factors that adversely impact the health of underserved and underrepresented groups such as African Americans. Stringent policies to address racism, discrimination, and adequate access to health care for the Black community must be implemented to decrease the presence of CVD as a health disparity. Without the presence of a social and physical environment that provides adequate resources, such as health care services, quality education to attain employment and be health literate, employment to afford access to health care, and the support to engage in preventive care, African Americans will continue to suffer from various health disparities, such as CVD, and have shorter life spans compared to other racial and ethnic groups.