COVID-19 and all-cause-mortality; differential trends between countries and selected states of India

By 22nd January 2021, the Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus had infected over 98 million and 10.6 million individuals globally and in India, with 1.7 million and 153, 067 deaths, respectively. Case Fatality rates (CFR) due to coronavirus disease 2019 (COVID 19) have varied significantly between countries. In order to understand the true impact of the pandemic, we should report coronavirus (COVID-19) mortality in the context of all-cause and non-COVID-19 mortality, and compare with previous years. The consequences of the pandemic have been, and will be, different in different settings within and across countries. To compare the all-cause mortality in the year 2020 with previous years in three selected states of India correlate it to the burden of COVID19 and compare all-cause mortality between three states of India to four high income countries. We provide quantitative data in three states across India (Himachal Pradesh, Kerala and Odisha) and compare with high-income countries to illustrate the importance of context-specific data monitoring and public health responses. There was a 1.9% increase in deaths, with 2.8% decrease in births in 2020, compared to 2019 in Himachal Pradesh, 13.3 and 9.2% decrease in Kerala and 16.7% and 21.4% decrease in Odisha. There was a direct correlation of all cause mortality to CFR on comparison between three states of India and despite the enormous burden of COVID19 in India all-cause mortality was lower compared to previous years in addition to the CFR due to COVID 19 being lower than in selected HICs.

COVID-19 and all-cause-mortality; differential trends between countries and selected states of India

Background: By 22nd January 2021, the SARS-CoV-2 virus had infected over 98 million and 10.6 million individuals globally and in India, with 1.7 million and 153, 067 deaths, respectively1. Case Fatality rates (CFR) due to COVID 19 have varied significantly between countries. In order to understand the true impact of the pandemic, we should report coronavirus (COVID-19) mortality in the context of all-cause and non-COVID-19 mortality, and compare with previous years. The consequences of the pandemic have been, and will be, different in different settings within and across countries.Objectives: To compare the all-cause mortality in the year 2020 with previous years in three selected states of India correlate it to the burden of COVID19 and compare all-cause mortality between three states of India to four high income countries.We also compared the number of cases, deaths, CFR, prevalence of NCDs per million and the proportion of population > age 65 in India to four high income countries (HIC)- the UK, US, Spain and Italy.Methods: We provide quantitative data in three states across India (Himachal Pradesh, Kerala and Odisha) and compare with high-income countries to illustrate the importance of context-specific data monitoring and public health responses.Results: There was a 1.9% increase in deaths, with 2.8% decrease in births in 2020, compared to 2019 in Himachal Pradesh, 13.3 and 9.2% decrease in Kerala and 16.7% and 21.4% decrease in Odisha.Conclusion: There was a direct correlation of all cause mortality to CFR on comparison between three states of India and despite the enormous burden of COVID19 in India all-cause mortality was lower compared to previous years in addition to the CFR due to COVID 19 being lower than in selected HICs.

Disaggregation of official demographic projections in sub-groups by education level: the neglected “composition effect” in the future path of life expectancy

We developed an innovative method to break down official population forecasts by educational level. The mortality rates of the high education group and low education group were projected using an iterative procedure, whose starting point was the life tables by education level for Italy, based on the year 2012. We provide a set of different scenarios on the convergence/divergence of the mortality differential between the high and low education groups. In each scenario, the demographic size and the life expectancy of the two sub-groups were projected annually over the period 2018–2065. We compared the life expectancy paths in the whole population and in the sub-groups. We found that in all of our projections, population life expectancy converges to the life expectancy of the high education group. We call this feature of our outcomes the “composition effect”, and we show how highly persistent it is, even in scenarios where the mortality differential between social groups is assumed to decrease over time. In a midway scenario, where the mortality differential is assumed to follow an intermediate path between complete disappearance in year 2065 and stability at the 2012 level, and in all the scenarios with a milder convergence hypothesis, our “composition effect” prevails over the effect of convergence for men and women. For instance, assuming stability in the mortality differential, we estimated a life expectancy increase at age 65 of 2.9 and 2.6 years for men, and 3.2 and 3.1 for women, in the low and high education groups, respectively, over the whole projection period. Over the same period, Italian official projections estimate an increase of 3.7 years in life expectancy at age 65 for the whole population. Our results have relevant implications for retirement and ageing policies, in particular for those European countries that have linked statutory retirement age to variations in population life expectancies. In all the scenarios where the composition effect is not offset by a strong convergence of mortality differentials, we show that the statutory retirement age increases faster than the group-specific life expectancies, and this finding implies that the expected time spent in retirement will shrink for the whole population. This potential future outcome seems to be an unintended consequence of the indexation rule.

The Intellectual Disability Mortality Disadvantage: Diminishing With Age?

On average, adults with intellectual disability (ID) have higher mortality risk than their peers in the general population. However, the effect of age on this mortality disadvantage has received minimal attention. Using data from the 1986–2011 National Health Interview Survey–Linked Mortality Files (NHIS–LMF), discrete time hazard models were used to compare mortality risk for adults with and without ID by age and gender. Increased mortality risk was present for all adults with ID, but was most pronounced among younger age females. The mortality differential between those with and without ID diminished with increased age for both females and males. Findings support the argument that heterogeneity of frailty may explain differences in mortality risk between those with and without ID.