Summertime, and the livin’ is easy: Winter and summer pseudoseasonal life expectancy in the United States

Identifying age- and sex-specific COVID-19 mortality trends over time in six countries

OBJECTIVES

The COVID-19 pandemic is characterized by successive waves that each developed differently over time and through space. We aim to provide an in-depth analysis of the evolution of COVID-19 mortality during 2020 and 2021 in a selection of countries.

METHODS

We focus on five European countries and the United States. Using standardized and age-specific mortality rates, we address variations in COVID-19 mortality within and between countries, and demographic characteristics and seasonality patterns.

RESULTS

Our results highlight periods of acceleration and deceleration in the pace of COVID-19 mortality, with substantial differences across countries. Periods of stabilization were identified during summer (especially in 2020) among the European countries analyzed but not in the United States. The latter stands out as the study population with the highest COVID-19 mortality at young ages. In general, COVID-19 mortality is highest at old ages, particularly during winter. Compared with women, men have higher COVID-19 mortality rates at most ages and in most seasons.

CONCLUSION

There is seasonality in COVID-19 mortality for both sexes at all ages, characterized by higher rates during winter. In 2021, the highest COVID-19 mortality rates continued to be observed at ages 75+, despite vaccinations having targeted those ages specifically.

Interpreting changes in life expectancy during temporary mortality shocks

BACKGROUND

Life expectancy is a pure measure of the mortality conditions faced by a population, unaffected by that population's age structure. The numerical value of life expectancy also has an intuitive interpretation, conditional on some assumptions, as the expected age at death of an average newborn. This intuitive interpretation gives life expectancy a broad appeal. Changes in life expectancy are also routinely used to assess mortality trends. Interpreting these changes is not straightforward as the assumptions underpinning the intuitive interpretation of life expectancy are no longer valid. This is particularly problematic during mortality 'shocks,' such as during wars or pandemics, when mortality changes may be sudden, temporary, and contrary to secular trends.

OBJECTIVE

This study aims to provide an alternative perspective on what changes in life expectancy measure that remains applicable during mortality shocks.

CONCLUSIONS

Returning to two different models that the period life table may represent, I show that a difference in life expectancy is typically interpreted from the synthetic cohort model as the difference in mean longevity between different birth cohorts. However, it can also be interpreted from the stationary population model as a measure of premature mortality in a death cohort. The latter, less common interpretation makes more sense for temporary declines in life expectancy induced by mortality shocks. The absolute change in life expectancy is then an age-standardized value of the average lifespan reduction for people dying during the mortality shock.

CONTRIBUTION

To clarify what a decline in life expectancy measures during mortality shocks is important, especially as demographers often assess the mortality impact of those shocks using this metric, which gets widely reported beyond demographers' inner circle.

Seasonal variation in infant mortality in India

Investigating seasonal variation in health helps us understand interactions between population, environment, and disease. Using information on birth month and year, survival status within the first year of life, and age at death (if applicable) of more than 330,000 children observed in four rounds of India's Demographic and Health Surveys, I estimate period mortality rates between birth and age one (₁m₀) by calendar month. Relative to spring months, infant mortality is higher in the summer, monsoon, and winter months. If spring mortality conditions had been prevalent throughout the year, mortality below age one would have been lower by 11.4 deaths per 1,000 in the early 1990s and 3.7 deaths per 1,000 in the mid-2010s. Seasonal variation in infant mortality has declined overall but remains higher among disadvantaged children. The results highlight the multiple environmental health threats that Indian infants face and the short time of year when these threats are less salient.

The Mean Unfulfilled Lifespan (MUL): A new indicator of the impact of mortality shocks on the individual lifespan, with application to mortality reversals induced by COVID-19

Declines in period life expectancy at birth (PLEB) provide seemingly intuitive indicators of the impact of a cause of death on the individual lifespan. Derived under the assumption that future mortality conditions will remain indefinitely those observed during a reference period, however, their intuitive interpretation becomes problematic when period conditions reflect a temporary mortality "shock", resulting from a natural disaster or the diffusion of a new epidemic in the population for instance. Rather than to make assumptions about future mortality, I propose measuring the difference between a period average age at death and the average expected age at death of the same individuals (death cohort): the Mean Unfulfilled Lifespan (MUL). For fine-grained tracking of the mortality impact of an epidemic, I also provide an empirical shortcut to MUL estimation for small areas or short periods. For illustration, quarterly MUL values in 2020 are derived from estimates of COVID-19 deaths that might substantially underestimate overall mortality change in affected populations. These results nonetheless illustrate how MUL tracks the mortality impact of the pandemic in several national and sub-national populations. Using a seven-day rolling window, the empirical shortcut suggests MUL peaked at 6.43 years in Lombardy, 8.91 years in New Jersey, and 6.24 years in Mexico City for instance. Sensitivity analyses are presented, but in the case of COVID-19, the main uncertainty remains the potential gap between reported COVID-19 deaths and actual increases in the number of deaths induced by the pandemic in some of the most affected countries. Using actual number of deaths rather than reported COVID-19 deaths may increase seven-day MUL from 6.24 to 8.96 years in Mexico City and from 2.67 to 5.49 years in Lima for instance. In Guayas (Ecuador), MUL is estimated to have reached 12.7 years for the entire month of April 2020.

The Mean Unfulfilled Lifespan (MUL): A new indicator of the impact of mortality shocks on the individual lifespan, with application to global 2020 quarterly mortality from COVID-19

Declines in period life expectancy at birth (PLEB) provide intuitive indicators of the impact of a cause of death on the individual lifespan. Derived under the assumption that future mortality conditions will remain indefinitely those observed during a reference period, however, the intuitive interpretation of a PLEB becomes problematic when that period conditions reflect a temporary mortality "shock", resulting from a natural disaster or the diffusion of a new epidemic in the population for instance. Rather than to make assumptions about future mortality, I propose measuring the difference between a period average age at death and the average expected age at death of the same individuals (death cohort): the Mean Unfulfilled Lifespan (MUL). For fine-grained tracking of the mortality impact of an epidemic, I also provide an empirical shortcut to MUL estimation for small areas or short periods. For illustration, quarterly MUL values in 2020 are derived from estimates of COVID-19 deaths in 159 national populations and 122 sub-national populations in Italy, Mexico, Spain and the US. The highest quarterly values in national populations are obtained for Ecuador (5.12 years, second quarter) and Peru (4.56 years, third quarter) and, in sub-national populations, for New York (5.52 years), New Jersey (5.56 years, second quarter) and Baja California (5.19 years, fourth quarter). Using a seven-day rolling window, the empirical shortcut suggests the MUL peaked at 9.12 years in Madrid, 9.20 years in New York, and 9.15 years in Baja California, and in Guayas (Ecuador) it even reached 12.6 years for the entire month of April. Based on reported COVID-19 deaths that might substantially underestimate overall mortality change in affected populations, these results nonetheless illustrate how the MUL tracks the mortality impact of the pandemic, or any mortality shock, retaining the intuitive metric of differences in PLEB, without their problematic underlying assumptions.