Interpreting changes in life expectancy during temporary mortality shocks

A historical syndemic? The impact of synergistic epidemics of measles and scarlet fever on life expectancy in Victoria, Australia (1860s-1870s)

OBJECTIVES

To explore whether synergistic epidemics of measles and scarlet fever in 1860s-1870s Victoria, Australia could be characterized as syndemics, we apply the methods of Sawchuk, Tripp, and Samakaroon (Social Science & Medicine 2022, 295, 112956) to quantify the impact of each of the two major co-occurring epidemic events (1867, 1875) in terms of life expectancy (LE) changes. Sawchuk et al. posit the presence of a harvesting effect, indicated by a statistically significant increase in LE in the immediate post-epidemic "fallow period", as a criterion for identification of a historical syndemic. We test an alternate hypothesis that the same methods can identify a short-term scarring effect.

MATERIALS AND METHODS

Using annual age- and cause-specific death statistics and census population data, we constructed abridged period life tables for baseline period, potential syndemic year, and "fallow" year for each of the two periods (1860s and 1870s). We compared LE at birth using Z-tests. We decomposed age-cause-specific mortality according to Arriaga's method to identify age-and cause-specific contributions to LE change.

RESULTS

LE was significantly lower than baseline (1864-1865) in 1867 but not in the "fallow" year (1869). LE in 1875 and the 1878 "fallow" year were both significantly below baseline (1871-1873). Age-cause-specific decomposition showed similar patterns for 1867 and 1875 for measles and scarlet fever combined effects.

DISCUSSION

Evidence of a scarring effect following the 1875 measles/scarlet fever combined peak supports the interpretation of this event as a syndemic. We suggest the short-term scarring effect can be a useful additional criterion for identifying historical syndemics.

Population age structure dependency of the excess mortality P-score

BACKGROUND

Since the outbreak of the COVID-19 pandemic, the excess mortality P-score has gained prominence as a measure of pandemic burden. The P-score indicates the percentage by which observed deaths deviate from expected deaths. As the P-score is regularly used to compare excess mortality between countries, questions arise regarding the age dependency of the measure. In this paper we present formal and empirical results on the population structure bias of the P-score with a special focus on cross-country comparisons during the COVID-19 pandemic in Europe.

METHODS

P-scores were calculated for European countries for 2021, 2022, and 2023 using data from the 2024 revision of the United Nations' World Population Prospects and the HMDs Short Term Mortality Fluctuations data series. The expected deaths for 2021, 2022, and 2023 were estimated using a Lee-Carter forecast model assuming pre-pandemic conditions. P-score differences between countries were decomposed using a Kitagawa-type decomposition into excess-mortality and structural components. To investigate the sensitivity of P-score cross-country rankings to differences in population structure we calculated the rank-correlation between age-standardized and classical P-scores.

RESULTS

The P-score is an average of age-specific percent excess deaths weighted by the age-distribution of expected deaths. It can be shown that the effect of differences in the distribution of deaths only plays a marginal role in a European comparison. In most cases, the excess mortality effect is the dominant effect. P-score rankings among European countries during the COVID-19 pandemic are similar under both age-standardized and classical P-scores.

CONCLUSIONS

Although the P-score formally depends on the age-distribution of expected deaths, this structural component only plays a minor role in a European comparison, as the distribution of deaths across the continent is similar. Thus, the P-score is suitable as a measure of excess mortality in a European comparison, as it mainly reflects the differences in excess mortality. However, this finding should not be extrapolated to global comparisons, where countries could have very different death distributions. In situations were P-score comparisons are biased age-standardization can be applied as a solution.

Spatial Variation in Excess Mortality Across Europe: A Cross-Sectional Study of 561 Regions in 21 Countries

OBJECTIVE

To measure the burden of the COVID-19 pandemic in 2020 at the subnational level by estimating excess mortality, defined as the increase in all-cause mortality relative to an expected baseline mortality level.

METHODS

Statistical and demographic analyses of regional all-cause mortality data provided by the vital statistics systems of 21 European countries for 561 regions in Central and Western Europe. Life expectancy losses at ages 0 and 60 for males and females were estimated.

RESULTS

We found evidence of a loss in life expectancy in 391 regions, whilst only three regions exhibit notable gains in life expectancy in 2020. For 12 regions, losses of life expectancy amounted to more than 2 years and three regions showed losses greater than 3 years. We highlight geographical clusters of high mortality in Northern Italy, Spain and Poland, whilst clusters of low mortality were found in Western France, Germany/Denmark and Norway/Sweden.

CONCLUSIONS

Regional differences of loss of life expectancy are impressive, ranging from a loss of more than 4 years to a gain of 8 months. These findings provide a strong rationale for regional analysis, as national estimates hide significant regional disparities.

The effect of the COVID-19 pandemic on life expectancy in the USA: An application of hybrid life expectancy

Pandemics are, by definition, temporary intervals of substantially increased mortality rates experienced across a wide geographic area. One way of assessing the magnitude of the COVID-19 pandemic in the USA has been to compute the differences in life expectancy at birth during a pandemic year and the year before the pandemic. Such comparisons are misleading because they do not account for the duration of the pandemic. The computation of life expectancy in 2019 assumes that people spend their entire lives experiencing prepandemic mortality rates. The computation of life expectancy in 2021 assumes that people live their entire lives in a permanent pandemic. However, people do not live their entire lives experiencing the elevated mortality rates of 2021. This article introduces a method for calculating life expectancy that reflects the experience of people enduring pandemic-level mortality rates for fixed durations. We call the new quantity hybrid life expectancy because it integrates both pandemic and prepandemic mortality rates. The difference in life expectancy at birth in the USA in 2019 with and without a 3-year-long pandemic is 0.01 years. This is because mortality rates at ages 0, 1, and 2 in the pandemic were essentially unchanged from their prepandemic levels. Life expectancy at age 65 incorporating a 3-year pandemic is 0.18 years lower than life expectancy would have been without it. Reductions in life expectancy due to the COVID-19 pandemic using hybrid life expectancy are dramatically lower than differences in life expectancy that do not take the duration of the pandemic into account.

Lifetime years married held steady for men with a BA degree since 1960 but dropped to lowest level since 1880 for men without a BA

Trends in life expectancy and marriage patterns work together to determine expected lifetime years married. In 1880, adult life expectancy was short and marriages were more likely to end by death than divorce. Since then, although there have been substantial life expectancy gains in adulthood, marriage has been increasingly delayed or forgone and cohabitation and divorce are far more prevalent. Whether adults today can expect to spend more or fewer years married than in the past depends on the relative magnitude of changes in mortality and marriage. We estimate trends in men's expected lifetime years married (and in other marital statuses) from 1880 to 2019 and by bachelor's degree (BA) status from 1960 to 2019. Our results show a rise in men's expected lifetime years married between 1880 and the Baby Boom era and a subsequent fall. There are large and growing differences by BA status. Men with a BA have had high and relatively stable expected lifetime years married since 1960. For men without a BA, expected lifetime years married has plummeted to lows not seen among men since 1880. Cohabitation accounts for a substantial fraction, although not all, of these declines. Our results demonstrate how increasing inequality in both life expectancy and marriage patterns combine to amplify educational differences in lifetime experiences of coresidential partnerships.