The Dangers of Drawing Cohort Profiles From Period Data: A Research Note.

Drawing cohort profiles and cohort forecasts from grids of age-period data is common practice in demography. In this research note, we (1) show how demographic measures artificially fluctuate when calculated from the diagonals of age-period rates because of timing and cohort-size bias, (2) estimate the magnitude of these biases, and (3) illustrate how prediction intervals for cohort indicators of mortality may become implausible when drawn from Lee-Carter methods and age-period grids. These biases are surprisingly large, even when the cohort profiles are created from single-age, single-year period data. The danger is that we overinterpret deviations from expected trends that were induced by our own data manipulation.

Diferenciais regionais da mortalidade no Brasil: contribuição dos grupos etários e de causas de óbito sobre a variação da esperança de vida e da dispersão da idade à morte entre 2008 e 2018 / Regional inequalities in mortality in Brazil: contributions of age and cause of death to changes in life expectancy and lifespan variation between 2008 and 2018 / Diferenciales regionales de mortalidad en Brasil: contribución de los grupos de edad y las causas de muerte en la variación de la esperanza de vida y la dispersión de la edad al morir entre 2008 y 2018

Resumo O Brasil é um país marcado por forte desigualdade socioeconômica entre as regiões, que, por sua vez, se traduz em diferenciais regionais de mortalidade. Para um bom monitoramento desses diferenciais, é importante uma análise não apenas dos níveis médios de mortalidade, mas também da variação da idade à morte na população. Esse artigo analisa a contribuição das causas de óbito sobre as mudanças na esperança de vida e na dispersão da idade à morte no Brasil e grandes regiões entre 2008 e 2018. Os resultados sugerem aumento dos diferenciais regionais na esperança de vida ao longo da década analisada. No entanto, as diferenças regionais na dispersão da idade à morte se mantiveram praticamente constantes. As mudanças na mortalidade por causa impactam de maneiras diferentes a dispersão da idade à morte em cada região: a redução da mortalidade por causas externas contribui substantivamente para diminuir a variação da idade à morte nas regiões Sul e Sudeste, enquanto a contribuição das mortes por afecções originadas no período perinatal foi substantiva apenas na região Nordeste. Por fim, reafirmamos a importância dos indicadores de dispersão da idade à morte para se ter uma visão mais ampla dos diferenciais regionais de mortalidade no Brasil.

Abstract Brazil is a country marked by substantial socioeconomic inequality among regions, which translates into regional differentials in mortality. For better monitoring these differentials, it is important to analyze not only population average mortality levels, but also the age at death variation. This article analyzes cause-of-death contributions to changes in life expectancy and age-at-death variation in Brazil and its regions between 2008 and 2018. Our results suggest an increase in regional inequalities in life expectancy over the decade. However, regional differences in age-at-death variation remained nearly constant. Changes in mortality by cause impact the age-at-death variation differently in each region: the reduction in mortality from external causes substantially contributed to decreasing the variation in age at death in the South and Southeast regions, whereas the contribution of deaths from conditions originating in the perinatal period was substantive only in the Northeast region. Finally, we reaffirm the importance of age-at-death dispersion indicators to have a broader view of Brazil's regional differentials in mortality.

Resumen Brasil es un país marcado por fuertes desigualdades socioeconómicas entre sus regiones, lo que traduce a su vez se en diferencias regionales en la mortalidad. Para un buen seguimiento de estos diferenciales es importante analizar no solo los niveles medios de mortalidad, sino también la variación de la edad de la muerte en la población. Este artículo analiza la contribución de los grupos de causas de defunción sobre los cambios en la esperanza de vida al nacer y la dispersión de la edad al morir en Brasil y las grandes regiones entre 2008 y 2018. Nuestros resultados sugieren un aumento de las diferencias regionales en la esperanza de vida a lo largo de la década. Sin embargo, las diferencias regionales en la dispersión de la edad al morir se mantuvieron prácticamente constantes. Los cambios en la mortalidad por causas repercuten de forma diferente en la dispersión de la edad al fallecer en cada región: la reducción de la mortalidad por causas externas contribuyó de forma sustantiva a disminuir la variación de la edad al morir en las regiones Sur y Sureste, mientras que la contribución de las muertes por afecciones originadas en el período perinatal fue sustantiva en la región Noreste. Por último, reafirmamos la importancia de los indicadores de dispersión de la edad al morir para tener una visión más general de los diferenciales regionales de mortalidad en Brasil.

Sensitivity Analysis of Excess Mortality due to the COVID-19 Pandemic.

Estimating excess mortality is challenging. The metric depends on the expected mortality level, which can differ based on given choices, such as the method and the time series length used to estimate the baseline. However, these choices are often arbitrary, and are not subject to any sensitivity analysis. We bring to light the importance of carefully choosing the inputs and methods used to estimate excess mortality. Drawing on data from 26 countries, we investigate how sensitive excess mortality is to the choice of the mortality index, the number of years included in the reference period, the method, and the time unit of the death series. We employ two mortality indices, three reference periods, two data time units, and four methods for estimating the baseline. We show that excess mortality estimates can vary substantially when these factors are changed, and that the largest variations stem from the choice of the mortality index and the method. We also find that the magnitude of the variation in excess mortality is country-specific, resulting in cross-country rankings changes. Finally, based on our findings, we provide guidelines for estimating excess mortality.

What should be the baseline when calculating excess mortality? New approaches suggest that we have underestimated the impact of the COVID-19 pandemic and previous winter peaks.

Excess mortality has been used to measure the impact of COVID-19 over time and across countries. But what baseline should be chosen? We propose two novel approaches: an alternative retrospective baseline derived from the lowest weekly death rates achieved in previous years and a within-year baseline based on the average of the 13 lowest weekly death rates within the same year. These baselines express normative levels of the lowest feasible target death rates. The excess death rates calculated from these baselines are not distorted by past mortality peaks and do not treat non-pandemic winter mortality excesses as inevitable. We obtained weekly series for 35 industrialized countries from the Human Mortality Database for 2000-2020. Observed, baseline and excess mortalities were measured by age-standardized death rates. We assessed weekly and annual excess death rates driven by the COVID-19 pandemic in 2020 and those related to seasonal respiratory infections in earlier years. There was a distinct geographic pattern with high excess death rates in Eastern Europe followed by parts of the UK, and countries of Southern and Western Europe. Some Asia-Pacific and Scandinavian countries experienced lower excess mortality. In 2020 and earlier years, the alternative retrospective and the within-year excess mortality figures were higher than estimates based on conventional metrics. While the latter were typically negative or close to zero in years without extraordinary epidemics, the alternative estimates were substantial. Cumulation of this "usual" excess over 2-3 years results in human losses comparable to those caused by COVID-19. Challenging the view that non-pandemic seasonal winter mortality is inevitable would focus attention on reducing premature mortality in many countries. As SARS-CoV-2 is unlikely to be the last respiratory pathogen with the potential to cause a pandemic, such measures would also strengthen global resilience in the face of similar threats in the future.

The Cross-sectional Average Inequality in Lifespan (CAL†): A Lifespan Variation Measure That Reflects the Mortality Histories of Cohorts.

Lifespan variation is a key metric of mortality that describes both individual uncertainty about the length of life and heterogeneity in population health. We propose a novel and timely lifespan variation measure, which we call the cross-sectional average inequality in lifespan, or CAL†. This new index provides an alternative perspective on the analysis of lifespan inequality by combining the mortality histories of all cohorts present in a cross-sectional approach. We demonstrate how differences in the CAL† measure can be decomposed between populations by age and cohort to explore the compression or expansion of mortality in a cohort perspective. We apply these new methods using data from 10 low-mortality countries or regions from 1879 to 2013. CAL† reveals greater uncertainty in the timing of death than the period life table-based indices of variation indicate. Also, country rankings of lifespan inequality vary considerably between period and cross-sectional measures. These differences raise intriguing questions as to which temporal dimension is the most relevant to individuals when considering the uncertainty in the timing of death in planning their life courses.