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.

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.

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 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.

Trends in healthy life expectancy among older Brazilian women between 1998 and 2008.

OBJECTIVE To analyze conditional and unconditional healthy life expectancy among older Brazilian women. METHODS This cross-sectional study used the intercensal technique to estimate, in the absence of longitudinal data, healthy life expectancy that is conditional and unconditional on the individual's current health status. The data used were obtained from the Pesquisa Nacional por Amostra de Domicílios (National Household Sample Survey) of 1998, 2003, and 2008.