Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: What can we learn from it?

Background: Age misreporting affects population estimates at older ages. In Brazil, every citizen must be registered and show an identity document to vaccinate against COVID-19. This requirement to present proof of age provides a unique opportunity for measuring the oldest-old population using novel administrative data. Objective: To offer critically assessed estimates of the Brazilian population aged 80 and older based on data from the vaccination registration system (VRS). To uncover discrepancies between the number of vaccinated oldest-old people and the projections used to estimate target populations for COVID-19 vaccination. Methods: We calculate data quality indicators based on data from the VRS - namely, 100+/80+ and 90+/80+ population proportions, sex ratios, and the Myers blended index - and compare them to those based on data on target populations from Brazilian censuses and demographic projections, and from Sweden - a country with high-quality data. We also estimate vaccination coverage ratios using population projections adjusted to excess deaths as the denominators. Results: Requiring documentation reduces age heaping, age exaggeration, and sex ratios marginally. However, it cannot solve the problem of the misreporting of birth dates due to the absence of long-standing birth registration systems in Brazil, particularly in the northern and central regions. In addition, we find a mismatch between the projected populations and numbers of vaccinated people across regions. Conclusions: Despite improvements in data quality in Brazil, we are still not confident about the accuracy of age reporting among the oldest old in the less advantaged Brazilian regions. The postponement of the 2020 census reduced the ability of authorities to define the target populations for vaccinations against COVID-19 and other diseases. Contribution: This is the first study to compare population estimates for the oldest old in administrative data and census data in Brazil. Age misreporting resulted in discrepancies that may have compromised the efficacy of the COVID-19 vaccination campaign

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

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

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) estimate the bias in the cohort TFR and life expectancies calculated from such data, and (2) demonstrate that cohort Lee-Carter forecasts drawn from an age-period grid have implausible prediction intervals, especially when drawn from the upper and lower bounds of the time series parameter. 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 in actual fact were induced by our own data manipulation

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

Data Resource Profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths

Riffe T, Acosta E, Aburto JM, et al. Data Resource Profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths. International Journal of Epidemiology. 2021;50(2):390-390f