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.

The curse of the plateau. Measuring confidence in human mortality estimates at extreme ages.

In recent years, the importance of describing mortality at the limits of the life span has led to a number of relevant and controversial studies. Whereas considerable efforts have been devoted to collecting data and estimating models on the oldest-old individuals, the testing of statistical confidence about the conclusions of analyses at extreme ages has been largely neglected. How certain can we be in saying that the risk of dying increases, levels out, or, paradoxically, decreases over age 105? Can we recognize particular mortality age patterns at such high ages? In this paper, it is shown that very little can be confidently asserted about mortality at extreme ages. Instead of analyzing actual data, we perform a series of simulation studies mimicking actual scenarios from controlled mechanisms. Our findings are thus robust with respect to factors such as particular observation schemes, heterogeneity, and data quality issues. Given the sample sizes currently available and the levels of mortality experienced in present populations, we show that before age 110, only a Gompertzian increase of mortality may be detected. Afterwards a plateau will be regularly recognized as the most suitable pattern, regardless of the complexity of the true underlying mortality.

Explaining regional differences in mortality during the first wave of Covid-19 in Italy.

Italy was hit harshly by the Covid-19 pandemic, registering more than 35,000 Covid-19 deaths between February and July 2020. During this first wave of the epidemic, the virus spread unequally across the country, with northern regions witnessing more cases and deaths. We investigate demographic and socio-economic factors contributing to the diverse regional impact of the virus during the first wave. Using generalized additive mixed models, we find that Covid-19 mortality at regional level is negatively associated with the degree of intergenerational co-residence, number of intensive care unit beds per capita, and delay in the outbreak of the epidemic. Conversely, we do not find strong associations for several variables highlighted in recent literature, such as population density or the share of the population who are older or have at least one chronic disease. Our results underscore the importance of context-specific analysis for the study of a pandemic.

Diversity of ageing across the tree of life.

Evolution drives, and is driven by, demography. A genotype moulds its phenotype's age patterns of mortality and fertility in an environment; these two patterns in turn determine the genotype's fitness in that environment. Hence, to understand the evolution of ageing, age patterns of mortality and reproduction need to be compared for species across the tree of life. However, few studies have done so and only for a limited range of taxa. Here we contrast standardized patterns over age for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga. Although it has been predicted that evolution should inevitably lead to increasing mortality and declining fertility with age after maturity, there is great variation among these species, including increasing, constant, decreasing, humped and bowed trajectories for both long- and short-lived species. This diversity challenges theoreticians to develop broader perspectives on the evolution of ageing and empiricists to study the demography of more species.

Modelling and forecasting adult age-at-death distributions.

Age-at-death distributions provide an informative description of the mortality pattern of a population but have generally been neglected for modelling and forecasting mortality. In this paper, we use the distribution of deaths to model and forecast adult mortality. Specifically, we introduce a relational model that relates a fixed 'standard' to a series of observed distributions by a transformation of the age axis. The proposed Segmented Transformation Age-at-death Distributions (STAD) model is parsimonious and efficient: using only three parameters, it captures and disentangles mortality developments in terms of shifting and compression dynamics. Additionally, mortality forecasts can be derived from parameter extrapolation using time-series models. We illustrate our method and compare it with the Lee-Carter model and variants for females in four high-longevity countries. We show that the STAD fits the observed mortality pattern very well, and that its forecasts are more accurate and optimistic than the Lee-Carter variants.

State transitions: a major mortality risk for seasonal species.

Ageing results from the accumulation of multifactorial damage over time. However, the temporal distribution of this damage remains unknown. In seasonal species, transitions between seasons are critical periods of massive physiological remodelling. We hypothesised that these recurrent peaks of physiological remodelling are costly in terms of survival. We tested whether captive small primates exposed to an experimentally increased frequency of seasonal transitions die sooner than individuals living under natural seasonality. The results show that experiencing one additional season per year increases the mortality hazard by a factor of 3 to 4, whereas the expected number of seasons lived is only slightly impacted by the seasonal rhythm. These results demonstrate that physiological transitions between periods of high and low metabolic activity represent a major mortality risk for seasonal organisms, which has been ignored until now.

Reconstructing Long-Term Coherent Cause-of-Death Series, a Necessary Step for Analyzing Trends.

Every time the classification of causes of death is changed, time series of deaths by cause are disrupted in more or less profound ways. When changes involve only the merging of several items or splitting a single item into several new categories, the problems caused by these ruptures are not too difficult to solve. A more or less severe imbroglio occurs, however, each time a new item results from recombining portions of different split items. Sometimes, but very rarely, some countries proceed to a bridge coding during the year of transition, which can help reconstruct coherent time series. This article first summarizes the general principles of the method developed for France by Meslé and Vallin to reconstruct complete series for France from 1925 to 1999 in the detailed list of the 9th WHO International Classification of Diseases (ICD), doing so by successively bridging a posteriori the five versions of the ICD that were in use during that period. Second, it reports on several methodological improvements that have been developed with the aim to reconstruct and analyze mortality trends by cause in sixteen industrialized countries.

Estimating subnational excess mortality in times of pandemic. An application to French départements in 2020.

The COVID-19 pandemic's uneven impact on subnational regions highlights the importance of understanding its local-level mortality impact. Vital statistics are available for an increasing number of countries for 2020, 2021, and 2022, facilitating the computation of subnational excess mortality and a more comprehensive assessment of its burden. However, this calculation faces two important methodological challenges: it requires appropriate mortality projection models; and small populations imply considerable, though commonly neglected, uncertainty in the estimates. We address both issues using a method to forecast mortality at the subnational level, which incorporates uncertainty in the computation of mortality measures. We illustrate our approach by examining French départements (NUTS 3 regions, or 95 geographical units), and produce sex-specific estimates for 2020. This approach is highly flexible, allowing one to estimate excess mortality during COVID-19 in most demographic scenarios and for past pandemics.

Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021).

Since its emergence in December 2019, the COVID-19 pandemic has resulted in a significant increase in deaths worldwide. This article presents a detailed analysis of the mortality burden of the COVID-19 pandemic across 569 regions in 25 European countries. We produce age and sex-specific excess mortality and present our results using Age-Standardised Years of Life Lost in 2020 and 2021, as well as the cumulative impact over the two pandemic years. Employing a forecasting approach based on CP-splines that considers regional diversity and provides confidence intervals, we find notable losses in 362 regions in 2020 (440 regions in 2021). Conversely, only seven regions experienced gains in 2020 (four regions in 2021). We also estimate that eight regions suffered losses exceeding 20 years of life per 1000 population in 2020, whereas this number increased to 75 regions in 2021. The contiguity of the regions investigated in our study also reveals the changing geographical patterns of the pandemic. While the highest excess mortality values were concentrated in the early COVID-19 outbreak areas during the initial pandemic year, a clear East-West gradient appeared in 2021, with regions of Slovakia, Hungary, and Latvia experiencing the highest losses. This research underscores the importance of regional analyses for a nuanced comprehension of the pandemic's impact.

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.

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.

Heterogeneity in subnational mortality in the context of the COVID-19 pandemic: the case of Belgian districts in 2020.

BACKGROUND: The COVID-19 pandemic has led to major shocks in mortality trends in many countries. Yet few studies have evaluated the heterogeneity of the mortality shocks at the sub-national level, rigorously accounting for the different sources of uncertainty. METHODS: Using death registration data from Belgium, we first assess change in the heterogeneity of districts' standardized mortality ratios in 2020, when compared to previous years. We then measure the shock effect of the pandemic using district-level values of life expectancy, comparing districts' observed and projected life expectancy, accounting for all sources of uncertainty (stemming from life-table construction at district level and from projection methods at country and district levels). Bayesian modelling makes it easy to combine the different sources of uncertainty in the assessment of the shock. This is of particular interest at a finer geographical scale characterized by high stochastic variation in annual death counts. RESULTS: The heterogeneity in the impact of the pandemic on all-cause mortality across districts is substantial: while some districts barely show any impact, the Bruxelles-Capitale and Mons districts experienced a decrease in life expectancy at birth of 2.24 (95% CI:1.33-3.05) and 2.10 (95% CI:0.86-3.30) years, respectively. The year 2020 was associated with an increase in the heterogeneity of mortality levels at a subnational scale in comparison to past years, measured in terms of both standardized mortality ratios and life expectancies at birth. Decisions on uncertainty thresholds have a large bearing on the interpretation of the results. CONCLUSION: Developing sub-national mortality estimates taking careful account of uncertainty is key to identifying which areas have been disproportionately affected.

The demography of COVID-19 deaths database, a gateway to well-documented international data.

National authorities publish COVID-19 death counts, which are extensively re-circulated and compared; but data are generally poorly sourced and documented. Academics and stakeholders need tools to assess data quality and to track data-related discrepancies for comparability over time or across countries. "The Demography of COVID-19 Deaths" database aims at bridging this gap. It provides COVID-19 death counts along with associated documentation, which includes the exact data sources and points out issues of quality and coverage of the data. The database - launched in April 2020 and continuously updated - contains daily cumulative death counts attributable to COVID-19 broken down by sex and age, place and date of occurrence of the death. Data and metadata undergo quality control checks prior to online release. As of mid-December 2021, it covers 21 countries in Europe and beyond. It is open access at a bilingual (English and French) website with content intended for expert users and non-specialists ( https://dc-covid.site.ined.fr/en/ ; figshare: https://doi.org/10.6084/m9.figshare.c.5807027 ). Data and metadata are available for each country separately and pooled over all countries.

Regularities in human mortality after age 105.

Empirical research on human mortality and extreme longevity suggests that the risk of death among the oldest-old ceases to increase and levels off at age 110. The universality of this finding remains in dispute because of two main reasons: i) high uncertainty around statistical estimates generated from scarce data, and ii) the lack of country-specific comparisons. In this article, we estimate age patterns of mortality above age 105 using data from the International Database on Longevity, an exceptionally large and recently updated database comprising more than 13,000 validated records of long-lived individuals from eight populations. We show that, in all of them, similar mortality trajectories arise, suggesting that the risk of dying levels off after age 105. As more high-quality data become available, there is more evidence in support of a levelling-off of the risk of dying as a regularity of longevous populations.