Demographic Research

Epidemiological studies suggest that age distribution of a population has a non-trivial effect on how morbidity rates, mortality rates and case fatality rates (CFR) vary when there is an epidemic or pandemic. We look at the empirical evidence from a large cohort of countries to see the sensitivity of Covid-19 data to their respective median ages. The insights that emerge could be used to control for age structure effects while investigating other factors like cross-protection, comorbidities, etc.

American mortality rates have diverged in recent years between those with and without a four-year college degree, and there are many reasons to expect the education-mortality gradient to have steepened during the pandemic. Those without a BA are more likely to work in frontline occupations, to rely on public transportation, and to live in crowded quarters, all of which are associated with an increase in infection risk, a risk that was zero prior to the pandemic. We use publicly available data from the National Center for Health Statistics on deaths by age, sex, education and race/ethnicity to assess the protective effect of a BA in 2020 compared to 2019. While the BA was strongly protective during 2020, the ratio of mortality rates between those with and without a degree was little changed relative to pre-pandemic years. Among 60 groups (gender by race/ethnicity by age) that are available in the data, the relative risk reduction associated with a BA fell for more than half the groups between 2019 and 2020, and increased by more than 5 percentage points for only five groups. Our main finding is not that the BA was protective against death in 2020, which has long been the case, but that the protective effect was little different than in 2019 and earlier years, in spite of the change in the pattern of risk by occupation and income. The virus maintained the mortality-education gradient that existed pre-pandemic, at least through the end of 2020. Our results suggest that changes in the risk of infection were less important in structuring mortality than changes in the risk of death conditional on infection.

It is natural to question the impact of COVID-19 on life expectancy. However, a newborn during the 2020-2021 period need not experience the same level of adult mortality found in 2020-2021 because there may be zero COVID-19 related deaths when the newborn reaches adulthood. Thus, life expectancy lost due to COVID-19 cannot be found simply by incorporating excess deaths due to COVID-19 and re-doing the life table computations because: (1) we know that the COVID-19 deaths need not occur every year for the next 20-25 years; and (2) once an adult, a newborn in 2021/2022 need not experience the same mortality rate that current middle and older aged COVID-19 patients experience. Using U.S. data as an example, we estimate an average of 29.68 years of life was lost to those aged 18-64 who died from COVID-19 in the U.S., noting that 74 % of the reported deaths of 18-64 occurred among 50-64 years and 10 % below 40 years. Instead of computing life expectancy years lost due to COVID-19, we recommend computing life years lost due to COVID-19.

BackgroundThe matrix model for kinship networks includes many demographic processes but is deterministic, projecting expected values of age-stage distributions of kin. It provides no information on (co)variances. Because kin populations are small, demographic stochasticity is expected to create appreciable inter-individual variation. ObjectivesTo develop a stochastic kinship model to project (co)variances of kin age-stage distributions, and functions thereof, including demographic stochasticity. MethodsKin populations are described by multitype branching processes. Means and covariances are projected using matrices that are generalizations of the deterministic model. The analysis requires only an age-specific mortality and fertility schedule. Both linear and non-linear transformations of the kin age distribution are treated as outputs accompanying the state equations. ResultsThe stochastic model follows the same mathematical framework as the deterministic model, modified to treat initial conditions as mixture distributions. Variances in numbers of most kin are compatible with Poisson distributions. Variances for parents and ancestors are compatible with binomial distributions. Prediction intervals are provided, as are probabilities of having at least one or two kin of each type. Prevalences of conditions are treated either as fixed or random proportions. Dependency ratios and their variances are calculated for any desired group of kin types. An example compares Japan under 1947 rates (high mortality, high fertility) and 2019 rates (low mortality, low fertility). ContributionPrevious versions of the kinship model have acknowledged their limitation to expected values. That limitation is now removed; means and variances are easily and quickly calculated with minimal modification of code.

Past pandemic experience can affect health outcomes in future pandemics. This paper focuses on the last major influenza pandemic in 1968 (H3N2), which killed up to 100,000 people in the US. We find that places with high influenza mortality in 1968 experienced 1-4% lower COVID-19 death rates. Our identification strategy isolates variation in COVID-19 rates across people born before and after 1968. In places with high 1968 influenza incidence, older cohorts experience lower COVID-19 death rates relative to younger ones. The relationship holds using county and patient-level data, as well as in hospital and nursing home settings. Results do not appear to be driven by systemic or policy-related factors, instead suggesting an individual-level response to prior influenza pandemic exposure. The findings merit investigation into potential biological and immunological mechanisms that account for these differences--and their implications for future pandemic preparedness.

BackgroundPrevious kinship models analyze female kin through female lines of descent, neglecting male kin and male lines of descent. Because males and females differ in mortality and fertility, including both sexes in kinship models is an important unsolved problem. ObjectivesThe objectives are to develop a kinship model including female and male kin through all lines of descent, to explore approximations when full sex-specific rates are unavailable, and to apply the model to several populations as an example. MethodsThe kin of a focal individual form an agexsex-classified population and are projected as Focal ages using matrix methods, providing expected age-sex structures for every type of kin at every age of Focal. Initial conditions are based on the distribution of ages at maternity and paternity. ResultsThe equations for two-sex kinship dynamics are presented. As an example, the model is applied to populations with large (Senegal), medium (Haiti), and small (France) differences between female and male fertility. Results include numbers and sex ratios of kin as Focal ages. An approximation treating female and male rates as identical provides some insight into kin numbers, even when male and female rates are very different. ContributionMany demographic and sociological parameters (e.g., aspects of health, bereavement, labor force participation) differ markedly between the sexes. This model permits analysis of such parameters in the context of kinship networks. The matrix formulation makes it possible to extend the two-sex analysis to include kin loss, multistate kin demography, and time varying rates.

Summary paragraphThe epidemiological transition from young to old deaths in high-income countries reduced mortality at all ages, but a major role was played by a decline of infant and child mortality from infectious diseases1,2 that greatly increased life expectancy at birth2,3. Over time, declines in infectious disease continue but chronic and degenerative causes persist4,5, so we might expect under-5 deaths to be concentrated in the first month of life. However, little is known about the age-pattern of this transition in early mortality or its potential limits. Here we first describe the limit using detailed data on Denmark, Japan, France, and the USA-- developed countries with low under-5 mortality. The limiting pattern of under-5 deaths concentrates in the first month, but is surprisingly dispersed over later ages: we call this the early rectangularization of mortality. Then we examine the progress towards this limit of 31 developing countries from sub-Saharan Africa (SSA)--the region with the highest under-5 mortality6. In these countries, we find that early deaths have large age-heterogeneities; and that the age patterns of death is an important marker of progress in the mortality transition at early ages. But a negative association between national income and under-5 mortality levels, confirmed here, does not help explain reductions in child mortality during the transition.

The COVID-19 pandemic, caused by tens of millions of SARS-CoV-2 infections world-wide, has resulted in considerable levels of mortality and morbidity. The United States has been hit particularly hard having 20 percent of the worlds infections but only 4 percent of the world population. Unfortunately, significant levels of misunderstanding exist about the severity of the disease and its lethality. As COVID-19 disproportionally impacts elderly populations, the false impression that the impact on society of these deaths is minimal may be conveyed by some because elderly individuals are closer to a natural death. To assess the impact of COVID-19 in the US, I have performed calculations of person-years of life lost as a result of 194,000 premature deaths due to SARS-CoV-2 infection as of early October, 2020. By combining actuarial data on life expectancy and the distribution of COVID-19 associated deaths we estimate that over 2,500,000 person-years of life have been lost so far in the pandemic in the US alone, averaging over 13.25 years per person with differences noted between males and females. Importantly, nearly half of the potential years of life lost occur in non-elderly populations. Issues impacting refinement of these models and the additional morbidity caused by COVID-19 beyond lethality are discussed.

Defining and quantifying exceptional familial human survival is a persistent challenge in longevity research. Traditional approaches rely on binary thresholds, arbitrary cutoffs, or simple descriptive measures, which discard information on variation among the oldest individuals, ignore differences in background mortality, and yield unstable family-level summaries. We propose a principled, model-based framework that transforms survival times into percentiles relative to population life tables, standardizing across birth cohorts, sexes, and populations. We extend beta mixed-effects regression to accommodate intentional left-censoring, which downweights early deaths while retaining their contribution to the likelihood, thereby focusing inference on extreme survival. Family-specific random effects provide interpretable, statistically grounded longevity scores, overcoming the limitations of ad hoc measures and enabling robust identification of long-lived families. Simulation studies and application to a large multigenerational Dutch cohort demonstrate that the method reliably identifies families enriched for longevity. This framework provides a flexible, interpretable, and robust tool for analyzing familial survival, offering a paradigm shift in the statistical study of exceptional human lifespan.

BackgroundCOVID-19 was first identified in Wuhan, the capital city of the province of Hubei in China. Due to the presentation of multiple symptoms at the same time, it is clinically important to understand the probability of dying from COVID-19 vs. the probability of dying from other causes. MethodsUsing data collected in Hubei that identified by age those who died of COVID-19 or its sequelae among the infected, we constructed a life table showing the conditional probability of dying at age x from COVID-19 and its sequela among those infected. Following the relative survival perspective, we also computed corresponding data for China that matched the format of the life table we constructed from the Hubei study. We then formed ratios of the 10-year conditional portability of dying at age x from COVID-19 for the Hubei COVID-19 victims to the ten-year conditional probability of dying at age x from all non-COVID-19 causes for those not infected by COVID-19 in China as a whole. FindingsAt every age, the conditional probability of dying from COVID-19 among those infected in Hubei is higher than the conditional probability of dying from all non-COVID-19 causes for China as a whole. Following a general age-related mortality pattern, the conditional probability of dying from COVID-19 from age 20 onward increases monotonically for those who are infected. Relative to the probability of dying in China from all other causes for those not infected, however, it declines monotonically from age 20 to age 70. InterpretationAt younger ages the relative conditional probability of dying from CVOD-19 among the infected is substantially higher than it is for those infected who dying of all other causes and while staying higher at all ages, it declines monotonically with age. The monotonic decline in the ratio from age 20 to age 70 is a result of the age-related increase in the probability of dying from one or more of a number of competing causes, which, in the case at hand is manifested in the fact that non-COVID-19 deaths in China among the uninfected were generally increasing at a faster age-related rate than were the COVID-19 deaths to the infected in Hubei.

OO_SCPLOWBJECTIVESC_SCPLOWAssess the impact of the illness designated COVID-19 during the first year of pandemic outbreak in Spain through age/sex-specific death rates. SO_SCPLOWTUDYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWDESIGNC_SCPLOWAge/sex-specific weeekly deaths in Spain were retrieved from Eurostat. Spanish resident population was obtained from the National Statistics Office. MO_SCPLOWETHODSC_SCPLOWGeneralized linear Poisson regressions were used to compute the contrafactual expected rates after one year (52 weeks or 364 days) of the pandemic onset. From this one-year age/sex-specific and age/sex-adjusted mortality excess rates were deduced. RO_SCPLOWESULTSC_SCPLOWFor the past continued 13 years one-year age/sex-adjusted death rates had not been as high as the rate observed on February 28th, 2021. The excess death rate was estimated as 1.790x10-3 (95 % confidence interval, 1.773x10-3 to 1.808x10-3; P-score = 20.2 % and z-score = 11.4) with an unbiased standard deviation of the residuals equal to 157x10-6. This made 84 849 excess deaths (84 008 to 85 690). Sex disaggregation resulted in 44 887 (44 470 to 45 303) male excess deaths and 39 947 (39 524 to 40 371) female excess deaths. CO_SCPLOWONCLUSIONC_SCPLOWWith 73 571 COVID-19 deaths and 9772 COVID-19 suspected deaths that occurred in nursing homes during the spring of 2020 it is only 1496 excess deaths (1.8 %, a z-score of 0.2) that remains unattributed. The infection rate during the first year of the pandemic is estimated in 16 % of population after comparing the ENE-COVID seroprevalence, the excess deaths at the end of the spring 2020 and the excess deaths at the end of the first year of the pandemic.

Gauging COVID-19s lethality, and how vaccination can reduce that lethality, has been challenging. Here, a new method, Gompertzian Analysis, counting cases and deaths, by age, and displaying them on logarithmic graphs, is outlined, and its first findings presented: FIRST, COVID-19 Gompertzian Lethality (Deaths/Cases) exhibits an ~10,000-fold exponential increase in the chance of death with age, the Gompertzian Force of Mortality, captured by the Gompertz Mortality Equation. SECOND, COVID-19 Pasteurian Infectivity (Cases/Population) occurs at similar rates across ages. THIRD, the same Gompertzian Force of Mortality characterizes other diseases and all-cause mortality, possibly from loss of Mitotic Dilution of toxic compounds due to decline in mitosis. FOURTH, resistance to COVID-19 infectivity and lethality appear to be separate processes. FIFTH, Over the past several years, Gompertzian Lethality, has declined, but not Pasteurian Infectivity. SIXTH, with each variant, Gompertzian Lethality has declined, but not Pasteurian Infectivity. SEVENTH, the unvaccinated have seen a decline in Gompertzian Lethality, less than the vaccinated, ascribable to infection, at the cost of lives lost. EIGHTH, different vaccines have different reductions in Gompertzian Lethality and Pasteurian Infectivity. NINTH, vaccination has reduced Pasteurian Infectivity, but not enough to suppress the pandemic. TENTH, vaccination has reduced Gompertzian Lethality, with sequential vaccination pointing linearly towards zero death after 3 or 4 boosters, without signs of waning. CONCLUSION: Gompertzian Analysis provides new, practical, actionable, information for understanding, and minimizing, the lethal burden of COVID-19 and other diseases.

BackgroundSubjective Survival Probabilities (SSP) are known to be associated with mortality but little is known about the relationship they might have with employment categories and job satisfaction. We assess such a relationship looking at the fifty-plus population in Japan that is characterized by a stratified labour market for the older workers and high working time intensity. MethodWe use the four waves (2007-2013) of the Japanese Study of Aging and Retirement (JSTAR), a panel dataset tracking 7,082 50-plus respondents in 10 Japanese prefectures. We use a mixed-effects quantile regression model to investigate the relationship between SSP and employment status (model 1) and job satisfaction (model 2). Both models additively control for demographic and socio-economic cofounders as well as other health measurements. Multiple imputations are used to correct sample attrition. ResultsIn model 1, retirement (-0.27, 95%CI =-0.51;-0.03) and contract work (-0.51, 95%CI=-0.79;-0.23) are negatively associated with SSP in comparison with full-time employment. In model 2, low job satisfaction appears to be strongly associated with SSP (-1.37, 95%CI=-1.84;-0.91) in comparison with high job satisfaction. The same trend is observed regardless of the way job satisfaction is calculated. Both working time and employment category are not significantly associated with SSP after controlling for job satisfaction which indicates that job satisfaction is a main driver of SSP discrepancies. DiscussionSSP variations can be explained by employment category with contract work more at risk. Job dissatisfaction is a main explanation of low SSP. Both work and employment explain SSP variations.

BACKGROUNDThe criteria used to allocate scarce COVID-19 vaccines are hotly contested. While some are pushing just to get vaccines into arms as quickly as possible, others advocate prioritization in terms of risk. OBJECTIVEOur aim is to use demographic models to show the enormous potential of vaccine risk-prioritization in saving lives. METHODSWe develop a simple mathematical model that accounts for the age distribution of the population and of COVID-19 mortality. This model considers only the direct live-savings for those who receive the vaccine, and does not account for possible indirect effects of vaccination. We apply this model to the United States, Japan, and Bangladesh. RESULTSIn the United States, we find age-prioritization would reduce deaths during a vaccine campaign by about 93 percent relative to no vaccine and 85 percent relative to age-neutral vaccine distribution. In countries with younger age structures, such as Bangladesh, the benefits of age-prioritization are even greater. CONTRIBUTIONFor policy makers, our findings give additional support to risk-prioritized allocation of COVID-19 vaccines. For demographers, our results show how the age-structures of the population and of disease mortality combine into an expression of risk concentration that shows the benefits of prioritized allocation. This measure can also be used to study the effects of prioritizing other dimensions of risk such as underlying health conditions.

Understanding the relationship between life expectancy at birth (e0) and the gross domestic product per capita (GDPpc) is relevant for cohesion policies in the European Union (EU), because it might imply that economic convergence (or divergence) is accompanied by narrowing (or widening) health gaps. Previous studies have studied the association between GDPpc and e0 almost exclusively based on national data. However, it is certainly relevant to add a subnational dimension, because levels and trends in both e0 and GDPpc vary substantially across Europes regions. Accordingly, the aim of our study is examining whether the economic performance of a region is correlated to their e0 level. To do so, we collected official mortality and population counts from national statistical offices and information on GDPpc from the Eurostat database for 506 regions in 21 European countries from 2008 to 2019. Using this data, we built Preston curves from regression models. Our results suggest that there is indeed a positive association between GDPpc and e0. Similarly to Prestons original analysis, we observe an upward shift in the curve, indicating that factors exogenous to a regions GDPpc level also play an important role in explaining e0 gains. Yet, the relationship differs between geographical areas, and we also find examples, such as women in Germany, Austria, Poland, and the Netherlands, where the relationship/pattern does not seem to hold.

Using anonymous publicly available data on COVID-19 infections and gross outcomes in India, the age and sex distribution of infections and fatalities is studied. The age structure in the count of infections is not proportional to that in the population, indicating the role of either co-morbidity or differential attack rate. There is a strong age structure in the sex ratio of cases, with the female to male ratio being about 50% on average. The ratio drops between puberty and menopause. No such structure is visible in the sex ratio of fatalities. The overall age distribution of fatalities is consistent with a model which uses the empirical age structure of infections and a previous determinations of age structured IFR. The average IFR for India is then expected to be 0.4% with a 95% CrI in [0.22%, 0.77%].

BackgroundLifespan inequality arises both from heterogeneity (e.g., in sex or race) and from unavoidable individual stochasticity. By treating a heterogeneous population as a mixture we can (and many have) partition variance in lifespan into a between-group component due to heterogeneity and a within-group component due to chance. Until now, such studies have treated factors singly. It is now possible to analyze multiple factors and their contributions to variance. ObjectiveThis paper is the first to exploit the new analysis for multi-factor studies. Multi-factor data are painfully rare, but a remarkable study by Bergeron-Boucher et al. presented U.S. life tables under all 54 combinations of four factors (sex, marital status, education, race). Our objective is to quantify the contributions of these factors and their interactions to lifespan inequality. MethodsThe population is treated as a mixture of 54 groups, with a mixture distribution either flat or proportional to population size of the different factor combinations. Components of the variance in remaining longevity, for starting ages from 30 to 85 years, are calculated using marginal mixture distributions. ResultsEven accounting for four factors and their interactions, between-group heterogeneity accounts for only 7% (population-weighted mixing) to 10% (flat mixing) of lifespan variance. Education and its interactions make the largest contribution. Contributions of two-way, three-way, and four-way interactions are orders of magnitude smaller. This suggests new ways of displaying, summarizing, and interpreting inequality as measured in multi-factor studies. ContributionMulti-factor studies can now be used to identify sources of variance in longevity and other demographic outcomes.

The growing number of series on COVID-19 deaths classified by age and sex, released by national health authorities, has allowed us to compute age and sex patterns of its mortality, based on 183,619 deaths from Western Europe and the USA. We highlight the specific age schedule of COVID-19 mortality and its pronounced excess male mortality and we then apply these COVID-19 death rates to world populations, in 2020. Our results underscore that considerable variations exist between world regions, as concerns the potential impact of COVID-19 mortality, because of their demographic structures. When compared to younger countries in Sub-Saharan Africa, the vulnerability to COVID-19 mortality is shown to be 17 times higher in several industrialized countries of East Asia and Europe. There is a high correlation (r2= .44) between demographic vulnerability to COVID-19 mortality and current COVID-19 death rates. AbstractCOVID-19, mortality, age structures, death rates, Europe, USA. BackgroundThe data available on infection and death rates from COVID-19 have pointed to the elderlys vulnerability to pandemics, especially elderly men. However, current models have not yet incorporated the growing volume of information on deaths by age and sex that is being released by statistical offices and health authorities. These newly available data allow us to examine the specific age and sex patterns of COVID-19 mortality and to estimate the impact of specific demographic structures on potential COVID-19 mortality worldwide. MethodsWe use the data available on May 15, 2020, from the nine countries with the largest series of deaths, disaggregated by age and sex: Belgium, France, Germany, Italy, Netherlands, Spain, Sweden, the UK, and the USA. Using 183,619 deaths (60.2% of all currently estimated COVID-19 deaths), we estimate the sex-specific death rates, by 5-year ranges, for two large death samples: USA and Western Europe. We compare these mortality rates with Gompertz models and with life tables of the worlds population, estimated by the United Nations. We apply these COVID-19 mortality rates by sex and 5-year group to the 2020 age and sex structures of world countries and regions, and obtain an index summarizing the relative magnitude of their potential vulnerability to COVID-19 mortality because of their demographic structures. FindingsCOVID-19 death rates cannot be computed below age 15. COVID-19 death rates from age 15-19 years to 90+ increase by a factor of 3 of every ten years, at a rate that is faster than general mortality. Male mortality from COVID-19 is systematically higher than female mortality, with a peak of excess male mortality occurring among 55-59-year-olds. Age and sex structures show considerable variations across countries, in terms of vulnerability to COVID-19 mortality. It transpires that the youngest countries in Central Africa are 17 times less vulnerable than aging countries, such as Japan. InterpretationWhereas the true intensity of the ongoing COVID-19 pandemic remains underestimated by existing statistics, this unique mortality dataset shows that the regularity of the distribution of COVID deaths by age and sex is in line with the standard Gompertz mortality equation; thus confirming the quality of the first death samples and the unique age and sex patterns of COVID-19 mortality. COVID-19 death rates tend to be negligible below age 15 and cannot be used for analysis. The rate of progression of death rates by age is faster than that of general mortality. This feature places the elderly population in a particularly vulnerable situation compared to younger adults. Male excess mortality from COVID-19 also appears far more pronounced than in general mortality patterns, with men aged 40-59 years being almost 2.5 times more likely to die than women of the same age. Our analysis also points to considerable variations between world regions, as concerns the potential impact of COVID-19 mortality, because of their demographic structures. The COVID-19 structural vulnerability index ranges from .28 in Western or Middle Africa to 2.6 in Southern Europe. There is a high correlation between demographic vulnerability to COVID-19 mortality and current COVID-19 death rates (r2= .44 for 188 countries).

The COVID-19 pandemic led to substantial life expectancy losses globally. Historically, life expectancy reversals have been followed by rapid returns to previous trajectories, but whether this is true for the COVID-19 pandemic is still unknown. We update life expectancy estimates through 2024 for 34 high-income countries and quantify annual and cumulative life expectancy "deficits" by comparing observed life expectancy with counterfactuals based on pre-pandemic trends. Five years after the pandemics onset, recovery remains incomplete in most countries. In 2024, 31 out of 34 countries still had lower life expectancy than expected. Across 2020-2024, cumulative deficits were statistically significant in nearly all countries. We identify four distinct life expectancy trajectories: (a) first wave peak (largest deficits in 2020 with gradual recovery); (b) second wave peak (largest deficits in 2021 with a sharper rebound); (c) late peak (minimal early impact followed by smaller deficits from 2022 onward); (d) prolonged depression (smaller but persistent deficits without a sharp peak). In general, countries with severe second-wave peaks (such as the USA and Bulgaria) had the largest cumulative deficits. In contrast, countries that delayed widespread infection (e.g., Norway, Japan) saw later deficits that persisted through 2024, but with lower cumulative mortality. Our findings suggest that COVID-19 was not a uniform, short-lived mortality shock. Instead, most high-income countries experienced multi-year disruptions to life expectancy trajectories, with variable patterns of recovery that continue to shape population health five years on.

Different estimation methods produce diverging accounts of racial/ethnic disparities in COVID-19 mortality in the United States. The CDCs decision to present the racial/ethnic distribution of COVID-19 deaths at the state level alongside re-weighted racial/ethnic population distributions--in effect, a geographic adjustment--makes it seem that Whites have the highest death rates. Age adjustment procedures used by others, including the New York City Department of Health and Mental Hygiene, lead to the opposite conclusion that Blacks and Hispanics are dying from COVID-19 at higher rates than Whites. In this paper, we use indirect standardization methods to adjust per-capita death rates for both age and geography simultaneously, avoiding the one-sided adjustment procedures currently in use. Using CDC data, we find age-and-place-adjusted COVID-19 death rates are 80% higher for Blacks and more than 50% higher for Hispanics, relative to Whites, on a national level, while there is almost no disparity for Asians. State-specific estimates show wide variation in mortality disparities. Comparison with non-epidemic mortality reveals potential roles for pre-existing health disparities and differential rates of infection and care.