Health expectancy (HE), commonly derived from cross-sectional prevalence data using the Sullivan method, serves as the most frequently used summary measure of population health. Like lifespan distribution statistics, which are often discussed alongside life expectancy (LE) in demographic studies, analogous statistics on healthy lifespans can provide valuable information on population health. We examine whether healthy lifespan distribution statistics beyond HE can be estimated based on cross-sectional prevalence data and the life table, the data inputs of the Sullivan method. To do so, we treat the Sullivan method as an extension of the stationary population model to health and distinguish between health conditions with and without recovery from the state of decreased health. Our empirical demonstration is based on the prevalence of chronic diseases in selected European countries in 2017 from the Survey of Health, Ageing and Retirement in Europe (SHARE), as well as on life tables from Eurostat. We find that the Sullivan method, when considered as an extension of the stationary population model to health, allows for the estimation of a healthy survival distribution and its statistics, beyond HE, for health characteristics with no recovery from the state of decreased health. We show that for such health conditions, the method requires that the number of persons in full health in a stationary population does not increase with age. We argue that for such health dimensions, HE conditional on being in good health at the life table radix age is of relevance for health policy interventions. In our empirical application, we show that the conditional and unconditional measures of HE can give substantially different pictures of population health. Furthermore, we show that across European countries, in contrast to the negative relationship between LE and lifespan inequality, higher HE is associated with greater inequality in healthy years lived when conditional on being healthy at age 50. Overall, the Sullivan method, when considered as an extension of the stationary population model, proves to be a valuable tool for deriving summary statistics of population health beyond HE, which are highly relevant to public policy.

Eurostat's official Healthy Life Years (HLY) estimates are based on European Union Statistics on Income and Living Conditions (EU-SILC) cross-sectional data. As EU-SILC has a rotational sample design, the largest part of the samples are longitudinal, health-related attrition constituting a potential source of bias of these estimates. Bland-Altman plots assessing the agreement between pairs of HLY based on total and new rotational, representative samples demonstrated no significant, systematic attrition-related bias. However, the wide limits of agreement indicate considerable uncertainty, larger than accounted for in the confidence intervals of HLY estimates.

Cross-sectional data from the Survey of Health, Ageing and Retirement in Europe (SHARE) are a common source of information in comparative studies of population health in Europe. In the largest part, these data are based on longitudinal samples, which are subject to health-specific attrition. This implies that estimates of population health based on cross-sectional SHARE datasets are biased as the data are selected on the outcome variable of interest. We examine whether cross-sectional datasets are selected based on health status. We compare estimates of the prevalence of full health, healthy life years at age 50 (HLY), and rankings of 18 European countries by HLY based on the observed, cross-sectional SHARE wave 7 datasets and full samples. The full samples consist of SHARE observed and attrited respondents, whose health trajectories are imputed by microsimulation. Health status is operationalised across the global index of limitations in activities of daily living (GALI). HLY stands for life expectancy free of activity limitations. Cross-sectional datasets are selected based on health status, as health limitations increase the odds of attrition from the panel in older age groups and reduce them in younger ones. In older age groups, the prevalence of full health is higher in the observed cross-sectional data than in the full sample in most countries. In most countries, HLY is overestimated based on the cross-sectional data, and in some countries, the opposite effect is observed. While, due to the small sample sizes of national surveys, the confidence intervals are large, the direction of the effect is persistent across countries. We also observe shifts in the ranking of countries according to HLYs of the observed data versus the HLYs of the full sample. We conclude that estimates on population health based on cross-sectional datasets from longitudinal, attrited SHARE samples are over-optimistic.

Family patterns in Western countries have changed substantially across birth cohorts. The spread of unmarried cohabitation, the decline and postponement of marriage and fertility, and the rise in nonmarital births, partnership instability, and repartnering lead to an increasing diversity in family life courses. In this paper we demonstrate how to set up a tool to explore family life trajectories. This tool models the changing family patterns, taking into account the complex interrelationships between childbearing and partnership processes. We build a microsimulation model parameterised using retrospective partnership and childbearing data. The data cover women born since 1940 in Italy, Great Britain, and two Scandinavian countries (Norway and Sweden), three significantly different cultural and institutional contexts of partnering and childbearing in Europe. We guide readers through the modelling of individual life events to obtain a set of aggregate estimates, providing information on the power, technical structure, and underlying assumptions of microsimulations. Validation of the simulated family life courses against their real-world equivalents shows that the simulations not only closely replicate observed childbearing and partnership processes, but also provide high quality predictions when compared to more recent fertility indicators. Using observed population estimates to systematically validate the results both validates our model and increases confidence that microsimulations satisfactorily replicate the behaviour of the original population. We create and validate a microsimulation model that can be used not only to explore mechanisms throughout the family life course but also to set up scenarios and predict future family patterns.

Using a highly stylized dynamic microsimulation model, we project the labor force of the United States up to the year 2060 and contrast these projections with projections for Germany to assess differential effects on outcomes. The projections are consistent with the US Census Bureau's and Eurostat's demographic projections. Our modeling approach allows to show and quantify how policy changes the future size of the labor force, which we assess with a series of what-if scenarios. Both the USA and Germany are expected to undergo demographic aging, but their demographic fundamentals differ starkly. This has strong implications for their labor force developments. According to our microsimulation, the US labor force will, despite population aging, increase by 16.2 percent in the age groups 15 to 74 (corresponding to 25.2 million workers) between 2020 and 2060, while Germany will experience a decline by 10.7 percent (4.4 million workers). In these baseline projections, improvements in the education structure will add about two million persons to the US labor force and about half a million persons to the German labor force by 2060. In the what-if scenarios, we examine the implications of improvements in the educational structure of the population and of policies which address the health impediments for labor force participation. Of the educational scenarios that we evaluate, increasing the number of persons who achieve more than lower education has the strongest positive impact on labor force participation, relative to the number of additional years of schooling implied by the various scenarios. Shifting people from intermediate to higher education levels also increases labor force participation in higher age groups, however, this is partially offset by lock in effects at younger ages. Our projections highlight that improvements in the labor market integration of people with health limitations provide a particularly promising avenue to increase labor force participation rates and thus help to address the challenges posed by demographic aging. If the health gap in participation rates in the United States were similar to that currently observed in Sweden, the labor force in 2060 would be larger by about 14.9 million persons.

Detailed socio-demographic projections are key for policy making and planning. In this paper we introduce the dynamic microsimulation platform DYNAMIS-POP. In its core, DYNAMIS-POP is a population projection model, able to reproduce existing macro (cohort-component based) population projections in their aggregate outcomes, but with the additional possibility to simulate in more detail a variety of geographic, education, ethnicity, child mortality, partnership status, fertility and health characteristics. DYNAMIS-POP is a continuous time interacting population model implemented in Modgen, a freely available programming technology developed at Statistics Canada. The code is also x-compatible with openM++, a platform-independent open-source implementation of Modgen. All components of DYNAMIS-POP are freely available and documented on line at www.dynamis.ihsn.org. Most statistical analysis scripts and scripts for post-processing and visualization of the results are implemented in R. Aiming to support portability, the model code and the R scripts are generic. Adaptation of the model to a specific country only requires adapting a single setup script and simulation module. The model is provided with test data of an imaginary country. Required data are available for most developing countries. To date, the model was tested using data from Mauritania, Nepal and Senegal. Designed as a modular and versatile microsimulation platform, DYNAMIS-POP can be adapted for a variety of applications related to population issues, education and health. In this paper we give an illustration from a study on child vaccination in Nepal.