Demand and Supply of Care Over the Life Course
We project the effects of changes in fertility and mortality rates on both the receipt and provision of care in the UK. We investigate the impact on the level and cost of care, as well as its share of total GDP, through the life course and across income and wealth distributions. SimPaths, an open-source dynamic microsimulation model, is employed to design different scenarios over a half-century period. This framework projects life histories over time, developing detailed representations of career paths, family and intergenerational relationships, health, and financial circumstances. Our estimates show that the value of care, as a share of GDP, almost doubles over the five decades of our analysis, with informal care accounting for most of the projected rise.
Read MoreDeveloping Reporting Standards for Population Health Microsimulation: A Scoping Review of Current Practices
Background Population health simulation models—including microsimulation, agent-based, system dynamics, and Markov models—are essential tools for understanding noncommunicable disease (NCD) burden and evaluating policy interventions. However, inconsistent reporting practices limit the transparency, reproducibility, and credibility of this work. Unlike clinical trials and observational studies, which benefit from established reporting guidelines (CONSORT, STROBE), no comprehensive standards exist for population health simulation models.
Read MoreForecasting ADRD in European Elderly Population Using Dynamic Microsimulation
Population ageing is rapidly increasing the prevalence of Alzheimer’s disease and related dementias (ADRD) across Europe, creating major challenges for health and long-term care systems. Existing European projections typically rely on static prevalence assumptions or self-reported diagnoses and rarely model individual cognitive trajectories within a unified, forward-looking framework. This paper presents a major extension of the European Future Elderly Model (EU-FEM), introducing a dedicated microsimulation module for dementia and cognitive decline applicable across multiple European countries. Using harmonized longitudinal data from SHARE Waves 1–9 (2004–2022), we develop and integrate a dynamic ADRD module that simulates transitions in cognitive status for individuals aged 50 and over. Cognitive decline status is defined using the Langa–Weir (LW) classification, adapted to the European context by combining episodic memory tests with functional limitations in instrumental activities of daily living (IADLs). Country-specific cut-offs are calibrated against OECD dementia prevalence benchmarks and tested for robustness across alternative SHARE waves. The classification algorithm incorporates deterministic and stochastic imputation procedures and enforces the absorptive nature of dementia over time. The ADRD module is embedded within EU-FEM’s first-order Markov Monte Carlo framework, allowing cognitive decline to evolve jointly with chronic conditions, demographic characteristics, and socioeconomic factors. Transition equations condition on prior Socio Economic Status (education, income, wealth, labour market status), health and cognition, enabling heterogeneous life-course trajectories. The enhanced model expands EU-FEM coverage to twelve European countries, including new Central and Eastern European populations, and produces internally consistent projections of dementia prevalence and cognitive trajectories. Validation exercises show close alignment with external epidemiological benchmarks and substantial improvements over self-reported dementia measures. This work demonstrates how dynamic microsimulation can be used to model cognitive decline in a harmonized, multi-country setting, providing a flexible platform for forecasting dementia prevalence and evaluating counterfactual scenarios involving risk-factor modification, prevention policies, and future care needs. The extended EU-FEM establishes a foundation for integrating cost-of-illness and long-term care modules, supporting policy analysis in ageing European societies.
Read MoreIntroducing an asset test into client fees for long-term social care: a simulation study using Finnish administrative data
As the population ages and the sustainability gap in public finances in Finland widens, new solutions are needed to ensure sufficient funding for public services. One potential solution is to place greater emphasis on private wealth in the financing of care services. At present, client fees for long-term social and health care services in Finland are determined based on clients’ income. This study examines the potential effects of also taking clients’ assets into account. We focus on the fiscal and distributional implications of such a reform. The analysis is based on the SOTE-SISU static microsimulation model and unique administrative register including wealth.
Read MoreMicrosimulation at Scale for Chronic Disease Modelling: Executing 100 Million Individual Life-Course Simulations in 100 Seconds
Microsimulation is a uniquely powerful technique for chronic disease modelling because it simulates outcomes at the level of the individual over time, capturing heterogeneity, history-dependent progression, multimorbidity, and complex clinical pathways that cohort averages cannot. In an era when chronic diseases account for the majority of global mortality and impose escalating pressure on health systems, decisions about their prevention, treatment, pricing, and resource allocation carry profound long-term clinical and financial consequences. Consequently, accurate long-horizon modelling of these diseases has become central to policy, reimbursement, and investment decisions. Historically, however, microsimulation has been constrained by computational performance. Statistical precision requires large, simulated populations to reduce Monte Carlo error, and probabilistic sensitivity analysis multiplies this burden through repeated parameter sampling. Many models built in spreadsheets or high-level languages require hours or days to run, limiting scenario exploration, delaying iteration, and reducing their practical utility in time-sensitive decision environments. To address these limitations, a legacy microsimulation stack was rebuilt into a high-performance platform capable of executing 100 million life-course simulations in approximately 100 seconds. Performance gains were achieved through several core engineering innovations. The microsimulation core was implemented in modern C++, enabling direct control over memory allocation, cache locality, and execution flow. Compared with interpreted (e.g. Python, R) or spreadsheet-based environments, compiled C++ dramatically reduces runtime overhead and enables predictable, deterministic execution, strengthening validation processes and supporting regulatory-grade transparency and auditability. Memory architecture was optimised to maximise Central Processing Unit (CPU) cache efficiency and minimise allocation costs. Modelled individuals’ attributes, state transitions, and event processes were encoded in compact, structured formats, allowing large virtual populations to be simulated without performance degradation. The engine exploited modern multi-core CPU architectures through multi-threading, allowing independent patient simulations to run concurrently. Because individual life trajectories are largely independent within Monte Carlo microsimulation, the model parallelises naturally, enabling near-linear scaling with available cores. Beyond single-machine performance, the system supports horizontal scaling via containerised simulation instances, allowing elastic expansion across the infrastructure based on workload demand, without reliance on specialised high-performance computing clusters. The platform includes integrated pipelines for data ingestion, preprocessing, simulation execution, and post-processing. Outputs are automatically aggregated into epidemiological, and economic metrics, including incidence, prevalence, costs, and healthcare resource use outcomes, ready for decision analysis. A user-facing interface abstracts technical complexity, allowing domain experts to configure scenarios and execute simulations without interacting directly with the code or infrastructure. The entire platform is securely hosted in the cloud, allowing for easy set up and access anywhere in the world. The system is comprised of cross-cloud components that allow it to be hosted in any of the major cloud providers. These advances represent a fundamental shift in capability: complex simulations once requiring hours or days may now be completed in seconds, enabling real-time exploration of uncertainty, and rapid scenario iteration to expedite decision-making. Microsimulation can therefore operate at the scale and speed demanded by modern policy, reimbursement, and investment strategies, amid growing chronic disease complexity and multimorbidity.
Read MoreModelling cancer incidences and mortality in the Austrian population using dynamic microsimulation
Population projections indicate that by 2045, the Austrian population aged 65 and older will increase by approximately 47% compared to 2023. Since the likelihood of a cancer diagnosis increases with age, a corresponding rise in cancer cases is expected. To address this and support evidence-based decision-making, a model has been developed on behalf of the Ministry of Health to project cancer incidence, prevalence, and mortality within the population up to the year 2045. Our cancer projection model builds on the microsimulation model used by Statistics Austria for official population projections (Pohl et al, 2025). It introduces a new module for calculating cancer diagnoses and refines existing ones, such as the module for calculating mortality. A key advantage of microsimulation is its ability to account for individual characteristics, allowing factors such as existing diagnoses to influence future disease states and determine cause specific mortality outcomes. In addition, microsimulation offers the possibility of further developing the model in the future, e.g. through extensions such as the consideration of risk factors, as is already done in well-known microsimulation models such as OncoSim. (Ruan et al., 2023). The model parameters are calculated using administrative register data, including the Central Population Register and Cause of Death Statistics, linked with the National Cancer Register – enabling detailed tracking of individual life histories.
Read MoreModelling future ambulatory care utilisation in Germany: A Microsimulation of patient demand and physician supply
Ensuring accessible and adequate outpatient healthcare close to patients homes is a central concern in Germanys health policy and societal debates. Against the backdrop of demographic change, an ageing medical profession, and changing professional priorities, the shortages in care are intensifying, particularly in rural regions. Concurrently, rising life expectancy is leading to an increase in age-associated, multimorbid conditions that require complex management. These developments affect both the supply of physicians and patient demand.
Read MorePopulations remember: projecting the intergenerational consequences of heat extremes
Extreme heat events cause substantial excess mortality, yet their long-term demographic consequences extend far beyond immediate death counts. Each heatwave creates demographic memory—the cascading effects of lost individuals who would have reproduced, aged, and shaped future population structures. In this work, I will develop a microsimulation framework to quantify how a single extreme heat event reshapes population trajectories over subsequent decades, comparing outcomes with and without the heatwave to isolate its lasting demographic imprint.
Read MoreProjecting Demand for Senior Day-Care Facilities in Slovakia
Population ageing represents one of the most significant long-term challenges for social and health care systems in Europe, with powerful implications for long-term care infrastructure. In Slovakia, demographic ageing is expected to intensify markedly over the coming decades, generating substantial pressure on the capacity and spatial distribution of residential care facilities for older persons. This paper aims to quantify the future demand for residential senior day-care facilities using the Slovak Labour Microsimulation Model (SLAMM). The analysis employs a dynamic microsimulation approach that models individual life-course transitions related to ageing, household structure, health status, and care dependency. Based on demographic projections of the Slovak population, the model simulates the evolution of the senior population and estimates the number of individuals requiring institutional or semi-institutional care services. The analysis is conducted at the district (LAU 1) level, allowing for the identification of significant regional disparities in both demographic ageing and projected care needs. The primary output of the model is a district-level forecast of demand for residential day-care facilities for seniors over the medium- and long-term horizon. Building on these demand estimates, the paper further quantifies the investment costs associated with expanding care infrastructure. Using unit cost estimates for the construction and capacity expansion of senior care facilities, the study derives projected capital expenditure requirements necessary to meet future demand under different demographic scenarios. The results provide a comprehensive picture of how population ageing will translate into spatially differentiated demand for senior care services in Slovakia. By linking microsimulation-based demand projections with infrastructure cost estimates, the paper offers an integrated analytical framework that supports evidence-based planning of long-term care capacity. The findings are particularly relevant for policymakers and regional authorities responsible for social service provision, as they highlight districts facing the most significant future capacity gaps and investment needs. Overall, the study demonstrates the usefulness of microsimulation modelling as a tool for strategic planning in the area of ageing and long-term care, enabling policymakers to anticipate demographic pressures and to design more efficient and equitable infrastructure investment strategies.
Read MoreRecent developments of the SimPaths dynamic microsimulation framework
SimPaths is an open-source framework for modelling individual and household life course events, jointly developed at the Centre for Microsimulation and Policy Analysis and the University of Glasgow (Bronka et al., 2025). The framework is designed to project life histories through time, building up a detailed picture of career paths, family (inter)relations, health, and financial circumstances. The modular nature of the SimPaths framework is designed to facilitate analysis of alternative assumptions concerning the tax and benefit system, sensitivity to parameter estimates and alternative approaches for projecting labour/leisure and consumption/savings decisions. SimPaths builds upon standardised assumptions and data sources, which facilitates adaptation to alternative countries.
Read MoreStrengthening Validation Frameworks in Dynamic Microsimulation: Evidence from SimPaths
Dynamic microsimulation models such as SimPaths are increasingly used to evaluate long-term policy impacts by generating synthetic trajectories for individuals and households. Their credibility, however, depends on rigorous validation: demonstrating that simulated outcomes can reliably reproduce observed data. Despite their growing role in policy analysis, validation practices remain fragmented and only partially automated (e.g., O’Donoghue et al., 2015; Gosseries & Van der Heyden, 2018). This paper presents ongoing work on strengthening validation frameworks in SimPaths, with a focus on discriminator-based methods and econometric consistency checks. First, we apply classifiers (e.g., Gradient Boosted Machines) to distinguish between simulated and survey data. Discriminator accuracy provides an interpretable quantitative score of similarity: the closer performance is to random guessing, the more realistic the simulated data. Second, we explore the re-estimation of key behavioural regressions using simulated data and assess parameter recovery. This helps identify whether discrepancies arise from implementation issues, estimation limitations, or structural differences between datasets. By combining machine-learning discriminators with regression-based diagnostics, the paper contributes to more automated, transparent, and reproducible validation practices for complex microsimulation models.
Read MoreThe future of long-term care in Europe. A microsimulation analysis of potential demand based on benefit eligibility rules
European long-term care (LTC) systems face mounting sustainability pressures as population ageing increases the number of older adults living with functional and cognitive limitations. Yet projections of future LTC needs often rely on simplified “need” definitions (e.g., at least one ADL/iADL limitation), overlooking a critical institutional determinant of public expenditure: eligibility rules. Access to publicly funded LTC is not automatic; it is regulated by national (and in some cases regional) legislation that combines multiple vulnerability dimensions—limitations in activities of daily living (ADL), instrumental ADL (iADL), cognition, behavioural issues, and medical needs—into non-linear thresholds for benefit entitlement. Because these rules differ markedly across Europe, they can generate substantially different trajectories of potential demand for publicly financed LTC even under identical demographic and epidemiological trends. This study investigates how the heterogeneity of LTC eligibility criteria shapes the evolution of potential demand for formal domiciliary LTC across European countries in the coming decades. We use longitudinal microdata from the Survey of Health, Ageing and Retirement in Europe (SHARE), Waves 1–9 to operationalize country-specific eligibility rules. Using SHARE’s information on ADL/iADL limitations, mobility constraints, depression symptoms, and cognitive impairment, we construct harmonized indicators of “objective vulnerability” consistent with the assessment-of-need frameworks embedded in legislation. These eligibility indicators are then embedded in a dynamic microsimulation model, the EU-FEM (the SHARE-based version of the Future Elderly Model), which simulates individual life courses through a first-order Markov Monte Carlo process. Transition models generate probabilities of changes in health and functional status over time, allowing heterogeneous trajectories by age, risk factors, and baseline conditions, while the simulated population evolves by ageing and cohort replacement. We produce projections of the population-level prevalence of eligibility for publicly funded domiciliary LTC—interpreted as potential demand—under a baseline “no policy change” scenario. We then conduct counterfactual exercises to disentangle the role of rules versus epidemiology: (i) applying alternative countries’ eligibility rules to the same underlying population trajectories; (ii) simulating a synthetic “single-country” setting to isolate institutional effects; and (iii) evaluating stylized healthy-ageing interventions that reduce the incidence of selected physical and mental limitations by 25% among individuals aged 65–75. The simulations highlight that eligibility-rule heterogeneity translates into markedly different projected pathways of potential LTC demand across Europe. Moreover, the same healthy-ageing intervention can yield very different reductions in projected eligibility depending on which functional domains are targeted and how each country’s rules weight physical, cognitive, and mental limitations. These findings imply that cross-country comparisons of future LTC burdens—and evaluations of prevention-oriented strategies—must explicitly account for institutional eligibility design. Ongoing work extends the framework by incorporating newer SHARE waves, improving harmonization with broader international platforms, and translating projected eligibility into cost trajectories of potential demand.
Read MoreThe Impact of Demographic Change on Spousal Caregiving and Future Gaps in Long-term Care: Microsimulation Projections for Austria and Italy
As populations age, the sustainability of long-term care systems increasingly depends on the availability of informal care, particularly from partners. This paper addresses the question of how much care we may expect partners to provide in the future by projecting demand for long-term care (LTC), the care supply mix based on current patterns, and the resulting care gaps up to 2070. Using a comparative dynamic microsimulation model, we contrast the results for Austria and Italy, two countries at very different stages in the ageing process and with pronounced institutional differences. Our results suggest that delayed widowhood due to improvements in mortality is a mitigating factor for the increased need for formal care in ageing societies, although it can only offset this increase to a limited extent. Even under optimistic assumptions, potential care gaps substantially increase in both countries, primarily due to demographic change. The size of these gaps is influenced by institutional settings, partnership patterns and gains in longevity, but no scenario reverses the overall upward trend. These findings emphasize the need for comprehensive LTC reforms that extend beyond merely promoting informal care and highlight the necessity for substantial investment in formal care infrastructure.
Read MoreUse of Microsimulation Methods to Assess Nutrition-associated Health Outcomes of Climate Change in Northwest Kenya
In the arid and semi-arid lands of northwest Kenya, climate change threatens to compound existing disparities in agricultural activities and health infrastructure and exacerbate food insecurity and malnutrition. To estimate the distribution of nutrition-associated health outcomes and assess projected disease burdens under climate change scenarios, a microsimulation model was combined with health impact assessment methods. A synthetic population covering Turkana, Samburu, and Laikipia counties was generated from census data and a survey conducted in the study region in 2025 provided socioeconomic attributes and baseline dietary intake data. Population growth estimates were obtained from the national statistics authority and forecasts of future edible food availability were estimated up to 2050 from a validated open-source agricultural land-use model. Relative risks of nutrition-associated health outcomes were simulated at the individual-level using standard exposure-response functions at baseline and then at five-year intervals under four representative concentration pathways. The attributable burden in disability-adjusted life years was then estimated to generate the projected morbidity burden in demographic groups. This work expands on an earlier microsimulation model framework developed to assess policies to promote the use of clean cooking fuels on individual exposure to ambient and household air pollution emissions and associated gender-specific mortality in three densely populated Kenyan municipalities. With this additional iteration, we demonstrate how geographic- and hazard-specific assessments of disease burdens in rural and urban populations may contribute toward an improved understanding of differential climate vulnerability in Kenya.
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