Dynamic Microsimulation Research Articles

The urban-rural gap in older Americans' healthy life expectancy.

Estimate health-quality-adjusted life expectancy (QALE) for Americans nearing retirement age and assess rural-urban disparities in QALE. We used a dynamic microsimulation model based on Health and Retirement Study data to estimate the quantity and health quality of expected future life years for rural and urban Americans ages 59-60 in 2014-2020. Cohort life expectancy at age 60 (LE) for urban and rural men was 22.9 and 20.9, respectively; for urban and rural women, LE was 25.6 and 25.0, respectively. Adjusting future life years to quality-adjusted life years, QALE was 17.5versus 15.7 for urban versus rural men, and 19.3versus 18.7 for women. Compared to a cohort in 1994-2000, the urban-rural QALE gap in 2014-2020 grew substantially for men; changes for women were smaller. Average QALE masked heterogeneity by race/ethnicity, education, and Census region. Counterfactual scenarios suggested eliminating smoking and managing obesity and prevalent heart conditions would be particularly beneficial for increasing rural QALE and reducing the urban-rural gap. Expected health quality, in addition to longevity, is an important factor when assessing rural disparities in older Americans' future life outcomes. Current chronic disease disparities are expected to translate to a widening urban-rural gap in QALE, particularly for men. Interventions earlier in life may be needed to fully address disparities in QALE at older ages.

How many people will live and die with serious illness in Ireland to 2040? Estimated needs and costs using microsimulation

As populations age, more people worldwide will live and die with serious illness like cancer, heart disease and dementia. Prior projections of serious illness prevalence and end-of-life care needs have typically used static population-level methods. We estimated future disease prevalence and healthcare costs by applying dynamic microsimulation models to high-quality individual-level panel data on older adults (aged 50 + ) in Ireland. We estimated that the number of people living and dying with serious illness will increase approximately 70 % over 20 years. Per-capita annual costs both at end of life and not at end of life increase substantially due to ageing populations and growing complexity. Total health system expenditures on care for people with serious illness are projected to double before accounting for rising cost of inputs in real terms. Decomposition of these estimates suggests that 39 % of additional costs are accounted for by rising absolute numbers of older people, 37 % by changing age distribution and growing life expectancy, and 23 % due to rising individual complexity including morbidity and functional limitations. Our results and methods will be of interest to other countries planning for the future population health needs, and formidable health system resources associated with these needs, in the coming years.

Using simulation modeling to inform intervention and implementation selection in a rapid stakeholder-engaged hybrid effectiveness-implementation randomized trial

BackgroundImplementation research generally assumes established evidence-based practices and prior piloting of implementation strategies, which may not be feasible during a public health emergency. We describe the use of a simulation model of the effectiveness of COVID-19 mitigation strategies to inform a stakeholder-engaged process of rapidly designing a tailored intervention and implementation strategy for individuals with serious mental illness (SMI) and intellectual/developmental disabilities (ID/DD) in group homes in a hybrid effectiveness-implementation randomized trial.MethodsWe used a validated dynamic microsimulation model of COVID-19 transmission and disease in late 2020/early 2021 to determine the most effective strategies to mitigate infections among Massachusetts group home staff and residents. Model inputs were informed by data from stakeholders, public records, and published literature. We assessed different prevention strategies, iterated over time with input from multidisciplinary stakeholders and pandemic evolution, including varying symptom screening, testing frequency, isolation, contact-time, use of personal protective equipment, and vaccination. Model outcomes included new infections in group home residents, new infections in group home staff, and resident hospital days. Sensitivity analyses were performed to account for parameter uncertainty. Results of the simulations informed a stakeholder-engaged process to select components of a tailored best practice intervention and implementation strategy.ResultsThe largest projected decrease in infections was with initial vaccination, with minimal benefit for additional routine testing. The initial level of actual vaccination in the group homes was estimated to reduce resident infections by 72.4% and staff infections by 55.9% over the 90-day time horizon. Increasing resident and staff vaccination uptake to a target goal of 90% further decreased resident infections by 45.2% and staff infections by 51.3%. Subsequent simulated removal of masking led to a 6.5% increase in infections among residents and 3.2% among staff. The simulation model results were presented to multidisciplinary stakeholders and policymakers to inform the “Tailored Best Practice” package for the hybrid effectiveness-implementation trial.ConclusionsVaccination and decreasing vaccine hesitancy among staff were predicted to have the greatest impact in mitigating COVID-19 risk in vulnerable populations of group home residents and staff. Simulation modeling was effective in rapidly informing the selection of the prevention and implementation strategy in a hybrid effectiveness-implementation trial. Future implementation may benefit from this approach when rapid deployment is necessary in the absence of data on tailored interventions.Trial registrationClinicalTrials.gov NCT04726371

The gender gap in lifetime earnings: A microsimulation approach

AbstractTo obtain a more complete understanding of the persisting gender earnings gap in Germany, this paper investigates both the cross‐sectional and lifetime dimension of gender inequalities. Based on a dynamic microsimulation model, we analyse how gender differences accumulate over work lives to examine the lifetime dimension of the gender gap. We estimate an average gender gap in lifetime earnings of 51.5 per cent for birth cohorts 1964–72. We show that this unadjusted gender lifetime earnings gap increases strongly with the number of children, ranging from 17.3 per cent for childless women to 68.0 per cent for women with three or more children. Results from a counterfactual analysis approach show an adjusted gender gap in lifetime earnings of around 10 per cent, suggesting that the gender gap in lifetime earnings is rather driven by gender differences in observable characteristics than by differences in rewards.

Can primary health care mitigate the effects of economic crises on child health in Latin America? An integrated multicountry evaluation and forecasting analysis

Latin American and Caribbean countries are dealing with the combined challenges of pandemic-induced socicoeconomic stress and increasing public debt, potentially leading to reductions in welfare and health-care services, including primary care. We aimed to evaluate the impact of primary health-care coverage on child mortality in Latin America over the past two decades and to forecast the potential effects of primary health-care mitigation during the current economic crisis. This multicountry study integrated retrospective impact evaluations in Brazil, Colombia, Ecuador, and Mexico from 2000 to 2019 with forecasting models covering up to 2030. We estimated the impact of coverage of primary health care on mortality rates in children younger than 5 years (hereafter referred to as under-5 mortality) across different age groups and causes of death, adjusting for all relevant demographic, socioeconomic, and health-care factors, with fixed-effects multivariable negative binomial models in 5647 municipalities with an adequate quality of vital statistics. We also performed several sensitivity and triangulation analyses. We integrated previous longitudinal datasets with validated dynamic microsimulation models and projected trends in under-5 mortality rates under alternative policy response scenarios until 2030. High primary health-care coverage was associated with substantial reductions in post-neonatal mortality rates (rate ratio [RR] 0·72, 95% CI 0·71-0·74), toddler (ie, aged between 1 year and <5 years) mortality rates (0·75, 0·73-0·76), and under-5 mortality rates (0·81, 0·80-0·82), preventing 305 890 (95% CI 251 826-360 517) deaths of children younger than 5 years over the period 2000-19. High primary health-care coverage was also associated with lower under-5 mortality rates from nutritional deficiencies (RR 0·55, 95% CI 0·52-0·58), anaemia (0·64, 0·57-0·72), vaccine-preventable and vaccine-sensitive conditions (0·70, 0·68-0·72), and infectious gastroenteritis (0·78, 0·73-0·84). Considering a scenario of moderate economic crisis, a mitigation response strategy implemented in the period 2020-30 that increases primary health-care coverage could reduce the under-5 mortality rate by up to 23% (RR 0·77, 95% CI 0·72-0·84) when compared with a fiscal austerity response, and this strategy would avoid 142 285 (95% CI 120 217-164 378) child deaths by 2030 in Brazil, Colombia, Ecuador, and Mexico. The improvement in primary health-care coverage in Brazil, Colombia, Ecuador, and Mexico over the past two decades has substantially contributed to improving child survival. Expansion of primary health-care coverage should be considered an effective strategy to mitigate the health effects of the current economic crisis and to achieve Sustainable Development Goals related to child health. UK Medical Research Council. For the Spanish and Portuguese translations of the abstract see Supplementary Materials section.

Private Wealth Over the Life Cycle: A Meeting Between Microsimulation and Structural Approaches

AbstractThis paper embeds a structural model of private wealth accumulation over the life cycle within a dynamic microsimulation model designed for long‐run projections of pensions. In such an environment, the optimal savings path results from consumption smoothing and bequests motives, on top of the mortality risk. Preferences are estimated based on a longitudinal wealth survey through a method of simulated moments. Simulations issued from these estimations replicate quite well a private wealth that is more concentrated than labor income. They enable us to compute “augmented” standards of living including capital income, hence to quantify both the countervailing role played by private wealth to earnings dropout after retirement and the impact of the mortality risk in this regard.

Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations

Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data. However, estimating such models on the regional level is often not feasible due to limited sample size and lack of geographic information. Simply applying the model estimated at the national level to all geographies leads to biased state transitions due to regional differences in level and distribution. In this paper, we introduce a model-based alignment method to adapt predicted probabilities obtained from a nationally estimated model to subregions by integrating known marginal distributions to re-introduce regional heterogeneity and create more realistic trajectories, particularly in small areas. We show that the model-adjusted transition probabilities can capture region-specific patterns and lead to improved projections. Our findings are useful to researchers who want to harmonise model outputs with external information, in particular for the field of microsimulation.

NiReMS: A regional model at household level combining spatial econometrics with dynamic microsimulation

ABSTRACT The heterogeneous spatial and individual impacts of the Great Recession, Brexit and COVID-19 have generated an important challenge for macroeconomic and regional/spatial modellers to consider greater integration of their approaches. Focusing on agent heterogeneity at the ITL 1 level in the UK, we propose the National Institute Regional Modelling System (NiReMS) – a synthesis of dynamic microsimulation with a spatial regional macroeconometric model. The model gives regional macro projections while allowing for household level inference. To showcase the model, we explore the impact of discontinuing the uplift in Universal Credit (UC) before the end of the pandemic and show that it led to more households consuming less. Importantly, the proposed framework highlights the unequal distributional impact across regions of the UK.

The Italian Treasury Dynamic Microsimulation Model: Data, Structure and Validation

The Italian Treasury Dynamic Microsimulation Model: Data, Structure and Validation

Dynamic Microsimulations of Regional Income Inequalities in Germany

Dynamic Microsimulations of Regional Income Inequalities in Germany

Projections of functional dependence among the late middle-aged and older population from 2018-2048 in China: a dynamic microsimulation

BackgroundThe population of China is aging rapidly. However, the long-term trajectories of functionally dependent late middle-aged and older Chinese people are currently absent. The present study aimed to estimate the population size and proportion of late middle-aged and older adults with difficulties and dependence on activities of daily living (ADL) and instrumental activities of daily living (IADL) in China from 2018 to 2048.MethodsWe constructed a dynamic microsimulation model to project the population size and proportions of late middle-aged and older Chinese people who have difficulty and dependence in ADL and IADL from 2018-2048. The model was populated with a representative sample of the target population and allowed individual-level interaction between risk factors, diseases, and health outcomes. Analyses by socioeconomic subgroups were also conducted.ResultsAlmost 25% and 38% of late middle-aged and older people in China will become ADL- and IADL-dependent by 2048, respectively. Also, 17% of the target population will be severely ADL-disabled by 2048. The inequity in functional status across subgroups by sex, educational level, and urban/rural residency will become substantial.ConclusionsThe numbers and percentages of China's functionally difficult and dependent late middle-aged and older population will increase by magnitudes as of the mid-21st century, the pressure of which is compounded by its disproportionate distribution across subgroups. To alleviate the overwhelming challenge, efforts to improve the functional status of the underserved subpopulation should also be iterated.

Current and Projected Mortality and Hospitalization Rates Associated With Conditional Cash Transfer, Social Pension, and Primary Health Care Programs in Brazil, 2000-2030

The health outcomes of increased poverty and inequalities in low- and middle-income countries (LMICs) have been substantially amplified as a consequence of converging multiple crises. Brazil has some of the world's largest conditional cash transfer (Programa Bolsa Família [PBF]), social pension (Beneficio de Prestacão Continuada [BPC]), and primary health care (Estratégia de Saúde da Família [ESF]) programs that could act as mitigating interventions during the current polycrisis era of increasing poverty, slow or contracting economic growth, and conflicts. To evaluate the combined association of the Brazilian conditional cash transfer, social pension, and primary health care programs with the reduction of morbidity and mortality over the last 2 decades and forecast their potential mitigation of the current global polycrisis and beyond. This cohort study used a longitudinal ecological design with multivariable negative binomial regression models (adjusted for relevant socioeconomic, demographic, and health care variables) integrating the retrospective analysis from 2000 to 2019, with dynamic microsimulation models to forecast potential child mortality scenarios up to 2030. Participants included a cohort of 2548 Brazilian municipalities from 2004 to 2019, projected from 2020 to 2030. Data analysis was performed from September 2022 to February 2023. PBF coverage of the target population (those who were poorest) was categorized into 4 levels: low (0%-29.9%), intermediate (30.0%-69.9%), high (70.0%-99.9%), and consolidated (≥100%). ESF coverage was categorized as null (0), low (0.1%-29.9%), intermediate (30.0%-69.9%), and consolidated (70.0%-100%). BPC coverage was categorized by terciles. Age-standardized, all-cause mortality and hospitalization rates calculated for the entire population and by age group (<5 years, 5-29 years, 30-69 years, and ≥70 years). Among the 2548 Brazilian municipalities studied from 2004 to 2019, the mean (SD) age-standardized mortality rate decreased by 16.64% (from 6.73 [1.14] to 5.61 [0.94] deaths per 1000 population). Consolidated coverages of social welfare programs studied were all associated with reductions in overall mortality rates (PBF: rate ratio [RR], 0.95 [95% CI, 0.94-0.96]; ESF: RR, 0.93 [95% CI, 0.93-0.94]; BPC: RR, 0.91 [95% CI, 0.91-0.92]), having all together prevented an estimated 1 462 626 (95% CI, 1 332 128-1 596 924) deaths over the period 2004 to 2019. The results were higher on mortality for the group younger than age 5 years (PBF: RR, 0.87 [95% CI, 0.85-0.90]; ESF: RR, 0.89 [95% CI, 0.87-0.93]; BPC: RR, 0.84 [95% CI, 0.82-0.86]), on mortality for the group aged 70 years and older, and on hospitalizations. Considering a shorter scenario of economic crisis, a mitigation strategy that will increase the coverage of PBF, BPC, and ESF to proportionally cover the newly poor and at-risk individuals was projected to avert 1 305 359 (95% CI, 1 163 659-1 449 256) deaths and 6 593 224 (95% CI, 5 534 591-7 651 327) hospitalizations up to 2030, compared with fiscal austerity scenarios that would reduce the coverage of these interventions. This cohort study's results suggest that combined expansion of conditional cash transfers, social pensions, and primary health care should be considered a viable strategy to mitigate the adverse health outcomes of the current global polycrisis in LMICs, whereas the implementation of fiscal austerity measures could result in large numbers of preventable deaths.

Evaluating the Impacts of Retirement Age Reform on Financial Status of the Iranian Social Security Organization Using a Dynamic Microsimulation Model

Evaluating the Impacts of Retirement Age Reform on Financial Status of the Iranian Social Security Organization Using a Dynamic Microsimulation Model

The Retirement Decision in Dynamic Microsimulation Models: An Exploratory Review

The Retirement Decision in Dynamic Microsimulation Models: An Exploratory Review

The combined effect of social pensions and cash transfers on child mortality: evaluating the last two decades in Brazil and projecting their mitigating effect during the global economic crisis

The world is currently experiencing multiple economic crises due to the COVID-19 pandemic, war in Ukraine, and inflation surge, which disproportionately affect children, especially in low- and middle-income countries (LMICs). We evaluated if the expansion of Social Assistance, represented by Social Pensions (SP) and Conditional Cash Transfers (CCT), could reduce infant and child mortality, and mitigate excess deaths among children in Brazil, one of the LMICs most affected by these economic crises. We conducted a retrospective impact evaluation in a cohort of Brazilian municipalities from 2004 to 2019 using multivariable fixed-effects negative binomial models, adjusted for relevant demographic, social, and economic factors, to estimate the effects of the SP and CCT on infant and child mortality. To verify the robustness of the results, we conducted several sensitivity and triangulation analyses, including difference-in-difference with propensity-score matching. These results were incorporated into dynamic microsimulation models to generate projections to 2030 of various economic crises and Social Assistance scenarios. Consolidated coverage of SP was associated with significant reductions in infant and child mortality rates, with a rate ratio (RR) of 0.843 (95% CI: 0.826-0.861) and 0.840 (95% CI: 0.824-0.856), respectively. Similarly, CCT consolidated coverages showed RRs of 0.868 (95% CI: 0.842-0.849) and 0.874 (95% CI: 0.850-0.899) for infant and child mortality, respectively. The higher the degree of poverty in the municipalities, the stronger the impact of CCT on reducing child mortality. Given the current economic crisis, a mitigation strategy that will increase the coverage of SP and CCT could avert 148,736 (95% CI: 127,148-170,706) child deaths up to 2030, compared with fiscal austerity measures. SP and CCT programs could strongly reduce child mortality in LMICs, and their expansion should be considered as an effective strategy to mitigate the impact of the current multiple global economic crises. Bill & Melinda Gates Foundation, Grant_Number:INV-027961. Medical Research Council(MRC-UKRI),Grant_Number:MC_PC_MR/T023678/1.

Challenges and Opportunities of Dynamic Microsimulation Modelling

Challenges and Opportunities of Dynamic Microsimulation Modelling

The Forgotten Middle: Worsening Health And Economic Trends Extend To Americans With Modest Resources Nearing Retirement.

In an aging US society, anticipating the challenges that future seniors will face is essential. This study analyzed the health and economic well-being of five cohorts of Americans in their mid-fifties between 1994 and 2018 using the Future Elderly Model, a dynamic microsimulation based on the Health and Retirement Study. We projected mortality, quality-adjusted life years, health expenditures, and income and benefits. We classified individuals by economic status and focused on the lower middle and upper middle of the economic distribution. Outcome disparities between people in these two groups widened substantially between the 1994 and 2018 cohorts. Quality-adjusted life expectancy increased (5percent) for the upper-middle economic status group but stagnated for their lower-middle peers. We found that the combined value of the current stock (financial and housing wealth) and the present value of the expected flow of resources (income, health expenditures, and quality-adjusted life-years) after age sixty grew 13percent for the upper-middle group between cohorts, whereas people in the lower-middle group in 2018 were left scarcely better off (3percent growth) than their peers two decades earlier. The relatively neglected "forgotten middle" group of near-retirees in the lower-middle group may require stronger supports than are currently available to them.

Evaluation and Forecasting Analysis of the Association of Conditional Cash Transfer With Child Mortality in Latin America, 2000-2030

Latin America has implemented the world's largest and most consolidated conditional cash transfer (CCT) programs during the last 2 decades. As a consequence of the COVID-19 pandemic, poverty rates have markedly increased, and a large number of newly low-income individuals, especially children, have been left unprotected. To evaluate the association of CCT programs with child health in Latin American countries during the last 2 decades and forecast child mortality trends up to 2030 according to CCT alternative implementation options. This cohort study used a multicountry, longitudinal, ecological design with multivariable negative binomial regression models, which were adjusted for all relevant demographic, socioeconomic, and health care variables, integrating the retrospective impact evaluations from January 1, 2000, to December 31, 2019, with dynamic microsimulation models to forecast potential child mortality scenarios up to 2030. The study cohort included 4882 municipalities from Brazil, Ecuador, and Mexico with adequate quality of civil registration and vital statistics according to a validated multidimensional criterion. Data analysis was performed from September 2022 to February 2023. Conditional cash transfer coverage of the target (lowest-income) population categorized into 4 levels: low (0%-29.9%), intermediate (30.0%-69.9%), high (70.0%-99.9%), and consolidated (≥100%). The main outcomes were mortality rates for those younger than 5 years and hospitalization rates (per 1000 live births), overall and by poverty-related causes (diarrheal, malnutrition, tuberculosis, malaria, lower respiratory tract infections, and HIV/AIDS), and the mortality rates for those younger than 5 years by age groups, namely, neonatal (0-28 days), postneonatal (28 days to 1 year), infant (<1 year), and toddler (1-4 years). The retrospective analysis included 4882 municipalities. During the study period of January 1, 2000, to December 31, 2019, mortality in Brazil, Ecuador, and Mexico decreased by 7.8% in children and 6.5% in infants, and an increase in coverage of CCT programs of 76.8% was observed in these Latin American countries. Conditional cash transfer programs were associated with significant reductions of mortality rates in those younger than 5 years (rate ratio [RR], 0.76; 95% CI, 0.75-0.76), having prevented 738 919 (95% CI, 695 641-782 104) child deaths during this period. The association of highest coverage of CCT programs was stronger with poverty-related diseases, such as malnutrition (RR, 0.33; 95% CI, 0.31-0.35), diarrhea (RR, 0.41; 95% CI, 0.40-0.43), lower respiratory tract infections (RR, 0.66, 95% CI, 0.65-0.68), malaria (RR, 0.76; 95% CI, 0.63-0.93), tuberculosis (RR, 0.62; 95% CI, 0.48-0.79), and HIV/AIDS (RR, 0.32; 95% CI, 0.28-0.37). Several sensitivity and triangulation analyses confirmed the robustness of the results. Considering a scenario of moderate economic crisis, a mitigation strategy that will increase the coverage of CCTs to protect those newly in poverty could reduce the mortality rate for those younger than 5 years by up to 17% (RR, 0.83; 95% CI, 0.80-0.85) and prevent 153 601 (95% CI, 127 441-180 600) child deaths by 2030 in Brazil, Ecuador, and Mexico. The results of this cohort study suggest that the expansion of CCT programs could strongly reduce childhood hospitalization and mortality in Latin America and should be considered an effective strategy to mitigate the health impact of the current global economic crisis in low- and middle-income countries.

Socio-economic inequality and healthcare costs over the life course – A dynamic microsimulation approach

ObjectivesAlthough there is well-established evidence for the existence of socio-economic inequalities in virtually all dimensions of health, little is known about the implications of these socio-economic disparities for healthcare costs from a cumulative life course perspective. Accounting for differentials in healthcare use patterns and mortality, we assess how lifetime costs differ between socio-economic groups. Study designThis study used dynamic microsimulation modelling. MethodsCombining price weights for healthcare services with information on healthcare consumption from the 2014 Austrian Health Interview Survey (n = 15,771), we calculated average cost profiles by gender, age and education consistent with aggregate System of Health Accounts. A dynamic microsimulation model was used to project cumulative healthcare costs over the entire lifecycle for the 2019 birth cohort in four different scenarios to illustrate the influence of the different cost determinants on lifetime costs. ResultsBefore considering social inequalities in mortality, men with compulsory education have close to 66% higher lifetime costs than men with tertiary education; for women, the difference is close to 20%. Accounting for longevity differentials reduces this gap to approximately 40% for men and 10% for women. Closing the gap in healthcare use and in longevity between socio-economic groups would reduce lifetime healthcare expenditure by 4.1% in the 2019 birth cohort and by 19% in the whole population. ConclusionsAccounting for mortality differentials between socio-economic groups has a large impact on estimated lifetime healthcare costs. Reducing social inequalities in health can contribute to containing healthcare expenditures against the backdrop of rising life expectancy.

The Impact of Health and Education on Labor Force Participation in Aging Societies: Projections for the United States and Germany from Dynamic Microsimulations

We project the labor force in the United States to 2060 and contrast the outcomes with comparative projections for Germany. In both countries, the population will age, but the demographic dynamics are fundamentally different. According to our dynamic microsimulations, the labor force in the U.S. will increase by 17 percent between 2020 and 2060 (about 29 million workers) despite population aging. In contrast, the labor force in Germany will decline by 11 percent (about 4.5 million workers). Our baseline projections indicate that an expansion of education will increase the labor force by about 3 million persons in the United States and about half a million persons in Germany by 2060. In several what-if scenarios, we examine the effects of further expanding education and of removing health barriers on labor force participation. Higher educational attainment among those with currently low education has the largest impact on labor force participation, relative to the additional years of schooling. However, health improvements and the labor market integration of people with health limitations suggest a larger increase in labor force participation rates. Using Sweden as a benchmark, we show that reducing the health participation gap would increase the U.S. labor force by as much as 13 million people in 2060 (+6.8 percent compared to our baseline).