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Comparing the Lifestyle Interventions for Prediabetes: An Integrated Microsimulation and Population Simulation Model.

ABSTRACT: We developed a model to compare the impacts of different lifestyle interventions among prediabetes individuals and to identify the optimal age groups for such interventions. A stochastic simulation was developed to replicate the prediabetes and diabetes trends (1997-2010) in the U.S. adult population. We then simulated the population-wide impacts of three lifestyle diabetes prevention programs, i.e., the Diabetes Prevention Program (DPP), DPP-YMCA, and the Healthy Living Partnerships to Prevent Diabetes (HELP-PD), over a course of 10, 15 and 30 years. Our model replicated the temporal trends of diabetes in the U.S. adult population. Compared to no intervention, the diabetes incidence declined 0.3 per 1,000 by DPP, 0.2 by DPP-YMCA, and 0.4 by HELP-PD over the 15-year period. Our simulations identified HELP-PD as the most cost-effective intervention, which achieved the highest 10-year savings of $38 billion for those aged 25-65, assuming all eligible individuals participate in the intervention and considering intervention achievement rates. Our model simulates the diabetes trends in the U.S. population based on individual-level longitudinal data. However, it may be used to identify the optimal intervention for different subgroups in defined populations.

SUBMITTER: Khademi A

PROVIDER: S-EPMC6695408 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Comparing the Lifestyle Interventions for Prediabetes: An Integrated Microsimulation and Population Simulation Model.

Khademi Amin A Shi Lu L Nasrollahzadeh Amir Ali AA Narayanan Hariharaprabhu H Chen Liwei L

Scientific reports 20190815 1

We developed a model to compare the impacts of different lifestyle interventions among prediabetes individuals and to identify the optimal age groups for such interventions. A stochastic simulation was developed to replicate the prediabetes and diabetes trends (1997-2010) in the U.S. adult population. We then simulated the population-wide impacts of three lifestyle diabetes prevention programs, i.e., the Diabetes Prevention Program (DPP), DPP-YMCA, and the Healthy Living Partnerships to Prevent ...[more]

PMID: 31417128

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Project description:IntroductionOsteoporosis is a systemic skeletal disease that is characterised by reduced bone strength and increased fracture risk. Osteoporosis-related fractures impose enormous disease and economic burden to the society. Although many treatments and health interventions are proven effective to prevent fractures, health economic evaluation adds evidence to their economic merits. Computer simulation modelling is a useful approach to extrapolate clinical and economic outcomes from clinical trials and it is increasingly used in health economic evaluation. Many osteoporosis health economic models have been developed in the past decades; however, they are limited to academic use and there are no publicly accessible health economic models of osteoporosis.Methods and analysisWe will develop the Australian osteoporosis health economic model based on our previously published microsimulation model of osteoporosis in the Chinese population. The development of the model will follow the recommendations for the conduct of economic evaluations in osteoporosis by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases and the US branch of the International Osteoporosis Foundation. The model will be a state-transition semi-Markov model with memory. Clinical parameters in the model will be mainly obtained from the Dubbo Osteoporosis Epidemiology Study and the health economic parameters will be collected from the Australian arm of the International Costs and Utilities Related to Osteoporotic Fractures Study. Model transparency and validates will be tested using the recommendations from Good Research Practices in Modelling Task Forces. The model will be used in economic evaluations of osteoporosis interventions including pharmaceutical treatments and primary care interventions. A user-friendly graphical user interface will be developed, which will connect the user to the calculation engine and the results will be generated. The user interface will facilitate the use of our model by people in different sectors.Ethics and disseminationNo ethical approval is needed for this study. Results of the model validation and future economic evaluation studies will be submitted to journals. The user interface of the health economic model will be publicly available online accompanied with a user manual.

Project description:BACKGROUND:Previous simulation studies estimating the impacts of lung cancer screening have ignored the changes in smoking prevalence over time in the United States. Our primary rationale was to perform, to our knowledge, the first simulation study that estimates the health outcomes of lung cancer screening with explicit modeling of smoking trends for the whole US population. METHODS/FINDINGS:Utilizing a well-validated microsimulation model, we estimated the benefits and harms of an annual low-dose computed tomography screening scenario with a realistic screening adherence rate versus a no-screening scenario for the US population from 2016-2030. The Centers for Medicare and Medicaid Services (CMS) eligibility criteria were applied: age 55-77 years at time of screening, history of at least 30 pack-years of smoking, and current smoker or former smoker with fewer than 15 years since quitting. In the screened population, cumulative mortality reduction was projected to reach 16.98% (95% CI 16.90%-17.07%). Cumulative mortality reduction was estimated to be 3.52% (95% CI 3.50%-3.53%) for the overall study population, with annual mortality reduction peaking at 4.38% (95% CI 4.36%-4.41%) in 2021 and falling to 3.53% (95% CI 3.50%-3.56%) by 2030. Lung cancer screening would save a projected 148,484 life-years (95% CI 147,429-149,540) across the total population through 2030. There were estimated to be 9,054 (95% CI 9,011-9,098) overdiagnosed cases among the 252,429 (95% CI 251,208-253,649) screen-detected lung cancer diagnoses, yielding an overdiagnosis rate of 3.59%. The limitations of our study are that we do not explicitly model race or socioeconomic status and our model was calibrated to data from studies performed in academic centers, both of which may impact the generalizability of our results. We also exclusively model the effects of the CMS guidelines for lung cancer screening and not any other screening strategies. CONCLUSIONS:The mortality reduction and life-years gained estimated by this study are lower than those of single birth cohort studies. Single cohort studies neglect the changing dynamics of smoking behavior across generations, whereas this study reflects the trend of decreasing smoking prevalence since the 1960s. Maximum benefit could be derived from lung cancer screening through 2021; in later years, mortality reduction due to screening will decline. If a comprehensive screening program is not implemented in the near future, the opportunity to achieve these benefits will have passed.

Project description:BackgroundMicrosimulation models of human immunodeficiency virus (HIV) disease that simulate individual patients one at a time and assess clinical and economic outcomes of HIV interventions often provide key details regarding direct individual clinical benefits ("individual benefit"), but they may lack detail on transmissions, and thus may underestimate an intervention's indirect benefits ("community benefit"). Dynamic transmission models can be used to simulate HIV transmissions, but they may do so at the expense of the clinical detail of microsimulations. We sought to develop, validate, and demonstrate a practical, novel method that can be integrated into existing HIV microsimulation models to capture this community benefit, integrating the effects of reduced transmission while keeping the clinical detail of microsimulations.MethodsWe developed a new method to capture the community benefit of HIV interventions by estimating HIV transmissions from the primary cohort of interest. The method captures the benefit of averting infections within the cohort of interest by estimating a corresponding gradual decline in incidence within the cohort. For infections averted outside the cohort of interest, our method estimates transmissions averted based on reductions in HIV viral load within the cohort, and the benefit (life-years gained and cost savings) of averting those infections based on the time they were averted. To assess the validity of our method, we paired it with the Cost-effectiveness of Preventing AIDS Complications (CEPAC) Model - a validated and widely-published microsimulation model of HIV disease. We then compared the consistency of model-estimated outcomes against outcomes of a widely-validated dynamic compartmental transmission model of HIV disease, the HIV Optimization and Prevention Economics (HOPE) model, using the intraclass correlation coefficient (ICC) with a two-way mixed effects model. Replicating an analysis done with HOPE, validation endpoints were number of HIV transmissions averted by offering pre-exposure prophylaxis (PrEP) to men who have sex with men (MSM) and people who inject drugs (PWID) in the US at various uptake and efficacy levels. Finally, we demonstrated an application of our method in a different setting by evaluating the clinical and economic outcomes of a PrEP program for MSM in India, a country currently considering PrEP rollout for this high-risk group.ResultsThe new method paired with CEPAC demonstrated excellent consistency with the HOPE model (ICC = 0.98 for MSM and 0.99 for PWID). With only the individual benefit of the intervention incorporated, a PrEP program for MSM in India averted 43,000 transmissions over a 5-year period and resulted in a lifetime incremental cost-effectiveness ratio (ICER) of US$2,300/year-of-life saved (YLS) compared to the status quo. After applying both the direct (individual) and indirect (community) benefits, PrEP averted 86,000 transmissions over the same period and resulted in an ICER of US$600/YLS.ConclusionsOur method enables HIV microsimulation models that evaluate clinical and economic outcomes of HIV interventions to estimate the community benefit of these interventions (in terms of survival gains and cost savings) efficiently and without sacrificing clinical detail. This method addresses an important methodological gap in health economics microsimulation modeling and allows decision scientists to make more accurate policy recommendations.

Project description:Background and objectiveSimulation models can project effects of tobacco use and cessation and inform tobacco control policies. Most existing tobacco models do not explicitly include relapse, a key component of the natural history of tobacco use. Our objective was to develop, calibrate and validate a novel individual-level microsimulation model that would explicitly include smoking relapse and project cigarette smoking behaviours and associated mortality risks.MethodsWe developed the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) model, in which individuals transition monthly between tobacco use states (current/former/never) depending on rates of initiation, cessation and relapse. Simulated individuals face tobacco use-stratified mortality risks. For US women and men, we conducted cross-validation with a Cancer Intervention and Surveillance Modeling Network (CISNET) model. We then incorporated smoking relapse and calibrated cessation rates to reflect the difference between a transient quit attempt and sustained abstinence. We performed external validation with the National Health Interview Survey (NHIS) and the linked National Death Index. Comparisons were based on root-mean-square error (RMSE).ResultsIn cross-validation, STOP-generated projections of current/former/never smoking prevalence fit CISNET-projected data well (coefficient of variation (CV)-RMSE≤15%). After incorporating smoking relapse, multiplying the CISNET-reported cessation rates for women/men by 7.75/7.25, to reflect the ratio of quit attempts to sustained abstinence, resulted in the best approximation to CISNET-reported smoking prevalence (CV-RMSE 2%/3%). In external validation using these new multipliers, STOP-generated cumulative mortality curves for 20-year-old current smokers and never smokers each had CV-RMSE ≤1% compared with NHIS. In simulating those surveyed by NHIS in 1997, the STOP-projected prevalence of current/former/never smokers annually (1998-2009) was similar to that reported by NHIS (CV-RMSE 12%).ConclusionsThe STOP model, with relapse included, performed well when validated to US smoking prevalence and mortality. STOP provides a flexible framework for policy-relevant analysis of tobacco and nicotine product use.

Dataset Information

Comparing the Lifestyle Interventions for Prediabetes: An Integrated Microsimulation and Population Simulation Model.

Publications

Comparing the Lifestyle Interventions for Prediabetes: An Integrated Microsimulation and Population Simulation Model.

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