Where can obesity management policy make the largest impact? Evaluating sub-populations through a microsimulation approach
| Autor: | Wenqing Su, Tracy Zvenyach, Timothy M. Dall, Fang Chen, Leigh Perreault, Theodore K. Kyle |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Cost-Benefit Analysis Population Microsimulation Severity of Illness Index Body Mass Index 03 medical and health sciences Sex Factors 0302 clinical medicine Weight loss Return on investment Absenteeism Diabetes Mellitus medicine Humans Computer Simulation Obesity 030212 general & internal medicine education education.field_of_study Cost–benefit analysis business.industry 030503 health policy & services Health Policy Age Factors Health Services Middle Aged medicine.disease Markov Chains Obesity Management Policy Socioeconomic Factors Health Resources Female medicine.symptom 0305 other medical science business Body mass index Models Econometric Demography |
| DOI: | 10.6084/m9.figshare.6743282.v2 |
| Popis: | Background: There is a critical need to focus limited resources on sub-groups of patients with obesity where we expect the largest return on investment. This paper identifies patient sub-groups where an investment may result in larger positive economic and health outcomes. Methods: The baseline population with obesity was derived from a public survey database and divided into sub-populations defined by demographics and disease status. In 2016, a validated model was used to simulate the incidence of diabetes, absenteeism, and direct medical cost in five care settings. Research findings were derived from the difference in population outcomes with and without weight loss over 15 years. Modeled weight loss scenarios included initial 5% or 12% reduction in body mass index followed by a gradual weight regain. Additional simulations were conducted to show alternative outcomes from different time courses and maintenance scenarios. Results: Univariate analyses showed that age 45–64, pre-diabetes, female, or obesity class III are independently predictive of larger savings. After considering the correlation between these factors, multivariate analyses projected young females with obesity class I as the optimal sub-group to control obesity-related medical expenditures. In contrast, the population aged 20–35 with obesity class III will yield the best health outcomes. Also, the sub-group aged 45–54 with obesity class I will produce the biggest productivity improvement. Each additional year of weight loss maintained showed increased financial benefits. Conclusions: This paper studied the heterogeneity between many sub-populations affected by obesity and recommended different priorities for decision-makers in economic, productivity, and health realms. |
| Databáze: | OpenAIRE |
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