Development and Validation of the US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) Model: Health Disparity and Economic Impact Model

David D. Kim, Lu Wang, Brianna N. Lauren, Junxiu Liu, Matti Marklund, Yujin Lee, Renata Micha, Dariush Mozaffarian and John B. Wong
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David D. Kim: Section of Hospital Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
Lu Wang: Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
Brianna N. Lauren: Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
Junxiu Liu: Department of Population Health Science and Policy, the Icahn School of Medicine at Mount Sinai, New York, NY, USA
Matti Marklund: The George Institute for Global Health, University of New South Wales, Sydney, Australia
Yujin Lee: Department of Food and Nutrition, Myongji University, Yongin, South Korea
Renata Micha: Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
Dariush Mozaffarian: Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
John B. Wong: Division of Clinical Decision Making, Tufts Medical Center, Boston, MA, USA

Medical Decision Making, 2023, vol. 43, issue 7-8, 930-948

Abstract: Background Few simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences. Methods We developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated. This model incorporates nationally representative data on baseline demographics, lifestyle, health, and cause-specific mortality; dynamic changes in modifiable risk factors over time; and parameter uncertainty using probabilistic distributions. Validation analyses included assessment of 1) population-level risk calibration and 2) individual-level risk discrimination. To illustrate the application of the DOC-M model, we evaluated the long-term cost-effectiveness of a national produce prescription program. Results Comparing the 15-y model-predicted population risk of primary outcomes among the 2001–2002 National Health and Nutrition Examination Survey (NHANES) cohort with the observed prevalence from age-matched cross-sectional 2003–2016 NHANES cohorts, calibration performance was strong based on observed-to-expected ratio and calibration plot analysis. In most cases, Brier scores fell below 0.0004, indicating a low overall prediction error. Using the Multi-Ethnic Study of Atherosclerosis cohorts, the c-statistics for assessing individual-level risk discrimination were 0.85 to 0.88 for diabetes, 0.93 to 0.95 for obesity, 0.74 to 0.76 for CVD history, and 0.78 to 0.81 for all-cause mortality, both overall and in three racial-ethnic groups. Open-source code for the model was posted at https://github.com/food-price/DOC-M-Model-Development-and-Validation . Conclusions The validated DOC-M model can be used to examine health, equity, and the economic impact of health policies and interventions on behavioral and clinical risk factors for obesity, diabetes, and CVD. Highlights We developed a novel microsimula’tion model for obesity, diabetes, and CVD, which intersect together and – critically for prevention and treatment interventions – share common lifestyle, biologic, and demographic risk factors. Validation analyses, including assessment of (1) population-level risk calibration and (2) individual-level risk discrimination, showed strong performance across the overall population and three major racial-ethnic groups for 6 outcomes (obesity, diabetes, CVD, and all-cause mortality, CVD- and DM-cause mortality) This paper provides a thorough explanation and documentation of the development and validation process of a novel microsimulation model, along with the open-source code (https://github.com/food-price/ DOCM_validation) for public use, to serve as a guide for future simulation model assessments, validation, and implementation.

Keywords: microsimulation model; validation; calibration; obesity; diabetes; cardiovascular disease (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:7-8:p:930-948

DOI: 10.1177/0272989X231196916

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