Development and Validation of the US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) Model: Health Disparity and Economic Impact Model
Authors: Kim David D. , Wang Lu , Lauren Brianna N. , Liu Junxiu , Marklund Matti , Lee Yujin , Micha Renata , Mozaffarian Dariush , Wong John B. 
Published: October 2023

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.