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This study aimed to develop a microsimulation model to estimate the health effects, costs, and cost-effectiveness of public health and clinical interventions for preventing/managing type 2 diabetes. We combined newly developed equations for complications, mortality, risk factor progression, patient utility, and cost—all based on US studies—in a microsimulation model. We performed internal and external validation of the model. To demonstrate the model's utility, we predicted remaining life-years, quality-adjusted life-years (QALYs), and lifetime medical cost for a representative cohort of 10 000 US adults with type 2 diabetes. We then estimated the cost-effectiveness of reducing hemoglobin A1c from 9% to 7% among adults with type 2 diabetes, using low-cost, generic, oral medications. The model performed well in internal validation; the average absolute difference between simulated and observed incidence for 17 complications was < 8%. In external validation, the model was better at predicting outcomes in clinical trials than in observational studies. The cohort of US adults with type 2 diabetes was projected to have an average of 19.95 remaining life-years (from mean age 61), incur $187 729 in discounted medical costs, and accrue 8.79 discounted QALYs. The intervention to reduce hemoglobin A1c increased medical costs by $1256 and QALYs by 0.39, yielding an incremental cost-effectiveness ratio of $9103 per QALY. Using equations exclusively derived from US studies, this new microsimulation model achieves good prediction accuracy in US populations. The model can be used to estimate the long-term health impact, costs, and cost-effectiveness of interventions for type 2 diabetes in the United States. • Existing type 2 simulation models implement risk equations derived from UK data. This model is based on newly derived risk equations developed for the United States. This model can be used to predict remaining life-years, costs, and quality-adjusted life-years for different cohorts of type 2 patients. • Existing type 2 simulation models are limited in their extendability. This microsimulation introduces a highly modular architecture that allows for easy addition of new modules (types of diabetes, complications). A wide variety of interventions can be implemented, informing policy decision making. [ABSTRACT FROM AUTHOR]

Background: Medications to treat opioid use disorder (OUD) including buprenorphine products are evidence-based and cost-effective tools for combating the opioid crisis. However, limited availability to buprenorphine is pervasive in the United States (US) and may serve to exacerbate the deadly epidemic. Although prior research points to rural counties as especially needy of strategies that improve buprenorphine availability, it is important to investigate the availability of waivered providers according to treatment need as defined by the county-level rate of opioid-overdose deaths (OOD). This study examined differences in buprenorphine provider availability relative to treatment need among rural and urban counties in the US. Methods: Buprenorphine provider availability relative to need in each county was defined as the number of waivered providers divided by the rate of OODs (i.e., number of OODs/100,000 population), according to 2018 data. Counties with ratios in the bottom tertile of their state were classified as buprenorphine undersupplied. We estimated logit models to statistically test the association of rurality and state main effects and their interaction terms (independent variables) and the county classified as buprenorphine undersupplied (dependent variable). Results: A total of 38 states and 2595 counties had sufficient non-suppressed data to remain in the analysis. A larger percent of urban counties (36.43%) than rural counties (32.01%) were classified as buprenorphine undersupplied (p = 0.001). The likelihood of a rural county being undersupplied varied considerably by state (Chi Square = 82.88, p = 0.000). All states with significant (p < 0.05 or p < 0.10) interaction terms showed lower likelihood of buprenorphine undersupply in rural counties. Conclusions: The rural–urban distribution in undersupply of waivered buprenorphine providers relative to need varied markedly by state. Strategies for improving access to buprenorphine-waivered providers should be state-centric and informed by county-specific indicators of need. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers. Agent-based models describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. They are increasingly used to study healthcare-associated infections, including Clostridioides difficile infection. The authors describe their methods of creating a geospatially explicit model of a major regional healthcare network using a synthetic population as microdata input. [ABSTRACT FROM AUTHOR]