Below are models that are being actively developed by our team. Click on each model name to find out more information, view documentation, and request access to the model.

iHOUSE

What if we could test the potential impacts of housing and health policy decisions before implementing them? 

iHOUSE (Improving Health and Housing Outcomes through a Simulation and Economic Model) is a simulation model designed to examine how changes in housing, health services, and treatment programs may affect people experiencing homelessness, particularly those with HIV and/or substance use disorders. Using longitudinal data and research evidence, the model simulates housing and health trajectories within Denver’s housing and homelessness system and allows researchers to compare potential interventions, investment strategies, and trade-offs over time. 

Community and stakeholder expertise has been central to model development. More than 100 stakeholders have contributed knowledge about how Denver’s housing and service systems operate in practice, helping refine model structure, assumptions, pathways, and priority outcomes. 

iHOUSE is designed as an analytic and decision-support tool that can support policy-relevant research, identify important evidence gaps, and help researchers and community partners explore real-world “what if?” questions. 

Interested in exploring the model, or discussing research and collaboration opportunities? Contact COPHI@cuanschutz.edu. 

The iHOUSE Model research project is supported by the National Institute on Drug Abuse (5R01DA061228; Principal Investigator: Joshua Barocas) within the National Institutes of Health.

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ReDUCE

Serious bacterial infections are among the most common medical complications of injection opioid use. Hospitalization for these infections is common and represents opportunities for intervention. Other infections typical of drug use include HIV and Hepatitis C (HCV), which further affect mortality and costs associated with injection opioid use. Further complicating the picture, sexually transmitted infections (i.e., syphilis, gonorrhea, and chlamydia) are prevalent among people who inject drugs (PWID) given the association with high-risk sexual practices.

The Reducing Infections Related to Drug Use Cost Effectiveness (ReDUCE) Model is a microsimulation model that simulates the natural history of injection drug use in the U.S. for the purpose of forecasting injection-related bacterial infections, HIV, and Hepatitis C (HCV). The model also incorporates sexual behaviors for the purpose of forecasting STIs in this population and to account for additional risk factors for HIV and HCV. The model uses injection frequency, injection practices, and sexual behaviors as the basis to estimate outcomes of interest including incidence of infections and deaths. This simulation modeling framework can help demonstrate how interventions might yield long-term benefits and to measure the potential magnitude of those benefits for people who inject drugs.