Health and Policy Associations of Homelessness in the United States

ImportanceHomelessness is a complex challenge with an estimated yearly economic burden of $6 billion in the United States. Mitigating homelessness requires an understanding of determinants of homelessness, their interaction with health factors, and quantification of impact. ObjectiveTo investigate the health, social and policy factors influencing homelessness in a longitudinal integrative machine learning analysis. Data Sources and Study designThis retrospective longitudinal study integrated Global Burden of Disease (GBD), Health Inequality, and Housing and Urban Development (HUD) datasets for 3131 counties in the United States. We used the disease burden data of 2014, health inequalities data of 2001-2014, and homelessness count of 2015. Primary Outcome and Measurement ResultsHomelessness, the burden of disease, health inequalities, economic policies, ethnic, social, and racial factors. MethodsSpearman rank correlation test was performed to check pairwise associations. A unified probabilistic model with temporal causality was fitted using a data-driven structure learning algorithm. The resulting associations adjusted for other variables in the network were quantified using network inference algorithms. Finally, counterfactual analysis was performed to quantify the potential impact of the learned interventions. ResultsThe total burden of homelessness was significantly (p<0.001) and positively associated with rates of HIV and hepatitis mortality. Inference from the unified probabilistic model indicated that a state with a high hepatitis mortality rate had a 9% higher homelessness. Further, the rate of rheumatic heart disease mortality had a 29% decrease with the provision of shelter in young adults experiencing homelessness (p<0.001). Finally, states with moderate tax progressivity had a mitigating effect on homelessness as compared to both high and low tax progressivity (2% and 5% respectively). We evaluated the counterfactual impact of policy interventions to provide more support to cancer patients to prevent homeless and provision of shelter to prevent rheumatic heart disease mortality in young adults experiencing homelessness. Conclusion and RelevanceControl of infectious diseases and the implementation of tax policies are critical interventions for the reduction of homelessness in the United States. Key PointsHomelessness, Bayesian Network, Counterfactual Analysis QuestionWhat are the health associations and determinants of homelessness in the United States? FindingIn this study on 3131 US Counties, we found infectious diseases mortality and tax progressivity to be strong determinants of homelessness using a Bayesian network model. MeaningThese findings suggest that decreasing the burden of infectious diseases and moderate tax progressivity are vital factors for mitigating homelessness in the United States.