Using Explainable-AI to Find Geospatial Environmental and Sociodemographic Predictors of Suicide Attempts

Despite a global decrease in suicide rates in recent years, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts in order to combat this growing epidemic. In this study, we use an explainable-artificial intelligence method, iterative Random Forest, to predict suicide attempts using data from the Million Veteran Program. Our predictive model incorporates multiple environmental variables (e.g., elevation, light wavelength absorbance, temperature, humidity, etc) at ZIP code-level geospatial resolution. We additionally consider demographic variables from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people in order to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by award #I01CX001729 from the Clinical Science Research and Development (CSR&D) Service of VHA ORD. This work was also supported by the joint U.S. Department of Veterans Affairs and US Department of Energy MVP CHAMPION program. No external funding was received. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The patients in this study were enrolled in the U.S. Department of Veterans Affairs (VA) Million Veteran Program (MVP). All subjects provided informed consent and the research presented here were approved by the VA Central Institutional Review Board. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors