Improving early intervention: identifying risk factors for UK military veterans that access military charities-a case-control study and an AI-powered predictive model.

Abstract

Some veterans face unique physical, mental, and social challenges, leading them to seek assistance from military charities. This case-control study uses data from the MONARCH Study and the tri-service food insecurity study, with the aim to identify key risk factors associated with charity usage among UK veterans. Cases (veterans who accessed charities in 2022) were compared to controls (veterans who did not access charities). Logistic regression and a random forest algorithm were used to identify risk factors for charity use. Several risk factors for charity use were identified: younger age, living alone, being a non-officer, and living in rented accommodation. Having dependents was found to be protective but emerged as a risk factor for veterans living alone and protective for veterans living with others. The use of a random forest algorithm confirmed the statistical importance of these variables, offering deeper insights into complex interactions. These results improve our understanding of the risk factors for charity usage by veterans and provide a predictive model that could be implemented in planning service provision in public health. Additionally, it could be used as the basis for the implementation of targeted preventive interventions, allowing for proactive measures to be taken to support veterans before they reach a point of needing charity services in a period of crisis. These predictive models could enable more efficient resource allocation and the development of tailored strategies to address the specific needs of at-risk veteran subgroups.