Modeling the impact of social determinants of health and public health interventions

Abstract

Social determinants of health (SDOH) are the non-medical factors that play a vital role in public health and form the basis of health policies. Building an effective public health policy is a complex endeavor that systematically understands multi-dimensional associations. The association of SDOH with public health outcomes is poorly understood due to the complex interplay of factors. Artificial Intelligence (AI) advancements have enabled models to make robust and explainable decisions in complex environments. Causal modeling and counterfactual analysis infer direct and indirect associations from observational data and rank policy indicators. Reinforcement learning (RL) is a paradigm for sequential decision-making under uncertainty that infers the policy after simulating sequential actions and accompanying rewards from the context. However, systematic application and utilization of these models are limited to public health settings. Through our work, we’ve made an integrative model that can measure complex interdependencies, bring together different kinds of knowledge about health system indicators, and use SDOH to guide public health interventions. In our first contribution, we contribute to the discovery of potential interventions using an integrative machine learning framework incorporating structural causal models, counterfactual analysis, and predictive modeling to discover policy interventions. Using this framework, we found policy solutions for three use cases presented, i.e. (i) vi antimicrobial resistance, (ii) mitigating the spread of HIV among women who work in the sex industry, and (iii) targeted interventions to improve mental health. In our second contribution, we built a novel framework for optimizing potential interventions using reinforcement learning. Here we showcase a model to optimally allocate COVID-19 in the context of different SDOHs for states of India. This use case also aims to generalize the reinforcement learning framework for optimizing healthcare resource allocation. Our final contribution is the real-world deployment of this policy-discovering and policy-optimizing AI model in response to COVID-19. Our machine learning models powered several web applications that provided forecasts of COVID-19 trajectory, mined emerging evidence from rapidly emerging COVID-19 literature, and for optimal allocation of COVID-19 vaccines.