Using data for addressing social determinants of health: Local public health workforce skills and perceptions

Abstract

Constraints in local health departments (LHDs), including lack of accessible, data, workforce training capacity, and funding limitations, hinder LHDs in their efforts to address social determinants of health (SDOH). Improved workforce capacity, including training and skill development, are needed to support LHDs in their efforts to address SDOH and health disparities. This cross-sectional study sought to examine latent structures of public health skills, the relationships between public health skills and staff’s support for affecting SDOH in their communities, and to develop a model for data-driven decision-making (DDDM). Three factors were identified in our factor analysis: 1) data and systems thinking, 2) planning and management, and 3) community collaboration. In our regression model, we found an interaction term between our two skills resulted in a negative association with between skills and staff’s support for addressing SDOH. Our DDDM model identified organization-level factors and local health department staff modifiable factors. Potential outcomes included: improved resource allocation, policy implementation, and targeted interventions for populations in greatest need, ultimately reducing county-level health disparities. Training evaluation data further supported various aspects of our model. Findings from this study can inform future training and implementation of programs and policies aimed at SDOH.