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Objectives: This study examined whether distinct factors exist among public health skills, measured through the Public Health Workforce Interests and Needs Survey (PH WINS). Understanding how workforce training needs group is important for developing targeted and appropriate public health workforce training sessions. Design: Exploratory factor analysis was used to examine public health skills among tier 1 staff (nonmanagers) and a combined group of tier 2 and 3 staff (managers and executives). Setting: Data for this study come from the 2017 PH WINS, which assessed public health workforce perceptions of training needs, workplace environment, job satisfaction, perceptions about national trends, and demographics. The analysis included 22 items. Participants: All public health staff in participating agencies were eligible to complete the survey. The national data set included participants from 47 state health agencies, 26 large local health departments (LHDs), and 71 mid-sized LHDs across all 10 Health and Human Services regions in the United States (including LHDs from all states). The analytic sample was n = 9630 in tier 1, n = 4829 in tier 2, and n = 714 in tier 3 staff. Main Outcome Measure: Three factors were identified within the skills portion of PH WINS, using exploratory factor analysis. To interpret retained factors, the following parameters were used: factor loadings greater than 0.4, factor cross-loadings less than 0.4 or higher than loadings on other factors, and communalities greater than 0.5. Results: Factors included (1) data and systems thinking, (2) planning and management, and (3) community collaboration, with slight variation in item loadings between tier 1 and tier 2 and 3 staff analyses. Conclusion: This study was the first known factor analysis of the training needs and workforce skills portion of PH WINS in the published literature. This study advances our conceptualization of public health workforce skills and has the potential to shape future critical workforce training development.

Introduction: Local health department (LHD) obesity prevention (OP) efforts, particularly by rural LHDs, are seemingly uncommon, in part, due to limited infrastructure, workforce capacity, accessible data, and available population-level interventions aimed at social determinants of health (SDOH). Methods: We conducted a scoping review to determine LHD roles in OP efforts and interventions. Inclusion criteria were articles including evidence-based OP and LHD leaders or staff. Articles were coded by type of LHD involvement, data use, intervention characteristics, use of an SDOH lens, and urban or rural setting. Results: We found 154 articles on LHD OP-52 articles met inclusion criteria. Typically, LHDs engaged in only surveillance, initial intervention development, or evaluation and were not LHD led. Data and SDOH lens use were infrequent, and interventions typically took place in urban settings. Conclusion: LHDs could likely play a greater role in OP and population-level interventions and use data in intervention decision making. However, literature is limited. Future research should focus on LHD capacity building, including academicpublic health partnerships. Studies should include rural populations, data, and SDOH frameworks addressing "upstream" factors. KEY WORDS: local health department, obesity prevention, public health data, rural health, social determinants of healthO besity in the United States has risen to 42.4% of adults in 2017-2018, 1 and obesity-related diseases (eg, diabetes, hypertension, cancer) 2 are the leading causes of preventable, premature death. 1,3 Obesity causes are complex; however, diet and physical activity (PA) are major associated factors. 4 Interventions that can be tailored

Recent frameworks, models, and reports highlight the critical need to address social determinants of health for achieving health equity in the United States and around the globe. In the United States, data play an important role in better understanding community‐level and population‐level disparities particularly for local health departments. However, data‐driven decision‐making—the use of data for public health activities such as program implementation, policy development, and resource allocation—is often presented theoretically or through case studies in the literature. We sought to develop a preliminary model that identifies the factors that contribute to data‐driven decision‐making in US local health departments and describe relationships between them. Guided by implementation science literature, we examined organizational‐level capacity and individual‐level factors contributing to using data for decision‐making related to social determinants of health and the reduction of county‐level disparities. This model has the potential to improve implementation of public health interventions and programs aimed at upstream structural factors, by elucidating the factors critical to incorporating data in decision‐making.