Using Machine Learning Methods to Examine Turnover Rates in State Health Agencies

CONTEXT: High turnover rates in the public health workforce pose ongoing challenges to maintain essential services and institutional knowledge. Recent studies indicate that job dissatisfaction, burnout, and structural barriers have intensified following the COVID-19 pandemic. While prior studies have identified key predictors of turnover intention, the potential of machine learning (ML) to improve predictive accuracy and guide targeted interventions remains underexplored. OBJECTIVE: This study applied ML techniques to examine the predictors of turnover intent and to simulate the impact of workplace satisfaction improvements among state health agency employees. METHODS: We used data from 4 waves of the nationally representative Public Health Workforce Interests and Needs Survey: 2014, 2017, 2021, and 2024. Focusing on state health agency central office staff, we trained 3 ML models-Lasso Regression, Random Forest, and Gradient Boosting-to predict intent to leave one's organization within the next year. Models were trained separately by year using cross-sectional data and evaluated. Variable importance was assessed, and a simulation was conducted to evaluate the potential reduction in predicted turnover following targeted improvements in job satisfaction, organizational satisfaction, and pay satisfaction. RESULTS: All models demonstrated strong performance, with area under the receiver operating characteristic curve values ranging from 0.78 to 0.85. Job satisfaction consistently emerged as the most important predictor across all models and years, followed by organizational and pay satisfaction. Lasso Regression generally achieved the highest sensitivity and accuracy. Simulation results showed that modest improvements in satisfaction variables could substantially reduce predicted turnover intent, particularly among early- and mid-career staff. CONCLUSION: This study highlights the value of ML for identifying key predictors of turnover intention. Findings reinforce the importance of job satisfaction, organizational climate, and compensation in retaining public health staff. ML-driven tools can support more proactive, data-informed retention strategies in the governmental public health system.