A county-level cross-sectional analysis of positive deviance to assess multiple population health outcomes in Indiana

Abstract

Objective: To test a positive deviance method to identify counties that are performing better than statistical expectations on a set of population health indicators. Design: Quantitative, cross-sectional county-level secondary analysis of risk variables and outcomes in Indiana. Data are analysed using multiple linear regression to identify counties performing better or worse than expected given traditional risk indicators, with a focus on ‘positive deviants’ or counties performing better than expected. Participants: Counties in Indiana (n=92) constitute the unit of analysis. Main outcome measures: Per cent adult obesity, per cent fair/poor health, low birth weight per cent, per cent with diabetes, years of potential life lost, colorectal cancer incidence rate and circulatory disease mortality rate. Results: County performance that outperforms expectations is for the most part outcome specific. But there are a few counties that performed particularly well across most measures. Conclusions: The positive deviance approach provides a means for state and local public health departments to identify places that show better health outcomes despite demographic, social, economic or behavioural disadvantage. These places may serve as case studies or models for subsequent investigations to uncover best practices in the face of adversity and generalise effective approaches to other areas.