Dataset Information

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.

SUBMITTER: Hendryx M

PROVIDER: S-EPMC5652502 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Hendryx Michael M Guerra-Reyes Lucia L Holland Benjamin D BD McGinnis Michael Dean MD Meanwell Emily E Middlestadt Susan E SE Yoder Karen M KM

BMJ open 20171011 10

<h4>Objective</h4>To test a positive deviance method to identify counties that are performing better than statistical expectations on a set of population health indicators.<h4>Design</h4>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 b ...[more]

PMID: 29025840

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Project description:ObjectivesWe aimed to examine the associations of both individual-level and county-level social capital with individual health in China during a period of rapid economic growth.Design and settingA serial cross-sectional study in China.Participants and methodsThe participants were 42 829 Chinese adults (aged ≥18 years) from the 2010, 2012, 2013 and 2015 Chinese General Social Survey. The outcomes were self-rated physical and mental health in all time points. We assessed social capital by the individual-level and county-level indicators, including frequency of socialising, civic participation and trust. We conducted multilevel binary logistic regression models to examine the associations of individual-level and county-level social capital with self-rated physical and mental health.ResultsAt the individual level, high frequency of socialising (2010-OR: 1.49, 95% CI: 1.33 to 1.66; 2012-OR: 1.39, 95% CI: 1.26 to 1.54; 2013-OR: 1.28, 95% CI: 1.15 to 1.42; 2015-OR: 1.36, 95% CI: 1.23 to 1.50) and high trust (2010-OR: 1.34, 95% CI: 1.22 to 1.47; 2012-OR: 1.30, 95% CI: 1.18 to 1.42; 2013-OR: 1.21, 95% CI: 1.10 to 1.33; 2015-OR: 1.41, 95% CI: 1.28 to 1.55) was significantly associated with good physical health in all years. At the individual level, high frequency of socialising (2010-OR: 1.27, 95% CI: 1.14 to 1.42; 2012-OR: 1.21, 95% CI: 1.09 to 1.34; 2013-OR: 1.30, 95% CI: 1.17 to 1.45; 2015-OR: 1.35, 95% CI: 1.22 to 1.50) and high trust (2010-OR: 1.47, 95% CI: 1.34 to 1.61; 2012-OR: 1.42, 95% CI: 1.30 to 1.56; 2013-OR: 1.36, 95% CI: 1.24 to 1.49; 2015-OR: 1.43, 95% CI: 1.30 to 1.57) was also significantly associated with good mental health in all years. No evidence showed that the associations of individual-level frequency of socialising and trust with physical and mental health changed over time. There were no consistent associations of individual-level civic participation or any county-level social capital indicators with physical or mental health.ConclusionThe positive associations of individual-level social capital in terms of socialising and trust with physical and mental health were robust during a period of rapid economic growth. Improving individual-level socialising and trust for health promotion could be a long-term strategy even within a rapidly developing society.

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Dataset Information

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

Objective

Design

Participants

Main outcome measures

Results

Conclusions

Publications

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

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