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

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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:OBJECTIVES:Patient experience is being widely considered in the evaluation of healthcare service quality, which is a key target for public hospitals under China's New Healthcare Reform. This study aimed to illustrate patients' experiences in county-level public hospitals, and identify aspects that need to be improved. SETTING AND PARTICIPANTS:Between 2016 and 2018, a cross-sectional study with 500 outpatients and 800 inpatients was conducted in 10 county-level public hospitals from Shandong Province, Hubei Province and Chongqing Municipality. METHOD:A three-part questionnaire was used to evaluate patients' experiences during their visits to hospitals. It comprised a questionnaire for basic information, the Picker Patient Experience (PPE-15) Questionnaire and the overall evaluation (a 3-point Likert scale to express patients' satisfaction and patient loyalty). Patients' experiences were classified according to six dimensions (information transmission and patient education, respect for patient preference, emotional support, physical comfort, involvement of family or friends and continuity of medical service). Both univariate and multivariate analyses were performed to evaluate patient experience. RESULTS:A total of 1241 valid questionnaires were analysed. The mean PPE-15 score was 41.33 (range, 23-56). The better the patient experience and satisfaction, the higher the patient loyalty (p<0.001). Except for hospital disparities, patients' age and occupation status had a significant impact on patient experience (p<0.05). Of the six dimensions, the physical comfort score was the highest, while the respect for patient preference score was the lowest. Additionally, a strong correlation was found between the respect for patient preference dimension and patients' overall satisfaction with their treatment experience. CONCLUSIONS:Hospital managers and staff members should pay close attention to the preferences of patients and their families to improve patient experience.

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.

Project description:ObjectiveThe purpose of this paper is to investigate the characteristics and determinants of inappropriate admission to hospital of elderly people in rural China.DesignA cross-sectional study of a comparison between the elderly and non-elderly groups of people.SettingThe survey was conducted on the largest county-level general hospitals in four counties in central and western China.ParticipantsA total of 652 rural patients admitted in hospitals were surveyed, who were divided into two groups according to age: elderly group (n=230, age ≥60 years) and non-elderly group (n=422, age <60 years).Primary measuresThe Chinese version of the appropriateness evaluation protocol was used to evaluate the inappropriate admission rates. The interactive regression models based on the relationship of age (elderly and non-elderly) with other factors and binary logistic regression models were used in the analysis of the specific factors and determinants of the inappropriate admission of elderly people.ResultThe inappropriate admission rate for the rural elderly was 30%, which was lower than that of the non-elderly people (40.8%). Compared with the non-elderly group, women in the elderly group (OR=0.33, 95% CI 0.15 to 0.73) had a lower incidence of inappropriate admission, and elderly people with chronic diseases (OR=3.33, 95% CI 1.23 to 9.04) were more prone to being inappropriately admitted than non-elderly people with chronic diseases. The binary logistic regression analysis shows that county, age (OR=0.94. 95% CI 0.90 to 0.99), gender (OR=0.49, 95% CI 0.25 to 2.98), department and response to doctor's admission request were the determinants of the inappropriate admission of elderly patients.ConclusionThe inappropriate admission rate of elderly people in rural China was high. We found that gender and chronic disease are the specific factors that were specific to non-elderly people. County, age, gender, department and response to a doctor's admission request had substantial influence on the inappropriate admission of the elderly in rural China.

Project description:ImportanceDespite substantial research, the drivers of the widening gap in life expectancy between rich and poor individuals in the United States-known as the longevity gap-remain unknown. The hypothesis of this study is that social mobility may play an important role in explaining the longevity gap.ObjectiveTo assess whether social mobility is associated with income-related differences in life expectancy in the United States.Design, setting, and participantsThis cross-sectional, ecological study used data from 1559 counties in the United States to assess the association of social mobility with average life expectancy at age 40 years by sex and income quartile among adult men and women over the period of January 2000 through December 2014. Bayesian generalized linear multilevel regression models were used to estimate the association, with adjustment for a range of socioeconomic, demographic, and health care system characteristics.ExposuresCounty-level social mobility, here operationalized as the association of the income rank of individuals born during the period of January 1980 through December 1982 (based on tax record data, averaged over the period January 2010 through December 2012) with the income ranks of their parents (averaged over the period January 1996 through December 2000) using the location where the parent first claimed the child as a dependent at age 15 years to identify counties.Main outcomes and measuresThe main outcome was life expectancy at age 40 years by sex and income quartile.ResultsThe sample consisted of 1559 counties, which represented 93% of the US population in 2000. Each 1-SD increase in social mobility-equivalent to the difference between a low-mobility state, such as Alabama (ranked 49th on this measure), and a higher-mobility state, such as Massachusetts (ranked 23rd on this measure)-was associated with a 0.38-year (95% credible interval [CrI], 0.29-0.47) and a 0.29-year (95% CrI, 0.21-0.38) increase in county-level life expectancy among men and women, respectively, in the lowest income quartile. Estimates for life expectancies among county residents in the highest income quartile were smaller in magnitude and not robust to covariate adjustment (men: 0.10-year [95% CrI, -0.02 to 0.22] increase; women: 0.08-year [95% CrI, -0.05 to 0.20] increase). Increasing social mobility in all counties to the value of the highest social mobility county was associated with decreases in the life expectancy gap between the highest and lowest income quartiles by 1.4 (95% CrI, 0.7-2.1) years for men and 1.1 (95% CrI, 0.5-1.6) years for women nationally, representing a 20% decrease.Conclusions and relevanceIn this cross-sectional study, higher county-level social mobility was associated with smaller county-level gaps in life expectancy by income. These findings motivate further investigation of causal relationships between policies that shift social mobility and health outcomes.

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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.

Similar Datasets

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