Sparse modeling of spatial environmental variables associated with asthma.

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

The Prevalence of Type 2 Diabetes Mellitus in a Wisconsin Hmong Patient Population.

PURPOSE: Wisconsin's largest Asian population, the Hmong, may be at high risk for type 2 diabetes. However, there are few population-based studies investigating the prevalence of diabetes in this population. This study compared the prevalence of diabetes between Hmong and non-Hispanic white patients of the University of Wisconsin departments of family medicine, pediatrics, and internal medicine clinics. METHODS: The study utilized data from the University of Wisconsin Electronic Health Record Public Health Information Exchange (UW eHealth--PHINEX). The proportion of Hmong patients diagnosed with diabetes was compared with the prevalence of diabetes in non-Hispanic white patients. Multivariate logistic regression was used to control for the differences in age, sex, body mass index (BMI), and health insurance between the two populations. RESULTS: The total prevalence of diabetes in the Hmong patient population was 11.3% compared to 6.0% in the non-Hispanic white patient population (P < 0.001). The prevalence of diabetes in Hmong adult patients was 19.1% compared to 7.8% in white adult patients (P =< 0.001). Compared with non-Hispanic whites, the odds ratio (95% CI) for diabetes, adjusted for age, sex, BMI, and insurance was 3.3 (2.6-4.1) for Hmong patients. CONCLUSION: Despite being one of Wisconsin's newest immigrant populations, who came from an area of the world with low rates of diabetes, the adjusted relative odds of diabetes in this clinic sample of Hmong patients is 3.3 times higher than its non-Hispanic white counterpart. The results support previous findings of significantly increased diabetes risk in the Hmong of Wisconsin.

Estimating Wisconsin asthma prevalence using clinical electronic health records and public health data.

OBJECTIVES: We compared a statewide telephone health survey with electronic health record (EHR) data from a large Wisconsin health system to estimate asthma prevalence in Wisconsin. METHODS: We developed frequency tables and logistic regression models using Wisconsin Behavioral Risk Factor Surveillance System and University of Wisconsin primary care clinic data. We compared adjusted odds ratios (AORs) from each model. RESULTS: Between 2007 and 2009, the EHR database contained 376,000 patients (30,000 with asthma), and 23,000 (1850 with asthma) responded to the Behavioral Risk Factor Surveillance System telephone survey. AORs for asthma were similar in magnitude and direction for the majority of covariates, including gender, age, and race/ethnicity, between survey and EHR models. The EHR data had greater statistical power to detect associations than did survey data, especially in pediatric and ethnic populations, because of larger sample sizes. CONCLUSIONS: EHRs can be used to estimate asthma prevalence in Wisconsin adults and children. EHR data may improve public health chronic disease surveillance using high-quality data at the local level to better identify areas of disparity and risk factors and guide education and health care interventions.

The theory and application of UW ehealth-PHINEX, a clinical electronic health record-public health information exchange.

BACKGROUND: Electronic health records (EHRs) hold the promise of improving clinical quality and population health while reducing health care costs. However, it is not clear how these goals can be achieved in practice. METHODS: Clinician-led teams developed EHR data extracts to support chronic disease use cases. EHRs were linked with community-level data to describe disease prevalence and health care quality at the patient, health care system, and community risk factor levels. Software was developed and statistical modeling included multivariate, mixed-model, longitudinal, data mining, and geographic information system (GIS)/spatial regression approaches. RESULTS: A HIPAA-compliant limited data set was created on 192,201 patients seen in University of Wisconsin Family Medicine clinics throughout Wisconsin in 2007-2009. It was linked to a commercially available database of approximately 6000 variables describing community-level risk factors at the census block group. Areas of increased asthma and diabetes prevalence have been mapped, identified, and compared to economic hardship. CONCLUSIONS: A comprehensive framework has been developed for clinical-public health data exchange to develop new evidence and apply it to clinical practice and health policy. EHR data at the neighborhood level can be used for future population studies and may enhance understanding of community-level patterns of illness and care.

Prevalence and Predictors of Unhealthy Weight Gain in Pregnancy.

IMPORTANCE: Weight gain during pregnancy affects obesity risk in offspring. OBJECTIVE: To assess weight gain among UW Health prenatal patients and to identify predictors of unhealthy gestational weight gain. METHODS: Retrospective cohort study of women delivering at UW Health during 2007-2012. Data are from the UW eHealth Public Health Information Exchange (PHINEX) project. The proportion of women with excess and insufficient (ie, unhealthy) gestational weight gain was computed based on 2009 Institute of Medicine guidelines. Multivariable logistic regression was used to identify risk factors associated with excess and insufficient gestational weight gain. RESULTS: Gestational weight gain of 7,385 women was analyzed. Fewer than 30% of prenatal patients gained weight in accordance with Institute of Medicine guidelines. Over 50% of women gained excess weight and 20% gained insufficient weight during pregnancy. Pre-pregnancy weight and smoking status predicted excess weight gain. Maternal age, race/ethnicity, smoking status, and having Medicaid insurance predicted insufficient weight gain. CONCLUSIONS AND RELEVANCE: Unhealthy weight gain during pregnancy is the norm for Wisconsin women. Clinical and community interventions that promote healthy weight gain during pregnancy will not only improve the health of mothers, but also will reduce the risk of obesity in the next generation.

Electronic health records and community health surveillance of childhood obesity.

BACKGROUND: Childhood obesity remains a public health concern, and tracking local progress may require local surveillance systems. Electronic health record data may provide a cost-effective solution. PURPOSE: To demonstrate the feasibility of estimating childhood obesity rates using de-identified electronic health records for the purpose of public health surveillance and health promotion. METHODS: Data were extracted from the Public Health Information Exchange (PHINEX) database. PHINEX contains de-identified electronic health records from patients primarily in south central Wisconsin. Data on children and adolescents (aged 2-19 years, 2011-2012, n=93,130) were transformed in a two-step procedure that adjusted for missing data and weighted for a national population distribution. Weighted and adjusted obesity rates were compared to the 2011-2012 National Health and Nutrition Examination Survey (NHANES). Data were analyzed in 2014. RESULTS: The weighted and adjusted obesity rate was 16.1% (95% CI=15.8, 16.4). Non-Hispanic white children and adolescents (11.8%, 95% CI=11.5, 12.1) had lower obesity rates compared to non-Hispanic black (22.0%, 95% CI=20.7, 23.2) and Hispanic (23.8%, 95% CI=22.4, 25.1) patients. Overall, electronic health record-derived point estimates were comparable to NHANES, revealing disparities from preschool onward. CONCLUSIONS: Electronic health records that are weighted and adjusted to account for intrinsic bias may create an opportunity for comparing regional disparities with precision. In PHINEX patients, childhood obesity disparities were measurable from a young age, highlighting the need for early intervention for at-risk children. The electronic health record is a cost-effective, promising tool for local obesity prevention efforts.