Accuracy of Electronic Health Record Food Insecurity, Housing Instability, and Financial Strain Screening in Adult Primary Care.

This study assesses the accuracy of electronic health record­based screening questionnaires about social risk factors using external single-domain questionnaires as a comparator.

Sensitivity and Specificity of Real-World Social Factor Screening Approaches.

Health care organizations are increasingly documenting patients for social risk factors in structured data. Two main approaches to documentation, ICD-10 Z codes and screening questions, face limited adoption and conceptual challenges. This study compared estimates of social risk factors obtained via screening questions and ICD-10 Z diagnoses coding, as used in clinical practice, to estiamtes from validated survey instruments in a sample of adult primary care and emergency department patients at an urban safety-net health system. Financial strain, transportation barriers, food insecurity, and housing instability were independently assessed using instruments with published reliability and validity. These four social factors were also being collected by the health system in screening questions or could be mapped to ICD-10 Z code diagnosis code concepts. Neither the screening questions nor ICD-10 Z codes performed particularly well in terms of accuracy. For the screening questions, the Area Under the Curve (AUC) scores were 0.609 for financial strain, 0.703 for transportation, 0.698 for food insecurity, and 0.714 for housing instability. For the ICD-10 Z codes, AUC scores tended to be lower in the range of 0.523 to 0.535. For both screening questions and ICD-10 Z codes, the measures were much more specific than sensitive. Under real world conditions, ICD-10 Z codes and screening questions are at the minimal, or below, threshold for being diagnostically useful approaches to identifying patients' social risk factors. Data collection support through information technology or novel approaches combining data sources may be necessary to improve the usefulness of these data.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review.

BACKGROUND: Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients' nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health. OBJECTIVE: This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources. METHODS: We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported. RESULTS: A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location. CONCLUSIONS: A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Physician Participation in Meaningful Use and Quality of Care for Medicare Fee-for-Service Enrollees.

OBJECTIVES: The Meaningful Use initiative has made nearly $30 billion available through incentive programs to encourage provider adoption and use of electronic health records. The objective of this study was to evaluate the impact of outpatient physicians' participation in Meaningful Use on the quality of care provided to Medicare fee-for-service (FFS) enrollees. DESIGN: Retrospective cohort study. SETTING: One hundred percent inpatient and outpatient Medicare FFS claims covering the period January 2010 through December 2012. PARTICIPANTS: 303,110 Medicare FFS enrollees from New York State. MEASUREMENTS: Hospitalizations and emergency department (ED) visits for ambulatory care sensitive conditions (ACSCs), diabetic retinopathy screening, diabetic nephropathy screening, colorectal cancer screening, and influenza vaccinations. RESULTS: Physician participation in the Meaningful Use initiative was not associated with reductions in ACSC-related hospitalizations (0.0 percentage points, 95% confidence interval (CI): 0.0-0.1) or ED visits (0.0 percentage points, 95% CI: 0.0-0.1) relative to the comparison group. Meaningful Use participation was associated with higher odds of colorectal cancer screening (odds ratio (OR): 1.2, 95% CI: 1.1-1.4) relative to the comparison group, but not for diabetic retinopathy screening (OR: 1.1, 95% CI: 1.0-1.2), diabetic nephropathy screening (OR: 1.0, 95% CI: 0.8-1.2), or influenza vaccinations (OR: 1.1, 95% CI: 1.0-1.2). Similar results were found in secondary analyses of dually-eligible beneficiaries participating in both Medicare and Medicaid. CONCLUSION: Physician participation in Meaningful Use was not associated with substantial improvements on six quality measures.

Use of Health Information Exchange and Repeat Imaging Costs.

PURPOSE: The aim of this study was to quantify the association between health information exchange (HIE) use and cost savings attributable to repeat imaging. METHODS: Imaging procedures associated with HIE were compared with concurrent controls on the basis of propensity score matching over the period from 2009 to 2010 in a longitudinal cohort study. The study sample (n = 12,620) included patients ages 18 years and older enrolled in the two largest commercial health plans in a 13-county region of western New York State served by the Rochester Regional Health Information Organization. The primary outcome was a continuous measure of costs associated with repeat imaging. The determinant of interest, HIE use, was defined as system access after the initial imaging procedure and before repeat imaging. RESULTS: HIE use was associated with an overall estimated annual savings of $32,460 in avoided repeat imaging, or $2.57 per patient. Basic imaging (radiography, ultrasound, and mammography) accounted for 85% of the estimated avoided cases of repeat imaging. Advanced imaging (CT and MRI) accounted for 13% of avoided procedures but constituted half of the estimated savings (50%). CONCLUSIONS: HIE systems may reduce costs associated with repeat imaging. Although inexpensive imaging procedures constituted the largest proportion of avoided repeat imaging in our study, most of the estimated cost savings were due to small reductions in repeated advanced imaging procedures. HIE systems will need to be leveraged in ways that facilitate greater reductions in advanced imaging to achieve appreciable cost savings.

Multistate analysis of factors associated with intimate partner violence.

BACKGROUND: Reports on prevalence estimates and risk factors of intimate partner violence (IPV) are limited in that they (1) focus on specific subgroup populations that are not representative of all women or (2) involve long questionnaires that are not useful as surveillance tools. OBJECTIVES: To report prevalence estimates and identify demographic and lifestyle factors associated with IPV in a large population-based sample of U.S. women using surveillance data. METHODS: Behavioral Risk Factor Surveillance System (BRFSS) data from eight U.S. states were analyzed individually and as a pooled sample (N=18,415). Multivariate logistic regression models were used to examine associations between IPV and the factors of interest. RESULTS: Factors consistently associated with IPV across the majority of states and in the pooled analysis included young age (pooled adjusted odds ratio [aOR], 3.07), single marital status (pooled aOR, 2.89), divorced/separated marital status (pooled aOR, 4.67), and annual household income <$25,000 (pooled aOR, 1.89). In addition, lack of health insurance, receipt of Medicaid, cigarette smoking, presence of children in the home, self-reported fair/poor health, and frequent mental distress were associated with IPV after adjustment for covariates. CONCLUSIONS: This study provides population-based estimates of IPV prevalence and factors associated with IPV using surveillance data. By pooling BRFSS data from individual states, the resulting large sample has adequate power to detect significant associations and has increased precision in the estimates of IPV risk. In addition, this study identifies high-risk populations to target for education and intervention programs and demonstrates the need for improved IPV surveillance.