Are Certain Health Centers Better Patient-Centered Medical Homes for People with Severe Mental Illness?

Patient-centered medical homes based at federally-qualified health centers (FQHCs) can benefit patients with complex health needs, such as severe mental illness (SMI). However, little is known about FQHC characteristics associated with changes in health care expenditures and utilization for individuals with SMI. Using North Carolina Medicaid claims and FQHC data from the Uniform Data System, multivariate regression identified FQHC characteristics associated with total expenditures, medication adherence and emergency department utilization among adults with SMI, controlling for time-invariant differences by health center. Few of the FQHC-level factors affected the outcomes-not even offering on-site behavioral health services. Although the FQHCs in the analysis sample exhibited considerable variation in the provision of specialty behavioral services and in staffing configurations, it may be the case that the examination of average effects across a heterogeneous group of adults with SMI mask benefits of FQHCs to certain subgroups. These findings support the conclusion that there is no "one-size-fits-all" model that works best for this diverse patient population. Study results are relevant for practices embarking on expanded medical home services for people with SMI.

Coincidence analysis: a new method for causal inference in implementation science.

BACKGROUND: Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches and can reveal new empirical findings related to implementation that might otherwise have gone undetected. METHODS: We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings. RESULTS: The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage. CONCLUSIONS: CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.

How Would Rural Hospitals Be Affected by Loss of the Affordable Care Act's Medicare Low-Volume Hospital Adjustment?

PURPOSE: The low-volume hospital (LVH) payment adjustment established in the Patient Protection and Affordable Care Act (ACA) of 2010 is scheduled to sunset on October 1, 2017. The purpose of this analysis was: (1) to estimate the effect of the ACA LVH adjustment on qualifying hospitals' profitability margins; and (2) to examine hospital and market characteristics of the hospitals that would be most adversely affected by the loss of the ACA LVH adjustment. METHODS: 2004-2015 data from the Hospital Cost Report Information System, Hospital Market Service Area File and Nielsen-Claritas Pop-Facts file were used to estimate difference-in-difference regression models with hospital-level random effects in order to determine whether the ACA LVH adjustment improved qualifying rural hospitals' profitability margins. Recycled predictions estimated the effect of losing the ACA LVH adjustment on profitability margins. Bivariate analyses explored associations between the predicted profitability margins and hospital and market characteristics. FINDINGS: The ACA LVH adjustment significantly improved Sole Community Hospitals' Medicare inpatient margins in the year they received the adjustment, and it had a large but statistically insignificant effect on the profitability margins of other rural hospitals. Hospitals that would be the most adversely affected by loss of the ACA LVH adjustment were more likely to be small, located in the South, and in high-poverty markets with higher proportions of black and uninsured individuals. CONCLUSIONS: Elimination of the ACA LVH adjustment would have differential effects on subgroups of hospitals, and those located in markets serving historically underserved populations would be the most adversely affected.