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.

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.

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.