Accounting for past patient composition in evaluations of quality reporting

Objective: To investigate whether accounting for past patient composition in evaluations of the association between public quality reports and patient selectivity changes findings and conclusions. Data Sources: Secondary data analysis of public reports of Assisted Reproductive Technology Clinic success rates between 2011 and 2018. Study Design: Two sets of fixed effects models, (1) a standard fixed-effects model (FE) and (2) a dynamic panel model using structural equation modeling estimated with maximum-likelihood (ML-SEM) with one- and two-year lagged patient characteristics, are compared. The outcome variables are patient composition features associated with success rates, including two age categories and eight diagnoses of infertility. Two-year lagged success rates for any live birth and a singleton live birth are central predictor variables. Data Collection/Extraction Methods: Clinics with complete records for the 2011-2018 period were included (N = 303). Principal Findings: For live birth success rates, the two models show increases in the two-year lagged success rate is associated with a reduction in (1) the transformed percentage of patients with endometriosis (FE: [beta] = -0.006, SE = 0.002, p < 0.01; ML-SEM: [beta] = -0.005, SE = 0.002, p < 0.01) and (2) the percentage of patients with tubal diagnoses (FE: [beta] = -0.090, SE = 0.017, p < 0.001; ML-SEM: [beta] = -0.064, SE = 0.027, p < 0.05). For singleton birth success rates, the models show positive associations between the two-year lagged success rate and the percent of patients over 35 years old (FE: [beta] = 0.219, SE = 0.033, p < 0.001; ML-SEM: [beta] = 0.095, SE = 0.047, p < 0.05). Overall, the FE models show numerous significant associations with the two-year lagged success rate not observed in the ML-SEM models. Thus, the preferred and theoretically appropriate model (ML-SEM) and the more commonly used model (FE) yield different results. Conclusions: Researchers and policymakers should use models that account for past patient composition when evaluating the associations between quality reports and patient selectivity. KEYWORDS ART, dynamic panel modeling, patient selection, quality reporting What is known on this topic * Public quality reporting is a common regulatory and incentivization tool in the United States. * Key concerns about public quality reporting include the possibility of changes in patient composition due to increased consumer choice and/or provider behavior. * Evidence of this kind of patient selection via the provider or patient mechanisms is mixed and often does not address how past patient composition may influence these associations. What this study adds * The comparison of two fixed-effects approaches, one that accounts for past patient composition and one that does not, demonstrates the importance of addressing past patient composition when investigating the association between past quality reports and current patient composition. * The paper employs a dynamic panel model using structural equation modeling estimated with the maximum likelihood that can be used to account for associations between lagged quality report metrics and patient composition changes while also accounting for previous patient composition.
1 | INTRODUCTION Quality reporting is an increasingly common mechanism used for regulation and incentivization in the US health care system. Often, these reports are made publicly available with the [...]