Accounting for past patient composition in evaluations of quality reporting

To investigate whether accounting for past patient composition in evaluations of the association between public quality reports and patient selectivity changes findings and conclusions.

Secondary data analysis of public reports of Assisted Reproductive Technology Clinic success rates between 2011 and 2018.

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.

Clinics with complete records for the 2011-2018 period were included (N = 303).

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: β = -0.006, SE = 0.002, p < 0.01; ML-SEM: β = -0.005, SE = 0.002, p < 0.01) and (2) the percentage of patients with tubal diagnoses (FE: β = -0.090, SE = 0.017, p < 0.001; ML-SEM: β = -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: β = 0.219, SE = 0.033, p < 0.001; ML-SEM: β = 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.

Researchers and policymakers should use models that account for past patient composition when evaluating the associations between quality reports and patient selectivity.