Accounting for past patient composition in evaluations of quality reporting

Katherine I. Tierney, Fishman S, Samuel Fishman
Health services research · 2022 · vol. 57(3) , pp. 668–680 · doi:10.1111/1475-6773.13942 · PMID:35060622 · PMC9108082 · W4205903288
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Abstract

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

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Condition tags

endometriosisinfertility

MeSH descriptors

Pregnancy, Multiple Reproductive Techniques, Assisted Adult Female Humans Live Birth Pregnancy

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