Usability of encounter data for Medicaid comprehensive managed care vs traditional Medicaid fee‐for‐service claims among pregnant women. Issue 1 (18th November 2019) - Choi, Yoonyoung; Park, Haesuk; Hampp, Christian; Brumback, Babette; Meissner, H. Cody; Li, Yan; Roussos‐Ross, Dikea; Zhu, Yanmin; Henriksen, Carl; Winterstein, Almut G.
Abstract: Background: The completeness of medical encounters capture among Medicaid enrollees in comprehensive managed care (CMC) has been shown to vary across states and years. CMC penetration has grown, and CMC encounter capture specific to pregnancy care is understudied. Objectives: To compare the completeness of encounter data for pregnant beneficiaries in CMC versus traditional fee‐for‐service (FFS) in Texas and Florida between 2007 and 2010. Methods: Using Medicaid Analytic eXtract (MAX) data linked to Florida and Texas birth certificate records, for each state and study year, we compared proportions using seven themes: (a) delivery; (b) prenatal visits; (c) dispensed prescriptions during pregnancy; (d) gestational diabetes and blood glucose testing; (e) antidiabetics and diagnosis of diabetes mellitus; (f) antibiotics for urinary tract infection and outpatient encounter; and (g) bacterial vaginosis and dispensing for metronidazole or clindamycin. We considered CMC data to be acceptable if proportions were no less than 10% below the corresponding (2007 to 2010) FFS control values. Results: Pregnancy‐related characteristics of FFS vs CMC denominators were comparable. Proportions for the seven measures among FFS controls ranged from 26% to 98%. In Texas, CMC encounter data met the thresholds for all measures between 2007 and 2010. Florida had usable CMC encounter data starting from 2009 with incomplete medical and pharmacy records in 2007 and 2008. Conclusions: The quality of CMC encounter data in MAX files for pregnant women varied in Florida and Texas and improved over time. Use of pregnancy‐specific measures can aid researchers in selecting states and years with acceptable encounter data quality.