![]() Odds ratios comparing the provider types were calculated via GEEs. Rates were calculated by using the aforementioned patient and provider type characteristics ( Table 1). Given stability in trends over time, all subsequent analyses were focused on 2017 data only. Under this general approach, estimates of metabolic monitoring rates (and 95% confidence intervals) were calculated by state and year. In all analyses, the provider clustering variable was defined by the most frequently seen outpatient provider in that year. All analyses, including calculation of metabolic monitoring rates, accounted for clustering of patients by provider by using generalized estimating equations (GEEs). Statistical analyses were performed by using SAS, version 9.4 (SAS Institute, Inc, Cary, NC) statistical significance was defined as P <. 20, 21 ArcGIS software was used with the child’s address information to calculate the geospatial variables. 19 Place of residence and SVI are both associated with barriers to accessing appropriate care. The SVI ranks census tracts on 15 social factors, including poverty, lack of vehicle access, and crowded housing. ![]() The covariates in all models were as follows: children’s sex, age, cardiometabolic diagnoses, enrollment in foster care, place of residence categorized by using the rural-urban commuting area codes (RUCCs), 18 and the social vulnerability index (SVI). The second set of regression models used provider specialty for the person who prescribed the antipsychotic most frequently during the year as the predictor variable. The predictor variable in the first set of models used provider specialty for the providers the child saw during the year in which the outcome was measured by using the previously described attribution strategies. ![]() 1, 17 Two sets of regression models were prepared for each state, along with a combined model. The measure was calculated by using an NCQA-certified software program. The outcome of interest was receipt of care for metabolic monitoring (yes or no). 6 Metabolic screening, which is conducted to obtain baseline information before the initiation of antipsychotic medications, is not part of the measure set. Receipt of metabolic monitoring is defined as youth who are 1 to 17 years of age, with ≥2 prescriptions for the same or different antipsychotics during the year in which the care was assessed, and who received 1 diabetes and 1 cholesterol monitoring test. 3, 4 The Metabolic Monitoring for Children and Adolescents on Antipsychotics measure remains an important priority nationally and is currently on the CMS Child Core Set, 5 which is used to annually assess state-specific performance on pediatric quality measures. Metabolic monitoring is a key component of the measure set because antipsychotic use places children at risk for increased BMI, impaired glucose metabolism, and hyperlipidemia. 1 The measure set was developed by the National Committee for Quality Assurance (NCQA) through the Pediatric Quality Measures Program, 2 led by the Agency for Healthcare Research and Quality, in collaboration with the CMS. In 2015, the Centers for Medicare and Medicaid Services (CMS) adopted a measure set entitled “Safe and Judicious Use of Antipsychotics in Children and Adolescents” to quantify the quality of care for children in state Medicaid programs taking antipsychotics.
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