RESUMO
The likelihood of clinicians prescribing direct-acting antiviral (DAA) therapy for patients with chronic hepatitis C virus (HCV) and substance use disorder (SUD) was assessed via a survey emailed throughout the United States to clinicians (physicians and advanced practice providers) in gastroenterology, hepatology, and infectious disease specialties. Clinicians' perceived barriers and preparedness and actions associated with current and future DAA prescribing practices of HCV-infected patients with SUD were assessed. Of 846 clinicians presumably receiving the survey, 96 completed and returned it. Exploratory factor analyses of perceived barriers indicated a highly reliable (Cronbach alpha = 0.89) model with five factors: HCV stigma and knowledge, prior authorization requirements, and patient- clinician-, and system-related barriers. In multivariable analyses, after controlling for covariates, patient-related barriers (P < 0.01) and prior authorization requirements (P < 0.01) were negatively associated with the likelihood of prescribing DAAs. Exploratory factor analyses of clinician preparedness and actions indicated a highly reliable (Cronbach alpha = 0.75) model with three factors: beliefs and comfort level; action; and perceived limitations. Clinician beliefs and comfort levels were negatively associated with the likelihood of prescribing DAAs (P = 0.01). Composite scores of barriers (P < 0.01) and clinician preparedness and actions (P < 0.05) were also negatively associated with the intent to prescribe DAAs. Conclusion: These findings underscore the importance of addressing patient-related barriers and prior authorization requirements-significant problematic barriers-and improving clinicians' beliefs (e.g., medication-assisted therapy should be prescribed before DAAs) and comfort levels for treating patients with HCV and SUD to enhance treatment access for patients with both HCV and SUD.
RESUMO
BACKGROUND: Application of latent variable models in medical research are becoming increasingly popular. A latent trait model is developed to combine rare birth defect outcomes in an index of infant morbidity. METHODS: This study employed four statewide, retrospective 10-year data sources (1999 to 2009). The study cohort consisted of all female Florida Medicaid enrollees who delivered a live singleton infant during study period. Drug exposure was defined as any exposure to Antiepileptic drugs (AEDs) during pregnancy. Mothers with no AED exposure served as the AED unexposed group for comparison. Four adverse outcomes, birth defect (BD), abnormal condition of new born (ACNB), low birth weight (LBW), and pregnancy and obstetrical complication (PCOC), were examined and combined using a latent trait model to generate an overall severity index. Unidimentionality, local independence, internal homogeneity, and construct validity were evaluated for the combined outcome. RESULTS: The study cohort consisted of 3183 mother-infant pairs in total AED group, 226 in the valproate only subgroup, and 43,956 in the AED unexposed group. Compared to AED unexposed group, the rate of BD was higher in both the total AED group (12.8% vs. 10.5%, P < .0001), and the valproate only subgroup (19.6% vs. 10.5%, P < .0001). The combined outcome was significantly correlated with the length of hospital stay during delivery in both the total AED group (Rho = 0.24, P < .0001) and the valproate only subgroup (Rho = 0.16, P = .01). The mean score for the combined outcome in the total AED group was significantly higher (2.04 ± 0.02 vs. 1.88 ± 0.01, P < .0001) than AED unexposed group, whereas the valproate only subgroup was not. CONCLUSIONS: Latent trait modeling can be an effective tool for combining adverse pregnancy and perinatal outcomes to assess prenatal exposure to AED, but evaluation of the selected components is essential to ensure the validity of the combined outcome.
