Assessing provider and racial/ethnic variation in response to the FDA antidepressant box warning

Abstract

Introduction After the 2004 FDA box warning raised concerns about increased suicidal ideation among youth taking antidepressants, antidepressant use decreased for White youth but slightly increased for Black and Latino youth. Better understanding of patient and provider factors contributing to these differences is needed to improve future risk warning dissemination. Methods We analyzed antidepressant prescriptions for youth aged 5‐17 in 2002‐2006 Medicaid claims data from four states (CA, FL, NC, and NY). In multilevel models, we assessed provider‐ and patient‐level contributions to changes in antidepressant use by race/ethnicity and compared responses to the box warning between providers with large (>2/3) and small (<1/3) proportions of minority patients. Results A significant amount of variance in overall prescribing patterns (calculated by the ICC) was explained at the provider level. Significant provider‐level variation was also identified in the differential effect of the box warning by racial/ethnic group. In a test of the influence of provider panel mix, we found that providers with large proportions of minority patients reduced antidepressant prescribing more slowly after the box warning than other providers. Discussion This study is the first to assess provider‐ and patient‐level variation in the impact of a health care policy change on treatment disparities. Black and Latino youth Medicaid beneficiaries were seen by largely different providers than their White counterparts, and these distinct providers were influential in driving antidepressant prescription patterns following the box warning. Concerted outreach to providers of minority beneficiaries is needed to ensure that risk warnings and clinical innovations diffuse swiftly across racial/ethnic minority groups.

Food Insufficiency Following Discontinuation of Monthly Child Tax Credit Payments Among Lower-Income US Households

“… 19 , 20 We estimated multivariable linear probability models with food insufficiency as the primary outcome. 21 , 22 The model included group fixed effects, time fixed effects, and a binary treatment variable (the interaction term) that varied at the group and time levels (eMethods 1 in the Supplement ). 23 We estimated unadjusted and adjusted models (with adjusted models including the covariates described previously) for 3 income strata: less than $50 000, less than $35 000, and less than $25 000 annually.…”

“…Difference-in-differences is a quasi-experimental technique that isolates the relationship between policy changes and outcomes across exposed (ie, treatment) and unexposed (ie, comparison) groups by accounting for secular trends . We estimated multivariable linear probability models with food insufficiency as the primary outcome . The model included group fixed effects, time fixed effects, and a binary treatment variable (the interaction term) that varied at the group and time levels (eMethods 1 in the Supplement).…”

Physician variation in the de‐adoption of ineffective statin and fibrate therapy

“…However, these studies did not explicitly model changes in levels or trends in use before and after a reversal in clinical evidence. Among the limited studies that have specifically looked at treatment use before and after a reversal in clinical evidence, several found evidence of de‐adoption, 9‐13 while another has not 14 …”

“…Howard and Hockenberry 6 found physician age was associated with evidence‐concordant obstetric care, though that analysis focused on a procedure where evidence had accumulated gradually as opposed to a reversal of evidence at a specific time. Cook and colleagues 10 modeled trends in anti‐depressant prescribing before and after an FDA warning and documented variation in physicians’ response to the warning, while also finding that the racial composition of a physician's patient panel was associated with prescribing patterns. However, they did not address how much of physicians’ variation was explained by the racial composition of a physician's panel or other physician characteristics.…”

“…These studies used many data resources, including insurance claims, 4,8,10,12,13 discharge databases, [5][6][7]9 patient registries, 11 and electronic health records. 14 Due largely to the structure of these data resources, to our knowledge, studies of de-adoption have not concurrently examined whether physicians exhibit the same patterns of de-adoption when treating patients from different insured populations.…”