The influence of social determinants of health on epilepsy treatment delays in an Arizona Medicaid population.

OBJECTIVE: The purpose of this study was to better understand the role of social determinants of health (SDoH) in both treatment delays and treatment gaps for individuals with epilepsy (IWE) enrolled in Arizona's Medicaid program using predictive models at the population and individual levels. METHODS: In this retrospective cohort study, two statistical regression models were developed using Arizona Medicaid medical and pharmacy claims records from 2015-2019 and selected census tract-level SDoH data. Three treatment outcomes were defined: timely treatment (treated within thirty days); delayed treatment (treated after thirty days); and untreated. For the first model, least squares regression was used to regress the epilepsy treatment delays on selected SDoH factors at the population-level. For the second model, multinomial logistic regression was used to estimate associations between epilepsy treatment delays and individual-level sociodemographic factors. RESULTS: Of the 5965 IWE identified with a new epilepsy diagnosis during the study period, 43.1% were treated with a mean delay of 180 days. Among the treated population, 42% received timely treatment. A treatment gap of at least 40.6% and potentially up to 56.9% was calculated. Individuals with epilepsy diagnosed in an inpatient setting or in emergency departments were more likely to be treated and receive timely treatment than those diagnosed in an office or clinic setting. Individuals with epilepsy diagnosed in "other" settings were more likely to go untreated or receive delayed treatment than a patient diagnosed in an office or clinic. Compared to IWE aged 31-50 years, IWE aged 0-30 years were more likely to receive timely treatment, IWE aged 51-64 years were more likely to receive delayed treatment, and IWE aged 65 years or older were more likely to go untreated. Widowed IWE were more likely to go untreated relative to single patients. Individuals with epilepsy experiencing homelessness were also more likely to go untreated. Unemployed IWE were more likely to go untreated or receive delayed treatment. Native American IWE were more likely to go untreated compared to White patients. CONCLUSIONS: Treatment gaps and treatment delays are experienced by IWE in the Arizona Medicaid population. The SDoH factors predicted to impact treatment delays include care setting, age, race, marital status, homelessness, and employment.

Looking the part (to me): effects of racial prototypicality on race perception vary by prejudice.

Less racially prototypic faces elicit more category competition during race categorization. Top-down factors (e.g. stereotypes), however, affect categorizations, suggesting racial prototypicality may enhance category competition in certain perceivers. Here, we examined how prejudice affects race category competition and stabilization when perceiving faces varying in racial prototypicality. Prototypically low vs high Black relative to White faces elicited more category competition and slower response latencies during categorization (Experiment 1), suggesting a pronounced racial prototypicality effect on minority race categorization. However, prejudice predicted the extent of category competition between prototypically low vs high Black faces. Suggesting more response conflict toward less prototypic Black vs White faces, anterior cingulate cortex activity increased toward Black vs White faces as they decreased in racial prototypicality, with prejudice positively predicting this difference (Experiment 2). These findings extend the literature on racial prototypicality and categorization by showing that relative prejudice tempers the extent of category competition and response conflict engaged when initially perceiving faces.