Comparing Pre- to Post-COVID-19 Health Disparities Between Black and White Female Connecticut Medicaid Beneficiaries in Behavioral Health Utilization
Notice bibliographique
Résumé
Background Broad health disparities (HDs) persist in Connecticut and the United States between minority and White patients, especially in access to and the utilization of behavioral telehealth services. Objective We aimed to determine the geographic distribution of HDs in Connecticut between Black female and White female adults in Medicaid behavioral telehealth utilization in 2019 and 2020. Methods We used the following spatial Connecticut data: (1) behavioral health utilization from Medicaid claims, from the Connecticut Department of Social Services, for the third quarters of 2019 and 2020; (2) mental health and drug and alcohol treatment facilities and ZCTA (ZIP Code Tabulation Area)-level descriptors from PolicyMap; and (3) Connecticut ZIP-to-ZCTA crosswalk data. Data were joined spatially, merged, and analyzed using spatial autoregressive models in Stata 17 (with outcome, predictors, and errors spatial lags). We computed ZCTA-level HDs comparing Black and White adult female Medicaid beneficiaries’ rates of face-to-face and telehealth behavioral services utilization. Spatial regressions were used to test spatial effects, which are extensions of classic regressions, that add neighbors’ effects to covariates. Results Distances to nearest treatment facility vary quite widely in Connecticut by ZCTAs, from 0.06 mile to 13.4 miles—3.5 miles on average. The overall White female versus Black female HDs in behavioral health care utilization were impacted by the distance to the nearest facility, such that ZCTAs farther away from the nearest facility display larger Black versus White HDs—nearly statistically significant effect in 2019 and significant effect in 2020. In 2020, in ZCTAs situated farther away from treatment facilities, both White female and Black female Medicaid patients had higher telebehavioral health utilization (spatial effects +1.3% points and +2.0% points, respectively, for 1 more mile farther away). The differential Black versus White female HDs in telebehavioral health care utilization were not impacted by distance to nearest facility, according to the total effect (direct and indirect through neighboring ZCTAs; P=.26). Conclusions Quantitative analyses indicate broad differences in Medicaid enrollment and the utilization of behavioral health services among Black and White female Medicaid recipients in Connecticut and that these differences were rather stable between 2019 and 2020. It appears that the expansion of telebehavioral health services in 2020 enhanced the access to treatment among residents who were located furthest away from providing facilities. Conflicts of Interest None declared.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».