Comparing Pre- to Post-COVID-19 Health Disparities Between Black and White Female Connecticut Medicaid Beneficiaries in Behavioral Health Utilization
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".