Intersectional Disparities in Mental Healthcare Utilization by Sex and Race/Ethnicity among US Adults: An NHANES Study
Bibliographic record
Abstract
ABSTRACT Introduction Mental healthcare utilization in the US remains low, with sociodemographic factors like sex and race/ethnicity influencing access across different population groups. However, how these factors interact to shape service utilization remains understudied. Methods Using data from the 2009–2018 cycles of the National Health and Nutrition Examination Survey (NHANES), we employed design-based log-binomial models to estimate the relative differences (prevalence ratios) of mental healthcare utilization across intersecting sex and race/ethnic groups. To assess the absolute differences, we used linear probability regression models to estimate the prevalence differences across these intersectional groups. We also conducted stratified analyses by education, income, health insurance, and depression status, to examine whether disparities persisted across socioeconomic and health-related subgroups. Results Overall, 9.1% of adults reported accessing mental health services in the past year. Hispanic males had the lowest utilization rates compared to Non-Hispanic (NH) White males, with an adjusted prevalence ratio (aPR) of 0.59 [95% CI: 0.47–0.73]. Among females, significant disparities were observed across all race/ethnic minority groups with NH Black and Hispanic females having significantly lower utilization rates compared to NH White females. Absolute prevalence differences mirrored the relative measures. Stratified analysis were largely consistent with primary results but revealed some variation across education, income and health insurance strata. Discussion These findings reflect how intersecting sociodemographic characteristics, specifically sex and race/ethnicity, influence utilization of mental health services. Stratified results suggest that socio-economic status may modify these disparities, pointing to the role played by systemic inequities. Hence, culturally informed strategies and wider structural interventions are needed to address these disparities. Future research should consider additional intersecting identities (e.g., sexual orientation and disability) and investigate socio-structural approaches to reducing these gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".