Intersectional disparities in mental healthcare utilization by sex and race/ethnicity among US adults: An NHANES study

Lotenna Olisaeloka; Esteban J Valencia; Gentille Musengimana; Daniel Vigo; Ehsan Karim

doi:10.1371/journal.pgph.0004682

. 2025 Oct 13;5(10):e0004682. doi: 10.1371/journal.pgph.0004682

Intersectional disparities in mental healthcare utilization by sex and race/ethnicity among US adults: An NHANES study

Lotenna Olisaeloka ^1,^2,^*, Esteban J Valencia ¹, Gentille Musengimana ¹, Daniel Vigo ^1,², Ehsan Karim ¹

Editor: Susan Julia Chand,³

PMCID: PMC12517490 PMID: 41082519

Abstract

Mental healthcare utilization in the US remains low, with persistent disparities observed across population groups. However, little is known about how sex and race/ethnicity jointly shape access to care. Intersectionality theory highlights the need to examine these dimensions together, as their combined influence may produce unique disadvantages not captured in single-axis analyses. This study utilized data from the 2009–2018 cycles of the National Health and Nutrition Examination Survey (NHANES). The relative differences (prevalence ratios) of mental healthcare utilization across intersecting sex and racial/ethnic groups were estimated using design-based log-binomial models. The absolute measure (prevalence differences) across these intersectional groups were obtained using linear probability regression models. Stratified analyses were conducted to examine how socioeconomic and need-related factors modified disparities. Overall, 9.1% of adults reported accessing mental health services in the preceding year. Marked disparities were observed across the intersectional groups. 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, all minority racial/ethnic groups reported lower utilization compared with NH White females with aPRs ranging from 0.73 to 0.81. Within racial/ethnic groups, women generally accessed care more than men, though the magnitude of sex differences varied. Stratified analyses showed that disparities were magnified among those without insurance and attenuated at higher income levels. These results show that sex and race/ethnicity jointly shape patterns of mental healthcare utilization in the United States, producing compounded disadvantages for specific groups such as Hispanic men. Stratified analyses suggest that socio-economic status may modify these disparities, pointing to the role played by systemic inequities. These findings underscore the importance of intersectional approaches in population mental health research and policy. Future research should consider additional intersecting identities including sexual orientation and disability.

Introduction

Mental health disorders affect approximately 1 in 8 people globally, with less than 75% of those affected receiving adequate care [1–3]. In the United States, the number of adults with any mental illness increased by 30% from 2008 to 2019, with over 50 million adults currently affected [4,5] Yet, more than half of them do not receive treatment [6,7]. Preventive mental health services like screening and early intervention are also critically lacking, especially for at-risk groups like individuals with chronic physical conditions [8]. Closing this persistent gap and doing so equitably represents an urgent public health priority.

Sociodemographic characteristics such as gender, race/ethnicity, sexual orientation and socioeconomic status are associated with mental health and access to care [9–11]. Whereas sociodemographic characteristics describe population groups and allow disparities to be quantified, social determinants of health (SDOH) represent the underlying societal conditions such as income, education, housing, and structural discrimination that generate inequities in the first place. The unequal distribution of these determinants helps explains why some populations experience greater barriers to mental healthcare and poorer outcomes [9,12]. Gender and race/ethnicity, in particular, are recognized as social constructs shaped by historical, cultural and political factors rather than biological differences. These categories often reflect unequal access to resources and opportunities, which in turn shape healthcare utilization and outcomes [13]. Although disparities by single-factor such as gender and race/ethnicity have been observed, how these factors intersect to influence mental healthcare utilization remains underexplored [14–19].

Intersectionality, a framework born out of Black women’s concurrent experiences of racism and sexism emphasizes the interconnected nature of social inequalities [13,20,21]. The rise of intersectionality in health equity research underscores the limitations of single-factor approaches which treat social categories as independent, and thus fail to capture how the complex interplay of factors shape disparities [20,22]. Although quantitative methods are crucial for identifying health disparities at the population level, intersectional research has remained largely qualitative due to challenges in translating theoretical constructs into statistical models [19,20]. This study addresses this gap in relation to mental healthcare utilization by operationalising intersectionality using regression models with interaction terms, as proposed by Bauer [20]. Specifically, it aimed to investigate disparities in mental healthcare utilization among U.S adults across intersections of sex and race/ethnicity. While two axes of identity are not exhaustive, they represent critical starting points for operationalizing intersectionality in healthcare access research [20]. Identifying disproportionately affected intersectional groups can inform targeted interventions and health equity strategies.

Methods

Ethics statement

The National Health and Nutrition Examination Survey (NHANES) receives ethical approval from the CDC Institutional Review Board and all participants gave informed consent [23]. This NHANES dataset is anonymous and publicly available, hence the study is exempt from institutional ethic review as outlined in the institution’s and Tri-Council Policy Statements (TCPS2) on Ethical Conduct for Research Involving Humans [24,25]. We assessed the survey data from 3rd November 2024 and did not have access to any information that could identify participants at any stage.

Data source

The study utilized aggregated data from five continuous cycles of the National Health and Nutrition Examination Survey (NHANES) from 2009-2018. The NHANES is an annual population-based, cross-sectional survey of civilian, non-institutionalized U.S. residents conducted by the Centers for Disease Control and Prevention (CDC) [23]. The survey collects sociodemographic, dietary, and health-related information from a nationally representative sample by employing a multi-stage probability sampling methodology. Detailed information on design, methodology and weighting are published on the CDC website.

Study sample

Aggregated data from the five NHANES cycles included 49,693 participants, of which only adults aged 18 years and over were included in the study (n = 30,352). Following the exclusion of individuals with missing values in exposure and outcome, the final analytical sample was 30,340. Fig 1 illustrates the process of creating the analytical sample.

Outcome

The outcome, ‘mental healthcare utilization’ reflected whether respondents accessed mental health services in the past twelve months and was ascertained by an affirmative response to the question: “During the last 12 months, have you seen or talked to a mental health professional about your health?” [26].

Exposure

The exposure variables were sex and race/ethnicity as collected by NHANES. Sex at birth was categorized as male or female, and participants were grouped by race/ethnicity variable (RIDRETH1) into four categories (Non-Hispanic White, Hispanic, Non-Hispanic Black and Non-Hispanic Other Race). The ‘Non-Hispanic Other Race’ category included individuals who identified as Non-Hispanic Asian, American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and multi-racial individuals. This grouping reflects NHANES’ racial and ethnic classifications and was used due to sample size limitations for more granular subgroup analysis.

While race, ethnicity, and sex are immutable characteristics, they serve as proxies for social experiences shaped by systemic factors such as structural racism, discrimination, and privilege. These variables were included to assess disparities in mental healthcare utilization, reflecting the influence of systemic and structural factors.

Covariate selection

A directed acyclic graph (DAG) [Dagitty (version 3.1)] [27] was used to illustrate covariate relationship and select the final adjustment set [28]. Based upon the literature, the following factors: age, education level, marital status, income level, health insurance, perceived health status, depression and chronic physical condition were considered [Fig 2] [10,11,14,29–31]. Income level (low, middle, high) was categorized using recommended thresholds of poverty income ratio (PIR) [32]. Perceived health status refers to how participants rated their current general health status (NHANES variable HSD010) [23]. Depression was categorized by severity (none, mild, moderate, moderately severe and severe) based on the Patient Health Questionnaire-9 (PHQ-9) scores [33]. Chronic condition indicated whether participants had diabetes, hypertension, cancer or chronic kidney disease. Statistical significance of baseline differences was not used to select covariates, but confounder selection followed a more robust strategy using the DAG. Hence, the final adjustment set included independent risk factors for the outcome not positioned along the exposure-outcome causal pathway [28].

Final Adjustment Set: As shown in the DAG, age was the sole variable that did not introduce bias when adjusted for [Fig 2]. Since the outcome of interest is the total disparity in mental healthcare utilization associated with sex and race/ethnicity, adjustment for downstream variables such as education, income, and health insurance were avoided. These variables plausibly represent socio-structural consequences of sex and race/ethnicity via structural racism, discrimination, and gendered socialization. Hence, adjusting for them would block causal pathways of interest and bias the total association toward the null (overadjustment) [28]. Therefore, only age which does not lie on the exposure-outcome pathway was adjusted for as a precision variable.

Statistical analysis

Descriptive statistics were conducted to summarize the study sample, with sociodemographic and health-related characteristics stratified by sex and race/ethnicity. Categorical variables were presented as counts with their respective weighted percentages. All analyses incorporated survey weights, clusters, and strata to account for the complex survey design [34].

Primary analysis

To operationalize intersectionality in this quantitative analysis, interaction terms were used between sex and race/ethnicity in the regression models. To examine whether utilization rates differed across intersectional groups, design-based log-binomial and linear probability models estimated interactions on the multiplicative and additive scales respectively [20,35]. Prevalence ratios (PR) and Prevalence Differences (PD), with their 95% confidence intervals (95% CI) and significance levels, were obtained via simple slope/effects analysis. Overall missingness and missingness within variables in the model were less than 5% respectively, hence a complete case analysis was conducted [36].

Stratified/Sensitivity analyses

To assess the consistency of the primary results as well as examine the role of socioeconomic and need-related factors in shaping the observed associations, stratified analyses were conducted by key variables: education level (high school and below vs. college and above), income level (low vs. middle vs. high), and health insurance status (insured vs. uninsured). Stratified analysis was also done by mental illness (depression – yes/no) to assess whether the observed results differed by mental health need. Depression severity (PHQ-9) was used as a proxy for clinical need, given the absence of validated measures for other mental disorders which precludes stratification for other conditions. Within each stratum, separate design-based log-binomial regression models including the interaction term between sex and race/ethnicity were fitted. The stratified analyses probed heterogeneity in the sex×race/ethnicity association without conditioning away the pathways that constitute the disparity itself. This approach allowed for an extension of the intersectional analysis beyond race/ethnicity and sex by evaluating whether the disparities observed in the primary analysis are modified by stratification variables.

To address missingness in key stratification variables, such as income level (10.5%) and depression (14.2%), we conducted multiple imputation. Little’s MCAR test (p < 0.001) and bivariate analyses respectively revealed that missingness was not completely at random (MCAR) but could be explained by observed data. Under the missing at random (MAR) assumption, our imputation model included variables from the primary model, those requiring imputation (income level, education level, health insurance, and depression), and auxiliary variables (annual family income, marital status, chronic condition, health status, and survey strata). Five imputations with 20 iterations each were performed using logistic regression for binary variables and polynomial regression for categorical variables. The number of iterations was determined by monitoring the trace plots until they demonstrated convergence. Individuals with missing outcome data (n = 12) were excluded, resulting in a final analytical sample of 30,340. Design-adjusted multivariable regression models were run on each imputed dataset, and results were pooled using Rubin’s rule which combines the within- and between- imputation variances [37].

All statistical analyses were conducted using R (v4.3.1). The survey package was used to fit the design-adjusted models [38], and mice package was used for missing data analysis. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines as well as widely adopted recommendations for reporting on sex/gender and race/ethncity [39–41].

Results

Sample characteristics

The study sample consisted of 30,340 participants, with a nearly even distribution of males (48.5%) and females (51.5%). Tables 1 and 2 present the characteristics of the participants stratified by sex and race/ethnicity respectively. The counts were from the eligible sample while the percentages were derived using survey features, hence represent the national distribution. Significant sex and race-ethnic differences were observed across most demographic and health-related characteristics including education, income level, marital status and health insurance. The sample was predominantly Non-Hispanic (NH) White (64.8%), with small but significant differences in racial/ethnic composition between sexes. Overall, 9.1% of the U.S population accessed mental health services. Females reported a significantly higher proportion of mental health service utilization than males (9.7% vs 8.4%). Stratification by race/ethnicity revealed distinct differences in sociodemographic characteristics and health outcomes. For example, Hispanics had the highest proportion of low-income individuals (30.9%), and NH Whites were significantly more likely to report excellent and very good health compared to other race/ethnic groups. Mental healthcare utilization was highest among NH Whites (9.8%) and lowest among Hispanic and Other Race groups (7.3% each) [Table 2].

Table 1. Sample characteristics by sex among US adults in the 2009-2018 NHANES.

Characteristic (%)	Overall 30,340	Male 14,707(48.5)	Female 15,633 (51.5)	p value
Age
18-39	11,147 (38.4)	5,418 (39.7)	5,729 (37.2)	0.017
40-59	9,442 (35.5)	4,488 (35.9)	4,954 (35.2)
60-79	7,810 (21.6)	3,887 (20.8)	3,923 (22.4)
80+	1,941 (4.4)	914 (3.5)	1,027 (5.3)
Ethnicity
Non-Hispanic White	11,699 (64.8)	5,793 (65.2)	5,906 (64.5)	0.003
Hispanic	7,671 (15.0)	3,598 (15.6)	4,073 (14.5)
Non-Hispanic Black	6,611 (11.5)	3,177 (10.7)	3,434 (12.3)
Other race*	4,359 (8.6)	2,139 (8.6)	2,220 (8.7)
Marital status
Married/with partner	16,864 (62.5)	8,881 (65.8)	7,983 (59.5)	<0.001
Divorced/Separated	4,141 (12.7)	1,697 (10.6)	2,444 (14.7)
Never married	5,453 (18.8)	2,769 (21.1)	2,684 (16.8)
Widowed	2,345 (5.9)	590 (2.5)	1,755 (9.0)
Education Level
High School or Less	7,235 (15.6)	3,672 (16.4)	3,563 (14.9)	<0.001
High School Graduate	7,447 (24.1)	3,780 (25.4)	3,667 (22.9)
Some College	8,639 (30.8)	3,843 (28.7)	4,796 (32.7)
College Graduate or Above	6,857 (29.5)	3,334 (29.4)	3,523 (29.6)
Income
Low Income	6,413 (15.8)	2,843 (14.2)	3,570 (17.03)	<0.001
Middle Income	14,157 (48.6)	6,937 (47.9)	7,220 (49.1)
High Income	65,888 (35.6)	3,400 (37.8)	3,188 (33.6)
Mental Healthcare Utilization
No	27,753 (90.9)	13,567 (91.6)	14,186 (90.3)	<0.001
Yes	2,587 (9.1)	1,140 (8.4)	1,447 (9.7)
Depression
None	1,9471 (76.4)	10,177 (80.8)	9,294 (72.3)	<0.001
Mild	4,220 (15.5)	1,772 (13.3)	2,448 (17.7)
Moderate	1,443 (5.1)	544 (3.7)	899 (6.4)
Severe	904 (3.0)	316 (2.2)	588 (3.7)
Chronic Conditions
No	15,662 (58.5)	7,607 (59.0)	8,055 (58.1)	0.485
Yes	13,194 (41.5)	6,353 (41.0)	6,841 (41.9)
Health status
Excellent	1,879 (8.7)	1,031 (9.0)	848 (8.3)	<0.001
Very good	6,830 (32.5)	3,393 (31.3)	3,437 (33.7)
Good	10,773 (40.5)	5,326 (41.8)	5,447 (39.2)
Fair	5,407 (15.7)	2,569 (15.7)	2,838 (15.8)
Poor	966 (2.6)	396 (2.1)	570 (3.0)
Health insurance
No	6,278 (17.2)	3,372 (19.5)	2,906 (15.0)	<0.001
Yes	24,004 (82.8)	11,310 (80.5)	12,694 (85.0)

Open in a new tab

* Other race includes Non-Hispanic Asians, individuals of other racial/ethnic identity (e.g., American Indian) and multiracial individuals.

Table 2. Sample characteristics stratified by race-ethnicity among US adults aged 18 years or more in the 2009-2018 NHANES.

Level (%)	Overall	Non-Hispanic White	Hispanic	Non-Hispanic Black	Other race	p-value
N	3,0340	11,699(64.8)	7,671(15.0)	6,611(11.5)	4,359 (8.6)
Sex						0.003
Male	14,707 (48.2)	5,793 (48.5)	3,598 (50.0)	3,177(44.7)	2,139 (47.8)
Female	15,633 (51.8)	5,906 (51.5)	4,073 (50.0)	3,434 (55.3)	2,220 (52.2)
Age						<0.001
18-39	11,147 (38.4)	3,886 (33.4)	3,040 (52.6)	2,367 (43.3)	1,854 (44.6)
40-59	9,442 (35.5)	3,382 (35.9)	2,433 (33.4)	2,132 (36.1)	1,495 (35.4)
60-79	7,810 (21.6)	3,065 (25.0)	2,004 (12.5)	1,867 (18.0)	874 (17.4)
80+	1,941 (4.4)	1,366 (5.7)	194 (1.5)	245 (2.6)	136 (2.6)
Marital status						<0.001
Divorced/Separated	4,141 (12.7)	1,607 (12.5)	1,062 (12.8)	1,141 (16.6)	331 (9.0)
Married/with partner	16,864 (62.5)	6,842 (65.2)	4,550 (64.2)	2,686 (42.6)	2,786 (66.0)
Never married	5,453 (18.8)	1,693 (15.7)	1,096 (19.4)	1,865 (34.2)	799 (20.9)
Widowed	2,345 (5.9)	1,139 (6.5)	460 (3.6)	551 (6.6)	195 (4.1)
Education level						<0.001
High School or Less	7,235 (15.6)	1,795 (10.1)	3,384 (38.4)	1,373 (18.8)	683 (13.9)
High School Graduate	7,447 (24.1)	2,999 (23.9)	1,745 (24.8)	1,898 (29.1)	805 (18.1)
Some College	8,639 (30.8)	3,754 (32.0)	1,689 (24.7)	2,195 (34.3)	1,001 (27.1)
College Graduate or above	6,857 (29.5)	3,121 (34.1)	788 (12.1)	1,093 (17.8)	1,855 (40.9)
Income level						<0.001
Low Income	6,413 (15.8)	1,902 (10.4)	2,142 (30.9)	1,647 (27.9)	722 (16.6)
Middle Income	14,157 (48.6)	5,692 (46.4)	3,546 (54.2)	3,072 (52.7)	1,847 (50.2)
High Income	6,588 (35.6)	3,329 (43.1)	874 (14.8)	1,112 (19.5)	1,273 (33.2)
Mental Healthcare Utilization						<0.001
No	27,753 (90.9)	10,522(90.2)	7,123 (92.7)	6,016 (91.1)	4,092 (92.7)
Yes	2,587 (9.1)	1,177 (9.8)	548 (7.3)	595 (8.9)	267 (7.3)
Chronic conditions						<0.001
No	15,662 (58.5)	5,790 (56.2)	4,268 (68.7)	2,989 (54.1)	2,615 (64.5)
Yes	13,194 (41.5)	5,499 (43.8)	2,903 (31.3)	3,286 (45.9)	1,506 (35.5)
Depression						<0.001
None	19,471 (76.4)	7,672 (77.1)	4,806 (74.9)	4,218 (73.7)	2,775 (77.7)
Mild	4,220 (15.5)	1,700 (15.3)	1,906 (16.1)	942 (17.0)	482 (14.3)
Moderate	1,443 (5.1)	594 (4.9)	389 (5.3)	315 (5.9)	145 (5.5)
Severe	904 (3.0)	368 (2.8)	278 (3.6)	189 (3.4)	69 (2.6)
Health status						<0.001
Excellent	2,416 (10.5)	1,059 (11.4)	489 (8.1)	462 (8.4)	406 (10.9)
Very good	6,830 (31.9)	3,447 (36.5)	1,065 (18.7)	1,303 (23.5)	1,015 (29.4)
Good	10,773 (39.7)	4,063 (38.2)	2,743 (42.7)	2,446 (42.5)	1,521 (42.2)
Fair	5,407 (15.4)	1,562 (11.9)	2,016 (26.5)	1,326 (22.3)	503 (14.8)
Poor	966 (2.5)	335 (2.0)	341 (4.1)	208 (3.3)	82 (2.7)
Health insurance						<0.001
No	6,278 (17.2)	1,638 (11.5)	2,666 (38.4)	1,265 (21.8)	709 (16.8)
Yes	24,004 (82.8)	10,049(88.5)	4,986 (61.6)	5,329 (78.2)	3,640 (83.2)

Open in a new tab

Mental healthcare utilization across intersectional groups

Significant disparities were observed in both relative and absolute prevalence estimates of mental healthcare utilization across sex and race/ethnic intersectional groups [Table 3]. Compared to NH White males, Hispanic males were the least likely (41% less likely, aPR = 0.59 [0.47–0.73]) to utilize services, followed by males in the Other race group (33% less likely, aPR = 0.66 [0.50–0.88]). NH Black males were also less likely to utilize mental health services compared to NH White males. However, this association was insignificant (aPR = 0.91 [0.75–1.12]). Among females, Hispanic, NH Blacks and women in the Other race category were significantly less likely (25%, 19% and 27% less likely respectively) to utilize mental health services compared to NH White females.

Table 3. Prevalence ratios (PR) and prevalence differences (PD) of mental healthcare utilization by sex and race/ethnicity (95% Confidence Intervals).

Race/Ethnicity PR & PD (95% CI)	Male	Female	Female vs Male (within strata of race-ethnicity)
Relative Scale Unadjusted PR
Non-Hispanic White	Ref^	Ref	1.14 (0.97, 1.32)
Hispanic	0.65 (0.52, 0.80)***	0.84 (0.70, 0.99)*	1.46 (1.20, 1.79)***
Non-Hispanic Black	0.97 (0.80, 1.19)	0.87 (0.73, 1.05)	1.02 (0.87, 1.20)
Other Race	0.70 (0.53, 0.92)**	0.79 (0.61, 1.00)*	1.27 (0.92, 1.75)
Adjusted PR
Non-Hispanic White	Ref	Ref	1.16 (1.00, 1.36)*
Hispanic	0.59 (0.47, 0.73)***	0.75 (0.63, 0.90)***	1.49 (1.22, 1.80)**
Non-Hispanic Black	0.91 (0.75, 1.12)	0.81 (0.68, 0.98)*	1.04 (0.89, 1.21)
Other Race	0.66 (0.50, 0.88)**	0.73 (0.57, 0.92)*	1.27 (0.92, 1.77)
Absolute Scale Unadjusted PD
Non-Hispanic White	Ref	Ref	0.01 (-0.00, 0.03)
Hispanic	-0.03 (-0.05, -0.02)***	-0.02 (-0.03, -0.00)*	0.03 (0.01, 0.04)***
Non-Hispanic Black	-0.00 (-0.02, 0.02)	-0.01 (-0.03, 0.00)	0.00 (-0.01, 0.02)
Other Race	-0.03 (-0.05, -0.01)**	-0.02 (-0.04, -0.00)*	0.02 (-0.01, 0.04)
Adjusted PD
Non-Hispanic White	Ref	Ref	0.01 (-0.00, 0.03)
Hispanic	-0.03 (-0.05, -0.01)***	-0.02 (-0.03, -0.00)**	0.03 (0.01, 0.04)***
Non-Hispanic Black	-0.00 (-0.02, 0.01)	-0.02 (-0.03, 0.00)	-0.00 (-0.01, 0.01)
Other Race	-0.03 (-0.05, -0.01)**	-0.02 (-0.04, -0.00)*	0.02 (-0.01, 0.04)

Open in a new tab

Adjusted estimates are adjusted for Age.

^Ref are the reference groups of comparisons. Within male and female categories, individuals in other intersectional groups are compared to Non-Hispanic Whites.

*indicates statistical significance at p values: * < 0.05, ** < 0.01 *** < 0.001

Within the same race-ethnic group, females were more likely to utilize services than males. While NH White (aPR = 1.16 [1.00, 1.36]) and Hispanic females (aPR = 1.49 [1.22, 1.80]) had significantly higher probabilities of utilizing services than their male counterparts, this association was not significant among NH Blacks and individuals in the NH Other race category.

Results on the absolute/additive scale were consistent with the relative findings but indicated smaller differences [Table 3].

Stratified analysis

The results of the stratified analyses were largely consistent with the primary analysis, although few subgroup differences emerged across education, income, health insurance, and depression status. [Table 4].

Table 4. Results of stratified analysis by education level, income level, health insurance and depression status showing adjusted prevalence ratios (PR) with 95% CIs.

Stratification Variable	Male aPR (95% CI)	Female aPR (95% CI)	Female vs Male aPR (95% CI)
Education High School or below
Non-Hispanic White	Ref	Ref	1.17 [0.91; 1.51]
Hispanic	0.58 [0.44,0.75]***	0.74 [0.58,0.93]**	1.51 [1.17,1.92]***
Non-Hispanic Black	1.26 [0.96, 1.65]	0.93 [0.72, 1.21]	0.87 [0.68; 1.12]
Other race	0.89 [0.59, 1.35]	0.94 [0.64, 1.38]	1.25 [0.73, 2.12]
College and above
Non-Hispanic White	Ref	Ref	1.14[0.96, 1.35]
Hispanic	0.73 [0.54,0.99]*	0.88[0.69,1.12]	1.38[1.02; 1.88]*
Non-Hispanic Black	0.70 [0.55, 0.88]**	0.78[0.63, 0.97]*	1.28[0.99; 1.67]
Other race	0.58[0.39, 0.86]**	0.66 [0.50, 0.87]**	1.31[0.83, 2.06]
Income Low Income
Non-Hispanic White	Ref	Ref	1.22 [0.99, 1.51]
Hispanic	0.46 [0.36, 0.60]***	0.47[0.36, 0.62]***	1.01 [0.73, 1.39]
Non-Hispanic Black	1.01 [0.77, 1.31]	0.67 [0.50, 0.99]**	0.72 [0.52, 0.99]
Other race	0.68 [0.43, 1.10]	0.62 [0.44, 0.90]**	1.06 [0.64, 1.75]
Middle Income
Non-Hispanic White	Ref	Ref	1.15 [0.89, 1.49]
Hispanic	0.56 [0.40, 0.79]***	0.75[0.46, 1.06]	1.62 [1.20, 2.18]**
Non-Hispanic Black	0.75 [0.55, 1.03]	0.75 [0.52, 1.57]	1.26 [0.93, 1.72]
Other race	0.45[0.28, 0.75]***	0.57 [0.29, 1.14]	1.65 [0.95, 2.82]
High Income
Non-Hispanic White	Ref	Ref	1.11 [0.84, 0.46]
Hispanic	0.73 [0.46, 1.15]	1.00[0.63, 0.90]	1.54 [0.89, 2.67]
Non-Hispanic Black	0.72 [0.49, 1.06]	0.95 [0.57, 1.57]	0.95 [0.57, 1.57]
Other race	0.76 [0.43, 1.36]	0.89 [0.44, 1.73]	0.87 [0.44, 1.73]
Insurance Uninsured
Non-Hispanic White	Ref	Ref	1.75[1.21; 2.53]**
Hispanic	0.52 [0.34,0.81]**	0.30 [0.21,0.41]***	1.00 [0.73,1.37]
Non-Hispanic Black	0.87[0.54, 1.37]	0.45[0.30,0.70]***	0.92[0.59; 1.43]
Other race	0.73 [0.33, 1.62]	0.43[0.25, 0.76]**	1.04[0.47, 2.32]
Insured
Non-Hispanic White	Ref	Ref	1.11[0.94, 1.28]
Hispanic	0.73 [0.57,0.94]*	1.00[0.84,1.20]	1.51[1.20; 1.92]***
Non-Hispanic Black	0.99[0.80, 1.12]	0.90[0.74, 1.08]	1.00[0.86; 1.17]
Other race	0.67[0.49, 0.90]**	0.78[0.79, 1.01]	1.28[0.89, 1.84]
Depression status No Depression
Non-Hispanic White	Ref	Ref	1.08[0.86; 1.37]
Hispanic	0.55[0.42,0.72]***	0.75[0.57,0.98]*	1.48[1.13,1.91]**
Non-Hispanic Black	0.88[0.66, 1.17]	0.73[0.52, 0.90]**	0.84[0.66; 1.09]
Other race	0.57 [0.34, 0.93]*	0.73 [0.52, 1.04]	1.40[0.80, 2.46]
Depression
Non-Hispanic White	Ref	Ref	0.99[0.81, 1.21]
Hispanic	0.61[0.46,0.81]***	0.70[0.57,0.87]***	1.14[0.89; 1.46]
Non-Hispanic Black	0.90[0.70, 1.15]	0.84[0.68, 1.03]	0.92[0.76; 1.12]
Other race	0.82[0.57, 1.17]	0.76 [0.57, 1.01]	0.91[0.62, 1.37]

Open in a new tab

*indicates statistical significance at p values: * < 0.05, ** < 0.01 *** < 0.001

Education

Compared to the primary analysis, stratification by education revealed notable differences among NH Black individuals. Among those with high school education or less, NH Black males had higher mental health service utilization than NH White males (aPR = 1.26, 95% CI: 0.96–1.65), contrasting with the overall finding of lower utilization among NH Black males in the primary analysis. NH Black males with high school education or less were also more likely to utilize services compared to their female counterparts (NH Black females) [Table 4]. Although these differences were not statistically significant, they indicate a directional reversal in disparities among lower-educated NH Black individuals compared to results from the primary analysis. Conversely, among those with higher education (college and above), findings mirror the main analysis with NH Black males having significantly lower utilization than NH White males, and NH Black females having higher utilization than NH Black males.

Income

Among individuals in the middle-income category, results from the stratified analysis aligned closely with the overall findings. However, among those with low income, NH Black females had lower utilization than NH Black males (aPR = 0.72 [0.52, 0.99]), again reversing the sex pattern from the primary analysis. This reversal is consistent with the pattern seen in the lower education stratum, suggesting a potential intersection of sex and socio-economic status in shaping service use. Among individuals with high-income, disparities across intersectional groups were not statistically significant. This contrasts with the significant disparities seen in the main analysis, suggesting that higher income may attenuate disparities.

Insurance

Stratified results by insurance status were broadly consistent with the primary analysis, particularly in terms of direction. However, disparities were more pronounced among uninsured females, with ethnic minority females having even lower utilization rates than in the main analysis. For example, uninsured Hispanic women had 70% lower utilization than uninsured NH White women (aPR = 0.30, 95% CI: 0.21–0.41), a sharper disparity than observed in the primary analysis (25% lower utilization, aPR = 0.75), indicating that lack of insurance may exacerbate disparities among women.

Depression

The adjusted prevalence ratios (aPRs) for mental healthcare utilization stratified by depression status were largely consistent with the primary analysis suggesting that depression status did not substantially alter the observed disparities.

Discussion

Primary findings

This study examined disparities in mental healthcare utilization among U.S adults across intersections of sex and race-ethnicity using nationally representative NHANES data. Only 9.1% of adults reported using mental health services in the prior year, underscoring the persistence of the treatment gap despite high levels of need [6,7]. The intersectional analysis revealed that Hispanic males had the lowest utilization rates, being 41% less like than NH White males to access care. Among females, all minority racial/ethnic groups had significantly lower utilization than NH White females. Within racial/ethnic groups, females generally accessed services more than males, although the magnitude of the sex gap differed across groups. Absolute prevalence differences mirrored relative measures but were smaller, reflecting the overall low baseline utilization rate.

These findings extend prior single-axis analyses by demonstrating how sex and race/ethnicity jointly shape access to care. For instance, the varying magnitude of sex gaps across racial/ethnic groups—from 49% higher rates for Hispanic females to only 4% higher for NH Black females relative to males—illustrates how sex and race/ethnicity interact differently across groups. Consistent with intersectionality theory, the disparities observed cannot be reduced to either race/ethnicity or sex alone but emerge from their combined influence [13,16,22].

The persistence of disparities even when stratified by depression status suggests that differences in need may not fully explain unequal utilization. This aligns with evidence showing that despite having comparable or higher prevalence of mental disorders, Hispanic and NH Blacks access care at lower rates than NH Whites [42,43]. Thus, intersecting social norms, systemic inequities, and structural barriers rather than need alone shape access among certain intersectional groups. For example, Hispanic men’s especially low utilization may reflect the interplay of traditional masculine norms that discourage help-seeking, alongside structural disadvantages including lower insurance coverage, language barriers, and immigration-related challenges [44–49]. This reinforces Crenshaw’s intersectionality framework and broader evidence that illustrate how healthcare access is shaped not only by clinical need but also by overlapping effects of social identities and systemic inequities [17,21,31].

Stratified analyses

Stratified analyses highlighted how socioeconomic status modifies observed disparities. The amplification of disparities among uninsured and low-income groups especially Hispanic and NH Blacks suggests that insurance coverage and income level are important structural factors. Furthermore, the absence of significant differences in utilization among high-income individuals suggests that socioeconomic advantages may attenuate disparities. Education also appeared to shape patterns among NH Blacks: Black men with lower education levels showed unexpectedly higher utilization relative to NH White men, while NH Black women in the same group reported lower utilization than NH Black men. This reversal may reflect pathways into treatment through coercive or involuntary routes, such as criminal justice referrals, which disproportionately affect lower-educated NH Black men [50,51]. Together, the primary and stratified study results demonstrate the utility of intersectional analyses for revealing heterogeneity within groups that single-factor approaches obscure.

Policy and practice implications

The study findings underscore the need for health policies and interventions that explicitly recognize intersecting inequities in access. The patterns observed suggest that culturally responsive and gender-sensitive strategies should be tested for effectiveness among key groups such as Hispanic men, where disparities are most pronounced. Promising approaches include the integration of mental health services into trusted community and faith-based organizations and the development of bilingual and culturally-competent outreach programs that challenge traditional masculine norms around help-seeking [29,49,52]. At the structural level, reforms that expand insurance coverage, reduce cost barriers, and improve geographic accessibility are likely to benefit underserved groups [4,52,53]. More broadly, policies addressing social determinants of health including education and employment are also essential to reducing disparities in mental healthcare utilization [52,54]. Intersectionality provides a framework for targeting interventions to the groups that are most disadvantaged at the overlap of multiple social identities.

Limitations

Several limitations should be considered when interpreting these findings. First, NHANES collects binary sex assigned at birth but does not capture gender identity [26]. While sex and gender are correlated, they are distinct constructs. Hence, the binary sex variable does not reflect the social, cultural, and individual experiences of gender identity and also limits the examination of disparities in gender-diverse populations. Likewise, the use of broad and heterogenous racial/ethnic categories such as “Non-Hispanic Other’ obscures important within-group differences including among equity-deserving groups like indigenous populations.

Second, sexual orientation – an important factor in intersectional analysis – could not be included in the analyses due to methodological challenges in the NHANES dataset including insufficient sample sizes for non-heterosexual groups and high rates of non-specific responses [23]. This highlights the need for improved data collection methods that better capture sexual orientation while maintaining statistical power for meaningful analysis. Furthermore, intersectionality is not limited to gender and race/ethnicity. Although the stratified analysis enabled consideration of other axes such as socioeconomic status, important factors like disability remain unexplored and can be the focus of future research.

Third, while stratification by depression severity provided useful insight, NHANES’ lack of consistently collected validated measures for other mental disorders restricted the scope of the need-based analyses. Fourth, the reliance on self-reported service utilization may be subject to recall and social desirability bias. Finally, these findings may not be generalizable outside the United States because while intersectionality is broadly applicable framework, the drivers of inequity differ across contexts. Despite these limitations, the large, nationally representative sample and use of intersectional analytic methods provide a robust contribution to understanding inequities in mental-healthcare utilization.

Conclusion

This study demonstrated significant intersectional disparities in mental health service utilization in the United States. Hispanic men and women from racial/ethnic minority groups had comparatively lower utilization rates, with socioeconomic position further shaping access. These results underscore the need to consider multiple, intersecting identities when designing policies and interventions to improve mental healthcare access.

Data Availability

The National Health and Nutrition Examination Survey (NHANES) dataset used for this study is publicly available from the National Center for Health Statistics repository on the Centre for Disease Control (CDC) website: https://www.cdc.gov/nchs/nhanes/index.html.

Funding Statement

The authors received no specific funding for this work.

References

1.Dattani S, Rodés-Guirao L, Ritchie H, Roser M. Mental Health. Our World Data [Internet]. 2023 Dec 28 [cited 2024 Jun 24]; Available from: https://ourworldindata.org/mental-health
2.Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Kessler RC, Demler O, Frank RG, Olfson M, Pincus HA, Walters EE, et al. Prevalence and treatment of mental disorders, 1990 to 2003. N Engl J Med. 2005;352(24):2515–23. doi: 10.1056/NEJMsa043266 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Exploring Barriers to Mental Health Care in the U.S. [Internet]. Research and Action Institute. [cited 2024 Dec 8]. Available from: https://www.aamcresearchinstitute.org/our-work/issue-brief/exploring-barriers-mental-health-care-us [Google Scholar]
5.2023 National Survey on Drug Use and Health (NSDUH) Releases [Internet]. [cited 2024 Dec 8]. Available from: https://www.samhsa.gov/data/release/2023-national-survey-drug-use-and-health-nsduh-releases
6.Mental Illness - National Institute of Mental Health (NIMH) [Internet]. [cited 2024 Nov 13]. Available from: https://www.nimh.nih.gov/health/statistics/mental-illness
7.Kohn R, Ali AA, Puac-Polanco V, Figueroa C, López-Soto V, Morgan K. Mental health in the Americas: an overview of the treatment gap. Rev Panam Salud Pública. 2018;42. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Liao B, Xu D, Tan Y, Chen X, Cai S. Association of mental distress with chronic diseases in 1.9 million individuals: a population-based cross-sectional study. J Psychosom Res. 2022;162:111040. [DOI] [PubMed] [Google Scholar]
9.Compton MT, Shim RS. The social determinants of mental health. Focus. 2015;13(4):419–25. [Google Scholar]
10.Dobalian A, Rivers PA. Racial and ethnic disparities in the use of mental health services. J Behav Health Serv Res. 2008;35(2):128–41. [DOI] [PubMed] [Google Scholar]
11.Wang J, Qiu Y, Zhu X. Trends of mental health care utilization among US adults from 1999 to 2018. BMC Psychiatry. 2023;23:665. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.World Report on Social Determinants of Health Equity [Internet]. [cited 2025 Sep 4]. Available from: https://www.who.int/teams/social-determinants-of-health/equity-and-health/world-report-on-social-determinants-of-health-equity
13.Funer F. Admitting the heterogeneity of social inequalities: intersectionality as a (self-)critical framework and tool within mental health care. Philos Ethics Humanit Med. 2023;18(1):21. doi: 10.1186/s13010-023-00144-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Walker ER, Cummings JR, Hockenberry JM, Druss BG. Insurance status, use of mental health services, and unmet need for mental health care in the United States. Psychiatr Serv. 2015;66(6):578–84. doi: 10.1176/appi.ps.201400248 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Byhoff E, Dinh DH, Lucas JA, Marino M, Heintzman J. Mental health care use by ethnicity and preferred language in a national cohort of community health center patients. Psychiatr Serv Wash DC. 2024;75(4):363–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Hahm HC, Cook BL, Ault-Brutus A, Alegría M. Intersection of race-ethnicity and gender in depression care: screening, access, and minimally adequate treatment. Psychiatr Serv. 2015;66(3):258–64. doi: 10.1176/appi.ps.201400116 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Manuel JI. Racial/Ethnic and Gender Disparities in Health Care Use and Access. Health Serv Res. 2018;53(3):1407–29. doi: 10.1111/1475-6773.12705 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Blaikie K, Mooney SJ, Hill HD, Rhew IC, Hajat A. Intersectional trends in poor mental health and health inequities across the US. SSM - Ment Health. 2024;6:100349. [Google Scholar]
19.Fagrell Trygg N, Gustafsson PE, Månsdotter A. Languishing in the crossroad? A scoping review of intersectional inequalities in mental health. Int J Equity Health. 2019;18(1):115. doi: 10.1186/s12939-019-1012-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Bauer GR. Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Soc Sci Med. 2014;110:10–7. [DOI] [PubMed] [Google Scholar]
21.Crenshaw K. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Feminist Legal Theories. Routledge. 1997. [Google Scholar]
22.Fagrell Trygg N, Månsdotter A, Gustafsson PE. Intersectional inequalities in mental health across multiple dimensions of inequality in the Swedish adult population. Soc Sci Med. 2021;283:114184. [DOI] [PubMed] [Google Scholar]
23.NHANES Survey Methods and Analytic Guidelines [Internet]. [cited 2024 Nov 8]. Available from: https://wwwn.cdc.gov/Nchs/Nhanes/AnalyticGuidelines.aspx
24.Human Research Policy (LR9) | Office of the University Counsel [Internet]. [cited 2024 Dec 9]. Available from: https://universitycounsel.ubc.ca/policies/human-research-policy//
25.Government of Canada IAP on RE. Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans – TCPS 2 (2022) [Internet]. 2023 [cited 2024 Nov 14]. Available from: https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2022.html
26.NHANES Questionnaires, Datasets, and Related Documentation [Internet]. [cited 2024 Sep 14]. Available from: https://wwwn.cdc.gov/nchs/nhanes//
27.DAGitty v3.1 [Internet]. [cited 2024 Nov 15]. Available from: https://dagitty.net/dags.html#
28.VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Stewart-Brown S, Samaraweera PC, Taggart F, Kandala NB, Stranges S. Socioeconomic gradients and mental health: implications for public health. Br J Psychiatry. 2015;206(6):461–5. https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/socioeconomic-gradients-and-mental-health-implications-for-public-health/F11FB267D70812FF5C014FD03EFDFAC7 [DOI] [PubMed] [Google Scholar]
30.Erving CL, Frazier C. The association between multiple chronic conditions and depressive symptoms: distinctions by race, gender, and age. Inno Aging. 2019;3(Supplement_1):S314–S314. doi: 10.1093/geroni/igz038.1148 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Byers AL, Lai AX, Nelson C, Yaffe K. Predictors of mental health services use across the life course among racially–ethnically diverse adults. Am J Geriatr Psychiatry. 2017;25(11). [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Jackson SL, Yang EC, Zhang Z. Income Disparities and Cardiovascular Risk Factors Among Adolescents. Pediatrics. 2018;142(5):e20181089. doi: 10.1542/peds.2018-1089 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16(9):606–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Fitting Regression Models to Survey Data on JSTOR [Internet]. [cited 2024 Nov 15]. Available from: https://www.jstor.org/stable/26408229
35.Naimi AI, Whitcomb BW. Estimating Risk Ratios and Risk Differences Using Regression. Am J Epidemiol. 2020;189(6):508–10. doi: 10.1093/aje/kwaa044 [DOI] [PubMed] [Google Scholar]
36.Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol. 2017;17(1):162. doi: 10.1186/s12874-017-0442-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Rubin DB. Multiple Imputation for Nonresponse in Surveys [Internet]. 1st ed. Wiley; 1987. [cited 2024 Dec 9]. (Wiley Series in Probability and Statistics). Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316696 [Google Scholar]
38.R: The R Project for Statistical Computing [Internet]. [cited 2024 Mar 22]. Available from: https://www.r-project.org//
39.Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31–4. doi: 10.4103/sja.SJA_543_18 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Clayton JA, Tannenbaum C. Reporting sex, gender, or both in clinical research?. JAMA. 2016;316(18):1863–4. [DOI] [PubMed] [Google Scholar]
41.Flanagin A, Frey T, Christiansen SL, AMA Manual of Style Committee. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA. 2021;326(7):621–7. [DOI] [PubMed] [Google Scholar]
42.Substance Abuse and Mental Health Services Administration. Key Substance Use and Mental Health Indicators in the United States: Results from the 2019 National Survey on Drug Use and Health. Secur Res Hub Rep [Internet]. 2020 Jan 1; Available from: https://digitalcommons.fiu.edu/srhreports/health/health/32
43.Wright E, Dore EC, Koenen KC, Mangurian C, Williams DR, Hamad R. Prevalence of and inequities in poor mental health across 3 US surveys, 2011 to 2022. JAMA Netw Open. 2025;8(1):e2454718. doi: 10.1001/jamanetworkopen.2025.2454718 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Chang C-W, Biegel DE. Factors affecting mental health service utilization among Latino Americans with mental health issues. J Ment Health. 2018;27(6):552–9. doi: 10.1080/09638237.2017.1385742 [DOI] [PubMed] [Google Scholar]
45.Villatoro AP, Mays VM, Ponce NA, Aneshensel CS. Perceived need for mental health care: the intersection of race, ethnicity, gender, and socioeconomic status. Soc Ment Health. 2018;8(1):1–24. doi: 10.1177/2156869317718889 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Derr AS. Mental health service use among immigrants in the United States: a systematic review. Psychiatr Serv. 2016;67(3):265–74. doi: 10.1176/appi.ps.201500004 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Ruiz E, Aguirre RTP, Mitschke DB. What leads non-U.S.-Born Latinos to access mental health care? Soc Work Health Care. 2013;52(1):1–19. doi: 10.1080/00981389.2012.733326 [DOI] [PubMed] [Google Scholar]
48.Pattyn E, Verhaeghe M, Bracke P. The gender gap in mental health service use. Soc Psychiatry Psychiatr Epidemiol. 2015;50(7):1089–95. doi: 10.1007/s00127-015-1038-x [DOI] [PubMed] [Google Scholar]
49.Swetlitz N, Hinton L, Rivera M, Liu M, Fernandez AC, Garcia ME. Barriers and facilitators to depression care among Latino men in a primary care setting: a qualitative study. BMC Prim Care. 2024;25:30. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Rainock M. Race-gender disparities in the criminalization and medicalization of mental illness. Soc Ment Health. 2025. doi: 21568693251335336 [Google Scholar]
51.Snowden LR, Hastings JF, Alvidrez J. Overrepresentation of black Americans in psychiatric inpatient care. Psychiatr Serv. 2009;60(6):779–85. doi: 10.1176/ps.2009.60.6.779 [DOI] [PubMed] [Google Scholar]
52.CDC. Protecting the Nation’s Mental Health [Internet]. Mental Health. 2024. [cited 2024 Dec 10]. Available from: https://www.cdc.gov/mental-health/about/what-cdc-is-doing.html [Google Scholar]
53.Guth M, Artiga S, Published OP. Effects of the ACA Medicaid Expansion on Racial Disparities in Health and Health Care [Internet]. KFF. 2020. [cited 2024 Dec 10]. Available from: https://www.kff.org/medicaid/issue-brief/effects-of-the-aca-medicaid-expansion-on-racial-disparities-in-health-and-health-care// [Google Scholar]
54.World mental health report: Transforming mental health for all [Internet]. World Health Organization. 2022. [cited 2022 Jul 14]. Available from: https://www.who.int/publications-detail-redirect/9789240049338 [Google Scholar]

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0004682.r001

Decision Letter 0

Susan Chand

6 Jul 2025

PGPH-D-25-01113

Intersectional Disparities in Mental Healthcare Utilization by Sex and Race/Ethnicity among US Adults: An NHANES Study

PLOS Global Public Health

Dear Dr. Olisaeloka,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 20 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Susan Julia Chand, PhD

Guest Editor

PLOS Global Public Health

Journal Requirements:

Additional Editor Comments (if provided):

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Global Public Health’s publication criteria?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

Reviewer #4: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #1: Yes

Reviewer #2: I don't know

Reviewer #3: No

Reviewer #4: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

Reviewer #1: This is a well-conceived and competently executed manuscript that applies intersectional theory to a vital public health issue: disparities in access to mental healthcare.

Key strengths include:

Use of a nationally representative dataset (NHANES, 2009–2018) and transparent methodology.

Operationalisation of intersectionality using interaction terms and both multiplicative and additive modelling.

Meaningful stratified analysis by socioeconomic and health-related variables (education, income, insurance, depression), which adds important nuance.

Clear policy implications, including recommendations for culturally responsive services and structural reform.

A few suggestions for improvement:

Clarify and define race/ethnicity categories early in the text, and consider commenting on the limitations of these broad categories (especially the heterogeneous “Other” group).

Provide greater clarity on limitations of sex vs. gender measures, and the consequences for interpretability, especially in light of intersectionality theory.

Strengthen the policy/practice recommendations by providing examples of successful interventions or promising approaches for specific subgroups (e.g., Hispanic men).

Conduct a final editorial proofread to catch minor inconsistencies and improve readability.

Reviewer #2: 1. Using personal pronouns (like "I" or "we") in the abstract of a research article is not recommended, especially in formal, academic, or scientific writing.

Passive voice or third-person constructions are often used. e.g Instead of “We analyzed the data,” write “The data were analyzed.”

2. Two different references style used - please stick to one

3. Please avoid personal pronouns, this is an academic article. its not standard practice

4. Passive voice or third-person constructions should be used

5. please include a table footer to show the significance level. e.g p<0.05.

Also an asterisk sign beside a significant value

6. Please do not use and cite publications over 10 years

Reviewer #3: Thank you for the opportunity to review this paper. This is an important topic and a useful way to look at the intersectionality of mental health services utilization. Here are my questions / comments on the paper:

1) Why was the adjusted model limited to age? It says that the other covariates introduced bias when they were adjusted for - but that is not well explained. Furthermore, this is a paper about intersectionality, but excluding overlapping variabilities without more sufficient explanation seems like a mistake both statistically and theoretically. Furthermore, the paper goes on to do sensitivity analyses, but again considers them each individually, which is a weakness of this paper, particularly in light of its focus on intersectionality.

2) Why is depression the only condition included in the analysis? It may be the only one with severity, but including other conditions would strengthen this. Or to be clear that the topic is depression - not mental health conditions more broadly.

3) The first sentence of the introduction could be stronger. Did the prevalence change 30 years ago? Is this time period relevant for treatment efforts?

4) The DAG requires more explanation - what do the lines mean and where is that data coming from?

5) The Table 1 data is quite extensive and probably too much. I would consider reorganizing or presenting it more clearly. Also, with the p values nearly all below 0.05, this is further evidence for including covariates in the analyses.

6) The discussion needs a lot of work. Both to community the findings more clearly, but also the related literature is not well organized and sometimes problematic. The results should be presented similarly (eg, 41% less likely vs 0.75-0.81), but there is far too much of restating the results and the linkages to related literature is weak. For example, the paragraph - "several factors might contribute to these observed.... has the quality of care from providers, stigma, and structural barriers. These are massive topics and then to jump to potential explanations of why Hispanic males might have lower access tried to cover too much space. I would recommend revising.

3) Building on the challenges of the discussion, the policy and practice implications lack focus and aren't meaningfully related to the findings of this paper.

7) Limitations: This is the first mention of LGBTQ+ people, so it seems a bit too much on this and does not reflect the other limitations of this paper (including that intersectionality is more than race and gender. Or that race is a social construct. Using a huge data set. Lack of data on other mental health conditions. Etc.).

Reviewer #4: Thank you for the opportunity to review this very informative and engaging article.

General overview:

The interaction of socio-demographic factors, especially age and socioeconomic status, bidirectionally (positively or negatively) influences MH service utilization not only in this study setting. This article provides a comprehensive overview of key background factors influencing mental health utilization worldwide. Inadvertently, the help-seeking behaviors, individualized multifactorial interplay indicated by these socio-demographics, and relevant socio-cultural-environmental, and systemic inequalities are key salient differences and factors in Mental health utilization. The evidence from this study on the intersectionality of socio-demographics and mental health service utilization is crucial for understanding the causes for low uptake of available health services in the US.

However, ” culturally informed strategies and wider structural interventions...(Abstract & conclusion)” is beautiful but outside of data. What informs this conclusion? It seems MH services are available, but the actual uptake is affected by the sociodemographic characteristics. Also, there could be other systemic factors, like a lack of task-shifted services, that are affecting access and utilization of the services, not necessarily culturally informed strategies.

‘Social determinants of health’, a close concept to socio-demographics, is mentioned in the introduction and discussion. Adding a sentence or two explaining distinct causative and impactful differences in mental health utilization would help provide more clarity in their contributions.

Recommendation:

Consider the ‘culturally informed strategies and structural interventions’ as a recommendation for further study.

Limitation:

Please add why this article can not be generalized to the LMICs, or provide a brief relevance of this article to the LMICs in the discussion

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: Yes: Simon Browes

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes: Josephine Akellot

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: PGPH-D-25-01113_reviewer.pdf

pgph.0004682.s001.pdf^{(1.5MB, pdf)}

PLOS Glob Public Health. 2025 Oct 13;5(10):e0004682. doi: 10.1371/journal.pgph.0004682.r002

Author response to Decision Letter 1

4 Sep 2025

Attachment

Submitted filename: Intersectionality Manuscript Response to Reviewers.docx

pgph.0004682.s002.docx^{(25.6KB, docx)}

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0004682.r003

Decision Letter 1

Susan Chand

22 Sep 2025

Intersectional Disparities in Mental Healthcare Utilization by Sex and Race/Ethnicity among US Adults: An NHANES Study

PGPH-D-25-01113R1

Dear Dr Olisaeloka,

We are pleased to inform you that your manuscript 'Intersectional Disparities in Mental Healthcare Utilization by Sex and Race/Ethnicity among US Adults: An NHANES Study' has been provisionally accepted for publication in PLOS Global Public Health.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact globalpubhealth@plos.org.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Global Public Health.

Best regards,

Susan Julia Chand, PhD

Guest Editor

PLOS Global Public Health

***********************************************************

Reviewer Comments (if any, and for reference):

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: PGPH-D-25-01113_reviewer.pdf

pgph.0004682.s001.pdf^{(1.5MB, pdf)}

Attachment

Submitted filename: Intersectionality Manuscript Response to Reviewers.docx

pgph.0004682.s002.docx^{(25.6KB, docx)}

Data Availability Statement

[pgph.0004682.ref001] 1.Dattani S, Rodés-Guirao L, Ritchie H, Roser M. Mental Health. Our World Data [Internet]. 2023 Dec 28 [cited 2024 Jun 24]; Available from: https://ourworldindata.org/mental-health

[pgph.0004682.ref002] 2.Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–50. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref003] 3.Kessler RC, Demler O, Frank RG, Olfson M, Pincus HA, Walters EE, et al. Prevalence and treatment of mental disorders, 1990 to 2003. N Engl J Med. 2005;352(24):2515–23. doi: 10.1056/NEJMsa043266 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref004] 4.Exploring Barriers to Mental Health Care in the U.S. [Internet]. Research and Action Institute. [cited 2024 Dec 8]. Available from: https://www.aamcresearchinstitute.org/our-work/issue-brief/exploring-barriers-mental-health-care-us [Google Scholar]

[pgph.0004682.ref005] 5.2023 National Survey on Drug Use and Health (NSDUH) Releases [Internet]. [cited 2024 Dec 8]. Available from: https://www.samhsa.gov/data/release/2023-national-survey-drug-use-and-health-nsduh-releases

[pgph.0004682.ref006] 6.Mental Illness - National Institute of Mental Health (NIMH) [Internet]. [cited 2024 Nov 13]. Available from: https://www.nimh.nih.gov/health/statistics/mental-illness

[pgph.0004682.ref007] 7.Kohn R, Ali AA, Puac-Polanco V, Figueroa C, López-Soto V, Morgan K. Mental health in the Americas: an overview of the treatment gap. Rev Panam Salud Pública. 2018;42. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref008] 8.Liao B, Xu D, Tan Y, Chen X, Cai S. Association of mental distress with chronic diseases in 1.9 million individuals: a population-based cross-sectional study. J Psychosom Res. 2022;162:111040. [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref009] 9.Compton MT, Shim RS. The social determinants of mental health. Focus. 2015;13(4):419–25. [Google Scholar]

[pgph.0004682.ref010] 10.Dobalian A, Rivers PA. Racial and ethnic disparities in the use of mental health services. J Behav Health Serv Res. 2008;35(2):128–41. [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref011] 11.Wang J, Qiu Y, Zhu X. Trends of mental health care utilization among US adults from 1999 to 2018. BMC Psychiatry. 2023;23:665. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref012] 12.World Report on Social Determinants of Health Equity [Internet]. [cited 2025 Sep 4]. Available from: https://www.who.int/teams/social-determinants-of-health/equity-and-health/world-report-on-social-determinants-of-health-equity

[pgph.0004682.ref013] 13.Funer F. Admitting the heterogeneity of social inequalities: intersectionality as a (self-)critical framework and tool within mental health care. Philos Ethics Humanit Med. 2023;18(1):21. doi: 10.1186/s13010-023-00144-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref014] 14.Walker ER, Cummings JR, Hockenberry JM, Druss BG. Insurance status, use of mental health services, and unmet need for mental health care in the United States. Psychiatr Serv. 2015;66(6):578–84. doi: 10.1176/appi.ps.201400248 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref015] 15.Byhoff E, Dinh DH, Lucas JA, Marino M, Heintzman J. Mental health care use by ethnicity and preferred language in a national cohort of community health center patients. Psychiatr Serv Wash DC. 2024;75(4):363–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref016] 16.Hahm HC, Cook BL, Ault-Brutus A, Alegría M. Intersection of race-ethnicity and gender in depression care: screening, access, and minimally adequate treatment. Psychiatr Serv. 2015;66(3):258–64. doi: 10.1176/appi.ps.201400116 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref017] 17.Manuel JI. Racial/Ethnic and Gender Disparities in Health Care Use and Access. Health Serv Res. 2018;53(3):1407–29. doi: 10.1111/1475-6773.12705 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref018] 18.Blaikie K, Mooney SJ, Hill HD, Rhew IC, Hajat A. Intersectional trends in poor mental health and health inequities across the US. SSM - Ment Health. 2024;6:100349. [Google Scholar]

[pgph.0004682.ref019] 19.Fagrell Trygg N, Gustafsson PE, Månsdotter A. Languishing in the crossroad? A scoping review of intersectional inequalities in mental health. Int J Equity Health. 2019;18(1):115. doi: 10.1186/s12939-019-1012-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref020] 20.Bauer GR. Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Soc Sci Med. 2014;110:10–7. [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref021] 21.Crenshaw K. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Feminist Legal Theories. Routledge. 1997. [Google Scholar]

[pgph.0004682.ref022] 22.Fagrell Trygg N, Månsdotter A, Gustafsson PE. Intersectional inequalities in mental health across multiple dimensions of inequality in the Swedish adult population. Soc Sci Med. 2021;283:114184. [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref023] 23.NHANES Survey Methods and Analytic Guidelines [Internet]. [cited 2024 Nov 8]. Available from: https://wwwn.cdc.gov/Nchs/Nhanes/AnalyticGuidelines.aspx

[pgph.0004682.ref024] 24.Human Research Policy (LR9) | Office of the University Counsel [Internet]. [cited 2024 Dec 9]. Available from: https://universitycounsel.ubc.ca/policies/human-research-policy//

[pgph.0004682.ref025] 25.Government of Canada IAP on RE. Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans – TCPS 2 (2022) [Internet]. 2023 [cited 2024 Nov 14]. Available from: https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2022.html

[pgph.0004682.ref026] 26.NHANES Questionnaires, Datasets, and Related Documentation [Internet]. [cited 2024 Sep 14]. Available from: https://wwwn.cdc.gov/nchs/nhanes//

[pgph.0004682.ref027] 27.DAGitty v3.1 [Internet]. [cited 2024 Nov 15]. Available from: https://dagitty.net/dags.html#

[pgph.0004682.ref028] 28.VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34(3). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref029] 29.Stewart-Brown S, Samaraweera PC, Taggart F, Kandala NB, Stranges S. Socioeconomic gradients and mental health: implications for public health. Br J Psychiatry. 2015;206(6):461–5. https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/socioeconomic-gradients-and-mental-health-implications-for-public-health/F11FB267D70812FF5C014FD03EFDFAC7 [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref030] 30.Erving CL, Frazier C. The association between multiple chronic conditions and depressive symptoms: distinctions by race, gender, and age. Inno Aging. 2019;3(Supplement_1):S314–S314. doi: 10.1093/geroni/igz038.1148 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref031] 31.Byers AL, Lai AX, Nelson C, Yaffe K. Predictors of mental health services use across the life course among racially–ethnically diverse adults. Am J Geriatr Psychiatry. 2017;25(11). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref032] 32.Jackson SL, Yang EC, Zhang Z. Income Disparities and Cardiovascular Risk Factors Among Adolescents. Pediatrics. 2018;142(5):e20181089. doi: 10.1542/peds.2018-1089 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref033] 33.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16(9):606–13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref034] 34.Fitting Regression Models to Survey Data on JSTOR [Internet]. [cited 2024 Nov 15]. Available from: https://www.jstor.org/stable/26408229

[pgph.0004682.ref035] 35.Naimi AI, Whitcomb BW. Estimating Risk Ratios and Risk Differences Using Regression. Am J Epidemiol. 2020;189(6):508–10. doi: 10.1093/aje/kwaa044 [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref036] 36.Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol. 2017;17(1):162. doi: 10.1186/s12874-017-0442-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref037] 37.Rubin DB. Multiple Imputation for Nonresponse in Surveys [Internet]. 1st ed. Wiley; 1987. [cited 2024 Dec 9]. (Wiley Series in Probability and Statistics). Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316696 [Google Scholar]

[pgph.0004682.ref038] 38.R: The R Project for Statistical Computing [Internet]. [cited 2024 Mar 22]. Available from: https://www.r-project.org//

[pgph.0004682.ref039] 39.Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31–4. doi: 10.4103/sja.SJA_543_18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref040] 40.Clayton JA, Tannenbaum C. Reporting sex, gender, or both in clinical research?. JAMA. 2016;316(18):1863–4. [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref041] 41.Flanagin A, Frey T, Christiansen SL, AMA Manual of Style Committee. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA. 2021;326(7):621–7. [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref042] 42.Substance Abuse and Mental Health Services Administration. Key Substance Use and Mental Health Indicators in the United States: Results from the 2019 National Survey on Drug Use and Health. Secur Res Hub Rep [Internet]. 2020 Jan 1; Available from: https://digitalcommons.fiu.edu/srhreports/health/health/32

[pgph.0004682.ref043] 43.Wright E, Dore EC, Koenen KC, Mangurian C, Williams DR, Hamad R. Prevalence of and inequities in poor mental health across 3 US surveys, 2011 to 2022. JAMA Netw Open. 2025;8(1):e2454718. doi: 10.1001/jamanetworkopen.2025.2454718 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref044] 44.Chang C-W, Biegel DE. Factors affecting mental health service utilization among Latino Americans with mental health issues. J Ment Health. 2018;27(6):552–9. doi: 10.1080/09638237.2017.1385742 [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref045] 45.Villatoro AP, Mays VM, Ponce NA, Aneshensel CS. Perceived need for mental health care: the intersection of race, ethnicity, gender, and socioeconomic status. Soc Ment Health. 2018;8(1):1–24. doi: 10.1177/2156869317718889 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref046] 46.Derr AS. Mental health service use among immigrants in the United States: a systematic review. Psychiatr Serv. 2016;67(3):265–74. doi: 10.1176/appi.ps.201500004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref047] 47.Ruiz E, Aguirre RTP, Mitschke DB. What leads non-U.S.-Born Latinos to access mental health care? Soc Work Health Care. 2013;52(1):1–19. doi: 10.1080/00981389.2012.733326 [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref048] 48.Pattyn E, Verhaeghe M, Bracke P. The gender gap in mental health service use. Soc Psychiatry Psychiatr Epidemiol. 2015;50(7):1089–95. doi: 10.1007/s00127-015-1038-x [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref049] 49.Swetlitz N, Hinton L, Rivera M, Liu M, Fernandez AC, Garcia ME. Barriers and facilitators to depression care among Latino men in a primary care setting: a qualitative study. BMC Prim Care. 2024;25:30. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pgph.0004682.ref050] 50.Rainock M. Race-gender disparities in the criminalization and medicalization of mental illness. Soc Ment Health. 2025. doi: 21568693251335336 [Google Scholar]

[pgph.0004682.ref051] 51.Snowden LR, Hastings JF, Alvidrez J. Overrepresentation of black Americans in psychiatric inpatient care. Psychiatr Serv. 2009;60(6):779–85. doi: 10.1176/ps.2009.60.6.779 [DOI] [PubMed] [Google Scholar]

[pgph.0004682.ref052] 52.CDC. Protecting the Nation’s Mental Health [Internet]. Mental Health. 2024. [cited 2024 Dec 10]. Available from: https://www.cdc.gov/mental-health/about/what-cdc-is-doing.html [Google Scholar]

[pgph.0004682.ref053] 53.Guth M, Artiga S, Published OP. Effects of the ACA Medicaid Expansion on Racial Disparities in Health and Health Care [Internet]. KFF. 2020. [cited 2024 Dec 10]. Available from: https://www.kff.org/medicaid/issue-brief/effects-of-the-aca-medicaid-expansion-on-racial-disparities-in-health-and-health-care// [Google Scholar]

[pgph.0004682.ref054] 54.World mental health report: Transforming mental health for all [Internet]. World Health Organization. 2022. [cited 2022 Jul 14]. Available from: https://www.who.int/publications-detail-redirect/9789240049338 [Google Scholar]

PERMALINK

Intersectional disparities in mental healthcare utilization by sex and race/ethnicity among US adults: An NHANES study

Lotenna Olisaeloka

Esteban J Valencia

Gentille Musengimana

Daniel Vigo

Ehsan Karim

Roles

Abstract

Introduction

Methods

Ethics statement

Data source

Study sample

Fig 1. Flowchart illustrating the sample selection process of adults aged 18+ from the NHANES cycles.

Outcome

Exposure

Covariate selection

Fig 2. Directed acyclic graph of the relationships between sex, race/ethnicity, mental health service utilization and associated factors.

Statistical analysis

Primary analysis

Stratified/Sensitivity analyses

Results

Sample characteristics

Table 1. Sample characteristics by sex among US adults in the 2009-2018 NHANES.

Table 2. Sample characteristics stratified by race-ethnicity among US adults aged 18 years or more in the 2009-2018 NHANES.

Mental healthcare utilization across intersectional groups

Table 3. Prevalence ratios (PR) and prevalence differences (PD) of mental healthcare utilization by sex and race/ethnicity (95% Confidence Intervals).

Stratified analysis

Table 4. Results of stratified analysis by education level, income level, health insurance and depression status showing adjusted prevalence ratios (PR) with 95% CIs.

Education

Income

Insurance

Depression

Discussion

Primary findings

Stratified analyses

Policy and practice implications

Limitations

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Susan Chand

Roles

Author response to Decision Letter 1

Decision Letter 1

Susan Chand

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases