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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: J Ment Health. 2018 Nov 10;28(5):536–545. doi: 10.1080/09638237.2018.1521917

Racial and Ethnic Differences in Mental Health Service Utilization among the Hawaii Medicaid Population

Eunjung Lim 1, Krupa Gandhi 1, Chathura Siriwardhana 1, James Davis 1, John J Chen 1
PMCID: PMC6510646  NIHMSID: NIHMS1514674  PMID: 30417731

Abstract

Background:

Racial/ethnic differences have not been adequately addressed in the utilization of mental health services among Medicaid populations.

Aim:

This study aimed to examine racial/ethnic differences in the utilization of health services for mental disorders in a Medicaid adult population aged between 21 and 64 years.

Methods:

Racial/ethnic differences in inpatient, outpatient, and emergency department services utilization were assessed using 2010 Hawaii Medicaid data. Zero-inflated negative binomial regressions were employed adjusting for age, sex, and residential area.

Results:

Among 73,200 beneficiaries, 29.7% were Whites, 28.5% Asians, 34.7% Native Hawaiians and Pacific Islanders (NHPIs); 60.2% were younger (21–44 years) and 58.6% were females. The patterns of utilization of health services differed across race/ethnicity. Compared to Whites, Asians and NHPIs were less likely to use outpatient services and had lower rates of inpatient visits. NHPIs were also estimated to have lower rates of outpatient and emergency department visits.

Conclusions:

Variation in the utilization of health services emphasizes the importance of race/ethnicity in mental health management. Various factors, such as language barriers and cultural differences, should be considered in developing clinical interventions or integrative health programs that aim to reduce racial/ethnic disparities among people with mental disorders.

Keywords: Race, ethnicity, utilization, Medicaid, mental disorder

Introduction

Racial/ethnic disparities have been widely investigated in mental health services utilization. The Institute of Medicine defines disparities in health care as “racial or ethnic differences in the quality of health care that are not due to access-related factors or clinical needs, preferences, and appropriateness of intervention” (Institute of Medicine Committee, 2003). The National Institute of Mental Health estimates that approximately 20–25% people suffer from diagnosable mental disorders (National Institute of Mental Health [NIMH], 2016a); however, only half receive treatment for their conditions (NIMH, 2016b). Studies found that racial/ethnic minority groups reported lower utilization rates for mental health services than Whites although the overall rates of mental disorders are approximately similar across races/ethnicities (Cooper-Patrick et al., 1999; Wang et al., 2005; Manseau & Case, 2014). This implies that racial/ethnic disparities could be interpreted as an unmet mental health service need. Further studies showed that racial/ethnic disparities are especially associated with socioeconomic status, limited access (e.g., insurance, cost of care) and suboptimal treatments for different racial/ethnic groups (Dinwiddie, Gaskin, Chan, Norrington, & McCleary, 2013; Freedenthal, 2007; Jimenez, Cook, Bartels, & Alegria, 2013; Lo, Cheng, & Howell, 2014; Ta, Juon, Gielen, Steinwachs, & Duggan, 2008).

Barriers to mental health services include socioeconomic status, societal stigma, cultural issues, language constraints, and patients’ fear and mistrust of treatment (McGuire, Alegria, Cook, Wells, & Zaslavsky, 2006). One of the major obstacles to mental health services utilization is limited resources such as lack of insurance, high medical cost, low income, and living in rural area (Dinwiddie et al., 2013). People with limited resources also incur an increased risk of developing mental disorders. For example, people who live in poverty may be subjected to adverse experiences such as housing instability, violence and food insecurity, which can lead to mental disorders (Chow, Jaffee, & Snowden, 2003; Ismayilova, Gaveras, Blum, To-Camier, & Nanema, 2016; Petering, Rhoades, Winetrobe, Dent, & Rice, 2017).

To address barriers to health services access and unmet needs among people with limited resources or disabilities, Medicaid was launched and has played a significant role in reimbursement of mental health services for people of all ages whose incomes are insufficient to pay for their healthcare (Centers for Medicare & Medicaid Services, 2017). Despite this national attention to disparity reduction, racial/ethnic disparities in access to mental health services persist (Alegria, Vallas, & Pumariega, 2010; Cook, Trinh, Li, Hou, & Progovac, 2016).

A substantial body of literature on racial/ethnic disparities in the utilization of mental health services exist but research studies have predominantly focused on Blacks and Hispanics (Manseau & Case, 2014; Horvitz-Lennon, McGuire, Alegria, & Frank, 2009; Samnaliev, McGovern, & Clark, 2009; Wu, Erickson, Piette, & Balkrishnan, 2012). Due to small population sizes on the mainland United States (US), Asians, and Native Hawaiians and Pacific Islanders (NHPIs) have frequently been ignored or collapsed into other racial/ethnic groups. Few studies have addressed disparities in inpatient and/or outpatient utilization of mental health services among Asians or NHPIs, and none have focused specifically on Medicaid beneficiaries in these ethnic groups (Augsberger, Yeung, Dougher, & Hahm, 2015; Sentell et al., 2013; Shin, 2009; Ta et al., 2008). As a result, studying utilization patterns of these minority ethnic groups in the Medicaid population is an important step to enhance the Medicaid program, particularly given that Asians and NHPIs are the fastest growing subpopulations in the US (Hoeffel, Rastogi, Kim, & Shahid, 2012).

Studies on racial/ethnic disparities in mental health services utilization have often focused on children and adolescents (Coker et al., 2009; Kataoka, Zhang, & Wells, 2002; Malhotra et al., 2015) or young adults like college students (Broman, 2012). Therefore, studies on adult populations with limited resources are needed, especially for understudied populations, such as Asian and NHPI. These ethnic groups tend to have significant language barriers compared to other races/ethnicities (Kim & Keefe, 2010), and have unique cultures and histories such as historical trauma due to colonialism (Evans-Campbell, 2008). In Asian cultures, mental disorders are often considered to be caused by evil spirits or lack of harmony in emotions (Gorman, 2015). Many Asians believe that family issues, medical illness and cultural differences cause mental disorders and they are unwilling to speak openly about their condition (Jimenez et al., 2013). This behavior may be due to the high stigmatization associated with mental disorders and the need to preserve a sense of dignity and social network (Kim, Sherman, Ko, & Taylor, 2006).

Similarly, Pacific Islanders believe that illness can occur from “ancestral-wrongness to the spirit” or physical, mental and spiritual disharmony, and that healing should occur holistically to achieve balance in all three areas (Wong, Taoka, Kuartei, Demei, & Soaladaob, 2004). NHPIs often seek the services of traditional healing practitioners and may be unwilling to tell their physicians that they are seeing a traditional healer for fear of alienating or angering their physicians (Mau, 2010).

To our knowledge, there are no published studies investigating how various types of mental healthcare utilization among Medicaid adult beneficiaries differ by race/ethnicity that include Asian and NHPI subpopulations. Although the urban-rural difference in health services utilization in mental disorders has been explored (Breslau, Marshall, Pincus, & Brown, 2014; Kiani, Tyrer, Hodgson, Berkin, & Bhaumik, 2013), health issues should be investigated in geographically defined regions to address etiologies and clinical implications specific to the regions (Posner & Goodman, 2014). With an increasing state role in regulating Medicaid expansion programs (Weil & Scheppach, 2010), it is essential for a state government to examine how mental health services are employed among its Medicaid beneficiaries. This concern applies in a state such as Hawaii, which has the most diverse understudied ethnic subpopulations in the US.

In this study, we examined racial/ethnic differences in inpatient (IP), outpatient (OP), and emergency department (ED) utilization for mental disorders using Hawaii Medicaid data on adults. We hypothesized that, stratified by service type, there are significant racial/ethnic differences in health service utilization and we expected that Asians and/or NHPIs receive lower rates of utilization compared to Whites. The identification of vulnerable racial/ethnic subpopulations will help the state government and healthcare professionals explore the specific barriers to access to mental health care in the state and develop integrated interventions or adjust healthcare policies specific to the disadvantaged racial/ethnic groups.

Methods

Data Source and Study Population

The Hawaii 2010 Medicaid claims data were utilized. Inclusion criteria were Hawaii residents, aged between 21 and 64 years (inclusive), and enrolled in Medicaid for all twelve months in 2010. Pregnant women were excluded as they might use health services for mental disorders related to pregnancy (e.g., antepartum or postpartum depression). The final dataset included 73,200 Medicaid beneficiaries. The study was approved by the University of Hawaii Institutional Review Board.

Outcome Measurements

Mental disorders were defined based on Clinical Classification Software (CCS) (Elixhauser, Steiner, & Palmer, 2015) developed by the Healthcare Cost and Utilization Project. CCS includes 285 mutually exclusive diagnosis categories based on the International Classification of Diseases, 9th Revision (ICD-9) codes. A patient was identified as using services for mental disorders if an ICD-9 code relevant to CCS 650–670 appeared on the patient’s claims record as either a principal or secondary diagnosis (Appendix 1).

The outcome variables were the number of IP, OP, and ED visits related to any mental disorder. An IP visit was defined as having a claim in the ‘Inpatient’ file. To assess the number of OP visits, we used the ‘Other Therapy’ file which contains a variety of service types such as outpatient, physician, home health, lab and X-ray, drug, dental, and hospice. An OP visit was defined as a claim for a physician, outpatient hospital, or psychiatric service in the file. An ED visit was defined using revenue center codes for ED claims from both the ‘Inpatient’ and ‘Other Therapy’ files. For each utilization type, the number of visits was defined as the total number of claims for the service in 2010. Multiple claim records for the same patient on the same date were considered as one single visit.

Race/Ethnicity

The primary racial/ethnic groups among Medicaid beneficiaries with mental disorders in Hawaii are White, Asian, and NHPI. Other small racial/ethnic groups in Hawaii (i.e., Hispanic, 5.1%; Black, 1.7%; American Indian/Alaskan Native, 0.2%; other, 0.1%) were combined into an ‘Other’ group, resulting in four racial/ethnic groups: White, Asian, NHPI, and Other. The final data included 21,727 Whites (29.7%), 20,872 Asians (28.5%), 25,414 NHPI (34.7%), and 5,187 Other (7.1%).

Control Variables

The control variables were sex, age, and residential area. Age was categorized into intervals of 21–44 and 45–64 years. Sex was categorized as male and female. Sex and age are factors known to affect mental health disorders (Afifi, 2007; Boyd et al., 2015; Gagne, Vasiliadis, & Preville, 2014; Le Querrec et al., 2015; Park et al., 2014; Riecher-Rossler, 2017). Additionally, we considered residential area as another control variable. Since all major acute care hospitals in Hawaii are on Oahu, we categorized residential area into Oahu (which is considered an urban area) and the other islands (which are considered rural areas) based on each beneficiary’s residential zip code. This categorization provided an indicator of ease of access to healthcare services. In the final data, 60.2% were aged 21–44 years, 58.6% were females, and 37.3% lived on Oahu.

Statistical Analyses

Descriptive statistics were reported using frequencies and percentages. Separate chi-square tests were conducted to assess bivariate associations between race/ethnicity and the control variables for each type (i.e., utilized or not) of utilization of mental health services. To address racial/ethnic differences in outcomes (i.e., number of visits for each health service), four statistical models for count data were considered: Poisson regression, negative binomial regression, zero-inflated Poisson regression, and zero-inflated negative binomial regression (Ismail & Zamani, 2013). Poisson regression models count data; negative binomial regression models count data with over-dispersion (i.e., presence of greater variability than expected from the Poisson model), and zero-inflated regressions account for excess zeros by estimating two regressions: one for modeling counts as the outcome (count model, either Poisson regression or negative binomial regression) and the other for modelling an excessive number of zero outcomes using logistic regression (zero model). Vuong’s tests (Vuong, 1989) and Clarke’s tests (Clarke, 2007) were used to compare the goodness of fit between pairs of the models and to select the best of the four models. All these models were adjusted for age, gender, and residential area. Due to the large sample size, a P-value less than 0.01 was considered statistically significant and should be cautiously interpreted. All the analyses were implemented in SAS version 9.4, and PROC COUNTREG was used to conduct the four models.

Results

A summary of the beneficiaries’ characteristics by race/ethnicity is presented in Table 1. Significant racial/ethnic differences were found for all the variables. There were more females in the Asian (60.3%) and NHPI (61.1%) groups than in the White group (54.5%) (P<0.001). Compared to Whites (51.9%), there were more beneficiaries aged 21–44 years in the NHPI (68.1%), Other (65.5%), and Asian (57.7%) groups. Asians (42.6%) or NHPIs (44.2%) were more likely to live on Oahu than Whites (24.8%) or Other (33.8%) (P<0.001).

Table 1.

Subject Characteristics by Race/Ethnicity, n (%)

Variable Total (n=73,200) White (n=21,727) Asian (n=20,872) NHPI (n=25,414) Other (n=5,187)
Age
 21–44 years 44,032 (60.2%) 11,269 (51.9%) 12,053 (57.7%) 17,312 (68.1%) 3,398 (65.5%)
 45–64 years 29,168 (39.8%) 10,458 (48.1%) 8,819 (42.3%) 8,102 (31.9%) 1,789 (34.5%)
Sex
 Male 30,277 (41.4%) 9,877 (45.5%) 8,288 (39.7%) 9,893 (38.9%) 2,219 (42.8%)
 Female 42,923 (58.6%) 11,850 (54.5%) 12,584 (60.3%) 15,521 (61.1%) 2,968 (57.2%)
Residential Area
 Other Island 45,928 (62.7%) 16,328 (75.2%) 11,987 (57.4%) 14,178 (55.8%) 3,435 (66.2%)
 Oahu 27,272 (37.3%) 5,399 (24.8%) 8,885 (42.6%) 11,236 (44.2%) 1,752 (33.8%)
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NHPI = Native Hawaiian and Pacific Islander.

All differences were significant (P<0.001) based on Chi-square test.

To identify the most frequently diagnosed mental disorders among the Medicaid beneficiaries, we computed the frequencies with each CCS for mental disorders by utilization type (Table 2). Regardless of type of mental disorder, there were 1,557 (2.1%) beneficiaries who utilized IP services, 15,709 (21.5%) beneficiaries who utilized OP services, and 3,722 (5.1%) beneficiaries who utilized ED services. The three most common mental disorders that Medicaid beneficiaries utilized IP services for were screening and history of mental health and substance abuse (SHMHSA) (0.9%), mood disorders (0.7%), and schizophrenia and other psychotic disorders (schizophrenia) (0.6%). Those for OP utilization were mood disorders (9.6%), anxiety disorders (6.7%), and schizophrenia (4.9%) and those for ED utilization were SHMHSA (2.4%), anxiety disorders (1.3%), and mood disorders (1.2%).

Table 2.

Mental Disorders Based on Clinical Classification Software by Health Service Type, n (%)

Clinical Classification Software (CCS) Health Service Type
Inpatient Outpatient Emergency
650: Adjustment disorders 40 (0.1%) 800 (1.1%) 90 (0.1%)
651: Anxiety disorders 266 (0.4%) 4,920 (6.7%) 948 (1.3%)
652: Attention-deficient, conduct, and disruptive behavior disorders 28 (0.04%) 485 (0.7%) 80 (0.1%)
653: Delirium, dementia, and amnestic and other cognitive disorders 76 (0.1%) 314 (0.4%) 69 (0.1%)
654: Developmental disorders 123 (0.2%) 833 (1.1%) 153 (0.2%)
655: Disorders usually diagnosed in infancy, childhood, or adolescence 11 (0.02%) 120 (0.2%) 18 (0.02%)
656: Impulse control disorders NR 70 (0.1%) NR
657: Mood disorders 496 (0.7%) 6,993 (9.6%) 914 (1.2%)
658: Personality disorders 67 (0.1%) 227 (0.3%) 73 (0.1%)
659: Schizophrenia and other psychotic disorders 429 (0.6%) 3,565 (4.9%) 690 (0.9%)
660: Alcohol-related disorders NR NR NR
661: Substance-related disorders 16 (0.02%) 165 (0.2%) 42 (0.1%)
662: Suicide and intentional self-inflicted injury 97 (0.1%) 222 (0.3%) 193 (0.3%)
663: Screening and history of mental health and substance abuse codes 690 (0.9%) 2,678 (3.7%) 1,760 (2.4%)
670: Miscellaneous disorders 43 (0.1%) 948 (1.3%) 75 (0.1%)
Any Mental Disorder 1,557 (2.1%) 15,709 (21.5%) 3,722 (5.1%)
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NR = Non-reportable due to small frequency (≤10).

% = Percentage of patients out of the total number of Medicaid beneficiaries (n=73,200).

The bold-faced numbers indicate the top three mental disorder groups for utilization of the respective health services among Medicaid beneficiaries.

Table 3 summarizes the frequency of beneficiaries who utilized health services for mental disorders at least one time. Regardless of the type of service, the utilization was higher among Whites compared to Asians or NHPIs: White 2.7% vs. Asian 1.9% and NHPI 1.9% for IP; White 27.5% vs. Asian 19.9% and NHPI 17.0% for OP; White 6.3% vs. Asian 3.6% and NHPI 5.0% for ED. In the bivariate associations, there were no significant differences between Oahu and the other islands in utilization by the types of mental health services.

Table 3.

Frequency and Percentage of Patients Who Used Mental Health Services, n (%)

Variable Number of Beneficiaries (n=73,200) Health Service Type
Inpatient (n=1,557) Outpatient (n=15,709) Emergency (n=3,722)
Age
 21–44 years 44,032 588 (1.3%) 7,551 (17.1%) 2,128 (4.8%)
 45–64 years 29,168 969 (3.3%) 8,158 (28.0%) 1,594 (5.5%)
Race/Ethnicity
 White 21,727 577 (2.7%) 5,970 (27.5%) 1,365 (6.3%)
 Asian 20,872 388 (1.9%) 4,160 (19.9%) 756 (3.6%)
 NHPI 25,414 487 (1.9%) 4,331 (17.0%) 1,264 (5.0%)
 Other 5,187 105 (2.0%) 1,248 (24.1%) 337 (6.5%)
Sex
 Male 30,277 740 (2.4%) 6,849 (22.6%) 1,728 (5.7%)
 Female 42,923 817 (1.9%) 8,860 (20.6%) 1,994 (4.6%)
Residential Area
 Other Island 45,928 955 (2.1%) 9,917 (21.6%) 2,306 (5.0%)
 Oahu 27,272 602 (2.2%) 5,792 (21.2%) 1,416 (5.2%)
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NHPI = Native Hawaiian and Pacific Islander.

% = Percentage of patients out of the total number of Medicaid beneficiaries in each category.

All differences were significant based on Chi-square tests (P-values<0.001) except residential area.

To investigate the effect of race/ethnicity on the number of visits for each type of service, we ran four regression models appropriate for count data adjusting for the control variables (Appendix 2). For all the types of services, zero-inflated negative binomial regressions were selected as the best model by goodness of fit criteria. The zero-inflated negative binomial results are presented in Table 4.

Table 4.

Zero-Negative Binomial Regression for the Utilization of Mental Health Services

Zero Model
 Count Model
Variable Parameter Estimate (SE) for Probability on Having Zero Utilization P-value OR (95% CI) for Having Non-Zero Utilization Parameter Estimate (SE) for Number of Utilization P-value Rate Ratio (95% CI) for Number of Utilization
Inpatient
Intercept −0.18 (0.30) 0.562 −3.09 (0.13) <0.001
Sex: Female vs. Male 0.70 (0.20) 0.001 0.50 (0.33–0.74) 0.03 (0.07) 0.666 NS
Age: 45–64 vs. 21–44 years −12.85 (393.58) 0.974 NS 0.08 (0.12) 0.495 NS
Race: Asian vs. White 0.01 (0.24) 0.982 NS −0.35 (0.09) <0.001 0.70 (0.59–0.83)
Race: NHPI vs. White 0.07 (0.22) 0.752 NS −0.23 (0.09) 0.007 0.79 (0.67–0.94)
Race: Other vs. White 0.30 (0.34) 0.374 NS −0.12 (0.14) 0.388 NS
Residential: Oahu vs. Other −0.14 (0.18) 0.436 NS 0.12 (0.07) 0.090 NS
Over-dispersion 15.39 (1.08) <0.001
Outpatient
Intercept −0.50 (0.12) <0.001 1.00 (0.05) <0.001
Sex: Female vs. Male −0.01 (0.04) 0.752 NS −0.20 (0.03) <0.001 0.82 (0.77–0.86)
Age: 45–64 vs. 21–44 years −0.90 (0.06) <0.001 2.46 (2.19–2.75) 0.33 (0.03) <0.001 1.39 (1.31–1.47)
Race: Asian vs. White 0.83 (0.07) <0.001 0.44 (0.38–0.50) 0.04 (0.04) 0.442 NS
Race: NHPI vs. White 0.94 (0.07) <0.001 0.39 (0.34–0.45) −0.14 (0.04) <0.001 0.87 (0.81–0.93)
Race: Other vs. White 0.26 (0.09) 0.004 0.77 (0.65–0.92) 0.01 (0.06) 0.793 NS
Residential: Oahu vs. Other −0.05 (0.04) 0.217 NS 0.09 (0.03) 0.003 1.09 (1.03–1.16)
Over-dispersion 6.17 (0.30) <0.001
Emergency
Intercept −2.00 (0.92) 0.029 −2.07 (0.12) <0.001
Sex: Female vs. Male 0.38 (0.21) 0.078 NS −0.18 (0.06) 0.003 0.84 (0.74–0.94)
Age: 45–64 vs. 21–44 years −0.56 (0.23) 0.015 NS −0.06 (0.07) 0.385 NS
Race: Asian vs. White 1.79 (0.68) 0.009 0.17 (0.04–0.64) −0.11 (0.10) 0.255 NS
Race: NHPI vs. White −0.15 (0.57) 0.797 NS −0.36 (0.08) <0.001 0.70 (0.60–0.82)
Race: Other vs. White 0.14 (0.66) 0.828 NS 0.07 (0.11) 0.528 NS
Residential: Oahu vs. Other 0.01 (0.22) 0.965 NS 0.10 (0.06) 0.088 NS
Over-dispersion 11.37 (1.20) <0.001
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NS = Not Significant in P−value<0.010.

NHPI = Native Hawaiian or Pacific Islander. SE = Standard Error. OR = Odds Ratio. CI = Confidence Interval.

Significant differences were found in IP utilization by sex and race/ethnicity. Females were less likely to use IP than males (Odds Ratio [OR]=0.50). Compared to Whites, we observed fewer IP visits by Asians and NHPIs (Asian: Rate Ratio [RR]=0.70; NHPI: RR=0.79).

Regarding OP utilization, significant disparities were found by sex, age, race/ethnicity, and residential area. Among the beneficiaries who used OP services at least one time, females had a lower rate of OP visits than males (RR=0.82). Compared to beneficiaries who were 21–44 years old, those 45–65 years of age were more likely to use OP services (OR=2.46); among the beneficiaries with at least one OP visit, older beneficiaries had a greater rate of OP visits compared to the younger beneficiaries (RR=1.39). Asians, NHPIs, and Others were less likely to use OP services (Asian: OR=0.44; NHPI: OR=0.39; Other: OR=0.77) compared to whites. The expected number of OP visits among NHPIs was lower compared to that of Whites (RR=0.87). The expected number of OP visits among other Hawaiian Islands residents was greater compared to the number of visits by those who lived on Oahu (RR=1.09).

Regarding ED utilization, significant differences were found by sex, age, and race/ethnicity. The expected number of ED visits was lower for females compared to males (RR=0.84). Older beneficiaries were more likely to use ED services (OR=1.75) than younger beneficiaries. Asians were less likely to use ED services than Whites (OR=0.17). The expected rate of ED visits among NHPIs was lower compared to that of Whites (RR=0.70).

Discussion

Employing Hawaii Medicaid data, we evaluated racial/ethnic differences in three types of health services utilization for mental disorders among adult beneficiaries. We found that the patterns of utilization of mental health services differed across race/ethnicity. Similar to the previous finding (Sentell et al., 2013), Asians or/and NHPIs were less likely to visit all three types of mental health services, compared to Whites.

The lower utilization rates of mental health services may not indicate that Asians and NHPIs have lower rates of mental disorders. The reported prevalence rates of mental disorders are inconsistent. Some studies using epidemiologic samples revealed lower rates of mental disorder for Asian-Americans (e.g., Huang et al., 2006; Breslau, Kendler, Su, Gaxiola-Aguilar, & Kessler, 2005), however, other studies reported similar or even higher prevalence rates of some mental disorders among NHPIs and Asians, compared to Whites. For example, Asian-Americans were more likely to be diagnosed general anxiety disorder (Asnaani, Richey, Dimaite, Hinton, & Hofmann, 2010), and mood and drug use disorders were comparable to other races/ethnicities (Huang et al., 2006). A National Latino and Asian American Study utilizing a diagnostic interview methodology showed a lifetime prevalence of 17.3% and 12-month prevalence of 9.2% for any mental disorder (Takeuchi et al., 2007). These rates were higher than Whites and other races in the National Comorbidity Study (Breslau et al., 2005). Asian-American college students were more likely to attempt suicide relative to Whites (Kisch, Leino, & Silverman, 2005). NHPI adults showed higher rates of depression and suicide attempts (Asian & Pacific Islander American Health Forum, 2010). Additionally, NHPIs showed increased prevalence rate of conduct disorder that are associated with higher rates of adult substance abuse and antisocial behaviors (Sakai, Risk, Tanaka, & Price, 2008). Therefore, further studies are needed to establish the prevalence rates of mental disorders, and to evaluate whether the low use of mental health services by Asian and NHPI reflects an unmet need.

The lower utilization rates of mental health services among Asians and NHPIs might be affected by various factors such as language barriers, low health literacy, cultural beliefs and lifestyle (e.g., pursuing acupuncture, alternative medicine, or self-diagnosis through the internet), migration, acculturation and socioeconomic status (Cook, John, Chung, Tseng, & Lee, 2017; Dinwiddie et al., 2013; Tran, Do, & Baccaglini, 2016). Limited English proficiency or low health literacy are major reasons preventing people with mental disorders from seeking mental health services (Cook et al., 2016; Kim & Keefe, 2010). Cultural differences can be another factor impeding utilization among these racial/ethnic groups. People with mental disorders in these ethnic groups may also suffer from societal stigma (e.g., stereotypes, prejudice, and discrimination). Hence, to avoid exposure of personal weakness or to maintain social and familial harmony, Asians and NHPIs with mental disorders might not seek health services or treatment (Gorman, 2015; Kramer, Kwong, Lee, & Chung, 2002; Riolo, Nguyen, Greden, & King, 2005).

In the adjusted models, residents of Oahu (considered urban) were more likely to visit OP compared to those living on the other islands. The rural residents might rely more on primary care providers than urban residents might. A study that examined the utilization of mental health care among Medicaid beneficiaries in Maine also found that urban residents have higher rates of mental health services utilization than rural residents (Lambert & Agger, 1995). Further study is needed to elucidate the reason for this regional disparity.

Clinical Implications

Our study has several clinical implications. Healthcare programs may require a language assistance plan to ensure equity of access to mental health services among minorities who have difficulty with the English language. Approximately eight million Asians and NHPIs speak their native tongue at home, and approximately half of them have reported that they do not speak English fluently (Association of Asian Pacific Community Health Organizations, 2010). Immigrants with limited English proficiency find it challenging to utilize mental health services because they have a difficult time making doctor appointments, acquiring knowledge about diseases, and understanding instructions by health professionals (Cook et al., 2016). For effective communication between healthcare providers and patients with mental disorders, the inclusion of language assistance services will help reduce racial/ethnic disparities in the utilization of mental health services and deliver high-quality care.

Another implication is that cultural differences may need to be considered when developing mental healthcare programs or interventions. Some mental disorders, known as culture-bound syndromes, are related to specific cultures (Tseng, 2006). Culture can also influence healthcare decision-making with respect to the types of services patients seek (e.g., traditional healer) and their coping styles (U.S. Department of Health and Human Services, 2001). A culturally competent intervention program – one with awareness of patients’ cultural identities and ability to engage in cultural knowledge, resources, and flexibility in health services – may be helpful for patients with mental disorders (Carlton et al., 2011). For instance, a culturally tailored program, Hūlili, includes Hawaiian cultural values in a program devised to engage participants in their learning and treatment (i.e., engagement), advance their insight into their lives (i.e., meaningfulness), and increase their positive capacity to cope with adversity (i.e., resilience) and wellness (Carlton et al., 2011). Evidence has revealed how Native Hawaiian adolescents with severe mental disorders can benefit from participation in the Hūlili program (Carlton et al., 2011). This culturally competent approach can be extended to NHPI and Asian adults with mental disorders.

In addition, health professionals may need to integrate alternative health care approaches (e.g., massage, psycho-spiritual methods, and herbal medicine) into mental health care plans. The need for integrative psychotherapy has been recognized among healthcare professionals in recent decades. Integrative mental health endorses the importance of good relationships between patients and healthcare professionals and assimilates alternative approaches (e.g., herbal and nutritional medicine, dietary modification and meditation) with standard interventions (e.g., pharmacologic treatments, psychotherapy) (Lake, Helgason, & Sarris, 2012). The development of psychiatric community centers that practice integrative mental health care may support minority patients with mental disorders in increasing adherence to their mental health care plan and improving their quality of life.

Furthermore, a strategic plan to provide mental health services to Asian and NHPI adults with mental disorders is essential. State governments should develop integrated services to allow Medicaid beneficiaries easy access to mental health services. This is feasible by integrating mental health services into primary health care and developing informal community mental health services (Word Health Organization, 2007). Primary care physicians or community counselors should include basic mental health screening tests as part of the regular checkup and provide informational brochures in various languages to increase health literacy and reduce cultural stigma toward mental disorder. This effort may help identify several early signs of mental disorders and reduce the stigma about mental disorder, as primary health care is not associated with any specific health conditions.

Limitations

Our study has several limitations. First, similar to other studies using claims databases, physician coding or data entry errors in ICD 9 codes can lead to misclassifications and yield unreliable estimates in utilization rates of mental health services.

Second, race/ethnicity was categorized based on the race variable available in the Medicaid data where major Asian ethnic groups in Hawaii (e.g., Chinese, Japanese, and Filipino) were merged into a single Asian group. Since Hawaii is one of the most racially/ethnically diverse states, more detailed race/ethnicity information could derive more in depth and judicious comparisons.

Third, our study population was restricted to Hawaii Medicaid beneficiaries fully enrolled in 2010. Therefore, our results may not be applicable to those beneficiaries who enrolled for only some part of the year.

Fourth, our findings may not be generalizable to other Asian or NHPI populations in mainland communities. The characteristics of Asians in Hawaii may differ from those of Asians in the US mainland, which could affect utilization patterns of mental health services.

Fifth, we could not explore other potential associations with the different types of utilization of health services other than age, sex, and residential area that were available in the database. Other important factors, such as social support and homelessness, could possibly account for the observed differences.

Sixth, our inclusion criteria led to list-wise deletion of cases with missing values. Although the overall missing rate was relatively small (<5%), this may limit the generalizability of the results.

Seventh, the utilization rate among Whites may not be the optimal level for mental health utilization, and the differences from Whites by race/ethnicity may not necessarily indicate racial/ethnic disparities or unmet need. Those who underutilize may simply be mentally healthy or tend to rely more on alternative or traditional treatment/medicine. Without accounting for alternative/complementary approaches, we could not determine whether the observed racial/ethnic differences truly indicate disparities or unmet need. Nevertheless, our findings from the count model examining the rates of visits may imply racial/ethnic disparities in unmet need because compared to Whites, Asians or/and NHPIs visited mental health services fewer times even among the beneficiaries who used health services. Further studies are needed to address this issue.

Despite these limitations, our study expands knowledge about racial/ethnic differences in use of three types of mental health services among the Medicaid population. Asians are the fastest-growing minorities in the US (Hoeffel et al., 2012). Exploring the utilization of different types of mental health services in the state with the largest Asian populations may not only provide the federal government a future direction for mental health services policy but also encourage the state government to develop effective interventions and management approaches to support Medicaid beneficiaries with mental disorders.

Conclusion

The study observed racial/ethnic differences in the patterns of utilization of different types of mental health services. The variation in health services utilization accentuates the importance of considering race/ethnicity in mental health management. Race/ethnicity may need to be considered in developing clinical interventions or integrative health programs that aim to reduce mental health disparities and improve the quality of life for individuals with mental disorders.

Acknowledgments

The authors thank Dr. Jill Miyamura of the Hawaii Health Information Corporation for providing access to the Hawaii Medicaid database. The authors thank Ms. Munirih Taafaki for reviewing and editing the manuscript.

Funding

This project was partially supported by the National Institutes of Health (NIH) under Grant U54MD007584, U54MD007601, P20GM103466, and U54GM104944. The content is solely the authors’ responsibility and does not necessarily reflect the official opinion of the NIH.

Appendix 1. Clinical Classification Software (CCS) Codes Defining Mental Disorders

CCS Description ICD-9 Codes
650 Adjustment disorders 309.0, 309.1, 309.22–309.24, 309.28–309.29, 309.3–309.4, 309.82, 309.83, 309.89, 309.9
651 Anxiety disorders 293.84, 300.0x, 300.10, 300.2x, 300.3, 300.5, 300.89, 300.9, 308.x, 309.81, 313.0, 313.1, 313.2x, 313.3, 313.82, 313.83
652 Attention-deficient, conduct, and disruptive behavior disorders 312.0–312.2, 312.4, 312.8–312.9, 313.81, 314.x
653 Delirium, dementia, and amnestic and other cognitive disorders 290.x, 293.0, 293.1, 294.x, 310.x, 331.0, 331.1x, 331.2, 331.82, 797
654 Developmental disorders 307.0, 307.9, 315.00, 315.x, 317–319, V40.0, V40.1
655 Disorders usually diagnosed in infancy, childhood, or adolescence 299.x, 307.2x, 307.3, 307.6, 307.7, 309.21, 313.23, 313.89, 313.9
656 Impulse control disorders 312.3x
657 Mood disorders 293.83, 296.x, 300.4, 311
658 Personality disorders 301.x
659 Schizophrenia and other psychotic disorders 293.81, 293.82, 295.x, 297.x, 298.x
660 Alcohol-related disorders 291.x, 303.x, 305.0x, 760.71, 980.0
661 Substance-related disorders 292.x, 304.x, 305.2–305.9, 648.3x, 655.5x, 760.72–760.73, 760.75, 779.5, 965.0x, V65.42
662 Suicide and intention self-inflicted injury E950-E959, V62.84
663 Screening and history of mental health and substance abuse codes 305.1x, 333.92, 357.5, 425.5, 535.3x, 571.0–571.3, 790.3, V11.x, V15.4x, V15.82, V62.85, V66.3, V70.1-V70.2, V71.01, V71.02, V71.09, V79.x
670 Miscellaneous disorders 293.89, 293.9, 300.11–300.16, 300.19, 300.6–300.7, 300.81, 300.82, 302.1–302.9, 306.x, 307.1, 307.4–307.5, 307.8, 310.1, 316, 648.4x, V40.2-V40.3, V40.9, V67.3
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Appendix 2. Model Fit Statistics between Models

Model Model Fit Statistic
-2LogLike AIC BIC
Inpatient
Poisson 19,320 19,334 19,399
Negative Binomial 17,032 17,048 17,122
Zero-inflated Poisson 17,150 17,179 17,308
Zero-inflated Negative Binomial 16,964 16,993 17,131
Vuong’s test ZINB > NB > ZIP > P
Clarke’s test ZINB > ZIP > NB > P
Outpatient
Poisson 541,100 541,115 541,179
Negative Binomial 164,882 164,897 164,971
Zero-inflated Poisson 260,312 260,340 260,469
Zero-inflated Negative Binomial 163,952 163,982 164,120
Vuong’s test ZINB > NB > ZIP > P
Clarke’s test ZINB > ZIP > NB > P
Emergency
Poisson 45,256 45,269 45,334
Negative Binomial 36,440 36,455 36,529
Zero-inflated Poisson 37,932 37,960 38,089
Zero-inflated Negative Binomial 36,384 36,414 36,552
Vuong’s test ZINB > NB > ZIP > P
Clarke’s test ZIP > NB > ZINB > P
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-2LogLike = (−2)* log likelihood. AIC = Akaike’s Information Criterion. BIC = Bayesian Information Criterion. P = Poisson regression. NB = negative binomial regression. ZIP = zero-inflated Poisson regression. ZINB = zero-inflated negative binomial regression.

In Vuong’s or Clarke’s test results, ‘=‘ indicates no significant difference; ‘>‘ indicates that the model on the left is significantly better than the model on the right. The bold was the selected model by criterion or test.

Footnotes

Disclosure Statement

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

  1. Association of Asian Pacific Community Health Organizations. (2010). Asian Americans, Native Hawaiians, Pacific Islanders (AA & NHOPI) in the United States Retrieved from http://www.aapcho.org/wp/wp-content/uploads/2012/02/AAPCHO_FactSheet-AANHOPI_in_US_2010.pdf
  2. Afifi M (2007). Gender differences in mental health. Singapore Medical Journal, 48(5), 385–391. Retrived from https://www.ncbi.nlm.nih.gov/pubmed/17453094 [PubMed] [Google Scholar]
  3. Alegria M, Vallas M, & Pumariega AJ (2010). Racial and ethnic disparities in pediatric mental health. Child and Adolescent Psychiatric Clinics of North America, 19(4), 759–774. doi: 10.1016/j.chc.2010.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Asian & Pacific Islander American Health Forum. (2010). Native Hawaiian and Pacific Islander health disparities Retrieved from www.apiahf.org/sites/default/files/NHPI_Report08a_2010.pdf
  5. Asnaani A, Richey JA, Dimaite R, Hinton DE, & Hofmann SG (2010). A cross-ethnic cimparison of lifetime prevalcence rates of anxiety disorders. The Journalof Nervous and Mental Diseases, 198(8), 551–555. doi: 10.1097/NMD.0b013e318ea169f [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Augsberger A, Yeung A, Dougher M, & Hahm HC (2015). Factors influencing the underutilization of mental health services among Asian American women with a history of depression and suicide. BioMed Central Health Service Research, 15, 542. doi: 10.1186/s12913-015-1191-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Boyd A, Van de Velde S, Vilagut G, de Graaf R, O’Neill S, Florescu S, … Kovess-Masfety V (2015). Gender differences in mental disorders and suicidality in Europe: results from a large cross-sectional population-based study. J Affect Disord, 173, 245–254. [DOI] [PubMed] [Google Scholar]
  8. Breslau J, Kendler KS, Su M, Gaxiola-Aguilar S, & Kessler RC (2005). Lifetime risk and persistence of psychiatric disorders across ethnic groups in the United States. Psychological Medicine, 35(3), 317–327. Retrived from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2748985/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Breslau J, Marshall GN, Pincus HA, & Brown RA (2014). Are mental disorders more common in urban than rural areas of the United States? Journal of Psychiatric Research, 56, 50–55. doi: 10.1016/j.jpsychires.2014.05.004 [DOI] [PubMed] [Google Scholar]
  10. Broman CL (2012). Race differences in the receipt of mental health services among young adults. Psychological Service, 9(1), 38–48. doi: 10.1037/a0027089 [DOI] [PubMed] [Google Scholar]
  11. Carlton B, Goebert DA, Bell CK, Horton M, Else IRN, Marcinowski M, & Yamada L (2011). Integration cultural values into mental health treatment. Hulili: Multidisciplinary Research on Hawaiian Well-Being, 7, 159–183. Retrived from http://www.ksbe.edu/_assets/spi/hulili/hulili_vol_7/7_Hulili_2011_Vol7_Carlton_et_al.pdf [Google Scholar]
  12. Centers for Medicare & Medicaid Services. (2017). Medicaid & CHIP: Strengthening coverage, improving health Retrieved from https://www.medicaid.gov/medicaid/program-information/downloads/accomplishments-report.pdf
  13. Chow JC, Jaffee K, & Snowden L (2003). Racial/ethnic disparities in the use of mental health services in poverty areas. American Journal of Public Health, 93(5), 792–797. Retrived from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447841/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Clarke KA (2007). A simple distribution-free test for nonnested model selection. Political Analysis, 15(3), 347–363. Retrived from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.102.4099&rep=rep1&type=pdf [Google Scholar]
  15. Coker TR, Elliott MN, Kataoka S, Schwebel DC, Mrug S, Grunbaum JA, … Schuster MA (2009). Racial/Ethnic disparities in the mental health care utilization of fifth grade children. Academic Pediatrics, 9(2), 89–96. doi: 10.1016/j.acap.2008.11.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cook BL, Trinh N-H, Li Z, Hou SS-Y, & Progovac AM (2016). Trends in racial-ethnic disparities in access to mental health care, 2004–2012. Psychiatric Services, 68(1): 9–16. doi: 10.1176/appi.ps.201500453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Cook WK, John I, Chung C, Tseng W, & Lee JP (2017). Medicaid expansion and healthcare access: lessons from Asian American and Pacific Islander experiences in California. Journal of Immigrant and Minority Health 19(4), 995–999. doi: 10.1007/s10903-016-0496-x [DOI] [PubMed] [Google Scholar]
  18. Cooper-Patrick L, Gallo JJ, Gonzales JJ, Vu HT, Powe NR, Nelson C, & Ford DE (1999). Race, gender, and partnership in the patient-physician relationship. Journal of the American Medical Association, 282(6), 583–589. Retived from https://jamanetwork.com/journals/jama/fullarticle/191132 [DOI] [PubMed] [Google Scholar]
  19. Dinwiddie GY, Gaskin DJ, Chan KS, Norrington J, & McCleary R (2013). Residential segregation, geographic proximity and type of services used: evidence for racial/ethnic disparities in mental health. Social Science & Medicine, 80, 67–75. doi: 10.1016/j.socscimed.2012.11.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Elixhauser A, Steiner C, & Palmer L (2015). Clincal Clssifications Software (CCS) Retrieved from http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp
  21. Evans-Campbell T (2008). Historical trauma in American Indian/Native Alaska communities: a multilevel framework for exploring impacts on individuals, families, and communities. Journal of Interpersonal Violence, 23(3), 316–338. doi: 10.1177/0886260507312290 [DOI] [PubMed] [Google Scholar]
  22. Freedenthal S (2007). Racial disparities in mental health service use by adolescents who thought about or attempted suicide. Suicide and Life-Threatening Behavior, 37(1), 22–34. doi: 10.1521/suli.2007.37.1.22 [DOI] [PubMed] [Google Scholar]
  23. Gagne S, Vasiliadis HM, & Preville M (2014). Gender differences in general and specialty outpatient mental health service use for depression. BioMed Central Psychiatry, 14, 135. doi: 10.1186/1471-244X-14-135 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Gorman A (2015, May 22). For many Asian Americans, depression is an unfamiliar word Retrieved from https://www.washingtonpost.com/national/health-science/for-many-asian-americans-depression-is-an-unfamiliar-word/2015/05/22/f186d140-e8f1-11e4-9767-6276fc9b0ada_story.html
  25. Hoeffel EM, Rastogi S, Kim MO, & Shahid H (2012). The Asian Population: 2010 Retrieved from https://www.census.gov/prod/cen2010/briefs/c2010br-11.pdf
  26. Horvitz-Lennon M, McGuire TG, Alegria M, & Frank RG (2009). Racial and ethnic disparities in the treatment of a Medicaid population with schizophrenia. Health Service Research, 44(6), 2106–2122. doi: 10.1111/j.1475-6773.2009.01041.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Huang B, Grant BF, Dawson DA, Stinson FS, Chou SP, Saha TD, Goldstein RB, Simth SM, Ruan WJ & Pickering RP (2006). Race-ethnicity and the prevalence and co-occurrence of diagnositc and statistical manual of mental disoders, fourth edition, alcohol and drug use disordes and axis I and II disorders: United States, 2001 to 2002. Comprehensive Psychiatry, 47(4), 252–257. doi: 10.1016/j.comppsych.2005.11.001 [DOI] [PubMed] [Google Scholar]
  28. Institute of Medicine Committee. (2003). Unequal treatment: confronting racial and ethnic disparities in health care: confronting racial and ethnic disparities in health care. In Smedley BD, Stith AY, & Nelson AR (Eds.). Washington (DC): National Academies Press (US) Retrived from https://www.ncbi.nlm.nih.gov/pubmed/25032386 [PubMed] [Google Scholar]
  29. Ismail N, & Zamani H (2013). Estimation of claim count data using negative binomial, generalized Poisson, zero-inflated negative binomial and zero-inflated generalized Poisson regression models. Casualty Actuarial Society E-Forum Retrived from https://www.casact.org/pubs/forum/13spforum/Ismail%20Zamani.pdf
  30. Ismayilova L, Gaveras E, Blum A, To-Camier A, & Nanema R (2016). Maltreatment and mental health outcomes among ultra-poor children in Burkina Faso: a latent class analysis. Public Library of Science One, 11(10), e0164790. doi: 10.1371/journal.pone.0164790 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jimenez DE, Cook B, Bartels SJ, & Alegria M (2013). Disparities in mental health service use of racial and ethnic minority elderly adults. Journal of the American Geriatrics Society, 61(1), 18–25. doi: 10.1111/jgs.12063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jimenez DE, Alegria M, Chen C-N, Chan D & Laderman M (2010). Prevalence of psychiatric illness among ethnic minority elderly. Journal of the American Geriatrics Society, 58(2), 256–264. doi: 10.1111/j.1532-5415.2009.02685.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kataoka SH, Zhang L, & Wells KB (2002). Unmet need for mental health care among U.S. children: variation by ethnicity and insurance status. The American Journal of Psychiatry, 159(9), 1548–1555. doi: 10.1176/appi.ajp.159.9.1548 [DOI] [PubMed] [Google Scholar]
  34. Kiani R, Tyrer F, Hodgson A, Berkin N, & Bhaumik S (2013). Urban-rural differences in the nature and prevalence of mental ill-health in adults with intellectual disabilities. Journal of Intellectual Disability Research, 57(2), 119–127. doi: 10.1111/j.1365-2788.2011.01523.x [DOI] [PubMed] [Google Scholar]
  35. Kim HS, Sherman DK, Ko D, & Taylor SE (2006). Pursuit of comfort and pursuit of harmony: culture, relationships, and social support seeking. Personality and Socical Psychology Bulliten, 32(12),1595–1607. doi: 10.1177/0146167206291991 [DOI] [PubMed] [Google Scholar]
  36. Kim W, & Keefe RH (2010). Barriers to healthcare among Asian Americans. Socical Work in Public Health, 25(3), 286–295. doi: 10.1080/19371910903240704 [DOI] [PubMed] [Google Scholar]
  37. Kisch J, Leino EV, & Silverman MM (2005). Aspects of suicidal behavior, depression, and treatment in college students: results from the spring 2000 national college health assessment survey. Suicide and Life-Threatening Behavior, 35(1), 3–13. do: 10.1521/suli.35.1.3.59263 [DOI] [PubMed] [Google Scholar]
  38. Kramer EJ, Kwong K, Lee E, & Chung H (2002). Cultural factors influencing the mental health of Asian Americans. Western Journal of Medicine, 176(4), 227–231. Retrived from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1071736/ [PMC free article] [PubMed] [Google Scholar]
  39. Lake J, Helgason C, & Sarris J (2012). Integrative Mental Health (IMH): paradigm, research, and clinical practice. Explore (NY), 8(1), 50–57. doi: 10.1016/j.explore.2011.10.001 [DOI] [PubMed] [Google Scholar]
  40. Lambert D, & Agger MS (1995). Access of rural AFDC Medicaid beneficiaries to mental health services. Health Care Financing Review, 17(1), 133–145. Retrived from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193566/ [PMC free article] [PubMed] [Google Scholar]
  41. Le Querrec F, Bounes V, Mestre ML, Azema O, Longeaux N, & Gallart JC (2015). Sex and age differences in ED patients with mental and behavioral disorders due to psychoactive substance use. The American Journal of Emergency Medicine, 33(11), 1612–1616. doi: 10.1016/j.ajem.2015.06.068 [DOI] [PubMed] [Google Scholar]
  42. Lo CC, Cheng TC, & Howell RJ (2014). Access to and utilization of health services as pathway to racial disparities in serious mental illness. Community Mental Health Journal, 50(3), 251–257. doi: 10.1007/s10597-013-9593-7 [DOI] [PubMed] [Google Scholar]
  43. Malhotra K, Shim R, Baltrus P, Heiman HJ, Adekeye O, & Rust G (2015). Racial/ethnic disparities in mental health service utilization among youth participating in negative externalizing behaviors. Ethnicity and Disease, 25(2), 123–129. Retrived from https://www.ethndis.org/edonline/index.php/ethndis/article/view/117/81 [PubMed] [Google Scholar]
  44. Manseau M & Case BG (2014). Racial-ethnic disparities in outpatient mental health visits to U.S. physicians, 1993–2008. Psychiatric Services, 65(1), 59–67. doi: 10.1176/appi.ps.201200528 [DOI] [PubMed] [Google Scholar]
  45. Mau M (2010). Health and health care of Native Hawaiian and other Pacific Islander older adults. eCampus Geriatrics Eds: Periyakoil VJ, Stannford CA. Retrived from http://www.healthalt.org/uploads/2/3/7/5/23750643/native_hawaiian_other_pacific_islander_older_adults.pdf [Google Scholar]
  46. McGuire TG, Alegria M, Cook BL, Wells KB, & Zaslavsky AM (2006). Implementing the Institute of Medicine definition of disparities: an application to mental health care. Health Services Research, 41(5), 1979–2005. doi: 10.1111/j.1475-6773.2006.00583.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. National Institute of Mental Health. (2016a). Any mental illness (AMI) among U.S. adults Retrieved from https://www.nimh.nih.gov/health/statistics/prevalence/any-mental-illness-ami-among-us-adults.shtml
  48. National Institute of Mental Health. (2016b). Use of mental health services and treatment among adults Retrieved from https://www.nimh.nih.gov/health/statistics/prevalence/use-of-mental-health-services-and-treatment-among-adults.shtml
  49. Park JE, Lee JY, Jeon HJ, Han KH, Sohn JH, Sung SJ, & Cho MJ (2014). Age-related differences in the influence of major mental disorders on suicidality: a Korean nationwide community sample. Journal of Affective Disorder, 162, 96–101. doi: 10.1016/j.jad.2014.03.012 [DOI] [PubMed] [Google Scholar]
  50. Petering R, Rhoades H, Winetrobe H, Dent D, & Rice E (2017). Violence, trauma, mental health, and substance use among homeless youth Juggalos. Child Psychiatry and Human Development 48(4), 642–650. doi: 10.1007/s10578-016-0689-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Posner SF, & Goodman RA (2014). Multimorbidity at the local level: implications and research directions. Mayo Clinic Proceedings, 89(10), 1321–1323. doi: 10.1016/j.mayocp.2014.08.007 [DOI] [PubMed] [Google Scholar]
  52. Riecher-Rossler A (2017). Sex and gender differences in mental disorders. Lancet Psychiatry, 4(1), 8–9. doi: 10.1016/S2215-0366(16)30348-0 [DOI] [PubMed] [Google Scholar]
  53. Riolo SA, Nguyen TA, Greden JF, & King CA (2005). Prevalence of depression by race/ethnicity: findings from the National Health and Nutrition Examination Survey III. American Journal of Public Health, 95(6), 998–1000. doi: 10.2105/AJPH.2004.047225 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Sakai JT, Risk NK, Tanaka CA, & Price RK (2008). Conduct disorder among Asians and Native Hawaiian/Pacific Islanders in the USA. Psycholgoical Medicine, 38(7), 1013–1025. doi: 10.1017/S0033291707001316 [DOI] [PubMed] [Google Scholar]
  55. Samnaliev M, McGovern MP, & Clark RE (2009). Racial/ethnic disparities in mental health treatment in six Medicaid programs. Journal of Health Care for the Poor and Underserved, 20(1), 165–176. doi: 10.1353/hpu.0.0125 [DOI] [PubMed] [Google Scholar]
  56. Sentell T, Unick GJ, Ahn HJ, Braun KL, Miyamura J, & Shumway M (2013). Illness severity and psychiatric hospitalization rates among Asian Americans and Pacific Islanders. Psychiatric Services, 64(11), 1095–1102. doi: 10.1176/appi.ps.201200408 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Shin JK (2009). Inpatient stays of Asian patients with psychiatric diagnoses in New York City. Issues in Mental Health Nursing, 30(2), 112–121. doi: 10.1080/01612840802597937 [DOI] [PubMed] [Google Scholar]
  58. Ta VM, Juon HS, Gielen AC, Steinwachs D, & Duggan A (2008). Disparities in use of mental health and substance abuse services by Asian and Native Hawaiian/other Pacific Islander women. The Journal of Behavioral Health Services and Research, 35(1), 20–36. doi: 10.1007/s11414-007-9078-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Takeuchi DT, Zane N, Hong S, Chae DH, Gong F, Gee GC et al. (2007). Immigration-related factors and mental disorders among Asian Amerians. American Jounral of Publich Health, 97(1), 84–90. doi: 10.2105/AJPH.2006.088401 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Tran H, Do V, & Baccaglini L (2016). Health care access, utilization, and management in adult Chinese, Koreans, and Vietnamese with cardiovascular disease and hypertension. Journal of Racial and Ethnic Health Disparities, 3(2), 340–348. doi: 10.1007/s40615-015-0155-2 [DOI] [PubMed] [Google Scholar]
  61. Tseung WS (2006). From peculair psychiatric disorders through culture-bound syndromes to culture-related specific syndromes 43(4), 554–576. doi: 10.1177/1363461506070781 [DOI] [PubMed] [Google Scholar]
  62. U.S. Department of Health and Human Services. (2001). Mental health: culture, race, and ethnicity-a supplement to mental health:a report of the surgeon general Rockville, MD: Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK44243/ [PubMed] [Google Scholar]
  63. Vuong QH (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 57(2), 307–333. Retrived from http://www.jstor.org/stable/1912557 [Google Scholar]
  64. Wang PS, Lane M, Olfson M, Pincus HA, Wells KB, & Kessler RC (2005). Twelve-month use of mental health services in the United States: results from the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 629–640. doi: 10.1001/archpsyc.62.6.629 [DOI] [PubMed] [Google Scholar]
  65. Weil A, & Scheppach R (2010). New roles for states in health reform implementation. Health Affairs, 29(6), 1178–1182. doi: 10.1377/hlthaff.2010.0448 [DOI] [PubMed] [Google Scholar]
  66. World Health Organization. (2007) The optimal mix of services for mental health. Mental Health Policy, Planning, & Service Development Information Sheet, Sheet2 Retrieved from http://www.who.int/mental_health/policy/services/2_Optimal%20Mix%20of%20Services_Infosheet.pdf
  67. Wong V, Taoka S, Kuartei S, Demei Y, & Soaladaob F (2004). Cancer in the Republic of Palau (Belau). Pacific Health Dialog, 11(2), 64–69. Retrived from https://www.researchgate.net/publication/7486092_Cancer_in_the_Republic_of_Palau_Belau [PubMed] [Google Scholar]
  68. Wu CH, Erickson SR, Piette JD, & Balkrishnan R (2012). Mental health resource utilization and health care costs associated with race and comorbid anxiety among Medicaid enrollees with major depressive disorder. Journal of the National Medical Association, 104(1–2), 78–88. doi: 10.1016/S0027-9684(15)30121-8 [DOI] [PubMed] [Google Scholar]

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