- Email: [email protected]
Variable reduction for past year alcohol and drug use in unmet need for mental health services among US adults
Journal of Affective Disorders 256 (2019) 110–116
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Variable reduction for past year alcohol and drug use in unmet need for mental health services among US adults Nianyang Wanga, Youssoufou Ouedraogob, Jun Chua, Ying Liub, Kesheng Wangb, Xin Xiec,
T
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a
Department of Health Services Administration, School of Public Health, University of Maryland, College Park, MD 20742, USA Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA c Department of Economics and Finance, College of Business and Technology, East Tennessee State University, PO Box 70686, 227 Sam Wilson Hall, Johnson City, TN 37614, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Mental health services Unmet need Alcohol use Drug use Cluster analysis Data mining
Background: No previous study has focused on the inter-relationship among alcohol and drug use variables in the past year. This study aimed to classify the past year alcohol and drug use variables and investigate the selected variables in past year alcohol and drug use with the unmet need for mental health services among US adults. Methods: Data came from the 2015 National Survey on Drug Use and Health (NSDUH). Oblique principal component cluster analysis (OPCCA) was used to classify 37 variables on alcohol and drug use in the past year into disjoint clusters. Weighted multiple logistic regression analysis was used to examine the associations of selected variables with the unmet need. Results: 37 alcohol and drug use variables were divided into 7 clusters. The variable with the lowest 1-R2 ratio (R2 is the squared correlation) from each cluster was selected as follows: tobacco use, pain reliever use, tranquilizer use, stimulant use, zolpidem products use, illicit drug and alcohol use, and benzodiazepine tranquilizers misuse. Multiple logistic regression analysis showed that pain reliever use (OR = 1.33, 95% CI = 1.17–1.50), tranquilizer use (OR = 2.49, 95% CI = 2.16–2.86), stimulant use (OR = 1.22, 95% CI = 1.01–1.47), and illicit drug and alcohol use (OR = 1.54, 95% CI = 1.34–1.77) revealed positive associations with the unmet need for mental health services. Conclusion: This is the first study using OPCCA to reduce the dominations of alcohol and drug use; several alcohol and drug use variables in the past year were associated with unmet need of mental health services.
1. Introduction In the United States (US), over 40 million people (about 20% of all adults) are affected by a mental health condition (Mental Health America, 2017). As mental health service use has been on the rise, the disparities among groups of people and their use of mental health services has also increased. The 2009 to 2011 National Survey on Drug Use and Health (NSDUH) reported that over half of adults with unmet mental health care cite affordability as a reason (Substance Abuse and Mental Health Services Administration, 2013). The number of adults that perceived unmet mental health care was 10 million in 2011 (Alang, 2015) and 11 million people reported an unmet mental health care in 2013 (Substance Abuse and Mental Health Services Administration, 2014). Previous studies have shown that social demographic factors such as gender (Roll et al., 2013), race (Barksdale et al., 2010; Derr, 2016; Lee et al., 2014; Roll et al., 2013; Tran and Ponce,
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2017), and age (Choi et al., 2014; Pottick et al., 2014) were associated with usage of mental health service (Alang, 2015; Golub et al., 2013). In addition, having low income and no coverage insurance were associated with increased odds of unmet mental health services (Keeler et al., 1988; Roll et al., 2013; Walker et al., 2015; Wu and Schlenger, 2004). Furthermore, adults with poor self-reported health and past-year mental health treatment use experienced higher odds of perceived unmet treatment (Choi et al., 2015; Roll et al., 2013). Both alcohol and drug use were significantly associated with mental problems in previous studies. For example, smoking and nicotine use has been found to be positively correlated with mental health problems such as depression and anxiety (Brown et al., 2015; Emre et al., 2014; Steinberg et al., 2015). Furthermore, alcohol and drug use were more prevalent in adults with mental problems than those without (Choi et al., 2015; Fink et al., 2015). One recent study found that homeless adults with drug abuse were significantly more likely than
Corresponding author. E-mail address: [email protected] (X. Xie).
https://doi.org/10.1016/j.jad.2019.05.069 Received 28 February 2019; Received in revised form 24 May 2019; Accepted 28 May 2019 Available online 28 May 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 256 (2019) 110–116
N. Wang, et al.
those without drug abuse to have suicidal ideation (Lee et al., 2017). However, few studies have focused on the association among alcohol, drugs, and the use of mental health services. The analysis of a community survey in New Zealand showed that people with alcohol abuse and/or dependence were more likely to have used mental health services (Strack et al., 1989); whereas the data from the National Comorbidity Survey in the US revealed that the majority of adults with a alcohol disorder in the past year did not seek help (Wu et al., 1999). Another study using the 2002 NSDUH data showed that 22.4% of substance abuse patients used mental health services (Mojtabai, 2005). However, there are significant differences in rates of use of psychiatric medication between those with and without alcohol dependence (Edlund and Harris, 2006); meanwhile there are racial differences for individuals with regards to frequency of drinking to intoxication and heavy episodic drinking as related to mental health service utilization (Minich et al., 2009). Alcohol and drug use variables may be correlated (Baz-Lomba et al., 2016; Button et al., 2007; Papazisis et al., 2018; Wang et al., 2018); while there is concurrent use of alcohol with other drugs, and comorbid alcohol use disorder (AUD) and drug use disorders (DUDs) (Borges et al., 2015; Cherpitel et al.,2007; Goldstein et al., 2012; Hakkarainen and Metso, 2009; Saha et al., 2018). However, no study has focused on the inter-relationship among alcohol and drug use and mental health service use. Variable cluster analysis, as implemented using PROC VARCLUS in SAS which borrows from factor analysis and combines with the methods from hierarchical clustering, is an alternative to traditional multivariate methods for variable reduction such as principal components and factor analysis (Aggarwal and Kosian, 2015; Nelson, 2001; Sanche and Lonergan, 2006). The PROC VARCLUS statement has been used in cluster analysis of dietary patterns (James, 2009), patterns of metabolic risk measures (Udo et al., 2014), Bern Psychopathology Scale (Lang et al., 2015), and measurements of patients' experiences of integrated care (Walker et al., 2016); however, no study has been performed to cluster alcohol and drug use variables. The aims of this study are to examine the factor structure of 37 variables pertaining to alcohol and drug use in past years by using the oblique principal component cluster analysis and to detect the associations of social-demographic and selected alcohol and drug use variables in the past year with unmet need of mental health services among US adults by using weighted multiple logistic regression models.
Table 1 Prevalence of unmet need of mental health services (%) within each group of demographic variables. Variable Gender Male Female Age group 18–25 years 26–34 years 35–49 years 50+ years Race White AA Other Hispanic Marital status Married Widowed/ divorced/ separated Never been married Income Less than $20,000 $20,000–$49,999 $50,000–$74,999 $75,000 or More Education ≤High school graduate >High school graduate Any health insurance Yes No Overall
Total (N)
Unmet
Prevalence (%)
95%CI
p-value
19,707 23,666
782 1936
3.0 6.2
2.7–3.3 5.8–6.7
<0.0001
14,465 9053 11,129 8726
1172 631 679 236
8.3 6.7 5.3 2.5
7.7–9.0 5.9–7.5 4.8–5.9 2.1–2.8
<0.0001
25,943 5470 4359 7601
1881 247 266 324
5.2 3.7 4.0 3.5
4.8–5.7 3.0–4.3 3.0–5.1 2.9–4.1
<0.0001
17,981 6433
742 457
3.0 5.4
2.7–3.3 4.7–6.1
<0.0001
18,959
1519
7.5
6.8–8.1
9642 13,947 6750 13,034
800 864 421 633
7.0 4.8 4.6 3.4
6.4–7.7 4.2–5.4 3.8–5.4 3.1–3.8
<0.0001
17,945
946
3.8
3.5–4.1
<0.0001
25,428
1772
5.3
4.8–5.7
37,736 5312 43,373
2365 333 2718
4.5 5.8 4.7
4.2–4.8 5.0–6.7 4.4–5.0
0.0007
Abbreviations: AA = African American, CI = Confidence interval, p-value is based on χ2 test.
2.2. Measures 2.2.1. Dependent variable The dependent variable, unmet need of mental health service use, is defined as feeling a perceived need for mental health treatment/counseling that was not received. This is often referred to as "unmet need."
2. Methods
2.2.2. Demographic variables Gender was self-reported as either male or female. Age was classified into four categories: 18–25, 26–34, 35–49, and 50+ years old. Race consisted of four subgroups: White, African American (AA), Hispanic, and Others. The four categories of annual income were less than $20,000, $20,000–$49,999, $50,000–$74,999 and $75,000 or more. Education indicated whether the participant had above a high school degree or not. There were three categories of marriage status: married; widowed/divorced/separated, and never been married. Demographic variables are listed in Table 1.
2.1. Sample This cross-sectional study used the 2015 NSDUH data. The NSDUH is a nationally representative survey that is conducted annually to assess the prevalence and correlates of drug use in the US for the population ages 12 and older (www.samhsa.gov). This survey series provides information about the use of illicit drugs, alcohol, and tobacco among members of the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several modules of questions pertaining to mental health issues such as anxiety disorder, depression, psychological distress, serious mental illness, and suicide variables. The NSDUH survey sample is completed based on a 50-state design with independent, multistage area probability sample for each of the 50 states and the District of Columbia. Data collection is performed by computer-assisted personal interviewing and audio computer-assisted self-interviewing methods. Details of the survey design and data collection methods of NSDUH 2015 were published elsewhere (Center for Behavioral Health Statistics and Quality, 2016). The total number of individuals in the 2015 NSDUH dataset is 57,147. The current analysis was restricted to 43,373 adult participants (aged 18 or above). There was an Institutional Review Board exemption due to secondary data analysis.
2.2.3. Insurance status A person has health insurance coverage if he/she has been covered by private insurance or other types of health insurance (Table 1). 2.2.4. Alcohol and drug use variables Thirty-seven alcohol and drug use variables including variable names and descriptions are listed in Table 3. All these variables are binary variables (yes, no) including alcohol, marijuana, cocaine, illicit drug, smoking and tobacco use, etc. 2.2.5. Alcohol use disorder and drug use disorders AUD and DUDs in the past year were considered as covariates 111
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Fig. 1. Oblique principal component cluster analysis of 37 alcohol and drug use variables.
clusters are as strongly correlated as possible with each other and as uncorrelated as possible with the variables in the other clusters (Aggarwal and Kosian, 2015; Nelson, 2001; Sanche and Lonergan, 2006). Considering the categorical variables, the polychoric correlation is applied to ordinal data (Lee et al., 1995). Higher squared correlation (R2) values with its own cluster, lower R2 values with next closest cluster, and lower 1-R2 ratios (the ratio of 1-R2 for a variable's own cluster to 1-R2 for its nearest cluster) indicate a good fit of the respective item.
pertaining to alcohol dependence or abuse and illicit drug abuse or dependence in the past year. Illicit drug or alcohol abuse or dependence in the past year was defined by being dependent on any illicit drug or alcohol in the past year or illicit drug or alcohol abuse in the past year. Illicit drug abuse is defined as abusing any of the following substances: marijuana, hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives. Illicit drug dependence was classified as being dependent on any of these following substances: marijuana, hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives.
1 − R2ratio = 2.3. Statistical analysis All the analyses were conducted with SAS (Statistical Analysis System, Version 9.4) (SAS Institute, Cary, NC, USA).
1 − R2own cluster 1 − R2next closest cluster
2.3.3. Weighted logistic regression analysis The surveylogistic procedure in SAS v.9.4 was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between potential factors and unmet need for mental health services. In model one, the univariate logistic regression was used to examine the role of potential risk factors in unmet need for mental health services. In model two, the multiple logistic regression was used to adjust for all potential risk factors of unmet need for mental health services (full model).
2.3.1. Descriptive statistics and prevalence The surveyfreq procedure in SAS v.9.4 was used to weight and estimate population proportions/prevalence. Then surveymeans procedure in SAS v.9.4 was used to estimate the overall prevalence while surveyfreq procedure in SAS v.9.4 was used to determine the prevalence of unmet need of mental health services in demographic variables. The Chi-square test was used to compare the prevalence of unmet need of mental health services across demographic variables.
3. Results 3.1. Prevalence
2.3.2. Variable cluster analysis using PROC VARCLUS The VARCLUS procedure in SAS v.9.4 was used to divide a set of numeric variables into disjoint clusters. The variables in the same
Table 1 details the prevalence of unmet need for mental health 112
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health services (OR = 1.89, 95% CI = 1.58–2.26 and OR = 2.12, 95% CI = 1.62–2.77, respectively).
Table 2 Oblique principal component cluster analysis of 37 alcohol and drug use variables. Cluster
Items (n)a
Proportion explained (%)b
4. Discussion
1 2 3 4 5 6 7
6 2 4 7 7 2 9
85.56 99.99 80.08 91.78 82.59 99.95 77.40
In the present study, we examined the prevalence of unmet need for mental health services using the 2015 NSDUH population-wide survey. Furthermore, we performed data mining using oblique principal component cluster analysis - The VARCLUS procedure in SAS v.9.4 was used to reduce the dimensions of alcohol and drug use variables in the past year. Using VARCLUS, the 37 alcohol and drug use variables were divided into seven clusters, then the variable with the lowest 1-R2 ratio from each cluster was selected for weighted multiple logistic regression analysis. Our results showed pain reliever use, tranquilizer use, stimulant use, illicit drug and alcohol use, alcohol dependence or abuse, and illicit drug dependence or abuse were positively associated with the unmet need for mental health services (p < 0.05). The overall prevalence of unmet mental health needs in our study was 4.7%, which is slightly higher than that in 2011 (4.6%) (Alang, 2015). The present study further added that there are differences in its prevalence across gender, age and race groups, marital status, education, and income levels (Table 1). For example, our results were consistent with previous studies by showing that gender, age group, race, family income, education, marital status, general health status were all associated with unmet mental health service need (Alang, 2015; Choi et al., 2015; Golub et al., 2013; Roll et al., 2013; Walker et al., 2015). In fact, using the 2011 NSDUH data, Alang (2015) found that women had higher rates of cost-related difficulties of obtaining mental health treatment compared to men, and in addition social stigma and structural barrier were reported more in Black adults versus White adults and in rural versus metropolitan citizens. Tran and Ponce (2017) have reported that men, Latinos, Asians, young people, older adults, people with less education, uninsured adults, and individuals with limited English proficiency were more likely to have an unmet need for mental health services. Having low income and no insurance coverage were associated with an increased probability of not using mental health services (Keeler et al., 1988; Roll et al., 2013; Tran and Ponce, 2017; Walker et al., 2015; Wu and Schlenger, 2004). For example, previous studies showed that the uninsured had more difficulties in obtaining mental health services (Walker et al., 2015; Wu and Schlenger 2004). In the present study, we found that individuals without any insurance had a higher prevalence of unmet mental health services than those with insurance (Table 1). Our findings further added that individuals without any insurance were more likely to have lower odds of unmet mental health services in our univariate logistic regression analysis; however, after adjusting for other variables, there is no association of insurance with unmet mental health services (Table 4). A study of the 2010 National Health Interview Survey (NHIS) showed that uninsured patients were about four times more likely to report unmet mental health care compared with private insurance (Roll et al., 2013), while our study produced more moderate results (OR = 0.82, 95%CI = 0.71–0.95 for people with private insurance comparing with uninsured). This discrepancy could be potentially explained by the fact that our study excluded children while Roll et al. (2013) included children. Interestingly, insured adults perceived higher levels of social stigma and minimization of mental disorders versus uninsured adults (Alang, 2015). A previous study using the 2004 to 2010 NSDUH data showed that veterans had an untreated serious psychological distress rate of 8% which is a similar rate compared to the nonveteran sample (Golub et al., 2013). Alcohol and drug use are risk factors for mental health problems such as depression and anxiety (Boden and Fergusson, 2011; Brown et al., 2015; Choi et al., 2015; Emre et al., 2014; Fink et al., 2015; Steinberg et al., 2015). For example, Boden and Fergusson (2011) examined the literature on the associations between alcohol use and major depression and concluded that increasing involvement with
a b
Number of variables in each cluster. Proportion of cluster variance explained by the included items (%).
services in 2015. The overall prevalence of unmet mental health services was 4.7% (3.0% for males and 6.2% for females). The young age groups revealed a higher prevalence of unmet mental health services. The prevalence of unmet mental health services was 5.2%, 3.7%, 3.5%, and 4.0% in Whites, AAs, Hispanics, and other ace (p < 0.0001), respectively. Those who are never married (7.5%) have a higher prevalence of unmet need for mental health services than the married (3.0%) and the windowed/divorced/separated (5.4%). Table 1 also indicates that the prevalence of unmet mental health services decreases as the income increases; whereas people with higher education also have a higher prevalence. In addition, the prevalence in no insurance was higher than that of insurance (5.8% vs. 4.5%). 3.2. Variable cluster analysis using PROC VARCLUS Thirty-seven alcohol and drug use variables were clustered into 7 clusters (6, 2, 4, 7, 7, 2 and 9 variables for each cluster, respectively) (Fig. 1 and Table 2). Fig. 1 displays 7 clusters while Table 2 shows the number of items in each cluster and proportion of cluster variance explained by the included items (%). About 85.15% of the total variation in the data could be accounted by the 7 clusters. Table 3 describes the variables in each cluster and the corresponding 1-R2 ratio values. Small values of the 1-R2 ratio indicate that the variable has a strong correlation with its own cluster and a weak correlation with the other clusters (Table 3). From each cluster, the variable with the lowest 1-R2 ratio was selected for further analysis. The following seven variables were selected: tobacco use, pain reliever use, tranquilizer use, stimulant use, zolpidem products use, illicit drug and alcohol use, and benzodiazepine tranquilizers misuse. 3.3. Logistic regression analysis Table 4 presents the results from both univariate and multiple logistic analyses. By using univariate analysis, all the factors were associated with the unmet need for mental health services (p < 0.05). After adjusting for all other variables, being female (OR = 2.30, 95% CI = 2.02–2.61), never married or widowed/divorced/separated (OR = 1.38, 95% CI = 1.15–1.65; OR = 1.39, 95% CI = 1.16–1.66, respectively), having above a high school education (OR = 1.43, 95% CI = 1.26–1.62), and lower income (OR = 1.85, 95% CI = 1.59–2.16; OR = 1.40, 95% CI = 1.19–1.64; OR = 1.43, 95% CI = 1.18–1.72, respectively) people had statistically significant rate of unmet need for mental health services. Regarding alcohol and drug use variables in the past year, pain reliever use (OR = 1.33, 95% CI = 1.17–1.50), tranquilizer use (OR = 2.49, 95% CI = 2.16–2.86), stimulant use (OR = 1.22, 95% CI = 1.01–1.47), and illicit drug and alcohol use (OR = 1.54, 95% CI = 1.34–1.77) revealed significantly positive associations with the unmet need for mental health services. In addition, having both alcohol dependence or abuse and illicit drug abuse or dependence in the past year were significantly associated with the unmet need for mental 113
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Table 3 R2 measures for alcohol and drug use in the past year. Cluster
Variable
Variable description (past year)
R2 with own cluster
R2 with next closest cluster
1 - R2 ratio
Cluster 1
ALCYR MRJYR COCYR HALLUCYR ILLANDALC ILLORALC TRQNMYR TRBENZPYMU CIGYR CGRYR SMKLSSYR TOBYR STMANYYR PSYCHYR AMMEPDAPYU AMPHETAPYU METHPDAPYU AMMEPDPYMU AMPHETPYMU TRQANYYR PSYANYYR ALPRPDAPYU LORAPDAPYU CLONPDAPYU DIAZPDAPYU MUSRLXAPYU SEDANYYR ZOLPPDAPYU PNRANYYR OXYCNANYYR PNRNMYR HYDCPDAPYU OXYCPDAPYU OXCNANYYR2 TRAMPDAPYU MORPPDAPYU OXYCPDPYMU
Alcohol use Marijuana use Cocaine use Hallucinogen use Illicit drug and alcohol use Illicit drug or alcohol use Tranquilizer misuse Benzodiazepine tranquilizers misuse Cigarette use Cigar use Smokeless tobacco use Any tobacco use Any stimulants use Any psychotherapeutics misuse Any amphetamine or methylphenidate products use Any amphetamine use Any methylphenidate products use Any amphetamine or methylphenidate products misuse Amphetamine misuse Any tranquilizers use Any psychotherapeutics use Any alprazolam products use Any lorazepam products use Any clonazepam products use Any diazepam products use Any muscle relaxants use Any sedatives use Any zolpidem products use Any pain reliever use OxyContin use Pain reliever misuse Any hydrocodone products use Any oxycodone products use Any OxyContin use Any tramadol products use Any morphine products Use Oxycodone products misuse
0.6405 0.8120 0.7413 0.7563 1.0000 1.0000 0.9999 0.9999 0.6572 0.6884 0.6355 1.0000 1.0000 0.7517 1.0000 0.9422 0.7198 0.9982 0.9923 1.0000 1.0000 0.6997 0.6516 0.6981 0.7337 0.6282 0.9995 0.9995 1.0000 0.8230 0.6572 0.6691 0.9296 0.8245 0.4513 0.5388 0.8152
0.1386 0.3321 0.3945 0.3851 0.5914 0.9981 0.6994 0.6932 0.2336 0.2299 0.0869 0.2757 0.2795 0.9981 0.3092 0.3241 0.2733 0.5957 0.5941 0.9981 1.2589 0.7400 0.2715 0.4538 0.4420 0.2469 0.3869 0.3595 0.3465 0.3211 0.4953 0.3432 0.3500 0.3211 0.3239 0.3236 0.5096
0.4173 0.2815 0.4273 0.3963 0.0000 0.0000 0.0002 0.0002 0.4473 0.4046 0.3991 0.0000 0.0000 1.1638 0.0000 0.0856 0.3856 0.0045 0.0190 0.0000 1.0000 1.1552 0.4782 0.5527 0.4773 0.4937 0.0008 0.0007 0.0000 0.2607 0.6791 0.5038 0.1083 0.2585 0.8117 0.6819 0.3768
Cluster 2 Cluster 3
Cluster 4
Cluster 5
Cluster 6 Cluster 7
Abbreviations: R2 = The squared correlation.
cluster analysis in VARCLUS and identified 7 clusters (Tables 2 and 3). Therefore, this study is the first attempt to use VARCLUS in SAS v.9.4 for data mining in the correlated alcohol and drug use variables. Our results further added that pain reliever use, tranquilizer use, stimulant use, and illicit drug and alcohol use had significantly positive associations with the unmet need for mental health services (Table 4). Our results provide useful insight for policy makers in making decisions regarding health insurance coverage in procedures covered with AUD and DUD treatments in the US. Furthermore, these findings highlight a need for interventions to target alcohol and drug users. In addition, it also calls for the reevaluation of AUD and DUD treatment components, and whether substance abuse treatments should include mental health screening and treatments for mental health conditions as a major component of the AUD and DUD treatment. Additionally, results from our study can provide strong supports to coordinated and managed cares that integrate mental health screening and treatment as part of the AUD and DUD treatment. Coordinated and managed care can effectively organize patients with both overall health care management, including mental health screenings and referrals, and substance abuse treatments such as wellness checkups. Future research should focus on the types of mental health services that patients with AUD and DUD desire; and also the effectiveness of having care coordinators from managed care programs for AUD and DUD treatments, such as improving patients’ overall treatment progress, reduce avoidable emergency department visits, and lower overall health care expenditures. Another recommendation is to have substance abuse treatment professionals take additional training in recognizing unmet mental
alcohol increases risk of depression. However, few studies have focused on the association of the use of alcohol and drug with mental health services. For example, Strack et al. (1989) analyzed a community survey data in New Zealand and found people with alcohol abuse and/ or dependence were more likely to have used mental health services than the population at large; however, 39% of those with an alcohol disorder had never used any form of mental health service. The present study using a large national survey data in the US showed that alcohol dependence or abuse and illicit drug abuse or dependence in the past year were significantly associated with the unmet need for mental health services (Table 4). Another previous US study found that comorbid psychiatric or alcohol disorders were stronger predictors of service utilization than a pure psychiatric or alcohol disorder; whereas the majority of the adults with a psychiatric or alcohol disorder in the past year did not seek help (Wu et al., 1999). In addition, a study using the 2002 NSDUH data showed that 22.4% of substance abuse patients used mental health services (Mojtabai, 2005). In the present study, we used AUD and DUDs including alcohol dependence or abuse and illicit drug abuse or dependence in the past year as covariates to investigate the variables in past year alcohol and drug use with the unmet need for mental health services. Alcohol and drug use may influence the unmet mental health services; whereas most of these alcohol and drug use variables may be inter-correlated. The VARCLUS procedure has not been applied to classify alcohol and drug use variables, even though it has been used in health sciences (James, 2009; Lang et al., 2015; Udo et al., 2014; Walker et al., 2016). The present study examined the factor structure of 37 variables on alcohol and drug use in the past years by using the oblique principal component
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Table 4 Univariate and multiple weighted logistic regression analyses. Variable
Crude OR
Gender (ref = male) Female 2.13 Age group (ref = 18–25 years) 26–34 years 0.77 35–49 years 0.61 50+ years 0.26 Race (ref = whites) AA 0.69 Other 0.77 Hispanic 0.66 Marital status (ref = married) Widowed/divorced/separated 1.77 Never married 2.63 Education (ref= ≤ high school) > high school 1.43 Income (ref =≥ $75,000) Less than $20,000 2.14 $20,000–$49,999 1.41 $50,000 - $74,999 1.40 Any health insurance Yes 1.31 Alcohol dependence or abuse (ABODALC) Yes 3.60 Illicit drug dependence or abuse (UDPYILL) Yes 6.24 Tobacco use (TOBYR) Yes 1.91 Pain reliever use (PNRANYYR) Yes 2.10 Tranquilizer use (TRQANYYR) Yes 3.84 Stimulant use (STMANYYR) Yes 3.33 Any zolpidem products use (ZOLPPDAPYU) Yes 2.06 Illicit drug and alcohol use (ILLANDALC) Yes 3.57 Benzodiazepine tranquilizers misuse (TRBENZPYMU) Yes 6.61
95% CI
p-value
Adjusted OR
95% CI
p-value
1.91–2.38
<0.0001
2.30
2.02–2.61
<0.0001
0.66–0.89 0.53–0.70 0.22–0.31
0.0003 <0.0001 <0.0001
0.92 0.88 0.37
0.77–1.10 0.73–1.06 0.29–0.48
0.3603 0.1786 <0.0001
0.56–0.85 0.58–1.03 0.54–0.80
0.0005 0.0748 <0.0001
0.61 0.80 0.62
0.49–0.75 0.61–1.06 0.50–0.78
<0.0001 0.1251 <0.0001
1.51–2.06 2.27–3.05
<0.0001 <0.0001
1.38 1.39
1.15–1.65 1.16–1.66
0.0004 0.0004
1.27–1.61
<0.0001
1.43
1.26–1.62
<0.0001
1.86–2.45 1.21–1.65 1.17–1.67
<0.0001 <0.0001 0.0003
1.85 1.40 1.43
1.59–2.16 1.19–1.64 1.18–1.72
<0.0001 <0.0001 0.0002
1.12–1.54
0.001
1.11
0.93–1.32
0.2485
3.12–4.15
<0.0001
1.89
1.58–2.26
<0.0001
5.04–7.72
<0.0001
2.12
1.62–2.77
<0.0001
1.67–2.19
<0.0001
1.06
0.89–1.26
0.5269
1.87–2.35
<0.0001
1.33
1.17–1.50
<0.0001
3.38–4.38
<0.0001
2.49
2.16–2.86
<0.0001
2.85–3.88
<0.0001
1.22
1.01–1.47
0.0418
1.66–2.56
<0.0001
1.11
0.88–1.40
0.3658
3.22–3.95
<0.0001
1.54
1.34–1.77
<0.0001
5.35–8.17
<0.0001
0.97
0.75–1.26
0.82838
Abbreviations: AA = African American; OR = Odds ratio; CI = Confidence interval.
5. Conclusions
health needs. Early detection is a key mechanism to preventing mental health condition deterioration among vulnerable population, and additional health expenditures. With frequent interactions and lengthy durations of treatments, substance abuse treatment professions can act as the frontline of defense against unmet mental health needs. Therapists and nursing staffs with knowledge of mental health needs detection can identify patients who have unmet needs, and suggest mental health services referrals to patients who are in need. Several strengths exist in this study. Firstly, the 2015 NSDUH data is a nationally representative population-based survey with large sample size in the US, which allows us to adjust for numerous potential factors. Second, to the best of our knowledge, this is the first study to use oblique principal component cluster analysis to investigate the relationship among correlated alcohol and drug use variables in the past year and reduce the dominations of alcohol and drug use variables. Third, we provided the updated prevalence of unmet need for mental health services in the US. In addition, we adjusted for AUD and DUDs including illicit drug or alcohol abuse or dependence in the past year as co-variates and found several alcohol and drug use variables in the past year are more likely to have an unmet need of mental health services. There are several limitations in this study. First, the nature of crosssectional design is not appropriate to determine a temporal or causal relationship between potential factors and unmet mental health services. Second, self-reported data from interviews may be subjected to response bias.
Using the data from NSDUH 2015, we examined the prevalence of the unmet need for mental health services. Furthermore, we reduced the dimensions of alcohol and drug use variables in the past year from 37 to 7 variables. Using weighted multiple logistic regression models, our results showed that pain reliever use, tranquilizer use, stimulant use, and illicit drug and alcohol use in the past year were positively associated with increased odds of the unmet need. These findings highlighted the complex relationship among alcohol and drug use variables and the complex effects of these factors on the unmet need for mental health services.
Funding No funding source is given for the present paper.
CRediT authorship contribution statement Nianyang Wang: Conceptualization, Formal analysis, Writing original draft. Youssoufou Ouedraogo: Writing - review & editing. Jun Chu: Writing - review & editing, Methodology. Ying Liu: Conceptualization, Data curation, Writing - review & editing. Kesheng Wang: Writing - original draft. Xin Xie: Conceptualization, Writing original draft. 115
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Declaration of Competing Interest
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The authors declare that they have no conflict of interest. Acknowledgments The authors would like to thank Substance Abuse and Mental Health Services Administration, US for providing data from 2015 National Survey on Drug Use and Health. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.05.069. References Aggarwal, V., Kosian, S., 2015. Feature selection and dimension reduction techniques in SAS. NESUG 38 (4), 293–299. Alang, S.M., 2015. Sociodemographic disparities associated with perceived causes of unmet need for mental health care. Psychiatr. Rehabil. J. 38, 293–299. Barksdale, C.L., Azur, M., Leaf, P.J., 2010. Differences in mental health service sector utilization among African American and Caucasian youth entering systems of care programs. J. Behav. Health Serv. Res. 37 (3), 363–373. Baz-Lomba, J.A., Salvatore, S., Gracia-Lor, E., Bade, R., Castiglioni, S., Castrignanò, E., Causanilles, A., Hernandez, F., Kasprzyk-Hordern, B., Kinyua, J., McCall, A.K., van Nuijs, A., Ort, C., Plósz, B.G., Ramin, P., Reid, M., Rousis, N.I., Ryu, Y., de Voogt, P., Bramness, J., Thomas, K., 2016. Comparison of pharmaceutical, illicit drug, alcohol, nicotine and caffeine levels in wastewater with sale, seizure and consumption data for 8 European cities. BMC Public Health 16 (1), 1035. Boden, J.M., Fergusson, D.M., 2011. Alcohol and depression. Addiction 106 (5), 906–914. Borges, G., Zemore, S., Orozco, R., Cherpitel, C.J., Ye, Y., Bond, J., Maxwell, J.C., Wallisch, L., 2015. Co-occurrence of alcohol, drug use, DSM-5 alcohol use disorder, and symptoms of drug use disorder on both sides of the U.S.-Mexico border. Alcohol Clin. Exp. Res. 39 (4), 679–687. Brown, C.H., Medoff, D., Dickerson, F.B., Fang, L.J., Lucksted, A., Goldberg, R.W., Kreyenbuhl, J., Himelhoch, S., Dixon, L.B., 2015. Factors influencing implementation of smoking cessation treatment within community mental health centers. J. Dual. Diagn. 11 (2), 145–150. Button, T.M., Rhee, S.H., Hewitt, J.K., Young, S.E., Corley, R.P., Stallings, M.C., 2007. The role of conduct disorder in explaining the comorbidity between alcohol and illicit drug dependence in adolescence. Drug Alcohol Depend. 87 (1), 46–53. Center for Behavioral Health Statistics and Quality,2016. 2015 National Survey on Drug Use and Health: sSummary of the effects of the 2015 NSDUH questionnaire redesign: iImplications for data users. Retrieved fromhttp://www.samhsa.gov/data/. Cherpitel, C.J., Robertson, M., Ye, Y., Borges, G., Bautista, C.F., Lown, A., Greenfield, T., Bond, J., 2007. Comorbidity for alcohol use disorders and drug use in Mexican-origin groups: comparison of data from National Alcohol Surveys in the U.S. and Mexico. Subst. Use Misuse. 42 (11), 1685–1703. Choi, N.G., Dinitto, D.M., Marti, C.N., 2015. Alcohol and other substance use, mental health treatment use, and perceived unmet treatment need: comparison between baby boomers and older adults. Am. J. Addict. 24 (4), 299–307. Choi, N.G., DiNitto, D.M., Marti, C.N., 2014. Treatment use, perceived need, and barriers to seeking treatment for substance abuse and mental health problems among older adults compared to younger adults. Drug Alcohol Depend. 145, 113–120. Derr, A.S., 2016. Mental health service use among immigrants in the United States: a systematic review. Psychiatr. Serv. 67 (3), 265–274. Edlund, M.J., Harris, K.M., 2006. Perceived effectiveness of medications among mental health service users with and without alcohol dependence. Psychiatr. Serv. 57 (5), 692–699. Emre, N., Topal, K., Bozkurt, N., Topaktas, E., 2014. Mental health screening and increased risk for anxiety and depression among treatment-seeking smokers. Tob. Induc. Dis. 12 (1), 20. Fink, D.S., Hu, R., Cerdá, M., Keyes, K.M., Marshall, B.D., Galea, S., Martins, S.S., 2015. Patterns of major depression and nonmedical use of prescription opioids in the United States. Drug Alcohol Depend. 153, 258–264. Goldstein, R.B., Dawson, D.A., Chou, S.P., Grant, B.F., 2012. Sex differences in prevalence and comorbidity of alcohol and drug use disorders: results from wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. J. Stud. Alcohol Drugs. 73 (6), 938–950. Golub, A., Vazan, P., Bennett, A.S., Liberty, H, J., 2013. Unmet need for treatment of substance use disorders and serious psychological distress among veterans: a nationwide analysis using the NSDUH. Mil. Med. 178 (1), 107–114.
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Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Variable reduction for past year alcohol and drug use in unmet need for mental health services among US adults Nianyang Wanga, Youssoufou Ouedraogob, Jun Chua, Ying Liub, Kesheng Wangb, Xin Xiec,
T
⁎
a
Department of Health Services Administration, School of Public Health, University of Maryland, College Park, MD 20742, USA Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA c Department of Economics and Finance, College of Business and Technology, East Tennessee State University, PO Box 70686, 227 Sam Wilson Hall, Johnson City, TN 37614, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Mental health services Unmet need Alcohol use Drug use Cluster analysis Data mining
Background: No previous study has focused on the inter-relationship among alcohol and drug use variables in the past year. This study aimed to classify the past year alcohol and drug use variables and investigate the selected variables in past year alcohol and drug use with the unmet need for mental health services among US adults. Methods: Data came from the 2015 National Survey on Drug Use and Health (NSDUH). Oblique principal component cluster analysis (OPCCA) was used to classify 37 variables on alcohol and drug use in the past year into disjoint clusters. Weighted multiple logistic regression analysis was used to examine the associations of selected variables with the unmet need. Results: 37 alcohol and drug use variables were divided into 7 clusters. The variable with the lowest 1-R2 ratio (R2 is the squared correlation) from each cluster was selected as follows: tobacco use, pain reliever use, tranquilizer use, stimulant use, zolpidem products use, illicit drug and alcohol use, and benzodiazepine tranquilizers misuse. Multiple logistic regression analysis showed that pain reliever use (OR = 1.33, 95% CI = 1.17–1.50), tranquilizer use (OR = 2.49, 95% CI = 2.16–2.86), stimulant use (OR = 1.22, 95% CI = 1.01–1.47), and illicit drug and alcohol use (OR = 1.54, 95% CI = 1.34–1.77) revealed positive associations with the unmet need for mental health services. Conclusion: This is the first study using OPCCA to reduce the dominations of alcohol and drug use; several alcohol and drug use variables in the past year were associated with unmet need of mental health services.
1. Introduction In the United States (US), over 40 million people (about 20% of all adults) are affected by a mental health condition (Mental Health America, 2017). As mental health service use has been on the rise, the disparities among groups of people and their use of mental health services has also increased. The 2009 to 2011 National Survey on Drug Use and Health (NSDUH) reported that over half of adults with unmet mental health care cite affordability as a reason (Substance Abuse and Mental Health Services Administration, 2013). The number of adults that perceived unmet mental health care was 10 million in 2011 (Alang, 2015) and 11 million people reported an unmet mental health care in 2013 (Substance Abuse and Mental Health Services Administration, 2014). Previous studies have shown that social demographic factors such as gender (Roll et al., 2013), race (Barksdale et al., 2010; Derr, 2016; Lee et al., 2014; Roll et al., 2013; Tran and Ponce,
⁎
2017), and age (Choi et al., 2014; Pottick et al., 2014) were associated with usage of mental health service (Alang, 2015; Golub et al., 2013). In addition, having low income and no coverage insurance were associated with increased odds of unmet mental health services (Keeler et al., 1988; Roll et al., 2013; Walker et al., 2015; Wu and Schlenger, 2004). Furthermore, adults with poor self-reported health and past-year mental health treatment use experienced higher odds of perceived unmet treatment (Choi et al., 2015; Roll et al., 2013). Both alcohol and drug use were significantly associated with mental problems in previous studies. For example, smoking and nicotine use has been found to be positively correlated with mental health problems such as depression and anxiety (Brown et al., 2015; Emre et al., 2014; Steinberg et al., 2015). Furthermore, alcohol and drug use were more prevalent in adults with mental problems than those without (Choi et al., 2015; Fink et al., 2015). One recent study found that homeless adults with drug abuse were significantly more likely than
Corresponding author. E-mail address: [email protected] (X. Xie).
https://doi.org/10.1016/j.jad.2019.05.069 Received 28 February 2019; Received in revised form 24 May 2019; Accepted 28 May 2019 Available online 28 May 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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those without drug abuse to have suicidal ideation (Lee et al., 2017). However, few studies have focused on the association among alcohol, drugs, and the use of mental health services. The analysis of a community survey in New Zealand showed that people with alcohol abuse and/or dependence were more likely to have used mental health services (Strack et al., 1989); whereas the data from the National Comorbidity Survey in the US revealed that the majority of adults with a alcohol disorder in the past year did not seek help (Wu et al., 1999). Another study using the 2002 NSDUH data showed that 22.4% of substance abuse patients used mental health services (Mojtabai, 2005). However, there are significant differences in rates of use of psychiatric medication between those with and without alcohol dependence (Edlund and Harris, 2006); meanwhile there are racial differences for individuals with regards to frequency of drinking to intoxication and heavy episodic drinking as related to mental health service utilization (Minich et al., 2009). Alcohol and drug use variables may be correlated (Baz-Lomba et al., 2016; Button et al., 2007; Papazisis et al., 2018; Wang et al., 2018); while there is concurrent use of alcohol with other drugs, and comorbid alcohol use disorder (AUD) and drug use disorders (DUDs) (Borges et al., 2015; Cherpitel et al.,2007; Goldstein et al., 2012; Hakkarainen and Metso, 2009; Saha et al., 2018). However, no study has focused on the inter-relationship among alcohol and drug use and mental health service use. Variable cluster analysis, as implemented using PROC VARCLUS in SAS which borrows from factor analysis and combines with the methods from hierarchical clustering, is an alternative to traditional multivariate methods for variable reduction such as principal components and factor analysis (Aggarwal and Kosian, 2015; Nelson, 2001; Sanche and Lonergan, 2006). The PROC VARCLUS statement has been used in cluster analysis of dietary patterns (James, 2009), patterns of metabolic risk measures (Udo et al., 2014), Bern Psychopathology Scale (Lang et al., 2015), and measurements of patients' experiences of integrated care (Walker et al., 2016); however, no study has been performed to cluster alcohol and drug use variables. The aims of this study are to examine the factor structure of 37 variables pertaining to alcohol and drug use in past years by using the oblique principal component cluster analysis and to detect the associations of social-demographic and selected alcohol and drug use variables in the past year with unmet need of mental health services among US adults by using weighted multiple logistic regression models.
Table 1 Prevalence of unmet need of mental health services (%) within each group of demographic variables. Variable Gender Male Female Age group 18–25 years 26–34 years 35–49 years 50+ years Race White AA Other Hispanic Marital status Married Widowed/ divorced/ separated Never been married Income Less than $20,000 $20,000–$49,999 $50,000–$74,999 $75,000 or More Education ≤High school graduate >High school graduate Any health insurance Yes No Overall
Total (N)
Unmet
Prevalence (%)
95%CI
p-value
19,707 23,666
782 1936
3.0 6.2
2.7–3.3 5.8–6.7
<0.0001
14,465 9053 11,129 8726
1172 631 679 236
8.3 6.7 5.3 2.5
7.7–9.0 5.9–7.5 4.8–5.9 2.1–2.8
<0.0001
25,943 5470 4359 7601
1881 247 266 324
5.2 3.7 4.0 3.5
4.8–5.7 3.0–4.3 3.0–5.1 2.9–4.1
<0.0001
17,981 6433
742 457
3.0 5.4
2.7–3.3 4.7–6.1
<0.0001
18,959
1519
7.5
6.8–8.1
9642 13,947 6750 13,034
800 864 421 633
7.0 4.8 4.6 3.4
6.4–7.7 4.2–5.4 3.8–5.4 3.1–3.8
<0.0001
17,945
946
3.8
3.5–4.1
<0.0001
25,428
1772
5.3
4.8–5.7
37,736 5312 43,373
2365 333 2718
4.5 5.8 4.7
4.2–4.8 5.0–6.7 4.4–5.0
0.0007
Abbreviations: AA = African American, CI = Confidence interval, p-value is based on χ2 test.
2.2. Measures 2.2.1. Dependent variable The dependent variable, unmet need of mental health service use, is defined as feeling a perceived need for mental health treatment/counseling that was not received. This is often referred to as "unmet need."
2. Methods
2.2.2. Demographic variables Gender was self-reported as either male or female. Age was classified into four categories: 18–25, 26–34, 35–49, and 50+ years old. Race consisted of four subgroups: White, African American (AA), Hispanic, and Others. The four categories of annual income were less than $20,000, $20,000–$49,999, $50,000–$74,999 and $75,000 or more. Education indicated whether the participant had above a high school degree or not. There were three categories of marriage status: married; widowed/divorced/separated, and never been married. Demographic variables are listed in Table 1.
2.1. Sample This cross-sectional study used the 2015 NSDUH data. The NSDUH is a nationally representative survey that is conducted annually to assess the prevalence and correlates of drug use in the US for the population ages 12 and older (www.samhsa.gov). This survey series provides information about the use of illicit drugs, alcohol, and tobacco among members of the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several modules of questions pertaining to mental health issues such as anxiety disorder, depression, psychological distress, serious mental illness, and suicide variables. The NSDUH survey sample is completed based on a 50-state design with independent, multistage area probability sample for each of the 50 states and the District of Columbia. Data collection is performed by computer-assisted personal interviewing and audio computer-assisted self-interviewing methods. Details of the survey design and data collection methods of NSDUH 2015 were published elsewhere (Center for Behavioral Health Statistics and Quality, 2016). The total number of individuals in the 2015 NSDUH dataset is 57,147. The current analysis was restricted to 43,373 adult participants (aged 18 or above). There was an Institutional Review Board exemption due to secondary data analysis.
2.2.3. Insurance status A person has health insurance coverage if he/she has been covered by private insurance or other types of health insurance (Table 1). 2.2.4. Alcohol and drug use variables Thirty-seven alcohol and drug use variables including variable names and descriptions are listed in Table 3. All these variables are binary variables (yes, no) including alcohol, marijuana, cocaine, illicit drug, smoking and tobacco use, etc. 2.2.5. Alcohol use disorder and drug use disorders AUD and DUDs in the past year were considered as covariates 111
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Fig. 1. Oblique principal component cluster analysis of 37 alcohol and drug use variables.
clusters are as strongly correlated as possible with each other and as uncorrelated as possible with the variables in the other clusters (Aggarwal and Kosian, 2015; Nelson, 2001; Sanche and Lonergan, 2006). Considering the categorical variables, the polychoric correlation is applied to ordinal data (Lee et al., 1995). Higher squared correlation (R2) values with its own cluster, lower R2 values with next closest cluster, and lower 1-R2 ratios (the ratio of 1-R2 for a variable's own cluster to 1-R2 for its nearest cluster) indicate a good fit of the respective item.
pertaining to alcohol dependence or abuse and illicit drug abuse or dependence in the past year. Illicit drug or alcohol abuse or dependence in the past year was defined by being dependent on any illicit drug or alcohol in the past year or illicit drug or alcohol abuse in the past year. Illicit drug abuse is defined as abusing any of the following substances: marijuana, hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives. Illicit drug dependence was classified as being dependent on any of these following substances: marijuana, hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives.
1 − R2ratio = 2.3. Statistical analysis All the analyses were conducted with SAS (Statistical Analysis System, Version 9.4) (SAS Institute, Cary, NC, USA).
1 − R2own cluster 1 − R2next closest cluster
2.3.3. Weighted logistic regression analysis The surveylogistic procedure in SAS v.9.4 was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between potential factors and unmet need for mental health services. In model one, the univariate logistic regression was used to examine the role of potential risk factors in unmet need for mental health services. In model two, the multiple logistic regression was used to adjust for all potential risk factors of unmet need for mental health services (full model).
2.3.1. Descriptive statistics and prevalence The surveyfreq procedure in SAS v.9.4 was used to weight and estimate population proportions/prevalence. Then surveymeans procedure in SAS v.9.4 was used to estimate the overall prevalence while surveyfreq procedure in SAS v.9.4 was used to determine the prevalence of unmet need of mental health services in demographic variables. The Chi-square test was used to compare the prevalence of unmet need of mental health services across demographic variables.
3. Results 3.1. Prevalence
2.3.2. Variable cluster analysis using PROC VARCLUS The VARCLUS procedure in SAS v.9.4 was used to divide a set of numeric variables into disjoint clusters. The variables in the same
Table 1 details the prevalence of unmet need for mental health 112
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health services (OR = 1.89, 95% CI = 1.58–2.26 and OR = 2.12, 95% CI = 1.62–2.77, respectively).
Table 2 Oblique principal component cluster analysis of 37 alcohol and drug use variables. Cluster
Items (n)a
Proportion explained (%)b
4. Discussion
1 2 3 4 5 6 7
6 2 4 7 7 2 9
85.56 99.99 80.08 91.78 82.59 99.95 77.40
In the present study, we examined the prevalence of unmet need for mental health services using the 2015 NSDUH population-wide survey. Furthermore, we performed data mining using oblique principal component cluster analysis - The VARCLUS procedure in SAS v.9.4 was used to reduce the dimensions of alcohol and drug use variables in the past year. Using VARCLUS, the 37 alcohol and drug use variables were divided into seven clusters, then the variable with the lowest 1-R2 ratio from each cluster was selected for weighted multiple logistic regression analysis. Our results showed pain reliever use, tranquilizer use, stimulant use, illicit drug and alcohol use, alcohol dependence or abuse, and illicit drug dependence or abuse were positively associated with the unmet need for mental health services (p < 0.05). The overall prevalence of unmet mental health needs in our study was 4.7%, which is slightly higher than that in 2011 (4.6%) (Alang, 2015). The present study further added that there are differences in its prevalence across gender, age and race groups, marital status, education, and income levels (Table 1). For example, our results were consistent with previous studies by showing that gender, age group, race, family income, education, marital status, general health status were all associated with unmet mental health service need (Alang, 2015; Choi et al., 2015; Golub et al., 2013; Roll et al., 2013; Walker et al., 2015). In fact, using the 2011 NSDUH data, Alang (2015) found that women had higher rates of cost-related difficulties of obtaining mental health treatment compared to men, and in addition social stigma and structural barrier were reported more in Black adults versus White adults and in rural versus metropolitan citizens. Tran and Ponce (2017) have reported that men, Latinos, Asians, young people, older adults, people with less education, uninsured adults, and individuals with limited English proficiency were more likely to have an unmet need for mental health services. Having low income and no insurance coverage were associated with an increased probability of not using mental health services (Keeler et al., 1988; Roll et al., 2013; Tran and Ponce, 2017; Walker et al., 2015; Wu and Schlenger, 2004). For example, previous studies showed that the uninsured had more difficulties in obtaining mental health services (Walker et al., 2015; Wu and Schlenger 2004). In the present study, we found that individuals without any insurance had a higher prevalence of unmet mental health services than those with insurance (Table 1). Our findings further added that individuals without any insurance were more likely to have lower odds of unmet mental health services in our univariate logistic regression analysis; however, after adjusting for other variables, there is no association of insurance with unmet mental health services (Table 4). A study of the 2010 National Health Interview Survey (NHIS) showed that uninsured patients were about four times more likely to report unmet mental health care compared with private insurance (Roll et al., 2013), while our study produced more moderate results (OR = 0.82, 95%CI = 0.71–0.95 for people with private insurance comparing with uninsured). This discrepancy could be potentially explained by the fact that our study excluded children while Roll et al. (2013) included children. Interestingly, insured adults perceived higher levels of social stigma and minimization of mental disorders versus uninsured adults (Alang, 2015). A previous study using the 2004 to 2010 NSDUH data showed that veterans had an untreated serious psychological distress rate of 8% which is a similar rate compared to the nonveteran sample (Golub et al., 2013). Alcohol and drug use are risk factors for mental health problems such as depression and anxiety (Boden and Fergusson, 2011; Brown et al., 2015; Choi et al., 2015; Emre et al., 2014; Fink et al., 2015; Steinberg et al., 2015). For example, Boden and Fergusson (2011) examined the literature on the associations between alcohol use and major depression and concluded that increasing involvement with
a b
Number of variables in each cluster. Proportion of cluster variance explained by the included items (%).
services in 2015. The overall prevalence of unmet mental health services was 4.7% (3.0% for males and 6.2% for females). The young age groups revealed a higher prevalence of unmet mental health services. The prevalence of unmet mental health services was 5.2%, 3.7%, 3.5%, and 4.0% in Whites, AAs, Hispanics, and other ace (p < 0.0001), respectively. Those who are never married (7.5%) have a higher prevalence of unmet need for mental health services than the married (3.0%) and the windowed/divorced/separated (5.4%). Table 1 also indicates that the prevalence of unmet mental health services decreases as the income increases; whereas people with higher education also have a higher prevalence. In addition, the prevalence in no insurance was higher than that of insurance (5.8% vs. 4.5%). 3.2. Variable cluster analysis using PROC VARCLUS Thirty-seven alcohol and drug use variables were clustered into 7 clusters (6, 2, 4, 7, 7, 2 and 9 variables for each cluster, respectively) (Fig. 1 and Table 2). Fig. 1 displays 7 clusters while Table 2 shows the number of items in each cluster and proportion of cluster variance explained by the included items (%). About 85.15% of the total variation in the data could be accounted by the 7 clusters. Table 3 describes the variables in each cluster and the corresponding 1-R2 ratio values. Small values of the 1-R2 ratio indicate that the variable has a strong correlation with its own cluster and a weak correlation with the other clusters (Table 3). From each cluster, the variable with the lowest 1-R2 ratio was selected for further analysis. The following seven variables were selected: tobacco use, pain reliever use, tranquilizer use, stimulant use, zolpidem products use, illicit drug and alcohol use, and benzodiazepine tranquilizers misuse. 3.3. Logistic regression analysis Table 4 presents the results from both univariate and multiple logistic analyses. By using univariate analysis, all the factors were associated with the unmet need for mental health services (p < 0.05). After adjusting for all other variables, being female (OR = 2.30, 95% CI = 2.02–2.61), never married or widowed/divorced/separated (OR = 1.38, 95% CI = 1.15–1.65; OR = 1.39, 95% CI = 1.16–1.66, respectively), having above a high school education (OR = 1.43, 95% CI = 1.26–1.62), and lower income (OR = 1.85, 95% CI = 1.59–2.16; OR = 1.40, 95% CI = 1.19–1.64; OR = 1.43, 95% CI = 1.18–1.72, respectively) people had statistically significant rate of unmet need for mental health services. Regarding alcohol and drug use variables in the past year, pain reliever use (OR = 1.33, 95% CI = 1.17–1.50), tranquilizer use (OR = 2.49, 95% CI = 2.16–2.86), stimulant use (OR = 1.22, 95% CI = 1.01–1.47), and illicit drug and alcohol use (OR = 1.54, 95% CI = 1.34–1.77) revealed significantly positive associations with the unmet need for mental health services. In addition, having both alcohol dependence or abuse and illicit drug abuse or dependence in the past year were significantly associated with the unmet need for mental 113
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Table 3 R2 measures for alcohol and drug use in the past year. Cluster
Variable
Variable description (past year)
R2 with own cluster
R2 with next closest cluster
1 - R2 ratio
Cluster 1
ALCYR MRJYR COCYR HALLUCYR ILLANDALC ILLORALC TRQNMYR TRBENZPYMU CIGYR CGRYR SMKLSSYR TOBYR STMANYYR PSYCHYR AMMEPDAPYU AMPHETAPYU METHPDAPYU AMMEPDPYMU AMPHETPYMU TRQANYYR PSYANYYR ALPRPDAPYU LORAPDAPYU CLONPDAPYU DIAZPDAPYU MUSRLXAPYU SEDANYYR ZOLPPDAPYU PNRANYYR OXYCNANYYR PNRNMYR HYDCPDAPYU OXYCPDAPYU OXCNANYYR2 TRAMPDAPYU MORPPDAPYU OXYCPDPYMU
Alcohol use Marijuana use Cocaine use Hallucinogen use Illicit drug and alcohol use Illicit drug or alcohol use Tranquilizer misuse Benzodiazepine tranquilizers misuse Cigarette use Cigar use Smokeless tobacco use Any tobacco use Any stimulants use Any psychotherapeutics misuse Any amphetamine or methylphenidate products use Any amphetamine use Any methylphenidate products use Any amphetamine or methylphenidate products misuse Amphetamine misuse Any tranquilizers use Any psychotherapeutics use Any alprazolam products use Any lorazepam products use Any clonazepam products use Any diazepam products use Any muscle relaxants use Any sedatives use Any zolpidem products use Any pain reliever use OxyContin use Pain reliever misuse Any hydrocodone products use Any oxycodone products use Any OxyContin use Any tramadol products use Any morphine products Use Oxycodone products misuse
0.6405 0.8120 0.7413 0.7563 1.0000 1.0000 0.9999 0.9999 0.6572 0.6884 0.6355 1.0000 1.0000 0.7517 1.0000 0.9422 0.7198 0.9982 0.9923 1.0000 1.0000 0.6997 0.6516 0.6981 0.7337 0.6282 0.9995 0.9995 1.0000 0.8230 0.6572 0.6691 0.9296 0.8245 0.4513 0.5388 0.8152
0.1386 0.3321 0.3945 0.3851 0.5914 0.9981 0.6994 0.6932 0.2336 0.2299 0.0869 0.2757 0.2795 0.9981 0.3092 0.3241 0.2733 0.5957 0.5941 0.9981 1.2589 0.7400 0.2715 0.4538 0.4420 0.2469 0.3869 0.3595 0.3465 0.3211 0.4953 0.3432 0.3500 0.3211 0.3239 0.3236 0.5096
0.4173 0.2815 0.4273 0.3963 0.0000 0.0000 0.0002 0.0002 0.4473 0.4046 0.3991 0.0000 0.0000 1.1638 0.0000 0.0856 0.3856 0.0045 0.0190 0.0000 1.0000 1.1552 0.4782 0.5527 0.4773 0.4937 0.0008 0.0007 0.0000 0.2607 0.6791 0.5038 0.1083 0.2585 0.8117 0.6819 0.3768
Cluster 2 Cluster 3
Cluster 4
Cluster 5
Cluster 6 Cluster 7
Abbreviations: R2 = The squared correlation.
cluster analysis in VARCLUS and identified 7 clusters (Tables 2 and 3). Therefore, this study is the first attempt to use VARCLUS in SAS v.9.4 for data mining in the correlated alcohol and drug use variables. Our results further added that pain reliever use, tranquilizer use, stimulant use, and illicit drug and alcohol use had significantly positive associations with the unmet need for mental health services (Table 4). Our results provide useful insight for policy makers in making decisions regarding health insurance coverage in procedures covered with AUD and DUD treatments in the US. Furthermore, these findings highlight a need for interventions to target alcohol and drug users. In addition, it also calls for the reevaluation of AUD and DUD treatment components, and whether substance abuse treatments should include mental health screening and treatments for mental health conditions as a major component of the AUD and DUD treatment. Additionally, results from our study can provide strong supports to coordinated and managed cares that integrate mental health screening and treatment as part of the AUD and DUD treatment. Coordinated and managed care can effectively organize patients with both overall health care management, including mental health screenings and referrals, and substance abuse treatments such as wellness checkups. Future research should focus on the types of mental health services that patients with AUD and DUD desire; and also the effectiveness of having care coordinators from managed care programs for AUD and DUD treatments, such as improving patients’ overall treatment progress, reduce avoidable emergency department visits, and lower overall health care expenditures. Another recommendation is to have substance abuse treatment professionals take additional training in recognizing unmet mental
alcohol increases risk of depression. However, few studies have focused on the association of the use of alcohol and drug with mental health services. For example, Strack et al. (1989) analyzed a community survey data in New Zealand and found people with alcohol abuse and/ or dependence were more likely to have used mental health services than the population at large; however, 39% of those with an alcohol disorder had never used any form of mental health service. The present study using a large national survey data in the US showed that alcohol dependence or abuse and illicit drug abuse or dependence in the past year were significantly associated with the unmet need for mental health services (Table 4). Another previous US study found that comorbid psychiatric or alcohol disorders were stronger predictors of service utilization than a pure psychiatric or alcohol disorder; whereas the majority of the adults with a psychiatric or alcohol disorder in the past year did not seek help (Wu et al., 1999). In addition, a study using the 2002 NSDUH data showed that 22.4% of substance abuse patients used mental health services (Mojtabai, 2005). In the present study, we used AUD and DUDs including alcohol dependence or abuse and illicit drug abuse or dependence in the past year as covariates to investigate the variables in past year alcohol and drug use with the unmet need for mental health services. Alcohol and drug use may influence the unmet mental health services; whereas most of these alcohol and drug use variables may be inter-correlated. The VARCLUS procedure has not been applied to classify alcohol and drug use variables, even though it has been used in health sciences (James, 2009; Lang et al., 2015; Udo et al., 2014; Walker et al., 2016). The present study examined the factor structure of 37 variables on alcohol and drug use in the past years by using the oblique principal component
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Table 4 Univariate and multiple weighted logistic regression analyses. Variable
Crude OR
Gender (ref = male) Female 2.13 Age group (ref = 18–25 years) 26–34 years 0.77 35–49 years 0.61 50+ years 0.26 Race (ref = whites) AA 0.69 Other 0.77 Hispanic 0.66 Marital status (ref = married) Widowed/divorced/separated 1.77 Never married 2.63 Education (ref= ≤ high school) > high school 1.43 Income (ref =≥ $75,000) Less than $20,000 2.14 $20,000–$49,999 1.41 $50,000 - $74,999 1.40 Any health insurance Yes 1.31 Alcohol dependence or abuse (ABODALC) Yes 3.60 Illicit drug dependence or abuse (UDPYILL) Yes 6.24 Tobacco use (TOBYR) Yes 1.91 Pain reliever use (PNRANYYR) Yes 2.10 Tranquilizer use (TRQANYYR) Yes 3.84 Stimulant use (STMANYYR) Yes 3.33 Any zolpidem products use (ZOLPPDAPYU) Yes 2.06 Illicit drug and alcohol use (ILLANDALC) Yes 3.57 Benzodiazepine tranquilizers misuse (TRBENZPYMU) Yes 6.61
95% CI
p-value
Adjusted OR
95% CI
p-value
1.91–2.38
<0.0001
2.30
2.02–2.61
<0.0001
0.66–0.89 0.53–0.70 0.22–0.31
0.0003 <0.0001 <0.0001
0.92 0.88 0.37
0.77–1.10 0.73–1.06 0.29–0.48
0.3603 0.1786 <0.0001
0.56–0.85 0.58–1.03 0.54–0.80
0.0005 0.0748 <0.0001
0.61 0.80 0.62
0.49–0.75 0.61–1.06 0.50–0.78
<0.0001 0.1251 <0.0001
1.51–2.06 2.27–3.05
<0.0001 <0.0001
1.38 1.39
1.15–1.65 1.16–1.66
0.0004 0.0004
1.27–1.61
<0.0001
1.43
1.26–1.62
<0.0001
1.86–2.45 1.21–1.65 1.17–1.67
<0.0001 <0.0001 0.0003
1.85 1.40 1.43
1.59–2.16 1.19–1.64 1.18–1.72
<0.0001 <0.0001 0.0002
1.12–1.54
0.001
1.11
0.93–1.32
0.2485
3.12–4.15
<0.0001
1.89
1.58–2.26
<0.0001
5.04–7.72
<0.0001
2.12
1.62–2.77
<0.0001
1.67–2.19
<0.0001
1.06
0.89–1.26
0.5269
1.87–2.35
<0.0001
1.33
1.17–1.50
<0.0001
3.38–4.38
<0.0001
2.49
2.16–2.86
<0.0001
2.85–3.88
<0.0001
1.22
1.01–1.47
0.0418
1.66–2.56
<0.0001
1.11
0.88–1.40
0.3658
3.22–3.95
<0.0001
1.54
1.34–1.77
<0.0001
5.35–8.17
<0.0001
0.97
0.75–1.26
0.82838
Abbreviations: AA = African American; OR = Odds ratio; CI = Confidence interval.
5. Conclusions
health needs. Early detection is a key mechanism to preventing mental health condition deterioration among vulnerable population, and additional health expenditures. With frequent interactions and lengthy durations of treatments, substance abuse treatment professions can act as the frontline of defense against unmet mental health needs. Therapists and nursing staffs with knowledge of mental health needs detection can identify patients who have unmet needs, and suggest mental health services referrals to patients who are in need. Several strengths exist in this study. Firstly, the 2015 NSDUH data is a nationally representative population-based survey with large sample size in the US, which allows us to adjust for numerous potential factors. Second, to the best of our knowledge, this is the first study to use oblique principal component cluster analysis to investigate the relationship among correlated alcohol and drug use variables in the past year and reduce the dominations of alcohol and drug use variables. Third, we provided the updated prevalence of unmet need for mental health services in the US. In addition, we adjusted for AUD and DUDs including illicit drug or alcohol abuse or dependence in the past year as co-variates and found several alcohol and drug use variables in the past year are more likely to have an unmet need of mental health services. There are several limitations in this study. First, the nature of crosssectional design is not appropriate to determine a temporal or causal relationship between potential factors and unmet mental health services. Second, self-reported data from interviews may be subjected to response bias.
Using the data from NSDUH 2015, we examined the prevalence of the unmet need for mental health services. Furthermore, we reduced the dimensions of alcohol and drug use variables in the past year from 37 to 7 variables. Using weighted multiple logistic regression models, our results showed that pain reliever use, tranquilizer use, stimulant use, and illicit drug and alcohol use in the past year were positively associated with increased odds of the unmet need. These findings highlighted the complex relationship among alcohol and drug use variables and the complex effects of these factors on the unmet need for mental health services.
Funding No funding source is given for the present paper.
CRediT authorship contribution statement Nianyang Wang: Conceptualization, Formal analysis, Writing original draft. Youssoufou Ouedraogo: Writing - review & editing. Jun Chu: Writing - review & editing, Methodology. Ying Liu: Conceptualization, Data curation, Writing - review & editing. Kesheng Wang: Writing - original draft. Xin Xie: Conceptualization, Writing original draft. 115
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Declaration of Competing Interest
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