Racial, ethnic, and age disparities in the association of mental health symptoms and polysubstance use among persons in HIV care

Thibaut Davy-Mendez; Varada Sarovar; Tory Levine-Hall; Alexandra N Lea; Amy S Leibowitz; Mitchell N Luu; Jason A Flamm; C Bradley Hare; Jaime Dumoit Smith; Esti Iturralde; James Dilley; Michael J Silverberg; Derek D Satre

doi:10.1371/journal.pone.0294483

. 2023 Nov 28;18(11):e0294483. doi: 10.1371/journal.pone.0294483

Racial, ethnic, and age disparities in the association of mental health symptoms and polysubstance use among persons in HIV care

Thibaut Davy-Mendez ^1,^2,^¤,^*, Varada Sarovar ², Tory Levine-Hall ², Alexandra N Lea ², Amy S Leibowitz ², Mitchell N Luu ³, Jason A Flamm ⁴, C Bradley Hare ⁵, Jaime Dumoit Smith ¹, Esti Iturralde ², James Dilley ¹, Michael J Silverberg ², Derek D Satre ^1,²

Editor: Kimberly Page⁶

¹Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States of America

²Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States of America

³Oakland Medical Center, Kaiser Permanente Northern California, Oakland, CA, United States of America

⁴Sacramento Medical Center, Kaiser Permanente Northern California, Sacramento, CA, United States of America

⁵San Francisco Medical Center, Kaiser Permanente Northern California, San Francisco, CA, United States of America

⁶University of New Mexico Health Sciences Center, UNITED STATES

Competing Interests: The authors have declared that no competing interests exist.

^¤

Current address: Division of Infectious Diseases and Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America

^✉

* E-mail: tdavy@med.unc.edu

Roles

Thibaut Davy-Mendez: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Varada Sarovar: Conceptualization, Data curation, Formal analysis, Writing – review & editing

Tory Levine-Hall: Conceptualization, Data curation, Formal analysis, Writing – review & editing

Alexandra N Lea: Conceptualization, Project administration, Supervision, Writing – review & editing

Amy S Leibowitz: Conceptualization, Investigation, Project administration, Writing – review & editing

Mitchell N Luu: Conceptualization, Investigation, Supervision, Writing – review & editing

Jason A Flamm: Conceptualization, Investigation, Supervision, Writing – review & editing

C Bradley Hare: Conceptualization, Investigation, Supervision, Writing – review & editing

Jaime Dumoit Smith: Conceptualization, Project administration, Supervision, Writing – review & editing

Esti Iturralde: Conceptualization, Investigation, Writing – review & editing

James Dilley: Conceptualization, Funding acquisition, Investigation, Writing – original draft, Writing – review & editing

Michael J Silverberg: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – review & editing

Derek D Satre: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

Kimberly Page: Editor

PMCID: PMC10684077 PMID: 38015975

Abstract

We characterized polysubstance use burden and associations with mental health problems across demographic subgroups of PWH. In 2018–2020, as part of a primary care-based intervention study, PWH in care at three medical centers in Kaiser Permanente Northern California were screened for depression (PHQ-9≥10), anxiety (GAD-2≥3), and substance use (Tobacco, Alcohol, Prescription medication, and other Substance use [TAPS]≥1 per substance). We used Poisson regression to estimate prevalence ratios (PRs) comparing polysubstance use prevalence (TAPS≥1 for ≥2 substances) between PWH with positive screens for depression or anxiety vs. neither, among all PWH, and stratified by race/ethnicity and age (restricted to men), adjusting for sociodemographics, CD4, and HIV load. Screened PWH (N = 2865) included 92% men, 56% White, 19% Black, and 15% Hispanic PWH, with a median age of 55 years. Overall, polysubstance use prevalence was 26.4% (95% CI 24.9%-28.1%). PWH with depression or anxiety (n = 515) had an adjusted polysubstance use PR of 1.26 (1.09–1.46) vs. PWH with neither (n = 2350). Adjusted PRs were 1.47 (1.11–1.96), 1.07 (0.74–1.54), and 1.10 (0.85–1.41) among Black, Hispanic, and White men, respectively. Adjusted PRs did not differ by age group. Interventions should consider jointly addressing mental health and substance use problems and potential drivers, e.g. stigma or socioeconomic factors.

Introduction

Persons with HIV (PWH) have a high prevalence of tobacco, alcohol, and other substance use, which can compromise HIV care engagement and viral suppression and contribute to adverse health outcomes [1–10]. PWH also have a high burden of mental health problems that frequently co-occur with substance use disorders (SUDs) and can complicate care and lead to worse health outcomes, such as higher mortality [2, 11–14]. Substance use can also affect mental health and vice-versa. PWH may use substances to cope with mental health symptoms, while reducing substance use is associated with lower depressive symptoms [15, 16]. PWH who use substances are less likely to receive depression treatment when they need it [13]. Evidence from the general population also shows that substance use reduces depression treatment adherence, and that depression may increase the risk of substance use relapse [17, 18]. Identifying PWH with co-occurring mental health and substance use problems is therefore essential to address both and improve health outcomes.

Few large or recent studies have examined the use of more than one substance, or polysubstance use, among PWH in clinical care [4, 16, 19–23]. Yet polysubstance use may affect up to 20% of PWH in care, and it can lead to worse adverse substance use effects, increased sexual risk behaviors, lower antiretroviral adherence, and lower SUD treatment rates [19, 21, 24–26]. Important knowledge gaps also exist related to polysubstance use among PWH. First, studies have often excluded tobacco or alcohol from polysubstance use definitions, and few have described co-used substances, such that little is known about polysubstance use combinations including all potentially harmful and commonly used substances [4, 19–22]. Second, polysubstance vs. single substance use comorbid with mental health disorders could have a different health impact, yet the association of mental health and polysubstance use problems among PWH has rarely been investigated [27]. Third, while polysubstance use is well-studied in persons at risk for HIV and vulnerable populations such as homeless persons, the demographic profile of PWH in care with polysubstance use and with comorbid mental health problems has not been well-described [24, 28–30]. In particular, mental health and substance use disparities exist between PWH of different gender, race, ethnicity, and age [19, 31, 32]. To inform care and understand racial, ethnic, and age-based health disparities among PWH, there is a need to characterize polysubstance use, co-occurrence with mental health problems, and PWH subgroups at greater risk of these.

In this study, we leveraged electronic screenings for mental health and substance use from an HIV primary care-based intervention to characterize in detail the prevalence and patterns of polysubstance use in a clinical population of PWH, estimate the association of depression and anxiety with polysubstance use, and determine if that association varied by race, ethnicity, and age.

Methods

Study setting and design

We used data from Promoting Access to Care Engagement (PACE), an intervention study evaluating electronic screening and treatment for depression, anxiety, and substance use disorders in HIV primary care [33]. PACE was based at three medical centers in Oakland, Sacramento, and San Francisco in Kaiser Permanente Northern California (KPNC), a private, not-for-profit, integrated health system providing care to >4.5 million members demographically similar to other privately insured adults in the region [34, 35].

As part of PACE, from 30 October 2018 to 17 July 2020, PWH ≥18 years attending outpatient primary care visits completed self-administered questionnaires every six months, via secure messaging or in clinic on a tablet. Results were displayed in the electronic health record (EHR) to enable providers to address problems and refer patients to behavioral health specialists. PACE was approved by the Institutional Review Boards of KPNC and the University of California, San Francisco with waivers of informed consent.

We conducted a cross-sectional, secondary data analysis of PWH who completed the initial screening questionnaire during PACE.

Study measures

Participants completed three questionnaires: the Patient Health Questionnaire (PHQ)-9, Generalized Anxiety Disorder (GAD)-2, and Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) Tool [36–38]. PACE defined a PHQ-9 score ≥10 as a positive screen for depression and GAD-2 score ≥3 as a positive screen for anxiety, based on validated thresholds [39, 40]. In this study, we divided PWH by mental health screening results: (1) PWH who screened positive for depression, anxiety, or both, and (2) PWH who screened negative for both.

The TAPS Tool captures 8 different substances: alcohol, tobacco, cannabis, illicit stimulants, heroin, prescription opioids, prescription sedatives, and prescription stimulants [38]. For each substance, TAPS includes a screener question with 5 responses ranging from “Never” to “Daily or almost daily”. For alcohol, the screener question is “In the past 12 months, how often have you had 5 or more drinks (men) / 4 or more drinks (women) containing alcohol in a day?” For other substances, the screener question is “In the past 12 months, how often have you used substance X?”. If the patient reports using more frequently than “Never”, they respond to additional questions on past-3-month use and substance-related problems, such as trying and failing to cut down or others expressing concern. For prescription medications, TAPS only ascertains misuse (using without a prescription or more than prescribed). For cannabis, TAPS does not differentiate between medicinal and recreational use.

TAPS provides a separate risk score for each examined substance. Scores range from 0 to 4 for alcohol and from 0 to 3 for other substances. For alcohol, a score of 1 represents endorsing using 5 or more drinks (men)/4 or more drinks (women) in one day in the past year (screener question), plus using alcohol in the past 3 months. A score >1 represents further endorsing at least one alcohol use-related problem, or using 5 or more/4 or more drinks in the past 3 months. For each other substance, a score of 1 represents endorsing use in the past 3 months, and a score >1 further endorsing at least one substance use-related problem. In these analyses, a single score for opioids was created by taking the highest of the scores for heroin and misused prescription opioids. We did not examine prescription stimulants as only seven patients endorsed prescription stimulant misuse.

In primary analyses, for each substance, we defined substance use as a TAPS score ≥1 for that substance, based on instrument validation against the modified Composite International Diagnostic Interview (CIDI) for identifying problem use [38]. Polysubstance use was defined as having a TAPS score ≥1 for two or more substances. In a sensitivity analysis, we defined polysubstance use as having a TAPS score ≥2 for two or more substances. A TAPS score ≥2 has been validated to screen for CIDI-defined SUD [38].

The other variables used in this analysis were extracted from the EHR and were sex, age, HIV risk group, race, ethnicity, CD4 cell count, HIV viral load (VL), health insurance type, and neighborhood deprivation index (NDI). NDI is a census tract-level summary measure of socioeconomic indicators, shown to be associated with health outcomes [41, 42]. NDI was calculated according to published methodology and divided into quartiles based on sample distribution [41]. Age, health insurance, and NDI were measured at screening. CD4 count and VL were the closest measurement six months prior to or following the screening date.

Statistical analysis

We estimated polysubstance use prevalence among all screened PWH and stratified by demographic and clinical characteristics listed above. We used Poisson regression models with robust variances to estimate prevalence ratios (PRs) comparing the polysubstance use prevalence by mental health status, adjusted for all covariates [43]. To evaluate whether associations between mental health and polysubstance varied by demographic characteristics, we conducted subgroup analyses stratified by race, ethnicity, and age group, restricting to PWH who were men and Black, Hispanic, or White, due to small numbers of women and PWH in other race and ethnicity groups in our sample. Analyses were conducted in SAS v9.4 (SAS Institute Inc., Cary, NC) using a pre-specified alpha of 0.05.

Results

Study sample

At 8954 eligible outpatient visits, 3904 (43%) screens were completed (S1 Fig). Of 4134 unique PWH eligible for screening, 2865 (69%) completed at least one screen and were included for analyses. PWH with no completed screen were less likely to be White (45% vs. 56%) and more likely to be Hispanic (22% vs. 15%) compared with PWH who had at least one screen (S1 Table).

Of the 2865 included PWH, most (92%) were men, 56% were White, 19% Black, 15% Hispanic, and 76% men who have sex with men (MSM) (Table 1). At screening, the median age was 55 years (interquartile range [IQR]) 46–62), the median CD4 count was 644 cells/μL (IQR 472–844), and 96% had a VL<200 copies/mL. PWH characteristics varied by race and ethnicity (S2 Table). Compared with the other racial and ethnic groups, Black PWH were less likely to be men (79% vs. 89%-96%), more likely to have heterosexual risk factor (37% vs. 12%-17%), and more likely to reside in the most deprived NDI quartile (44% vs. 18%-25%).

Table 1. Demographic and clinical characteristics of 2865 persons with HIV screened for depression, anxiety, and substance use disorders, Kaiser Permanente Northern California, 2018–2020.

		Positive mental health screening ^a
Characteristic	All patients N = 2865	Depression, anxiety, or both N = 515	Neither N = 2350
Men	2629 (92%)	465 (90%)	2164 (92%)
Race and ethnicity
Asian or Pacific Islander	198 (7%)	36 (7%)	162 (7%)
Black	558 (19%)	107 (21%)	451 (19%)
Hispanic	419 (15%)	70 (14%)	349 (15%)
White	1602 (56%)	284 (55%)	1318 (56%)
Other/unknown	88 (3%)	18 (3%)	70 (3%)
Age, years	55 (46, 62)	52 (41, 60)	56 (47, 63)
HIV risk group
MSM	2170 (76%)	385 (75%)	1785 (76%)
IDU	185 (6%)	45 (9%)	140 (6%)
Heterosexual or other	510 (18%)	85 (17%)	425 (18%)
CD4 count, ^b cells/μL	644 (472, 844)	644 (468, 857)	644 (474, 841)
HIV RNA <200 copies/mL ^b	2678 (96%)	466 (90%)	2212 (94%)
Insurance type
Private	1968 (69%)	318 (62%)	1650 (70%)
Medicare	732 (26%)	148 (29%)	584 (25%)
Medicaid	142 (5%)	44 (9%)	98 (4%)
Other	23 (1%)	5 (1%)	18 (1%)
NDI quartile ^c
1 (least deprived)	717 (25%)	111 (22%)	606 (26%)
2	726 (25%)	143 (28%)	583 (25%)
3	704 (25%)	127 (25%)	577 (25%)
4 (most deprived)	712 (25%)	133 (26%)	579 (25%)
TAPS score ≥1 ^d
Alcohol	1101 (38%)	209 (41%)	892 (38%)
Cannabis	988 (34%)	215 (42%)	773 (33%)
Tobacco	457 (16%)	115 (22%)	342 (15%)
Stimulant	174 (6%)	58 (11%)	116 (5%)
Sedative	37 (1%)	11 (2%)	26 (1%)
Opioid	23 (1%)	7 (1%)	16 (1%)

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Numbers are N (%) or median (IQR). Abbreviations: IDU, injection drug use; IQR, interquartile range; MSM, men who have sex with men; NDI, neighborhood deprivation index; TAPS, Tobacco, Alcohol, Prescription medication, and other Substance Use tool.

^a Depression was defined as a PHQ-9 score ≥10. Anxiety was defined as a GAD-2 score ≥3.

^b Closest measurement within six months before or after screening date. CD4 count was missing for 468 patients (74 [14%] with depression, anxiety, or both, and 394 [17%] without) and HIV RNA was missing for 77 patients (17 [3%] with depression, anxiety, or both, and 60 [3%] without).

^c Calculated according to Messer et al. and divided in quartiles based on the distribution of the entire patient sample. NDI was missing for six patients [41].

^d A TAPS score ≥1 has been validated to screen for problem use against the modified Composite International Diagnostic Interview (CIDI) [38].

On the initial screen (Table 1), 515 (18%) PWH had a positive screen for depression or anxiety, including 140 with a positive screen for depression only, 146 for anxiety only, and 229 for both. PWH who screened positive for depression or anxiety were younger (median 52 vs. 56 years), less likely to have a VL<200 copies/mL (90% vs. 94%), and more likely to be covered by Medicare (29% vs. 25%) or Medicaid (9% vs. 4%), compared with PWH who screened negative for both (Table 1). Racial and ethnic distribution did not differ by mental health status.

Polysubstance use

Polysubstance use prevalence was 26.4% overall (95% confidence interval [CI] 24.9%-28.1%) and varied by sex, race, ethnicity, age, VL, and insurance type (Fig 1, all P<0.05). Polysubstance use prevalence was highest among PWH who were Black (32%) or Hispanic (33%), 18–29 (61%) or 30–39 (46%) years old, had a VL ≥200 copies/mL (39%), with Medicaid (41%), or in the most deprived NDI quartile (30%). PWH with polysubstance use (N = 757) used a median of 2 substances (IQR 2–3; range 2–6), most commonly alcohol/cannabis (43%), followed by alcohol/cannabis/tobacco (12%), alcohol/tobacco (11%), and cannabis/tobacco (10%) (Fig 2).

Fig 2 — A positive screen was defined as a score ≥1 on the Tobacco, Alcohol, Prescription medication and other Substance use (TAPS) questionnaire. Only combinations accounting for >1% of patients are displayed.

Associations of mental health and polysubstance use

PWH who screened positive for depression or anxiety had a higher polysubstance use prevalence than those who screened negative for both, with estimates of 34.2% vs. 24.7%, respectively, and an unadjusted PR of 1.38 (95% CI 1.20–1.59) (Table 2). The adjusted PR was 1.26 (1.09–1.46).

Table 2. Polysubstance prevalence and prevalence ratios comparing persons with HIV by mental health screening results, Kaiser Permanente Northern California, 2018–2020.

Positive mental health screen ^a	N	Polysubstance Use Prevalence ^b (95% CI)	Unadjusted PR (95% CI) ^c	Adjusted PR (95% CI) ^c^, ^d
Depression, anxiety, or both	515	34.2% (30.3%, 38.5%)	1.38 (1.20, 1.59)	1.26 (1.09, 1.46)
Neither	2350	24.7% (23.0%, 26.5%)	1 (ref.)	1 (ref.)

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Abbreviations: CI, confidence interval; PR, prevalence ratio.

^a Depression was defined as a PHQ-9 score ≥10. Anxiety was defined as a GAD-2 score ≥3.

^b Polysubstance use was defined as having a score ≥1 for two or more substances on the Tobacco, Alcohol, Prescription medication and other Substance use (TAPS) Tool.

^c Estimates and 95% CIs were obtained from a Poisson regression model with robust variance.

^d Adjusted for sex, race, ethnicity, age, HIV risk group, HIV viral load, CD4 count, Neighborhood Deprivation Index (NDI), and insurance.

Among PWH who were men and Black, Hispanic, or White (N = 2371), associations between mental health and polysubstance use varied by race and ethnicity (Fig 3A). Among Black men, those screening positive for depression or anxiety vs. neither had a polysubstance use PR of 1.73 (1.34–2.24) in unadjusted analyses, and 1.47 (1.11–1.96) in adjusted analyses. The adjusted PR was 1.07 (0.74–1.54) among Hispanic men and 1.10 (0.85–1.41) among White men. For all three race/ethnicity groups, lower age was associated with higher polysubstance use prevalence, and age was the strongest confounder of the association between mental health screening and polysubstance use, with a change-in-estimate ranging 8.0%–11.4% (S3 and S4 Tables).

In the same subgroup of Black, Hispanic, or White men, among those aged ≥60 years (Fig 3B), there was no difference in polysubstance use prevalence by mental health status (adjusted PR 1.06; 95% CI 0.67–1.68). Among other age groups, we observed small differences with wide CIs, for example with an adjusted PR of 1.26 (0.91–1.75) among PWH aged 30–39 years. An adjusted PR could not be estimated for PWH aged 18–29 years.

Sensitivity analysis

Using a TAPS score ≥2 to identify substance use, polysubstance use prevalence was 7.8% (6.8%-8.8%) overall, and this varied by race, ethnicity, age, HIV risk group, VL, insurance type, and NDI (S2 Fig, all P<0.05). Polysubstance use prevalence was highest for PWH who were Black (12%), 18–29 (23%) or 30–39 (15%) years old, with injection drug use risk factor (15%), VL≥200 copies/mL (17%), or Medicaid coverage (17%).

In this sensitivity analysis, screening positive for depression or anxiety was more strongly associated with polysubstance use, with an adjusted PR of 2.05 (95% CI 1.54–2.72) (S5 Table). In subgroup analyses (S3 Fig), the strongest unadjusted associations between having depression or anxiety and polysubstance use were observed among Black men (PR 3.14; 95% CI 1.99–4.94) and men aged 40–49 (PR 3.04; 95% CI 1.71–5.39). In adjusted analyses, the PR among Black men was 2.45 (95% CI 1.42–4.23), and the adjusted PR among most age groups was not estimable due to small sample sizes.

Discussion

Among >2800 PWH screened in primary care, polysubstance use prevalence was 26%, most commonly with alcohol/cannabis, and highest among men, younger, Black, and Hispanic PWH. Having depression or anxiety was associated with a 26% increase in adjusted polysubstance use prevalence among all PWH, but a 47% increase among Black men.

Studies in PWH have used various polysubstance use definitions. In CNICS, a multisite US clinical cohort, 20% of PWH screened positive for two or more SUDs including alcohol and illicit drugs, using the Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) and Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) [19]. In ASTRA, a study of MSM with HIV in the United Kingdom, 51% reported any substance use, of whom 68% used two or more, or approximately a 35% polysubstance use prevalence, examining cannabis, stimulants, and opioids, amyl nitrites, and erectile dysfunction medications, but not alcohol [20]. In a Washington, DC HIV cohort, 8% were diagnosed with two or more SUDs; this study did not examine cannabis [22]. None of these studies examined tobacco use.

This study extends prior work by providing recent polysubstance use estimates from a large clinical sample of PWH, including all potentially harmful legal and illicit substances and detailing combinations. Our findings highlight the importance of including tobacco, alcohol, and cannabis in polysubstance use studies, as they are commonly used by PWH and can lead to considerable disease burden and adverse outcomes [1, 6, 7, 10]. In this study, the two most frequent polysubstance use combinations, together accounting for 55% of PWH with polysubstance use, included both alcohol and cannabis. Prior work has shown that the prevalence of substance use is generally highest for alcohol and cannabis, individually [3, 19]. However, past studies that have examined polysubstance use in PWH have shown a greater preponderance of stimulant use [20–22]. Several polysubstance use combinations in our study included stimulants, together accounting for almost 20% of PWH with polysubstance use. These differences may be explained in part by geographic variations in substance use [3]. Future studies should further examine polysubstance use typologies among PWH and evaluate whether they have different mental health risk profiles.

This study also used a novel instrument, TAPS, developed as a screening tool brief enough for primary care and sufficiently detailed to inform clinical decisions [38]. We examined polysubstance use based both on TAPS score cut-offs of ≥1 and ≥2 for each substance, which identify possible problem use and possible SUD, respectively. With the higher cut-off, which may be most comparably to the CNICS study definition, polysubstance use prevalence was 8%, suggesting that higher-risk polysubstance use may be less common in our sample than in some other clinical settings [19].

We found that screening positive for depression or anxiety was associated with polysubstance use. While studies have reported associations between mental health and single or any substance use [12, 14, 44, 45], there have been few reports on mental health and polysubstance use in PWH. In one study of PWH aged 18–24 years, those with polysubstance use had greater mental health symptoms, including depression and anxiety [27]. In our study, mental health-polysubstance use associations were also stronger when using the higher cut-off of a TAPS score ≥2 for a given substance, indicating possible SUD. Clinical management of PWH with both mental health and SUDs is more complex than those with either [46]. Mental health and SUDs can have a greater impact together, including reduced HIV care engagement, lower viral suppression, and higher mortality [11, 12, 47]. Integrated screening strategies and co-management may prove highly beneficial, as addressing one issue can improve other conditions [15, 48]. Co-occurring mental health and polysubstance use problems therefore have important implications for HIV, mental health, and SUD treatment outcomes.

Our findings highlighted disparities by race and ethnicity. Polysubstance use prevalence was highest for Black and Hispanic PWH, consistent with some prior reports [22, 23]. We also found that screening positive for depression or anxiety was more strongly associated with polysubstance use among Black than among White or Hispanic men. There are several potential explanations. Black PWH are less likely to receive mental health and SUD treatment than White PWH [31, 49]. Untreated mental health problems could contribute to greater polysubstance use as a coping strategy, and untreated SUD to more severe mental health symptoms. Structural factors, like experiences of racism and socioeconomic deprivation, may also be drivers of both mental health symptoms and polysubstance use among Black men. In our study, Black PWH were more likely from deprived neighborhoods and covered by Medicaid. Black men may also experience stronger forms of stigma related to HIV status or being MSM [50, 51]. In addition to addressing mental health and substance use problems jointly, addressing drivers, for example via trauma-informed care, may provide additional benefits for Black or other PWH of color.

In our study, polysubstance use prevalence was also higher among younger PWH. CNICS and ASTRA reported that lower age was associated with substance use, including polysubstance use in ASTRA [19, 20]. In contrast, in the DC cohort, substance use was associated with older age [22]. Our findings highlight the urgency of addressing polysubstance use in younger PWH, with 61% prevalence in those aged 18–29 years when using a TAPS score of ≥1 for each substance, and 23% when using a score ≥2, which suggests a quarter of PWH in this age group may be experiencing ≥2 SUDs. Nonetheless, screening and treatment are needed at all ages among PWH. Prevalence estimates (with TAPS≥1) were 23% and 17% for those aged 50–59 and ≥60 years in our study, respectively. Older PWH may also have unique drivers of polysubstance use, such as chronic pain, social isolation, or survivor guilt. Despite the association of age with polysubstance use, we did not find strong evidence that mental health-polysubstance use associations varied by age, indicating that PWH with depression or anxiety in a given age group in our study were not more likely to have concurrent polysubstance use than PWH with depression or anxiety in a different age group.

This study had a large and racially and ethnically diverse sample of PWH in clinical care at three KPNC sites in Oakland, Sacramento, and San Francisco, three of the 50 priority jurisdictions of the national End the HIV Epidemic (EHE) initiative [52]. Given that mental health and polysubstance use problems can compromise viral suppression and increase sexual risk behaviors [8–10, 21, 26], our findings provide important evidence that can inform EHE efforts in these and similar priority jurisdictions. Our data are recent and captured several months of COVID-19-related social distancing and shelter-in-place policies that could have exacerbated mental health and substance use problems [53]. We leveraged a rich data source including mental health and substance use screens and EHR data on sociodemographic and clinical characteristics, including NDI based on geocoded residence. Another strength is the use of the novel TAPS tool, which identifies both problem use and SUD for all potentially harmful substances including tobacco and is promoted by the NIH for implementation in health settings [54]. The implementation of EHR-integrated screening tools such as TAPS at primary care visits shows promise for increasing screening, identification, and counseling of substance use problems [55].

One important consideration is that there were few women in our study, and subgroup analyses only examined men. While our overall sample was representative of PWH in Northern California, our findings may not be generalizable to women with HIV or PWH receiving care in other settings. In addition, 31% of eligible PWH did not complete a screen. High response rates are important to provide adequate care to PWH. Future qualitative analyses from the PACE trial may shed light on drivers of non-response in this patient population. In a recent study, PWH who refused to complete a screening questionnaire cited not believing it was needed or useful as one of the reasons for non-completion; however, >80% of those who completed it felt it improved their care [56]. Communication with patients, including via providers, may help impress the importance of screening measures to improve care quality and their own satisfaction. While a completion level of 31% is similar to that of other primary care-based screening studies [55, 57], our findings could have been impacted. If PWH who did not complete a screen were more likely to have mental health and substance use problems, our findings could underestimate their prevalence in this population, particularly among Hispanic PWH, as PWH without a screen were more likely to be Hispanic. Furthermore, the estimated association between mental health and polysubstance use among Hispanic men with HIV may have been biased as a result, most likely towards the null.

Another important consideration is that our study used a cross-sectional design to characterize associations of mental health and polysubstance use problems. Longitudinal studies are needed to better understand the link between mental health and polysubstance use in PWH, including the degree to which mental health symptoms may lead to polysubstance use, e.g. as a coping strategy, polysubstance use may worsen mental health symptoms, and common drivers may contribute to both worse mental health symptoms and increased polysubstance use.

Conclusions

PWH in KPNC had high polysubstance use prevalence which was associated with having elevated depression or anxiety symptoms, especially among Black men. Research and interventions should jointly consider mental health and substance use problems as well as possible drivers of both conditions.

Supporting information

S1 Table. Characteristics of 4134 persons with HIV eligible for screening, stratified by screening completion, Kaiser Permanente Northern California, 2018–2020.

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S2 Table. Demographic and clinical characteristics stratified by race and ethnicity.

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S3 Table. Unadjusted and adjusted prevalence ratios (95% confidence intervals) for the association with polysubstance use, stratified by race/ethnicity, among men with HIV.

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S4 Table. Prevalence ratios comparing the probability of polysubstance use between PWH with positive screens for depression, anxiety, or both and PWH with no positive mental health screen, unadjusted and adjusted for a single covariate at a time, with the percentage change from unadjusted estimate.

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S5 Table. Polysubstance use prevalence and prevalence ratios comparing persons with HIV by mental health screening results, in a sensitivity analysis using a TAPS score ≥2.

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S1 Fig. Flowchart of patient inclusion.

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S2 Fig. Prevalence of polysubstance use by clinical and demographic characteristics in a sensitivity analysis using a TAPS score ≥2.

Abbreviations: API, Asian or Pacific Islander; Hetero., heterosexual; IDU, injection drug use; MSM, men who have sex with men; NDI, neighborhood deprivation index. * denotes P <0.05.

(DOCX)

Click here for additional data file.^{(281KB, docx)}

S3 Fig

Prevalence of polysubstance among patients screening positive for either depression or anxiety versus neither, stratified by (A) race and ethnicity and (B) age, in a sensitivity analysis using a TAPS score ≥2.

(DOCX)

Click here for additional data file.^{(371.3KB, docx)}

Data Availability

This study used sensitive data and access has been restricted by the Kaiser Permanente Northern California (KPNC) Institutional Review Board (IRB). Given the sensitive nature of analytic data sets, data sharing must take place through active partnerships between external researchers and the study team. External investigators can contact either study MPI (Derek D. Satre or Michael J. Silverberg) to initiate a request for study data to support new study proposals or manuscripts. Approval of such requests and initiation of collaborations will consider several criteria including but not limited to the following: proposed project must be of high scientific merit and consistent with the overall goals and objectives of the parent study; the proposed project’s investigators must plan for adequate resources to effectively complete the project. In addition, external investigators can submit a request for collaboration on the Kaiser Permanente Research Collaboration Portal at https://rcp.kaiserpermanente.org/.

Funding Statement

This study was supported by the National Institute on Drug Abuse (grant number R01DA043139 to M.J.S and D.D.S.; nida.nih.gov). D.D.S was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (grant number K24AA025703; niaaa.nih.gov). T.D.-M. was supported by grants from the National Institute on Drug Abuse (grant number T32DA007250; nida.nih.gov) and the National Heart, Lung, and Blood Institute (grant number K01HL169020; nhlbi.nih.gov). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0294483.r001

Decision Letter 0

Kimberly Page

4 Sep 2023

PONE-D-23-17520Racial, ethnic, and age disparities in the association of mental health symptoms and polysubstance use among persons in HIV carePLOS ONE

Dear Dr. Davy-Mendez,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. I regret that we were not able to secure a second reviewer for this manuscript. Thus, in the interest of time (the manuscript was submitted many months ago), I, as Academic Editor, undertook the second review. My expertise in substance use research, mental health, and my independence from any contributing authors qualify me for this. This is a nice manuscript that describes baseline characteristics of a cohort being followed as part of an intervention to implement, assess efficacy, and cost-effectiveness of electronic screening modalities for mental health in HIV primary care clinics at three Kaiser Permanente Northern California locations. I agree with Reviewer 1 that the manuscript provides noteworthy and actionable information, especially among persons engaged in care. This largely descriptive manuscript adds to the literature regarding the prevalence of substances used and indicators of potential excess risk of polysubstance use. One recognizes that the 'actionable' factors will be further explored as the trial progresses and ends. To that end, I disagree with several of Reviewer #1's suggestions, including conducting latent class analysis, bidirectional models, and causal inference methods. The papers' contribution is to describe the population and many of the factors this paper is exploring will either be done as part of outcome analyses of the trial. Latent class analysis is valuable to identify patient populations that have similar symptom profiles and have the potential ot further inform the field. However, I believe that this analysis may be more valuable when examining symptom profiles that contribute to the low (or high) efficacy of the intervention. Further, the measures used in this study to screen for mental health symptoms and substance use are being increasingly used in primary health settings across the country. LCA may help to identify homogeneous groups, but are not likely to be useful for providers in busy primary care settings who likely don't have the time to categorize patients in these groups, whereas the screening instruments have defined cut-offs, and validated performance characteristics. LCA may be interesting to conduce when the trial is over and additional exploratory analyses are conducted with the trial outcomes. In my mind this paper does accomplish the main goal of describing the prevalence and excess risk of polysubstance use in the sample. For these reasons, I also do not endorse assessing bidirectional models, or using inverse probability weighting at this stage. This may be reflective of my own training in epidemiology. I applaud the authors for presenting prevalence and prevalence ratios and value this as a contribution to the field. Some minor comments: 1. I also agree that the findings regarding Black patients are important. I agree that some additional analyses would be relevant here, however my suggestion is to assess what is confounding the aPRs in the assocation between having depression/anxiety and polysubstance use: what factors made the adjusted risk estimates so much higher? 2. I also agree that some additional brief information regarding the TAPs instrument would be helpful. Especially as the authors believe that this is a novel factors of the study.3. Please clarify if there were measures of opioid use in the survey of substances. 4. It is not clear why the authors refer to the the data being collected in the priority jurisdictions of the End the HIV Epidemic initiative. How does this study related to that initiative? Thank you for your patience in the processing of this manuscript.

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Reviewer #1: This is an interesting manuscript describing the association of screening positive for a mental health disorder (i.e., depression or anxiety) with polysubstance use assessed via the TAPS. There are several strengths from this large sample of PWH who are engaged in care that provide opportunities to guide expanded efforts to optimize screening and treatment for co-occurring mental health and substance use disorders. Below I provide some feedback that may assist with further refining this contribution.

1) TAPS - It would be useful to have a bit more information regarding the TAPS measure and some indication for what the thresholds of 1 (Problem Use) and 2 (Higher Risk) are indexing for each substance. I was also confused by the >= 1 at various points in the manuscript, but it became clear that this was for two substances. Consider referring to the TAPS total scores (i.e., >= 2 and >=4) at some point to make this more clear.

2) Analysis - The analytic approach is generally sound but one wonders if a Latent Class Analysis would not provide more nuanced information regarding polysubstance use typologies that can then be examined as correlates. Figure 2 was particularly helpful in this regard but LCA would assist with empirically derived typologies.

3) Stimulants - It seems worth noting that 15% of participants had some polysubstance pattern that involved stimulants and this is without assessing substances that are commonly co-used among MSM (e.g., GHB, amyl nitrites, ketamine, erectile dysfunction medications). Stimulants are strongly associated with depression and other forms of negative affect, and again one wonders if there are distinct polysubstance use typologies that are driving observed associations with mental health indicators.

4) Negative Reinforcement Models - The authors note that substance may be used as a means of coping, consistent with the self-medication hypothesis. It may also be that using substances induces symptoms of withdrawal (including depression and anxiety). Addressing the potentially bi-directional connections between substance use and mental health seems worthwhile.

5) Black Race - In my opinion, these are the most interesting and timely findings. It would be interesting to know how the social determinants of health examined in this study (e.g., Area Deprivation Index, Medicaid) could account for or moderate observed associations with Black race. It would be useful to have a table reported the bivariate and adjusted associations for each of the models (i.e., completing screening and polysubstance use outcomes).

6) Screening Rates - It is noteworthy that approximately one-third of potentially eligible individuals did not complete the screening measures, and some systemic predictors of completing screening were identified. This seems to be an important finding in and of itself for two reasons. First, from a clinical perspective we need patients to complete these measures to even identify that they are in need of treatment for mental health or substance use disorders. Some discussion of ways to optimize screening efforts such as contingency management (patients) of pay for performance (providers) would be useful. Second, the issue of non-response introduces systematic biases in efforts to estimate the prevalence of polysubstance use and its association with mental health. Consider, deploying causal inference methods to adjust for predictors of non-response (e.g., inverse probability weighting).

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Reviewer #1: Yes: Adam Carrico

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PLoS One. 2023 Nov 28;18(11):e0294483. doi: 10.1371/journal.pone.0294483.r002

Author response to Decision Letter 0

20 Oct 2023

EDITOR COMMENTS

This is a nice manuscript that describes baseline characteristics of a cohort being followed as part of an intervention to implement, assess efficacy, and cost-effectiveness of electronic screening modalities for mental health in HIV primary care clinics at three Kaiser Permanente Northern California locations. I agree with Reviewer 1 that the manuscript provides noteworthy and actionable information, especially among persons engaged in care. This largely descriptive manuscript adds to the literature regarding the prevalence of substances used and indicators of potential excess risk of polysubstance use. One recognizes that the 'actionable' factors will be further explored as the trial progresses and ends.

To that end, I disagree with several of Reviewer #1's suggestions, including conducting latent class analysis, bidirectional models, and causal inference methods. The papers' contribution is to describe the population and many of the factors this paper is exploring will either be done as part of outcome analyses of the trial. Latent class analysis is valuable to identify patient populations that have similar symptom profiles and have the potential ot further inform the field. However, I believe that this analysis may be more valuable when examining symptom profiles that contribute to the low (or high) efficacy of the intervention. Further, the measures used in this study to screen for mental health symptoms and substance use are being increasingly used in primary health settings across the country. LCA may help to identify homogeneous groups, but are not likely to be useful for providers in busy primary care settings who likely don't have the time to categorize patients in these groups, whereas the screening instruments have defined cut-offs, and validated performance characteristics. LCA may be interesting to conduce when the trial is over and additional exploratory analyses are conducted with the trial outcomes. In my mind this paper does accomplish the main goal of describing the prevalence and excess risk of polysubstance use in the sample. For these reasons, I also do not endorse assessing bidirectional models, or using inverse probability weighting at this stage. This may be reflective of my own training in epidemiology. I applaud the authors for presenting prevalence and prevalence ratios and value this as a contribution to the field.

Thank you for the feedback regarding the contribution of the analyses conducted in this manuscript and actionable implications for patients engaged in care. We are also grateful for the helpful suggestions for improvement, and the guidance regarding analyses suggested by the Reviewer 1. The analyses suggested by the Reviewer 1 such as LCA could yield informative findings and we plan to integrate such approaches in future analyses using outcome data from the completed trial. Please see also our specific replies to Reviewer 1, below.

Some minor comments:

1. I also agree that the findings regarding Black patients are important. I agree that some additional analyses would be relevant here, however my suggestion is to assess what is confounding the aPRs in the assocation between having depression/anxiety and polysubstance use: what factors made the adjusted risk estimates so much higher?

We agree that the findings by race/ethnicity merit additional detail in the manuscript given their importance. We have added two supplemental tables. S3 Table displays unadjusted and adjusted prevalence ratios (PRs) for all covariates, stratified by race/ethnicity. S4 Table displays the change in PR estimate for the association of mental health and polysubstance use, adjusting for only one covariate at a time, stratified by race/ethnicity. In addition, we have added the following statements to describe these findings:

Results, page 11, lines 243–246:

“For all three race/ethnicity groups, lower age was associated with higher polysubstance use prevalence, and age was the strongest confounder of the association between mental health screening and polysubstance use, with a change-in-estimate ranging 8.0%–11.4% (S3 and S4 Tables).”

2. I also agree that some additional brief information regarding the TAPs instrument would be helpful. Especially as the authors believe that this is a novel factors of the study.

We have added additional details on the TAPS instrument, including its structure and scoring system, in the Methods, pages 5–6, lines 114–142:

“The TAPS Tool captures 8 different substances: alcohol, tobacco, cannabis, illicit stimulants, heroin, prescription opioids, prescription sedatives, and prescription stimulants [38]. For each substance, TAPS includes a screener question with 5 responses ranging from “Never” to “Daily or almost daily”. For alcohol, the screener question is “In the past 12 months, how often have you had 5 or more drinks (men) / 4 or more drinks (women) containing alcohol in a day?” For other substances, the screener question is “In the past 12 months, how often have you used substance X?”. If the patient reports using more frequently than “Never”, they respond to additional questions on past-3-month use and substance-related problems, such as trying and failing to cut down or others expressing concern. For prescription medications, TAPS only ascertains misuse (using without a prescription or more than prescribed). For cannabis, TAPS does not differentiate between medicinal and recreational use.

3. Please clarify if there were measures of opioid use in the survey of substances.

We have added details about how TAPS and our analyses capture opioid use in the revisions described in response to Editor comment #2. These are highlighted below:

Methods, page 5, line 114:

“The TAPS Tool captures 8 different substances: alcohol, tobacco, cannabis, illicit stimulants, heroin, prescription opioids, prescription sedatives, and prescription stimulants.38”

Methods, page 6, line 122:

“For prescription medications, TAPS only ascertains misuse (using without a prescription or more than prescribed).”

Methods, page 6, line 132:

“In these analyses, a single score for opioids was created by taking the highest of the scores for heroin and misused prescription opioids.”

4. It is not clear why the authors refer to the data being collected in the priority jurisdictions of the End the HIV Epidemic initiative. How does this study related to that initiative?

We have clarified this point in the Discussion, pages 15–16, lines 359–364:

“This study had a large and racially and ethnically diverse sample of PWH in clinical care at three KPNC sites in Oakland, Sacramento, and San Francisco, three of the 50 priority jurisdictions of the national End the HIV Epidemic (EHE) initiative [52]. Given that mental health and polysubstance use problems can compromise viral suppression and increase sexual risk behaviors [8-10, 21, 26], our findings provide important evidence that can inform EHE efforts in these and similar priority jurisdictions.”

REVIEWER COMMENTS

We thank the reviewer for their comments and suggestions. We have clarified the language around TAPS scoring throughout the manuscript. We have also added additional details on the TAPS instrument in the Methods. See our response to the Editor’s comment #2 above.

The primary objective of this study was to characterize associations of mental health and polysubstance use problems across demographic groups at baseline in the PACE trial. Details on the combinations of substances used by participants are provided in Figure 2 for descriptive purposes. We agree with the reviewer that more nuanced typologies of polysubstance use would be informative, and we appreciate the suggestion of suing LCA to examine these typologies. Following guidance from the Editor, and given that examining these typologies fell outside the scope of the present study, we plan to incorporate LCA approaches and examinations of typologies in future analyses that will include follow up screening and outcome data from the trial. See also our response to the Editor, above. We have added statements regarding the importance of polysubstance use typologies in response to this comment and comment #3 (see below).

We have expanded the following paragraph in the Discussion section, page 13, lines 292–305:

“This study extends prior work by providing recent polysubstance use estimates from a large clinical sample of PWH, including all potentially harmful legal and illicit substances and detailing combinations. Our findings highlight the importance of including tobacco, alcohol, and cannabis in polysubstance use studies, as they are commonly used by PWH and can lead to considerable disease burden and adverse outcomes [1, 6, 7, 10]. In this study, the two most frequent polysubstance use combinations, together accounting for 55% of PWH with polysubstance use, included both alcohol and cannabis. Prior work has shown that the prevalence of substance use is generally highest for alcohol and cannabis, individually [3, 19]. However, past studies that have examined polysubstance use in PWH have shown a greater preponderance of stimulant use [20-22]. Several polysubstance use combinations in our study included stimulants, together accounting for almost 20% of PWH with polysubstance use. These differences may be explained in part by geographic variations in substance use [3]. Future studies should further examine polysubstance use typologies among PWH and evaluate whether they have different mental health risk profiles.”

We agree that the potential bi-directional connections between substance use and mental are important to highlight. Given the cross-sectional nature of this study, we could not examine the timing of mental health relative to substance use and vice versa. We have added the following statements to the Discussion, page 17, lines 393–398:

“Another important consideration is that our study used a cross-sectional design to characterize associations of mental health and polysubstance use problems. Longitudinal studies are needed to better understand the link between mental health and polysubstance use in PWH, including the degree to which mental health symptoms may lead to polysubstance use, e.g. as a coping strategy, polysubstance use may worsen mental health symptoms, and common drivers may contribute to both worse mental health symptoms and increased polysubstance use.”

We have added two supplemental tables. S3 Table displays unadjusted and adjusted prevalence ratios (PRs) for all covariates, stratified by race/ethnicity. S4 Table displays the change in PR estimate for the association of mental health and polysubstance use, adjusting for only one covariate at a time, stratified by race/ethnicity. As part of the planned future analyses of this hybrid implementation/effectiveness trial, we will examine how computerized screening (versus usual care by providers) may have resulted in increased identification of substance use and mental health problems. We will also examine whether patient factors, including but not limited to neighborhood deprivation, age, and race/ethnicity, were associated with receipt of screening and behavioral interventions, and these analyses are ongoing. For this reason, examining associations with completing a screen was not an objective of the current analysis. Differences between patients who were screened vs. not screened were provided descriptively in S1 Table. No additional tables were added as there were no relevant modeling analyses to report.

We have added the following statements to describe the findings reported in S3 and S4:

Results, page 11, lines 243–246:

We agree with the reviewer that understanding patterns of screening are important, and the PACE trial includes analyses of qualitative interviews as well as quantitative data on screening completion. As noted in response #5, these analyses are in process. Given the cross-sectional design and largely descriptive nature of our study objective, we feel that methods intended to answer causal questions using longitudinal data are outside the scope of this study. However, we agree with the reviewer about the important implications of non-response both for clinical care and potential limitations of our analyses. We have expanded the section on non-response, including discussion of possible bias, in the Discussion, pages 16–17, lines 378–391:

“In addition, 31% of eligible PWH did not complete a screen. High response rates are important to provide adequate care to PWH. Future qualitative analyses from the PACE trial may shed light on drivers of non-response in this patient population. In a recent study, PWH who refused to complete a screening questionnaire cited not believing it was needed or useful as one of the reasons for non-completion; however, >80% of those who completed it felt it improved their care [56]. Communication with patients, including via providers, may help impress the importance of screening measures to improve care quality and their own satisfaction. While a completion level of 31% is similar to that of other primary care-based screening studies [55, 57], our findings could have been impacted. If PWH who did not complete a screen were more likely to have mental health and substance use problems, our findings could underestimate their prevalence in this population, particularly among Hispanic PWH, as PWH without a screen were more likely to be Hispanic. Furthermore, the estimated association between mental health and polysubstance use among Hispanic men with HIV may have been biased as a result, most likely towards the null.”

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PLoS One. doi: 10.1371/journal.pone.0294483.r003

Decision Letter 1

Kimberly Page

2 Nov 2023

Racial, ethnic, and age disparities in the association of mental health symptoms and polysubstance use among persons in HIV care

PONE-D-23-17520R1

Dear Dr. Davy-Mendez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kimberly Page, PhD, MPH

Academic Editor

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Additional Editor Comments (optional): I commend the authors on their revisions. I think this paper will be highly cited as the field moves forward in examining polysubstance use and the associated health outcomes.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0294483.r004

Acceptance letter

Kimberly Page

17 Nov 2023

PONE-D-23-17520R1

Racial, ethnic, and age disparities in the association of mental health symptoms and polysubstance use among persons in HIV care

Dear Dr. Davy-Mendez:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

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Associated Data

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

Supplementary Materials

S1 Table. Characteristics of 4134 persons with HIV eligible for screening, stratified by screening completion, Kaiser Permanente Northern California, 2018–2020.

(DOCX)

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S2 Table. Demographic and clinical characteristics stratified by race and ethnicity.

(DOCX)

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S3 Table. Unadjusted and adjusted prevalence ratios (95% confidence intervals) for the association with polysubstance use, stratified by race/ethnicity, among men with HIV.

(DOCX)

Click here for additional data file.^{(36KB, docx)}

(DOCX)

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S5 Table. Polysubstance use prevalence and prevalence ratios comparing persons with HIV by mental health screening results, in a sensitivity analysis using a TAPS score ≥2.

(DOCX)

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S1 Fig. Flowchart of patient inclusion.

(DOCX)

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S2 Fig. Prevalence of polysubstance use by clinical and demographic characteristics in a sensitivity analysis using a TAPS score ≥2.

Abbreviations: API, Asian or Pacific Islander; Hetero., heterosexual; IDU, injection drug use; MSM, men who have sex with men; NDI, neighborhood deprivation index. * denotes P <0.05.

(DOCX)

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S3 Fig

(DOCX)

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Attachment

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Data Availability Statement

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PERMALINK

Racial, ethnic, and age disparities in the association of mental health symptoms and polysubstance use among persons in HIV care

Thibaut Davy-Mendez

Varada Sarovar

Tory Levine-Hall

Alexandra N Lea

Amy S Leibowitz

Mitchell N Luu

Jason A Flamm

C Bradley Hare

Jaime Dumoit Smith

Esti Iturralde

James Dilley

Michael J Silverberg

Derek D Satre

Roles

Abstract

Introduction

Methods

Study setting and design

Study measures

Statistical analysis

Results

Study sample

Table 1. Demographic and clinical characteristics of 2865 persons with HIV screened for depression, anxiety, and substance use disorders, Kaiser Permanente Northern California, 2018–2020.

Polysubstance use

Fig 1. Prevalence of polysubstance use stratified by clinical and demographic characteristics, among 2865 persons with HIV in Kaiser Permanente Northern California, 2018–2020.

Fig 2. Polysubstance combinations among 757 persons with HIV screening positive for problem substance use for two or more substances.

Associations of mental health and polysubstance use

Table 2. Polysubstance prevalence and prevalence ratios comparing persons with HIV by mental health screening results, Kaiser Permanente Northern California, 2018–2020.

Fig 3.

Sensitivity analysis

Discussion

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Kimberly Page

Roles

Author response to Decision Letter 0

Decision Letter 1

Kimberly Page

Roles

Acceptance letter

Kimberly Page

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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