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Gurnik H, Engstrom CW, McCabe SE, Evans-Polce RJ. Differences in HIV testing among sexual orientation subgroups in the United States: A national cross-sectional study. Prev Med Rep 2023; 34:102230. [PMID: 37252072 PMCID: PMC10209872 DOI: 10.1016/j.pmedr.2023.102230] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/26/2023] [Accepted: 05/07/2023] [Indexed: 05/31/2023] Open
Abstract
Understanding differences in HIV testing among US adults is a crucial step for HIV prevention. This study used cross-sectional data to assess whether HIV testing varies across sexual orientation subgroups and by important psychosocial factors. Data were from the National Epidemiological Survey on Alcohol and Related Conditions-III (NESARC-III; n = 36 309, response rate = 60.1%), a nationally representative survey of the US non-institutionalized adult population. Using logistic regression, we examined HIV testing among heterosexual concordant, heterosexual discordant, gay/lesbian, and bisexual adults. Psychosocial correlates included adverse childhood experiences (ACEs), discrimination, educational attainment, social support, and substance use disorders (SUDs). Bisexual (77.0%) and gay/lesbian (65.4%) women had a greater prevalence of HIV testing than concordant heterosexual women (51.6%), and bisexual women had a significantly higher testing prevalence than discordant heterosexual women (54.8%). Gay (84.0%) and bisexual (72.1%) men also had a significantly higher testing prevalence than discordant (48.2%) and concordant (49.4%) heterosexual men. In multivariable models, bisexual men and women (AOR = 1.8; 95% CI = 1.3-2.4) and gay men (AOR = 4.7; 95% CI = 3.2-7.1) had significantly greater odds of HIV testing than heterosexual concordant adults. A higher number of ACEs, greater social support, history of SUDs, and higher educational attainment were positively associated with HIV testing. HIV testing prevalence varied across sexual orientation subgroups; discordant heterosexual men had the lowest prevalence. Health care providers should consider a person's sexual orientation, ACEs, educational attainment, social support, and history of SUDs when evaluating HIV testing needs in the US.
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Affiliation(s)
- Holly Gurnik
- Center for the Study of Drugs, Alcohol, Smoking and Health, Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Curtiss W. Engstrom
- Center for the Study of Drugs, Alcohol, Smoking and Health, Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- Program in Survey and Data Science, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
| | - Sean Esteban McCabe
- Center for the Study of Drugs, Alcohol, Smoking and Health, Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- Institute for Research on Women and Gender, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Sexuality and Health Disparities, Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rebecca J. Evans-Polce
- Center for the Study of Drugs, Alcohol, Smoking and Health, Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
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Hasin DS, Aharonovich E, Zingman BS, Stohl M, Walsh C, Elliott JC, Fink DS, Knox J, Durant S, Menchaca R, Sharma A. HealthCall: A randomized trial assessing a smartphone enhancement of brief interventions to reduce heavy drinking in HIV care. J Subst Abuse Treat 2022; 138:108733. [PMID: 35131124 PMCID: PMC9167215 DOI: 10.1016/j.jsat.2022.108733] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 01/07/2022] [Accepted: 01/25/2022] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Heavy drinking among people living with HIV (PLWH) worsens their health outcomes and disrupts their HIV care. Although brief interventions to reduce heavy drinking in primary care are effective, more extensive intervention may be needed in PLWH with moderate-to-severe alcohol use disorder. Lengthy interventions are not feasible in most HIV primary care settings, and patients seldom follow referrals to outside treatment. Utilizing visual and video features of smartphone technology, we developed the "HealthCall" app to provide continued engagement after brief intervention, reduce drinking, and improve other aspects of HIV care with minimal demands on providers. We conducted a randomized trial of its efficacy. METHODS The study recruited alcohol-dependent PLWH (n = 114) from a large urban HIV clinic. Using a 1:1:1 randomized design, the study assigned patients to: Motivational Interviewing (MI) plus HealthCall (n = 39); NIAAA Clinician's Guide (CG) plus HealthCall (n = 38); or CG-only (n = 37). Baseline MI and CG interventions took ~25 min, with brief (10-15 min) 30- and 60-day booster sessions. HealthCall involved daily use of the smartphone app (3-5 min/day) to report drinking and health in the prior 24 h. Outcomes assessed at 30 and 60 days and at 3, 6 and 12 months included drinks per drinking day (DpDD; primary outcome) and number of drinking days, analyzed with generalized linear mixed models and pre-planned contrasts. RESULTS Study retention was excellent (85%-94% across timepoints). At 30 days, DpDD among patients in MI + HealthCall, CG + HealthCall, and CG-only was 3.80, 5.28, and 5.67, respectively; patients in MI + HealthCall drank less than CG-only and CG + HealthCall (IRRs = 0.62, 95% CI = 0.46, 0.84, and 0.64, 95% CI = 0.48, 0.87, respectively). At 6 months (end-of-treatment), DpDD was lower in CG + HealthCall (DpDD = 4.88) than MI + HealthCall (DpDD = 5.88) or CG-only (DpDD = 6.91), although these differences were not significant. At 12 months, DpDD was 5.73, 5.31, and 6.79 in MI + HealthCall, CG + HealthCall, and CG-only, respectively; DpDD was significantly lower in CG + HealthCall than CG-only (IRR = 0.71, 95% CI = 0.51, 0.98). CONCLUSIONS During treatment, patients in MI + HealthCall had lower DpDD than patients in other conditions; however, at 12 months, drinking was lowest among patients in CG + HealthCall. Given the importance of drinking reduction and the low costs/time required for HealthCall, pairing HealthCall with brief interventions merits widespread consideration.
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Affiliation(s)
- Deborah S Hasin
- Department of Psychiatry, Columbia University Irving Medical Center, 1051 Riverside Drive, New York, NY 10032, USA; New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA; Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, USA.
| | - Efrat Aharonovich
- Department of Psychiatry, Columbia University Irving Medical Center, 1051 Riverside Drive, New York, NY 10032, USA; New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA.
| | - Barry S Zingman
- Montefiore Medical Center and Albert Einstein College of Medicine, 3444 Kossuth Ave, The Bronx, NY 10467, USA.
| | - Malka Stohl
- New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA.
| | - Claire Walsh
- New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA.
| | - Jennifer C Elliott
- Department of Psychiatry, Columbia University Irving Medical Center, 1051 Riverside Drive, New York, NY 10032, USA; New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA.
| | - David S Fink
- New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA.
| | - Justin Knox
- New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA.
| | - Sean Durant
- Montefiore Medical Center and Albert Einstein College of Medicine, 3444 Kossuth Ave, The Bronx, NY 10467, USA.
| | - Raquel Menchaca
- Montefiore Medical Center and Albert Einstein College of Medicine, 3444 Kossuth Ave, The Bronx, NY 10467, USA.
| | - Anjali Sharma
- Montefiore Medical Center and Albert Einstein College of Medicine, 3444 Kossuth Ave, The Bronx, NY 10467, USA.
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Hochheimer M, Sacco P, Ware OD. Latent classes of lifetime drug use disorder in national epidemiological survey on alcohol and related conditions - III. Addict Behav 2020; 106:106379. [PMID: 32199206 DOI: 10.1016/j.addbeh.2020.106379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/06/2020] [Accepted: 02/28/2020] [Indexed: 10/24/2022]
Abstract
AIMS/BACKGROUND People diagnosed with multiple drug use disorders are high-risk subpopulations, but changes in diagnostic classification and drug use prevalence mean patterning of drug use disorders has changed in the past decade. We analyzed comorbidity patterns of lifetime drug use disorder in a general population sample. DESIGN Using latent class analysis, we derived lifetime drug use disorder classes based on dichotomous indicators of sedative, cannabis, opioid, cocaine, stimulant, hallucinogen, inhalant/solvent, club drug, heroin, and other drug use disorders in the National Epidemiologic Survey on Alcohol and Related Conditions-III (n = 36,309). Multinomial models assessed associations between sociodemographic and clinical correlates and latent class status. RESULTS Four latent classes of lifetime substance use disorder were identified: A very low risk class, a class with high opioid, sedative and heroin comorbidity, a class based on cocaine and stimulant comorbidity, and class with high likelihood of multiple lifetime drug use disorders. All higher risk classes were associated with higher risk of lifetime personality disorder and mood disorder. Conduct disorder was also associated with higher risk, but level of risk varied by class. Opioid and sedative class was associated with higher odds of lifetime eating disorder diagnosis. CONCLUSIONS Comorbidity of drug use disorders is associated with a range of lifetime mental health disorder diagnoses. Unlike previous research, we did not identify a cannabis use class, possibly due to changes in diagnostic criteria and cannabis prevalence rates.
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