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Beckham SW, Glick JL, Schneider KE, Allen ST, Shipp L, White RH, Park JN, Sherman SG. Latent Classes of Polysubstance Use and Associations with HIV Risk and Structural Vulnerabilities among Cisgender Women Who Engage in Street-Based Transactional Sex in Baltimore City. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19073783. [PMID: 35409469 PMCID: PMC8997521 DOI: 10.3390/ijerph19073783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 03/14/2022] [Accepted: 03/20/2022] [Indexed: 02/04/2023]
Abstract
We describe patterns of polysubstance use and associations with HIV risk-related behaviors among women engaged in street-based transactional sex, an understudied yet important population and area of research. This sample was restricted to cisgender women who reported drug use (n = 244) in the baseline of the longitudinal SAPPHIRE cohort study. Latent class analysis (LCA) was conducted using drug use measures (route of administration (injection/non-injection); type of drug (specific opioids, stimulants)) and selection based on fit statistics and qualitative interpretation of the classes. Polysubstance use was prevalent (89% ≥ 2), and 68% had injected drugs in the past 3 months. A three-class solution was selected: Class 1 ("heroin/cocaine use", 48.4% of sample), Class 2 ("poly-opioid use", 21.3%), and Class 3 ("poly-route, polysubstance use", 30.3%). Class 3 was significantly younger, and Class 2 was disproportionately non-White. Women reported high levels of housing (63%) and food (55%) insecurity, condomless sex with clients (40%), and client-perpetrated violence (35%), with no significant differences by class. Obtaining syringes from syringe services programs differed significantly by class, despite injection behaviors in all classes. Tailored HIV and overdose prevention programming that considers drug use patterns would strengthen their impact.
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Affiliation(s)
- Sam Wilson Beckham
- Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (J.L.G.); (S.T.A.); (R.H.W.); (J.N.P.); (S.G.S.)
- Correspondence:
| | - Jennifer L. Glick
- Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (J.L.G.); (S.T.A.); (R.H.W.); (J.N.P.); (S.G.S.)
| | - Kristin E. Schneider
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21201, USA;
| | - Sean T. Allen
- Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (J.L.G.); (S.T.A.); (R.H.W.); (J.N.P.); (S.G.S.)
| | - Lillian Shipp
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21201, USA;
| | - Rebecca Hamilton White
- Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (J.L.G.); (S.T.A.); (R.H.W.); (J.N.P.); (S.G.S.)
| | - Ju Nyeong Park
- Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (J.L.G.); (S.T.A.); (R.H.W.); (J.N.P.); (S.G.S.)
| | - Susan G. Sherman
- Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (J.L.G.); (S.T.A.); (R.H.W.); (J.N.P.); (S.G.S.)
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Li Y, Ragland M, Austin E, Young K, Pratte K, Hokanson JE, Beaty TH, Regan EA, Rennard SI, Wern C, Jacobs MR, Tal-Singer R, Make BJ, Kinney GL. Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene ® Study. Clin Epidemiol 2020; 12:1171-1181. [PMID: 33149694 PMCID: PMC7602898 DOI: 10.2147/clep.s279075] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/06/2020] [Indexed: 01/21/2023] Open
Abstract
PURPOSE Medication patterns include all medications in an individual's clinical profile. We aimed to identify chronic co-morbidity treatment patterns through medication use among COPDGene participants and determine whether these patterns were associated with mortality, acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and quality of life. MATERIALS AND METHODS Participants analyzed here completed Phase 1 (P1) and/or Phase 2 (P2) of COPDGene. Latent class analysis (LCA) was used to identify medication patterns and assign individuals into unobserved LCA classes. Mortality, AECOPD, and the St. George's Respiratory Questionnaire (SGRQ) health status were compared in different LCA classes through survival analysis, logistic regression, and Kruskal-Wallis test, respectively. RESULTS LCA identified 8 medication patterns from 32 classes of chronic comorbid medications. A total of 8110 out of 10,127 participants with complete covariate information were included. Survival analysis adjusted for covariates showed, compared to a low medication use class, mortality was highest in participants with hypertension+diabetes+statin+antiplatelet medication group. Participants in hypertension+SSRI+statin medication group had the highest odds of AECOPD and the highest SGRQ score at both P1 and P2. CONCLUSION Medication pattern can serve as a good indicator of an individual's comorbidities profile and improves models predicting clinical outcomes.
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Affiliation(s)
- Yisha Li
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Margaret Ragland
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Erin Austin
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
| | - Kendra Young
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - John E Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Terri H Beaty
- Bloomberg School of Public Health, University of John Hopkins, Baltimore, MD, USA
| | | | - Stephen I Rennard
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NB, USA
| | - Christina Wern
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | | | - Gregory L Kinney
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - On Behalf of theCOPDGene investigators
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
- National Jewish Health, Denver, CO, USA
- Bloomberg School of Public Health, University of John Hopkins, Baltimore, MD, USA
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NB, USA
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- School of Pharmacy, Temple University, PA, Pennsylvania, USA
- COPD Foundation, Washington, D.C., USA
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