A latent class analysis of alcohol and drug use immediately before or during sex among women

Reynolds, Grace L.; Fisher, Dennis G.

doi:10.1080/00952990.2018.1528266

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…While people who use multiple substances through injection generally face greater health-related risks, these studies show that non-injecting classes also face unique risks and vulnerabilities that should be addressed [ 38 , 42 ]. Furthermore, while LCAs exploring substance use have found polysubstance use to be associated with sex work [ 26 , 42 ], we found few LCA studies exploring substance use specifically among women who sell sex [ 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

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

Beckham

Glick

Schneider

et al. 2022

IJERPH

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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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Section: Introductionmentioning

confidence: 99%

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

Beckham

Glick

Schneider

et al. 2022

IJERPH

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“…Collins et al [21] described the whole process of fitting LCA including the key features, although most of those key features are also available in other packages, nowadays. Listed are a couple of examples using Proc LCA: Reynolds et al [22], and Ryoo et al [23], they explored gifted children's victimization and bullying by using Proc LCA. As of Jan, 2022, Proc LCA is still requiring additional installation within SAS.…”

Section: Proc Lcamentioning

confidence: 99%

gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

Ryoo¹,

Park²,

Kim³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become increasingly popular along with the popularity of machine learning techniques. Although there are many software packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural equation modeling framework. The package, gscaLCA which is implemented in the R statistical computing environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling. More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis (GSCA), the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities, which is applicable in mixture modeling such as latent class analysis in statistics. As a hybrid model between data clustering in classifications and model-based mixture modeling approach, fuzzy clusterwise GSCA, denoted as gscaLCA, encompasses many advantages from both methods: (1) soft partitioning from FC and (2) efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA. The main function, gscaLCA, works for both binary and ordered categorical variables. In addition, gscaLCA can be used for latent class regression as well. Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA. This paper contributes to providing a methodological tool, gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.

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<p>Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene<sup>®</sup> Study</p>

Li¹,

Ragland²,

Austin³

et al. 2020

CLEP

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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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A latent class analysis of alcohol and drug use immediately before or during sex among women

Cited by 3 publications

References 47 publications

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

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

gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

<p>Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene<sup>®</sup> Study</p>

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