A Latent Class Analysis of Stigmatizing Attitudes and Knowledge of HIV Risk among Youth in South Africa

Abstract

BackgroundThe current study aims to investigate how the ability to accurately gauge risk factors associated with contracting HIV while taking into consideration various individual and community level socio-demographic characteristics (e.g., race and poverty) predicts the nature of stigmatizing attitudes toward persons with HIV.MethodsData from a sample of 1,347 Cape Town area youth who participated in the Cape Area Panel Study (CAPS) Wave 2a were used. Latent Class Analysis was conducted to ascertain whether response patterns regarding knowledge of HIV contraction suggest the presence of subgroups within the sample.ResultsFindings indicate that there are four latent classes representing unique response pattern profiles regarding knowledge of HIV contraction. Additionally, our results suggest that those in South Africa who are classified as “white,” live in more affluent communities, and have more phobic perceptions of HIV risk are also more likely to have the most stigmatizing attitudes toward those who are HIV positive.ConclusionImplications of these findings include extending HIV knowledge, education, and awareness programs to those who are not traditionally targeted in an attempt to increase levels of knowledge about HIV and, consequently, decrease stigma.

Patterns of RSA and observed distress during the still‐face paradigm predict later attachment, compliance and behavior problems: A person‐centered approach

“…Latent profile analysis is a statistical procedure that can be used to classify individuals into latent types using a set of continuous indicators (Geiser, ). The optimal number of profiles is determined using the statistical indices such as the Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), Adjusted Bayesian Information Criterion (ABIC), entropy, and the Lo‐Mendell‐Rubin LRT likelihood (LMR) test and Vuong‐Lo‐Mendell Rubin likelihood ratio (VLMR) test as well as by considering the principle of parsimony and model interpretation (i.e., considering substantive theory, profile size, meaningfulness of each profile) (Brinkley‐Rubinstein & Craven, ; Chung, Anthony, & Schafer, ; Muthén, ; Nylund, Asparouhov, & Muthén, ). Generally, smaller BIC, AIC and ABIC and larger entropy values indicate better model fit (Geiser, ).…”

Evaluation of Hiv / Aids Information and Stigmatization Level and Related Factors

“…[8,10] Damgalama korkusu, HIV/AIDS için riskli davranış sergileyen kişilerin HIV testi yaptırmalarını veya seropozitif durumlarını cinsel eşlere, aileye ve arkadaşlara açıklamalarını engelleyebilmektedir. [6,11,12] HIV/AIDS tanısı konmuş kişilerde damgalama, enfekte bireylerin sağlık kurumlarına bu sorunla ile ilgili problemler için zamanında tıbbi tedavi başvurusunu tamamen engelleyerek veya geciktirerek bu kişilerin yeterli tıbbi bakım almasını önlemektedir. Damgalama ayrıca hastanın ilaç rejimlerine uyumuna müdahale edebilmektedir.…”

“…HIV/AIDS hakkında bilgi düzeyi az olan kişilerde HIV/AIDS hakkında damgalama düzeyinin daha yüksek olması beklenmektedir. [12,20,21] Bu çalışmada HIV/AIDS ile ilgili bilgi düzeyi arttıkça damgalamanın azaldığı bulundu. Daha önce yapılan araştırmalarda da, bilgi düzeyi artışının, damgalama düzeyini azalttığı bildirilmiştir.…”

Substance use, mental illness, and familial conflict non-negotiation among HIV-positive African-Americans: latent class regression and a new syndemic framework

“…Several previous investigations of syndemic classes have utilized LCA as a meaningful way to classify subtypes of individuals based on their unique behavioral patterns of HIV risk, to inform treatment interventions (Keiser et al, 2012; Noor et al, 2014; Brinkley-Rubinstein & Craven, 2014; Halkitis et al, 2012; Konda et al, 2011). In the present LCA model, the largest syndemic class, Class 1, also had the lowest syndemic burden of moderate mental illness (43%; N =351).…”