Anticipated HIV Stigma and Delays in Regular HIV Testing Behaviors Among Sexually-Active Young Gay, Bisexual, and Other Men Who Have Sex with Men and Transgender Women

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Abstract

Young gay, bisexual and other men who have sex with men (YGBMSM) and young transgender women are disproportionately affected by HIV/AIDS. The success of biomedical prevention strategies is predicated on regular HIV testing; however, there has been limited uptake of testing among YGBMSM and young transgender women. Anticipated HIV stigma—expecting rejection as a result of seroconversion- may serve as a significant barrier to testing. A cross-sectional sample of YGBMSM ( n=719, 95.5%) and young transgender women ( n = 33, 4.4%) ages 15 to 24 were recruited to participate in a one-time survey. Approximately one-third of youth had not tested within the last 6 months. In a multivariable model, anticipated HIV stigma and reporting a non-gay identity were associated with an increased odds of delaying regular HIV testing. Future research and interventions are warranted to address HIV stigma, in order to increase regular HIV testing among YGBMSM and transgender women.

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Transgender stigma and health: A critical review of stigma determinants, mechanisms, and interventions.

Jaclyn M White Hughto, Sari Reisner, John E Pachankis (2015)

Transgender people in the United States experience widespread prejudice, discrimination, violence, and other forms of stigma.

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Record: found
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Article: not found

From conceptualizing to measuring HIV stigma: a review of HIV stigma mechanism measures.

Valerie Earnshaw, Stephenie Chaudoir (2009)

Recent analyses suggest that lack of clarity in the conceptualization and measurement of HIV stigma at an individual level is a significant barrier to HIV prevention and treatment efforts. In order to address this concern, we articulate a new framework designed to aid in clarifying the conceptualization and measurement of HIV stigma among individuals. The HIV Stigma Framework explores how the stigma of HIV elicits a series of stigma mechanisms, which in turn lead to deleterious outcomes for HIV uninfected and infected people. We then apply this framework to review measures developed to gauge the effect of HIV stigma since the beginning of the epidemic. Finally, we emphasize the utility of using three questions to guide future HIV stigma research: who is affected by, how are they affected by, and what are the outcomes of HIV stigma?

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To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.

Alan Hubbard, Jennifer Ahern, Nancy L Fleischer … (2010)

Two modeling approaches are commonly used to estimate the associations between neighborhood characteristics and individual-level health outcomes in multilevel studies (subjects within neighborhoods). Random effects models (or mixed models) use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. These methods are used in place of basic regression approaches because the health of residents in the same neighborhood may be correlated, thus violating independence assumptions made by traditional regression procedures. This violation is particularly relevant to estimates of the variability of estimates. Though the literature appears to favor the mixed-model approach, little theoretical guidance has been offered to justify this choice. In this paper, we review the assumptions behind the estimates and inference provided by these 2 approaches. We propose a perspective that treats regression models for what they are in most circumstances: reasonable approximations of some true underlying relationship. We argue in general that mixed models involve unverifiable assumptions on the data-generating distribution, which lead to potentially misleading estimates and biased inference. We conclude that the estimation-equation approach of population average models provides a more useful approximation of the truth.