The impact of the COVID-19 pandemic is far reaching, with devastating effects on individuals, communities, and societies across the world. People with chronic health conditions may be at greater risk of contracting or experiencing complications from COVID-19. In addition to illness or death for those who contract the virus, the physical distancing required to flatten the curve of new cases is having a negative impact on the economy, the effects of which intersect with mental health and other existing health concerns, thus affecting marginalized communities. Given that HIV also has a disproportionate impact on marginalized communities, COVID-19 is affecting people with HIV (PWH) in unique ways and will continue to have an impact on HIV research and treatment after the COVID-19 crisis passes. Using the biopsychosocial framework to contextualize the impact of COVID-19 on PWH, the purpose of this review article is to: (1) outline the similarities and differences between the COVID-19 and HIV pandemics; (2) describe the current and future impact of COVID-19 on PWH; and (3) outline a call to action for scientists and practitioners to respond to the impact of COVID-19 on HIV prevention and treatment.

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Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

Given the adverse impact of stigma on efforts to increase access to health care services (e.g., HIV screening and care, mental health), it is imperative that researchers not further stigmatize their study participants. The history of unethical research on marginalized communities necessitates careful consideration for researchers who hope to address issues of stigma among historically marginalized groups. This article will provide a guide to research conceptualization and an exploration of ethical considerations to minimize stigmatizing experiences for vulnerable populations participating in qualitative research. The authors aim to inform and prepare qualitative researchers for the complexities of conceptualizing and implementing research among communities experiencing stigma. The article will include a discussion of potential biases, positionality and reflexivity, possible ethical dilemmas, and participatory methodologies, as well as provide recommendations for specific safeguards that may be appropriate for studying minority populations using examples from previous qualitative research with vulnerable and stigmatized communities.

Clear disparities exist in risk factors, cervical cancer characteristics, and survival time between WH and WNH women.