Field studies indicate that people may form impressions about potential partners' HIV risk, yet lack insight into what underlies such intuitions. The present study examined which cues may give rise to the perception of riskiness. Towards this end, portrait pictures of persons that are representative of the kinds of images found on social media were evaluated by independent raters on two sets of data: First, sixty visible cues deemed relevant to person perception, and second, perceived HIV risk and trustworthiness, health, and attractiveness. Here, we report correlations between cues and perceived HIV risk, exposing cue-criterion associations that may be used to infer intuitively HIV risk. Second, we trained a multiple cue-based model to forecast perceived HIV risk through cross-validated predictive modelling. Trained models accurately predicted how 'risky' a person was perceived (r = 0.75) in a novel sample of portraits. Findings are discussed with respect to HIV risk stereotypes and implications regarding how to foster effective protective behaviors.
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HIV Med
January 2025
National Centre for Epidemiology, Carlos III Health Institute, Madrid, Spain.
Objectives: We aimed to describe health-related quality of life (HRQoL), overall and across its dimensions, identify associated factors, and assess changes over time among people with HIV (PWH) from the Spanish multicentre CoRIS cohort.
Methods: We developed a mobile app to collect HRQoL data every 3 months using the WHOQOL-HIV-BREF questionnaire (31 items across six domains), among PWH followed in CoRIS in 2021-2023. Factors associated with good/very good global HRQoL and with domain-specific mean scores were identified using multivariable logistic and linear regression, respectively.
Front Nephrol
January 2025
Department of Nephrology, Nephrology Vanderbilt Institute for Global Health (VIGH), Nashville, TN, United States.
Introduction: Antiretroviral therapy (ART) increases the life expectancy of persons living with HIV (PLWH), but not without potentially serious adverse effects. Tenofovir disoproxil fumarate (TDF) can cause nephrotoxicity, manifesting as acute kidney injury (AKI) that may persist after treatment discontinuation. Kidney injury biomarkers such as kidney injury molecule-1 (KIM-1), retinol-binding protein-4 (RBP-4), interleukin-18 (IL-18), and neutrophil gelatinase-associated lipocalin (NGAL) can aid early diagnosis and predict TDF-associated nephrotoxicity.
View Article and Find Full Text PDFInt J Public Health
January 2025
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States.
Objectives: This study analyzes survey data across 21 countries to explore correlations between delays in blood testing and the prevalence of seven health conditions: thalassaemias, sickle cell disorders, malaria, HIV, high fasting plasma glucose, impaired kidney function, and high LDL cholesterol.
Methods: We analyzed Pandemic Recovery Survey data via multivariable logistic regression to compare blood test delays between individuals with and without medical conditions, while adjusting for sociodemographic factors. We also examined the disease burden using disability-adjusted life years (DALYs) and summary exposure values (SEV) rates.
AIDS Res Treat
January 2025
Department of Biomedical Sciences, School of Medicine, Debre Markos University, Debre Markos, Ethiopia.
Atherogenic index of plasma (AIP) and high-sensitivity C-reactive protein (hsCRP) levels which are strong predictors of the risk of cardiovascular disease (CVD) seen elevated in the serum of people living with HIV (PLWH) on HAART and in those with low cluster of differentiation-4 (CD4) cell counts. Thus, this study aimed to evaluate AIP and hsCRP levels among PLWH on dolutegravir (DTG) and ritonavir-boosted atazanavir-based (ATV/r) antiretroviral therapy (ART) and their correlations to CD4 cell counts. The study design was an institutional-based comparative cross-sectional study conducted from November 4, 2021, to January 4, 2022.
View Article and Find Full Text PDFPublic Health Rep
January 2025
Division of Injury Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Data science is an emerging field that provides new analytical methods. It incorporates novel data sources (eg, internet data) and methods (eg, machine learning) that offer valuable and timely insights into public health issues, including injury and violence prevention. The objective of this research was to describe ethical considerations for public health data scientists conducting injury and violence prevention-related data science projects to prevent unintended ethical, legal, and social consequences, such as loss of privacy or loss of public trust.
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