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The HIV epidemic affects men who have sex with men (MSM) disproportionally in Brazil, and pre-exposure prophylaxis (PrEP) is effective for preventing HIV in this population. However, low perceived risk of HIV may influence the acceptability and decision to use PrEP. This study estimated the association between self-perception of HIV risk and acceptability of daily oral PrEP among Brazilian MSM. Respondent-driven sampling (RDS) was used for behavioral and biological surveillance to recruit 4,176 MSM 18 years or over in 12 Brazilian cities in 2016. Results were weighted using Gile's estimator in RDS Analyst software. Adjusted odds rations (OR) with 95% confidence intervals were calculated using multivariate logistic regression. Acceptability of daily oral PrEP was high (69.7%) among the 3,544 MSM available for analysis. Most participants self-reported low or moderate risk of HIV infection (67.2%) and a small proportion (9.3%) reported high risk. A dose–response relationship was observed between acceptability of PrEP and self-reported risk: PrEP acceptability was 1.88 times higher (OR 1.8; 95% CI: 1.24–2.85) among MSM whose perceived risk of HIV infection was low or moderate, and 5 times higher (OR 5.68; 95% CI: 2.54–12.73) among those who self-reported high risk compared to MSM reporting no HIV risk. MSM with the highest risk perception of HIV reported higher rates of PrEP acceptability. Given the availability of daily oral PrEP in the public health care system in Brazil, we suggest emphasizing counseling about self-perception of HIV risk as part of routine HIV prevention services. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected. [ABSTRACT FROM AUTHOR]

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