RESUMEN
Inadequate knowledge is one of the principal obstacles for preventing HIV/AIDS spread. Worldwide, it is reported that adolescents and young people have a higher vulnerability of being infected. Thus, the need to understand youths' knowledge towards HIV/AIDS becomes crucial. This study aimed to identify the determinants and develop a predictive model to estimate HIV/AIDS knowledge among this target population in Peru. Data from the 2019 DHS Survey were used. The software RStudio and RapidMiner were used for quasi-binomial logistic regression and computational model building, respectively. Five classification algorithms were considered for model development and their performance was assessed using accuracy, sensitivity, specificity, FPR, FNR, Cohen's kappa, F1 score and AUC. The results revealed an association between 14 socio-demographic, economic and health factors and HIV/AIDS knowledge. The accuracy levels were estimated between 59.47 and 64.30%, with the random forest model showing the best performance (64.30%). Additionally, the best classifier showed that the gender of the respondent, area of residence, wealth index, region of residence, interviewee's age, highest educational level, ethnic self-perception, having heard about HIV/AIDS in the past, the performance of an HIV/AIDS screening test and mass media access have a major influence on HIV/AIDS knowledge prediction. The results suggest the usefulness of the associations found and the random forest model as a predictor of knowledge of HIV/AIDS and may aid policy makers to guide and reinforce the planning and implementation of healthcare strategies.
Asunto(s)
Síndrome de Inmunodeficiencia Adquirida , Infecciones por VIH , Humanos , Adolescente , Adulto Joven , Modelos Logísticos , Perú/epidemiología , Síndrome de Inmunodeficiencia Adquirida/epidemiología , Algoritmos , Aprendizaje Automático , Infecciones por VIH/epidemiología , Infecciones por VIH/prevención & control , Conocimientos, Actitudes y Práctica en SaludRESUMEN
The COVID-19 crisis has produced worldwide changes from people's lifestyles to travel restrictions imposed by world's nations aiming to keep the virus out. Several countries have created digital information applications to help control and manage the COVID-19 crisis, such as the creation of contact tracing apps. The Peruvian government in collaboration with several institutions developed PerúEnTusManos, an epidemiological tracing application. The application uses georeferencing to study users' movements and creates individual mobility patterns from the Peruvian citizens as well as detects crowds. In this article, we present a process to detect possible infected individuals based on probabilities assigned to people that had contact with someone who tested positive for COVID-19, using data collected from PerúEnTusManos. The preliminary evaluation shows promising results when detecting probabilities of possible infected individuals as well as the most infected districts in Peru. The ultimate goal of the application in Peru is to provide reliable information to health authorities to make informed decisions about the assignations of the available clinical tests and the economic re-activation.