Continuidade do cuidado na atenção primária e profissionais de saúde entre adultos e idosos na cidade Pelotas, 2021

O presente estudo teve como objetivo avaliar, segundo a interseccionalidade de sexo, cor da pele, escolaridade e classificação econômica, a continuidade do cuidado de adultos e idosos, residentes no município de Pelotas-RS no ano de 2021, com um serviço de saúde da atenção básica e/ou um profissional de saúde

Iniquidades em saúde e multimorbidade na perspectiva da interseccionalidade: ELSI-Brasil 2015-2016

O presente estudo teve como objetivo avaliar a associação entre iniquidades em saúde e multimorbidade, em idosos, sob a perspectiva da interseccionalidade

Emergency department use and Artificial Intelligence in Pelotas: design and baseline results

RESUMO Objetivo: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil. Methods: The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year. Results: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension. Conclusion: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state