ABSTRACT
This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil's COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil's primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil's elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country's National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country's COVID-19 response.
O objetivo deste artigo é analisar o uso da inteligência artificial espacial no contexto da imunização contra COVID-19 para a seleção adequada dos recursos necessários. Trata-se de estudo ecológico de caráter transversal baseado em uma abordagem espaço-temporal utilizando dados secundários, em Unidades Básicas de Saúde do Brasil. Foram adotados quatro passos analíticos para atribuir um volume de população por unidade básica, aplicando algoritmos de inteligência artificial a imagens de satélite. Em paralelo, as condições de acesso à internet móvel e o mapeamento de tendências espaço-temporais de casos graves de COVID-19 foram utilizados para caracterizar cada município do país. Cerca de 18% da população idosa brasileira está a mais de 4 quilômetros de distância de uma sala de vacina. No total, 4.790 municípios apresentaram tendência de agudização de casos de Síndrome Respiratória Aguda Grave. As regiões Norte e Nordeste apresentaram o maior número de Unidades Básicas de Saúde com mais de 5 quilômetros de distância de antenas de celular. O Plano nacional de vacinação requer o uso de estratégias inovadoras para contornar os desafios do país. O uso de metodologias baseadas em inteligência artificial espacial pode contribuir para melhoria do planejamento das ações de resposta à COVID-19.
Subject(s)
COVID-19 Vaccines , COVID-19 , Aged , Artificial Intelligence , Brazil , Cities , Cross-Sectional Studies , Humans , Intelligence , SARS-CoV-2 , VaccinationABSTRACT
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.
Subject(s)
Artificial Intelligence , Obesity , Adult , Female , Humans , Middle Aged , Male , Socioeconomic Factors , Brazil , Emergency Service, HospitalABSTRACT
INTRODUCTION: Since 2020, the world has been going through a viral pandemic with a high morbidity and mortality rate along with the potential to evolve from an acute infection to post-acute and long-COVID, which is still in the process of elucidation. Diagnostic and prognostic research is essential to understand the complexity of factors and contexts involving the illness's process. This protocol introduces a study strategy to analyse predictors, sequelae, and repercussions of COVID-19 in adults and older adults with different disease severities in the State of Paraná, Brazil. METHODS AND ANALYSIS: A mixed-methods sequential explanatory design. The quantitative data will be conducted by an ambispective cohort study, which will explore the manifestations of COVID-19 for 18 months, with nearly 3000 participants with confirmed diagnoses of COVID-19 (reverse transcription-PCR test) between March and December of 2020, retrieved from national disease reporting databases, over 18 years old, living in a Brazilian State (Paraná) and who survived the viral infection after being discharged from a health service. Data collection will be conducted through telephone interviews, at two different occasions: the first will be a recall 12 months after the acute phase as a retrospective follow-up, and the second will be another prospective interview, with data of the following 6 months. For the qualitative step, Grounded Theory will be used; participants will be selected from the cohort population. The first sample group will be composed of people who were discharged from the intensive care unit, and other sample groups will be composed according to theoretical saturation. The qualitative data will follow the temporal design and classification of the disease provided for in the cohort. ETHICS AND DISSEMINATION: Ethics approval was granted by the State University of Maringá, under opinion number: 4 165 272 and CAAE: 34787020.0.0000.0104 on 21 July 2020, and Hospital do Trabalhador (Worker's Hospital), which is accountable for the Health Department of the State of Paraná, under opinion number: 4 214 589 and CAAE: 34787020.0.3001.5225 on 15 August 2020. The participants will verbally consent to the research, their consent will be recorded, and the informed consent form will be sent by mail or email. Outcomes will be widely disseminated through peer-reviewed manuscripts, conference presentations, media and reports to related authorities.
Subject(s)
COVID-19 , Humans , Aged , Adolescent , SARS-CoV-2 , Brazil/epidemiology , Post-Acute COVID-19 Syndrome , Cohort Studies , Prospective Studies , Retrospective StudiesABSTRACT
AIMS AND OBJECTIVES: This paper aims to: (a) determine the personal, sociodemographic, clinical, behavioural, and social characteristics of older Brazilians with clinical evidence of long COVID; (b) evaluate perceived quality of life and determine its association with personal, sociodemographic, behavioural, clinical and social variables; and (c) assess significant predictors of high perceived QoL. BACKGROUND: Given the inherent vulnerabilities of the ageing process, the older people are an at-risk group for both contagion of SARS-CoV-2 and the perpetuation of residual symptoms after infection, the so-called long COVID or post-COVID syndrome. DESIGN: A cross-sectional survey design using the STROBE checklist. METHODS: Brazilian older people with long COVID syndrome (n = 403) completed a phone survey measuring personal, sociodemographic, behavioural, clinical, and social characteristics, and perceived Quality of Life (QoL). Data were collected from June 2021-March 2022. A multiple linear regression model was performed to identify salient variables associated with high perceived QoL. RESULTS: The mean age of participants was 67.7 ± 6.6 years old. The results of the multivariate regression model showed that race, home ownership, daily screen time, musculoskeletal and anxiety symptoms, and work situation were the significant predictors of QoL among COVID-19 survivors. CONCLUSIONS: Knowledge about the persistence of physical, emotional, and social symptoms of COVID-19 can help nurses and other healthcare providers to improve the management of survivors, bringing benefits to the whole society. RELEVANCE TO CLINICAL PRACTICE: Given the novelty of long-COVID and its heterogeneous trajectory, interventions focusing on the repercussions and requirements unique to more vulnerable older persons should be developed and these aspects should be included in public health recommendations and policymakers' concerns. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution was required to design, to outcome measures or undertake this research. Patients/members of the public contributed only to the data collection.
ABSTRACT
Introduction Since 2020, the world has been going through a viral pandemic with a high morbidity and mortality rate along with the potential to evolve from an acute infection to post-acute and long-COVID, which is still in the process of elucidation. Diagnostic and prognostic research is essential to understand the complexity of factors and contexts involving the illness’s process. This protocol introduces a study strategy to analyse predictors, sequelae, and repercussions of COVID-19 in adults and older adults with different disease severities in the State of Paraná, Brazil. Methods and analysis A mixed-methods sequential explanatory design. The quantitative data will be conducted by an ambispective cohort study, which will explore the manifestations of COVID-19 for 18 months, with nearly 3000 participants with confirmed diagnoses of COVID-19 (reverse transcription-PCR test) between March and December of 2020, retrieved from national disease reporting databases, over 18 years old, living in a Brazilian State (Paraná) and who survived the viral infection after being discharged from a health service. Data collection will be conducted through telephone interviews, at two different occasions: the first will be a recall 12 months after the acute phase as a retrospective follow-up, and the second will be another prospective interview, with data of the following 6 months. For the qualitative step, Grounded Theory will be used;participants will be selected from the cohort population. The first sample group will be composed of people who were discharged from the intensive care unit, and other sample groups will be composed according to theoretical saturation. The qualitative data will follow the temporal design and classification of the disease provided for in the cohort. Ethics and dissemination Ethics approval was granted by the State University of Maringá, under opinion number: 4 165 272 and CAAE: 34787020.0.0000.0104 on 21 July 2020, and Hospital do Trabalhador (Worker’s Hospital), which is accountable for the Health Department of the State of Paraná, under opinion number: 4 214 589 and CAAE: 34787020.0.3001.5225 on 15 August 2020. The participants will verbally consent to the research, their consent will be recorded, and the informed consent form will be sent by mail or email. Outcomes will be widely disseminated through peer-reviewed manuscripts, conference presentations, media and reports to related authorities.
ABSTRACT
OBJECTIVE: To analyze the spatial correlation between confirmed cases of covid-19 and the intensive care unit beds exclusive to the disease in municipalities of Paraná. METHODS: This is an epidemiological study of ecological type which used data from the Epidemiological Report provided by the Department of Health of Paraná on the confirmed cases of covid-19 from March 12, 2020, to January 18, 2021. The number of intensive care beds exclusive to covid-19 in each municipality of Paraná was obtained by the Cadastro Nacional de Estabelecimentos de Saúde (CNES - National Registry of Health Establishments), provided online by the Departamento de Informática do Sistema Único de Saúde (Datasus - Informatics Department of the Brazilian Unified Health System). The Bivariate Moran's Index (local and global) was used to analyze the intensive care bed variable and spatial correlation, with a 5% significance level. LISA Map was used to identify critical and transition areas. RESULTS: In the analyzed period, we found 499,777 confirmed cases of covid-19 and 1,029 intensive care beds exclusive to the disease in Paraná. We identified a positive spatial autocorrelation between the confirmed cases of covid-19 (0.404-p ≤ 0.001) and intensive care beds exclusive to the disease (0.085-p ≤ 0.001) and disparities between the regions of Paraná. CONCLUSION: Spatial analysis indicated that confirmed cases of covid-19 are related to the distribution of intensive care beds exclusive to the disease in Paraná, allowing us to find priority areas of care in the state regarding the dissemination and control of the disease.