Background Coronavirus disease 2019 (COVID-19) has spread worldwide, causing a high burden of morbidity and mortality, and has affected the various health service systems in the world, demanding disease monitoring and control strategies. The objective of this study was to identify risk areas using spatiotemporal models and determine the COVID-19 time trend in a federative unit of northeastern Brazil. Methods An ecological study using spatial analysis techniques and time series was carried out in the state of Maranhão, Brazil. All new cases of COVID-19 registered in the state from March 2020 to August 2021 were included. Incidence rates were calculated and spatially distributed by area, while the spatiotemporal risk territories were identified using scan statistics. The COVID-19 time trend was determined using Prais–Winsten regressions. Results Four spatiotemporal clusters with high relative risks for the disease were identified in seven health regions located in the southwest/northwest, north and east of Maranhão. The COVID-19 time trend was stable during the analysed period, with higher rates in the regions of Santa Inês in the first and second waves and Balsas in the second wave. Conclusions The heterogeneously distributed spatiotemporal risk areas and the stable COVID-19 time trend can assist in the management of health systems and services, facilitating the planning and implementation of actions toward the mitigation, surveillance and control of the disease.

Background The detection of spatiotemporal clusters of deaths by coronavirus disease 2019 (COVID-19) is essential for health systems and services, as it contributes to the allocation of resources and helps in effective decision making aimed at disease control and surveillance. Thus we aim to analyse the spatiotemporal distribution and describe sociodemographic and clinical and operational characteristics of COVID-19-related deaths in a Brazilian state. Methods A descriptive and ecological study was carried out in the state of Maranhão. The study population consisted of deaths by COVID-19 in the period from 29 March to 31 July 2020. The detection of spatiotemporal clusters was performed by spatiotemporal scan analysis. Results A total of 3001 deaths were analysed with an average age of 69 y, predominantly in males, of brown ethnicity, with arterial hypertension and diabetes, diagnosed mainly by reverse transcription polymerase chain reaction in public laboratories. The crude mortality rates the municipalities ranged from 0.00 to 102.24 deaths per 100 000 inhabitants and three spatiotemporal clusters of high relative risk were detected, with a mortality rate ranging from 20.25 to 91.49 deaths per 100 000 inhabitants per month. The headquarters was the metropolitan region of São Luís and municipalities with better socio-economic and health development. Conclusions The heterogeneous spatiotemporal distribution and the sociodemographic and clinical and operational characteristics of deaths by COVID-19 point to the need for interventions.

Objetivou-se analisar a produção científica nacional e internacional acerca das técnicas de análise espacial utilizadas na detecção da coinfecção tuberculose/HIV no mundo. Trata-se de uma revisão integrativa da literatura, que utilizou a estratégia PICo para elaboração da pergunta norteadora: Quais são as evidências científicas relacionadas à utilização de técnicas de análise espacial na detecção da coinfecção tuberculose/HIV no mundo? Foram realizadas buscas nas bases de dados Lilacs, PubMed® e Medline®, e na biblioteca eletrônica SciELO em janeiro de 2020, utilizando os descritores “Tuberculosis (TB) HIV coinfection”, “spatial analysis”, “disease notification”, “risk area”, “TB/HIV”, “spatiotemporal” isolados e/ou combinados. Foram incluídos artigos disponíveis na íntegra nos idiomas português, inglês e espanhol, sendo excluídas dissertações, teses, revisões de literatura e notas editoriais. Foram analisados 16 artigos, publicados a partir de 2006. Todos utilizaram dados secundários, com delineamento ecológico. Técnicas de análise de dados de área, análise bayesiana, Moran global e local, estatística de varredura, modelo geostatístico gaussiano e Krigagem foram utilizadas para determinar a distribuição heterogênea da coinfecção, com destaque para os coeficientes de prevalência e mortalidade, principalmente em cenários como Brasil e África. A diversidade de técnicas de análise espacial identificadas, com destaque para análise de dados de área e análise bayesiana, contribuiu para a avaliação e a identificação de fatores de risco dos indicadores de morbimortalidade pela coinfecção, alvos de ação e planejamento das políticas públicas de saúde, possibilitando a instituição de ações voltadas para além de questões curativas e preventivas, pautadas na redução das desigualdades socioespaciais nos diferentes cenários.

Objetivou-se detectar aglomerados espaciais e espaço-temporais de tuberculose em município do nordeste brasileiro prioritário para o controle da doença. Trata-se de um estudo ecológico, no qual foram considerados os casos novos de tuberculose ocorridos em Imperatriz (MA), entre 2009 e 2018, coletados juntos ao Sistema de Informação de Agravos de Notificação. Utilizou-se da técnica de estatística de varredura para a detecção dos aglomerados espaciais e espaço-temporais dos casos. Foram identificados três aglomerados espaciais de alto risco relativo (RR): aglomerado 1 (RR=2,10), aglomerado 2 (RR= 2,20) e aglomerado 3 (RR=2,70). A análise espaço-temporal evidenciou dois aglomerados de alto risco relativo, o aglomerado 1 (RR=2,80) que ocorreu entre 01/01/2009 a 31/12/2013 e o aglomerado 2 (RR= 3,40) com ocorrência entre 01/01/2009 a 31/12/2010. Tais achados apontam para a necessidade da elaboração de estratégias para o combate e controle nas áreas de risco, considerando as evidentes desigualdades socioespaciais presentes no município sob investigação.

Introduction: The analysis of factors associated with multibacillary leprosy is important for the development of strategies to mitigate the disease, which persists as a public health problem in Brazil and the world. The objective of this study was to verify the associations between sociodemographic and clinical-epidemiological variables and multibacillary leprosy in the state of northeastern Brazil. Methodology: This is a cross-sectional, analytical, and retrospective study, with a quantitative approach, carried out in 16 municipalities in the southwest of Maranhão State, northeastern Brazil. All cases of leprosy reported between January 2008 and December 2017 were considered. Sociodemographic and clinical-epidemiological variables were analyzed using descriptive statistics. The identification of the risk factors associated with multibacillary leprosy was conducted using Poisson regression models. The prevalence ratios and respective 95% confidence intervals were estimated using regression coefficients at a 5% significance level. Results: A total of 3,903 leprosy cases were analyzed. Individuals older than 15 years, males, with less than 8 years of education, with level I, II, or "not evaluated" disability, and with type 1 or 2 or both reactional states were more likely to have multibacillary leprosy. Therefore, these characteristics may be considered risk factors. No protective factors were identified. Conclusions: The investigation revealed important associations between risk factors and multibacillary leprosy. The findings can be considered during the creation of strategies to control and combat the disease.