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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Congenital syphilis is an infectious disease that affects 1 million children a year worldwide. Methods The objective of this study was to describe a spatial analysis of the epidemiological characteristics of congenital syphilis in the state of Maranhão, Brazil from 2007 to 2018. This was an ecological study using data obtained in May 2020 from three Brazilian information systems. The spatial correlation was analyzed according to local and global Moran indexes. Results During the 12-y period, 1 426 177 children were born. Of these children, 3684 acquired congenital syphilis (0.26%; mean annual rate of 2.62/1000 live births) and 70 died (1.90%; mean annual rate 0.05). The clusters were statistically significant according to the global Moran index of 0.40 (p=0.01). Spearman's correlation coefficient between the rate of detection of syphilis in pregnant women and family health strategy teams was strong, positive and significant (ρ=0.73; p<0.00). Most of the variables studied showed a trend of annual increase (p<0.05). Conclusions The use of spatial analysis tools have made it possible to detect areas with both a greater and a lesser need for intervention, and to more effectively improve and monitor those areas to change the epidemiological profile of the disease. [ABSTRACT FROM AUTHOR]

Introduction: There is a need to improve knowledge and understanding of the factors associated with mortality from COVID-19 so that managers and decision-makers can implement strategies to mitigate and control the severe forms of the disease. This study aimed to determine the factors associated with deaths from COVID-19 in the state of Maranhão, in northeastern Brazil. Methodology: This is a cross-sectional and analytical study with patients with a confirmed diagnosis of COVID-19 who died from March 2020 to January 2022. Simple and multiple logistic regression models were used to assess the association between clinical-epidemiological characteristics and death. The odds ratios were expressed using a 95% confidence interval and a 5% significance level. Results: A total of 386,567 cases of COVID-19 were registered in the period, of which 10,986 died. Risk factors associated with deaths from COVID-19 were male sex, age over 30 years, positive reverse transcriptase-polymerase chain reaction (RT-PCR) result, positive CT scan, and having one or more associated comorbidities. The three comorbidities linked to the highest propensity to die were diabetes mellitus, neurological disease, and obesity. Conclusions: The study findings support the implementation of strategic actions by health care and surveillance professionals and managers towards reducing the incidence of the risk factors for mortality by COVID-19 in Maranhão. [ABSTRACT FROM AUTHOR]

Introduction: Epidemiological investigations on tuberculosis-diabetes comorbidity using spatial analysis should be encouraged towards a more comprehensive view of the health of individuals affected by such comorbidity in different contexts. This study analyzes the territories vulnerable to tuberculosis-diabetes comorbidity in a municipality in northeastern Brazil using spatial analysis techniques. Methods: An ecological study was carried out in Imperatriz, Maranhão, Brazil. Tuberculosis-diabetes cases reported in the Brazilian Notifiable Diseases Information System between 2009 and 2018 were analyzed. Kernel density estimation and spatial scanning techniques were used to identify the areas with the greatest occurrence of spatial clusters. Results: A heterogeneous spatial distribution was found, ranging from 0.00 to 4.12 cases/km2. The spatial scanning analysis revealed three high-risk spatial clusters with statistical significance (p < 0.05), involving eleven strictly urban sectors with a relative risk of 4.00 (95% CI: 2.60-6.80), 5.10 (95% CI: 2.75-7.30), and 6.10 (95% CI: 3.21-8.92), indicating that the population living in these areas had a high risk of tuberculosis-diabetes comorbidity. Conclusions: The highest concentration of cases/km², as well as risk clusters, were found in areas with high circulation of people and socioeconomic and environmental vulnerabilities. Such findings reinforce the need for public health interventions to reduce social inequalities. [ABSTRACT FROM AUTHOR]