Factors associated with deaths from COVID-19 in a region of northeastern Brazil.

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.

Spatiotemporal risk assessment and COVID-19 trend estimation in a federative unit in northeastern Brazil.

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.

Suitability and Allocation of Protein-Containing Foods According to Protein Tolerance in PKU: A 2022 UK National Consensus.

Introduction: There is little practical guidance about suitable food choices for higher natural protein tolerances in patients with phenylketonuria (PKU). This is particularly important to consider with the introduction of adjunct pharmaceutical treatments that may improve protein tolerance. Aim: To develop a set of guidelines for the introduction of higher protein foods into the diets of patients with PKU who tolerate >10 g/day of protein. Methods: In January 2022, a 26-item food group questionnaire, listing a range of foods containing protein from 5 to >20 g/100 g, was sent to all British Inherited Metabolic Disease Group (BIMDG) dietitians (n = 80; 26 Inherited Metabolic Disease [IMD] centres). They were asked to consider within their IMD dietetic team when they would recommend introducing each of the 26 protein-containing food groups into a patient's diet who tolerated >10 g to 60 g/day of protein. The patient protein tolerance for each food group that received the majority vote from IMD dietetic teams was chosen as its tolerance threshold for introduction. A virtual meeting was held using Delphi methodology in March 2022 to discuss and agree final consensus. Results: Responses were received from dietitians from 22/26 IMD centres (85%) (11 paediatric, 11 adult). For patients tolerating protein ≥15 g/day, the following foods were agreed for inclusion: gluten-free pastas, gluten-free flours, regular bread, cheese spreads, soft cheese, and lentils in brine; for protein tolerance ≥20 g/day: nuts, hard cheeses, regular flours, meat/fish, and plant-based alternative products (containing 5−10 g/100 g protein), regular pasta, seeds, eggs, dried legumes, and yeast extract spreads were added; for protein tolerance ≥30 g/day: meat/fish and plant-based alternative products (containing >10−20 g/100 g protein) were added; and for protein tolerance ≥40 g/day: meat/fish and plant-based alternatives (containing >20 g/100 g protein) were added. Conclusion: This UK consensus by IMD dietitians from 22 UK centres describes for the first time the suitability and allocation of higher protein foods according to individual patient protein tolerance. It provides valuable guidance for health professionals to enable them to standardize practice and give rational advice to patients.

Tuberculosis/HIV co-infection in Northeastern Brazil: Prevalence trends, spatial distribution, and associated factors.

INTRODUCTION: The objective was to analyze the prevalence trend, spatial distribution, and TB-HIV co-infection-associated factors in an endemic scenario for TB in Northeastern Brazil. METHODS: An ecological and temporal series study was conducted based on secondary data obtained from the Brazilian Notifiable Diseases Information System between January 2008 and December 2019. The prevalence rates were determined for each year and the average for the period. Prais-Winsten regressions were used for temporal variation analysis, scanning techniques were used to detect spatial clusters, and the Poisson regression model was used to explore the factors associated with the outcome. RESULTS: A total of 947 TB cases were reported, of which 501 (52.9%) underwent HIV testing, and of these, 73 were positive. The average prevalence was 20.0%, ranging from 1.5% in 2018 to 44.4% in 2009. A decreasing trend was found. Sixty-seven cases (92%) were geocoded, and two statistically significant (p < 0.005) high relative risk (RR) spatial clusters were detected. Statistically significant associations (p < 0.05) between the co-infection and variables such as male gender, living in the urban area, entry due to relapse, and case closure due to loss to follow-up were evidenced, and these variables constituted risk factors. CONCLUSIONS: A decreasing prevalence of TB-HIV co-infection has been found, as well as a heterogeneous spatial distribution with the formation of spatial clusters in urban areas characterized by socio-spatial inequalities associated with clinical-epidemiological factors. Such findings provide subsidies for rethinking health care activities and improving public policies for vulnerable populations.

Vulnerable territories to tuberculosis-diabetes mellitus comorbidity in a northeastern Brazilian scenario.

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 socio-economic and environmental vulnerabilities. Such findings reinforce the need for public health interventions to reduce social inequalities.

Deaths due to COVID-19 in a state of northeastern Brazil: spatiotemporal distribution, sociodemographic and clinical and operational characteristics.

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.