Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil

OBJECTIVES

In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal.

STUDY DESIGN

Ecological retrospective study.

METHODS

We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables.

RESULTS

We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality.

CONCLUSIONS

The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.

Tuberculosis risks and socio-economic level: a case study of a city in the Brazilian south-east, 1998-2004

OBJECTIVES

To explore tuberculosis (TB) risks in relation to potential determinants in the city of São José do Rio Preto, São Paulo State, Brazil; to analyse morbidity and mortality indicators in São José do Rio Preto, and to determine the relationship between the risk of TB and socio-economic level (SEL) using a geo-referenced information system (GIS) and the national census for 2000.

METHOD

Standardised incidence rates and TB incidence and mortality rates were calculated. Socio-economic variables were determined using the statistical technique of principal component analysis. Data sources were the São Paulo State Data Analysis System (SEADE), the TB Notification Database (EPI-TB), the Information Department of the Brazilian Health Ministry (DATASUS), and the Brazilian Institute of Geography and Statistics (IBGE). New cases reported in 1998-1999 and 2003-2004 in the urban area of the city were geo-referenced and analysed.

RESULTS

TB risk in the city is twice as high in areas of lower SEL than in areas with higher SEL.

CONCLUSION

The identification of areas with different levels of risk enables the Municipal Health Department to propose innovative interventions to minimise the risk of disease at both individual and population level.