Additive Effect of Wildfires on Hospital Admission in the Pantanal Wetland

Additive Effect of Wildfires on Hospital Admission in the Pantanal Wetland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Additive Effect of Wildfires on Hospital Admission in the Pantanal Wetland André Calixto Goncalves, Marcelo Marques de Magalhães, Kaylane de Almeida Faria, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5388967/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Forest fires produce toxic emissions that are detrimental to human health. Currently, the Pantanal, the largest continuous wetland in the world, is facing intense fires. This situation may contribute to increased hospital admissions for respiratory and cardiovascular diseases. To assess the additive effect between active fire hotspots and hospitalizations of residents in the Pantanal, a generalized linear model was used, incorporating geolocalized covariates such as air quality and climatic conditions. Our findings show a consistent worsening, when humans are exposed to 10 additional wildfire hotspots, the daily risk of admissions for respiratory diseases increases by 23,2% and 22,3% for cardiovascular diseases in 10-year analysis. These results can stimulate preventive measures to mitigate forest fires and also early preparation of the health system. Figures Figure 1 Figure 2 Figure 3 Brazilian Features Brazil, with its vast vegetation cover, is one of the largest countries in the world. In 2022, nearly 495.8 million hectares of vegetation covered approximately 58.3% of its territory, encompassing major biomes such as the Amazon Rainforest, the Caatinga, the Atlantic Rainforest, the Cerrado, the Pampa, and the Pantanal ( 1 , 2 ). Their preservation helps mitigate carbon emissions, playing a crucial role in promoting global sustainability ( 3 – 5 ). The expansion of large-scale economic activities has adversely impacted the conservation of the biomes remaining forests. As a result, the annual rate of burned area per square kilometer has escalated significantly. Between 2003 and 2022, the cumulative burned area reached nearly 6.6 million square kilometers, with an average of 327,000 square kilometers per year. Among the biomes experiencing heightened environmental pressures is the Pantanal, a region shared by Brazil, Bolivia, and Paraguay. In Brazil alone, the Pantanal covered approximately 150,000 square kilometers in 2022, supporting a population of around 731,000 individuals. Over the past four years (2019–2022), the average annual burned area per square kilometer within the Brazilian Pantanal has increased by approximately 82% compared to the 2003–2018 period. ( 1 , 2 , 6 ) In addition, its territory has a very low preservation rate, with only 4.6% of its area protected under conservation units, of which only 2.9% correspond to integral protection and 1.7% to sustainable use, which places the Pantanal biome among those with the highest environmental risk on the planet. ( 2 , 7 ) On the Brazilian side, Pantanal is made up of 21 municipalities, located in Mato Grosso and Mato Grosso do Sul states, in 2022 had approximately 90 tons of plant extraction, 911.000 hectares of municipal agricultural production and 11.5 million animals in the herd, with a total growth between 2003 and 2022 of 25%, 94.77% and 6.32% respectively. ( 6 , 8 ) The consequences of economic expansion around the Pantanal biome, coupled with the loosening of environmental legislation from 2019 to 2022, have significantly increased the number of fires in the region (Fig. 1 ). ( 10 – 12 ) The impact of this situation extends far beyond climate change, as studies worldwide have shown. ( 9 , 13 – 15 , 17 , 18 ) These studies highlight that the smoke released from burning forests has a significant effect on human health, primarily due to the emission of pollutants contained in the smoke from burning biological matter. The main particles released are fine and coarse particulate matter (PM2.5 and PM10). ( 9 , 13 , 14 , 17 – 22 ) Although the composition of particles from burning biological forest material is generally similar, studies indicate that the concentration of pollutants emitted can vary depending on the type of plant burned. This suggests that the location of the forest and the specific type of material burned may have differing impacts on the health of populations worldwide. ( 27 , 28 ) There is a consensus in studies conducted on this matter that inhaling these emissions in concentrated quantities has serious effects on local populations. Research from different regions indicates the occurrence of diseases such as asthma, pneumonia, and bronchitis, as well as an increase in deaths from cardiovascular diseases, with varying levels of impact depending on the concentration of local pollutants. Additionally, there is evidence of an increased risk of illness in children and the elderly, with varying significance across different seasons of the year. ( 13 – 18 ) The epidemiological scenario in the Pantanal, as can be seen in Fig. 2 , points to a drop in hospitalizations in recent years, while there has been a sharp rise in the number of deaths from both respiratory and cardiovascular diseases, suggesting the hypothesis of lethality caused by inhaling these particles, as reflected in other studies around the world on the effect of inhaling smoke from fires on human health. ( 13 , 14 , 17 , 18 ) Inhaling the materials present in these emissions can have a local and systemic inflammatory effect that penetrates deep into the human body. This makes it a risk for all the inhabitants of the region, especially those who already suffer from chronic lung diseases and socially vulnerable people such as children and the elderly. ( 13 , 16 , 23 ) The Pantanal region’s climate tends to become drier from July onwards, coinciding with the southern hemisphere's winter (June to August). This aridity, combined with the increased frequency of forest fires, contributes to a rise in illnesses. The biome’s flat terrain, surrounded by mountains, creates a greenhouse effect due to smoke, exacerbating the health consequences of these fires. ( 24 – 27 ) The objective of this study was to verify the additive effect of the increase in wildfires on hospitalizations for respiratory and cardiovascular diseases. Database The databases used in this study cover the years 2010 to 2019, on a daily basis, and 21 municipalities that make up the Pantanal biome. The daily hospitalization admissions were obtained from the DataSUS, the Brazilian Unified Health System (SUS) database at the Hospital Information System (SIH) level. This dataset includes information on the admission itself (date in/out, ICD-10 diagnostics), and on the patient characteristics, such as age, sex, race, and subsectors’s zip code. The latter allows for the geolocation of them. This dataset represents 71.6% of the care provided in the state of Mato Grosso do Sul and 80.2% of the care provided in the state of Mato Grosso. According to the IBGE's National Health Survey for 2019. ( 6 , 31 ) The hospital admissions diagnostics corresponding to the ICD-10 Chap. 10 codes (J00-J99), such as Pneumonia, Bronchitis, and Asthma were grouped as respiratory diseases. The circulatory system diseases were grouped collecting the Chap. 9 codes (I00-I99), such as Pulmonary Embolism, Cerebral Infarction, and Heart Failure. ( 6 , 31 ) Respiratory diseases hospitalizations regularly increase as the temperature and the humidity fall. A season variable was introduced in the dataset to tackle those effects, classifying summer from December to February, autumn from March to May, winter from June to August, and spring from September to November. The zip code geolocation coordinates, municipal shapes and population (by year), and biome shapes were obtained from the Brazilian Institute of Geography and Statistics (IBGE), including the Demographic Census, and the National Register of Addresses for Statistical Purposes. ( 6 ) Fire active hotspots for vegetation, and covariates for air quality (PM2.5, PM10, NO2, SO2), and climate (temperature and relative humidity) were obtained from the National Aeronautics and Space Administration (NASA) products. The estimates for each variable were done at zip code level, accounting for at least the 9 nearest observations, which results in a zip code buffer of 4,071.5 km 2 . The fire active hotspots were obtained from the MODIS Collection 6 Hotspot Active Fire Detections (MCD14DL-NRT, 1 km 2 grid), distributed by the Fire Information for Resource Management System (FIRMS). The particulate matter (PM2.5 and PM10), the sulfur dioxide (SO2) surface mass concentrations, the mean air temperature, and the air relative humidity were obtained from the MERRA-2 Collection (0.25 degree grid), distributed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). The nitrogen dioxide (NO2) surface mass concentration was obtained from the OMI/Aura NO2 Total and Tropospheric Column L3 V3 (0.25 degree grid), distributed by GES DISC." ( 32 – 35 ) The dataset was aggregated daily, according to the patient region zip code. This resulted in a time series of cross-sectional data for each specific zip code, counting for 3652 days and 2902 zip codes. Methods The effects of air pollutants on respiratory and circulatory system diseases are usually delayed in time, so they require the use of statistical models flexible enough to describe the time dimension additional to the exposure-response relationship. The association relationship of forest active fire hotspots and the daily hospital admissions was assessed by a Poisson regression, using a generalized linear model (GLM), with distributed lag non-linear effects (DLNM) on the delayed exposure-response in time ( 15 , 39 ). The daily respiratory and circulatory system hospitalizations admissions by zip code were evaluated in distinct regressions. Daily active fire hotspots are given by a counting number by zip code. Air quality (PM 2.5 , PM 10 , SO 2 , NO 2 concentrations in µ/m³) and climate (mean temperature in Celsius and relative humidity in %) covariates were added to mitigate the confounding effects over the fire-hospital admissions associations. Seasonal effects were mitigated by the introduction of a season factor (summer as basis, autumn, winter, spring), and a smooth function was introduced to correct for long-time trend. The zip code centroid coordinates given by its longitude and latitude were introduced to control for fixed effects of the zip code's spatial specificities. The relative risk of hospital admissions given the number of daily active fire hotspots, was assessed by independent Poisson regressions for groups of respiratory and circulatory system diseases, considering two time-window datasets (2010-19 and 2015-19), following the simplified equation: Log [ E (y i ) ] = β 0 + β 1 X i + ∑γ j Z ij where, E (y i ) is the odds-rate for the daily hospital admissions count, and (i) refers to the zip code; X i is the active fire hotspots smoothed delayed effect’s function; Z ij is the set of (j) covariates, seasonal, and fixed effects components; and the coefficients parameters to estimate are β 0 for the intercept, β 1 for the active fire hotspots, and γ j for the set of (j) covariates, seasonal and fixed effects components. The active fire hotspots smoothed function corresponds to the “cross-basis” linear function S (Fire i , df) of the daily counting of active fire hotspots (in the dimension i ) and the lag dimension of its occurrence in time (t) up to 10 days, defined by a polynomial function of degree 4. The air pollutant covariates components Zij of (PM2.5, PM10, SO2, NO2) follows the same smoothing function definition given to Fire i . The delayed effect of daily mean temperature in Zij is smoothed by two lag strata (0 and 1–3), assuming constant effects within each stratum (of 1 Celsius), and the relative humidity is introduced without transformation or lagged effects in Z ij . The season factor of Z ij is given by dummies for autumn, winter, spring, setting summer as the basis. The long-time trend smoothed function is given by a natural spline of degree 5 or 10, according to the dataset period (i.e., 5 or 10 years). The zip code centroid coordinates given by Lon i and Lat i are the two last components of Z ij . ( 9 , 15 , 16 , 36 , 37 ) The predicted effects of the active fire hotspots over the relative risk of daily hospital admissions (RR) were obtained considering an increase of 10-units of active fire hotspots, accumulated across 10 days of lagged effects. The exceeding relative risk (ERR) is obtained by subtracting one from RR. The predictions, and their corresponding 95% confidence interval, were estimated by group of diseases, respiratory and circulatory system, and by the time windows, 2010-19 and 2015-19. Results To gain a clearer understanding of the statistical analysis results, it is essential to examine how fires behaved in the Pantanal biome over the decade (2010–2019). Movie S1 depicts this behavior for the six municipalities that encompass the majority of the biome's area. In total, the Pantanal is composed of 21 municipalities, but six of them together account for 87.21% of the biome's total area: Corumbá (44.74%), Poconé (10.11%), Cáceres (10.21%), Aquidauana (9.36%), Barão de Melgaço (7.80%), and Santo Antônio de Leverger (4.99%). ( 6 ) Movie S1: The Behavior of Forest Fires in the Pantanal Municipalities from 2010 to 2019. Source: Data from the IBGE and NASA data produced by the authors ( 6 , 35 ) An analysis of forest fire behavior in the six municipalities depicted in Movie S1 reveals a trend of increasing fire hotspots over the years, with a peak occurring between 2015 and 2019 for all municipalities studied. As a result, the air quality indicators varied significantly over the analyzed period of 3,652 days. It was found that in 25% of these days, the concentration of PM10 exceeded 13.31 µg/m³, which is very close to the limit defined by the WHO. Another important aspect was the variation in relative humidity, which fell below 68.83% on 50% of the days and below 52.57% on 25% of the days, both well below the WHO-recommended limit of 70% ( 43 ) Another indication that forest fires could impact hospitalizations is the data on maximum hospitalizations per day, with some days recording 169 hospitalizations for respiratory diseases and 114 for cardiovascular diseases. These descriptive statistics highlight the need to assess the relative risk of forest fires contributing to an increase in hospitalizations for respiratory and cardiovascular diseases, as shown in Fig. 3 : Source: produced by the authors As shown in Fig. 3 , the 10-year data analysis (2010–2019) indicates an adjusted prediction value of 1.232047 for respiratory diseases. This can be interpreted as a 23.20% increase in the risk of hospitalizations for respiratory diseases and an approximate 22.37% increase in the risk of hospitalizations for cardiovascular diseases in the population living in the 21 municipalities that make up the Pantanal biome. These results are similar to those from another study carried out in Brazil on forest fires in general, which estimated an increase of 23% (95%CI: 12%-33%) in the risk of hospitalizations for respiratory diseases due to forest fires in Brazil between 2008 and 2018. The estimated increase in the risk of cardiovascular hospitalizations was 21% (95%CI: 8%-35%). These average results are very close to those obtained in our study ( 44 ). The analysis of the 5-year period (2015–2019), a time of heightened fire activity in the biome as depicted in Fig. 3 , reveals that the risk of hospitalizations for cardiovascular diseases surged by 29.30%, and the risk of hospitalizations for respiratory diseases climbed by 33.84%. When analyzing only respiratory diseases without accounting for seasonal bias, slightly stronger results are observed. For the 5-year period from 2015 to 2019, the percentage rises to 34.11%, with an upper limit of 35.34% An examination of the studies on which this analysis is based reveals that children aged 0 to 4 and the elderly aged 65 and over have the highest incidence of hospitalizations for diseases caused by forest fire smoke contamination ( 9 , 15 , 16 ). The demographic composition of the 21 municipalities in the Pantanal shows that children aged 0 to 4 make up 7.43% of the population, or 54,328 individuals, and the elderly population aged 65 or over constitutes 9.33%, or 68,236 people ( 6 ) The findings of this study, combined with the demographic characteristics of the region and the results of previous research ( 9 , 15 , 16 ) strongly suggest that the increase in forest fires is indeed harming the health of people living near the biome, particularly those who are most vulnerable due to their age. Discussion The main findings of this study highlight the sharp increase in hospitalization rates for respiratory and cardiovascular diseases as a result of forest fires. They demonstrate that this impact is of significant concern to society and public authorities, who must be prepared to equip the health system to manage high-magnitude events, such as those experienced in Brazil in the Pantanal biome in the final months of 2024. The results of this study demonstrate the need to mitigate the effects of the expansionist logic of the agricultural economy around the biome, which, in pursuit of profit, puts the entire local population at risk and significantly contributes to climate change, making it a problem not only for Brazil but for the rest of the world as well. It is important to note that the findings of this study are similar to those of other significant research on the impact of wildfires on human health, particularly the study conducted in California on the large-scale fires of 2003 and their effects on hospitalizations for respiratory and cardiovascular diseases ( 9 ). The similarities between this study and the California study lie in their conclusions that forest fires increase hospitalizations for respiratory diseases. However, the primary difference is in the scale of the impact observed in each case. The hypothesis for this difference involves two main factors: the first relates to the geographical characteristics of California and the Pantanal. While California consists of several mountain ranges that can help disperse the smoke, the Pantanal is a flat plain surrounded by mountains, creating a greenhouse effect. The second factor is the type of material burned, as the vegetation in California's forests is significantly different from that in the Pantanal, which could account for the varying impacts observed. This study combined the most important and short-time feasible data sources available. The combination of DataSUS patients zip codes and IBGE data on zip code centroids is a key feature of this study. It allows us to grab a reasonable accurate level of matching with data available by NASA air quality and climate products. Collecting sparse information raises many challenges, the NASA datasets are estimated at a global level, and the active fire hotspots are defined in the same way. The global thresholds can underestimate/overestimate the number of active fire spots for the specific region of Pantanal biome. This could be solved by defining local thresholds and/or validating the data in loco, which is out of our scope. The zip code aggregated information from DataSUS is the nearest proxy for the patient data individualization. It brings loss of accuracy but produces much better results than the regular studies made at the municipal level, with monthly aggregated data. Still zip codes can have different areas, which does not affect the air quality and climate indicators aggregations but can introduce a scale bias for the active fire hotpots accounting, resulting in greater effects for zip codes with larger areas. The model brings a superposition of active fire hotspots and particulate matter (PM 2.5 and PM 10 ). In a certain way it controls for effects of regular days particulate pollutants, without active fire hotspots. A regression of active fire hotspots on particulate matter could solve this issue. This would affect the most part of the population, who lives in downtown areas. The appropriate definition of the smoothed functions and the time lag effects for each parameter requires an extensive exercise of sensitivity analysis over different periods of lags, and levels of degrees of freedom for the polynomial’s terms, for each parameter. This should improve the results robustness, but we have no computational resources to develop it by now. In this regard, our study makes a significant contribution to science by striving to bring the analysis as close as possible to a controlled study and to individualize the exposure to smoke from wildfires. However, a controlled study with a smaller number of patients, testing exposure to these toxins at the lowest possible level, could more robustly demonstrate the impact of emissions from burning biological material on human health. Conclusion World society as we know it is at risk. The effects of climate change have increased the frequency of extreme weather events worldwide, with more and more victims on different continents having their lives transformed by these changes There seems to be no end to this cycle of natural destruction. Protecting forests from the expansionist logic of the economy is becoming increasingly urgent. Forests need to be valued as the planet’s greatest economic asset, both from Brazil’s perspective and from a global standpoint. The aim of this study was to raise awareness among the key players in this scenario, from politicians responsible for safeguarding natural heritage to those responsible for starting forest fires, of the far-reaching consequences of these fires. The smoke spares no one, and its impacts on health are countless. Moreover, not only humans suffer the consequences; the rich fauna and flora of the Pantanal could disappear if no measures are taken. Countless scientific discoveries remain to be uncovered in these forests, and their preservation is a duty for all of us. Declarations Acknowledgments Funding: There is no funding information to declare Authors contributions: All authors contributed equally to this work Competing interests: There is no competing interests to declare Data and Materials Availability: All data are available in the manuscript or the supplementary materials. If you need to check the raw data, you can access it publicly at the following URLs: https://datasus.saude.gov.br/transferencia-de-arquivos/ (for health data) https://giovanni.gsfc.nasa.gov/giovanni/ (for climate data) https://www.ibge.gov.br/estatisticas/sociais/populacao/38734-cadastro-nacional-de-enderecos-para-fins-estatisticos.html?=&t=downloads (for zip codes) Supplementary Materials: Materials and Methods Figs. S1 to S41 Tables S1 to S31 Movie S1 References (9, 15, 16, 36, 37) References Brazil (2024) a . Serviço Florestal Brasileiro., “Sistema Nacional de Informações Florestais” Brazil (2024) b . Ministério do Meio Ambiente - “Biomas” Barros, F.V., Lewis, K., Robertson A.D., Pennington, R.T., Hill, T.C. et al, Cost-effective restoration for carbon sequestration across Brazil's biomes, Science of The Total Environment, Volume 876, 2023, 162600, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2023.162600. Delgado, R. 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Lung-fei Lee, Jihai Yu, Estimation of spatial autoregressive panel data models with fixed effects, Journal of Econometrics, Volume 154, Issue 2, 2010, Pages 165-185, ISSN 0304-4076, https://doi.org/10.1016/j.jeconom.2009.08.001. Badi H. Baltagi, Seuck Heun Song, Won Koh, Testing panel data regression models with spatial error correlation, Journal of Econometrics, Volume 117, Issue 1, 2003, Pages 123-150, ISSN 0304-4076, https://doi.org/10.1016/S0304-4076(03)00120-9. World Health Organization, & World Health Organization. (2021). WHO Global Air Quality Guidelines: Particulate Matter (PM2. 5 and PM10). Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide, 267 Requia, W.J., Amini, H., Mukherjee, R. et al. Health impacts of wildfire-related air pollution in Brazil: a nationwide study of more than 2 million hospital admissions . Additional Declarations No competing interests reported. Supplementary Files MovieS1TheBehaviorofForestFiresinthePantanalMunicipalitiesfrom2010to20192.gif Movie S1: The Behavior of Forest Fires in the Pantanal Municipalities from 2010 to 2019 Source: Data from the IBGE and NASA data produced by the authors (6, 35) SuplementaryMaterialPantanal4.pdf Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Jan, 2025 Reviews received at journal 08 Jan, 2025 Reviews received at journal 18 Dec, 2024 Reviewers agreed at journal 17 Dec, 2024 Reviewers agreed at journal 05 Dec, 2024 Reviewers invited by journal 02 Dec, 2024 Editor assigned by journal 29 Nov, 2024 Editor invited by journal 26 Nov, 2024 Submission checks completed at journal 25 Nov, 2024 First submitted to journal 04 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5388967","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400277704,"identity":"e9f5b08c-2e26-427e-9191-05811540dafa","order_by":0,"name":"André Calixto Goncalves","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYHACAzDJxwwiKxgY2JgPgFgHCGthY2ZgbGA4A2SwJRCrhQGohbENxCKghX9287YPHxjs5NnYecwffJx3OI+Pjfnw5wKGO/m4tEjcOVY8cwZDsmEbM1ti48xth4vZ2NjSpGcwPLNswKXnRo4xMw/DAcY2ZuaDzbzbDie2yfeYAUUOG+DSIQ/S8ofhgH0bM2NjM+8coBY2/s+f8WkxAGkBejYRYksDSAsPgzQ+LYY30ooZewySk0F+mTnjWDpQC5uZ9AyDZzi1yN1I3szwo8LOtp//jMGHDzXWifPbmB9/Lqi4g1ML1HlofGYMEYKAmVQNo2AUjIJRMKwBALPjUC3qgacLAAAAAElFTkSuQmCC","orcid":"","institution":"CECS - Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo","correspondingAuthor":true,"prefix":"","firstName":"André","middleName":"Calixto","lastName":"Goncalves","suffix":""},{"id":400277705,"identity":"37145671-8473-4312-a084-44fa97966ad7","order_by":1,"name":"Marcelo Marques de Magalhães","email":"","orcid":"","institution":"Department of Agricultural and Environmental Sciences, São Paulo State University Júlio de Mesquita Filho: Tupã, SP","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"Marques","lastName":"de Magalhães","suffix":""},{"id":400277706,"identity":"515acec1-d406-49c2-90c8-a4c89c91a010","order_by":2,"name":"Kaylane de Almeida Faria","email":"","orcid":"","institution":"Department of Pharmaceutical Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Kaylane","middleName":"de Almeida","lastName":"Faria","suffix":""},{"id":400277707,"identity":"8ed54676-6d07-4831-ac0e-5b837de0334f","order_by":3,"name":"Gabriel Alexandre dos Santos","email":"","orcid":"","institution":"Department of Pharmaceutical Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"Alexandre dos","lastName":"Santos","suffix":""},{"id":400277708,"identity":"513b2b84-3295-4179-910f-6bcbc038e61d","order_by":4,"name":"Rodolfo Valentim","email":"","orcid":"","institution":"Department of Physics, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Rodolfo","middleName":"","lastName":"Valentim","suffix":""},{"id":400277709,"identity":"99966dc6-6b32-4999-ac96-4ccbe3711349","order_by":5,"name":"Ivan Filipe Fernandes","email":"","orcid":"","institution":"CECS - Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo","correspondingAuthor":false,"prefix":"","firstName":"Ivan","middleName":"Filipe","lastName":"Fernandes","suffix":""},{"id":400277710,"identity":"111600e4-4295-4e4c-b2e5-d33793be429a","order_by":6,"name":"Gabriela Moraes do Nascimento","email":"","orcid":"","institution":"Department of Pharmaceutical Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Moraes do","lastName":"Nascimento","suffix":""},{"id":400277711,"identity":"d4a413a3-0d9b-43b2-a97b-dcd4e3f54a3c","order_by":7,"name":"Francisco Aparecido Rodrigues","email":"","orcid":"","institution":"Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Aparecido","lastName":"Rodrigues","suffix":""},{"id":400277712,"identity":"9c72d454-956d-47ed-9112-216cd654659d","order_by":8,"name":"Ricardo Ceneviva","email":"","orcid":"","institution":"CECS - Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Ceneviva","suffix":""},{"id":400277713,"identity":"1efac11f-8136-47b1-82d2-878298cdadf4","order_by":9,"name":"Maria Clara Mendes Stama","email":"","orcid":"","institution":"Department of Pharmaceutical Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Clara Mendes","lastName":"Stama","suffix":""},{"id":400277714,"identity":"326bf7de-57e2-4bb7-a477-82cecf9e7c8c","order_by":10,"name":"Daniel Tetsuo G. Mori","email":"","orcid":"","institution":"Department of Chemistry, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Tetsuo G.","lastName":"Mori","suffix":""},{"id":400277715,"identity":"15238959-6bfc-4873-9e3d-c93c850d4385","order_by":11,"name":"Carolina Nascimento Capellini","email":"","orcid":"","institution":"Department of Environmental Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"Nascimento","lastName":"Capellini","suffix":""},{"id":400277716,"identity":"a6ed1879-ac85-4c91-b511-5044375f085b","order_by":12,"name":"Maria Eduarda Feres Garcia","email":"","orcid":"","institution":"Department of Environmental Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Eduarda Feres","lastName":"Garcia","suffix":""},{"id":400277717,"identity":"e22cbbd2-36c1-4606-9d86-0acf318eb737","order_by":13,"name":"Thiago Bruschi","email":"","orcid":"","institution":"Department of Pharmaceutical Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"","lastName":"Bruschi","suffix":""},{"id":400277718,"identity":"8be625bd-b7ef-4a6f-af61-993f5e516e64","order_by":14,"name":"Gabriel Poveda Gonçalves","email":"","orcid":"","institution":"Serviço Nacional de Aprendizagem Comercial – SENAC, São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"Poveda","lastName":"Gonçalves","suffix":""},{"id":400277719,"identity":"d97dfc49-09b0-4fee-9fa3-f03808a12d6e","order_by":15,"name":"Lais Costa Brito","email":"","orcid":"","institution":"Department of Pharmaceutical Sciences, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Lais","middleName":"Costa","lastName":"Brito","suffix":""},{"id":400277720,"identity":"db64bbe2-88f0-482b-9168-7aa02ce7c9a5","order_by":16,"name":"Djeansy Djarny Etchiamiadzy Toussaint","email":"","orcid":"","institution":"Department of Chemistry Engineering, Federal University of São Paulo, Diadema","correspondingAuthor":false,"prefix":"","firstName":"Djeansy","middleName":"Djarny Etchiamiadzy","lastName":"Toussaint","suffix":""},{"id":400277721,"identity":"583143ae-7658-441c-97a8-eb27d01de86b","order_by":17,"name":"Rejane Calixto Gonçalves","email":"","orcid":"","institution":"Department of Preventive Medicine, USP Medical School","correspondingAuthor":false,"prefix":"","firstName":"Rejane","middleName":"Calixto","lastName":"Gonçalves","suffix":""},{"id":400277722,"identity":"c9755c68-07a5-41df-a7bd-288cf39431d9","order_by":18,"name":"Olinda do Carmo Luiz","email":"","orcid":"","institution":"Department of Preventive Medicine, USP Medical School","correspondingAuthor":false,"prefix":"","firstName":"Olinda","middleName":"do Carmo","lastName":"Luiz","suffix":""}],"badges":[],"createdAt":"2024-11-04 14:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5388967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5388967/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-13257-z","type":"published","date":"2025-07-29T16:38:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73646853,"identity":"9d534bc3-56db-46c6-adde-62994e0be965","added_by":"auto","created_at":"2025-01-13 08:56:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165870,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the last 20 years of the Evolution of Livestock, Area Under production, Plant extraction and Burned area\u003c/p\u003e\n\u003cp\u003eSource: Data from the IBGE Sidra System and the INPE Burning System produced by the authors (30)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5388967/v1/33dd92862c2ae248cf4ac8d4.png"},{"id":73647259,"identity":"bf777749-4302-45e1-9fad-400ddc0e6742","added_by":"auto","created_at":"2025-01-13 09:04:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":192936,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the last 20 years of Hospital admissions and deaths due to respiratory and circulatory diseases in Pantanal cities\u003c/p\u003e\n\u003cp\u003eSource: Data from the DataSUS produced by the authors. (31)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5388967/v1/bd263d205e5d1abc1b01a215.png"},{"id":73647257,"identity":"34a9d0af-0258-4de1-9ab8-47272e6f57a8","added_by":"auto","created_at":"2025-01-13 09:04:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202304,"visible":true,"origin":"","legend":"\u003cp\u003eRelative risk of hospitalizations for respiratory and cardiovascular diseases in a 10-day lag in relation to active fire hotspots\u003c/p\u003e\n\u003cp\u003eSource: produced by the authors\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5388967/v1/eb4e77834b7623184c786bd2.png"},{"id":88268495,"identity":"2834d62a-f018-40b2-8d8c-568dd232fef4","added_by":"auto","created_at":"2025-08-04 16:52:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5388967/v1/05359729-3748-4412-9c68-87a4a697dcd2.pdf"},{"id":73646857,"identity":"b98986d1-b62e-47b9-8d17-7b22bf545c5e","added_by":"auto","created_at":"2025-01-13 08:56:25","extension":"gif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3835295,"visible":true,"origin":"","legend":"\u003cp\u003eMovie S1: The Behavior of Forest Fires in the Pantanal Municipalities from 2010 to 2019\u003c/p\u003e\n\u003cp\u003eSource: Data from the IBGE and NASA data produced by the authors (6, 35)\u003c/p\u003e","description":"","filename":"MovieS1TheBehaviorofForestFiresinthePantanalMunicipalitiesfrom2010to20192.gif","url":"https://assets-eu.researchsquare.com/files/rs-5388967/v1/5cf6073ad16a92c42013e997.gif"},{"id":73646869,"identity":"b1a51793-c5c9-40a1-93af-5f6f29732f20","added_by":"auto","created_at":"2025-01-13 08:56:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46641420,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementaryMaterialPantanal4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5388967/v1/8bcb53f1cf0cc2a8dc0e8290.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Additive Effect of Wildfires on Hospital Admission in the Pantanal Wetland","fulltext":[{"header":"Brazilian Features","content":"\u003cp\u003eBrazil, with its vast vegetation cover, is one of the largest countries in the world. In 2022, nearly 495.8\u0026nbsp;million hectares of vegetation covered approximately 58.3% of its territory, encompassing major biomes such as the Amazon Rainforest, the Caatinga, the Atlantic Rainforest, the Cerrado, the Pampa, and the Pantanal (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e). Their preservation helps mitigate carbon emissions, playing a crucial role in promoting global sustainability (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe expansion of large-scale economic activities has adversely impacted the conservation of the biomes remaining forests. As a result, the annual rate of burned area per square kilometer has escalated significantly. Between 2003 and 2022, the cumulative burned area reached nearly 6.6 million square kilometers, with an average of 327,000 square kilometers per year. Among the biomes experiencing heightened environmental pressures is the Pantanal, a region shared by Brazil, Bolivia, and Paraguay. In Brazil alone, the Pantanal covered approximately 150,000 square kilometers in 2022, supporting a population of around 731,000 individuals. Over the past four years (2019\u0026ndash;2022), the average annual burned area per square kilometer within the Brazilian Pantanal has increased by approximately 82% compared to the 2003\u0026ndash;2018 period. (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eIn addition, its territory has a very low preservation rate, with only 4.6% of its area protected under conservation units, of which only 2.9% correspond to integral protection and 1.7% to sustainable use, which places the Pantanal biome among those with the highest environmental risk on the planet. (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eOn the Brazilian side, Pantanal is made up of 21 municipalities, located in Mato Grosso and Mato Grosso do Sul states, in 2022 had approximately 90 tons of plant extraction, 911.000 hectares of municipal agricultural production and 11.5\u0026nbsp;million animals in the herd, with a total growth between 2003 and 2022 of 25%, 94.77% and 6.32% respectively. (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThe consequences of economic expansion around the Pantanal biome, coupled with the loosening of environmental legislation from 2019 to 2022, have significantly increased the number of fires in the region (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e) The impact of this situation extends far beyond climate change, as studies worldwide have shown. (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e) These studies highlight that the smoke released from burning forests has a significant effect on human health, primarily due to the emission of pollutants contained in the smoke from burning biological matter. The main particles released are fine and coarse particulate matter (PM2.5 and PM10). (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eAlthough the composition of particles from burning biological forest material is generally similar, studies indicate that the concentration of pollutants emitted can vary depending on the type of plant burned. This suggests that the location of the forest and the specific type of material burned may have differing impacts on the health of populations worldwide. (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThere is a consensus in studies conducted on this matter that inhaling these emissions in concentrated quantities has serious effects on local populations. Research from different regions indicates the occurrence of diseases such as asthma, pneumonia, and bronchitis, as well as an increase in deaths from cardiovascular diseases, with varying levels of impact depending on the concentration of local pollutants. Additionally, there is evidence of an increased risk of illness in children and the elderly, with varying significance across different seasons of the year. (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThe epidemiological scenario in the Pantanal, as can be seen in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, points to a drop in hospitalizations in recent years, while there has been a sharp rise in the number of deaths from both respiratory and cardiovascular diseases, suggesting the hypothesis of lethality caused by inhaling these particles, as reflected in other studies around the world on the effect of inhaling smoke from fires on human health. (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eInhaling the materials present in these emissions can have a local and systemic inflammatory effect that penetrates deep into the human body. This makes it a risk for all the inhabitants of the region, especially those who already suffer from chronic lung diseases and socially vulnerable people such as children and the elderly. (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThe Pantanal region\u0026rsquo;s climate tends to become drier from July onwards, coinciding with the southern hemisphere\u0026apos;s winter (June to August). This aridity, combined with the increased frequency of forest fires, contributes to a rise in illnesses. The biome\u0026rsquo;s flat terrain, surrounded by mountains, creates a greenhouse effect due to smoke, exacerbating the health consequences of these fires. (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to verify the additive effect of the increase in wildfires on hospitalizations for respiratory and cardiovascular diseases.\u003c/p\u003e\n\u003ch3\u003eDatabase\u003c/h3\u003e\n\u003cp\u003eThe databases used in this study cover the years 2010 to 2019, on a daily basis, and 21 municipalities that make up the Pantanal biome. The daily hospitalization admissions were obtained from the DataSUS, the Brazilian Unified Health System (SUS) database at the Hospital Information System (SIH) level. This dataset includes information on the admission itself (date in/out, ICD-10 diagnostics), and on the patient characteristics, such as age, sex, race, and subsectors’s zip code. The latter allows for the geolocation of them. This dataset represents 71.6% of the care provided in the state of Mato Grosso do Sul and 80.2% of the care provided in the state of Mato Grosso. According to the IBGE's National Health Survey for 2019. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe hospital admissions diagnostics corresponding to the ICD-10 Chap.\u0026nbsp;10 codes (J00-J99), such as Pneumonia, Bronchitis, and Asthma were grouped as respiratory diseases. The circulatory system diseases were grouped collecting the Chap.\u0026nbsp;9 codes (I00-I99), such as Pulmonary Embolism, Cerebral Infarction, and Heart Failure. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRespiratory diseases hospitalizations regularly increase as the temperature and the humidity fall. A season variable was introduced in the dataset to tackle those effects, classifying summer from December to February, autumn from March to May, winter from June to August, and spring from September to November.\u003c/p\u003e \u003cp\u003eThe zip code geolocation coordinates, municipal shapes and population (by year), and biome shapes were obtained from the Brazilian Institute of Geography and Statistics (IBGE), including the Demographic Census, and the National Register of Addresses for Statistical Purposes. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFire active hotspots for vegetation, and covariates for air quality (PM2.5, PM10, NO2, SO2), and climate (temperature and relative humidity) were obtained from the National Aeronautics and Space Administration (NASA) products. The estimates for each variable were done at zip code level, accounting for at least the 9 nearest observations, which results in a zip code buffer of 4,071.5 km\u003csup\u003e2\u003c/sup\u003e. The fire active hotspots were obtained from the MODIS Collection 6 Hotspot Active Fire Detections (MCD14DL-NRT, 1 km\u003csup\u003e2\u003c/sup\u003e grid), distributed by the Fire Information for Resource Management System (FIRMS). The particulate matter (PM2.5 and PM10), the sulfur dioxide (SO2) surface mass concentrations, the mean air temperature, and the air relative humidity were obtained from the MERRA-2 Collection (0.25 degree grid), distributed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). The nitrogen dioxide (NO2) surface mass concentration was obtained from the OMI/Aura NO2 Total and Tropospheric Column L3 V3 (0.25 degree grid), distributed by GES DISC.\" (\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe dataset was aggregated daily, according to the patient region zip code. This resulted in a time series of cross-sectional data for each specific zip code, counting for 3652 days and 2902 zip codes.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eThe effects of air pollutants on respiratory and circulatory system diseases are usually delayed in time, so they require the use of statistical models flexible enough to describe the time dimension additional to the exposure-response relationship. The association relationship of forest active fire hotspots and the daily hospital admissions was assessed by a Poisson regression, using a generalized linear model (GLM), with distributed lag non-linear effects (DLNM) on the delayed exposure-response in time (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The daily respiratory and circulatory system hospitalizations admissions by zip code were evaluated in distinct regressions. Daily active fire hotspots are given by a counting number by zip code. Air quality (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e concentrations in µ/m³) and climate (mean temperature in Celsius and relative humidity in %) covariates were added to mitigate the confounding effects over the fire-hospital admissions associations. Seasonal effects were mitigated by the introduction of a season factor (summer as basis, autumn, winter, spring), and a smooth function was introduced to correct for long-time trend. The zip code centroid coordinates given by its longitude and latitude were introduced to control for fixed effects of the zip code's spatial specificities. The relative risk of hospital admissions given the number of daily active fire hotspots, was assessed by independent Poisson regressions for groups of respiratory and circulatory system diseases, considering two time-window datasets (2010-19 and 2015-19), following the simplified equation:\u003c/p\u003e\u003cp\u003eLog [ E (y\u003csub\u003ei\u003c/sub\u003e) ] = β\u003csub\u003e0\u003c/sub\u003e + β\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003ei\u003c/sub\u003e + ∑γ\u003csub\u003ej\u003c/sub\u003e Z\u003csub\u003eij\u003c/sub\u003e\u003c/p\u003e\u003cp\u003ewhere, E (y\u003csub\u003ei\u003c/sub\u003e) is the odds-rate for the daily hospital admissions count, and (i) refers to the zip code; X\u003csub\u003ei\u003c/sub\u003e is the active fire hotspots smoothed delayed effect’s function; Z\u003csub\u003eij\u003c/sub\u003e is the set of (j) covariates, seasonal, and fixed effects components; and the coefficients parameters to estimate are β\u003csub\u003e0\u003c/sub\u003e for the intercept, β\u003csub\u003e1\u003c/sub\u003e for the active fire hotspots, and γ\u003csub\u003ej\u003c/sub\u003e for the set of (j) covariates, seasonal and fixed effects components. The active fire hotspots smoothed function corresponds to the “cross-basis” linear function S (Fire\u003csub\u003ei\u003c/sub\u003e, df) of the daily counting of active fire hotspots (in the dimension \u003cem\u003ei\u003c/em\u003e) and the lag dimension of its occurrence in time (t) up to 10 days, defined by a polynomial function of degree 4. The air pollutant covariates components Zij of (PM2.5, PM10, SO2, NO2) follows the same smoothing function definition given to Fire\u003csub\u003ei\u003c/sub\u003e. The delayed effect of daily mean temperature in Zij is smoothed by two lag strata (0 and 1–3), assuming constant effects within each stratum (of 1 Celsius), and the relative humidity is introduced without transformation or lagged effects in Z\u003csub\u003eij\u003c/sub\u003e. The season factor of Z\u003csub\u003eij\u003c/sub\u003e is given by dummies for autumn, winter, spring, setting summer as the basis. The long-time trend smoothed function is given by a natural spline of degree 5 or 10, according to the dataset period (i.e., 5 or 10 years). The zip code centroid coordinates given by Lon\u003csub\u003ei\u003c/sub\u003e and Lat\u003csub\u003ei\u003c/sub\u003e are the two last components of Z\u003csub\u003eij\u003c/sub\u003e. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe predicted effects of the active fire hotspots over the relative risk of daily hospital admissions (RR) were obtained considering an increase of 10-units of active fire hotspots, accumulated across 10 days of lagged effects. The exceeding relative risk (ERR) is obtained by subtracting one from RR. The predictions, and their corresponding 95% confidence interval, were estimated by group of diseases, respiratory and circulatory system, and by the time windows, 2010-19 and 2015-19.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo gain a clearer understanding of the statistical analysis results, it is essential to examine how fires behaved in the Pantanal biome over the decade (2010\u0026ndash;2019). Movie S1 depicts this behavior for the six municipalities that encompass the majority of the biome's area.\u003c/p\u003e \u003cp\u003eIn total, the Pantanal is composed of 21 municipalities, but six of them together account for 87.21% of the biome's total area: Corumb\u0026aacute; (44.74%), Pocon\u0026eacute; (10.11%), C\u0026aacute;ceres (10.21%), Aquidauana (9.36%), Bar\u0026atilde;o de Melga\u0026ccedil;o (7.80%), and Santo Ant\u0026ocirc;nio de Leverger (4.99%). (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMovie S1: The Behavior of Forest Fires in the Pantanal Municipalities from 2010 to 2019.\u003c/p\u003e \u003cp\u003eSource: Data from the IBGE and NASA data produced by the authors (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAn analysis of forest fire behavior in the six municipalities depicted in Movie S1 reveals a trend of increasing fire hotspots over the years, with a peak occurring between 2015 and 2019 for all municipalities studied.\u003c/p\u003e \u003cp\u003eAs a result, the air quality indicators varied significantly over the analyzed period of 3,652 days. It was found that in 25% of these days, the concentration of PM10 exceeded 13.31 \u0026micro;g/m\u0026sup3;, which is very close to the limit defined by the WHO. Another important aspect was the variation in relative humidity, which fell below 68.83% on 50% of the days and below 52.57% on 25% of the days, both well below the WHO-recommended limit of 70% (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAnother indication that forest fires could impact hospitalizations is the data on maximum hospitalizations per day, with some days recording 169 hospitalizations for respiratory diseases and 114 for cardiovascular diseases. These descriptive statistics highlight the need to assess the relative risk of forest fires contributing to an increase in hospitalizations for respiratory and cardiovascular diseases, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: produced by the authors\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the 10-year data analysis (2010\u0026ndash;2019) indicates an adjusted prediction value of 1.232047 for respiratory diseases. This can be interpreted as a 23.20% increase in the risk of hospitalizations for respiratory diseases and an approximate 22.37% increase in the risk of hospitalizations for cardiovascular diseases in the population living in the 21 municipalities that make up the Pantanal biome. These results are similar to those from another study carried out in Brazil on forest fires in general, which estimated an increase of 23% (95%CI: 12%-33%) in the risk of hospitalizations for respiratory diseases due to forest fires in Brazil between 2008 and 2018. The estimated increase in the risk of cardiovascular hospitalizations was 21% (95%CI: 8%-35%). These average results are very close to those obtained in our study (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis of the 5-year period (2015\u0026ndash;2019), a time of heightened fire activity in the biome as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, reveals that the risk of hospitalizations for cardiovascular diseases surged by 29.30%, and the risk of hospitalizations for respiratory diseases climbed by 33.84%.\u003c/p\u003e \u003cp\u003eWhen analyzing only respiratory diseases without accounting for seasonal bias, slightly stronger results are observed. For the 5-year period from 2015 to 2019, the percentage rises to 34.11%, with an upper limit of 35.34%\u003c/p\u003e \u003cp\u003eAn examination of the studies on which this analysis is based reveals that children aged 0 to 4 and the elderly aged 65 and over have the highest incidence of hospitalizations for diseases caused by forest fire smoke contamination (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The demographic composition of the 21 municipalities in the Pantanal shows that children aged 0 to 4 make up 7.43% of the population, or 54,328 individuals, and the elderly population aged 65 or over constitutes 9.33%, or 68,236 people (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe findings of this study, combined with the demographic characteristics of the region and the results of previous research (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) strongly suggest that the increase in forest fires is indeed harming the health of people living near the biome, particularly those who are most vulnerable due to their age.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of this study highlight the sharp increase in hospitalization rates for respiratory and cardiovascular diseases as a result of forest fires. They demonstrate that this impact is of significant concern to society and public authorities, who must be prepared to equip the health system to manage high-magnitude events, such as those experienced in Brazil in the Pantanal biome in the final months of 2024.\u003c/p\u003e \u003cp\u003eThe results of this study demonstrate the need to mitigate the effects of the expansionist logic of the agricultural economy around the biome, which, in pursuit of profit, puts the entire local population at risk and significantly contributes to climate change, making it a problem not only for Brazil but for the rest of the world as well.\u003c/p\u003e \u003cp\u003eIt is important to note that the findings of this study are similar to those of other significant research on the impact of wildfires on human health, particularly the study conducted in California on the large-scale fires of 2003 and their effects on hospitalizations for respiratory and cardiovascular diseases (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe similarities between this study and the California study lie in their conclusions that forest fires increase hospitalizations for respiratory diseases. However, the primary difference is in the scale of the impact observed in each case. The hypothesis for this difference involves two main factors: the first relates to the geographical characteristics of California and the Pantanal. While California consists of several mountain ranges that can help disperse the smoke, the Pantanal is a flat plain surrounded by mountains, creating a greenhouse effect. The second factor is the type of material burned, as the vegetation in California's forests is significantly different from that in the Pantanal, which could account for the varying impacts observed.\u003c/p\u003e \u003cp\u003eThis study combined the most important and short-time feasible data sources available. The combination of DataSUS patients zip codes and IBGE data on zip code centroids is a key feature of this study. It allows us to grab a reasonable accurate level of matching with data available by NASA air quality and climate products. Collecting sparse information raises many challenges, the NASA datasets are estimated at a global level, and the active fire hotspots are defined in the same way. The global thresholds can underestimate/overestimate the number of active fire spots for the specific region of Pantanal biome. This could be solved by defining local thresholds and/or validating the data in loco, which is out of our scope.\u003c/p\u003e \u003cp\u003eThe zip code aggregated information from DataSUS is the nearest proxy for the patient data individualization. It brings loss of accuracy but produces much better results than the regular studies made at the municipal level, with monthly aggregated data. Still zip codes can have different areas, which does not affect the air quality and climate indicators aggregations but can introduce a scale bias for the active fire hotpots accounting, resulting in greater effects for zip codes with larger areas.\u003c/p\u003e \u003cp\u003eThe model brings a superposition of active fire hotspots and particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e). In a certain way it controls for effects of regular days particulate pollutants, without active fire hotspots. A regression of active fire hotspots on particulate matter could solve this issue. This would affect the most part of the population, who lives in downtown areas.\u003c/p\u003e \u003cp\u003eThe appropriate definition of the smoothed functions and the time lag effects for each parameter requires an extensive exercise of sensitivity analysis over different periods of lags, and levels of degrees of freedom for the polynomial\u0026rsquo;s terms, for each parameter. This should improve the results robustness, but we have no computational resources to develop it by now.\u003c/p\u003e \u003cp\u003eIn this regard, our study makes a significant contribution to science by striving to bring the analysis as close as possible to a controlled study and to individualize the exposure to smoke from wildfires. However, a controlled study with a smaller number of patients, testing exposure to these toxins at the lowest possible level, could more robustly demonstrate the impact of emissions from burning biological material on human health.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWorld society as we know it is at risk. The effects of climate change have increased the frequency of extreme weather events worldwide, with more and more victims on different continents having their lives transformed by these changes\u003c/p\u003e \u003cp\u003eThere seems to be no end to this cycle of natural destruction. Protecting forests from the expansionist logic of the economy is becoming increasingly urgent. Forests need to be valued as the planet\u0026rsquo;s greatest economic asset, both from Brazil\u0026rsquo;s perspective and from a global standpoint.\u003c/p\u003e \u003cp\u003eThe aim of this study was to raise awareness among the key players in this scenario, from politicians responsible for safeguarding natural heritage to those responsible for starting forest fires, of the far-reaching consequences of these fires. The smoke spares no one, and its impacts on health are countless. Moreover, not only humans suffer the consequences; the rich fauna and flora of the Pantanal could disappear if no measures are taken.\u003c/p\u003e \u003cp\u003eCountless scientific discoveries remain to be uncovered in these forests, and their preservation is a duty for all of us.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere is no funding information to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed equally to this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThere is no competing interests to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Materials Availability:\u0026nbsp;\u003c/strong\u003eAll data are available in the manuscript or the supplementary materials.\u003c/p\u003e\n\u003cp\u003eIf you need to check the raw data, you can access it publicly at the following URLs:\u003c/p\u003e\n\u003cp\u003ehttps://datasus.saude.gov.br/transferencia-de-arquivos/ (for health data)\u003c/p\u003e\n\u003cp\u003ehttps://giovanni.gsfc.nasa.gov/giovanni/ (for climate data)\u003c/p\u003e\n\u003cp\u003ehttps://www.ibge.gov.br/estatisticas/sociais/populacao/38734-cadastro-nacional-de-enderecos-para-fins-estatisticos.html?=\u0026amp;t=downloads (for zip codes)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaterials and Methods\u003c/p\u003e\n\u003cp\u003eFigs. S1 to S41\u003c/p\u003e\n\u003cp\u003eTables S1 to S31\u003c/p\u003e\n\u003cp\u003eMovie S1\u003c/p\u003e\n\u003cp\u003eReferences (9, 15, 16, 36, 37)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrazil (2024) \u003cstrong\u003ea\u003c/strong\u003e. Servi\u0026ccedil;o Florestal Brasileiro., \u0026ldquo;Sistema Nacional de Informa\u0026ccedil;\u0026otilde;es Florestais\u0026rdquo;\u0026lt;https://snif.florestal.gov.br/pt-br/\u0026gt;\u003c/li\u003e\n \u003cli\u003eBrazil (2024) \u003cstrong\u003eb\u003c/strong\u003e. Minist\u0026eacute;rio do Meio Ambiente - \u0026ldquo;Biomas\u0026rdquo; \u0026lt;https://antigo.mma.gov.br/biomas.html\u0026gt;\u003c/li\u003e\n \u003cli\u003eBarros, F.V., Lewis, K., Robertson A.D., Pennington, R.T., Hill, T.C. et al, Cost-effective restoration for carbon sequestration across Brazil\u0026apos;s biomes, Science of The Total Environment, Volume 876, 2023, 162600, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2023.162600.\u003c/li\u003e\n \u003cli\u003eDelgado, R. C., et al, Degradation of South American biomes: What to expect for the future?, Environmental Impact Assessment Review, Volume 96, 2022, 106815, ISSN 0195-9255, https://doi.org/10.1016/j.eiar.2022.106815.\u003c/li\u003e\n \u003cli\u003eSobral-Souza, T., et al, Efficiency of protected areas in Amazon and Atlantic Forest conservation: A spatio-temporal view, Acta Oecologica, Volume 87, 2018, Pages 1-7, ISSN 1146-609X, https://doi.org/10.1016/j.actao.2018.01.001.\u003c/li\u003e\n \u003cli\u003eBrazil (2024) \u003cstrong\u003ec\u003c/strong\u003e IBGE. Sidra: Banco de Tabelas Estat\u0026iacute;sticas. Dispon\u0026iacute;vel em: https://sidra.ibge.gov.br/home/ipca/brasil\u003c/li\u003e\n \u003cli\u003eWolfgang J. Junk, Catia Nunes de Cunha, Pantanal: a large South American wetland at a crossroads, Ecological Engineering, Volume 24, Issue 4, 2005, Pages 391-401, ISSN 0925-8574, https://doi.org/10.1016/j.ecoleng.2004.11.012.\u003c/li\u003e\n \u003cli\u003eBartelet, H.A., Barnes, M.L. \u0026amp; Cumming, G.S. Determinants, outcomes, and feedbacks associated with microeconomic adaptation to climate change. Reg Environ Change 22, 59 (2022). https://doi.org/10.1007/s10113-022-01909-z\u003c/li\u003e\n \u003cli\u003eDelfino RJ, Brummel S, Wu J, Stern H, Ostro B, Lipsett M, Winer A, Street DH, Zhang L, Tjoa T, Gillen DL. The relationship of respiratory and cardiovascular hospital admissions to the southern California wildfires of 2003. Occup Environ Med. 2009 Mar;66(3):189-97. https://doi.org/10.1136/oem.2008.041376\u003c/li\u003e\n \u003cli\u003eThomaz, S. M., Barbosa, L. G., de Souza Duarte, M. C., \u0026amp; Panosso, R. (2020). Opinion: The future of nature conservation in Brazil. Inland Waters, 10(2), 295\u0026ndash;303. https://doi.org/10.1080/20442041.2020.1750255\u003c/li\u003e\n \u003cli\u003eVale, Mariana V. et al, The COVID-19 pandemic as an opportunity to weaken environmental protection in Brazil, Biological Conservation, Volume 255, 2021, 108994, ISSN 0006-3207, https://doi.org/10.1016/j.biocon.2021.108994\u003c/li\u003e\n \u003cli\u003eMenezes, R.G., Barbosa Jr., R. Environmental governance under Bolsonaro: dismantling institutions, curtailing participation, delegitimising opposition. Z Vgl Polit Wiss 15, 229\u0026ndash;247 (2021). https://doi.org/10.1007/s12286-021-00491-8\u003c/li\u003e\n \u003cli\u003eHaikerwal, A., et al, Impact of smoke from prescribed burning: Is it a public health concern? Journal of the Air \u0026amp; Waste Management Association, 65(5), 592\u0026ndash;598. (2015). https://doi.org/10.1080/10962247.2015.1032445\u003c/li\u003e\n \u003cli\u003eMachado-Silva, F., Drought and fires influence the respiratory diseases hospitalizations in the Amazon, Ecological Indicators, Volume 109, 2020, 105817, ISSN 1470-160X,\u003cu\u003e \u003c/u\u003ehttps://doi.org/10.1016/j.ecolind.2019.105817\u003c/li\u003e\n \u003cli\u003e\u0026Ccedil;apraz, O., Deniz, A., Assessment of hospitalizations from asthma, chronic obstructive pulmonary disease and acute bronchitis in relation to air pollution in İstanbul, Turkey, Sustainable Cities and Society, Volume 72, 2021, 103040, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2021.103040\u003c/li\u003e\n \u003cli\u003eHe, Y., Jiang, W., Liao, JQ. et al. Short-term effects of air pollutants on hospital admissions for acute bronchitis in children: a multi-city time-series study in Southwest China. World J Pediatr 18, 426\u0026ndash;434 (2022).\u003cu\u003e \u003c/u\u003ehttps://doi.org/10.1007/s12519-022-00537-1\u003c/li\u003e\n \u003cli\u003eTakaro, T. K., Henderson, S. B., Climate Change and the New Normal for Cardiorespiratory Disease, Canadian Respiratory Journal, 22, 361687, 3 pages, 2015. https://doi.org/10.1155/2015/361687\u003c/li\u003e\n \u003cli\u003eAdaji, E.E., Ekezie, W., Clifford, M. et al. Understanding the effect of indoor air pollution on pneumonia in children under 5 in low- and middle-income countries: a systematic review of evidence. Environ Sci Pollut Res 26, 3208\u0026ndash;3225 (2019). https://doi.org/10.1007/s11356-018-3769-1\u003cu\u003e \u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eBarros, B., Oliveira, M., \u0026amp; Morais, S. (2023). Continent-based systematic review of the short-term health impacts of wildfire emissions. Journal of Toxicology and Environmental Health, Part B, 26(7), 387\u0026ndash;415. https://doi.org/10.1080/10937404.2023.2236548\u003c/li\u003e\n \u003cli\u003eWei, Jing et al. Long-term mortality burden trends attributed to black carbon and PM2\u0026middot;5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study The Lancet Planetary Health, Volume 7, Issue 12, e963 - e975 https://doi.org/10.1016/S2542-5196(23)00235-8\u003c/li\u003e\n \u003cli\u003eBurke, M., et al, The changing risk and burden of wildfire in the United States. Proc Natl Acad Sci U S A. 2021 Jan 12;118(2):e2011048118. https://doi.org/10.1073/pnas.2011048118\u003c/li\u003e\n \u003cli\u003eD\u0026rsquo;Amato, Gennaro et al. Climate change, allergy and asthma, and the role of tropical forests World Allergy Organization Journal, Volume 10, 11\u003cu\u003e \u003c/u\u003ehttps://doi.org/10.1186/s40413-017-0142-7\u003c/li\u003e\n \u003cli\u003eBernstein, Aaron S. et al Lungs in a Warming World CHEST, Volume 143, Issue 5, 1455 - 1459 (2013) https://doi.org/10.1378/chest.12-2384\u003c/li\u003e\n \u003cli\u003eLeal Filho et al. Fire in Paradise: Why the Pantanal is burning, Environmental Science \u0026amp; Policy, Volume 123, 2021, Pages 31-34, ISSN 1462-9011, https://doi.org/10.1016/j.envsci.2021.05.005\u003c/li\u003e\n \u003cli\u003eMenezes, Lucas S. et al Lightning patterns in the Pantanal: Untangling natural and anthropogenic-induced wildfires, Science of The Total Environment, Volume 820, 2022, 153021, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2022.153021\u003c/li\u003e\n \u003cli\u003eBiggerstaff, M., Cauchemez, S., Reed, C. et al. Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature. BMC Infect Dis 14, 480 (2014).\u003cu\u003e \u003c/u\u003ehttps://doi.org/10.1186/1471-2334-14-480\u003cu\u003e\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eGhimire, B., C. A. Williams, G. J. Collatz, and M. Vanderhoof (2012), Fire-induced carbon emissions and regrowth uptake in western U.S. forests: Documenting variation across forest types, fire severity, and climate regions, J. Geophys. Res., 117, G03036, https://doi.org/10.1029/2011JG001935\u003c/li\u003e\n \u003cli\u003eRey-Salgueiro, L., Mart\u0026iacute;nez-Carballo, E., Merino, A., Vega, J. A., Fonturbel, M. T., and Simal-Gandara, J. (2018) Polycyclic Aromatic Hydrocarbons in Soil Organic Horizons Depending on the Soil Burn Severity and Type of Ecosystem. Land Degrad. Develop., 29: 2112\u0026ndash;2123. https://doi.org/10.1002/ldr.2806\u003c/li\u003e\n \u003cli\u003eBoin, M.N., Martins, P.C.S., da Silva, C.A., Salgado, A.A.R. (2019). Pantanal: The Brazilian Wetlands. In: Salgado, A., Santos, L., Paisani, J. (eds) The Physical Geography of Brazil . Geography of the Physical Environment. Springer, Cham. https://doi.org/10.1007/978-3-030-04333-9_5\u003cu\u003e \u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eBrazil (2024) \u003cstrong\u003ed \u003c/strong\u003eINPE. Banco de Dados de queimadas. Dispon\u0026iacute;vel em: http://www.inpe.br/queimadas/bdqueimadas\u003c/li\u003e\n \u003cli\u003eBrazil (2024) \u003cstrong\u003ee\u003c/strong\u003e Minist\u0026eacute;rio da Sa\u0026uacute;de. DATASUS. Tabnet. Bras\u0026iacute;lia, DF: Minist\u0026eacute;rio da Sa\u0026uacute;de, 2022 https://datasus.saude.gov.br/\u003c/li\u003e\n \u003cli\u003eAhmad, S.P., P. F. Levelt, P. K. Bhartia, E. Hilsenrath, G. W. Leppelmeierd, and J. E. Johnson. Atmospheric products from the ozone monitoring instrument (OMI). In Proc. SPIE, Earth Observing Systems VIII, volume 5151, pages 619\u0026ndash;630. William L. Barnes, 2003.\u003c/li\u003e\n \u003cli\u003eBoersma, F., E. Bucsela, E. Brinksma, and J.F. Gleason, \u0026ldquo;NO2,\u0026rdquo; Algorithm Theoretical Baseline Document: OMI Trace Gas Algorithms, K. Chance (ed.), Vol. IV, ATBD-OMI-04, Version 2.0, Aug. 2002.\u003c/li\u003e\n \u003cli\u003eCarn, S.A., N.A. Krotkov, K. Yang, R.M. Hoff, A.J. Prata, A.J. Krueger, S.C. Loughlin, and P.F. Levelt (2007b). \u0026ldquo;Extended observations of volcanic SO2 and sulfate aerosol in the stratosphere,\u0026rdquo; Atmos. Chem. Phys. 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A.,A distributed lag approach to fitting non-linear dose\u0026ndash;response models in particulate matter air pollution time series investigations, Environmental Research, Volume 104, Issue 2, 2007, Pages 193-200, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2007.01.009\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eLung-fei Lee, Jihai Yu, Estimation of spatial autoregressive panel data models with fixed effects, Journal of Econometrics, Volume 154, Issue 2, 2010, Pages 165-185, ISSN 0304-4076, https://doi.org/10.1016/j.jeconom.2009.08.001.\u003c/li\u003e\n \u003cli\u003eBadi H. Baltagi, Seuck Heun Song, Won Koh, Testing panel data regression models with spatial error correlation, Journal of Econometrics, Volume 117, Issue 1, 2003, Pages 123-150, ISSN 0304-4076,\u003cu\u003e \u003c/u\u003ehttps://doi.org/10.1016/S0304-4076(03)00120-9.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization, \u0026amp; World Health Organization. (2021). WHO Global Air Quality Guidelines: Particulate Matter (PM2. 5 and PM10). Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide, 267\u003c/li\u003e\n \u003cli\u003eRequia, W.J., Amini, H., Mukherjee, R. et al. Health impacts of wildfire-related air pollution in Brazil: a nationwide study of more than 2 million hospital admissions .\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5388967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5388967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForest fires produce toxic emissions that are detrimental to human health. Currently, the Pantanal, the largest continuous wetland in the world, is facing intense fires. This situation may contribute to increased hospital admissions for respiratory and cardiovascular diseases. To assess the additive effect between active fire hotspots and hospitalizations of residents in the Pantanal, a generalized linear model was used, incorporating geolocalized covariates such as air quality and climatic conditions. Our findings show a consistent worsening, when humans are exposed to 10 additional wildfire hotspots, the daily risk of admissions for respiratory diseases increases by 23,2% and 22,3% for cardiovascular diseases in 10-year analysis. These results can stimulate preventive measures to mitigate forest fires and also early preparation of the health system.\u003c/p\u003e","manuscriptTitle":"Additive Effect of Wildfires on Hospital Admission in the Pantanal Wetland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 08:56:20","doi":"10.21203/rs.3.rs-5388967/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-10T05:43:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-08T22:48:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-18T17:32:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333953264628461981555632632383595695236","date":"2024-12-17T08:35:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241695987203300284823695091636255077167","date":"2024-12-05T22:36:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-02T08:29:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-29T10:13:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-26T10:06:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-26T04:24:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-04T14:34:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6b022bf-6a3e-4d31-86e1-0181e57372b6","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:47:08+00:00","versionOfRecord":{"articleIdentity":"rs-5388967","link":"https://doi.org/10.1038/s41598-025-13257-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-29 16:38:14","publishedOnDateReadable":"July 29th, 2025"},"versionCreatedAt":"2025-01-13 08:56:20","video":"","vorDoi":"10.1038/s41598-025-13257-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-13257-z","workflowStages":[]},"version":"v1","identity":"rs-5388967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5388967","identity":"rs-5388967","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}