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Cited by 122Air Quality and Health ImpactsClimate Change and Health ImpactsFire effects on ecosystems

Health impacts of wildfire-related air pollution in Brazil: a nationwide study of more than 2 million hospital admissions between 2008 and 2018

Open access
Requia, Weeberb J., Amini, Heresh, Mukherjee, Rajarshi, Gold, Diane R., Schwartz, Joel D.·2021·Nature Communications
DOI 10.1038/s41467-021-26822-7PMC PMC8589982
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Abstract

We quantified the impacts of wildfire-related PM 2.5 on 2 million hospital admissions records due to cardiorespiratory diseases in Brazil between 2008 and 2018. The national analysis shows that wildfire waves are associated with an increase of 23% (95%CI: 12%–33%) in respiratory hospital admissions and an increase of 21% (95%CI: 8%–35%) in circulatory hospital admissions. In the North (where most of the Amazon region is located), we estimate an increase of 38% (95%CI: 30%–47%) in respiratory hospital admissions and 27% (95%CI: 15%–39%) in circulatory hospital admissions. Here we report epidemiological evidence that air pollution emitted by wildfires is significantly associated with a higher risk of cardiorespiratory hospital admissions.

AI snapshot

Written from the paper’s full text.

TL;DR

This nationwide Brazilian study found that wildfire-related PM2.5 during wildfire waves was associated with higher cardiorespiratory hospital admissions from 2008 to 2018. The strongest national associations were a 23% increase in respiratory admissions and a 21% increase in circulatory admissions.

Key findings

  • Across Brazil, wildfire waves were associated with a 23% increase in respiratory hospital admissions (95% CI: 12–33%) at a 5-day moving average and a 21% increase in circulatory hospital admissions (95% CI: 8–35%) at a 2-day moving average.
  • In the North region, where most of the Amazon is located, the estimated increases were 38% for respiratory admissions (95% CI: 30–47%) and 27% for circulatory admissions (95% CI: 15–39%).
  • Regional heterogeneity was substantial: the Midwest had a 20% increase in respiratory admissions (95% CI: 13–26%) at 5 days and a 56% increase in circulatory admissions (95% CI: 36–76%) at 2 days.
  • The study included 2,044,038 cardiorespiratory hospital admissions; 50.2% were respiratory and 49.8% circulatory.

Methods

  • Time-stratified case-crossover design using conditional logistic regression, comparing exposure on hospital admission days with control days matched by day of week, month, and calendar year.
  • Data covered 2,044,038 hospital admissions for respiratory (ICD-10 J00–J99) and circulatory (ICD-10 I00–I99) diseases in Brazil from 2008 to 2018, with wildfire records from INPE and PM2.5, other pollutants, and weather from ECMWF/CAMS ensemble models.
  • “Wildfire wave” was defined as any day when wildfire records and PM2.5 concentration exceeded the 99th percentile of the 2008–2018 regional time series; region-specific odds ratios were meta-analyzed with random effects to estimate national averages.

Limitations

  • The authors state that care-seeking behavior likely led to underestimation of cases, that the fixed annual definition of wildfire waves may cause misclassification, that predicted air pollution and meteorological variables may introduce exposure measurement error, and that hospital admission forms may contain errors.
  • The text also makes evident that some subgroup analyses by region, sex, and age had very large groups of ties and could not be computed because the Survival package algorithm may fail due to overflow.
  • The authors note that regional heterogeneity may reflect cultural, social, behavioral, environmental, and healthcare-access differences, including unequal distribution of health facilities across Brazil.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41467-021-26822-7

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

21 of 24 cited sources checked · 10 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    In Brazil, reducing wildfire-related particulate emissions by 30% would prevent about 400 to 1700 premature adult deaths annually.

    “In Brazil, the reduction of wildfire-related particulate emissions by 30% indicates a health improvement by preventing about 400 to 1700 premature adult deaths annually8.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [8] Reddington, C. L. et al. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nat. Geosci. 8, 768–771, https://doi.org/10.1038/ngeo2535 (2015). source

      The paper reports that a 40% reduction in Brazil’s deforestation rates is estimated to prevent 1,060 premature adult deaths annually across South America, with a 95% confidence interval of 388–1,721 (Abstract; page not provided). Its 30% figure refers to lower dry-season PM2.5 concentrations in low-deforestation years, not a 30% reduction in particulate emissions; the mortality estimate is also for South America, not Brazil alone. (LLM, conf=0.90)

      Experimental — accuracy not yet measured
  • Premisecitation

    There is considerable difference in the quality of health/environment and healthcare across different populations in Brazil, which is an important determinant of the health impacts of wildfire-related air pollution.

    “where there is a considerable difference in the quality of health/environment and healthcare across different populations (influencing health/environment equity in negative ways), which is an important determinant of the health impacts of wildfire-related air pollution20”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [20] Rappold, A. G. et al. Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health. Environ. Heal 11, 71, https://doi.org/10.1186/1476-069X-11-71 (2012). source

      The Background and Discussion say community health and socioeconomic conditions can modify health risks from wildfire-smoke exposure. But the Methods place the study in North Carolina, not Brazil, and the Discussion says access and quality of care were not observed to determine susceptibility at the community level. (LLM, conf=0.87)

      Experimental — accuracy not yet measured
  • Study design and conductcitation

    ECMWF is a reliable source for daily temporal changes in air pollution, as shown by validation studies.

    “However, we highlight that in our study we were interested in temporal changes of air pollution (daily variation), and therefore, European Centre for Medium-Range Weather Forecasts (ECMWF) seems to be a reliable source of providing such data as shown by the validation studies mentioned in the “Methods” section.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Inness et al. [36] Inness A., et al. 2018. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. Discuss. 1–55; https://doi.org/10.5194/acp-2018-1078 (2019). source

      The paper’s Abstract describes ECMWF’s CAMS reanalysis as time-consistent atmospheric-composition data with hourly surface forecast fields, and its Nitrogen dioxide section reports validation against observations at European stations. That validation says the reanalyses reproduce observed mean values and seasonal variability well, but it does not establish reliability specifically for day-to-day changes in air pollution. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    AERONET observation sites in South America have significant representativity for AOD measured by MODIS.

    “Other studies have shown that AERONET observation sites in South America has significant representativity for AOD measured by MODIS, aboard TERRA and AQUA satellites38.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [38] Hoelzemann, J. J. et al. Regional representative of AERONET observation sites during the biomass burning season in South America determined by correlation studies with MODIS Aerosol Optical Depth. J. Geophys. Res. Atmos. 114, 1–20, https://doi.org/10.1029/2008JD010369 (2009). source

      Section 5.3 reports moderate to strong correlations between AERONET and MODIS AOD for most studied sites and concludes that AERONET observations characterize a significant area of South America. The citing sentence omits the study’s biomass-burning-season focus and generalizes to AERONET sites in South America, although Section 5.3 excludes three sites from the regional analysis because their data were insufficient. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
  • Interpretationcitation

    A portion of the regional heterogeneity in the impact of wildfire PM2.5 exposure can be explained by cultural, social, behavioral, and environmental/geographical conditions.

    “A portion of the regional heterogeneity of the impact of wildfire PM2.5 exposure indicated by our results can be explained by the cultural, social, behavioral, and environmental/geographical conditions10,20,24.”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [10] Reid, C. E. et al. Health impacts of wildfire smoke. Environ. Health Perspect. 124, 1334–1343 (2016b). source

      The Results (Vulnerable Populations; page unavailable) describe differences associated with socioeconomic status and ethnicity, while the Discussion says that different fire types could produce different health effects because of smoke composition. The review does not establish that cultural or behavioral conditions explain regional heterogeneity, and it presents the fire-type explanation as a possibility rather than a demonstrated cause. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [20] Rappold, A. G. et al. Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health. Environ. Heal 11, 71, https://doi.org/10.1186/1476-069X-11-71 (2012). source

      The Results report that wildfire-smoke PM2.5 risks differed between North Carolina counties grouped by socioeconomic and physical-environment measures, and the Discussion identifies socioeconomic factors as the strongest modifiers. This supports a role for social and environmental conditions in differing impacts, but the paper does not establish cultural or geographical conditions as explanations; its Discussion also cautions that county-level associations cannot establish individual-level causes. Page numbers are unavailable in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [24] Reid, C. E. et al. Critical review of health impacts of wildfire smoke exposure. Environ. Health Perspect. 124, 1334–1343, https://doi.org/10.1289/ehp.1409277 (2016a). source

      The Results (Vulnerable Populations; page unavailable) report differences in wildfire-smoke effects by socioeconomic status and suggest that social characteristics may help explain differences between population groups. The Discussion (page unavailable) says different types of fires could produce different health effects because smoke constituents vary. The review does not establish that cultural or behavioral conditions explain regional heterogeneity in wildfire PM2.5 impacts, and it presents the environmental explanation as a possibility rather than a demonstrated cause. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Classified as probably not a substantial claim. 15
  • May combine two claims. 12

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    About 12-16% of global wildfire-related particulate emissions occur across Brazil.

    “About 12–16% of global wildfire-related particulate emissions occur across Brazil8.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [8] Reddington, C. L. et al. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nat. Geosci. 8, 768–771, https://doi.org/10.1038/ngeo2535 (2015). source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    A critical review of 53 epidemiological studies shows that wildfire exposure is associated with respiratory diseases and growing evidence suggests associations for specific cardiovascular endpoints.

    “Regarding the health impacts of wildfire smoke exposure, a critical review of 53 epidemiological studies10 shows that wildfire exposure is associated with respiratory diseases and growing evidence suggests associations for specific cardiovascular endpoints.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [10] Reid, C. E. et al. Health impacts of wildfire smoke. Environ. Health Perspect. 124, 1334–1343 (2016b). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Liu et al. estimated a 7.2% increase in risk for respiratory admissions during smoke wave days with high wildfire-specific PM2.5 during 2004 and 2009 in the Western United States.

    “Liu et al.11 estimated a 7.2% increase in risk for respiratory admissions during smoke wave days with high wildfire-specific PM2.5 during 2004 and 2009 in the Western United States.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [11] Liu, J. C. et al. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 28, 77–85, https://doi.org/10.1097/EDE.0000000000000556 (2017). source

  • May combine two claims.

    Findingown data

    Only four studies on wildfire, air pollution, health effects, human exposure, and Brazil were found in PubMed and Web of Science, and three of them focused only on the Amazon region.

    “We found only four studies8,16–18 and three of them focused only on the Amazon region.”
    Quote in the paperSubstantial claim4 of 4 sources checked
    • cites [8] Reddington, C. L. et al. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nat. Geosci. 8, 768–771, https://doi.org/10.1038/ngeo2535 (2015). source
    • cites [16] Do Carmo, C. N. et al. Association between particulate matter from biomass burning and respiratory diseases in the southern region of the Brazilian Amazon. Rev. Panam. Salud Public. 27, 10–16, https://doi.org/10.1590/s1020-49892010000100002 (2010). source
    • cites [17] Ignotti, E. et al. Impact on human health of particulate matter emitted from burnings in the Brazilian Amazon region. Rev. Saúde. Pública 44, 121–130 (2010b). source
    • cites [18] Jacobson L. S. V., et al. 2014. Acute effects of particulate matter and black carbon from seasonal fires on peak expiratory flow of schoolchildren in the Brazilian Amazon. PLoS ONE 9, https://doi.org/10.1371/journal.pone.0104177 (2014). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The concept of wildfire wave defined in this study is similar to concepts of extreme air pollution events from wildfires defined in previous studies.

    “The concept of wildfire wave defined in our study is similar to the concepts of extreme air pollution events from wildfires defined in previous studies11,15.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [11] Liu, J. C. et al. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 28, 77–85, https://doi.org/10.1097/EDE.0000000000000556 (2017). source
    • cites [15] Johnston, F., Hanigan, I., Henderson, S., Morgan, G. & Bowman, D. Extreme air pollution events from bushfires and dust storms and their association with mortality in Sydney, Australia 1994-2007. Environ. Res 111, 811–816, https://doi.org/10.1016/j.envres.2011.05.007 (2011). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In South America, the CAMS satellite instrument data have an approximate bias of −0.006 ± 0.128 relative to AERONET.

    “In South America, the data from satellite’s instruments are slightly smaller, with an approximate bias of −0.006 ± 0.128.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Inness et al. [36] Inness A., et al. 2018. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. Discuss. 1–55; https://doi.org/10.5194/acp-2018-1078 (2019). source

      The Aerosols section and Figure 22 caption show that the paper compares reanalysis aerosol optical depth with AERONET observations in South America. The passages available from our copy do not give the South American bias of −0.006 ± 0.128; they also describe reanalysis output, not a direct bias of satellite-instrument data. Searches for the value and the South America entry of Table 8 did not resolve those points. (LLM, conf=0.78)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The validation for the CAMS global model is reported by Inness et al.36.

    “The validation for the CAMS global model is reported by Inness et al.36.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Inness et al. [36] Inness A., et al. 2018. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. Discuss. 1–55; https://doi.org/10.5194/acp-2018-1078 (2019). source

  • May combine two claims.

    Premisecitation

    The association between AERONET data and AOD from MODIS is significant during biomass burning seasons in South America, with R2 higher than 0.85 for most AERONET stations in Brazil.

    “This association between AERONET data and AOD from MODIS is significant during the biomass burning seasons in South America, which the R2 (coefficient of determination) for most of the AERONET stations in Brazil was higher than 0.8538.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [38] Hoelzemann, J. J. et al. Regional representative of AERONET observation sites during the biomass burning season in South America determined by correlation studies with MODIS Aerosol Optical Depth. J. Geophys. Res. Atmos. 114, 1–20, https://doi.org/10.1029/2008JD010369 (2009). source

      Section 5.2 reports strong AERONET–MODIS AOD correlations during the August–October burning seasons and says this pattern applies to the biomass-burning-influenced sites. It gives R² > 0.7, however, not R² > 0.85 for most Brazilian stations; the Conclusions likewise report generally good correlations at R² > 0.7. (LLM, conf=0.87)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    The study found the same variation in subgroup analysis by age as Haikerwal et al. but not for subgroup analysis by sex.

    “We found the same variation for the subgroup analysis by age but not for the subgroup analysis by sex.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Our results show high concentration of PM2.5 in the Amazon region, whereas the regions with high density of wildfire are mostly located in part of the North and Northeast in Brazil.

    “Our results show high concentration of PM2.5 in the Amazon region, whereas the regions with high density of wildfire are mostly located in part of the North and Northeast in Brazil.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The mean concentration of PM2.5 over the study period in Brazil was ~15 μg m−3, exceeding the WHO air quality guideline of 10 μg m−3 annual mean.

    “The mean concentration of PM2.5 over the study period in Brazil was ~15 μg m−3, which exceed the air quality guidelines from the World Health Organization (10 μg m−3 annual mean).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Wildfires emit substantial amounts of air pollutants that can travel over large distances, affecting air quality and human health far from the originating fires.

    “Wildfires emit substantial amounts of air pollutants that can travel over large distances, affecting air quality and human health far from the originating fires4.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [4] Youssouf, H. et al. Quantifying wildfires exposure for investigating health-related effects. Atmos. Environ. 97, 239–251 (2014). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The health data may contain errors because hospital admission data are based on Hospital Admission Authorization Forms filled out by hospital staff or doctors.

    “Finally, given that the health data is based on filling out of Hospital Admission Authorization Forms (HAAFs), we have to consider a possibility of errors when the hospital staffs/doctors are filling these forms.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    The literature has reported PM2.5 as the major pollutant emitted by wildfires.

    “As shown in the “Introduction” section, the literature has reported PM2.5 as the major pollutant emitted by wildfires.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Region-specific odds ratios were meta-analyzed to estimate national average hospital admissions associated with wildfire-related PM2.5 in wildfire waves, accounting for intra- and inter-region variability with random effects.

    “Then, region-specific OR were meta-analyzed to estimate national average hospital admissions associated with wildfire-related PM2.5 in wildfire waves. We accounted for intra- and inter-region variability by applying regression meta-analysis with random effects.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Numerous effect modification and sensitivity analyses were conducted by stratifying by sex, age, excluding control pollutants, excluding race, excluding spatial terms, and combining cardiorespiratory admissions.

    “We conducted numerous effect modification and sensitivity analysis by stratifying the analyses by sex, by age (0–5 years old, 35–64 years old, and >64 years old), by excluding the control pollutants from the model (CO, NO2, and O3), by excluding race, by excluding some spatial terms (state and latitude/longitude), and by accounting for cardiorespiratory hospital admissions (cardiovascular and”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The study used a modeling approach that indicates extreme episodes of wildfire, defined as wildfire waves.

    “Third, we used a modeling approach that indicates the extreme episodes of wildfire, which we defined as wildfire waves.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductreasoning

    The matching study design controls for individual-level confounding factors because patients are considered as their own controls.

    “We highlight that the matching study design controls for individual-level confounding factors as the patients are considered as their controls.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Our findings add strength to the evidence that wildfire pollution, such as pollution from other sources, has adverse cardiac and respiratory effects, likely through similar biological mechanisms as shown in toxicological literature.

    “First, our findings add strength to the evidence that wildfire pollution, such as pollution from other sources, has adverse cardiac and respiratory effects, likely through similar biological mechanisms as shown in toxicological literature.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The case-crossover design defines patients as their own controls, allowing control of all individual-level potential confounders except those that change rapidly.

    “Also, people who had the health event were defined as their own controls, allowing for control of all individual-level potential confounders (e.g., socio-economic status, smoking history, pre-existing medical conditions) by design, except for ones that change rapidly.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The primary analysis and all sensitivity analyses were conducted individually for each of the five Brazilian regions to capture regional heterogeneity of landscape.

    “The primary analysis and all the sensitivity analyses were conducted individually for each one of the five Brazilian regions (Appendix 1 shows the spatial distribution of these regions). We performed this subgroup analysis by region to capture the regional heterogeneity of landscape in Brazil (e.g., Amazon Forest, Atlantic Forest, Pantanal, etc.), which is strongly correlated with wildfire occ”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The conditional logistic regression model was adjusted for other air pollutants, meteorological variables, elevation, spatial terms, and hospital stay duration.

    “We adjusted the model for several control/confounding variables, including other air pollutants emitted by wildfires (CO, NO2, and O3), meteorological variables (temperature, relative humidity, precipitation, wind speed, wind direction), topographic variable represented by elevation, spatial terms (latitude, longitude, state, and a binary variable representing the municipalities that are the capit”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The definition of wildfire waves may be a source of misclassification because the number of smoke days is fixed over a year.

    “Second, the definition of wildfire waves may be a source of misclassification, because the number of smoke days will be fixed over a year.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The study has the largest sample size and the largest study period in Brazil among studies on this topic.

    “To our knowledge, this is the study with the largest sample size and the largest study period in Brazil.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The study accounted for exposure on the day of the health event and on non-event days with time-stratified sampling, reducing confounding related to seasonal trend by controlling for time-dependent/independent risk factors.

    “Fourth, we accounted for a strong quantitative component in our analyses, looking at the exposure on the day of the health event and the exposure on non-event days with a time-stratified sampling. As described in the “Methods” sections, this reduced the effects of confounding related to the seasonal trend by controlling for time-dependent/independent risk factors.”
    Quote in the paperSubstantial claim
25 of 25

  • The paper relies on this without support.

    Fine particulate matter (PM2.5) is the major pollutant emitted by wildfires.

    “Fine particulate matter (PM2.5) is the major pollutant emitted by wildfires.”

  • The paper relies on this without support.

    Brazil is a very fire-prone region.

    “Given that Brazil is very fire-prone region”

  • The paper relies on this without support.

    Different types of biomass in Brazil are strongly correlated with wildfire events over space and time.

    “where there are different types of biomass (Amazon Forest, Cerrado, Atlantic Forest, Caatinga, Pampa, and Pantanal) that are strongly correlated with wildfire events over space and time”

  • The paper relies on this without support.

    There is a critical challenge related to land use in Brazil that correlates to wildfire occurrence.

    “where there is a critical challenge related to land use (e.g., agriculture, deforestation etc.), which also correlates to wildfire occurrence”

  • The paper relies on this without support.

    The distribution of health facilities varies drastically by region in Brazil, with more per capita in the South than in the Amazon region, including the North.

    “In Brazil, the distribution of health facilities varies drastically by region. For example, there are many more health facilities per capita in the South than in the Amazon region, including the North.”

  • The paper relies on this without support.

    People with cardiorespiratory symptoms during wildfire episodes may not seek medical care, leading to underascertainment of hospital admissions.

    “given the presence of the individual perceptions and decisions to seek medical care after symptoms of cardiorespiratory diseases during wildfire episodes (as mentioned above), there will be different types of persons.”
6 of 6

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Discussion's 1-day circulatory increase conflicts with Table 3

    Minor

    The discussion reports a 14.9% increase for the 1-day moving average, but Table 3 gives an odds ratio of 1.14, corresponding to a 14% increase. An increase of 14.9% would not round to the table's odds ratio.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Ignotti, E. et al. Impact on human health of particulate matter emitted from burnings in the Brazilian Amazon region. Rev. Saude Publica 44, 121–130 (2010a). · DOI · Paper page
  2. Reid, C. E. et al. Critical review of health impacts of wildfire smoke exposure. Environ. Health Perspect. 124, 1334–1343, https://doi.org/10.1289/ehp.1409277 (2016a). · DOI · Paper page

Cited by

Sorted by citations

Related

Authors
Requia, W. J., Amini, H., Mukherjee, R., Gold, D. R., & Schwartz, J. D.
Affiliations
  • University of Copenhagen (DK)
  • Harvard University (US)
  • Brigham and Women's Hospital (US)
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-11-12
Volume
12
Issue
1
Pages
6555-6555
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-26822-7
PMC
PMC8589982
OpenAlex
W3213522055
Cited by
122
References
37
Citations by year
2026: 30 · 2025: 30 · 2024: 23 · 2023: 23 · 2022: 16
FWCI
6.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
Keywords
wildfire smoke, air pollution health effects, respiratory hospital admissions, Amazon wildfires, Northern Brazil, particulate matter exposure
MeSH terms
Air Pollutants, Air Pollution, Brazil, Hospitalization, Humans

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