Keywords
asthma, forest fires, California, acres burned
asthma, forest fires, California, acres burned
We have updated the limitations paragraph to address important points raised by the reviewer. For example, we included recommendations for what variables could have improved the sensitivity of our statistical analysis.
See the authors' detailed response to the review by Brian G. Oliver
Forest fires are devastating events causing widespread damage and contributing enormous environmental pollution1,2. According to the National Interagency Fire Center, in 2017 there were 9,988 fires within the state of California. While the immediate effects of forest fires are well known, the lingering implications are less apparent. As some of the main by-products of forest fires are CO2 and PM2.53,4, questions remain with regards to how forest fires contribute to the incidence of respiratory diseases.
Asthma is a chronic respiratory illness characterized by the constriction of the bronchi, leading to difficult breathing and sometimes irreversible respiratory tissue remodeling5. According to the US Center of Disease and Control and Prevention, there is an asthma prevalence of 7.7% among the population in California. Because asthma symptoms can be exacerbated by airborne pollutants, such as CO2 and PM2.56,7, we hypothesized that an increase in acres of forest burned would be linked to increased emergency department (ED) visits due to asthma.
Data was retrieved from the California Environmental Health Tracking Program. The inclusion criteria to extract the data included ED visits due to asthma, all ages, all races/ethnicities, and all genders.
Reported forest fires were collected from the Historical Wildfire Activity Statistics section (HWAS) of the California Department of Forestry and Fire Protection. Only fires greater than 300 acres were included in the final dataset; as per National’s Park Services terminology, fires above 300 acres burned are considered large fire events. For this data, during some years, some counties had missing values because they did not reach the 300 acres threshold.
Both datasets were inclusive to an 11-year period (January to December, 2005–2015).
Statistical analysis was performed with R (version 3.5.0, https://www.r-project.org/). California’s counties were grouped into regions (Figure 1). Given the size of the state and no present guidelines for grouping of each county, we grouped counties by proximity into North, Coastal, Central, Motherload, and South regions. This classification was carried out within the R script included in Supplementary File 1. A pairwise t-test, with Bonferroni adjustment, was implemented to compare both the acres burned and the asthma rate, respectively, per region. A linear regression model, adjusted by year and region, was elaborated to evaluate the relationship between acres burned and ED asthma visits. p < 0.05 was considered statistically significant.
Each county was assigned to a region based on geography and proximity. As existing maps were unable to distribute California’s counties evenly, we used a breakdown of regions (Figure 1). Table 1 provides the mean acres burned and mean asthma rates per region. The ED asthma visits rates were above 50% except for the South and Motherload regions. A pairwise t-test with Bonferroni adjustment did not detect statistically significant differences in acres burned between the regions (p = 0.38 to 1.0; Table 2).
Region | Mean acres burned | Mean asthma rates (%) |
---|---|---|
North | 26137 | 53.7 |
South | 17541 | 48.8 |
Central | 16167 | 52.2 |
Coastal | 12553 | 50.1 |
Motherload | 9758 | 43.0 |
On the contrary, a statistically significant difference (p = 0.003) was found between the South (48.8%) and the Motherload (43.0%) region with regards to asthma rates. A linear regression model, adjusting for years and region, was employed to evaluate the magnitude of relationship between acres burned and ED asthma visits (Figure 2). In this model, and although statistically significant (p = 0.005), forest fires only explained less than 5% of the variability of the ED asthma visits between the years 2006 to 2015 (r2 = 0.047). These results suggest that although regions within California may differ in ED asthma visits, forest fires did not explain these differences.
In this study, data on forest fires within the state of California was compiled to address the potential relationship with ED asthma visits. As per our linear regression model, there was no link between the ED asthma visits and acres of forest burned. Future research should account for the additional variables related to asthma as well as forest fires.
In the current study, forest fires barely explained the ED visits from asthma during the years 2006 to 2015. It is possible that these ED asthma visits may have resulted from other stimuli, such as biological and non-biological indoor and outdoor air pollution besides forest fires8–10. Also, California participates in different programs that may reduce ED visits like the National Asthma Control Initiative, which was developed by the US National Institute of Health.
Interestingly, asthma rates varied across all regions. The Motherload region had significantly lower mean asthma rates than the North region. This finding further warrants additional investigations into the asthma rates within states susceptible to natural disasters and diverse geographic and topography, such as the state of California. Future studies may shed light on the interplay of forest fires, environmental variables, and chronic respiratory diseases, such as asthma.
There were several limitations in this study. First, fires are known to often cross county lines, which may difficult assigning over or underestimate the values per county. Also, the dataset did not include assessment of air pollution (i.e. PM2.5, PM10), which prevented us from evaluating air particulate matter as a confounder. Another limitation was that the data did not include years with no fires as a baseline value for ED asthma visits. Also, the data on asthma rates was available by year, not by month, thus reducing our ability to match forest fires and asthma rates by week or month. Finally, it is possible that ED asthma visits may also be modified by another variable. For example, tracing the sale and use of asthma medication could provide more reliable information of asthma exacerbation rates as the result of forest fires. Another point worth mentioning was that the Alpine county was not include due to the lack of data relating to asthma.
In summary, findings from the current study warrant examination of additional variables that could potentially contribute to environmental air pollution, besides forest fires, and are also linked to exacerbations of asthma. These include sociodemographics, medication sales due to asthma, and others that could shed light on the activities that the people of California implement during forest fires events.
F1000Research Dataset 1: Forest fires acres burned and emergency department visits due to asthma for California, January-December 2005–2015. DOI, 10.5256/f1000research.15839.d21339711
Data for emergency department visits due to asthma: http://www.cehtp.org/page/asthma/query
Data for forest fires: http://www.fire.ca.gov/fire_protection/fire_protection_fire_info_redbooks
The full dataset is available as Dataset 1.
R code for performing the analysis is available in Supplementary File 1.
Supplementary File 1: R code used for analysis of asthma and fires in California.
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Competing Interests: No competing interests were disclosed.
Reviewer Expertise: respiratory diseases
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: respiratory diseases
At the request of the author(s), this article is no longer under peer review.
Alongside their report, reviewers assign a status to the article:
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Version 2 (revision) 15 Mar 19 |
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Version 1 10 Aug 18 |
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Click here to access the data.
Spreadsheet data files may not format correctly if your computer is using different default delimiters (symbols used to separate values into separate cells) - a spreadsheet created in one region is sometimes misinterpreted by computers in other regions. You can change the regional settings on your computer so that the spreadsheet can be interpreted correctly.
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