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      Effect of a vegetation fire event ban on hospital visits for respiratory diseases in Upper Northern Thailand

      1 , 2 , 2 , 3 , 1 , 4 , 1 , 4
      International Journal of Epidemiology
      Oxford University Press (OUP)

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          Abstract

          Background

          Upper Northern Thailand (UNT) has been episodically affected by air pollution from vegetation burning, which causes adverse respiratory health effects. However, no study has evaluated the effect of regulatory actions to prohibit vegetation burning on respiratory morbidity. We examined the effect of a burning ban enforced in May 2016 on hospital visits for respiratory diseases in UNT.

          Methods

          This study used data from eight provinces in UNT. Analyses were conducted for January to April of 2014–2016 (before ban enforcement) and January to April of 2017–2018 (after ban enforcement). Particulate matter of 10 microns in diameter or smaller (PM10) concentrations, numbers of satellite fire hotspots and age-standardized rates of hospital visits for respiratory diseases before and after ban enforcement were compared. The effect of the ban on hospital visits for respiratory diseases was evaluated using an interrupted time-series analysis controlled for season-specific temporal trends, day of week, public holiday, temperature, relative humidity, number of hospitals and offset population, with gastrointestinal diseases as a negative control. A meta-analysis was performed to pool province-specific effect estimates.

          Results

          The daily average PM10 concentration and the number of fire hotspots decreased after ban enforcement in all provinces in UNT, with percent changes ranging from 5.3 to 34.3% and 14.3 to 81.5%, respectively. The adjusted pooled effect estimates of hospital visits for respiratory diseases decreased by 9.1% (95% CI: 5.1, 12.9), whereas a null association was observed for gastrointestinal diseases.

          Conclusion

          The burning ban had a positive impact on both air pollution levels and rates of hospital visits for respiratory diseases in UNT.

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          Most cited references35

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          Conducting Meta-Analyses inRwith themetaforPackage

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            Is Open Access

            Time series regression studies in environmental epidemiology

            Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed (‘lagged’) associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.
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              Estimated Global Mortality Attributable to Smoke from Landscape Fires

              Background: Forest, grass, and peat fires release approximately 2 petagrams of carbon into the atmosphere each year, influencing weather, climate, and air quality. Objective: We estimated the annual global mortality attributable to landscape fire smoke (LFS). Methods: Daily and annual exposure to particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) from fire emissions was estimated globally for 1997 through 2006 by combining outputs from a chemical transport model with satellite-based observations of aerosol optical depth. In World Health Organization (WHO) subregions classified as sporadically affected, the daily burden of mortality was estimated using previously published concentration–response coefficients for the association between short-term elevations in PM2.5 from LFS (contrasted with 0 μg/m3 from LFS) and all-cause mortality. In subregions classified as chronically affected, the annual burden of mortality was estimated using the American Cancer Society study coefficient for the association between long-term PM2.5 exposure and all-cause mortality. The annual average PM2.5 estimates were contrasted with theoretical minimum (counterfactual) concentrations in each chronically affected subregion. Sensitivity of mortality estimates to different exposure assessments, counterfactual estimates, and concentration–response functions was evaluated. Strong La Niña and El Niño years were compared to assess the influence of interannual climatic variability. Results: Our principal estimate for the average mortality attributable to LFS exposure was 339,000 deaths annually. In sensitivity analyses the interquartile range of all tested estimates was 260,000–600,000. The regions most affected were sub-Saharan Africa (157,000) and Southeast Asia (110,000). Estimated annual mortality during La Niña was 262,000, compared with 532,000 during El Niño. Conclusions: Fire emissions are an important contributor to global mortality. Adverse health outcomes associated with LFS could be substantially reduced by curtailing burning of tropical rainforests, which rarely burn naturally. The large estimated influence of El Niño suggests a relationship between climate and the burden of mortality attributable to LFS.
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                Author and article information

                Journal
                International Journal of Epidemiology
                Oxford University Press (OUP)
                0300-5771
                1464-3685
                April 01 2022
                May 09 2022
                February 04 2022
                April 01 2022
                May 09 2022
                February 04 2022
                : 51
                : 2
                : 514-524
                Affiliations
                [1 ]Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Kyoto, Japan
                [2 ]Department of Hygiene, Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
                [3 ]School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
                [4 ]Graduate School of Global Environmental Studies, Kyoto University, Kyoto, Japan
                Article
                10.1093/ije/dyac005
                35134940
                15acc023-792e-4d61-b563-a7c4ac4b8003
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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