Did Fine Particulate Matter from the Summer 2016 Landscape Fires in Tasmania Increase Emergency Ambulance Dispatches? A Case Crossover Analysis

Short-term Exposure to Wildfire-Specific PM2.5 and Diabetes Hospitalization: A Study in Multiple Countries and Territories

OBJECTIVE

To evaluate associations of wildfire fine particulate matter ≤2.5 mm in diameter (PM2.5) with diabetes across multiple countries and territories.

RESEARCH DESIGN AND METHODS

We collected data on 3,612,135 diabetes hospitalizations from 1,008 locations in Australia, Brazil, Canada, Chile, New Zealand, Thailand, and Taiwan during 2000-2019. Daily wildfire-specific PM2.5 levels were estimated through chemical transport models and machine-learning calibration. Quasi-Poisson regression with distributed lag nonlinear models and random-effects meta-analysis were applied to estimate associations between wildfire-specific PM2.5 and diabetes hospitalization. Subgroup analyses were by age, sex, location income level, and country or territory. Diabetes hospitalizations attributable to wildfire-specific PM2.5 and nonwildfire PM2.5 were compared.

RESULTS

Each 10 µg/m3 increase in wildfire-specific PM2.5 levels over the current day and previous 3 days was associated with relative risks (95% CI) of 1.017 (1.011-1.022), 1.023 (1.011-1.035), 1.023 (1.015-1.032), 0.962 (0.823-1.032), 1.033 (1.001-1.066), and 1.013 (1.004-1.022) for all-cause, type 1, type 2, malnutrition-related, other specified, and unspecified diabetes hospitalization, respectively. Stronger associations were observed for all-cause, type 1, and type 2 diabetes in Thailand, Australia, and Brazil; unspecified diabetes in New Zealand; and type 2 diabetes in high-income locations. An estimate of 0.67% (0.16-1.18%) and 1.02% (0.20-1.81%) for all-cause and type 2 diabetes hospitalizations were attributable to wildfire-specific PM2.5. Compared with nonwildfire PM2.5, wildfire-specific PM2.5 posed greater risks of all-cause, type 1, and type 2 diabetes and were responsible for 38.7% of PM2.5-related diabetes hospitalizations.

CONCLUSIONS

We show the relatively underappreciated links between diabetes and wildfire air pollution, which can lead to a nonnegligible proportion of PM2.5-related diabetes hospitalizations. Precision prevention and mitigation should be developed for those in advantaged communities and in Thailand, Australia, and Brazil.

Climate Change, Landscape Fires, and Human Health: A Global Perspective

Landscape fires are an integral component of the Earth system and a feature of prehistoric, subsistence, and industrial economies. Specific spatiotemporal patterns of landscape fire occur in different locations around the world, shaped by the interactions between environmental and human drivers of fire activity. Seven distinct types of landscape fire emerge from these interactions: remote area fires, wildfire disasters, savanna fires, Indigenous burning, prescribed burning, agricultural burning, and deforestation fires. All can have substantial impacts on human health and well-being directly and indirectly through (a) exposure to heat flux (e.g., injuries and destructive impacts), (b) emissions (e.g., smoke-related health impacts), and (c) altered ecosystem functioning (e.g., biodiversity, amenity, water quality, and climate impacts). Minimizing the adverse effects of landscape fires on population health requires understanding how human and environmental influences on fire impacts can be modified through interventions targeted at individual, community, and regional levels.

Ambulance dispatches and heatwaves in Tasmania, Australia: A case-crossover analysis

BACKGROUND

Climate change is causing an increase in the frequency and severity of heatwave events, with a corresponding negative impact on human health. Health service utilisation during a heatwave is increased, with a greater risk of poor health outcomes identified for specific population groups. In this study, we examined the impact of heatwave events on ambulance dispatches in Tasmania, Australia from 2008 to 2019 to explore health service utilisation and identify the most vulnerable populations at a local level.

METHODS

We used a time-stratified case-crossover analysis with conditional logistic regression to examine the association between ambulance dispatches and three levels of heatwave events (extreme, severe, and low-intensity). We examined the relationship for the whole study population, and by age, gender, socio-economic advantage and clinical diagnostic group.

RESULTS

We found that ambulance dispatches increase by 34% (OR 1.34, 95% CI 1.18-1.52) during extreme heatwaves, by 10% (OR 1.10, 95% CI 1.05-1.15) during severe heatwaves and by 4% (OR 1.04, 95% CI 1.02-1.06) during low-intensity heatwaves. We found significant associations for the elderly (over 65), the young (5 and under) and for regions with the greatest socio-economic disadvantage.

CONCLUSION

Heatwaves were associated with increased demands on ambulance services in Tasmania. In subgroups of people aged over 65 or under 5 years of age, and those from areas of higher disadvantage, we generally observed greater effect sizes than for the population as a whole.

AQVx-An Interactive Visual Display System for Air Pollution and Public Health

Fine particulate matter emissions (PM_2.5) from landscape biomass fires, both prescribed and wild, pose a significant public health risk, with smoke exposure seasonally impacting human populations through both highly concentrated local plumes, and more dispersed regional haze. A range of technologies now exist for mapping and modeling atmospheric particulate concentration, including low-cost mobile monitors, dispersion and chemical transport modeling, multi-spectral earth observation satellites, weather radar, as well as publicly available real-time data feeds from agencies providing information about fire activity on the ground. Ubiquitous smart phone availability also allows instant public reporting of both health symptoms and smoke exposure. We describe a web-based visual display interface, Air Quality Visualization (AQVx), developed to allow the overlaying, synchronization and comparison of a range of maps and data layers, in order to both assess the potential public health impact of landscape fire smoke plumes, and the accuracy of dispersion models. The system was trialed in the state of Victoria, in south-eastern Australia, within the domain of the AQFx chemical transport model, where large-scale annual prescribed burning operations (~11,000 km² yr) are carried out, and where extreme wildfires frequently occur during the summer months. AQVx, coupled with the ARSmoke smart phone application, allowed managers to rapidly validate modeled smoke transport against satellite imagery, and identify potential exposure risks to populated areas.

The Value of Local Heatwave Impact Assessment: A Case-Crossover Analysis of Hospital Emergency Department Presentations in Tasmania, Australia

Heatwaves have been identified as a threat to human health, with this impact projected to rise in a warming climate. Gaps in local knowledge can potentially undermine appropriate policy and preparedness actions. Using a case-crossover methodology, we examined the impact of heatwave events on hospital emergency department (ED) presentations in the two most populous regions of Tasmania, Australia, from 2008–2016. Using conditional logistic regression, we analyzed the relationship between ED presentations and severe/extreme heatwaves for the whole population, specific demographics including age, gender and socio-economic advantage, and diagnostic conditions that are known to be impacted in high temperatures. ED presentations increased by 5% (OR 1.05, 95% CI 1.01–1.09) across the whole population, by 13% (OR 1.13, 95% CI 1.03–1.24) for children 15 years and under, and by 19% (OR 1.19, 95% CI 1.04–1.36) for children 5 years and under. A less precise association in the same direction was found for those over 65 years. For diagnostic subgroups, non-significant increases in ED presentations were observed for asthma, diabetes, hypertension, and atrial fibrillation. These findings may assist ED surge capacity planning and public health preparedness and response activities for heatwave events in Tasmania, highlighting the importance of using local research to inform local practice.

Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.