Abstract
Background: The modular British Columbia Asthma Prediction System (BCAPS) is designed to reduce information burden during wildfire smoke events by automatically gathering, integrating, generating, and visualizing data for public health users. The BCAPS framework comprises five flexible and geographically scalable modules: (1) historic data on fine particulate matter (PM2.5) concentrations; (2) historic data on relevant health indicator counts; (3) PM2.5 forecasts for the upcoming days; (4) a health forecasting model that uses the relationship between (1) and (2) to predict the impacts of (3); and (5) a reporting mechanism.Methods: The 2018 wildfire season was the most extreme in British Columbia history. Every morning BCAPS generated forecasts of salbutamol sulfate (e.g., Ventolin) inhaler dispensations for the upcoming days in 16 Health Service Delivery Areas (HSDAs) using random forest machine learning. These forecasts were compared with observations over a 63-day study period using different methods including the index of agreement (IOA), which ranges from 0 (no agreement) to 1 (perfect agreement). Some observations were compared with the same period in the milder wildfire season of 2016 for context.Results: The mean province-wide population-weighted PM2.5 concentration over the study period was 22.0 μg/m3, compared with 4.2 μg/m3 during the milder wildfire season of 2016. The PM2.5 forecasts underpredicted the severe smoke impacts, but the IOA was relatively strong with a population-weighted average of 0.85, ranging from 0.65 to 0.95 among the HSDAs. Inhaler dispensations increased by 30% over 2016 values. Forecasted dispensations were within 20% of the observed value in 71% of cases, and the IOA was strong with a population-weighted average of 0.95, ranging from 0.92 to 0.98. All measures of agreement were correlated with HSDA population, where BCAPS performance was better in the larger populations with more moderate smoke impacts. The accuracy of the health forecasts was partially dependent on the accuracy of the PM2.5 forecasts, but they were robust to over- and underpredictions of PM2.5 exposure.Conclusions: Daily reports from the BCAPS framework provided timely and reasonable insight into the population health impacts of predicted smoke exposures, though more work is necessary to improve the PM2.5 and health indicator forecasts.
Highlights
Transient wildfire smoke causes episodes of the worst air quality that many populations will ever experience
British Columbia is the westernmost province of Canada, with a total land area of 925,186 km2, and a population approaching 5.0 million people
British Columbia is divided into 16 Health Service Delivery Areas (HSDAs) for the purposes of health administration, and these are used as the geographic units of analysis for British Columbia Asthma Prediction System (BCAPS) (Figure 2)
Summary
Transient wildfire smoke causes episodes of the worst air quality that many populations will ever experience. The public health response to smoke events could be improved by automated surveillance systems that collate the relevant data and provide useful information about the health impacts of observed and predicted smoke exposures for specific populations. The modular British Columbia Asthma Prediction System (BCAPS) is designed to reduce information burden during wildfire smoke events by automatically gathering, integrating, generating, and visualizing data for public health users. The BCAPS framework comprises five flexible and geographically scalable modules: [1] historic data on fine particulate matter (PM2.5) concentrations; [2] historic data on relevant health indicator counts; [3] PM2.5 forecasts for the upcoming days; [4] a health forecasting model that uses the relationship between [1] and [2] to predict the impacts of [3]; and [5] a reporting mechanism
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