Feasibility of Deploying Inhaler Sensors to Identify the Impacts of Environmental Triggers and Built Environment Factors on Asthma Short-Acting Bronchodilator Use

Abstract

Background: Epidemiological asthma research has relied upon self-reported symptoms or healthcare utilization data, and used the residential address as the primary location for exposure. These data sources can be temporally limited, spatially aggregated, subjective, and burdensome for the patient to collect.

Objectives: First, we aimed to test the feasibility of collecting rescue inhaler use data in space-time using electronic sensors. Second, we aimed to evaluate whether these data have the potential to identify environmental triggers and built environment factors associated with rescue inhaler use and to determine whether these findings would be consistent with the existing literature.

Methods: We utilized zero-truncated negative binomial models to identify triggers associated with inhaler use, and implemented three sensitivity analyses to validate our findings.

Results: Electronic sensors fitted on metered dose inhalers tracked 5,660 rescue inhaler use events in space and time for 140 participants from 13 June 2012 to 28 February 2014. We found that the inhaler sensors were feasible in passively collecting objective rescue inhaler use data. We identified several environmental triggers with a positive and significant association with inhaler use, including: AQI, PM10, weed pollen, and mold. Conversely, the spatial distribution of tree cover demonstrated a negative and significant association with inhaler use.

Conclusions: Utilizing a sensor to capture the signal of rescue inhaler use in space-time offered a passive and objective signal of asthma activity. This approach enabled detailed analyses to identify environmental triggers and built environment factors that are associated with asthma symptoms beyond the residential address. The application of these new technologies has the potential to improve our surveillance and understanding of asthma. Citation: Su JG, Barrett MA, Henderson K, Humblet O, Smith T, Sublett JW, Nesbitt L, Hogg C, Van Sickle D, Sublett JL. 2017. Feasibility of deploying inhaler sensors to identify the impacts of environmental triggers and built environment factors on asthma short-acting bronchodilator use. Environ Health Perspect 125:254-261; http://dx.doi.org/10.1289/EHP266.

Figure 1

The relative location of Jefferson County, Kentucky, and its air pollution, pollen and weather monitoring sites. For the rescue inhaler use location marked on the map (solid circle), the estimation of PM₁₀ concentration as applied in Equation 3 can be obtained using the following method: where d_i and c_i are respectively the distance to and concentration at the ith monitoring site. A hypothetical example is used here to demonstrate how we estimated exposure at a rescue inhaler use location through the inverse distance weighting algorithm. The figure was created by the authors of this paper using ArcGIS software (V9.3; Environmental Systems Research Institute, Redland, CA).

Figure 2

Hotspots of rescue inhaler use events in Jefferson County, Kentucky, generated using a kernel density function based on the locations where rescue inhaler use events occurred from June 2012 to February 2014. The figure was created by the authors of this paper using ArcGIS software (version 9.3; Environmental Systems Research Institute, Redland, CA).