Effects of an air pollution personal alert system on health service usage in a high-risk general population: a quasi-experimental study using linked data

Abstract

BackgroundThere is no evidence to date on whether an intervention alerting people to high levels of pollution is effective in reducing health service utilisation. We evaluated alert accuracy and the effect of a targeted personal air pollution alert system, airAware, on emergency hospital admissions, emergency department attendances, general practitioner contacts and prescribed medications.MethodsQuasi-experimental study describing accuracy of alerts compared with pollution triggers; and comparing relative changes in healthcare utilisation in the intervention group to those who did not sign-up. Participants were people diagnosed with asthma, chronic obstructive pulmonary disease (COPD) or coronary heart disease, resident in an industrial area of south Wales and registered patients at 1 of 4 general practices. Longitudinal anonymised record linked data were modelled for participants and non-participants, adjusting for differences between groups.ResultsDuring the 2-year intervention period alerts were correctly issued on 208 of 248 occasions; sensitivity was 83.9% (95% CI 78.8% to 87.9%) and specificity 99.5% (95% CI 99.3% to 99.6%). The intervention was associated with a 4-fold increase in admissions for respiratory conditions (incidence rate ratio (IRR) 3.97; 95% CI 1.59 to 9.93) and a near doubling of emergency department attendance (IRR=1.89; 95% CI 1.34 to 2.68).ConclusionsThe intervention was associated with increased emergency admissions for respiratory conditions. While findings may be context specific, evidence from this evaluation questions the benefits of implementing near real-time personal pollution alert systems for high-risk individuals.

City housing atmospheric pollutant impact on emergency visit for asthma: A classification and regression tree approach

“…It would be interesting to set up an alert system for patients with asthma, using a lower threshold than the current WHO recommendations. A recent quasi-experimental study reported a doubling of emergency admissions for all relevant conditions (asthma, chronic obstructive pulmonary disease or coronary heart disease) and a fourfold increase in admissions for respiratory conditions after implementation for 2 years an alert system automatically triggered by high pollution levels [40]. We might envisage a personalized alert system for each asthmatic patient to trigger an action (for example: to avoid strenuous physical activity, individual adaptation of treatment …) when daily 1-hour maximum of NO 2 exceeds 12 mm/m 3 , threshold found in CART method, in the PACA region.…”

Associations between air pollution and cardio-respiratory physiological measures in older adults exercising outdoors

“…With respect to effects on incidence of adverse health effects, one study found increased healthcare use following air quality advisories (Lyons et al 2016), while another, looking at the association between an alert service and COPD, found no change in admissions (Maheswaran et al 2010). Chen et al reported that air quality advisories reduced asthma-related emergency room admissions by 25%, while there was no impact on visits for cardiovascular outcomes (Chen et al 2018).…”

“…These findings provided evidence supporting the utility of the AQHI in predicting health risks for diverse health outcomes and types of communities not accounted for in developing the AQHI, which was based on the effects of air pollution on mortality in large urban centres. However, empirical evidence to support the effectiveness of AQIs and advisories in actually reducing exposures and health risks is mixed (Bickerstaff and Walker 2001;Semenza et al 2008;Stieb et al 2008b;Smallbone 2009Smallbone , 2015Wen et al 2009;Neidell 2009;Maheswaran et al 2010;Licskai et al 2013;Mullins and Bharadwaj 2015;Radisic et al 2016;Lyons et al 2016;D'Antoni et al 2017;Chen et al 2018) and to our knowledge there have been no previous experimental studies based on individual-level data. The present study was designed as a randomized controlled trial of the AQHI in which the intervention comprised advising participants to exercise indoors rather than outdoors on days when the maximum AQHI was forecast to be 5 or higher.…”

A mobile phone-based program to promote healthy behaviors among adults with prediabetes: study protocol for a pilot randomized controlled trial

“…We will thus collect data about the intervention’s feasibility and acceptability, and we will apply learned lessons to the subsequent larger study. Second, despite numerous mHealth applications to promote behavioral change and chronic disease management, rates of engagement with these applications over time are low [ 56 ], and use of mHealth devices may have unpredictable effects on the behaviors and outcomes they aim to modify [ 57 , 58 ]. It is unknown whether study participants will engage with JOOL Health or whether use of the app will stimulate positive behavioral change among the study population.…”