Factors influencing health behaviours in response to the air quality health index: a cross-sectional study in Hamilton, Canada

Abstract

Research associating adverse health effects with air pollution exposure is robust. Public health authorities recognize the need to implement population health strategies that protect public health from air pollution exposure. The Air Quality Health Index (AQHI) is a public health initiative that is intended to protect the public's health from exposure to air pollution. The aim of this research was to identify and explain factors influencing AQHI adoption at the individual level and to establish intervention strategies. A cross-sectional survey with both quantitative and qualitative questions was administered in Hamilton, Ontario, Canada, during the months of June to October 2012. Logistic regression and the Health Belief Model are used to explore the data. Demographics (gender, age, education, and area of residence), knowledge/understanding, and individual risk perceptions (neighbourhood air effects on health) were found to be significant predictors of AQHI adoption. The perceived benefits of AQHI adoption included protection of health for self and those cared for via familial and (or) occupational duties, whereas the perceived barriers of AQHI adoption included lack of knowledge about where to check and lack of time required to check and follow AQHI health messages. Also, self-efficacy was uncovered as a factor influencing AQHI adoption. Accordingly, increases in AQHI adoption could be achieved via increasing AQHI knowledge among low socioeconomic status females, communicating the benefits of AQHI adoption to “at-risk” populations and implementing supports for males to follow AQHI health messages.

Psychosocial and demographic predictors of adherence and non-adherence to health advice accompanying air quality warning systems: a systematic review

“…The vast majority of the included studies were cross-sectional surveys [ 15 , 24 – 26 , 29 – 35 , 37 , 38 , 40 , 42 , 43 ], whereas the remaining studies adopted a cross-sectional quasi-experimental design with quasi-randomisation and a control group [ 27 , 28 ], a quasi-experimental design using linked data [ 36 ], and a one-group pre- post-test design [ 39 ]. Although there was only one qualitative study [ 41 ], some studies also collected qualitative data in the form of self-reported reasons for adherence and/or non-adherence [ 24 , 30 , 40 , 42 ]. The studies included samples of general public, service users of air alert systems, asthmatic patients and people with other respiratory and/or heart conditions, elderly, people who spend most of their time working in busy streets, communities involved in wildfire events, parents of healthy and parents of asthmatic children, and health care professionals.…”

“…): with respiratorycondition = 33.3% (n = 2); FG: with respiratory condition: 34% (n = 15). Radisic et al [ 40 ] JA CSS Hamilton, Ontario, Jun - Oct 2012 N = 707 General public Age (groups) 18–34 = 25% ( n = 174); 35–54 = 45% ( n = 318); ≥ 55 = 30% ( n = 215); Gender: female n = 479 (68%); missing = 3% ( n = 24); Health: self-reported respiratory condition: 25% ( n = 179); Missing: 1% (n = 3); cardiovascular condition: 9% ( n = 63); Missing: 1% (n = 8). Reams et al [ 29 ] JA CSS Upper Industrial CorridorLouisiana 2011 N = 64 Attendees of 3 East Baton Rouge Metropolitan Council meetings Age: ≥50 age group = 80%; Gender: female = 54%.…”

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

“…However, those receiving air quality data directly were more aware of the relationship between health and the air quality and more likely to modify their behavior (Smallbone 2009). Radisic et al also found that lack of knowledge was an important barrier, as well as lack of time and dependence on one's own perceptions and media reports (Radisic et al 2016). Though only about a third of participants in an asthma study said they would change their behaviour when aware of an air quality advisory, Wen et al suggested this percentage could be increased with the addition of health-care professional advice (Wen et al 2009).…”

“…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 Air Pollution Monitoring Data Set

“…The study area is Hamilton, Ontario, Canada, which has received many studies on its air quality [7,13,[18][19][20][21]. Hamilton is developed around an industrial core, which has traditionally focused on steel production.…”