Air quality alerts benefit asthmatics – Authors' reply
Bibliographic record
Abstract
We appreciate the comments by Kaylee Ho and colleagues1Ho K Paez J Liu B Air quality alerts benefit asthmatics.Lancet Planet Health. 2018; 2: 518Summary Full Text Full Text PDF PubMed Scopus (2) Google Scholar on our study,2Chen H Li Q Kaufman JS et al.Effect of air quality alerts on human health: a regression discontinuity analysis in Toronto, Canada.Lancet Planet Health. 2018; 2: e19-e26Summary Full Text Full Text PDF PubMed Scopus (48) Google Scholar in which we evaluated the influence of an air quality alert programme on a wide array of health outcomes in Toronto, Canada. The Correspondence by Ho and colleagues presented an analysis using the 2007–10 National Health and Nutrition Examination Survey in the USA, showing that individuals who had asthma were more likely to change their behaviours in response to deterioration in air quality than those without asthma.3Zipf G Chiappa M Porter KS Ostchega Y Lewis BG Dostal J National health and nutrition examination survey: plan and operations, 1999–2010.Vital Health Stat 1. 2013; 56: 1-37Google Scholar Their finding adds to the ongoing scientific discussion as to whether an air quality alert programme might encourage avoidance behaviours (eg, reducing outdoor travelling and physical activities) on days with high levels of pollution. Although we applaud the efforts made by Ho and colleagues to provide an explanation to one of our findings that air quality alerts were related to some reduction in asthma-related emergency department visits, their method is somewhat unclear. Without presenting the number of non-asthmatics, there is some ambiguity as to whether the four strata (lifetime asthma, current asthma, asthma attack in the past year, and asthma emergency department visit in the past year) were indeed mutually exclusive. In addition, it is uncertain whether the question about behavioural changes was responded to by both asthmatics and non-asthmatics, or only by individuals with asthma. Furthermore, Ho and colleagues compared the proportion of individuals who changed activity because of poor air quality between asthmatics and non-asthmatics within each of the four strata. This appears to imply that individuals who do not have asthma were also included in the four strata. Despite our observation that air quality alerts were related to some decreases in asthma emergency department visits, we did not detect a reduction in any of the other health outcomes examined. One important implication of these findings is that measures focusing only on days with the highest levels of pollution are unlikely to help to address the most harmful effects of air pollution, given the role of long-term air pollution exposure as a risk factor for major chronic diseases such as cancer, cardiovascular diseases, and respiratory diseases. Although there is an increasing interest in developing innovative programmes, such as personalised systems to protect high-risk subgroups from air pollution, converging scientific evidence over the past decades suggests that air pollution problems are best addressed through collective and enforceable actions. Such actions could be improvement in fuel standards and emission control, especially targeting specific sources such as power generation and industries that contribute substantially to air pollution, and improvement in urban and transportation planning, rather than advising individuals and leaving to them to protect themselves from the harmful effects of air pollution. We declare no competing interests. Air quality alerts benefit asthmaticsWe have read with interest the Article by Hong Chen and colleagues,1 particularly the evidence of reductions in asthma-related emergency-department visits associated with air quality alert programmes in Toronto (ON, Canada). We have compared changes in activity between people with asthma and people who do not have asthma using representative data from the 2007–10 National Health and Nutrition Examination Survey in the USA.2 Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".