Scott Weichenthal1, Patrick Bélisle2, Eric Lavigne3, Paul J Villeneuve4, Amanda Wheeler3, Xiaohong Xu5, Lawrence Joseph6. 1. Air Health Effects Science Division, Health Canada, Ottawa, Canada; Department of Environmental and Occupational Health, University of Montreal, Montreal, Canada. Electronic address: scott.weichenthal@hc-sc.gc.ca. 2. McGill University Health Center, Division of Clinical Epidemiology, Montreal, Canada. 3. Air Health Effects Science Division, Health Canada, Ottawa, Canada. 4. Institute of Health: Science, Technology and Policy, Carleton University, Ottawa, Ontario, Canada. 5. Department of Civil and Environmental Engineering, University of Windsor, Windsor, Canada. 6. McGill University Health Center, Division of Clinical Epidemiology, Montreal, Canada; McGill University, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, Canada.
Abstract
BACKGROUND: We examined the impact of data source and exposure measurement error for ambient NO2 on risk estimates derived from a case-crossover study of emergency room visits for asthma in Windsor, Canada between 2002 and 2009. METHODS: Paired personal and fixed-site NO2 data were available from an independent population (47 children and 48 adults) in Windsor between 2005 and 2006. We used linear regression to estimate the relationship and measurement error variance induced between fixed site and personal measurements of NO2, and through a series of simulations, evaluated the potential for a Bayesian model to adjust for this change in scale and measurement error. Finally, we re-analyzed data from the previous case-crossover study adjusting for the estimated change in slope and measurement error. RESULTS: Correlations between paired NO2 measurements were weak (R(2)≤0.08) and slopes were far from unity (0.0029≤β≤0.30). Adjusting the previous case-crossover analysis suggested a much stronger association between personal NO2 (per 1ppb) (Odds Ratio (OR)=1.276, 95% Credible Interval (CrI): 1.034, 1.569) and emergency room visits for asthma among children relative to the fixed-site estimate (OR=1.024, 95% CrI 1.004-1.045). CONCLUSIONS: Our findings suggest that risk estimates based on fixed-site NO2 concentrations may differ substantially from estimates based on personal exposures if the change in scale and/or measurement error is large. In practice, one must always keep the scale being used in mind when interpreting risk estimates and not assume that coefficients for ambient concentrations reflect risks at the personal level. Crown
BACKGROUND: We examined the impact of data source and exposure measurement error for ambient NO2 on risk estimates derived from a case-crossover study of emergency room visits for asthma in Windsor, Canada between 2002 and 2009. METHODS: Paired personal and fixed-site NO2 data were available from an independent population (47 children and 48 adults) in Windsor between 2005 and 2006. We used linear regression to estimate the relationship and measurement error variance induced between fixed site and personal measurements of NO2, and through a series of simulations, evaluated the potential for a Bayesian model to adjust for this change in scale and measurement error. Finally, we re-analyzed data from the previous case-crossover study adjusting for the estimated change in slope and measurement error. RESULTS: Correlations between paired NO2 measurements were weak (R(2)≤0.08) and slopes were far from unity (0.0029≤β≤0.30). Adjusting the previous case-crossover analysis suggested a much stronger association between personal NO2 (per 1ppb) (Odds Ratio (OR)=1.276, 95% Credible Interval (CrI): 1.034, 1.569) and emergency room visits for asthma among children relative to the fixed-site estimate (OR=1.024, 95% CrI 1.004-1.045). CONCLUSIONS: Our findings suggest that risk estimates based on fixed-site NO2 concentrations may differ substantially from estimates based on personal exposures if the change in scale and/or measurement error is large. In practice, one must always keep the scale being used in mind when interpreting risk estimates and not assume that coefficients for ambient concentrations reflect risks at the personal level. Crown
Authors: Tahir Taj; Ebba Malmqvist; Emilie Stroh; Daniel Oudin Åström; Kristina Jakobsson; Anna Oudin Journal: Int J Environ Res Public Health Date: 2017-05-31 Impact factor: 3.390
Authors: Ching-Yen Kuo; Ren-Hao Pan; Chin-Kan Chan; Chiung-Yi Wu; Dinh-Van Phan; Chien-Lung Chan Journal: Int J Environ Res Public Health Date: 2018-03-31 Impact factor: 3.390
Authors: Ching-Yen Kuo; Chin-Kan Chan; Chiung-Yi Wu; Dinh-Van Phan; Chien-Lung Chan Journal: Int J Environ Res Public Health Date: 2019-01-12 Impact factor: 3.390