Volume 10 Supplement 2

Canadian Society of Allergy and Clinical Immunology and AllerGen Abstracts 2014

Open Access

Impact of air pollution on physician office visits for common childhood conditions in Ontario, Canada

  • Laura Feldman1, 2Email author,
  • Chenwei Gao1, 3,
  • Jingqin Zhu1, 3,
  • Jacqueline Simatovic1 and
  • Teresa To1, 2, 3
Allergy, Asthma & Clinical Immunology201410(Suppl 2):A54

https://doi.org/10.1186/1710-1492-10-S2-A54

Published: 18 December 2014

Background

Children are particularly sensitive to air pollutants, due to factors such as ongoing lung development and choice of activities [1]. We evaluated the impact of fine particulate matter (PM2.5) on physician office visits for common conditions in children in Ontario, Canada.

Methods

PM2.5 and temperature measurements were obtained from satellite data for all of Ontario [2]. Physician office visits were stratified into two groups based on the literature: air pollution-sensitive (acute respiratory infections, allergic rhinitis, asthma, bronchiolitis, diabetes, otitis media) and air pollution-insensitive (gastroenteritis, injuries). Claims data were obtained for every month in 2010 from health administrative databases for children 0-14 years of age. Age- and sex-standardized morbidity ratios (SMRs) were calculated by region in Ontario. Spatial Poisson regression models were used to analyze the relationship between PM2.5 and physician office visits, with temperature as a covariate.

Results

Crude rates of physician office visits are presented in Table 1. As expected, fine particulate was significantly associated with monthly rates of physician office visits for air pollution-sensitive conditions, and not for insensitive conditions. Fitted SMRs for air pollution-sensitive conditions are presented in Figure 1. SMRs for sensitive and insensitive conditions were strongly positively correlated (r = 0.53), and data were spatially autocorrelated. This suggests an underlying spatial process that influences physician office visit rates for common childhood conditions, both for air pollution-sensitive and -insensitive conditions.
Table 1

Crude rates of air pollution-sensitive and air pollution-insensitive conditions in Ontario for each month in 2010

 

Crude rates of physician office visitsa

 

Jan

Feb

Mar

Apr

May

June

July

Aug

Sept

Oct

Nov

Dec

Air pollution-sensitive

8.05

8.84

8.94

7.80

7.21

6.55

5.15

4.73

6.57

7.52

9.27

10.89

Air pollution-insensitive

1.48

1.52

1.61

1.55

1.63

1.63

1.34

1.29

1.34

1.38

1.54

1.22

aNumber of claims per 100 population aged 0-14 years.

Figure 1

Distribution of (a) fine particulate matter (PM2.5, in μg/m3) and (b) fitted sex-standardized morbidity ratios (SMRs) from spatial Poisson regression for physician office visits for air pollution-sensitive conditions; all by Forward Sortation Area (FSA) in Southern Ontario in July 2010

Conclusions

In this analysis PM2.5, was significantly associated with physician office visits for air pollution-sensitive conditions. Areas with high PM2.5 levels and SMRs higher than 1 were identified; children with air pollution-sensitive conditions in these areas may benefit from targeted air pollution reduction interventions. Additionally, future analysis should evaluate the role of household income and access to care in influencing the spatial pattern of primary health care utilization for common childhood conditions across Ontario.

Authors’ Affiliations

(1)
Child Health Evaluative Sciences, The Hospital for Sick Children
(2)
University of Toronto
(3)
Institute for Clinical Evaluative Sciences

References

  1. WHO-Europe: Effects of Air Pollution on Children's Health and Development. A Review of the Evidence. Special Programme on Health and Environment. 2005, Bonn: World Health Organization, European Centre for Environment and HealthGoogle Scholar
  2. Battelle Memorial Institute, Center for International Earth Science Information Network CIESIN - Columbia University: Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD). 2013, Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC)Google Scholar

Copyright

© Feldman et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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