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Residential Air Pollution and Otitis Media During the First Two Years of Life

MacIntyre, Elaina A.a,b; Karr, Catherine J.c; Koehoorn, Mieked; Demers, Paul A.a; Tamburic, Lilliane; Lencar, Cornela; Brauer, Michaela

doi: 10.1097/EDE.0b013e3181fdb60f
Air Pollution: Original Article

Background: Otitis media is the leading reason young children receive antibiotics or visit a physician. We evaluated the impact of ambient air pollution on outpatient physician visits for otitis media in a population-based birth cohort.

Methods: All children born in southwestern British Columbia during 1999-2000 were followed until the age of 2 years. Residential air pollution exposures were estimated for the first 24 months of life by inverse-distance weighting of monitor data (CO, NO, NO2, O3, PM2.5, PM10, SO2), temporally adjusted land-use regression models (NO, NO2, PM2.5, black carbon, woodsmoke), and proximity to roads and point sources. We used generalized estimating equations to longitudinally assess the relationship between physician visits for otitis media (ICD-9) and average pollutant exposure in the 2 months prior to the visit, after adjustment for covariates.

Results: Complete exposure and risk-factor data were available for 45,513 children (76% of all births). A total of 42% of subjects had 1 or more physician visits for otitis media during follow-up. Adjusted estimates for NO, PM2.5, and woodsmoke were consistently elevated (eg, relative risk of 1.10 [95% confidence interval = 1.07-1.12] per interquartile range [IQR] increase in NO; 1.32 [1.27-1.36] per IQR increase in days of woodsmoke exposure). No increased risks were observed for the remaining pollutants (eg, 1.00 [0.98-1.03] per IQR increase in PM10; 0.99 [0.97-1.01] per IQR increase in black carbon).

Conclusions: Modest but consistent associations were found between some measures of air pollution and otitis media in a large birth cohort exposed to relatively low levels of ambient air pollution.


From the aSchool of Environmental Health, University of British Columbia, Vancouver, British Columbia, Canada; bInstitute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany; cDepartment of Environmental and Occupational Health Sciences, University of Washington, Washington, USA; dSchool of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada; and eCentre for Health Services and Policy Research, University of British Columbia, Vancouver, British Columbia, Canada.

Submitted October 19, 2009; accepted July 31, 2010; posted 28 October 2010.

Supported by the British Columbia Centre for Disease Control via an agreement with Health Canada under the Border Air Quality Strategy (GEH0402). Additional support was provided by the Center for Health and Environment Research at UBC, funded by the Michael Smith Foundation for Health Research (MSFHR). The Canadian Institutes of Health Research and MSFHR supported E.M. through a UBC Bridge Strategic Training Fellowship, Senior Graduate Studentship and Frederick Banting and Charles Best Canada Graduate Scholarship. M.K. was supported in part by a MSFHR Scholar Award.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (

Correspondence: Elaina MacIntyre, Institute of Epidemiology, German Research Center for Environmental Health, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany. E-mail:

Otitis media is the main reason children under 5 years receive antibiotics, and a leading reason for physician visits.1,2 Those diagnosed with otitis before 6 months of age are at increased risk for recurrent and chronic otitis media, and the resultant economic burden is comparable with that of chronic respiratory diseases.3

Ambient air pollutants can adversely affect respiratory defense mechanisms, making the respiratory system more susceptible to infection.4–8 Nitric oxide (NO), nitrogen dioxide (NO2), and ozone (O3) have been shown to exacerbate viral inflammation, impair mucosal cilia clearance, and increase the susceptibility of epithelial cells to viral injury. Particulate matter (PM) can induce oxidative stress in bronchial epithelial cells, leading to increased susceptibility to infection9 and may reduce viral uptake by alveolar macrophages.10 Environmental tobacco smoke is a strong risk factor for otitis media,11 likely due to alteration of immune defenses12,13 and increased susceptibility following inflammation of the respiratory epithelium.14 Few studies have examined ambient air pollution in relation to otitis, but indoor biomass burning has been identified as a risk factor,15,16 an association with traffic-related air pollution has been observed in Europe,17 and a recent study has reported a link between ambient air pollution and emergency room visits for otitis media in Canada.18

As part of the Border Air Quality Study, we evaluated the relationship between ambient air pollution and otitis media in southwestern Canada. Although this region has lower air pollution concentrations than other urban areas across Canada and the United States,18 ambient air pollution has been associated with small-for-gestational-age and premature births, infant bronchiolitis, and childhood asthma in this region.19–21

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Study Area and Population

This retrospective study included all singleton births in the Georgia Basin Airshed (including the metropolitan areas of Vancouver and Victoria) in 1999 and 2000, based on maternal residential postal code and date of birth listed on birth certificates (n = 59,917). Using the universal healthcare system, the cohort was established and followed through the first 24 months of life by extracting data from a series of linked administrative datasets obtained from the British Columbia Ministries of Health, Vital Statistics Agency, and Perinatal Database Registry. The Centre for Health Services and Policy Research at the University of British Columbia completed all data linkages via personal health number or by geographical region when individual linkage was not possible. Personal identifiable information (full date of birth, postal code and personal health number) was removed prior to release of the linked dataset. The University of British Columbia Behavioral Research Ethics Board (B05-0123) approved this study.

To account for changes in residence, we monitored residential postal codes recorded at each physician visit or hospitalization during the first 24 months of life. Where there was a change in postal code, the move date was set as the midpoint between contacts if nonoverlapping, or at the first date of the new address if overlapping. The first month of life was excluded from analysis due to the difficulty in diagnosing otitis media in newborns and to create a postnatal exposure window.22 We excluded children who died during follow-up, moved outside the study region, or had missing covariate information.

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The British Columbia Ministries of Health provided information on outpatient physician visits coded as otitis media (ICD-9 codes: 381-nonsuppurative otitis media and eustachian tube disorders, 381.0-acute nonsuppurative otitis media, 382-suppurative and unspecified otitis media, 382.0-acute suppurative otitis media, 382.9-unspecified otitis media). Data were extracted and linked based on each child's unique personal health number. Details on the use of these data for otitis media diagnoses have been described in detail elsewhere.23 To ensure that follow-up visits were not counted as separate infections, any visit within 14 days of a previous visit was excluded.

We obtained information on antibiotic prescriptions from the British Columbia College of Pharmacists. Data were linked to physician visits based on personal health number and service date, allowing for up to 4 days between physician and pharmacy visit. Otitis media visits with a linked antibiotic prescription were considered “acute otitis media” and used for sensitivity analyses.

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Data on sex, month and year of birth, aboriginal status, older siblings, maternal smoking during pregnancy, birth weight, gestational age, breast-feeding initiation, and maternal age were linked to cohort members at the individual level by personal health number. Data on neighborhood income, neighborhood female education, and rural residence were linked based on census dissemination area. Dissemination areas range in size depending on population density; in urban regions, these areas are approximately 4 square blocks. Details on the strength of these risk factors for otitis-media incidence and recurrence for this population are provided in detail elsewhere23 and summarized in the results section.

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Air Pollution Exposure

Exposures were estimated at the center of each postal code and linked to children based on their residential history (measured in months). Postal codes typically correspond to 1 block-face in urban areas, but may be considerably larger in rural areas with low population density. For each child-month, we assigned 2-month exposure windows by averaging the current and previous months' estimated exposure (for continuous variables). Air pollution exposures were estimated using 3 approaches: regulatory monitoring networks operated by the British Columbia Ministry of Environment and Metro Vancouver, temporally adjusted land-use regression models of source-specific pollution, and measures of proximity to highways/major roads and point sources. Exposures were updated using the residential-history file and weighted by time spent living at multiple residences.

Regulatory monitors collected information on NO/NO2 (n = 22 locations), carbon monoxide (CO) (n = 19), PM2.5 (n = 7), PM10 (n = 19), sulfur dioxide (SO2) (n = 14), and O3 (n = 24). We assigned monthly average exposures to postal codes by calculating an inverse-distance weighted average using the 3 closest monitors within 50 km of the postal code. Monthly averages were considered missing if 5 consecutive days or 10 days in total were missing for that month.

The traffic-related air pollution land-use regression models were developed after measuring NO/NO2 (n = 116), PM2.5 (n = 25), and black carbon (n = 39) at sites throughout the study area.24,25 The final models (R 2: NO = 0.62, NO2 = 0.56, PM2.5 = 0.52, black carbon = 0.56) included combinations of population density, elevation, land use, and road characteristics. In regions where all predictor variables were missing or zero, the resulting air pollution concentration was zero. Comparisons with additional measurements and cross-validation analysis indicated greater accuracy for the NO, NO2, and black carbon models than for PM2.5. Spatial surfaces of predicted (annual average) concentrations at a resolution of 10 m were smoothed (Focal Statistics, ArcGis Spatial Analyst; ArcGIS) to provide a more accurate reflection of the measured effect of proximity to roadways.26 For each land-use regression model, we fit the corresponding monitor data with a monthly dummy variable and a covariate for linear trend (Times Series Forecasting System, version 9; SAS Institute Inc., Cary, NC) and a month-year adjustment factor was applied to estimate monthly average concentrations. PM2.5 trends were used to develop adjustments for black carbon.

The woodsmoke surface24 was developed after mobile monitoring of particle light scattering throughout the study area on cold, clear winter nights (n = 19). We combined these measurements with fixed-location monitoring of levoglucosan (a biomass combustion tracer compound) and PM2.5 to develop a spatial woodsmoke model for the study area. Postal codes in the top tertile of model-estimated woodsmoke PM2.5, were classified as woodsmoke areas. Heating-degree days (based on daily temperature data) were used to identify time periods during which elevated concentrations of woodsmoke would be present in these areas.27 We calculated the number of woodsmoke days for each month of follow-up.

Proximity to point sources was estimated using an index assigned to each point source in the study area that reported yearly emissions to the federal government. The pollutant contribution for each point source was assessed in relation to other point sources in the study area, and a proximity-weighted summation of emissions was assigned to each residential postal code using a circular buffer of 10 kilometers.27 Proximity of residential postal codes to expressways, primary highways, secondary highways, and major arterial roads was measured using 50 m and 150 m buffers (DMTI Arc View street file dataset for BC, Canmap Streetfiles, v2006.3, 2006). Road type proximity was coded as a categorical variable.

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Statistical Analysis

The association between residential air pollution exposure and physician diagnosis of otitis media was assessed longitudinally using generalized estimating equations with a logit link function and an autoregressive working correlation matrix to account for correlation between months for the same child. We used directed acyclic graphs (DAGs) to identify potential confounders. For each analysis, children were excluded if they had missing exposure data for any month during follow-up. An otitis-media-event month was defined as having a physician diagnosis of otitis media on any day in that month. Analyses were stratified by sex, aboriginal status, maternal smoking, older siblings, and otitis media season. Otitis media season was a 4-level categorical variable based on the otitis media rate in each of the 47 months of the study, with cut-offs at the 25th, 50th, and 75th percentiles. Continuous measures of exposure were converted into quartiles to investigate possible nonlinear associations between air pollution and otitis media, and multipollutant models were used to test the robustness of associations. Finally, we conducted sensitivity analyses restricted to event-months with a physician visit linked to an antibiotic prescription. SAS 9.1 was used for all statistical analyses (SAS Institute, Cary, NC, 2002).

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There was complete residential history, outcome, and potential confounder information for 45,513 children (76% of all births). There were no differences in the distributions of available covariates or exposures or in outcomes between the original cohort and the reduced cohort used in the analyses (data not shown).

The cohort was 52% male, 54% had older siblings, 2% were aboriginal, 92% were breast-fed, and 10% had mothers who reported smoking during pregnancy. Maternal age ranged from 14 to 51 years (mean = 31 [SD = 5.4]) and birth weight ranged from 450 to 6072 g (mean = 3451 [SD = 353]). The proportion of women in each dissemination area with a postsecondary education ranged from 7% to 100% (mean = 37.3; SD = 11.7); 11% lived in rural areas as defined by Census Canada.

During the first 2 years of life, 42% (19,115) of the cohort had at least 1 physician visit for otitis media, 27% (12,369) had at least 1 visit with a linked antibiotic prescription, and 17% (7740) moved at least once. Otitis media incidence peaked at 8-10 months of age and during the winter.23 The Figure illustrates the trend over time for otitis-media physician visits and selected exposures. Risk of otitis media was increased for boys (odds ratio [OR] = 1.23 [95% confidence interval {CI} = 1.19-1.27]), aboriginal children (1.47 [1.30-1.65]), those with older siblings (1.18 [1.14-1.22]) and those whose mothers smoked during pregnancy (1.16 [1.09-1.23]). Risk estimates were higher with younger mothers (<20 years, 1.34 [1.20-1.49]; 30-34 years, 0.93 [0.89-0.97]). Neighborhoods with lower female education had higher risk (low, 1.30 [1.23-1.37]; medium, 1.10 [1.05-1.06]). Risk was lower with lower neighborhood income (low, 0.83 [0.78-0.88]; medium, 0.92 [0.87-0.97]).23



Mean air pollution concentrations (Table 1) were relatively low compared with regulatory guidelines. Correlations between the ambient monitoring data were 0.73 to 0.84 for NO, NO2, and CO; 0.32 to 0.67 for PM2.5, PM10, SO2; and −0.72 to 0.24 for O3 and all other pollutants. Correlations between land-use regression estimates were 0.73 for NO and NO2; and 0.56 for PM2.5 and black carbon. Across these 2 approaches (inverse-distance weighting vs. land-use regression) for the same pollutant, correlations were 0.64 for NO, 0.62 for NO2, and 0.18 for PM2.5.



Table 2 provides the crude and adjusted risk estimates and 95% confidence intervals from the longitudinal analysis. Although risk estimates were generally robust to adjustment for otitis-media risk factors, some were highly sensitive to adjustment for otitis media season—specifically, those with strong seasonal trends (Figure, and eFigure, Upon adjusting for otitis media season (Table 2), estimates for NO, woodsmoke, and CO decreased; estimates for NO2 changed direction; and estimates for PM2.5 (using inverse-distance weighting), PM2.5 (using land-use regression), black carbon, and PM10 increased. There was little difference in the estimates for O3 after adjusting for season. Protective associations were found for O3, as expected, given its strong negative correlations with traffic-related pollutants (CO, NO, and NO2). Protective associations were initially observed with all particle and point-source proximity measures, but estimates for PM2.5 and PM10 increased after adjustment for season. Residential woodsmoke was strongly associated with otitis media in all models. Adjusted estimates for road proximity were inconsistent, and did not follow the expected dose-response patterns relating exposure intensity to road type (expressway > primary highway > secondary highway > major road), although there were relatively few children living close to the first 3 road types.



Table 3 presents results from multipollutant models for woodsmoke, NO, and PM2.5 (only when assessed by inverse density weighting). NO assessed by inverse-distance weighting was sensitive to adjustment for other pollutants, but estimates for NO assessed by land-use regression, as well as PM2.5 and woodsmoke, were similar to those of the single-pollutant models.



Stratification by aboriginal status and maternal smoking during pregnancy are presented in the eAppendix ( Effect estimates for aboriginal children (n = 1309) were elevated for all land-use regression measures of air pollution and for O3 and PM10. Estimates for children whose mothers smoked during pregnancy were elevated for black carbon. There were no differences by sex or the presence of older siblings.

Analyses by quartile of exposure for continuous measures (adjusted for otitis media season) confirmed the results in Table 2, with increasing risks by increasing quartile of NO and PM2.5 (inverse distance weighted) and decreasing risks by increasing quartile of black carbon, NO2, SO2, O3, and point source (results not shown). Sensitivity analyses using acute otitis media (physician visit linked with antibiotic prescription) yielded results similar to those in Table 2 but with slightly larger confidence intervals (eAppendix,

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This is the largest and most comprehensive study to date examining the association between air pollution and otitis media. We identified every child born in our study region over a 2-year period, and followed their healthcare visits through the first 2 years of life using available registries. Linked administrative data allowed us to include a number of known otitis media risk factors as potential confounders. The population-based design reduced the possibility of selection bias.

We observed positive associations of otitis media with some markers of vehicle exhaust (NO, CO, PM2.5) and woodsmoke. Associations for other pollutants were largely protective or null. Despite our focus on spatial contrasts in exposure, increased otitis media risk was mainly associated with pollutants that had seasonal patterns similar to that of otitis media (Figure). The effect of seasonal adjustment in the NO2 models suggests that either the univariate association between NO2 and otitis media was due to their temporal correlation (Figure), or that the seasonal adjustment over-corrected for the effects of air pollutants with strong seasonal trends. Moreover, these findings suggest that the timing of exposure is critical in that elevated risks are seen mainly when the spatial and temporal variability of air pollution are considered together. Although the road proximity measures do not include a temporal aspect, these results were difficult to interpret, as very small numbers of children lived in close proximity to expressways and highways, and findings were not consistent across various metrics. Poor monitor coverage for PM2.5 and SO2 may have resulted in inadequate spatial variability, and estimates for CO suffered from a large number of missing data.

Theoretically, exposures estimated from ambient monitoring data should best reflect temporal variation, while exposures estimated by land-use regression models also incorporated spatial variation. Regulatory monitors are strategically located to capture regional and urban background pollution but not neighborhood pollution, while land-use regression is ideally suited to capture near-source effects.28 Of the pollutants with estimates from both ambient monitors and land-use regression models (NO, NO2, and PM2.5), otitis media risk estimates were consistent across the various exposure metrics for NO and NO2 but not PM2.5. The PM2.5 land-use regression model performed worse than those for NO, NO2, and black carbon. Further, multipollutant results (Table 3) suggest that NO assessed by inverse-distance weighting was sensitive to inclusion of other pollutants, while NO assessed by land use regression was not. A possible explanation may be the temporal variability in the inverse-distance-weighted NO data.

The protective associations for ozone are not surprising in that ozone had a strong negative correlation with NO, NO2, and CO, and there is a strong inverse seasonality trend between ozone and otitis media (Figure). When analyses were stratified by event season, the adjusted estimate for ozone was 1.24 (1.16-1.31) for events during the summer (July-September) and 1.20 (1.14-1.25) during the winter (January-March). Additionally, stratification by otitis media season identified associations with PM10 and PM2.5 (land-use regression) in the summer; possibly because children spend more time outdoors (results not shown).

This study was completed as part of the Border Air Quality Study, which established and followed a birth cohort of 120,000 children born during 1999-2002. Previous publications have assessed birth outcomes,19 infant bronchiolitis,20 and asthma21 in relation to ambient air pollution exposures. Table 4 provides a comparison of results from these previous studies and those presented here. Given the different analytical strategies and exposure windows used in analyses of the various outcomes, it is hard to draw firm conclusions from these comparisons. Overall, findings were consistent across multiple outcomes for NO, CO, and O3, and to a lesser degree for PM2.5, black carbon, and woodsmoke.



Previous literature examining the relationship between air pollution and otitis media is sparse. Furthermore, studies are limited by sample size, relatively crude exposure assessment, inadequate adjustment of potential confounders, and study design. In a cross-sectional study in Spain, children residing in regions of high air pollution had greater odds of acute otitis media during the first year of life (OR = 2.01 [95% CI = 1.05-3.84]) after adjustment for sex, older siblings, smoking, socioeconomic status, and breast-feeding.29 However, it is unclear what pollutants or sources were most responsible for this finding. A recent ecologic study in the United States attributed reductions in the frequency of otitis media during 1997-2006 in part to reductions in ambient air pollutants (CO, NO2, SO2, and PM), although potential confounding was noted as an important limitation.30 In another regional comparison, the prevalence of pediatric otitis media decreased from 31% to 26% with decreases in total suspended particulate and SO2 in a series of repeated cross-sectional surveys following German reunification.31 In the Netherlands,17 a 2-year birth cohort of nearly 3000 children found an increased risk of otitis media for exposure to PM2.5 (OR = 1.13 [95% CI = 1.00-1.27]), black carbon (1.10 [1.00-1.22]), and NO2 (1.14 [1.03-1.27]) based on annual averaged exposures estimated from land-use regression models. Finally, a recent analysis from Edmonton, Canada reported associations between daily variability in levels of CO and NO2 with otitis media emergency department visits.18 Although we did not find an association with black carbon, our findings for NO and (inverse distance weighted) PM2.5 support an association with combustion sources.

We found robust associations between exposure to residential woodsmoke and otitis media. This finding may represent causal links consistent with the toxicologic effects of woodsmoke components on respiratory health, or it may be an artifact of the strong temporal correlation between wood- burning and otitis media. A similar association (although reflecting considerably higher exposures) was reported in a case-control study of children up to age 2 years in Mozambique, which reported elevated odds for otitis media (OR = 3.09 [95% CI = 2.00-4.78]) if families used charcoal or wood as sources of energy.15 A case-control study in rural New York reported an odds ratio of 1.73 (95% CI = 1.03-2.89) for otitis media in children whose families used wood-burning stoves,32 although a second study examining otitis media in the first year of life for over 900 infants found no such association after adjustment.33 An important distinction is that the above-mentioned studies identified wood-burning in the home, while our study modeled ambient residential woodsmoke at the neighborhood level.

While some findings from our study and others suggest an association between air pollution exposure and otitis media, a causal relationship requires a plausible biologic mechanism. Acute otitis media is caused by bacterial or viral pathogens that migrate from the nasopharynx into the eustachian tube and middle ear. Tubal obstruction leads to the accumulation of mucosal secretions and growth of pathogens, resulting in acute signs and symptoms of infection.1 An episode of otitis media often follows a viral upper-respiratory tract infection, due to the disruption of mucociliary clearance and tubal occlusion caused by enlarged adenoids during infection.13 The exact mechanism by which air pollutant exposures may lead to otitis media is not well understood. Because air pollutants cause inflammation, mucosal swelling, decreased cilia clearance, and decreased eustachian tube patency,7,34–37 the mechanism might be similar to that of prior viral infection, in which the disruption of homeostasis impairs host response,38–40 and promotes pathogen proliferation.

This study was population-based, and, although we used detailed individual spatiotemporal exposure estimates accounting for residential mobility, there are nonetheless several limitations to our methodology. The study region has lower levels of air pollution than most urban areas, and the exposure range for some pollutants may have been too narrow to detect a signal. Additional limitations arise from our use of administrative data to create the cohort, compile residential histories, and obtain outcome/covariate information. Although we included a large number of children, privacy legislation prohibited examination of exposure windows less than 2 months (only the month and year of birth was released for each child).

There has been no research examining the diagnostic accuracy of otitis media in the study area. Over diagnosis due to physician/caregiver knowledge or vigilance would bias the estimates toward the null. Physician visit, while an imperfect measure is more objective and less biased than parental reporting of symptoms, which has been used in previous studies. There was potential for misclassification of disease in this study because both acute otitis media (ICD-9: 381.0 and 382.0) and broader or unspecified otitis media (ICD-9: 381, 382, and 382.9) codes were used. It was necessary to include the broad 3-digit codes in our case definition because 93% of our outpatient physician visits were coded to only 3-digits. In all, 35% of children with an otitis-media physician visit did not have a linked antibiotic prescription, and so it is likely that some of these were visits for eustachian tube disorders and chronic otitis media (a consequence of using the broad ICD-9 381 and 382 codes), while the remainder were probably acute otitis that resolved without antibiotics. This limitation is inherent in using administrative databases for population-based research.

Due to the nature of the administrative data available for this study, we could not account for emergency room visits. However, it is standard practice for emergency room physicians to recommend that young children be followed up by their regular physician, and this follow-up visit would be captured in our data. Although changes in residence were accounted for in this study, there were no data available on time spent in other locations such as daycare. This study could not assess indoor sources of air pollution, daycare attendance, duration of breast-feeding or ethnicity (with the exception of aboriginal status), as information on these potential risk factors was not available at the individual level. The positive association between otitis media and neighborhood income was unexpected and difficult to interpret.

We observed associations between increased physician visits for otitis media and some common air pollutants, specifically woodsmoke and primary traffic emissions. Findings for other pollutants were either protective or null, and the impact of adjusting for otitis media season highlighted the complexity of assessing relationships between air pollution and seasonal infections. Our findings suggest that air pollution reduction strategies may be successful in reducing the overall burden of this common and costly childhood disease, particularly in regions where levels of woodsmoke or traffic-related pollutants are elevated or increasing. Future studies should consider short-term exposure windows, adjust for indoor sources of air pollution, and collect information on relevant microenvironments.

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We gratefully acknowledge the expertise of John Petkau in completing the statistical analysis.

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