The study population is a particularly sensitive group – infants with at least one sibling with physician-diagnosed asthma. In fact, we found that infants with at least one parent with asthma history had higher severity of wheeze symptoms (). This could be explained by genetics or exposures that are common to family members (Bisgaard et al., 2009
). While some variables could be independent risk factors for developing wheeze symptoms (e.g., infants with parental history of asthma, presence of persistent mold), we found that living in a more urban area was associated with higher risk of wheeze symptoms, after adjustment for other variables.
There are multiple mechanisms through which urban land-use could affect wheeze symptoms, such as air pollution, noise, and stress (Klinnert et al., 2003
; Ryan et al., 2005
). Our original hypothesis was that the fraction of urban land-use around residence would be associated with severity of infants’ respiratory symptoms, with the concept that urban land-use captures multiple aspects of urban burdens, including traffic and other pollutants, noise and other factors relating to urbanicity. An association has been identified between factors reflecting urbanicity and adverse respiratory symptoms (Wright, 2007
). While our study examined urbanicity in general, we were able to address some of these factors.
Traffic-related outdoor air pollution is anticipated to be higher in urban centers because of high traffic volume and concentrated roadways. Several studies used developed land-use as a marker of traffic emissions (Clougherty et al., 2008
; Henderson et al., 2007
). Many used shorter distances than we used in this work (e.g., 500m compared to our selected buffer of 1,540m); however, the nature of the buffer differs as many such studies consider only the presence or absence of a specified land-use within a certain distance of the residence, whereas we considered the degree of land-use (e.g., fraction of area) within a specified distance (English et al., 1999
; Zhou and Levy, 2007
We selected buffer size based on the value that maximized the log-likelihood and performed sensitivity analysis with other sizes. Results were essentially unchanged (OR 1.02 to 1.09) when we modified the buffer size from 100m to 2,000m, although estimates were not statistically significant for the first a few hundred meters. This might be explained by two reasons. First, traffic-related outdoor NO2
level could affect infants’ health further than the distance many studies considered. A recent study reported that NO2
levels could be explained in part by traffic emissions and land-use within 6,000m from a residence (Skene et al., 2010
). Another possible explanation is that urbanicity may capture factors relevant to human health other than traffic pollutants. Our analysis suggested the fraction of urban land-use within the 1,540m buffer around residences is somewhat correlated with estimated traffic-related outdoor NO2
levels (0.63). This correlation was similar (0.51 to 0.64) across a range of buffer sizes of 100–2,000m. When adjusted for estimated NO2
levels, the association between urban land-use and severity of wheeze lost statistical significance although the central estimate was similar (OR of 1.07 with NO2
adjustment, 1.09 without NO2
adjustment). The NO2
levels were based on a GIS traffic model that incorporated data on highway patterns and traffic flow, and did not explicitly use land-use categories as inputs; however, urbanicity and traffic are linked, as evidenced by the correlation between NO2
estimates and the urban land-use category.
Thus, findings indicate that the observed association between urban land-use and infants’ respiratory symptoms incorporates some effects of traffic-related outdoor air pollution, although these results cannot be explained solely by traffic. Highway and non-highway vehicles account for over 90% of emissions for CO, about 36% for volatile organic compounds (VOCs), and over 70% for nitrogen oxides. Area point sources (e.g., small commercial and industrial firms) contribute about 40% of VOC emissions, whereas stationary point sources (e.g., utilities, industry) contribute about 13% of nitrogen oxide emissions (CT Dept. of Environmental Protection, 2005
). Another interesting finding is the clear trend between the different levels of urban intensity and severity of wheeze symptoms. High intensity urban areas, pixels with 80% or more impervious surfaces, are more likely to contain industrial centers and be surrounded by heavy traffic, while low intensity urban areas, pixels with 20% or more and less than 50% impervious surfaces, are more likely to be residential, less polluted areas.
Our results imply that the association between urban land-use and severity of wheeze was higher for infants of lower SES families. One potential explanation is housing characteristics. Families with higher income may be more likely to afford home air conditioning (AC). Earlier work showed lower effect estimates for air pollution for communities or individuals with central AC, which may relate to changes in the penetration of ambient air pollutants indoors or filtration (Bell et al., 2009
; Medina-Ramon et al., 2006
; Waring and Siegel, 2008
). The correlation between county’s median income and AC prevalence in Connecticut was 0.65 (US Census Bureau, 2000
; US Department of Commerce, 1997
). Another explanation could be that susceptibility to environmental stressors from urban environments may be related to baseline healthcare status or healthcare, which could be related to SES (O’Neill et al., 2007
A key strength of this study is the cohort design; we were able to adjust for infants’ race, parental asthma history, mother’s educational attainment, smoking in the home, presence of persistent mold, and family income on an individual level, and base exposure on residential location. We were able to estimate a marker for traffic-related outdoor air pollutants, NO2
, using a GIS integrated traffic exposure model, for the study time period and area where monitoring data were unavailable. Although actual measurements would be preferable, such information is often prohibited by cost or data availability. The methods used in this study to estimate NO2
based on a GIS model and to obtain land-use based on satellite imagery could be applied to other studies, and in fact related approaches have been used (Jerrett et al., 2005
Limitations of this study include the potential misclassification of land-use categories and sub-scale heterogeneity given land-use resolution of 30×30m. However, we anticipate that any such misclassification would be non-differential and drive effect estimates towards the null. Additional refinements to land-use categorization methods and satellite imagery, perhaps with higher spatial resolution, would be beneficial. While we addressed traffic-related outdoor air pollution, this issue warrants further attention. Our ability to separate effects of traffic and other urban characteristics is limited because of their moderate correlation. Also, our method using NO2
as an indicator of overall traffic-related outdoor air pollution may not fully capture the effect of some pollutants or other sources on adverse respiratory symptoms. Traffic contributes to a variety of pollutants including NO2
, and CO, and previous research indicated that other air pollutants, such as PM2.5
, are associated with respiratory outcomes (Dominici et al., 2006
). Other sources such as industry also contribute to NO2
). Although our method has the spatial advantage of individual-level exposure, the NO2
traffic approach has a temporal limitation due to the use of annual averages, which were the only available form of traffic data. Additional research in this area is warranted. Future research could investigate actual observed air pollutants, rather than estimated values, and other environmental stressors in relation to urban land-use and respiratory symptoms. Additionally, although the NO2
estimation model performs well, it could be improved with consideration of seasonal traffic volume and non-highway roads for which data were unavailable, and wind direction which was not incorporated in the exposure model (Holford et al., 2010
In conclusion, we found an association between urban land-use and severity of wheeze symptoms for infants, with higher effect estimates with higher degree of urbanicity. Our cohort subjects are at high risk of adverse respiratory outcomes, and further work could investigate whether results apply to a more general population. Urban land-use represents several aspects of urban environment. Urbanicity may affect health not only through traffic-related outdoor air pollution, but also through other pathways, and its effect may be modified by SES. Further research is needed to investigate the various characteristics of an urban environment that affect respiratory health, given the environmental justice implications.