Southern California Children's Health Research Articles

Parental Stress, Traffic, and New Onset Asthma in a Cohort of Children

SM4-PD-10 Recent literature suggests that both traffic-related pollutant exposure and psychosocial stress are risk factors for childhood asthma. However, there has been little study of synergistic effects or of associations of these risk factors with new onset asthma in prospective studies. We examined the relationship of local traffic-related exposure and of parental stress to the risk of incident asthma in the southern California Children's Health Study. A cohort of 5- to 7-year-old children was recruited in 2003, and a parent of each child completed a questionnaire on the child's history and symptoms of asthma and relevant risk factors. Parental stress at study entry was assessed using a well-validated 4-question perceived stress scale. The child's residential exposure was assessed by modeling exposure to local traffic-related pollutants. A yearly parent-completed questionnaire identified children with new-onset asthma through 2005. The relative risk of new onset childhood asthma associated with parental stress was 1.19 over the interquartile range of stress in the study population (95% CI, 1.00–1.41). There was no association of incident asthma with traffic modeled pollution by itself, but there was a significant interaction of parental stress with the child's residential traffic exposure. The relative risk of asthma associated with the interquartile range of traffic-modeled pollutant exposure was 2.48 (1.37–4.50) among children with parents in the upper quartile of perceived stress. The pattern of effects was similar among children with no lifetime history of wheeze at study entry, indicating that the associations with incident asthma did not merely reflect exacerbation of preexisting (but undiagnosed) asthma. These results suggest that parental stress may make children more susceptible to traffic-related asthma.

Health effects of the 2003 Southern California wildfires on children.

In late October 2003, Southern California wildfires burned more than 3,000 km2. The wildfires produced heavy smoke that affected several communities participating in the University of Southern California Children's Health Study (CHS). To study the acute effects of fire smoke on the health of CHS participants. A questionnaire was used to assess smoke exposure and occurrence of symptoms among CHS high-school students (n = 873; age, 17-18 yr) and elementary-school children (n = 5,551; age, 6-7 yr), in a total of 16 communities. Estimates of particulate matter (PM10) concentrations during the 5 d with the highest fire activity were used to characterize community smoke level. All symptoms (nose, eyes, and throat irritations; cough; bronchitis; cold; wheezing; asthma attacks), medication usage, and physician visits were associated with individually reported exposure differences within communities. Risks increased monotonically with the number of reported smoky days. For most outcomes, reporting rates between communities were also associated with the fire-related PM10 levels. Associations tended to be strongest among those without asthma. Individuals with asthma were more likely to take preventive action, such as wearing masks or staying indoors during the fire. Exposure to wildfire smoke was associated with increased eye and respiratory symptoms, medication use, and physician visits.

Bayesian Modeling of Air Pollution Health Effects with Missing Exposure Data

The authors propose a new statistical procedure that utilizes measurement error models to estimate missing exposure data in health effects assessment. The method detailed in this paper follows a Bayesian framework that allows estimation of various parameters of the model in the presence of missing covariates in an informative way. The authors apply this methodology to study the effect of household-level long-term air pollution exposures on lung function for subjects from the Southern California Children's Health Study pilot project, conducted in the year 2000. Specifically, they propose techniques to examine the long-term effects of nitrogen dioxide (NO2) exposure on children's lung function for persons living in 11 southern California communities. The effect of nitrogen dioxide exposure on various measures of lung function was examined, but, similar to many air pollution studies, no completely accurate measure of household-level long-term nitrogen dioxide exposure was available. Rather, community-level nitrogen dioxide was measured continuously over many years, but household-level nitrogen dioxide exposure was measured only during two 2-week periods, one period in the summer and one period in the winter. From these incomplete measures, long-term nitrogen dioxide exposure and its effect on health must be inferred. Results show that the method improves estimates when compared with standard frequentist approaches.

Predicting residential ozone deficits from nearby traffic

Oxides of nitrogen in fresh traffic exhaust are known to scavenge ambient ozone. However, there has only been little study of local variation in ozone resulting from variation in vehicular traffic patterns within communities. Homes of 78 children were selected from a sample of participants in 3 communities in the southern California Children's Health Study. Twenty-four hour ozone measurements were made simultaneously at a home and at a community central site monitor on two occasions between February and November 1994. Homes were geo-coded, and local residential nitrogen oxides (NO x ) above regional background due to nearby traffic at each participant's home were estimated using a line source dispersion model. Measured home ozone declined in a predictable manner as modeled residential NO x increased. NO x modeled from local traffic near homes accounted for variation in ozone concentrations of as much as 17 parts per billion. We conclude that residential ozone concentrations may be over- or underestimated by measurements at a community monitor, depending on the variation in local traffic in the community. These findings may have implications for studies of health effects of traffic-related pollutants.

Seasonal and spatial trends in particle number concentrations and size distributions at the children's health study sites in Southern California

Continuous measurements of particle number (PN), particle mass (PM(10)) and gaseous copollutants (NO(x), CO and O3) were obtained at eight sites (urban, suburban and remote) in Southern California during years 2002 and 2003 in support of University of Southern California Children's Health Study. We report the spatial and temporal variation of PNs and size distributions within these sites. Higher average total PN concentrations are found in winter (November to February), compared to summer (July to September) and spring (March to June) in all urban sites. Contribution of local vehicular emissions is most evident in cooler months, whereas effects of long-range transport of particles are enhanced during warmer periods. The particle size profile is most represented by a combination of the spatial effects, for example, sources, atmospheric processes and meteorological conditions prevalent at each location. Afternoon periods in the warmer months are characterized by elevated number concentrations that either coincide or follow a peak in ozone concentrations, suggesting the formation of new particles by photochemistry. Results show no meaningful correlation between PN and mass, indicating that mass based standards may not be effective in controlling ultrafine particles. The study of the impact of the Union worker's strike at port of Long Beach in October 2002 revealed statistically significant increase in PN concentrations in the 60-200 nm range (P<0.001), which are indicative of contributions of emissions from the idling ships at the port.

A three‐level model for binary time‐series data: the effects of air pollution on school absences in the Southern California Children's Health Study

A three-level model is proposed to simultaneously examine the effects of daily exposure to air pollution and individual risk factors on health outcomes without aggregating over subjects or time. We used a logistic transition model with random effects to take into account heterogeneity and overdispersion of the observations. A distributed lag structure for pollution has been included, assuming that the event on day t for a subject depends on the levels of air pollution for several preceding days. We illustrate this proposed model via detailed analysis of the effect of air pollution on school absenteeism based on data from the Southern California Children's Health Study.

Development of an individual exposure model for application to the Southern California children's health study

The Southern California Children's Health Study (CHS) investigated the relationship between air pollution and children's chronic respiratory health outcomes. Ambient air pollutant measurements from a single CHS monitoring station in each community were used as surrogates for personal exposures of all children in that community. To improve exposure estimates for the CHS children, we developed an Individual Exposure Model (IEM) to retrospectively estimate the long-term average exposure of the individual CHS children to CO, NO2, PM10, PM2.5, and elemental carbon (EC) of ambient origin. In the IEM, pollutant concentrations due to both local mobile source emissions (LMSE) and meteorologically transported pollutants were taken into account by combining a line source model (CALINE4) with a regional air quality model (SMOG). To avoid double counting, local mobile sources were removed from SMOG and added back by CALINE4. Limited information from the CHS survey was used to group each child into a specific time-activity category, for which corresponding Consolidated Human Activity Database (CHAD) time-activity profiles were sampled. We found local traffic significantly increased within-community variability of exposure to vehicle-related pollutants. PM-associated exposures were influenced more by meteorologically transported pollutants and local non-mobile source emissions than by LMSE. The overall within-community variability of personal exposures was highest for NO2 (±20–40%), followed by EC (±17–27%), PM10 (±15–25%), PM2.5 (±15–20%), and CO (±9–14%). Between-community exposure differences were affected by community location, traffic density, and locations of residences and schools in each community. Proper siting of air monitoring stations relative to emission sources is important to capture community mean exposures.

Associations between Particle Number and Gaseous Co-Pollutant Concentrations in the Los Angeles Basin

Continuous measurements of particle number (PN), particle mass (PM10), and gaseous pollutants [carbon monoxide (CO), nitric oxide (NO), oxides of nitrogen (NOx), and ozone (O3)] were performed at five urban sites in the Los Angeles Basin to support the University of Southern California Children's Health Study in 2002. The degree of correlation between hourly PN and concentrations of CO, NO, and nitrogen dioxide (NO2) at each site over the entire year was generally low to moderate (r values in the range of 0.1–0.5), with a few notable exceptions. In general, associations between PN and O3 were either negative or insignificant. Similar analyses of seasonal data resulted in levels of correlation with large variation, ranging from 0.0 to 0.94 depending on site and season. Summertime data showed a generally higher correlation between the 24-hr average PN concentrations and CO, NO, and NO2 than corresponding hourly concentrations. Hourly correlations between PN and both CO and NO were strengthened during morning rush-hour periods, indicating a common vehicular source. Comparing hourly particle number concentrations between sites also showed low to moderate spatial correlations, with most correlation coefficients below 0.4. Given the low to moderate associations found in this study, gaseous co-pollutants should not be used as surrogates to assess human exposure to airborne particle number concentrations.

A two-stage model for multiple time series data of counts.

We propose a two-stage model for time series data of counts from multiple locations. This method fits first-stage model(s) using the technique of iteratively weighted filtered least squares (IWFLS) to obtain location-specific intercepts and slopes, with possible lagged effects via polynomial distributed lag modeling. These slopes and/or intercepts are then taken to a second-stage mixed-effects meta-regression model in order to stabilize results from various locations. The representation of the models from the stages into a combined mixed-effects model, issues of inference and choices of the parameters in modeling the lag structure are discussed. We illustrate this proposed model via detailed analysis on the effect of air pollution on school absenteeism based on data from the Southern California Children's Health Study.