Factors affecting ozone related asthma responses among middle school students with asthma

Outdoor environmental risk factors for asthma have been extensively researched, even though the majority of a person's daily activity occurs indoors. There is limited evidence linking personal exposure concentrations of ozone, pollen, mold, temperature, and humidity to childhood asthma. ^ The current study consisted of a secondary, more complex analysis of the data from the Houston Air Toxics and Asthma in Children (ATAC) Study to further investigate the association of personal ozone exposure on asthma outcome variability among middle school children with asthma. The ATAC Study primarily investigated the association between selected oxygenated air toxics and indicators of asthma variability (PEFR, FEV1, asthma symptoms, and rescue medication usage) among 30 labile and persistent Houston middle-school children with diagnosed asthma. This panel study used a repeated measurements design of four separate 10-day sampling periods that extended over a 20 month period. The secondary analysis included aggregate regression models that were constructed with two different estimates of ozone exposure (daily maximum hourly outdoor concentration and daily maximum hourly personal exposure), with three different estimates of personal environmental temperature and humidity exposures (daily average, intraday difference, and interday difference), and for thee different time periods [same day of exposure (lag 0), one day after initial exposure (lag 1), and two days after initial exposure (lag 2)]. ^ Overall, the models using daily maximum hourly personal ozone exposures in combination with intraday and interday personal temperature and humidity differences produced more significant plausible associations than models using daily maximum hourly personal ozone exposures with personal average temperature and humidity exposures. Significant associations were identified between daily maximum hourly personal ozone exposure and clinical indicators of asthma variability. The increasing effect on rescue medication usage from daily maximum hourly personal ozone exposure were identified as soon as the same day of exposure (lag 0; p=0.0072), and the same effects were delayed until the second next day (lag 2; p= 0.0026). The increasing effect on asthma symptoms were identified on the second next day after initial exposure (lag 2; p= 0.0024). There was a consistent inverse relationship between personal relative humidity exposure and indicators of asthma variability. Decreasing effects on daily FEV1 variability from personal relative humidity exposure were identified on the same day of exposure (lag 0; p= 0.034), increasing effects on morning PEFR were identified on the next day after initial exposure (lag 1; p= 0.0001), and decreasing effects on overnight PEFR variability were identified on the second next day after the initial exposure (lag 2; p= 0.007). With the conclusion of this research, there are opportunities for future similar studies in the preventive management of asthma in children living in high-ozone areas.^

Statistical issues on studies of mixed longitudinal design

Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^