for repeated observations on individuals and due to the extreme non-normality of these measures. Readings below the limit of detection for any assay were arbitrarily assigned a value corresponding to 50% of the limit of detection to avoid division by zero in any subsequent calculation. Longitudinal data was cleaned to exclude spurious or physiologically impossible data points by an a priori defined protocol. Peak flow and FEV1 data for each student was expressed as a Z-score relative to the student's mean score over the period of the study. Longitudinal data analysis exploring bivariate association between lung function (Z-scores of PEFR or FEV1) or symptom data and pooled air pollution measures (both indoor and outdoor) and climate related measures were conducted using the non-parametric Spearman's correlation coefficient. The air pollution measures were lagged daily out to seven days and correlated with lung function and symptom scores. In this manner the potential for any lagged effect of air pollution on these measures was analysed. Variables showing some association (p < 0.15) with lung function or symptom data from these analyses were then entered into a mixed-model linear regression analysis which included the daily use of a reliever as a covariate to confirm the independent association with lung function or symptoms. These analyses were undertaken for the dataset as a whole and repeated separately for asthmatic and non-asthmatic students.
In addition, mean levels of each of the longitudinal outcome variables for each student were calculated within moderate/high (24 hour average> 20 μm/m3 outdoor) pollution days and low pollution days. These levels were compared using Mann-Whitney U tests. Longitudinal data analysis exploring bivariate association between lung function (Z-scores of PEFR or FEV1) and pooled air pollution measures on moderate/high pollution (24 hour average> 20 μm/m3 outdoor) days alone were then conducted using the non-parametric Spearman's correlation coefficient.
The power of the study was of necessity dependant on the number and magnitude of high pollution days during the study period. In addition, limiting the sampling to one school (to increase exposure validity) placed limitations on potential numbers of students willing/able to participate. Retrospective power analysis was therefore undertaken for lung function scores (expressed as Z-scores) between moderate/high (24 hour average> 20 μm/m3 outdoor) pollution days and low pollution days. Assuming a 2:1 split in air pollution days, and given n = 80 students, the study had an 80% power to detect differences in Z-score values of 0.30 in FEV1 and 0.27 in PEFR with α = 0.05 (two-tailed). In addition, with 100 time points a correlation between Z-scores and pollution of >0.28 would have been detected as statistically significant i.e. R2 >10%.
Students' baseline demographics and lung function
A total of 93 students took part in the study, from a total potential boarding house population of 240 students. Eighty-nine students identified themselves as New Zealand European, whilst four (4.3%) came from Asia. There were no students who identified themselves as Maori. In 2001, 6.9% of the population of Christchurch identified themselves as Maori, while 5.5% identified themselves as Asian . Twenty-six (28%) of the students had a doctor diagnosis of asthma. The prevalence of asthma in this age group in New Zealand is 24.4% . Seventeen of these students used short-acting beta agonists on an as-required basis, while eight students were taking regular inhaler corticosteroids. Mean age of the students at study start was 14 years 5 months (range 12 years 9 months – 17 years 6 months). There were no significant age differences between students with asthma and those without asthma. There was no significant difference in % predicted FEV1 between asthmatics and non-asthmatics (97.54% ± 2.481 (mean ± 1 s.e.) n = 26, vs 95.49% ± 1.635 n = 57), though FEV1/FVC ratio was significantly lower in the asthmatic students (FEV1/FVC ratio asthma group 0.803 ± 0.013 n = 26 vs no