Table 5 shows the results of two alternative
assumptions that give the likely highest
and lowest PM10 effects and reflect alternative
approaches of accounting for time trends in
the data (Figure 1). The first specification
includes age, sex, educational level, having a
chronic respiratory condition, and having no
air conditioning in the home, but does not
include any terms for time and temperature
(which generally increased over time). This
model reflects the hypothesis that the decreasing
trend in symptoms is entirely attributable
to the decreasing trend in PM10 concentrations
during the study period. At the other
extreme, loess smoothers of symptoms over
both time and temperature are added to the
model. The smooth of symptoms over time
causes a significant attenuation in the estimated
odds ratio for PM10 for all three of the
panels. In this model the downward trend in
symptoms is captured by the smooth variable,
essentially implying only a small portion of
the downward trend is attributable to
decreasing concentrations of PM10. It is
notable that even with this extreme assumption,
a statistically significant association
between symptoms and PM10 is still observed
for the Odean Circle adult panel.
Dichotomous samplers located at Odean
Circle and at Chulalongkorn Hospital during
part of the study period provided a limited
number of days of PM2.5 concentrations. We
therefore estimated a PM2.5 coefficient and a
PM10 coefficient for the same days to compare
their associations with symptoms. As
summarized in Table 6, statistically significant
associations were found between respiratory
symptoms and both PM10 and PM2.5,
measured as 4-day moving averages, for both
adult panels. The odds ratios for interquartile
ranges (45 μg/m3 for PM10 and 26 μg/m3 for
PM2.5) are comparable in magnitude for all
the comparisons. For children, however, the
PM2.5 results are not statistically significant.