The daily PM10 concentrations at
Chulalongkorn were highly correlated with
those at Odean Circle (r = 0.95). This correlation
suggests the curbside measurements are
reflecting the general day-to-day fluctuations
in PM10 concentrations over a reasonably
wide area in the city, because these locations
were a few miles apart. PM10 concentrations
at both sites were moderately inversely correlated
with daily temperature (r = –0.38 and
r = –0.32, respectively) and humidity (r =
–0.55 and r = –0.47, respectively), at Odean
Circle and Chulalongkorn.
Table 3 summarizes the impact of alternative
lags on the PM10 variable using the
basic logistic regression model for lower and
upper respiratory symptoms for Odean
Circle adults, nurses, and children. For
Odean Circle adults, this model controls for
a subject’s age, sex, educational level, having
a chronic respiratory condition, having no air
conditioning in the home, and daily average
temperature. For the nurses, there was no
variation in sex, education, or air conditioning,
so these were not included in the model.
For children, the model includes age, sex, having
a chronic respiratory condition, having no
air conditioning in the home, daily average
temperature, and daily average humidity. Lags
of up to 3 days and moving averages of up to
4 days (i.e., the average of the current day’s
PM10 concentration and the concentrations
on the three previous days) were examined in
these basic models. For all three panels and
both outcomes, a 4-day moving average generated
the strongest associations with PM10.
However, positive associations were indicated
for all of the lags examined, and statistically
significant results were obtained for all three
moving average measures. Based on these
results, the 4-day moving average was selected
as the basic measure of PM10 for subsequent
sensitivity analyses.
All the individual characteristics shown in
Table 1 were included in preliminary analyses,
but only those with statistically significant
relationships with symptoms were retained in
the basic model. Having a household member
who smokes (none of the subjects smoked) or
using charcoal for cooking were not significant
for the adults or for children, except for
upper respiratory symptoms in children,
which showed a higher frequency for those
who had a smoker in the house. However, the
PM10 coefficient for upper respiratory symptoms
in children was not changed when the
household smoker variable was added to the
model. Having no air conditioning had an
unexpected negative sign on symptom frequencies
in the children, but had the
expected positive sign for adults. The result
for children may have been due to correlation
with socioeconomic status rather than an
actual beneficial respiratory effect of having
no air conditioning.
Those with a chronic respiratory condition
were much more likely to have symptoms, but
interactions with the PM10 variable were not
statistically significant, suggesting those with a
chronic respiratory condition were no more
likely to be affected by daily fluctuations in
PM10 than those without a chronic condition.
Interactions between PM10 and other variables,
including no air conditioning and presence
of household smoker, were also tested
and none were found to be statistically significant.
It is important to note these are simple
binary variables for each subject and do not
reflect the potential impact of day-to-day fluctuations
in such exposures or differences in
the amount of exposure for subjects who are
exposed. These findings, therefore, suggest
only those exposed to environmental tobacco
smoke or to charcoal smoke in the home show
no evidence of a different reaction to fluctuations
in daily concentrations of outdoor
PM10. They should not be interpreted as
showing no effect of these indoor exposures
on daily symptoms, because they were not
measured as daily exposures.
The PM10 effects in the basic model and
sensitivity analyses are summarized in Table
4. First, the results for the basic model are
reported with and without a variable controlling
for the impact of daily average temperature
(unlagged). Adding temperature to the
model attenuated the effect of PM10 somewhat
for the adult panels, but caused a slight
increase in the estimated PM10 effect for children.
Temperature was negatively associated
with symptoms (i.e., fewer symptoms were
reported on hotter days). For Odean Circle