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We controlled for the effect of temperature by modeling high and low temperatures separately. For high temperatures we calculated the average temperature on the current and previous day (lag 0–1) and fit a natural spline with 3 df on the lagged variable only for days on which the lag 0–1 temperature was higher than the median annual temperature for the city as a whole. Similarly, we adjusted for low temperatures by fitting a natural spline with 2 df for the average temperature on previous 6 days (lag 1–6) only for days on which the lag 1–6 temperature was below the median annual value for the city (). This method accounts for differences in the lag structures and effects of cold and warm temperatures on hospitalizations while reducing the correlation between the two spline terms. We also performed an analysis that adjusted for potentially prolonged effects of warm temperatures on hospitalizations by replacing the lag 0–1 temperature term from the base model with the lag 0–6 average.
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Epidemiological studies on the short-term effects of PM2.5–10 on hospitalizations are few and inconsistent. In a large U.S. study of 108 counties (), a 10-μg/m3 increase in PM2.5–10 was associated with a 0.36% (95% posterior interval: 0.05, 0.68) increase in cardiovascular disease admissions on the same day. However, when adjusted for PM2.5, the association was still positive but no longer statistically significant. Two studies conducted in the United Kingdom (; ) did not identify any evidence of effects of coarse particles on hospital admissions for either cardiovascular or respiratory causes. A study conducted in six French cities () found no association between coarse PM and cardiovascular or respiratory admissions, and reported only one statistically significant association with ischemic heart disease in the elderly. We identified positive associations of coarse particles with both cardiovascular (0.46% increase; 95% CI: 0.10, 0.82 with a 6.3-μg/m3 increase in lag 0–1 PM2.5–10) and respiratory admissions (1.24% increase; 95% CI: –0.32, 2.82) with a 6.3-μg/m3 increase in lag 0–5 PM2.5–10). Corresponding associations with a 10-μg/m3 increase in PM2.5–10 were higher than previously reported (0.73%; 95% CI: 0.16, 1.30%, and 1.95%; 95% CI: –0.51, 4.48%, respectively). However, because PM2.5–10 is obtained as the difference between PM10 and PM2.5, it is affected by measurement error from two sources, and the direction and magnitude of the resulting bias in the associations with hospitalizations cannot be predicted.