Adjusting for temporal trend in the logistic regression analysis yielded similar findings to the unadjusted results (Table 2).
We analyzed daily deaths and emergency hospitalizations for CVDs among persons ≥ 40 years of age for associations with PM2.5, its chemical components, nitrogen dioxide (NO2), carbon monoxide, and sulfur dioxide for the years 2000 2006 using a Poisson time-series model adjusting for temporal and seasonal trends, temperature effects, and day of the week.
We estimated percent excess risk (%ER) for air pollutants using a Poisson time-series model, adjusting for temporal trends and seasonal cycles, immediate and delayed temperature effects, and day of the week.
The LOS effects were robust in terms of the sensitivity analysis concerning the different statistical calculation models (fixed versus random effects model) and the Year of publication, adjusting for temporal trends.
Furthermore, sensitivity analyses were performed to test whether the effect size varied by the countries where the study was carried out (adjusting for market forces) and the year of publication, adjusting for temporal trends.
After adjusting for temporal trends, a significantly higher post-campaign colonoscopy rate was sustained for 9 months [ 19].
Annual averages were calculated by adjusting for temporal variations using measurements obtained from a reference site located in urban background.
To account for temporal effects, we included the term experimental run as a random effect.
Such methods have been applied to adjust for temporal trends in estimating air pollution effects (Peng et al. 2006; Touloumi et al. 2006).
Therefore, a null-effect SR was calculated to adjust for temporal trends.
We adjusted for temporal trends in neonatal respiratory admissions, including day of the week, and effects of weather on neonatal respiratory admissions using piecewise constant functions that varied by 30, 90, 180, 270, and 365 days throughout the study period.
