Suchergebnisse

AbstractThere is growing evidence that air pollutants might affect human behavior. This study assesses the associations between air pollution concentrations and emergency department (ED) visits for abuse of psychoactive substances. 28,745 such ED visits were identified and retrieved from a health database containing diagnosed visits from five hospitals in Edmonton (Canada) over 10 years. The ED visits were analyzed as daily counts. Conditional Poisson regression models were used to estimate the associations between the number of ED visits and concentration levels of gaseous air pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3)) and particulate matters (PM2.5 and PM10, fine and coarse, respectively). Air pollutants and weather factors in the realized statistical models were lagged by the same number of days, from 0 to 5 days. The associations were estimated in the form of concentration-response functions. The results show relative risks and their 95% confidence intervals. Positive and statistically significant associations were obtained for CO for all patients (lags from 0 to 5), males (lags 1 and 3–5), and females (lag 4). For NO2, exposure lagged by 1 and 2 days has a positive statistically significant association for all and male patients. PM10 shows the same type of associations lagged by 2 and 3 days. PM2.5 (lag 2) is associated only in females. The results indicate that urban air pollution may have an impact on the abuse of psychoactive substances.

AbstractIn this work, a new technique is proposed to study short-term exposure and adverse health effects. The presented approach uses hierarchical clusters with the following structure: each pair of two sequential days in 1 year is embedded in the year. We have 183 clusters per year with the embedded structure <year:2 days>. Time-series analysis is conducted using a conditional Poisson regression with the constructed clusters as a stratum. Unmeasured confounders such as seasonal and long-term trends are not modelled but are controlled by the structure of the clusters. The proposed technique is illustrated using four freely accessible databases, which contain complex simulated data. These data are available as the compressed R workspace files. Results based on the simulated data were very close to the truth based on the presented methodology. In addition, the case-crossover method with 1-month and 2-week window, and a conditional Poisson regression on 3-day clusters as a stratum, was also applied to the simulated data. Difficulties (high type I error rate) were observed for the case-crossover method in the presence of high concurvity in the simulated data. The proposed methods using various forms of a stratum were further applied to the Chicago mortality data. The considered methods have often different qualitative and quantitative estimations.

AbstractTo investigate the acute impact of various air pollutants on various disease groups in the urban area of the city of Toronto, Canada. Statistical models were developed to estimate the relative risk of an emergency department visit associated with ambient air pollution concentration levels. These models were generated for 8 air pollutants (lagged from 0 to 14 days) and for 18 strata (based on sex, age group, and season). Twelve disease groups extracted from the International Classification of Diseases 10th Revision (ICD-10) were used as health classifications in the models. The qualitative results were collected in matrices composed of 18 rows (strata) and 15 columns (lags) for each air pollutant and the 12 health classifications. The matrix cells were assigned a value of 1 if the association was positively statistically significant; otherwise, they were assigned to a value of 0. The constructed matrices were totalized separately for each air pollutant. The resulting matrices show qualitative associations for grouped diseases, air pollutants, and their corresponding lagged concentrations and indicate the frequency of statistically significant positive associations. The results are presented in colour-gradient matrices with the number of associations for every combination of patient strata, pollutant, and lag in corresponding cells. The highest number of the associations was 8 (of 12 possible) obtained for the same day exposure to carbon monoxide, nitrogen dioxide, and days with elevated air quality health index (AQHI) values. For carbon monoxide, the number of the associations decreases with the increasing lags. For this air pollutant, there were almost no associations after 8 days of lag. In the case of nitrogen dioxide, the associations persist even for longer lags. The numerical values obtained from the models are provided for every pollutant. The constructed matrices are a useful tool to analyze the impact of ambient air pollution concentrations on public health.