Dong GH, Qian ZM, Xaverius PK, Trevathan E, Maalouf S, Parker J, et al. 2013. Association between long-term air pollution and increased blood pressure and hypertension in China. Hypertension 61:578–584.
Coogan PF, White LF, Jerrett M, Brook RD, Su JG, Seto E, et al. 2012. Air pollution and incidence of hypertension and diabetes mellitus in black women living in Los Angeles. Circulation 125:767–772.
This study has several strengths. The exposure assessment, one of the most critical aspects of this kind of study, was performed in a rigorous way with standardized procedures across all study areas (; ). The LOOCV R2 coefficients from the PM2.5 LUR models ranged from 0.53 in Finland to 0.79 in Germany (Ruhr area), denoting strong discriminatory power of the spatial attributes used in these models to capture the spatial contrasts of exposures within the study areas (). An additional point of merit is the extensive list of variables available for confounding adjustment, including cardiovascular mediators and confounders, road traffic noise exposure at each residence and the urban/rural indicator used to characterize the degree of urbanization of each study area. Also, the statistical modeling was rigorously standardized between cohorts and addressed several methodological issues, including the potential for spatial autocorrelation of the study outcomes, and the linearity of the relationship between long-term air pollution exposure and stroke incidence.
An air pollution measurement system that is sufficient in terms of spatial and pollutant analysis, and is relatively low-priced and autonomous is the priority.
A few limitations of the present study should be mentioned. First, air pollution measurement campaigns were implemented between 2008 and 2011, after the follow-up period of most cohorts (; ). As a consequence, this study relies on the assumption that the intracohort spatial distribution of air pollution has not dramatically changed in the last 10–15 years and that the land-use model predictions are thus representative of the baseline spatial contrasts for all the cohorts investigated. Several studies in the literature support this assumption over periods of about 10 years (; ). In addition, within the ESCAPE project many efforts were made to back-extrapolate air pollution concentrations, taking into account long-term time trends (see Supplemental Material, “Methods,” pp. 3–13), and analyses relating back-extrapolated data to the health outcomes showed no clear differences in the results compared with original data (data not shown). We also performed an exploratory analysis to evaluate whether the association between PM2.5 and stroke incidence differed according to accrual time, under the hypothesis that the assumption of stable spatial distribution of air pollution over time could be more valid for more recent cohorts: We did not find meaningful differences in the association estimates across cohorts according to accrual time (data not shown). Second, our approach exploited only within-study area contrasts, which limited the exposure contrast, but decreased the risk of potential confounding when comparing diverse cohorts from different countries. Third, the data available to adjust for confounding were somewhat different from cohort to cohort, allowing the possibility of different degrees of residual confounding in the cohort-specific results. However, the most relevant cardiovascular risk factors (smoking, diabetes or hypertension, BMI, physical activity) were available in almost all the cohorts, and thus severe bias in the effect estimates due to residual confounding is unlikely. Finally, we did not consider the possible impact of loss to follow-up (drop out or death) on the findings. Air pollution exposure is an established cause of mortality, so that older participants are likely to represent a population that is “selected” such that those who sustained higher exposures are more likely to have characteristics (genetic or otherwise) that place them at lower risk for stroke, resulting in underestimation of the causal relation of exposure with stroke risk. However, given the small relative risk of the air pollution–mortality association (i.e., HR 3 increase in PM2.5 or 10-μg/m3 increase in PM10 or NO2 as reported by ), this underestimation is likely to be small.
A map of the study areas and further details on individual and area-level characteristics are available in Supplemental Material, Figure S1 and Table S1. Exposure levels and ranges were generally higher in Italy than in the other areas (). More details on air pollution exposures are reported in the Supplemental Material, Tables S2 and S3.
A total of 111,931 participants were under study. After the exclusion of the prevalent cases and of participants with missing exposure, a total of 105,025 participants remained. However, 5,579 participants had missing information on any of the variables in the main model; therefore, 99,446 participants (88.8% of the original study population, and 92.4% of the original cohorts after exclusion of prevalent cases) were included in the analyses, providing ≥ 1 million person-years of observation. 3,086 incident stroke events were registered during the follow-up. The majority of the stroke cases with defined etiology were coded as ischemic stroke; however, 43% of all cases were undefined, thus precluding the possibility of analyzing different types of stroke separately. The baseline age distribution was heterogeneous across cohorts, with mean values ranging from 44 years (two Italian cohorts) to 74 years (a Swedish cohort), whereas sex, education, and occupation had less variability. The percentage of current smokers at baseline was the highest in southern Europe and the lowest in Sweden and Germany (). The comparison of the studied population before and after the exclusion of the participants with missing data on the confounders did not show differences in relation to air pollution exposure (i.e., PM2.5) and occurrence of the study outcome.
The major causes and impacts of Japan air pollution will be addressed and highlighted in details; air pollution have its significant health implication on humans, as well as the environment, the effects and damages may extend beyond any measure of tolerance.
For example, hair spray usage indoor is polluting the air inside because of the emissions of toxic chemicals used to create the product are present within a spray of it....
Correcting for the effect of antihypertensive medication. To account for the influence of BPLM intake on the level of measured BP, we assessed the effect of air pollution on BP in participants taking BPLM (“medicated”) and in participants not taking BPLM (“nonmedicated”) separately. To increase power, we calculated results in subgroups of medicated and nonmedicated in the whole cohort, using an interaction term, exposure × BPLM intake. The analysis model was
Associations with particulate air pollutants. Modeled PM concentrations were not clearly associated with any of the studied outcomes in the single-pollutant models (, and ). We found a 0.20-mmHg (95% CI: –0.76, 1.16) and a 0.98-mmHg (95% CI: –0.35, 2.31) increase in systolic BP per 5-μg/m3 increase in PM2.5 in nonmedicated and medicated participants, respectively. The pinteraction for PM2.5 × BPLM intake was 0.25. Similar results were found for diastolic BP: an increase of 0.14 mmHg in nonmedicated (95% CI: –0.57, 0.85) and by 0.59 mmHg in medicated (95% CI: –0.19, 1.37) participants per 5-μg/m3 increase in PM2.5; the pinteraction was 0.26. The ORs for hypertension and BPLM intake per 5-μg/m3 of PM2.5 were 1.07 (95% CI: 0.95, 1.21) and 1.06 (95% CI: 0.96, 1.17), respectively. Similarly, elevated, but nonsignificant, estimates were observed for PM2.5 absorbance, PMcoarse, and PM10. Results across studies were somewhat heterogeneous for PM2.5 and PMcoarse (), displaying relatively large positive point estimates in some cohorts and inverse associations in others.