Atmospheric circulation types and daily mortality in Athens, Greece

We investigated the short-term effects of synoptic and mesoscale atmospheric circulation types on mortality in Athens, Greece. The synoptic patterns in the lower troposphere were classified in 8 a priori defined categories. The mesoscale weather types were classified into 11 categories, using meteorologic parameters from the Athens area surface monitoring network; the daily number of deaths was available for 1987-1991. We applied generalized additive models (GAM), extending Poisson regression, using a LOESS smoother to control for the confounding effects of seasonal patterns. We adjusted for long-term trends, day of the week, ambient particle concentrations, and additional temperature effects. Both classifications, synoptic and mesoscale, explain the daily variation of mortality to a statistically significant degree. The highest daily mortality was observed on days characterized by southeasterly flow [increase 10%; 95% confidence interval (CI), 6.1-13.9% compared to the high-low pressure system), followed by zonal flow (5.8%; 95% CI, 1.8-10%). The high-low pressure system and the northwesterly flow are associated with the lowest mortality. The seasonal patterns are consistent with the annual pattern. For mesoscale categories, in the cold period the highest mortality is observed during days characterized by the easterly flow category (increase 9.4%; 95% CI, 1.0-18.5% compared to flow without the main component). In the warm period, the highest mortality occurs during the strong southerly flow category (8.5% increase; 95% CI, 2.0-15.4% compared again to flow without the main component). Adjusting for ambient particle levels leaves the estimated associations unchanged for the synoptic categories and slightly increases the effects of mesoscale categories. In conclusion, synoptic and mesoscale weather classification is a useful tool for studying the weather-health associations in a warm Mediterranean climate situation

The role of a Mediterranean diet on the risk of oral and pharyngeal cancer.

BACKGROUND: The Mediterranean diet has a beneficial role on various neoplasms, but data are scanty on oral cavity and pharyngeal (OCP) cancer. METHODS: We analysed data from a case-control study carried out between 1997 and 2009 in Italy and Switzerland, including 768 incident, histologically confirmed OCP cancer cases and 2078 hospital controls. Adherence to the Mediterranean diet was measured using the Mediterranean Diet Score (MDS) based on the major characteristics of the Mediterranean diet, and two other scores, the Mediterranean Dietary Pattern Adherence Index (MDP) and the Mediterranean Adequacy Index (MAI). RESULTS: We estimated the odds ratios (ORs), and the corresponding 95% confidence intervals (CI), for increasing levels of the scores (i.e., increasing adherence) using multiple logistic regression models. We found a reduced risk of OCP cancer for increasing levels of the MDS, the ORs for subjects with six or more MDS components compared with two or less being 0.20 (95% CI 0.14-0.28, P-value for trend <0.0001). The ORs for the highest vs the lowest quintile were 0.20 (95% CI 0.14-0.28) for the MDP score (score 66.2 or more vs less than 57.9), and 0.48 (95% CI 0.33-0.69) for the MAI score (score value 2.1 or more vs value less 0.92), with significant trends of decreasing risk for both scores. The favourable effect of the Mediterranean diet was apparently stronger in younger subjects, in those with a higher level of education, and in ex-smokers, although it was observed in other strata as well. CONCLUSIONS: Our study provides strong evidence of a beneficial role of the Mediterranean diet on OCP cancer

The impact of measurement error in modelled ambient particles exposures on health effect estimates in multi-level analysis: a simulation study.

Background: Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects. Methods: We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the “true” underlying daily exposure surfaces for PM10 and PM2.5 for 2009–2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. Results: For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from −11% (underestimate) to 20% (overestimate) for PM10 and of −20% to 17% for PM2.5. Integration of models performed best in almost all cases. Conclusions: No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate

Saharan dust and association between particulate matter and case-specific mortality: a case-crossover analysis in Madrid (Spain)

<p>Abstract</p> <p>Background</p> <p>Saharan dust intrusions are a common phenomenon in the Madrid atmosphere, leading induce exceedances of the 50 μg/m³- EU 24 h standard for PM₁₀.</p> <p>Methods</p> <p>We investigated the effects of exposure to PM₁₀between January 2003 and December 2005 in Madrid (Spain) on daily case-specific mortality; changes of effects between Saharan and non-Saharan dust days were assessed using a time-stratified case-crossover design.</p> <p>Results</p> <p>Saharan dust affected 20% of days in the city of Madrid. Mean concentration of PM₁₀was higher during dust days (47.7 μg/m³) than non-dust days (31.4 μg/m³). The rise of mortality per 10 μg/m³PM₁₀concentration were always largely for Saharan dust-days. When stratifying by season risks of PM₁₀, at lag 1, during Saharan dust days were stronger for respiratory causes during cold season (IR% = 3.34% (95% CI: 0.36, 6.41) versus 2.87% (95% CI: 1.30, 4.47)) while for circulatory causes effects were stronger during warm season (IR% = 4.19% (95% CI: 1.34, 7.13) versus 2.65% (95% CI: 0.12, 5.23)). No effects were found for cerebrovascular causes.</p> <p>Conclusions</p> <p>We found evidence of strongest effects of particulate matter during Saharan dust days, providing a suggestion of effect modification, even though interaction terms were not statistically significant. Further investigation is needed to understand the mechanism by which Saharan dust increases mortality.</p

Investigating regional differences in short-term effects of air pollution on daily mortality in the APHEA project: A sensitivity analysis for controlling long-term trends and seasonality

Short-term effects of air pollution on daily mortality in eight western and five central-eastern European countries have been reported previously, as part of the APHEA project. One intriguing finding was that the effects were lower in central-eastern European cities. The analysis used sinusoidal terms for seasonal control and polynomial terms for meteorologic variables, but this is a more rigid approach than the currently accepted method, which uses generalized additive models (GAM). We therefore reanalyzed the original data to examine the sensitivity of the results to the statistical model. The data were identical to those used in the earlier analyses. The outcome was the daily total number of deaths, and the pollutants analyzed were black smoke (BS) and sulfur dioxide (SO(2)). The analyses were restricted to days with pollutant concentration < 200 microg/m(3) and < 150 microg/m(3) alternately. We used Poisson regression in a GAM model, and combined individual city regression coefficients using fixed and random-effect models. An increase in BS by 50 microg/m(3) was associated with a 2.2% and 3.1% increase in mortality when analysis was restricted to days < 200 microg/m(3) and < 150 microg/m(3), respectively. The corresponding figures were 5.0% and 5.6% for a similar increase in SO(2). These estimates are larger than the ones published previously: by 69% for BS and 55% for SO(2). The increase occurred only in central-eastern European cities. The ratio of western to central-eastern cities for estimates was reduced to 1.3 for BS (previously 4.8) and 2.6 for SO(2) (previously 4.4). We conclude that part of the heterogeneity in the estimates of air pollution effects between western and central-eastern cities reported in previous publications was caused by the statistical approach used and the inclusion of days with pollutant levels above 150 microg/m(3). However, these results must be investigated further

Short-term exposure to traffic-related air pollution and daily mortality in London, UK.

Epidemiological studies have linked daily concentrations of urban air pollution to mortality, but few have investigated specific traffic sources that can inform abatement policies. We assembled a database of >100 daily, measured and modelled pollutant concentrations characterizing air pollution in London between 2011 and 2012. Based on the analyses of temporal patterns and correlations between the metrics, knowledge of local emission sources and reference to the existing literature, we selected, a priori, markers of traffic pollution: oxides of nitrogen (general traffic); elemental and black carbon (EC/BC) (diesel exhaust); carbon monoxide (petrol exhaust); copper (tyre), zinc (brake) and aluminium (mineral dust). Poisson regression accounting for seasonality and meteorology was used to estimate the percentage change in risk of death associated with an interquartile increment of each pollutant. Associations were generally small with confidence intervals that spanned 0% and tended to be negative for cardiovascular mortality and positive for respiratory mortality. The strongest positive associations were for EC and BC adjusted for particle mass and respiratory mortality, 2.66% (95% confidence interval: 0.11, 5.28) and 2.72% (0.09, 5.42) per 0.8 and 1.0 μg/m(3), respectively. These associations were robust to adjustment for other traffic metrics and regional pollutants, suggesting a degree of specificity with respiratory mortality and diesel exhaust containing EC/BC

Long-term exposure to traffic-related air pollution and stroke: a systematic review and meta-analysis

Background Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke. Methods PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies. Results Nineteen studies were included. The meta-analytic relative risks (and 95% confidence intervals) were: 1.03 (0.98-1.09) per 1 μg/m3 EC, 1.09 (0.96-1.23) per 10 μg/m3 PM10, 1.08 (0.89-1.32) per 5 μg/m3 PM2.5, 0.98 (0.92; 1.05) per 10 μg/m3 NO2 and 0.99 (0.94; 1.04) per 20 μg/m3 NOx with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively. Conclusion The available literature provides low to moderate evidence for an association of TRAP with stroke

Challenges to evidence synthesis and identification of data gaps in human biomonitoring

The increasing number of human biomonitoring (HBM) studies undertaken in recent decades has brought to light the need to harmonise procedures along all phases of the study, including sampling, data collection and analytical methods to allow data comparability. The first steps towards harmonisation are the identification and collation of HBM methodological information of existing studies and data gaps. Systematic literature reviews and meta-analyses have been traditionally put at the top of the hierarchy of evidence, being increasingly applied to map available evidence on health risks linked to exposure to chemicals. However, these methods mainly capture peer-reviewed articles, failing to comprehensively identify other important, unpublished sources of information that are pivotal to gather a complete map of the produced evidence in the area of HBM. Within the framework of the European Human Biomonitoring Initiative (HBM4EU) initiative—a project that joins 30 countries, 29 from Europe plus Israel, the European Environment Agency and the European Commission—a comprehensive work of data triangulation has been made to identify existing HBM studies and data gaps across countries within the consortium. The use of documentary analysis together with an up-to-date platform to fulfil this need and its implications for research and practice are discussed

Measurement error correction methods for the effects of ambient air pollution on mortality and morbidity using the UK Biobank cohort: the MELONS study

Epidemiological cohort studies associating long-term exposure to ambient air pollution with health outcomes most often do not account for individually assigned exposure measurement error. Here, we implemented Cox proportional hazards models to explore the relationships between NO2, PM2.5 and ozone exposures with the incidence of natural-cause mortality and several morbidity outcomes in 61,797 London-dwelling respondents of the UK Biobank cohort. Data from an existing personal monitoring campaign was used as an external validation dataset to estimate measurement error structures between “true” personal exposure and several surrogate (measured and modelled) estimates of assigned exposure, allowing for the application of two health effect estimate correction methodologies: regression calibration (RCAL) and simulation extrapolation (SIMEX). Uncorrected hazard ratios (HRs) suggested an increase in the risk of natural-cause mortality for modelled NO2 estimates (HR: 1.028 [0.983, 1.074] per IQR increment of 14.54 μg/m3) and no statistically significant association was observed for PM2.5 surrogate exposure measures. Measurement error corrected HRs were generally larger in magnitude, although exhibited wider confidence intervals than uncorrected effect estimates. Chronic obstructive pulmonary disease (COPD) was associated with increased exposure to modelled NO2 (1.087 [1.022, 1.155]). Both RCAL and SIMEX correction resulted in increased HRs (1.254 [1.061, 1.482] and 1.192 [1.093, 1.301], respectively). SIMEX correction of modelled PM2.5 (IQR: 1.72 μg/m3) associations with COPD increased the HR (1.079 [1.001, 1.164]) in comparison to uncorrected (1.042 [0.988, 1.099]). These findings suggest that health effect estimates not corrected for exposure measurement error may lead to underestimation in the magnitude of effects