A case-control study of lactation and cancer of the breast.

We have examined the relation of lactation, by total duration, with breast cancer risk among pre- and post-menopausal women. In a hospital-based case-control study conducted in Athens (1989-91), involving 820 patients with confirmed breast cancer and 795 orthopaedic patient controls and 753 hospital visitor controls, logistic regression was used to analyse the data controlling for demographic, nutritional and reproductive factors, including parity and age at any birth. Among post-menopausal women, there was no association between breastfeeding and breast cancer risk, but among premenopausal women those who has breastfed for > or = 24 months had an odds ratio of 0.50 (95% confidence interval 0.23-1.41). A reduction of the odds ration was also evident among premenopausal women who had breastfed between 12 and 23 months (odds ratio 0.70; 95% confidence interval 0.34-1.60). In conjunction with several other recent reports these results support the hypothesis that breastfeeding of prolonged duration may reduce the risk of breast cancer among premenopausal women but not among post-menopausal women. The biology underlying this different effect remains unknown, and the practical implication of the finding is a marginal importance

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 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

Relationship between ozone and temperature during the 2003 heat wave in France: consequences for health data analysis

BACKGROUND: PAPRICA is a research program designed to estimate the impact on the health of patients with chronic respiratory insufficiency of a prevention strategy based on notification of ozone pollution. The first year of this study was conducted during the 2003 heat wave, and high temperatures were therefore considered as a confounding factor in the data analysis. The aim of the present study was to assess the relationship between ozone and temperature in order to propose a methodology to distinguish between the effects of ozone and temperature on the impact of a prevention strategy with regard to ozone pollution. METHODS: Multivariate analyses were used to identify associated climate and ozone pollution profiles. This descriptive method is of great value to highlight the complexity of interactions between these parameters. RESULTS: Ozone concentration and temperature were strongly correlated, but the health impact of ozone pollution alone will be evaluated by focusing on situations characterized by ozone concentrations above 110 μg/m(3)/8h (air quality guidelines to protect human health defined by the French legislation) and temperatures lower than 26°C, below the discomfort threshold. CONCLUSION: The precise relationship between ambient ozone concentration and temperature identified during the PAPRICA 2003 study period will be used in analysing the PAPRICA health data