Results from the Cox’s regression model: HRs of the association between education and mortality of residents in Rome stratified by age group at inclusion, gender, and birthplace, 2001–2012.

<p>Results from the Cox’s regression model: HRs of the association between education and mortality of residents in Rome stratified by age group at inclusion, gender, and birthplace, 2001–2012.</p

Education and Mortality in the Rome Longitudinal Study

<div><p>Background</p><p>A large body of evidence supports an inverse association between socioeconomic status and mortality. We analysed data from a large cohort of residents in Rome followed-up between 2001 and 2012 to assess the relationship between individual education and mortality. We distinguished five causes of death and investigated the role of age, gender, and birthplace.</p><p>Methods</p><p>From the Municipal Register we enrolled residents of Rome on October 21^st 2001 and collected information on educational level attained from the 2001 Census. We selected Italian citizens aged 30–74 years and followed-up their vital status until 2012 (n = 1,283,767), identifying the cause of death from the Regional Mortality Registry. We calculated hazard ratios (HRs) for overall and cause-specific mortality in relation to education. We used age, gender, and birthplace for adjusted or stratified analyses. We used the inverse probability weighting approach to account for right censoring due to emigration.</p><p>Results</p><p>We observed an inverse association between education (none vs. post-secondary+ level) and overall mortality (HRs(95%CIs): 2.1(1.98–2.17), males; 1.5(1.46–1.59), females) varying according to demographic characteristics. Cause-specific analysis also indicated an inverse association with education, in particular for respiratory, digestive or circulatory system related-mortality, and the youngest people seemed to be more vulnerable to low education.</p><p>Conclusion</p><p>Our results confirm the inverse association between education and overall or cause-specific mortality and show differentials particularly marked among young people compared to the elderly. The findings provide further evidence from the Mediterranean area, and may contribute to national and cross-country comparisons in Europe to understand the mechanisms generating socioeconomic differentials especially during the current recession period.</p></div

Results from the Cox’s regression model: HRs of the association between education and cause specific mortality of residents in Rome, stratified by age group at inclusion and adjusted for birthplace.

<p>§ Estimate based on 49 cases.</p><p>Males, 2001–2012.</p

Distribution of baseline characteristics, and crude overall and cause-specific mortality rates of residents in Rome, 2001–2012.

<p>Distribution of baseline characteristics, and crude overall and cause-specific mortality rates of residents in Rome, 2001–2012.</p

Results from the Cox’s regression model: HRs of the association between education and cause specific mortality of residents in Rome, stratified by age group at inclusion and adjusted for birthplace.

<p># Estimate based on 36 cases.</p><p>¶ Estimate based on 71 cases.</p><p>Females, 2001–2012.</p

Additional file 1 of Equity in the recovery of elective and oncological surgery volumes after the COVID-19 lockdown: a multicentre cohort study in Italy

Supplementary Material

Development of Land Use Regression Models for Elemental, Organic Carbon, PAH, and Hopanes/Steranes in 10 ESCAPE/TRANSPHORM European Study Areas

Land use regression (LUR) models have been used to model concentrations of mainly traffic-related air pollutants (nitrogen oxides (NO_<i>x</i>), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon (EC/OC) content of PM_2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16–40 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (<i>R</i>²) was found for EC – 84%. The median <i>R</i>² was 51% for OC, 67% for benzo­[a]­pyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM_2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies

Evaluation of Land Use Regression Models for NO₂ and Particulate Matter in 20 European Study Areas: The ESCAPE Project

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO₂₎ and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO₂. LUR models have been developed for NO₂, PM_2.5 absorbance, and copper (Cu) in PM₁₀ based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and “hold-out evaluation (HEV)” using the correlation of predicted NO₂ or PM concentrations with measured NO₂ concentrations at the 20 additional NO₂ sites in each area. For NO₂, PM_2.5 absorbance and PM₁₀ Cu, the median LOOCV <i>R</i>²s were 0.83, 0.81, and 0.76 whereas the median HEV <i>R</i>² were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV <i>R</i>² and HEV <i>R</i>² for PM_2.5 absorbance and PM₁₀ Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV <i>R</i>²s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites

Development of Land Use Regression Models for PM_2.5, PM_2.5 Absorbance, PM₁₀ and PM_coarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM_2.5, PM_2.5 absorbance, PM₁₀, and PM_coarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (<i>R</i>²) was 71% for PM_2.5 (range across study areas 35–94%). Model <i>R</i>² was higher for PM_2.5 absorbance (median 89%, range 56–97%) and lower for PM_coarse (median 68%, range 32– 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower <i>R</i>² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation <i>R</i>² results were on average 8–11% lower than model <i>R</i>². Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE

Long-term exposure to air pollution and liver cancer incidence in six European cohorts

Particulate matter air pollution and diesel engine exhaust have been classified as carcinogenic for lung cancer, yet few studies have explored associations with liver cancer. We used six European adult cohorts which were recruited between 1985 and 2005, pooled within the “Effects of low-level air pollution: A study in Europe” (ELAPSE) project, and followed for the incidence of liver cancer until 2011 to 2015. The annual average exposure to nitrogen dioxide (NO2), particulate matter with diameter <2.5 μm (PM2.5), black carbon (BC), warm-season ozone (O3), and eight elemental components of PM2.5 (copper, iron, zinc, sulfur, nickel, vanadium, silicon, and potassium) were estimated by European-wide hybrid land-use regression models at participants' residential addresses. We analyzed the association between air pollution and liver cancer incidence by Cox proportional hazards models adjusting for potential confounders. Of 330 064 cancer-free adults at baseline, 512 developed liver cancer during a mean follow-up of 18.1 years. We observed positive linear associations between NO2 (hazard ratio, 95% confidence interval: 1.17, 1.02-1.35 per 10 μg/m3), PM2.5 (1.12, 0.92-1.36 per 5 μg/m3), and BC (1.15, 1.00-1.33 per 0.5 10−5/m) and liver cancer incidence. Associations with NO2 and BC persisted in two-pollutant models with PM2.5. Most components of PM2.5 were associated with the risk of liver cancer, with the strongest associations for sulfur and vanadium, which were robust to adjustment for PM2.5 or NO2. Our study suggests that ambient air pollution may increase the risk of liver cancer, even at concentrations below current EU standards