Department and worker characteristics of the analyzed population.

<p>Data are median (IQR) and n (%) unless otherwise indicated.</p><p>TTH = tension-type headache, M = migraine, MP = myogenous neck/shoulder pain.</p

Effects of the intervention (change from baseline in the number of days with pain or drug consumption at month 7) on the study outcomes.

<p>*Adjusted by age, sex, neck/shoulder pain (when analyzing headache and analgesic drug consumption), headache (when analyzing neck/shoulder pain and analgesic drug consumption), education level, and job activity.</p

Mean differences (days/month) between groups in the changes from baseline (month 7 vs. month 1) of the frequency of headache (panel A), neck/shoulder pain (panel B), headache and/or neck/shoulder pain (panel C), by subgroups.

<p>Mean differences (days/month) between groups in the changes from baseline (month 7 vs. month 1) of the frequency of headache (panel A), neck/shoulder pain (panel B), headache and/or neck/shoulder pain (panel C), by subgroups.</p

Comparison of the proportion of subjects with (“improved”^*) or without (“not improved” ^‡) reduction in pain frequency or drug consumption of ≥50% at the end of follow-up (responder rates), between IG and control group.

<p>*“Improved”: subjects with a baseline frequency of ≥4 days/month with pain (or drug consumption) that had a reduction in pain frequency or drug consumption of ≥50% at the end of follow-up.</p>‡<p>“Not improved”: includes subjects with ≥4 days/month with pain (or drug consumption) at the baseline with less than 50% of reduction in pain frequency or drug consumption at the end of follow-up, and those subjects that had a baseline frequency of less than 4 days with pain/drug consumption independently from their results.</p>§<p>adjusted by age, sex, neck/shoulder pain (when analyzing headache and analgesic drug consumption), headache (when analyzing neck/shoulder pain and analgesic drug consumption), education level, job activity and baseline value of each subject.</p

Flow chart.

<p>Flow chart.</p

Results (responder rates) of the sensitivity analyses performed on the whole randomized population according to two different <i>scenarios</i>.

<p>*Scenario 1: the probability of improvement observed in the control group was assigned to the workers not completing the baseline and/or the follow-up diary in both the IG and the control group.</p><p>**Scenario 2 (<i>worst scenario</i>): the probability of improvement observed in the IG was assigned to the workers not completing the baseline and/or the follow-up diary in the control group, whereas the probability of improvement observed in the control group was assigned to the workers not completing the baseline and/or the follow-up diary in the IG.</p

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