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The European Union (EU) Ambient Air Quality Directive (AAQD) and Drinking Water Directive (DWD) are aimed at maintaining and improving air quality and ensuring high standards for potable water across the EU. Besides several other indicators, the AAQD and DWD consist of chemical parameters (substances or substance groups) that are regulated within this framework. All the substances are associated with various health outcomes, and many of them are classified as carcinogenic or probably/possibly carcinogenic with causal links. To quantify the health burden of the chemical substances included in the AAQD and DWD, we need information regarding population exposure, current baseline mortality/morbidity rates in the populations, and exposure-response functions (ERFs) or unit risks (URs) from previous epidemiological studies. During this study, we analyzed the availability of ERFs or URs and discussed their applicability in health impact assessments (HIAs). From the HIA perspective, ERFs─in terms of relative risk (RR), standardized mortality ratio (SMR), odds ratio (OR), or UR data─were available for many of the analyzed substances. However, for some substances such as acrylamide, antimony, boron, chlorate and chlorite, copper, microcystin-LR, and selenium, no risk measures could be identified. The aim of this study is to derive ERFs, which will allow HIAs for a larger number of chemicals when exposure data and baseline mortality/morbidity data are available. Currently, HIAs have largely focused only on main ambient pollutants such as particulate matter (PM10), fine particulate matter (PM2.5), nitrogen oxides (NOX), and ozone (O3). In contrast, health risks related to exposure to chemicals are much more diverse, and the health burdens should be quantified to a much greater extent.

Background:The impact of air pollution and road traffic noise on depression remains inconclusive. This study examined long-term residential exposure to air pollutants and road traffic noise in relation to dispensed antidepressant medications.

Methods:A cohort study was conducted using the Swedish Malmö Diet and Cancer Study, with the outcome defined as a first dispensed antidepressant (ATC: N06A) and/or a first clinical depression diagnosis (ICD-10: F32/F33) during follow-up (2007–2011). Exposure was assessed through modelled annual mean concentrations of particulate matter (PM2.5, PM10), nitrogen oxides (NOx), and road traffic noise (Lden). Cox proportional hazards regression was applied, adjusted for potential confounders including sociodemographic and lifestyle factors.

Results:A total of 18 373 individuals were included, among whom 2224 with the outcome (89 % had dispensed antidepressant, 6 % both clinical diagnosis and antidepressant, and 5 % clinical diagnosis) during follow-up. Increased hazard ratios (HRs) were observed in association with long-term air pollution exposure, but only among women. Increased but statistically non-significant HRs were found for road traffic noise in women, while no associations were found in men. For lag1-5 (1–5 years exposure window prior to the event) in women, HRs per 10-unit increase were: PM2.5, 1.29 (95 % CI, 1.09–1.52); PM10, 1.10 (1.03–1.17); NOx, 1.15 (1.04–1.26); and Lden, 1.06 (0.99–1.13). Similar patterns were found for lag1 and in the two-pollutant models.

Conclusion:Long-term exposure to air pollution was positively associated with dispensed antidepressant medications in women but not in men. For road traffic noise, evidence was less clear, but a potential link was seen in women. Though associations were seen only in women, the findings suggest that air quality improvements and possibly also noise reductions may help reduce health burdens related to depressive symptoms.

Background: Studies on long-term exposure to air pollution at high levels suggest an association with increased risk of metabolic syndrome (MetS). We aimed to study this relationship at very low levels, less well-studied, yet of public health importance.

Methods: The Swedish CArdioPulmonary bioImage Study (SCAPIS) consists of randomly selected subjects aged 50–64 years from six large cities in Sweden (n = 30,154). Participants underwent medical examination and answered a health-related questionnaire at enrolment. MetS was defined using established criteria. Residential exposure to PM2.5 and NO2 was modelled annually for each subject over 10 years before enrolment. Adjusted prevalence ratios (adj PR) were calculated both by quartiles of exposure and per 1 μg/m3 increment in PM2.5 and NO2, with adjustments for age, lifestyle and socioeconomic factors.

Results: Full information on exposure, covariates, and MetS was obtained for 13,997 (90.26 %) females and 12,978 (88.61 %) males, respectively. The 10-years mean exposures of PM2.5 and NO2 before enrolment were 6.57 and 11.47 μg/m3, respectively. The prevalence of MetS was 28.75 % in females and 38.20 % in males, respectively. Quartile analysis of PM2.5 did not reveal significant increased adj PR for MetS, neither in females nor in males. For females the adj PR per 1 μg/m3 was 0.91 (0.89–0.94) and for males 0.95 (0.92–0.97), respectively. Similar, but somewhat weaker, associations were seen for NO2.

Conclusion: Some inverse associations between air pollution and MetS were observed, but these results should be interpreted with caution due to limitations in the cross-sectional design. Results should not be interpreted beyond our studied exposure range.

Trafikverket har beviljat Institutionen för folkhälsa och klinisk medicin, Umeå universitet ett FOI-bidrag för projektet Reviderat underlag om hälsokonsekvenser av luftföroreningar, vilket ska komplettera underlagen om luftföroreningars hälsoeffekter till grund för rekommendationer i rapporten Analysmetod och samhällsekonomiska kalkylvärden för transportsektorn (ASEK-rapporten).

Projektets fyra delmoment syftar till

• att analysera hur morbiditetseffekter av kvävedioxid i sig kan uppskattas med direkta beräkningar
• att studera hur de i ASEK beräknade ohälsokostnaderna för slitageemissioner som PM10 påverkas av senare års resultat om sambanden mellan PM10, slitagepartiklar och mortalitet
• att bedöma vilka effekter av vägtrafikens föroreningar, specifikt kväveoxider och partiklar, på barns hälsa som det finns olika grad av evidens för och hur konsekvenserna kan uppskattas och värderas
• att föreslå en metod att uppskatta kostnader för emissioner genererande exponering för sekundärt bildade partiklar

Background: The association between socioeconomic status (SES) and environmental burden is context-dependent. Those with low SES may be more likely to live near major roads and industries where environmental exposures are high but may also be more likely to live further away from city centers. The aim of this study was to investigate the association between SES and environmental burden in Sweden.

Methods: The Swedish CArdioPulmonary bioImage Study (SCAPIS) cohort recruited participants from six Swedish cities during 2013–2018. Residential environmental exposures (particulate matter <2.5 µm [PM2.5], road traffic noise, and lack of greenspace) and neighborhood-level SES factors (proportions with low-income, low education, unemployment, rental units, foreign born) were assessed from participants’ addresses in 2018. Individual-level SES factors (financial buffer, education, occupation, type of living, foreign born) were obtained from a questionnaire and neighborhood data from Statistics Sweden. Linear regression models were used to analyze the link between environmental exposures and SES factors. Correlations between SES factors were analyzed using Cramér's V and Spearman rank correlations.

Results: The study included 23 320 SCAPIS participants in 1939 neighborhoods. The explanatory power of SES factors differed substantially between factors and cities. A model with all ten SES factors could account for 25–88 %, 36–60 %, and 49–81 % of the spatial variance in PM2.5, noise, and lack of greenspace respectively. Neighborhoods with more low-income earners, rental units and foreign born had more environmental burden. Neighborhoods with more inhabitants with low education and unemployment had less environmental burden. Associations were generally non-linear. Individual-level SES factors were not associated with environmental burdens after adjusting for neighborhood SES factors. Individual and neighborhood SES were weakly correlated.

Conclusion: Neighborhood SES factors accounted for a large proportion of the variance in environmental burdens, whereas individual SES factors did not. The relationship between SES and environmental burden differed greatly between indicators and cities.

Objectives: Socioeconomic status (SES) is in many cases related to air pollution exposure, but less is known about its effects on susceptibility to air pollution. The main aim of this study was to analyse the impact of SES on health effects associated with exposure to fine particles (PM_2.5).

Methods: Firstly, a systematic literature review of studies analysing the impacts of SES on health effects related to air pollution exposure was carried out. Secondly, a meta-analysis was performed by analysing studies on long-term mortality associated with exposure to PM_2.5 divided into different SES groups.

Results: The meta-analysis showed that the relative risk (RR) for all-cause mortality associated with PM_2.5 did not depend on individual education or income. It also revealed that adjustment for individual lifestyle factors (such as smoking, alcohol intake, physical activity, eating behaviours, and body mass index), in addition to adjustment for SES, did not significantly change the RR.

Conclusion: The association between all-cause mortality and PM_2.5 did not depend on education or individual income. Due to the high heterogeneity observed, further studies are required to draw firm conclusions.

The aim of this study was to investigate the relationship between source-specific ambient particulate air pollution concentrations and the incidence of dementia. The study encompassed 70,057 participants from the Västerbotten intervention program cohort in Northern Sweden with a median age of 40 years at baseline. High-resolution dispersion models were employed to estimate source-specific particulate matter (PM) concentrations, such as PM10 and PM2.5 from traffic, exhaust, and biomass (mainly wood) burning, at the residential addresses of each participant. Cox regression models, adjusted for potential confounding factors, were used for the assessment. Over 884,847 person-years of follow-up, 409 incident dementia cases, identified through national registers, were observed. The study population’s average exposure to annual mean total PM10 and PM2.5 lag 1–5 years was 9.50 µg/m3 and 5.61 µg/m3, respectively. Increased risks were identified for PM10-Traffic (35% [95% CI 0–82%]) and PM2.5-Exhaust (33% [95% CI − 2 to 79%]) in the second exposure tertile for lag 1–5 years, although no such risks were observed in the third tertile. Interestingly, a negative association was observed between PM2.5-Wood burning and the risk of dementia. In summary, this register-based study did not conclusively establish a strong association between air pollution exposure and the incidence of dementia. While some evidence indicated elevated risks for PM10-Traffic and PM2.5-Exhaust, and conversely, a negative association for PM2.5-Wood burning, no clear exposure–response relationships were evident.

Background: Many studies reported associations between long-term exposure to environmental factors and mortality; however, little is known on the combined effects of these factors and health. We aimed to evaluate the association between external exposome and all-cause mortality in large administrative and traditional adult cohorts in Europe.

Methods: Data from six administrative cohorts (Catalonia, Greece, Rome, Sweden, Switzerland and the Netherlands, totaling 27,913,545 subjects) and three traditional adult cohorts (CEANS-Sweden, EPIC-NL-the Netherlands, KORA–Germany, totaling 57,653 participants) were included. Multiple exposures were assigned at the residential addresses, and were divided into three a priori defined domains: (1) air pollution [fine particulate matter (PM2.5), nitrogen dioxide (NO₂), black carbon (BC) and warm-season Ozone (warm-O3)]; (2) land/built environment (Normalized Difference Vegetation Index—NDVI, impervious surfaces, and distance to water); (3) air temperature (cold- and warm-season mean and standard deviation). Each domain was synthesized through Principal Component Analysis (PCA), with the aim of explaining at least 80% of its variability. Cox proportional-hazards regression models were applied and the total risk of the external exposome was estimated through the Cumulative Risk Index (CRI). The estimates were adjusted for individual- and area-level covariates.

Results: More than 205 million person-years at risk and more than 3.2 million deaths were analyzed. In single-component models, IQR increases of the first principal component of the air pollution domain were associated with higher mortality [HRs ranging from 1.011 (95% CI: 1.005–1.018) for the Rome cohort to 1.076 (1.071–1.081) for the Swedish cohort]. In contrast, lower levels of the first principal component of the land/built environment domain, pointing to reduced vegetation and higher percentage of impervious surfaces, were associated with higher risks. Finally, the CRI of external exposome increased mortality for almost all cohorts. The associations found in the traditional adult cohorts were generally consistent with the results from the administrative ones, albeit without reaching statistical significance.

Discussion: Various components of the external exposome, analyzed individually or in combination, were associated with increased mortality across European cohorts. This sets the stage for future research on the connections between various exposure patterns and human health, aiding in the planning of healthier cities.

Air pollution's short-term effects on a wide range of health outcomes have been studied extensively, primarily focused on vulnerable groups (e.g., children and the elderly). However, the air pollution effects on the adult working population through sick leave have received little attention. This study aims to 1) estimate the associations between particulate matter ≤2.5 μm³ (PM_2.5) and sick leave episodes and 2) calculate the attributable number of sick leave days and the consequential productivity loss in the City of Stockholm, Sweden. Individual level daily sick leave data was obtained from Statistics Sweden for the years 2011–2019. Daily average concentrations of PM_2.5 were obtained from the main urban background monitoring station in Stockholm. A case-crossover study design was applied to estimate the association between short-term PM_2.5 and onset of sick leave episodes. Conditional logistic regression was used to estimate the relative increase in odds of onset per 10 μg/m³ of PM_2.5, adjusting for temperature, season, and pollen. A human capital method was applied to estimate the PM2.5 attributable productivity loss. In total, 1.5 million (M) individual sick leave occurrences were studied. The measured daily mean PM_2.5 concentration was 4.2 μg/m³ (IQR 3.7 μg/m³). The odds of a sick leave episode was estimated to increase by 8.5% (95% CI: 7.8–9.3) per 10 μg/m³ average exposure 2–4 days before. Sub-group analysis showed that private sector and individuals 15–24 years old had a lower increase in odds of sick leave episodes in relation to PM_2.5 exposure. In Stockholm, 4% of the sick leave episodes were attributable to PM_2.5 exposure, corresponding to €17 M per year in productivity loss. Our study suggests a positive association between PM_2.5 and sick leave episodes in a low exposure area.

Aims: Patient characteristics and treatment setting are potential predictors of premature dropout from lifestyle interventions, but their relative importance is unknown.

Methods: From the quality registry of the unit for behavioral medicine, Umeå University hospital, we identified 2589 patients who had been enrolled in a multimodal lifestyle intervention for cardiometabolic risk reduction between 2006 and 2015. Baseline characteristics predicting dropout before 1-year follow-up were selected by a stepwise logistic regression algorithm.

Results: Better physical health and older age predicted full participation, with odds ratios for premature dropout (ORs) of.44 (95% confidence interval (CI).31-.63), and.47 (95% CI.34-.65) in the highest compared to the lowest quartile, respectively. Odds of premature dropout were also lower among female participants,.71 (95% CI.58-.89). Premature dropout was predicted by higher BMI, snuffing tobacco, and smoking, with ORs of 1.53 (95% CI 1.13-2.08) in the highest compared to the lowest quartile of BMI, 1.37 (95% CI 1.03-1.81) comparing snuff user with non-users and 2.53 (95% CI 1.79-3.61) comparing smokers with non-smokers. Odds ratio for premature dropout among inpatients compared with outpatients was.84 (95% CI.68-1.04).

Conclusion: Higher risk at baseline predicts premature dropout.