To assess the annual consultation prevalence and new onset consultation rate for doctor-diagnosed shoulder pain conditions.

The aim of this study was to investigate the risk of developing preeclampsia (PE) associated with gestational exposure to ambient air pollutants in southern Sweden, a low-exposure area. We used a cohort of 43,688 singleton pregnancies and monthly mean exposure levels of black carbon (BC), local and total particulate matter (PM_2.5 and PM₁₀), and NO_X at the maternal residential address estimated by Gaussian dispersion modeling from 2000 to 2009. Analyses were conducted using binary logistic regression. A subtype analysis for small-for-gestational age (SGA) was performed. All analyses were adjusted for obstetrical risk factors and socioeconomic predictors. There were 1286 (2.9%) PE cases in the analysis. An adjusted odds ratio (AOR) of 1.35 with a 95% confidence interval (CI) of 1.11–1.63 was found when comparing the lowest quartile of BC exposure to the highest quartile in the third trimester The AOR for PE associated with each 5 µg/m³ increase in locally emitted PM_2.5 was 2.74 (95% CI: 1.68, 4.47) in the entire pregnancy. Similar patterns were observed for each 5 µg/m³ increment in locally emitted PM₁₀. In pregnancies complicated by PE with SGA, the corresponding AOR for linear increases in BC was 3.48 (95% CI: 1.67, 7.27). In this low-level setting, maternal exposure to ambient air pollution during gestation was associated with the risk of developing PE. The associations seemed more pronounced in pregnancies with SGA complications, a finding that should be investigated further.

Health impact assessments of differential air pollution rely on epidemiologically established relationships between concentration levels where people are exposed and adverse health outcomes. To assess air pollution concentrations, land use regression is commonly used. However, an alternative tool is dispersion modelling, where a detailed inventory of pollution sources together with meteorological data drives calculations of compound dispersion. With this, both spatial and temporal variation can be assessed. In this study, we evaluated results of a Gaussian dispersion model applied to an emissions inventory for Scania, the southernmost county in Sweden. The dispersion considered was particulate matter of aerodynamic diameter < 10 µm (PM₁₀), particulate matter of aerodynamic diameter < 2.5 µm (PM_2.5) and black carbon (BC) during an 11-year period (2000–2011). Mean concentrations and 95th percentiles expressed in µg/m³ ranged from 10.1 to 12.6 and 16.6 to 20.7 for PM_2.5 and from 14.0 to 18.8 and 22.6 to 27.0 for PM₁₀, respectively. Seven monitoring stations were used for evaluation. Correlations (R²) ranged from 0.44 to 0.86 for PM_2.5 (mean bias from − 9.0 to 0.1 µg/m³) and from 0.46 to 0.83 for PM₁₀ (mean bias − 6.1 to 3.5 µg/m³). An evaluated database of PM and BC concentrations for Scania is now available for future exposure assessment projects. Calculations were based on a well-known dispersion model with detailed emission data as input. The evaluation showed correlation coefficients for PM in line with previous literature. The data on PM₁₀, PM_2.5 and BC concentrations will, therefore, be used in subsequent studies, epidemiological as well as health impact assessments.

A health impact assessment (HIA) is an important tool for making informed decisions regarding the design and evaluation of environmental interventions. In this study, we performed a quantitative HIA for the population of Scania (1,247,993), the southernmost county in Sweden, in 2016. The impact of annual mean concentrations of particulate matter with an aerodynamic diameter <2.5 µm (PM_2.5), modeled at their home residences for the year 2011, on mortality, asthma, dementia, autism spectrum disorders, preeclampsia and low birth weight (LBW) was explored. Concentration–response (C-R) functions were taken from epidemiological studies reporting meta-analyses when available, and otherwise from single epidemiological studies. The average level of PM_2.5 experienced by the study population was 11.88 µg/m³. The PM_2.5 exposure was estimated to cause 9–11% of cases of LBW and 6% of deaths from natural causes. Locally produced PM_2.5 alone contributed to 2–9% of the cases of diseases and disorders investigated. Reducing concentrations to a maximum of 10 µg/m³ would, according to our estimations, reduce mortality by 3% and reduce cases of LBW by 2%. Further analyses of separate emission sources’ distinct effects were also presented. Reduction of air pollution levels in the study area would, as expected, have a substantial effect on both mortality and adverse health outcomes. Reductions should be aimed for by local authorities and on national and even international levels.

While prenatal exposure to ambient air pollution has been shown to be associated with reduced birth weight, there is substantial heterogeneity across studies, and few epidemiological studies have utilized source-specific exposure data. The aim of the present study was, therefore, to investigate the associations between local, source-specific exposure to fine particulate matter (PM_2.5) during pregnancy and birth weight. An administrative cohort comprising 40,245 singleton births from 2000 to 2009 in Scania, Sweden, was combined with data on relevant covariates. Investigated sources of PM_2.5 included all local sources together as well as tailpipe exhaust, vehicle wear-and-tear, and small-scale residential heating separately. The relationships between these exposures, represented as interquartile range (IQR) increases, and birth weight (continuous) and low birth weight (LBW; <2500 g) were analyzed in crude and adjusted models. Each local PM_2.5 source investigated was associated with reduced birth weight; average decreases varied by source (12–34 g). Only small-scale residential heating was clearly associated with LBW (adjusted odds ratio: 1.14 (95% confidence interval: 1.04–1.26) per IQR increase). These results add to existing evidence that prenatal exposure to ambient air pollution disrupts fetal growth and suggest that PM_2.5 from both vehicles and small-scale residential heating may reduce birth weight.

In this study, the long-term mortality effects associated with exposure to PM₁₀ (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM_2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NO_x (nitrogen oxides) were analyzed in a cohort in southern Sweden during the period from 1991 to 2016. Participants (those residing in Malmö, Sweden, born between 1923 and 1950) were randomly recruited from 1991 to 1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish National Cause of Death Register. The modeled concentrations of PM₁₀, PM_2.5, BC, and NO_x at the cohort participants’ home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM₁₀, PM_2.5, BC, and NO_x and the time until death among the participants during the period from 1991 to 2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1–5 years (lag1–5), and 6–10 years (lag6–10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM₁₀, PM_2.5, BC, and NO_x in the single-pollutant models at lag1–5 were 1.06 (95% CI: 1.02–1.11), 1.01 (95% CI: 0.95–1.08), 1.07 (95% CI: 1.04–1.11), and 1.11 (95% CI: 1.07–1.16) per IQR increase, respectively. The HRs, in most cases, decreased with the inclusion of a larger number of covariates in the models. The most robust associations were shown for NO_x, with statistically significant positive HRs in all the models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.