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BACKGROUND: Risk factors for malignant tumours of the central nervous system (CNS) are largely unknown. METHODS: We pooled six European cohorts (N = 302,493) and assessed the association between residential exposure to nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), ozone (O3) and eight elemental components of PM2.5 (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) and malignant intracranial CNS tumours defined according to the International Classification of Diseases ICD-9/ICD-10 codes 192.1/C70.0, 191.0-191.9/C71.0-C71.9, 192.0/C72.2-C72.5. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level. RESULTS: During 5,497,514 person-years of follow-up (average 18.2 years), we observed 623 malignant CNS tumours. The results of the fully adjusted linear analyses showed a hazard ratio (95% confidence interval) of 1.07 (0.95, 1.21) per 10 μg/m³ NO2, 1.17 (0.96, 1.41) per 5 μg/m³ PM2.5, 1.10 (0.97, 1.25) per 0.5 10-5m-1 BC, and 0.99 (0.84, 1.17) per 10 μg/m³ O3. CONCLUSIONS: We observed indications of an association between exposure to NO2, PM2.5, and BC and tumours of the CNS. The PM elements were not consistently associated with CNS tumour incidence.

Childhood obesity is an increasingly severe public health problem, with a prospective impact on health. We propose an exposome approach to identifying actionable risk factors for this condition. Our assumption is that relationships between external exposures and outcomes such as rapid growth, overweight or obesity in children can be better understood through a “meet-in-the-middle” model. This is based on a combination of external and internal exposome-based approaches, i.e. the study of multiple exposures (in our case dietary patterns) and molecular pathways (metabolomics and epigenetics). This may strengthen causal reasoning by identifying intermediate markers that are associated with both exposures and outcomes. Our biomarker-based studies in the STOP consortium suggest (in several ways, including mediation analysis) that Branched-Chain Amino Acids (BCAAs) could be mediators of the effect of dietary risk factors on childhood overweight/obesity. This is consistent with intervention and animal studies showing that higher intake of BCAAs has a positive impact on body composition, glycemia and satiety. Concerning food, of particular concern is the trend of increasing intake of ultra-processed food (UPF), including among children. Several mechanisms have been proposed to explain the impact of UPF on obesity and overweight, including nutrient intake (particularly proteins), changes in appetite or the role of additives. Research from the ALSPAC cohort has shown a relationship between UPF intake and trajectories in childhood adiposity, while UPF was related to lower blood levels of BCAAs. We suggest that an exposome-based approach can help strengthening causal reasoning and support policies. Intake of UPF in children should be restricted to prevent obesity.

Background: Low Emission Zones (LEZ) and Congestion Charging Zones (CCZ) have been implemented in several cities globally. We systematically reviewed the evidence on the impacts of these air pollution and congestion reduction schemes on a range of physical health outcomes. Methods: We searched MEDLINE, Embase, Web of Science, IDEAS, Greenfile, and TRID databases through 4 January 2023. We included studies that evaluated the effect of the implementation of a LEZ or CCZ on air pollution-related health outcomes, including cardiovascular and respiratory diseases, birth outcomes, dementia, lung cancer and diabetes, or road traffic injuries (RTI) using longitudinal study designs. We excluded studies without empirical health data. Two authors independently assessed papers for inclusion. Results were narratively synthesised and visualised using harvest plots. Risk of bias was assessed using the Graphic Appraisal Tool for Epidemiological studies for correlation studies. The protocol was registered with PROSPERO (CRD42022311453). Findings: Of 2,279 studies screened, sixteen were included. Eight studies assessed LEZ in London, Milan, Tokyo, and several German cities, seven assessed the London CCZ and one the Stockholm CCZ. Several LEZ studies identified positive effects on air pollution-related outcomes, with reductions in at least some cardiovascular disease subcategories in five out of six studies investigating this outcome, although results for other health outcomes were less consistent. Six out of seven studies on the London CCZ reported reductions in total and/or car RTIs, although one reported an increase in cyclist/motorcyclist injuries and one reported an increase in serious/fatal injuries. Interpretation: Studies were limited in number and varied in design, intervention, and outcomes. Current evidence suggests LEZ schemes reduce air pollution related health outcomes, with the strongest impact on cardiovascular disease. Evidence on CCZ is mainly limited to London but suggests they reduce overall RTIs.

Background: Myocardial infarction (MI) mortality varies substantially within high-income countries. There is limited guidance on what interventions – primary and secondary prevention and/or improving care pathways and quality – can reduce and equalise MI mortality. Our aim was to understand the contribution of incidence (event rate), pre-hospital deaths and hospital case-fatality, to how MI mortality varies within England. Methods: We used linked data on hospitalisation and deaths from 2015-2018 with geographical identifiers to estimate MI death and event rates, pre-hospital deaths and hospital case fatality for men and women aged 45 years and older in 326 districts in England. Data were analysed in a Bayesian spatial model that accounted for similarities and differences in spatial patterns of fatal and non-fatal MI. Results: The 99th to 1st percentile ratio of age-standardised MI death rate was 2.63 (95% credible interval 2.45-2.83) in women and 2.56 (2.37-2.76) in men across districts, with death rate highest in north of England. The main contributor to this variation was MI event rate, with a 99th to 1st percentile ratio of 2.55 (2.39-2.72) (women) and 2.17 (2.08-2.27) (men) across districts. Pre-hospital mortality was greater than hospital case fatality in every district. Prehospital mortality had a 99th to 1st percentile ratio 1.60 (1.50-1.70) in women and 1.75 (1.66- 1.86) in men across districts and made a greater contribution to case-fatality variation than hospital case fatality which had a 99th to 1st percentile ratio of 1.39 (1.29-1.49) (women) and 1.49 (1.39-1.60) (men). The contribution of case fatality to variation in deaths across districts was largest in middle ages. Pre-hospital mortality was slightly higher in men than women in most districts and age groups, whereas hospital case fatality was higher in women in virtually all districts at ages up to and including 65-74 years; after this age, it became similar between the sexes. 3 Interpretation: Most of the variation in MI death rate is due to variation in MI event rate, with a smaller role for case fatality. The majority of variation in case fatality occurs before rather than after hospital admission. Reducing subnational variations in MI mortality requires interventions that reduce event rate and pre-hospital deaths.

Background: There is limited data with high spatial granularity on how mortality and longevity have changed in English communities. We estimated trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6,791 English middle-layer super output areas (MSOAs). Methods: We used de-identified data for all deaths in England from 2002 to 2019 with information on age, sex and MSOA of residence, and population counts by age, sex and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA. Results: In 2002-2006 and 2006-2010, the vast majority of MSOAs experienced a life expectancy increase for both sexes. In 2010-2014, female life expectancy decreased in 351 (5%) of MSOAs. By 2014-2019, the number of MSOAs with declining life expectancy was 1,270 (19%) for women and 784 (12%) for men. The life expectancy increase from 2002 to 2019 was smaller where life expectancy had been lower in 2002, mostly northern urban MSOAs, and larger where life expectancy had been higher in 2002, mostly MSOAs in and around London. As a result of these trends, the gap between the 1st and 99th percentiles of MSOA life expectancy for women increased from 10.7 (95% credible interval 10.4-10.9) in 2002 to reach 14.2 (13.9-14.5) years in 2019, and from 11.5 (11.3-11.7) years to 13.6 (13.4-13.9) years for men. Interpretation: In many English communities, life expectancy declined in the years prior to the Covid-19 pandemic. To ensure that this trend does not continue there is a need for pro-equity economic and social policies, and greater investment on public health and healthcare.

Background: We evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence). Methods: We applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects. Results: Effect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates’ standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter. Conclusions: Our study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data. © 2021 The Authors

Background: Long-term exposure to ambient air pollution has been linked to childhood-onset asthma, although evidence is still insufficient. Within the multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), we examined the associations of long-term exposures to particulate matter with a diameter

BACKGROUND: Inconsistent associations between long-term exposure to particles with an aerodynamic diameter ≤2:5 lm [fine particulate matter (PM2:5)] components and mortality have been reported, partly related to challenges in exposure assessment. OBJECTIVES: We investigated the associations between long-term exposure to PM2:5 elemental components and mortality in a large pooled European cohort; to compare health effects of PM2:5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms. METHODS: We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985–2005). Residential exposure to 2010 annual average concentration of eight PM2:5 components [copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)] was estimated with Europe-wide SLR and RF models at a 100 × 100 m scale. We applied Cox proportional hazards models to investigate the associations between components and natural and cause-specific mortality. In addition, two-pollutant analyses were conducted by adjusting each component for PM2:5 mass and nitrogen dioxide (NO2) separately. RESULTS: We observed 46,640 natural-cause deaths with 6,317,235 person-years and an average follow-up of 19.5 y. All SLR-modeled components were statistically significantly associated with natural-cause mortality in single-pollutant models with hazard ratios (HRs) from 1.05 to 1.27. Similar HRs were observed for RF-modeled Cu, Fe, K, S, V, and Zn with wider confidence intervals (CIs). HRs for SLR-modeled Ni, S, Si, V, and Zn remained above unity and (almost) significant after adjustment for both PM2:5 and NO2. HRs only remained (almost) significant for RF-modeled K and V in two-pollutant models. The HRs for V were 1.03 (95% CI: 1.02, 1.05) and 1.06 (95% CI: 1.02, 1.10) for SLR-and RF-modeled exposures, respectively, per 2 ng=m3, adjusting for PM2:5 mass. Associations with cause-specific mortality were less consistent in two-pollutant models. CONCLUSION: Long-term exposure to V in PM2:5 was most consistently associated with increased mortality. Associations for the other components were weaker for exposure modeled with RF than SLR in two-pollutant models. https://doi.org/10.1289/EHP8368. © 2021, Public Health Services, US Dept of Health and Human Services. All rights reserved.

BACKGROUND: Long-term exposure to outdoor air pollution increases the risk of cardiovascular disease, but evidence is unclear on the health effects of exposure to pollutant concentrations lower than current EU and US standards and WHO guideline limits. Within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), we investigated the associations of long-term exposures to fine particulate matter (PM2·5), nitrogen dioxide (NO2), black carbon, and warm-season ozone (O3) with the incidence of stroke and acute coronary heart disease. METHODS: We did a pooled analysis of individual data from six population-based cohort studies within ELAPSE, from Sweden, Denmark, the Netherlands, and Germany (recruited 1992-2004), and harmonised individual and area-level variables between cohorts. Participants (all adults) were followed up until migration from the study area, death, or incident stroke or coronary heart disease, or end of follow-up (2011-15). Mean 2010 air pollution concentrations from centrally developed European-wide land use regression models were assigned to participants' baseline residential addresses. We used Cox proportional hazards models with increasing levels of covariate adjustment to investigate the association of air pollution exposure with incidence of stroke and coronary heart disease. We assessed the shape of the concentration-response function and did subset analyses of participants living at pollutant concentrations lower than predefined values. FINDINGS: From the pooled ELAPSE cohorts, data on 137 148 participants were analysed in our fully adjusted model. During a median follow-up of 17·2 years (IQR 13·8-19·5), we observed 6950 incident events of stroke and 10 071 incident events of coronary heart disease. Incidence of stroke was associated with PM2·5 (hazard ratio 1·10 [95% CI 1·01-1·21] per 5 μg/m3 increase), NO2 (1·08 [1·04-1·12] per 10 μg/m3 increase), and black carbon (1·06 [1·02-1·10] per 0·5 10-5/m increase), whereas coronary heart disease incidence was only associated with NO2 (1·04 [1·01-1·07]). Warm-season O3 was not associated with an increase in either outcome. Concentration-response curves indicated no evidence of a threshold below which air pollutant concentrations are not harmful for cardiovascular health. Effect estimates for PM2·5 and NO2 remained elevated even when restricting analyses to participants exposed to pollutant concentrations lower than the EU limit values of 25 μg/m3 for PM2·5 and 40 μg/m3 for NO2. INTERPRETATION: Long-term air pollution exposure was associated with incidence of stroke and coronary heart disease, even at pollutant concentrations lower than current limit values. FUNDING: Health Effects Institute. Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Background: An association between long-term exposure to fine particulate matter (PM2.5) and lung cancer has been established in previous studies. PM2.5 is a complex mixture of chemical components from various sources and little is known about whether certain components contribute specifically to the associated lung cancer risk. The present study builds on recent findings from the “Effects of Low-level Air Pollution: A Study in Europe” (ELAPSE) collaboration and addresses the potential association between specific elemental components of PM2.5 and lung cancer incidence. Methods: We pooled seven cohorts from across Europe and assigned exposure estimates for eight components of PM2.5 representing non-tail pipe emissions (copper (Cu), iron (Fe), and zinc (Zn)), long-range transport (sulfur (S)), oil burning/industry emissions (nickel (Ni), vanadium (V)), crustal material (silicon (Si)), and biomass burning (potassium (K)) to cohort participants’ baseline residential address based on 100 m by 100 m grids from newly developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). Results: The pooled study population comprised 306,550 individuals with 3916 incident lung cancer events during 5,541,672 person-years of follow-up. We observed a positive association between exposure to all eight components and lung cancer incidence, with adjusted HRs of 1.10 (95% CI 1.05, 1.16) per 50 ng/m3 PM2.5 K, 1.09 (95% CI 1.02, 1.15) per 1 ng/m3 PM2.5 Ni, 1.22 (95% CI 1.11, 1.35) per 200 ng/m3 PM2.5 S, and 1.07 (95% CI 1.02, 1.12) per 200 ng/m3 PM2.5 V. Effect estimates were largely unaffected by adjustment for nitrogen dioxide (NO2). After adjustment for PM2.5 mass, effect estimates of K, Ni, S, and V were slightly attenuated, whereas effect estimates of Cu, Si, Fe, and Zn became null or negative. Conclusions: Our results point towards an increased risk of lung cancer in connection with sources of combustion particles from oil and biomass burning and secondary inorganic aerosols rather than non-exhaust traffic emissions. Specific limit values or guidelines targeting these specific PM2.5 components may prove helpful in future lung cancer prevention strategies. © 2020 Elsevier Inc.

Background: Air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD), but evidence is sparse and inconsistent. Objectives: We examined the association between long-term exposure to low-level air pollution and COPD incidence. Methods: Within the ‘Effects of Low-Level Air Pollution: A Study in Europe’ (ELAPSE) study, we pooled data from three cohorts, from Denmark and Sweden, with information on COPD hospital discharge diagnoses. Hybrid land use regression models were used to estimate annual mean concentrations of particulate matter with a diameter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in 2010 at participants’ baseline residential addresses, which were analysed in relation to COPD incidence using Cox proportional hazards models. Results: Of 98,058 participants, 4,928 developed COPD during 16.6 years mean follow-up. The adjusted hazard ratios (HRs) and 95% confidence intervals for associations with COPD incidence were 1.17 (1.06, 1.29) per 5 µg/m3 for PM2.5, 1.11 (1.06, 1.16) per 10 µg/m3 for NO2, and 1.11 (1.06, 1.15) per 0.5 10−5m−1 for BC. Associations persisted in subset participants with PM2.5 or NO2 levels below current EU and US limit values and WHO guidelines, with no evidence for a threshold. HRs for NO2 and BC remained unchanged in two-pollutant models with PM2.5, whereas the HR for PM2.5 was attenuated to unity with NO2 or BC. Conclusions: Long-term exposure to low-level air pollution is associated with the development of COPD, even below current EU and US limit values and possibly WHO guidelines. Traffic-related pollutants NO2 and BC may be the most relevant. © 2020

Data Resource Basics: The Avon Longitudinal Study of Parents and Children (ALSPAC) have built a dataset of physical and social environmental data which – when linked with the wider ALSPAC resource - can provide a platform for environmental epidemiology. ALSPAC is a multi-generational cohort, comprising 15 247 enrolled index participants and their family members from an eligible sample defined as all pregnant women living in and around the city of Bristol (South West UK) and due to deliver between April 1991 and December 1992.Data Collected: ALSPAC have collected self-reported data about the home (e.g. damp, new furnishings and carpets, type of cooking fuel used), modes of transport; neighbourhood perceptions, and parental occupational history. ALSPAC fieldworkers have collected air pollution data from sub-groups of participants using sensors and assayed participant biosamples (e.g. venous blood caboxyhaemoglobin and methaemoglobin). Biological measures of lead, calcium and mercury have been measured from antenatal blood sample, cord blood and in child blood (at age 30m). Child activity has been measured at various time points using sensors. Participant residential and school attended address records have been geocoded to property and postcode level. The ALGAE software has been developed to assign spatial data to participants and across periods of participant’s life (e.g. pregnancy trimesters) (Figure 1). Meaning that study collected data have been (or have the potential to be) augmented by any specially-indexed routine records (e.g. PM2.5, PM10, NO2 air pollution; green space; neighbourhood socio-demographic factors; distance to services or facilities). Examples of linkage to date include: calculating downwind proximity to powerlines; calculating the density of fast-food outlets in participants’ neighbourhoods; modelling PM2.5 and PM10 exposures; and, mapping participants to radon air pollution estimates.Data Resource Use: The physical and social environment records have been used to investigate associations between: 1) domestic, workplace and outdoor exposures and health phenotypic and ‘omic outcomes, using both directly recorded exposure information and information derived from assayed biosamples; 2) residential location and migration on health and social outcomes; 3) movement between places on health and behavioural outcomes. These records have also been used to inform wider research: informing purposeful sampling strategies and to test for confounding relating to social position. Furthermore, ALSPAC has been a test-bed for developing new data collection and analysis methodologies.Reasons to be cautious: ALSPAC is a regionally sampled cohort, the resulting participant clustering provides advantages in terms of testing locality-based effects and enabling multi-level modelling but is not well suited to studying issues relating to national variation. The quality and completeness of the ALSPAC address database (and thus geocoded resource) is impacted by study attrition: the rates of which are differentiated by health and social status.Collaboration and data access: Individual level-data are available upon request from the ALSPAC Executive (www.bristol.ac.uk/alspac). To protect participant confidentiality, the use of some environmental data are subject to disclosure controls or are only handled by the ALSPAC data managers (who facilitate the investigation).

Urban areas in Europe are facing a range of environmental public health challenges, such as air pollution, traffic noise and road injuries. The identification and quantification of the public health risks associated with exposure to environmental conditions is important for prioritising policies and interventions that aim to diminish the risks and improve the health of the population. With this purpose in mind, the EURO-HEALTHY project used a consistent approach to assess the impact of key environmental risk factors and urban environmental determinants on public health in European metropolitan areas. A number of environmental public health indicators, which are closely tied to the physical and built environment, were identified through stakeholder consultation; data were collected from six European metropolitan areas (Athens, Barcelona, Lisbon, London, Stockholm and Turin) covering the period 2000–2014, and a health impact assessment framework enabled the quantification of health effects (attributable deaths) associated with these indicators. The key environmental public health indicators were related to air pollution and certain urban environmental conditions (urban green spaces, road safety). The air pollution was generally the highest environmental public health risk; the associated number of deaths in Athens, Barcelona and London ranged between 800 and 2300 attributable deaths per year. The number of victims of road traffic accidents and the associated deaths were lowest in the most recent year compared with previous years. We also examined the positive impacts on health associated with urban green spaces by calculating reduced mortality impacts for populations residing in areas with greater green space coverage; results in Athens showed reductions of all-cause mortality of 26 per 100,000 inhabitants for populations with benefits of local greenspace. Based on our analysis, we discuss recommendations of potential interventions that could be implemented to reduce the environmental public health risks in the European metropolitan areas covered by this study. © 2018 Elsevier B.V.

Background: Some studies have reported associations between municipal waste incinerator (MWI) exposures and adverse birth outcomes but there are few studies of modern MWIs operating to current European Union (EU) Industrial Emissions Directive standards. Methods: Associations between modelled ground-level particulate matter ≤10 μm in diameter (PM10) from MWI emissions (as a proxy for MWI emissions) within 10 km of each MWI, and selected birth and infant mortality outcomes were examined for all 22 MWIs operating in Great Britain 2003–10. We also investigated associations with proximity of residence to a MWI. Outcomes used were term birth weight, small for gestational age (SGA) at term, stillbirth, neonatal, post-neonatal and infant mortality, multiple births, sex ratio and preterm delivery sourced from national registration data from the Office for National Statistics. Analyses were adjusted for relevant confounders including year of birth, sex, season of birth, maternal age, deprivation, ethnicity and area characteristics and random effect terms were included in the models to allow for differences in baseline rates between areas and in incinerator feedstock. Results: Analyses included 1,025,064 births and 18,694 infant deaths. There was no excess risk in relation to any of the outcomes investigated during pregnancy or early life of either mean modelled MWI PM10 or proximity to an MWI. Conclusions: We found no evidence that exposure to PM10 from, or living near to, an MWI operating to current EU standards was associated with harm for any of the outcomes investigated. Results should be generalisable to other MWIs operating to similar standards. Keywords: Environment, Birth weight, Stillbirth, Infant mortality, Municipal Waste Incinerator, Epidemiology

We established air pollution modelling to study particle (PM10) exposures during pregnancy and infancy (1990–1993) through childhood and adolescence up to age ~15 years (1991–2008) for the Avon Longitudinal Study of Parents And Children (ALSPAC) birth cohort. For pregnancy trimesters and infancy (birth to 6 months; 7 to 12 months) we used local (ADMS-Urban) and regional/long-range (NAME-III) air pollution models, with a model constant for local, non-anthropogenic sources. For longer exposure periods (annually and the average of birth to age ~8 and to age ~15 years to coincide with relevant follow-up clinics) we assessed spatial contrasts in local sources of PM10 with a yearly-varying concentration for all background sources. We modelled PM10 (μg/m3) for 36,986 address locations over 19 years and then accounted for changes in address in calculating exposures for different periods: trimesters/infancy (n = 11,929); each year of life to age ~15 (n = 10,383). Intra-subject exposure contrasts were largest between pregnancy trimesters (5th to 95th centile: 24.4–37.3 μg/m3) and mostly related to temporal variability in regional/long-range PM10. PM10 exposures fell on average by 11.6 μg/m3 from first year of life (mean concentration = 31.2 μg/m3) to age ~15 (mean = 19.6 μg/m3), and 5.4 μg/m3 between follow-up clinics (age ~8 to age ~15). Spatial contrasts in 8-year average PM10 exposures (5th to 95th centile) were relatively low: 25.4–30.0 μg/m3 to age ~8 years and 20.7–23.9 μg/m3 from age ~8 to age ~15 years. The contribution of local sources to total PM10 was 18.5%–19.5% during pregnancy and infancy, and 14.4%–17.0% for periods leading up to follow-up clinics. Main roads within the study area contributed on average ~3.0% to total PM10 exposures in all periods; 9.5% of address locations were within 50 m of a main road. Exposure estimates will be used in a number of planned epidemiological studies.

We established air pollution modelling to study particle (PM10) exposures during pregnancy and infancy (1990–1993) through childhood and adolescence up to age ~15 years (1991–2008) for the Avon Longitudinal Study of Parents And Children (ALSPAC) birth cohort. For pregnancy trimesters and infancy (birth to 6 months; 7 to 12 months) we used local (ADMS-Urban) and regional/long-range (NAME-III) air pollution models, with a model constant for local, non-anthropogenic sources. For longer exposure periods (annually and the average of birth to age ~8 and to age ~15 years to coincide with relevant follow-up clinics) we assessed spatial contrasts in local sources of PM10 with a yearly-varying concentration for all background sources. We modelled PM10 (μg/m3) for 36,986 address locations over 19 years and then accounted for changes in address in calculating exposures for different periods: trimesters/infancy (n = 11,929); each year of life to age ~15 (n = 10,383). Intra-subject exposure contrasts were largest between pregnancy trimesters (5th to 95th centile: 24.4–37.3 μg/m3) and mostly related to temporal variability in regional/long-range PM10. PM10 exposures fell on average by 11.6 μg/m3 from first year of life (mean concentration = 31.2 μg/m3) to age ~15 (mean = 19.6 μg/m3), and 5.4 μg/m3 between follow-up clinics (age ~8 to age ~15). Spatial contrasts in 8-year average PM10 exposures (5th to 95th centile) were relatively low: 25.4–30.0 μg/m3 to age ~8 years and 20.7–23.9 μg/m3 from age ~8 to age ~15 years. The contribution of local sources to total PM10 was 18.5%–19.5% during pregnancy and infancy, and 14.4%–17.0% for periods leading up to follow-up clinics. Main roads within the study area contributed on average ~3.0% to total PM10 exposures in all periods; 9.5% of address locations were within 50 m of a main road. Exposure estimates will be used in a number of planned epidemiological studies.

Objective To investigate the relation between exposure to both air and noise pollution from road traffic and birth weight outcomes.Design Retrospective population based cohort study.Setting Greater London and surrounding counties up to the M25 motorway (2317 km2), UK, from 2006 to 2010.Participants 540 365 singleton term live births.Main outcome measures Term low birth weight (LBW), small for gestational age (SGA) at term, and term birth weight.Results Average air pollutant exposures across pregnancy were 41 μg/m3 nitrogen dioxide (NO2), 73 μg/m3 nitrogen oxides (NOx), 14 μg/m3 particulate matter with aerodynamic diameter 13.8 μg/m3during pregnancy.Conclusions The findings suggest that air pollution from road traffic in London is adversely affecting fetal growth. The results suggest little evidence for an independent exposure-response effect of traffic related noise on birth weight outcomes.

Municipal Waste Incineration (MWI) is regulated through the European Union Directive on Industrial Emissions (IED), but there is ongoing public concern regarding potential hazards to health. Using dispersion modeling, we estimated spatial variability in PM10 concentrations arising from MWIs at postcodes (average 12 households) within 10 km of MWIs in Great Britain (GB) in 2003–2010. We also investigated change points in PM10 emissions in relation to introduction of EU Waste Incineration Directive (EU-WID) (subsequently transposed into IED) and correlations of PM10 with SO2, NOx, heavy metals, polychlorinated dibenzo-p-dioxins/furan (PCDD/F), polycyclic aromatic hydrocarbon (PAH) and polychlorinated biphenyl (PCB) emissions. Yearly average modeled PM10 concentrations were 1.00 × 10–5 to 5.53 × 10–2 μg m–3, a small contribution to ambient background levels which were typically 6.59–2.68 × 101 μg m–3, 3–5 orders of magnitude higher. While low, concentration surfaces are likely to represent a spatial proxy of other relevant pollutants. There were statistically significant correlations between PM10 and heavy metal compounds (other heavy metals (r = 0.43, p =

Background: Deprivation indices have been widely used in healthcare research and planning in the United Kingdom. Existing indices, however, are dominated by characteristics of urban populations that may be less relevant in capturing the nature of rural deprivation. We explore if deprivation indices can be modified to make them more sensitive to displaying rural disadvantage in England. Methods: The analysis focussed on the 2011 Carstairs Index (Carstairs2011) and the 2010 English Index of Multiple Deprivation (IMD2010). We removed all urban areas as identified by the Office for National Statistics Rural–Urban Area Classifications and mapped the Carstairs2011 and IMD2010 across the remaining rural areas using rural-specific quintiles. Results: Our method was effective in displaying much greater heterogeneity in rural areas than was apparent in the original indices. We received positive feedback from Directors of Public Health who confirmed that the observed patterns mirror their experiences and first-hand knowledge on the ground. Conclusions: Our maps of Carstairs2011 and IMD2010 for rural areas might strengthen the evidence base for rural planning and service provision. The modified deprivation indices, however, were not specifically formulated for rural populations and further work is needed to explore alternative input variables to produce a more rural-specific measure of deprivation.

Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Design/methods: Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates.

Evidence on the effects of long-term exposure to traffic pollution on health is inconsistent. In Greater London we examined associations between traffic pollution and emergency hospital admissions for cardio-respiratory diseases by applying linear and piecewise linear Poisson regression models in a small-area analysis. For both models the results for children and adults were close to unity. In the elderly, linear models found negative associations whereas piecewise models found non-linear associations characterized by positive risks in the lowest and negative risks in the highest exposure category. An increased risk was observed among those living in areas with the highest socioeconomic deprivation. Estimates were not affected by adjustment for traffic noise. The lack of convincing positive linear associations between primary traffic pollution and hospital admissions agrees with a number of other reports, but may reflect residual confounding. The relatively greater vulnerability of the most deprived populations has important implications for public health.