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Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide. [ABSTRACT FROM AUTHOR]

Background: Little is known about how asymptomatic testing as a method to control transmission of COVID-19 can be implemented, and the prevalence of asymptomatic infection within university populations. The objective of this study was to investigate how to effectively set-up and implement a COVID-19 testing programme using novel reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) technology and to quantify the scale of asymptomatic infection on a university campus. Methods: An observational study to describe the set-up and implementation of a novel COVID-19 testing programme on a UK university campus between September and December 2020. RT-LAMP testing was used to identify asymptomatic cases. Results: A total of 1,673 tests were performed using RT-LAMP during the study period, of which 9 were positive for COVID-19, giving an overall positivity rate of 0.54%, equivalent to a rate in the tested population of 538 cases per 100,000 over the duration of testing. All positive tests were found to be positive on RT-PCR testing, giving a false positive rate of 0%. Conclusions: This study shows that it is possible to rapidly setup a universal university testing programme for COVID-19 in collaboration with local healthcare providers using RT-LAMP testing. Positive results were comparable to those in the local population, though with a different peak of infection. Further research to inform the design of the testing programme includes focus groups of those who underwent testing and further interrogation of the demographics of those opting to be tested to identify potential access problems or inequalities. [ABSTRACT FROM AUTHOR]

The article discusses the findings of a large-scale analysis of the association between air pollutants and leucocyte telomere length in the UK Biobank (UKB). Topics covered include the recruitment and phenotyping of UKB participants, the assessment of the correlation coefficients between air pollutants and with socioeconomic status and the limitations of the study.

Introduction: Birth weight is a strong predictor of infant mortality, morbidity and later disease risk. Previous work from the 1980s indicated a shift in the UK towards heavier births; this descriptive analysis looks at more recent trends.Methods: Office for National Statistics (ONS) registration data on 17.2 million live, single births from 1986 to 2012 were investigated for temporal trends in mean birth weight, potential years of birth weight change and changes in the proportions of very low (<1500 g), low (<2500 g) and high (≥4000 g) birth weight. Analysis used multiple linear and logistic regression adjusted for maternal age, marital status, area-level deprivation and ethnicity. Additional analyses used the ONS NHS Numbers for Babies data set for 2006-2012, which has information on individual ethnicity and gestational age.Results: Over 27 years there was an increase in birth weight of 43 g (95% CI 42 to 44) in females and 44 g (95% CI 43 to 45) in males, driven by birth weight increases between 1986-1990 and 2007-2012. There was a concurrent decreased risk of having low birth weight but an 8% increased risk in males and 10% increased risk in females of having high birth weight. For 2006-2012 the birth weight increase was greater in preterm as compared with term births.Conclusions: Since 1986 the birth weight distribution of live, single births in England and Wales has shifted towards heavier births, partly explained by increases in maternal age and non-white ethnicity, as well as changes in deprivation levels. Other potential influences include increases in maternal obesity and reductions in smoking prevalence particularly following the introduction of legislation restricting smoking in public places in 2007. [ABSTRACT FROM AUTHOR]

Road traffic gives rise to noise and air pollution exposures, both of which are associated with adverse health effects especially for cardiovascular disease, but mechanisms may differ. Understanding the variability in correlations between these pollutants is essential to understand better their separate and joint effects on human health. We explored associations between modelled noise and air pollutants using different spatial units and area characteristics in London in 2003–2010. We modelled annual average exposures to road traffic noise (L Aeq,24 h , L den , L Aeq,16 h , L night ) for ~ 190,000 postcode centroids in London using the UK Calculation of Road Traffic Noise (CRTN) method. We used a dispersion model (KCLurban) to model nitrogen dioxide, nitrogen oxide, ozone, total and the traffic-only component of particulate matter ≤ 2.5 μm and ≤ 10 μm. We analysed noise and air pollution correlations at the postcode level (~ 50 people), postcodes stratified by London Boroughs (~ 240,000 people), neighbourhoods (Lower layer Super Output Areas) (~ 1600 people), 1 km grid squares, air pollution tertiles, 50 m, 100 m and 200 m in distance from major roads and by deprivation tertiles. Across all London postcodes, we observed overall moderate correlations between modelled noise and air pollution that were stable over time (Spearman's rho range: | 0.34–0.55 |). Correlations, however, varied considerably depending on the spatial unit: largest ranges were seen in neighbourhoods and 1 km grid squares (both Spearman's rho range: | 0.01–0.87 |) and was less for Boroughs (Spearman's rho range: | 0.21–0.78 |). There was little difference in correlations between exposure tertiles, distance from road or deprivation tertiles. Associations between noise and air pollution at the relevant geographical unit of analysis need to be carefully considered in any epidemiological analysis, in particular in complex urban areas. Low correlations near roads, however, suggest that independent effects of road noise and traffic-related air pollution can be reliably determined within London. [ABSTRACT FROM AUTHOR]

Health impact assessment (HIA) aims to make the health consequences of decisions explicit. Decision‐makers need to know that the conclusions of HIA are robust. Quantified estimates of potential health impacts may be more influential but there are a number of concerns. First, not everything that can be quantified is important. Second, not everything that is being quantified at present should be, if this cannot be done robustly. Finally, not everything that is important can be quantified: rigorous qualitative HIA will still be needed for a thorough assessment. This paper presents the first published attempt to provide practical guidance on what is required to perform robust, quantitative HIA. Initial steps include profiling the affected populations, obtaining evidence for postulated impacts, and determining how differences in subgroups' exposures and susceptibilities affect impacts. Using epidemiological evidence for HIA is different from carrying out a new study. Key steps in quantifying impacts are mapping the causal pathway, selecting appropriate outcome measures and selecting or developing a statistical model. Evidence from different sources is needed. For many health impacts, evidence of an effect may be scarce and estimates of the size and nature of the relationship may be inadequate. Assumptions and uncertainties must therefore be explicit. Modelled data can sometimes be tested against empirical data but sensitivity analyses are crucial. When scientific problems occur, discontinuing the study is not an option, as HIA is usually intended to inform real decisions. Both qualitative and quantitative elements of HIA must be performed robustly to be of value. [ABSTRACT FROM PUBLISHER]

Hospital activity data can be accessed from a variety of sources ranging from hospitals to the Department of Health. These data provide valuable and widely used information, but care is needed in their use and interpretation. Hospital activity rates reflect not only the underlying prevalence and severity of disease, individual factors and referral practices, but also variations related to provider-specific factors: the 'provider effect'. This includes completeness in the data, supply of hospital beds, admission policies, hospital access and distance from hospital. The provider effect can be controlled to a certain extent in statistical analyses. Although data quality has improved considerably in the last decade, this should still be investigated where trusts are being compared and in small area studies because missing data may lead to artefactual differences in rates. 'Dump' postcodes, where missing or unknown postcodes are assigned to a local postcode such as that of the hospital, may affect small area analyses and linkage if a proxy patient identifier is constructed that includes postcode. [ABSTRACT FROM PUBLISHER]

The UK implemented a lockdown in Spring (2020) to curtail the person-to-person transmission of the SARS-CoV-2 virus. Measures restricted movements to one outing per day for exercise and shopping, otherwise most people were restricted to their dwelling except for key workers (e.g. medical, supermarkets, and transport). In this study, we quantified changes to air quality across the United Kingdom from 30/03/2020 to 03/05/2020 (weeks 14–18), the period of most stringent travel restrictions. Daily pollutant measurements of NO 2 , O 3 and PM 2.5 from the national network of monitoring sites during this period were compared with measurements over the same period during 2017–19. Comparisons were also made with predicted concentrations for the 2020 period from business-as-usual (BAU) modelling, where the contributions of normal anthropogenic activities were estimated under the observed meteorological conditions. During the lockdown study period there was a 69% reduction in traffic overall (74% reduction in light and 35% in heavy vehicles). Measurements from 129 monitoring stations, identified mean reductions in NO 2 of 38.3% (−8.8 μg/m3) and PM 2.5 of 16.5% (−2.2 μg/m3). Improvements in NO 2 and PM 2.5 were largest at urban traffic sites and more modest at background locations where a large proportion of the population live. In contrast, O 3 concentrations on average increased by 7.6% (+4.8 μg/m3) with the largest increases at roadside sites due to reductions in local emissions of NO. A lack of VOC monitoring limited our capacity to interpret changes in O 3 at urban background locations. BAU models predicted comparable NO 2 reductions and O 3 gains, although PM 2.5 episodes would have been more prominent without lockdown. Results demonstrate the relatively modest contribution of traffic to air quality, suggesting that sustained improvements in air quality require actions across various sectors, including working with international and European initiatives on long-range transport air pollutants, especially PM 2.5 and O 3. Image 1 • Monthly-average daily traffic counts in April-2020 fell by 69% compared to April-2019. • NO 2 and PM 2.5 concentrations fell respectively by 38.3% (8.8 μg/m3) and 16.5% (2.2 μg/m3). • O 3 increased by 7.6% overall as the atmospheric chemistry changed. • NO 2 and PM 2.5 improvements were largest at urban traffic sites. • Weather conditions contributed to particulate episodes that were seen despite traffic reductions. [ABSTRACT FROM AUTHOR]

Modeling historic air pollution exposures is often restricted by availability of monitored concentration data. We evaluated back-extrapolation of land use regression (LUR) models for annual mean NO2 concentrations in Great Britain for up to 18 years earlier. LUR variables were created in a geographic information system (GIS) using land cover and road network data summarized within buffers, site coordinates, and altitude. Four models were developed for 2009 and 2001 using 75% of monitoring sites (in different groupings) and evaluated on the remaining 25%. Variables selected were generally stable between models. Within year, hold-out validation yielded mean-squared-error-based R² (MSE-R²) (i.e., fit around the 1:1 line) values of 0.25-0.63 and 0.51-0.65 for 2001 and 2009, respectively. Back-extrapolation was conducted for 2009 and 2001 models to 1991 and for 2009 models to 2001, adjusting to the year using two background NO2 monitoring sites. Evaluation of back-extrapolated predictions used 100% of sites from an historic national NO2 diffusion tube network (n = 451) for 1991 and 70 independent sites from automatic monitoring in 2001. Values of MSE-R² for back-extrapolation to 1991 were 0.42-0.45 and 0.52-0.55 for 2001 and 2009 models, respectively, but model performance varied by region. Back-extrapolation of LUR models appears valid for exposure assessment for NO2 back to 1991 for Great Britain. [ABSTRACT FROM AUTHOR]

Robust methods to estimate historic population air pollution exposures are important tools for epidemiological studies evaluating long-term health effects. We developed land use regression (LUR) models for NO 2 exposure in Great Britain for 1991 and explored whether the choice of year-specific or back-extrapolated LUR yields 1) similar LUR variables and model performance, and 2) similar national and regional address-level and small-area concentrations. We constructed two LUR models for 1991using NO 2 concentrations from the diffusion tube monitoring network, one using 75% of all available measurement sites (that over-represent industrial areas), and the other using 75% of a subset of sites proportionate to population by region to study the effects of monitoring site selection bias. We compared, using the remaining (hold-out) 25% of monitoring sites, the performance of the two 1991 models with back-extrapolation of a previously published 2009 model, developed using NO 2 concentrations from automatic chemiluminescence monitoring sites and predictor variables from 2006/2007. The 2009 model was back-extrapolated to 1991 using the same predictors (1990 & 1995) used to develop 1991 models. The 1991 models included industrial land use variables, not present for 2009. The hold-out performance of 1991 models (mean-squared-error-based-R 2 : 0.62–0.64) was up to 8% higher and ~ 1 μg/m 3 lower in root mean squared error than the back-extrapolated 2009 model, with best performance from the subset of sites representing population exposures. Year-specific and back-extrapolated exposures for residential addresses (n = 1.338,399) and small areas (n = 10.518) were very highly linearly correlated for Great Britain (r > 0.83). This study suggests that year-specific model for 1991 and back-extrapolation of the 2009 LUR yield similar exposure assessment. [ABSTRACT FROM AUTHOR]

Land-use regression modeling was used to develop maps of annual average black smoke (BS) and sulfur dioxide (SO2) concentrations in 1962,1971,1981, and 1991 for Great Britain on a 1 km grid for use in epidemiological studies. Models were developed in a GIS using data on land cover, the road network, and population, summarized within circular buffers around air pollution monitoring sites, together with altitude and coordinates of monitoring sites to consider global trend surfaces. Models were developed against the log-normal (LN) concentration, yielding R² values of 0.68 (n = 534), 0.68 (n = 767), 0.41 (n = 771), and 0.39 (n = 155) for BS and 0.61 (n = 482), 0.65 (n = 733), 0.38 (n = 756), and 0.24 (n = 153) for SO2 in 1962, 1971, 1981, and 1991, respectively. Model evaluation was undertaken using concentrations at an independent set of monitoring sites. For BS, values of R² were 0.56 (n = 133), 0.41 (n = 191), 0.38 (n = 193), and 0.34 (n = 37), and for SO2 values of R² were 0.71 (n = 121), 0.57 (n = 183), 0.26 (n = 189), and 0.31 (n = 38) for 1962, 1971, 1981, and 1991, respectively. Models slighdy underpredicted (fractional bias: 0∼-0.l) monitored concentrations of both pollutants for all years. This is the first study to produce historic concentration maps at a national level going back to the 1960s. [ABSTRACT FROM AUTHOR]

[Display omitted] • Gene-air pollution interaction effects on lung function are poorly understood. • Seven new potential gene-air pollution interaction signals identified. • Interaction effects identified are large enough to be clinically relevant. • Observed interactions include one previously identified lung function signal. • High-risk genetic subgroups potentially more susceptible to outdoor air pollution. Impaired lung function is predictive of mortality and is a key component of chronic obstructive pulmonary disease. Lung function has a strong genetic component but is also affected by environmental factors such as increased exposure to air pollution, but the effect of their interactions is not well understood. To identify interactions between genetic variants and air pollution measures which affect COPD risk and lung function. Additionally, to determine whether previously identified lung function genetic association signals showed evidence of interaction with air pollution, considering both individual effects and combined effects using a genetic risk score (GRS). We conducted a genome-wide gene-air pollution interaction analysis of spirometry measures with three measures of air pollution at home address: particulate matter (PM 2.5 & PM 10) and nitrogen dioxide (NO 2), in approximately 300,000 unrelated European individuals from UK Biobank. We explored air pollution interactions with previously identified lung function signals and determined their combined interaction effect using a GRS. We identified seven new genome-wide interaction signals (P < 5 × 10 - 8), and a further ten suggestive interaction signals (P < 5 × 10 - 7). Additionally, we found statistical evidence of interaction for FEV 1 /FVC between PM 2.5 and previously identified lung function signal, rs10841302, near AEBP2 , suggesting increased susceptibility as copies of the G allele increased (but size of the impact was small - interaction beta: -0.363 percentage points, 95% CI: -0.523, -0.203 per 5 µg/m3). There was no observed interaction between air pollutants and the weighted GRS. We carried out the largest genome-wide gene-air pollution interaction study of lung function and identified potential effects of clinically relevant size and significance. We observed up to 440 ml lower lung function for certain genotypes when exposed to mean levels of outdoor air pollution, which is approximately equivalent to nine years of average normal loss of lung function in adults. [ABSTRACT FROM AUTHOR]

Little is known about how asymptomatic testing as a method to control the transmission of COVID-19 can be successfully implemented, and the prevalence of asymptomatic infection within university populations. The aim of this study was to describe the methodology of implementing a novel asymptomatic mass testing programme, and to report the number of positive cases diagnosed during the study period. To our knowledge, this study is the first to report prevalence of asymptomatic COVID-19 infection within a UK university population using reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) as a molecular diagnostic tool. An observational study was undertaken to describe the set-up and implementation of a novel COVID-19 testing programme on a UK university campus between Sept 28 and Dec 18, 2020. Students and staff members volunteered for testing throughout the term. The programme used RT-LAMP testing to identify asymptomatic cases within the population. Any positive cases received RT-PCR testing to confirm the result using the current gold-standard testing methodology. 1673 tests were done using RT-LAMP during the study period, of which nine were positive for COVID-19. This gave an overall positivity rate of 0·54%, equivalent to a rate in the tested population of 538 cases per 100 000 over the duration of testing. All positive tests were also found to be positive on RT-PCR testing, giving a false positive rate of 0%. Uptake was affected by changes to delivery of university teaching, leading to lower attendance on campus throughout the term. This study shows that it is possible to rapidly set up a universal university testing programme for COVID-19 in collaboration with local health-care providers using RT-LAMP testing, with full concordance between RT-LAMP testing and RT-PCR testing on positive RT-LAMP results. Positive results were similar to those in the local population, although with a different weekly peak of infection. None. [ABSTRACT FROM AUTHOR]

Many studies, including the HYENA and the DEBATS studies, showed a significant association between aircraft noise exposure and the risk of hypertension. Few studies have considered aircraft noise annoyance and noise sensitivity as factors of interest, especially in relation to hypertension risk, or as mediating or modifying factors. The present study aims 1) to investigate the risk of hypertension in relation to aircraft noise annoyance or noise sensitivity; and 2) to examine the role of modifier or mediator of these two factors in the association between aircraft noise levels and the risk of hypertension. This study included 6,105 residents of ten European airports from the HYENA and DEBATS studies. Information on aircraft noise annoyance, noise sensitivity, and demographic, socioeconomic and lifestyle factors was collected during an interview performed at home. Participants were classified as hypertensive if they had either blood pressure levels above the WHO cut-off points or physician-diagnosed hypertension in conjunction with the use of antihypertensive medication. Outdoor aircraft noise exposure was estimated for each participant's home address. Poisson regression models with adjustment for potential confounders were used. Interactions between noise exposure and country were tested to consider possible differences between countries. An increase in aircraft noise levels at night was weekly but significantly associated with an increased risk of hypertension (RR = 1.03, 95% CI 1.01–1.06 for a 10-dB(A) increase in L night). A significant association was found between aircraft noise annoyance and hypertension risk (RR = 1.06, 95%CI 1.00–1.13 for highly annoyed people compared to those who were not highly annoyed). The risk of hypertension was slightly higher for people highly sensitive to noise compared to people with low sensitivity in the UK (RR = 1.29, 95%CI 1.05-1.59) and in France (RR = 1.11, 95%CI 0.68-1.82), but not in the other countries. The association between aircraft noise levels and the risk of hypertension was higher among highly sensitive participants (RR = 1.00, 95%CI 0.96–1.04; RR = 1.03, 95%CI 0.90–1.11; RR = 1.12, 95%CI 1.01–1.24, with a 10-dB(A) increase in L night for low, medium, and high sensitive people respectively) or, to a lesser extent, among highly annoyed participants (RR = 1.06, 95%CI 0.95-1.18 for a 10-dB(A) increase in L night among highly annoyed participants, and RR = 1.02, 95%CI 0.99–1.06 among those not highly annoyed). The present study confirms findings in the small number of available studies to date suggesting adverse health effects associated with aircraft noise annoyance and noise sensitivity. The findings also indicate possible modifier effects of aircraft noise annoyance and noise sensitivity in the relationship between aircraft noise levels and the risk of hypertension. However, further investigations are needed to better understand this role using specific methodology and tools related to mediation analysis and causal inference. • Aircraft noise levels are associated, albeit weakly, with the risk of hypertension. • Aircraft noise annoyance is significantly associated with hypertension risk. • Hypertension risk is slightly higher in people highly sensitive to noise. • Annoyance modifies the relationship between aircraft noise and hypertension risk. • Noise sensitivity also modifies this association. [ABSTRACT FROM AUTHOR]

Background: Environmental stressors such as transport noise may contribute to development of obesity through increased levels of stress hormones, sleep deprivation and endocrine disruption. Epidemiological evidence supporting an association of road traffic noise with obesity markers is still relatively scant and confined to certain geographical regions. We aimed to examine the cross-sectional associations between road traffic noise and obesity markers in three large European cohorts involving nearly 500,000 individuals. Methods: Three population-based cohorts (UK Biobank, Lifelines, HUNT3) were established between 2006 and 2013 in the UK, the Netherlands and Norway respectively. For all three cohorts, residential 24-h road traffic noise (Lden) for 2009 was modelled from a standardised European noise assessment framework. Residential exposures to NO2 for 2007 and PM2.5 for 2010 were estimated from Europe-wide land use regression models. Obesity markers including body mass index and waist circumference were measured at recruitment. Obesity and central obesity status were subsequently derived. Regression models were fitted in each cohort, adjusting for a harmonised set of demographic and lifestyle covariates, with further adjustments for air pollution in the main model. Results: The main analyses included 412,934 participants of UK Biobank, 61,032 of Lifelines and 30,305 of HUNT3, with a mean age of 43–56 years and Lden ranging 42–89 dB(A) across cohorts. In UK Biobank, per 10 dB(A) higher of Lden: BMI was higher by 0.14kg/m2 (95%CI: 0.11–0.18), waist circumference higher by 0.27 cm (95%CI: 0.19–0.35), odds of obesity was 1.06 (95%CI: 1.04–1.08) and of central obesity was 1.05 (95%CI: 1.04–1.07). These associations were robust to most other sensitivity analyses but attenuated by further adjustment of PM2.5 or area-level socioeconomic status. Associations were more pronounced among women, those with low physical activity, higher household income or hearing impairment. In HUNT3, associations were observed for obesity or central obesity status among those exposed to Lden greater than 55 dB(A). In contrast, no or negative associations were observed in the Lifelines cohort. Conclusions: This largest study to date providing mixed findings on impacts of long-term exposure to road traffic noise on obesity, which necessitates future analyses using longitudinal data to further investigate this potentially important epidemiological link. • The largest study to date involving nearly 500,000 adults from three European cohorts. • In UK Biobank, higher road-noise exposure was associated with obesity/central obesity status. • These associations were attenuated by further adjustment for PM2.5. • In HUNT3, only exposure to road-noise >55 dB was associated with obesity/central obesity status. • No or negative associations were observed in the Lifelines cohort. [ABSTRACT FROM AUTHOR]

Background: Nighttime aircraft noise may affect people's sleep, yet large-scale evidence using objective and subjective measures remains limited., Objective: Our aim was to investigate associations between nighttime aircraft noise exposure and objectively measured sleep disturbance using a large UK cohort., Methods: We used data from 105,770 UK Biobank cohort participants exposed and unexposed to aircraft noise who lived in 44 local authority districts near 4 international airports in England. We used a generalized linear regression model to examine cross-sectional associations between aircraft noise L night (23:00 hours-07:00 hours) and 7-d actimetric measures collected 2013-2015 ( n = 22,102 ). We also used Logit and generalized estimating equations models to examine associations between L night and self-reported sleep measures at enrollment (2006-2010) and follow-up (2012-2013). This approach allowed us to compare and contrast the results and support potential future meta-analyses on noise-related sleep disturbance., Results: Cross-sectional analyses of actimetric data suggested sleep disturbance associated with L night , showing higher level of movements during the least active continuous 8-h time period [ β : 0.12 milligravitational units; 95% confidence interval (CI): 0.013, 0.23]. We also saw disrupted sleep-wake cycles as indicated by index scores of lower relative amplitude ( β : - 0.006 ; 95% CI: - 0.007 , - 0.005 ), poorer interdaily stability ( β : - 0.010 ; 95% CI: - 0.014 , - 0.006 ), and greater intradaily variability ( β : 0.021; 95% CI: 0.019, 0.023), comparing L night ≥ 55 dB with < 45 dB. Repeated cross-sectional analyses found a 52% higher odds of more frequent daytime dozing [odds ratio (OR) = 1.52 ; 95% CI: 1.32, 1.75] for L night ≥ 55 dB in comparison with < 45 dB, whereas the likelihood for more frequent sleeplessness was more uncertain ( OR = 1.13 ; 95% CI: 0.92, 1.39). Higher effect sizes were seen in preidentified vulnerable groups, including individuals > 65 y of age and those with diabetes or dementia., Conclusion: Individuals exposed to higher levels of aircraft noise experienced objectively higher levels of sleep disturbance and changes in sleep-wake cycle. https://doi.org/10.1289/EHP14156.

Exposure to pollen and fungal spores can trigger asthma/allergic symptoms and affect health. Rising temperatures from climate change have been associated with earlier seasons and increasing intensity for some pollen, with weaker evidence for fungal spores. It is unclear whether climate change has resulted in changes in the exposure-response function between temperature and pollen/fungal spore concentrations over time. This study examined associations between temperature and pollen/fungal spores in different time periods and assessed potential adaptation using the longest pollen/fungal spore dataset in existence (52 years). Daily concentrations of pollen (birch and grass) and fungal spores (Cladosporium, Alternaria, Sporobolomyces and Tilletiopsis) collected between April and October from Derby (1970-2005) and Leicester (2006-2021), UK, were analysed. Cumulative seasonal concentrations (seasonal integral) and start-of-season were calculated and linked to seasonal mean temperatures (Tmeans) using generalized additive models. Daily concentrations were evaluated against daily Tmean with distributed lagged nonlinear models. Models were adjusted for precipitation, relative humidity, long-term trend and location. Seasonal and daily analyses were respectively stratified into two periods (1970-1995, 1997-2021) and five decades. Warmer seasonal Tmeans were associated with higher seasonal integral for birch, Cladosporium and Alternaria, as well as earlier start-of-season for birch, grass and Cladosporium. There were indications of changing associations with temperature in the recent decades. A warmer January was associated with higher seasonal integral for grass in 1997-2021, but not in 1970-1995. In 2000-2021, daily concentrations of birch pollen tended to remain at higher levels, vs. decrease during 1990s, when Tmean was between 13 and 15 °C. Our study suggests higher temperatures experienced in recent decades are associated with higher overall abundance of some pollen/fungal spores, which may increase future disease burdens of allergies. The changing responses of some pollen to higher temperatures over time may indicate adaptation to increasing temperatures and should be considered in climate change mitigation and adaptation planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Aim: To explore the health risk of living near permitted composting sites (PCSs) on disease severity in children and adults with cystic fibrosis (CF) across the UK.  METHODS: A semi-individual cross-sectional study was used to examine the risk of disease severity in people with CF (pwCF) within and beyond 4 km of PCSs in the UK in 2016. All pwCF registered in the UK CF Registry were eligible for this study. Linear and Poisson regressions, adjusted for age, gender, genotype, BMI, Pseudomonas aeruginosa and deprivation, were used to quantify associations between distance to a PCS and percent predicted forced expiratory volume in one second (ppFEV 1 ), pulmonary exacerbations (#IVdays), and fungal and bacterial infections., Results: The mean age of the 9,361 pwCF (3,931 children and 5,430 adults) studied was 20.1 (SD = 14.1) years; 53.3% were male; and 49.2% were homozygous F508del. Over 10% of pwCF (n = 1,015) lived within 4 km of a PCS. We found no statistically significant difference in ppFEV 1 and #IVdays/year in children. However, in adults, ppFEV 1 was -1.07% lower (95% confidence interval (CI): -2.29%, 0.16%) and #IVdays/year were 1.02 day higher (95%CI: 1.01, 1.04) within 4 km of a PCS. Furthermore, there were statistically significant differences in mean ppFEV 1 in CF adults with Aspergillus fumigatus (58.2.% vs 62.0%, p = 0.005) and Candida spp. (56.9% vs 59.9%, p = 0.029) residing within 4 km of a PCS. No associations were identified for allergic bronchopulmonary aspergillosis, P. aeruginosa or Staphylococcus aureus., Conclusions: This novel national study provides evidence that adults with CF living near a PCS may experience small reductions in lung function, an increased risk of pulmonary exacerbations, and more frequent fungal infections. If confirmed by studies using refined exposure assessment methods accounting for bioaerosol dispersion, these results could have important implications for the living environment of pwCF., (© 2022. The Author(s).)

Background: Long-term exposure to air pollution concentrations is known to be adversely associated with a broad range of single non-communicable diseases, but its role in multimorbidity has not been investigated in the UK. We aimed to assess associations between long-term air pollution exposure and multimorbidity status, severity, and patterns using the UK Biobank cohort., Methods: Multimorbidity status was calculated based on 41 physical and mental conditions. We assessed cross-sectional associations between annual modeled particulate matter (PM) 2.5 , PM coarse , PM 10 , and nitrogen dioxide (NO 2 ) concentrations (μg/m 3 -modeled to residential address) and multimorbidity status at the baseline assessment (2006-2010) in 364,144 people (mean age: 52.2 ± 8.1 years, 52.6% female). Air pollutants were categorized into quartiles to assess dose-response associations. Among those with multimorbidity (≥2 conditions; n = 156,395) we assessed associations between air pollutant exposure levels and multimorbidity severity and multimorbidity patterns, which were identified using exploratory factor analysis. Associations were explored using generalized linear models adjusted for sociodemographic, behavioral, and environmental indicators., Results: Higher exposures to PM 2.5 , and NO 2 were associated with multimorbidity status in a dose-dependent manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) [PM 2.5 : adjusted odds ratio (adjOR) = 1.21 (95% CI = 1.18, 1.24); NO 2 : adjOR = 1.19 (95 % CI = 1.16, 1.23)]. We also observed dose-response associations between air pollutant exposures and multimorbidity severity scores. We identified 11 multimorbidity patterns. Air pollution was associated with several multimorbidity patterns with strongest associations (Q4 vs. Q1) observed for neurological (stroke, epilepsy, alcohol/substance dependency) [PM 2.5 : adjOR = 1.31 (95% CI = 1.14, 1.51); NO 2 : adjOR = 1.33 (95% CI = 1.11, 1.60)] and respiratory patterns (COPD, asthma) [PM 2.5 : adjOR = 1.24 (95% CI = 1.16, 1.33); NO 2 : adjOR = 1.26 (95% CI = 1.15, 1.38)]., Conclusions: This cross-sectional study provides evidence that exposure to air pollution might be associated with having multimorbid, multi-organ conditions. Longitudinal studies are needed to further explore these associations., Competing Interests: RS declares research support received in the last 36 months from Janssen, GSK, and Takeda. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ronaldson, Arias de la Torre, Ashworth, Hansell, Hotopf, Mudway, Stewart, Dregan and Bakolis.)

Background and Aims: To describe sociodemographic, lifestyle, environmental and traditional clinical risk factor differences between ethnic groups and to investigate the extent to which such differences confound the association between ethnic groups and the risk of cardiovascular disease (CVD) METHODS AND RESULTS: A total of 440,693 white European (55.9% women), 7305 South Asian (48.6%) and 7628 black African or Caribbean (57.7%) people were included from UK Biobank. Associations between ethnicity and cardiovascular outcomes (composite of non-fatal stroke, non-fatal myocardial infarction and CVD death) were explored using Cox-proportional hazard models. Models were adjusted for sociodemographic, lifestyle, environmental and clinical risk factors. Over a median (IQR) of 12.6 (11.8, 13.3) follow-up years, there were 22,711 (5.15%) cardiovascular events in white European, 463 (6.34%) in South Asian and 302 (3.96%) in black African or Caribbean individuals. For South Asian people, the cardiovascular hazard ratio (HR) compared to white European people was 1.28 (99% CI [1.16, 1.43]). For black African or Caribbean people, the HR was 0.80 (0.66, 0.97). The elevated risk of CVD in South Asians remained after adjusting for differences in sociodemographic, lifestyle, environmental and clinical factors, whereas the lower risk in black African or Caribbean was largely attenuated., Conclusions: South Asian, but not black African or Caribbean individuals, have a higher risk of CVD compared to white European individuals. This higher risk in South Asians was independent of sociodemographic, lifestyle, environmental and clinical factors., Competing Interests: Declaration of competing interest TY, MJD are supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC). KK is Director for the University of Leicester Centre for Ethnic Health Research, Trustee of the South Asian Health Foundation, national NIHR Applied Research Collaborations – East Midlands (ARC-EM) lead for Ethnicity and Diversity and a member of SAGE and Chair of the SAGE subgroup on ethnicity and COVID-19. AH acknowledges funding from the NIHR Health Protection Research Unit in Environmental Exposures and Health Development Award at University of Leicester. Other authors declare no conflicts of interests., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Rationale : Exposure to air pollution during intrauterine development and through childhood may have lasting effects on respiratory health. Objectives : To investigate lung function at ages 8 and 15 years in relation to air pollution exposures during pregnancy, infancy, and childhood in a UK population-based birth cohort. Methods : Individual exposures to source-specific particulate matter ≤10 μm in aerodynamic diameter (PM 10 ) during each trimester, 0-6 months, 7-12 months (1990-1993), and up to age 15 years (1991-2008) were examined in relation to FEV 1 % predicted and FVC% predicted at ages 8 ( n  = 5,276) and 15 ( n  = 3,446) years using linear regression models adjusted for potential confounders. A profile regression model was used to identify sensitive time periods. Measurements and Main Results : We did not find clear evidence of a sensitive exposure period for PM 10 from road traffic. At age 8 years, 1 μg/m 3 higher exposure during the first trimester was associated with lower FEV 1 % predicted (-0.826; 95% confidence interval [CI], -1.357 to -0.296) and FVC% predicted (-0.817; 95% CI, -1.357 to -0.276), but similar associations were seen for exposures for other trimesters, 0-6 months, 7-12 months, and 0-7 years. Associations were stronger among boys, as well as children whose mother had a lower education level or smoked during pregnancy. For PM 10 from all sources, the third trimester was associated with lower FVC% predicted (-1.312; 95% CI, -2.100 to -0.525). At age 15 years, no adverse associations with lung function were seen. Conclusions : Exposure to road-traffic PM 10 during pregnancy may result in small but significant reductions in lung function at age 8 years.

The Rapid Inquiry Facility 4.0 (RIF) is a new user-friendly and open-access tool, developed by the UK Small Area Health Statistics Unit (SAHSU), to facilitate environment public health tracking (EPHT) or surveillance (EPHS). The RIF is designed to help public health professionals and academics to rapidly perform exploratory investigations of health and environmental data at the small-area level (e.g. postcode or detailed census areas) in order to identify unusual signals, such as disease clusters and potential environmental hazards, whether localized (e.g. industrial site) or widespread (e.g. air and noise pollution). The RIF allows the use of advanced disease mapping methods, including Bayesian small-area smoothing and complex risk analysis functionalities, while accounting for confounders. The RIF could be particularly useful to monitor spatio-temporal trends in mortality and morbidity associated with cardiovascular diseases, cancers, diabetes and chronic lung diseases, or to conduct local or national studies on air pollution, flooding, low-magnetic fields or nuclear power plants., (© The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Ambient air pollution increases the risk of respiratory mortality, but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors.We used UK Biobank data on 303 887 individuals aged 40-69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of land use regression-based estimates of particulate matter (particles with a 50% cut-off aerodynamic diameter of 2.5 and 10 µm: PM 2.5 and PM 10 , respectively; and coarse particles with diameter between 2.5 μm and 10 μm: PM coarse ) and nitrogen dioxide (NO 2 ) concentrations with forced expiratory volume in 1 s (FEV 1 ), forced vital capacity (FVC), the FEV 1 /FVC ratio and COPD (FEV 1 /FVC -3 increase in PM 2.5 concentration was associated with lower FEV 1 (-83.13 mL, 95% CI -92.50- -73.75 mL) and FVC (-62.62 mL, 95% CI -73.91- -51.32 mL). COPD prevalence was associated with higher concentrations of PM 2.5 (OR 1.52, 95% CI 1.42-1.62, per 5 µg·m -3 ), PM 10 (OR 1.08, 95% CI 1.00-1.16, per 5 µg·m -3 ) and NO 2 (OR 1.12, 95% CI 1.10-1.14, per 10 µg·m -3 ), but not with PM coarse Stronger lung function associations were seen for males, individuals from lower income households, and "at-risk" occupations, and higher COPD associations were seen for obese, lower income, and non-asthmatic participants.Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study., Competing Interests: Conflict of interest: D. Doiron has nothing to disclose. Conflict of interest: K. de Hoogh has nothing to disclose. Conflict of interest: N. Probst-Hensch has nothing to disclose. Conflict of interest: I. Fortier has nothing to disclose. Conflict of interest: Y. Cai has nothing to disclose. Conflict of interest: S. De Matteis has nothing to disclose. Conflict of interest: A.L. Hansell has nothing to disclose., (Copyright ©ERS 2019.)

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 (PM 10 ) 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 PM 10 or proximity to an MWI., Conclusions: We found no evidence that exposure to PM 10 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., (Copyright © 2018. Published by Elsevier Ltd.)

Background: This study aimed to investigate the effects of long-term exposure to road traffic noise and air pollution on incident cardiovascular disease (CVD) in three large cohorts: HUNT, EPIC-Oxford and UK Biobank., Methods: In pooled complete-case sample of the three cohorts from Norway and the United Kingdom (N = 355,732), 21,081 incident all CVD cases including 5259 ischemic heart disease (IHD) and 2871 cerebrovascular cases were ascertained between baseline (1993-2010) and end of follow-up (2008-2013) through medical record linkage. Annual mean 24-hour weighted road traffic noise (Lden) and air pollution (particulate matter with aerodynamic diameter ≤ 10 μm [PM10], ≤2.5 μm [PM2.5] and nitrogen dioxide [NO2]) exposure at baseline address was modelled using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU) and European-wide Land Use Regression models. Individual-level covariate data were harmonised and physically pooled across the three cohorts. Analysis was via Cox proportional hazard model with mutual adjustments for both noise and air pollution and potential confounders., Results: No significant associations were found between annual mean Lden and incident CVD, IHD or cerebrovascular disease in the overall population except that the association with incident IHD was significant among current-smokers. In the fully adjusted models including adjustment for Lden, an interquartile range (IQR) higher PM10 (4.1 μg/m3) or PM2.5 (1.4 μg/m3) was associated with a 5.8% (95%CI: 2.5%-9.3%) and 3.7% (95%CI: 0.2%-7.4%) higher risk for all incident CVD respectively. No significant associations were found between NO2 and any of the CVD outcomes., Conclusions: We found suggestive evidence of a possible association between road traffic noise and incident IHD, consistent with current literature. Long-term particulate air pollution exposure, even at concentrations below current European air quality standards, was significantly associated with incident CVD., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Chronic obstructive pulmonary disease (COPD) is an important cause of mortality with marked geographic variations in Great Britain. Additional factors beyond cigarette smoking are likely to influence these variations, but direct information on smoking by area is not readily available. We compared methods of jointly modeling the spatial distribution of mortality from COPD and lung cancer, using the latter as a proxy for smoking, to identify areas in which risk factors other than smoking may be important., Methods: We obtained district-level mortality and population data for men aged 45 years or older in 1981-1999 in Great Britain. Three models were compared: Bayesian ecological regression using observed (model 1) or spatially smoothed (model 2) lung cancer standardized mortality ratio (SMR) as a smoking proxy, and bivariate regression (model 3) treating smoking as a spatial latent variable common to both diseases., Results: Model selection criteria favored models 2 and 3 over model 1. Between 9% (model 3) and 25% (model 2) of spatial variation in COPD mortality was estimated to be unrelated to smoking. After adjustment for lung cancer as a proxy for smoking, both models showed similar geographic patterns of higher COPD mortality in conurbation and mining areas, historically associated with heavy industry and higher air pollution levels., Conclusions: Joint modeling of multiple diseases can be used to investigate geographic variations in risk. These models reveal patterns that are adjusted for the effects of shared area-level risk factors for which no direct data are available.