Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology.

BACKGROUND: In England there are four national routinely collected data sets on births: Office for National Statistics (ONS) births based on birth registrations; Hospital Episode Statistics (HES) deliveries (mothers' information); HES births (babies' information); and NHS Numbers for Babies (NN4B) based on ONS births plus gestational age and ethnicity information. This study describes and compares these data, with the aim of recommending the most appropriate data set(s) for use in epidemiological research and surveillance. METHODS: We assessed the completeness and quality of the data sets in relation to use in epidemiological research and surveillance and produced detailed descriptive statistics on common reproductive outcomes for each data set including temporal and spatial trends. RESULTS: ONS births is a high quality complete data set but lacks interpretive and clinical information. HES deliveries showed good agreement with ONS births but HES births showed larger amounts of missing or unavailable data. Both HES data sets had improved quality from 2003 onwards, but showed some local spatial variability. NN4B showed excellent agreement with ONS and HES deliveries for the years available (2006-2010). Annual number of births increased by 17.6% comparing 2002 with 2010 (ONS births). Approximately 6% of births were of low birth weight (2.6% term low birth weight) and 0.5% were stillbirths. CONCLUSIONS: Routinely collected data on births provide a valuable resource for researchers. ONS and NN4B offer the most complete and accurate record of births. Where more detailed clinical information is required, HES deliveries offers a high quality data set that captures the majority of English births

Bayesian spatial modelling for quasi-experimental designs: An interrupted time series study of the opening of Municipal Waste Incinerators in relation to infant mortality and sex ratio.

BACKGROUND: There is limited evidence on potential health risks from Municipal Waste Incinerators (MWIs), and previous studies on birth outcomes show inconsistent results. Here, we evaluate whether the opening of MWIs is associated with infant mortality and sex ratio in the surrounding areas, extending the Interrupted Time Series (ITS) methodological approach to account for spatial dependencies at the small area level. METHODS: We specified a Bayesian hierarchical model to investigate the annual risks of infant mortality and sex-ratio (female relative to male) within 10 km of eight MWIs in England and Wales, during the period 1996-2012. We included comparative areas matched one-to-one of similar size and area characteristics. RESULTS: During the study period, infant mortality rates decreased overall by 2.5% per year in England. The opening of an incinerator in the MWI area was associated with -8 deaths per 100,000 infants (95% CI -62, 40) and with a difference in sex ratio of -0.004 (95% CI -0.02, 0.01), comparing the period after opening with that before, corrected for before-after trends in the comparator areas. CONCLUSION: Our method is suitable for the analysis of quasi-experimental time series studies in the presence of spatial structure and when there are global time trends in the outcome variable. Based on our approach, we do not find evidence of an association of MWI opening with changes in risks of infant mortality or sex ratio in comparison with control areas

Software application profile: the Rapid Inquiry Facility 4.0: an open access tool for environmental public health tracking

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

Local- and regional-scale air pollution modelling (PM10) and exposure assessment for pregnancy trimesters, infancy, and childhood to age 15 years: Avon Longitudinal Study of Parents And Children (ALSPAC).

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

Fetal growth, stillbirth, infant mortality and other birth outcomes near UK municipal waste incinerators; retrospective population based cohort and case-control study.

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

Estimating Particulate Exposure from Modern Municipal Waste Incinerators in Great Britain.

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 = <0.001)), PAHs (r = 0.20, p = 0.050), and PCBs (r = 0.19, p = 0.022). No clear change points were detected following EU-WID implementation, possibly as incinerators were operating to EU-WID standards before the implementation date. Results will be used in an epidemiological analysis examining potential associations between MWIs and health outcomes

Long-term exposure to road traffic noise, ambient air pollution, and cardiovascular risk factors in the HUNT and lifelines cohorts

Aims: Blood biochemistry may provide information on associations between road traffic noise, air pollution, and cardiovascular disease risk. We evaluated this in two large European cohorts (HUNT3, Lifelines). Methods and results: Road traffic noise exposure was modelled for 2009 using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU). Annual ambient air pollution (PM10, NO2) at residence was estimated for 2007 using a Land Use Regression model. The statistical platform DataSHIELD was used to pool data from 144 082 participants aged ≥20 years to enable individual-level analysis. Generalized linear models were fitted to assess cross-sectional associations between pollutants and high-sensitivity C-reactive protein (hsCRP), blood lipids and for (Lifelines only) fasting blood glucose, for samples taken during recruitment in 2006-2013. Pooling both cohorts, an inter-quartile range (IQR) higher day-time noise (5.1 dB(A)) was associated with 1.1% [95% confidence interval (95% CI: 0.02-2.2%)] higher hsCRP, 0.7% (95% CI: 0.3-1.1%) higher triglycerides, and 0.5% (95% CI: 0.3-0.7%) higher high-density lipoprotein (HDL); only the association with HDL was robust to adjustment for air pollution. An IQR higher PM10 (2.0 µg/m3) or NO2 (7.4 µg/m3) was associated with higher triglycerides (1.9%, 95% CI: 1.5-2.4% and 2.2%, 95% CI: 1.6-2.7%), independent of adjustment for noise. Additionally for NO2, a significant association with hsCRP (1.9%, 95% CI: 0.5-3.3%) was seen. In Lifelines, an IQR higher noise (4.2 dB(A)) and PM10 (2.4 µg/m3) was associated with 0.2% (95% CI: 0.1-0.3%) and 0.6% (95% CI: 0.4-0.7%) higher fasting glucose respectively, with both remaining robust to adjustment for air/noise pollution. Conclusion: Long-term exposures to road traffic noise and ambient air pollution were associated with blood biochemistry, providing a possible link between road traffic noise/air pollution and cardio-metabolic disease risk

Prospective study design and data analysis in UK Biobank

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.</p

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease