Lower Layer Super Output Areas Research Articles

A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning

We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution.

Forecasting Knife Homicide Risk from Prior Knife Assaults in 4835 Local Areas of London, 2016\u20132018

Research QuestionHow accurately can all recorded locations of 97 knife homicides in one year be forecast across all 4835 Lower Layer Super Output Areas (LSOAs) of London, based only upon all 3506 known locations of nonfatal knife injury assaults in the preceding year?DataAll recorded “knife crimes” in the Metropolitan Police Service (MPS) area of London in the financial year 2016–2017 (year 1) were manually reviewed to identify all 3506 reported locations of knife-enabled (KE) but nonfatal injuries, as distinct from other events digitally coded as “knife crimes”, such as displaying, threatening with, or carrying knives. All KE homicides in 2017–2018 (year 2) were then added to the database.MethodsEach KE injury assault in year 1 was classified for occurrence in one of London’s 4835 LSOAs. The total N of such crimes within each LSOA was summed across all records to divide all LSOAs into seven categories of frequency of KE injury assaults in 2016/2017: 0, 1, 2, 3, 4, 5, and 6 or more. We then divided the N of LSOAs in each category in 2016/2017 (year 1) into the total N of knife homicides in 2017/2018 (year 2). False positives, false negatives, and percentage of homicides targeted were calculated for six different targeting plans.FindingsOver two thirds (69%) of KE homicides in 2017/2018 (year 2) occurred in just 67 (1.4%) of all 4835 LSOAs, comprising 3.3% of the 2048 LSOAs that had had one or more of the 3506 KE injury assaults mapped in 2016/2017 (year 1). The proportion of LSOAS with a KE homicide in year 2 was higher where there were higher numbers of KE injury assaults per LSOA in year 1. Among LSOAs with zero assaults in year 1, only 1% had a KE homicide in year 2. Among LSOAs with six or more KE injury assaults in year 1, 15% had a KE homicide in year 2. While the risk of homicide was 1400% higher in the hottest spots of knife assaults than in the coolest spots, the absolute number of year two KE homicides in those hottest areas was only 6% of the total. All LSOAs with one or more year 1 KE assaults had three times as much KE homicide risk than LSOAs with no year 1 KE assaults. Targeting all 2048 areas that had one or more year 1 KE assaults would have yielded a true positive rate of over 3% and covered 69% of actual homicide locations. The true negative rate for 2781 areas that would not have been targeted based on having no year 1 KE injuries would have been 99%.ConclusionsPredicting which local areas are most likely to suffer knife-enabled homicides, based only on recent nonfatal knife injuries, can pinpoint risks of homicide in local areas that are up to 1400% higher than in most local areas, offering a range of strategies for resource allocation.

Mapping Soil Heavy Metal Levels in England and Wales for Application in Public Health

Background/Aim: Heavy metals, especially Lead (Pb), Cadmium (Cd) and metalloid Arsenic (As), have known health impacts. Using extensive soil sampling data from the British Geological Survey (BGS) we aimed to generate reliable soil metal concentration surfaces, with accompanying measures of uncertainty, to identify areas exceeding health-based guideline values.Methods: The BGS soil metals data for Pb, Cd and As comprise an amalgamation of six different surveys collected over a 34-year period and have up to four samples per km2. Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK) were applied to an 80% stratified sample (i.e. 80% random sample of each survey, comprising 36,802 ‘training samples’). The remaining 20% ‘test sample’ (9,202 samples) was used for cross-validation. The predicted standard error generated via the EBK approach was mapped to quantify the uncertainty/error in the interpolated surface.Results: The OK and EBK interpolation methods generated similar soil concentration surfaces, and cross validation with the held-back test data indicated moderate to good model performance: Spearman’s rank correlation coefficients 0.83 (Pb), 0.72 (As) and 0.57 (Cd). Overall, the models tended to underestimate metal concentrations, although at lower levels (where the majority of the samples clustered), the models both over and under-estimated metal concentrations. For each metal, the areas of high uncertainty (i.e. high standard errors) were located in the areas with high soil metal concentrations. In terms of small areas exceeding health based guideline values, almost 4% of Lower Layer Super Output Areas (LSOAs) exceeded the UK soil guideline value for As, and 3.6% exceeded the USA EPA hazard standard for Pb for residential children’s play areas. No LSOAs exceeded the UK soil guideline value for Cd.Conclusions: These soil metal concentration surfaces can be used to identify areas exceeding health based guidelines.

The United Kingdom National Neonatal Research Database: A validation study.

BackgroundThe National Neonatal Research Database (NNRD) is a rich repository of pre-defined clinical data extracted at regular intervals from point-of-care, clinician-entered electronic patient records on all admissions to National Health Service neonatal units in England, Wales, and Scotland. We describe population coverage for England and assess data completeness and accuracy.MethodsWe determined population coverage of the NNRD in 2008–2014 through comparison with data on live births in England from the Office for National Statistics. We determined the completeness of seven data items on the NNRD. We assessed the accuracy of 44 data items (16 patient characteristics, 17 processes, 11 clinical outcomes) for infants enrolled in the multi-centre randomised controlled trial, Probiotics in Preterm Study (PiPs). We compared NNRD to PiPs data, the gold standard, and calculated discordancy rates using predefined criteria, and sensitivity, specificity and positive predictive values (PPV) of binary outcomes.ResultsThe NNRD holds complete population data for England for infants born alive from 25+0 to 31+6 (completed weeks) of gestation; and 70% and 90% for those born at 23 and 24 weeks respectively. Completeness of patient characteristics was over 90%. Data were linked for 2257 episodes of care received by 1258 of the 1310 babies recruited to PiPs. Discordancy rates were <5% for 13/16 patient characteristics (exceptions: mode of delivery 8.7%; maternal ethnicity 10.2%, Lower layer Super Output Area 16.5%); <5% for 9/16 processes (exceptions: medical treatment for Patent ductus arteriosus 6.1%, high-dependency days 10.2%, central line days 11.2%, type of first milk 22.3%; and during first 14 days, summary of types of milk 13.8%; number of days of antibiotics 9.0%; whether antacid given 5.1%); and <5% for 10/11 clinical outcomes (exception: Bronchopulmonary dysplasia, defined as oxygen dependency at 36 weeks postmenstrual age 3.3%). The specificity of NNRD data was >85% for all outcomes; sensitivity ranged from 50–100%; PPV ranged from 58.8 (95% CI 40.8–75.4%) for porencephalic cyst to 99.7 (95% CI 99.2, 99.9%) for survival to discharge.ConclusionsThe completeness and quality of data held in the NNRD is high, providing assurance in relation to use for multiple purposes, including national audit, health service evaluations, quality improvement, and research.

Comparative morbidities and the share of emergencies in hospital admissions in deprived areas: a method and evidence from English administrative data

ObjectiveVarious studies find that the share of emergencies in hospital admissions is higher in deprived areas, but both the explanation and policy implications are unclear. We estimate the extent to...

Cutting care clusters: the creation of an inverse pharmacy care law? An area-level analysis exploring the clustering of community pharmacies in England

ObjectivesTo (1) explore the clustering of community pharmacies in England and (2) determine the relationship between community pharmacy clustering, urbanity and deprivation.DesignAn area-level analysis spatial study.SettingEngland.Primary and secondary outcome measuresCommunity...

Measuring Active Living Environments: An international comparison between Canada and Wales

BackgroundThe impacts of the built environment on health is a widely studied international area of research. One area of research is how urban morphology (e.g. active living environments, also known as neighbourhood walkability) may promote healthy behaviour within a population. However urban morphology and data relating to the built environment varies across different countries.&#x0D; ObjectivesOne of the challenges in international studies is producing consistent, comparable measures of the built environment, in this case active living environments. As part of a study which compares the impact of neighbourhood environments on health outcomes for patients with type 2 diabetes (T2D), neighbourhood-level measures for walkable environments were derived for Canada and Wales using Geographic Information Systems (GIS).&#x0D; MethodsUsing method based upon the Canadian Active Living Environments Database (Can-ALE) we created walkability indicators for Wales, UK. We created GIS models using OpenStreetMap and Office for National Statistics (ONS) Open Data to produce walkability metrics for each Lower Layer Super Output Area (LSOA) in Wales for linkage into the SAIL databank. We compared the GIS generated walkability metrics for Wales with those produced for Canada to evaluate whether the GIS methods are internationally transferable in the context of generating walkability indictors and associations with T2D.&#x0D; FindingsThis work highlights the challenges in creating internationally comparable environmental exposure metrics. The differences in urban morphology and scale in Canada and Wales are significant, however this work demonstrates how with considered methodological choices these differences can be overcome to generate comparable built environment indicators.&#x0D; ConclusionsThe generation of comparable walkability indicators for the built environment has allowed subsequent analysis into hospital admissions for people living with T2D in Caranda and Wales. This study has wider implications for international research into the impacts of the built environment on population health and are reproducible on future studies.

Targeting and modelling urban energy retrofits using a city-scale energy mapping approach

City authorities, community groups and retrofit installers need to identify suitable local areas and dwellings for installing energy retrofit measures. This paper presents a localised Geographical Information System (GIS) based approach that utilizes publicly-available national and local datasets on housing and energy to provide targeted low carbon measures across UK cities. The study uses a rapid city-level energy assessment approach to spatially identify suitable neighbourhoods for particular retrofit measures, based on relative energy use and fuel poverty ratings. A GIS-based carbon mapping model (called DECoRuM) is then used to estimate energy use and potential for energy reduction on a house-by-house level. The improvement measures are aggregated to encourage bulk installations and drive down installation costs.To identify an appropriate neighbourhood case study area, publicly available datasets were assessed for the town of Bicester (Oxfordshire, UK), which included Ordnance Survey Mastermap, Energy Performance Certificate data (EPC) and sub-national energy statistics available at lower layer super output area (LSOA). When the EPC data for Bicester were compared with the sub-national statistics for Bicester, the average difference was found to be ∼800 kWh. This is interesting as EPCs represent dwelling specific but modelled data whereas sub-national datasets represent actual but aggregated data. Superimposing the above datasets, a neighbourhood in southwest Bicester was selected as having the highest percentage of dwellings with energy consumption >300 kWh/m2/yr (EPC), most dwellings in need of wall insulation (EPC), second highest mean total energy consumption (sub-national), and third highest percentage of fuel poor dwellings (sub-national). House-level energy assessment in the selected area using DECoRuM showed that a package based approach comprising fabric and heating system upgrade and solar PVs emerged as the most effective.

An Exploration of Crime Prediction Using Data Mining on Open Data

The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.

A cross-sectional analysis of green space prevalence and mental wellbeing in England

BackgroundWith urbanisation increasing, it is important to understand how to design changing environments to promote mental wellbeing. Evidence suggests that local-area proportions of green space may be associated with happiness and life satisfaction; however, the available evidence on such associations with more broadly defined mental wellbeing in still very scarce. This study aimed to establish whether the amount of neighbourhood green space was associated with mental wellbeing.MethodsData were drawn from Understanding Society, a national survey of 30,900 individuals across 11,096 Census Lower-Layer Super Output Areas (LSOAs) in England, over the period 2009–2010. Measures included the multi-dimensional Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) and LSOA proportion of green space, which was derived from the General Land Use Database (GLUD), and were analysed using linear regression, while controlling for individual, household and area-level factors.ResultsThose living in areas with greater proportions of green space had significantly higher mental wellbeing scores in unadjusted analyses (an expected increase of 0.17 points (95% CI 0.11, 0.23) in the SWEMWBS score for a standard deviation increase of green space). However, after adjustment for confounding by respondent sociodemographic characteristics and urban/rural location, the association was attenuated to the null (regression coefficient B = − 0.01, 95% CI -0.08, 0.05, p = 0.712).ConclusionsWhile the green space in an individual’s local area has been shown through other research to be related to aspects of mental health such as happiness and life satisfaction, the association with multidimensional mental wellbeing is much less clear from our results. While we did not find a statistically significant association between the amount of green space in residents’ local areas and mental wellbeing, further research is needed to understand whether other features of green space, such as accessibility, aesthetics or use, are important for mental wellbeing.

Dying in hospital: socioeconomic inequality trends in England.

ObjectiveTo describe trends in socioeconomic inequality in the proportion of deaths occurring in hospital, during a period of sustained effort by the NHS in England to improve end of life care.MethodsWhole-population, small area longitudinal study involving 5,260,871 patients of all ages who died in England from 2001/2002 to 2011/2012. Our primary measure of inequality was the slope index of inequality. This represents the estimated gap between the most and least deprived neighbourhood in England, allowing for the gradient in between. Neighbourhoods were geographic Lower Layer Super Output Areas containing about 1500 people each.ResultsThe overall proportion of patients dying in hospital decreased from 49.5% to 43.6% during the study period, after initially increasing to 52.0% in 2004/2005. There was substantial ‘pro-rich’ inequality, with an estimated difference of 5.95 percentage points in the proportion of people dying in hospital (confidence interval 5.26 to 6.63), comparing the most and least deprived neighbourhoods in 2011/2012. There was no significant reduction in this gap over time, either in absolute terms or relative to the mean, despite the overall reduction in the proportion of patients dying in hospital.ConclusionsEfforts to reduce the proportion of patients dying in hospital in England have been successful overall but did not reduce inequality. Greater understanding of the reasons for such inequality is required before policy changes can be determined.

Outcomes and inequalities in diabetes from 2004/2005 to 2011/2012: English longitudinal study.

Outcomes of diabetes care are unequal and the NHS has a duty to consider reducing inequality in healthcare outcomes. To quantify trends in socioeconomic inequality and diabetes outcomes. Whole-population longitudinal study of 32 482 neighbourhoods (Lower Layer Super Output Areas [LSOAs]) in England between 2004/2005 and 2011/2012. Slope indices of inequality (SIIs) between neighbourhoods of great and little deprivation were measured annually for: glycated haemoglobin control in people with diabetes; emergency hospitalisation for diabetes; and amenable mortality from diabetes. From 2004/2005 to 2011/2012 glycaemic control improved in all social groups, regardless of deprivation level, although inequality was unchanged as measured by the SII (0.04, 95% confidence interval [CI] = -0.43 to 0.52). Diabetes-related amenable mortality improved in all social groups, but decreased at a faster rate in neighbourhoods of greater deprivation. Inequality in diabetes-related amenable mortality improved, with the SII falling by 2.68 (95% CI = 1.93 to 3.43), resulting in 594 (95% CI = 420 to 767) fewer deaths. In contrast, emergency hospitalisations for diabetes increased in all social groups, with faster growth in neighbourhoods of greater deprivation. The socioeconomic gradient increased with the SII widening by 19.59 admissions for diabetes per 100 000 (95% CI = 16.00 to 23.17), resulting in an increase of 5991 (95% CI = 5084 to 6899) excess admissions associated with socioeconomic inequality during the study period. In diabetes, mortality declined faster, but emergency hospitalisation grew faster in more deprived neighbourhoods. Unequal growth in emergency hospitalisation for diabetes is likely to be partly due to increased diabetes prevalence and patients living longer, but may also be due to overuse of glycaemic control medication.

Resource endowment and opportunity cost effects along the stages of entrepreneurship

In this paper, the start-up process is split conceptually into four stages: considering entrepreneurship, intending to start a new business in the next 3 years, nascent entrepreneurship and owning-managing a newly established business. We investigate the determinants of all of these jointly, using a multinomial logit model; it allows for the effects of resources and capabilities to vary across these stages. We employ the Global Entrepreneurship Monitor database for the years 2006–2009, containing 8269 usable observations from respondents drawn from the Lower Layer Super Output Areas in the East Midlands (UK) so that individual observations are linked to space. Our results show that the role of education, experience, and availability of ‘entrepreneurial capital’ in the local neighbourhood varies along the different stages of the entrepreneurial process. In the early stages, the negative (opportunity cost) effect of resources endowment dominates, yet it tends to reverse in the advanced stages, where the positive effect of resources becomes stronger.

The costs of inequality: whole-population modelling study of lifetime inpatient hospital costs in the English National Health Service by level of neighbourhood deprivation

BackgroundThere are substantial socioeconomic inequalities in both life expectancy and healthcare use in England. In this study, we describe how these two sets of inequalities interact by estimating the social...

Sunshine, Sea, and Season of Birth: MS Incidence in Wales.

Maternal sun exposure in gestation and throughout the lifetime is necessary for vitamin D synthesis, and living near the sea is a population level index of seafood consumption. The aim of this study was to estimate the incidence rate of multiple sclerosis (MS) in Wales and examine its association with sun exposure, coastal living, and latitude. The study used a database of MS hospital visits and admissions in Wales between 2002 and 2013. For the 1,909 lower layer super output areas (LSOAs) in Wales, coastal status, population, longitude/latitude, and average sunshine hours per day were obtained. Age-specific and age-standardised MS incidence were calculated and modelled using Poisson regression. The distribution of births by month was compared between MS cases and the combined England and Wales population. There were 3,557 new MS cases between 2002 and 2013, with an average annual incidence of 8.14 (95% CI: 7.69–8.59) among males and 12.97 (95% CI: 12.44–13.50) among females per 100,000 population. The female-to-male ratio was 1.86:1. For both sexes combined, the average annual incidence rate was 9.10 (95% CI: 8.80–9.40). All figures are age-standardized to the 1976 European standard population. Compared to the combined England and Wales population, more people with MS were born in April, observed-to-expected ratio: 1.21 (95% CI: 1.08–1.36). MS incidence varied directly with latitude and inversely with sunshine hours. Proximity to the coast was associated with lower MS incidence only in easterly areas. This study shows that MS incidence rate in Wales is comparable to the rate in Scotland and is associated with environmental factors that probably represent levels of vitamin D.

Change in alcohol outlet density and alcohol-related harm to population health (CHALICE): a comprehensive record-linked database study in Wales

BackgroundExcess alcohol consumption has serious adverse effects on health and results in violence-related harm.ObjectiveThis study investigated the impact of change in community alcohol availability on alcohol consumption and alcohol-related harms to health, assessing the effect of population migration and small-area deprivation.DesignA natural experiment of change in alcohol outlet density between 2006 and 2011 measured at census Lower Layer Super Output Area level using observational record-linked data.SettingWales, UK; population of 2.5 million aged ≥ 16 years.Outcome measuresAlcohol consumption, alcohol-related hospital admissions, accident and emergency (A&amp;E) department attendances from midnight to 06.00 and violent crime against the person.Data sourcesLicensing Act 2003 [Great Britain.Licensing Act 2003. London: The Stationery Office; 2003. URL:www.legislation.gov.uk/ukpga/2003/17/contents(accessed 8 June 2015)] data on alcohol outlets held by the 22 local authorities in Wales, alcohol consumption data from annual Welsh Health Surveys 2008–12, hospital admission data 2006–11 from the Patient Episode Database for Wales (PEDW) and A&amp;E attendance data 2009–11 were anonymously record linked to the Welsh Demographic Service age–sex register within the Secure Anonymised Information Linkage Databank. A final data source was recorded crime 2008–11 from the four police forces in Wales.MethodsOutlet density was estimated (1) as the number of outlets per capita for the 2006 static population and the per quarterly updated population to assess the impact of population migration and (2) using new methods of network analysis of distances between each household and alcohol outlets within 10 minutes of walking and driving. Alcohol availability was measured by three variables: (1) the previous quarterly value; (2) positive and negative change over the preceding five quarters; and (3) volatility, a measure of absolute quarterly changes during the preceding five quarters. Longitudinal statistical analysis used multilevel Poisson models of consumption and Geographically Weighted Regression (GWR) spatial models of binge drinking, Cox regression models of hospital admissions and A&amp;E attendance and GWR models of violent crime against the person, each as a function of alcohol availability adjusting for confounding variables. The impact on health inequalities was investigated by stratifying models within quintiles of the Welsh Index of Multiple Deprivation.ResultsThe main finding was that change in walking outlet density was associated with alcohol-related harms: consumption, hospital admissions and violent crime against the person each tracked the quarterly changes in outlet density. Alcohol-related A&amp;E attendances were not clinically coded and the association was less conclusive. In general, social deprivation was strongly associated with the outcome measures but did not substantially modify the associations between the outcomes and alcohol availability. We found no evidence for an important effect of population migration.LimitationsLimitations included the absence of any standardised methods of alcohol outlet data collation, processing and validation, and incomplete data on on-sales and off-sales. We were dependent on the quality of clinical coding and administrative records and could not identify alcohol-related attendances in the A&amp;E data set.ConclusionThis complex interdisciplinary study found that important alcohol-related harms were associated with change in alcohol outlet density. Future work recommendations include defining a research standard for recording outlet data and classification of outlet type, the methodological development of residence-based density measures and a health economic analysis of model-predicted harms.FundingThe National Institute for Health Research Public Health Research programme. Additional technical and computing support was provided by the Farr Institute at Swansea University, made possible by the following grant:Centre for the Improvement of Population Health through E-records Research (CIPHER) and Farr Institute capital enhancement. CIPHER and the Farr Institute are funded by Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Chief Scientist Office (Scottish Government Health Directorates), the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute for Health Research, the National Institute for Social Care and Health Research (Welsh Government) and the Wellcome Trust (grant reference MR/K006525/1).

Land use mix and five-year mortality in later life: Results from the Cognitive Function and Ageing Study

This study explores the potential modifying effect of age and mediation effect of co-morbidity on the association between land use mix, a measure of neighbourhood walkability, and five-year mortality among the 2424 individuals participating in the year-10 follow-up of the Cognitive Function and Ageing Study in England. Postcodes of participants were mapped onto Lower-layer Super Output Areas, a small area level geographical unit in the UK, and linked to Generalised Land Use data. Cox regression models were fitted to investigate the association. For the younger older age group (75–79 years), the effect of high land use mix on an elevated risk of mortality was mediated by co-morbidity. For older old age groups (80–84, 85+ years), a higher land use mix was directly associated with a 10% lower risk of five-year mortality. The findings suggest differential impacts of land use mix on the health of the younger and older old.

Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies

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 (LAeq,24h, Lden, LAeq,16h, Lnight) 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 ≤2.5μm and ≤10μm. 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), 1km grid squares, air pollution tertiles, 50m, 100m and 200m 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 1km 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.

Cars and socio-economics: understanding neighbourhood variations in car characteristics from administrative data

There were 30.7 million registered cars in Great Britain in 2011, outnumbering the total number of households recorded by the census. Despite this, the Driving and Vehicle Licensing Agency’s (DVLA) database of car model registrations remains underexplored as an indicator of socio-economic characteristics. In the past, car ownership itself has been frequently considered as a census proxy variable for affluence. However, this is an increasingly dated interpretation as ownership has become more widespread across society and the value of cars varies considerably. Understanding the geography of different car types, however, is likely to be more informative of local population characteristics as the choice of model is dependent on several factors, notably including the cost and the purpose of the vehicle. In partnership with the Department for Transport (DfT), a car segmentation was produced that grouped every car model registered in England and Wales in 2011 into 10 distinctive categories based on the vehicle’s key characteristics. Data representing the total number of registered cars for each car segment and three age groups were made available at a small area geography (known as lower layer super output areas – LSOAs) to be analysed for this study. It revealed that each car segment is uniquely distributed across London, and the rest of England and Wales. The patterns were then compared with key 2011 Census variables on socio-economics to understand the extent to which spatial patterns of broad car characteristics correspond with variances in indicators of social make-up.

Unequal socioeconomic distribution of the primary care workforce: whole-population small area longitudinal study

ObjectiveTo measure changes in socioeconomic inequality in the distribution of family physicians (general practitioners (GPs)) relative to need in England from 2004/2005 to 2013/2014.DesignWhole-population small area longitudinal data linkage study.SettingEngland...