Lower Layer Super Output Areas Research Articles

THE IMPACT OF SOCIOECONOMIC STATUS ON OUTCOMES FROM TREATMENT FOR FEMOROACETABULAR IMPINGEMENT

There is little known about how patient socioeconomic status impacts clinical outcomes in hip preservation surgery. The aim of this study was to evaluate the relationship between indices of multiple deprivation, funding provider (NHS Funded or Private Funded) and clinical outcomes following surgery for femoroacetabular impingement (FAI)The study analysed the data of 5590 patients recorded in the NAHR who underwent primary hip arthroscopic treatment for FAI between November 2013 and July 2023. Records were matched to the UK National index of multiple deprivation using the lower layer super output area. Using iHOT12 score, patient reported outcome measures were analysed at base line and 1 year following surgery.2358 records were matched to LLSOA deciles. Between the lowest (most deprived) 3 deciles and the highest (least deprived) the average baseline iHOT12 score was 28.98 (n=366) and 35.33 (n=821). The proportion of patients receiving treatment through NHS funding compared to independent funding for the most deprived, 292 (90%) 37 (10%) respectively compared to the least deprived 515 (70%) and 244 (30%) respectively. At 1year, iHOT12 scores for each group were 51.64 (29.1 SD) compared to 61.5 (28.06 SD) respectively.The study demonstrates that patients from lower socioeconomic backgrounds had poorer baseline and one-year post-surgery iHOT12 scores compared to those from higher socioeconomic strata. Furthermore, a higher reliance on NHS funding was observed among the most deprived, while more affluent patients predominantly opted for private funding. These findings underscore the significant influence of socioeconomic status on both the quality of healthcare received and recovery outcomes in hip preservation surgery, calling attention to the need for more equitable healthcare solutions.

Social and spatial inequalities of contemporary food deserts: A compound of store and online access to food in the United Kingdom

This research studies the social and spatial inequalities of contemporary food deserts in England and Wales. Two indices of the Store Food Desert Index and Online Food Desert Index are measured to examine store access and online access to food at the Lower Layer Super Output Areas geographical level. A detailed analysis is conducted to (i) identify “priority areas” where there is relatively lower store access and online access to food, (ii) synthesize population to understand the characteristics of “at-risk communities” that are disproportionately affected by food deserts, and (iii) examine the association between self-reported health conditions and a compound of store and online access to food. Three insights are provided. First, there is a clear urban-rural divide in which urban areas generally benefit from better provision of both store and online access to food. Second, about 23% of the population (13.8 million people) live in priority areas with limited access to both physical stores and online groceries. Within this group, 0.06% (8000 individuals) do not have cars, 2.17% (300,000 individuals) are Black, and 14.5% (2 million individuals) reside in deprived neighborhoods. This is an alarming picture. Third, people residing in areas with high store and online access to food reported better health conditions, and online access is as effective as store access in improving health conditions. The findings highlight the potential of combining online and store access to bridge geographical and economic gaps, thereby providing healthier food options to those in food deserts.

Patient-reported outcome and experience measures (POEM) of a community-based glaucoma clinic in Cambridge, UK: an observational study

ObjectivesGlaucoma care demand in UK hospitals has exploded in recent years. This has resulted in a push to community (shared, virtual, etc) care models to reduce the burden on hospital...

An ethnic group specific deprivation index for measuring neighbourhood inequalities in England and Wales

AbstractThe measurement of deprivation for small areas in the UK has provided the basis for the development of policies and targeting of resources aimed at reducing spatial inequalities. Most measures summarise the aggregate level of deprivation across all people in a given area, and no account is taken of differences between people with differing characteristics, such as age, sex or ethnic group. In recognition of the marked inequalities between ethnic groups in the UK, and the distinctive geographies of these inequalities, this paper presents a new ethnic group‐specific neighbourhood deprivation measure—the Ethnic Group Deprivation Index (EGDI). This index, using a custom cross‐tabulated 2021 Census dataset on employment, housing tenure, education and health by ethnic group, reveals the small area geographies of ethnic inequalities that have to date received scant attention, and yet have profound impacts on life chances and well‐being. Drawing on the methodological framework of the widely used English Index of Multiple Deprivation (IMD) and for the same geographies (Lower Layer Super Output Areas), the EGDI measures deprivation for each ethnic group using data from the 2021 Census of England and Wales. The EGDI reveals the complex geographies of ethnic inequality and demonstrates that while one ethnic group in a neighbourhood may have high relative levels of deprivation, another ethnic group in that same neighbourhood may experience very low relative levels. The EGDI explores ethnic inequalities within and between neighbourhoods, complementing and augmenting existing measures by offering an important means of better understanding ethnic inequalities. The EGDI can be used to help shape locally and culturally sensitive policy development and resource allocation.

Association between distance to nearest automated external defibrillator and socioeconomic deprivation in Great Britain

Abstract Background Disparities in socioeconomic status manifest in death from out of hospital cardiac arrest (OHCA). Inequalities in access to automated external defibrillators (AED) due to socioeconomically determined location of AEDs may be associated with OHCA survival. Purpose To investigate distances between nearest AED location and postcode by socioeconomic deprivation for the population of Great Britain (GB). Methods AED location data for 2022 were drawn from the British Heart Foundation The Circuit registry. All 1,677,466 unit postcodes in Great Britain were linked to the Index of Deprivation for the corresponding Lower Layer Super Output Area using the 2019 Ministry of Housing Communities and Local Government tool. The median distance by road from the centre of each postcode to the nearest AED was calculated using geographic information systems. The proportion available 24/7 was also reported. Multilevel linear mixed regression models fit by country and adjusted for urban/rural status and clustering to calculate predicted marginal mean distances to the nearest active AED, and nearest AED accessible 24/7 by deprivation decile. Results 78,425 unique AED locations were included in the analysis (Table 1). The median distance to nearest AED was 762.1m (IQR 410.8 - 1221.0) in GB; 742.7m (407.2 - 1340.7) in Scotland, 738.8m (422.6 - 1223.5) in England, and 511.5m (282.9 - 937.3) in Wales. For 24/7 access AEDs, median distances were greater: 964.0m (524.7 - 1667.5), 994.1m (538.5 – 1832.3), 990.8m (544.9 - 1683.3), and 569.7m (316.8 - 1033.4), respectively. Overall, there was little evidence of a trend in nearest AED distance by deprivation (fig 1). In Scotland the nearest AED was, on average, 108m further away in the most compared with the least deprived areas. In England, the nearest AED was closer to postcodes in the least deprived areas of England (predicted mean 20m) and Wales (predicted mean 95m). In England, AEDs with 24/7 access were further from postcodes with increasing deprivation (predicted mean 99m further than least deprived areas). In Scotland, nearest 24/7 access AEDs were 317m further away in the most deprived areas, and 62m closer in Wales, but with no evidence of a linear trend. At a typical walking speed (1.31m/sec), on average in GB it would take 17 minutes to retrieve an AED and 21 minutes out of hours. In England and Scotland out of hours, those in the most deprived areas would be delayed access to an AED by 2.5 and 8 minutes respectively compared the least deprived. Conclusions Overall, the median distance to nearest AED across postcodes in GB is at odds with the likelihood of successful resuscitation. Graded social inequalities were not observed in AED access across GB. However, substantive differences were observed in 24/7 AED access between most and least deprived areas of England and Scotland. Greater and more equitable access to AEDs has the potential to save lives and improve outcomes for people with OHCA.

Prepayment meters strongly associated with multiple types of deprivation and emergency respiratory hospital admissions: an observational, cross-sectional study

BackgroundPrepayment meters (PPMs) require energy to be paid in advance. Action groups and media contend that PPMs are concentrated in the most vulnerable groups, prone to run out of credit...

Predicting health related deprivation using loyalty card digital footprints

Predicting health related deprivation using loyalty card digital footprints

Social Statistics Transformation: Understanding the population through the production of income by ethnicity statistics from administrative data

ObjectivesThe lack of an income question on the Census has meant the production of multivariate income by ethnicity statistics has not been possible in census outputs to date. Our ambition is to provide individual-level records for every member of the usually resident population of England and Wales using admin data.
 MethodExciting progress has been made in the development of admin-based characteristics measures, including the ongoing feasibility research to produce admin-based datasets on ethnic group and income. Demonstrated improved results include the coverage of the population steadily increasing and methods to create these datasets gradually improving.
 Access to these record-level administrative datasets has allowed us to combine admin-based income and ethnicity measures developed in previous research, linking individuals between the two. We review the coverage of the combined dataset and the feasibility of producing multivariate statistics at subnational levels in England and Wales for the first time.
 ResultsThis presentation will showcase our innovative progress so far. By combining admin-based income and admin-based ethnicity datasets, we established an income and a stated ethnicity for 77.1% of people in England and 82.1% of people in Wales aged 16 years and over in the admin-based Statistical Population Dataset (our population base).
 For the first time, we have produced income percentiles for ethnic groups at different levels of geography in England and Wales including national figures, regional figures and figures for local authorities and lower layer super output areas; although statistical disclosure control means that some of the figures have been suppressed.
 We will highlight some of the challenges in using administrative data sources to produce these statistics and in assessing their statistical quality.
 ConclusionOur research developing these statistics is truly novel and shows much promise. Future work will include research to improve the univariate admin-based measures that are used, to continue to explore the limitations of the combined dataset, to explore the data by occupied address, and explore methods to adjust for missingness.

Socioeconomic Deprivation as a Predictor of Lower Limb Amputation in Diabetic Foot Disease.

Objective The prevalence of diabetes and its complications are on the rise worldwide. This is particularly prevalent in low- and middle-income countries. The Index of Multiple Deprivation (IMD) is a measure of relative social deprivation. This index classifies England into small subsets called lower layer super output areas (LSOAs) and then ranks these LSOAs into deciles from the most deprived to the least deprived area. We used this to analyse the relationship between deprivation and outcomes in diabetic foot disease (DFD). Methods We evaluated patients admitted to our multidisciplinary unit with DFD over a five-year period. Their postal codes were used to classify these patients into IMD deciles with decile 1 representing the 10% of most deprived LSOAs and decile 10 the least deprived areas in the country. We used this classification to analyse the relationship between deprivation and its influence on surgical outcomes and lower limb amputation. We specifically compared those falling in the top five against the bottom five deciles. Results Our cohort consisted of 70 patients with diabetes who had surgery on their diabetic foot. Of this cohort, 33 (47%) of these underwent amputation. The majority of these procedures were minor amputations conducted on the forefoot (75.6%) and there were no below knee amputations. Of those requiring an amputation, 27 patients (81.8%) had an IMD decile of 5 or below, and only six (18.2%) patients had above 5. Conclusion Our study shows that the likelihood of amputations related to diabetic foot disease is inversely proportional to the index of multiple deprivation.

Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London

Cycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to road safety. This research addresses the identification of cyclists’ risk factors using deep learning techniques applied to a Google Street View (GSV) imagery dataset. The research utilizes a case study approach, focusing on London, and applies object detection and image segmentation models to extract cyclists’ risk factors from GSV images. Two state-of-the-art tools, You Only Look Once version 5 (YOLOv5) and the pyramid scene parsing network (PSPNet101), were used for object detection and image segmentation. This study analyzes the results and discusses the technology’s limitations and potential for improvements in assessing cyclist safety. Approximately 2 million objects were identified, and 250 billion pixels were labeled in the 500,000 images available in the dataset. On average, 108 images were analyzed per Lower Layer Super Output Area (LSOA) in London. The distribution of risk factors, including high vehicle speed, tram/train rails, truck circulation, parked cars and the presence of pedestrians, was identified at the LSOA level using YOLOv5. Statistically significant negative correlations were found between cars and buses, cars and cyclists, and cars and people. In contrast, positive correlations were observed between people and buses and between people and bicycles. Using PSPNet101, building (19%), sky (15%) and road (15%) pixels were the most common. The findings of this research have the potential to contribute to a better understanding of risk factors for cyclists in urban environments and provide insights for creating safer cities for cyclists by applying deep learning techniques.

O020 Comparison of indices of deprivation and screening uptake within a local AAA screening programme

Abstract Introduction The NHS Abdominal Aortic Aneurysm Screening Programme (NAAASP) was established nationally to reduce the number of deaths caused by ruptured AAA. NAAASP invites all men in their 65th year for a simple abdominal ultrasound scan. Across the country uptake to the programme varies greatly. The aim of this study is to determine the impact of deprivation on local screening programme attendance. Methods The attendance for patients invited to screening within the Leicester, Leicestershire and Rutland screening NAAASP programme between the years 2016 and 2021 was compared to indices of multiple deprivation (IMD) (derived from their Lower Layer Super Output Area (LSOA) for each patient). Multiple logistic regression was used to determine the most significant component contributing to the IMD score. Results 28,730 men in total were invited to attend. Mean IMD for those that attended screening was 15.6 versus 20.6 for non-attenders (P value <0.001). Multiple regression revealed the IMD domains most associated with non-attendance to screening to be; Health Deprivation and Disability domain (P<0.001), Outdoor living environment domain (P<0.001) and Adult skills and English language proficiency domain (P<0.001). Barriers to housing and service domain including ‘geographical barriers’ which relate to the physical proximity of local services and employment domains did not show any association with attendance. Conclusion In line with the contemporary medical literature, social deprivation affects the uptake of the local National Abdominal Aortic Aneurysm Screening Programme (NAAASP). Health deprivation and disability, Outdoor living environment and adult education domains are most contributory to non-attendance.

Modelling socio-economic mortality at neighbourhood level

AbstractIn this study, we quantify the relationship between socio-economic status and life expectancy and identify combinations of socio-economic variables that are particularly useful for explaining mortality differences between neighbourhoods in England. We achieve this by examining socio-economic variation in mortality experiences across small areas in England known as lower layer super output areas (LSOAs). We then consider 12 socio-economic variables that are known to have a strong association with mortality. We estimate the relationship between those variables and mortality rates using a random forest algorithm. Based on the resulting estimate, we then create a new socio-economic mortality index – the Longevity Index for England (LIFE). The index is constructed in a way that eliminates the impact of care homes that might artificially increase mortality rates in LSOAs with care homes compared to LSOAs that do not contain a care home. Using mortality data for different age groups, we make the index age-dependent and investigate the impact of specific socio-economic characteristics on the age-specific mortality risk. We compare the explanatory power of the LIFE index to the English Index of Multiple Deprivation (IMD) as predictors of mortality. While we find that the IMD can explain regional mortality differences to some extent, the LIFE index has significantly greater explanatory power for mortality differences between regions. Our empirical results also indicate that income deprivation amongst the elderly and employment deprivation are the most significant socio-economic factors for explaining mortality variation across LSOAs in England.

Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data.

London has outperformed smaller towns and rural areas in terms of life expectancy increase. Our aim was to investigate life expectancy change at very-small-area level, and its relationship with house prices and their change. We performed a hyper-resolution spatiotemporal analysis from 2002 to 2019 for 4835 London Lower-layer Super Output Areas (LSOAs). We used population and death counts in a Bayesian hierarchical model to estimate age- and sex-specific death rates for each LSOA, converted to life expectancy at birth using life table methods. We used data from the Land Registry via the real estate website Rightmove (www.rightmove.co.uk), with information on property size, type and land tenure in a hierarchical model to estimate house prices at LSOA level. We used linear regressions to summarise how much life expectancy changed in relation to the combination of house prices in 2002 and their change from 2002 to 2019. We calculated the correlation between change in price and change in sociodemographic characteristics of the resident population of LSOAs and population turnover. In 134 (2.8%) of London's LSOAs for women and 32 (0.7%) for men, life expectancy may have declined from 2002 to 2019, with a posterior probability of a decline >80% in 41 (0.8%, women) and 14 (0.3%, men) LSOAs. The life expectancy increase in other LSOAs ranged from <2 years in 537 (11.1%) LSOAs for women and 214 (4.4%) for men to >10 years in 220 (4.6%) for women and 211 (4.4%) for men. The 2.5th-97.5th-percentile life expectancy difference across LSOAs increased from 11.1 (10.7-11.5) years in 2002 to 19.1 (18.4-19.7) years for women in 2019, and from 11.6 (11.3-12.0) years to 17.2 (16.7-17.8) years for men. In the 20% (men) and 30% (women) of LSOAs where house prices had been lowest in 2002, mainly in east and outer west London, life expectancy increased only in proportion to the rise in house prices. In contrast, in the 30% (men) and 60% (women) most expensive LSOAs in 2002, life expectancy increased solely independently of price change. Except for the 20% of LSOAs that had been most expensive in 2002, LSOAs with larger house price increases experienced larger growth in their population, especially among people of working ages (30-69 years), had a larger share of households who had not lived there in 2002, and improved their rankings in education, poverty and employment. Large gains in area life expectancy in London occurred either where house prices were already high, or in areas where house prices grew the most. In the latter group, the increases in life expectancy may be driven, in part, by changing population demographics. Wellcome Trust; UKRI (MRC); Imperial College London; National Institutes of Health Research.

Household wealth, neighbourhood deprivation and frailty amongst middle-aged and older adults in England: a longitudinal analysis over 15 years (2002–2017)

frailty is a condition of reduced function and health due to ageing processes and is associated with a higher risk of falls, hospitalisation, disability and mortality. to determine the relationship between household wealth and neighbourhood deprivation with frailty status, independently of demographic factors, educational attainment and health behaviours. population-based cohort study. communities in England. in total 17,438 adults aged 50+ from the English Longitudinal Study of Ageing. multilevel mixed-effects ordered logistic regression was used in this study. Frailty was measured using a frailty index. We defined small geographic areas (neighbourhoods) using English Lower layer Super Output Areas. Neighbourhood deprivation was measured by the English Index of Multiple Deprivation, grouped into quintiles. Health behaviours included in this study are smoking and frequency of alcohol consumption. the proportion of respondents who were prefrail and frail were 33.8% [95% confidence interval (CI) 33.0-34.6%] and 11.7 (11.1-12.2)%, respectively. Participants in the lowest wealth quintile and living in the most deprived neighbourhood quintile had 1.3 (95% CI = 1.2-1.3) and 2.2 (95% CI = 2.1-2.4) times higher odds of being prefrail and frail, respectively, than the wealthiest participants living in the least deprived neighbourhoods Living in more deprived neighbourhood and poorer wealth was associated with an increased risk of becoming frail. Those inequalities did not change over time. in this population-based sample, living in a deprived area or having low wealth was associated with frailty in middle-aged and older adults. This relationship was independent of the effects of individual demographic characteristics and health behaviours.

Deprivation based inequality in NOx emissions in England

Deprivation based inequality in NOx emissions in England, based on index of multiple deprivation decile and mean emissions per lower layer super output area.

Prediction of diabetes prescription volumes of various geographies using regression techniques.

Background: Increasing diabetes prevalence is a major public health concern. In this study we ask whether linked open data can be used to predict prescription volumes of drugs used in the treatment of diabetes across small geographies of England. Methods: We propose and demonstrate a methodology of utilising publicly available open data to infer the geo-spatial distribution of prescribed drugs for diabetes, at the lower layer super output area level. Multiple datasets are acquired, processed, and linked together, enabling a more in-depth analysis. Combining these linked datasets with published deprivation factors of geographies across England, we build highly predictive regression models. Results: Regression models were trained and are capable of accurately predicting diabetes prescribing volumes based on deprivation indicators of various geographies across England. Models built with data covering the city of Bradford, England, produced a predicted against actual correlation value of R2 = 0.672 using multiple linear regression and 0.775 using Least Absolute Shrinkage and Selection Operator (LASSO). Median age and air quality factors proved to be significant markers for diabetes prescribing. Conclusions: The results of this study suggest our methodology is robust and accurate. Such predictive models are useful to health authorities in light of increasing costs and increasing prevalence of diabetes. While using publicly available open data negates any issues of data privacy.

Influence of maternal socioeconomic deprivation and living environment on newborn bloodspot 25-hydroxyvitamin D levels.

Vitamin D deficiency in neonates can have life-threatening consequences, hence the knowledge of risk factors is essential. This study aimed to explore the effect of maternal socioeconomic status (SES) on newborn 25-hydroxyvitamin D (25OHD) concentrations. Over two 1-week periods (winter and summer of 2019), 3000 newborn heel prick dried blood spots (DBS) and additional data of newborns, from a regional newborn screening laboratory (52° N) in the West Midlands, UK, were gathered. Post code was replaced with lower layer super output area (LSOA). Index of Multiple Deprivation (IMD) quintiles for the corresponding LSOA was used to assess SES [quintile one (Q1): most deprived 20%, quintile five (Q5): least deprived 20%]. Each of the seven domains of deprivation were examined (income, employment, education, health, barriers to housing and services, crime and living environment). 25OHD was measured on 6mm sub-punch from DBS using quantitative liquid chromatography tandem mass spectrometry and equivalent plasma values were derived. In total 2999 (1500 summer-born, 1499 winter-born) newborn DBS (1580 males) were analysed. Summer-born newborns had significantly higher 25OHD (IQR) concentrations [49.2 (34.3; 64.8) nmol/l] than winter-born newborns [29.1 (19.8; 40.6) nmol/l, p<0.001].25OHD levels varied significantly between the different IMD quintiles in the whole (p<0.001) and summer-born cohort (p<0.001), but not in the winter-born cohort (p=0.26), whereby Q1 had the lowest 25OHD concentrations. Among the domains of deprivation, living environment had a significant influence on 25OHD levels (β=0.07, p=0.002). In this subdomain, 25OHD levels varied significantly between quintiles in the whole (p<0.001) and summer-born cohort (mean 25OHD Q1 46.45 nmol/l, Q5 54.54 nmol/l; p<0.001) but not in the winter-born cohort (mean 25OHD Q1 31.57 nmol/l, Q5 31.72 nmol/l; p=0.16). In a regression model, living environment was still significant (p=0.018), albeit less than season of birth and ethnicity. Among the seven domains of deprivation, maternal living environment had the greatest effect on newborn 25OHD levels. Whilst improved living environment positively influenced vitamin D status in the summer-born babies, winter-born had low 25OHD levels irrespective of the environment. Strategies such as enhanced supplementation and food fortification with vitamin D should be considered to overcome the non-modifiable main risk factors for vitamin D deficiency.

Exploring the heterogeneous effects of zonal factors on bicycle injury severity: latent class clustering analysis and partial proportional odds models

Despite the benefits of cycling being widely accepted, bicycle safety—especially severe injury—has received increasing attention due to the vulnerability of bicyclists on the road. Factors contributing to varying bicycle injury severity have been identified in the literature. For the zonal factors, variables related to sociodemographic and household characteristics, built environments, land use, and traffic conditions are considered. However, it is rare that the heterogeneity and hierarchal features of bicycle injury severity are simultaneously considered. This study contributes to the literature by investigating the heterogeneous effects of zonal factors on varying bicycle injury severity, using a 3-year crash data set from the Lower Layer Super Output Areas of London. A combination of latent class clustering and partial proportional odds methods was developed. First, five subgroups of bicycle crashes were identified based on the latent class clustering method. Afterward, partial proportional models were developed separately for different clusters. Results indicate that a series of factors is found to be associated with the occurrence of severe bicycle injuries. However, effects of these factors could be distinctive among different clusters. For example, some factors only have significant impacts in the specific crash clusters. Furthermore, heterogeneous effects of the same factors in one or different clusters are discovered. The findings of this study can be helpful for the development of cycle infrastructures, traffic management, and safety education that can enhance the risk perception of bicyclists and reduce the occurrence of severe bicycle injuries.

Autism incidence and spatial analysis in more than 7 million pupils in English schools: a retrospective, longitudinal, school registry study

Understanding how certain factors affect autism incidence can help to identify inequities in diagnostic access. We aimed to investigate the incidence of autism in England as a function of geography and sociodemographics, examining spatial distribution across health service boundaries. In this retrospective, longitudinal, school registry study, we sourced data for the years 2014-17 from the summer school census, which is a component of the National Pupil Database, a government registry of pupils under state education in England. Our main outcome was the incidence of autism in the English state-funded education system, defined by the amount of new autism-specific Education, Health and Care Plans or autism-specific special education needs and disability support recorded during each summer school census year since the 2014 baseline. After excluding prevalent cases in 2014, we calculated unadjusted incidence and age-adjusted, sex-adjusted incidence per 100 000 person-years per subsequent school year and by various sociodemographic categories and local authority districts. We report spatial effects using local indicators of spatial association. We used a three-level mixed-effects logistic regression model with two random intercepts (lower-layer super output area [a geographical area in England containing 1000-3000 residents] and pupil identifier) to calculate odds ratios (ORs) for autism incidence, adjusting for age, sex, ethnicity, claimed eligibility for free school meals, ethnic density quintile, Index of Multiple Deprivation quintile, first language spoken at home, and year, with our reference category being White girls without claimed eligibility for free school meals who speak English as their first language. Between 2014 and 2017, our total sample included 31 580 512 person-years and 102 338 newly diagnosed autistic pupils, corresponding to an unadjusted annual autism incidence of 429·1 cases per 100 000 person-years (95% CI 426·4-431·7) and an age-adjusted, sex-adjusted annual incidence of 426·9 cases per 100 000 person-years (423·5-430·4). The adjusted incidence of autism was slightly higher in 2014-15 than in 2015-16 or 2016-17, and, of the age groups, pupils aged 1-3 years, 4-6 years, and 10-12 years had the highest incidence of autism. Adjusted autism incidence in boys was 3·9-times the incidence in girls (668·6 cases per 100 000 person-years [95% CI 662·5-674·6] vs 173·2 cases per 100 000 person-years [170·1-176·3]). Across ethnic groups, adjusted incidence was highest in pupils who had an unclassified ethnicity (599·4 cases per 100 000 person-years [574·5-624·3]) or were Black (466·9 cases per 100 000 person-years [450·8-483·0]). However, in our fully adjusted mixed-effects logistic regression model, we observed lower odds of autism among Asian (OR 0·65 [0·59-0·71]), Black (0·84 [0·77-0·92]), and Chinese (0·62 [0·42-0·92]) girls compared with White girls when these groups had not claimed free school meals and spoke English as a first language. Boys from all ethnicities irrespective of first language spoken and free school meals status had increased odds of autism compared with White girls with no claimed eligibility for free school meals who spoke English as their first language. We also found that claimed free school meal eligibility, first language spoken, sex, and ethnicity differentially impacted the odds of autism. Our spatial analysis showed significant spatial autocorrelation across lower-layer super output areas in England, with 2338 hotspots (high-incidence areas surrounded by other high-incidence areas). The incidence of autism varies across sex, age, ethnicity, and geographical location. Environmental and social factors might interact with autism aetiology. Speaking a language other than English and economic hardship might increase access barriers to autism diagnostic services, autism-specific Education, Health and Care Plans, and school-level support. The Commonwealth Fund, the Institute for Data Valorization, the Fonds de recherche du Québec-Santé, Calcul Quebec, the Digital Research Alliance of Canada, the Wellcome Trust, the Innovative Medicines Initiative, the Autism Centre of Excellence, the Simons Foundation Autism Research Initiative, the Templeton World Charitable Fund, the Medical Research Council, the National Institute for Health and Care Research Cambridge Biomedical Research Centre, and the National Institute for Health and Care Research Applied Research Collaboration East of England-Population Evidence and Data Science.

A crash feature-based allocation method for boundary crash problem in spatial analysis of bicycle crashes

In conventional safety analysis, traffic and crash data are often aggregated at the geographical units like census tracts, street blocks, and traffic analysis zones, which are often delineated by roads and other physical entities. A considerable proportion of crashes may occur at or near the boundary of geographical units. Such the crashes, also known as boundary crashes, can correlate with the explanatory variables of neighboring geographical units, regardless of the spatial proximity. This could then bias the parameter estimation of crash frequency model. In this study, a novel data-driven approach is developed for the allocation of boundary crashes. For example, crash severity and bicyclist characteristics are considered in the crash feature-based allocation. An illustrative case study based on built environment, population, traffic and bicycle crash data from 289 Lower Layer Super Output Areas (LSOAs) of London in the period 2017–2019 was conducted. Results indicate that high matching percentages of boundary crash allocation can be achieved. Furthermore, prediction performances, in terms of root mean square error (RMSE) and mean absolute error (MAE), of the crash frequency models based on the proposed crash feature-based allocation method is superior, compared to that based on conventional boundary crash allocation methods like half-and-half and iterative assignment approaches. Last but not least, more influencing factors that affect the bicycle crash frequency at macroscopic level can be identified. Findings should be indicative to the spatial safety analysis for different geographical configurations.