Lower Layer Super Output Areas Research Articles

Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London

Delineating urban functional use plays a key role in understanding urban dynamics, evaluating planning strategies and supporting policymaking. In recent years, Points of Interest (POI) data, with precise geolocation and detailed attributes, have become the primary data source for exploring urban functional use from a bottom-up perspective, using local, highly disaggregated, big datasets. Previous studies using POI data have given insufficient consideration to the relationship among POI classes in the spatial context, and have failed to provide a straightforward means by which to classify urban functional areas. This study proposes an approach for delineating urban functional use at the scale of the Lower Layer Super Output Area (LSOA) in Greater London by integrating the Doc2Vec model, a neural network embedding method commonly used in natural language processing for vectoring words and documents from their context. In this study, the neural network vectorises both POI classes (‘Word’) and urban areas (‘Document’) based on their functional context by learning features from the spatial distribution of POIs in the city. Specifically, we first construct POI sequences based on the distribution of POI classes, and add their LSOA IDs as ‘document’ tags. By utilising these constructed POI–LSOA sequences, the Doc2Vec model trains the vectors of 574 POI classes (word vectors) and 4836 LSOAs (document vectors). The vectors of POI classes are then used in calculating the functional similarity scores based on their cosine distance, with the vectors of LSOAs grouped into clusters (i.e., functional areas) via the k-means clustering algorithm. We also identify latent functions in each cluster of LSOAs by performing topic modelling and enrichment factor. Compared with TF–IDF, LDA and Word2Vec models, the Doc2Vec model obtains the highest accuracy when classifying functional areas. This study proposes a straightforward approach in which the model directly trains vectors for urban areas, subsequently using them to classify urban functional areas. By employing the enhanced neural network model with low-cost and ubiquitous POI datasets, this study provides a potential tool with which to monitor urban dynamics in a timely and adaptive manner, thereby providing enhanced, data-driven support to urban planning, development and management.

Home Energy Efficiency and Subjective Health in Greater London

The UK has introduced legislation that requires net-zero greenhouse gas emissions to be achieved by 2050. Improving the energy efficiency of homes is a key objective to help reach this target, and the UK government’s Clean Growth Strategy aims to get many homes up to an Energy Performance Certificate (EPC) Band of C by 2035. The relationship between home energy-efficiency and occupant health and wellbeing remains an area of ongoing research. This paper explores the nexus between home energy efficiency, energy consumption and self-reported health—an indicator of the general health and wellbeing of the population. We focus on Greater London through secondary data analysis. Energy-efficiency ratings and air infiltration rates of dwellings, derived from EPCs, were aggregated and matched to local area self-reported health and energy consumption data obtained from the Greater London Authority’s (GLA) Lower Layer Super Output Area (LSOA) Atlas database. Our regression model indicates that improving the energy efficiency (SAP) rating by 10 points for a typical home may reduce household gas consumption by around 7% (95% CIs: 2%, 14%). Beta regression finds a positive, but not statistically significant association between median SAP rating and the proportion of the population reporting ‘good or very good’ health when considering all Greater London LSOAs (z score = 0.60, p value = 0.55). A statistically significant positive association is observed however when repeating the analysis for the lowest income quartile LSOAs (z score = 2.03, p value = 0.04). This indicates that the least well-off may benefit most from home energy efficiency programs. A statistically significant positive association is also observed for the relationship between self-reported health and air infiltration rates (z score = 2.62, p value = 0.01). The findings support existing evidence for the predominantly naturally ventilated UK housing stock, suggesting that home energy efficiency measures provide a co-benefit for occupant health provided that adequate air exchange is maintained.

Association of socioeconomic deprivation with opioid prescribing in primary care in England: a spatial analysis

BackgroundThe increasing trends in opioid prescribing and opioid-related deaths in England are concerning. A greater understanding of the association of deprivation with opioid prescribing is needed to guide policy responses...

Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis

Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014–2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: −0.2%, 1.2%) and 1.4% (95% CrI: −2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.

Multiple deprivation and geographic distance to community physical activity events — achieving equitable access to parkrun in England

ObjectivesTo evaluate geographic access to free weekly outdoor physical activity events (‘parkrun’) in England, with a particular focus on deprived communities, and to identify optimal locations for future events to further maximise access. Study designThis study is a cross-sectional ecological analysis of the socio-economic disparities in geographic access to parkrun events in England in late 2018. MethodsWe combined geolocation data on all English Lower Layer Super Output Areas and parkrun events to calculate geodesic distances to the nearest event for more than 32,000 communities in England. We use this measure of geographic access to summarise the relationship between access and socio-economic deprivation, measured using the index of multiple deprivation. We then used geographic coordinates of public green spaces in England to conduct a simple location-allocation analysis to identify 200 locations for future event locations that would maximise access. ResultsIn England, 69% of the population live within 5 km of one of the 465 parkrun events. There is a small negative correlation between distance and deprivation, indicating that access is slightly better in more socio-economically deprived areas. Setting up an additional 200 events in optimal locations would improve access: the average distance to the nearest parkrun event would improve by 1.22 km, from 4.65 km to 3.43 km, and approximately 82% of the English population would live within 5 km of a parkrun event. ConclusionOver two-thirds of the English population live within 5 km of a parkrun event, and contrary to our expectation, we find that geographic access is slightly better for those living in more deprived communities. Creating additional events may improve geographic access, but effective strategies will still be needed to increase engagement in new and existing events by those living in socio-economically deprived areas.

The evolution and stability of multi-ethnic residential neighbourhoods in England.

This paper analyses the most ethnically diverse spaces in England. We define multi-ethnic neighbourhoods as spaces where no one group is in a majority and at least five ethnic groups have representation. Around 4% of all English neighbourhoods (Lower Layer Super Output Areas) met these criteria in 2011. Often mislabelled as “segregated” spaces, the growth of ethnically diverse neighbourhoods helps benchmark increased inter-ethnic contact, yet we know very little about their spatial extent and the dynamics of their expansion. We use Census data for 1991, 2001, and 2011 to consider how neighbourhood-level diversity has changed during a period of substantial increase in ethnic diversity at the national scale. To what extent did these highly diverse areas grow, and what is the geography of that growth? Which types of areas did these neighbourhoods transition from? For example, were multi-ethnic neighbourhoods formerly low or moderately diverse, and which groups dominated these locales? We also consider if multi-ethnic neighbourhoods are here to stay, or if they are compositionally unstable. We reveal a surprising aspect in England’s neighbourhood transitions: multi-ethnic neighbourhoods are highly stable, and increasingly so. Some 88% of neighbourhoods that were multi-ethnic in 1991 retained their high-diversity status in 2001, while over 95% of 2001 multi-ethnic neighbourhoods remained highly diverse by 2011. This is a different story to that of the USA, where high-diversity neighbourhoods have received more scholarly attention, and where these neighbourhoods have high attrition rates, functioning as stepping stones to another type of space. We explore the demographic and housing dynamics associated with this stability.

Effect of London cycle hire scheme on bicycle safety

This study evaluates the effect of London Cycle Hire scheme (LCH) on bicycle crashes, based on the data from 333 Lower Layer Super Output Areas (LSOAs) in the period 2011–2012. The Propensity Score Matching method (PSM) is applied to evaluate the effects of policy interventions (‘treatment’) on bicycle safety, with which the effects of confounding factors on the treatment effects are accounted, using the systematically established untreated groups. Covariates including land use, traffic and population characteristics are considered when selecting the untreated group for each treated unit. Results of PSM indicated that numbers of overall and slight injury bicycle crashes increased by 37.7% and 31.8% when LCH was introduced. Additionally, the interaction by another transport management policy – London Congestion Charging scheme (LCC) – on the effects of LCH on bicycle crash was estimated. Numbers of overall and slight injury bicycle crash further increased by 59.1% and 57.8% because of the implementation of LCC. For the killed or seriously injured (KSI) bicycle crashes, increases were observed in both cases (i.e. 81% for LCH and 66% for LCC), despite that they are not statistically significant. Results are indicative to the design and planning of bicycle infrastructure that could enhance the overall bicycle safety in London.

Small-area level socio-economic deprivation and tuberculosis rates in England: An ecological analysis of tuberculosis notifications between 2008 and 2012.

BackgroundTuberculosis (TB) rates in England are among the highest in high-income countries. Poverty and historic and current immigration from high TB incidence parts of the world are two major drivers of tuberculosis in England. However, little has been done in recent years to examine socio-economic trends in TB rates in England, and to disentangle the role of deprivation from that of place of birth in the current TB epidemiology.ObjectivesTo assess the association between England’s 2008–2012 TB notification rates and small area-level deprivation, together and separately in the UK-born and foreign-born populations.MethodsEcological analysis of the association between quintiles of England’s 2010 Index of Multiple Deprivation (IMD) and TB rates at the Lower-layer Super Output Area (LSOA; average population ~1500) level, using negative binomial and zero-inflated negative binomial regression models, adjusting for age, sex, urban/rural area classification, and area-level percentage of non-White residents.ResultsThere was a log-linear gradient between area-deprivation levels and TB rates, with overall TB rates in the most deprived quintile areas three times higher than the least deprived quintile after adjustment for age and sex (IRR = 3.35; 95%CI: 3.16 to 3.55). The association and gradient were stronger in the UK-born than the foreign-born population, with UK-born TB rates in the most deprived quintiles about two-and-a-half times higher than the least deprived quintile (IRR = 2.39; 95%CI: 2.19 to 2.61) after controlling for age, sex, urban/rural classification and percentage of non-White residents; whereas the comparable figure for foreign-born persons was 80% higher (IRR = 1.78; 95%CI: 1.66 to 1.91).ConclusionsSocio-economic deprivation continues to play a substantial role in sustaining the TB epidemic in England, especially in the UK-born population. This supports the case for further investigations of the underlying social- determinants of TB.

Field validity and spatial accuracy of Food Standards Agency Food Hygiene Rating scheme data for England.

BackgroundThe study aimed to evaluate the validity and spatial accuracy of the Food Standards Agency Food Hygiene Rating online data through a field audit.MethodsA field audit was conducted in five Lower Layer Super Output Areas (LSOAs) in the North East of England. LSOAs were purposively selected from the top and bottom quintiles of the Index of Multiple Deprivation and from urban and rural areas. The FHRS data validity against the field data was measured as Positive Predictive Values (PPV) and sensitivity. Spatial accuracy was evaluated via mean difference in straight line distances between the FHRS coordinates and the field coordinates.ResultsIn all, 182 premises were present in the field, of which 162 were in the FHRS data giving a sensitivity of 89%. Eight outlets recorded in the FHRS data were absent in the field, giving a PPV of 95%.The mean difference in the geographical coordinates of the field audit compared to the FHRS was 110 m, and <100 m for 77% of outlets.ConclusionsAfter an evaluation of the validity and spatial accuracy of the FHRS data, the results suggest that it is a useful dataset for surveillance of the food environment and for intervention evaluation.

Who is responsible? Defining a hospital catchment population in the English National Health Service

Abstract Background There is a growing emphasis on National Health Service hospitals in England promoting population health. Patients can access any hospital, making it complex to define the population a hospital is responsible for. Defining this 'catchment' population is fundamental to provide a population denominator from which to evaluate service provision such as unmet need and the effect of prevention initiatives. Using Chelsea and Westminster Hospital NHS Foundation Trust (CWFT), a large hospital in London as a case study, methods to define the population that has potential to attend the hospital were compared. Methods Inpatient, outpatient and emergency attendances were identified using Hospital Episode Statistics from 1st April 2017-31st March 2018. Lower Layer Super Output Areas (LSOAs), consisting of 1,500 people on average, were used as the geographic unit. Catchment populations were constructed using 3 different methods. Under First-Past-The-Post (FPTP), LSOAs were allocated if a greater proportion of patients attended CWFT than any other hospital trust. Under 30% Proportional Flow (30PF), LSOAs were allocated if more than 30% of patients attended CWFT, while under Stratified Proportional Allocation (SPA), patients were assigned in accordance with the proportion from each LSOA that attended CWFT, by gender and 5-year age strata. Results Under FPTP, 30PF and SPA, a total of 303, 326 and 10,636 LSOAs were assigned to CWFT, respectively, with corresponding populations of 530,980, 569,682, and 484,249 and median ages of 36, 36 and 29 years. Under FPTP, the catchment area did not overlap with that of any other hospital, while under 30PF, 13.2% of the LSOAs were also allocated to another hospital catchment. Maps were constructed for FPTP and 30PF. Conclusions The 3 methods produced different catchment populations, with differing characteristics. Understanding the relative merits of each method has implications for hospitals in how they engage in and evaluate population health. Key messages Engagement in and evaluation of public health activities requires knowledge of ‘who’ the baseline population denominator is, but there is no consensus on determining this at the hospital level. Comparing 3 methods to define a hospital catchment, these differ by total population, median age and geography, the choice of which impacts on how hospitals engage in population health.

Neighbourhood unemployment and other socio-demographic predictors of emergency hospitalisation for infectious intestinal disease in England: A longitudinal ecological study

Previous studies have observed that infectious intestinal disease (IID) related hospital admissions are higher in more deprived neighbourhoods. These studies have mainly focused on paediatric populations and are cross-sectional in nature. This study examines recent trends in emergency IID admission rates, and uses longitudinal methods to investigate the effects of unemployment (as a time varying measure of neighbourhood deprivation) and other socio-demographic characteristics on IID admissions for adults and children in England. A longitudinal ecological analysis was performed using Hospital Episode Statistics on emergency hospitalisations for IID, collected over the time period 2012-17 across England. Analysis was conducted at the neighbourhood (Lower-layer Super Output Area) level for three age groups (0-14; 15-64; 65+ years). Mixed-effect Poisson regression models were used to assess the relationship between trends in neighbourhood unemployment and emergency IID admission rates, whilst controlling for measures of primary and secondary care access, underlying morbidity and the ethnic composition of each neighbourhood. From 2012-17, declining trends in emergency IID admission rates were observed for children and older adults overall, while rates increased for some sub-groups in the population. Each 1 percentage point increase in unemployment was associated with a 6.3, 2.4 and 4% increase in the rate of IID admissions per year for children [IRR=1.06, 95%CI 1.06-1.07], adults [IRR=1.02, 95%CI 1.02-1.03] and older adults [IRR=1.04, 95%CI 1.036-1.043], respectively. Increases in poor primary care access, the percentage of people from a Pakistani ethnic background, and the prevalence of long-term health problems, in a neighbourhood, were also associated with increases in IID admission rates. Increasing trends in neighbourhood deprivation, as measured by unemployment, were associated with increases in emergency IID admission rates for children and adults in England, despite controlling for measures of healthcare access, underlying morbidity and ethnicity. Research is needed to improve understanding of the mechanisms that explain these inequalities, so that effective policies can be developed to reduce the higher emergency IID admission rates experienced by more disadvantaged communities.

An examination of trends in antibiotic prescribing in primary care and the association with area-level deprivation in England

BackgroundInternationally, there are growing concerns about antimicrobial resistance. This has resulted in increased scrutiny of antibiotic prescribing trends – particularly in primary care where the majority of prescribing occurs. In England, antibiotic prescribing targets are set nationally but little is known about the local context of antibiotic prescribing. This study aimed to examine trends in antibiotic prescribing (including broad-spectrum), and the association with area-level deprivation and region in England.MethodsAntibiotic prescribing data by GP surgery in England were obtained from NHS Business Service Authority for the years 2014–2018. These data were matched with the Index of Multiple Deprivation (IMD) 2015 at the Lower Layer Super Output Area level Lower Layer Super Output Area (LSOA) level. Linear regression methods were employed to explore the relationship between antibiotic use and area-level deprivation as well as region, after controlling for a range of other confounding variables, including health need, rurality, and ethnicity.ResultsOver time, the amount of antibiotic prescribing significantly reduced from 1.11 items per STAR-PU to 0.96 items per STAR-PU – a reduction of 13.6%. The adjusted models found that, at LSOA level, the most deprived areas of England had the highest levels of antibiotic prescribing (0.03 items per STAR-PU higher). However, broad spectrum antibiotic prescribing exceeding 10% of all antibiotic prescribing within a GP practice was higher in more affluent areas. There were also significant regional differences – with the North East and the East of England having the highest levels of antibiotic prescribing (by 0.16 items per STAR-PU).ConclusionAlthough antibiotic prescribing has reduced over time, there remains significant variation in by area-level deprivation and region in England – with higher antibiotic prescribing in more deprived areas. Future prescribing targets should account for local factors to ensure the most deprived communities are not inappropriately penalised.

Identifying naturally occurring communities of primary care providers in the English National Health Service in London

ObjectivesPrimary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that PCNs should represent ‘natural’...

Safety impacts of the discrepancies and accesses between adjacent traffic analysis zones

The analyses of the safety effects of the road network features can help to understand the risk factors in road crashes and provide useful policy implications. In macro-level analyses, adjacent traffic analysis zones (TAZs) could influence each other on road safety. Therefore, it is necessary to move beyond the common practice of just examining the road network of the TAZ itself and analyze how and to what extent the interrelated factors of the objective TAZ and its adjacent TAZs interact to affect crash frequency. This study aims to investigate the safety impacts of the discrepancies and accesses between adjacent TAZs. Based on data from London at the level of Lower Layer Super Output Area (LSOA), the Bayesian model was employed to analyze the safety effects of various factors related to the discrepancies and accesses (i.e., access number, discrepancies of road network density). A spatial weight based on the access number was applied when establishing the Bayesian spatial models. Results suggest that TAZs with greater discrepancies and more accesses from adjacent TAZs experience more crashes.

Forecasting spatial, socioeconomic and demographic variation in COVID-19 health care demand in England and Wales

BackgroundCOVID-19 poses one of the most profound public health crises for a hundred years. As of mid-May 2020, across the world, almost 300,000 deaths and over 4 million confirmed cases were registered. Reaching over 30,000 deaths by early May, the UK had the highest number of recorded deaths in Europe, second in the world only to the USA. Hospitalization and death from COVID-19 have been linked to demographic and socioeconomic variation. Since this varies strongly by location, there is an urgent need to analyse the mismatch between health care demand and supply at the local level. As lockdown measures ease, reinfection may vary by area, necessitating a real-time tool for local and regional authorities to anticipate demand.MethodsCombining census estimates and hospital capacity data from ONS and NHS at the Administrative Region, Ceremonial County (CC), Clinical Commissioning Group (CCG) and Lower Layer Super Output Area (LSOA) level from England and Wales, we calculate the number of individuals at risk of COVID-19 hospitalization. Combining multiple sources, we produce geospatial risk maps on an online dashboard that dynamically illustrate how the pre-crisis health system capacity matches local variations in hospitalization risk related to age, social deprivation, population density and ethnicity, also adjusting for the overall infection rate and hospital capacity.ResultsBy providing fine-grained estimates of expected hospitalization, we identify areas that face higher disproportionate health care burdens due to COVID-19, with respect to pre-crisis levels of hospital bed capacity. Including additional risks beyond age-composition of the area such as social deprivation, race/ethnic composition and population density offers a further nuanced identification of areas with disproportionate health care demands.ConclusionsAreas face disproportionate risks for COVID-19 hospitalization pressures due to their socioeconomic differences and the demographic composition of their populations. Our flexible online dashboard allows policy-makers and health officials to monitor and evaluate potential health care demand at a granular level as the infection rate and hospital capacity changes throughout the course of this pandemic. This agile knowledge is invaluable to tackle the enormous logistical challenges to re-allocate resources and target susceptible areas for aggressive testing and tracing to mitigate transmission.

Spatial heterogeneity and socioeconomic determinants of opioid prescribing in England between 2015 and 2018

BackgroundOpioid overdoses have had a serious impact on the public health systems and socioeconomic welfare of several countries. Within this broader context, we focus our study on primary care opioid prescribing in England from 2015 to 2018, particularly the patterns of spatial variations at the community level and the socioeconomic and environmental factors that drive consumption.MethodsLeveraging open data sources, we combine prescription records with aggregated data on patient provenance and build highly granular maps of Oral Morphine Equivalent (OME) prescribing rates for Lower Layer Super Output Areas (LSOA). We quantify the strength of spatial associations by means of the Empirical Bayes Index (EBI) that accounts for geographical variations in population density. We explore the interplay between socioeconomic and environmental determinants and prescribing rates by implementing a multivariate logistic regression model across different temporal snapshots and spatial scales.ResultsWe observe, across time and geographical resolutions, a significant spatial association with the presence of localized hot and cold spots that group neighboring areas with homogeneous prescribing rates (e.g., EBI = 0.727 at LSOA level for 2018). Accounting for spatial dependency effects, we find that LSOA with both higher employment deprivation (OR = 62.6, CI 52.8–74.3) and a higher percentage of ethnically white (OR = 30.1, CI 25.4–35.7) inhabitants correspond to higher prescribing rates. Looking at educational attainment, we find LSOA with the prevalent degree of education being apprenticeship (OR = 2.33, CI 1.96–2.76) a risk factor and those with level 4+ (OR = 0.41, CI 0.35–0.48) a protective factor. Focusing on environmental determinants, housing (OR = 0.18, CI 0.15–0.21) and outdoor environment deprivation (OR = 0.62, CI 0.53–0.72) indices capture the bi-modal behavior observed in the literature concerning rural/urban areas. The results are consistent across time and spatial aggregations.ConclusionsFailing to account for local variations in opioid prescribing rates smooths out spatial dependency effects that result in underestimating/overestimating the impact on public health policies at the community level. Our study suggests a novel approach to inform more targeted interventions toward the most vulnerable population strata.

Abstract MP05: Impact of Gentrification on Cardiovascular Risk Factors and Outcomes in England: 2004-2018

Introduction: Dynamic changes to neighborhoods due to forces such as gentrification impact the health of residents. Much of this research has been conducted within the United States, where racial disparities and access to healthcare impact risk for health outcomes. Internationally, other factors may play a more prominent role in the association between gentrification and cardiovascular risk factors and outcomes. Hypothesis: Residents living in gentrified vs. non-gentrified neighborhoods will have lower odds of diabetes (DM), hypertension (HTN), depression and cardiovascular disease (CVD). Methods: We defined gentrification using changes in domains of the Index of Multiple Deprivation (IMD) at the level of the Lower Layer Super Output Areas (LSOA) in England from 2004-2010. We used all IMD domains (income, employment, education, crime, barriers to housing, and living environment), except the health domain, from 2004 and 2010 to define LSOA deprivation. The IMD for each LSOA was standardized to the mean IMD of England using z-scores. LSOAs were eligible to be gentrified if they had a positive z-score in 2004 and were considered to gentrify if they had a negative change in the transformed IMD from 2004 to 2010. We linked these data to individual participants in the Understanding Society Study (USS). The USS is a nationally representative cohort study of 60,000 United Kingdom residents started in 2009 with follow-up ongoing. We limited the analysis to residents in England who lived in top and bottom 25% deprived LSOAs (n=8782). We used multivariable logistic regression to calculate the odds ratio for self-reported DM, HTN, depression, and CVD in residents in neighborhoods that did and did not gentrify, adjusting for race, sex, length of residence (LOR), baseline IMD score, and baseline prevalence of health conditions. Results were stratified by age (&lt;65 &amp; &gt;=65 years) and median LOR (&lt;13 &amp; &gt;=13 years). Results: At baseline, 8782 participants had a median age of 43 years, 4% were black and 55% were female. There was no significant difference in the prevalence of DM, HTN, depression, or CVD at baseline. At follow-up, overall, there were no significant difference in the odds of DM, HTN, or CVD between residents living in gentrified vs. non-gentrified neighborhoods. Residents in neighborhoods that gentrified had a 39% lower odds of depression as compared to participants living in neighborhoods that did not gentrify (p=0.01). Results were not significantly modified by age or length of residence. Conclusions: Residents living in gentrified neighborhoods did not have differential risk for most CVD risk factors and outcomes as compared to residents living in neighborhoods that did not gentrify. However, the impact of gentrification on health is not uniform across all conditions. The positive health impact seen may suggest gentrification increases access to resources not present prior to gentrification.

Area Deprivation, Urbanicity and Severe Mental Illness – A Population-Based Linkage Study Using Routinely Collected Primary and Secondary Care Data

Background There is a long-standing debate regarding the associations between area deprivation, urbanicity and elevated risk of severe mental illnesses (SMIs).&#x0D; Main Aim We investigated the associations between area deprivation, urbanicity and risk of SMIs in a population cohorts in Wales.&#x0D; Methods/Approach We extracted primary and secondary care electronic health records from 2004 to 2015 from Wales’s population. We identified prevalent and incident individuals with SMIs (schizophrenia related disorders and bipolar disorder) and their level of deprivation and urbanicity. We used the Welsh Index of Multiple Deprivation (WIMD) and urban/rural indicator to measure the level of area deprivation and urbanicity respectively for all lower layer super output areas, the geographic units used in the reporting of small area statistics comprised of approximately 1,500 individuals.&#x0D; Results Prevalence and incidence of SMIs is not evenly distributed in Wales. Increased prevalence and incidence of SMIs occur in more deprived and urban areas. Such associations occur for both schizophrenia related disorders and bipolar disorder and in both the primary and secondary care cohorts.&#x0D; Conclusion These findings have implications for resource allocation, service configuration and access to services in deprived communities, as well as, for broader public health interventions addressing poverty, and social and environmental contexts.

Associations of area characteristics at birth with overweight and obesity among school-aged children in the south of England: an analysis of a population-based cohort

BackgroundGeographical inequalities in the prevalence of overweight and obesity among children are well established in cross-sectional research. We aimed to examine how area-level characteristics at birth are related to these outcomes in childhood. MethodsAnonymised antenatal and birth data recorded by University Hospital Southampton (2003–18) linked to school-measured weight and height data for children within Southampton, UK were used (14 084 children at ages 4–5 years, and 5 637 at ages 10–11 years). Children's home address at birth was provided at the lower layer super output area level (areas with average populations of 1500). Area-level indices (walkability, unhealthy or healthy food outlets, and spaces for social interaction), natural greenspace coverage, supermarket density, and measures of air pollution (PM2·5, PM10, and NOx) were constructed for lower layer super output areas using data from Ordnance Survey and open sources. Overweight or obesity was defined as a BMI greater than the 85th centile for sex and age. Population-average generalised estimating equations estimated the risk of being overweight or obese for children at both time points. Confounders accounted for included maternal BMI and smoking in early pregnancy, educational attainment, ethnicity, and parity. This project was approved by the NHS Health Research Authority (242031). FindingsNo significant associations were noted between area characteristics at birth and overweight or obesity at ages 4–5 years. The proportion of the lower layer super output area covered by greenspace was negatively associated with the risk of being overweight or obese at ages 10–11 years (relative risk ratio 0·997, 95% CI 0·995–0·999). This finding was consistent in a subgroup of children who moved residence (between different lower layer super output areas) between birth and time of measurement. InterpretationLocal access to natural greenspaces at the time of birth was negatively associated with becoming overweight or obese by the end of primary school, regardless of migration. However, the size of this protective effect is small. Access to and protection of greenspace might have a role in early prevention of childhood obesity. FundingSupported by an Academy of Medical Sciences and Wellcome Trust grant to NAA and the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre.

MicroEnv: A microsimulation model for quantifying the impacts of environmental policies on population health and health inequalities

The Sustainable Development Goals (SDGs) recognise the critical need to improve population health and environmental sustainability. This paper describes the development of a microsimulation model, MicroEnv, aimed at quantifying the impact of environmental exposures on health as an aid to selecting policies likely to have greatest benefit. Its methods allow the integration of morbidity and mortality outcomes and the generation of results at high spatial resolution. We illustrate its application to the assessment of the impact of air pollution on health in London. Simulations are performed at Lower Layer Super Output Area (LSOA), the smallest geographic unit (population of around 1500 inhabitants) for which detailed socio-demographic data are routinely available in the UK. The health of each individual in these LSOAs is simulated year-by-year using a health-state-transition model, where transition probabilities from one state to another are based on published statistics modified by relative risks that reflect the effect of environmental exposures. This is done through linkage of the simulated population in each LSOA with 1 × 1 km annual average PM2.5 concentrations and area-based deprivation indices. Air pollution is a leading cause of mortality and morbidity globally, and improving air quality is critical to the SDGs for Health (Goal 3) and Cities (Goal 11). The evidence of MicroEnv is aimed at providing better understanding of the benefits for population health and health inequalities of policy actions that affect exposure such as air quality, and thus to help shape policy decisions. Future work will extend the model to integrate other environmental determinants of health.