Middle Layer Super Output Area Research Articles

A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy.

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005–1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024–1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129–1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009–1.0036], percentage of adults aged 45–64 years old [RR = 1.0031, 95% CI 1.0024–1.0039], and particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PM}_{2.5}$$\\end{document}) concentrations [RR = 1.0126, 95% CI 1.0083–1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Identifying employee, workplace and population characteristics associated with COVID-19 outbreaks in the workplace: a population-based study

ObjectivesTo identify risk factors that contribute to outbreaks of COVID-19 in the workplace and quantify their effect on outbreak risk.MethodsWe identified outbreaks of COVID-19 cases in the workplace and investigated...

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...

Validation of a dynamic method of measuring households and populations from primary care Electronic Health Records: Cross-sectional comparison with Office for National Statistics Census 2021 estimates

ObjectivesWe have developed a dynamic method for identifying household members from Electronic Health Records (EHR). We compared the 2021 Census estimates of household number and demography with similar estimates derived from primary care EHRs on the Census date using primary care EHRs for the population of north east London (NEL).
 MethodWe included 2,115,017 patients registered with a general practitioner on the 2021 Census date in NEL and assigned households from encrypted Unique Property Reference Numbers. We compared household number and size by Local Authority (LA), Middle Layer Super Output Area (MSOA) and area’s Index of Multiple Deprivation quintiles (IMDq) to Office for National Statistics (ONS) 2021 Census estimates and by LA to ONS Admin Based Housing Stock (ABHS) 2020 estimates. We assessed differences in EHR and Census 2021 populations by sex, age, LA, MSOA and IMDq. Sensitivity analyses will exclude those without a recent recorded clinical encounter.
 ResultsEHR population estimates (2,115,017) were 116,346 (5.8%) higher than Census estimates (1,998,671), higher among men (9.2%) than women (2.5%) in almost all age groups, especially men aged 30-50 years and higher in the most (8.7%), than in the least (2.5%) deprived IMDq. EHR household estimates (660,789) were 68,047 (9.3%) lower than Census estimates (728,836), and 19,719 (3.1%) higher than ABHS occupied addresses (641,070). EHR household size estimates were 15.6%, 29.2%,12.5% and 8.4% lower for household sizes 1,2 3 and 4, and 13.3%, 42.1%, 82.1% and 195.8% higher for household sizes 5, 6,7 and 8 respectively when compared to Census estimates. EHR population and household estimates were respectively 5-10% higher and 5-11% lower for almost all NEL local authorities.
 ConclusionEHR- and Census-derived population and household estimates differ, mainly in the prevalence of larger households. While data were extracted on the same date, person-level validation was not possible. Differences may reflect deregistration delay in EHR when changing residence. Analyses based on clinical encounters recency may identify registered patients who are no longer residents.

Twitter mobility dynamics during the COVID-19 pandemic: A case study of London.

The current COVID-19 pandemic has profoundly impacted people's lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people's travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 -Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale.

Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities

This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city.

Impact of cuts to local authority spending on cultural, environmental and planning services on inequalities in childhood obesity in England: A longitudinal ecological study.

Almost 20% of children in England are living with obesity by the end of primary school, with marked and growing inequalities driven by increasing prevalence in more deprived areas. Neighbourhood environments are upstream determinants of childhood weight status. Cultural, Environmental and Planning (CEP) services delivered by local authorities (LAs) in England include various services that contribute to these local environments, e.g. leisure centres, parks, playgrounds, libraries, community safety and environmental protection. Children in deprived areas potentially benefit most from the provision of these universal services. Spending on CEP services has been cut dramatically over the past decade, especially in more deprived areas. Given the potential link between these services and childhood obesity, we examined whether recent cuts in LA spending on CEP services are associated with trends and inequalities in obesity. We compiled annual data (2009-2017) on CEP spending in 324 LAs in England, from Ministry of Housing, Communities and Local Government reports. Obesity prevalence data for Year 6 children were obtained from the National Child Measurement Programme, for LAs and Middle-layer Super Output Areas (MSOAs). Following descriptive and pooled OLS analyses, we used fixed effects panel regression to estimate associations between CEP spending and obesity prevalence, within LAs over time, adjusting for potential confounding by local economic conditions and spending on other public services. Final models included an interaction term between area deprivation (2015 IMD) and year to account for differential background trends in obesity across deprivation levels. We tested for effect modification by deprivation and, using MSOA-level obesity data, explored associations between spending and within-LA obesity inequalities. In unadjusted pooled OLS analyses, areas with higher CEP spending had higher prevalence of obesity, reflecting the strong social gradient in childhood obesity and the higher levels of central government funding allocated to more deprived areas. Deprivation, other spend, and local economic conditions explained this relationship. In the fixed effects analysis, designed to isolate average within-area change in obesity associated with changing spend, we observed a 0.10 percentage point increase in obesity prevalence for each 10% reduction in spend (95%CI: 0.04,0.15; p<0.001), but this disappeared after accounting for differential background trends in obesity across deprivation levels (-0.02; 95%CI: 0.07,0.03; p=0.39). Similar results were observed for obesity inequalities, although sensitivity analyses suggest spending on Environmental Services in particular may affect inequalities in urban local authorities. CEP spending levels may influence local childhood obesity risk, but the increasing prevalence and widening inequalities in obesity of the past decade seem to have been driven mainly by factors other than CEP spending cuts, that are also unevenly distributed across deprivation levels. The influence of specific services might be obscured by grouping CEP services for analysis.

Development of a composite regional vulnerability index and its relationship with the impacts of the COVID-19 pandemic

The interactions between vulnerability and human activities have largely been regarded in terms of the level of risk they pose, both internally and externally, for certain groups of disadvantaged individuals and regions/areas. However, to date, very few studies have attempted to develop a comprehensive composite regional vulnerability index, in relation to travel, housing, and social deprivation, which can be used to measure vulnerability at an aggregated level in the social sciences. Therefore, this research aims to develop a composite regional vulnerability index with which to examine the combined issues of travel, housing and socio-economic vulnerability (THASV index). It also explores the index’s relationship with the impacts of the COVID-19 pandemic, reflecting both social and spatial inequality, using Greater London as a case study, with data analysed at the level of Middle Layer Super Output Areas (MSOAs). The findings show that most of the areas with high levels of composite vulnerability are distributed in Outer London, particularly in suburban areas. In addition, it is also found that there is a spatial correlation between the THASV index and the risk of COVID-19 deaths, which further exacerbates the potential implications of social deprivation and spatial inequality. Moreover, the results of the multiscale geographically weighted regression (MGWR) show that the travel and socio-economic indicators in a neighbouring district and the related vulnerability indices are strongly associated with the risk of dying from COVID-19. In terms of policy implications, the findings can be used to inform sustainable city planning and urban development strategies designed to resolve urban socio-spatial inequalities and the potential related impacts of COVID-19, as well as guiding future policy evaluation of urban structural patterns in relation to vulnerable areas.

Is there greater ethnic mixing in residential or workplace spaces?

ABSTRACT While much of the ethnic and racial segregation literature concentrates on residential neighbourhoods, a growing body of research demonstrates how levels of inter-ethnic mixing vary between home and other spaces, including workplace environments. Using alternative population bases from the 2011 Census for England and Wales, we explore how levels of ethnic segregation and diversity compare for working-age people in residential (‘nighttime’), workday (‘daytime’) and workplace (‘employment’) environments. Analyses of Middle Layer Super Output Areas reveal that a larger number of these zones had low residential ethnic diversity than had low workday or workplace diversity. We also find higher levels of residential segregation than workplace segregation for all ethnic groups, including the White British (majority) and minoritised groups. Notable differences in segregation between residential and workplace spaces for the Pakistani and Bangladeshi ethnic groups, for example, demonstrate that more commonly applied measures of residential segregation fail to capture the extent of inter-ethnic mixing across spaces, and at different times of the day. Commuting data are analysed to explore the role of daily mobilities in shaping these patterns. The findings nuance understandings of levels of ethnic mixing, and emphasise the need for segregation analyses across multiple spatio-temporal contexts.

Analysis of short-term heat effects on cardiopulmonary mortality using national and regional small area data – Results of the EXHAUSTION project

Background and Aim: The adverse effects of extreme air temperatures on mortality have been widely documented in major cities, while little is known about suburban or rural areas. We investigated the association between high air temperature and all-cause and cardiopulmonary mortality in the warm period (May-September) by small area levels in five European countries. Methods: We applied a novel two-stage design with two hierarchical aggregation levels, assigning mortality counts and air temperature at the finer level and performing the statistical analyses by using multiple series at the coarser level. The finer level was defined as municipalities in four countries (Norway, Germany, Italy and Greece) and as Lower Layer Super Output Area (LSOA) in England and Wales. The coarser level was defined as provinces (four countries) or Middle Layer Super Output areas (MSOA) (England and Wales). In each country, we applied area-specific Distributed Lag Nonlinear Models Poisson regression. Exposure was modelled at lag 0-1 days. Then, area-specific estimates were pooled in random-effects meta-analysis to obtain national and multi-country effects. Results: The pooled relative risk (RR) of mean temperature increases from the 75th to 99th percentile on all-cause mortality was 1.15 (95% confidence interval (CI): 1.08, 1.23) with RRs ranging from 1.05 (CI95%. 1.03-1.08) in Norway to 1.22 (95%CI. 1.21-1.24) in Italy. In Norway, England and Wales, and Greece, we detected much higher associations with respiratory mortality. Cause-specific analyses in Germany and Italy are ongoing. We also evaluated effect modification by age and sex, and found a greater risk among the elderly (75+ years, RR: 1.19, 95%CI: 1.10-1.28) and females (RR: 1.20, 95%CI: 1.09-1.32). Conclusions: We found evidence of adverse effects of high temperatures on cardiopulmonary mortality in five countries using an innovative approach. Differentials urban/rural are object of current investigation. Keywords: Mortality, Short-term Exposure, Temperature, Small-area, Europe.

Evaluating the risk of accessing green spaces in COVID-19 pandemic: A model for public urban green spaces (PUGS) in London

The pandemic caused by SARS-CoV-2 (COVID-19) at the beginning of 2020 has restricted the human population indoor with some allowance for recreation in green spaces for social interaction and daily exercise. Understanding and measuring the risk of COVID-19 infection during public urban green spaces (PUGS) visits is essential to reduce the spread of the virus and improve well-being. This study builds a data-fused risk assessment model to evaluate the risk of visiting the PUGS in London. Three parameters are used for risk evaluation: the number of new cases at the middle-layer super output area (MSOA) level, the accessibility of each public green space and the Indices of Multiple Deprivation at the lower-layer super output area (LSOA) level. The model assesses 1357 PUGS and identifies the risk in three levels, high, medium and low, according to the results of a two-step clustering analysis. The spatial variability of risk across the city is demonstrated in the evaluation. The evaluation of risk can provide a better metric to the decision-making at both the individual level, on deciding which green space to visit, and the borough level, on how to implement restricting measures on green space access.

Do vaccinations reduce inequality in Covid-19 mortality? Evidence from England

The Covid-19 pandemic has affected countries and regions to varying degrees. In this paper, I examine the socioeconomic determinants of Covid-19 mortality and study whether vaccinations have affected the relationship between those determinants and Covid-19 mortality rates across local areas in England. I use monthly data for 6791 Middle Layer Super Output Areas for the period from March 2020 to April 2021 and estimate a spatial correlations model with local authority and time fixed effects. To study whether vaccinations have affected the relationship between socioeconomic determinants and Covid-19 mortality, I extend the model to include interactions between socioeconomic variables and the lagged cumulative vaccination rate. I find that Covid-19 mortality is higher in areas that have an older population, a larger share of Asian population, higher population density, lower income, poorer pre-existing health and a larger share of employment in health and social care occupations. Vaccinations have weakened the links between mortality and these socioeconomic characteristics. These findings highlight the importance of making vaccines widely available and encouraging take-up, to reduce inequality in Covid-19 mortality across socioeconomic groups.

Neighbourhood school environment and dental care needs amongst 5-to-11-year-old children attending a local dental facility in South-East England

AimTo investigate the relationship between neighbourhood school environment and dental care needs of 5-to-11-year-olds attending a local dental facility in Portsmouth, South-East England.MethodsSecondary analyses were undertaken using three cross-sectional, open-access datasets. Data included 4 years of children’s electronic dental records from the University of Portsmouth Dental Academy comprising age, gender, tooth extraction history and residential postcodes converted to Index of Multiple Deprivation (IMD) quintiles for census-based geographies called middle-layer super output areas (MSOAs). Additionally, overall effectiveness scores (OES) (1=Outstanding to 4=Inadequate) from Office for Standards in Education, Children’s Services and Skills of neighbouring schools were computed to ‘population-weighted mean OES’. Descriptive, univariate logistic regression and multilevel-modelling analyses investigated contextual-level influence of school-OES on tooth extraction.ResultsThere were 429-patients [mean-age 7.78 years (SD 1.97 years), female 50.1%] living across 23-MSOAs. Seventy had undergone tooth extraction treatment. Population-weighted mean OES range was 1.74–3.00, while 3.5% and 48% of patients belonged to the most and least deprived IMD-quintiles, respectively. Univariate models revealed age and population-weighted mean OES as statistically significant predictors of tooth extraction (p < 0.05). Multilevel modelling, controlling for age and population-weighted mean OES indicated likelihood of tooth extraction increased by 15% with increasing age and by 161% for patients living in MSOAs with higher population-weighted mean OES (i.e. poor school-performance).ConclusionsSchool effectiveness scores remained a significant predictor of tooth extraction in our study when controlling for individual predictors of dental health. Further research is required to assess the role of neighbourhood school environment in predicting child dental health at the national level.

High-resolution spatiotemporal analysis in 6,791 English areas reveals declines in life expectancy

BackgroundHealth inequalities in the UK and other advanced economies are receiving renewed attention by the public and governments in light of the Covid-19 pandemic. Current data on mortality and longevity in England lack precision either in space or in time. We estimated trends from 2002 to 2019 in life expectancy for all 6,791 English Middle-layer Super Output Areas (MSOAs, median population 7,985 in 2019).MethodsWe used data for all deaths in England from 2002 to 2019 with information on age, sex and MSOA of residence and data on population by age, sex and MSOA. We used a Bayesian hierarchical model to obtain stable estimates of age-specific death rates by sharing information across age groups, MSOAs and years. We used life-table methods to calculate life expectancy at birth by sex and MSOA.ResultsIn 2002-2006 and 2006-2010, the vast majority of MSOAs experienced a life expectancy increase for both sexes. In 2010-2014, female life expectancy decreased in 322 (5%) of MSOAs for women. By 2014-2019, the number of MSOAs with declining life expectancy was 1,178 (17%) for women and 635 (9%) for men. The life expectancy increase from 2002 to 2019 was smaller where life expectancy had been lower in 2002, mostly northern urban MSOAs, and larger where life expectancy had been higher in 2002, mostly MSOAs in and around London. As a result of these trends, the gap between the lowest and highest MSOA life expectancy increased from 2002 to 2019, to reach 20.6 (95% credible interval 17.3-24.4) years for women and 27.2 (23.4-31.7) years for men.ConclusionsThis study highlights not only rising inequality, but also declining life expectancy in a large proportion of English communities. The post-Covid rebuild policies in England must include pro-equity economic and educational policies, and expansion of public health and healthcare.Key messages Between 2014 and 2019, 17% and 9% of communities in England underwent a decrease in life expectancy for women and men respectively, highlighting the immediate need for equity-focussed policies.For women, 2% of small areas in England have experienced a long-term decline in life expectancy between 2002 and 2019.

Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data

SummaryBackgroundHigh-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England.MethodsWe performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA.FindingsIn 2002–06 and 2006–10, all but a few (0–1%) MSOAs had a life expectancy increase for female and male sexes. In 2010–14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014–19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4–10·9) in 2002 to reach 14·2 years (13·9–14·5) in 2019, and for men increased from 11·5 years (11·3–11·7) in 2002 to 13·6 years (13·4–13·9) in 2019.InterpretationIn the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country.FundingWellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.

Bayesian Modelling for Spatially Misaligned Health Areal Data: A Multiple Membership Approach

AbstractDiabetes prevalence is on the rise in the United Kingdom, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to London data on diabetes prevalence and mortality. In order to improve the estimation of relative risks, we analyse jointly prevalence and mortality data to ensure borrowing strength over the two outcomes. The available data involve two spatial frameworks, areas (Middle Layer Super Output Areas, MSOAs) and general practices (GPs) recruiting patients from several areas. This raises a spatial misalignment issue that we deal with by employing the multiple membership principle. Specifically, we translate areal spatial effects to explain GP practice prevalence according to proportions of GP populations resident in different areas. A sparse implementation in RStan of both the multivariate conditional autoregressive (MCAR) and generalised MCAR (GMCAR) with multiple membership allows the comparison of these bivariate priors as well as exploring the different implications for the mapping patterns for both outcomes. The necessary causal precedence of diabetes prevalence over mortality allows a specific conditionality assumption in the GMCAR, not always present in the context of disease mapping. Additionally, an area-locality comparison is considered to locate high versus low relative risk clusters.

POS-513 RISK OF COVID-19 DISEASE, DIALYSIS UNIT ATTRIBUTES AND INFECTION CONTROL STRATEGY AMONG LONDON IN-CENTRE HAEMODIALYSIS PATIENTS: A RETROSPECTIVE COHORT STUDY

Patients with end-stage kidney disease experience some of the highest rates of COVID-19 disease. Alongside demographic and cardio-metabolic risks, those receiving in-centre haemodialysis (ICHD) treatment face a unique set of challenges surrounding the requirement to regularly attend for life-extending treatment, thereby preventing self-isolation. We aimed to characterise the impact of community disease burden, dialysis unit attributes and the range of infection control strategies, on rates of COVID-19 disease among patients receiving ICHD in London, UK. We used data collected by the Pan-London COVID-19 Renal Audit Group which included all adult patients receiving long-term ICHD provided by all seven London renal centres on 2 March 2020. A discrete-time multi-variable multi-level time-to-event analysis was used to examine associations with the risk of testing positive for SARS-Cov-2 infection, and admission to hospital with COVID-19 over 13 weeks. Data on 5,755 patients dialysing in 51 ICHD units from the 7 London renal centres were analysed. Of these, 981 (17.0%) patients tested positive and 465 (8.2%) were admitted with suspected COVID-19. Alongside established risk factors for disease, we observed a strong relationship between ICHD infection rates and rates reported in patients’ local communities. In addition, dialysis unit size was associated with outcomes, as was layout, specifically the number of side rooms (hazard ratio, HR: 0.86, 95%CI: 0.80 to 0.93 for test positivity or admission per side room/20 stations). Reduced risk of admission was observed two weeks following institution of mask wearing in asymptomatic patients (HR: 0.63, 95%CI: 0.43 to 0.93). Rates of staff illness paralleled that of patients, but no associations were seen between rates of disease in patients and the different units’ mitigation strategies. Figure: Geographical distribution of cases by dialysis unit by week for the first 8 weeks of the pandemic and at the final follow-up week. Each ICHD unit is represented by a circle. Circle size: number of patients dialysed; Circle colour: cumulative proportion of patients test positive for COVID-19 or admitted with suspected COVID-19. Underlying green intensity reflects number of cases in each middle layer super output area (not necessarily reflective of patients' home locality as, most, but not all, patients dialyse in their closest unit). Three units are not represented as they fall outside the area of the map. Dates represent the first day of the week represented.. These findings suggest the high risk of COVID-19 in the ICHD population relates to individual factors, and underlying community transmission, along with increased unit size and unfavourable unit layout. The results support interventions aimed at reducing transmission from asymptomatic patients and highlight the importance of preventing infection in nursing staff.

Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England

COVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space–time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial–temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

Shedding new light on residential property price variation in England: A multi-scale exploration

Exploring the nature of spatial and temporal variation in house prices is important because it can help better understand such issues as affordability and equity of access to housing. In the UK, research on house price variation has been hindered by a lack of extensive data linking the prices of properties at different places and times to their physical attributes. This paper addresses this gap through using a new dataset linking Land Registry Price Paid Data to attribute data from Ordnance Survey and Energy Performance Certificates datasets. The new data are used to investigate spatial disparities in England’s house prices at four geographical scales (from local authority to individual address) between 2009 and 2016 – a period of sustained price rises after the global financial crisis of 2008. We selected two housing price measures for comparison, namely transaction price and the house price per square metre. Multilevel variance components models are used to estimate variation in the two house price measures at four different spatial scales and we compare spatial disparities in the two measures at these different scales. Our results suggest that accounting for the size of properties by using house price per square metre offers a more accurate picture of house price variation than does the use of transaction prices at the same geographic scale. Spatial disparities in house price per square metre are more apparent and are seen to be clustered at local authority level and highly clustered at Middle Layer Super Output Area level, with imbalances increasing during this eight-year period and highlighting the strong and growing influence of London on the national housing market.