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Summary: Background: Lower socioeconomic status, as measured by the Index of Multiple Deprivation (IMD), is associated with higher rates of smoking-related disease mortality, and with poor uptake of cancer screening. Here we explore whether socioeconomic status impacts the effectiveness of a single round of low-dose-CT screening, or impacts other causes of death, in the UKLS LDCT screening trial. Methods: IMD quintiles were defined according to UK-wide data, with the deprived group defined as the lower two quintiles (Q1-2) and the less deprived as Q3-5. Follow-up data was obtained for lung cancer diagnosis (median follow-up 9.1 years) and cause of death (median follow-up 9.9 years). Outcomes were compared based on IMD group and trial arm (CT or control). Findings: More deprived quintiles were less likely to respond to the questionnaire, but this population was more likely to be selected for screening by the LLP risk model. Lower IMD quintiles benefitted from low-dose-CT screening in terms of lung cancer survival (HR 1.89, 95% CI 1.16–3.08) to the same extent as upper quintiles (HR 1.87, 95% CI 1.07–3.26). However, there was a bigger impact on deaths due to COPD and emphysema in more deprived quintiles. Interpretation: Whilst LDCT screening benefit for lung cancer was similar, significant impact on the rates of death from other smoking-related diseases, notably COPD and emphysema, was seen primarily in lower socioeconomic groups. Future research is required to confirm how lung cancer screening benefits other disease outcomes. Funding: NIHR Health Technology Assessment Programme; NIHR Policy Research programme; Roy Castle Lung Cancer Foundation.

Recent studies have sought to explore the relationship between environmental and financial performance, in particular the relationship between the energy efficiency level of a building and its financial value. The present real estate dataset contains 43 variables of repeat sales transactions, energy performance certificate (EPC) rating, index of multiple deprivation (IMD), and geographical location of properties in England and Wales involved in a total of 4,201 transactions from 1995 to 2012. This dataset enables researchers and practitioners to further explore important questions regarding the nexus between the real estate industry, sustainability levels, and socio-economic aspects. Due to the scarcity of publicly available quality real estate data, the dataset detailed in this article may play a relevant role by becoming easily discoverable, clearly explained, and structured to be ready to be used by researchers, analysts, and policymakers. The empirical analysis of the economic case for energy-efficient dwellings in the UK private rental market performed in Fuerst, et al. [1] is based on this dataset.

While social and spatial determinants of biomarkers have been reported, no previous study has examined both together within an intersectional perspective. We present a novel extension of quantitative intersectional analyses using cross-classified multilevel models to explore how intersectional positions and neighbourhood deprivation are associated with biomarkers, using baseline UK Biobank data (collected from 2006 to 2010). Our results suggest intersectional inequalities in biomarkers of healthy ageing are mostly established by age 40-49, but different intersections show different relationships with deprivation. Our study suggests that certain biosocial pathways are more strongly implicated in how neighbourhoods and intersectional positions affect healthy ageing than others., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Hip fracture risk varies by geography and by levels of deprivation. We examined the effect of local area-level deprivation on hip fracture incidence across nine regions in England, using 14 years of hospital data, to determine whether inequalities in hip fracture incidence rates vary across geographic regions in England., Study Design: Sequential annual cross-sectional studies over 14 years., Methods: We used English Hospital Episodes Statistics (2001/02-2014/15) to identify hip fractures in adults aged 50+ years and mid-year population estimates (2001-2014) from the Office for National Statistics. The Index of Multiple Deprivation was used to measure local area deprivation. We calculated age-standardised hip fracture incidence rates per 100,000 population, stratified by gender, geographic region, deprivation quintiles and time-period, using the 2001 English population as the reference population. Using Poisson regression, we calculated age-adjusted incidence rate ratios (IRRs) for hip fracture, stratified as above., Results: Over 14 years, we identified 747,369 hospital admissions with an index hip fracture. Age-standardised hip fracture incidence was highest in the North East for both men and women. In North England (North East, North West and Yorkshire and the Humber), hip fracture incidence was relatively higher in more deprived areas, particularly among men: IRR most vs least deprived quintile 2.06 (95% confidence interval [CI] = 2.00-2.12) in men, 1.62 (95% CI 1.60-1.65) in women. A relationship, albeit less marked, between deprivation and hip fracture incidence was observed among men in the Midlands and South, but with no clear pattern among women., Conclusions: Regional variation in hip fracture incidence exists across England, with the greatest absolute burden of incident hip fractures observed in the North East for both men and women. Across local areas in North England, absolute and relative inequalities in hip fracture incidence were greater than in other regions. Our findings highlight the need for improved fracture prevention programmes that aim to reduce regional and social inequalities in hip fracture incidence., (Copyright © 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in the presence of (i) high correlation between the predictors and (ii) spatial correlation for the response. Covariates which are highly correlated create collinearity problems when used in a standard multiple regression model. Many methods have been proposed in the literature to address this issue. A very common approach is to create an index which aggregates all the highly correlated variables of interest. For example, it is well known that there is a relationship between social deprivation measured through the Multiple Deprivation Index (IMD) and air pollution; this index is then used as a confounder in assessing the effect of air pollution on health outcomes (e.g. respiratory hospital admissions or mortality). However it would be more informative to look specifically at each domain of the IMD and at its relationship with air pollution to better understand its role as a confounder in the epidemiological analyses. In this paper we illustrate how the complex relationships between the domains of IMD and air pollution can be deconstructed and analysed using profile regression, a Bayesian non-parametric model for clustering responses and covariates simultaneously. Moreover, we include an intrinsic spatial conditional autoregressive (ICAR) term to account for the spatial correlation of the response variable., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)