The insignificant evolution of the richness-mass relation of galaxy clusters

We analysed the richness--mass scaling of 23 very massive clusters at $0.15<z<0.55$ with homogenously measured weak-lensing masses and richnesses within a fixed aperture of $0.5$ Mpc radius. We found that the richness--mass scaling is very tight (the scatter is $<0.09$ dex with 90 \% probability) and independent of cluster evolutionary status and morphology. This implies a close association between infall and evolution of dark matter and galaxies in the central region of clusters. We also found that the evolution of the richness-mass intercept is minor at most, and, given the minor mass evolution across the studied redshift range, the richness evolution of individual massive clusters also turns out to be very small. Finally, it was paramount to account for the cluster mass function and the selection function. Ignoring them would led to biases larger than the (otherwise quoted) errors. Our study benefits from: a) weak-lensing masses instead of proxy-based masses thereby removing the ambiguity between a real trend and one induced by an accounted evolution of the used mass proxy; b) the use of projected masses that simplify the statistical analysis thereby not requiring consideration of the unknown covariance induced by the cluster orientation/triaxiality; c) the use of aperture masses as they are free of the pseudo-evolution of mass definitions anchored to the evolving density of the Universe; d) a proper accounting of the sample selection function and of the Malmquist-like effect induced by the cluster mass function; e) cosmological simulations for the computation of the cluster mass function, its evolution, and the mass growth of each individual cluster.Comment: A&A, in press. Fixed pdf generation proble

Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes

Background: Enhanced quality of care and improved access are central to effective primary care management of long term conditions. However, research evidence is inconclusive in establishing a link between quality of primary care, or access, and adverse outcomes, such as unplanned hospitalisation. Methods: This paper proposes a structural equation model for quality and access as latent variables affecting adverse outcomes, such as unplanned hospitalisations. In a case study application, quality of care (QOC) is defined in relation to diabetes, and the aim is to assess impacts of care quality and access on unplanned hospital admissions for diabetes, while allowing also for socio-economic deprivation, diabetes morbidity, and supply effects. The study involves 90 general practitioner (GP) practices in two London Clinical Commissioning Groups, using clinical quality of care indicators, and patient survey data on perceived access. Results: As a single predictor, quality of care has a significant negative impact on emergency admissions, and this significant effect remains when socio-economic deprivation and morbidity are allowed. In a full structural equation model including access, the probability that QOC negatively impacts on unplanned admissions exceeds 0.9. Furthermore, poor access is linked to deprivation, diminished QOC, and larger list sizes. Conclusions: Using a Bayesian inference methodology, the evidence from the analysis is weighted towards negative impacts of higher primary care quality and improved access on unplanned admissions. The methodology of the paper is potentially applicable to other long term conditions, and relevant when care quality and access cannot be measured directly and are better regarded as latent variables

Spatially Interpolated Disease Prevalence Estimation Using Collateral Indicators of Morbidity and Ecological Risk

This paper considers estimation of disease prevalence for small areas (neighbourhoods) when the available observations on prevalence are for an alternative partition of a region, such as service areas. Interpolation to neighbourhoods uses a kernel method extended to take account of two types of collateral information. The first is morbidity and service use data, such as hospital admissions, observed for neighbourhoods. Variations in morbidity and service use are expected to reflect prevalence. The second type of collateral information is ecological risk factors (e.g., pollution indices) that are expected to explain variability in prevalence in service areas, but are typically observed only for neighbourhoods. An application involves estimating neighbourhood asthma prevalence in a London health region involving 562 neighbourhoods and 189 service (primary care) areas

Geographical Aspects of Recent Trends in Drug-Related Deaths, with a Focus on Intra-National Contextual Variation.

BACKGROUND: Recent worldwide estimates are of 53 million users of opioids annually, and of 585,000 drug-related deaths, of which two thirds are due to opioids. There are considerable international differences in levels of drug death rates and substance abuse. However, there are also considerable variations within countries in drug misuse, overdose rates, and in drug death rates particularly. Wide intra-national variations characterize countries where drug deaths have risen fastest in recent years, such as the US and UK. Drug deaths are an outcome of drug misuse, which can ideally be studied at a relatively low spatial scale (e.g., US counties). The research literature suggests that small area variations in drug deaths to a considerable degree reflect contextual (place-related) factors as well as individual risk factors. METHODS: We consider the role of area social status, social cohesion, segregation, urbanicity, and drug supply in an ecological regression analysis of county differences in drug deaths in the US during 2015-2017. RESULTS: The analysis of US small area data highlights a range of factors which are statistically significant in explaining differences in drug deaths, but with no risk factor having a predominant role. Comparisons with other countries where small area drug mortality data have been analyzed show differences between countries in the impact of different contextual factors, but some common themes. CONCLUSIONS: Intra-national differences in drug-related deaths are considerable, but there are significant research gaps in the evidence base for small area analysis of such deaths

Assessing persistence in spatial clustering of disease, with an application to drug related deaths in scottish neighbourhoods

© 2019, Prex S.p.A. All rights reserved. Background: The upward trend in drug related deaths in some countries is a major public health concern. Regarding geographic location within countries, many studies report spatial clustering in drug related deaths. We consider drug related deaths in Scottish small areas, and investigate probabilities that clusters of adjacent neighbourhoods have elevated risk. We focus especially on assessing persistence in spatial clustering, relevant to prioritising area based interventions. We assess impacts of area risk factors on drug deaths, finding a strong link to poverty, and a clear overlap between drug death clustering and spatial poverty clustering. Methods: We analyse drug related deaths in 1279 Scotland neighbourhoods over two periods, 2009-13 and 2014-18, during which drug related mortality in Scotland has more than doubled. A fully Bayesian approach is used to identify zones with high mortality risk in both a neighbourhood and its spatial lag (“high-high” clusters), and extended to identify recurring high risk clustering over more than one period. Estimation of mortality risks, and of cluster probabilities through periods, is developed in conjunction with a regression model including area risk factors such as deprivation. Results: Persistent clustering is concentrated in major urban centres, for example, Glasgow and Dundee. Deprivation is the paramount observed risk factor underlying elevated mortality risk, and persistent clustering in drug related mortality shows strong overlaps with poverty clustering. Social fragmentation modifies the paramount influence of poverty on drug mortality risk. Conclusion: Cluster persistence is a central feature in small area variability in drug related death risk in Scotland intermediate zones, especially in some urban areas

Obesity and Urban Environments

Obesity is a major public health issue, affecting both developed and developing societies. Obesity increases the risk for heart disease, stroke, some cancers, and type II diabetes. While individual behaviours are important risk factors, impacts on obesity and overweight of the urban physical and social environment have figured large in the recent epidemiological literature, though evidence is incomplete and from a limited range of countries. Prominent among identified environmental influences are urban layout and sprawl, healthy food access, exercise access, and the neighbourhood social environment. This paper reviews the literature and highlights the special issue contributions within that literature

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK.

Background: The evolution of the COVID-19 epidemic has been accompanied by efforts to provide comparable international data on new cases and deaths. There is also accumulating evidence on the epidemiological parameters underlying COVID-19. Hence, there is potential for epidemic models providing mid-term forecasts of the epidemic trajectory using such information. The effectiveness of lockdown or lockdown relaxation can also be assessed by modelling later epidemic stages, possibly using a multiphase epidemic model. Methods: Commonly applied methods to analyse epidemic trajectories or make forecasts include phenomenological growth models (e.g., the Richards family of densities) and variants of the susceptible-infected-recovered (SIR) compartment model. Here, we focus on a practical forecasting approach, applied to interim UK COVID data, using a bivariate Reynolds model (for cases and deaths), with implementation based on Bayesian inference. We show the utility of informative priors in developing and estimating the model and compare error densities (Poisson-gamma, Poisson-lognormal, and Poisson-log-Student) for overdispersed data on new cases and deaths. We use cross validation to assess medium-term forecasts. We also consider the longer-term postlockdown epidemic profile to assess epidemic containment, using a two-phase model. Results: Fit to interim mid-epidemic data show better fit to training data and better cross-validation performance for a Poisson-log-Student model. Estimation of longer-term epidemic data after lockdown relaxation, characterised by protracted slow downturn and then upturn in cases, casts doubt on effective containment. Conclusions: Many applications of phenomenological models have been to complete epidemics. However, evaluation of such models based simply on their fit to observed data may give only a partial picture, and cross validation against actual trends is also valuable. Similarly, it may be preferable to model incidence rather than cumulative data, although this raises questions about suitable error densities for modelling often erratic fluctuations. Hence, there may be utility in evaluating alternative error assumptions