Peter Congdon

Modelling inequality in reported long term illness in the UK: combining individual and area characteristics.

STUDY OBJECTIVE: To assess the nature of the relation between health and social factors at both the aggregated scale of geographical areas and the individual scale. DESIGN AND SETTING: The individual data are derived from the sample of anonymised records (SAR) from the census of 1991 in Great Britain, and are combined with area data from this census. The ecological setting (context) was defined using multivariate methods to classify the 278 districts of residence identifiable in the SAR. The outcome health variable is the 1991 census long-term limiting illness question. Health variations were analysed by multilevel logistic regression to examine the compositional variation (at the level of the individual) and the contextual variation (variability operating at the level of districts) in reported illness. PARTICIPANTS: 10 per cent randomised subsample of the SAR who are aged 16+ and are resident in households. MAIN RESULTS: The multi-level modelling revealed that area factors have a significant association with individual health outcome but their effect is smaller than that of individual attributes. The results show evidence for both compositional and contextual effects in the pattern of variation in propensity to report illness. CONCLUSIONS: The results suggest generally higher levels of ill health for individuals who are older, not married, in a semi/unskilled manual social class, and socioeconomically deprived (as measured by a composite deprivation score). All individuals living in areas with high levels of illness (which tend to be more deprived areas) show greater morbidity, even after allowing for their individual characteristics. However, within affluent areas, where morbidity was generally lower, the health inequality (health gradient) between rich and poor individuals was particularly strong. We consider the implications of these findings for health and resource allocation policy.

Suicide and Parasuicide in London: A Small-area Study

This study discusses variability in the spatial prevalence of suicide and parasuicide across small areas in London in relation to the social and demographic composition of their populations. The focus is on the relative importance in explaining suicidal outcomes of variables representing respectively social deprivation, psychiatric morbidity and anomie (or community fragmentation), and of differentiation in the effects of these factors across sub-populations. There is strong evidence for such contextual effects—namely, varying effects of these socio-economic factors according to geographical setting—as well as for differential associations by age group, sex and type of outcome (suicide vs parasuicide).

Bayesian model choice based on Monte Carlo estimates of posterior model probabilities

A range of approximate methods have been proposed for model choice based on Bayesian principles, given the problems involved in multiple integration in multi-parameter problems. Formal Bayesian model assessment is based on prior model probabilities P(M=j) and posterior model probabilities P(M=j|Y) after observing the data. An approach is outlined here that produces posterior model probabilities and hence Bayes factor estimates but not marginal likelihoods. It uses a Monte Carlo approximation based on independent MCMC sampling of two or more different models. While parallel sampling of the models is not necessary, such a form of sampling facilitates model averaging and assessing the impact of individual observations on the overall estimated Bayes factor. Three worked examples used before in model choice studies illustrate application of the method.

The development of gravity models for hospital patient flows under system change: a Bayesian modelling approach.

This paper discusses models for the impact on patient referral flows from homes to hospitals of reconfigurations of emergency hospital services. Such system change might involve new hospital sites, or expanded bed numbers at some sites, together with possible closure of emergency units at other hospitals. In terms of a gravity model for the flows of patients, this corresponds to removing a destination or adding a new one. While retaining a gravity type approach to this problem, the relevance of supply weighting by distance within the broader accessibility concept is stressed since the independence from irrelevant alternatives property is generally inapplicable. This accessibility based approach may be implemented as a general linear model, with a Poisson outcome, and a Bayesian estimation and predictive approach is adopted. In the context of patient hospitalisations, this facilitates prediction of new patient flows following hospital reconfiguration. A UK based case study of small residential areas and hospitals in North East London and Essex is presented within the context of a review of emergency hospital siting in London.

Toxocara infection in the United States: the relevance of poverty, geography and demography as risk factors, and implications for estimating county prevalence

ObjectiveTo estimate Toxocara infection rates by age, gender and ethnicity for US counties using data from the National Health and Nutrition Examination Survey (NHANES).MethodsAfter initial analysis to account for missing data, a binary regression model is applied to obtain relative risks of Toxocara infection for 20,396 survey subjects. The regression incorporates interplay between demographic attributes (age, ethnicity and gender), family poverty and geographic context (region, metropolitan status). Prevalence estimates for counties are then made, distinguishing between subpopulations in poverty and not in poverty.ResultsEven after allowing for elevated infection risk associated with poverty, seropositivity is elevated among Black non-Hispanics and other ethnic groups. There are also distinct effects of region. When regression results are translated into county prevalence estimates, the main influences on variation in county rates are percentages of non-Hispanic Blacks and county poverty.ConclusionsFor targeting prevention it is important to assess implications of national survey data for small area prevalence. Using data from NHANES, the study confirms that both individual level risk factors and geographic contextual factors affect chances of Toxocara infection.

The epidemiology of suicide in London

The paper considers trends in the incidence of suicide in London in relation to national trends and highlights the distinctive fall in its suicide levels. Within London differentials across boroughs and wards are investigated, with particular regard to contextual variability across subpopulations in the impact of social deprivation and social fragmentation (anomie). Patterns of causation also differ for suicide disaggregated by age group and sex. The relevance of these differentials for explaining the distinctive trend in Londons overall suicide rate is discussed.

Assessing the impact of socioeconomic variables on small area variations in suicide outcomes in England.

Ecological studies of suicide and self-harm have established the importance of area variables (e.g., deprivation, social fragmentation) in explaining variations in suicide risk. However, there are likely to be unobserved influences on risk, typically spatially clustered, which can be modeled as random effects. Regression impacts may be biased if no account is taken of spatially structured influences on risk. Furthermore a default assumption of linear effects of area variables may also misstate or understate their impact. This paper considers variations in suicide outcomes for small areas across England, and investigates the impact on them of area socio-economic variables, while also investigating potential nonlinearity in their impact and allowing for spatially clustered unobserved factors. The outcomes are self-harm hospitalisations and suicide mortality over 6,781 Middle Level Super Output Areas.

Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes

Abstract.A methodology is proposed for modelling spatially varying predictor effects on a disease or mortality count outcome. The methodology may be extended to multivariate outcomes, so that one may assess the similarity of spatial patterning of regression effects between outcomes. Another extension involves longitudinal data, where a number of modelling structures are possible. The methodology is illustrated by suicide mortality in 32 London Boroughs over the period 1979–1993, in terms of area deprivation and a measure of social fragmentation.

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 lead to larger biases 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.

The Spatial Pattern of Suicide in the US in Relation to Deprivation, Fragmentation and Rurality

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.