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"Congdon, P"
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Applied Bayesian Modelling, 2nd Edition
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.
eBook
Does depression diagnosis and antidepressant prescribing vary by location? Analysis of ethnic density associations using a large primary-care dataset
Studies have linked ethnic differences in depression rates with neighbourhood ethnic density although results have not been conclusive. We looked at this using a novel approach analysing whole population data covering just over one million GP patients in four London boroughs.
Using a dataset of GP records for all patients registered in Lambeth, Hackney, Tower Hamlets and Newham in 2013 we investigated new diagnoses of depression and antidepressant use for: Indian, Pakistani, Bangladeshi, black Caribbean and black African patients. Neighbourhood effects were assessed independently of GP practice using a cross-classified multilevel model.
Black and minority ethnic groups are up to four times less likely to be newly diagnosed with depression or prescribed antidepressants compared to white British patients. We found an inverse relationship between neighbourhood ethnic density and new depression diagnosis for some groups, where an increase of 10% own-ethnic density was associated with a statistically significant (p < 0.05) reduced odds of depression for Pakistani [odds ratio (OR) 0.81, 95% confidence interval (CI) 0.70-0.93], Indian (OR 0.88, CI 0.81-0.95), African (OR 0.88, CI 0.78-0.99) and Bangladeshi (OR 0.94, CI 0.90-0.99) patients. Black Caribbean patients, however, showed the opposite effect (OR 1.26, CI 1.09-1.46). The results for antidepressant use were very similar although the corresponding effect for black Caribbeans was no longer statistically significant (p = 0.07).
New depression diagnosis and antidepressant use was shown to be less likely in areas of higher own-ethnic density for some, but not all, ethnic groups.
Journal Article
Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.
Journal Article
Applied Bayesian Modelling
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.
eBook
Spatial heterogeneity in Bayesian disease mapping
Disease mapping applications generally assume homogeneous regression effects and use random intercepts to account for residual spatial dependence. However, there may be local variation in the association between disease and area risk factors. We consider implications for model fit, estimated regression coefficients, and substantive inferences of allowing spatial variability in impacts of area risk factors. An application to suicide in 6791 English small areas shows that average regression coefficients and substantive inferences (e.g. about relative risk) may be considerably affected by allowing spatially varying predictor effects, while fit is improved.
Journal Article
Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach
Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.
Journal Article
Explaining variations in obesity and inactivity between US metropolitan areas
This paper discusses measurement of the main dimensions of the urban environment that have been proposed as relevant to explaining geographic variations in obesity and inactivity. It considers urban sprawl, food access and exercise access as latent constructs, defined by sets of observed indicators for areas. In an application to 993 US metropolitan counties, the paper shows how these latent constructs may be incorporated in an ecological (area-scale) model, which recognizes spatial aspects in the patterning of both outcomes and environmental factors. Urban sprawl and area socioeconomic status emerge from regression modelling as leading influences on obesity and inactivity.
Journal Article
Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes
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.
Journal Article
Size and strength : do we need both to measure vocabulary knowledge?
This article describes the development and validation of a test of vocabulary size and strength. The first part of the article sets out the theoretical rationale for the test, and describes how the size and strength constructs have been conceptualized and operationalised. The second part of the article focuses on the process of test validation, which involved the testing of the hypotheses implicit in the test design, using both unidimensional and multifaceted Rasch analyses. Possible applications for the test include determining the status of a learner's vocabulary development as well as screening and placement. A model for administering the test in computer adaptive mode is also proposed. The study has implications both for the design and delivery of this test as well as for theories of vocabulary acquisition. [Author abstract]
Journal Article