Biomedical subjects

A B Lawson

Publications and source records attributed to A B Lawson.

15 recordsLinked to original sources

PubMed2005

Spatial statistical modeling of disease outbreaks with particular reference to the UK foot and mouth disease (FMD) epidemic of 2001.

In this paper we examine issues relating to the analysis of spatially-referenced disease data. Initially, we discuss the use of exploratory statistical tools such as density estimation and nonparametric regression. We then consider the need for descriptive epidemic models in space, time, and space-time models for epidemic dynamics. Implicitly space-time must be considered in any analysis of the spatial structure of epidemics. The use of Bayesian models for disease spread is discussed and applied to the recent foot and mouth outbreak in the UK.

Animals↗

PubMed2005

Bayesian hierarchical modelling to enhance the epidemiological value of abattoir surveys for bovine fasciolosis.

Four classes of Bayesian hierarchical models were evaluated using an historical dataset from an abattoir survey for fasciolosis conducted in Victoria, Australia. The purpose of this analysis was to identify areas of high prevalence and to explain these in terms of environmental covariates. The simplest of the Bayesian models, with a single random effect, validated the use of smoothed maps for cartographic display when the sample sizes vary. The model was then extended to partition the random effect into spatially structured and unstructured components, thus allowing for spatial autocorrelation. Rainfall, irrigation, temperature-adjusted rainfall and a remotely sensed surrogate for rainfall, the normalised difference vegetation index (NDVI), were then introduced into the models as explanatory variables. The variable that best explained the observed distribution was irrigation. Associations between prevalence and both rainfall and NDVI that were significant in fixed effects models were shown to be due to spatial confounding. Nevertheless, provided they are used cautiously, confounded variables may be valid predictors for the prevalence of disease.

Abattoirs↗

PubMed2005

Area-level risks for BSE in British cattle before and after the July 1988 meat and bone meal feed ban.

In this paper we investigate area-level risk factors for BSE for the cattle population present in Great Britain between 1986 and 1997. By dividing this population into two birth cohorts, those born before the July 1988 ban on feeding ruminant-derived meat and bone meal to ruminants and those born after, second-order regional influences are distinguished from the strong first-order south-to-north gradient of area-level BSE risk using Bayesian hierarchical models that account for structured (spatially correlated) and unstructured heterogeneity in the data. For both cohorts area-level risk of BSE was increased by a more southerly location and greater numbers of dairy cattle, relative to non-dairy cattle. For the cohort of cattle born after the July 1988 ban on feeding ruminant-derived meat and bone meal area-level BSE risk was additionally associated with greater numbers of pigs, relative to cattle. These findings support the role of low level cross-contamination of cattle feed by pig feed as an influence on BSE incidence risk as the epidemic evolved. Prior to the 1988 meat and bone meal ban unexplained BSE risk was relatively uniformly distributed across the country whereas after the ban there were spatially aggregated areas of unexplained risk in the northern and eastern regions of England suggesting that local influences allowed BSE control measures to be less-successfully applied in these areas, compared with the rest of the country. We conclude that spatially localised influences were operating in divergent ways during the two phases of the epidemic.

Animal Feed↗

PubMed2001

Disease map reconstruction.

The analysis of the geographical distribution of disease incidence or prevalence is now of considerable importance for public health workers and epidemiologists alike. Important disease variations often have a spatial expression and so spatial analysis methods are an important additional tool in this connection. In this tutorial I have aimed to highlight the main issues relating to the analysis of disease where the goal is the reduction in noise in a disease map. This area is sometimes simply called disease mapping. A number of modelling approaches to disease mapping are considered and a case study highlighting the methods advocated is also included.

Bayes Theorem↗

PubMed2000

Descriptive spatial analysis of the epidemic of bovine spongiform encephalopathy in Great Britain to June 1997.

This was a spatial analysis of the epidemic of bovine spongiform encephalopathy (BSE) in Great Britain, based on agricultural census data collected between 1986 and 1996 and BSE case data collected up to June 1997. Kernel smoothing techniques were used to plot the distribution of BSE-positive cattle holdings per 100 holdings per square kilometre and the distribution of confirmed BSE cases per 100 head of cattle per square kilometre. In the early stages of the epidemic reported BSE cases were scattered widely throughout Great Britain, with no clearly identifiable focus. By June 1997, a statistically significant cluster of BSE-positive holdings was identifiable in the eastern part of the South west region of England. During the epidemic the highest densities of confirmed BSE cases per 100 cattle per square kilometre occurred in the greater part of the South west region of England and within Dyfed in the south west of Wales. In Wales, a small number of holdings experienced large numbers of confirmed BSE cases. In the South west region of England a large number of holdings experienced small numbers of confirmed cases. By June 1997, the distribution of BSE-positive holdings across Great Britain was largely determined by factors that influenced the amount of recycled infectious material they were exposed to.

Animals↗

PubMed2000

Approaches to the space-time modelling of infectious disease behaviour.

A new approach to the space-time modelling of infectious diseases is considered. A modulated heterogeneous Poisson process with intensity defined as a function of a two-dimensional susceptibility field is proposed. The model is fitted to a measles epidemic using a proportional hazards approximation.

Adolescent↗

PubMed1993

Applications of extraction mapping in environmental epidemiology.

This paper discusses a new method which allows the extraction of a background disease rate from a data set consisting of spatial co-ordinates of morbidity or mortality events. We demonstrate its application with two data sets, one based on cancer registry data and the other on death certificates.

Air Pollutants↗

PubMed1993

On the analysis of mortality events associated with a prespecified fixed point.

"A model-based approach to the analysis of disease incidence around a fixed point is presented by considering the radial and directional effects to be expected from emissions from a putative source. In addition we present some score statistics which can be used to test for spatial effects." The methods discussed are applied to the analysis of bronchitis mortality around a reprocessing plant in Bonnybridge, Scotland.

Cause of Death↗

PubMed1992

Low sex ratios of births in areas at risk from air pollution from incinerators, as shown by geographical analysis and 3-dimensional mapping.

Previous research in environmental and occupational health has suggested that fluctuations in the sex ratios of births might provide a useful early warning to the possible health effects of toxins or other stresses in the environment. To examine further this hypothesis, we investigated the sex ratios of births in an area in central Scotland which contained two incineration plants. Analyses of the sex ratios, at various levels of geographical detail and using 3-dimensional mapping techniques, in the residential areas at risk from airborne pollution from these incinerators showed locations with statistically significant excesses of female births.

Air Pollution↗

PubMedDate not supplied

Disease mapping models: an empirical evaluation. Disease Mapping Collaborative Group.

The analysis of small area disease incidence has now developed to a degree where many methods have been proposed. However, there are few studies of the relative merits of the methods available. While many Bayesian models have been examined with respect to prior sensitivity, it is clear that wider comparisons of methods are largely missing from the literature. In this paper we present some preliminary results concerning the goodness-of-fit of a variety of disease mapping methods to simulated data for disease incidence derived from a range of models. These simulated models cover simple risk gradients to more complex true risk structures, including spatial correlation. The main general results presented here show that the gamma-Poisson exchangeable model and the Besag, York and Mollie (BYM) model are most robust across a range of diverse models. Mixture models are less robust. Non-parametric smoothing methods perform badly in general. Linear Bayes methods display behaviour similar to that of the gamma-Poisson methods.

Algorithms↗

PubMedDate not supplied

Cluster modelling of disease incidence via RJMCMC methods: a comparative evaluation. Reversible jump Markov chain Monte Carlo.

The spatial modelling of small area health data has, for some time, included spatial autocorrelation as a random effect. This effect is non-specific and global and does not address the location of clusters of disease (a specific task). This paper addresses the need for specific and non-specific random effects within spatial epidemiology. In addition, individual frailty is also considered important and a computational algorithm based on reversible jump Markov chain Monte Carlo (RJMCMC) methods is described.

Algorithms↗

PubMedDate not supplied

Spatial competing risk models in disease mapping.

Often it is required that the 'health status' of an area must be assessed and this involves the analysis of a range of different diseases within one study window. This often arises, for example, when disease cluster alarms are sounded and there is a need to provide a general overview of health in the vicinity of he cluster area. Our approach leads to the consideration of the joint spatial distribution of a 'basket' of diseases. We examine the use of weighting schemes within our general formulation, and extensions to count data and spatio-temporal modelling.

Air Pollution↗

PubMedDate not supplied

The power of focused tests to detect disease clustering.

Statistical tests have been proposed for determining whether incident cases of adverse health effects are 'clustered' together. Several procedures, termed 'focused', specifically analyse disease surveillance data around pre-specified putative sources of environmental hazard. Little has been done to compare the performance of various proposed methods on actual models of clustering. Analytic power functions are derived for three tests of focused clustering. These functions are based on the probabilistic structure of the clustering tests and do not require simulation. The three tests are compared with respect to statistical power on hypothetical data where monotone multiplicative increases in disease risk near a putative hazard define disease clusters of varying intensity.

Cluster Analysis↗

PubMedDate not supplied

Tests for directional space-time interaction in epidemiological data.

Spatial incidence of disease is often recorded with time of occurrence as an ordering label. This ordering can be used to provide distance based tests for joint clustering of cases in space and time. Two different tests are proposed: one where a control disease is available, and the other where only standardized rates within census regions are available.

Child↗

PubMedDate not supplied

MCMC methods for putative pollution source problems in environmental epidemiology.

This paper demonstrates the use of the Gibbs Sampler and other Markov Chain Monte Carlo (MCMC) methods in two applications in environmental epidemiology. The first example concerns the application of a Metropolis-Hastings/Gibbs sampler to a Cox process with a direction-dependent cluster variance parameter. The second example consists of the estimation of the posterior (spatial) distribution of a putative location.

Algorithms↗