Biomedical subjects
Publications and source records attributed to A B Lawson.
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.
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.
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.
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.
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.