Biomedical subjects

Andrew B Lawson

Publications and source records attributed to Andrew B Lawson.

10 recordsLinked to original sources

PubMed2006

Dairy, magnesium, and calcium intake in relation to insulin sensitivity: approaches to modeling a dose-dependent association.

Dairy intake has been inversely associated with insulin resistance, which may be partly due to the specific effects of calcium and magnesium. Data from the Insulin Resistance Atherosclerosis Study (1992-1999) for 1,036 US adults without diabetes at baseline were examined to evaluate the cross-sectional association of habitual dairy, magnesium, and calcium intake with insulin sensitivity at baseline and after 5 years of follow-up. Insulin sensitivity was directly measured with a validated, 12-sample, insulin-enhanced, intravenous glucose tolerance test with minimal model analysis. Dietary intake was assessed by a validated food frequency interview, and dietary supplement dose was confirmed by reviewing the supplement label. Several statistical approaches were used to ensure appropriate modeling of the dose-dependent association. No association was found between dairy intake and insulin sensitivity (p=0.41); however, associations were positive for magnesium and calcium intake (p=0.016) after adjusting for demographic, nondietary lifestyle and dietary factors, and food groups. Furthermore, magnesium intake was associated with insulin sensitivity in a threshold fashion, with a Bayesian method-estimated threshold (325 mg) (beta=0.0607/100 mg, p=0.0008 for <325 mg of magnesium/day; and beta=-0.001/100 mg, p=0.82 for >or=325 mg of magnesium/day). This study suggests that magnesium and calcium intake specifically, but not dairy intake, is associated with insulin sensitivity.

Adult↗

PubMed2006

Cluster detection diagnostics for small area health data: with reference to evaluation of local likelihood models.

The focus of this paper is the development of a range of cluster detection diagnostics that can be used to assess the degree to which a clustering method recovers the true clustering behaviour of small area data. The diagnostics proposed range from individual region specific diagnostics to neighbourhood diagnostics, and assume either individual region risk as focus, or concern areas of maps defined to be clustered and the recovery ability of methods. A simulation-based comparison is made between a small set of count data models: local likelihood, BYM and Lawson and Clark. It is found that local likelihood has good performance across a range of criteria when a CAR prior is assumed for the lasso parameter.

Algorithms↗

PubMed2006

Disease cluster detection: a critique and a Bayesian proposal.

This paper reviews issues in the analysis of non-focussed clustering, and proposes a novel approach to cluster modelling that can be used in a surveillance context. The novel approach involves the use of local likelihood models for the analysis of clustering in small area health data. Local likelihood is used when interdependence between data events at locations is modelled directly, as opposed to the modelling of a hidden process of cluster centres. This approach allows the use of conventional posterior sampling. It also allows a less parameterized approach to the form of clusters detected. The idea of a spatially dependent lasso which provides the local maxima for the aggregation of locations is considered as an approximation. The methods are applied to a well known data set and compared with Satscan, and a conditional logistic Bayesian model.

Air Pollutants↗

PubMed2006

Monitoring changes in spatio-temporal maps of disease.

The object of statistical surveillance is to detect a change in a process accurately and quickly as new observations keep adding to the observed part of the process. In this paper we discuss methodological issues in developing a rapid response in a spatial surveillance system. Simple exploratory statistical methods together with more sophisticated methods, based on hierarchical space-time models defined at small area level, are considered.

Bayes Theorem↗

PubMed2006

Online updating of space-time disease surveillance models via particle filters.

Online surveillance of disease has become an important issue in public health. In particular, the space-time monitoring of disease plays an important part in any syndromic system. However, methodology for these systems is generally lacking. One approach to space-time monitoring of health data is to consider the space-time model parameters as the focus and to monitor their changes as multivariate time series (Lawson AB. Some considerations in spatial-temporal analysis of public health surveillance data. In Brookmeyer R, Stroup DF eds. Monitoring the Health of Populations. Oxford University Press, 2004; Vidal Rodeiro CL, Lawson AB. Monitoring changes in spatio-temporal maps of disease. Biometrical Journal 2006; to appear). However with complex space-time models, this becomes very time consuming. Some simplifications may be necessary and these can be made in a number of ways. In this article, the focus is on particle filters that can be used to resample the history of the process and thereby reduce computation time. This article describes a particular case of particle filters, the resample-move algorithm, proposed by Gilks and Berzuini (Gilks WR, Berzuini C. Following a moving target--Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society, Series B 2001; 63: 127-46), in the context of disease map surveillance. This is followed by an application to a real data set in which a comparison between the use of Markov chain Monte Carlo methods and the resample-move algorithm is carried out.

Algorithms↗

PubMed2006

Surveillance of individual level disease maps.

Methods for the production of individual (address) level disease maps are often retrospective; they estimate a map of the average relative risk of disease over a study period. However, recently, epidemiologists have started to look at weekly or monthly reports of disease and assess them for any change in the distribution of relative risk. For example, in the United States of America, the Centre for Disease Control and Prevention now routinely collects information on over 50 notifiable diseases every week. In this paper we present a method for the detection of a sudden change in the geographical distribution of the disease in a prospective study. The method is based on an estimate of the directional derivative of the conditional probability of a case, given either a case or control has occurred. It is based on standard kernel approaches to nonparametric regression and it is readily applied in any standard statistical software package. Two simulated examples of sudden clustering around a fixed point are provided.

Humans↗

PubMed2005

Scale and shape issues in focused cluster power for count data.

BACKGROUND: Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Evaluations of statistical power provide important information for the selection of an appropriate statistical method in environmentally-related disease cluster investigations. Published power evaluations have not yet addressed the use of models for focused cluster detection and have not fully investigated the issues of disease cluster scale and shape. As meteorological and other factors can impact the dispersion of environmental toxicants, it follows that environmental exposures and associated diseases can be dispersed in a variety of spatial patterns. This study simulates disease clusters in a variety of shapes and scales around a centrally located single pollution source. We evaluate the power of a range of focused cluster tests and generalized linear models to detect these various cluster shapes and scales for count data. RESULTS: In general, the power of hypothesis tests and models to detect focused clusters improved when the test or model included parameters specific to the shape of cluster being examined (i.e. inclusion of a function for direction improved power of models to detect clustering with an angular effect). However, power to detect clusters where the risk peaked and then declined was limited. CONCLUSION: Findings from this investigation show sizeable changes in power according to the scale and shape of the cluster and the test or model applied. These findings demonstrate the importance of selecting a test or model with functions appropriate to detect the spatial pattern of the disease cluster.

Journal Article↗

PubMed2005

Current practices in cancer spatial data analysis: a call for guidance.

There has long been a recognition that place matters in health, from recognition of clusters of yellow fever and cholera in the 1800s to modern day analyses of regional and neighborhood effects on cancer patterns. Here we provide a summary of discussions about current practices in the spatial analysis of georeferenced cancer data by a panel of experts recently convened at the National Cancer Institute.

Journal Article↗

PubMed2003

Evaluation of systemic insecticides as a treatment option in integrated pest management of the elm leaf beetle, Xanthogaleruca luteola (Müller) (Coleoptera: Chrysomelidae).

This study evaluates the efficacy of two systemic insecticides (imidacloprid and abamectin) in an operational setting and their suitability to be incorporated into an integrated pest management program. Elm leaf beetle abundance and leaf damage were compared between treated trees and untreated control trees from 1995 through 1999. Laboratory bioassays using first-instar larvae were also used to measure the toxicity of leaves collected from treated trees at varying times after treatment. Trunk injections of abamectin and imidacloprid reduced the defoliation caused by elm leaf beetle when applied after monitoring at the peak density of elm leaf beetle eggs. Treatment in the first generation appeared to provide protection against damage in that generation as well as the second and third beetle generations. Both of these materials become active within the tree canopy very quickly and are therefore compatible with a management program that determines the need for treatment based on monitoring for egg clusters at peak density of eggs. Laboratory bioassays showed no toxicity of leaves in the year following treatment.

Animals↗

PubMed2002

Spatial mixture relative risk models applied to disease mapping.

An important issue within health services research is the correct allocation of resources within health authority regions and the capability of public health professionals to make such allocation appropriately. This allocation is often based on a mapping of relevant disease incidence and the assessment of the geographical distribution of relative risk of disease in small areas within the health authority administrative domain. Existing methods for the statistical analysis of small area risk are mostly based on smoothing methods. However, these methods often smooth over large discontinuities in the risk surface which might be important to maintain for the purposes of resource allocation. In this paper we propose a method that involves the use of spatial mixtures of components that can provide a balance between smoothness and the maintenance of discontinuity. The method is applied to a sudden infant death incidence data set.

Epidemiologic Methods↗