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Thomas Webster

Publications and source records attributed to Thomas Webster.

6 recordsLinked to original sources
PubMed2006

Method for mapping population-based case-control studies: an application using generalized additive models.

BACKGROUND: Mapping spatial distributions of disease occurrence and risk can serve as a useful tool for identifying exposures of public health concern. Disease registry data are often mapped by town or county of diagnosis and contain limited data on covariates. These maps often possess poor spatial resolution, the potential for spatial confounding, and the inability to consider latency. Population-based case-control studies can provide detailed information on residential history and covariates. RESULTS: Generalized additive models (GAMs) provide a useful framework for mapping point-based epidemiologic data. Smoothing on location while controlling for covariates produces adjusted maps. We generate maps of odds ratios using the entire study area as a reference. We smooth using a locally weighted regression smoother (loess), a method that combines the advantages of nearest neighbor and kernel methods. We choose an optimal degree of smoothing by minimizing Akaike's Information Criterion. We use a deviance-based test to assess the overall importance of location in the model and pointwise permutation tests to locate regions of significantly increased or decreased risk. The method is illustrated with synthetic data and data from a population-based case-control study, using S-Plus and ArcView software. CONCLUSION: Our goal is to develop practical methods for mapping population-based case-control and cohort studies. The method described here performs well for our synthetic data, reproducing important features of the data and adequately controlling the covariate. When applied to the population-based case-control data set, the method suggests spatial confounding and identifies statistically significant areas of increased and decreased odds ratios.

Case-Control Studies↗
PubMed2005

Cluster detection methods applied to the Upper Cape Cod cancer data.

BACKGROUND: A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. METHODS: We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. RESULTS: The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. CONCLUSION: The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Adult↗
PubMed2005

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: an application of generalized additive models to case-control data.

BACKGROUND: The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed. METHODS: We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983-1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk. RESULTS: Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation. DISCUSSION: Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling.

Adult↗
PubMed2004

Environmental and endogenous peroxisome proliferator-activated receptor gamma agonists induce bone marrow B cell growth arrest and apoptosis: interactions between mono(2-ethylhexyl)phthalate, 9-cis-retinoic acid, and 15-deoxy-Delta12,14-prostaglandin J2.

The common commercial use of phthalate esters has resulted in significant human exposure to these bioactive compounds. The facts that phthalate ester metabolites, like endogenous PGs, are peroxisome proliferator-activated receptor (PPAR) agonists, and that PPARgamma agonists induce lymphocyte apoptosis suggest that phthalate esters are immunosuppressants that could act together with PGs to modulate early B cell development. In this study we examined the effects of a metabolite of one environmental phthalate, mono(2-ethylhexyl)phthalate (MEHP), and 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)), on developing B cells. MEHP inhibited [(3)H]thymidine incorporation by primary murine bone marrow B cells and a nontransformed murine pro/pre-B cell line (BU-11). Cotreatment with a retinoid X receptor alpha ligand, 9-cis-retinoic acid, decreased [(3)H]thymidine incorporation synergistically, thereby implicating activation of a PPARgamma-retinoid X receptor alpha complex. These results were similar to those obtained with the natural PPARgamma ligand 15d-PGJ(2). At moderate MEHP concentrations (25 or 100 microM for primary pro-B cells and a pro/pre-B cell line, respectively), inhibition of [(3)H]thymidine incorporation resulted primarily from apoptosis induction, whereas at lower concentrations, the inhibition probably reflected growth arrest without apoptosis. Cotreatment of bone marrow B cells with 15d-PGJ(2) and MEHP significantly enhanced the inhibition of [(3)H]thymidine incorporation seen with MEHP alone, potentially mimicking exposure in the bone marrow microenvironment where PG concentrations are high. Finally, MEHP- and 15d-PGJ(2)-induced death does not result from a decrease in NF-kappaB activation. These data demonstrate that environmental phthalates can cooperate with an endogenous ligand, 15d-PGJ(2), to inhibit proliferation of and induce apoptosis in developing bone marrow B cells, potentially via PPARgamma activation.

Alitretinoin↗
PubMed2002

A method for spatial analysis of risk in a population-based case-control study.

Spatial distributions of disease occurrence and risk have traditionally served as a tool for identifying exposures of public health concern. Current software for manipulating geographic data (GIS) now allows many kinds of analyses not feasible before. This paper presents a method for producing a "picture" of disease risks based on residential history data from a population based case-control study. We illustrate the method using geographically coded data on individual-level risk factors, such as age and smoking, from a cancer study of the Upper Cape Cod region of Massachusetts for 1983-1986. Our results show the association between lung cancer and residential location as an indicator of exposure. Crude and adjusted odds ratios were estimated by adaptive rate stabilization and mapped using kriging as an interpolation method to examine the risk of lung cancer in the region. The crude and adjusted surfaces for various smoothing parameters were compared to identify areas of increased lung cancer not explained by standard risk factors. Such spatial patterns of disease risk may provide clues to exposures of importance or confirm associations with previously suspected exposures.

Adult↗