Skip to main content

COVID-19 Resources

What people with cancer should know: https://www.cancer.gov/coronavirus

Guidance for cancer researchers: https://www.cancer.gov/coronavirus-researchers

Get the latest public health information from CDC: https://www.cdc.gov/coronavirus

Get the latest research information from NIH: https://www.covid19.nih.gov

Grant Details

Grant Number: 5R01CA092693-03 Interpret this number
Primary Investigator: Wartenberg, Daniel
Organization: Univ Of Med/Dent Nj-R W Johnson Med Sch
Project Title: Geographic Tools for Surveillance and Study of Disease
Fiscal Year: 2003


Abstract

Disease mapping and cluster detection methods are statistical approaches to identify and summarize geographic patterns of disease occurrence. With the widespread availability and use of geographic information systems (GIS) and disease and environmental data, it is important that the analytic tools provide clear, accurate, precise and interpretable results. Toward that end, the proposed project will address three critical issues in geographical analysis. First, we will investigate a variety of methods for accommodating instability in rates from regions with small populations at risk. If not address adequately, maps may display spurious peaks (i.e., clusters) and valleys that can lead to misinterpretation. Traditional approaches include empirical Bayes mapping (i.e., smoothing) and grouping of neighboring geographical units. Second, we will continue our work on the development of geographic surveillance tools. One main goal of surveillance is to identify important changes in rates or patterns of disease occurrence for disease prevention and control activities by reviewing routinely collected data on an on-going basis. However, most approaches consider only temporal changes. Yet, perceptions of clusters are often spatial, and environmental pollutants typically are described in terms of the spatial or space-time distribution. This project will extend surveillance methods for use with spatial and space-time data, and will develop approaches for prospective rather than only retrospective evaluation. Third, we will extend our work on methods for analyzing geographic data when information is missing for some of the geographic units. Often, due to administrative or jurisdictional limitations, data are not available for an entire study region. However, there may be a need to be able to estimate rates for the entire region, or for those geographical units for which data are unavailable. We will explore methods for imputing values and adjusting for edge effects using methods including Markov Chain Monte Carlo simulations.



Publications

Some simple tests for spatial effects around putative sources of health risk.
Authors: Lawson A.B. , Williams F.L. , Liu Y. .
Source: Biometrical journal. Biometrische Zeitschrift, 2007 Aug; 49(4), p. 493-504.
PMID: 17638283
Related Citations

Online updating of space-time disease surveillance models via particle filters.
Authors: Vidal Rodeiro C.L. , Lawson A.B. .
Source: Statistical methods in medical research, 2006 Oct; 15(5), p. 423-44.
PMID: 17089947
Related Citations

Surveillance of individual level disease maps.
Authors: Clark A.B. , Lawson A.B. .
Source: Statistical methods in medical research, 2006 Aug; 15(4), p. 353-62.
PMID: 16886736
Related Citations

Monitoring changes in spatio-temporal maps of disease.
Authors: Vidal Rodeiro C.L. , Lawson A.B. .
Source: Biometrical journal. Biometrische Zeitschrift, 2006 Jun; 48(3), p. 463-80.
PMID: 16845909
Related Citations

Statistical methods for the detection of spatial clustering in case-control data.
Authors: Rogerson P.A. .
Source: Statistics in medicine, 2006-03-15; 25(5), p. 811-23.
PMID: 16453374
Related Citations

Recent changes in the spatial pattern of prostate cancer in the U.S.
Authors: Rogerson P.A. , Sinha G. , Han D. .
Source: American journal of preventive medicine, 2006 Feb; 30(2 Suppl), p. S50-9.
PMID: 16458790
Related Citations

Scale and shape issues in focused cluster power for count data.
Authors: Puett R.C. , Lawson A.B. , Clark A.B. , Aldrich T.E. , Porter D.E. , Feigley C.E. , Hebert J.R. .
Source: International journal of health geographics, 2005-03-31; 4(1), p. 8.
EPub date: 2005-03-31.
PMID: 15801981
Related Citations

Comparison of office visit and nurse advice hotline data for syndromic surveillance--Baltimore-Washington, D.C., metropolitan area, 2002.
Authors: Henry J.V. , Magruder S. , Snyder M. .
Source: MMWR supplements, 2004-09-24; 53, p. 112-6.
PMID: 15714639
Related Citations

Approaches to syndromic surveillance when data consist of small regional counts.
Authors: Rogerson P.A. , Yamada I. .
Source: MMWR supplements, 2004-09-24; 53, p. 79-85.
PMID: 15714634
Related Citations

Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches.
Authors: Rogerson P.A. , Yamada I. .
Source: Statistics in medicine, 2004-07-30; 23(14), p. 2195-214.
PMID: 15236425
Related Citations

Childhood leukaemia incidence and the population mixing hypothesis in US SEER data.
Authors: Wartenberg D. , Schneider D. , Brown S. .
Source: British journal of cancer, 2004-05-04; 90(9), p. 1771-6.
PMID: 15150603
Related Citations




Back to Top