• ISES-ISEE 2010: 2010 Joint Conference of International Society of Exposure Science & International Society for Environmental Epidemiology (Seoul, South Korea, 28 August - 1 September, 2010)

• 28 August 2010: We will run a half day workshop on Disease Mapping using Generalized Additive Models at ISES/ISEE 2010.

• 19 July 2010: Two new papers examine the statistical properties of using GAMs for mapping of disease. GAMs compare favorably with the widely used spatial scan statistic SaTScan in many cases.

• Young RL, Weinberg J, Ozonoff A, Vieira V, Webster TF. Generalized Additive Models and Inflated Type I Error Rates of Smoother Significance Tests. Computational Statistics & Data Analysis. In press. doi:10.1016/j.csda.2010.05.004.

• Young RL, Weinberg J, Vieira V, Ozonoff A, Webster TF. A power comparison of generalized additive models and the spatial scan statistic in a case-control setting. International Journal of Health Geographics 2010, 9:37. [Online 19 July 2010]. The full text is freely available doi:10.1186/1476-072X-9-37

• Young RL, Weinberg J, Vieira VM, Aschengrau A, Webster TF. A multilevel non-hierarchical study of birth weight and socioeconomic status. International Journal of Health Geographics 2010, 9:36 [Online 9 July 2010]. The full text is freely available doi:10.1186/1476-072X-9-36.

• Vieira VM, Hart JE, Webster TF, Weinberg J, Puett R, Laden F, Costenbader KH, Karlson EW. Association between Residences in U.S. Northern Latitudes and Rheumatoid Arthritis: A Spatial Analysis of the Nurses’ Health Study. Environ Health Perspect. 2010; 957–961 [Online 25 March 2010]. The full text is freely available doi:10.1289/ehp.0901861.

• Gallagher LG, Webster TF, Aschengrau A, Vieira VM. Using Residential History and Groundwater Modeling to Examine Drinking Water Exposure and Breast Cancer. Environ Health Perspect. 2010; 118(6):118:749–755 [Online 17 February 2010]. The full text is freely available doi:10.1289/ehp.0901547.

We subscribe to the philosophy of the BU Superfund Research Program (BUSRP) to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here. See also our BUSRP research translation core.

• Young RL, Weinberg J, Ozonoff A, Vieira V, Webster TF. Generalized Additive Models and Inflated Type I Error Rates of Smoother Significance Tests. Computational Statistics & Data Analysis. In press. doi: 10.1016/j.csda.2010.05.004.

• Young RL, Weinberg J, Ozonoff A, Vieira V, Webster TF. Generalized Additive Models and Inflated Type I Error Rates of Smoother Significance Tests. Computational Statistics & Data Analysis. In press.

• Tom Webster, D.Sc.

• Robin Young (former graduate student)

• 26 March 2010: Graduate student Robin Young defends her dissertation "Properties of Hypothesis Tests Using Generalized Additive Models with Smoothers of Geographic Location in Spatial Statistics"
• 25 March 2010: New paper on the spatial distribution of rheumatoid arthrititis in the US.

• 26 March 2010: Graduate student Robin Young defends her dissertation
• 25 March 2010: New paper on the spatial distribution of rheumatoid arthritis in the US.

• Vieira VM, Hart JE, Webster TF, Weinberg J, Puett R, Laden F, Costenbader KH, Karlson EW. Association between Residences in U.S. Northern Latitudes and Rheumatoid Arthritis: A Spatial Analysis of the Nurses’ Health Study. Environ Health Perspect. In press. [Online 25 March 2010]. The full text is freely available doi:10.1289/ehp.0901861.

• Gallagher LG, Webster TF, Aschengrau A, Vieira VM. Using Residential History and Groundwater Modeling to Examine Drinking Water Exposure and Breast Cancer. Environ Health Perspect. In press. [Online 17 February 2010]. The full text is freely available doi:10.1289/ehp.0901547.
• Hoffman K, Webster TF, Weinberg JM, Aschengrau A, Janulewicz PA, White RF, Vieira VM. Spatial analysis of learning and developmental disorders in upper Cape Cod, Massachusetts using generalized additive models. International Journal of Health Geographics 2010, 9:7. The full text is freely available doi:10.1186/1476-072X-9-7.

• Vieira V, Webster T, Weinberg J, Hart J, Laden F, Costenbader K, Karlson E. Spatial Analysis of Rheumatoid Arthritis in the Nurses' Health Study. Presented at ISEE 2009. Dublin, Ireland, 26-29 August 2009.
• Young RL, Weinberg J, Vieira V, Webster TF. A Power Comparison of Generalized Additive Models and the Spatial Scan Statistic Under Simple Alternative Hypotheses. Presented at the Joint Statistical Meetings (JSM) 2009 (Washington DC, 1-6 August 2009).

• Young RL, Weinberg J, Ozonoff A, Vieira V, Webster TF. Generalized Additive Models and Inflated Type I Error Rates of Smoother Hypothesis Tests. Western North American Region of the International Biometric Society (WNAR). Portland OR, 16 June 2009.

• Vieira VM, Webster TF, Weinberg J, Aschengrau A. Spatial analysis of bladder, kidney, and pancreatic cancer on upper Cape Cod: An application of generalized additive models to case-control data. Environmental Health; 2009, 8:3. [Online 10 February 2009]. The full text is freely available doi: 10.1186/1476-069X-8-3

• Webster TF, Vieira VM, Weinberg J, Aschengrau A. Ecologic Measures of Exposure: Can They Cause Ecologic Bias? Presented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Vieira V, Fraser A, Webster T, Howard GJ, Bartell S. Accuracy of Automated and E911 Geocoding Methods for Rural Addresses. Presented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Vieira V, Webster T, Weinberg J, Aschengrau A. Spatial Analysis of Kidney, Pancreatic, and Bladder Cancer on Upper Cape Cod using Generalized Additive Modeling. Presented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Gallagher L, Ozonoff D, Webster T, Vieira V, Weinberg J, Aschengrau A. Reanalysis of the Risk of Breast Cancer and Tetrachloroethylene-Contaminated Drinking Water in Cape Cod, Massachusetts Using a Modified Exposure Assessment. Presented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)

• Webster TF, Vieira VM, Weinberg J, Aschengrau A. Ecologic Measures of Exposure: Can They Cause Ecologic Bias? resented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Vieira V, Fraser A, Webster T, Howard GJ, Bartell S. Accuracy of Automated and E911 Geocoding Methods for Rural Addresses. resented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Vieira V, Webster T, Weinberg J, Aschengrau A. Spatial Analysis of Kidney, Pancreatic, and Bladder Cancer on Upper Cape Cod using Generalized Additive Modeling.resented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Gallagher L, Ozonoff D, Webster T, Vieira V, Weinberg J, Aschengrau A. Reanalysis of the Risk of Breast Cancer and Tetrachloroethylene-Contaminated Drinking Water in Cape Cod, Massachusetts Using a Modified Exposure Assessment. resented at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)
• Vieira V, Webster T, Weinberg J, Aschengrau A. Spatial-temporal analysis of breast cancer as a function of residential history. 2008 Meeting of the Association of American Geographers. 16 April 2008. Boston. MA. AAG 2008

• ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)

• 13 October 2008: Our recent time-space analysis of breast cancer was covered in the Cape Cod Times.

• 13 October 2008: Our recent time-space analysis of breast cancer was covered in the Cape Cod Times.'''

• 22 September 2008: Our multilevel analysis of breast cancer was featured in a National Institute of Environmental Health Sciences (NIEHS) press release on spatial epidemiology.

• Webster TF, Vieira VM, Weinberg J, Aschengrau A. Ecologic Measures of Exposure: Can They Cause Ecologic Bias?
• Vieira V, Fraser A, Webster T, Howard GJ, Bartell S. Accuracy of Automated and E911 Geocoding Methods for Rural Addresses.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Spatial Analysis of Kidney, Pancreatic, and Bladder Cancer on Upper Cape Cod using Generalized Additive Modeling.
• Gallagher L, Ozonoff D, Webster T, Vieira V, Weinberg J, Aschengrau A. Reanalysis of the Risk of Breast Cancer and Tetrachloroethylene-Contaminated Drinking Water in Cape Cod, Massachusetts Using a Modified Exposure Assessment.

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on upper Cape Cod discussed in our new publication, Vieira et al 2008. The movie is a Windows Media file (1 mb). Click to view

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on upper Cape Cod discussed in our new publication, Vieira et al 2008. The movie is a Windows Media file (1 mb). Click to view|

• 13 August 2008: New publication on time-space clustering of breast cancer on Cape Cod, including a cool animation, Vieira et al 2008.
• 25 April 2008: New publication on multilevel modeling of breast cancer and socioeconomic status on Cape Cod, Webster et al 2008.

Disease registry data are often mapped by town or county of diagnosis and contain limited data on possible confounders. These maps often possess poor spatial resolution, the potential for spatial confounding, and the inability to consider latency. Population-based case-control studies can, on the other hand, provide detailed information on residential history and covariates. We are developing and applying methods for mapping case-control and cohort data while adjusting for risk factors and latency. We map point patterns using generalized additive models (GAMs), a statistical framework which allows us to analyze binary outcome data, adjust for covariates, smooth on space (with optimal degree of smoothing), and perform hypothesis tests. For example, the map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis. Our latest work has focused on time-space clustering.

• 13 August 2008: New publication on time-space clustering of breast cancer on Cape Cod, including a cool animation Vieira et al 2008.

• 25 April 2008: New publication on multilevel modeling of breast cancer and socioeconomic status on Cape Cod. Webster et al 2008.

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on upper Cape Cod discussed in our new publication, Vieira et al 2008. The movie is a Windows Media file (1 mb). Attach:timespacemovie.wmv?

• Vieira VM, Webster TF, Weinberg JM, Aschengrau A. Spatial-temporal analysis of breast cancer on Upper Cape Cod, Massachusetts. International Journal of Health Geographics 2008, 7:46. The full text is freely available here.

We subscribe to the philosophy of the BU Superfund Basic Research Program (BUSBRP) to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here. See also our BUSBRP research translation core.

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on upper Cape Cod. The movie is a Windows Media file (1 mb). Attach:timespacemovie.wmv?

Upcoming conferences

• Look for us at ISEE-ISEA 2008 (Pasadena, CA, 12-16 October 2008)

• 25 April 2008: New publication. Webster TF, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Environ Health Perspect 2008; 116(8):1125-1129. doi:10.1289/ehp.10818. [Online 25 April 2008]. The full text is freely available here.

• Webster TF, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Environ Health Perspect 2008; 116(8):1125-1129. doi:10.1289/ehp.10818. [Online 25 April 2008]. The full text is freely available here.

• 25 April 2008: New publication. Webster TF, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Environ Health Perspect 116(8):1125-1129. doi:10.1289/ehp.10818. [Online 25 April 2008]. The full text is freely available here.

• Webster TF, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Environ Health Perspect 116(8):1125-1129. doi:10.1289/ehp.10818. [Online 25 April 2008]. The full text is freely available here.

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on upper Cape Cod. The movie is a Windows Media file (1 mb). Attach:timespacemovie.wmv

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on upper Cape Cod. The movie is a Windows Media file (1 mb). movie.wmv

The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on Upper Cape Cod. The movie is a Windows Media file (1 mb). movie.wmv

View a movie of breast cancer time-space analysis
The movie shows the risk of breast cancer diagnosis 1983-1993 based on residential history on Upper Cape Cod. The movie is a windows media file (1 mb). Attach:movie.wmv

Many diseases are associated with an individual's socioeconomic status (SES), also known as socioeconomic position (SEP). Community level SES is often associated with disease risk as well. However, despite our knowledge of these separate associations, most previous research has not examined individual and community SES simultaneously. As a result, it is unclear if the greater disease incidence in certain communities is related to the SES of the individuals who live there (composition) or because some aspect of living in a the community confers a greater risk of disease, regardless of their SES (context). A recent paper investigated this question for breast cancer on Cape Cod.

• Robin Young, doctoral student

• Vieira V, Webster T, Weinberg J, Aschengrau A. Spatial-temporal analysis of breast cancer as a function of residential history. 2008 Meeting of the Association of American Geographers. 16 April 2008. Boston. MA. AAG 2008

• 25 April 2008: New publication. Webster TF, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Environ Health Perspect. In press. doi:10.1289/ehp.10818. [Online 25 April 2008]. The full text is freely available here.

• Webster TF, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Environ Health Perspect. In press. doi:10.1289/ehp.10818. [Online 25 April 2008]. The full text is freely available here.

• Google street view outside the beautiful Talbot building where we are located

Where are we? Google street view outside the beautiful Talbot building where we are located:look here

Google street view outside the beautiful Talbot building where we are located: look here

Google street view outside our building: look here

'''Google street view outside our building: look here

Google street view outside our building: here

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• Webster, T. Individual studies with ecologic exposure measures. Environmental Statistics Seminar, Harvard School of Public Health. 7 December 2007.

• Webster, T. Overview: Studies that combine individual and group-level data. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. more information
• Webster, T. Individual studies with ecologic measures of exposure. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. more information

• Webster, T. Overview: Studies that combine individual and group-level data. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. more information
• Webster, T. Individual studies with ecologic measures of exposure. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. more information

• Webster, T. Overview: Studies that combine individual and group-level data. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. [http://dimacs.rutgers.edu/Workshops/Ecologic|more information]
• Webster, T. Individual studies with ecologic measures of exposure. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. [http://dimacs.rutgers.edu/Workshops/Ecologic|more information]

• Webster, T. Overview: Studies that combine individual and group-level data. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. [[http://dimacs.rutgers.edu/Workshops/Ecologic|more information]
• Webster, T. Individual studies with ecologic measures of exposure. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University. [[http://dimacs.rutgers.edu/Workshops/Ecologic|more information]

• Webster, T. Individual studies with ecologic measures of exposure. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University.

• Webster, T. Overview: Studies that combine individual and group-level data. DIMACS Workshop on Ecologic Inference. 28 - 30, November 2007, Rutgers University.

 *Webster, T. Individual studies with ecologic measures of exposure. DIMACS Workshop on Ecologic Inference.  28 - 30, November 2007, Rutgers University.

email: Dr. Tom Webster, Dept. Environmental Health

• Veronica Vieira, D.Sc.

• Tom Webster, D.Sc., Project Leader

• Veronica Vieira, D.Sc. Dept. Environmental Health

• Tom Webster, D.Sc., Dept. Environmental Health

• Workshop on ecologic inference: 28-30 November 2007, DIMACS, Rutgers University\\

The workshop is part of the DIMACS Special Focus on Computational and Mathematical Epidemiology. The focus will be on study designs combining individual and group level data. More information on the workshop

• Workshop on ecologic inference: 28-30 November 2007, DIMACS, Rutgers University
  The workshop is part of the DIMACS Special Focus on Computational and Mathematical Epidemiology. The focus will be on study designs combining individual and group level data.

 More information on the workshop

• More information on ecologic studies.

• DIMACS Workshop on Ecologic Inference, 28-30 November 2007
• International Society for Environmental Epidemiology: ISEE 2007

Individual-level studies collect information on exposure, outcome and covariates for each individual; purely ecologic studies collect group-level (aggregate) information for these variables. Ecologic bias can occur when aggregate data are used to make inferences about individuals. We are interested in comparing the direction and magnitude of ecologic bias compared with biases occuring on the individual-level. Such information is useful in designing ecologic studies, doing sensitivity analyses of ecologic studies, and understanding what happens when a group-level variable is used in an otherwise individual-level study (e.g., as a proxy for exposure on the individual-level). For more information, look here.

• 5 July 2007: New publication. Webster TF. Bias magnification in ecologic studies: a methodological investigation. Environmental Health 2007; 6:17. The full text is freely available here.

Individual-level studies collect information on exposure, outcome and covariates for each individual; purely ecologic studies collect group-level (aggregate) information for these variables. Ecologic bias can occur when aggregate data are used to make inferences about individuals. We are interested in comparing the direction and magnitude of ecologic bias compared with biases occuring on the individual-level. Such information is useful in designing ecologic studies, doing sensitivity analyses of ecologic studies, and understanding what happens when a group-level variable is used in an otherwise individual-level study (e.g., as a proxy for exposure on the individual-level). For more information, look here.

• Webster T,Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Society for Epidemiologic Research. Boston, 19-22 June 2007. American Journal of Epidemiology 2007; 165(11) Suppl: S145.

• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The impact of community and individual-level socioeconomic status on the risk of breast cancer: Multi-level modeling in Massachusetts. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France. Epidemiology 2006; 17(6) Suppl:S210

• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-spatial analysis of breast cancer risk on upper Cape Cod, Massachusetts, USA. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France. Epidemiology 2006; 17(6) Suppl:S484

• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-spatial analysis of breast cancer risk on upper Cape Cod, Massachusetts, USA. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France. Epidemiology 2006; 17(6) Suppl:S484

• Gallagher L, Ozonoff D, Webster T, Vieira V, Aschengrau A. Use of a water distribution model to assess exposure to tetachloroethylene-contaminated drinking water. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France. Epidemiology 2006; 17(6) Suppl:S470-S471.

• Gallagher L, Ozonoff D, Webster T, Vieira V, Aschengrau A. Use of a water distribution model to assess exposure to tetachloroethylene-contaminated drinking water. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France. Epidemiology 2996; 17(6) Suppl:S470-S471.

• 5 July 2007: New publication. Webster TF. Bias magnification in ecologic studies: a methodological investigation. Environmental Health 2007; 6:17.

The Spatial Epidemiology Group at Boston University School of Public Health has several interests:

Disease Mapping and Clusters \\

Ecologic bias \\

Multi-level studies \\

News & Awards (For details, look here)

• 5 July 2007: New publication. Webster TF. Bias magnification in ecologic studies: a methodological investigation. Environmental Health 2007; 6:17 (5 July 2007).

Recent publications:

Recent conference presentations:

Recent conferences

How do we make these maps? \\

Spatial Epidemiology Group at Boston University School of Public Health

For more information: \\

• International Society for Environmental Epidemiology: ISEE 2007

• Webster T,Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Society for Epidemiologic Research. Boston, 19-22 June 2007.
• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The impact of community and individual-level socioeconomic status on the risk of breast cancer: Multi-level modeling in Massachusetts. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France.
• Gallagher L, Ozonoff D, Webster T, Vieira V, Aschengrau A. Use of a water distribution model to assess exposure to tetachloroethylene-contaminated drinking water. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-spatial analysis of breast cancer risk on upper Cape Cod, Massachusetts, USA. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France.

• Webster T,Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. ''Society for Epidemiologic Research'. Boston, 19-22 June 2007.
• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The impact of community and individual-level socioeconomic status on the risk of breast cancer: Multi-level modeling in Massachusetts. Presented at 'ISEE/ISEA', 2-6 September 2006, Paris, France.
• Gallagher L, Ozonoff D, Webster T, Vieira V, Aschengrau A. Use of a water distribution model to assess exposure to tetachloroethylene-contaminated drinking water. Presented at 'ISEE/ISEA', 2-6 September 2006, Paris, France.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-spatial analysis of breast cancer risk on upper Cape Cod, Massachusetts, USA. Presented at 'ISEE/ISEA', 2-6 September 2006, Paris, France.

Ecologic bias
Individual-level studies collect information on exposure, outcome and covariates for each individual; purely ecologic studies collect group-level (aggregate) information for these variables. Ecologic bias can occur when aggregate data are used to make inferences about individuals. We are interested in comparing the direction and magnitude of ecologic bias compared with biases occuring on the individual-level. Such information is useful in designing ecologic studies, doing sensitivity analyses of ecologic studies, and understanding what happens when a group-level variable is used in an otherwise individual-level study (e.g., as a proxy for exposure on the individual-level).

Ecologic bias
Individual-level studies collect information on exposure, outcome and covariates for each individual; purely ecologic studies collect group-level (aggregate) information for these variables. Ecologic bias can occur when aggregate data are used to make inferences about individuals. We are interested in comparing the direction and magnitude of ecologic bias compared with biases occuring on the individual-level. Such information is useful in designing ecologic studies, doing sensitivity analyses of ecologic studies, and understanding what happens when a group-level variable is used in an otherwise individual-level study (e.g., as a proxy for exposrue on the individual-level).

• Webster TF. Bias magnification in ecologic studies: a methodological investigation. Environmental Health 2007; 6:17 (5 July 2007). The full text is freely available here.

• * Webster TF. Bias magnification in ecologic studies: a methodological investigation. Environmental Health 2007; 6:17 (5 July 2007). The full text is freely available here.

• Webster TF. Bias Magnification in Ecologic Studies: A Methodological Investigation. Accepted for Environmental Health.

• Webster T,Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Society for Epidemiologic Research. Boston, 19-22 June 2007.

• Webster TF. Bias Magnification in Ecologic Studies: A Methodological Investigation. Submitted.

• test

• Webster T, Vieira V; Weinberg J; Aschengrau A. Method for mapping population-based case-control studies using Generalized Additive Models. International Journal of Health Geographics 2006, 5:26 (9 June 2006).The full text is freely available here.

• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-Spatial Analysis of Breast Cancer on Upper Cape Cod, Massachusetts. Presented at Spatial Epidemiology 2006, London.

• Webster T. Bias Magnification in Ecologic Studies: A Methodological Investigation. Submitted.

• Webster T, Hoffman K, Weinberg J, Vieira V, Aschengrau A. Community and Individual-Level Socioeconomic Status and Breast Cancer Risk: Multi-level Modeling on Cape Cod, MA. Submitted.

(:title Spatial Epidemiology at Boston University School of Public Health:)

return to Tom Webster

return to Tom Webster

• Ann Aschengrau, Sc.D.

• Al Ozonoff, Ph.D.

• Janice Weinberg, Sc.D.

• Veronica Vieira, D.Sc.

We subscribe to the philosophy of the BU Superfund Basic Research Program (BUSBRP) to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here. See also our BUSBRP research translation core.

Many diseases are associated with an individual's socioeconomic status (SES), also known as socioeconomic position (SEP). Community level SES is often associated with disease risk as well. However, despite our knowledge of these separate associations, most previous research has not examined individual and community SES simultaneously. As a result, it is unclear if the greater disease incidence in certain communities is related to the SES of the individuals who live there (composition) or because some aspect of living in a the community confers a greater risk of disease, regardless of their SES (context). A current project is investigating this question for breast cancer on Cape Cod.

• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The impact of community and individual-level socioeconomic status on the risk of breast cancer: Multi-level modeling in Massachusetts. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France.
• Gallagher L, Ozonoff D, Webster T, Vieira V, Aschengrau A. Use of a water distribution model to assess exposure to tetachloroethylene-contaminated drinking water. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-spatial analysis of breast cancer risk on upper Cape Cod, Massachusetts, USA. Presented at ISEE/ISEA, 2-6 September 2006, Paris, France.

Recent conferences

• Tom Webster, D.Sc.

We subscribe to the philosophy of the BU Superfund Basic Research Program (BUSBRP) to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here. See also our BUSBRP research translation core.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here. See also our research translation core.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here?. See also our research translation core.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here. See also our research translation core.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: here]. See also our [[http://www.busbrp.org/projects/project2.html|research translation core.

• Webster T, Vieira V; Weinberg J; Aschengrau A. Method for mapping population-based case-control studies using Generalized Additive Models. International Journal of Health Geographics 2006, 5:26 (9 June 2006).The full text is freely available here.

• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The Impact of Community and Individual-Level Socioeconomic Status on the Risk of Breast Cancer: Multi-level Modeling among Residents of Cape Cod, MA (USA). Presented at Spatial Epidemiology 2006, London.
• Webster T. Cross-level bias in partially ecologic studies. Presented at Spatial Epidemiology 2006, London.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-Spatial Analysis of Breast Cancer on Upeer Cape Cod, Massachusetts. Presented at Spatial Epidemiology 2006, London.

(:title Spatial Epidemiology:)

• Tom Webster, D.Sc.

• Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the Upper Cape Cod cancer data. Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005). The full text is freely available here
• Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005). The full text is freely available here.
• Webster, T. Commentary: Does the spectre of ecologic bias haunt epidemiology? International Journal of Epidemiology 2002; 31:161-162. The full text is freely available here.

Many diseases are associated with an individual's socioeconomic status (SES), also known as socioeconomic position (SEP). Community level SES is often associated with disease risk as well. However, despite our knowledge of these separate associations, most previous research has not examined individual and community SES simultaneously. As a result, it is unclear if the greater disease incidence in certain communities is related to the SES of the individuals who live there (composition) or because some aspect of living in a the community confers a greater risk of disease, regardless of their SES (context). A current CIREEH-sponsored pilot project is investigating this question for breast cancer on Cape Cod.

Multi-level studies \\

• Webster, T. Commentary: Does the spectre of ecologic bias haunt epidemiology? International Journal of Epidemiology 2002; 31:161-162. The full text is freely available at http://ije.oxfordjournals.org/cgi/content/full/31/1/161

• Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the Upper Cape Cod cancer data. Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005). The full text is freely available at http://www.ehjournal.net/content/4/1/19
• Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005). The full text is freely available at http://www.ehjournal.net/content/4/1/11
• Webster, T. Commentary: Does the spectre of ecologic bias haunt epidemiology? International Journal of Epidemiology 2002; 31:161-162. The full text is freely available at http://ije.oxfordjournals.org/cgi/content/full/31/1/161

• Webster, T. Commentary: Does the spectre of ecologic bias haunt epidemiology? International Journal of Epidemiology 2002; 31:161-162.

• Webster T, Vieira V; Weinberg J; Aschengrau A. Method for mapping population-based case-control studies using Generalized Additive Models. International Journal of Health Geographics [accepted for publication].
• Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the Upper Cape Cod cancer data. Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005). The full text is available at http://www.ehjournal.net/content/4/1/19

• Webster, T. Commentary: Does the specter of ecologic bias haunt epidemiology? International Journal of Epidemiology 2002; 31:161-162.

Many diseases are associated with an individual's socioeconomic status (SES), also known as socioeconomic position (SEP). It has also recognized that community level SES is associated with disease. However, despite our knowledge of these separate associations, most previous research has not examined individual and community SES simultaneously. As a result, it is unclear if the greater disease incidence in certain communities is related to the SES of the individuals who live there (composition) or because some aspect of living in a the community confers a greater risk of disease, regardless of their SES (context). A current CIREEH-sponsored pilot project is investigating this question for breast cancer on Cape Cod.

• the use of combinations of individual- and group-level data (multi-level studies)

Mulit-level studies
Many diseases are associated with an individual's socioeconomic status (SES), also known as socioeconomic position (SEP). It has also recognized that community level SES is associated with disease. However, despite our knowledge of these separate associations, most previous research has not examined individual and community SES simultaneously. As a result, it is unclear if the greater disease incidence in certain communities is related to the SES of the individuals who live there (composition) or because some aspect of living in a the community confers a greater risk of disease, regardless of their SES (context). A current CIREEH-sponsored pilot project is investigating this question for breast cancer on Cape Cod.

Recent publications:

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out, courtesy of our research translation core.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and ArcView (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html.

Upcoming conferences

• Spatial Epidemiology Conference 2006

Upcoming conferences
* International Conference on Environmental Epidemiology and Exposure: ISEE/ISEA 2006

• Kate Hoffman, doctoral student

• the use of Geographic Information Systems (GIS) in exposure assessment

• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The Impact of Community and Individual-Level Socioeconomic Status on the Risk of Breast Cancer: Multi-level Modeling among Residents of Cape Cod, MA (USA). Presented at Spacial Epidemiology 2006, London.
• Webster T. Cross-level bias in partially ecologic studies. Presented at Spacial Epidemiology 2006, London.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-Spatial Analysis of Breast Cancer on Upeer Cape Cod, Massachusetts. Presented at Spacial Epidemiology 2006, London.

Recent conference presentations:

• Webster T, Weinberg J, Vieira V, Hoffman K, Aschengrau A. The Impact of Community and Individual-Level Socioeconomic Status on the Risk of Breast Cancer: Multi-level Modeling among Residents of Cape Cod, MA (USA). Presented at "Spacial Epidemiology 2006", London.
• Webster T. Cross-level bias in partially ecologic studies. Presented at "Spacial Epidemiology 2006", London.
• Vieira V, Webster T, Weinberg J, Aschengrau A. Temporal-Spatial Analysis of Breast Cancer on Upeer Cape Cod, Massachusetts. Presented at "Spacial Epidemiology 2006", London.

Disease Mapping and Clusters \\

The Spatial Epidemiology Group at Boston University School of Public Health has several interests:

• disease mapping and clusters
• time-space analysis
• ecologic bias
• the use of combinations of individual- and group-level data
• the use of Geographic Informaiton Systems (GIS) in exposure assessment

The Spatial Epidemiology Group at Boston University School of Public Health has several interests: disease mapping and clusters, ecologic bias, and the use of combinations of individual- and group-level data.

Disease Mapping and Clusters Disease registry data are often mapped by town or county of diagnosis and contain limited data on possible confounders. These maps often possess poor spatial resolution, the potential for spatial confounding, and the inability to consider latency. Population-based case-control studies can, on the other hand, provide detailed information on residential history and covariates. We are developing and applying methods for mapping case-control and cohort data while adjusting for risk factors and latency. We map point patterns using generalized additive models (GAMs), a statistical framework which allows us to analyze binary outcome data, adjust for covariates, smooth on space (with optimal degree of smoothing), and perform hypothesis tests. For example, the map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis.

Recent publications on disease mapping:

The Spatial Epidemiology Group at Boston University School of Public Health has several projects, including the development and application of methods for mapping case-control and cohort data while adjusting for risk factors and latency. For example, the map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis. We map point patterns using generalized additive models (GAMs), a statistical framework which allows us to analyze binary outcome data, adjust for covariates, smooth on space (with optimal degree of smoothing), and perform hypothesis tests.

For more information:
email: Dr. Tom Webster

How do we make these maps? \\

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license. We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and Arview (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are also connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are connected, i.e., making results and products freely available where possible through open-access publications and open source software available under general public license.

• Janice Weinberg, Sc.D.

• Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the Upper Cape Cod cancer data. Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005). The full text is available at http://www.ehjournal.net/content/4/1/19

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are connected, i.e., making results and products freely available where possible through open-access publications and open source sfotware available under general public license.

The Spatial Epidemiology Group at Boston University School of Public Health has several projects, including the development and application of methods for mapping case-control and cohort data while adjusting for risk factors and latency. For example, the map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis. We map point patterns using generalized additive models (GAMs), a statistical framework which allows us to analyze binary outcome data, adjust for covariates, smooth on space (with optimal degree of smoothing), and perform hypothesis tests,

• Janice Weinberg

• Al Ozonoff, Ph.D.

• [[http://sph.bumc.bu.edu/directory/displayDetails.aspx?INDEX=12088|Al Ozonoff, Ph.D.]

• [http://sph.bumc.bu.edu/directory/displayDetails.aspx?INDEX=4965|Janice Weinberg]]

• Ann Aschengrau, Sc.D.

• Ann Aschengrau

• Veronica Vieira, D.Sc.

• Tom Webster, D.Sc.

• Tom Webster, D.Sc.

We are currently creating maps using S-Plus (to calculate surfaces using GAMs) and Arview (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html.

Spatial Epidemiology Group at Boston University School of Public Health

• Ann Aschengrau
• Veronica Vieira
• Janice Weinberg
• Tom Webster, D.Sc.

We are currently creating maps using S-Plus (fto calculate surfaces using GAMs) and Arview (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html.

We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are connected, i.e., making results and products freely available where possible through open-access publications and open source sfotware available under general public license.

We are currently creating maps using S-Plus (fto calculate surfaces using GAMs) and Arview (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html. We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are connected, i.e., making results and products freely available where possible through open-access publications and open source sfotware available under general public license.

We are currently creating maps using S-Plus (fto calculate surfaces using GAMs) and Arview (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html. We subscribe to the philosophy of the ? to which we are connected, i.e., making results and products freely available where possible through open-access publications and open source sfotware available under general public license.

We are currently creating maps using S-Plus (fto calculate surfaces using GAMs) and Arview (for mapping the surfaces). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html. We subscribe to the philosophy of the BU Superfund Basic Research Program to which we are connected, i.e., making results and products freely available where possible through open-access publications and open source sfotware available under general public license.

How do we make these maps?

• Webster T, Vieira V; Weinberg J; Aschengrau A. Method for mapping population-based case-control studies using Generalized Additive Models. International Journal of Health Geographics [accepted for publication].

• Webster T, Vieira V; Weinberg J; Aschengrau A. Method for mapping population-based case-control studies using Generalized Additive Models. "International Journal of Health Geographics"" [accepted for publication].

We are currently creating maps using SPlus (for gams) and Arview (for maps). R may provide a useful alternative for gams. Our code is freely available, as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html

For more information, email: Dr. Tom Webster

For more information, email Tom Webster

For more information, contact: Tom Webster

Recent publications :

How do we make these maps? :

For more information , contact: Tom Webster

• Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the Upper Cape Cod cancer data. Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005). The full text is available at http://www.ehjournal.net/content/4/1/19

• Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005). The full text is available at http://www.ehjournal.net/content/4/1/11
• Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A.

Computer code: We are currently creating maps using SPlus (for gams) and Arview (for maps). R may provide a useful alternative for gams. Our code is freely available as is some synthetic data for trying it out: http://www.busbrp.org/projects/project2.html

For more information, contact: Tom Webster

For more information, contact / / Tom Webster

For more information, contact \\\ Tom Webster

For more information, contact // Tom Webster

The Spatial Epidemiology Group at Boston University School of Public Health has several projects, including the development and application of methods for mapping case-control and cohort data while adjusting for risk factors and latency. For example, the map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis. We map point patterns using generalized additive models (GAMs), a statistical framework which allows us to analyze binary outcome data, adjust for covariates, smooth on space (with optimaed degree of smoothing), and perform hypothesis tests,

For more information, contact \\ Tom Webster

Recent publications:

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005). The full text is available at http://www.ehjournal.net/content/4/1/11

Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005). The full text is available at http://www.ehjournal.net/content/4/1/19

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005) \ \ The full text is available at: http://www.ehjournal.net/content/4/1/11

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005) \\ The full text is available at: http://www.ehjournal.net/content/4/1/11

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005) Full text?

Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005) Full text?

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005) \\ http://www.ehjournal.net/content/4/1/11

Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005) \\ http://www.ehjournal.net/content/4/1/19

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005) // http://www.ehjournal.net/content/4/1/11

Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005) // http://www.ehjournal.net/content/4/1/19

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005)

Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005)

Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the Upper Cape Cod cancer data. Environmental Health: A Global Access Science Source 2005, 4:19 (15 September 2005) http://www.ehjournal.net/content/4/1/19

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005)

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

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D Environmental Health: A Global Access Science Source 2005, 4:11 (14 June 2005) http://www.ehjournal.net/content/4/1/11

http://www.google.com

Vieira et al 2006?

The Spatial Epidemiology Group at Boston University School of Public Health has several projectsw, including the development and application of methods for mapping case-control and cohort data while adjusting for risk factors and latency. For example, the map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis. We map point patterns using generalized additive models (GAMs), a statistical framework which allows us to analyze binary outcome data, adjust for covariates, smooth on space (with optimaed degree of smoothing), and perform hypothesis tests,

A few recent publications:

The Spatial Epidemiology Group at Boston University School of Public Health is developing and applying methods for mapping case-control and cohort data while adjusting for risk factors and latency. Failure to adjust for covariates may either cause or hide disease clusters. Failure to take latency into account may hide spatial patterns. The map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis.

The Spatial Epidemiology Group at Boston University School of Public Health is developing and applying methods for mapping case-control and cohort data while adjusting for risk factors and latency. Failure to adjust for covariates may either cause or hide disease clusters. Failure to take latency into account may hide spatial patterns. The map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis.

The >>red<<Spatial Epidemiology Group at Boston University School of Public Health>><< is developing and applying methods for mapping case-control and cohort data while adjusting for risk factors and latency. Failure to adjust for covariates may either cause or hide disease clusters. Failure to take latency into account may hide spatial patterns. The map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis.

The Spatial Epidemiology Group at Boston University School of Public Health is developing and applying methods for mapping case-control and cohort data while adjusting for risk factors and latency. Failure to adjust for covariates may either cause or hide disease clusters. Failure to take latency into account may hide spatial patterns. The map on the left above shows breast cancer risk at the time of diagnosis; the map on the right shows maps residential location twenty years prior to diagnosis.

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Spatial Epidemiology at Boston University School of Public Health