# Presentation: 131031

## Long Island Breast Cancer Studyand the GIS-H (Health)

**Edward J. Trapido, Sc.D.**
**Associate Director**
**Epidemiology and Genetics Research Program, DCCPS/NCI**

**COMPREHENSIVE APPROACHES TO CANCER CONTROL**
**September, 2003 Atlanta, GA **

## The northeastern United States has had high rates of breast cancer.

- The Long Island Breast Cancer Study Project (LIBCSP) focuses on Long Island (Nassau and Suffolk counties) in New York.

## Long Island Breast Cancer Study Project

**Grew out of community’s                               concern**
**A multistudy investigation of  environmental factors and breast cancer**
**NCI developed the GIS-H in response to a law passed in 1993**

**Notes:**
Grew out of the community’s concern
It’s a multistudy investigation of  environmental factors and breast cancer in New York and Connecticut  
NCI developed the GIS-H in response to a law passed in 1993.  That law 

directed that research be conducted on environmental factors that may contribute to breast cancer, and 
mandated the creation of a GIS for breast cancer studies on Long Island.   

## Public Law 103-43June 10, 1993

**“****The director of the NCI. . .shall conduct a case-control study [of] factors contributing to the incidence of breast cancer in:**
**The counties of Nassau and Suffolk, and**
**The 2 counties in the northeastern U.S. [that] had the highest age-adjusted mortality rate of such cancer. . .”**

## Public Law 103-43,GIS Requirement

**Certain elements of the study ... shall include the use of a geographic system to evaluate the current and past exposure of individuals, including direct monitoring and cumulative estimates of exposure, to:**
**contaminated drinking water **
**sources of indoor and ambient air pollution, including emissions from aircraft **
**electromagnetic fields **
**pesticides, and other toxic chemicals **
**hazardous and municipal waste**
**other factors as appropriate.**

## A Tool for Studying Environment & Breast Cancer

## Geographic Extent

******	****Nassau and Suffolk counties (red) - detailed health, demographic, environmental data.**
******	****Buffer counties within 50 km - additional environmental data (less precision, detail)**
******	****Extended area within100 miles of mid-point of counties' boundary line (limited data)**

**Notes:**
[red] Nassau and Suffolk counties (red) - detailed health, demographic, environmental data
[gold] Buffer counties within 50 km - additional environmental data (less precision, detail)
[grey] Extended area within100 miles of mid-point of counties' boundary line (limited data)

## www.healthgis-li.com

**Notes:**
This is the webpage. Please note the website address.

## Website

**Two Sections**
**Public Use**
**Researcher’s Use**

**Public**

**Research**

**Notes:**
Purpose of public site- window into how GIS-H took operates, in addition to being able to examine many issues

## Levels of Access

**Public**
**Public data**
**Secure (for researchers)**
**All public data**
**Protocol restricted data**
**Requires approval for each researcher and project**

**Notes:**
There are two levels of access to the GIS-H
Public
Secure
Generally speaking, all approved researchers will have access to all public data as well as restricted data.  Examples of restricted data may include
HCFA data
Tumor Registry data
Cancer incidence
Which means any information about an individual or specific site would be restricted, however when the information is aggregated up, it would not be restricted.

## Public Use 

**ArcExplorer allows the public to:**
**create their own maps using publicly available data**
**use additional interactive features and flexibility, including unique combinations of layers**

**Notes:**
As you can see, ArcExplorer allows the public to...

## Public Use

**16 interactive maps with up to 9 environmental exposure layers.**
**Each will be on the public website.**
**Map topics and exposures numbers reflect the interests and concerns of community members.**

## For Researchers

**Enable researchers to:**
**Explore and synthesize available information on potential exposures**
**Generate hypotheses**
**Identify spatial and temporal clusters of disease**
**Evaluate risk factors for breast cancer and other health outcomes (with your addition of data)**
**Address methodological issues**
**Identify gaps in available information**

**Notes:**
Now, for researchers,...

## What Questions Can Be Addressed?

**What are the rates of breast cancer in the community (overall, in smaller areas)?**
**Can we identify clusters of cases, or areas with significantly higher rates?**
**Where might exposures of interest (to scientists, to the community) come from?**
**Are there correlations -- spatial relationships -- between disease and potential exposures?**
**More sophisticated: Are potential environmental exposures linked with breast cancer, taking other factors into account?**

## Data Included in the GIS-H

**Geospatial**
**Demographic and **** Behavioral**
**Health**
**Environmental**

## Geospatial

**Base Maps*****:  ***
*** *****Cadastral* data (tax lots, parcels)**
** ****Political boundaries**
** ****Roads**
** ****Railroads**
** ****Hydrology (water supply, rivers, streams)**
** ****Aerial photography and satellite imagery**

- * Showing property boundaries, subdivision lines, etc.

## Slide 17

## Demographic and Lifestyle

**Census Data:**** **
**Counts of the population**
**Descriptive information about individuals **
**Age, Race****, ****Gender, Income groupings****	**** **
**Households**
**Type and age of housing**
**Rural or urban**
**National Nutritional Health and Lifestyle Survey**

## Slide 19

## Slide 20

## Health

**Medical outcomes**
**State Cancer Registry (yes and no)**
**Rates by zip available for 1993-97**
**Others available from registry**
**Medicare**
**Hospital discharges**
**Medical facilities**

## Slide 22

## Environmental

***Environmental***

**Air quality monitoring results**
**Drinking water analysis and water use**
**Industrial sites, industrial releases and hazardous materials**
**Radioactive sites or materials**
**Land use and land cover**
**Traffic volume**
**Weather and climate information**
**Other: weather, satellite image maps**

**Notes:**
VOC Detections in Suffolk County Wells  This map displays volatile organic chemical compound detections data extracted from results of individual samples drawn from supply wells for community, non-community and private water systems in Suffolk County. Detection information is aggregated to the level of place (as defined by the US Census Bureau). Individual color coded layers are provided for cis-1,2-dichloroethene, MTBE, tetrachloroethene, tetrachloroterephthalic, and 1,2-Dichloroethane. TRI Water Release sites, petroleum tank sites, gas stations,and dry cleaners are also provided for comparison with potential sources of contamination.

## Data Sources 

**County**** Water Authorities and Departments of Health**
**State**** Departments of Environmental Conservation, Health, Labor, and Public Service**
**Federal**** Centers for Disease Control, National Center for Health Statistics, Environmental Protection Agency, Nuclear Regulatory Commission, Geological Survey, Census, Department of Agriculture**

## Metadata

**Need to understand data limitations**
**For each dataset, information on...**
**Identification**
**Data quality**
**Spatial data organization**
**Spatial reference information**
**Entity and attribute overview**
**Distribution **

## Slide 27

## Researcher’s Toolbox

**ArcView, Spatial Analyst and 3D Analyst**
**Extensions developed especially for GIS-H**
**Add Database Theme and Table Tools**
**Case File Formatter**
**Data query wizard**
**Disease Rate Calculator (graphic)**
**Areal Interpolator  (graphic)**
**Cluster Analysis Tool (to facilitate using SaTScan)**
**Empirical Bayes Tool**
**Geographic masking**

**Notes:**
Spatial Analyst- spatial modeling and analysis
surface and  terrain analyses
Map algebra

ArcView- GIS software- visualization, query, analysis, integration capabilities along with the ability to create and edit geographic data

3 D analysts
Allows users to visualize and analyze surface data from multiple viewpoints or chosen location on a surface

SaTScan is used to 
To evaluate reported spatial or space-time disease clusters, to see if they are statistically significant. 
To test whether a disease is randomly distributed over space, over time or over space and time. 
To perform geographical surveillance of disease, to detect areas of significantly high or low rates. 
To perform repeated time-periodic disease surveillance for the early detection of disease outbreaks. 
SaTScan uses either a Poisson-based model, where the number of events in an area is Poisson distributed according to a known underlying population at risk; a Bernoulli model, with 0/1 event data such as cases and controls; or a space-time Permutation model, using only case data. For all models, SaTScan adjusts for the underlying in homogeneity of a background population. For the Poisson model, it can also 

Cluster analysis is an exploratory data analysis tool for solving classification problems. Its object is to sort cases (people, things, events, clusters) into groups so that the degree of association is strong between members of the same cluster and weak between different clusters. 

## Researcher’s Toolbox

**Additional software for researchers' use**
**Adobe Photoshop**
**ArcInfo**
**SAS**
**S-Plus**
**WinBUGS **
**User's guide**

**Notes:**
SPLUS
S-PLUS is an integrated suite of software facilities for data manipulation, calculation and graphical display. Among other things it has 
an effective data handling and storage facility, 
a suite of operators for calculations on arrays, in particular matrices, 
a large, coherent, integrated collection of intermediate tools for data analysis, 
graphical facilities for data analysis and display either at a workstation or on hardcopy, and 
a well developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities. (Indeed most of the system supplied functions are themselves written in the S-PLUS language.) 
The term ``environment'' is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software. 
S-PLUS is very much a vehicle for newly developing methods of interactive data analysis. As such it is very dynamic, and new releases have not always been fully upwardly compatible with previous releases. Some users welcome the changes because of the bonus of new technology and new methods that come with new releases; others seem to be more worried by the fact that old code no longer works. Although S-PLUS is intended as a programming language, in my view one should regard programmers written in S-PLUS as essentially ephemeral. 

WinBUgs
Bayesian inference Using Gibbs Sampling is a piece of computer software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods

Random data perturbation is used to prevent inference of sensitive information about individuals from legitimate sum entries.

## Disease Rate Calculator

**Calculating directly-adjusted rate for selected census tracts.**

## Areal Interpolator

**Interpolating zip code population from census tract population.**

## Cluster Analysis

**Checking for clusters of sample cases**
**uses SatScan software as cluster analysis engine.**

## Geographic Masking

**Masking selected (in yellow) sample cases **
**using random perturbation method.**

**Notes:**
Moves values into adjacent cells to prevent researchers from working backwards from aggregate results to individual cases.

## Examples of Maps

**Notes:**
What the slides as a whole are showing is how you can create a map with different layers, representing different data.  They show how you can turn layers visibility on/off.  They show some simple analysis through "Identifying" layer features, and viewing their attributes (tabular table).  Buffering around features is also shown in slide 5.

Map terms that you need to be familiar with in using web maps and services: 
Map Layer: A spatial dataset containing a common feature type. Layers are also referred to as coverages or themes. 
Active layer: The query, search, and identify tools all return data from the active layer. Only one layer can be specified as active at a time. 
Visible layer: Layers may be turned on or off. Visible layers will be shown on the map. 
Query: The process of selecting information from a GIS by asking spatial or logical questions of the geographic data. Spatial query is the process of selecting features based on location or spatial relationship (e.g., select all features within 300 feet of another; point at a set of features to select them). Logical query is the process of selecting features whose attributes meet specific logical criteria (e.g., select all polygons whose value for AREA is greater than 10,000 or select all streets whose name is 'Main St.'). Once selected, additional operations can be performed, such as drawing them, listing their attributes or summarizing attribute values. 
Identify: This function allows you to click on the map and have data returned about that feature of the active layer. 
Buffer: A zone of a specified distance around a selected feature. The resulting buffer zones form polygonal coverages. 
Pan: This function gives you the ability to manually move a map in any direction but does not change the map extent. 
Zoom: This function gives you the ability to change the map extent. You may zoom in or zoom out. 
Map Services: A map service is a grouping of layers. These layers typically will have a common theme. Map services may contain just one layer. 

## Slide 35

**Notes:**
LI Breast Cancer Rates 1993-97

## Slide 36

**Notes:**
Long Island TRI Ground Release Sites  This map displays Toxic Release Inventory sites that report ground release of contaminants on Long Island. Users may query the TRI Ground Release layer to display the locations of a wide range of compounds and substances.

## Slide 37

**Notes:**
This is an example of the TRI Air and Land Data Warehouse, to give you some notion of how much information there is.

## Slide 38

**Notes:**
Inactive Hazardous Waste Sites
Hazardous Waste Sites on Long Island  This map displays a variety of hazardous waste and materials sites on Long Island. It includes both active and inactive waste sites in addition to facilities involved in waste storage and handling. Users may query the maps to find specific information about the sites.

## Slide 39

**Notes:**
Buffered TRI Sites- This looks at whether if one provides a radius around the tri sites, can breast cancer rates be explained. Not really.

## Slide 40

**Notes:**
This looks back at long island, and I will focus on the area where the hand it.

## Slide 41

**Notes:**
This adds waste sites, and still doesn’t appear to explain very much.

## Slide 42

**Notes:**
This one, on the other hand, suggests that inactive waste sites could account for some of the high rates.

## Slide 43

**Notes:**
Are Disease Rates “Near” Waste Sites the Same as in Areas Outside “Buffers”? Again, the answer is not entirely clear, although there may be something worth investigating further.

## What is Availability of GIS-H?

**Available now to researchers with approved projects**
**Public mapping features available soon**

## Important Issues

**Data are imperfect**
**Examples:  addresses, sparse data, data collected for other purposes**
**Potential exposure not necessarily actual exposure**
**Time frame and latency of cancer**
**Substitutions and additions may be recommended as we go along**
**The website will not include software to keep**
**The eye is not a good analytic tool**
**Confidentiality**

## Slide 46

## In Summary, the GIS-H is...

**Comprehensive, integrated data warehouse (> 80 datasets)**
**Flexible and expandable**
**Can integrate external datasets**
**Sophisticated researcher’s toolbox**
**Community input and access**
**Systematic attempt to include high quality data, comprehensive metadata**
**A prototype and resource for future studies**

**Notes:**
The GIS-H has a number of unique features.
First, we have a rich data warehouse of over 80 datasets
It is flexible and expandable to better respond to research needs.  As additional relevant exposure data become available, they will be added to allow researchers to explore important exposure-disease relationships
The GIS-H system integrates numerous geospatial and relational datasets into a single integrated database. This integrated repository of data is called the GIS-H Data Warehouse. While the terminology may be new to traditional GIS technologists, the concept is extremely flexible and efficient, allowing the continued incorporation of new datasets and the refresh of existing datasets without the rebuilding of the database. Data warehouses are intended to simplify the organization of the data for effective use by nontechnical end users. 
The GIS-H Data Warehouse comprises 3 fundamental data structures: facts, dimensions, and geographical dimensions (geodimensions). Each is a table in which data are organized into rows and columns. In general, fact tables contain the quantitative information associated with a dataset. The fact tables are linked to dimensions tables that provide qualitative information about the facts. Each fact table is also linked to a geodimension table that integrates the factual information with geospatial data stored in the database. The GIS-H is organized into a collection of fact and dimension schemas (also referred to as star schemas because of the geometric representation of fact in the middle with dimensions as satellites). Each star schema comprises one fact table, one or more dimension tables, and one geodimension table. The fact table is linked through key fields to its associated dimension tables. Note the link field names are shown next to the relationship lines linking the fact table to the dimension tables. Every fact table in the GIS-H will be similarly linked to dimension tables that qualify the facts. This star schema provides the GIS-H user with the population of every block group for every educational group for which census statistics were collected in 1990. Education and block groups qualify the population information in the fact table. The user can select and display a subset of the educational groupings represented in the "Education Dimensions" in conjunction with a subset of the block groups contained in the "Block Group 90 Geodimensions" table. The geographic granularity of the data in this schema is population by block group. The organization of the data into facts, dimensions, and geodimensions provides unusual flexibility for the GIS-H users. 
Each source dataset that contains quantitative information (or facts) is transformed into one or more star schemas, depending on the complexity of the facts in the dataset. In some cases, the source dataset is not associated with any quantitative facts. This is often true of geospatial source datasets that contribute to base map information, such as a hydrology dataset that serves simply to provide the hydrology of a given area. The Data Source Matrix on the Metadata Browser source datasets home page provides a Matrix on the Metadata Browser home page that links the source datasets to the facts and geographic dimensions that were derived from it. Selecting from the GIS-H Data Warehouse directory displays a diagram of the data. The user can then click on a fact or dimension table to retrieve a report providing the table definition and column definition for the table. 

## Apply!

**Access to researcher site is limited to investigators with approved protocols**
**For additional information, visit GIS-H website**
**www.healthgis-li.com**

***Inquiries may be directed to:***

**Burdette (Bud) Erickson, M.Sc.**
**301.435.4913**
_**berikso@mail.nih.gov**_

## Thank You! 

***Thank You! ***