Detecting Significant Spatial Patterns of Survival from Breast and Prostate Cancer in Michigan

1
Detecting Significant Spatial Patterns of
Survival from Breast and Prostate Cancer in
Michigan

• Glenn Copeland1, Jaymie Meliker2,3, May Yassine4
• 1 Michigan Cancer Surveillance Program, Lansing,
  MI
• 2 BioMedware, Inc., Ann Arbor, MI
• 3 Graduate Program in Public Health, Department
  of Preventive Medicine, Stony Brook University
  (SUNY), Stony Brook, NY
• 4 Michigan Public Health Institute, Okemos, MI

Denver, CO NAACCR June 12th, 2008
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Geocoding of Cancers - Issues

• Con
• Effort and Dollars
• No Denominators
• Difficult to Analyze
• Can Easily Mislead
• Confidentiality
• Disconnect with Causation

• Pro
• Corrects Residence
• Identifies Census Tract
• Enables Mapping
• Really Sells!
• Tool for Cluster Studies

3
Eureka Moment

• Study Proposal
• Examine Association of Arsenic in Groundwater and
  Bladder Cancer
• Collaborative between
• U of Michigan
• David Schottenfeld, Jerome Nriagu, Jaymie Meliker
• BioMedware
• Geoffery Jacquez, Pierre Goovaerts
• Complex Study Design
• Establish Connection between Exposure and Cancer
• Exposed to Broader View of Geospatial Analysis
• Merging of Analytical and Spatial Tools
• STIS - Space-Time Intelligence System

4
Naturally Occurring Arsenic in Groundwater
5
Perhaps in place of slide 4?
Arsenic in Public Water Supplies
50 40 30 20 10 0
µg/L
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Collaborate with BioMedware

• Turn Focus from Cluster to Control
• Address Denominator Problem
• Develop Measures (largely) Immune from Reporting
  Issue
• Resolve Disconnect between Cause/Effect and
  Geographic Association with Outcome

7
Why map disease patterns?
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Easily communicate trends and differences across
regions

• Identify patterns and factors potentially
  contributing to those patterns
• Most efforts have sought to address etiologic
  questions
• Given latency concerns, we set out to explore
  spatial pattern to help understand more immediate
  factors that might influence stage at diagnosis
  and survival once diagnosed

9
Why statistically analyze disease patterns?

• Human tendency to see patterns/clusters where
  they may not exist
• Can lead to false positives raise inappropriate
  concern

10
In 2004 a decade-old grilled cheese sandwich said
to bear an image of the Virgin Mary sold on eBay
for 28,000
Courtesy G. Jacquez
11
The Nun Bun, from the Bongo Java coffee shop in
Nashville, Tennessee (Owner Bob Bernstein)
Source Prof. John K. Kruschke,
http//www.indiana.edu/jkkteach/P335/nunbun.html
Courtesy G. Jacquez
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Courtesy G. Jacquez
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Select Statistical Measures

• Denominator within the Registry
• Less Subject to Reporting Bias
• Meaning to Cancer Control
• Survival Rates
• Percent Early Stage
• Percent Late Stage

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Data

• Cases of Breast and Prostate Cancer from 1985 to
  2002
• Cancer cases were defined as early stage, late
  stage, or unknown using the SEER General Summary
  Stage classification
• Early stage consisted of local and in situ cases
  late stage consisted of regional and distant
  metastatic cancer
• Cases with unknown stage of diagnosis were not
  included in the analysis (lt5)
• Approximately 92 of breast and 90 of prostate
  cancer cases were successfully geocoded to
  residence at time of diagnosis
• Breast cancer
• 72,216 early stage cases 29,202 late stage cases
• 87,807 survived 5 years 13,611 not survive 5
  years
• Prostate cancer
• 79,661 early stage cases 19,544 late stage cases
• 76,639 survived 5 years 22,566 not survive 5
  years
• The percentage of addresses successfully geocoded
  did not differ by stage, survival, or year of
  diagnosis
• Use state legislative districts for presenting
  results ? compelling to legislators
• Maps and analyses performed with Space-Time
  Information System software (STISTM, TerraSeer,
  Ann Arbor, Michigan) and SatScanTM

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Analytic Approaches Considered

• Examine trends using aggregated data in polygons
• Trends observed--but are arbitrary polygon shapes
  influencing the results?
• Compare point data with aggregated data
• Compare SatScan vs Nearest Neighbor methods for
  analyzing point data

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Disparity in Early Stage Prostate Cancer
Detroit Neighborhoods 1995-2003
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Breast Cancer Survival (5 Years)
Proportion Survived
(quintiles)
9-Year moving windows
Improvements over time clearly demonstrated
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Prostate Cancer Survival (5 Years)
Proportion Survived
(quintiles)
9-Year moving windows
Improvements over time clearly demonstrated
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Analytic Approaches Considered

• Examine trends using aggregated data in polygons
• Trends observed--but are arbitrary polygon shapes
  influencing the results?
• Compare spatial clustering analyses of point data
  vs. aggregated data
• Compare SatScan vs Nearest Neighbor methods for
  analyzing point data

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Clusters of Early Stage Breast Cancer Cases
1994-2002

• SatScan spatial clustering approach
• Considered current state-of-the art method for
  analyzing aggregated data
• Points, polygons of census tracts and State House
  Legislative Districts
• Significant results only detected using point
  data
• Plausible because of ecologic fallacy and
  modifiable area unit problem (MAUP)

Methods description Bernoulli model run on point
data, Poisson model on polygon data search
window equivalent to 4 of population (more than
twice as large as the largest house polygon, 8
times as large as largest tract polygon). No
pairs of centers both in each other's cluster.
Purely spatial analysis. 999 replicates. Circular
window. Took more than 9 hours to run SatScan on
the point dataset.
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Analytic Approaches Considered

• Examine trends using aggregated data in polygons
• Trends observed--but are arbitrary polygon shapes
  influencing the results?
• Compare results of point data with aggregated
  data
• Point data appear more sensitive (not
  surprisingly)
• Compare SatScan vs Nearest Neighbor methods for
  analyzing point data

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What is the most appropriate method for
identifying clusters in point data?

• We compared two most common methods
• SatScan
• Circular scan window of varying diameter
• Takes many hours to run on 50,000 points
• Cuzick-Edwards Test
• Nearest neighbor approach
• Quick (only a few minutes)
• Must repeat analyses across different numbers of
  nearest neighbors (k) to identify consistency

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Clusters of Early Stage Breast Cancer Cases
2000-2002

• Limited to only 3 years of data because of speed
  of analysis in SatScan
• Clusters (shown in blue) identified using
  Cuzick-Edwards
• K 200 nearest neighbors (1 of total sample
  size)
• No clusters identified with SatScan
• Which method is correct?

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Evaluating Cuzick-Edwards Results

• Are clusters of early stage cases associated with
  known risk factors?
• Yes!
• Fully adjusted logistic regression models
• Odds of being in early stage breast cancer
  cluster
• Proximity to mammography clinics as distance
  increases by 5 km OR0.93 CI (0.90, 0.95)
• Poverty in 2000 Census Tracts as poverty
  increases by 1 OR0.92 CI (0.91, 0.93)
• Race if black, compared with white OR0.47 CI
  (0.39, 0.56)
• Simulation results also encouraging
• Not presented today

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Risk Estimates from Cuzick-Edwards Test
Influence of Choice of k Nearest Neighbors
Proportion Surviving at least 5 Years in k
Nearest Neighbor Windows
k100 k200
k300
Upper 5 of data Next 20 Middle 50 Next
20 Bottom 5
Choice of k does not meaningfully influence the
results
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Analytic Approaches Considered

• Examine trends using aggregated data in polygons
• Trends observed--but are arbitrary polygon shapes
  influencing the results?
• Compare results of point data with aggregated
  data
• Point data appear more sensitive (not
  surprisingly)
• Compare SatScan vs Nearest Neighbor methods for
  analyzing point data
• Nearest neighbor approach looks most promising
  (Cuzick-Edwards clustering method)

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Early Stage Breast Cancer Time Series
1985-87 1992-94
2000-02
Upper 5 of data Next 20 Middle 50 Next
20 Bottom 5
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Breast Cancer 5 Year Survival Time Series
1985-87 1992-94
2000-02
Upper 5 of data Next 20 Middle 50 Next
20 Bottom 5
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Early Stage Diagnosis (Left) and 5 Year Survival
(Right) Breast Cancer, 1985-87
Breast Cancer 5 Yr Survival, 1985-1987
Early Stage Breast Cancer, 1985-1987
Highest proportion Lowest proportion
Highest proportion Lowest proportion
proportion of nearest 200 neighbors (bluetop
5, red bottom 5)
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Early Stage Diagnosis (Left) and 5 Year Survival
(Right) Breast Cancer, 1992-94
Breast Cancer 5 Yr Survival, 1992-1994
Early Stage Breast Cancer, 1992-1994
Highest proportion Lowest proportion
Highest proportion Lowest proportion
proportion of nearest 200 neighbors (bluetop
5, red bottom 5)
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Early Stage Diagnosis (Left) and 5 Year Survival
(Right) Breast Cancer, 2000-02
Breast Cancer 5 Yr Survival, 2000-2002
Early Stage Breast Cancer, 2000-2002
Highest proportion Lowest proportion
Highest proportion Lowest proportion
proportion of nearest 200 neighbors (bluetop
5, red bottom 5)
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Summary

• Spatial analyses of point data are more sensitive
  than analyses relying on data aggregated into
  polygons
• In point data analyses, Cuzick-Edwards Test is
  more sensitive than the SaTScan spatial scan
  statistic
• The Cuzick-Edwards Test allowed identification
  of an association between early stage breast
  cancer and proximity to mammography clinics,
  helping to justify this important public health
  measure
• Methods were applied to identify clusters of
  early stage breast cancer through time, and also
  shown to be applicable for finding spatial
  patterns of survivors of breast cancer
• The approaches outlined here should serve cancer
  control efforts
• Application to other cancers
• Enhancing the investigation of public health
  factors responsible for spatial patterns in
  cancer outcomes

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Acknowledgments

• NPCR funded the project
• Redirected unobligated funds
• We thank Geoffrey Jacquez, Pierre Goovaerts, and
  Gillian AvRuskin of BioMedware for their
  assistance with these maps and analyses
• Terraseer.com