Statistical approaches for detecting unexplained clusters of disease.

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• Statistical approaches for detecting unexplained
  clusters of disease.
• Spatial Aggregation
• Thomas Talbot
• New York State Department of Health
• Environmental Health Surveillance Section
• Albany School of Public Health
• GIS Public Health Class
• March 3, 2009

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Cluster

• A number of similar things grouped closely
  together
  
  Websters Dictionary
• Unexplained concentrations of health eventsin
  space and/or time
• 
  Public Health Definition

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• Occupation
• Sex, Age
• Socioeconomic class
• Behavior (smoking)
• Race
• Time
• Space

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Spatial Autocorrelation
Everything is related to everything else, but
near things are more related than distant
things.
- Toblers first law of geography








Positive autocorrelation

• Negative autocorrelation

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Morans I

• A test for spatial autocorrelation in disease
  rates.
• Nearby areas tend to have similar rates of
  disease. Moran I is greater than 1, positive
  spatial autocorrelation.
• When nearby areas are dissimilar Moran I is less
  than 1, negative spatial autocorrelation.

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Detecting Clusters

• Consider scale
• Consider zone
• Control for multiple testing

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Talbot
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Cluster Questions

• Does a disease cluster in space?
• Does a disease cluster in both time and space?
• Where is the most likely cluster?
• Where is the most likely cluster in both time and
  space?

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More Cluster Questions

• At what geographic or population scale do
  clusters appear?
• Are cases of disease clustered in areas of high
  exposure?

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Nearest Neighbor AnalysisCuzick Edwards Method

• Count the the number of cases whose nearest
  neighbors are cases and not controls.
• When cases are clustered the nearest neighbor to
  a case will tend to be another case, and the test
  statistic will be large.

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Nearest Neighbor Analyses
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Advantages

• Accounts for the geographic variation in
  population density
• Accounts for confounders through judicious
  selection of controls
• Can detect clustering with many small clusters

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Disadvantages

• Must have spatial locations of cases controls
• Doesnt show location of the clusters

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Spatial Scan StatisticMartin Kulldorff

• Determines the location with elevated rate that
  is statistically significant.
• Adjust for multiple testing of the many possible
  locations and area sizes of clusters.
• Uses Monte Carlo testing techniques

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The Space-Time Scan Statistic

• Cylindrical window with a circular geographic
  base and a height corresponding to time.
•  
• Cylindrical window is moved in space and time.
• P value for each cylinder calculated.

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Knox Method test for space-time interaction

• When space-time interaction is present cases near
  in space will be near in time, the test statistic
  will be large.
• Test statistic The number of pairs of cases that
  are near in both time and space.

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Focal tests for clustering

• Cross sectional or cohort approach Is there a
  higher rate of disease in populations living in
  contaminated areas compared to populations in
  uncontaminated areas? (Relative risk)
• Case/control approach Are there more cases than
  controls living in a contaminated area? (Odds
  ratio)

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Focal Case-Control Design
500 m.
250 m.
Case
Control
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Regression Analysis

• Control for know risk factors before analyzing
  for spatial clustering
• Analyze for unexplained clusters.
• Follow-up in areas with large regression
  residuals with traditional case-control or cohort
  studies
• Obtain additional risk factor data to account for
  the large residuals.

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At what geographic or population scale do
clusters appear?Multiresolution mapping.
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• A cluster of cases in a neighborhood provides a
  different epidemiological meaning then a cluster
  of cases across several adjacent counties.
• Results can change dramatically with the scale of
  analysis.

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1995-1999
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Interactive Selections by rate, population and p
value
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References

• Talbot TO, Kulldorff M, Forand SP, and Haley VB.
  Evaluation of Spatial Filters to Create Smoothed
  Maps of Health Data.  Statistics in Medicine.
  2000, 192451-2467
• Forand SP, Talbot TO, Druschel C, Cross PK. Data
  Quality and the Spatial Analysis of Disease
  Rates Congenital Malformations in New York.
  2002. Health and Place.  2002, 8191-199
• Haley VB, Talbot TO. Geographic Analysis of Blood
  Lead Levels in New York State Children Born
  1994-1997.  Environmental Health Perspectives
  2004, 112(15)1577-1582
• Kuldorff M, National Cancer Institute. SatScan
  User Guide www.satscan.org

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Geographic Aggregation of Health DatabyThomas
TalbotNYS Department of HealthEnvironmental
Health Surveillance Section

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Health data can be shown at different geographic
scales

• Residential address
• Census blocks, and tracts
• Towns
• Counties
• State

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Concerns about release of small area data

• Risk of disclosure of confidential information.
• Rates of disease are unreliable due to small
  numbers.

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Rate maps with small numbers provide very little
information.
http//www.nyhealth.gov/statistics/ny_asthma/hosp/
zipcode/hamil_t2.htm http//www.nyhealth.gov/stat
istics/ny_asthma/hosp/zipcode/pdf/hamil_m2.pdf
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Disclosure of confidential information
Census Blocks
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Smoothed or Aggregated Count Rate Maps

• Protect Confidentiality so data can be shared.
• Reduce random fluctuations in rates due to small
  numbers.

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Smoothed Rate Maps

• Borrow data from neighboring areas to provide
  more stable rates of disease.
• Shareware tools available
• Empirical or Hierarchal Bayesian approaches
• Adaptive Spatial Filters
• Head banging
• etc.

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from Talbot et al., Statistics in Medicine, 2000
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Problems with smoothing

• Does not provide counts rates for defined
  geographic areas.
• Not clear how to link risk factor data with
  smoothed health data.
• Methods are sometimes difficult to understand -
  black boxes
• Does not meet requirements of some recent New
  York policies legislation.

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Environmental Facilities Cancer Incidence Map
Law, 2008 3-0317

• Plot cancer cases by census block, except in
  cases where such plotting could make it possible
  to identify any cancer patient.
• Census blocks shall be aggregated to protect
  confidentiality.

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Environmental Justice Permitting NYSDEC
Commissioner Policy 29

• Incorporate existing human health data into the
  environmental review process.
• Data will be made available at a fine geographic
  scale (ZIP code or ZIP Code Groups).

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Aggregated Count or Rate Maps

• Merge small areas with neighboring areas to
  provide more stable rates of disease and/or
  protect confidentiality.
• Aggregation can be done manually.
• Existing automated tools were difficult to use.

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Original ZIP Codes3 Years Low Birth Weight
Incidence Ratios
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Aggregated to 250 Births per ZIP Code Group
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Goal
Our Tool Requirements

• Aggregate small areas into larger ones.
• User decides how much aggregation is needed.
• Works with various levels of geography.
• census blocks, tracts, towns, ZIP codes etc.
• can nest one level of geography in another
• Uses software which is readily available in
  NYSDOH (SAS)?
• Can output results for use in mapping programs.

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Aggregation Tool
Regions
Original Block Data
SAS Tool
Simulated data
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Aggregation Process

• Populated blocks with the fewest cases are merged
  first.
• If there is a tie the program starts with the
  block with the fewest neighbors.
• Selected block then is merged with the closest
  neighbor in the same census block group.
• After merging the first block the list of
  neighbors is updated.
• Process repeats until all regions have a minimum
  number of cases
• program can also merge to user specified
  population

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Special Situations

• Tool tries to avoid merging blocks in different
  census areas
• Census block groups
• Census tracts (homogeneous population
  characteristics).
• Counties
• Tool tries to avoid merging blocks across major
  water bodies
• e.g. Finger lakes, Hudson River, Atlantic Ocean

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Water
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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9 Cases 98 Population
Simulated data
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New York StateDescriptive StatisticsYear 2000
populated census blocks
NY number of cases 470,000 NY population
18,976,457
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Performance Measures

• Compactness
• Homogeneity with respect to demographic factors
  (measured as index of dissimilarity)
• Similar population sizes.
• Number of aggregated areas.
• Aggregated zones are completely contained within
  larger areas.
• e.g. blocks aggregation areas contained within
  tracts

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Index of dissimilaritythe percentage of one
group that would have to move to a different area
in order to have a even distribution
Wikipedia
bi the minority population of the ith
area, e.g. census tract B the total minority
population of the large geographic entity for
which the index of dissimilarity is being
calculated. wi the non-minority population of
the ith area W the total non-minority
population of the large geographic entity for
which the index of dissimilarity is being
calculated.
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The End