Download 9 Cases 98 Population

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
Cluster
• A number of similar things grouped closely
together
Webster’s Dictionary
• Unexplained concentrations of health events
in space and/or time
Public Health Definition
• Occupation
• Sex, Age
• Socioeconomic class
• Behavior (smoking)
• Race
• Time
• Space
Spatial Autocorrelation
“Everything is related to everything else, but near things are more
related than distant things.”
- Tobler’s first law of
geography
Positive
autocorrelation
Negative autocorrelation
Moran’s 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.
Detecting Clusters
• Consider scale
• Consider zone
• Control for multiple testing
Talbot
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?
More Cluster Questions
• At what geographic or population scale
do clusters appear?
• Are cases of disease clustered in areas
of high exposure?
Nearest Neighbor Analysis
Cuzick & 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.
Nearest Neighbor Analyses
Advantages
• Accounts for the geographic variation in
population density
• Accounts for confounders through
judicious selection of controls
• Can detect clustering with many small
clusters
Disadvantages
• Must have spatial locations of cases &
controls
• Doesn’t show location of the clusters
Spatial Scan Statistic
Martin 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
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.
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.
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)
Focal Case-Control Design
500 m.
250 m.
Case
Control
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.
At what geographic or
population scale do clusters
appear?
Multiresolution mapping.
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.
1995-1999
Interactive Selections by rate, population and p value
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, 19:2451-2467
•
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, 8:191-199
•
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
Geographic Aggregation
of Health Data
by
Thomas Talbot
NYS Department of Health
Environmental Health Surveillance Section
Health data can be shown at
different geographic scales
•
•
•
•
•
Residential address
Census blocks, and tracts
Towns
Counties
State
Concerns about release
of small area data
• Risk of disclosure of confidential
information.
• Rates of disease are unreliable due to
small numbers.
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/statistics/ny_asthma/hosp/zipcode/pdf/hamil_m2.pdf
Disclosure of confidential information
Census
Blocks
Smoothed or Aggregated
Count & Rate Maps
• Protect Confidentiality so data can be
shared.
• Reduce random fluctuations in rates due
to small numbers.
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.
from Talbot et al., Statistics in Medicine, 2000
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.
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.
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).
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.
Original ZIP Codes
3 Years Low Birth Weight Incidence Ratios
Aggregated to 250 Births per ZIP Code Group
Our Tool Requirements
Goal
• 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.
Aggregation Tool
Regions
Original Block Data †
Block
Block
Cases
Region
122300/2004
10
A
122300/2005
11
A
014500/3005
3
B
Cases
122300/2004
10
122300/2005
11
014500/3005
3
014500/3007
4
014500/3007
4
B
014500/3008
0
014500/3008
0
B
014500/3009
1
014500/3009
1
B
014500/3010
15
014500/3010
15
B
103202/2001
8
103202/2001
8
C
103202/2002
6
103202/2002
6
C
† Simulated
data
SAS Tool
Cases
Region
21
A
23
B
14
C
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
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
Water
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
† Simulated
data
9
Cases
98
Population
New York State
Descriptive Statistics
Year 2000 populated census blocks
New Regions:
Level of Aggregation
Statistic (calculated using
populated regions only)
Original Census
Blocks
6 cases
12 cases 24 cases
225,167
39,748
21,525
11,381
84
477
882
1,667
Median cases
1
10
20
38
Median Census blocks
1
4
7
14
Number
Average Population
NY number of cases
NY population
470,000
18,976,457
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
Index of dissimilarity
the 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.
The End