PHC6194 SPATIAL EPIDEMIOLOGY
Disease Clustering
Hui Hu Ph.D.
Department of Epidemiology
College of Public Health and Health Professions & College of Medicine
February 26, 2020
Introduction
Global Clustering
Local Clustering
Lab: Disease Clustering
Introduction
Spatial Patterns
Random
Cluster
Regular
Disease Cluster
 The occurrence of a greater than expected number of cases of a disease within a group of people, a geographic area, or a period of time.
 A collection of disease occurrence:
 of sufficient size and concentation to be unlikely to have occurred by chance, or
 related to each other through some social or biological mechanism, or having a common relationship with some other events or circumstance
 Spatial aggregation of disease events may only be a function of the distribution of population
 Disease cluster: residual spatial variation in risk after known influence have been accounted for
Purposes of Disease Cluster Detection
 Confirmatory purpose
 verify if a perceived cluster exists:
e.g. excess risk reported by citizens
 Exploratory purpose
 search for spatial patterns
 Identification of clusters can lead to interventions
Methods of Disease Cluster Detection
 Global clustering:
 nonspecific methods
 only detect if cluster exists, without specific location
 Local clustering:
 specific methods
 shows the specific locations where clusters exist
 two methods: nonfocused and focused
Global Clustering
Global Clustering (Nonspecific Methods)

Evaluate whether clustering exist as a global phenomena throughout the study region, without pinpointing the location of specific cluster
 e.g. the analysis of overall clustering tendency of some disease incidence in a study region
Tests for Global Clustering
 Over 100 different testing methods for global clustering in the field
 Some widelyused methods:
 for aggregated data:
Moran's I
Geary's C
 for points data:
KNN
Moran's I
 Moran's I is a global index of spatial autocorrelation
 to quantify the similarity of an variable among areas that are defined as spatially related
 Calculation:
 N: number of spatial units indexed by i and j
 X: the variable of interest
 wij: a matrix of spatial weights
I={{N}\over {\sum_i(X_i\bar X)^2}}\times {{\sum_i\sum_jw_{ij}(X_i\bar X)(X_j\bar X)}\over {\sum_i\sum_jw_{ij}}}
Moran's I (cont'd)
 Moran's I coefficient of autocorrelation is similar to Pearson's correlation coefficient
 I>0
 positive spatial autocorrelation
 neighboring regions tend to have similar values
 I<0
 negative spatial autocorrelation
 neighboring regions tend to have inverse values
 Results will depend on specification of the weight matrix
Geary's C
 Also called Geary's contiguity ratio
 Another widely used global index of spatial autocorrelation
 Calculation:
 N: number of spatial units indexed by i and j
 X: the variable of interest
 wij: a matrix of spatial weights
C={{N1}\over {2\sum_i(X_i\bar X)^2}}\times {{\sum_i\sum_jw_{ij}(X_iX_j)^2}\over {\sum_i\sum_jw_{ij}}}
Geary's C (cont'd)
 Geary's C ranges from 0 to 2
 Low value of Geary's C denote positive autocorrelation
 0 indicates perfect positive spatial autocorrelation
 High value of Geary's C denote negative autocorrelation
 2 indicates perfect negative spatial autocorrelation
 1 indicates no autocorrelation
KNN
 Proposed by Cuzick and Edward
 To detect the possible clustering of subpopulations within a clustered or nonuniformlyspread overall population
 Based on the locations of cases and randomly selected controls from a specified region
KNN (cont'd)
 Central idea of the method: to find how many of the K nearest neighbors of a cases that are also cases
 A weight matrix based on KNN
 wij=1 if location j is among k nearest neighbors of location i
 The test statistics:
 𝛿=1 if the point is a case, 𝛿=0 if the point is a control
T_k=\sum_{i=1}^n \sum_{j=1}^n w_{ij}\delta_i\delta_j
KNN (cont'd)
 Monte Carlo test under the random labeling hypothesis is used to test the significance
 The rank of the test statistic is based on the data observed among the values from the randomly labeled data, which allows calculation of the pvalue
Local Clustering
Local Clustering Test
 Additionally specify the location and can be extended to also consider temporal patterns
 Focused tests:
 investigate whether there is an increased risk of disease around a predetermined point
 e.g. Superfund site; A nuclear power plant; A waste dumping site
 Nonfocused tests:
 identify the location of all potential clusters in the study region
Focused Tests
 H0: there is no cluster of cases around the foci
 The Lawson Waller test
 also called Berman's Z1 test
 H0: yi~ Poisson(ni*r)
 H1: yi~ Poisson(ni*r (1+εθi)), where θi represents exposure to the foci experienced by population in region i; ε represents a small, positive constant
 what is the relative risk comparing people in region i with people with no exposure

The Lawson Waller score
 where θi is defined by the inverse distance of each region from the foci
 usually standardized to range from 0 to 1
T_{sc}=\sum_{i=1}^N\theta_i(y_irn_i)
Nonfocused Tests
 Aggregated data:
 Local Indicators of Spatial Autocorrelation (LISA)
 Local GetisOrd G statistics
 Spatial scan statistics
 Point data:
 Openshaw's Geographical analysis Machine (GAM)
 Turnbull's cluster evaluation permutation procedure (CEPP)
 Spatial scan statistics
LISA
 Also called Local Moran's I
 LISA values allow for the computation of its similarity with its neighbors and also to test its significance
 LISA divides the study region into 5 categories:
 highhigh locations: also known as hot spots
 lowlow locations: also known as cold spots
 highlow locations: potential spatial outliers
 lowhigh locations: potential spatial outliers
 locations with no significant local autocorrelation
Local GetisOrd G Statistic
 The proportion of all x values in the study area accounted for by the neighbors of location i
 G will be high where high values cluster (hot spot)
 G will be low where low values cluster (cold spot)
G_i(d)={{\sum_jw_{ij}x_j}\over {\sum_jx_j}}
Spatial Scan Statistic
 Steps:
 search over a given set of spatial regions
 find those regions which are most likely to be clusters
 correctly adjust for multiple hypothesis testing
Search Over a Given Set of Spatial Regions
 Create a regular or irregular grid of centroids covering the whole study area
 Create an infinite number of circles around each centroid, with the radius ranging from 0 to a maximum which includes at most 50% of the population
 A circular scanning window is placed at different coordinates with radius that vary from 0 to some set upper limit.
Find Regions that are Most Likely to be Clusters
 For each location and size of window
H = elevated risk within window as compared to outside of window
 Is there any region with disease rates significantly higher inside the circle than outside the circle ?
 For each circle, obtain the actual and expected number of cases inside and outside the circle, and calculate likelihood function
A
Find Regions that are Most Likely to be Clusters (cont'd)
 Generate random replicas of the dataset under the nullhypothesis of no clusters (Monte Carlo sampling)
 Compare most likely clusters in real and random datasets (likelihood ratio test)
Properties of Spatial Scan Statistics
 Adjusts for inhomogeneous population density
 Simultaneously tests for clusters of any size and any location, by using circular windows with continuously variable radius
 Accounts for multiple testing
 Possibility to include confounding variables
 Can be used with both aggregated and point data
Lab: Disease Clustering
git pull
PHC6194Spring2020Lecture8
By Hui Hu
PHC6194Spring2020Lecture8
Slides for Lecture 8, Spring 2020, PHC6194 Spatial Epidemiology
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