Cluster Analysis Using SaTScan - Notes - bidankomunitas · 28 September 2007 APHEO Conference 2007 4 What is a cluster? An unusual collection of events Unusual aggregation of real

Cluster Analysis using SaTScan

Patrick DeLuca, M.A.APHEO 2007 Conference, Ottawa

October 16th, 2007

28 September 2007 APHEO Conference 2007 2

Outline

Clusters & Cluster Detection Spatial Scan StatisticCase Study

Clusters & Cluster Detection


What is a cluster?

An unusual collection of eventsUnusual aggregation of real or perceived health eventsA geographically and/or temporally bounded collection of occurrences:

of a disease already known to occur typically in clusters, orof sufficient size and concentration to be unlikely to have occurred by chance, orrelated to each other through some social or biological mechanism, or having a common relationship with some other event or circumstance (Knox, 1989)


Cluster Detection

GIS can be used as a quick screening tool to identify clusters

Identification of clusters can lead to interventions, even when etiology is uncertain


Interesting Questions to be Asked

Over what scale does any clustering occur?Are clusters merely a result of some obvious a priori heterogeneity in the study region?Are they associated with proximity to other features of interest, such as transport arteries or possible point sources of pollution?Are events that cluster in space also clustered in time?


Global and Local Tests

Global tests detect the presence or absence of clustering over the whole study region without specifying the spatial location.Local tests additionally specify the location and if extended to consider temporal patterns, can specify spatio-temporal clusters.

A special case of local tests is the focused test which is used to detect raised incidence of disease around some pre-specified source, such as an incinerator.


Point Based Methods

Global MeasuresRipley’s K Function (and its variants)

CrimeStat, Splus/R

Local MeasuresKernel Density Estimation

ArcGIS, CrimeStat, Splus

Kuldorff’s Spatial Scan Statistic


Area Based Methods

Global MeasuresMoran’s I

ArcGIS, GeoDA, CrimeStat, Splus/R

Local MeasuresLocal Moran’s I

ArcGIS, GeoDa, CrimeStat, R

Kuldorff’s Spatial Scan Statistic

SaTScan 7.0.3


Introduction to SaTScan 7.0.3

Analyzes spatial, temporal and spatio-temporal data through a scan statisticQuick assessment of potential clustersDeveloped by Martin Kuldorff for the National Cancer InstituteFreely available from www.satscan.org


Objectives of the software (1)

To perform geographical surveillance of disease, to detect areas of significantly high or low rates

To test whether a disease is randomly distributed over space, over time or over space and time.


To evaluate reported spatial or space-time disease clusters, to see if they are statistically significant

To perform repeated time-periodic disease surveillance for the early detection of disease outbreaks.

Objectives of the software (2)


How does it work? (1)

A circular scanning window is placed at different coordinates with radii that vary from 0 to some set upper limit.

For each location and size of window:

HA = elevated risk within window as compared to outside of window





Rainy River can be the centerof 14 potential clusters



Likelihood function is created depending on model selected (more later…..)

Likelihood = hypothetical probability that an event that has already occurred would yield a specific outcomeFunction is maximized over all window locations and sizes

The one with the maximum likelihood is most likely cluster (least likely to have occurred by chance)Likelihood ratio for this window becomes maximum likelihood ratio test statisticA p-value is obtained for the cluster by Monte Carlo hypothesis testing



Indicates whether there is clusteringShows us where it is (text description)Evaluates its statistical significanceProduces a relative risk for the cluster

Which can be low or high riskSolves the Texas Sharpshooter problem --does not rely on a priori knowledge of any cluster


Types of Analysis & Models


Poisson models

Should be used when background population reflects a certain risk mass such as total persons in an area

Expected number of cases is directly proportional to the total number of persons

Example – # cases of cancer per 100,000 persons


How the Poisson model works

How the scan works in this caseLikelihood function:

C = total number of casesc = observed number of cases in windowE[c] = covariate adjusted expected number of cases in windowI() = indicator function


Output

Need to import this into ArcView forvisualization.


Visualization

*Only p < 0.05 displayed


Alternative visualization


Alternative visualization


Bernoulli models

Bernoulli process is a discrete-time stochastic process based on Bernoulli trials

An experiment whose outcome is random and can be either of two possible outcomes, “success” and “failure”Values expressed as 0 or 1 (non-cases or cases)


Scan similar to Poisson, visiting each eventLikelihood function:

C = total number of cases in datasetc = observed number of cases in windown = total # of cases and controls in windowN = total number of cases and controls in dataset

How the Bernoulli model works


Result of non-focused test


Focused tests

Focused tests can be conducted by providing the coordinates of one or more point sources. In these instances, the scan will only evaluate clusters around these sources and bypass the rest of the coordinates


Result of focused test


Elliptical Scanning Window

Well, sort of elliptical!

Case Study(Suppressed)

QUESTIONS?

Documents

Cluster Analysis Using SaTScan - Notes - bidankomunitas · 28 September 2007 APHEO Conference 2007 4 What is a cluster? An unusual collection of events Unusual aggregation of real