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SaTScanJ User Guide

1. Additional Output Files In addition to the standard results file that is automatically shown at the completion of the calculations it is possible to request four additional output files with different types of information e Cluster Information with each row containing summary information for each cluster e Cluster Cases Information with data set and ordinal category specific information for each cluster concerning observed and expected cases their ratio and the relative risk This file is primarily used for the ordinal model or when there are multiple data sets For other analyses this file is redundant as it contains a subset of the information already in the Cluster Information file e Location Information with each row containing information about a particular location and its cluster membership e Risk Estimates for Each Location e Simulated Log Likelihood Ratios You must manually open all these files after the run is completed They are provided in either ASCII or dBase format so that they can be easily imported into spreadsheets geographical information systems or other database software Related Topics Output Tab Results of Analysis Cluster Information File Location Information File Risk Estimates for Each Location Simulated Log Likelihood Ratios SaTScan User Guide v7 0 42 Advanced Features While most SaTScan analyses can be performed using the features on the three basic tabs for input analysis and output
2. Miscellaneous New Versions To check whether there is a later version than the one you are currently using simply click on the update button 3 on the tool bar If a newer version exists you will be asked whether you want to automatically download and install it At any given time it is also possible to download the latest version of the SaTScan from the World Wide Web at http www satscan org Related Topics Download and Installation Analysis History File In the analysis history file SaTScan automatically maintains a log of all the SaTScan analyses conducted Included in the log is an assigned analysis number together with information about the time of the analysis parameter settings a very brief summary of the results as well as the name of the standard results file created The analysis history is in a dBase file with the name AnalysisHistory dbf located in the same directory as the SaTScan executable It can be opened and read using most database and spreadsheet software including Excel You can erase the file at any time A new file will them be created the next time you run SaTScan starting the list of analyses from scratch Related Topics Running SaTScan Results of Analysis Random Number Generator The choice of random number generator is critical for any software creating simulated data SaTScan uses a Lehmer random number generator with modulus 2 1 2147483647 and multiplier 48271 which is known
3. Related Topics Advanced Features Analysis Tab Temporal Window Tab Maximum Spatial Cluster Size Include Purely Temporal Clusters Maximum Spatial Cluster Size The program will scan for clusters of geographic size between zero and some upper limit defined by the user The upper limit can be specified either as a percent of the population used in the analysis as a percent of some other population defined in a max circle size file or in terms of geographical size using the circle radius The maximum can also be defined using a combination of these three criteria The recommended choice is to specify the upper limit as a percent of the population at risk and to use 50 as the value It is possible to specify a maximum that is less than 50 but not more than 50 A cluster of larger size would indicate areas of exceptionally low rates outside the circle rather than an area of exceptionally high rate within the circle or vice versa when looking for clusters of low rates When SaTScan User Guide v7 0 46 in doubt choose a high percentage since SaTScan will then look for clusters of both small and large sizes without any pre selection bias in terms of the cluster size When calculating the percentage SaTScan uses the population defined by the cases and controls for the Bernoulli model the covariate adjusted population at risk from the population file for the Poisson model the cases for the space time permutation ordinal exponential and nor
4. Sometimes it is interesting to simultaneously search for and evaluate clusters in more than one data set For example one may be interested in spatial clusters with excess incidence of leukemia only of lymphoma only or of both simultaneously As another example one may be interested in detecting a gastrointestinal disease outbreak that effect children only adults only or both simultaneously If SaTScan is used to analyze one single combined data set one may miss a cluster that is only present in one of the subgroups On the other hand if two SaTScan analyses are performed one for each data set there is a loss of power if the true cluster is about equally strong in both data sets A SaTScan analysis with multiple data sets and the multivariate scan option solves this problem The multivariate scan statistic with multiple data sets works as follows when searching for clusters with high rates 1 For each window location and size the log likelihood ratio is calculated for each data set 2 The log likelihood ratios for the data sets with more than expected number of cases is summed up and this sum is the likelihood for that particular window 3 The maximum of all the summed log likelihood ratios taken over all the window locations and sizes constitutes the most likely cluster and this is evaluated in the same way as for a single data set When searching for clusters with low rates the same procedure is performed except that we inst
5. McGrath G Martin SW Bovine tuberculosis in badgers in four areas in Ireland does tuberculosis cluster Preventive Veterinary Medicine 59 103 111 2003 Joly DO Ribic CA Langenberg JA Beheler K Batha CA Dhuey BJ Rolley RE Bartelt G Van Deelen TR Samual MD Chronic wasting disease in free ranging Wisconsin white tailed deer Emerging Infectious Disease 9 599 601 2003 online Miller MA Grigg ME Kreuder C James ER Melli AC Crosbie PR Jessup DA Boothroyd JC Brownstein D Conrad PA An unusual genotype of Toxoplasma gondii is common in California sea otters Enhydra lutris nereis and is a cause of mortality International Journal for Parasitology 34 275 284 2004 Olea Popelka FJ Flynn O Costello E McGrath G Collins JD O Keeffe JO Kelton DF Berke O Martin SW Spatial relationship between Mycobacterium bovis strains in cattle and badgers in four areas in Ireland Preventive Veterinary Medicine 71 57 70 2005 Demography 134 Collado Chaves A Fecundidad adolescente en el gran area metropolitana de Costa Rica Poblacion y Salud en Mesoam rica 1 4 2003 online Forestry 135 136 Coulston JW Riitters KH Geographic Analysis of Forest Health Indicators Using Spatial Scan Statistics Environmental Management 31 764 773 2003 Riitters KH Coulston JW Hot spots of perforated forest in the eastern United States Environmental Management 35 483 492 2005 SaTScan User Guide v7 0 89 Toxicolo
6. Tab pete tpe et ehe rd ettet eet dedica 45 Spatial Window Tab eerte iR cus e met edu EENE re erbe estan 46 Temporal Window Tab oen eue eem ee ee 48 Spatial and Temporal Adjustments Tab sse nee enne enne 50 Inference T b x 5 eoe e t e UR Os reatu bed Rees 22 Clusters Reported Tab teen Ie eee eres 54 FOIDUHTANDE INICIO 57 Specifying Analysis and Data Options sese neen rennen enne tene tenete 57 SaTScan User Guide v7 0 Launching the Analysis tone inerte eter Pre peo te ten pee edipi 57 St t s Messages C S EES 58 Warnings and EfFors eU et p SE EA EERE eE pU i Ue E REE 58 Saving Analysis Parameters nri iras i E E A nenne tne tren nennen nennen eterne rennen nennen 59 Parallel ProcessOrs ssi niter trt eerte Rr EE UR TH SEX leere t E REEF reet 60 Batch Mode stereo beret m aut a penton S Rte E store unto e cR Pole OE on oie 60 Computing Time o eee teen eI Rte tei Ie rie io ae ats 61 Memory Requirements epe Yero ese ee tette pee etre oreet need 62 PRTIHIEEOSUEIEICETESTETI OTT LS sess sanacdesas seas scbatessandesponssedsandswassesesssecnseasSonsoussen 65 Standard Results File out cccsscccssssececsssceceeseececssaeecessseeeceeaeeecsecaeeessueeecsesaeeesseeaeeseseeeenaas 65 Cluster Information File col ccccccccssssscecssccececssececsesececsecceceseeecsesaececsesaeeesueeecsenaeeeesenaee
7. population in some areas grows faster than in others This is typically not a problem if the total study period is less than a year However the user is advised to be very careful when using this method for data spanning several years If the background population increases or decreases faster in some areas than in others there is risk for population shift bias which may produce biased p values when the study period is SaTScan User Guide v7 0 10 longer than a few years For example if a new large neighborhood is developed there will be an increase in cases there simply because the population increases and using only case data the space time permutation model cannot distinguish an increase due to a local population increase versus an increase in the disease risk As with all space time interaction methods this is mainly a concern when the study period is longer than a few years If the population increase or decrease is the same across the study region that is okay and will not lead to biased results Related Topics Likelihood Ratio Test Analysis Tab Probability Model Comparison Methodological Papers Ordinal Model With the ordinal model each observation is a case and each case belongs to one of several ordinal categories If there are only two categories the ordinal model is identical to the Bernoulli model where one category represents the cases and the other category represent the controls in the Bernoulli model The case
8. this option allows one to keep the original maximum on the circle size for the statistical inference and at the same time limit the size of the clusters reported This will typically result in SaTScan reporting many clusters that were not reported from the original analysis due to the fact that they shared the centroid of a more likely cluster SaTScan User Guide v7 0 55 The unit by which to define the maximum size of the reported cluster is the same as the unit used to define the maximum cluster size for inference purposes as defined on the Spatial Window Tab Related Topics Advanced Features Inference Tab Results of Analysis Criteria for Reporting Secondary Clusters Maximum Spatial Cluster Size Log Likelihood Ratio Standard Results File SaTScan User Guide v7 0 56 Running SaTScan Specifying Analysis and Data Options The SaTScan program requires that you specify parameters defining input analysis and output options for the analysis you wish to conduct A tabbed dialog is provided for this purpose To access the parameter tab dialog either press the button or select the File New menu item Specify the parameters for your session on the following tabs e Input Tab e Analysis Tab e Output Tab See the section on Basic SaTScan Features for instructions on how to fill in these tabs Most analyses can be performed using only these three tabs For each tab there are additional features that can be selected by first clicking
9. 0 29 Grid File The optional grid file defines the centroids of the circles used by the scan statistic If no grid file is specified the coordinates given in the coordinates file are used for this purpose Each line in the file represents one circle centroid There should be at least two variables representing Cartesian standard X y coordinates or exactly two variables representing latitude and longitude The choice between Cartesian and latitude longitude must coincide with the coordinates file as must the number of dimensions Related Topics nput Tab Grid File Name Coordinates Cartesian Coordinates Latitude and Longitude Coordinates File SaTScan Import Wizard SaTScan ASCII File Format Computing Time Neighbors File This is an optional file It cannot be defined using the SaTScan Import Wizard but has to be specified using the ASCII file format With this option the coordinates and grid files are not needed and ignored if provided With the standard parameter settings SaTScan uses the coordinates file to determine which locations are closest to the center of each circle constructed This is done using Euclidean distances In essence for each centroid SaTScan finds the closest neighbor the second closest and so on until it reaches the maximum window size With the neighbors file it is possible for the user to specify these neighbor relations in any way without being constrained to Euclidean distances For example the neigh
10. AR Rufenacht J Zurbriggen A Heim D Geographical clustering of cases of bovine spongiform encephalopathy BSE born in Switzerland after the feed ban Veterinary Record 151 467 472 2002 Perez AM Ward MP Torres P Ritacco V Use of spatial statistics and monitoring data to identify clustering of bovine tuberculosis in Argentina Preventive Veterinary Medicine 56 63 74 2002 Schwermer H Rufenacht J Doherr MG Heim D Geographic distribution of BSE in Switzerland Schweizer Archiv fur Tierheilkunde 144 701 708 2002 Ward MP Clustering of reported cases of leptospirosis among dogs in the United States and Canada Preventive Veterinary Medicine 56 215 226 2002 Falconi F Ochs H Deplazes P Serological cross sectional survey of psoroptic sheep scab in Switzerland Veterinary Parasitology 109 119 127 2002 Knuesel R Segner H Wahli T A survey of viral diseases in farmed and feral salmonids in Switzerland Journal of Fish Diseases 26 167 182 2003 Berke O Grosse Beilage E Spatial relative risk mapping of pseudorabies seropositive pig herds in an animal dense region Journal of Veterinary Medicine B50 322 325 2003 Abrial D Calavas D Lauvergne N Morignat E Ducrot C Descriptive spatial analysis of BSE in western France Veterinary Research 34 749 60 2003 Sheridan HA McGrath G White P Fallon R Shoukri MM Martin SW A temporal spatial analysis of bovine spongiform encephalopathy in Irish cattle herds from
11. Bernoulli model there are cases and non cases represented by a 0 1 variable These variables may represent people with or without a disease or people with different types of disease such as early and late stage breast cancer They may reflect cases and controls from a larger population or they may together constitute the population as a whole Whatever the situation may be these variables will be denoted as cases and controls throughout the user guide and their total will be denoted as the population Bernoulli data can be analyzed with the purely temporal the purely spatial or the space time scan statistics Example For the Bernoulli model cases may be newborns with a certain birth defect while controls are all newborns without that birth defect The Bernoulli model requires information about the location of a set of cases and controls Separate locations may be specified for each case and each control or the data may be aggregated for states provinces counties parishes census tracts postal code areas school districts households etc with multiple cases and controls at each data location To do a temporal or space time analysis it is necessary to have a time for each case and each control as well Related Topics Likelihood Ratio Test Analysis Tab Probability Model Comparison Methodological Papers SaTScan User Guide v7 0 9 Poisson Model With the Poisson model the number of cases in each location is Poisson distribu
12. Brown BW Holly EA A test to detect clusters of disease Biometrika 114 631 635 1987 SaTScan User Guide v7 0 92
13. Coordinates File Grid File Coordinates Specify the type of coordinates used by the coordinates file and the grid file as either Cartesian or latitude longitude Cartesian is the mathematical name for the regular x y coordinate system taught in high school Related Topics Cartesian Coordinates Latitude Longitude Coordinates File Grid File SaTScan User Guide v7 0 37 Analysis Tab Input Analysis Output r Type of Analysis M Probability Model gt Scan for Areas with 1 Retrospective Analyses Poisson High Rates C Purely Spatial r Louise C Bernoulli Purely Temporal C High or Low Rates Space Time Permutation Space Time TUM Time Aggregation C Ordinal pene Prospective Analyses Units Year C Mo C Purely Temporal C Exponential I Space Time Normal Length E Years Monte Carlo Replications 0 9 999 or value ending in 999 399 Advanced gt gt Analysis Tab Dialog Box The Analysis Tab is used to set various analysis options Additional features are available by clicking on the Advanced button in the lower right corner Related Topics Basic SaTScan Features Statistical Methodology Spatial Window Tab Temporal Window Tab Spatial and Temporal Adjustments Tab Inference Tab Type of Analysis SaTScan may be used for a purely spatial purely temporal or space time analyses A purely spatial analysis ignores the time of cases even when such data are provided A purely tempo
14. Data For temporal and space time data there is an additional difference among the probability models in the way that the temporal data is handled With the Poisson model population data may be specified at one or several time points such as census years The population is then assumed to exist between such time points as well estimated through linear interpolation between census years With the Bernoulli space time permutation ordinal exponential and normal models a time needs to be specified for each case and for the Bernoulli model for each control as well Related Topics Bernoulli Model Poisson Model Space Time Permutation Model Likelihood Ratio Test Methodological Papers Spatial Temporal and Space Time Scan Statistics Spatial Scan Statistic The standard purely spatial scan statistic imposes a circular window on the map The window is in turn centered on each of several possible grid points positioned throughout the study region For each grid point the radius of the window varies continuously in size from zero to some upper limit specified by the user In this way the circular window is flexible both in location and size In total the method creates an infinite number of distinct geographical circles with different sets of neighboring data locations within them Each circle is a possible candidate cluster The user defines the set of grid points used through a grid file If no grid file is specified the grid points are
15. SaTScan User Guide v7 0 43 Data sets are added by first clicking on the Add button and then entering the file names by either typing it in the text box by using the browser button E or through the SaTScan Import Wizard Import 8H We s File 2 button Remove a data set by selecting it and clicking on the Remove button Multiple data sets can be used for two different purposes One purpose is when there are different types of data and we want to know if there is a cluster in either one or more of the data sets The evidence for a cluster could then come exclusively from one data set or it may use the combined evidence from two or more data sets The other purpose is to adjust for covariates In this case the evidence of a cluster is based on all data sets The difference is discussed in more detail in the statistical methodology section Warning The computing time is considerably longer when analyzing multiple data sets as compared to a single data set Hence it is not recommended to use multiple data sets when there are many locations in the coordinates file Related Topics Advanced Features Input Tab Multivariate Scan with Multiple Data Sets Covariate Adjustments Using Multiple Data Sets Computing Time Case File Control File Population File Data Checking Tab Advanced Input Features Multiple Data Sets Data Checking Non Eucledian Neighbors Study Period Check Check to ensure that cases and controls ar
16. SaTScan can evaluate more different cluster locations and sizes without restrictions imposed by administrative geographical boundaries minimizing assumptions about the geographical cluster location and size 4 If my data is sparse won t the rates be statistically unstable The stability of rates does not depend on the geographical resolution of the input data but on the population size of the circles constructed by SaTScan 5 Whatis the minimum number of spatial locations needed to run SaTScan The purely temporal scan statistic can be run with only one geographical location The space time scan statistic needs at least two locations With only two locations the space time scan statistic will look for temporal clusters in either or both of the locations Technically the purely spatial scan statistic can also be run using only two geographical locations providing correct inference There is no point using a purely spatial scan statistic for such data though for which a regular chi square statistic can be used instead as there is no multiple testing to adjust for With three locations or more the fundamental scan statistic concept of including different combinations of locations into the potential clusters is being utilized In most practical applications though the spatial and space time scan statistics are used for data sets with SaTScan User Guide v7 0 73 hundreds or thousands of geographical locations If there is a choice less sp
17. a percent then for the Bernoulli and Poisson models it can be at most 90 percent and for the space time permutation model at most 50 percent The recommended value is 50 percent Related Topics Temporal Window Tab Maximum Spatial Cluster Size Include Purely Spatial Clusters Flexible Temporal Window Definition Time Aggregation Include Purely Spatial Clusters In addition to the maximum temporal cluster size it is also possible to allow clusters to contain the whole time period under study In this way purely spatial clusters are included among the evaluated windows The purpose of specifying a maximum temporal size but still including purely spatial clusters is to eliminate clusters containing the whole study period except a small time period at the very beginning or at the very end of the study period Note When adjusting for purely spatial clusters using stratified randomization all purely spatial clusters are adjusted away and this parameter has no effect on the analysis Related Topics Temporal Window Tab Maximum Temporal Cluster Size Include Purely Temporal Clusters Spatial Adjustment Flexible Temporal Window Definition For retrospective analyses SaTScan will evaluate all temporal windows less than the specified maximum and for prospective analyses the same is true with the added restriction that the end of the window is identical to the study period end date When needed SaTScan can be more flexible than that and
18. adjusted for by using multiple data sets which limits the number of covariate categories that can be defined or through a pre processing regression analysis done before running SaTScan All probability models can be used for either individual locations or aggregated data With the Poisson model population data is only needed at selected time points and the numbers are interpolated in between A population time must be specified even for purely spatial analyses Regardless of model used the time of a case or control need only be specified for purely temporal and space time analyses The space time permutation model automatically adjusts for purely spatial and purely temporal clusters For the Poisson model purely temporal and purely spatial clusters can be adjusted for in a number of different ways For the Bernoulli ordinal exponential and normal models spatial and temporal adjustments can be done using multiple data sets but it is limited by the number of different data sets allowed and it is also much more computer intensive Few Cases Compared to Controls In a purely spatial analysis where there are few cases compared to controls say less than 10 percent the Poisson model is a very good approximation to the Bernoulli model The former can then be used also for 0 1 Bernoulli type data and may be preferable as it has more options for various types of adjustments including the ability to adjust for covariates specified in the case and
19. and Newell Cuzick and Edwards Diggle and ChetwyndP Grimson 5 Moran Ranta Tango Walter and Whittemore et al These methods test for clustering throughout the study region without the ability to pinpoint the location of specific clusters As such these tests and the spatial scan statistic complement each other since they are useful for different purposes Global Space Time Interaction Tests Knox Mantel Diggle et al Jacquez Baker and Kulldorff and Hjalmars have proposed different tests for space time interaction Like the space time permutation version of the space time scan statistic these methods are designed to evaluate whether cases that are close in space are also close in time and vice versa adjusting for any purely spatial or purely temporal clustering Being global in nature these other tests are useful when testing to see if there is clustering throughout the study region and time period and the preferred method when for example trying to determine whether a disease is infectious Unlike the space time permutation based scan statistic though they are unable to detect the location and size of clusters and to test the significance of those clusters Related Topics Likelihood Ratio Test SaTScan Methodology Papers SaTScan User Guide v7 0 25 Input Data Data Requirements Required Files The input data should be provided in at least two different files a case file and a coordi
20. and Suggestions Feedback from users is greatly appreciated Very valuable suggestions concerning the SaTScan software have been received from many individuals including Allyson Abrams Harvard Medical School amp Harvard Pilgrim Health Care Frank Boscoe New York State Health Department Eric Feuer National Cancer Institute Laurence Freedman National Cancer Institute David Gregorio University of Connecticut Goran Gustafsson Karolinska Institute Sweden Jessica Hartman New York Academy of Medicine Richard Heffernan New York City Department of Health Kevin Henry New Jersey Department of Health Ulf Hjalmars Ostersund Hospital Sweden Richard Hoskins Washington State Department of Health Lan Huang National Cancer Institute Ahmedin Jemal American Cancer Society Inkyung Jung Harvard Medical School amp Harvard Pilgrim Health Care Ann Klassen Johns Hopkins University Ken Kleinman Harvard Medical School amp Harvard Pilgrim Health Care Kristina Metzger New York City Department of Health Barry Miller National Cancer Institute Farzad Mostashari New York City Department of Health Karen Olson Children s Hospital Boston SaTScan User Guide v7 0 71 Linda Pickle National Cancer Institute Tom Richards Centers for Disease Control and Prevention Gerhard Rushton University of Iowa Joeseph Sheehan University of Connecticut Tom Talbot New York State Health Department Toshiro Tango National Institute of Public
21. between them Related Topics Covariate Adjustment Using the Input Files Covariate Adjustment using Statistical Regression Software Covariate Adjustment Using Multiple Data Sets Methodological Papers Covariate Adjustment Using the Input Files With the Poisson and space time permutation models it is possible to adjust for multiple categorical covariates by specifying the covariates in the input files To do so simply enter the covariates as extra columns in the case file all models and the population file Poisson model There is no need to enter any information on any of the window tabs For the Poisson model the expected number of cases in each area under the null hypothesis is calculated using indirect standardization Without covariate adjustment the expected number of cases in a location is spatial analysis E c p C P where c is the observed number of cases and p the population in the location of interest while C and P are the total number of cases and population respectively Let c pj C and P be defined in the same way but for covariate category i The indirectly standardized covariate adjusted expected number of cases spatial analysis is Ele M Elci Xipi Ci Pi The same principle is used when calculating the covariate adjusted number of cases for the space time scan statistic although the formula is more complex due to the added time dimension SaTScan User Guide v7 0 18 Since the space time permuta
22. cluster detection was proposed by Rushton and Lolonis 66 who used p value contour maps to depict the clusters rather than overlapping circles As with GAM it does not adjust for the multiple testing inherent in the many potential cluster locations evaluated Cluster Detection Tests The spatial scan statistic is a cluster detection test A cluster detection test is able to both detect the location of clusters and evaluate their statistical significance without problems with multiple testing In 1990 Turnbull et al proposed the first such test using overlapping circles with fixed population size assigning the circle with the most cases as the detected cluster SaTScan User Guide v7 0 24 The spatial scan statistic was in part inspired by the work of Openshaw et al and Turnbull et al By applying a likelihood ratio test it was possible to evaluate clusters of different sizes as Openshaw et al did while at the same time adjusting for the multiple testing as Turnbull et al did In a power comparison it was shown that Turnbull s method has higher power if the true cluster size is within about 20 percent of what is specified by that method while the spatial scan statistic has higher power otherwise Note that the cluster size in Turnbull s method must be specified before looking at the data or the procedure is invalid Focused Cluster Tests Focused tests should be used when there is a priori knowledge about the locatio
23. clustering Statistical Methods in Medical Research 4 124 136 1995 153 Glaz J Balakrishnan N editors Scan Statistics and Applications Birkhauser Boston 1999 154 Glaz J Naus JI Wallenstein S Scan Statistics Springer Verlag New York 2001 155 Grimson RC A versatile test for clustering and a proximity analysis of neurons Methods of Information in Medicine 30 299 303 1991 156 Jacquez GM A k nearest neighbor test for space time interaction Statistics in Medicine 15 1935 1949 1996 157 Knox G The detection of space time interactions Applied Statistics 13 25 29 1964 158 Kulldorff M Statistical Methods for Spatial Epidemiology Tests for Randomness in GIS and Health in Europe L yt nen M and Gatrell A eds London Taylor amp Francis 1998 159 Kulldorff M Hjalmars U The Knox method and other tests for space time interaction Biometrics 9 621 630 1999 160 Lawson AB On the analysis of mortality events associated with a pre specified fixed point Journal of the Royal Statistical Society Series A 156 363 377 1993 161 Mantel N The detection of disease clustering and a generalized regression approach Cancer Research 27 209 220 1967 162 Moran PAP Notes on continuous stochastic phenomena Biometrika 37 17 23 1950 163 Naus J The distribution of the size of maximum cluster of points on the line Journal of the American Statistical Association 60 532 538 1965 164 Openshaw S Cha
24. compared to the baseline Setting a value of one is equivalent of not doing any adjustments A value of greater than one is used to adjust for an increased risk and a value of less than one to adjust for lower risk A relative risk of zero is used to adjust for missing data for that particular time and location start time Optional The start of the time period to be adjusted using this relative risk end time Optional The end of the time period to be adjusted using this relative risk If no start and end times are given the whole study period will be adjusted for that location If All is selected instead of a location ID but no start or end times are given that has the same effect as when no adjustments are done The name of the adjustments file is specified on the Analysis Tab gt Advanced Features gt Risk Adjustments Note Assigning a relative risk of x to half the locations is equivalent to assigning a relative risk of 1 x to the other half Assigning the same relative risk to all locations and time periods has the same effect as not adjusting at all Note It is permissible to adjust the same location and time periods multiple times through different rows with different relative risks SaTScan will simply multiply the relative risks For example if you adjust location A with a relative risk of 2 for all time periods and you adjust 1990 with a relative risk of 2 for all locations then the 1990 entry for location A will be ad
25. on the Advanced button in the lower right corner of the tab These additional features may be useful in special circumstances The available choices for some features may depend on what was selected in other places For example if a purely spatial analysis is chosen the space time permutation model is not available and vice versa Related Topics Basic SaTScan Features Input Tab Analysis Tab Output Tab Advanced Features Launching the Analysis Launching the Analysis Once the data input files have been created and the parameters defining the input analysis and output options have been specified select the Execute button to launch the analysis and produce the results file A special job status window will appear containing status warning and or error messages Once the analysis has been completed the standard results file will appear in the job status window Multiple parameter session windows may be opened simultaneously for data entry and multiple analyses may be run concurrently If you are running multiple analyses concurrently please verify that the output files have different names Related Topics Input Data Data Requirements Specifying Analysis and Data Options Status Messages Warnings and Errors Computing Time Batch Mode SaTScan User Guide v7 0 57 Status Messages Status messages are displayed as the program executes the analysis as the data is read and at each step of the analysis Normal status messag
26. population files As an approximation for Bernoulli type data the Poisson model produces slightly conservative p values Bernoulli versus Ordinal Model The Bernoulli model is mathematically a special case of the ordinal model when there are only two categories The Bernoulli model runs faster making it the preferred model to use when there are only two categories Normal versus Exponential Model Both the normal and exponential models are meant for continuous data The exponential model is primarily designed for survival time data but can be used for any data where all observations are positive It is especially suitable for data with a heavy right tail The normal model can be used for continuous data that takes both positive and negative values While still formally valid results from the normal model are sensitive to extreme outliers SaTScan User Guide v7 0 13 Normal versus Ordinal Model The normal model can be used for categorical data when there are very many categories As such it is sometimes a computationally faster alternative to the ordinal model There is an important difference though With the ordinal model only the order of the observed values matters For example the results are the same for ordered values 1 2 3 4 and 1 10 100 1000 With the normal model the results will be different as they depend on the relative distance between the values used to define the categories Temporal
27. sets This option is not available for the Bernoulli ordinal exponential or space time permutation models in the latter case because the method automatically adjusts for any purely spatial clusters Note It is not possible to simultaneously adjust for spatial clusters and purely temporal clusters using stratified randomization and if both types of adjustments are desired the space time permutation model should be used instead Related Topics Spatial and Temporal Adjustments Tab Poisson Model Adjusting for Temporal Trends Adjusting for Known Relative Risks Sometimes it is known a priori that a particular location and or time has a higher or lower risk of known magnitude and we want to detect clusters above and beyond this or in other words we want to adjust for this known excess lower risk One way to do this is to simply change the population at risk numbers in the population file A simpler way is to use the adjustments file In this file a relative risk is specified for any location and time period combination The expected counts are then multiplied by this relative risk for that location and time For example if it is known from historical data that a particular location typically have 50 percent more cases during the summer months June to August then for each year one would specify a relative risk of 1 5 for this location and these months A summer cluster will then only appear in this location if the excess risk is more than 50 p
28. south distance from the equator and locations south of the equator should be entered as negative numbers Longitude represents the east west distance from the Prime Meridian Greenwich England and locations west of the Prime Meridian should be entered as negative numbers For example the National Institutes of Health in Bethesda Maryland which is located at 39 00 degrees north and 77 10 degrees west should be reported as 39 00 and 77 10 respectively Latitudes and longitudes can for the purpose of this program not be specified in degrees minutes and seconds Such latitudes and longitudes can easily be converted into decimal numbers of degrees DND by the simple formula DND degrees minutes 60 seconds 3600 If latitude longitude coordinates are used the coordinates file should contain the following information location id Any numerical value or string of characters Empty spaces may not form part of the id latitude Latitude in decimal number of degrees longitude Longitude in decimal number of degrees Note When coordinates are specified in latitudes and longitudes SaTScan does not perform a projection of these coordinates onto a planar space Rather SaTScan draws perfect circles on the surface of the spherical earth Related Topics Input Tab Coordinates File Coordinates Cartesian Coordinates Latitude and Longitude Grid File SaTScan Import Wizard SaTScan ASCII File Format Computing Time SaTScan User Guide v7
29. tests whether it is 17 If I am interested in whether there is spatial auto correlation in the data why should I use the spatial scan statistic rather than a traditional spatial auto correlation test If you are only interested in whether there is spatial auto correlation or not but don t care about cluster locations there are tests for spatial auto correlation global clustering that have higher power than the spatial scan statistic and should be used instead The spatial scan statistic should be used when you are interested in the detection and statistical significance of local clusters 18 In spatial statistics is it not always important to adjust for spatial auto correlation This cannot be done in SaTScan Whether to adjust for spatial auto correlation depends on the question being asked from the data As an example let s assume that we have geographical data on people who get sick due to food poisoning In such data there is clearly spatial auto correlation since bad food sold at restaurants or grocery stores are often sold to multiple customers many of who will live in the same neighborhood If we are doing spatial regression trying to determine what neighborhood characteristics such as mean income house values educational levels or ethnic origin contribute to a higher risk for food poisoning it is critical to adjust for the spatial auto correlation in the data If not the confidence in the risk relationships will be overestima
30. there is no need for any modification of the input data Poisson Model To adjust the Poisson model for missing data use the adjustments file to define the location and time combinations for which the data is missing and assign a relative risk of zero to those location time combinations Space Time Permutation Model It is a little more complex to adjust for missing data in the space time permutation model but still possible First add day of week as a covariate in the analysis file When a particular location time period is missing then for that location remove all data for the days of the week for which any data is missing For example if data from Thursday 10 23 and Friday 10 24 are missing for zip code area A and data from Saturday 10 25 are missing from area B remove data from all Thursdays and Fridays for area A and data from all Saturdays from area B while retaining all data from Saturdays through Wednesdays for area A and all data except Saturdays from area B For all other zip code areas retain all data for all days Note that in addition to adjusting for the missing data this approach will also adjust for any day of week by spatial interaction effects The same approach can be used with other categorization of the data as long as the categorizations is in some time periodic unit that occur several times and is evenly spread out over the study period For example it is okay to categorize into months if the study period span
31. time time Optional Time may be specified in years months or days All control times must fall within the study period as specified on the Analysis tab The format of the times must be the same as in the case file Note Multiple lines may be used for different controls with the same location time and attributes SaTScan will automatically add them Related Topics Input Tab Control File Name Multiple Data Sets Tab SaTScan Import Wizard SaTScan ASCII File Format SaTScan User Guide v7 0 27 Population File The population file is used for the Poisson model providing information about the background population at risk This may be actual population count from a census or it could be for example covariate adjusted expected counts from a statistical regression model It should contain the following information location id Any numerical value or string of characters Empty spaces may not form part of the id time The time to which the population size refers May be specified in years months or days If the population time is unknown but identical for all population numbers then a dummy year must be given the choice not affecting result population Population size for a particular location year and covariate combination If the population size is zero for a particular location year and set of covariates then it should be included in the population file specified as zero The population can be specified as a decimal number t
32. to find Communications of the ACM 31 1192 1201 1988 SaTScan User Guide v7 0 80 Visualization and Mapping 22 Boscoe FP McLaughlin C Schymura MJ Kielb CL Visualization of the spatial scan statistic using nested circles Health and Place 9 273 277 2003 Methods Evaluations and Comparisons 23 24 235 26 27 28 29 30 31 32 Kulldorff M Tango T Park P Power comparisons for disease clustering tests Computational Statistics and Data Analysis 42 665 684 2003 Song C Kulldorff M Power evaluation of disease clustering tests International Journal of Health Geographics 2 9 2003 online Kulldorff M Zhang Z Hartman J Heffernan R Huang L Mostashari F Evaluating disease outbreak detection methods Benchmark data and power calculations Morbidity and Mortality Weekly Report 53 144 151 2004 online Nordin J Goodman M Kulldorff M Ritzwoller D Abrams A Kleinman K Levitt MJ Donahue J Platt R Using modeled anthrax attacks on the Mall of America to assess sensitivity of syndromic surveillance Emerging Infectious Diseases 11 1394 1398 2005 online Ozdenerol E Williams BL Kang SY Magsumbol MS Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters International Journal of Health Geographics 4 19 2005 online Costa MA Assun o RM A fair comparison between the spatial scan and Besag Newell disease clustering test
33. to perform well Related Topics Monte Carlo Replications Contact Us Please direct technical questions about installation and running the program as well as the web site to techsupport satscan org Please direct substantive questions about the statistical methods and suggestions about new features to Martin Kulldorff Associate Professor Biostatistician SaTScan User Guide v7 0 70 Department of Ambulatory Care and Prevention Harvard Medical School and Harvard Pilgrim Health Care 133 Brookline Avenue 6th Floor Boston MA 02215 USA Email kulldorff 9 satscan org Acknowledgements Financial Support National Cancer Institute Division of Cancer Prevention Biometry Branch SaTScan v1 0 2 0 2 1 National Cancer Institute Division of Cancer Control and Population Sciences Statistical Research and Applications Branch SaTScan v3 0 part v6 1 part Alfred P Sloan Foundation through a grant to the New York Academy of Medicine Farzad Mostashari PI SaTScan v3 0 part 3 1 4 0 5 0 5 1 Centers for Disease Control and Prevention through Association of American Medical Colleges Cooperative Agreement award number MM 0870 SaTScan v6 0 v6 1 part National Institute of Child Health and Development through grant RO1HD048852 7 0 Their financial support is greatly appreciated The contents of SaTScan are the responsibility of the developer and do not necessarily reflect the official views of funders Comments
34. whether the case file and the control file when applicable contain information about the time of each case and control and if so whether the precision should be read as days months or years If the time precision is specified to be days but the precision in the case or control file is in month or year then there will be an error If the time precision is specified as years but the case or control file includes some dates specified in terms of the month or day then the month or day will be ignored For a purely spatial analysis the case and control file need not contain any times If they do it has to be specified that they do contain this information but the information is ignored Note The choice defines only the precision for the times in the case and control files The precision of the times in the population file can be different Related Topics nput Tab Case File Control File Study Period Time Aggregation Study Period Specify the start and end date of the time period under study This must be done even for a purely spatial analysis in order to calculate the expected number of cases correctly Allowable years are those between 1753 and 9999 All times in the case and control files must fall on or between the start and end date of the study period Dates in the population file are allowed to be outside the start and end date of the study period Start Date The earliest date to be included in the study period End Date Th
35. 0 Hayran M Analyzing factors associated with cancer occurrence A geographical systems approach Turkish Journal of Cancer 34 67 70 2004 online Sheehan TJ DeChello LM A space time analysis of the proportion of late stage breast cancer in Massachusetts 1988 to 1997 International Journal of Health Geographics 4 15 2005 online Fukuda Y Umezaki M Nakamura K Takano T Variations in societal characteristics of spatial disease clusters examples of colon lung and breast cancer in Japan International Journal of Health Geographics 4 16 2005 online Ozonoff A Webster T Vieira V Weinberg J Ozonoff D Aschengrau A Cluster detection methods applied to the Upper Cape Cod cancer data Environmental Health A Global Access Science Source 4 19 2005 online Klassen A Curriero F Kulldorff M Alberg AJ Platz EA Neloms ST Missing stage and grade in Maryland prostate cancer surveillance data 1992 1997 American Journal of Preventive Medicine 30 877 87 2006 online Pollack LA Gotway CA Bates JH Parikh Patel A Richards TB Seeff LC Hodges H Kassim S Use of the spatial scan statistic to identify geographic variations in late stage colorectal cancer in California United States Cancer Causes and Control 17 449 457 2006 Cardiology 81 Kuehl KS Loffredo CA A cluster of hypoplastic left heart malformation in Baltimore Maryland Pediatric Cardiology 27 25 31 2006 Rheumatology Auto Immune Diseases 8
36. 1996 to 2000 Canadian Journal of Veterinary Research 69 19 25 2005 online Guerin MT Martin SW Darlington GA Rajic A A temporal study of Salmonella serovars in animals in Alberta between 1990 and 2001 Canadian Journal of Veterinary Research 69 88 89 2005 online SaTScan User Guide v7 0 88 Veterinary Medicine Wildlife 126 127 128 129 130 131 132 133 Smith KL DeVos V Bryden H Price LB Hugh Jones ME Keim P Bacillus anthracis diversity in Kruger National Park Journal of Clinical Microbiology 38 3780 3784 2000 online Berke O von Keyserlingk M Broll S Kreienbrock L On the distribution of Echinococcus multilocularis in red foxes in Lower Saxony identification of a high risk area by spatial epidemiological cluster analysis Berliner und Munchener Tierarztliche Wochenschrift 115 428 434 2002 Miller MA Gardner IA Kreuder C Paradies DM Worcester KR Jessup DA Dodd E Harris MD Ames JA Packham AE Conrad PA Coastal freshwater runoff is a risk factor for Toxoplasma gondii infection of southern sea otters Enhydra lutris nereis International Journal for Parasitology 32 997 1006 2002 Hoar BR Chomel BB Rolfe DL Chang CC Fritz CL Sacks BN Carpenter TE Spatial analysis of Yersinia pestis and Bartonella vinsonii subsp berkhoffii seroprevalence in California coyotes Canis latrans Preventive Veterinary Medicine 56 299 311 2003 Olea Popelka FJ Griffin JM Collins JD
37. 2 83 84 85 Walsh SJ Fenster JR Geographical clustering of mortality from systemic sclerosis in the Southeastern United States 1981 90 Journal of Rheumatology 24 2348 2352 1997 Walsh SJ DeChello LM Geographical variation in mortality from systemic lupus erythematosus in the United States Lupus 10 637 646 2001 L pez Abente G Morales Piga A Bachiller Corral FJ Illera Mart n O Mart n Domenech R Abraira V Identification of possible areas of high prevalence of Paget s disease of bone in Spain Clinical and Experimental Rheumatology 21 635 368 2003 Donnan PT Parratt JDE Wilson SV Forbes RB O Riordan JI Swingler RJ Multiple sclerosis in Tayside Scotland detection of clusters using a spatial scan statistic Multiple Sclerosis 11 403 408 2005 Neurological Diseases 86 Sabel CE Boyle PJ L yt nen M Gatrell AC Jokelainen M Flowerdew R Maasilta P Spatial clustering of amyotrophic lateral sclerosis in Finland at place of birth and place of death American Journal of Epidemiology 157 898 905 2003 SaTScan User Guide v7 0 85 Liver Diseases 87 Ala A Stanca CM Bu Ghanim M Ahmado I Branch AD Schiano TD Odin JA Bach N Increased prevalence of primary biliary cirrhosis near superfund toxic waste sites Hepatology 43 525 531 2006 Diabetes 88 Green C Hoppa RD Young TK Blanchard JF Geographic analysis of diabetes prevalence in an urban area Social Science and Medicine 57 551
38. 560 2003 Pediatrics see also cancer cardiology 89 90 9 92 93 94 95 96 97 98 99 Kharrazi M et al Pregnancy outcomes around the B K K landfill West Covina California An analysis by address California Department of Health Services 1998 Sankoh OA Ye Y Sauerborn R Muller O Becher H Clustering of childhood mortality in rural Burkina Faso International Journal of Epidemiology 30 485 492 2001 online George M Wiklund L Aastrup M Pousette J Thunholm B Saldeen T Wernroth L Zaren B Holmberg L Incidence and geographical distribution of sudden infant death syndrome in relation to content of nitrate in drinking water and groundwater levels European Journal of Clinical Investigation 31 1083 1094 2001 Bell S Spatial Analysis of Disease Applications In Beam C ed Biostatistical Applications in Cancer Research Boston Kluwer p151 182 2002 online Forand SP Talbot TO Druschel C Cross PK Data quality and the spatial analysis of disease rates congenital malformations in New York State Health and Place 8 191 199 2002 Colorado Department of Public Health and Environment Analysis of birth defect data in the vicinity of the Redfield plume area in southeastern Denver county 1989 1999 Colorado Department of Public Health and the Environment 2002 online Boyle E Johnson H Kelly A McDonnell R Congenital anomalies and proximity to landfill sites Irish Medical J
39. 8 2001 Chaput EK Meek JI Heimer R Spatial analysis of human granulocytic ehrlichiosis near Lyme Connecticut Emerging Infectious Diseases 8 943 948 2002 online Huillard d Aignaux J Cousens SN Delasnerie Laupretre N Brandel JP Salomon D Laplanche JL Hauw JJ Alperovitch A Analysis of the geographical distribution of sporadic Creutzfeldt Jakob disease in France between 1992 and 1998 International Journal of Epidemiology 31 490 495 2002 online Mostashari F Kulldorff M Hartman JJ Miller JR Kulasekera V Dead bird clustering A potential early warning system for West Nile virus activity Emerging Infectious Diseases 9 641 646 2003 online Ghebreyesus TA Byass P Witten KH Getachew A Haile M Yohannes M Lindsay SW Appropriate Tools and Methods for Tropical Microepidemiology a Case study of Malaria Clustering in Ethiopia Ethiopian Journal of Health Development 17 1 8 2003 Sauders BD Fortes ED Morse DL Dumas N Kiehlbauch JA Schukken Y Hibbs JR Wiedmann M Molecular subtyping to detect human listeriosis clusters Emerging Infectious Diseases 9 672 680 2003 online Brooker S Clarke S Njagi JK Polack S Mugo B Estambale B Muchiri E Magnussen P Cox J Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya Tropical Medicine and International Health 9 757 766 2004 Washington CH Radday J Streit TG Boyd HA Beach MJ Addiss DG Lovin
40. Ds have exactly the same coordinates then the data for the two are combined and treated as a single location A coordinates file is not needed for purely temporal analyses Related Topics nput Tab Coordinates File Name Coordinates Cartesian Coordinates Latitude and Longitude Grid File SaTScan User Guide v7 0 28 Cartesian Coordinates Cartesian is the mathematical name for the regular planar x y coordinate system taught in high school These may be specified in two three or any number of dimensions The SaTScan program will automatically read the number of dimensions which must be the same for all coordinates If Cartesian coordinates are used the coordinates file should contain the following information location id Any numerical value or string of characters Empty spaces may not form part of the id coordinates The coordinates must all be specified in the same units There is no upper limit on the number of dimensions x and y coordinates Required z1 zN coordinates Optional Note If you have more than 10 dimensions you cannot use the SaTScan Import Wizard for the coordinates and grid files but must specify them using the SaTScan ASCII file format Related Topics nput Tab Coordinates Latitude and Longitude Coordinates File Grid File SaTScan Import Wizard SaTScan ASCII File Format Latitude and Longitude Latitudes and longitudes should be entered as decimal number of degrees Latitude represents the north
41. Health Japan Jean Francois Viel Universit de Franche Comt France SaTScan User Guide v7 0 72 Frequently Asked Questions Input Data 1 I tried running SaTScan using one of the sample data sets and all went well but when I try it on my own data there is an error What should I do SaTScan makes sure that the input data is compatible with each other and with the options specified on the windows interface For example it complains if there is a location ID in the case file that is not present in the coordinates file as it must know where to localize those cases For most data sets there is some need for data cleaning and SaTScan is designed to help with this process by spotting and pointing out any inconsistencies found 2 I have constructed the ASCII input files exactly according to the description in the SaTScan User Guide but SaTScan complains that they are not in the correct format What is wrong The most likely explanation is that the files are in UNICODE rather than ASCII format Just convert to ASCII and it should work 3 In my data there are zero or only one case in most locations Can I use SaTScan for such sparse data Yes you certainly can One of the main reasons for using SaTScan is to avoid arbitrary geographical aggregation of the data letting the scan statistic consider different smaller or larger aggregations through its continuously moving window With finer geographical resolution of the input data
42. M Heffernan R Hartman J Assun o RM Mostashari F A space time permutation scan statistic for the early detection of disease outbreaks PLoS Medicine 2 216 224 2005 online Ordinal Model 6 Jung I Kulldorff M Klassen A A spatial scan statistic for ordinal data Statistics in Medicine 2006 in press online Exponential Model 7 Huang L Kulldorff M Gregorio D A spatial scan statistic for survival data Biometrics 2006 in press online Normal Model 8 Kulldorff M et al 2006 manuscript in preparation Multivariate Scan Statistic 9 Kulldorff M Mostashari F Duczmal L Yih K Kleinman K Platt R Multivariate spatial scan statistics for disease surveillance Statistics in Medicine 2006 in press online Elliptic Scanning Window 10 Kulldorff M Huang L Pickle L Duczmal L An elliptic spatial scan statistic Statistics in Medicine 2006 epub ahead of print SaTScan User Guide v7 0 79 Monte Carlo Hypothesis Testing 11 Dwass M Modified randomization tests for nonparametric hypotheses Annals of Mathematical Statistics 28 181 187 1957 Recurrence Intervals 12 Kleinman K Lazarus R Platt R A generalized linear mixed models approach for detecting incident clusters of disease in small areas with an application to biological terrorism American Journal of Epidemiology 159 217 24 2004 Adjustments Adjusting for Covariates 13 Kulldorff M A spatial scan statistic Communications in Stat
43. N coordinate gt Neighbors File Format nbr location ID location ID of closest neighbor location ID of 2 closest neighbor etc Special Max Circle Size File Format max location ID lt population gt Adjustment File Format adj lt location ID relative risk start time end time Time Formats Times must be entered in a specific format The valid formats are 2003 2003 10 2003 10 24 2003 10 2003 10 24 10 2003 10 24 2003 10 2003 10 24 2003 Single digit days and months may be specified with one or two digits For example September 9 2002 can be written as 2002 9 9 2002 09 09 2002 09 9 2002 9 09 2002 9 9 etc Note SaTScan v7 0 also support a couple of other time formats used in earlier versions but they are no longer recommended Related Topics Input Tab Case File Control File Population File Coordinates File Grid File Max Circle Size File Neighbors File Adjustments File SaTScan Import Wizard SaTScan User Guide v7 0 34 Basic SaTScan Features Most SaTScan analyses can be performed using the basic analysis and data features The users specify these on three different window tabs for input analysis and output options respectively These contain all required specifications for a SaTScan analysis as well as a few optional ones Additional features all optional can be specified on the advanced features tabs Related Topics Statistical Methodology Input Tab Analy
44. P VALUE 10 10 13 10 10 14 10 10 10 Observed Cases oBSERVED 11 11 14 11 11 15 t1 Expected Cases EXPECTED 12 12 15 12 12 16 esenea epea fone H3 H8 38 pe pepr 124 Relative Risk REL RISK 14 14 37 18 Mean Inside _ amp MEKANIN 12 Mean Outside mean oor 1 1 1 1 1 173 Unexplained Variance VARIANCE 14 Standard deviation DEVIATION 15 Table 3 Content of the cluster information output file with dBase variable names and examples of column ordering for a few different types of analyses Related Topics Cluster Cases Information File Location Information File Output Tab Results of Analysis Standard Results File SaTScan User Guide v7 0 67 Cluster Cases Information File cci In the cluster cases information file there is one line for each ordinal category in each data set for each cluster For each cluster category data set combination there is one column for the observed number of cases the expected number of cases observed divided by expected and the relative risk If neither the ordinal model nor multiple data sets are used then there is only one line for each cluster and there is no information in this file that is not also in the Cluster Information File The file will have the same name as t
45. SaTScan User Guide for version 7 0 By Martin Kulldorff August 2006 http www satscan org Contents jn M 4 The SaTScan SoftWare cett uox HUI er etre oa dua et ertet tr toe EEE eben 4 Download and Installation sees nennen nennen rene rennen enne tren nenne 5 Test Run itte nete BO E PRO E A TE E E E E EEEE teases sights 5 SEWQURMBEACANT I pH PE gt Niro coated n ra AM 9 Bernoulli Model in eet eh hk ARAL RS IR Rei 9 Poisson Model sisi tet bee en ta dede iioi rp 10 Space Time Permutation Modlel e nette nette rete t RI ERG RS Keis 10 Ordinal Model eie rt ed lee deut ed eet e cte dd pee 11 Exponential Model eet ro reset ere QU Me YE Pe ee EUER Ree er togeeebeubeveres seuauubanecbaass 11 Normal Model nene eH IO RE 12 Probability Model Comparison eese nennen ene neeneeeeeenetent trennen trennen 13 Spatial Temporal and Space Time Scan Statistics sse 14 Fikelihood Ratio Test eee teat tete ie Ie ic erre ie tebe ed E ee c ay 15 Secondary C lust ts 1 tee rebate o iba eR e tbe een 17 Adjusting for More Likely Clusters temeritate e ter ipo Eees I VERESS tos 17 Covariate Adjustments aeoe eee a e e dedu eer ed o e needs 17 Spatial and Temporal Adjustments essere nnne 20 Missing Data reseni
46. a In the latter case the percent increase or decrease must be calculated using standard statistical regression software such as SAS or S plus and then inserted on the Risk Adjustments Tab SaTScan User Guide v7 0 20 For space time analyses it is also possible to adjust for a temporal trend non parametrically This adjusts the expected count separately for each aggregated time interval removing all purely temporal clusters The randomization is then stratified by time interval to ensure that each time interval has the same number of events in the real and random data sets The ability to adjust for temporal trends is much more limited for the Bernoulli ordinal and exponential models as none of the above features can be used Instead the time must be divided into discrete time periods with the cases and controls in each period corresponding to a separate data set with separate case and control files The analysis is then done using multiple data sets Related Topics Spatial and Temporal Adjustments Tab Time Aggregation Poisson Model Adjusting for Purely Spatial Clusters In a space time analysis with the Poisson model it is also possible to adjust for purely spatial clusters in a non parametric fashion This adjusts the expected count separately for each location removing all purely spatial clusters The randomization is then stratified by location ID to ensure that each location has the same number of events in the real and random data
47. a that is not true Does this mean that the null hypothesis is wrong When accepting the notion of statistical hypothesis testing one must also accept the fact that the null hypothesis is never true For example when comparing the efficacy of two different surgical procedures in a clinical trial we know for sure that their efficacy cannot be equal but we still use equality as the null hypothesis since we are interested in finding out whether one is better than the other Likewise with geographical data we know that disease risk is not the same everywhere but we still use it as the null hypothesis since we are interested in finding locations with excess risk Hence the null hypothesis is wrong in the sense that we know it is not true but it is not wrong in the sense that we should not use it 16 Does SaTScan assume that there is no spatial auto correlation in the data Note Spatial auto correlation means that the location of disease cases is dependent on the location of other disease cases such as with an infectious disease where an infected individual is likely to infect those living close by SaTScan User Guide v7 0 75 No SaTScan does not assume that there is no spatial auto correlation in the data Rather it is a test of whether there is spatial auto correlation or other divergences from the null hypothesis In this sense it is equivalent to a statistical test for normality which does not assume that the data is normally distributed but
48. anagement Services Inc SaTScan v7 0 Software for the spatial and space time scan statistics http www satscan org 2006 Users of SaTScan should in any reference to the software note that SaTScan is a trademark of Martin Kulldorff The SaTScan software was developed under the joint auspices of i Martin Kulldorff ii the National Cancer Institute and iii Farzad Mostashari of the New York City Department of Health and Mental Hygiene Related Topics SaTScan Bibliography Methodological Papers SaTScan User Guide v7 0 78 SaTScan Methodology Papers Statistical Methodology General Statistical Theory Bernoulli and Poisson Models 1 Kulldorff M A spatial scan statistic Communications in Statistics Theory and Methods 26 1481 1496 1997 online Spatial Scan Statistic Bernoulli Model 2 Kulldorff M Nagarwalla N Spatial disease clusters Detection and Inference Statistics in Medicine 14 799 810 1995 online Retrospective Space Time Scan Statistic 3 Kulldorff M Athas W Feuer E Miller B Key C Evaluating cluster alarms A space time scan statistic and brain cancer in Los Alamos American Journal of Public Health 88 1377 1380 1998 online Prospective Space Time Scan Statistic 4 Kulldorff M Prospective time periodic geographical disease surveillance using a scan statistic Journal of the Royal Statistical Society A164 61 72 2001 online Space Time Permutation Model 5 Kulldorff
49. ar window plus five different elliptic shapes where the ratio of the longest to the shortest axis of the ellipse is 1 5 2 3 4 or 5 For each shape a different number of angles of the ellipse are used equal to 4 6 9 12 and 15 respectively The north south axis is always one of the angles included and the remainder is equally spaced around the circle For each shape and angle all possible sizes of the ellipses are used up to an upper limit specified by the user in the same way as for the circular window When using an elliptic window shape it is possible to request a non compactness eccentricity penalty which will favor more compact over less compact ellipses even when they have slight lower likelihood ratios but the less compact ellipses when the difference is larger The formula for the penalty is 4s s4 1 where s is the elliptic window shape defined as the ratio of the length of the longest to the shortest axis of the ellipse With a strong penalty a with a medium penalty a 2 and with no penalty a 0 Note In batch mode it is possible to request SaTScan to use any other collection of ellipses to define the scanning window and any value of the eccentricity penalty parameter greater than zero Note The elliptic window option can only be used when regular two dimensional Cartesian coordinates are used but not when they are specified as latitude longitude If you have the latter you must first do a planar map projection from th
50. ars months or days All case times must fall within the study period as specified on the Input Tab attribute A variable describing some characteristic of the case These may be covariates Poisson and space time permutation models category ordinal model survival time exponential model censored exponential model or a continuous variable value normal model The covariates are optional variables and any number of categorical covariates may be specified as either numbers or through characters The categories for the ordinal model can be specified as any positive or negative numerical value The survival times must be positive numbers Censored is a 0 1 variable with censored 1 and uncensored 0 Example If on April 1 2004 there were 17 male and 12 female cases in New York the following information would be provided NewYork 12 2004 4 1 Female NewYork 17 2004 4 1 Male Note Multiple lines may be used for different cases with the same location time and attributes SaTScan will automatically add them Related Topics Input Tab Case File Name Multiple Data Sets Tab Covariate Adjustment Using Input Files SaTScan Import Wizard SaTScan ASCII File Format Control File The control file is only used with the Bernoulli model It should contain the following information location id Any numerical value or string of characters Empty spaces may not form part of the id controls The number of controls for the specified location and
51. ata an exponential model for survival time data with or without censored variables or a normal model for other types of continuous data The data may be either aggregated at the census tract zip code county or other geographical level or there may be unique coordinates for each observation SaTScan adjusts for the underlying spatial inhomogeneity of a background population It can also adjust for any number of categorical covariates provided by the user as well as for temporal trends known space time clusters and missing data It is possible to scan multiple data sets simultaneously to look for clusters that occur in one or more of them Developers and Funders The SaTScan software was developed by Martin Kulldorff together with Information Management Services Inc Financial support for SaTScan has been received from the following institutions e National Cancer Institute Division of Cancer Prevention Biometry Branch v1 0 2 0 2 1 e National Cancer Institute Division of Cancer Control and Population Sciences Statistical Research and Applications Branch v3 0 part v6 1 part e Alfred P Sloan Foundation through a grant to the New York Academy of Medicine Farzad Mostashari PI v3 0 part 3 1 4 0 5 0 5 1 e Centers for Disease Control and Prevention through Association of American Medical Colleges Cooperative Agreement award number MM 0870 v6 0 6 1 part e National Institute of Child Health and Development 7 0 T
52. atial aggregation of the data is typically better which means more geographical locations Analysis 6 With latitude longitude coordinates what planar projection is used No projection is used SaTScan draws perfect circles on the spherical surface of the earth 7 When should I use the Bernoulli versus the Poisson model Use the Bernoulli model when you have binary data such as cases and controls late and early stage cancer or people with and without a disease Use the Poisson model when you have cases and a background population at risk such as population numbers from the census 8 SaTScan adjusts for categorical covariates but I want to adjust for a continuous variable Is that possible One way to do this is to categorize the continuous variable A better approach is to 1 calculate the adjustment using a regular statistical software package such as SAS ii use the result from that analysis to calculate the covariate adjusted expected number of cases at each location and iii use these expected values instead of the population in the population file With this approach there should not be any covariates in either the case or the population files 9 What should I use as the maximum geographical cluster size Is that an arbitrary choice If you don t want to be arbitrary choose 50 of the population as the maximum geographical cluster size SaTScan will then evaluate very small and very large clusters and everything in betwe
53. bors may be sorted according to distance along a subway network or a water distribution system The first column of this file contains the location IDs defining the centroids of the scanning window The subsequent entries on each row are then the centroids neighbors in order of closeness The scanning window will expand in size until there are no more neighbors provided for that row That means that this file also defines the maximum window size It is allowed to have multiple rows for the same location ID centroid each with a different set of closest neighbors Related Topics Coordinates File Input Tab Non Euclidean Neighbors Tab SaTScan ASCII File Format Max Circle Size File This optional file is used to determine the maximum circle size of the scanning window when the maximum is defined as a percentage of the population Normally the percentage is based on the population in the population file but by using the max circle size file a different population can be specified for this purpose One important reason for using the max circle size file is for prospective space time analyses where the regular population file may change over time but one wants to evaluate the same set of geographical circles each time This is critical in order to properly adjust the prospective space time scan statistic for earlier analyses It can also be used for other purposes The file should contain one line for each location with the following inf
54. cations specified on the Analysis Tab Note With this option the p values obtained after an early termination will be slightly conservative The interpretation does not change for p values obtained from a full run Related Topics Inference Tab Monte Carlo Replications Results of Analysis Computing Time SaTScan User Guide v7 0 52 Adjust for Earlier Analyses in Prospective Surveillance When doing prospective purely temporal or prospective space time analyses repeatedly in a time periodic fashion it is possible to adjust the statistical inference p values for the multiple testing inherent in the repeated analyses done To do this simply mark the adjust for earlier analyses box and specify the date for which you want to adjust for all subsequent analyses This date must be greater or equal to the study period start date and less than or equal to the study period end date as specified on the Input Tab For the adjustment to be correct it is important that the scanning spatial window is the same for each analysis that is performed over time This means that the grid points defining the circle centroids must remain the same If the location IDs in the coordinates file remain the same in each time periodic analysis then there is no problem On the other hand if new IDs are added to the coordinates file over time then you must use a special grid file and retain this file through all the analyses Also when you adjust for earlier anal
55. ce R Lovegrove MC Lafontant JG Lammie PJ Hightower AW Spatial clustering of filarial transmission before and after a Mass Drug Administration in a setting of low infection prevalence Filaria Journal 3 3 2004 online Dreesman J Scharlach H Spatial statistical analysis of infectious disease notification data in Lower Saxony Gesundheitswesen 66 783 789 2004 Bakker MI Hatta M Kwenang A Faber WR van Beers SM Klatser PR Oskam L Population survey to determine risk factors for Mycobacterium leprae transmission and infection International Journal of Epidemiology 33 1329 1336 2004 Jennings JM Curriero FC Celentano D Ellen JM Geographic identification of high gonorrhea transmission areas in Baltimore Maryland American Journal of Epidemiology 161 73 80 2005 Polack SR Solomon AW Alexander NDE Massae PA Safari S Shao JF Foster A Mabey DC The household distribution of trachoma in a Tanzanian village an application of GIS to the study of trachoma Transactions of the Royal Society of Tropical Medicine and Hygiene 99 218 225 2005 Wylie JL Cabral T Jolly AM Identification of networks of sexually transmitted infection a molecular geographic and social network analysis J Infect Diseases 191 899 906 2005 SaTScan User Guide v7 0 82 4T 48 49 50 51 52 53 Moore GE Ward MP Kulldorff M Caldanaro RJ Guptill LF Lewis HB Glickman LT Identification of a space time cluster of cani
56. cts a certain risk mass such as total person years lived in an area The cases are then included as part of the population count Bernoulli Model The Bernoulli model should be used when the data set contains individuals who may or may not have a disease and for other 0 1 type variables Those who have the disease are cases and should be listed in the case file Those without the disease are controls listed in the control file The controls could be a random set of controls from the population or better the total population except for the cases The Bernoulli model is a special case of the ordinal model when there are only two categories Space Time Permutation Model The space time permutation model should be used when only case data is available and when one wants to adjust for purely spatial and purely temporal clusters Ordinal Model The ordinal model is used when individuals belong to one of three or more categories and when there is an ordinal relationship between those categories such as small medium and large When there are only two categories the Bernoulli model should be used instead Exponential Model The exponential model is used for survival time data to search for spatial and or temporal clusters of exceptionally short or long survival The survival time is a positive continuous variable Censored survival times are allowed for some but not all individuals Normal Model The normal model is used for continuous data Obser
57. de v7 0 Statistical Methodology Scan statistics are used to detect and evaluate clusters in a temporal spatial or space time setting This is done by gradually scanning a window across time and or space noting the number of observed and expected observations inside the window at each location In the SaTScan software the scanning window is either an interval in time a circle or an ellipse in space or a cylinder with a circular or elliptic base in space time Multiple different window sizes are used The window with the maximum likelihood is the most likely cluster that is the cluster least likely to be due to chance A p value is assigned to this cluster The general statistical theory behind the spatial and space time scan statistics used in the SaTScan software is described in detail by Kulldorff 1997 for the Bernoulli and Poisson models by Kulldorff et al 2005 for the space time permutation model by Jung et al 2006 for the ordinal model by Huang et al 2006 for the exponential model and by Kulldorff et al 2006 for the normal model Here we give a brief non mathematical description For all probability models the scan statistic adjusts for the uneven geographical density of a background population and the analyses are conditioned on the total number of cases observed Related Topics 7he SaTScan Software Basic SaTScan Features Advanced Features Analysis Tab Methodological Papers Bernoulli Model With the
58. e is that it gives some information about the alternatives for which the test can be expected to have good power 21 For the exponential model it is assumed that the survival times follow an exponential distribution Are the results biased if the survival times follow a different distribution No matter which distribution generated the survival times the p values from the statistical inference are still valid and unbiased no matter which distribution This 1s because rather than generating the random data from an exponential distribution each random data is a spatial permutation of the survival times A greatly missspecified distribution may lead to a loss in power though For example if the data is Bernoulli distributed the exponential model has less power to detect a cluster than the Bernoulli model For continuous distributions such as gamma and lognormal the exponential model has been shown to work well Operating Systems 22 Is SaTScan available for Linux machines A Linux version of SaTScan is available It can be downloaded from the www satscan org web site 23 Is SaTScan available for Unix machines There is a Unix version of SaTScan available for Solaris It has not been thoroughly tested so it may not work on all computers Anyone interested in trying this version should send an email to kulldorff satscan org SaTScan User Guide v7 0 77 SaTScan Bibliography Different SaTScan analysis options were developed a
59. e latest date to be included in the study period Note The start and end dates cannot be specified to a higher precision than the precision of the times in the case and control files SaTScan User Guide v7 0 36 If the user does not specify month then by default it will be set to January for the start date and to December for the end date Likewise if day is not specified then by default it will be set to the first of the month for the start date and the last of the month for the end date Related Topics nput Tab Case File Control File Time Precision Time Aggregation Population File Name Specify the name of the input file with population data This file is only used for analyses using the Poisson probability model Related Topics Input Tab Population File Coordinates File Name Specify the name of the input file with geographical coordinates of all the locations with data on the number of cases controls and or population When multiple data sets are used the coordinates file must include the coordinates for all locations found in any of the data sets Related Topics nput Tab Coordinates Coordinates File Grid File Name Specify the name of the optional grid file with the coordinates of the circle centroids used by the spatial and space time scan statistics If no special grid file is specified then the coordinates in the coordinates file are used are used for this purpose Related Topics Input Tab Coordinates
60. e latitude longitude coordinates of which there are many different ones proposed in the geography literature Related Topics Advanced Features Computing Time Include Purely Spatial Clusters Likelihood Ratio Test Maximum Spatial Cluster Size Spatial Temporal and Space Time Scan Statistics Spatial Window Tab Temporal Window Tab Advanced Analysis Features Spatial Window Temporal Window Space and Time Adjustments Inference Maximum Temporal Cluster Size is 50 percent of the study period lt 90 default 50 C is years Include Purely Spatial Clusters Temporal Size 100 Set Defaults Flexible Temporal Window Definition Include only windows with Start time in range 2006 fr to 2500 fiz E End time in range 2006 fi to 2000 fiz 7 SaTScan User Guide v7 0 48 Temporal Window Tab Dialog Box Use the Temporal Window Tab to define the exact nature of the scanning window with respect to time Related Topics Advanced Features Analysis Tab Spatial Window Tab Maximum Temporal Cluster Size Include Purely Spatial Clusters Flexible Temporal Window Definition Maximum Temporal Cluster Size For purely temporal and space time analyses the maximum temporal cluster size can be specified in terms of a percentage of the study period as a whole or as a certain number days months or years The maximum must be at least as large as the length of aggregated time interval length If specified as
61. e no reported cluster with its center contained in a subsequently reported less likely cluster No Pairs of Centers Both in Each Others Clusters Secondary clusters are not centered in a previously reported cluster that contains the center of a previously reported cluster This means that there will be no pair of reported clusters each of which contain the center of the other No Restrictions Most Likely Cluster for Each Grid Point The most extensive option is to all present clusters in the list with no restrictions This option reports the most likely cluster for each grid point This means that the number of clusters reported is identical to the number of grid points Note The criteria for determining overlap is based only on geography and ignoring time Hence Warning No Restrictions may create output files that are huge in size Related Topics Advanced Features Inference Tab Results of Analysis Maximum Spatial Cluster Size Report Only Small Clusters Report Only Small Clusters When the most likely clusters are very large in size it is sometimes of interest to know whether they contain smaller clusters that are statistically significant on their own strength One way to find such clusters is to play around with the maximum circle size parameter but that leads to incorrect statistical inference as the maximum circle size is then chosen based on the results of the analysis leading to pre selection bias To avoid this problem
62. e of the population then the special max circle size file must be used This is to ensure that the evaluated geographical circles do not change over time Related Topics Advanced Features Spatial Window Tab Max Circle Size File Include Purely Temporal Clusters Computing Time Include Purely Temporal Clusters A purely temporal cluster is one that includes the whole geographic area but only a limited time period When doing a space time analysis it is possible to allow potential clusters to contain the whole geographical area under study as an exception to the maximum spatial cluster size chosen In this way purely temporal clusters are included among the collection of windows evaluated Note This option is not available for the space time permutation model as that model automatically adjusts for purely temporal clusters When adjusting for purely temporal clusters using stratified randomization all purely temporal clusters are adjusted away and this parameter has no effect on the analysis Related Topics Advanced Features Spatial Window Tab Maximum Spatial Cluster Size Include Purely Spatial Clusters Temporal Trend Adjustment Computing Time Elliptic Scanning Window As an advanced option it is possible to use a scanning window that is consists not only of circles but also of ellipses of different shapes and angles When the elliptic spatial scan statistic is requested SaTScan SaTScan User Guide v7 0 47 uses the circul
63. e scan statistic with an application to syndromic surveillance Epidemiology and Infection 2005 133 409 419 110 Nordin JD Goodman MJ Kulldorff M Ritzwoller DP Abrams AM Kleinman K Levitt MJ Donahue J Platt R Simulated anthrax attacks and syndromic surveillance Emerging Infectious Diseases 2005 11 1394 98 online SaTScan User Guide v7 0 87 111 112 Yih K Abrams A Kleinman K Kulldorff M Nordin J Platt R Ambulatory care diagnoses as potential indicators of outbreaks of gastrointestinal illness Minnesota Morbidity and Mortality Weekly Report 54 Suppl 157 62 2005 online Besculides M Heffernan R Mostashari F Weiss D Evaluation of school absenteeism data for early outbreak detection New York City BMC Public Health 2006 5 105 online Veterinary Medicine Domestic Animals 113 114 115 116 117 118 119 120 121 122 123 124 125 Norstr m M Pfeiffer DU Jarp J A space time cluster investigation of an outbreak of acute respiratory disease in Norwegian cattle herds Preventive Veterinary Medicine 47 107 119 2000 Ward MP Blowfly strike in sheep flocks as an example of the use of a time space scan statistic to control confounding Preventive Veterinary Medicine 49 61 69 2001 United States Department of Agriculture West Nile virus in equids in the Northeastern United States in 2000 USDA APHIS Veterinary Services 2001 online Doherr MG Hett
64. e within the Study Period C Ignore cases and controls that are outside the Study Period Geographical Coordinates Check f Check to ensure that all locations in the case control and population files are present in the coordinates or neighbors file C Ignore data in the case control and population files that do not correspond to a location ID listed in the coordinates or neighbors file Data Checking Tab Dialog Box SaTScan User Guide v7 0 44 Study Period Check By default SaTScan will check that all the cases and all the controls are within the specified temporal study period On this tab it is possible to turn this off Cases and controls outside the study period will then be ignored This may be used if for example you only want to analyze a temporal subset of the data in the case and control input files Geographical Coordinates Check By default SaTScan will check that all the cases controls and population numbers are within one of the locations specified in the coordinates file On this tab it is possible to turn this off Data in other locations not present in the coordinates file are then ignored This may be used if for example you only want to analyze a geographical subset of the data in which case only the geographical coordinates file has to be modified while the other files can be used as they are Related Topics Advanced Features Case File Input Tab Study Time Period Non Euclidean Neighbors Tab Adva
65. ead sum up the log likelihood ratios of the data sets with fewer than expected number of cases within the window in question When searching for both high and low clusters both sums are calculated and the maximum of the two is used to represent the log likelihood ratio for that window Note All data sets must use the same probability model and the same geographical coordinates file Related Topics Multiple Data Sets Tab Covariate Adjustment Using Multiple Data Sets Coordinates File SaTScan User Guide v7 0 23 Comparison with Other Methods Scan Statistics Scan statistics were first studied in detail by Joseph Naus A major challenge with scan statistics is to find analytical results concerning the probabilities of observing a cluster of a specific magnitude and there is a beautiful collection of subsequent mathematical theory obtaining approximations and bounds for these probabilities under a variety of settings Excellent reviews of this theory have been provided by Glaz and Balakrishnan and Glaz Naus and Wallenstein Two common features for most of this work are 1 they use a fixed size scanning window and ii they deal with count data where under the null hypothesis the observed number of cases follow a uniform distribution in either a continuous or discrete setting so that the expected number of cases in an area is proportional to the size of that area In disease surveillance neither of these assumptions is met si
66. eeeeene eene nhennren eren rennen enne tnne nnne 79 Selected SaTScan Applications by Field of Study sse 81 Other References in the User Guides erre Eee EUR reve Ue eene es 90 SaTScan User Guide v7 0 Introduction The SaTScan Software Purpose SaTScan is a free software that analyzes spatial temporal and space time data using the spatial temporal or space time scan statistics It is designed for any of the following interrelated purposes e Perform geographical surveillance of disease to detect spatial or space time disease clusters and to see if they are statistically significant e Test whether a disease is randomly distributed over space over time or over space and time e Evaluate the statistical significance of disease cluster alarms e Perform repeated time periodic disease surveillance for early detection of disease outbreaks The software may also be used for similar problems in other fields such as archaeology astronomy botany criminology ecology economics engineering forestry genetics geography geology history neurology or zoology Data Types and Methods SaTScan uses either a Poisson based model where the number of events in a geographical area is Poisson distributed according to a known underlying population at risk a Bernoulli model with 0 1 event data such as cases and controls a space time permutation model using only case data an ordinal model for ordered categorical d
67. eis IA Almeida MC Homicide clusters and drug traffic in Belo Horizonte Minas Gerais Brazil from 1995 to 1999 Cadernos de Sa de Publica 17 1163 1171 2001 online 145 Ceccato V Haining R Crime in border regions The Scandinavian case of Oresund 1998 2001 Annals of the Association of American Geographers 94 807 826 2004 Related Topics Methodological Papers SaTScan Bibliography Suggested Citation Other References in the User Guide 146 Alt KW Vach W The reconstruction of genetic kinship in prehistoric burial complexes problems and statistics In Bock HH Ihm P eds Classification data analysis and knowledge organization Berlin Springer Verlag 1991 SaTScan User Guide v7 0 90 147 Baker RD Testing for space time clusters of unknown size Journal of Applied Statistics 23 543 554 1996 148 Besag J Newell J The detection of clusters in rare diseases Journal of the Royal Statistical Society A154 143 155 199 149 Bithell JF The choice of test for detecting raised disease risk near a point source Statistics in Medicine 14 2309 2322 1995 150 Cuzick J Edwards R Spatial clustering for inhomogeneous populations Journal of the Royal Statistical Society B52 73 104 1990 151 Diggle PJ Chetwynd AD Second order analysis of spatial clustering for inhomogeneous populations Biometrics 47 1155 1163 199 152 Diggle P Chetwynd AG H ggkvist R Morris SE Second order analysis of space time
68. en 10 Why can t I select a maximum geographical cluster size that is larger than 50 of the population Clusters of excess risk that are larger than 50 of the population at risk are better viewed as cluster with lower risk outside the scanning window and the area outside will always have a very irregular geographical shape If there is interest in clusters with lower risk than expected it is more appropriate to select the low rates option on the analysis tab Results 11 I get an error stating that the output file could not be created Why Windows 2000 and Windows XP have tighter default security settings than Windows 95 98 NT ME and under these newer versions of Windows permission to write to the Program Files folder is given only to administrators and power users of that machine If the output file path includes the Program Files folder and you do not have administrative or power user privileges on your computer Windows prevents SaTScan from creating the output file in the SaTScan User Guide v7 0 74 designated location The solution is to specify a different output file name using a different directory 12 Since the SaTScan results are based on Monte Carlo simulated random data why are the p values the same when I run the analysis twice All computer based simulations are based on pseudo random number generators When the same seed is used exactly the same sequence of pseudo random numbers will be generated Since SaTScan us
69. er P values listed for secondary clusters are calculated in the same way as for the most likely cluster by comparing the log likelihood ratio of secondary clusters in the real data set with the log likelihood ratios of the most likely cluster in the simulated data sets This means that if a secondary cluster is significant it can reject the null hypothesis on its own strength without help of any other clusters It also means that these p values are conservative PARAMETER SETTINGS A reminder of the parameter settings used for the analysis Additional results files The name and location of additional results files are provided when applicable Related Topics Output Tab Clusters Reported Tab Cluster Information File Location Information File Risk Estimates for Each Location Simulated Log Likelihood Ratios Cartesian Coordinates Additional Output Files Cluster Information File col In the cluster information file each cluster 1s on one line with different information about the cluster in different columns For each cluster there is information about the location and size of the cluster its log likelihood ratio and the p value Except for the ordinal model and when multiple data sets are used there is also information about the observed and expected number of cases observed expected and relative risk For the ordinal model and multiple data sets these numbers depend on the data set and or category and the information is ins
70. ercent This option is only available for the Poisson model Related Topics Adjustments File Spatial and Temporal Adjustments Tab Time Aggregation Poisson Model Missing Data SaTScan User Guide v7 0 21 Missing Data If there is missing data for some locations and times it is important to adjust for that in the analysis If not you may find statistically significant low rate clusters where there is missing data or statistically significant high rate clusters in other locations even though these are simply artifacts of the missing data Bernoulli Model To adjust a Bernoulli model analysis for missing data do the following If cases are missing for a particular location and time period remove the controls for that same location and time Likewise if controls are missing for a particular location and time remove the cases for that same location and time This needs to be done before providing the data to SaTScan If both cases and controls are missing for a location and time you are fine and there is no need for any modification of the input data Ordinal Model To adjust an ordinal model analysis for missing data do the following If one or more categories are missing for a particular location and time period remove all cases in the remaining categories from that same location and time This needs to be done before providing the data to SaTScan If all cases in all categories are missing for a location and time you are fine and
71. ermutation and exponential models b 16 for the Bernoulli model b 4 for the ordinal model b 20 for the normal model CAT the number of actegories in the ordinal model CAT 1 for other models CONT 3 for exponential model CONT 4 for normal model and CONT 1 for all other models C the total number of cases for the ordinal model or multiple data sets C20 R 1 when scanning for high rates only or low rates only R22 when scanning for either high or low rates D number of data sets P number of processors available on the computer for SaTScan use SaTScan User Guide v7 0 62 For purely spatial analyses and most space time analyses TI is much less than G and so it is the expression to the left of the first plus sign above that is critical in terms of memory requirements Table 2 provides estimates of the memory requirements when G L mg 0 5 and TI 1 SG L Memory SG L Memory Needed Needed 3 500 32Mb 44 000 2Gb 6 500 64Mb 63 000 4Gb 10 000 128Mb 89 000 16Gb 15 000 256Mb 126 000 32Gb 22 000 512Mb 178 000 64Gb 32 000 1Gb 250 000 128Gb Table 2 Approximate memory requirements for a purely spatial analysis when the maximum geographical cluster size is 50 of the population Special Memory Allocation When the number of locations is very large while the number of cases time intervals and simulations are not SaTScan sometimes uses an alternati
72. ers smaller than 50 percent of the population at risk lt 50 default 50 lv 50 percent of the population defined in the max circle size file lt 50 iw acircle with a fi kilometer radius Clusters Reported Tab Dialog Box This tab is reached by clicking the Advanced button in the lower right corner of the Output Tab Related Topics Advanced Features Output Tab Results of Analysis Criteria for Reporting Secondary Clusters Report Only Small Clusters Criteria for Reporting Secondary Clusters SaTScan evaluates an enormous amount of different circles cylinders in order to find the most likely cluster All of these may be considered secondary clusters with either a high or a low rate To present all of these secondary clusters is impractical and unnecessary since many of them will be very similar to each other For example to add one location with a very small population to the most likely cluster will not decrease the likelihood very much even if that location contains no additional cases Such a secondary cluster is not interesting even though it could have the second highest likelihood among all the clusters evaluated Rather than reporting information about all evaluated clusters SaTScan only reports a limit number of secondary clusters using criteria specified by the user A three stage procedure is used to select the secondary clusters to report 1 For each circle centroid SaTScan will only consider the cluster wit
73. es are displayed in the top box of the job status window Warnings and error messages are displayed in the bottom box of the job status window Upon successful completion of the calculations the standard results file will be shown in the job status window Related Topics Launching the Analysis Warnings and Errors Warnings and Errors You are running SaTScan v4 0 Beta 98 SaTScan is free available for download from http www satscan org It may be used free of charge as long as proper citations are given ito both the SaTScan software and the underlying statistical methodology Reading the geographic coordinates file Reading the population file Reading the case file Job cancelled Please review Warnings Errors window below Warnings Errors Error Date 1977 in record 1 of case file is not within study period beginning 1900 1 1 and ending 1900 12 31 Error Date 1974 in record 2 of case file is not within study period beginning 1900 1 1 and ending 1900 12 31 zl E Mail Print Close SaTScan Status Messages and Warnings Errors Dialog Box Warning Messages SaTScan may produce warnings as the job is executing If a warning occurs a message is displayed in the Warnings Errors box on the bottom of the job status window A warning will not stop the execution of the analysis If a warning occurs please review the message and access the help system if further information is required If y
74. es the same seed for every run you obtain the same result for two runs when the input data is the same 13 I ran exactly the same data using two different versions of SaTScan v2 1 and SaTScan v3 0 3 1 4 0 5 0 5 1 6 0 7 0 but the p values are different Why Which one is the correct one Compared to v2 1 the pseudo random number generation is done slightly differently in SaTScan v3 0 and later typically resulting in slightly different p values While both are valid and correct only one p value should be used We recommend always using the p value that was calculated first 14 I ran exactly the same data using SaTScan v2 1 3 0 3 1 4 0 and SaTScan v5 0 5 1 6 0 7 0 but the results are different Why In earlier version SaTScan defined overlapping clusters based on whether the two circles where overlapping In SaTScan v5 0 and later two clusters overlap if they have at least one location ID in common These two definitions are usually the same but in rare cases they may be different If you were running the Poisson model another possible reason for the difference is that SaTScan v5 0 and later uses a more precise algorithm for calculating the expected number of cases when the population dates in the population file are specified using days rather than months or years Interpretation 15 In SaTScan after adjusting for population density and covariates such as age the null hypothesis is complete spatial randomness For most disease dat
75. ess 66 Cluster Cases Information File cci ccccccccsssccesseececeesececseseececseeeecsesaececseceeeeesueeeesesaeeeeseeaeeess 68 Location Information File gis eaer iaie a E EEE RE E E E EE EE 68 Risk Estimates for Each Location File rr ccccccsscccssssececeesceceesnececsesaececseseeeesaeeecnesaeeesseeeeeees 68 Simulated Log Likelihood Ratios File Ilr eese eese eee nnne 69 zn 70 NEW Versions uei pe REPE npe e REDE 70 Analysis History File ite pner ee Donostia otim pedes sets 70 Random Number Generator coit inosine eroe a iieis iii ii 70 Contact US fi ice EROR EEE EE eo USER PEU QUEE SERERE E EO EAEE EE e ues 70 Acknowledgements ttt Hee p eee bre ep teen sas EP ERU E eo Foret EA 71 Frequently Asked Questions eee eee esee essen ena en enata tuse ta sins ta sts tasses suse tasse ta sens enses tasses sone soseo 73 Input Data cosets toe Rote RU eed eet see RU Ite On deeem E o INTER EUE 73 LUCR Mc sudeusceenncubie Woke doeviaueebebawiaceteneecud etebecoaiieesveeb inte 74 Results eene eer eth tiere E ntque roter tet di ie istius 74 Interpretation otio e Determine sees 75 Operating Systems ose ete ted tree p tef otn eroe te nc tede TI SaTScan Bibliography RR 78 Suggested Citations i eit o Disk Lesa bese d eite wach oth e pete i etur Siete eve ceri estan 78 SaTScan Methodology Papers ssessssseeeeee
76. eters with a prm file extension The parameter file is stored in an ASCII text file format To save analysis parameters 1 If the parameters have not previously been saved select Save As from the File menu A Save Parameter File As dialog will open 2 Select a directory location from the Save In drop down menu at the top of the dialog box 3 Enter a name for your parameter file in the File Name text box It is recommended that the Save As Type selection remain as Parameter Files prm 4 Press the Save button Once the parameter file is initially saved save changes to the file by selecting Save on the File menu The file will save without opening the Save Parameter File As dialog To open a saved parameter file 1 Select Open from the File menu or click on the button in the toolbar A Select Parameter File dialog will open 2 Locate the desired file using the Look in drop down menu 3 Once the file is located highlight the file name by clicking on it 4 Press the Open button A Parameter tab dialog will open containing the saved parameter settings The location and name of the parameter file is listed in the title bar of this dialog Related Topics Specifying Analysis and Data Options Basic SaTScan Features Advanced Features Batch Mode SaTScan User Guide v7 0 59 Parallel Processors If you have parallel processors on your computer SaTScan can take advantage of this by
77. g the study period Annual rate per 100 000 Poisson model This is calculated taking leap years into account and is based on the average length of a year of 365 2425 If calculated by hand ignoring leap years the numbers will be slightly different but not by much Variance normal model This is the variance for all observations in the data assuming a common mean MOST LIKELY CLUSTER Summary information about the most likely cluster that is the cluster that is least likely to be due to chance Radius When latitude and longitude are used the radius of the circle is given in kilometers When regular Cartesian coordinates are used the radius of the circle is given in the same units as those used in the coordinates file Population This is the average population in the geographical area of the cluster The average is taken over the whole study period even when it is a space time cluster whose temporal length is only a part of the study period Unexplained Variance normal model This is the estimated common variance for all observations in the data that cannot be explained by this particular cluster It is calculated by using the different estimated means inside and outside the cluster P value The p values are adjusted for the multiple testing stemming from the multitude of circles cylinders corresponding to different spatial and or temporal locations and sizes of potential SaTScan User Guide v7 0 65 clusters evaluated This
78. gy 137 Sudakin DL Horowitz Z Giffin S Regional variation in the incidence of symptomatic pesticide exposures Applications of geographic information systems Journal of Toxicology Clinical Toxicology 40 767 773 2002 Psychology 138 Margai F Henry N A community based assessment of learning disabilities using environmental and contextual risk factors Social Science and Medicine 56 1073 1085 2003 Brain Imaging 139 Yoshida M Naya Y Miyashita Y Anatomical organization of forward fiber projections from area TE to perirhinal neurons representing visual long term memory in monkeys Proceedings of the National Academy of Sciences of the United States of America 100 4257 4262 2003 online History 140 Witham CS Oppenheimer C Mortality in England during the 1783 4 Laki Craters eruption Bulletin of Volcanology 67 15 25 2004 Criminology 141 Jefferis ES A multi method exploration of crime hot spots SaTScan results National Institute of Justice Crime Mapping Research Center 1998 142 Kaminski RJ Jefferis ES Chanhatasilpa C A spatial analysis of American police killed in the line of duty In Turnbull et al eds Atlas of crime Mapping the criminal landscape Phoenix AZ Oryx Press 2000 143 LeBeau JL Demonstrating the analytical utility of GIS for police operations A final report National Criminal Justice Reference Service 2000 online 144 Beato Filho CC Assun o RM Silva BF Marinho FC R
79. h the highest likelihood among those that share that same centroid grid point 2 These clusters will be ordered in descending order by the value of their log likelihood ratios creating a list with the same number of clusters as there are grid points SaTScan User Guide v7 0 54 3 The most likely cluster will always be reported Options for reporting secondary clusters follow Except under the last option secondary clusters will only be reported if p lt 1 No Geographical Overlap Default Secondary clusters will only be reported if they do not overlap with a previously reported cluster that is they may not have any location IDs in common Therefore no overlapping clusters will be reported This is the most restrictive option presenting the fewest number of clusters No Cluster Centers in Other Clusters Secondary clusters are not centered in a previously reported cluster and do not contain the center of a previously reported cluster While two clusters may overlap there will be no reported cluster with its centroid contained in another reported cluster No Cluster Centers in More Likely Clusters Secondary clusters are not centered in a previously reported cluster This means that there will be no reported cluster with its center contained in a previously reported more likely cluster No Cluster Centers in Less Likely Clusters Secondary clusters do not contain the center of a previously reported cluster This means that there will b
80. he standard results file but with the extensions cci txt and cci dbf respectively and will be located in the same directory Related Topics Cluster Information File Location Information File Output Tab Results of Analysis Standard Results File Location Information File gis As an option a special output file may be created describing the various clusters in a way that is easy to incorporate into a geographical information system GIS This file may be requested in ASCII and or dBase format and can be accessed using any text editor or spreadsheet program It will have the same name as the results file but with the extensions gis txt and gis dbf respectively and it will be located in the same directory This file has one row for each location belonging to a cluster The columns shown depends on the chosen analysis including among other the following information Location ID Cluster Number P Value of Cluster Observed Cases in Cluster Expected Cases in Cluster Observed Expected in Cluster Observed Cases in Location Expected Cases in Location Observed Expected in Location Note The second third fourth fifth and sixth column entries are the same for all locations belonging to the same cluster Related Topics Output Tab Results of Analysis Standard Results File Cluster Information File Risk Estimates for Each Location File rr If the option to include risk estimates fo
81. heir financial support is greatly appreciated The contents of SaTScan are the responsibility of the developer and do not necessarily reflect the official views of the funders SaTScan User Guide v7 0 4 Related Topics Statistical Methodology SaTScan Bibliography Download and Installation To install SaTScan go to the SaTScan Web site at http www satscan org and select the SaTScan download link After downloading the SaTScan installation executable to your PC click on its icon and install the software by following the step wise instructions Related Topics New Versions Test Run Before using your own data we recommend trying one of the sample data sets provided with the software Use these to get an idea of how to run SaTScan To perform a test run 1 Click on the SaTScan application icon 2 Click on Open Saved Session 3 Select one of the parameter files for example nm prm Poisson model NHumberside prm Bernoulli model or NYCfever prm space time permutation model 4 Click on the Execute button A new window will open with the program running in the top section and a Warnings Errors section below When the program finishes running the results will be displayed Note The sample files should not produce warnings or errors Related Topics Sample Data Sets Sample Data Sets Six different sample data sets are provided with the software They are automatically downloaded to your compu
82. hman ST MacDougal L Danley R Mrosszczyk M Sorensen AM Kulldorff M Geographical surveillance of breast cancer screening by tracts towns and zip codes Journal of Public Health Management and Practice 6 48 57 2001 New York State Department of Health Cancer Surveillance Improvement Initiative 2001 online Gregorio DI Kulldorff M Barry L Samociuk H Zarfos K Geographic differences in primary therapy for early stage breast cancer Annals of Surgical Oncology 2001 8 844 849 2001 online Roche LM Skinner R Weinstein RB Use of a geographic information system to identify and characterize areas with high proportions of distant stage breast cancer Journal of Public Health Management and Practice 8 26 32 2002 Jemal A Kulldorff M Devesa SS Hayes RB Fraumeni JF A geographic analysis of prostate cancer mortality in the United States International Journal of Cancer 101 168 174 2002 Michelozzi P Capon A Kirchmayer U Forastiere F Biggeri A Barca A Perucci CA Adult and childhood leukemia near a high power radio station in Rome Italy American Journal of Epidemiology 155 1096 1103 2002 Zhan FB Lin H Geographic patterns of cancer mortality clusters in Texas 1990 to 1997 Texas Medicine 99 58 64 2003 Thomas AJ Carlin BP Late detection of breast and colorectal cancer in Minnesota counties an application of spatial smoothing and clustering Statistics in Medicine 22 113 127 2003 Buntinx F Geys H Lousbe
83. i AAT D XR tec ede abu I EGRE E E ES 22 Multivariate Scan with Multiple Data Sets sese 23 Comparison with Other Methods eee e esee eee ee eene tenentes thats tuse ta suse ta sene ta statu sees tosta sse ta an 24 Nur lE 24 Spatial and Space Time Clustering eese ene entren trennen rennen 24 I idirirec e 26 D ta Requirements eee ober poU TU RHET regt tae edes 26 erum 27 Control M S 27 Population File uice eren PIRA UD E 28 Coordinates Eie nre et ee e PR PU DR e RR HEN Ihre Pee te eerte egens 28 Sung EE 30 Neighbots File i casters kat cole RUE t ieee hws deii e Ie Bava 30 Max Citcle Size Filen onte babeo Benet ebur eet iban e 30 Adjustments File one ete e poit e eR PRU oi tete UR sey 31 SaT Scan Import Wizards isee ed ete ee ded rd be ated nc eyes 32 SaTScan ASCII File Format tet tenir steer epe ee testet ee rete ceva erbe taie 33 Basic SaTScan T 35 Iput Tab M 35 Analysis T b z oe teo ERU SR UU eR eco isti ite s 38 Output Tab e hee et ety ete Nia i e ret Ee a cand 41 Advanced Feature M 43 Multiple Data Sets Tab 5i Reap Ee eS RS 43 Data Checking Tab rechnen oU D InRERIPR DEDI Dae ess 44 Non E chd an Neighbors
84. ined by repeating the same analytic exercise on a large number of random replications of the data set generated under the null hypothesis The p value is obtained through Monte Carlo hypothesis testing by comparing the rank of the maximum likelihood from the real data set with the maximum likelihoods from the random data sets If this rank is R then p R 1 simulation In order for p to be a nice looking number the number of simulations is restricted to 999 or some other number ending in 999 such as 1999 9999 or 99999 That way it is always clear whether to reject or not reject the null hypothesis for typical cut off values such as 0 05 0 01 and 0 001 The SaTScan program scans for areas with high rates clusters for areas with low rates or simultaneously for areas with either high or low rates The latter should be used rather than running two separate tests for high and low rates respectively in order to make correct statistical inference The most common analysis is to scan for areas with high rates that is for clusters Non Compactness Penalty Function When the elliptic window shape is used there is an option to use a non compactness eccentricity penalty to favor more compact clusters The main reason for this is that the elliptic scan statistic will under the null hypothesis typically generate an elliptic most likely cluster since there are more elliptic than circular cluster evaluated At the same time the concept of clus
85. ional variables that you selected have been matched click on the Execute button to import the file This will create a temporary file in SaTScan ASCII file format 5 If the input file has headings that are exactly the same as the SaTScan variable names you can click on the Auto Align button to match these automatically When importing the case file the variables to match varies depending on the probability model used By selecting the probability model at the top of the w the import wizard will only display the variables relevant to that model Step 4 Saving the Imported File The imported file which is in SaTScan ASCII file format must be saved at least temporarily The default is to save it to the TEMP directory and after the analysis is completed you may erase the file You can also save it to some other directory of your choice and use it for future analyses without having to recreate it by using the Import Wizard again Related Topics Input Tab Case File Control File Population File Coordinates File Grid File Max Circle Size File Adjustments File SaTScan ASCII File Format As an alternative to using the SaTScan Import Wizard is it also possible to directly write the name of the input files in the text fields provided on the Input Tab or to browse the file directories for the desired input files using the button to the right of that box The files must then be in SaTScan file format which are space delimited ASCII files
86. istical Regression Software SaTScan cannot in itself do an adjustment for continuous covariates Such adjustments can still be done for the Poisson model 5 but it is a little more complex The first step is to calculate the covariate adjusted expected number of cases for each location ID and time using a standard statistical regression software package like SAS These expected numbers should then replace the raw population numbers in the population file while not including the covariates themselves The use of external regression software is also an excellent way to adjust for covariates in the exponential model The first step is to fit an exponential regression model without any spatial information in order to obtain risk estimates for each of the covariates The second step is to adjust the survival and censoring time up or down for each individual based on the risk estimates his or her covariates For the normal model covariates can be adjusted for by first performing a linear regression model using standard statistical software and then replacing the observed value with their residuals Related Topics Covariate Adjustments Covariate Adjustment Using the Input Files Covariate Adjustment Using Multiple Data Sets Exponential Model Methodological Papers Poisson Model Population File Covariate Adjustment Using Multiple Data Sets It is also possible to adjust for categorical covariates using multiple data sets The cases and c
87. istics Theory and Methods 26 1481 1496 1997 online 14 Kulldorff M Feuer EJ Miller BA Freedman LS Breast cancer in northeastern United States A geographical analysis American Journal of Epidemiology 146 161 170 1997 online 15 Kleinman K Abrams A Kulldorff M Platt R A model adjusted space time scan statistic with an application to syndromic surveillance Epidemiology and Infection 2005 133 409 419 16 Klassen A Kulldorff M Curriero F Geographical clustering of prostate cancer grade and stage at diagnosis before and after adjustment for risk factors International Journal of Health Geographics 2005 4 1 online 17 Huang L Kulldorff M Gregorio D A spatial scan statistic for survival data Biometrics 2006 in press online Adjusting for More Likely Clusters 18 Zhang Z Kulldorff M Assun o R Spatial scan statistics adjusted for multiple clusters Manuscript under review Computational Aspects Algorithms 19 Kulldorff M Spatial scan statistics Models calculations and applications In Balakrishnan and Glaz eds Recent Advances on Scan Statistics and Applications Boston USA Birkhauser 1999 online Random Number Generator 20 Lehmer DH Mathematical methods in large scale computing units In Proceedings of the second symposium on large scale digital computing machinery Cambridge USA Harvard Univ Press 1951 2 Park SK Miller KW Random number generators Good ones are hard
88. it is possible to define the scanning window as any time period that start within a predefined start range and ends within a predefined end range This option is only available when a retrospective purely temporal or a retrospective space time analysis is selected on the Analysis Tab Related Topics Temporal Window Tab Maximum Temporal Cluster Size Include Purely Spatial Clusters Study Period Time Aggregation SaTScan User Guide v7 0 49 Spatial and Temporal Adjustments Tab Advanced Analysis Features Spatial Window Temporal Window Space and Time Adjustments Inference Temporal Adjustment Close None C Nonparametric with time stratified randomization _Set Defauis Log linear trend with 5 X per year Log linear with automatically calculated trend Spatial Adjustments None C Nonparametric with spatial stratified randomization Temporal Spatial and or Space Time Adjustments Adjust for known relative risks Adjustments File CASaTScan User Guide 7 0 adjustments adj a4 Spatial and Temporal Adjustments Tab Dialog Box Covariates are adjusted for either by including them in the case and population files or by using multiple data sets depending on the probability model used The features on this tab are used to adjust for temporal spatial and space time trends and variation They are only available when using the Poisson probability model Related Topics Advanced Fea
89. justed with a relative risk of 2 2 4 SaTScan User Guide v7 0 31 Related Topics Adjustments with Known Relative Risk Missing Data Spatial and Temporal Adjustments Tab SaTScan Import Wizard SaTScan ASCII File Format SaTScan Import Wizard The SaTScan Import Wizard can be used to import dBase comma delimited or space delimited files It works for all import files except the optional Neighbors File Launch the Import Wizard by clicking on the File Import 49 button to the right of the text field for the file that you want to import Use the Next and Previous buttons to navigate between the dialogs Follow the steps below to import files Step 1 Selecting the Source File 1 3 At the bottom of the Select Source File dialog select the file type extension you are looking for If you are unsure select the All Files option Browse the folders and highlight the file you want to open It will appear in the File Name text field Click on Open The SaTScan Import Wizard will now appear Step 2 Specifying the File Format If you are importing a dBase file this step is automatically skipped For all other source files you need to specify the file structure using the File Format dialog box 1 5 First specify whether you have a character delimited or fixed column file format using the radio buttons under the Source File Type heading If there are extraneous lines in the beginning of the file type the number lines that
90. lthough it could be used for other continuous type data as well Each observation is a case and each case has one continuous variable attribute as well as a 0 1 censoring designation For survival data the continuous variable is the time between diagnosis and death or depending on the application between two other types of events If some of the data is censored due to loss of follow up the continuous variable is then instead the time between diagnosis and time of censoring The 0 1 censoring variable is used to distinguish between censored and non censored observations SaTScan User Guide v7 0 11 Example For the exponential model the data may consist of everyone diagnosed with prostate cancer during a ten year period with information about either the length of time from diagnosis until death or from diagnosis until a time of censoring after which survival is unknown When using the temporal or space time exponential model for survival times it is important to realize that there are two very different time variables involved The first is the time the case was diagnosed and that is the time that the temporal and space time scanning window is scanning over The second is the survival time that is time between diagnosis and death or for censored data the time between diagnosis and censoring This is an attribute of each case and there is no scanning done over this variable Rather we are interested in whether the scanning window includes exce
91. mal models When there are multiple data sets the maximum is defined as a percentage of the combined total population cases in all data sets It is also possible to specify the maximum circle size in terms of actual geographical size rather than population If latitude longitude coordinates are used then the maximum radius should be specified in kilometers If Cartesian coordinates are used the maximum radius should be specified in the same units as the Cartesian coordinates Alternatively for either probability model it is possible to specify a max circle size file to define the maximum circle size This file must contain a population for each location and the maximum circle size is then defined as a percentage of this population rather than the regular one This feature may be used when for example you want to define the circles in the Bernoulli or space time population models based on the actual population rather than the locations of cases and controls It may also be used if you want the geographical circles to include for example at most 10 counties out of a total of 100 irrespectively of the population in those counties This is accomplished by assigning a population of 1 to each county in the special max circle size file and then set the maximum circle size to be 10 of this population If a prospective space time analysis is performed adjusting for earlier analyses and if the max circle size is defined as a percentag
92. means that under the null hypothesis of complete spatial randomness there is a 5 chance that the p value for the most likely cluster will be smaller than 0 05 and a 95 chance that it will be bigger Under the null hypothesis there will always be some area with a rate higher than expected just by chance alone Hence even though the most likely cluster always has an excess rate when scanning for areas with high rates the p value may actually be very close or identical to one Recurrence Interval For prospective analyses the recurrence interval or null occurrence rate is shown as an alternative to the p value The measure reflects how often a cluster of the observed or larger likelihood will be observed by chance assuming that analyses are repeated on a regular basis with a periodicity equal to the specified time interval length For example if the observed p value is used as the cut off for a signal and if the recurrence interval is once in 14 months than the expected number of false signals in any 14 month period is one If no adjustments are made for earlier analysis then the recurrence interval is once in D p days where D is the number of days in each time interval If adjustments are made for a number of earlier analyses then the recurrence interval is once every D 1 1 p days SECONDARY CLUSTERS Summary information about other clusters detected in the data The information provided is the same as for the most likely clust
93. memory for the 32 bit windows version of SaTScan The Linux version of SaTScan can be used to analyze larger data sets Related Topics Coordinates File Grid File Spatial Temporal and Space Time Scan Statistics Spatial Window Tab Temporal Window Tab Monte Carlo Replications Multiple Data Sets Tab Warnings and Errors SaTScan User Guide v7 0 64 Results of Analysis As output SaTScan creates one standard text based results file in ASCII format and up to five different optional output files in column format that can be generated in either ASCII or dBase format Some of the optional files are useful when exporting output from SaTScan into other software such as a spreadsheet or a geographical information system Related Topics Output Tab Clusters Reported Tab Standard Results File Cluster Information File Location Information File Risk Estimates for Each Location Simulated Log Likelihood Ratios Analysis History File Standard Results File out The standard results file is automatically shown after the calculations are completed It is fairly self explanatory but for proper interpretation it is recommended to read either the section on the statistical method or even better one of the methodological papers listed in the bibliography SUMMARY OF DATA Use this to check that the input data files contain the correct number of cases locations etc Total population Poisson model This is the average population durin
94. mes in between those dates SaTScan will estimate the population through linear interpolation If all population counts have the same date the population is assumed to be constant over time Multiple Data Sets It is possible to specify multiple case files each representing a different data set with information about different diseases or about men versus women respectively For the Bernoulli model each case file must be accompanied with its own control file and for the Poisson model each case file must be accompanied with its own population file The maximum number of data sets that SaTScan can analyze is twelve Covariate Adjustments With the Poisson and space time permutation models it is possible to adjust for multiple categorical covariates by including them in the case and population files For the Bernoulli ordinal or exponential models covariates can be adjusted for using multiple data sets Related Topics nput Tab Multiple Data Sets Tab Case File Control File Population File Coordinates File Grid File SaTScan Import Wizard SaTScan ASCII File Format Covariate Adjustments SaTScan User Guide v7 0 26 Case File The case file provides information about cases It should contain the following information location id Any numerical value or string of characters Empty spaces may not form part of the id cases The number of cases for the specified location time and covariates time Optional May be specified in ye
95. morrhagic fever with renal syndrome in China BMC Infectious Diseases 6 77 2006 online Cancer 54 55 56 57 58 59 Hjalmars U Kulldorff M Gustafsson G Nagarwalla N Childhood leukemia in Sweden Using GIS and a spatial scan statistic for cluster detection Statistics in Medicine 15 707 715 1996 Kulldorff M Feuer EJ Miller BA Freedman LS Breast cancer in northeastern United States A geographical analysis American Journal of Epidemiology 146 161 170 1997 online Imai J Spatial disease clustering in Kochi prefecture in Japan National Institute of Public Health Epidemiology and Biostatistics Research 57 96 1998 in Japanese VanEenwyk J Bensley L McBride D Hoskins R Solet D McKeeman Brown A Topiwala H Richter A Clark R Addressing community health concerns around SeaTac Airport Second Report Washington State Department of Health 1999 online Hjalmars U Kulldorff M Wahlquist Y Lannering B Increased incidence rates but no space time clustering of childhood malignant brain tumors in Sweden Cancer 85 2077 2090 1999 Viel JF Arveux P Baverel J Cahn JY Soft tissue sarcoma and non Hodgkin s lymphoma clusters around a municipal solid waste incinerator with high dioxin emission levels American Journal of Epidemiology 152 13 19 2000 SaTScan User Guide v7 0 83 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 Sheehan TJ Gers
96. n of the hypothesized cluster For example a cluster around a toxic waste site in one country may spur an investigation about clusters around a similar toxic waste site in another country The spatial scan statistic or other cluster detection tests should then not be used as they will have low power due to the evaluation of all possible locations even though the hypothesized location is already known Examples of focused tests are Stone s Test 9 Lawson Waller s Score Test and Bithell s Test Focused tests should never be used when the foci were defined using the data itself This would lead to pre selection bias and the resulting p values would be incorrect It is then better to use the spatial scan statistic If on the other hand the point source was defined without looking at the data than it is better to use the focused test rather than the spatial scan statistic as the former will have higher power as it focuses on the location of interest In addition to various scan statistics the SaTScan software can also be used to do a focused test in order to evaluate whether there is a disease cluster around a pre determined focus ref 2 p809 This is done by using a grid file with only a single grid point reflecting the coordinates of the focus of interest Global Clustering Tests Most proposed tests for spatial clustering are tests for global clustering These include among many others the methods proposed by Alt and Vach 5 Besag
97. nates file The Poisson model also requires a population file while the Bernoulli model requires a control file Optional Files One may also specify an optional special grid file that contains geographical coordinates of the centroids defining the circles used by the scan statistic If such a file is not specified the coordinates in the coordinate file will be used for that purpose As part of the advanced features there is also an optional max circle size file and an optional adjustments file File Format The data input files must be in SaTScan ASCII file format or you may use the SaTScan import wizard for dBase comma delimited or space delimited files Using such files the wizard will automatically generate SaTScan file format files Both options are described below Spatial Resolution Separate data locations may be specified for individuals or data may be aggregated for states provinces counties parishes census tracts postal code areas school districts households etc Temporal Information To do a temporal or a space time analysis it is necessary to have a time related to each case and if the Bernoulli model is used for each control as well This time can be specified as a day month or year When the Poisson model is used the background denominator population is assumed to exist continuously over time although not necessarily at a constant level The population file requires a date to be specified for each population count For ti
98. nce we do not know the size of a cluster a priori and since the population at risk is geographically inhomogeneous Under the null hypothesis of equal disease risk one expects to see more disease cases in a city compared to a similar sized area in the country side just because of the higher population density in the city The scan statistics in the SaTScan software were developed to resolve these two problems Since no analytical solutions have been found to obtain the probabilities under these more complex settings Monte Carlo hypothesis testing is instead used to obtain the p values Spatial and Space Time Clustering Descriptive Cluster Detection Methods In 1987 Openshaw et al developed a Geographical Analysis Machine GAM that uses overlapping circles of different sizes in the same way as the spatial scan statistic except that the circle size does not vary continuously With the GAM a separate significance test is made for each circle leading to multiple testing and in almost any data set there will be a multitude of significant clusters when defined in this way This is because under the null hypothesis each circle has a 0 05 probability of being significant at the 0 05 level and with 20 000 circles we would expect 1 000 significant clusters under the null hypothesis of no clusters GAM is hence very useful for descriptive purposes but should not be used for hypothesis testing Another nice method for descriptive
99. nced Input Features Multiple Data Sets Data Checking Non Eucledian Neighbors Special Neighbor File Specify geographical neighbors through a user defined file l Non Euclidean Neighbors Tab Dialog Box Rather than using circles or ellipses defined by the Euclidean distances between the locations specified in the coordinates and grid files it is possible to manually specify a neighborhood matrix For each centroid its closest 2 closest 3 closest neighbors are specified in turn and so on This option is activated by checking the box on this tab and specifying the name of the neighbors file containing the SaTScan User Guide v7 0 45 neighbor matrix information The format of the neighbors file is described in the ASCII File format section Related Topics Advanced Features Input Tab Neighbors File ASCII File Format Spatial Window Tab Advanced Analysis Features Spatial Window Temporal Window Space and Time Adjustments Inference gt Maximum Spatial Cluster Size ercent of the population at risk lt 50 default 50 RO E e Set Defaults Iv 50 percent of the population defined in the max circle size file lt 502z E s WV isacircle with a 1 kilometer radius Include Purely Temporal Clusters Spatial Size 100 Spatial Window Shape Circular Spatial Window Tab Dialog Box Use the Spatial Window Tab to define the exact nature of the scanning window with respect to space
100. nd the maximum of the two is used to represent the log likelihood ratio for that window Related Topics Multiple Data Sets Tab Covariate Adjustment Covariate Adjustment Using the Input Files Covariate Adjustment using Statistical Regression Software Methodological Papers Bernoulli Model Spatial and Temporal Adjustments Adjusting for Temporal Trends If there is an increasing temporal trend in the data then the temporal and space time scan statistics will pick up that trend by assigning a cluster during the end of the study period If there is a decreasing trend it will instead pick up a cluster at the beginning of the time period Sometimes it is of interest to test whether there are temporal and or space time clusters after adjusting for a temporal trend For the space time permutation model the analysis is automatically adjusted for both temporal trends and temporal clusters and no further adjustments are needed For the Poisson model the user can specify whether a temporal adjustment should be made and if so whether to adjust with a percent change or non parametrically Sometimes the best way to adjust for a temporal trend is by specifying the percent yearly increase or decrease in the rate that is to be adjusted for This is a log linear adjustment Depending on the application one may adjust either for a trend that SaTScan estimates from the data being analyzed or from the trend as estimated from national or other similar dat
101. ne rabies vaccine associated adverse events using a very large veterinary practice database Vaccine epub ahead of print 2005 Gosselin PL Lebel G Rivest S Fradet MD The Integrated System for Public Health Monitoring of West Nile Virus ISPHM WNV a real time GIS for surveillance and decision making International Journal of Health Geographics 4 21 2005 online Gaudart J Poudiougou B Ranque S Doumbo O Oblique decision trees for spatial pattern detection optimal algorithm and application to malaria risk BMC Medical Research Methodology 5 22 2005 online Nisha V Gad SS Selvapandian D Suganya V Rajagopal V Suganti P Balraj V Devasundaram J Geographical information system GIS in investigation of an outbreak of dengue fever Journal of Communicable Diseases 37 39 43 2005 Jones RC Liberatore M Fernandez JR Gerber SI Use of a prospective space time scan statistic to prioritize shigellosis case investigations in an urban jurisdiction Public Health Reports 121 133 9 2006 Pearl DL Louie M Chui L Dore K Grimsrud KM Leedell D Martin SW Michel P Svenson LW McEwen SA The use of outbreak information in the interpretation of clustering of reported cases of Escherichia coli O157 in space and time in Alberta Canada 2000 2002 Epidemiology and Infection pud ahead of print 2006 Fang L Yan L Liang S de Vlas SJ Feng D Han X Zhao W Xu B Bian L Yang H Gong P Richardus JH Cao W Spatial analysis of he
102. ntensive than the analysis of a single data set Except for the ordinal model the computing time for two data sets is much more than twice the time for a single data set The computing time for s gt 2 data sets is approximately s 2 times longer than the computing time for two data sets Related Topics Coordinates File Grid File Spatial Window Tab Temporal Window Tab Monte Carlo Replications Early Termination of Simulations Multiple Data Sets Tab Memory Requirements SaTScan uses dynamic memory allocation Depending on the nature of the input data SaTScan will automatically choose one of two memory allocation schemes the standard one and a special one for data sets with very many spatial locations but few time intervals and few simulations Standard Memory Allocation Using the standard memory allocation scheme the amount of memory needed for large data sets is approximately 2xLxGxmg bo 4x CONT x P xLxTIx CATxD 8xCxRxP bytes if L lt 65 536 and 4xLxGxmg b 4x CONT x P x Lx TIx CATxD 8xCxRxP bytes if L265 536 where L the number of location IDs in the coordinates file G the number of coordinates in the grid file G L if no grid file is specified mg maximum geographical cluster size as a proportion of the population 0 lt mg mg 1 fora purely temporal analysis TI number of time intervals into which the temporal data is aggregated TI 1 for a purely spatial analysis b 12 for the Poisson space time p
103. o reflect a population size at risk rather than an actual number of people covariates Optional Any number of categorical covariates may be specified each represented by a different column separated by empty spaces May be specified numerically or through characters The covariates must be the same as in the case file Example If age and sex are the covariates included with 18 different age groups then there should be 18x2 36 rows for each year and census area With 3 different census years and 32 census areas the file will have a total of 3456 rows and 5 columns Note Multiple lines may be used for different population groups with the same location time and covariate attributes SaTScan will automatically add them Note For a purely temporal analysis with the Poisson model it is not necessary to specify a population file if the population is constant over time Related Topics nput Tab Population File Name Multiple Data Sets Tab Covariate Adjustment Using Input Files Max Circle Size File SaTScan Import Wizard SaTScan ASCII File Format Coordinates File The coordinates file provides the geographic coordinates for each location ID Each line of the file represents one geographical location Area based information may be aggregated and represented by one single geographical point location Coordinates may be specified either using the standard Cartesian coordinate system or in latitude and longitude If two different location I
104. o Test Analysis Tab Probability Model Comparison Methodological Papers Space Time Permutation Model The space time permutation model requires only case data with information about the spatial location and time for each case The number of observed cases in a cluster is compared to what would have been expected if the spatial and temporal locations of all cases were independent of each other so that there is no space time interaction Therefore we get a cluster in a geographical area if during a specific time period that area has a high proportion of excess cases or a smaller deficiency of cases than surrounding areas This means that if during a specific week all geographical areas have twice the number of cases than normal none of these areas constitute a cluster On the other hand if one geographical area has twice the number of cases while other areas have a normal amount of cases then there will be a cluster in that first area The space time permutation model automatically adjusts for both purely spatial and purely temporal clusters Hence there are no purely temporal or purely spatial versions of this model Example In the space time permutation model cases may be daily occurrences of ambulance dispatches to stroke patients It is important to realize that space time permutation clusters may be due either to an increased risk of disease or to different geographical population distribution at different times where for example the
105. on Input data Related Topics Spatial and Temporal Adjustment Tab Spatial and Temporal Adjustments Temporal Trend Adjustment Spatial Adjustment Adjustments File Poisson Model SaTScan User Guide v7 0 51 Inference Tab Advanced Analysis Features Spatial Window Temporal Window Space and Time Adjustments Inference Early Termination jw Terminate the analysis early for large p values Set Defaults Prospective Surveillance r Critical Values Iterative Scan r Inference Tab Dialog Box This tab is reached by clicking the Advanced button in the lower right corner of the Analysis Tab Related Topics Advanced Features Analysis Tab Early Termination of Simulations Adjust for Earlier Analyses in Prospective Surveillance Early Termination of Simulations With more Monte Carlo replications the power of the scan statistic is higher but it is also more time consuming to run When the p value is small this is often worth the effort but for large p values it is often irrelevant whether for example p 0 7535 or p 0 8545 SaTScan provides the option to terminate the simulations early when the p value is large With this option SaTScan will terminate after 99 simulations when p gt 0 5 at that time after 199 simulations when p gt 0 4 after 499 simulations when p gt 0 2 and after 999 simulations when p gt 0 1 If it passes all of these without terminating early it will run the full length with the number of Monte Carlo repli
106. one by generating simulated data from the normal distribution but rather by permuting the space time locations and the continuous attribute e g birth weight of the observations While still being formally valid the results can be greatly influenced by extreme outliers though so it may be wise to truncate such observations before doing the analysis Note If all values are multiplied with or added to the same constant the statistical inference will not change meaning that the same clusters with the same log likelihoods and p values will be found Only the estimated means and variances will differ Related Topics Likelihood Ratio Test Analysis Tab Probability Model Comparison Methodological Papers SaTScan User Guide v7 0 12 Probability Model Comparison For count data there are three different probability models available in SaTScan Poisson Bernoulli and space time permutation The ordinal model is designed for categorical data with an inherent ordering from for example low to high There are two models for continuous data Normal and Exponential The latter is primarily designed for survival type data The Poisson model is usually the fastest to run The ordinal model is typically the slowest With the Poisson and space time permutations models an unlimited number of covariates can be adjusted for by including them in the case and population files With the Bernoulli ordinal exponential and normal models covariates can be
107. ontrols population are then divided into categories and a separate data set is used for each category This type of covariate adjustment is computationally much slower than the one using the input files and is not recommended for large data sets One advantage is that it can be used to adjust the ordinal model for covariates for which other adjustment procedures are unavailable A disadvantage is that since the SaTScan User Guide v7 0 19 maximum number of data sets allowed by SaTScan is twelve the maximum number of covariate categories is also twelve The adjustment approach to multiple data sets is as follows when searching for clusters with high rates 1 For each window location and size the log likelihood ratio is calculated for each data set 2 The log likelihood ratio for all data sets with less than expected number of cases in the window is multiplied with negative one 3 The log likelihood ratios are then summed up and this sum is the combined log likelihood for that particular window 4 The maximum of all the combined log likelihood ratios taken over all the window locations and sizes constitutes the most likely cluster and this is evaluated in the same way as for a single data set When searching for clusters with low rates the same procedure is performed except that it is then the data sets with more than expected cases that we multiply by one When searching for both high and low clusters both sums are calculated a
108. order of Lx SG x mg x TIS x mt x MC P where L number of geographical data locations in the coordinates file L 1 for purely temporal analyses SG number of geographical coordinates in the special grid file If there is no such file SG L mg maximum geographical cluster size as a proportion of the population 0 lt mg mg 1 fora purely temporal analysis TI number of time intervals into which the temporal data is aggregated TI 1 for a purely spatial analysis mt maximum temporal cluster size as a proportion of the study period 0 mt 0 9 mt 1 for purely spatial analysis MC z number of Monte Carlo replications P number of processors available on the computer for SaTScan use k 0 for a purely spatial analysis k 1 for prospective temporal and prospective space time analyses without adjustments for earlier analyses k 2 for retrospective temporal and retrospective space time analyses The unit of the above formula depends on the probability model used and on the speed of the computer When the total number of cases is very large compared to the number of locations and time intervals the computing time is instead on the order of CxMC P Where C the total number of cases MC z number of Monte Carlo replications P number of processors available on the computer for SaTScan use SaTScan User Guide v7 0 61 Multiple Data Sets An analysis using multiple data sets is considerably more computer i
109. ormation SaTScan User Guide v7 0 30 location id Any numerical value or string of characters Empty spaces may not form part of the id population Any non negative number The name of the special max circle size file is specified on the Analysis Tab Advanced Features Spatial Window Tab Note If a location ID is missing from this file the population is assumed to be zero If a location ID occurs more than once the population numbers will be added Related Topics Input Tab Population File Spatial Window Tab SaTScan Import Wizard SaTScan ASCII File Format Adjustments File The adjustments file can be used to adjust a Poisson model analysis for any temporal spatial and space time anomalies in the data with a known relative risk It can for example be used to adjust for missing or partially missing data Note Covariates are adjusted for by using the case and population files or by analyzing multiple data sets not with this file The adjustments file should contain one or more lines for each location for which adjustments are warranted with the following information location id Any numerical value or string of characters Empty spaces may not form part of the id Alternatively it is possible to specify All in which all location will be adjusted with the same relative risk relative risk Any non negative number The relative risk representing how much more common disease is in this location and time period
110. ou do not want to see the warning messages they can be turned off by clicking Session gt Execute Options gt Do not report warning messages Error Messages If a serious problem occurs during the run an error message will be displayed in the Warnings Errors box on the bottom of the job status window and the job will be terminated The user may resolve most errors by reviewing the message and using the help system SaTScan User Guide v7 0 58 If the error message cannot be resolved you may press the email button on the job status window This will generate an automatic email message to SaTScan technical support The contents of the Warnings Errors box will be automatically placed in the e mail message All a user needs to do is press their e mail Send key Users may also print the contents of the Warnings Errors box and even select copy ctrl c and paste ctrl v the contents if necessary One of the most common errors is that the input files are not in the required format or that the file contents are incompatible with each other When this occurs an error message will be shown specifying the nature and location of the problem Such error messages are designed to help with data cleaning Related Topics nput Data Data Requirements SaTScan Support Saving Analysis Parameters Analysis parameters specified on the Parameter tab dialog can be saved and reused for future analyses It is recommended that you save the param
111. ournal 97 16 18 2004 Andrade AL Silva SA Martelli CM Oliveira RM Morais Neto OL Siqueira Junior JB Melo LK Di Fabio JL Population based surveillance of pediatric pneumonia use of spatial analysis in an urban area of Central Brazil Cadernos de Sa de P blica 20 411 421 2004 online Ozdenerol E Williams BL Kang SY Magsumbol MS Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters International Journal of Health Geographics 4 19 2005 online Viel JF Floret N Mauny F Spatial and space time scan statistics to detect low clusters of sex ratio Environmental and Ecological Statistics 12 289 299 2005 Ali M Asefaw T Byass P Beyene H Karup Pedersen F Helping northern Ethiopian communities reduce childhood mortality population based intervention trial Bulletin of the World Health Organization 83 27 33 2005 online SaTScan User Guide v7 0 86 Geriatrics 100 Yiannakoulias N Rowe BH Svenson LW Schopflocher DP Kelly K Voaklander DC Zones of prevention the geography of fall injuries in the elderly Social Science and Medicine 57 2065 73 2003 Parasitology 101 Enemark HL Ahrens P Juel CD Petersen E Petersen RF Andersen JS Lind P Thamsborg SM Molecular characterization of Danish Cryptosporidium parvum isolates Parasitology 125 331 341 2002 102 Washington CH Radday J Streit TG Boyd HA Beach MJ Addiss DG Lovince R Lovegrove MC Lafon
112. ower In SaTScan the number of replications must be at least 999 to ensure excellent power for all types of data sets For small to medium size data sets 9999 replications are recommended since computing time is not a major issue Related Topics Analysis Tab Likelihood Ratio Test Computational Speed Random Number Generator SaTScan User Guide v7 0 40 Output Tab Input Analysis Output Results File ess aTScanM ser Guide 7 0 results txt ptional Output Files mE ia iu e m Cluster Information Cluster Case Information r rc lv Location Information Risk Estimates for Each Location H i Simulated Log Likelihood Ratios T est Statistics Advanced Output Tab Dialog Box Use the Output Tab is used to set parameters defining the output information provided by SaTScan Related Topics Results of Analysis Standard Results File Results File Name Additional Output Files Clusters Reported Tab Results File Name Specify the output file name to which the results of the analysis are to be written This is the standard results file automatically shown after the completion of the calculations Four optional output files may also be created but must be opened manually by the user Warning If you specify the name of a file that already exits the old file will be overwritten and lost Related Topics Output Tab Additional Output Files Standard Results File SaTScan User Guide v7 0 41
113. parameters additional options are warranted for some types of analyses and these are available as advanced features These features are reached through the Advanced button on the lower right corner of each of the three main tabs Advanced should be interpreted as additional or uncommon rather than complex difficult or better Since many of the advanced options depend on the selections made on the Input and Analysis Tabs it is recommended that those two tabs be filled in first Related Topics Basic SaTScan Features Multiple Data Sets Tab Spatial Window Tab Temporal Window Tab Spatial and Temporal Adjustments Tab Inference Tab Clusters Reported Tab Multiple Data Sets Tab Advanced Input Features Multiple Data Sets Data Checking Non Eucledian Neighbors Additional Input Data Sets Case File za 3d Control File Bernoulli Madel Population File Poisson Model Sd Multiple Data Sets Tab Dialog Box It is possible to seach and evaluate clusters in multiple data sets as described in the Statistical Methodology section The first data set is defined on the Input Tab Up to eleven additional data sets can be defined on the Multiple Data Sets Tab These files must be of the same class as the first one That is if the first data set consists of a case and a control file so must all the others as well The time precision and study period must also be the same as on the Input Tab
114. ptionally many cases with a small or large value of this attribute It is important to note that while the exponential model uses a likelihood function based on the exponential distribution the true survival time distribution must not be exponential and the statistical inference p value is valid for other survival time distributions as well The reason for this is that the randomization is not done by generating observations from the exponential distribution but rather by permuting the space time locations and the survival time censoring attributes of the observations Related Topics Likelihood Ratio Test Analysis Tab Probability Model Comparison Methodological Papers Normal Model The normal model is designed for continuous data For each individual called a case there is a single continuous attribute that may be either negative or positive The model can also be used for ordinal data when there are very many categories That is ties are allowed Example For the normal model the data may consist of the birth weight and residential census tract for all newborns with an interest in finding clusters with lower birth weight It is important to note that while the normal model uses a likelihood function based on the normal distribution the true distribution of the continuous attribute must not be normal The statistical inference p value is valid for any continuous distribution The reason for this is that the randomization is not d
115. r a single retrospective analysis using historic data or for time periodic prospective surveillance where the analysis is repeated for example every day week month or year Related Topics Analysis Tab Spatial Window Tab Temporal Window Tab Temporal Scan Statistic The temporal scan statistic uses a window that moves in one dimension time defined in the same way as the height of the cylinder used by the space time scan statistic This means that it is flexible in both start and end date The maximum temporal length is specified on the Temporal Window Tab Related Topics Analysis Tab Temporal Window Tab Space Time Scan Statistic Likelihood Ratio Test For each location and size of the scanning window the alternative hypothesis is that there is an elevated risk within the window as compared to outside Under the Poisson assumption the likelihood function for a specific window is proportional to c C c aa a E c C E c where C is the total number of cases c is the observed number of cases within the window and E c is the covariate adjusted expected number of cases within the window under the null hypothesis Note that since the analysis is conditioned on the total number of cases observed C E c is the expected number of cases outside the window I is an indicator function When SaTScan is set to scan only for clusters with high rates I is equal to 1 when the window has more cases than expected under the null h
116. r each location is selected a file with a list of all data locations and the corresponding number of observed cases number of expected cases the observed expected ratio and the relative risk for each location is provided This may be useful when examining a cluster area in SaTScan User Guide v7 0 68 more detail The information is purely descriptive There is one line for each Location ID and the content of the five columns is as follows Location ID gt lt Observed Cases Expected Cases gt lt Observed Expected gt lt Relative Risk This file may be accessed using any text editor or spreadsheet program It will have the same name as the results file but with the extension rr txt or rr dbf and it will be located in the same directory Related Topics Output Tab Results of Analysis Standard Results File Simulated Log Likelihood Ratios File Ilr The log likelihood ratio test statistics from the random data sets are not provided as part of the standard output If desired they can be printed to a special file which by default has the same name as the output file but with the extension Ilr txt or llr dbf There is typically no need for this file but it can be useful for statistical researchers who may be interested in the distributional properties of the scan statistic under various scenarios Related Topics Output Tab Results of Analysis Standard Results File Monte Carlo Replications SaTScan User Guide v7 0 69
117. ral analysis ignores the geographical location of cases even when such information is provided Purely temporal and space time data can be analyzed in either retrospective or prospective fashion In a retrospective analysis the analysis is done only once for a fixed geographical region and a fixed study period SaTScan scans over multiple start dates and end dates evaluating both alive clusters lasting until the study period and date as well as historic clusters that ceased to exist before the study period end date The prospective option is used for the early detection of disease outbreaks when analyses are SaTScan User Guide v7 0 38 repeated every day week month or year Only alive clusters clusters that reach all the way to current time as defined by the study period end date are then searched for Related Topics Spatial Temporal and Space Time Scan Statistics Analysis Tab Methodological Papers Computing Time Spatial Window Tab Temporal Window Tab Time Aggregation Probability Model There are five different probability models that can be used Poisson Bernoulli space time permutation ordinal and exponential For purely spatial analyses the Poisson and Bernoulli models are good approximations for each other in many situations Temporal data are handled differently so the models differ more for temporal and space time analyses Poisson Model The Poisson model should be used when the background population refle
118. rgh D Broeders G Cloes E Dhollander D Op De Beeck L Vanden Brande J Van Waes A Molenberghs G Geographical differences in cancer incidence in the Belgian province of Limburg European Journal of Cancer 39 2058 72 2003 Santamaria Ulloa C Evaluaci n de alarmas por c ncer utilizando an lisis espacial una aplicaci n para Costa Rica Reivista Costarricense de Salud P blica 12 18 22 2003 online Sheehan TJ DeChello LM Kulldorff M Gregorio DI Gershman S Mroszezyk M The geographic distribution of breast cancer incidence in Massachusetts 1988 1997 adjusted for covariates International Journal of Health Geographics 2004 3 17 online Fang Z Kulldorff M Gregorio DI Brain cancer in the United States 1986 95 A geographic analysis Neuro Oncology 2004 6 179 187 Hsu CE Jacobson HE Soto Mas F Evaluating the disparity of female breast cancer mortality among racial groups a spatiotemporal analysis International Journal of Health Geographics 3 4 2004 online Han DW Rogerson PA Nie J Bonner MR Vena JE Vito D Muti P Trevisan M Edge SB Freudenheim JL Geographic clustering of residence in early life and subsequent risk of breast cancer United States Cancer Causes and Control 15 921 929 2004 Campo J Comber H Gavin A T All Ireland Cancer Statistics 1998 2000 Northern Ireland Cancer Registry National Cancer Registry 2004 online SaTScan User Guide v7 0 84 TX 76 77 78 79 8
119. rlton M Wymer C Craft AW A mark 1 analysis machine for the automated analysis of point data sets International Journal of Geographical Information Systems 1 335 358 1987 SaTScan User Guide v7 0 91 165 Ranta J Pitkniemi J Karvonen M et al Detection of overall space time clustering in non uniformly distributed population Statistics in Medicine 15 2561 2572 1996 166 Rushton G Lolonis P Exploratory Spatial Analysis of Birth Defect Rates in an Urban Population Statistics in Medicine 7 717 726 1996 167 Stone RA Investigation of excess environmental risk around putative sources statistical problems and a proposed test Statistics in Medicine 7 649 660 1988 168 Tango T A class of tests for detecting general and focused clustering of rare diseases Statistics in Medicine 14 2323 2334 1995 169 Tango T A test for spatial disease clustering adjusted for multiple testing Statistics in Medicine 19 191 204 2000 170 Turnbull B Iwano EJ Burnett WS et al Monitoring for clusters of disease application to Leukemia incidence in upstate New York American Journal of Epidemiology 132 8136 8143 1990 171 Waller LA Turnbull BW Clark LC Nasca P Chronic disease surveillance and testing of clustering of disease and exposure Environmetrics 3 281 300 1992 172 Walter SD A simple test for spatial pattern in regional health data Statistics in Medicine 13 1037 1044 1994 173 Whittemore AS Friend N
120. running different Monte Carlo simulations using different processors thereby increasing the speed of the calculations The default is that SaTScan will use all processors that the computer has If you want to restrict the number you can do that by clicking on Session Execute Options and selecting the maximum number of processors that SaTScan is allowed to use Batch Mode SaTScan is most easily run by clicking the Execute button at the top of the SaTScan window after filling out the various parameter fields in the Windows interface An alternative approach is to skip the windows interface and launch the SaTScan calculation engine directly by either 1 Dragging a parameter file onto the SaTScanBatch exe executable 2 Writing SaTScanBatch exe prm in a batch file or at the command prompt where prm is the name of the parameter file Using the batch mode version it is possible to write special software that incorporates the SaTScan calculation engine with other applications such as an automated daily surveillance system for the early detection of disease outbreaks To use SaTScan in this manner requires a reasonable amount of computer skills and sophistication When running SaTScan in batch mode the parameter file may still be changed using the SaTScan windows interface It is also possible to change the parameter manually using any text editor or automatically by using some other software product When the batch mode
121. s Environmental and Ecological Statistics 12 301 319 2005 Tango T Takahashi K A flexibly shaped spatial scan statistic for detecting clusters International Journal of Health Geographics 4 11 2005 online Kulldorff M Song C Gregorio D Samociuk H DeChello L Cancer map patterns Are they random or not American Journal of Preventive Medicine 30 837 49 2006 online Duczmal L Kulldorff M Huang L Evaluation of spatial scan statistics for irregular shaped clusters Journal of Computational and Graphical Statistics 15 428 442 2006 Aamodt G Samuelsen SO Skrondal A A simulation study of three methods for detecting disease clusters International Journal of Health Geographics 5 15 2006 online Related Topics SaTScan Bibliography Selected Applications by Field of Study Suggested Citation Selected SaTScan Applications by Field of Study Infectious Diseases 33 Cousens S Smith PG Ward H Everington D Knight RSG Zeidler M Stewart G Smith Bathgate EAB Macleod MA Mackenzie J Will RG Geographical distribution of variant Creutzfeldt Jakob disease in Great Britain 1994 2000 The Lancet 357 1002 1007 2001 SaTScan User Guide v7 0 81 34 35 36 37 38 39 40 41 42 43 44 45 46 Fevre EM Coleman PG Odiit M Magona JW Welburn SC Woolhouse MEJ The origins of a new Trypanosoma brucei rhodesiense sleeping sickness outbreak in eastern Uganda The Lancet 358 625 62
122. s in the ordinal model may be a sample from a larger population or they may constitute a complete set of observations Ordinal data can be analyzed with the purely temporal the purely spatial or the space time scan statistics Example For the ordinal model the data may consist of everyone diagnosed with breast cancer during a ten year period with three different categories representing early medium and late stage cancer at the time of diagnosis The ordinal model requires information about the location of each case in each category Separate locations may be specified for each case or the data may be aggregated for states provinces counties parishes census tracts postal code areas school districts households etc with multiple cases in the same or different categories at each data location To do a temporal or space time analysis it is necessary to have a time for each case as well With the ordinal model it is possible to search for high clusters with an excess of cases in the high valued categories for low clusters with an excess of cases in the low valued categories or simultaneously for both types of clusters Reversing the order of the categories has the same effect as changing the analysis from high to low and vice versa Related Topics Likelihood Ratio Test Analysis Tab Bernoulli Model Probability Model Comparison Methodological Papers Exponential Model The exponential model is designed for survival time data a
123. s in the specified units Example If interval units are years and the length is two then the time intervals will be two years long Note If the time interval length is not a fraction of the length of the whole study period the earliest time interval will be the remainder after the other intervals have received their proper length Hence the first time interval may be shorter than the specified length Important For prospective space time analyses the time interval must be equal to the length between the time periodic analyses performed So if the time period analyses are performed every week then the time interval should be set to 7 days Related Topics Analysis Tab Time Precision Study Period Computational Speed Monte Carlo Replications For hypothesis testing the SaTScan program generates a number of random replications of the data set under the null hypothesis The test statistic is then calculated for each random replication as well as for the real data set and if the latter is among the 5 percent highest then the test is significant at the 0 05 level This is called Monte Carlo hypothesis testing and was first proposed by Dwass Irrespective of the number of Monte Carlo replications chosen the hypothesis test is unbiased resulting in a correct significance level that is neither conservative nor liberal nor an estimate The number of replications does affect the power of the test with more replications giving slightly higher p
124. s several years but not if you only have one years worth of data Two more crude approaches to deal with missing data in the space time permutation model is to remove all data for a particular location if some data are missing for that location or to remove all data for a particular time period for dates on which there is missing data in any location The latter is especially useful in prospective surveillance for missing data during the beginning of the study period to avoid removing recent data that are the most important for the early detection of disease outbreaks SaTScan User Guide v7 0 22 Note When there are location time combinations with missing data either remove the whole row from the case file or assign zero cases to that location time combination If you only remove the number of cases but retain the location ID and time information there will be a file reading error Warning The adjustment for missing data only works if the locations and times for which the data is missing is independent of the number of cases in that location and time For example if data is missing for all locations with less than five observed cases the adjustment procedures described above will not work properly Related Topics Adjustments File Adjusting for Known Relative Risks Bernoulli Model Ordinal Model Poisson Model Space Time Permutation Model Spatial and Temporal Adjustments Tab Time Aggregation Multivariate Scan with Multiple Data Sets
125. set to be identical to the coordinates of the location IDs defined in the coordinates file The latter option ensures that each data location is a potential cluster in itself and it is the recommended option for most types of analyses As an alternative to the circle it is also possible to use an elliptic window shape in which case a set of ellipses with different shapes and angles are used as the scanning window together with the circle This provides slightly higher power for true clusters that are long and narrow in shape and slightly lower power for circular and other very compact clusters Related Topics Analysis Tab Coordinates File Elliptic Scanning Window Grid File Maximum Spatial Cluster Size Spatial Window Tab SaTScan User Guide v7 0 14 Space Time Scan Statistic The space time scan statistic is defined by a cylindrical window with a circular or elliptic geographic base and with height corresponding to time The base is defined exactly as for the purely spatial scan statistic while the height reflects the time period of potential clusters The cylindrical window is then moved in space and time so that for each possible geographical location and size it also visits each possible time period In effect we obtain an infinite number of overlapping cylinders of different size and shape jointly covering the entire study region where each cylinder reflects a possible cluster The space time scan statistic may be used for eithe
126. sis Tab Output Tab Advanced Features Input Tab Input Analysis Dutput Case File Control File Bernoulli Model Study Period Year Month Da Year Month Da Start Date 2000 m i End Date 2000 fiz EN Population File Poisson Model nee S Bd m a Coordinates 24 Cartesian mi a4 Latitude Longitude Advanced Coordinates File Grid File optional Input Tab Dialog Box The Input Tab is used to specify the names of the input data files as well as the nature of the data in these files If the files are in SaTScan ASCII file format they may be specified either by writing the name in the text box or by using the browse button EI If they are not in SaTScan ASCII file format they must amp be specified using the SaTScan import wizard by clicking on the File Import ad button Both the SaTScan ASCII file format and the SaTScan import wizard are described in the Input Data section Related Topics Basic SaTScan Features Input Data Multiple Data Sets Tab SaTScan User Guide v7 0 35 Case File Name Specify the name of the input file with case data This file is required for all analyses irrespectively of the probability model used Related Topics nput Tab Case File Control File Name Specify the name of the input file with control data This file is only used for analyses with the Bernoulli probability model Related Topics Input Tab Control File Time Precision Indicate
127. so of interest to evaluate secondary clusters after adjusting for other clusters in the data As an advanced option SaTScan is able to adjust the inference of secondary clusters for more likely clusters in the data This is done in an iterative manner In the first iteration SaTScan runs the standard analysis but only reports the most likely cluster That cluster is then removed from the data set including all cases and controls Bernoulli model in the cluster while the population Poisson model is set to zero for the locations and the time period defining the cluster In a second iteration a completely new analysis is conducted using the remaining data This procedure is then repeated until there are no more clusters with a p value less than a user specified maxima or until a user specified maximum number of iterations have been completed whichever comes first For purely spatial analyses it has been shown that the resulting p values for secondary clusters are quite accurate and at most marginally biased Related Topics Clusters Reported Tab Criteria for Reporting Secondary Clusters Iterative Scan Likelihood Ratio Test Secondary Clusters Standard Results File Covariate Adjustments A covariate should be adjusted for when all three of the following are true e The covariate is related to the disease in question SaTScan User Guide v7 0 17 e The covariate is not randomly distributed geographically e You want to find clusters tha
128. stry Aggregation 191 Postal Codes most with only a single individual Precision of case and control times None Coordinates Cartesian Covariates None Data source Drs Ray Cartwright and Freda Alexander Published by J Cuzick and R Edwards Journal of the Royal Statistical society B 52 73 104 1990 Space Time Permutation Model Hospital Emergency Room Admissions Due to Fever at New York City Hospitals Case file NYCfever cas Format lt zip gt lt cases 1 gt lt date gt Coordinates file NYCfever geo Format lt zip gt lt latitude gt lt longitude gt SaTScan User Guide v7 0 6 Study period Nov 1 2001 Nov 24 2001 Aggregation Zip code areas Precision of case times Days Coordinates Latitude Longitude Covariates None Data source New York City Department of Health Ordinal Model Purely Spatial Education Attainment Levels in Maryland Case file MarylandEducation cas Format county lt individuals category gt Coordinates file MarylandEducation geo Format county latitude longitude Study period 2000 Aggregation 24 Counties and County Equivalents Precision of case times None Coordinates Latitude Longitude Covariates None Categories 1 Less than 9 grade 2 9 to 12 grade but no high school diploma 3 High school diploma but no bachelor degree 4 Bachelor or higher degree Data source United States Census Bureau Information about education comes from the long Cen
129. sus 2000 form filled in by about 1 6 households Note Only people age 25 and above are included in the data For each county the census provides information about the percent of people with different levels of formal education The number of individuals reporting different education levels in each county was estimated as this percentage times the total population age 25 divided by six to reflect the 1 6 sampling fraction for the long census form Exponential Model Space Time Artificially Created Survival Data Case file SurvivalFake cas Format lt location id gt lt individuals gt lt time of diagnosis gt lt survival time gt lt censored gt Coordinates file SurvivalFake geo Format lt location id gt lt x coordinate gt lt y coordinate gt Study period 2000 2005 Aggregation 5 Locations Precision of times of diagnosis Year Precision of survival censoring times Day SaTScan User Guide v7 0 7 Coordinates Cartesian Covariates None Data source Artificially created data Normal Model Purely Spatial Artificially Created Continuous Data Case file NormalFake cas Format location id lt individuals weight increase Coordinates file NormalFake geo Format location id lt x coordinate gt lt y coordinate gt Study period 2006 Aggregation 26 Locations Coordinates Cartesian Covariates None Data source Artificially created data Related Topics Test Run Input Data SaTScan User Gui
130. t cannot be explained by that covariate Here are three examples e If you are studying cancer mortality in the United States you should adjust for age since i older people are more likely to die from cancer ii some areas such as Florida have a higher percent older people and iii you are presumably interested in finding areas where the risk of cancer is high as opposed to areas with an older population e f you are interested in the geographical distribution of birth defects you can but do not need to adjust for gender While birth defects are not equally likely in boys and girls the geographical distribution of the two genders is geographically random at time of birth e If you are studying the geography of lung cancer incidence you should adjust for smoking if you are interested in finding clusters due to non smoking related risk factors but you should not adjust for smoking if you are interested in finding clusters reflecting areas with especially urgent needs to launch an anti smoking campaign When the disease rate varies for example with age and the age distribution varies in different areas then there is geographical clustering of the disease simply due to the age covariate When adjusting for categorical covariates the SaTScan program will search for clusters above and beyond that which is expected due to these covariates When more than one covariate is specified each one is adjusted for as well as all the interaction terms
131. t different times and they are described in different scientific publications The following bibliography contains selected papers and reports intended to help you find information on the following 1 Find the methodological paper s in which the various analysis options are presented and discussed in more detail than what is available here in the SaTScan User Guide 2 Find applications in different scientific areas 3 Determine the relevant scientific papers to cite Suggested Citations The SaTScan software may be used freely with the requirement that proper references are provided to the scientific papers describing the statistical methods For the most common analyses the suggested citations are Bernoulli and Poisson Models Kulldorff M A spatial scan statistic Communications in Statistics Theory and Methods 26 1481 1496 1997 online Space Time Permutation Model Kulldorff M Heffernan R Hartman J Assun o RM Mostashari F A space time permutation scan statistic for the early detection of disease outbreaks PLoS Medicine 2 216 224 2005 online Ordinal Model Jung I Kulldorff M Klassen A A spatial scan statistic for ordinal data Manuscript 2005 online Exponential Model Huang L Kulldorff M Gregorio D A spatial scan statistic for survival data Manuscript 2005 online Normal Model Manuscript in preparation Until available please cite this User Guide Software Kulldorff M and Information M
132. t exceptional since the City has about 3 percent of the U S population If we accept that there is a cluster in Seattle though and if we adjust for that by removing Seattle from the analysis then 30 cases in the City out of 30 nationwide is statistically significant This is similar to a regular multiple regression where if we adjust for one variable another variable may suddenly become statistically significant Note that SaTScan User Guide v7 0 76 the opposite is also true If we remove an area with significantly fewer cases than expected than a significant cluster with an excess number of cases may become non significant 20 For count data the spatial scan statistic uses a particular alternative hypothesis with an excess risk in a circular cluster where the number of cases follows a Poisson or Bernoulli distribution Does this mean that it can only be used to detect such alternative hypotheses Many proposed and widely used test statistics do not specify an alternative hypothesis at all This neither means that they cannot be used for any alternative hypotheses nor that they are good for all alternatives Likewise if an explicit alternative is defined as with the spatial scan statistic that does not mean that it cannot be used for other alternative hypotheses as well It is simply a question of the test statistic having good power for some alternative hypotheses and low power for other The advantage of having a well specified alternativ
133. tant JG Lammie PJ Hightower AW Spatial clustering of filarial transmission before and after a Mass Drug Administration in a setting of low infection prevalence Filaria Journal 3 3 2004 online 103 Odoi A Martin SW Michel P Middleton D Holt J Wilson J Investigation of clusters of giardiasis using GIS and a spatial scan statistic International Journal of Health Geographics 3 11 2004 online 104 Reperant LA Deplazes P Cluster of Capillaria hepatica infections in non commensal rodents from the canton of Geneva Switzerland Parasitology Research 96 340 342 2005 Alcohol and Drugs 105 Hanson CE Wieczorek WF Alcohol mortality a comparison of spatial clustering methods Social Science and Medicine 55 791 802 2002 Accidents 106 Nkhoma ET Hsu CE Hunt VI Harris AM Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties U S 1980 2001 International Journal of Health Geographics 3 25 2004 online Syndromic Surveillance 107 Heffernan R Mostashari F Das D Karpati A Kulldorff M Weiss D Syndromic surveillance in public health practice The New York City emergency department system Emerging Infectious Diseases 10 858 864 2004 online 108 Minnesota Department of Health Syndromic Surveillance A New Tool to Detect Disease Outbreaks Disease Control Newsletter 32 16 17 2004 online 109 Kleinman K Abrams A Kulldorff M Platt R A model adjusted space tim
134. tead provided in the Cluster Cases Information File The exact columns shown depend on the chosen analysis as shown in Table 3 The file will have the same name as the standard results file but with the extensions col txt and col dbf respectively and will be located in the same directory SaTScan User Guide v7 0 66 s 5 z 5 E 2 Ee EA Bernoulli Circular Cartesian One Data Set Bernoulli Circular Lat Long Mulitple Data Sets Poisson Bernoulli Circular Cartesian 5 Dimensions ace T ime Permutation Circular Lat Long Output Variable 2 Laiude ATTUDE 3 Xcoordinate K O e Yoona eooo eaa SEO i 1 51 1 um ECCO ES Circie Radius mous 5 5 8 5 5 T IE MNOR Ea Exponential Circular Lat Long One Data Set Poisson Bernoulli Elliptic Cartesian One Data Set Ordinal Circular Lat Long One Data Set Normal Circular Lat Long One Data Set Sp acento em Bus TIERE HERR HET HE HEEERRH EHE ES s HESSE Ellipse Length of Minor Axis Ellipse Length of Major Axis Ellipse Angle Ex 1 3 EI EN MEESESERERESRREREREN ClusterStatDate STARTDATE 8 8 9 815 9 51 5 15 RM Oae Lxx owe AFER ER E Location IDs numee Loc e a 8 8 8 8 5 Log Likelihood Ratio LR amp amp 9 9 12 jej t2 e 98 e Test Statistic TEST_STAT 9 13 P ValueofCluster
135. ted Under the null hypothesis and when there are no covariates the expected number of cases in each area is proportional to its population size or to the person years in that area Poisson data can be analyzed with the purely temporal the purely spatial or the space time scan statistic Example For the Poisson model cases may be stroke occurrences while the population is the combined number of person years lived calculated as 1 for someone living in the area for the whole time period and 4 for someone dying or moving away in the middle of the time period The Poisson model requires case and population counts for a set of data locations such as counties parishes census tracts or zip code areas as well as the geographical coordinates for each of those locations The population data need not be specified continuously over time but only at one or more specific census times For times in between SaTScan does a linear interpolation based on the population at the census times immediately proceeding and immediately following For times before the first census time the population size is set equal to the population size at that first census time and for times after the last census time the population is set equal to the population size at that last census time To get the population size for a given location and time period the population size as defined above is integrated over the time period in question Related Topics Likelihood Rati
136. ted with biased p values that are too small providing statistically significant results when none exist Here the null hypothesis should be that there is spatial auto correlation and the alternative hypothesis that there are geographical differences in the risk of food poisoning On the other hand if we are interested in quickly detecting food poisoning outbreaks we should not adjust for the spatial auto correlation since we are interested in detecting clusters due to such correlation and if they are adjusted away important clusters may go undetected Here the null hypothesis is that the food poisoning cases are geographically randomly distributed adjusted for population density etc and the alternative hypothesis is that there is some clustering either due to differences in underlying risk factors or spatial auto correlation Once the location of a cluster has been detected it is for the local health officials to determine the source of the cluster to prevent further illness 19 If there are multiple clusters in the data does that mean that the p values are more likely to be significant than their 0 05 nominal significance level suggests so that chance clusters are detected too often No The opposite is actually true Looking at United States mortality suppose we have 1000 cases of a disease in Seattle and 30 in New York City Seattle is clearly a significant cluster but 30 cases in New York City out of 1030 in all of the USA is no
137. ter together with the software itself These and other sample data sets are also available at http www satscan org datasets Poisson Model Space Time Brain Cancer Incidence in New Mexico Case file nm cas Format lt county gt lt cases 1 gt lt year gt lt age group gt lt sex gt Population file nm pop Format lt county gt lt year gt lt population gt lt age group gt lt sex gt Coordinates file nm geo SaTScan User Guide v7 0 5 Format county x coordinate y coordinate Study period 1973 1991 Aggregation 32 counties Precision of case times Years Coordinates Cartesian Covariate 1 age groups 0 4 years 2 5 9 years 18 85 years Covariate 2 gender 1 male 2 female Population years 1973 1982 1991 Data source New Mexico SEER Tumor Registry This is a condensed version of a more complete data set with the population given for each year from 1973 to 1991 and with ethnicity as a third covariate The complete data set can be found at http www satscan org datasets Bernoulli Model Purely Spatial Childhood Leukemia and Lymphoma Incidence in North Humberside Case file NHumberside cas Format lt location id gt lt cases gt Control file Nhumberside ctl Format lt location id gt lt controls gt Coordinates file Nhumberside geo Format lt location id gt lt x coordinate gt lt y coordinate gt Study period 1974 1986 Controls Randomly selected from the birth regi
138. tering is based on a compactness criterion in the sense that the cases in the cluster should be close to each other so that we may be more interested in more compact clusters When the non compactness penalty is used the pure likelihood ratio is no longer used as the test statistic Rather the test statistic is defined as the log likelihood ratio multiplied with a non compactness penalty of the form 4s s 1 where s is the elliptic window shape defined as the ratio of the length of the longest to the shortest axis of the ellipse For the circle s The parameter a is a penalty tuning parameter With a 0 the penalty function is always 1 irrespectively of s so that there is never a penalty When a goes to infinity the penalty function goes to 0 for all s gt 1 so that only circular clusters are allowed Other than this there is no clear intuitive meaning of the penalty tuning parameter a In SaTScan it is possible to use either a strong penalty a 1 or a medium size penalty a 1 2 Related Topics Batch Mode Bernoulli Model Covariate Adjustments Elliptic Scanning Window Exponential Model Monte Carlo Replications Ordinal Model Poisson Model Secondary Clusters Space Time Permutation Model Standard Results File SaTScan User Guide v7 0 16 Secondary Clusters For purely spatial and space time analyses SaTScan also identifies secondary clusters in the data set in addition to the most likely cluster and orders them according
139. tion model automatically adjusts for purely spatial and purely temporal variation there is no need to adjust for covariates in order to account for different spatial or temporal densities of these covariates For example there is no need to adjust for age simply because some places have a higher proportion of old people than other Rather covariate adjustment is used if there is space time interaction due to this covariate rather than to the underlying disease process For example if children get sick mostly in the summer and adults mostly in the winter then there will be age generated space time interaction clusters in areas with many children in the summer and vice versa When including child adult as a covariate these clusters are adjusted away Note Too many covariate categories can create problems For the space time permutation model the adjustment is made at the randomization stage so that each covariate category is randomized independently If there are too many covariate categories so that all or most cases in a category belong to the same spatial location or the same aggregated time interval then there is very little to randomize and the test becomes meaningless Related Topics Covariate Adjustments Covariate Adjustment using Statistical Regression Software Covariate Adjustment Using Multiple Data Sets Methodological Papers Poisson Model Space Time Permutation Model Case File Population File Covariate Adjustment Using Stat
140. to their likelihood ratio test statistic There will almost always be a secondary cluster that is almost identical with the most likely cluster and that have almost as high likelihood value since expanding or reducing the cluster size only marginally will not change the likelihood very much Most clusters of this type provide little additional information but their existence means that while it is possible to pinpoint the general location of a cluster its exact boundaries must remain uncertain There may also be secondary clusters that do not overlap with the most likely cluster and they may be a great interest The user must decide to what extent overlapping clusters are reported in the results files The default is that the geographically overlapping clusters are not reported For purely temporal analyses only the most likely cluster is reported Related Topics Adjusting for More Likely Clusters Likelihood Ratio Test Clusters Reported Tab Criteria for Reporting Secondary Clusters Standard Results File Adjusting for More Likely Clusters When there are multiple clusters in the data set the secondary clusters are evaluated as if there were no other clusters in the data set That is they are statistically significant if and only if they are able to cause a rejection of the null hypothesis on their own strength whether or not the other clusters are true clusters or not That is often the desired type of inference Sometime though it is al
141. ts away all such clusters to see if there are any space time clusters not explained by purely spatial clusters This is done in a non parametric fashion through stratified randomization by location so that the total number of cases in each specific location is the same in the real and random data sets That is only the time of a case is randomized The default is no spatial adjustment Note It is not possible to simultaneously adjust for spatial clusters and purely temporal clusters using stratified randomization If both types of adjustments are desired the space time permutation model should be used instead It is possible to adjust for purely spatial clusters with stratified randomization together with a temporal adjustment using a log linear trend Related Topics Spatial and Temporal Adjustment Tab Spatial and Temporal Adjustments Temporal Trend Adjustment Adjustment with Known Relative Risk Poisson Model Adjustment with Known Relative Risks The most flexible way to adjust a Poisson model analysis is to use the special adjustments file In this file a relative risk is specified for any location and time period combination and SaTScan will adjust the expected counts up or down based on this relative risk One use of this option is to adjust for missing data by specifying a zero relative risk for those location and time combinations for which data is missing The required format of the Adjustments File is described in the section
142. tures Analysis Tab Spatial and Temporal Adjustments Temporal Trend Adjustment Spatial Adjustment Adjustment with Known Relative Risk Poisson Model Temporal Trend Adjustment Temporal trends can be adjusted for in three different ways Non parametric When the adjustment is non parametric SaTScan adjusts for any type of purely temporal variation This is done by stratifying the randomization by the aggregated time intervals so that each time interval has the same number of cases in the real and random data sets That is it is only the spatial location of a case that is randomized SaTScan User Guide v7 0 50 Log linear trend specified by user Specify an annual percent increase or decrease in the risk A decreasing trend is specified with a negative number For example if the rate decreases by 1 4 percent per year then write 1 4 in the 46 per year box Log linear trend automatically calculated Rather than the user specifying the adjusted relative risk SaTScan can calculate the observed trend in the data and then adjust for exactly that amount of increase or decrease The default is no temporal trend adjustment Related Topics Spatial and Temporal Adjustment Tab Spatial and Temporal Adjustments Spatial Adjustment Adjustment with Known Relative Risk Poisson Model Spatial Adjustment When a purely spatial analysis is performed the purpose is to find purely spatial clusters For space time analyses this feature adjus
143. vations may be either positive or negative Related Topics Analysis Tab Bernoulli Model Exponential Model Methodological Papers Ordinal Model Poisson Model Probability Model Comparison Space Time Permutation Model Scan for High or Low Rates It is possible to scan for areas with high rates only clusters for areas with low rates only or simultaneously for areas with either high or low rates The most common analysis is to scan for areas with high rates only that is for clusters For the exponential model high corresponds to short survival For the ordinal and normal models high corresponds to large value categories observations Related Topics Analysis Tab Likelihood Ratio Test Methodological Papers SaTScan User Guide v7 0 39 Time Aggregation Space time analyses are sometimes very computer intensive To reduce the computing time case times may be aggregated into time intervals Another reason for doing so is to adjust for cyclic temporal trends For example when using intervals of one year the analysis will automatically be adjusted for seasonal variability in the counts and when using time intervals of 7 days it will automatically adjust for weekday effects Units The units in which the length of the time intervals are specified This can be in years months or days The units of the time intervals cannot be more precise than the time precision specified on the input tab Length The length of the time interval
144. ve memory allocation scheme to reduce the total memory requirement This selection is done automatically The amount of memory needed for large data sets is then approximately 4xLxTIx CAT x EXP xD x MC 8xMC xC x R xP bytes where MC is the number of Monte Carlo simulations and the other variables are defined as above Insufficient Memory If there is insufficient memory available on the to run the analysis using either memory allocation scheme there are several options available for working around the limitation e Close other applications e Aggregate the data into fewer data locations reduce L e Decrease the number of circle centroids in the special grid file reduce G e Reduce the upper limit on the circle size reduce mg e Run the program on a computer with more memory It is highly desirable that there is sufficient RAM to cover all the memory needs as SaTScan runs considerable slower when the swap file is used so these technciques may also be used to avoid the swap file Not all of these above options will work for all data sets Please note that the following SaTScan options do not influence the demand on memory SaTScan User Guide v7 0 63 e The length of the study period e The maximum temporal cluster size e Type of space time clusters to include in the analysis Note The 32 bit windows operating system can allocate a maximum of 2 GBytes of memory to a single application and that is hence the upper limit on the
145. version of SaTScan is run the standard results file does not automatically pop up on the screen but must be opened manually using any available text editor such as Notepad Opportunity There are some parameter options that are not allowed when SaTScan is run under the windows interface but which can be set when run in batch mode A few such examples are the number of Monte Carlo replications the use ellipses rather than circles and an unlimited number of multiple data sets Parameter options not allowed by the windows interface have not all been thoroughly tested though so there is some risk involved when running such analyses Related Topics Launching the Analysis Basic SaTScan Features Advanced Features Saving Analysis Parameters SaTScan User Guide v7 0 60 Computing Time The spatial and space time scan statistics are computer intensive to calculate The computing time depends on a wide variety of variables and depending on the data set and the analytical options chosen it could range from a few seconds to several days or weeks The ordinal model is in general much more computer intensive than the other probability models Other than that the three main things that increase the computing time is the number of locations in the coordinates and special grid files the number of time intervals for space time analyses and the number of data sets used Single Data Set For a single data set the computing time is approximately on the
146. with one row for each location covariate combination and with columns as defined below Such files can be created using any text editor and most spreadsheets The order of the columns in the file is very important but the rows can be in any order The optional variables defined above are optional columns in the SaTScan file format Case File Format cas location id lt cases gt time lt attribute 1 gt lt attribute N gt The number of attributes and their meaning depends on the probability model as shown in Table 1 Probability Model attribute 1 attribute 2 attribute N Poisson covariate covariate 2 covariate N Bernoulli not used not used not used Space Time Permutation covariate covariate 2 covariate N Ordinal category not used not used SaTScan User Guide v7 0 33 Exponential survival time censored not used Normal continuous variable not used not used Table 1 Attributes used for different probability models Control File Format ctl location ID lt controls gt time Population File Format pop location ID time population lt covariate 1 gt lt covariate N gt Coordinates File Formats geo location ID latitude longitude OR location ID x coordinate y coordinate lt z1 coordinate gt lt zN coordinate gt Special Grid File Formats grd latitude longitude OR x coordinate lt y coordinate gt z1 coordinate lt z
147. you would like to ignore in the text field in the upper right corner If you have a character delimited file use the scrolling menus to select the field separator to be either a comma a semicolon or white space If you have a fixed column file define the fields using the Field Information box For each field type the name the start column and the length maximum number of characters into the appropriate spaces Click on the Add button to add another field The information will appear in the panel on the right Continue adding fields until you have the appropriate number To change the information in the right panel highlight the line you want to change The information will appear in the Field Information box Edit the information and click on the Update button when you are done The updated information will appear in the right panel Click on Next to proceed to the next dialog box Step 3 Matching Source File Variables with SaTScan Variables The top grid in this dialog box links the SaTScan variables with the input file variables from the source file The bottom grid displays sample data from the chosen input file SaTScan User Guide v7 0 32 1 If there are headers in your file click the checkbox in the lower left corner 2 To match the variables click on one of the places where it says unassigned 3 Select the appropriate variable form the input file to go with the chosen SaTScan variable 4 When all the required and opt
148. ypothesis and 0 otherwise The opposite is true when SaTScan is set to scan only for clusters with low rates When the program scans for clusters with either high or low rates then I 21 for all windows The space time permutation model uses the same function as the Poisson model Due to the conditioning on the marginals the observed number of cases is only approximately Poisson distributed Hence it is no longer a formal likelihood ratio test but it serves the same purpose as the test statistic For the Bernoulli model the likelihood function is SaTScan User Guide v7 0 15 Gt e C c e n C 2 10 n n N n N n where c and C are defined as above n is the total number of cases and controls within the window while N is the combined total number of cases and controls in the data set The likelihood function for the ordinal exponential and normal models are more complex due to the more complex nature of the case data We refer to papers by Jung Kulldorff and Klassen Huang Kulldorff and Gregorio and Kulldorff et al for the likelihood functions for these models The likelihood function is maximized over all window locations and sizes and the one with the maximum likelihood constitutes the most likely cluster This is the cluster that is least likely to have occurred by chance The likelihood ratio for this window constitutes the maximum likelihood ratio test statistic Its distribution under the null hypothesis is obta
149. yses and if the max circle size is defined as a percentage of the population then the special max circle size file must be used Related Topics Inference Tab Computing Time Type of Analysis Spatial Temporal and Space Time Scan Statistics Iterative Scan The iterative scan option is used to adjust the p values of secondary clusters for more likely clusters that are found and reported This is done by doing the analysis in several iteration removing the most likely cluster found in each iteration and then reanalyzing the remaining data The user must specify the maximum number of iterations allowed in the range 1 32000 The user may also request that the iterations stop when the cluster has a p value greater than a specified lower bound In terms of computing time each iteration takes approximately the same amount of time as a regular analysis with the same parameters Related Topics Adjusting for More Likely Clusters Inference Tab Computing Time SaTScan User Guide v7 0 53 Clusters Reported Tab Advanced Output Features Clusters Reported Criteria for Reporting Secondary Clusters No Geographical Overlap C No Cluster Centers in Other Clusters Set Defaults C No Cluster Centers in More Likely Clusters No Cluster Centers in Less Likely Clusters C No Pairs of Centers Both in Each Others Clusters C No Restrictions Most Likely Cluster for Each Grid Point Maximum Reported Spatial Cluster Size jw Report only clust

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