From healthmaps@home.com Fri Jul 13 12:49:07 2001 Received: from mxu4.u.washington.edu (mxu4.u.washington.edu [140.142.33.8]) by lists.u.washington.edu (8.11.2+UW01.01/8.11.2+UW01.04) with ESMTP id f6DJn4061250 for ; Fri, 13 Jul 2001 12:49:04 -0700 Received: from femail13.sdc1.sfba.home.com (femail13.sdc1.sfba.home.com [24.0.95.140]) by mxu4.u.washington.edu (8.11.2+UW01.01/8.11.2+UW01.04) with ESMTP id f6DJn4s05565 for ; Fri, 13 Jul 2001 12:49:04 -0700 Received: from c501552d ([24.19.225.248]) by femail13.sdc1.sfba.home.com (InterMail vM.4.01.03.20 201-229-121-120-20010223) with SMTP id <20010713194900.FSVR27910.femail13.sdc1.sfba.home.com@c501552d> for ; Fri, 13 Jul 2001 12:49:00 -0700 From: "Richard Hoskins" To: Subject: RE: Dengue & population Date: Fri, 13 Jul 2001 12:49:00 -0700 Message-ID: MIME-Version: 1.0 Content-Type: text/plain; charset="US-ASCII" Content-Transfer-Encoding: 7bit X-Priority: 3 (Normal) X-MSMail-Priority: Normal X-Mailer: Microsoft Outlook IMO, Build 9.0.2416 (9.0.2910.0) X-MimeOLE: Produced By Microsoft MimeOLE V5.50.4522.1200 In-Reply-To: Basil: I think one very effective way to get the information you need is to use a spatial scan statistic approach. Your data is made for it. You do not need to test the hypothesis of E vs W, and of course likely there are more cases where there are more people - I suspect the mossies like areas where there is lots of food(people). I think you need to know where the rates are higher than anywhere else, if the idea is to deal with those areas first. Also it can tell you where areas are lower than expected and give an easy to understand way to determine if the rates are really elevated or not. The spatial scan statistic can be easily calculated. The background is http://www.sph.umich.edu/~lestberg/GeoMed/Scan/ScMain.htm http://sun2539.sph.umich.edu:2000/geomed/stats/kullscan/scan.html http://sun2539.sph.umich.edu:2000/geomed/stathelp/advisor.html http://dcp.nci.nih.gov/bb/SaTScan.html has free software and there is a commercial product now which does cluster calculations http://www.terraseer.com/clusterseer.html which has a whole lot of cluster tests bundled in one place. A link directly to ArcView http://www.phrl.org/REGS/Order.htm Dick Hoskins WA State Dept of Health -----Original Message----- From: WAPHGIS-owner@u.washington.edu [mailto:WAPHGIS-owner@u.washington.edu]On Behalf Of Basil_LOH@env.gov.sg Sent: Friday, July 13, 2001 1:36 AM To: waphgis@u.washington.edu; ai-geostats@unil.ch; fnpbb@diamond.mahidol.ac.th; getis@mail.sdsu.edu; owner-health-gis@who.ch Subject: Dengue & population Hi list members, I am new to geo/spatial statistics and I don't have any expert spatial epidemiologist in my country. So I thought I'd run through what I have done with you all, and check whether I am on the right track. If anyone has any better ideas, please feel free to let me know too! I am working on the following questions: How can I test the hypothesis that most of the dengue cases are located where most of the population are? How can I test the hypothesis that significantly more dengue cases are located in the east than in the west of my country? How can I detect if there are any clustering or any other spatial trends of dengue in relation to population? In my Arcview GIS 3.2, I have a polygon layer of population according to postal sectors (83 polygons with sizes ranging from 0.3 km(superscript: 2) to 33 km(superscript: 2)). I also have a point layer of dengue cases. What I've done so far: Correlated number of dengue cases with population in each postal sector polygon. Correlated number of dengue cases with population density (i.e. population/ area) in each postal sector. Correlated dengue morbidity rate (i.e. no. of cases/ population) with population in each postal sector. Correlated dengue morbidity rate with population density in each postal sector. What more can I do? Thanks very much in advance. Best wishes. Basil Vector Control & Research Dept Singapore .