From healthmaps@home.com Mon Feb 19 07:46:41 2001 Received: from mxu1.u.washington.edu (mxu1.u.washington.edu [140.142.32.8]) by lists.u.washington.edu (8.9.3+UW00.05/8.9.3+UW00.12) with ESMTP id HAA136226 for ; Mon, 19 Feb 2001 07:46:40 -0800 Received: from femail4.sdc1.sfba.home.com (femail4.sdc1.sfba.home.com [24.0.95.84]) by mxu1.u.washington.edu (8.9.3+UW00.02/8.9.3+UW99.09) with ESMTP id HAA20318 for ; Mon, 19 Feb 2001 07:46:40 -0800 Received: from c501552d ([24.19.225.248]) by femail4.sdc1.sfba.home.com (InterMail vM.4.01.03.00 201-229-121) with SMTP id <20010219154418.XGLJ6532.femail4.sdc1.sfba.home.com@c501552d> for ; Mon, 19 Feb 2001 07:44:18 -0800 From: "Health Maps" To: Subject: RE: Re: testing spatial association Date: Mon, 19 Feb 2001 07:46:38 -0800 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) In-Reply-To: X-MimeOLE: Produced By Microsoft MimeOLE V5.00.3018.1300 The problem you will have with this study is that although many cases may live near the big roads, a lot of people will live near little roads that may also or may even be more likely to be adding to a respiratory disease process. More people tend to live near smaller roads than the large highways, and if the traffic density is high enough, their exposure may be greater. This is because there are more smaller roads and likely because of highway right-of-way rules people are allowed to live nearer non-major high traffic highways than big highways. For example, in the US although Interstates may have very high traffic densities, and although many people may live near those roads in urban areas, they are farther from the center line of the highways than those people that live on lesser roads which may or may not have even higher traffic densities. The diffusion process is inverse to the square root of the molecular weight of the pollutant and exponential, that is, ~ 1/distance. How far one lives from the road may be very important. This is not exactly a problem of confounding, but of mixed effect. Proximity to any road, not just the big ones needs to be separated out or delineated in some way. The confounding comes in if there is a socioeconomic difference in who lives near a big highway and who does not. If you do not know other exposures that can lead to respiratory diseases, then you may have a very big problem. For example do you know the smoking status of the cases? And would you know the smoking status of any control group you choose? The confounding part comes in because different socioeconomic classes tend to have different smoking rates, and different socioeconomic classes tend to have a differential probability of living near large roads - in my area. Richard Hoskins healthmaps@home.com GMT -8 To subscribe to WAPHGIS, Washington Public Health GIS listserve, send a message to listproc@u.washington.edu with the request "subscribe waphgis" followed by your name in the body of the message, like so: subscribe waphgis Your Name -----Original Message----- From: WAPHGIS-owner@u.washington.edu [mailto:WAPHGIS-owner@u.washington.edu]On Behalf Of AZucchi@asl.bergamo.it Sent: Monday, February 19, 2001 1:11 AM To: waphgis@u.washington.edu Subject: Rif: Re: testing spatial association Dear colleagues, first of all thanks to all who kindly replied to my question. I I become aware of having been somewhat naive in putting the question and showing the cases map, it was just to exemplify; I obviously have all single administrative in-boundaries prevalence rates, and the map looks substantially the same. The boundaries are representing the administrative city or village boundaries; from north to south the highest distance is 40 kilometers. I was not interested in searching clusters (as with SatScan) tout court, but, as Dick Hoskins correctly defined, to look for clusters/aggregation/association along the roads (not radially, but along a linear -or, almost linear- feature). Professor Jim Case suggests to simply divide the case population into two classes: near a road- not near a road (after a sound definiton of the two, of the distance, and so on), and then to test the differences. Very correct is the suggestion to adopt empirical Bayes estimates to adjust the small numbers. I will do it. Could you suggest some similar published study to use as an example to follow? Thank you Alberto Zucchi .