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Your Environment. Your Health.

Progress Reports: Boston University: Analyzing Patterns in Epidemiologic and Toxicologic Data

Superfund Research Program

Analyzing Patterns in Epidemiologic and Toxicologic Data

Project Leader: Veronica M. Vieira (University of California-Irvine)
Grant Number: P42ES007381
Funding Period: 1995-2017
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Progress Reports

Year:   2016  2015  2014  2013  2012  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996  1995 

Routinely collected disease data are often mapped by town or county. Disease registries typically record residence at diagnosis, but this can obscure spatial patterns because some diseases take years to develop. Registries have information on very few risk factors. An area of elevated risk may be due to the presence of many people with an unrecorded risk factor, e.g., smoking. Attempts to relate town disease rates to average exposures in the town - "ecologic" studies - can produce very misleading results. Maps based on suitably conducted studies of individuals can solve these problems. Project investigators have developed statistical methods to map individual level data while accounting for known risk factors. After testing the method using synthetic data, they investigated the association between residential history and colorectal, lung and breast cancer on Upper Cape Cod, Massachusetts. Rather than causing "hot spots," adjusting for known risk factors sometimes revealed them. Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated northeast of the Massachusetts Military Reservation (MMR). Breast cancer hot spots tended to increase in magnitude as latency increased. Significant hot spots were located near two pollution plumes and the MMR. The basic pattern remained after multiple residences were taken into account. These results are being checked using other cluster detection methods. Comparisons of individual and group-level analysis of the breast cancer data examined two major sources of ecologic bias.

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