Literature DB >> 10670998

Subject loss in spatial analysis of breast cancer.

D I Gregorio1, E Cromley, R Mrozinski, S J Walsh.   

Abstract

Possible selection bias from assignment of latitude-longitude coordinates to the place of residence of all Connecticut women diagnosed with breast cancer from 1992 to 1995 (N = 11,470) was evaluated. Exact address-matching was accomplished for 8,121 records (70.8%) and an additional 1,722 records (15.0%) were matched using relaxed criteria. We did not address-match 1,627 records (14.1%) due to missing address information or limitations of the Geographic Information Systems street file. The age-adjusted likelihood of address-matching records was significantly greater for women of color, those born within Connecticut, residents of urban locales or census tracts with low median family incomes and those cases diagnosed nearer to 1992. Few differences in address-matching were attributable to tumor characteristics or therapeutic modality.

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Mesh:

Year:  1999        PMID: 10670998     DOI: 10.1016/s1353-8292(99)00004-0

Source DB:  PubMed          Journal:  Health Place        ISSN: 1353-8292            Impact factor:   4.078


  16 in total

1.  Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse.

Authors:  Bret J Gardner; Jay G Pedersen; Mary E Campbell; James C McClay
Journal:  J Am Med Inform Assoc       Date:  2019-04-01       Impact factor: 4.497

2.  A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes.

Authors:  Jennifer S Sonderman; Michael T Mumma; Sarah S Cohen; Elizabeth L Cope; William J Blot; Lisa B Signorello
Journal:  Geospat Health       Date:  2012-05       Impact factor: 1.212

3.  Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering.

Authors:  Dale L Zimmerman; Jie Li; Xiangming Fang
Journal:  Stat Med       Date:  2010-01-19       Impact factor: 2.373

4.  Using imputation to provide location information for nongeocoded addresses.

Authors:  Frank C Curriero; Martin Kulldorff; Francis P Boscoe; Ann C Klassen
Journal:  PLoS One       Date:  2010-02-10       Impact factor: 3.240

5.  The effects of local street network characteristics on the positional accuracy of automated geocoding for geographic health studies.

Authors:  Dale L Zimmerman; Jie Li
Journal:  Int J Health Geogr       Date:  2010-02-16       Impact factor: 3.918

6.  Geographic variability in geocoding success for West Nile virus cases in South Dakota.

Authors:  Christine L Wey; Jennifer Griesse; Lon Kightlinger; Michael C Wimberly
Journal:  Health Place       Date:  2009-06-12       Impact factor: 4.078

7.  Relationship between physician supply and breast cancer survival: a geographic approach.

Authors:  Jay M Fleisher; Jennie Q Lou; Maria Farrell
Journal:  J Community Health       Date:  2008-08

8.  Geographic bias related to geocoding in epidemiologic studies.

Authors:  M Norman Oliver; Kevin A Matthews; Mir Siadaty; Fern R Hauck; Linda W Pickle
Journal:  Int J Health Geogr       Date:  2005-11-10       Impact factor: 3.918

9.  Geo-relationship between cancer cases and the environment by GIS: a case study of Trabzon in Turkey.

Authors:  Tahsin Yomralioglu; Ebru H Colak; Arif C Aydinoglu
Journal:  Int J Environ Res Public Health       Date:  2009-12-11       Impact factor: 3.390

10.  A case-referent study: light at night and breast cancer risk in Georgia.

Authors:  Sarah E Bauer; Sara E Wagner; Jim Burch; Rana Bayakly; John E Vena
Journal:  Int J Health Geogr       Date:  2013-04-17       Impact factor: 3.918

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