| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Oliver, M Norman Matthews, Kevin A Siadaty, Mir Hauck, Fern R Pickle, Linda W |
| Abstract | Background This article describes geographic bias in GIS analyses with unrepresentative data owing to missing geocodes, using as an example a spatial analysis of prostate cancer incidence among whites and African Americans in Virginia, 1990–1999. Statistical tests for clustering were performed and such clusters mapped. The patterns of missing census tract identifiers for the cases were examined by generalized linear regression models. Results The county of residency for all cases was known, and 26,338 (74%) of these cases were geocoded successfully to census tracts. Cluster maps showed patterns that appeared markedly different, depending upon whether one used all cases or those geocoded to the census tract. Multivariate regression analysis showed that, in the most rural counties (where the missing data were concentrated), the percent of a county's population over age 64 and with less than a high school education were both independently associated with a higher percent of missing geocodes. Conclusion We found statistically significant pattern differences resulting from spatially non-random differences in geocoding completeness across Virginia. Appropriate interpretation of maps, therefore, requires an understanding of this phenomenon, which we call \"cartographic confounding.\" |
| Related Links | https://ij-healthgeographics.biomedcentral.com/counter/pdf/10.1186/1476-072X-4-29.pdf |
| Ending Page | 9 |
| Page Count | 9 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| DOI | 10.1186/1476-072X-4-29 |
| Journal | International Journal of Health Geographics |
| Issue Number | 1 |
| Volume Number | 4 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2005-11-10 |
| Access Restriction | Open |
| Subject Keyword | Public Health Health Informatics Human Geography Epidemiology Health Promotion and Disease Prevention Medical Geography bias (epidemiology) confounding factors geographic information systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Public Health, Environmental and Occupational Health Business, Management and Accounting |
| Journal Impact Factor | 3/2023 |
| 5-Year Journal Impact Factor | 4.1/2023 |
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