The impact of data quality on spatial analysis of cancer registry data: The example of missing stage at diagnosis and late-stage colorectal cancer

Recinda Sherman, Kevin Henry, David J Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Most disease surveillance systems currently geocode case data. This, coupled with advances in geographic analysis tools, has led to a rise in epidemiologic studies on distribution of disease that rely on analysis of secondary data, e.g. from cancer registries. However, while the data and tools are available for performing geospatial analyses, there are challenges with which methodologies to apply, how to interpret and translate results, and how results are impacted by data quality. The issue of data quality is the subject of this paper. Mapping cancer rates highlights spatial patterns that can help elucidate environmental, clinical, or social causality pathways that drive differences in disease burden by geographic locations. Locating areas with high rates of cancer incidence or variations by stage at diagnoses can help prioritize cancer control efforts. Once the geographic patterns of cancer are mapped, the ideal action is to follow with effective public health interventions for the high risk communities. However, before using results of spatial research to inform public health response, it is important to consider whether the results are spurious due to methodological issues, such as data quality. Missing or incorrect data can distort research conclusions and result in ineffective public health policy. Using colorectal cancer (CRC) as an example, the impact of missing stage at diagnosis on late-stage at diagnosis cluster detection is evaluated. The impact on cluster detection, areabased modeling, and distance from services analysis is described.

Original languageEnglish
Title of host publicationHealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems
PublisherAssociation for Computing Machinery
Pages18-26
Number of pages9
DOIs
StatePublished - Jan 1 2013
Event2nd ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, HealthGIS 2013 - In Conjunction with the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Orlando, FL, United States
Duration: Nov 5 2013Nov 5 2013

Other

Other2nd ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, HealthGIS 2013 - In Conjunction with the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CountryUnited States
CityOrlando, FL
Period11/5/1311/5/13

Fingerprint

Public health

Keywords

  • Area-based measures
  • Cluster detection
  • Colorectal cancer
  • Data quality
  • Screening disparities
  • Stage at diagnosis

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Sherman, R., Henry, K., & Lee, D. J. (2013). The impact of data quality on spatial analysis of cancer registry data: The example of missing stage at diagnosis and late-stage colorectal cancer. In HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems (pp. 18-26). Association for Computing Machinery. https://doi.org/10.1145/2535708.2535714

The impact of data quality on spatial analysis of cancer registry data : The example of missing stage at diagnosis and late-stage colorectal cancer. / Sherman, Recinda; Henry, Kevin; Lee, David J.

HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems. Association for Computing Machinery, 2013. p. 18-26.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sherman, R, Henry, K & Lee, DJ 2013, The impact of data quality on spatial analysis of cancer registry data: The example of missing stage at diagnosis and late-stage colorectal cancer. in HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems. Association for Computing Machinery, pp. 18-26, 2nd ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, HealthGIS 2013 - In Conjunction with the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, United States, 11/5/13. https://doi.org/10.1145/2535708.2535714
Sherman R, Henry K, Lee DJ. The impact of data quality on spatial analysis of cancer registry data: The example of missing stage at diagnosis and late-stage colorectal cancer. In HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems. Association for Computing Machinery. 2013. p. 18-26 https://doi.org/10.1145/2535708.2535714
Sherman, Recinda ; Henry, Kevin ; Lee, David J. / The impact of data quality on spatial analysis of cancer registry data : The example of missing stage at diagnosis and late-stage colorectal cancer. HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems. Association for Computing Machinery, 2013. pp. 18-26
@inproceedings{9c4b2413317b4fa08776dc119c683662,
title = "The impact of data quality on spatial analysis of cancer registry data: The example of missing stage at diagnosis and late-stage colorectal cancer",
abstract = "Most disease surveillance systems currently geocode case data. This, coupled with advances in geographic analysis tools, has led to a rise in epidemiologic studies on distribution of disease that rely on analysis of secondary data, e.g. from cancer registries. However, while the data and tools are available for performing geospatial analyses, there are challenges with which methodologies to apply, how to interpret and translate results, and how results are impacted by data quality. The issue of data quality is the subject of this paper. Mapping cancer rates highlights spatial patterns that can help elucidate environmental, clinical, or social causality pathways that drive differences in disease burden by geographic locations. Locating areas with high rates of cancer incidence or variations by stage at diagnoses can help prioritize cancer control efforts. Once the geographic patterns of cancer are mapped, the ideal action is to follow with effective public health interventions for the high risk communities. However, before using results of spatial research to inform public health response, it is important to consider whether the results are spurious due to methodological issues, such as data quality. Missing or incorrect data can distort research conclusions and result in ineffective public health policy. Using colorectal cancer (CRC) as an example, the impact of missing stage at diagnosis on late-stage at diagnosis cluster detection is evaluated. The impact on cluster detection, areabased modeling, and distance from services analysis is described.",
keywords = "Area-based measures, Cluster detection, Colorectal cancer, Data quality, Screening disparities, Stage at diagnosis",
author = "Recinda Sherman and Kevin Henry and Lee, {David J}",
year = "2013",
month = "1",
day = "1",
doi = "10.1145/2535708.2535714",
language = "English",
pages = "18--26",
booktitle = "HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - The impact of data quality on spatial analysis of cancer registry data

T2 - The example of missing stage at diagnosis and late-stage colorectal cancer

AU - Sherman, Recinda

AU - Henry, Kevin

AU - Lee, David J

PY - 2013/1/1

Y1 - 2013/1/1

N2 - Most disease surveillance systems currently geocode case data. This, coupled with advances in geographic analysis tools, has led to a rise in epidemiologic studies on distribution of disease that rely on analysis of secondary data, e.g. from cancer registries. However, while the data and tools are available for performing geospatial analyses, there are challenges with which methodologies to apply, how to interpret and translate results, and how results are impacted by data quality. The issue of data quality is the subject of this paper. Mapping cancer rates highlights spatial patterns that can help elucidate environmental, clinical, or social causality pathways that drive differences in disease burden by geographic locations. Locating areas with high rates of cancer incidence or variations by stage at diagnoses can help prioritize cancer control efforts. Once the geographic patterns of cancer are mapped, the ideal action is to follow with effective public health interventions for the high risk communities. However, before using results of spatial research to inform public health response, it is important to consider whether the results are spurious due to methodological issues, such as data quality. Missing or incorrect data can distort research conclusions and result in ineffective public health policy. Using colorectal cancer (CRC) as an example, the impact of missing stage at diagnosis on late-stage at diagnosis cluster detection is evaluated. The impact on cluster detection, areabased modeling, and distance from services analysis is described.

AB - Most disease surveillance systems currently geocode case data. This, coupled with advances in geographic analysis tools, has led to a rise in epidemiologic studies on distribution of disease that rely on analysis of secondary data, e.g. from cancer registries. However, while the data and tools are available for performing geospatial analyses, there are challenges with which methodologies to apply, how to interpret and translate results, and how results are impacted by data quality. The issue of data quality is the subject of this paper. Mapping cancer rates highlights spatial patterns that can help elucidate environmental, clinical, or social causality pathways that drive differences in disease burden by geographic locations. Locating areas with high rates of cancer incidence or variations by stage at diagnoses can help prioritize cancer control efforts. Once the geographic patterns of cancer are mapped, the ideal action is to follow with effective public health interventions for the high risk communities. However, before using results of spatial research to inform public health response, it is important to consider whether the results are spurious due to methodological issues, such as data quality. Missing or incorrect data can distort research conclusions and result in ineffective public health policy. Using colorectal cancer (CRC) as an example, the impact of missing stage at diagnosis on late-stage at diagnosis cluster detection is evaluated. The impact on cluster detection, areabased modeling, and distance from services analysis is described.

KW - Area-based measures

KW - Cluster detection

KW - Colorectal cancer

KW - Data quality

KW - Screening disparities

KW - Stage at diagnosis

UR - http://www.scopus.com/inward/record.url?scp=84898985202&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898985202&partnerID=8YFLogxK

U2 - 10.1145/2535708.2535714

DO - 10.1145/2535708.2535714

M3 - Conference contribution

AN - SCOPUS:84898985202

SP - 18

EP - 26

BT - HealthGIS 2013 - Proc. of the 2nd ACM SIGSPATIAL Int. Workshop on the Use of GIS in Public Health, In Conjunction with the 21st ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems

PB - Association for Computing Machinery

ER -