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Int J Health Geogr. 2016 Jul 1;15(1):21. doi: 10.1186/s12942-016-0049-5.

Spatial measurement errors in the field of spatial epidemiology.

Author information

1
Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, 200032, China. epistat@gmail.com.
2
Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China. epistat@gmail.com.
3
Department of Health Sciences, Northeastern University, Boston, MA, 02115, USA.
4
Department of Epidemiology and the Center for Communicable Disease Dynamics, School of Public Health, Harvard University, Boston, MA, 02115, USA.
5
Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA, 02115, USA.
6
Harvard Medical School, Boston, MA, 02115, USA.
7
Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, 200032, China.
8
Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China.

Abstract

BACKGROUND:

Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data.

METHODS:

Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.

RESULTS:

We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.

CONCLUSION:

Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

KEYWORDS:

Environmental epidemiology; GIS; Geographical epidemiology; Measurement error; Misclassification; Spatial epidemiology

PMID:
27368370
PMCID:
PMC4930612
DOI:
10.1186/s12942-016-0049-5
[Indexed for MEDLINE]
Free PMC Article

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