Spatial measurement errors in the field of spatial epidemiology

Abstract

BackgroundSpatial 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. MethodsGoogle 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.ResultsWe 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.ConclusionSpatial 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.

Spatial clustering of childhood leukaemia with the integration of the Paediatric Environmental History

“…Similar results were found in Zhang et al (2016) showing a decrease in p-values as the temporal lag between the prenatal and diagnostic address increase. However, Chen et al (2010) explored the extent of ambient air pollutant exposure measurement error due to maternal residential mobility during pregnancy within a New York birth cohort, but no significant impact was found.…”

Measurement Error and Environmental Epidemiology: a Policy Perspective

“…First, with improvements in GIS technology, there is increasing focus on accounting for measurement error arising spatial misalignment (61). As in settings where exposure measurement is based on a job exposure matrix, measurement error in of spatial exposures often has both Berkson-like and Classical-like components (28,62). For example, air pollution studies rely on estimates of individual exposure based on measurements from individual air monitors, which may be subject to classical error, and a spatial model assigning exposure to individuals located at specific points in space, which may be subject to Berkson error.…”

Statistical evaluation of spatial uncertainty in schistosomiasis mapping

“…Uncertainty sources derived from spatial data -covariate and survey dataarise due to random errors, such as equipment limitations or unfavourable environment conditions during data capture; and systematic errors, such as sampling design (Rothman, 2012) and measurement errors (Zhang et al, 2016). Sampling design errors relate to insufficiently large sample sizes, the type of survey , the selected morbidity indicator (Soares Magalhães et al, 2014), and limited access to geographic areas , among others.…”

“…Sampling design errors relate to insufficiently large sample sizes, the type of survey , the selected morbidity indicator (Soares Magalhães et al, 2014), and limited access to geographic areas , among others. Examples of measurement errors are positional measurement error (Zhang et al, 2016), geo-coding errors (Atkinson and Graham, 2006), non-calibrated equipment or data (i.e. coordinate inaccuracies) (Curran et al, 2000).…”

“…Pure specification bias is a type of ecological fallacy that arises when aggregated survey or covariate data are used for individual-level inferences (Wakefield and Shaddick, 2006;Zhang et al, 2016). Ecological fallacy is an important source of uncertainty in spatial epidemiological studies as any direct link between exposure and health outcomes is imperfectly measured (King, 2013).…”