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Use of attribute association error probability estimates to evaluate quality of medical record geocodes

Overview of attention for article published in International Journal of Health Geographics, September 2015
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Title
Use of attribute association error probability estimates to evaluate quality of medical record geocodes
Published in
International Journal of Health Geographics, September 2015
DOI 10.1186/s12942-015-0019-3
Pubmed ID
Authors

Christian A. Klaus, Luis E. Carrasco, Daniel W. Goldberg, Kevin A. Henry, Recinda L. Sherman

Abstract

The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Unspecified 2 10%
Other 1 5%
Lecturer 1 5%
Lecturer > Senior Lecturer 1 5%
Other 4 19%
Unknown 8 38%
Readers by discipline Count As %
Unspecified 2 10%
Social Sciences 2 10%
Engineering 2 10%
Medicine and Dentistry 2 10%
Decision Sciences 1 5%
Other 3 14%
Unknown 9 43%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2015.
All research outputs
#11,160,853
of 12,545,316 outputs
Outputs from International Journal of Health Geographics
#400
of 475 outputs
Outputs of similar age
#201,955
of 244,641 outputs
Outputs of similar age from International Journal of Health Geographics
#1
of 5 outputs
Altmetric has tracked 12,545,316 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 475 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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