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Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping

Identifieur interne : 004679 ( Ncbi/Merge ); précédent : 004678; suivant : 004680

Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping

Auteurs : Maria T. Patterson ; Robert L. Grossman

Source :

Big Data [ 2167-6461 ] ; 2017.

RBID : PMC:5647508

Abstract

We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ∼100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. We show that this method yields results in good agreement with established methods for detecting spatial autocorrelation (Moran's I method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647508

DOI: 10.1089/big.2017.0028
PubMed: 28933946
PubMed Central: 5647508

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 method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.</p>
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  <front><journal-meta><journal-id journal-id-type="nlm-ta">Big Data</journal-id>
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<article-categories><subj-group subj-group-type="heading"><subject>Original Articles</subject>
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<title-group><article-title>Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Patterson</surname>
<given-names>Maria T.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1,</sup>
</xref>
<xref ref-type="author-notes" rid="fn1"><sup>*</sup>
</xref>
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<contrib contrib-type="author"><name><surname>Grossman</surname>
<given-names>Robert L.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1,</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2,</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3,</sup>
</xref>
<xref ref-type="aff" rid="aff4"><sup>4,</sup>
</xref>
<xref ref-type="corresp" rid="corr1"><sup>†</sup>
</xref>
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<aff id="aff1"><label><sup>1</sup>
</label>
Center for Data Intensive Science,<institution>University of Chicago</institution>
, Chicago, Illinois.</aff>
<aff id="aff2"><label><sup>2</sup>
</label>
Computation Institute,<institution>University of Chicago</institution>
, Chicago, Illinois.</aff>
<aff id="aff3"><label><sup>3</sup>
</label>
Section of Computational Biomedicine and Biomedical Data Science, Department of Medicine,<institution>University of Chicago</institution>
, Chicago, Illinois.</aff>
<aff id="aff4"><label><sup>4</sup>
</label>
Institute for Genomics and Systems Biology,<institution>University of Chicago</institution>
, Chicago, Illinois.</aff>
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<author-notes><fn id="fn1" fn-type="other"><label><sup>*</sup>
</label>
<p>Current affiliation: Department of Astronomy, University of Washington, Seattle, Washington.</p>
</fn>
<corresp id="corr1"><label><sup>†</sup>
</label>
Address correspondence to: <italic>Robert L. Grossman, Center for Data Intensive Science, University of Chicago, 900 East 57th Street, KCBD 10142 Chicago, IL 60637,</italic>
 E-mail: <email xlink:href="mailto:robert.grossman@uchicago.edu">robert.grossman@uchicago.edu</email>
</corresp>
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<pub-date pub-type="ppub"><day>01</day>
<month>9</month>
<year>2017</year>
<pmc-comment>string-date: September 2017</pmc-comment>
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<pub-date pub-type="epub"><day>01</day>
<month>9</month>
<year>2017</year>
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<month>9</month>
<year>2017</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the . </pmc-comment>
      <volume>5</volume>
<issue>3</issue>
<fpage>213</fpage>
<lpage>224</lpage>
<permissions><copyright-statement>© Maria T. Patterson and Robert L. Grossman 2017; Published by Mary Ann Liebert, Inc.</copyright-statement>
<copyright-year>2017</copyright-year>
<license license-type="open-access"><license-p> This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="big.2017.0028.pdf"></self-uri>
<abstract><title>Abstract</title>
<p>We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ∼100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. We show that this method yields results in good agreement with established methods for detecting spatial autocorrelation (Moran's <italic>I</italic>
 method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.</p>
</abstract>
<kwd-group kwd-group-type="author"><title><bold>Keywords:</bold>
 </title>
<kwd>geospatial variation of disease incidence</kwd>
<kwd>geospatial correlation</kwd>
<kwd>electronic medical records</kwd>
</kwd-group>
<counts><fig-count count="5"></fig-count>
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<tree><noCountry><name sortKey="Grossman, Robert L" sort="Grossman, Robert L" uniqKey="Grossman R" first="Robert L." last="Grossman">Robert L. Grossman</name>
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Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping

Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping

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