Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions
Increasing attention is being paid to the marked disparities in diabetes prevalence and health outcomes in the United States. There is a need to identify the small-area geographic variation in diabetes risk and related outcomes, a task that current health surveillance methods, which often rely on a...
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| creator | Gabert, Rose Thomson, Blake Gakidou, Emmanuela Roth, Gregory |
| description | Increasing attention is being paid to the marked disparities in diabetes prevalence and health outcomes in the United States. There is a need to identify the small-area geographic variation in diabetes risk and related outcomes, a task that current health surveillance methods, which often rely on a self-reported diagnosis of diabetes, are not detailed enough to achieve. Broad adoption of electronic health records (EHR) and routine centralized reporting of patient-level data offers a new way to examine diabetes risk and highlight hotspots for intervention.
We examined small-area geographic variation in hemoglobin A1c (HgbA1C) levels in three counties though a retrospective observational analysis of the complete population of diabetic patients receiving at least two ambulatory care visits for diabetes in three counties (two urban, one rural) in Minnesota in 2013, with clinical performance measures re-aggregated to patient home zip code area. Patient level performance measures included HgbA1c, blood pressure, low-density lipoprotein cholesterol and smoking. Diabetes care was provided to 63,053 patients out of a total population of 1.48 million people aged 18-74. Within each zip code area, on average 4.1% of the population received care for diabetes. There was significant and largely consistent geographic variation in the proportion of patients within their zip code area of residence attaining HgbA1C |
| doi_str_mv | 10.1371/journal.pone.0159227 |
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We examined small-area geographic variation in hemoglobin A1c (HgbA1C) levels in three counties though a retrospective observational analysis of the complete population of diabetic patients receiving at least two ambulatory care visits for diabetes in three counties (two urban, one rural) in Minnesota in 2013, with clinical performance measures re-aggregated to patient home zip code area. Patient level performance measures included HgbA1c, blood pressure, low-density lipoprotein cholesterol and smoking. Diabetes care was provided to 63,053 patients out of a total population of 1.48 million people aged 18-74. Within each zip code area, on average 4.1% of the population received care for diabetes. There was significant and largely consistent geographic variation in the proportion of patients within their zip code area of residence attaining HgbA1C <8.0%, ranging from 59-90% of patients within each zip code area (interquartile range (IQR) 72.0%-78.1%). Attainment of performance measures for a zip code area were correlated with household income, educational attainment and insurance coverage for the same zip code area (all p < .001).
We identified small geographic areas with the least effective control of diabetes. Centrally-aggregated EHR provides a new means of identifying and targeting at-risk neighborhoods for community-based interventions.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0159227</identifier><identifier>PMID: 27463641</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adult ; Aged ; Biology and Life Sciences ; Blood pressure ; Census of Population ; Cholesterol ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus - epidemiology ; Diabetes Mellitus - prevention & control ; Diabetics ; Disease control ; Disease prevention ; Documentation ; Economic indicators ; Electronic Health Records ; Electronic medical records ; Emergency medical services ; Female ; Health maintenance organizations ; Health risks ; Health surveillance ; Hemoglobin ; HMOs ; Humans ; Internet ; Low density lipoprotein ; Male ; Management ; Medical care ; Medical records ; Medicine and Health Sciences ; Middle Aged ; Minnesota - epidemiology ; Neighborhoods ; Patients ; People and places ; Postal codes ; Prevention ; Public health ; Quality ; Research and Analysis Methods ; Residence Characteristics ; Residential density ; Retrospective Studies ; Risk ; Risk Factors ; Rural areas ; Smoking ; Surveillance ; Systematic review ; Tobacco ; Variation ; Young Adult</subject><ispartof>PloS one, 2016-07, Vol.11 (7), p.e0159227-e0159227</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Gabert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Gabert et al 2016 Gabert et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-8aa1423738876412b152e2f85d3cd99958e53e64f2f2d25e9e219608d98c72493</citedby><cites>FETCH-LOGICAL-c725t-8aa1423738876412b152e2f85d3cd99958e53e64f2f2d25e9e219608d98c72493</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963128/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963128/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27463641$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gorlova, Olga Y</contributor><creatorcontrib>Gabert, Rose</creatorcontrib><creatorcontrib>Thomson, Blake</creatorcontrib><creatorcontrib>Gakidou, Emmanuela</creatorcontrib><creatorcontrib>Roth, Gregory</creatorcontrib><title>Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Increasing attention is being paid to the marked disparities in diabetes prevalence and health outcomes in the United States. There is a need to identify the small-area geographic variation in diabetes risk and related outcomes, a task that current health surveillance methods, which often rely on a self-reported diagnosis of diabetes, are not detailed enough to achieve. Broad adoption of electronic health records (EHR) and routine centralized reporting of patient-level data offers a new way to examine diabetes risk and highlight hotspots for intervention.
We examined small-area geographic variation in hemoglobin A1c (HgbA1C) levels in three counties though a retrospective observational analysis of the complete population of diabetic patients receiving at least two ambulatory care visits for diabetes in three counties (two urban, one rural) in Minnesota in 2013, with clinical performance measures re-aggregated to patient home zip code area. Patient level performance measures included HgbA1c, blood pressure, low-density lipoprotein cholesterol and smoking. Diabetes care was provided to 63,053 patients out of a total population of 1.48 million people aged 18-74. Within each zip code area, on average 4.1% of the population received care for diabetes. There was significant and largely consistent geographic variation in the proportion of patients within their zip code area of residence attaining HgbA1C <8.0%, ranging from 59-90% of patients within each zip code area (interquartile range (IQR) 72.0%-78.1%). Attainment of performance measures for a zip code area were correlated with household income, educational attainment and insurance coverage for the same zip code area (all p < .001).
We identified small geographic areas with the least effective control of diabetes. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gabert, Rose</au><au>Thomson, Blake</au><au>Gakidou, Emmanuela</au><au>Roth, Gregory</au><au>Gorlova, Olga Y</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-07-27</date><risdate>2016</risdate><volume>11</volume><issue>7</issue><spage>e0159227</spage><epage>e0159227</epage><pages>e0159227-e0159227</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Increasing attention is being paid to the marked disparities in diabetes prevalence and health outcomes in the United States. There is a need to identify the small-area geographic variation in diabetes risk and related outcomes, a task that current health surveillance methods, which often rely on a self-reported diagnosis of diabetes, are not detailed enough to achieve. Broad adoption of electronic health records (EHR) and routine centralized reporting of patient-level data offers a new way to examine diabetes risk and highlight hotspots for intervention.
We examined small-area geographic variation in hemoglobin A1c (HgbA1C) levels in three counties though a retrospective observational analysis of the complete population of diabetic patients receiving at least two ambulatory care visits for diabetes in three counties (two urban, one rural) in Minnesota in 2013, with clinical performance measures re-aggregated to patient home zip code area. Patient level performance measures included HgbA1c, blood pressure, low-density lipoprotein cholesterol and smoking. Diabetes care was provided to 63,053 patients out of a total population of 1.48 million people aged 18-74. Within each zip code area, on average 4.1% of the population received care for diabetes. There was significant and largely consistent geographic variation in the proportion of patients within their zip code area of residence attaining HgbA1C <8.0%, ranging from 59-90% of patients within each zip code area (interquartile range (IQR) 72.0%-78.1%). Attainment of performance measures for a zip code area were correlated with household income, educational attainment and insurance coverage for the same zip code area (all p < .001).
We identified small geographic areas with the least effective control of diabetes. Centrally-aggregated EHR provides a new means of identifying and targeting at-risk neighborhoods for community-based interventions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27463641</pmid><doi>10.1371/journal.pone.0159227</doi><tpages>e0159227</tpages><oa>free_for_read</oa></addata></record> |
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| issn | 1932-6203 1932-6203 |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adolescent Adult Aged Biology and Life Sciences Blood pressure Census of Population Cholesterol Diabetes Diabetes mellitus Diabetes Mellitus - epidemiology Diabetes Mellitus - prevention & control Diabetics Disease control Disease prevention Documentation Economic indicators Electronic Health Records Electronic medical records Emergency medical services Female Health maintenance organizations Health risks Health surveillance Hemoglobin HMOs Humans Internet Low density lipoprotein Male Management Medical care Medical records Medicine and Health Sciences Middle Aged Minnesota - epidemiology Neighborhoods Patients People and places Postal codes Prevention Public health Quality Research and Analysis Methods Residence Characteristics Residential density Retrospective Studies Risk Risk Factors Rural areas Smoking Surveillance Systematic review Tobacco Variation Young Adult |
| title | Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T12%3A52%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identifying%20High-Risk%20Neighborhoods%20Using%20Electronic%20Medical%20Records:%20A%20Population-Based%20Approach%20for%20Targeting%20Diabetes%20Prevention%20and%20Treatment%20Interventions&rft.jtitle=PloS%20one&rft.au=Gabert,%20Rose&rft.date=2016-07-27&rft.volume=11&rft.issue=7&rft.spage=e0159227&rft.epage=e0159227&rft.pages=e0159227-e0159227&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0159227&rft_dat=%3Cgale_plos_%3EA459324626%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1807273113&rft_id=info:pmid/27463641&rft_galeid=A459324626&rft_doaj_id=oai_doaj_org_article_e93c121263ce499099ba071a8048d6a9&rfr_iscdi=true |