Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity
Electronic medical records (EMRs) used in primary care contain a breadth of data that can be used in public health research. Patient data from EMRs could be linked with other data sources, such as a postal code linkage with Census data, to obtain additional information on environmental determinants...
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| Veröffentlicht in: | BMC medical informatics and decision making 2016-03, Vol.16 (32), p.32-32, Article 32 |
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| creator | Biro, Suzanne Williamson, Tyler Leggett, Jannet Ann Barber, David Morkem, Rachael Moore, Kieran Belanger, Paul Mosley, Brian Janssen, Ian |
| description | Electronic medical records (EMRs) used in primary care contain a breadth of data that can be used in public health research. Patient data from EMRs could be linked with other data sources, such as a postal code linkage with Census data, to obtain additional information on environmental determinants of health. While promising, successful linkages between primary care EMRs with geographic measures is limited due to ethics review board concerns. This study tested the feasibility of extracting full postal code from primary care EMRs and linking this with area-level measures of the environment to demonstrate how such a linkage could be used to examine the determinants of disease. The association between obesity and area-level deprivation was used as an example to illustrate inequalities of obesity in adults.
The analysis included EMRs of 7153 patients aged 20 years and older who visited a single, primary care site in 2011. Extracted patient information included demographics (date of birth, sex, postal code) and weight status (height, weight). Information extraction and management procedures were designed to mitigate the risk of individual re-identification when extracting full postal code from source EMRs. Based on patients' postal codes, area-based deprivation indexes were created using the smallest area unit used in Canadian censuses. Descriptive statistics and socioeconomic disparity summary measures of linked census and adult patients were calculated.
The data extraction of full postal code met technological requirements for rendering health information extracted from local EMRs into anonymized data. The prevalence of obesity was 31.6 %. There was variation of obesity between deprivation quintiles; adults in the most deprived areas were 35 % more likely to be obese compared with adults in the least deprived areas (Chi-Square = 20.24(1), p |
| doi_str_mv | 10.1186/s12911-016-0272-9 |
| format | Article |
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The analysis included EMRs of 7153 patients aged 20 years and older who visited a single, primary care site in 2011. Extracted patient information included demographics (date of birth, sex, postal code) and weight status (height, weight). Information extraction and management procedures were designed to mitigate the risk of individual re-identification when extracting full postal code from source EMRs. Based on patients' postal codes, area-based deprivation indexes were created using the smallest area unit used in Canadian censuses. Descriptive statistics and socioeconomic disparity summary measures of linked census and adult patients were calculated.
The data extraction of full postal code met technological requirements for rendering health information extracted from local EMRs into anonymized data. The prevalence of obesity was 31.6 %. There was variation of obesity between deprivation quintiles; adults in the most deprived areas were 35 % more likely to be obese compared with adults in the least deprived areas (Chi-Square = 20.24(1), p < 0.0001). Maps depicting spatial representation of regional deprivation and obesity were created to highlight high risk areas.
An area based socio-economic measure was linked with EMR-derived objective measures of height and weight to show a positive association between area-level deprivation and obesity. The linked dataset demonstrates a promising model for assessing health disparities and ecological factors associated with the development of chronic diseases with far reaching implications for informing public health and primary health care interventions and services.</description><identifier>ISSN: 1472-6947</identifier><identifier>EISSN: 1472-6947</identifier><identifier>DOI: 10.1186/s12911-016-0272-9</identifier><identifier>PMID: 26969124</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Adult ; Aged ; Algorithms ; Body mass index ; Canada ; Care and treatment ; Census of Population ; Censuses ; Chronic Disease - epidemiology ; Chronic diseases ; Chronic illnesses ; Complications and side effects ; Datasets ; Development and progression ; Disease ; Electronic health records ; Electronic Health Records - statistics & numerical data ; Feasibility Studies ; Female ; Humans ; Male ; Medical Record Linkage - methods ; Medical records ; Medical research ; Middle Aged ; Obesity ; Obesity - epidemiology ; Patient outcomes ; Patients ; Physicians ; Primary care ; Primary Health Care - statistics & numerical data ; Privacy ; Research ethics ; Social Class ; Socioeconomic factors ; Software ; Surveillance ; Young Adult</subject><ispartof>BMC medical informatics and decision making, 2016-03, Vol.16 (32), p.32-32, Article 32</ispartof><rights>COPYRIGHT 2016 BioMed Central Ltd.</rights><rights>Copyright BioMed Central 2016</rights><rights>Biro et al. 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c494t-7467b320f7a83baccf9b44c683c998f6baf52238730d2e31dc3663661bbea0683</citedby><cites>FETCH-LOGICAL-c494t-7467b320f7a83baccf9b44c683c998f6baf52238730d2e31dc3663661bbea0683</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/PMC4788841/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788841/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26969124$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Biro, Suzanne</creatorcontrib><creatorcontrib>Williamson, Tyler</creatorcontrib><creatorcontrib>Leggett, Jannet Ann</creatorcontrib><creatorcontrib>Barber, David</creatorcontrib><creatorcontrib>Morkem, Rachael</creatorcontrib><creatorcontrib>Moore, Kieran</creatorcontrib><creatorcontrib>Belanger, Paul</creatorcontrib><creatorcontrib>Mosley, Brian</creatorcontrib><creatorcontrib>Janssen, Ian</creatorcontrib><title>Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity</title><title>BMC medical informatics and decision making</title><addtitle>BMC Med Inform Decis Mak</addtitle><description>Electronic medical records (EMRs) used in primary care contain a breadth of data that can be used in public health research. Patient data from EMRs could be linked with other data sources, such as a postal code linkage with Census data, to obtain additional information on environmental determinants of health. While promising, successful linkages between primary care EMRs with geographic measures is limited due to ethics review board concerns. This study tested the feasibility of extracting full postal code from primary care EMRs and linking this with area-level measures of the environment to demonstrate how such a linkage could be used to examine the determinants of disease. The association between obesity and area-level deprivation was used as an example to illustrate inequalities of obesity in adults.
The analysis included EMRs of 7153 patients aged 20 years and older who visited a single, primary care site in 2011. Extracted patient information included demographics (date of birth, sex, postal code) and weight status (height, weight). Information extraction and management procedures were designed to mitigate the risk of individual re-identification when extracting full postal code from source EMRs. Based on patients' postal codes, area-based deprivation indexes were created using the smallest area unit used in Canadian censuses. Descriptive statistics and socioeconomic disparity summary measures of linked census and adult patients were calculated.
The data extraction of full postal code met technological requirements for rendering health information extracted from local EMRs into anonymized data. The prevalence of obesity was 31.6 %. There was variation of obesity between deprivation quintiles; adults in the most deprived areas were 35 % more likely to be obese compared with adults in the least deprived areas (Chi-Square = 20.24(1), p < 0.0001). Maps depicting spatial representation of regional deprivation and obesity were created to highlight high risk areas.
An area based socio-economic measure was linked with EMR-derived objective measures of height and weight to show a positive association between area-level deprivation and obesity. The linked dataset demonstrates a promising model for assessing health disparities and ecological factors associated with the development of chronic diseases with far reaching implications for informing public health and primary health care interventions and services.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Body mass index</subject><subject>Canada</subject><subject>Care and treatment</subject><subject>Census of Population</subject><subject>Censuses</subject><subject>Chronic Disease - epidemiology</subject><subject>Chronic diseases</subject><subject>Chronic illnesses</subject><subject>Complications and side effects</subject><subject>Datasets</subject><subject>Development and progression</subject><subject>Disease</subject><subject>Electronic health records</subject><subject>Electronic Health Records - statistics & numerical data</subject><subject>Feasibility Studies</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Medical Record Linkage - methods</subject><subject>Medical records</subject><subject>Medical research</subject><subject>Middle Aged</subject><subject>Obesity</subject><subject>Obesity - epidemiology</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Physicians</subject><subject>Primary care</subject><subject>Primary Health Care - statistics & numerical data</subject><subject>Privacy</subject><subject>Research ethics</subject><subject>Social Class</subject><subject>Socioeconomic factors</subject><subject>Software</subject><subject>Surveillance</subject><subject>Young Adult</subject><issn>1472-6947</issn><issn>1472-6947</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNptUk2LFDEQbURx19Ef4EUCXrz0mnTSSceDsAx-wYIX9xzS6eqZrN3JmKTV-WX-PWuYcd0VSSBF1XuvUsWrqueMXjDWydeZNZqxmjJZ00Y1tX5QnTOBgdRCPbwTn1VPcr6hlKmOt4-rs0ZqqVkjzqtf18VPvuxJHMnkw1cfNmSX_GzTnjibgMAErqQYvCMzDN7ZiSRwMQ2Z_PBlS9Y22MHbQByEvGQy2GJJiSSXZdiTsgUyQIE0-2BDyYc2bnuUG3wGm-ENQTL8tPNuAtJjYiAxkBydj9gnxBmhudiC2jZgrYeM_31aPRrtlOHZ6V1V1-_ffVl_rK8-f_i0vryqndCi1EpI1fOGjsp2vLfOjboXwsmOO627UfZ2bJuGd4rToQHOBselxMv6HixF2Kp6e9TdLT3Oj0OWZCdzWpGJ1pv7leC3ZhO_G6G6rhMMBV6dBFL8tkAuZvbZwTTZAHHJhinFBacS266ql_9Ab-KSAo6HKK06KVjb_kVt7ATGhzFiX3cQNZdCSN4qqhWiLv6DwjMALjQGGD3m7xHYkeBSzDnBeDsjo-bgNnN0m0G3mYPbjEbOi7vLuWX8sRf_DX2S04g</recordid><startdate>20160311</startdate><enddate>20160311</enddate><creator>Biro, Suzanne</creator><creator>Williamson, Tyler</creator><creator>Leggett, Jannet Ann</creator><creator>Barber, David</creator><creator>Morkem, Rachael</creator><creator>Moore, Kieran</creator><creator>Belanger, Paul</creator><creator>Mosley, Brian</creator><creator>Janssen, Ian</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160311</creationdate><title>Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity</title><author>Biro, Suzanne ; Williamson, Tyler ; Leggett, Jannet Ann ; Barber, David ; Morkem, Rachael ; Moore, Kieran ; Belanger, Paul ; Mosley, Brian ; Janssen, Ian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c494t-7467b320f7a83baccf9b44c683c998f6baf52238730d2e31dc3663661bbea0683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Body mass index</topic><topic>Canada</topic><topic>Care and treatment</topic><topic>Census of Population</topic><topic>Censuses</topic><topic>Chronic Disease - epidemiology</topic><topic>Chronic diseases</topic><topic>Chronic illnesses</topic><topic>Complications and side effects</topic><topic>Datasets</topic><topic>Development and progression</topic><topic>Disease</topic><topic>Electronic health records</topic><topic>Electronic Health Records - statistics & numerical data</topic><topic>Feasibility Studies</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Medical Record Linkage - methods</topic><topic>Medical records</topic><topic>Medical research</topic><topic>Middle Aged</topic><topic>Obesity</topic><topic>Obesity - epidemiology</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Physicians</topic><topic>Primary care</topic><topic>Primary Health Care - statistics & numerical data</topic><topic>Privacy</topic><topic>Research ethics</topic><topic>Social Class</topic><topic>Socioeconomic factors</topic><topic>Software</topic><topic>Surveillance</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Biro, Suzanne</creatorcontrib><creatorcontrib>Williamson, Tyler</creatorcontrib><creatorcontrib>Leggett, Jannet Ann</creatorcontrib><creatorcontrib>Barber, David</creatorcontrib><creatorcontrib>Morkem, Rachael</creatorcontrib><creatorcontrib>Moore, Kieran</creatorcontrib><creatorcontrib>Belanger, Paul</creatorcontrib><creatorcontrib>Mosley, Brian</creatorcontrib><creatorcontrib>Janssen, Ian</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biological Sciences</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC medical informatics and decision making</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Biro, Suzanne</au><au>Williamson, Tyler</au><au>Leggett, Jannet Ann</au><au>Barber, David</au><au>Morkem, Rachael</au><au>Moore, Kieran</au><au>Belanger, Paul</au><au>Mosley, Brian</au><au>Janssen, Ian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity</atitle><jtitle>BMC medical informatics and decision making</jtitle><addtitle>BMC Med Inform Decis Mak</addtitle><date>2016-03-11</date><risdate>2016</risdate><volume>16</volume><issue>32</issue><spage>32</spage><epage>32</epage><pages>32-32</pages><artnum>32</artnum><issn>1472-6947</issn><eissn>1472-6947</eissn><abstract>Electronic medical records (EMRs) used in primary care contain a breadth of data that can be used in public health research. Patient data from EMRs could be linked with other data sources, such as a postal code linkage with Census data, to obtain additional information on environmental determinants of health. While promising, successful linkages between primary care EMRs with geographic measures is limited due to ethics review board concerns. This study tested the feasibility of extracting full postal code from primary care EMRs and linking this with area-level measures of the environment to demonstrate how such a linkage could be used to examine the determinants of disease. The association between obesity and area-level deprivation was used as an example to illustrate inequalities of obesity in adults.
The analysis included EMRs of 7153 patients aged 20 years and older who visited a single, primary care site in 2011. Extracted patient information included demographics (date of birth, sex, postal code) and weight status (height, weight). Information extraction and management procedures were designed to mitigate the risk of individual re-identification when extracting full postal code from source EMRs. Based on patients' postal codes, area-based deprivation indexes were created using the smallest area unit used in Canadian censuses. Descriptive statistics and socioeconomic disparity summary measures of linked census and adult patients were calculated.
The data extraction of full postal code met technological requirements for rendering health information extracted from local EMRs into anonymized data. The prevalence of obesity was 31.6 %. There was variation of obesity between deprivation quintiles; adults in the most deprived areas were 35 % more likely to be obese compared with adults in the least deprived areas (Chi-Square = 20.24(1), p < 0.0001). Maps depicting spatial representation of regional deprivation and obesity were created to highlight high risk areas.
An area based socio-economic measure was linked with EMR-derived objective measures of height and weight to show a positive association between area-level deprivation and obesity. The linked dataset demonstrates a promising model for assessing health disparities and ecological factors associated with the development of chronic diseases with far reaching implications for informing public health and primary health care interventions and services.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>26969124</pmid><doi>10.1186/s12911-016-0272-9</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | BMC medical informatics and decision making, 2016-03, Vol.16 (32), p.32-32, Article 32 |
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| source | SpringerOpen; MEDLINE; SpringerLink (Online service); PubMed Central; Directory of Open Access Journals; EZB Electronic Journals Library; PubMed Central Open Access |
| subjects | Adult Aged Algorithms Body mass index Canada Care and treatment Census of Population Censuses Chronic Disease - epidemiology Chronic diseases Chronic illnesses Complications and side effects Datasets Development and progression Disease Electronic health records Electronic Health Records - statistics & numerical data Feasibility Studies Female Humans Male Medical Record Linkage - methods Medical records Medical research Middle Aged Obesity Obesity - epidemiology Patient outcomes Patients Physicians Primary care Primary Health Care - statistics & numerical data Privacy Research ethics Social Class Socioeconomic factors Software Surveillance Young Adult |
| title | Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity |
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