Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data
Background Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clin...
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| Veröffentlicht in: | International Journal of Obesity 2020-08, Vol.44 (8), p.1753-1765 |
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| creator | Campbell, Elizabeth A. Qian, Ting Miller, Jeffrey M. Bass, Ellen J. Masino, Aaron J. |
| description | Background
Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts.
Methods
EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children’s Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar’s test was used to assess the statistical significance of pattern prevalence differences between the case and control populations.
Results
SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (
p
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| doi_str_mv | 10.1038/s41366-020-0614-7 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7381422</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A630528971</galeid><sourcerecordid>A630528971</sourcerecordid><originalsourceid>FETCH-LOGICAL-c596t-1b5702285ead6b3c2f0ea429de63f6f2ff03ae10f2c3ab1464ffc33e63201fe53</originalsourceid><addsrcrecordid>eNqFkk1v1DAQhi0EomXhB3BBlpAQl5TxR5zkglRVfFSqxAXOluOMd10l9hI7RZX48Xi7pe0iEMohGs8zb_KOX0JeMjhhINp3STKhVAUcKlBMVs0jcsxko6pads1jcgwCmgpqVR-RZyldAkBdA39KjgSXnQShjsnP8wFD9s5bk30MNDqacdrG2YzUxjD4m9OtyRnnkKhJKVpvMg70h88busWhVLO3NPaYfL6mPlhfJC3SJfmwpgm_Lzfl5MOuNmGgvV_TwWTznDxxZkz44va9It8-fvh69rm6-PLp_Oz0orJ1p3LF-roBztsazaB6YbkDNJJ3AyrhlOPOgTDIwHErTM-kks5ZIUqXA3NYixV5v9fdLv2Egy2Oiz-9nf1k5msdjdeHneA3eh2vdCNaJjkvAm9vBeZY7KSsJ58sjqMJGJekuYROiVbJrqCv_0Av4zKHYq9QjWqkZPAfiqsGZCvEPbU2I2ofXCx_Z3ef1qdKQM3brmGFOvkLVZ4BJ1_uEJ0v5wcDbx4MbNCMeZPiuOyuOh2CbA_aOaY0o7tbGQO9S6DeJ1CXBOpdAsvCVuTVw13fTfyOXAH4HkilFdY431v_t-ovFdnmeg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2426704833</pqid></control><display><type>article</type><title>Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data</title><source>MEDLINE</source><source>Nature Journals Online</source><source>Alma/SFX Local Collection</source><creator>Campbell, Elizabeth A. ; Qian, Ting ; Miller, Jeffrey M. ; Bass, Ellen J. ; Masino, Aaron J.</creator><creatorcontrib>Campbell, Elizabeth A. ; Qian, Ting ; Miller, Jeffrey M. ; Bass, Ellen J. ; Masino, Aaron J.</creatorcontrib><description>Background
Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts.
Methods
EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children’s Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar’s test was used to assess the statistical significance of pattern prevalence differences between the case and control populations.
Results
SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (
p
< 0.05). Asthma and allergic rhinitis were strongly associated with childhood obesity incidence, particularly during the pre-index and index visits. Seven conditions were commonly diagnosed for cases exclusively during pre-index visits, including ear, nose, and throat disorders and gastroenteritis.
Conclusions
The novel application of SPADE on a large retrospective dataset revealed temporally dependent condition associations with obesity incidence. Allergic rhinitis and asthma had a particularly high prevalence during pre-index visits. These conditions, along with those exclusively observed during pre-index visits, may represent signals of future obesity. While causation cannot be inferred from these associations, the temporal condition patterns identified here represent hypotheses that can be investigated to determine causal relationships in future obesity research.</description><identifier>ISSN: 0307-0565</identifier><identifier>ISSN: 1476-5497</identifier><identifier>EISSN: 1476-5497</identifier><identifier>DOI: 10.1038/s41366-020-0614-7</identifier><identifier>PMID: 32494036</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/499 ; 692/699/2743/393 ; 692/700/1720 ; Adolescent ; Algorithms ; Allergic rhinitis ; Asthma ; Asthma - epidemiology ; Big Data ; Body weight ; Case-Control Studies ; Causation ; Child ; Child, Preschool ; Children ; Comparative analysis ; Electronic Health Records ; Electronic medical records ; Epidemiology ; Female ; Gastroenteritis ; Health aspects ; Health Promotion and Disease Prevention ; Humans ; Incidence ; Internal Medicine ; Male ; Medical records ; Medical research ; Medicine ; Medicine & Public Health ; Medicine, Experimental ; Metabolic Diseases ; Obesity ; Obesity in children ; Patients ; Pediatric Obesity - epidemiology ; Pediatrics ; Philadelphia - epidemiology ; Population ; Population (statistical) ; Population control ; Population studies ; Prevention ; Public Health ; Retrospective Studies ; Rhinitis ; Rhinitis, Allergic - epidemiology</subject><ispartof>International Journal of Obesity, 2020-08, Vol.44 (8), p.1753-1765</ispartof><rights>The Author(s) 2020</rights><rights>COPYRIGHT 2020 Nature Publishing Group</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c596t-1b5702285ead6b3c2f0ea429de63f6f2ff03ae10f2c3ab1464ffc33e63201fe53</citedby><cites>FETCH-LOGICAL-c596t-1b5702285ead6b3c2f0ea429de63f6f2ff03ae10f2c3ab1464ffc33e63201fe53</cites><orcidid>0000-0002-2684-0548</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32494036$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Campbell, Elizabeth A.</creatorcontrib><creatorcontrib>Qian, Ting</creatorcontrib><creatorcontrib>Miller, Jeffrey M.</creatorcontrib><creatorcontrib>Bass, Ellen J.</creatorcontrib><creatorcontrib>Masino, Aaron J.</creatorcontrib><title>Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data</title><title>International Journal of Obesity</title><addtitle>Int J Obes</addtitle><addtitle>Int J Obes (Lond)</addtitle><description>Background
Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts.
Methods
EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children’s Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar’s test was used to assess the statistical significance of pattern prevalence differences between the case and control populations.
Results
SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (
p
< 0.05). Asthma and allergic rhinitis were strongly associated with childhood obesity incidence, particularly during the pre-index and index visits. Seven conditions were commonly diagnosed for cases exclusively during pre-index visits, including ear, nose, and throat disorders and gastroenteritis.
Conclusions
The novel application of SPADE on a large retrospective dataset revealed temporally dependent condition associations with obesity incidence. Allergic rhinitis and asthma had a particularly high prevalence during pre-index visits. These conditions, along with those exclusively observed during pre-index visits, may represent signals of future obesity. While causation cannot be inferred from these associations, the temporal condition patterns identified here represent hypotheses that can be investigated to determine causal relationships in future obesity research.</description><subject>692/499</subject><subject>692/699/2743/393</subject><subject>692/700/1720</subject><subject>Adolescent</subject><subject>Algorithms</subject><subject>Allergic rhinitis</subject><subject>Asthma</subject><subject>Asthma - epidemiology</subject><subject>Big Data</subject><subject>Body weight</subject><subject>Case-Control Studies</subject><subject>Causation</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Children</subject><subject>Comparative analysis</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Gastroenteritis</subject><subject>Health aspects</subject><subject>Health Promotion and Disease Prevention</subject><subject>Humans</subject><subject>Incidence</subject><subject>Internal Medicine</subject><subject>Male</subject><subject>Medical records</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Medicine, Experimental</subject><subject>Metabolic Diseases</subject><subject>Obesity</subject><subject>Obesity in children</subject><subject>Patients</subject><subject>Pediatric Obesity - epidemiology</subject><subject>Pediatrics</subject><subject>Philadelphia - epidemiology</subject><subject>Population</subject><subject>Population (statistical)</subject><subject>Population control</subject><subject>Population studies</subject><subject>Prevention</subject><subject>Public Health</subject><subject>Retrospective Studies</subject><subject>Rhinitis</subject><subject>Rhinitis, Allergic - epidemiology</subject><issn>0307-0565</issn><issn>1476-5497</issn><issn>1476-5497</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkk1v1DAQhi0EomXhB3BBlpAQl5TxR5zkglRVfFSqxAXOluOMd10l9hI7RZX48Xi7pe0iEMohGs8zb_KOX0JeMjhhINp3STKhVAUcKlBMVs0jcsxko6pads1jcgwCmgpqVR-RZyldAkBdA39KjgSXnQShjsnP8wFD9s5bk30MNDqacdrG2YzUxjD4m9OtyRnnkKhJKVpvMg70h88busWhVLO3NPaYfL6mPlhfJC3SJfmwpgm_Lzfl5MOuNmGgvV_TwWTznDxxZkz44va9It8-fvh69rm6-PLp_Oz0orJ1p3LF-roBztsazaB6YbkDNJJ3AyrhlOPOgTDIwHErTM-kks5ZIUqXA3NYixV5v9fdLv2Egy2Oiz-9nf1k5msdjdeHneA3eh2vdCNaJjkvAm9vBeZY7KSsJ58sjqMJGJekuYROiVbJrqCv_0Av4zKHYq9QjWqkZPAfiqsGZCvEPbU2I2ofXCx_Z3ef1qdKQM3brmGFOvkLVZ4BJ1_uEJ0v5wcDbx4MbNCMeZPiuOyuOh2CbA_aOaY0o7tbGQO9S6DeJ1CXBOpdAsvCVuTVw13fTfyOXAH4HkilFdY431v_t-ovFdnmeg</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Campbell, Elizabeth A.</creator><creator>Qian, Ting</creator><creator>Miller, Jeffrey M.</creator><creator>Bass, Ellen J.</creator><creator>Masino, Aaron J.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><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>7T2</scope><scope>7TK</scope><scope>7TS</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2684-0548</orcidid></search><sort><creationdate>20200801</creationdate><title>Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data</title><author>Campbell, Elizabeth A. ; Qian, Ting ; Miller, Jeffrey M. ; Bass, Ellen J. ; Masino, Aaron J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c596t-1b5702285ead6b3c2f0ea429de63f6f2ff03ae10f2c3ab1464ffc33e63201fe53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>692/499</topic><topic>692/699/2743/393</topic><topic>692/700/1720</topic><topic>Adolescent</topic><topic>Algorithms</topic><topic>Allergic rhinitis</topic><topic>Asthma</topic><topic>Asthma - 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epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Campbell, Elizabeth A.</creatorcontrib><creatorcontrib>Qian, Ting</creatorcontrib><creatorcontrib>Miller, Jeffrey M.</creatorcontrib><creatorcontrib>Bass, Ellen J.</creatorcontrib><creatorcontrib>Masino, Aaron J.</creatorcontrib><collection>SpringerOpen website</collection><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>Health and Safety Science Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Physical Education Index</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database (Proquest)</collection><collection>ProQuest SciTech 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>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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 Health & Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Psychology Database (ProQuest)</collection><collection>Biological Science 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 One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International Journal of Obesity</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Campbell, Elizabeth A.</au><au>Qian, Ting</au><au>Miller, Jeffrey M.</au><au>Bass, Ellen J.</au><au>Masino, Aaron J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data</atitle><jtitle>International Journal of Obesity</jtitle><stitle>Int J Obes</stitle><addtitle>Int J Obes (Lond)</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>44</volume><issue>8</issue><spage>1753</spage><epage>1765</epage><pages>1753-1765</pages><issn>0307-0565</issn><issn>1476-5497</issn><eissn>1476-5497</eissn><abstract>Background
Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts.
Methods
EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children’s Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar’s test was used to assess the statistical significance of pattern prevalence differences between the case and control populations.
Results
SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (
p
< 0.05). Asthma and allergic rhinitis were strongly associated with childhood obesity incidence, particularly during the pre-index and index visits. Seven conditions were commonly diagnosed for cases exclusively during pre-index visits, including ear, nose, and throat disorders and gastroenteritis.
Conclusions
The novel application of SPADE on a large retrospective dataset revealed temporally dependent condition associations with obesity incidence. Allergic rhinitis and asthma had a particularly high prevalence during pre-index visits. These conditions, along with those exclusively observed during pre-index visits, may represent signals of future obesity. While causation cannot be inferred from these associations, the temporal condition patterns identified here represent hypotheses that can be investigated to determine causal relationships in future obesity research.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32494036</pmid><doi>10.1038/s41366-020-0614-7</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2684-0548</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0307-0565 |
| ispartof | International Journal of Obesity, 2020-08, Vol.44 (8), p.1753-1765 |
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| subjects | 692/499 692/699/2743/393 692/700/1720 Adolescent Algorithms Allergic rhinitis Asthma Asthma - epidemiology Big Data Body weight Case-Control Studies Causation Child Child, Preschool Children Comparative analysis Electronic Health Records Electronic medical records Epidemiology Female Gastroenteritis Health aspects Health Promotion and Disease Prevention Humans Incidence Internal Medicine Male Medical records Medical research Medicine Medicine & Public Health Medicine, Experimental Metabolic Diseases Obesity Obesity in children Patients Pediatric Obesity - epidemiology Pediatrics Philadelphia - epidemiology Population Population (statistical) Population control Population studies Prevention Public Health Retrospective Studies Rhinitis Rhinitis, Allergic - epidemiology |
| title | Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data |
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