Effect of ICD‐9‐CM to ICD‐10‐CM coding system transition on identification of common conditions: An interrupted time series analysis

Purpose To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database. Methods Using the National Health Insurance Research Database, we estimated the 3‐month prevalence of recorded diagnosis of 32 conditio...

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Veröffentlicht in:Pharmacoepidemiology and drug safety 2021-12, Vol.30 (12), p.1653-1674
Hauptverfasser: Hsu, Meng‐Chen, Wang, Chi‐Chuan, Huang, Ling‐Ya, Lin, Chih‐Ying, Lin, Fang‐Ju, Toh, Sengwee
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container_issue 12
container_start_page 1653
container_title Pharmacoepidemiology and drug safety
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creator Hsu, Meng‐Chen
Wang, Chi‐Chuan
Huang, Ling‐Ya
Lin, Chih‐Ying
Lin, Fang‐Ju
Toh, Sengwee
description Purpose To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database. Methods Using the National Health Insurance Research Database, we estimated the 3‐month prevalence of recorded diagnosis of 32 conditions based on the ICD‐9‐CM codes in 2014–2015 and the ICD‐10‐CM codes in 2016–2017. Two algorithms were assessed for ICD‐10‐CM: validated ICD‐10 codes in the literature and codes translated from ICD‐9‐CM using an established mapping algorithm. We used segmented regression analysis on time‐series data to examine changes in the 3‐month prevalence (both level and trend) before and after the ICD‐10‐CM implementation. Results Significant changes in the level were found in 19 and 11 conditions when using the ICD‐10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD‐10 codes from the literature or mapping algorithm. Conclusions Less than half of the 32 conditions studied had a smooth transition between the ICD‐9‐CM and ICD‐10‐CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.
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Methods Using the National Health Insurance Research Database, we estimated the 3‐month prevalence of recorded diagnosis of 32 conditions based on the ICD‐9‐CM codes in 2014–2015 and the ICD‐10‐CM codes in 2016–2017. Two algorithms were assessed for ICD‐10‐CM: validated ICD‐10 codes in the literature and codes translated from ICD‐9‐CM using an established mapping algorithm. We used segmented regression analysis on time‐series data to examine changes in the 3‐month prevalence (both level and trend) before and after the ICD‐10‐CM implementation. Results Significant changes in the level were found in 19 and 11 conditions when using the ICD‐10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD‐10 codes from the literature or mapping algorithm. Conclusions Less than half of the 32 conditions studied had a smooth transition between the ICD‐9‐CM and ICD‐10‐CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.</description><identifier>ISSN: 1053-8569</identifier><identifier>EISSN: 1099-1557</identifier><identifier>DOI: 10.1002/pds.5330</identifier><identifier>PMID: 34258812</identifier><language>eng</language><publisher>Chichester, UK: John Wiley &amp; Sons, Inc</publisher><subject>Abuse ; Alcohol abuse ; Algorithms ; Anemia ; Clinical Coding ; Collagen ; Coronary artery disease ; Databases, Factual ; diagnostic coding transition ; Drug abuse ; Heart diseases ; Humans ; ICD‐10‐CM ; ICD‐9‐CM ; International Classification of Diseases ; Interrupted Time Series Analysis ; Liver cancer ; Liver diseases ; Mapping ; Metastases ; Prevalence ; Psychosis ; Rheumatoid arthritis ; Time series ; Vascular diseases</subject><ispartof>Pharmacoepidemiology and drug safety, 2021-12, Vol.30 (12), p.1653-1674</ispartof><rights>2021 John Wiley &amp; Sons Ltd.</rights><rights>2021 John Wiley &amp; Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3490-dff40de5c75294b19d4e11e15a1791873981bb462216d1b5a7cd454bbfdf41a3</citedby><cites>FETCH-LOGICAL-c3490-dff40de5c75294b19d4e11e15a1791873981bb462216d1b5a7cd454bbfdf41a3</cites><orcidid>0000-0002-4597-4859 ; 0000-0002-8249-7481 ; 0000-0002-5160-0810</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fpds.5330$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fpds.5330$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34258812$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hsu, Meng‐Chen</creatorcontrib><creatorcontrib>Wang, Chi‐Chuan</creatorcontrib><creatorcontrib>Huang, Ling‐Ya</creatorcontrib><creatorcontrib>Lin, Chih‐Ying</creatorcontrib><creatorcontrib>Lin, Fang‐Ju</creatorcontrib><creatorcontrib>Toh, Sengwee</creatorcontrib><title>Effect of ICD‐9‐CM to ICD‐10‐CM coding system transition on identification of common conditions: An interrupted time series analysis</title><title>Pharmacoepidemiology and drug safety</title><addtitle>Pharmacoepidemiol Drug Saf</addtitle><description>Purpose To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database. Methods Using the National Health Insurance Research Database, we estimated the 3‐month prevalence of recorded diagnosis of 32 conditions based on the ICD‐9‐CM codes in 2014–2015 and the ICD‐10‐CM codes in 2016–2017. Two algorithms were assessed for ICD‐10‐CM: validated ICD‐10 codes in the literature and codes translated from ICD‐9‐CM using an established mapping algorithm. We used segmented regression analysis on time‐series data to examine changes in the 3‐month prevalence (both level and trend) before and after the ICD‐10‐CM implementation. Results Significant changes in the level were found in 19 and 11 conditions when using the ICD‐10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD‐10 codes from the literature or mapping algorithm. Conclusions Less than half of the 32 conditions studied had a smooth transition between the ICD‐9‐CM and ICD‐10‐CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.</description><subject>Abuse</subject><subject>Alcohol abuse</subject><subject>Algorithms</subject><subject>Anemia</subject><subject>Clinical Coding</subject><subject>Collagen</subject><subject>Coronary artery disease</subject><subject>Databases, Factual</subject><subject>diagnostic coding transition</subject><subject>Drug abuse</subject><subject>Heart diseases</subject><subject>Humans</subject><subject>ICD‐10‐CM</subject><subject>ICD‐9‐CM</subject><subject>International Classification of Diseases</subject><subject>Interrupted Time Series Analysis</subject><subject>Liver cancer</subject><subject>Liver diseases</subject><subject>Mapping</subject><subject>Metastases</subject><subject>Prevalence</subject><subject>Psychosis</subject><subject>Rheumatoid arthritis</subject><subject>Time series</subject><subject>Vascular diseases</subject><issn>1053-8569</issn><issn>1099-1557</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc1q3DAQx0VpaT5a6BMUQS-5ONVY0trKLWw-IaWF5m5kaVQUbGsjyYS95QF66DPmSaLNblsoFCQ0mvnxY-BPyAdgx8BY_Xll07HknL0i-8CUqkDK5vWmlrxq5ULtkYOU7hgrMyXekj0uatm2UO-Tn-fOock0OHq9PHt6_KXKXX6hOez-wLYNE6yfftC0ThlHmqOeks8-TLQcb3HK3nmjtx1X6HEslQmTfaHSCT0t3JQxxnmV0dLsR6QJo8dE9aSHdfLpHXnj9JDw_e49JLcX57fLq-rm6-X18vSmMlwoVlnnBLMoTSNrJXpQViAAgtTQKGgbrlroe7Goa1hY6KVujBVS9L2zToDmh-Roq13FcD9jyt3ok8Fh0BOGOXW1lCBbEDUr6Kd_0Lswx7LuhlKCLwSX8FdoYkgpoutW0Y86rjtg3SagrgTUbQIq6MedcO5HtH_A34kUoNoCD37A9X9F3bez7y_CZ-iAnP8</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Hsu, Meng‐Chen</creator><creator>Wang, Chi‐Chuan</creator><creator>Huang, Ling‐Ya</creator><creator>Lin, Chih‐Ying</creator><creator>Lin, Fang‐Ju</creator><creator>Toh, Sengwee</creator><general>John Wiley &amp; Sons, Inc</general><general>Wiley Subscription Services, Inc</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>7TK</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4597-4859</orcidid><orcidid>https://orcid.org/0000-0002-8249-7481</orcidid><orcidid>https://orcid.org/0000-0002-5160-0810</orcidid></search><sort><creationdate>202112</creationdate><title>Effect of ICD‐9‐CM to ICD‐10‐CM coding system transition on identification of common conditions: An interrupted time series analysis</title><author>Hsu, Meng‐Chen ; Wang, Chi‐Chuan ; Huang, Ling‐Ya ; Lin, Chih‐Ying ; Lin, Fang‐Ju ; Toh, Sengwee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3490-dff40de5c75294b19d4e11e15a1791873981bb462216d1b5a7cd454bbfdf41a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abuse</topic><topic>Alcohol abuse</topic><topic>Algorithms</topic><topic>Anemia</topic><topic>Clinical Coding</topic><topic>Collagen</topic><topic>Coronary artery disease</topic><topic>Databases, Factual</topic><topic>diagnostic coding transition</topic><topic>Drug abuse</topic><topic>Heart diseases</topic><topic>Humans</topic><topic>ICD‐10‐CM</topic><topic>ICD‐9‐CM</topic><topic>International Classification of Diseases</topic><topic>Interrupted Time Series Analysis</topic><topic>Liver cancer</topic><topic>Liver diseases</topic><topic>Mapping</topic><topic>Metastases</topic><topic>Prevalence</topic><topic>Psychosis</topic><topic>Rheumatoid arthritis</topic><topic>Time series</topic><topic>Vascular diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hsu, Meng‐Chen</creatorcontrib><creatorcontrib>Wang, Chi‐Chuan</creatorcontrib><creatorcontrib>Huang, Ling‐Ya</creatorcontrib><creatorcontrib>Lin, Chih‐Ying</creatorcontrib><creatorcontrib>Lin, Fang‐Ju</creatorcontrib><creatorcontrib>Toh, Sengwee</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Pharmacoepidemiology and drug safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hsu, Meng‐Chen</au><au>Wang, Chi‐Chuan</au><au>Huang, Ling‐Ya</au><au>Lin, Chih‐Ying</au><au>Lin, Fang‐Ju</au><au>Toh, Sengwee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of ICD‐9‐CM to ICD‐10‐CM coding system transition on identification of common conditions: An interrupted time series analysis</atitle><jtitle>Pharmacoepidemiology and drug safety</jtitle><addtitle>Pharmacoepidemiol Drug Saf</addtitle><date>2021-12</date><risdate>2021</risdate><volume>30</volume><issue>12</issue><spage>1653</spage><epage>1674</epage><pages>1653-1674</pages><issn>1053-8569</issn><eissn>1099-1557</eissn><abstract>Purpose To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database. Methods Using the National Health Insurance Research Database, we estimated the 3‐month prevalence of recorded diagnosis of 32 conditions based on the ICD‐9‐CM codes in 2014–2015 and the ICD‐10‐CM codes in 2016–2017. Two algorithms were assessed for ICD‐10‐CM: validated ICD‐10 codes in the literature and codes translated from ICD‐9‐CM using an established mapping algorithm. We used segmented regression analysis on time‐series data to examine changes in the 3‐month prevalence (both level and trend) before and after the ICD‐10‐CM implementation. Results Significant changes in the level were found in 19 and 11 conditions when using the ICD‐10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD‐10 codes from the literature or mapping algorithm. Conclusions Less than half of the 32 conditions studied had a smooth transition between the ICD‐9‐CM and ICD‐10‐CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.</abstract><cop>Chichester, UK</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>34258812</pmid><doi>10.1002/pds.5330</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-4597-4859</orcidid><orcidid>https://orcid.org/0000-0002-8249-7481</orcidid><orcidid>https://orcid.org/0000-0002-5160-0810</orcidid></addata></record>
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source MEDLINE; Wiley Online Library Journals Frontfile Complete
subjects Abuse
Alcohol abuse
Algorithms
Anemia
Clinical Coding
Collagen
Coronary artery disease
Databases, Factual
diagnostic coding transition
Drug abuse
Heart diseases
Humans
ICD‐10‐CM
ICD‐9‐CM
International Classification of Diseases
Interrupted Time Series Analysis
Liver cancer
Liver diseases
Mapping
Metastases
Prevalence
Psychosis
Rheumatoid arthritis
Time series
Vascular diseases
title Effect of ICD‐9‐CM to ICD‐10‐CM coding system transition on identification of common conditions: An interrupted time series analysis
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