Healthcare system engagement and algorithm‐identified cancer incidence following initiation of a new medication
Purpose Implausibly high algorithm‐identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation (“catch‐up care”) that increases diagnostic opportunity. Understanding the relationships between HU prior to and...
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Veröffentlicht in: | Pharmacoepidemiology and drug safety 2023-03, Vol.32 (3), p.321-329 |
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creator | D'Arcy, Monica E. Stürmer, Til Sandler, Robert S. Baron, John A. Jonsson‐Funk, Michele L. Troester, Melissa A. Lund, Jennifer L. |
description | Purpose
Implausibly high algorithm‐identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation (“catch‐up care”) that increases diagnostic opportunity. Understanding the relationships between HU prior to and around initiation and subsequent cancer rates and timing is important to avoiding protopathic bias.
Methods
We identified a cohort of 417 458 Medicare beneficiaries (2007–2014) aged ≥66 initiating an antihypertensive (AHT) after ≥180 days of non‐use. Initiators were stratified into groups of 0, 1, 2–3, and ≥4 outpatient visits (OV) 60–360 days before initiation. We calculated algorithm‐identified colorectal cancer (aiCRC) rates stratified by OVs and time since AHT initiation: (0–90, 91–180, 181–365, 366–730, and 731+ days). We summarized HU ‐360/+60 days around AHT initiation by aiCRC timing: (0–29, 30–89, 90–179, and ≥180 days).
Results
AiCRC incidence (311 per 100 000 overall) peaked in the first 0–90 days, was inversely associated with HU before initiation, and stabilized ≥180 days after AHT initiation. Catch‐up care was greatest among persons with aiCRCs identified |
doi_str_mv | 10.1002/pds.5556 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9931672</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2737471088</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4046-f6d0082c737fd0c053594cdf38e3e53a447139657a1b579f0c0832a1395a707f3</originalsourceid><addsrcrecordid>eNp1kd1qFTEUhYMotlbBJ5CAN95MTSaTZHIjSP2pUFBQr0Oa2ZmTMpOcJnN6OHc-gs_ok7hPW-sPeJWw9sfK2lmEPOXsmDPWvlwP9VhKqe6RQ86MabiU-v7-LkXTS2UOyKNaLxjDmekekgOhhOl43x6Sy1Nw07LyrgCtu7rATCGNboQZ0kJdGqibxlzispp_fPseB1RjiDBQ75KHQmPye9EDDXma8jamEbW4RLfEnGgO1NEEWzrDEP219pg8CG6q8OT2PCJf3739cnLanH18_-Hk9VnjO9apJqiBsb71WugwMI-bSNP5IYgeBEjhuk5zYZTUjp9LbQIivWgdatJppoM4Iq9ufNebc3zdY_LiJrsucXZlZ7OL9u9Jiis75itrjOBKt2jw4tag5MsN1MXOsXqYJpcgb6ptMRqGYH2P6PN_0Iu8KQnXQ0qrVuG_69-GvuRaC4S7MJzZfY8We7T7HhF99mf4O_BXcQg0N8A2TrD7r5H99ObzteFPk-6pmg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2776269997</pqid></control><display><type>article</type><title>Healthcare system engagement and algorithm‐identified cancer incidence following initiation of a new medication</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><creator>D'Arcy, Monica E. ; Stürmer, Til ; Sandler, Robert S. ; Baron, John A. ; Jonsson‐Funk, Michele L. ; Troester, Melissa A. ; Lund, Jennifer L.</creator><creatorcontrib>D'Arcy, Monica E. ; Stürmer, Til ; Sandler, Robert S. ; Baron, John A. ; Jonsson‐Funk, Michele L. ; Troester, Melissa A. ; Lund, Jennifer L.</creatorcontrib><description>Purpose
Implausibly high algorithm‐identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation (“catch‐up care”) that increases diagnostic opportunity. Understanding the relationships between HU prior to and around initiation and subsequent cancer rates and timing is important to avoiding protopathic bias.
Methods
We identified a cohort of 417 458 Medicare beneficiaries (2007–2014) aged ≥66 initiating an antihypertensive (AHT) after ≥180 days of non‐use. Initiators were stratified into groups of 0, 1, 2–3, and ≥4 outpatient visits (OV) 60–360 days before initiation. We calculated algorithm‐identified colorectal cancer (aiCRC) rates stratified by OVs and time since AHT initiation: (0–90, 91–180, 181–365, 366–730, and 731+ days). We summarized HU ‐360/+60 days around AHT initiation by aiCRC timing: (0–29, 30–89, 90–179, and ≥180 days).
Results
AiCRC incidence (311 per 100 000 overall) peaked in the first 0–90 days, was inversely associated with HU before initiation, and stabilized ≥180 days after AHT initiation. Catch‐up care was greatest among persons with aiCRCs identified <30 days in follow‐up. Catch‐up care magnitude decreased as time to the aiCRC date increased, with aiCRCs identified ≥180 days after AHT initiation exhibiting similar HU compared with the full cohort.
Conclusion
Lower HU before—and increased HU around AHT initiation—seem to drive excess short‐term aiCRC incidence. Person‐time and case accrual should only begin when incidence stabilizes. When comparison groups within a study differ by HU, outcome‐detection bias may exist. Similar observations may exist in other settings when typical HU is delayed (e.g., cancer screening during SARS‐CoV‐2).</description><identifier>ISSN: 1053-8569</identifier><identifier>ISSN: 1099-1557</identifier><identifier>EISSN: 1099-1557</identifier><identifier>DOI: 10.1002/pds.5556</identifier><identifier>PMID: 36394182</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Aged ; algorithm ; Algorithms ; Antihypertensives ; Cancer ; Cancer screening ; Colorectal cancer ; Colorectal carcinoma ; COVID-19 ; Delivery of Health Care ; Health care ; healthcare delivery ; Humans ; Incidence ; Medical screening ; Medicare ; Neoplasms ; new user study ; outcome detection bias ; protopathic bias ; SARS-CoV-2 ; Severe acute respiratory syndrome coronavirus 2 ; United States - epidemiology</subject><ispartof>Pharmacoepidemiology and drug safety, 2023-03, Vol.32 (3), p.321-329</ispartof><rights>2022 John Wiley & Sons Ltd.</rights><rights>2023 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4046-f6d0082c737fd0c053594cdf38e3e53a447139657a1b579f0c0832a1395a707f3</citedby><cites>FETCH-LOGICAL-c4046-f6d0082c737fd0c053594cdf38e3e53a447139657a1b579f0c0832a1395a707f3</cites><orcidid>0000-0002-1586-9089 ; 0000-0002-1108-0689 ; 0000-0002-9204-7177</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.5556$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fpds.5556$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36394182$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>D'Arcy, Monica E.</creatorcontrib><creatorcontrib>Stürmer, Til</creatorcontrib><creatorcontrib>Sandler, Robert S.</creatorcontrib><creatorcontrib>Baron, John A.</creatorcontrib><creatorcontrib>Jonsson‐Funk, Michele L.</creatorcontrib><creatorcontrib>Troester, Melissa A.</creatorcontrib><creatorcontrib>Lund, Jennifer L.</creatorcontrib><title>Healthcare system engagement and algorithm‐identified cancer incidence following initiation of a new medication</title><title>Pharmacoepidemiology and drug safety</title><addtitle>Pharmacoepidemiol Drug Saf</addtitle><description>Purpose
Implausibly high algorithm‐identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation (“catch‐up care”) that increases diagnostic opportunity. Understanding the relationships between HU prior to and around initiation and subsequent cancer rates and timing is important to avoiding protopathic bias.
Methods
We identified a cohort of 417 458 Medicare beneficiaries (2007–2014) aged ≥66 initiating an antihypertensive (AHT) after ≥180 days of non‐use. Initiators were stratified into groups of 0, 1, 2–3, and ≥4 outpatient visits (OV) 60–360 days before initiation. We calculated algorithm‐identified colorectal cancer (aiCRC) rates stratified by OVs and time since AHT initiation: (0–90, 91–180, 181–365, 366–730, and 731+ days). We summarized HU ‐360/+60 days around AHT initiation by aiCRC timing: (0–29, 30–89, 90–179, and ≥180 days).
Results
AiCRC incidence (311 per 100 000 overall) peaked in the first 0–90 days, was inversely associated with HU before initiation, and stabilized ≥180 days after AHT initiation. Catch‐up care was greatest among persons with aiCRCs identified <30 days in follow‐up. Catch‐up care magnitude decreased as time to the aiCRC date increased, with aiCRCs identified ≥180 days after AHT initiation exhibiting similar HU compared with the full cohort.
Conclusion
Lower HU before—and increased HU around AHT initiation—seem to drive excess short‐term aiCRC incidence. Person‐time and case accrual should only begin when incidence stabilizes. When comparison groups within a study differ by HU, outcome‐detection bias may exist. Similar observations may exist in other settings when typical HU is delayed (e.g., cancer screening during SARS‐CoV‐2).</description><subject>Aged</subject><subject>algorithm</subject><subject>Algorithms</subject><subject>Antihypertensives</subject><subject>Cancer</subject><subject>Cancer screening</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>COVID-19</subject><subject>Delivery of Health Care</subject><subject>Health care</subject><subject>healthcare delivery</subject><subject>Humans</subject><subject>Incidence</subject><subject>Medical screening</subject><subject>Medicare</subject><subject>Neoplasms</subject><subject>new user study</subject><subject>outcome detection bias</subject><subject>protopathic bias</subject><subject>SARS-CoV-2</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>United States - epidemiology</subject><issn>1053-8569</issn><issn>1099-1557</issn><issn>1099-1557</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kd1qFTEUhYMotlbBJ5CAN95MTSaTZHIjSP2pUFBQr0Oa2ZmTMpOcJnN6OHc-gs_ok7hPW-sPeJWw9sfK2lmEPOXsmDPWvlwP9VhKqe6RQ86MabiU-v7-LkXTS2UOyKNaLxjDmekekgOhhOl43x6Sy1Nw07LyrgCtu7rATCGNboQZ0kJdGqibxlzispp_fPseB1RjiDBQ75KHQmPye9EDDXma8jamEbW4RLfEnGgO1NEEWzrDEP219pg8CG6q8OT2PCJf3739cnLanH18_-Hk9VnjO9apJqiBsb71WugwMI-bSNP5IYgeBEjhuk5zYZTUjp9LbQIivWgdatJppoM4Iq9ufNebc3zdY_LiJrsucXZlZ7OL9u9Jiis75itrjOBKt2jw4tag5MsN1MXOsXqYJpcgb6ptMRqGYH2P6PN_0Iu8KQnXQ0qrVuG_69-GvuRaC4S7MJzZfY8We7T7HhF99mf4O_BXcQg0N8A2TrD7r5H99ObzteFPk-6pmg</recordid><startdate>202303</startdate><enddate>202303</enddate><creator>D'Arcy, Monica E.</creator><creator>Stürmer, Til</creator><creator>Sandler, Robert S.</creator><creator>Baron, John A.</creator><creator>Jonsson‐Funk, Michele L.</creator><creator>Troester, Melissa A.</creator><creator>Lund, Jennifer L.</creator><general>John Wiley & 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><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1586-9089</orcidid><orcidid>https://orcid.org/0000-0002-1108-0689</orcidid><orcidid>https://orcid.org/0000-0002-9204-7177</orcidid></search><sort><creationdate>202303</creationdate><title>Healthcare system engagement and algorithm‐identified cancer incidence following initiation of a new medication</title><author>D'Arcy, Monica E. ; Stürmer, Til ; Sandler, Robert S. ; Baron, John A. ; Jonsson‐Funk, Michele L. ; Troester, Melissa A. ; Lund, Jennifer L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4046-f6d0082c737fd0c053594cdf38e3e53a447139657a1b579f0c0832a1395a707f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aged</topic><topic>algorithm</topic><topic>Algorithms</topic><topic>Antihypertensives</topic><topic>Cancer</topic><topic>Cancer screening</topic><topic>Colorectal cancer</topic><topic>Colorectal carcinoma</topic><topic>COVID-19</topic><topic>Delivery of Health Care</topic><topic>Health care</topic><topic>healthcare delivery</topic><topic>Humans</topic><topic>Incidence</topic><topic>Medical screening</topic><topic>Medicare</topic><topic>Neoplasms</topic><topic>new user study</topic><topic>outcome detection bias</topic><topic>protopathic bias</topic><topic>SARS-CoV-2</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>D'Arcy, Monica E.</creatorcontrib><creatorcontrib>Stürmer, Til</creatorcontrib><creatorcontrib>Sandler, Robert S.</creatorcontrib><creatorcontrib>Baron, John A.</creatorcontrib><creatorcontrib>Jonsson‐Funk, Michele L.</creatorcontrib><creatorcontrib>Troester, Melissa A.</creatorcontrib><creatorcontrib>Lund, Jennifer L.</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 & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Pharmacoepidemiology and drug safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>D'Arcy, Monica E.</au><au>Stürmer, Til</au><au>Sandler, Robert S.</au><au>Baron, John A.</au><au>Jonsson‐Funk, Michele L.</au><au>Troester, Melissa A.</au><au>Lund, Jennifer L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Healthcare system engagement and algorithm‐identified cancer incidence following initiation of a new medication</atitle><jtitle>Pharmacoepidemiology and drug safety</jtitle><addtitle>Pharmacoepidemiol Drug Saf</addtitle><date>2023-03</date><risdate>2023</risdate><volume>32</volume><issue>3</issue><spage>321</spage><epage>329</epage><pages>321-329</pages><issn>1053-8569</issn><issn>1099-1557</issn><eissn>1099-1557</eissn><abstract>Purpose
Implausibly high algorithm‐identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation (“catch‐up care”) that increases diagnostic opportunity. Understanding the relationships between HU prior to and around initiation and subsequent cancer rates and timing is important to avoiding protopathic bias.
Methods
We identified a cohort of 417 458 Medicare beneficiaries (2007–2014) aged ≥66 initiating an antihypertensive (AHT) after ≥180 days of non‐use. Initiators were stratified into groups of 0, 1, 2–3, and ≥4 outpatient visits (OV) 60–360 days before initiation. We calculated algorithm‐identified colorectal cancer (aiCRC) rates stratified by OVs and time since AHT initiation: (0–90, 91–180, 181–365, 366–730, and 731+ days). We summarized HU ‐360/+60 days around AHT initiation by aiCRC timing: (0–29, 30–89, 90–179, and ≥180 days).
Results
AiCRC incidence (311 per 100 000 overall) peaked in the first 0–90 days, was inversely associated with HU before initiation, and stabilized ≥180 days after AHT initiation. Catch‐up care was greatest among persons with aiCRCs identified <30 days in follow‐up. Catch‐up care magnitude decreased as time to the aiCRC date increased, with aiCRCs identified ≥180 days after AHT initiation exhibiting similar HU compared with the full cohort.
Conclusion
Lower HU before—and increased HU around AHT initiation—seem to drive excess short‐term aiCRC incidence. Person‐time and case accrual should only begin when incidence stabilizes. When comparison groups within a study differ by HU, outcome‐detection bias may exist. Similar observations may exist in other settings when typical HU is delayed (e.g., cancer screening during SARS‐CoV‐2).</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><pmid>36394182</pmid><doi>10.1002/pds.5556</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-1586-9089</orcidid><orcidid>https://orcid.org/0000-0002-1108-0689</orcidid><orcidid>https://orcid.org/0000-0002-9204-7177</orcidid></addata></record> |
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source | MEDLINE; Access via Wiley Online Library |
subjects | Aged algorithm Algorithms Antihypertensives Cancer Cancer screening Colorectal cancer Colorectal carcinoma COVID-19 Delivery of Health Care Health care healthcare delivery Humans Incidence Medical screening Medicare Neoplasms new user study outcome detection bias protopathic bias SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 United States - epidemiology |
title | Healthcare system engagement and algorithm‐identified cancer incidence following initiation of a new medication |
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