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
Hauptverfasser: D'Arcy, Monica E., Stürmer, Til, Sandler, Robert S., Baron, John A., Jonsson‐Funk, Michele L., Troester, Melissa A., Lund, Jennifer L.
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container_end_page 329
container_issue 3
container_start_page 321
container_title Pharmacoepidemiology and drug safety
container_volume 32
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
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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 &lt;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 &amp; 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 &amp; Sons Ltd.</rights><rights>2023 John Wiley &amp; 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 &lt;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 &amp; 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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 &lt;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 &amp; 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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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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