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 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).