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Background: The objective was to assess changes over time in prescriptions filled for nonopioid analgesics for older postoperative patients in the immediate postdischarge period. The authors hypothesized that the number of patients who filled a nonopioid analgesic prescription increased during the study period., Methods: The authors performed a population-based cohort study using linked health administrative data of 278,366 admissions aged 66 yr or older undergoing surgery between fiscal year 2013 and 2019 in Ontario, Canada. The primary outcome was the percentage of patients with new filled prescriptions for nonopioid analgesics within 7 days of discharge, and the secondary outcome was the analgesic class. The authors assessed whether patients filled prescriptions for a nonopioid only, an opioid only, both opioid and nonopioid prescriptions, or a combination opioid/nonopioid., Results: Overall, 22% (n = 60,181) of patients filled no opioid prescription, 2% (n = 5,534) filled a nonopioid only, 21% (n = 59,608) filled an opioid only, and 55% (n = 153,043) filled some combination of opioid and nonopioid. The percentage of patients who filled a nonopioid prescription within 7 days postoperatively increased from 9% (n = 2,119) in 2013 to 28% (n = 13,090) in 2019, with the greatest increase for acetaminophen: 3% (n = 701) to 20% (n = 9,559). The percentage of patients who filled a combination analgesic prescription decreased from 53% (n = 12,939) in 2013 to 28% (n = 13,453) in 2019. However, the percentage who filled both an opioid and nonopioid prescription increased: 4% (n = 938) to 21% (n = 9,880) so that the overall percentage of patients who received both an opioid and a nonopioid remained constant over time 76% (n = 18,642) in 2013 to 75% (n = 35,391) in 2019., Conclusions: The proportion of postoperative patients who fill prescriptions for nonopioid analgesics has increased. However, rather than a move to use of nonopioids alone for analgesia, this represents a shift away from combination medications toward separate prescriptions for opioids and nonopioids., (Copyright © 2022, the American Society of Anesthesiologists. All Rights Reserved.)

There are an increasing number of "big data" studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers' Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. When such a trial is infeasible, observational studies using robust regression techniques may be able to advance knowledge. Logistic regression is used when the outcome is binary (e.g., intracranial hemorrhage: yes or no), by modeling the natural log for the odds of an outcome. Because outcomes are influenced by many factors, we commonly use multivariable logistic regression to estimate the unique influence of each factor. From this tutorial, one should acquire a clearer understanding of how to perform and assess multivariable logistic regression.