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Background Increased illness due to antigenically drifted A(H3N2) clade 3C.3a influenza viruses prompted concerns about vaccine effectiveness (VE) and vaccine strain selection. We used US virologic surveillance and US Influenza Vaccine Effectiveness (Flu VE) Network data to evaluate consequences of this clade. Methods Distribution of influenza viruses was described using virologic surveillance data. The Flu VE Network enrolled ambulatory care patients aged ≥6 months with acute respiratory illness at 5 sites. Respiratory specimens were tested for influenza by means of reverse-transcriptase polymerase chain reaction and were sequenced. Using a test-negative design, we estimated VE, comparing the odds of influenza among vaccinated versus unvaccinated participants. Results During the 2018–2019 influenza season, A(H3N2) clade 3C.3a viruses caused an increasing proportion of influenza cases. Among 2763 Flu VE Network case patients, 1325 (48%) were infected with A(H1N1)pdm09 and 1350 (49%) with A(H3N2); clade 3C.3a accounted for 977 (93%) of 1054 sequenced A(H3N2) viruses. VE was 44% (95% confidence interval, 37%–51%) against A(H1N1)pdm09 and 9% (−4% to 20%) against A(H3N2); VE was 5% (−10% to 19%) against A(H3N2) clade 3C.3a viruses. Conclusions The predominance of A(H3N2) clade 3C.3a viruses during the latter part of the 2018–2019 season was associated with decreased VE, supporting the A(H3N2) vaccine component update for 2019–2020 northern hemisphere influenza vaccines.

During 2013-2014, IIV was significantly effective against A(H1N1)pdm09. Lack of LAIV4 effectiveness in children highlights the importance of continued annual monitoring of effectiveness of influenza vaccines in the United States.

Background Diagnostic errors in primary care are harmful but difficult to detect. We tested an electronic health record (EHR)-based method to detect diagnostic errors in routine primary care practice. Methods We conducted a retrospective study of primary care visit records “triggered” through electronic queries for possible evidence of diagnostic errors: Trigger 1: A primary care index visit followed by unplanned hospitalization within 14 days; and Trigger 2: A primary care index visit followed by ≥ 1 unscheduled visit(s) within 14 days. Control visits met neither criterion. Electronic trigger queries were applied to EHR repositories at two large healthcare systems between October 1, 2006 and September 30, 2007. Blinded physician-reviewers independently determined presence or absence of diagnostic errors in selected triggered and control visits. An error was defined as a missed opportunity to make or pursue the correct diagnosis when adequate data was available at the index visit. Disagreements were resolved by an independent third reviewer. Results Queries were applied to 212,165 visits. On record review, we found diagnostic errors in 141 of 674 Trigger 1-positive records (PPV=20.9%, 95% CI, 17.9%-24.0%) and 36 of 669 Trigger 2-positive records (PPV=5.4%, 95% CI, 3.7%-7.1%). The control PPV of 2.1% (95% CI, 0.1%-3.3%) was significantly lower than that of both triggers (P ≤ .002). Inter-rater reliability was modest, though higher than in comparable previous studies (κ = 0.37 [95% CI=0.31-0.44]). Conclusions While physician agreement on diagnostic error remains low, an EHR-facilitated surveillance methodology could be useful for gaining insight into the origin of these errors.