Modeling the Impacts of Clinical Influenza Testing on Influenza Vaccine Effectiveness Estimates.

BACKGROUND: Test-negative design studies for evaluating influenza vaccine effectiveness (VE) enroll patients with acute respiratory infection. Enrollment typically occurs before influenza status is determined, resulting in over-enrollment of influenza-negative patients. With availability of rapid and accurate molecular clinical testing, influenza status could be ascertained before enrollment, thus improving study efficiency. We estimate potential biases in VE when using clinical testing. METHODS: We simulate data assuming 60% vaccinated, 25% of those vaccinated are influenza positive, and VE of 50%. We show the effect on VE in 5 scenarios. RESULTS: Vaccine effectiveness is affected only when clinical testing preferentially targets patients based on both vaccination and influenza status. Vaccine effectiveness is overestimated by 10% if nontesting occurs in 39% of vaccinated influenza-positive patients and 24% of others. VE is also overestimated by 10% if nontesting occurs in 8% of unvaccinated influenza-positive patients and 27% of others. Vaccine effectiveness is underestimated by 10% if nontesting occurs in 32% of unvaccinated influenza-negative patients and 18% of others. CONCLUSIONS: Although differential clinical testing by vaccine receipt and influenza positivity may produce errors in estimated VE, bias in testing would have to be substantial and overall proportion of patients tested would have to be small to result in a meaningful difference in VE.

Incorporating Real-time Influenza Detection Into the Test-negative Design for Estimating Influenza Vaccine Effectiveness: The Real-time Test-negative Design (rtTND).

With rapid and accurate molecular influenza testing now widely available in clinical settings, influenza vaccine effectiveness (VE) studies can prospectively select participants for enrollment based on real-time results rather than enrolling all eligible patients regardless of influenza status, as in the traditional test-negative design (TND). Thus, we explore advantages and disadvantages of modifying the TND for estimating VE by using real-time, clinically available viral testing results paired with acute respiratory infection eligibility criteria for identifying influenza cases and test-negative controls prior to enrollment. This modification, which we have called the real-time test-negative design (rtTND), has the potential to improve influenza VE studies by optimizing the case-to-test-negative control ratio, more accurately classifying influenza status, improving study efficiency, reducing study cost, and increasing study power to adequately estimate VE. Important considerations for limiting biases in the rtTND include the need for comprehensive clinical influenza testing at study sites and accurate influenza tests.

Influenza Vaccine Effectiveness for Prevention of Severe Influenza-Associated Illness Among Adults in the United States, 2019-2020: A Test-Negative Study.

BACKGROUND: Influenza vaccine effectiveness (VE) against a spectrum of severe disease, including critical illness and death, remains poorly characterized. METHODS: We conducted a test-negative study in an intensive care unit (ICU) network at 10 US hospitals to evaluate VE for preventing influenza-associated severe acute respiratory infection (SARI) during the 2019-2020 season, which was characterized by circulation of drifted A/H1N1 and B-lineage viruses. Cases were adults hospitalized in the ICU and a targeted number outside the ICU (to capture a spectrum of severity) with laboratory-confirmed, influenza-associated SARI. Test-negative controls were frequency-matched based on hospital, timing of admission, and care location (ICU vs non-ICU). Estimates were adjusted for age, comorbidities, and other confounders. RESULTS: Among 638 patients, the median (interquartile) age was 57 (44-68) years; 286 (44.8%) patients were treated in the ICU and 42 (6.6%) died during hospitalization. Forty-five percent of cases and 61% of controls were vaccinated, which resulted in an overall VE of 32% (95% CI: 2-53%), including 28% (-9% to 52%) against influenza A and 52% (13-74%) against influenza B. VE was higher in adults 18-49 years old (62%; 95% CI: 27-81%) than those aged 50-64 years (20%; -48% to 57%) and ≥65 years old (-3%; 95% CI: -97% to 46%) (P = .0789 for interaction). VE was significantly higher against influenza-associated death (80%; 95% CI: 4-96%) than nonfatal influenza illness. CONCLUSIONS: During a season with drifted viruses, vaccination reduced severe influenza-associated illness among adults by 32%. VE was high among young adults.

Influenza vaccine acceptance and hesitancy among adults hospitalized with severe acute respiratory illnesses, United States 2019-2020.

INTRODUCTION: Understanding patient factors associated with not being vaccinated is essential for successful implementation of influenza vaccination programs. METHODS: We enrolled adults hospitalized with severe acute respiratory illness at 10 United States (US) hospitals during the 2019-2020 influenza season. We interviewed patients to collect data about influenza vaccination, sociodemographic characteristics, and vaccine perceptions. RESULTS: Among 679 participants, 264 (38.9%) reported not receiving influenza vaccination. Among those not vaccinated, 135 (51.1%) reported choosing not to receive a vaccine because of perceived ineffectiveness (36.7%) or risk (14.4%) of influenza vaccination. Sociodemographic factors associated with not being vaccinated included no medical insurance (aOR = 6.42; 95% CI: 2.52-16.38) and being non-White or Hispanic (aOR = 1.54, 95% CI: 1.02-2.32). CONCLUSIONS: Optimizing uptake of influenza vaccination in the US may be improved by educational programs regarding vaccine safety and effectiveness and enhancing vaccine access, particularly among non-White and Hispanic Americans and those without medical insurance.

Comparison of clinical prediction models for resistant bacteria in community-onset pneumonia.

OBJECTIVES: Six recently published algorithms classify pneumonia patients presenting from the community into high- and low-risk groups for resistant bacteria. Our objective was to compare performance of these algorithms for identifying patients infected with bacteria resistant to traditional community-acquired pneumonia antibiotics. METHODS: This was a retrospective study of consecutive adult patients diagnosed with pneumonia in an emergency department and subsequently hospitalized. Each patient was classified as high or low risk for resistant bacteria according to the following algorithms: original health care-associated pneumonia (HCAP) criteria, Summit criteria, Brito and Niederman strategy, Shorr model, Aliberti model, and Shindo model. The reference for comparison was detection of resistant bacteria, defined as methicillin-resistant Staphylococcus aureus or Gram-negative bacteria resistant to ceftriaxone or levofloxacin. RESULTS: A total of 614 patients were studied, including 36 (5.9%) with resistant bacteria. The HCAP criteria classified 304 (49.5%) patients as high risk, with an area under the receiver operating characteristic curve (AUC) of 0.63 (95% confidence interval [CI] = 0.54 to 0.72), sensitivity of 0.69 (95% CI = 0.52 to 0.83), and specificity of 0.52 (95% CI = 0.48 to 0.56). None of the other algorithms improved both sensitivity and specificity or significantly improved the AUC. Compared to the HCAP criteria, the Shorr and Aliberti models classified more patients as high risk, resulting in higher sensitivity and lower specificity. The Shindo model classified fewer patients as high risk, with lower sensitivity and higher specificity. CONCLUSIONS: All algorithms for identification of resistant bacteria included in this study had suboptimal performance to guide antibiotic selection. New strategies for selecting empirical antibiotics for community-onset pneumonia are necessary.