Influenza vaccination is recommended for healthcare workers (HCWs), but coverage is often low. We reviewed studies evaluating interventions to increase seasonal influenza vaccination coverage in HCWs, including a meta-regression analysis to quantify the effect of each component. Fourty-six eligible studies were identified. Domains conferring a high risk of bias were identified in most studies. Mandatory vaccination was the most effective intervention component (Risk Ratio of being unvaccinated [RR unvacc ] D 0.18, 95% CI: 0.08-0.45), followed by "soft" mandates such as declination statements (RR unvacc D 0.64, 95% CI: 0.45-0.92), increased awareness (RR unvacc D 0.83, 95% CI: 0.71-0.97) and increased access (RR unvacc D 0.88, 95% CI: 0.78-1.00). For incentives the difference was not significant, while for education no effect was observed. Heterogeneity was substantial (t 2 D 0.083). These results indicate that effective alternatives to mandatory HCWs influenza vaccination do exist, and need to be further explored in future studies.

Background Starting in late February 2020, Greece is experiencing a coronavirus disease 2019 (COVID-19) epidemic. Healthcare personnel (HCP) were disproportionately affected, accounting for approximately 10% of notified cases. Exclusion from work for 7 days was recommended for HCP with high-risk occupational exposure. Our aim was to evaluate the 7-day exclusion from work policy for HCP with high-risk exposure. Methods HCP with a history of occupational exposure to COVID-19 were notified to the Hellenic National Public Health Organization, regardless of their exposure risk category. Exposed HCP were followed for 14 days after last exposure. Results We prospectively studied 3398 occupationally exposed HCP; nursing personnel accounted for most exposures (n=1705; 50.2%). Of the 3398 exposed HCP, 1599 (47.1%) were classified as low-risk, 765 (22.5%) as moderate-risk, and 1031 (30.4%) as high-risk exposures. Sixty-six (1.9%) HCP developed COVID-19 at a mean of 3.65 days (range: 0-17 days) post-exposure. Of the 66 HCP with COVID-19, 46, 7, and 13 had a history of high-, moderate- or low-risk exposure (4.5%, 0.9%, and 0.8% of all high-, moderate-, and low-risk exposures, respectively). Hospitalization and absenteeism were more prevalent among HCP with high-risk exposure. A logistic regression analysis showed that the following variables were significantly associated with an increased risk for the onset of COVID-19: male, administrative personnel, underlying disease and high-risk exposure. Conclusion HCP with high-risk occupational exposure to COVID-19 had increased probability of serious morbidity, healthcare seeking, hospitalization and absenteeism. Our findings justify the 7-day exclusion from work policy for HCP with high-risk exposure.

Background The available evidence on whether neuraminidase inhibitors reduce mortality in patients with influenza is inconclusive and focuses solely on influenza A/H1N1pdm09. We assessed whether early oseltamivir treatment (≤48 hours from symptom onset) decreases mortality compared to late treatment in a large cohort of critically ill patients with influenza of all types. Methods The study included all adults with laboratory-confirmed influenza hospitalized in intensive care units (ICUs) in Greece over 8 seasons (2010–2011 to 2017–2018) and treated with oseltamivir. The association of early oseltamivir with mortality was assessed with log-binomial models and a competing risks analysis estimating cause-specific and subdistribution hazards for death and discharge. Effect estimates were stratified by influenza type and adjusted for multiple covariates. Results A total of 1330 patients were studied, of whom 622 (46.8%) died in the ICU. Among patients with influenza A/H3N2, early treatment was associated with significantly lower mortality (relative risk, 0.69 [95% credible interval {CrI}, .49–.94]; subdistribution hazard ratio, 0.58 [95% CrI, .37–.88]). This effect was purely due to an increased cause-specific hazard for discharge, whereas the cause-specific hazard for death was not increased. Among survivors, the median length of ICU stay was shorter with early treatment by 1.8 days (95% CrI, .5–3.5 days). No effect on mortality was observed for A/H1N1 and influenza B patients. Conclusions Severely ill patients with suspected influenza should be promptly treated with oseltamivir, particularly when A/H3N2 is circulating. The efficacy of oseltamivir should not be assumed to be equal against all types of influenza.

Introduction Estimating the contribution of influenza to excess mortality in the population presents substantial methodological challenges. Aim In a modelling study we combined environmental, epidemiological and laboratory surveillance data to estimate influenza-attributable mortality in Greece, over four seasons (2013/14 to 2016/17), specifically addressing the lag dimension and the confounding effect of temperature. Methods Associations of influenza type/subtype-specific incidence proxies and of daily mean temperature with mortality were estimated with a distributed-lag nonlinear model with 30 days of maximum lag, separately by age group (all ages, 15–64 and ≥ 65 years old). Total and weekly deaths attributable to influenza and cold temperatures were calculated. Results Overall influenza-attributable mortality was 23.6 deaths per 100,000 population per year (95% confidence interval (CI): 17.8 to 29.2), and varied greatly between seasons, by influenza type/subtype and by age group, with the vast majority occurring in persons aged ≥ 65 years. Most deaths were attributable to A(H3N2), followed by influenza B. During periods of A(H1N1)pdm09 circulation, weekly attributable mortality to this subtype among people ≥ 65 years old increased rapidly at first, but then fell to zero and even negative, suggesting a mortality displacement (harvesting) effect. Mortality attributable to cold temperatures was much higher than that attributable to influenza. Conclusions Studies of influenza-attributable mortality need to consider distributed-lag effects, stratify by age group and adjust both for circulating influenza virus types/subtypes and daily mean temperatures, in order to produce reliable estimates. Our approach addresses these issues, is readily applicable in the context of influenza surveillance, and can be useful for other countries.

Objectives: Excess population mortality during winter is most often associated with influenza A(H3N2), though susceptibility differs by age. We examined differences between influenza types/subtypes in their association with severe laboratory-confirmed cases, overall and by age group, to determine which type is the most virulent. Methods: We used nine seasons of comprehensive nationwide surveillance data from Greece (2010 e2011 to 2018e2019) to examine the association, separately for influenza A(H1N1)pdm09, A(H3N2) and B, between the number of laboratory-confirmed severe cases (intensive care hospitalizations or deaths) per type/subtype and the overall type-specific circulation during the season (expressed as a cumulative incidence proxy). Quasi-Poisson models with identity link were used, and multiple imputation to handle missing influenza A subtype. Results: For the same level of viral circulation and across all ages, influenza A(H1N1)pdm09 was associated with twice as many intensive care hospitalizations as A(H3N2) (rate ratio (RR) 1.89, 95% CI 1.38 e2.74) and three times more than influenza B (RR 3.27, 95%CI 2.54e4.20). Similar associations were observed for laboratory-confirmed deaths. A(H1N1)pdm09 affected adults over 40 years at similar rates, whereas A(H3N2) affected elderly people at a much higher rate than younger persons (65 vs. 40 e64 years, RR for intensive care 5.42, 95% CI 3.45e8.65, and RR for death 6.19, 95%CI 4.05e9.38). Within the 40e64 years age group, A(H1N1)pdm09 was associated with an approximately five times higher rate of severe disease than both A(H3N2) and B. Discussion: Influenza A(H1N1)pdm09 is associated with many more severe laboratory-confirmed cases, likely due to a more typical clinical presentation and younger patient age, leading to more testing. A(H3N2) affects older people more, with cases less often recognized and confirmed.