Background Although molecular tests are considered the gold standard for coronavirus disease 2019 (COVID-19) diagnostics, serological and immunological tests may be useful in specific settings. Objectives This review summarises the underlying principles and performance of COVID-19 serological and immunological testing. Sources Selected peer-reviewed publications on COVID-19 related serology and immunology published between December 2019 and March 2021. Content Serological tests are highly specific but heterogeneous in their sensitivity for the diagnosis of COVID-19. For certain indications, including delayed disease presentations, serological tests can have added value. The presence of antibodies against SARS-CoV-2 may indicate a recent or past COVID-19 infection. Lateral flow immunoassay (LFIA) antibody tests have the advantages of being easy and fast to perform, but many have a low sensitivity in acute settings. Enzyme-linked immunosorbent assay (ELISA) and chemiluminescence immunoassays (CLIA) have higher sensitivities. Besides humoral immunity, cellular immunity is also essential for successful host defences against viruses. Enzyme-linked immunospot (ELISpot) assays can be used to measure T-cell responses against SARS-CoV-2. The presence of cross-reactive SARS-CoV-2-specific T-cells in never exposed patients suggests the possibility of cellular immunity induced by other circulating coronaviruses. T-cell responses against SARS-CoV-2 have also been detected in recovered COVID-19 patients with no detectable antibodies. Implications Serological and immunological tests are primarily applied for population-based seroprevalence studies to evaluate the effectiveness of COVID-19 control measures and increase our understanding of the immunology behind COVID-19. Combining molecular diagnostics with serological tests may optimise the detection of COVID-19. As not all infected patients will develop antibodies against SARS-CoV-2, assessment of cellular immunity may provide complementary information on whether a patient has been previously infected with COVID-19. More studies are needed to understand the correlations of these serological and immunological parameters with protective immunity, taking into account the different circulating virus variants.

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Objective Influenza virus infections cause a high disease and economic burden during seasonal epidemics. However, there is still a need for reliable disease burden estimates to provide a more detailed picture of the impact of influenza. Therefore, the objectives of this study is to estimate the incidence of hospitalisation for influenza virus infection and associated hospitalisation costs in adult patients in the Netherlands during two consecutive influenza seasons. Methods We conducted a retrospective study in adult patients with a laboratory confirmed influenza virus infection in three Dutch hospitals during respiratory seasons 2014-2015 and 2015-2016. Incidence was calculated as the weekly number of hospitalised influenza patients divided by the total population in the catchment populations of the three hospitals. Arithmetic mean hospitalisation costs per patient were estimated and included costs for emergency department consultation, diagnostics, general ward and/or intensive care unit admission, isolation, antibiotic and/or antiviral treatment. These hospitalisation costs were extrapolated to national level and expressed in 2017 euros. ResultsThe study population consisted of 380 hospitalised adult influenza patients. The seasonal cumulative incidence was 3.5 cases per 10,000 persons in respiratory season 2014-2015, compared to 1.8 cases per 10,000 persons in 2015-2016. The arithmetic mean hospitalisation cost per influenza patient was €6128 (95% CI €4934-€7737) per patient in 2014-2015 and €8280 (95% CI €6254-€10,665) in 2015-2016, potentially reaching total hospitalisation costs of €28 million in 2014-2015 and €20 million in 2015-2016. Conclusions Influenza virus infections lead to 1.8-3.5 hospitalised patients per 10,000 persons, with mean hospitalisation costs of €6100-€8300 per adult patient, resulting in 20-28 million euros annually in The Netherlands. The highest arithmetic mean hospitalisation costs per patient were found in the 45-64 year age group. These influenza burden estimates could be used for future influenza cost-effectiveness and impact studies.

Background Potentially unnecessary antibiotic use for community‐acquired pneumonia (CAP) contributes to selection of antibiotic‐resistant pathogens. Cytokine expression at the time that treatment is started may assist in identifying patients not requiring antibiotics. We determined plasma cytokine patterns in patients retrospectively categorized as strict viral, pneumococcal or combined viral‐bacterial CAP. Objective To investigate whether cytokine‐based prediction models can be used to differentiate strict viral CAP from other aetiologies at admission. Methods From 344 hospitalized CAP patients, 104 patients were categorized as viral CAP (n = 17), pneumococcal CAP (n = 48) and combined bacterial‐viral CAP (n = 39). IL‐6, IL‐10, IL‐27, IFN‐γ and C‐reactive protein (CRP) were determined on admission in plasma. Prediction of strict viral aetiology was explored with two multivariate regression models and ROC curves. Results Viral pneumonia was predicted by logistic regression using multiple cytokine levels (IL‐6, IL‐27 and CRP) with an AUC of 0.911 (95% CI: 0.852‐0.971, P < .001). For the same patients the AUC of CRP was 0.813 (95% CI: 0.728‐0.898, P < .001). Conclusions This study demonstrated differences in cytokine expression in selected CAP patients between viral and bacterial aetiology. Prospective validation studies are warranted.

The elderly are more susceptible to infections, which is reflected in the incidence and mortality of lower respiratory tract infections (LRTIs) increasing with age. Several aspects of antimicrobial use for LRTIs in elderly patients should be considered to determine appropriateness. We discuss possible differences in microbial etiology between elderly and younger adults, definitions of inappropriate antimicrobial use for LRTIs currently found in the literature, along with their results, and the possible negative impact of antimicrobial therapy at both an individual and community level. Finally, we propose that both antimicrobial stewardship interventions and novel rapid diagnostic techniques may optimize antimicrobial use in elderly patients with LRTIs.

Introduction: The safety of de-escalation of empirical antimicrobial therapy is largely based on observational data, with many reporting protective effects on mortality. As there is no plausible biological explanation for this phenomenon, it is most probably caused by confounding by indication. Areas covered: We evaluate the methodology used in observational studies on the effects of deescalation of antimicrobial therapy on mortality. We extended the search for a recent systematic review and identified 52 observational studies. The heterogeneity in study populations was large. Only 19 (36.5%) studies adjusted for confounders and four (8%) adjusted for clinical stability during admission, all as a fixed variable. All studies had methodological limitations, most importantly the lack of adjustment for clinical stability, causing bias toward a protective effect. Expert opinion: The methodology used in studies evaluating the effects of de-escalation on mortality requires improvement. We depicted all potential confounders in a directed acyclic graph to illustrate all associations between exposure (de-escalation) and outcome (mortality). Clinical stability is an important confounder in this association and should be modeled as a time-varying variable. We recommend to include de-escalation as time-varying exposure and use inverse-probability-of-treatment weighted marginal structural models to properly adjust for time-varying confounders.