Considering potential benefits, as well as harms, from the COVID-19 disruption to cancer screening and other healthcare services.

Since 2020, hundreds of thousands of more deaths than expected have been observed across the globe. Amid the coronavirus 2019 (COVID-19) pandemic, current research priorities are to control the spread of infection and minimise loss of life. However, there may be future opportunities to learn from the pandemic to build a better healthcare system that delivers maximum health benefits with minimum harm. So far, much research has focused on foregone benefits of healthcare services such as cancer screening during the pandemic. A more balanced approach is to recognise that all healthcare services have potential harms as well as benefits. In this way, we may be able to use pandemic 'natural experiments' to identify cases where a reduction in a healthcare service has not been harmful to the population and some instances where this may have even been beneficial.

Electronic and animal noses for detecting SARS‐CoV‐2 infection

Objectives This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To assess the diagnostic test accuracy of eNoses to screen for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection in public places, such as airports. To assess the diagnostic test accuracy of sniffer animals, and more specifically dogs, to screen for SARS‐CoV‐2 infection in public places, such as airports. To assess the diagnostic test accuracy of eNoses for SARS‐CoV‐2 infection or COVID‐19 in symptomatic people presenting in the community, or in secondary care. To assess the diagnostic test accuracy of sniffer animals, and more specifically dogs, for SARS‐CoV‐2 infection or COVID‐19 in symptomatic people presenting in the community, or in secondary care. Secondary objectives If sufficient data are available, we will investigate the accuracy (either by stratified analysis, or by subgroup analysis) according to specific eNose technology or animal, and according to whether those who are tested are symptomatic or not. We will also investigate whether eNose brand, reference standard, and healthcare setting are associated with differences in diagnostic test accuracy.

Learning from the pandemic: mortality trends and seasonality of deaths in Australia in 2020.

AIM: To assess whether the observed numbers and seasonality of deaths in Australia during 2020 differed from expected trends based on 2015-19 data. METHODS: We used provisional death data from the Australian Bureau of Statistics, stratified by state, age, sex and cause of death. We compared 2020 deaths with 2015-19 deaths using interrupted time series adjusted for time trend and seasonality. We measured the following outcomes along with 95% confidence intervals: observed/expected deaths (rate ratio: RR), change in seasonal variation in mortality (amplitude ratio: AR) and change in week of peak seasonal mortality (phase difference: PD). RESULTS: Overall 4% fewer deaths from all causes were registered in Australia than expected in 2020 [RR 0·96 (0·95-0·98)] with reductions across states, ages and sex strata. There were fewer deaths from respiratory illness [RR 0·79 (0·76-0·83)] and dementia [RR 0·95 (0·93-0·98)] but more from diabetes [RR 1·08 (1·04-1·13)]. Seasonal variation was reduced for deaths overall [AR 0·94 (0·92-0·95)], and for deaths due to respiratory illnesses [AR 0·78 (0·74-0·83)], dementia [AR 0.92 (0.89-0.95)] and ischaemic heart disease [0.95 (0.90-0.97)]. CONCLUSIONS: The observed reductions in respiratory and dementia deaths and the reduced seasonality in ischaemic heart disease deaths may reflect reductions in circulating respiratory (non-SARS-CoV-2) pathogens resulting from the public health measures taken in 2020. The observed increase in diabetes deaths is unexplained and merits further study.

Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: Systematic review.

BACKGROUND: Accurate seroprevalence estimates of SARS-CoV-2 in different populations could clarify the extent to which current testing strategies are identifying all active infection, and hence the true magnitude and spread of the infection. Our primary objective was to identify valid seroprevalence studies of SARS-CoV-2 infection and compare their estimates with the reported, and imputed, COVID-19 case rates within the same population at the same time point. METHODS: We searched PubMed, Embase, the Cochrane COVID-19 trials, and Europe-PMC for published studies and pre-prints that reported anti-SARS-CoV-2 IgG, IgM and/or IgA antibodies for serosurveys of the general community from 1 Jan to 12 Aug 2020. RESULTS: Of the 2199 studies identified, 170 were assessed for full text and 17 studies representing 15 regions and 118,297 subjects were includable. The seroprevalence proportions in 8 studies ranged between 1%-10%, with 5 studies under 1%, and 4 over 10%-from the notably hard-hit regions of Gangelt, Germany; Northwest Iran; Buenos Aires, Argentina; and Stockholm, Sweden. For seropositive cases who were not previously identified as COVID-19 cases, the majority had prior COVID-like symptoms. The estimated seroprevalences ranged from 0.56-717 times greater than the number of reported cumulative cases-half of the studies reported greater than 10 times more SARS-CoV-2 infections than the cumulative number of cases. CONCLUSIONS: The findings show SARS-CoV-2 seroprevalence is well below "herd immunity" in all countries studied. The estimated number of infections, however, were much greater than the number of reported cases and deaths in almost all locations. The majority of seropositive people reported prior COVID-like symptoms, suggesting that undertesting of symptomatic people may be causing a substantial under-ascertainment of SARS-CoV-2 infections.

Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: Systematic review and meta-analysis.

Background: Knowing the prevalence of true asymptomatic coronavirus disease 2019 (COVID-19) cases is critical for designing mitigation measures against the pandemic. We aimed to synthesize all available research on asymptomatic cases and transmission rates. Methods: We searched PubMed, Embase, Cochrane COVID-19 trials, and Europe PMC for primary studies on asymptomatic prevalence in which (1) the sample frame includes at-risk populations and (2) follow-up was sufficient to identify pre-symptomatic cases. Meta-analysis used fixed-effects and random-effects models. We assessed risk of bias by combination of questions adapted from risk of bias tools for prevalence and diagnostic accuracy studies. Results: We screened 2,454 articles and included 13 low risk-of-bias studies from seven countries that tested 21,708 at-risk people, of which 663 were positive and 111 asymptomatic. Diagnosis in all studies was confirmed using a real-time reverse transcriptase-polymerase chain reaction test. The asymptomatic proportion ranged from 4% to 41%. Meta-analysis (fixed effects) found that the proportion of asymptomatic cases was 17% (95% CI 14% to 20%) overall and higher in aged care (20%; 95% CI 14% to 27%) than in non-aged care (16%; 95% CI 13% to 20%). The relative risk (RR) of asymptomatic transmission was 42% lower than that for symptomatic transmission (combined RR 0.58; 95% CI 0.34 to 0.99, p = 0.047). Conclusions: Our one-in-six estimate of the prevalence of asymptomatic COVID-19 cases and asymptomatic transmission rates is lower than those of many highly publicized studies but still sufficient to warrant policy attention. Further robust epidemiological evidence is urgently needed, including in subpopulations such as children, to better understand how asymptomatic cases contribute to the pandemic.

Historique: Il est essentiel de connaître la prévalence des véritables cas asymptomatiques de maladie à coronavirus 2019 (COVID-19) pour concevoir des mesures d'atténuation de la pandémie. Les chercheurs ont voulu synthétiser toutes les recherches disponibles sur les cas asymptomatiques et les taux de transmission. Méthodologie: Les chercheurs ont fouillé les bases de données PubMed, Embase, Cochrane pour trouver les études sur la COVID-19, et Europe PMC pour colliger les études primaires sur la prévalence des cas asymptomatiques dans lesquelles 1) le cadre d'échantillonnage incluait une population à risque et 2) le suivi était suffisant pour dépister les cas présymptomatiques. La méta-analyse a fait appel à des modèles d'effets fixes et d'effets aléatoires. Nous avons évalué le risque de biais par une combinaison de questions adaptées d'outils sur les risques de biais des études de prévalence et de précision diagnostique. Résultats: Les chercheurs ont extrait 2 454 articles, dont 13 études à faible risque de biais de sept pays dans lesquelles 21 708 personnes à risque ont subi le test de dépistage, soit 663 cas positifs et 111 cas asymptomatiques. Dans toutes les études, le diagnostic a été confirmé au moyen du test d'amplification en chaîne par polymérase après transcriptase inverse en temps réel. La proportion de cas asymptomatiques se situait entre 4 % et 41 %. La méta-analyse (à effets fixes) a établi que la proportion de cas asymptomatiques s'élevait à 17 % (IC à 95 %, 14 % à 20 %) dans l'ensemble, mais qu'elles étaient plus élevées dans les soins aux aînés (20 %; IC à 95 %, 14 % à 27 %) qu'auprès du reste de la population (16 %; IC à 95 %, 13 % à 20 %). Le risque relatif [RR] de transmission de cas asymptomatiques était plus faible de 42 % que celui de cas symptomatiques (RR combiné de 0,58; IC à 95 %, 0,34 à 0.99, p = 0,047). Conclusions: L'évaluation de la prévalence d'un sixième de cas asymptomatiques de COVID-19 et de taux de transmission de cas asymptomatiques est inférieure à celle de nombreuses études hautement publicisées, mais suffit tout de même pour justifier l'intérêt de la santé publique. D'autres données épidémiologiques solides s'imposent de toute urgence, y compris dans des sous-populations comme les enfants, pour mieux comprendre l'effet des cas asymptomatiques sur la pandémie.

Setting minimum clinical performance specifications for tests based on disease prevalence and minimum acceptable positive and negative predictive values: Practical considerations applied to COVID-19 testing.

OBJECTIVES: Several guidelines for the evaluation of laboratory tests for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection have recommended establishing an a priori definition of minimum clinical performance specifications before test selection and method evaluation. METHODS: Using positive (PPV) and negative predictive values (NPV), we constructed a spreadsheet tool for determining the minimum clinical specificity (conditional on NPV or PPV, sensitivity and prevalence) and minimum clinical sensitivity (conditional on NPV or PPV, specificity and prevalence) of tests. RESULTS: At a prevalence of 1%, there are no minimum sensitivity requirements to achieve a desired NPV of 60%-95% for a given clinical specificity above 20%. It is not possible to achieve 60-95% PPV even with 100% clinical sensitivity, except when the clinical specificity is near 100%. The opposite trend is seen in high prevalence settings (60%), where a relatively low minimum clinical sensitivity is required to achieve a desired PPV for a given clinical specificity, and a higher minimum clinical specificity is required to achieve a desired NPV for a given clinical sensitivity. DISCUSSION: The selection of laboratory tests and the testing strategy for SARS-CoV-2 involves delicate trade-offs between NPV and PPV based on prevalence and clinical sensitivity and clinical specificity. Practitioners and health authorities should carefully consider the clinical scenarios under which the test result will be used and select the most appropriate testing strategy that fulfils the a priori defined clinical performance specification.