The COVID-19 pandemic and government lockdown restrictions have had an impact on children and young people worldwide. In this editorial, we explore how and why referrals to UK children and adolescent mental health services (CAMHS) have changed during the pandemic and summarise the emerging data on the potential reasons behind this.

Recent epidemiological studies analysing sex-disaggregated patient data of coronavirus disease 2019 (COVID-19) across the world revealed a distinct sex bias in the disease morbidity as well as the mortality – both being higher for the men. Similar antecedents have been known for the previous viral infections, including from coronaviruses, such as severe acute respiratory syndrome (SARS) and middle-east respiratory syndrome (MERS). A sound understanding of molecular mechanisms leading to the biological sex bias in the survival outcomes of the patients in relation to COVID-19 will act as an essential requisite for developing a sex-differentiated approach for therapeutic management of this disease. Recent studies which have explored molecular mechanism(s) behind sex-based differences in COVID-19 pathogenesis are scarce; however, existing evidence, for other respiratory viral infections, viz. SARS, MERS and influenza, provides important clues in this regard. In attempt to consolidate the available knowledge on this issue, we conducted a systematic review of the existing empirical knowledge and recent experimental studies following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The qualitative analysis of the collected data unravelled multiple molecular mechanisms, such as evolutionary and genetic/epigenetic factors, sex-linkage of viral host cell entry receptor and immune response genes, sex hormone and gut microbiome-mediated immune-modulation, as the possible key reasons for the sex-based differences in patient outcomes in COVID-19.

We update our previous insights into COVID-19 vaccine acceptance and hesitancy in Finland. Vaccine acceptance increased from 64% (November/December 2020) to 74% (April 2021). However, there was a group of participants that were preferring to wait to get vaccinated ranging from 6% of over-64-years-olds to 29% of under-30-years-olds. The previously identified enablers convenience (below-50-years-olds), worry about severe disease and protection for oneself (above-50-years-olds) were no longer significantly associated with increased vaccine acceptance. Understanding barriers and enablers behind vaccine acceptance is decisive in ensuring a successful implementation of COVID-19 vaccination programs, which will be key to ending the pandemic.

Pooling of samples in detecting the presence of virus is an effective and efficient strategy in screening carriers in a large population with low infection rate, leading to reduction in cost and time. There are a number of pooling test methods, some being simple and others being complicated. In such pooling tests, the most important parameter to decide is the pool or group size, which can be optimised mathematically. Two pooling methods are relatively simple. The minimum numbers required in these two tests for a population with known infection rate are discussed and compared. Results are useful for identifying asymptomatic carriers in a short time and in implementing health codes systems.

COVID-19 research has been produced at an unprecedented rate and managing what is currently known is in part being accomplished through synthesis research. Here we evaluated how the need to rapidly produce syntheses has impacted the quality of the synthesis research. Thus, we sought to identify, evaluate and map the synthesis research on COVID-19 published up to 10 July 2020. A COVID-19 literature database was created using pre-specified COVID-19 search algorithms carried out in eight databases. We identified 863 citations considered to be synthesis research for evaluation in this project. Four-hundred and thirty-nine reviews were fully assessed with A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) and rated as very low-quality (n = 145), low-quality (n = 80), medium-quality (n = 208) and high-quality (n = 151). The quality of these reviews fell short of what is expected for synthesis research with key domains being left out of the typical methodology. The increase in risk of bias due to non-adherence to systematic review methodology is unknown and prevents the reader from assessing the validity of the review. The responsibility to assure the quality is held by both producers and publishers of synthesis research and our findings indicate there is a need to equip readers with the expertise to evaluate the review conduct before using it for decision-making purposes.

Evidence that more people in some countries and fewer in others are dying because of the pandemic, than is reflected by reported coronavirus disease 2019 (Covid-19) mortality rates, is derived from mortality data. Using publicly available databases, deaths attributed to Covid-19 in 2020 and all deaths for the years 2015–2020 were tabulated for 35 countries together with economic, health, demographic and government response stringency index variables. Residual mortality rates (RMR) in 2020 were calculated as excess mortality minus reported mortality rates due to Covid-19 where excess deaths were observed deaths in 2020 minus the average for 2015–2019. Differences in RMR are differences not attributed to reported Covid-19. For about half the countries, RMR's were negative and for half, positive. The absolute rates in some countries were double those in others. In a regression analysis, population density and proportion of female smokers were positively associated with both Covid-19 and excess mortality while the human development index and proportion of male smokers were negatively associated with both. RMR was not associated with any of the investigated variables. The results show that published data on mortality from Covid-19 cannot be directly comparable across countries. This may be due to differences in Covid-19 death reporting and in addition, the unprecedented public health measures implemented to control the pandemic may have produced either increased or reduced excess deaths due to other diseases. Further data on cause-specific mortality is required to determine the extent to which residual mortality represents non-Covid-19 deaths and to explain differences between countries.

This study aimed to analyse the geographical distribution of coronavirus disease 2019 (COVID-19) and to identify high-risk areas in space and time for the occurrence of cases and deaths in the indigenous population of Brazil. This is an ecological study carried out between 24 March and 26 October 2020 whose units of analysis were the Special Indigenous Sanitary Districts. The Getis-Ord General G and Getis-Ord Gi* techniques were used to verify the spatial association of the phenomena and a retrospective space–time scan was performed. There were 32 041 confirmed cases of COVID-19 and 471 deaths. The non-randomness of cases (z score = 5.40; P < 0.001) and deaths (z score = 3.83; P < 0.001) were confirmed. Hotspots were identified for cases and deaths in the north and midwest regions of Brazil. Sixteen high-risk space–time clusters were identified for the occurrence of cases with a higher RR = 21.23 (P < 0.001) and four risk clusters for deaths with a higher RR = 80.33 (P < 0.001). These clusters were identified from 22 May and were active until 10 October 2020. The results indicate critical areas in the indigenous territories of Brazil and contribute to better directing the actions of control of COVID-19 in this population.

We performed surveillance for hospital-acquired COVID-19 (HA-COVID-19) and compared time-based, electronic definitions to real-time adjudication of the most likely source of acquisition. Without real-time adjudication, nearly 50% of HA-COVID-19 cases identified using electronic definitions were misclassified. Both electronic and traditional contact tracing methods likely underestimated the incidence of HA-COVID-19.