Top 10 Most Read in September: Vaccine acceptance, delirium in critically ill COVID-19 patients and symptom scores to predict COVID-19 positivity.

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Image credit: Pixabay Vaccine Vaccination Covid-19

 

September sees just three new entries in our top 10 most read articles, all on coronavirus:

Public acceptance of COVID-19 vaccines 

Our highest new entry is a large online survey of COVID-19 vaccine acceptance by Lindholt and colleagues from July, the first time the paper’s been in our top 10 since its publication. The authors examined vaccine acceptance across eight Western democracies that differed in terms of the severity of the pandemic and their response. The authors found wide variations in the level of vaccine acceptance across each country, from 83% in Denmark to as low as 47% in France and Hungary. Key individual drivers of acceptance of an approved COVID-19 vaccine were (1) trust in the national health authorities and scientists and (2) personal health concerns.

For a further discussion of vaccine uptake and public attitudes towards vaccination, see also our Q&A with Dr Samantha Vanderslott from the Oxford Vaccine Group posted earlier this year.

Delirium and neuropsychological outcomes in critically Ill patients with COVID-19 

Our second new entry, published in September, investigated the clinical course of delirium for patients with COVID-19 in an intensive care unit in the United States. The authors found delirium was a common complication, affecting more than 70% of patients, and likely to involve multiple contributing factors including sedation regimens, inflammation, delirium prevention protocol deviations and hypoxic-ischaemic injury.

Symptom scores to predict COVID-19 positivity in Nigeria 

Our final new entry, also published in September, is a study from Nigeria aiming to develop and validate a symptom prediction tool for COVID-19 test positivity. Laboratory capacity to test for SARS-CoV-2 is a major challenge in Nigeria. Here the authors evaluated the predictive capacity of clinical symptoms to predict COVID-19 as a possible approach to aid prompt recognition of COVID-19 by frontline healthcare workers. The best individual symptom predictors of COVID-19 positivity in children, adult and elderly patients were loss of smell, either fever or cough and difficulty in breathing, respectively. However, none of the symptom scores had good enough discrimination to use in practice, emphasising the need to invest in molecular diagnostics for COVID-19 in Nigeria.

Here is the full list of most read papers in BMJ Open during September 2021:

Rank Author(s) Title
1 Li et al. Impact of COVID-19 on female fertility: a systematic review and meta-analysis protocol
2 Gadermann et al. Examining the impacts of the COVID-19 pandemic on family mental health in Canada: findings from a national cross-sectional study
3 Espiritu et al. The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA study): a protocol study
4 Lindholt et al. Public acceptance of COVID-19 vaccines: cross-national evidence on levels and individual-level predictors using observational data
5 Dost et al. Perceptions of medical students towards online teaching during the COVID-19 pandemic: a national cross-sectional survey of 2721 UK medical students
6 King et al. Mobility study of young women who exchange sex for money or commodities using Google Maps and qualitative methods in Kampala, Uganda
7 Ragheb et al. Delirium and neuropsychological outcomes in critically Ill patients with COVID-19: a cohort study
8 Dennis et al. Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study
9 Elimian et al. Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study
10 Dahlen et al. Intrapartum interventions and outcomes for women and children following induction of labour at term in uncomplicated pregnancies: a 16-year population-based linked data study

*Most read figures are based on pdf downloads and full text views. Abstract views are excluded.

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