Bringing evidence from press release to the clinic in the era of COVID-19

The urgent need to develop effective therapeutics and disseminate information from clinical studies has led to data from clinical trials being made available by alternate methods prior to peer-reviewed publication, including press releases, social media and pre-print papers. While this allows clinicians more open access to these data, a trust has to be placed with the investigators releasing these data without the availability of scientifically rigorous peer review. The examples of results from trials studying dexamethasone and hydroxychloroquine for treatment of COVID-19 have had contrasting outcomes, including the potential for significant numbers of lives saved with the early release of results from the RECOVERY trial studying dexamethasone contrasting with unsubstantiated data being presented from trials studying hydroxychloroquine. Clinicians and researchers must maintain a healthy scepticism when reviewing results prior to peer-reviewed publication, but also consider when these opportunities may allow for early implementation of potentially lifesaving interventions for people infected with COVID-19.

Brick plots: an intuitive platform for visualizing multiparametric immunophenotyped cell clusters

BACKGROUND

The advent of mass cytometry has dramatically increased the parameter limit for immunological analysis. New approaches to analysing high parameter cytometry data have been developed to ease analysis of these complex datasets. Many of these methods assign cells into population clusters based on protein expression similarity.

RESULTS

Here we introduce an additional method, termed Brick plots, to visualize these cluster phenotypes in a simplified and intuitive manner. The Brick plot method generates a two-dimensional barcode that displays the phenotype of each cluster in relation to the entire dataset. We show that Brick plots can be used to visualize complex mass cytometry data, both from fundamental research and clinical trials, as well as flow cytometry data.

CONCLUSION

Brick plots represent a new approach to visualize complex immunological data in an intuitive manner.

Using Baidu search index to monitor and predict newly diagnosed cases of HIV/AIDS, syphilis and gonorrhea in China: estimates from a vector autoregressive (VAR) model

OBJECTIVES

Internet search engine data have been widely used to monitor and predict infectious diseases. Existing studies have found correlations between search data and HIV/AIDS epidemics. We aimed to extend the literature through exploring the feasibility of using search data to monitor and predict the number of newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China.

METHODS

This paper used vector autoregressive model to combine the number of newly diagnosed cases with Baidu search index to predict monthly newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China. The procedures included: (1) keywords selection and filtering; (2) construction of composite search index; (3) modelling with training data from January 2011 to October 2016 and calculating the prediction performance with validation data from November 2016 to October 2017.

RESULTS

The analysis showed that there was a close correlation between the monthly number of newly diagnosed cases and the composite search index (the Spearman's rank correlation coefficients were 0.777 for HIV/AIDS, 0.590 for syphilis and 0.633 for gonorrhoea, p<0.05 for all). The R² were all more than 85% and the mean absolute percentage errors were less than 11%, showing the good fitting effect and prediction performance of vector autoregressive model in this field.

CONCLUSIONS

Our study indicated the potential feasibility of using Baidu search data to monitor and predict the number of newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China.

Reporting guidelines for health research: protocol for a cross-sectional analysis of the EQUATOR Network Library

INTRODUCTION

Transparency and completeness of health research is highly variable, with important deficiencies in the reporting of methods and results of studies. Reporting guidelines aim to improve transparency and quality of research reports, and are often developed by consortia of journal editors, peer reviewers, authors, consumers and other key stakeholders. The objective of this study will be to investigate the characteristics of scientific collaboration among developers and the citation metrics of reporting guidelines of health research.

METHODS AND ANALYSIS

This is the study protocol for a cross-sectional analysis of completed reporting guidelines indexed in the Enhancing the QUAlity and Transparency Of health Research Network Library. We will search PubMed/MEDLINE and the Web of Science. Screening, selection and data abstraction will be conducted by one researcher and verified by a second researcher. Potential discrepancies will be resolved via discussion. We will include published papers of reporting guidelines written in English. Published papers will have to meet the definition of a reporting guideline related to health research (eg, a checklist, flow diagram or explicit text), with no restrictions by study design, medical specialty, disease or condition. Raw data from each included paper (including title, publication year, journal, subject category, keywords, citations, and the authors' names, author's affiliated institution and country) will be exported from the Web of Science. Descriptive analyses will be conducted (including the number of papers, citations, authors, countries, journals, keywords and main collaboration metrics). We will identify the most prolific authors, institutions, countries, journals and the most cited papers. Network analyses will be carried out to study the structure of collaborations.

ETHICS AND DISSEMINATION

No ethical approval will be required. Findings from this study will be published in peer-reviewed journals. All data will be deposited in a cross-disciplinary public repository. It is anticipated the study findings could be relevant to a variety of audiences.

The burden of cancer attributable to modifiable risk factors: the Australian cancer-PAF cohort consortium

PURPOSE

To estimate the Australian cancer burden attributable to lifestyle-related risk factors and their combinations using a novel population attributable fraction (PAF) method that accounts for competing risk of death, risk factor interdependence and statistical uncertainty.

PARTICIPANTS

365 173 adults from seven Australian cohort studies. We linked pooled harmonised individual participant cohort data with population-based cancer and death registries to estimate exposure-cancer and exposure-death associations. Current Australian exposure prevalence was estimated from representative external sources. To illustrate the utility of the new PAF method, we calculated fractions of cancers causally related to body fatness or both tobacco and alcohol consumption avoidable in the next 10 years by risk factor modifications, comparing them with fractions produced by traditional PAF methods.

FINDINGS TO DATE

Over 10 years of follow-up, we observed 27 483 incident cancers and 22 078 deaths. Of cancers related to body fatness (n=9258), 13% (95% CI 11% to 16%) could be avoided if those currently overweight or obese had body mass index of 18.5-24.9 kg/m2. Of cancers causally related to both tobacco and alcohol (n=4283), current or former smoking explains 13% (11% to 16%) and consuming more than two alcoholic drinks per day explains 6% (5% to 8%). The two factors combined explain 16% (13% to 19%): 26% (21% to 30%) in men and 8% (4% to 11%) in women. Corresponding estimates using the traditional PAF method were 20%, 31% and 10%. Our PAF estimates translate to 74 000 avoidable body fatness-related cancers and 40 000 avoidable tobacco- and alcohol-related cancers in Australia over the next 10 years (2017-2026). Traditional PAF methods not accounting for competing risk of death and interdependence of risk factors may overestimate PAFs and avoidable cancers.

FUTURE PLANS

We will rank the most important causal factors and their combinations for a spectrum of cancers and inform cancer control activities.