Online dissemination of Cochrane reviews on digital health technologies: a cross-sectional study.

BACKGROUND: This cross-sectional study investigated the online dissemination of Cochrane reviews on digital health technologies. METHODS: We searched the Cochrane Database of Systematic Reviews from inception up to May 2023. Cochrane reviews with any population (P), intervention or concept supported by any digital technology (I), any or no comparison (C), and any health outcome (O) were included. Data on review characteristics (bibliographic information, PICO, and evidence quality) and dissemination strategies were extracted and processed. Dissemination was assessed using review information on the Cochrane website and Altmetric data that trace the mentions of academic publications in nonacademic online channels. Data were analysed using descriptive statistics and binary logistic regression analysis. RESULTS: Out of 170 records identified in the search, 100 Cochrane reviews, published between 2005 and 2023, were included. The reviews focused on consumers (e.g. patients, n = 86), people of any age (n = 44), and clinical populations (n = 68). All reviews addressed interventions or concepts supported by digital technologies with any devices (n = 73), mobile devices (n = 17), or computers (n = 10). The outcomes focused on disease treatment (n = 56), health promotion and disease prevention (n = 27), or management of care delivery (n = 17). All reviews included 1-132 studies, and half included 1-10 studies. Meta-analysis was performed in 69 reviews, and certainty of evidence was rated as high or moderate for at least one outcome in 46 reviews. In agreement with the Cochrane guidelines, all reviews had a plain language summary (PLS) that was available in 3-14 languages. The reviews were disseminated (i.e. mentioned online) predominantly via X/Twitter (n = 99) and Facebook (n = 69). Overall, 51 reviews were mentioned in up to 25% and 49 reviews in 5% of all research outputs traced by Altmetric data. Dissemination (i.e. higher Altmetric scores) was associated with bibliographic review characteristics (i.e. earlier publication year and PLS available in more languages), but not with evidence quality (i.e. certainty of evidence rating, number of studies, or meta-analysis performed in review). CONCLUSIONS: Online attention towards Cochrane reviews on digital health technologies is high. Dissemination is higher for older reviews and reviews with more PLS translations. Measures are required to improve dissemination of Cochrane reviews based on evidence quality. SYSTEMATIC REVIEW REGISTRATION: The study was prospectively registered at the Open Science Framework ( https://osf.io/mpw8u/ ).

Impact of the COVID-19 pandemic on oncological care in Germany: rapid review.

OBJECTIVES: The COVID-19 pandemic affected medical care for chronic diseases. This study aimed to systematically assess the pandemic impact on oncological care in Germany using a rapid review. METHODS: MEDLINE, Embase, study and preprint registries and study bibliographies were searched for studies published between 2020 and 2 November 2022. Inclusion was based on the PCC framework: population (cancer), concept (oncological care) and context (COVID-19 pandemic in Germany). Studies were selected after title/abstract and full-text screening by two authors. Extracted data were synthesized using descriptive statistics or narratively. Risk of bias was assessed and summarized using descriptive statistics. RESULTS: Overall, 77 records (59 peer-reviewed studies and 18 reports) with administrative, cancer registry and survey data were included. Disruptions in oncological care were reported and varied according to pandemic-related factors (e.g., pandemic stage) and other (non-pandemic) factors (e.g., care details). During higher restriction periods fewer consultations and non-urgent surgeries, and delayed diagnosis and screening were consistently reported. Heterogeneous results were reported for treatment types other than surgery (e.g., psychosocial care) and aftercare, while ongoing care remained mostly unchanged. The risk of bias was on average moderate. CONCLUSIONS: Disruptions in oncological care were reported during the COVID-19 pandemic in Germany. Such disruptions probably depended on factors that were insufficiently controlled for in statistical analyses and evidence quality was on average only moderate. Research focus on patient outcomes (e.g., longer term consequences of disruptions) and pandemic management by healthcare systems is potentially relevant for future pandemics or health emergencies.

A semiparametric approach for meta-analysis of diagnostic accuracy studies with multiple cut-offs.

The accuracy of a diagnostic test is often expressed using a pair of measures: sensitivity (proportion of test positives among all individuals with target condition) and specificity (proportion of test negatives among all individuals without target condition). If the outcome of a diagnostic test is binary, results from different studies can easily be summarized in a meta-analysis. However, if the diagnostic test is based on a discrete or continuous measure (e.g., a biomarker), several cut-offs within one study as well as among different studies are published. Instead of taking all information of the cut-offs into account in the meta-analysis, a single cut-off per study is often selected arbitrarily for the analysis, even though there are statistical methods for the incorporation of several cut-offs. For these methods, distributional assumptions have to be met and/or the models may not converge when specific data structures occur. We propose a semiparametric approach to overcome both problems. Our simulation study shows that the diagnostic accuracy is under-estimated, although this underestimation in sensitivity and specificity is relatively small. The comparative approach of Steinhauser et al. is better in terms of coverage probability, but may lead to convergence problems. In addition to the simulation results, we illustrate the application of the semiparametric approach using a published meta-analysis for a diagnostic test differentiating between bacterial and viral meningitis in children.