routinely collected data
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2022 ◽  
Author(s):  
Masudah Paleker ◽  
Mary-Ann Davies ◽  
Peter Raubenheimer ◽  
Jonathan Naude ◽  
Andrew Boulle ◽  
...  

Fewer COVID-19 deaths have been reported in this fourth wave, with clinicians reporting less admissions due to severe COVID-19 pneumonia when compared to previous waves. We therefore aimed to rapidly compare the profile of deaths in wave 4 with wave 3 using routinely collected data on admissions to public sector hospitals in the Western Cape province of South Africa. Findings show that there have been fewer COVID-19 pneumonia deaths in the Omicron-driven fourth wave compared to the third wave, which confirms anecdotal reports and lower bulk oxygen consumption by hospitals in the province.


2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mei Liu ◽  
Wen Wang ◽  
Mingqi Wang ◽  
Qiao He ◽  
Ling Li ◽  
...  

Abstract Background In recent years, studies that used routinely collected data (RCD), such as electronic medical records and administrative claims, for exploring drug treatment effects, including effectiveness and safety, have been increasingly published. Abstracts of such studies represent a highly attended source for busy clinicians or policy-makers, and are important for indexing by literature database. If less clearly presented, they may mislead decisions or indexing. We thus conducted a cross-sectional survey to systematically examine how the abstracts of such studies were reported. Methods We searched PubMed to identify all observational studies published in 2018 that used RCD for assessing drug treatment effects. Teams of methods-trained collected data from eligible studies using pilot-tested, standardized forms that were developed and expanded from “The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology” (RECORD-PE) statement. We used descriptive analyses to examine how authors reported data source, study design, data analysis, and interpretation of findings. Results A total of 222 studies were included, of which 118 (53.2%) reported type of database used, 17 (7.7%) clearly reported database linkage, and 140 (63.1%) reported coverage of data source. Only 44 (19.8%) studies stated a predefined hypothesis, 127 (57.2%) reported study design, 140 (63.1%) reported statistical models used, 142 (77.6%) reported adjusted estimates, 33 (14.9%) mentioned sensitivity analyses, and 39 (17.6%) made a strong claim about treatment effect. Studies published in top 5 general medicine journals were more likely to report the name of data source (94.7% vs. 67.0%) and study design (100% vs. 53.2%) than those in other journals. Conclusions The under-reporting of key methodological features in abstracts of RCD studies was common, which would substantially compromise the indexing of this type of literature and prevent the effective use of study findings. Substantial efforts to improve the reporting of abstracts in these studies are highly warranted.


2021 ◽  
Vol 4 (12) ◽  
pp. e2139748
Author(s):  
Charlie Harper ◽  
Marion Mafham ◽  
William Herrington ◽  
Natalie Staplin ◽  
William Stevens ◽  
...  

Health Equity ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 801-817
Author(s):  
Kevin Morisod ◽  
Xhyljeta Luta ◽  
Joachim Marti ◽  
Jacques Spycher ◽  
Mary Malebranche ◽  
...  

Author(s):  
Alexander J. Fowler ◽  
M.A. Hussein Wahedally ◽  
Tom E.F. Abbott ◽  
Melanie Smuk ◽  
John R. Prowle ◽  
...  

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Van Thu Nguyen ◽  
Mishelle Engleton ◽  
Mauricia Davison ◽  
Philippe Ravaud ◽  
Raphael Porcher ◽  
...  

Abstract Background To assess the completeness of reporting, research transparency practices, and risk of selection and immortal bias in observational studies using routinely collected data for comparative effectiveness research. Method We performed a meta-research study by searching PubMed for comparative effectiveness observational studies evaluating therapeutic interventions using routinely collected data published in high impact factor journals from 01/06/2018 to 30/06/2020. We assessed the reporting of the study design (i.e., eligibility, treatment assignment, and the start of follow-up). The risk of selection bias and immortal time bias was determined by assessing if the time of eligibility, the treatment assignment, and the start of follow-up were synchronized to mimic the randomization following the target trial emulation framework. Result Seventy-seven articles were identified. Most studies evaluated pharmacological treatments (69%) with a median sample size of 24,000 individuals. In total, 20% of articles inadequately reported essential information of the study design. One-third of the articles (n = 25, 33%) raised some concerns because of unclear reporting (n = 6, 8%) or were at high risk of selection bias and/or immortal time bias (n = 19, 25%). Only five articles (25%) described a solution to mitigate these biases. Six articles (31%) discussed these biases in the limitations section. Conclusion Reporting of essential information of study design in observational studies remained suboptimal. Selection bias and immortal time bias were common methodological issues that researchers and physicians should be aware of when interpreting the results of observational studies using routinely collected data.


2021 ◽  
Author(s):  
Katherine G Young ◽  
Timothy J McDonald ◽  
Beverley M Shields

Data linkage of cohort or RCT data with routinely collected data is becoming increasingly commonplace, and this often involves combining biomarker measurements from different sources. However, sources may have different biases due to differences in assay method and calibration. Combining these measurements, or diagnoses based on these measurements, is therefore not always valid. We highlight an example using glycated haemoglobin A1c (HbA1c) test results from two different sources in UK Biobank data.


Author(s):  
Hayato Yamana ◽  
Asuka Tsuchiya ◽  
Hiromasa Horiguchi ◽  
Shigeki Morita ◽  
Tamotsu Kuroki ◽  
...  

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