Previous work has identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors: participation, attrition, prognostic factor measurement, confounding measurement and account, outcome measurement, and analysis and reporting. This article describes the Quality In Prognosis Studies tool, which includes questions related to these areas that can inform judgments of risk of bias in prognostic research.A working group comprising epidemiologists, statisticians, and clinicians developed the tool as they considered prognosis studies of low back pain. Forty-three groups reviewing studies addressing prognosis in other topic areas used the tool and provided feedback. Most reviewers (74%) reported that reaching consensus on judgments was easy. Median completion time per study was 20 minutes; interrater agreement (κ statistic) reported by 9 review teams varied from 0.56 to 0.82 (median, 0.75). Some reviewers reported challenges making judgments across prompting items, which were addressed by providing comprehensive guidance and examples. The refined Quality In Prognosis Studies tool may be useful to assess the risk of bias in studies of prognostic factors.
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http://dx.doi.org/10.7326/0003-4819-158-4-201302190-00009 | DOI Listing |
Orphanet J Rare Dis
January 2025
Department of Pediatric Gastroenterology and Nutrition, Amsterdam UMC, Emma Children's Hospital, Vrije Universiteit, Amsterdam, The Netherlands.
Background: Achalasia is a rare esophageal motility disorder with an estimated annual incidence of 1-5/100.000 and a mean age at diagnosis > 50 years of age. Only a fraction of the patients has an onset during childhood (estimated incidence of 0.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Amref Health Africa in Ethiopia, EPI Technical Assistant at West Gondar Zonal Health Department, SLL Project, COVID-19 Vaccine, Gondar, Ethiopia.
Background: Ethiopian healthcare relies heavily on Health Extension Workers (HEWs), who deliver essential services to communities nationwide. By analyzing existing research, the authors explore how prevalent job satisfaction is and what factors affect it. This comprehensive analysis aims to improve HEW satisfaction through targeted interventions, ultimately leading to a more effective healthcare workforce and better health outcomes in Ethiopia.
View Article and Find Full Text PDFClin Epigenetics
January 2025
Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait.
View Article and Find Full Text PDFJ Expo Sci Environ Epidemiol
January 2025
Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Background: Preterm birth (PTB) is a common pregnancy complication associated with significant neonatal morbidity. Prenatal exposure to environmental chemicals, including toxic and/or essential metal(loid)s, may contribute to PTB risk.
Objective: We aimed to summarize the epidemiologic evidence of the associations among levels of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), lead (Pb), and zinc (Zn) assessed during the prenatal period and PTB or gestational age at delivery; to assess the quality of the literature and strength of evidence for an effect for each metal; and to provide recommendations for future research.
Sci Rep
January 2025
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany.
The characteristics of data produced by omics technologies are pivotal, as they critically influence the feasibility and effectiveness of computational methods applied in downstream analyses, such as data harmonization and differential abundance analyses. Furthermore, variability in these data characteristics across datasets plays a crucial role, leading to diverging outcomes in benchmarking studies, which are essential for guiding the selection of appropriate analysis methods in all omics fields. Additionally, downstream analysis tools are often developed and applied within specific omics communities due to the presumed differences in data characteristics attributed to each omics technology.
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