Resources Repository
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ArticlePublication 2020Bayes' Theorem, COVID-19, and Screening Tests
This article reviews the implications of increased testing for COVID-19 using reverse transcriptase polymerase chain …
This article reviews the implications of increased testing for COVID-19 using reverse transcriptase polymerase chain reaction (rRT-PCR) through the application of Bayes’ Theorem for three hypothetical, stylized case scenarios. The scenarios involve three patients with a low, moderate, and high pre-test probability of COVID-19 infection. The category of low probability would include "asymptomatic individuals in a presumed low prevalence environment" and might vary from 10 to 20%. The category of moderate probability would include "individuals…
Test Performance | Probability/Bayes | Infectious Diseases | Clinical Care | Health/Medicine -
Tools/ModelsInteractive, Teaching Resource 2020Interactive Graphic: Interpreting a COVID-19 Test Result
Currently, the most common diagnostic test for COVID-19 relies on reverse transcriptase polymerase chain reaction …
Currently, the most common diagnostic test for COVID-19 relies on reverse transcriptase polymerase chain reaction (RT-PCR), and most often uses samples obtained from the respiratory tract by nasopharyngeal swab. This interactive graphic demonstrates the influence of the prior probability of COVID-19, the test sensitivity (i.e., the probability of a positive test conditional on disease presence), and the test specificity (i.e., the probability of a negative test conditional on disease absence) on the post-test probability of…
Test Performance | Probability/Bayes | Infectious Diseases | Clinical Care | Health/Medicine | Science/Technology | High School | College | Graduate | Doctoral | Professional | Graphics/Visualization | Quantitative Literacy -
ArticlePublication 2020Interpreting COVID-19 Test Results: A Bayesian Approach
This article considers the following question with respect to interpreting the results of polymerase chain reaction …
This article considers the following question with respect to interpreting the results of polymerase chain reaction (PCR) assays from nasal and pharyngeal swabs for COVID-19 to inform clinical decision making: "While a positive result in an acutely ill patient is straightforward, how should physicians interpret negative tests in patients with suspected COVID-19 infection?" Using an assumption of near-perfect specificity of PCR assays for COVID-19, the authors acknowledge the uncertainty of test sensitivity. They consider two clinical scenarios…
Test Performance | Probability/Bayes | Infectious Diseases | Health/Medicine | Science/Technology | Global -
ArticlePublication 2020Translating Population Evidence to Individual Patients
In this paper, the authors describe the differences in population level outcomes compared to individual …
In this paper, the authors describe the differences in population level outcomes compared to individual patients and discuss ways that these are differences. The authors cover topics including the difference between relative and absolute risk and benefit. They use an example of the decision to start anticoagulation in new-onset atrial fibrillation to discuss translating population level evidence to treatment of an individual. These options include generalizability, subgroup analysis, prediction rules, following response to therapy, and even…
Test Performance | Evidence Synthesis | Health Systems | Clinical Care | Health/Medicine -
ArticlePublication 2020Clinical Decision Making: Using a Diagnostic Test
This article is part of a 6-part series on clinical decision making. The authors use …
This article is part of a 6-part series on clinical decision making. The authors use two clinical examples to review the principles of interpreting diagnostic test results. They outline an approach that can be used to determine how to select and apply tests and their results to the practice of internal medicine. Topics covered in the two case studies include sensitivity and specificity, positive predictive and negative predictive value of tests, and how to estimate…
Test Performance | Probability/Bayes | Clinical Care | Health/Medicine -
ArticlePublication 2019Measuring Progress Towards Universal Health Coverage
This article, published in BMJ Global Health, aims to estimate the 2015 national and subnational …
This article, published in BMJ Global Health, aims to estimate the 2015 national and subnational universal health coverage (UHC) service coverage status for Ethiopia. The UHC service coverage index is constructed from the geometric means of component indicators: first, within each of four major categories and then across all components to obtain the final summary index. The authors estimate UHC service coverage at the subnational level using a variety of surveys and routinely collected administrative…
Test Performance | Health Systems | Health/Medicine | Sub-Saharan Africa -
Teaching PackPublication, Teaching Resource 2022Teaching Pack: Probability Revision and Bayes
In this teaching pack on Probability Revision and Bayes, students are introduced to the implications …
In this teaching pack on Probability Revision and Bayes, students are introduced to the implications of imperfect information, acquire a conceptual understanding of Bayes theorem, and gain practical skills in performing probability revision. Materials include an instructor's note, videos, companion slides, a glossary, an annotated bibliography, sample exercises, and additional support. Learning Objectives Demonstrate a conceptual understanding of Bayes’ theorem and probability revision. Differentiate between test characteristics (e.g., probability of positive test given…
Test Performance | Probability/Bayes | Decision Analysis | Environmental Health | Health/Medicine | College | Graduate | Critical Thinking/Analysis | Graphics/Visualization | Quantitative Literacy -
Teaching PackPublication, Teaching Resource 2022Teaching Pack: Using Test Information I
In this teaching pack on Using Test Information I, students review the performance of a …
In this teaching pack on Using Test Information I, students review the performance of a dichotomous test and the relationship between sensitivity and specificity, calculate likelihood ratios to describe test performance, and conduct probability revision using the odds-LR form of Bayes. Materials include an instructor's note, videos, companion slides, a glossary, an annotated bibliography, and sample exercises. Learning Objectives Calculate four conditional probabilities describing the performance of a dichotomous test, and explain the…
Test Performance | Probability/Bayes | Decision Analysis | Child/Nutrition | Health/Medicine | College | Graduate | Doctoral | Professional | Critical Thinking/Analysis | Graphics/Visualization | Quantitative Literacy -
Teaching PackPublication, Teaching Resource 2022Teaching Pack: Using Test Information II
In this teaching pack on Using Test Information II, students are introduced to tests with …
In this teaching pack on Using Test Information II, students are introduced to tests with continuous or categorical results, and calculate the sensitivity and specificity conditional on different 'cutoff points' or 'positivity criterion'. They are introduced to ROC curves and explore the implications of operating at different parts of the curve for any single test. They calculate the optimal positivity criterion given information on the prior, the test performance, and the relative consequences of true…
Test Performance | Probability/Bayes | Decision Analysis | Health/Medicine | College | Graduate | Doctoral | Critical Thinking/Analysis | Quantitative Literacy