scholarly journals On the formalism of the screening paradox

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256645
Author(s):  
Jacques Balayla
Keyword(s):  
Predictive Value ◽  
Disease Process ◽  
Population Level ◽  
Bayes Theorem ◽  
Disease Prevalence ◽  
Screening Tests ◽  
Screening Process ◽  
Screening Programs ◽  
Closed Systems ◽  
The Mathematical Model

Bayes’ Theorem imposes inevitable limitations on the accuracy of screening tests by tying the test’s predictive value to the disease prevalence. The aforementioned limitation is independent of the adequacy and make-up of the test and thus implies inherent Bayesian limitations to the screening process itself. As per the WHO’s Wilson − Jungner criteria, one of the prerequisite steps before undertaking screening is to ensure that a treatment for the condition screened for exists. However, when applying screening programs in closed systems, a paradox, henceforth termed the “screening paradox”, ensues. If a disease process is screened for and subsequently treated, its prevalence would drop in the population, which as per Bayes’ theorem, would make the tests’ predictive value drop in return. Put another way, a very powerful screening test would, by performing and succeeding at the very task it was developed to do, paradoxically reduce its ability to correctly identify individuals with the disease it screens for in the future—over some time t. In this manuscript, we explore the mathematical model which formalizes said screening paradox and explore its implications for population level screening programs. In particular, we define the number of positive test iterations (PTI) needed to reverse the effects of the paradox. Given their theoretical nature, clinical application of the concepts herein reported need validation prior to implementation. Meanwhile, an understanding of how the dynamics of prevalence can affect the PPV over time can help inform clinicians as to the reliability of a screening test’s results.

scholarly journals Bayesian updating and sequential testing: overcoming inferential limitations of screening tests

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jacques Balayla
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Bayesian Updating ◽  
Bayes Theorem ◽  
Disease Prevalence ◽  
Positive Test ◽  
Screening Tests ◽  
Sequential Testing ◽  
Widespread Application ◽  
Test Probability

Abstract Background Bayes’ theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian limitations and thus improves the reliability of screening tests. Methods We use Bayes’ theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive predictive value, represented graphically by the tablecloth function. Results For a given PPV ($$\rho$$ ρ ) approaching k, the number of positive test iterations needed given a prevalence of disease ($$\phi$$ ϕ ) is: $$n_i =\lim _{\rho \rightarrow k}\left\lceil \frac{ln\left[ \frac{\rho (\phi -1)}{\phi (\rho -1)}\right] }{ln\left[ \frac{a}{1-b}\right] }\right\rceil \qquad \qquad (1)$$ n i = lim ρ → k l n ρ ( ϕ - 1 ) ϕ ( ρ - 1 ) l n a 1 - b ( 1 ) where $$n_i$$ n i = number of testing iterations necessary to achieve $$\rho$$ ρ , the desired positive predictive value, ln = the natural logarithm, a = sensitivity, b = specificity, $$\phi$$ ϕ = disease prevalence/pre-test probability and k = constant. Conclusions Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a $$\rho (\phi )$$ ρ ( ϕ ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. Clinical validation of these concepts needs to be obtained prior to its widespread application.

scholarly journals Bayesian Updating and Sequential Testing: Overcoming Inferential Limitations of Screening Tests

2021 ◽  
Author(s):  
Jacques Balayla
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Bayesian Updating ◽  
Bayes Theorem ◽  
Disease Prevalence ◽  
Positive Test ◽  
Screening Tests ◽  
Sequential Testing ◽  
Widespread Application ◽  
Test Probability

Abstract Background: Bayes’ Theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian lim- itations and thus improves the reliability of screening tests. Methods: We use Bayes’ Theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive pre- dictive value, represented graphically by the tablecloth function. Results: For a given PPV ρ approaching k, the number of positive test iterations given a prevalence φ needed is: [see equation], where ni = number of testing iterations necessary to achieve ρ, the desired positive predictive value, ln = the natural logarithm, a = sensitivity, b = specificity, φ = disease prevalence/pre-test probability and k = constant. Conclusions: Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a ρ(φ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. Clinical vali- dation of these concepts needs to be obtained prior to its widespread application.

Newborn Genetic Screening: Blessing or Curse?

1999 ◽  
Vol 18 (7) ◽  
pp. 11-19 ◽  
Author(s):  
Carole Kenner ◽  
Stephanie Amlung
Keyword(s):  
Newborn Screening ◽  
Genetic Screening ◽  
Ethical Issues ◽  
Ethical Dilemmas ◽  
Standard Of Care ◽  
Screening Tests ◽  
Screening Process ◽  
Screening Programs ◽  
Screening Procedures

Newly discovered genes and advances in genetic screening programs prompt many questions reflecting the kinds of ethical dilemmas that go hand in hand with life-changing discoveries. Neonatal genetic screening has been a standard of care for some time, but as our knowledge in the field of genetics expands, should we continue with the same approach? What newborn genetic screening tests should be mandatory, and what are the long-range consequences associated with testing? This article reviews genetic modes of inheritance, outlines and explains the most common newborn screening tests, and enumerates the ethical issues associated with these screening procedures. The role of the neonatal nurse in the newborn genetic screening process is discussed.

scholarly journals The positive predictive value of genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Disease Prevalence ◽  
Screening Tests ◽  
Clinical Indication ◽  
Confirmatory Test ◽  
Pathogenic Variants ◽  
Test Population ◽  
Health Communities ◽  
Asymptomatic Individuals

As the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities. However, the application of screening principles to inherited diseases with unknown prevalence in an unselected (low-risk) test population raises unique challenges. This paper examines the effect of disease prevalence on the positive predictive value of a test result. Many inherited conditions have very low prevalence in an unselected population, which increases the probability that some likely pathogenic variants may be false positives. In situations where the adverse impact of a false positive result is significant, laboratories should address this issue by either increasing the interpretive specificity of the test, performing a clinical confirmatory test to establish the presence of disease, or restricting the test to a population with increased disease prevalence. Here, we review the statistical concepts relevant to screening tests, apply these concepts to genetic disease screening, create a model to estimate prevalence and positive predictive value, and provide a framework for further discussion.

scholarly journals A Model-Based Meta-Analysis of Willingness to Participate in Cancer Screening

2021 ◽  
Vol 18 (5) ◽  
pp. 2580
Author(s):  
Guangchao Charles Feng ◽  
Zhiliang Lin ◽  
Wanhua Ou ◽  
Xianglin Su ◽  
Qing Yan
Keyword(s):  
Structural Equation ◽  
Meta Analysis ◽  
Health Belief ◽  
Screening Tests ◽  
Equation Modeling ◽  
Willingness To Participate ◽  
Early Screening ◽  
The Public ◽  
Screening Programs ◽  
Belief Model

Although early screening tests are beneficial for the detection and treatment of cancers, many people have failed to participate in screening tests. The present study aims to explore the theoretical underpinning of low participation in screening programs using the method of meta-analytic structural equation modeling. It was found that the health belief model is the most adopted theoretical framework. Moreover, the intended uptake of screening was positively predicted only by cues to action, health literacy, and perceived susceptibility. As a result, a health intention model, including the three significant variables, is proposed. The practical implications of the findings are that health communication campaigns should focus on enlightening and engaging the public through all necessary means to raise awareness and transfer knowledge in relation to screening procedures as well as cancers per se.

scholarly journals Family history assessment significantly enhances delivery of precision medicine in the genomics era

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yasmin Bylstra ◽  
Weng Khong Lim ◽  
Sylvia Kam ◽  
Koei Wan Tham ◽  
R. Ryanne Wu ◽  
...  
Keyword(s):  
Family History ◽  
Cancer Risk ◽  
Clinical Care ◽  
Genomic Analysis ◽  
Population Level ◽  
Cancer Genes ◽  
Family History Information ◽  
Screening Programs ◽  
Family History Assessment ◽  
Risk Of Cancer

Abstract Background Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection. Methods To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes. Results Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant. Conclusions These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs. Trial registration ClinicalTrials.gov NCT02791152. Retrospectively registered on May 31, 2016.

scholarly journals Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 503
Author(s):  
Thomas F. Monaghan ◽  
Syed N. Rahman ◽  
Christina W. Agudelo ◽  
Alan J. Wein ◽  
Jason M. Lazar ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Medical Literature ◽  
Disease Prevalence ◽  
Positive Test Result ◽  
Predictive Values ◽  
Sensitivity Specificity ◽  
Test Result ◽  
Test Probability

Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.

scholarly journals Comparison between Enzyme Immunoassays Performed on Samples of Dried Blood and Serum for Toxoplasmosis Prenatal Screening: Population-based Study

2021 ◽  
Vol 43 (05) ◽  
pp. 351-356
Author(s):  
Bárbara Araújo Marques ◽  
Ericka Vianna Machado Carellos ◽  
Vânia Maria Novato Silva ◽  
Fernando Henrique Pereira ◽  
Maria Regina Lage Guerra ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Prenatal Screening ◽  
Dried Blood Spots ◽  
Population Based ◽  
Minas Gerais ◽  
Immunoglobulin M ◽  
Screening Tests ◽  
Reference Test ◽  
Screening Programs ◽  
Blood Spots

Abstract Objective Most prenatal screening programs for toxoplasmosis use immunoassays in serum samples of pregnant women. Few studies assess the accuracy of screening tests in dried blood spots, which are of easy collection, storage, and transportation. The goals of the present study are to determine the performance and evaluate the agreement between an immunoassay of dried blood spots and a reference test in the serum of pregnant women from a population-based prenatal screening program for toxoplasmosis in Brazil. Methods A cross-sectional study was performed to compare the immunoassays Imunoscreen Toxoplasmose IgM and Imunoscreen Toxoplasmose IgG (Mbiolog Diagnósticos, Ltda., Contagem, Minas Gerais, Brazil)in dried blood spots with the enzyme-linked fluorescent assay (ELFA, BioMérieux S.A., Lyon, France) reference standard in the serum of pregnant women from Minas Gerais Congenital Toxoplasmosis Control Program. Results The dried blood spot test was able to discriminate positive and negative results of pregnant women when compared with the reference test, with an accuracy of 98.2% for immunoglobulin G (IgG), and of 95.8% for immunoglobulin M (IgM). Conclusion Dried blood samples are easy to collect, store, and transport, and they have a good performance, making this a promising method for prenatal toxoplasmosis screening programs in countries with continental dimensions, limited resources, and a high prevalence of toxoplasmosis, as is the case of Brazil.

PP58 The Alliance Between Health Technology Assessment And Public Health In National Screening Policies

2019 ◽  
Vol 35 (S1) ◽  
pp. 48-48
Author(s):  
Leonor Varela-Lema ◽  
Janet Puñal-Riobóo ◽  
Paula Cantero-Muñoz ◽  
Maria José Faraldo-Vallés
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Newborn Screening ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
National Health System ◽  
Health Technology ◽  
Screening Tests ◽  
Special Focus ◽  
Screening Programs

IntroductionDecision making regarding national population-based prenatal and newborn screening policies is recognized to be highly challenging. This paper aims to describe the formalized collaboration that has been established between the Spanish National Public Health Screening Advisory Committee (PHSAC) and the Spanish Network of Health Technology Assessment (HTA) agencies to support the development of evidence- and consensus-based recommendations to support this process.MethodsIn-depth description and analysis of the strategic and methodological processes that have been implemented within the Spanish National Health System prenatal and newborn screening frameworks, with special emphasis on the role, actions, and responsibilities of HTA agencies.ResultsThe role of HTA agencies is threefold: (i) support the PHSAC by providing evidence on safety, effectiveness and cost/effectiveness of the screening tests/strategies, as well as contextualized information regarding costs, organizational, social, legal and ethical issues; (ii) collaborate with the PHSAC in the development of formal evidence- and consensus-based recommendations for defining population screening programs, when required; (iii) analyze real-world data that is generated by piloted programs. This paper will provide real-life examples of how these processes were implemented in practice, with a special focus on the development of the non-invasive prenatal testing (NIPT) policy. Recommendations for NIPT were developed by a multidisciplinary group based on the European network for Health Technology Assessment (EUnetHTA) rapid assessment report and the predictive models that were built using national statistics and other contextualized data.ConclusionsThe current work represents an innovative approach for prenatal and newborn screening policymaking, which are commonly difficult to evaluate due to the low quality of evidence and the confounding public health issues. The paper raises awareness regarding the importance of joint collaborations in areas where evidence is commonly insufficient for decision making.

scholarly journals Positive predictive value highlights four novel candidates for actionable genetic screening from analysis of 220,000 clinicogenomic records

2021 ◽  
Author(s):  
Kelly M. Schiabor Barrett ◽  
Alexandre Bolze ◽  
Yunyun Ni ◽  
Simon White ◽  
Magnus Isaksson ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Genetic Screening ◽  
Predictive Value ◽  
Rare Variants ◽  
Large Population ◽  
Population Screening ◽  
Cystic Kidney Disease ◽  
Cystic Kidney ◽  
Screening Programs ◽  
Thalassemia Minor

Abstract Purpose To identify conditions that are candidates for population genetic screening based on population prevalence, penetrance of rare variants, and actionability. Methods We analyzed exome and medical record data from >220,000 participants across two large population health cohorts with different demographics. We performed a gene-based collapsing analysis of rare variants to identify genes significantly associated with disease status. Results We identify 74 statistically significant gene–disease associations across 27 genes. Seven of these conditions have a positive predictive value (PPV) of at least 30% in both cohorts. Three are already used in population screening programs (BRCA1, BRCA2, LDLR), and we also identify four new candidates for population screening: GCK with diabetes mellitus, HBB with β-thalassemia minor and intermedia, PKD1 with cystic kidney disease, and MIP with cataracts. Importantly, the associations are actionable in that early genetic screening of each of these conditions is expected to improve outcomes. Conclusion We identify seven genetic conditions where rare variation appears appropriate to assess in population screening, four of which are not yet used in screening programs. The addition of GCK, HBB, PKD1, and MIP rare variants into genetic screening programs would reach an additional 0.21% of participants with actionable disease risk, depending on the population.

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