Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis

<p>Abstract</p> <p>Background</p> <p>With today’s rapid advances in technology and understanding of disease, more screening and diagnostic tests have become available in a variety of sociodemographic and clinical settings. This analysis quantifies the impact of varying...

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Main Authors: Bentley Tanya GK, Catanzaro Antonino, Ganiats Theodore G
Format: Article
Language:English
Published: BMC 2012-10-01
Series:BMC Research Notes
Subjects:
Online Access:http://www.biomedcentral.com/1756-0500/5/563
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spelling doaj-f27309c740304d05ae669f7212b7c5322020-11-25T02:51:59ZengBMCBMC Research Notes1756-05002012-10-015156310.1186/1756-0500-5-563Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosisBentley Tanya GKCatanzaro AntoninoGaniats Theodore G<p>Abstract</p> <p>Background</p> <p>With today’s rapid advances in technology and understanding of disease, more screening and diagnostic tests have become available in a variety of sociodemographic and clinical settings. This analysis quantifies the impact of varying prevalence rates on test performance for given sensitivity and specificity values.</p> <p>Methods</p> <p>Using a worked example of latent tuberculosis infection, we compared true-positive (TP) and false-positive (FP) results when varying prevalence and test sensitivity and specificity. We used estimates from published literature to estimate two tests’ sensitivity (81%, QuantiFERON<sup>®</sup>-TB Gold In-Tube; 88%, T-SPOT<sup>®</sup>.<it>TB</it>) and specificity (99%; 88%), and we used World Health Organization data to estimate disease prevalence in five countries.</p> <p>Results</p> <p>Varying sensitivity impacted outcomes most in high-prevalence settings; change in specificity had greater impact in low-prevalence settings. In switching from QuantiFERON-TB to T-SPOT.<it>TB</it> (higher sensitivity, lower specificity), trade-offs between increasing case identification (TPs) and decreasing unnecessary treatments (FPs) varied dramatically with prevalence. Lower-prevalence settings paid a greater “price” of more FPs for each TP gained, with 37.7 FPs per TP in the United States (5% prevalence) versus 2.5 in the Ivory Coast (55% prevalence).</p> <p>Conclusions</p> <p>Prevalence affects test performance for given sensitivity and specificity values. To optimize test performance, disease prevalence should be incorporated in testing decisions, and sensitivity and specificity should be set locally, not globally. In lower-prevalence settings, using highly specific assays may optimize outcomes.</p> http://www.biomedcentral.com/1756-0500/5/563Disease prevalenceTestingScreeningTest thresholdsSensitivitySpecificityTuberculosisOutcomes
collection DOAJ
language English
format Article
sources DOAJ
author Bentley Tanya GK
Catanzaro Antonino
Ganiats Theodore G
spellingShingle Bentley Tanya GK
Catanzaro Antonino
Ganiats Theodore G
Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
BMC Research Notes
Disease prevalence
Testing
Screening
Test thresholds
Sensitivity
Specificity
Tuberculosis
Outcomes
author_facet Bentley Tanya GK
Catanzaro Antonino
Ganiats Theodore G
author_sort Bentley Tanya GK
title Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
title_short Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
title_full Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
title_fullStr Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
title_full_unstemmed Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
title_sort implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis
publisher BMC
series BMC Research Notes
issn 1756-0500
publishDate 2012-10-01
description <p>Abstract</p> <p>Background</p> <p>With today’s rapid advances in technology and understanding of disease, more screening and diagnostic tests have become available in a variety of sociodemographic and clinical settings. This analysis quantifies the impact of varying prevalence rates on test performance for given sensitivity and specificity values.</p> <p>Methods</p> <p>Using a worked example of latent tuberculosis infection, we compared true-positive (TP) and false-positive (FP) results when varying prevalence and test sensitivity and specificity. We used estimates from published literature to estimate two tests’ sensitivity (81%, QuantiFERON<sup>®</sup>-TB Gold In-Tube; 88%, T-SPOT<sup>®</sup>.<it>TB</it>) and specificity (99%; 88%), and we used World Health Organization data to estimate disease prevalence in five countries.</p> <p>Results</p> <p>Varying sensitivity impacted outcomes most in high-prevalence settings; change in specificity had greater impact in low-prevalence settings. In switching from QuantiFERON-TB to T-SPOT.<it>TB</it> (higher sensitivity, lower specificity), trade-offs between increasing case identification (TPs) and decreasing unnecessary treatments (FPs) varied dramatically with prevalence. Lower-prevalence settings paid a greater “price” of more FPs for each TP gained, with 37.7 FPs per TP in the United States (5% prevalence) versus 2.5 in the Ivory Coast (55% prevalence).</p> <p>Conclusions</p> <p>Prevalence affects test performance for given sensitivity and specificity values. To optimize test performance, disease prevalence should be incorporated in testing decisions, and sensitivity and specificity should be set locally, not globally. In lower-prevalence settings, using highly specific assays may optimize outcomes.</p>
topic Disease prevalence
Testing
Screening
Test thresholds
Sensitivity
Specificity
Tuberculosis
Outcomes
url http://www.biomedcentral.com/1756-0500/5/563
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AT catanzaroantonino implicationsoftheimpactofprevalenceontestthresholdsandoutcomeslessonsfromtuberculosis
AT ganiatstheodoreg implicationsoftheimpactofprevalenceontestthresholdsandoutcomeslessonsfromtuberculosis
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