Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis.
Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis.
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DOI：
10.1186/1756-0500-5-563
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发表时间：
2012-10-10
影响因子：
1.8
通讯作者：
Ganiats, Theodore G
中科院分区：
文献类型：
--
作者：
Bentley, Tanya G K;Catanzaro, Antonino;Ganiats, Theodore G
背景：随着当今技术的快速进步和对疾病的了解，越来越多的筛查和诊断测试已经在各种社会人口学和临床环境中可用。该分析量化了不同流行率对给定敏感性和特异性值的测试性能的影响。方法：使用一个潜伏性结核感染的工作实例，我们比较了真阳性（TP）和假阳性（FP）的结果在不同的患病率和测试的敏感性和特异性。我们使用已发表文献的估计来估计两种检测方法的灵敏度（81%,QuantiFERON-TB Gold In-Tube; 88%, T-SPOT）。结核病)和特异性（99%;88%），我们使用世界卫生组织的数据来估计五个国家的疾病患病率。结果：不同的敏感性对高患病率环境的结果影响最大；特异性的改变在低患病率环境中有更大的影响。从QuantiFERON-TB切换到T-SPOT。结核病（敏感性较高，特异性较低），在增加病例识别（TPs）和减少不必要治疗（FPs）之间的权衡随着患病率的不同而显著不同。低流行环境为每获得一个TP付出更多FPs的“代价”，美国为37.7 FPs / TP（患病率5%），而科特迪瓦为2.5 FPs / TP（患病率55%）。结论：患病率影响给定敏感性和特异性值的检测性能。为优化检测性能，应将疾病流行情况纳入检测决策，并应在当地而非全球设定敏感性和特异性。在低患病率环境中，使用高度特异性的检测方法可以优化结果。
BACKGROUND: 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.METHODS: 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-TB Gold In-Tube; 88%, T-SPOT.TB) and specificity (99%; 88%), and we used World Health Organization data to estimate disease prevalence in five countries.RESULTS: 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.TB (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).CONCLUSIONS: 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.