Baum Sarah E, Pelissari Daniele M, Dockhorn Costa Fernanda, Harada Luiza O, Sanchez Mauro, Bartholomay Patricia, Cohen Ted, Castro Marcia C, Menzies Nicolas A
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Health and Environment Surveillance Secretariat, Ministry of Health, Brasília, Brazil.
PLoS Comput Biol. 2024 Dec 23;20(12):e1012662. doi: 10.1371/journal.pcbi.1012662. eCollection 2024 Dec.
Effectively responding to drug-resistant tuberculosis (TB) requires accurate and timely information on resistance levels and trends. In contexts where use of drug susceptibility testing has not been universal (i.e. not all patients are offered testing), surveillance for rifampicin-resistance-one of the core drugs in the TB treatment regimen-has relied on resource-intensive and infrequent nationally-representative prevalence surveys. The expanded availability of rapid diagnostic tests (RDTs) over the past decade has increased testing coverage in many settings. However, RDT data collected in the course of routine (but not universal) use may provide biased estimates of resistance if the subset of patients receiving RDTs is not representative of the overall cohort. Here, we developed a method that attempts to correct for non-random use of RDT testing in the context of routine TB diagnosis to recover unbiased estimates of resistance among new and previously treated TB cases. Specifically, we employed statistical corrections to model rifampicin resistance among TB notifications with observed Xpert MTB/RIF (a WHO-recommended RDT) results using a hierarchical generalized additive regression model, and then used model output to impute results for untested individuals. We applied this model to 2017-2023 case-level data on over 800,000 patients from Brazil. Modeled estimates of the prevalence of rifampicin resistance were substantially higher than naïve estimates, with estimated prevalence ranging between 28-44% higher for new cases and 2-17% higher for previously treated cases. Our estimates of RR-TB incidence were estimated with narrower uncertainty intervals relative to WHO estimates for the same time period, and were robust to alternative model specifications. Our approach provides a generalizable method to leverage routine RDT data to derive timely estimates of RR-TB prevalence among notified TB cases in settings where testing for TB drug resistance is not universal.
有效应对耐药结核病需要有关耐药水平和趋势的准确及时信息。在药敏试验尚未普及的情况下（即并非所有患者都接受检测），对利福平耐药性（结核病治疗方案中的核心药物之一）的监测依赖于资源密集且不频繁的全国代表性患病率调查。在过去十年中，快速诊断检测（RDT）的可用性不断扩大，在许多情况下增加了检测覆盖率。然而，如果接受RDT检测的患者子集不能代表整个队列，那么在常规（但非普遍）使用过程中收集的RDT数据可能会提供有偏差的耐药性估计。在此，我们开发了一种方法，试图在常规结核病诊断的背景下纠正RDT检测的非随机使用情况，以恢复新发病例和既往治疗病例中无偏差的耐药性估计。具体而言，我们采用统计校正方法，使用分层广义相加回归模型，根据观察到的Xpert MTB/RIF（世界卫生组织推荐的RDT）结果，对结核病报告病例中的利福平耐药性进行建模，然后使用模型输出为未检测个体推算结果。我们将此模型应用于来自巴西的80多万患者的2017 - 2023年病例级数据。利福平耐药率的建模估计值显著高于未经校正的估计值，新发病例的估计患病率高出28 - 44%，既往治疗病例高出2 - 17%。相对于世界卫生组织对同一时期的估计，我们对RR-TB发病率的估计具有更窄的不确定性区间，并且对替代模型规范具有稳健性。我们的方法提供了一种可推广的方法，用于利用常规RDT数据，在结核病耐药性检测不普遍的情况下，及时推算已报告结核病病例中RR-TB的患病率。
