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Multidrug Resistance Among New Tuberculosis Cases: Detecting Local Variation Through Lot Quality-assurance Sampling

Hedt, Bethany Lynna; van Leth, Frankb,c; Zignol, Matteod; Cobelens, Frankb; van Gemert, Wayned; Nhung, Nguyen Viete; Lyepshina, Svitlanaf; Egwaga, Saidig; Cohen, Tedh,i

doi: 10.1097/EDE.0b013e3182459455
Infectious Disease

Background: Current methodology for multidrug-resistant tuberculosis (MDR TB) surveys endorsed by the World Health Organization provides estimates of MDR TB prevalence among new cases at the national level. On the aggregate, local variation in the burden of MDR TB may be masked. This paper investigates the utility of applying lot quality-assurance sampling to identify geographic heterogeneity in the proportion of new cases with multidrug resistance.

Methods: We simulated the performance of lot quality-assurance sampling by applying these classification-based approaches to data collected in the most recent TB drug-resistance surveys in Ukraine, Vietnam, and Tanzania. We explored 3 classification systems— two-way static, three-way static, and three-way truncated sequential sampling—at 2 sets of thresholds: low MDR TB = 2%, high MDR TB = 10%, and low MDR TB = 5%, high MDR TB = 20%.

Results: The lot quality-assurance sampling systems identified local variability in the prevalence of multidrug resistance in both high-resistance (Ukraine) and low-resistance settings (Vietnam). In Tanzania, prevalence was uniformly low, and the lot quality-assurance sampling approach did not reveal variability. The three-way classification systems provide additional information, but sample sizes may not be obtainable in some settings. New rapid drug-sensitivity testing methods may allow truncated sequential sampling designs and early stopping within static designs, producing even greater efficiency gains.

Conclusions: Lot quality-assurance sampling study designs may offer an efficient approach for collecting critical information on local variability in the burden of multidrug-resistant TB. Before this methodology is adopted, programs must determine appropriate classification thresholds, the most useful classification system, and appropriate weighting if unbiased national estimates are also desired.

From the aDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA; bDepartment of Global Health, Academic Medical Centre, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands; cKNCV Tuberculosis Foundation, The Hague, The Netherlands; dStop TB Department, World Health Organization, Geneva, Switzerland; eNational Tuberculosis Control Program, National Lung Hospital, Hanoi, Vietnam; fDonetsk State Medical University, Donetsk, Ukraine; gNational Tuberculosis Control Program, Tanzania; hDivision of Global Health Equity, Brigham and Women's Hospital, Boston, MA; and iDepartment of Epidemiology, Harvard School of Public Health, Boston, MA.

Submitted: 29 March 2011; accepted: 25 October 2011; posted online 16 January 2012.

B.L.H. received support from National Institutes of Health Grant R56 EB006195. F.v.L. received funding from the UK Department for International Development (DFID) for his work in the Tanzania survey. T.C. received finding from National Institute of Health grants DP2OD006663 and U54GM088558. Financial support for the survey in Donetsk, Ukraine, was provided by the United States Agency for International Development for the survey in Vietnam, from The Netherlands Government Grant for National Strategic Plan of the NTP for the period 2001–2005 and for the survey in Tanzania, from the World Health Organization. The authors reported no other financial interests related to this research.

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Correspondence: Bethany L. Hedt, Department of Global Health and Social Medicine, 641 Huntington Ave, Boston, MA 02115. E-mail:

© 2012 Lippincott Williams & Wilkins, Inc.