Journal article
F. Ginting, A. Sugianli, G. Bijl, Restuti Hidayani Saragih, R. Kusumawati, I. Parwati, M. D. de Jong, C. Schultsz, F. van Leth
American Journal of Epidemiology, 2019
American Journal of Epidemiology, 2019
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DOI
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APA
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Ginting, F., Sugianli, A., Bijl, G., Saragih, R. H., Kusumawati, R., Parwati, I., … van Leth, F. (2019). Rethinking Antimicrobial Resistance Surveillance: A Role for Lot Quality Assurance Sampling. American Journal of Epidemiology.
Chicago/Turabian
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Ginting, F., A. Sugianli, G. Bijl, Restuti Hidayani Saragih, R. Kusumawati, I. Parwati, M. D. de Jong, C. Schultsz, and F. van Leth. “Rethinking Antimicrobial Resistance Surveillance: A Role for Lot Quality Assurance Sampling.” American Journal of Epidemiology (2019).
MLA
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Ginting, F., et al. “Rethinking Antimicrobial Resistance Surveillance: A Role for Lot Quality Assurance Sampling.” American Journal of Epidemiology, 2019.
BibTeX Click to copy
@article{f2019a,
title = {Rethinking Antimicrobial Resistance Surveillance: A Role for Lot Quality Assurance Sampling},
year = {2019},
journal = {American Journal of Epidemiology},
author = {Ginting, F. and Sugianli, A. and Bijl, G. and Saragih, Restuti Hidayani and Kusumawati, R. and Parwati, I. and de Jong, M. D. and Schultsz, C. and van Leth, F.}
}
Abstract
Abstract Global surveillance of antimicrobial resistance (AMR) is a key component of the 68th World Health Assembly Global Action Plan on AMR. Laboratory-based surveillance is inherently biased and lacks local relevance due to aggregation of data. We assessed the feasibility, sensitivity, and affordability of a population-based AMR survey using lot quality assurance sampling (LQAS), which classifies a population as having a high or low prevalence of AMR based on a priori defined criteria. Three studies were carried out in Medan and Bandung, Indonesia, between April 2014 and June 2017. LQAS classifications for 15 antibiotics were compared with AMR estimates from a conventional population-based survey, with an assessment of the cost of a single LQAS classification using microcosting methodology, among patients suspected of urinary tract infection at 11 sites in Indonesia. The sensitivity of LQAS was above 98%. The approach detected local variation in the prevalence of AMR across sites. Time to reach LQAS results ranged from 47 to 138 days. The average cost of an LQAS classification in a single facility was US$466. The findings indicate that LQAS-based AMR survey is a feasible, sensitive, and affordable strategy for population-based AMR surveys, providing essential data to inform local empirical treatment guidelines and antimicrobial stewardship efforts.