Confidence interval methods for antimicrobial resistance surveillance data

  16 June 2021

Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents.

General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.

Author(s): Erta Kalanxhi, Gilbert Osena, Geetanjali Kapoor & Eili Klein
Effective Surveillance  


Unrestricted financial support by:

Antimicrobial Resistance Fighter Coalition


JSS University


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