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Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

We used the following formula for sample size estimation for a proportion: sample size = p(1-p)×(z/e)^2×n/[(1+(n-1))×r] where z is the z score corresponding to the desired confidence level (in this case z=1.96 for a 95% CI), p is the estimated prevalence of antibiotic use (using 74% as the prevalence determined by Kiggundu et al [36] in 13 hospitals in Uganda), e is the margin of error expressed as a proportion (assumed to be 0.03), n is the number of clusters (in this case, the 9 surveillance sites), and r

Jonathan Mayito, Conrad Tumwine, Ronald Galiwango, Elly Nuwamanya, Suzan Nakasendwa, Mackline Hope, Reuben Kiggundu, Dathan M Byonanebye, Flavia Dhikusooka, Vivian Twemanye, Andrew Kambugu, Francis Kakooza

JMIR Res Protoc 2024;13:e58116

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