Community-Based Surveillance of Antimicrobial Use and ...

Community-based surveillance of antimicrobial useand resistance in resource-constrained settings2.4 Lessons learnt from this site2.4.1 AMRThis site demonstrated the usefulness of using E. coli from urine as an indicator organism.The method allowed the collection of both commensals and pathogens, the former beingused for surveillance and the latter being of direct clinical benefit to patients and prescribers.Obtaining urine samples from pregnant women is also practical, as they routinely visithealth facilities that have access to laboratory services. They are also not likely to bereceiving ABM, but can be questioned about such use when providing the sample. However,as shown in the early months of the study, the yield of isolates may still be lower thanexpected.2.4.2 ABM useAt this site, measures of trends in ABM consumption based on the percentage ofprescriptions with an ABM and those on the DDD prescribed per 100 patients correlated to agreat extent. Determination of the former measure is far easier, and the chances of calculationerror are much lower. The amount of data to be recorded and captured is also far less.However, if some of the interventions to be assessed are dependent on reducing the quantityof ABM prescribed (for example, advocating short‐course ABM for UTI in otherwise healthywomen), then measures that take quantity into account (such as DDD per 100 patients) willbe important. However, care must be taken to:• examine sufficient numbers of prescriptions to obtain sufficient ABM‐containingprescriptions;• determine the total number of patients seen in the time required to obtain sufficient exitinterviews from patients receiving ABM;• define the correct DDD for combination products, such as those containing bothampicillin and cloxacillin, and in capturing all ABM prescribed for a single patient.Although appealingly simple, measuring consumption on the basis of purchase records maybe misleading. What is purchased may not be consumed or may be consumed months afterpurchase. Such methods are also challenging in terms of the denominator to be applied, ifDDD per 1000 population is to be used as the measure of exposure. Since differentdenominators were used, no comparison could be made between consumption measuredaccording to bulk purchase data and exiting patient interviews.This site utilized only one type of facility (private pharmacies), and so no comparison withother settings or types of prescribers (such as public sector clinic personnel) could be made.26