Does locally relevant, real-time infection epidemiological data improve clinician management and antimicrobial...

Antimicrobial resistance is a significant threat to public health. Diagnostic uncertainty is a key driver of antimicrobial prescribing. We sought to determine whether locally relevant, real-time syndromic or microbiological infection epidemiology can improve prescribing by reducing diagnostic uncertainty.

Eligible studies investigated effects on primary care prescribing for common infections in Organisation For Economic Co-Operation And Development countries. We searched Medline, Embase, Cumulative index to nursing and allied health literature, Web of Science, grey literature sources, thesis databases and trial registries.

We identified 9548 reports, of which 17 were eligible, reporting 12 studies, of which 3 reported relevant outcomes. The first (observational) showed antibacterial prescribing for upper respiratory infections reduced from 26.4% to 8.6% (P = 0.01). The second (observational) showed antibacterial prescribing reduced during influenza pandemic compared with seasonal influenza periods [odds ratio (OR) 0.72 (95% CI, 0.68 to 0.77), P < 0.001], while antiviral prescribing increased [OR 6.43 (95% CI, 5.02 to 8.25), P < 0.001]. The likelihood of prescribing also decreased as the number of infection cases a physician saw increased in the previous week [OR 0.57 (95% CI, 0.51 to 0.63), P < 0.001 for ≥12 versus ≤1 patient). The third (randomized-controlled trial) showed an absolute reduction in antibacterial prescribing of 5.1% during a period of moderate influenza activity (P < 0.05). We did not find measures of diagnostic certainty, harms or costs.

There is promising evidence that epidemiological syndromic and microbiological data can reduce primary care antimicrobial prescribing. Future research should use randomized designs of behaviourally informed interventions, investigate costs and harms, and establish mechanisms of behaviour change.

CRD42016038871.