• J. Antimicrob. Chemother. · Oct 2013

    Development of a risk-adjustment model for antimicrobial utilization data in 21 public hospitals in Queensland, Australia (2006-11).

    • M Rajmokan, A Morton, J Marquess, E G Playford, and M Jones.
    • Centre for Healthcare Related Infection Surveillance and Prevention (CHRISP), Queensland Health, Herston, QLD 4006, Australia.
    • J. Antimicrob. Chemother. 2013 Oct 1; 68 (10): 2400-5.

    ObjectivesMaking valid comparisons of antimicrobial utilization between hospitals requires risk adjustment for each hospital's case mix. Data on individual patients may be unavailable or difficult to process. Therefore, risk adjustment for antimicrobial usage frequently needs to be based on a hospital's services. This study evaluated such a strategy for hospital antimicrobial utilization.MethodsData were obtained on five broad subclasses of antibiotics [carbapenems, β-lactam/β-lactamase inhibitor combinations (BLBLIs), fluoroquinolones, glycopeptides and third-generation cephalosporins] from the Queensland pharmacy database (MedTrx) for 21 acute public hospitals (2006-11). Eleven clinical services and a variable for hospitals from the tropical region were employed for risk adjustment. Multivariable regression models were used to identify risk and protective services for these antibiotics. Funnel plots were used to display hospitals' antimicrobial utilization.ResultsTotal inpatient antibiotic utilization for these antibiotics increased from 130.6 defined daily doses (DDDs)/1000 patient-days in 2006 to 155.8 DDDs/1000 patient-days in 2011 (P < 0.0001). Except for third-generation cephalosporins, the average utilization rate was higher for intensive care, renal/nephrology, cardiac, burns/plastic surgery, neurosurgery, transplant and acute spinal services than for the respective reference group (no service). In addition, oncology, high-activity infectious disease and coronary care services were associated with higher utilization of carbapenems, BLBLIs and glycopeptides.ConclusionsOur model predicted antimicrobial utilization rates by hospital services. The funnel plots displayed hospital utilization data after adjustment for variation among the hospitals. However, the methodology needs to be validated in other populations, ideally using a larger group of hospitals.

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