Chris Curtis

Is there an association between referral population deprivation and antibiotic prescribing in primary and secondary care

Objective The study was designed to explore the presence of any relationship between NHS secondary care antibiotic prescribing rates or primary care antibiotic prescribing rates and the levels of deprivation experienced within the referred primary care population. The study also aimed to determine whether the antibiotic prescribing rates for each care sector were correlated.

An evaluation of quinolone prescribing in a group of acute hospitals: development of an objective measure of usage

Objectives: To develop an objective measure to enable hospital Trusts to compare their use of antibiotics.Design:Self-completion, postal questionnaire with telephone follow up.Sample: 4 hospital trusts in the English Midlands.Results: The survey showed that it was possible to collect data concerning the number of Defined Daily Doses (DDDs) of quinolone antibiotic dispensed per Finished Consultant Episode (FCE) in each Trust. In the 4 trusts studied the mean DDD/FCE was 0.197 (range 0.117 to 0.258). This indicates that based on a typical course length of 5 days, 3.9% of patient episodes resulted in the prescription of a quinolone antibiotic. Antibiotic prescribing control measures in each Trust were found to be comparable. Conclusion: The measure will enable Trusts to objectively compare their usage of quinolone antibiotics and use this information to carry out clinical audit should differences be recorded. This is likely to be applicable to other groups of antibiotics.

A multisite analysis of missed doses of antibiotics administered in hospital care

Objectives The study aimed to quantify the incidence of missed antibiotic doses in acute hospitals and identify the underlying reasons using data from electronic prescribing and medicines administration systems (EPMAS), thus establishing the feasibility of determining a minimal acceptable range for missed antibiotic doses. Methods Prescribing and administration data for antimicrobials were extracted from three hospital EPMAS (1 April 2010–31 March 2011). Data transfer protocols were developed and context mapping undertaken to ensure consistent analysis and interpretation. Total and missed numbers of antibiotic doses were calculated. The top 20 prescribed antibiotics were determined and the reasons for missed antibiotic doses were recorded. Results A data set of 1u2005157u2005576 antibiotic administration events was compiled. The percentage of missed antibiotic doses ranged from 5.90% to 10.26% of the total. The percentage use of the ‘Medicine unavailable’ reason for missed antibiotic doses was a very small proportion of the total numbers of antibiotic doses prescribed (range 0.64–0.98%). Conclusions This study has demonstrated that large data sets from different EPMAS can be used to quantify the incidence of missed antibiotic doses in acute hospitals. It is proposed that the numbers of ‘Nil By Mouth’ and ‘appropriate clinical reasons’ for dose omission provide an estimate of the maximum proportion of valid missed antibiotic doses within any hospital (range 0.3–4.6% of total antibiotic doses prescribed), so it is suggested that the acceptable level of missed antibiotics should be approximately 5% of the total number of doses intended. Active intervention by senior hospital management appears to lead to the reduction in numbers of missed antibiotic doses. Medicines supply failure was not a significant reason for missed antibiotic administration.

The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review

Background Inappropriate antimicrobial use has been shown to be an important determinant of the emergence of antimicrobial resistance (AMR). Health information technology (HIT) in the form of Computerised Decision Support (CDS) represents an option for improving antimicrobial prescribing and containing AMR. Objectives To evaluate the evidence for CDS in improving quantitative and qualitative measures of antibiotic prescribing in inpatient hospital settings. Methods A systematic literature search was conducted of articles published from inception to 20th December 2014 using eight electronic databases: MEDLINE, EMBASE, PUBMED, Web of Science, CINAHL, Cochrane Library, HMIC and PsychINFo. An updated systematic literature search was conducted from January 1st 2015 to October 1st 2016 using PUBMED. The search strategy used combinations of the following terms: (electronic prescribing) OR (clinical decision support) AND (antibiotic or antibacterial or antimicrobial) AND (hospital or secondary care or inpatient). Studies were evaluated for quality using a 10-point rating scale. Results Eighty-one studies were identified matching the inclusion criteria. Seven outcome measures were evaluated: adequacy of antibiotic coverage, mortality, volume of antibiotic usage, length of stay, antibiotic cost, compliance with guidelines, antimicrobial resistance, and CDS implementation and uptake. Meta-analysis of pooled outcomes showed CDS significantly improved the adequacy of antibiotic coverage (n = 13; odds ratio [OR], 2.11 [95% CI, 1.67 to 2.66, p ≤ 0.00001]). Also, CDS was associated with marginally lowered mortality (n = 20; OR, 0.85 [CI, 0.75 to 0.96, p = 0.01]). CDS was associated with lower antibiotic utilisation, increased compliance with antibiotic guidelines and reductions in antimicrobial resistance. Conflicting effects of CDS on length of stay, antibiotic costs and system uptake were also noted. Conclusions CDS has the potential to improve the adequacy of antibiotic coverage and marginally decrease mortality in hospital-related settings.

Indicators for the monitoring of antibiotic use

Increasing rates of antibiotic resistance have been related to increased rates of antibiotic prescribing. This paper describes the use of indicators to measure and compare antibiotic use and enable hospitals to benchmark their practice. The Defined Daily Dose measure may be combined with a number of denominators that quantify patient activity in order to correct for workload variations between hospitals. This may be combined with data from mandatory surveillance schemes for various infections to inform prescribing practice.