Delayed antibiotic prescribing for respiratory tract infections: protocol of an individual patient data meta-analysis

Introduction Delayed prescribing can be a useful strategy to reduce antibiotic prescribing, but it is not clear for whom delayed prescribing might be effective. This protocol outlines an individual patient data (IPD) meta-analysis of randomised controlled trials (RCTs) and observational cohort studies to explore the overall effect of delayed prescribing and identify key patient characteristics that are associated with efficacy of delayed prescribing. Methods and analysis A systematic search of the databases Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Ovid Embase, EBSCO CINAHL Plus and Web of Science was conducted to identify relevant studies from inception to October 2017. Outcomes of interest include duration of illness, severity of illness, complication, reconsultation and patient satisfaction. Study authors of eligible papers will be contacted and invited to contribute raw IPD data. IPD data will be checked against published data, harmonised and aggregated to create one large IPD database. Multilevel regression will be performed to explore interaction effects between treatment allocation and patient characteristics. The economic evaluation will be conducted based on IPD from the combined trial and observational studies to estimate the differences in costs and effectiveness for delayed prescribing compared with normal practice. A decision model will be developed to assess potential savings and cost-effectiveness in terms of reduced antibiotic usage of delayed prescribing and quality-adjusted life years. Ethics and dissemination Ethical approval was obtained from the University of Southampton Faculty of Medicine Research Ethics Committee (Reference number: 30068). Findings of this study will be published in peer-reviewed academic journals as well as General Practice trade journals and will be presented at national and international conferences. The results will have important public health implications, shaping the way in which antibiotics are prescribed in the future and to whom delayed prescriptions are issued. PROSPERO registration number CRD42018079400.

[1]  C. Del Mar,et al.  Delayed antibiotic prescriptions for respiratory infections. , 2017, The Cochrane database of systematic reviews.

[2]  K. McGuigan,et al.  Scaling up: Lessons from a feasibility study involving people with type 2 diabetes and their families , 2017 .

[3]  P. Little,et al.  Understanding the delayed prescribing of antibiotics for respiratory tract infection in primary care: a qualitative analysis , 2016, BMJ Open.

[4]  T. Peters,et al.  Development and internal validation of a clinical rule to improve antibiotic use in children presenting to primary care with acute respiratory tract infection and cough: a prognostic cohort study , 2016, The Lancet. Respiratory medicine.

[5]  M. Hernán,et al.  ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions , 2016, British Medical Journal.

[6]  J. Sterne,et al.  Improving the Diagnosis and Treatment of Urinary Tract Infection in Young Children in Primary Care: Results from the DUTY Prospective Diagnostic Cohort Study , 2016, The Annals of Family Medicine.

[7]  H. Goossens,et al.  Cost effectiveness of amoxicillin for lower respiratory tract infections in primary care: an economic evaluation accounting for the cost of antimicrobial resistance. , 2016, The British journal of general practice : the journal of the Royal College of General Practitioners.

[8]  Thomas Agoritsas,et al.  Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches , 2015, Systematic Reviews.

[9]  Cliodna McNulty,et al.  Narrative review of primary care point-of-care testing (POCT) and antibacterial use in respiratory tract infection (RTI) , 2015, BMJ Open Respiratory Research.

[10]  P. Shekelle,et al.  Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation , 2015, BMJ : British Medical Journal.

[11]  D. Mant,et al.  Antibiotic prescription strategies for acute sore throat: a prospective observational cohort study. , 2014, The Lancet. Infectious diseases.

[12]  C. Mcnulty,et al.  Clinical score and rapid antigen detection test to guide antibiotic use for sore throats: randomised controlled trial of PRISM (primary care streptococcal management) , 2013, BMJ.

[13]  B. Arroll,et al.  Antibiotics for the common cold and acute purulent rhinitis. , 2013, The Cochrane database of systematic reviews.

[14]  Richard Smith,et al.  The true cost of antimicrobial resistance , 2013, BMJ.

[15]  Douglas G. Altman,et al.  Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice , 2012, PloS one.

[16]  Catrin Tudur Smith,et al.  Individual participant data meta-analyses compared with meta-analyses based on aggregate data , 2011, The Cochrane database of systematic reviews.

[17]  C. Chew‐Graham,et al.  Managing self-limiting respiratory tract infections: a qualitative study of the usefulness of the delayed prescribing strategy. , 2011, The British journal of general practice : the journal of the Royal College of General Practitioners.

[18]  G. Guyatt,et al.  GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. , 2011, Journal of clinical epidemiology.

[19]  Alastair D Hay,et al.  Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis , 2010, BMJ : British Medical Journal.

[20]  R. Riley,et al.  Meta-analysis of individual participant data: rationale, conduct, and reporting , 2010, BMJ : British Medical Journal.

[21]  D. Altman,et al.  Assessing Risk of Bias in Included Studies , 2008 .

[22]  L. Dooley,et al.  Delayed antibiotics for respiratory infections. , 2007, The Cochrane database of systematic reviews.

[23]  Richard D Riley,et al.  Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. , 2007, Journal of clinical epidemiology.

[24]  P. Little.  Delayed prescribing of antibiotics for upper respiratory tract infection , 2005, BMJ : British Medical Journal.

[25]  Harold I Feldman,et al.  Individual patient‐ versus group‐level data meta‐regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head , 2002, Statistics in medicine.

[26]  Ralph B. D'Agostino.  Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. , 1998, Statistics in medicine.

[27]  Isabelle Boutron,et al.  A revised tool for assessing risk of bias in randomized trials , 2016 .

[28]  J. Chemali,et al.  Summary and discussion of “ The central role of the propensity score in observational studies for causal effects , 2014 .

[29]  Katja Jasinskaja,et al.  Elaboration and Explanation ⋆ , 2011 .

[30]  P. Glasziou,et al.  Antibiotics for sore throat. , 2004, The Cochrane database of systematic reviews.

[31]  P C Lambert,et al.  A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. , 2002, Journal of clinical epidemiology.