ObjectivesThis paper describes the methods used in the International Cancer Benchmarking Partnership Module 4 Survey (ICBPM4) which examines time intervals and routes to cancer diagnosis in 10 jurisdictions. We present the study design with defining and measuring time intervals, identifying patients with cancer, questionnaire development, data management and analyses.Design and settingRecruitment of participants to the ICBPM4 survey is based on cancer registries in each jurisdiction. Questionnaires draw on previous instruments and have been through a process of cognitive testing and piloting in three jurisdictions followed by standardised translation and adaptation. Data analysis focuses on comparing differences in time intervals and routes to diagnosis in the jurisdictions.ParticipantsOur target is 200 patients with symptomatic breast, lung, colorectal and ovarian cancer in each jurisdiction. Patients are approached directly or via their primary care physician (PCP). Patients’ PCPs and cancer treatment specialists (CTSs) are surveyed, and ‘data rules’ are applied to combine and reconcile conflicting information. Where CTS information is unavailable, audit information is sought from treatment records and databases.Main outcomesReliability testing of the patient questionnaire showed that agreement was complete (κ=1) in four items and substantial (κ=0.8, 95% CI 0.333 to 1) in one item. The identification of eligible patients is sufficient to meet the targets for breast, lung and colorectal cancer. Initial patient and PCP survey response rates from the UK and Sweden are comparable with similar published surveys. Data collection was completed in early 2016 for all cancer types.ConclusionAn international questionnaire-based survey of patients with cancer, PCPs and CTSs has been developed and launched in 10 jurisdictions. ICBPM4 will help to further understand international differences in cancer survival by comparing time intervals and routes to cancer diagnosis.

The results of the study suggest that TREAT can improve the appropriateness of antimicrobial therapy and reduce the cost of side effects in regions with a low prevalence of resistant pathogens, however, at the expense of increased use of antibiotics.

Summary.A problem in clinical microbiology is that of inappropriate antibiotic therapy. Various decision-support systems have been proposed to aid physicians in this domain, and we discuss the a priori advantages of using a probabilistic network over other approaches. The Treat project uses a probabilistic network to combine clinical signs, symptoms and laboratory results, and we discuss the problem of obtaining probabilities for the network. Finally, we consider how such a system can be tested in clinical practice and outline the results of our tests.

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A practical method for predicting probability for antimicrobial susceptibility could be developed based on a semi-naïve Bayesian approach using statistical data on cross-susceptibilities and cross-resistances. The reduction in Brier distance from 37.7% to 25.3%, indicates a significant advantage to the proposed min2max2 method (p<10(99)).