Methods
This was an exploratory cross-sectional analysis of data from the Registrar Clinical Encounters in Training (ReCEnT) study. ReCEnT is an ongoing, prospective cohort study of registrars in general practice training. The ReCEnT study protocol is described in detail elsewhere.25
Registrars from regional training providers (RTPs; 2010–2015) and regional training organisations (RTOs; 2016–2018) across Australia (New South Wales, Queensland, Victoria, South Australia, Tasmania and the Australian Capital Territory) were included. RTPs/RTOs were/are geographically defined not-for-profit GP education and training organisations. There was a change in Australian GP specialist vocational training in 2015–2016 from RTPs to RTOs. RTOs and RTPs operate in a similar manner but with different geographic boundaries (and a reduction from 17 to 9 organisations). Registrars collect data as part of their educational training requirements and are provided with individualised feedback reports to promote reflection on their clinical experiences.26 Registrars may provide informed written consent for their ReCEnT data to be also used for research.
Initially, registrar education, work experience and demographics, plus the characteristics of their current place of practice are collected. Data collection occurs once every 6 months midtraining term (for three terms) and entails recording details of 60 consecutive clinical consultations on hardcopy case report forms. Data collection is designed to reflect a typical week in office-based general practice; it includes in-practice consultations excluding specialty clinics (such as immunisations and cervical screening) and excludes home visits and nursing home visits.
Outcome factor
The outcome factor was whether a dizziness-related or vertigo-related problem/diagnosis was a specific vertigo provisional diagnosis or a non-specific symptom/problem formulation. In ReCEnT, registrars are asked to provide the single most likely diagnosis for each problem dealt with. If they feel unable to provide a specific provisional diagnosis (eg, ‘vestibular neuritis’), they are asked to be as specific as they can (in this example, ‘vertigo’). Provisional diagnoses/problems are coded according to the International Classification of Primary Care, second edition (ICPC-2). Our outcome factor was defined by 23 ICPC-2 codes (see online supplemental material 1). Determination of ICPC-2 codes as being related to vertigo/dizziness and, then, classification of each of these as specific vertigo provisional diagnoses or as non-specific symptom/problem formulations was by a panel of one senior GP (PM) and one senior neurologist (CL).
Independent variables
Independent variables were related to patient, registrar, practice, consultation and consultation action factors.
Patient factors were patient age group, patient gender, identification as Aboriginal and/or Torres Strait Islander, non-English speaking background (NESB) and patient/practice status (whether the patient was an existing patient, new to the registrar or new to the practice).
Registrar factors were gender, part-time or full-time status, term of registrar training and whether they obtained their primary medical degree in Australia or obtained it overseas.
Practice factors included size of practice (as determined by number of full-time equivalent GPs), bulk billing practice (does the practice routinely charge the patient no consultation fee), rurality (based on the Australian Standard Geographical Classification Remoteness Area classification), Socioeconomic Index for Area – Index of Relative Social Disadvantage (SEIFA-IRSD) decile (where 1 is the most disadvantaged and 10 the least disadvantaged), and RTP/RTO region.
Consultation factors were duration of consultation, number of problems/diagnoses addressed in each consultation and if the registrar sought information or assistance for diagnosis and/or management of the problem (if they consulted their supervisor and/or other sources of information).
The consultation action factors were if medication was prescribed, pathology or imaging ordered, referrals made, follow-up organised and if any learning goals were generated by the registrar.
Statistical analysis
Analysis was at the level of the individual problem/diagnosis and performed on data from 2010 to 2018, equivalent to 18 six monthly rounds of data collection.
Frequency of dizziness-related and vertigo-related problems/diagnoses and proportions of these problems/diagnoses that were a specific provisional diagnosis and that were a non-specific symptom/problem formulation were calculated with 95% CIs, adjusted for clustering of observations within registrars.
The analyses were restricted to new (first presentation) problems/diagnoses involving dizziness and vertigo (ie, our 23 adjudicated ICPC-2 codes). The primary analysis addressed the research question: what are the associations of seeing a patient and making a new specific vertigo diagnosis compared with a diagnosis/problem formulation of a new vertigo/dizziness symptom?
We performed a sensitivity analysis with the analysis confined to ICPC-2 codes entailing ‘true vertigo’ and excluding from the analysis ‘not-obviously-vertiginous dizziness’ (as determined by our expert panel). See figure 1 for the flow chart of problems/diagnoses included in our primary and sensitivity analyses. The rationale for the primary and sensitivity analyses was the difficulty of eliciting and interpreting history and examination in vertiginous/dizzy presentations. In many instances vertiginous symptoms are subtle or difficult to differentiate or are difficult for patients to verbalise. Thus, it is likely that a proportion of true vertigo presentations have been coded as other presentations of dizziness. An inclusive primary analysis with a restrictive sensitivity analysis addressed this inherent imprecision.
Figure 1Flow chart of problems.
For both the primary and sensitivity analyses, univariate and multivariable logistic regression was conducted, within the generalised estimating equations framework to account for repeated measures within registrars. An exchangeable working correlation structure was assumed.
The multivariable regression was carried out as two models. In the first, ‘patient’, ‘registrar’, ‘practice’ and ‘consultation’ factors with p<0.20 were entered in the model. In the second model, all these variables were entered in the model along with consultation action factors fwith p<0.20. The rationale was that the first model provided evidence of associations of the diagnosis/problem formulation being made, unaffected by registrar actions taken as a result of the diagnosis/problem formulation made. The second model provided evidence of registrar actions taken, adjusted for the prior variables.
Covariates with a univariate p<0.2 in univariate analysis were included in the multiple regression model, which was then assessed for model reduction. Covariates that were no longer significant (at p<0.2) were tested for removal from the model and removed if any covariate in resulting model did not substantively change (by >10%). The Hosmer-Lemeshow test was used to assess model goodness of fit.
The sensitivity analysis was conducted in the same manner.
In post hoc analyses, the number per 100 problems/diagnoses of pathology and imaging tests ordered and procedures performed were calculated, and the duration of consultation was compared with that of consultations in the entire ReCEnT dataset (with Kruskal-Wallis rank test).
Statistical analyses were programmed using STATA V.14.1 and SAS V.9.4. P values <0.05 were considered statistically significant.