Original research••

Influenza presentations and use of neuraminidase inhibitors by Australian general practice registrars: a cross-sectional analysis from the ReCEnT study

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Abstract

Objective This study aims to establish prevalence and associations of (1) influenza and influenza-like illness (IILI) presentations to Australian general practice (GP) registrars (trainees) and (2) the use of neuraminidase inhibitors (NAIs) by GP registrars for new presentations of IILI, for the 10 years leading up to the COVID-19 pandemic in Australia (2010–2019).

Design This was a cross-sectional analysis of the Registrar Clinical Encounters in Training ongoing inception cohort study of the in-consultation experience and clinical behaviours of GP registrars. Data are collected by individual registrars three times (from 60 consecutive consultations each time) at 6 monthly intervals. Data include diagnoses/problems managed and medicines prescribed, along with multiple other variables. Univariate and multivariable logistic regression was used to establish associations of registrars seeing patients with IILI and of prescribing NAIs for IILI.

Setting Teaching practices within the Australian general practitioner specialist vocational training programme. Practices were located in five of the six Australian states (plus one territory).

Participants GP registrars in each of their three compulsory 6-month GP training terms.

Results From 2010 to 2019, 0.2% of diagnoses/problems seen by registrars were IILI. 15.4% of new IILI presentations were prescribed an NAI. IILI diagnoses were less likely in younger (0–14) and older (65+) age groups, and more likely in an area of higher socioeconomic advantage. There was considerable variation in NAI prescribing between regions. There was no significant association of prescribing NAIs with age or Aboriginal and/or Torres Strait Islander patients.

Conclusions IILI presentations were more likely among working-age adults and not among those groups at higher risk. Similarly, high-risk patient groups who would benefit most were not more likely to receive NAIs. The epidemiology and management of IILI has been distorted by the COVID-19 pandemic, but the burden of influenza in vulnerable populations must not be overlooked. Appropriately targeted antiviral therapy with NAIs influences outcomes for vulnerable patients. General practitioners manage the majority of IILI in Australia, and understanding GP IILI presentation and NAI prescribing patterns is a key first step to enabling sound and rational prescribing decisions for better patient outcomes.

What is already known on this topic

Neuraminidase inhibitors are most beneficial in those at higher risk of complications of influenza.

What this study adds

The quality of use of medical resources and influenza antiviral prescribing in real-world evaluation and management of influenza by general practice registrars is examined in this paper.

How this study might affect research, practice or policy

The use of appropriately targeted neuraminidase inhibitors should be a practice focus in at-risk patient cohorts while the epidemiology of seasonal influenza continues to be unpredictable after the COVID-19 pandemic. The barriers to seeking medical care and providing intervention against influenza must be defined with further research.

Introduction

Influenza and influenza-like illness (IILI) affects all age groups and is responsible for a substantial burden of illness.1 2 Severe outcomes of influenza illness in Australia are below the global average but both the morbidity and mortality are still considerable. The Global Burden of Disease Study 2017 estimated global deaths and hospitalisations from influenza-related lower respiratory tract infection to be 1.9 and 123.8 per 100 000, respectively, compared with 0.9 and 38.3 per 100 000 in Australia.3 However, seasonal variation in influenza cases and outcomes can vary widely.4 Influenza cases and hospitalisations in Australia were substantially reduced during the COVID-19 pandemic,4–6 with the WHO reporting no southern hemisphere influenza season and a fourfold reduction in IILI rates at Australian sentinel locations.7 Coinciding with the reduction of COVID-19 public health measures during 2022, there was a rapid early increase in reported influenza cases that year, peaking much earlier in the influenza season than is usually observed.8 The COVID-19 pandemic demonstrated the importance of understanding the presentation of respiratory illnesses, and the importance of well-managed antiviral therapy in protecting vulnerable individuals and the healthcare system in Australia. COVID-19 and influenza share some transmission characteristics,9 and meaningful lessons can be drawn from what is known about past influenza pandemics.10

Influenza is often a self-limiting illness in healthy adults,11 and in many cases, it is appropriate to take a conservative approach to treatment with supportive care only.11 The greater burden of illness is borne by adults aged over 65 years, and children under 5 years, as well as pregnant women and those with chronic conditions.2 12–14 Aboriginal and/or Torres Strait Islander peoples are also at risk of more severe influenza illness.15 High-risk groups account for a majority of primary care presentations, hospital admissions and deaths from influenza,12 13 with the highest mortality in infants less than 6 months of age.11 A 2008 analysis estimated an average cost of US$115 million (range US$72.3–US$170.1 million) spent in Australia on healthcare alone for a typical influenza season.16

Individuals at risk of complications from influenza, those in close contact with at-risk individuals, and those with severe influenza may benefit from antiviral treatment with a neuraminidase inhibitor (NAI).11 17 NAIs have shown variable effect, and benefit appears to be dependent on age, background and comorbidities.18–20 Influenza can develop resistance to NAIs and more effective treatments are under development.21 Patients aged over 65 years or under 5 years of age, and those with high-risk comorbidities benefit most.18–20 22 The benefit of NAI treatment is dependent on initiating treatment within the first 2 days of illness,17 18 23 although one study of hospitalised patients found an ongoing but less pronounced benefit if started within 3–5 days of illness onset.24 Current Australian guidelines recommend that NAI use is targeted towards younger children (<5 years), older adults (>65 years), Aboriginal and/or Torres Strait Islander patients, those with high-risk comorbidities, and all those who are hospitalised for IILI.25

Clinical practice patterns of general practice (GP) registrars (vocational trainees in specialist family medicine/GP), including prescribing behaviours, are relevant as these clinical behaviours may persist into subsequent careers as independent practitioners (noting that GPs’ established antibiotic prescribing patterns may be stable over time26). Registrars operate in an apprenticeship-like model of education/training but have prescribing, billing and referral rights equivalent to those of established senior GPs. GP training is thus a window of opportunity to influence developing clinical behaviour.

In this study, we aim to describe the prevalence and associations of presentations of IILI to Australian GP registrars, and of GP registrars’ treatment with an NAI, in the 10 years prior to the COVID-19 pandemic, to inform our understanding of the usual primary care approach to the presentation and antiviral management of a highly transmissible respiratory viral illness.

Methods

The Registrar Clinical Encounters in Training (ReCEnT) study methodology has been described previously.27 In summary, ReCEnT is an ongoing inception cohort study of the in-consultation clinical and educational experiences of Australian GP registrars. It is currently conducted in three Australian states and a territory, and previously also conducted in two other states. The current analysis includes data from 2010 to 2019.

During the period included in this analysis, ReCEnT employed a paper-based self-report methodology to systematically collect characteristics of registrars and their practices and record clinical and educational details of 60 consecutive consultations in each of three 6-month registrar training terms in GP, equivalent to approximately 1 week of patient exposure, each term, for a full-time registrar. A wide range of variables are recorded by registrars in ReCEnT. This includes demographic information on the registrars themselves and on the practice in which the registrar trains. In-consultation data collected relates to patient demographics, problems or diagnoses addressed in the consultation, investigations undertaken, referrals made, medicines prescribed, procedures undertaken, sources of information consulted (including supervisor advice) and learning goals. Registrar actions are linked in data collection to the problem or diagnosis they relate to. Registrars’ problems and diagnoses are coded by the research team to the International Classification of Primary Care plus (ICPC-plus) classification system.28 Medicines are coded to the Anatomical Therapeutic Chemical (ATC) classification.29 Only office-based consultations are included and dedicated ‘clinics’ (eg, immunisation clinics) are excluded.

ReCEnT is a routine component of all registrars’ educational programmes at ReCEnT-participating training organisations,30 31 and registrars can also provide informed consent to their data being used for research purposes.

Outcomes

Outcome factors were (1) a problem/diagnosis being IILI and (2) a new (first presentation) IILI problem/diagnosis being treated with NAIs.

Included ICPC codes were ‘influenza’, ‘influenza-like-illness’, ‘Flu’ and ‘Influenza; H1N1’.

Within the new influenza problems, we included the prescription of antivirals (per ATC codes), oseltamivir (J05AH02) and zanamivir (J05AH01).

Independent variables

Independent variables included patient, registrar, practice-level and consultation-level factors. See online supplemental appendix 1 for a list of independent variables.

Statistical analyses

For this study, complete-case analysis was used. Analyses were conducted at the level of problem/diagnosis and included all problems/diagnoses for our first analysis (influenza presentations), and all new IILI problems/diagnoses for our second analysis (NAIs prescribed for IILI).

For both outcomes, logistic regression was used within the generalised estimating equations framework to account for repeated measures within registrars. An exchangeable working correlation structure was assumed.

Our second analysis, with outcome ‘prescription of NAIs for IILI presentations’, had a low number of observations due to the exclusion criteria used. These low numbers caused some zero cells in cross-tabulations. The low number of observations also meant that clustering of observations by registrar was minimal. Models were run to assess differences between models with and without clustering by registrar (excluding the variables with zero cells). Comparison of the resulting models confirmed that clustering by registrar was not necessary, and Firth’s correction was used to overcome the problems caused by zero cells.

In both analyses, univariate analysis was conducted on each covariate, with the outcome. Covariates with a univariate p<0.20 were considered for inclusion in the multiple regression model.32

Once the model with all significant covariates was fitted, model reduction was assessed. Covariates which were no longer significant (at p<0.2) in the multivariable models were tested for removal from the model. If the covariate’s removal did not substantively change the resulting model (defined as a change in the OR of any covariate of greater than 10%), the covariate was removed from the final model, and diagnostic tests were conducted to assess goodness of fit.

For our multivariable analyses with outcome ‘a problem/diagnosis being IILI’, three regression models were built sequentially. To examine the question of associations of a problem/diagnosis being IILI, patient, practice and registrar independent variables were entered in an initial logistic regression model. To examine the question of how consultation content for IILI diagnoses/problems differs from other diagnoses/problems, the above variables were entered in a second model along with the ‘consultation variables’. To examine the question of whether actions arising from IILI problems/diagnoses differ from those arising from other problems/diagnoses, all variables entered in the previous two models were entered in a third model along with ‘consultation actions variables’. The rationale for this sequential approach is that whether a patient presents with an IILI problem/diagnosis will be influenced by patient, registrar and practice factors, but evaluation of these influences may be distorted by inclusion in the multivariable model of factors operating once the consultation is progressing. Similarly, consideration of consultation content related to IILI care may be distorted by the inclusion in this model of actions arising from the consultation.

For our multivariable analysis with outcome ‘prescription of NAIs for IILI presentations’, only one model (including all the above independent variables) was undertaken.

Diagnostic tests were conducted to assess goodness of fit of each model: the Hosmer-Lemeshow (H-L) test, approximate linearity of continuous variables, and influential observations.

Analyses were programmed using STATA V.16.0 and SAS V.9.4. Significance was declared at the conventional 0.05 level, with the magnitude and precision of effect estimates also used to interpret results.

Patient and public involvement

There was no patient involved. The participants in the ReCEnT study are registrars. Registrars were involved in the design of the study (in 2009) and participating registrars regularly provide feedback on the ongoing study.

Results

A total of 2839 registrars (response rate 95.5%) provided data from 413 306 consultations, with 645 767 individual problems/diagnoses. See table 1 for the demographics of participating registrars and their practices.

Table 1

Characteristics of participating registrars and practices

A problem/diagnosis being influenza or influenza-like illness

In 0.2% (1473) of problems/diagnoses, the diagnosis was IILI. Of these, 1130 (85%) were a new problem. A total of 578 pathology tests were ordered for new IILI presentations, of which 123 were for PCR tests.

Characteristics associated with a problem/diagnosis being IILI are presented in table 2. The univariate and multivariable logistic regression models with outcome factor ‘problem/diagnosis being IILI’) are presented in table 3. Regression diagnostics showed no violations of the assumptions of heteroscedasticity or normality for the three models. Goodness-of-fit tests showed a good fit for each of the three model (χ²=27.9, p=0.06; χ²=20.3, p=0.32; χ²=25.9, p=0.10) and there were no influential observations.

Table 2

Characteristics associated with seeing a patient with a diagnosis of influenza (n=645 767*)

Table 3

Univariate and multivariable associations of a problem/diagnosis seen by a registrar being influenza

Statistically significant associations on multivariable analysis of a problem/diagnosis being IILI include patients aged 0–14 and aged 65+ being less likely than those aged 15–34, (OR 0.58 (95% CI 0.48 to 0.71), p<0.001, and OR 0.47 (95% CI 0.37 to 0.59) p<0.001, respectively). Females were less likely to present with IILI (OR 0.83 (95% CI 0.73 to 0.94), p=0.003 for female gender), and an IILI presentation is more likely in a higher socioeconomic area as measured by the SEIFA-IRSD index (OR 1.06 (95% CI 1.02 to 1.09) p<0.001, for each SEIFA-IRSD decile). There was evidence for IILI encounters being less complex, with less in-consultation assistance or information sought (OR 0.46 (95% CI 0.37 to 0.56) p<0.001), fewer other problems dealt with in the consultation (OR 0.50 (95% CI 0.46 to 0.55), p<0.001) and fewer referrals made (OR 0.18 (95% CI 0.12 to 0.27) p<0.001).

New influenza or influenza-like illness problems/diagnoses being prescribed an NAI

Of 1130 new IILI problems/diagnoses, 174 (15.4%) were prescribed an NAI. One prescription was for zanamivir, and the remainder for oseltamivir. Characteristics associated with a problem/diagnosis being IILI are presented in table 4.

Table 4

Characteristics associated with an antiviral medication being prescribed for a new influenza diagnosis (n=1130*)

The univariate and multivariable logistic regression models with outcome factor ‘new influenza problems/diagnoses being prescribed an NAI’ are presented in table 5. Regression diagnostics showed no violations of the assumptions of heteroscedasticity or normality. Goodness-of-fit tests showed the model was a good fit (χ²=13.6, p=0.76) and there were no influential observations.

Table 5

Univariate and multivariable associations of an antiviral medication being prescribed for a new influenza diagnosis

Statistically significant multivariable associations of a new IILI problem/diagnosis being prescribed an NAI include ordering pathology (OR 2.76 (95% CI 1.86 to 4.09), p<0.001), and seeking assistance from a supervisor (OR 2.89 (95% CI 1.18 to 7.09), p=0.020) or from another source (OR 8.67 (95% CI 4.61 to 16.3), p<0.001). There was also considerable variation in NAI prescribing between regions. There was no significant association of prescribing NAIs with age or Aboriginal and/or Torres Strait Islander patients.

Discussion

Summary of main findings

This descriptive analysis examined the characteristics of IILI presentations to GP registrars, and the NAI prescribing habits within those consultations. Considered here is the 10 years of data leading up to the outbreak of the COVID-19 pandemic in 2020.

Presentations of IILI comprised 0.2% of all problems/diagnoses seen by registrars. In IILI encounters, patients were more likely to be male, more likely to attend a practice in an area with a higher SEIFA-IRSD index, indicating a higher socioeconomic status, and less likely to be a child ≤14 years or an older adult ≥65 years old.

Only 123 PCR tests were ordered for 1130 new IILI presentations (10.9%) which implies that registrars are making a majority of IILI diagnoses based on clinical findings without confirmatory pathology.

An NAI was prescribed for 15.4% of new presentations of IILI. NAI prescribing was strongly associated with seeking in-consultation assistance (OR 2.89 (95% CI 1.18 to 7.09), p=0.020) and information (OR 8.67 (95% CI 4.61 to 16.3), p<0.001). The reasons for registrars seeing assistance or information from resources was not captured but may include confirmation of indications for prescribing or dosing information. There was marked inter-regional variability in NAI prescribing, but no associations with age or Aboriginal and/or Torres Strait Islander patients (see table 4).

One notable finding is that although practices located within an area with a lower SEIFA index were less likely to have patients present with IILI (OR 1.06 (95% CI 1.02 to 1.09) p<0.001), there was no significant difference in the rates of NAI prescribing in these regions (OR 1.05 (95% CI 0.97 to 1.14) p=0.20) for patients with IILI (see table 4).

Comparison with previous literature

Bernado et al33 examined NAI prescribing in Australia over the period of 2015–2017 using the MedicineInsight database. Consistent with the findings in our study, they found that IILI consultation rates were lower in women, children and older adults, and those in areas of socioeconomic disadvantage.33 The average rate of NAI prescribing reported by Bernado et al was 25.0%, well above our finding of 15.4%, and this may be due to the differing definitions of IILI presentation between the studies. Similar to our study, the findings from the MedicineInsight database show significant differences in NAI prescribing between states, with rates ranging from 8.5% in the Northern Territory to 31% in New South Wales.28

A multicentre US surveillance study of antiviral medication for IILI between 2009 and 201634 found that use of NAIs was more likely if patients presented in the first 2 days of illness (26.4% vs 9.9%; p<0.001),34 which is in line with current guidelines.25 NAI prescribing rates for children <2 years were similar to our overall findings at 14%, but 31% of adults ≥65 years were prescribed an NAI, almost double the overall rate in our study.33

Another US study which examined 5 years of outpatient antiviral prescribing for acute respiratory illness35 found that NAIs were infrequently prescribed for high-risk patients who would benefit most.35 This is similar to our study in which it appears that vulnerable patient groups (the very young, older adults and Aboriginal and/or Torres Strait Islander patients) are not being targeted for NAI use.

A retrospective UK study reviewed primary care data from the 2009 H1N1 influenza pandemic in children <17 years and found an overall prescribing rate of 24.9% in this group,23 compared with the overall NAI prescribing rate of 15.4% in this report (with no increase in NAI prescribing in children). Similar to our findings, this study found that the overwhelming majority of NAI prescriptions were for oseltamivir (99.8%).23 The authors found a significant reduction in complications of pneumonia and hospitalisation with early prescription of NAIs.

While the place of NAIs in IILI is controversial,17 36 37 a recent large multicentre European RCT confirms earlier trials demonstrating a clear benefit in early illness,18 and Australian Therapeutic Guidelines 37^,25 recommend oseltamivir in early disease in those with risk factors, and in all hospitalised patients. This controversy may have influenced prescribing habits of registrars.

Strengths and limitations

This study’s strengths include the size of the data set, the high response rate38 and the close within-consultation linkage of contemporaneously recorded data (including linkage of registrars’ clinical actions with the problem/diagnosis which prompted them). The participating registrars broadly reflect national registrar demographics across metropolitan, regional and remote areas of Australia. It is important to note, though, that presentation for IILI symptoms in Australia is likely to have been, at least in part, driven by the need for sickness certification.

The COVID-19 pandemic caused significant distortion to the epidemiology of influenza and extraordinary disruption to the system of healthcare in Australia and around the world.5 6 8 39 This description of the patterns of presentation and treatment of influenza in Australia specifically explores the prepandemic state of IILI presentation and NAI use decision-making by GP registrars to inform an evidence-based approach to primary care management of IILI in the future.

This study is limited by the relatively small number of Aboriginal and/or Torres Strait Islander patients presenting with IILI. While Aboriginal and/or Torres Strait Islander patients were less than one third as likely to be prescribed NAIs as other patients, the CIs of this association were wide and did not reach statistical significance (OR 0.28 (95% CI 0.01 to 5.86), p=0.41). Our participating practices did not include Aboriginal and Torres Strait Islander health services and the small numbers of Aboriginal and Torres Strait Islander patients in our study means we cannot draw conclusions about any association of Aboriginal and Torres Strait Islander status and NAI prescription. Aboriginal and/or Torres Strait Islander patients represent an at-risk group for severe outcomes of influenza, with one study reporting hospitalisation rates up to six times higher during the 2009 H1N1 influenza pandemic.40 Targeted use of NAIs in line with Australian guidance25 would be expected to produce a statistically significant positive association. A dedicated study of this important population may produce a more robust statistical analysis of the use of NAIs.

Clinical information on other risks for severe disease (pregnancy and chronic diseases) was not available, and neither were the results of the 123 PCR tests sent. Ordering pathology was associated with prescription of an NAI (OR 2.76 (95% CI 1.86 to 4.09), p<0.001). As only new presentations of IILI were being considered, registrar ordering pathology and prescribing NAIs happened within the same consultation, without pathology results being available at the time. This suggests a strong suspicion of influenza based on the clinical presentation at the time. A prospective study directed towards decision-making in NAI prescribing may help to define what features of an IILI presentation direct a prescriber towards NAI use. Unfortunately, comparison with some previous literature is limited by the lack of data in our study on duration of IILI symptoms prior to presentation.34 41

Implications for clinical and educational practice

Registrars appeared confident in managing IILI—being less likely to seek in-consultation advice or assistance or make a referral for IILI presentations. When registrars did prescribe an NAI, they were much more likely to have sought advice or further information. This may suggest a need for specific education around NAI prescribing.

The lack of association of NAI prescription with age, or with Aboriginal and/or Torres Strait Islander patients is notable, although the limited number of Aboriginal and/or Torres Strait Islander patients in this study means that definitive conclusions cannot be drawn. Registrars may benefit from targeted education as current guidelines recommend consideration of NAI in Aboriginal and/or Torres Strait Islander patients, as well as older patients ≥65 years, or children <5 years.

The reasons for the marked regional variability in NAI prescribing are not entirely clear. However, we note that the two regions with markedly higher odds of prescribing comprise capital cities, which may reflect better access to testing and medicines in these locations rather than any factor amenable to education. The ability to receive a PCR test result in a reasonable time frame may affect the prescribing habits of registrars and GPs. There was some increased accessibility of PCR testing from 2010 to 2019 (the period of our study) and the widespread community exposure to PCR testing during the COVID-19 pandemic may have changed substantively the expectations of the community, and of general practitioners around testing and treatment of respiratory illnesses since the prepandemic era.42

Implications for future research

This study found that IILI problems/diagnoses were less likely to be recorded by registrars in areas of socioeconomic disadvantage. It is unclear whether this is due to patient behaviours, access to care or GP trainees’ diagnostic processes.⁴³

In our study, NAIs are not consistently being prescribed in line with guideline recommendations. NAIs are not subsidised by the PBS in Australia and this likely represents a barrier to access for many Australians. Prolonged turnaround times for results from PCR testing in less urbanised areas can also delay diagnosis beyond the target window for their use, losing their potential benefits and making consideration of NAI use redundant.

Further research which prospectively measures barriers to access for healthcare and medications could explore the reduced number of IILI presentations in disadvantaged areas, and the regional and demographic variation in prescribing that has been observed in our study.

Conclusions

In this analysis, IILI encounters were mostly managed conservatively by GP registrars. There appeared to be a lack of antiviral therapy targeted towards patients who would most benefit, as well as significant regional variation.

Further research into how to optimise the quality use of NAIs, and barriers to healthcare access in primary care management of respiratory illness would be valuable to general practitioners.

The future state of respiratory illness presentation and management, including IILI, is uncertain following the onset of the COVID-19 pandemic. Primary care practitioners manage the majority of respiratory illnesses in Australia and have the opportunity to learn from our past knowledge of IILI to make better use of our resources in the future.

Contributors: The authors, CM, MvD, AD, ATa, EGH, AF, JD, JB, AR, ATu, KM, NS, KF and PM, have contributed substantially to the writing of this manuscript. CM, PM and MvD participated in the conception and design of the analyses of data from the ReCEnT study reported in the manuscript. PM, ATa, KM, NS and KF participated in the acquisition of data. ATa, JB and EGH participated in the data analysis. CM, PM, ATu, AF, JD, AD, AR, MvD, EGH and KF participated in the interpretation of the data. CM, PM, MvD, JD, AF, ATa and ATu drafted the manuscript. All authors reviewed the manuscript for important intellectual content, read and approved the final manuscript and agree to be accountable for all aspects of the work.
Funding: This Study was funded by Department of Health and Aged Care, Australian Government (Not applicable/NA).
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

No data are available. Not applicable.

Ethics statements

Patient consent for publication:

Ethics approval:

This study involves human participants and ethics approval is from the University of Newcastle Human Research Ethics Committee, Reference H-2009-0323. Participants gave informed consent to participate in the study before taking part.

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Overview

Abstract
Introduction
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