Look no further than GPs for Medical Home

GPs already perform many of the functions of a Medical Home, and should be at the centre of any move to formalise such an arrangement in Australia, the AMA has said.

As Health Minister Sussan Ley contemplates the findings and recommendations of the primary health review led by former AMA President Dr Steve Hambleton, the AMA has issued a Position Statement advising that any proposal to adopt a Medical Home approach in Australia must have GPs at its core.

Internationally, the term Medical Home is used to refer to a model of primary care that is patient-centred, comprehensive, team-based, coordinated, accessible and focused on quality and safety.

AMA Vice President Dr Stephen Parnis said in Australia these attributes were already embodied in general practice.

“The concept of the Medical Home already exists in Australia, to some extent, in the form of a patient’s usual GP,” Dr Parnis said. “If there is to be a formalised Medical Home concept in Australia, it must be general practice. GPs are the only primary health practitioners with the skills and training to provide holistic care for patients.”

Evidence suggest patients with a usual GP or Medical Home have better health outcomes, and 93 per cent of Australians have a usual general practice, and 66 per cent have a family doctor.

Dr Parnis said the Medical Home concept had the potential to deliver improved support for GPs in providing well-coordinated and integrated multi-disciplinary care for patients with chronic and complex disease, and it made sense for this to be the focus of Government thinking on adopting the Medical Home idea in Australia.

“You can’t just transplant models of health care from other countries without acknowledgement of local conditions and what is already working well,” he said.

“Australia needs to build on what works, and ensure that a local version of the Medical Home is well-designed and relevant.”

The AMA said this should involve additional funding to enable GPs to deliver comprehensive and ongoing care, including patient education, improved coordination and targeting of services, and activity that does not require face-to-face contact.

Establishing a Medical Home arrangement in Australia was likely to involve formally linking a patient with their nominated GP or medical practice through registration, and the AMA said this should be voluntary for both patients and doctors.

In addition, the peak medical group said fee-for-service must remain the predominant funding mechanism for doctors, though it acknowledged that the Medical Home could also involve a blended funding model that rewarded the delivery of services over a period of time.

The AMA Position Statement on the Medical Home can be viewed at: position-statement/ama-position-statement-medical-home

Adrian Rollins

AMA in the News – 2 February 2016

Your AMA has been active on policy and in the media on a range of issues crucial to making our health system better. Below is a snapshot of recent media coverage.

Print/Online

Timing of Medicare cuts announcement criticised, The Age, 29 December 2015
Doctors have criticised the Turnbull government for using the Christmas-New Year holiday period to reveal the first tranche of items to be dropped from the government-subsidised Medicare Benefits Schedule. AMA President Professor Brian Owler said the proposed cuts would make the common tonsillectomy procedure marginally more expensive due to fewer individual parts of the operation being funded by Medicare.

Take care morning after the big night, Adelaide Advertiser, 1 January 2016
Health and safety experts are urging people to be careful embracing life the morning after a big night. AMA President Professor Brian Owler urged people to take it easy with water sports and even sunbaking over summer if they have consumed alcohol.

Anti-vax nuts try to cheat jab laws, The Sunday Telegraph, 3 January 2016
Anti-vaxers are trying to manipulate the new “no jab no pay” laws in a bid to gain taxpayer-funded rebates available only to those who vaccinate their children. AMA President Professor Brian Owler said the attempt is hurting only the child involved.

Threats to handouts prompts jab boosts, The Sunday Telegraph, 17 January 2016
Doctors have noticed a significant boost in the number of parents bringing their children in for vaccinations as the new “No Jab, No Play” laws start to bite. AMA President Professor Brian Owler said the laws were already having a beneficial effect on immunisation numbers.

Warning over autism doctor shopping, The Australian, 19 January 2016
GPs should be given stronger guidance about how to diagnose autism. AMA President Brian Owler said that having consistent guidelines would make things easier for doctors during diagnosis, but added that the emphasis should remain on assessing children early.

Doctors warn of busy emergency facilities, Australian Financial Review, 28 January 2016
The AMA Public Hospital Report Card found the performance of the public hospital system has stagnated, and even declined in some areas. AMA President Professor Brian Owler placed the blame for the declining public hospital performance firmly on the Federal Government’s reduced rate of health funding which would lead to a funding “black hole” in 2017.

Hospitals faced with funding ‘black hole’, Sydney Morning Herald, 28 January 2016
The Federal Government is under pressure to reform taxes following a report card on public hospitals that shows the most urgent patients are waiting longer at emergency departments. AMA President Professor Brian Owler said hospitals would be insufficiently funded to meet the rising demands from 2017, when the states and territories were facing a “black hole”.

State looks sick, Herald Sun, 29 January 2016
Victorian emergency patients are paying the price for a “funding crisis” in the nation’s public hospitals, and doctors warn the worst is yet to come. The AMA warned that a further $57 billion of Commonwealth funding was expected to be lost from hospital coffers over seven years starting next year, by indexing funding growth to CPI and population expansion.

Radio

Professor Brian Owler, 774 ABC Melbourne, 29 December 2015
AMA President Professor Brian Owler discussed recent cuts to the Medicare Benefits Scheme. Professor Owler said it was clearly a cost saving exercise by the Federal Government.

Professor Brian Owler, Radio National, 29 December 2015
AMA President Professor Brian Owler talked about new cuts to the MBS. Professor Owler said the AMA has supported the Medicare Benefits Schedule review from the outset, on the basis there were no cuts to access to patient services.

Dr Stephen Parnis, 4BC Brisbane, 7 January 2016
AMA Vice President Dr Stephen Parnis dismissed claims that pap smears would cost women $30. Dr Parnis said cuts to Medicare have resulted in reports of overpriced pap smears.

Dr Stephen Parnis, Tipple J Sydney, 25 January 2016
AMA Vice President Dr Stephen Parnis discussed the use of so-called “hangover clinics”. Dr Parnis said the treatments they offered were a placebo, and he questioned whether their operations were ethical.

Professor Brian Owler, Radio National, 28 January 2016
AMA President Professor Brian Owler discussed the latest AMA Public Hospital Report Card which revealed a public hospital funding ‘black hole’ as Commonwealth funding cuts hit the States and Territories.

Professor Brian Owler, 2GB Sydney, 28 January 2016
AMA President Professor Brian Owler talked about a report from the AMA showing emergency department waiting times has worsened for the first time in seven years.

Professor Brian Owler, 774 ABC Melbourne, 28 January 2016
AMA President Professor Brian Owler talked about the AMA Public Hospital Report Card and said longer waits for elective surgery and emergency rooms often resulted in more health problems.

Television

Professor Brian Owler, ABC News 24, 28 December 2015
AMA President Professor Brian Owler talked about Health Minister Sussan Ley’s proposed removal of 23 items from the Medicare Benefits Schedule.

Dr Stephen Parnis, ABC News 24, 1 January 2016
AMA Vice President Dr Stephen Parnis talked about how parents who refused to vaccinate their children would be stripped of childcare benefits by the Federal Government under new laws. Dr Parnis said public health was a major government responsibility, and vaccination rates were not as high as health experts would like them to be.

Professor Brian Owler, The Today Show, 14 January 2016
AMA President Professor Brian Owler discussed the importance of safe work environments for emergency workers after a police officer was allegedly shot by a patient with a history of ice addiction at a Sydney hospital.

Professor Brian Owler, Channel 7 Melbourne, 26 January 2016
Medibank says it is passing savings onto its members, but there are concerns more affordable premiums might mean cuts in benefits. AMA President Professor Brian Owler said doctors did not want to see people taking out cheaper premiums and policies and then realising that their private health insurance was not worth it.

Professor Brian Owler, The Today Show, 28 January 2016
The AMA Public Hospital Report Card 2016 showed that, against key measures, the performance of public hospitals is virtually stagnant, and even declining in key areas. AMA President Brian Owler said unless the Government looked at the way it funded public hospitals, people were likely to wait longer in emergency departments and for elective surgery.

Professor Brian Owler and Dr Stephen Parnis, Channel 9, 28 January 2016
The AMA released its new Public Hospital Report Card and the figures revealed that scores of patients were not being treated within recommended times. Doctors fear the situation is only going to get worse.

Professor Brian Owler, ABC News 24, 28 January 2016
The AMA has warned that public hospitals are facing a funding crisis. AMA President Professor Brian Owler said hospitals faced a crisis due to the funding fight between Federal and State governments.

Anti-vax dodge dismissed by Commonwealth

The Federal Government has confirmed that a form being circulated by anti-vaccination campaigners attempting to circumvent new ‘No Jab, No Pay’ laws has no legal standing, backing AMA advice that doctors are under no obligation to sign it.

Social Services Minister Christian Porter has written to AMA President Brian Owler confirming that medical practitioners were under no obligation to sign the form, which asks doctors to acknowledge the ‘involuntary consent’ of a parent to the vaccination of their children, and which is deemed to be ineffective in any case.

“I am able to advise you that under the No Jab, No Pay Act, immunisation providers are not obligated to sign such declarations,” Mr Porter wrote. “This statutory declaration is not relevant evidence for the purposes of family assistance payments, [so that] even if such a form were signed by a doctor…it would not in any circumstances make the relevant parent eligible for payments that would otherwise be suspended.”

The form has been circulated by anti-vaccination campaigners following Federal Government welfare changes aimed at denying certain welfare payments to parents who refuse to vaccinate their child.

Under the No Jab, No Pay laws, from 1 January this year parents of children whose vaccination is not up-to-date are no longer eligible for the Family Tax Benefit Part A end-of-year supplement, or for Child Care Benefit and Child Care Rebate payments. The only exemption will be for children who cannot be vaccinated for medical reasons.

The new laws were introduced amid mounting concern that vaccination rates in some areas were slipping to dangerously low levels, increasing the risk of a sustained outbreak of potentially deadly diseases such as measles.

The Australian Childhood Immunisation Register shows there has been a sharp increase in the proportion of parents registering a conscientious objection to the vaccination of their child, from just 0.23 per cent in late 1999 to 1.77 per cent by the end of 2014.

In all, around a fifth of all young children who are not fully immunised are that way because of the conscientious objection of their parents.

The form being circulated by anti-vaccination groups, headed “Acknowledgement of involuntary consent to vaccination”, is intended to circumvent the No Jab, No Pay laws and allow conscientious objectors to receive Government benefits without allowing the vaccination of their children.

But Mr Porter said the aim of the new laws was to boost immunisation rates “by providing a level of encouragement and incentive for families to more thoroughly inform themselves about the importance of immunising their children”.

The Minster said the Government recognised the right of parents to decide not to vaccinate their children, but the new laws meant there would be consequences.

“An individual is not prohibited in any way from maintaining their vaccination objection; it is simply the case they will not receive some of their family assistance,” he said. “This is a relatively small financial cost, particularly when compared to the cost that the spread of crippling, debilitating and deadly diseases has on our health system and community.”

“It is the Government’s view that when an individual decides not to vaccinate their child, they are putting their child and the community at risk of infectious diseases.”

Last month, the AMA’s senior legal adviser John Alati advised that, where there was no medical reason for vaccination exemption, the doctor’s job was to outline the relevant facts about immunisation and to provide vaccination where consent was given. Where it was withheld, “the doctor should not perform the procedure as it might constitute trespass to the person”.

His advice was backed by Mr Porter, who said that “the appropriate path for a doctor or medical profession who may be requested to sign [the form being circulated by anti-vaccination campaigners] is simply to vaccinate where there is consent, and decline where consent is absent”.

Adrian Rollins

Timeliness of lung cancer care in Victoria: a retrospective cohort study

More Australian men and women die from lung cancer than from any other cancer.1 It is the fourth most common neoplasm in both men and women, and in 2014 more than 11 000 people were diagnosed with lung cancer in Australia.1 The 5-year survival rate is 14%;1 the median survival time for patients with non-small cell lung cancer (NSCLC) is 6.9 months, and for small cell lung cancer (SCLC) it is 7.2 months.2

A recent Victorian study found that only 30% of patients with NSCLC received treatment with curative intent, and only 33% were discussed in a multidisciplinary team meeting.2 While 26% of patients presented with stage III disease, only 8% had invasive staging of the mediastinum, highlighting a potential discordance in staging of the cancer. The study did not assess delays in the care pathway.

In addition to obvious psychological distress, delay in managing lung cancer increases the potential for disease progression before treatment and may reduce the capacity for treatment with curative intent.3 Brocken and colleagues categorised delay as either “first-line”, caused by delays in the patient seeking medical advice or a delay in management by the general practitioner, or “second-line”, caused by delays in referral (time lag between the hospital receiving a referral and accessing a specialist) and treatment delivery (time lag between diagnosis and the start of treatment).4

Systematic reviews have identified organisational factors that affect the timeliness of care, including whether surgery was undertaken in a teaching hospital, whether the patient was initially referred to someone other than a respiratory physician, and the increasing number of diagnostic tests and hospitals attended to achieve a diagnosis.5 Patient-level factors associated with second-line delays include presentation with atypical symptoms,6 fewer years of education, lower disposable income, and multiple comorbidities,7 as well as symptoms that suggest less advanced disease.8 The reported impact of the age of the patient on timeliness of care is variable.5

In this article we examine second-line delays in the management of NSCLC in Victorian hospitals.

Materials and methods

Patients

Data were sourced from the Victorian Lung Cancer Registry (VLCR). This initiative operates in eight Victorian hospitals (six public hospitals, including four metropolitan and two regional centres, and two private hospitals) and captures about 25% of Victorian lung cancer notifications.9 Patients were recruited to the registry if they were at least 18 years old and presented with an incident case of lung cancer identified by ICD-10 (International Classification of Diseases, 10th revision) lung cancer codes (C34.0–C34.9, Z85.1, Z85.2) based on either a clinical or pathology diagnosis. Patients were excluded if they had secondary lung cancer or mesothelioma.

Patients diagnosed with NSCLC between July 2011 and October 2014 were assessed for eligibility. A waiver of consent enabled details about deceased patients to be collected. Of the eligible 1863 patients, 446 (24%) were excluded: 267 (14.3%) had been diagnosed by doctors who had not consented to participation in the registry, 67 (3.6%) had a carcinoid tumour, 14 (0.7%) had mesothelioma, and 98 (5.3%) declined participation.

Data collection

Hospital lung cancer notifications were provided by participating hospitals each month. Medical record review was undertaken 4 months after diagnosis. Confirmation of management was obtained when registry staff contacted patients by telephone 6 and 12 months after diagnosis. The medical records of patients who died after diagnosis but before follow-up were used as the only source of information for these patients.

Statistical considerations

Categorical data are presented as absolute numbers and percentages. Continuous variables are presented as means and standard deviations for normally distributed data and medians and interquartile ranges (IQR) for non-parametric data. Time intervals were recorded as medians, IQRs and means.

Patient factors that were analysed included sex, age, country of birth, preferred language, smoking status, TNM stage of disease at diagnosis,10 Eastern Cooperative Oncology Group (ECOG) performance status,11 and major comorbidities extracted from their medical records. Age groups were categorised by quartiles. Patients with diabetes mellitus were defined as those with insulin-dependent or oral hypoglycaemic disease; patients with renal disease were defined as those requiring dialysis; patients with cardiovascular disease were defined as those with a previous myocardial infarction or coronary intervention; patients with respiratory disease were defined as those with a functional expiratory volume of less than 66%; and patients with neoplastic disease were defined as those with any past history of cancer other than lung cancer. The Colinet simplified comorbidity score (SCS) is a weighted index with a range of 0 (no comorbidities) to 20.12 The index was dichotomised into two categories (> 9 v ≤ 9), in line with evidence that an SCS greater than 9 predicts worse survival for patients with NSCLC.13 The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD), which rates the socio-economic status of the patient on the basis of their residential postcode, was categorised into deciles; a low score indicates relatively greater disadvantage and a lack of advantage in general.14

Disease management factors included in our analysis included whether the patient received surgery, radiotherapy or chemotherapy as their initial treatment, and whether the intent was curative or palliative. Organisational factors included hospital type (private v public) and location (metropolitan v regional) for the hospital where diagnosis and initial definitive management were provided.

Three main outcomes were investigated:

the interval between initial referral for management and diagnosis (“referral to diagnosis”);
the interval between diagnosis and initial surgery, chemotherapy, radiotherapy or referral to palliative care (“diagnosis to initial definitive management”); and
the interval between referral and initial definitive management.

The referral date was the date recorded on the referral letter to the hospital or diagnosing clinician. Survival was censored at the date of the event of interest or the date on which the patient died; otherwise, the last known follow-up date (30 December 2014) was used. The log-rank test was used to compare survival in subgroups.

Both univariate and separate multivariate Cox regression analyses were performed to identify independent and significant factors associated with each time interval. For the multivariate model, we started with all significant variables identified in the univariate analysis and applied the stepwise method to determine the final list of variables included in the multivariate model. The likelihood ratio test was performed, with the probability of entry and removal of the variables set at 0.01 and 0.05 respectively. The median time to event was computed from the survival curve (along with the IQR). The proportional hazards assumption was tested using the Schoenfeld test. Data analysis was performed in Stata 13.0 (StataCorp).

Ethics approval

Ethics approval was provided by the Monash University Human Research Ethics Committee (reference CF11/1693–2011000940).

Results

The demographic and clinical characteristics of the 1417 patients included in our analysis are summarised in Box 1. The mean age of the cohort was 71.3 ± 11.4 years. The mean SCS was 7.6 ± 2.9. There were 682 deaths in the cohort (48%).

The median interval between referral and diagnosis was 15 days (IQR, 5–36 days), between diagnosis and initial definitive management 30 days (IQR, 6–84 days), and between referral and definitive management 53 days (IQR, 25–106 days) (Box 2). Socio-demographic factors associated with the length of the intervals between referral and diagnosis, diagnosis and initial definitive management, and referral and initial definitive management are summarised in Box 2. Box 3 includes the clinical factors associated with more timely diagnosis after referral. Hospital-related factors are summarised in Box 4.

Box 5 summarises the results of the stepwise selection process, and details the factors associated with overall delay for each of the three time intervals. The proportional hazards assumption was not violated in the final multivariate model, except for the interval between diagnosis and first management (P < 0.001). This violation was largely associated with the wider differences in the survival slope during the early periods of follow-up for the surgery and no-surgery groups, but, as the plots did not cross at any time point, we decided to retain this variable in the model because it was deemed a clinically important factor.

A longer interval between referral and diagnosis was associated with being born overseas; having early stage disease (stage I or II) or not having the stage documented; notification by a public hospital; receiving curative treatment; and either declining or not receiving palliative care. Patients waited significantly longer for initial definitive management after diagnosis if their ECOG performance status was not documented; having stage II or III disease, or the disease stage at diagnosis was not documented; receiving subsequent treatment in a public hospital; and not undergoing surgery. Overall, a longer interval between referral and initial definitive management was associated with being managed in a public hospital and not receiving (or declining) palliative care.

Discussion

Using a clinical quality registry, we reviewed care provided to a large cohort of patients with NSCLC who were managed in Victorian hospitals. We found several disparities. Patients born overseas experienced delays in receiving a diagnosis after referral, but not in treatment after diagnosis; overall, their treatment path took longer than that of Australian-born patients. Patients with advanced disease received prompt diagnosis, but then experienced delays in further definitive management. This may be explained by patients only needing palliative care after the diagnosis was confirmed. Conversely, patients with early stage disease waited longer to receive a diagnosis but, once diagnosed, received more timely initial definitive management than patients with more advanced disease. Patients managed in public hospitals waited longer than patients managed in the private sector to receive either a diagnosis or initial definitive management. Patients receiving chemotherapy in regional hospitals waited longer than those in metropolitan hospitals. Any active treatment took longer to commence than palliation.

The association between timeliness of care delivery and health outcomes has been investigated in many studies, often with paradoxical results.15 This is partly explained by evidence that patients with more advanced disease are often fast-tracked for referral and definitive management, but are less likely to survive than patients with less advanced disease. Of concern is the accumulating evidence that delay in treatment allows time for tumour growth and may result in patients becoming ineligible for curative treatment. Upstaging rates of 17% within a period of 20 days16 and gross tumour volume increases of 35% over a median time of 13 days17 have been reported.

A systematic review published in 2009 reported that all eight studies which had examined time from referral to first respiratory specialist visit had found median waiting times of no more than 14 days.5 The only published Australian study identified a median time between referral and initial consultation of 5 days, and a further 14 days between initial consultation and a management decision.18 In contrast, median waiting times in our study were as high as 34 days for patients with early stage disease, and patients referred to a metropolitan hospital in which they subsequently underwent surgery waited a median 29 days after referral for a diagnosis.

The time between diagnosis and initiation of treatment for patients with lung cancer has been studied in a number of countries, and the median was found to range between 12 and 52 days.5 Large cohort studies in Canada have identified waiting times of 42 days for curative radiotherapy and 39 days for surgery. Closer to home, the median overall waiting time from diagnosis to initiation of radiation therapy in Queensland was 33 days; this was not affected by distance to the treating hospital.19 Median waiting times were longer for patients with early stage disease (stage I and II NSCLC; median, 48 days) than for those with more advanced disease (stages III and IV disease; median, 34 and 26 days respectively). Our finding that the median waiting time from diagnosis to initiation of radiotherapy in both private and public hospitals was 28 days is comparable with these Queensland findings. However, when our analysis was confined to waiting times in public hospitals, the median waiting time was 30 days, compared with 12 days for patients treated in private facilities.

Dutch quality indicators suggest that organisations should provide a diagnosis within 21 days, and commence treatment within 35 days of the first visit to a specialist.20 The Danish Lung Cancer Registry has established targets of 28 days for the period from referral to diagnosis, 14 days from diagnosis to initial treatment, and 42 days from referral to initial treatment.21 Current standards set by the British National Health Service (NHS) require that all patients with suspected cancer be seen by a specialist within 14 days, and that those diagnosed with cancer be treated within 31 days of the decision to treat and within 62 days of referral.22 While setting a generic waiting time for all cancer management has been criticised as not considering the different levels of priority for the treatment of the various cancer types,23 such targets acknowledge the psychological impact of delay on patients, regardless of the prognosis of the cancer. A study conducted in two Australian radiotherapy centres found that delay between a decision to give radiotherapy and starting treatment (ie, between diagnosis and treatment) caused a higher level of concern than a delay between referral and diagnosis.24

Our finding that patients with less advanced disease waited longer for a diagnosis than patients with advanced disease is consistent with a United Kingdom study of general practitioner referral patterns which showed that a greater proportion of urgent than of non-urgent referrals involved patients with advanced lung cancer disease (higher TNM stage, and extensive v limited stage).25 However, we found that the interval between diagnosis and treatment was shorter for patients with early stage disease (stage I), suggesting that they were given priority for treatment over patients with stage II or III disease.

Our finding that overseas-born patients waited, on average, 5 days longer than their Australian-born counterparts for a diagnosis after referral may reflect organisational (eg, access to translators and services) or patient-level barriers (eg, cultural and health literacy). Cultural barriers explained a 30% reduction in African Americans receiving cancer stage-appropriate treatment in four United States hospitals; African Americans were more likely than their white counterparts to report fatalism, negative surgical beliefs and mistrust of doctors.26 Further work is required to unravel the reasons for the delays experienced by overseas-born patients presenting with suspected lung cancer in Australia.

The finding that patients managed in public hospitals waited more than twice as long as those treated in private hospitals for diagnosis and treatment is disappointing, Particularly unsatisfactory was that the median time from referral to initiation of definitive treatment was 61 days in public hospitals, only just within the British NHS target of a maximum 62 days between referral and first definitive treatment of cancer patients.22 The overall proportion of patients who waited longer than 62 days in our study was 42%, but for patients treated in public hospitals it was 48%.

There were several limitations to our study. First, 20% of patients were excluded from analysis because either the patient or their treating doctor declined participation in the registry. We cannot ascertain whether there were systematic differences in disease management patterns of these patients. Second, caution should be exercised when extrapolating our findings to Victorian hospitals that do not contribute to the VLCR. Patients notified from regional hospitals are under-represented in the registry (15% of cases in our registry were notified from regional Victorian hospitals, while 33% of lung cancer notifications in Victoria over the past 5 years were by regional hospitals27). Finally, the IRSAD score provides socio-economic characteristics of areas in which patients lived at the time of their diagnosis; we were unable to investigate the impact of an individual’s socio-economic status on the timeliness of care.

Improving timeliness of care requires political will and investment in resources to redesign care processes. Interventions that improve timeliness and appropriateness of care, including rapid access lung cancer clinics28 and process restructuring that enables rapid diagnostic imaging, biopsy collection and progression to discussion at multidisciplinary team meetings29 have produced impressive results, and warrant investigation in Victorian public hospitals. Any intervention should be underpinned by systematic monitoring of its impact on the quality of care and feedback to clinical units, such as that provided by the VLCR.

Box 1 –
Demographic and clinical characteristics of the 1417 patients

Characteristics	Categories	Number of patients (percentage)

Age (mean, 71.3 ± 11.4 years)	≤ 64 years	369 (26.0%)
	65–72 years	370 (26.1%)
	73–80 years	358 (25.3%)
	≥ 81 years	320 (22.6%)
Sex	Male	832 (58.7%)
Born overseas		719 (50.7%)
Preference for language other than English		86 (6.1%)
Ever smoked		1139 (90%)
Notifying hospital type	Metropolitan	1215 (85.7%)
	Regional	202 (14.3%)
	Private	428 (30.2%)
	Public	989 (69.8%)
Comorbidities (ascertained from medical record review)	Diabetes mellitus	195 (13.8%)
	Renal disease	24 (1.7%)
	Cardiovascular disease	244 (17.2%)
	Respiratory disease	206 (14.5%)
	Neoplastic disease	292 (20.6%)
SCS (mean, 7.6 ± 2.9)	> 9	550 (38.8%)
ECOG score	< 2	528 (37.3%)
	2–4	194 (13.7%)
	Not available/not stated	695 (49.1%)
Disease stage at diagnosis*	I	122 (8.6%)
	II	135 (9.5%)
	III	201 (14.2%)
	IV	253 (17.9%)
	Not available/not stated	706 (49.8%)
IRSAD, percentiles (mean score: 1020 [SD, 73])	1%–20%	206 (14.6%)
	21%–40%	184 (13.0%)
	41%–60%	246 (17.4%)
	61%–80%	276 (19.5%)
	81%–100%	502 (35.5%)
First treatment intent was curative		461 (44.2%)
Surgery performed		459 (32.4%)
Chemotherapy performed		548 (38.7%)
Radiotherapy performed		487 (34.4%)
Palliative care	Referral	404 (28.5%)
	Not performed/declined	835 (58.9%)
	Not stated	178 (12.6%)
Treating hospital for surgery	Metropolitan	437 (95.2%)
	Regional	22 (4.8%)
	Private	179 (39.0%)
	Public	280 (61.0%)
Treating hospital for chemotherapy	Metropolitan	406 (75.9%)
	Regional	129 (24.1%)
	Private	177 (33.1%)
	Public	358 (66.9%)
Treating hospital for radiotherapy	Metropolitan	367 (77.4%)
	Regional	107 (22.6%)
	Private	71 (15.0%)
	Public	403 (85.0%)

ECOG = Eastern Cooperative Oncology Group11; IRSAD = Index of Relative Socio-economic Advantage and Disadvantage14; SCS = Colinet simplified comorbidity score (SCS)12; SD = standard deviation. * TNM classification of malignant tumours (7th edition).10

Box 2 –
Socio-demographic factors associated with length of the time intervals (in days) between referral and diagnosis, diagnosis and first management, and referral and first definitive management

Characteristic

Referral to diagnosis

Diagnosis to initial definitive management

Referral to initial definitive management

Median (IQR)

Overall

15 (5–36)

30 (6–84)

53 (25–106)

Age group

0.133

< 0.001

≤ 64 years

14 (5–32)

24 (5–60)

43 (20–81)

65–72 years

16 (5–41)

25 (0–66)

47 (23–100)

73–80 years

16 (6–40)

31 (3–75)

56 (32–112)

≥ 81 years

13 (4–31)

46 (14–NC)

68 (31–421)

Sex

Female

13 (5–34)

0.498

31 (5–97)

0.271

52 (24–120)

0.282

Male

16 (5–37)

30 (7–75)

53 (25–102)

Place of birth

Australia

13 (4–33)

0.001

26 (1–82)

0.063

46 (20–102)

0.019

Overseas

18 (6–40)

33 (10–88)

57 (31–112)

Language preference

English

15 (5–35)

0.031

30 (6–84)

0.491

51 (23–105)

0.031

Other language

20 (7–61)

36 (14–72)

70 (42–131)

Smoking history

Never smoked

12 (4–31)

0.398

27 (1–59)

0.456

43 (17–87)

0.175

Ever smoked

16 (6–38)

28 (4–70)

53 (26–105)

IRSAD deciles

0.533

0.089

0.068

19 (7–40)

34 (7–80)

56 (29–105)

27 (11–44)

50 (4–434)

73 (34–261)

15 (8–34)

26 (0–63)

52 (3–96)

14 (6–40)

51 (7–731)

62 (33–147)

17 (6–45)

29 (1–88)

61 (20–111)

14 (5–35)

28 (3–88)

43 (24–100)

16 (7–32)

29 (4–69)

59 (26–98)

13 (4–37)

28 (6–59)

43 (21–89)

13 (3–39)

29 (9–90)

59 (26–118)

11 (3–25)

25 (8–63)

43 (19–94)

IRSAD = Index of Relative Socio-economic Advantage and Disadvantage; NC = not computable. * Log-rank test comparison across each category for the respective variable.

Box 3 –
Clinical factors associated with length of time intervals (in days) between referral to diagnosis, diagnosis to first management, and referral to first definitive management

Characteristic

Referral and diagnosis

Diagnosis and initial definitive management

Referral and initial definitive management

Median (IQR)

Comorbidities^†

Diabetes mellitus present

18 (5–44)

0.395

29 (3–58)

0.048

55 (31–91)

0.635

Diabetes mellitus absent

14 (5–35)

30 (6–90)

52 (24–108)

Renal disease present

15 (2–40)

0.746

31 (19–49)

0.876

49 (32–182)

0.679

Renal disease absent

15 (5–36)

30 (6–84)

53 (24–106)

Cardiovascular disease present

20 (6–50)

0.024

25 (4–59)

0.023

55 (27–106)

0.910

Cardiovascular disease absent

14 (5–34)

31 (6–88)

52 (24–106)

Respiratory disease present

25 (10–51)

< 0.001

40 (12–85)

0.121

69 (40–129)

< 0.001

Respiratory disease absent

14 (4–33)

28 (6–84)

50 (22–102)

Neoplastic disease present

16 (5–40)

0.156

30 (4–77)

0.516

55 (24–128)

0.653

Neoplastic disease absent

15 (5–36)

30 (6–85)

52 (25–104)

SCS < 9

15 (6–36)

0.345

30 (4–77)

0.281

54 (26–111)

0.191

SCS ≥ 9

15 (4–36)

30 (8–94)

50 (22–102)

ECOG score < 2

14 (5–34)

< 0.001

24 (1–51)

< 0.001

47 (22–87)

< 0.001

ECOG score 2–4

9 (3–22)

40 (12–140)

56 (24–244)

ECOG score missing

18 (6–44)

37 (2–218)

59 (29–129)

Disease stage at diagnosis^‡

< 0.001

0.078

34 (16–61)

0 (0–37)

56 (33–103)

29 (11–60)

28 (0–58)

59 (33–102)

III

17 (7–28)

38 (15–64)

55 (34–83)

10 (4–23)

30 (12–72)

44 (22–91)

Not available/not stated

13 (4–34)

32 (7–339)

54 (20–161)

First treatment intent non-curative

12 (4–24)

< 0.001

11 (25–47)

< 0.001

37 (19–63)

0.017

First treatment intent curative

28 (11–58)

0 (0–31)

45 (22–78)

Surgery performed

28 (10–56)

< 0.001

0 (0–22)

< 0.001

42 (18–73)

< 0.001

Surgery not performed

11 (4–25)

48 (21–393)

61 (30–230)

Chemotherapy performed

13 (4–30)

< 0.001

24 (7–47)

< 0.001

38 (19–68)

< 0.001

Chemotherapy not performed

16 (6–42)

41 (4–NC)

69 (32–414)

Radiotherapy performed

13 (4–25)

< 0.001

28 (11–52)

< 0.001

42 (21–73)

< 0.001

Radiotherapy not performed

17 (5–43)

32 (0–739)

63 (30–181)

Palliative care referral

11 (3–22)

< 0.001

43 (19–255)

< 0.001

56 (26–175)

0.007

Palliative care not performed

19 (7–46)

23 (0–55)

51 (27–97)

Not stated

8 (2–24)

54 (10–NC)

44 (17–NC)

ECOG = Eastern Cooperative Oncology group11; IRSAD = Index of Relative Socio-economic Advantage and Disadvantage14; NC = not computable; SCS = Colinet simplified comorbidity score (SCS).12 * Log-rank test comparison across each category for the respective variable. The difference associated with ECOG scores (< 2 v 2–4) was also significant if the not available/not stated group was excluded (P < 0.001). The comparisons for comorbidities are between those who had the comorbidity versus those who did not. † Comorbidities ascertained from medical record review. ‡ TNM classification of malignant tumours (7th edition).10

Box 4 –
Hospital-related factors associated with length of time intervals (in days) between referral and diagnosis, diagnosis and first management, and referral and first definitive management

Characteristic

Referral to diagnosis

Diagnosis to initial definitive management

Referral to initial definitive management

Median (IQR)

Notifying hospital

Metropolitan hospital

15 (5–38)

0.289

Regional hospital

18 (10–29)

Private hospital

7 (2–19)

< 0.001

Public hospital

19 (8–43)

Treating hospital

Private

15 (1–52)

30 (13–76)

Public

36 (11–91)

< 0.001

61 (35–118)

< 0.001

Treating hospital for surgery

Metropolitan

29 (9–59)

0.250

0 (0–20)

0.065

41 (18–73)

0.741

Regional

23 (16–44)

21 (0–58)

44 (34–72)

Private

15 (5–38)

< 0.001

0 (0–16)

0.040

22 (10–46)

< 0.001

Public

35 (15–70)

0 (0–28)

51 (30–91)

Treating hospital for chemotherapy

Metropolitan

11 (3–90)

0.010

20 (6–40)

0.001

37 (17–65)

0.068

Regional

20 (10–31)

32 (11–58)

43 (28–79)

Private

5 (1–16)

< 0.001

14 (4–30)

< 0.001

20 (11–38)

< 0.001

Public

19 (8–37)

29 (9–51)

50 (30–78)

Treating hospital for radiotherapy

Metropolitan

12 (4–25)

0.964

26 (10–49)

0.099

41 (20–70)

0.515

Regional

16 (6–26)

35 (15–61)

50 (23–78)

Private

3 (1–8)

< 0.001

12 (6–31)

0.005

20 (12–43)

< 0.001

Public

15 (7–29)

30 (13–54)

46 (25–76)

NA = not available. * Log-rank test comparison across each category for the respective variable.

Box 5 –
Multivariate analysis of factors associated with lengths of intervals

Characteristics

Hazard ratio (95% CI)*

P^†

Characteristics affecting time from referral to diagnosis

Place of birth

Australia

Overseas

0.84 (0.72–0.99)

0.035

Disease stage at diagnosis^‡

0.58 (0.43–0.78)

0.000

0.66 (0.49–0.89)

0.006

III

0.92 (0.72–1.18)

0.529

Not available/not stated

0.74 (0.59–0.93)

0.010

Notifying hospital

Private

Public

0.50 (0.41–0.60)

< 0.001

First treatment intent

Non-curative

Curative

0.73 (0.61–0.89)

0.002

Palliative Care

Yes

No/declined

0.64 (0.52–0.79)

< 0.001

Not stated

1.22 (0.87–1.71)

0.245

Factors affecting time from diagnosis to initial definitive management

ECOG performance status

< 2

2–4

0.95 (0.77–1.16)

0.615

Not available/not stated

0.84 (0.74–0.96)

0.011

Disease stage at diagnosis^†

0.60 (0.46–0.79)

< 0.001

III

0.61 (0.47–0.78)

< 0.001

0.81 (0.63–1.05)

0.118

Not available/not stated

0.79 (0.63–0.99)

0.044

Treating hospital type

Private

Public

0.80 (0.69–0.92)

0.002

Surgery performed

Yes

No/declined

0.54 (0.47–0.62)

< 0.001

Factors affecting time from referral to initial definitive management

Palliative care

Yes

No/declined

0.73 (0.62–0.86)

< 0.001

Not stated

1.03 (0.78–1.35)

0.849

Treating hospital type

Private

Public

0.55 (0.48–0.64)

< 0.001

ECOG = Eastern Cooperative Oncology group. * A positive hazard ratio corresponds to shorter time to event. † Cox proportional hazards model. ‡ TNM classification of malignant tumours (7th edition).10

A survey of Sydney general practitioners’ management of patients with chronic hepatitis B

In Australia, the prevalence of chronic hepatitis B (CHB) infection has increased over the past decade, with an estimated 218 000 Australians living with the disease.1 The annual number of deaths attributable to CHB is also expected to rise from 450 in 2008 to 1550 in 2017.2 Cost-effective treatments to reduce morbidity and mortality are available;2–4 however, up to 44% of infected Australians remain undiagnosed1,5 and only 2%–13% of those infected are receiving adequate treatment.2,6

The highest prevalence of CHB in New South Wales is in the Sydney and South Western Sydney Local Health Districts (LHDs), with respective estimated prevalence rates of 1.67% and 1.61% (the NSW average is 1.11%).7 In these LHDs, a large proportion of the population was born in countries with an intermediate or high prevalence of CHB.8,9 To relieve the pressure on specialist liver services, the National Hepatitis B Strategy 2014–20175 recommends an increased role for general practitioners in the management of CHB. We therefore examined the CHB assessment and management practices of GPs in the two LHDs, and the confidence that these GPs have in different models of care.

Methods

We used a descriptive cross-sectional study design to survey GPs about case management. A questionnaire (Appendix) was developed by a steering group that included hepatologists, nurses, public health physicians, an infectious diseases physician and a GP. The survey also included a separate section on contact management; this is not discussed in this article.

Eligible GPs were those practising in Sydney LHD (SLHD) or South Western Sydney LHD (SWSLHD) who had had at least one patient aged 18 years or over who had been notified as having CHB to the Public Health Unit under the NSW Public Health Act 2010 between 1 June 2012 and 31 May 2013. A survey was posted to each GP, and those who had not returned it within 4 weeks received a telephone call and another copy of the survey. GPs were excluded if they no longer practised at the same location.

Returned surveys were coded and the data entered into Excel 2010 (Microsoft) and analysed with Excel 2010 (Microsoft), SAS Enterprise Guide 6.1 (SAS Institute) and Stata 10.0 (StataCorp). Blank responses were coded as “unknown”. Demographic information for all GPs in SLHD and SWSLHD was obtained from the Inner West Sydney and South Western Sydney Medicare Locals.

Human research ethics approval was granted by the SLHD Ethics Review Committee (RPAH Zone), protocol number X13-0035.

Results

Completed questionnaires were returned by 123 of 213 eligible GPs (57.7% response rate), with no statistically significant difference in response rate between SLHD and SWSLHD GPs (P = 0.41).

There were significant differences in sex, age distribution, and type of practice between the study participants and those of all GPs in SLHD and SWSLHD (Box 1). The average number of patients with CHB notified by responding GPs during the study period was 1.88, compared with 1.96 for non-responders (P = 0.73). Most GPs (97 of 123, 78.9%) estimated that they cared for 50 or fewer patients with CHB. GPs from SWSLHD were more likely than SLHD GPs to have cared for more than 50 patients with CHB (odds ratio [OR], 3.24; 95% CI, 1.08–9.68).

GPs were asked how confident they were about different aspects of CHB assessment and management (Box 2). GPs who reported that they were “not very” or “not at all” confident were more likely than GPs who were “very” or “reasonably confident” to have cared for 50 or fewer patients (OR, 1.26; 95% CI, 1.14–1.40).

Box 3 summarises responses by GPs who were asked how comfortable they would be managing a patient with CHB in a number of different scenarios. GPs who were at least reasonably confident without specialist or hepatitis nurse input were more likely than those who were not to have cared for more than 50 patients with CHB (OR, 4.68; 95% CI, 1.28–17.16).

Discussion

This is the largest survey of Australian GPs to have examined their CHB assessment and management practices, and their views about specific models of care. Our results have important implications for service development. We found that GPs were generally confident about diagnosing and managing CHB, and were most comfortable with a model of care that included an initial specialist review. However, a significant number of GPs were not confident about managing CHB, particularly without the support of a specialist. If there is to be a successful shift toward a CHB model of care in which primary health care plays an increased role,5 this problem will need to be addressed by policy makers and medical educators. A framework that provides GPs with the support and resources necessary for appropriate CHB management is needed.

Most GPs felt confident about CHB management, but it is notable that almost one-fifth were “not very” or “not at all” confident. These GPs were more likely to have had a lower CHB patient load, and may thus have had less experience in this area. Previous surveys of Australian GPs have identified knowledge gaps about different aspects of CHB management.8,10,11 Our findings are consistent with these reports, but also indicate that a supportive CHB model that enables GPs to easily access appropriate resources and specialised advice is required.

The current Australian CHB model of care is focused on specialist hepatological care; however, these services are facing huge demands, and it has been suggested that increased involvement of GPs is needed to deal with the growing burden of CHB,2,5,12 as well as integrated nursing models and an exploration of the role of nurse practitioners.5 The majority of surveyed GPs were most comfortable with a care model that included initial review by a specialist and continuing GP management, with less support for a model in which there was no specialist input, and a reluctance to accept review by a hepatitis clinical nurse consultant alone. The stated preference of GPs in our study for specialist input in CHB management has implications for future health service planning. If nursing support for GPs is to be successful in an alternative CHB model of care, background specialist support needs to be clearly promoted to gain the confidence of GPs and to optimise the management of CHB.

Our study has limitations. While the response rate compares favourably with other recent written GP surveys about CHB,11,13 the possibility of response bias cannot be excluded. The significant difference in sex, age distribution, and type of practice between study participants and all GPs in the surveyed LHDs affects the external validity of our findings. While the steering group provided GP input into questionnaire development to improve its face validity for GPs, we did not test the questionnaire on another group of GPs; the applicability of the instrument to other settings is therefore unclear. Closed-ended and multiple-choice questions were used to facilitate the comparability of responses; however, their use may have prevented GPs from expressing other views.

This study identified that some GPs working in areas where the prevalence of CHB is high lack confidence about managing CHB. GPs in areas where CHB is less prevalent may encounter these problems to a greater extent, but further research is necessary to confirm this assumption and to thereby inform educational programs and service planning. As the CHB burden in Australia rises and the capacity of specialist liver services is tested, a new model of care focusing on primary health care needs to be developed, but must be considered carefully, noting the clear preference of GPs for specialist support. Our results suggest that well designed and targeted support programs that include specialist support are needed as part of a model of care which ensures that GPs feel confident about managing CHB.

Box 1 –
Demographic characteristics of the study participants (n = 123) and of all general practitioners in the Sydney and South Western Sydney Local Health Districts (n = 1135)

	Study participants	All GPs	P (χ² test)

Sex			< 0.001
Female	31.7%	40.3%
Male	67.5%	59.7%
Not recorded	0.8%	0
Age group			< 0.001
< 30 years	0	0
30–39 years	10.6%	6.2%
40–49 years	26.0%	16.6%
50–59 years	32.5%	18.2%
≥ 60 years	30.1%	22.2%
Not recorded	0.8%	36.7%
Local Health District			NA
Sydney	48.0%
South Western Sydney	52.0%
Type of practice			< 0.001
Solo	35.8%	19.0%
Group	63.4%	80.8%
Not recorded	0.8%	0.3%

NA = not applicable.

Box 2 –
Confidence of general practitioners (n = 123) about different aspects of the assessment and management of patients with chronic hepatitis B (CHB)

	Very confident	Reasonably confident	Not very confident	Not at all confident	Unknown

Identifying patients at risk of CHB	49.6%	48.8%	1.6%	0	0
Screening patients at risk of CHB	52.0%	45.5%	1.6%	0	0.8%
Ordering appropriate tests for diagnosing CHB	57.7%	39.0%	2.4%	0	0.8%
Interpreting hepatitis B serology and DNA results	43.1%	47.2%	7.3%	1.6%	0.8%
Managing patients with CHB	22.8%	56.1%	17.9%	1.6%	1.6%
Undertaking surveillance of liver cancer	30.9%	57.7%	8.9%	1.6%	0.8%
Referring for fibroscan	12.2%	35.8%	35.0%	16.3%	0.8%

Box 3 –
Confidence of general practitioners (n = 123) about managing patients with chronic hepatitis B in various models of care

	Very	Reasonably	Not very	Not at all	Unknown

With no specialist input	8.9%	48.0%	29.3%	12.2%	1.6%
Initial referral to a specialist for assessment, then managed by GP	43.1%	45.5%	8.9%	0.8%	1.6%
Initial referral to a specialist for assessment, then managed by GP with support from a hepatitis clinical nurse consultant	37.4%	45.5%	9.8%	5.7%	1.6%
Initial review by a hepatitis clinical nurse consultant, then managed by GP	18.7%	40.7%	22.8%	16.3%	1.6%

Acting on potentially inappropriate care

Measuring inappropriate care should rely on evidence available at the time, with anomalous practices evaluated by clinical panels

Our recent MJA article1 and accompanying report2 set out a model for harnessing clinical expertise and government resources to address the issue of inappropriate care.

This model involves four steps. First, a credible, independent body would review clinical evidence to identify potentially inappropriate treatments. Second, the same body would monitor use of those treatments. Hospitals providing the treatments more frequently than the national average rate would be advised of that fact. Third, if a hospital’s abnormal practice persisted, they would face an external clinical review in which they could justify their practice to peers. Finally, if the reviewers found this justification unsatisfactory, only then might there be financial consequences.

It is hard to see how this cautious and clinician-led model is punitive, as Sherlock labels it.3 We do not recommend taking potentially inappropriate treatment options away from clinicians. Instead, we recommend monitoring their use and initiating clinician-led reviews of unusual practice patterns, recognising that even do-not-do treatments may be justified in some circumstances.

Our model recognises that health care is dynamic and the evidence often changes, so that procedures seen as inappropriate in one year could be regarded as good practice the next. We took care in our study to ensure that we were only analysing treatment choices that were inconsistent with the prevailing evidence at the time our data were collected. Evidence that was outdated or published after our study year was not taken into account. Therefore, we disagree with the critiques of our findings by Sherlock3 and Clark.4

Clark4 questions our identification of vertebroplasty for osteoporotic spinal fractures as potentially inappropriate, but the current NICE guidance (https://www.nice.org.uk/guidance/ta279) and forthcoming trial that they refer to were not available during our study year. Although 2003 NICE guidance (https://www.nice.org.uk/guidance/ipg12) supporting use of vertebroplasty was available during our study year, it had been contradicted by the more recent landmark trials published in 2009.5,6 This change in prevailing evidence was reflected in the subsequent recommendation by the Medical Services Advisory Committee (MSAC) to defund the procedure.7

Sherlock likewise refers to evidence that was not current during our study year. MSAC report 1054.18 was published too late to influence clinicians’ treatment choices when our data were collected, while MSAC report 10549 recommendation of time-limited support for certain conditions requiring hyperbaric oxygen treatment had lapsed well before our study year.

We accept that the 4500 admissions that we identified did not involve 4500 people; unfortunately, our data did not allow us to identify the number of individuals treated. Our dataset also did not include Medicare Benefits Schedule item numbers. We therefore relied on the International Classification of Diseases 10th revision (ICD-10)-coded diagnosis and Australian Classification of Health Interventions procedure codes available in the hospital discharge abstract.

We were conservative in our approach to identifying potentially inappropriate care, excluding all cases where a patient had a diagnosis that was an indication for the treatment. To use Sherlock’s example, a patient with a primary diagnosis of “waiting for residential care” who also had a diagnosis of diabetic ulcers would not have been counted as a do-not-do patient in our study, as they had a comorbidity (diabetic ulcers) that may have legitimated hyperbaric oxygen treatment.

Despite our exclusions, we remained wary of coding errors, and designed our analysis and recommendations to take them into account. We excluded hospitals with very low rates of relevant procedures or patients. As a result, an isolated coding error could not result in an above average rate of questionable treatments. If numerous coding errors did lead to a false positive, these would have been uncovered during our proposed clinical reviews. If the result of adopting our model is more accurate coding, in addition to appropriate care, that is an added benefit.

We do not believe that the criticisms from Sherlock and Clark invalidate our findings, but we do welcome debate about our approach. It shows that there is a strong interest in making sure that measurement of practice variation and potentially ineffective treatment is robust and clinically meaningful.

We have proposed a practical method for moving from talking about the issue to acting on it. We do so in a way that involves significant clinical input, as we acknowledge that overcoming the problem of inappropriate clinical variation requires strong clinical leadership.10 Doing nothing is not an option. Provision of clinically inappropriate care incurs a cost to both patients and the health system. Not acting on our findings means that people will suffer and we waste resources.

Vertebroplasty is not a do-not-do treatment

Vertebroplasty has been controversial but remains clinically useful and new evidence awaits publication

Duckett and colleagues have classified vertebroplasty as a do-not-do treatment.1 They referenced two randomised controlled trials (RCTs)2,3 as definitive proof of this. However, the authors failed to heed our clinical opinion published in the MJA that these two trials were “not relevant to the patient group that we treat with vertebroplasty”.4 We have the largest clinical vertebroplasty experience in Australia, yet our published advice was apparently ignored. In the article by Duckett and colleagues, Box 1 illustrated the selection process that the authors used to determine do-not-do procedures. The process supposedly excluded evidence which was “contested” or “which was not supported by consulted clinical experts”. Accordingly, vertebroplasty should have been deleted from the list.

The authors used the United Kingdom National Institute for Health and Care Excellence (NICE) for clinical guidance. Current NICE guidance5 states that “vertebroplasty and kyphoplasty can be considered appropriate interventions for people with recent, unhealed osteoporotic vertebral compression fractures in whom the pain is severe and ongoing despite optimal pain management”.

From 1208 potential treatments, the authors excluded 1200, leaving five apparently incontrovertible do-not-do treatments. The fact that at least one of the five is wrongly included (by the authors’ own criteria) demonstrates the failure of the proposed model and the danger of adopting this kind of formula to influence clinical practice in hospitals.

The evidence for and against vertebroplasty is inconclusive. There is disparity in measured outcomes between blinded RCTs2,3 of vertebroplasty for fractures up to 12 months old and a larger, open-label RCT6 of fractures less than 6 weeks in duration. The blinded trials found no significant benefit of vertebroplasty over placebo, whereas the open-label RCT found significant benefit of vertebroplasty over conservative care. This disparity is well described in the NICE guidance.5

For the past 10 years, my vertebroplasty practice has been confined to treating fractures less than 6 weeks old.7 It is clear to me that the published blinded trials tested a different approach and are not relevant to the patient group that my practice treats with vertebroplasty for two principal reasons: the fractures were mostly non-acute; and the volume of cement used in these trials (2.6 cm³ on average in both trials) would have been insufficient to stabilise an acutely collapsing vertebral fracture.

Attempting to answer the acute fracture conundrum, the authors of the blinded RCTs published a meta-analysis of 52 patients from both trials with fractures less than 6 weeks duration.8 Only outcomes at 2 weeks and 1 month were presented and the evidence is hardly definitive.

The onus was placed on vertebroplasty practitioners to provide high-quality blinded data in this group of patients. For this purpose, my co-investigators and I embarked on the Vertebroplasty for Acute Painful Osteoporotic fractURes (VAPOUR) trial.9 Five years ago, we reconfigured the protocol from the INvestigational Vertebroplasty Efficacy and Safety Trial (INVEST),2,10 the larger of the two blinded vertebroplasty trials. We excluded crossover, which was permitted at 1 month in INVEST. We changed the selection criteria to include only fractures less than 6 weeks duration (average fracture age in the VAPOUR trial was 2.6 weeks compared with 18 weeks in INVEST) with pain scores greater than 7/10 and with either magnetic resonance imaging or single-photon emission computed tomography evidence of acute fracture. In-patients, already hospitalised with acute fractures, comprised 59% of the VAPOUR trial enrolment but were excluded in INVEST. The procedural technique was different in the VAPOUR trial, where we attempted maximum fill of the vertebral body to stabilise the fracture and prevent ongoing collapse. The average cement volume of 7.5 cm³ in the VAPOUR trial was three times that in INVEST. The method of blinding and data collection was similar for the two trials.

Our trial team included four Sydney centres with established vertebroplasty programs. The VAPOUR trial completed enrolment of 120 patients in December 2014 and is the largest RCT and the only acute fracture RCT of vertebroplasty in Australia. Statistical assessments of outcomes are nearing completion and the results of the trial will soon be published.

Inappropriate care in medicine

Non-clinicians have based their claims of inappropriate care in hyperbaric medicine on flawed methods

In a recent article in the MJA, Duckett and colleagues presented “a model to measure potentially inappropriate care in Australian hospitals”.1 The article was a summary of their report for the Grattan Institute.2,3 However, with regard to hyperbaric oxygen treatment (HBOT), the summary concealed fundamental flaws in their source data collection and their methods that have resulted in misleading conclusions. Neither Duckett and colleagues nor the accompanying editorial4 cited the two source documents on which the articles were based.2,3 A critical reading of the source documents, cross-referenced with the relevant Medical Services Advisory Committee (MSAC) reports, has identified errors in method and interpretation that invalidate the findings of Duckett et al.2–7

The use of HBOT has been subject to three MSAC reviews since 1998.5–7 As a result of these evidence-based reviews, eight conditions have been accepted for funding by Medicare, including non-neurological soft tissue radiation injury. By exclusion, no other conditions are funded by Medicare. This does not mean there is no supporting evidence for other conditions, as many have not been formally reviewed by MSAC, an important distinction when defining do-not-do treatments.

MSAC reports 1054 and 1054.1 specifically dealt with HBOT for non-diabetic problem wounds and soft tissue radiation injury. MSAC report 1054.1 was inaccurately referenced by Duckett et al, who omitted soft tissue radiation injuries from the title of the report. This omission meant that they included soft tissue radionecrosis in their list of conditions that are inappropriate for HBOT (set out in Box 2 of their article) without alerting the reader to the contradiction.1 The inclusion of soft tissue radiation injury as a do-not-treat condition led to an overestimation in their calculations (soft tissue radiation injury represents 60% of the case load at our hospital), resulting in HBOT accounting for 79% of their total of inappropriate care tally.

By not accurately interpreting their references, Duckett et al allowed errors in data collection to compound into major flaws in their method and conclusions. In the financial year of analysis, 2010–11, there were 15 579 HBOT procedures (Australia-wide) under the relevant Medicare item numbers.8 Of these, 8910 were under item 13015, which represents the combined total for soft tissue radiation injury and non-diabetic problem wounds. Medicare data confirm that both soft tissue radiation injury and non-diabetic problem wounds were funded and appropriate when Duckett’s group collected data and inaccurately defined them as do-not-do treatments. Both the Medicare data and the source data used by Duckett et al recorded the total number of procedures, not the number of patients treated. As each patient received 17.2 HBOTs on average,6 the total number of patients receiving HBOT in Australia in 2010–11 was fewer than 1000 — far less than the number alleged to have received inappropriate treatment.1,2,6 The original Grattan Institute report asserted that “more than 4500 people a year get hyperbaric oxygen therapy when they do not need it”, and “one in four hyperbaric oxygen treatments should not happen”. Both statements are unfounded and incorrect.2

To identify procedures, Duckett et al used the Australian Classification of Health Interventions (ACHI) codes (which have limited clinical relevance and refer only to time for funding purposes), then cross-referenced them against ICD-10 (International Statistical Classification of Diseases and Related Health Problems, 10th Revision) codes, rather than using Medicare Benefits Schedule (MBS) codes, which are linked to conditions.3 ACHI code 9619100 is not on the MBS; it describes HBOT ≤ 90 minutes, which is not used in Australia by any Medicare-funded facility. Any 9619100 descriptors detected by the study would have demonstrated coding errors. The magnitude of that error cannot be measured owing to lack of detail.

A further source of error was the linking of ACHI codes to ICD-10 codes for comorbidities to discover the clinical condition being treated. HBOT is often implemented as part of multidisciplinary treatment processes in tertiary hospitals. Many patients who receive HBOT have multiple comorbidities. Duckett and colleagues admitted that a limitation of their study was the justified coding errors produced from “a rare combination of patient characteristics”. This comment reflects lack of clinical knowledge — these characteristics are far more common than the authors acknowledge — which has led to underestimation of the magnitude of coding errors.

As an illustration, a retrospective review of all patients receiving HBOT at Royal Brisbane and Women’s Hospital during the relevant study period shows that 25% of the coding was incorrect. One patient was recorded as receiving HBOT for ≤ 90 minutes for their primary diagnosis of “waiting for residential care” when the treatment was actually for a diabetic ulcer.

The conclusions drawn by Duckett et al are at best misleading.1 It is of great concern that non-clinicians are proposing this analysis to inform health policy and are recommending actions based on flawed methods and misuse of data.

A multidisciplinary renal genetics clinic improves patient diagnosis

Developments in genomic science are disproportionately in advance of their translational clinical application. Multidisciplinary clinics are proposed to overcome this1 in many medical fields.2 This is especially so in nephrology, which is typified by significant community disease burden3 and heritability.4 Several renal genetics clinics (RGCs) operate overseas, although their models and outcomes are largely unreported. The first multidisciplinary RGC in Australasia commenced at the Royal Brisbane and Women’s Hospital in August 2013, involving a clinical geneticist, nephrologist, genetic counsellor, and ancillary clinical and diagnostic services. The departments of clinical genetics and nephrology jointly operate the RGC. The clinical geneticist and nephrologist see families in the same appointment, maximising use of time. In this article, we report this clinical service’s initial outcomes and model for mainstreaming genetic medicine.

We undertook a retrospective cohort study of patients who attended the Royal Brisbane and Women’s Hospital Renal Genetics Clinic during its first 2 years of operation (1 August 2013 to 31 August 2015; ethics approval reference, HREC/14/QRBW/187). During this period, 108 patients from 100 families were seen; the median age was 41 years (range, 13–86 years). Most patients were referred by a public or private sector nephrologist (47% [51/108] and 28% [30/108], respectively), and 81% (87/108) had an existing genetic renal diagnosis, 45% (49/108) had extra-renal clinical features and 65% (70/108) had a family history of renal disease. Existing renal diagnoses were diverse, and the most common were autosomal dominant polycystic kidney disease (34% [37/108]), Alport syndrome (17% [18/108]) and focal segmental glomerulosclerosis (7% [8/108]).

The overlapping reasons for referral were for a diagnosis (67% [72/108]), a discussion about a diagnosis (27% [29/108]) and genetic counselling (81% [87/108]). Clinical and family histories and results of clinical investigations were reviewed. Differential diagnoses were discussed for 68% of patients (73/108), disease information was provided to 89% (96/108), and genetic counselling provided to 67% (72/108).

Genetic testing was ordered for 69% of patients (75/108). Of a total of 83 tests, results were positive for 39% (32/83), negative for 30% (25/83), “variant of uncertain significance” for 7% (6/83) and pending for 24% (20/83). Negative genetic test results have enabled 12 of the families to enrol in a research study.5 To date, the clinical diagnosis has changed for 27 of the 108 patients (25%) (Box), enabling correct diagnosis, accurate genetic counselling, identification of at-risk individuals, access to assisted reproductive technologies and altered medical management.

This RGC model is novel in Australasia and its results are among the first to be reported internationally. In its first 2 years of operation, patients underwent clinical appraisal and a tailored combination of differential diagnosis discussion, disease information provision and genetic counselling. Genetic testing was often, but not always, used, with results confirming or clarifying a diagnosis for about half of the patients. Overall, the diagnosis was changed in a quarter of patients. This clinic model is inclusive, flexible and multidisciplinary while demonstrably improving patient diagnosis and care. We believe that it is a viable, translational and patient-focused clinical template for effective introduction of genetics and genomics into everyday clinical practice.

Box –
Changes in clinical diagnosis at the Royal Brisbane and Women’s Hospital Renal Genetics Clinic (green, unchanged diagnosis; blue, changed diagnosis)

Hospitals face funding ‘black hole’

Almost a third of Emergency Department patients in need of urgent treatment are being forced to wait more than 30 minutes to be seen, while thousands of others face months-long delays for elective surgery as under-resourced public hospitals struggle to cope with increasing demand.

The AMA’s latest snapshot of the health of the nation’s public hospital system shows that improvements in performance have stalled following a sharp slowdown in Federal Government funding, underlining doctor concerns that patients are paying a high price for Budget austerity.

“By any measure, we have reached a crisis point in public hospital funding,” AMA President Professor Brian Owler said. “The states and territories are facing public hospital funding black hole from 2017 when growth in Federal funding slows to a trickle.”

The Federal Government will have slashed $454 million from hospital funding by 2017-18, and a downshift in the indexation of spending from mid-2018 will reduce its contribution by a further $57 billion by 2024-25.

Professor Owler said the consequences of Commonwealth cutbacks were already showing up in hospital performance, and the steep slowdown in funding growth in coming years will further exacerbate the situation.

“Public hospital funding is about to become the single biggest challenge facing State and Territory finances, and the dire consequences are already starting to show,” the AMA President said. “Without sufficient funding to increase capacity, public hospitals will never meet the targets set by governments, and patients will wait longer for treatment.”

The AMA’s Report Card, drawing on information from the Australian Institute of Health and Welfare, the Council of Australian Governments Reform Council and Treasury, shows the performance of public hospitals against several key indicators has plateaued and, by some measures, is declining.

In terms of hospital capacity, the long-term trend toward fewer beds per capita is continuing. The decline is even more marked when measured in terms of the number of beds for every 1000 people aged 65 years of older – a fast growing age group with the highest demand for hospital services.

In 1993 there were almost 30 beds for every 1000 older people, but by 2013-14 that had virtually halved to around 17 beds.

Alongside a relative decline in capacity, there are signs the hospitals are struggling under the pressure of growing demand.

Emergency departments, often seen as the coal face of hospital care, the proportion of urgent Category 3 patients seen within the clinically recommended 30 minutes fell back to 68 per cent in 2014-15 – a two percentage point decline from the previous year, and a result that ended four years of unbroken improvement.

The national goal that 80 per cent of all ED patients are seen within clinically recommended times appears increasingly unlikely, as does the COAG target that 90 per cent of all ED patients be admitted, referred or discharged within four hours. For the last two years, the ratio has been stuck at 73 per cent.

The outlook for patients needing elective surgery is similarly discouraging.

The AMA report found that although there was slight reduction in waiting times for elective surgery in 2014-15, patients still faced a median delay of 35 days, compared with 29 days a decade earlier.

It appears very unlikely the goal that by 2016 all elective surgery patients be treated within clinically recommended times will be achieved. Less than 80 per cent of Category 2 elective surgery patients were admitted within 90 days in 2014-15 – a figure that has barely budged in 12 years.

The Commonwealth argues it has had to wind back hospital spending because of unsustainable growth in the health budget.

But Professor Owler said the evidence showed the opposite was the case.

The Government’s own Budget Papers show total health expenditure grew 1.1 per cent in 2012-13 and 3.1 per cent the following year – well below long-term average annual growth of 5 per cent.

Furthermore, health is claiming a shrinking share of the total Budget. In 2015-16, it accounted for less than 16 per cent of the Budget, down from more than 18 per cent a decade ago.

“Clearly, total health spending is not out of control,” Professor Owler said, and criticised what he described as a retreat by the Commonwealth Government from its responsibility for public hospital funding.

“There is no greater role for governments than protecting the health of the population,” he said. “Public hospitals are the foundation of our health care system. Public hospital funding and improving hospital performance must be a priority for all governments.”

In a statement to Fairfax Media, Health Minister Sussan Ley declined to specifically address the issues raised in the AMA Report Card.

Instead, the Minister pointed out that Commonwealth funding for hospitals was increasing on an annual basis, and there had been no policy anouncements in last year’s Budget or MYEFO affecting that. While technically correct, the Minister’s comments brush over the big changes announced in the first Hockey BUdget in 2014-15, including a massvie slowdown in the growth of Federal funding for hospitals.

The issue of hospital funding is set to loom large when the nation’s leaders meet in March to discuss reform of the Federation.

Already, several premiers are pushing for an overhaul of taxation arrangements to provide the states with a better growth revenue stream than the Goods and Services Tax.

South Australian Premier Jay Weatherill has proposed that the Commonwealth hold on to GST revenue and, in return, give the states and territories a slice of income tax receipts.

Adrian Rollins