Changing education model key to stamping out bullying in medicine

Creating new policies and guidelines isn’t enough to stamp out sexual harassment and bullying in the medical world, according to a Perspective published today in the Medical Journal of Australia.

Professor Merrilyn Walton, Professor of Medical Education (Patient Safety) at the University of Sydney, says the entire design of medical education needs to be overhauled.

She writes in her article that “being a senior doctor is not a qualification for teaching in itself”.

Instead, she feels that supervisors should be accredited in order to reinforce the potential of all trainees.

She feels that this change in educational structure might help women learn better and decrease instances of bullying in medicine.

“Women may be at a disadvantage their learning approaches and styles may not be as suited to the opportunistic supervision learning method used in hospitals that requires an assertive personality and a “can do” attitude that are not necessarily the best for patient care, but are best for progress in specialty training,” Professor Walton wrote.

Related: Opinion: Surgery support

She feels particularly in areas of surgery where currently only 10% are women, encouraging female surgeons to become clinical supervisors would help.

She also feels colleges need to change their culture when it comes to reporting harassment and bullying in medicine.

“College policies and guidelines about harassment and discrimination alone will not change the culture — these must be accompanied by swift and strong action by College representatives when instances are brought to their attention.”

Read the full perspective on the Medical Journal of Australia website.

The AMA recently made a submission to the Royal College of Surgeons to look into issues of discrimination, bullying and harassment.

The AMA submission offers explanations as to why discrimination, bullying, and harassment are a problem in the profession of surgery, however don’t believe they should be excuses for unacceptable standards of behaviour.

“There is no one solution that will fix these issues,” AMA President Professor Brian Owler said.

“We need to target areas such as the working environment, training and education, mentoring and role models, the elevation of more to women leadership roles, policies, complaints processes, and reporting.

“And we need to create an environment where people should not be afraid to come forward with complaints.”

Read the submission on the AMA website.

We’re overdosing on medicine – it’s time to embrace life’s uncertainty

The more we learn about the problem of too much medicine and what’s driving it, the harder it seems to imagine effective solutions. Winding back unnecessary tests and treatments will require a raft of reforms across medical research, education and regulation.

But to enable those reforms to take root, we may need to cultivate a fundamental shift in our thinking about the limits of medicine. It’s time to free ourselves from the dangerous fantasy that medical technology can deliver us from the realities of uncertainty, ageing and death.

We’re all ill now

A growing body of evidence shows that when it comes to health care, we may simply be getting too much of a good thing. In the United States, it’s estimated that more than US$200 billion a year is squandered on unnecessary tests and treatments. In the United Kingdom, senior medical groups are calling on doctors to reduce all the wasteful things they do. And in Australia, the Choosing Wisely campaign recently kicked off with lists of unnecessary and harmful health care.

Not only are we overusing pills and procedures, we’re creating even more problems with “overdiagnosis” by labelling more and more healthy people with diseases that will never harm them.

Screening programs targeting the healthy can detect potentially deadly cancers and extend lives. But they can also find many early abnormalities that are then treated as cancers, even though they would never have caused anyone any symptoms if left undetected.

The common ups and downs of our sex lives are often re-labelled as medical dysfunctions. Older people who are simply at risk of future illness – those with high cholesterol, for instance, or reduced kidney function, or low bone mineral density – are portrayed as if they were diseased.

The doctors expanding disease definitions and lowering the thresholds at which diagnoses are made are often being paid directly by the companies that stand to benefit from turning millions more people into patients.

What’s driving all this excess is a toxic combination of good intentions, wishful thinking and vested interests – fuelled by sophisticated diagnostic technology that often offers the illusion of more certainty about the causes of our suffering. It’s as if we’re seeking technical fixes for the fundamental reality of human existence – uncertainty, ageing and death.

Fundamental shifts in thinking

Indeed, intolerance of uncertainty has been suggested as among the most important drivers of medical excess. Doctors order ever more tests to try, often in vain, to be sure about what they’re seeing – to be more certain. But disease and the benefits and harms of treating it are inevitably fraught with uncertainty because we’re trying to apply knowledge derived from populations to unique individuals.

More broadly, uncertainty is the basis of all scientific creativity, intellectual freedom and political resistance. We should nurture uncertainty, treasure it and teach its value, rather than be afraid of it.

No matter how much the marketers of medicines try to make us feel broken by the mere passing of time, ageing is not a disease. Disease definitions that equate “normal” with being young are fundamentally flawed and require urgent review.

The doctors who defined osteoporosis, for instance, arbitrarily decided the bones of a young woman were normal, automatically classifying millions of older women as “diseased”. Similarly, those who defined “chronic kidney disease” have classified the normal changes in kidney function that happen as many of us age as somehow abnormal. Brace yourself for the impending arrival of pre-dementia, the latest attempt to medicalise the ageing process.

In all cases, the people who wrote these definitions included those with ties to pharmaceutical companies – reinforcing the need for much greater independence between doctors and the industries that benefit from expanding medical empires.

Rays of hope

Everyone must die and everyone, patients and doctors alike, is more or less fearful of dying. So, it’s perhaps not surprising that we so often turn to biotechnical approaches rather than paying real attention to the care of the dying – a core purpose of medicine.

We're overdosing on medicine – it's time to embrace life's uncertainty - Featured Image

What we tend to forget is that medicine cannot save lives – it can only postpone death. Yet we persuade ourselves it might somehow keep extending our lives, and we come to view almost every death as a failure of medicine.

Doctors persist with treatments for the dying well after these have become obviously futile, often with the support of patients or their families. Deep, difficult and necessary conversations about death and dying are only possible in a context of trust, which becomes increasingly difficult as health-care systems are ever more fragmented.

But, there are many positive signs of change within medicine. The Choosing Wisely campaign mentioned above is a partnership between doctors and wider civil society. And it’s now an international movement to wind back excess medicine.

A new approach called shared decision making is promoting much more honest conversations between doctors and the people they care for, embracing uncertainty about benefits and harms, rather than peddling false hopes. Another new approach among GPs called quaternary prevention is urging doctors to protect people from unnecessary medical labels and unwarranted tests and treatments.

Perhaps all these new movements will re-establish doctor-patient trust, helping us reduce fear and embrace uncertainty, and end the pretence that medicine can cure ageing and even death. Biomedical science has made our lives immeasurably better, but it’s time to accept that too much medicine can be as harmful as too little.

Former president of the UK Royal College of General Practitioners, Dr Iona Heath, co-authored this article. Dr Heath will deliver a free public lecture on the problem of “Too Much Medicine” at the University of Sydney this Wednesday night, August 5.

Ray Moynihan is Senior Research Fellow at Bond University.

This article was originally published on The Conversation.
Read the original article.

Splenic abscess complicating gastroenteritis due to Salmonella Virchow in an immunocompetent host

Clinical record

A 20-year-old man was admitted to a regional hospital with fevers, rigors, anorexia and left upper quadrant pain. It was his fourth presentation to the emergency department in the preceding 10 days. On the first two presentations, he had been sent home with a provisional diagnosis of renal colic. After review by his general practitioner, he had undergone outpatient imaging that identified filling defects in the pulmonary arteries of his left lower lobe, which were reported as being consistent with pulmonary emboli. In addition, two hypodense splenic lesions were identified, as well as collapse and possible consolidation of the left lower lobe. His GP had referred him to the emergency department for further review (his third presentation), after which he had commenced therapeutic anticoagulation for a presumed diagnosis of pulmonary emboli.

The patient’s history was notable for a self-limiting episode of gastroenteritis 6 weeks before his initial presentation, with sick family contacts. On his fourth presentation, he described progressive left upper quadrant and flank pain over the preceding 10 days, with intermittent fevers and rigors. He had no other focal infective symptoms on review.

On examination, he was found to have a fever (temperature, 39.3°C), sinus tachycardia (heart rate, 154 beats/min), tachypnoea (respiratory rate, 28 breaths/min), hypotension (blood pressure, 97/66 mmHg), decreased breath sounds at the left base of his lung fields and mild left upper quadrant tenderness. Investigations showed a white cell count of 16 × 109/L (reference interval [RI], 4.0–11.0 × 109/L), with a predominant neutrophilia (neutrophils, 14 × 109/L [RI, 2.0–7.0 × 109/L]). Results of his liver function tests and electrolyte, urea and creatinine levels were all within reference intervals.

A computed tomography scan of the chest and upper abdomen again showed two low-density lesions of unclear aetiology in the spleen, as well as a left-sided pleural effusion and collapse of the left lower lobe. Given the possibility that the hypodense splenic lesions represented septic emboli from a cardiac source, the patient was treated empirically with benzylpenicillin, flucloxacillin and gentamicin for a provisional diagnosis of endocarditis. However, a transthoracic echocardiogram performed the next day did not support this diagnosis, with no abnormalities detected. Beyond the radiological findings described, there were no other clinical grounds to support a diagnosis of endocarditis.

Blood cultures taken on Day 1 of admission were positive for gram-negative bacilli, with confirmation of a non-typhoidal Salmonella species (later confirmed as Salmonella Virchow) the following day. This allowed targeted antibiotic therapy, once susceptibilities were known, with ampicillin (2 g every 6 hours). Cultures of stool samples taken at admission were positive for the same isolate, consistent with the patient’s self-limiting episode of gastroenteritis 6 weeks before his first presentation.

Magnetic resonance imaging of the abdomen suggested that the two splenic lesions were likely to represent abscesses in this clinical context (Figure). Given our patient’s ongoing sepsis, a decision was made to perform a laparoscopic splenectomy for source control on Day 5 of admission. Surgical specimens tested positive for Salmonella Virchow. Histopathological testing identified cystic lymphangiomas of the spleen. Despite problems with postoperative pain and a prolonged ileus, the patient made a full recovery. He received appropriate post-splenectomy vaccinations, along with a total of 2 weeks’ intravenous ampicillin, followed by a 2-week course of oral amoxicillin.

Non-typhoidal salmonellae are common foodborne pathogens. In Australia, they are the second most frequent bacterial isolates identified in cases of acute gastroenteritis, after Campylobacter jejuni. In 2010, OzFoodNet sites reported 11 992 cases of Salmonella infection, a rate of 53.7 cases per 100 000.1 Salmonella Virchow was the third most common isolate, after Salmonella Typhimurium and Salmonella Enteritidis. Non-typhoidal Salmonella infection outbreaks are most commonly associated with consumption of poultry and eggs, but have also been linked to fresh produce and, increasingly, contact with pet reptiles.2

Up to 8% of patients with gastroenteritis secondary to non-typhoidal Salmonella infection develop bacteraemia.3 Risk factors for invasive infection include extremes of age, immunosuppressed states, malignancy, HIV infection and use of tumour necrosis factor-blocking medication.4 Our case is unusual in that bacteraemia occurred in an otherwise immunocompetent host.

Extraintestinal focal infections have been reported to occur in 5% to 10% of patients with non-typhoidal Salmonella bacteraemia.3 The best recognised complications are endovascular infections, most commonly involving the aorta, that result from seeding of atherosclerotic plaques and aneurysms.5 However, focal infections of almost all organ systems have been reported.

Splenic abscesses are most commonly seen as a complication of infective endocarditis, occurring in about 5% of patients.6,7 They are also found as a rare complication of non-typhoidal Salmonella infections. In one case series of 49 patients from southern Taiwan, Salmonella species were the third most common pathogens isolated from splenic abscesses, accounting for 11% of cases.8 The most common presentations among the 49 patients with splenic abscesses were fever (47 patients), abdominal pain confined to the left upper quadrant (33 patients), left pleural effusion and splenomegaly (both 27 patients), all of which were present in our patient.

About 50% of patients presenting with splenic abscesses have pre-existing anatomical abnormalities.9 The cystic lymphangiomas identified in our patient almost certainly predisposed him to developing splenic abscesses.

According to the literature, the mainstay of treatment for splenic abscesses is splenectomy. Data from 287 cases published between 1987 and 1995 suggested that non-operative management, which included invasive treatment with percutaneous aspiration and catheter drainage, had a success rate of less than 65%.10 The same series suggested that antimicrobial therapy alone had a success rate of less than 50%. Salvage splenectomy, however, was not shown to result in increased mortality. Another retrospective study of 51 patients in a tertiary hospital between 1998 and 2003 reported survival rates of 48% with antimicrobial therapy alone, 45% with pigtail catheter insertion and drainage in addition to antimicrobial therapy, and 100% with splenectomy and antimicrobial therapy.11 These results may be influenced by selection bias but do suggest improved outcomes with splenectomy over less invasive strategies.

Lessons from practice

Splenic abscesses are a rare but potentially life-threatening complication of non-typhoidal Salmonella bacteraemia.
Splenic abscesses should be considered as a possible source of infection in patients presenting with unexplained fevers and left upper quadrant or left flank pain.
Splenectomy plus appropriate antimicrobial therapy remains the mainstay of treatment for splenic abscesses.
Interventional radiological techniques should be considered as a spleen-preserving strategy on a case-by-case basis and where experienced radiologists are available.

Splenic abscesses are a rare but serious complication of non-typhoidal Salmonella bacteraemia that may occur in otherwise immunocompetent individuals. Splenic abscesses should be suspected in patients with unexplained fevers and left upper quadrant pain. The mainstay of treatment is splenectomy with appropriate antimicrobial therapy.

A: Axial T2-weighted magnetic resonance image (MRI) of the abdomen, without contrast, showing an abscess in the inferior pole of the spleen (circle).

B: Saggital T2-weighted MRI of the abdomen, without contrast, showing two splenic abscesses (circles).

General practitioner understanding of abbreviations used in hospital discharge letters

The transition from hospital to the community is a potentially dangerous time for patients.1 It often involves a change in medical management, with potential for error. Hospital discharge letters aim to facilitate safe transition of patients into the community. To be effective, discharge letters must reach the general practitioner in a timely manner and contain easily understandable information. These are essential ingredients in effective continuity of care.

Deficits in discharge letters can contribute to a failure of information transfer. Studies have found high rates of omissions and errors in such letters.2–4 This contributes to errors in care after discharge. One study found that 49.5% of patients discharged from a large academic medical centre experienced at least one medical error relating to change of care on discharge.2

In this article, we focus on the potential danger of using abbreviations (shortened forms of words or phrases5) in medical communication. Abbreviations used in medical communications are either acronyms or initialisms. Acronyms use the initial letters of words and are pronounced as words (eg, ASCII, NASA); initialisms use initial letters pronounced separately (eg, BBC).5 Abbreviations are commonly used in medical specialties, but may not be understood by the broader profession. Doctors are under pressure to complete discharge letters in a timely fashion, and abbreviations may be used to facilitate this process.

We identified few published studies of the frequency of abbreviations in discharge letters.6,7,8 Some reported that abbreviation use is increasing and identified this as a concern. A recent audit at Royal Melbourne Hospital reported that 20.1% of all words in discharge letters were abbreviations.8 Another study audited abbreviation use in inpatient medical records and surveyed members of an inpatient multidisciplinary team for their understanding of abbreviations.9 The mean correct response rate was 43%, with Postgraduate Year 1 doctors posting the best scores (57%) and dietitians posting the worst (20%).

However, we identified no published studies determining whether the abbreviations used in hospital discharge letters are understood by GPs, who are usually the recipients of discharge letters.

Methods

We retrospectively analysed 200 electronic hospital discharge letters (eDLs) of patients discharged from Nepean Hospital, Sydney, a tertiary referral centre, from 31 December 2012, working backwards to 18 December 2012. We stopped at this point because few new abbreviations were being identified. To be included in the audit, an eDL had to be addressed to a GP.

We chose 31 December to begin the analysis to provide a representative sample of junior doctors who had a minimum of almost a year of hospital experience.

The meaning of each abbreviation was inferred from the surrounding text, and abbreviations were categorised as shown in Box 1.

Survey of GPs

From the audit, we developed a survey using the 15 most commonly used abbreviations plus five less frequently used but clinically important abbreviations. We determined that abbreviations of investigations, management or services were likely to be most clinically significant, based on our clinical experience and the potential consequences of misinterpretation. We defined commonly used abbreviations as those that were used at least 20 times in the audit. In the resulting survey of GPs, each abbreviation was provided in the context of a phrase in which it had been used in a discharge letter (Appendix).

To provide adequate precision, we aimed for 100 GP responses. The survey was mailed to all 240 GPs listed in the 2014 edition of the Medical Practitioners’ Directory for the Nepean, Blue Mountains and Hawkesbury areas. This was the most extensive directory of GPs in this area available to us. Responses were returned in a coded envelope inside a postage-paid envelope. GPs who did not respond were resent surveys on up to two additional occasions.

Outcome measures

Survey responses were analysed to determine what proportion of GPs understood each abbreviation.

Ethics approval

The study was approved by the Nepean Blue Mountains Local Health District Human Research Ethics Committee.

Results

Electronic discharge letter audit

We found 321 different abbreviations in the 200 eDLs audited (a rate of 1.6 new abbreviations per eDL and 7.1 total abbreviations per eDL); most were initialisms. The frequency of abbreviations in eDLs is shown in Box 2.

Hospital coding-approved abbreviations accounted for 62.6% of all abbreviations identified. Seven unapproved abbreviations (2.2%) were in common use (ie, found more than 20 times in the audit).

GP survey

The response rate was 55% (132 of 240 GPs). No abbreviation was correctly interpreted by all GPs, but 10 abbreviations (50%) were interpreted correctly by 97.0% of GPs (128).

The frequency of incorrect interpretation of all abbreviations in the survey is shown in Box 3. Box 4 shows the range and frequency of individual GP scores.

Discussion

The results of our survey show that there is poor understanding among GPs of abbreviations used in hospital discharge letters. The response rate to our survey was fair, so our results are likely to be representative of GPs in the area.

Worryingly, more than half of the abbreviations we found related to investigations, management or services that we considered to be the most clinically significant categories. Misinterpretation of abbreviations by GPs can adversely affect patient care through duplication of investigations, failing to institute treatment based on investigation results or failing to follow up with recommended management. We could find no studies that identified which types of abbreviations confer the worst outcomes if misinterpreted. Also of concern is that almost half of the abbreviations we identified were used only once in the 200 eDLs.

The difference identified in the use of abbreviations by junior doctors and understanding of abbreviations by GPs suggests a lack of consistency between the language commonly used in hospitals and that used by GPs. It is uncertain how well understood these same abbreviations are by hospital doctors in different specialty areas. The language of abbreviations may also vary between hospitals. Common abbreviations found previously in Royal Melbourne Hospital discharge letters8 were different from those we found. The five most common inappropriate ambiguous or unknown abbreviations in the Royal Melbourne Hospital audit were not found in any eDL in our audit. Their abbreviation rate was higher, with a mean of 10.5 new abbreviations per discharge letter compared with our rate of 1.6. Widespread use of abbreviations in paediatric medical notes with no standardisation and difficulty in interpretation by health care professionals has also been previously reported.11

Our study has some limitations. Non-responding GPs might have scored differently on the survey compared with those who responded. Also, we did not ascertain GP demographic characteristics such as length of career outside the hospital setting. GPs with more recent hospital practice may better understand these abbreviations. In addition, we could not assess GPs’ understanding of most abbreviations we identified in the eDL audit because of the large number identified. However, we expect that understanding of these less frequently used abbreviations would be poorer than for the 20 we included in our survey. Also, this study was conducted in a single centre, so the results may not be generalisable to other centres. However, junior doctors are drawn from many universities and it is likely that discharge practices are similar in other hospitals.

Conclusion

Discharge letters are an essential means of communication between hospitals and GPs to facilitate optimal care of patients when they return to the community. All abbreviations used should be understood by all GPs. Strategies to improve communication by means of discharge letters are urgently needed. Potential solutions include banning the use of abbreviations in eDLs or using only a limited number of hospital-approved abbreviations and providing GPs with an approved abbreviation list. Another option would be use of computer software to auto-complete mutually exclusive abbreviations (ie, allowing only one possible meaning for each).

1
Categorisation of the 321 abbreviations used in 200 sequential electronic hospital discharge letters

Type of abbreviation	Number	% of total	Representation of the types of abbreviation in the survey

Investigations	102	31.8%	30%
Physical examination finding	56	17.5%	30%
Management	56	17.5%	5%
Service*	22	6.9%	5%
Patient history	20	6.2%	30%
Other	65	20.1%	0
Total	321	100.0%	100%

*A hospital outpatient service such as outreach or outpatient clinics.

2
Frequency with which the 321 abbreviations were used in 200 sequential electronic hospital discharge letters

Frequency	Number (%)

> 20 times	17 (5.3%)
15–19 times	5 (1.6%)
10–14 times	14 (4.4%)
5–9 times	32 (10.0%)
0–4 times	253 (78.8%)

3
Frequency of incorrect interpretation by general practitioners of 20 common or clinically significant abbreviations

Abbreviations

GPs misinterpreting abbreviation

Number

Percentage (95% CI)10

SNT

47.0% (38.5%–55.5%)

TTE*

33.3% (25.3%–41.3%)

EST*

33.3% (25.3%–41.3%)

NKDA

32.6% (24.6%–40.6%)

CTPA*

31.1% (23.2%–39.0%)

ORIF*

28.0% (20.4%–35.7%)

HSDNM

23.5% (16.3%–30.7%)

B/G

23.5% (16.3%–30.7%)

GCS*

18.2% (11.6%–24.8%)

ADLs

13.6% (7.8%–19.5%)

PMHx

3.0% (0.1%–6.0%)

ECG

3.0% (0.1%–6.0%)

CXR

3.0% (0.1%–6.0%)

O/E

3.0% (0.1%–6.0%)

2.3% (0–4.8%)

GORD

2.3% (0–4.8%)

1.5% (0–3.6%)

1.5% (0.–3.6%)

ADLs = activities of daily living. B/G = background. BP = blood pressure. CT = computed tomography. CTPA = computed tomographic pulmonary angiography. CXR = chest x-ray. ECG = electrocardiogram. ED = emergency department. EST = exercise stress testing. GCS = Glasgow coma scale. GORD = gastro-oesophageal reflux disease. HR = heart rate. HSDNM = heart sounds dual and no murmur. NKDA = no known drug allergies. O/E = on examination. ORIF = open reduction and internal fixation. PMHx = past medical history. RR = respiratory rate. SNT = soft, non-tender. TTE = transthoracic echocardiogram.
*Less common but clinically significant abbreviations.

4
Proportion of general practitioners receiving particular survey scores for correct interpretation of abbreviations

Geographical mobility of general practitioners in rural Australia

Key to improving the poorer health status that characterises people in rural areas is ensuring equitable access to appropriate health care.1–3 However, this requires recruiting and retaining an adequate supply of appropriate health workers, which is known to be difficult in rural and remote areas.4,5 While considerable research has been conducted on the factors and barriers that facilitate and impede medical workforce supply in rural areas, there is a dearth of quantitative empirical evidence relating to the dynamics of general practitioner mobility patterns — specifically, which doctors move where, at what frequency, and why.

Understanding GP mobility is important because of its impact on workforce availability — both in the origin area (place from which the doctor moved) and the destination area. Considerable investment is made by governments into health programs specifically oriented towards improving the recruitment and retention of doctors in rural areas, with the goal of maximising movement into and minimising movement away from rural areas.

Despite a large body of social sciences literature on both inter- and intraregional migration, its applicability to the health workforce is not clear. Unfortunately, research literature focusing specifically on medical and health workforce mobility is scant. Internationally, sentinel works related to doctors include one 20-year national study in the United States on the volume and location of rural moves, although covariate analysis was not reported.6 Subsequent publications from the same dataset have focused on mobility in and out of areas of high need and between four major regions of the US.7,8 Similarly, a few Canadian studies have focused specifically on interregional (large distance) migration patterns of doctors without a focus on rural areas per se.9,10 Much of the extant health mobility literature has concentrated on the international migration of doctors from developing to developed countries,11 focusing particularly on ethical issues, the impact of the loss of doctors on reduced access to health care in origin countries, and the roles of these international medical graduates (IMGs) in destination countries.12–14

Associations between mobility and covariates have rarely been quantified,9,15,16 with younger age being the dominant common factor linked with increased mobility. Much less has been written about the mobility of Australian doctors,17,18 and no specific mobility data have been published for the non-medical health workforce. The reasons for this lack of literature include limited access to data at a suitable geographical scale; lack of longitudinal studies from which to monitor doctors’ movements; inherent difficulties of tracking individual doctors without linked datasets; and insufficient numbers of moves to generate valid and reliable results.

In an attempt to redress this paucity of evidence, we aimed to describe the geographical mobility of GPs in Australia both within rural areas and between rural and metropolitan areas. We describe where doctors are moving to and from, how many doctors are moving, and the characteristics of doctors who move. Such research helps to provide the basis for better understanding the role of push and pull factors behind why doctors move and what has influenced their decision to move. This in turn assists policymakers to design policies targeting medical workforce maldistribution in rural and remote areas.

Methods

We used data from the large Medicine in Australia: Balancing Employment and Life (MABEL) survey, conducted within the Centre for Research Excellence in Medical Workforce Dynamics. MABEL is Australia’s national longitudinal survey of doctors, which collects similar data in annual waves from mostly the same panel of doctors (https://mabel.org.au). MABEL was approved by the University of Melbourne Faculty of Business and Economics Human Ethics Advisory Group (Reference 0709559) and the Monash University Standing Committee on Ethics in Research Involving Humans (Reference CF07/1102 – 2007000291).

Study participants

The first wave of the MABEL survey, in 2008, invited the participation of the entire medical workforce, and 3906 GPs (19% of Australia’s GP workforce) completed the initial survey. Subsequent annual waves of previous respondents saw a 70%–80% retention rate, including the annual addition of new GPs to the dataset and returning participants who missed at least 1 year. This study used data from waves 1–5 (2008 to 2012), comprising 3502 (wave 2), 3514 (wave 3), 3287 (wave 4) and 3361 (wave 5) responses. Detailed non-response bias was conducted for waves 1 and 2.19,20 The most notable observable bias was a significant increase in the number of responses from doctors in remote areas, attributable to a financial incentive ($100 honorarium) to maximise participation of these GPs. GP registrars were excluded, because many do not have autonomy over their work location during their fellowship training.

Locational measures

Locational data were geocoded to a specific town or suburb. Each GP’s self-reported work location (< 1% missing data) was used to calculate mobility, by comparing their location between each annual wave. Mobility was classified using the seven-category Modified Monash Model scale,21 which combines population size of settlements (< 5000; 5000–15 000; >15 000–50 000; and > 50 000) with the Australian Statistical Geography Standard — Remoteness Areas (ASGS-RA) classification, to define a geographical classification of most relevance to Australian GPs.22 Locational changes within the same rural town or within metropolitan areas were all classified as “no change”.

Statistical analysis

Analysis was conducted in two distinct parts. First, all GP respondents were analysed using only their origin location and destination location, aggregated using the Modified Monash Model. GPs who participated in all five waves thus contributed four origin–destination pairs. Second, mobility outcomes were assessed for their association with additional key covariates of age (< 40, 40–54, ≥ 55 years), sex, having a life partner, IMG status and location restrictions as part of their registration, business relationship within the practice, and length of stay in that location. For this analysis, mobility was categorised as no change (metropolitan), no change (rural), change from rural to metropolitan, change from metropolitan to rural and change within rural areas (where “rural” encompasses all six non-metropolitan categories, from regional to very remote). Annual “risk” of moving between rural and metropolitan locations was measured using total number of observed years. Panel (clustered) logit models were additionally used to measure the associations between these risk factors and either leaving rural areas (models 1 and 2) or leaving metropolitan areas (models 3 and 4). Length of stay was removed from models 2 and 4 because of its strong multicollinearity. All calculations were performed using StataSE 12 (StataCorp) with a 5% significance level.

Results

Between wave 1 and wave 5, a total of 5844 GPs completed at least one MABEL survey. Of these, 1810 GPs completed all five waves, providing 7240 mobility observations. A further 805 GPs missed one survey (2415 mobility observations), 786 completed three out of five waves (1572 observations) and 887 completed only two waves. Additionally, 1470 GPs completed only one MABEL survey, contributing no mobility data. In total, there were 12 114 mobility observations, which decreased to 10 900 after GP registrars were removed from the dataset.

Overall, fully trained GPs were observed to have a mobility rate of about 4.6% (507/10 900). In comparison, GP registrars had a mobility rate of 21% (253/1214).

Box 1 summarises the number of locational changes for the five waves (2008–2012). Cells along the main diagonal represent GPs who did not change their location between waves. This approximation of retention within each category shows a decreasing rate as the degree of geographical rurality or remoteness increases. In cells to the right of the diagonal, the destination location is increasingly remote compared with the origin location (eg, large rural to remote), and cells to the left of the diagonal capture GPs who have moved to decreasingly remote locations (eg, small rural to medium rural). The first row captures all GPs (103) who moved from a metropolitan origin to a non-metropolitan destination during the five-wave period; 98.6% (7015) stayed within a metropolitan location. The first column of Box 1 captures all GPs who moved from a regional, rural or remote origin to a metropolitan destination (133). Just under half of all observed location changes were between non-metropolitan and metropolitan locations (236). There were 478 observations originating from a remote or very remote location, with 417 (87%) GPs remaining within the same location in the next year, and most (45 [74%]) of the 61 movers remaining within a non-metropolitan area.

Aggregate counts of GPs and the characteristics of the movers and stayers are summarised in Box 2. The observed risk (per observed year) of moving to a non-metropolitan area was 1 in 75 for metropolitan GPs. In contrast, the risk of losing non-metropolitan GPs to metropolitan areas was 1 in 31. Of the 271 GPs who moved within non-metropolitan Australia, 77 moved to regional centres (population over 50 000), but only 24 left regional centres for a smaller rural or remote location. A further 18 GPs moved from a rural to a remote location and 35 moved from remote practice to small or large rural locations.

Box 2 also shows the characteristics of GPs who moved compared with GPs who stayed in their original location. There was a small increased risk of moving for the youngest group of GPs, while sex and having a life partner had minimal association with increased mobility. IMGs had an increased risk of moving; even more so for the subgroup who were restricted in their location choice. GPs who were also principals of their practice were much less likely to move, while contract employees were highly mobile away from regional, rural and remote areas.

Box 3 shows the association between observed significant location changes and GP characteristics, with two binary outcomes tested (leaving rural and leaving metropolitan practice). Younger rural GPs were significantly more likely to leave rural practice than older rural GPs. There were no other significant associations between GP mobility and age or between GP mobility and sex and family status. The risk of moving to a metropolitan area was 2.5 to three times higher for rural GPs in their first 3 years in a location than for those who had been in a location for 4 years or more. Both contract employees and salaried employees were highly likely to leave rural practice, while salaried employees were most likely to leave metropolitan areas. Compared with GPs in regional centres, those in small and medium rural towns were significantly more likely to leave rural practice, while GPs in very remote areas had a lower risk of moving to a metropolitan area. The omission of length of stay strengthened the mobility odds ratio of all employment types, compared with practice principal or partners, and the association between small population size and an increased risk of turnover in rural areas. Additionally, IMGs restricted in their practice location had a higher risk of moving than Australian-trained, unrestricted GPs.

Discussion

This study provides the first national evidence of rural GP mobility over an extended period. Moreover, we investigated whether individual- and practice-level covariates were associated with the propensity to move. We used the seven-category Modified Monash Model to show which groups of GPs exhibit the highest mobility and are most at risk of leaving rural practice, or most likely to leave metropolitan areas for rural practice.

GPs in small rural towns and remote areas had higher mobility rates. While remote and very remote GPs had the highest mobility rate, this group was not significantly at increased risk of leaving non-metropolitan practice completely. Rural GPs practising in small towns (less than 5000 residents) and in medium-sized towns (up to 15 000 residents) were most at risk of moving to metropolitan areas. These results further support the need for policies to better target GPs in small rural communities and differentiate them from GPs in large regional centres.21–23

GPs most at risk of moving, both from and to rural areas, are those who have only been in their current location for up to 3 years, similar to recent findings in rural New South Wales.24 That is, once a GP has been settled in either a rural or metropolitan location for at least 3 years they are less likely to move.

Additionally, younger GPs (under 40 years) and those working as either salaried or contract employees are more likely to be mobile. Sex and family status were not associated with mobility.

When more data become available, we plan to investigate whether there is any association between mobility and GPs’ satisfaction with the schools that are available for their dependents.

Unrestricted rural IMGs had a slightly but non-significantly increased risk of leaving rural practice compared with locally trained unrestricted GPs. Further investigation of the strength of association between mobility and changed restriction (overseas trained) or bonding (Australian trained) status is also planned.

Our study was strengthened by the removal of GP registrars. GP registrars frequently have minimal control over their training locations, and so their moves are not equivalent to observed moves of GPs who have independently chosen to practise in a specific location. GP registrars are highly mobile, both between metropolitan and rural areas as well as between different rural areas. In this study, GP registrars were observed to have a mobility rate about five times higher than the annual mobility rate for fully trained GPs.

The main limitation of this study was our use of a self-selected cohort. Annually, the MABEL survey includes about 16%–19% of all Australian GPs, with 75%–80% of participants returning each year (potential selection or attrition bias). Despite having five annual waves of data, the number of GPs observed changing work location was still relatively small. In total, only 236 GPs were observed moving in either direction between rural and metropolitan areas. Larger numbers of moves are observable using more complete datasets like the Australian Health Practitioner Regulation Agency dataset,18 but this approach only provides very limited covariate information and includes large bias from the highly mobile GP registrar subgroup. The true mobility rate of rural–urban relocations for Australia’s GP population may be different to the 3.2% (rural origin) and 1.3% (urban origin) observed annual rates in our study. More observed moves and sophisticated panel data analysis will be possible as additional MABEL data become available.

Increasing workforce supply and maintaining the existing rural medical workforce remains a key health issue in improving rural health in Australia. For several decades now, the Australian Government has made considerable investment in training more medical graduates, exposing these new doctors to more rural experience during their training, and increasing GP fellowships, rural bonded scholarships and rural retention payments.25 Nonetheless, little longitudinal evidence exists on how long to expect GPs to remain in different locations, what locations GPs move from and to, and personal and organisational factors associated with mobility. Most existing evidence comprises only cross-sectional data on retrospectively identified factors and prospective intention.

Using the best available GP data, this study helps to understand who is most likely to move each year, how often moves occur and where they might move to and from. In particular, these results both highlight and quantify the strong association between mobility propensity and increasing rurality and remoteness of practice locations. Such evidence is useful in guiding more effective targeting of rural health policies and workforce planning and incentives.

1 Summary of origin–destination work location changes* for all non-registrar general practitioners (annual survey, 2008–2012)

	Destination location, no. (%)
Origin location	Metropolitan	Regional centre	Large rural	Medium rural	Small rural	Remote	Very remote	Total observations (n = 10 900)

Metropolitan	7015 (98.6%)	38 (0.5%)	9 (0.1%)	11 (0.2%)	30 (0.4%)	10 (0.1%)	5 (0.1%)	7118 (100%)
Regional centre	15 (2.0%)	695 (94.7%)	4 (0.5%)	6 (0.8%)	7 (1.0%)	4 (0.5%)	3 (0.4%)	734 (100%)
Large rural	28 (3.5%)	20 (2.5%)	741 (91.6%)	6 (0.7%)	10 (1.2%)	2 (0.3%)	2 (0.3%)	809 (100%)
Medium rural	26 (3.7%)	21 (3.0%)	5 (0.7%)	633 (89.3%)	17 (2.4%)	3 (0.4%)	4 (0.6%)	709 (100%)
Small rural	48 (4.6%)	27 (2.6%)	14 (1.3%)	13 (1.2%)	943 (89.6%)	4 (0.4%)	3 (0.3%)	1052 (100%)
Remote	14 (4.0%)	3 (0.9%)	5 (1.4%)	7 (2.0%)	5 (1.4%)	311 (89.1%)	4 (1.2%)	349 (100%)
Very remote	2 (1.6%)	6 (4.7%)	2 (1.6%)	4 (3.1%)	3 (2.3%)	6 (4.7%)	106 (82.2%)	129 (100%)

* There were an additional 51 work location changes observed within non-metropolitan areas where the location category was unchanged.

2 Characteristics of general practitioners who remain in or change their work location (annual survey, 2008–2012)

						Per-year “risk”
Origin location characteristic	No change: metropolitan	Metropolitan to rural*	Rural to metropolitan	No change: rural	Moved within rural	Rural to metropolitan	Metropolitan to rural

Total observations	7015	103	133	3378	271	3.20% (1 in 31)	1.34% (1 in 75)
Age group
< 40	751 (11%)	16 (16%)	28 (21%)	335 (10%)	44 (17%)	6.3% (1 in 16)	1.9% (1 in 53)
40–54 years	3171 (46%)	41 (40%)	68 (52%)	1722 (52%)	136 (52%)	3.2% (1 in 31)	1.2% (1 in 85)
55+ years	3006 (43%)	45 (44%)	35 (27%)	1281 (38%)	84 (32%)	2.3% (1 in 44)	1.4% (1 in 73)
Sex and family circumstances
Male and partner	3007 (45%)	40 (42%)	64 (50%)	1835 (58%)	136 (54%)	2.9% (1 in 35)	1.2% (1 in 83)
Female and partner	2783 (42%)	38 (40%)	51 (40%)	1024 (32%)	87 (35%)	4.0% (1 in 25)	1.3% (1 in 79)
Male and no partner	306 (5%)	8 (8%)	5 (4%)	141 (4%)	17 (7%)	2.7% (1 in 37)	2.4% (1 in 42)
Female and no partner	517 (8%)	10 (10%)	8 (6%)	180 (6%)	10 (4%)	3.8% (1 in 27)	1.8% (1 in 57)
Training location and place restriction†
Local, unrestricted	5282 (80%)	66 (71%)	70 (54%)	2226 (70%)	145 (56%)	2.6% (1 in 38)	1.1% (1 in 87)
IMG, restricted	295 (4%)	11 (12%)	35 (27%)	416 (13%)	60 (23%)	6.2% (1 in 16)	3.3% (1 in 30)
IMG, unrestricted	1022 (15%)	16 (17%)	24 (19%)	519 (16%)	54 (21%)	3.6% (1 in 28)	1.4% (1 in 72)
Business relationship
Principal, partner	2119 (33%)	15 (16%)	16 (13%)	1211 (39%)	38 (18%)	1.2% (1 in 87)	0.6% (1 in 155)
Associate	664 (10%)	11 (12%)	13 (11%)	469 (15%)	20 (9%)	2.4% (1 in 41)	1.5% (1 in 67)
Salaried employee	371 (6%)	13 (14%)	15 (13%)	307 (10%)	36 (17%)	3.9% (1 in 26)	3.1% (1 in 32)
Contract employee	3261 (51%)	52 (57%)	76 (63%)	1086 (35%)	119 (56%)	5.4% (1 in 19)	1.5% (1 in 68)
Length of stay in origin location
≤ 1 year	533 (8%)	24 (26%)	40 (33%)	416 (13%)	68 (29%)	6.8% (1 in 15)	3.8% (1 in 26)
2–3 years	722 (11%)	18 (19%)	32 (26%)	414 (13%)	50 (21%)	5.8% (1 in 17)	2.2% (1 in 45)
4–6 years	894 (14%)	14 (15%)	11 (9%)	406 (13%)	47 (20%)	2.2% (1 in 46)	1.4% (1 in 70)
7–10 years	917 (14%)	8 (9%)	12 (10%)	396 (13%)	25 (11%)	2.6% (1 in 39)	0.8% (1 in 125)
11+ years	3491 (53%)	30 (32%)	26 (21%)	1521 (48%)	48 (20%)	1.5% (1 in 67)	0.8% (1 in 125)

IMG = international medical graduate. * Rural includes regional, rural or remote. † Locally trained and restricted doctors (that is, Australian-trained graduates who are bonded to initially work in a rural area) have been removed from this analysis because there were very few observations in this group.

3 Panel logit models of general practitioners who move between metropolitan and rural work locations (annual survey, 2008–2012)

Origin location characteristic

Leaving rural,* model 1† (OR [95% CI])

Leaving rural, model 2† (OR [95% CI])

Leaving metropolitan, model 3‡ (OR [95% CI])

Leaving metropolitan, model 4‡ (OR [95% CI])

Age group (reference: 55+ years)

< 40 years

2.06 (0.79–5.34)

2.85 (1.09–7.43)**

0.62 (0.30–1.27)

1.11 (0.57–2.14)

40–54 years

1.62 (0.75–3.48)

1.68 (0.76–3.72)

0.61 (0.35–1.04)

0.76 (0.45–1.26)

Sex and family status (reference: male and partner)

Female and partner

1.07 (0.54–2.10)

1.13 (0.56–2.27)

0.97 (0.57–1.63)

0.90 (0.54–1.50)

Male and no partner

1.35 (0.35–5.27)

1.51 (0.37–6.14)

1.32 (0.50–3.46)

1.56 (0.64–3.79)

Female and no partner

0.62 (0.15–2.47)

1.07 (0.29–3.91)

1.26 (0.56–2.80)

1.20 (0.54–2.65)

Training location and place restriction§ (reference: Australia, unrestricted)

International, restricted

0.91 (0.41–2.05)

1.57 (0.73–3.36)

1.59 (0.74–3.41)

2.65 (1.29–5.43)††

International, unrestricted

1.39 (0.58–3.32)

1.92 (0.79–4.66)

1.02 (0.54–1.92)

1.33 (0.73–2.41)

Business relationship (reference: principal, partner)

Associate

1.18 (0.39–3.61)

1.76 (0.58–5.40)

1.83 (0.76–4.37)

2.43 (1.05–5.60)**

Salaried employee

3.89 (1.15–13.16)**

7.23 (1.94–26.89)††

3.22 (1.33–7.82)††

4.94 (2.14–11.40)††

Contract employee

5.18 (1.98–13.56)††

8.38 (2.72–25.78)††

1.47 (0.74–2.92)

2.20 (1.16–4.19)**

Length of stay in origin location (reference: 11+ years)¶

≤ 1 year

3.40 (1.34–8.62)††

—

4.46 (2.11–9.42)††

—

2–3 years

3.73 (1.41–9.85)††

—

2.63 (1.25–5.53)**

—

4–6 years

1.03 (0.36–2.94)

—

2.16 (1.04–4.48)**

—

7–10 years

0.86 (0.30–2.48)

—

1.24 (0.54–2.85)

—

Modified Monash rural scale (reference: regional centre)

Large rural

2.25 (0.78–6.50)

2.68 (0.88–8.20)

Medium rural

3.04 (1.02–9.09)**

3.46 (1.09–10.99)**

Small rural

3.96 (1.41–11.14)††

5.03 (1.61–15.78)††

Remote

1.22 (0.34–4.34)

1.27 (0.34–4.81)

Very remote

0.20 (0.01–3.03)

0.23 (0.01–4.17)

* Rural includes regional, rural and remote. † Dependent variable outcome = moves from rural/remote to metropolitan (n = 133). ‡ Dependent variable outcome = moves from metropolitan to rural/remote (n = 103). § Locally trained and restricted doctors (that is, local graduates who are bonded to initially work in a rural area) have been removed from this analysis because there were very few observations in this group. ¶ Length of stay was removed from Models 2 and 4 because of its strong multicollinearity. ** P < 0.05. †† P < 0.01.

State-based legal requirement for Schedule 8 prescriptions: why so complicated?

Inconsistent prescription requirements between Australian states and territories create unnecessary complexity for health professionals

In Australia, medicines defined as Schedule 8 (S8) under the Standard for the Uniform Scheduling of Medicines and Poisons are strictly regulated because of the high risk of misuse and/or physical and psychological dependence associated with them.1 They have to be prescribed, dispensed, documented and destroyed in specific ways that are in compliance with each state and territory’s different drug regulations. S8 medicines are under stricter control than Schedule 4 (S4) medicines (other prescription-only drugs), for which requirements have been standardised between states and territories.2,3

Australia has no central body to regulate the handling of S8 drugs. Although the Therapeutic Goods Administration (TGA) is the national body for the regulation of medicines, each state and territory self-regulates under the general principles established by the TGA and has its own interpretation and legislation regarding S8 drugs, resulting in varied prescribing requirements. The legal requirements for obtaining authority and writing prescriptions for S8 medicines are listed in Box 1 and Box 2: they are often difficult to find and are long and daunting to read.

Impact on practice

The establishment of a national registration agency, the Australian Health Practitioner Regulation Agency (AHPRA), in 2010 meant that Australian health professionals were allowed to freely practise in any state or territory. Greater mobility of health practitioners between jurisdictions has been accompanied by new problems.

First, to the best of our knowledge, prescribers newly relocated to a different state or who practise across more than one jurisdiction have no single, clear resource that documents the slight nuances in each state or territory’s regulations. Legal requirements for prescribing S8 drugs are not accessible in a prescriber-friendly manner. Pharmacists can guide prescribers on the regulations and legality of prescriptions; yet the same confusion applies to pharmacists who move interstate.

Second, travelling patients bringing an S8 prescription interstate might discover that a legal prescription in one state is not legal in another. The dispensing pharmacist would need to contact the medical practitioner in the patient’s home state to find a solution. If this could not be done, treatment would be delayed until a local prescription was obtained from a medical practitioner in the state the patient was visiting.

What can we do?

It may be impractical to unify health care legislation in Australia to eliminate the complexity. However, all states and territories could maintain individual regulations but unify the S8 legal requirements. Given that S4 requirements are standardised between the different states and territories, why are S8 requirements treated differently?

For the moment, resources highlighting state-based S8 requirements for prescribers should be made readily available. A comprehensive quick-reference guide, such as the table we provide here, minimises the ambiguity in legal requirements for health practitioners, and its use may also reduce the amount of time spent by pharmacists and doctors in correcting non-compliant prescriptions.

1 Current requirements of Australian states and territories for obtaining authority to prescribe Schedule 8 medicines

State or territory

Required authority

Australian Capital Territory4

Write “Standing short term approval” for treatment of less than 2 months. For treatment of longer than 2 months, write “CHO approval number” followed by approval number from the ACT Chief Health Officer

New South Wales5

From Director-General NSW Health for psychostimulants, alprazolam, methadone, buprenorphine, flunitrazepam and hydromorphone

Northern Territory6

None

Queensland7

None

South Australia8

From SA Minister for Health for more than 2 months of treatment13

Tasmania9

From Tas Secretary for Health for more than 2 months of treatment14,15 (1 month for alprazolam, prior approval for psychostimulants, fentanyl and hydromorphone)

Victoria10,11

May need a Drugs and Poisons Regulation Group permit to prescribe to drug-dependent patients

Western Australia12

From WA Department of Health Chief Executive Officer for more than 2 months of treatment

2 Current legal requirements for prescribing Schedule 8 medicines in each state of Australia

Australian Capital Territory4

New South Wales5

Northern Territory6

Queensland7

South Australia8

Tasmania9

Victoria10,11

Western Australia12

Prescriber

Name

✓

Address

✓

Phone no.

✓

Qualification

✓

Signature

Patient

Name

✓

H (with initials)

✓

Address

✓

Date of birth

✓

Medicine

Name

✓

H (description of the medicine)

✓

H (description of the medicine)

Form

✓

Not specified

✓

Not specified

Strength

✓

Not specified

Quantity

✓

H (in words and figures)

✓ (in words and figures)

H (in words and figures)

✓ (in words and figures)

H (in words and figures)

Direction

✓

No. of repeats

✓

H (in words and figures)

Interval for repeats

✓

Date

✓

Only one S8 drug per prescription*

Multiple items allowed

✓

Not specified

✓

Multiple items allowed

✓

✓ = required. x = not required. H = information that must be written in the doctor’s own handwriting. * Exceptions apply: different forms of the same drug are acceptable.

Coordinated care versus standard care in hospital admissions of people with chronic illness: a randomised controlled trial

Chronic, non-communicable diseases including cardiovascular diseases, oral health care, mental disorders and musculoskeletal diseases comprised 85% of the total burden of illness in Australia and New Zealand in the 2008–09 financial year, incurring direct health care costs of $27 billion.1 Respiratory illness, heart disease and diabetes comprised 80% of the total burden of illness and injury and 70% of health expenditure in Australia in 2004.2,3

Fragmentation of health care with poor coordination and communication among care agencies and a lack of continuity of care are noted as problems.4 As a consequence, some consumers rely heavily on local hospital emergency departments (EDs) to provide ongoing care. Although Australian and overseas studies have emphasised coordination problems in the management of chronic care, little is known about what defines well coordinated care, and what comprises an effective program.5–7

Australian coordinated care experiments between 1997 and 20058 often ended up costing more than standard care, and fewer than half showed an improvement in patient wellbeing.8–10

Western Sydney’s health services to older people and those with chronic illness were reviewed by the (then) Sydney West Area Health Service’s Service Redesign Unit and PricewaterhouseCoopers in 2007.11 The resulting Care Navigation (CN) framework was intended to help patients with chronic illness access services and providers in a more coordinated and timely way, using alternatives to hospital admission where possible for patients with acute deterioration. Those presenting to the ED would have their care more completely coordinated.

We conducted a randomised controlled trial (RCT) to test the hypotheses that, compared with standard care, CN would:

be superior for participants with complex chronic illness, improve quality of life, and reduce emergency re-presentations and hospital readmissions;
extend time to first re-presentation and first readmission, and reduce length of stay; and
have no effect on the mortality rate.

Methods

The study protocol has been published elsewhere.12 Ethics approval was granted by Sydney West Area Health Service Human Research Ethics Committee – Nepean Campus (HREC/09/NEPEAN/55), and ratified by the University of Sydney Research Integrity office.

We conducted a pragmatic RCT. Researchers who collected outcome data or performed statistical analyses were blinded to treatment allocation. Patients and CN nurses were not blinded owing to the nature of the intervention.

Eligible patients who presented to Nepean Hospital ED between 17 May 2010 and 25 February 2011 were identified by an algorithm implemented in the ED patient tracking system, and were approached to consent to participate in the trial. The inclusion algorithm identified patients who had three or more unplanned admissions to a Sydney West Area Health Service hospital in any previous 12-month period and were either aged ≥ 70 years or aged ≥ 45 years if they were of Aboriginal or Torres Strait Islander descent; or aged 16–69 years with at least one admission for a respiratory- or cardiology-related condition. Patients were also eligible if a CN nurse determined that a patient would benefit from receiving CN.

Patients were ineligible if they had previously received CN; were medically unable to participate in study activities (questionnaire completion); were admitted to hospital more than one CN business day before randomisation; or did not provide consent.

Randomisation was stratified by age (≥ 70 years; 16–69 years), and participants were randomly allocated 1 : 1 to CN and standard care. The sequence of treatment allocation was determined by block design. A phone-based randomisation service provided by the National Health and Medical Research Council Clinical Trials Centre was used to allocate treatment arms to participants after consent was given. Participants were followed up for 24 months after randomisation.

Intervention

Three nursing roles were allocated: Inbound, Inflight and Outbound. Two full-time nurses were employed to conduct CN through the recruitment period and for 24 months of follow-up. One nurse conducted the Inbound role — managing patients at presentation to the ED, assessing their current health status and risk of readmission, and directing them to the best method of care in the hospital or community. A second nurse carried out the Inflight role — monitoring the progress of patients’ care and minimising delays to discharge from the hospital ward. The second CN nurse also carried out the Outbound role — reviewing patients’ hospital stay, assessing the need for out-of-hospital care facilities and making arrangements for ongoing care after departure from hospital.

CN nurses used an electronic assessment form to identify medical and psychosocial risks of readmission, and to identify patients in the ED who might not require hospital admission if community-based care could be organised instead.

Data collection

Baseline demographics were collected from New South Wales Health’s Health Information Exchange (HIE) system.

The three primary outcomes of the trial were a reflection of the aims of CN: i) number of re-presentations to a Western Sydney or Blue Mountains EDs; ii) number of readmissions to a Western Sydney or Blue Mountains hospital; and iii) quality of life. Re-presentation and readmission data were collected electronically from the HIE database. Participants completed the EQ-5D-3L questionnaire13 at baseline, 12 and 24 months.

Mortality data were obtained from the National Death Index maintained by the Australian Institute of Health and Welfare. HIE data were used to investigate the time that participants spent in and between hospital visits.

Allied health referral data were obtained from the NSW Health Cerner database. Community health service referral data were obtained from the Community Health Information Management Enterprise (CHIME) and provided by Western Sydney/Nepean Blue Mountains Local Health District Community Health, Information Management and Logistical Support. Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data were provided by Medicare Australia Statistics.

Statistical analyses

Primary analyses were intention-to-treat. The main outcomes were analysed using negative binomial models to estimate the incidence rate ratios of re-presentations and readmissions, and change in EQ-5D score from baseline at 24 months using an analysis of covariance (ANCOVA).

Other outcomes were analysed using negative binomial generalised estimating equation models (length of stay in ED, in ward, and total length of hospital stay; time from arrival in ED to first seen by doctor, and to first allied health referral). For the time-to-event outcomes, time to first ED re-presentation and time to first hospital readmission, we used Kaplan–Meier curves and a Cox proportional hazards model to estimate the hazard ratio.

The total follow-up for each patient was used as an offset. All regression models included treatment arm and the stratification variable (age group) as explanatory variables. Further adjusted analyses were conducted for all outcomes, for sex and the number of ED presentations in the 12 months before randomisation (quartiles). Post-hoc subgroup analyses were conducted on the primary outcomes with respect to age strata; number of ED presentations or hospital admissions in the 12 months before randomisation; whether participants were identified as appropriate for CN by clinician flagging; or whether participants had a carer. A two-sided P of 0.05 or less was considered significant. Data were analysed using SAS, version 9.3 (SAS Institute).

Power calculation

We planned to recruit 500 patients over 12 months and expected a 20% loss to follow-up, leaving a final sample size of 400 with 90% power to detect a 20% reduction in hospital readmissions (rate ratio of 0.8), assuming a 5% significance level and a Poisson distribution with an average of 2.5 admissions per patient over 24 months in the control group, compared with 2.0 in the intervention group. A sample of 400 gave 80% power to detect a 15% reduction in hospital readmissions and a clinically significant difference in presentations. It also allowed us to detect a mean difference of 10 points on the EQ-5D scale, with about 80% power at a 5% significance level. This calculation is based on pilot data that estimated standard deviation of EQ-5D scores to be 35 points.6

Besides the quantitative studies of the effect of CN, a process evaluation gave qualitative insights into the process of the provision of care. Extensive interviews with service providers included tracking how the model of care changed over the course of the intervention. These data will be presented in a subsequent publication.

Results

Five hundred patients were recruited to the study between May 2010 and February 2011. Box 1 shows the flow of participants’ progress through the study. Participant baseline demographic information by study arm is presented in Box 2. Randomisation provided an even distribution between study arms for all demographic variables except sex — the CN group had 55% women compared with 45% in the standard care group. Three-quarters of participants were born in Australia, and four of these were reported in the hospital patient database as being Indigenous. Most participants presented to the ED on a weekday, during the daytime, and 88% were admitted to hospital at their randomisation visit.

Primary outcomes

The comparison of outcomes by treatment type is shown in Box 3. The mean number of ED re-presentations during the 24-month follow-up period was not statistically significantly reduced in the CN group (6.28; 95% CI, 5.44–7.26) compared with the standard care group (7.57; 95% CI, 6.55–8.74). This corresponds to a 17% reduction in re-presentation (95% CI, − 1% to 32%; P = 0.07). Similarly, there was no significant reduction in the mean number of hospital readmissions during the follow-up period in the CN group (4.38; 95% CI, 3.79–5.06) compared with the standard care group (5.16; 95% CI, 4.46–5.96). This corresponds to a 15% reduction (95% CI, − 4% to 30%; P = 0.11). Quality of life at 24 months did not differ significantly between the CN and standard care groups, with a mean difference of zero (95% CI, − 0.10 to 0.09; P = 0.93). Further analyses adjusted for sex and ED presentations before randomisation were similar.

CN had no significant treatment effect on any primary outcome in any of the subgroups analysed (results not shown).

Secondary outcomes

CN did not affect the time to first re-presentation after randomisation (hazard ratio, 1.01; 95% CI, 0.84–1.23; P = 0.89; Box 4A), or the time to first readmission (hazard ratio, 0.93; 95% CI, 0.77–1.13; P = 0.47; Box 4B). CN had no effect on the mean number of hours spent in the ED at the randomisation visit (rate ratio, 0.95; 95% CI, 0.82–1.11; P = 0.54) or over the subsequent 24 months (rate ratio, 0.99; 95% CI, 0.90–1.08; P = 0.80; Box 3). CN did not significantly reduce the mean number of days admitted to a ward at the randomisation visit (rate ratio, 1.2; 95%, CI, 0.82–1.76; P = 0.36) or over the subsequent 24 months (rate ratio, 0.98; 95% CI, 0.82–1.17; P = 0.82; Box 3). CN had no effect on mortality (hazard ratio, 0.92; 95% CI, 0.67–1.26; P = 0.60; Box 4C).

Process outcomes

More than six times the number of patients in the CN group (119/247 [48%]; 95% CI, 42–54) had their medications reviewed by a hospital pharmacist when presenting to hospital than those in the standard care group (19/245 [8%], 95% CI, 5–12); the overall difference was statistically significant (rate ratio, 6.35; 95% CI, 4.03–10.02; P < 0.001). However, there was no difference in the number of prescription medications dispensed over the 24-month follow-up period. CN had no effect on any other inhospital allied health or diagnostic services (results not shown).

Patients in the CN group received more services per year from community health (rate, 13.80; 95% CI, 10.69–17.8) than standard care patients (rate, 7.10; 95% CI, 5.46–9.23); the overall difference was statistically significant (rate ratio, 1.94; 95% CI, 1.35–2.81; P < 0.001). Most of these services were the result of referrals from hospitals (CN rate, 1.00 per year; 95% CI, 0.88–1.13 v standard care rate, 0.38; 95% CI, 0.32–0.45; P < 0.001). CN did not change the number of service payments claimed from the Medicare Benefits Schedule by general practitioners, non-hospital allied health professionals or consultant physicians (results not shown).

Delivery of intervention

CN began in May 2010. Nursing personnel was reduced from two nurses to one nurse on 9 November 2011. The remaining CN nurse reviewed existing risk assessments, updating participants’ requirements where required, but did not carry out any other part of the Inbound CN role due to availability of time and a lack of expertise in ED nursing. CN ceased at Nepean Hospital on 4 April 2012, when the remaining CN nurse left the position. Box 5 depicts the availability of CN nurses along with the number of participants actively in the study in the intervention arm throughout the study period. Per-protocol analyses based on 12 months of follow-up or the period when CN nurses were available demonstrated no difference between standard care and CN in any of the primary or secondary outcomes (results not shown).

Discussion

CN did not improve quality of life or reduce unplanned hospital presentations or admissions despite community health services almost doubling. This study sought to establish whether an energetic hospital care coordination program could enable patients admitted with an exacerbation of chronic illness to receive sufficient assistance in hospital and in the community, to reduce their need for future readmission.

There is a growing body of evidence that outcomes for people living with chronic illness can be improved, and hospital attendances reduced, by redesign of the health care delivery system across primary, secondary and acute sectors to ensure equitable, structured, proactive, coordinated, culturally sensitive care; decision support and clinical information systems that support this care; case management for complex patients; empowerment and support for self-management by patients and their carers; and community mobilisation.4,14,15 The impact of these changes is greatest when multiple, integrated improvements are made in care delivery.16

CN was an attempt to organise these services from a hospital base. However, it was no more effective than the existing processes of care at Nepean Hospital in improving self-reported quality of life, reducing hospital presentations or admissions, reducing the time patients spent in hospital or delaying readmission. CN had no effect on mortality. No intervention effect was detected in any of the subgroups analysed. However, CN did have an impact on the processes of care following discharge. Patients in the intervention group received more services from community health agencies, mainly nursing services.

Patients in the CN group spent the same amount of time in hospital and were referred to inhospital allied health or diagnostic services at the same rates as the standard care group. Delivery of CN was largely within the hospital, with limited arrangements made for ongoing care after departure. While these arrangements presumably reflected the care navigators’ assessment of the participants’ current and expected needs at that time, subsequent changes in their clinical needs would have been managed by health service structures and services that were similar in the two arms of the trial.

Attempts to formally evaluate interventions in health care systems are fraught by changes in the environment of care as staff change, funding sources change, and higher service priorities come to dominate the care scene. CN suffered the effects of all these real-world variations.

While study recruitment achieved the predetermined target of 500 participants and complete data were available for analysis from 492 (98%) at the end of the study, implementation of the intervention varied during the study; in particular, the number of CN nurses reduced from two to one 18 months after recruitment commenced. The second nurse left 4.5 months later, when CN ceased at the hospital, and the final 10 months of the study period had no CN. However, analysis limited to the period when both nurses were available showed no intervention effect on any of the primary or secondary outcomes.

CN during hospital admission with increased referrals for community health services after discharge was too small an intervention in the overall health system to have an impact. Future service development should explore the potential benefits of linking navigated intrahospital care to ongoing, proactive care planning and delivery in the community.

1 Flowchart of participants’ progress through a randomised controlled trial comparing Care Navigation (CN) with standard care for patients with chronic illness, Nepean Hospital, Sydney, May 2010 – February 2013

2 Participant baseline demographic information by study arm

Demographic variable	Care Navigation (n = 247)	Standard care (n = 245)

Age in years at randomisation, mean (SD)	73.3 (12.3)	74.9 (11.8)
Age at randomisation by strata, no. (%)
≥ 70 years	171 (69%)	171 (70%)
16–69 years	76 (31%)	74 (30%)
Sex, no. (%)
Female	135 (55%)	110 (45%)
Male	112 (45%)	135 (55%)
Country or region of birth, no. (%)
Australia	188 (76%)	183 (75%)
Europe	40 (16%)	46 (19%)
Other/not stated	19 (8%)	16 (7%)
Preferred language, no. (%)
English	232 (94%)	219 (89%)
Non-English	10 (4%)	13 (5%)
Not stated	5 (2%)	13 (5%)
Marital status, no. (%)
Married or de facto	117 (47%)	127 (52%)
Single, widowed, separated or divorced	129 (52%)	116 (47%)
Not stated	1 (< 1%)	2 (1%)
Funding source for services (in addition to Medicare), no. (%)
None	166 (67%)	166 (68%)
Private health insurance	10 (4%)	13 (5%)
Department of Veterans’ Affairs card, all types	21 (9%)	12 (4%)
Compensation	2 (1%)	2 (1%)
Not stated	48 (19%)	52 (21%)
Primary SRG assigned to hospital admissions in the 12 months before randomisation, no. (%)*
Cardiology	85 (34%)	89 (36%)
Surgery	58 (23%)	39 (16%)
Respiratory	38 (15%)	49 (20%)
Other†	107 (43%)	97 (40%)
No. of emergency department presentations in the 12 months before randomisation, mean (SD)
1	33 (13)	47 (19)
2–3	92 (37)	88 (36)
4–5	68 (28)	67 (27)
≥ 6	54 (22)	43 (18)
No. of unplanned hospital admissions in the 12 months before randomisation, mean (SD)
0	7 (3)	13 (5)
1	53 (21)	50 (20)
2	53 (21)	45 (18)
3–4	83 (34)	76 (31)
≥ 5	51 (21)	61 (25)
Eligibility criteria used at randomisation visit, no. (%)
Electronic algorithm	181 (73%)	170 (69%)
Clinician flag	66 (27%)	75 (31%)
Unplanned hospital admissions at randomisation, no. (%)	222 (90%)	209 (85%)

SRG = service-related group. * Percentages exceed 100% as some participants with more than one previous admission were listed under more than one primary SRG. † Including gastroenterology; geriatrics; cancer; neurology; renal medicine; rehabilitation; immunology and infectious diseases; endocrinology; non-subspecialty medicine; ear, nose and throat; psychiatry – acute, maintenance, drug and alcohol, unallocated, pain management; renal dialysis; palliative care; gynaecology; or dermatology.

3 Comparison of outcomes of Care Navigation and standard care for the 24 months after randomisation

Outcome

Care Navigation

Standard care

RR/HR/MD (95% CI)

Primary

Mean no. of re-presentations (95% CI)

6.28 (5.44–7.26)

7.57 (6.55–8.74)

RR, 0.83 (0.68–1.01)

0.07

Mean no. of readmissions (95% CI)

4.38 (3.79–5.06)

5.16 (4.46–5.96)

RR, 0.85 (0.70–1.04)

0.11

Quality of life 24 months after randomisation —
mean change in EQ-5D scores (95% CI)

0.14 (0.08–0.21)

0.15 (0.08–0.22)

MD, 0 (− 0.10 to 0.09)

0.93

Secondary

Median time from randomisation to first ED re-presentation, days (IQR)

111 (89–143)

103 (72–148)

HR, 1.01 (0.84–1.23)

0.89

Median time from randomisation to first hospital readmission, days (IQR)

155 (121–205)

144 (102–178)

HR, 0.93 (0.77–1.13)

0.47

Median time from randomisation to death, days (IQR)

—

HR, 0.92 (0.67–1.26)

0.60

Mean length of ED stay, hours (95% CI)

To departure-ready

5.73 (5.37–6.1)

6.81 (5.74–8.08)

RR, 0.84 (0.69–1.02)

0.08

Actual

10.58 (9.91–11.3)

10.71 (10.03–11.44)

RR, 0.99 (0.90–1.08)

0.80

Mean length of stay admitted to a ward, days (95% CI)

5.46 (4.86–6.14)

5.57 (4.76–6.53)

RR, 0.98 (0.82–1.17)

0.82

Mean length of ED stay at randomisation visit, hours (95% CI)

All participants

12.91 (11.59–14.39)

13.55 (12.01–15.28)

RR, 0.95 (0.82–1.11)

0.54

Participants not admitted to a ward

7 (4.69–10.44)

6.52 (5.28–8.07)

RR, 1.07 (0.65–1.76)

0.78

Participants admitted to a ward

13.61 (12.2–15.18)

14.74 (13.01–16.7)

RR, 0.92 (0.79–1.08)

0.32

Length of stay in a ward at randomisation visit

7.01 (4.52–10.87)

5.86 (4.7–7.31)

RR, 1.2 (0.82–1.76)

0.36

ED = emergency department. HR = hazard ratio. IQR =interquartile range. MD = mean difference. RR = rate ratio. All analyses were adjusted for stratification at randomisation (age: ≥ 70 years; 16–69 years). — = Median survival cannot be obtained as cumulative survival did not fall below 50% during the study period.

4 Kaplan–Meier curves by treatment group in the 24 months after randomisation

A. Time to first emergency department re-presentation. B. Time to first hospital readmission. C. Time to death.

5 Number of participants in the intervention group and the availability of the Care Navigation (CN) nurses throughout the study period

Communicating the health effects of air pollution

To the Editor: Air pollution causes 3000 deaths each year in Australia.1 To put this in perspective: the national road toll in 2014 was 1153. Deaths from air pollution are due to both acute and chronic effects, and the principal diagnoses are of cardiac and respiratory disease. Reform of Australia’s national air quality standards and regulatory mechanisms is currently underway, with the federal government calling for public consultation in the revision of the National Environmental Protection Measure (Ambient Air Quality) for particulates. Engagement in this consultation process requires that the risks be communicated in a way that the public can understand.

A large published cohort study can be of assistance.2 The mortality impacts of both particulate air pollution less than 2.5 µm in diameter (PM2.5) and of cigarette smoking for a large American cohort were followed up for 16 years. The relative risk of all-cause mortality associated with smoking 22 cigarettes a day was 2.58, while the relative risk associated with each 10 µg/m3 of PM2.5 exposure was 1.04 (95% CI, 1.01–1.08), making smoking 24 times as risky to survival (relative risks combine exponentially; 1.04 raised to the power of 24 = 2.58). Assuming that the effects are linear, breathing air including 10 µg/m3 PM2.5 is equivalent in mortality terms to smoking 22/24 of a cigarette each day. The Australian ambient air quality standard for PM2.5 exposure is set at an annual average of 8 µg/m3, which in this metric would be equivalent to 0.73 cigarettes a day. This calculation is based on mortality alone, and says nothing about the amount of particulate matter in cigarette smoke. In 2013, the New South Wales Environmental Protection Authority monitor at Beresfield, near the coal corridor to Newcastle, recorded annual average PM2.5 exposure of 8.3 µg/m3, so every adult and child in that district is exposed to this risk.

Debate continues as to whether there is a safe PM2.5 level, below which there is no health effect. Using the smoking analogy again, we cannot point to a cohort study showing health damage from smoking one cigarette a day, but no one would suggest that this level of smoking is safe.

There are many opportunities to improve air quality in Australia, such as restricting the mining and transport of coal in close proximity to residential areas, phasing out wood-burning heaters, shifting the electricity supply away from coal, and enforcing Australian emissions standards on ships in port. These improvements will not eventuate unless the public demands them, and better risk communication allows public participation in this technically complex area.

Changes in psychological distress and psychosocial functioning in young people visiting headspace centres for mental health problems

Improving the mental health and wellbeing of adolescents and young adults is receiving increasing attention throughout the world.1 The Australian Government was the first to invest significant funds in a practical and systematic response to this challenge, initiating a national reform process that created new service platforms for young people through its founding of headspace, the National Youth Mental Health Foundation.2

The initiative commenced in 2006, establishing an initial 10 centres and is set to increase to a network of 100 centres across Australia by 2016. headspace centres are one-stop entry points offering a mix of the services that young people need most. Centres provide early intervention by responding to early presentations of mental health problems and by assisting young people at greater risk of developing mental disorders. Being youth-friendly and non-stigmatising are priorities, and centre activities are founded on youth participation and engagement at all levels.3

From the beginning, the headspace initiative has evaluated its activities, despite the significant challenges inherent in determining the outcomes of such a complex, long-term, real-world, system-wide intervention. A preliminary external evaluation in 2009 showed that young people approved the approach used by the initial centres.4 At that time, however, it was still too early, in terms of implementation of the headspace initiative, to assess outcomes for the clients.

To facilitate investigation of the impact of the headspace centres, an innovative routine data capture system was introduced in 2013. This system collects information each time a young person accesses a headspace centre for service, and attempts to follow them up after they have finished engaging with the centre. Analysis of the dataset has shown that young people presenting to headspace centres have a wide range of mental health concerns, and are typically in the early stages of the development of a mental disorder.5 Further analyses have explored the types of service young people receive at the centres. In the companion paper to this article, we report that most of the young people seeking help at headspace centres present with mental health concerns, that they generally receive a timely response, and receive assessment and mental health care services. We also found that the initiative is primarily supported by funding from the headspace grant and by the Australian Government Medical Benefits Schedule.6

The current study reports the main clinical outcomes for young people who had presented to headspace centres for mental health concerns. The primary aim was to determine the extent to which psychological distress was reduced and psychosocial functioning improved in headspace clients.

Methods

Participants and procedure

Participants were all clients who had commenced an episode of care at a headspace centre for mental health reasons between 1 April 2013 and 31 March 2014. Young people who initially visited headspace for other reasons (situational, physical or sexual health, alcohol or other drug, or vocational reasons) were excluded from analyses. This selection was made because young people presenting with mental health concerns comprise the vast majority of those who seek help at headspace centres and definitely use their mental health care services; young people primarily presenting for other reasons may not have used mental health care services (see the companion paper to this article6). Analyses were limited to a young person’s first episode of care during the 12-month data collection period.

The procedure for the routine collection of data provided by the young people and service providers to the headspace Minimum Data Set is described elsewhere.5 Data related to psychological distress were collected from young people immediately before their first, third, sixth, 10th and 15th visits, as well as at follow-up. Measures of psychosocial functioning were recorded by service providers at each occasion of service.

Young people were invited to consent to being followed up when they first attended headspace. They provided an email address, and data were solicited after a 90-day pause in service provision by sending an email with a link to the follow-up questions. Young people could choose to answer these questions electronically, and responses were uploaded into the headspace data warehouse. Ethics approval for the follow-up was obtained from Melbourne Health Quality Assurance Review.

Measures

The primary presenting concern was categorised according to the clinical presentation features as determined by clinicians. These did not comprise diagnoses, but were rather the main symptoms evident at the initial presentation that were indicative of mental health problems.
Treatment services were recorded by clinicians, and were categorised as: cognitive behaviour therapy (CBT), interpersonal therapy, acceptance and commitment therapy, psychoeducation (including skills training and relaxation strategies), general and supportive counselling, mindfulness-based therapies, motivational interviewing, problem-solving therapy, and other interventions.
Client outcomes that were assessed were:
- the level of psychological distress, based on self-reports according to the 10-item Kessler Psychological Distress Scale (K10);7 and
- overall psychosocial functioning, assessed by service providers using the Social and Occupational Functioning Assessment Scale (SOFAS).8

Appendix 1 presents the number of clients for whom data were available at key time points.

Statistical analyses

IBM SPSS Statistics 21 was used for statistical analyses. Frequencies of each primary presenting concern were calculated, and age group and sex differences were assessed by χ2 analyses with Bonferroni correction for multiple comparisons.

Changes in each of the outcome measures over time were analysed in two ways.9 First, mixed-model repeated measures analysis of variance (ANOVA) was used to assess aggregate changes over time in K10 and SOFAS scores according to time point, number of service sessions, age group and sex. The statistical relationship between K10 and SOFAS scores was expressed as a Pearson product-moment correlation coefficient (r). Differences between the characteristics of clients who provided follow-up data and those who did not were analysed by logistic regression.

Second, significant change, reliable change and clinically significant change scores were calculated for the K10 and SOFAS data, as increasingly conditional indicators of change. The criterion for significant change was a moderate effect size (0.5) or greater for the degree of change.10 The reliable change index (RCI) (indicating reliable improvement or decline) and clinically significant change index (CSI) (cut-off point at which the person is more likely to belong to a non-clinical rather than a clinical population) were determined using Jacobson and Truax’s method.11

For the K10 scores, the RCI was estimated as a 6.73-point change (rounded to 7 points) using reliability coefficients reported for an Australian normative group (age group, 16–24 years) in the 2007 National Survey of Mental Health and Wellbeing.12 Using the same norms, the CSI cut-off was estimated to be 22.56 points (rounded to 23 points). For the SOFAS data, an RCI score of 10 was used; this was based on comparable outpatient psychiatric services data using the Global Assessment of Functioning scale as an equivalent. The CSI for the same comparison group was a score of 69 (Söderberg and Tungström [2006], cited by Falkenström13).

Results

The participants were 24 034 clients from the 55 headspace centres fully operational during the study period. Almost two-thirds of clients were female (62.7%), 36.9% were male and 0.4% were intersex or transgender. The mean age was 17.8 years (SD, 3.3), with 16.7% aged 12–14 years, 35.0% aged 15–17 years, 25.7% aged 18–20 years, and 22.6% aged 21–25 years.

Follow-up data were collected between June 2013 and August 2014. Of the total sample, 20 903 clients (87.0%) were eligible to provide follow-up data; the remaining 13.0% were still receiving headspace services or had not yet had a 90-day service-free period. Only 3.1% of eligible young people (651 clients) responded to the follow-up survey.

Presenting concern and treatment services

The most common mental health problems at initial presentation were depressive symptoms and anxiety, which together accounted for more than two-thirds of presentations. These were the most common presenting reasons for all age/sex groups, with the exception of 12–14-year-old boys, who presented most frequently with anxiety and anger problems and less frequently for depressive symptoms (Appendix 2).

Age and sex differences among those presenting with mental health concerns were indicated by χ2 analysis (χ2 [70] = 3300.57, P < 0.001). The proportions of younger males (12–14 years of age) presenting for anger or behavioural problems was greater than for other age/sex groups. Younger females (12–14 years of age) had higher presentation rates for deliberate self-harm than other groups (Appendix 2).

The most common treatment provided for all primary presenting concerns was CBT; for example, 43.6% of service provided to clients presenting with depressive symptoms involved CBT. A similar pattern of treatments was evident for all primary presenting concerns, with the second most common treatment being supportive counselling (except for borderline personality trait presentations). Psychoeducation was ranked third for most mental health problems (Box 1).

Mean changes in outcomes over time

Changes in the two outcome scores over time are depicted in Box 2 and Box 3. These plot the mean scores at each session that they were recorded, according to the total number of sessions attended. The sample sizes for each point declined as the number of sessions attended increased (Appendix 3). The follow-up data analyses were based on a particularly small sample size; further, no clinician-rated measures were available at this point, as the follow-up was based solely on self-report.

For the change in K10 between initial presentation and last recorded assessment, the factor with the greatest effect size was time, which explained 10.8% of the variance (Appendix 4, ANOVA 1; Box 2). Including the 3-month follow-up in the analysis showed that the time effect remained significant and explained 12.5% of the variance (Appendix 4, ANOVA 2). On average, there was a 3-point improvement in K10 scores from first to last assessment, and a further 3-point improvement from last service to follow-up for the small proportion of young people who provided follow-up data.

It is, however, important to note that the group of clients who provided follow-up data was significantly different from the much larger group of those who did not (χ2 [17] = 153.43, P < 0.001, Nagelkerke R2 = 0.062). Those who provided follow-up data were more likely to be female (odds ratio [OR], 1.63; 95% CI, 1.27–2.11), older (OR, 1.07; 95% CI, 1.04–1.11), have attended a greater number of service sessions (OR, 1.59; 95% CI, 1.39–1.82) and had better psychosocial functioning at exit (OR, 1.03; 95% CI, 1.02–1.05).

For change in SOFAS scores, time was again the strongest factor, but explained only 4.5% of the variance in this outcome measure Appendix 3, ANOVA 3; Box 3).

Significant, reliable and clinically significant change

The percentages of young people showing significant, reliable and clinically significant change between their first and last recorded assessments (not including follow-up) are presented in Box 4. Of the young people for whom data were available, psychological distress was significantly reduced in 36%, was reliably improved in 26%, and clinically significantly improved (by crossing the threshold distinguishing a clinical from a non-clinical population) in 21%. In 13% of clients, K10 scores significantly worsened, and in 8% they reliably deteriorated. According to clinician ratings of psychosocial functioning, significant and clinically significant improvement were each evident for 37% of the assessed clients, while 31% reliably improved. In contrast, function significantly declined in almost a fifth of clients, and reliably declined in 15%.

For 9957 clients, both K10 and SOFAS change data were available. Of these, 59.9% significantly improved and 49.2% reliably improved on at least one of the two scales, while 40.4% of those in the clinical group showed clinically significantly improvement on one or both of the scales.

It is important to note that the K10 and SOFAS scales measure different aspects of mental health, and that psychological distress (K10) was self-reported by young people, while social and occupational functioning (SOFAS) was assessed by a clinician. K10 and SOFAS scores were weakly correlated at presentation (r = − 0.19, P < 0.001) and at final assessment (r = − 0.23, P < 0.001).

There were statistically significant differences between those who improved and those who did not (significant improvement on at least one measure: χ2 [15] = 1168.48, P < 0.001, Nagelkerke R2 = 0.153). Improvement was predicted by greater distress (OR, 1.03; 95% CI, 1.02–1.04) and lower psychosocial functioning (OR, 0.94; 95% CI, 0.94–0.95) at service entry, and by attending a greater number of service sessions (OR, 1.16; 95% CI, 1.10–1.22). Age, sex and primary presenting concern did not predict improvement.

Discussion

This article reports the first outcome data for young people who have accessed the national headspace centre network for mental health problems. The analyses focused on the two key clinical outcomes, psychological distress and psychosocial functioning. The results show that psychological distress was significantly reduced in more than one-third of clients for whom data were available, and psychosocial functioning improved in a similar proportion. If improvement in either measure is considered, 60% of clients experienced significant change. Improvements in young people with greater distress and poorer functioning at service entry were noted in those who engaged well with the service (ie, attended more health care sessions). The findings are consistent with those reported from a single Sydney-based headspace service that found both symptomatic and functional improvements in its clients.14

Comparative data that would help determine whether these outcomes are acceptable are difficult to find. headspace clients present for a wide range of reasons and attend for varying numbers of sessions; although only outcomes for mental health clients were examined here, these young people still constitute a diverse group.6 Comparisons with outcomes from highly controlled clinical studies are therefore inappropriate. A study of psychotherapeutic outcomes in similarly aged young people attending a mental health clinic in the Netherlands, where the clients also presented with a variety of mental health concerns and received varying amounts of service, found that psychosocial functioning reliably improved in 19% of clients.13 This compares with the considerably higher rate of 31% that we have reported.

Comparative Australian data are scarce. Public tertiary mental health services use age bands of 0–17 and 18–64 years in their outcomes reports, and these are not comparable with either the age range of clients in these analyses or with the enhanced primary care service model of headspace. The most recent report from the National Outcomes and Casemix Collection (NOCC), which used the Health of the Nation Outcome Scales (HoNOS) family of outcome measures, showed that 37% of those aged 0–17 years and 24% of those aged 18–64 years using community-based public mental health services reported a significant improvement between the first and last occasions of service.15 The outcomes in young people reported here are similar to the child and adolescent results of the NOCC report, but much better than its findings for adults. However, the degree to which HoNOS outcomes are comparable with K10 and SOFAS scores is unclear, and the lack of directly comparable age groups makes interpretation difficult.

Drawing conclusions from the current study is restricted by several limitations. Primarily, the absence of a control group and other limitations inherent to observational studies means that the changes in scores reported cannot be attributed to headspace care.16 Further, most of the outcome data were derived from the last recorded assessment point for each client, but for many young people this was not at the completion of treatment. Our results are therefore likely to underestimate psychological and psychosocial gains in the course of treatment.

The follow-up rate was disappointing, although wholly expected, and highlights the considerable challenges in persuading young people to provide follow-up information after they have stopped attending for service. Without committing substantial resources to maintaining contact with people after leaving a health service, obtaining longer-term outcomes from real-world interventions will always be a major hurdle. Nevertheless, the headspace initiative has developed a process that attempts to routinely follow up young people after the end of service, and this may be unique in service delivery outside a well resourced prospective clinical trial. Over time, this follow-up database will grow and yield a rich source of information, even though there will be inevitable bias in those who provide follow-up data.

Another limitation is that the data cannot clearly determine the extent to which headspace clients received sufficient and appropriately matched “doses” of evidence-based therapies for different presenting problems and diagnoses, although it is evident that most clients did receive evidence-based therapies. headspace centres differ considerably in both their priorities and their capacity as a result of the diverse community and workforce contexts in which they are embedded,17 although all centres pursue a common vision of youth-focused, evidence-based, early intervention.3 The complexity and severity of young people’s presenting concerns also varies, with a substantial subset of young people who need, but are unable to gain, access to specialised tertiary services,18 which may have an impact on average improvement scores for the total client group.

Nevertheless, this article demonstrates that headspace is committed to examining and reporting outcomes for young people using its services, and that the headspace centre initiative is associated with improved mental health outcomes for a large number of young people assisted by this network across Australia.

1 Most common types of mental health care service received by headspace clients, according to the primary presenting problem*

Total sessions

Treatment services type rank

Presenting concern

Depressive symptoms

25 708

CBT
(43.6%)

Supportive counselling
(18.6%)

Psycho-education
(8.2%)

IPT
(7.5%)

ACT
(4.8%)

Anxiety symptoms

21 516

CBT
(47.0%)

Supportive counselling
(14.6%)

Psycho-education
(9.7%)

ACT
(7.5%)

IPT
(4.9%)

Anger problems

3859

CBT
(36.7%)

Supportive counselling
(21.3%)

Psycho-education
(16.6%)

IPT
(6.8%)

Motivational interviewing
(3.3%)

Stress related

3521

CBT
(34.0%)

Supportive counselling
(21.9%)

Psycho-education
(12.1%)

IPT
(7.2%)

ACT
(5.5%)

Suicidal thoughts or behaviour

2355

CBT
(36.9%)

Supportive counselling
(19.5%)

IPT
(9.6%)

Psycho-education
(9.2%)

ACT
(5.1%)

Behavioural problems

1389

CBT
(32.1%)

Supportive counselling
(23.3%)

Psycho-education
(18.8%)

IPT
(4.7%)

ACT
(3.6%)

Deliberate self-harm

1479

CBT
(36.3%)

Supportive counselling
(22.4%)

Psycho-education
(11.8%)

IPT
(6.6%)

ACT
(5.8%)

Eating disorder related

1159

CBT
(47.9%)

Supportive counselling
(12.9%)

Psycho-education
(8.4%)

IPT
(7.1%)

ACT
(6.0%)

Psychotic symptoms

531

CBT
(33.5%)

Supportive counselling
(23.0%)

Other
(18.8%)

Psycho-education
(12.2%)

IPT
(7.9%)

Borderline personality traits

523

CBT
(31.4%)

Other
(18.2%)

Supportive counselling
(17.8%)

Psycho-education
(11.1%)

IPT
(7.6%)

All presenting concerns

63 221

CBT
(42.8%)

Supportive counselling
(17.9%)

Psycho-education
(9.9%)

IPT
(6.5%)

ACT
(5.6%)

CBT = cognitive behaviour therapy. IPT = interpersonal therapy. ACT = acceptance and commitment therapy.

* Percentages refer to proportion of total mental health care sessions received by clients presenting with the respective concern. Percentages in rows do not add to 100% as other treatment modes were possible.

2 Mean psychological distress scores (K10) at different time points

3 Mean psychosocial functioning scores (SOFAS) at different time points

4 Proportion of young people showing significant, reliable and clinical change in psychological distress and psychosocial functioning between first and last service ratings

Measure	Method	Number of clients	Change category
Measure	Method	Number of clients	Improvement	No change	Deterioration
K10	Significant change (effect size ≥ 0.5)	10 228	36.1%	50.9%	13.0%
	Reliable change	10 228	26.2%	65.9%	8.0%
	Clinically significant change*	8205	21.1%	78.9%	NA
SOFAS	Significant change (effect size ≥ 0.5)	15 496	37.1%	43.4%	19.5%
	Reliable change	15 496	30.9%	53.6%	15.5%
	Clinically significant change*	9556	37.0%	63.0%	NA

K10 = Kessler Psychological Distress Scale. SOFAS = Social and Occupational Functioning Assessment Scale. NA = not applicable: young people in the clinical population are, by definition, not able to deteriorate, but rather remain in the clinical population. * It was not possible to assess the clinical improvement of young people who were in the non-clinical population at the first time point (19.8% of total sample for K10 and 38.3% of total sample for SOFAS); they were therefore excluded from this analysis.

Securing a rural health workforce for the next generation of rural Australians

The value of investing in advancing rural training pathways should not be forgotten

The establishment of rural-based academic networks in health was a key initiative of the incoming federal Coalition government in 1996 to help overcome the shortage of medical and other health professionals in rural and remote Australia. University departments of rural health1 were initially established in medical schools to increase the capacity of rural health services to provide high-quality education and clinical training of students from multiple disciplines, and support the delivery of clinical services to communities. In a second phase of development from 1999, the addition of rural clinical schools expanded the network to provide at least 1 year of clinical training for 25% of Australian medical students in a rural community.

Now, 18 years on, rural Australia has access to an unprecedented network of 28 academic departments invested in clinical training. This represents a substantial footprint across each state and the Northern Territory. The rural academic network delivers rural-based clinical training in both hospital and community settings for more than 5500 medical, nursing and allied health students annually and includes access to interprofessional learning and simulation training. In some university departments of rural health and rural clinical schools, there are opportunities for students to contribute to service delivery to underserved rural communities through service learning. In this educational approach, students deliver clinical care under supervision to patients with high health need and poor access to services, in student-led clinics or in an assisting role.2 Progressively, academic teams have also formed to undertake research, evaluation and development work on the rural health agenda, including carrying out studies on the effectiveness of rural training in delivering rural health workforce outcomes and health service redesign for rural and remote communities.

Developing rural health as a career path

Academic departments contribute to the development of rural training pathways using multiple strategies that are sequenced through the different phases of career development; from promoting health careers to secondary school students in rural communities, delivering rural-based clinical training for undergraduate students, through to providing professional development for rurally located practitioners across the different disciplines. Vocational training in medicine for a career in rural health has benefited from the expansion of prevocational training opportunities managed by rural health services, the regionalisation of general practice training,3 and growing acceptance of rural generalism as an occupational classification and potential career path for medical graduates. The rural training of medical students is integral to the pathway and is starting to deliver workforce outcomes for rural health services and communities, as evidenced by the increasing number of these graduates undertaking prevocational and vocational training in rural areas.4,5 The development of rural training pathways for nursing, dentistry, pharmacy and other allied health disciplines to build a generalist workforce is not as well advanced.

The 2013 Review of Australian Government Health Workforce Programs6 reported on the importance of creating a coherent pathway for rural and regional education and training — for medical training in the first instance and then for nursing, allied health and dentistry. The report concluded that these developments had the potential to achieve better health workforce outcomes for rural Australia, as well as promote generalist medicine and integrated primary care. The policy direction is clear, yet there is uncertainty in an environment where there will be increasing competition for funds. How the establishment of primary care networks and rebuilding of general practice training will affect the ongoing development of rural training pathways has yet to be determined. The decision to invest during a previous period of fiscal constraint delivered an internationally regarded rural academic network; it is timely to now invest in the advancement of training pathways to secure a rural health workforce for the next generation of rural Australians.