Preventing type 2 diabetes: scaling up to create a prevention system

Every day an estimated 280 Australians develop type 2 diabetes.1 By 2023, type 2 diabetes is predicted to become the number one specific cause of burden of disease in Australia.2 Policies and programs to support the prevention of type 2 diabetes need to be scaled up urgently if Australia hopes to limit or reduce the enormous negative impact this serious and complex condition has on individuals, families, employers, businesses and governments. An estimated 1.5 million Australians have type 2 diabetes, and this is predicted to increase to 3.3 million by 2031.3 Prediabetes, which includes impaired glucose tolerance and impaired fasting glucose, is estimated to affect 2 million Australians, putting them at high risk of developing type 2 diabetes.4

Type 2 diabetes is a serious and progressive condition. If not identified and well managed it can lead to many complications, including macrovascular complications (heart attacks, strokes, amputations); microvascular complications (eye, kidney and nerve damage); and mental health problems (depression, anxiety and distress). Importantly, the macrovascular complications may begin early in the prediabetes stage. In Australia, diabetes accounts for one-third of all preventable hospital admissions coupled with longer than average stays. The economic cost is increasing dramatically and is estimated at $14.6 billion per year.5

There can be no doubt that this is a serious epidemic that will have a large negative impact on health and productivity. However, despite strong evidence and many powerful tools to help prevent the future growth of type 2 diabetes, efforts to establish a sustained and effective prevention system in Australia have been patchy, project oriented and subject to frequent funding disruption due to government change and short-term thinking.

The International Diabetes Federation consensus statement6 on prevention of type 2 diabetes published in 2007 clearly identified two key target activities for prevention to be undertaken simultaneously — a high-risk population approach combined with an entire population approach. However, few countries have implemented this combined approach on a scale large enough to create a prevention system that can work effectively.

In Australia, the state of Victoria is the most advanced, with the combination of a statewide, high-risk population, diabetes prevention program (Life! program) combined with an entire population and systems approach across 12 local government areas (Healthy Together Victoria), as well as the LiveLighter campaign to encourage Victorians to live healthier lifestyles.

So what works and how do we scale up to create effective prevention?

High-risk population prevention

Up to 58% of type 2 diabetes developing from the prediabetes population can be prevented through intensive, structured lifestyle interventions.7,8 There is strong evidence from randomised controlled trials (RCTs) around the world to support this approach. Furthermore, cost-effectiveness studies and community-based evaluations conclude that these interventions are able to be implemented, and are effective and cost-effective.9,10

The effect of lifestyle intervention has an impact lasting up to 20 years after the active intervention.7 The drug metformin can also prevent type 2 diabetes in individuals at high risk.11 While it is not as effective as a lifestyle modification program, metformin is an appropriate and effective intervention for some people.12 Lifestyle intervention alone is less effective in those with severe obesity, whereas bariatric surgery13 has been shown to be effective.

Translational studies including the Greater Green Triangle Diabetes Prevention Project,14 Sydney Diabetes Prevention Program (SDPP),15 and the large-scale implementation of the Victorian Life! program16,17 have built a strong case that the RCT evidence can be translated into community-based programs. These programs have included face-to-face group interventions and individual telephone interventions, both with 5–6 structured sessions and trained facilitators. Maintaining the fidelity of these lifestyle interventions through accreditation or certification is important.

The questionnaire-based Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK) has made screening to identify and recruit individuals at high risk feasible and effective at a community level. The Workhealth program in Victoria (no longer funded) used the AUSDRISK tool for over 500 000 worker health checks, and around 23% of workers were found to be at high risk.18 Lamentably, most were not systematically referred for any follow-up or prevention intervention. The Life! program has seen over 59 000 people with prediabetes referred to lifestyle-based prevention courses. This has been achieved by establishing multiple recruitment methods that include: integrated social marketing techniques using mass media and online media; effective risk messaging about the seriousness and personal relevance; workplace and community promotions; and a call-to-action response system with a telephone response line similar to Quitline.

A framework for scaling up high-risk population prevention in Australia should be based on international experience (United States Centers for Disease Control National Diabetes Prevention Program) and local implementation models (SDPP and Victorian Life! program). Importantly, a national approach should reflect a national commitment and integrate national and state or territory government funding with private health insurance funding, employer funding (for workplace-based elements) and individual contribution. This partnership of funders has been established in the US program.

A national type 2 diabetes high-risk prevention program should include six key strategies:

Marketing risk messages with a clear call to action as well as a telephone response and support line;
Systematically identifying high-risk individuals in community settings, online and in primary care;
Intervention based on the most appropriate pathway for prevention;
- Intensive lifestyle programs (face-to-face groups, telephone, webinar groups, commercial programs)
- Medication (metformin)
- Surgery (for those with severe obesity)
Building a prevention workforce;
Continuous improvement, evaluation and innovation;
National coordination and collaboration.

Entire population approach

Driving the development and increasing prevalence of type 2 diabetes is the increase in modifiable risk factors, particularly unhealthy diets, physical inactivity, weight gain and overweight and obesity. There is a seven times greater risk of diabetes among obese people compared with those of healthy weight, with a threefold increase in risk among overweight people.19 The modifiable risk factors for type 2 diabetes are common risk factors for cardiovascular disease, kidney disease and many cancers.

Entire population prevention action must be multi-faceted and include legislation or regulation, fiscal incentives, social marketing, health promotion and the provision of public health services.20 It should target policy, structural and environmental factors to reduce the proportion of people shifting from healthy weight to overweight to obese, and low risk to high risk.21

The United Kingdom Foresight report22 provided a clear framework for national action. The experts and evidence pointed to an environments, systems and behaviour approach that reflects the following elements:

Food environment (production and formulation, labelling and consumption, marketing)
Activity environment (built environment, transport)
Societal influences (attitudes, education, cultures)
Individual influences (behaviour, choices, families)
Biological influences (genetics and epigenetics)

National Preventative Health Taskforce report

The evidence has also been reviewed in Australia and an excellent evidence-based framework for national action was proposed in the National Preventative Health Taskforce report.23 However, as described below, implementation by either national or state or territory governments has been scarce.

Driving change in the food supply to increase the availability and demand for healthier food products and decrease the availability and demand for unhealthy food products. There has been some work on improving food labelling to support and empower consumers to make healthier choices. Agreement has been reached around a new interpretive front-of-pack healthy star labelling system that aims to empower people to make healthier choices. Adoption of the system will be supported by a social marketing campaign, and its success will be enhanced if use is widespread. Agreement has also been reached that high-level health claims will not be able to be used on foods that are determined to be unhealthy overall. Next steps could include the development of a nutrition policy to set out strategies for improving diets. Much more needs to be done towards achieving reformulation of processed foods to reduce added sugar, salt and fat.

Reduce the exposure of children and others to the marketing and promotion of unhealthy foods. Australia relies to a large extent on a self-regulatory system; however, there is little evidence that this has significantly reduced the power and volume of marketing to which children are exposed. Increasingly, digital and social media, which are harder for parents to monitor, are being integrated into marketing campaigns. A single agreed definition of unhealthy foods that cannot be marketed to children has not been developed. The excessive marketing and consumption of sugar-sweetened beverages, which especially affects children and young people, and the continuing expansion of this category beyond traditional soft drinks are a particular focus of national and international public health advocacy.

Drive environmental changes throughout the community to increase levels of physical activity and reduce sedentary behaviours. There has been little policy or systematic effort to drive this, and high-level leadership and development of a national framework for active living is still needed.

Embed physical activity and healthy eating in everyday life, including workplaces, school and communities. Some jurisdictions have established a systematic approach; for example, South Australia has established the OPAL (Obesity Prevention and Lifestyle) program and Victoria the Healthy Together Victoria initiative, which target a range of settings.

Encourage people to improve physical activity and healthy eating through social marketing. Some efforts commenced at the national level with the Measure Up and Swap It, Don’t Stop It campaigns, but these are no longer funded. A number of jurisdictions have rolled out the LiveLighter campaign in order to target diets and sedentary behaviour through small, sustainable changes.

Strengthen skill and support primary health care and the public health workforce to support people making healthy choices. Since this recommendation was made, in 2013 the National Health and Medical Research Council developed clinical practice guidelines24 to support the management of overweight and obesity by health professionals.

The entire population approach also requires a focus on low-income communities and cultural and linguistic diversity.

Two population groups requiring special consideration

Indigenous Australians are three times more likely than non-Indigenous Australians to have type 2 diabetes and more likely to develop complications.25 Prevention efforts needed for Indigenous Australians require urgent attention but were beyond the scope of our review.

Women who develop gestational diabetes (about 5%–10% of pregnancies26) have an increased future risk of developing type 2 diabetes, and their children also have an increased health risk. Increasing evidence for the importance of in utero factors to the risk of type 2 diabetes in adult life owing to epigenetic mechanisms27 suggests that further examination of diabetes prevention strategies specific to maternal and child health is also required.

Conclusions

For decades Australia has led the way in large-scale approaches in some key areas of prevention. A prominent example is the international leadership shown in tobacco control through the combination of social marketing, evidence-based interventions, and regulatory and policy initiatives, together with monitoring and evaluation.

It is over 10 years since the first international RCTs reported that we can prevent the development of type 2 diabetes in the high-risk population (with prediabetes). It is over 5 years since the National Preventative Health Taskforce report. Yet there has been little national action.

In 2013, Diabetes Australia called for a new national diabetes strategy with prevention as the focus. It also pointed to the need for sustained national action for prevention of type 2 diabetes through a combined approach of a national diabetes prevention program (targeting the high-risk population) concurrent with serious national, societal change to create healthy food and activity environments and support healthy choices by making them affordable and accessible.

It is encouraging that the Australian Government is proceeding with its commitment to develop a new national diabetes strategy and has formed a national advisory group and established a timeline for completion in early 2015.

Short-term pilot projects will not be enough. Small-scale programs will not be enough. Prevention of type 2 diabetes is proven, possible and powerful — but to achieve this we need to scale up our national effort and create a sustained prevention system for the next decade and beyond.

Full medical program fees and medical student career intention

Admission to Australian medical schools is highly competitive, despite an increase in capacity from about 1200 places in 1999 to 3500 places in 2013.1 Most of these places are Commonwealth supported; in 2013, students paid A$9450 for each of the 4–6 years of medical tuition via the HECS-HELP scheme (Higher Education Contribution Scheme, Higher Education Loan Program).2

Tuition fees can be paid in advance or deferred until an annual income threshold is reached (A$51 309 in the 2013–14 financial year); deferred payments are interest-free but adjusted according to the consumer price index.3 In 2011, 56% of medical school entrants had a prior degree that may have also generated HECS-HELP debt.4

Medical students enrolled in a Commonwealth-supported place (CSP) may accrue a debt of A$36 000 to A$63 000, depending on course structure and prior degrees (4–7 years of fees), plus living expenses (about A$15 000 per year if away from home2). Concerns have been raised about the potential inequity of pricing medical education beyond the range that is practical for students from lower socioeconomic backgrounds.5,6

Subsidies for tuition fees are available through Bonded Medical Places (18.8% of students), Medical Rural Bonded Scholarships (3.0%), Australian Defence Force (ADF) scholarships (0.2%),4 and state-based rural scholarships, all of which require return of service in areas of need (usually non-metropolitan communities or the ADF). CSP students can also reduce HECS-HELP debts by one year’s fees for each year of work in an area of need.

Between 2005 and 2009, some universities introduced additional places that required payment of the full cost of medical education.7 Bond University commenced a full-fee paying (FFP) undergraduate medical course7 and some other medical schools offered small numbers of domestic FFP places to cater for surplus student demand.

Most medical schools also offer FFP courses for international students, providing a valuable source of income and contributing to export of higher education.8 About 700 international places were available annually until recent competition for intern places reduced this to about 550 places.

While fees for international FFP students vary (A$30 000 to A$60 000 per year), the financial investment by graduation by these students is much higher than that for domestic FFP students. Domestic FFP students may access partial loans under the FEE-HELP loan scheme (a lifetime maximum of A$116 507 in 2013)9 and may apply for ADF scholarships and state-based rural scholarships. International FFP students do not have these options.

FFP students (domestic and international) can therefore graduate with substantial debts, raising concerns about workforce outcomes. The debts can be comparable to those in the United States, where a private education system is well established.10–12 In 2012, 86% of US medical graduates reported substantial debts, 30% as high as US$200 000.11 Follow-up studies show a clear association between debt, anticipated income, personal value placed on money and choice of higher paid specialties.11,13,14

Choice of specialty is a complex decision, influenced by extrinsic and intrinsic factors.15 Extrinsic factors include work culture, postgraduate work experience, opportunities for flexible hours and perceived prestige of certain specialties.15 Prestige is characterised by longer and more intensive training periods, more competitive selection, higher income, and perceived greater societal status.16 These perceptions remain constant from undergraduate training through to early postgraduate training, although they may be challenged by clinical experiences during clerkships and residency.16

Intrinsic factors include student demographics, personal attributes and preferences, and the clinical work environment. Student demographic profiles have significant influences on specialty choice.17 Socioeconomic status and background appear to be important; students from less wealthy families place greater weight on debt and pursuing higher incomes. Students from a rural background are more likely to pursue a rural career.12,18 Sex is associated with differences in motivation for particular career choices, with women more likely to pursue general practice careers because of shorter, more flexible training and better working conditions.15 Men who place added value on personal lifestyle factors also prefer general practice.19

The three most influential personal determinants of specialty choice appear to be “appraisal of own skills and aptitudes”, “intellectual content of the specialty”, and “interest in helping people”.15 Lifestyle and finances are increasingly reported as major considerations by graduates.10–12,18 While debt load may influence students towards a preference for higher-paying careers, this may be more significant only for students who place greater importance on socioeconomic status as overall career preferences between FFP students and their publicly subsidised counterparts are similar.12

Although privately funded medical education continues to grow overseas, the influence of financial burden on career choice in Australia (where the geographic and specialty distribution of graduates is of greater concern than total numbers) has not been studied in depth. We compared the geographic and specialty career intentions of CSP and FFP medical students, domestic and international.

Methods

Data were accessed from the Medical Schools Outcomes Database and Longitudinal Tracking (MSOD) Project, a project of Medical Deans Australia and New Zealand that is funded by Health Workforce Australia as a means of evaluating rural medical education initiatives.19 Questionnaires are administered to all medical students on entry to and graduation from Australian medical schools. We collected data from these questionnaires for the period 2008 (when the MSOD Project commenced, with four participating medical schools) to 2011 (when 18 medical schools participated).

Dependent variables

Work location preference data were collected in five categories: small community; smaller town; regional city or large town; major urban centre; and capital city. The first three and last two categories were collapsed to create a binary variable for preference for rural practice versus urban practice.

Preferences for future medical specialty were analysed according to:

first choice selection of one of the five highest paid specialties in Australia — identified by the 2010 Medicine in Australia: Balancing Employment and Life study20 of specialty income (emergency medicine; surgery; radiology; obstetrics and gynaecology; intensive care medicine)21
selection of any of the top six in-need specialties — those predicted by Health Workforce Australia to be in short supply in 2025 (general practice; psychiatry; obstetrics and gynaecology; pathology; ophthalmology; radiology)22
first choice selection of any of the top six in-need specialties
first, second or third choice selection of any of the top six in-need specialties.

Independent variables

A binary variable was created to represent whether each student was a domestic FFP student or a CSP student (the latter was defined as a student receiving Commonwealth, state or university support). This variable excluded international FFP students, and a second binary variable was created to contrast international FFP students with CSP students. Additional binary variables were created for sex, marital status (single versus married or living with a partner) and rural background. Age at entry to medical school, in whole years, was also included.

Statistical analysis

Logistic regression models were run initially to analyse (i) rural future practice intentions, (ii) preferences for the top five income specialties and (iii) preferences for the top six in-need specialties. Two models were run for each: for domestic FFP students versus CSP students, and for international FFP students versus CSP students. Another six logistic regression models were then run to further analyse preferences for the top six in-need specialties, for domestic FFP students versus CSP students only.

Sex, age, marital status and rural background were initially included in all regression models together with the independent variable of whether students were domestic FFP or international FFP. Two-way interaction terms were included for interactions between the control variables and the independent variable of interest, and then removed using a backwards stepwise approach until the model contained only statistically significant interaction effects (those contributing a significant improvement in the model [a 2 log likelihood]).

Results

Data were available for 5688 students who graduated from 2008 to 2011: 262 in 2008; 894 in 2009; 1978 in 2010; and 2554 in 2011. The overall response rate was 83% (4704/5688) for those who completed the entrance questionnaire and 80% (4461/5568) for those who completed both entrance and exit questionnaires, although there were missing data for each of the variables, in particular specialty preferences.

A summary of the data collected from the exit questionnaires is shown in Box 1. A majority of students were young (mean age, 22 years), female, single and from urban backgrounds, and a minority were domestic FFP students (9.1%) and international FFP students (14.7%).

Preference for rural versus urban future practice

Domestic FFP students were significantly more likely than CSP students to prefer an urban future practice location (OR, 5.58; 95% CI, 2.04–15.26; P < 0.001). However, there was a significant domestic FFP student by marital status interaction effect, such that domestic FFP students who were married or partnered on exit from medical school were more likely to prefer a rural location (OR, 0.64; 95% CI, 0.44–0.95; P < 0.05).

Having a rural background had a strong effect on preference for a rural future practice (OR, 0.18), as did being older on entry to medical school (OR, 0.96). International FFP students were more likely to state a preference for urban practice (OR, 1.79). These data are shown in Box 2.

Preferences for medical specialty

Students who were married or partnered were less likely to select one of the top five income specialties as their first preference (OR, 0.77) and domestic FFP students were more likely to select one of the top five income specialties as their first preference (OR, 1.37). International FFP students were not more likely to select one of the top five income specialties as their first preference (OR, 1.18). These data are summarised in Box 3.

Domestic FFP students, but not international FFP students, were less likely than CSP students to have a first preference for an in-need specialty (OR, 0.72; 95% CI, 0.52–1.00; P < 0.05). All of the control variables were significant in the models as main effects: men were less likely to select an in-need specialty (OR, 0.44; 95% CI, 0.37–0.53; P < 0.001), while older (OR, 1.03; 95% CI, 1.01–1.04; P < 0.01), married or partnered (OR, 1.23; 95% CI, 1.14–1.34; P < 0.001) and rural background (OR, 1.28; 95% CI, 1.04–1.57; P < 0.05) graduates were more likely to select one of the in-need specialties.

Analyses of each of the predicted top six in-need specialities for 2025 were performed separately and focused on first preferences. Domestic FFP students were less likely than CSP students to select general practice (OR, 0.71), but domestic FFP students were no more or less likely than CSP students to select the remaining five in-need specialties. The results of the analysis of preference for general practice are shown in Box 4.

However, there was no significant difference between domestic FFP students and CSP students in terms of including any of the top six in-need specialties in their first three preferences (OR, 0.89; 95% CI, 0.71–1.11).

Discussion

We found that Australian domestic FFP students as a whole were more likely to state a preference for higher-paid specialties in urban locations, and were less likely to state a first choice preference for one of the six specialties predicted to be in need in 2025. However, the only specialty that was less popular was general practice; other associations were not uniform throughout the cohort.

Domestic FFP students who were married or partnered were notably different in that they were more likely to prefer rural practice settings. Further, when students’ first three specialisation preferences were analysed, there were no significant differences between domestic FFP and CSP students in preference for a top six in-need specialty.

These differences might be partly explained by student demographics and medical school locations. A higher proportion of FFP students, both domestic and international, are from urban backgrounds and most medical schools are in metropolitan areas or large urban centres. The outcomes may, therefore, be similar to those for other urban background students in urban medical schools.

On the other hand, rural clinical school experience in one metropolitan medical school program has been shown to be at least as influential as rural background,23 suggesting that rural initiatives may be effective for FFP students.

The implications of our findings for Australia’s medical workforce planning could be relatively minor. Domestic FFP students currently comprise a small minority of Australia’s medical students. For international students, a majority of whom want to stay in Australia for at least some postgraduate experience and training, encouragement can be drawn from the finding that specialty choice is similar to that for CSP students. If the number of FFP student places is increased through changes in government policy or establishment of more private medical schools, strategies to increase the focus on rural and other underserved populations might be needed. Unless rural initiatives are extended across public and private medical schools, FFP student places (domestic or international) are unlikely to address the current rural and regional medical workforce shortages or affect the intention to further develop primary care medicine.

The possibility of increased fees for CSP students, and therefore increased debt levels, begs consideration of the potential impact on career preferences. Higher fees might drive interest towards higher-paid, metropolitan specialties. Such an effect might, however, be mitigated by continuing the apparently successful rural medical education initiatives that, although costly, are effective in promoting rural recruitment.23,24

Our study has some limitations. Several initiatives may influence choice of rural locations in the early graduate period. For example, CSP students with rural scholarships are required to work at least initially in non-metropolitan communities, and may also reduce HECS-HELP debt by working in a defined area of need, usually non-metropolitan. Hence it is difficult to be confident that any changes in career preference relate to fee levels or to the existence of fewer rural medical education initiatives for FFP students. Also, we were not able to collect data on debt, and it is possible that students paying high fees could graduate with no debt. Finally, career preference takes several years to become final, so longer follow-up (at least 10 years) is required to produce a more robust analysis based on final career outcomes.

Our data show differences in specialty and location preferences between domestic FFP and CSP students. Consideration should be given to extending rural medical education initiatives across public and private medical school places to ensure that all medical students are exposed to rural and more generalist career attractions.

1 Descriptive statistics for study variables for students who completed the Medical Schools Outcomes Database and Longitudinal Tracking Project exit questionnaire, 2008–2011*

Domestic FFP students (n = 408)

International FFP students (n = 655)

CSP students (n = 3398)

Age in years at entry to medical school, mean (range; SD)

22 (16–53; 4.6)

21 (15–36; 3.0)

22 (15–68; 5.0)

Women

219/408 (53.7%)

371/654 (56.7%)

1924/3398 (56.6%)

Marital status single

318/408 (77.9%)

527/655 (80.5%)

2325/3229 (72.0%)

Urban background

371/408 (90.9%)

596/655 (91.0%)

2650/3398 (78.0%)

Preference for urban future practice

364/390 (93.3%)

481/521 (92.3%)

2686/3258 (82.4%)

First preference for any of the top five income specialties

153/322 (47.5%)

235/552 (42.6%)

1115/2929 (38.1%)

First preference for any of the top six in-need specialties†

54/322 (16.8%)

129/552 (23.4%)

737/2929 (25.2%)

First to third preference for any of the top six in-need specialties†

151/322 (46.9%)

243/552 (44.0%)

1377/2929 (47.0%)

* Data are number/denominator (%) unless otherwise indicated. † Top six specialties predicted to be in need in 2025.

2 Logistic regression analysis of preference on exit from medical school for rural versus urban future practice (n = 3314)*

Odds ratio (95% CI)†

Age at entry to medical school

0.96 (0.95–0.98)

< 0.001

Sex

1.24 (1.01–1.53)

< 0.05

Marital status

1.91 (0.70–5.23)

Not significant

Rural background

0.18 (0.15–0.22)

< 0.001

Domestic FFP student by marital status interaction

0.35 (0.14–0.90)

< 0.05

Domestic FFP student

3.36 (1.75–6.46)

< 0.001

International FFP student‡

1.79 (1.19–2.72)

< 0.01

CSP = Commonwealth-supported place. FFP = full fee-paying. * Independent variable: CSP students versus domestic FFP students (CSP = 0, FFP = 1). Control variables: age at entry to medical school, sex (female = 1, male = 2), marital status (unmarried = 0, married = 1) and rural background of the student (no = 0, yes = 1). † Higher odds ratio indicates preference for urban future practice. ‡ International students were entered into a separate model with the control variables.

3 Logistic regression analysis of first preference on exit from medical school for any of top five income specialties (n = 2945)*

Odds ratio (95% CI)†

Age at entry to medical school

1.00 (0.98–1.02)

Not significant

Sex

1.55 (1.33–1.80)

< 0.001

Marital status

0.77 (0.64–0.92)

< 0.01

Rural background

0.93 (0.77–1.12)

Not significant

Domestic FFP student

1.37 (1.07–1.75)

< 0.05

International FFP student‡

1.18 (0.96–1.47)

Not significant

CSP = Commonwealth-supported place. FFP = full fee-paying. * Independent variable: CSP students versus domestic FFP students (CSP = 0, FFP = 1). Control variables: age at entry to medical school, sex (female = 1, male = 2), marital status (unmarried = 0, married = 1) and rural background of the student (no = 0, yes = 1). † Higher odds ratio indicates greater likelihood of choosing high-income specialty. ‡ International students were entered into a separate model with the control variables.

4 Logistic regression analysis of first preference on exit from medical school for working in general practice (n = 2945)*

Odds ratio (95% CI)†

Age at entry to medical school

1.03 (1.01–1.05)

< 0.01

Sex

0.44 (0.37–0.53)

< 0.001

Marital status

1.55 (1.28–1.89)

< 0.001

Rural background

1.28 (1.04–1.57)

< 0.05

Domestic FFP student

0.71 (0.52–0.99)

< 0.05

International FFP student‡

0.99 (0.76–1.26)

Not significant

CSP = Commonwealth-supported place. FFP = full fee-paying. * Independent variable: CSP students versus domestic FFP students (CSP = 0, FFP = 1). Control variables: age at entry to medical school, sex (female = 1, male = 2), marital status (unmarried = 0, married = 1) and rural background of the student (no = 0, yes = 1). † Higher odds ratio indicates greater likelihood of choosing general practice. ‡ International students were entered into a separate model with the control variables.

All about John

John Kitzhaber is a medical man — once an emergency medicine physician — and yet a man who has devoted most of his working life to politics in the United States. Doctor changed to Governor when he won office in Oregon in 1995, after one term in the lower house and three successive terms as a state senator.

I have known John Kitzhaber for about 20 years, and hearing that he had decided to run for an unprecedented fourth term as Governor, I decided to visit Oregon before the election and sample opinion. I wondered whether training as a doctor had given him a magic touch that assured him automatic re-election.

As I went around the countryside, I met another John — son of a Danish immigrant. He did not think much of Governor Kitzhaber. In this John’s view, living with a woman unmarried flew in the face of Bible teaching. I gently reminded him that the Governor and his partner were engaged to be married. He went on to say that Kitzhaber had been in office far too long; however, Wayne Morse was his hero. Morse epitomised that Oregon independence which had seen him elected successively as a Republican, Independent and Democrat United States senator over 24 years. Kitzhaber was so far only striving for 16 as Governor. I bit my tongue.

Oregonians have an idiosyncratic streak. On becoming the 33rd State of the Union in 1859, they rejected the blandishments of both the North and the South to join the Civil War.

As an aside, Kitzhaber was born in Colfax, Washington, named after Schuyler Colfax, the Republican Speaker of the House of Representatives who famously announced the passage of the constitutional amendment effectively abolishing slavery in the US and considered it the happiest day of his life. Ironically, the same belief in equality and equity that epitomised the Republican Colfax in 1865 resurfaced strongly in the character of the Democrat John Kitzhaber a century later.

Oregon is a multicultural community of 4 million people. The waiter at the Benson Hotel in Portland was proud of his Chinese heritage and the fact that his forebears were Chinese herbal doctors. Chinese immigrants were the backbone of Oregon’s salmon cannery industry.

Oregon is also where the community stopped logging to preserve the northern spotted owl in the 1990s, and where, in the 1960s, the community stopped privatisation of their wide, sweeping beaches that the Native Americans and then the early settlers used as highways.

Kitzhaber entered politics in 1978. His major interest then was public education. He became nationally prominent with his development of the Oregon Health Plan in the 1990s. The Medicaid allowance provided to the state by the federal government had paid for all health care for the indigent but excluded the marginally poor from state health care support. Kitzhaber’s plan had simple aims: he set out to make essential health care more widely available and more inclusive by rationing services, excluding those that were not cost-effective. The idea was bold and the planning and consultation broad, but like all health care legislation in the US, it ended up entangled in complexity.1 Kitzhaber admits that he should have paid as much attention to the political aspects of the health care system as to its funding.

In Portland, it was clear to me that I was in Kitzhaber territory, confirming what was obvious in 2010 when, for much of election night, he had been trailing his Republican opponent until the votes from Portland came in — like the Pacific Ocean crashing into the mouth of the Columbia River — ensuring his third term.

There was yet another John, who drove me around Portland, who had been an environmental activist in his youth but now exhibited a complete disenchantment with politics. Once he would have been part of the Kitzhaber constituency, but no longer. The problem with disappointed romantics is they make the worst cynics — and they stay at home on election day.

Kitzhaber as a political figure raises the question of the place of doctors in politics. Those who know of Earl Page in Australia and David Owen in Great Britain might well say they made valuable contributions — Page by his involvement in the first national health scheme with the passage of the National Health Act in 1953; Owen as British Foreign Secretary. What makes for such success, and how much does it depend on political longevity?

Two qualities that favour success in politics derive from being medically trained. The first is grace under pressure, which Kitzhaber has repeatedly demonstrated; and the second is a clear understanding of the importance of maintaining the currency of your knowledge and skills.

Whether or not he sees governing the state as a continuous ward round, he should recognise the sentiment expressed by a woman in the Oregon country town of John Day. Her father had been a child in an Okie family, who fled to Oregon from the Oklahoma dust bowl 80 years ago.2 She had lived in rural poverty. The people now were sick of politics, she said: “too much politics”. People wanted politicians to stop media posturing and get on with the job. She raised a sceptical eyebrow at the mention of the governor’s challenger; it settled back when Kitzhaber was mentioned.

Whatever else he may be, Kitzhaber is a man for whom policy is the prime consideration.3 Political storms have to be handled by knowing first when to don raincoat and gumboots — admittedly over his trademark jeans, cowboy buckle and tan Western boots. And second, knowing when to empty the teacup once the storm has passed.

So what has happened since my visit?

Governor Kitzhaber was elected for his fourth term on 4 November 2014 with a greatly increased majority of 80 000. The Oregon electorate ran counter to what happened elsewhere in the United States. Democrat Jeff Merkley was re-elected to the US Senate in a landslide. The Democrats achieved an increased majority in both houses of the state legislature.

Given that majority, it will be interesting to see whether in 4 years Kitzhaber can fully implement Obamacare in Oregon, removing any bureaucratic glitches. Perhaps, despite so much asymmetry in the information available to date, he will be able to demonstrate to a country divided over this issue that universal health care is not only affordable, but cost-effective.

Can Australia’s clinical practice guidelines be trusted?

Trustworthy clinical practice guidelines can improve the health of the population and lead to more efficient and effective investment in health care. Take cerebral palsy, for example, a condition that, in 2008, was estimated to cost the Australian taxpayer $3.87 billion per annum (http://www.adelaide.edu.au/arch/antenatalMagneiumSulphateGuidlines.pdf). The Australian antenatal magnesium sulfate guidelines, approved by National Health and Medical Research Council and released in 2010, have been implemented successfully in 90% of tertiary maternity hospitals, and are estimated to be preventing more than 150 new cases of cerebral palsy from occurring each year (Aust N Z J Obstet Gynaecol 2013; 53: 86-89). Considering the estimated annual cost to the community of $115 000 per person with cerebral palsy, the cumulative long-term savings from this single guideline will be substantial.

Significant time, effort and resources are spent developing clinical practice guidelines for use in Australia. Between 2005 and 2013, 1046 such guidelines were published. Most, however, are of disappointingly poor quality and should not be trusted to inform decision making. Most do not describe the processes used to develop them and lack transparency around who authored them, with one in three containing no information at all about the guideline development group or the authors. Fewer than one in five guidelines make any reference to the evidence underpinning the recommendations made, and fewer than one in 10 are informed by systematic reviews of the evidence. Possibly of greatest concern is that 93% do not have clearly documented processes for identifying and managing conflicts of interest (https://www.nhmrc.gov.au/_files_nhmrc/publications/attachments/nh165_2014_nhmrc_clinical_guidelines_annual_report_140805.pdf).

This situation cannot continue. Poor-quality guidelines lead to poor-quality decisions and will have a direct, negative impact on the health of Australians. The NHMRC is working with the Australian Commission on Safety and Quality in Health Care (http://www.safetyandquality.gov.au) and the Commonwealth Department of Health on a more sensible national framework for developing more reliable and trustworthy clinical practice guidelines and clinical care standards for Australia.

What influences doctors to work in rural locations?

Student background and clinical education act synergistically

We are fortunate that there is increasing evidence available on which to base policy decisions for building the rural medical workforce. The concept of the rural pipeline1 provides a framework in which to consider who we admit to medical programs, where we can best deliver medical education that is motivating towards rural practice and why graduates then choose to work in rural locations.

Two new sources of evidence are helping to address the “who” and “where” considerations. The Medical Schools Outcomes Database and Longitudinal Tracking (MSOD) Project has provided evidence of medical student career intentions on commencement and completion of undergraduate education, and data on location of practice in the early postgraduate years. In this issue of the Journal, Hays and colleagues describe their analysis of national MSOD data to determine where full-fee paying (FFP) students intend to practise.2 They conclude that FFP students are significantly more likely to practise in an urban area.

Their findings should cause us to pause and reflect on the question of who to admit. If we no longer have a shortage of medical practitioners in Australia (compared with other OECD [Organisation for Economic Co-operation and Development] countries3) but the challenge of distributing practitioners to rural areas is ongoing, FFP students with urban practice intentions are not likely to help address the maldistribution challenge.

Also in this issue, Kondalsamy-Chennakesavan and colleagues, who studied determinants of rural practice in University of Queensland medical graduates,4 affirmed that rural background is one of the greatest predictors of rural practice.5 Owing to the maturity of the University of Queensland Rural Clinical School (UQRCS) program, the authors could show a direct relationship between length of rural background (before medical school entry) and likelihood of rural practice. Those who had attended UQRCS as undergraduates for 1–2 years had increased odds of rural practice, and more time spent at UQRCS increased the odds. The effect of rural background plus rural undergraduate clinical education was also affirmed as very influential for recruiting future rural practitioners.4,6

We now need to build on these promising findings from over a decade of federal government support for the Rural Clinical Training and Support program and explore why graduates go on to practise in rural locations. The next stage in the vertical continuum of the medical education pipeline, including the social issues that affect practice location, surely deserves attention.

Postgraduate education occurs at a crucial time in a trainee’s personal and professional life. For most specialties, this training is currently based in large urban centres, and trainees spend very little time in rural settings. Thus mentor–trainee relationships, life partnerships, work opportunities for partners, purchases of homes, and stable child care or schooling arrangements become established in urban centres.

Reversing this trend offers promise of influencing trainees to train in rural locations and then remain there. This may be attractive to older or married students, who are more likely to choose a rural location.4 The considerable investment in infrastructure and human resources for undergraduate education in rural Australia can then be leveraged to build regional local postgraduate training networks. Start-up funding will help establish the necessary partnerships with postgraduate training providers and colleges for regionally based postgraduate education.

Medical school debt and financial incentives potentially also influence decisions regarding rural practice. Hays et al highlight the large debts that many FFP students incur by graduation, and suggest that these might motivate choice of urban practice in lucrative subspecialties.2 In the future, increasing debts incurred by Commonwealth-supported place (CSP) students, owing to proposed changes to the HECS-HELP scheme (Higher Education Contribution Scheme, Higher Education Loan Program), might dissuade doctors from working in rural locations and in the needed vocation of general practice, despite the HECS Reimbursement Scheme for rural service.7 Evidence for the influence of medical school selection on rural medical workforce is growing. Recruiting CSP students from rural and lower socioeconomic backgrounds may prove to be an influential strategy.8

Proposed changes to the District of Workforce Shortage, Australian Standard Geographical Classification – Remoteness Area (ASGC-RA) system and Bonded Medical Places Scheme should encourage more doctors to work in rural locations. The Modified Monash Model (MMM) — the updated geographical classification system) — will focus support and resources on small rural and remote communities (where the need is greatest), and any bonded student will be able to complete their return of service obligation in any small town (< 15 000 population). Application of MMM classifications to the General Practice Rural Incentives Programme is planned to ensure that incentive payments are targeted to relocating doctors to the areas of most need and retaining them there. Better targeting of incentives should provide a synergistic strategy9 and, when used in conjunction with key strategies associated with rural background, rural education and rural service obligations, should increase the number of doctors working in rural locations.

Determinants of rural practice: positive interaction between rural background and rural undergraduate training

In developed countries, including the United States, Canada and Australia, a fifth to a third of the population live in rural areas yet the number of medical practitioners employed per 100 000 population in those areas is about half that of major cities.1–4 It has long been recognised that rural doctors are more likely to have a rural background and to have had some medical training (undergraduate or postgraduate) in rural areas,5–16 although the effect of undergraduate rural exposure has been questioned.17 Other factors associated with rural practice include being single, having children, having a partner with a rural background, rural primary and secondary education, intention or desire to practise rurally, sex, age, having a bonded scholarship, and medical school attended.18–20

From the early 1990s, the Australian Government introduced national initiatives aimed at encouraging rural practice. This included funding medical schools to increase enrolment of students with a rural background and provide short-term undergraduate rural exposure.21,22 In 2001, to increase the duration of rural exposure, universities began to establish rural clinical schools (RCSs); by 2008, 17 were operating. Participating universities are funded to train 25% of domestic students who are publicly funded (those have a Commonwealth-supported place) in a rural area, defined by Australian Standard Geographical Classification – Remoteness Area (ASGC-RA) categories ASGC-RA2 to ASGC-RA5.23

To track students through medical school and training, the Australian Government has supported a large prospective cohort study involving all Australian medical students — the Medical Schools Outcomes Database and Longitudinal Tracking Project.24 In the future, this study will generate robust data (currently, most participants are undergraduates or are in early postgraduate training).

The University of Queensland Rural Clinical School (UQRCS) was established in 2002. It is one of the largest RCSs in Australia and has teaching sites in four regional cities situated 130–650 km from Brisbane. The University of Queensland (UQ) School of Medicine also has eight metropolitan clinical schools (MCSs).

We aimed to quantify determinants of rural practice and interactions between them, particularly the role of rural background and years of RCS training, for UQ medical graduates. We hypothesised that attendance at UQRCS is an independent predictor of rural practice, after adjusting for confounders, and that a positive interaction between UQRCS attendance and rural background enhances the effect.

Methods

This study was part of the UQ Medical Graduates Cohort Study — a retrospective cohort study of UQ medical graduates who graduated during the period 2002–2011. Lists of eligible participants (those who had been domestic students [ie, Australian citizens and permanent residents]) and their current contact details, if available, were obtained from UQ. If not available, current suburb and postcode were sought by searching the Australian Health Practitioner Regulation Agency (AHPRA) register of practitioners. This information was used to narrow telephone directory and internet searches. Potential participants were invited by email, post or telephone and sent a link to an online questionnaire, or a hard copy if requested. Since UQRCS attendance was a critical exposure variable but only 20% of potential participants would have attended UQRCS, we targeted this group to improve the power and efficiency of the study.

The questionnaire comprised questions on demographics, family information (parents’ rural background, partnership status and partner’s rural background), residential history (birth place and location during preschool, primary school, high school and post-school years), boarding school attendance, gap year after high school, scholarships (including bonded scholarships), rural health club membership, tertiary education and postgraduate training, year of medical school graduation, and location of current clinical practice (primary location for those with more than one). The outcome of interest was location of current clinical practice categorised as rural. The primary predictor variables of interest were attendance at UQRCS and rural background; the latter was defined as having resided in a rural area in Australia for at least 5 years since commencing primary school and before commencing UQ medical degree, as per funding parameters.

Statistical analyses

Descriptive statistics were used to report location of current clinical practice by ASGC-RA categories and UQRCS status. The category ASGC-RA1 was considered metropolitan, and categories ASGC-RA2 to ASGC-RA5 were considered rural (RA2, inner regional; RA3, outer regional; RA4, remote; RA5, very remote). If location of current clinical practice was overseas, it was categorised as metropolitan.

Univariate and multivariate logistic regression analyses were used to identify factors predictive of rural clinical practice. Multivariate models were adjusted for potential confounding factors: parent’s rural background, partnership status, partner’s rural background, bonded scholarship, boarding school attendance and gap year after high school. Interactions between these determinants were evaluated and included in the final model if statistically significant.

Stata for Mac (version 12.1, SE [StataCorp]) was used for statistical analyses and P < 0.05 was considered statistically significant.

The UQ Behavioural and Social Sciences Ethical Review Committee approved the study.

Results

Of 2833 medical graduates who graduated during the period 2002–2011, 2478 were domestic students. Of these, contact information (email address, postal address or telephone number) was available for 1714. Of these potentially contactable graduates, 142 were initially excluded — emails bounced for 127 (no other contact details were available) and 15 declined to participate. The questionnaire was sent to the remaining 1572, of whom 754 completed it during the period December 2012 to October 2013 (response rate, 48.0% [likely to be an underestimate as the status of sent emails could not be verified]). Characteristics of the participants are shown in Appendix 1.

Of the 754 respondents, 31.3% (236) had a rural background, 36.6% (276) attended UQRCS for 1 or 2 years and 27.2% (205) were currently practising in a rural area — 18.8% (90/478) of those who attended an MCS and 41.7% (115/276) of those who attended UQRCS (P < 0.001). Of those categorised as currently practising in a metropolitan area, 20 were practising overseas.

The proportion of participants currently practising in a rural area was lowest for those with a metropolitan background who attended a metropolitan clinical school (reference group; 16.9% [61/361]), intermediate for those with a rural background who attended a metropolitan school (24.8% [29/117]) and for those with metropolitan background who attended UQRCS (26.8% [42/157]), and highest for those with a rural background who attended UQRCS (61.3% [73/119]). For all rural ASGC-RA categories, the proportion of practitioners who had attended UQRCS was about twice that of those who attended an MCS (RA2, 9.2% [44/478] v 22.5% [62/276]; RA3, 7.5% [36/478] v 14.5% [40/276]; RA4, 1.0% [5/478] v 2.5% [7/276]; RA5, 1.0% [5/478] v 2.2% [6/276]). Also, the geographic distribution of UQRCS graduates matched the distribution of Queensland’s general population more closely than that of MCS graduates (Box 1).

Associations between rural practice and potential predictors of rural practice are shown in Appendix 2. On univariate analyses, the following variables showed an odds ratio (OR) of at least 2.0: UQRCS attendance; rural background; father’s, mother’s and partner’s rural background; any scholarship; bonded scholarship; and rural health club membership. There was no association between sex and rural practice.

Two multivariate models predicting rural practice are shown in Box 2. In the model with main effects (without interaction terms), the following variables were independent predictors: UQRCS attendance (1 or 2 years); rural background; partner with rural background; being single; and bonded scholarship. With the exception of the interaction between UQRCS attendance and rural background, all other two-way and three-way interactions between UQRCS exposure, rural background and partner’s rural background were not statistically significant and were not included in the final multivariate model. The variables relating to rural background exhibited multicollinearity, so parents’ backgrounds were not included in the final multivariate model.

To simplify the interpretation of interaction between UQRCS attendance and rural background, the participants were grouped into six categories. The model that included interaction between UQRCS attendance and rural background shows that a substantial positive interaction exists (Box 2). Compared with the reference group, participants with a rural background who attended UQRCS for 1 and 2 years were 4.44 and 7.09 times as likely, respectively, to practise in a rural area after adjusting for partner with a rural background, being single and bonded scholarship.

To explore the effect of the duration of rural background on the adjusted predictive probability of current rural practice, we developed a logistic regression model with explanatory variables: UQRCS attendance v MCS attendance, years spent in a rural area (since primary school and before commencing UQ medical degree) as a continuous variable, and an interaction term between these two variables (Box 3). The predicted probabilities are divergent across the range of years spent in a rural area. In UQRCS attendees with 10 and 20 years of rural exposure, the predicted probabilities of rural practice are 54% (95% CI, 46%–62%) and 79% (95% CI, 69%–89%), respectively.

To determine the representativeness of our sample, we linked 2360 of the 2478 domestic medical graduates (95.2%) to the AHPRA database to determine current practice location. Characteristics of these two groups and crude ORs of rural practice for them are shown in Appendix 3. All characteristics for these two groups are similar except there was a higher proportion who attended UQRCS in the group of 2478 students from which we recruited participants than the group of 2360 for whom data linkage was done (36.6% v 19.8% [476]), which is consistent with our recruitment strategy.

Discussion

Our study shows that rural background and 1 or 2 years of UQRCS training are independent predictors of subsequent rural practice after adjusting for confounders including partnership status, partner’s rural background and bonded scholarship. For UQRCS attendees, a positive, nearly linear, correlation exists between the probability of rural practice and duration of rural background over a range of 0–20 years. In addition, there was a similar beneficial effect across inner regional, outer regional, remote and very remote areas on rural practice.

It also shows that there is a strong positive interaction between rural background and UQRCS attendance in enhancing the probability of rural practice. In contrast, training students with a rural background at an MCS or training students with a metropolitan background at UQRCS does not have a statistically significant effect on probability of rural practice (although trends exist [ORs, 1.46–1.83]). Our finding that having a bonded scholarship is associated with rural practice is not unexpected given that these graduates are financially committed to working in a rural area or an underserviced area, many of which are rural.

Few studies have used multivariate analysis to identify independent determinants of rural practice. A retrospective survey of 264 rural and 179 urban practitioners in Ontario, Canada, found that rural background and any rural undergraduate training were independent predictors of rural practice (ORs, 3.31 and 2.46, respectively), as were rural postgraduate training, medical school location and being male.19 In this survey, the main effects were reported but the interactions were not.

An Australian survey of 268 rural and 236 metropolitan general practitioners found statistically significant associations between current rural practice and rural background, rural school education and partner with a rural background.20 In the multivariate analysis, only rural primary school and partner with a rural background were independently associated with rural practice (ORs, 2.43 and 3.14, respectively).

A multivariate logistic regression analysis with seven variables using a sample of 359 medical graduates in the US found that the only independent predictors of rural practice were rural background and intention at medical school entry to become a family physician.18 Interaction between the variables was not reported.

At the University of Minnesota (UMN) Medical School, 27% of students had one or two different rural exposures — first 2 years of undergraduate training in a regional city (UMN-Duluth) and/or 9 months with a primary care preceptor in a rural community in their third year (Rural Physician Associate Program [RPAP]).25 In multivariate analysis for the outcome of current rural practice, RPAP, UMN-Duluth and rural background were independent predictors of rural practice (ORs, 4.6, 4.1 and 2.8, respectively). Other confounders were not assessed. When two- and three-way interactions were assessed, only UMN-Duluth training plus rural background was statistically significant and the interaction was negative (OR, 0.56 [95% CI, 0.33–0.96]), in contrast to our finding.

In a recent data-linkage study conducted by the Rural Clinical School of Western Australia (RCSWA), rural practice of 1017 medical graduates who graduated during the period 2002–2009 was determined from the AHPRA register and rural background was defined by medical school entry through a quarantined rural pathway.26 In a multivariate model with interactions, the OR for rural practice for students with rural background who attended RCSWA compared with those with metropolitan background who did not attend RCSWA was 7.5 (95% CI, 3.5–15.8) — a very similar result to ours.

Although the study did adjust for age and sex, no adjustments were made for other confounders. Further, the proportion of graduates practising rurally in the Western Australian and Queensland cohorts is markedly different (7.7% and 27.2%, respectively), which may be partly due to the lower proportion of the population living, over the study period, in areas categorised as RA2 to RA5 in Western Australia compared with Queensland (27% v 42%).

Our study has strengths and limitations. The main strength is that extensive data were available on sufficient numbers of graduates with different exposures to provide power for multivariate analyses with interactions on the outcome of rural practice. The study was conducted in a single large medical school with a uniform curriculum across 4 years, except that in year 3 and/or year 4 clinical training was delivered at UQRCS or an MCS. The findings may be generalisable to other Australian medical schools that offer MCS and RCS placements but perhaps not to regional medical schools that have much higher proportions of rural background students and routinely deliver rural exposure across multiple years of their courses.27

Our sample represents only 30.4% of domestic 2002–2011 medical graduates from UQ, so there could have been participation bias. However, a previous study with a response rate of 64% found that 40% of UQRCS attendees were in rural practice28 — a very similar result to ours (42%). In addition, the consistency of the result for rural practice between 2007–2011 graduates and 2002–2006 graduates (Appendix 2) suggests no participation bias relating to time since graduation. Further, our analysis of representativeness suggests that sampling would not have caused significant bias in the results.

Our results may be affected by self-selection bias regarding clinical school attended — that is, students who attend UQRCS may do so because they intend to enter rural practice. In the first few years of teaching at UQRCS, most students were conscripted, but in recent years 70%–90% have had UQRCS placement as their first preference. However, other factors influence students’ first preferences, such as free or highly subsidised accommodation, academic reputation, patient and teacher access, lifestyle and work opportunities.24,29 Rural intention has been shown to be associated with other rural exposures (rural background or rural upbringing, having a spouse who had lived in a rural area),30,31 which we adjusted for. Also, other unknown confounders may have influenced our results.

Between 2002 and 2012, the number of full-time equivalent doctors practising in Australia per 100 000 population (FTE rate) increased by 33% in metropolitan areas but increased by 50%–75% in rural areas,32,33 indicating reversal of the previous downward trend seen for GPs.1 Nevertheless, the FTE rate remains 33%–39% lower in rural areas for all doctors and 48%–78% lower for specialists, so more needs to be done.

Rural clinical placements are limited and more expensive than metropolitan clinical placements, so policy measures that maximise the cost-effectiveness of RCS programs are warranted. Our results suggest that the probability of rural practice could be increased by policies that increase the proportion of RCS attendees who have a rural background and who attend for more than one year. In addition, preferential recruitment of students with a background of longer-term rural residence should be considered.

1 Location of current clinical practice for study participants (n = 754) by clinical school attended, and distribution of Queensland’s general population*

MCS = metropolitan clinical school. UQRCS = University of Queensland Rural Clinical School. * Distribution of Queensland population according to Australian Bureau of Statistics data for 2010.

2 Multivariate models predicting rural practice

Odds ratio (95% CI)

Model with main effects

UQRCS attendance

MCS attendee

Reference group

UQRCS attendee (1 year)

1.84 (1.21–2.82)

0.005

UQRCS attendee (2 years)

2.71 (1.65–4.45)

< 0.001

Background of participant

Metropolitan background

Reference group

Rural background*

2.30 (1.57–3.36)

< 0.001

Background of partner

Metropolitan background

Reference group

Rural background

3.08 (1.96–4.84)

< 0.001

Not applicable (single)

1.98 (1.28–3.06)

0.002

Bonded scholarship

No bonded scholarship

Reference group

Bonded scholarship

2.34 (1.37–3.98)

0.002

Model with interaction between UQRCS attendance and rural background†

UQRCS attendance and background of participant

MCS attendee, metropolitan background

Reference group

MCS attendee, rural background

1.61 (0.94–2.75)

0.08

UQRCS attendee (1 year), metropolitan background

1.46 (0.85–2.51)

0.17

UQRCS attendee (1 year), rural background

4.44 (2.38–8.29)

< 0.001

UQRCS attendee (2 years), metropolitan background

1.83 (0.91–3.67)

0.09

UQRCS attendee (2 years), rural background

7.09 (3.57–14.10)

< 0.001

Background of partner

Metropolitan background

Reference group

Rural background

3.14 (1.99–4.96)

< 0.001

Not applicable (single)

2.02 (1.30–3.12)

0.002

Bonded scholarship

No bonded scholarship

Reference group

Bonded scholarship

2.27 (1.32–3.90)

0.003

UQRCS = University of Queensland Rural Clinical School. MCS = metropolitan clinical school. * Rural background was defined as at least 5 years since primary school and before commencing University of Queensland medical degree spent a rural area (areas in Australian Standard Geographical Classification – Remoteness Area [ASGC-RA] categories ASGC-RA2 to ASGC-RA5). † Other two-way and three-way interactions (among UQRCS status, rural background status and partnership status) were not statistically significant and were not included in the model.

3 Predicted probabilities of current rural practice, according to duration of rural background, by clinical school attended*

MCS = metropolitan clinical school. UQRCS = University of Queensland Rural Clinical School. * Based on a logistic regression model with the following as explanatory variables: UQRCS attendance v MCS attendance, years spent in a rural area (since primary school and before commencing University of Queensland medical degree) as a continuous variable, and an interaction term between these two variables. Shaded bands represent 95% CIs.

Maximising the commercial benefits of research

It is reasonable to expect that the returns from research contribute economically to generate Australia’s national wealth, given that Australian Government-funded research, such as through the National Health and Medical Research Council (NHMRC), is supported by public taxes. Australia’s national wealth is enhanced through translating research findings into new commercial products, companies or industries. This concept was at the centre of the Wills Review more than 15 years ago, and represents a virtuous cycle of investing in research to create knowledge and translating that knowledge to reap patient and economic benefits that lead to further investment.

NHMRC-funded research has contributed to commercial benefits including the cochlear implant, cervical cancer vaccine and sleep apnoea devices, in addition to other examples such as the research of David Anderson, who has created a simple blood test for HIV patient monitoring in developing countries (with funding from the NHMRC and the Bill and Melinda Gates Foundation).

At the NHMRC, we do much to foster collaborations between research and industry to boost the commercial returns of research. We have an industry stream as part of our Career Development Fellowships, supporting researchers who conduct research with strong industry links. While any research can result in commercial gains, we also have a specific research funding scheme — the Development Grants Scheme — to support research at proof-of-concept (for commercialisation) stage. A review of this scheme found that of the 40 completed projects studied, over half were under some form of possible commercial development and six had resulted in a product to market or were awaiting regulatory approval (http://www.nhmrc.gov.au/_files_nhmrc/file/media/media/rel12/nhmrc_development_grants_review_april_public_121122.pdf). In November 2014, we hosted a workshop to link our funded researchers with commercialisation advisors.

Improving patient care and human health will always be the main role of research, but we need to continue to support the commercial benefits of medical research in our country.

Copayments and the evidence-base paradox

When is evidence merely opinion? The evidence-base paradox is particularly relevant to the GP copayment

The proposed introduction by the Australian Government of a copayment for visits to general practitioners has received much attention. In academic journals and op-ed pages, evidence is cited that supposedly shows that the copayment is a “bad idea”.1 However, is the evidence cited relevant to this particular policy of a copayment of $5 to $7?

The RAND Health Insurance Experiment

From 1971 to 1982, the RAND Health Insurance Experiment (HIE) in the United States randomly allocated families to health insurance plans with zero, 25%, 50% or 95% copayments.2 The study found that “Cost sharing in general had no adverse effects on participant health”. This finding might support a copayment. However, the study also provides evidence against copayments by emphasising the finding that “The poorest and sickest 6 percent of the sample at the start of the experiment had better outcomes under the free plan for 4 of the 30 conditions measured”.

Participants in the RAND HIE were also grouped by the maximum percentage of their income that they were expected to contribute. Thus, even the poorest in the RAND study could be charged copayments ranging from 5%–15% of their gross income, capped at up to US$4000 per year in today’s money (whichever was less). So, is this finding about the poor from the RAND study relevant to a proposed $7 copayment, capped at $70 for the year for holders of concession cards?

Longer term consequences of the copayment

It has been theorised that a copayment will cost the federal government more money in the long run; that it would prevent people from getting necessary outpatient care, leading to a need for more expensive inpatient care. To support this, a 2010 study is often quoted.3 However, this study was limited to people aged over 65 years, and participants already paid copayments which thereafter increased for both primary and specialist care by between 33% and 150% with no yearly cap.

Importantly, a 2014 study specifically looked at the impact of cost-sharing by patients on “low-income populations”.4 The results of this study “largely confirm the conclusions of the RAND Health Insurance Experiment” in regard to the impact of copayments on demand for health care, but specifically found “no statistically significant evidence for ‘offset effects’ that would indicate that reduced use of outpatient services led to increased demand for hospital services.”4 Therefore, the data are mixed. Offset effects might then depend on other policy settings.

Impact on affordability and access to health care

Another widely quoted finding is that 16% of Australians are already unable to see a doctor because of the cost. This implies that people are already going without necessary care, and that this will inevitably be exacerbated by the copayment. This claim is based on a survey of health consumers in 11 countries,5 and to be included in that Australian 16%, individuals had to have, once in the past year, not filled a prescription, not visited a doctor while experiencing a medical problem, and/or not received recommended care because of cost. But is this finding relevant to the currently proposed copayment?

Twenty-five per cent of Australian survey participants already had to pay at least $1000 out-of-pocket for health-related expenses for the year. Therefore, the 16% would include private patients baulking at an out-of-pocket expense. Furthermore, this same survey showed that 14% of Australian participants waited 6 days or more for an appointment to see a doctor or nurse when they were sick or needed care, and half waited more than a month and 18% more than 2 months to see a specialist. Presumably this percentage would be higher for public patients.

Waiting times are a product of a system that is overwhelmed. Waiting leads to loss of economic productivity and quality of life and even death.6 It could be argued that the waiting times listed above indicate a failure to have a truly universal health care system in Australia; more so than would the introduction of a copayment. A copayment might reduce demand2,4 and allow resources to be allocated to reducing waiting time. Thus, accusations that a copayment is immoral7 are unjustified.

Impact on outcomes

A Consumers Health Forum of Australia report acknowledges that “there is very little robust research on the impact of co-payments on the longer term health outcomes of consumers. This makes it very difficult to draw any conclusions about the overall impact of co-payments on health status as the relationship between access to health care, the provision of care and health outcomes is very complex”.8 Particularly relevant then is the Oregon Health Insurance Experiment, in which low-income adults with no health insurance at all (which would require an extreme degree of copayment) were randomly selected (from a state ballot) to receive government health care with “relatively comprehensive benefits with no consumer cost sharing”.9 Those who received free health care predictably increased their use of health services, including primary and preventive care, but this did not lead to actual improvements in basic objective health indicators such as blood pressure, cholesterol levels or diabetes control.10 Despite increased access to primary and preventive care, those with insurance had a 35% increase in hospital admissions10 and a 40% increase in emergency department visits.11 These data are also relevant to studies such as the RAND study cited above, in which it was found that both necessary and unnecessary health care were reduced;2 that is, the RAND HIE demonstrates the complexity of predicting what really is necessary care in relation to actual health outcomes.

Where lies the truth?

Health care is complex, entwined with biology, clinical medicine, politics, ideology, sociology, psychology and patient belief systems. The copayment is an example of what has been described as the fact-checking paradox (or evidence-base paradox);12 that is, the more something really needs the scrutiny of fact-checking, the less it is possible to impartially decipher the facts free from ideological bias.

Enabling the success of academic health science centres in Australia: where is the leadership?

Lack of policy development hinders the effective integration of research, education and health care delivery

The expanding health care demands of our community require that our health system have an expanding knowledge base, enhanced capability, greater process efficiency and more targeted application of clinical interventions. The search for new groundbreaking discoveries should continue unabated (for example, in replicating the success of statins in ameliorating coronary heart disease or antiretroviral therapy in controlling HIV infection). However, there is an equally important, immediate and ongoing daily need for all patients to receive better, safer and more efficient care from highly competent health professionals using existing knowledge and resources. This responsibility must be shared between health administrators, front-line health professionals, and academic teachers and researchers.

In recognition of this shared responsibility, at least four academic health science centres (AHSCs) have been established in Australia in the past 5 years. They comprise partnerships or collaborations between universities and their affiliated research institutes and health service organisations.

There is no universally agreed definition of an AHSC, but most are alliances of geographically co-located entities, with varying descriptions of what they actually do or hope to achieve. However, all AHSCs are committed to a tripartite mission of advancing research, education, and patient care. This mission presents challenges for AHSCs worldwide in responding to demands for high-value, patient-centred care and improved population health. Historically, the research stakeholders within AHSCs have attracted funding primarily to conduct basic research and biomedical studies aimed at new diagnostic and therapeutic discoveries, with less emphasis on education, patient care (especially primary care and preventive medicine), and health services research.1 However, this is likely to change as AHSCs realise they must match their pre-eminence in the science of discoveries with equal commitment to translational and implementation science focused on health system improvement.2 Can AHSCs truly claim to excel in scientific discovery if they are not researching ways of making clinical services more reliably excellent? Can they truly claim to teach high-quality medicine without consistently providing high-quality care? The AHSC needs to be defined as a centre of learning committed to improving health and health care by advancing, applying and disseminating knowledge through a learning health system.

Defining stakeholder roles and functions

Putting this ubiquitous aim of high-quality care for all into operation is the prime responsibility of health departments and health services, and is exacted by multiple key performance indicators (KPIs) and contractual obligations applied to their staff. Universities and research institutes are also subject to a regulatory system, but one that involves different performance measures centred on research output and academic excellence. These different objectives constrain the development of integrated health, education and research partnerships, which need to share a set of common objectives, incentivised by funding arrangements that all parties in the alliance can sign up to.

Such convergence is possible and necessary. In exploring new and better models of care, managers are seeking front-line clinician–researchers with leadership skills who can assist in the process. Engaging practising health professionals in the science of health service innovation presents a challenge. Specialty colleges and other professional bodies need to view and endorse AHSCs as one means of engaging their clinician constituency in health system improvement and the acquisition of requisite skills.3 Front-line consumers of health care must also be involved in determining priorities for research and service delivery.

In turn, senior government representatives and policymakers must show leadership in endorsing and resourcing ASHCs as vehicles for drawing together, with appropriate balance, all of the endeavours directed towards improving health care for, and the health of, the community. Health service managers need to make their services more research-friendly by actively facilitating research governance, ethics approvals, participant access and recruitment, and data collection systems.

The success of AHSCs as vehicles underpinning learning health systems requires structural alignment and functional integration of research, education and clinical service delivery. Accountability for each of these three elements, which are currently held by different agencies (traditionally universities and research institutes for research, teaching hospitals for education, and hospitals and health services, including primary care, for clinical services), must be brought together under one integrated learning health framework. This will not be easy. It requires both bottom-up leadership by local academic and clinical leaders and top-down leadership from government departments, statutory bodies and health service administrations. The boards of AHSCs must overcome the current physical, financial, administrative, professional, legal and historical factors that currently constrain research, education and service excellence within the individual partnering organisations. The operations of these new partnerships must be aligned so that new and better ideas and technologies that solve priority population health problems can be introduced more quickly, efficiently and effectively.

Encouragingly, there is evidence that the need for such alignment is recognised and is beginning to happen within AHSCs in the United States,4 United Kingdom5 and Canada,6 driven in no small measure by government policies, such as the Affordable Care Act 2010 in the US, and the Health and Social Act 2012 in the UK. In particular, AHSCs in the UK are now being surrounded by academic health science networks to ensure broader implementation of knowledge into patient care.5 In Australia, calls for recognition of the value of university teaching hospitals7 have drawn attention to our nation lagging behind international developments in integrating science and clinical service delivery,8 and have advocated for government action in developing AHSCs.

The research community perspective

In late 2010, the National Health and Medical Research Council (NHMRC) released a discussion paper that proposed to “invite consortia of universities, hospitals and medical research institutes to apply for recognition for excellence in research and research translation”.9 The NHMRC proposed that such centres be designated “Advanced Health Research Centres”. This descriptor was criticised by deans from the Group of Eight Universities8 for its eschewing of academia. The apparent tight alignment of “excellence” with basic science research, disproportionately rewarded by the NHMRC project grants system,1 also constrained any significant shift of academic mindsets towards applied clinical research and implementation of knowledge. This shortcoming was further profiled in the McKeon Review of health and medical research (HMR) in Australia,10 commissioned by the federal government. That review involved a wide diversity of stakeholders, and it proposed “an overarching message . . . [about the] lack of a sufficiently strong connection between HMR and the delivery of healthcare services”.10 It highlighted the need for an academic leadership body, as well as financial commitment and closer integration of research centres, if research was to be better embedded in the health care system. It also recognised the need for more commercialisation of research in parallel with translating evidence into practice. The review was released in February 2013, but the federal government is yet to formulate a policy and a structure for meeting these identified needs.

This shift towards closer integration between those who generate and those who use research has continued with the establishment of an NHMRC research translation faculty and, more recently, calls for submissions from academic and health care precincts to be recognised as Advanced Health Research and Translation Centres (AHRTCs).11 This new concept places yet more emphasis on how the scientific output of AHRTCs directly influences clinical practice and teaching, health care policies, and population health outcomes, both locally and more broadly.

Uniting for a common cause

We acknowledge that the prevailing uncertainties in the absence of a national plan for AHSCs may make whole-hearted commitment to comprehensive integration of academic and service organisations more difficult. Universities and biomedical research institutes are concerned that some of their research funding may be diverted to health service delivery, while health services have concerns that the reverse could occur, especially given the potentially large scope of clinical and health services research that will be required to drive evidence implementation and innovation across the entire health care system.

This uncertainty impedes a concerted effort to bring applied clinical and health services research into both mainstream academia and service delivery, as evidenced by the relatively few centres of clinical effectiveness or health service evaluation in this country. Although the science of implementation is receiving increasing attention internationally,12 maximal benefit from clinical research, knowledge translation and service innovation will only be realised by collaborative academic–service partnerships that cover the whole spectrum, from basic science to front-line patient care. Within such partnerships, the mindsets of all agencies and individuals involved, including those of practising clinicians, must converge on creating learning health care systems that aspire to deliver the best possible health care within declared financial constraints. Strategies for facilitating such convergence in our AHSC13 are provided in the Appendix.

The mission for AHSCs and AHRTCs is to serve as vehicles for integrating academia and service delivery for the benefits of the community. Whether they succeed will depend on whether the partnering organisations within them, government departments, the NHMRC and the health professions believe in their worth and are prepared to openly support them with the required resources and governance frameworks. We challenge government and all key stakeholders to step forward and develop policies for ensuring their creativity, relevance, and sustainability.

The future of Queensland’s rural medical workforce

An update from the Rural Doctors Association of Queensland

The Rural Doctors Association of Queensland (RDAQ) is optimistic about the future of rural medicine. Our 25th anniversary conference in June was an outstanding celebration of bush camaraderie, the RDAQ’s rich history, and the intellectual contribution of the giants of rural medicine on whose shoulders we stand (all 23 past presidents were present). Colleagues from across the state, their families and students attended, and relaxed to covers by the famous Rural Rednecks.

However, the rural workforce faces major challenges. Like the three-legged stool of rural clinical placements — accommodation, clinical activity and clinical teachers — the rural workforce has three pillars that are similarly interdependent. All three pillars — supply, training and the working environment (closely linked to retention) — must be strong to ensure that the structure does not collapse.

Regarding the first pillar, the problems of inadequate supply, insufficient succession planning, and overreliance on international medical graduates (IMGs) and short-term locums are well documented. Recent data demonstrate our dependence on overseas-trained colleagues — just 53% of Queensland’s regional, rural and remote medical workforce was trained locally.1 We are a long way from being self-sufficient.

Queensland has a substantial rural and remote population, many mining towns, a large Indigenous population, and a mix of private and public health care funding. Although Queensland Health has reconfirmed commitment to staffing and funding small rural hospitals,2 this support needs to be delivered flexibly, tailored to local needs. State and federal lines of responsibility should not be rigid and bureaucratic; they need to enable creative and innovative solutions that meet community needs and deliver appropriate workforce models. The hospital and health service roll-out3 has the potential to promote local innovation and problem solving, but we must ensure that the needs of rural communities are addressed and that the momentum of statewide reform is maintained.

We are encouraged by the fact that rural health matters are on the agenda; we have the ear of government and we have a strong portfolio of rurally focused data, policies, organisations, events, education and family support. The Queensland Minister for Health, Lawrence Springborg, is committed to reopening maternity services, starting with Beaudesert and then Cooktown, Weipa and Ingham.4 The RDAQ is engaged in this discussion and working closely with the Hospital and Health Services and local clinicians.

It is apparent that much of the growth in numbers of health care students, registrars and education providers in Queensland is occurring in regional and rural areas. We need to create additional generalist-focused training places in non-metropolitan locations to match training to workforce needs and ensure the communities most in need benefit from this workforce supply. Regionally based training models are demonstrating strikingly different outcomes compared with traditional models. For example, two-thirds of James Cook University medical graduates undertake non-metropolitan internships compared with one in six medical graduates from other universities. Across eight postgraduate years, more than two-thirds of James Cook University medical graduates are practising outside metropolitan areas, compared with 20% of all Australian clinicians.5,6

Regarding the second pillar, training, can we train the increasing numbers of rural students and registrars who are interested in rural practice? We are encouraged by students’ commitment to social accountability. There is no shortage of altruistic young people committed to making a difference. The National Rural Health Students’ Network (http://www.nrhsn.org.au) — the future of rural health — aims to harness good intent by networking with student rural health clubs such as Rural Health in the Northern Outback (Club RHINO). Dhoom Medical Charity, Supporting All Nations Towards Equality (SANTE), Insaka and Run to Better Days are other examples of student organisations committed to social justice.

While innovations abound and Australian training is world class, we need to maintain attractive, properly resourced training pathways. The acclaimed Queensland Rural Generalist Pathway is expanding and attracting international recognition.7 The pathway has attracted more graduates rurally, especially to generalist positions in rural hospitals. We must ensure that strengthening public career pathways does not attract registrars away from private general practice, a cornerstone of rural clinical care and training. We need more rural doctors but must also achieve “An equilibrium balancing public and private medical workforce”.8

Looking at the third pillar, the working environment, raises the question of whether there are good jobs and good places to work in rural areas. The Statewide Rural and Remote Clinical Network, chaired by RDAQ past president Bruce Chater, does ground-breaking work guiding the effective and safe delivery of rural and remote health services. Key resources include Better health for the bush, which describes “safe, applicable healthcare for rural and remote Queensland”, and the Queensland Rural and Remote Health Service Framework, which outlines clinical services that communities can expect locally.2,9 These initiatives provide clarity around the environment in which tomorrow’s workforce will train and work. They present a consistent approach to classification and terminology which can, therefore, “provide a general overview of the service mix, service capability and workforce profile for each classification of rural and remote health facility”.2

Each of the three interdependent pillars is vital: if one collapses, the whole structure will fall. And although they are all strong, we must remain vigilant to keep them strong. Finally, we must remember that rural health matters are everyone’s business. Where does our food come from? What sustains our economy? As a South African medical officer recently observed about compulsory rural service, “It is the rural experience which gives doctors the humanity our patients yearn for in us”.10