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Preference: General Medicine

312

Reporting rural workforce outcomes of rural-based postgraduate vocational training

To the Editor: Following calls to bridge the evidence gap regarding rural exposure and uptake into rural medical practice,1 Rural Clinical Schools (RCS) regularly report graduate rural career outcomes. This permits comparison of approaches and quality improvement of the program.

Comparatively little is reported from the postgraduate rural-based vocational training programs. Just as the evidence gap for RCS required bridging, a similar approach should be adopted as standing key performance outcome indicators of regional training providers.

Ministerial review of general practice training previously identified a chronic undersupply of rural doctors as a “pressing concern” for government.2 Rural vocational training needs were determined as a priority in establishing the regionalised Australian General Practice Training (AGPT) program in 2002. The program requires 50% of vocational training to be conducted in rural areas to improve distribution of general practitioners into rural and regional areas.3 Yet, unlike RCS, in the AGPT program, training providers are not required to routinely report their rural workforce outcomes.

Two key performance outcome indicators are proposed: rural retention rate (RRR) and advanced rural skills proportion (ARSP). RRR reflects the number of registrars in rural practice 1 or more years after completing training. Advanced rural skill training acquisition is not available to RCS, but has been identified as increasing rural retention,4 and is intimately related to rural retention and workforce outcomes. ARSP is the proportion of all completing registrars who achieved Fellowship in Australian College of Rural and Remote Medicine or Fellowship in Advanced Rural General Practice.

These are both reasonable and appropriate measures of rural workforce contribution. Such sentinel measures would permit comparison between programs, leading to further improvement of vocational rural medical education.

In one dedicated rural medical vocational training program in Queensland, the RRR is 75% (38/51 registrars since the exclusive rural pathway was delivered) and the ARSP is 49% (25/51).

Dedicated rural medicine training contributes to the short- and intermediate-term rural medical workforce. This contribution should be measured using key sentinel measures in addition to detailed multivariate analyses.

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)†

P

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)†

P

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)†

P

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.

Toxic epidermal necrolysis — an investigation to dye for?

We report the first case in Australia, as confirmed by the Therapeutic Goods Administration, of toxic epidermal necrolysis associated with the iodinated contrast medium iopamidol. It serves as a warning about the use of contrast in imaging and cardiac catheterisation and a reminder of the need for increased awareness of the issue.

Clinical record

A 44-year-old woman presented to the emergency department with a 3-day history of a progressive rash, fever, malaise and mucosal ulceration. She met the diagnosis of toxic epidermal necrolysis (TEN) based on the following criteria: bullae and desquamation affecting about 84% of the body surface (Box 1 and Box 2), buccal and vaginal ulceration, a positive Nikolsky sign (this is a useful sign in bullous skin diseases and can be demonstrated by rubbing the skin surface, which will blister within a few minutes if the sign is positive), fever, tachycardia and mild hypotension. She also had abnormal results of liver function tests: bilirubin level, 69 µmol/L (reference interval [RI], < 20 µmol/L); alkaline phosphatase level, 180 U/L (RI, 25–100 U/L); γ-glutamyl transferase level, 499 U/L (RI, < 30 U/L); alanine aminotransferase level, 1730 U/L (RI, < 30 U/L); and aspartate aminotransferase level, 638 U/L (RI, < 30 U/L). She had a white cell count of 4.3 × 109/L (RI, 4.0–10.0 × 109/L) and a raised C-reactive protein level of 53.1 mg/L (RI, < 5 mg/L).

The patient was immediately transferred to the burns unit and managed with nanocrystalline silver dressings, intravenous immunoglobulin, aggressive fluid and electrolyte balance therapy, analgesia and intravenous antibiotics. She was discharged home on Day 15.

Histopathological examination (Box 3) showed extensive epidermal necrosis and subepidermal clefting with a sparse superficial perivascular infiltrate of lymphocytes, occasional neutrophils and eosinophils, and exocytosis of cells into the epidermis. Results of staining for immunofluorescence were negative. This was consistent with the clinical diagnosis of TEN, and the possibility of pemphigus vulgaris was excluded.

The patient’s only recent exposure to medications included 150–200 µg of thyroxine sodium daily for 19 years and 2.5 mg of indapamide daily for 6 months. The patient underwent a computed tomography (CT) neck scan with the contrast medium iopamidol about 4 weeks before the development of symptoms. The patient had recently (3 weeks before onset of rash) stopped taking a herbal “liver cleanser”. She had been taking this intermittently for 2 months. She had no recent travel history or vaccinations.

The patient recalled having a previous CT scan of the neck before her surgery 17 years ago. Unfortunately, however, any records of this had been destroyed.

The patient’s past medical history was notable for mild stable hypertension, hypothyroidism and a benign mixed salivary gland tumour electively excised 17 years earlier.

Discussion

Toxic epidermal necrolysis (TEN), or Lyell syndrome, is a rare and life-threatening severe systemic condition associated with dramatic cutaneous sloughing of up to 100% of the body surface area. The incidence of TEN is two cases per million person-years.1 It is characterised by necrosis and subsequent detachment of the epidermis from the dermis in more than 30% the body surface. If not treated and managed promptly, the consequences can be fatal; patients are vulnerable to infections and sepsis leading to death. The mortality associated with TEN is high, at 30%–40%.2

At the other end of the spectrum, and more common, are mild-to-moderate skin reactions to contrast media (CM). These include, in increasing severity, lichenoid reaction, erythema multiforme and Stevens–Johnson syndrome.3 Patients at risk of late skin reactions are those with a previous history of CM reactions.3,4

TEN is attributed to medications in 80% of cases.1 The most commonly associated medications include sulfonamides, penicillin and other antibiotics, anticonvulsants, oxicam nonsteroidal anti-inflammatory drugs, allopurinol and corticosteroids.5 TEN commonly occurs 1–3 weeks after the start of therapy. Other triggers include infections, malignancy and vaccination.1

The dermatological reactions caused by CM can be classified as early or late reactions. Early reactions occur soon after injection of the contrast medium, and late reactions occur within a week. The incidence of late adverse reactions is 2%.3,6 They commonly present as maculopapular erythema, angioedema and urticaria. Evidence to date suggests that late reactions are more common with non-ionic CM, in particular dimers,3 despite non-ionic CM being touted as having fewer adverse reactions. Late reaction incidence with non-ionic CM varies between 8% and 71%.3

Iodinated CM can be divided into ionic and non-ionic contrast medium. The ionicity pertains to the osmolality the CM create in blood; ionic CM create higher osmolality leading to CM reaction. The move from the use of ionic CM to non-ionic CM was based on the need for an agent with fewer adverse effects and equal or slightly improved diagnostic efficacy.7 The morbidity and mortality associated with non-ionic CM were less than for ionic CM.8 Although non-ionic CM have these advantages, ionic CM are still in use today. A recent study highlighted that although non-ionic CM are the best tolerated in the early phase, they are associated with a higher level of adverse effects such as late skin reactions.8 Iopamidol is a non-ionic contrast medium.

There have been several cases of TEN caused by CM reported in the literature. Commonly, the cases have involved repeated exposure or sensitisation to the CM in the cardiac catheter laboratory over a period of days to even years.9–11 TEN occurs with subsequent exposures to the CM administered. In our case report, there was a history of prior CT scan of the neck; however, records of the scan are no longer held by the radiologist to verify the date and contrast medium used.

TEN has also been shown to be caused by gastrointestinal oral CM.12

Only two published cases of TEN have been attributed to the administration of iopamidol. The first case is of a young boy with subsequent exposure to iopamidol.10 The case was not biopsy-proven TEN, but was based on clinical diagnosis. The second case is that of a patient who underwent intravenous urography for investigation of systemic lupus erythematosus with renal involvement.13 The patient died despite intensive care and support. Our case report would be the third reported case of TEN caused by iopamidol.

Although it is difficult to be sure that iopamidol was responsible for the development of TEN in our patient, it is highly likely to be the cause. She had been on indapamide for a period and had been taking the herbal “liver cleanser” intermittently. One recent case of herbal medicines and TEN has been reported.14 However, as noted by the author, it was difficult to determine the causative agent.

It is important to be aware of the risk of CM and to think twice about the necessity of CM in imaging. Although rare, life-threatening adverse effects such as TEN should lead to reconsideration of contrast dyes, as patients may suffer unnecessarily or lose their lives.

1 Bullae and desquamation of back

2 Desquamation of both feet

3 Skin punch biopsy sample showing extensive epidermal necrosis (bracket) and subepidermal clefting (asterisk) with mixed inflammatory infiltrate and negative results of staining for immunofluorescence

Roadmap for physician trainees’ Everest

ALTHOUGH NOT DIRECTLY endorsed by the Royal Australasian College of Physicians (RACP), Examination medicine is widely considered to be the definitive manual for approaching the exam that many physicians describe as the most stressful experience of their careers. The 7th edition retains the overall structure of its previous incarnation, with the early chapters providing an overview of physician training in Australia and New Zealand and touching briefly on the FRACP written exam. The book remains dedicated to how candidates for the FRACP examination should approach the long and short case formats that make up the exam.

This new edition makes even greater use of tables and figures to present key information in ways that are relatively simple to absorb. Clinical images abound and are now predominantly in colour. Useful tips and potential pitfalls for the unwary candidate are flagged. A welcome new chapter has been added that contains examples of potential long cases — highlighting how an examiner may approach a case and what discussion points are likely to arise. Additional online video content is included with this edition in the form of a model long and short case.

The 7th edition builds on the strengths of previous editions and hence remains an invaluable reference, not only for FRACP candidates but also for advanced trainees and consultants looking to refresh and revise their clinical skills. Professor Talley and Dr O’Connor are well placed to comment on the standard expected at the exam being, at the time of publication, the President of the RACP and a member of the Senior Examination Panel, respectively. Medical students and clinicians from other specialties will also benefit from the systematic and thorough approach to clinical assessment and diagnosis presented in this book, although they may find the level of detail surplus to requirement.

A guide to OSCEs: my findings on examination

With over 25 years of development and review contributing to Talley and O’Connor’s Clinical examination, it is no wonder that the educational guide, now in its 7th edition, has become an essential reference for medical students.

While maintaining its dependable content, the new edition has added depth, particularly regarding investigations useful for the diagnosis and ongoing evaluation of common differential diagnoses. Examples include indications for and interpretation of echocardiograms, abdominal computed tomography scans and lumbar punctures. This is consistent with a clear shift towards supporting students in their preparation for assessment, which involves more than physical examination.

The exam focus is substantiated by the addition of “OSCE (objective structured clinical examination) revision topics”, “T&O’C essentials” and a far more reader-friendly format. While the book preserves its system-based structure, the clear division of content ensures information is significantly more accessible.

The guide takes a further step towards preparing students for their future medical practice, with additional chapters on the assessment of the geriatric patient, of the acutely ill patient and of death. While the latter begins to extend beyond the immediate scope of a student, the topics are only covered superficially and, at most, would serve as a starting point for supplemental reading in emergency and end-of-life care. It is important to bear in mind that these topics are not a pressing concern to students in their current clinical roles and, as such, would not justify significantly greater coverage.

There is no doubt that Clinical examination is the cornerstone reference for medical students studying the skill of physical diagnosis. The systematic guide also provides experienced physicians with a reliable and thorough resource on examination findings and patient interactions. It is highly recommended to all those seeking to improve their understanding of, or requiring succinct templates for, structured physical examinations.

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)

P

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.

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

Rural dental health care and the workforce challenges

To the Editor: Rural Australians have poorer oral health than metropolitan Australians,1 and this oral health gap is not decreasing.2 Possible reasons for poor oral health among rurally located people are that they tend to be of lower socioeconomic status,3 which is known to be linked with a marked inequality in oral health,4 and that rural people have a different concept of health,5 which may influence their use of health services.

Another reason that has been suggested for poor oral health among rural people is poor access to dental care due to an inequitable geographic distribution of the dental workforce,6 with progressively fewer dentists as remoteness from major cities increases. It is not surprising, then, that dentists practising in non-capital city areas have been supplying more patient visits per year, and were more likely to be busier than they would like to be, compared with dentists in capital cities.7

Since 2005, four new dental schools have opened in Australia, at Griffith, La Trobe, James Cook and Charles Sturt universities; many of the existing dental schools have increased their student numbers; and there has been a large increase in the number of dentists coming from overseas. Anecdotal evidence indicates that rural dental practices, which have had trouble attracting dental practitioners in the past, are now receiving cold calls from new graduates looking for work. The problem of the rural undersupply of dentists may be correcting itself.

However, a continuous churn of highly skilled and experienced practitioners being replaced by less experienced ones could result, since practitioners tend to move back to capital cities once they gain some experience.8 A lack of experienced dentists could lead to rural patients being caught up in a repeat restoration cycle9 of having teeth filled and later having the same teeth refilled as restorations collapse or more decay occurs. Although ensuring that rural towns have fluoridated water supplies would be the most cost-effective way to reduce the incidence and prevalence of dental caries among rural people10 and to limit the repeat restoration cycle, enabling rural people to access dental care will decrease the effects of the disease, particularly if treated in its early stages.

Despite an increase in interest among new graduates and overseas-trained dentists in working in rural areas, there is still a great need for specialist dental services, such as oral and maxillofacial surgery, oral medicine specialist care, special needs dentistry, periodontics, endodontics and orthodontics, in rural areas. People in rural areas have high medical and dental needs and require access to experienced health practitioners. One way to provide this and to ensure a continuation of dental care in rural communities is to develop an advanced rural dentist program similar to the rural medical generalist program.11 The program could cover some procedures of specialist dental practice, some areas of practice outside of dentistry and research into rural oral health, and it could also teach the skills to create stronger links and cooperation between rural health care practitioners.

A new blood glucose management algorithm for type 2 diabetes: a position statement of the Australian Diabetes Society

Large randomised controlled trials (RCTs) have demonstrated that controlling blood glucose level (BGL) in people with type 2 diabetes (T2D) is important for preventing microvascular complications.1,2 Lowering BGL may also reduce myocardial infarction, though not necessarily stroke or all-cause mortality.3

In 2009, the Australian Diabetes Society (ADS) published a position statement recommending individualisation of glycaemic targets.4 The general glycated haemoglobin (HbA1c) target for most people with T2D is ≤ 53 mmol/mol (7%), however:

  • in people without known cardiovascular disease, a long duration of diabetes, severe hypoglycaemia or another contraindication,4 the target is ≤ 48 mmol/mol (6.5%);
  • in people with reduced hypoglycaemia awareness or major comorbidities, the target may increase to ≤ 64 mmol/mol (8%);
  • in people with limited life expectancy, aim for symptom control; and
  • in women planning a pregnancy, aim for the tightest achievable control without severe hypoglycaemia; preferably ≤ 42 mmol/mol (6.0%).

Despite the increasing range of therapies available, achieving glycaemic targets can be difficult. To assist with selecting glucose-lowering pharmacotherapy for people with T2D, the ADS developed this position statement (Box 1). Its aim is to describe the place of the various drug classes in the glucose-lowering therapeutic algorithm (Box 2), drug options in the setting of renal impairment (Appendix 1) or hepatic impairment, the Pharmaceutical Benefits Scheme (PBS) prescribing restrictions for obtaining subsidised products in Australia (Appendix 2), and the current PBS-subsidised and unsubsidised costs (Appendix 3). A more detailed version of the statement that will be updated annually is available on the ADS website (http://www.diabetessociety.com.au).

Drugs

Metformin

Metformin decreases hepatic glucose output, lowers fasting glucose levels and is generally weight-neutral. It decreases HbA1c level by up to 15–22 mmol/mol.5 Gastrointestinal side effects are common. Metformin should be started at low doses and titrated. People with gastrointestinal side effects should be offered one of the slow-release formulations, which cost about 40% more per dose (Appendix 3). Metformin is contraindicated for patients who have severe renal, hepatic or cardiac failure (Appendix 1).

Sulfonylureas

Sulfonylureas have favourable long-term safety and outcome data and are cheap and effective, with decreases in HbA1c level of up to 7–16 mmol/mol when combined with metformin.6–8 They trigger insulin release in a glucose-independent manner. Their main side effects are hypoglycaemia and weight gain. The risk of hypoglycaemia is highest with sulfonylureas with long half-lives and renally excreted active metabolites, such as glibenclamide.7

Dipeptidyl peptidase-4 inhibitors

Dipeptidyl peptidase-4 (DPP-4) inhibitors decrease inactivation of glucagon-like peptide-1 (GLP-1), thereby increasing its availability. GLP-1 improves β-cell function and insulin secretion and slows gastric emptying. A meta-analysis reported decreases in HbA1c level of 7–8 mmol/mol, except for vildagliptin (11 mmol/mol).9 Common side effects include gastrointestinal disturbance and nasopharyngitis, which often subside over 10–14 days. Rash is a rare but potentially serious side effect. Postmarketing surveillance has reported an association between DPP-4 inhibitors and pancreatitis. People with diabetes have an increased background risk of pancreatitis and it remains unclear whether DPP-4 inhibitors augment this risk.10

At the time of writing, there were five DPP-4 inhibitors available in Australia that were approved for PBS-subsidised use with either metformin or a sulfonylurea but not both. Triple therapy and monotherapy are not PBS-subsidised; nor is dual therapy with insulin or sodium–glucose cotransporter 2 (SGLT2) inhibitors (Appendix 2).

Thiazolidinediones

Thiazolidinediones (TZDs) are transcription factor peroxisome proliferator-activated receptor PPARγ agonists, which lower BGL through insulin sensitisation. Side effects include weight gain, fluid retention, heart failure and an increased risk of non-axial fractures in women.11 Pioglitazone is associated with an increased risk of bladder cancer. In a limited number of people, TZDs combine well with metformin and sulfonylureas.

Acarbose

Acarbose is an α-glucosidase inhibitor that slows intestinal carbohydrate absorption and reduces postprandial BGL. Its main side effects are bloating and flatulence, which lead to discontinuation in up to 25% of people. If tolerated, it can be effective, particularly when combined with metformin.

Sodium–glucose cotransporter 2 inhibitors

Two SGLT2 inhibitors (canagliflozin and dapagliflozin) were listed on the PBS in 2013. These drugs inhibit a renal sodium–glucose cotransporter to produce urinary glucose loss and decrease BGL. Urinary glucose loss is the mechanism for the weight loss associated with these drugs. Side effects include dehydration, dizziness and increased risk of genitourinary infections. The first two can be prevented with adequate fluid intake, and the latter diminished with meticulous hygiene. Use with loop diuretics should be avoided. SGLT2 inhibitors have diminished efficacy in people with renal impairment. PBS reimbursement requires use with metformin or a sulfonylurea but not both.

Glucagon-like peptide-1 receptor agonists

GLP-1 receptor agonists (GLP-1RAs) are administered by subcutaneous injection. They stimulate β-cell insulin release and slow gastric emptying, which contributes to weight loss but may cause nausea and vomiting. Effects on BGL are slightly superior to those of oral agents. An increased risk of pancreatitis (about 50% above a baseline of one to two episodes per 1000 patient-years in T2D) has been reported.10 These agents should be avoided in patients with a history of pancreatitis or pancreatic malignancy.

Insulin

Insulin has extensive effects on metabolism and is generally necessary for cellular glucose uptake. Short-, intermediate- and long-acting insulins are available, as well as premixed preparations. The major side effects are hypoglycaemia and weight gain. In many people, insulin is initiated only after an unnecessarily prolonged period of hyperglycaemia. Insulin is the most potent glucose-lowering agent. With adequate dosage and dietary adherence, it can almost always achieve target BGL. Insulin should be considered early if BGL is very high.

Treatment algorithm

The algorithm in Box 2 summarises the available clinical evidence for pharmacotherapeutic strategies to achieve target HbA1c levels in people with T2D. At each step, there are proven and effective approaches. Indications that may be PBS-subsidised are highlighted in the algorithm by a red border. It should be noted that use of a medication outside PBS-approved indications requires that it be purchased through private prescription. The current cost of these medications is given in Appendix 3. The algorithm is structured with a usual approach to treatment initiation, and intensification and alternative approaches at each stage.

First-line treatment

First-line treatment is appropriate diet and exercise. This should be reinforced at every stage. Both weight loss and prevention of weight gain are important.

When lifestyle measures alone no longer achieve desired targets, metformin should be added if there is no contraindication. If metformin is not tolerated or is contraindicated, a sulfonylurea should be used. Other medications are also available but, apart from acarbose and insulin, are not PBS-subsidised as initial treatment.

Second-line treatment

If glucose control is not achieved with a single agent, there are many second-line treatment options. Sulfonylureas are good second-line agents, achieving similar decreases in HbA1c level as other second-line oral agents, for about 25% of the daily cost (Appendix 3). For patients who experience problematic hypoglycaemia, weight gain or other side effects, an alternative agent should be considered. The most common alternative second-line agent is a DPP-4 inhibitor. These are available in combination tablets with metformin, which may improve compliance.

SGLT2 inhibitors are another option and are PBS-subsidised with either metformin or a sulfonylurea. Addition of acarbose is another second-line alternative. GLP-1RAs can also be used with metformin or a sulfonylurea as second-line therapy. As always, insulin is an option that should be considered, especially if the HbA1c level is > 75 mmol/mol (9%) after use of an oral agent, as the likelihood of achieving good glycaemic control with oral agents alone becomes lower.

Third-line treatment

After failure of dual oral therapy, treatment options become more complex. Metformin should be continued for its insulin-sensitising effects unless contraindications develop. Ineffective therapies should be ceased and substituted with a different medication. Comparative evidence from RCTs to inform prescribing is scarce. The options are triple oral therapy or the addition of a GLP-1RA or insulin.

Triple oral therapy

Metformin, sulfonylurea and DPP-4 inhibitor: Limited RCTs examining the addition of sitagliptin12 and linagliptin13 to metformin–sulfonylurea have demonstrated reductions in HbA1c level of 7–10 mmol/mol versus baseline and placebo, respectively,12,13 but with small increases in weight that are significant in some studies. With triple therapy, the advantage of lower risk of hypoglycaemia with DPP-4 inhibitors appears to be lost.12,13 It is reasonable to escalate from metformin and a sulfonylurea or metformin and a DPP-4 inhibitor to triple therapy with all three. However, this combination is not currently PBS-subsidised.

Metformin, sulfonylurea and thiazolidinedione: Trials of triple therapy with metformin, a sulfonylurea and a TZD show HbA1c lowering of about 11 mmol/mol, but with an increase in weight of 3–5 kg and increased hypoglycaemia.14 Pre-emptive decreases in the sulfonylurea dose should be considered. Pioglitazone is PBS-subsidised for triple oral therapy with metformin and a sulfonylurea.

Metformin, sulfonylurea and SGLT2 inhibitor: Limited trials indicate that triple therapy with metformin, a sulfonylurea and an SGLT2 inhibitor may be an effective combination, but this is not PBS-subsidised. A 52-week RCT comparing the addition of canagliflozin or a DPP-4 inhibitor to metformin–sulfonylurea found that HbA1c level decreased by 11 mmol/mol with the SGLT2 inhibitor versus 7 mmol/mol with the DPP-4 inhibitor. There were greater reductions in weight and blood pressure with the SGLT2 inhibitor, and no difference between groups in hypoglycaemia.12

Metformin, sulfonylurea and acarbose: Acarbose is PBS-approved for triple therapy with metformin and a sulfonylurea. One double-blinded crossover trial of this combination reported a 1.9% decrease in weight and a 15 mmol/mol decrease in HbA1c level with the addition of acarbose.15 Acarbose in other dual-therapy combination studies more commonly decreases HbA1c level by about 6–9 mmol/mol.16

Injected agents

Metformin, sulfonylurea and GLP–1RA: Triple-therapy studies of metformin, a sulfonylurea and a GLP-1RA are few but show reductions in HbA1c level of about 11 mmol/mol, with a little weight loss.17–19 Hypoglycaemia is common but may be attenuated by reducing sulfonylurea dosage. The available GLP-1RAs have approval from the Therapeutic Goods Administration for use with metformin and sulfonylurea, but only exenatide is PBS-subsidised.

Insulin: Insulin can be used at any stage in the treatment cascade. It is commonly initiated as once-daily basal insulin added to oral drugs, particularly metformin. Alternatively, it can be initiated as once- or twice-daily premixed insulin, again usually in combination with metformin. Insulin therapy can be intensified by combining long-acting insulin with multiple injections of short-acting insulin.

Recent studies have explored combining insulin with newer therapies, including DPP-4 inhibitors, SGLT2 inhibitors and GLP-1RAs, with good effect. These combinations are not PBS-subsidised.

Glucose-lowering therapy in people with renal or hepatic impairment

In the setting of renal dysfunction, pharmacokinetic changes develop that may increase the risk of hypoglycaemia and side effects. Changes are most evident with chronic kidney disease stages 4 and 5, and must be considered when deciding on therapy (Appendix 1). Drug doses may need reduction or cessation. Among the oral agents, glipizide and gliclazide can be used at a reduced dose up to stage 4, as can sitagliptin. Linagliptin is acceptable without dose adjustment, even with dialysis.

Liver disease may also affect pharmacokinetics. Most drugs do not need discontinuation in the setting of mild–moderate hepatic impairment. Drug half-lives, doses, interactions and risks of drug-specific adverse effects should be considered. There is little experience with use of the newer drug classes in people with advanced hepatic impairment.

Glucose-lowering therapy in older people

In older people, glycaemic targets need to be moderated in light of life expectancy, cognitive impairment or frailty.4 This may mean that the glycaemic target should be symptom control only, or ≤ 64 mmol/mol (8%). Assessment of estimated glomerular filtration rate is important, as serum creatinine level is not as reliable a marker of renal function. Cardiac dysfunction is more common in older people, and severe congestive cardiac failure is a contraindication to metformin. Use of multiple drugs should prompt regular review, to minimise polypharmacy. Drugs should be used at the minimum doses needed to achieve practical and safe glycaemic targets.

Conclusions

Diabetes is a progressive condition and, as such, glycaemic targets should be reviewed at regular intervals. The importance of diet and exercise should be reinforced at each step of the therapeutic pathway.

With the range of therapies now available in Australia, there is considerable scope for individualising therapy. If the patient experiences adverse effects with one glucose-lowering agent, another should be substituted. This will often take the form of combination therapy. With diabetes education and an engaged patient, it is now possible to achieve good glycaemic control in more people with T2D.

Abbreviations

ADS

Australian Diabetes Society

BGL

Blood glucose level

DPP-4

Dipeptidyl peptidase-4

GLP-1

Glucagon-like peptide-1

GLP-1RA

GLP-1 receptor agonist

HbA1c

Glycated haemoglobin

PBS

Pharmaceutical Benefits Scheme

RCT

Randomised controlled trial

SGLT2

Sodium–glucose cotransporter 2

TZD

Thiazolidinedione

1 Position statement development process

The Australian Diabetes Society (ADS) council appointed the authors to draft the position statement, with a focus on results of recent trials of triple therapy and newer agents.

The draft statement was reviewed by the current ADS council and revised, then sent to all ADS members for comment and revision.

2 Australian management algorithm for lowering blood glucose level in people with type 2 diabetes*

HbA1c = glycated haemoglobin. UKPDS = United Kingdom Prospective Diabetes Study. DPP-4 = dipeptidyl peptidase-4. SGLT2 = sodium–glucose cotransporter 2. TZD = thiazolidinedione. GLP1-RA = glucagon-like peptide-1 receptor agonist.

* Blue boxes indicate usual therapeutic strategy; white boxes indicate alternative approaches; and red borders indicate therapies that are potentially subsidised by the Pharmaceutical Benefits Scheme. Compliance should be assessed before changing or adding new therapies, and therapies that do not improve glycaemic control should be ceased.

† Unless metformin is contraindicated or not tolerated, it is often therapeutically useful to continue it in combination with insulin in people with type 2 diabetes.

‡ Switching to another oral agent is likely to have the smallest impact on glycaemia.

§ Basal plus refers to continuing oral agents.