Type 2 diabetes is one of the fastest growing chronic diseases internationally . It is estimated that world prevalence of diabetes in adults will rise from 6.4% in 2010 to 7.7% by 2030 . Long-term complications associated with diabetes include macrovascular disease, coronary heart disease, stroke, retinopathy, and kidney diseases . Those diagnosed with diabetes have double the risk of developing cardiovascular disease (CVD) and have a higher mortality due to their first CVD event [3, 4]. Diabetic nephropathy affects approximately 10% of people with diabetes, is the most common cause of end-stage kidney disease, and diabetes is a major contributor to lower limb amputations and visual loss . The direct and indirect costs of diabetes and its associated complications to the health system and to individuals is substantial .
The health complications associated with type 2 diabetes can be prevented, delayed, or lessened if diabetes is diagnosed early and is well controlled. While there are many factors important to diabetes control, metabolic markers of poor diabetes control such as elevated blood glucose, blood lipids, cholesterol, and urinary albumin indicate increased risk of diabetic complications [6–11]. Interventions designed to impact on multiple risk factors in people with diabetes can reduce risk of diabetes complications [6, 12]. However, achieving optimal diabetes management at a population-level remains challenging.
General practitioners (GPs), or primary care physicians, play a major role in diabetes management . In an effort to improve diabetes management, national Clinical Practice Guidelines for general practice specify the requirements for testing of blood lipids, HbA1c, and urinary albumin [6, 13]. Target levels for each of these metabolic indicators are provided, and follow-up care is recommended if results fall outside these parameters . While these metabolic markers cannot assess the quality of an individual’s diabetes care, they represent key objective measures of the management of diabetes at a population level. While there were about 818,000 Australians diagnosed with diabetes in 2009 and 2010, it appears that a minority have received the recommended annual testing of metabolic markers .
In Australia, people living in regional, rural, and remote regions have mortality rates between 10% and 70% higher than those living in the major cities . Rural and remote communities are of particular importance because rates of diabetes consultations are higher than in other areas of Australia . Rates of diabetes-related hospitalisation rise with increasing remoteness of residence , as do death rates due to diabetes . The proportion of diabetic patients meeting targets for total cholesterol, triglycerides, and blood pressure levels is also lower in rural areas compared with urban areas . The role of the GP is critical in rural settings, because there may be less access to specialist services. In Australia, organisations such as the Rural Health Education Foundation run distance education programs, including internet-based programs for rural GPs; however, these have not been evaluated for effect on quality of care or patient outcomes.
Continuing medical education (CME) is a commonly employed mechanism to improve clinical practice . There is growing evidence that CME approaches that involve multiple exposures to educational material over time and a variety of educational techniques are effective at improving doctors’ knowledge, attitudes, and patient outcomes . Web-based CME is increasing in popularity and is of particular relevance to GPs in non-metropolitan locations where face-to-face training opportunities are less accessible. However, while some studies have examined the effect of CME on participant satisfaction , few have examined the effect on patient outcomes, and none at the population level.
Studies that examine the effect of provider-change strategies on patient outcomes have done so by looking at patients linked to providers who have participated in the study [20–22]. The outcomes at the patient level, therefore, do not include the patients of providers who choose not to participate, patients who are ‘missed’ or excluded by their provider, patients who do not provide consent, and providers or patients lost to follow-up. While these methodological flaws are often unavoidable, they severely limit the generalisability of the results. This project will use objective data to examine uptake and effectiveness of CME and additional intervention strategies at the population level in the rural setting. To meet this objective, the research design uses communities as the unit of analysis and administrative data sets (whole town de-identified pathology data) without the usual problems of generalisability resulting from enrolling practices and patients.
In addition to drawing on best evidence for CME  the intervention will be multifaceted; including an on-line Active Learning Module (ALM) attracting CME points, and strategies designed to improve adherence to guideline recommendations such as performance feedback [23, 24]. Non-metropolitan GPs are likely to be receptive to the web-based aspects of our proposed approach because computer use by GPs in rural and remote areas is higher than that of GPs in metropolitan areas . Rural towns also offer a greater chance of capturing the whole population than does a selected group of GPs.
Aims and objectives
To test whether population-level improvements can be achieved by implementing evidence-based practice change strategies for GP care and management of diabetes in rural communities.
To test whether a rural GP-focused intervention involving Continuing Medical Education (CME), reminders and feedback can improve clinical outcomes as measured by glycaemic control.
To examine whether the above-mentioned intervention can:
Improve patterns of diabetes care as measured by whether the frequency of testing for hemoglobin A1c, blood lipids and urinary albumin meets NHMRC (National Health and Medical Research Council) guidelines.
Improve other clinical outcomes as measured by blood lipid control and urinary albumin control.