Timothy A Welborn

Sir Charles Gairdner Hospital, Perth City, Western Australia, Australia

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Publications (64)320.2 Total impact

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    Louise Gek Huang Goh, Timothy Alexander Welborn, Satvinder Singh Dhaliwal
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    ABSTRACT: Background We conducted an independent external validation of three cardiovascular risk score models (Framingham risk score model and SCORE risk charts developed for low-risk regions and high-risk regions in Europe) on a prospective cohort of 4487 Australian women with no previous history of heart disease, diabetes or stroke. External validation is an important step to evaluate the performance of risk score models using discrimination and calibration measures to ensure their applicability beyond the settings in which they were developed. Methods Ten year mortality follow-up of 4487 Australian adult women from the National Heart Foundation third Risk Factor Prevalence Study with no baseline history of heart disease, diabetes or stroke. The 10-year risk of cardiovascular mortality was calculated using the Framingham and SCORE models and the predictive accuracy of the three risk score models were assessed using both discrimination and calibration. Results The discriminative ability of the Framingham and SCORE models were good (area under the curve > 0.85). Although all models overestimated the number of cardiovascular deaths by greater than 15%, the Hosmer-Lemeshow test indicated that the Framingham and SCORE-Low models were calibrated and hence suitable for predicting the 10-year cardiovascular mortality risk in this Australian population. An assessment of the treatment thresholds for each of the three models in identifying participants recommended for treatment were found to be inadequate, with low sensitivity and high specificity resulting from the high recommended thresholds. Lower treatment thresholds of 8.7% for the Framingham model, 0.8% for the SCORE-Low model and 1.3% for the SCORE-High model were identified for each model using the Youden index, at greater than 78% sensitivity and 80% specificity. Conclusions Framingham risk score model and SCORE risk chart for low-risk regions are recommended for use in the Australian women population for predicting the 10-year cardiovascular mortality risk. These models demonstrate good discrimination and calibration performance. Lower treatment thresholds are proposed for better identification of individuals for treatment.
    BMC Women's Health 09/2014; 14(1):118. DOI:10.1186/1472-6874-14-118 · 1.66 Impact Factor
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    ABSTRACT: The objectives of this study were to determine whether the cross-sectional associations between anthropometric obesity measures, body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR), and calculated 10-year cardiovascular disease (CVD) risk using the Framingham and general CVD risk score models, are the same for women of Australian, UK and Ireland, North European, South European and Asian descent. This study would investigate which anthropometric obesity measure is most predictive at identifying women at increased CVD risk in each ethnic group.
    BMJ Open 05/2014; 4(5):e004702. DOI:10.1136/bmjopen-2013-004702 · 2.06 Impact Factor
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    Satvinder S Dhaliwal, Timothy A Welborn, Peter A Howat
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    ABSTRACT: To assess the role of body adiposity index (BAI) in predicting cardiovascular disease (CVD) and coronary heart disease (CHD) mortality, in comparison with body mass index (BMI), waist circumference (WC), and the waist circumference to hip circumference ratio (WHR). This study was a prospective 15 year mortality follow-up of 4175 Australian males, free of heart disease, diabetes and stroke. The Framingham Risk Scores (FRS) for CHD and CVD death were calculated at baseline for all subjects. Multivariable logistic regression was used to assess the effects of the measures of obesity on CVD and CHD mortality, before adjustment and after adjustment for FRS. The predictive ability of BAI, though present in the unadjusted analyses, was generally not significant after adjustment for age and FRS for both CVD and CHD mortality. BMI behaved similarly to BAI in that its predictive ability was generally not significant after adjustments. Both WC and WHR were significant predictors of CVD and CHD mortality and remained significant after adjustment for covariates. BAI appeared to be of potential interest as a measure of % body fat and of obesity, but was ineffective in predicting CVD and CHD.
    PLoS ONE 04/2014; 9(4):e94560. DOI:10.1371/journal.pone.0094560 · 3.53 Impact Factor
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    ABSTRACT: Although elevated cardiovascular disease (CVD) risk factors are associated with a higher risk of developing heart conditions across all ethnic groups, variations exist between groups in the distribution and association of risk factors, and also risk levels. This study assessed the 10-year predicted risk in a multiethnic cohort of women and compared the differences in risk between Asian and Caucasian women. Information on demographics, medical conditions and treatment, smoking behavior, dietary behavior, and exercise patterns were collected. Physical measurements were also taken. The 10-year risk was calculated using the Framingham model, SCORE (Systematic COronary Risk Evaluation) risk chart for low risk and high risk regions, the general CVD, and simplified general CVD risk score models in 4,354 females aged 20-69 years with no heart disease, diabetes, or stroke at baseline from the third Australian Risk Factor Prevalence Study. Country of birth was used as a surrogate for ethnicity. Nonparametric statistics were used to compare risk levels between ethnic groups. Asian women generally had lower risk of CVD when compared to Caucasian women. The 10-year predicted risk was, however, similar between Asian and Australian women, for some models. These findings were consistent with Australian CVD prevalence. In summary, ethnicity needs to be incorporated into CVD risk assessment. Australian standards used to quantify risk and treat women could be applied to Asians in the interim. The SCORE risk chart for low-risk regions and Framingham risk score model for incidence are recommended. The inclusion of other relevant risk variables such as obesity, poor diet/nutrition, and low levels of physical activity may improve risk estimation.
    International Journal of Women's Health 03/2014; 6:259-67. DOI:10.2147/IJWH.S55225
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    ABSTRACT: It is important to ascertain which anthropometric measurements of obesity, general or central, are better predictors of cardiovascular disease (CVD) risk in women. 10-year CVD risk was calculated from the Framingham risk score model, SCORE risk chart for high-risk regions, general CVD and simplified general CVD risk score models. Increase in CVD risk associated with 1 SD increment in each anthropometric measurement above the mean was calculated, and the diagnostic utility of obesity measures in identifying participants with increased likelihood of being above the treatment threshold was assessed. Cross-sectional data from the National Heart Foundation Risk Factor Prevalence Study. Population-based survey in Australia. 4487 women aged 20-69 years without heart disease, diabetes or stroke. Anthropometric obesity measures that demonstrated the greatest increase in CVD risk as a result of incremental change, 1 SD above the mean, and obesity measures that had the greatest diagnostic utility in identifying participants above the respective treatment thresholds of various risk score models. Waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-stature ratio had larger effects on increased CVD risk compared with body mass index (BMI). These central obesity measures also had higher sensitivity and specificity in identifying women above and below the 20% treatment threshold than BMI. Central obesity measures also recorded better correlations with CVD risk compared with general obesity measures. WC and WHR were found to be significant and independent predictors of CVD risk, as indicated by the high area under the receiver operating characteristic curves (>0.76), after controlling for BMI in the simplified general CVD risk score model. Central obesity measures are better predictors of CVD risk compared with general obesity measures in women. It is equally important to maintain a healthy weight and to prevent central obesity concurrently.
    BMJ Open 01/2014; 4(2):e004138. DOI:10.1136/bmjopen-2013-004138 · 2.06 Impact Factor
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    Satvinder S Dhaliwal, Timothy A Welborn, Peter A Howat
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    ABSTRACT: The role of physical activity in preventing CVD has been highlighted by Professor Jerry Morris in the 1950's. We report outcome of a 15-year prospective study with the aim to identify whether physical activity showed cardiovascular benefit independent of common risk factors and of central obesity. Baseline data of 8662 subjects, with no previous history of heart disease, diabetes or stroke, were obtained from an age- and gender- stratified sample of adults in Australian capital cities and were linked with the National Death Index to determine the causes of death of 610 subjects who had died to 31 December 2004. The study consisted of 4175 males (age 42.3±13.1 years) and 4487 females (age 42.8±13.2 years). Fasting serum lipid levels, systolic and diastolic blood pressure and smoking habits at baseline were recorded. The Framingham Risk Scores of 15-year mortality due to CHD and CVD were calculated using established equations. Subjects were also asked if they engaged in vigorous exercise, less vigorous exercise or walk for recreation and exercise in the past 2 weeks. Subjects in the high recreational physical activity category were 0.16 (0.06-0.43; p<0.001) and 0.12 (0.03-0.48; p = 0.003) times as likely as subjects in the low category for CVD and CHD mortality respectively. After adjusting for both the Framingham Risk Score and central obesity (Waist circumference to Hip circumference Ratio), those in the high recreational physical activity group were 0.35 (0.13-0.98) times less likely compared to the low category for CVD mortality. Recreational physical activity independently predicted reduced cardiovascular mortality over fifteen years. A public health focus on increased physical activity and preventing obesity is required to reduce the risk of CVD and CHD.
    PLoS ONE 12/2013; 8(12):e83435. DOI:10.1371/journal.pone.0083435 · 3.53 Impact Factor
  • Timothy A Welborn, Satvinder S Dhaliwal
    The Medical journal of Australia 04/2011; 194(8):429-30. · 3.79 Impact Factor
  • Timothy A Welborn, Satvinder S Dhaliwal
    New England Journal of Medicine 02/2011; 364(8):781; author reply 782-3. DOI:10.1056/NEJMc1014730#SA1 · 54.42 Impact Factor
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    ABSTRACT: To assess the adequacy of self-reported weight and height as indicators for BMI in community-based obesity control programs. Self-reported and measured weight and height and calculated BMI in 6979 adults were assessed using analysis of covariance. Prevalence of obesity (BMI > 25 kg/m(2)) and overweight (25-29.9 kg/m(2)) was lower using self-reported values by 3.2% and 5.0%, respectively. Females underreported BMI more than males did; and older subjects, more than younger subjects. Self-reported weight and height measurements may be used for the evaluation of community-based obesity control programs with the application of correction factors. This will minimize costs associated with physical measurements.
    American journal of health behavior 04/2010; 34(4):489-99. DOI:10.5993/AJHB.34.4.10 · 1.31 Impact Factor
  • Satvinder S Dhaliwal, Timothy A Welborn
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    ABSTRACT: Our objective is to develop a parsimonious model to predict coronary heart disease (CHD) and cardiovascular disease (CVD) deaths using individual components of the Framingham risk score plus measures of central obesity. 15 year mortality follow-up of 8662 representative Australian adults in the National Heart Foundation Risk Factor Prevalence Survey of 1989, excluding those with a baseline history of heart disease, stroke or diabetes. Measures included blood pressure, fasting lipids, smoking history, body mass index (BMI), waist circumference (WC) and waist to hip ratio (WHR). Multivariable logistic regression was used to assess the effects of the Framingham risk variables and central obesity variables on cardiovascular disease mortality. Smoking status, high density lipoprotein cholesterol (HDL-C) and the total cholesterol (TC) to HDL-C ratio were significant univariate predictors of CHD deaths. These together with systolic blood pressure were significant predictors of CVD deaths. The obesity measures of WC and WHR were significant univariate predictors but BMI was not. In multivariable analyses, only smoking status and waist to hip ratio were identified as key independent risk factors for CHD and CVD deaths, although TC to HDL-C ratio contributed minimally to CHD deaths. Receiver operator characteristic (ROC) curves for the Framingham risk score in comparison to the WHR plus smoking model were virtually identical, with no added effect of the lipid ratio. The preferred model for predicting CHD and CVD deaths uses central obesity plus smoking with no added influence of measured lipids or blood pressure. A public health focus on identifying and modifying central obesity is at least as important as the measurement and treatment of lipids and hypertension.
    Preventive Medicine 09/2009; 49(2-3):153-7. DOI:10.1016/j.ypmed.2009.07.019 · 2.93 Impact Factor
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    ABSTRACT: To provide an estimate of the morbidity and mortality resulting from abdominal overweight and obesity in the Australian population. Prospective, national, population-based study (the Australian Diabetes, Obesity and Lifestyle [AusDiab] study). 6072 men and women aged>or=25 years at study entry between May 1999 and December 2000, and aged<or=75 years, not pregnant and for whom there were waist circumference data at the follow-up survey between June 2004 and December 2005. Incident health outcomes (type 2 diabetes, hypertension, dyslipidaemia, the metabolic syndrome and cardiovascular diseases) at 5 years and mortality at 8 years. Comparison of outcome measures between those classified as abdominally overweight or obese and those with a normal waist circumference at baseline, and across quintiles of waist circumference, and (for mortality only) waist-to-hip ratio. Abdominal obesity was associated with odds ratios of between 2 and 5 for incident type 2 diabetes, dyslipidaemia, hypertension and the metabolic syndrome. The risk of myocardial infarction among obese participants was similarly increased in men (hazard ratio [HR], 2.75; 95% CI, 1.08-7.03), but not women (HR, 1.43; 95% CI, 0.37-5.50). Abdominal obesity-related population attributable fractions for these outcomes ranged from 13% to 47%, and were highest for type 2 diabetes. No significant associations were observed between all-cause mortality and increasing quintiles of abdominal obesity. Our findings confirm that abdominal obesity confers a considerably heightened risk for type 2 diabetes, the metabolic syndrome (as well as its components) and cardiovascular disease, and they provide important information that enables a more precise estimate of the burden of disease attributable to obesity in Australia.
    The Medical journal of Australia 09/2009; 191(4):202-8. · 3.79 Impact Factor
  • Satvinder S Dhaliwal, Timothy A Welborn
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    ABSTRACT: Methods of estimating central obesity are important because of the increasing frequency of obesity related diseases worldwide. Here we evaluate the precision of measuring waist circumference and the waist to hip ratio with comparisons across ethnic groups. The third Australian Risk Factor Prevalence Study (1989) of 9279 adults recorded height, weight, and Body Mass Index together with duplicate measurements of the waist and hip circumferences, the waist to hip ratio, and blood pressure levels using clearly defined survey techniques. Measurement error and precision for these variables were calculated, and comparisons were made across ethnic groups. Coefficients of variation for the waist circumference and the waist to hip ratio were less than 1% indicating good precision in comparison with quite large variability for systolic and diastolic pressure readings. Waist circumference showed increased variability in subjects with larger body build in comparison with waist to hip ratio. Equivalence tests across ethnic groups indicated that the waist to hip ratio was independent of ethnicity. Waist to hip ratio provides a superior measure of central obesity with low measurement error, high precision, and no bias over a wide range of ethnic groups. We believe that it is essential to standardize methods in the assessment of central obesity. Assessment criteria should be based on waist to hip ratio rather than waist circumference.
    Preventive Medicine 08/2009; 49(2-3):148-52. DOI:10.1016/j.ypmed.2009.06.023 · 2.93 Impact Factor
  • Satvinder S Dhaliwal, Timothy A Welborn
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    ABSTRACT: To evaluate the role of measurements of central obesity in the multivariable prediction of cardiovascular risk using the Framingham risk scores, we studied 4,175 representative men from Australian cities, free of heart disease, stroke, and diabetes in 1989, and followed the cohort for mortality to 2004. Baseline lipids, blood pressure, and current cigarette smoking were recorded. Obesity was assessed by body mass index, waist circumference (WC), and waist-to-hip ratio (WHR) by strictly standardized methods. The Framingham equations were strong predictors of coronary heart disease (CHD) and cardiovascular disease (CVD) deaths. Of the obesity measurements, WHR and WC predicted deaths using Cox proportional hazards regression but body mass index did not. In the multivariable analyses, WHR was an independent predictor of CHD deaths, and WHR and WC were independent predictors of CVD deaths. There was little or no attenuation of hazard ratios for WHR and WC after correction for the Framingham scores. The 2 measurements of central obesity were more strongly predictive of CHD and CVD deaths in subjects at the lower levels of Framingham risk. In contrast, cigarette smoking risk appeared to contribute more in the higher Framingham risk categories. In conclusion, central obesity significantly and independently contributes to cardiovascular outcomes and to residual risk after accounting for the Framingham equations.
    The American journal of cardiology 06/2009; 103(10):1403-7. DOI:10.1016/j.amjcard.2008.12.048 · 3.43 Impact Factor
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    ABSTRACT: Framingham risk functions are widely used for prediction of future cardiovascular disease events. They do not, however, include anthropometric measures of overweight or obesity, now considered a major cardiovascular disease risk factor. We aimed to establish the most appropriate anthropometric index and its optimal cutoff point for use as an ancillary measure in clinical practice when identifying people with increased absolute cardiovascular risk estimates. Analysis of a population-based, cross-sectional survey was carried out. The 1991 Framingham prediction equations were used to compute 5 and 10-year risks of cardiovascular or coronary heart disease in 7191 participants from the Australian Diabetes, Obesity and Lifestyle Study (1999-2000). Receiver operating characteristic curve analysis was used to compare measures of body mass index (BMI), waist circumference, and waist-to-hip ratio in identifying participants estimated to be at 'high', or at 'intermediate or high' absolute risk. After adjustment for BMI and age, waist-to-hip ratio showed stronger correlation with absolute risk estimates than waist circumference. The areas under the receiver operating characteristic curve for waist-to-hip ratio (0.67-0.70 in men, 0.64-0.74 in women) were greater than those for waist circumference (0.60-0.65, 0.59-0.71) or BMI (0.52-0.59, 0.53-0.66). The optimal cutoff points of BMI, waist circumference and waist-to-hip ratio to predict people at 'high', or at 'intermediate or high' absolute risk estimates were 26 kg/m2, 95 cm and 0.90 in men, and 25-26 kg/m2, 80-85 cm and 0.80 in women, respectively. Measurement of waist-to-hip ratio is more useful than BMI or waist circumference in the identification of individuals estimated to be at increased risk for future primary cardiovascular events.
    European Journal of Cardiovascular Prevention and Rehabilitation 01/2008; 14(6):740-5. DOI:10.1097/HJR.0b013e32816f7739 · 3.69 Impact Factor
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    ABSTRACT: Diabetes mellitus increases the risk of cardiovascular disease (CVD) and all-cause mortality. The relationship between milder elevations of blood glucose and mortality is less clear. This study investigated whether impaired fasting glucose and impaired glucose tolerance, as well as diabetes mellitus, increase the risk of all-cause and CVD mortality. In 1999 to 2000, glucose tolerance status was determined in 10,428 participants of the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). After a median follow-up of 5.2 years, 298 deaths occurred (88 CVD deaths). Compared with those with normal glucose tolerance, the adjusted all-cause mortality hazard ratios (HRs) and 95% confidence intervals (CIs) for known diabetes mellitus and newly diagnosed diabetes mellitus were 2.3 (1.6 to 3.2) and 1.3 (0.9 to 2.0), respectively. The risk of death was also increased in those with impaired fasting glucose (HR 1.6, 95% CI 1.0 to 2.4) and impaired glucose tolerance (HR 1.5, 95% CI 1.1 to 2.0). Sixty-five percent of all those who died of CVD had known diabetes mellitus, newly diagnosed diabetes mellitus, impaired fasting glucose, or impaired glucose tolerance at baseline. Known diabetes mellitus (HR 2.6, 95% CI 1.4 to 4.7) and impaired fasting glucose (HR 2.5, 95% CI 1.2 to 5.1) were independent predictors for CVD mortality after adjustment for age, sex, and other traditional CVD risk factors, but impaired glucose tolerance was not (HR 1.2, 95% CI 0.7 to 2.2). This study emphasizes the strong association between abnormal glucose metabolism and mortality, and it suggests that this condition contributes to a large number of CVD deaths in the general population. CVD prevention may be warranted in people with all categories of abnormal glucose metabolism.
    Circulation 08/2007; 116(2):151-7. DOI:10.1161/CIRCULATIONAHA.106.685628 · 14.95 Impact Factor
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    ABSTRACT: Insulin resistance is considered a core component in the pathophysiology of the metabolic syndrome. Some clinicians measure serum insulin concentrations in the mistaken belief that they can be used to diagnose insulin resistance. Serum insulin levels are poor measures of insulin resistance. Furthermore, there is no clinical benefit in measuring insulin resistance in clinical practice. Measurements of fasting serum insulin levels should be reserved for large population-based epidemiological studies, where they can provide valuable data on the relationship of insulin sensitivity to risk factors for diabetes and cardiovascular disease. Clinicians should shift from identifying "insulin resistance" to identifying risk factors, such as fasting glucose and lipid levels, hypertension and central obesity. These proven risk factors converge within the metabolic syndrome. Individuals "at risk" of diabetes and atherosclerotic cardiac disease can be identified simply and inexpensively, using classic clinical techniques, such as history-taking, physical examination, and very basic investigations.
    The Medical journal of Australia 09/2006; 185(3):159-61. · 3.79 Impact Factor
  • Diabetes Care 07/2005; 28(6):1490-2. DOI:10.2337/diacare.28.6.1490 · 8.57 Impact Factor
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    ABSTRACT: The aim of this study is to compare the effect of orlistat vs. placebo on the predicted 10-year cardiovascular disease (CVD) risk in obese people with one or more cardiovascular risk factors treated for 12 months, in conjunction with a fat-reduced, but otherwise ad libitum, diet. A double-blind, randomized, placebo-controlled, parallel study was performed in conjunction with a fat-reduced diet and physical activity advice for 1 year. Participants (n = 339) from eight centres in Australia and New Zealand were randomized to either orlistat (120 mg) three times daily (n = 104 women, 66 men; mean +/- s.d. age = 52.0 +/- 7.5 years, body mass index (BMI) = 37.6 +/- 5.1 kg/m(2)) or placebo three times daily (n = 89 women, 80 men; age = 52.5 +/- 7.4 years, BMI = 38.0 +/- 4.9 kg/m(2)). The primary efficacy criterion was the 10-year risk of developing CVD calculated from the Framingham equation. Secondary efficacy criteria were body weight, waist circumference, blood pressure and serum concentrations of triglycerides, cholesterol (total, LDL and HDL), glucose, insulin and glycated haemoglobin and quality of life. There was no difference in the change in 10-year CVD risk between orlistat and placebo groups over 1 year. The orlistat group, however, had significant favourable changes in many of the individual CVD risk factors (total cholesterol, LDL-cholesterol, glucose, glycated haemoglobin, insulin, body weight and waist circumference) and one of the domains of quality of life measured by means of the SF-36 questionnaire (vitality), compared to the placebo group. Significant reductions in medication use for hypertension and diabetes were observed in the orlistat group, compared to those in placebo, but there were no significant differences in medication use for blood lipids. Orlistat may have reduced CVD risk, as judged by the favourable changes in individual risk factors and reductions in medication use, but the method used in order to measure absolute CVD risk in this study (Framingham CVD equation) was not sensitive enough to detect the changes in this relatively low-risk group (approximately 10% of risk of a CVD event over 10 years).
    Diabetes Obesity and Metabolism 06/2005; 7(3):254-62. DOI:10.1111/j.1463-1326.2004.00467.x · 5.46 Impact Factor

Publication Stats

4k Citations
320.20 Total Impact Points

Institutions

  • 1979–2014
    • Sir Charles Gairdner Hospital
      Perth City, Western Australia, Australia
  • 2010
    • Curtin University Australia
      • School of Public Health
      Bentley, Western Australia, Australia
  • 2009
    • Baker IDI Heart and Diabetes Institute
      • Clinical Diabetes and Epidemiology Research Group
      Melbourne, Victoria, Australia
  • 1985–2009
    • University of Western Australia
      • School of Population Health
      Perth City, Western Australia, Australia
  • 2005
    • The Queen Elizabeth Hospital
      Tarndarnya, South Australia, Australia
  • 2004
    • University of Melbourne
      Melbourne, Victoria, Australia
  • 2002–2004
    • Deakin University
      Geelong, Victoria, Australia
    • University of Vic
      Vic, Catalonia, Spain
  • 2003
    • Monash University (Australia)
      • Department of Epidemiology and Preventive Medicine
      Melbourne, Victoria, Australia
  • 1981
    • Royal Perth Hospital
      Perth City, Western Australia, Australia