19 research outputs found
The association of smoking status with glycemic control, metabolic profile and diabetic complications:Results of the Australian National Diabetes Audit (ANDA).
Background: Tobacco smoking and diabetes mellitus contribute significantly to the overall health burden and mortality of Australians. We aimed to assess the relationship of smoking with glycemic control, metabolic profile and complications in Australian patients living with diabetes. Methods: We analysed the 2011–2017 biennial Australian National Diabetes Audit cross-sectional data. Patients were classified as current, past or never smokers. Linear (or quantile) and logistic regression models were used to assess for associations. Results: Data from 15,352 patients were analysed, including 72.2% with type 2 diabetes. Current smokers comprised 13.5% of the study population. Current and past smokers had a median HbA1c that was 0.49% and 0.14% higher than never smokers, respectively, as well as higher triglyceride and lower HDL levels (all p values < .0001). Compared to never smokers, current smokers had higher odds of severe hypoglycemia and current and past smokers had higher odds of myocardial infarction, stroke, peripheral vascular disease, lower limb amputation, erectile dysfunction and peripheral neuropathy (all p values ≤.001), with no significant change over time. Conclusion: When compared to never smokers, current and past smokers had poorer glycemic and lipid control and higher odds of macrovascular and microvascular complications. Despite this, current smoking remains prevalent among Australians with diabetes
Medical nutrition therapy for gestational diabetes mellitus in Australia : what has changed in 10 years and how does current practice compare with best practice?
Background: The present study aimed to report Australian dietetic practice regarding management of gestational diabetes mellitus (GDM) and to make comparisons with the findings from a 2009 survey of dietitians and with the Academy of Nutrition and Dietetics Evidence-Based Nutrition Practice Guidelines (NPG). Methods: Cross-sectional surveys were conducted in 2019 and 2009 of dietitians providing medical nutrition therapy (MNT) to women with GDM in Australia. The present study compares responses on demographics, dietetic assessment and interventions, and guideline use in 2019 vs. 2009. Results: In total, 149 dietitians (2019) and 220 (2009) met survey inclusion criteria. In both surveys >60% of respondents reported dietary interventions aiming for >45% energy from carbohydrate, 15%–25% energy from protein and 15%–30% energy from fat. Many variations in MNT found in 2009 continued to be evident in 2019, including the percentage of energy from carbohydrate aimed for (30%–65% in 2019 vs. 20%–75% in 2009) and the wide range in the recommended minimum daily carbohydrate intake (40–220 and 60–300 g). Few dietitians reported aiming for the NPG minimum of 175 g of carbohydrate daily in both surveys (32% in 2019 vs. 26% in 2009). There were, however, some significant increases in MNT consistent with NPG recommendations in 2019 vs. 2009, including the minimum frequency of visits provided (49%, n = 61 vs. 33%, n = 69; p < 0.001) and provision of gestational weight gain advice (59%, n = 95 vs. 40%, n = 195; p < 0.05). Conclusions: Although many dietitians continue to provide MNT consistent with existing NPG, there is a need to support greater uptake, especially for recommendations regarding carbohydrate intake
The ADIPS pilot national diabetes in pregnancy benchmarking programme
Background: To test the feasibility of benchmarking the care of women with pregnancies complicated by hyperglycaemia. Methods: A retrospective audit of volunteer diabetes services in Australia and New Zealand involving singleton pregnancies resulting in live births between 2014 and 2020. Ranges are shown and compared across services. Results: The audit included 10,144 pregnancies (gestational diabetes mellitus (GDM) = 8696; type 1 diabetes (T1D) = 435; type 2 diabetes (T2D) = 1013) from 11 diabetes services. Among women with GDM, diet alone was used in 39.4% (ranging among centres from 28.8-57.3%), metformin alone in 18.8% (0.4-43.7%), and metformin and insulin in 10.1% (1.5-23.4%); when compared between sites, all p 6.5% (48 mmol/mol)), 78.4% and 54.6%, respectively (p < 0.001). Conclusion: Management of maternal hyperglycaemia and pregnancy outcomes varied significantly. The maintenance and extension of this benchmarking service provides opportunities to identify policy and clinical approaches to improve pregnancy outcomes among women with hyperglycaemia in pregnancy
Survey on testing for gestational diabetes mellitus in Australia
We surveyed members of National Association of Diabetes Centres (NADC) assessing use of new Australasian Diabetes In Pregnancy Society (ADIPS) and Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) Gestational Diabetes Mellitus (GDM) diagnostic guidelines in Australia. We found piecemeal adoption of recommended changes, with cessation of the 50 g glucose challenge test (GCT) universal, early screening implementation common, but by varied methodologies, and new diagnostic criteria acceptance far from complete with significant workload increases almost universal
Successful implementation of diabetes audits in Australia : the Australian National Diabetes Information Audit and Benchmarking (ANDIAB) initiative
Aim: We developed and implemented a national audit and benchmarking programme to describe the clinical status of people with diabetes attending specialist diabetes services in Australia. Methods: The Australian National Diabetes Information Audit and Benchmarking (ANDIAB) initiative was established as a quality audit activity. De-identified data on demographic, clinical, biochemical and outcome items were collected from specialist diabetes services across Australia to provide cross-sectional data on people with diabetes attending specialist centres at least biennially during the years 1998 to 2011. Results: In total, 38 155 sets of data were collected over the eight ANDIAB audits. Each ANDIAB audit achieved its primary objective to collect, collate, analyse, audit and report clinical diabetes data in Australia. Each audit resulted in the production of a pooled data report, as well as individual site reports allowing comparison and benchmarking against other participating sites. Conclusions: The ANDIAB initiative resulted in the largest cross-sectional national de-identified dataset describing the clinical status of people with diabetes attending specialist diabetes services in Australia. ANDIAB showed that people treated by specialist services had a high burden of diabetes complications. This quality audit activity provided a framework to guide planning of healthcare services. What's new? The Australian National Diabetes Information Audit and Benchmarking (ANDIAB) initiative collected comprehensive health data from Australian diabetes specialist services. Individual participating sites could compare and benchmark their practice findings. A large national de-identified dataset describing diabetes care status was developed. Findings from ANDIAB highlight the high burden of diabetes complications among these individuals and can be used to help plan improvements in diabetes healthcare services
The diagnosis and management of diabetes in Australia : does the “Rule of Halves” apply?
The Rule of Halves (ROH) is a theoretical framework which states that roughly half of all people with a condition are diagnosed; half of those diagnosed receive care; half of those who receive care achieve their treatment targets; and half of those who reach their targets achieve the desired treatment outcomes. This review examined the applicability of the ROH to diabetes in Australia. Five databases were searched for articles and government reports published between January 2000 and August 2019. Data was extracted for each level of the framework, and pooled sample proportions calculated for the total population and sub-group analysis by ethnicity and type of diabetes. The results showed that 72% of people with diabetes are diagnosed. Approximately 50% are receiving standard care, and 40–60% are meeting treatment targets for HbA1c, blood pressure and lipid levels. Overall, Australia is doing better than the ROH when it comes to achieving treatment outcomes; prevalence of microvascular complications was 20–30% and cardiovascular disease 62%. Indigenous people were less likely to meet treatment targets and more likely to experience adverse complications of diabetes. This review indicates the ROH does generally apply for diabetes care and management in Australia, highlighting the need for further improvement
Metabolic syndrome in type 1 diabetes and its association with diabetes complications
Aim: To assess the prevalence of metabolic syndrome in type 1 diabetes, and its age-related association with diabetes complications. Methods: Australian National Diabetes Information Audit and Benchmarking (ANDIAB) was a well-established quality audit programme. It provided cross-sectional data on people attending specialist diabetes services across Australia. We determined the prevalence of metabolic syndrome (WHO criteria) in adults with type 1 diabetes and its associations with diabetes complications across age groups. Results: Metabolic syndrome prevalence was 30% in 2120 adults with type 1 diabetes. Prevalence increased with age: 21% in those aged 60 years (P60 years (all P < 0.05). Metformin use was higher in those with metabolic syndrome (16% vs 4%; P<0.001). Conclusions: In this large Australian cohort, metabolic syndrome was common in type 1 diabetes and identified people at increased risk of the spectrum of diabetes complications, particularly in young to middle-aged adults. Potential clinical implications are that therapies targeting insulin resistance in this high-risk group may reduce diabetes complications and should be explored
Metabolic syndrome in type 1 diabetes and its association with diabetes complications
Aim: To assess the prevalence of metabolic syndrome in type 1 diabetes, and its age-related association with diabetes complications. Methods: Australian National Diabetes Information Audit and Benchmarking (ANDIAB) was a well-established quality audit programme. It provided cross-sectional data on people attending specialist diabetes services across Australia. We determined the prevalence of metabolic syndrome (WHO criteria) in adults with type 1 diabetes and its associations with diabetes complications across age groups. Results: Metabolic syndrome prevalence was 30% in 2120 adults with type 1 diabetes. Prevalence increased with age: 21% in those aged 60 years (P60 years (all P < 0.05). Metformin use was higher in those with metabolic syndrome (16% vs 4%; P<0.001). Conclusions: In this large Australian cohort, metabolic syndrome was common in type 1 diabetes and identified people at increased risk of the spectrum of diabetes complications, particularly in young to middle-aged adults. Potential clinical implications are that therapies targeting insulin resistance in this high-risk group may reduce diabetes complications and should be explored
Initial group dietary education compared to individual education in gestational diabetes mellitus management : do outcomes differ?
Aims: To assess the effectiveness of Initial Group versus Initial Individual GDM dietary education in terms of insulin requirements and pregnancy outcomes. Methods: A retrospective audit of clinical data was conducted where English speaking women who received initial education in a group setting (01-2-2012 to 01-2-2014) (Group), were compared to women who received initial individual education with a dietitian (1-2- 2010 to 31-1-2012) (Individual), all followed by one individual dietitian appointment. The same dietary information was provided in both settings. Data collected included: attendance rates, insulin requirements, maternal weight gain, and rates of adverse birth outcomes. Data were compared by t-test or Chi-squared test. Multivariable logistic regression analysis was conducted to determine independent predictors of insulin therapy. Results: Of 743 women; (362 Group and 381 Individual), Group women had a lower HbA1c at GDM diagnosis 5.3 ± 0.6% versus 5.5 ± 0.5% (34 ± 6.6 mmol/mol versus 37 ± 5.5 mmol/mol p < 0.0001). There were no other differences in baseline characteristics. More Group women required insulin (42.0% versus 34.6%, p = 0.048). Group education was found to be an independent predictor of insulin therapy (OR = 1.9 [1.29, 2.75] p < 0.001). Conclusions: Despite adjusting for all known potential confounders, unlike Individual education, Group education remained a significant predictor of insulin therapy (but resulted in similar therapeutic and pregnancy outcomes to Individual education)
Are the Institute of Medicine weight gain targets applicable in women with gestational diabetes mellitus?
Aims/hypothesis: Our aim was to study the relationship between excessive gestational weight gain (GWG) according to Institute of Medicine (IOM) targets and perinatal outcomes, and examine whether modifying targets may improve outcomes in women with gestational diabetes mellitus (GDM). Methods: This was a retrospective cohort study of all GDM pregnancies from 1992 to 2013. ORs were calculated for associations between excessive GWG (EGWG) using IOM targets and adverse pregnancy outcomes. ORs were then adjusted for maternal age, gestational age at diagnosis, prepregnancy BMI, gravidity, parity, ethnicity, antenatal fasting blood glucose level (BGL), 2 h BGL and HbA1c. BMI was categorised into underweight (<18.5 kg/m2), healthy weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) and obese (≥30 kg/m2). Large for gestational age (LGA) was defined as birthweight above the 90th percentile, small for gestational age (SGA) was birthweight below the 10th percentile, macrosomia was birthweight >4000 g, and preterm delivery was delivery prior to 37 weeks’ gestation. Modified GWG targets were derived by: (1) subtracting 2 kg from the upper IOM target only; (2) subtracting 2 kg from both upper and lower targets; (3) using the interquartile range of maternal GWG of women with infants who were appropriate for gestational age per BMI category; and (4) restricting GWG to 0–4 kg in women with BMI ≥35 kg/m2. Results: Among 3095 GDM pregnancies, only 31.7% had GWG within IOM guidelines. Adjusted ORs for women who exceeded GWG were Caesarean section (1.5; 95% CI 1.2, 1.9), LGA (1.8; 95% CI 1.4, 2.4) and macrosomia (2.3; 95% CI 1.6, 3.3); there was a lower risk of SGA (adjusted OR 0.5; 95% CI 0.3, 0.7). Conclusions/interpretation: EGWG according to IOM targets was associated with Caesarean section, LGA and macrosomia. Modification of IOM criteria, including more restrictive targets, did not improve perinatal outcomes