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 (p <0.0001 for all). Compared to never smokers, current smokers had higher odds of severe hypoglycaemia 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 ≤0.001), 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.
BackgroundAcute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies.MethodsA time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected.ResultsForty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017.ConclusionsPrehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.
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