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

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins

Geographical variation of diabetic emergencies attended by prehospital Emergency Medical Services is associated with measures of ethnicity and socioeconomic status

Abstract Geographical variation of diabetic emergencies attended by prehospital emergency medical services (EMS) and the relationship between area-level social and demographic factors and risk of a diabetic emergency were examined. All cases of hypoglycaemia and hyperglycaemia attended by Ambulance Victoria between 1/01/2009 and 31/12/2015 were tabulated by Local Government Area (LGA). Conditional autoregressive models were used to create smoothed maps of age and gender standardised incidence ratio (SIR) of prehospital EMS attendance for a diabetic emergency. Spatial regression models were used to examine the relationship between risk of a diabetic emergency and area-level factors. The areas with the greatest risk of prehospital EMS attendance for a diabetic emergency were disperse. Area-level factors associated with risk of a prehospital EMS-attended diabetic emergency were socioeconomic status (SIR 0.70 95% CrI [0.51, 0.96]), proportion of overseas-born residents (SIR 2.02 95% CrI [1.37, 2.91]) and motor vehicle access (SIR 1.47 95% CrI [1.08, 1.99]). Recognition of areas of increased risk of prehospital EMS-attended diabetic emergencies may be used to assist prehospital EMS resource planning to meet increased need. In addition, identification of associated factors can be used to target preventative interventions tailored to individual regions to reduce demand

Time series modelling to forecast prehospital EMS demand for diabetic emergencies

Abstract Background Acute 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. Methods A 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. Results Forty-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. Conclusions Prehospital 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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Transport frequency and unadjusted and adjusted odds of transport to hospital.

<p>Transport frequency and unadjusted and adjusted odds of transport to hospital.</p

Characteristics of patients who utilise EMS for hyperglycaemia.

<p>Characteristics of patients who utilise EMS for hyperglycaemia.</p

EMS attendances by age group and diabetes type.

<p>EMS attendances by age group and diabetes type.</p

Annual case number and annual case rate of EMS-attended hyperglycaemia.

<p>During 2014, 3 months of data was unavailable due to lapse in electronic data collection.</p