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Background: Effective prevention of type 2 diabetes (T2D) requires early identification of high-risk individuals who might benefit from intervention. We sought to determine whether low serum testosterone, a novel risk factor for T2D in men, adds clinically meaningful information beyond current T2D risk models. Methods: The Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) study population consists of 2563 community-dwelling men aged 35-80 years in Adelaide, Australia. Of the MAILES participants, 2038 (80.0 %) provided information at baseline (2002-2006) and follow-up (2007-2010). After excluding participants with diabetes (n = 317), underweight (n = 5), and unknown BMI status (n = 11) at baseline; and unknown diabetes status (n = 50) at follow-up; 1655 participants were followed for 5 years. T2D at baseline and follow-up was defined by self-reported diabetes, or fasting plasma glucose (FPG) ≥7.0 mmol/L (126.1 mg/dL), or glycated haemoglobin (HbA1c) ≥6.5 %, or diabetes medications. Risk models were tested using logistic regression models. Sensitivity, specificity, positive predictive values (PPV) were used to identify the optimal cut-off point for low serum testosterone for incident T2D and the area under the receiver operating characteristic (AROC) curve was used to summarise the predictive power of the model. 15.5 % of men had at least one missing predictor variable; addressed through multiple imputation. Results: The incidence rate of T2D was 8.9 % (147/1655) over a median follow-up of 4.95 years (interquartile range: 4.35-5.00). Serum testosterone level predicted incident T2D (relative risk 0.96 [95 % CI: 0.92,1.00], P = 0.032) independent of current risk models including the AUSDRISK, but did not improve corresponding AROC statistics. A cut-off point of <16 nmol/L for low serum testosterone, which classified about 43 % of men, returned equal sensitivity (61.3 % [95 % CI: 52.6,69.4]) and specificity (58.3 % [95 % CI: 55.6,60.9) for predicting T2D risk, with a PPV of 12.9 % (95 % CI: 10.4,15.8). Conclusions: Low serum testosterone predicts an increased risk of developing T2D in men over 5 years independent of current T2D risk models applicable for use in routine clinical practice. Screening for low serum testosterone in addition to risk factors from current T2D risk assessment models or tools, including the AUSDRISK, would identify a large subgroup of distinct men who might benefit from targeted preventive interventions. [ABSTRACT FROM AUTHOR]

Objective. To understand what impact hospital inpatient occupancy levels have on patient throughput by analysing one hospital's occupancy levels and the rate of patient discharge. Methods. A four-stage model was fit to hospital admission and separation data and used to analyse the per-capita separation rate according to the patient load and the impact of hospital over-census actions. Results. Per-capita separation rates are significantly higher on days when the hospital declares an over-census due to emergency department crowding. Per-capita separation rates are also higher or lower on days with 8-10% higher or lower patient loads, respectively, but the response is not nearly as strong as the response to an over-census declaration, and is limited to patients with an elapsed stay of 10 days or more. Within the medical division there is an increase in per-capita separation rates on over-census days, but no significant difference in per-capita release rates for different patient loads. Within the surgical division there is no significant difference in per-capita separation rates on over-census days compared with other days, but the patient load does make a significant difference. Conclusion. Staff do discharge a greater proportion of long-stay patients when the hospital is experiencing high demand and a lower proportion when occupancy is low, but the reasons driving those changes remains unclear. What is known about the topic? The evidence regarding safe and efficient levels of hospital occupancy is limited. There is minimal empirical evidence that confirms the relationship between occupancy and discharge rates. What does the paper add? Per-capita separation rates increase strongly on over-census days. The hospital increases per-capita separation rates on days of high occupancy and reduces it on days of low occupancy, mostly for long-stay patients with over 10 days of elapsed stay. The response to high occupancy is not as strong as the response to over-census. The medical division responds strongly to the over-census and the surgical division does not. The surgical division responds more to occupancy levels within its own division than does the medical division. What are the implications for practitioners? The implementation of over-census-type responses to periods of high occupancy may result in increased per-capita separation rate. Using mathematical analysis to understand patient load on per capita separation is important to create a better understanding of health service delivery, thereby aiding hospital managers, and has the potential to guide system improvement. The clinical drivers for these changes and the service design implications require further investigation. [ABSTRACT FROM AUTHOR]

Objective. To obtain prevalence estimates of clinical features of obstructive sleep apnoea (OSA) and identify the dimensions of the public health problem requiring further investigation for an Australian population. Methods. The South Australian Health Omnibus Survey is an annual representative population survey of South Australians aged ≥15 years, conducted via interviewer-administered questionnaire. In 2009, 3007 participants were asked the STOP-BANG instrument measure of obstructive sleep apnoea risk, which includes symptoms of loud snoring, frequent tiredness during daytime, observed apnoea, and high blood pressure (STOP), and measured body mass index, age, neck circumference and gender (BANG). Three or more positive response categorises a person at high risk for OSA. Results. Snoring was reported by 49.7% of adults. Tiredness after sleep more than 3 -- 4 times per week was reported by 24.8%, and during wake-time by 27.7% of adults, with 8.8% reporting having fallen asleep while driving. Over half of the surveyed men (57.1%, n = 566) and 19.3% (n = 269) of the women were classified at high-risk of OSA with the STOP-BANG measure. In multivariable models, high risk was associated with less education, lower income, and residence in a regional rather than metropolitan area. Conclusion. The high prevalence of adults at risk for OSA suggests that the capacity currently available within the healthcare system to investigate and diagnose OSA is likely to be inadequate, particularly outside urban areas. This highlights an important public health problem that requires further detailed study and trials of new models of care. [ABSTRACT FROM AUTHOR]