Summary Background Elevated blood pressure and glucose, serum cholesterol, and body mass index (BMI) are risk factors for cardiovascular diseases (CVDs); some of these factors also increase the risk of chronic kidney disease (CKD) and diabetes. We estimated CVD, CKD, and diabetes mortality attributable to these four cardio-metabolic risk factors for all countries and regions between 1980 and 2010. Methods We used data on risk factor exposure by country, age group, and sex from pooled analysis of population-based health surveys. Relative risks for cause-specific mortality were obtained from pooling of large prospective studies. We calculated the population attributable fractions (PAF) for each risk factor alone, and for the combination of all risk factors, accounting for multi-causality and for mediation of the effects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specific PAFs by the number of disease-specific deaths from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all inputs to the final estimates. Findings In 2010, high blood pressure was the leading risk factor for dying from CVDs, CKD, and diabetes in every region, causing over 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths; and cholesterol for 10%. After accounting for multi-causality, 63% (10.8 million deaths; 95% confidence interval 10.1–11.5) of deaths from these diseases were attributable to the combined effect of these four metabolic risk factors, compared with 67% (7.1 million deaths; 6.6–7.6) in 1980. The mortality burden of high BMI and glucose nearly doubled between 1980 and 2010. At the country level, age-standardised death rates attributable to these four risk factors surpassed 925 deaths per 100,000 among men in Belarus, Mongolia, and Kazakhstan, but were below 130 deaths per 100,000 for women and below 200 for men in some high-income countries like Japan, Singapore, South Korea, France, Spain, The Netherlands, Australia, and Canada. Interpretations The salient features of the cardio-metabolic epidemic at the beginning of the twenty-first century are the large role of high blood pressure and an increasing impact of obesity and diabetes. There has been a shift in the mortality burden from high-income to low- and middle-income countries.

BackgroundHuntington's disease (HD) is a fatal inherited neurodegenerative disease, caused by a

Objective: To examine the relationship between self-reported and clinical measurements for height and weight in adults aged 18 years and over and to determine the bias associated with using household telephone surveys. Conclusion:Self-report is important in monitoring overweight and obesity; however, it must be recognised that prevalence estimates obtained are likely to understate the problem. Implications:The public health focus on obesity is warranted, but self-report estimates, commonly used to highlight the obesity epidemic, are likely to be underestimations. Self-report would be a more reliable measure if people did not round their measurements and if older persons more accurately knew their height.

Heat waves are considered a health risk and they are likely to increase in frequency, intensity and duration as a consequence of climate change. The effects of heat waves on human health could be reduced if individuals recognise the risks and adopt healthy behaviours during a heat wave. The purpose of this study was to determine the predictors of risk perception using a heat wave scenario and identify the constructs of the health belief model that could predict adaptive behaviours during a heat wave. A cross-sectional study was conducted during the summer of 2012 among a sample of persons aged between 30 to 69 years in Adelaide. Participants’ perceptions were assessed using the health belief model as a conceptual frame. Their knowledge about heat waves and adaptive behaviours during heat waves was also assessed. Logistic regression analyses were performed to determine the predictors of risk perception to a heat wave scenario and adaptive behaviours during a heat wave. Of the 267 participants, about half (50.9%) had a high risk perception to heat waves while 82.8% had good adaptive behaviours during a heat wave. Multivariate models found that age was a significant predictor of risk perception. In addition, participants who were married (OR = 0.21; 95% CI, 0.07–0.62), who earned a gross annual household income of ≥$60,000 (OR = 0.41; 95% CI, 0.17–0.94) and without a fan (OR = 0.29; 95% CI, 0.11–0.79) were less likely to have a high risk perception to heat waves. Those who were living with others (OR = 2.87; 95% CI, 1.19–6.90) were more likely to have a high risk perception to heat waves. On the other hand, participants with a high perceived benefit (OR = 2.14; 95% CI, 1.00–4.58), a high “cues to action” (OR = 3.71; 95% CI, 1.63–8.43), who had additional training or education after high school (OR = 2.65; 95% CI, 1.25–5.58) and who earned a gross annual household income of ≥$60,000 (OR = 2.66; 95% CI, 1.07–6.56) were more likely to have good adaptive behaviours during a heat wave. The health belief model could be useful to guide the design and implementation of interventions to promote adaptive behaviours during heat waves.

No abstract

The Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) Study was established in 2009 to investigate the associations of sex steroids, inflammation, environmental and psychosocial factors with cardio-metabolic disease risk in men. The study population consists of 2569 men from the harmonisation of two studies: all participants of the Florey Adelaide Male Ageing Study (FAMAS) and eligible male participants of the North West Adelaide Health Study (NWAHS). The cohort has so far participated in three stages of the MAILES Study: MAILES1 (FAMAS Wave 1, from 2002-2005, and NWAHS Wave 2, from 2004-2006); MAILES2 (FAMAS Wave 2, from 2007-2010, and NWAHS Wave 3, from 2008-2010); and MAILES3 (a computer-assisted telephone interview (CATI) survey of all participants in the study, conducted in 2010). Data have been collected on a comprehensive range of physical, psychosocial and demographic issues relating to a number of chronic conditions (including cardiovascular disease, diabetes, arthritis and mental health) and health-related risk factors (including obesity, blood pressure, smoking, diet, alcohol intake and inflammatory markers), as well as on current and past health status and medication.

COVID-19 is a strong disruptive force that has not only influenced our global health and economy but also has changed the way we teach, learn and communicate with our students. It has disturbed the regular education pattern and the standard practices that we adapted over many years. The challenge is beyond changing the mode of delivering instructions from face to face to online. The real challenge is in creating a culture that supports the adoption of innovative practices, which require different skills and competences from the teacher, student, mentor and administrator, and at the same time maintaining the quality of the products. In other words, changing what was exceptional to be the norm over a short period of time. This article describes our approach "Open Learning" in managing such change. Our over-riding philosophy is about ensuring that students have high quality resources, and the enthusiasm and learning skills to benefit from them. At the same time we want to optimise the use of the available online applications and learning management system so that their use is within the capability of our faculty. This paper describes the evolution of our approach and the principles upon which it has been based. Our experiences over the past few months will transform the educational experience of our students over the years to come.

SummaryBackgroundDiabetes has been defined on the basis of different biomarkers, including fasting plasma glucose (FPG), 2-h plasma glucose in an oral glucose tolerance test (2hOGTT), and HbA1c. We assessed the effect of different diagnostic definitions on both the population prevalence of diabetes and the classification of previously undiagnosed individuals as having diabetes versus not having diabetes in a pooled analysis of data from population-based health examination surveys in different regions.MethodsWe used data from 96 population-based health examination surveys that had measured at least two of the biomarkers used for defining diabetes. Diabetes was defined using HbA1c (HbA1c ≥6·5% or history of diabetes diagnosis or using insulin or oral hypoglycaemic drugs) compared with either FPG only or FPG-or-2hOGTT definitions (FPG ≥7·0 mmol/L or 2hOGTT ≥11·1 mmol/L or history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated diabetes prevalence, taking into account complex survey design and survey sample weights. We compared the prevalences of diabetes using different definitions graphically and by regression analyses. We calculated sensitivity and specificity of diabetes diagnosis based on HbA1c compared with diagnosis based on glucose among previously undiagnosed individuals (ie, excluding those with history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated sensitivity and specificity in each survey, and then pooled results using a random-effects model. We assessed the sources of heterogeneity of sensitivity by meta-regressions for study characteristics selected a priori.FindingsPopulation prevalence of diabetes based on FPG-or-2hOGTT was correlated with prevalence based on FPG alone (r=0·98), but was higher by 2–6 percentage points at different prevalence levels. Prevalence based on HbA1c was lower than prevalence based on FPG in 42·8% of age–sex–survey groups and higher in another 41·6%; in the other 15·6%, the two definitions provided similar prevalence estimates. The variation across studies in the relation between glucose-based and HbA1c-based prevalences was partly related to participants' age, followed by natural logarithm of per person gross domestic product, the year of survey, mean BMI, and whether the survey population was national, subnational, or from specific communities. Diabetes defined as HbA1c 6·5% or more had a pooled sensitivity of 52·8% (95% CI 51·3–54·3%) and a pooled specificity of 99·74% (99·71–99·78%) compared with FPG 7·0 mmol/L or more for diagnosing previously undiagnosed participants; sensitivity compared with diabetes defined based on FPG-or-2hOGTT was 30·5% (28·7–32·3%). None of the preselected study-level characteristics explained the heterogeneity in the sensitivity of HbA1c versus FPG.InterpretationDifferent biomarkers and definitions for diabetes can provide different estimates of population prevalence of diabetes, and differentially identify people without previous diagnosis as having diabetes. Using an HbA1c-based definition alo...