Dr Benjamin Ewald

Purpose: Rural areas may face under-recognized threats to air quality. We tested 2 hypotheses that 1) rural areas in New South Wales, Australia, would have better air quality than metropolitan Sydney, and that 2) the rural Upper Hunter region characterized by coal mining and coal combustion would have worse air quality than other rural areas of the state. Methods: We analyzed 2017 daily mean values for New South Wales, Australia, for particulate matter (PM2.5 and PM10), sulfur dioxide (SO ), nitric oxide (NO), nitrogen dioxide (NO ), and NOx (sum of NO and NO ). Forty-six air monitoring stations were grouped into 6 rural and urban regional areas. Linear regression models examined pollution levels in association with rural and urban regions and meteorological covariates. Results: Findings show that daily mean pollutant levels in the rural Upper Hunter were the highest of all regions, and were significantly higher than metropolitan Sydney, with and without control for weather conditions, for every pollutant. For example, daily mean PM2.5 was 8.64 µg/m in the rural Upper Hunter, compared to 7.23 µg/m in metropolitan Sydney. Conclusions: Results highlight the need to consider both urban and rural sources of pollution in air quality studies, and appropriate policy steps to address likely rural air pollution from coal mining. 2 2 2 3 3

Objective: Interventions to promote physical activity for sedentary patients seen in general practice may be a way to reduce the burden of chronic disease. Coaching by an exercise physiologist is publicly funded in Australia, but cost effectiveness has not been documented. Methods: In a three-arm randomised controlled trial, face-to-face coaching and telephone coaching over 12 weeks were compared with a control group using the outcome of step count for one week at baseline, three months and twelve months. Program costs and time-based costs were considered. Quality of life was measured as a secondary outcome. Results: At 12 months, the intervention groups were more active than controls by 1,002 steps per day (95%CI 244, 1,759). This was achieved at a cost of AUD$245 per person. There was no change in reported quality of life or utility values. Conclusion: Coaching achieved a modest increase in activity equivalent to 10 minutes walking per day, at a cost of AUD$245 per person. Face-to-face and telephone counselling were both effective. Implication for public health: Persistence of increases nine months after the end of coaching suggests it creates long-term change and is a good value health intervention.

Background and objective It is helpful for general practitioners (GPs) and their patients to understand the amount of health benefit expected from different preventive activities to enable a thoughtful choice of which to adopt first. The aim of this article is to illustrate how it might be possible to quantify the mortality benefit for cancer screening, quitting smoking, losing weight and treating lipids, which are preventive activities from The Royal Australian College of General Practitioners' (RACGP's) Guidelines for preventive activities in general practice (Red Book). Methods A sample of common preventive activities was taken, with an outcome for each selected for fair comparison, and benefits and harms were estimated. Results For a man aged 50 years, the benefit in terms of reduced risk of dying is greatest for quitting smoking (at 24 fewer deaths/1000/decade), which is approximately 10 times the benefit of lowering lipids in a man with metabolic syndrome and about 50 times greater than from participating in regular colorectal cancer screening. Benefits for women are generally lower, as their baseline risk is lower. Discussion It is feasible to quantify the benefits of some preventive activities, although estimating them is not straightforward and requires several assumptions. Nevertheless, extending estimates such as these to the items in the RACGP's Red Book would assist GPs and their patients' preventive activity prioritisation.

Objectives: To determine the extent to which physical activity reduces the number of hospital bed-days for Australians over 55, using an objective measure of activity. Design, setting and participants: 9784 Newcastle residents aged 55 years or more were invited to participate. 3253 responders were eligible and wore pedometers for one week during 2005e2007; their hospital data from recruitment to 31 March 2015 were analysed (mean follow-up time: 8.2 years). Complete data for 2110 people were available for analysis. Main outcome measures: Mean annual hospital bed-days, according to individual step count. Results: There was a statistically significant reduction in the number of hospital bed-days associated with higher step counts; the incidence rate ratio per extra 1000 steps per day at baseline was 0.91 (95% CI, 0.90e0.94). The disease-specific reductions were significant for admissions for cancer and diabetes, but not for cardiovascular disease. The difference between 4500 and 8800 steps per day (the upper and lower quartile boundaries for step count) was 0.36 bed-days per person per year, after adjusting for age, sex, number of medications, number of comorbidities, smoking and alcohol status, and education. When analysis was restricted to hospital admissions after the first 2 years of follow-up, the difference was 0.29 bed-days per person per year. Conclusions: More active people require less hospital care, and an achievable extra 4300 steps per day would result in an average of one less day in hospital for each 3 years of life.

One Health is a concept which fosters collaborative relationships between human health, animal health and environmental health partners. Diseases are emerging and re-emerging in South Asia due to poor sanitation, close proximity of people to livestock, deforestation, porous borders, climate change, changes in human behaviour and unhygienic food preparation and consumption practices. This review was completed in two stages. First, we conducted a review of peer-reviewed literature and grey literature available in Google search engine related to One Health in four countries (Bangladesh, Bhutan, India and Nepal). Second, we used a structured questionnaire completed by the key stakeholders working on One Health for the collection of information related to the challenges in implementing One Health. Most of the One Health activities in South Asia are determined by donor preferences. Bangladesh and India did considerable work in advancing One Health with limited support from the government agencies. Weak surveillance mechanisms, uncertain cost-effectiveness of One Health compared with the existing approach, human resources and laboratory capacity are some of the factors hindering implementation of the One Health concept. Implementation of One Health is growing in the South Asia region with limited or no government acceptance. To institutionalise it, there is a need for leadership, government support and funding.

Introduction Primary care physicians are well placed to offer physical activity counseling, but insufficient time is a barrier. Referral to an exercise specialist is an alternative. In Australia, exercise specialists are publicly funded to provide face-to-face counseling to patients who have an existing chronic illness. This trial aimed to (1) determine the efficacy of primary care physicians¿ referral of insufficiently active patients for counseling to increase physical activity, compared with usual care, and (2) compare the efficacy of face-to-face counseling with counseling predominantly via telephone. Study design Three-arm pragmatic RCT. Setting/participants Two hundred three insufficiently active (<7,000 steps/day) primary care practice patients (mean age 57 years; 70% female) recruited in New South Wales, Australia, in 2011¿2014. Intervention (1) Five face-to-face counseling sessions by an exercise specialist, (2) one face-to-face counseling session followed by four telephone calls by an exercise specialist, or (3) a generic mailed physical activity brochure (usual care). The counseling sessions operationalized social cognitive theory via a behavior change counseling framework. Main outcome measures Change in average daily step counts between baseline and 12 months. Data were analyzed in 2016. Results Forty (20%) participants formally withdrew; completion rates at 3 and 6 months were 64% and 58%, respectively. Intervention attendance was high (75% received five sessions). The estimated mean difference between usual care and the combined intervention groups at 12 months was 1,002 steps/day (95% CI=244, 1,759, p=0.01). When comparing face-to-face with predominantly telephone counseling, the telephone group had a non-significant higher mean daily step count (by 619 steps) at 12 months. Conclusions Provision of expert physical activity counseling to insufficiently active primary care patients resulted in a significant increase in physical activity (approximately 70 minutes of walking per week) at 12 months. Face-to-face only and counseling conducted predominantly via telephone were both effective. This trial provides evidence to expand public funding for expert physical activity counseling and for delivery via telephone in addition to face-to-face consultations. Trial registration This trial is registered at www.anzctr.org.au/ ACTRN12611000884909.

Background: Although an overall public health target of 10,000 steps per day has been advocated, the dose-response relationship for each health benefit of physical activity may differ. Methods: A representative community sample of 2458 Australian residents aged 55-85 wore a pedometer for a week in 2005-2007 and completed a health assessment. Age-standardized steps per day were compared with multiple markers of health using locally weighted regression to produce smoothed dose-response curves and then to select the steps per day matching 60% or 80% of the range in each health marker. Results: There is a linear relationship between activity level and markers of inflammation throughout the range of steps per day; this is also true for BMI in women and high density lipoprotein in men. For other markers, including waist:hip ratio, fasting glucose, depression, and SF-36 scores, the benefit of physical activity is mostly in the lower half of the distribution. Conclusions: Older adults have no plateau in the curve for some health outcomes, even beyond 12,000 steps per day. For other markers, however, there is a threshold effect, indicating that most of the benefit is achieved by 8000 steps per day, supporting this as a suitable public health target for older adults. © 2014 Human Kinetics, Inc.

New drugs are usually compared to a placebo. Sometimes it may be unethical to give patients a placebo, so the new drug is compared with standard treatment. Trials which compare treatments may not be designed to show that one treatment is superior. These are known as non-inferiority or equivalence trials. Non-inferiority trials aim to show that the new drug is no worse than standard treatment. Equivalence trials aim to show the new treatment is no better and no worse. An equivalence boundary should be set before the trial. This is the definition of what would be the minimum important difference between the treatments. There are several traps in the interpretation of trials of non-inferiority or equivalence. The results can be influenced by many factors including the size of the equivalence boundary and whether an intention-to-treat or 'per protocol' analysis is used.