Tobacco smoking is a leading cause of cardiovascular disease (CVD) morbidity and mortality. Evidence on the relation of smoking to different subtypes of CVD, across fatal and non-fatal outcomes, is limited.A prospective study of 188,167 CVD- and cancer-free individuals aged ≥ 45 years from the Australian general population joining the 45 and Up Study from 2006 to 2009, with linked questionnaire, hospitalisation and death data up to the end of 2015. Hazard ratios (HRs) for hospitalisation with or mortality from CVD among current and past versus never smokers were estimated, including according to intensity and recency of smoking, using Cox regression, adjusting for age, sex, urban/rural residence, alcohol consumption, income and education. Population-attributable fractions were estimated.During a mean 7.2 years follow-up (1.35 million person-years), 27,511 (crude rate 20.4/1000 person-years) incident fatal and non-fatal major CVD events occurred, including 4548 (3.2) acute myocardial infarction (AMI), 3991 (2.8) cerebrovascular disease, 3874 (2.7) heart failure and 2311 (1.6) peripheral arterial disease (PAD) events. At baseline, 8% of participants were current and 34% were past smokers. Of the 36 most common specific CVD subtypes, event rates for 29 were increased significantly in current smokers. Adjusted HRs in current versus never smokers were as follows: 1.63 (95%CI 1.56-1.71) for any major CVD, 2.45 (2.22-2.70) for AMI, 2.16 (1.93-2.42) for cerebrovascular disease, 2.23 (1.96-2.53) for heart failure, 5.06 (4.47-5.74) for PAD, 1.50 (1.24-1.80) for paroxysmal tachycardia, 1.31 (1.20-1.44) for atrial fibrillation/flutter, 1.41 (1.17-1.70) for pulmonary embolism, 2.79 (2.04-3.80) for AMI mortality, 2.26 (1.65-3.10) for cerebrovascular disease mortality and 2.75 (2.37-3.19) for total CVD mortality. CVD risks were elevated at almost all levels of current smoking intensity examined and increased with smoking intensity, with HRs for total CVD mortality in current versus never smokers of 1.92 (1.11-3.32) and 4.90 (3.79-6.34) for 4-6 and ≥ 25 cigarettes/day, respectively. Risks diminished with quitting, with excess risks largely avoided by quitting before age 45. Over one third of CVD deaths and one quarter of acute coronary syndrome hospitalisations in Australia aged < 65 can be attributed to smoking.Current smoking increases the risk of virtually all CVD subtypes, at least doubling the risk of many, including AMI, cerebrovascular disease and heart failure. Paroxysmal tachycardia is a newly identified smoking-related risk. Where comparisons are possible, smoking-associated relative risks for fatal and non-fatal outcomes are similar. Quitting reduces the risk substantially. In an established smoking epidemic, with declining and low current smoking prevalence, smoking accounts for a substantial proportion of premature CVD events.
Abstract Background Aboriginal Australians have a life expectancy more than ten years less than that of non-Aboriginal Australians, reflecting their disproportionate burden of both communicable and non-communicable disease throughout the lifespan. Little is known about the health and health trajectories of Aboriginal children and, although the majority of Aboriginal people live in urban areas, data are particularly sparse in relation to children living in urban areas. Methods/Design The Study of Environment on Aboriginal Resilience and Child Health (SEARCH) is a cohort study of Aboriginal children aged 0-17 years, from urban and large regional centers in New South Wales, Australia. SEARCH focuses on Aboriginal community identified health priorities of: injury; otitis media; vaccine-preventable conditions; mental health problems; developmental delay; obesity; and risk factors for chronic disease. Parents/caregivers and their children are invited to participate in SEARCH at the time of presentation to one of the four participating Aboriginal Community Controlled Health Organisations at Mount Druitt, Campbelltown, Wagga Wagga and Newcastle. Questionnaire data are obtained from parents/caregivers and children, along with signed permission for follow-up through repeat data collection and data linkage. All children have their height, weight, waist circumference and blood pressure measured and complete audiometry, otoscopy/pneumatic otoscopy and tympanometry. Children aged 1-7 years have speech and language assessed and their parents/caregivers complete the Parental Evaluation of Developmental Status. The Study aims to recruit 1700 children by the end of 2010 and to secure resources for long term follow up. From November 2008 to March 2010, 1010 children had joined the study. From those 446 children with complete data entry, participating children ranged in age from 2 weeks to 17 years old, with 144 aged 0-3, 147 aged 4-7, 75 aged 8-10 and 79 aged 11-17. 55% were male and 45% female. Discussion SEARCH is built on strong community partnerships, under Aboriginal leadership, and addresses community priorities relating to a number of under-researched areas. SEARCH will provide a unique long-term resource to investigate the causes and trajectories of health and illness in urban Aboriginal children and to identify potential targets for interventions to improve health.
Simulation models of smoking behaviour provide vital forecasts of exposure to inform policy targets, estimates of the burden of disease, and impacts of tobacco control interventions. A key element of useful model-based forecasts is a clear picture of uncertainty due to the data used to inform the model, however, assessment of this parameter uncertainty is incomplete in almost all tobacco control models. As a remedy, we demonstrate a Bayesian approach to model calibration that quantifies parameter uncertainty. With a model calibrated to Australian data, we observed that the smoking cessation rate in Australia has increased with calendar year since the late 20th century, and in 2016 people who smoked would quit at a rate of 4.7 quit-events per 100 person-years (90% equal-tailed interval (ETI): 4.5–4.9). We found that those who quit smoking before age 30 years switched to reporting that they never smoked at a rate of approximately 2% annually (90% ETI: 1.9–2.2%). The Bayesian approach demonstrated here can be used as a blueprint to model other population behaviours that are challenging to measure directly, and to provide a clearer picture of uncertainty to decision-makers.
Background: We investigated risk factors for fracture among young adults, particularly body mass index (BMI) and physical activity, which although associated with fracture in older populations have rarely been investigated in younger people.Methods: In 2009, 4 years after initial recruitment, 58 204 Thais aged 19 to 49 years were asked to self-report fractures incident in the preceding 4 years. Conditional logistic regression was used to calculate odds ratios (ORs) and 95% CIs for associations of fracture incidence with baseline BMI and physical activity.Results: Very obese women had a 70% increase in fracture risk (OR = 1.73, 95% CI 1.21–2.46) as compared with women with a normal BMI. Fracture risk increased by 15% with every 5-kg/m2 increase in BMI. The effects were strongest for fractures of the lower limbs. Frequent purposeful physical activity was also associated with increased fracture risk among women (OR = 1.52, 95% CI 1.12–2.06 for 15 episodes/week vs none). Neither BMI nor physical activity was associated with fracture among men, although fracture risk decreased by 4% with every additional 2 hours of average sitting time per day (OR = 0.96, 95% CI 0.93–0.99).Conclusions: The increase in obesity prevalence will likely increase fracture burden among young women but not young men. While active lifestyles have health benefits, our results highlight the importance of promoting injury prevention practices in conjunction with physical activity recommendations, particularly among women.
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