COPD in Never-Smokers: BOLD Australia Study.

Purpose: Tobacco smoking is the major risk factor for COPD, and it is common for other risk factors in never-smokers to be overlooked. We examined the prevalence of COPD among never-smokers in Australia and identified associated risk factors. Methods: We used data from the Australia Burden of Obstructive Lung Disease (BOLD) study, a cross-section of people aged ≥40 years from six sites. Participants completed interviews and post-bronchodilator spirometry. COPD was primarily defined as an FEV1/FVC ratio <0.70 and secondarily as the ratio less than the lower limit of normal (LLN). Results: The prevalence of COPD in the 1656 never-smokers who completed the study was 10.5% (95% CI: 9.1-12.1%) [ratio

Characteristics in Stages of Change and Decisional Balance among Smokers: The Burden of Obstructive Lung Diseases (BOLD)-Australia Study.

Smoking cessation remains a health promotion target. Applying the Transtheoretical Model to Australian Burden of Obstructive Lung Diseases (BOLD) data, we examined differences in stages of change (SoC) and readiness to quit decisional behaviours. Factors were identified likely to influence readiness of smokers, ≥40 years old, to quit. Analysis was restricted to current smokers classified to one of three stages: pre-contemplation (PC), contemplation (C) or preparation (P) to quit. Their ability to balance positive and negative consequences was measured using decisional balance. Among 314 smokers, 43.0% females and 60.8% overweight/obese, the distribution of SoC was: 38.1% PC, 38.3% C and 23.5% P. Overweight/obesity was associated with readiness to quit in stages C and P and there were more negative than positive attitudes towards smoking in those stages. Males were significantly heavier smokers in PC and C stages. Females used smoking cessation medication more frequently in PC stage, were more embarrassed about smoking and had greater negative reinforcements from smoking. Age started smoking and factors related to smoking history were associated with readiness to quit and increased the odds of being in stage C or P. An overweight/obese smoker was likely to be contemplating or preparing to quit. In these stages, smokers have more negative attitudes toward smoking. Starting smoking later, taking advice on cessation from health providers and using quit medications indicate increased readiness to quit. Evaluating these factors in smokers and developing cessation gain-framed messages may prove useful to healthcare providers.

Leading Causes of Cancer Mortality - Caribbean Region, 2003-2013.

Cancer is one of the leading causes of deaths worldwide (1); in 2012, an estimated 65% of all cancer deaths occurred in the less developed regions of the world (2). In the Caribbean region, cancer is the second leading cause of mortality, with an estimated 87,430 cancer-related deaths reported in 2012 (3). The Pan American Health Organization defines the Caribbean region as a group of 27 countries that vary in size, geography, resources, and surveillance systems.* CDC calculated site- and sex-specific proportions of cancer deaths and age-standardized mortality rates (ASMR) for 21 English- and Dutch-speaking Caribbean countries, the United States, and two U.S. territories (Puerto Rico and the U.S. Virgin Islands [USVI]), using the most recent 5 years of mortality data available from each jurisdiction during 2003-2013. The selection of years varied by availability of the data from the countries and territories in 2015. ASMR for all cancers combined ranged from 46.1 to 139.3 per 100,000. Among males, prostate cancers were the leading cause of cancer deaths, followed by lung cancers; the percentage of cancer deaths attributable to prostate cancer ranged from 18.4% in Suriname to 47.4% in Dominica, and the percentage of cancer deaths attributable to lung cancer ranged from 5.6% in Barbados to 24.4% in Bermuda. Among females, breast cancer was the most common cause of cancer deaths, ranging from 14.0% of cancer deaths in Belize to 29.7% in the Cayman Islands, followed by cervical cancer. Several of the leading causes of cancer deaths in the Caribbean can be reduced through primary and secondary preventions, including prevention of exposure to risk factors, screening, early detection, and timely and effective treatment.

Assessing the performance of two lung age equations on the Australian population: using data from the cross-sectional BOLD-Australia study.

INTRODUCTION: Lung age, a simple concept for patients to grasp, is frequently used as an aid in smoking cessation programs. Lung age equations should be continuously updated and should be made relevant for target populations. We observed how new lung age equations developed for Australian populations performed when utilizing the Burden of Obstructive Lung Disease (BOLD)-Australia dataset compared to more commonly used equations. METHODS: Data from a cross-sectional population study of noninstitutionalized Australians aged ≥40 years with analysis restricted to Caucasians <75 years. Lung age calculated using equations developed by Newbury et al. and Morris and Temple was compared with chronological age by smoking status and within smoking status. RESULTS: There were 2,793 participants with a mean age of 57 (±10 SD) years. More than half (52%) ever smoked, and 10.4% were current smokers. Prevalence of chronic obstructive pulmonary disease stage I or higher was 13.4% (95% confidence interval = 12.2, 14.7). For both genders, newer Newbury equations estimated lung ages significantly higher than actual age across all smoking groups (p < .05). Morris and Temple equations resulted in lung age estimates significantly lower than chronological age for nonsmokers (p < .05) but no difference among current smokers. Both equations showed exposure to smoking had lung ages higher than never-smokers (p < .001). Lung age also increased with increased pack-years. CONCLUSIONS: This supports the use of updated equations suited to the population of interest. The Australian Newbury equations performed well in the BOLD-Australia dataset, providing more meaningful lung age profile compared to chronological age among smokers. Using equations not developed or ideally suited for our population is likely to produce misleading results.