Reductions in cardiovascular, cerebrovascular, and respiratory mortality following the national Irish smoking ban: Interrupted time-series analysis

Copyright @ 2013 Stallings-Smith et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Background: Previous studies have shown decreases in cardiovascular mortality following the implementation of comprehensive smoking bans. It is not known whether cerebrovascular or respiratory mortality decreases post-ban. On March 29, 2004, the Republic of Ireland became the first country in the world to implement a national workplace smoking ban. The aim of this study was to assess the effect of this policy on all-cause and cause-specific, non-trauma mortality. Methods: A time-series epidemiologic assessment was conducted, utilizing Poisson regression to examine weekly age and gender-standardized rates for 215,878 non-trauma deaths in the Irish population, ages ≥35 years. The study period was from January 1, 2000, to December 31, 2007, with a post-ban follow-up of 3.75 years. All models were adjusted for time trend, season, influenza, and smoking prevalence. Results: Following ban implementation, an immediate 13% decrease in all-cause mortality (RR: 0.87; 95% CI: 0.76-0.99), a 26% reduction in ischemic heart disease (IHD) (RR: 0.74; 95% CI: 0.63-0.88), a 32% reduction in stroke (RR: 0.68; 95% CI: 0.54-0.85), and a 38% reduction in chronic obstructive pulmonary disease (COPD) (RR: 0.62; 95% CI: 0.46-0.83) mortality was observed. Post-ban reductions in IHD, stroke, and COPD mortalities were seen in ages ≥65 years, but not in ages 35-64 years. COPD mortality reductions were found only in females (RR: 0.47; 95% CI: 0.32-0.70). Post-ban annual trend reductions were not detected for any smoking-related causes of death. Unadjusted estimates indicate that 3,726 (95% CI: 2,305-4,629) smoking-related deaths were likely prevented post-ban. Mortality decreases were primarily due to reductions in passive smoking. Conclusions: The national Irish smoking ban was associated with immediate reductions in early mortality. Importantly, post-ban risk differences did not change with a longer follow-up period. This study corroborates previous evidence for cardiovascular causes, and is the first to demonstrate reductions in cerebrovascular and respiratory causes

A randomized trial to assess the impact of opinion leader endorsed evidence summaries on the use of secondary prevention strategies in patients with coronary artery disease: the ESP-CAD trial protocol [NCT00175240]

BACKGROUND: Although numerous therapies have been shown to be beneficial in the prevention of myocardial infarction and/or death in patients with coronary disease, these therapies are under-used and this gap contributes to sub-optimal patient outcomes. To increase the uptake of proven efficacious therapies in patients with coronary disease, we designed a multifaceted quality improvement intervention employing patient-specific reminders delivered at the point-of-care, with one-page treatment guidelines endorsed by local opinion leaders ("Local Opinion Leader Statement"). This trial is designed to evaluate the impact of these Local Opinion Leader Statements on the practices of primary care physicians caring for patients with coronary disease. In order to isolate the effects of the messenger (the local opinion leader) from the message, we will also test an identical quality improvement intervention that is not signed by a local opinion leader ("Unsigned Evidence Statement") in this trial. METHODS: Randomized trial testing three different interventions in patients with coronary disease: (1) usual care versus (2) Local Opinion Leader Statement versus (3) Unsigned Evidence Statement. Patients diagnosed with coronary artery disease after cardiac catheterization (but without acute coronary syndromes) will be randomly allocated to one of the three interventions by cluster randomization (at the level of their primary care physician), if they are not on optimal statin therapy at baseline. The primary outcome is the proportion of patients demonstrating improvement in their statin management in the first six months post-catheterization. Secondary outcomes include examinations of the use of ACE inhibitors, anti-platelet agents, beta-blockers, non-statin lipid lowering drugs, and provision of smoking cessation advice in the first six months post-catheterization in the three treatment arms. Although randomization will be clustered at the level of the primary care physician, the design effect is anticipated to be negligible and the unit of analysis will be the patient. DISCUSSION: If either the Local Opinion Leader Statement or the Unsigned Evidence Statement improves secondary prevention in patients with coronary disease, they can be easily modified and applied in other communities and for other target conditions

Consequences of EU Biofuel Policies on Agricultural Production and Land Use

Some recent discussions of adverse human health effects of active and passive smoking have suggested that low levels of exposure are disproportionately dangerous, so that “The effects of even brief (minutes to hours) passive smoking are often nearly as large (averaging 80% to 90%) as chronic active smoking” (Barnoya and Glantz, 2005). Recent epidemiological evidence (Teo et al., 2006) suggests a more linear relation. This paper reexamines the empirical relation between self-reported low levels of active smoking and risk of coronary heart disease (CHD) in public-domain data from the National Health and Nutrition Examination Survey (NHANES). Consistent with biological evidence on J-shaped and U-shaped relations between smoking-associated risk factors and CHD risks, we find that low levels of active smoking do not appear to be associated with increased CHD risk. Several methodological challenges in epidemiology may explain how model-derived estimates can predict low-dose linear or concave dose-response estimates, even if the empirical (i.e., data-based) relation does not show a clear increased risk at the lowest doses