The COVID-19 pandemic has caused devastating impacts globally. To mitigate virus spread, Israel imposed severe restrictions during March–April 2020. An online cross-sectional survey was conducted in April 2020 among current and ex-smokers to explore changes in smoking behaviour and home-smoking rules during this period. Bivariate analysis and multivariate logistic regression examined associations between sociodemographic characteristics and perceived risk of infection and quitting smoking during the initial COVID-19 period. Current smoking was reported by 437 (66.2%) of the 660 participants, 46 (7%) quit during the initial restriction period, and 177 (26.8%) were ex-smokers. Nearly half (44.4%) of current smokers intensified their smoking, and 16% attempted to quit. Quitting during the COVID-19 period was significantly associated with higher education (adjusted odds ratio (aOR): 1.97, 95% CI: 1.0–3.8), not living with a smoker (aOR: 2.18, 95% CI: 1.0–4.4), and having an underlying chronic condition that increases risk for COVID-19 complications (aOR: 2.32, 95% CI: 1.1–4.6). Both an increase in smoking behaviour and in attempts to quit smoking during the initial COVID-19 pandemic were evident in this sample of adult Israeli smokers. Governments need to use this opportunity to encourage smokers to attempt quitting and create smoke-free homes, especially during lockdown conditions, while providing mental and social support to all smokers.

Clements' approach to process capability analysis for skewed distributions, based on fitting the Pearson distribution system to data, is widely used in industry. In this paper we compare the accuracy of the Pearson system and the RMM (response modeling methodology) distribution, as distributional models for process capability analysis of non-normal data. The accuracy of the estimates of C p and C PU is measured by the relative mean square errors. Three factors that may affect the accuracy of RMM and Pearson are examined: the data-generating distribution (Weibull, lognormal, gamma), the skewness (0.5, 1.25, 2) and the sample size (50, 300, 2000). The results show that RMM consistently outperforms Pearson, even for samples from gamma, which is a special case of Pearson. This implies that when observations are visibly skewed yet their underlying distribution is unknown, RMM estimators for C p and C PU take account of the information stored in the data more precisely than the Pearson model, and may therefore constitute a preferred distributional model to pursue in process capability analysis.

The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.