To examine the relationship between smoking and Alzheimer's disease (AD) after controlling for study design, quality, secular trend, and tobacco industry affiliation of the authors, electronic databases were searched; 43 individual studies met the inclusion criteria. For evidence of tobacco industry affiliation, http://legacy.library.ucsf.edu was searched. One fourth (11/43) of individual studies had tobacco-affiliated authors. Using random effects meta-analysis, 18 case control studies without tobacco industry affiliation yielded a non-significant pooled odds ratio of 0.91 (95% CI, 0.75-1.10), while 8 case control studies with tobacco industry affiliation yielded a significant pooled odds ratio of 0.86 (95% CI, 0.75-0.98) suggesting that smoking protects against AD. In contrast, 14 cohort studies without tobacco-industry affiliation yielded a significantly increased relative risk of AD of 1.45 (95% CI, 1.16-1.80) associated with smoking and the three cohort studies with tobacco industry affiliation yielded a non-significant pooled relative risk of 0.60 (95% CI 0.27-1.32). A multiple regression analysis showed that case-control studies tended to yield lower average risk estimates than cohort studies (by -0.27 +/- 0.15, P=0.075), lower risk estimates for studies done by authors affiliated with the tobacco industry (by -0.37 +/- 0.13, P=0.008), no effect of the quality of the journal in which the study was published (measured by impact factor, P=0.828), and increasing secular trend in risk estimates (0.031/year +/- 0.013, P=0.02). The average risk of AD for cohort studies without tobacco industry affiliation of average quality published in 2007 was estimated to be 1.72 +/- 0.19 (P< 0.0005). The available data indicate that smoking is a significant risk factor for AD.

Purpose/Objectives To develop an instrument to measure the stigma perceived by people with lung cancer based on the HIV Stigma Scale. Design Psychometric analysis. Setting Online survey. Sample 186 patients with lung cancer. Methods An exploratory factor analysis with a common factor model using alpha factor extraction. Main Research Variables Lung cancer stigma, depression, and quality of life. Findings Four factors emerged: stigma and shame, social isolation, discrimination, and smoking. Inspection of un-rotated first-factor loadings showed support for a general stigma factor. Construct validity was supported by relationships with related constructs: self-esteem, depression, social support, and social conflict. Coefficient alphas ranging from 0.75–0.97 for the subscales (0.96 for stigma and shame, 0.97 for social isolation, 0.9 for discrimination, and 0.75 for smoking) and 0.98 for the 43-item Cataldo Lung Cancer Stigma Scale (CLCSS) provided evidence of reliability. The final version of the CLCSS was 31 items. Coefficient alpha was recalculated for the total stigma scale (0.96) and the four subscales (0.97 for stigma and shame, 0.96 for social isolation, 0.92 for discrimination, and 0.75 for smoking). Conclusions The CLCSS is a reliable and valid measure of health-related stigma in this sample of people with lung cancer. Implications for Nursing The CLCSS can be used to identify the presence and impact of lung cancer stigma and allow for the development of effective stigma interventions for patients with lung cancer.

Purpose In 2010, lung cancer is expected to be the leading cause of cancer death in both men and women. Because survival rates are increasing, an evaluation of the effects of treatment on quality of life (QOL) is an important outcome measure. In other diseases, stigma is known to have a negative impact on health status and QOL and be amenable to intervention. This is the first study to compare levels of lung cancer stigma (LCS) and relationships between LCS, depression, and QOL in ever and never smokers. Method A total of 192 participants with a self-report diagnosis of lung cancer completed questionnaires online. Results Strong associations in the expected directions, were found between LCS and depression (r = 0.68, p < 0.001) and QOL (r = −0.65, p < 0.001). No significant differences were found in demographic characteristics or study variables between ever smokers and never smokers. A simultaneous multiple regression with 5 independent variables revealed an overall model that explained 62.5% of the total variance of QOL (F5,168 = 56.015, P < 0.001). Conclusions After removing age, gender, and smoking status, depression explained 22.5% of the total variance of QOL (F4,168 = 100.661, p < 0.001). It is expected that depression and LCS would share some of the explanation of the variance of QOL, the correlation between LCS and depression is 0.629 (p < 0.001), however, LCS provides a unique and significant explanation of the variance of QOL over and above that of depression, age, gender, and smoking status, by 2.1% (p < 0.001).

Because multiple symptoms associated with “sickness behavior” have a negative impact on functional status and quality of life, increased information on the mechanisms that underlie inter-individual variability in this symptom experience is needed. The purposes of this study were to determine: if distinct classes of individuals could be identified based on their experience with pain, fatigue, sleep disturbance, and depression; if these classes differed on demographic and clinical characteristics; and if variations in pro- and anti- inflammatory cytokine genes were associated with latent class membership. Self-report measures of pain, fatigue, sleep disturbance, and depression were completed by 168 oncology outpatients and 85 family caregivers (FCs). Using latent class profile analysis (LCPA), three relatively distinct classes were identified: those who reported low depression and low pain (83%), those who reported high depression and low pain (4.7%), and those who reported high levels of all four symptoms (12.3%). The minor allele of IL4 rs2243248 was associated with membership in the “All high” class along with younger age, being White, being a patient (versus a FC), having a lower functional status score, and having a higher number of comorbid conditions. Findings suggest that LPCA can be used to differentiate distinct phenotypes based on a symptom cluster associated with sickness behavior. Identification of distinct phenotypes provides new evidence for the role of IL4 in the modulation of a sickness behavior symptom cluster in oncology patients and their FCs.

Study purposes were to determine the prevalence of persistent pain in the breast; characterize distinct persistent pain classes using growth mixture modeling, and evaluate for differences among these pain classes in demographic, preoperative, intraoperative, and postoperative characteristics. In addition, differences in the severity of common symptoms and quality of life outcomes measured prior to surgery, among the pain classes, were evaluated. Patients (n=398) were recruited prior to surgery and followed for six months. Using growth mixture modeling, patients were classified into no (31.7%), mild (43.4%), moderate (13.3%), and severe (11.6%) pain groups based on ratings of worst breast pain. Differences in a number of demographic, preoperative, intraoperative, and postoperative characteristics differentiated among the pain classes. In addition, patients in the moderate and severe pain classes reported higher preoperative levels of depression, anxiety, and sleep disturbance than the no pain class. Findings suggest that approximately 25% of women experience significant and persistent levels of breast pain in the first six months following breast cancer surgery.

This study investigated lung cancer stigma, anxiety, depression and quality of life (QOL), and validated variable similarities between ever and never smokers. Patients took online self-report surveys. Variable contributions to QOL were investigated using hierarchical multiple regression. Patients were primarily Caucasian females with smoking experience. Strong negative relationships emerged between QOL and anxiety, depression and lung cancer stigma. Lung cancer stigma provided significant explanation of the variance in QOL beyond covariates. No difference emerged between smoker groups for study variables. Stigma may play a role in predicting QOL. Interventions promoting social and psychological QOL may enhance stigma resistance skills.

Lung cancer is the leading cause of cancer death in the US. About 50% of lung cancer patients are current smokers at the time of diagnosis and up to 83% continue to smoke after diagnosis. A recent study suggests that people who continue to smoke after a diagnosis of early-stage lung cancer almost double their risk of dying. Despite a growing body of evidence that continued smoking by patients after a lung cancer diagnosis is linked with less effective treatment and a poorer prognosis, the belief prevails that treating tobacco dependence is useless. With improved cancer treatments and survival rates, smoking cessation among lung cancer patients has become increasingly important. There is a pressing need to clarify the role of smoking cessation in the care of lung cancer patients. Objective: This paper will report on the benefits of smoking cessation for lung cancer patients and the elements of smoking cessation treatment, with consideration of tailoring to the needs of lung cancer patients. Results: Given the significant benefits of smoking cessation and that tobacco dependence remains a challenge for many lung cancer patients, cancer care providers need to offer full support and intensive treatment with a smoking cessation program that is tailored to lung cancer patients’ specific needs. Conclusion: A tobacco dependence treatment plan for lung cancer patients is provided.

E-cigarettes are promoted as healthier alternatives to conventional cigarettes. Many cigarette smokers use both products. It is unknown whether the additional use of e-cigarettes among cigarette smokers (dual users) is associated with reduced exposure to tobacco-related health risks. Cross-sectional analysis was performed using baseline data from the Health eHeart Study, among English-speaking adults, mostly from the United States. Cigarette use (# cigarettes/day) and/or e-cigarette use (# days, # cartridges, and # puffs) were compared between cigarette only users vs. dual users. Additionally, we examined cardiopulmonary symptoms/ conditions across product use: no product (neither), e-cigarettes only, cigarettes only, and dual use. Among 39,747 participants, 573 (1.4%) reported e-cigarette only use, 1,693 (4.3%) reported cigarette only use, and 514 (1.3%) dual use. Dual users, compared to cigarette only users, reported a greater median (IQR) number of cigarettes per day, 10.0 (4.0–20.0) vs. 9.0 (3.0–15.0) (p < .0001), a lower (worse) median (IQR) SF-12 general health score, 3.3 (2.8–3.8) vs. 3.5 (2.8–3.9) (p = .0014), and a higher (worse) median (IQR) breathing difficulty score in the past month, 2.0 (1.0–2.0) vs. 1.0 (1.0–2.0) (p = .001). Of the 19 cardiopulmonary symptoms/ conditions, having a history of arrhythmia was significantly different between cigarette only users (14.2%) and dual users (17.8%) (p = .02). In this sample, dual use was not associated with reduced exposure to either (i) cigarettes, compared to cigarette only users or (ii) e-cigarettes, compared to e-cigarette only users. E-cigarette only use, compared to no product use, was associated with lower general health scores, higher breathing difficulty scores (typically and past month), and greater proportions of those who responded ‘yes’ to having chest pain, palpitations, coronary heart disease, arrhythmia, COPD, and asthma. These data suggest the added use of e-cigarettes alone may have contributed to cardiopulmonary health risks particularly respiratory health risks.