Associations of sustained smoking and smoking cessation with work-related outcomes: a longitudinal analysis.

PURPOSE: The aim of this study was to assess the association between sustained smoking and quitting with work-related outcomes among older workers. METHODS: We categorized a sample of older employees into non-smokers, sustained smokers and quitters. Multivariable regression models were used to test longitudinal associations of sustained smoking and smoking cessation with sickness absence, productivity loss and work ability. RESULTS: We included 3612 non-smokers, 673 sustained smokers and 246 quitters. Comparing sustained smokers to non-smokers, we found higher (but not statistically significant) sickness absence for sustained smokers [1.01, 95% confidence interval (CI) - 0.16-2.17]. We did not find differences in productivity loss (OR 0.82, 95% CI 0.60-1.13) and work ability (0.05, 95% CI -0.05-0.15). For employees with a relatively high physical health at baseline, comparing quitters to sustained smokers, we found higher (but not statistically significant) productivity loss for quitters (OR 2.23, 95% CI 0.94-5.31), and no difference in sickness absence (0.10, 95% CI - 2.67-2.87), and work ability (- 0.10, 95% CI -  0.36-0.16). For employees with a relatively low physical health at baseline, comparing quitters to sustained smokers, we found a statistically significant lower work ability (- 0.31, 95% CI - 0.57-0.05), and no difference in sickness absence (2.53, 95% CI - 1.29-6.34) and productivity loss (OR 1.26, 95% CI 0.66-2.39). CONCLUSIONS: We found no evidence that sustained smokers have less favorable work-related outcomes than non-smokers or that quitters have more favorable work-related outcomes than sustained smokers. The benefits of smoking cessation for employers might take a longer time to develop.

"Like you are fooling yourself": how the "Stoptober" temporary abstinence campaign supports Dutch smokers attempting to quit.

BACKGROUND: The Stoptober temporary abstinence campaign challenges smokers to engage in a collective quit attempt for 28 days. The campaign is based on social contagion theory, SMART (i.e., Specific, Measurable, Attainable, Realistic and Time-sensitive) goal setting and PRIME (i.e., Plans, Responses, Impulses, Motives and Evaluations) theory. Although Stoptober was found to yield impressive 28-day quit rates, relapse rates remained substantial. Therefore, we examined how Stoptober supported smokers in their attempt to quit and how the campaign's effectiveness could be strengthened. METHODS: In 2016, we conducted semi-structured interviews with 23 Stoptober participants in the Netherlands. Data were analyzed thematically. RESULTS: Respondents explained how social contagion-based components had familiarized them with Stoptober, motivated them to participate, and created a pro-smoking cessation social norm. Setting SMART goals was reported as "fooling yourself", since it distracted respondents from their goal of quitting for good and helped them perceive that temporary abstinence was achievable. Respondents also illustrated the usefulness of PRIME theory. They typically used an individual selection of available supports that varied over time. To achieve long-term abstinence, respondents expressed the need for additional social network support and interactive, personalized and professional support during and after the campaign. CONCLUSIONS: Stoptober supports smokers in their attempts to quit and generally according to the campaign's theoretical principles. Added to available evidence, this finding supports the continuation and wider implementation of Stoptober, while connecting the campaign to social networks and regular smoking-cessation services to help improve long-term abstinence rates.

Smoking and sickness absence: a systematic review and meta-analysis.

Objectives Evidence on the effect of smoking on sickness absence could guide workplace smoking cessation interventions and encourage employers to promote smoking cessation among their employees. This systematic review and meta-analysis aimed to summarize evidence on the association between smoking and sickness absence and determine whether there are differences in this association for study design, methodology, and sample characteristics. Methods We searched for studies that reported on smoking status and sickness absence, used empirical data, were published in a peer-reviewed journal in the last 25 years, and written in English. We conducted pooled analyses in which uni- and multivariate generalized linear regression models were applied. Results After screening 2551 unique records, 46 articles from 43 studies were included, of which 33 studies (with 1 240 723 participants) could be included in the pooled analyses. Smoking was associated with an 31% increase in risk of sickness absence compared to non-smoking (95% confidence interval (CI) 1.24-39). We did not find statistically significant different effect sizes for study location, gender, age, occupational class, study design, assessment of sickness absence, short- versus long-term sickness absence, and adjustment for relevant confounders. Furthermore, smoking was associated with 2.89 more sickness absence days per year compared to non-smoking (95% CI 2.08-3.70). Conclusions We found robust evidence showing that smoking increases both the risk and number of sickness absence days in working populations, regardless of study location, gender, age, and occupational class. Encouraging smoking cessation at the workplace could therefore be beneficial for employers and employees.

Multimorbidity is common among young workers and related to increased work absenteeism and presenteeism: results from the population-based Raine Study cohort.

Objectives This study aimed to determine the extent of both multimorbidity and work productivity loss among young adults with paid work and to analyze their association. Methods We included 604 participants from a follow-up of the Raine Study that comprised a cohort who were 22 years at the time (Gen2-22). Information on 36 health conditions, grouped into 10 condition categories, was collected through questionnaires and physical assessments (for body mass index only). Quarterly questionnaires about work productivity, including total absenteeism, sickness absenteeism, and total presenteeism, were distributed electronically over the subsequent 12 months. Descriptive statistics were used to determine the prevalence of health conditions, condition categories, multimorbidity, and work productivity. Zero-inflated negative binomial regression analyses were used to assess the association of multimorbidity with productivity loss. Results Multimorbidity prevalence was substantially higher in females (63%) compared to males (41%). Productivity loss increased as the number of condition categories increased. For example, total absenteeism was associated with an increase in the number of health condition categories in males [incidence rate ratio (IRR) 1.20, 95% confidence interval (CI) 1.05-1.36] and females (IRR 1.13, 95% CI 1.04-1.24). Similar results were found for sickness absenteeism and presenteeism. The highest burden of productivity loss was found for musculoskeletal disorders (42 444 hours/1000 workers/year), sleep problems (33 581 hours/1000 workers/year), mental and neurological conditions (15 650 hours/1000 workers/year), and 'other' medical conditions (22 519 hours/1000 workers/year). Conclusions Multimorbidity appears to be highly prevalent among young adults with paid work and is strongly related to work productivity loss. Therefore, young workers should be targeted in interventions aiming to reduce multimorbidity and its impact on work productivity.

Effectiveness of a Large, Nation-Wide Smoking Abstinence Campaign in the Netherlands: A Longitudinal Study.

From 2014, the 28-day smoking abstinence campaign 'Stoptober' is held in the Netherlands. Each year, more than 50,000 people participate in what has become a nation-wide collective cessation attempt. This study aims to determine the short-term effects of 'Stoptober' on participants' smoking behavior and behavioral determinants. Stoptober participants completed online surveys before the start of the campaign (n = 6856) and three months later (n = 1127). Descriptive statistics and t-tests were performed to determine changes in smoking and behavioral determinants. Logistic regression analyses were used to identify differences between subgroups. After three months, 71.8% of respondents had quit smoking and consumption was reduced among sustained smokers. Cessation rates were similar for subgroups by age, sex and educational level. Cessation was positively associated with confidence and self-efficacy at baseline and negatively associated with past year quit attempts and addiction level at baseline. For quitters, we found favorable changes in attitude towards cessation related stress, social norms, social pressure to smoke, self-efficacy to quit, smoking habit strength and smoker identity. For sustained smokers, we found favorable changes in attitude towards cessation related stress, self-efficacy and smoking habit strength. These results suggest that an abstinence campaign with a wide reach in a national population may be effective in decreasing smoking prevalence and cigarette consumption among a broad range of participants.

Associations of the Stoptober smoking cessation program with information seeking for smoking cessation: A Google trends study.

INTRODUCTION: The national smoking cessation program Stoptober was introduced in October 2012 in England and in October 2014 in the Netherlands. There is little evidence on the extent to which the Stoptober program has an impact on smoking-related outcomes at national levels. We aimed to measure the magnitude and timing of the associations of the Dutch Stoptober program with searching for smoking cessation on the internet. METHODS: An interrupted time series analysis was used on Google search queries. Data were seasonally adjusted and analyzed using autoregressive integrated moving average (ARIMA) modelling. To examine the magnitude and timing of the program, nine potential intervention periods around early October were analyzed simultaneously, with control for national tobacco control policies. Parallel analyses were made of Belgium as a control group. RESULTS: The 2014-2016 Dutch Stoptober programs were associated with a significant increase in relative search volume (RSV) in the week the challenge starts (11%, 95% CI: 1-21), the next week (22%, 95% CI: 12-33) and the week afterward (17%, 95% CI: 8-27). A smaller, non-significant increase was observed in the two weeks before the challenge. No substantial increases were found in the Belgian control group. CONCLUSIONS: In the Netherlands, the Stoptober program was associated with a substantial short-term increase in information seeking for smoking cessation. This suggests that Stoptober may be able to affect smoking-related outcomes in national populations at large.

Did the introduction of pictorial health warnings increase information seeking for smoking cessation?: Time-series analysis of Google Trends data in six countries.

INTRODUCTION: Pictorial health warnings (PHW) can influence smoking cessation rates and precursors thereof. However, both the magnitude and duration of their impact, in national populations, remain uncertain because of limitations of the available data. In this study we used Google Trends data from six European countries to evaluate whether the implementation of PHW was followed by a short-term increase in online searches on smoking cessation. METHODS: We applied an interrupted time-series design using ARIMA models. We used weekly or monthly data on the relative search volume (RSV) for search terms about smoking cessation. First, RSV trends were seasonally adjusted and adjusted for autocorrelation. Next, regression models were fitted that included terms for the potential effect of PHW in month 1, months 2-3, and months 4-6 after implementation. RESULTS: Our findings for France and the United Kingdom were partly in line with our initial expectations. In France, a 4% increase (95% CI: -2% - 11%) occurred in the first month after implementation, but not later. In the UK, a 3% increase (95% CI: 1% - 6%) in 'quit smoking' searches occurred in months 2-3. No increases were observed for any other periods for France, the UK, Ireland, Norway, Denmark or Switzerland. CONCLUSIONS: We found no consistent support that the implementation of PHW was associated with increased internet searches for smoking cessation. Further studies are needed to assess and understand the magnitude and duration of population-wide impacts of PHW.

Effect of Tobacco Control Policies on Information Seeking for Smoking Cessation in the Netherlands: A Google Trends Study.

BACKGROUND: The impact of tobacco control policies on measures of smoking cessation behaviour has often been studied, yet there is little information on their precise magnitude and duration. This study aims to measure the magnitude and timing of the impact of Dutch tobacco control policies on the rate of searching for information on smoking cessation, using Google Trends search query data. METHODS: An interrupted time series analysis was used to examine the effect of two types of policies (smoke-free legislation and reimbursement of smoking cessation support (SCS)) on Google searches for 'quit smoking'. Google Trends data were seasonally adjusted and analysed using autoregressive integrated moving average (ARIMA) modelling. Multiple effect periods were modelled as dummy variables and analysed simultaneously to examine the magnitude and duration of the effect of each intervention. The same analysis was repeated with Belgian search query data as a control group, since Belgium is the country most comparable to the Netherlands in terms of geography, language, history and culture. RESULTS: A significant increase in relative search volume (RSV) was found from one to four weeks (21-41%) after the introduction of the smoking ban in restaurants and bars in the Netherlands in 2008. The introduction of SCS reimbursement in 2011 was associated with a significant increase of RSV (16-22%) in the Netherlands after 3 to 52 weeks. The reintroduction of SCS in 2013 was associated with a significant increase of RSV (9-21%) in the Netherlands from 3 to 32 weeks after the intervention. No effects were found in the Belgian control group for the smoking ban and the reintroduction of SCS in 2013, but there was a significant increase in RSV shortly before and after the introduction of SCS in 2011. CONCLUSIONS: These findings suggest that these tobacco control policies have short-term or medium-term effects on the rate of searching for information on smoking cessation, and therefore potentially on smoking cessation rates.