Simulating the impact of changing trends in smoking and obesity on productivity of an industrial population: an observational study

ObjectiveThe increasingly high levels of overweight and obesity among the workforce are accompanied by a hidden cost burden due to losses in productivity. This study reviews the extent of indirect cost of overweight and obesity.MethodsA systematic search was conducted in eight electronic databases (PubMed, Cochrane Library, Web of Science Core Collection, PsychInfo, Cinahl, EconLit and ClinicalTrial.gov). Additional studies were added from reference lists of original studies and reviews. Studies were eligible if they were published between January 2000 and June 2017 and included monetary estimates of indirect costs of overweight and obesity. The authors reviewed studies independently and assessed their quality.ResultsOf the 3626 search results, 50 studies met the inclusion criteria. A narrative synthesis of the reviewed studies revealed substantial costs due to lost productivity among workers with obesity. Especially absenteeism and presenteeism contribute to high indirect costs. However, the methodologies and results vary greatly, especially regarding the cost of overweight, which was even associated with lower indirect costs than normal weight in three studies.ConclusionThe evidence predominantly confirms substantial short-term and long-term indirect costs of overweight and obesity in the absence of effective customised prevention programmes and thus demonstrates the extent of the burden of obesity beyond the healthcare sector.

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“… 21 Another long-term study evaluated the yearly cost of a 20 000 workforce over 30 years at $6.6 million. 28 …”

Section: Resultsmentioning

confidence: 99%

“… 64 Economic loss due to premature death was estimated for Sweden at $4.8 million for overweight and $383 million for obesity. 28 …”

Section: Resultsmentioning

confidence: 99%

Productivity loss due to overweight and obesity: a systematic review of indirect costs

2017

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“…For five studies (27,29,41,56,58), we did not have sufficient information to estimate either relative risk of sickness absence or number of sickness absence days. This was due to a lack of reporting of standard errors and CI of the outcome measure (27,29,41,58) or no information on number or proportion of smokers in the research population (56). The corresponding authors of these studies were contacted but either did not respond or were unable to provide the required information.…”

Section: Studies Not Included In Pooled Analysesmentioning

confidence: 99%

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

Troelstra¹,

Coenen²,

Boot³

et al. 2019

Scand J Work Environ Health

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Smoking and sickness absence: a systematic review and meta-analysis by Troelstra SA, Coenen P, Boot CRL, Harting J, Kunst AE, van der Beek AJSmoking increased both the risk of sickness absence and the number of sickness absence days, regardless of gender, age, study location, and occupational class. This suggests that all employers and employees, irrespective of these characteristics, could equally benefit from smoking cessation, and therefore that all employee populations should equally be offered the opportunity to participate in smoking cessation interventions. Troelstra SA. Coenen P, Boot CRL, Harting J, Kunst AE, van der Beek AJ. Smoking and sickness absence: a systematic review and meta-analysis. Scand J Work Environ Health. 2020;46(1):5-18. doi:10.5271/sjweh.3848Objectives 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. MethodsWe 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. ResultsAfter 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). ConclusionsWe 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.

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“…Genome-wide association studies (GWAS) have identified numerous variants associated with body mass index (BMI) [1] , but a substantial proportion of the estimated heritability remains to be accounted for. At the same time, a number of modifiable environmental factors have been identified that influence BMI, with cigarette smoking a strong lifestyle influence on BMI [2] . In a previous Mendelian randomisation analysis, we used a single nucleotide polymorphism in the CHRNA5-A3–B4 gene cluster associated with heaviness of smoking within smokers [3] to confirm the causal effect of smoking in reducing BMI [4] .…”

Section: Introductionmentioning

confidence: 99%

Stratification by Smoking Status Reveals an Association of CHRNA5-A3-B4 Genotype with Body Mass Index in Never Smokers

Taylor

Morris²,

Fluharty

et al. 2014

PLoS Genet

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We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00×10−10), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38×10−5). An interaction test confirmed that these estimates differed from each other (P = 4.95×10−13). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.

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Simulating the impact of changing trends in smoking and obesity on productivity of an industrial population: an observational study

Cited by 7 publications

References 31 publications

Productivity loss due to overweight and obesity: a systematic review of indirect costs

Productivity loss due to overweight and obesity: a systematic review of indirect costs

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

Stratification by Smoking Status Reveals an Association of CHRNA5-A3-B4 Genotype with Body Mass Index in Never Smokers

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