Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/373241

Association between activities outside work and presenteeism among Korean wage workers : using nationwide survey

Jung, S. W.·Lee, J.-H.·Lee, K.-J.
View original source ↗Export metadata ↓

Abstract

IntroductionPresenteeism is a significant global health issue nowadays and can influence to producitivity loss. So, there were many studies to analyse relationship between workplace factor and presenteeism. But a few studies have considered non-occupational factor. The purpose of this study was to examine the association between presenteeism and activities outside work, such as, volunteering, self-development, leisure/sports, gardening & house repair activity, in Korean wage workers.\n\nMethodsThis study analysed the 4th Korean Working Condition Survey(KWCS) and overall 19,294 wage workers participated. To find the relationship between presenteeism and activities outside work, multivariate logistic regression analysis was used after adjusting for general and occupational characteristics.\n\nResultsAmong self-development, leisure/sports and gardening & house repair activities significantly increased the odds ratio of presenteeism (Odds ratio[OR] =1.33, 95% confidence interval[CI]: 1.23-1.43, OR = 1.40, 95% CI: 1.29-1.51 and OR = 1.09, 95% CI: 1.01-1.18, respectively).\n\nConclusionsSome of activities outside work were related to presenteeism in Korean wage workers. Although many of previous studies addressed positive effect of those activities for health, this study showed negative effect of activities oudside work for health. And we should consider Korean organizational culture for this reason and need more structural studies to find out specific factors.

Source connections

Explore related subjects

epidemiologyPreprint

Keep this discovery

BibTeXRIS

Jung, S. W., Lee, J.-H., Lee, K.-J.. 2018-07-20. Association between activities outside work and presenteeism among Korean wage workers : using nationwide survey. https://doi.org/10.1101/373241

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2018

Translating surveillance data into incidence estimates

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

epidemiology↗
bioRxiv2018

The Swiss Primary Ciliary Dyskinesia registry: objectives, methods and first results

Primary Ciliary Dyskinesia (PCD) is a rare hereditary, multi-organ disease caused by defects in ciliary structure and function. It results in a wide range of clinical manifestations, most commonly in the upper and lower airways. Central data collection in national and international registries is essential to studying the epidemiology of rare diseases and filling in gaps in knowledge of diseases such as PCD. For this reason, the Swiss Primary Ciliary Dyskinesia Registry (CH-PCD) was founded in 2013 as a collaborative project between epidemiologists and adult and paediatric pulmonologists.\n\nThe registry records patients of any age, suffering from PCD, who are treated and resident in Switzerland. It collects information from patients identified through physicians, diagnostic facilities, and patient organisations. The registry dataset contains data on diagnostic evaluations, lung function, microbiology and imaging, symptoms, treatments, and hospitalizations.\n\nBy May 2018, CH-PCD has contacted 566 physicians of different specialties and identified 134 patients with PCD. At present this number represents an overall 1 in 63,000 prevalence of people diagnosed with PCD in Switzerland. Prevalence differs by age and region; it is highest in children and adults younger than 30 years, and in Espace Mittelland. The median age of patients in the registry is 25 years (range 5-73), and 49 patients have a definite PCD diagnosis based on recent international guidelines. Data from CH-PCD are contributed to international collaborative studies and the registry facilitates patient identification for nested studies.\n\nCH-PCD has proven to be a valuable research tool that already has highlighted weaknesses in PCD clinical practice in Switzerland. Development of centralised diagnostic and management centres and adherence to international guidelines are needed to improve diagnosis and management--particularly for adult PCD patients.

epidemiology↗
bioRxiv2018

Perfect Counterfactuals for Epidemic Simulations

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervetnion, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a single world approach to matching simulations of controlled epidemics to their exact uncontrolled counterfac-tual. Our method borrows concepts from percolation approaches prune out possible epidemic histories and create potential epidemic graph that can be realized to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models, and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids non-sensical inferences.

epidemiology↗