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

ABSTRACT: To estimate the impact of trends in smoking and obesity prevalence on productivity loss among petrochemical employees from 1980 to 2009.Smoking and obesity informations were collected during company physical examinations. Productivity loss was calculated as differential workdays lost between smokers and non-smokers, and obese and normal-weight employees.During 1980-2009, smoking prevalence decreased from 32% to 17%, while obesity prevalence increased from 14% to 42%. In 1982, lost productivity from obesity was an estimated 43 days/100 employees, and for smoking, 65 days/100 employees, but by 1987, workdays lost due to obesity exceeded that attributable to smoking. In 2007, workdays lost from obesity were 3.7 times higher than for smoking.Owing to the increasing trend in obesity, the productivity impact on employers from obesity will continue to rise without effective measures supporting employee efforts to achieve healthy weight through sustainable lifestyle changes.

SUBMITTER: Bhojani FA

PROVIDER: S-EPMC3996817 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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

Bhojani Faiyaz A FA Tsai Shan P SP Wendt Judy K JK Koller Kim L KL

BMJ open 20140419 4

<h4>Objective</h4>To estimate the impact of trends in smoking and obesity prevalence on productivity loss among petrochemical employees from 1980 to 2009.<h4>Methods</h4>Smoking and obesity informations were collected during company physical examinations. Productivity loss was calculated as differential workdays lost between smokers and non-smokers, and obese and normal-weight employees.<h4>Results</h4>During 1980-2009, smoking prevalence decreased from 32% to 17%, while obesity prevalence incre ...[more]

PMID: 24747795

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Project description:BackgroundUnderstanding the mechanisms that influence the population dynamics and spatial genetic structure of the vectors of pathogens infecting humans is a central issue in tropical epidemiology. In view of the rapid changes in the features of landscape pathogen vectors live in, this issue requires new methods that consider both natural and human systems and their interactions. In this context, individual-based model (IBM) simulations represent powerful yet poorly developed approaches to explore the response of pathogen vectors in heterogeneous social-ecological systems, especially when field experiments cannot be performed.Methodology/principal findingsWe first present guidelines for the use of a spatially explicit IBM, to simulate population genetics of pathogen vectors in changing landscapes. We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil. We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure. Optimized for T. brasiliensis using field data pairwise fixation index (FST) from microsatellite loci, our simulations confirmed the importance of these three variables to understand vector genetic structure at the landscape level. We then ran prospective scenarios accounting for land-use change (deforestation and urbanization), which revealed that human-induced land-use change favored higher genetic diversity among sampling points.Conclusions/significanceOur work shows that mechanistic models may be useful tools to link observed patterns with processes involved in the population genetics of tropical pathogen vectors in heterogeneous social-ecological landscapes. Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

Dataset Information

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

Publications

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

Similar Datasets

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