Modeling long-term average exposure in occupational exposure-response analysis

Preller, Liesbeth; Kromhout, Hans; Heederik, Dick; Tielen, Mjm

doi:10.5271/sjweh.67

Cited by 76 publications

(71 citation statements)

References 15 publications

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“…For the purpose of the present study, dummy variables were used; a characteristic was present or not present, depending on the amount of time a farmer worked in compart ments with particular characteristics. The methods used to estimate exposure to farm characteristics for the same study population are fully described by Preller and co workers [38],…”

Section: Farm Characteristicsmentioning

confidence: 99%

“…Analyses on endotoxin content were performed with a modified kinetic Limulus Amoebocyte Lisate test. Long-term average exposure to dust and endotoxins was estimated by using a mathematical modelling technique described previously [38]. Number of animals was not analysed in the present study as a risk factor, but was important in the modelling to calculate long term average exposure.…”

Section: Personal Exposurementioning

confidence: 99%

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Bronchial hyperresponsiveness and exposure in pig farmers

Vogelzang

Gulden

Preller

et al. 1997

International Archives of Occupational and Environmental Health

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Please be advised that this information was generated on 2018-05-09 and may be subject to change.Int Arch Occiip Environ Health (1997) Abstract Objective: To study the effect of exposure on bronchial responsiveness in pig farmers. Method: A group of 196 pig farmers were tested for lung function and bronchial responsiveness to histamine in the sum mer of 1992. To achieve sufficient contrast in respira tory morbidity and exposure, 96 of the farmers were selected because they had chronic respiratory symp toms and the remaining 100 because they were free from any respiratory symptoms. Personal exposure to dust, endotoxins and ammonia was measured during 1 working day in the summer of 1991 and 1 day in the winter of 1992. Data on farm characteristics were gathered in the same period. Results: After adjusting for age and smoking behaviour, mild bronchial re sponsiveness, defined as PCi0 ^ 16 mg/ml, was asso ciated with the use of quaternary ammonium com pounds as disinfectant [prevalence odds ratio (POR) 6.7, 95% confidence interval (Cl) 1.4-32.8], use of wood-shavings as bedding (POR 13.3, Cl 1.3-136.7), use of automated dry feeding (POR 2.8, Cl 1.0-7.8), use of pellets as feeding material (POR 4.8, Cl L I-21.1) and location of air exhaust via pit or roof in the confinement units (POR 2.7, CI 1.2-6.3). The associa- tion with the use of disinfectants other than quaternary ammonium compounds was not significant (POR 2.4,. No associations between bronchial re sponsiveness and measured exposure to dust, endo toxins or ammonia were discernible. Conclusion: Pro tective measures, designed to prevent airway disease in confinement farming, should be based on information about the operational and other characteristics of farms that are related to high exposure and health ef fects. Specifically, the use of quaternary ammonium compounds as disinfectant, the use of wood-shavings as bedding and the use of automated dry feeding should be discouraged.

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Section: Farm Characteristicsmentioning

confidence: 99%

Section: Personal Exposurementioning

confidence: 99%

Bronchial hyperresponsiveness and exposure in pig farmers

Vogelzang

Gulden

Preller

et al. 1997

International Archives of Occupational and Environmental Health

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“…Thus the job-exposure estimate would be based on a taskexposure matrix (12) rather than on a job-exposure matrix (13,14). A task-based modeling approach has been suggested for several occupational and environmental exposures (15)(16)(17)(18)(19). As concerns mechanical exposures, task-based modeling has been used to assess energy expenditure at work (20,21), vibration (22), work postures (23)(24)(25), and low-back load in existing (26) and hypothetical jobs (27).…”

mentioning

confidence: 99%

Task-based estimation of mechanical job exposure in occupational groups

Mathiassen¹,

Nordander²,

Svendsen³

et al. 2005

Scand J Work Environ Health

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“…They suggest that if direct measurements are sparse or unreliable then empirical models could be used to augment the data. Preller et al (1995), for example, uses multiple regression modelling to achieve this from a set of readily observable factors such as location, activity or job title.…”

Section: Variability In Exposure Modelsmentioning

confidence: 99%

Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

Lloyd

Forster

2014

Computational Statistics & Data Analysis

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Several thousand people die as a result of a road accident each year in Great Britain and the trend in the number of fatal accidents is monitored closely to understand increases and reductions in the number of deaths. Results from analysis of these data directly influence Government road safety policy and ensure the introduction of effective safety interventions across the country.Overall accident numbers are important, but when disaggregating into various characteristics, accident risk (defined as the number of accidents relative to an exposure measure) is a better comparator. The exposure measure used most commonly for accident rate analysis is traffic flow which can be disaggregated into vehicle types, road type, and year. Here we want to assess the accident risk across different car types and car ages, and therefore alternative exposure sources are required. We disaggregate exposure to a further extent than possible with currently available data in order to take the increased variability within these new factors into account.Exposure data sources are mainly based on sample surveys and therefore have some associated uncertainty, however previous accident risk analysis has not, in general, taken this into account. For an explicit way to include this uncertainty we use a Bayesian analysis to combine three sources of exposure using a log-Normal model with model priors representing our uncertainty in each data source.Using further Bayesian models, we propagate this uncertainty through to accident rates and accident severity, determining important factors and interrelationships between factors to identify key features affecting accident trends, and we make the first exploration of the effect of the recent recession on road accidents.i ii

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Modeling long-term average exposure in occupational exposure-response analysis

Cited by 76 publications

References 15 publications

Bronchial hyperresponsiveness and exposure in pig farmers

Bronchial hyperresponsiveness and exposure in pig farmers

Task-based estimation of mechanical job exposure in occupational groups

Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

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