Distribution and skewness of occupational exposure sets of measurements in the Norwegian industry

Osvoll, Peter

doi:10.1016/s0003-4878(99)00029-0

Cited by 5 publications

(4 citation statements)

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“…It is also common with hetero-scedasticity where the variance increases with the expected value. Such data can often be described by a log-normal or quasi-log-normal distribution [1].…”

Section: Introductionmentioning

confidence: 99%

Linear Maximum Likelihood Regression Analysis for Untransformed Log-Normally Distributed Data

Gustavsson¹,

Johannesson²,

Sällsten³

et al. 2012

OJS

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Medical research data are often skewed and heteroscedastic. It has therefore become practice to log-transform data in regression analysis, in order to stabilize the variance. Regression analysis on log-transformed data estimates the relative effect, whereas it is often the absolute effect of a predictor that is of interest. We propose a maximum likelihood (ML)-based approach to estimate a linear regression model on log-normal, heteroscedastic data. The new method was evaluated with a large simulation study. Log-normal observations were generated according to the simulation models and parameters were estimated using the new ML method, ordinary least-squares regression (LS) and weighed least-squares regression (WLS). All three methods produced unbiased estimates of parameters and expected response, and ML and WLS yielded smaller standard errors than LS. The approximate normality of the Wald statistic, used for tests of the ML estimates, in most situations produced correct type I error risk. Only ML and WLS produced correct confidence intervals for the estimated expected value. ML had the highest power for tests regarding β<sub>1</sub>

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“…It is also common with hetero-scedasticity where the variance increases with the expected value. Such data can often be described by a log-normal or quasi-log-normal distribution [1].…”

Section: Introductionmentioning

confidence: 99%

Linear Maximum Likelihood Regression Analysis for Untransformed Log-Normally Distributed Data

Gustavsson¹,

Johannesson²,

Sällsten³

et al. 2012

OJS

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“…There exist different types of sources for occupational exposure data. Examples of national exposure databases in Europe include NEDB in the United Kingdom [46], COLCHIC in France [47], MEGA in Germany [48], and EXPO in Norway [49]. In addition, there are industry-specific multinational exposure databases such as PAPDEM for the paper and pulp industry [50], EXASRUB for the rubber manufacturing industry [51], and AWE for the asphalt industry [52].…”

Section: рис 1 формирование когорты работников хризотиловой промышлен...mentioning

confidence: 99%

“…В мире существуют различные типы источников данных о воздействии производственных факторов. Примеры национальных баз данных по уровням экспозиции в Европе включают «NEDB» в Великобритании [46], «COLCHIC» во Франции [47], «MEGA» в Германии [48] и «EXPO» в Норвегии [49]. Кроме того, существуют отраслевые многонациональные базы данных об уровнях воздействия, такие как «PAPDEM» для целлюлозно-бумажной промышленности [50], «EXASRUB» для резиновой промышленности [51] и «AWE» для промышленности, связанной с производством асфальта [52].…”

Section: рис 1 формирование когорты работников хризотиловой промышлен...unclassified

Strategies of the International Agency for Research on Cancer (IARC/WHO) to reduce the occupational cancer burden

Олссон

Шубауер-Бериган

Шюц

2021

Med. tr. prom. èkol.

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The main mission of the International Agency for Research on Cancer (IARC/WHO) is to conduct research for cancer prevention worldwide. The strategies of the IARC to reduce the occupational cancer burden include enhancing cancer surveillance, evaluating and classifying potential human carcinogens, and conducting epidemiological research to fill gaps in knowledge on occupationally related cancers. Beyond the IARC work, it is essential to systematically monitor occupational exposures in workplaces, in order to effectively protect workers. There are multiple sources of information about occupational exposures in workplaces, but they are often not used for hazard surveillance or for research. The Russian Federation has great potential to advance research and worker protection due to their strong tradition to monitor and record exposure concentrations in workplaces. Currently most evidence regarding occupational cancer burden comes from Western Europe and North America. Estimation of the burden of occupational cancer requires accurate data from local settings as extrapolating data from other settings may be misleading due to major differences in exposures, exposure pathways and baseline cancer risks. To fill this knowledge gap, it is important to conduct exposure surveillance and epidemiological studies on occupational cancer in the Russian Federation.

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“…National databases containing occupational exposure data exist in several countries such as the National Exposure Database (NEDB) in Britain (Burns and Beaumont, 1989), COLCHIC in France (Vincent and Jeandel, 2001), MEGA in Germany (Stamm, 2001), and EXPO in Norway (Osvoll and Woldbaek, 1999). Additionally, industry-specific databases are available such as PAPDEM for the paper and pulp industry (Kauppinen et al, 1997), AWE for the asphalt industry (Burstyn et al, 2000), EXASRUB for the rubber manufacturing industry (de Vocht et al, 2005), and the SVF exposure database for the glass wool insulation industry (Marchant et al, 2009).…”

Section: Identification Of Sourcesmentioning

confidence: 99%

The Advanced REACH Tool (ART): Incorporation of an Exposure Measurement Database

Schinkel

Ritchie

Goede³

et al. 2013

The Annals of Occupational Hygiene

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This article describes the structure, functionalities, and content of the Advanced REACH Tool (ART) exposure database (version 1.5). The incorporation of the exposure database into ART allows users who do not have their own measurement data for their exposure scenario, to update the exposure estimates produced by the mechanistic model using analogous measurement series selected from the ART exposure measurement database. Depending on user input for substance category and activity (sub)classes, the system selects exposure measurement series from the exposure database. The comprehensive scenario descriptions and summary statistics assist the user in deciding if the measurement series are indeed fully analogous. After selecting one or more analogous data sets, the data are used by the Bayesian module of the ART system to update the mechanistically modeled exposure estimates. The 1944 exposure measurements currently stored in the ART exposure measurement database cover 9 exposure situations for handling solid objects (n = 65), 42 situations for handling powders, granules, or pelletized material (n = 488), 5 situations for handling low-volatility liquids (n = 88), 35 situations for handling volatile liquids (n = 870), and 26 situations for handling liquids in which powders are dissolved or dispersed (resulting in exposure to mist) (n = 433). These 117 measurement series form a good basis for supporting user exposure estimates. However, by increasing the diversity of exposure situations and the number of measurement series in the database, the usefulness of the ART system will be further improved. Suggestions to stimulate the process of sharing exposure measurement data both to increase the available data in the ART and for other purposes are made.

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Distribution and skewness of occupational exposure sets of measurements in the Norwegian industry

Cited by 5 publications

References 0 publications

Linear Maximum Likelihood Regression Analysis for Untransformed Log-Normally Distributed Data

Linear Maximum Likelihood Regression Analysis for Untransformed Log-Normally Distributed Data

Strategies of the International Agency for Research on Cancer (IARC/WHO) to reduce the occupational cancer burden

The Advanced REACH Tool (ART): Incorporation of an Exposure Measurement Database

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