Rating exposure control using Bayesian decision analysis.
A model is presented for applying Bayesian statistical techniques to the problem of determining, from the usual limited number of exposure measurements, whether the exposure profile for a similar exposure group can be considered a Category 0, 1, 2, 3, or 4 exposure. The categories were adapted from...
| Publicado en: | Journal of Occupational & Environmental Hygiene Vol. 3; no. 10; pp. 568 - 582 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct2006
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106247446&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106247446 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15459624 V1L jtl: Journal of Occupational & Environmental Hygiene issn: 15459624 maglogo: Y pubinfo: dt: Oct2006 vid: 3 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 106247446 2009298752 10.1080/15459620600914641 NLM16998991 106247446 ppf: 568 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Rating exposure control using Bayesian decision analysis. aug: au: Hewett P Logan P Mulhausen J Ramachandran G Banerjee S affil: Exposure Assessment Solutions, Inc., Morgantown, West Virginia 26508, USA. phewett_2006_07@oesh.com sug: subj: Models, Statistical Occupational Exposure Standards Confidence Intervals Occupational Exposure Occupational Exposure Analysis Occupational Health Probability Risk Assessment Human ab: A model is presented for applying Bayesian statistical techniques to the problem of determining, from the usual limited number of exposure measurements, whether the exposure profile for a similar exposure group can be considered a Category 0, 1, 2, 3, or 4 exposure. The categories were adapted from the AIHA exposure category scheme and refer to (0) negligible or trivial exposure (i.e., the true X[0.95] < or =1%OEL), (1) highly controlled (i.e., X[0.95] < or =10%OEL), (2) well controlled (i.e., X[0.95] < or =50%OEL), (3) controlled (i.e., X[0.95] < or =100%OEL), or (4) poorly controlled (i.e., X[0.95] > or =1%OEL) exposures. Unlike conventional statistical methods applied to exposure data, Bayesian statistical techniques can be adapted to explicitly take into account professional judgment or other sources of information. The analysis output consists of a distribution (i.e., set) of decision probabilities: e.g., 1%, 80%, 12%, 5%, and 2% probability that the exposure profile is a Category 0, 1, 2, 3, or 4 exposure. By inspection of these decision probabilities, rather than the often difficult to interpret point estimates (e.g., the sample 95th percentile exposure) and confidence intervals, a risk manager can be better positioned to arrive at an effective (i.e., correct) and efficient decision. Bayesian decision methods are based on the concepts of prior, likelihood, and posterior distributions of decision probabilities. The prior decision distribution represents what an industrial hygienist knows about this type of operation, using professional judgment; company, industry, or trade organization experience; historical or surrogate exposure data; or exposure modeling predictions. The likelihood decision distribution represents the decision probabilities based on an analysis of only the current data. The posterior decision distribution is derived by mathematically combining the functions underlying the prior and likelihood decision distributions, and represents the final decision probabilities. Advantages of Bayesian decision analysis include: (a) decision probabilities are easier to understand by risk managers and employees; (b) prior data, professional judgment, or modeling information can be objectively incorporated into the decision-making process; (c) decisions can be made with greater certainty; (d) the decision analysis can be constrained to a more realistic 'parameter space' (i.e., the range of plausible values for the true geometric mean and geometric standard deviation); and (e) fewer measurements are necessary whenever the prior distribution is well defined and the process is fairly stable. Furthermore, Bayesian decision analysis provides an obvious feedback mechanism that can be used by an industrial hygienist to improve professional judgment. For example, if the likelihood decision distribution is inconsistent with the prior decision distribution then it is likely that either a significant process change has occurred or the industrial hygienist's initial judgment was incorrect. In either case, the industrial hygienist should readjust his judgment regarding this operation. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|