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Biomedical subjects

M Nicas

Publications and source records attributed to M Nicas.

25 records · Page 2Linked to original sources
PubMed1992

A probability model for assessing exposure among respirator wearers: Part I--Description of the model.

The basic respirator equation states that the contaminant level inside a respirator (CI) is the product of the contaminant level outside the respirator (CO) and the decimal fraction penetration (P). On the basis of this relation, the authors present a probability model for the lognormal total distribution of CI levels among a respirator-wearing population; the model accounts for between-wearer and within-wearer variability in both CO levels and P values. The assumptions underlying the model are shown to be consistent with current knowledge about the variability in CO levels and P values. The model provides the basis for assessing the probability of overexposure to acute toxicants and to chronic toxicants among a respirator-wearing population.

Air Pollutants, Occupational↗
PubMed1992

A probability model for assessing exposure among respirator wearers: Part II-Overexposure to chronic versus acute toxicants.

A model describing the lognormal total distribution of contaminant levels inside a respirator (CI) is applied to assessing the probability of toxicant overexposure among a population of respirator wearers; the model accounts for between-wearer and within-wearer variability in ambient exposure levels (CO) and decimal fraction respirator penetration (P) values. The three exceedance probabilities are defined as PrI, the proportion of all CI levels over the permissible exposure limit (PEL); PrII, the proportion of wearers with an arithmetic mean CI level over the PEL; and PrIII, the proportion of wearers with a 95th percentile CI value over the PEL. PrII is considered that fraction of the population overexposed to a chronic toxicant; PrIII is considered that fraction overexposed to an acute toxicant. The behavior of PrII and PrIII over a range of exposure parameters is explored. An important observation is that a respirator-wearing population can have a substantial fraction of toxicant overexposure even though two conditions are met: (1) the P values for the population satisfy the criterion for the assigned protection factor (APF); and (2) the population arithmetic mean CO level is at or below the maximum use concentration (MUC), defined as APF x PEL. The authors recommend that the current MUCs for air-purifying respirators be reduced by one-half to reduce the potential respirator-wearing population fraction of overexposure and that appropriate exposure surveillance programs for all wearers be mandated.

Acute Disease↗
PubMed1991

Environmental versus analytical variability in exposure measurements.

Measurements of 8-hr time-weighted average (TWA) exposures are subject to environmental variability and collection and analytical error. Environmental variability can be represented by the geometric standard deviation (GSD) of the lognormally distributed 8-hr TWAs; analytical variability can be represented by the coefficient of variation (CV) of the normally distributed collection and analytical errors. A mathematical expression is derived for the variance of the measured 8-hr TWAs as a function of the GSD of the true daily average exposures and the total CV of the industrial hygiene method used in monitoring. For typical values of the GSD and CV, environmental variability is far more important than analytical variability in determining the variance of the measured 8-hr TWAs. A resulting policy implication is that the Occupational Safety and Health Administration inappropriately focuses on analytical variability when determining compliance with its permissible exposure limits.

Bias↗
PubMedDate not supplied

Regulating the risk of tuberculosis transmission among health care workers.

The 1994 Centers for Disease Control and Prevention guidelines on preventing tuberculosis (TB) transmission among health care workers (HCWs), and the 1997 Occupational Safety and Health Administration (OSHA) proposed TB standard, do not address the issue of acceptable risk. Further, many infection control personnel oppose OSHA's promulgating a standard because they believe most TB infections among HCWs are nonoccupational in origin. This article examines the relationship between TB infection and disease rates, and introduces a probability framework to apportion infection risk between occupational and nonoccupational exposure. It is argued that most TB infections among HCWs are work-related. A 0.2% overall annual risk of TB infection (accounting for both workplace and community exposure) is proposed as acceptable, because in the context of an infection surveillance program it limits an HCW's cumulative disease risk close to the value for the general United States population. Based on the probability framework, an estimate of the background community infection rate, and the traditional Wells-Riley risk model, it is shown that a target workplace infection risk value can be derived and expressed in terms of an expected pulmonary dose. The latter target dose informs risk management decision-making.

Health Personnel↗
PubMedDate not supplied

Markov modeling of contaminant concentrations in indoor air.

Most models for contaminant dispersion in indoor air are deterministic and do not account for the probabilistic nature of the pollutant concentration at a given room position and time. Such variability can be important when estimating concentrations involving small numbers of contaminant particles. This article describes the use of probabilistic models termed Markov chains to account for a portion of this variability. The deterministic and Markov models are related in that the former provide the expected concentration values. To explain this relationship, a single-zone (well-mixed room) scenario is described as a Markov chain. Subsequently, a two-zone room is cast as a Markov model, and the latter is applied to assessing a health care worker's risk of tuberculosis infection. Airborne particles carrying Mycobacterium tuberculosis bacilli are usually present in small numbers in a room occupied by an infectious tuberculosis patient. For a given scenario, the Markov model permits estimates of variability in exposure intensity and the resulting variability in infection risk.

Air Microbiology↗
PubMedDate not supplied

Modeling turbulent diffusion and advection of indoor air contaminants by Markov chains.

Turbulent eddy diffusion models are used to describe a continuous concentration gradient with distance from an in-room contaminant emission source. A refined diffusion model termed the Drivas model also accounts for contaminant reflection by wall surfaces and partially accounts for removal by exhaust air. This article develops two models based on Markov chains to describe indoor air contaminant dispersion by turbulent diffusion and advection, and removal by the exhaust airflow. Markov model I is equivalent to the Drivas model and is computationally simple. Markov model II can provide more realism by accounting for the locations of air inlets and outlets, advective flow patterns, in-room reflective surfaces, and contaminant removal mechanisms at specific room positions. The price paid for this greater realism is greater computational complexity. Both Markov models are explicitly probabilistic and estimate the expected concentration values at given room positions.

Air Pollution, Indoor↗