Reducing environmental surface contamination in healthcare settings: A statewide collaborative.

To help reduce healthcare-associated infection (HAI) rates across the state, the Maryland Patient Safety Center's Clean Collaborative (Collaborative) supported 17 acute care hospitals, 3 long-term care facilities, and 4 ambulatory surgical centers in improving environmental surface cleaning, with the goal of reducing rates of Clostridium difficile infection, which the Collaborative team selected as a proxy for HAIs. Eighty-eight percent of participating facilities achieved the program goal of a 10% reduction in relative light units from the baseline month to the final month of the Collaborative. In addition, participating facilities achieved a 14.2% decrease in C. difficile rates compared to only a 5.9% decrease among non-participating facilities (in Maryland).

A comparison study using a mathematical model and actual exposure monitoring for estimating solvent exposures during the disassembly of metal parts.

The objective of this research was to compare the airborne solvent concentrations measured during the disassembly of solvent-coated metal parts with concentrations predicted by a mathematical model. The study involved three test simulations where cyclohexane, used as a penetrating solvent, was squirted onto a gate valve while the valve was subsequently disassembled. Three test simulations were performed to evaluate the effect of varying the speed of random air movement in the work area. For statistical considerations, six replicate solvent application trials were conducted for each simulation. Area and personal air samples were collected during the trials performed under each test simulation. Cyclohexane was applied to the valve at a consistent rate to obtain, to the greatest extent possible, a constant generation rate of solvent vapors. The Near Field-Far Field (NF-FF) model, applied using a constant solvent generation rate, was selected to predict the solvent vapor concentrations, and Monte Carlo analysis was used to quantify uncertainty in the input parameters of the model. Solvent concentration predictions obtained from the modeling process were within a multiplicative factor of 0.1 to 1.5 of the arithmetic mean of the actual air sample results for all three NF and FF conditions in each simulation. Application of the NF-FF model under the conditions described suggests there is a reasonable degree of reliability in forecasting airborne contaminant levels in the workplace environment. Given the limited resources faced today by many industrial hygienists, exposure modeling can serve as a valuable tool for generating the information needed to make informed decisions about employee exposure.

Estimating benzene exposure at a solvent parts washer.

A mathematical model is described for estimating benzene exposure at a parts washer using petroleum distillates solvent containing benzene. The basic assumptions are that the benzene mass emission rate exponentially decreases over time, and that the air above the parts washer basin to which a worker is exposed is part of a well-mixed air zone termed the near field (relative to the source location). Two previously conducted simulations of the parts washer process are described. A single 1-hour time-weighted average (TWA) benzene concentration was measured during Simulation #1, and two 4-hour TWA benzene concentrations were measured during Simulation #2. The initial benzene concentrations in the solvents were known, and the exponential loss rate constants were estimated from subsequent determinations of the benzene concentrations. Values for the interzonal airflow rate were estimated based on the conceptual geometry of the near field zone and sparse information on air speed near the parts washers. Minimum values for the room supply/exhaust air rate were estimated based on the room volumes and ventilation conditions. The modeled benzene concentrations were within a multiplicative range of one-half to twofold the measured concentrations. Uncertainty in a model estimate was quantified by Monte Carlo analysis; the distributions of model estimates exhibited coefficients of variation of approximately 40%. Issues related to uncertainty in exposure estimates made by mathematical modeling are discussed.