Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-9 (of 9 Records) |
Query Trace: Bailer AJ[original query] |
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Visualizing the NIOSH Pocket Guide: open-source web application for accessing and exploring the NIOSH Pocket Guide to Chemical Hazards
Lucas L , Whittaker C , Bailer AJ . J Occup Environ Hyg 2023 1-11 The NIOSH Pocket Guide to Chemical Hazards is a trusted resource that displays key information for a collection of chemicals commonly encountered in the workplace. Entries contain chemical structures, occupational exposure limit information ranging from limits based on full-shift time-weighted averages to acute limits such as short-term exposure limits and immediately dangerous to life or health values, as well as a variety of other data such as chemical-physical properties and symptoms of exposure. The NIOSH Pocket Guide (NPG) is available as a printed, hardcopy book, a PDF version, an electronic database, and a downloadable application for mobile phones. All formats of the NIOSH Pocket Guide allow users to access the data for each chemical separately, however, the guide does not support data analytics or visualization across chemicals. This project reformatted existing data in the NPG to make it searchable and compatible with exploration and analysis using a web application. The resulting application allows users to investigate the relationships between occupational exposure limits, the range and distribution of occupational exposure limits, and specialized sorting of chemicals by health endpoint or to summarize information of particular interest. These tasks would have previously required manual extraction of the data and analysis. The usability of this application was evaluated among industrial hygienists and researchers and while the existing application seems most relevant to researchers, the open-source code and data are amenable to modification by users to increase customization. |
Quantal risk assessment database: A database for exploring patterns in quantal dose-response data in risk assessment and its application to develop priors for Bayesian dose-response analysis
Wheeler MW , Piegorsch WW , Bailer AJ . Risk Anal 2018 39 (3) 616-629 Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose-response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose-response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose-response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose-response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database. |
Bayesian quantile impairment threshold benchmark dose estimation for continuous endpoints
Wheeler MW , Bailer AJ , Cole T , Park RM , Shao K . Risk Anal 2017 37 (11) 2107-2118 Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose-response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure-response quantile relationship, which gives the model flexibility to estimate the quantal dose-response function. We describe this methodology and apply it to both epidemiology and toxicology data. |
Historical context and recent advances in exposure-response estimation for deriving occupational exposure limits
Wheeler MW , Park RM , Bailer AJ , Whittaker C . J Occup Environ Hyg 2015 12 Suppl 1 0 Virtually no occupational exposure standards specify the level of risk for the prescribed exposure, and most occupational exposure limits are not based on quantitative risk assessment (QRA) at all. Wider use of QRA could improve understanding of occupational risks while increasing focus on identifying exposure concentrations conferring acceptably low levels of risk to workers. Exposure-response modeling between a defined hazard and the biological response of interest is necessary to provide a quantitative foundation for risk-based occupational exposure limits; and there has been considerable work devoted to establishing reliable methods quantifying the exposure-response relationship including methods of extrapolation below the observed responses. We review of several exposure-response modeling methods available for QRA, and demonstrate their utility with simulated data sets. |
An empirical comparison of low-dose extrapolation from points of departure (PoD) compared to extrapolations based upon methods that account for model uncertainty
Wheeler MW , Bailer AJ . Regul Toxicol Pharmacol 2013 67 (1) 75-82 Experiments with relatively high doses are often used to predict risks at appreciably lower doses.A point of departure (PoD) can be calculated as the dose associated with a specified moderate response level that is often in the range of experimental doses considered. A linear extrapolation to lower doses often follows.An alternative to the PoD method is to develop a model that accounts for the model uncertainty in the dose-response relationship and to use this model to estimate the risk at low doses.Two such approaches that account for model uncertainty are model averaging (MA) and semi-parametric methods.We use these methods, along with the PoD approach in the context of a large animal (40,000+ animal) bioassay that exhibited sub-linearity. When models are fit to high dose data and risks at low doses are predicted, the methods that account for model uncertainty produce dose estimates associated with an excess risk that are closer to the observed risk than the PoD linearization.This comparison provides empirical support to accompany previous simulation studies that suggest methods that incorporate model uncertainty provide viable, and arguably preferred, alternatives to linear extrapolation from a PoD. |
Monotonic bayesian semiparametric benchmark dose analysis
Wheeler M , Bailer AJ . Risk Anal 2012 32 (7) 1207-18 Quantitative risk assessment proceeds by first estimating a dose-response model and then inverting this model to estimate the dose that corresponds to some prespecified level of response. The parametric form of the dose-response model often plays a large role in determining this dose. Consequently, the choice of the proper model is a major source of uncertainty when estimating such endpoints. While methods exist that attempt to incorporate the uncertainty by forming an estimate based upon all models considered, such methods may fail when the true model is on the edge of the space of models considered and cannot be formed from a weighted sum of constituent models. We propose a semiparametric model for dose-response data as well as deriving a dose estimate associated with a particular response. In this model formulation, the only restriction on the model form is that it is monotonic. We use this model to estimate the dose-response curve from a long-term cancer bioassay, as well as compare this to methods currently used to account for model uncertainty. A small simulation study is conducted showing that the method is superior to model averaging when estimating exposure that arises from a quantal-linear dose-response mechanism, and is similar to these methods when investigating nonlinear dose-response patterns. |
Worker injuries and safety equipment in Ohio nursing homes
Stanev S , Bailer AJ , Straker JK , Mehdizadeh S , Park RM , Li HJ . J Gerontol Nurs 2012 38 (6) 47-56 A survey of Ohio nursing homes was conducted in 2007 to examine whether injury rates were related to facility characteristics and availability of safety equipment. The median rate of injury in the 898 facilities was 5.7 injuries per 100 workers per year. Although 95% of the facilities had written resident lifting policies, only 22% of these were zero-lift policies. Gait transfer belts (99%) and portable total-lift hoists (96%) were common, whereas ceiling-mounted total-lift hoists were rarely reported (7%). In a multivariable analysis, injury rate ratios increased with the proportion of residents using wheelchairs and were lower in smaller facilities. Facilities without a lifting policy had a higher estimated injury rate than facilities without such a policy; however, none of the safety equipment was associated with significant changes in injury rates. More information, such as frequency of use and access to versus availability of equipment, may be needed to better understand the impact of safety equipment on nursing home worker injury rates. |
Political economy of US states and rates of fatal occupational injury
Loomis D , Schulman MD , Bailer AJ , Stainback K , Wheeler M , Richardson DB , Marshall SW . Am J Public Health 2009 99 (8) 1400-8 OBJECTIVES: We investigated the extent to which the political economy of US states, including the relative power of organized labor, predicts rates of fatal occupational injury. METHODS: We described states' political economies with 6 contextual variables measuring social and political conditions: "right-to-work" laws, union membership density, labor grievance rates, state government debt, unemployment rates, and social wage payments. We obtained data on fatal occupational injuries from the National Traumatic Occupational Fatality surveillance system and population data from the US national census. We used Poisson regression methods to analyze relationships for the years 1980 and 1995. RESULTS: States differed notably with respect to political-economic characteristics and occupational fatality rates, although these characteristics were more homogeneous within rather than between regions. Industry and workforce composition contributed significantly to differences in state injury rates, but political-economic characteristics of states were also significantly associated with injury rates, after adjustment accounting for those factors. CONCLUSIONS: Higher rates of fatal occupational injury were associated with a state policy climate favoring business over labor, with distinct regional clustering of such state policies in the South and Northeast. |
Impact of publicly sponsored interventions on musculoskeletal injury claims in nursing homes
Park RM , Bushnell PT , Bailer AJ , Collins JW , Stayner LT . Am J Ind Med 2009 52 (9) 683-97 BACKGROUND: The rate of lost-time sprains and strains in private nursing homes is over three times the national average, and for back injuries, almost four times the national average. The Ohio Bureau of Workers' Compensation (BWC) has sponsored interventions that were preferentially promoted to nursing homes in 2000-2001, including training, consultation, and grants up to $40,000 for equipment purchases. METHODS: This study evaluated the impact of BWC interventions on back injury claim rates using BWC data on claims, interventions, and employer payroll for all Ohio nursing homes during 1995-2004 using Poisson regression. A subset of nursing homes was analyzed with more detailed data that allowed estimation of the impact of staffing levels and resident acuity on claim rates. Costs of interventions were compared to the associated savings in claim costs. RESULTS: A $500 equipment purchase per nursing home worker was associated with a 21% reduction in back injury rate. Assuming an equipment life of 10 years, this translates to an estimated $768 reduction in claim costs per worker, a present value of $495 with a 5% discount rate applied. Results for training courses were equivocal. Only those receiving below-median hours had a significant 19% reduction in claim rates. Injury rates did not generally decline with consultation independent of equipment purchases, although possible confounding, misclassification, and bias due to non-random management participation clouds interpretation. In nursing homes with available data, resident acuity was modestly associated with back injury risk, and the injury rate increased with resident-to-staff ratio (acting through three terms: RR = 1.50 for each additional resident per staff member; for the ratio alone, RR = 1.32, 95% CI = 1.18-1.48). In these NHs, an expenditure of $908 per resident care worker (equivalent to $500 per employee in the other model) was also associated with a 21% reduction in injury rate. However, with a resident-to-staff ratio greater than 2.0, the same expenditure was associated with a $1,643 reduction in back claim costs over 10 years per employee, a present value of $1,062 with 5% discount rate. CONCLUSIONS: Expenditures for ergonomic equipment in nursing homes by the Ohio BWC were associated with fewer worker injuries and reductions in claim costs that were similar in magnitude to expenditures. Un-estimated benefits and costs also need to be considered in assessing full health and financial impacts. Am. J. Ind. Med. 52:683-697, 2009. (c) 2009 Wiley-Liss, Inc. |
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