Monika Vadali - Academia.edu (original) (raw)

Papers by Monika Vadali

Research paper thumbnail of www.mdpi.com/journal/ijerph Article Traffic, Air Pollution, Minority and Socio-Economic Status: Addressing Inequities in Exposure and Risk

Abstract: Higher levels of nearby traffic increase exposure to air pollution and adversely affect... more Abstract: Higher levels of nearby traffic increase exposure to air pollution and adversely affect health outcomes. Populations with lower socio-economic status (SES) are particularly vulnerable to stressors like air pollution. We investigated cumulative exposures and risks from traffic and from MNRiskS-modeled air pollution in multiple source categories across demographic groups. Exposures and risks, especially from on-road sources, were higher than the mean for minorities and low SES populations and lower than the mean for white and high SES populations. Owning multiple vehicles and driving alone were linked to lower household exposures and risks. Those not owning a vehicle and walking or using transit had higher household exposures and risks. These results confirm for our study location that populations on the lower end of the socio-economic spectrum and minorities are disproportionately exposed to traffic and air pollution and at higher risk for adverse health outcomes. A major s...

Research paper thumbnail of Professional judgment and decision making in occupational exposure assessment

Research paper thumbnail of Traffic, Air Pollution, Minority and Socio-Economic Status: Addressing Inequities in Exposure and Risk

International Journal of Environmental Research and Public Health, 2015

Research paper thumbnail of Bayesian hierarchical framework for occupational hygiene decision making

The Annals of occupational hygiene, 2014

A hierarchical Bayesian framework has been developed for exposure assessment that makes use of st... more A hierarchical Bayesian framework has been developed for exposure assessment that makes use of statistical sampling-based techniques to estimate the posterior probability of the 95th percentile or arithmetic mean of the exposure distribution being located in one of several exposure categories. The framework can synthesize professional judgment and monitoring data to yield an updated posterior exposure assignment for routine exposure management. The framework is versatile enough that it can be modified for use in epidemiological studies for classifying the arithmetic mean instead of the 95th percentile into several exposure categories. Various physico-chemical exposure models have also been incorporated in the hierarchical framework. The use of the framework in three settings has been illustrated. First, subjective judgments about exposure magnitude obtained from industrial hygienists for five tasks were treated as priors in the Bayesian framework. Monitoring data for each task were ...

Research paper thumbnail of Exposure Modeling in Occupational Hygiene Decision Making

Journal of Occupational and Environmental Hygiene, 2009

The primary objective was to develop a framework for using exposure models in conjunction with tw... more The primary objective was to develop a framework for using exposure models in conjunction with two-dimensional Monte Carlo methods for making exposure judgments in the context of Bayesian decision analysis. The AIHA exposure assessment strategy will be used for illustrative purposes, but the method has broader applications beyond these specific exposure assessment strategies. A two-dimensional Monte Carlo scheme by which the exposure model output can be represented in the form of a decision chart is presented. The chart shows the probabilities of the 95th percentile of the exposure distribution lying in one of the four exposure categories relative to the occupational exposure limit (OEL): (1) highly controlled (<10% of OEL), (2) well controlled (10-50% of OEL), (3) controlled (50-100% of OEL), and (4) poorly controlled (>100% of OEL). Such a decision chart can be used as a "prior" in the Bayesian statistical framework, which can be updated using monitoring data to arrive at a final decision chart. Hypothetical examples using commonly used exposure models are presented, along with a discussion of how this framework can be used given a hierarchy of exposure models.

Research paper thumbnail of Effect of Training, Education, Professional Experience, and Need for Cognition on Accuracy of Exposure Assessment Decision-Making

The Annals of Occupational Hygiene, 2011

Research paper thumbnail of Effect of Training on Exposure Judgment Accuracy of Industrial Hygienists

Journal of Occupational and Environmental Hygiene, 2012

Results are presented from a study that investigated the effect of data interpretation training o... more Results are presented from a study that investigated the effect of data interpretation training on exposure judgment accuracy of industrial hygienists across several companies in different industry sectors. Participating companies provided monitoring information on specific exposure tasks. Forty-nine hygienists from six companies participated in the study, and 22 industrial tasks were evaluated. The number of monitoring data points for individual tasks varied between 5 and 24. After reviewing all available basic characterization information for the job, task, and chemical, hygienists were asked to provide their judgment on the probability of the 95th percentile of the underlying exposure distribution being located in one of four exposure categories relative to the occupational exposure limit as outlined in the AIHA exposure assessment strategy. Ninety-three qualitative judgments (i.e., without reviewing monitoring data) and 2142 quantitative judgments (i.e., those made after reviewing monitoring data) were obtained. Data interpretation training, with simple rules of thumb for estimating 95th percentiles, was provided to all hygienists. A data interpretation test was administered before and after training. All exposure task judgments were collected before and after training. Data interpretation test accuracy for the hygienists increased from 48% to 67% after training (p < 0.001) and a significant underestimation bias was removed. Hygienist quantitative task judgment accuracy improved from 46% to 69% (p < 0.001) post-training. Accuracy results showed good improvement in industrial hygienists' quantitative judgments as a result of training. Hence, the use of statistical tools is promoted to improve judgments based on monitoring data and provide feedback and calibration to improve qualitative judgments. It may be worthwhile to develop standard training programs to improve exposure judgments.

Research paper thumbnail of www.mdpi.com/journal/ijerph Article Traffic, Air Pollution, Minority and Socio-Economic Status: Addressing Inequities in Exposure and Risk

Abstract: Higher levels of nearby traffic increase exposure to air pollution and adversely affect... more Abstract: Higher levels of nearby traffic increase exposure to air pollution and adversely affect health outcomes. Populations with lower socio-economic status (SES) are particularly vulnerable to stressors like air pollution. We investigated cumulative exposures and risks from traffic and from MNRiskS-modeled air pollution in multiple source categories across demographic groups. Exposures and risks, especially from on-road sources, were higher than the mean for minorities and low SES populations and lower than the mean for white and high SES populations. Owning multiple vehicles and driving alone were linked to lower household exposures and risks. Those not owning a vehicle and walking or using transit had higher household exposures and risks. These results confirm for our study location that populations on the lower end of the socio-economic spectrum and minorities are disproportionately exposed to traffic and air pollution and at higher risk for adverse health outcomes. A major s...

Research paper thumbnail of Professional judgment and decision making in occupational exposure assessment

Research paper thumbnail of Traffic, Air Pollution, Minority and Socio-Economic Status: Addressing Inequities in Exposure and Risk

International Journal of Environmental Research and Public Health, 2015

Research paper thumbnail of Bayesian hierarchical framework for occupational hygiene decision making

The Annals of occupational hygiene, 2014

A hierarchical Bayesian framework has been developed for exposure assessment that makes use of st... more A hierarchical Bayesian framework has been developed for exposure assessment that makes use of statistical sampling-based techniques to estimate the posterior probability of the 95th percentile or arithmetic mean of the exposure distribution being located in one of several exposure categories. The framework can synthesize professional judgment and monitoring data to yield an updated posterior exposure assignment for routine exposure management. The framework is versatile enough that it can be modified for use in epidemiological studies for classifying the arithmetic mean instead of the 95th percentile into several exposure categories. Various physico-chemical exposure models have also been incorporated in the hierarchical framework. The use of the framework in three settings has been illustrated. First, subjective judgments about exposure magnitude obtained from industrial hygienists for five tasks were treated as priors in the Bayesian framework. Monitoring data for each task were ...

Research paper thumbnail of Exposure Modeling in Occupational Hygiene Decision Making

Journal of Occupational and Environmental Hygiene, 2009

The primary objective was to develop a framework for using exposure models in conjunction with tw... more The primary objective was to develop a framework for using exposure models in conjunction with two-dimensional Monte Carlo methods for making exposure judgments in the context of Bayesian decision analysis. The AIHA exposure assessment strategy will be used for illustrative purposes, but the method has broader applications beyond these specific exposure assessment strategies. A two-dimensional Monte Carlo scheme by which the exposure model output can be represented in the form of a decision chart is presented. The chart shows the probabilities of the 95th percentile of the exposure distribution lying in one of the four exposure categories relative to the occupational exposure limit (OEL): (1) highly controlled (<10% of OEL), (2) well controlled (10-50% of OEL), (3) controlled (50-100% of OEL), and (4) poorly controlled (>100% of OEL). Such a decision chart can be used as a "prior" in the Bayesian statistical framework, which can be updated using monitoring data to arrive at a final decision chart. Hypothetical examples using commonly used exposure models are presented, along with a discussion of how this framework can be used given a hierarchy of exposure models.

Research paper thumbnail of Effect of Training, Education, Professional Experience, and Need for Cognition on Accuracy of Exposure Assessment Decision-Making

The Annals of Occupational Hygiene, 2011

Research paper thumbnail of Effect of Training on Exposure Judgment Accuracy of Industrial Hygienists

Journal of Occupational and Environmental Hygiene, 2012

Results are presented from a study that investigated the effect of data interpretation training o... more Results are presented from a study that investigated the effect of data interpretation training on exposure judgment accuracy of industrial hygienists across several companies in different industry sectors. Participating companies provided monitoring information on specific exposure tasks. Forty-nine hygienists from six companies participated in the study, and 22 industrial tasks were evaluated. The number of monitoring data points for individual tasks varied between 5 and 24. After reviewing all available basic characterization information for the job, task, and chemical, hygienists were asked to provide their judgment on the probability of the 95th percentile of the underlying exposure distribution being located in one of four exposure categories relative to the occupational exposure limit as outlined in the AIHA exposure assessment strategy. Ninety-three qualitative judgments (i.e., without reviewing monitoring data) and 2142 quantitative judgments (i.e., those made after reviewing monitoring data) were obtained. Data interpretation training, with simple rules of thumb for estimating 95th percentiles, was provided to all hygienists. A data interpretation test was administered before and after training. All exposure task judgments were collected before and after training. Data interpretation test accuracy for the hygienists increased from 48% to 67% after training (p < 0.001) and a significant underestimation bias was removed. Hygienist quantitative task judgment accuracy improved from 46% to 69% (p < 0.001) post-training. Accuracy results showed good improvement in industrial hygienists' quantitative judgments as a result of training. Hence, the use of statistical tools is promoted to improve judgments based on monitoring data and provide feedback and calibration to improve qualitative judgments. It may be worthwhile to develop standard training programs to improve exposure judgments.