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Articles by James A. Deddens
Total Records ( 5 ) for James A. Deddens
  Cynthia J. Hines , James A. Deddens , Larry B. Jaycox , Ronnee N. Andrews , Cynthia A. F. Striley and Michael C. R. Alavanja
  Pesticide exposure assessment in the Agricultural Health Study (AHS) has relied upon two exposure metrics: lifetime exposure days and intensity-weighted lifetime exposure days, the latter incorporating an intensity score computed from a questionnaire-based algorithm. We evaluated this algorithm using actual fungicide exposure measurements from AHS private orchard applicators. Captan was selected as a marker of fungicide exposure. Seventy-four applicators from North Carolina and Iowa growing apples and/or peaches were sampled on 2 days they applied captan in 2002 and 2003. Personal air, hand rinse, 10 dermal patches, a pre-application first-morning urine and a subsequent 24-h urine sample were collected from each applicator per day. Environmental samples were analyzed for captan, and urine samples were analyzed for cis-1,2,3,6-tetrahydrophthalimide (THPI). Task and personal protective equipment information needed to compute an individual’s algorithm score was also collected. Differences in analyte detection frequency were tested in a repeated logistic regression model. Mixed-effects models using maximum-likelihood estimation were employed to estimate geometric mean exposures and to evaluate the measured exposure data against the algorithm. In general, captan and THPI were detected significantly more frequently in environmental and urine samples collected from applicators who used air blast sprayers as compared to those who hand sprayed. The AHS pesticide exposure intensity algorithm, while significantly or marginally predictive of thigh and forearm captan exposure, respectively, did not predict air, hand rinse or urinary THPI exposures. The algorithm’s lack of fit with some exposure measures among orchard fungicide applicators may be due in part to the assignment of equal exposure weights to air blast and hand spray application methods in the current algorithm. Some modification of the algorithm is suggested by these results.
  Cynthia J. Hines , Nancy B. Nilsen Hopf , James A. Deddens , Antonia M. Calafat , Manori J. Silva , Ardith A. Grote and Deborah L. Sammons
  Phthalates are used as plasticizers and solvents in industrial, medical and consumer products; however, occupational exposure information is limited. We sought to obtain preliminary information on occupational exposures to diethyl phthalate (DEP), di-n-butyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP) by analyzing for their metabolites in urine samples collected from workers in a cross-section of industries. We also obtained data on metabolites of dimethyl phthalate (DMP), benzylbutyl phthalate (BzBP), di-isobutyl phthalate and di-isononyl phthalate. We recruited 156 workers in 2003-2005 from eight industry sectors. We assessed occupational contribution by comparing end-shift metabolite concentrations to the US general population. Evidence of occupational exposure to DEHP was strongest in polyvinyl chloride (PVC) film manufacturing, PVC compounding and rubber boot manufacturing where geometric mean (GM) end-shift concentrations of DEHP metabolites exceeded general population levels by 8-, 6- and 3-fold, respectively. Occupational exposure to DBP was most evident in rubber gasket, phthalate (raw material) and rubber hose manufacturing, with DBP metabolite concentrations exceeding general population levels by 26-, 25- and 10-fold, respectively, whereas DBP exposure in nail-only salons (manicurists) was only 2-fold higher than in the general population. Concentrations of DEP and DMP metabolites in phthalate manufacturing exceeded general population levels by 4- and >1000-fold, respectively. We also found instances where GM end-shift concentrations of some metabolites exceeded general population concentrations even when no workplace use was reported, e.g. BzBP in rubber hose and rubber boot manufacturing. In summary, using urinary metabolites, we successfully identified workplaces with likely occupational phthalate exposure. Additional work is needed to distinguish occupational from non-occupational sources in low-exposure workplaces.
  Yan Jin , Misty J. Hein , James A. Deddens and Cynthia J. Hines
  Studies of determinants of occupational exposure frequently involve left-censored lognormally distributed data, often with repeated measures. Left censoring occurs when observations are below the analytical limit of detection (LOD); repeated measures data results from taking multiple measurements on the same worker. A common method of dealing with this type of data has been to substitute a value (such as LOD/2) for the censored data followed by statistical analysis using the ‘usual’ methods. Recently, maximum likelihood estimation (MLE) methods have been employed to reduce bias associated with the substitution method. We compared substitution and MLE methods using simulated lognormally distributed exposure data subjected to varying amounts of censoring using two procedures available in SAS: LIFEREG and NLMIXED. In these simulations, the MLE method resulted in less bias and performed well even for censoring up to 80%, whereas the substitution method resulted in considerable bias. We illustrate the NLMIXED procedure using a dataset of chlorpyrifos air measurements collected from termiticide applicators on consecutive days over a 5-day workweek. We provide sample SAS code for several situations including one and two groups, with and without repeated measures, random slopes, and nested random effects.
  Cynthia J. Hines , James A. Deddens , Joseph Coble , Freya Kamel and Michael C. R. Alavanja
  Objectives: To identify and quantify determinants of captan exposure among 74 private orchard pesticide applicators in the Agricultural Health Study (AHS). To adjust an algorithm used for estimating pesticide exposure intensity in the AHS based on these determinants and to compare the correlation of the adjusted and unadjusted algorithms with urinary captan metabolite levels.

Methods: External exposure metrics included personal air, hand rinse, and dermal patch samples collected from each applicator on 2 days in 2002-2003. A 24-h urine sample was also collected. Exposure determinants were identified for each external metric using multiple linear regression models via the NLMIXED procedure in SAS. The AHS algorithm was adjusted, consistent with the identified determinants. Mixed-effect models were used to evaluate the correlation between the adjusted and unadjusted algorithm and urinary captan metabolite levels.

Results: Consistent determinants of captan exposure were a measure of application size (kilogram of captan sprayed or application method), wearing chemical-resistant (CR) gloves and/or a coverall/suit, repairing spray equipment, and product formulation. Application by airblast was associated with a 4- to 5-fold increase in exposure as compared to hand spray. Exposure reduction to the hands, right thigh, and left forearm from wearing CR gloves averaged ~80%, to the right and left thighs and right forearm from wearing a coverall/suit by ~70%. Applicators using wettable powder formulations had significantly higher air, thigh, and forearm exposures than those using liquid formulations. Application method weights in the AHS algorithm were adjusted to nine for airblast and two for hand spray; protective equipment reduction factors were adjusted to 0.2 (CR gloves), 0.3 (coverall/suit), and 0.1 (both).

Conclusions: Adjustment of application method, CR glove, and coverall weights in the AHS algorithm based on our exposure determinant findings substantially improved the correlation between the AHS algorithm and urinary metabolite levels.

  Matthew M. Dahm , Douglas E. Evans , Mary K. Schubauer-Berigan , M. Eileen Birch and James A. Deddens
  Research Significance: Toxicological evidence suggests the potential for a wide range of health effects from exposure to carbon nanotubes (CNTs) and carbon nanofibers (CNFs). To date, there has been much focus on the use of direct-reading instruments (DRIs) to assess multiple airborne exposure metrics for potential exposures to CNTs and CNFs due to their ease of use and ability to provide instantaneous results. Still, uncertainty exists in the usefulness and interpretation of the data. To address this gap, air-monitoring was conducted at six sites identified as CNT and CNF manufacturers or users and results were compared with filter-based metrics.

Methods: Particle number, respirable mass, and active surface area concentrations were monitored with a condensation particle counter, a photometer, and a diffusion charger, respectively. The instruments were placed on a mobile cart and used as area monitors in parallel with filter-based elemental carbon (EC) and electron microscopy samples. Repeat samples were collected on consecutive days, when possible, during the same processes. All instruments in this study are portable and routinely used for industrial hygiene sampling.

Results: Differences were not observed among the various sampled processes compared with concurrent indoor or outdoor background samples while examining the different DRI exposure metrics. Such data were also inconsistent with results for filter-based samples collected concurrently at the same sites [Dahm MM, Evans DE, Schubauer-Berigan MK et al. (2012) Occupational exposure assessment in CNT and nanofiber primary and secondary manufacturers. Ann Occup Hyg; 56: 542-56]. Significant variability was seen between these processes as well as the indoor and outdoor backgrounds. However, no clear pattern emerged linking the DRI results to the EC or the microscopy data (CNT and CNF structure counts).

Conclusions: Overall, no consistent trends were seen among similar processes at the various sites. The DRI instruments employed were limited in their usefulness in assessing and quantifying potential exposures at the sampled sites but were helpful for hypothesis generation, control technology evaluations, and other air quality issues. The DRIs employed are nonspecific, aerosol monitors, and, therefore, subject to interferences. As such, it is necessary to collect samples for analysis by more selective, time-integrated, laboratory-based methods to confirm and quantify exposures.

 
 
 
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