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Journal articles on the topic 'Exposure index'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Moschandreas, D., S. Karuchit, M. Lebowitz, M. OʼRourke, and S. Gordon. "THE TOTAL EXPOSURE INDEX SCHEME." Epidemiology 9, Supplement (July 1998): S133. http://dx.doi.org/10.1097/00001648-199807001-00449.

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2

Marshall, John T., and Art Mundt. "Dow's chemical exposure index guide." Process Safety Progress 14, no. 3 (July 1995): 163–70. http://dx.doi.org/10.1002/prs.680140305.

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3

Sohn, Young-Soo, Moon-Gyu Sung, Young-Mi Lee, Eun-Mi Lee, Jin-Kyung Oh, Sung-Hwan Byun, Yeon-Un Jeong, et al. "Photoresist Exposure Parameter Extraction from Refractive Index Change during Exposure." Japanese Journal of Applied Physics 37, Part 1, No. 12B (December 30, 1998): 6877–83. http://dx.doi.org/10.1143/jjap.37.6877.

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4

Hayes, Miles O. "An exposure index for oiled shorelines." Spill Science & Technology Bulletin 3, no. 3 (January 1996): 139–47. http://dx.doi.org/10.1016/s1353-2561(96)00014-x.

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5

Huda, W. "A radiation exposure index for CT." Radiation Protection Dosimetry 157, no. 2 (May 19, 2013): 172–80. http://dx.doi.org/10.1093/rpd/nct128.

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6

Guðjónsdóttir, J., K. E. Paalsson, and G. P. Sveinsdóttir. "Are the target exposure index and deviation index used efficiently?" Radiography 27, no. 3 (August 2021): 903–7. http://dx.doi.org/10.1016/j.radi.2021.02.012.

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7

Zachariah, Philip, Lisa Saiman, Jianfang Liu, and Elaine Larson. "Measuring Multiple Dimensions of Cumulative Antibiotic Exposure: The Antibiotic Exposure Index." Open Forum Infectious Diseases 4, suppl_1 (2017): S165—S166. http://dx.doi.org/10.1093/ofid/ofx163.289.

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8

Wheeler, David C., Salem Rustom, Matthew Carli, Todd P. Whitehead, Mary H. Ward, and Catherine Metayer. "Bayesian Group Index Regression for Modeling Chemical Mixtures and Cancer Risk." International Journal of Environmental Research and Public Health 18, no. 7 (March 27, 2021): 3486. http://dx.doi.org/10.3390/ijerph18073486.

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There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.

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9

SCOGGINS, AMANDA, and GAVIN FISHER. "Air Pollution Exposure Index for New Zealand." New Zealand Geographer 58, no. 2 (October 2002): 56–64. http://dx.doi.org/10.1111/j.1745-7939.2002.tb01635.x.

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10

JAMES, FRANKLIN J. "A New Generalized “Exposure-Based” Segregation Index." Sociological Methods & Research 14, no. 3 (February 1986): 301–16. http://dx.doi.org/10.1177/0049124186014003005.

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11

Chapman, M. D. "Guanine ? an adequate index of mite exposure?" Allergy 48, no. 5 (July 1993): 301–2. http://dx.doi.org/10.1111/j.1398-9995.1993.tb02395.x.

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12

Rydock, James P., Kim Robert Lisø, Eirik J. Førland, Kristine Nore, and Jan Vincent Thue. "A driving rain exposure index for Norway." Building and Environment 40, no. 11 (November 2005): 1450–58. http://dx.doi.org/10.1016/j.buildenv.2004.11.018.

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13

IMBRIANI, Marcello, Qiao NIU, Sara NEGRI, and Sergio GHITTORI. "Trichloroethylene in Urine as Biological Exposure Index." INDUSTRIAL HEALTH 39, no. 3 (2001): 225–30. http://dx.doi.org/10.2486/indhealth.39.225.

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14

CHERRIE, J. "Are task-based exposure levels a valuable index of exposure for epidemiology?" Annals of Occupational Hygiene 40, no. 6 (December 1996): 715–17. http://dx.doi.org/10.1016/s0003-4878(96)00037-3.

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15

Serrano, Helena C., Melanie Köbel, José Palma-Oliveira, Pedro Pinho, and Cristina Branquinho. "Mapping Exposure to Multi-Pollutants Using Environmental Biomonitors—A Multi-Exposure Index." Journal of Toxicology and Environmental Health, Part A 80, no. 13-15 (June 1, 2017): 710–18. http://dx.doi.org/10.1080/15287394.2017.1286930.

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16

Lowry, Larry K. "The biological exposure index: Its use in assessing chemical exposures in the workplace." Toxicology 47, no. 1-2 (December 1987): 55–69. http://dx.doi.org/10.1016/0300-483x(87)90160-0.

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17

Reichler, Mary R., Awal Khan, Yan Yuan, Bin Chen, James McAuley, Bonita Mangura, Timothy R. Sterling, et al. "Duration of Exposure Among Close Contacts of Patients With Infectious Tuberculosis and Risk of Latent Tuberculosis Infection." Clinical Infectious Diseases 71, no. 7 (February 11, 2020): 1627–34. http://dx.doi.org/10.1093/cid/ciz1044.

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Abstract Background Predictors of latent tuberculosis infection (LTBI) among close contacts of persons with infectious tuberculosis (TB) are incompletely understood, particularly the number of exposure hours. Methods We prospectively enrolled adult patients with culture-confirmed pulmonary TB and their close contacts at 9 health departments in the United States and Canada. Patients with TB were interviewed and close contacts were interviewed and screened for TB and LTBI during contact investigations. Results LTBI was diagnosed in 1390 (46%) of 3040 contacts, including 624 (31%) of 2027 US/Canadian-born and 766 (76%) of 1013 non-US/Canadian-born contacts. In multivariable analysis, age ≥5 years, male sex, non-US/Canadian birth, smear-positive index patient, and shared bedroom with an index patient (P < .001 for each), as well as exposure to >1 index patient (P < .05), were associated with LTBI diagnosis. LTBI prevalence increased with increasing exposure duration, with an incremental prevalence increase of 8.2% per 250 exposure hours (P < .0001). For contacts with <250 exposure hours, no difference in prevalence was observed per 50 exposure hours (P = .63). Conclusions Hours of exposure to a patient with infectious TB is an important LTBI predictor, with a possible risk threshold of 250 hours. More exposures, closer exposure proximity, and more extensive index patient disease were additional LTBI predictors.

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18

Kim, Hyun Soo, Jae Ho Jeong, and Jong Woong Lee. "Research on Image Quality and Effective dose by Exposure Index Variation." Journal of the Korean Society of Radiology 7, no. 1 (February 28, 2013): 63–69. http://dx.doi.org/10.7742/jksr.2013.7.1.063.

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19

Franzblau, Alfred, Steven P. Levine, Richard M. Schreck, James B. D'Arcy, and Qing-Shan Qu. "Use of Urinary Formic Acid as a Biologic Exposure Index of Methanol Exposure." Applied Occupational and Environmental Hygiene 7, no. 7 (July 1992): 467–71. http://dx.doi.org/10.1080/1047322x.1992.10390191.

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20

Creeden, A., and M. Curtis. "Optimising default radiographic exposure factors using Deviation Index." Radiography 26, no. 4 (November 2020): 308–13. http://dx.doi.org/10.1016/j.radi.2020.02.009.

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21

Liu, Honghu, Nicole Prause, Gail E. Wyatt, John K. Williams, Dorothy Chin, Teri Davis, Tamra Loeb, Erica Marchand, Muyu Zhang, and Hector F. Myers. "Development of a composite trauma exposure risk index." Psychological Assessment 27, no. 3 (September 2015): 965–74. http://dx.doi.org/10.1037/pas0000069.

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22

Lanca, L., and A. Silva. "Evaluation of exposure index (IgM) in orthopaedic radiography." Radiation Protection Dosimetry 129, no. 1-3 (February 18, 2008): 112–18. http://dx.doi.org/10.1093/rpd/ncn143.

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23

McCauley, Clark, Mary Plummer, Sophia Moskalenko, and J. Toby Mordkoff. "The exposure index: A measure of intergroup contact." Peace and Conflict: Journal of Peace Psychology 7, no. 4 (2001): 321–36. http://dx.doi.org/10.1207/s15327949pac0704_03.

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24

Ariga, Eiji. "Evaluation of Calibration Error for Applying Exposure Index." Japanese Journal of Radiological Technology 67, no. 11 (2011): 1433–37. http://dx.doi.org/10.6009/jjrt.67.1433.

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25

Wang, S. W., H. Qian, C. Weisel, C. Nwankwo, and N. Fiedler. "Development of Solvent Exposure Index for Construction Painters." Journal of Occupational and Environmental Hygiene 8, no. 6 (June 2011): 375–86. http://dx.doi.org/10.1080/15459624.2011.583488.

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26

Thomas, Martin L. H. "A physically derived exposure index for marine shorelines." Ophelia 25, no. 1 (March 1986): 1–13. http://dx.doi.org/10.1080/00785326.1986.10429719.

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27

Hatsusaka, Natsuko, Yusuke Seki, Norihiro Mita, Yuki Ukai, Hisanori Miyashita, Eri Kubo, David Sliney, and Hiroshi Sasaki. "UV Index Does Not Predict Ocular Ultraviolet Exposure." Translational Vision Science & Technology 10, no. 7 (June 1, 2021): 1. http://dx.doi.org/10.1167/tvst.10.7.1.

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28

Offiah, A. C. "Quality assurance: using the exposure index and the deviation index to monitor radiation exposure for portable chest radiographs in neonates." Yearbook of Diagnostic Radiology 2012 (January 2012): 179–80. http://dx.doi.org/10.1016/j.yrad.2012.03.011.

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29

Cohen, Mervyn D., Matt L. Cooper, Kelly Piersall, and Bruce K. Apgar. "Quality assurance: using the exposure index and the deviation index to monitor radiation exposure for portable chest radiographs in neonates." Pediatric Radiology 41, no. 5 (December 30, 2010): 592–601. http://dx.doi.org/10.1007/s00247-010-1951-9.

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30

Hirose, Shinichiro, Hiroaki Matsuzawa, Asako Hirose, and Kiyosumi Kawamoto. "Study on the Effect of Setting the Region of Interest for Exposure Index Computing." Japanese Journal of Radiological Technology 71, no. 1 (2015): 7–11. http://dx.doi.org/10.6009/jjrt.2015_jsrt_71.1.7.

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31

Irsal, Muhammad. "Exposure Factor Control with Exposure Index Guide As Optimizing Efforts in Chest Pa Examination." Journal of Physics: Conference Series 1842, no. 1 (March 1, 2021): 012059. http://dx.doi.org/10.1088/1742-6596/1842/1/012059.

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32

Kurniawati, Dartini, Ary. "Differences of Radiographic Quality and Exposure Index on Computed Radiography Using Imaging Plate with Different Reading Time Period." Journal of Medical Science And clinical Research 05, no. 06 (June 22, 2017): 23664–69. http://dx.doi.org/10.18535/jmscr/v5i6.143.

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33

Nurrokhim, Mukhammad Lutfan, Dwi Rochmayanti, and Ari Budiono. "Standarisasi Indeks Eksposur untuk Memenuhi Kriteria Anatomi dan Aspek Teknis pada Radiografi Thorax Pediatrik." Jurnal Imejing Diagnostik (JImeD) 7, no. 1 (February 2, 2021): 22–27. http://dx.doi.org/10.31983/jimed.v7i1.5937.

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Background: Computed Radiography has an exposure index that used as an exposure indicator. But on radiographic examination, exposure index value sometimes ignored, and in the preliminary survey of pediatric chest examination resulting a large exposure index range. The aim of this study is to know the profile of exposure index value and the setting of the exposure factors, the assessment of anatomy criteria and technical aspect, and the right exposure factors such as kV and mAs on pediatric chest examination.Methods: The type of this research is descriptive quantitative. The research was done by collecting data related to pediatric chest radiograph (≤ 2 years) the value of exposure index was recorded, then the radiograph was assessed using questionnaires that filled by one respondent who is a radiologist. The data was analyzed by displaying the data of exposure index and anatomy criteria from questionnaires into the table form, and then conducted a descriptive analysis to be drawn conclusions.Results: The results showed the profile of exposure index value and the setting of the exposure factor has a fairly large exposure index range of 1084 – 2175, using 40 kV and 10 mAs and the collimation still often exceeds the object. Then for the assessment of the thorax anatomical criteria and the technical aspect overall was “Good Enough”, and the right exposure factors, that is: at 6 and 7 cm chest thickness was using 60 kV and 1,6 mAs; at 11 cm chest thickness was using 60 kV and 2 mAs, FFD 100 cm, and the setting of collimation as wide as object, the exposure index generated in the normal range that is 1251 – 1382.Conclusion: The right exposure factors on pediatric chest examination, that is: at 6 and 7 cm chest thickness was using 60 kV and 1,6 mAs; at 11 cm chest thickness was using 60 kV and 2 mAs, FFD 100 cm, and the setting of collimation as wide as object.

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34

Boori, M. S., V. Vozenilek, and K. Choudhary. "Exposer Intensity, Vulnerability Index And Landscape Change Assessment In Olomouc, Czech Republic." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 771–76. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-771-2015.

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The objective of this study is vulnerability and exposer intensity due to land use change in Olomouc, Czech Republic. Vulnerability assessment with exposer intensity to land use/cover change is an important step for enhancing the understanding and decision-making to reduce vulnerability. This study work includes quantification of Exposure Index (EI), Sensitivity Index (SI) and Adaptive Capacity Index (AI). EI is based on intensity of land use/cover change, SI and AI based on natural factors such as elevation, slope, vegetation and land use/cover. Vulnerability Index (VI) derived on the quantification of SI and AI and compared from 1991, 2001 and 2013. Comparing of EI and VI for last three decades, settlements have highest vulnerability index due to high socio-economic activities and water have lowest vulnerability index due to less human interferences. Agriculture has highest exposer index and second highest vulnerability, which show its high rate of exploitation and production. In the study areas, vulnerability tends to increase with the increase of exposure to land use change, but can peak off once the land use start to benefit socio-economically from development. Only in this way we can enhance the adaptive capacity of study area to use change of land.

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35

Virji, M. Abbas, Christine R. Schuler, Jean Cox-Ganser, Marcia L. Stanton, Michael S. Kent, Kathleen Kreiss, and Aleksandr B. Stefaniak. "Associations of Metrics of Peak Inhalation Exposure and Skin Exposure Indices With Beryllium Sensitization at a Beryllium Manufacturing Facility." Annals of Work Exposures and Health 63, no. 8 (September 4, 2019): 856–69. http://dx.doi.org/10.1093/annweh/wxz064.

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Abstract Objectives Peak beryllium inhalation exposures and exposure to the skin may be relevant for developing beryllium sensitization (BeS). The objective of this study was to identify risk factors associated with BeS to inform the prevention of sensitization, and the development of chronic beryllium disease (CBD). Methods In a survey of short-term workers employed at a primary beryllium manufacturing facility between the years 1994–1999, 264 participants completed a questionnaire and were tested for BeS. A range of qualitative and quantitative peak inhalation metrics and skin exposure indices were created using: personal full-shift beryllium exposure measurements, 15 min to 24 h process-specific task and area exposure measurements, glove measurements as indicator of skin exposure, process-upset information gleaned from historical reports, and self-reported information on exposure events. Hierarchical clustering was conducted to systematically group participants based on similarity of patterns of 16 exposure variables. The associations of the exposure metrics with BeS and self-reported skin symptoms (in work areas processing beryllium salts as well as in other work areas) were evaluated using correlation analysis, log-binomial and logistic regression models with splines. Results Metrics of peak inhalation exposure, indices of skin exposure, and using material containing beryllium salts were significantly associated with skin symptoms and BeS; skin symptoms were a strong predictor of BeS. However, in this cohort, we could not tease apart the independent effects of skin exposure from inhalation exposure, as these exposures occurred simultaneously and were highly correlated. Hierarchical clustering identified groups of participants with unique patterns of exposure characteristics resulting in different prevalence of BeS and skin symptoms. A cluster with high skin exposure index and use of material containing beryllium salts had the highest prevalence of BeS and self-reported skin symptoms, followed by a cluster with high inhalation and skin exposure index and a very small fraction of jobs in which beryllium salts were used. A cluster with low inhalation and skin exposure and no workers using beryllium salts had no cases of BeS. Conclusion Multiple pathways and types of exposure were associated with BeS and may be important for informing BeS prevention. Prevention efforts should focus on controlling airborne beryllium exposures with attention to peaks, use of process characteristics (e.g. the likelihood of upset conditions to design interventions) minimize skin exposure to beryllium particles, and in particular, eliminate skin contact with beryllium salts to interrupt potential exposure pathways for BeS risk.

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36

Stanišić, Michał-Goran, Natalia Majewska, Marcin Makałowski, Robert Juszkat, Magdalena Błaszak, and Wacław Majewski. "Patient radiation exposure during carotid artery stenting." Vascular 23, no. 2 (June 25, 2014): 154–60. http://dx.doi.org/10.1177/1708538114540641.

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Objectives The main purpose of this study was to document the radiation doses to patients during carotid stenting. Material and method Fluoroscopy and exposure time, air kerma and dose-area product during carotid artery stenting in 160 patients were retrospectively reviewed with regard to body mass index, degree of stenosis and use of cerebral protection devices. Results Total air kerma was lower than 0.5 Gy in 80%, 0.5–1 Gy in 17% and higher than 1 Gy (maximum 1.2) in 3% of patients. Mean total dose-area product value for carotid stenting was 54 Gy cm2. The mean air kerma (fluoroscopy), air kerma (exposure), total air kerma and dose-area product (fluoroscopy), dose-area product (exposure), total dose-area product of patients with body mass index within the range 25–29.9 and with body mass index >30 were significantly increased compared to that of patients with body mass index 18–24.9 (H = 40.2, df = 2; p = 0.0000001 and p = 0.000003, respectively). Conclusion Carotid artery stenting is a relatively safe radiological procedure in terms of the radiation dose acquired by the patient. The main factors contributing to possible radiation overdosing are body mass index value and complexity of the carotid lesion. Proper preoperative planning in obese and complicated patients may reduce the fluoroscopy time and contribute to reduced dose acquisition.

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37

Yoon, Dokyoung, Michael E. Ginevan, and Nathan J. Karch. "The Validity of the Stellman Exposure Opportunity Index Model." Epidemiology 22 (January 2011): S269. http://dx.doi.org/10.1097/01.ede.0000392524.29430.f1.

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38

Seixas, Noah S., Marty Cohen, Brian Zevenbergen, Michael Cotey, Stephanie Carter, and Joel Kaufman. "Urinary Fluoride as an Exposure Index in Aluminum Smelting." AIHAJ 61, no. 1 (January 2000): 89–94. http://dx.doi.org/10.1202/0002-8894(2000)061<0089:ufaaei>2.0.co;2.

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39

Kobayashi, Hiroshi, and Toshiyuki Horiuchi. "Novel Projection Exposure System Using Gradient-Index Lens Array." Japanese Journal of Applied Physics 47, no. 7 (July 11, 2008): 5702–7. http://dx.doi.org/10.1143/jjap.47.5702.

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40

Leung, Tim, and Brian Ward. "Dynamic Index Tracking and Risk Exposure Control Using Derivatives." Applied Mathematical Finance 25, no. 2 (March 4, 2018): 180–212. http://dx.doi.org/10.1080/1350486x.2018.1507750.

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41

Lowry, Larry K. "Biological Exposure Index as a Complement to the TLV." Journal of Occupational and Environmental Medicine 28, no. 8 (August 1986): 578–82. http://dx.doi.org/10.1097/00043764-198608000-00011.

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42

Szigeti, F., and R. Bauer. "Stellenwert des Standard Exposure Index in der klinischen Routine." Radiopraxis 8, no. 02 (June 22, 2015): 95–105. http://dx.doi.org/10.1055/s-0034-1391890.

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43

Kaur, Kulwinder, Surinder Pal Singh, Kailash Meena, Prabhnoor Kaur Saraya, and Kirandeep Kaur. "Smoking index- A measure to quantify cumulative smoking exposure." Panacea Journal of Medical Sciences 10, no. 2 (August 15, 2020): 71–74. http://dx.doi.org/10.18231/j.pjms.2020.018.

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44

Khobragade, P., Franco Rupcich, Jiahua Fan, Dominic J. Crotty, Naveen M. Kulkarni, Stacy D. O'Connor, W. Dennis Foley, and Taly Gilat Schmidt. "CT automated exposure control using a generalized detectability index." Medical Physics 46, no. 1 (December 4, 2018): 140–51. http://dx.doi.org/10.1002/mp.13286.

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45

Smith, Kirk R., and Dilip R. Ahuja. "Toward a greenhouse equivalence index: The total exposure analogy." Climatic Change 17, no. 1 (August 1990): 1–7. http://dx.doi.org/10.1007/bf00148996.

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46

Seixas, Noah S., Marty Cohen, Brian Zevenbergen, Michael Cotey, Stephanie Carter, and Joel Kaufman. "Urinary Fluoride as an Exposure Index in Aluminum Smelting." AIHAJ - American Industrial Hygiene Association 61, no. 1 (January 2000): 89–94. http://dx.doi.org/10.1080/15298660008984520.

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47

Scott, Alexander W., Yifang Zhou, Janet Allahverdian, Jessica L. Nute, and Christina Lee. "Evaluation of digital radiography practice using exposure index tracking." Journal of Applied Clinical Medical Physics 17, no. 6 (November 2016): 343–55. http://dx.doi.org/10.1120/jacmp.v17i6.6082.

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48

Vano, E., D. Martinez, J. M. Fernandez, J. M. Ordiales, C. Prieto, A. Floriano, and J. I. Ten. "Paediatric entrance doses from exposure index in computed radiography." Physics in Medicine and Biology 53, no. 12 (June 3, 2008): 3365–80. http://dx.doi.org/10.1088/0031-9155/53/12/020.

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49

Kendall, G. M., and J. S. Hughes. "Occupational exposure and the Central Index of Dose Information." British Journal of Radiology 63, no. 756 (December 1990): 963–65. http://dx.doi.org/10.1259/0007-1285-63-756-963.

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50

Coulston, D. J., and R. Strong. "Occupational exposure and the central index of dose information." British Journal of Radiology 64, no. 764 (August 1991): 772. http://dx.doi.org/10.1259/0007-1285-64-764-772.

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