Simultaneous modeling of detection rate and exposure concentration using semi-continuous models to identify exposure determinants when left-censored data may be a true zero.

Details

• Personal Author:
  Albert PS ; Choo-Wosoba H ; Deziel NC ; Dopart P ; Friesen MC ; Hwang J ; Lavoué J ; Russ DE ; Sarazin P ; Zhu B
• Description:
  Background: Most methods for treating left-censored data assume the analyte is present but not quantified. Biased estimates may result if the analyte is absent such that the unobserved data represents a mixed exposure distribution with an unknown proportion clustered at zero. Objective: We used semi-continuous models to identify time and industry trends in 52,457 OSHA inspection lead sample results. Method: The first component of the semi-continuous model predicted the probability of detecting concentrations ≥ 0.007 mg/m3 (highest estimated detection limit, 62% of measurements). The second component predicted the median concentration of measurements ≥ 0.007 mg/m3. Both components included a random-effect for industry and fixed-effects for year, industry group, analytical method, and other variables. We used the two components together to predict median industry- and time-specific lead concentrations. Results: The probabilities of detectable concentrations and the median detected concentrations decreased with year; both were also lower for measurements analyzed for multiple (vs. one) metals and for those analyzed by inductively-coupled plasma (vs. atomic absorption spectroscopy). The covariance was 0.30 (standard error = 0.06), confirming the two components were correlated. Significance: We identified determinants of exposure in data with over 60% left-censored, while accounting for correlated relationships and without assuming a distribution for the censored data. [Description provided by NIOSH]
• Subjects:
  Biomarkers Environmental Exposure Epidemiology Lead Occupational Health Safety
• Keywords:
  Author Keywords: Statistical Modeling; Left-censored Data; Occupational Lead Exposure Statistical Analysis; Statistical Quality Control; Epidemiology; Exposure Assessment; Environmental Exposure; Lead; Sampling Methods; Biomarkers;
• ISSN:
  1559-0631
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Connecticut ; Maryland ; Ohio ; Oklahoma ; OSHA Region 1 ; OSHA Region 3 ; OSHA Region 5 ; OSHA Region 6
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  31
• Issue:
  6
• NIOSHTIC Number:
  nn:20064988
• Citation:
  J Expo Sci Environ Epidemiol 2021 Nov; 31(6):1047-1056
• Contact Point Address:
  Melissa C. Friesen, National Cancer Institute, Division of Cancer Epidemiology & Genetics, Occupational and Environmental Epidemiology Branch, Rockville, MD, USA
• Email:
  friesenmc@mail.nih.gov
• CAS Registry Number:
  Lead (CAS RN 7439-92-1)
• Federal Fiscal Year:
  2022
• Performing Organization:
  University of Cincinnati
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Journal of Exposure Science and Environmental Epidemiology
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:8634a5321bae669ffb4a9226c4de282dd7e35113094ef638a98e27a7546e5a4c59b79dfb355d6111654e391119283a698131385173a9ed9e431a92c2c05a1308
• Download URL:
  https://stacks.cdc.gov/view/cdc/249295/cdc_249295_DS1.pdf
• File Type:
  [PDF - 576.26 KB ]