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1.
J Occup Environ Med ; 60(6): 496-506, 2018 06.
Article in English | MEDLINE | ID: mdl-29443707

ABSTRACT

OBJECTIVES: The butter flavoring additive, diacetyl (DA), can cause bronchiolitis obliterans (BO) by inhalation. A risk assessment was performed using data from a microwave popcorn manufacturing plant. METHODS: Current employees' medical history and pulmonary function tests together with air sampling over a 2.7-year period were used to analyze forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). The exposure responses for declining pulmonary function and for possible early onset of BO were estimated using multiple regression methods. Several exposure metrics were investigated; benchmark dose and excess lifetime risk of impairment were calculated. RESULTS: Forty-six percent of the population had less than 6 months exposure to DA. Percent-of-predicted FEV1 declined with cumulative exposure (0.40 per ppm-yr, P < 10) as did percent FEV1/FVC (0.13 per ppm-yr, P = 0.0004). Lifetime respiratory impairment prevalence of one per thousand resulted from 0.005 ppm DA and one per thousand lifetime incidence of impairment was predicted for 0.002 ppm DA. CONCLUSION: DA exposures, often exceeding 1 ppm in the past, place workers at high risk of pulmonary impairment.


Subject(s)
Air Pollutants, Occupational/toxicity , Diacetyl/toxicity , Occupational Exposure/adverse effects , Occupational Exposure/analysis , Adult , Bronchiolitis Obliterans/epidemiology , Bronchiolitis Obliterans/etiology , Cross-Sectional Studies , Female , Food-Processing Industry , Forced Expiratory Volume , Humans , Male , Middle Aged , Risk Assessment , United States/epidemiology , Vital Capacity , Young Adult
2.
Radiat Res ; 168(6): 757-63, 2007 Dec.
Article in English | MEDLINE | ID: mdl-18088178

ABSTRACT

Errors in the estimation of exposures or doses are a major source of uncertainty in epidemiological studies of cancer among nuclear workers. This paper presents a Monte Carlo maximum likelihood method that can be used for estimating a confidence interval that reflects both statistical sampling error and uncertainty in the measurement of exposures. The method is illustrated by application to an analysis of all cancer (excluding leukemia) mortality in a study of nuclear workers at the Oak Ridge National Laboratory (ORNL). Monte Carlo methods were used to generate 10,000 data sets with a simulated corrected dose estimate for each member of the cohort based on the estimated distribution of errors in doses. A Cox proportional hazards model was applied to each of these simulated data sets. A partial likelihood, averaged over all of the simulations, was generated; the central risk estimate and confidence interval were estimated from this partial likelihood. The conventional unsimulated analysis of the ORNL study yielded an excess relative risk (ERR) of 5.38 per Sv (90% confidence interval 0.54-12.58). The Monte Carlo maximum likelihood method yielded a slightly lower ERR (4.82 per Sv) and wider confidence interval (0.41-13.31).


Subject(s)
Monte Carlo Method , Occupational Exposure/statistics & numerical data , Radiation Monitoring/statistics & numerical data , Uncertainty , Research Design , Time Factors
3.
J Acoust Soc Am ; 113(2): 871-80, 2003 Feb.
Article in English | MEDLINE | ID: mdl-12597181

ABSTRACT

Variability in background risk and distribution of various risk factors for hearing loss may explain some of the diversity in excess risk of noise-induced hearing loss (NIHL). This paper examines the impact of various risk factors on excess risk estimates of NIHL using data from the 1968-1972 NIOSH Occupational Noise and Hearing Survey (ONHS). Previous analyses of a subset of these data focused on 1172 highly "screened" workers. In the current analysis, an additional 894 white males (609 noise-exposed and 285 controls), who were excluded for various reasons (i.e., nonoccupational noise exposure, otologic or medical conditions affecting hearing, prior occupational noise exposure) have been added 2066) to assess excess risk of noise-induced material impairment in an unscreened population. Data are analyzed by age, duration of exposure, and sound level (8-h TWA) for four different definitions of noise-induced hearing impairment, defined as the binaural pure-tone average (PTA) hearing threshold level greater than 25 dB for the following frequencies: (a) 1-4 kHz (PTA1234), (b) 1-3 kHz (PTA123), (c) 0.5, 1, and 2 kHz (PTA512), and (d) 3, 4, and 6 kHz (PTA346). Results indicate that populations with higher background risks of hearing loss may show lower excess risks attributable to noise relative to highly screened populations. Estimates of lifetime excess risk of hearing impairment were found to be significantly different between screened and unscreened population for noise levels greater than 90 dBA. Predicted age-related risk of material hearing impairment in the ONHS unscreened population was similar to that predicted from Annex B and C of ANSI S3.44 for ages less than 60 years. Results underscore the importance of understanding differential risk patterns for hearing loss and the use of appropriate reference (control) populations when evaluating risk of noise-induced hearing impairment among contemporary industrial populations.


Subject(s)
Hearing Loss, Noise-Induced/etiology , Noise, Occupational/adverse effects , Occupational Diseases/etiology , Adult , Aged , Audiometry, Pure-Tone , Auditory Threshold , Female , Health Surveys , Hearing Loss, Noise-Induced/epidemiology , Hearing Loss, Noise-Induced/prevention & control , Humans , Male , Mass Screening , Middle Aged , National Institute for Occupational Safety and Health, U.S. , Occupational Diseases/epidemiology , Occupational Diseases/prevention & control , Risk Factors , Sound Spectrography , United States
4.
Am J Ind Med ; 42(1): 1-10, 2002 Jul.
Article in English | MEDLINE | ID: mdl-12111685

ABSTRACT

BACKGROUND: Standardized mortality ratios (SMRs) and other measures of relative risk by themselves may not suffice as descriptors of occupational hazards for many audiences including decision-makers and those at direct risk from hazardous work. To explore other approaches, we calculated excess years of potential life lost and excess lifetime risk for both lung diseases and fatal injuries in a cohort of uranium miners with historical records of exposure to radon gas. METHODS: We used relatively simple life table (SMR) methods and also analyzed lung cancer mortality with Poisson regression methods permitting control for smoking. RESULTS: Among uranium miners hired after 1950, whose all-cause SMR was 1.5, 28 percent would experience premature death from lung diseases or injury in a lifetime of uranium mining. On average, each miner lost 1.5 yr of potential life due to mining-related lung cancer, or almost 3 months of life for each year employed in uranium mining. As a consequence of all excess lung disease and injury risks combined, a year of mining was associated with 5.9 months loss of potential life. For each year actually working underground, miners lost more than 8 months of potential life. When controlled for smoking (and healthy worker effect) with Poisson regression, the estimates for radon-related lung cancer effects were slightly larger. Although chronic disease deaths dominated in excess years of life lost (due to radon, silica and possibly other exposures), more years were lost on average per individual injury death (38 yr), than per excess lung cancer (20 yr) or other lung disease death (18 yr). Fatal-injury dominated the potential years of life lost up to about age 40. CONCLUSIONS: Years of life lost per years employed provides another, more intuitive summary of occupational mortality risk.


Subject(s)
Life Expectancy , Lung Neoplasms/etiology , Lung Neoplasms/mortality , Mining , Occupational Exposure , Quality-Adjusted Life Years , Uranium , Wounds and Injuries/mortality , Accidents, Occupational , Adolescent , Adult , Aged , Aged, 80 and over , Cohort Studies , Colorado/epidemiology , Humans , Life Tables , Middle Aged , Poisson Distribution , Radon , Regression Analysis , Risk , Time Factors
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