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HEARING LOSS IN AIRCRAFT MAINTENANCE TECHNICIANS
Maya Guest1, May Boggess2
1 Faculty of Health, University of Newcastle, Australia. 2 Department of Statistics, Texas A&M University, College Station, USA
Deseal, then reseal a fuel tank
The fuel tanks were a confined space They were very cramped, with tradespeople
crawling around the braces
Health Concerns
Concerns about various symptoms experienced by workers were raised in early 1999 with symptoms including: memory loss fatigue neurological problems eg. colour vision
Deseal/reseal activities ceased in 2000 The SHOAMP Study
The SHOAMP Study
Aims compare series of general health, medical and
neurophysiological outcomes between F-111 deseal/reseal personnel and appropriate comparisons
Research Question Is there an association between adverse health
status and an involvement in F-111 deseal/reseal activities?
Study Design Retrospective cohort; postal questionnaire & exam
Study Group
Exposed group in Deseal/Reseal program at Amberley
N = 616
Two comparison groups same time, but in non-technical at Amberley
N=406
same time, but in technical at Richmond N=516
Total health exams N=1538
Measuring Hearing Thresholds
Pure-tone audiometry at the frequencies of 0.5, 1, 2, 3, 4, 6, 8 kHz for air conduction
Australian Standard AS1269.4.1998 by trained nurses
Measures threshold dB (smaller is better)
How to compare groups?
Treat each frequency separately
Do 95% confidence intervals for each group overlap?
Problems
16 observations on single person will be correlated
Distribution heavily skewed Multiple test correction (eg. Bonferroni)
needed to control overall error rate Other factors need to be controlled for
AGE!
The ISO-7029
The ISO-7029: statistical distribution of hearing thresholds as a function of age provides by gender the expected median value of hearing thresholds relative to the median threshold at the age of 18 years and the statistical distribution above and below the median value for the range of audiometric frequencies from 125 Hz to 8000 Hz for populations of otologically normal persons of given age between 18 and 70 years
Quantile model?
Mean regression: coefficients estimated by minimizing the sum of the squares of the residuals
Quantile regression: coefficients estimated by minimizing the sum of the absolute values of the residuals
Quantile model?
Mean regression: 1821 Gauss showed it was ML, least variance IF residuals are normal.
Quantile regression: 1818 Laplace showed it had smaller variance than mean for certain distributions with long tails.
Central Limit Theorem is not a cure all.
Statistical Analysis: quantile model to compare to normal population Response: hearing threshold (dB) Explanatory variables:
Frequency, Age Posting category, Rank category Alcohol consumption category, Smoking status Diabetes status SSRI’s (anti-depressants), malaria medication Ringing in the ears Exposure group, civilian solvent exposure
Bootstrap standard errors: correlation within person.
Statistical Analysis: quantile model to compare to normal population Statistically significant explanatory variables:
Frequency, Age Smoking status, Diabetes status SSRI’s (anti-depressants) Ringing in the ears Exposure group
Clinically significant variables: Frequency Age
Result table------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------_Ifrequen~10 | -1.051308 .3152126 -3.34 0.001 -1.669113 -.4335024_Ifrequen~15 | -.6029429 .3331257 -1.81 0.070 -1.255857 .0499715_Ifrequen~20 | -1.881937 .3809262 -4.94 0.000 -2.628539 -1.135336_Ifrequen~30 | .8669362 .4485159 1.93 0.053 -.0121388 1.746011_Ifrequen~40 | 7.536735 .4582282 16.45 0.000 6.638624 8.434846_Ifrequen~60 | 8.997418 .3901236 23.06 0.000 8.23279 9.762046_IfreXg~40_2 | -2.720623 .9044762 -3.01 0.003 -4.493363 -.9478817_IfreXg~60_2 | -2.208105 .8227658 -2.68 0.007 -3.820696 -.5955134_IfreXg~80_2 | -3.32165 .7481134 -4.44 0.000 -4.787925 -1.855374 age | -.4541155 .2376783 -1.91 0.056 -.9199565 .0117254 age2 | .006715 .002729 2.46 0.014 .0013662 .0120638 frequency | .2533864 .1252859 2.02 0.043 .0078305 .4989422 fa | -.0225578 .0058333 -3.87 0.000 -.0339908 -.0111248 fa2 | .0004074 .000067 6.08 0.000 .0002761 .0005387_Ismoke_ca~3 | 1.7394 .5696168 3.05 0.002 .6229713 2.855828 diabetes | 5.374096 1.413524 3.80 0.000 2.60364 8.144552 ssri | 5.298353 1.240201 4.27 0.000 2.867604 7.729102 ringing | 3.350579 .4439917 7.55 0.000 2.480371 4.220787 _cons | 18.82656 4.990258 3.77 0.000 9.045838 28.60729------------------------------------------------------------------------------
Conclusion
Need to reconsider noise exposure limits if workers are additionally exposed to chemicals
Need to reconsider the efficacy of hearing protectors in combined exposures
The TUNRA Study TeamPrincipal Investigators
Catherine D’Este, Associate Professor in Biostatistics, Centre for Clinical Epidemiology & Biostatistics, The University of Newcastle.
John Attia, Senior Lecturer in Epidemiology, Centre for Clinical Epidemiology & Biostatistics, The University of Newcastle; Academic Consultant, Hunter Area Health Service
Anthony Brown, Director of Primary Health Care and Population Health, Macquarie Area Health Service; Conjoint Associate Professor, Environmental and Occupational Health, The University of Newcastle.
Julie Byles, B.Med, PhD, Professor and Director, Centre for Research and Education in Ageing (CREA), Faculty of Health, The University of Newcastle.
Associate Investigator Robert Gibberd, Associate Professor,
Centre for Clinical Epidemiology & Biostatistics, The University of Newcastle.
CEO of TUNRA Ltd Soozy Smith, PhD, TUNRA Ltd, The
University of Newcastle.
Project Support Meredith Tavener, Project Manager. Richard Gibson, Associate Lecturer in
Biostatistics (Research), Centre for Clinical Epidemiology & Biostatistics, The University of Newcastle, Project Statistician.
Maya Guest,. Research Higher Degree candidate, PhD Fellow for SHOAMP.