European Journal of Epidemiology

, Volume 24, Issue 8, pp 469–475

Validity of self-reported occupational noise exposure

  • Klaus Schlaefer
  • Brigitte Schlehofer
  • Joachim Schüz
OCCUPATIONAL EPIDEMIOLOGY

Abstract

In all epidemiological studies the validity of self-reported questionnaire data is an important issue as the exposure assessment based on such data is a major source of bias in the risk estimation. A validation study was conducted based on a case–control study including 94 acoustic neuroma cases and 191 matched controls from the German Interphone Study to investigate the level of agreement between self-reported occupational noise exposure and a job-exposure-matrix (JEM) on noise exposure derived from a lifetime occupation calendar. The JEM was generated based on measurement data collected in the literature for various occupations. Level of agreement was investigated by using sensitivity, specificity, kappa coefficient and the Youden-Index. The receiver operating characteristics curve yielded an optimal cut point of 80 decibel(Acoustic) (dB(A)) to dichotomize noise exposure, displaying a moderate agreement between self-reported exposure and the JEM-based exposure (kappa of 0.53) that was slightly higher for cases than controls (kappas of 0.62 and 0.48). The agreement was only slightly lower if the longest held job or the last held job were used instead of the loudest job of the lifetime job history. The cut point of 80 dB(A) corresponds with regulations for workers safety with a recommendation to wear noise protection. The good levels of agreement between self-reported high occupational noise exposure compared with JEM-data, together with no substantial differences between cases and controls, suggest that self-reported data on occupational noise exposure is a valid exposure metric. Noise exposure appears to be appropriate if only exposure information on the last or the longest held job is available.

Keywords

Occupational noise exposure Job exposure matrix Data validity Recall bias Interphone study 

References

  1. 1.
    Rothman KJ. Modern epidemiology. Toronto: Little, Brown and Company; 2008. p. 102–12.Google Scholar
  2. 2.
    Adegoke OJ, Blair A, Ou SX, Sanderson M, Addy CL, Dosemeci M, et al. Agreement of job-exposure matrix (JEM) assessed exposure and self-reported exposure among adult leukemia patients and controls in Shanghai. Am J Ind Med. 2004;45:281–8. doi:10.1002/ajim.10351.PubMedCrossRefGoogle Scholar
  3. 3.
    Brigham J, Lessov-Schlaggar CN, Javitz HS, McElroy M, Krasnow R, Swan GE. Reliability of adult retrospective recall of lifetime tobacco use. Nicotine Tob Res. 2008;10:287–99. doi:10.1080/14622200701825718.PubMedCrossRefGoogle Scholar
  4. 4.
    Schuz J, Spector LG, Ross JA. Bias in studies of parental self-reported occupational exposure and childhood cancer. Am J Epidemiol. 2003;158:710–6. doi:10.1093/aje/kwg192.PubMedCrossRefGoogle Scholar
  5. 5.
    Cardis E, Richardson L, Deltour I, Armstrong B, Feychting M, Johansen C, et al. The Interphone study: design, epidemiological methods, and description of the study population. Eur J Epidemiol. 2007;22:647–64. doi:10.1007/s10654-007-9152-z.PubMedCrossRefGoogle Scholar
  6. 6.
    Samkange-Zeeb F, Berg G, Blettner M. Validation of self-reported cellular phone use. J Expo Anal Environ Epidemiol. 2004;14:245–8. doi:10.1038/sj.jea.7500321.PubMedCrossRefGoogle Scholar
  7. 7.
    Parslow RC, Hepworth SJ, McKinney PA. Recall of past use of mobile phone handsets. Radiat Prot Dosimetry. 2003;106:233–40.PubMedGoogle Scholar
  8. 8.
    Vrijheid M, Deltour I, Krewski D, Sanchez M, Cardis E. The effects of recall errors and of selection bias in epidemiologic studies of mobile phone use and cancer risk. J Expo Sci Environ Epidemiol. 2006;16:371–84. doi:10.1038/sj.jes.7500509.PubMedCrossRefGoogle Scholar
  9. 9.
    Vrijheid M, Armstrong BK, Bedard D, Brown J, Deltour I, Iavarone I, et al. Recall bias in the assessment of exposure to mobile phones. J Expo Sci Environ Epidemiol; 2008.Google Scholar
  10. 10.
    Hepworth SJ, Bolton A, Parslow RC, van Tongeren M, Muir KR, McKinney PA. Assigning exposure to pesticides and solvents from self-reports collected by a computer assisted personal interview and expert assessment of job codes: the UK Adult Brain Tumour Study. Occup Environ Med. 2006;63:267–72. doi:10.1136/oem.2005.021022.PubMedCrossRefGoogle Scholar
  11. 11.
    Miller MH, Doyle TJ, Geier SR. Acoustic neurinoma in a population of noise exposed workers. Laryngoscope. 1981;91:363–71.PubMedGoogle Scholar
  12. 12.
    Preston-Martin S, Thomas DC, Wright WE, Henderson BE. Noise trauma in the aetiology of acoustic neuromas in men in Los Angeles County, 1978–1985. Br J Cancer. 1989;59:783–6.PubMedGoogle Scholar
  13. 13.
    Edwards CG, Schwartzbaum JA, Nise G, Forssen UM, Ahlbom A, Lonn S, et al. Occupational noise exposure and risk of acoustic neuroma. Am J Epidemiol. 2007;166:1252–8. doi:10.1093/aje/kwm217.PubMedCrossRefGoogle Scholar
  14. 14.
    Schlehofer B, Schlaefer K, Blettner M, Berg G, Bohler E, Hettinger I, et al. Environmental risk factors for sporadic acoustic neuroma (Interphone study Group, Germany). Eur J Cancer. 2007;43:1741–7. doi:10.1016/j.ejca.2007.05.008.PubMedCrossRefGoogle Scholar
  15. 15.
    Daniel E. Noise and hearing loss: a review. J Sch Health. 2007;77:225–31. doi:10.1111/j.1746-1561.2007.00197.x.PubMedCrossRefGoogle Scholar
  16. 16.
    Noise and Hearing Loss. In: NIH Consensus Statement 1990, 8. NIH Consensus Development Program—Consensus Development Conference Reports; 1990, 1–24.Google Scholar
  17. 17.
    European Council. Directive 2003/10/EC of the European parliament and of the council of 6 Feb 2003 on the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents; 2008.Google Scholar
  18. 18.
    Knipfer C, Funke HW. BIA-Report 2/97 Lärmbelastung an Baustellenarbeitsplätzen Part V. St. Augustin, Germany, Hauptverband der gewerblichen Berufsgenossenschaften (HVGB); 1997.Google Scholar
  19. 19.
    SUVA/Akustik Lärmtabellen 86201.D/F/I—86495.D/F/I. 2002-2004. 2004. Lucerne, Switzerland, Swiss Accident Insurance Fund.Google Scholar
  20. 20.
    Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007;96:644–7. doi:10.1111/j.1651-2227.2006.00178.x.PubMedCrossRefGoogle Scholar
  21. 21.
    Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J. 2008;50:419–30. doi:10.1002/bimj.200710415.PubMedCrossRefGoogle Scholar
  22. 22.
    Kirkwood B. Sterne JA. Wiley-Blackwell: Essential Medical Statistics; 2003.Google Scholar
  23. 23.
    Landis JR, Koch GG. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics. 1977;33:363–74. doi:10.2307/2529786.PubMedCrossRefGoogle Scholar
  24. 24.
    Tielemans E, Heederik D, Burdorf A, Vermeulen R, Veulemans H, Kromhout H, et al. Assessment of occupational exposures in a general population: comparison of different methods. Occup Environ Med. 1999;56(3):145–51. doi:10.1136/oem.56.3.145.PubMedCrossRefGoogle Scholar
  25. 25.
    Mannetje A, Kromhout H. The use of occupation and industry classifications in general population studies. Int J Epidemiol. 2003;32(3):419–28. doi:10.1093/ije/dyg080.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Klaus Schlaefer
    • 1
  • Brigitte Schlehofer
    • 1
  • Joachim Schüz
    • 2
  1. 1.Unit of Environmental EpidemiologyGerman Cancer Research CentreHeidelbergGermany
  2. 2.Institute of Cancer EpidemiologyDanish Cancer SocietyCopenhagenDenmark

Personalised recommendations