An educational intervention to improve knowledge about prevention against occupational asthma and allergies using targeted maximum likelihood estimation

Abstract

Purpose

Occupational asthma and allergies are potentially preventable diseases affecting 5–15% of the working population. However, the use of preventive measures is often insufficient. The aim of this study was to estimate the average treatment effect of an educational intervention designed to improve the knowledge of preventive measures against asthma and allergies in farm apprentices from Bavaria (Southern Germany).

Methods

Farm apprentices at Bavarian farm schools were asked to complete a questionnaire evaluating their knowledge about preventive measures against occupational asthma and allergies (use of personal protective equipment, personal and workplace hygiene measures). Eligible apprentices were randomized by school site to either a control or an intervention group. The intervention consisted of a short educational video about use of preventive measures. Six months after the intervention, subjects were asked to complete a post-intervention questionnaire. Of the 116 apprentices (70 intervention group, 46 control group) who answered the baseline questionnaire, only 47 subjects (41%; 17 intervention group, 30 control group) also completed the follow-up questionnaire. We, therefore, estimated the causal effect of the intervention using targeted maximum likelihood estimation. Models were controlled for potential confounders.

Results

Based on the targeted maximum likelihood estimation, the intervention would have increased the proportion of correct answers on all six preventive measures by 18.4% (95% confidence interval 7.3–29.6%) had all participants received the intervention vs. had they all been in the control group.

Conclusions

These findings indicate the improvement of knowledge by the educational intervention.

This is a preview of subscription content, log in to check access.

Fig. 1

References

  1. Ahern J, Karasek D, Luedtke AR et al (2016) Racial/ethnic differences in the role of childhood adversities for mental disorders among a nationally representative sample of adolescents. Epidemiology 27:697–704. https://doi.org/10.1097/EDE.0000000000000507

    Article  Google Scholar 

  2. Ameille J, Hamelin K, Andujar P et al (2013) Occupational asthma and occupational rhinitis: the United Airways disease model revisited. Occup Environ Med 70:471–475. https://doi.org/10.1136/oemed-2012-101048

    Article  Google Scholar 

  3. Angrist JD (2003) Treatment effect heterogeneity in theory and practice. Institute for the Study of Labor (IZA), Bonn

    Google Scholar 

  4. Ayres JG, Boyd R, Cowie H, Hurley JF (2011) Costs of occupational asthma in the UK. Thorax 66:128–133. https://doi.org/10.1136/thx.2010.136762

    Article  Google Scholar 

  5. Baur X, Sigsgaard T, Aasen TB et al (2012) Guidelines for the management of work-related asthma. Eur Respir J 39:529–545. https://doi.org/10.1183/09031936.00096111

    Article  CAS  Google Scholar 

  6. Bayerisches Landesamt für Statistik und Datenverarbeitung (2014) Berufliche Schulen in Bayern. In: Schuljahr 2013/14. Bayerisches Landesamt für Statistik und Datenverarbeitung, München

    Google Scholar 

  7. Bettinghaus EP (1986) Health promotion and the knowledge-attitude-behavior continuum. Prev Med 15:475–491. https://doi.org/10.1016/0091-7435(86)90025-3

    Article  CAS  Google Scholar 

  8. Bonow CA, Cezar-Vaz MR, Almeida MCV de et al (2013) Risk perception and risk communication for training women apprentice welders: A challenge for public health nursing. Nurs Res Pract 2013:386260. https://doi.org/10.1155/2013/386260

    Article  Google Scholar 

  9. Breen R, Choi S, Holm A (2015) Heterogeneous causal effects and sample selection bias. Sociol Sci 2:351–369. https://doi.org/10.15195/v2.a17

    Article  Google Scholar 

  10. Burney PG, Luczynska C, Chinn S, Jarvis D (1994) The European Community Respiratory Health Survey. Eur Respir J 7:954–960

    Article  CAS  Google Scholar 

  11. Cezar-Vaz MR, Bonow CA, Vaz JC (2015) Risk communication concerning welding fumes for the primary preventive care of welding apprentices in southern Brazil. Int J Environ Res Public Health 12:986–1002. https://doi.org/10.3390/ijerph120100986

    Article  CAS  Google Scholar 

  12. Colson KE, Rudolph KE, Zimmerman SC et al (2016) Optimizing matching and analysis combinations for estimating causal effects. Sci Rep 6:23222. https://doi.org/10.1038/srep23222

    Article  CAS  Google Scholar 

  13. Crippa M, Torri D, Fogliata L et al (2007) Implementation of a health education programme in a sample of hairdressing trainees. Med Lav 98:48–54

    Google Scholar 

  14. Díaz I, Colantuoni E, Rosenblum M (2016) Enhanced precision in the analysis of randomized trials with ordinal outcomes. Biometrics 72:422–431. https://doi.org/10.1111/biom.12450

    Article  Google Scholar 

  15. Fewell Z, Davey Smith G, Sterne JAC (2007) The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol 166:646–655. https://doi.org/10.1093/aje/kwm165

    Article  Google Scholar 

  16. Fischer K, White IR (2012) Causal inference in clinical trials. In: Berzuini C, Dawid P, Bernardinelli L (eds) Causality: statistical perspectives and applications. Wiley, Ltd, pp 310–326

    Google Scholar 

  17. Grimmer J, Messing S, Westwood SJ (2017) Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods. Polit Anal 25:413–434. https://doi.org/10.1017/pan.2017.15

    Article  Google Scholar 

  18. Gruber S, Laan MJ van der (2010) A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome. Int J Biostat. https://doi.org/10.2202/1557-4679.1260 (Article 26)

    Article  Google Scholar 

  19. Gruber S, Laan MJ van der (2012) Tmle: an R package for targeted maximum likelihood estimation. J Stat Softw 51:1–35

    Article  Google Scholar 

  20. Hainich R (2010) Fehlerkultur in der Ausbildung auf der Intensivstation. Intensiv 18:62–69. https://doi.org/10.1055/s-0030-1251485

    Article  Google Scholar 

  21. Imai K, Ratkovic M (2013) Estimating treatment effect heterogeneity in randomized program evaluation. Ann Appl Stat 7:443–470. https://doi.org/10.1214/12-AOAS593

    Article  Google Scholar 

  22. Kim J, Arrandale VH, Kudla I et al (2012) Educational intervention among farmers in a community health care setting. Occup Med (Lond) 62:458–461. https://doi.org/10.1093/occmed/kqs129

    Article  CAS  Google Scholar 

  23. Kütting B, Weistenhöfer W, Baumeister T et al (2009) Current acceptance and implementation of preventive strategies for occupational hand eczema in 1355 metalworkers in Germany. Br J Dermatol 161:390–396. https://doi.org/10.1111/j.1365-2133.2009.09085.x

    Article  Google Scholar 

  24. Laan MJ van der, Rose S (2011) Targeted learning: causal inference for observational and experimental data. Springer Science & Business Media, Berlin

    Google Scholar 

  25. Laan MJ van der, Gruber S (2012) Targeted minimum loss based estimation of causal effects of multiple time point interventions. Int J Biostat 8(1)9. https://doi.org/10.1515/1557-4679.1370

    Article  Google Scholar 

  26. Laan MJ van der, Polley EC, Hubbard AE (2007) Super learner. Stat Appl Genet Mol Biol. https://doi.org/10.2202/1544-6115.1309 (Article25)

    Article  Google Scholar 

  27. Lee BK, Lessler J, Stuart EA (2010) Improving propensity score weighting using machine learning. Stat Med 29:337–346. https://doi.org/10.1002/sim.3782

    Article  Google Scholar 

  28. Lendle S (2015a) Tmlecte: estimates the NDE and ATT with TMLE. R package available at https://github.com/lendle/tmlecte

  29. Lendle SD (2015b) Targeted minimum loss based estimation: applications and extensions in causal inference and big data. PhD thesis, UC Berkeley

  30. Lendle SD, Fireman B, Laan MJ van der (2013a) Targeted maximum likelihood estimation in safety analysis. J Clin Epidemiol 66:S91–S98. https://doi.org/10.1016/j.jclinepi.2013.02.017

    Article  Google Scholar 

  31. Lendle SD, Subbaraman MS, Laan MJ van der (2013b) Identification and efficient estimation of the natural direct effect among the untreated. Biometrics 69:310–317. https://doi.org/10.1111/biom.12022

    Article  Google Scholar 

  32. Levesque DL, Arif AA, Shen J (2012) Effectiveness of pesticide safety training and knowledge about pesticide exposure among Hispanic farmworkers. J Occup Environ Med 54:1550–1556. https://doi.org/10.1097/JOM.0b013e3182677d96

    Article  Google Scholar 

  33. Ling TC, Coulson IH (2002) What do trainee hairdressers know about hand dermatitis? Contact Derm 47:227–231

    Article  CAS  Google Scholar 

  34. Luque-Fernandez MA, Schomaker M, Rachet B, Schnitzer ME (2018) Targeted maximum likelihood estimation for a binary treatment: A tutorial. Stat Med 37:2530–2546. https://doi.org/10.1002/sim.7628

    Article  Google Scholar 

  35. Mahmud N, Schonstein E, Schaafsma F et al (2010) Pre-employment examinations for preventing occupational injury and disease in workers. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD008881

    Article  Google Scholar 

  36. Moscato G, Pala G, Boillat MA et al (2011) EAACI position paper: Prevention of work-related respiratory allergies among pre-apprentices or apprentices and young workers. Allergy 66:1164–1173. https://doi.org/10.1111/j.1398-9995.2011.02615.x

    Article  CAS  Google Scholar 

  37. Muth T, Bahemann A, Voß HJ, Borsch-Galetke E (2005) Gesundheitlich begründete Ausbildungsabbrüche. Arbeitsmedizin, Sozialmedizin, Umweltmedizin 40:182

    Google Scholar 

  38. Nixon R, Roberts H, Frowen K, Sim M (2006) Knowledge of skin hazards and the use of gloves by Australian hairdressing students and practising hairdressers. Contact Derm 54:112–116. https://doi.org/10.1111/j.0105-1873.2006.00790.x

    Article  Google Scholar 

  39. Patuzzi M (2012) Ausbildungsreport 2012 Bayern. DGB-Jugend Bayern, Munich

    Google Scholar 

  40. Peden D, Reed CE (2010) Environmental and occupational allergies. J Allergy Clin Immunol 125:S150–S160. https://doi.org/10.1016/j.jaci.2009.10.073

    Article  Google Scholar 

  41. Peres F, Rodrigues KM, Silva Peixoto Belo MS da et al (2013) Design of risk communication strategies based on risk perception among farmers exposed to pesticides in Rio de Janeiro State, Brazil. Am J Ind Med 56:77–89. https://doi.org/10.1002/ajim.22147

    Article  Google Scholar 

  42. Petersen ML, Porter KE, Gruber S et al (2012) Diagnosing and responding to violations in the positivity assumption. Stat Methods Med Res 21:31–54. https://doi.org/10.1177/0962280210386207

    Article  Google Scholar 

  43. Pirracchio R, Petersen ML, Laan M van der (2015) Improving propensity score estimators’ robustness to model misspecification using super learner. Am J Epidemiol 181:108–119. https://doi.org/10.1093/aje/kwu253

    Article  Google Scholar 

  44. Polley E, LeDell E, Laan M van der (2016) SuperLearner: super learner prediction. R package documentation available at https://cran.r-project.org/web/packages/SuperLearner/index.html

  45. Pounds L, Duysen E, Romberger D et al (2014) Social marketing campaign promoting the use of respiratory protection devices among farmers. J Agromed 19:316–324. https://doi.org/10.1080/1059924X.2014.917350

    Article  Google Scholar 

  46. R Core Team (2016) R: a language and environment for statistical computing. Available at: https://www.rproject.org/

  47. Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55. https://doi.org/10.1093/biomet/70.1.41

    Article  Google Scholar 

  48. Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66:688–701. https://doi.org/10.1037/h0037350

    Article  Google Scholar 

  49. Rubin DB (1978) Bayesian inference for causal effects: the role of randomization. Ann Stat 6:34–58

    Article  Google Scholar 

  50. Samii C, Paler L, Daly SZ (2016) Retrospective causal inference with machine learning ensembles: an application to anti-recidivism policies in Colombia. Polit Anal 24:434–456. https://doi.org/10.1093/pan/mpw019

    Article  Google Scholar 

  51. Schnitzer ME, Laan MJ van der, Moodie EEM, Platt RW (2014) Effect of breastfeeding on gastrointestinal infection in infants: a targeted maximum likelihood approach for clustered longitudinal data. Ann Appl Stat 8:703–725

    Article  Google Scholar 

  52. Seifried J, Baumgartner A (2009) Lernen aus Fehlern in der betrieblichen Ausbildung—Problemfeld und möglicher Forschungszugang. In: bwp@ Berufs- und Wirtschaftspädagogik online. 17

  53. Tarlo SM, Lemiere C (2014) Occupational asthma. N Engl J Med 370:640–649. https://doi.org/10.1056/NEJMra1301758

    Article  CAS  Google Scholar 

  54. Tarlo SM, Liss GM (2005) Prevention of occupational asthma–practical implications for occupational physicians. Occup Med (Lond) 55:588–594. https://doi.org/10.1093/occmed/kqi182

    Article  Google Scholar 

  55. Vandenplas O, Dressel H, Wilken D et al (2011) Management of occupational asthma: cessation or reduction of exposure? A systematic review of available evidence. Eur Respir J 38:804–811. https://doi.org/10.1183/09031936.00177510

    Article  CAS  Google Scholar 

  56. Vanderweele TJ, Arah OA (2011) Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology 22:42–52. https://doi.org/10.1097/EDE.0b013e3181f74493

    Article  Google Scholar 

  57. Xie Y, Brand JE, Jann B (2012) Estimating heterogeneous treatment effects with observational data. Sociol Methodol 42:314–347

    Article  Google Scholar 

Download references

Acknowledgements

Special thanks to Jenny Schlichtiger for her valuable collaboration and hard work in the data collection process, and to Iven-Alex Heim for acting in the educational videos. The authors would also like to thank all farm apprentices who kindly participated in our study. Furthermore, we would like to thank Thomas Brendel and Thomas Bischoff from the Instituf für Didaktik und Ausbildungsforschung in der Medizin (LMU) for their help in creating the educational video.

Funding

This study was funded by Gesund.Leben.Bayern. http://www.gesundheit.bayern.de.

Author information

Affiliations

Authors

Contributions

Study conception and design: KR, CR, SB and VK; fieldwork and data collection: SB; data analysis: DRM and RH; interpretation of the data: DRM, KR and RH; drafting of the manuscript: DRM and KR. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Daloha Rodríguez-Molina.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This study was approved by the Ethics Committee of the University of Munich (LMU) (Project no. 6–14). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 260 KB)

Supplementary material 2 (PDF 308 KB)

Online Resource 3 Video 1, shown as part of the intervention (MP4 14105 KB)

Online Resource 4 Video 2, shown as part of the intervention (MP4 18398 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rodríguez-Molina, D., Barth, S., Herrera, R. et al. An educational intervention to improve knowledge about prevention against occupational asthma and allergies using targeted maximum likelihood estimation. Int Arch Occup Environ Health 92, 629–638 (2019). https://doi.org/10.1007/s00420-018-1397-1

Download citation

Keywords

  • Occupational asthma and allergies
  • Educational intervention
  • Targeted maximum likelihood estimation
  • Preventive measures
  • Causal effect