An educational intervention to improve knowledge about prevention against occupational asthma and allergies using targeted maximum likelihood estimation
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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).
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.
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.
These findings indicate the improvement of knowledge by the educational intervention.
KeywordsOccupational asthma and allergies Educational intervention Targeted maximum likelihood estimation Preventive measures Causal effect
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.
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.
This study was funded by Gesund.Leben.Bayern. http://www.gesundheit.bayern.de.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
- Bayerisches Landesamt für Statistik und Datenverarbeitung (2014) Berufliche Schulen in Bayern. In: Schuljahr 2013/14. Bayerisches Landesamt für Statistik und Datenverarbeitung, MünchenGoogle Scholar
- Crippa M, Torri D, Fogliata L et al (2007) Implementation of a health education programme in a sample of hairdressing trainees. Med Lav 98:48–54Google Scholar
- Lendle S (2015a) Tmlecte: estimates the NDE and ATT with TMLE. R package available at https://github.com/lendle/tmlecte
- Lendle SD (2015b) Targeted minimum loss based estimation: applications and extensions in causal inference and big data. PhD thesis, UC BerkeleyGoogle Scholar
- Muth T, Bahemann A, Voß HJ, Borsch-Galetke E (2005) Gesundheitlich begründete Ausbildungsabbrüche. Arbeitsmedizin, Sozialmedizin, Umweltmedizin 40:182Google Scholar
- Patuzzi M (2012) Ausbildungsreport 2012 Bayern. DGB-Jugend Bayern, MunichGoogle Scholar
- 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
- R Core Team (2016) R: a language and environment for statistical computing. Available at: https://www.rproject.org/
- Seifried J, Baumgartner A (2009) Lernen aus Fehlern in der betrieblichen Ausbildung—Problemfeld und möglicher Forschungszugang. In: bwp@ Berufs- und Wirtschaftspädagogik online. 17Google Scholar