ISSN: 2161-0711

Medicina comunitaria y educación para la salud

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Abstracto

Assessment of Self-Awareness and Subjective Prevalence of Noise-Induced Hearing Loss among Traffic Policemen in a Belgaum, India

Reshma Susan Mathew

Context: Noise-induced hearing loss is a major preventable occupational hazard with variousill-effects. We studied NIHL in the vulnerable traffic police-personnel, on Indian population in a growing city

Aim: To find subjective prevalence of noise-induced hearing loss among traffic police-personnel using a structured questionnaire. To identify awareness of the ill-effects of noise pollution on hearing.

Settings and design aone-yearcommunity-based cross-sectional study, conducted in traffic police-personnel in Belgaum, from January-December 2019.

Materials and Methods: 80 male traffic police-personnel aged 18 yrs to 50 yrs included. The questionnaire had 17 questions regarding the self-assessment of traffic policemen about their hearing, exposure to loud sound, use ofpersonal protective devices and the Smith hearing severity questionnaire.

Statistical analysis asthe data analysis was carried out using SPSS and included calculation of percentagesandproportions, application of the test of significance and chi-square test.

Results: Prevalence of NIHL was 56.25% based on the questionnaire and were categorized as mild, moderate and severe hearing loss in 35.56%, 53.33% and 11.11% of subjects respectively. Most participants rated no knowledge and use of any ear protection devices. Hearing loss was regarded as an important issue by majority. Among the participants, 20% already suffered from tinnitus. Only 11.25% used earplugs to protect their hearing.

Conclusion: The negative consequences of noise exposure are shown to be present among the subjects bythis study.

Hence, there is a need to educate them about hazardous effect of noise exposure. Also, further studies for those the early detection and practicing preventive measures are pivotal.

Descargo de responsabilidad: este resumen se tradujo utilizando herramientas de inteligencia artificial y aún no ha sido revisado ni verificado.