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Prevalence and Predictors of Respiratory Diseases Among Coal-Based Sponge Iron Plant Workers: A Cross-Sectional Study in Barjora, India
BACKGROUND: During the last decade, coal-based sponge iron plants, a highly polluted industry, have grown rapidly in Barjora, India. The toxic effects of particulate matters and gaseous pollutants are often rapid and include respiratory diseases (such as asthma and rhinoconjunctivitis). OBJECTIVES A...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Ubiquity Press
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997524/ https://www.ncbi.nlm.nih.gov/pubmed/30741515 http://dx.doi.org/10.5334/aogh.2424 |
Sumario: | BACKGROUND: During the last decade, coal-based sponge iron plants, a highly polluted industry, have grown rapidly in Barjora, India. The toxic effects of particulate matters and gaseous pollutants are often rapid and include respiratory diseases (such as asthma and rhinoconjunctivitis). OBJECTIVES AND METHODS: A cross-sectional study was conducted among 258 coal-based sponge iron plant workers in Barjora to assess the prevalence of respiratory diseases (self-reported) and to determine the associated factors. FINDINGS: The percentage of participants with any chronic respiratory disease, asthma and rhinoconjuctivitis were 25.5%, 8.9%, and 17.1%, respectively. The odds of any chronic respiratory disease were lower in participants with family history of any chronic respiratory disease (odds ratio [OR] 0.47, 95% confidence interval [CI] 0.24–0.91, P = 0.024). The odds of asthma were lower in participants living in a room with less than 3 people (OR 2.86, 95% CI 1.16–7.07, P = 0.023) and with family history (OR 0.20, 95% CI 0.08–0.53, P = 0.001). The odds of rhinoconjuctivitis were lower in illiterate participants (OR 0.34, 95% CI 0.12–0.94, P = 0.038) and those with pucca/semipucca house type (OR 2.44, 95% CI 1.11–5.39, P = 0.027). CONCLUSION: Many coal-based sponge iron plant workers in Barjora report the presence of respiratory diseases, and the predictors such as overcrowding and poor quality housing were identified. |
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