Introduction: Respiratory disorders pose a serious health risk for quarry workers exposed to dust, as they are a leading source of morbidity and mortality globally, often resulting in irreversible lung conditions. This study assessed the prevalence and determinants of restrictive disorder among quarry workers in Umuoghara quarry site, Ebonyi State.
Methods: This study was done on quarry workers at the Umuoghara quarry site, Ebonyi State. An analytical cross-sectional study design was adopted. Data was collected using a pre-tested semi-structured questionnaire among 300 quarry workers selected by simple random sampling method. Lung function test was performed using a spirometer- spirovit SPI schiller and data was analyzed with the use of IBM SPSS version 23.0. Pearson's chi-square test was used to find associations between variables. Binary logistic regression (multivariate analysis) was used to find the determinants of restrictive lung disorder at p < 0.05.
Results: The prevalence of restrictive disorder was 14.3%. Working for more than 5 years (AOR = 2.880 at 95% CI = 1.234-6.720), Working for more than 10 years (AOR = 9.645 at 95% CI = 2.601-35.766), smoking (AOR = 3.558 at 95% CI = 1.631-7.762) and non-use of protective measures (AOR = 0.114; 95% CI = 0.050-0.262) were the determinants of restrictive lung disorder in quarry workers.
Conclusion: There is an increased risk of developing respiratory problems among quarry workers exposed to quarry dust. It is recommended that employees receive thorough education on the dangers of this exposure, and that employers be mandated to provide protective equipment and strictly enforce its use among workers.
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http://dx.doi.org/10.1186/s12890-025-03497-0 | DOI Listing |
BMC Pulm Med
January 2025
Institute of Public Health, University of Nigeria, Enugu, Nigeria.
Introduction: Respiratory disorders pose a serious health risk for quarry workers exposed to dust, as they are a leading source of morbidity and mortality globally, often resulting in irreversible lung conditions. This study assessed the prevalence and determinants of restrictive disorder among quarry workers in Umuoghara quarry site, Ebonyi State.
Methods: This study was done on quarry workers at the Umuoghara quarry site, Ebonyi State.
Am J Ind Med
January 2025
Occupational Cancer Research Centre, Ontario Health, Toronto, Ontario, Canada.
Introduction: Raynaud's phenomenon (RP) is linked to occupational exposures such as vibration, cold temperature, and chemicals. However, large cohort studies examining RP by occupation and sex are scarce. To address this gap, this study aimed to assess risk of RP by both occupation and sex in a large cohort of workers in Ontario, Canada.
View Article and Find Full Text PDFInt Arch Occup Environ Health
January 2025
Coordination for the Innovation and Application of Science and Technology (CIACYT), Autonomous University of San Luis Potosi, Sierra Leona Avenue No. 550, Lomas Second Section, San Luis Potosi, C.P. 78210, SLP, Mexico.
Purpose: Individuals in occupational environments are particularly susceptible to the impacts of pollutants; making it crucial to assess systemic inflammation markers. The study aimed to evaluate the immune response to inflammation through the assessment of a cytokine profile in individuals working in vulnerable conditions exposed to heavy metals.
Methods: A total of 137 adults participated in this study from three work environments: brickyards, waste scavenging and quarries.
West Afr J Med
November 2024
Family Medicine Department, Federal Medical Centre, Gusau, Zamfara State.
Background: Workplace hazards are produced in the process of quarrying stones and include heavy metals like Lead, Cadmium, Nickel, etc. These hazards are harmful to the workers whenever they accumulate above the maximum permissible level in the quarries.
Objectives: This study assessed the Lead level in dust samples and housekeeping practices in stone quarries in Gusau.
Sci Rep
October 2024
Imagen Technologies, 224 W 35th St Ste 500, New York, NY, 10001, USA.
Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted by physicians. Here, we present an FDA-cleared, artificial intelligence (AI) system which uses a deep learning algorithm to assist physicians in the comprehensive detection and localization of abnormalities on chest X-rays. We trained and tested the AI system on a large dataset, assessed generalizability on publicly available data, and evaluated radiologist and non-radiologist physician accuracy when unaided and aided by the AI system.
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