Prevalence and predictors of high-frequency hearing loss among mine workers in Gujarat, western India: a cross-sectional study on the need to implement a comprehensive hearing conservation program.

BACKGROUND: High-frequency hearing loss (HFHL) stands as a prevalent occupational morbidity globally, with numerous associated risk factors, some of which are modifiable. In the context of a comprehensive hearing conservation program, the initial steps involve early screening and identification of workers with these modifiable risk factors, aiming to reduce the prevalence of hearing loss. Our objective was to estimate the prevalence of HFHL and determine its predictors among mine workers. METHODS: We conducted a cross-sectional study among 226 mine workers in ten open-cast mines in Gujarat state, the western part of India, in November 2020. We collected data on socio-demography, addiction, occupation history and comorbidities, along with anthropometric, blood pressure, and blood sugar measurements. Audiometric evaluations using a portable diagnostic audiometer were employed to assess HFHL, defined as a hearing threshold exceeding 25 decibels (dB) at high frequencies (3000, 4000, 6000, and 8000 Hz). A generalized linear model (GLM) with a binomial family was performed to determine the predictors significantly predicting HFHL after adjusting for confounding variables. RESULTS: The prevalence of HFHL was 35% (95% CI: 29-42%) in our study setting. Office workers demonstrated a prevalence of 19%, whereas other job categories displayed a higher prevalence of 42%, resulting in a significant prevalence difference of 23% and a prevalence ratio of 2.2. The GLM analysis revealed that variables, such as noise exposure during work [adjusted prevalence ratio (aPR) 2.3 (95% CI: 1.2-4.7, p = 0.018)] and noise exposure duration [aPR 1.1 (95% CI: 1.0-1.1, p = 0.042)], were significant predictors of HFHL. CONCLUSIONS: In our study setting, mine workers exhibited a high prevalence of HFHL, with exposure to workplace noise and duration being modifiable predictors. Because HFHL advances slowly and is generally undetected by the individual, we recommend periodic testing using audiometry to identify it among mine workers and, if possible, shifting them from mining activities to office. Furthermore, we advocate for the implementation of a comprehensive hearing conservation program to the extent possible.

Determinants of discomfort from combined exposure to noise and vibration in dumper operators of mining industry in India.

In the mining industry, dumper operators are exposed to combined noise and vibration, leading to discomfort. Dumpers are heavy earth-moving machines that are used for carrying bulky material in mining industries. Dumper operators are exposed to physical hazards such as vibration, noise, heat, and humidity, throughout their lifetime of work. Fifty-four dumper operators working in mines were selected for this study. Noise was measured as per the guidelines of the Directorate General Mining Safety, India, and whole-body vibration was measured as per the guidelines of ISO 2631-1::1997. Noise Pro DLX, Type 2 noise dosimeters were used for the measurement of personal noise exposure while SV-106 six channels vibration meters were used for whole body vibration (WBV) exposure measurement. Discomfort was calculated using the regression equation developed by Huang and Griffin (2014). The total discomfort level of mine operators was about 192. A predictive equation was derived by using a regression model to determine the contribution of individual variables causing discomfort. It was observed that for every unit increase in noise (LAeq), discomfort increased by 10.20 units, a one-unit increase in vibration (A (8)) led to a 51.7-unit increase in discomfort, while an increase of one unit of exposure time increased the discomfort level by 5.24 units.

Artificial intelligence in advancing occupational health and safety: an encapsulation of developments.

OBJECTIVES: In an era characterized by dynamic technological advancements, the well-being of the workforce remains a cornerstone of progress and sustainability. The evolving industrial landscape in the modern world has had a considerable influence on occupational health and safety (OHS). Ensuring the well-being of workers and creating safe working environments are not only ethical imperatives but also integral to maintaining operational efficiency and productivity. We aim to review the advancements that have taken place with a potential to reshape workplace safety with integration of artificial intelligence (AI)-driven new technologies to prevent occupational diseases and promote safety solutions. METHODS: The published literature was identified using scientific databases of Embase, PubMed, and Google scholar including a lower time bound of 1974 to capture chronological advances in occupational disease detection and technological solutions employed in industrial set-ups. RESULTS: AI-driven technologies are revolutionizing how organizations approach health and safety, offering predictive insights, real-time monitoring, and risk mitigation strategies that not only minimize accidents and hazards but also pave the way for a more proactive and responsive approach to safeguarding the workforce. CONCLUSION: As industries embrace the transformative potential of AI, a new frontier of possibilities emerges for enhancing workplace safety. This synergy between OHS and AI marks a pivotal moment in the quest for safer, healthier, and more sustainable workplaces.

Predictors of pulmonary dysfunction vis-à-vis the hypertension status of mine workers in Gujarat, western India: A cross-sectional study on the need to further investigate the role of anti-hypertensive drugs.

BACKGROUND: Mine workers face various health risks from occupational hazards, notably dust-related pulmonary dysfunction. This dysfunction is also attributed to diverse risk factors and health conditions. Despite the variety of underlying mechanisms, conflicting evidence persists regarding hypertension as a potential risk factor for such dysfunction. OBJECTIVE: To determine the predictors of pulmonary dysfunction vis-à-vis the hypertension status of mine workers. METHODS: We conducted a cross-sectional study among 444 mine workers from ten open-cast mines in Gujarat state (western part of India) from November 2020 to February 2022. We collected data on demographics, occupation, addiction, and comorbidities, including measurements like anthropometry, blood pressure, blood sugar, haemoglobin, and lipid levels. Hypertension was confirmed based on self-reported history and/or onsite blood pressure measurement, while pulmonary functions were assessed using a spirometer (expressed as forced expiratory volume in the first second FEV1 and forced vital capacity FVC). Multiple linear regression analysis was performed to determine the significant predictor of FEV1 or FVC vis-à-vis the hypertension status after adjusting for confounding variables. In addition, we assessed the effect of anti-hypertensive medications on pulmonary dysfunction. RESULTS: A total of 41% (95% CI: 36-45%) of mine workers were suffering from hypertension. On multiple linear regression, only being a male and work experience duration were the significant predictors of FEV1 [0.900 (0.475-1.092), p=<0.001; -0.029 (-0.034 - -0.021, p=<0.001] and FVC [1.088 (0.771-1.404), p=<0.001; -0.031 (-0.038 - -0.024, p = 0.001] respectively. While unadjusted analysis indicated hypertension led to FEV1 and FVC reduction, this effect lost significance after adjusting for confounders. Nevertheless, subgroup analysis revealed those on antihypertensive medications had reductions in FEV1 and FVC by -0.263 (95% CI: -0.449 - -0.078, p = 0.006) L and -0.271 (95% CI: -0.476 - -0.067, p = 0.009) L respectively. CONCLUSION: In our study among mine workers, alterations in lung function (FEV1 and FVC) on spirometry were predicted by gender and duration of work experience, while hypertension did not serve as a predictor. It is noteworthy that antihypertensive drugs were found to reduce lung functions on spirometry, highlighting the need for further research.

A mixed-methods study on impact of active case finding on pulmonary tuberculosis treatment outcomes in India.

BACKGROUND: Tuberculosis (TB) remains a significant public health burden in India, with elimination targets set for 2025. Active case finding (ACF) is crucial for improving TB case detection rates, although conclusive evidence of its association with treatment outcomes is lacking. Our study aims to investigate the impact of ACF on successful TB treatment outcomes among pulmonary TB patients in Gujarat, India, and explore why ACF positively impacts these outcomes. METHODS: We conducted a retrospective cohort analysis in Gujarat, India, including 1,638 pulmonary TB cases identified through ACF and 80,957 cases through passive case finding (PCF) from January 2019 to December 2020. Generalized logistic mixed-model compared treatment outcomes between the ACF and PCF groups. Additionally, in-depth interviews were conducted with 11 TB program functionaries to explore their perceptions of ACF and its impact on TB treatment outcomes. RESULTS: Our analysis revealed that patients diagnosed through ACF exhibited 1.4 times higher odds of successful treatment outcomes compared to those identified through PCF. Program functionaries emphasized that ACF enhances case detection rates and enables early detection and prompt treatment initiation. This early intervention facilitates faster sputum conversion and helps reduce the infectious period, thereby improving treatment outcomes. Functionaries highlighted that ACF identifies TB cases that might otherwise be missed, ensuring timely and appropriate treatment. CONCLUSION: ACF significantly improves TB treatment outcomes in Gujarat, India. The mixed-methods analysis demonstrates a positive association between ACF and successful TB treatment, with early detection and prompt treatment initiation being key factors. Insights from TB program functionaries underscore the importance of ACF in ensuring timely diagnosis and treatment, which are critical for better treatment outcomes. Expanding ACF initiatives, especially among hard-to-reach populations, can further enhance TB control efforts. Future research should focus on optimizing ACF strategies and integrating additional interventions to sustain and improve TB treatment outcomes.

Physicochemical, thermal, pasting, morphological, functional and bioactive binding characteristics of starches of different oat varieties of North-Western Himalayas.

Starches were isolated from five oat varieties (SFO-1, SFO-3, Sabzar, SKO-20 and SKO-96) grown in North-Western Himalayas of India. Moisture content of the varieties ranged from 9.25 ± 0.09 to 13.21 ± 0.11 %, indicating their shelf-stability. Results suggested >90 % purity of starches as was evident from values of ash, proteins, and lipids. Amylose content results showed that all starches fall within category of intermediate-amylose starches. Lambdamax, blue value and OD620/550 were found significantly (p ≤ 0.05) higher in SKO-20. Sabzar exhibited higher starch hydrolysis percentage of 85.16 % whereas, lowest was observed in SKO-20 (78.12 %). Degree of syneresis was higher in SKO-20 however, its freeze-thaw stability was lesser. Wide peak in FTIR spectra at 3320 cm-1 confirms nature of starches. SKO-20 exhibited significantly higher onset gelatinization temperature (65.19 ± 1.06 °C) and enthalpy (15.78 ± 0.15 J/g) whereas, Sabzar exhibited lowest enthalpy. Pasting characteristics indicated lowest and highest final viscosity in SKO-20 (341.30 ± 2.11 mPas) and SKO-96 (1470 ± 4.56 mPas), respectively. SEM results indicated irregular and polygonal shape of starches with size <10 µm. SKO-20 exhibited lowest disintegration time of 2.08 ± 0.01 min and Sabzar showed highest (3.31 ± 0.07 min). SKO-20 released more curcumin (71.28 %) whereas, Sabzar released less. This suggests that SKO-20 could be used as better excipient for delivery of curcumin at target site.