As the world celebrates the centenary of quantum science in 2025, quantum technologies (QTs) are emerging as a transformative force across multiple sectors, including occupational safety and health (OSH). Far from being a source of risk, QTs have the potential to significantly enhance OSH outcomes through ultra-sensitive quantum sensors for environmental and health monitoring, secure quantum communication for protected data transmission, quantum simulations for ergonomic workplace design, and advanced predictive analytics for risk prevention. This paper explores how these innovations can revolutionize workplace safety and health infrastructure, especially in high-risk environments like mining, manufacturing, chemical plants, health monitoring, and military settings. Through strategic policy integration, safety-by-design principles, and interdisciplinary collaboration, the quantum revolution can be harnessed not only to redefine technological capabilities, but also to create safer, smarter, and more resilient occupational ecosystems. By proposing quantum-specific safety classifications, interdisciplinary oversight, and international collaboration, this paper calls for embedding safety-by-design principles into the core of quantum Research and Development (R and D). As quantum technologies become foundational to future industries, worker safety must advance in tandem framing quantum not merely as a technological revolution, but as a unique opportunity to reimagine the relationship between innovation and human well-being.

BackgroundObesity and job stress are established contributors to oxidative stress, which is associated with various adverse health outcomes. However, the moderating role of job stress in the relationship between obesity and oxidative stress remains inadequately understood.ObjectivesThis study explored whether job stress, specifically the extrinsic and intrinsic components of the Effort Reward Imbalance (ERI) model, moderates the relationship between obesity and oxidative stress among female nurses.MethodsWe conducted an exploratory analysis from a cross-sectional study involving female nurses at a tertiary hospital in Western India. The study assessed the prevalence of obesity and overweight based on the suggested criteria for the Asian Indian population. We examined correlations among obesity markers and oxidative stress markers and explored if there is a moderator role of job stress on the relationship between obesity and oxidative stress.ResultsThe combined prevalence of overweight and obesity in the study population was 74%. Significant positive correlations were found between age, obesity-related parameters (Body-mass Index, Percentage Body Fat, Waist-Hip Ratio), and oxidative stress markers (Protein carbonyl content and Glutathione S-Transferase). The relationship between obesity and oxidative stress markers was moderated by overcommitment. Nurses with marked overcommitment exhibited stronger associations between age, obesity, and oxidative stress markers compared to those without marked overcommitment.ConclusionsOur findings suggest that overcommitment moderates the relationship between obesity and oxidative stress, highlighting the need to address intrinsic as well as extrinsic work-related factors in interventions targeting obesity among nurses. Future studies should confirm these findings and explore mechanisms to develop targeted interventions.

Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care and reduce the burden on healthcare facilities. This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. Accuracy of models were evaluated using Mean Absolute Percentage Error, and Diebold-Mariano test for assessing differences in predictive performance. Further, we forecast the bed occupancy, providing crucial insights for healthcare administrators in capacity planning and resource allocation, supporting data-driven decisions and enhancing the quality of mental health services in India.

Mental health disorders affect over 15% of the global working-age population, contributing to an annual economic loss of approximately USD 1 trillion due to diminished productivity and increased healthcare expenditures. In India, the post-pandemic surge in hospitalizations has placed additional strain on mental health infrastructure, exacerbating an already significant treatment gap. Overcrowding and inadequate forecasting mechanisms have resulted in occupancy rates that exceed hospital capacity, underscoring the urgent need for predictive tools to support admission planning and resource allocation. This study introduces a novel forecasting framework that applies Bayesian Model Averaging (BMA) with Zellner’s g-prior used here for the first time alongside deep learning models for predicting weekly bed occupancy at India’s second-largest mental health hospital. Time series data from 2008 to 2024 were used to train six models: Time Delay Neural Networks (TDNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU). Model performance was optimized using random search (RS) and grid search (GS) hyperparameter tuning, allowing the framework to account for model uncertainty while improving predictive accuracy and consistency. Among all models, BiLSTM with GS tuning and BMA-GS model showed the best forecasting performance for bed-occupancy, achieving 98.06% accuracy (MAPE: 1.939%) and effectively capturing weekly fluctuations within ±13 beds. In contrast, RS-tuned models yielded higher errors (MAPE: 2.331%). Moreover, the average credible interval width decreased from 16.34 under BMA-RS to 13.28 with BMA-GS, indicating improved forecast precision and reliability. This study demonstrates that embedding Bayesian statistics specifically BMA with Zellner’s g-prior into deep learning architectures offers a robust and scalable solution for forecasting hospital bed occupancy. The proposed framework enhances predictive accuracy and reliability, supporting data-driven planning for hospital administrators and policymakers. It aligns with the objectives of India’s National Mental Health Programme (NMHP) and Sustainable Development Goal 3, advancing equitable and efficient access to mental healthcare.

Objectives: Work-related musculoskeletal disorders (WMSDs) are among the most common occupational diseases, affecting various sectors such as agriculture, small-scale industries, handicrafts, construction, and banking. These disorders, caused by overexertion and repetitive motion, lead to work absenteeism, productivity loss, and economic impacts. The aim of the study was to determine the magnitude of musculoskeletal disorders among different occupational workers in India.Methods: We identified studies reporting the prevalence of WMSDs using the Nordic Musculoskeletal Questionnaire in different databases between 2005 and 2023 through searches on SCOPUS, PubMed Central, and Google Scholar. The required information was then extracted. A random effects model was used to pool estimates of prevalence with 95% CIs. Publication bias was assessed by applying funnel plots.Results: The 12-month prevalence of WMSDs was reported across several occupational groups, and the meta or the pooled prevalence was estimated as 0.76 (95% CI, 0.70 to 0.82) along with substantial variability in the prevalence estimates between different industries and studies. The meta-prevalence for low back pain was estimated as 0.60 (95% CI, 0.54 to 0.66). The meta-prevalence for neck pain was estimated as 0.40 (95% CI, 0.34 to 0.47) whereas for shoulder pain it was estimated as 0.36 (95% CI, 0.30 to 0.42), respectively. The risk of bias was statistically nonsignificant, and overall publication bias was low as per visual inspections from funnel plots.Conclusions: WMSDs are prevalent across various Indian industries in significant proportions, particularly in agriculture, health care, and mining, leading to significant productivity loss and economic impact. The variation in prevalence highlights the need for sector-specific interventions. Addressing WMSDs requires comprehensive ergonomic and policy measures. Effective strategies are essential to mitigate these disorders' widespread impact. Key points What is already known on this topic: Work-related musculoskeletal disorders (WMSDs) are a significant concern for India's workforce, directly impacting productivity and economic output. Previous research has shown that occupational risks in Southeast Asia contribute to a substantial disease burden, with 2.04 million disability-adjusted life years attributed to these risks. Over 90% of Indian workers are in the informal economy, lacking adequate workplace health protections, including insurance. Prior studies on WMSDs in India have been fragmented, occupation-specific, and reported varying prevalence rates across sectors, such as agriculture (76% to 99%), manufacturing (47% to 92%), and health care (51% to 100%). However, there has been a lack of national-level meta-estimates, limiting informed decision-making for worker welfare.What this study adds: This study provides a comprehensive epidemiological overview of WMSDs across various occupational groups in India, a region underrepresented in global assessments. By conducting a systematic review and meta-analysis, the research offers robust and generalizable prevalence estimates across different occupations. The study identifies the sectors with the highest prevalence of WMSDs and outlines the primary ergonomic and risk factors contributing to these disorders. These data fill a critical gap in occupational health literature, offering valuable insights into the burden of WMSDs in India.How this study might affect research, practice, or policy: The findings highlight the need for targeted ergonomic interventions and preventive strategies in high-risk sectors like health care and agriculture. These insights can guide policymakers and occupational health practitioners in developing tailored health programs, potentially reducing WMSD incidence and improving workforce productivity. Implementing these measures could reduce the incidence of WMSDs, lower the economic burden associated with these disorders, and improve overall workforce well-being.

Job stress by effort-reward imbalance (ERI) is a predictor of burnout. Job stress is associated with inflammation, that is a forerunner of distal outcomes, including mortality. Sleep quality, an important association between job stress and inflammation has not been extensively studied. A cross-sectional study was conducted to examine the relationship between job stress, sleep quality, and inflammation among female nurses. As the primary outcome measure, a composite inflammation score was constructed from five interleukins (IL-6, 8, 10, 1β, TNF-α). Among fifty participants (mean age 32±7 years, work experience 105±8 months), there was poor sleep quality among the high ERI group (p=0.021). Overcommitment(OC), an intrinsic component of the ERI, was related to poor sleep quality (β =0.21, p =0.025). High OC (β =2.4, p = 0.025) and increased sleep latency (β = 8.3, p =0.027) were associated with elevated inflammation. There was a significant interaction between ERI and OC on inflammation (β =5.186, p =0.017) and conditional effects of ERI on OC to inflammation only in the high ERI group (p =0.002), not in the low ERI group (p = 0.839). Composite inflammation scores from various inflammatory markers may be potential indicators of adverse outcomes in burnout studies among healthcare workers.

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. Artificial Intelligence (AI) is an evolving field that is rapidly integrating into traditional applications of occupational health and safety. However, there is a noticeable deficiency in systematic summarization on this subject. The present study illuminates cutting-edge AI solutions for the detection of occupational lung diseases through the utilization of chest X-ray algorithms and technological innovations aimed at enhancing worker safety. This synopsis delivers a thorough and current assessment of AI-driven solutions that are beneficial for safeguarding health and minimizing the risk of injury. It offers invaluable insights for practitioners, policymakers, and researchers operating within this domain.

Objectives: This study explored whether job stress, specifically the extrinsic and intrinsic components of the Effort Reward Imbalance (ERI) model, moderates the relationship between obesity and oxidative stress among female nurses. Methods: We conducted an exploratory analysis from a cross-sectional study involving female nurses at a tertiary hospital. The study assessed the prevalence of obesity and overweight, examined correlations among obesity markers and oxidative stress markers, and explored if there is a moderator role of job stress on the relationship between obesity and oxidative stress. Results: The combined prevalence of overweight and obesity in the study population was 74%. Significant positive correlations were found between age, obesity-related parameters (BMI, PBF, WHR), and oxidative stress markers (Protein carbonyl content - PCC and Glutathione S-Transferase - GST). Higher levels of PCC and lower levels of GST were associated with higher BMI, PBF, and WHR. The relationship between obesity and oxidative stress markers was moderated by overcommitment. Nurses with high overcommitment exhibited stronger associations between age, obesity, and oxidative stress markers compared to those with low overcommitment. Conclusions: Our findings suggest that intrinsic job stress, particularly overcommitment, moderates the relationship between obesity and oxidative stress. This indicates that job stress should be considered in interventions targeting obesity among nurses. Further studies with larger samples are needed to confirm these findings and develop effective interventions addressing job stress and overcommitment.

The primary objective of occupational health and safety is to protect the well-being, health, and safety of workers at the workplace. This involves creating and maintaining safe and healthy work environments, preventing accidents and injuries, and addressing the physical, mental, and social aspects of worker health. The overarching principle is to prioritize the welfare of workers and ensure their rights to a safe and healthy work environment. In the era of artificial intelligence (AI), it is essential to respond promptly to potential threats and enhance security measures. No industry is exempt from the risks of employee harm and even well-managed companies encounter obstacles that can hinder their competitiveness and workplace safety. Employing AI provides several benefits in enhancing workplace safety. Through the utilization of machine learning algorithms, AI can effectively learn and identify patterns, facilitating the detection of hazardous situations. For example, AI-powered image analysis can identify employees who fail to wear mandatory protective gear such as helmets, goggles, or masks and detect the safe proximity distances. Embracing AI in the realm of occupational health enables organizations to shift from a reactive to a proactive approach. The integration of AI technologies enables swift identification of potential threats, timely implementation of interventions, and continuous enhancement of safety practices. The AI tools offer huge potential gains for chest-x-ray analysis with high precision and accuracy to identify Pneumoconiosis progression in dust-exposed workers a critical offering by AI in the absence of radiologists. The effectiveness of health and safety training programs can be exponentially increased by the use of Vernacular Intelligent Virtual Assistant to achieve the highest compliance among workers to reduce the chances of workplace accidents. Ultimately, leveraging AI to reimagine occupational health holds the promise of fostering safer, healthier, and more productive workplaces for employees.

CRISPR-Cas system has emerged as one of the most powerful genome editing tool owing to the fact that it is faster and cheaper than its counterparts. This technology has immense potential for improvising human health, agriculture, and environment. Profitable applications have attracted not only researchers but also the commercial entities to deploy this technology in ongoing processes to maximize output. However, the uncertainty around ownership of CRISPR-Cas gene editing technology and its associated patents presents a challenging situation over the prospects of research and technology developments along with possible financial liabilities to commercial users. Powerful gene editing abilities of CRISPR-Cas technology also raises ethical concerns not only for humans, but other species and environment which make researchers to think on setting limits on utilization of methodology to edit the genomes of any organism. This chapter outlines the landscape around these contemporary issues to understand the commercial future of this technology.

Job stress by effort-reward imbalance (ERI) is a predictor of burnout. Job stress is associated with inflammation, that is a forerunner of distal outcomes, including mortality. Sleep quality, an important association between job stress and inflammation has not been extensively studied. A cross-sectional study was conducted to examine the relationship between job stress, sleep quality, and inflammation among female nurses. As the primary outcome measure, a composite inflammation score was constructed from five interleukins (IL-6, 8, 10, 1β, TNF-α). Among fifty participants (mean age 32±7 years, work experience 105±8 months), there was poor sleep quality among the high ERI group (p=0.021). Overcommitment(OC), an intrinsic component of the ERI, was related to poor sleep quality (β =0.21, p =0.025). High OC (β =2.4, p = 0.025) and increased sleep latency (β = 8.3, p =0.027) were associated with elevated inflammation. There was a significant interaction between ERI and OC on inflammation (β =5.186, p =0.017) and conditional effects of ERI on OC to inflammation only in the high ERI group (p =0.002), not in the low ERI group (p = 0.839). Composite inflammation scores from various inflammatory markers may be potential indicators of adverse outcomes in burnout studies among healthcare workers.

Reproducibility is a preferred aim in any scientific research, including occupational health research. Datamanagement is an important and essential step in marching towards reproducibility. A good datamanagement helps us stay organized, improve transparency, quality and fosters collaboration. Here we discuss how to organize and prepare for data management, how data management facilitates interoperability and accessibility, followed by storing and dissemination of data. We wrap up by providing pointers on what needs to be included in the data management plans.

Smokeless tobacco (ST) consumption keeps human oral health at high risk which is one of the major reasons for oral tumorigenesis. The chemical constituents of the ST products have been well discussed; however, the inhabitant microbial diversity of the ST products is less explored especially from south Asian regions. Therefore, the present investigation discusses the bacteriome-based analysis of indigenous tobacco products. The study relies on 16S amplicon-based bacteriome analysis of Indian smokeless tobacco (ST) products using a metagenomic approach. A total of 59,15,143 high-quality reads were assigned to 34 phyla, 82 classes, 176 orders, 256 families, 356 genera, and 154 species using the SILVA database. Of the phyla (> 1%), Firmicutes dominate among the Indian smokeless tobacco followed by Proteobacteria, Bacteroidetes, and Actinobacteria (> 1%). Whereas, at the genera level (> 1%), Lysinibacillus , Dickeya , Terribacillus , and Bacillus dominate. The comparative analysis between the loose tobacco (LT) and commercial tobacco (CT) groups showed no significant difference at the phyla level, however, only three genera ( Bacillus, Aerococcus, and Halomonas ) were identified as significantly different between the groups. It indicates that CT and LT tobacco share similar bacterial diversity and poses equal health risks to human oral health. The phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt 2.0) based analysis uncovered several genes involved in nitrate/nitrite reduction, biofilm formation, and pro-inflammation that find roles in oral pathogenesis including oral cancer. The strong correlation analysis of these genes with several pathogenic bacteria suggests that tobacco products pose a high bacterial-derived risk to human health. The study paves the way to understand the bacterial diversity of Indian smokeless tobacco products and their putative functions with respect to human oral health. The study grabs attention to the bacterial diversity of the smokeless tobacco products from a country where tobacco consumers are rampantly prevalent however oral health is of least concern.

Introduction For effective prevention of occupational disease and injuries, accurate and timely reporting on the occurrence of occupational disease and injuries is critical. Disease and patient registries have been proven a rich sources of information for improvement in decision making health areas. Currently, there is no such accessible database in OSH domain in India. In view of unavailability of any national or regional database for occupational diseases and injuries, a paper-based registry was initiated at Employees State Insurance Corporation (ESIC) Model Hospital, Ahmedabad situated in highly industrialized state in India. Material and Methods The study involves hospital-based surveillance for disease and injuries among admitted (In-Patient Department) workers at ESIC hospital. The paper-based registry was initiated in December 2018 at ESIC Model Hospital, Ahmedabad which is administered by Employees' State Insurance Corporation, Ministry of Labour. The data was collected from 600 workers using a pre-structured questionnaire. Results & Conclusion Maximum hospitalizations of workers were related to respiratory (34.83%) and cardiovascular (12.33%) illnesses. Nearly 10% workers reported with problems related to musculoskeletal disorders(MSD), among whom lower back pain was the highly prevalent problem. Injury(non-fatal) at the workplace was recorded as 6.6% among hospitalized workers. Most of the injuries were in form of bone fractures (42.50%) of lower extremities and burn (22.50%) while performing duties at workplace. The median absenteeism for the hospitalized workers was found to be 11 (1, 41) days. Data revealed that compliance and knowledge of safety measure was low among injured workers, nearly half of injured workers were not using safety gears or had proper knowledge of preventive measures. These workers were engaged in work continuously for more than 8 hours before injury. A large number of hospitalized workers reported a noisy environment (60.16%) at workplace, and nearly 65.50% workers reported the presence of dust/odour/smoke, and excessive heat at work environment.

Introduction Data availability for occupational disease and injury statistics is crucial for policy perspective, and for the evaluation of implementation of occupational health and safety(OSH) standards in industrial settings of a country.The Factories Act 1948 require notifiable diseases to be informed in prescribed format to the concerned authority for the reporting purpose, however, the mechanism for the dissemination of this information is not clearly defined which leads to delay in public access to this information. A registry of occupational diseases and injuries has a great potential in fulfilling this data gap. Under Digital India scheme, multiple electronic health identities being generated for Indian citizens, which presents a beneficial opportunity to integrate notification system with a registry database. Material and Methods Using a structured validated questionnaire, representative institutions catering to Occupational diseases/disorders across the country are being contacted. The focus of enquiry includes the nature of records maintained, the different variables being included, the follow-up and continuity of care variables, mechanism of reporting and collating data, compensation related issues and concerns, etc., In addition, the extent of digitisation of records and scope and status of computerisation will be documented. Wherever feasible visits will be made to ascertain. The country will be divided into 6 regions and at least 6 to 10 institutions of varying nature will be contacted for purposes of this activity. Results & Conclusion Currently, there is no registry in India for Occupational Diseases and Injuries, this study explores a potential model for online registry in India which has large pool of networked hospitals serving the industrial workers through government welfare schemes. Data insights from registry can be utilized in policy making in area of OSH, and to plan and intervene in form of medical strategies for protection and welfare of workers.

Arsenic and chromium are the most common environmental toxicants prevailing in nature. Hence, the present study endeavors to investigate the salutary effects of Coenzyme Q10 (CoQ10), Biochanin A (BCA), and Phloretin (PHL) on the combined neurotoxic impact of arsenic and chromium in the Swiss albino mice (Mus musculus). Sodium meta-arsenite (100 ppm) and potassium dichromate (75 ppm) were given orally in conjugation with CoQ10 (10 mg/kg), BCA & PHL (50 mg/kg each) in accordance with body weight per day for the 2 weeks experimental duration. Weight reduction was figured out in the exposed toxic group of arsenic and chromium in contrast with the comparison group (control), and with the selected anti-oxidants treatment, it rose significantly to the basal status (p < 0.05). The concentration of arsenic and chromium was reduced significantly (p < 0.001) amidst all the natural compounds co-medicated groups. Anti-oxidant indicators, viz. lipid peroxidation (LPO) and protein carbonyl content (PCC), were found elevated, with reduction observed in the levels of superoxide dismutase (SOD), reduced glutathione (GSH), glutathione s-transferase (GST), and total thiols (TT) in the arsenic and chromium, co-exposed mice. The alterations in redox homeostasis were well corroborated with the estimations of cholinesterase's enzymes (p < 0.05) along with DNA fragmentation assay and altered Nrf2 signaling. The administration of CoQ10, BCA, and PHL ameliorated the effects of arsenic and chromium induced oxidative stress in the exposed mice. Our research unfolds the remedial outcome of these natural compounds contrary to the combined arsenic and chromium associated-neurotoxicity in the experimental model.

Rising global energy demands and climate crisis has created an unprecedented need for the bio-based circular economy to ensure sustainable development with the minimized carbon footprint. Along with conventional biofuels such as ethanol, microbes can be used to produce advanced biofuels which are equivalent to traditional fuels in their energy efficiencies and are compatible with already established infrastructure and hence can be directly blended in higher proportions without overhauling of the pre-existing setup. Metabolic engineering is at the frontiers to develop microbial chassis for biofuel bio-foundries to meet the industrial needs for clean energy. This review does a thorough inquiry of recent developments in metabolic engineering for increasing titers, rates, and yields (TRY) of biofuel production by engineered microorganisms.

Blood lead level (BLL) is the primary biomarker for lead-exposure monitoring in occupationally exposed workers. We evaluated occupational lead-exposure (OE) impact on cardiopulmonary functions in lead-acid battery recycling unit workers. Seventy-six OE cases and 30 control subjects were enrolled for questionnaire-based socio-demographic, dietary, tobacco usage, and medical history data. Anthropometric measurements, systolic and diastolic blood pressure (SBP and DBP), and pulmonary function tests were performed. Venous blood was collected for BLL, hematological analysis, and biochemical analysis. OE caused a significant increase in BLL, SBP, DBP, and small airways obstruction in lung function tests. It also impaired platelet indices, affected renal and liver biochemical measurements, and promoted oxidative stress and DNA damage. Multilinear regression analysis suggested that BLL affected SBP (β = 0.314, p = .034) and increased small airways obstruction (FEV1/FVC, β = −0.37, p = .05; FEV25–75%, β = −0.351, p = .016). Higher BLL appears to be an independent modulator of hypertension and poor pulmonary function upon occupational lead exposure in lead-acid battery recyclers.

Veterinarians experience different types of health hazards from their occupation. Studies on the prevalence and occurrence of biological health hazards in veterinary medicine in India are scant and probably underreported. Thus, we sought to assess the biological health hazards and infection control practices (ICPs) among veterinarians from the states of Gujarat and Maharashtra, India. A cross-sectional survey was conducted among veterinarians (n = 562) from Gujarat and Maharashtra states in India to identify biological health hazards and ICPs for the prevention of occupational health hazards during 2016–2017 by personally contacting them. Responses regarding a biological hazard and ICPs were recorded. Descriptive analysis was attempted, and continuous variables are presented as the mean ± SD. Categorical variables are reported as counts and percentages (%). Most of the veterinarians (49.3%) worked in the field and were continuously exposed to different types of biological health hazards, especially zoonoses, ranging from mild and self-limiting to fatal diseases (e.g., brucellosis (subclinical and clinical form) and rabies (fatal)) without common prophylactic vaccinations, such as rabies and tetanus. While inquiring medical health status of the veterinarians, only 35.8% of the total respondents underwent a routine medical health checkup within the past year, and 56.9% did not receive a routine dose of an anthelmintic for deworming. Forty-nine percent of the respondents took all necessary precautions, including wearing an apron, facemask, and gloves. In contrast, 10.2% of the respondents wore only an apron, and 8.4% of respondents did not take any precautions while performing their day-to-day work. In total, 40.2% of the respondents followed the proper method of handwashing, that is, washing hands between patient examinations. In contrast, 27.9% of the respondents washed their hands once after completing the work. The majority of the respondents (87.7%) reported an urgent need for occupational hazards and safety (OHS) training in continued veterinary education (CVE) programs. The present study demonstrates that veterinarians in the states of Gujarat and Maharashtra in India pay less attention to their own health that may increase the risk of occupation-related biological health hazards. These results suggest that safety and ICPs are not prioritized, which are serious concerns. These findings may be useful for developing policies to prevent occupationally related biological health hazards among veterinarians in India.