[Current status and prospects of health hazards database for chemical hazards].

In order to facilitate technical personnel related to occupational health and safety production to search, obtain, and master information on the hazard classification and health effects of chemical hazards, this article surveyed 14 commonly used foreign databases and 9 commonly used domestic databases, analyzed the characteristics, main content, scope of application, and network resources of each database, and considered the development of database for occupational health hazard of chemical hazards.

Methodology and data for quantifying storm erosion potential considering sea level rise.

This dataset quantifies storm intensity of approximately 130 unique historical storms along the New Jersey coastline from 1980 to 2014 for three separate sea level conditions. Namely, (1) as observed in the historical record; (2) detrended to 1997 mean sea level and (3) adjusted to the 2050 and 2100 sea level rise scenarios presented in the International Panel on Climate Change's (IPCC) Sixth Assessment Report (AR6). Projected sea level scenarios are adjusted to include local vertical land movement. Storm intensity is quantified in terms of erosion potential, considering the combination of total water level, wave heights, and storm duration. The observational dataset includes both tropical and extratropical storms and quantifies both the cumulative (duration) and peak (single hour) storm intensity for each storm and sea level rise (SLR) condition. Additionally, hourly time series of wave characteristics and water levels are provided at 13 locations along the New Jersey coast, facilitating hydrodynamic forcing of nearshore models. The dataset provides the means and methods to directly compare historical storms under future SLR conditions.

[Occupational hazards of crystalline silicon solar cell manufacturing industry].

The wide use of crystalline silicon solar cells in the field of new energy is an important boost for China to achieve the environmental protection goal as soon as possible. However, the production and manufacturing processes of these cells give rise to various occupational hazards at workplace, thus posing health risks to workers. This review provided an overview of production processes of crystalline silicon solar cells, the characteristics of occupational health hazards (productive dust; physical factors, productive toxicant) and proposed occupational protection suggestions.

Assessment of potentially toxic element contents in chickens and poultry feeds from Bangladesh markets: Implications for human health risk.

Chicken (Gallus domesticus) is a significant source of animal protein for the people of Bangladesh. However, anthropogenic activity may contaminate chicken meat with potentially toxic elements (PTEs) despite the nutritional benefits. Current work aims to determine the accumulated content of PTEs (Pb, Cd, Cr, As, and Hg) in chickens and poultry feeds commercially sold in Bangladesh markets and compare with WHO, FAO, EU, EC, FSANZ standards. Three different chicken varieties, native (local variety, freehand raised), poultry (raised for meat only), and layer chicken (commercially raised for eggs and later used for meat), were investigated, and commercial poultry feeds were used to raise the latter two varieties. The Pb, Cd, Cr, As, and Hg contents (mg kg-1 fresh weight (f.w.) were 0.481-1.067, 0.025-0.118, 0.069-0.319, 0.007-0.071, 0.002-0.019, respectively. In addition, associated health risks due to the PTEs in different varieties of chicken organs, e.g., meat, liver, and kidney, were evaluated. The study suggests that the poultry feeds should be carefully monitored regarding PTEs content to avoid potential human health risks due to chicken consumption in Bangladesh.

Increasing extreme precipitation variability plays a key role in future record-shattering event probability.

Climate events that break records by large margins are a threat to society and ecosystems. Climate change is expected to increase the probability of such events, but quantifying these probabilities is challenging due to natural variability and limited data availability, especially for observations and very rare extremes. Here we estimate the probability of precipitation events that shatter records by a margin of at least one pre-industrial standard deviation. Using large ensemble climate simulations and extreme value theory, we determine empirical and analytical record shattering probabilities and find they are in high agreement. We show that, particularly in high emission scenarios, models project much higher record-shattering precipitation probabilities in a changing relative to a stationary climate by the end of the century for almost all the global land, with the strongest increases in vulnerable regions in the tropics. We demonstrate that increasing variability is an essential driver of near-term increases in record-shattering precipitation probability, and present a framework that quantifies the influence of combined trends in mean and variability on record-shattering behaviour in extreme precipitation. Probability estimates of record-shattering precipitation events in a warming world are crucial to inform risk assessment and adaptation policies.

Analysis of fall risk factors and environmental hazards for older adults in Japan: assessing fall situations and locations.

[Purpose] This study aimed to examine falls among older adults in Japanese households and determine the risk associated with each fall location. [Participants and Methods] This study included 99 participants (249 fall events) who received daycare rehabilitation at a nursing care facility. Data on fall circumstances were collected from the medical records and accident reports. The analyzed variables included age, medical status, level of care required, fall history, location, and mode of transportation during the falls. [Results] Falls occurred most commonly in bedrooms. Falls at an entrance were associated with no assistive device (OR: 1.76, 95% CI: 1.06-1.80) and 1 history of falls (OR: 1.22, 95% CI: 1.03-3.10). Risk factors for falls in bedrooms included Parkinson's disease (OR: 1.83, 95% CI: 1.11-1.87), orthopedic disease (OR: 1.11, 95% CI: 1.15-3.43), and cane walking (OR: 1.08, 95% CI: 1.33-4.13). Falls in a hallway were associated with no assistive device (OR: 1.75, 95% CI: 1.15-1.91). [Conclusion] Bedrooms and hallways in Japanese households were identified as locations with a high risk of falls. The unique architectural and cultural features of Japanese homes may contribute to this risk. Rehabilitation programs should consider individual fall histories, medical conditions, and differences in mobility.

Users' Perspective on Revised Simple Simulator for Calculating Nutritional Food Stocks in Preparation for Large-scale Disasters.

OBJECTIVE: In 2020, Japan's Ministry of Health, Labour and Welfare developed an Excel workbook entitled "Simple Simulator for calculating nutritional food stocks in preparation for large-scale disasters." In September 2021, it was modified as the "Revised Simulator" to plan food stockpiles in normal times and post-disaster meals. This study aimed to further improve the Revised Simulator. METHODS: Eight group interviews were conducted with 12 public health dietitians, 9 disaster management officers, and 2 public health nurses from September to November 2021. They provided nutritional support during previous disasters or prepared for predicted future disasters. Qualitative analysis was conducted on interview transcriptions, then the Revised Simulator was improved based on their feedback. RESULTS: The Revised Simulator was improved to the "Simulator for calculating nutritional food stocks and meals for large-scale disasters" with significant changes such as adding specific tags in the food list to denote long shelf life and elderly-friendly foods, as well as displaying bar graphs to visualize the required and supplied amounts of energy and nutrients. CONCLUSIONS: The Revised Simulator was upgraded for planning and assessing stockpiles and meals in ordinary conditions and emergencies. This study will contribute to enhancing the quality and quantity of food supplies during disasters.

Competing Risks in Clinical Trials: Do They Matter and How Should We Account for Them?

During patient follow-up in a randomized trial, some deaths may occur. Where death (or noncardiovascular death) is not part of an outcome of interest it is termed a competing risk. Conventional analyses (eg, Cox proportional hazards model) handle death similarly to other censored follow-up. Patients still alive are unrealistically assumed to be representative of those who died. The Fine and Gray model has been used to handle competing risks, but is often used inappropriately and can be misleading. We propose an alternative multiple imputation approach that plausibly accounts for the fact that patients who die tend also to be at high risk for the (unobserved) outcome of interest. This provides a logical framework for exploring the impact of a competing risk, recognizing that there is no unique solution. We illustrate these issues in 3 cardiovascular trials and in simulation studies. We conclude with practical recommendations for handling competing risks in future trials.

A mentally healthy framework to guide employers and policy makers.

Mental health problems among the working population represent a growing concern with huge impacts on individuals, organizations, compensation authorities, and social welfare systems. The workplace presents both psychosocial risks and unique opportunities for intervention. Although there has been rapid expansion of workplace mental health interventions over recent decades, clear direction around appropriate, evidence-based action remains limited. While numerous workplace mental health models have been proposed to guide intervention, general models often fail to adequately consider both the evidence base and where best-practice principles alone inform action. Further, recommendations need to be updated as new discoveries occur. We seek to update the Framework for Mentally Healthy Workplaces based on new evidence of intervention effectiveness while also incorporating evidence-based principles. The updated model also integrates concepts from existing alternate models to present a comprehensive overview of strategies designed to enhance wellbeing, minimize harm, and facilitate recovery. Examples of available evidence and obstacles to implementation are discussed. The Framework is designed to support employers and managers in determining which strategies to apply and to guide future avenues of research.

Sample size calculation for mixture cure model with restricted mean survival time as a primary endpoint.

It is not uncommon for a substantial proportion of patients to be cured (or survive long-term) in clinical trials with time-to-event endpoints, such as the endometrial cancer trial. When designing a clinical trial, a mixture cure model should be used to fully consider the cure fraction. Previously, mixture cure model sample size calculations were based on the proportional hazards assumption of latency distribution between groups, and the log-rank test was used for deriving sample size formulas. In real studies, the latency distributions of the two groups often do not satisfy the proportional hazards assumptions. This article has derived a sample size calculation formula for a mixture cure model with restricted mean survival time as the primary endpoint, and did simulation and example studies. The restricted mean survival time test is not subject to proportional hazards assumptions, and the difference in treatment effect obtained can be quantified as the number of years (or months) increased or decreased in survival time, making it very convenient for clinical patient-physician communication. The simulation results showed that the sample sizes estimated by the restricted mean survival time test for the mixture cure model were accurate regardless of whether the proportional hazards assumptions were satisfied and were smaller than the sample sizes estimated by the log-rank test in most cases for the scenarios in which the proportional hazards assumptions were violated.

Exact test and exact confidence interval for the Cox model.

The Cox proportional hazards model is commonly used to analyze time-to-event data in clinical trials. Standard inference procedures for the Cox model are based on asymptotic approximations and may perform poorly when there are few events in one or both treatment groups, as may be the case when the event of interest is rare or when the experimental treatment is highly efficacious. In this article, we propose an exact test of equivalence and efficacy under a proportional hazard model with treatment effect as the only fixed effect, together with an exact confidence interval that is obtained by inverting the exact test. The proposed test is based on a conditional error method originally proposed for sample size reestimation problems. In the present context, the conditional error method is used to combine information from a sequence of hypergeometric distributions, one at each observed event time. The proposed procedures are evaluated in simulation studies and illustrated using real data from an HIV prevention trial. A companion R package "ExactCox" is available for download on CRAN.

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset. Unlike traditional models, GCNs leverage structural relationships within the data, enhancing predictive accuracy and robustness. By integrating demographic and laboratory data into a GCN framework, our approach addresses class imbalance and demonstrates significant improvements in prediction accuracy. METHODS: The cohort included all consecutive positive COVID-19 patients fulfilling study criteria admitted to 42 public hospitals in Hong Kong between January 23 and December 31, 2020 (n = 7,606). We proposed the population-based graph convolutional neural network (GCN) model which took blood test results, age and sex as inputs to predict the survival outcomes. Furthermore, we compared our proposed model to the Cox Proportional Hazard (CPH) model, conventional machine learning models, and oversampling machine learning models. Additionally, a subgroup analysis was performed on the test set in order to acquire a deeper understanding of the relationship between each patient node and its neighbours, revealing possible underlying causes of the inaccurate predictions. RESULTS: The GCN model was the top-performing model, with an AUC of 0.944, considerably outperforming all other models (p < 0.05), including the oversampled CPH model (0.708), linear regression (0.877), Linear Discriminant Analysis (0.860), K-nearest neighbours (0.834), Gaussian predictor (0.745) and support vector machine (0.847). With Kaplan-Meier estimates, the GCN model demonstrated good discriminability between low- and high-risk individuals (p < 0.0001). Based on subanalysis using the weighted-in score, although the GCN model was able to discriminate well between different predicted groups, the separation was inadequate between false negative (FN) and true negative (TN) groups. CONCLUSION: The GCN model considerably outperformed all other machine learning methods and baseline CPH models. Thus, when applied to this imbalanced COVID survival dataset, adopting a population graph representation may be an approach to achieving good prediction.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

The effect of adaptive capacity on resilience to the COVID-19 pandemic: A cross-country analysis.

The COVID-19 pandemic's profound impacts on global health, driven by preparedness gaps and systemic risks, underscore the need to enhance societies' ability to manage both predictable risks and uncertainties inherent in disasters. While disaster research emphasises risk management for predictable threats and adaptive capacity for unexpected challenges, there is a lack of empirical examination of the impact of adaptive capacity on disaster resilience. This study addresses this gap by identifying three key adaptive capacities - quality of institutions, collaborative governance, and social capital - and examining their effects on COVID-19 resilience outcomes, measured by the ability to reduce excess mortality. Analysing secondary data from 129 nations using partial least squares structural equation modelling, the research finds significant positive effects of institutional quality and social capital on resilience outcomes. Conversely, collaborative governance shows a significant negative association, suggesting potentially intricate impacts beyond initial expectations. The findings highlight the need to enhance institutional quality and social capital to address preparedness gaps and unexpected challenges posed by biological hazards such as COVID-19. Future research should explore collaborative governance using a disaggregated approach that considers the roles of different stakeholders in various disaster phases. Contribution: This study advances disaster research by presenting practical methodologies for operationalising adaptive capacities and empirically examining their effects on disaster resilience. For practitioners and policymakers, it highlights the need to adopt a long-term perspective in building disaster resilience, focussing on improving institutional quality and social capital to manage the uncertainties and complexities inherent in disaster scenarios effectively.

Impact of Health Care Management Workshops on the Health-Seeking Behavior of Traffic Police in Bhubaneswar.

Background: Sustainable development goals (SDGs) 3.9.1 and 11.6.2 call for a reduction in deaths and illnesses from air pollution, improving the air quality of cities. The above goals motivate us to organize workshops to improve the health of traffic police, who bear the brunt of air pollution. The paper examines the effect of workshops on the health-seeking behavior of the traffic police in Bhubaneswar city. Methods: The study conducted two workshops as a quasi-experimental, single-group study at an academic institution in Bhubaneswar. It included 20 traffic police officers (11 male and 9 female). The Kirkpatrick 4-level model was used to assess the effectiveness of the workshops. A paired t-test was used to compare pre- and postworkshop scores. Results: Thirteen traffic police officers rated the workshop sessions as excellent. The score before the workshop ranged from zero to three, with a mean (standard deviation [SD]) of 2.81 (1.0). The postworkshop score had a minimum to maximum score of three to five with a mean (SD) of 4.41 (0.7) (P < 0.005). The effect size dcohen (confidence interval [CI]) was 1.87 (3.27-4.71). The mean (SD) of absolute and relative gain was 1.6 (1.0) and 0.93 (1.02), respectively. All 20 traffic police officers showed improvement in health-seeking behavior. The significant lifestyle changes after the workshops ranged between 5% and 75%. Conclusion: The test scores indicated statistically significant improvement as the P value recorded was smaller than 0.05. This concludes that the improvement in understanding of the session was statistically significant because of the training imparted.

Assessment of Meat Content and Foreign Object Detection in Cattle Meatballs Using Ultrasonography, Radiography, and Electrical Impedance Tomography Imaging.

Meat content and physically hazardous contaminants in the internal section of meatballs cannot be detected by the naked eye or surface detectors. This study is aimed at analyzing the meat content of cattle meatballs and detecting foreign objects using ultrasonography (USG), digital radiography (DR), and electrical impedance tomography (EIT). Meatballs were produced using four different meat formulations (0%, 25%, 50%, and 75% meat) and three treatments (no preservative (control), borax, and formalin preservatives). Cast iron and plastic beads were used as models of foreign objects embedded in the samples. The echogenicity, opacity, and resistivity values of each sample were evaluated and compared across groups. The results showed that the shelf life of the control meatballs was shorter than that of meatballs with preservatives. The echogenicity and opacity values for the different meat formulations were hypoechoic in USG and grey in DR. USG was able to distinguish between control and preservative-treated meatballs but could not differentiate meat content and detect foreign objects. Conversely, DR effectively assessed meat content and detected iron-based foreign objects, while EIT showed higher resistivity values for iron and plastic beads compared to the meatball bodies.

Association between homocysteine levels and hyperlipidemia prevalence as well as all-cause mortality of hyperlipidemia patients in the US population: results from NHANES database.

Objective: Several studies have investigated the correlation between blood lipids and homocysteine, but no clear conclusions have been defined yet. Therefore, we utilized data from National Health and Nutrition Examination Survey (NHANES) to explore the correlation between serum homocysteine (Hcy) levels and hyperlipidemia, which is determined by the levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). We believe this study can provide a scientific basis for the prevention and treatment of lipid abnormalities. Methods: The data used in this study were sourced from NHANES 1999-2006, linked with National Death Index mortality data from January 1999 to December 2019. We employed logistic regression to assess the associations between Hcy levels and the presence of hyperlipidemia. Additionally, survival analysis using Kaplan-Meier estimate and Cox proportional hazards regression model was conducted to evaluate the associations between Hcy levels and all-cause mortality in the hyperlipidemia population. Results: (1) A total of 13,661 subjects were included in the study. There were statistically significant differences in Hcy levels across different groups based on gender, age, race, marital status, education level, hypertension status, diabetes status, and Body Mass Index (BMI) (P < 0.05). (2) In the overall population, hyperhomocysteinemia (HHcy) was associated with an increased risk of high-TC hyperlipidemia (P < 0.05). Subgroup analysis by gender showed that HHcy in females was associated with an increased risk of dyslipidemia (OR = 1.30, 95% CI: 1.07-1.59, P < 0.05) and high-LDL-C hyperlipidemia (OR = 1.30, 95% CI: 1.00-1.68, P < 0.05). In addition, subgroup analysis by age revealed that HHcy in middle-aged people was associated with an increased risk of high-TC hyperlipidemia (OR = 1.21, 95% CI: 1.03-1.41, P < 0.05) and high-LDL-C hyperlipidemia (OR = 1.23, 95% CI: 1.06-1.43, P < 0.05). (3) HHcy was consistently associated with an increased mortality risk in the hyperlipidemia population (HR = 1.49, 95% CI: 1.35-1.65, P < 0.05). Conclusion: There was positive correlation between Hcy levels and the presence of hyperlipidemia. In the overall population, HHcy was associated with an increased risk of high-TC hyperlipidemia. Among females, HHcy is linked to an increased risk of dyslipidemia and high-LDL-C hyperlipidemia. In middle-aged people, HHcy was associated with an elevated risk of high-TC hyperlipidemia and high-LDL-C hyperlipidemia. In addition, HHcy increased the all-cause mortality rate in hyperlipidemia patients.

Frequency of occupational health hazards and factors responsible among the waste handlers at the tertiary care hospitals of Karachi.

Background & Objectives: Hospital waste handlers (HWHs) are in contact with contaminated waste that put them at risk for occupational health hazards. The objective of the study was to determine the frequency of occupational health hazards and identify factors contributing to them among the HWHs at tertiary care hospitals of Karachi. Methods: A cross sectional survey was conducted from January 2021 till June 2022 on 417 conveniently selected HWHs of the public and private tertiary care hospitals of the Karachi including three Public sector hospitals (Civil Hospital Karachi, National Institute of Child Health, Jinnah Post Graduate Medical Center) and five private sector hospitals (Sohail University Hospital, Darulsehat Hospital, Kharadar General Hospital, Patel Hospital and Hamdard University Hospital) using a structured questionnaire. Chi Square test was applied to determine the differences in occurrence of different hazardous outcomes (Needle stick injury, Sharp Injury, Eye Symptoms, Skin symptoms, Cough) between different groups of age, gender, type of hospitals and status of being trained in Hospital Waste Management (HWM). Results: Around half of the HWHs (52.6%) labeled the bins of the waste according to their level of hazard. Only 17.9% disinfected the infected waste. The proportion of participants who experienced needle stick and sharp injury in the last six months was 16.3% and 15.8% respectively. Majority of them used disposable gloves (95.7%) and face masks (94.3%). One thirds had access to aprons while only 10.5% had access to protective shoes at their work place. HWHs of private sector were significantly less likely to experience Needle stick injuries, skin symptoms, cough, breathing difficulty and throat burning. Conclusion: The HWM practices in tertiary care hospitals of Karachi is far from being satisfactory. HWHs must be trained and monitored for safe disposal of waste.

Pan India fluoride hazard assessment in groundwater.

Fluoride (F¯) contamination in groundwater in India has gained global attention due to human health hazards. India's hydrogeological heterogeneity, spatio-temporal variability of F¯, and health hazards due to geogenic and geo-environmental control pose unique challenges. Addressing these with only a single region-specific study is not possible. Therefore, this study provides an in-depth, holistic analysis of pan India F¯ contamination, controlling factors, and health hazards using a coupled advanced geostatistical and geospatial approach. Alarming F¯ contaminations are identified in Rajasthan, Telangana, Western Andhra Pradesh, Eastern Karnataka, Parts of Haryana, Gujarat, Madhya Pradesh, Tamil Nadu, Uttar Pradesh, Jharkhand, Bihar, and Chhattisgarh. Probabilistic health-risk evaluation using hot-spot, showed similar spatio-temporal distribution of F¯ contamination. The hazard quotient (HQ) for high F¯ shows more adversity to children than adults. Nationally, 8.65 % and 7.10 % of pre- and post-monsoon sites exceed the recommended safe limit of 1.50 mg/L. The highest average F¯ concentration is in Rajasthan. Very high-risk skeletal fluorosis is possible at around ≤ 2 %, whereas dental caries due to deficiency in F¯ concentration is approximately 40 %. A decisive hierarchy of lithology, geomorphology, soils, and lineaments control are identified on F¯ contamination. Climatic conditions are pivotal in governing all these controlling variables. Thus, in arid/semi-arid dry western regions, F¯ contamination is much higher than in the humid areas. Integration of strengths, weaknesses, opportunities, and threats (SWOT) analysis with the results can aid policymakers and government authorities in achieving sustainable remedial measures for future adaptability.

Identifying Work-Related Injuries Among Healthcare Workers in Makkah Hospitals, Saudi Arabia: A Cross-Sectional Study in 2024.

Background Work-related injuries (WRIs) are a major occupational health issue among healthcare workers (HCWs) worldwide. HCWs face numerous daily hazards including needlestick injuries, chemical exposures, ergonomic strains, and psychological stressors crucial for their health and healthcare system functionality. In Makkah, Saudi Arabia, healthcare infrastructure advances raise concerns about work-related injuries among HCWs. This study in Makkah hospitals aims to identify, understand, and manage WRIs for improved occupational health guidelines and strategies. Methods This descriptive cross-sectional study on HCWs was conducted at Makkah hospitals using an electronic questionnaire that investigated the demographics, work-related injuries, and occupational hazards. The data collected from the retrieved questionnaires were analyzed using the IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States). Results Among 379 enrolled HCWs, 172 (49.3%) were physicians and 89 (19.8%) were nurses; 304 (80.2%) of the total participants knew about occupational safety. The total incidence of WRIs was 67.8%. WRIs were significantly associated with age (P˂0.001), gender (P=0.02), educational level (P˂0.001), profession (P˂0.001), working hours (P˂0.001), and shift time (P˂0.001). Conclusion WRIs were highly prevalent among HCWs with varying rates based on the type of injury and the frequency of injury. WRIs were associated with various factors including age, gender, education, profession, working house, and shift time of the participants.