Global trend according to estimated number of occupational accidents and fatal work-related diseases at region and country level

Background

Although occupational accidents and work-related diseases have been of interest for a long time, due to lack of proper recording and notification systems the official numbers of occupational accidents and work-related diseases are missing for many countries. Presently, the demand for effectiveness and an interest in the economic aspects of accidents have increased prevention activities at company and country levels.

Methods

Occupational accident data of selected countries and of World Health Organization regional divisions together with the global burden of disease were used in estimating global occupational accidents and fatal work-related diseases. The trend of global occupational accidents and work-related diseases is presented at region and country levels. The years 1998, 2001, and 2003 are compared in the case of occupational accidents and the years 2000 and 2002 in the case of work-related diseases.

Results

The total number of occupational accidents and fatal work-related diseases has increased, but the fatality rates per 100,000 workers have decreased. There were almost 360,000 fatal occupational accidents in 2003 and almost 2 million fatal work-related diseases in 2002. Every day more than 960,000 workers get hurt because of accidents. Each day 5,330 people die because of work-related diseases.

Conclusions

Information on occupational accidents and work-related diseases is needed so that countries may understand better the importance of occupational health and safety at country and company level. Especially companies in developing countries are not familiar with occupational safety and health. Statistical data is essential for accident prevention; it is a starting point for the safety work.     

Differences in urban and rural accident characteristics and medical service utilization for traffic fatalities in less-motorized societies

Introduction

Fatalities from traffic accidents in less-motorized societies are an important global issue. We aimed to characterize the geographic differences of fatalities in such societies to facilitate the development of targeted interventions.

Method

This study linked police reports, hospital data, and vital registration data from Taiwan with special reference to accident factors in pre-hospital deaths and medical care in hospital deaths.

Results

A higher percentage of pre-hospital deaths were observed following rural as compared to urban traffic accidents. The deaths due to rural accidents can be attributed to lower use of restraints (i.e., helmets or seat belts), lower percentage of motorcyclists, and more highway accidents. A higher percentage of victims in rural accidents were transported to distant medical centers rather than to local hospitals.

Conclusion

Specific interventions, such as intelligent emergency medical systems, campaigns for helmets and seat belt usage, enforcement of helmets and seat belt use, and speed control measures should be targeted to rural areas.

Impact on industry

Cooperation between the vehicle industry and emergency medical providers in rural traffic accident rescue teams may decrease the numbers of deaths in these regions.     

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey

Introduction

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey.

Method

Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P < 0.05 with spatial autocorrelation analyses.

Results

Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P < 0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R² values. Geographically weighted regression provided values of 0.89-0.99 adjusted R² for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions.

Impact on industry

Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.     

Have government regulations improved workplace safety? : A test of the asynchronous regulatory effects in China's coal industry, 1995-2006

Problem

Empirical studies on the effectiveness of workplace safety regulations are inconclusive. This study hypothesizes that the asynchronous effects of safety regulations occur because regulations need time to become effective. Safety regulations will work initially by reducing the most serious accidents, and later by improving overall safety performance.

Method

The hypothesis is tested by studying a provincial level aggregate panel dataset for China's coal industry using two different models with different sets of dependent variables: a fixed-effects model on mortality rate, which is defined as fatalities per 1,000 employees; and a negative binominal model on the annual number (frequency) of disastrous accidents.

Results

Safety regulations can reduce the frequency of disastrous accidents, but have not reduced mortality rate, which represents overall safety performance.

Discussion and summary

Policy recommendations are made, including shifting production from small to large mines through industrial consolidation, improving the safety performance of large mines, addressing consequences of decentralization, and facilitating the implementation of regulations through carrying on institutional actions and supporting legislation.

Impact on industry

Until recently, about 4,000 coal miners perished annually in China, demonstrating that workplace safety in China's coal industry is an urgent and important issue. This research provides evidence that safety regulations have asynchronous effects and identifies the priorities in improving safety in China's current coal mining. This may assist the Chinese government to design more effective safety improvement policies and improve the effectiveness of safety regulations and safety performance.     

Injury severity analysis of accidents involving young male drivers in Great Britain

Introduction

Young male drivers are over-represented in traffic accidents; they were involved in 14% of fatal accidents from 1991 to 2003 while holding only 8% of all drivers licenses in the UK. In this study, a subset of the UK national road accident data from 1991 to 2003 has been analyzed. The primary aim is to determine how to best use monetary and progressive resources to understand how road safety measures will reduce the severity of accidents involving young male drivers in both London and Great Britain.

Method

Ordered probit models were used to identify specific accident characteristics that increase the likelihood of one of three categorical outcomes of accident severity: slight, serious, or fatal.

Results

Characteristics found to lead to a higher likelihood of serious and fatal injuries are generally similar across Great Britain and London but are different from those predicted to lead to a higher likelihood of slight injuries. Those characteristics predicted to lead to serious and fatal injuries include driving in darkness, between Friday and Sunday, on roads with a speed limit of 60 mph, on single carriageways, overtaking, skidding, hitting an object off the carriageway, and when passing the site of a previous accident. Characteristics predicted to lead to slight injuries include driving in daylight, between Monday and Thursday, on roads with a speed limit of 30 mph or less, at a roundabout, waiting to move, and when an animal is on the carriageway.

Impact on Industry

These results aid the selection of policy options that are most likely to reduce the severity of accidents involving young male drivers.     

The role of production and teamwork practices in construction safety: A cognitive model and an empirical case study

Problem

In construction, the challenge for researchers and practitioners is to develop work systems (production processes and teams) that can achieve high productivity and high safety at the same time. However, construction accident causation models ignore the role of work practices and teamwork. This study investigates the mechanisms by which production and teamwork practices affect the likelihood of accidents.

Method

The paper synthesizes a new model for construction safety based on the cognitive perspective (Fuller's Task-Demand-Capability Interface model, 2005) and then presents an exploratory case study. The case study investigates and compares the work practices of two residential framing crews: a 'High Reliability Crew' (HRC)—that is, a crew with exceptional productivity and safety over several years, and an average performing crew from the same company.

Results

The model explains how the production and teamwork practices generate the work situations that workers face (the task demands) and affect the workers ability to cope (capabilities). The case study indicates that the work practices of the HRC directly influence the task demands and match them with the applied capabilities. These practices were guided by the 'principle' of avoiding errors and rework and included work planning and preparation, work distribution, managing the production pressures, and quality and behavior monitoring.

Summary

The Task Demand-Capability model links construction research to a cognitive model of accident causation and provides a new way to conceptualize safety as an emergent property of the production practices and teamwork processes. The empirical evidence indicates that the crews' work practices and team processes strongly affect the task demands, the applied capabilities, and the match between demands and capabilities.

Impact on Industry

The proposed model and the exploratory case study will guide further discovery of work practices and teamwork processes that can increase both productivity and safety in construction operations. Such understanding will enable training of construction foremen and crews in these practices to systematically develop high reliability crews.     

Are drivers' comparative risk judgments about speeding realistic?

Introduction

This study focused on comparative judgments about speeding risks among young drivers who have a high risk of being involved in a traffic accident.

Method

We examined (a) how these drivers assess their risk of sanctions and their risk of causing an automobile crash because of speeding in comparison to the estimated risks of other drivers, and (b) how realistic their comparative risk judgments are. We measured the relationship between the drivers' comparative risk judgments, self-reported speeding, and driving-related sensation-seeking. We hypothesized that (a) they would think they have less risk of sanctions and of causing a car accident than others, and (b) their comparative judgments of speeding risks would be linked to self-reported speeding and driving-related sensation-seeking. The study was based on a computerized questionnaire survey conducted with 3,002 young drivers (mean age = 22.3) administered by professional investigators.

Results

The results confirmed our hypotheses.

Impact on Industry

In order to improve the effectiveness of prevention measures and to evaluate the effect of them, road-safety interventions should take into account comparative risk judgments about the targeted risk behavior.     

Blood on the coal: The effect of organizational size and differentiation on coal mine accidents

Introduction

Each year, there are at least 100,000,000 occupational accidents and 100,000 occupational deaths in the world. In the United States, one of the safest countries in the world in which to work, there were more than 5,400 workplace fatalities and 5.9 million workplace injuries in 2007. The cost to American industry and taxpayers is estimated to be at least $170 billion per year. Further, as illustrated by accidents such as Three Mile Island and Bhopal, industrial accidents potentially impact a much wider sphere than that of the injured worker and his or her employer. As the repercussions of organizational accidents reverberate through organizations and are felt from human resources to accounting, firms are beginning to incorporate messages of safety in their missions and strategies. As firms organize to achieve safer work environments, they are faced with decisions on how to structure their activities in terms of, among other things, size and differentiation.

Method

This paper explores the impact on accident rates of size and differentiation at the corporate and mine levels of mining companies in an effort to create a framework for thinking about organizational accidents from a structural perspective.

Results

The results suggest that larger mines are safer than smaller mines, and that mines with less task diversity are safer than mines with greater task diversity. The results also suggest that at the corporate level, task diversity decreases mine accidents. These results may help mining executives and engineers structure their corporate activities and individual mines more effectively to help reduce accidents.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

Comparison of risk factors for falls from height between commercial and residential roofers

Introduction

The purpose of this investigation was to compare commercial roofers and residential roofers in terms of their behaviors, beliefs, working conditions, and attitudes toward the use of fall protection devices, which could lead to fall accidents.

Methods

A cross-sectional sample of 252 roofers participated in the survey in the Midwest (Wisconsin, Illinois, Michigan, Indiana, and Iowa).

Results

Residential roofers were more likely to fall (adjusted prevalence ratio = 1.57, 95% CI = 0.86, 2.27) than commercial roofers. Existence of fall protection programs, enforcement of fall protection device use, actual use of fall protection devices, work type, company size, and race/ethnicity were significantly associated with fall accidents.

Impact on industry

This study adds insight into fall accidents from roofs in the construction industry and provides industry-specific cautions against fall accidents that can be reflected in regulatory agency implementation.     

Effect of a nurse back injury prevention intervention on the rate of injury compensation claims

Problem

This study evaluated the effect of introducing a No Lifting policy on back injuries to nurses, across an entire health care system.

Methods

Methods included: analysis of the data for all public health agencies in the Australian state of Victoria; compensation data from the Victorian Workcover Authority; data about workforce and program implementation from a retrospective survey of agencies; longitudinal analysis of standardized workers compensation claim rates for back injuries before, during and after the intervention.

Results

A statistically significant decline in back injury claim rates during implementation contrasted with no statistically significant trends within the periods before and after the intervention. A statistically significant reduction occurred in mean quarterly standard back injury claim incidence rates per 1,000 equivalent fulltime nursing staff (EFTNS), representing a 24% reduction in standard back injury claims/1000 EFTNS.

Discussion

Ergonomics principles encourage changing the work environment to suit the worker. This approach delivered a significant improvement in the immediate term.

Impact of industry

The substantial decline in back injury rates signifies a major improvement in the safety of a critical aspect of the work environment for nurses.     

Predicting driver drowsiness using vehicle measures: Recent insights and future challenges

Introduction

Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.

Method

The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.

Results

Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.

Conclusions

Further research is necessary to examine more complex functions, as well as individual differences between drivers.

Impact on Industry

A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

Validity of police-reported alcohol involvement in fatal motor carrier crashes in the United States between 1982 and 2005

Objective

To examine the validity of police-reported alcohol data for drivers involved in fatal motor carrier crashes.

Material and Methods

We determined the availability of blood alcohol concentration (BAC) and police-reported alcohol data on 157,702 drivers involved in fatal motor carrier crashes between 1982 - 2005 using Fatality Analysis and Reporting System (FARS) data. Drivers were categorized as motor carrier drivers if they operated a vehicle with a gross vehicle weight rating of greater than 26,000 pounds. Otherwise, they were classified as non motor carrier drivers. The sensitivity and specificity of police-reported alcohol involvement were estimated for both driver types.

Results

Of the 157,702 drivers, 18% had no alcohol information, 15% had BAC results, 42% had police-reported alcohol data, and 25% had both. Alcohol information varied significantly by driver, crash, and vehicle characteristics. For example, motor carrier drivers were significantly more likely (51%) to have BAC testing results compared to non motor carrier drivers (31%) (p < 0.001). The sensitivity of police-reported alcohol involvement for a BAC level ≥ 0.08 was 83% (95% CI 79%, 86%) for motor carrier drivers and 90% (95% CI 89%, 90%) for non motor carrier drivers. The specificity rates were 96% (95% CI 95%, 96%) and 91% (95% CI 90%, 91%), respectively.

Conclusions

The sensitivity and specificity of police-reported alcohol involvement are reasonably high for drivers involved in fatal motor carrier crashes. Further research is needed to determine the extent to which the accuracy of police-reported alcohol involvement may be overestimated because of verification bias.

Impact on the Industry

Based on the results of this study, the federal government should continue to work with states to strengthen their strategies to increase chemical testing of all drivers involved in fatal crashes.     

Epidemiology of subway-related fatalities in New York City, 1990-2003

Problem

Subway transit is a relatively safe mode of transportation, yet compared to all other forms of mass transit in the United States (U.S.), subways have the highest fatality rate. The aim of this paper is to characterize subway-related fatalities in order to identify opportunities for risk reduction.

Method

Medical examiner records for all New York City (NYC) subway-related deaths (1990-2003) were reviewed. Data were abstracted on decedents' demographics and autopsy findings, including laboratory findings.

Results

There were 668 subway-related fatalities, of these, 10 (1.5%) were homicides, 343 (51.3%) were determined to be suicides, and 315 (47.2%) were accidental. Although decedent characteristics varied between fatality categories, they were not particularly informative with regard to prevention.

Conclusion

Prevention strategies that focus on structural controls are likely to be most efficacious in improving the overall safety of the NYC subway systems.

Impact on industry

These findings suggest that structural rather than individual-level interventions would be most successful in preventing subway fatalities.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

The effect of work hours on adverse events and errors in health care

Introduction

We studied the relationship between registered nurses' extended work duration with adverse events and errors, including needlestick injuries, work-related injuries, patient falls with injury, nosocomial infections, and medication errors.

Method

Using bivariate and multivariate logistic regression, this secondary analysis of 11,516 registered nurses examined nurse characteristics, work hours, and adverse events and errors.

Results

All of the adverse event and error variables were significantly related to working more than 40 hours in the average week. Medication errors and needlestick injuries had the strongest and most consistent relationships with the work hour and voluntary overtime variables.

Discussion

This study confirms prior findings that increased work hours raise the likelihood of adverse events and errors in healthcare, and further found the same relationship with voluntary overtime.

Impact on Industry

Legislation has focused on mandatory overtime; however, this study demonstrated that voluntary overtime could also negatively impact nurse and patient safety.