4 Practical Compendium
Occupational Accidents, Workplace Accidents, Accidents at work, Workplace injuries, Determinants, Factors, Cost, Occupational Safety, Occupational Risk, Commuting Accidents, Accident Frequency, Accident Severity
4.1 General figures before proceeding to the study’s most important insights
This section provides information that may be generally relevant. We provide an overview of key numbers illustrating the scope of Occupational Accident (OA) between 2014 and 2023.
When two factors are found to be related, it does not mean that one causes the other. For instance, if people with higher wages report more occupational accidents, it doesn’t mean that higher wages lead to more occupational accidents. It simply shows that these two things happen together, not that one leads to the other.
For clarity, we have rounded numerical values typically downward, to avoid overstating the findings.
The final dataset covers 8 million monthly observations and 32,000 reported OA.
Some key numbers:
- There are 4 OA notifications for every 1,000 work months.
- For every 100 notifications, insurers reject slightly fewer than 9, meaning that just over 91 out of 100 notifications are accepted as OA.
- Of the accepted OA, roughly 15 out of 100 happen while commuting and 85 occur at the workplace.
- And of those workplace OA, almost 7 out of 100 are classified as severe.
The exact percentages are:
4.2 Key insights for employers
4.2.1 Medium-sized companies: a safety advantage?
The number of accepted OA appears to be linked to the company size. Employees in medium-sized companies (20 - 49 employees) report the fewest accepted workplace OA. In contrast, employees in both small companies (fewer than 5 employees) and large companies (more than 200 employees) show higher OA rates. To put in perspective: For every 10 accepted workplace OA among employees in small companies, there are 7 among employees in medium companies and 17 among employees in large companies. In other words, as an employee of a small company, you are 1.2 times more likely to be involved in a workplace OA compared to someone in a medium-sized company. Meanwhile, an employee working in a large company is 2.5 times more likely to be involved in a workplace OA compared to someone in a medium-sized company.
This suggests that safety and prevention policies should be tailored to company size. Small companies may benefit from easy-to-access safety tools and training that fit their limited resources. Larger companies, on the other hand, may require more comprehensive systems to manage complex risks. It’s also worth exploring why medium-sized companies report fewer OA, as this could reflect differences in reporting practices or safety approaches that merit further investigation.
4.2.2 Smaller companies face longer absences after OAs.
The length of absence following a workplace OA appears to vary by company size. Employees in companies with fewer than 5 employees show the longest average absence. For example, where an employee in a company with 50 - 99 employees reports 10 absence days, an employee in the smallest companies report slightly over 13 days.
It is also worth considering the role of social dynamics within companies. In smaller companies, employees may feel social or managerial pressure not to take time off after an accident unless absolutely necessary. This could lead to underreporting of minor incidents or shorter absences. In contrast, employees in larger companies might feel more comfortable taking time off, which could influence the average length of absence following an OA.
Combined with the higher rate of OA in very small companies, this underscores the need for targeted prevention strategies. Tailored support, simplified procedures, and accessible safety resources can help address the specific challenges these companies face.
4.2.3 Workplace safety starts with onboarding. A focus on vulnerable groups.
Less experienced employees tend to report more workplace OA. For every 10 notifications among the most experienced workers, there are approximately 14 among the least experienced. A similar trend appears when comparing contract types: for every 10 notifications among workers with unlimited contracts, there are about 12 among those with fixed-term contracts. This pattern is consistent for accepted workplace OA as well.
One group particularly at-risk consists of less experienced workers on fixed-term (‘interim’) contracts. In this group, for every 10 notifications among experienced workers with unlimited contracts, there are around 18 notifications.
This highlights the importance for employers to provide thorough onboarding and clear communication about workplace risks, especially for new and temporary staff.
4.2.4 Demographics shape risk: the link between age, gender and workplace OA rates.
Some personal characteristics, like gender and age, are linked to workplace OA rates. For every 10 female employees with accepted OA, there are about 13 male employees. Similarly, employees between the ages of 20 and 59 have more OA than those younger than 20 or older than 60, roughly 13 workplace OA for every 10 in the younger and older age groups.
While gender and age are not factors employers can influence, it’s still useful to understand how these factors relate to workplace OA.
4.2.5 Company practices matter: up to 20% of OA differences are linked to company-level factors.
Not all companies are the same. Even after accounting for sector, job type, and wages, up to one fifth of the differences in accepted OA seem to come down to company-specific practices. In other words, some companies manage safety better than others, possibly due to stronger safety culture or safer work environments.
To improve overall safety, it would be valuable for companies to share best practices. Initiatives like inter-company safety days can help facilitate the exchange of effective strategies that others can adopt.
4.2.7 Why workplace OA can be costlier than you think.
Another way to assess the severity of OAs is by examining the employee absence duration. We compared two data sources: official insurer records (via Federaal agentschap voor beroepsrisico’s (FEDRIS)) and our own Liantis Payroll Services (PS) operational data.
Insurer documents showed a median absence of 5 days and a mean of 24 days per accident. In contrast, our payroll records indicated a median of 10 days and a mean of 33 days. Although part of the costs are covered by OA insurers, practical challenges and productivity losses still fall on the employer. This notable discrepancy suggests that the actual impact of OA, especially in terms of absence and wage cost, is often underestimated.
The gap between reported and actual absence durations highlights a potential blind spot in how companies assess the cost and severity of workplace OA. To gain a more accurate understanding and improve risk management, organizations should complement insurer data with internal or external records. This approach could lead to more accurate budgeting, improved prevention budget allocation, and a clearer picture of the real cost, possibly up to twice as high as currently estimated.
4.2.8 Understanding the challenges of preventing commuting accidents.
Commuting OA are particularly challenging for employers to prevent as they are largely influenced by individual differences. Factors such as job type, sector, or nationality do not explain much of the variation. Instead, personal characteristics, possibly including personality traits, attitudes toward driving or unique circumstances unique, seem to play a more significant role, even though these are not directly measured.
This makes it challenging to design collective prevention strategies, as the risk of commuting OA is more closely tied to these individual differences. What works for one employee may not be effective for another.
One clear pattern does emerge: seasonality. Commuting OA are more frequent in certain months. For instance, an employee is twice as likely to be involved in a commuting OA in January as in August.
4.2.9 Why frequency and severity degree metrics may mislead prevention policy.
When we try to predict the frequency and severity degree of OA at company-level, the results are very random. Even when we look at the same company over time, we can’t explain more than 10% of the differences. This means that a company’s score in one year doesn’t tell us much about the next year.
This randomness makes these indicators unreliable for evaluating the effectiveness of prevention policies. Other researchers have noted similar concerns. For example, to make a metric like Total Recordable Incident Rate (TRIR) statistically meaningful, you’d need over million hours of work (Hallowell et al., 2021), far beyond what most companies ever reach.
Despite this, these metrics are embedded in legislation. Companies must report them, and may even face financial penalties if their scores are worse than sector benchmarks. While these sector averages may be more stable due to large aggregated work hours, individual company scores can be easily inflated by just one OA. The high degree of randomness in frequency and severity degree metrics undermines their usefulness for company-level policy evaluation.
Policymakers should reconsider the importance given to these indicators and explore alternative approaches. Companies, in turn, are advised to use caution when relying on these scores to guide prevention efforts. Instead, they should be encouraged to focus on qualitative insights, root cause analyses, and targeted risk assessments. Monitoring indicators of safety culture and engagement, such as near-miss reporting frequency, onboarding and safety training program completion rates, audits and inspections, hazard and risk correction rates, and other organizational indicators, can offer more meaningful insights into safety engagement and awareness and can guide more effective prevention strategies.
4.2.10 Facilitate and promote the reporting of occupational incidents and accidents.
Significant progress has been made in safety practices through the investigation of OA. The preventive measures that have emerged, and continue to emerge, from these investigations have contributed to meaningful behavioural changes and a shift in company culture, with increased focus on workplace safety.
However, it is important to emphasize that reported OA represent only the tip of the iceberg. Many incidents remain formally unreported, and valuable insights often lie hidden within day-to-day occurrences that do not meet the threshold for mandatory reporting. Organizations should invest in improved incident recording systems or seek external support to do so. Encourage employees to report incidents and near misses, and foster a psychologically safe, open-minded culture where everyone feels empowered to learn from one another.
When an incident escalates into an OA, be aware of the simplified declaration process. Currently used in fewer than 10% of cases, this pathway appears underutilized, especially considering that nearly half of workplace accidents result in fewer than four days of absence. Stakeholders are encouraged to explore ways to reduce administrative burden and raise awareness of this option. Promoting appropriate use of simplified declarations can streamline reporting and enhance efficiency without compromising data quality.
4.2.11 Blue-collar workers report twice as many accidents as white-collar workers. Gap widens for accepted cases.
It should come as no surprise that there is a significant difference between white-collar and blue-collar workers. For every 10 OA notifications among white-collar workers, there are about 20 among blue-collar workers. This gap becomes even more pronounced when considering accepted OAs, with approximately 25 accepted cases among blue-collar workers for every 10 among white-collar workers.
This suggests that, despite advances in workplace safety, there is still considerable room for improvement in blue-collar work environments to ensure safer working conditions.
4.3 Key insights for employees
4.3.1 Workplace versus commuting OAs.
Around 85% of accepted OAs happen in the workplace, not during commuting. This means that most risks are present on-site, and staying alert during your workday is essential. This distinction is important for understanding where preventive efforts should be focused.
4.3.2 Your first days matter: stay alert, stay safe.
Starting a new job or working on a fixed-term (‘interim’) contract? You are statistically more likely to have a workplace OA. For every 10 notifications among the most experienced workers, there are about 14 among the least experienced. Similarly, for every 10 notifications from permanent staff, there are around 12 from those with fixed-term contracts. The highest risk group? Less experienced workers on fixed-term contracts, who report up to 18 notifications for every 10 among experienced permanent staff.
That’s why proper onboarding is so important. Make sure you understand the safety procedures, ask questions if anything is unclear, and speak up about risks. Being proactive in your first days or weeks can make a real difference in staying safe.
If you are an experienced employee, your role in workplace safety goes beyond your own actions. New or temporary colleagues may not yet be familiar with the risks or procedures. Take a moment to check in, offer guidance, and share practical tips. Even a small gesture, like pointing out a hazard or explaining a safety routine, can help prevent accidents and create a safer, more supportive workplace for everyone.
4.3.3 Workplace safety: a priority for blue-collar roles.
If you work in a blue-collar job, you are statistically more likely to have a workplace OA. For every 10 accident notifications among white-collar workers, there are about 20 among blue-collar workers. This gap widens even further when looking at accepted cases, with roughly 25 accepted accidents among blue-collar workers for every 10 among white-collar staff.
This does not mean OA are unavoidable, it means staying alert and following safety procedures is even more important. Take time to understand the risks in your work environment, use protective gear correctly, and speak up if something feels unsafe. Your awareness and actions are key to protect yourself and those around you.
4.3.4 Accidents and demographics.
Workplace OA can happen to anyone, but some groups face higher risks. Data shows that male employees report more OA than female employees, about 13 for every 10. Age also plays a role, especially in recovery time. For example, a 60-year-old may need four times more days off than an 18-year-old after an OA.
This does not mean older or male workers are less careful. It means that the consequences of OA can vary depending on your age and physical resilience. If you are in an older age group, it’s especially important to stay alert, follow safety procedures closely, and report any incident, even minor ones. Your health and safety matter, and being informed helps you protect yourself better.
4.3.5 Winter commutes: higher risk, greater caution.
Commuting OA do not happen evenly throughout the year. Data shows a clear seasonal pattern: for every OA reported in August, there are about twice as many in January. This increase may be linked to winter weather, poor visibility, or more stressful travel conditions.
To reduce your risk, give yourself enough time to travel, wear visible clothing if you’re biking or riding a motorbike,, especially in the dark, and be cautious in slippery conditions. A few small changes can make a big difference in getting to work safely.
4.4 Key insights for policy makers
4.4.2 Insurer acceptance rates: adjusted data shows equity.
We looked at how often insurers accept claims for workplace OA. At first, the acceptance rates seemed different between insurers. But when we adjusted for key factors like the sector or company size, those differences disappeared.
This suggests that, overall, there is a relatively fair playing field among insurers in terms of claim outcomes. Still, it is important to maintain transparency and continue monitoring insurer practices to ensure fairness remains consistent.
4.4.3 Supporting vulnerable workers through stronger onboarding.
Our analysis shows that vulnerable workers report more workplace OA. For every 10 notifications from experienced workers, there are approximately 14 from less experienced ones. Similarly, workers with fixed-term contracts report more OA than those with permanent contracts. The most at-risk group consists of inexperienced workers on fixed-term (‘interim’) contracts. This group reports significantly more workplace OA, about 18 for every 10 reported by experienced employees with permanent contracts.
This highlights the need for policy measures that support thorough onboarding and clear communication about workplace risks, especially for new and temporary workers. The government can play a key role by setting minimum onboarding standards, encouraging sector-specific safety guidelines, and supporting initiatives that improve access to safety information for all workers, regardless of contract type or experience level.
4.4.4 Sector-specific: a call for targeted prevention.
Our analysis shows that workers in some sectors report more workplace OA than in others. The lowest numbers are found in accommodation and food services, as well as in information and communication. In contrast, manufacturing, construction, and health & social care report the highest numbers. For every 10 notifications in the lower-risk sectors, there are approximately 26 in construction and health & social activities, and slightly more than 23 in manufacturing. These differences become even more pronounced when looking at accepted OA, rising to 36 and 33, respectively.
Importantly, this pattern persists even after adjusting for several factors, indicating that sector-specific risks are not solely explained by company size, job type, or other contextual factors.
The severity of OA also varies. In sectors like real estate and professional services workers are absent for a short time. But in manufacturing and construction, the number of absence days per OA is about twice as high. This suggests that both the frequency and impact of OA are sector-dependent.
For policymakers, this underscores the need to move beyond one-size-fits-all prevention strategies. Sector-specific approaches, such as tailored safety guidelines, targeted inspection capacity, and focused awareness campaigns, are essential to address the unique risk profiles of different industries. Tailoring prevention efforts to the realities of different sectors can lead to more effective safety policies.
4.4.5 Supporting small businesses in workplace safety.
Employees in very small companies (with 5 or fewer employees) appear to be 1.2 times more likely to have a workplace OA compared to those in medium-sized companies (20-49 employees). These OA also tend to lead to longer absences in these very small companies. This suggests that small enterprises may face specific challenges.
Tailored prevention strategies, simplified administrative processes, and accessible safety resources could help address challenges unique to small companies. If policymakers support small companies in these ways, it could help reduce both the human and financial impact of workplace OA.
4.4.6 Tackling underreporting to improve workplace safety.
Notification numbers of OA appear significantly lower among low-wage workers, but this likely reflects underreporting rather than safer jobs. For every notification in the lowest wage group, there are slightly more than 4 in the middle category and just over 2 in the highest category. The same pattern appears in accepted workplace OA. This suggests that workers with lower wages, who may be more vulnerable, are less likely to report incidents. Reasons could be fear of financial repercussions or lack of awareness about reporting procedures. It is therefore essential that policy efforts explicitly account for underreporting as a real issue that can affect data and analyses. To ensure fair and effective prevention strategies, all workers must be informed, supported, and empowered to report workplace accidents without hesitation. Government and other stakeholders play a key role in promoting awareness, simplifying reporting procedures, and fostering a safety culture where every incident is acknowledged and addressed.
4.4.8 Seasonal patterns in commuting (and workplace) accidents: align safety campaigns strategically.
Commuting OA rise sharply in winter: January sees nearly twice as many reported commuting OA as August. These variations are likely influenced by factors such as bad weather and reduced daylight. Additionally, workplace OAs tend to peak around June, September, and October.
For policymakers, these patterns present a strategic opportunity to time safety campaigns more effectively. Focused efforts, such as promoting visibility and caution in slippery conditions, should be boosted during high-risk months. Winter commuting, in particular, warrants special attention due to elevated commuting OA risks. Aligning prevention campaigns with seasonal trends can more effectively reduce commuting-related injuries and promote safer travel to and from work.
4.4.9 Rethinking frequency and severity degree metrics in OA policy.
Traditional safety metrics such as OA frequency and severity degrees are deeply embedded in legislation and widely used for benchmarking company safety performance. However, recent analyses show that these indicators offer limited value for policy. Our analysis shows they’re highly unpredictable, with less than 10% of the variation in these scores explained. A company’s score in one year does not reliably predict its score in the next, making these metrics unsuitable for hard benchmarking or evaluating prevention efforts.
Despite their statistical instability, companies are often required to report these metrics and may face financial consequences if their scores exceed sector benchmarks. While such benchmarks may be meaningful at the aggregated sector level, they are not reliable at the individual company level, where a single OA can disproportionately skew results. Researchers have noted that to make these metrics statistically robust, data from millions of work hours would be needed (Hallowell et al., 2021), far beyond the scope of most companies.
Policymakers should reconsider the weight given to frequency and severity metrics in regulatory frameworks and explore alternative approaches that better reflect safety performance. Companies, in turn, should be encouraged to focus on qualitative insights, root cause analyses, and targeted risk assessments. Monitoring indicators of safety culture and engagement, such as near-miss reporting frequency, onboarding and safety training program completion rates, audits and inspections, hazard and risk correction rates, and other organizational indicators, can offer more meaningful insights into safety engagement and awareness and can guide more effective prevention strategies.
One of the most informative indicators is the number of days an employee is absent following an OA. However, current reporting practices may significantly underestimate this impact. A comparison between insurer records and internal payroll data revealed notable discrepancies: while insurer documents reported a median of 5 days and a mean of 24 days per accident, payroll data showed a median of 10 days and a mean of 33 days. This suggests that the true cost, particularly in terms of wage loss and productivity, may be up to twice as high as officially recorded.
To address this blind spot, policymakers should operationalize access to validated and improved absence data. Our approach has demonstrated improved explanatory power and model quality. Without an official data flow this approach remains difficult to use outside of research contexts, limiting its potential to inform policy and practice. Establishing a formal mechanism for sharing validated absence data with relevant stakeholders would enable more accurate budgeting, targeted prevention efforts, and better-informed reintegration strategies.
Recognizing and integrating these more reliable data sources into the policy framework is a critical step toward understanding the full impact of OAs and designing interventions that reflect their true cost.
4.4.10 Strengthening data governance and communication in OA systems.
Effective prevention and reintegration strategies rely on high-quality, transparent, and well-structured data flows. However, the current system for reporting, accepting and compensating OAs remains complex, fragmented, and vulnerable to inconsistencies. To improve reliability and usability, it is essential to simplify this system and ensure that each OA is uniquely and consistently identified across all stakeholders. Distributed ledger technologies may offer a promising solution by streamlining data exchange and maintaining a validated, up-to-date record for each case.
Clear communication is equally vital. When technical changes are made to OA data flows, particularly to variables that are business-critical for stakeholders, a formal and well-communicated process must be in place. FEDRIS and/or Kruispuntbank Sociale Zekerheid (CBSS in English) (KSZ) should ensure that such processes are properly documented and shared with all relevant parties. Moreover, discrepancies in variable definitions and labelling, as highlighted in our data quality report, underscore the need for improved and regularly updated documentation of OA variables within KSZ systems.
4.4.11 Improving transparency and utility of OA data for prevention and reintegration.
To enable evidence-based prevention and more effective reintegration, stakeholders need access to key outcome data such as the number of days absent, total accident cost, final case status, and severity of the OA. Structurally sharing (parts of) the final validated OA record with relevant actors, including prevention advisors, OA insurers, and researchers, can significantly enhance the impact of interventions and policy decisions.
A unified approach to assess the severity of OA is also recommended. Currently, multiple actors (External Service for Prevention and Protection at work (EDPB in Dutch) (ESPP), FEDRIS, OA insurers) apply the same rules independently, leading to fragmented evaluations. Storing the result of the evaluation as the final validated message, combined with centralized quality control could improve consistency, fairness, and trust in the system.
4.4.12 Optimizing the simplified declaration pathway and exposure metrics.
The simplified OA declaration pathway, which is designed to reduce administrative burden for minor accidents, appears to be underutilized. Although fewer than 10% of cases currently follow this route, nearly half of all reported accidents involve less than four days of absence. This suggests a missed opportunity to streamline reporting. Stakeholders should explore ways to increase awareness and promote appropriate use of this pathway.
Finally, greater clarity is needed regarding the calculation of exposure metrics, such as the number of workers at work. Current methods based on Déclaration multifonctionelle / multifunctionele Aangifte (DmfA) data may overestimate actual exposure by including individuals on leave, long-term sick leave, or maternity leave. This could distort risk assessments and policy evaluations. Transparent documentation and refined methodologies are essential to ensure accurate exposure estimates.
List Of Acronyms
- DmfA
- Déclaration multifonctionelle / multifunctionele Aangifte
- ESPP
- External Service for Prevention and Protection at work (EDPB in Dutch)
- FEDRIS
- Federaal agentschap voor beroepsrisico’s
- KSZ
- Kruispuntbank Sociale Zekerheid (CBSS in English)
- OA
- Occupational Accident
- PS
- Payroll Services
- TRIR
- Total Recordable Incident Rate