Underreporting study

EUROPEAN COMMISSION EUROSTAT Directorate F: Social statistics Unit F-5: Education, health and social protection

Doc ESTAT/F5/ESAW/2021/4

Underreporting study

Item 4 of the agenda

Meeting of the Working Group

European Statistics on Accidents at Work (ESAW)

Virtual meeting

Luxembourg, 20 October 2021

Methodological study on under-reporting of occupational accidents in European Union

Abstract

3 Methodological study on under-reporting of occupational accidents in European Union

Abstract: Background: Occupational accidents or occupational injuries caused by accidents at work have

been target for prevention for well over 100 years. Fatal and serious accidents have been better

covered while less serious accidents may not be properly notified, reported, recorded and

compensated. Fatal accidents are clearly important as they cause a high number of years of lost life

and lower the life expectancy; the average age of a victim is in the range of 30–35 years (1).

However, the sheer high number of non-fatal cases is a cause of shorter or longer disability to work

and often considerably high number of losses to both the individual and the society and leads to

permanent reduction of workability as measured by Disability Adjusted Life Years. This is important

in terms of both ethical and economic losses to workers themselves, employers and the society as a

whole. A fatal accident is less frequent however; thousands of less serious injuries and incidents

usually precede it. These are good indicators for identifying risks and for the prevention of both fatal

and non-fatal accidents.

Objective: The objective of this study was to identify the level of under-reporting of non-fatal

accidents based on the assumption of better reported/recorded fatal cases in the European Union.

Based on existing numerical evidence a methodology could be established to work out a comparable

and adjustable mechanism for defining the magnitude and the number of non-fatal accidents in EU

Member States. This would be particularly interesting in the attempt of reaching the objective of the

study of providing a level of the existing underreporting.

Methods: Depending on several factors, such as the economic sector, gender, age and cultures in

EU Member States the reporting is varying widely at overall level and at sectoral fatal/non-fatal rates:

from 1 fatal/2 000 non-fatal reported cases to 1 fatal/10 non-fatal and an average EU rate in the

range of 1/1250. This wide range is reflected also in various sectors such as construction,

manufacturing and services. The particularities and characteristics of the sectors are reflected by

varying rates of fatal/non-fatal accidents. Further, certain countries have been selected as

benchmark countries and priority variables have been selected and have been used in consecutive

imputations with the purpose of replacing poor reported values until observed changes were

negligible.

Results: While there are genuine differences between EU Member States in reporting of fatal

accidents in overall and in different economic sectors, the adjusted numbers for less reported non-

fatal cases in EU are in reality much closer to the best reporting countries. Differences result from

selected technical processes, tools, methods of work, level of mechanization, automation, and the

level of labour intensive jobs in Member States. Administrative reporting practices from workplaces

are equally different. According to the available Eurostat data, we arrived to the result that the overall

number of non-fatal accidents goes from a range of reported 2.4 million cases to 6.9 million of such

injuries per year in question. Individual country under-reporting varies even more drastically. This

would have a major impact on the priorities for prevention and possibly could have significant effects

to insurance and social support systems for victims of occupational accidents. The relatively rare

fatal accidents will not provide a sufficient base of knowledge for heading for the long-term ‘vision

zero’ and ‘zero harm’ efforts.

Discussion: A way to check the level of reporting compared to the adjusted numbers could also be

the 2020 ad-hoc module of the labour force survey ‘Accidents at work and work related health

problems’ (2) even though the classification between formal reporting and ad-hoc labour-force

surveys are not entirely comparable. The average level and number of non-fatal injury cases in

relation to fatal ones tend to raise when economies are shifting from production based to service-

(1) Finnish Workers Compensation Centre, Statistics Report ‘Occupational accidents in 2009–2018’ -pdf -file, annex on

p.4 excel table in Finnish, (https://www.tvk.fi/tietopalvelu-ja-julkaisut/tilastot/tyotapaturmatilastot/) (2) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_labour_force_survey_-_ad_hoc_modules#Overview_of_the_ad_hoc_modules

https://www.tvk.fi/tietopalvelu-ja-julkaisut/tilastot/tyotapaturmatilastot/

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_labour_force_survey_-_ad_hoc_modules#Overview_of_the_ad_hoc_modules

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_labour_force_survey_-_ad_hoc_modules#Overview_of_the_ad_hoc_modules

Abstract


oriented occupations as hazardous situations at workplaces decreases. This study provides

consistent data also in terms of rates of non-fatal accidents by 100 000 workers and the past ad-hoc

labour force survey (2013?).

Conclusion: Adjusting incidence of non-fatal accidents with the proposed methodology reflects the

actual risk level in Member States considerably better and provides a solid and research based

background for preventing risks at places of work.

Keywords: occupational accidents, accident-reporting systems, statistical methods, adjusting and

standardizing data.

Authors: Gianni Betti (3), Ivars Vanadzins (4), Hionia Vlachou (5) and Jukka Takala (6) (7) (8)

Acknowledgement: This work has been carried out under the supervision of the Eurostat project

manager Matyas Meszaros. Special thanks goes to Silvia Crintea-Rotaru for helpful comments.

(3) University of Siena; Department of Economics and Statistics, Italy, [email protected] (4) Riga Stradins University, Institute of Occupational safety and environmental health, [email protected] (5) Gopa GOPA Consultants, Senior Statistician, [email protected] (6) ICOH c/o INAIL Italy (7) Tampere University, Faculty of Engineering and Natural Sciences/Faculty of Social Sciences, Finland (8) Correspondence: [email protected], [email protected]

mailto:[email protected]





Table of contents


Table of contents

Introduction and objectives ....................................................................................... 8

1. Introduction and objectives .............................................................................................. 8

1.1 Evidence for the existence of under-reporting ................................................................ 9

1.2 Objective ....................................................................................................................... 11

Materials and Methods ............................................................................................. 12

2. Materials and Methods .................................................................................................... 12

2.1 Materials and existing data ........................................................................................... 12

2.2 Description of methodology .......................................................................................... 14

Results....................................................................................................................... 19

3. Results .............................................................................................................................. 19

Discussion ................................................................................................................ 24

4. Discussion ........................................................................................................................ 24

Conclusions .............................................................................................................. 25

5. Conclusions ...................................................................................................................... 25

Annex ........................................................................................................................ 27

Abbreviations


Abbreviations ESAW European Statistics on Accidents at Work

ILO International Labour Organization

AHMs Ad-Hoc Modules

BSN Baltic Sea Network countries

EFTA European Free Trade Association

ESAW European Statistics on Accidents at Work

EU European Union

Eurobase Eurostat's dissemination database

Eurostat Statistical office of the European Union

EU-OSHA European Union Agency for Safety and Health at Work

MS Member States

ISCO International Standard Classification of Occupations

LFS Labour Force Survey

NACE Statistical classification of economic activities in the European Community

WHO World Health Organization

ILO International Labour Organisation

SRMI sequential regression multivariate imputation

MAR Missing at Random

Country codes EU-28 European Union (28 countries)

EU-25 European Union (25 countries)

BE Belgium

BG Bulgaria

CZ Czechia

DK Denmark

DE Germany

EE Estonia

IE Ireland

EL Greece

ES Spain

FR France

HR Croatia

IT Italy

CY Cyprus

LV Latvia

LT Lithuania

LU Luxembourg

HU Hungary

MT Malta

NL Netherlands

Abbreviations


AT Austria

PL Poland

PT Portugal

RO Romania

SI Slovenia

SK Slovakia

FI Finland

SE Sweden

UK United Kingdom

1 Introduction and objectives


1. Introduction and objectives European Statistics on Accidents at Work (ESAW) is an administrative data collection. The legal

basis for ESAW data is the European Commission Regulation (EU) No 349/2011(9) as regards the

statistics on accidents at work. It defines the variables, the breakdowns and the metadata information

that the Member States have to deliver to Eurostat providing reliable and comparable information on

accidents at work. The data contains information about the reporting organisation, about the

characteristics of the victim, characteristics of the accident and certain causes and circumstances of

accidents.

According to the ESAW regulation mentioned above, an accident at work means a discrete

occurrence in the course of work which leads to physical or mental harm. The phrase in the course of

the work means whilst engaged in an occupational activity or during the time spent at work. This

includes road traffic accidents that occur in the course of the work but exclude commuting accidents

i.e. road accidents that occur during the journey between home and workplace.

Accidents at work are disaggregated into ‘non-fatal’ and ‘fatal’ and is defined as follows:

‘A non-fatal accident at work’ is an accident which a victim survives and may result in one or more

days of absence from work. A serious non-fatal accident at work is an accident at work resulting in

more than three days' of absence from work.

The Labour Force Survey modules on accidents at work and other work-related health problems

include self-reported data on all non-fatal accidents (including the possibility to exclude those

accidents with less than four days of absence).

The scope of the administrative data collection ESAW 'European Statistics on Accidents at Work' is

referring only to data on accidents with four days or more of absence as well as fatal accidents at

work. Only full calendar days of absence from work have to be considered, excluding the day of the

accident. Consequently, ‘more than three calendar days’ means ‘at least four calendar days’, which

implies that only if the victim resumes work on the fifth (or subsequent) working day after the date on

which the accident occurred should the incident be included.’ (10)

‘A fatal accident at work refers to an accident at work which leads to the death of a victim within one

year of the accident.’ (11)

These definitions are slightly different from the one provided by the ILO (1988), where: an

occupational accident is an unexpected and unplanned occurrence, including acts of violence,

(9) https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32011R0349&qid=1556019536013&from=EN

(10) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Non-fatal_accident_at_work

(11) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Fatal_accident_at_work


https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Accident_at_work

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Accident_at_work

https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32011R0349&qid=1556019536013&from=EN

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Non-fatal_accident_at_work

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Fatal_accident_at_work



arising out of or in connection with work which results in one or more workers incurring a personal

injury, disease or death. As occupational accidents are to be considered travel, transport or road

traffic accidents in which workers are injured and which arise out of or in the course of work, i.e.

while engaged in an economic activity, or at work, or carrying on the business of the employer.

1.1 Evidence for the existence of under-reporting

Several studies have provided evidence for the existence of under-reporting of both fatal and non-

fatal work accidents in the European Union; in particular, the under-reporting in the case of non-fatal

work accidents could be of high level and differences of magnitude between EU countries significant.

Takala et al. (2017) showed that EU-28 rates are quite low compared to other countries globally

however within the EU the variation is relatively high, see Figure 1. which could signal potential

issues in the reporting of accidents at work. The implementation of systematic accident prevention

programs produced a positive impact and the fatal accident rates, in general, continue to decrease.

However, this trend is not observable for non-fatal accidents. An adjustment in order reach

comparability within the countries, is could be envisaged and would mean adjusting by the original

fatal accident/non-fatal injury rates. The adjustment will take into account the difference of the

economic structure composition and further other variables of the EU-28 as compared to their

original structure.

Actions on the prevention of fatal accidents arising from both work-related traffic injuries and all other

accidents has been challenging due to missing data e.g. from the UK for traffic injuries (12). When

only other accidents than traffic accidents are compared it has been easier. The countries with a low

incidence of accidents at work included major countries such as the UK with 0.74 fatal injuries per

100 000 employed and Germany with 0.9 per 100 000. Comparative global outcomes are based

further on ILO estimates (Hämäläinen et al. 2017) as a source for global data. Data including work-

related traffic accidents in the estimates significantly increases the rates including those in the EU-28

up to double of those presented in Figure 1.

Standardized incidence rates, in Figure 1, include adjustments based on average industry structures

in the EU. Countries that have a relatively high level of activity in high-risk industries, such as

construction, would otherwise show much higher rates as compared to those with a high service

industry component even though within each economic sector, their safety levels and rates would be

equal to those in an another country. While Eurostat rates have been standardized, the non-EU

country rates could not be adjusted due to lack of comparable data. Fatal injury rates in industrialized

countries are gradually decreasing (Takala 2019), partly due to a shift in the countries’ economic

structure from dangerous sectors to less risky ones, such as the services sector as well as

technological developments such as automatisation.

A number of scientific reports (Kurppa 2015, Hämäläinen 2017) showed that similar patterns should

be equally visible for non-fatal accidents.

(12) Health and Safety Executive , United Kingdom, 2011 , see further in Takala et al. 2017



Figure 1: Standardised incidence rates (per 100 000 workers) of fatal accidents at work for 2011 (13)

Takala et al. (2014) specified that accident prevention cannot start from the fatal injuries on top of the

pyramid, see Figure 2, without proper knowledge of the non-fatal injuries and hazards at work as

they represent the majority of accidents at work. The existing hazards — according to accident

causation theories — will end up in a more serious outcome when several contributing factors are

present simultaneously. Fatal outcomes are such rare events and can only be eliminated or reduced

when the individual hazards and exposures are known, and then are gradually and continually

eliminated or reduced, in a priority order, based on risk assessment.

Figure 2: Accidents at work for every fatal accident (14)

(13) Eurostat, Takala et al. 2017, Note: Singapore, Malaysia and World rates not standardised

(14) German figures are from German Statutory Accident Insurance System (DGUV), EU-28 and Singapore fatal injuries are taken from official statistics and non-fatal cases from ad-hoc labour force surveys, Source: Takala et al. JOEH: http://www.tandfonline.com/doi/pdf/10.180/15459624.20003.863131

http://www.tandfonline.com/doi/pdf/10.180/15459624.20003.863131



1.2 Objective

The objective of the present project is to provide further evidence for the existence and the scale of

under-reporting of non-fatal and fatal accidents in the EU. Based on that the study aims at

developing and testing of a new methodology for a possible adjustment and standardisation of the

level of non-fatal accidents for all EU Member States.

Estimates of the magnitude of non-fatal accidents at work based on fatal work injuries are likewise

required to provide a quantitative base for calculating the economic costs of work injuries to

employees, employers, industries, insurances systems and the government. A prerequisite is that the

statistical information is reliable and comparable. Serious under-reporting of work injuries could result

in mistaken policy measures and heavily flawed economic estimations (Kurppa 2015).

2 Materials and Methods


2. Materials and Methods

2.1 Materials and existing data

The data presently available in Eurobase (Eurostat’ dissemination database) demonstrates a wide

magnitude of difference between the EU Member States.

By inverting the numbers in Figure 2, reported here in EU Member States in Figure 3, it is evident

that the annual rates of fatal/non-fatal accidents vary from year to year significantly due to the fact

that fatal accidents are statistically rare events. In particular, in small countries a rolling average of at

least three years and sometimes more years should be used to avoid false interpretation based on

possible random annual fluctuation. The difference of the vertical scale from this Figure 3 as

compared to the next Figure 4 should be noted.

Figure 3: Number of fatal accidents per 1 000 non-fatal accidents (>3 days lost) in better reporting countries and selected other EU countries (1st group) for all NACE Rev. 2 branches A – U/UNK, 2008–14

The magnitude of difference in these rates are about 102 -fold. The annual fluctuation of the rates in

smaller countries compared to EU-15 total are large and often random. Data for EU-15 are used to

accent the significant differences in incidence rates as data from EU-28 would also include countries

0.0

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0.2

0.3

0.4

0.5

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0.7

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EU15 ES DK DE FI

2008 2009 2010 2011 2012 2013 2014

Source: Eurostat (online data code: hsw_n2_02; hsw_n2_01)




with suspected poorer reporting of non-fatal accidents at work.

Figure 4: Number of fatal accidents per 1 000 non-fatal accidents (>3 days lost) in better

reporting (15),and selected other EU countries (2nd group) for all NACE Rev. 2 branches A –

U/UNK, 2008–14

Another major difference of the fatal/non-fatal accidents can be seen when analysing various

sectoral accident rates shown in Figure 5. This means that adjusting the values should take into

account several variables such as economic sector (NACE), age, sex, rolling average of the years

etc.

(15) The country selection is simply due to level of reporting not necessarily between EU-15 and the rest. Also later

division of groups in Table 2 is based on WHO classification due to estimates made by ILO using WHO regions. WHO High Income includes Slovenia (not EU-15) higher income.

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EU15 RO BG LT LV

2008 2009 2010 2011 2012 2013 2014




Figure 5: Number of fatal accidents per 1 000 non-fatal accidents (>3 days lost) for selected NACE Rev. 2 divisions in EU-15, 2014 (ratio)

2.2 Description of methodology

The methodology proposed is based on applying of different methods in order to statistically

determine the size of under-reporting of non-fatal accidents in the EU.

The unique previous tentative effort for assessing the size of underreporting of non-fatal accidents

was carried out by Kurppa (2015), whose aim was to provide estimates in order to determine the

magnitude in size of occurrence and levels of reporting and underreporting of non-fatal work

accidents in the Baltic Sea Network (BSN) countries, including eight out of 28 EU countries. The

study conducted semi-quantitative data analyses for monitoring statistics that have been submitted

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by national statistical authorities to the ILO during a 5-year period between 2003 and 2007.

Kurppa (2015), adopted two main schemes for providing estimates of the order of magnitude about

the size of occurrence and levels of reporting and underreporting of non-fatal work accidents:

In the first scheme, incidence rates of accidents at work of benchmark countries were applied to the

workforce of a country. Then the calculated numbers of accidents were compared with the numbers

registered in the official statistics.

In the second scheme, the expected numbers of non-fatal work accidents were calculated by

multiplying the registered number of fatal work accidents in a country by an external coefficient (ratio

between fatal and non-fatal accidents) of a benchmark country.

Based on previous analysis, Kurppa (2015) noted that the reporting of non-fatal work accidents is

nearly complete in Finland and Germany, where the compensation schemes encourage reporting by

providing positive incentives.

In the current paper, statistical indicators for Finland, Germany, and the EU-15 average were used

as benchmarks against the indicator data of work accidents of other countries were compared.

Germany and Finland show no or very low levels of underreporting; for those reasons the first step of

the proposed methodology consists in selecting Germany and Finland as ‘benchmark’ countries.

In Kurppa (2015) incidence rates have been estimated by gender and by branch of economic activity;

however, evidence shows that such rates may vary according also to other characteristics of the

labour market and due to administrative requirements. For these reasons, in the present

methodology, we aimed to investigate which could be the variables or characteristics having an

impact in the estimation of incidence rates. For meeting such an objective, an iterative imputation

procedure for correcting non-fatal accidents percentages/ratios was proposed. In synthesis, the

proposal is based on an iterative procedure of one model with three consecutive steps, as follows:

Step 1. Following proposal of Kurppa (2015), choosing ‘benchmark’ countries.

Step 2. Estimate linear regression models of non-fatal / fatal accidents ratios in such countries in

function of some selected explanatory variables.

Step 3. Impute non-fatal / fatal accidents ratios in other countries; consider such estimate as

Estimate [0]

Repeat Steps 2–3 in iterative way until convergence of the regression parameters in step 2 is

reached.

A suitable method is to use the ratio between fatal accidents and non-fatal accidents as long as

relatively reliable data for fatal cases is available. In general, fatal cases are much better reported

whereas reporting of the non-fatal ones have some major shortcomings. While in the ILO’s Global

Estimates by Hämäläinen et al. (2017) an overall estimate were used for all industries – and in some

cases in three major sectorial groups (agriculture, industry & construction, services) this could be

done at least for those economic sectors where sufficient data is available. Said that, the two main

decisions for the regression model in step 2 are questioned as follows:

1. Which type of model should be used linear /non-linear, including the dependent variable and

which regressors? The interpretation and estimation of linear models it is easier compared

to non-linear models. However, the dependent variable should have a non-skewed

distribution, although it will be difficult to get a normal distribution of the residual. For these

considerations, a logarithmic transformation of ratios of non-fatal versus fatal percentages

has been selected. The logarithm of ratios could be also seen as difference of logarithm of

percentages.

2. The number and type of statistical units; the list of regression could be the following:



NACE groups (16);

gender;

age groups;

part of the body injured;

type of injury.

The number of statistical units depends on the number of years and the number of sub-groups to be

analyzed. There is a trade off in the choice of the number of the statistical units: on one side, the

higher the number (pattern ‘A’ in figure 6a), the higher the degrees of freedom, and the estimates

would be more efficient (lower variability of parameters); on the other side, the smaller the number

(pattern ‘E’ in figure 6a), the less biased is the calculation of non-fatal / fatal percentage ratio within

the sub-group. Since such sub-groups could be seen as a ‘pseudo-panel’ from a statistical point of

view, here it is proposed to adopt the method of Veerbek and Ninjam (1990, 1992, 1994) for

identifying the optimal number of statistical units.

The optimal solution consists in ‘cumulating’ information over 3 years; in particular, by adding a new

and fresh year when available, in this way new estimates might be calculated for every year (pattern

C2 in Figure 6b; this has also been suggested and applied by Betti et al., 2002 and Betti and

Gagliardi, 2018).

Figure 6a: Possible patterns of cumulation over years

(16) https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HIERARCHIC

2010 2011 2012 2013 2014 2015 2016 2017

A X X X X X X X X

B X X X X X X X X

C X X X X X X X X

D X X X X X X X X

E X X X X X X X X

https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HIERARCHIC

https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HIERARCHIC



Figure 7b: Possible patterns of cumulation over 3 years

Once the number of statistical units has been identified, the proposed imputation procedure is a

‘light’ and ad hoc version of the ‘sequential regression multivariate imputation’ (SRMI) approach

adopted by the imputation SAS software (IVE-ware). The later method, proposed by the authors of

the software (Raghunathan, Lepkowski, Van Hoewyk, and Solenberger, 2001), constructs the

imputed values by fitting a sequence of regression models and drawing values from the

corresponding predictive distribution, under the hypothesis of Missing at Random (MAR) mechanism,

infinite sample size and simple random sampling.

Taking into account all the aspects discussed above, the proposed model is described by the

equation (1):

𝑙𝑜𝑔 (% 𝑛𝑜𝑛−𝑓𝑎𝑡𝑎𝑙

% 𝑓𝑎𝑡𝑎𝑙)

𝑖𝑡= 𝛼 + 𝛽1𝑋1𝑖𝑡 + 𝛽2𝑋2𝑖𝑡 + ⋯ + 𝛽𝑘𝑋𝑘𝑖𝑡 + 𝜀𝑖𝑡 (1)

Where 𝑋𝑘𝑖𝑡 are the regressors to be included in the model, while 𝜀𝑖𝑡 is an error defined by two or

three variance components. This model is estimated for NACE groups, gender, age group, part of

the body injured, type of injury.

In particular, we propose to implement the ‘non-fatal accidents percentages/ratios’, estimated under

section 2, in order to estimate the level of incidence rates in Member States, where with the

proposed method it was identified to have under-reporting issues of non-fatal accidents at work.

Example of implementation of the proposed statistical methodology:

An iterative procedure will be performed for Steps 2 and 3. At every iteration, we estimate a set of

parameters 𝛽1, 𝛽2, … 𝛽𝑘; we compare such parameters with those in the previous iteration; when the

difference is less than a small value chosen, the iterative process ends. Then Task 3 is performed as

follows:

multiply such final set of parameters 𝛽1, 𝛽2, … 𝛽𝑘; by the corresponding set of

regressors/variables for a specific country, in a year;

in this way, we get the estimated log of the non-fatal/fatal ratio: 𝑙𝑜𝑔 (% 𝑛𝑜𝑛−𝑓𝑎𝑡𝑎𝑙

% 𝑓𝑎𝑡𝑎𝑙)

𝑖𝑡 (2)

calculate the non-fatal/fatal ratio by the exponential function;

multiply such ratio by the total number of fatal accidents in that country in that year;

2010 2011 2012 2013 2014 2015 2016 2017

C1 X X X X X X X X

C2 X X

X X X

X X X

X X X

X X X

X X X

X X X



While there are several variables used – NACE groups, gender, age groups, body part, type of injury

— obviously not all have the same influence to the outcome. As a result, the biggest impact comes

from the sectoral variable — NACE groups. The order of the imputation process does not have an

impact on the outcome. The model is exactly the same for all variable treatments. The reason of the

selecting this method is explained above in point 2.2.

3 Results


3. Results There is a wealth of information and reports that fatal accidents are much better reported than no-

fatal accidents in the majority of EU countries. This under-reporting study used several variables for

adjusting the poorly reported non-fatal accidents, as explained based on reporting from the

benchmark countries, using available variables, in particular, the sectoral rates of fatal and non-fatal

accidents, the age groups, the type of injury and the part of body injured. The regression results of

model in equation (1) in Section 2 are estimated for: NACE groups, age groups, part of the body

injured, type of injury and are reported in tables 1a and 1b. All four models are very good, and R-

squared ranging from 42 % to 79 %.

The selection of the two benchmark countries is based on the highest rank of reporting non- fatal

accident/fatal accident rates. Adding more Member States that have presumable lower level

reporting rates would dilute the outcome. On the other hand, for the selected benchmark countries

(Germany and Finland), there are no major reasons to assume over-reporting. Furthermore, there

may be still some level of under-reporting in these countries that is more likely for non-fatal minor

accidents as compared to fatal ones. The later variables such as part of the body injured and age

have actually very minor impact on the outcome.

The ILO Estimates (Hämäläinen, Takala et al. 2017) on the non-fatal number of accidents is the only

so far carried out similar effort worldwide. While it is based on a rough grouping of economic sectors

to only three: 1) agriculture, fishing, forestry, 2) industry and construction and 3) service industries, it

has the same baseline thinking than this study. Due to lack of global detailed data that outcome is

less reliable compared to that of the process in this study but points out to the same direction.

ILO 2014 data is the latest available outcome used for such process. Due to wide data collection

covering both occupational accidents and work-related diseases, the WHO data grouping available

was used by the ILO study. This is based on WHO regions called ‘WHO High Income’ and ‘WHO

EURO’ Region. As a result, the table lists first the WHO EURO Region Countries, followed by the

WHO High Income Region (starting by Austria). The WHO Regions are based on general health

factors in addition to occupational ones but it appears to be comparable also to accident reporting

due to differences in administrative practices in the two regions.

Table 1a: Results of regression models for NACE groups, age groups

Parameters Model NACE p-values Parameters

Model AGE p-values

Intercept 8 <.0001 Intercept 7 <.0001 NACE_A – Agriculture, forestry and fishing

0 <.0001 AGE18_34 0 <.0001

NACE_C – Manufacturing 0 1 AGE35_54 1 0

NACE_F – Construction 0 <.0001 AGEGE_55 0 1

NACE_H – Transportation and storage 0 <.0001

R-squared 1 R-squared 0

3 Results

3 Results


The numbers estimated from the model in equation (2) in Section 2 are considerably different from

the raw reported numbers from countries, and in most cases also show significantly higher figures as

compared to ILO estimates based on rough country adjustments.

Figure 7 reflects the size of countries however also other factors clearly influence the outcomes of

this under-reporting study. It is demonstrating the outcome from the construction sector (NACE

rev. 2 - F). While reports show that the construction sector is not just a very risky one and also has a

high number of workers involved. The imminent risk from falling from elevated levels is a key cause

for high number of fatal injuries, but there are also a high number of non-fatal cases as well.

However, the high number of fatal cases causes a different rate between fatal and non-fatal

accidents. On other hand, the services sector and other selected industries have an opposite trend

as the fatal accidents are relatively rare events.

Figure 8: Number of All accidents, annual average in 2015–17, adjusted by NACE sectors

While for the full number of non-fatal cases it is important to estimate the magnitude and level of

risks, the comparison between countries needs to take into account their sectoral distribution and to

some level also their technological matureness and peculiarities of workforce. To compare just

consolidated numbers would give a picture that countries with high level of activities in construction,

manufacturing, mining, agriculture, forestry and fishing are more dangerous than those where less

risky jobs are more frequent even if the incidence rates in these industries are actually comparable.

Furthermore, the rate of fatal accidents/100 000 workers (see Figure 1) as well as the adjusted rate

of non-fatal cases/100 000 workers provide a relatively fair comparison between countries. However,

e.g. Luxembourg may have had some of the random fatal cases within the period under

consideration that may need more comprehensive number of years of adjustment. A best picture is

obtained with all these adjustments together including access to full data, see Figures 8 and 9.

0

200 000

400 000

600 000

800 000

1 000 000

1 200 000

AT BEBGCY CZ DEDK EE ES FI FRGRHRHU IE IT LT LU LV MT NL PL PTROSE SI SK UK

3 Results


Figure 9: Number of non-fatal accidents in construction sector in EU-28 annual average, 2015–17 (NACE F), adjusted

Figure 10: Rate of non-fatal accidents/employment, annual average in 2015–2017, adjusted by NACE Sector and by best reporting countries, EU-28 divided into 2 separate WHO Regions. (%)

0

20 000

40 000

60 000

80 000

100 000

120 000

140 000

AT BE BGCY CZ DE DK EE ES FI FRGRHRHU IE IT LT LU LV MT NL PL PT ROSE SI SK UK

0%

2%

4%

6%

8%

10%

12%

14%

Bu

lgaria

Cro

atia

Czech

Repu

blic

Esto

nia

Hu

nga

ry

La

tvia

Lithu

ania

Po

land

Ro

mania

Slo

vakia

Au

str

ia

Be

lgiu

m

Cypru

s

De

nm

ark

Fin

land

Fra

nce

Germ

an

y

Gre

ece

Ire

land

Ita

ly

Lu

xe

mbou

rg

Malta

Ne

therla

nds

Po

rtug

al

Slo

ven

ia

Sp

ain

Sw

ede

n

Un

ited

Kin

gd

om

EU

28

3 Results


Table 2: Comparative analysis of fatal and non-fatal accidents in EU-28 based on ILOSTAT,

EUROSTAT data (17) C

ou

ntr

y

To

tal

em

plo

ym

en

t

Occu

pa

tio

nal in

juri

es in

2014 r

ep

ort

ed

to

IL

O

Fata

l

Occu

pa

tio

na

l in

juri

es in

2014 r

ep

ort

ed

to

IL

O

No

n-f

ata

l

Accid

en

ts a

t w

ork

in

2014 r

ep

ort

ed

to

Eu

ros

tat

Fa

tal

Accid

en

ts a

t w

ork

in

2014 r

ep

ort

ed

to

Eu

ros

tat

No

n-f

ata

l

Glo

bal

esti

mate

s o

f o

ccu

pa

tio

nal

accid

en

ts

(A

t le

ast

fou

r d

ays a

bs

en

ce)

Lo

wer

lim

it (

Eq

. j)

(0.1

4)

ILO

2014

Glo

bal

esti

mate

s o

f o

ccu

pa

tio

nal

accid

en

ts

(At

least

fou

r d

ays a

bs

en

ce)

Up

pe

r lim

it (

Eq

. j)

(0.0

8 IL

O 2

014

Glo

bal

esti

mate

s o

f o

ccu

pa

tio

nal

accid

en

ts (

At

least

fou

r d

ays a

bs

en

ce)

Sele

cte

d I

LO

2014

No

n-f

ata

l accid

en

ts b

ased

on

th

e E

U28 U

nd

er-

rep

ort

ing

stu

dy,

all s

ecto

rs,

an

nu

al

avera

ge o

f

years

2015

-2017

EU-28 218 336 000 3 379 2 414 073 2 413 571 4 051 944 3 548 302 6 936 092

Bulgaria 2 981 400 115 2289 110 1 772 78 571 137 500 108 036 175 403

Croatia 1 565 700 36 13785 22 8 999 15 714 27 500 21 607 65 206

Czech Republic 4 974 300 45058 101 36 622 72 143 126 250 99 196 217 135

Estonia 624 800 16 4619 13 5 393 9 286 16 250 12 768 33 255

Hungary 4 100 800 78 19583 74 15 918 52 857 92 500 72 679 162 362

Latvia 884 600 39 1 409 27 857 48 750 38 304 55 425

Lithuania 1 319 000 60 3232 51 2 599 36 429 63 750 50 089 79 551

Poland 15 861 500 225 59 414 160 714 281 250 220 982 532 730

Romania 8 613 700 224 3351 253 3 101 180 714 316 250 248 482 494 258

Slovakia 2 363 100 39 7 365 27 857 48 750 38 304 93 244

Austria 4 113 700 119 52 968 85 000 132 222 108 611 221 047

Belgium 4 544 500 45 46 704 32 143 50 000 46 704 121 935

Cyprus 362700 5 1 613 4 1 359 2 857 4 444 4 563 7 173

Denmark 2 714 100 28 31 770 20 000 31 111 31 770 58 685

Finland 2 447 200 28 42 162 20 000 31 111 42 162 41 773

France 26 396 400 517 467 869 369 286 574 444 471 865 776 729

Germany 39 871 300 639 955 280 471 704 819 336 429 523 333 704 819 862 016

Greece 3 536 200 25 3 152 17 857 27 778 22 817 60 641

Ireland 1 913 900 45 13 103 32 143 56 250 44 196 88 028

Italy 22 278 900 396 305 246 459 251 769 327 857 573 750 388 929 983 300

Luxembourg 245 600 10 6 154 7 143 12 500 9 821 29 343

Malta 181 700 4 2 273 2 857 5 000 3 929 8 477

Netherlands 8 236 100 39 56 377 27 857 48 750 56 377 232 132

Portugal 4 499 500 148 111 134 105 714 185 000 145 357 286 253

(17) Adjusted by the EU-28 Under-reporting Study. ILO data is classified by WHO Regions (alphabetical in two groups).

Year 2014 is used as the latest available international reference.

3 Results


Co

un

try

To

tal

em

plo

ym

en

t

Occu

pa

tio

nal in

juri

es in

2014 r

ep

ort

ed

to

IL

O

Fata

l

Occu

pa

tio

na

l in

juri

es in

2014 r

ep

ort

ed

to

IL

O

No

n-f

ata

l

Accid

en

ts a

t w

ork

in

2014 r

ep

ort

ed

to

Eu

ros

tat

Fa

tal

Accid

en

ts a

t w

ork

in

2014 r

ep

ort

ed

to

Eu

ros

tat

No

n-f

ata

l

Glo

bal

esti

mate

s o

f o

ccu

pa

tio

nal

accid

en

ts

(A

t le

ast

fou

r d

ays a

bs

en

ce)

Lo

wer

lim

it (

Eq

. j)

(0.1

4)

ILO

2014

Glo

bal

esti

mate

s o

f o

ccu

pa

tio

nal

accid

en

ts

(At

least

fou

r d

ays a

bs

en

ce)

Up

pe

r lim

it (

Eq

. j)

(0.0

8 IL

O 2

014

Glo

bal

esti

mate

s o

f o

ccu

pa

tio

nal

accid

en

ts (

At

least

fou

r d

ays a

bs

en

ce)

Sele

cte

d I

LO

2014

No

n-f

ata

l accid

en

ts b

ased

on

th

e E

U28 U

nd

er-

rep

ort

ing

stu

dy,

all s

ecto

rs,

an

nu

al

avera

ge o

f

years

2015-2

017

Slovenia 916 700 25 12

914 20 10 016 14 286 25 000 24 554 34 559

Spain 17 344 200 246 423 106 247 287 809 176 429 308 750 287 809 624 018

Sweden 4 772 100 41 30

319 36 21 343 25 714 45 000 40 268 74 986

United Kingdom 30 672 300 207 160 700 147 857 258 750 203 304 516 428

Table 2 shows the approximate outcomes related to the comparative analysis. The reference

numbers from the year 2014 from ILOSTAT and Eurostat will be more reliable for bigger Member

States while in small countries the random character of fatal accidents in any year could be balanced

by a taking average values also for the reference values of ILOSTAT/EUROSTAT. The ILO

Estimates (Hämäläinen, Takala et al. 2017) have already used an adjustment of the non-fatal cases

and these are already better than raw reporting data. The results of this under-reporting study show

clearly higher numbers. These are practically more than doubling the raw reported numbers from 2.4

million to 6.9 million of non-fatal accidents in EU-28 causing more than three days of absence from

work. The ILO Estimates for EU in 2017 is in the middle of the two different numbers or 3.6 million

occupational accidents with a lower and higher estimates and confidence interval of 2.5 – 4.1 million.

The authors of the ILO Estimate plan to use the methodology of this paper in near future.

The non-fatal accident numbers and level is important to highlight sectors, workplaces and jobs that

need special attention and provides much more information that the relatively rare fatal cases for

prevention. Furthermore, the overall costs to workers and their families, employers and the society

has been shown to be highest by those non-fatal injuries that are resulting in serious injuries,

extensive and permanent harm and lifetime inabilities to work. The level and numbers of those is

much higher than those of fatal cases and consequently results in highest costs and loss of Disability

Adjusted Life Years.

4 Discussion


4. Discussion Different industries may show different patterns of work injuries because of industry-specific

circumstances. This is influencing also the ratio between fatal and non-fatal work injuries, which may

be inherently different across branches of economy (Kurppa 2015). Furthermore, stability of the ratio

of fatal/non-fatal accidents changes, in particular, for smaller countries where the number of fatal

accidents may vary significantly from one year to another. Therefore, it is essential to cover more

years, e.g. by using a rolling average of several years. The numbers of accidents, both fatal and non-

fatal, in most EU countries goes gradually down due to shift from more dangerous jobs to less

dangerous ones and general improvements in control of workplace risks, as increasing number of

workers are employed in service occupations that are less hazardous, in particular, for fatal

accidents risks.

We do not imply that the proposed approach and calculated numbers are precise due to 28 different

accident registration systems in EU. Different practices, administrative requirements and incentive

systems exist for workers reporting their accidents to their employer and further employers reporting

to authorities, and then finally countries reporting to EU. However, this method gives a much more

reliable and comparable data than reporting of just the raw data of today and thus could be used

both at national level and internationally for planning of effective and justified preventive measures.

Such methodology for calculations could also be used by governments to analyse the potential

effects of accidents on social security systems and economics in general.

4 Discussion

6 Annex


5. Conclusions We are convinced that there is a solid and scientific solution to counter the obvious under-reporting

of non-fatal accidents (injuries) in EU Member State country statistics. The applied outcome of this

study is feasible and based on solid evidences. This study proposes a scientific solution and tries to

find an optimal method for estimating the level of under-reporting. It is a first comprehensive and

statistically tested effort based also on Kurppa 2015 and Hämäläinen, Takala 2017 and covers the

full EU level.

The system should take into account the economic structure and sectors of individual Member

States, the differing rates of fatal to non-fatal injuries in various sectors, and other parameters and

variables described earlier. Fatal outcomes are still rare events and the annual numbers in small

Member States need rolling average adjustments from year to year.

While fatal injuries are better recorded and reported than non-fatal ones, also the fatal cases may not

be reported in a uniform manner from original sources across countries. The quality and type of

various registration and compensation systems in Member States varies widely, and this may result

of under-reporting of fatal cases.

The target to standardize registration and compensation systems in EU is challenging as policy

makers mostly believe in their own country numbers. Convincing and explaining the need and

evidence for further adjustments and standardization is important to compensate for the EU-level

differences to provide reliable data.

The proposed model and methodology may also be used for calculation of so far non-covered

injuries, notably the continuously growing group of self-employed workers, non-wage farmers and

other non-wage worker groups. Estimations and calculation for accident level in the informal sector

should follow.

We have used selected country data notably those of Germany and Finland as references and

baseline for standardization. However, it should be kept in mind that Denmark has started currently

to work in the area of under-reporting, which was presented in the Luxembourg Working Group

meeting of last 16 October 2019. For this reason, in the near future, the reference base may be

broadened.

5 Conclusions

6 Annex


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Work in 2012’, Journal of Occupational and Environmental Hygiene, 11, pp. 326–337.

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6 Annex


Classification of the different variables follow the Annex I of European Statistics on Accidents at

Work (ESAW)- Summary methodology, 2013 edition (18).

Figure 11: Results by NACE. Number of All accidents, sum of the years in 2015–17 and average annual number, adjusted by NACE sectors and by best reporting countries, and rate of under-reporting

(non_fatal_final = total estimate of the years, average 2015–17 = rolling average over the three years)

Country non_fatal fatal NACE non_fatal_final rate (%) of

underreporting average 2015–17

AT 12 307 144 A 51 908 76 17 303

BE 1 129 4 A 2 460 54 820

BG 161 17 A 15 384 99 5 128

CY 100 2 A 476 79 159

CZ 7 511 34 A 44 907 83 14 969

DE 179 040 216 A 179 040 0 59 680

DK 2 863 24 A 4 895 42 1 632

EE 429 4 A 5 175 92 1 725

ES 84 473 126 A 147 840 43 49 280

FI 11 329 17 A 11 329 0 3 776

FR 32 637 74 A 66 555 51 22 185

GR 276 7 A 5 142 95 1 714

HR 1 927 11 A 11 391 83 3 797

HU 2 210 36 A 19 748 89 6 583

IE 3 831 70 A 23 224 84 7 741

IT 87 978 233 A 351 325 75 117 108

LT 446 17 A 11 968 96 3 989

LU 396 2 A 1 949 80 650

LV 213 16 A 7 949 97 2 650

MT 62 0 A 321 81 107

NL 12 3 A 8 266 100 2 755

PL 3 731 48 A 29 486 87 9 829

(18) https://ec.europa.eu/eurostat/documents/3859598/5926181/KS-RA-12-102-EN.PDF/56cd35ba-1e8a-4af3-9f9a-b3c47611ff1c

6 Annex

https://ec.europa.eu/eurostat/documents/3859598/5926181/KS-RA-12-102-EN.PDF/56cd35ba-1e8a-4af3-9f9a-b3c47611ff1c

https://ec.europa.eu/eurostat/documents/3859598/5926181/KS-RA-12-102-EN.PDF/56cd35ba-1e8a-4af3-9f9a-b3c47611ff1c

6 Annex




PT 17 143 74 A 44 179 61 14 726

RO 438 125 A 62 756 99 20 919

SE 1 617 24 A 4 013 60 1 338

SI 1 025 6 A 3 433 70 1 144

SK 1 494 12 A 17 569 91 5 856

UK 21 317 110 A 58 208 63 19 403

AT 41 388 25 C 137 849 70 45 950

BE 29 727 30 C 51 146 42 17 049

BG 1 800 50 C 135 817 99 45 272

CY 995 1 C 3 740 73 1 247

CZ 59 597 64 C 281 381 79 93 794

DE 627 460 223 C 627 460 0 209 153

DK 16 673 9 C 22 507 26 7 502

EE 3 540 9 C 33 722 90 11 241

ES 209 131 164 C 289 029 28 96 343

FI 18 109 11 C 18 109 0 6 036

FR 159 324 168 C 256 569 38 85 523

GR 2 435 17 C 35 827 93 11 942

HR 9 749 12 C 45 507 79 15 169

HU 25 327 39 C 178 717 86 59 572

IE 8 778 5 C 42 015 79 14 005

IT 198 877 250 C 627 148 68 209 049

LT 3 220 19 C 68 233 95 22 744

LU 2 134 4 C 8 293 74 2 764

LV 1 702 12 C 50 161 97 16 720

MT 1 212 2 C 4 949 76 1 650

NL 233 88 C 126 747 100 42 249

PL 83 431 171 C 520 672 84 173 557

PT 102 168 66 C 207 924 51 69 308

RO 4 400 124 C 497 831 99 165 944

SE 15 045 12 C 29 482 49 9 827

SI 12 727 13 C 33 657 62 11 219

SK 11 777 23 C 109 366 89 36 455

UK 98 424 75 C 212 232 54 70 744

AT 35 079 51 F 136 862 74 45 621

BE 24 041 42 F 48 453 50 16 151

BG 566 62 F 50 027 99 16 676

CY 578 2 F 2 545 77 848

CZ 7 923 67 F 43 819 82 14 606

DE 346 671 251 F 346 671 0 115 557

DK 16 186 14 F 25 594 37 8 531

6 Annex




EE 877 12 F 9 786 91 3 262

ES 141 872 190 F 229 682 38 76 561

FI 19 340 14 F 19 340 0 6 447

FR 154 009 230 F 290 519 47 96 840

GR 1 172 26 F 20 200 94 6 733

HR 3 303 29 F 18 060 82 6 020

HU 2 327 62 F 19 235 88 6 412

IE 5 119 24 F 28 703 82 9 568

IT 90 362 337 F 333 794 73 111 265

LT 1 125 26 F 27 925 96 9 308

LU 6 583 9 F 29 969 78 9 990

LV 413 12 F 14 258 97 4 753

MT 858 7 F 4 104 79 1 368

NL 84 47 F 53 526 100 17 842

PL 16 042 179 F 117 274 86 39 091

PT 56 888 127 F 135 617 58 45 206

RO 1 438 170 F 190 588 99 63 529

SE 13 212 21 F 30 329 56 10 110

SI 4 034 11 F 12 497 68 4 166

SK 1 330 25 F 14 468 91 4 823

UK 72 775 147 F 183 821 60 61 274

AT 14 514 45 H 55 989 74 18 663

BE 23 197 40 H 46 225 50 15 408

BG 943 55 H 82 410 99 27 470

CY 404 1 H 1 759 77 586

CZ 12 725 61 H 69 585 82 23 195

DE 219 312 247 H 219 312 0 73 104

DK 11 024 15 H 17 235 36 5 745

EE 864 14 H 9 533 91 3 178

ES 88 711 204 H 141 999 38 47 333

FI 12 341 16 H 12 341 0 4 114

FR 116 911 182 H 218 054 46 72 685

GR 1 143 8 H 19 478 94 6 493

HR 2 656 12 H 14 359 82 4 786

HU 8 719 49 H 71 258 88 23 753

IE 3 809 8 H 21 118 82 7 039

IT 89 958 188 H 328 557 73 109 519

LT 1 141 28 H 28 003 96 9 334

LU 1 863 14 H 8 386 78 2 795

LV 740 29 H 25 259 97 8 420

MT 841 0 H 3 977 79 1 326

6 Annex




NL 110 44 H 69 304 100 23 101

PL 18 702 143 H 135 179 86 45 060

PT 26 543 55 H 62 563 58 20 854

RO 1 181 103 H 154 762 99 51 587

SE 10 301 25 H 23 378 56 7 793

SI 2 934 12 H 8 987 67 2 996

SK 2 714 43 H 29 191 91 9 730

UK 85 087 205 H 212 499 60 70 833

AT 84 834 74 Other 280 533 70 93 511

BE 127 338 71 Other 217 520 41 72 507

BG 3 238 85 Other 242 572 99 80 857

CY 3 483 5 Other 12 998 73 4 333

CZ 45 164 107 Other 211 712 79 70 571

DE 1 213 566 356 Other 1 213 566 0 404 522

DK 78 958 28 Other 105 824 25 35 275

EE 4 393 12 Other 41 549 89 13 850

ES 775 053 273 Other 1 063 502 27 354 501

FI 64 081 35 Other 64 081 0 21 360

FR 937 234 537 Other 1 498 490 37 499 497

GR 6 933 35 Other 101 277 93 33 759

HR 22 937 36 Other 106 300 78 35 433

HU 28 280 63 Other 198 128 86 66 043

IE 31 357 28 Other 149 023 79 49 674

IT 418 115 500 Other 1 309 077 68 436 359

LT 4 873 32 Other 102 523 95 34 174

LU 10 219 16 Other 39 431 74 13 144

LV 2 346 16 Other 68 646 97 22 882

MT 2 980 4 Other 12 080 75 4 027

NL 812 174 Other 438 552 100 146 184

PL 128 399 276 Other 795 577 84 265 192

PT 202 158 117 Other 408 475 51 136 158

RO 5 135 236 Other 576 838 99 192 279

SE 70 806 33 Other 137 758 49 45 919

SI 17 178 11 Other 45 103 62 15 034

SK 11 837 40 Other 109 138 89 36 379

UK 412 227 255 Other 882 525 53 294 175

6 Annex


Figure 12: Results by age groups, in 2015–17 adjusted by age of the victim and best reporting

countries

Country non_fatal fatal AGE non_fatal_final rate (%) of

underreporting

AT 6 746 0 Other 26 531 75

AT 73 206 55 AGE18 243 044 70

AT 84 171 147 AGE35 300 901 72

AT 23 999 137 AGEGE 92 665 74

BE 2 146 0 Other 4 278 50

BE 77 227 57 AGE18 129 949 41

BE 104 199 91 AGE35 188 796 45

BE 21 860 39 AGEGE 42 780 49

BG 8 0 Other 689 99

BG 1 672 43 AGE18 121 584 99

BG 3 395 155 AGE35 265 830 99

BG 1 633 71 AGEGE 138 106 99

CY 19 0 Other 82 77

CY 2 179 2 AGE18 7 941 73

CY 2 403 7 AGE35 9 430 75

CY 959 2 AGEGE 4 065 76

CZ 816 7 Other 4 456 82

CZ 48 076 65 AGE18 221 640 78

CZ 63 456 164 AGE35 315 005 80

CZ 20 572 97 AGEGE 110 302 81

DE 31 859 2 Other 31 859 0

DE 922 665 232 AGE18 922 665 0

DE 1 150 797 592 AGE35 1 150 797 0

DE 480 728 467 AGEGE 480 728 0

DK 2 713 3 Other 4 192 35

DK 37 959 18 AGE18 49 516 23

DK 59 139 38 AGE35 83 066 29

DK 25 892 31 AGEGE 39 281 34

EE 16 0 Other 176 91

EE 3 934 8 AGE18 36 448 89

EE 3 955 22 AGE35 39 456 90

EE 2 198 21 AGEGE 23 684 91

ES 1 575 0 Other 2 519 37

ES 361 823 156 AGE18 488 474 26

ES 758 533 593 AGE35 1 102 665 31

ES 177 308 208 AGEGE 278 395 36

FI 382 0 Other 382 0

FI 39 335 18 AGE18 39 335 0

FI 58 457 41 AGE35 58 457 0

6 Annex



underreporting

FI 27 026 34 AGEGE 27 026 0

FR 14 460 17 Other 26 921 46

FR 537 690 177 AGE18 845 063 36

FR 676 978 630 AGE35 1 145 661 41

FR 170 987 368 AGEGE 312 542 45

GR 2 0 Other 34 94

GR 3 502 18 AGE18 50 044 93

GR 7 038 58 AGE35 108 295 94

GR 1 417 17 AGEGE 23 550 94

HR 64 8 Other 343 81

HR 13 751 14 AGE18 62 297 78

HR 20 486 56 AGE35 99 935 80

HR 6 271 22 AGEGE 33 041 81

HU 386 0 Other 3 132 88

HU 23 814 45 AGE18 163 125 85

HU 32 248 116 AGE35 237 856 86

HU 10 415 88 AGEGE 82 973 87

IE 1 047 0 Other 5 833 82

IE 19 118 27 AGE18 89 907 79

IE 26 284 44 AGE35 133 097 80

IE 6 444 64 AGEGE 35 246 82

IT 1 913 4 Other 7 013 73

IT 206 386 231 AGE18 638 706 68

IT 496 255 765 AGE35 1 653 673 70

IT 180 736 508 AGEGE 650 509 72

LT 69 1 Other 1 687 96

LT 3 772 20 AGE18 77 847 95

LT 4 514 62 AGE35 100 313 96

LT 2 450 39 AGEGE 58 806 96

LU 42 0 Other 194 78

LU 6 030 6 AGE18 23 521 74

LU 12 758 28 AGE35 53 584 76

LU 2 365 11 AGEGE 10 729 78

LV 14 0 Other 477 97

LV 2 016 15 AGE18 57 965 97

LV 2 148 40 AGE35 66 501 97

LV 1 236 30 AGEGE 41 331 97

MT 38 1 Other 181 79

MT 2 261 5 AGE18 9 097 75

MT 2 726 4 AGE35 11 810 77

MT 928 3 AGEGE 4 342 79

6 Annex



underreporting

NL 26 5 Other 15 841 100

NL 335 53 AGE18 172 303 100

NL 544 171 AGE35 301 280 100

NL 346 127 AGEGE 206 972 100

PL 257 1 Other 1 825 86

PL 87 721 169 AGE18 525 881 83

PL 118 550 399 AGE35 765 260 85

PL 43 777 248 AGEGE 305 223 86

PT 7 949 7 Other 18 675 57

PT 120 513 62 AGE18 239 014 50

PT 213 790 238 AGE35 456 565 53

PT 62 647 132 AGEGE 144 503 57

RO 13 0 Other 1 702 99

RO 3 890 145 AGE18 429 814 99

RO 6 854 429 AGE35 815 454 99

RO 1 835 184 AGEGE 235 806 99

SE 129 2 Other 288 55

SE 34 271 24 AGE18 64 666 47

SE 49 502 47 AGE35 100 577 51

SE 27 080 42 AGEGE 59 428 54

SI 39 0 Other 119 67

SI 12 955 11 AGE18 33 430 61

SI 20 724 29 AGE35 57 583 64

SI 4 180 13 AGEGE 12 545 67

SK 73 0 Other 780 91

SK 10 520 35 AGE18 94 898 89

SK 13 693 69 AGE35 133 004 90

SK 4 866 39 AGEGE 51 051 90

UK 10 505 12 Other 26 105 60

UK 220 323 186 AGE18 462 184 52

UK 325 251 336 AGE35 734 680 56

UK 133 751 258 AGEGE 326 317 59

6 Annex


Figure 13: Results by body part of the injured in 2015–17 and best reporting countries

Country non_fatal fatal BODY non_fatal_final underreporting

AT 23 701 225 BODY1_4_7 123 639 81

AT 561 2 Other 3 476 84

AT 100 958 3 BODY5_6 314 292 68

BE 36 039 61 BODY1_4_7 89 330 60

BE 3 141 59 Other 9 248 66

BE 95 578 3 BODY5_6 141 379 32

BG 1 321 181 BODY1_4_7 144 983 99

BG 1 0 Other 130 99

BG 3 198 7 BODY5_6 209 458 98

CY 996 6 BODY1_4_7 5 442 82

CY 11 0 Other 71 85

CY 2 653 0 BODY5_6 8 651 69

CZ 15 790 159 BODY1_4_7 114 642 86

CZ 827 64 Other 7 132 88

CZ 71 021 4 BODY5_6 307 716 77

DE 333 602 846 BODY1_4_7 333 602 0

DE 23 988 12 Other 23 988 0

DE 1 365 477 22 BODY5_6 1 365 477 0

DK 37 466 28 BODY1_4_7 68 240 45

DK 3 316 26 Other 7 175 54

DK 58 341 2 BODY5_6 63 414 8

EE 1 494 21 BODY1_4_7 21 469 93

EE 9 4 Other 154 94

EE 5 219 0 BODY5_6 44 756 88

ES 283 051 660 BODY1_4_7 557 909 49

ES 3 874 0 Other 9 071 57

ES 580 262 1 BODY5_6 682 536 15

FI 21 714 49 BODY1_4_7 21 714 0

FI 1 039 7 Other 1 039 0

FI 61 341 2 BODY5_6 61 341 0

FR 234 148 211 BODY1_4_7 504 295 54

FR 33 911 381 Other 86 757 61

FR 382 386 3 BODY5_6 491 473 22

GR 1 614 60 BODY1_4_7 36 192 96

GR 0 0 Other 0

GR 6 358 0 BODY5_6 85 080 93

HR 7 120 22 BODY1_4_7 46 642 85

HR 1 576 45 Other 12 264 87

HR 18 613 0 BODY5_6 72 764 74

HU 8 664 134 BODY1_4_7 92 765 91

6 Annex



HU 314 26 Other 3 994 92

HU 35 023 6 BODY5_6 223 781 84

IE 15 721 80 BODY1_4_7 99 889 84

IE 1 684 7 Other 12 707 87

IE 21 401 3 BODY5_6 81 149 74

IT 156 074 738 BODY1_4_7 721 651 78

IT 17 044 260 Other 93 613 82

IT 416 205 29 BODY5_6 1 148 436 64

LT 1 592 78 BODY1_4_7 51 170 97

LT 25 0 Other 955 97

LT 5 647 0 BODY5_6 108 317 95

LU 4 027 22 BODY1_4_7 23 390 83

LU 63 1 Other 435 86

LU 9 953 0 BODY5_6 34 499 71

LV 1 007 33 BODY1_4_7 43 294 98

LV 30 13 Other 1 532 98

LV 2 567 1 BODY5_6 65 860 96

MT 1 110 5 BODY1_4_7 6 566 83

MT 120 1 Other 843 86

MT 2 905 0 BODY5_6 10 255 72

NL 228 82 BODY1_4_7 145 841 100

NL 241 10 Other 183 117 100

NL 388 141 BODY5_6 148 109 100

PL 35 858 541 BODY1_4_7 334 829 89

PL 17 16 Other 189 91

PL 130 393 17 BODY5_6 726 598 82

PT 80 566 294 BODY1_4_7 230 024 65

PT 11 735 3 Other 39 799 71

PT 177 566 4 BODY5_6 302 541 41

RO 2 602 480 BODY1_4_7 420 668 99

RO 96 22 Other 18 436 99

RO 5 706 20 BODY5_6 550 512 99

SE 22 871 34 BODY1_4_7 60 898 62

SE 4 749 43 Other 15 019 68

SE 45 503 1 BODY5_6 72 303 37

SI 5 385 39 BODY1_4_7 21 589 75

SI 54 0 Other 257 79

SI 20 297 0 BODY5_6 48 560 58

SK 3 731 77 BODY1_4_7 52 736 93

SK 141 20 Other 2 367 94

SK 15 466 1 BODY5_6 130 457 88

UK 162 661 426 BODY1_4_7 485 920 67

6 Annex



UK 10 392 103 Other 36 876 72

UK 289 613 11 BODY5_6 516 301 44

6 Annex


Figure 14: Results by injury type as adjusted by the best reporting countries, total EU data not available due to missing reporting by Member States

Country non_fatal fatal INJ non_fatal_final underreporting

AT 4 903 114 INJ70_120 31 933 85

AT 113 684 38 INJ10_30 373 917 70

AT 4 769 52 INJ40_60 23 591 80

AT 3 539 1 Other 17 870 80

BE 4 860 22 INJ70_120 15 622 69

BE 119 296 7 INJ10_30 193 647 38

BE 11 393 33 INJ40_60 27 814 59

BE 6 020 61 Other 15 002 60

BG 63 89 INJ70_120 8 986 99

BG 3 697 26 INJ10_30 266 300 99

BG 658 59 INJ40_60 71 284 99

BG 0 0 Other 0

CY 134 5 INJ70_120 951 86

CY 3 470 1 INJ10_30 12 440 72

CY 335 1 INJ40_60 1 806 81

CY 29 0 Other 160 82

CZ 536 47 INJ70_120 4 655 88

CZ 70 659 26 INJ10_30 309 885 77

CZ 4 526 23 INJ40_60 29 853 85

CZ 14 570 105 Other 98 098 85

DE 20 638 59 INJ70_120 20 638 0

DE 1 032 496 197 INJ10_30 1 032 496 0

DE 651 601 558 INJ40_60 651 601 0

DE 36 773 29 Other 36 773 0

DK 4 644 7 INJ70_120 10 833 57

DK 52 359 2 INJ10_30 61 682 15

DK 5 293 11 INJ40_60 9 378 44

DK 13 126 42 Other 23 740 45

EE 99 26 INJ70_120 1 853 95

EE 6 457 2 INJ10_30 61 040 89

EE 408 4 INJ40_60 5 801 93

EE 16 2 Other 232 93

ES 16 896 414 INJ70_120 44 974 62

ES 776 413 17 INJ10_30 1 043 644 26

ES 70 435 182 INJ40_60 142 396 51

ES 21 745 0 Other 44 873 52

FI 982 19 INJ70_120 982 0

FI 67 131 0 INJ10_30 67 131 0

6 Annex


FI 12 591 25 INJ40_60 12 591 0

FI 2 427 14 Other 2 427 0

FR 90 636 79 INJ70_120 222 710 59

FR 334 430 14 INJ10_30 414 990 19

FR 217 257 49 INJ40_60 405 464 46

FR 107 346 454 Other 204 498 48

GR 49 12 INJ70_120 1 406 97

GR 7 453 11 INJ10_30 108 006 93

GR 598 41 INJ40_60 13 034 95

GR 110 1 Other 2 447 96

HR 1 582 25 INJ70_120 13 083 88

HR 20 740 5 INJ10_30 86 618 76

HR 1 489 8 INJ40_60 9 353 84

HR 3 616 32 Other 23 185 84

HU 741 55 INJ70_120 10 235 93

HU 42 693 28 INJ10_30 297 810 86

HU 2 206 49 INJ40_60 23 144 90

HU 377 31 Other 4 037 91

IE 370 52 INJ70_120 3 366 89

IE 30 671 1 INJ10_30 141 054 78

IE 959 19 INJ40_60 6 632 86

IE 4 214 14 Other 29 751 86

IT 25 105 101 INJ70_120 154 342 84

IT 529 221 592 INJ10_30 1 643 066 68

IT 17 239 14 INJ40_60 80 496 79

IT 18 563 258 Other 88 478 79

LT 121 47 INJ70_120 5 042 98

LT 6 895 11 INJ10_30 145 103 95

LT 475 17 INJ40_60 15 034 97

LT 27 2 Other 872 97

LU 182 14 INJ70_120 1 420 87

LU 12 528 5 INJ10_30 49 347 75

LU 908 6 INJ40_60 5 379 83

LU 218 7 Other 1 318 83

LV 21 22 INJ70_120 1 174 98

LV 3 098 2 INJ10_30 87 465 96

LV 285 8 INJ40_60 12 102 98

LV 301 27 Other 13 046 98

MT 35 1 INJ70_120 287 88

MT 3 456 0 INJ10_30 14 288 76

MT 163 7 INJ40_60 1 014 84

MT 10 0 Other 63 84

NL 241 10 INJ70_120 190 949 100

6 Annex


NL 428 169 INJ10_30 171 252 100

NL 54 16 INJ40_60 32 496 100

NL 135 51 Other 82 927 100

PL 4 310 286 INJ70_120 50 942 92

PL 149 200 29 INJ10_30 890 552 83

PL 14 910 196 INJ40_60 133 849 89

PL 5 2 Other 46 89

PT 5 346 129 INJ70_120 20 339 74

PT 221 476 17 INJ10_30 425 530 48

PT 16 913 128 INJ40_60 48 873 65

PT 26 786 4 Other 79 010 66

RO 398 168 INJ70_120 79 461 99

RO 6 187 74 INJ10_30 623 799 99

RO 1 711 201 INJ40_60 259 455 99

RO 383 34 Other 59 283 99

SE 4 481 41 INJ70_120 15 164 70

SE 52 152 1 INJ10_30 89 124 41

SE 4 162 14 INJ40_60 10 696 61

SE 13 824 25 Other 36 269 62

SI 191 17 INJ70_120 795 76

SI 11 267 4 INJ10_30 23 696 52

SI 673 3 INJ40_60 2 129 68

SI 13 319 6 Other 43 003 69

SK 493 45 INJ70_120 8 762 94

SK 17 676 9 INJ10_30 158 651 89

SK 1 189 23 INJ40_60 16 050 93

SK 547 11 Other 7 537 93

UK 3 844 182 INJ70_120 15 554 75

UK 370 923 25 INJ10_30 757 999 51

UK 22 657 199 INJ40_60 69 635 67

UK 55 399 126 Other 173 803 68

6 Annex


Commentary to the 4 annexed tables

Some clarification on symbols on the NACE groups used at the EU:

A Agriculture

C Manufacturing

F Construction

H Transport and Storage

Other Other economic sectorsBackground = theme colour 20%

The imputation based on the NACE Groups and age groups of the injured match perfectly well

overall. Summary data of injury type and injured body are provided for each country while total

numbers cannot be estimated due to some non-reported data.

As a whole the imputation method adjusting non-fatal accident numbers provides a reliable method

of estimating the level of under-reporting. The rate of under-reporting varies by country and in

average is 58.1 %. Instead of 2.9 million annual non-fatal accidents that are reported to EUROSTAT

we estimated that a realistic number covering also the non-reported cases was 6.9 million accidents

in average over the period of 2015–15.

See also comparison to earlier estimates made by the ILO, in 2017, based on latest data then

available of the reference year 2014 that indicated 3.55 million non-fatal accidents in EU-28 in 2014

with a range up to 4.0 million accidents. The method was a simplified one based on less reliable data

from other countries globally.

Based on global data other industrialised countries tend to follow the same tendencies in reporting –

and under-reporting – fatal and non-fatal accidents.

The variable non_fatal_final is built by the total estimate of the years 2015–17.

The variable average 2015–17 consists of the rolling average over the three years.

The variable age as classified

Code Label

00 Less than 1 year

01 1 year old

02 2 years

… etc

10 10 years

… etc

90 90 years

98 Above 90 years of age

99 Age unknown

is grouped as follows: group 1 — 18–34 , group 2 — 35–54, group 3 — 55, group 4 — other

The variable body ‘part of body injured’ as classified in Annex I is grouped into group body 5_6

(Upper Extremities, not further specified, Lower Extremities, not further specified), body 1_4_7

6 Annex


(Head, not further specified , Torso and organs, not further specified, Whole body and multiple sites,

not further specified) and other including the remaining categories.

The variable injuries ‘type of injuries’ is grouped into:

INJ10_30 (010 Wounds and superficial injuries, 020 Bone fractures and 030 Dislocations, sprains

and strains)

INJ40_60 (040 Traumatic amputations (Loss of body parts), 050 Concussion and internal injuries,

060 Burns, scalds and frostbites)

INJ70_120 (070 Poisonings and infections, 080 Drowning and asphyxiation, 090 Effects of sound,

vibration and pressure, 100 Effects of temperature extremes, light and radiation, 110 Shock, 120

Multiple injuries)

Other (999 Other specified injuries not included under other headings)

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Underreporting study

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