research article variation of occupational accidents with

International Academic Journal of Education & Literature ISSN Print : 2708-5112 | ISSN Online : 2708-5120 Frequency : Bi-Monthly Language : English Origin : Kenya Website : https://www.iarconsortium.org/journal-info/IAJEL

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Research Article

Variation of Occupational Accidents with Time of Day and Worker

Fatigue in Kenya

Abstract: Background: The current study investigated the status of workplace accidents

in relation to time of day and the contribution of fatigue in the frequency of occurrence of

occupational accident in Kenya between June 2008 and June 2009. Materials and Methods:

Data was obtained from accident reporting forms (DOSH Form 1) in the Directorate of

Occupational Safety and Health Services (DOSHS) and 320 subjects in 8 occupations in the

Nairobi metropolitan were interviewed through 320 questionnaires with a return rate of

98.7%. Results: It was established that 1472 occupational accidents had been reported in

Kenya in that year from 88 industries. From a low in the morning the highest number of

accidents occurred between 1000 hours and 1200 hours, and between 1600 to 1700 hours,

with peaks of 226 accidents, between 1100 and 1200 hours, and 182 between 1500 and 1700

hours. Conclusion: The study concludes that the rate of occupational accidents in Kenya is

high and that occurrence of accidents varies with time of day at the workplace. We

recommend that DOSHS should strive to understand the underlying factors responsible for

high number of accidents and ensure employers put in place mechanisms to minimize

accidents including worker training programs.

Keywords: accidents, fatigue, time of day.

INTRODUCTION

The International Labour Organization (ILO) estimates that some 2.3

million women and men around the world succumb to work-related

accidents or diseases every year, corresponding to over 6000 deaths every

single day. Worldwide, there are around 340 million occupational

accidents and 160 million victims of work-related illnesses annually

(1996-2020), according to ILO (2012). Many government and industries

have widely acknowledged workplace fatigue as a significant

occupational health and safety risk (Flecher A. et al., 2005). This has been particularly evident in several high consequence industries like

transportation, medicine manufacturing, etc.

In Kenya, however the impact of time factor and fatigue on

occurrence of accident has never been taken into consideration. The Kenya law on safety and health (OSHA 2007) is

completely silent on issues of fatigue, working hours and shift work. The directorate of Occupational Safety and Health

(DOSH) is responsible for regulation and monitoring work related accidents in the country

The Directorate of Occupational Safety and Health Services has been investigating and recommending preventive

measures on occupational accidents since the inception of the Occupational Safety and Health Act (OSHA, 2007), but the

accident statistics shows that the numbers of occupational accidents have been on the increase over the years. DOSHS has been for a long time focusing its attention on the immediate cause of accident and neglecting the root causes. It is

obvious that if DOSH aims to reduce occupational accident the department needs to look beyond the obvious factors and

take into account all the various ways in which the human element can contribute to accidents. In the absence of the

knowledge of the causes of accidents, prevention of the same is extremely difficult or impossible. It is necessary to gain

more insight into human factor and characteristics that might be a cause of occupational accidents. Fatigue may be one of

the human factors that have been ignored for a long time, hence the lack of success in reducing workplace accidents.

Until now very little research has been done on the causes of workplace accident and the role of fatigue in the

accident occurrence in Kenya. Research has been done by Kemei and Nyerere (2016), Mwangi and Oduor ((2016), and

Makori et al (2018) on accidents in construction sites, but construction is a unique field and does not represent all other

workplaces, thus leaving a gap in scholarly knowledge. The main objective of this study was to establish the relationship of accidents with time of a working day, and to identify factors that contribute to accidents, including the influence of

fatigue on the frequency of occurrence of occupational accidents in Kenya.

Article History

Received: 22.12.2020

Revision: 04.01.2021

Accepted: 18.01.2021

Published: 30.01.20021

Author Details Joseph T. Mailutha1 and Ali M. Nadoboi2

Authors Affiliations 1Division of Administration, Kisii University, Kenya 2Directorate of Occupational Safety and Health Services, Kenya

Corresponding Author* Joseph. T. Mailutha

How to Cite the Article: Joseph T. Mailutha & Ali M. Nadoboi (2021); Var iat ion of Occupat ional Accident s wi th Time of Da y and

Worker Fa t igue in Kenya . Int Aca J Edu Lte. 2(1); 43-50. Copyright @ 2021: This is an open-access article distributed under the terms of the Creative Commons Attribution license which permits unrestricted use, distribution, and reproduction in any medium for non commercial use (NonCommercial, or CC-BY-NC) provided the original author and source are credited.

Joseph T. Mailutha & Ali M. Nadoboi, Int Aca J Edu Lte; Vol-2, Iss- 1 (Jan-Feb, 2021): 43-50.

44

To achieve this objective the study set out the

following specific objectives 1) to determine the

number of accidents that occurred in Nairobi metropolitan between 2008 and 2009, 2) to evaluate the

variation of time and frequency of accident occurrence,

3) to establish whether there is any correlation between

accident frequencies and the fatigue level of workers. In

this paper we report only results of the first two

objectives, and the third objective will be reported in

subsequent papers.

Occupational accidents are unplanned occurrences

that occur in the course of duty of a worker, with a

consequence of injuries, fatalities, or damage to

property and loss of production. Accidents cannot be eliminated, but risks can be reduced. However, to

reduce occurrence of accidents there must be

understanding of the causes of accidents. Many

attempts have been made to develop a prediction theory

of accident causation, but so far no theory can be taken

as a universally accepted model of accident prediction

because each accident may occur under unique

circumstances and environments.

Multiple causation theory, ILO, (2011), which is an

outgrowth of the domino theory, postulates that for a single accident there may be many contributory factors,

causes and sub-causes, and that certain combinations of

these give rise to accidents. According to this theory,

the contributory factors can be grouped into the

following two categories, behavioural, includes factors

pertaining to the worker, such as improper attitude, lack

of knowledge, lack of skills and inadequate physical

and mental condition; environmental, includes improper

guarding of other hazardous work elements and

degradation of equipment through use and unsafe

procedures, and the administrative causes may include inadequate staffing, limited amounts of breaks and

unrealistic output demands, and general work

environment.

Statistics published by Statista Research

Department, (2016), shows the annual number of work-

related deaths and accidents worldwide as of 2016,

there were approximately 313 million accidents and 2.3

million deaths at the workplace per year. Fatigue is

many times considered as the major cause of accident,

but fatigue is a consequence of underlying factors.

Although work shifts give some sort of break for workers to rest, prolonged shifts, and irregular or

insufficient sleep may also be associated with fatigue.

In workplace, feelings of fatigue may arise from high

temperatures, high noise levels, dim lighting or poor

visibility, work tasks that are long, repetitive, paced,

difficult, boring, or monotonous

According to ILO Encyclopedia, 2005, the

traditional view of occupational accidents so states that

accidents are produced by technological as well as

individual human failures. Nevertheless, during the

three last decades researchers have increasingly

recognized that industrial accidents are caused by a dynamic interaction between factors in the social and

physical environments, that is, characteristics of the

individual and the organization as well as technical

forces.

Demographic and lifestyle factors including age,

gender, alcohol consumption and smoking; work

environment and safety culture and climate fields

comprising physical and psychosocial work

environment factors such as noise, temperature and

machinery, workplace and ergonomic conditions, work

support and work pressure, risk perception and competence, are likely to favour accident occurrences

and need to be studied, because by studying such

factors, the root cause of accidents can be isolated and

necessary steps taken to prevent the recurrence of the

accidents. In their work, Hsiao and Simeonov (2001)

presented a model for structuring factors that play a role

in falls from roofs, which can also be applied to

occupational accidents in general. Their model consists

of three categories of factors: work environment, task

related factors, and personal factors. The work

environment includes factors such as noise and machinery, the physical environment in which tasks are

performed. Task related factors describe the tasks of a

certain job (for example, load handling, physical

exertion, and complexity of the task). Personal factors

include, for instance, age, training, and sensory

interface with protective equipment.

In their model, Hsiao and Simeonov (2001) classify

fatigue as a task related factor only, implying that

performing tasks at a given intensity and duration can

lead to fatigue. Fatigue may then decrease the ability of the worker to process important visual and perceptive

information relevant to avoiding an accident. In

contrast, fatigue can also be viewed as having a multi

factorial origin, affected by non-work related

circumstances and personal characteristics, with a

prolonged character that may affect the individual’s

performance and ability to function at work. To date, it

is unknown whether fatigue is a mediator between work

related factors and the occurrence of accidents, or

whether fatigue is an independent risk factor (Swaen et

al., 2003)

2.0 Materials and Methods

The study used two sources of data, secondary

occupational accident data from all the Work Injury

Benefit Acts forms (WIBA) was obtained from the

Directorate of Occupational Safety and Health Services

(DOSH) by analyzing accident reporting forms,(DOSH

1, Appendix I) for the period between June 2008 and

June 2009. In Kenya it is mandatory for all institutions employing staff to report accidents arising from work to


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the directorate. For each accident reported a

comprehensive compilation of the circumstances

surrounding it, including age of the injured worker,

length of time on the job, occupation of the worker, the machine or tools involved, name and location of

workplace and the time of the day when the accident

occurred is required.

This data was analysed in Microsoft excel in order

to determine the number of accidents reported in one

year, the organizations that had reported, and the

workplaces that had reported the highest number of

accidents. Ten percent (10%) of all the total number of

workplaces that had reported accidents through WIBA

forms from June 2008 to June 2009 were picked for

primary data. Within the 8 workplaces a sample of the workers (n = 320) were randomly picked to fill the

questionnaires. workplaces that had reported highest

number of accidents were picked for the purposive

sampling. Respondents were sought from each of the 8

workplaces that had reported the highest number of

accidents in the year under review. Only workers who

worked continuously for 8-9 hours daily and doing day

shift were considered for the study.

The field study covered the Nairobi Metropolitan,

which Nairobi City County and its satellite towns of Kitengela, Athi River, Thika and Kiambu, and covering

a total of eight different industries. The choice of

workplaces was largely based on the data that was

analyzed from the DOSHS reports. Nairobi was

selected for the study because it has the largest

concentration of industries and has all representatives of

industries. Besides the all the industries that had

reported the highest number of accident were found in

the metropolitan. The area has a diverse nature of

workplaces which includes steel rolling mills,

horticulture, and manufacturing among others.

Workers present during the visits were approached

at random to participate in the filling of the

questionnaire. Each respondent was required to answer

two sets of questions, one on their demographic data

and the other on factors of subjective fatigue, Checklist

Individual Stregth (CIS checklist). The research

assistants were available throughout the exercise to

answer or clarify any questions or difficulties from the

respondents, especially of the CIS checklist due to

literacy levels associated with the kind of workforce in

various workplaces. The sample consisted of 320 individuals, 298 men and 22 women, aged between 18

and 60 years, in employment in different organization.

The choice of age bracket was guided by the fact that

persons below the age of 18 years are not employable

under the Kenyan law, and the retirement age in Kenya is 60 years.

Out of the eight different occupations selected based

on the number of accidents reported, four were steel

making industries based in Nairobi and Athi River with

a combined total of about 1000 workers and had

reported a total of 187 accidents, plastic making

industry with 415 workers, having reported 45

accidents, paper making industry with 205 workers with

34 reported accidents, printing industry with 180

workers having reported 30 accidents and horticultural

industry employing 365 workers with 30 reported accidents. In total the eight industries had reported 326

accidents.

Descriptive statistical analysis method including

percentages means and standard deviations were used,

and the results were presented in form of tables. Further

analysis of data was done by use of SPSS where

variances, significant levels and correlation factors were

analyzed and tabulated. The accident frequency data

from the DOSH1 form was being linked to the data on

the level of subjective fatigue of workers at different times of the day and analysis on relationship between

the two was assessed.

RESULTS

3.1 The wider objective of this study was to

establish the relationship of accidents with time of a

working day, and to identify factors that contribute to

accidents, including the influence of fatigue on the

frequency of occurrence of occupational accidents in

Kenya. To achieve this objective the study relied on

both desktop study and field data under the following

specific objectives 1) to determine the number of accidents that occurred in Nairobi metropolitan between

2008 and 2009, 2) to evaluate the variation of time and

frequency of accident occurrence, 3) to establish

whether there is any correlation between accident

frequencies and the fatigue level of workers. In this

paper we report mainly results of the first two

objectives,whose data was mainly from desktop study

and partly field data. the third objective will be reported

in another upcoming paper.


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Accident as a Function of Time

Table 1: Variation of accident occurrence with time of day

Time of the day Number of

Accident

Reported

Percentage

0700-0759 36 2.45

0800-0859 58 3.94

0900-0959 138 9.38

1000-1059 214 14.54

1100-1159 226 15.35

1200-1259 166 11.28

1300-1359 74 5.03

1400-1459 110 7.47 1500-1559 156 10.60

1600-1659 182 12.36

1700-1759 80 5.43

1800-1859 32 2.17

Total 1472 100

The results from DOSHS revealed that a total of

1472 occupational accidents had been reported in

Kenya from 88 workplaces between June 2008 and June

2009. The results further showed that the highest

number of accident was reported in eight occupations.

Table 1 shows the frequency of accident occurrence at

different times of the day.

From table 1, it is noted that majority of accidents

occurred between 10.00 am and 12.59 pm, when a total

of 606 (41.17%) occurred with a peak of 226 (15.35%),

occurring between 1100 and 1159 hours. This is the

period when workers are most active and already

getting fatigued by the job. There is a sharp decline

between 1300 and 1359 hours with only 74 (5.03%)

accidents reported.

The rise in accidents again picked from 1400 to 1759 hours when a total of 448 (30.43%) accidents

were recorded with a peak of 182 (12.36%) between

1600 and 1700 hours. This is the time when workers

have been re-energized and rested.

The types of industries sampled

Table 2: The nature of industries that were evaluated

Type of industry Number of accident

reported

Total number of

workers

Number of workers

interviewed

Large scale steel making 48 205 40 steel rolling mill 41 316 40

steel fabricating 56 300 40

plastic making 45 415 40

paper making 34 205 40

printing 30 180 40

horticultural 30 365 40

Small and medium scale steel

making

42 278 40

Total 326 2264 320

Table 2 shows the types of industries that were

picked for the field study and the number of accidents

that had been reported from each industry. From the

table it is observed that steel related industries reported

the highest number of occupational accidents. Large

scale steel making, steel rolling mill and steel

fabricating accounted for 48, 41 and 56 accidents

respectively.


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Demographic profiles of workers

Table 3: Profiles of respondents

Item Number %

Gender Male

female

294 22

93% 7%

Age (yr) 15-25 26-35 36-45

46-55 Above 55

74

156 58

22 6

24% 49% 18%

7% 2%

Education level Primary

Secondary college

88

195 36

28% 61% 11%

Nature of work Manual worker

Supervisor

office

264 33

19

84% 10%

6% Mode of payment

Monthly Weekly

Daily

141 95 80

45% 30% 25%

Time of starting work 0730 0800

0900

120 196

0

38% 62%

0% Time of ending work

0500 After 0500

270 46

85% 15%

Time for break

30 minutes break 45 minutes break

1hour break More than I hour

65

171

80 0

21% 54%

25% 0%

Number of hours for sleep Less 8 hours

8 hours More than 8 hours

264 50 2

83.4% 16% 0.6%

Number off-days per week One day a week

Two days a week

290

26

92%

8%

Table 3 shows the profiles of the respondents. From

the table, it can be observed that the largest number of workers were males. The majority of the workers were

aged between 26 to 35 years, representing 49%.

From the same table it is observed that manual

workers occupy 84% of the workforce in the sampled

industries.

Most workers usually worked for between 8 and 9

hours or more in a day, with only 15% working for

eight hours or less. 61% of the respondents have

reached primary school level, 8% secondary level, and

only 11% having college education. This can be explained by the fact that employees in these industries

are mainly blue colour workers. The majority of

workers interviewed were manual labourers 84%, 10%

being supervisors with just 6% being office workers.

Breaks during work varied with the different

workplaces. Most of the workers got less than one hour

break, with 54% getting 45 minutes break, 21% getting

30 minutes break, and only a quarter getting one hour

break. None of the workers reported getting more than

one hour break.

The study shows average hours of sleep obtained in

24 hours was 6.5 hours. 83.4% of respondents getting

less than 8 hours sleep, with 16% reported getting 8

hours of sleep and less than one percent (0.6%) reported more than 8 hours of sleep per night. Further the

majority of workers, 92%, reported having to work six


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days a week, and only 8% having at least two days off

in a week.

In the sampled workplaces the most common method of payment was monthly rate, 45% with 30%

receiving weekly wages and 25% getting paid on daily

basis.

DISCUSSION

From table 1, it is noted that majority of accidents

occurred between 10.00 am and 12.59 pm. This is the

period when workers are most active and already

getting fatigued by the job. The sharp decline between

1300 and 1359 hours can be explained by the fact that

this is the lunch hour in Kenya and this can explain the

small number. Most workplaces that do manufacturing,

workers go for lunch in shifts between 1200 to 1400 hours to avoid disruption of work. The second rise

from 1400 to 1759 hours coincides with the time when

workers have been re-energized and rested, whereas the

minimum for the day between 1800 and 1859 hours

comes at the time when most of the workers have either

left their workplaces for home or a fresh shift may be

starting for the day.

These trends are in agreement with O’Neill and

Panuwatwanich (2013), where they reported that

productivity starts with a low at the beginning of a day,

peaks in the late morning then drops off towards the end of the day. In the same study they concluded that a

higher level of fatigue is associated with lower level of

productivity (lower LUF). In a study cited by Gatonye

Gathura in the Standard newspaper of 13th Nov 2017,

“It’s established that most accidents occur around just

before the workers take a lunch break and it has been

called 'lunch time effect',” The study came up with 10

main reasons for the increasing number of accidents

at construction sites, top of these being reluctance to

invest in safety, lack of training in safety and failure by

regulators to enforce safety regulations.

Similar observations were reported by Kerstin

Hanecke, et al (1998), where they reported accident

peaks at 1000 and 1100hours, a drop between 1200 and

1300hours, and a second peak at 1400 and 1500hours

for the German working population. They stated that

they could assign any specific reason that could be

assigned to the findings of their research. It is not easy

to specify a single reason due to varied nature of the

workplace environments and work culture. Mark

Weycer (2019), argues that time of day, the time an

employee spends on a certain task or performs repetitive tasks, environmental factors, such as poor

indoor air quality and noise, worker’s personal factors,

as well as a person’s age and health are factors that can

lead to accident.

In an investigation of occupational accidents among

factory workers in Babadogo Industrial Area in Ruaraka

-Nairobi, between September 1998 and December 1998,

Boaz (1998) found out that up to 47.2% of respondents

have had occupational accidents in the previous twelve

months under study, while 2.8% have had accidents at least 15 times or more and at least 38% of the injured

workers did not report accidents to their employers. He

also reported that accidents rates vary with time of the

day with peaks at 12.00 noon (18.1%) and at 3.00 p.m.

(21.0%). In the same study it was also shown that a

significant number of workers had low level of

education and they recommended training of workers,

as this could be a contributing factor in accident

causation. In their study on causes of accidents and time

of day, Williamson and Feyer (2007) have stated that in

terms of absolute numbers, fatalities were most

common in the late morning or early afternoon. They further found out that behavioural factors were the most

common causes of fatalities at all times, but most

common in the early hours of the morning.

The high response return rate of questionnaires from

the field study, where a total of 316 questionnaires were

returned out of the 320 sent out (98.7%) was due to the

presence of assistants during the filling of the

questionnaires. The observation that steel fabrication

and large scale steel making are the industries with the

highest number of occupational accidents may be attributed to the nature of work and the type of

machines involved in those occupations, where there

are many mobile machines and large amounts of metal

cutting activities.

It was observed that the largest number of workers

was males, and 49% of the total respondents were aged

between 26 to 35 years. This is logical because majority

of industrial workers in Kenya are males due to the

physical nature of work involved. This also coincides

with the 7% were office workers, which is the same percentage of female respondents. In these types of

industries female workers are deployed mainly in

offices as secretaries and clerks. Employees in these

industries are mainly blue colour workers, which

explain the reason for most workers usually working for

between 8 and 9 hours or more in a day, with only 15%

working for eight hours or less. Majority of the

respondents have only basic educations level with only

11% having college education. This explains why the

large number of manual workers a small percentage

being office workers. The college educated staff are

mainly at the supervisory level.

The prolonged performance of repetitive tasks

without the adequate chance of rest and recovery,

coupled with low level of education, may result in an

occupational overuse injury. Most of the workers got

less than one hour break, and of the workers getting

more than one hour break. These are factors that lead to

fatigue which is known to be detrimental to work

performance and a risk factor for accidents because a

fatigued worker has a reduced cognitive capacity.


49

Fatigue reduces a person’s power of judgment and

can lead to accidents because it directly affects a

number of key physical and mental abilities needed to carry out even basic motor tasks e.g impaired

concentration, poor judgment, reduced hand and eye

coordination and reduced vigilance and slowed reaction

time.These effects may be reduced or eliminated when

a person has had adequate break and is fully rested.

Sufficient time spent away from the work environment

allows partial recovery from fatigue, which

subsequently improves performance and reduce risks to

accidents.

The study shows average hours of sleep obtained in

24 hours was 6.5 hours, and the majority of workers, have to work six days a week. This means the worker

has very little time to recover thus raising the risk of

fatigue. The short sleep hours may arise from waking

early so as to report to work by the reporting time.

Many workers commute long distances from their

residences to places of work and traffic in Nairobi is

chaotic during rush hours.

It is known that inadequate sleep and inadequate

break time may increase levels of fatigue due to

insufficient recovery time. Mark Weycer (2019) reported that sleep deprivation is a cause of workplace

fatigue. He further argued that a fatigued worker is far

more likely to miss critical steps in a safety process,

forget safety precautions, and misjudge or completely

overlook hazardous conditions. Phil La Duke (2014)

reports that a sleep-deprived worker is also far more

likely to misjudge the height at which he/she is

working, how much clutter could cause a trip, the speed

of a forklift, and the traction of a work surface.

Multiple sources quoted by Chan (2010), found in Phil La Duke (2014)), list fatigue as one of the top five

causal factors in workplace incidents, so while experts

may attribute upward of 90% of workplace injuries to

unsafe behavior, most fail to answer the question of

why a worker behaved unsafely. This study also

showed most (63%) of the workers felt that fatigue does

not affect the quality of their work and even fewer

(26%) workers felt that fatigue affected their safety at

work. This could be interpreted that, either the workers

felt job insecurity if they reported that they were

fatigued or the workers are not aware of factors that can

cause accidents in the workplaces.

The type of contract engagement terms and mode of

work compensation may also contribute to risks of

accidents. For the workers who are paid per day the

amounts usually depends on how much work was

accomplished. Piece work for this category of workers,

meeting or even surpassing the target is their driving

force. These categories of workers more often than not

are willing to work extra hours to earn more. They

sometimes even forego or shorten their breaks in order

to get more work done and get maximum pay.

CONCLUSIONS AND RECOMMENDATIONS

The study concludes that the prevalence of

occupational accidents is high and accidents occurrence varies with time of working day starting from low in the

peaks in the late morning and late afternoon. We further

conclude that fatigue is a risk factor in occupational

accidents. We recommend that DOSHS should sponsor

research to highlight the underlying factors in

occupational accidents, and ensure training of workers

in basic accident preventive mechanisms.

Acknowledgement

The authors wish to appreciate the Directorate of

Occupational Safety and Health Services for allowing and availing their accident reporting forms (DOSH 1).

Funding: This research did not have external funding.

It was funded from own resources.

REFERENCES

1. Gathura, G. (2017 13th November). Standard

Newspaper.

2. Government of Kenya. (2007). Occupational Safety and Health ACT 2007.

3. Hänecke, K., Tiedemann, S., Nachreiner, F., &

Grzech-Šukalo, H. (1998). Accident risk as a

function of hour at work and time of day as

determined from accident data and exposure

models for the German working

population. Scandinavian journal of work,

environment & health, 43-48.

4. Hsiao, H., & Simeonov, P. (2001). Preventing falls

from roofs: a critical review. Ergonomics, 44(5),

537-561. 5. International Labour Organization Encyclopedia

(2005).

6. Kemei, R., & Nyerere, J. (2016). Occupational

accident patterns and prevention measures in

construction sites in Nairobi County

Kenya. American Journal of Civil

Engineering, 4(5), 254-263.

7. Kenya Government. (2007). The Work Injury

Benefits Act.

8. Makori, G. O., Mamati, E. G., & Njoroge, J. B.

(2018). Evaluation of factors that contribute to

occurrence of accidents at construction sites in Nairobi County. Scientific Research Journal

(SCIRJ), VI (VIII), August 2018.

9. Ministry of Labour, Directorate of Occupational

Safety & Health Services (DOSHS)


50

10. Mwangi, F.N., & Oduor, R. (2016). An

investigation of the causes of accidents and health

hazards on construction sites and their management in kenya (case study of Nairobi

County). Unpublished thesis of the Department of

real estate and construction management, School

of built environment, University of Nairobi.

11. O’Neill, C., & Panuwatwanich, K. (2013). The

impact of fatigue on labour productivity: case

study of dam construction project in

Queensland. Proceedings from EPPM.

12. Phil La Duke, (2014 April 25,). Industrial Safety

and Hygiene News

13. Samwel Boaz, O. (2012). Accidents as an

occupational health problem, a case of Babadogo industrial area, Nairobi, Kenya. http://ir-

library.ku.ac.ke/handle/123456789/2875.

14. Statista Research Department. (2016). Annual

number of deaths and accidents at work

worldwide, Jan 15, 2016.

15. Swaen, G. M. H., Van Amelsvoort, L. G. P. M., Bültmann, U., & Kant, I. J. (2003). Fatigue as a

risk factor for being injured in an occupational

accident: results from the Maastricht Cohort

Study. Occupational and environmental

medicine, 60(suppl 1), i88-i92.

16. Weycer, M. (2019). The Impact of Worker Fatigue

on Workplace Injuries and Employment-Related

Accidents. Construction Site Injuries, Personal

Injury,| Mar 11, 2019 |.

17. Williamson, A. M., & Feyer, A. M. (2007).

Causes of accidents and the time of day, Pages

158-164 | published online: 25 Sep 2007.

Appendix 1 DOSH From 1

research article variation of occupational accidents with

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