journal of occupational safety and health, december 2013, vol 10, no. 2

JOURNAL OFOCCUPATIONALSAFETY AND HEALTH

National Institute of Occupational Safety and Health

National Institute of Occupational Safety and Health (NIOSH)Ministry of Human Resources Malaysia

December 2013, Vol 10, No. 2ISSN 1675-5456PP13199/12/2012(032005)

Journal of OccupationalSafety and Health

Editor-in-chiefIr. Haji Rosli bin Hussin

Executive DirectorNIOSH, Malaysia

Secretariat

Editorial Board

Prof. Dr. Krishna Gopal RampalUniversiti Kebangsaan Malaysia

NIOSH, MalaysiaIr. Daud Sulaiman

Fadzil OsmanNIOSH, MalaysiaRaemy Md. ZeinNIOSH, Malaysia

The Journal

- Aims to serve as a forum for the sharing of research findings and information across broad areas in Occupational Safety and Health. - Publishes original research reports, topical article reviews, book reviews, case reports, short communications, invited editorial and letters to editor. - Welcomes articles in Occupational Safety and Health related fields.

Associate Editors

Prof. Dr. Ismail BahriUniversiti Kebangsaan MalaysiaDr. Jeffereli Shamsul BahrinBASF East Asia Regional Headquartes Ltd.Dr. Abu Hasan SamadPrince Court Medical Centre

Mohd Rashidi RohmadRoslina Md HusinNor Akmar Yussuf

i

December 2013 Vol.10 No.2

Contents

Journal of Occupational Safety and Health

1. The Origin of Workers Hearing Level – A Finding Based on Chances of OccurrenceBy Gan Chun Chet, MSc (UK), BSc (Hons) (UK), Peng

2. Occupational Lung Carcinogens and Factors in Relation to Lung Cancer RiskFauziah Nordin1,3, Richard Booton2, Paul O’Donnell2, Philip Barber2, Andrew Povey1 1Centre for Occupational & Environmental Health, Faculty of Medical & Human Sciences, The University of Manchester, United Kingdom 2North West Lung Centre, Wythenshawe Hospital, Manchester, United Kingdom 3Institute for Public Health, Ministry of Health Malaysia, WP Kuala Lumpur, MalaysiaCorresponding author:Dr Fauziah Nordin,Institute for Public Health,Ministry of Health Malaysia,Jalan Bangsar, 50590 WP Kuala Lumpur,Malaysia(Tel: +60322979400, Fax: +60322823114, email: [email protected])

3. OHSAS 18001 and MS 1722 Certification Initiatives Prove the Commitment to SustainabilityWai Onn HongProcessing Department, Genting Plantations Berhad,10th Floor, Wisma Genting, Jalan Sultan Ismail, Kuala Lumpur, MalaysiaTel: +60 3 2333 6506 Fax: + 60 3 2161 9689 Email: [email protected]

4. The Extent of Predictability of Noise-Induced Problems – A Cross-Over from the Healthy Limit to Off-Limit ConditionsBy Ir. Gan Chun ChetMSc (UK), BSc (Hons) (UK), PEng

5. Prevalence Of Work Related Musculoskeletal Disorder Among Port Workers: Quantitative Analysis At The Physiotherapy Centre Of Malaysian Shipping Industry, SelangorIzham Zain¹, Azrul Anuar¹, Asrina Asri¹, Shamsul Azhar²¹ KPJ Healthcare University College² Physiotherapy Department, Malaysia Shipping Industry, SelangorCorresponding author: [email protected]

1 - 11

13 - 25

27 - 36

37 - 50

51 - 66

This page has been intentionally left blank


1 1

The Origin of Workers Hearing Level – A Finding Based on

Chances of Occurrence

By Gan Chun Chet, MSc (UK), BSc (Hons) (UK), Peng

_____________________________________________________________________

Abstract

The paper writes on the possible origin of off-limit cases found in a noise

project conducted internally in a factory in Malaysia. Out of 691 sampled workers’

that attended audiometric test results (some repeated), it was found that the mode of

hearing ability is between 20 to 30 dB depending on individual worker’s age ranging

from 20 to 55 years. Out of the total results, approximately 100 workers are above a

limit defined here in this paper as the off-limit condition. The chance of a worker

originating from a good condition to an unhealthy condition is about 1 percent. The

data are tabulated to show that a sway pattern could be an explanation of workers’

origin. Although the data is profound, there is no evidence of a trace due to a short

test period. Possibilities are highlight here to outline the severity of a cross over to the

unhealthy condition (here defined as the off-limit condition). Some advises are

mentioned here with individual susceptibility on the matter though there is no data to

substantiate. Further findings are required to show a trace. In conclusion, the severity

is highlight. A chart, developed to know the limits of hearing ability, is illustrated

based the findings.

_____________________________________________________________________

Introduction

Scientific data induced to formulate a theory or depicted from an observation

and subsequently utilizing an existing theory to explain a fact, are used as a base for

an answer or a comparison. However, scientific disillusionment exists, drawing from

the base of data in question where different views exist from the same set of

observations. While scientific breakthroughs are remarkable yet a theory is falsifiable

if other research is concluded differently. This leave a myth to the underlying truth

based on the truth of facts due to a possible change in a hypothetical assumption or a

change in a theory, etc. Is this possible?

The following are findings of a real case on noise induced problems in a

factory. The trace of historical origin of these workers’ hearing ability is unknown.

The data is profound because it shows the actual truth but unknown to others. After

going through the analysis, my personal opinion is that the truth lies within these

individuals. Here defined the off limit cases (red line) as shown in the diagram below,


2 2

grids with utterly puzzled and astonished findings. A possible explanation to the

situation is that sway pattern might had happened and a possible origin based on the

possibilities might be the explanation. This article tries to uncover the origin of their

hearing abilities based on the chances of occurrence in each of the sway pattern are

illustrated in the sections.

Background of the Research Data

The total number of workers attended the test, including repeated cases,

amounts to 691 in number. The hearing abilities of the workers are checked using an

audiogram by an independent test company. The workers average hearing abilities at

500, 1000, 2000 and 3000Hz were plotted against age; regardless of the number of

year of service with the company. This is shown in graph 1 (Right Ear). This article

investigates the possible origin of hearing ability of Right Ear when they are at the

age of 16 to 20 years old (started and joined the industry). Some of these workers

discovered at later age that they had hearing problem. The findings found that about

10 – 16 percent of the workers are able to hear loud noises (possibly with hearing

impaired). Regardless of the area noise in this factory or area noise in previous

company, the graph below shows their hearing abilities.

Graph 1 : Workers Hearing

Abilities (Right Ear Only)


3 3

Methodology

The method is based on possible sway patterns of a worker that might not have

eluded noisy area. However, a worker might be employed with a noise problem.

Unfortunately nearing to the end of employment, between the age of 40 to 55,

problems were noticed. This is shown in the graph. It is thoughtful to know but the

actual origin (occurrence) cannot be traced. Possibly these patterns exists, as shown in

the graph below (2 to 6). The possible path called the “sway patterns”. In the diagram

below, the ability either started off from a healthy condition or an off limit condition.

Both these conditions are the possible origin. At the end of employment is here called

the “end destination” of a sway pattern. The numbers of off limit cases were counted

to calculate the possible occurrences shown in a grid matrix below. The findings are

as shown in the next section.

2

grids with utterly puzzled and astonished findings. A possible explanation to the

situation is that sway pattern might had happened and a possible origin based on the

possibilities might be the explanation. This article tries to uncover the origin of their

hearing abilities based on the chances of occurrence in each of the sway pattern are

illustrated in the sections.

Background of the Research Data

The total number of workers attended the test, including repeated cases,

amounts to 691 in number. The hearing abilities of the workers are checked using an

audiogram by an independent test company. The workers average hearing abilities at

500, 1000, 2000 and 3000Hz were plotted against age; regardless of the number of

year of service with the company. This is shown in graph 1 (Right Ear). This article

investigates the possible origin of hearing ability of Right Ear when they are at the

age of 16 to 20 years old (started and joined the industry). Some of these workers

discovered at later age that they had hearing problem. The findings found that about

10 – 16 percent of the workers are able to hear loud noises (possibly with hearing

impaired). Regardless of the area noise in this factory or area noise in previous

company, the graph below shows their hearing abilities.

Graph 1 : Workers Hearing

Abilities (Right Ear Only)


4

4

Category 3

Category 2

Category 1

A B C

2

Graph 3 : From Category 3 (Sheet 2)

Category 3

Category 2

Category 1

A B C

1

Very rare Graph 2 : From Individual Categories (Category 1, 2 or 3) (Sheet 1)

Category 3 : Caution Level Category 2 : Healthy Level Category 1 : Very Good Level



5

5

Category 3

Category 2

Category 1

A B C

3




A B C

4



6

6

Findings

The following are the findings from graph 2 to 6. The numbers of hearing

ability in each of the possible pattern are counted. This is tabulated in table 1 below.

Separated by “below 10 counts”, “10 to 19 counts” and “above or equal to 20 counts”,

most of these workers are in the two circles shown the table below.

The most probable occurrence is 40 cases as defined here by the count of

occurrences above the limit (red line), could originated from Category 2 and swayed

to C. The second most probable occurrence is 38, with the origin from Category 1 and

the end destination is C. The probable occurrences are tabulate in table 2 together

Category 3

Category 2

Category 1

Very low level Graph 6: From Off Limit (Sheet 5)


7 7

with the number of occurrences in descending order. The high chances of origin

shown in the table might have originated from Category 1 or 2 defined here applicable

to this situation.

Discussion

The numbers shows the count in each of the pattern. It does not tell that a

person hearing ability originated from a point above the off limit condition or from

any point on the Y-axis of the graphs (2 to 6). In addition, it is not possible to say that

a person will move to a point with certainty after exposure to noise years later. It is to

note that a sway of a possible situation might have originated from these defined

limits, with the possibility that each of the condition is considered independent from

each other.

The pattern cannot compute specifically which employee has a good hearing

ability and later have a problem at the age of 40 to 50. Neither does the pattern shows

that a person in a good condition, as defined, ended up with a problem due to noise

problem in the plant. The patterns are the possibilities of an origin by the count of

occurrence in the sway pattern.

By counting the possibility of an occurrence, out of an estimate off limit cases

with reference to the mode occurrence, the chance on one person originated from

either side of the limit is approximately 1 percent. Out of the number of workers (691

records), about 109 records a fifty fifty chance on either sides. Half of which might be

healthy, with an increment of approximately 1 percent on an addition case.

The Development of A Chart Defining the Possibilities of An Origin

The chart as shown below represents and shows the origin in a graphical form.

It could be used as to explain a point of reference origin in this situation.

6

Findings

The following are the findings from graph 2 to 6. The numbers of hearing

ability in each of the possible pattern are counted. This is tabulated in table 1 below.

Separated by “below 10 counts”, “10 to 19 counts” and “above or equal to 20 counts”,

most of these workers are in the two circles shown the table below.

The most probable occurrence is 40 cases as defined here by the count of

occurrences above the limit (red line), could originated from Category 2 and swayed

to C. The second most probable occurrence is 38, with the origin from Category 1 and

the end destination is C. The probable occurrences are tabulate in table 2 together

Category 3

Category 2

Category 1

Very low level Graph 6: From Off Limit (Sheet 5)


8 8

Category Possibly From The Following Sound Limit

Off Limit 21 and 40 dB

Category 3 17 to 20 dB



Low Level Less than 8dB!

Category of age range

[O] – 16 to 20 years old

[A] – 30 to 40 years old

[B] – 40 to 50 years old

[C] – 50 to 60 years old

[D] – More than 60

Diagram 1: A Pattern that is to Be Avoided

From

Category 3

Category 2

Category 1 10

20

View 1

Off Limit

A

Age

40

0

10

20

0

30 40

0

50 60

B C D O Noise, dB

See View

1

To

Limit


9 9

The shaded area, as shown in the graphic above, shows the sway pattern of a

worker from healthy condition (below the red line) to either [A], [B] or [C]. This is to

be avoided. The [O]s are unknown condition as the problem occurs at a very early

age. The [D] end destination are old or elderly people.

The hearing level should be below the limit by avoiding exposure to unwanted

sound.

• Some Advises regarding Noise Problem Based on this Situation

Different factory conditions will have different impact on the workers. The

hearing ability of every worker is different, whether they are new or an existing

worker. A few advises as listed below.

Opinion alone not substantiated by data is not real. A change in theory is a

change of a hypothetical question forming a paradox. In this case, the origin of

workers noise level, based by factual data by the count of workers falling in the sub-

diagram, is in fact forming a set of suggestions and fitting it into the box.

In this article, it is only to suggest that there might be possibilities that the off

limit workers might have came from an off limit condition or a healthy condition

(whether Category 1 then Category 2, etc) in the order. It is difficult to conclude that

this is where the workers condition came from.

Disclaiming the facts, that workers are from healthy condition and the cause of

their hearing disability is from the plant, the point of origin cannot be traced exactly.

In fact, in my opinion, it can only be know of possible origins. This is only one plant

that encountered this problem. What about others? The truth lies in the workers

themselves.

8

Category Possibly From The Following Sound Limit

Off Limit 21 and 40 dB




Low Level Less than 8dB!

Category of age range

[O] – 16 to 20 years old

[A] – 30 to 40 years old

[B] – 40 to 50 years old

[C] – 50 to 60 years old

[D] – More than 60

Diagram 1: A Pattern that is to Be Avoided

From

Category 3

Category 2

Category 1 10

20

View 1

Off Limit

A

Age

40

0

10

20

0

30 40

0

50 60

B C D O Noise, dB

See View

1

To

Limit


10 10

The Line below the Limit (Red Line)

The conditions below the limit are healthy conditions. This line is drawn based

on the majority of the workers being below this limit in clusters due to employment

years shown in graph 1. The general view shows that the workers are healthy below

the red line in the diagram below. The equation to this line is calculated. With this

line, different factors to categorize healthy workers are possible, with a caution region

to warn the workers that the condition might cross above the limit. If this happens,

then the group will be in the possible pattern of origin as highlighted in this article.

Ideally, workers should come in healthy and maintain a healthy condition at

later stage of their employment. Noise induced problems are caused by prolong

exposure of unwanted sound into the ear. Age related losses might be the reason for

the increased in hearing ability of the workers at later age. The problem about noise is

that if it is detected will cause a failure in hearing ability. If it is purely due to age

related reasons, as already known, then the reason of workers moving up to a new

level of hearing ability is due to an over exposure can be identify, assuming that there

is no disease to the ears or other medical reasons linked to this, which requires

qualified medical practitioner to inform and a cure.

The plot of audiometric results shows the location of a person hearing level.

The results from the workers in the graph above (graph 1) show remarkable truth in it.

Worker’s Age (Year)

20

dB

30

dB

20 60

10

dB

30 40 50

Normal Hearing

Ability

Category 3

Category 2

Category 1

Category 1 : Very Good

Category 2 : Good

Category 3 : Caution

Chart 1: A Noise Chart of Healthy Workers


11 11

The inadequacy of health information might be the reason to a high number of off

limit cases. It is might be only known at that instant of time that the ears have been

affected that medical examiners are only able to comfort these patience. It might be

that the workers are not aware of the problem and have caused a shift in hearing.

How is it possible to trace the point of origin at a test or a series of test within

the short time frame? If an earlier test was conducted, there might be able to locate the

origin, subject also to age related losses and other unknown factors like susceptibility,

etc. In this article, it is stated by rough approximation that the count of numbers in the

pattern of possible sway from the start of employment might have happened.

Otherwise, without the count, it is just a guess.

In Conclusion

The purpose of the article is to highlight the severity of the problem. Noise

induced problem should be avoided. From a layman point of view, it is advised to

consult an expert in this area or an ear specialist should problem arise.

Please note that the off limit in this article is based on the general mode limit

of a plant. It does not mean that it is applicable to other situations. Please refer to an

ear consultant for medical advice for the permissible levels.

(Note : The data is also key in by the author to analyze the problem)

The author expresses his personal opinions on the above out of interest to the topic and

indemnifies himself from the readers for any charges. It is not to depict any information from

this article and is only to be referred to a qualified medical practitioner for expert advice if a

problem is encountered. The article writes on the possible origin based on data collected to

help other to avoid a hearing problem.

Author Contact:

[email protected]

Info to Reviewer:

Qualifications:

MSc (UK), University of Manchester Institute of Science and Technology in Operations

Management

BSc (Hons) (UK), University of Manchester in Mechanical Engineering

Professional Registration:

PEng Registration, Board of Engineers Malaysia, Mechanical Branch (Registration No.

12539)

10

The Line below the Limit (Red Line)

The conditions below the limit are healthy conditions. This line is drawn based

on the majority of the workers being below this limit in clusters due to employment

years shown in graph 1. The general view shows that the workers are healthy below

the red line in the diagram below. The equation to this line is calculated. With this

line, different factors to categorize healthy workers are possible, with a caution region

to warn the workers that the condition might cross above the limit. If this happens,

then the group will be in the possible pattern of origin as highlighted in this article.

Ideally, workers should come in healthy and maintain a healthy condition at

later stage of their employment. Noise induced problems are caused by prolong

exposure of unwanted sound into the ear. Age related losses might be the reason for

the increased in hearing ability of the workers at later age. The problem about noise is

that if it is detected will cause a failure in hearing ability. If it is purely due to age

related reasons, as already known, then the reason of workers moving up to a new

level of hearing ability is due to an over exposure can be identify, assuming that there

is no disease to the ears or other medical reasons linked to this, which requires

qualified medical practitioner to inform and a cure.

The plot of audiometric results shows the location of a person hearing level.

The results from the workers in the graph above (graph 1) show remarkable truth in it.

Worker’s Age (Year)

20

dB

30

dB

20 60

10

dB

30 40 50

Normal Hearing

Ability

Category 3

Category 2

Category 1

Category 1 : Very Good

Category 2 : Good

Category 3 : Caution

Chart 1: A Noise Chart of Healthy Workers


13

Occupational Lung Carcinogens and Factors in Relation to Lung

Cancer Risk

Fauziah Nordin

1,3, Richard Booton

2, Paul O’Donnell

2, Philip Barber

2, Andrew Povey

1

1Centre for Occupational & Environmental Health, Faculty of Medical & Human Sciences, The

University of Manchester, United Kingdom 2North West Lung Centre, Wythenshawe Hospital,

Manchester, United Kingdom 3Institute for Public Health, Ministry of Health Malaysia, WP Kuala

Lumpur, Malaysia

Corresponding author:

Dr Fauziah Nordin,

Institute for Public Health,

Ministry of Health Malaysia,

Jalan Bangsar, 50590 WP Kuala Lumpur,

Malaysia

(Tel: +60322979400, Fax: +60322823114, email: [email protected] )

_______________________________________________________________________________

Abstract

Introduction:

Although smoking is the most important cause of lung cancer, occupational factors can also play

an important role. Worldwide, approximately 10% of lung cancer deaths in men (88,000 deaths)

and 5% in women (14,300 deaths) were attributable to exposure to occupational carcinogens,

referred to the report on the global burden of disease due to occupational carcinogens

Methods:

We examined the risks associated with occupational exposures in a case-referent study of lung

cancer that was carried out between November 1998 to March 2000. Cases were patients attended

a bronchoscopy clinic at the North West Lung Centre, Wythenshawe Hospital in Manchester

during that period who were subsequently found to have lung cancer. Referents were patients

found not to have lung cancer at bronchoscopy.

Results:

There were 121 subjects in the study (39 cases, 82 referents). Smoking status was significantly

associated with lung cancer risk: the odds ratio of having lung cancer in ever smokers (vs never

smokers) was 3.21 (95% CI: 1.02 - 10.07). There were also significant association between

number of cigarettes smoked (p = 0.01) and years smoked (p = 0.04) with lung cancer risk.


14 2

Years of exposure to occupational carcinogens was also associated with the development of lung

cancer (p = 0.02). Workers who were exposed for 45 years or more, had an increase risk when

compared to those who had worked for less than 17 years (OR, 95% CI = 2.54, 1.12 – 6.34). Job

category was found to be borderline significant with lung cancer risk. The adjusted odds ratio of

having lung cancer among unskilled manual job worker (vs management, professional & associate

professional) was 4.75 (95% CI: 1.06 - 21.4).

Conclusion:

This study shows an exposure to occupational carcinogens was associated with an elevated lung

cancer risk. Unskilled manual job workers had a higher lung cancer risk compared with other

categories, such as management, professional & associate professional.

Keywords:

Occupational Lung Carcinogens, Lung Cancer, Smoking

_______________________________________________________________________________

1. INTRODUCTION

1.1. Occupational exposure to known lung carcinogens

Lung cancer is the second ranked after bladder cancer among all occupational cancers worldwide

(Hansen, 2008). The risk of occupational substances causing lung cancer depends on certain

occupational characteristics, including the nature of work or job category (direct or indirect

exposure), how much exposure (the quantities), for how long (age of employment, length of

exposure, frequency been exposed), types of the substance (gas or mist form, individual or mixed

form) and whether the substance is organic or non-organic (Hansen, 2008).

The carcinogen list based on IARC category (“Lists of IARC evaluations according to IARC

monographs - International occupational safety & health information centre,” n.d.) such as list A

(confirmed human carcinogen) and list B (suspected human carcinogen), is still being updated

periodically to uncover the harmful effects particularly for those where there are still substantial

uncertainties. A recent population-based study found an increased risk of lung cancer in list A

occupation category with OR 1.74 (95% CI 1.27 – 2.38) compared with list B category. Lung

cancer risk in increased in several industrial sectors; the ceramic and refractory brick sector (OR,

95% CI = 2.64, 1.13– 6.19) and nonferrous metals industry (OR, 95% CI = 2.45, 1.31– 4.60)

(Consonni et al., 2010). They estimated that 4.9 % (95% CI 2.0 – 7.8) of lung cancers in men were

attributable to occupation (Consonni et al., 2010).


15 3

1.2. Risk factors and specific occupational lung carcinogen

Based on a recent number of occupations or occupational exposures listed by IARC (“Lists of

IARC evaluations according to IARC monographs - International occupational safety & health

information centre,” n.d.) studies have reported on the magnitude of the association between

specific occupational carcinogen and lung cancer risk. As reported in the IARC and the National

Toxicology Program (NTP) (“Lists of IARC evaluations according to IARC monographs -

International occupational safety & health information centre,” n.d.), workers in a specific job

category and particular type of industry are often at an increased risk of lung cancer. For example,

workers in shoe manufacturing are exposed to a group of carcinogenic substances such as organic

solvents (toluene, xylene, methyl ethyl ketone, formaldehyde), chromium, nickel, arsenic, vinyl

chloride or others (Galán Dávila, Romero Candeira, Sánchez Payá, Orts Giménez, & Llorca

Martínez, 2005).

A cohort study of 7828 workers in a shoe manufacturing plant in USA found a significant excess

of lung cancer deaths with a SMR = 1.36 (95% CI 1.19-1.54)(Lehman & Hein, 2006). This was

associated with exposure to chronic, low levels of organic solvents and the finding has persisted

with increasing years of follow up of the cohort. The evidence regarding the risk of lung cancer

related to solvents continues to emerge. Another study with 6000 subjects in European countries

looking at the exposure to specific organic solvents (acrylnitrile, vinyl chloride and styrene)

reported a significant increase in the risk of lung cancer for ever exposure to acrylnitrile (OR, 95%

CI = 2.20, 1.11 – 4.36) and vinyl chloride (OR, 95% CI = 1.05, 0.68– 1.62). There was a positive

dose-response relationship although not significant, between estimated cumulative exposure

(maximum cumulative exposure compared to non-exposed) and lung cancer risk for both

acrylnitrile (OR, 95% CI = 2.87, 0.85 – 9.66) and vinyl chloride (OR, 95% CI = 1.51, 0.65– 3.47)

(“Lists of IARC evaluations according to IARC monographs - International occupational safety &

health information centre,” n.d.)

Two occupations met the criteria of having sufficient evidence of carcinogenicity for the human

lung, namely painters and welders (“Lists of IARC evaluations according to IARC monographs -

International occupational safety & health information centre,” n.d.). A significant association

between risk of lung cancer and occupational exposure to paint dust (RR, 95% CI = 2.48, 0.88-

6.97) and welding fumes (RR, 95% CI = 1.73, 1.05 – 2.85) has been reported in a large cohort

study in the Netherlands with 58,279 participants (van Loon et al., 1997). The same finding was

2

Years of exposure to occupational carcinogens was also associated with the development of lung

cancer (p = 0.02). Workers who were exposed for 45 years or more, had an increase risk when

compared to those who had worked for less than 17 years (OR, 95% CI = 2.54, 1.12 – 6.34). Job

category was found to be borderline significant with lung cancer risk. The adjusted odds ratio of

having lung cancer among unskilled manual job worker (vs management, professional & associate

professional) was 4.75 (95% CI: 1.06 - 21.4).

Conclusion:




Keywords:

Occupational Lung Carcinogens, Lung Cancer, Smoking

_______________________________________________________________________________

1. INTRODUCTION

1.1. Occupational exposure to known lung carcinogens

Lung cancer is the second ranked after bladder cancer among all occupational cancers worldwide

(Hansen, 2008). The risk of occupational substances causing lung cancer depends on certain

occupational characteristics, including the nature of work or job category (direct or indirect

exposure), how much exposure (the quantities), for how long (age of employment, length of

exposure, frequency been exposed), types of the substance (gas or mist form, individual or mixed

form) and whether the substance is organic or non-organic (Hansen, 2008).

The carcinogen list based on IARC category (“Lists of IARC evaluations according to IARC

monographs - International occupational safety & health information centre,” n.d.) such as list A

(confirmed human carcinogen) and list B (suspected human carcinogen), is still being updated

periodically to uncover the harmful effects particularly for those where there are still substantial

uncertainties. A recent population-based study found an increased risk of lung cancer in list A

occupation category with OR 1.74 (95% CI 1.27 – 2.38) compared with list B category. Lung

cancer risk in increased in several industrial sectors; the ceramic and refractory brick sector (OR,

95% CI = 2.64, 1.13– 6.19) and nonferrous metals industry (OR, 95% CI = 2.45, 1.31– 4.60)

(Consonni et al., 2010). They estimated that 4.9 % (95% CI 2.0 – 7.8) of lung cancers in men were

attributable to occupation (Consonni et al., 2010).


16 4

reported in a recent meta-analysis study on lung cancer and welding with 60 studies of welders of

shipyard, mild steel and stainless steel. The combined relative risks (CRR) for lung cancer in all

the welders as compared with non-welders was 1.26 (95% CI 1.20 – 1.32) (Ambroise, Wild, &

Moulin, 2006).

Figure 1: Forest plot of lung cancer risk with occupational exposure

(van Loon et al., 1997),(Ambroise et al., 2006),(Berry, 2004),(Cassidy et al., 2007),(Olsson et

al., 2010),(Scélo et al., 2004)

However the study failed to detect any dose-response relationship between lung cancer incidence

and cumulative exposure to chromium and nickel in welders (Ambroise et al., 2006). The

magnitude of association between occupational exposure and lung cancer risk from previous

studies is summarized in figure 1.

In another study in the Netherlands an increased risk of lung cancer was reported for employment

of 15 years and more in blue collar jobs in the “electronics and optical instruments” industry (RR,

5.0

2.0

_________________

Asbestos (OR, 95% CI = 1.85, 1.07-3.21)

Vinyl chloride (OR, 95% CI = 1.05, 0.68– 1.62)

Acrylnitrile (OR, 95% CI = 2.20, 1.11 – 4.36)

PAHs (OR, 95% CI = 1.97, 1.16 – 3.35)

Silica (OR, 95% CI = 1.37, 1.14-1.65)

Welding fumes (RR, 95% CI = 1.73, 1.05 – 2.85)

Paint dust (RR, 95% CI = 2.48, 0.88-6.97)

Occupational Exposure

Magnitude of association

____________________

_______________________

____________

____________________________

_______________

1.0


17 5

95% CI = 1.99, 1.18 – 3.35), “construction and homebuilding business” industry (RR, 95% CI =

1.64, 1.21 – 2.22) and “railway company” (RR, 95% CI = 2.40, 1.00 – 5.73) (Preller, Balder,

Tielemans, Brandt, & Goldbohm, 2008). The risk of lung cancer was observed for lengthy

employment in certain high-risk industries and research on specific occupational category is

needed to further investigate causative factors.

The aim of this study was to examine occupational exposures and lung cancer risk in a North West

population. The ultimate aim of this work is to compare the occupational exposure and the

development of lung cancer in previous studies carried out in Wythenshawe, which have been

looking at the factors and determinants for lung cancer risk and susceptibility.

2. METHODOLOGY

2.1. Study population

Self-reported occupational histories and exposures were collected in a case-referent study that was

carried out between November 1998 to March 2000. Cases were patients attended a bronchoscopy

clinic at the North West Lung Centre, Wythenshawe Hospital who were subsequently found to

have lung cancer. Referents were patients found not to have lung cancer at bronchoscopy.

2.2. Occupational exposure analysis

The occupational history was assessed by questions on the employment status, the title of the job,

period of employment and working duration (hours) per week. For each person, information of a

maximum of five occupations was registered, starting with the current or most recent job first and

working backwards. In the few cases where more than five occupations were mentioned, similar

consecutive jobs for different employers were deleted. If more than five jobs still remained, the job

with the least information provided was omitted unless it lasted for a very long time.

The information on occupational exposure was obtained by asking the participants whether they

were exposed to smoke, dust, fumes or asbestos. The type of industry was coded according to the

UK Standard Industrial Classification (2003) and occupation was coded according to the Standard

Occupational Classification (2000), both from the UK Office for National Statistics (ONS) (Office

for National Statistics, 2010).

4

reported in a recent meta-analysis study on lung cancer and welding with 60 studies of welders of

shipyard, mild steel and stainless steel. The combined relative risks (CRR) for lung cancer in all

the welders as compared with non-welders was 1.26 (95% CI 1.20 – 1.32) (Ambroise, Wild, &

Moulin, 2006).

Figure 1: Forest plot of lung cancer risk with occupational exposure

(van Loon et al., 1997),(Ambroise et al., 2006),(Berry, 2004),(Cassidy et al., 2007),(Olsson et

al., 2010),(Scélo et al., 2004)

However the study failed to detect any dose-response relationship between lung cancer incidence

and cumulative exposure to chromium and nickel in welders (Ambroise et al., 2006). The

magnitude of association between occupational exposure and lung cancer risk from previous

studies is summarized in figure 1.

In another study in the Netherlands an increased risk of lung cancer was reported for employment

of 15 years and more in blue collar jobs in the “electronics and optical instruments” industry (RR,

5.0

2.0

_________________

Asbestos (OR, 95% CI = 1.85, 1.07-3.21)

Vinyl chloride (OR, 95% CI = 1.05, 0.68– 1.62)

Acrylnitrile (OR, 95% CI = 2.20, 1.11 – 4.36)

PAHs (OR, 95% CI = 1.97, 1.16 – 3.35)

Silica (OR, 95% CI = 1.37, 1.14-1.65)

Welding fumes (RR, 95% CI = 1.73, 1.05 – 2.85)

Paint dust (RR, 95% CI = 2.48, 0.88-6.97)

Occupational Exposure

Magnitude of association

____________________

_______________________

____________

____________________________

_______________

1.0


18 6

2.3. Statistical analysis

Frequencies are presented for categorical data and means with standard deviations for continuous

data. All statistical analysis was carried out in SPSS (version 15.0). Comparisons were made

between two groups (e.g. cases and referents) to determine the risk of lung cancer. χ2-test was

used for 2 X 2 table and binary logistic regression was used for variable with 2 or more categorical

groups. Odds Ratio (OR) and its 95% confidence interval (96% CI) was measured to determine the

magnitude of association for occupational and other factors (smoking, alcohol consumption and

family history of lung cancer) with lung cancer risk. The variables were further stratified for

smoking status to control for confounding factor and adjusted odds ratio were then measured.

3. RESULTS


There are 121 subjects in the study (39 cases, 82 referents), which 74 (61.2%) of them were men

with a mean age of total study population was 61.1 + 13.7 years old. (Table 1)

Table 1.Frequency distribution of lung cancer incidence by gender and age group

Variable

Lung Cancer

Yes No

n (%) n (%)

Total n (%)

Gender

Male

Female

Age group (years)

< 55

55 - 64

65 - 74

≥ 75

(mean + SD)

29 (74.4)

10 (25.6)

6 (17.1)

10 (28.6)

14 (40.0)

5 (14.3)

64.6 + 10.7

45 (54.9)

37 (45.1)

32 (40.5)

15 (19.0)

18 (22.8)

14 (17.7)

59.7 + 14.6

74 (61.2)

47 (38.8)

38 (33.3)

25 (21.9)

32 (28.1)

19 (16.7)

61.1 + 13.7

3.2. Smoking characteristics and alcohol intake with lung cancer

Smoking status was significantly associated with lung cancer risk: the odds ratio (OR) of having

lung cancer in ever smokers (vs. never smokers) were 3.21 (95% CI: 1.02 - 10.07). The duration of

years cigarette smoked was longer in the lung cancer group with mean of 42.7 + 13.1 years

compared with mean of 32.1 + 16.0 for referents, and was significantly associated with lung cancer

risk. Other characteristics were not significantly associated with lung cancer risk (Table 2)


19 7

Table 2. Smoking characteristics and alcohol intake in cases and referents

Variable

Lung Cancer

Yes No n (%) n (%)

Crude Odds Ratio

(95% CI)a

Ever smoking

Yes

Nob

Smoking status

Current smoker

Ex-smokerb

Age smoking began

(years)

< 15b

15 - 19

20 - 24

≥ 25

(mean + SD)

Cigarettes smoked

(per day)

1 - 9

10 - 19

20 - 39

≥ 40 b

(mean + SD)

Years smoked

1 - 9

10 - 29

30 - 49

≥ 50b

(mean + SD)

Passive smoker

Yes

Nob

Ever drink alcohol

Yes

Nob

Alcohol intake

(units per week)

0b

1 - 13

14 - 27

≥ 28

(mean + SD)

35 (89.7)

4 (10.3)

17 (48.6)

18 (51.4)

22 (62.9)

10 (28.6)

3 (8.6)

0 (0)

15.2 + 2.9

0 (0)

10 (28.6)

18 (51.4)

7 (20.0)

24.0 + 12.6

0 (0)

5 (14.7)

17 (50.0)

12 (35.3)

42.7 + 13.1

17 (43.6)

22 (56.4)

29 (74.4)

10 (25.6)

10 (25.6)

17 (43.6)

6 (15.4)

6 (15.4)

14.5 + 16.1

60 (73.2)

22 (26.8)

23 (38.3)

37 (61.7)

28 (46.7)

23 (38.3)

7 (11.7)

2 (3.3)

16.4 + 3.3

8 (13.6)

11 (18.6)

34 (57.6)

6 (10.2)

21.0 + 12.4

3 (5.9)

19 (37.3)

19 (37.3)

10 (19.6)

32.1 + 16.0

34 (41.5)

48 (58.5)

66 (80.5)

16 (19.5)

17 (21.0)

39 (48.1)

17 (21.0)

8 (9.9)

12.3 + 12.2

3.21 (1.02-10.07)

1*

1.52 (0.65-3.53)

1

1

0.55 (0.22-1.40)

0.54 (0.13-2.36)

0.78 (0.19-3.11)

0.45 (0.13-1.55)

1

0.22 (0.60-0.80)

0.75 (0.26-2.16)

1*

1.09 (0.50-2.36)

1

0.70 (0.29-1.73)

1

1

0.74 (0.28-1.94)

0.60 (0.18-2.02)

1.23 (0.34-4.75)

* significant difference p<0.05. aOdds ratio is for incidence of lung cancer in each group versus incidence in

patients in groupb

6

2.3. Statistical analysis

Frequencies are presented for categorical data and means with standard deviations for continuous

data. All statistical analysis was carried out in SPSS (version 15.0). Comparisons were made

between two groups (e.g. cases and referents) to determine the risk of lung cancer. χ2-test was

used for 2 X 2 table and binary logistic regression was used for variable with 2 or more categorical

groups. Odds Ratio (OR) and its 95% confidence interval (96% CI) was measured to determine the

magnitude of association for occupational and other factors (smoking, alcohol consumption and

family history of lung cancer) with lung cancer risk. The variables were further stratified for

smoking status to control for confounding factor and adjusted odds ratio were then measured.

3. RESULTS


There are 121 subjects in the study (39 cases, 82 referents), which 74 (61.2%) of them were men

with a mean age of total study population was 61.1 + 13.7 years old. (Table 1)

Table 1.Frequency distribution of lung cancer incidence by gender and age group

Variable

Lung Cancer

Yes No

n (%) n (%)

Total n (%)

Gender

Male

Female

Age group (years)

< 55

55 - 64

65 - 74

≥ 75

(mean + SD)

29 (74.4)

10 (25.6)

6 (17.1)

10 (28.6)

14 (40.0)

5 (14.3)

64.6 + 10.7

45 (54.9)

37 (45.1)

32 (40.5)

15 (19.0)

18 (22.8)

14 (17.7)

59.7 + 14.6

74 (61.2)

47 (38.8)

38 (33.3)

25 (21.9)

32 (28.1)

19 (16.7)

61.1 + 13.7

3.2. Smoking characteristics and alcohol intake with lung cancer

Smoking status was significantly associated with lung cancer risk: the odds ratio (OR) of having

lung cancer in ever smokers (vs. never smokers) were 3.21 (95% CI: 1.02 - 10.07). The duration of

years cigarette smoked was longer in the lung cancer group with mean of 42.7 + 13.1 years

compared with mean of 32.1 + 16.0 for referents, and was significantly associated with lung cancer

risk. Other characteristics were not significantly associated with lung cancer risk (Table 2)


20 8

3.3. Occupational exposure and lung cancer risk

The variables of occupational characteristic were stratified for smoking status to control for

confounding factor and adjusted odds ratio (aOR) are tabulated in Table 3. Lung cancer risk varied

with job category with an aOR among unskilled manual job workers (vs. management,

professional & associate professional) of 4.75 (95% CI: 1.06-21.36). Exposure to occupational

carcinogens was associated with an elevated but non-significant lung cancer risk: the aOR in ever

exposed (vs never exposed) was 1.93 (95% CI: 0.77-4.82). There was no association with duration

of exposure. Workers who were exposed to smoke or asbestos, had an increased risk when

compared to those who had no exposure: aOR = 3.56 (95% CI: 0.96-13.13) for smoke and aOR =

4.00 (95% CI: 1.10-14.47) for asbestos.

Table 3. Occupational characteristics in cases and referents

Variable

Lung Cancer

Yes No n (%) n (%)

Crude Odds Ratio

(95% CI)a

Adjusted Odds Ratio

(95% CI)c

Employment status

Employed

Not employedb

Job category

Management, professional &

associate professionalb

Clerical and secretarial

Sales and service

Skilled manual

Unskilled manual

Exposure to carcinogen

Yes

Nob

Years of exposure

1 - 17b

18 - 32

33 - 44

≥ 45

(mean + SD)

Exposure to specific carcinogen

No exposureb

Exposed to smoke

Exposed to dust

Exposed to fumes

Exposed to asbestos

5 (12.8)

34 (87.2)

8 (20.5)

7 (17.9)

8 (20.5)

8 (20.5)

8 (20.5)

30 (76.9)

9 (23.1)

13 (43.3)

11 (36.7)

4 (13.3)

2 (6.7)

20.6 + 13.7

9 (23.1)

9 (23.1)

6 (15.4)

6 (15.4)

9 (23.1)

27 (31.7)

55 (68.3)

26 (32.1)

12 (14.8)

24 (29.6)

15 (18.5)

4 (4.9)

46 (56.1)

36 (43.9)

26 (56.5)

10 (21.7)

9 (19.6)

1 (2.2)

18.1 + 13.3

36 (43.9)

9 (11.0)

15 (18.3)

14 (17.1)

8 ( 9.8)

0.30 (0.10-0.85)

1*

1

1.89 (0.56-6.44)

1.08 (0.35-3.34)

1.73 (0.54-5.57)

6.50(1.54-27.4)

2.61 (1.10-6.18)

1*

1

2.20 (0.74-6.51)

0.89 (0.23-3.44)

4.00 (0.33-48.3)

1

4.00 (1.23-13.0)

1.60 (0.48-5.29)

1.71 (0.51-5.71)

4.50 (1.35-14.9)

0.33 (0.11-0.99)

1*

1

2.38 (0.63-9.02)

0.96 (0.27-3.42)

1.97 (0.56-6.94)

4.75 (1.06-21.4)

1.93 (0.77-4.82)

1

1

2.73 (0.81-9.15)

1.09 (0.26-4.55)

1.91 (0.11-33.5)

1

3.56 (0.96-13.1)

0.89 (0.23-3.49)

1.11 (0.30-4.05)

4.00 (1.10-14.5)


patients in groupb .

cAdjusted odds ratio (aOR) for smoking status


21 9

3.4. Family history of lung cancer and lung cancer risk

Table 4 shows the number of patients with a family history of lung cancer. The majority of the

lung cancer patients had no family history of lung cancer (94.9%) and there was no significant

association with lung cancer risk.

Table 4. Family history of lung cancer in cases and referents

Variable

Lung Cancer

Yes No n (%) n (%)

Odds Ratio

(95% CI)a

Relatives with lung cancer

No

Yesb

Unknown

37 (94.9)

2 (5.1)

0 (0)

75 (91.5)

6 (7.3)

1 (1.2)

1

0.68 (0.13-3.51)

aOdds ratio is for incidence of lung cancer in each group versus incidence in patients in groupb

4. DISCUSSION

In this study, we examined the risks associated with occupational exposures in a case-referent

study of lung cancer. The completed self-reported occupational histories and exposures were

analysed. This is a small pilot study using general job questionnaires to assess the occupational

exposure and the findings will be used as part of the reference for the main study that will be

undertaken in the same population.

From this study, smoking status was significantly associated with lung cancer risk, with three-fold

higher risk to get lung cancer. The risk also associated with the duration of years smoked. The

findings were consistent with the other studies (Doll & Hill, 1950), (Peto, Lopez, Boreham, Thun,

& Heath, 1992) which indicating the duration of smoking is one of the strongest determinants of

lung cancer risk in smokers. The risk increases with the number of years a person has smoked and

also the number of cigarettes smoked (Lubin & Caporaso, 2006).

Different job category having different types of exposure to occupational hazards particularly

occupational carcinogens. In this study, unskilled manual job workers had a higher lung cancer

risk compared with other categories, such as management, professional & associate professional. It

is suggested that those who worked in the unskilled manual workers are prone to be more exposed

to different kind of occupational carcinogens.

8

3.3. Occupational exposure and lung cancer risk

The variables of occupational characteristic were stratified for smoking status to control for

confounding factor and adjusted odds ratio (aOR) are tabulated in Table 3. Lung cancer risk varied

with job category with an aOR among unskilled manual job workers (vs. management,

professional & associate professional) of 4.75 (95% CI: 1.06-21.36). Exposure to occupational

carcinogens was associated with an elevated but non-significant lung cancer risk: the aOR in ever

exposed (vs never exposed) was 1.93 (95% CI: 0.77-4.82). There was no association with duration

of exposure. Workers who were exposed to smoke or asbestos, had an increased risk when

compared to those who had no exposure: aOR = 3.56 (95% CI: 0.96-13.13) for smoke and aOR =

4.00 (95% CI: 1.10-14.47) for asbestos.

Table 3. Occupational characteristics in cases and referents

Variable

Lung Cancer

Yes No n (%) n (%)

Crude Odds Ratio

(95% CI)a

Adjusted Odds Ratio

(95% CI)c

Employment status

Employed

Not employedb

Job category

Management, professional &

associate professionalb

Clerical and secretarial

Sales and service

Skilled manual

Unskilled manual

Exposure to carcinogen

Yes

Nob

Years of exposure

1 - 17b

18 - 32

33 - 44

≥ 45

(mean + SD)

Exposure to specific carcinogen

No exposureb

Exposed to smoke

Exposed to dust

Exposed to fumes

Exposed to asbestos

5 (12.8)

34 (87.2)

8 (20.5)

7 (17.9)

8 (20.5)

8 (20.5)

8 (20.5)

30 (76.9)

9 (23.1)

13 (43.3)

11 (36.7)

4 (13.3)

2 (6.7)

20.6 + 13.7

9 (23.1)

9 (23.1)

6 (15.4)

6 (15.4)

9 (23.1)

27 (31.7)

55 (68.3)

26 (32.1)

12 (14.8)

24 (29.6)

15 (18.5)

4 (4.9)

46 (56.1)

36 (43.9)

26 (56.5)

10 (21.7)

9 (19.6)

1 (2.2)

18.1 + 13.3

36 (43.9)

9 (11.0)

15 (18.3)

14 (17.1)

8 ( 9.8)

0.30 (0.10-0.85)

1*

1

1.89 (0.56-6.44)

1.08 (0.35-3.34)

1.73 (0.54-5.57)

6.50(1.54-27.4)

2.61 (1.10-6.18)

1*

1

2.20 (0.74-6.51)

0.89 (0.23-3.44)

4.00 (0.33-48.3)

1

4.00 (1.23-13.0)

1.60 (0.48-5.29)

1.71 (0.51-5.71)

4.50 (1.35-14.9)

0.33 (0.11-0.99)

1*

1

2.38 (0.63-9.02)

0.96 (0.27-3.42)

1.97 (0.56-6.94)

4.75 (1.06-21.4)

1.93 (0.77-4.82)

1

1

2.73 (0.81-9.15)

1.09 (0.26-4.55)

1.91 (0.11-33.5)

1

3.56 (0.96-13.1)

0.89 (0.23-3.49)

1.11 (0.30-4.05)

4.00 (1.10-14.5)


patients in groupb .

cAdjusted odds ratio (aOR) for smoking status


22 10

Exposure to occupational carcinogens was associated with an elevated lung cancer risk; however

there was no association with duration of exposure. Workers who were exposed to smoke or

asbestos had an increased risk when compared to those who had no exposure. It is consistent with

other studies which showed workers who are exposed to smoke or asbestos, which include in the

list A IARC list are in the higher risk to get the occupational lung cancer (Driscoll et al., 2005),

(Berry, 2004).

5. CONCLUSION





23 11

6. REFERENCES

Ambroise, D., Wild, P., & Moulin, J.-J. (2006). Update of a meta-analysis on lung cancer and

welding. Scandinavian Journal of Work, Environment & Health, 32(1), 22–31.

doi:10.5271/sjweh.973

Berry, G. (2004). The Interaction of Asbestos and Smoking in Lung Cancer: A Modified Measure

of Effect. Annals of Occupational Hygiene, 48(5), 459–462. doi:10.1093/annhyg/meh023

Cassidy, A., ’t Mannetje, A., van Tongeren, M., Field, J. K., Zaridze, D., Szeszenia-Dabrowska,

N., Boffetta, P. (2007). Occupational exposure to crystalline silica and risk of lung cancer: a

multicenter case-control study in Europe. Epidemiology (Cambridge, Mass.), 18(1), 36–43.

doi:10.1097/01.ede.0000248515.28903.3c

Consonni, D., De Matteis, S., Lubin, J. H., Wacholder, S., Tucker, M., Pesatori, A. C., … Landi,

M. T. (2010). Lung cancer and occupation in a population-based case-control study. American

Journal of Epidemiology, 171(3), 323–333. doi:10.1093/aje/kwp391

Doll, R., & Hill, A. B. (1950). Smoking and Carcinoma of the Lung. British Medical Journal,

2(4682), 739–748.

Driscoll, T., Nelson, D. I., Steenland, K., Leigh, J., Concha-Barrientos, M., Fingerhut, M., &

Prüss-Ustün, A. (2005). The global burden of disease due to occupational carcinogens.

American Journal of Industrial Medicine, 48(6), 419–431. doi:10.1002/ajim.20209

Galán Dávila, A., Romero Candeira, S., Sánchez Payá, J., Orts Giménez, D., & Llorca Martínez, E.

(2005). Lung Cancer Risk in Shoe Manufacturing. Archivos de Bronconeumología ((English

Edition)), 41(4), 202–205. doi:10.1016/S1579-2129(06)60426-6

10

Exposure to occupational carcinogens was associated with an elevated lung cancer risk; however

there was no association with duration of exposure. Workers who were exposed to smoke or

asbestos had an increased risk when compared to those who had no exposure. It is consistent with

other studies which showed workers who are exposed to smoke or asbestos, which include in the

list A IARC list are in the higher risk to get the occupational lung cancer (Driscoll et al., 2005),

(Berry, 2004).

5. CONCLUSION





24 12

Hansen, H. (Ed.). (2008). Textbook of Lung Cancer, Second Edition (2nd ed.). Informa

Healthcare.

Lehman, E. J., & Hein, M. J. (2006). Mortality of workers employed in shoe manufacturing: an

update. American Journal of Industrial Medicine, 49(7), 535–546.

Lists of IARC evaluations according to IARC monographs - International occupational safety &

health information centre. (n.d.). Retrieved May 1, 2012, from

http://www.ilo.org/legacy/english/protection/safework/cis/products/safetytm/iarclist.htm

Lubin, J. H., & Caporaso, N. E. (2006). Cigarette smoking and lung cancer: modeling total

exposure and intensity. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the

American Association for Cancer Research, Cosponsored by the American Society of

Preventive Oncology, 15(3), 517–523. doi:10.1158/1055-9965.EPI-05-0863

Office for National Statistics, E. L. and S. A. (2010, June 23). Standard Occupational

Classification 2010 (SOC2010). Office for National Statistics. Text. Retrieved July 29, 2013,

from http://www.ons.gov.uk/ons/guide-method/classifications/current-standard-

classifications/soc2010/index.html

Olsson, A. C., Fevotte, J., Fletcher, T., Cassidy, A., ’t Mannetje, A., Zaridze, D., Boffetta, P.

(2010). Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: a

multicenter study in Europe. Occupational and Environmental Medicine, 67(2), 98–103.

doi:10.1136/oem.2009.046680

Peto, R., Lopez, A. D., Boreham, J., Thun, M., & Heath, C., Jr. (1992). Mortality from tobacco in

developed countries: indirect estimation from national vital statistics. Lancet, 339(8804),

1268–1278.


25

13

Preller, L., Balder, H. F., Tielemans, E., Brandt, P. A. van den, & Goldbohm, R. A. (2008).

Occupational lung cancer risk among men in the Netherlands. Occupational and

Environmental Medicine, 65(4), 249–254. doi:10.1136/oem.2006.030353

Scélo, G., Constantinescu, V., Csiki, I., Zaridze, D., Szeszenia-Dabrowska, N., Rudnai, P., …

Boffetta, P. (2004). Occupational Exposure to Vinyl Chloride, Acrylonitrile and Styrene and

Lung Cancer Risk (Europe). Cancer Causes & Control, 15(5), 445–452.

Van Loon, A. J., Kant, I. J., Swaen, G. M., Goldbohm, R. A., Kremer, A. M., & van den Brandt, P.

A. (1997). Occupational exposure to carcinogens and risk of lung cancer: results from The

Netherlands cohort study. Occupational and Environmental Medicine, 54(11), 817–824.

12

Hansen, H. (Ed.). (2008). Textbook of Lung Cancer, Second Edition (2nd ed.). Informa

Healthcare.

Lehman, E. J., & Hein, M. J. (2006). Mortality of workers employed in shoe manufacturing: an

update. American Journal of Industrial Medicine, 49(7), 535–546.

Lists of IARC evaluations according to IARC monographs - International occupational safety &

health information centre. (n.d.). Retrieved May 1, 2012, from

http://www.ilo.org/legacy/english/protection/safework/cis/products/safetytm/iarclist.htm

Lubin, J. H., & Caporaso, N. E. (2006). Cigarette smoking and lung cancer: modeling total

exposure and intensity. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the

American Association for Cancer Research, Cosponsored by the American Society of

Preventive Oncology, 15(3), 517–523. doi:10.1158/1055-9965.EPI-05-0863

Office for National Statistics, E. L. and S. A. (2010, June 23). Standard Occupational

Classification 2010 (SOC2010). Office for National Statistics. Text. Retrieved July 29, 2013,

from http://www.ons.gov.uk/ons/guide-method/classifications/current-standard-

classifications/soc2010/index.html

Olsson, A. C., Fevotte, J., Fletcher, T., Cassidy, A., ’t Mannetje, A., Zaridze, D., Boffetta, P.

(2010). Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: a

multicenter study in Europe. Occupational and Environmental Medicine, 67(2), 98–103.

doi:10.1136/oem.2009.046680

Peto, R., Lopez, A. D., Boreham, J., Thun, M., & Heath, C., Jr. (1992). Mortality from tobacco in

developed countries: indirect estimation from national vital statistics. Lancet, 339(8804),

1268–1278.


27 1

OHSAS 18001 and MS 1722 Certification Initiatives Prove the

Commitment to Sustainability

Wai Onn Hong

Processing Department, Genting Plantations Berhad,

10th

Floor, Wisma Genting, Jalan Sultan Ismail, Kuala Lumpur, Malaysia

Tel: +60 3 2333 6506 Fax: + 60 3 2161 9689 Email: [email protected]

__________________________________________________________________________________

Abstract

Malaysia’s palm oil industry is growing in complexity and successively to succeed on the global

level by accounts for about 36% of the word production of palm oil [1]. But, Occupational Health

and Safety (OHS) issues are still problematic areas that need to be addressed by all parties

concerned in this industry. In the olden days, unlike construction or manufacturing industry, palm

oil industry was green in OHS management system. However, due to stringent in the legislative

enforcement in the past few years, it has lead some of the plantation companies to develop OHS

management system, which are based on Occupational Health and Safety Assessment Series

(OHSAS), towards corporate sustainability. Sustainability is not about paying lip-service to the

latest corporate buzzword; neither is it about superficially meeting minimum requirements for the

sake of compliance. Rather, sustainability is a core value that lies at the heart of the companies’

business conduct. In practical terms, this means strive to operate with due consideration for the

interest of all stakeholders by making the health and safety of all workers a priority. This paper

describes the certification of OHSAS 18001 and MS 1722 in Genting Plantations Berhad (GENP)

prove the commitment to sustainability by forming guiding principle on safety management.

Further, this paper also demonstrates that the implementation of safety management can help to

reduce the accident rate, especially fatal accident.

Keywords: palm oil industry, OHSAS 18001, MS 1722, safety management, sustainability

_________________________________________________________________________________

Introduction

The working population is a valuable asset to our nation especially palm oil industry, therefore we

cannot afford to have many accidents which will eventually jeopardize our valued human resources.

Workers involved in palm oil industry can be divided into two broad categories: those working in

the plantations and those employed to work in the palm oil mill. The former are mainly the

harvesters who harvest the fresh fruit bunches while the second category includes the workers

employed to operate and maintain machineries in palm oil mills. Accidents involving both of these

categories are not rare in Malaysia. Statistic of occupational accidents in the country published by


28 2

Department of Occupational Safety and Health (DOSH) show that the total number of accidents as

well as the number of fatalities has not much improvement between 2007 and 2011 (Figure 1).

In view of OHS issues still remain an important matter in palm oil industry throughout the decade,

government has in fact stringent in the legislative enforcement since recent years. It is at a time like

this that the palm oil industry needs to consolidate and be proactive in meeting upcoming

challenges. The palm oil industry also needs to meet challenges with more evidence of sustainable

safety management system throughout the implementation.

This paper not only describes the certification of OHSAS 18001 and MS 1722 in Genting

Plantations Berhad proves the commitment to sustainability by forming guiding principle on safety

management, but also demonstrates that the implementation of safety management can help to


The OHSAS 18001 and the MS 1722 standard enable an organization to manage its OHS risks and

improve its OHS performance. The requirements of the standard are intended to address OHS for

employees, temporary employees, contractors and other personnel on site rather than the safety of

products and services. The standards provide a more effective method of protecting employees and

others from workplace injuries and illnesses and demonstrate management commitment in meeting

OHS requirements [2, 3, 4].


29 3

Figure 1: National accident rate per 1,000 workers and fatality rate per 100,000 workers from year 2007 – 2011

GENP’s Response and Initiative

In Malaysia, legislation concerning OHS for palm oil industry comprises the following Acts and

Regulations:

a) Occupational Safety and Health Act 1994

b) Factories and Machineries Act 1967

c) Electricity Supply Act 1990

d) Fire Services Act 1994

Although with all these Acts and Regulations in placed, it is always an argument that who should

responsible and accountable for accident prevention, employers, employees or relevant authorities.

GENP’s commitment to these areas, which are of paramount importance to the Group’s overall

sustainability agenda, was displayed clearly through important certification initiatives undertaken at

the palm oil mill level. As part of the palm oil mill improvement efforts, GENP’s palm oil mills

embarked on a third party, independent verification exercise of their OHS Management System,

guided by a road map began in year 2010. Under the standards subscribes, OHS management

system composed of standards, procedures and monitoring arrangement that aim at promoting the

OHS of people at workplace and to protect the public from accident shall be established and

implemented.

2

Department of Occupational Safety and Health (DOSH) show that the total number of accidents as

well as the number of fatalities has not much improvement between 2007 and 2011 (Figure 1).

In view of OHS issues still remain an important matter in palm oil industry throughout the decade,

government has in fact stringent in the legislative enforcement since recent years. It is at a time like

this that the palm oil industry needs to consolidate and be proactive in meeting upcoming

challenges. The palm oil industry also needs to meet challenges with more evidence of sustainable

safety management system throughout the implementation.

This paper not only describes the certification of OHSAS 18001 and MS 1722 in Genting

Plantations Berhad proves the commitment to sustainability by forming guiding principle on safety

management, but also demonstrates that the implementation of safety management can help to


The OHSAS 18001 and the MS 1722 standard enable an organization to manage its OHS risks and

improve its OHS performance. The requirements of the standard are intended to address OHS for

employees, temporary employees, contractors and other personnel on site rather than the safety of

products and services. The standards provide a more effective method of protecting employees and

others from workplace injuries and illnesses and demonstrate management commitment in meeting

OHS requirements [2, 3, 4].


30 4

Implementation of OHSAS 18001 and MS 1722

The initiatives on certification of OHSAS 18001 and MS 1722 started with gap analysis at GENP’s

oil mills to determine the status of existing OHS processes and controls in place. Recommendations

were provided to bridge the gaps in that analysis. Having completed the gap analysis, a series of

training were held to cover variety of topics, including ISO Awareness, Hazard Identification, Risk

Assessment and Risk Control, Safe Operating Procedure and Emergency Preparedness. This was

followed by the challenging task of preparing documentations in accordance with the unique

features of each palm oil mill and these have been structured into four levels as follows:

• Level 1 - Manual. This document gives an overview of the OHS Management Systems, includes

the policies and all the non-operations procedures. It also outlines the structure of the

documentation used in the OHS Management System.

• Level 2 – System Procedure. These documents specify principles, strategies and the general

procedures of operations related actions (system process).

• Level 3 – Operations Procedure Documents. These documents specify in details the current

practices or processes in any operations related action (core process).

• Level 4 – Records, forms and checklists. These documents further specify the manner of

processes in an action. They also demonstrate conformance to specified OHS Management

Systems.

The pyramid of OHS management system documentation is shown in Figure 2.

Figure 2: The pyramid of OHS management system documentation


31 5

Meanwhile, at the operation sites, proactive measures were taken to improve the safety and health

performances. At the same time, safety and health awareness was also actively promoted at the

palm oil mills during daily morning muster and periodically training. Last stage of this project is to

conduct internal auditing of OHS performance. The internal auditors aim to ensure compliance with

OHSAS 18001 and MS 1722 standards thereby ensuring the success of OHS programs through the

implementation of safety management system. In additional, internal audit also serves as a platform

to identify OHS opportunities for continual improvement. After about one year implementation

period, SIRIM QAS International Sdn Bhd, the country’s leading and internationally-recognized

certification, inspection and testing body, was engaged to carry out a series of audits, culminate in

all palm oil mills successfully securing recommendation for certification of their Health and Safety

Management System under OHSAS 18001 and MS 1722 by the end of January 2011.

Implementation stages of OHSAS 18001 and MS 1722 was summarized in Figure 3.

Figure 3: Road map for implementation of OHSAS 18001 and MS 1722

Guiding Principle on Safety Management

OHSAS 18001 and MS 1722 implemented by GENP is applicable company-wide and information

is disseminated to all employees in order to ensure successful implementation. A generic safety

management system has been established in order to sustain OHSAS 18001 and MS 1722. In this

management system, a number of important elements are specified and these are related to the

setting of policy and creation of plans and organizational capacity to realize that policy (Plan), the

analysis of hazards and effects leading to planning and implementation of those plans in order to

manage the risks (Do) and the control on the effective performance of those steps (Check). A

4

Implementation of OHSAS 18001 and MS 1722

The initiatives on certification of OHSAS 18001 and MS 1722 started with gap analysis at GENP’s

oil mills to determine the status of existing OHS processes and controls in place. Recommendations

were provided to bridge the gaps in that analysis. Having completed the gap analysis, a series of

training were held to cover variety of topics, including ISO Awareness, Hazard Identification, Risk

Assessment and Risk Control, Safe Operating Procedure and Emergency Preparedness. This was

followed by the challenging task of preparing documentations in accordance with the unique

features of each palm oil mill and these have been structured into four levels as follows:

• Level 1 - Manual. This document gives an overview of the OHS Management Systems, includes

the policies and all the non-operations procedures. It also outlines the structure of the

documentation used in the OHS Management System.

• Level 2 – System Procedure. These documents specify principles, strategies and the general

procedures of operations related actions (system process).

• Level 3 – Operations Procedure Documents. These documents specify in details the current

practices or processes in any operations related action (core process).

• Level 4 – Records, forms and checklists. These documents further specify the manner of

processes in an action. They also demonstrate conformance to specified OHS Management

Systems.

The pyramid of OHS management system documentation is shown in Figure 2.

Figure 2: The pyramid of OHS management system documentation


32 6

feedback loop is in placed to enable all the information gained are sent to management for their

respond (Act/Feedback). [2, 3, 4, 5]. Further, there is an element extended out of the loop where

the organization has to establish an OHS management system with continual improvement activities

in order to ensure the sustainability of OHSAS 18001 and MS 1722 subscribes. This safety

management system is simplified in Figure 4.

Figure 4: Generic OHS management system with elements of Plan-Do-Check-Act

Evolution of Safety Culture

The systematic approach to safety management in OHSMS is not the end the journey as

management system is a primarily rational inventions, defined on paper in offices and capable of

objective in audits. The next stage is to build generative safety culture. As the premier level in safety

cultures, generative safety culture is the situation where people carry out what they know has to be

done not because they have to, but they want to. In other words, it is where the safe behavior is fully

integrated into everything the organization does [5].


33 7

In order to promote generative safety culture, GENP encouraging to have combination of a top-

down commitment to improve and the realization that the workforce is where that improvement has

to take place. To have this premier safety culture implant, information is actively sought and

responsibilities are shared among employees and employers. Furthermore, new ideas are welcomed

in generative safety culture regardless it is from which level of employees, workers, staffs or

executives.

Figure 5 shows the evolution of safety culture in GENP. Initial stage of safety culture at individual

oil mill might vary from reactive to proactive due to different level of safety awareness.

Nevertheless, all have successfully built and implant premier level of generative safety culture after

implementation of OHSMS.

Figure 5: Evolution of safety culture in GENP after implementing safety management

The effect of Implementing OHSAS 18001 and MS 1722

The most important and measurable benefit resulting from safety management system and evolution

to generative safety culture is decrease in occupational accident rate and fatality rate. Figure 6

summarizes the statistic of occupational accidents recorded during last five years in GENP oil mills.

2007 2008 2009 2010 2011 2012*

Number of

accidents 5 9 13 6 4 3

Number of

fatalities 2 2 1 0 0 0

6

feedback loop is in placed to enable all the information gained are sent to management for their

respond (Act/Feedback). [2, 3, 4, 5]. Further, there is an element extended out of the loop where

the organization has to establish an OHS management system with continual improvement activities

in order to ensure the sustainability of OHSAS 18001 and MS 1722 subscribes. This safety

management system is simplified in Figure 4.

Figure 4: Generic OHS management system with elements of Plan-Do-Check-Act

Evolution of Safety Culture

The systematic approach to safety management in OHSMS is not the end the journey as

management system is a primarily rational inventions, defined on paper in offices and capable of

objective in audits. The next stage is to build generative safety culture. As the premier level in safety

cultures, generative safety culture is the situation where people carry out what they know has to be

done not because they have to, but they want to. In other words, it is where the safe behavior is fully

integrated into everything the organization does [5].


34 8

Accident rate

per 100

workers

0.35 0.62 0.85 0.38 0.25 0.19

Fatality rate

per 100

workers

0.14 0.14 0.07 0.00 0.00 0.00

Remark: Data updated as at June 2012.

Figure 6: Statistic of occupational accidents recorded during last five years in GENP oil mills

Figure 7 clearly shows that there was significant decrease in accident rate since year 2009 after

implementation of Occupational Health and Safety Management System. Furthermore, the accident

rate of 0.38 and 0.25 per 100 workers recorded in year 2010 and 2011 were greatly lower than

national accident rate for the same period, 0.65 and 0.62 per 100 workers respectively.

Figure 8 shows the fatality rate per 100 workers from year 2007 to 2012 (June). It was clearly shows

that implementation of OHSMS proves the commitment to sustainability as GENP oil mills

continuously recorded zero fatal accident since year 2009.

Figure 7: Accident rate per 100 workers from year 2007 – 2012 (June)


35 9

Figure 8: Fatality rate per 100 workers from year 2007 – 2012 (June)

Conclusion

Owing to increase in complexity of operations, the palm oil industry has become more challenging

than ever before. Plantation companies are faced with the challenge of having to close monitor their

business to minimize occupational hazards, while simultaneously trying to sustain profits in a

competitive marketplace. In Malaysia, government agencies such as DOSH have done their part to

promote safety awareness in the industry in order to reduce accidents rate in workplace. However,

the key to proper safety execution is neither through strict guidelines nor stringent in enforcement,

but through an effective safety management initiative, first approved by an organization’s top

management, then integrated via specific safety management implementation tools and system, and

finally by continuous follow up and monitoring to ensure quality and continuous improvement. In

order to prove the commitment to sustainability, GENP has to ensure consistency in the adoption

and implementation of the OHSMS among Group operating units as only those companies that take

on aggressive safety management will prove the commitment to sustainability and guarantee the

improvement of work conditions, the decrease of occupational accident rate as well as lowering of

the occupational fatality rate.

Acknowledgement

The writer would like to thank the Senior Vice President – Group Processing, Genting plantations

Berhad for permission to present this paper...

8

Accident rate

per 100

workers

0.35 0.62 0.85 0.38 0.25 0.19

Fatality rate

per 100

workers

0.14 0.14 0.07 0.00 0.00 0.00

Remark: Data updated as at June 2012.

Figure 6: Statistic of occupational accidents recorded during last five years in GENP oil mills

Figure 7 clearly shows that there was significant decrease in accident rate since year 2009 after

implementation of Occupational Health and Safety Management System. Furthermore, the accident

rate of 0.38 and 0.25 per 100 workers recorded in year 2010 and 2011 were greatly lower than

national accident rate for the same period, 0.65 and 0.62 per 100 workers respectively.

Figure 8 shows the fatality rate per 100 workers from year 2007 to 2012 (June). It was clearly shows

that implementation of OHSMS proves the commitment to sustainability as GENP oil mills

continuously recorded zero fatal accident since year 2009.

Figure 7: Accident rate per 100 workers from year 2007 – 2012 (June)


36 10

References

[1] Foreign Agricultural Service, United States Department of Agriculture, 2012, Oilseeds: World

Markets and Trade.

[2] Department of Standards Malaysia, 2005, Occupational Safety and Health Management Systems,

Part 1: Requirements.


Part 2: Guidelines.

[4] OHSAS Project Group, 2007, Occupational Health and Safety Management Systems –

Requirements.

[5] Patrick Hudson, 2001, Safety Management and Safety Culture The Long, Hard and Winding Road

(Edited by Warwick Pearse, Clare Gallagher and Liz Bluff). Occupational Health & Safety

Management Systems, 3-32.


37 10

References

[1] Foreign Agricultural Service, United States Department of Agriculture, 2012, Oilseeds: World

Markets and Trade.


Part 1: Requirements.


Part 2: Guidelines.

[4] OHSAS Project Group, 2007, Occupational Health and Safety Management Systems –

Requirements.

[5] Patrick Hudson, 2001, Safety Management and Safety Culture The Long, Hard and Winding Road

(Edited by Warwick Pearse, Clare Gallagher and Liz Bluff). Occupational Health & Safety

Management Systems, 3-32.

The Extent of Predictability of Noise-Induced Problems – A

Cross-Over from the Healthy Limit to Off-Limit Conditions

By Ir. Gan Chun Chet

MSc (UK), BSc (Hons) (UK), PEng

___________________________________________________________________________

Abstract

The paper examines the method of predicting noise induced problems among factory

workers. The pattern of an impaired condition above the off-limit condition defined here is

exponential. The age-related losses are linear with approximately 3.3 dB decibel loss below

the age of 35 at an interval of 10 years on the job, and 4 decibel dB loss above the age of 35

(inclusive) at the same interval.

The equations of age losses show that hearing losses due to noise exposure are caused

by hearing deterioration. The projection based on the data shows the extent of the prediction.

The extent of predictability for off-limit conditions derived from the records depicted shows

that it is applicable to some situations but not all. It is impossible to postulate all types of

patterns, but the pattern as shown, exponential in nature, can be utilised by other similar

occurrences.

The facts derived from the data show that hearing loss is inevitable, noise exposure

among workers must be avoided. The number of workers with hearing level higher than the

normal level, between 10 to 16 percent of the total number of data sampled from the factory

workers, is an indication that the noise-induced problem exists. It is applicable to many other

factories and we hope that in a range of other situations, such problems can be prevented.

Due to these facts, the extent of predictability of these equations are worth noting. The

aim of this paper to show that the data points can be traced, with the explanation that the

sway patterns occurred as denoted by the exponential growth pattern of defined interval.

That could account for the data observed.

___________________________________________________________________________

Introduction

The issue of whether a cross-over does occur during the course of a workers’ duration

on the job is relevant when the hearing condition deteriorated from a normal condition to a

worst condition. The extent of the deterioration after an exposure which caused the

impairment is subject to the variable of the accumulated amount of noise that a person is

exposed to during his or her work duration. Due to this, the starting condition, i.e. the initial

state of an individual at start off is important to note. The matter of an impairment arises


38

when heavy exposure to noise causes hearing deterioration, so that the condition of the

person at work is a possible explanation of the deterioration. It is important then to establish

whether the person is susceptible to the given noise level while others are not.

This study seeks to provide a possible basis for prediction. However, subject to the

condition of the factory or the activity of a particular worker, the mathematical equation

derived from the test result of a scatter plot (based on real audiometric test result) points to a

possible explanation that an exponential equation that can be extended forward or backwards

to find out the person past or a possible future condition. This is applicable to off-limit

conditions.

Age-related losses are definitely a factor to be considered. In relation to the facts as

shown in the scatter plot, the drift from a normal hearing level to off-limit level defined the

need to inform others of the susceptibility of workers. Due to this, keeping a good record of

the audiometric test result is suggested here to ensure that workers hearing levels are

protected. It was also noticed that there might be cases where the hearing condition has been

affected due the work environment. This paper points to a “cross-over” range that noise

custodians should be aware of should this be the situation.

Thus, due to the range that some might question and the subjective initial point, the

equation, an exponential growth in nature, postulates the situation, assuming that a healthy

condition is the healthy beginning and that the work condition affects the worker (this does

not rule out the possibility that the worker had the impairment already when entering the

firm). The result as shown is important: there are many precautions that can be taken to

prevent the shift, but susceptibility is noticed from the scattered plot where the same scatter

pattern below 35 years old or above 35 years old shows that workers in the same age group

have the same deterioration pattern (as contrasted with a slightly lower level for workers

under age 35). Thus, one should take special note of a possible increase regardless of age,

where exposure to noise is dangerous, which will cause this pattern as shown.


39

when heavy exposure to noise causes hearing deterioration, so that the condition of the

person at work is a possible explanation of the deterioration. It is important then to establish

whether the person is susceptible to the given noise level while others are not.

This study seeks to provide a possible basis for prediction. However, subject to the

condition of the factory or the activity of a particular worker, the mathematical equation

derived from the test result of a scatter plot (based on real audiometric test result) points to a

possible explanation that an exponential equation that can be extended forward or backwards

to find out the person past or a possible future condition. This is applicable to off-limit

conditions.

Age-related losses are definitely a factor to be considered. In relation to the facts as

shown in the scatter plot, the drift from a normal hearing level to off-limit level defined the

need to inform others of the susceptibility of workers. Due to this, keeping a good record of

the audiometric test result is suggested here to ensure that workers hearing levels are

protected. It was also noticed that there might be cases where the hearing condition has been

affected due the work environment. This paper points to a “cross-over” range that noise

custodians should be aware of should this be the situation.

Thus, due to the range that some might question and the subjective initial point, the

equation, an exponential growth in nature, postulates the situation, assuming that a healthy

condition is the healthy beginning and that the work condition affects the worker (this does

not rule out the possibility that the worker had the impairment already when entering the

firm). The result as shown is important: there are many precautions that can be taken to

prevent the shift, but susceptibility is noticed from the scattered plot where the same scatter

pattern below 35 years old or above 35 years old shows that workers in the same age group

have the same deterioration pattern (as contrasted with a slightly lower level for workers

under age 35). Thus, one should take special note of a possible increase regardless of age,

where exposure to noise is dangerous, which will cause this pattern as shown.

Methodology

The mathematical equation suggested here is an exponential curve for off-limit cases

defined here as workers with hearing ability above the normal hearing limit. The workers

initial condition started off healthy (below the healthy limit) and swayed to an off-limit

condition known as the limit line. The high points observed in the scatter plot show that it is

exponential behaviour. The starting point began from a healthy condition due to the steep

exponential curve as shown here, and the increase is significant. The cross-over ranges for

two data points are estimated here in Graph 1a to show that it is true in this condition where

even younger workers (early 20s, see Graph 1b) are found with the same condition as

compared to older workers; this is reflected in the scattered plot. As for older workers in their

30s and 40s, these cross-over ranges as shown here deteriorate exponentially to their

conditions as plotted, and the situation as shown is significantly high.

The method employed here shows the growth patterns of deterioration for these off-

limit workers. With the equations derived (typically exponential in nature), the projection is

feasible and the starting point assured, with probability of percentage based on counts of

these data points discussed in a later section. Considering that the pattern is true, then it

should be exponential, subject to these deterioration factors (that the sign of aging exists in

these off-limit cases).

The exponential equation has three (3) variables showing the sign of deteriorated hearing.

The equation is as follows:

y = A + B exp (C * x) – (1) where y is the backward or forward projection of worker

hearing level in decibel dB, x is the age of the worker, A,B or C are the variables that signify

(a) the starting hearing level (A+B at x=17), (b) the hearing deterioration (the invert of a

growth pattern) C at x=current age/result,

These aging losses can then be subtracted from y depending on the workers’ age to calculate

the impact of noise on their hearing.

The predictability of normal limit shows that it is an increment of factored linear lines

due to age-related losses, within the intervals (of 10 years), and it is significant that the

hearing losses show about 3.3 decibel dB below the age of 35 and about 4 decibel dB above

the age of 35 (inclusive). This paper proposes that susceptibility be investigated by recording

the test result and the equations verified (or confirmed) by other results to prove that these


40

could be the projected patterns. With this, a plausible explanation is elaborated, although the

equations might not be applicable to some situations. The equations derived from the scatter

plot are explained in the next section.

Derivation of The Mathematical Equations

‐ Off-Limit Conditions

The equation that represents this pattern is derived to be exponential, as stated in the

previous section. It has an age-related loss factor which is embedded into this equation

(equation 1). By removing the age losses symptom, the equation becomes as follows:

y = { A + B exp (C * x) } – { 0.3 x + 16.5 } + 22.1 ; for less than 35 years of age - (2)

y = { A + B exp (C * x) } – { 0.4 x + 14 } + 22.1 ; for more than 35 years of age - (3)

(inclusive of 35)

; where A, B and C are variable coefficients depending on individual case

The first part of the equation is exponential, whereas the second part of the equation is

linear due to age-related losses and the third part of the equation is the baseline of a person

(the starting point at 17 years old, or just having joined the industry as an employee. Equation

(2) and (3) are corrected due to age-related losses. Omitting the first part and the second part,

this will signify the present reading at x is equal to the age of the person at the time of the

test.

The exponential curve shown here is based on an observed pattern on the scattered

plot. The so-called sway pattern is explained elsewhere [1]. These are the possible

explanations for these situations based on observation. Below are the explanations for

workers with healthy limits.

By shifting the curve away from the y-axis to a starting age, the equation that includes

this is

y = A + B exp [C * (x-21)] ; where 21 is the age at which a person that enters the

company with baseline checked. – [equation 2/3 shift]


41

could be the projected patterns. With this, a plausible explanation is elaborated, although the

equations might not be applicable to some situations. The equations derived from the scatter

plot are explained in the next section.

Derivation of The Mathematical Equations

‐ Off-Limit Conditions

The equation that represents this pattern is derived to be exponential, as stated in the

previous section. It has an age-related loss factor which is embedded into this equation

(equation 1). By removing the age losses symptom, the equation becomes as follows:

y = { A + B exp (C * x) } – { 0.3 x + 16.5 } + 22.1 ; for less than 35 years of age - (2)

y = { A + B exp (C * x) } – { 0.4 x + 14 } + 22.1 ; for more than 35 years of age - (3)

(inclusive of 35)

; where A, B and C are variable coefficients depending on individual case

The first part of the equation is exponential, whereas the second part of the equation is

linear due to age-related losses and the third part of the equation is the baseline of a person

(the starting point at 17 years old, or just having joined the industry as an employee. Equation

(2) and (3) are corrected due to age-related losses. Omitting the first part and the second part,

this will signify the present reading at x is equal to the age of the person at the time of the

test.

The exponential curve shown here is based on an observed pattern on the scattered

plot. The so-called sway pattern is explained elsewhere [1]. These are the possible

explanations for these situations based on observation. Below are the explanations for

workers with healthy limits.

By shifting the curve away from the y-axis to a starting age, the equation that includes

this is

y = A + B exp [C * (x-21)] ; where 21 is the age at which a person that enters the

company with baseline checked. – [equation 2/3 shift]

‐ Below the Healthy Limit

The limit line defined here is the off-limit reading of the workers where there is no

“cross-over” above this limit line. The result is clustered below this defined limit line. Due to

this, there are certain factors that are considered to be true, i.e. that an age-related loss will

occur and in this internal study, it was found that the hearing level will deteriorate about 3.3

dB among workers below age 35 and 4 dB above 35 years of age (inclusive of 35) at an

interval of 10 years. In fact, every year, the hearing ability of a worker deteriorates on

average of 0.33 dB below 35 years old or 0.4 decibel above 35 years of age (inclusive 35).

Thus, the fact that workers’ hearing deteriorates is an age-related loss. It needs to be

subtracted from the equation as shown here and the loss in decibel level in this study is also

noted. But the reason of noise exposure caused by the noise in the factory or from outside the

factory could not be shown here except for a similar pattern of points recorded leaving the

cluster group.

The situation of a cross-over below the healthy limit is due to access noise exposure

or an accumulation of noise affecting the ear as shown in an increase in the hearing level,

signified by a cross-over between healthy limits. Whereas age loss is significant after 10

years, a cross-over between healthy limit in 1 to 2 years is a sign of a significant loss. Thus, it

is important to calculate and determine the significant loss related to over-exposure.

The equations of healthy hearing limits with respect to age are shown below:

y1 = 0.3 x + 16.5; for less than 35 years of age - (4) - Off-limit

y1’ = 0.86y1 - (3 (i)) - Caution limit

y1’’ = 0.7y1 - (3 (ii)) - Good limit

y1’’’ = 0.5y1 - (3 (iii)) - Very good limit

y2 = 0.4 x + 14; for more than 35 years of age (inclusive of 35) – (5) - Off-limit

y2’ = 0.83y2 - (4 (i)) - Caution limit

y2’’ = 0.68y2 - (4 (ii)) - Good limit

y2’’’ = 0.48y2 - (4 (iii)) - Very good limit

<graph 1>, <graph 2>, <graph 3>

The cross-over point is merely the intersection between the exponential curve (1 - 2,

3) and the off-limit line (4) & (5) for workers with long exposure. And an arithmetic

subtraction of increment shows workers within the healthy limit.


42

The Sampling of Workers in the Audiometric Test

The estimated sample of audiometric test records of the workers who took the test

with the hearing level is given in the table below. These figures show that the number of

workers with hearing level above 30 decibel dB is approximately 10 – 16 percent of the total.

A rough approximation shows that workers’ hearing level does occur above the normal

hearing level. This estimate indicates that a benchmark is required based on data from other

factories. The estimated count in this factory is as follows:

<Table 1>

Taking note that there are hidden points in the cluster below the healthy limit as

described in the section below, this shows that will appear in the off-limit conditions. Thus,

the 10 – 16 percent as counted might not depict the true numbers. This figure shows only

point records.

Discussion

Since the current points of off-limit cases are observed to be exponential on the scatter

plot, the forward projection of 5 intervals is acceptable for some applications. This means that

a noise-induced problem faced by factory workers that is caused by excessive noise exposure,

showing signs of deterioration in hearing ability, is an empirically verified fact that must be

looked into seriously. Some workers might be vulnerable to the same noise. On the other

hand, noise-induced problems might be due to other noise factors external to the factory.

However, in this internal study analysis, a similar pattern of two distinctive age groups

(above 35-years-old or below 35 years old) is noticed, one above the cluster and the other

below that cluster We have found that the record points as shown on the plot are either dotted

plots above the cluster groups (which are substantially above the healthy limit) or solid dotted

plots inside the cluster group.

Thus, the forward projection is valid for application where there is an observed point

of distinct high level distinguishable or separated from the main cluster group of normal

hearing level. This is shown in Figure 1. Similar patterns are shown by the two cluster groups

in Figure 2. From here, the two equations that show two rough interception curves of the

approximate records, where a few points way above the two clusters, known as the points

leaving the cluster group, are exponential. That is shown in the equations below:


43

The Sampling of Workers in the Audiometric Test

The estimated sample of audiometric test records of the workers who took the test

with the hearing level is given in the table below. These figures show that the number of

workers with hearing level above 30 decibel dB is approximately 10 – 16 percent of the total.

A rough approximation shows that workers’ hearing level does occur above the normal

hearing level. This estimate indicates that a benchmark is required based on data from other

factories. The estimated count in this factory is as follows:

<Table 1>

Taking note that there are hidden points in the cluster below the healthy limit as

described in the section below, this shows that will appear in the off-limit conditions. Thus,

the 10 – 16 percent as counted might not depict the true numbers. This figure shows only

point records.

Discussion

Since the current points of off-limit cases are observed to be exponential on the scatter

plot, the forward projection of 5 intervals is acceptable for some applications. This means that

a noise-induced problem faced by factory workers that is caused by excessive noise exposure,

showing signs of deterioration in hearing ability, is an empirically verified fact that must be

looked into seriously. Some workers might be vulnerable to the same noise. On the other

hand, noise-induced problems might be due to other noise factors external to the factory.

However, in this internal study analysis, a similar pattern of two distinctive age groups

(above 35-years-old or below 35 years old) is noticed, one above the cluster and the other

below that cluster We have found that the record points as shown on the plot are either dotted

plots above the cluster groups (which are substantially above the healthy limit) or solid dotted

plots inside the cluster group.

Thus, the forward projection is valid for application where there is an observed point

of distinct high level distinguishable or separated from the main cluster group of normal

hearing level. This is shown in Figure 1. Similar patterns are shown by the two cluster groups

in Figure 2. From here, the two equations that show two rough interception curves of the

approximate records, where a few points way above the two clusters, known as the points

leaving the cluster group, are exponential. That is shown in the equations below:

y = 18 + 5 exp (0.1x) - (5); above cluster

y = 21 + exp (0.09x) - (6); above cluster

The above equations represent the two sway patterns, with their hearing ability

deteriorating as shown in Figure 2; these indicate the starting point of about 20 to 23 decibels.

A rectification factor on the overall equation (5) to increase the steepness is multiplied as

shown in Figure 3. This represents a slow deterioration at an early age and presumed to lead

to a more rapid increase in hearing deterioration at a later age.

y = 0.9 (18 + 5 exp (0.1x)) - (7); to curve the radius near the y-axis with steeper slope as x

(age) increases.

<figure 1, 2 & 3>

With these off-limit cases, the limit of healthy workers is linear as age increases

instead of an exponential curve. However, it is noticed that the age -related losses lie between

0.33 to 0.4 dB annually, depending on the age. If it is below 35 years old, the increase is 0.33

dB every year. If it is above 35 years old, the increase is 0.4 every year. Thus, if the increase

is higher than this figure, then most probably an exposure to noise is probably the factor - the

noise levels at their work place have harmed healthy hearing capacity. It is advisable to have

the ear checked early and to stay below the permissible exposure level as stated in Factories

Machinery Act 1967, Noise Regulation 1989, with the risk of a possible cross-over or

deterioration due to noise exposure.

Where it is noted that a person’s susceptible limit is different from another person’s

limit, the risk here is whether that person will have the same deterioration pattern with

another person, given the same noise exposure. Although there is no substantial evidence

from this study, this should be explored in future research.

With regard to individual susceptibility, it is found that the deterioration patterns are

signified by the coefficient B and C in equations 1, 2 & 3. Noteworthy is that the individual

sway patterns of these off-limit cases differ in all cases. This could be an age-related

increment due to the age factor. The losses are more severe after a time period. Losses are

less at the beginning stage, with a possible double age effect factor due to severe weakness

and the possibility of an immediate loss pattern occurring.

y = B * x’ * exp (C * (x – 16)) – (8) ; age incremental factor, or double the age effect 2x’.


44

Conclusion

Workers are at risk at their work place when an increase in their hearing limit (i.e.

hearing ability during the test) is recorded. Long exposure to noise is very dangerous, as it

will damage hearing. The equations here can help to trace the origin of a worker’s hearing

condition (including a cross-over range). It is also able to a certain extent to predict the

deterioration caused by noise, exponential for off-limit cases and linear due to age-related

losses. Thus, it is important to take note of the work environment (the noise levels), to have

noise test results be recorded (susceptibility) and to avoid noise exposure where possible.

Age-related losses are natural but the impact of noise after exposure will cause substantial

additional deterioration.

References

[1] C.C. Gan, The Cause of Damage in Workers’ Hearing Levels – A Finding Based on The

Chances of Occurrence in the Possible Sway Patterns, NIOSH Journal,? 2010

[2] Factories and Machinery Act, 1967, Noise Regulation 1989.


45

Conclusion

Workers are at risk at their work place when an increase in their hearing limit (i.e.

hearing ability during the test) is recorded. Long exposure to noise is very dangerous, as it

will damage hearing. The equations here can help to trace the origin of a worker’s hearing

condition (including a cross-over range). It is also able to a certain extent to predict the

deterioration caused by noise, exponential for off-limit cases and linear due to age-related

losses. Thus, it is important to take note of the work environment (the noise levels), to have

noise test results be recorded (susceptibility) and to avoid noise exposure where possible.

Age-related losses are natural but the impact of noise after exposure will cause substantial

additional deterioration.

References

[1] C.C. Gan, The Cause of Damage in Workers’ Hearing Levels – A Finding Based on The

Chances of Occurrence in the Possible Sway Patterns, NIOSH Journal,? 2010

[2] Factories and Machinery Act, 1967, Noise Regulation 1989.

Graphs

Graph 1a : Off-limit Conditions and tThe Variables of the Exponential Equations

12.8 initial point - intersection at y-axis

A B C

11.5 1.3 0.08

y = A + B * e (c*x)

Sway Pattern - based on highest possible condition, "Category 2" to "C".

- the exponential curve from the highest count in the pattern. Approx 40 out of

100.

10.2 initial point - intersection at y-axis

A B C

9.2 1.0 0.07

y = A + B * e (c*x)

Sway Pattern - based on 2nd highest possible condition, "Category 1" to "C".

- the exponential curve from the second highest count in the pattern. Approx 38 out of 100.


46

Graph 1b : Off-limit Conditions (Workers in their early 20s)

Lower Range

15 dB

initial point, intersect with earliest start

age at 16 years old.

A B C Overall Factor

-20 2.5 0.1 1.2

y = A + B * e (c*x)

1.1y - (Lower - i)

Upper Range

18 dB

initial point – start age at 27 yrs old when problem

occurs, presumably 15dB at 16 years old (intersection

with lower range), with hearing level 19.1dB. Cross

over at the same age, indicating drastic deterioration.


-50 10 0.1 2

y = A + B * e (c*x)

2y - (Lower - ii)


47

Graph 1b : Off-limit Conditions (Workers in their early 20s)

Lower Range

15 dB

initial point, intersect with earliest start

age at 16 years old.


-20 2.5 0.1 1.2

y = A + B * e (c*x)

1.1y - (Lower - i)

Upper Range

18 dB

initial point – start age at 27 yrs old when problem

occurs, presumably 15dB at 16 years old (intersection

with lower range), with hearing level 19.1dB. Cross

over at the same age, indicating drastic deterioration.


-50 10 0.1 2

y = A + B * e (c*x)

2y - (Lower - ii)

Graph 2 : The Healthy Limits (off-limit, caution, good and very good)

Graph 3 : The Scatter Plot of the Workers’ Audiometric Test Results


48

Table 1: Statistics of Sampled Workers

(please consider the numbers as an estimate for a rough indication of percentage instead of

the exact number)

AudiometricTestResultsabove30dB

20–35yrsold 35–55yrsold

7 10

1214

10%

30yrsoldandabove

18

21

16%

>=40

<40&>=30

dB No.No.

Lessthan10yearsofservice Morethan10yearsofservice

7 (2%)

10 (2%)

26 (6%)

397 (90%)

Less than 10 years of

service

60 dB

40 dB

30 dB

4 (2%)

14 (6%)

21 (8%)

212 (84%)

More than 10 years

of service

60 dB

40 dB

30 dB


49

Table 1: Statistics of Sampled Workers

(please consider the numbers as an estimate for a rough indication of percentage instead of

the exact number)

AudiometricTestResultsabove30dB

20–35yrsold 35–55yrsold

7 10

1214

10%

30yrsoldandabove

18

21

16%

>=40

<40&>=30

dB No.No.

Lessthan10yearsofservice Morethan10yearsofservice

7 (2%)

10 (2%)

26 (6%)

397 (90%)

Less than 10 years of

service

60 dB

40 dB

30 dB

4 (2%)

14 (6%)

21 (8%)

212 (84%)

More than 10 years

of service

60 dB

40 dB

30 dB

Figure 1: Clusters and Points Record Leaving Cluster Group

Figure 2: Sway Patterns of Workers Noise Hearing Level to the Off-Limit Conditions.

Highdistinctpoints


50

Figure 3: Corrected to Show Slow Increase at Early Stage and Deteriation Pattern at Later

Stage


51

Figure 3: Corrected to Show Slow Increase at Early Stage and Deteriation Pattern at Later

Stage

1

PREVALENCE OF WORK RELATED MUSCULOSKELETAL

DISORDER AMONG PORT WORKERS: QUANTITATIVE ANALYSIS

AT THE PHYSIOTHERAPY CENTRE OF MALAYSIAN SHIPPING

INDUSTRY, SELANGOR

Izham Zain¹, Azrul Anuar¹, Asrina Asri¹, Shamsul Azhar²

¹ KPJ Healthcare University College

² Physiotherapy Department, Malaysia Shipping Industry, Selangor

Corresponding author: [email protected]

Abstract

The objective of this study is to identify the type of occupational related musculoskeletal

disorder among Malaysian Shipping Industry workers and to determine the relationship

between workers sosio demographic factors with occupational related musculoskeletal

disorder and injuries. This is a cross sectional, retrospective study using secondary data that

is available at the physiotherapy centre of Malaysia Shipping in Selangor. The study

population is the shipping port workers received physiotherapy treatment from 2011 and

2012. A total of 90 samples comprise of 85 male workers and the remaining is female. The

mean age is 34.1 (±7.36). Crane operator is the largest number of workers seeks for

physiotherapy treatment (68), office (15) and 7 from maintenance. The mean of employment

duration is 8.02 (±4.47) years with the maintenance group of workers have longest working

duration of 9 years. Muscle and ligament sprain strain known to be the commonest condition

(80%) refer for physiotherapy rehabilitation, tendinitis (14%) and fracture (6%). Young age

group of workers were significantly 9 times higher (95% CI 1.83 – 40.35) of getting back

injuries. The prevalence of musculoskeletal disorder based on work categories vary with

office type workers has 4.5 times higher (95% CI 1.06 – 19.7) on hand injuries. This study

has revealed that workers age, different type of work categories, working experience, and

body mass composition were associates with the occupational related injuries. The training

programme emphasise on preventive measures should be tailored to empower the employee

on safety measures at work.

Keywords: Shipping Industry Workers, Physiotherapy, Occupational related injuries


52

2

Introduction.

According to the World Health Organisation, work-related musculoskeletal disorders arise

when exposed to work activities and work conditions that significantly contribute to

development of such derangement. (World Health Organization, 1985). The scientific

committee for musculoskeletal disorders of the International Commission on Occupational

Health (ICOH) describe work-related musculoskeletal disorders as a wide range of

inflammatory and degenerative diseases and disorders that result in pain and functional

impairment (Kilbom et al., 1996). Musculoskeletal disorders are known to be most common

work-related illness, it representing a third or more of all listed occupational diseases in the

developed countries (National research council. 2001). In the United States, Canada, Finland,

Sweden, and England, musculoskeletal disorders cause more work absenteeism or disability

with substantial costs and negative impact on quality of life than any other group of diseases.

There is a conclusive evidence indicate that organizations who fail to control workplace

injuries will experience an increase in loss-related expenses, mainly on workers

compensation insurance premiums. The current insurance premiums are determined based on

organization’s reported on work related injuries yearly. A report on business metal industry

performance in United State indicate that the cost of insurance premiums for the workers of

heavy steel industry is escalating gradually for the past 5 years. Therefore it is important for

companies to be in constant control of its human-related losses in order to secure reasonably

profit (Spengler et al 1986). A Korea Occupational safety and health Agency reported in

2005, a total of 9114 employees in Korea received workers compensation due to illness. Out

of these, 6223 cases (68.3%) were work related injuries. Highly physical demanding task are

known to be high risk sector of developing musculoskeletal injuries. Upper extremity work

related musculoskeletal disorders are highly found in manual-intensive type of occupations,

such as clerical work, postal service, cleaning, industrial inspection and packaging (Rampel

and Punner. 1997). Back and lower limb disorders occur more commonly on truck drivers,

warehouse workers, airplane baggage handlers, construction trades, nurses, other patient-care

workers, and operators of cranes or other large vehicles driver (Pope et al. 1991). The

European survey on working conditions ( 2001) estimated 45.5% of workers reported

working in painful or tiring positions, 35% were required to handle heavy loads in their work

and 62.3% reported repetitive hand or arm movements. All of such activities were known to

correlates closely with musculoskeletal disorders. In 2011, occupational injuries following

transportation activities, storage and courier services contribute 13.3% of total injury in


53

2

Introduction.

According to the World Health Organisation, work-related musculoskeletal disorders arise

when exposed to work activities and work conditions that significantly contribute to

development of such derangement. (World Health Organization, 1985). The scientific

committee for musculoskeletal disorders of the International Commission on Occupational

Health (ICOH) describe work-related musculoskeletal disorders as a wide range of

inflammatory and degenerative diseases and disorders that result in pain and functional

impairment (Kilbom et al., 1996). Musculoskeletal disorders are known to be most common

work-related illness, it representing a third or more of all listed occupational diseases in the

developed countries (National research council. 2001). In the United States, Canada, Finland,

Sweden, and England, musculoskeletal disorders cause more work absenteeism or disability

with substantial costs and negative impact on quality of life than any other group of diseases.

There is a conclusive evidence indicate that organizations who fail to control workplace

injuries will experience an increase in loss-related expenses, mainly on workers

compensation insurance premiums. The current insurance premiums are determined based on

organization’s reported on work related injuries yearly. A report on business metal industry

performance in United State indicate that the cost of insurance premiums for the workers of

heavy steel industry is escalating gradually for the past 5 years. Therefore it is important for

companies to be in constant control of its human-related losses in order to secure reasonably

profit (Spengler et al 1986). A Korea Occupational safety and health Agency reported in

2005, a total of 9114 employees in Korea received workers compensation due to illness. Out

of these, 6223 cases (68.3%) were work related injuries. Highly physical demanding task are

known to be high risk sector of developing musculoskeletal injuries. Upper extremity work

related musculoskeletal disorders are highly found in manual-intensive type of occupations,

such as clerical work, postal service, cleaning, industrial inspection and packaging (Rampel

and Punner. 1997). Back and lower limb disorders occur more commonly on truck drivers,

warehouse workers, airplane baggage handlers, construction trades, nurses, other patient-care

workers, and operators of cranes or other large vehicles driver (Pope et al. 1991). The

European survey on working conditions ( 2001) estimated 45.5% of workers reported

working in painful or tiring positions, 35% were required to handle heavy loads in their work

and 62.3% reported repetitive hand or arm movements. All of such activities were known to

correlates closely with musculoskeletal disorders. In 2011, occupational injuries following

transportation activities, storage and courier services contribute 13.3% of total injury in

3

Malaysia. Lifting, poor working posture, performing repetitive movements are among the

known caused of occupational related musculoskeletal injuries. Treatment and recovery are

often unsatisfactory especially for chronic injuries. The end result can be permanent disability

and sometimes with the loss of employment. Study on the occupational related

musculoskeletal injuries among port workers especially in Malaysia is still limited. Therefore

this study will explore the patterns and trends of occupational related injury that has been

treated at the physiotherapy department. The objective of this study is to identify the type of

occupational related musculoskeletal disorder among Malaysian Shipping Industry workers

and to determine the relationship between workers sosiodemographic factors with

occupational related musculoskeletal disorder and injuries. The outcome from this study will

give some insight and be able to suggest preventive and advice suitable corrective measures.

Literature review.

Cross sectional study conducted by Azmi Hassan and Rampal (1995) to determine the

prevalence of back pain among the bus drivers and office workers in a Bus company in

Kelantan found that majority of workers (60.8%) complaining of neck and back pain. The

prevalence of neck and back pain is significantly associated with unsuitable sitting posture

and time spending on driving a bus. The work nature seems to be consistent with the port

workers engaged with prolong sitting posture and it should have the same effects on them.

There are limited study that has examined the impact of work duration on the musculoskeletal

disorders and injuries. Azmi and Rampal (1995) examine the association of musculoskeletal

injuries with time spending on driving a bus. There’s a statistically significant relation of

back pain and time spending on driving. Even though majority (32.2%) of bus drivers

complains of low back pain however the onset is strongly associated with the uncomfortable

driver seat. Prevalence study conducted by Evangelos et al (2006) on musculoskeletal

disorders in shipyard industry found that 25% of total workers (998) have an episode of

musculoskeletal injury. Data collection was done through questionnaires circulated to the

respective employee. They found that, employees with middle age category of 31 – 44 years

old were prone to have musculoskeletal injuries (OR= 5.1, 95% CI 1.19 – 10.34) compare

with younger and older age group. The blue collar workers were 9 times higher to encounter

occupational related injury compare to other type of work category. Most of the data were

based on self reports and it indicates elements of biasness since the employee needs to recall


54

4

the musculoskeletal injuries episode. In order to minimize the internal validity on data

collection exercise, the researcher should utilize the secondary data collection method

through employee medical records. Marlise et al (2012) conducted a prevalence study on

musculoskeletal diseases among dock workers. This is a retrospective and quantitative study. The

data were taken from medical files of dockworkers from 2000 to 2009. Individuals older than 52

years of age and with more than 21 years working experience in the field were predominated.

The most frequent musculoskeletal diseases included low back pain (38.8%), tendinitis

(19.7%), and neck pain (12.5%). The author belief that age and working experience were

important factors in the development of musculoskeletal related injury among dock workers.

However there are other contributing factors that might trigger the musculoskeletal symptoms

among dock workers such as types of work categories, working hours, and the effects of

work related vibration. All these elements in combination with each other were likely to

increase the incidence of work related injuries and disorders. Habibi et al (2008) conducted a

Prevalence study on Musculoskeletal Disorders and associated lost work days in Steel

Making Industry, they found that workers age between 25 – 31 years old were among the

highest age group of people absent from work due to the occupational injuries. There is

numerous risk factors that might contribute to the absenteeism among them, namely because

of poor posture at work, lack of job rotation, poor work station and the effects of vibrating

tools. The implications are seemed to be serious once the workers reach to older age because

the untreated occupational related disorders will effects workers productivities. Therefore it is

important to consider different age of groups as variable to enable an appropriate measures be

carried out.

Methodology.

Malaysia shipping Industry situated in Selangor and is a multicargo port which handles all

types of cargoes in containers, dry bulk, liquid bulk, vehicles and other conventional cargoes.

The industry has more than 4,000 staff responsible in ensuring the smooth running of port

activities. To ensure the optimum health of staff the port management provide Health clinic

and Physiotherapy centre that operate during office hours and runs by qualified medical

personnel. This is a retrospective study, using secondary data (Physiotherapy treatment card)

that is available at the Physiotherapy centre of Malaysia Shipping Industry. It involves port

workers that underwent physiotherapy treatment of the year 2011. The eligibility criteria to

enable the samples to enrol in this study are it should be port workers and had been diagnosed


55

5

of musculoskeletal disorders / injuries and being referred for physiotherapy treatment. The

exclusion criteria, been diagnosed as non musculoskeletal disorders / injuries and has

underwent surgery due to motor vehicles accident. The patient name list was obtained from

the physiotherapy registration book of the year 2011. The head of physiotherapy service of

Westport physiotherapy centre will assist the researcher to trace the physiotherapy treatment

card. The researcher will evaluate each treatment card to determine it eligibility prior to data

collection exercise. The data collection exercise will be carried out by the researcher using

study data collection form. This study has received approval and permission from the

Malaysia port authority management and University research ethic committee to enable the

researcher to conduct a data collection exercise.

Results.

Majority of samples were male employee, it represents 85 (94%) of total samples and the

remaining 5% were female (Graph 1). The mean age of study sample is 34.1 (±7.36) with the

youngest age is 21 and the oldest is 55 years old. Employment duration is varies with

minimum duration of 1 years and the maximum employment duration is 17 years. The mean

employment duration is 8.02 (±4.47) years. Maintenance personnel are known to have

longest employment duration of 9 years and the shortest duration is employee working and

attach to office related task. Majority (82%) of port workers were obese with mean BMI is

25.8. (Table 1)

Majority of crane related task operator were from younger aged group (72%) and 17%

engage in office related task. Interestingly out of 15 workers engaged in office related task,

14 of them were overweight category. (Table 2)

Descriptive statistical tool was used to describe the diagnosis of musculoskeletal injuries

among port workers. Sprain and strain are the commonest injury on workers follow by

tendinitis and fracture. It represents 80%, 14% and 6% respectively. The anatomical structure

involves varies. Sprain and strain were more obvious on workers with back and knee injuries.

A total of 46% (n=41) cases referred for physiotherapy rehabilitation are due to such injury.

Prevalence of such injury on knee and neck area is 12% (n=11) and 10% (9) respectively.

Tendinitis is significantly higher over hand (n=5) and shoulder (n=4) area. Fracture cases is

more common on hand (n=3) and only 1 fracture cases found on shoulder and ankle area

respectively (Table 3). The Odd ratio was used to determine the association between

exposure and outcome. Pearson correlation analysis was conducted to assess the intensity and


56

6

direction of the association between variables. Workers age category of 21 – 39 years old

seems to have 9 times (95% CI 1.83 – 40.55) higher suffering back injury. However, workers

with aged category of 40 – 55 years old were significantly 5 times higher of getting hand

injury (p < 0.05. 95% CI 1.42 – 22.6) and 2 time higher on elbow injury (p<0.05. 95% CI

0.19 – 25.0). The relationship between older age group workers with elbow and hand injury is

strongly associated and consistent. The prevalence of occupational related injury based on

work category is varies. Workers doing office type of task is significantly high on hand injury

(p <0.05) with 4 times higher compared to other work categories. However, maintenance

group of workers was associated with an approximately 4 times increase on elbow injury

(95% CI 1.06 – 19.7). Back pain was significantly more common among workers with

working experience of 1 – 7 years. The prevalence of such injury is 3 times higher compared

to those of more than 8 years of working experience. However, for workers with more than 8

years working experience, neck pain seems to be more dominant. The incidence is 5 times

(95% CI 0.43 – 69.3) higher compared to younger age category. There is no statistically

relationship between excessive BMI with occupational related injury. In this prevalence

study, BMI less that 23.9 kg / m² were found to have 2 times higher of elbow pain (p< 0.05.

95% CI 0.20 – 28.2). However, relatively excessive BMI were known to have any of the

musculoskeletal injuries regardless the anatomical area. (Table 4.1 and Table 4.2)

Discussion.

This prevalence study reveal that 75% (n=68) of total patients coming to physiotherapy

department for rehabilitation purposes is from crane operator work category and subsequently

the small percentage were from office and maintenance category. This finding is similar with

study of Evangelos et al (2006) who reported, blue collar workers were 9 times higher to

encounter occupational related disorders. However, the recurrence rate of occupational

related injuries and diseases after receiving physiotherapy rehabilitation were not reported

because it was not within the scope of this study.

Limitation of study.

There is a limitation of the study need to be considered in the interpretation of the result. This

is a cross sectional study using secondary data that is available at the physiotherapy centre.

Obviously secondary data is limited in details and therefore it difficult to draw a

comprehensive conclusion of prevalence on occupational related injuries. This study is not


57

7

able to evaluate workers knowledge on health and safety aspects at work place because of

limitation mention. Therefore the association of occupational injuries and knowledge on

health and safety at work place cannot be drawned. Although this limitation cannot be

excluded, our findings are in line with other studies on prevalence of occupational related

injuries. The strength of this study, there is no evidence of bias in data collection since it

involves secondary data collection that available at the physiotherapy centre.

Work categories and occupational related disorders.

Marlise et al (2012) found that 38.8% of port workers would suffer from low back pain.

Interestingly the data from this study show that there is no statistical association between

back pain and different category of port workers. However, the statistic is relatively indicate

that prevalence of back pain is high on crane operator and office workers. Interestingly, crane

operator (n=68) were the majority group of workers seeks physiotherapy treatment. This

finding may be due to vibration effects from heavy vehicle and prolong sitting duration at

work. Hand injuries were significantly high on office work category with odd ratio of 4 time

higher compare to other categories. The effects of using lots of hand and finger movement

during computer work might contribute to such injuries. This finding is consistent with

numerous of study conducted in evaluating the association of work categories and

occupational related injuries (Christina et al. 2005).

Age and Occupational related musculoskeletal disorders.

Obviously younger age group workers (81%) were the most group to encounter an episode of

back pain compare to older age group. It is found that the younger age group workers were

involve in a high demand of physical related task and the continuous vibration effects from

the heavy vehicle itself (Azmi and Rampal.1995). This study were not evaluating the

contributing factors of occupational related injuries and diseases, however such effects on the

younger aged group workers cannot be denied. Interestingly, the older aged group workers

were significantly high of getting hand and elbow pain. This finding is not consistent with the

study conducted by Papageorgiou et al (1996) which found that the highest incidence of back

pain among workers were between 49 – 59 years old. The high percentage of younger aged

worker suffering from occupational related disorders cannot be used to inference the true

picture of occupational related injuries among port workers because of limitation in samples


58

8

size spectrum. Majority of samples were within younger age group, it represents 73% and the

remaining percentage were samples of 40 – 55 years old. Obviously, the age distributions

were not equal between 2 age groups. In order to have a clearer picture of occupational

related trends of injuries, the study should include the data available at the health centre.

However, this study was conducted within the physiotherapy centre therefore it cannot

represent the whole population of such organization.

BMI and work related disorders.

There is no statistical significant indicate the relation of BMI and work related disorders.

However, descriptive statistic showed that 93% of office workers were overweight. These

phenomena may be due their work nature of less active and small physical movement. If this

trend continues without preventive intervention and active involvement from both employees

and employer, it can contribute to an increase number of non communicable diseases in

future. Few studies have examined the relation of excessive body weight with occupational

related diseases and suggested that excessive body weight increased the risk of

musculoskeletal injuries and cardiovascular disorders (Paul et al.2007). The combination

effects of excessive body weight with occupational related stress and working environment

will increase the risk of occupational related disorders. Therefore, it can conclude that obesity

should consider a significant occupation hazards. Since there are complete gymnasium

equipments and it is easily access by employees, the BMI might not be a good assessment

tools to assess the excessive fat composition. The possibility of excessive body composition

may be due to increase muscle flash resulting from weight training programme. The

alternative measurement of taking fat composition should consider in order determining the

actual scenario. The best option available is through body fat calipers which are feasible to do

by taking primary data method.

Work experience and occupational related injuries.

Noorul Huda (2012) found that there is strong association between work experience and

occupational injuries. Majority of samples were working as crane operator. Workers with 8 –

17 years working experience were significantly 5 times higher suffering from neck pain.

However, those workers with less than 8 years were significantly 3 times higher suffering

back pain. In this prevalence study, samples knowledge on occupational related injuries and


59

9

working experience were not measures and therefore the relation and association between

these two factors were unable to rule out. There are numerous studies indicate that there are

significant relations between knowledge and occupational related injuries. In fact working

experience has a significant impact in improving knowledge, skills and even positive

behaviour towards safety at work (Hong Wang et al. 2003). However in this prevalence study

such variables were not measure because of limitation in data collection itself. Evaluating the

knowledge is best to carry out through collection of primary data.

Conclusion

This study has revealed that workers age, different type of work categories, working

experience, and body mass composition were associates with the occupational related

injuries. The preventive programme should be tailored to empower the employee on safety

measures at work place. The training on preventive aspect of occupational injuries can be

conducted to employee in order to equip them with adequate knowledge on safety and

preventive measures at work place. The preventive measure is known to be a most valuable

strategy rather that cure. The physiotherapy department should play an important role in

assisting and organizing such training session. Both parties, employer and employee should

involve actively and work together to achieve this aim


60

10

Reference.

Azmi Hassan, K.G. Rampal (2006). Prevalence of Back pain and risk factors Among bus

driver in Kelantan 1995. Jurnal Kesihatan Masyarakat, 12 (1).

Christina F Lassen, Sigurd Mikkelsen, Ann I Kryger, Johan H Andersen (2005). Risk

factors for persistent elbow, forearm and hand pain among computer workers. Scand J Work

Environ Health ;31(2):122–131

Rempel R M, Punnett. L, (1997). Epidemiology of wrist and hand disorders, in:

M. Nordin, G.B. Andersson, M.H. Pope (Eds.), Mosby-Year Book, Inc, Philadelphia, PA,

1997, pp. 421–430.

European Foundation for the Improvement of Living and Working Conditions.(2001). Ten

years of working conditions in the European Union.

(http://www.eurofound.europa.eu/publications) (accessed February 2012)

European Agency for safety and health at work (2010). European Risk Observation Report.

Publications office of European Union, Luxembourg.

Evangelos C Alexopoulos, Dimitra Tanagra, Eleni Konstantinou and Alex Burdorf (2006).

Musculoskeletal disorders in shipyard industry: prevalence, health

care use, and absenteeism. Biomed Centre . 7 : 88.

Habibi, Fereidan, Molla aghababai, Pourabdian (2008). Prevalence of Musculoskeletal

Disorders and Associated Lost Work Days in Steel Making Industry. Iranian J Publ Health

;Vol. 37 : 83 – 91

National Research Council, (2001). The Institute of Medicine. Musculoskeletal disorders and

the workplace: Low back and upper extremities. National Academy Press, Washington, DC.

National Research Council, Institute of Medicine. (2001) Musculoskeletal Disorders and the

Workplace: Low Back and Upper Extremities. Washington, DC: The National Academies

Press.

Paul A. Schulte, Gregory R. Wagner, Aleck Ostry, Laura A. Blanciforti, Robert G. Cutlip,

Kristine M. Krajnak. (2007). Work, Obesity, and Occupational Safety and Health. American

Journal of Public Health : Vol 97, No. 3

Spengler DM, Bigos SJ, Martin NA.(1986) Back Injuries in industry : A retrospective study :

Overview and cost analysis. Spine; 241 – 245.

Rempel DM, Punnett L, (1997). Epidemiology of wrist and hand disorders, in: M. Nordin,

G.B. Andersson, M.H. Pope (Eds.), Mosby-Year Book, Inc, Philadelphia, PA, : 421–430.


61

11

Ling Lei, Youxin Ling (2002). Prevalence and risk factors of musculoskeletal disorders of

foundry workers. International Journal of Industrial Ergonomics; Vol 35 (3) : 197 - 204

Work Related Musculoskeletal Disorders. Occupational Health Clinics for Ontario Workers

Inc. (http://www.ohcow.on.ca/uploads/Resource/General%20Handouts/pdf) (accessed Feb 2012)

Pope M H, Andersson G B, Frymoyer J W, Chaffin D B. (2007). Mosby-Year Book, Inc, St

Louis, MO, 1991. Young workers – facts and figures. European Agency for safety and Health

at Work, EU-OSHA.

( http://osha.europa.eu/en/publications/reports/7606507) (accessed March 2012)

Noorul Huda Zakaria, Norudin Mansor, Zalinawati Abdullah (2012). Workplace Accident

in Malaysia: Most Common Causes and Solutions. Business and Management Review Vol.

2(5) pp. 75 – 88 July, 2012

Health and Safety Executive. Safety of Industrial Lift Trucks: A Survey of Investigated

Accidents and Incidents (April 1997 – March 2001). , Special Inspector Reports Number 60.

2003

Gyekye, S.A. (2010). Occupational safety management: The role of causal attribution.

International Journal Of Psychology; 45 (6): 405–416.

Schulte PA. (1986). Problems in notification and screening of workers at high risk of disease.

Journal of Occupational Medicine ; 28(10) : 951-7.

Papageorgiou A, Croft P, Thomas E, Ferry S, Jayson M, Silman A.(1996) Influence of

previous pain experience on the episodic incidence of low back pain. Results from the South

Manchester back pain study. Pain journal; 66 :181-5.

Paul A. Schulte, Gregory R. Wagner, Aleck Ostry, Laura A. Blanciforti, (2007). Work,

Obesity, and Occupational Safety and Health. American Journal of Public Health; 97 (3) :

428 – 434.

Hong Wang, Kristoper Femmie, Guoping He, Jane Burgess (2003). A training programme for

prevention of occupational exposure to bloodborne pathogens: impact on knowledge,

behaviour and incidence of needle stick injuries among student nurses in Changsha, People's

Republic of China. Journal of Advanced Nursing ; 41 : 187 - 194

Fredriksson K, Alfredsson L, Ahlberg G, Josephson M, Kilbom A, Wigaeus Hjelm E,

Wiktorin C, Vingard E. (2002). Musculoskeletal Intervention Centre. Work environment and

neck and shoulder pain: the influence of exposure time. Results from a population based

case-control study. Occup Environ Med ; 59 (3):182-188.


62

12

Tables and Figure.

Table 1: Social demography data on port workers.

Baseline data N (%) Mean (SD)

Age (years old)

34.1 (±7.36)

21 - 39 73 (81%) 31.4 (±4.68)

40 - 55 17 (19%) 45.8 (±4.60)

Employment duration (Years) 8.02 (±4.47)

Crane operator 68 (76%) 8.37 (±4.57)

Office 15 (16%) 5.93 (±3.85)

Maintenance 7 (8%) 9.14 (±1.94)

BMI (kg / m²) 24.9 (±2.76)

Ideal body weight (<22.9) 16 (18%) 21.3 (±3.01)

Overweight (>23.0) 74 (82%) 25.8 (±1.94)

Table 2: Sociodemography data distribution based on the different work categories

Work Category N

Crane Operator

(N= 68)

Office

(N=15)

Port Maintenance

(N=7)

Age (years old)

21 - 39 53 13 7

40 - 55 15 2 -

Work experience

1 – 7 years 32 13 4

8 – 17 years 36 2 3

BMI (kg / m²)

Ideal (<22.9) 14 1 1


63

13

Overweight (>23.0) 54 14 6

Table 3: Types of occupational related injuries and it anatomical areas

Area of Injury Sprain / strain Tendinitis Fracture N

Back

41

-

-

41

Knee 11 2 - 13

Hand 2 5 3 10

Neck 9 - - 9

Shoulder 3 4 1 8

Ankle 3 1 1 5

Elbow 2 1 - 3

Hip 1 - - 1


64

14

Tab

le 4

.1:

Pre

val

ence

dat

a of

area

of

occ

upat

ional

rel

ated

musc

ulo

skel

etal

dis

ord

er a

nd s

oci

odem

ogra

phy d

ata.

Ba

ck

pa

in (

N=

41

)

Kn

ee P

ain

(N

=1

1)

Neck

pa

in (

N=

9)

Ha

nd

In

jury

(N

=1

0)

OR

95

% C

I

p v

alu

e

OR

95

% C

I

p v

alu

e

OR

95

% C

I

p v

alu

e

OR

95

% C

I

p v

alu

e

Ag

e (

Yea

rs

old

)

0.0

5*

0.5

4

0

.08

0.0

1*

21

– 3

9

9

1.8

3 –

40

.35

1.3

0

.27

– 6

.65

0.4

0

.09

– 1

.87

0.2

0

.04

– 0

.70

40

- 5

5

0.1

0

.03

– 0

.55

0.8

0

.15

– 3

.76

2.4

0

.53

– 1

0.7

5.7

1

.42

– 2

2.6

Wo

rk

ca

teg

ory

0

.60

0

.31

0

.28

0

.05

*

Cra

ne

op

erat

or

1

0.3

3 –

3.1

2

0

.5

0.1

2 –

2.3

1

2

0

.22

– 1

6.2

0.2

0

.05

– 0

.94

Off

ice

1

0.3

2 –

3.0

2

2

0

.43

– 8

.11

0.5

0

.06

– 4

.64

4.5

1

.06

– 1

9.7

Mai

nte

nan

ce

0.5

0

.82

– 2

.48

3

0.4

9 –

18

.1

-

-

2

0.2

1 –

21

.0

Wo

rk

ex

perie

nce

0.0

2*

0

.36

0

.02

*

0.2

4

1 –

7 y

ears

3

1

.21

– 6

.83

0.7

0

.21

– 2

.21

0.2

0

.04

– 1

.06

2

0.5

4 –

8.7

5

8 –

17

yea

rs

0.3

0

.15

– 0

.83

1.5

0

.45

– 4

.80

5

0.9

5 –

24

.7

0

.5

0.1

1 –

1.9

6

BM

I (K

g /

m²)

0

.55

0

.23

0

.70

0

.57

(<2

3.9

) 1

0

.31

– 2

.71

2.5

0

.64

– 9

.10

1.4

0

.26

– 7

.30

1.2

0

.23

– 0

.57

(>2

4.0

)

1.1

0

.37

– 3

.25

0.4

0

.11

– 1

.57

0.7

0

.14

– 3

.90

0.8

5

0.1

6 –

4.4

3

OR

=O

dd

s r

atio

, 9

5%

CI

= 9

5%

Co

nfid

en

ce

In

terv

al. S

ign

ific

an

t d

iffe

ren

ce

p <

0.0

5.

Sta

tistica

l te

st

= P

ea

rso

n c

orr

ela

tio

n


65

15

Tab

le 4

.2:

Pre

val

ence

dat

a of

area

of

occ

upat

ional

rel

ated

musc

ulo

skel

etal

dis

ord

er a

nd s

oci

odem

ogra

phy d

ata.

S

ho

uld

er p

ain

(N

=8

)

An

kle

pa

in (

N=

5)

Elb

ow

pa

in (

N=

3)

Hip

In

jury

(N

=1

)

OR

95

% C

I

p v

alu

e

OR

95

% C

I

p v

alu

e

OR

95

% C

I

p v

alu

e

OR

95

% C

I

p v

alu

e

Ag

e (

Yea

rs

old

)

0.3

1

0.5

2

0.0

5*

-

21

– 3

9

0.7

1

.23

- 3

.66

0.3

0

.05

– 2

.10

0.4

0

.04

– 5

.28

0.9

0

.96

– 1

.01

40

- 5

5

1.5

0

.27

-8

.11

3

0.4

8 –

20

.3

2

.2

0.1

9 –

25

.0

-

-

Wo

rk

ca

teg

ory

0

.73

0.0

5*

-

Cra

ne

op

erat

or

0.9

0

.83

– 0

.97

0.9

0

.87

– 1

.0

0.2

3

0.9

0

.93

– 1

.01

0.2

0

.05

– 0

.94

Off

ice

- -

- 1

.3

1.1

1 –

1.4

0

-

-

- -

Mai

nte

nan

ce

1.4

0

.15

– 1

3.8

1

1.0

1 –

1.2

0

5

0

.43

– 6

9.3

- -

Wo

rk

ex

perie

nce

0.5

2

0.6

5

-

-

1 –

7 y

ears

0

.8

0.1

9 –

3.5

1

0

.2

0.0

2 –

1.8

0

-

-

2.1

0

.51

– 8

.75

8 –

17

yea

rs

1.2

0

.30

– 5

.20

5.2

0

.57

– 4

8.4

0.9

0

.85

– 1

.0

-

-

BM

I (K

g /

m²)

0

.43

0

.13

0

.02

*

-

(<2

3.9

) 1

.2

1.1

2 –

1.4

0

1

1

.01

– 1

.14

2

0.2

0 –

28

.2

-

-

(>2

4.0

) 0

.9

0.8

2 –

0.9

6

0

.9

0.8

7 –

1.0

0.4

0

.04

– 4

.90

0.8

0

.16

– 4

.43

OR

=O

dd

s r

atio

, 9

5%

CI

= 9

5%

Co

nfid

en

ce

In

terv

al. S

ign

ific

an

t d

iffe

ren

ce

p <

0.0

5.

Sta

tistica

l te

st

= P

ea

rso

n c

orr

ela

tio

n


66

16

Graph 1: Numbers of samples based on gender

n=85

n=5

0

10

20

30

40

50

60

70

80

90

male female

References: All references must be formatted in accordance with the Publication Manual of the American Psychological Association (APA), Fifth Edition. For example: Journal Articles: Smith, A.B., Adams, K.D., & Jones, L.J. (1992). The hazards of living in a volcano. Journal of Safety Research, 23(1),81-94. Book: Perez, A.K., Little, T.H., & Brown, Y.J. (1999). Safety in numbers. Itasca, IL: National Safety Council. On-line Publication: National Institute of Occupational Safety and Health. Sick Building Syndrome. www.niosh.com.my/safetytips.asp?safetyid=1 (accessed October 2004) Government Publication: Ministry of Health Malaysia & Academy of Medicine Malaysia (2003). Clinical Practise Guidelines on Management of Obesity 2003. Tables and Figures: Tables and figures should be on separate sheets from the text, in accordance with APA style, numbered consecutively and given a short but explicit title. Title for table should be above table. Title for figures should be below figure, Figures must be supplied as glossy photographs or professionally or electronically drawn artwork using heavy white paper and black ink. A notation should be made in the text showing approximately where each table or figure should appear (e.g., Insert Table 3 here). When referring to a particular table or figure in the text always use its number. All tables will be re-set in the production process. All figures will be scanned from the original. Computer Disks: If you send a computer disk with your submission, please label it with the author(s) name(s) and manuscript title. Disks will not be returned. Only Microsoft Word format is accepted. Contributor’s copy: Each author will receive 1 copy of the journal.

Secretariat Address National Institute of Occupational Safety and Health

Lot 1, Jalan 15/1, Section 15, 43650 Bandar Baru Bangi Selangor Darul Ehsan, Malaysia

Tel.: 603-8911 3879 / 3867 / 3871 Fax.: 603-8926 5655 Email: [email protected] Website: www.niosh.com.my

Subscription InformationJournal of Occupational Safety and Health (ISSN 1675-5456) is published bi-annually by

Communication, Business and Information Dissemination Division (CBID), NIOSH,Malaysia. Subscription is free.

To subscribe, kindly contact Roslina / Nor Akmar / 603-8911 3879 / 3867 [email protected]

journal of occupational safety and health, december 2013, vol 10, no. 2

Documents

ministry of health malaysia

malaysia tel

public health

health editor

malaysia raemy

malaysia shipping industry

malaysia corresponding

health related elds