International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 5 – Nov 2014
Effect of Occupational Diseases & Work Injury
among Employees in a Cement Industry
Madhav. K #1, R. Mahesh Rengaraj. #2
Production and Industrial Engineering Department.
SCMS School of Engineering & Technology, Ernakulam, Kerala, India.
#
Abstract— Safety is a priority of any industrial activity.
Workers in the cement sector are exposed to many occupational
hazards which may contribute to diseases and work injuries.
This study was conducted among employees in a cement
industry in Kerala. Occupational diseases and injuries data were
collected by using questionnaires and personnel interview of the
workers in different parts cement industries. A descriptive crosssectional study was carried out. 38 questions were administered
to workers who consented to the study. Hundred questionnaires
were fully completed and used for analysis.
This study had two goals; to define the work-related diseases
occurring among workers in the cement industry, and to find the
distribution of occupational injuries and common risk factors of
these injuries among workers in the cement industry. Data
analysis revealed that a lot of fatal occupational diseases and
illnesses exist in the industries; the possible sources of these
diseases and illnesses are also numerous.
Keywords— cement industry, work related diseases.
I. INTRODUCTION
Cement is an essential component of infrastructure
development and most important input of construction
industry, particularly in the government’s infrastructure and
housing programs, which are necessary for the country’s
socioeconomic growth and development. It is also the second
most consumed material on the planet [1]. The Indian cement
industry is the second largest producer of cement in the world
just behind China, but ahead of the United States and Japan. It
is consented to be a core sector accounting for approximately
1.3% of GDP and employing over 0.14 million people [2].
Safe work creates no obstacles to being competitive and
successful. In fact, no country – and no company in the long
run – has been able to jump to a high level of productivity
without making sure that the work environment is safe.
An injury or illness is considered by the Occupational
Safety and Health Administration to be work-related if an
event or exposure in the work environment either caused or
contributed to the resulting condition or significantly
aggravated a pre-existing condition.
Skin diseases or disorders are illnesses involving the
worker's skin that are caused by work exposure to chemicals,
plants or other substances.
Respiratory conditions are illnesses associated with
breathing hazardous biological agents, chemicals, dust, gases,
vapours, or fumes at work [3].
ISSN: 2231-5381
II. METHODOLOGY
The primary objective of the study was to identify the
factors responsible for the work injury. An instrument was
developed using these factors and reliability of the measuring
instrument was tested. The analysis was carried out using
software SPSS 20.
A. Survey Instrument
The questionnaire contained 38 questions to measure the
perceptions of the employees about the occupational diseases
and injury. This was prepared based on review of related
literature [4]. The contents of this draft questionnaire were
discussed with senior safety professional in the industry. A
pilot survey was conducted on a selected sample of 10
workers to get the feedback about the clarity of the items. The
questionnaire is then given to the workers. Each item was
measured on a Likert scale. Likert scale is a type of response
scale often used in questionnaires, and is the most widely used
scale in questionnaire survey.
In this study respondents were asked to give their
preference on a five point Likert scale in order to evaluate the
respondent’s level of agreement with each item. The four page
questionnaire consisted of two parts. Six demographic
questions about the age, sex, designation, experience,
qualification and income as well as one question regarding
injury history of the employee. Space was provided beside
each statement to mark the preference in the 5-point Likert
scale. To maintain anonymity of the respondent information
such as name, signatures etc. were avoided in the
questionnaire.
B. Population and Sample
This study was conducted in a cement industry. After
getting permission from the management the questionnaire is
distributed personally to the employees and explained the
purpose of study. Completed questionnaires were personally
collected from the participants and a total of 100 completed
response sheets were received.
C. Data Analysis
Descriptive statistics and correlations of the studied
variables were first analysed. Confirmatory factor analysis
http://www.ijettjournal.org
Page 208
International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 5 – Nov 2014
was used to verify the reliability. Regression analysis was
conducted to test the goodness of fit of the various models.
D. Factor Analysis
TABLE IIIII
KMO and Bartlett's Test
III. RESULTS & INTERPRETATION
KMO and Bartlett's Test
A. Occupational Diseases
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
.706
Approx. Chi-Square
609.425
df
66
Sig.
.000
Bartlett's Test of
Sphericity
The Kaiser-Meyer-Olkin measure should be greater
than 0.70 and is inadequate if less than 0.50. The KMO test
tells one whether or not enough items are predicted by each
factor. The Bartlett test should be significant (i.e., a
significance value of less than 0.05); this means that the
variables are correlated highly enough to provide a reasonable
basis for factor analysis.
TABLE IVVVI
Total Variance Explained
Fig.1. Occupational Diseases.
The most common work-related diseases among cement
industry workers were skin allergy, eye irritation and
shortness of breath.
Fa
ctor
B. Reliability Analysis
TABLE I
Reliability Statistics
Reliability Statistics
1
Cronbach's Alpha
.806
Cronbach's Alpha
Based on
Standardized Items
N of Items
.807
12
Cronbach's alpha is the most common measure of internal
consistency. It is most commonly used when we have multiple
Likert questions in a survey/questionnaire that form a scale
and we wish to determine if the scale is reliable. Here
Cronbach’s alpha is 0.806, which indicates a high level of
internal consistency for our scale. An alpha value of 0.6 or
above is considered as significant [5].
C. Correlation Analysis
The Pearson product-moment correlation coefficient
(Pearson’s correlation, for short) is a measure of the strength
and direction of association that exists between two variables
measured on at least an interval scale.
ISSN: 2231-5381
Total Variance Explained
Extraction
Initial
Sums of
Rotation Sums of
Eigenvalues
Squared
Squared Loadings
Loadings
%
Cu
%
Cu
%
of
mu
of
mu
of
Cum
Tot
Tot
Tot
Va lati
Va lati
Va
ulati
al
al
al
ria
ve
ria
ve
ria ve %
nce
%
nce
%
nce
32.
3.9
32. 3.4 29. 29. 2.4 20. 20.78
501
00
501 92 098 098
95 789
9
2
2.3
71
3
1.5
95
4
1.1
06
5
.77
7
6
.61
0
7
.55
1
8
.33
8
19.
756
13.
289
9.2
20
6.4
74
5.0
84
4.5
94
2.8
19
http://www.ijettjournal.org
52.
258
1.9
75
16.
462
45.
560
2.4
80
20.
671
41.45
9
65.
546
1.2
81
10.
671
56.
232
1.7
73
14.
773
56.23
2
74.
767
81.
241
86.
325
90.
919
93.
738
Page 209
International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 5 – Nov 2014
9
.23
7
1.9
76
.22
8
.16
9
1.9
00
1.4
04
one dependent and at least two independent variable [6]. A
linear relationship between the dependent and independent
variables is assumed. A t test is conducted for testing the
significance of the individual regression coefficients. The
overall fit of the regression is given by R² that is called
coefficient of determination and is a measure of the
explanatory power of the model. The value of R² lies between
0 and 1. The closer the value of R² to 1, the better is the
goodness of fit. The significance of R² is carried out by using
the f statistic.
95.
715
97.
615
99.
11
020
100
.11 .98
12
.00
8
0
0
Extraction Method: Principal Axis Factoring.
10
TABLE VIIV
TABLE V
Rotated Factor Matrix
Model Summary
Rotated Factor Matrixa
Model Summary
Factor
1
Repetitive and awkward
movements
.851
Wear PPE during work
.836
Failure to follow safety rules
.806
Lack of attention
.549
2
3
R Square
Adjusted R
Square
Std. Error of
the Estimate
.891a
.794
.792
1.58270
b
.812
.808
1.52156
Model
R
1
2
.901
a. Predictors: (Constant), Human factor
b. Predictors: (Constant), Human factor, Mechanical factor
Hazardous chemicals
.806
Misplaced objects
.801
Slipping floors
.698
Proper lighting
arrangements
.671
Work place is hot and humid
.422
This table provides the R and R2 values. The R value
represents the simple correlation (the "R" Column), which
indicates a high degree of correlation. The R2 value (the "R
Square" column) indicates how much of the total variation in
the dependent variable can be explained by the independent
variable.
TABLE 6
ANOVA
Heavy tools
.926
Unsafe tools
.707
Rapidly moving parts
.462
ANOVAa
Extraction Method: Principal Axis Factoring.
Rotation Method: Varimax with Kaiser Normalization.
Model
Sum of
Squares
df
Mean
Square
F
Sig.
Regression
948.515
1
948.515
378.656
.000b
Residual
245.485
98
2.505
Total
1194.000
99
Regression
969.431
2
484.715
209.367
.000c
Residual
224.569
97
2.315
Total
1194.000
99
a. Rotation converged in 4 iterations.
For this analysis we use an orthogonal rotation (varimax).
This means that the final factors will be as uncorrelated as
possible with each other. As a result we can assume that the
information explained by one factor is independent of the
information in the other factors. We rotate the factors so that
they are easier to interpret. Rotation makes it so that, as much
as possible, different items are predicted by different
underlying factors, and each factor explains more than one
item. This is a condition called simple structure. The three
factors identified are Environmental factors, Human factors
and Mechanical factors.
1
E. Regression Analysis
c. Predictors: (Constant), Human factor, Mechanical factor
2
a. Dependent Variable: Accidents
b. Predictors: (Constant), Human factor
In a simple regression, there is one dependent and one
independent variable whereas in multiple regressions there is
ISSN: 2231-5381
http://www.ijettjournal.org
Page 210
International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 5 – Nov 2014
The ANOVA table, which reports how well the regression
equation fits the data (i.e., predicts the dependent variable).
This table indicates that the regression model predicts the
dependent variable significantly well.
IV. CONCLUSION
A lot of occupational diseases and illnesses exist in
manufacturing industries. More work-related diseases and
illnesses are even likely to emerge as innovations in
technology continue. The possible sources of industrial
diseases and illnesses are also numerous such that the
industrial workers are not even fully aware of the health
hazards surrounding their work.
This study has been conducted by taking some variables
into considerations to get an overall view of occupational
diseases and injuries among workers in the cement industry.
The most common work-related diseases among cement
industry workers were skin allergy, eye irritation and
shortness of breath. Factor analysis is done to find out the
factors that are responsible for accidents. The factors
responsible for accidents are Environmental factors, Human
factors and Mechanical factors.
Safety measures at the workplace should be put into place
and then examined regularly to avoid the risk of injuries.
REFERENCES
[1]. WBCSD 2002 page -1.
[2]. L. G Burange, S Yamini, “Performance of Indian cement industry: the
competitive landscape”. University of Mumbai, 25/ (9)/3/2008, April 2008.
[3]. BLS November 20, 2012.
[4]. R.M.A. Alazab, Work-related diseases and occupational injuries among
workers in the construction industry.
[5]. Michael R. Prone. Predictors of Work Injuries among Employed
Adolescents. Journal of Applied Psychology 1998, Vol. 83, No. 4, 565 -576.
[6]. Deepak Chawla, Neena Sondhi. Research Methodology- Concepts and
Cases.
ISSN: 2231-5381
http://www.ijettjournal.org
Page 211