View of A Cross Sectional Study on Occupational Health Hazards and its Correlates among Workers in Small Scale Factories, Puducherry

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A Cross Sectional Study on Occupational Health Hazards and its Correlates among Workers in Small Scale Factories, Puducherry

Ponmalar M¹. Rajini S², Uma Devi R³

1,2,3 Department of Community Medicine, Sri Lakshmi Narayana Institute of Medical Sciences Affiliated to Bharath

Institute of Higher Education and Research, Chennai, Tamil Nadu, India.

To assess the health status of the workers in small scale factories in the study area. To identify the exposure of workers to the occupational hazards in them workplace. To study the association between occupational hazards and systemic health problems among the workers.

1. Introduction

The Industrial Revolution showed a conspicuous phase of ripening in the hindmost fragment of the early 19^th century that transmogrified bucolic societies of Europe and America into Industrialised civic ones. Commodities that had been assiduously crafted by hand, kicked off to be fabricated in abundance by machines in Industries. This was boarded up by the ground breaking use of steam power thus setting the Revolution in Industries in Britain, mushrooming to the rest of the World by 1830s & 40s. Concurrently even as industrialisation escalated the Economic yield and enhanced the quality of living for the middle and upper class, Poverty striken and working class people resumed to grapple. The Labour initiated by the technological upheaval had made performance in factories progressively monotonous and at times treacherous. Many workers were exacted to work long hours for paltry wages, heading to exploitation of labour, resulting in conflict between the bourqeois and the Proletariat1.

As the Industrial Revolution moved along, Socialist pundit reproached Capitalism for the hardship of the Proletariat, following which Communism grew out in the 19th century. Ensuing World War II in the mid-20^th century, people began to substantiate Globalization of World Economy, where it is no secret that the World of Work is abstruse. Globalisation influences the design of workplaces, the course of action being executed and occupational safety and health (OSH). Regardless of considerable treads ameliorating OSH since the past centenary, 317 million non-lethal occupational injuries and 321,000 occupational fatalities has been conjectured Globally every year, which means 151 workers sustain a work associated accident every 15 seconds. Impoverished workplace safety and health adds to substantial freight on employers.

International Security Association has insuinated that expenditure affliated with nonfatal workplace accidents alone twins roughly 4% of World GDP each year2(ISSA 2014: safe work 2012).

Albeit practically every single job entails certain likelihood for injury, the immensity of risk extends amply across job limits, Geographic precincts and Although the sequel of Mondialisation has been mixed, Occupational Injury rates have shown to soar in Low and Middle Income countries and fade in high Income Countries. India has reported 17 million Occupational Non- fatal injuries (17% of the World) and 45,000 fatal injuries (45% of the total deaths due to occupational Injuries in World) each year. Out of 11 million cases of Occupational diseases in the

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World, 1.9 million cases (17%) are contributed by India and out of 0.7 million deaths in the World 0.12(17%) is contributed by India3.

In Developing Country Such as India, Small and Medium Enterprises are often the paramount of the economy. Small & Medium Enterprises alone contribute to 7% of India’s Gross Domestic Product. They account 90% of the Industrial component and bestow 35% of India’s Exports. The SME sector of India is contemplated as of Economy granting employment to about 60 million people, creating 1.3million jobs every year. As per Annual report 2018-2019 on MSME, distribution of small sector enterprises is 3.31 lakh with 0.78 lakh belonging to rural and 2.53 lakh in Urban.

The Educational level and Socio-Economic status of SSE workers vary comprehensively but on many occasions lower than the averages for the whole workplace. Of specific significance, the owners/Managers may have had little knock up in operation and management and even less in the recognition, prevention and control of Occupational health risks and environment. Even where pertinent educational resources are made available, they often have the paucity of time, energy and resources to make use of them. With the esteem of being a backer amiable, the UT of Puducherry has authentic documentation of alluring surfeit outlay and has witnessed electrifying Industrial Growth over the years. Puducherry has 6964 SSE4 which are deemed to be the life belt of Puducherry Economy. While Evolution in Technology have minimized some hazards at the workplace, Occupational injury, illness and workplace facilities are important Public health concerns. Many studies have chronicled that the burden of Occupational injuries and illness is not in alike dispersal across the Labour force.

Consideration of Vulnerability only in terms of Individual Demographic, Job or Workplace characteristics is skimpy and absurd as it doesn’t fairly consider how the distinct circumstances of the workers put up on their Occupational health and safety. This study computed the vulnerability compendiously and scrutinize its relation with demographic profile, Behavioral determinants, Physical and mental status of workers in Small Scale Factories.

It is a known fact that workers in small scale factories have a hard physically challenging job.

Working under machines, being on their feet all day and straining their backs nd muscles, workers face a number of occupational hazards on daily basis. According to WHO, annually 2.9 billion workers across the Globe are exposed to workplace hazards. In addition to injuries, nearly 100 occupational diseases have been classified according to the tenth revision of International Classification of Diseases and related health problems (ICD-10). Broadly these include respiratory, musculoskeletal, skin and psychological disorders5. These on long term leads to sickness absenteeism and loss of productivity resulting in Economic loss.

This study was conducted to assess health status, environment and to figure out the association between occupational hazards and work profile of the workers in Small Scale Industries, Puducherry. Various researchers all over World, has studied the occupational hazards in small scale industries. However, there are not many studies done in Puducherry related to these aspects.

This study would give an insight to fill the gap existing in the occupational health system and thus exploring ways to bridge this gap.

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2. Materials And Methods Study design:

This was a cross- sectional study conducted among workers in small scale factories at Puducherry.

Study setting and study period:

The study was conducted from September 10th 2018 to June 31st 2019 in the small scale factories at Villianur, which is one of the catchment area of SLIMS, Puducherry.

Study population:

The workers employed in the small scale factories.

Sample size:

The sample size was calculated based on the previous study conducted by Prabha Thangaraj et al31, where the prevalence of occupational health hazards recorded in the study was 58.8%. This was taken as the reference value for calculating sample size for this study and was calculated using the formula,

N= Zα2pq/(L)2 Where,

Z = 1.96 at a confidence interval of 95%

P = 58.8 q = 100 - 58.8 = 41.2

L = relative precision, which is assigned as 10% of p for this study

= 5.88

Substituting the values in the formula, N = (1.96 * 1.96* 58.8*41.2) / (5.88*5.88)

= 9306.5064 / 34.57

=269

Considering, 10% for the Non response rate N = 295

The sample size calculated was 269. By adding 10% for non-response rate and the final sample size derived were 295 which was rounded off to 300. [N = 300]

Inclusion criteria:

The workers present in the factory during the survey.

Exclusion criteria:

The workers who didn’t give their consent for the study were excluded.

Sampling technique:

Villianur Taluk is located in Puducherry District. It is one among the 4 Taluks. The area of Villianur Taluk is 130.40 sq.km with population density of 1636 per sq.km. registered clusters of micro and small enterprises are there in UT of Puducherry. Some potential clusters available includes Plastic, Corrugated box, Fragrance industries. Considering approximately 50 small scale enterprises in Villianur, 9 factories were chosen by systematic random sampling method. 35 samples from each Industry were selected using simple random sampling method using the

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lottery method with the help attendance registry maintained in the factories to arrive at the sampling frame.

Study tool:

A pretested questionnaire was used as a study stool for the data collection. The validity of the tool was assessed by consultation with expert opinion. The questionnaire was prepared in English and was translated to the local language during the interview and the responses were collected by the interview herself. The questionnaire comprised of six sections.

Section i: socio demography details.

This section comprised of Personal details, including age, gender, religion, education status and the sociodemographic details such as Income, marital status, type of family, family members and the state of origin.

Section ii: behavioural determinants.

This includes the behavioral determinants of the workers which include the habits of smoking, alcohol intake, and dietary habits and sleep pattern.

The AUDIT - C was used to assess alcohol dependence. The AUDIT-C is an alcohol screen that helps to identify persons who are hazardous drinkers or have active alcohol use disorders (including alcohol abuse or dependence). The AUDIT-C is a modified version of the 10 question AUDIT instrument.

Audit-c questionnaire:

1. How often do you have a drink containingalcohol?

a. Never

b. Monthly orless

c. 2-4 times aweek

d. 2-3 times aweek

e. 4 or more times aweek

2. How many standard drinks containing alcohol do you have on a typicalday?

a. 1 or 2

b. 3 or 4

c. 5 or 6

d. 7 or 9

e. 10 ormore

3. How often do you have six or more drinks on one occasion?

a. Never

b. Less thanmonthly

c. Monthly

d. Weekly

e. Daily or almost daily Scoring:

The AUDIT-C is scored on a scale of 0-12

Each AUDIT-C question has 5 answer choices. Points allotted are: a = 0 points, b = 1 point, c = 2 points, d = 3 points, e = 4 points

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In MEN, a score of 4 or more is considered positive, optimal for identifying hazardous drinking or active alcohol use disorders.

In WOMEN, a score of 3 or more is considered positive (same as above).

However, when the points are all from question #1 alone (#2 & #3 are zero), it can be assumed that the patient is drinking below the recommended limits and it is suggested that the provider review the patient’s alcohol intake over the past few months to confirm accuracy.

Generally, the higher the score, the more likely it is that the patient’s drinking is affecting his or her safety.

Similarly, Fagerstrom scale was used to assess the Nicotine Dependence. The questionnaire is as follows,

Do you currently smoke cigarettes?

1) No 2) Yes

1. How soon after you wake up do you smoke your firstcigarette?

1) Within5 minutes 3) 31 - 60minutes

2) 6 -30 minutes 4) after 60minutes

2. Do you find it difficult to refrain from smoking in places where it is forbidden (e.g., in church, at the library, in thecinema)?

1) No 2) Yes

3. Which cigarette would you hate most to giveup?

1) The first one inthemorning 2) Anyother

4. How many cigarettes per day do yousmoke?

1) 10orless 3) 21 to 30

2) 11 to 20 4) 31 ormore

5. Do you smoke more frequently during the first hours after waking than during the rest of theday?

1) No 2) Yes

2) Do you smoke when yoy are so ill that you are in bed most of theday?

3) No 3) Yes

Section iii: Work Profile

This section comprises of the working nature of the study population comprising of place of occupation, type of work, Technical qualification, experience of work, work shift, nature of work, pre-employment medical screening and periodic medical check-up.

Section IV: Workplace Hazards.

This section asks the kinds of health and safety hazards that the workers are exposed in their job.

The questions in this section are based on the OHS vulnerability measure developed by the Institute of Work & Health, a not-for-profit organization based in Toronto, Canada. The main aim of this measure is to promote, protect and improve the safety and health of working people.

Section V: Physical Health

It includes the self-reporting of ocular problems, oral cavity problems, ENT related complaints, cardiac problems, respiratory related problems, gastro intestinal problems, nervous problems, urinary problems, musculoskeletal problems, skin problems.

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Section VI: Mental Health Status

This section assesses the mental health status of the workers, which is based on “THE WORKPLACE STRESS SCALE”, of The Marlin Company, North Haven, CT and the American Institute of stress, Yonkers, NY.

QUESTIONS

Never Rarely Sometimes Often Very Often

1. Conditions at work are unpleasant or sometimes even unsafe.

2. I feel that my job is negatively affecting my physical or emotional well being

3. I have too much work to do and/or too many unreasonable deadliness

4.I find it difficult to express my opinions or feelings about my job conditions to my superiors.

5. I feel that job pressures interfere with my family or personal life.

6. I have adequate control or input over my work duties.

7.I receive appropriate recognition or rewards for good performance.

8.I am able to utilize my skills and talents to the fullest extent at work.

Interpretation:

 Total score of 15or less - chilled out or relativelycalm

 Total score of 16to 20 - Fairlylow

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 Total score of 21to 25 - Moderatestress

 Total score of 26to 30 -Severe

 Total score of 31to 40 - Stress level is potentiallyhigh Section VI: Job Satisfaction Assessment

This section consists of questions to assess the satisfactory level of their job which is based on The Generic Job Satisfaction scale37.

Interpretation:

Total score of 42to 50 - Very high Total score of 39 to 41 - High Total score of 32 to 38 - Average Total score of 27 to 31 - Low Total score of 10 to 20 - Very low Informed Consent:

The nature of the study was explained to the workers and the consent, which was prepared in the local language, was obtained from each participant prior to the interview session.

Ethical approval:

The proposal of the study was presented and was approved by the Institutional Ethics Committee prior to the pretesting. The approval letter is enclosed in [Annexure - 1].

Data collection period:

Data was collected from the study participants for a period of 3 months from 2nd December 2018 to 28th February 2019.

Data collection method:

The data was collected by interviewing the workers as per our inclusion criteria using the proforma in the respective factories. The questionnaire was prepared in English and orally translated to local language (Tamil) while conducting the interview. The interview was conducted by the investigator himself and their responses were recorded in the questionnaire [Annexure III]

Operational definitions:

Age: Age was recorded to the nearest completed year as per information provided by the study subject.

1. Religion: The subject’s religion was noted and was grouped as “Hindu”,

“Muslim”, “Christian”

2. Education Classification:³³

Education was classified based on modified kuppusamy classification 2017 as illiterate, primary school, middle school, high school, high secondary school, graduate, postgraduate and professional.

3. FamilyType:³³

Family type was divided into 3 categories as nuclear family, joint family and three generation family.

4. Socioeconomic status:³⁴

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Socioeconomic status was classified based on Modified BG Prasad’s classification 2017 as upper, upper middle, middle, lower middle and lower.

5. Migrant:³⁵

Persons who are outside the territory of the state of which they are nationals or citizens are not subject to its legal protection and are in the territory of another state.

6. Occupationalhazard:³⁶

The potential risks to life or functioning of an individual that is inherently associated with his occupation or work environment. Some of these hazards resulted in contraction of a disease or the loss of functionality or death.

3. Statistical analysis:

The statistical analysis of data was done using descriptive and analytical statistics. The descriptive statistics analyzed were presented in the form of frequency distribution and percentage. The analytical statistics used were chi- square. The association of occupational hazards with work profile of the workers in small scale industries was assessed. P value < 0.05 was considered to be statistically significant. Data was entered in Microsoft Excel and analyzed using the software SPAA, version 22.

TABLE 1: Distribution Of Sociodemographic Profile Of The Study Population S

NO

VARIABLES FREQUENCY

(N=300)

PERCENTAGE 1. AGE CATEGORY 15 - 24 Yrs 46 15.3%

25 - 34 Yrs 122 40.7%

35 - 44 Yrs 92 30.7%

45 - 54 Yrs 35 11.7%

>= 55 Yrs 5 1.7%

2. GENDER MALE 169 55.7%

FEMALE 133 44.3%

3. RELIGION HINDU 283 94.3%

CHRISTIAN 11 3.6%

MUSLIM 6 2%

4. EDUCATION ILLITERATE 29 9.7%

PRIMARY 14 4.7%

MIDDLE 45 15%

HIGH 87 29%

HIGHER SECONDARY

53 17.7%

GRADUATE 72 24%

5. SOCIOECONOMIC STATUS

CLASS I 28 9.3%

CLASS II 107 35.7%

CLASS III 123 41%

CLASS IV 38 12.7%

CLASS V 4 1.3%

6. TYPE OF FAMILY NUCLEAR 251 83.7%

JOINT 21 7%

THREE 28 9.3%

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GENERATION

7. MARITAL STATUS MARRIED 213 71%

UNMARRIED 80 26.7%

DIVORCED 1 0.3%

WIDOWER 6 2%

The Table 1 shows the Distribution of Sociodemographic profile of the study participants. 40.7%

(n=122) ,30.7% (n=92), were under the age group of 25-34yrs and 35-44yrs respectively. The mean age was 29.8. Around 55.7% (n=167) of the study participants were male and the remaining 44.3% (n=133) were females. Regarding the educational qualification, 29% (n=87) had a high school education, 24%(n=72) with graduate degree and around 9.7%(n=29) were illiterates. 83.7%(n=251) belonged to Nuclear family and 9.3%(n=28) to three generation family.

Among the Study participants, 71% (n=213) were married, 26.7%(n=80) were unmarried and 2%(n=6) were widower.

TABLE 2: Distribution Of Work Profile Of The Study Participants

WORK PROFILE FREQUENCY( N=300) PERCENTAGE

PLACE OF

OCCUPATION

PRODUCTION 212 70.7%

TECHNICAL 70 23.3%

MANAGEMENT 18 6%

NATURE OF WORK

MECHANICAL 103 34.3%

MANUAL 197 65.7%

TYPE OF

WORK

SKILLED 84 28%

SEMI-SKILLED 120 40%

UNSKILLED 96 32%

WORK SHIFT DAY 64 21.3%

NIGHT 3 1%

GENERAL 233 77.7%

WORK

EXPERIENCE

<5 YRS 139 46.3%

5 - 10 YRS 103 34.3%

>10 YRS 58 19.3%

As shown in Table 2, 70.7%(n=212) were in the Production department and the remaining 23.3%

(n=70) and 6% (n=18) were in the Technical and Management departments. 34.3% (n=103) of

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Distribution of Pre-employment medical screening among the study participants

134

166

yes no

the Participants did mechanical work and the 65.7%(n=197) did manual type of work. Around 40% (n=120) were semi-skilled and 32% (n=96) were unskilled and 28% (n=84) were skilled.

Nearly 77.7% (n=233) of the participants belonged to the General shift. 46.3%(n=139) had the work experience for less than 5 yrs, 34.3%(n=103) had an experience for 5-10 yrs and 19.3%

(n=58) for more than 10 yrs.

FIG 1: Distribution Of Pre-Employment Medical Screening Among The Study Participants.

From the Pie diagram, it is observed that 55.3%(n=166) had Pre- employment medical screening and the remaining 44.7%(n=134) had not undergone any medical screening

FIGURE 2: Distribution Of Periodic Medical Check Up Among The Study Participants

Among the study participants the above bar diagram shows that nearly 59.7% (n=179) had undergone periodic medical checkup and 40.3% (n=121) did not have any periodic checkup.

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TABLE 3: Distribution Of Nicotine Dependence Among The Study Participants

NICOTINE DEPENDENCE MEN ( N=167) WOMEN (N=133)

Frequency Percentage Frequency Percentage

Yes Low 0 0 0 0

Low -

Moderate

2 1.197% 1 0.75%

Moderate 69 41.31% 4 3%

High 10 5.98% 0 0

No 86 51.49% 128 96.24%

The table is showing the distribution of Nicotine dependence which was assessed using the Fagerstrom scale40. 41.31% (n=69) had moderate dependence for Nicotine among the Males and 3% (n=4) in females. 5.98% (n=10) of male Participants reported HIgh Nicotine Dependence.

TABLE 4: Distribution Of Alcohol Dependence Among The Study Participants

ALCOHOL DEPENDENCE MEN ( N=167) WOMEN (N=133)

Frequency Percentage Frequency Percentage

Yes Not a

Hazardous drinker

67 40.11% 0 0

Hazardous Drinker

24 14.37% 3 2.25%

No 76 45.50% 130 97.7%

The table 4 shows the Distribution of Alcohol Dependence and 14.37% (n=24) among the males reported being a hazardous drinker which was assessed using the AUDIT-C questionnaire41.

TABLE 5: Distribution Of Workplace Hazards Among The Study Participants

WORKPLACE MEN ( N=167) WOMEN (N=133)

Frequency Percentage Frequency Percentage Exposed

124 74.25% 102 76.69%

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Not Exposed 43 25.74% 31 23.30%

The above table is showing the Distribution of Workplace hazards among the study participants.

Nearly 74.25% (n=124) in Males and 76.69% (n=102) were exposed to the risk of Occupational hazards and the remaining 25.74% (n=43) in Males and 23.30% (n=31) in Females were not exposed.

FIG 3: Distribution Of Ocular Complaints Among The Study Population

The distribution of Ocular complaints among the Participants is shown in the bar diagram.15.3%(n=46), 6.3% (n=19) and 4.7%(n=14) had reported with the complaints of headache, itching & watering of eyes and blurred vision respectively.

FIG 4: Distribution Of Oral Cavity Complaints Among The Study Population

The Pie diagram is showing the distribution of the Oral complaints and only 2% (n=6) of the participants had reported with toothache and gum bleeding each.

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FIG 5: Distribution Of Ent Complaints Among The Study Population

Among the participants, the distribution of ENT complaints had reported with complaints of 3.7% (n=11) with defective hearing and 1% (n=3) with ear ache.

FIG 6: Distribution Of Cardiovascular Complaints Among The Study Population

Figure 6 shows the distribution of Cardiovascular complaints among the study participants and it is found to be, that 4.3% (n=13) had the complaints of chest discomfort, 0.7% (n=2) had reported of Breathlessness.

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Distribution of Respiratory complaints among the study

2.70% population

6% 2%

Normal Cough

89.30%

Breathing Difficulty FIG 7: Distribution Of Respiratory Complaints Among The Study Population

From the pie diagram 6% (n=18), 2.7% (n=8), 2% (n=6) of the study participants had the complaint of cough, breathing difficulty and wheezing respectively.

FIG 8: Distribution Of Gastrointestinal Tract Complaints Among The Study Population

From the above bar diagram it is evident that 4.7% (n=14) reported with the complaints of gastritis, 2% reported with constipation and 1.3% (n=4) with the complaints of abdominal pain.

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Distribution of Urinary complaints among theStudy Population

4

Normal

Burning Micturition 96

Distribution of Nervous complaints among the study Population

95.30%

2.30% 1% 1.30%

Normal Giddiness Burning sensation in limbs Numbness

FIG 9: Distribution Of Urinary Complaints Among The Study Population

Among the participants the Pie diagram is showing the distribution of urinary complaints where 4% (n=12) had reported with burning micturition.

FIG 10: Distribution Of Nervous Complaints Among The Study Population

The bar diagram is showing the distribution of nervous complaints among the study population where 2.3% (n=7) had reported with giddiness.

TABLE 6: Distribution Of Musculoskeletal Complaints Among The Study Population

COMPLAINTS FREQUENCY ( N = 300) PERCENTAGE

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NORMAL 185 61.7%

LOW BACK ACHE 64 21.3%

JOINT PAIN 29 9.7%

NECK PAIN 22 7.3%

The distribution of musculoskeletal complaints among the study population reported, 21.3%

(n=64), 9.7% (n=29), 7.3% (n=22) with low back pain, joint pain and Neck pain respectively.

TABLE 7: Distribution Of Dermatology Complaints Among The Study Population

COMPLAINTS FREQUENCY ( N = 300) PERCENTAGE

NORMAL 266 88.7%

ITCHING 21 7%

PIGMENTED PATCHES 10 3.3%

ULCERS 3 1%

Table 7 is showing the distribution of dermatologic complaints .7% (n=21) had the complaints of itching, 3.3% (n=10) had pigmented patches and 1% (n=3) with ulcers.

TABLE 8: Distribution Of Work Stress Scale Among The Study Population

CATEGORY FREQUENCY ( N = 300) PERCENTAGE

CHILLED OUT/ CALM 126 42%

FAIRLY LOW 119 39.7%

MODERATE STRESS 34 11.35%

SEVERE STRESS 16 5.3%

POTENTIALY HIGH

STRESS

5 1.7%

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In the distribution of work stress among the study population 39.7% (n=119) reported with fairly low stress, 11.35% (n=34) with moderate stress and 5.3% (n=16) with severe stress among the study participants.

TABLE 9: Distribution Of Job Satisfaction Scale Among The Study Population

SATISFACTION LEVEL FREQUENCY ( N

= 300)

PERCENTAGE

VERY HIGH 267 89%

HIGH 28 9.3%

AVERAGE 5 1.7%

LOW - -

VERY LOW - -

As shown in the table among the study participants, 89% (n=267) reported with very high satisfaction regarding their occupation, 9.3% (n= 28) reported with high and 1.7%(n=5) with average satisfaction.

TABLE 10: Association Between Sociodemographic Profile And Workplace Hazards

S NO VARIABLES OCCUPATIONAL

HAZARDS X2 P VALUE

EXPOSED NOT EXPOSED 1. AGE

CATEGORY

15 - 24 Yrs 36(12%) 10(3.33%)

9.401 0.052 25 - 34 Yrs 85(28.33%) 37(12.33%)

35 - 44 Yrs 72(24%) 20(6.66%) 45 - 54 Yrs 31(10.3%) 4(1.33%)

>= 55 Yrs 2(0.66%) 3(1%)

2. GENDER MALE 124(41.3%) 43(14.33%)

0.237 0.626 FEMALE 102(34%) 31(10.33%)

3. RELIGION HINDU 214(71.3%) 69(23%)

2.410 0.661 CHRISTIAN 8(2.66%) 3(1%)

MUSLIM 4(1.33%) 2(0.66%)

4. EDUCATION ILLITERATE 25(8.33%) 4(1.33%)

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PRIMARY 12(4%) 2(0.66%)

15.66 0.008*

MIDDLE 35(11.66%) 10(3.33%) HIGH 70(23.33%) 17(5.66%) HIGHER

SECONDARY

42(14%) 11(3.66%) GRADUATE 42(14%) 30(10%) 5. SOCIO

ECONOMIC STATUS

CLASS I 21(7%) 7(2.33%)

1.868 0.760 CLASS II 82(27.33%) 25(8.33%)

CLASS III 91(30.33%) 32(10.66%) CLASS IV 30(10%) 8(2.66%) CLASS V 2(0.66%) 2(0.66%)

6. TYPE OF

FAMILY

NUCLEAR 188(62.6%) 63(21%)

3.459 0.177

JOINT 19(6.3%) 2(0.66%)

THREE

GENERATION

19(6.3%) 9(3%)

7. MARITAL STATUS

MARRIED 159(53%) 54(18%)

2.956 0.398 UNMARRIED 63(21%) 17(5.66%)

DIVORCED 1(0.33%) 0

WIDOWER 3(1%) 3(1%)

The Table 10 shows the association of the sociodemographic profile with occupational hazards.

Regarding the Educational qualification, 23.33%(n=70) were exposed to occupational hazards who had high school education, 11.66% exposed to hazards had completed middle school education. The table shows the association of education with the occupational hazards (p= 0.008, X2 = 15.667) and is statistically significant. There has been no association of occupational hazards with the other sociodemographic variables.

TABLE 11: Association Between The Work Profile And Alcohol Dependence

WORK PROFILE ALCOHOL

DEPENDENCE

CHI SQUARE

P VALUE

YES NO

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PLACE OF OCCUPATION

PRODUCTION 18(6%) 194(64.6%)

28.288 0.029*

TECHNICAL 5(1.66%) 65(21.66%) MANAGEMENT 2(0.66%) 16(5.33%)

NATURE OF

WORK

MECHANICAL 3(1%) 100(33.3%)

19.933 0.011*

MANUAL 22(7.3%) 175(58.3%)

TYPE OF

WORK

SKILLED 5(1.66%) 79(26.33%)

16.641 0.409 SEMI-SKILLED 13(4.3%) 107(35.6%)

UNSKILLED 7(2.33%) 89(29.66)

WORK SHIFT

DAY 6(2%) 58(19.33%)

28.31 0.029*

NIGHT 1(0.33%) 2(0.66%) GENERAL 18(6%) 215(71.6%)

WORK

EXPERIENCE

<5 YRS 13(4.3%) 126(42%)

50.144 0.000*

5 - 10 YRS 6(2%) 97(32.33%)

>10 YRS 6(2%) 52(17.33%)

* P< 0.05, Statistically significant at 95% CI (confidence interval)

The Table 11, is showing the association of work profile and its dependence on alcohol. The place of occupation has shown a association with p value of 0.029

(X2 = 28.288) and is statistically significant. Similarly, the nature of work, work shift and working experience has also been statistically significant with a p value < 0.05

TABLE 12: Association Between The Work Profile And Nicotine Dependence

WORK PROFILE NICOTINE DEPENDENCE CHI

SQUARE

P VALUE

LOW LOW-

MOD

MOD HIGH

PLACE OF

PRODUCTION 154

(51.3%) 2 (0.6%)

49 (16.3%)

7

(2.33%) 25.99 0.026*

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OCCUPATION TECHNICAL 42

(14%)

0 26

(8.66%) 2 (0.66%)

MANAGEMENT 12

(4%)

2 (0.6%)

4 (1.33%)

0

NATURE OF

WORK

MECHANICAL 64

(21.3%) 1 (0.3%)

34 (11.3%)

4

(1.33%) 13.06 0.071

MANUAL 150

(50%) 2 (0.6%)

39 (13%)

6 (2%) TYPE

OF WORK

SKILLED 46

(15.3%) 1 (0.3%)

35 (11.6%)

2

(0.66%) 27.47 0.017*

SEMI SKILLED 87 (29%)

2 (0.6%)

25 (8.33%)

6 (2%)

UNSKILLED 81

(27%)

0 13

(4.33%) 2 (0.66%) WORK SHIFT

DAY 57

(19%)

0 5

(1.66%) 2

(0.66%) 30.49 0.007*

NIGHT 2

(0.66%)

0 1

(0.33%) 0

GENERAL 155

(51.6%) 3 (1%)

67 (22.3%)

8 (2.66%) WORK

EXPERIENCE

<5 YRS 96

(32%) 2 (0.6%)

35 (11.6%)

6

(2%) 7.986 0.890

5 - 10 YRS 76

(25.33

%)

0 25

(8.33%) 2 (0.66%)

>10 YRS 42

(14%) 1 (0.3%)

13 (4.33%)

2 (0.66%)

From the study population it is observed that there has been a association of work profile and their dependence on nicotine with a p value < 0.05 and is found statistically significant.

TABLE 13: Association Between The Work Profile And The Work Stress.

WORK PROFILE

WORK STRESS SCALE

CHI SQUA RE

P

VALU E

CALM LOW MOD SEVER E HIGH

PLACE OF OCCUPATION

PRODUCTION 97 (32.3%)

80 (6.66%)

24 (8%)

4 (1.3%)

7 (2.3%)

14.94 0.060 TECHNICAL

24 (8%)

29 (9.66%)

7 (2.3%)

1 (0.3%)

9 (3%)

5 10 3 0 0

(21)

MANAGEMENT (1.66%) (3.33%) (1%)

NATURE OF

WORK

MECHANICAL 52 (17.3%)

36 (12%)

10 (3.3%)

0 5

(1.6%)

6.615 0.158

MANUAL 74

(24.6%) 83 (27.6%)

24 (8%)

5 (1.6%)

11 (3.6%) TYPE

OF WORK

SKILLED

43 (14.3%)

24 (8%)

12 (4%)

0 5

(1.6%)

25.32 0.001*

SEMI SKILLED 57 (19%)

44 (14.6%)

7 (2.3%)

3 (1%)

9 (3%)

UNSKILLED

26 (8.66%)

51 (17%)

15 (5%)

2 (0.6%)

2 (0.6%) WORK SHIFT

DAY 19

(6.33%) 31 (10.3%)

9 (3%)

0 5

(1.6%)

17.14 0.029*

NIGHT 1

(0.33%)

0 2

(0.6%)

0 0

GENERAL 106

(35.3%) 86 (28.6%)

25 (8.3%)

5 (1.6%)

11 (3.6%) WORK

EXPERIENCE

<5 YRS 54 (18%)

60 (20%)

15 (5%)

3 (1%)

7 (2.3%)

3.997 0.857 5 - 10 YRS 44

(14.6%) 37 (12.3%)

13 (4.3%)

2 (0.6%)

7 (2.3%)

>10 YRS 28 (9.33%)

22 (7.33%)

6 (2%)

0 2

(0.6%)

It is found that the type of work has influenced stress among the study participants with a p value 0.001 (X2 = 25.329). The work shift has also shown statistically significant results and thus has proved its association with stress related to the job.

TABLE 14: Association Between The Work Profile And The Job Satisfaction Scale

WORK PROFILE JOB SATISFACTION SCALE CHI

SQUARE

P VALUE VERY

HIGH

HIGH AVERAGE

PLACE OF

PRODUCTION 196 (65.33%)

15 (5%)

1 (0.33%)

(22)

OCCUPATION TECHNICAL 56 (18.66%)

11 (3.66%)

3 (1%)

11.589 0.021*

MANAGEMENT 15 (5%)

2 (0.66%)

1 (0.33%)

NATURE OF

WORK

MECHANICAL 90 (30%)

11 (3.66%)

2

(0.66%) 0.422 0.810

MANUAL 177

(59%)

17 (5.66%)

3 (1%)

TYPE OF

WORK

SKILLED 74

(27%) 8 (2.66%)

2 (0.66%)

2.635 0.621 SEMI-SKILLED 105

(35%)

12 (4%)

3 (1%) UNSKILLED 88

(29.33%) 8 (2.66%)

0

WORK SHIFT

DAY 56

(18.66%) 6 (2%)

2 (0.66%)

20.260 0.000*

NIGHT 2

(0.66%)

0 1

(0.33%)

GENERAL 209

(69.66%) 22 (7.33%)

2 (0.66%) WORK

EXPERIENCE

<5 YRS 122 (40.66%)

15 (5%)

2 (0.66%)

7.208 0.125 5 - 10 YRS 88

(29.33%) 12 (4%)

3 (1%)

>10 YRS 58 (19.33%)

0 0

From the above table it is observed that the work profile of the study participants has a strong association with their job satisfaction with a p value < 0.05 and hence being statistically significant.

TABLE 15: Association Between Alcohol Dependence And Work Stress Among The Study Participants.

ALCOHOL DEPENDENCE

WORK STRESS SCALE

CHI SQUAR E

P VALUE

YES

6 (2%)

108 (36%)

30 (10%)

2 (0.66%)

15 (5%)

57.285 0.004*

NO

120 (40%)

11 (3.66%)

4 (1.3%)

3 (1%)

1 (0.33%)

(23)

The table 15 is showing that behavior of alcohol dependence among the study participants has been strongly associated with stress in their workplace with a p value of 0.004 (X2 = 57.285).

TABLE 16: Association Between The Alcohol Dependence And Job Satisfaction Scale.

ALCOHOL DEPENDENCE

JOB SATISFACTION SCALE CHI

SQUARE

P VALUE

VERY HIGH HIGH AVERAGE

YES

21 (7%)

3 (1%)

1 (0.33%)

14.191 0.585 NO

246 (82%)

25 (8.33%)

4 (1.33%)

From the above mentioned table alcohol dependence is found to have no association with their job satisfaction.

TABLE 17: Association Between The Nicotine Dependence And Work Stress NICOTINE

DEPENDENCE

WORK STRESS SCALE

CHI SQUA RE

P VALUE

LOW 87 86 23 5 13

(29%) (28.6%) (7.6%) (1.66%) (4.33%)

LOW - MOD 2 1 0 0 0

(0.6%) (0.33%) 21.711 0.794

MOD 32 31 8 0 2

(10.6% (10.3%) (2.6%) (0.66%) )

HIGH 5 1 3 0 1

(1.66) (0.33%) (1%) (0.33%)

Table 17 shows that there exists no association between nicotine dependence and the work stress among the participants.

(24)

TABLE 18: Association Between The Nicotine Dependence And Job Satisfaction Scale.

NICOTINE DEPENDENCE

JOB SATISFACTION SCALE CHI

SQUARE

P VALUE VERY

HIGH

HIGH AVERAGE

LOW 191

(63.66%) 21 (7%)

2 (0.66%)

9.035 0.829 LOW - MOD 3

(1%)

0 0

MOD 64

(21.33%) 6 (2%)

3 (1%)

HIGH 9

(3%)

1 (0.33%)

0

There is no association between nicotine dependence with the job satisfaction among the study participants in their work place.

TABLE 19: Association Between Occupational Hazards And Dependence On Alcohol.

ALCOHOL DEPENDENCE

OCCUPATIONAL HAZARDS

CHI SQUARE

P VALUE

EXPOSED NOT EXPOSED

YES

23(7.66%) 72(24%)

5.368 0.718 NO

203(67.66%) 2(0.66%)

From the above table it is found that there has been no association between occupational hazards with alcohol dependence behavior of the participants.

TABLE 20: Association Between Occupational Hazards And Dependence On Nicotine.

NICOTINE DEPENDENCE

OCCUPATIONAL HAZARDS

CHI SQUARE

P VALUE

EXPOSED NOT EXPOSED

LOW 161(53.66%) 53(17.66%)

10.796 0.148 LOW-

MODERATE

1(0.33%) 2(0.66%)

MODERATE 58(19.33%) 15(5%)

HIGH 6(2%) 4(1.33%)

(25)

The table is showing the association between the occupational hazards and the dependence of study participants in alcohol intake and has been found to be not statistically significant and hence not associated.

TABLE 21: Asssociation Between Work Profile And Ocular Complaints Among The Study Participants.

WORK PROFILE

OCULAR COMPLAINTS

CHI SQUAR E

P

VALUE

YES NO

PLACE OF

OCCUPATION

PRODUCTION 57(19%) 155(51.66%)

7.013 0.320 TECHNICAL 15(5%) 55(18.33%)

MANAGEMEN T 7(2.33%) 11(3.66%)

NATURE OF

WORK

MECHANICAL 19(6.33%) 84(28%)

5.116 0.164

MANUAL 60(20%) 137(45.66%)

TYPE OF WORK

SKILLED 17(5.66%) 67(22.33%)

6.203 0.401 SEMI-SKILLE

D

6(2%) 84(28%) UNSKILLED 36(12%) 70(23.33%) WORK SHIFT

DAY 17(5.66%) 47(15.66%)

25.045 0.000*

NIGHT 2(0.66%) 1(0.33%)

GENERAL 60(20%) 173(57.66%) WORK

EXPERIENCE

<5 YRS 33(11%) 106(35.33%)

6.907 0.329 5 - 10 YRS 27(9%) 76(25.33%)

>10 YRS 19(6.33%) 39(13%) TECHNICAL

QUALIFICATION

YES 14(4.66%) 61(20.33%)

5.601 0.133

NO 65(21.66%) 160(53.33%)

Among the study participants it is observed that the work shift has strong association with their ocular complaints with p value of 0.000 (X2 = 25.045). No statistical significance is found with other domains of work profile of the study participants.

(26)

TABLE 22: Asssociation Between Work Profile And Ent Complaints Among The Study Population.

WORK PROFILE

ENT

COMPLAINTS CHI

SQUAR E P

VALUE YES

NO

PLACE OF

OCCUPATION

PRODUCTION 9(3%) 203(67.66%)

8.762 0.187 TECHNICAL 6(2%) 64(21.33%)

MANAGEMENT 1(0.33%) 17(5.66%)

NATURE OF

WORK

MECHANICAL 4(1.33%) 99(33%)

0.362 0.948

MANUAL 12(4%) 185(61.66%)

TYPE OF WORZ

SKILLED 8(2.66%) 76(25.33%)

13.551 0.035*

SEMI-SKILLED 4(1.33%) 116(38.66%) UNSKILLED 4(1.33%) 92(30.66%) WORK SHIFT

DAY 3(1%) 61(20.33%)

9.517 0.147 NIGHT 1(0.33%) 2(0.66%)

GENERAL 12(4%) 221(73.66%)

WORK

EXPERIENCE

<5 YRS 9(3%) 130(43.33%)

6.221 0.339 5 - 10 YRS 6(2%) 97(32.33%)

>10 YRS 1(0.66%) 57(19%) TECHNICAL

QUALIFICATION

YES 8(2.66%) 67(22.33%)

10.725 0.013*

NO 8(2.66%) 217(72.33%)

The type of work and the technical qualification of the workers has shown an association with their ENT complaints with p value <0.05 and hence been statistically significant.

TABLE 23: Asssociation Between Work Profile And Cardiovascular Complaints Among The Study Population

WORK PROFILE

CARDIOVASCULAR

COMPLAINTS CHI P

(27)

YES NO SQUA

RE

VALUE

PLACE OF

OCCUPATION

PRODUCTION 11(3.66%) 201(67%)

5.696 0.458 TECHNICAL 7(2.33%) 63(21%)

MANAGEMENT 2(0.66%) 16(5.33%)

NATURE OF

WORK

MECHANICA L 7(2.33%) 96(32%)

0.362 0.948 MANUAL 13(4.33%) 184(61.33%)

TYPE OF

WORK

SKILLED 9(3%) 75(25%)

11.864 0.065 SEMI-SKILLE D 7(2.33%) 113(37.66%)

UNSKILLED 4(1.33%) 92(30.66%) WORK SHIFT

DAY 4(1.33%) 60(20%)

2.086 0.912

NIGHT 0 3(1%)

GENERAL 16(5.33%) 217(72.33%) WORK

EXPERIENCE

<5 YRS 5(1.66%) 134(41.33%)

8.994 0.174 5 - 10 YRS 9(3%) 94(31.33%)

>10 YRS 6(2%) 52(17.33%) TECHNICAL

QUALIFICATI ON

YES 8(2.66%) 67(22.33%)

10.941 0.012*

NO 12(4%) 213(71%)

The association of cardiovascular complaints of the study participants with their work profile has shown that technical qualification of the workers has an association with p value 0.012 (X2 = 10.941) and hence proven to be statistically significant.

TABLE 24: Asssociation Between Work Profile And Respiratory Complaints Among The Study Population

WORK PROFILE

RESPIRATORY

COMPLAINTS CHI

SQUARE

P VALUE

YES NO

(28)

PLACE OF

OCCUPATION

PRODUCTION 18(6%) 194(64.6%)

12.754 0.047*

TECHNICAL 13(4.33%) 57(19%) MANAGEMENT 1(0.66%) 17(5.66%)

NATURE OF

WORK

MECHANICAL 12(4%) 91(30.3%)

1.884 0.597

MANUAL 20(6.66%) 177(59%)

TYPE OF WORK

SKILLED 13(4.33%) 71(23.66%)

8.238 0.221 SEMI-SKILLED 10(3.33%) 110(36.6%)

UNSKILLED 9(3%) 87(29%)

WORK SHIFT

DAY 0 62(20.66%)

18.431 0.005*

NIGHT 1(0.66%) 2(0.66%)

GENERAL 29(9.66%) 204(68%)

WORK

EXPERIENCE

<5 YRS 20(6.66%) 119(39.6%)

6.310 0.389 5 - 10 YRS 10(3.33%) 93(31%)

>10 YRS 2(0.66%) 56(18.66%) TECHNICAL

QUALIFICATION

YES 14(4.66%) 61(20.33%)

7.235 0.065

NO 18(6%) 207(69%)

The table is showing no association between the place of occupation and their work shift with p value >0.05 and hence not found to be statistically significant.

TABLE 25: Asssociation Between Work Profile And Gastrointestinal Complaints Among The Study Population

WORK PROFILE

GASTROINTESTINAL

COMPLAINTS CHI

SQUARE

P VALUE YES

NO PRODUCTION 11(3.6%) 201(67%)

(29)

PLACE

OF OCCUPATION

TECHNICAL 12(4%) 58(19.33%) 14.511 0.024*

MANAGEMENT 1(0.66%) 17(5.66%)

NATURE OF

WORK

MECHANICAL 8(2.66%) 95(31.66%0

4.447 0.217 MANUAL 16(5.3%) 181(60.33%)

TYPE OF WORK

SKILLED 10(3.3%) 74(24.66%)

8.202 0.224 SEMI-SKILLED 12(4%) 108(36%)

UNSKILLED 2(0.66) 94(31.33%)

WORK SHIFT

DAY 1(0.33%) 63(21%)

5.151 0.525

NIGHT 0 3(1%)

GENERAL 23(7.6%) 210(70%)

WORK

EXPERIENCE

<5 YRS 18(6%) 121(40.33%)

11.448 0.075 5 - 10 YRS 5(1.66%) 98(32.66%)

>10 YRS 1(0.33%) 57(19%) TECHNICAL

QUALIFICATION

YES 9(3%) 66(22%)

3.602 0.308

NO 15(5%) 210(70%)

It is observed from the table that there has been an association between the place of occupation and the gastrointestinal complaints with p value 0.024 (X2 = 14.511).

TABLE 26: Asssociation Between Work Profile And Nervous Complaints Among The Study Population

WORK PROFILE

NERVOUS

COMPLAINTS CHI

SQUARE

P VALUE

YES NO

PLACE OF

OCCUPATION

PRODUCTION 7(2.33%) 205(68.3%)

8.056 0.234 TECHNICAL 6(2%) 64(21.3%)

(30)

MANAGEMENT 1(0.66%) 17(5.66%)

NATURE OF

WORK

MECHANICAL 4(1.33%) 99(33%)

5.761 0.124 MANUAL 10(3.33%) 187(62.3%)

TYPE OF WORK

SKILLED 10(3.33%) 74(24.66%)

18.733 0.005*

SEMI-SKILLED 1(0.33%) 119(39.6%)

UNSKILLED 3(1%) 93(31%)

WORK SHIFT

DAY 1(0.33%) 63(21%)

26.664 0.000*

NIGHT 1(0.33%) 2(0.66%)

GENERAL 12(4%) 221(73.6%)

WORK

EXPERIENCE

<5 YRS 7(2.33%) 132(44%)

11.068 0.086 5 - 10 YRS 6(2%) 97(32.33%)

>10 YRS 1(0.33%) 57(19%) TECHNICAL

QUALIFICATION

YES 8(2.66%) 67(22.33%)

26.664 0.000*

NO 6(2%) 219(73%)

The table is showing strong association between the work profile and nervous complaints with p value < 0.05 and thus found to be statistically significant.

TABLE 27: Asssociation Between Work Profile And Musculoskeletal Complaints Among The Study Population

WORK PROFILE

MUSCULOSKELETAL

COMPLAINTS CHI

SQUARE

P VALUE YES

NO

PLACE OF OCCUPATION

PRODUCTION 79(26.3%) 133(44.3%)

8.330 0.215 TECHNICAL 26(8.66%) 44(14.66%)

(31)

MANAGEMENT 10(3.33%) 8(2.66%)

NATURE OF

WORK

MECHANICAL 27(9%) 76(25.33%)

9.966 0.019*

MANUAL 88(29.3%) 109(36.3%) TYPE OF WORK

SKILLED 26(8.66%) 58(19.33%)

9.920 0.218 SEMI-SKILLED 42(14%) 78(26%)

DAY 29(9.66%) 35(11.66%)

49.642 0.000*

NIGHT 3(1%) 0

GENERAL 83(27.6%) 150(50%) WORK

EXPERIENCE

<5 YRS 51(17%) 88(29.33%)

19.914 0.003 5 - 10 YRS 52(17.3%) 51(17%)

>10 YRS 12(4%) 46(15.33%) TECHNICAL

QUALIFICATION

YES 21(7%) 54(18%)

4.807 0.187

NO 94(31.3%) 131(43.6%)

The study participants have shown strong association between type of work and the musculoskeletal disorders with p value 0.019 (X2 = 9.966). Work shift has also been association with musculoskeletal disorders with p value 0.000.

TABLE 28: Asssociation Between Work Profile And Dermatology Complaints Among The Study Population

WORK PROFILE

DERMATOLOGY

COMPLAINTS CHI

SQUARE

P VALUE

YES NO

PLACE OF

OCCUPATION

PRODUCTION 24(8%) 188(62.6%)

3.949 0.684 TECHNICAL 10(3.33%) 60(20%)

MANAGEMENT 0 18(6%)

NATURE OF

WORK

MECHANICAL 11(3.66%) 92(30.66%)

2.129 0.546

MANUAL 23(7.66%) 174(58%)

TYPE OF WORK

SKILLED 9(3%) 75(25.66%)

4.829 0.566 SEMI-SKILLED 15(5%) 105(35%)

(32)

DAY 8(2.66%) 56(18.66%)

33.037 0.000*

NIGHT 1(0.66%) 2(0.66%)

GENERAL 25(8.33%) 208(69.3%) WORK

EXPERIENCE

<5 YRS 17(5.66%) 122(40.6%)

1.953 0.924 5 - 10 YRS 13(4.33%) 90(30%)

>10 YRS 4(1.33%) 54(18%) TECHNICAL

QUALIFICATION

YES 9(3%) 66(22%)

6.907 0.329

NO 25(8.33%) 200(66.6%)

From the above table it is evident that there has been association between dermatologic complaints and work profile with p value of 0.000.

4. Discussion

In this following study, socio demographic details, work profile and its association with occupational hazards, work stress and job satisfaction has been discussed in comparison with other studies conducted elsewhere. In the study, majority of the study participants, 40.7%

(n=122),30.7% (n=92), 15.3% (n=46) were under the age group of 25-34 yrs, 35-44 yrs, 15-24 yrs respectively. The overall mean age was 29.8, ranging between 25-34 yrs. In a study conducted by Tadesse et al 9, 60% of the respondents were young, belonging to the age group of 14-29 yrs. Another study done by Saha et al14, 80.36% were under the age group of 15-45 yrs.

Joshi et al 16, in their study observed 55% of the respondents in the age group 10-15 yrs and the mean age being 28.68 yrs, which is in close range with our study. The reason for this is, majority of the enterprises prefer younger age group, which would aid in quality working thus leading to increased production in their factories.

It is observed in the study that, nearly 55.7% (n=169) were males and 44.3% (n=133) were females. Joshi et al16 in their study, reported 84.7% as males and the remaining 15.3% as females, which is in concordance with a study done by Nakata et al22, where the males comprised of 70.33% and the females 29.66%. This is because, the work being carried out in factories are so tedious and hence men were usually preferred by the factories. Among the study participants, 29% had a high school educational qualification, 24% were graduates, 17.7% had completed their higher secondary school education, 15% middle school education and 9.7% were illiterates. In a study, done by Nakata et al22, 48.16% had completed their high school education, 28.73% were graduates. Amaravathi et al30, study observed that 39.3% were qualified with high school, 1.4% were graduates, 9.3% with higher secondary school education and 4.3% were illiterates.

In the study 70.7% were involved in the production, 23.3% in the technical wing. Around 34.3%

dealt with mechanical works and 65.7% had manual type of work. 52% of the workers were

(33)

unskilled. 77.7% of the respondents had a general shift in their factories and 46.3% of the participants had less than 5 yrs of working experience, 34.3% with 5-10yrs. In a study done by Nakata et al²², it is observed that 54.3% involved in production, 4.5% in the technical side. 68.3%

had a working experience of more than 7 yrs. Amaravathi et al³⁰, observed that 37% had a work experience of 1-3 yrs and 21.4% of the workers had morning shift. There has been a strong association between the work profile and psychosocial hazards (p=0.001, X²=25.329). It is evident that there exists an association between the work profile and the ocular complaints with p value of 0.00. Majority of the workers had MSD and is in strong association with the work shift (p = 0.00, X2 = 49.642) and the nature of work (p= 0.019, X2 = 9.966).

5. Risk factors and health problems of workers in small scale factories.

In our present study, 74.25% of the males and 76.69% of the females were exposed to the risk of occupational hazards and 25.74% in males and 23.30% in females were not at the risk of exposure. Similarly, the prevalence of occupational injury in a study conducted by Nakata et al22, reported as 35.6% (male = 43% & females= 17.9%). The prevalence of alcohol dependence in our study was 14.37%. The study reported that around 41.31% of the study participants had moderate nicotine dependence and 5.98% had high dependence. Similarly, Bandyopadhyay10, study observed 38.4% of their study participants with tobacco and alcohol addiction. In addition to this, pyschosocial hazards are also in association with alcohol dependence (p value-0.004, X2=57.285). This is similar to the study conducted by Nakata et al22, where association is observed between smoking and hazards related to their workplace.

In the study, 11.35% of the study participants reported with moderate stress, 39.7% reported fairly low and 42% were calm. It has been evident that the job stress is strongly in association with the type of work and work shift. This is similar to the study conducted by Lai et al 12, where work load, good work relationships, poor communication are being strongly associated with job stress in the small scale enterprises. Our findings for the moderate stress level can be partly explained by the fact that small scale enterprises have a limited workforce and the tasks are incompatible.

In the ocular complaints observed in our study, 15.3% reported with headache, 6.3% with itching and watering of eyes, 4.7% with blurred vision. Similarly, in a study conducted by Bandyopadhyay10, reported with 15.7% of visual difficulties. The study 16 done by Joshi reported that 40.42% of the respondents had complaints of headache. Another study conducted by Chohan 15, reported with 32.5% of eye infections among the study participants. The study found out that the prevalence of oral cavity problems was 2%. In the study done by Kamble et al in Pune among workers in automobile industry; they reported 17. 9 % had oral cavity problems38.

Present study, among the workers participated, 5.3 % had ENT problems which was similar to findings in the study done by Kamble et al in Pune where 6.4 % had ENT disorder38.

The study reported that 6.7% had cardiovascular problems, where as in a study done by Vyas et al39 only 3% had cardio vascular problems. This is due to age difference in the study population and the risk factors such as smoking and nicotine dependence also leads to cardiovascular problems among the workers in this study.

Among the workers participated, 10.70% had respiratory problems. The other studies done by Shinde et al45 (2015), Vyas H et al 39, Philip et al 42, Selvithangaraj et al 43 and Kamble et al 38 showed a prevalence of respiratory problem as 22.2%, 20%, 17.9%, 11.3% and 1.1%

respectively. This is due to inhalation of the fumes which comes from the chemicals in their workplace, which causes the respiratory problems. In this study it has been found that there was a

(34)

significant association between working profile of the participants and respiratory problem.

Similar to this study association between exposure to chemicals and respiratory problem was documented in the study done by Philip et al42.

The study participants have shown the prevalence of gastro intestinal problem as8% in the present study. Similar findings were described in the study done by Shinde et al (2015) where the prevalence of gastro intestinal problem was 36. 2%45 . In the other studies done by Philip et al, the prevalence of gastro intestinal problem was 29 . 2%42. In the study done by Selvi Thangaraj et al the prevalence of gastro intestinal problem was 10%43. In this study the working profile of the participants had strong association with gastro intestinal problem.

The prevalence of nervous problem among the study participants in the study has been reported as 4.6%. And only 1.3% of the study participants had numbness problem. This was very less when compared to the study done by Philip et al42 and Shinde et al 45 where the prevalence is 46.2% and 58.5% respectively. The low prevalence in the present study is due to the fact that most of the workers were not exposed to heavy works.

Present study, prevalence of genito urinary problem among the study participants was 4% which is due to socio demographic risks factors such age, diet, and personal habits. A 10 years follow up study among automobile repair workers in The Netherlands, to assess cause specific mortality done by Eva S Hansen showed an increased mortality due to urinary tract cancer47.

6. Musculoskeletal

The reported prevalence of musculoskeletal problem in the present study was28.3%. Similar findings in the study done by Shinde et al 3 0 and Selvi thangaraj et al 3 3 where the prevalence of musculoskeletal problem is 54. 9% and 62% respectively. In the other studies done by Nasarudden et al, Akter et al and Philip et al 42 the prevalence has been reported as 87.4%, 77%, 44.3% respectively. The prevalence is comparatively low than the other studies as the duration of work differs and is in turn being influenced by the job nature. In this study the workers who were working with manual which has postural difficulties had 29.33% of musculoskeletal problem when compared to workers who were working with machinery tools (9%) and there was a statistically significant association between working manually which is a physical risk factor and musculoskeletal problem. Similar to this study statistically significant association between physical risk factor and musculoskeletal problem was found in the study done by Akter S 50.

Among the workers participated in the study 11.3% had skin problems. Similar findings were seen in the study done by Shinde et al 45. In the other studies done by Philip et al42 and Vyas et al 39, the prevalence of skin problem was 16. 1%, 8% respectively. This is because of handling the machines and exposure of skin to chemicals during their work, which leads to itching and on chronic exposure causes various skin problems

From the participantsit is reported that 39.7%, 11.35%, 5.3% and 1.7% experienced fairly low, moderate stress, severe stress and potentially high stress. In the study done by Edimansyah et al, showed prevalence of self - perceived depressionand anxiety as 35 .4% and 47. 2%

respectively48. This difference in our study is because the mental health problem has been assessed using “THE WORKPLACE STRESS SCALE”, of The Marlin company, North Haven, CT and the American Institute of stress, Yonkers, NY and not by the self-perceived problems as done in the study by Edimansyah et al 48. The study results found that there was a statistically significant association between the working shift and mental health problem. Working in both day and night is an indirect cause of mental health problem, the workers who are working in day and night shift had altered sleep pattern and sleep disruption leads to mental health problems. The