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  Table of Contents 
ORIGINAL ARTICLE
Year : 2016  |  Volume : 20  |  Issue : 1  |  Page : 10-13
 

Impact of social determinants on well-being of urban construction workers of Hyderabad


1 Department of Community Medicine, Deccan College of Medical Sciences, Hyderabad, Telangana, India
2 Department of Community Medicine, Catholic Health Association of India, Secunderabad, Telangana, India
3 Department of Community Medicine, Gandhi Medical College, Secunderabad, Telangana, India
4 Department of Community Medicine, Osmania Medical College, Hyderabad, Telangana, India

Date of Web Publication10-Jun-2016

Correspondence Address:
Dr. Sudha Bala
Flat No. 301, Shubodaya Enclave, Bagh Amberpet, Hyderabad - 500 013, Telangana
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0019-5278.183828

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  Abstract 

Background: Hyderabad has witnessed one of the largest labor immigration in recent years and these construction workers are highly vulnerable in terms of health. "Social determinants of health" (SDH) arise from conditions in which they live and these factors interact with each other to produce direct impact on health. Objectives: (1) To evaluate the sociodemographic and job characteristics of the construction workers. (2) To assess the impact of social determinants on well-being. Materials and Methods: A sample size of 135 construction workers working at three sites of HITEC city were interviewed using semi-structured questionnaire. Health perception and the impact on well-being was measured using the Healthy Days Module and Kessler's Psychological Distress Scale. SDH were measured on a 27-item questionnaire with responses on a Likert scale ranging from 0 to 4. Proportions, percentages, P values, and mean scores were obtained. Results: The mean age of the sample was 35.4 ± 11.94 years. Seventeen (12.6%) of the workers reported a high risk score on the Kessler's Psychological Distress Scale. Binary logistic regression analysis was used to identify significant domains of social determinants independently associated with the well being of construction workers and significant among the nine domains of social determinants were addiction score domain with odds of 2.259 and a P value of 0.015 and the distress domain with odds of 1.108 and a P < 0.001. Conclusions: There is a significant impairment of physical and mental health due to various factors including SDH, such as addictive habits and psychological distress, which are amenable to prevention.


Keywords: Construction workers, social determinants, well-being


How to cite this article:
Bala S, Valsangkar S, Lakshman Rao RL, Surya Prabha ML. Impact of social determinants on well-being of urban construction workers of Hyderabad. Indian J Occup Environ Med 2016;20:10-3

How to cite this URL:
Bala S, Valsangkar S, Lakshman Rao RL, Surya Prabha ML. Impact of social determinants on well-being of urban construction workers of Hyderabad. Indian J Occup Environ Med [serial online] 2016 [cited 2019 Oct 21];20:10-3. Available from: http://www.ijoem.com/text.asp?2016/20/1/10/183828



  Introduction Top


Hyderabad, an Information and communication technology hub of Telangana, has witnessed one of the largest labor immigration in recent years and these construction workers are highly vulnerable in terms of health. With an estimated workforce of 11.6 million construction workers in India, it is paramount to implement measures to promote health and well-being. [1] "Social determinants of health" (SDH) arise from the social and economic conditions in which they live and these factors interact with each other to produce direct impact on health and predict the greatest proportion of health status variance. The World Health Organization established the Commission on Social Determinants of Health (CSDH), on the premise that action on SDH is the most effective way to improve the health of all people and reduce inequalities. [2]

A range of factors has been identified as SDH and these generally include the following: The wider socioeconomic context; inequality; poverty; social exclusion; socioeconomic position; income; public policies; health services; employment; education; housing; transport; the built environment; health behaviors or lifestyles-diet, smoking, and alcohol (substance abuse); and psychosocial factors-social and community support networks and stress. A life course perspective provides a framework for understanding how these SDH shape and influence an individual's health from birth to old age. [3]

There is a paucity of studies among the most disadvantaged group, i.e., construction migrants that examine the entire ambit of social determinants affecting health and well-being. To understand this, the present study was conducted with an objective to assess the impact of social determinants on well-being (general, physical, mental health, and activity limitation).


  Materials and Methods Top


The sample size of 136 construction workers was estimated using 4pq/l 2 formula; prevalence of sickness absenteeism as 10% (according to Annual Labour Report 2013) and 5% precision was taken. [4] The study was conducted over a period of three months April-June 2014. Study participants working at three sites located at HITEC city were interviewed using a semi-structured questionnaire that was divided into the following parts, namely (1) sociodemographic characteristics; (2) job characteristics including work type, nature, and duration; 3) psychological distress using Kessler's Psychological Distress Scale consisting 10 questions on nonspecific psychological distress and is about the level of anxiety and depressive symptoms a person may have experienced in the most recent 4-week period. The response for each of the 10 items are categorized using a Likert scale 1-5; 10 items are summed to give scores ranging between 10 and 50 to classify as low risk (score 10-15), medium risk (score 16-29), and high risk (score 30-50); [5] (4) health perception and the impact on well-being were measured using the Healthy Days Module Centre for disease control and prevention (CDC); [6] and (5) SDH grouped into nine domains-social gradient, job, housing, nutrition, water and sanitation, health-care access, life-course perspective with provident environment for self and family, community support, addiction, and stress scores were measured on a 27-item questionnaire with categorical responses or responses on a Likert scale ranging from 0 to 4. According to this scale, the lower score indicated not affected on their overall well-being but except for the scores of addiction and distress. Well-being was defined based on both a subjective evaluation of health that was obtained on a binary scale and an objective measurement of health based on past physical illness within the last month. Informed consent of the participants and  Institutional ethical clearance for the study were obtained.

Data analysis was done using Statistical Package for the Social Sciences (SPSS) version 17 (SPSS-Inc., Chicago, IL). The statistical measures obtained were proportions, percentages, P values, and mean scores. Binary logistic regression was performed with overall health as binary outcome and SDH as predictors.


  Results Top


Sociodemographic characteristics

A total of 136 construction workers were included in the study. Mean age was 35.5 ± 11.94 years. Males constituted 69 (50.4%) and females constituted 67 (49.6%) of the sample. Most of the respondents had nuclear family [92 (67.6%)], followed by joint family [42 (30.9%)] and three-generation family [2 (1.5%)]. According to their marital status, 112 (82.4%) were married, 6 (4.4%) were unmarried, 16 (11.8%) were widows, and 2 (1.5%) were widowers. Majority of them 112 (82.2%) belonged to upper lower class and 82 (60.7%) were manual laborers as depicted in [Figure 1] and [Figure 2].
Figure 1: Socioeconomic status according to modified Kuppuswamy classification (2013)

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Figure 2: Job characteristics of construction workers

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Physical health

Prevalence of self-reported physical illness was found in 51 (37.47%) subjects. Among this, the most common was musculoskeletal disorders [26 (19.11%)] followed by hypertension [10 (7.35%)], pelvic inflammatory disease [6 (4.41%)] and skin infections [2 (1.47%)] [Table 1].
Table 1: Prevalence of self-reported physical illness


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Psychological distress

Using Kessler's Psychological Distress Scale, it was found that 17 (13%) and 53 (39%) were at high and medium risk, respectively, as depicted in [Figure 3].
Figure 3: Psychological distress scale

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Healthy days measures-days affected

The core healthy days measures assess a persons perceived sense of well-being during the past 30 days. Among the affected group, the number of days lost due to the physical illness was 3.251 ± 1.32 days, mental illness was 0.2 ± 0.146 days, and disability limitation was 0.438 ± 0.06 days [Table 2].
Table 2: Healthy days measures - days affected


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Social determinants and well-being

Mean scores of the SDH were analyzed with the binary outcome of well-being into affected and not affected groups [Table 3]. Lower social gradient score, lower employment score, and lower social and community support were found in the affected group. In these, we also found higher addiction and distress scores that were confirmed by binary logistic regression analysis. On logistic regression analysis, the significant predictors of poor well-being among the social determinants were addiction score with an odds of 2.259 and psychological distress on Kessler's Psychological Distress Scale with an odds of 1.108 [Table 4].
Table 3: Mean scores of social determinants of health


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Table 4: Significant predictors of health on binary logistic regression analysis among social determinants


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  Discussion Top


The present study sample constituted of 50.4% males and 49.6% females. Most of the respondents belonged to a nuclear family-67.6%, joint family-30.9%, and three-generation family-1.5%. According to their marital status, 82.4% were married, 4.4% were unmarried, 11.8% were widows, and 1.5% were widowers. Majority of them [112 (82.2%)] belonged to upper lower class and 96 (60.7%) were manual laborers. Similarly, Tiwari et al. in their study done at urban areas of Kolkata depicted that 96.5% were males and the remaining 3.5% were females. Among them 60.7% were married and 36.5% were unmarried. Of them, 58.4% had nuclear type of family and the remaining 41.6% had adopted joint type of family system. And majority of them (48.2%) were helpers and 57.2% were earning less than Rs. 5,000. [7]

It was found that the most common physical health problem found was musculoskeletal disorders (19.11%) followed by hypertension (7.35%), pelvic inflammatory disease (4.41%), and skin infections (1.47%). Similar results have been found in the study conducted by Mohopatra that also found that musculoskeletal disorders constituted 40%. [8] These findings were also supported by Valsangkar and Sai in their study conducted at Karimnagar. [9] Contrary to the present study, Sarika Manhas in her study done in Jammu found that eye problems as the most common health problem among the construction workers. [10]

In the present study, Kessler's Psychological Distress Scale 10 was used to assess the mental health status, using this it was found that 13% and 39% were at high and medium risk, respectively. Similarly Sandra Mary et al. in their study conducted in Bangalore among low-income group women found that nearly 50% were diagnosed with moderate to severe mental disorder using the same Kessler's Psychological Distress Scale. [11] Gaurav et al. in his study conducted among construction workers in Vadodara city revealed that 40.7% and 35.8% of the participants have extreme high and high levels of stress, respectively, using stress inventory scale. [12]

Mean scores of social determinants in the present study revealed lower social gradient score, lower employment score, lower social and community support , and higher addiction and distress scores in the affected group. Among these, addiction and distress scores were the predictors on the binary logistic regression analysis. Ajay Pawar et al. in their study conducted among construction workers in Surat found that the major social determinant scores having an influence on morbidity status were social gradient, stress, social exclusion and support, physical activity, and suboptimal behavior score on binary logistic regression model. [13] Contrary to this, Valsangkar et al. obtained large variation in the means on the domains of job security, availability of food, and water and community support among the affected group. [9]


  Conclusions Top


Construction workers are a vulnerable group with significant impairment of physical and mental health. Health is affected among these workers due to a myriad variety of factors including SDH such as addictive habits and psychological distress. These are amenable to prevention through simple ergonomic measures such as periodic examination and counseling.

Limitations

Physical health problems are self-reported and thus subject to measurement errors. Moreover, qualitative methods of analysis are required to identify the reasons for various components of well-being.

Acknowledgments

We are grateful to Dr. K.V.S. Murthy, Professor, Community Medicine, Gandhi Medical College, Secunderabad, and all the construction workers who had participated in our study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Census of India: Economic activity. 2011. Available from: Censusindia.gov.in/Census_And_You/economic_activity.aspx. [Last accessed on 2014 Mar 7].  Back to cited text no. 1
    
2.
World Health Organization. Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health. Report from the Commission on Social Determinants of Health. Geneva: WHO; 2008.  Back to cited text no. 2
    
3.
Farrell C, McAvoy H, Wilde J; Combat Poverty Agency. Tackling Health Inequalities-An All-Ireland Approach to Social Determinants. Dublin: Combat Poverty Agency/Institute of Public Health in Ireland; 2008. p. 18.  Back to cited text no. 3
    
4.
Annual report 2012-2013. Ministry of labour and employment. Government of India. pg151. Available from: http://www.labour.nic.in . [Last accessed on 2013 Dec 6].  Back to cited text no. 4
    
5.
The Kessler Psychological Distress scale (K10). Department of Health. Population Research and outcome studies. Brief Reports. Number 2002-14.  Back to cited text no. 5
    
6.
Measuring Healthy Days: Population Assessment of Health-Related Quality of Life. US department of health and human services. Centre for disease control and prevention. Atlanta, Georgia: CDC; 2000. p. 8.  Back to cited text no. 6
    
7.
Tiwari G, Gangopadhyay PK, Biswas S, Nayak K, Chakraborty D, Halder LC. Psychological stress of the building construction workers. Human Biology Review 2013;2:207-22.  Back to cited text no. 7
    
8.
Mohopatra R. Occupational Health Hazards in Small Scale and Other Industries. 1 st ed. New Delhi: Jaypee Brothers Medical Publishers Pvt Ltd; 2002. p. 54-5.  Back to cited text no. 8
    
9.
Valsangkar S, Surendranath Sai K. Impact of musculoskeletal disorders and social determinants on health in construction workers. Int J Biol Med Res 2012;3:1727- 30.  Back to cited text no. 9
    
10.
Manhas S. Assessment of physical health status of female construction workers of Kathau district, J and K. International Organization of Scientific Research-Journal of Humanities and Social Sciences 2014;19:19-24.  Back to cited text no. 10
    
11.
Travasso SM, Rajaraman D, Heymann SJ. A qualitative study of factors affecting mental health amongst low-income working mothers in Bangalore, India. BMC Womens Health 2014;14:22.  Back to cited text no. 11
    
12.
Gaurav GD, Krushna GM, Trivedi AA. Stress among unorganized sector workers in Vadodara city. Int J Res Dev Health 2013;1:183-90.  Back to cited text no. 12
    
13.
Pawar AB, Mohan PV, Bansal RK. Social determinants, suboptimal health behavior, and morbidity in urban slum population: An Indian perspective. J Urban Health 2008;85:607-18.  Back to cited text no. 13
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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