|Year : 2016 | Volume
| Issue : 1 | Page : 14-20
Chronic disease risk factors among hotel workers
Nilesh Chandrakant Gawde1, Prashika R Kurlikar2
1 Centre for Public Health, School of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, Maharashtra, India
2 Department of Population Studies, International Institutes for Population Sciences, Mumbai, Maharashtra, India
|Date of Web Publication||10-Jun-2016|
Dr. Nilesh Chandrakant Gawde
Centre for Public Health, School of Health Systems Studies, Tata Institute of Social Sciences, Deonar, Mumbai - 400 088, Maharashtra
Source of Support: None, Conflict of Interest: None
Context: Non-communicable diseases have emerged as a global health issue. Role of occupation in pathogenesis of non-communicable diseases has not been explored much especially in the hospitality industry. Aims: Objectives of this study include finding risk factor prevalence among hotel workers and studying relationship between occupational group and chronic disease risk factors chiefly high body mass index. Settings and Design: A cross-sectional study was conducted among non-managerial employees from classified hotels in India. Materials and Methods: The study participants self-administered pre-designed pilot-tested questionnaires. Statistical analysis used: The risk factor prevalence rates were expressed as percentages. Chi-square test was used for bi-variate analysis. Overweight was chosen as 'outcome' variable of interest and binary multi-logistic regression analysis was used to identify determinants. Results: The prevalence rates of tobacco use, alcohol use, inadequate physical activity and inadequate intake of fruits and vegetables were 32%, 49%, 24% and 92% respectively among hotel employees. Tobacco use was significantly common among those in food preparation and service, alcohol use among those in food service and security and leisure time physical activity among front office workers. More than two-fifths (42.7%) were overweight. Among the hotel workers, those employed in food preparation and security had higher odds of 1.650 (CI: 1.025 - 2.655) and 3.245 (CI: 1.296 - 8.129) respectively of being overweight. Conclusions: Prevalence of chronic disease risk factors is high among hotel workers. Risk of overweight is significantly high in food preparation and security departments and workplace interventions are necessary to address these risks
Keywords: Hotel workers, overweight, prevalence, risk factors
|How to cite this article:|
Gawde NC, Kurlikar PR. Chronic disease risk factors among hotel workers. Indian J Occup Environ Med 2016;20:14-20
| Introduction|| |
Noncommunicable diseases have emerged as a major cause of mortality globally, accounting for 38 million deaths annually.  Many of these deaths occur before the age of 70 years, i.e., among economically productive individuals. Low- and middle-income countries have a disproportionately higher burden of these premature deaths. High blood pressure, blood glucose, serum cholesterol, and body mass index (BMI) have been identified as major metabolic risk factors driving this epidemic.  Dietary and behavioral risk factors including smoking, alcohol use, diet, and physical activity have been identified as major underlying determinants. 
Compared to these established risk factors, occupation has been less extensively studied as an underlying determinant of noncommunicable diseases. Physical activity requirement, dietary practices, and work-related stress vary among occupational groups. Due to a variation in lifestyle factors across occupational groups, certain occupations are more at risk of metabolic risk factors and consequent noncommunicable diseases. , Many researchers have examined the role of work-associated physical activity as factor intermediary in the relationship between occupation and chronic diseases. Consequently, most studies on the prevalence of risk factors such as physical inactivity have been conducted among industries or occupational groups that are "sedentary" in nature. There is dearth of research on behavioral and metabolic risk factors among other industries and occupational groups including the hospitality industry.
Hospitality is a rapidly growing industry in emerging economies such as India. The human workforce in India's hospitality industry is nearly four million with nearly half working in more than 130,000 hotels and motels, 5% of which are classified as star hotels.  In the past, occupational health has focused more on physical and chemical hazards that are specific to occupational setting. Less attention has been paid to cardiovascular and other noncommunicable diseases, which are often thought to be associated with the lifestyle of an individual rather than her/his occupation. This holds true for hospitality despite it being an industry that manufactures and/or serves food including fast food that is linked to obesity. Chronic disease risk factors among hotel workers need to be studied and this study is among the first of such efforts.
Within every industry, occupations can be further classified as administrative, executive, clerical, skilled, semiskilled, and unskilled labor. It was originally thought that higher grades are more at risk because they have sedentary work but later it was found that occupation may also act independent of these intermediary behavior risk factors. Lower occupational grades have been associated with higher chances of mortality due to coronary artery disease.  Lower occupational grade is associated with lower income and lesser social recognition. People with lower socioeconomic status are more vulnerable to conditions of ill-health and often do not have resources to deal with the consequences of diseases resulting in disparities in health. In India also, a recent study found an association between lower occupational status and the higher prevalence of both behavioral and metabolic risk factors. , This study has been conducted among nonmanagerial workers. A hotel does not have a homogenous group of employees but includes several occupations including housekeeping, front office, food preparation, food service, and administration. This study covers employees across the various departments of classified hotels. It aims to find the risk factor prevalence among hotel workers and to study the relationship between occupational group and chronic disease risk factors, chiefly high BMI.
| Materials and Methods|| |
This paper is part of a larger study, which covers chronic disease risk factors, musculoskeletal conditions, mental well-being, and common morbidities among hotel employees. Methods relevant to chronic disease risk factors, which are relevant to this paper are detailed here.
Conceptual framework and study design
The literature was reviewed to identify the relevant theory and conceptual framework that could be used to meet the stated objectives. Punnett et al.  had conceptualized the association between occupation and health domains including cardiovascular health. This model suggests that cardiovascular health is determined by the socioeconomic position, working conditions, and health behaviors. The model also suggests that health behaviors are shaped by the socioeconomic position and working conditions. This framework was in line with the previous literature mentioned earlier in the introduction section and was used for this study. Prevalence as well as association between cause and effect can be studied by cross-sectional studies. Although the usefulness of cross-sectional design for later purposes is limited, in view of the resource constraints, the cross-sectional study design was chosen.
Study settings and sampling
The study focuses more on the luxury hotels from Mumbai, Maharashtra, India but a smaller sample from luxury and budget hotels out of Mumbai, Maharashtra, India was also taken. Permission from the respective management of these hotels was sought. A total of three luxury hotels were selected from Mumbai, Maharashtra, India. All employees (approximately 2,500) excluding the top managerial employees from the three Mumbai hotels were invited to participate in the study. Three luxury and two budget hotels outside Mumbai, Maharashtra, India were included from one metropolitan city and two nonmetropolitan cities. For these hotels outside Mumbai, Maharashtra, India a total of 200 and 100 employees were randomly selected from the luxury and budget hotels, respectively, and were invited to participate in the study.
Study variables and tools
Socioeconomic variables included age, sex, education, marital status, income, and type of house. Data on years in service, type of occupation (housekeeping, food preparation, etc.) and average number of extra hours of work per week were collected for assessing work conditions. Behavioral factors included the use of tobacco, alcohol, number of servings of fruits and vegetables per day, skipping of breakfast, and leisure-time physical activity. Given the resource constraints, biological measurement of metabolic risk factors, namely, high blood pressure, blood sugar, and BMI could not be conducted during the study. For these metabolic risk factors, self-reported data were collected. A predesigned self-administered tool was prepared in English by two researchers. Comments were invited from two medical professionals providing clinical services to hotel workers as well as from two human resource professionals working in the hotel industry. An academician with experience in occupational and environmental medicine was also consulted. The revised study tool was administered to 14 employees in one luxury hotel (part of the sample). However, these 14 employees were asked not to be part of the final survey. Based on the results of the pilot study, the tools were further modified and in the final version, each question was in both English and Hindi.
A team of two research officers was appointed to oversee the data collection process. These two research officers were involved in pilot testing of the study tools, supervising the data collection process, data entry, and analysis. As stated already, the employees were invited to participate in the study and a dedicated place within the hotel was reserved for the same. Interested participants were provided with informed consent forms. After reading and signing the informed consent forms, participants filled self-administered questionnaires. The data were collected in the presence of research officers who could clarify doubts of employees regarding any of the questions.
Research officers checked all the questionnaires manually first and then entered the data in Statistical Package for the Social Sciences (SPSS) version 16, IBM. Data were cleaned and analyzed with frequency and percentages. Occupation was coded into one of the eight categories, namely, housekeeping, desk work, food preparation, food service, front office, engineering, security, and others. Bivariate analysis was conducted for behavioral and metabolic risk factors with occupation as an independent variable. Leisure-time physical activity was considered adequate if the person was engaging in moderate physical activity (fast walk, running, etc.) for at least 30 min on at least for 3 days in 1 week. A person consuming five servings of fruits and vegetables per day was considered to have an adequate intake of fruits and vegetables. Among the metabolic risk factors, data on BMI (calculated from height and weight) were available for most participants but not for the others. Hence, multivariate analysis (multiple binary logistic regressions) was limited to the outcome "overweight."
| Results|| |
A total of 1,183 hotel workers including 953 from Mumbai, Maharashtra, India and 230 from outside the city of Mumbai participated in the study. Their sociodemographic characteristics have been described in [Table 1]. More than half were aged less than 35 years and in terms of numbers, the workforce was dominated by men. Data on residence and income showed that they were from the lower middle class. Most employees were from food preparation, food service, and housekeeping departments [Figure 1].
|Figure 1: Percentage distribution of study participants according to type of occupation. It shows that food service, food preparation, and housekeeping are three prominent labor-intensive departments in a hotel as they accounted for the majority of the respondents in the study. There were some departments such as security, engineering, desk work, and engineering|
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|Table 1: Socio-demographic characteristics of study respondents (N=1183*) |
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Behavioral risk factors
[Table 2] shows the prevalence of behavioral risk factors among hotel workers. Tobacco use was reported by nearly one-third and alcohol use by nearly half of the participants. Only a quarter was having adequate leisure-time physical activity and less than 10% were taking at least five servings of vegetables and fruits daily. The prevalence of behavioral risk factors varied among occupational groups significantly. An exception to this was an adequate intake of fruits and vegetables, which was very low among all groups. Employees from food preparation, food service, and others had higher reported consumptions of tobacco and alcohol. Front office and food service employees had higher prevalence of inadequate physical activity. Two hundred and thirty five (20%) reported not taking breakfast on a regular basis; the proportion was higher among those in food preparation (23%), food service (24%), front office (25%), and others (30%). Almost one-fifth (229; 19.5%) reported that work causes them to overeat. This was reported more frequently by those involved in housekeeping (25.5%), others (20.6%), food preparation (19.7%), and food service (19.2%).
|Table 2: Prevalence of behavioural risk factors among hotel workers according to socio-demographic variables |
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Among other sociodemographic factors [Table 2], agewise analysis shows that risk factors including tobacco use and alcohol use are common since young age although the proportion of regular consumption of alcohol increased by 35 years. Younger employees were less likely to have adequate leisure-time physical activities. On the other hand, adequate consumption of fruits and vegetables was common among younger employees. Substance use was significantly less common among women but physical activity and fruit and vegetable intakes were also lower among them. Education, income, and type of family did not have any relation with risk factor prevalence in this study. The only exception is the prevalence of regular consumption of alcohol, which was higher among those from nuclear families and higher income quintiles.
Metabolic risk factors
One hundred and fifteen (9.7%) employees reported having hypertension, 80 (6.8%) reported diabetes, and 20 (1.7%) reported heart disease. Of the hypertensive patients, 105 reported having checked their blood pressure. Among others, 375 (35.2%) had checked their blood pressure in the previous year. Similarly, 69 diabetics had checked their blood sugar and among others, 293 (26.6%) had checked their blood sugar levels. Among those aged 35 years and above and not diagnosed with hypertension, 199 (44.2%) reported checking their blood pressure levels. One hundred and seventy six (36.7%) employees aged 35 years and above with no diagnosed diabetes had checked their blood sugar. Two hundred and fifteen (21.3%) employees without any diagnosed chronic condition (hypertension, diabetes, or heart disease) had checked their cholesterol levels; the proportion was a little higher (122, 30.8%) among those aged 35 years and above.
The percentage of overweight people increased as age advanced [Figure 2]. However, even among those aged less than 25 years, nearly one-third was overweight. The prevalence of obesity did not vary much and this was common among younger employees as well. Unadjusted analysis found age and occupation to be associated with overweight status [Table 3]. Adjusted analysis showed that those employed in food preparation, food service, security, and others were significantly more likely than the housekeeping staff to be overweight. Females were at a lesser risk of being overweight and income had a positive effect on weight.
|Figure 2: Percentage of overweight people increased as age advanced [Figure 2]. However, even among those aged less than 25 years, nearly one-third were overweight. Prevalence of obesity did not vary much and this was common among younger employees as well. Unadjusted analysis found age and occupation to be associated with overweight status|
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| Discussion|| |
This is first study on the prevalence of risk factors among hotel employees in India. Both tobacco use and alcohol use were very common. Only a quarter had adequate physical activity and the rates for adequate consumption of fruits and vegetables were further low. More importantly, these factors existed at a young age. A high prevalence of overweight existed even before 35 years of age. The presence of risk factors at an earlier age is likely to result in the early onset of chronic conditions and interventions need to focus on preventive actions. It is important to reduce this risk by promoting behavior change. Workplace interventions including health promotion and behavior change programs to address chronic disease risk factors are becoming common in high-income countries. Reviews of such interventions found that most workplace interventions were successful in bringing a positive change in various behaviors including smoking, physical activity, and eating behavior. , This evidence-based practice needs to be replicated in the Indian hospitality industry as well. Behavior change can minimize risk but not eliminate it. Screening for the presence of metabolic risk factors such as high blood pressure and blood sugar is equally important. This is because these conditions are often asymptomatic and remain hidden for a long period of time. This study found that a majority of the employees, even those above 35 years of age, had not checked their blood pressure or blood sugar level during the previous year. It is important to screen the employees for early detection of these conditions. There is evidence that early detection and appropriate treatment including lifestyle modification can prevent early deaths, reduce morbidities, and minimize cost. Thus, a mix of behavioral and clinical interventions is needed.
Prevalence rates of risk factors found in this study were compared with the rates in the general population to examine whether hotel workers were more at risk. National level data on two behavioral factors, namely, tobacco use and alcohol use are available in India. Recent global adult tobacco survey found that nearly half of the Indian men were using tobacco  but the rates were lower in urban areas and in middle-income groups. In this study, tobacco use was reported by one-third of male employees, which was not much different from that in the general community. Prevalence among female employees also was similar to the one among urban females.  Regular use of alcohol was comparable to the national statistics for urban residents, both for men and women. However, the occasional use of alcohol was higher among hotel employees compared to the national statistics for both the gender groups.  Studies have shown that nearly half of the urban adults in India are overweight and this study also showed similar findings. , Although physical activity patterns at subnational level were available from a study,  no comparison was done due to differences in definition and measurement of adequate physical activity.
Although the prevalence of risk factors among hotel employees appeared to be similar to that in the general population, there was a considerable variation within the hotel employees. Among various occupational groups, those involved in food preparation, food service, and security had a higher prevalence of behavioral risk factors. Chances of overweight were also significantly higher among those in food preparation and security. This higher risk persisted even after adjusting for other factors. In the adjusted analysis, the most confounding factors were considered except for the intake of calories and occupation-associated physical activity. These two factors may be mediating the risk associated with these occupational groups. Those involved in food preparation and service reported that work causes them to overeat primarily because tasting the food during its preparation is part of their job responsibilities. Alcohol use was common among security and food service employees. In continuation with this, it is worth noting here that food additives are commonly used for the preparation of food in hotels. The kitchen and bakery staff is consuming such additives in much higher quantity compared to the general population. "Chemical obesogen" hypothesis has been proposed, which is based on the findings that many environmental contaminants including food additives dysregulate endocrine function, insulin signaling, and/or adipocyte function.  Although the connection between food additives and obesity has not been conclusively proven, this is a distinct possibility and it may explain the higher risk of obesity among kitchen and bakery staff. A study among security personnel linked higher BMI with shift work and the effect was thought to be mediated by stress.  The present study did not measure work-related stress but that could be a possible mechanism as well. Occupation-associated physical activity does not seem to explain this because food preparation as well as security involve moderate levels of activity. The occupational category of others included employees from different departments who could not be classified into any of the seven occupational categories. This was a heterogeneous group and had variance in work responsibilities; hence, the higher risk among them is not being commented upon.
The study found that certain occupational groups had higher odds of being overweight. However, the cause-effect relationship cannot be conclusively proven due to the cross-sectional nature of the study. There were other study limitations as well. Measurement of blood pressure and sugar was not conducted and the true prevalence of these risk factors could not be found. For multivariate analysis, behavioral risk factors were classified as binary (yes and no) variables and dose-response relationships were not studied. Occupation-associated physical activity and dietary intakes were not measured. Findings of this study cannot be generalized for the entire industry as the study included only star (classified) hotels. Nevertheless, the study highlights the need of workplace interventions.
Interventions, whether behavioral or clinical, need organizational framework. Typically, such interventions both the behavioral and clinical are planned and implemented by the occupational health and safety department. However, the Indian legislation (the Factories Act, 1948) does not warrant such department or infrastructure for the hospitality industry. Consequently, even the star hotels do not have an occupational safety and health department. Some employers may proactively implement interventions because in the long run, this will minimize medical costs. However, for the industry as a whole, policy and legislative reform are needed to pave way for workplace interventions. Pilot interventions need not wait for policy and legislative reform. Reviews of workplace interventions have shown that for interventions to be successful, a right mix of appropriate strategies need to be in place (Allison 2011). Pilot workplace interventions should take this into consideration while designing the program. To summarize, the prevalence of chronic disease risk factors is high among hotel workers. The risk of overweight is significantly high in food preparation and security departments and workplace interventions are necessary to address these risks.
The authors thank Prof. C. A. K. Yesudian for his inputs during study design and administration phase. We also thank Dr. Lucky Singh who provided key technical support during planing and implementation of the study.
Financial support and sponsorship
The study was completed with financial support from the Tata Group.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Danaei G, Lu Y, Singh GM, Carnahan E, Stevens GA, Cowan MJ, et al
. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment. Lancet Diabetes Endocrinol 2014;2:634-47.
Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al
. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2224-60.
Allman-Farinelli MA, Chey T, Merom D, Bauman AE. Occupational risk of overweight and obesity: An analysis of the Australian Health Survey. J Occup Med Toxicol 2010;5:14.
Bonauto DK, Lu D, Fan ZJ. Obesity prevalence by occupation in Washington state, Behavioral Risk Factor Surveillance System. Prev Chronic Dis 2014;11:130219.
Market Plus. Study to Assess the Requirement of Manpower in Hospitality and Travel Trade Sector. Delhi: Government of India, Ministry of Tourism; 2012. p. 6.
Marmot MG, Rose G, Shipley M, Hamilton PJ. Employment grade and coronary heart disease in British civil servants. J Epidemiol Community Health 1978;32:244-9.
Gupta R, Deedwania PC, Sharma K, Gupta A, Guptha S, Achari V, et al
. Association of educational, occupational and socioeconomic status with cardiovascular risk factors in Asian Indians: A cross-sectional study. PLoS One 2012;7:e44098.
Gupta R, Sharma KK, Gupta A, Agrawal A, Mohan I, Gupta VP, et al
. Persistent high prevalence of cardiovascular risk factors in the urban middle class in India: Jaipur Heart Watch-5. J Assoc Physicians India 2012;60:11-6.
Punnett L, Cherniack M, Henning R, Morse T, Faghri P; CPH-NEW Research Team. A conceptual framework for integrating workplace health promotion and occupational ergonomics programs. Public Health Rep 2009;124(Suppl 1):16-25.
Fishwick D, Carroll C, McGregor M, Drury M, Webster J, Bradshaw L, et al
. Smoking cessation in the workplace. Occup Med (Lond) 2013;63:526-36.
Allison KR, Dwyer JJ, MacGillivray A, Hawrychuk S; Ontario Agency for Health Protection and Promotion. A review of workplace and community interventions for adults: Effects of physical activity, eating behaviours and BMI-risk factors for diabetes. Toronto, ON: Queen′s Printer for Ontario; 2011. p. 2-3.
Palipudi K, Rizwan SA, Sinha DN, Andes LJ, Amarchand R, Krishnan A, et al
. Prevalence and sociodemographic determinants of tobacco use in four countries of the World Health Organization: South-East Asia region: Findings from the Global Adult Tobacco Survey. Indian J Cancer 2014;51(Suppl 1):S24-32.
International Institute for Population Sciences (IIPS) and Macro International. National Family Health Survey (NFHS-3), 2005-06: India: Mumbai: International Institute for Population Sciences (IIPS); 2007. p. 426-32.
Millett C, Agrawal S, Sullivan R, Vaz M, Kurpad A, Bharathi AV, et al
. Associations between active travel to work and overweight, hypertension, and diabetes in India: A cross-sectional study. Plos Med 2013;10:e1001459.
Anjana RM, Pradeepa R, Das AK, Deepa M, Bhansali A, Joshi SR, et al
. ICMR- INDIAB Collaborative Study Group. Physical activity and inactivity patterns in India-results from the ICMR-INDIAB study (Phase-1) [ICMR-INDIAB-5]. Int J Behav Nutr Phys Act 2014;11:26.
Simmons AL, Schlezinger JJ, Corkey BE. What are we putting in our food that is making us fat? Food additives, contaminants, and other putative contributors to obesity. Curr Obes Rep 2014;3:273-85.
Abedini R, Soltanzadeh A, Faghih MA, Mohammadi H, Kamalinia M, Mohraz MH, et al
. Health consequences of shift-work: The case of Iranian Hospital Security Personnel. Work 2015;50:305-11.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]