A Comparative Study of Non Communicable Disease Risk Factors Among Gulf Migrant Workers and Non Migrant Workers of Malappuram District, Kerala

A COMPARATIVE STUDY OF NON COMMUNICABLE DISEASE RISK FACTORS AMONG GULF MIGRANT WORKERS AND NON MIGRANT WORKERS OF MALAPPURAM DISTRICT, KERALA

DR. SHAMIM BEGAM N.

Dissertation submitted in partial fulfillment of the requirement for the award of the degree of MASTER OF PUBLIC HEALTH

2013

Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology Thiruvananthapuram, Kerala, India

DEDICATION

The work embodied in this dissertation is dedicated to my family, especially

to my husband Dr. Reji and my children Mehreen and Mehak.

Their unconditional support made this work possible.

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to Dr K Srinivasan, my guide for the guidance and support all throughout this study.

I would like to thank Dr. Dileep Damodaran (Surgical Oncologist, MIMS) whose inspirations were pivotal for my aspiration to pursue Masters in Public Health.

I would also thank my senior and dear friend Dr. Praveen G Pai for his constant support.

My heartfelt thanks to Dr. GK Mini who patiently listened to all my doubts and guided me at every stage of this study even in her busy schedule.

At this time, I would also like to express my sincere gratitude to Dr V Raman Kutty for his Valuable suggestions and to Dr.PS Sharma for clarifying my doubts each time I needed.

I would like to thank all other faculty of AMCHSS for their support and guidance during this study period.

I would also like to thank Dr Mohammed Sameer PT, Former President Indian Dental Association Malappuram District, for his constant support throughout the study period.

I would like to thank those who help in data collection especially in identifying and accessing study subjects and i specially thank each participant of this study.

I take this opportunity to thank every individual who directly or indirectly helped me to carry out this study successfully.

Above all, I would like to thank God Almighty who has been with me through out, as a constant source of encouragement and support.

DECLARATION

I hereby declare that the work embodied in this dissertation titled, ‘A comparative study of non communicable disease risk factors among gulf migrant workers and non migrant population of Malappuram district Kerala’ is the result of original research and has not been submitted for any degree in any other university or institution.

Dr Shamim Begam.N, MPH 2012, Achutha Menon Center for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum - 11

Date: 31 October 2013

iii

CERTIFICATE

I hereby certify that the work embodied in this dissertation titled, ‘A comparative study of non communicable disease risk factors among gulf migrant workers and non migrant population of Malappuram district Kerala’ is a bonafide record of original research work undertaken by Dr. Shamim Begam in partial fulfillment of the requirement for the award of the ‘Master of Public Health’ degree, under my guidance and supervision.

Dr Kannan Srinivasan, Associate Professor, Achutha Menon Center for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum - 11

Date: 31 October

CONTENTS

Dedication …………………………………………………………………………………………..………………………. i

Acknowledgements………………………………………………………………..…………..………………………. ii

Declaration ………………………………………………………………………………………..………………………. iii

Certificate – Guide …………………………………………………..………………………..………………………. iv

Glossary of Abbreviations ………………………………………………………………………………………... viii

List of Tables and Figures ………………………………………………………………………………………... ix

Abstract ……………………………………………………………………………………………………………………… x

1. Introduction …………………………………………………………………………………………………………… 1 2. Literature Review

2.1. Global scenario of NCDs and risk factors …………………………………………………. 4

2. 2. Indian scenario ………………………………………………………………………………………….. 9

2.3. Kerala Scenario ………………………………………………………………………………………….. 13

2.4 Occupation related factors …………………………………………………………………………. 15

2.5. Migration and NCDs …………………………………………………………………………………. 15

2.6. Studies on NCD prevalence in Indian immigrants ………………………………… 16

2.7. Rationale for the study ……………………………………………………………………………… 17

2.8. Objective of the study ………………………………………………………………………………… 18

3. Methodology

3.1. Study setting ………………………………………………………………………………………………. 19 3.2. Study design ………………………………………………………………………………………………. 19 3.3. Sample size estimation ………………………………………………………………………………. 20 3.4. Sample selection procedure ……………………………………………………………………….. 20 3.5. Study Participants ……………………………………………………………………………………… 22 3.5.1. Inclusion criteria ……………………………………………………………………………... 22 3.5.2. Exclusion criteria …………………………………………………………………………….. 22 3.6. Data collection techniques ……………………………………………………….………………... 22 3.7. Participant flow …………………………………………………………………………………………. 23 3.8 Data storage ………………………………………………………………………….…………………….. 23 3.9 Data Analysis and Statistical Measures ……………...………………….…………………… 24 3.10. Variables in the study ………………………………………………………………………… 24

3.10.1 .STEP 1 Variables ……………………………………………………………………………. 24

3.10.2. STEP 2 Variables …………………………………………..………………………………. 26

3.11. Definitions of STEP 2 Variables ……………………………………………………… 26

3.12. Ethical considerations ………………………………………………………………………... 27 4. Results

4.1 Baseline characteristics and Magnitude of risk factors …………………………………... 28

4.1.1. Migration characteristics ………………………………………………………..……………. 29

4.1.2. Age distribution ………………………………………………………………………………… 30

4.1.3. Socio demographic profile …………………………………………………………………… 31

4.1.4. Occupation related factors …………………………………………………………………… 32

4.1.5. Personal family history of NCDs ……………………………………………………… 34

4.1.6. Tobacco use ………………………………………………………………………………………….. 34

4.1.7. Passive smoking …………………………………………………………………………………… 36

4.1.8. Alcohol use …………………………………………………………………………………………… 37

4.1.9. Dietary habits ………………………………………………………………………………………... 37

4.1.10. Physical activity ……………………………………………………………………………………. 38

4.1.11. Sedentary Habits ………………………………………………………………………………….. 39

4.1.12. Waist circumference, Weight, height, and BMI …………………………………… 40

4.1.13. Abdominal obesity and overweight …………………………………………………… 40

4.1.14. Systolic pressure and diastolic pressure ……………………………………………… 41

4.1.15. Hypertension ………………………………………………………………………………………… 42

4.1.16. Awareness treatment and control of hypertension ………………………….. 42

4.1.17. Self reported diabetes ………………………………………………………………………… 43

4.1.18. Crude odds ratios of anthropometric risk factors ……………………………. 44

4.2. Logistic regressions ……………………………………………………………………………………… 45

4.2.1. Univariate logistic regression with hypertension as outcome …………. 45

4.2.2. Multi variate logistic regression with hypertension as outcome …….. 45

4.2.3. Univariate logistic regression with abdominal obesity as outcome … 47 4.2.4. Multi variate logistic regression with abdominal obesity as outcome 47

4.3. Duration of migration and anthropometric risk factors ………………………….. 50

5. Discussion

5.1. Sample characters………………………………………………………………………………….………... 51

5.2. Behavioral Risk factors ………………………………………………………………………………… 51

5.3. Tobacco use ……………………………………………………………………………………………………. 51

5.4. Alcohol use …………………………………………………………………………………………………….. 52

5.5. Dietary Habits ……………………………………………………………………………………………….. 53

5.6. Physical activity and sedentary habits ………………………………………………………. 53

5.7. Occupational categories ………………………………………………………………………………. 54

5.8. Working days in a typical week…………………………………………………………………… 55

5.9. Working hours on a typical day ………………………………………………………………….. 55

5.10. Sleeping hours on a typical day ………………………………………………………………… 55

5.12. Hypertension ………………………………………………………………………………………………... 56

5.13. Abdominal Obesity and overweight …………………………………………………………. 57

5.14. Awareness, treatment and control of hypertension ……………………………………… 59

5.15. Self reported diabetes ………………………………………………………………………………….. 60

5.16. Duration of migration and Anthropometric risk factors ……………………………… 61

5.17. Strengths of the study …………………………………………………………………………………. 63

5.18. Limitations of the study ………………………………………………………………………………. 63

5.19. Conclusions ………………………………………………………………………………………………….. 64

5.20. Recommendations ……………………………………………………………………………………….. 65

References ………………………………………………………………………………………………………………… 66 Annexures Annex 1: Informed Consent Annex 2: Interview Schedule

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GLOSSARY OF ABBREVIATIONS

BMI : Body Mass Index

COPD : Chronic Obstructive Pulmonary Diseases

CHD : Chronic Heart Disease

CVD : Cardio Vascular Disease

GDP : Gross Domestic Product

LMIC : Low and Middle Income Countries

KSA : Kingdom of Saudi Arabia

NCDS : Non Communicable Diseases

NRI : Non Resident Indian

SCTIMST : Sree Chitra Tirunal Institute for Medical Sciences and Technology

T2DM : Type 2 Diabetes Mellitus

UNDP : United Nations Development Programme

UAE : United Arab Emirates

UK : United Kingdom

USA : United States of America

WHO : World Health Organization

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LIST OF TABLES AND FIGURES

Table 1. : Duration of migration 30

Table 2. : Age distribution 30

Table 3. : Socio demographic profile of the participants 32

Table 4. : Occupation related factors 33

Table 5. : Personal and family history of Non communicable diseases(NCDs) 34

Table 6. : Tobacco use 35

Table 7. : Passive smoking 36

Table 8. : Alcohol use 37

Table 9. : Dietary habits 38

Table 10. : Physical activity 39

Table 11. : Mean sedentary habits: Time spent sitting or reclining per day (minutes) 39

Table 12. : Mean waist circumference, weight , height and BMI 40

Table 13. : Abdominal obesity and overweight 41

Table14. : Mean systolic blood pressure diastolic blood pressure 41

Table15. : Hypertension 42

Table16. : Awareness, treatment and control of hypertension 43

Table17. : History and treatment of Self reported diabetes 43

Table.18. : Bivariate analysis with migration status as outcome variable 44

Table.19. : Univariate logistic regression analysis results (Hypertension as outcome) 46

Table.20. : Multivariate logistic regression analysis results (Hypertension as outcome) 47

Table.21. : Univariate logistic regression analysis results (obesity as outcome) 48

Table.22 : Multivariate logistic regression analysis results (obesity as outcome) 49

Figure 1 : Schematic Representation of sample selection 21

Figure 2 : Changes in anthropometric risk factors with duration of migration 50

ABSTRACT

Introduction: Data on risk factors of non communicable diseases are very limited on gulf migrant population in India. The objective of this study was to compare the magnitude of NCD risk factors between gulf migrant workers and non migrant workers of Malappuram district, Kerala.

Methods: Using a multistage cluster sampling technique, 384 adults between 25-65 years of age (191 gulf migrant workers and 193 non migrant workers) were selected. Information on risk factors of NCDs were collected using the World Health Organization (WHO) STEPS questionnaire. Weight, height, waist circumference and Blood pressure were measured using the WHO protocol. Data were analyzed using SPSS software

Results: Prevalence of current tobacco use was 21.4% among migrants and 16.6% among non migrants, exposure to passive smoking was 46% among migrants and 19.7% among non migrants(p= <0.001),current alcohol use was 8.9% among migrants and 12.4% among non migrants, physical inactivity was 26.7% among migrants and 23.8% among non migrants, diet with less than 5 servings of fruits and vegetables per day was 86.9 % among migrants and 76.2% among non migrants(p = <0.05),personal history of chronic diseases was 37% among migrants and 21% among non migrants(p= <0.001) ,occupation related risk factors: working all 7 days per week was 35% among migrants and 1% among non migrants (p= <0.001) , working more than 8 hrs per day was 76.9% among migrants and 33.1% among non migrants(p= <0.001) , sleeping less than 6 hrs per day was 41.3% among migrants and 14% among non migrants (p= <0.001),Prevalence of Hypertension was 59.7% among migrants and 29.8% among non migrants( p=<0.001),when adjusted for other factors hypertension was 2.5 (95% CI=1.38-4.46) times higher among migrants compared to non migrants, abdominal obesity was 79.5% among migrants and 44.5% among non migrants(p=<0.001) when adjusted for other factors abdominal obesity was 2.41 times higher among migrants compared to non migrants (95% CI=1.35-4.31 ),overweight was 66% among gulf migrants and 46% among non migrants(p=<0.001), Prevalence of self reported diabetes among migrants was 38.2 % and among non migrants 19.17%(p=<0.001). Among hypertensive migrants 43.5% were aware, 33.9 % were on treatment and 12.2% achieved adequate control, among hypertensive non migrants the corresponding figures were 56.9 %, 53.4 %, and 48.3 % respectively (p= <0.001). Prevalence of all the anthropometric risk factors considered increased with duration of migration.

Conclusion: The burden of NCD risk factors is significantly high among gulf migrant workers compared to non migrant workers. No much difference is seen in most of the behavioral risk factors (STEP 1) of migrants and non migrants, but anthropometric risk factors like hypertension, overweight and abdominal obesity and self reported diabetes are significantly higher among gulf migrant population which needs further exploration. These data may serve to impel efforts to lower the heavy burden of NCD risk factors among gulf migrant workers compared to non migrant workers in particular, and migrant population in general.

1 INTRODUCTION

Non-communicable diseases (NCDs) are diseases of long duration and usually these diseases are slowly progressing. The four main types of NCDs are cardiovascular diseases, cancers, chronic respiratory diseases and diabetes. Ageing, urbanization, and unhealthy lifestyles are some of the factors driving these diseases. The vulnerability of NCDs and risk factors is spread over all age groups including Children, adults and the elderly .There is a shift noticed from communicable diseases in children to NCDs in adults globally.1, 2

The high incidence of NCDs and risk factors is very much related to growing life style changes. Such changes are more significant in affluent societies; each of these risk factor increases the risk for NCDs through independent mechanisms. In spite of being related to each other the occurrence of one risk factor makes way for another leading to greater risk for developing NCDs.3,4

Present Population assessments of these diseases and risk factors are useful to describe the distribution of these in future. It has been evident that the major risk factors associated with

NCDs are tobacco use, harmful alcohol intake, unhealthy diet (low fruits and vegetable consumption) and low physical activity. These are well modifiable through life style changes and primary prevention methods. Unlike diseases, risk factors are open to interventions giving a greatest impact on reducing NCD mortality and morbidity.12 So prevention, early detection and management of these risk factors would be An effective option in controlling these NCD and to reduce the disease burden.5,6

Globally, for the last three decades NCDs have been the commonest cause of death and disability. NCDs contribute a third of the disability-adjusted life year burden even in sub-Saharan

Africa. However, very less resource is allocated to NCD research in developing countries. High prevalence of non communicable disease is a big challenge in most of the developing countries.

According to the global risk report states that threat of chronic NCDs is rated above the current global financial crisis and the probable cost has been estimated from $250 billion to $1trillion.7

Studies suggest that despite repeat calls for action, the NCD burden is increasing and remaining unattended in developing countries.8

World health Organization (WHO) has declared NCDs as a growing threat globally especially in developing countries as an extra burden due to NCDs over other communicable diseases in such developing countries.9 Out of the total 60% of deaths due to NCDs, 80% occur in developing countries. NCDs are responsible for maximum number of deaths in working age group globally affecting more of younger age group in poor countries than in the rich countries.

It suggests that the potential of young workers of a poor country becomes unavailable due to the higher incidence of NCDs among them pointing to the seriousness of the situation.10

The rapidly increasing burdens of NCDs in the developing countries have thus contributed significantly to the increasing poverty in these regions. Prevalence of NCDs in these countries is also a major barrier in developmental and poverty reduction initiatives. This threat of NCDs is a major challenge in the path of achieving Millennium development goals (MDGs). The greater risk of socially disadvantaged people getting exposed to harmful products, like tobacco, unhealthy food, having limited access to health services makes them victims of morbidities and mortalities of NCDs faster than people of higher socio economic status. Thus NCDs also creates an increase in social inequalities by effecting poorer people.1,11 In spite of its great relevance taking into account its contribution to the major portion of the global disease burden, chronic diseases have not been given the necessary attention.

Migrant workers from the Indian subcontinent have been streaming steadily in to Arabian

Gulf for over four decades driven by poverty, perceived wage differences and constant demand for cheap labor. Middle-east migration has already entered the policy agenda but the focus has been limited only to the economic aspects. Very little attention has been offered to the adaptation and survival of migrants in the host country and associated health impacts.13

In this scenario it is essential to explore the levels of non communicable disease risk factors in the community especially of the migrant workers who spent considerable part of their life time in foreign countries and contributing extensively to our national and state economy. They are highly vulnerable to the disease risk factors due to the forced adaptation to the different cultures, lifestyles living and working environments of the host country.

2. REVIEW OF LITERATURE:

2.1. Global scenario NCDs and risk factors

As we advance further into the twenty first century, it is found that the challenges posed by NCDs present an impending threat to the people worldwide. The maximum effects of these risk factors fall mainly on the developing countries, and comparatively among poorer people reflecting the underlying socioeconomic determinants. NCDs are the major causes of death in all regions. It is predicted that, by 2020 largest increases in deaths due to

NCDs will be in Africa. The cost of NCDs due to lengthy and expensive treatment and loss of breadwinners are very high. As a result of this almost 100 million people are forced into poverty annually.14

These behavioral risk factors lead to four key physiological changes that increase the risk of

NCDs like raised blood pressure, overweight/obesity, hyperglycemia and hyperlipidemia.

The leading NCD risk factors are elevated blood pressure to which 13% of global deaths can be attributed, tobacco use to which 9% of deaths can be attributed, raised blood glucose to which 6% of deaths can be attributed, physical inactivity to which 6% of deaths can be attributed and overweight and obesity to which 5% of global deaths can be attributed.1,15

In 2005, WHO had re-emphasized to address chronic NCDs as it has been a neglected health issue.16 It has been estimated that an additional 2% reduction of yearly mortality rates from NCDs would avert 24 million deaths by 2015, leading to 80% of life years gained from the life saved in those who are younger than 70yrs. This can also contribute to savings of almost 10% of expected loss in income in developing countries.17

It is predicted that, by 2020 NCDs disease will contribute to 80% of the global burden of diseases and will also be responsible for seven out of every ten deaths in developing countries. Cardiovascular diseases are reported to be the major cause of deaths worldwide.

The death rates of cardio vascular diseases (CVDs) have shown a declining figure in developed countries and an opposite scenario in developing countries. There is a sharp increase in death rates due to cardio vascular diseases with an estimated rate of 80% burden of CVDs. These are contributing to an estimated rate of 17 million deaths per year globally.18

Hypertension is a highly prevalent cardiovascular risk factor worldwide because of increasing factors like obesity and longevity. Even though the treatment of hypertension has prevented cardiovascular diseases and enhanced life, hypertension is still remaining inadequately managed. Globally, high blood pressure accounted for more than 20% of all health loss in adults aged 70 years and older in 2010, and around 15% in those aged 50-69 yrs contributing to highest number of DALYs. It has been estimated that 62% of cerebrovascular disease and 49% ischemic heart diseases are due to high to systolic blood pressure.2 It is estimated that by 2025, the total number of adults with hypertension will become 1.56 billion indicating an increase of 60% of which maximum will be contributed by developing countries. Developed countries are expected to have an increase of 24% in the total number of hypertensive from 333 million to 413 million. It is also expected that there will be a sharp raise of 80% in the total number of people with hypertension indicating that by 2025 three quarter of the world’s hypertensive population will live in developing countries. It is also noticed that the incidence of cardiovascular diseases are at an younger age in developing countries compared to developed countries compared to developed

5 countries. It is reported that deaths due to CVDs are 46.7% in developing countries compared to 26.5% in developed countries in the year 1990.19

It is estimated that the prevalence of diabetes in 2011 is 366 milion and this number is expected to increase to 552 million by the year 2030. Among this 80% of the diabetics are contributed by low and middle income countries .in the in the year 2011 the deaths due to diabetes is estimated to be 4.6 million. There is a worldwide increase noticed in the incidence of type2 diabetes. It is estimated that 78,000 children develop type 1 diabetes per year. 20

According to WHO reports its evident by the year 2030 diabetes will be the 7th leading cause of death it is also estimated that in the year 2004 diabetes and its complications killed

3.4 million people globally having more than 80% of these deaths from low and middle income countries. It is understood that modifying behavioral risk factors and maintaining adequate body weight can prevent or postpone the incidence of type-2 diabetes. 21

It was estimated that the prevalence of diabetes for all age-groups is up to 2.8% in

2000 which is projected to become 4.4% in 2030. It is also reported that the total number of people with diabetes in 2000 was 171 million which is projected to raise up to 366 million in

2030 Increasing proportion of aged people and sharp growth in urban life are important demographic changes influencing prevalence of diabetes.22 In 2007, it was estimated that 246 million people are living with diabetes globally. The steadily increasing prevalence of diabetes mellitus is a matter of particular concern that by 2030 there will be 438 million people with diabetes and the most affected age group is between 40-59 years. 23,24

The analysis of global burden of disease risk factors reports that tobacco use including second hand smoking is the second leading risk factor contributing to global disease burden and remained the foremost risk factor in high income parts of America and

Europe in 2010.2 Tobacco is the forth common risk factor for non communicable disease and second major cause of death worldwide. Tobacco is smoked, chewed or inhaled. More than one billion people use tobacco everyday and almost 15000 people die because of the diseases resulting from tobacco use. Tobacco is found to be a significant risk factor of tuberculosis, also accounts for almost half of tuberculosis induced deaths in India. 25 Out of the 1.3 billion smokers in the world 82% are living in developing countries. Mortality due to tobacco use is more among men compared to women. But the female mortality is predicted to increase considerably in developed countries due to the sharp increase in tobacco use among women.

26 Tobacco accounts for almost six million deaths every year out of which 5.1 million deaths are from direct tobacco smoking and more than half a million deaths are from exposure to second-hand smoking. By the year 2030 the tobacco induced deaths are projected to increase up to 8 million contributing 10% of all global deaths.1 Tobacco accounts for an estimated loss of $200 billion yearly, 4.9 million deaths and a loss of 59.1 million DALYs. The number of deaths is predicted to rise up to 10 million by 2020 and seven million of these deaths will be contributed by developing countries especially by India and China alone.27 By successfully reducing the prevalence of tobacco use by 5% by 2020 we will be able to prevent a minimum of 100 million premature deaths. 28

According to the analysis of attributable risk factors alcohol use is reported to be the third major risk factor contributing to global disease burden and in 2010.2 Developing countries are having a higher rate of increase in alcohol consumption. Out of the 2.3 million annual deaths from harmful drinking half are contributed by NCDs.1 The pattern and volume of alcohol consumption vary geographically. High alcohol intake (75 gms or more per day) is an independent risk factor for all NCDs and also damages the organs like liver and pancreas

7 and increases the risk for diabetes and obesity.29 Harmful use of alcohol contributes to 2.5 million deaths per year globally.30 WHO reports that a minimum of ten thousand million people regularly use alcohol globally and alcohol use accounts for 3.2%of all deaths 4%of disease burden and 3.5% of DALYs lost due to all causes globally.31

High body mass index is yet another significant risk factor for NCDs. Its reported that there is an increasing Prevalence of high body mass index globally mainly in the high income regions of the world.2 Obesity is among the commonest expression of unhealthy diet and most prevalent malnutrition all over the world. Obesity is usually associated with lack of physical activity and unhealthy diet. There has been a dramatic increase in the prevalence of obesity over the past two decades globally. WHO reports that over 1 billion adults are overweight and about 300 million of these are obese.32 There has been a sharp increase in the number of overweight and obese people in the developing countries especially in South East

Asia compared to Europe and united states.33 Countries with fast growing economies are having high incidence of obesity. Developing countries contributes about 250 million people with obesity and 12.6% of women and 9.3% men are obese in India.34

Diet with low fruits and vegetables is ranked fifth globally among risk factors attributable to global burden of diseases, it is ranked third among attributable risk factors in east and south east Asia indicating its importance in Asian countries.2 Over the past few decades there has been a massive shift in dietary patterns mainly in developing countries.

Economic, technological and social changes happening in these countries are the factors influencing transition of these dietary habits and these changes are more striking in the developing countries.33 Globally Almost 1.7 Million (2.8%) deaths and 16.0 million 1.0% disability adjusted life years are attributable to low fruit and vegetable consumption per year.

Adequate consumption of fruit and vegetables can reduce the risk for cardiovascular diseases, stomach cancer and colorectal cancer.35

Lack of physical activity is another important and independent risk factor for chronic non communicable disease also the fourth leading risk factor for mortality.1 It is the important determinant of energy expenditure and overweight.36,37 low physical activity is ranked 10th according to attributable risk factor survey globally but ranked 4th in high income Asia

Pacific regions.2 In developing world, burden of physical inactivity and sedentary life styles are shifting from rich to poor.33 About 3.2 million deaths and 32.1 million DALYs representing about 2.1% of global DALYs annually can be attributed to insufficient physical activity. Lack of physical activity can contribute 20% to 30% higher risk for all-cause mortality. It is estimated that 150 minutes of moderate physical activity a week or its equivalent can reduce the risk of ischemic heart disease approximately by 30%, the risk of diabetes by 27% and the risk of breast and colon cancer by 21%-25%. Developed countries are found to have more than double the prevalence of physical inactivity compared to developing countries among both sexes. In developed countries 41% of men and 48% of women are insufficiently physically active compared to 18% of men and 21% of women in developing countries.37

2. 2. Indian scenario

Studies have shown that there is an increasing burden of NCDs in India, 53% of all deaths and 44% of the disability adjusted life years lost can be attributed to NCDs.38 It has been understood that a major share of out of pocket expenditure of Indian households goes for NCDs pointing towards the growing burden of NCD’s and the financial impact.39 It was found that in India, cardio vascular diseases cause 29% of all deaths. India has the maximum

9 number of potential life years lost in the world which is estimated as 9.2 million in the year

2000 and this situation is expected to further get aggravated up to 17.9 million years by the year 2030.38

It is estimated that in 2003 there was 29.8 million people with cardiovascular diseases killing 1.5 million people every year.40 In India the cases of cardiovascular diseases are is expected to raise from 29 million in 2000 to 64 million in 2015 with a higher prevalence in young adults.41

There is a massive increase in the prevalence of hypertension in India in the last three decades up to 30 times more among the people living in urban area and 10 times among those in rural areas of the country. In 1980 the prevalence of hypertension among urban population was 6.6% which increased to 36.4% in 2003. However studies show that the prevalence is different among different communities and varies across different populations. it was estimated that the prevalence of hypertension is 73% among Parsi community in western India in the age group equivalent to 70 years, 51.8% among Keralites in southern

India and 63.6% among Assamese in north eastern state of India.42 In the year 2000 hypertension was found to be affecting 118 million population in India and it is estimated to grow up to 214 million by 2025.43 A study in “Kerala state” which is considered as the harbinger of the future for rest of India, reported high prevalence of hypertension (30%) comparable to that in united states.58

The trend of diabetes in India has changed alarmingly in the recent years. There is a sharp increase in the Prevalence of diabetes also associated with an increase in prevalence of impaired glucose tolerance as indicated by the recent studies. It’s reported that Prevalence of diabetes varies 16% in urban India to 3% in rural India. Like other developing countries in

India the prevalence of diabetes is higher among affluent urban living people different from the trends of diabetes in west. From the year 1980, the slow increase in the trend of diabetes came up to 11% among obese sedentary urban Indians. The prevalence is widely different among different groups of people.44, 45 India is leading the world having largest number of diabetic people, hence termed as the “diabetic capital of the world”. According to Diabetes

Atlas 2011 published by the international Diabetes Federation, in India there are 61.3 million diabetics and is expected to raise 101.2 million by 2030 unless effective preventive measures are taken.20 ,46

Tobacco use is a major public health problem in India where 57% of men and 10.8% of women in the age group of 15-49 years are using tobacco in some form.48 Studies report a constant increase in tobacco use in any form. The prevalence of smoking was around 34% and that of smokeless tobacco was up to 38%.47 Other than smoking, India is characterized by the use of smokeless tobacco. Common usages of smokeless tobacco is chewing ,applying to teeth or gums or sniffing .The most prevalent forms of smokeless tobacco are betel quid with tobacco, Gutkha , Khaini and Panmasala.51 Studies report that the prevalence of tobacco use in men and boys aged 12-60 years is 55.8% ranging from 21.6% in those aged 12-18 years up to 71.5% among 51-60 years of age group.49 A study reports that prevalence of smoking varies from 40% among men to 30% among women, and prevalence of the smokeless tobacco is 7.1% among men and 1.2% among women.50 Global adult Tobacco Survey-2010 reports users of smokeless tobacco among males increased from 24% to 36% between NFHS

2 and NFHS 3.51

Use of alcohol largely varies among different regions of India. A study reports that about 2.5% of men are reported to be heavy drinkers drinking more than five standard drinks

11 in a week.55 In Gujarat one of the western state of India, the prevalence of alcohol consumption is 7% and 75% in Arunachal Pradesh which is a north eastern state of India.

The prevalence of alcohol consumption was found to be significantly higher among tribal, rural and low socio economic urban sections.52 Nearly 62.5 million people drink alcohol in

India and the per capita consumption is around four liters per adult per year. The average age of alcohol consumption has found to be declining by nine years and reached to 19 years of age. It is also predicted that the age of first alcohol drink will reach to fifteen years.53

It’s reported that the prevalence of low physical activity is 12.4% with an increasing prevalence as the age increases; it is also found that urban living individuals are two times more likely to have low physical activity compared to rural living individuals.55 Studies report that only 14% of the people were involved in regular non occupational physical activity.54 It was found that 61-66% of men and 51-75% of women were having low physical activity while 56% of urban population and 18.35% of rural population did not have adequate physical activity. It was estimated that 7.3% of the population are overweight and 1.2% of the population are Obese in India. Upper income groups, urban living and elderly are found to be more prone to physical inactivity and obese. 55

There is a huge shift noticed in the dietary pattern of Indian’s in the past century. It is reported that there is a shift towards high fat low carbohydrate diet in India. Consumption of coarse grains like rye, millet, maize etc has been shifted to rice and wheat among all economic groups but more prominent in high income and urban population. The fat consumption including dairy products, highly saturated ghee, and sugar products have increased across all income groups in the country.56

2.3. Kerala Scenario

Kerala is the state with best health indicators in the country the sharp declines in fertility and mortality rates in the state has given rise to epidemiological transition with high mortality and morbidity from chronic diseases. Kerala state also has high literacy, high life expectancy and socioeconomic progress. But the prevalence of lifestyle diseases like obesity, high blood pressure, heart disease and diabetes are high. This is resulting in a very high mortality and morbidity from NCDs.57 Kerala is one of the most advanced states in rapid health transition. High burden of NCD risk factors is comparable to the United States of

America according to a large community based study conducted during 2005-2008. High prevalence of NCD risk factors was found across all the socioeconomic and demographic categories of population in Kerala.58 A study done in 2002 reveals that 49% of the deaths in the community are due to cardiovascular diseases and high prevalence of chronic diseases and risk factors.59 Prevalence of high blood pressure is found very high up to 30-36% which is comparable to the prevalence estimates in United States Of America (USA).58

Kerala is the diabetes capital of India with a prevalence of 20% which is the double the national average of 8%. In a large multi-center study, the prevalence of diabetes in

Thiruvananthapuram was found as 17% compared to 15% in Hyderabad and New Delhi, 4% in Nagpur and 3% in Dibrugarh.46, 60 A study describing the findings from the evaluation of a

Diabetes Prevention Program in Kerala, India (K-DPP) reports very high Prevalence of risk factors for diabetes and in an increasing pattern in Kerala. This can be largely attributable to the rapid changes in the lifestyle of people. This study provides strong support for undertaking more research into the conduct of community-based diabetes prevention in the rural areas of Kerala.61 In a community based study, high prevalence of smoking and alcohol

13 consumption was found among males. About two fifths (40%) of the people were current smokers and current users of alcohol (41%). Both of these habits were initiated at an early age of 21yrs.62 The study done in 2002 reveals that prevalence of Tobacco use is 46.7% and prevalence alcohol consumption is 31.3% and both risk factors were found more among men compared to women.59 A study done under IDSP project in Kerala reports the prevalence of any tobacco use among men as 29% and current alcohol use as 24%64 Tobacco use was found high in Keralites having 45% among rural men and 43% among urban men. Kerala also has the highest rates among slum dwellers (63%). Smoking in Kerala is almost double that of national average as well as the US average.58,63

A study done in Kerala reports that prevalence of physical inactivity is 9.5% in urban areas, 6.3% in rural areas and a total prevalence of 6.9%. This study also reports that Physical inactivity was associated with greater prevalence of overweight, abdominal obesity and hypertension.58 Physical inactivity was noticed in almost a quarter of the target population including 23% males and 22% females. 33% of females and 17% of males were found obese and 23% reported stress. Smoking, alcohol consumption, stress and unhealthy diet was found considerably high among low socio-economic group.62 Another study reports the prevalence of low physical activity in Kerala 76% ranging from 79% among urban and 75% among rural population.64

It is reported that the prevalence of low fruits and vegetable consumption is ranges from 35-70% in Kerala compared to national prevalence of 80-90%.63 Study conducted by ministry of family health and welfare reports the prevalence of low fruit and vegetable consumption as 87%.64 A study reports the 38% of urban living and 40.3% of people living in rural area are having low fruits and vegetable consumption. High BMI, waist

14 circumference, and obesity were found more prevalent in urban men and women compared with their rural counterparts. The overall prevalence of risk factors is noticed 50-100% higher in Kerala than the national average with narrow urban-rural gradient.58,63 Another study reports the Prevalence of central obesity in Kerala as 43% ranging from 60% among females and 23% among males. This study also reports the prevalence of overweight as

27/%.64

High prevalence of NCD risk factors were found across all the socioeconomic and demographic categories of population in Kerala as reported in a study conducted by Ministry

Of Health And Family Welfare In 2007-2008.64 The high prevalence, poor detection and control of NCDs and its risk factors in a state like Kerala with the highest standards of health care and literacy calls for an immediate public health attention.

2.4 Occupation related factors:

Evidence substantiates that long working hours work and Inadequate sleep contributes to cardiovascular risk factors like hypertension, diabetes, atherosclerosis and compromise mental health resulting in premature mortality. 92,94,96 Over time work can in turn cause less sleeping hours. Lack of adequate sleep is reported to trigger the sympathetic nervous system resulting in increased heart rate; blood pressure and cardio vascular risk factors. Individuals who sleep less than six hours/day are reported to be at higher risk for heart diseases compared to those who sleep more than six hours/day.93, 95, 96

2.5. Migration and NCDs:

Migration is an important issue that affects health globally. It has been proved that migration is an important factor in developing NCDs and risk factors. In 2010 it was estimated the international migrant population of 214 million are living outside their country

15 of birth which contributes to 3.1 % of the global population. It’s estimated that 86% of the total population in Qatar, 70 % of the total population in United Arab Emirates, 69 % of the total population in Kuwait, and up to 10 % of the population of other high income countries are contributed my international migrants. There is an increase of international migration at

2.9 % of annual growth rate with different types of migration (labour migration, refugees etc). These factors complicates the situation further calling for an immediate need for policy interventions. High and challenging prevalence of NCDs such as cardiovascular diseases

(CVD), diabetes and risk factors has been established by the research findings in the countries of origin of key migrant categories.65,66

2.6. Studies on NCD prevalence in Indian immigrants:

Studies on immigrant Asian Indians in other countries including US and UK and the comparison with their contemporaries in India showed Higher prevalence of cholesterol, diabetes, hypertension and obesity.67,68 A review of the evidence suggests important role of environmental factors making an increased risk of obesity and Type 2 Diabetes Mellitus

(T2DM). Urbanization, mechanization, and changes in nutrition and lifestyle behaviors are said to be the contributory factors for this phenomenon. However, the role of stress and unknown factors are still not explored.69,70

South Asian ethnicity has been identified as a major risk factor for Chronic Heart

Disease (CHD) and risk factors.71 South Asians were to have a one in three lifetime risk for the development of diabetes, also vulnerability for developing the condition, ten years earlier than Europeans. Along with the ethnic predisposition, westernized lifestyle after immigration resulted in an early onset and high prevalence rates of diabetes and Metabolic Syndrome among Asian Indians in United States (US).72 There is high prevalence of adverse body fat ,

16 high lipid profile and insulin resistance beginning at a young age. This finding has been consistently recorded in Asian Indians in all geographic locations.72,73 Indian immigrants were found to have a high incidences of overweight, with minimal exercises while in the host country as per studies done in US.74 A population based study done in Singapore showed, one in three migrant Indians are having diabetes and was found to be significantly higher than those reported in India.75 A systemic review of literature shows that the average values of Body Mass Index (BMI), blood pressure, lipids and blood glucose of the migrant Asian

Indians were higher compared to their counterparts in India.72,73,76,77 A study done on migrant

Asian Indians living in the United Kingdom (UK) found levels of blood pressure, obesity, blood glucose, total cholesterol, and insulin resistance higher than their siblings in Punjab.78

In a comparative study done between migrant and non migrant Gujaratis, it was found that the NCD risk factors such as high BMI, blood pressure , dietary intake of calorie and fat were high among the migrants.79 It has been observed in a study that the migrant Asians in the US were least likely to be taking regular exercise, and had a lower awareness of cholesterol or an appropriate diet in spite of many efforts for public awareness.80 A comparative study on

London migrants with native Indians reports that the migrants were on an average 10 kg heavier and had much higher prevalence of hypertension compared to the native counterparts. 97,98

2.7. Rationale for the study

Chronic anxiety, homesickness and isolation are leading to depression as well as

stress related health issues in migrants. This can increase their risk for chronic diseases and

risk factors pushing them in to morbidity and mortality. Migrants usually gets excluded

from studies and their issues remain unreported.65,66, It is evident that the migrant Indians

are having higher prevalence of NCDs and NCD risk factors compared to the native

population from many of the studies done in the US and Europe.73,74,75,78,79,80 It is also

understood from the literatures that that the lifestyles in the middle east has changed over

the period of time and traditional healthy diet and practices are replaced by unhealthy

lifestyles and food habits. Hence, in the process of acculturation, migrants from south Asia

to these Middle East countries could have an increased risk of developing NCDs and risk

factors.99, 100, 101

There is a high prevalence of hypertension, dyslipidaemia, diabetes and risk factors

among gulf migrant workers according to observations done by practicing doctors in the

community and there are anecdotal reports supporting this. Therefore it is essential that the

health issues of migrants have to be identified and prioritized in national health policies and

laws. Taking into consideration the large number of Keralites working in gulf countries

since 1950’s and the economic contributions they had been making to our national and state

economy makes the need of research more convincing. 83, 84 To the best of my knowledge,

there are no available literatures on the health issues of gulf migrant workers from India.

Understanding the pattern of NCDs and risk factors among the gulf migrants is essential to

address the health issues of this population.

2.8. Objective of the study

To compare the prevalence of Non communicable disease risk factors among gulf migrant workers and non migrant workers of Malappuram district, Kerala

3 METHODOLOGY

3.1 Study setting

Malappuram is one of the backward districts of Kerala. According to 2011 census total population of the district is 41,10, 956 spread over an area of 3550 km2. Malappuram

District has 5 municipalities, 14 block Panchayats and 100 Grama Panchayats 85,86. Migrants to gulf countries (Gulf countries in this study is operationalized as countries in the Middle

East region including UAE, KSA, Qatar, Oman, Kuwait and Bahrain) accounts for 95.9 percent of the total international migrants from Kerala. 94.8% of Keralite migrants to gulf consist of males. Malappuram is the district with maximum number of gulf migrant workers in the state making up to 18.2 percent of the total number of migrants from Kerala. The remittance received by Keralalites from emigrants to the Gulf countries is popularly known as “Gulf

Money”. This is an important contribution to the economy of the State. The most important factor for the development in Malappuram was the remittance of Gulf migrants. Malappuram received nearly 20 percent of the total remittance to Kerala in 2011. Tirur taluk is having maximum number of gulf migrants and the largest remittance in Malappuram district. There are 53.4 emigrants corresponding to every 100 house hold in Malappuram. Migrants to gulf countries are mainly unskilled laborers and 64.1% of them are literates with schooling below

SSLC, also 91.3 percent of the gulf migrant did not have their families with them in the working country 83,84

3.2 Study design

Comparative cross sectional study

3.3 Sample size estimation

The sample size was calculated in Open Epi software version 2. Based on the pilot

study. The prevalence of hypertension among gulf migrant workers and non migrant

workers was taken as 30% and 53% respectively. Taking these prevalences the sample

size was calculated. The alpha error was fixed at 5% with 80% power. Since the cluster

sampling method was used the sample size was adjusted for a design effect of 2, and

sample size was calculated as 320. Accounting for a non-response rate of 20%, the final

sample size was calculated as 384. This was rounded as 400 with an 1:1 ratio in both

groups (Migrants 200 and non migrants 200)

3.4. Sample selection procedure

Multi stage cluster Sampling was performed. In the first stage of sampling Out of

the 15 Block Panchayats of Malappuram district, Tanur Block Panchayat was randomly

selected for the study. In the second stage of sampling out of the total 9 Grama-Panchayats

of Tanur Block Panchayat, four Grama Panchayats (Thanaloor, Tanur, Ponmundam, and

Cheriyamundam) were randomly selected. In the third stage of sampling, from each of

these selected Panchayats five wards were randomly selected which was the study area 87.

Two clusters of 10 individuals were identified for both gulf migrant workers and non

migrant workers from each of these selected wards. From a randomly selected household of

each selected ward all consecutive households were screened until 10 subjects meeting

inclusion criteria for both gulf migrant worker and non migrant worker was recruited for

the study making a cluster in both groups. From one house hold one subject was recruited 20

.The study was conducted in the period of July-September which is the vacation time in gulf countries and Ramadan festival time. Most of the gulf migrant workers had come on vacation which made the sample selection of gulf migrants easier 88.

Figure 1 Schematic Representation of sample selection

MALAPPURAM DISTRICT

TIRUR TALUK

TANUR BLOCK PANCHAYAT

TANUR TANALUR CHERIYAMUNDAM PONMUNDAM PANCHAYAT PANCHAYAT PANCHAYAT PANCHAYATH

WARDS - 1, 2, WARDS - 5, 6, WARDS - 1, 2, WARDS - 6, 7, 5, 7, 12 7, 12, 17 4, 15, 16 8, 10, 15

10 migrants & 10 migrants & 10 migrants & 10 migrants & 10 non migrants 10 non migrants 10 non migrants 10 non migrants from each ward from each ward from each ward from each ward

Migrants- 50 Migrants- 50 Migrants- 50 Migrants- 50

Non migrants- 50 Non migrants- Non migrants- 50 Non migrants- 50 50

Total no. of migrants selected (N) = 200 21

Total no. of non-migrants selected (N) = 200

3.5. Study Participants

3.5.1. Inclusion criteria

Gulf migrant workers: Individuals in the age group of 25-64 years; Permanent residents of Malappuram district, Non Resident Keralites worked in gulf countries for a minimum of five years and those who have returned from gulf within one month of the survey

Non migrant workers: Individuals in the age group of 25-64 years, Permanent residents of Malappuram district (residing in the district for a minimum of last five years) and those who have not worked outside Kerala

3.5.2. Exclusion criteria

Those who are not willing to provide a written informed consent and those who are suffering from debilitating diseases

3.6. Data collection techniques

The data collection was conducted for a period of three months from June15 2013 to September 15th 2013. The data were collected by the principal investigator with the help of a male assistant who was trained using WHO STEPS training Manual prior to the starting of data collection. Demographic information, behavioral risk factor profile and history of hypertension and diabetes (STEP 1 Variables) were obtained using interview technique (STEP 1 questionnaire)

Anthropometric measurements (STEP 2) in which height, weight, waist circumference and blood pressure were measured as per the guidelines given by WHO

STEPS instrument for chronic disease risk factor surveillance. As recommended by 22

STEPS Manual the physical measurements (Step 2) were taken immediately after the

behavioral measurements (Step 1) as the participants were already seated for at least 15

minutes while collecting Step1. BP was measured using OMRON automatic BP

apparatus (Total three reading were recorded, the mean values of second and third

readings were taken for data analysis). Height was measured using Stadiometer Weight

was measured using SECA weighing machine and Waist-circumference was measured

using SECA constant tension tape.

3.7. Participant flow

Gulf Migrants: out of the total 224 gulf migrants approached 200 consented for participating in the study. From this 9 were eliminated due to inconsistencies and incompleteness resulting in an effective sample size of 191

Non migrants: out of the total 228 non migrants approached 200 consented for participating in the study. From this 7 were eliminated due to inconsistencies and incompleteness resulting in an effective sample size of 193

3.8 Data storage

All the data are kept with the principal investigator who is bearing the complete responsibility of the safety and confidentiality of the data. All the hard copies of the filled interview schedule and consent form are strictly confined to the personal locker of the principal investigator in sealed covers. All the soft copies are safely stored in a computer hard drive after entry with password encryption of the file. Data will be kept with the principal investigator for any future references. 23

3.9 Data Analysis and Statistical Measures

The data was entered in Microsoft excel (2007 version) and then scrutinized in the same software. It was then imported in to SPSS for window version 17.0 for analysis.

Statistical significance was fixed at P value < 0.05. Descriptive analysis was done to study the sample characteristics and to compare the prevalence of risk factors in both gulf migrants and non migrants. Student’s t-test was used for testing statistical significance of continuous variables. Chi-square test used for was used for testing statistical significance of categorical variables. Univariate Logistic Regression is used to test the significance and to estimate the odds ratios of the relevant variables. Multivariate Logistic regression is used to fit the model, all significant variables in the univariate analysis and biologically plausible variables affecting the outcome variable were included in the model.

3.10. Variables in the study

3.10.1 .STEP 1 Variables: a. Socio-demographic variables: Age, sex, education, caste, monthly income, marital status, working country, duration of migration. b. Occupation related variables: Type of the current job

Number of working days in a typical week, Number of working hours on a typical day and sleeping hours on typical day were assessed as from the formative research it was understood that gulf migrant workers are forced to work on holidays and daily overtime which could contribute to the personal stress making them vulnerable to NCDs and risk factors82,92,96,99,100 24

Behavioral variables c. Tobacco use variables:

Ever smoked tobacco: Ever smoked any tobacco products such as cigarette, bidis etc

Current smokers: Smoked cigarettes, bidis etc in the last one month

Ever used smokeless tobacco: Ever used any smokeless tobacco such as snuff, chewing tobacco, betel etc

Current smokeless tobacco: Used any smokeless tobacco such as snuff, chewing tobacco, betel etc during the last one month

Current use of both smoked and smokeless tobacco: Used both smoked and smokeless tobacco during last one month d. Alcohol use variables:

Ever consumed alcohol: Ever consumed an alcoholic drink such as beer, wine, spirits, fermented cider, etc

Current alcohol use: Consumed an alcoholic drink with in the past 30 days e. Diet variables

Less than five servings of (< 5) fruits and vegetables and more than or equal to five servings of (≥ 5) fruit and vegetables servings per day based on WHO guidelines f. Physical activity

Physical activity was captured in three domains of activity at work; travel to and from

places and physical activity at leisure time. Based on MET Minutes per week. Physical

activity was classified in to three groups’ viz. High, Moderate and Low. This was 25

calculated using the Metabolic Equivalents (MET). The calculation is done using formulas taking in to account the number of days involved in physical activity and the time spent for each activity

High physical activity: More than or equal to 3000 MET Minutes per week

Moderate physical activity: 600 to 2999 MET Minutes per week

Low physical activity: Less than 600 MET Minutes per week g. Sedentary habits : Time spend sitting or reclining on chair per day (in Minutes ) h. History of hypertension: Awareness, treatment and control of hypertension i. History of diabetes : History and treatment of self reported diabetes

3.10.2. STEP 2 Variables

1. Abdominal obesity

2. Overweight

3. Hypertension

3.11. Definitions of STEP 2 Variables

Abdominal obesity: Abdominal obesity was defined as waist circumference > 90 cm for males and ≥ 80 cm for females*

Overweight: Overweight was defined as BMI > 25 Kg/m2**

Hypertension: Hypertension was defined as systolic blood pressure ≥ 140 mm of

Hg or diastolic blood pressure ≥ 90 mm of Hg or currently taking any medication for hypertension***

(* International diabetes federation ** WHO *** JNC 7) 26

3.12 Ethical considerations

Ethical clearance was obtained from institutional ethics committee (IEC) of SCTIMST.

Confidentiality: The identity of each participant was kept anonymous from the stage of data collection as in the entry form, where only the dummy ID numbers are shown.

Privacy: The interview was conducted at household of the respondent, as per the convenience of the respondent. Visual and auditory privacy was strictly ensured

Consent: Informed consent was obtained from the subjects prior to the start of the interview

[in local language – Malayalam] after being read, understood and any doubts clarified to the subject. The subject had the freedom to refuse at the outset or even withdraw from the study at any stage and to refuse answering to any of the questions. No monetary benefits or favors in any form was offered for participating in the study

4. RESULTS

In this chapter the results of data analysis is described. The data was analyzed using

SPSS for windows version 17. Comparison of non communicable disease risk factors among gulf migrant workers and non migrant workers was done after identifying the baseline characteristics in both groups.

Sample information: Out of the total 452 selected participants 400 consented to participate in the study with a response rate of 88.45%. The total sample consisted of 191(49.7 %) migrants (response rate 89.3%) and 193 (50.3%) of non migrants with a response rate of

87.2%

4.1 Baseline characteristics and Magnitude of risk factors:

Baseline characteristics and comparison of prevalence of risk factors in the study sample is described under the following domains. The details of each variable are provided for both gulf migrants and non migrants simultaneously. Whenever the comparison is required the test for statistical significance test is performed.

4.1.1. Migration characteristics 4.1.2. Age distribution 4.1.3. Socio demographic profile 4.1.4. Occupation related factors 4.1.5. Personal and family history of NCDs 4.1.6. Tobacco use 4.1.7. Passive smoking 4.1.8. Alcohol use 4.1.9. Dietary habits 4.1.10. Physical activity 4.1.11. Sedentary Habits 28

4.1.12. Weight, height and waist circumference 4.1.13. Abdominal obesity and overweight 4.1.14. Systolic pressure, diastolic pressure 4.1.15. Hypertension 4.1.16. Awareness treatment and control of hypertension 4.1.17. History and treatment of self reported diabetes 4.1.18. Crude odds ratios of Anthropomertic risk factors 4.2. Logistic regressions

4.2.1. Univariate logistic regression with hypertension as outcome

4.2.2. Multi variate logistic regression with hypertension as outcome

4.2.3. Univariate logistic regression with abdominal obesity as outcome

4.2.4. Multi variate logistic regression with abdominal obesity as outcome

4.3. Duration of migration and anthropometric risk factors

4.1.1 Migration Characteristics (Table 1)

Out of the total migrants (N=191) 53.4% were working in UAE; 21.4% were from

KSA; 11% were from Qatar, 7.8% from Oman and 6.28% were working in Kuwait and

Bahrain. Duration of migration was categorized as less than 10 yrs, 10-20 yrs and more than

20 yrs. 43.5% of the migrants were working in gulf for less than10 yrs , 38.2% of the migrants were working in gulf for 10-20 yrs and 18.3% of the migrants were working in gulf for more than 20 yrs . The mean duration of years working in gulf was 14.59 years with a minimum of 5 years and maximum of 38 years.

Table 1. Duration of migration

Duration of migration (in years) <10 yrs 10-20 yrs >20 yrs

n (%) 83 (43.5) 73 (38.2) 35 (18.3) Source: Primary survey, 2013 Malappuram

4.1.2 Age distribution (Table 2)

The overall mean age of the participants was 40.63 yrs with a minimum age of

25years and maximum age of 60yrs. Among migrants the mean age is 41.7years and in non migrants 39.5 yrs. In the total sample 52.9 % belonged to less than or equal to mean age and

47.1 % belonged to more than mean age. Among migrants 45% belonged to less than or equal to mean age and 55% belonged to more than mean age. Among non migrants 60.6% belonged to less than or equal to mean age and 39.4% belonged to more than mean age.

Table 2. Age distribution

Age (in years) Mean (SD) Minimum Maximum P value* Migrants (N=191) 41.7 (8.7) 25 60 < 0.05 Non-migrants (N=193) 39.5 (10.4) 26 60 Total (N=384) 25 40.6 (9.6) 60

Age groups Migrants Non-migrant Total P value** N=191 N=193 N=384 n (%) n (%) n (%) 1 ≤ Mean age 86 (45) 117 (60.6) 203 (52.9) < 0.05 1 > Mean age 105(55) 76 (39.4) 181 (47.1) Source: Primary survey, 2013 Malappuram 1 For migrants, mean age = 41 yrs. For non migrants, mean age = 39 yrs *student’s t test **chi square test

4.1.3 Socio demographic profile (Table 3)

The population of Malappuram district is predominantly of Muslims. In the study sample majority was Muslims and other religions mainly Hindus and a very few Christians.

Hindus and Christians were clubbed together categorized as “others”. 71% of the total samples were Muslims and the remaining 28.38% belonged to “others”. Among Non migrants 71.6% were Muslims and 35.2% belonged to “others”. Among migrants 78.53% were Muslims and the remaining 21.4% belonged to “others” category.

In the educational status was categorized as low (up to high school level) medium (up to higher secondary school) and high (university educated ) among migrants 55.5% had low level education, 24% had medium level education and 20.4 % were highly educated. Among non migrants 58 % had low level education, 18.1% had medium level education and 23.83% were highly educated. In both groups more than half of the samples belonged to low education group.

Socio economic status was assessed by taking monthly income of the participants. As expected gulf migrants were found to have higher monthly income than non migrants due to the currency conversion rates of gulf. However the Socio economic status was comparable in both groups as all of the study subjects had Pucca houses with independent living. 29.84% of the migrants and 75% of the non migrants were in low income groups.17.8% of migrants and

18.65% of non migrants belonged to middle income group and 52.4% of migrants and only

6.2% of non migrants belonged to high income group. Monthly income was found significantly different in both groups

Table 3. Socio demographic profile of the participants

Migrant Non-migrant Total Variable N=191 N=193 N=384 P value* n (%) n (%) n (%) Muslims 150 (78.53) 125 (71.62) 275(71.62) Religion < 0.05 Others 41(21.47) 68 (35.2) 109 (28.38)

Married 177 (92.7) 167 (86.5) 344(89.58) Marital status 0.490 Others 14 (7.3) 26 (13.5) 40 (10.42)

Low 106 (55.5) 112 (58.04) 218 (56.77) Educational Medium 46 (24.08) 35 (18.13) 81 (21.09) 0.329 status High 39 (20.42) 46 (23.83) 85 (22.14) Source: Primary survey, 2013 Malappuram *chi square test

4.1.4 Occupation related factors (Table 4)

Occupation was categorized as unskilled, semi skilled, professionals and those who could not be categorized in any of these categories were categorized as “others”. Among migrants 46.6% were unskilled 32% were semi skilled, 10% were professionals and 11.5% are coming under “others” category. Among non migrants 43% were unskilled 32.6% were semi skilled, almost 11% is professionals and 14% is coming under “others” category.

Similar distribution was found in different occupational categories in both migrants and non migrants.

Among gulf migrants 35 % were reported working all 7 days whereas only 1% of non migrants were found working all 7 days in a typical week. This was significantly different among both groups. The working hours in a typical day was categorized as less than or equal to 8 hrs and more 8 hrs considering that 8hrs per day is the standard working time. Among

migrants 77 % were found working more than 8 hrs, whereas among non migrants only 33% was working more 8 hrs on a typical day. This was significantly different among both groups

Considering that 6 hrs of minimum sleep is required for an individual per day, sleeping hours was categorized as less than 6 hrs per day and more than or equal to 6 hrs per day. 41 % of migrants were sleeping less than 6 hrs per day where as among non migrants only 14% were found to be sleeping less than 6 hrs on a typical day. This was significantly different among both groups.

Table 4. Occupation related factors

Variable name Migrants Non migrants Total P value* N=191 N=193 N=384 n (%) n (%) n (%) Occupation category Unskilled 89 (46.6) 82 (42.5) 171(44.53) 0.821 Semi-skilled 61 (32) 63 (32.6) 124(32.29) Professionals 19 (10) 21 (10.9) 40(10.42) Others 22 (11.5) 27 (14) 49(12.76)

Number of working days < 7 days 124 (65) 191 (99) 315 (82) <0.001 on a typical week All 7 days 67 (35) 2 (1) 69 (18)

Working hours on a ≤8 hrs 44 (23) 129 (67) 173 (45) <0.001 typical day > 8 hrs 147 (77) 64 (33) 211(55)

Duration of sleeping on < 6 hrs 79 (41) 27 (14) 106 (23.4) <0.001 a typical day ≥ 6 hrs 112 (59) 166 (86) 278 (76.6) Source: Primary survey, 2013 Malappuram *chi square test

4.1.5: Personal and family history of non communicable diseases (Table 5)

Among migrants 37 % were having history of non communicable diseases compared to 21 % of non migrants and 57.6 % of migrants were found to have family history of non communicable diseases compared to 50 % of non migrants. Personal history of chronic diseases was significantly different in both groups (P value <0.001).

Table 5. Personal and family history of Non communicable diseases(NCDs)

Migrants Non migrants Total Variable N=191 N=193 N=384 P value* n (%) n (%) n (%) Personal history of Yes 71 (37.17) 41 (21.24) 112 (29.17) <0.001 chronic diseases No 120 (62.83) 152 (78.76) 272 (70.83)

Family history of Yes 110 (57.6) 97 (50.26) 207 (54) 0.150 chronic diseases No 81 (42.41) 96 (49.74) 177 (47) Source: Primary survey, 2013 Malappuram *chi square test

4.1.6: Tobacco use (Table 6)

Among migrants 37.2% were ever smokers of tobacco and among non migrants

24.3% were ever smokers. This was significantly different among both groups. 19.3% of the ever smokers were currently smokers among migrants and 15 % of the ever smokers were currently smokers among non migrants. Ever users of smokeless tobacco were 16.7% among migrants and 14.5% among non migrants Current users of smokeless tobacco was 2.6% among migrants and 3.6% among non migrants. Prevalence of current users of any tobacco was 21.4% of migrants and 16.8% of non migrants which was not significantly different

among both groups. 48.7% of migrants had ever used any form of tobacco whereas only

32.6% of non migrants had ever used any form of tobacco. This was significantly different in both groups. The mean age of initiation of using tobacco products was found to be 20 yrs among migrants and 21 yrs among non migrants which is similar in both groups.

Table 6. Tobacco use

Migrants Non-migrants Total Variable N=191 N=193 N=384 P value* n (%) n (%) n (%) Ever smokers Yes 71 (37.17) 47 (24.3) 118 (30.7) < 0.05 No 120 (62.83) 146(75.65) 266 (69.3)

Current smokers Yes 37 (19.3) 29 (15) 66 (17) 0.259 No 154 (80.63) 164 (85) 318 (83)

Ever users of Yes 32 (16.7) 28 (14.5) 60 (15.6) 0.544 Smokeless tobacco No 159 (83.3) 165 (85.5) 324 (84.4)

Current users Of Yes 5 (2.6) 7 (3.6) 12 (3) 0.570 smokeless tobacco No 186 (97.4) 186 (96.4) 327 (97)

Current users of Any Yes 41 (21.4) 32 (16.6) 73 (19) 0.222 tobacco No 150 (78.53) 161(83.4) 311 (81)

Ever use of any form Yes 93 (48.7) 63 (32.6) 156 (40.6) <0.001 of tobacco No 98 (51.3) 130 (67.4) 228(59.4) Source: Primary survey, 2013 Malappuram * Chi square test

4.1.7 Passive smoking (Table 7)

Among migrants exposure to Passive smoking at home was 38.2% and at workplace was 28.8% and among non migrants 15% were exposed to passive smoking at home and almost 11% at work place. 21% of migrants and 6% of non migrants were exposed to passive smoking both at home and work place. 46% of migrants and almost 20% of non migrants were victims of passive smoking either at work place or at home. Passive smoking was significantly different in both groups

Table 7. Passive smoking

Migrants Non-migrants Total P value* Variable N=191 N=193 N=384 n (%) n (%) n (%) Passive smoking at Yes 55 (28.8) 29 (15) 84 (22) <0.001 work place No 136 (71.2) 164(85) 300(78)

Passive smoking at Yes 73 (38.2) 21 (10.9) 94 (24.5) <0.001 home No 118 (62) 172 (89.1) 290 (75.5)

Passive smoking at Yes 40 (21) 12 (6 ) 52 (13.5) <0.001 work place and No 151 (79) 181(94) 332 (86.5) home

Passive smoking at Yes 88 (46) 38 (19.7) 126 (32.8) <0.001 home or work place No 103 (54) 155 (80.3) 258 (67.2) Source: Primary survey, 2013 Malappuram * Chi square test

4.1.8 Alcohol use (Table 8)

The overall prevalence of current alcohol use among participants was 15%. Ever use of alcohol among migrants was 16.2% and among non migrants was 14%.The prevalence of current alcohol use among migrants and non migrants was 8.9 % and 12.4 % respectively.

Table 8. Alcohol use

Migrants Non migrants Total Variable N=191 N=193 N=384 P value* n (%) n (%) n (%) Ever users of Yes 31 (16.3) 27 (14) 58 (15) 0.540 alcohol No 160 (83.7) 166 (86) 326 (85)

Current users Yes 17 (8.9) 24 (12.4) 41 (10.7) 0.262 of alcohol No 174 (91.1) 169 (87.6) 343 (89.3) Source: Primary survey, 2013 Malappuram *Chi square test

4.1.9. Dietary habits (Table 9)

The overall prevalence of low fruit and vegetable consumption is found to be very high as 81.5%. Among migrants only 86.5% were consuming less than 5 servings of fruits and vegetable per day and among non migrants 76.2% were consuming less than 5 servings of fruits and vegetable per day. This was significantly different among both groups

Table 9.Dietary habits

Migrants Non-migrants Total Dietary habits N=179 N=181 N=384 P value* n(%) n(%) n(%) ≥ 5 fruit and 25 (13.1) 46 (23.8) 71 (18.5) vegetable servings per day < 0.05

<5 fruit and 166 (86.9) 147 (76.2) 313 (81.5) vegetable servings per day Source: Primary survey, 2013 Malappuram *Chi square test

4.1.10. Physical activity (Table 10)

Physical activity was categorized in to three categories namely low, medium and high

Physical activity was found similar in both groups. Among migrants 39.8 % had high level of physical activity and 33.5% were moderately active and 26.7 had low level of physical activity. Among non migrants 37.4% had high level of physical activity and 38.8 % were moderately active and 23.8% had low level of physical activity. This was not significantly different among both groups.

Table 10. Physical activity

Migrants Non migrants Total

N=191 N=193 N=384 Variable P value* n (%) n(%) n (%) Physical activity (MET min/week) High 76 (39.8) 72 (37.4) 148 (38.5) ( ≥ 3000)

Moderate 64 (33.5) 75 (38.8) 139 (36.2) 0.542 (600-2999) Low 51 (26.7) 46 (23.8) 93 (25.26) (<600) Source: Primary survey, 2013 Malappuram *Chi square test

4.1.11 .Sedentary habits (Table 11)

The mean time spent sitting or reclining per day is higher among migrants compared to non migrants. The mean time spent sitting or reclining per day among migrants was 103.8 minutes with a minimum of 10 minutes and maximum of 300 minutes and among non migrants the mean time spent sitting or reclining per day was found to be 63.17 minutes with a minimum of 5 minutes and maximum of 360 minutes .This was significantly different among both groups

Table 11. Mean sedentary habits: Time spent sitting or reclining per day (minutes)

Variable Mean (SD) Minimum Maximum P value*

Migrants 103.82 (66.7) 10 300 <0.001 Non migrants 63.17 (55.36) 5 360 Source: Primary survey, 2013 Malappuram * Student’s t test

4.1.12. Waist circumference, weight , height and BMI (Table 12)

Among migrants the mean of waist circumference, weight, height and BMI among migrants was found to be 96.7 Cms, 75.9 Kg, 166.9 Cms, and 27.3 Kg/m2 respectively

Among non migrants the mean of waist circumference weight height and BMI was found to be 89.98 Cms, 67.27Kg, 164.7 Cms and 24.8 Kg/m2. Each of this was higher among migrants and significantly different among both groups.

Table 12. Mean waist circumference, weight , height and BMI

Migrants Non migrants Total Variable (N=191) (N=193) (N=384) P value* Mean (SD) Mean (SD) Mean (SD) Waist circumference (Cms) 96.7 (9.07) 89.98 (9.9) 93.32 (10) < 0.001

Weight (Kg) 75.97 (11.7) 67.27 (10.5) 71.6 (12) < 0.001

Height (Cms) 166.9 (6.2) 164.7 (5.6) 165.8 (6.2) < 0.001

BMI (Kg/m2) 27.32 (4.4) 24.8 (3.8) 26 (4.3) < 0.001 Source: Primary survey, 2013 Malappuram * Student’s t test

4.1.13. Abdominal obesity and over weight (Table 13)

Abdominal obesity and overweight was found very high among migrants compared to non migrants. Abdominal obesity was 79.5%, and overweight was 66 % among migrants whereas among non migrants abdominal obesity, overweight was 44.5 % and 46.1%. Both abdominal obesity, overweight are significantly different in both groups.

Table 13.Abdominal obesity and overweight

Migrants Non migrants Total Variable N=191 N=193 N=384 P value* n (%) n (%) n(%) Abdominal Yes 152 (79.58) 86 (44.56) 238 (62) <0.001 obesity1 No 39 (20.42) 107 (55.44) 146 (38)

Overweight2 Yes 126 (65.99) 89 (46.11) 215 (56) <0.001 No 65 (34) 104 (53.89) 169 (44) Source: Primary survey, 2013 Malappuram 1 waist circumference ≥90 Cms 2 BMI ≥25 Kg/m2 *chi square test

4.1.14. Systolic blood pressure and diastolic blood pressure (Table 14)

The mean systolic blood pressure and diastolic blood pressure among migrants was

154.7 and 92.4 respectively. The mean systolic blood pressure and diastolic blood pressure among non migrants was found to be 139.2 and 86.4 respectively. Both mean systolic and diastolic blood pressure in higher among migrants compared to non migrants and the difference is statistically significant

Table14. Mean systolic blood and pressure diastolic blood pressure

Variable Migrants Non migrants Total P value* (N=191) (N=193) (N=384) Mean (SD) Mean (SD) Mean (SD)

Systolic blood 154.7 (16.5) 139.2 (13.3) 149.53 (17.2) <0.001 Pressure

Diastolic blood pressure 92.4 (5.4) 86.45 (5.6) 90.44 (6.2) <0.001

Source: Primary survey, 2013 Malappuram * Student’s t test

4.1.15. Hypertension (Table 15)

The prevalence of hypertension among migrants is found two times higher among migrants compared to non migrants. The prevalence of hypertension is 60.2% among gulf migrants and 30.05 % among non migrants. This was significantly different in both groups

Table15. Hypertension

Migrants Non-migrants Total Hypertension (N=191) (N=193) (N=384) P value* n(%) n(%) n(%)

Yes 115 (60.21) 58 (30) 173 (45) <0.001

No 76 (39.79) 135 (70) 211(55)

Source: Primary survey, 2013 Malappuram *chi square test

4.1.16. Awareness, treatment and control of hypertension: (Table16)

Among hypertensives awareness, treatment and control of hypertension is higher among migrants compared to non migrants. Among hypertensive migrants 43.5% were aware, 33.9% were under treatment and only 12.2% had achieved control of hypertension.

Among hypertensive non migrants 57% were aware 53.4% were on treatment and 48.3% had controlled hypertension. The treatment and control of hypertension was significantly different in both groups.

Table16. Awareness, treatment and control of hypertension

Migrants Non-migrants Total Hypertension (N=115) (N=58) (N=173) P value* n(%) n(%) n(%)

Aware1 50 (43.5) 33 (56.9) 83 (48) 0.095

Treated2 39 (33.9) 31 (53.4) 70 (40.4) 0.013

Control3 14 (12.2) 28 (48.3) 42(24.2) <0.001

Source: Primary survey, 2013 Malappuram *chi square test 1 Those who reported the history of hypertension 2 Those who are under treatment for hypertension 3 Those who had SBP<140 and DBP<90

4.1.17. Self reported diabetes : (Table17)

History and treatment of diabetes is higher among migrants compared to non migrants. Among migrants 38.2% reported history of diabetes and 23.6% reported taking treatment for diabetes. Among non migrants 19.7% reported history of diabetes and 12.4% reported taking treatment for diabetes. History and treatment of diabetes is significantly different in both groups

Table17. History and treatment of Self reported diabetes

Migrants Non-migrants Total Diabetes (N=191) (N=193) (N=384) P value* n(%) n(%) n (%)

History1 Yes 73 (38.22) 37 (19.17) 110 (28.65) <0.001

No 118 (61.78) 156 (80.83) 274 (71.35)

Treatment 2 Yes 45 (23.6) 24 (12.44) 69 (18) <0.001

No 146 (76.4) 169 (87.56) 315 (82) *chi square test Source: Primary survey, 2013 Malappuram 1 Those who reported the history of diabetes 2 Those who are under treatment for diabetes 43

4.1.18. Crude odds ratios of anthropometric risk factors and self reported diabetes with migration status as outcome: (Table 18)

Crude odds ratio of the important physiologic risk factors is analyzed below.

Compared to non migrants, migrants are having 3.52 times higher risk of having hypertension, 2.27 times higher risk of having abdominal obesity , 4.84 times higher risk of overweight and 2.6 times higher risk of having diabetes. All these were statistically significant

Table.18 :Bivariate analysis results with migration status as outcome

Variable Crude Odds ratio 95% CI of OR P Value Hypertension Non Migrant* 1 Migrant 3.52 2.31-5.38 <0.001 Abdominal obesity Non Migrant* 1 Migrant 2.27 3.08-7.62 <0.001 Over weight Non Migrant* 1 Migrant 4.84 1.50-3.42 <0.001 Self reported diabetes Non Migrant* 1 Migrant 2.6 1.64-4.14 <0.001 Source: Primary survey, 2013 Malappuram *Reference category

As diabetes was self reported (not measured) it was not considered for further analysis. Out of the anthropometric risk factors which are measured, since abdominal obesity and overweight are well correlated (Pearson’s r = 0.6) we choose abdominal obesity for further analysis. Hypertension was also selected for further analysis. Univariate logistic

regression and multiple logistic regressions were attempted taking hypertension and abdominal obesity as outcome variables.

4.2 Logistic regressions

4.2.1. Univariate logistic regression with hypertension as outcome: (Table 19)

Univariate logistic regression was done taking hypertension as outcome variable with all independent variables which can be the predictors of hypertension. Non migrants were significantly at higher risk of having hypertension compared to migrants. Age, income, working hours per day, working days per week, sleeping hours per day, self reported diabetes ;overweight, and fruits and vegetable consumption were the other variables significant in univariate logistic regression analysis.

4.2.2 Multiple logistic regression with hypertension as outcome: (Table 20)

Multi-variate logistic regression analysis was done to estimate the adjusted odds ratio of migrants to have hypertension compared to non migrants adjusted for other variables.

Statistically significant variables in the univariate logistic regression and biologically plausible variables which can affect the outcome variable were considered in the model..

After adjusting for other variables gulf migrants were having 2.5 times higher risk of having hypertension compared to non migrant workers. Subjects above mean age had 2.5 times higher risk compared to those below mean age, and subjects who reported having diabetes had 2 times higher risk of having hypertension compared to those who did not report history of diabetes. This model has Cox and Snell R square value 0.213 meaning that this model explains 21.3%of the hypertension in this sample.

Table.19 : Univariate logistic regression analysis results (Hypertension as outcome)

Un adjusted 95% CI Variable Odds Ratio of OR P Value Non migrants* 1 Migrants 3.54 2.31 - 5.38 <0.001

Age (40 yrs) ≤ 40* 1 2.58 - 6.06 <0.001 > 40 3.96

Monthly come 1Low * 1 1.06 - 2.39 <0.05 2 High 1.59

Passive smoking No* 1 Yes 1.06 0.623 - 1.83 0.808

Current use of No* 1 any tobacco Yes 1.433 0.877 - 2.34 0.151

Current alcohol use No* 1 Yes 1.098 0.625 - 1.92 0.746

Physical activity High* 1 Moderate 0.95 0.56 - 1.61 0.212 Low 1.40 0.84 - 2.35

Working hours on a ≤ 8 hrs* 1 typical day >8 hrs 2.38 1.57 - 3.60 <0.001

Working days on a <7 days* 1 typical week All 7 days 3.77 2.14 - 6.65 <0.001 Sleeping hours on a ≥ 6 hrs* 1 typical day < 6 hours 1.71 1.09 - 2.69 0.019

Self reported diabetes No* 1 2.54 - 6.55 <0.001 Yes 4.08

Over weight No* 1 Yes 1.76 1.17 - 2.66 <0.05

Fruits and vegetable ≥ 5servings* 1 consumption < 5 servings 1.09 - 2.69 0.019 1.71

Source: Primary survey, 2013 Malappuram 1 ≤ 10000 Rs/month 2 > 10,000 Rs/month * Reference category

Table.20 : Multi variate logistic regression analysis results (Hypertension as outcome)

Variable Adjusted Odds ratio 95% CI of OR P Value Non migrants* 1 1.38-4.46 <0.05 Migrants 2.48

Age (in years) ≤ 40 * 1

>40 2.47 1.50-4.07 <0.001

Self reported No* 1 diabetes Yes 1.95 1.12-3.40 0.018

Source: Primary survey, 2013 Malappuram * Reference category Other variables considered in the model are monthly income, current use of any tobacco , passive smoking, fruits and vegetable consumption , physical activity, sleeping hours on a typical day, working hours on a typical day, over weight and current alcohol use

4.2.3. Univariate logistic regression with abdominal obesity as outcome:(Table 21)

Univariate logistic regression was done taking abdominal obesity as outcome variable with all independent variables which can be the predictors of abdominal obesity. Migrants had significantly higher risk for abdominal obesity compared to non migrants. Age, income, working hrs on a typical day, working days on a typical week, history of diabetes and hypertension are the other variables significant in univariate logistic regression analysis.

4.2.4 Multiple logistic regression with Abdominal obesity as outcome (Table 22)

Multi variate logistic regression analysis was done to estimate the adjusted odds ratio of the migrants to have abdominal obesity compared to non migrants adjusted for other variables.

Statistically significant variables in the univairte logistic regression and biologically plausible variables which can affect the outcome variable were considered in the model. Gulf migrants were having 2.4 times higher risk of having abdominal obesity compared to non migrant workers.

Subjects in high income category were having 3 times higher risk compared to those in low income category. Subjects with hypertension were having 1.7 times higher risk for having abdominal obesity compared to non hypertensives. Subjects working more than 8 hrs on a typical day were

having 2 times higher risk compared to those who are working less than or equal to 8 hrs on a typical day. Subjects who reported history of diabetes and tobacco users were having higher risk of having abdominal obesity with P values<0.1. This model has Cox and Snell R square value 0.207 meaning that this model explains 20.7% of the abdominal obesity in this sample.

Table.21. Univariate logistic regression analysis results (Abdominal obesity as outcome)

Variable name Un adjusted 95% CI of OR P Value Odds Ratio Migrants* 1 Non migrants 4.85 3.09 - 7.62 <0.001

Age (in years) ≤ 40 * 1 1.22 - 2.83 <0.05 > 40 1.86

Monthly come 1Low* 1 2.92 - 7.24 <0.001 2High 4.59

Any tobacco use No* 1 Yes 1.96 1.27 - 3.03 <0.027

Passive smoking No* 1 Yes 1.67 1.06 - 2.63 0.151

Current alcohol use No* 1 Yes 0.922 0.521 - 1.63 0.781

Physical activity High* 1 Moderate 0.04 0.65 - 1.68 0.634 Low 1.29 0.75 - 2.19

Working hours ≤ 8 hrs* 1 on a typical day >8 hrs 3.30 2.15 - 5.08 <0.001

Working days <7 days* 1 on a typical week All 7 days 2.83 1.51 - 5.31 <0.001

Sleeping hours ≥ 6 hrs* 1 on a typical day < 6 hours 1.35 0.84 - 2.16 0.213

Self reported diabetes No* 1 1.62 - 4.46 <0.001 Yes 2.69

Fruits and vegetable ≥ 5 servings* 1 Consumption < 5 servings 1.33 0.79 - 2.49 0.279

Hypertension No 1 1.77-4.24 <0.001 Yes 2.74 1 ≤ 10000 Rs/month Source: Primary survey, 2013 Malappuram 2 > 10,000 Rs/month * Reference category 48

Table.22: Multi variate logistic regression analysis results (Abdominal obesity as outcome)

Variable name Adjusted Odds 95% CI of OR P Value ratio Non migrants* 1 Migrants 2.41 1.35-4.31 <0.05

Monthly income Low * 1 High 3.08 1.82-5.20 <0.001

Hypertension No* 1 Yes 1.73 1.02-2.98 <0.05

Working hours on a ≤ 8 hrs* 1 typical day >8 hrs 2.03 1.21-3.40 <0.05

Self reported diabetes No* 1 Yes 1.76 0.95-3.32 0.070

Current use of any No* 1 form of tobacco Yes 1.57 0.94-2.61 0.084 Source: Primary survey, 2013 Malappuram * Reference category Other variables considered in the model are: age, passive smoking, fruits and vegetable consumption , physical activity, sleeping hours on a typical day, current alcohol use and hypertension

Duration of migration and anthropometric risk factors

When we examined duration of migration and anthropometric risk factors there was an increasing trend noticed in the prevalence of these risk factors and duration of migration

Overweight, Abdominal obesity and Hypertension, was found to be increasing with the duration of gulf migration. This is represented graphically below:

Figure. 2. Changes in anthropometric risk factors with duration of migration

CHANGES IN ANTHROPOMETRIC RISK FACTORS WITH DURATION OF MIGRATION 100 80 60 Hypertension 40 Overweight Abdominal obesity 20

Anthropometric Risk Factors (%) Factors Risk Anthropometric 0 ≤ 10 11 to 20 > 20 Duration of migration (years)

5 Discussions

This study was conducted to compare the prevalences of non communicable disease risk factors among gulf migrant workers and non migrant workers of Malappuram district and to test the hypothesis that gulf migrant workers are having a high prevalence of NCD risk factors compared to non migrant workers.

5.1. Sample characters

As WHO STEPS recommends the age group of 25-65yrs we selected for this study

5.2. Behavioral Risk factors

As the study is conducted in Malappuram district and 72% of the subjects were from

Muslim community. Tobacco use and alcohol use is considered to be against the religious beliefs and social norms in this community which could have resulted in the under reporting of the same. The study was conducted in the month of “Ramadan” and adjacent moths which are believed to be the auspicious months for Muslim community. Abstinence of tobacco and alcohol during this month could be a probable reason for lower prevalence of current use of alcohol and tobacco compared to ever use of the same.

5.3. Tobacco use

The prevalence of tobacco use was 19.3% among migrants and 15% among non migrants which is much lower than the national average 37% according to NFHS3. Other

studies done in Kerala reports the prevalence of smoking among men varying from 40% to

42% 58,62 which is higher than the findings of this study.

The prevalence of smokeless tobacco use is 16.7% among migrants and 14.5% among non migrants. This is much lower than that of the national average of smokeless tobacco use which is 38%. The prevalence of smokeless tobacco use among men was 7% according another study done in Kerala. 47,64 Exposure to passive smoking was found higher among gulf migrants (46%) compared to non migrants (19.7%). This was significantly different in both groups. As most of the gulf migrants in the study (79%) were unskilled and semi skilled workers. They are forced live in joint and shared accommodations and work under the dictatorship of private parties and companies 82,99,100. This makes them vulnerable for the exposure to passive smoking at home and work place.

5.4. Alcohol use

The prevalence of current alcohol use was 9% among migrants and 12.4% among non migrants. Prevalence of ever alcohol use is 16% among migrants and 14% non migrants. This is much lower than the national prevalence 32%.47 Other studies in Kerala reports high prevalence of alcohol (24%) use in the community.64 A study done in Kerala reports 41% of the samples are current users of alcohol.62 Another study reports the prevalence of alcohol use 26%among urban men and 20.9%among rural men. 58 These are much higher than the prevalence found in this study.

5.5. Dietary Habits

Fruits and vegetable consumption is found to be considerably less among both migrant and non migrant population as per WHO recommendation (≥5 servings)

Percentage of migrants consuming less than 5 servings of fruits and vegetables per day is 86.9% and 76.2% among non migrants. This is higher than the prevalence found by other studies which reports 47% and 39.7% of low fruits and vegetable consumption in

Kerala. 44,58

A study done under IDSP project reports 87.5% prevalence of low fruits and vegetable consumption which is similar and comparable to the finding of this study. 64 Other studies had higher prevalence of low fruits and vegetable consumption across India. A study in the northern India reports that about 90% of prevalence of low fruits and vegetables which is comparable to the prevalence in this study68. Only 13 % of migrants and 23% of non migrant population is consuming fruits and vegetables as per WHO recommendation. Gulf

Migrants are having significantly lower consumption of adequate fruits and vegetables compared to non migrants. This could be because living away from families they largely depend on restaurant food which was understood from formative research and literatures. 82,99

5.6. Physical activity and sedentary habits

Prevalence of physical activity in all categories were similar and almost equally distributed among migrants and non migrants in this study. Among migrants 39.8 % of high level of physical activity and 33.5% of moderate physical activity, among non migrants

37.4% of high level of physical activity and 38.8 % of moderate physical activity was found.

26.7 % of migrants were physically inactive and 23.8% of the non migrants were physically in active.

A study in Kerala had reported 23.7% of prevalence of high level of physical activity and

69.5% of moderate level of physical activity and 6.8% of physical inactivity. 58 Another study done under IDSP project reports 76% prevalence of low physical activity in Kerala which is higher the findings of this study.64A study in Delhi reports 17.8% of the respondents were involved in moderate level of physical activity 103. In sufficient physical activity of 18.35% in rural population and 56% in urban population was reported in another study 104 . The findings of these studies are different from each other and from the finding of present study possibly due to lack of validation of self reported information.

The mean sedentary habit is higher among migrants compared to non migrants. The mean time spent sitting or reclining per day among migrants was 103.8 minutes compared to be 63.17 minutes among non migrants. This could be due to lack of recreational activities for gulf migrants in working countries due to which on free times migrant workers tend to spend time sitting in chair or watching TV , it could also be due to long sitting hours for semiskilled workers because of overtime work as understood from the formative research and literatures.82,99,100

5.7. Occupational categories

Distribution of subjects in different occupational categories were similar in both groups studied, almost half of the subjects in both gulf migrants and non migrants belonged to unskilled category.

5.8. Working days in a typical week

Among gulf migrants 35% reported working every day in a week compared to 1% of non migrants. This shows that 35% of gulf migrants do not get a single holiday in a typical week. This can contribute to the stress faced by migrants in the working country.

5.9. Working hours on a typical day

Among gulf migrants 77% were found working more than 8 hrs typical a day compared to 33% of non migrants. This can cause extra burden on these migrant workers increasing their stress in the working countries predisposing them to NCDs and risk factors.

5.10. Sleeping hours on a typical day

41.3% of the gulf migrants reported to be sleeping less than 6 hours on a typical day compared to 14% of non migrants. This is another factor which can increase the stress among these migrant population who live away from the family and native country. Adequate sleep is a biologic necessity and basic right of every individual. Inadequate sleep and long working hours are reported to contribute to cardiovascular risk factors and compromise mental health resulting in premature mortality. 92,93, 94, 95, 96

Working days on a typical week, working hours on a typical day, and sleeping hours on a typical day are found significantly different in migrants compared to non migrants even though these factors became insignificant when adjusted for other factors in multivariate analysis which could be due to the insufficient sample size to prove association.

Gulf migrants are reported to be having lack of sleep due to long working hours and interrupted sleep. It’s also reported that gulf migrants are forced to work overtime without adequate payment and they are not given adequate resting time. 82,99,100

5.11. Hypertension

Gulf migrants are found having high prevalence of hypertension compared to their non migrant contemporaries. Prevalence of hypertension among gulf migrants is 60.2% and among non migrants is 30% which is two times higher among gulf migrants. This was significantly different in both groups. Gulf migrants were having 2.5 times higher risk of having hypertension compared to non migrants. This difference in prevalence is comparable to another study on British Gujaratis and their contemporaries in village of origin which reports prevalence of 23.7% of hypertension among Gujaratis in Navsari and 46.9% among

Gujaratis at Sanwell indicating a double prevalence of hypertension among British migrants79. Another study on Coronary risk factors in people from the Indian subcontinent living in west London and their siblings in India found that Indian men in west London had significantly higher systolic and diastolic blood pressure compared to their siblings in

Punjab. 78 Another study done on Indian migrants to London and natives in India which reports prevalence of hypertension 23% in migrants to Southall compared to 1.4% prevalence in natives of Punjab. 96 A study on a sample of female Punjabi Indians in Southall concludes that the prevalence of hypertension among women above age 40 yrs is 60% which is same as the prevalence found among gulf migrants in this study. 97 The findings of these studies are consistent with the findings of the present study.

Even though gulf migrants are different from the migrants referred in these studies who relocates themselves in the emigrating country, a similar pattern of disease incidence can be noticed (Gulf migrants are denied of citizenship and they are like guest workers in the working country having only temporary residence ).

The prevalence of hypertension in non migrants is same as the prevalence reported by another study in Kerala which reports nearly 30% prevalence of hypertension. 58 A study reports the prevalence of hypertension ranging from 20 to 40%among urban adults. 33

Another study in south India reports the prevalence of hypertension among urban and rural population as 34.9% and 32.5% respectively. These are comparable to the prevalence of hypertension among non migrants in the present study.

The higher prevalence of hypertension among Gulf migrants could be due to the lifestyle changes and stress migrant population face in the working countries in the process of acculturation. This requires further exploration.

5.12. Abdominal Obesity and overweight

The prevalence of abdominal obesity was 79.5% and 44.5% among non migrants showing almost double prevalence of abdominal obesity among migrants compared to non migrants. Migrants were found to have 2.4 times higher risk of having abdominal obesity compared to non migrants. Prevalence of overweight and obesity is high among migrants compared to non migrants. Among migrants 66% of and among non migrants 46% of were having overweight. These prevalences were significantly different in both groups.

A study on Coronary risk factors in people from the Indian subcontinent living in

West London and their siblings in India found that Indian men in West London had greater 57

body mass index and other Coronary risk factors compared to their siblings in Punjab. 78 A comparative study on London migrants with native Indians reports that the migrants were on an average 10 kg heavier than the native counterparts and the measure of obesity was strongly related to blood pressure elevation, which are consistent with the findings of this study. 96 .

A study on British Gujaratis and their contemporaries in village of origin reporting a higher prevalence of BMI and Waist: hip ratio among Gujaratis at Sanwell compared to Gujaratis in

Navsari. This study also concludes that exposure to increased fat intake and obesity is related to migration and is likely to explain the high prevalence of CHD risk factor among British

Gujaratis. 79 Studies in gulf countries report that with increased affluence traditional food habits are taken over by high fat and calorie rich diet in the country .These findings are comparable to the findings of present study, also it can be understood that after migration gulf migrants tend to adapt the unhealthy diet pattern of the working countries making them highly vulnerable to the incidence of overweight and obesity. 81,82,99 Overall prevalence of excess adiposity among migrants is comparable to United States where prevalence of overweight is reported to be more than 60%, and Prevalence of abdominal obesity 40% to

60%. 90 Unhealthy diet, stress and inadequate physical activity may have contributed to the higher prevalence of excess adiposity among gulf migrants. 82,99

The prevalence of abdominal obesity among non migrants in this study is comparable to other study in Kerala which reports the prevalence of abdominal obesity as 41.9% among urban and 33.9% among rural population44 A large community based study in Kerala reports the prevalence of abdominal obesity as 34% and prevalence of overweight as 25% 58

which are lower than the corresponding prevalences among non migrants found in present study. 58

Another study in Kerala found the prevalence of overweight as 20.9% and 36.3% in rural and urban areas respectively.44 A study done under IDSP project in Kerala reports high prevalence of overweight across all age groups except younger age. 64 A study done in urban

Delhi reports high prevalence of overweight varying from 35.1% among men to 47.6% among women89. These are comparable to the findings of the present study.

5.13. Awareness, treatment and control of hypertension

Awareness, treatment and control of hypertension are significantly lower among gulf migrants compared to non migrants. Among hypertensive non migrants 56.9% were aware and among migrants 43.5% were aware about their hypertension, among non migrants 53.4% were receiving medication whereas among migrants only 33.9% were receiving medication for hypertension, also among non migrants 48.3% had achieved adequate control but among migrants only 12.2% had achieved adequate control of hypertension.

A population based survey on Punjabi females living in Southall, west London reports that two third of the hypertensive were unaware of their high blood pressure and among those who were aware 75% were taking medication for the condition which indicates a low awareness compared to the findings of this study, where as prevalence of those receiving treatment for hypertension is higher compared to the findings of this study.97

Better awareness and adequate control of hypertension among non migrants could be due to the NCD clinics under NPCDCS programme from sub-centre level which is active since 2012 in the district. A study on NCD risk factors in Kerala reports 59% of awareness which is similar and comparable to the awareness among non migrants of this study.64 59

Another study in Kerala reported 36.9% of awareness, 26.9% were taking treatment for hypertension and 8.6% had achieved control which is lower than the corresponding prevalences among non migrants in the present study .58

It can be noticed that there is a large difference among migrants compared to non migrants in achieving adequate control of hypertension in spite of receiving treatment. The inadequate treatment and control of hypertension among gulf migrants can be due to the lack of knowledge about prevention and treatment of NCDs and its health impacts. Non provision for medical care by the employers and high cost for medical care in the gulf countries could be another reason for this, as understood from the formative research. However these issues need further exploration in order to address this from a public health perspective.

5.14 Self reported diabetes

Those who reported history of diabetes among gulf migrants is found to be much higher among migrants (38.2%) compared to non migrants (19.1%). Among migrants 23.6% were taking treatment for diabetes and among non migrants 12.4% were taking treatment for diabetes. A similar pattern is reported in another study on migrants from the Indian subcontinent of Punjabi origin living in west London and their siblings living in Punjab reports fivefold increase in blood glucose due to insulin resistance among Indian migrants. 79

A research funded by US congress and conducted by Indian American physicians reports that 17.4% prevalence of diabetes among NRIs compared to 9% prevalence in rural

India; also among diabetic Indians nearly one fifth were not aware of the disease. This difference in prevalence of diabetes among NRIs and Indian citizens is similar to the findings

of the present study. This study also reports that NRIs are highly prone to diabetes and metabolic syndrome due to the mechanized lifestyle they tend to live in foreign countries. 73

A community based study done in Kerala reports that the prevalence of diabetes varies from 12.3% in urban men to 19% in rural men. This is comparable to the prevalence among non migrants in this study58 A study done in India on rural urban difference in prevalence of self reported diabetes reports the prevalence varies from 3.1% in rural areas to

7.3% in urban areas 91 also a study done in rural Kerala reports the prevalence of self reported diabetes as 13.1% . These prevalence are lower than the findings of this study.100

5.15. Duration of migration and Anthropometric risk factors

There is an increasing trend noticed in the prevalence of hypertension, overweight and abdominal obesity with duration of migration indicating an association between duration of gulf migration and incidence of these risk factors.

The study on migrant Indians living in Southall, west London reports that there is a steady and significant increase in blood pressure with increasing age which is comparable with the findings of this study. 96 Studies report that on migration Indian migrants acquire

CHD risk profile similar to that of the host country unmasking the under laying genetic risk of coronary heart disease. 78 Higher prevalence of diabetes and premature coronary heart diseases and risk factors in migrant South Asian populations has been a consistent finding in many studies. 73,78,79,97,98

Literatures substantiates that South Asian ethnicity is a major risk factor for

Chronic Heart Disease (CHD) and risk factors , also reports that on migration, migrants

acquire NCD risk factor profile similar to that of host country exposing their underlying 61

genetic risk for coronary heart disease. 71,78 Studies in gulf countries reports high incidence of diabetes and clustering of other NCD risk factors associated with rapid life style changes in the country. 81 Along with the ethnic predisposition, lifestyle changes after migration explains the higher prevalence of NCD risk factors among gulf migrants compared to their non migrant contemporaries.

Challenges to Physical, emotional and psychological wellbeing of gulf migrants,

lack of resources for adequate health care and host countries negligence of basic health

needs are creating serious damage to the physical and mental health gulf migrants also

undermining the most fundamental human rights principles. Dictatorship of the job

sponsors, exploitations, labour abuse, and unhealthy living and working conditions

among gulf migrants were reported previously. 82,99,100,101 While adapting to the different

culture, living and working environments of the host country, stress and unhealthy diet

could be counterproductive to the health of gulf migrants. These factors are making gulf

migrants vulnerable to the high incidence of NCDs and risk factors calling for an

immediate public health attention.

5.16. Strengths of the study:

 There are limited studies among migrants on NCD risk factors in India

 To the best of my knowledge this the first comparative prevalence study on NCD Risk

factors among gulf migrant workers and non migrant contemporaries using WHO

STEPS

 This study uncovers the health challenges among gulf migrant population which cause

mortality , morbidity and loss of DALYs among this group of population

 This study gives baseline data and scope for future studies to understand the factors

associated with the high incidence of NCD risk Factors among gulf migrant workers This

study provides strong justification for the health profession to step up its health advocacy

with respect to policies to reduce rates of NCD risk factors among gulf migrant workers.

5.17. Limitations of the study

 Sample size was calculated to detect the prevalence differences. This paused problem

while establishing association between risk factors.

 Religious beliefs and social norms against alcohol and tobacco use in the study

population could have resulted in under reporting of the same.

 Self reported information on tobacco, diet and physical activity was not validated using

any other methods.

5.18. Conclusion

The burden of NCD risk factors are significantly higher among the gulf Migrant workers compared to non migrant workers. Hypertension and abdominal obesity is found to be 2.5 times higher among migrants compared to non migrants. Among migrants prevalences of risk factors increased with an increase in the duration of migration. Most of the behavioral risk factors (STEP 1) were not significantly different among both groups.

Occupation related risk factors such as daily working hours, increased number of working days per week, and lack of adequate sleep are found to be high among migrants compared to non migrants making them vulnerable to NCDs and it’s risk factors. Comparatively high prevalence of passive smoking and unhealthy diet among gulf migrant workers has to be considered as an important issue. Treatment and control of hypertension is very low among gulf migrants compared to non migrants. Most of the gulf migrants are unskilled and semi skilled workers with lower education, who need to be imparted awareness prevention and treatment of NCDs and related risk factors. It is high time that migrants recognize the need for the prevention and control of these diseases themselves and adapt healthy life styles.

Gulf migrants are ignored by both government and health system in spite of repeated calls from WHO about the growing threat of NCDs in developing countries.

None of the national programmes or policies addresses these vulnerable groups of migrants who spend their most productive life time in gulf countries living behind their families and contribute in a larger extend to our economy. Prevention and treatment of

NCDs and risk factors can reduce the morbidity mortality among this group considerably. 64

A comprehensive approach is necessary to promote interventions for prevention and control of NCDs and it’s risk factors among this high risk group who contribute to the considerable proportion of our population. These data may serve to impel multi-sectoral efforts to lower the burden of NCD risk factors among migrant population in general, and in gulf migrants, in particular.

5.19. Recommendations

 Call for further exploration of the associated factors on NCD risk factors among gulf

migrants using more qualitative information to facilitate prevention and treatment

programmes.

 Gulf Migrant workers are at high risk for other chronic diseases as well, Special focus

should be given by the health department on the health issues of gulf migrant workers

who contribute to the national and state economy.

 There should be stringent system for continues monitoring of health status of gulf

migrant workers.

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ANNEXURE

ANNEXURE 1

CONSENT FORM

Hello, I am Dr. Shamim Begam, a Master of Public Health (MPH) scholar at Achutha Menon Centre for Health Science Studies, Sree Chitra Thirunal Institute for Medical Sciences and Technology, Thiruvananthapuram. As a part of my curriculum, I am conducting a study titled “A comparative study of non communicable disease risk factors among gulf migrant workers and non migrant workers of Malappuram district, Kerala” . I request you to spend some time & participate in the study. Purpose of the study: There has been a sharp increase in occurrence of non communicable disease (disease which are not infections and largely determined by some life style related behaviors such as tobacco, harmful alcohol use, physical activity, low fruit and vegetable consumption) like heart diseases, stroke, chronic obstructive lung diseases, etc in both common population and migrated population of India. So, I plan to do this study to find out the burden of NCD risk factors among male gulf migrant population and compare it with the common male population who has not worked outside Kerala. I sincerely hope that this study will help in future to plan proper health programmes to address this problem among gulf migrant workers of our state. Participation is required from your side: For this purpose I will ask you a set of questions regarding some behaviors like smoking, chewing tobacco, drinking alcohol, diet/food, physical activities which are related to increased risk of chronic diseases and I will take measurement of your height, weight, waist circumference, and blood pressure ( 3 readings). I will also ask questions enquiring about your work, health care, diabetes (sugar disease), cholesterol (fat) and hypertension (pressure). It will involve no invasive procedure like blood withdrawal .The whole procedure will take about 30- 45 minutes.

Benefits from participation: There is no direct benefit except measuring your blood pressure, height, weight and waist circumference for you from the study but public health programme as a whole may benefit. Discomfort/ harm from participation: Participation in the study will not impose any risk to health. Some questions related to your health, behaviors and personal history as well as measurements of blood pressure, height, weight and waist circumference may cause little bit discomfort. Voluntariness: Your participation in the study is voluntary and you can withdraw from the study at any point of time and refusal to participate will not involve any form of penalty. Confidentiality: The confidentiality of the information provided will be maintained. Your personal identity will not be revealed to anyone. However some of the information will be shared with people who are associated with study. All the copies of filled interview schedules and consent forms will be kept under the custody of principal investigator and will be destroyed when they are deemed no longer needed. The study will be published in scientific journal but your identity will not be revealed. If you need any further clarification about study you may contact directly to me, my phone number is 9567295299. For any queries related to the authenticity you may contact to my research guide Dr. Kannan srinivasan, Associate Professor, AMCHSS, SCTIMST (PH: 0471 - 2524243) or Dr. Anoop Kumar T, Member secretary, Institutional Ethics Committee, SCTIMST (Ph: 0471-2520256/257)

Thanking you.

Dr. Shamim Begam

CONSENT FORM TO TAKE PART IN THE STUDY A comparative study of non communicable disease risk factors among gulf migrant workers and non migrant workers in Malappuram district, Kerala

I ………………………………………………………………………….. Son of ………………………………………………………………………... aged …………… Declare that (please tick the boxes) 1. I have read the information sheet provided to me regarding this study and have clarified all doubts that I had. [ ] 2. I also understand that my participation in this study is entirely voluntary and I am free to withdraw permission to continue to participate at any time. [ ] 3. Understand that my identity will not be revealed in any information released to third parties or published. [ ] 4. I voluntarily agree to take part in this study. [ ]

Signature/ thumb impression: Name: ………………………………………..

Name of witness: ……………………………

Relation to participant: ……………………….

Date: ……………………………………… Annexure II – Data Collection Tools INTERVIEW SCHEDULE

A COMPARATIVE STUDY OF NON COMMUNICABLE DISEASE RISK FACTORS AMONG GULF MIGRANT WORKERS AND NON-MIGRANT WORKERS OF MALAPPURAM DISTRICT, KERALA

Participant identification Number

Panchayat code

Ward code

Participant type

Date of interview

Day Month Year

Contact number

Time of the interview

Participant working in Gulf: YES NO (if “no”, then go directly to step-1) Sl.No Question Response Code 1 Which gulf country are you [1] KSA working? [2] UAE [3] Oman [4] Qatar G1 [5] Kuwait [6] Bahrain [7] Others……………………………… 2 Since how many years are you Years G2 working in gulf? STEP 1: DEMOGRAPHIC INFORMATION 3 How old are you? Years C1 (age in completed years) 4 Sex [1] Male (If male, go to C4) C2 [2] Female (If female go to C3) 5 Have you attained menopause? [1] Yes [3] Others (specify) C3 [2] No 6 What is the highest level of [1] No formal schooling education you have completed? [2] Elementary school completed [3] High school completed [4] Higher secondary school C4 [5] University /college completed [6] Postgraduate degree [7] Others(specify)………………… 7 What is your religion [1] Muslim [2] Hindu [3] Christian C5 [4] Others (specify)………………… 8 What is your marital status? [1] Never married [2] Currently married [3] Divorced/separated C6 [4] Widowed [5] cohabitating

Socio-economic Status 9 How much is your monthly [1] Upto Rs.3000 household income(in Rs) [2] Rs. 3001 – 7500 [3] Rs. 7501 – 10000 C7 [4] Rs. 10000 – 25000 [5] More than 25000 10 Type of residential arrangement [1] Own house [2] Rented house C8 [3] Dependent on others 11 Type of housing [1] Kucha [2] Semi pucca C9 [3] Pucca

Occupation related factors 12 What is your current occupation [1] Professional category? [2] Skilled [3] Semi Skilled [4Unskilled/Man Labour O1 [5] Others (Specify)……………………..

13 Since how long are you doing the Years O2 current occupation 14 On a typical week how many days Days O3 do you work? 15 On a typical week what is your Hours average working hours per day [ it O4 excludes time for rest, food & others]

Personal and Family History

16 Did you ever have any of the [1] Heart disease O5 chronic diseases? [2] stroke [3] cancer [4] COPD [5] No [6] Don’t know [7] Others…………………………………… 17 Were either of your parents or F1 siblings have any of the given [1] Yes chronic disease conditions [ self [2] No reported/ medically diagnosed/on If no, go to T1 medications]

18 If Yes, what are they diagnosed [1] Father as? [2] Mother F2 [3] Both [4] Siblings [5] None [6] Don’t Know

Tobacco use

19 Do you currently smoke any T1 tobacco products such as [1] Yes cigarettes/Bidi/others? [2] No If no, go to T5 20 Do you currently smoke tobacco [1] Yes T2 products daily? [2] No If no, go to T5 21 How old were you when you first Age in years T3 started smoking daily? [77] Don’t know 22 How many of the following do you [1] Cigarette/day T4 smoke each day? [2] Bidi/day [3] Others/day 23 During past week on how many Number of days T5 days did someone smoke in closed Don’t know areas, in your work place when you were present? 24 During past week how many days No. of days T6 did someone in your home smoke, in your presence? [77] Don’t know 25 Do you currently use any [1] Yes T7 smokeless tobacco products? [2] No If no, go to A1 26 Do you currently use any [1] Yes T8 smokeless tobacco products such [2] No as (snuff, chewing tobacco, betel) If no, go to A1 daily? 27 How old were you when you first Age in years T9 started using smokeless tobacco daily? [77] Don’t know 28 How many of the following [1] Pan Masala - Packet/day smokeless tobacco products do [2] Paan /day T10 use daily on an average? [3] Gutkha - Packet /day [4] Snuff - times /day [5] Betel with tobacco/day [6] Others (specify) ------

Alcohol consumption 29 Have you ever consumed an [1] Yes A1 alcoholic drink such as beer, wine, [2] No whisky, locally prepared alcohol? If no, go to D1 30 Have you ever consumed an [1] Yes A2 alcoholic drink such as beer, wine, [2] No whisky, locally prepared alcohol, in the past 12 months? 31 If yes, how frequently you [1] 1-3 days/month A3 consumed at least one drink of [2] 1-4 days/week alcohol in past 12 months? [3] 5-6 days/week [4] Daily 32 How many years you have been Years A4 taking alcohol as whole (Years)? 33 Have you consumed alcohol in the [1] Yes A5 past 30 days? [2] No If no, go to D1 34 During the past 30 days when you Number A6 drank alcohol, on an average how many standard alcoholic drinks did [77] Don’t know you have during one occasion? 35 During each of past 7 days, how [1] Monday A7a many standard alcoholic drinks did [2] Tuesday A7b you have each day? [3] Wednesday A7c

[4] Thursday A7d

(Standard drink: One standard [5] Friday A7e glass of beer, wine or any form of [6] Saturday A7f spirit. 1 Std glass – 8-13 grams of ethanol.) [7] Sunday A7g

Diet 36 In a typical week, on how many Number of days D1 days do you eat fruit? [77] Don’t know

If Zero days go to D3 37 How many servings of fruit do you Number of servings D2 eat on one of those days? [77] Don’t know

38 In a typical week, on how many Number of days D3 days do you eat vegetables? [77] Don’t know

If Zero days go to D6 39 How servings of vegetables do you Number of servings D4 eat on one of those days? [77] Don’t know

Physical Activity 40 Does your work involve vigorous- P1 intensity activity that causes large [1] Yes increase in breathing or heart rate [2] No (like carrying or lifting heavy If no, go to P4 loads, digging or construction Work) for at least 10 minutes continuously? 41 In a typical week, on how many P2 days do you do vigorous-intensity Number of days activities as part of your work? 42 How much time do you spend P3 doing vigorous-intensity activities at work on a typical day? Hours Minutes 43 Does your work involve P4 moderate-intensity activity that [1] Yes causes small increase in breathing [2] No or heart rate (like brisk walking If no, go to P7 or carrying light loads) for at least 10 minutes continuously? 44 In a typical week, on how many P5 days do you do moderate- Number of days intensity activities as part of your work? 45 How much time do you spend P6 doing moderate-intensity activities at work on a typical day? Hours Minutes Travel to and from places 46 Do you walk or use a bicycle (pedal P7 cycle) for at least 10 minutes [1] Yes continuously to get to and from [2] No places? If no, go to P10 47 In a typical week, on how many days P8 do you walk or bicycle for at least 10 Number of days minutes continuously to get to and from places? 48 How much time do you spend P9 walking or bicycling for travel on a typical day? Hours Minutes Recreational / sports/leisure time physical activities 49 Do you do any vigorous-intensity P10 sports, fitness or recreational [1] Yes (leisure) activities that cause large [2] No increase in breathing or heart rate If no, go to P13 ( like running or Football) for at least 10 minutes continuously? 50 In a typical week, on how many P11 days do you do vigorous-intensity Number of days sports, fitness or recreational (leisure) activities?

51 How much time do you spend P12 doing vigorous-intensity sports, fitness or recreational activities Hours Minutes on typical day? 52 Do you do any moderate-intensity P13 sports, fitness or recreational [1] Yes (leisure) activities that cause [2] No small increase in breathing or

heart rate (like brisk walking, cycling, and volleyball) for at least 10 minutes continuously? 53 In a typical week, on how many P14 days do you do moderate- Number of days intensity sports, fitness or recreational (leisure) activities? 54 How much time do you spend P15 doing moderate-intensity sports, fitness or recreational (leisure) Hours Minutes activities on a typical day? Sedentary behavior 55 How much time to you usually P16 spend sitting or reclining on a typical day? Hours Minutes 56 How many hours do you usually P17 sleep in a night or day [if nights shift work]? Hours Minutes

History of Raised blood pressure

57 Have you ever had your blood H1 pressure measured by a doctor or [1] Yes other health worker? [2] No If no, go to H3 58 Have you ever been told by a H2a doctor or other health worker [1] Yes that you have raised blood [2] No pressure or hypertension If no, go to H3 (pressure)? 59 Are you currently taking any H2b medicine for raised blood [1] Yes pressure? [2] No

History of Diabetes 60 Have you ever had your blood H3 sugar measured by a doctor or [1] Yes other health worker? [2] No If no, go to M1

61 Have you ever been told by a H4a doctor or other health worker [1] Yes that you have raised blood sugar [2] No or diabetes? If no, go to M1 62 Are you currently taking any [1] Yes H4b treatment for diabetes? [2] No

Step 2 Physical measurements 63 Height [rounded to nearest cm] In Centimeters(Cm) M1

64 Weight In Kilograms (Kg) M2

65 Waist circumference In Centimeters (Cm) M3

66 Blood pressure: Reading 1 Systolic (mmHg) M4a

Diastolic (mmHg) M4b

Reading 2 Systolic (mmHg) M5a

Diastolic (mmHg) M5b

Reading 3 Systolic (mmHg) M6a

Diastolic (mmHg) M6b

Outcome of the Interview Completed Incomplete

Reasons for incompleteness Name of the Interviewer Signature of the Interviewer Date Any other comment