Case Report Open Access
Clustering of Cardiovascular Diseases Risk Factors among Manufacturing Employees in Sousse, Tunisia
Ezzi Olfa1*, Maatoug Jihene1, Chebil Dhekra1, Harrabi Imed1, Amimi Souad2, Mrizek Nejib3 and Ghannem Hassen1
1Department of Epidemiology, University Hospital Farhat Hached, Sousse, Tunisia
2Group of Occupational Medicine, Sousse, Tunisia
3Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
*Corresponding author: Dr. Ezzi Olfa, Department of Epidemiology, Farhat Hached University Hospital, Ibn Jazzar Street, Sousse, Tunisia, Tel: 21697775705; Fax: 21673226702; E-mail: @
Received: February 08, 2018; Accepted: March 02, 2018; Published: March 09, 2018
Citation: Ezzi Olfa, Jihene M, Dhekra C, et al. (2018) Clustering of Cardiovascular Diseases Risk Factors among Manufacturing Employees in Sousse, Tunisia. J Endocrinol Diab 5(1): 1-7. DOI: 10.15226/2374-6890/5/1/00196
Abstract
Objective: To estimate the prevalence of multiple risk factors for cardiovascular diseases (CVD) among manufacturing employees in Sousse, Tunisia and to determine factors associated with this clustering.

Methods: A cross-sectional study was carried out to estimate the prevalence of CVD risk factors in workplaces and their clustering. We used data from a workplace-based intervention which took place in six companies of the governorate of Sousse in Tunisia, Tunisia. Results: A total of 2113 employees were surveyed. The prevalence of having four or five risk factors tended to be higher among male employees, those with higher education level and those who were part of managerial staff.

Conclusion: Screening and targeted health promotion initiatives should be launched in worksite targeting the modifiable factors to avert the excessive risk for CVD.
Introduction
There is increasing epidemiological evidence that elevated risk of cardiovascular diseases (CVD) mortality is associated with certain lifestyle habits [1,2]. Smoking, physical inactivity, low consumption of vegetables and fruit, high blood pressure and high body mass index are associated with an increased risk of CVD (3). In contrast, having a healthy lifestyle could potentially prevent more than three-quarters of the risks of CVD [2,4,5].

Despite the new interest in and emphasis on public health and disease prevention in developing countries, it appears that the challenge of controlling CVD remains [3]. In recent years, the clustering of lifestyle risk factors has gained much attention. Many CVD risk factors are not randomly distributed across the population, but occur in combination with others. The clustering of risk factors is usually associated with a higher risk of diseases than can be expected from the added individual effects alone [6,7]. In particular, it can be used to identify risk factors which lead to other unhealthy habits. Previous studies have shown that the prevalence of multiple risk factors patterns differs between socio-demographic groups and regions.

Tunisia is now facing the phenomenon of epidemiologic transition where total mortality is decreasing, life expectancy is increasing, and lifestyles associated with chronic disease particularly diabetes and CVD, are being adopted [3,8–10].

Moreover, CVD are the first leading causes of death in Tunisia. CVD mortality accounted for one-third of total mortality in 2006 [11,12].

According to the World Health Organization, the most cost-effective methods of reducing risk among an entire population are population-wide interventions, combining effective policies and broad health promotion policies [13]. In this context, the workplace offers several advantages in that a substantial number of the working population can be reached and multiple levels of influence on behavior can be targeted [14].

In order to develop effective health promotion interventions in worksite, it is important to identify subgroups of employees who are more at risk for CVD than others.

To our knowledge, there is no previous study that provided the existence of clustering of different sets of CVD risk factors and their specific socio-demographic attributes among manufacturing employees in Tunisia.

Our objective is through a representative sample of manufacturing employees aged 18-67 years in Sousse, Tunisia, to determine the prevalence and socio demographic correlates of multiple occurrences of risk factors for CVD.
Methods
Study Design:
A cross-sectional study was carried out to determinate the prevalence of CVD risk factors in workplaces and their clustering.
Population:
we used data from a workplace-based intervention which took place in the manufacturing sector in six factories spread across three delegations of the region of Sousse. All employees in these six factories were included.
Data collection:
Socio-demographic characteristics and lifestyle data have been collected by a pre-tested questionnaire in Arabic.

Socio-demographic characteristics measured included age, sex, marital status, educational level and profession. Lifestyle items were composed of smoking status, daily cigarette consumption, eating habits, physical activity and alcohol consumption. All physical assessments (height, weight and blood pressure) were conducted by trained research assistants in a standardized manner.
Measures and Variables:
a.NCD risk factors were defined as follows:
*Tobacco use:
smokers were the participants who responded YES to the question: do you smoke any kind of tobacco (cigarettes, cigar, pipe or water-pipe)?

*Unhealthy diet:
when participant responded NO to the question: do you eat 5 or more portions of fruits and legumes a day?

*Physical inactivity:
when participants responded NO to the question: do you spend 30 minutes or more of moderate to vigorous activity per day during 5 days a week.

*High body mass index:
Body mass index (BMI) was determined as the body weight in kilograms divided by squared height in meters (kg/m2). High body mass index was defined as a BMI≥30 kg/m2.

*High blood pressure (HBP):
defined as a systolic blood pressure number of 140 or higher and/or diastolic blood pressure number of 90 or higher. Both systolic and diastolic pressures were measured at two occasions and the averages were recorded. Participants reporting current use of anti-hypertensive drugs were considered hypertensive regardless their blood pressure readings.
b.Socioeconomic status
it was based on the Asset index which has been used by researchers since 1998. Researchers use data on household assets to describe household welfare instead of using household income or expenditure data. The World Bank usually encourages researchers to utilize the asset index to classify household socioeconomic position in middle- and low-income countries where household income and expenditure data are unreliable (15,16).

In our survey, participants were asked about the availability of eleven household items in their household and its quantity.

These household items were: 1) Flushable toilet 2) electricity, 3) refrigerator, 4) central air conditioning (AC) or central heating, 5) air-cooling unit that moves and cools air, 6) washing machine, 7) television (TV), 8) telephone/mobile phone, 9) computer with Internet connection, 10) water safe for drinking, and 11) automobile/car.

Factor analysis was used to give different weights for different household items and to develop a comprehensive asset index (first extracted component in the analysis), which was used as a proxy of the socioeconomic status. Then Ward method was performed in order to obtain three hierarchical socioeconomic level; low, medium and high socioeconomic level.
Analysis
Statistical analysis was performed using the SPSS 10.0 software. Data were presented as frequencies, means and standard deviations. The chi-square test was used to compare different clusters with the categorical variables. The level of significance was 0.05.
Ethical consideration:
The study was approved by the Ethics Committee of the University Hospital Farhat Hached, Tunisia. It does not represent any risk for participants who gave their informed consent before responding to the questionnaire.
Results
Descriptive Analysis
A total of 2113 employees were surveyed. The response rate was 71.9%. The participants consisted of 1342 (63.5%) men and 775 (36.5%) women. The mean age of employees was 36.28±8.79 years. Majority of the respondents were workers (79.5%) with medium socioeconomic status (78.7%) as indicated in Table 1.

The most common NCD risk factor was physical inactivity with a prevalence of 59 % (Table 2). Unhealthy diet was the second most common risk factor (41.9%), followed by smoking (34%), high BMI (23.7%) and HBP (19.82%).

The proportions of respondents with 0, 1, 2, 3or ≥ 4 risk factors were respectively 11.54%, 29.68%, 34.03%, 19.12% and 5.63%% (Table 2).
Cluster analysis
As showed in Table 3, the combination of all five risk factors showed clustering with an O/E ratio of 1.9. The greatest degree of clustering occurred in two patterns, the first with two risk factors (smoking and high BMI) and the second with three risk factors (smoking, high BMI and HBP) (O/E: 2.23).

Among the four risk factors patterns, the combination of smoking, physical inactivity, high BMI and HBP and the combination of unhealthy diet, physical inactivity, high BMI and HBP were more prevalent than expected.

Among the three risk factors patterns, the association between smoking, unhealthy diet and physical inactivity, between smoking, high BMI and HBP, between unhealthy diet, physical inactivity and high BMI and between physical inactivity, high BMI and HBP were more prevalent than expected
Table 1: Socio-demographic characteristics of Tunisian employees aged 18–67 years (N=2113)

n

Percentage (%)

Age

18-29

531

25.1

30-39

876

41.5

40-49

517

24.5

≥ 50

189

9

Gender

Male

1342

63.5

Female

775

36.5

Education level

Primary

462

21.9

Secondary

1356

64.2

University

295

14

Marital Status

Married 

1439

68.1

Not married

674

31.9

Employment status

Workers

1680

79.5

Technician

231

10.9

Managerial staff

202

9.6

Employment status

Workers

1680

79.5

Technician

231

10.9

Managerial staff

202

9.6

Socioeconomic status

Low

117

5.5

Medium

1663

78.7

High

333

15.8

Table 2: NCD risk factors among Tunisian employees aged 18–67 years (N=2113)

n

Percentage (%)

CVD risk factors

Physical inactivity

1247

59

Unhealthy diet

885

41.9

Smoking

718

34

High BMI

500

23.66

HBP

419

19.82

Number of CVD risk factors

0

244

11.54

1

627

29.68

2

719

34.03

3

404

19.12

4 or 5

119

5.63

Among the two risk factors patterns, the association between smoking and BMI, between smoking and HBP, between unhealthy diet and physical inactivity, between high BMI and HBP were more prevalent than expected (Table 3).
Relationship between NCD risk factors clusters and socio Demographic characteristics
The prevalence of having four or five risk factors tended to be higher among male employees, those with higher education level and those who were part of managerial staff (Table 4).

Employees aged 40 and more were more likely to have four or five risk factors (23 versus 32.8%) with no significant difference.

Employees with high economics level were more likely to have four or five risk factors (14.3 versus 21.8%) with no significant difference.
Discussion
This is the first report of the prevalence, clustering and socioeconomic distribution of CVD risk factors in a representative sample of Tunisian employees.

This study sought to identify patterns of individual and concurrent CVD risk factors, with an emphasis on the number and type of risk factors, to support future intervention strategies.

Three major findings can be highlighted. First, the occurrence of the studied risk factors in the population study is high: 88.5% of Tunisian employees reported at least one risk factor for CVD. Second, the behavior pattern that indicated a greater increase than that expected at random was the simultaneous occurrence of obesity and smoking with or not HBP. Finally, the most vulnerable groups to the simultaneous occurrence of four or five risk factors for CVD were identified: male employees, those with higher education level and those who were part of managerial staff.

Physical inactivity was the most prevalent risk factor among Tunisian employees, followed by the low consumption of fruits and vegetables. Studies that evaluated risk factors clustering among employees using similar criteria, supported our findings [17,18].

In fact, the epidemiological transition in Tunisia is related to prominent life style changes, particularly changes in food consumption patterns [19,20]. Food transition has led to the shift from a traditional diet rich in cereals, fruits and vegetables to a diet rich in animal products, with increased energy intake. In 20 years, the daily ration has increased by 140 calories per person on average, from 2294 kcal a day in 1975 to 2434 kcal a day in 1995 [21].

HBP and obesity prevalence among employees sample were lower than national prevalence which can be explicated by the Health worker effect , while smoking prevalence was higher than national one which can be explicated by the sex ratio of our employees sample. Indeed, according to a study involving 402 teachers of Kalaa Kebira in Sousse in 1992, the global prevalence of tobacco use was lower, estimated to 29.3%. However, many studies [18,19,22,23,25-27] reported that it was rather
Table 3: Clustering pattern of NCD risk factors in Tunisian employees aged 18–67 years (N=2113)

Risk factor

Smoking

Unhealthy diet

Physical inactivity

High BMI

HBP

n

Observed %

Expected %

O/E

5

+

+

+

+

+

16

0.76

0.4

1.9

Total

16

0.76

0.4

1.9

4

+

+

+

+

-

25

1.18

1 .61

0.73

+

+

+

-

+

29

1.37

1.28

1.07

+

+

-

+

+

4

0.18

0.28

0 .64

+

-

+

+

+

17

0.82

0.55

1.49

-

+

+

+

+

28

1.32

0.78

1.69

Total

103

4.87

4.9

0.99

3

+

+

+

-

-

146

6.91

5.12

1.35

+

+

-

+

-

10

0.47

1.12

0.42

+

+

-

-

+

15

0.71

0.89

0.8

+

-

+

+

-

18

0.85

2.23

0.38

+

-

+

-

+

23

1.09

1.76

0.62

+

-

-

+

+

18

0. 85

0.38

2.23

-

+

+

+

-

75

3.55

3.14

1.13

-

+

+

-

+

42

1.99

2.48

0.8

-

+

-

+

+

8

0.38

0.54

0.7

-

-

+

+

+

49

2.32

1.08

2.15

Total

404

19.12

18.74

1.02

2

+

+

-

-

-

63

3

3.45

0.87

+

-

+

-

-

125

5.91

7.07

0.83

+

-

-

+

-

18

0.85

0.38

2.23

+

-

-

-

+

34

1.61

1.22

1.32

-

+

+

-

-

269

12.73

9 .94

1.28

-

+

-

+

-

26

1.23

2.18

0.56

-

+

-

-

+

28

1.32

1.72

0.77

-

-

+

+

-

92

4.35

7.48

0.58

-

-

+

-

+

37

1.75

3.43

0.51

-

-

-

+

+

27

1.28

0.75

1 .70

Total

719

34.03

37.62

0.9

1

+

-

-

-

-

157

7.43

4.91

1.51

-

+

-

-

-

101

4.78

6.91

0.69

-

-

+

-

-

256

12.11

13.73

0.88

-

-

-

+

-

69

3.26

3

1.08

-

-

-

-

+

44

2.1

2.38

0.88

Total

627

29.68

30.93

0.96

0

-

-

-

-

-

244

11.54

9.54

1.21

Total

244

11.54

9.54

1.21

Table 4: Prevalence (%) of multiple NCD risk factors in Tunisian employees aged 18–67 years (N=2113)

Number of risk factors

0 n (%)

1 n (%)

2 n (%)

3 n (%)

4 or 5 n (%)

p-value

Age

18-29

59 (24.2)

163 (26)

190 (26.4)

95 (23.5)

24 (20.2)

0.49

30-39

108 (44.3)

266 (42.4)

295 (41.0)

160 (39.6)

47 (39.5)

40-49

56 (23.0)

140 (22.3)

177 (24.6)

105 (26.0)

39 (32.8)

≥ 50

21 (8.6)

58 (9.3)

57 (7.8)

44 (10.9)

9 (7.6)

Gender

Male

135 (55.3)

367 (58.5)

429 (59.7)

311 (77.0)

100 (84.0)

< 10-3

Female

109 (44.7)

260 (41.5)

290 (40.3)

93 (23.0)

19 (16.0)

Education level

Primary

57 (23.4)

156 (24.9)

153 (21.3)

80 (19.8)

16 (13.4)

0.035

Secondary

153 (62.7)

404 (64.4)

460 (64.0)

256 (63.4)

83 (69.7)

University

34 (13.9)

67 (10.7)

106 (14.7)

68 (16.8)

20 (16.8)

Marital Status

Married

82 (33.7)

200 (32.1)

237 (33.1)

119 (29.5)

28 (23.7)

0.244

Not  married

161 (66.3)

424 (67.9)

480 (66.9)

284 (70.5)

90 (76.3)

Employment status

Workers

192 (78.7)

507 (80.9)

578 (80.4)

315 (78.0)

88 (73.9)

< 10-3

Technician

38 (15.6)

75 (12.0)

70 (9.7)

36 (8.9)

12 (10.1)

Managerial staff

14 (5.7)

45 (7.2)

71 (9.9)

53 (13.1)

19 (16.0)

Socioeconomic status

Low

10 (4.1)

35 (5.6)

37 (5.1)

32 (7.9)

3 (2.5)

0.185

Middle

199 (81.6)

498 (79.4)

566 (78.7)

310 (76.7)

90 (75.5)

High

36 (14.3)

94 (15.0)

116 (16.1)

62 (15.3)

26 (21.8)

unemployment which was associated with higher tobacco use prevalence, independently of educational and economic status.

Furthermore, Tunisian employees smoking prevalence seems to be higher than prevalence found in a Brazilian study conducted in 47,477 workers located in 2775 Brazilian industries where the prevalence was only 13% (18) and higher than the prevalence found among employees at a Saudi University where daily smokers accounted for 22.7% [17]. This finding confirm that smoking still represent an ongoing and dire public health threat in Tunisia despite national efforts to address this epidemic.

Our study also revealed that 59% of Tunisian employees had two or more CVD risk factors, including 24.75% with at least three risk factors. Only 11.5% of Tunisian employees did not have any of the five risk factors. The most frequent number of risk factors among employees was two reinforcing findings from other studies [17,18].

The clustering between obesity with smoking, associated or not with HBP had a higher observed prevalence than expected. It represents an increase of 123%. The Brazilian study showed that is rather aggregation between smoking and alcohol consumption which was more observed than expected among employees (28).

As in studies that assessed individual risk behaviors (29), simultaneous risk factors are associated to socio demographic variables (30). In this study, male employees with a higher education level and who were parts of managerial staff were more likely to have multiple risk factors.

Indeed, it is well known that men are less likely to seek formal health care than women are, especially in the pre-symptomatic phase of chronic diseases (31). Avoiding the primary care system, men are deprived of protection required to preserve their health, including support to achieve and sustain healthy behaviors [18].

In our study, employees who are part of managerial staff were more likely to have higher number of CVD risk factors. This could be explicated by the fact that this category of employees may be occupied with busy work and engaged in more social activities, like eating out for business or with friends. It is difficult to make the healthiest choices on the menu and resist the temptation to overeat [32]. Indeed, it was revealed that worksite interventions were more effectives for workers as compared to managers in promoting the recommended guidelines for both fruit and vegetable intake and physical activity [14].

Contrary to our expectation, socioeconomic status was not associated significantly with more clustering of risk factors. In fact, it was well established that people who are socially advantaged have more access to resources and are more able to take advantage of opportunities to be healthy compared to those who are socially disadvantaged [33]. However, it is important to recognize the limitation of using the asset index. The asset index is better thought of as a proxy for long-term household wealth rather than current per capita consumption [34]. Nonetheless, the strong correlation between asset index and money metric measures like income and expenditure was not consistently supported [32].

It is the first piece of novel research to investigate the distribution of CVD risk factors in a specific group of employed adults in Tunisia. To our knowledge, this analysis was unprecedented in our country.

Strengths of this study included the use of a large, randomly selected sample of workplaces that enabled a heterogeneous sample of employees to be surveyed. Objective measurements of weight and blood tension are the strength of this research; the ascertainment of these measurements did not rely on selfreported data.

Some limitations need to be considered when interpreting the findings. Firstly, participants were recruited from six factories spread across three delegations of the region of Sousse which would not be representative for the general population or the general working population. The ‘Healthy worker effect’ is a common effect in studies with occupational samples and is reflected in the better health status of employed people relative to the general population [35]. Therefore the generalizability of the prevalence estimates of the CVD risk factors to the general population may be limited. Data collected were self-reported and therefore subject to recall or response bias including social desirability bias. To minimize this bias, standardized data collection procedures were followed and participants were assured that their data would remain confidential. As a crosssectional study, the present analysis is however, limited in its ability to elucidate a causal relationship.
Conclusion
This study was useful for identifying groups that are generally more at risk and developing tailored intervention activities. There should be Tunisian government and private sector support to strengthen the involvement of communities, with the aims of combating the current surge in chronic diseases. Screening and health promotion initiatives in workplace should be launched targeting the modifiable risk factors to avert the excessive risk for CVD among this important subgroup of the population.
Acknowledgment
This manuscript was based on a project grant funded by ‘‘UnitedHealth Group’’ and by the Research Unit ‘‘Santé UR12SP28’’entitled: Epidemiologic transition and prevention of chronic disease of the Ministry of Higher Education, Tunisia
References
  1. Knoops KT, De Groot LC, Kromhout D, Perrin AE, Moreiras-Varela O, Menotti A, et al. Mediterranean diet, lifestyle factors, and 10-year mortality in elderly European men and women: the HALE project. JAMA. 2004;292(12):1433‑1439.
  2. King DE, Mainous AG, Geesey ME. Turning Back the Clock: Adopting a Healthy Lifestyle in Middle Age. Am J Med. 2007;120(7):598‑603. Doi: 10.1016/j.amjmed.2006.09.020
  3. Ghannem H. The challenge of preventing cardiovascular disease in Tunisia. Prev Chronic Dis. 2006;3(1):A13.
  4. King DE, Mainous AG, Carnemolla M, Everett CJ. Adherence to healthy lifestyle habits in US adults, 1988-2006. Am J Med. 2009;122(6):528‑534. Doi: 10.1016/j.amjmed.2008.11.013
  5. Chiuve SE, McCullough ML, Sacks FM, Rimm EB. Healthy Lifestyle Factors in the Primary Prevention of Coronary Heart Disease Among Men: Benefits Among Users and Nonusers of Lipid-Lowering and Antihypertensive Medications. Circulation. 2006;114(2):160‑167. Doi:  10.1161/CIRCULATIONAHA.106.621417
  6. Lv J, Liu Q, Ren Y, Gong T, Wang S, Li L, Community Interventions for Health (CIH) collaboration. Socio-demographic association of multiple modifiable lifestyle risk factors and their clustering in a representative urban population of adults: a cross-sectional study in Hangzhou, China. Int J Behav Nutr Phys Act. 2011;8(1):40. Doi: 10.1186/1479-5868-8-40
  7. Poortinga W. The prevalence and clustering of four major lifestyle risk factors in an English adult population. Prev Med. 2007;44(2):124‑128.  Doi: 10.1016/j.ypmed.2006.10.006
  8. Alaya NB, Delpeuch F, Romdhane HB. Modèle causal des cardiopathies ischémiques en Tunisie. Options Mediterr Ser B Etudes Rech. 2002;41.
  9. Ben Romdhane H, Haouala H, Belhani A,  et al. La transition épidemiologique ses déterminants et son impact sur les systèmes de santé à travers l’analyse de la tendance des maladies cardiovasculaires en Tunisie. Tunis Médicale. 2005;83(5):1‑7.
  10. Ghannem H. Epidémiologie du diabète sucré dans le Sahel tunisien. Sante Publique. 1992;4(3):29‑32.
  11. Ben Romdhane H, Bougatef S, Skhiri H, et al. Le registre des maladies coronaires en Tunisie : organisation et premiers résultats. Rev Epidemiol Sante Publique. 2004;52:558-564.
  12. Elasmi M, Feki M, Sanhaji H, et al. Prévalence des facteurs de risque cardiovasculaires conventionnels dans la population du Grand Tunis. Rev Epidemiol Sante Publique. 2009;57(2):87‑92.
  13. Kwauk CT. Obesity and the healthy living apparatus: discursive strategies and the struggle for power. Crit Discourse Stud. 2012;9(1):39‑57. Doi: 10.1080/17405904.2011.632139
  14. Quintiliani L, Sattelmair J, Sorensen G. The workplace as a setting for interventions to improve diet and promote physical activity. World Health Organization 2008.
  15. Filmer D, Pritchett LH. Estimating wealth effects without expenditure Data—Or tears: An application to educational enrollments in states of india. Demography. 2001;38(1):115-132.
  16. Morris SS, Carletto C, Hoddinott J, Christiaensen LJ. Validity of rapid estimates of household wealth and income for health surveys in rural Africa. J Epidemiol Community Health. 2000;54(5):381‑387.
  17. Amin TT, Al Sultan AI, Mostafa OA, Darwish AA, Al-Naboli MR. Profile of non-communicable disease risk factors among employees at a Saudi university. Asian Pac J Cancer Prev APJCP. 2014;15(18):7897‑7907.
  18. Del Duca GF, Silva KS, Garcia LMT, De Oliveira ESA, Nahas MV. Clustering of unhealthy behaviors in a Brazilian population of industrial workers. Prev Med. 2012;54(3-4):254‑258. Doi: 10.1016/j.ypmed.2012.02.005
  19. Ghannem H, Fredj AH. Transition épidémiologique et facteurs de risque cardiovasculaire en Tunisie. Rev Epidemiol Sante Publique. 1997;45(4):286‑292.
  20. Ben Romdhane H, Khaldi R, Oueslati A, Skhiri H. Transition épidémiologique et transition alimentaire et nutritionnelle en Tunisie. Options Méditerranéennes B. 2002;41:7-27
  21. Institut national de statistique. Enquête budget et consommation et niveau de vie des ménages. Tunis. Institut national de statistique. 1995: 127.
  22. El Ati J, Traissac P, Delpeuch F, et al. Gender obesity inequities are huge but differ greatly according to environment and socio-economics in a North African setting: a national cross-sectional study in Tunisia. PloS One. 2012;7(10):e48153. Doi: 10.1371/journal.pone.0048153
  23. Ben Romdhane H, Ben Ali S, Skhiri H, et al. Hypertension among Tunisian adults: results of the TAHINA project. Hypertens Res Off J Jpn Soc Hypertens. 2012;35(3):341‑347. Doi: 10.1038/hr.2011.198
  24. Thygesen LC, Hvidtfeldt UA, Mikkelsen S, Brønnum-Hansen H. Quantification of the healthy worker effect: a nationwide cohort study among electricians in Denmark. BMC Public Health. 2011;11(1):571. Doi: 10.1186/1471-2458-11-571
  25. Fakhfakh R, Hsairi M, Maalej M, Achour N, Nacef T. Tobacco use in Tunisia: behaviour and awareness. Bull World Health Organ. 2002;80(5):350‑356.
  26. Fakhfakh R, Hsairi M, Achour N. Epidemiology and prevention of tobacco use in Tunisia: a review. Prev Med. 2005;40(6):652‑657. Doi:  10.1016/j.ypmed.2004.09.002
  27. Giatti L, Barreto SM. Tabagismo, situação no mercado de trabalho e gênero: análise da PNAD 2008. Cad Saúde Pública. 2011;27(6):1132‑1142.  Doi: 10.1590/S0102-311X2011000600010
  28. Tracey ML, Fitzgerald S, Geaney F, Perry IJ, Greiner B. Socioeconomic inequalities of cardiovascular risk factors among manufacturing employees in the Republic of Ireland: A cross-sectional study. Prev Med Rep. 2015;2:699‑703. Doi: 10.1016/j.pmedr.2015.08.003
  29. Maatoug J, Harrabi I, Hmad S, et al. Clustering of Risk Factors With Smoking Habits Among Adults, Sousse, Tunisia. Prev Chronic Dis. 2013;10.
  30. Ferreira da Costa F, Benedet J, Leal DB, Altenburg de Assis MA. Clustering of risk factors for non communicable diseases in adults from Florianopolis, SC. Rev Bras Epidemiol Braz J Epidemiol. 2013;16(2):398‑408.
  31. Hunt K, Adamson J, Hewitt C, Nazareth I. Do women consult more than men? A review of gender and consultation for back pain and headache. J Health Serv Res Policy. 2011;16(2):108‑117. Doi: 10.1258/jhsrp.2010.009131
  32. Lv J, Liu Q, Ren Y, Gong T, Wang S, Li L,et al. Socio-demographic association of multiple modifiable lifestyle risk factors and their clustering in a representative urban population of adults: a cross-sectional study in Hangzhou, China. Int J Behav Nutr Phys Act. 2011;8(1):40. Doi:  10.1186/1479-5868-8-40.
  33. Stringhini S, Sabia S, Shipley M,  Brunner E, Nabi H, Kivimaki M, et al. Association of socioeconomic position with health behaviors and mortality. JAMA. 2010;303(12):1159‑1166. Doi: 10.1001/jama.2010.297
  34. Sahn DE, Stifel D. Exploring Alternative Measures of Welfare in the Absence of Expenditure Data. Rev Income Wealth. 2003;49(4):463‑489.
  35. Li CY, Sung FC. A review of the healthy worker effect in occupational epidemiology. Occup Med. 1999;49(4):225‑229.
 
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