Abstract
Hypertension affects more than a quarter of the world adult population, with ruralurban disparities. In Cote d’Ivoire, the prevalence was 21.7% in 2005. The aim of this study was to determine factors associated with hypertension in a peri-urban community in Abidjan. A cross-sectional study was conducted at Anonkoi 3 a peri-urban area in Abidjan. The sample was of 360 subjects aged 18 and older. Behavioral, anthropometric and blood pressure characteristics were determined using WHO STEPS questionnaire and multivariate logistic regression was performed. Prevalence of hypertension was 18.61%. Subjects were low fruit and vegetable consumption (3.3%), low level of physical activity (64.2%) and abdominal obesity at 40%. The risk of hypertension was significant from age 45, in subjects living with a partner and in those with low level of physical activity. Health education programs are essential to prevent cardiovascular risks.
Key words: Hypertension, prevalence, risk factor
Introduction
Hypertension is responsible for at least 45% of deaths from cardiovascular diseases and 51% of stroke deaths worldwide.1 More than a quarter of the global adult population is hypertensive and this proportion is expected to reach 29.2% by 2025, or nearly 1.6 billion people.2 In sub-Saharan Africa, this condition currently affects approximately 27-28% of the adult population aged 20 years and over.2 In addition, there are significant rural-urban disparities.3 Studies in sub-Saharan Africa reported low prevalence (<10%) of arterial hypertension in predominantly rural populations.4 In Côte d’Ivoire, according to the estimates of the World Health Organization in relation to the STEPS study in 2005,5 the overall prevalence of arterial hypertension was 21.7% and this was significantly more important in rural areas with 29.6% against 21% in urban areas. There is significant urbanization in this country and the capital Abidjan in its extension, occupies a part of the surrounding rural territories, thus favoring the creation of suburban districts.6 These territories combine the characteristics of rural areas with low economic potential and those of the city. This environment determines the vulnerability of peri-urban populations to non-communicable diseases mainly related to lifestyle. Several neighborhoods of the Abobo commune, in the north part of the district of Abidjan are built on the territory of indigenous villages with the typology of suburban neighborhoods.5 In 2016, the incidence rate of hypertension in the health district of AboboWest was 18.01 ‰, one of the highest in the Abidjan 2 health region.7 These data from health facilities can be underestimated because the use of the health facilities is still low (45.26%) at the national level.7 In addition, there is few data on arterial hypertension in Côte d’Ivoire. The aim of the present study was to determine the prevalence and risk factors associated with hypertension in Anonkoi 3, one of the peri-urban neighborhoods of Abobo West.
Materials and Methods
Study design and setting
The study design was a cross-sectional survey conducted in November 2017. The study was carried out in Anonkoi 3. Anonkoi 3 is a peri-urban area belonging to the health district of Abobo-West. In the general census of the population in 2014, its population was estimated at 4814 inhabitants and the neighborhood had 754 households.8 In the West Abobo health district, there is one referral hospital and the entire population lives within 5 km of a health center.7
Participants and Sampling
The study population consisted of adults aged 18 years and older who had been resident in Anonkoi 3 for at least six months and who had given oral consent. Pregnant and breastfeeding women were not included in the study because of the potential hypertensive disorders associated with pregnancy and postpartum.9 The sample size was determined from the Schwartz formula.10 The prevalence of hypertension used was 21.7% from the WHO STEPS report in Côte d’Ivoire in 2005, the only available value for the entire country. The number of subjects obtained after calculation was 261. We extrapolated it to 360 subjects by taking into account possible non-responses. The subjects included in the study were selected by a two-degree random sample. We subdivided the neighborhood into 30 sectors using a plan. Households in these 30 sectors were identified by the team. For each sector, the first 12 randomly selected households were visited and one adult was interviewed per household. The choice of adult to be interviewed per household was random.
Data collection
A team of six trained investigators performed an electronic data collection using the MAGPIR. The interview, anthropometric measurements and blood pressure were conducted at the participant’s home. The questionnaire used for the interview was the WHO STEPS questionnaire.5 The subject surveyed was seated during the entire interview which lasted 20 minutes on average. The questions focused on sociodemographic characteristics (age, gender, educational level, living with a partner, occupational category, number of children), family history of hypertension, behavioral risk factors (consumption of tobacco, alcohol, fruit and vegetables, level of physical activity). Each individual surveyed systematically had three blood pressure measurements taken by the same investigator. Systolic and diastolic blood pressure were taken in the right arm after 5 minutes of rest. At the end of the interview with each participant, the average of the blood pressure measurements was calculated. In case the PAS ≥ 140 mm Hg and/or the PAD ≥ 90 mm Hg, the participant was invited to contact the health structure in his district for a diagnosis and possibly treatment.
Subjects were considered as hypertensive when they had previously been diagnosed by a health professional or had ongoing antihypertensive therapy, or when they had a systolic blood pressure greater than or equal to 140 mmHg and/or diastolic blood pressure greater than or equal to 90 mmHg.11 The weight was measured in kilograms (kg) using a TANITAR weighing scale to the nearest .1 kg. The measurement of participants height was done in centimeters using a stadiometer to the nearest .1cm. Concerning the body mass index (BMI), the following BMI categories were defined: underweight (BMI<18.5), normal weight (18.5≤BMI <25), overweight (25≤BMI <30), obesity (BMI ≥ 30).12Waist circumference was measured in cm in the narrowest sections of the trunk between the costal margin and the iliac crest; and the hip circumference as the widest section of the buttocks over the greater trochanters. Waist– to– hip Ratio (WHR) was determined by dividing waist circumference in cm by hip circumference in cm. A WHR of more than .8 for females and .95 for males was considered as abdominal obesity.12 Smoking was assessed by defining the past and current smoking status of the participants interviewed. 13 Smoking was defined as individuals smoking daily or having smoked daily in the past. Non-smokers were those who smoked experimentally or not at all. Alcohol consumption was measured using the AUDIT-C test, which is an international screening test developed by WHO.14 The AUDIT-C test refers to the last 12 months of the person’s life. The AUDIT-C test was interpreted using the scores obtained: a score greater than or equal to 4 for men, 3 for women signs a Heavy drinking and a score greater than or equal to 5 for men, 4 for women signs an active alcohol dependence. 14,15 All subjects with Heavy drinking and active alcohol dependence were categorized as alcohol consumers. And those who were not classified in these groups as nondrinkers. Fruit and vegetable consumption was assessed on the basis of 5 portion of fruits and vegetables, ie 400g recommended per day.16 Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ).17
Data analysis
The data was entered into the ExcelR 2016 and then analyzed with Stata 13R. Qualitative variables were presented as numbers and percentages. Mean and standard deviation of the quantitative variables were calculated using a Chi-square or Fisher tests were used to assess differences in the proportion of hypertension. The associations between sociodemographic, BMI categories, abdominal obesity and health behavior variables and hypertension were assessed in a two step procedure where individuals with hypertension were compared separately with those having no hypertension. In the first step, each variable was evaluated independently in a bivariate multinomial logistic regression analysis with hypertension as dependent variable to generate unadjusted OR. Those variables with Pvalues less than 0.2 were retained and entered into multinomial logistic regression model to determine variables that were independently associated with hypertension. A probability level of P<0.05 was considered significant.
Table 1.
Socio-demographic, behavioral and metabolic characteristics (n=360).
| Variables | Frequencies (n) | % |
|---|---|---|
| Sociodemographic characteristics | ||
| Age (years) | ||
| 18 – 35 | 226 | 62.8 |
| 35 – 45 | 74 | 20.5 |
| ≥ 45 | 60 | 16.7 |
| Gender | ||
| Male | 146 | 40.6 |
| Female | 214 | 59.4 |
| Level of education | ||
| None | 71 | 19.7 |
| Primary school | 106 | 29.4 |
| High school | 136 | 37.8 |
| University | 47 | 13.1 |
| Main occupation | ||
| Unemployed | 121 | 33.6 |
| Employees | 79 | 21.9 |
| Informal private sector | 160 | 44.4 |
| Living with a partner | ||
| No | 212 | 58.9 |
| Yes | 148 | 41.1 |
| Behavioral characteristics | ||
| Tobacco consumption | ||
| No | 326 | 90.6 |
| Yes | 34 | 9.4 |
| Alcohol consumption | ||
| No | 212 | 58.9 |
| Yes | 148 | 41.1 |
| Fruits and vegetables consumption | ||
| Less than 5 portions/day | 348 | 96.7 |
| 5 portions and more/day | 12 | 3.3 |
| Physical activity | ||
| Intense | 18 | 5.0 |
| Moderate | 111 | 30.8 |
| Low | 231 | 64.2 |
| Family History of HBP | ||
| No | 247 | 68.6 |
| Yes | 113 | 31.4 |
| Metabolic characteristics | ||
| BMI | ||
| BMI < 18.5 | 31 | 8.6 |
| 18.5 ≤ BMI < 25 | 201 | 55.8 |
| 25 ≤ BMI < 30 | 67 | 18.6 |
| BMI ≥ 30 | 61 | 16.9 |
| Abdominal obesity | ||
| No | 216 | 60.0 |
| Yes | 144 | 40.0 |
BMI, body mass index; HBP, high blood pressure.
Table 2.
Prevalence of hypertension based on participants' sociodemographic, behavioral and nutritional characteristics (n = 360).
| No Hypertensive n (%) | Hypertensive n (%) | P-value | |
|---|---|---|---|
| Sociodemographic factors | |||
| Gender | |||
| Male | 123(84.24) | 23(15.8) | 0.2499 |
| Female | 170(79.4) | 44(20.6) | |
| Age (years) | |||
| 18 – 35 | 200(88.5) | 26(11.5) | < 0.001 |
| 35 – 45 | 59(79.7) | 15(20.3) | |
| ≥ 45 | 34(56.7) | 26(43.3) | |
| Main occupation | |||
| Unemployed | 98(81) | 23(19.01) | 0.9724 |
| Employees | 65(82.28) | 14(17.72) | |
| Informal private sector | 130(81.25) | 30(18.75) | |
| Level of education | |||
| None | 55(77.46) | 16(22.54) | 0.089 |
| Primary school | 80(75.47) | 26(24.53) | |
| High school | 116(85.29) | 20(14.71) | |
| University | 42(89.36) | 5(10.64) | |
| Living with a partner | |||
| Yes | 104(70.27) | 44(29.7) | < 0.001 |
| No | 189(89.15) | 23(10.8) | |
| Behavioral Factors | |||
| Tobacco consumption | |||
| No | 269(81.5) | 61(18.48) | 0.8382 |
| Yes | 10(33.33) | 20(66.67) | |
| Alcohol consumption | |||
| No | 170(80.19) | 42(19.81) | 0.484 |
| Yes | 123(83.11) | 25(16.89) | |
| Fruits and vegetables consumption | |||
| Less than 5 portions/day | 283(81.32) | 65(18.68) | 1.000 |
| 5 portions and more/day | 10(83.33) | 2(16.67) | |
| Physical activity | |||
| Intense | 11(61.11) | 7(38.89) | 0.045 |
| Moderate | 95(85.59) | 16(14.41) | |
| Low | 187(80.95) | 44(19.05) | |
| Family History of HBP | |||
| No | 208(84.2) | 39(15.79) | 0.042 |
| Yes | 85(75.22) | 28(24.78) | |
| Metabolic Factors | |||
| BMI | 26(83.87) | 5(16.13) | 0.002 |
| BMI < 18,5 | 169(84.08) | 32(15.92) | |
| 18,5 ≤ BMI < 25 | 59(88.06) | 8(11.94) | |
| 25 ≤ BMI < 30 | 39(63.93) | 22(36.07) | |
| BMI ≥ 30 | |||
| Abdominal obesity | |||
| No | 185(85.65) | 31(14.35) | 0.011 |
| Yes | 108(75.00) | 36(25.00) | |
BMI, body mass index; HBP, high blood pressure.
Ethical considerations
The study protocol was submitted to the scientific committee of the Training and Research Unit Pharmaceutical and Biological Sciences, which validated the methodological and ethical aspects. We obtained the prior authorization of community keepers and leaders. Oral consent was sought for each participant.
Results
Socio-demographic, behavioral and metabolic characteristics
As described in Table 1, the average age of participants was 33.51 (±13.07) years. The 25 to 34 age group was the most represented (36.9%). The sex ratio (M/F) was 0.68 denoting a female predominance. The level of education was low, participants with a university education level only represented 13.1% of the sample. More than half of the participants lived with a partner. As regards economic activities, the informal sector was the most represented with 44.4%.
The proportion of participants consuming at least 5 portions of fruits and vegetables per day was very low (3.3%). In addition, the majority of the participants had a low level of physical activity (64.2%). In contrast, the participants practically did not consume tobacco (90.6%), and did not consume alcohol in 58.9% of the cases.
Abdominal obesity was found in 2 out of 5 participants. Moreover, one out of five had obesity and overweight.
Prevalence and associated factors of hypertension
The prevalence of hypertension was 18.61% (95% CI : 14.7-23.0). In addition, a family history of hypertension was reported in 31.4% of the cases. Twenty-one participants were on anti-hypertensives. Table 2 presents the univariate analysis of the prevalence of hypertension by socio-demographic, behavioral and metabolic characteristics. The socio-demographic characteristics associated with hypertension were age (P <0.001), a live with a partner (P<0.001), a family history of hypertension (P=0.042). Regarding the behavioral data, only the level of physical activity was associated with high blood pressure (P=0.045). The metabolic factors studied were all associated with hypertension. After logistic regression with adjustment for all covariates, participants aged 45 or older were 3 times more likely to have an hypertension (OR =3.44, 95% CI : 1.60-7.43). For those living with a partner, this risk was two times higher compared to participants living without a partner (OR = 2.10, 95% CI : 1.08-4.11) (Table 3).
Discussion
The prevalence of hypertension at Anonkoi 3 was relatively high. This prevalence reached the prevalence of 20.4% reported in 2014 in the city of Adzopé (south-east of Côte d’Ivoire).18 In Burkina Faso, a study conducted in peri-urban areas in 2014 revealed a lower prevalence of hypertension of 11%.3 In peri-urban areas, people are experiencing changes in lifestyle due to urbanization. The association between hypertension and age was found in our study. It is thought to be related to the increase in aortic stiffness observed with aging.13,19 Doulougou in Burkina Faso also observed an increase in the prevalence of hypertension with age.3 In addition, participants living with a partner were more likely to have hypertension. This could be justified by the stress of the negative quality of a life with a partner.20 Similarly, Onwuchekwa had shown in a study in rural Nigeria that a high proportion of married people were hypertensive.21 For him, being married did not offer the protection you would expect from having a companion.21 Our study did not reveal a significant association between hypertension and smoking, alcoholism and non-consumption of fruits and vegetables; the proportion of subjects with these behavioural risk factors was low in the study population. Other studies in Africa had also revealed this lack of association. 22,23 The effect of obesity on hypertension was not found in our study. Similarly, some authors in sub-Saharan Africa had made the same observation.3,21 However, other studies showed an independent association between the Body Mass Index and hypertension.14,24 On the other hand, low levels of physical activity did not increase the risk of high blood pressure. The proportions of physically active subjects were low in our study, which may justify this result. However, it has been described in the literature that regular physical activity would significantly reduce the risk of high blood pressure.25
Table 3.
Factors associated with hypertension and corresponding multivariate Odds Ratio in the Anonkoi 3 adult population (n=360).
| Hypertensive n(%) | Unadjusted OR (95%CI) | P-value | Adjusted OR (95%CI) | P-value | |
|---|---|---|---|---|---|
| Gender | |||||
| Female | 44 (20.6) | 1 | - | - | - |
| Male | 23 (15.8) | 0.72 (0.41-1.25) | 0.251 | - | - |
| Age (years) | |||||
| [18 – 35[ | 26 (11.5) | 1 | 1 | - | |
| [35 – 44[ | 15 (20.3) | 1,96 (0.95-3.90) | 0.060 | 1.28 (0.58-2.73) | 0.535 |
| ≥ 45 | 26 (43.3) | 5.88 (3.07-11.39) | <0.001 | 3.44 (1.60-7.43) | 0.002 |
| Level of education | |||||
| None | 16 (22.5) | 1 | - | 1 | - |
| Primary school | 26 (24.5) | 1.12 (0.55-2.31) | 0.760 | 0.95 (0.43-2.13) | 0.896 |
| High school | 20 (14.7) | 0.59 (0.29-1.24) | 0.161 | 0.73 (0.32-1.68) | 0.450 |
| University | 5 (10.6) | 0.41 (0.13-1.14) | 0.105 | 0.71 (0.20-2.21) | 0.568 |
| Main occupation | |||||
| Informal private sector | 30 (18.8) | 1 | - | - | - |
| Unemployed | 23 (19.0) | 1.02 (0.55-1.85) | 0.956 | - | - |
| Employees | 14 (17.7) | 0.93 (0.45-1.85) | 0.847 | - | - |
| Living with a partner | |||||
| No | 23 (10.8) | 1 | - | 1 | - |
| Yes | 44 (29.7) | 3.48(2.01-6.16) | <0.001 | 2.10(1.08-4.11) | 0.029 |
| Tobacco consumption | |||||
| No | 60 (18.4) | 1 | - | - | - |
| Yes | 7 (20.6) | 1.15 (0.44-2.63) | 0.756 | - | - |
| Alcohol consumption | |||||
| No | 42 (19.8) | 1 | - | - | - |
| Yes | 25 (16.9) | 0.82 (0.47-1.41) | 0.484 | - | - |
| Fruits and vegetables consumption | |||||
| 5 portions and more/day | 2 (16.7) | 1 | - | - | - |
| Less than 5 portions/day | 65 (18.7) | 1.15 (0.29-7.59) | 0.860 | - | - |
| Family History of HBP | |||||
| No | 39 (15.8) | 1 | - | 1 | - |
| Yes | 28 (24.8) | 1.76 (1.01-3.03) | 0.044 | 1.74 (0.94-3.22) | 0.076 |
| BMI | |||||
| Normal | 32 (15.9) | 1 | - | 1 | - |
| Obese | 22 (36.1) | 2.98 (1.55-5.68) | 0.001 | 1.84 (0.86-3.93) | 0.117 |
| Underweight | 5 (16.1) | 1.02 (0.32-2.65) | 0.976 | 1.23 (0.36-3.55) | 0.721 |
| Overweight | 8 (11.9) | 0.72 (0.29-1.57) | 0.430 | 0.49 (0.19-1.17) | 0.127 |
| Abdominal obesity | |||||
| No | 31 (14.4) | 1 | - | 1 | - |
| Yes | 36 (25.0) | 1.99 (1.17-3.41) | 0.012 | 1.29 (0.67-2.48) | 0.444 |
| Physical Activity | |||||
| Low | 44 (19.0) | 1 | - | 1 | - |
| Intense | 7(38.9) | 2.70 (0.95-7.27) | 0.052 | 2.63 (0.82-8.14) | 0.094 |
| Moderate | 16 (14.4) | 0.72 (0.37-1.31) | 0.293 | 0.67 (0.33-1.31) | 0.254 |
BMI, body mass index; HBP, high blood pressure; OR, odds ratio; CI, confidence interval.
Study limitation
The blood pressure measurement was based on a single visit, which could have overestimated the prevalence of hypertension in our study. The rest period between measurements could have minimized this effect.
Conclusions
The prevalence of hypertension in the peri-urban community of Anonkoi 3 was high. Populations of this peri-urban environment are exposed to this disease. Some predictors of high blood pressure have been identified in the community, including older age, living with a partner, and low levels of physical activity. Health education programs targeting at-risk populations should be intensified with emphasis on life styles to prevent cardiovascular risk. Thus, our study has enabled us to mobilize useful information for effective intervention in the field in the fight against high blood pressure.
Acknowledgments
The authors thank the National Program for Prevention and Control of Metabolic Diseases of Cote d'Ivoire for its material contribution and the National Red Cross of Cote d'Ivoire, in particular its local committee of Abobo for its technical assistance.
Funding Statement
Funding: None.
References
- 1.Organisation Mondiale de la Sante. Panorama mondial de l’hypertension arterielle: Un ≪ tueur silencieux ≫ responsable d’une crise de sante publique [Internet]. Geneve (Suisse); 2013. [cite 7 janv 2016] p. 1-40. Report No. : WHO/DCO/WHD/2013.2. Disponible sur : http://apps.who.int/iris/bitstream/handle/10665/85334/WHO_DCO_WHD_2013.2_fre.pdf [Google Scholar]
- 2.Kearney PM, Whelton M, Reynolds K, et al. Global burden of hypertension: analysis of worldwide data. Lancet 2005;365:217-23. [DOI] [PubMed] [Google Scholar]
- 3.Doulougou B. Hypertension arterielle dans la population adulte du Burkina Faso: prevalence, detection, traitement et controle. These PhD Sante Publique option epidemiologie, Universite de Montreal, Quebec; 2014. [Google Scholar]
- 4.Agyemang C, Bruijnzeels MA, Owusu-Dabo E. Factors associated with hypertension awareness, treatment, and control in Ghana, West Africa. J Hum Hypertens 2006;20:67-71. [DOI] [PubMed] [Google Scholar]
- 5.Organisation Mondiale de la Sante. Enquete sur les facteurs de risque des maladies non transmissibles [Internet]. Cote d’Ivoire, Regions Sanitaires des Lagunes 2005; [cite 14 mai 2016] p. 1-165. Disponible sur : http://www.who.int/ncds/surveillance/steps/Cotedivoire_2005.pdf [Google Scholar]
- 6.Koffi A. Mutations sociales et gestion de l’espace rural en pays ebrie (Sud-Est de la Cote d’Ivoire. These Geographie humaine, Universite de Paris 1 Pantheon-Sorbonne, France; 2007. [Google Scholar]
- 7.Ministere de la Sante et de l’Hygiene Publique. Rapport Annuel sur la Situation Sanitaire 2016. [Internet]. Republique de Cote d’Ivoire; 2017 mai [cite 25 juin 2018]. 351p. Disponible sur : https://dipe.info/index.php/fr/component/jdownloads/send/6-rapportannuel-sur-la-situation-sanitaire/52-rapport-annuel-sur-la-situation-sanitaire-rass?Itemid=0 [Google Scholar]
- 8.Institut National de Statistique. Recensement general de la population et de l’habitat 2014 : Anonkoi 3-Abobo. Ministere du Plan et du Developpement-RCI; 2014. [Google Scholar]
- 9.Pradervand P, Feihl F, Waeber B, Vial Y. Hypertension et grossesse. Rev Med Suisse 2009;5:1758-62. [PubMed] [Google Scholar]
- 10.Schwartz D. Methodes statistiques a l’usage des medecins at des biologistes. Paris: Flammarion Medecine-Sciences; 2006. [Google Scholar]
- 11.Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 2003;289:2560-72. [DOI] [PubMed] [Google Scholar]
- 12.Organisation Mondiale de la Sante. Obesite: prevention et prise en charge de l’epidemie mondiale; rapport d’une consultation de l’OMS. Geneve 1997: Organisation Mondiale de la Sante; 284 p. Disponible sur: http://www.who.int/iris/handle/10665/42734 [Google Scholar]
- 13.Global Adult Tobacco Survey Collaborative Group. Questions sur le tabagisme a utiliser dans les enquetes : sous-ensemble de questions essentielles tirees de l’enquete mondiale sur le tabagisme chez les adultes (GATS), [Internet]. Atlanta : Centers for Disease Control and Prevention; 2011. p. 1-41. Disponible sur: https://www.who.int/tobacco/publications/surveillance/tqs_fr_final.pdf [Google Scholar]
- 14.Saunders JB, Aasland OG, Babor TF, et al. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II. Addiction 1993;88:791-804. [DOI] [PubMed] [Google Scholar]
- 15.Bush K, Kivlahan DR, McDonell MB, et al. The AUDIT alcohol consumption questions (AUDIT-C) : an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med 1998;158:1789-95. [DOI] [PubMed] [Google Scholar]
- 16.Oyebode O, Gordon-Dseagu V, Walker A, Mindell JS. Fruit and vegetable consumption and all-cause, cancer and CVD mortality: analysis of Health Survey for England data. J Epidemiol Commun Health 2014;68:856-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutrition 2006;9 [consulte le 21 janv 2018] Disponible sur : http://www.journals.cambridge.org/abstract_S1368 980006001261 [DOI] [PubMed] [Google Scholar]
- 18.Attoh T, N’guessan K, Okoubo G, et al. Determinants de l’hypertension arterielle a Adzope-Cote d’Ivoire. Revue Bio- Africa 2016;15:17-25. [Google Scholar]
- 19.Alikor C, Emem-Chioma P, Odia O. Hypertension in a Rural Community in Rivers State, Niger Delta Region of Nigeria : Prevalence and Risk Factors. Nigerian Health J 2013;1:18-25. [Google Scholar]
- 20.Birditt KS, Newton NJ, Cranford JA, Ryan LH. Stress and Negative Relationship Quality among Older Couples: Implications for Blood Pressure. J Gerontol B Psychol Sci Soc Sci 2016;71:775-85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Onwuchekwa AC, Mezie-Okoye MM, Babatunde S. Prevalence of hypertension in Kegbara-Dere, a rural community in the Niger Delta region, Nigeria. Ethn Dis 2012;22:340-6. [PubMed] [Google Scholar]
- 22.Ntuli ST, Maimela E, Alberts M, et al. Prevalence and associated risk factors of hypertension amongst adults in a rural community of Limpopo Province, South Africa. Afr J Prim Health Care Fam Med 2015;7:847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Addo J, Amoah AGB, Koram KA. The changing patterns of hypertension in Ghana : a study of four rural communities in the Ga District. Ethn Dis 2006;16:894-9. [PubMed] [Google Scholar]
- 24.Mulenga D, Siziya S, Rudatsikira E, et al. District specific correlates for hypertension in Kaoma and Kasama rural districts of Zambia. Rural and Remote Health (Internet) 2013;13:2345 Available from: http://www.rrh.org.au/articles/subviewnew.asp?ArticleID=2345 (Accessed 19 May 2016) [PubMed] [Google Scholar]
- 25.Sosner P, Gremeaux V, Bosquet L, Herpin D. Hypertension arterielle et exercice physique – Mise au point pratique. Ann Cardiol Angeiol 2014;63: 197-203. [DOI] [PubMed] [Google Scholar]