The magnitude of hypertension and its risk factors in southern Ethiopia: A community based study

Alemayehu Zekewos; Tariku Egeno; Eskindir Loha

doi:10.1371/journal.pone.0221726

Abstract

Background

Prevention and control of hypertension has not been given due attention though previous studies indicated that hypertension is growing public health problem.

Objective

This study aimed to determine the prevalence of hypertension and associated factors in Bona district, southern Ethiopia.

Methods

A community based cross-sectional study was conducted on 1952 participants aged ≥25 years in Bona District, southern Ethiopia. Data were collected from consented participants recruited using multistage sampling technique. Data were entered, checked for quality and analyzed by SPSS for Windows version 20.0. Since the outcome variables were ordered categorical, we used multinomial logistic regression model to identify associated factors. Among the independent variables included in the model no multicolinearity was observed. The level of significance was set at P value ≤ 0.05.

Results

The observed prevalence of hypertension (21.8%) was remarkable in rural setting. Out of hypertensive participants, 195 (45.9%) were newly diagnosed. About one third of the participants (31.4%) had central obesity measured by waist-to-height ratio ≥0.50. Being male, age advancement, high BMI (≥25.0 kg/m²) and central obesity (waist-to-height ratio ≥0.50) were positively associated with both systolic and diastolic hypertension. Systolic hypertension was negatively associated with high family income. The likely hood of developing diastolic hypertension increased in participants with family history of hypertension.

Conclusion

The overall prevalence of hypertension, 21.8%, is alarmingly high that it can be said that hypertension is becoming a silent epidemic in Ethiopia. Nationwide survey is needed to get the clear magnitude of hypertension so that early detection and management strategies can be enforced.

Citation: Zekewos A, Egeno T, Loha E (2019) The magnitude of hypertension and its risk factors in southern Ethiopia: A community based study. PLoS ONE 14(8): e0221726. https://doi.org/10.1371/journal.pone.0221726

Editor: Wei Wang, Edith Cowan University, AUSTRALIA

Received: April 23, 2019; Accepted: August 13, 2019; Published: August 28, 2019

Copyright: © 2019 Zekewos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Hypertension is an important determinant of cardiovascular disease (CVD) and mortality, and accounts for 7.5 million (12.8%) of deaths per year [1,2]. Risk Assessment Collaborating group has identified hypertension as the leading global risk factor for mortality and the third leading risk factor for disease burden [2,3]. The incidence of hypertension is increasing globally due to current nutritional transition, sedentary lifestyle, excessive body weight and other modifiable risk factors. Different studies have indicated that it is increasing drastically in recent years in developing countries while it remained stable and decreased in developed countries [4]. Over 25% of world’s adult population was affected by hypertension in 2000 and projected to raise to 30% in 2025 [2].

In Africa, the national prevalence of hypertension in age group of 25–65 years ranges from 25% to 35% and it remains the most important contributor for increased mortality from cardiovascular diseases [5,6]. Kearaney et al estimated that hypertension affected 639 million people in developing countries in 2000 and this value is projected to rise to 1.15 billion by 2025 [7]. Though the prevalence of hypertension is increasing in developing countries due attention has not been given regarding strategies for prevention and control since these countries are overwhelmed by health needs of communicable diseases [2,4].

Like other developing countries, Ethiopia is increasingly being affected by hypertension. For instance, as high as 31.5% and 28.9% in male and females, respectively in Addis Ababa were reported [8]. Another study reported 19.1% prevalence of hypertension among bank workers and teachers in Addis Ababa, with higher (22%) in men than in women (14.9%) in 2009 [9]. Two other studies reported 13.2% hypertension prevalence in southwest Ethiopia [10] and 28.3% hypertension prevalence in Gondar town [11]. In a more recent study conducted in Addis Ababa in 2014 the prevalence of hypertension found to be 25.0% with significantly higher prevalence of hypertension in males (30.2%) than females (21.2%) [12]. However, due attention has not been given to prevent and control hypertension though studies indicated hidden epidemic of hypertension in Ethiopian population.

The risk factors for increasing prevalence of hypertension include population growth, aging and easily modifiable risky behaviors, like unhealthy diet, harmful use of alcohol, smoking, lack of physical activity, overweight/obesity and longstanding stress [12,13]. Studies in Ethiopian populations have shown that the odds of developing hypertension is higher in male sex, advancing age, being overweight/obese, being physically inactive, high salt intake, family history of hypertension, and being urban dweller [8,10,11,14].

To the best of our knowledge, data on incidence of hypertension in Southern Ethiopia is scarce. Two studies reported, one hospital based and the other institution based, reported 18.8% overall prevalence of hypertension in diabetic and non-diabetic controls [15] and 19.7% crude prevalence of hypertension, respectively [16]. Therefore, this study aimed to assess the prevalence of hypertension and associated risk factors in Bona district, southern Ethiopia, at community level.

Materials and methods

Study setting and design

This is a cross-sectional community based study conducted on 1952 participants aged 25 years and above, in Bona district, South Ethiopia. Bona district (‘woreda’ in Ethiopia setting) is one of the 19 districts in Sidama zone, South Nations Nationalities and People’s state. The district has 20 villages (‘kebele’, the smallest administrative unit in Ethiopia). The data were collected from February to June 2016 from 2670 participants aged 15–110 years for diabetes prevalence study. In this study, data for participants of age ≥25 years were included in analysis.

Residents of the Bona district, who have lived at least half a year in the study area and who gave informed consent were included in the study. Pregnant women, severely ill patients at the time of data collection and individuals with fever, infection and congestive heart failure were excluded from the study.

Sampling techniques

The sample size was estimated by taking 0.50 proportion since national prevalence for hypertension is lacking. The calculated sample size at 95% confidence interval and absolute precision (d) of 0.02 was 2401. Multistage sampling technique was applied to select study participants. From 20 villages of the district, 10 were selected by lottery method, and each village was allocated sample size proportional to the total households in each village. A constant number k was obtained by dividing the total household in each selected village by the sample size allotted to the respective village. Then, the first household was selected from each selected village randomly by lottery method, and subsequent households were selected by taking consecutive k^th households until the allotted sample size for each village was obtained. Only one participant was recruited into the study from a household, by using lottery method in households where there were more than one eligible individuals.

Data collection

The data were collected by data collectors consisted of general practitioners and nurses who were able to speak and write the local language. The data collectors were given one day training about the study and the data collection process by the principal investigator. To ensure data quality, the questionnaire was standardized by 5% pretesting and random supervisions were done by investigators. The general practitioners made physical examination for clinical conditions for consented individuals before they were interviewed by trained nurses using structured questionnaire. The data were collected in three categories: socio-demographic information, previous history of hypertension and treatment, and family history of hypertension. After the interview, anthropometric parameters and blood pressure were measured for each participant.

Blood pressure was measured after at least 5 minutes of rest using an appropriate mercury sphygmomanometer and expressed in mmHg. Hypertension was defined based on WHO criteria, systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90mmHg or reported regular use of anti-hypertensive drug [2]. The measurements were taken two times and the average value was recorded.

Weight was measured to the nearest 0.1 kilogram (kg) using a person scale when the participants were in light indoor clothing and bare feet. Height was measured to the nearest 0.01 meter (m) by stadiometer when the participants were in erect position without shoes. Body mass index (BMI) was calculated by dividing weight in kilograms by square of height in meters. BMI < 18.0kg/m² is taken as underweight, 18.0–24.9kg/m² as normal, 25.0–29.9kg/m² as overweight and ≥30kg/m² as obese [17]. Waist circumference was measured to the nearest of 0.01 meter (m) by placing a tape meter horizontally, midway between the 12^th rib and the iliac crest on the mid-axillary line. Waist-to-height ratio (WHtR) was calculated by dividing waist circumference in meters by height in meters. Both male and female participants with WHtR ≥0.50 were considered as having central obesity based on suggestions from previous studies [18,19].

Data analysis

Data were entered, cleaned, coded and analyzed by using SPSS for Windows version 20.0 (IBM, USA). The data were cleaned by using sort cases tool and whenever missing and/or unexpected values were identified, that value was checked in the filled hardcopy data collection questionnaire to correct data entry mistakes. Continuous variables were expressed in mean and standard deviation of the mean or median and inter-quartile range. Since the outcome variable was ordered categorical, we used multinomial logistic regression model to identify associated factors. Collinearity diagnostics was done, and there was no multicollinearity among the independent variables included in the model. An odds ratio (OR) with 95% CI was reported, while the level of significance was set at p < 0.05.

Ethical approval

The research protocol was approved by the South Nations, Nationalities and Peoples’ Regional State Health Bureau Ethical Review Committee. Every effort was made to keep personal information in the research record private and confidential. Written informed consent was obtained from each participant before data collection.

Result

Characteristics of study participants

Data were collected from total of 2670 study participants, from which data of 1952 participants, aged 25 years and above, were included in the analysis. Table 1 depicts the socio-demographic and anthropometric characteristics of the study participants. More than half of the participants (53.4%) were male while the rest 46.6% were females. Most (96.8%) of the participants are of Sidama ethnic group and the rest 3.2% were from others. Most (85.0%) of them were married, followed by 10.1%, 3.1%, and 1.8% of widowed, single and divorced, respectively.

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Table 1. Sociodemographic and anthropometric characteristics of study participants (N = 1952).

https://doi.org/10.1371/journal.pone.0221726.t001

The median age of the study participants was 40 (30–54) years and more than one third (35.0%) of the participants were in the age range of 25–34 years, followed by 25.2%, 15.1%, 13.8%, and 11.0%, in the age range of 35–44, 45–54, ≥65, and 55–64 years, respectively. Regarding education level, 39.1% of the study participants had elementary education, followed by 35.5%, 19.1%, and 6.2% of illiterate, secondary, and postsecondary education, respectively.

The average family size is 6.7 (2.9), which is similar to family size commonly observed in developing countries. When grouped into two categories, 66.9% of the participants live in families of more than 5 members and the rest 33.1% live in families of five or less members. The rough median family income was 475.00 (200.00–1000.00) Birr and more than half (58.3%) of the participants reported to have monthly income of 500 or less Birr and 41.7% reported that they have more than 500 Birr monthly family income.

The mean BMI was 20.93 (2.99) kg/m², with most of the study participants (79.1%) had normal body mass index (BMI), remarkable proportion (12.9%) of them were underweight and 8.0% of them were overweight and obese. Nearly one third (31.4%) of participants had central obesity, as measured by WHtR, and it was significantly higher in females (40.6%, P<0.001) compared to that in males (23.3%). Only 6.7% of the participants reported that they have family history of hypertension.

Prevalence of hypertension and associated factors

The mean systolic and diastolic blood pressure, respectively were 117.0 (21.1) mmHg and 75.5 (13.3) mmHg. In our study hypertension was defined based on the WHO criteria, systolic blood pressure 140mmHg and above or diastolic blood pressure 90mmHg and above or reported regular use of anti-hypertensive drugs [2]. Out of the participants included in analysis, 21.8% had hypertension either by systolic or diastolic blood pressure, 12.3% in males and 9.4% in females. Out of the hypertensive group, 47.1% (200) were hypertensive both by systolic and diastolic blood pressure that constituted 10.2% of the total participants. From the hypertensive group, 27.3% and 25.6% were hypertensive by systolic and diastolic blood pressure alone, respectively. One hundred night five participants (45.9%) of the hypertensive participants were newly diagnosed. About 10.6% of the participants had prehypertension.

Identifying the predictors of hypertension is important for taking corrective measures to prevent the alarmingly increasing prevalence of hypertension in all populations. As indicated in Table 2, since the dependent variable is ordered categorical, we used multinomial logistic regression to identify predictors of hypertension. Being male (16.6%) is of higher risk of developing systolic hypertension than being female (15.7%), OR 1.37 (95% CI: 1.00, 1.89, P<0.050). Systolic hypertension was significantly increased progressively with age advancement that its prevalence in participants with age of 65 years and above (35.7%) is significantly higher than that in participants with age range of 25–34 years (7.5%), OR 6.21 (95% CI: 3.99, 9.68, P<0.001).

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Table 2. Estimation of systolic hypertension and ordinal logistic regression analysis of factors associated with systolic blood pressure in study participants (N = 1952).

https://doi.org/10.1371/journal.pone.0221726.t002

The other predictor of systolic hypertension was BMI. The prevalence of systolic hypertension in participants with BMI ≥ 25 kg/m² (28.8%) was significantly higher than that of participants with BMI 18.0–24.9kg/m² (15.3%), OR 2.60 (95% CI: 1.43, 4.71, P<0.002). Nearly one third of the study participants had central obesity (as measured by WHtR ≥0.50) and the prevalence of systolic hypertension is significantly higher in participants with WHtR ≥0.5 (23.9%) compared to 12.7% of participants with WHtR <0.50, OR 1.83 (95% CI: 1.36, 2.45, P<0.001). Systolic hypertension was negatively associated with increased family income. The association of systolic hypertension with educational levels, family size, family income, and family history of hypertension was not statistically significant.

Like for systolic hypertension, multinomial logistic regression was done to identify risk factors for diastolic hypertension and results were depicted in Table 3. Diastolic hypertension was significantly associated with male sex, older age, high BMI, central obesity, and family hypertension history.

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Table 3. Estimation of diastolic hypertension and ordinal logistic regression analysis of factors associated with systolic blood pressure in study participants (N = 1952).

https://doi.org/10.1371/journal.pone.0221726.t003

Being male sex sets higher risk of developing diastolic hypertension in our study, OR 1.38 (95% CI: 1.01, 1.90, P<0.044). Diastolic hypertension increases with age advancement; in participants aged 65 years and above the risk of developing diastolic hypertension significantly increased, OR 3.70 (95% CI: 2.40, 5.70, P<0.001).

The other predictor of diastolic hypertension was high BMI in which its prevalence in participants with BMI ≥25kg/m² (34.0%) is significantly higher compared to participants with normal BMI (12.4%), OR 2.52 (95% CI: 1.41, 4.48, P<0.002). Central obesity was measured by WHtR in our study and we found that participant with high WHtR have higher risk of developing diastolic hypertension, OR 1.88 (95% CI: 1.40, 2.51, P<0.001). The prevalence of diastolic hypertension was significantly higher in participants with family history of hypertension (24.4%) compared to participants without family history of hypertension (15.2%), OR 1.79 (95%: 1.15, 2.80, P<0.010).

The rest of socio-demographic and economic variables included in the regression model were not significantly associated with diastolic hypertension.

Discussion

It has been well established that hypertension increases the risk of cardiovascular disease and mortality [1,2]. The detection, prevention, management and control of hypertension is insufficient in low income countries [20] in face of higher burden of hypertension in low income countries than in high income countries [21]. The findings of this study strengthens reports from previous studies [8,10–12,14,16] that hypertension is becoming a silent epidemic in the country and calling policymakers for timely intervention in terms of creating awareness, early detection and management. We estimated the magnitudes of systolic, diastolic and combined hypertension in rural setting. Out of 1952 participants, 425 (21.8%) had hypertension either by systolic or diastolic blood pressure from which 47.1% (200) had combined hypertension, indicating that hypertension may be an important public health concern in study area. The risk factors for both systolic and diastolic hypertension were being male sex, age advancement, high body mass index and central obesity. In addition, family history of hypertension is risk for high diastolic blood pressure.

The observed prevalence of hypertension, 21.8%, seems remarkably higher in rural setting. Such a high prevalence of hypertension may indicate the disorder became a hidden epidemic in the community. In addition, nearly half (45.9%) of the hypertensive participants were newly diagnosed, indicating that blood pressure checkup level is low in the studied population. The prevalence of hypertension in our study is similar to the prevalence in rural Kenya, 21.4% [22], urban Kenya, 22.8% [23], urban Malawi, 22.5% [24], and Durame town (southern Ethiopia), 22.4% [14]. The high prevalence of hypertension in our study, among other studies in Ethiopia and other African countries, may be due to age advancement, nutritional transition and changing lifestyle like physical inactivity. However, the prevalence of hypertension observed in our study is higher than reported from other studies in Ethiopia in urban settings and other African countries in rural settings; 13.2% in southwest Ethiopia that had included both rural and urban participants [10], 19.7% among workers of Hawassa University [16], 19.3% in rural Nigeria, and 14.5% in rural Malawi [24]. In contrary, the observed hypertension in our study is lower than that reported from Ethiopia and other African countries. For example, two studies in Addis Ababa, 30.3% by Tesfaye et al [8] and 25% by Abdissa et al [12], and 28.3% in Gondar town [11] reported higher prevalence of hypertension than ours. Moreover, the prevalence of hypertension in our study is lower than 23.6% [25] and 24.8% [26] prevalence of hypertension in rural Nigeria and 24.1% in rural and 32.9% in semi-urban settings of Ghana [27]. These discrepancies in prevalence of hypertension may be due to differences in socio-demographic settings, physical activity, nutritional status, economic status, ethnic groups, and age of participants included.

As observed in previous studies [8,9,12,28] hypertension was higher in males (12.3%) than in females (9.4%) in our study. Systolic hypertension as well as diastolic hypertension were significantly higher in males (16.6% systolic and 17.3% diastolic) than females (15.7% systolic and 14.2% diastolic), which is consistent with the previous reports in Ethiopia [8] and other African countries [29]. For instance, prevalence of systolic and diastolic hypertension in males (24.9% systolic and 21.8% diastolic), which is significantly higher than that in female (20.3% systolic and 18.0% diastolic) was reported in Addis Ababa [8]. The higher prevalence of hypertension in males than in females may be due to sex-difference in effect of genetic doses and hormones such as rennin-angiotensin-aldosterone system and gonadal hormones [30,31].

As expected, prevalence of systolic as well as diastolic hypertension increased progressively with age advancement, which leads to increased arterial stiffness. The highest prevalence of both systolic hypertension and diastolic hypertension were observed in age of 65 years and above, 35.7% (P<0.001) and 28.3% (P<0.001), respectively. This is in agreement to previous studies in Ethiopia [8,11,12,16] and elsewhere in other African countries [23,27]. In fact, the high prevalence of hypertension our study may due to age advancement since 39.8% of study participants were in age of 45 years and above. Therefore, it is advisable to design methods to promote blood pressure check up as age advances to ensure early detection and management of hypertension. Other studies also advice early detection and management of hypertension [11,22].

The other risk factor for development of hypertension was increased BMI, which is measure of body fat load. Both systolic hypertension (28.8%, P<0.002) and diastolic hypertension (34.0%, P<0.002) in participants with BMI ≥25.0 kg/m² were significantly higher respectively compared to 15.3% and 14.6% prevalence in participants with BMI 18.0–24.9 kg/m². Other studies in Ethiopia [8] and other African countries [32] reported similar finding. The prevalence of central obesity as measured by WHtR ≥0.5 (31.4%) is remarkably high in rural setting; with significantly higher prevalence in females (40.6%, χ² = 67.5, P<0.001) compared to that in males (23.3%). High WHtR is positively and significantly associated with both systolic hypertension (23.9%, P<0.001) and diastolic hypertension (24.3%, P<0.009). Thus, it can be inferred that the observed high prevalence may be due to increased central obesity in the studied population. Previous studies reported that increased WHtR strongly predicted the probability of developing hypertension in both children and adults [33,34].

The association of hypertension with family size, educational level and family income is not significant. However, the prevalence of systolic hypertension is highest in illiterates and high family income is inversely associated with both systolic and diastolic hypertension. Family history of hypertension significantly increases the chance of developing diastolic hypertension in this study (OR 1.79 (95% CI: 1.15, 2.80), P<0.010), while the prevalence of systolic hypertension in participants with family history of hypertension (19.8%) is not significantly different from those without family history of hypertension (15.9%). Previous studies in different parts of the country [10,11] reported similar findings.

There were potential limitations of our study. One of the limitations is limited demographic and anthropometric measurements incorporated in our study. For example, to measure central obesity we used only waist-to-height ratio while using waist-to-hip ratio may better complement our findings. The other limitation of our study is dependence on only two blood pressure measurements on one instance to diagnose hypertension. Guidelines for diagnosis of hypertension recommend measurements of blood pressure on two or more instances [35]. Our study is limited to small locality so that it may not be generalized to the total population of Ethiopia.

Conclusion

The overall prevalence of hypertension, 21.8%, is alarmingly high that it can be said that hypertension is becoming a silent epidemic in Ethiopia. This could be due to not making necessary adjustment in modifiable risk factors like physical inactivity, overweight and obesity and central obesity. Being male, age advancement, high BMI, central obesity and family history of hypertension are risk factors for both systolic and diastolic hypertension. Nationwide survey is needed to get the clear magnitude of hypertension so that early detection and management early detection and management strategies can be enforced.

Acknowledgments

The authors heartily thank Bona district health bureau for its support during data collection. We are also grateful to the study participants and data collectors for their cooperation.

References

1. Alwan A (2010) Global status report on noncommunicable diseases 2010. Geneva, Switzerland: World Health Organization: 2–17.
- View Article
- Google Scholar
2. Mathers C, Stevens G, Mascarenhas M (2009) Global health risks: mortality and burden of disease attributable to selected major risks. Geneva, Switzerland: World Health Organization.
3. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJL (2002) Comparative risk assessment collaborative group: selected major risk factors and global and regional burden of disease. Lancet 360: 1347–1360. pmid:12423980
- View Article
- PubMed/NCBI
- Google Scholar
4. Kearney PM, Wheltona M, Reynolds K, Munter P, Whelton PK, He J (2004) Worldwide prevalence of hypertension: a systematic review. J Hypertens 22: 11–19. pmid:15106785
- View Article
- PubMed/NCBI
- Google Scholar
5. WHO AFRO (2005) Cardiovascular diseases in the African Region: Current Situation and Perspectives. Report of the Regional Director. Fifty-fifth session. Maputo, Mozambique.
6. World Health Organization (2002) World Health Report 2002. Reducing risks, promoting healthy life. Geneva: WHO.
7. Kearney PM, Whelton M, Reynolds K, Munter P, Whelton PK, He J (2005) Global burden of hypertension: analysis of worldwide data. Lancet 365: 217–223. pmid:15652604
- View Article
- PubMed/NCBI
- Google Scholar
8. Tesfaye F, Byass P, Wall S (2009) Population based prevalence of high blood pressure among adults in Addis Ababa: uncovering a silent epidemic. BMC Cardiovascular Disorders 9: 1–10. pmid:19126206
- View Article
- PubMed/NCBI
- Google Scholar
9. Nshissoa LD, Reese A, Gelaye B, Lemma S, Berhane Y, Williams MA (2012) Prevalence of Hypertension and Diabetes among Ethiopian Adults. Diabetes Metab Syndr 6: 36–41. pmid:23014253
- View Article
- PubMed/NCBI
- Google Scholar
10. Gudina EK, Michael Y, Assegid S (2013) Prevalence of hypertension and its risk factors in southwest ethiopia: a hospital-based cross-sectional survey. Integrated Blood Pressure Control 6: 111–117. pmid:23986649
- View Article
- PubMed/NCBI
- Google Scholar
11. Awoke A, Awoke T, Alemu S, Megabiw B (2012) Prevalence and associated factors of hypertension among adults in Gondar, Northwest Ethiopia: a community based cross-sectional study. BMC Cardiovascular Disorders 12.
- View Article
- Google Scholar
12. Abdissa SG, Feleke Y, Awol M (2015) Prevalence of hypertension and pre-hypertension in Addis Ababa, Ethiopia: A survey done in recognition of World Hypertension Day, 2014. Ethiopian Journal of Health Development 29: 22–30.
- View Article
- Google Scholar
13. WHO (2008) Raised blood pressure. Situation and trends. Global Health Observatory (GHO) data.
14. Helelo TP, Gelaw YA, Adane AA (2014) Prevalence and Associated Factors of Hypertension among Adults in Durame Town, Southern Ethiopia. PLoS ONE 9.
- View Article
- Google Scholar
15. Giday A, Wolde M, Yihdego D (2010) Hypertension, obesity and central obesity in diabetics and non diabetics in Southern Ethiopia. Ethiop J Health Dev 24: 145–147.
- View Article
- Google Scholar
16. Esaiyas A, Teshome T, Kassa D (2018) Prevalence of Hypertension and Associate Risk Factors among Workers at Hawassa University, Ethiopia: An Institution Based Cross Sectional Study. J Vasc Med Surg 6.
- View Article
- Google Scholar
17. Lau DCW, Douketis J, Morrison KM, Hramiak IM, Sharma AM, Ur E (2007) 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children [summary]. CMAJ 176: S1–S13.
- View Article
- Google Scholar
18. Browning LM, Hsieh S, Ashwell M (2010) A systematic review of waist to height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr Res Rev 23: 247–269. pmid:20819243
- View Article
- PubMed/NCBI
- Google Scholar
19. Yoo EG (2016) Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Korean J Pediatr 59: 425–431. pmid:27895689
- View Article
- PubMed/NCBI
- Google Scholar
20. Pereira M, Lunet N, Azevedo A, Barros H (2009) Differences in prevalence, awareness, treatment and control of hypertension between developing and developed countries. J Hypertens 27: 963–975. pmid:19402221
- View Article
- PubMed/NCBI
- Google Scholar
21. Ibrahim MM, Damasceno A (2012) Hypertension in developing countries. Lancet 380: 611–619. pmid:22883510
- View Article
- PubMed/NCBI
- Google Scholar
22. Hendriks ME, Wit FW, Roos MT, Brewster LM, Akande TM, de Beer IH, et al (2012) Hypertension in sub-Saharan Africa: cross-sectional surveys in four rural and urban communities. PLoS ONE 7: e32638. pmid:22427857
- View Article
- PubMed/NCBI
- Google Scholar
23. Joshi MD, Ayah R, Njau EK, Wanjiru R, Kayima JK, Njeru EK, et al (2014) Prevalence of hypertension and associated cardiovascular risk factors in an urban slum in Nairobi, Kenya: A population-based survey. BMC Public Health 14: 1177. pmid:25407513
- View Article
- PubMed/NCBI
- Google Scholar
24. Price AJ, Crampin AC, Amberbir A, Kayuni-Chihana N, Musicha C, Tafatatha T, et al (2018) Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol 6: 208–222. pmid:29371076
- View Article
- PubMed/NCBI
- Google Scholar
25. Andy JJ, Peters E, Ekrikpo UE, Akapan NA, Unadike BC, Ekott JU (2012) Prevalence and correlates of hypertension among the Ibibio/Annangs, Efiks and Obolos: a cross sectional community survey in rural South-South Nigeria. Ethn Dis 22: 335–339. pmid:22870578
- View Article
- PubMed/NCBI
- Google Scholar
26. Isezuo SA, Sabir A, Ohwovorilole AE, Fasanmade OA (2011) Prevalence, associated factors and relationship between prehypertension and hypertension: a study of two ethnic African Populations in Northern Nigeria. J human Hypertens 25: 224–230.
- View Article
- Google Scholar
27. Cappuccio PF, Micah F, Emmett L, Kerry SM, Antwi S, Martin-Peprah R, et al (2004) Prevalence, management and control of hypertension in Ashanti, West Africa. Hypertension 43: 1017–1022. pmid:15037552
- View Article
- PubMed/NCBI
- Google Scholar
28. Tesfaye F, Nawi NG, Van Minh H, Byass P, Berhane Y, Bonita R, et al (2007) Association between body mass index and blood pressure across three populations in Africa and Asia. Journal of Human Hypertension 21: 28–37. pmid:17066088
- View Article
- PubMed/NCBI
- Google Scholar
29. Lawoyin TO, Asuzu MC, Kaufman J, Rotimi C, Owoaje E, Johnson L, et at (2002) Prevalence of cardiovascular risk factors in an African urban inner city community. West Afr J Med 21: 208–211. pmid:12744569
- View Article
- PubMed/NCBI
- Google Scholar
30. Sandberg K, Ji H (2012) Sex differences in primary hypertension. Biology of Sex Differences 3.
- View Article
- Google Scholar
31. Ji H, Zheng W, Wu X, Liu J, Ecelbarger CM, Watkins R, et al (2010) Sex chromosome effects unmasked in angiotensin IIinduced hypertension. Hypertension 55: 1275–1282. pmid:20231528
- View Article
- PubMed/NCBI
- Google Scholar
32. Bover P, Ross AG, Gervasoni JP, Mkamba M, Mtasiwa DM, Lengeler C, et al (2002) Distribution of blood pressure, body mass index and smoking habits in the urban population of Dar es Salaam, Tanzania, and associations with socioeconomic status. Int J Epidemiol 1: 240–247.
- View Article
- Google Scholar
33. Choi JR, Koh SB, Choi E (2018) Waist-to-height ratio index for predicting incidences of hypertension: the ARIRANG study. BMC Public Health 18: 767. pmid:29921256
- View Article
- PubMed/NCBI
- Google Scholar
34. Chen TL, Choy CS, Chan WY, Chen CH, Liao CC (2012) Waist-to-Height Ratio and Elevated Blood Pressure Among Children in Taiwan. INDIAN PEDIATRICS 49: 463–466. pmid:22317985
- View Article
- PubMed/NCBI
- Google Scholar
35. Chobanian AV, Bakris G, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al (2003) The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 289: 2560–2572. pmid:12748199
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Alwan A (2010) Global status report on noncommunicable diseases 2010. Geneva, Switzerland: World Health Organization: 2–17.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Mathers C, Stevens G, Mascarenhas M (2009) Global health risks: mortality and burden of disease attributable to selected major risks. Geneva, Switzerland: World Health Organization.

[ref3] 3. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJL (2002) Comparative risk assessment collaborative group: selected major risk factors and global and regional burden of disease. Lancet 360: 1347–1360. pmid:12423980
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref4] 4. Kearney PM, Wheltona M, Reynolds K, Munter P, Whelton PK, He J (2004) Worldwide prevalence of hypertension: a systematic review. J Hypertens 22: 11–19. pmid:15106785
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref5] 5. WHO AFRO (2005) Cardiovascular diseases in the African Region: Current Situation and Perspectives. Report of the Regional Director. Fifty-fifth session. Maputo, Mozambique.

[ref6] 6. World Health Organization (2002) World Health Report 2002. Reducing risks, promoting healthy life. Geneva: WHO.

[ref7] 7. Kearney PM, Whelton M, Reynolds K, Munter P, Whelton PK, He J (2005) Global burden of hypertension: analysis of worldwide data. Lancet 365: 217–223. pmid:15652604
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref8] 8. Tesfaye F, Byass P, Wall S (2009) Population based prevalence of high blood pressure among adults in Addis Ababa: uncovering a silent epidemic. BMC Cardiovascular Disorders 9: 1–10. pmid:19126206
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref9] 9. Nshissoa LD, Reese A, Gelaye B, Lemma S, Berhane Y, Williams MA (2012) Prevalence of Hypertension and Diabetes among Ethiopian Adults. Diabetes Metab Syndr 6: 36–41. pmid:23014253
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref10] 10. Gudina EK, Michael Y, Assegid S (2013) Prevalence of hypertension and its risk factors in southwest ethiopia: a hospital-based cross-sectional survey. Integrated Blood Pressure Control 6: 111–117. pmid:23986649
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref11] 11. Awoke A, Awoke T, Alemu S, Megabiw B (2012) Prevalence and associated factors of hypertension among adults in Gondar, Northwest Ethiopia: a community based cross-sectional study. BMC Cardiovascular Disorders 12.
View Article
Google Scholar

[32] View Article

[33] Google Scholar

[ref12] 12. Abdissa SG, Feleke Y, Awol M (2015) Prevalence of hypertension and pre-hypertension in Addis Ababa, Ethiopia: A survey done in recognition of World Hypertension Day, 2014. Ethiopian Journal of Health Development 29: 22–30.
View Article
Google Scholar

[35] View Article

[36] Google Scholar

[ref13] 13. WHO (2008) Raised blood pressure. Situation and trends. Global Health Observatory (GHO) data.

[ref14] 14. Helelo TP, Gelaw YA, Adane AA (2014) Prevalence and Associated Factors of Hypertension among Adults in Durame Town, Southern Ethiopia. PLoS ONE 9.
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref15] 15. Giday A, Wolde M, Yihdego D (2010) Hypertension, obesity and central obesity in diabetics and non diabetics in Southern Ethiopia. Ethiop J Health Dev 24: 145–147.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref16] 16. Esaiyas A, Teshome T, Kassa D (2018) Prevalence of Hypertension and Associate Risk Factors among Workers at Hawassa University, Ethiopia: An Institution Based Cross Sectional Study. J Vasc Med Surg 6.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref17] 17. Lau DCW, Douketis J, Morrison KM, Hramiak IM, Sharma AM, Ur E (2007) 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children [summary]. CMAJ 176: S1–S13.
View Article
Google Scholar

[48] View Article

[49] Google Scholar

[ref18] 18. Browning LM, Hsieh S, Ashwell M (2010) A systematic review of waist to height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr Res Rev 23: 247–269. pmid:20819243
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref19] 19. Yoo EG (2016) Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Korean J Pediatr 59: 425–431. pmid:27895689
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref20] 20. Pereira M, Lunet N, Azevedo A, Barros H (2009) Differences in prevalence, awareness, treatment and control of hypertension between developing and developed countries. J Hypertens 27: 963–975. pmid:19402221
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref21] 21. Ibrahim MM, Damasceno A (2012) Hypertension in developing countries. Lancet 380: 611–619. pmid:22883510
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref22] 22. Hendriks ME, Wit FW, Roos MT, Brewster LM, Akande TM, de Beer IH, et al (2012) Hypertension in sub-Saharan Africa: cross-sectional surveys in four rural and urban communities. PLoS ONE 7: e32638. pmid:22427857
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref23] 23. Joshi MD, Ayah R, Njau EK, Wanjiru R, Kayima JK, Njeru EK, et al (2014) Prevalence of hypertension and associated cardiovascular risk factors in an urban slum in Nairobi, Kenya: A population-based survey. BMC Public Health 14: 1177. pmid:25407513
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref24] 24. Price AJ, Crampin AC, Amberbir A, Kayuni-Chihana N, Musicha C, Tafatatha T, et al (2018) Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol 6: 208–222. pmid:29371076
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref25] 25. Andy JJ, Peters E, Ekrikpo UE, Akapan NA, Unadike BC, Ekott JU (2012) Prevalence and correlates of hypertension among the Ibibio/Annangs, Efiks and Obolos: a cross sectional community survey in rural South-South Nigeria. Ethn Dis 22: 335–339. pmid:22870578
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref26] 26. Isezuo SA, Sabir A, Ohwovorilole AE, Fasanmade OA (2011) Prevalence, associated factors and relationship between prehypertension and hypertension: a study of two ethnic African Populations in Northern Nigeria. J human Hypertens 25: 224–230.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref27] 27. Cappuccio PF, Micah F, Emmett L, Kerry SM, Antwi S, Martin-Peprah R, et al (2004) Prevalence, management and control of hypertension in Ashanti, West Africa. Hypertension 43: 1017–1022. pmid:15037552
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref28] 28. Tesfaye F, Nawi NG, Van Minh H, Byass P, Berhane Y, Bonita R, et al (2007) Association between body mass index and blood pressure across three populations in Africa and Asia. Journal of Human Hypertension 21: 28–37. pmid:17066088
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref29] 29. Lawoyin TO, Asuzu MC, Kaufman J, Rotimi C, Owoaje E, Johnson L, et at (2002) Prevalence of cardiovascular risk factors in an African urban inner city community. West Afr J Med 21: 208–211. pmid:12744569
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref30] 30. Sandberg K, Ji H (2012) Sex differences in primary hypertension. Biology of Sex Differences 3.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref31] 31. Ji H, Zheng W, Wu X, Liu J, Ecelbarger CM, Watkins R, et al (2010) Sex chromosome effects unmasked in angiotensin IIinduced hypertension. Hypertension 55: 1275–1282. pmid:20231528
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref32] 32. Bover P, Ross AG, Gervasoni JP, Mkamba M, Mtasiwa DM, Lengeler C, et al (2002) Distribution of blood pressure, body mass index and smoking habits in the urban population of Dar es Salaam, Tanzania, and associations with socioeconomic status. Int J Epidemiol 1: 240–247.
View Article
Google Scholar

[105] View Article

[106] Google Scholar

[ref33] 33. Choi JR, Koh SB, Choi E (2018) Waist-to-height ratio index for predicting incidences of hypertension: the ARIRANG study. BMC Public Health 18: 767. pmid:29921256
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref34] 34. Chen TL, Choy CS, Chan WY, Chen CH, Liao CC (2012) Waist-to-Height Ratio and Elevated Blood Pressure Among Children in Taiwan. INDIAN PEDIATRICS 49: 463–466. pmid:22317985
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref35] 35. Chobanian AV, Bakris G, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al (2003) The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 289: 2560–2572. pmid:12748199
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

Figures

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Materials and methods

Study setting and design

Sampling techniques

Data collection

Data analysis

Ethical approval

Result

Characteristics of study participants

Prevalence of hypertension and associated factors

Discussion

Conclusion

Acknowledgments

References