Journal of Scientific Research and Studies Vol. 4(4), pp. 73-85, April, 2017 ISSN 2375-8791 Copyright © 2017 Author(s) retain the copyright of this article http://www.modernrespub.org/jsrs/index.htm

Full Length Research Paper

Prevalence and awareness of hypertension among urban and rural traders in Hohoe Municipality, Ghana

Margaret Kweku1, Fafali Bani1, Wisdom Axame1, Richard Owusu1, Mohammed Takase2, Martin Adjuik1 and Elvis Tarkang1*

1School of Public Health, University of Health and Allied Sciences, Ho, Volta Region, Ghana.

2School of Biological Sciences, University of Cape Cast, Cape Coast, Central Region, Ghana.

*Corresponding author. E-mail: ebeyang1@yahoo.com

Accepted 09 April, 2017

Recent studies have shown an increasing prevalence of hypertension in rural than urban communities. This study was undertaken to examine the prevalence of hypertension and its awareness among urban and rural traders in the Hohoe Municipality. The methodology used was a population-based cross-sectional study involving 289 urban and 280 rural traders aged 18-65 years, collecting data through face-to-face interview using semi-structured questionnaire. Blood pressure and anthropometric measurements were taken; t-test, Chi-square and multivariable logistic regression were performed. Results show that hypertension prevalence was 34.1%, with 32.2% among rural and 36.1% in urban traders. Diagnosed hypertension was 28.7% among urban and 32.9% in rural traders. Uncontrolled hypertension had a higher prevalence in rural (56.5%) than urban (48.2%). Traders aged 40-49, 50-59 and 60+ were 4 times more likely to become hypertensive as compared to those less than 30 years [OR=4.10, p<0.001], [OR=4.69 p<0.001] and [OR=4.06, p=0.002] respectively. Traders in stores were 10.22 times more likely to become hypertensive compared to those at home [OR=10.22, p=0.034]. Traders involved in physical activity more than three times per week, and those who consumed vegetables 1-3 times per week were 84% and 93% less likely to become hypertensive, [OR=0.16, p=0.024] and [OR=0.07, p=0.041] respectively. In conclusion, there is increasing prevalence of Hypertension in rural than urban due to higher uncontrolled hypertension, sedentary nature of traders in stores and increasing age among rural traders’ population. Targeted periodic screening and creation of awareness on hypertension are needed to enhance the prevention and control of hypertension among traders in Hohoe. Key words: Hypertension, diagnosed, undiagnosed, controlled, uncontrolled, urban, rural, traders, Hohoe municipality, Ghana.

Abbreviations: BP, Blood Pressure; VRHD, Volta Regional Health Directorate; mmHg, Millimeters of Mercury; OPD, Out Patient Department; NCDS, Non-Communicable Diseases; ISH, International Society of Hypertension; GHS, Ghana Health Service; HBP, High Blood Pressure; PI, Principal Investigator; NIH, National Institute of Health; RCH, Reproductive and Child Health; WHO, World Health Organization; DALYs, Disability-Adjusted Life-Years; BMI, Body Mass Index; CHPS, Community Health Planning and Services; CI, Confidence Interval; WC, Waist Circumference; HC, Hip Circumference; WHR, Waist-Hip Ratio; HMHD, Hohoe Municipal Health Directorate; GHS ERC, Ghana Health Service Ethical Review Committee.

INTRODUCTION World Health Organization (WHO) defined hypertension (HPT) as a systolic blood pressure (BP) equal to or

above 140 mmHg and/or diastolic BP equal to or above 90 mmHg (WHO, 2013). Hypertension is among the

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74 J. Sci. Res. Stud. leading causes of the burden of non-communicable diseases in developing countries (WHO, 2010) and it was considered to be non-existent in most African societies. The global prevalence of HPT among the adult population is expected to increase from 26% (972 million) in 2000 to 29% (1.56 billion) by 2025 with associated cardiovascular complications (Kearney et al., 2005; Mensah and Bakris, 2011). The overall prevalence of HPT (including those on medication for high blood pressure) in adults aged 25 years and above was about 40% in 2008 (WHO, 2010). Both men and women have high rates of BP in the African region, with prevalence rates over 40% (WHO, 2003).

Reports from rural Thailand indicated high BP among adults was 27%, with 15% being hypertensive and 12% being pre-hypertensive (Myo et al., 2010). In rural Nigeria, the prevalence of hypertension was found to be 37.6% (Isara and Okundia, 2015). A survey of 20 rural Ghanaian villages in 1973 found the prevalence of 2-5% and concluded that HPT was not a significant health problem in rural Ghana (Pobee et al., 1977). Recently, in rural Ghana, the prevalence of HPT was 35% (Hana et al., 2012). A systematic analysis report from Ghana, however, showed the prevalence of HPT between 25 and 48%, with the prevalence higher in urban populations than in rural populations (Bosu, 2010). Another study has also shown a growing trend of HPT in urban communities compared to rural communities (Awuah et al., 2014).

Studies have shown that awareness about HPT is low (Myo et al., 2010). In Ghana, reports have shown that many Ghanaians living with HPT are not aware of it. In Accra, Ghana, HPT prevalence was 34%, and 15% had been previously diagnosed with HPT whilst 19% were undiagnosed (Aryeetey and Ansong, 2011). Elevated body mass index (BMI) has been identified as the main risk factor of HPT (Duah et al., 2013). A recent study in Ghana found that over a third of the study population was either overweight or obese (Aikins et al., 2014). A cross-sectional study in four rural communities in the Ga District of Ghana showed that risk factors associated with high BP include increasing BMI, increased salt consumption, family history of hypertension and excessive alcohol intake (Addo et al., 2012).

Data from the Hohoe Municipal Hospital (HMH) for the past four years indicates that there is an increase in the detection of HPT cases in the municipality. The high prevalence of both out-patient and in-patient HPT cases has led to the establishment of an HPT clinic in the HMH in June 2011. Statistics from the Hohoe Municipal Hospital HPT clinic shows that over the past four years, diagnosis of HPT cases has increased starting from 254 cases in 2012, to 5,287 cases in 2014 (Hohoe Municipality, 2012).

This increasing HPT trend in the municipality informed our choice of the study among traders who are next to farming and contribute significantly to economic development in the informal sector in the municipality.

This study was set out to assess the prevalence of HPT and Pre-HPT and also determine awareness and predictors of HPT among traders in urban and rural areas of the Hohoe municipality. MATERIALS AND METHODS Study site The study was carried out in the Hohoe Municipality, one of the twenty-five administrative districts in the Volta Region of Ghana. It is located at longitude 0° 15’’ East and 0° 45’’ East and latitude 6° 45’’ North and 7° 15’’ North. It shares boundary to the East with Togo, forming part of the International borders, on the southeast with the Afadjato South District and the southwest with Kpando Municipal, on the northwest with Jasikan District and on the North West with Biakoye District. The Municipality is located in the central part of Volta Region with a population of 167,743 people, of which 48.1% are males and 51.9% are females (Ghana Statistical Service, 2010). Hohoe is the Municipal capital with a population of 63,000 people. The municipality covers an area of 1,403 sq. km and has been divided into 7 sub-Municipalities namely; Hohoe, Gbi-Rural, Alavanyo, Agumatsa, Likpe, Akpafu/Santrokofi and Lolobi. The main economic activity is farming and about 55% of the population grows cash crops such as cocoa, maize, cassava, rice, yam and vegetables whilst trading forms about 25%, livestock rearing 15% and other industrial activities represent 5%. The major ethnic groups in the Municipality are Ewes, Akpafu/Lolobi, Santrokofi and Likpe. There are 21 health facilities in the Municipality comprising Municipal hospital (1), health centres (14), Reproductive Child Health (RCH) (1) and Community-Based Health Planning and Services (CHPS) compounds (5) (Addo et al., 2012). Study design This was a cross-sectional study carried out in April, 2015 among 569 traders with 289 from urban and 280 from rural communities within the Hohoe municipality (Figure 1). A pre-tested, semi-structured questionnaire, modified from the WHO STEPwise approach to non-communicable disease risk factor surveillance (STEPS) was used to obtain information on the socio-demographic characteristics and anthropometric indices. Study population The study population were traders (defined in the study as people selling at home, in shops, market and those moving around to sell their wares), who were aged 18 to 65 years, resident in the Municipality for at least three

Kweku et al. 75

Figure 1. Showing sampling and outcome of HPT survey.

months and consented to participate. Traders, not resident in the Municipality, pregnant, and could not give consent were excluded.

Sampling

A multi-stage sampling technique was used to select the communities and the participants. This included stratified sampling, thus the names of all the communities within Hohoe were listed to form a sampling frame of clusters stratified into urban and rural based on the population density. The corresponding total population was noted per strata to enable proportionate calculation of the population. The names of the communities were written on pieces of paper and folded, then grouped into corresponding strata and shaken to ensure they mixed well. Using lottery method, three persons were blinded and they randomly selected one community from each stratum. Individual units were interviewed in the selected communities. Systematic sampling technique was used to select every k

th unit starting with a unit which

corresponds to the number r chosen at random from 1 to k, where “k” is an integer such that k≈ N/n. A list of traders including market women and shop owners was obtained from the Hohoe Municipal Assembly. The sampling procedure is denoted as follows; r, r+k, r+2k, ... r+(n-1)k. Thus, this procedure was repeated until the sample size required was obtained.

Sample size determination

The required sample size was determined using a sample size calculation formula obtained from a book entitled ‘Biostatistics for Health Science Students’: lecture

note series (Degu and Tessema, 2005). Z score of 1.96 at 95% confidence level, the margin of error of 4.13% and proportion of 40% were entered into the formula to determine a minimum sample size of 540. However, a non-response rate of 5% was applied to the minimum sample size which was increased to 567≈569 traders.

Data collection

A pre-tested, semi-structured questionnaire was used to collect information on the socio-demographic characteristics and awareness about hypertension. Data was collected through one-on-one interview. Arterial blood pressure and anthropometric measurement of height, weight, hip and waist circumference were also measured. Qualified health personnel were trained to assist in the data collection. Data quality control was ensured by calibrating all data collection tools for measurements before use.

Blood pressure measurement Arterial blood pressure was measured at rest using a digital sphygmomanometer MOTECH™®TrueScan™ (Digital/Automatic Blood Pressure Monitor, Germany). Repeated measurements were taken in triplicate at 5 min interval, and the averages of the two nearest measurements were recorded to the nearest 1 mmHg.

Anthropometric measurements

Weight measurements were taken with an electronic bathroom weighing scale (Seca Personen wage Clara

Total adults surveyed (N=569)

Ever been Diagnosed HPT

175 (30.8%)

Never been diagnosed

HPT 394 (69.2%)

Overall prevalence of HPT

including those on

treatment 277(48.7%)

Uncontrolled HPT

92(52.6%)

Rural (n=280) Urban (n=289)

Controlled HPT 83

(47.4%)

Rural prevalence 101

(36.1%)

Urban prevalence 93

(32.2%)

Prevalence of HPT during the survey 194

(34.1%)

76 J. Sci. Res. Stud. 803 Medical Scales and Measuring Systems, Hamburg, Germany). Weight was taken with participants wearing light clothing without shoes and values obtained were recorded to the nearest 0.5 kg. Heights of the traders were taken with a stadiometer while standing upright to the nearest 0.1 cm. Waist Circumference (WC) and Hip Circumference (HC) were measured to the nearest 0.1 cm using an inextensible tape measure and the measurements were done at the naval region for WC and at the level of the greater trochanter for HC. Classification of hypertension Hypertension was classified based on recommended cut-offs (WHO, 2016) as follows: Normal: (Systolic BP <120 and Diastolic BP <80 mmHg); Pre-hypertension: (Systolic BP = 120-139 and/or Diastolic BP = 80-89 mmHg); Hypertension- Stage I hypertension: (Systolic BP = 140-159 and/or Diastolic BP = 90-99 mmHg) and Stage II hypertension: (Systolic BP > 160 and/or Diastolic BP > 100 mmHg). Classification of physical activity Physical activity was estimated by quantifying activities such as carrying light loads, washing clothes, brisk walking to the farm or to the market, scrubbing floor sweeping inside or around the home. Physical activity was re-categorized as ≥3 days in a week and <3 days in a week. Statistical analysis Data was entered into Epi Info 7 and analyzed with STATA 14. Body mass index (BMI) was calculated based on WHO criteria as weight (kg) divided by height squared (m

2). Waist-to-Hip Ratio (WHR) was calculated by

dividing WC by HC. BMI and WHR were classified based on WHO recommendations. Participants were classified as hypertensive using BP threshold of 140/90 mmHg. Frequencies and percentages were used to summarize categorical variables (sex, educational background, ethnicity, religion) whilst means and standard deviations were used for continuous variables (BMI and BP). Chi-square analysis was used to test for the association between HPT and background characteristics. Pearson product moment correlation coefficient was used to determine the direction and strength of the relation between blood pressure (BP) and BMI, and between BP and WHR. Multivariate Logistic regression was used to determine the strength of association between some independent variables (age, sex, educational level,

marital status business location, ever been diagnosed hypertensive etc.) and HPT (dependent variable). The statistical significance was set at p-value < 0.05. Ethical considerations Ethical approval for the study was sought from the Ghana Health Service (GHS) Ethical Review Committee (ERC) with an ID number: GHS-ERC 10/04/15. Permission was also sought from the Hohoe Municipal Health Directorate (HMHD) of the GHS. Each respondent was informed prior to the interview that they were under no obligation to take part in the study, that they can withdraw at any time and that all answers will be treated with paramount confidentiality. All the traders who agreed to be part of the study signed an informed consent form before being interviewed and blood samples were taken. Persons with high BP were asked to go to the HPT clinic at the Municipal hospital for further investigations and care. RESULTS Table 1 summarizes the background characteristics of the traders (participants). Overall, five hundred and sixty-nine (569) participants were surveyed. This included 289 from urban and 280 from rural communities. Most participants were aged between 30-59 years with majority 166 (29.2%) aged between 40-49 years, 134 (23.6%) between 30-39 years, and 127 (22.4%) between 50-59 years. Only 70 (12.3%) were between 18-29 years and 71 (12.5%) were 60 years and above. Of the 569 participants, 67 (11.8%) did not have any formal education. The highest level of education attained by most participants was Junior High School (JHS)/Middle school 363 (63.8%), followed by primary school 67 (11.8%). Only 59 (10.4%) of the participants had Senior High School (SHS) and 13 (2.3 %) had tertiary education.

Most participants 353 (62.0%) were married or co-habiting with their partners followed by those who were divorced 87 (15.3%), widowed 72 (12.7%) and the least were single 57 (10.0%). The majority of the participants 420 (73.8%) were Ewes, followed by the Guans 101 (17.8%), Akans 18 (3.2%) and others 16 (2.8%) from the northern and other parts of Ghana. Over 90% of the participants 538 (94.6%) were Christians, 25 (4.4%) were Muslims and 4 (0.7%) were traditionalist.

Majority 255 (44.8%) of the participants conduct their trade in the market, followed by stores 120 (21.1%) and at home 117 (20.6%). The rest 71 (12.5%) move from house to house or combine their trading locations 6 (1.1%).

Table 1 shows that age, educational level and ethnicity were significantly associated with HPT (χ2 =11.49, p=0.022), (χ2 =24.95, p<0.001) and (χ2 =63.28, p<0.001) respectively. There was also a significant association

Kweku et al. 77

Table 1. Background characteristics and location of respondents.

Characteristics

Urban

[N=289]

n (%)

Rural

[N=280]

n (%)

Total

N=569 χ

2 P- Value

Sex

Male 43 (14.9) 59 (21.1) 102 (17.9)

Female 246 (85.1) 221 (78.9) 467 (82.1) 3.71 0.054

Age group

18-29 31 (10.7) 39 (14.0) 70 (12.3)

30-39 77 (26.6) 57 (20.3) 134 (23.5)

40-49 86 (29.8) 80 (28.6) 166 (29.2)

50-59 70 (24.2) 57 (20.3) 127 (22.4)

60 & above 25 (8.7) 47 (16.8) 72(12.6) 11.49 0.022

Educational level

None 42 (14.5) 25 (8.9) 67 (11.8)

Primary 49 (17.1) 18 (6.4) 67 (11.8)

JHS/middle 166 (57.4) 197 (70.4) 363 (63.8)

SHS 29 (10.0) 30 (10.7) 59 (10.3) 24.95 <0.001

Tertiary 3 (1.0) 10 (3.6) 13 (2.3)

Marital status

Single 24 (8.3) 33 (11.8) 57 (10.0)

Married/co-habiting 177 (61.2) 176 (62.8) 353 (62.0)

Divorced 47 (16.3) 40 (14.3) 87 (15.3)

Widowed 41 (14.2) 31 (11.1) 72 (12.7) 3.23 0.357

Ethnicity

Ewe 233 (80.6) 187 (66.8) 420 (73.8)

Guan 18 (6.2) 83 (29.6) 101 (17.7)

Akan 15 (5.2) 3 (1.1) 18 (3.2)

Others 23 (8.0) 7 (2.5) 30 (5.3) 63.28 <0.001

Religion

Christianity 264 (91.3) 274 (97.8) 538 (94.5)

Islamic religion 21 (7.3) 4 (1.4) 25 (4.4)

Traditionalist 3 (1.0) 1 (0.4) 4 (0.7)

Others 1 (0.4) 1 (0.4) 2 (0.4) 12.61 0.006

Business location

Home 24 (8.3) 93 (33.2) 117 (20.6)

Store 53 (18.3) 67 (23.9) 120 (21.1)

Market 181 (62.7) 74 (26.4) 255 (44.8)

house to house 27 (9.3) 44 (15.7) 71 (12.5)

Others 4 (1.4) 2 (0.7) 6 (1.0) 91.84 <0.001

Type of trade

Static\sedentary 225 (77.8) 220 (78.6) 445 (78.2)

Mobile 64 (22.2) 60 (21.4) 124 (21.8) 0.04 0.836

between religion and business location and HPT (χ2 =12.61, p=0.006) and (χ2 =91.84, p<0.001) respectively. Anthropometric indices and lifestyle of respondents Table 2 shows that overall underweight was 2.8% with

0.7% from urban and 5.0% from rural areas. Overweight was similar among urban and rural participants (31.5%) and (31.4%) respectively. Obesity was significantly higher among urban (41.5%) than rural (20.0%) participants (Figure 2). Prevalence of high-risk WHR was 48 (8.4%) and moderate risk was 76 (13.4%) and was similar in both urban and rural participants (p=0.460) and (p=0.881)

78 J. Sci. Res. Stud.

Table 2. Anthropometric indices and lifestyle of respondents.

Characteristics

Urban

[N=289]

n (%)

Rural

[N=280]

n (%)

Total

N=569 χ

2 P- Value

BMI status

Underweight 2 (0.7) 14 (5.0) 16 (2.8)

Normal 76 (26.3) 122 (43.6) 198 (34.8)

Overweight 91 (31.5) 88 (31.4) 179 (31.5)

Obese 120 (41.5) 56 (20.0) 176 (30.9) 42.88 <0.001

BP

Systolic BP (Mean) (SD) 127 (19.0) 131.8 (20.9) - - -

Diastolic BP (mean0 (SD 83.7 (12.1) 84.3 (12.9) - - -

WHR all

Low risk 222 (76.8) 223 (79.6) 445 (78.7)

Moderate risk 42 (14.5) 34 (12.1) 76 (13.4)

High risk 25 (8.7) 23 (8.3) 48 (8.4) 0.79 0.675

Salt consumption

Never 2 (0.7) 138 (49.3) 140 (24.6)

Sometimes 202 (69.9) 99 (35.3) 301 (52.9)

Always 85 (29.4) 43 (15.4) 128 (22.5) 181.04 <0.001

Consume alcohol

No 168 (58.3) 152 (54.3) 320 (56.3)

Yes 120 (41.7) 128 (45.7) 248 (43.7) 0.95 0.331

Smoke tobacco

No 279 (96.5) 264 (94.3) 543 (95.4)

Yes 10 (3.5) 16 (5.7) 26 (4.6) 1.66 0.198

Physical activity per week

No physical activity 171 (59.2) 138 (49.3) 309 (54.3)

1-3 times per week 79 (27.3) 94 (33.6) 173 (30.4)

More than 3 times per week 39 (13.5) 48 (17.1) 87 (15.3) 5.61 0.060

Fruit consumption per week

No fruits per week 27 (9.3) 55 (19.6) 82 (14.4)

1-3 days per week 172 (59.6) 169 (60.4) 341 (59.9)

More than 3 days per 90 (31.1) 56 (20.0) 146 (25.7) 17.37 <0.001

Vegetable consumption

No vegetables 14 (4.8) 1 (0.4) 15 (2.6)

1-3 days per week 161 (55.7) 102 (36.4) 263 (46.2)

More than 3 days per week 114 (39.5) 177 (63.2) 291 (51.2) 37.64 <0.001

Time of supper

Before 5pm 18 (6.2) 43 (15.4) 61 (10.7)

5-6pm 49 (17.0 49 (17.5) 98 (17.2)

6-7pm 81 (28.0) 90 (32.1) 171 (30.1)

After 7pm 141 (48.8) 98 (35.0) 239 (42.0) 18.32 <0.001

respectively (Figure 3).

Table 2 shows that BMI and salt consumption were associated with HPT. (χ2 =42.88, α=5% and p<0.001) and (χ2 =181.04, α=5% and p<0.001) respectively. Fruit consumption, vegetable consumption and time for supper were also associated with HPT (χ2 =17.37, α=5% and p<0.001), (χ2 =37.64, α=5% and p<0.001) and (χ2

=18.32, α=5% and p<0.001) respectively (Table 2). Underweight was significantly higher among rural participants than urban (p=0.002) whilst obesity was significantly higher among urban participants than rural (p<0.001) (Figure 2). Moderate and high-risk WHR were similar in both urban and rural participants (p=0.460) and (p=0.881) respectively (Table 2 and Figure 3).

Kweku et al. 79

Figure 2. BMI classification by urban and rural communities.

Figure 3. WHR for risk identification by urban and rural communities.

Prevalence of Hypertension and awareness of hypertension status Overall HPT including those who have been diagnosed and were on treatment was 277 (48.7%). At the time of the survey, HPT was 194 (34.1%) with 93 (32.2%) from urban and 101(36.1%) from rural communities. Hypertension which has never been diagnosed (Undiagnosed HPT) was 111 (26.8%) (Table 3). Prevalence of pre-HPT was 214 (37.6%) with 109 (37.7%) among urban and 105 (37.5%) among rural participants. The mean Systolic BP among urban traders was 127±19.0 and diastolic was 83.7±12.1mmHg. Among rural traders, the mean systolic BP was 131.8±20.9 mmHg and diastolic was 84.34.3±12.9 mmHg (Table 3).

Table 3 shows that 175 (30.8%) of the participants had ever been diagnosed to be hypertensive, out of which 83 (28.7%) were from urban and 92 (32.9%) from rural communities. Approximately 194 (34.1%) of the

participants had HPT (defined as Systolic BP≥140 mmHg and, Diastolic ≥ 90 mmHg), with 93 (32.2%) from urban and 101 (36.1%) from rural communities. Pre-hypertension (defined as: Systolic BP = 120-139 and/or Diastolic BP = 80-89 mmHg) was 214 (37.6%) with 109 (37.7%) from urban and 105 (37.5%) from rural communities. Out of the 175 participants who had ever been diagnosed, 92 (52.6%) did not have their BP under control (uncontrolled HPT), of whom 40 (48.2%) were urban and 52 (56.5) were rural participants.

A total of 394 participants were not aware of their HPT status. Prevalence of HPT among participants who have never been diagnosed was 102 (25.8%) with 53 (25.6) from urban and 49 (26.1) from rural communities. Out of the 206 participants who have never been diagnosed from urban communities, 83 (40.1%) were pre-hypertensive and 53 (25.6%) were hypertensive. Out of the 188 participants from rural communities who have never been diagnosed, 76 (40.4%) were pre-hypertensive

80 J. Sci. Res. Stud.

Table 3. Prevalence and classification of hypertension.

Characteristics

Urban

[N=289]

n (%)

Rural

[N=280]

n (%)

Total

N=569 χ

2 p-value

Blood pressure (BP)

Systolic BP (Mean) (SD) 127 (19.0) 131.8 (20.9) -

Diastolic BP (mean0 (SD) 83.7 (12.1) 84.3 (12.9) -

Overall prevalence of HPT including those on treatment

Ever been diagnosed +never been diagnosed HPT

136 (47.0) 141 (50.4) 277 (48.7) 0.734

Ever been diagnosed of HPT

Yes 83 (28.7) 92 (32.9) 175 (30.8)

No 206 (71.3) 188 (67.1) 394 (69.2) 1.60 0.205

Classification of HPT at the time of the survey

Normal 87 (30.1) 74 (26.4) 161 (28.3)

Pre-HPT 109 (37.7) 105 (37.5) 214 (37.6)

HPT 93 (32.2) 101 (36.1) 194 (34.1) 1.31 0.519

Classification of HPT among those ever been diagnosed (diagnosed)

n=83 n=92 N=175

Normal 17 (20.5) 11 (12.0) 28 (16.0)

Pre-hypertensive 26 (31.3) 29 (31.5) 55 (31.4)

Hypertensive (uncontrolled HPT) 40 (48.2) 52 (56.5) 92 (52.6) 2.56 0.278

Never been diagnosed Classification of HPT (undiagnosed)

n=206 n=188 N=395

Normal 71 (34.3) 63 (33.5) 134 (33.9)

Pre-hypertensive 83 (40.1) 76 (40.4) 159 (40.3)

Hypertensive 53 (25.6) 49 (26.1) 102 (25.8) 0.03 0.986

Figure 4. Physical activities done by participants per week.

and 49 (26.1%) were hypertensive (Table 3). Lifestyle of respondents More than half of the participants did not do any physical activity 309 (54.3%) per week. No physical activity was

significantly higher among urban participants 171 (59.2%) than rural 138 (49.3%) (Figure 4). Even though doing physical activity 3 times or more per week was higher among rural participants 48 (17.1%) than urban 39 (13.5%), the difference was not significant (p=0.245) (Table 2 and Figure 4).

No fruit consumption per week was significantly higher

Kweku et al. 81

Figure 5. Fruit consumption by participants per week.

Figure 6. Vegetable consumption by participants per week.

among rural traders than urban (19.6% vs. 9.3%; p<0.001). Fruit consumption more than 3 times per week was significantly higher among urban than rural traders (31.1% vs. 20.0%; p=0.003) (Table 2 and Figure 5).

No vegetable consumption was significantly higher among urban traders than rural (4.8% vs. 0.4%; p=0.001). Similarly, consumption of vegetables 1-3 times per week was significantly higher among urban than rural traders (55.7% vs. 36.4; p<0.001). Interestingly, consumption of vegetables more than 3 times per week was significantly higher among rural than urban traders (63.3% vs.39.5%; p<0.001) (Table 2 and Figure 6). Predictors of Hypertension Table 4 summarizes some of the predictors of HPT. Female traders were 0.68 times less likely to become hypertensive, however, this was not statistically significant [OR=0.68 (95% CI: (0.41, 1.12); p=0.126].

There was a significant association between increasing age among participants compared to the younger age group. Participants who were aged 40-49, 50-49 years and 60 years and above were 4.10, 4.69 and 4.06 times more likely to become hypertensive as compared to those aged 18-29 years [OR=4.10 (95% CI:1.98, 8.48); p<0.001], and [OR=4,69 (95% CI: 2.19, 10.05); p<0.001] and [OR=4.06 (95% CI: 1.71, 9.67); p=0.002] respectively.

Even though not statistically significant, participants with SHS and tertiary educational level were 1.46 and 1.30 times more likely to become hypertensive as compared to those with no education [OR=1.46 (95% CI: 0.69-3.11); p=0.322] and [OR=1.30 (95% CI: 0.37-4.53); p=0.683] respectively. Muslim participants were 0.04 times less likely to become hypertensive as compared to Christians [OR=0.04 (95% CI: 0.00-0.62); p=0.022]. Those who conduct their trading activities in stores were 10.22 times more likely to become hypertensive as compared to those who sell at home [OR=10.22 (95% CI:

82 J. Sci. Res. Stud.

Table 4. Anthropometric indices (BMI/WHR), Lifestyle and the odds of Hypertension.

Characteristics

Urban

[N=289]

n (%)

Rural

[N=280]

n (%)

OR (95% CI) P- Value

Sex

Male 43 (14.9) 59 (21.1)

Female 246 (85.1) 221 (78.9) 0.68 (0.41, 1.12) 0.126

Age group

18-29 31 (10.7) 39 (14.0)

30-39 77 (26.6) 57 (20.4) 2.01 (0.98, 4.14) 0.058

40-49 86 (29.8) 80 (28.7) 4.10 (1.98, 8.48) <0.001

50-59 70 (24.2) 57 (20.4) 4.69 (2.19, 10.05) <0.001

60 & above 25 (8.7) 47 (16.8) 4.06 (1.71, 9.67) 0.002

Educational Level

None 42 (14.5) 25 (8.9)

Primary 49 (17.1) 18 (6.4) 1.05 (0.53, 2.07) 0.890

JHS 198 (68.5) 237 (84.6) 1.15 (0.67, 1.97) 0.614

SHS 29 (10.0) 30 (10.7) 1.46 (0.69, 3.11) 0.322

Tertiary 3 (1.0) 10 (3.6) 1.30 (0.37, 4.53) 0.683

Religion

Christianity 264 (91.3) 274 (97.8)

Muslim 21 (7.3) 4 (1.4) 0.04 (0.00, 0.62) 0.022

Traditionalist 3 (1.0) 1 (0.4) 0.09 (0.00, 2.28) 0.146

Business location

Home 24 (8.3) 93 (33.2)

Store 53 (18.3) 67 (23.9) 10.22 (1.20,7.24) 0.034

Market 181(62.7) 74 (26.4) 2.45 (0.34,17.59) 0.373

House to house 27 (9.3) 44 (15.7) 2.81 (0.33,23.65) 0.343

Physical activity

No physical activity 171 (59.2) 138 (49.3)

1-3 times per week 79 (27.3) 94 (33.6) 0.39 (0.12,1.35) 0.139

More than 3 times per week 39 (13.5) 48 (17.1) 0.16 (0.03,0.79) 0.024

Vegetable consumption

No vegetables 14 (4.8) 1 (0.4)

1-3 days per week 160 (55.7) 102 (36.4) 0.07 (0.01, 0.89) 0.041

More than 3 days per week 114 (39.5) 177 (63.2) 0.18 (0.01, 2.43) 0.197

BMI status

Underweight 2 (0.7) 14 (5.0)

Normal 76 (26.3) 122 (43.6) 2.71 (0.05,134.37) 0.617

Overweight 91(31.5) 88 (31.4) 7.07 (0.15,324.12) 0.316

Obese 120 (41.5) 56 (20.0) 3.15 (0.07,151.81) 0.561

WHR all

Low risk 222 (76.8) 223 (79.6)

Moderate risk 42 (14.5) 34(12.1) 0.69 (0.14,3.39) 0.650

High risk 25 (8.7) 23 (8.3) 0.79 (0.27,2.32) 0.662

1.20-7.24); p=0.034]. Lifestyle, physical activity, vegetable consumption and the odds of Hypertension of traders Participants who did physical activities more than 3 times

per week were 0.16 times less likely to become hypertensive [OR=0.16 (95% CI: 0.03-0.79); p=0.024]. Even though those who did physical activities 1-3 times a week were 0.39 times less likely to become hypertensive, the difference was not statistically significant [OR=0.39 (95% CI: 0.12-1.35); p=0.139].

Kweku et al. 83

Table 5. Correlation between blood pressure and body mass index and waist to hip ratio for men and women.

Variable Blood pressure

r* p-value

BMI 0.09 0.038

WHR-all 0.08 0.043

WHR-men 0.07 0.479

WHR-women 0.09 0.059

* Pearson product moment correlation coefficient

Participants who consumed vegetables 1-3 days per week were 0.07 times less likely to become hypertensive compared to those who did not consume vegetables [OR=0.07 (95% CI: 0.01-0.89); p= 0.041]. Even though participants who consumed vegetables more than 3 days per week were 0.18 times less likely to become hypertensive compared to those who did not consume, the difference was not statistically significant [OR=0.18 (95% CI: 0.01-2.43); p= 0.197]. Anthropometric indexes (BMI/WHR) and the odds of Hypertension of traders Table 4 shows that even though participants who were overweight and obese were 7.07 and 3.15 times more likely to become hypertensive when compared to those who were underweight, the difference was however, not statistically significant [OR= 7.07 (95% CI: (0.15-324.12); p=0.316] and [OR= 3.15 (95% CI: 0.07-151.81); p=561] respectively. Participants with moderate and high risk WHR were 0.69 and 0.79 times less likely to become hypertensive when compared to those with low risk, but the difference was also not statistically significant [OR= 0.69 (95% CI: 0.14-3.39); p=0.650] and [OR= 0.79 (95% CI: 0.27-2.32); p= 0.662] respectively (Table 4). Correlation between blood pressure and body mass index and waist-to-hip ratio Pearson product moment correlation coefficient was computed to measure the strength and direction of the relationship between BP vs. BMI, and BP vs. WHR. Table 5 shows a statistically significant but weak positive linear relationship between BP and BMI (r=0.09, p<0.038, α=0.05). There was also a statistically significant weak positive relationship between BP and overall Waist to Hip Ratio (WHR-all), (r=0.08, p=0.043, α=0.05). A weak positive linear relationship was also observed between BP and Waist to Hip Ratio for men (WHR-men) and women (WHR-women) (r=0.07, p=0.479, α=0.05) and (r=0.09, p=0.059, α=0.05) respectively. Since all the correlation coefficients computed were positive, the

variables (BP vs. BMI, BP vs. WHR-all, BP vs. WHR-men and WHR-women) were directly related (Table 5). DISCUSSION It was found in this study that even though not statistically significant, there was a higher prevalence of Hypertension among traders in rural than in urban areas (36.1 vs. 32.2; p=0.519). The higher rural prevalence is consistent with studies by Akpan et al. (2015) in southern Nigeria which demonstrated a higher prevalence of HPT in rural than urban societies. In that study, they found 44.3% prevalence of HPT in the rural society compared to 27.5% in urban (p<0.001). This is in contrast with findings reported by Addo et al. (2012) in Ghana showing a higher prevalence in urban than rural. This increased prevalence of HPT in rural areas in this study may be attributed to a rise in westernized lifestyle among the rural dwellers. It was also found that more traders in the rural (33.2%) areas sell at home than urban (8.3%). Similarly, more rural traders sell in the store (23.9%) compared to urban traders (18.3%) while more traders in the urban (62.7%) sell in the market compared to rural dwellers (26.4%). In rural areas, the stores are very close to the home and therefore, very little walking is required. The high prevalence of HPT in this study group may be explained in part by their sedentary lifestyle since most traders sit in one place to conduct their businesses and do not have regular exercise.

Overall, undiagnosed and uncontrolled HPT was high among traders (25.8 and 52.6%) respectively. Undiagnosed HPT was similar among urban and rural traders, however, uncontrolled HPT was higher in rural (56.5%) than in urban (48.2%). This partly explains why HPT is higher among rural than urban traders.

This study revealed that age, the location of trade, physical activity, and consumption of vegetables were the most important predictors of hypertension in both rural and urban communities. Increasing age has been found to be a single predictor of hypertension (Akpan et al., 2015; Adebayo et al., 2013; Sola et al., 2013) and is an important cause of morbidity and mortality worldwide.

The key finding of this study shows an overall

84 J. Sci. Res. Stud. prevalence of 48.7% HPT cases among traders including those on treatment. At the time of the survey, the prevalence of HPT was 34.1% with 36.1% rural and 32.2% from urban areas. This also agrees with findings by Bosu (2010), which revealed that the prevalence of HPT ranged from 19 to 48%.

According to Asekun-Olarinmoye et al. (2013), a significant association existed between age older than 40 years and having HPT. This study also found that in both urban and rural, the prevalence of HPT increases with age, with the highest prevalence among the age group of 40-49 (urban 29.8% and rural 28.7%).

This study found a statistically significant association between HPT and conducting trading activities in stores. Participants trading in stores were 10.22 times more likely to become hypertensive. This could be because they may be involved in other physical activities. This study also revealed that HPT was significantly lower among those involved in physical activities three or more times per week compared to those who did not, and HPT was significantly lower among traders who consumed vegetables compared to those who did not. Conclusion There is an epidemiologic change in the prevalence of HPT in the rural communities in Ghana. The overall prevalence of hypertension in this study was high (48.7%). There was a high prevalence of HPT and Pre-HPT among traders in this study in both urban and rural settings. Uncontrolled HPT was higher among rural than urban traders. Over a quarter of the traders’ population were walking about with HPT and were not aware in both urban and rural communities. Prevalence was higher in the rural than urban areas and this could be attributable to a higher sedentary life of trading in stores, uncontrolled HPT, lack of physical activities, and not consuming fruits and vegetables.

Recommendations It is therefore recommended that health education should be intensified among the population sub-group as a crucial weapon in reducing the prevalence of HPT. It is also essential to provide information about how to control HPT. Large-scale population screening for HPT and pre-HPT is needed and adequate BP control is imperative to mitigate the mortality and morbidity associated with HPT. During screening programmes, information must be provided to alert people to seek timely medical attention as needed to reduce complications associated with HPT. Regular physical exercise, at least thrice weekly is recommended for every adult in both urban and rural settings. This requires health education in public places and institutions and via the media to curb the impending global epidemic of hypertension.

Availability of data and material: Available upon request. Competing interests: The authors declare that they have no competing interests. Funding: None Authors’ contributions MK and FB conceived the study, MK, WT, MA, WA and RO did the data analysis and wrote the methods section. MK, FB, MT and ET were responsible for the initial draft of the manuscript. All authors reviewed and approved the final version of the manuscript.

ACKNOWLEDGEMENT We are grateful to the staff of the University of Health and Allied Sciences. We are also grateful to Dr Felix Doe and the staff of the Hohoe Municipal Health Directorate and the Hohoe Municipal Assembly. We would like to thank the interviewers and the traders who participated in the study. REFERENCES Addo J, Agyemang C, Smeeth L, de-Graft Aikins A, Edusei AK,

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