International Journal of Hypertension
Volume 2018 (2018), Article ID 6959165, 7 pages
https://doi.org/10.1155/2018/6959165

Research Article

Hypertension and Associated Factors in Rural and Urban Areas Mali: Data from the STEP 2013 Survey

Hamidou Oumar Bâ,¹ Youssouf Camara,² Ichaka Menta,¹ Ibrahima Sangaré,¹ Noumou Sidibé,¹ I. B. Diall,³ Souleymane Coulibaly,³ Maiga Asmaou Kéita,⁴ and Georges Rosario Christian Millogo⁵

¹CHU Gabriel Touré Bamako, Bamako, Mali
²CHU Kati, Kati, Mali
³CHU Point G, Bamako, Mali
⁴CHU Mère-Enfant, Bamako, Mali
⁵CHU YO, Ouagadougou, Burkina Faso

Correspondence should be addressed to Hamidou Oumar Bâ; bhamiba@yahoo.fr

Received 2 September 2017; Revised 24 November 2017; Accepted 20 December 2017; Published 21 January 2018

Academic Editor: Tomohiro Katsuya

Copyright © 2018 Hamidou Oumar Bâ et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Our study aims to estimate hypertension (HTN) prevalence and its predictors in rural and urban area. Methods. We conducted a cross-sectional population-based study involving subjects aged 15 to 65 years. Collected data (sociodemographic, blood pressure, weight, height, and blood glucose) were analyzed using SPSS version 20. A logistic regression was conducted to look for factors associated with HTN. Results. Mean was 47 years. High blood pressure (HBP) prevalence was 21.1 and 24.7%, respectively, in rural and urban setting. In rural area age group significantly predicted hypertension with age of 60 years having more-than-4-times risk of hypertension, whereas, in urban area age group, sex and body mass index were predictors with OR: HTN raising from 2.06 [1.24–3.43] for 30–44 years old to 7.25 [4.00–13.13] for 60 years and more using <30 years as reference. Female sex was protective with OR of 0.45 [0.29–0.71] and using normal weight as reference OR for overweight was 1.54 [1.04–2.27] and 2.67 [1.64–4.36] for obesity. Conclusion. Hypertension prevalence is high and associated factors were age group in rural area and age group, female sex, and body mass index in urban area.

1. Introduction

Hypertension (HTN) as leading risk factor for cardiovascular diseases has been largely described by many authors in the world [1–4]. As in other parts of the world HTN is a public health problem of growing concern in Low- and Middle-Income Countries (LMIC) [5, 6] and particularly in Africa [7, 8]. Newer African data on prevalence from community-based studies are high and above 25% [9, 10] and up to 53% [11]. Many factors have been described as associated with HTN [2, 12, 13].

Published data and mostly those from population-based studies are either old or rare in Mali. Prevalence data based on a 2002 conducted survey found 26% in the urban setting Bamako [14]. Recent data are therefore needed as well in urban as in rural areas. Our study aims to estimate hypertension prevalence and its risk factors in some rural and urban areas based on 2013 step survey data.

2. Materials and Methods

We conducted a cross-sectional population-based study, whose data stemmed from the 2013 STEPS-Survey in urban and rural areas. This approach has been described by the World Health Organization (WHO) [15].

2.1. Sampling and Data Collection

The study sample is based on the last STEPS-Survey which was conducted in 2013 with 2102 subjects aged from 15 to 65 years of whom 1543 (73.4%) in urban and 559 (26.6%) in rural areas.

It was conducted in administrative units known as communes and within each commune in towns/villages.

A two-stage cluster sampling method was used to select subjects from urban and rural areas. First clusters were obtained among communes in the three involved regions and second clusters from quartiers within these communes. Households were then randomly selected and all eligible adults in the household were interviewed and underwent physical exam and measurements.

The sample size was calculated using the formula

with = sample size, Z = 1,96, = 40% = 0.40, , = 3% = 0.03, .

A total of 30 clusters as given in Table 1 were obtained.

Table 1

Commune
Number of clusters
Type of area
Quartiers/villages

II (Bamako)
3
Urban
Bakaribougou, Médina coura; Niarela

III (Bamako)
3
Urban
Bamakocourabolibana,
Dravelila, N’Tomicorobougou

VI (Bamako)
6
Urban
Banankabougou, Magnanbougou,
Missabougou, Niamakoro, Sogoniko; Yirimadjo

Koulikoro
5
Urban
Katibougou, Kasso, Kolebougou, Koulikoroba, Koulikoro garre I

Kati central
3
Rural
Kati coura, Malibougou, Sébénicoro

Ouélessébougou
2
Rural
Fanicodiana, Tinkele

Ségou
4
Urban
Bagadadji, Darsalam, Mission, Sidosonicoura

Sikasso
4
Urban
Boula hameau, Lafiabougou koko, Quartuier résidentiel, Wayerma I

Total
30

2.2. Data Collection

The following data were recorded for each study participant:

(i)Sociodemographic(ii)3 blood pressures and heart rate measures in 5 min interval, using their mean as systolic, diastolic blood pressure, respectively, SBP, DBP, and HR(iii)Fasting/postprandial glycemia(iv)Weight in kilogram (Kg) and height in centimeter (cm) allowing calculation of body mass index (BMI) in Kg/m² as weight (Kg)/height (cm)²(v)Waist circumference (WC) and hip circumference (HC) all in cm(vi)Waist-to-hip ratio (rWH) as WC in cm divided by HC in cm(vii)Mean blood pressure from 3 measures

For the filling of the survey formulary and performing measurements, medical teams were built and trained for the household visit (and second visit in case household members were not present). Each subject was interviewed and different measures were obtained according to guidelines of the WHO STEPS approach for chronic disease surveillance [15].

Interviews were performed by medical staff and focused on sociodemographic characteristics, lifestyle, cardiovascular risk factors, personal and family history of cardio-vascular diseases, or other chronic illnesses.

Height was measured without shoes to the nearest centimeter with subject stand on the footplate with back against stadiometer rule.

Weight was measured to the nearest 0.1 kg on an electronic scale with the subject wearing light clothing and no shoes.

Waist circumference (WC) and hip circumference (HC) were measured with a stretch‐resistant tape that is wrapped snugly around the subject, but not constricting.

WC was measured at the midpoint between the lower border of the rib cage and the iliac crest with the subject being light clothed.

HC was measured around the widest portion of the buttocks.

We used for blood pressure measurements a Frangly® aneroid sphygmomanometer with a medium and large cuff size and performed measures at rest, the subject being in sitting position on the right arm. The mean of the two blood pressure readings was used for each subject in this study. A third measure was performed in some cases if blood pressure values were borderline.

2.3. Definitions

Education level was graded as follows:

(i)Level 0: no school attending(ii)Level 1: school attending for 1–6(iii)Level 2: school attending for 7–9(iv)Level 3: school attending for 10–12(v)Level 4: school attending for 12 and more years

Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg or self-reported use of antihypertensive drug irrespective of measured blood pressure [16].

Mean arterial blood pressure (mBP) was calculated with the following formula: .

General obesity was defined by body mass index (BMI) and further central obesity through the waist circumference (WC) [17].

BMI served to define weight disorders as follows:

(i)Underweight (UW): <18.5(ii)Normal weight (NW): 18.5–24.99(iii)Overweight (OW): ≥25.00–29.99(iv)Obesity (OB): ≥30.00

Based on waist circumference, OW was defined as WC ≥90 cm and ≥80 respectively for men and women and OB as waist ≥102 cm for men and ≥88 cm for women [18].

For waist-to-hip ratio (rWH) Men with a rWH 0.90–0.99 and women with a rWH 0.80–0.84 were classified as overweight, whilst men with a rWH ≥ 1.00 and women with a rWH ≥ 0.85 were classified as obese [18].

Diabetes was assessed with a glucometer (one-touch ultra Bayer®), fastened or postprandial with cutting values of 1.26 and 2 g, respectively, and the use of antidiabetic medicine.

2.4. Data Analysis

Statistical analysis was done using analytical software SPSS version 20. Results were expressed as either mean values (standard deviation) or proportions. -test for continuous variables and chi-square or Fisher test analysis for categorical variables were used. Level of significance was set at 0.05.

After a prevalence estimation in rural and urban setting, hypertensive subjects were selected to compare them regarding sociodemographics and descriptives variables.

Finally a logistic regression was conducted to look for predictors of hypertension in rural and in urban setting using age group, marital status, sex, educational level, tobacco smoking, alcohol consumption, body mass index, waist circumference, and waist-to-hip ratio and diabetes as independent variables.

For logistic regressions the following references were used: <30 years, single, Female, unschooled, nonsmoker, no alcohol consumption, normal weight for BMI, WC, and rWH, no diabetes, and resting HR > 90/min

3. Results

From the 2102 study participants, 495 were hypertensive subjects of whom 118 (23.8%) resided in rural area.

Mean age of the sample was ( in rural and in urban area with). Height and Glycemia were slightly higher in urban areas with 167.60 versus 165.76 cm and 120.18 versus 116.23 mg/l with value of 0.074 and 0.536. rWH was not significantly different with 0.91 in urban and 0.92 in rural area and value of 0.634 (Table 2).

Table 2: Description of sociodemographic characteristics of 495 hypertensive subjects in the 2013 STEP survey in Mali.

Variables
Setting
Total

Rural
Urban

%

%

%

Age groups (years)

    16–29
14
2.8
41
8.3
55
11.1
0.845

    30–44
28
5.7
97
19.6
125
25.3

    45–59
47
9.5
136
27.5
183
37.0

    60 and more
29
5.9
103
20.8
132
26.7

Sex

    Male
39
7.9
158
31.9
197
39.8
0.086

    Female
79
16.0
219
44.2
298
60.2

Marital status

    Unmarried
8
1.6
32
6.5
40
8.1
0.676

    Divorced
0
0.0
1
0.2
1
0.2

    Married
95
19.2
308
62.2
403
81.4

    Widowers
15
3.0
36
7.3
51
10.3

Education level

    Level 0
79
16.0
165
33.3
244
49.3
<0.0001

    Level 1
11
2.2
84
17.0
95
19.2

    Level 2
14
2.8
51
10.3
65
13.1

    Level 3
13
2.6
63
12.7
76
15.4

    Level 4
1
0.2
14
2.8
15
3.0

Weight, BMI, WC, and HC were higher in urban areas compared to those in rural areas, 75.47 versus 68.50 kg, 26.29 versus 24.83 kg/m², 92.79 versus 85.45 cm, and 102.2 versus 93.45  cm, respectively. All values were highly significant with 0.0001 (Table 2).

Age group showed the same profile, increasing proportion up to 59 years in urban as in rural areas. Age group 45–59 y made 37% (9.5 in rural and 27.5% in urban areas). But differences did not reach significant level (Table 3).

Table 3: Description of continuous variables of 495 hypertensive subjects in rural and urban setting in Mali.

Rural
Urban
Total

Mean

SD
Mean

SD
Mean

SD

Age
47.00
118
13.214
48.03
377
13.243
47.78
495
13.230
0.451

Weight (Kg)
68.50
116
13.305
75.47
376
16.510
73.83
492
16.076
<0.0001

Height (cm)
165.76
118
09.708
167.60
375
09.711
167.16
493
9.732
0.074

BMI
24.83
116
04.346
26.29
374
05.516
26.41
490
5.330
<0.0001

WC
85.48
114
11.819
92.79
368
14.123
91.06
482
13.952
<0.0001

HC
93.50
115
12.914
102.31
334
15.315
100.05
449
15.218
<0.0001

rWH
0.92
111
0.087
0.91
328
0.108
0.92
439
0.103
0.634

Glycemia
116.23
117
29.638
120.18
363
66.882
119.22
480
59.968
0.536

BMI: body mass index; WC: waist circumference; HC: hip circumference; rWH: waist-to-hip ratio.

Female sex and the status married represented 60.2 and 81.4% of the sample, respectively, but with value of 0.086 and 0.676 whereas almost half of the sample was unschooled () (Table 3).

HBP prevalence was 21.1 and 24.7%, respectively, in rural and urban setting (Figure 1).

Figure 1: Blood pressure profile in rural and urban area.

Among blood pressure parameters, heart rate was lower in urban areas (78.8%) versus 81.25% in rural areas with a value of 0.040. Systolic, diastolic, and mean arterial pressure were slightly higher in urban areas with, respectively, 151.23 mmHg, 94.62 mmHg, and 113.49 mmHg versus 150.94 mmHg, 94.10 mmHg, and 113.04 mmHg in rural areas. Corresponding values were, respectively, 0.900, 0.675, and 0.753 (Table 4). Pulsed pressure was in the same level with 56.84 mmHg in rural areas and 56.61 mmHg in urban areas ().

Table 4: Blood pressure related parameters in the sample of 495 hypertensive subjects.

Rural
Urban
Total

Mean

SD
Mean

SD
Mean

SD

SBP
150.94
118
22.734
151.23
377
21.630
151.16
495
21.875
0.900

DBP
94.10
118
13.193
94.62
377
11.310
94.49
495
11.774
0.675

HR
81.25
118
16.177
78.03
377
14.368
78.80
495
14.866
0.040

PP
56.84
118
17.421
56.61
377
18.224
56.67
495
18.019
0.904

mBP
113.04
118
14.862
113.49
377
12.939
113.38
495
13.408
0.753

SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; PP: pulsed pressure; mBP: mean blood pressure.

Using logistic regression, age group was found to be a significant predictor for hypertension (, , and ) in rural areas. The OR were 2.60, 4.97, and 9.70 for 30–44 y, 45–59 y, and above 60 years old, respectively, with under 30 years as reference (Table 5).

Table 5: Logistic regression predicting hypertension in rural setting.

Variables

S.E.
df
Sig.
OR
95% CI for OR

Lower
Upper

Age group: <30 years as reference

3
.000

    30–44
.955
.462
1
.039
2.60
1.05
6.42

    45–59
1.603
.468
1
.001
4.97
1.99
12.42

    60 and more
2.272
.535
1
.000
9.70
3.40
27.66

Marital status: single as reference

2
.904

    Married
.018
.544
1
.974
1.02
0.35
2.96

    Widower
.220
.706
1
.755
1.25
0.31
4.97

Sex
−.811
.414
1
.050
0.44
0.20
1.00

Educational level: unschooled as reference

4
.595

    Level 1
−.336
.441
1
.446
0.71
0.30
1.70

    Level 2
−.123
.403
1
.761
0.88
0.40
1.95

    Level 3
.008
.426
1
.986
1.01
0.44
2.32

    Level 4
−1.654
1.092
1
.130
0.19
0.02
1.63

Tobacco smoking
.042
.389
1
.914
1.04
0.49
2.24

Alcohol consumption
−.242
.713
1
.735
0.79
0.19
3.18

BMI: Underweight as reference

3
.887

    Normal weight
.395
.661
1
.550
1.49
0.41
5.43

    Overweight
.537
.702
1
.445
1.71
0.43
6.78

    Obesity
.411
.803
1
.608
1.51
0.31
7.28

WC: normal as reference

2
.211

    Overweight
.390
.350
1
.265
1.48
0.74
2.94

    Obesity
.711
.408
1
.082
2.04
0.91
4.53

rWH: normal as reference

2
.054

    Overweight
.296
.431
1
.491
1.35
0.58
3.13

    Obesity
1.066
.465
1
.022
2.90
1.17
7.23

Diabetes
.133
.383
1
.728
1.14
0.54
2.42

Resting HR > 90/min
1.551
.652
1
.017
4.72
1.32
16.94

Whereas in urban area age group, sex, and BMI were found as predictors for hypertension, hypertension increased in age group from 2.06 CI [95% 1.24–3.43] for 30–44 y to 7.25 CI [95% 4.00–13.13] for 60 y and above (). Female sex was protective with OR of 0.45 IC [95% 0.29–0.71] and BMI showed an increase of hypertensive subjects with increasing BMI. OR for overweight was 1.54 IC [95% 1.04–2.27] and 2.67 IC [95% 1.64–4.36] for obesity.

4. Discussion

In the study with a large data sample, we first estimated HTN prevalence in rural and urban area and then conducted a logistic regression to look for predictors of HTN. To our knowledge it is the first time that community-based data are analyzed in such a way. Previous studies are either community-based in urban area [14] or from hospital based data.

4.1. Prevalence of Hypertension

HTN prevalence remains high in rural and urban setting with values above 20% as shown in Figure 1. There is more HTN in urban area than in rural area as found by many other authors [13, 19, 20]. Previous data [14] was conducted only in the urban city of Bamako. Compared to others parts of the world [5] our prevalence is relatively low. Trends over the time due to unavailability of data cannot be assessed as done for west African countries [21].

4.2. Hypertension in Rural Area

Our data suggest high prevalence of HTN as found in other rural areas in Africa [19, 22] but remains low compared to data from Ghana [23] with near twice our prevalence. This is opposed to data from rural areas in Sudan with near 16% [24].

Our high prevalence could be due to either a real increase in HTN prevalence due to changing in behaviors (less physical activity, diet modifications) or modification in the population.

Looking for predictive factors, we found that only age group and resting heart rate significantly predicted HTN, with OR for age group increases with increasing age from 1.05 to 3.4 as shown in Table 5. This also conforms to what is known about HTN and it increases with aging. Many factors are found to be associated with HTN such that in our study (Table 6) age and BMI were predictors for HTN as found by Abebe et al. [13] and Neupane et al. [25]. Fasting glucose [13] and income and educational level [23] were factors found by these authors.

Table 6: Logistic regression predicting hypertension in urban setting.

Variables

S.E.
df
Sig.
OR
95% CI for OR

Lower
Upper

Age group: <30 years as reference

3
.000

    30–44
.724
.259
1
.005
2.06
1.24
3.43

    45–59
1.459
.261
1
.000
4.30
2.58
7.18

    60 and more
1.981
.303
1
.000
7.25
4.00
13.13

Marital status: single as reference

3
.369

    Married
.224
.269
1
.404
1.25
0.74
2.12

    Widower
.420
.408
1
.303
1.52
0.68
3.38

    Divorced
−1.346
1.121
1
.230
0.26
0.03
2.34

Female sex
−.789
.228
1
.001
0.45
0.29
0.71

Educational level: unschooled as reference

4
.284

    Level 1
−.079
.209
1
.705
0.92
0.61
1.39

    Level 2
−.030
.257
1
.907
0.97
0.59
1.61

    Level 3
−.372
.223
1
.096
0.69
0.44
1.07

    Level 4
−.622
.370
1
.093
0.54
0.26
1.11

Tobacco smoking
.087
.261
1
.738
1.09
0.65
1.82

Alcohol consumption
.083
.334
1
.803
1.09
0.57
2.09

BMI: Normal as reference

2
.000

    Overweight
.429
.200
1
.032
1.54
1.04
2.27

    Obesity
.983
.249
1
.000
2.67
1.64
4.36

WC: normal as reference

2
.054

    Overweight
.451
.258
1
.080
1.57
0.95
2.60

    Obesity
.665
.281
1
.018
1.95
1.12
3.37

rWH: normal as reference

2
.407

    Overweight
.199
.232
1
.391
1.22
0.77
1.92

    Obesity
.334
.250
1
.182
1.40
0.86
2.28

Diabetes
.069
.225
1
.760
1.07
0.69
1.67

Resting HR > 90/min
.274
.476
1
.564
1.32
0.52
3.35

4.3. Hypertension in Urban Area

The same finding of a high HTN prevalence was confirmed with our data. Our prevalence of 24.7% is practically equal to that found on data from the 2002 study which was conducted using the same WHO STEPwise approach and had involved only population of the urban city of Bamako.

In their systematic analysis Adeloye and Basquill [8] gave prevalence for countries of different regions Africa. Our prevalence was similar to that of sub-Saharan Africa as found by Adeloye and Basquill. Elsewhere, we see large differences in prevalence, but increase is constant [25, 26]. Some authors reported stable prevalence like in Switzerland [27], Turkey [28].

Opposed to finding in rural area, age, female sex, and body mass index were found to be predictive for HTN.

For age group the OR increases with age from 2.06 (30–44 years) to 7.25 (60 and more) which is similar finding in most studies [10, 13, 23] in Africa.

Female sex appeared to be protective in our study with an OR of 0.45.

Lastly BMI was the third predictive factor that we found with OR 1.54 for overweight and 2.67 of obese subjects. This increasing of HTN with increasing BMI has been described by many authors [13, 23].

4.4. Strength and Limits

Our data provide recent and community-based data on hypertension prevalence in rural and urban areas in Mali, using the WHO STEPwise survey strategy, and contribute to covering the need of such data.

We faced some difficulties which can be considered as limits. First, due to financial reason, lipid profile was not included in the 2013 STEPS-Survey. Second we also have been unable to take into account income and lastly comparison was difficult due to the lack of previous data.

5. Conclusion

Hypertension prevalence is high in rural and urban area and associated factors were age group in rural area and age group, female sex, and body mass index in urban area. Our data could be used as reference and serve to initiate more extensive studies permitting to get robust data for deciders. It is also important to find financial support to include lipid profile in the next STEPS-Survey.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

Thanks are due to Dr. Nazoum Diarra, who led for years the Non-Communicable Disease section at the Ministry of Health, for giving us opportunity to publish these data.

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