Open Access

Profile of people with hypertension in Nairobi’s slums: a descriptive study

  • Annelieke Hulzebosch1Email author,
  • Steven van de Vijver2, 3,
  • Samuel O. Oti3,
  • Thaddaeus Egondi3 and
  • Catherine Kyobutungi3
Globalization and Health201511:26

https://doi.org/10.1186/s12992-015-0112-1

Received: 18 January 2015

Accepted: 11 June 2015

Published: 27 June 2015

Abstract

Background

Cardiovascular disease (CVD) is a rising health burden among the world’s poor with hypertension as the main risk factor. In sub-Saharan Africa, hypertension is increasingly affecting the urban population of which a substantial part lives in slums. This study aims to give insight into the profile of patients with hypertension living in slums of Nairobi, Kenya.

Methods

Sociodemographic and anthropometric data as well as clinical measurements including BP from 440 adults with hypertension aged 35 years and above living in Korogocho, a slum on the eastern side of Nairobi, Kenya, will be collected at baseline and at the first clinic visit.

Conclusion

The study population showed high prevalence of overweight and abdominal obesity as well as behavioral risk factors such as smoking, alcohol and a low vegetable and fruit intake. Furthermore, the majority of hypertensive patients do not take anti-hypertensive medication and the ones who do show little adherence.

Trial registration

Current controlled trials ISRCTN84424579.

Keywords

Hypertension Slums Sub-Saharan Africa Overweight Awareness Drug coherence Diabetes

Background

In 2000, nearly one billion of the world’s population (over 25 % at that time) had hypertension and this is expected to increase to almost 30 % by 2025 [1]. Hypertension is the single most important risk factor for cardiovascular disease (CVD) [2, 3], the leading cause of death worldwide [3]. The World Health Organization estimated that CVD caused 13.4 million deaths in 2008, 80 % of which occurred in developing countries [4, 5].

In sub-Saharan Africa (SSA), the epidemiological transition of diseases causing mortality from predominantly infectious diseases to non-communicable diseases (NCDs) like CVD is attributable largely to urbanization and industrialization [6]. The changes in lifestyle associated with this transition may lead to an increase in the occurrence of behavioral risk factors for CVD such as smoking, physical inactivity and an unhealthy diet. Increases in mean blood pressure (BP) have already been observed in SSA. In Kenya for example, the average systolic BP has risen from 127 in 1990 to 132 mmHg in 2010 [7]. The prevalence of risk factors for CVD including hypertension is higher in urban than in rural areas [8, 9]. It is expected that the urban population in Africa will increase by half in the next decades [10]. The urban population growth rate in Nairobi is 4.2 %, which is almost double the national growth rate of 2.4 % [11]. Nearly 60 % of the urban population in Nairobi lives in slums or slum-like conditions [12, 13]. Slums are typically characterized by poor living conditions with limited access to quality healthcare [14]. The prevalence of risk factors for CVD is high in slums and the associated psychosocial burden of insecurity, violence and stress may cause an increased risk of CVD [15]. Therefore a large part of the CVD burden is on the urban poor, who may not have the financial resources or adequate health literacy to adopt healthier lifestyles to adopt preventive measures [16, 17].

Despite the large extent of the epidemic, there is a paucity of documentation on the profile of the growing group of urban poor people with hypertension in SSA. This descriptive study aims to give an insight into patients with hypertension in a slum in Nairobi. What are the characteristics of these people, their behavioral risk factors, medication use and comorbidities?

Methods

Study population and design

The study was conducted in Korogocho, a large slum with approximately 72.000 residents in the outskirts of Nairobi, the capital city of Kenya. Korogocho is under surveillance as part of the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). The NUHDSS is operated by the African Population and Health Research Center (APHRC), a regional research institution. Details of the operation of the NUHDSS have been published elsewhere [18].

Data from 440 individuals were collected between August 2012 and December 2013. The data-collection was done as part of a published cross-sectional survey called the SCALE UP project, which aimed to develop and introduce a cost-effective and scalable CVD prevention model among the urban poor [19]. The goal of the prevention model was to raise awareness of hypertension through mass screening of adult slum residents aged 35 years and older, to improve access to treatment and promote adherence to medication. This paper examines adults with hypertension detected through the SCALE UP study and referred to local primary health care facility for treatment and follow-up.

As part of the SCALE UP study, 40 interviewers and 40 community health workers (CHWs) trained and employed by APHRC performed household visits to screen participants for hypertension and risk factors. The interviewers administered the questionnaire in the local lingua franca – Kiswahili. This questionnaire was adapted from the WHO STEPs instrument and gathered information on demographics, education level, wealth and behavioral risk factors [20]. After the questionnaires were administered, the CHWs proceeded to take anthropometric measurements from all consenting participants including weight, height and waist circumference. While an elevated BMI is a well-established contributor to the etiology of CVD, studies have shown that abdominal obesity are more closely related to CVD morbidity and mortality than BMI alone [20]. Therefore, both measures have been included in this study. All measurements were taken in line with the STEPS protocol [21]. Stage one hypertension was defined as mean systolic blood pressure (SBP) at least 140 mmHg or diastolic blood pressure (DBP) at least 90 mmHg, or self-reported previous diagnosis and current use of antihypertensive medication. Stage two hypertension was defined as mean SBP at least 160 mmHg or DBP at least 100 mmHg [2]. Mean arterial pressure was diastolic pressure plus one third of systolic BP. Treatment control was defined as BP of less than 140/90 mmHg for those participants currently taking anti-hypertensive medication. Participants with hypertension detected through the household visits were referred to a local primary health clinic known as Provide International, where they were interviewed again trained community health workers for risk re-assessment purposes. Participants were then assessed by clinical officers who confirmed their diagnoses. Those confirmed with hypertension and who provided informed consent became subjects of this current study. Overall, three levels of information were collected from participants; a questionnaire to gather information about history of hypertension and comorbidities, basic anthropometric and clinical measurements and a random blood glucose test.

Average monthly income was categorized into four groups, where 1000 KES equals $11.10. Alcohol consumption was classified as low (<1 standard unit of alcohol per week), moderate (1–14 per week for males and 1–7 per week for females) and high (>14 per week for males and >7 per week for females). Adequate physical activity was defined as more than 150 min of moderate intensity activity or more than 75 min vigorous intensity activity per week [22, 23]. Adequate fruit and vegetable intake was defined as 5 servings of fruit or vegetables per day, using a serving size of 80 g and the daily minimum of 400 g [2426].

Field supervisors verified the data quality. Questionnaires that did not meet the standards were corrected in the field. The completed questionnaires were then transported to a central location for double-data entry using MySQL data entry screens with Microsoft Access database backend.

Statistical analysis

Frequency distributions of sociodemographic, behavioral and clinical characteristics of study subjects by gender were examined. Continuous variables were expressed as means with standard errors of means. Categorical variables were expressed as numbers with percentages. For those variables where over 10 % of results were missing, ‘missing’ was listed as a category and thus percentages adjusted accordingly.

Association of the study participants’ sociodemographic and behavioral risk factors by sex was conducted using chi square tests for significance. A value of P < 0.05 was considered significant. All analyses were performed with STATA 12 (Stata Corp, College Station, Texas).

Ethical approval

The study protocol was approved by the Kenya Medical Research Institute (KEMRI)/National Ethical Review Committee (NON-SSC Protocol No.339).

Results

There were 256 women (58 %) and 184 men (42 %) in the study. It is noteworthy that although a total of 976 were referred from the screening during the household visits, only 440 (46 %) who attended the local primary clinic subsequently as at the time of conducting this analysis were included.

Table 1 shows sociodemographic variables as well as behavioral risk factors. Men were significantly more educated than women and reported a higher average monthly income. Gender disparities were also observed in CVD risk factors, significantly more men smoked and consumed alcohol. There was no significant difference in reported physical activity and daily fruit and vegetable intake between genders. Overall individuals demonstrated a high level of physical activity but a very low level of fruit and vegetable intake. As can be seen in Table 2, women had a higher BMI with 63 % being overweight or obese. Men had a significantly larger waist circumference; almost half of them fit the criteria for abdominal obesity. The mean SBP was 149.2 mmHg and the mean DBP was 95.2 mmHg. Women had a lower SBP than men. Over 40 % of individuals fit the criteria for stage two hypertension. Table 3 shows the history of the participants’ hypertension. Almost 70 % were diagnosed in the last two years and 68 % were diagnosed through a CHW visiting their home as part of the SCALE UP study. Only a quarter of the participants with hypertension ever used medication for their condition. Of the people who did, the majority (78 %) used the medication daily. The reported total costs of anti-hypertensive medication vary but over 75 % spent more than 100 KES per month (about $1.15). Treatment control was 53.8 % (95 % CI 34.7–73.0). Almost half of the medication users are currently not taking the medication anymore. Over a third of patients felt better and therefore believed they did not need the medication anymore, and almost half of the group reported they stopped using the anti-hypertensive drugs because they could no longer afford it. Overall 10 % of the patients with hypertension were found to have diabetes. Only 6.8 % reported to be aware of this condition prior to the CHW’s home visit. Table 4 shows other reported comorbidities.
Table 1

Description of study participants by gender

 

All (N = 440)

Women (N = 256)

Men (N = 184)

 

N

%

95 % CI

N

%

95 % CI

N

%

95 % CI

Age (years)

         

<45

67

15.2

12.2–18.9

41

16.0

12.0–21.1

26

14.1

9.8–20.0

45–54

154

35.0

30.7–39.6

92

35.9

30.3–42.0

62

33.7

27.2–40.9

55–64

102

23.2

19.5–27.4

51

19.9

15.5–25.3

51

27.7

21.7–34.7

65–74

78

17.7

14.4–21.6

46

18.0

13.7–23.2

32

17.4

12.6–23.6

≥75

39

8.9

6.5–11.9

26

10.2

7.0–14.5

13

7.1

4.1–11.8

Education P < 0,01

         

Not finished primary school

99

22.5

 

66

25.8

 

38

17.9

 

Primary school

171

38.9

 

89

34.8

 

82

44.6

 

Secondary school and higher

54

12.3

 

16

6.3

 

33

20.7

 

Missing

116

26.4

 

85

33.2

 

31

16.8

 

Marital status P < 0,01

         

Currently married

269

61.3

 

108

42.2

 

161

88.0

 

Never married

30

6.8

 

23

9.0

 

7

3.8

 

Widowed

89

20.3

 

80

31.3

 

9

4.9

 

Divorced/separated

51

11.6

 

45

17.6

 

6

3.3

 

Monthly income P < 0,01

         

<1000 KES

37

8.4

 

29

11.3

 

8

4.3

 

1000–4999 KES

180

40.9

 

119

46.5

 

61

33.2

 

5000–9999 KES

88

20.0

 

31

12.1

 

57

31.0

 

≥10 000 KES

23

5.2

 

5

2.0

 

18

9.8

 

Missing

112

25.5

 

72

28.1

 

40

21.7

 

Smoking status P < 0,01

         

Current smoker

37

8.4

 

5

2.0

 

32

17.5

 

Previous smoker

52

11.8

 

18

7.0

 

34

18.6

 

Non-smoker

350

79.7

 

233

91.0

 

117

63.9

 

Alcohol consumption P < 0,01

         

Low

373

84.8

 

237

92.6

 

136

73.9

 

Moderate

30

6.8

 

9

3.5

 

21

11.4

 

High

37

8.4

 

10

3.9

 

27

14.7

 

Physical activity P = 0.17

         

Adequate

401

91.8

 

231

90.2

 

170

93.9

 

Inadequate

36

8.2

 

25

9.8

 

11

6.1

 

Daily fruit and vegetable intake P = 0.12

         

Adequate

76

17.3

 

49

19.1

 

27

14.7

 

Inadequate

299

68.0

 

176

68.8

 

123

66.8

 

Missing

65

14.8

 

31

12.1

 

34

18.5

 

Missing data: marital status 1, smoking status 1, physical activity 3. P values for difference between genders derived using chi-squared test

Table 2

Anthropometric and biochemical measurements

 

All (N = 440)

Women (N = 256)

Men (N = 184)

 

N

%

95 % CI

N

%

95 % CI

N

%

95 % CI

BMI P < 0.01

         

Underweight (<18.5)

26

6.0

4.1–8.7

13

5.2

3.0–8.8

12

7.2

4.2–12.0

Normal weight (18.5–24.9)

184

42.8

38.2–47.5

79

31.7

26.2–37.8

105

58

50.7–65.0

Overweight (25–29.9)

130

30.2

26.1–34.8

88

35.3

29.6–41.5

42

23.2

17.6–29.9

Obese (≥30)

90

20.9

17.3–25.1

69

27.7

22.5–33.6

21

11.6

7.7–17.2

Waist circumference P < 0.01

         

Normal

293

67.8

63.3–72.1

200

79.7

74.2–84.2

93

51.4

44.1–58.6

Abdominal obesity

139

32.2

27.9–36.7

51

20.3

15.8–25.8

88

48.6

41.4–55.9

Diabetes mellitus

41

10.0

7.5–13.4

26

11.0

7.6–15.6

15

8.8

5.3–14.1

Blood pressure

      

Mean SBP

149.2

147.1–151.3

147.3

144.4–150.1

151.9

148.7–155.1

Mean DBP

95.2

94.0–96.4

95.5

94.0–97.0

94.8

92.9–96.7

Mean arterial pressure

145.0

143.2–146.7

144.6

142.4–146.9

145.4

142.7–148.2

Stage two hypertension

177

40.2

35.7–44.9

100

39.1

33.3–45.2

77

41.8

34.9–49.1

Missing data: BMI 10, waist circumference 8, diabetes mellitus 32. P values for difference between genders derived using chi-squared test and t-test in the case of blood pressure

Table 3

History of hypertension

 

All (N = 440)

 

N

%

95 % CI

Time since diagnosis (years)

   

<1

14

3.2

1.9–5.3

1–2

285

64.8

60.2–69.1

2–5

56

12.7

9.9–16.2

5–10

39

8.9

6.5–11.9

>10

46

10.5

7.9–13.7

Place of diagnosis

   

CHW visit

298

68.5

 

Private healthcare facility

92

21.1

 

Public healthcare facility

45

10.3

 

Ever used anti-hypertensive drugs

   

No

336

76.5

 

Yes

103

23.5

 

Medication use

   

Daily

80

77.7

 

On most days

10

9.7

 

On some days

7

6.8

 

When I felt bad

6

5.8

 

Total cost of anti-hypertensive drugs per month

   

No cost, free

13

12.6

 

<100 KES

11

10.7

 

100–299 KES

30

29.1

 

300–499 KES

28

27.2

 

≥500 KES

21

20.4

 

Currently taking the anti-hypertensive drugs

   

Yes

54

52.4

 

No

49

47.6

 

Reason for stopping the anti-hypertensive drugs

   

Could no longer afford the medication

25

51.0

 

I felt better, did not need further treatment

15

30.6

 

Other

9

18.4

 

Missing data: place of diagnosis 2, ever used anti-hypertensive drugs 1

Table 4

Self reported history of comorbidities

 

All (N = 440)

Women (N = 256)

Men (N = 184)

 

N

%

95 % CI

N

%

95 % CI

N

%

95 % CI

Diabetes

30

6.8

4.8–9.6

23

9.0

6.0–13.2

7

3.8

1.8–7.8

Stroke

7

1.6

0.8–3.3

3

1.2

0.4–3.6

4

2.2

0.8–5.7

Angina

5

1.1

0.5–2.7

3

1.2

0.4–3.6

2

1.1

0.3–4.3

Missing data: diabetes, stroke and angina 1

Discussion

This study describes the profile of hypertensive patients living in an informal settlement in Kenya. Compared to a recent population wide survey in Korogocho, our study participants smoke and consume more alcohol, and there is more obesity and less fruit and vegetable intake [15]. The differences could be explained by the type of study participants, our study only included hypertensive patients aged 35 years and older whereas the previous survey included a representative urban slum population of adults aged 18 years and older. So compared to a largely healthy population, patients with hypertension will expectedly show different characteristics. For example, the percentage of men in our study with abdominal obesity (48.6 %) is very high compared to the aforementioned survey in the same slum where only 11.6 % of men fit the criteria.

Our study shows a higher rate of participants classifying as overweight or obese than a recent study on overweight in Korogocho that found overweight and obesity rates similar to that of a national survey; around 40 % in women and 17 % in men [27, 28]. Studies from Tanzania and Cameroon show roughly the same gender disparities in prevalence of excess bodyweight [29, 30]. In our study we have found overweight and obesity prevalence to be much higher at 63 % and 34.8 % for women and men respectively, but again, our study sample consists of selected individuals with known hypertension. The high proportion of overweight and obesity specifically among women could be explained by different socio-cultural factors such as gender specific patterns of work activities and cultural standards of physical attractiveness [31].

Currently the estimated prevalence of diabetes in Africa is 1–3 % in rural areas and 5–6 % in urban SSA, but country reports have varied widely [32]. The prevalence of diabetes is likely to be higher in people with hypertension than at a population level. A study in Nigeria found that 4.6 % of hypertensive patients had diabetes [33]. Our study found this number to be over twice as high (10 %).

Evidence shows that CVD risk factors occur predominantly in clusters [34]. People with diabetes are more likely to also suffer from hypertension and/or dyslipidemia. This is likely to also occur in our study population, given the high obesity and abdominal obesity prevalence suggesting a strong influence of metabolic syndrome in this population.

Recommendations

There should be more attention paid to the large and growing epidemic of CVD in SSA and a joint effort to stop it. Policy makers and health providers need to work together. This paper shows that weight control is an important priority in both men and women and that cost of medication plays a major role in compliance. A possible solution could be a community health insurance or drug revolving funds [35].

Levels of health literacy have to increase in order for people to change their lifestyle, get diagnosed or comply with therapy. The WHO developed”best buys” for policy makers to tackle the main non-communicable diseases. These “best buys” state that providing counseling and multi-drug therapy for people with high risk of CVD can be used together with population-level interventions like taxes on tobacco to decentivize smoking, legislation to promote reduced salt foods for mitigating hypertension and improving the public awareness of the role of diet and physical activity on health through mass media. These interventions have been found to have a significant impact and be highly cost-effective. The implementation of these initiatives in a slum context is urgently needed.

Strengths and limitations of the study

To the best of our knowledge this is the first study on the profile of hypertensive patients in a Kenyan urban informal settlement. Our study adds to the limited body of evidence on hypertension in Kenya and gives an insight to the patients’ living standards and their rationale. This study has several limitations. One is the self-reporting of patients’ wealth, education, behavioral risk factors (smoking, alcohol misuse, diet and physical activity), comorbidities and use of medication. Self-reporting can lead to inaccurate reporting due to lack of awareness, misinterpretation of questions, or concern for judgement and affect our study conclusions. Additionally, our study did not capture over 50 % of patients during household screening visits or clinic visits. Follow up studies in this sub-population of patients would be very helpful. Despite these limitations, this study provides important data regarding the profile of patients with hypertension in an African urban slum.

Conclusions

The study population showed high prevalence of overweight and abdominal obesity as well as behavioral risk factors such as smoking, alcohol and a low vegetable and fruit intake. Furthermore, the vast majority of hypertensive patients do not take anti-hypertensive medication and the ones who do show little adherence.

Abbreviations

CVD: 

Cardiovascular disease

SSA: 

Sub-Saharan Africa

NCDs: 

Non-communicable diseases

BP: 

Blood pressure

NUHDSS: 

Nairobi Urban Health and Demographic Surveillance System

CHW: 

Community health worker

SBP: 

Systolic blood pressure

DBP: 

Diastolic blood pressure

KEMRI: 

Kenya medical research institute

Declarations

Acknowledgments

We would like to thank the following people and organizations for their support in this study: the staff of APHRC for their support through drivers, field interviewers, research assistants and statisticians; staff of AIGHD for their practical and scientific support. We thank Dr. Tuinenburg of the UMC Utrecht for his general advice. Financial support for the execution of the project was from the Academic Medical Center (AMC) Foundation Amsterdam. This work was also made possible by core funding for APHRC from The William and Flora Hewlett Foundation (grant no. 2009–40510), the Swedish International Cooperation Agency (SIDA) (grant no. 2011–001578) and the Rockefeller Foundation (grant no. 2009SCG302).

Authors’ Affiliations

(1)
Utrecht University
(2)
Department of Global Health, Academic Medical Center, University of Amsterdam
(3)
African Population and Health Research Center

References

  1. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005;365:217–23.PubMedView ArticleGoogle Scholar
  2. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, et al. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension. 2003;42:1206–52.PubMedView ArticleGoogle Scholar
  3. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747–57.PubMedView ArticleGoogle Scholar
  4. World Health Organization. The global burden of disease: 2004 update. Geneva 2008. Available at: http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/ [Accessed 12 November 2014]
  5. Gaziano TA, Opie LH, Weinstein MC. Cardiovascular disease prevention with a multidrug regimen in the developing world: a cost-effectiveness analysis. Lancet. 2006;368:679–86.PubMed CentralPubMedView ArticleGoogle Scholar
  6. Godfrey R, Julien M. Urbanisation and health. Clin Med J R Coll Phys. 2005;5:137–41.Google Scholar
  7. Imperial College London. Global burden of metabolic risk factors of chronic diseases. 2012. Available at: http://www.imperial.ac.uk/medicine/globalmetabolics/. [Accessed 12 November 2014]
  8. Cardiovascular Diseases in the African Region: Current Situation and Perspectives, WHO, 17 June 2005. Available at: http://www.afro.who.int/index.php?option=com_docman&task=doc_download&gid=2305- [Accessed 24 March 2014]
  9. Fuentes R, Ilmaniemi N, Laurikainen E, Tuomilehto J, Nissinen A. Hypertension in developing economies: a review of population-based studies carried out from 1980 to 1998. J Hypertens. 2000;18:521–9.PubMedView ArticleGoogle Scholar
  10. United Nations. World Urbanization Prospects The 2007 Revision Highlights. New York: ESA/P/WP/2; 2007. p. 883.Google Scholar
  11. Kenya National Bureau of Statistics (KNBS) and ICF Macro. 2010 Kenya Demographic and Health Survey 2008–09. Calverton, Maryland: KNBS and ICF Macro. Available at: \ http://dhsprogram.com/pubs/pdf/FR229/FR229.pdf [accessed 24 december 2014]
  12. UN Habitat, the State of African Cities: Nairobi, available at: http://www.ruaf.org/sites/default/files/Habitat%20state%20of%20African%20cities.pdf [accessed 12 March 2014]
  13. United Nations Human Settlements Programme (UN-HABITAT). Nairobi urban sector profile. Nairobi: UN-HABITAT; 2006.Google Scholar
  14. Lamba D. The forgotten half; environmental health in Nairobi’s poverty areas. Environ Urban. 1994;6(1):164–73.View ArticleGoogle Scholar
  15. Van de Vijver SJM, Oti SO, Agyemang C, Gomez GB, Kyobutungi C. Prevalence, awareness, treatment and control of hypertension among slum dwellers in Nairobi Kenya. J Hypertens. 2013;31(5):1018–24.PubMedView ArticleGoogle Scholar
  16. Popkin BM, Gordon-Larsen P. The nutrition transition: worldwide obesity dynamics and their determinants. Int J Obes Relat Metab Disord. 2004;28(3):S2–9.PubMedView ArticleGoogle Scholar
  17. Nyaruhucha CNM, Achen JH, Msuya JM, Shayo NB, Kulwa KBM. Prevalence and awareness of obesity among people of different age groups in educational institutions in Morogoro Tanzania. East Afr Med J. 2003;80:68–72.PubMedGoogle Scholar
  18. Emina J, Beguy D, Zulu EM, Ezeh AC, Muindi K, Elung’ata P, et al. Monitoring of health and demographic outcomes in poor urban settlements: evidence from the Nairobi Urban Health and Demographic Surveillance System. J Urban Health: Bull N Y Acad Med. 2011;88(2):S200–18.View ArticleGoogle Scholar
  19. Oti SO, van de Vijver SJM, Kyobutungi C, Gomez GB, Agyemang C, Van Charante EPM, et al. A community-based intervention for primary prevention of cardiovascular diseases in the slums of Nairobi: the SCALE UP study protocol for a prospective quasi-experimental community-based trial. Trials. 2013;14:409.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Lee CMY, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008;61(7):646–53.PubMedView ArticleGoogle Scholar
  21. The WHO STEPwise approach to chronic disease risk factor surveillance, version 3.0, June 2013, available at: http://www.who.int/chp/steps/instrument/STEPS_Instrument_V3.0.pdf?ua=1 [accessed on 15 April 2014]
  22. World Health Organization, Global Strategy on Diet, Physical Activity and Health: Intensity of physical activity, available at: http://www.who.int/dietphysicalactivity/physical_activity_intensity/en/ [accessed 24 March 2014]
  23. World Health Organization, Physical activity recommendations. http://www.who.int/dietphysicalactivity/physical-activity-recommendations-18-64years.pdf?ua=1 [accessed 24 March 2014].
  24. Agudo A. World health organization background paper: measuring intake of fruit and vegetables, available at: http://www.who.int/dietphysicalactivity/publications/f&v_intake_measurement.pdf [accessed on 25 March 2014]
  25. World Health Organization technical report series: diet, nutrition and the prevention of chronic diseases, available at: http://whqlibdoc.who.int/trs/who_trs_916.pdf [accessed on 25 March 2014]
  26. NHS rough guide: fruit and vegetable portion size, available at: http://www.nhs.uk/livewell/5aday/documents/downloads/5aday_portion_guide.pdf [accessed on 25 March 2014]
  27. Ettarh R, Van de Vijver S, Oti S, Kyobutungi C. Overweight, obesity, and perception of body image among slum residents in Nairobi, Kenya, 2008–2009. Prev Chronic Dis. 2013;10:E212.PubMed CentralPubMedView ArticleGoogle Scholar
  28. Ziraba AK, Fotso JC, Ochako R. Overweight and obesity in urban Africa: a problem of the rich or the poor? BMC Public Health. 2009;9:465. doi:10.1186/1471-2458-9-465.PubMed CentralPubMedView ArticleGoogle Scholar
  29. Njelekela MA, Mpembeni R, Muhihi A, Mligiliche NL, Spiegelman D, Hertzmark E, et al. Gender-related differences in the prevalence of cardiovascular disease risk factors and their correlates in urban Tanzania. BMC Cardiovasc Disord. 2009;9:30.PubMed CentralPubMedView ArticleGoogle Scholar
  30. Sobngwi E, Mbanya J-CN, Unwin NC, Kengne AP, Fezeu L, Minkoulou EM, et al. Physical activity and its relationship with obesity, hypertension and diabetes in urban and rural Cameroon. Int J Obes Relat Metab Disord. 2002;26:1009–16.PubMedView ArticleGoogle Scholar
  31. Ayah R, Joshi MD, Wanjiru R, Njau EK, Otieno CF, Njeru EK, et al. A population-based survey of prevalence of diabetes and correlates in an urban slum community in Nairobi Kenya. BMC Public Health. 2013;13:371.PubMed CentralPubMedView ArticleGoogle Scholar
  32. Siervo M, Grey P, Nyan OA, Prentice AM. A pilot study on body image, attractiveness and body size in Gambians living in an urban community. Eat Weight Disord. 2006;11:100–9.PubMedView ArticleGoogle Scholar
  33. Mbanya JCN, Motala AA, Sobngwi E, Assah FK, Enoru ST. Diabetes in sub-Saharan Africa. Lancet. 2010;375(9733):2254–66.PubMedView ArticleGoogle Scholar
  34. Ogunleye OO, Ogundele SO, Akinyemi JO, Ogbera O. Clustering of hypertension, diabetes mellitus and dyslipidemia in a Nigerian population: a cross sectional study. Afr J Med Med Sci. 2012;41(2):191–5.PubMedGoogle Scholar
  35. Hendriks ME, Wit FW, Akande TM, Kramer B, Osagbemi GK, Tanovic Z, et al. Effect of health insurance and facility quality improvement on blood pressure in adults with hypertension in Nigeria: a population-based study. JAMA Intern Med. 2014;174(4):555–63.PubMedView ArticleGoogle Scholar

Copyright

© Hulzebosch et al. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.