Abstract
Objective
Rapid urbanization is changing the epidemiology of non-communicable diseases in sub-Saharan Africa. We aimed to identify the determinants of raised blood pressure in urban Uganda to highlight targets for preventive interventions.
Design
Case-control.
Setting
Three community-based sites in Kampala, the capital of Uganda.
Participants
Participants were eligible to enroll if they were aged ≥18 years and not pregnant.
Methods
450 cases with raised blood pressure were frequency matched by sex and age to 412 controls. Unconditional logistic regression was used to evaluate the association of socio-demographic, lifestyle, anthropometric, and laboratory variables with the outcome of raised blood pressure. Cases currently treated with antihypertensive medication and cases not treated with antihypertensive medication were analyzed separately.
Results
Significantly increased odds of raised blood pressure were associated with overweight body mass index (BMI) (25 kg/m2 ≤ BMI < 30 kg/m2), obese BMI (BMI ≥ 30 kg/m2) and hemoglobin A1c ≥ 6.5%. Significantly decreased odds of raised blood pressure were associated with moderate-to-vigorous work-related physical activity of >4 hours/week. No significant associations were found between raised blood pressure and marital status, education level, car or flush toilet ownership, dietary habits, alcohol consumption, smoking habits, moderate-to-vigorous leisure-related physical activity > 4 hours/week, waist-to-hip ratio, or total cholesterol levels.
Conclusions
Targeted interventions are needed to address the key modifiable risk factors for raised blood pressure identified in this study, namely elevated BMI and regular physical activity, in order to reduce the burden of cardiovascular disease in urban Uganda.
Keywords: Hypertension, Cardiovascular Disease, Stroke, Obesity, Uganda, Africa
Introduction
High blood pressure is the leading risk factor globally for premature death and disability as measured in disability-adjusted life years (DALYs).1 More than one-quarter of the world’s adult population has hypertension and this proportion is expected to increase as a result of epidemiological transitions and demographic changes, eg, urbanization, particularly in low- and middle-income countries (LMIC).2 Hypertension is the leading modifiable risk factor for mortality from cardiovascular disease (CVD) including stroke and ischemic heart disease.3,4 Age-standardized mortality rates for stroke decreased globally from 1990 to 2013 but did not decline in sub-Saharan Africa (SSA) where deaths from stroke exceeded deaths from ischemic heart disease in 2013.5 A systematic analysis of cross-sectional studies of hypertension in Africa reported a pooled crude prevalence of 30.8% and a pooled awareness rate of 33.7% in 2010.6 This analysis included four studies from Uganda reporting crude prevalence rates of hypertension ranging from 20.5% to 30.4%. A national survey of hypertension conducted in 2014 in Uganda found a crude prevalence of 26.4% and an awareness rate of only 7.7%.7
The anticipated rise in the burden of CVD in LMIC can be attenuated by primordial and primary prevention at the population and individual levels.8 Investigations of the proximal determinants of key risk factors for CVD will empower public health policymakers with the data needed to make informed decisions for program planning and resource allocation for preventive interventions. Previous studies have evaluated the risk factors for hypertension in rural, urban, and mixed rural-urban communities in Uganda.7,9-15 We undertook this study to identify the determinants of raised blood pressure in an exclusively urban population in Kampala, the capital of Uganda.
Methods
Study Population and Setting
Residents of Kampala, the capital of Uganda, aged ≥18 years were recruited from three sites of religious worship in central Kampala: Rubaga Cathedral (Catholic), Namirembe Cathedral (Protestant) and the Old Kampala National Mosque (Muslim). Study recruitment tables were set up during times of worship and individuals were self-referred for participation. Recruitment was conducted on a weekly basis from February to September 2014.
Measurements
Blood pressure (BP) was measured twice (> 2 minutes apart) using a battery-operated automatic blood pressure monitor (Microlife BP3NB1-1X or Homedics BPA-060). A large adult cuff was utilized as needed. Participants were at rest for at least 5 minutes prior to measurement and seated with their forearm resting comfortably on a table and their legs uncrossed. The lower of the two readings was used to determine case status (raised blood pressure present) or control status (raised blood pressure not present and not treated with antihypertensive medication). Raised blood pressure was present if the systolic BP was ≥ 140 mm Hg and/or the diastolic BP was ≥ 90 mm Hg or if the participant was currently treated with antihypertensive medication. Data on socio-demographic characteristics, dietary habits, smoking habits, alcohol consumption, and work-related and leisure-related physical activity of study participants were collected by trained enumerators using standardized questionnaires adapted from the WHO STEPS Instrument.16 Alcohol intake, smoking status, and regular physical activity were categorized according to the approach employed by the INTERSTROKE study of risk factors for ischemic and hemorrhagic stroke.17
Body mass index (BMI) was calculated as weight in kilograms over height in meters squared and categorized as normal (BMI < 25 kg/m2) overweight (25 kg/m2≤ BMI < 30 kg/m2) and obese (BMI ≥ 30 kg/m2). Height was measured to the nearest tenth of a centimeter using a portable stadiometer and recorded as the maximum distance from the floor to the highest point on the head of the barefoot participant standing as fully extended as possible. Well-calibrated scales were used to measure weight to the nearest tenth of a kilogram and adjusted to zero before each measurement. Waist-to-hip ratio (WHR) was used as an indicator of abdominal obesity (≥ .9 for men and ≥ .85 for women). Waist circumference was measured midway between the lowest rib and the iliac crest. Hip circumference was measured at the level of the greater trochanters with the legs close together. Hemoglobin A1c (HbA1c) levels were measured to diagnose diabetes (HbA1c ≥ 6.5%) and pre-diabetes (5.6% < HbA1c < 6.5%). Total cholesterol levels were measured and categorized as high (≥6.2 mmol/L), borderline high (5.0 mmol/L - <6.2 mmol/L), and desirable (<5.0 mmol/L).
Statistical Analysis
Because of the uncertainty of self-reported age, especially for older individuals, we used frequency matching for sex and age band (18-25, 26-35, 36-45, 46-55, 56-65, >65 years) to obtain a similar distribution of cases and controls. Due to differing levels of participation at each site, we were not able to recruit equal numbers of cases and controls from each site. Data analysis was conducted as for an unmatched case-control study using Stata/IC 11.2. Cases currently treated with antihypertensive medication (n=148) and cases not treated with antihypertensive medication (n=302) were analyzed separately. Continuous variables were expressed as means ± standard deviation while categorical variables were expressed as percentages. Descriptive statistics for all variables were assessed using the two-tailed unpaired Student’s t test for continuous variables and the chi-square statistic with Yates’ correction for categorical variables. A total of 145 missing values (1% of all values) were imputed by multiple imputation by chained equations (10 imputations).
Unconditional logistic regression with frequency matching variables (age and sex) and recruitment site included as covariates was used to evaluate the association of socio-demographic, lifestyle, anthropometric and laboratory variables with the outcome of raised blood pressure. Backward stepwise selection was employed (P cutoffs; Pr = .05, Pe = .049) to identify variables for inclusion in the multivariable logistic regression model. Additional variables known to be associated with raised blood pressure were forced into the model (education level, HbA1c, WHR, alcohol consumption). Multicollinearity of independent variables was evaluated by examining the variance inflation factor (VIF) of each variable. All VIF values were <1.53. Estimated odds ratios (OR) were reported with 95% confidence intervals (CI) and a two-tailed P=.05 was used as the cut-off point for statistical significance.
Results
Characteristics of Study Participants
We enrolled 450 cases (148 currently treated with antihypertensive medication) and 412 controls at three distinct sites of religious worship in central Kampala: Rubaga Cathedral (202 cases, 130 controls), Namirembe Cathedral (77 cases, 88 controls) and the Old Kampala National Mosque (171 cases, 194 controls). These sites were chosen because of their size and to ensure a diverse sample of study participants representative of the population of Kampala. Tables 1 and 2 compare the characteristics of the cases (cases treated and cases not treated) and controls. Although we used frequency matching of cases by sex and age band, the mean age of cases exceeded controls due to challenges finding older study participants without hypertension. The lower of two recorded blood pressure readings was used to classify participants as cases or controls in order to reduce the possibility of over-diagnosing raised blood pressure. Average systolic and diastolic blood pressures of cases were significantly higher than controls.
Table 1. Sociodemographic and lifestyle characteristics of participants.
| Variable | Prevalence, n (%) | ||
| Controls, n=412 | Cases not treated, n=302 | Cases treated, n=148 | |
| Age, mean ± SD | 42.1 ± 13.5 | 46.1 ± 13.3 a | 56.0 ± 13.8 a |
| Sex | |||
| Male | 249 (60.4) | 208 (68.9) a | 69 (46.6) a |
| Female | 163 (39.6) | 94 (31.1) | 79 (53.4) |
| Systolic blood pressure, mean ± SD | 122.5 ± 11.3 | 152.6 ± 17.6 a | 149.4 ± 20.5 a |
| Diastolic blood pressure, mean ± SD | 78.0 ± 7.8 | 97.1 ± 10.9 a | 92.2 ± 13.8 a |
| Education | |||
| < Primary/none | 46 (11.2) | 26 (8.6) | 13 (8.8) |
| Primary completed | 99 (24.2) | 66 (21.9) | 30 (20.3) |
| ≥ Secondary completed | 265 (64.6) | 209 (69.5) | 105 (70.9) |
| Marital status | |||
| Never married/widowed/divorced | 158 (38.3) | 94 (31.2) | 60 (40.5) |
| Currently married | 254 (61.7) | 207 (68.8) | 88 (59.5) |
| Car ownership | |||
| No | 339 (82.5) | 235 (77.8) | 96 (66.2) a |
| Yes | 72 (17.5) | 67 (22.2) | 49 (33.8) |
| Flush toilet ownership | |||
| No | 278 (67.6) | 200 (66.7) | 79 (54.1) a |
| Yes | 133 (32.4) | 100 (33.3) | 67 (45.9) |
| Smoking status | |||
| Never | 370 (91.4) | 256 (84.8) a | 133 (89.9) |
| Current (any smoking in past 12 months) | 13 (3.2) | 14 (4.6) | 2 (1.4) |
| Former (quit > 12 months ago) | 22 (5.4) | 32 (10.6) | 13 (8.7) |
| Physical activity - work | |||
| Moderate/vigorous ≤ 4 hours/week | 243 (59.6) | 204 (68.7) a | 110 (76.9) a |
| Moderate/vigorous > 4 hours/week | 165 (40.4) | 93 (31.3) | 33 (23.1) |
| Physical activity - leisure | |||
| Moderate/vigorous ≤ 4 hours/week | 374 (91.0) | 278 (92.1) | 142 (95.9) |
| Moderate/vigorous > 4 hours/week | 37 (9.0) | 24 (7.9) | 6 (4.1) |
a. P<.05 compared with controls, chi-square test or t-test (see Methods section)
Table 2. Dietary, anthropometric, and laboratory characteristics of participants.
| Variable | Prevalence, n (%) | ||
| Controls, n=412 | Cases not treated, n=302 | Cases treated, n=148 | |
| Alcohol consumption | |||
| None | 312 (75.7) | 218 (72.4) a | 99 (66.9) |
| 1-30 drinks/month | 82 (19.9) | 55 (18.3) | 38 (25.7) |
| >30 drinks/month | 18 (4.4) | 28 (9.3) | 11 (7.4) |
| Fruit consumption | |||
| ≤ 6 servings/week | 288 (70.1) | 212 (70.2) | 96 (65.3) |
| ≥ 7 serving/week | 123 (29.9) | 90 (29.8) | 51 (34.7) |
| Vegetable consumption | |||
| ≤ 6 servings/week | 280 (68.1) | 217 (71.9) | 83 (56.1) a |
| ≥ 7 servings/week | 131 (31.9) | 85 (28.1) | 65 (43.9) |
| Meat consumption | |||
| ≤ 3 servings/week | 293 (71.6) | 227 (75.2) | 122 (82.4) a |
| ≥ 4 servings/week | 116 (28.4) | 75 (24.8) | 26 (17.6) |
| Body mass index (BMI) | |||
| < 25 kg/m2 | 208 (50.5) | 109 (36.7) a | 42 (28.4) a |
| 25 kg/m2 - < 30 kg/m2 | 123 (29.9) | 108 (36.3) | 54 (36.5) |
| ≥ 30 kg/m2 | 81 (19.6) | 80 (27.0) | 52 (35.1) |
| Waist-to-hip ratio (WHR) | |||
| < .9 (men)/< .85 (women) | 229 (55.9) | 139 (47.1) a | 55 (37.4) a |
| ≥ .9 (men)/> .85 (women) | 181 (44.1) | 156 (52.9) | 92 (62.6) |
| Hemoglobin A1c (HbA1c) | |||
| ≤ 5.6% | 318 (81.1) | 227 (80.2) | 99 (68.8) a |
| > 5.6 - < 6.5 % | 40 (10.2) | 36 (12.7) | 19 (13.2) |
| ≥ 6.5% | 34 (8.7) | 20 (7.1) | 26 (18.0) |
| Total cholesterol | |||
| < 5.0 mmol/L | 180 (45.9) | 105 (37.1) | 50 (34.7) a |
| 5.0 - < 6.2 mmol/L | 123 (31.4) | 105 (37.1) | 48 (33.3) |
| ≥ 6.2 mmol/L | 89 (22.7) | 73 (25.8) | 46 (32.0) |
a. P<.05 compared with controls, chi-square test or t-test (see Methods section).
Factors Associated with Raised Blood Pressure
We assessed for independent predictors of raised blood pressure using unconditional multivariable logistic regression adjusted for frequency matching variables (age and sex) and recruitment site (Table 3). Cases currently treated with antihypertensive medication and cases not treated with antihypertensive medication were analyzed separately. Significantly higher odds of raised blood pressure were associated with overweight BMI (25 kg/m2 ≤ BMI < 30 kg/m2) (cases not treated), obesity BMI (BMI ≥ 30 kg/m2) (cases treated and cases not treated), and hemoglobin A1c ≥ 6.5% (cases treated). Moderate to vigorous work-related physical activity > 4 hours/week was associated with significantly lower odds of raised blood pressure (cases treated and cases not treated).
Table 3. Predictors of raised blood pressure in study participants.
| Variable | Cases not treated, OR (95% CI) a | P | Cases treated, OR (95% CI) a | P | |
| Education | |||||
| < Primary/none | 1 | 1 | |||
| Primary completed | 1.29 (.71 – 2.35) | .408 | 2.03 (.84 – 4.90) | .114 | |
| ≥ Secondary completed | 1.41 (.81 – 2.45) | .218 | 2.11 (.93 – 4.81) | .076 | |
| Car ownership | |||||
| No | 1 | ||||
| Yes | 1.63 (.94 – 2.82) | .081 | |||
| Alcohol consumption | |||||
| None | 1 | 1 | |||
| 1-30 drinks/month | .75 (.48 – 1.18) | .219 | 1.29 (.71 – 2.34) | .84 | |
| >30 drinks/month | 1.82 (.92 – 3.59) | .083 | 1.35 (.50 – 3.59) | .554 | |
| Physical activity - work | |||||
| Moderate/vigorous ≤ 4 hours/week | 1 | 1 | |||
| Moderate/vigorous > 4 hours/week | .69 (.50 – .97) | .030 | .47 (.28 – .78) | .004 | |
| Body mass index (BMI) | |||||
| < 25 kg/m2 | 1 | 1 | |||
| 25 kg/m2 - < 30 kg/m2 | 1.67 (1.14 – 2.44) | .009 | 1.59 (.90 – 2.81) | .111 | |
| ≥ 30 kg/m2 | 2.45 (1.55 – 3.87) | <.001 | 2.81 (1.50 – 5.27) | .001 | |
| Waist-to-hip ratio (WHR) | |||||
| < .9 (men)/< .85 (women) | 1 | 1 | |||
| ≥ .9 (men)/≥ .85 (women) | 1.19 (.85 – 1.66) | .310 | 1.30 (.80 – 2.12) | .292 | |
| Hemoglobin A1c (HbA1c) | |||||
| ≤ 5.6% | 1 | 1 | |||
| > 5.6% - < 6.5% | 1.25 (.76 – 2.08) | .381 | 1.39 (.67 – 2.86) | .373 | |
| ≥ 6.5% | .62 (.33 – 1.16) | .133 | 2.03 (1.03 – 4.00) | .040 | |
a. Adjusted for age, sex, recruitment site, and variables shown (see Methods section).
Discussion
To our knowledge, this study is the first investigation of the determinants of raised blood pressure in an exclusively urban population in Uganda. We found significantly increased odds of raised blood pressure associated with overweight BMI (25 kg/m2 ≤ BMI < 30 kg/m2), obesity BMI (BMI ≥ 30 kg/m2), and hemoglobin A1c ≥ 6.5%, and significantly decreased odds of raised blood pressure associated with moderate-to-vigorous work-related physical activity of > 4 hours/week. No significant associations were found between raised blood pressure and marital status, education level, car ownership, flush toilet ownership, dietary habits, alcohol consumption, smoking habits, moderate to vigorous leisure-related physical activity >4 hours/week, WHR, or total cholesterol levels. Due to the low prevalence rates in cases and controls, our study may have been underpowered to detect a significant association between moderate-to-vigorous leisure-related physical activity or smoking with raised blood pressure.
Previous studies of risk factors for hypertension in rural, urban, and mixed rural-urban populations in Uganda7, 9-15 also reported significant associations of higher BMI with hypertension while certain studies reported significant associations of random or fasting blood glucose levels,10,12,15 alcohol consumption,9,12,15 WHR,10,15 or education level9,10 with hypertension. Notably, our study is the first investigation in Uganda to find significantly reduced odds of raised blood pressure with regular physical activity. Since our case-control study design employed frequency matching for age and sex, we could not evaluate the association of these two variables with raised blood pressure. One limitation of our study design is the self-referral of participants, which may have resulted in an indeterminate recruitment bias between cases and controls. Although we recruited participants from three distinct sites of religious worship to obtain a broadly representative sample of study participants, cases and controls were not recruited equally from the three sites. Further, adult residents who do not engage in religious worship were not included in our study sample.
According to the Global Burden of Disease Study 2013, CVD caused more than one million deaths in sub-Saharan Africa (SSA) in 2013, which represents 11.3% of annual deaths from all causes in that region.5 Deaths from stroke exceeded those from ischemic heart disease by 58%. Age-adjusted mortality rates from CVD in SSA have remained unchanged from 1990 to 2013 in contrast the sharp declines seen in many other regions of the world.5 Hypertension is the leading risk factor for stroke in SSA and a leading risk factor for ischemic heart disease in SSA.17,18 The age-standardized prevalence rate of hypertension in SSA in 2010 was 25.9%, a rate comparable to other developing regions of the world.6 However, rates of awareness, treatment and control of hypertension in SSA are very low due to weak health systems for the management of non-communicable diseases.2,6 The burden of CVD in SSA is expected to rise as a result of population growth and aging as well as advancing urbanization, which is driving an increased prevalence of the key risk factors for CVD including hypertension.2,19-21
Conclusion
Population-level and individual-level interventions are needed to address the key modifiable risk factors for raised blood pressure identified in our study. In particular, interventions to reduce elevated BMI and increase regular physical activity are needed to reduce the prevalence of raised blood pressure and the burden of CVD in urban Uganda. Domestic health sector policies and budgets should include programs for the provision of community-based screening and counseling for raised blood pressure to increase awareness and to promote healthy lifestyle modifications, such as weight reduction, regular physical activity, moderation of alcohol consumption, smoking cessation, and reduced salt intake.
Acknowledgments and Compliance with Ethical Standards
Our research was approved by the Makerere University School of Medicine Research and Ethics Committee and by University of California at Berkeley Committee for Protection of Human Subjects and all procedures followed were in accordance with the ethical standards of the IRB and the Helsinki Declaration of 1975, as revised in 2000. Informed written consent was obtained from each study participant.
The study investigators thank John Mujuni and the volunteers of the Alliance for Stroke Awareness and Prevention Project (ASAPP) for their assistance with the recruitment of study participants. Jerome H. Chin thanks Lee Riley at the University of California, Berkeley for his guidance and support.
This study was supported by the Fogarty International Center at the U.S. National Institutes of Health (NIH); Global Health Equity Scholars Fellowship (JHC) and Medical Education Partnership Initiative (MEPI) Linked Award: Building Capacity for Cardiovascular Research and Training in Uganda (AT, JK, RK). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
- 1.Forouzanfar MH, Afshin A, Alexander LT, et al. ; GBD 2015 Risk Factors Collaborators . Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1659-1724. 10.1016/S0140-6736(16)31679-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mills KT, Bundy JD, Kelly TN, et al. Global Disparities of Hypertension Prevalence and Control: A Systematic Analysis of Population-Based Studies From 90 Countries. Circulation. 2016;134(6):441-450. 10.1161/CIRCULATIONAHA.115.018912 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Roth GA, Nguyen G, Forouzanfar MH, Mokdad AH, Naghavi M, Murray CJL. Estimates of global and regional premature cardiovascular mortality in 2025. Circulation. 2015;132(13):1270-1282. 10.1161/CIRCULATIONAHA.115.016021 [DOI] [PubMed] [Google Scholar]
- 4.Feigin VL, Roth GA, Naghavi M, et al. ; Global Burden of Diseases, Injuries and Risk Factors Study 2013 and Stroke Experts Writing Group . Global burden of stroke and risk factors in 188 countries, during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 2016;15(9):913-924. 10.1016/S1474-4422(16)30073-4 [DOI] [PubMed] [Google Scholar]
- 5.Mensah GA, Roth GA, Sampson UKA, et al. ; GBD 2013 Mortality and Causes of Death Collaborators . Mortality from cardiovascular diseases in sub-Saharan Africa, 1990-2013: a systematic analysis of data from the Global Burden of Disease Study 2013. Cardiovasc J Afr. 2015;26(2)(suppl 1):S6-S10. 10.5830/CVJA-2015-036 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Adeloye D, Basquill C. Estimating the prevalence and awareness rates of hypertension in Africa: a systematic analysis. PLoS One. 2014;9(8):e104300. 10.1371/journal.pone.0104300 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Guwatudde D, Mutungi G, Wesonga R, et al. The Epidemiology of Hypertension in Uganda: Findings from the National Non-Communicable Diseases Risk Factor Survey. PLoS One. 2015;10(9):e0138991. 10.1371/journal.pone.0138991 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Weintraub WS, Daniels SR, Burke LE, et al. ; American Heart Association Advocacy Coordinating Committee; Council on Cardiovascular Disease in the Young; Council on the Kidney in Cardiovascular Disease; Council on Epidemiology and Prevention; Council on Cardiovascular Nursing; Council on Arteriosclerosis; Thrombosis and Vascular Biology; Council on Clinical Cardiology, and Stroke Council . Value of primordial and primary prevention for cardiovascular disease: a policy statement from the American Heart Association. Circulation. 2011;124(8):967-990. 10.1161/CIR.0b013e3182285a81 [DOI] [PubMed] [Google Scholar]
- 9.Wamala JF, Karyabakabo Z, Ndungutse D, Guwatudde D. Prevalence factors associated with hypertension in Rukungiri district, Uganda--a community-based study. Afr Health Sci. 2009;9(3):153-160. [PMC free article] [PubMed] [Google Scholar]
- 10.Maher D, Waswa L, Baisley K, Karabarinde A, Unwin N. Epidemiology of hypertension in low-income countries: a cross-sectional population-based survey in rural Uganda. J Hypertens. 2011;29(6):1061-1068. 10.1097/HJH.0b013e3283466e90 [DOI] [PubMed] [Google Scholar]
- 11.Mayega RW, Makumbi F, Rutebemberwa E, et al. Modifiable socio-behavioural factors associated with overweight and hypertension among persons aged 35 to 60 years in eastern Uganda. PLoS One. 2012;7(10):e47632. 10.1371/journal.pone.0047632 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kotwani P, Kwarisiima D, Clark TD, et al. ; SEARCH Collaboration . Epidemiology and awareness of hypertension in a rural Ugandan community: a cross-sectional study. BMC Public Health. 2013;13(1):1151. 10.1186/1471-2458-13-1151 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Musinguzi G, Nuwaha F. Prevalence, awareness and control of hypertension in Uganda. PLoS One. 2013;8(4):e62236. 10.1371/journal.pone.0062236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kayima J, Nankabirwa J, Sinabulya I, et al. Determinants of hypertension in a young adult Ugandan population in epidemiological transition-the MEPI-CVD survey. BMC Public Health. 2015;15(1):830. 10.1186/s12889-015-2146-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Nakibuuka J, Sajatovic M, Nankabirwa J, et al. Stroke-Risk Factors Differ between Rural and Urban Communities: Population Survey in Central Uganda. Neuroepidemiology. 2015;44(3):156-165. 10.1159/000381453 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.WHO The STEPS Instrument. Available: http://www.who.int/chp/steps/instrument/en/
- 17.O’Donnell MJ, Chin SL, Rangarajan S, et al. ; INTERSTROKE investigators . Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet. 2016;388(10046):761-775. 10.1016/S0140-6736(16)30506-2 [DOI] [PubMed] [Google Scholar]
- 18.Yusuf S, Hawken S, Ôunpuu S, et al. ; INTERHEART Study Investigators . Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937-952. 10.1016/S0140-6736(04)17018-9 [DOI] [PubMed] [Google Scholar]
- 19.Poulter NR, Prabhakaran D, Caulfield M. Hypertension. Lancet. 2015;386(9995):801-812. 10.1016/S0140-6736(14)61468-9 [DOI] [PubMed] [Google Scholar]
- 20.Roth GA, Forouzanfar MH, Moran AE, et al. Demographic and epidemiologic drivers of global cardiovascular mortality. N Engl J Med. 2015;372(14):1333-1341. 10.1056/NEJMoa1406656 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Siddharthan T, Ramaiya K, Yonga G, et al. Noncommunicable Diseases In East Africa: Assessing The Gaps In Care And Identifying Opportunities For Improvement. Health Aff (Millwood). 2015;34(9):1506-1513. 10.1377/hlthaff.2015.0382 [DOI] [PMC free article] [PubMed] [Google Scholar]