Abstract
Introduction:
Hypertension (HTN) among adolescents is common in high-income countries, and leads to increased premature cardiovascular diseases (CVD). In sub-Saharan Africa (SSA), the prevalence of HTN among adolescents, associated risk factors and CVD complications are not well-described. Such data is needed for planning public health programs to prevent premature CVD in SSA.
Methods:
We systematically searched 5 databases (MEDLINE, Embase, Google Scholar, Web of Science, and African Index Medicus) from their establishment to December 2021. Key search terms were: adolescent, arterial hypertension, and names of the 48 countries in SSA. We used Covidence® to manage the search results. The review was registered in the Open Science Framework (OSF) https://osf.io/p5sbt/.
Results:
We identified 4,008 articles out of which we screened 3,088 abstracts, and reviewed 583 full-text articles. We finally included 92 articles that were published between 1968 to December 2021. The majority were cross-sectional studies (80%) and conducted in school settings (78%). The risk of bias was low for 59 studies (64.1%), moderate for 29 studies (31.5%), and high for 4 studies (4.3%). Overall, the prevalence of HTN varied widely from 0.18% to 34.0% with a median (IQR) of 5.5% (3.1%, 11.1%). It was relatively higher in studies using automated blood pressure (BP) devices, and in studies defining HTN using thresholds based on percentile BP distribution for one's height, age, and sex. In addition, the prevalence of HTN was significantly higher in studies from Southern Africa region of SSA and positively correlated with the year of publication. Across studies, traditional risk factors such as age, sex, body mass index, and physical inactivity, were commonly found to be associated with HTN. In contrast, non-traditional risk factors related to poverty and tropical diseases were rarely assessed. Only three studies investigated the CVD complications related to HTN in the study population.
Conclusion:
The prevalence of HTN among adolescents in SSA is high indicating that this is a major health problem. Data on non-traditional risk factors and complications are scarce. Longitudinal studies are needed to clearly define the rates, causes, and complications of HTN.
Systematic Review Registration:
https://osf.io/p5sbt/, identifier (10.17605/OSF.IO/P5SBT).
Introduction
Hypertension (HTN) among adolescents is common globally, but most data come from high-income countries (1). Much less information is available from sub–Saharan Africa (SSA) where adolescents constitute about 18% of the population (2). In high-income countries, obesity is the major risk factor for HTN, and other traditional risk factors such as physical inactivity, unhealthy diet, and family history of HTN are also common (3, 4). In SSA, potential risk factors related to poverty and tropical diseases, also referred to as “non-traditional” risk factors, may be common. They include low birth weight, malnutrition, stunted growth, malaria, and/or other tropical infectious diseases. However, there is limited information describing the contribution of non-traditional risk factors for HTN among adolescents in Africa (3, 5–9).
Increasing evidence from long-term follow-up studies in high-income countries shows that HTN during adolescence is strongly associated with premature onset of cardiovascular diseases (CVD) including left ventricular hypertrophy, chronic kidney disease, decline in cognitive function, and type-2-diabetes mellitus (10–13). By comparison, little is known about blood pressure (BP) trajectory and its potential complications among adolescents in SSA (14, 15).
We are aware of three systematic reviews (and meta-analyses) describing the burden of HTN in children and adolescents in Africa (16–18). The reviews used published data from 1996 to 2017 and from 2017 to 2020, and from 2010 to 2021with the pooled estimates for prevalence of HTN of 5.5% and 7.5% and 9.9% respectively. The major limitations of two of these reviews were that they combined BP measurements for adolescents with those of children as young as 2 years. Also, neither of the reviews reported non-traditional risk factors and complications of HTN. Since the epidemiology of HTN varies between adolescents and children, studies that focus on adolescents only may provide useful information specific for this population.
Therefore, the current systematic review aims to describe the burden, risk factors, and complications of HTN among adolescents aged 10–19 years in SSA. Findings from this study will inform (1) programs promoting regular and proper BP measurements among adolescents; (2) primary CVD preventive interventions targeting adolescents; and (3) the design of new studies aimed to bridge existing gaps in the literature.
Methods
Databases and search strategy
We systematically searched for studies on HTN among adolescents aged 10–19 years in SSA, published in any language, from a range of databases from their inception until December 2021. Two investigators (MKN and RHA) were supported by a librarian (AH) to develop and conduct the search strategy. We searched MEDLINE, Embase, and African Index Medicus to identify relevant published literature. We searched Embase and Web of Science Core collection to identify studies that had been presented as conference abstracts but not yet published as full-length journal articles. We searched Google Scholar to identify potentially relevant information apart from the published research articles (19). The search strategy consisted of three concepts, combined with Boolean operators: adolescents, sub-Saharan Africa (names of the 48 countries in SSA), and hypertension. Both controlled vocabulary and free-text keywords were identified using the Yale MeSH Analyzer (20). Proximity operators were incorporated to retrieve variations of phrases. No limits were used for any of the databases. Terms used for HTN were “arterial hypertension”, “hypertension” and “blood pressure”. Terms for adolescent were “adolescent”, “teen”, and “youth”. The specific search strategies used for each of the databases are attached in Supplementary Appendix 1.
Selection criteria
Using Covidence (Melbourne, VIC 3000, Australia), two investigators (MKN and RHA) independently assessed the articles for eligibility in two steps. First, they independently screened the titles and abstracts of the retrieved articles. Then, they obtained full texts of potentially eligible articles and independently reviewed them for possible inclusion. A third reviewer (GAK) resolved any disagreements between the two investigators. Throughout this selection process, duplicates were removed both by Covidence and the two reviewers. We used Google Translate for studies published in languages other than English.
We included studies if they were: observational (cross-sectional, case-control, and cohort studies) or experimental studies, enrolled adolescents residing in SSA, reported at least one of the outcomes of interest (incidence/prevalence, risk factors, and complications of HTN), and reported data for a group or subgroup of participants within the ages of 10–19 years.
We excluded studies if they were animal studies, only reported non-systemic HTN (e.g., portal hypertension, pulmonary hypertension, and intracranial hypertension), case studies, case series, studies with sample size less than 5, qualitative studies and systematic reviews, studies including only participants with high BP, studies including only adolescents from a single high-risk group (e.g., those with HIV, sickle cell disease, malnutrition), and studies including adolescents with secondary HTN.
Data extraction procedures
The two investigators (MKN and RHA) independently extracted data using a pre-validated data extraction table. This process did not use any automation tools. Extracted data included: the author's name, year of publication, country of study, study design, participant's age range, average age, sample size, type of BP measurement device used (automated vs. manual), criteria used for defining HTN, prevalence and/or incidence estimates, risk factors and complication of HTN. We only extracted data for the eligible age range. We hand-calculated proportion of adolescents with HTN in the eligible age range if this information was not reported and where the numerator and the corresponding denominator were available. For cohort studies, we used prevalence estimates from baseline data and incidence from follow-up visits. We left blank any information which was either missing or not reported for the eligible age range. A third reviewer (GAK) resolved any discrepancies in the extracted data.
Evaluation of risk of bias
The two investigators (MKN and RHA) independently assessed each of the included studies for risk of bias using the Newcastle-Ottawa Scale (NOS) for assessment of the quality of non-randomized studies (21). The assessment was done on three domains: (1) selection of study participants, (2) comparability of participants in different outcome groups, and (3) ascertainment of study outcomes or exposure status. Across the three domains, a range of “high-quality” items could score a star (depending on the study design). Case-control and cohort studies have a total of 8 items with a maximum score of 9 stars. And cross-sectional studies have a total of 7 items with a maximum score of 10 stars. Studies with a total score of ≥7 stars were regarded as of high quality, 4–6 stars as of moderate quality, and 0–3 stars as of low quality (high risk of bias). We resolved any disagreements on the assessment of the risk of bias by consensus.
Data synthesis and analysis
We summarized the study selection process using a PRISMA flow diagram (22). We used tables to show characteristics of included studies, prevalence and/or incidence of HTN, and associated risk factors. We summarized our findings using proportions (for categorical data), and mean and/or median (for continuous data). We assessed the association between prevalence of HTN and each of the following study characteristics: year of publication, study settings (rural, rural/urban and urban), sub Saharan Africa region from which the studies came from (West, South, East, and Central), type of BP measurement device used (manual vs. automated), and criteria for defining HTN (cut offs based on a fixed vs. percentile BP values). We used meta-regression analysis to assess the adjusted association between prevalence of HTN and all the five study characteristics mentioned above. We did not perform pooled analysis due to heterogeneity in methods as well settings/context across the studies.
Ethical considerations
This study was exempted from ethical approval as it involved a review of published data. To our knowledge, all included studies obtained ethics clearance before data collection. The protocol for this review was registered in the Open Science Framework (OSF) https://osf.io/p5sbt/.
Role of funding organization
The funders had no role in the study design, data collection, data analysis, data interpretation, and writing of this manuscript.
Results
We identified 4,008 articles, and after removing 920 duplicates, we screened the title and abstract of the remaining 3,088 articles and selected 583 articles for full-text review. We could not retrieve the full texts of 15 publications (Supplementary Appendix 2). Of the remaining 568 articles, 476 were excluded after a full-text review (their exclusion reasons are summarized in the Figure 1 PRISMA flow diagram below. We finally included 92 articles in our systematic review.
Figure 1
Characteristics of the included studies
Table 1 shows the characteristics of the 92 included studies. The studies were published from 1968 to 2021, with the majority (75%) published between 2010 and 2021. The studies came from 14 (29%) out of the 48 countries in SSA. The majority of the studies came from the west (46.2%), followed by the south (29.7%), east (22.0%), and central (2.2%) African regions. Of the 92 studies, the majority were cross-sectional (80.0%), and conducted in schools (70.3%) and in urban settings (65.9%). They included a total of 89,599 subjects with sample sizes ranging from 53 to 7,746 (median = 670, IQR: 375, 1,080). The risk of bias was low for 59 studies (64.1%), moderate for 29 studies (31.5%), and high for 4 studies (4.3%).
Table 1
| Author | Year of publication | Country | Study design | Age range | Average age | Sample size | Female (%) | Setting | Urban/Rural |
|---|---|---|---|---|---|---|---|---|---|
| Akinkugbe OO (23) | 1968 | Nigeria | Cross-sectional | 10–19 | 1,119 | 48.8 | Community | Rural | |
| Johnson TO (24) | 1971 | Nigeria | Cross-sectional | 10–19 | 370 | 59.5 | Community | Urban | |
| Akinkugbe A (25) | 1976 | Nigeria | Cross-sectional | 15–19 | 103 | 100 | Community | Rural | |
| Ayobanji Ayoola E (26) | 1979 | Nigeria | Cross-sectional | 11–19 | 487 | 39.6 | School | Urban | |
| Abu-Bakare A (27) | 1983 | Nigeria | Cross-sectional | 11–18 | 14.3 | 684 | 100 | School | Urban |
| Idahosa PE (28) | 1985 | Nigeria | Cross-sectional | 15–19 | 109 | 0 | Community | Urban | |
| Eferakeya AE (29) | 1986 | Nigeria | Cross-sectional | 10–15 | 12.5 | 679 | 45.5 | School | Urban & Rural |
| Adams-Campbell LL (30) | 1987 | Nigeria | Cross-sectional | 12–17 | 118 | 34.8 | School | Urban | |
| Kitange HM (31) | 1993 | Tanzania | Cross-sectional | 15–19 | 16.8 | 1,673 | 55.2 | Community | Urban & Rural |
| Muraguri PW (32) | 1997 | Kenya | Cross-sectional | 13–18 | 403 | 49.4 | School | Urban | |
| Kane A (33) | 1998 | Senegal | Cross-sectional | 12–19 | 132 | Community | Rural | ||
| Longo-Mbenza B (34) | 1999 | Zaire | Cross-sectional | 13–16 | 471 | 33.1 | School | Urban & Semi-Urban | |
| Schutte AE (#167) (35) | 2003 | South Africa | Cross-sectional | 10–19 | 631 | 53.1 | School | Urban & Rural | |
| Schutte AE (#168) (36) | 2003 | South Africa | Cross-sectional | 10–15 | 695 | 53.8 | School | Urban & Rural | |
| Schutte AE (#1885) (35) | 2003 | South Africa | Cross-sectional | 10–15 | 631 | 53.1 | School | Urban & Rural | |
| VanRooyen JM (37) | 2005 | South Africa | Cross-sectional | 10–15 | 12.4 | 775 | 53.1 | School | Urban & Rural |
| Chiolero A (38) | 2007 | Seychelles | Cross-sectional | 12–15 | 7,746 | 51.4 | School | ||
| Thurston SW (39) | 2007 | Seychelles | Cohort | 12–15 | 524 | 53.4 | Community | Urban | |
| Mijinyawa MS (39) | 2008 | Nigeria | Cross-sectional | 13–19 | 15.9 | 968 | 50.8 | School | Urban |
| Monyeki KD (40) | 2008 | South Africa | Cross-sectional | 10–13 | 1,307 | 49.3 | School | Rural | |
| Cournil A (41) | 2009 | Senegal | Cohort | 16–19 | 17.9 | 1,288 | 46.4 | Community | |
| Mijinyawa MS (42) | 2009 | Nigeria | Cross-sectional | 13–18 | 15.6 | 718 | 56.9 | School | Urban |
| Odey F (43) | 2009 | Nigeria | Cross-sectional | 10–18 | 14.6 | 375 | 61 | School | Urban |
| Ansa VO (44, 45) | 2010 | Nigeria | Cross-sectional | 10–17 | 13.4 | 964 | 51 | School | Urban |
| Ejike C (45) | 2010 | Nigeria | Cross-sectional | 13–18 | 15.2 | 843 | 47.3 | School | Urban & Semi-Urban |
| Kruger HS (46) | 2010 | South Africa | Cross-sectional | 13–18 | 15.6 | 178 | 60.9 | School | |
| Chiolero A (47) | 2011 | Seychelles | Cohort | 12–15 | 12.5 | 3,461 | 51.8 | School | |
| Hawkesworth S (48) | 2011 | Gambia | Interventional: randomized trial | 11–17 | 14.1 | 1,267 | 48.2 | Community | Rural |
| Mamabolo RL (49) | 2011 | South Africa | Cross-sectional | 12–19 | 307 | 56.7 | School | ||
| Meehan KA (50) | 2011 | Sierra Leone | Cross-sectional | 15–16 | 407 | Hospital | Urban | ||
| Bukabau JB (51) | 2012 | DRC/Zaire | Cross-sectional | 15–20 | 18.7 | 524 | 49.8 | School | Urban |
| Griffiths PL (52) | 2012 | South Africa | Cohort | 16 | 16 | 358 | 47 | Community | Urban |
| Okoh BA (53) | 2012 | Nigeria | Cross-sectional | 10–12 | 483 | 56.7 | School | Urban | |
| Okpere AN (54) | 2012 | Nigeria | Cross-sectional | 10–19 | 13.6 | 615 | 51.4 | School | Urban |
| Oyewole OO (55) | 2012 | Nigeria | Cross-sectional | 12–18 | 14.9 | 1638 | 51.7 | School | Urban |
| Goon D (56) | 2013 | South Africa | Cross-sectional | 10–13 | 140 | 45.7 | School | Rural | |
| Lyngdoh T (57) | 2013 | Seychelles | Cohort | 12–15 | 407 | 54.1 | School | Urban | |
| Okpere AN (58) | 2013 | Nigeria | Cross-sectional | 10–19 | 820 | 49.9 | School | Urban | |
| Ujunwa FA (59) | 2013 | Nigeria | Cross-sectional | 10–18 | 15 | 2,694 | 52 | School | Urban |
| Mushengezi B (60) | 2014 | Tanzania | Cross-sectional | 12–19 | 16.5 | 582 | 47.9 | School | Urban |
| Okagua J (61) | 2014 | Nigeria | Cross-sectional | 10–18 | 14.5 | 1,080 | 49 | School | Urban |
| Kagura J (14) | 2015 | South Africa | Cohort | 18 | 18 | 1,587 | 51.7 | Community | Urban |
| Nkeh-Chungag B (62) | 2015 | South Africa | Cross-sectional | 13–17 | 15.1 | 392 | 69.9 | School | Semi-Urban |
| Odunaiya NA (63) | 2015 | Nigeria | Cross-sectional | 15–18 | 16.4 | 1,079 | 53.5 | School | Rural |
| Oyeyemi AY (64) | 2015 | Nigeria | Cross-sectional | 13–18 | 15.6 | 1,048 | 37.7 | School | Urban |
| Ratovoson R (65) | 2015 | Madagascar | Cross-sectional | 15–17 | 638 | Community | Urban & Rural | ||
| Awotidebe A (66) | 2016 | South Africa | Cross-sectional | 13–14 | 13.9 | 310 | 61.9 | School | |
| Munthali RJ (67) | 2016 | South Africa | Cohort | 1,589 | 51.7 | Community | Urban | ||
| Strassman BI (68) | 2016 | Mali | Cohort | ||||||
| Uwaezuoke SN (69) | 2016 | Nigeria | Cross-sectional | 10–19 | 14.8 | 2,419 | 48.7 | School | Urban |
| Alicke M (70) | 2017 | Ghana | Cross-sectional | 14–15 | 15.2 | 188 | 50 | Hospital | Rural |
| Bedu-Addo G (6) | 2017 | Ghana | Cohort | 14–15 | 14.8 | 155 | 47 | Community | Rural |
| Etyang AO (71) | 2017 | Kenya | Cross-sectional | 11–17 | 13.3 | 686 | 55.7 | Community | Urban |
| Etyang AO (72) | 2018 | Kenya | Cross-sectional | 11–17 | 13.3 | 609 | 55.5 | Community | Urban |
| Ezeudu CE (73) | 2018 | Nigeria | Cross-sectional | 10–19 | 14.6 | 984 | 52.2 | School | Rural |
| Hendry LM (74) | 2018 | South Africa | Cohort | 17.9 | 1,947 | 73.3 | Community | Urban | |
| Isezuo KO (75) | 2018 | Nigeria | Cross-sectional | 10–18 | 14.5 | 800 | 47 | School | Urban |
| Leyvraz M (76) | 2018 | Seychelles | Cohort | 12–15 | 15.6 | 5,967 | 51.4 | School | Urban & Rural |
| Masocha V (77) | 2018 | South Africa | Cohort | 14 | 14.9 | 289 | 59.9 | School | Urban |
| Nakiriba R (78) | 2018 | Uganda | Cross-sectional | 12–18 | 15.4 | 688 | 100 | School | Urban |
| Omisore AG (79) | 2018 | Nigeria | Cross-sectional | 10–19 | 13.7 | 1,000 | 49 | School | Urban & Rural |
| Abu OO (#201) (80) | 2019 | Nigeria | Cross-sectional | 10–19 | 14 | 420 | 57.4 | School | Urban |
| Adeomi AA (81) | 2019 | Nigeria | Cross-sectional | 10–19 | 14.4 | 313 | 58.5 | School | Urban |
| Amponsem-Boateng C (82) | 2019 | Ghana | Cross-sectional | 15–17 | 571 | 61.2 | School | ||
| Chungag A (83) | 2019 | South Africa | Cross-sectional | 10–14 | 11.9 | 540 | 53.7 | School | |
| Frigati L (84) | 2019 | South Africa | Case-control | 9–14 | 12 | 620 | 49.8 | Community | Urban |
| Lule SA (#101) (85) | 2019 | Uganda | Cohort | 10–11 | 10.2 | 1,119 | 47.9 | Community | Urban & Rural |
| Lule SA (#3098) (86) | 2019 | Uganda | Cohort | 10–11 | 10.4 | 815 | 49 | Community | Urban & Rural |
| Lule SA (#409) (87) | 2019 | Uganda | Cohort | 10–11 | 10.2 | 1,119 | 47.9 | Community | Urban & Rural |
| Nkwana MR (88) | 2019 | South Africa | Cross-sectional | 11–15 | 591 | 46.7 | School | Urban | |
| Nsanya MK (89) | 2019 | Tanzania, Uganda | Cross-sectional | 12–15 | 827 | 50.4 | School | Urban | |
| Abiodun O (90) | 2019 | Nigeria | Cross-sectional | 15–19 | 16.51 | 6,980 | 56.2 | School | |
| Azupogo F (91) | 2020 | Ghana | Cross-sectional | 15–19 | 16.9 | 1,727 | 49.6 | Community | Urban & Rural |
| Katamba G (92) | 2020 | Uganda | Cross-sectional | 12–19 | 15.6 | 616 | 65.6 | School | Urban |
| Masocha V (93) | 2020 | South Africa | Cohort | 14–16 | 14.9 | 186 | 56.5 | School | Urban |
| Mokgwathi M (94) | 2020 | Botswana | Cross-sectional | 17.1 | 252 | 68.3 | School | Urban & Rural | |
| Raphadu TT (95) | 2020 | South Africa | Cross-sectional | 13–19 | 218 | 55.5 | School | Urban & Rural | |
| Sungwa EE (96) | 2020 | Tanzania | Cross-sectional | 10–16 | 350 | School | Urban | ||
| Ugochukwu EF (97) | 2020 | Nigeria | Cross-sectional | 10–17 | 593 | 51.2 | School | Urban | |
| Ukoh U (98) | 2020 | Nigeria | Cross-sectional | 10–19 | 15.1 | 2,401 | 50.2 | School | Urban |
| Akinbodewa AA (99) | 2021 | Nigeria | Cross-sectional | 10–17 | 12.9 | 53 | 52.8 | Community | Rural |
| Ayogu RN (100) | 2021 | Nigeria | Cross-sectional | 10–19 | 401 | 53.1 | Community | Rural | |
| Chungag A (101) | 2021 | South Africa | Cohort | 10–14 | 411 | 54.3 | School | Urban & Rural | |
| DuPlessis JP (102) | 2021 | South Africa | Cross-sectional | 13–14 | 15.2 | 172 | 61 | School | Urban |
| Engwa G (103) | 2021 | South Africa | Cross-sectional | 13–16 | 234 | School | Rural | ||
| Letswalo BP (104) | 2021 | South Africa | Cross-sectional | 13–16 | 14.1 | 232 | 78.4 | School | Rural |
| Lwabukuna WC (105) | 2021 | Tanzania | Cross-sectional | 14–19 | 16.9 | 217 | 68 | School | Urban |
| Meer R (106) | 2021 | South Africa | Cohort | 5–18 | 1,891 | 51.7 | Community | Urban | |
| Nganou-Gnindjio CN (107) | 2021 | Cameroon | Cross-sectional | 12–19 | 1,392 | School | Urban & Semi-Urban | ||
| Nsanya MK (4) | 2021 | Tanzania | Cross-sectional | 11–15 | 13.9 | 500 | 56.6 | School | Urban |
| Sekokotla AM (108) | 2021 | South Africa | Case-control | 13–17 | 76 | 100 | School | Urban | |
| Shokunbi OS (109) | 2021 | Nigeria | Cross-sectional | 10–19 | 488 | 64.3 | School | Urban |
Characteristics of studies with data on hypertension among adolescents aged 10–19 years in sub-Saharan Africa (N = 92).
Methods of BP measurements
The studies used different devices for BP measurements, and the definition of HTN was not consistent across studies (Table 2).
Table 2
| Author | Year of publication | Device for BP meas. | Days for BP meas. | Number of BP meas. | Calculation of final BP meas. | HTN definition or guideline | Pre-HTN (%) | HTN (%) | Elevated BP (%) | Incidence | Mean SBP (SD) | Mean DBP (SD) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Akinkugbe OO (23) | 1968 | Mercury | 1 | 1 | WHO, BP ≥ 140/90 | 2.8 | 106.4 | 65.6 | ||||
| Johnson TO (24) | 1971 | Mercury | 1 | 1 | WHO, BP ≥ 160/95 | 0.8 | 117.9 | 74.6 | ||||
| Ayobanji Ayoola E (26) | 1979 | Mercury | 1 | 3 | Average | BP > + 2 SD | 3.3 | 106.5 (11.8) | 61 (9) | |||
| Abu-Bakare A (27) | 1983 | WHO, BP ≥ 133/90 | 6.8 | 110.6 (11.1) | 69.5 (10.5) | |||||||
| Idahosa PE (28) | 1985 | Automated | 1 | 2 | Average | WHO, BP ≥ 160/95 | 11.9 | 1.8 | 122 (15.7) | 63 (11.1) | ||
| Eferakeya AE (29) | 1986 | Finapres | 1 | Continuous 5 min | Average | BP > + 2 SDs | 4.47 | |||||
| Adams-Campbell LL (30) | 1987 | Mercury | 1 | 3 | Average of 2nd & 3rd | WHO, BP ≥ 140/90 | 2.3 | |||||
| Kitange HM (31) | 1993 | Mercury | 1 | 2 | Average | WHO, BP ≥ 160/95 | 0.4 | 114.1 (12.4) | 66.1 (9.5) | |||
| Muraguri PW (32) | 1997 | Mercury | 1 | 3 | Average | Second Report | 1 | 109.5 (9.9) | 66.5 (6.5) | |||
| Kane A (33) | 1998 | Aneroid | 1 | 1 | Third Report | 1.5 | 108.9 (12.2) | 64.3 (10.1) | ||||
| Schutte AE (35) (#167) | 2003 | Finapres | 1 | Continous | Average | Third Report | 20.7 | |||||
| Schutte AE (36) (#168) | 2003 | Finapres | 1 | Continous | Average | Third Report | 17.3 | 20.8 | ||||
| Schutte AE (35) (#1885) | 2003 | Finapres | 1 | Continous | Average | Third Report | 15.5 | 18.4 | ||||
| Chiolero A (38) | 2007 | Automated | 1 | 2 | Average | Fourth Report | 110.5 (12) | 67.2 (8.5) | ||||
| Monyeki KD (40) | 2008 | Automated | 1 | 3 | Average | Fourth Report | 4.9 | |||||
| Cournil A (41) | 2009 | Mercury | 1 | 3 | WHO, BP ≥ 140/90 | 12.2 | 120.5 (12.8) | 72.5 (12.4) | ||||
| Mijinyawa MS (42) | 2009 | Mercury | 1 | 3 | Average | Third Report | 4.5 | 111 (13.8) | 73 (9.5) | |||
| Odey F (43) | 2009 | Mercury | 1 | 3 | Average | Fourth Report | 7.5 | 6.7 | 114.6 (12.6) | 62.9 (8.7) | ||
| Ansa VO (44) | 2010 | Mercury | 1 | 2 | Average | Fourth Report | 1.8 | |||||
| Ejike C (45) | 2010 | Mercury | 1 | 2 | 2nd reading | BP > mean + 2SD | 23.4 | 10.1 | ||||
| Kruger HS (46) | 2010 | Mercury | 1 | 2 | Average | Fourth Report | 26.4 | |||||
| Chiolero A (47) | 2011 | Automated | 1 | 2 | Average | Fourth Report | 7.3 | 109.8 (11.4) | 66.8 (8.2) | |||
| Hawkesworth S (74) | 2011 | Automated | 1 | 3 | Third Report | 5.46 | 110.5 (9.0) | 64.7 (7.6) | ||||
| Meehan KA (50) | 2011 | Mercury | 1 | 1 | WHO-ISH 1999, JNC7 | 1.2 | 104.2 (12.3) | 64.3 (8.4) | ||||
| Bukabau JB (51) | 2012 | Aneroid | 1 | 3 | Average | WHO, BP ≥ 140/90 | 3.1 | 107.3 (11.3) | 70.9 (8.1) | |||
| Griffiths PL (52) | 2012 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 11.2 | 1.9 | 114.8 (10.4) | |||
| Okoh BA (53) | 2012 | Mercury | 1 | 3 | Average | Fourth Report | 3.9 | |||||
| Okpere AN (54) | 2012 | Mercury | 1 | 3 | Average | Second Report | 2.8 | |||||
| Oyewole OO (55) | 2012 | Aneroid | 1 | 1 | Fourth Report & JNC7 | 1.5 | 0.18 | 81.4 (14.3) | 48.5 (9.4) | |||
| Goon D (56) | 2013 | Automated | 1 | Fourth Report | 0.6 | 99.4 (9.4) | 54.8 (5.7) | |||||
| Okpere AN (58) | 2013 | Mercury | 1 | 3 | Average | Fourth Report | 3.2 | 114.2 (9.3) | 70.4 (7.8) | |||
| Ujunwa FA (59) | 2013 | Mercury | 3 | 3 | Average | Fourth Report | 13.5 | 12 | 108.31 (11.8) | 71.2 (7.9) | ||
| Mushengezi B (60) | 2014 | Automated | 1 | 2 | Average | Fourth Report & WHO | 4.0 | 120 (11) | 69 (8) | |||
| Okagua J (61) | 2014 | Mercury | 1 | 3 | Average | Fourth Report | 4.3 | 111.1 (14.7) | 66.3 (11.7) | |||
| Kagura J (14) | 2015 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 12.2 | 15.7 | 117.9 (11.0) | 71.0 (9.0) | ||
| Nkeh-Chungag B (62) | 2015 | Automated | 1 | 3 | Average | Fourth Report | 12.3 | 21.2 | 119.5 (1.5) | 72.0 (1) | ||
| Oyeyemi AY (64) | 2015 | Mercury | 1 | 3 | Average of 2nd & 3rd | WHO, BP ≥ 125/80 | 11.2 | 2.4 | ||||
| Ratovoson R (65) | 2015 | Automated | 1 | 2 | Average | JNC 7 | 7.4 | |||||
| Awotidebe A (66) | 2016 | Mercury | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 8.7 | 4.3 | 105.3 (11.4) | 68.6 (8.9) | ||
| Munthali RJ (67) | 2016 | Automated | 1 | 2 | Average | Fourth Report & JNC7 | 34.9 | |||||
| Uwaezuoke SN (69) | 2016 | Mercury | 1 | 3 | Average | Fourth Report & JNC7 | 10.7 | |||||
| Alicke M (70) | 2017 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 9 | 110 | 68 | |||
| Bedu-Addo G (6) | 2017 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 10 | 110 (11) | 68 (9) | |||
| Etyang AO (71) | 2017 | Automated | 1 | 3 | Average of 2nd & 3rd | AHA 2014 | 4.2 | 117 (12) | 64 (8) | |||
| Etyang AO (72) | 2018 | Automated + 24 h ABPM | 2 | 3 | Average of 2nd & 3rd | AHA 2014 | 3.9 | 117 (11) | 64 (7) | |||
| Ezeudu CE (73) | 2018 | Automated | 2 | 3 | Average of 2nd & 3rd | Fourth Report | 6.3 | 110.5 (10.2) | 71.5 (8.5) | |||
| Isezuo KO (75) | 2018 | Mercury | 3 | 3 | Third measure | Fourth Report | 7.5 | 3.1 | 111.7 (13.2) | 67.7 (9.6) | ||
| Leyvraz M (76) | 2018 | Automated | 1 | 2 | Average | Fourth Report | 8.5 | 111.6 (11.7) | 68.4 (8.5) | |||
| Masocha V (77) | 2018 | Automated | 1 | 2 | Average | IDF | 9.7 | 5% over 3 years | 103.2 (9.8) | 67.7 (8.0) | ||
| Nakiriba R (78) | 2018 | Automated | 1 | 3 | Average | 11.6 | ||||||
| Omisore AG (79) | 2018 | Mercury | 1 | 2 | Average | Fourth Report | 4.1 | 102.9 (13.5) | 66.0 (9.9) | |||
| Abu OO (80) (#201) | 2019 | 1 | Fourth Report | 8.8 | 6.9 | 104.8 (14.8) | 67.1 (10.5) | |||||
| Adeomi AA (81) | 2019 | Automated | 1 | 2 | Average | Fourth Report | 32.9 | |||||
| Amponsem-Boateng C (82) | 2019 | Automated & Mercury | 1 | Average | JNC 7 | 35 | 3.1 | |||||
| Chungag A (83) | 2019 | Automated | 1 | 3 | Average | AAP 2017 | 12.2 | 20.7 | 111.5 (0.65) | 71.9 (0.45) | ||
| Lule SA (#101) (85) | 2019 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 10.5 | 8.4 | 105.9 (8.3) | 65.2 (7.3) | ||
| Abiodun O (90) | 2019 | Mercury | 1 | 3 | Average of 2nd & 3rd | AAP 2017 | 25.3 | 25.1 | 118.6 (11.8) | 68.3 (8.8) | ||
| Nkwana MR (88) | 2019 | Automated | 1 | 2 | Average | 26.7 | 108.8 (13.5) | 71.7 (11.3) | ||||
| Nsanya MK (89) | 2019 | Automated | 1 | 3 | Average | Fourth Report | 22.0 | 15.0 | 36.8 | |||
| Frigati L (84) | 2019 | Automated | 1 | Fourth Report | 13.9 | 105.5 | 67.4 | |||||
| Azupogo F (91) | 2020 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report | 0.2 | 20.4 | 110.5 | 68.5 | ||
| Katamba G (92) | 2020 | Automated | 1 | 3 | Average of 2nd & 3rd | Fourth Report, ESH 2009, AHA 2017 | 7.1 | 3.1 | 113.3 (9) | 66.5 (8.1) | ||
| Masocha V (93) | 2020 | Automated | 1 | 2 | Average | IDF | 5 | 103.5 (10.5) | 66.7 (8.4) | |||
| Mokgwathi M (94) | 2020 | Automated | 2 | 2 | Average | Fourth Report, JNC8 | 15.5 | 13.1 | 118 (13.2) | 71.8 (9.5) | ||
| Raphadu TT (95) | 2020 | Automated | 1 | 2 | Average | Fourth Report | 27.1 | 8.7 | ||||
| Ugochukwu EF (97) | 2020 | Mercury | 1 | 3 | Average | Third Report | 11.0 | 8.5 | 110.6 (12.6) | 70.5 (8.5) | ||
| Ukoh U (98) | 2020 | Automated & Mercury | 1 | 3 | Average | Fourth Report & JNC 7 | 4.6 | 106.7 (11.4) | 63.6 (7.3) | |||
| Sungwa EE (96) | 2020 | Automated | 1 | 3 | Average | Fourth Report | 21.4 | 113.4 | 65.4 | |||
| Akinbodewa AA (99) | 2021 | Mercury | 1 | 1 | Fourth Report | 13.2 | 13.2 | 106.2 (15.5) | 63.7 (10.7) | |||
| Ayogu RN (100) | 2021 | Automated | 1 | 2 | Average | AAP 2017 | 10.7 | 19 | ||||
| Chungag A (101) | 2021 | Automated | 1 | 3 | Average | AAP 2017 | 5.1 | 32.8 | 111.8 | 72.1 | ||
| DuPlessis JP (102) | 2021 | Finapres | 1 | Continuous | Average | AAP 2017 | 19 | 34 | 117 (13.3) | 77 (9.2) | ||
| Letswalo BP (104) | 2021 | Automated | 1 | 3 | Average | Fourth Report | 15.5 | 23.3 | 115.9 (9.0) | 72.7 (6.3) | ||
| Lwabukuna WC (105) | 2021 | Mercury | 1 | 2 | Average | IDF | 2.3 | 111.5 (8.7) | 65.2 (5.9) | |||
| Meer R (106) | 2021 | Automated | 3 | Average | Fourth Report | 57 cases/1000 pyrs | ||||||
| Nganou-Gnindjio CN (107) | 2021 | Automated | 1 | 3 | Average | AAP 2017/JNC8 | 11.1 | |||||
| Nsanya MK0 (4) | 2021 | Automated + 24 h ABPM | 2 | 2 | Average | AAP 2017 | 10.2 | |||||
| Sekokotla AM (108) | 2021 | Automated | 1 | 3 | Average | 11.8 | 19.7 | 31.6 | 117 | 71 | ||
| Shokunbi OS (109) | 2021 | Mercury | 1 | 3 | Average | AAP 2017 | 10.5 | 19.3 | 110.6 (13.7) | 71.9 (11.0) |
Studies that reported prevalence and incidence of hypertension among adolescents aged 10–19 years in sub-Saharan Africa (N = 78).
Second Report: National Heart, Lung and Blood Institute (NHLBI) task force report on blood pressure control in children (1987), children and adolescents up to 18 years.
Third Report: National Heart, Lung and Blood Institute (NHLBI) update on the 1987 task force report on high blood pressure in children and adolescents up to 18 years.
Fourth Report: National Heart, Lung and Blood Institute (NHLBI) task force report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents up to 17 years.
ESH 2009: European Society of Hypertension (ESH) on management of high blood pressure in children and adolescents up to 17 years.
AHA 2014: American Heart Association (AHA) update, A scientific statement on ambulatory BP monitoring in children and adolescents up to 17 years.
AAP 2017: American Academy of Paediatric—clinical practice guideline for screening and management of high blood pressure in children and adolescents up to 12 years.
JNC7: The Joint National Committee on prevention, detection, evaluation and treatment of high blood pressure “he seventh report”—(adults ≥18years).
JNC8: The Joint National Committee on prevention, detection, evaluation and treatment of high blood pressure “he eighth report”—(adults ≥18years).
WHO/ISH 1999: The World Health Organization—International Society of Hypertension Guidelines for Management of hypertension—(adults ≥18years).
IDF: The International Diabetes Federation (IDF) consensus definition metabolic syndrome—(adults ≥18years).
Of 79 studies shown in Table 2, 39 studies (49.4%) used automated BP machines, 33 studies (41.7%) used manual (mercury or aneroid) BP machines, 2 studies (2.6%) used both automated and manual BP machines, and 4 studies (5.1%) used Finapres (Finger artery pressure)—an automated device which records BP measurements “continuously” from a finger artery. Only 3 studies used 24-h ambulatory BP monitoring for confirming HTN (4, 71, 72).
Of the 79 studies, 71 (89.8%) had their BP measurements obtained in one day. Of those 71 studies, the number of BP measurement obtained in one day ranged from one in 6 studies (8.5%), two in 19 studies (26.8%) and three in 37 studies (52.1%). In addition, 5 studies (7.0%) collected more than 3 BP measurements (“continuous”), and this information was not reported in 4 studies (5.6%).
Of the 68 studies with more than one BP measurements, most studies 51(75.0%) used an average of all BP measurements to obtain final BP measure. In addition, 14 studies (20.5%) used an average of second and third BP measurements, 1 study (1.5%) only used second measurement, 1 study (1.5%) only used third measurement and this information was not reported in another 1 study (1.5%).
Definitions of hypertension
Of the 77 studies with information on standard guidelines used for defining HTN (Table 2), 58 (75.3%) used one of the six American guidelines for the diagnosis and management of HTN in children and adolescents. The 2004 “Fourth Report” was the most commonly used version in 39 studies (50.6%) (110). In addition, the fixed BP cut-offs for defining HTN in adults were used in 10 studies (13.0%), often in older adolescents aged 18 years and above. Fewer studies used the z-distribution (n = 3) and the International Diabetes Federation definitions (n = 3).
Guidelines for defining HTN among adolescents have evolved over the years (Supplementary Appendix 3) provides a summary of the guidelines that were used for diagnosing HTN in the included studies. Overall, the aim for guideline revisions over the years was to account for age, height, and sex differences in BP thresholds for HTN. Also, in particular to American guidelines, the reference population size has increased from about 9,000 in 1977 to nearly 50,000 adolescents with normal BMI and percentile thresholds for HTN have decreased in the most recent guidelines (11).
Prevalence and incidence of HTN
Of the 92 studies, 78 studies (84.6%) reported estimates for prevalence of pre-hypertension and/or elevated BP and/or HTN with only 2 studies reporting incidence of HTN (Table 2). The incidence of HTN was estimated at 5% over 2 years (77) and 57 cases per 1,000 person-years (106).
The prevalence of pre-hypertension was reported in 27 studies and ranged from 1.5% to 35%, with median (IQR) = 11.8% (8.7%, 15.5%). The prevalence of HTN was reported in 65 studies and ranged from 0.18% to 34.0%, with median (IQR) = 5.5% (3.1%, 11.1%); while the prevalence of elevated BP (combined pre-hypertension and HTN) was reported in 18 studies and ranged from 0.6% to 36.8%, and median (IQR) = 20.8% (10.2%, 31.6%).
Prevalence of HTN across the four African regions in sub-Saharan Africa
Of all 65 studies with information on prevalence of HTN, studies from south African region (n = 13) had higher HTN prevalence, median (IQR) of 13.9% (5.1% to 21.2%), followed by west African region with a median (IQR) of 4.5% (2.6% to 9.5%), east African region with a median (IQR) of 4.2% (3.1% to 8.4%), and central African region with a median (IQR) of 3.1% (3.1% to 3.1%).
Factors associated with prevalence of HTN
Overall, studies in which BP measurements were obtained using automated BP devices (n = 31) had significantly higher mean HTN prevalence (11.1% (95% CI: 8.2%–14.1%)), than those using manual devices—Mercury or Aneroid BP machines (n = 31) (5.4% (95% CI, 3.6%–7.2%)) (p = 0.0006) (Figure 2).
Figure 2
Similarly, studies in which HTN was defined using cut offs based on a percentile BP distribution according to age, sex, and height (n = 43) had significantly higher mean HTN prevalence (8.7% (95% CI: 6.7%–10.7%)), than those using cut-offs based a fixed BP measure (n = 14), (3.8% (95% CI: 1.9%–5.6%)) (p = 0.004) (Figure 3).
Figure 3
There were statistically significant differences between mean HTN prevalence across the four sub Saharan Africa regions (F = 7.49, p = 0.0002). However, there was no significant difference in mean HTN prevalence across study settings (rural, rural/urban and urban) (Figures 4A,B).
Figure 4
There was a significant positive correlation between HTN prevalence, and year of study publication (n = 64, Pearson Correlation coefficient “r” = 0.4, p = 0.0009) (Figure 5).
Figure 5
In an adjusted meta regression analysis model (including year of publication, study settings, African region, type of BP measurement device used, and criteria for defining HTN), the year of publication (β = 0.14%, 95% CI: −0.003% to 0.28%, p = 0.05) and African regions (F = 3.95, p = 0.01) remained significantly associated with higher prevalence of hypertension. (Adjusted R2 = 0.38). Additionally, on average HTN prevalence estimates from Southern Africa were higher by 6.05%, 95% CI (1.97% to 10.13%, p = 0.005) than those from Western Africa.
Risk factors for hypertension
Table 3 summarizes common risk factors for HTN among adolescents in SSA. Of all 92 studies, 79 (85.9%) assessed at least one kind of traditional and/or non-traditional risk factor, and a majority (79.7%) were cross-sectional studies. Most of the reported risk factors were significantly associated with HTN although some of the associations were in an unexpected direction (82, 91, 100).
Table 3
| Traditional risk factors | ||
|---|---|---|
| Risk factor | With significant association | Without significant association |
| Body Mass Index (BMI) and other anthropometric measures {n = 60} | Overweight/Obesity {n = 23} (39, 45, 46, 52, 54, 57, 58, 71–73, 76, 77, 82, 85, 88, 90, 91, 100, 102, 104, 106, 108, 109) Higher BMI {n = 12} (38, 59, 62, 66, 69, 79, 81, 83, 89, 96, 97, 106) Waist circumference: Higher waist circumference {n = 6} (46, 66, 69, 83, 90, 104) Waist to height ratio: Higher waist to height ratio {n = 4} (66, 81, 90, 104) Waist to hip ratio: Higher waist to hip ratio {n = 1} (69) Neck circumference: increasing neck circumference {n = 1} (92) Thigh circumference: higher thigh circumference {n = 1} (104) Body fat percentage: higher body fat percentage {n = 2} (46, 66) | BMI {n = 9} (4, 32, 43, 63, 70, 92, 94, 98, 107) Stunted growth {n = 1} (37) |
| Sex {n = 53} | Female {n = 16} (39, 43, 58, 64, 70, 72, 73, 76, 83, 85, 91, 94, 97, 107, 109) Male {n = 9} (24, 71, 75, 89–92, 94, 95) | {n = 27} (24, 26, 30, 31, 36, 38, 41, 42, 45, 52–54, 60, 66, 75, 77, 79, 81, 88, 90, 95, 96, 99, 106, 108, 111) |
| Age {n = 45} | Older {n = 31} (31, 32, 37, 41, 43, 53, 63, 64, 70, 94, 98, 99, 107) Younger {n = 1} (100) | {n = 13} (24, 38, 45, 56, 66, 75, 81, 85, 90, 91, 95, 108) |
| Substance use/abuse {n = 8} | Cigarette smoking {n = 1} (94); Alcohol drinking {n = 4} (81, 92, 98, 100) | Cigarette smoking {n = 1} (59) Alcohol drinking {n = 1} (89) |
| Diet {n = 9} | Macronutrients: Deficiency in protein; Skipping meals daily (35, 100) Micronutrients: Deficiency in biotin B7, folic acid, pantothenic acid B5, zinc, vitamin & Energy; vitamin A, vitamin C, vitamin E, nicotinic acid, vitamin B12, biotin and phosphorus (35, 70) Sugar, salt and fat: Adding table salt; higher egg consumption; polyunsaturated fats; fried food; soft drinks; high sugar consumption > 10 teaspoons per day; eating fried/baked snacks (4, 96, 100, 109) Vegetables and fruits: low fruit and vegetable consumption; fibre (35, 89, 109) | |
| Physical inactivity {n = 7} | Physical inactivity {n = 4} (38, 77, 82, 97) Higher frequency of watching Television—protective among girls {n = 1} (91) | {n = 2} (4, 100) |
| Cardiometabolic risk factors {n = 7} | Cardiometabolic risks tend to cluster among females {n = 1} (70); lower eGFR {n = 1} (71); Higher pulse wave velocity {n = 2} (71, 104); Higher fasting glucose {n = 1} (104); high resting pulse >90bpm {n = 1} (4) | |
| Heredity {n = 8} | Genes—NOSIAP, MYRE, POC1B {n = 1} (74) Family history of hypertension {n = 5} (39, 54, 85, 92, 107) | Genes {n = 1} (86) Family history of hypertension {n = 1} (58) |
| Non-traditional risk factors | ||
| Socioeconomic factors (n = 21) | Higher SES: Higher parental education {n = 2} (30, 85); higher socioeconomic class {n = 3} (42, 75, 98); parent income {n = 1} (64); Attending private school {n = 2} (73, 96); living in urban area {n = 2} (45, 79); owning land—protective {n = 1}; (91) Lower SES: Living in rural area {n = 1} (96); Lower socioeconomic status {n = 4} (43, 52, 91, 97); Environmental factor: Winter weather {n = 1} (101) | Parental education {n = 1} (64); parent occupation {n = 1} (64); higher socioeconomic class {n = 1} (29); Attending private school {n = 1} (58); lower social class {n = 1} (58); indoor particulate matter pollution {n = 1} (101); Living in rural area {n = 1} (101) |
| Maternal and Natal factors {n = 15} | Maternal factors: Maternal exposure to MeHg {n = 1} (111); Maternal hypertension {n = 1} (111); Malaria in pregnancy {n = 1} (6); Younger maternal age {20—29years}—protective {n = 1} (52); higher maternal gestation BMI {n = 1} (85); Natal factors: Low birth weight {n = 1} (34); Weight gain in first year of life {n = 1} (47); Postnatal and childhood weight gain {n = 2} (41, 87); Born post-term {n = 1} (52); High BP at young ages {n = 2} (14, 76); | Birth weight {n = 1} (47); Maternal supplementation with calcium and protein from second trimester {n = 1} (48); stunted growth {n = 1} (37) |
| Blood disorders {n = 5} | Higher haemoglobin concentration {n = 1} (90); low serum magnesium and low-grade inflammation—high-sensitive C-reactive protein {n = 1} (108); low serum potassium {n = 1}; blood homocysteine {n = 1} (102) | Sickle cell {n = 1} (72); Alpha thalassemia {n = 1} (71) |
| Endemic infections {n = 3} | HIV infection—lower SBP {n = 1} (84); Childhood Malaria infection—lower SBP {n = 1} (47); Whipworm infestation—higher SBP {n = 1} (87) | |
Risk factors for hypertension among adolescents aged 10–19 years in sub-Saharan Africa.
Traditional risk factors
Sex (n = 53), age (n = 45), body mass index (n = 45), and other anthropometric measures (n = 15) were the most commonly studied traditional risk factors for HTN. Other important traditional risk factors include alcohol/tobacco use (n = 8), diet (n = 9), level of physical activity (n = 7), genetic (n = 7), and cardiometabolic risk factors (n = 7)—Table 3.
Sex: Of 53 studies, nearly half 26 (49%) showed a significant association with HTN. Of those 26 studies, a slight majority 16 (62%) showed that females had a higher risk of HTN than males, and obesity and an unhealthy diet were the major factors to account for the gender differences.
Age: Of 45 studies, a majority 32 (71%) showed a significant association with HTN, and 97% of these 32 studies showed that older age was associated with HTN.
Body Mass Index (BMI) and other anthropometric measures: Of 59 studies, a majority 49 (83%) had a significant association with HTN. However, 10 (17%) studies did not find a significant association with HTN. These 10 studies were all cross-sectional, school-based, and the majority were conducted in urban settings.
Diet: All 9 studies reporting on various dietary factors found significant associations with HTN. The studies assessed a deficiency of macronutrients, micronutrients, and minerals; excessive consumption of sugar, salt, and fats; and low consumption of vegetables and fruits.
Physical inactivity: Of 7 studies, 4 (71%) found a significant association between physical inactivity and HTN. However, 1 study (14%) surprisingly showed that physical inactivity was associated with normal BP.
Cardiometabolic risk factors: All 7 studies reporting various cardiometabolic risks found significant associations with HTN. The identified cardiometabolic risk factors were lower eGFR, higher pulse wave velocity, high fasting glucose, high resting pulse, and higher blood homocysteine, and these risk factors tended to cluster in females.
Heredity: Of 8 studies, 6 (75%) showed a significant association with HTN. However, the majority 5 (83%) relied on self-reported family history of HTN. Importantly, one study using human genome data from the Birth to Twenty cohort (South Africa) showed that the NOSIAP, MYRE, and POCIB genes were associated with HTN in South African adolescents.
Non-traditional risk factors
These are factors related to socio-economic environmental and endemic diseases which are related to HTN.
Socioeconomic status and environmental factors (n = 21) as well as maternal and natal factors (n = 15) were the most commonly reported non-traditional risk factors. Other important non-traditional risks were chronic inflammation, blood composition, diseases of the blood (n = 5), and endemic infections (n = 3) (Table 3).
Socioeconomic and environmental factors: Of 21 studies, 17 (81%) found a significant association between socioeconomic and environmental factors with HTN. Of the 17 studies, 12 (71%) found higher socioeconomic status and 5 (29%) found lower socioeconomic as a significant risk for HTN.
Maternal and natal factors: Of 15 studies, majority 12 (80%) found a significant association between maternal and natal factors with HTN. Of the 12 studies, 5 (42%) assessed maternal factors, including events before or during pregnancy, including maternal exposure to mercury, maternal HTN, and malaria in pregnancy. Seven studies (58%) assessed the association between events occurring during or after birth, including low birth weight, weight gain after birth and during early childhood, and higher BP at a young age.
Blood disorders: Of 5 studies, 3 (60%) found a significant association with HTN. Of the 3 studies, the reported risks were higher hemoglobin concentration (90), lower serum magnesium (108), lower serum potassium, and higher C-reactive protein levels (102, 109).
Endemic infections: Of the 3 studies on HIV, malaria and whipworm infestation, only whipworm infestation was significantly associated with HTN (85).
Complications of hypertension
Only 3 studies assessed potential complications of HTN among adolescents in SSA. Two studies found no significant association between HTN and cognitive decline (57), proteinurias, left ventricular hypertrophy, and retinopathy (39). One study found a significant association between HTN and microalbuminuria (54) (Table 4).
Table 4
| Author | Year | Sample size | Prevalence of complication (end organ damage) | Association with hypertension |
|---|---|---|---|---|
| Lyngdoh (57) | 2013 | 407 | Cognitive decline using multiple cognitive tests | Not significant |
| Mijinyawa (39) | 2008 | 968 | Proteinuria (30 mg/dl)—present in 15 (21.4%) out of 70 with high BP. | Not significant |
| Left ventricular hypertrophy—present in 0 (0%) out of 41 with high BP and available for ECG. | ||||
| Retinopathy—present in 0 (0%) out of 70 with high BP and available for fundoscopy. | ||||
| Okpere (54) | 2012 | 615 | Microalbuminuria—present in 12 (70.6%) out of 17 with hypertension | Significant (p = 0.001) |
Summary of studies on complication of hypertension among adolescents aged 10–19 years in sub-Saharan Africa.
Discussion
In this systematic review, we aimed to determine the burden, risk factors, and complications of HTN among adolescents in SSA.
The prevalence of HTN among adolescents in SSA was found to be high with varying estimates across studies. Overall, prevalence estimates from recently published studies were higher than those published in the past. This finding persisted even after taking into account of other possible reasons including differences in the devices used to measure BP and guidelines for defining HTN. In addition, studies with significantly higher prevalence estimates came from Southern Africa region, and overweight/obesity among girls was the major reported risk factor (62, 83, 88, 103, 104). Similar findings were observed in the previous systematic reviews (17, 18). The finding suggests that the burden of HTN is increasing and could explain the rising burden of CVD among young adults (17, 18, 112–114).
Nearly 90% of the included studies had their BP measurements obtained in one day contrary to the standard recommendations which require taking multiple BP measurements on at least 2 separate occasions (days) (11, 110). This raises concern about the accuracy of the HTN prevalence estimates and a potential possibility of an overestimated HTN burden (3). Adolescents are more likely than adults to experience white coat HTN (4, 115, 116). Moreover, majority of adolescents in SSA have never had their BP measured and are therefore more prone to the “white coat effect”, particularly when the BP measurements are taken on one occasion (4). This observation underscores the importance of following the standard BP measurement procedures particularly in this population to ensure accurate estimates.
Non-traditional risk factors related to poverty and tropical diseases may be important drivers of HTN among adolescents in SSA despite being under-reported and/or their role being less acknowledged (6, 15). We found relatively fewer studies that assessed/reported non-traditional risk factors, likely reflecting low awareness of their potential role on HTN in this population. In addition, the scarcity of long-term cohort studies among adolescents in SSA could partly explain this finding since most of the non-traditional risks involve tracking long term exposures occurring in one's lifetime (5, 15, 85, 87). However, it is worth noting that majority of studies assessing non-traditional risk factors often reported significant associations with HTN. This observation underscores the importance of the non-traditional risk factors although we cannot rule out publication bias.
The majority of the included studies were conducted in school settings among apparently healthy adolescents. This finding may reflect the willingness of schools and/or students to participate in research and health related interventions. It highlights the importance of schools as a potentially suitable platform for CVD prevention interventions targeting adolescents (117). Such interventions could be integrated into existing school health programs, and address multiple aspects of cardiovascular health including a healthy diet, physical exercise, and body weight control (117). BP measurement in schools might also help to raise awareness of the relevance of cardiovascular health for adolescents among teachers, parents, and entire communities. In addition, this finding points to the scarcity of hospital-based studies among adolescents in SSA where the epidemiology of HTN could be different.
Only three studies assessed/reported potential complications of HTN among adolescents in SSA (39, 54, 57), and mostly they found little or no complications. This may be due to using less-sensitive tools for the detection of HTN-related complications. For instance, in one of the three studies, left ventricular hypertrophy was assessed by electrocardiography which is known to have a sensitivity of less than 35% (39, 118). Tools with higher sensitivity and specificity to increase our awareness and knowledge of complications of HTN will help to determine the public health importance of HTN among adolescents in SSA.
Our study should be viewed in the context of the following strengths and limitations. A strength of our study is that we searched from multiple databases since their inception (from 1946). The wider review time frame allowed us to retrieve more studies published at widely ranging time points, hence giving a wider scope on how HTN estimates and their determinants have evolved over time. Our review focused on studies involving adolescents, and our findings provide useful information specific to this population. This is the first review to look at potential role of non-traditional risk factors and complications of hypertension among adolescents in SSA.
Our study has limitations. First, the retrieved studies came from only 14 (29%) of all 48 countries in SSA. The majority of studies came from Nigeria and South Africa, which are the two most advanced economies in SSA. Further, there were significant differences in prevalence estimates by African regions from which the studies came from. In this regard, our findings may not be generalizable to all countries in SSA. Secondly, a majority of studies were cross-sectional in design and therefore it is difficult to determine the temporal association with the reported risk factors. Thirdly, we cannot rule out publication bias, particularly for studies assessing/reporting non-traditional risk factors. And lastly, we did not conduct meta-analysis due to heterogeneity in study methods and settings/geography among the included studies.
Added value and implications of these findings
To our understanding, this is the second systematic review including adolescent only data in SSA region (16). Our review is updating the previous three reviews and has mapped available literature in SSA and highlighted existing knowledge/data gaps (16–18). These findings underscore the importance of accurate BP measurement and diagnosis of HTN in adolescents, both in research and clinical settings. Since HTN during adolescence is associated with the growing HTN/CVD epidemic among adults, broadly these findings will help us to design a CVD prevention intervention targeting adolescents in SSA, particularly in school settings.
Conclusion
HTN among adolescents in SSA is high, and non-traditional risk factors may be an important driver. Longitudinal observational and interventional studies are needed to clearly define the causes and complications of HTN in adolescents in SSA. In addition, governments and healthcare systems should provide the resources and accountability necessary for regular and proper BP measurements for adolescents in health facilities.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
All authors had access to data extraction sheets and data analysis outputs. All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1251817/full#supplementary-material
SUPPLEMENTARY DATA SHEET 1
Search methods for rates, risk factors, and consequences of high blood pressure among adolescents in sub-Saharan Africa.
SUPPLEMENTARY DATA SHEET 2
List of publications whose full texts were not immediately available for review.
SUPPLEMENTARY DATA SHEET 3
Summary table of hypertension guidelines used in published studies among adolescents aged 10–19 years in sub-Saharan Africa.
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Summary
Keywords
hypertension, blood pressure, risk factors, adolescents, sub-Saharan Africa, cardiovascular complications
Citation
Nsanya MK, Abramson R, Kisigo GA, Hickner A, Nyanza EC, Peck RN and Kapiga SH (2023) Hypertension among adolescents in sub-Saharan Africa: a systematic review. Front. Cardiovasc. Med. 10:1251817. doi: 10.3389/fcvm.2023.1251817
Received
02 July 2023
Accepted
18 October 2023
Published
07 December 2023
Volume
10 - 2023
Edited by
Okechukwu Ogah, University of Ibadan, Nigeria
Reviewed by
Bert-Jan Van Den Born, Amsterdam University Medical Center, Netherlands
Maria Grazia Modena, University of Modena and Reggio Emilia, Italy
Paolo Alberto Gasparini, University Hospital of Modena, Italy, in collaboration with reviewer [MGM]
Updates
Copyright
© 2023 Nsanya, Abramson, Kisigo, Hickner, Nyanza, Peck and Kapiga.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mussa K. Nsanya mussa.nsanya@mitu.or.tz
Disclaimer
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