The Prevalence Rates of Acute Rheumatic Fever in Africa: A Systematic Review and Meta-Analysis

Abstract

Background:

Acute rheumatic fever (ARF) is an autoimmune disease that affects children.

Objectives:

The meta-analysis aimed to establish pooled prevalence values and heterogeneity in the prevalence values.

Methods:

The study’s published articles were searched using PubMed, the Cochrane Database of Systematic Reviews, Google Scholar, and the Institute for Scientific Information (ISI) Web of Science.

Results:

The pooled total sample size was 386, 875 and the mean (SD) of the pooled sample size was 15 475 ± 24 765.988 at 95% intervals. The analysis revealed an estimated overall prevalence of acute rheumatic fever (ARF) in Africa of 1% under both the common and random effects models. The between-study variance (τ²) was 2.5043, and the I² statistics- representing the percentage of total variability attributable to heterogeneity was extremely high at 98.2%.

Conclusion:

There is a clear statistical difference in prevalence rates and a high degree of heterogeneity in individual studies.

Keywords

heterogeneity meta-analysis children ARF nations

Introduction

Acute rheumatic fever is a delayed autoimmune reaction and may occur in children who are genetically predisposed to Group Aβ- hemolytic Streptococcal pharyngitis.¹ Rheumatic heart disease, a sequel of ARF is a chronic disease of children with Sub-Saharan Africa being the epicenter with a prevalence of 5·7 per 1000 especially in children aged 5 to 14 years in the last decade.¹ Acute rheumatic fever is a major public health concern in developing countries and the prevalence rates/ vary considerably across the continent, countries and regions. Lahiri et al.² have noted the prevalence of acute rheumatic fever (ARF) as 8 to 51 per 100 000 cases.² The disease commonly affects children from the age of 5 to 15 years of age but reports have shown children with ARF who are 1 year of age.² The disease commonly affects children from the age of 5 to 15 years of age. Poor socioeconomic status and overcrowding have been noted as possible triggers.² There is a spurious rise in the global burden of illnesses caused by rheumatic fever (RF) and rheumatic heart disease (RHD) in Africa. For instance, ARF occurs in 0.4% to 3.0% of cases of group A β-hemolytic streptococcal throat infection in children as many as 39% of patients with ARF have been noted to develop severe cardiac disease.³ The rising trends of ARF were also reported by the World Health Organization (WHO) from 100 countries between 1970 and 2009 based on population-based screening, prospective disease surveillance, national health registries, surgical interventions, and autopsy findings.⁴

Over 10 years, the prevalence in Africa has experienced rising trends. For instance, in Mozambique,⁵ a South African country, prevalence values of 30 per 1000 were elicited among school children and rates of 15 per 1000 children were reported in Uganda.⁶ Besides, the prevalence of clinical sequel from ARF has also been documented.⁷ For example, in Soweto, the burden of heart failure from rheumatic fever was noted to be 30 per 100 000 per year in children aged 14 to 19 years. A study in Senegal, West Africa, showed a high fetal loss from pregnant mothers who had rheumatic heart disease.⁸ Reports on the prevalence of ARF in various African countries have been done but there is a need to synthesize pooled prevalence values and to document trends of this disease. It is also important to establish if there is any heterogeneity in the report of prevalence values. Besides, work done on a meta-analysis of ARF in children of African descent combined the prevalence rates of children and adults, some focused mostly on the Eastern and West African countries, with a paucity of studies on the burden in the Northern part of the continent. This may not give a definite prevalence rate in children. This study aimed to evaluate the actuarial prevalence rates in children with ARF in Africa. Furthermore, these reports will help in making policies, especially in the African continent as regards the control of the disease and reduction of the burden of the complications.

What is Already Known on This Topic

Acute rheumatic fever is an important public health issue. There is a gap in knowledge regarding the epidemiology of ARF and its attendant impact on children of African descent.

What This Study Adds

There is a rising trend in the prevalence of ARF in Africa.

Heterogeneity in the reported prevalence values in African children has been established.

How This Study Might Affect Research, Practice, or Policy

These findings provide reports on pooled prevalence rates of ARF in Africa which will help in making policies, especially in the African continent as regards the control of the disease and reduction of the burden of the disease in children.

Methods

Study Participants

The meta-analysis involves children who had ARF aged 1 year to 19 years.

Search Strategy

Methods

Search engines for the published articles included in the study were PubMed, the Cochrane Database of Systematic Reviews, Google Scholar, the Institute for Scientific Information (ISI) “Web of Science,” and Medline. Articles published between 1978 and 2025 were included in the study. The last search was done in April 2025. Search was restricted to children from 1 year to the age of 19 years. Besides, a manual search was done to screen articles that fulfilled the inclusion criteria. Selected studies for the meta-analysis were defined and delineated with the aid of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Heterogeneity was assessed using the I² statistics. Keywords such as Acute rheumatic fever, rheumatic heart disease; children; echocardiography, microbiological assay; and prevalence rates were used in the course of the study.

Study Selection

Children with ARF who fulfilled the inclusion criteria were selected in the study. This includes: (1) population: children aged 1 year to 19 years (2) Epidemiology: Documented prevalence of ARF (2) Reported outcome on gender bias, (3) Reported methodology used for the meta-analysis, (4) Studies that documented prevalence of ARF within the given age using echocardiography and microbiological assay. Work excluded in the meta-analysis included; children more than 19 years of age, review articles, case series, or case reports, a study involving both children and adults, duplicated studies and letters to the editor, studies from Zambia, Cameroon, Ivory Coast and Tunisia were excluded because the age limit used in the study was beyond 19 years of age. The literature search in the meta-analysis was reviewed by 2 researchers and any disagreement between the researchers shall be resolved by a third party. Any missing data was sorted out by the corresponding author.

Data Extraction

Socio-demographic and epidemiological data were extracted from the meta-analysis. Duplicated articles or articles with similar authors were not encountered in the meta-analysis. Studies including ARF associated with HIV were also excluded. The studies were conducted in the Western, Central, Eastern, Southern and Northern African regions. A significant number of the studies were in Ethiopia, East Africa, and a few from the Central African Republic.

Risk of Bias Assessment

This was used to reduce the risk of bias and errors in the selected studies. Errors from the prevalence (p) of ARF and the sample size (SS), the precision (C), or the margin of error for included studies were eliminated with the following formula:

S = z 2 \times p \times (1 - p) / d 2,

Z = the value fixed at 1.96 across studies (corresponding to 95% confidence interval). The desirable margin of error is less than and equal to 5% (0.05).

The research on children with ARF who fulfilled the inclusion criteria was screened for bias using the modified Newcastle-Ottawa scale for cohort studies.⁹ This scale constitutes the following 8 domains, namely: cohort size (more than 100 participants = 1 point, between 50 and 99 participants = 0.5 points, fewer than 50 subjects = 0 points); Number of children in the study population who had ARF (study conducted in a given population = 1 point; multi-center = 0.5 points; single center = 0 points); reported information on the epidemiology of ARF (well-defined information = 1 point, information fairly clear but still have some gray areas = 0.5 points, unclear = 0); reported information on clinical outcome and gender correlates of ARF (yes = 1 point, no = 0 points); scores of more than 6 in 0 to 10 scale are taken as high-quality, 3 to 4 as medium quality, and <3 as low-quality, respectively.

Statistical Analysis

A meta-analysis of the prevalence of ARF from the different studies was carried out using the meta package in R. The provided data represents a meta-analysis of 30 studies, with 253 016 observations and 574 events. The primary goal of this meta-analysis is to estimate the overall prevalence rates of ARF while accounting for heterogeneity between studies.

IBM SPSS version 20 software was used to analyze the pooled mean prevalence of ARF. The pooled mean sample size and standard deviation were evaluated using the student t-test. The analysis methods used in the meta-analysis include standard techniques for combining and evaluating study results while accounting for potential heterogeneity. The statistical soundness of the included analysis was guaranteed by the conclusions drawn from the meta-analysis. It ensures that Conclusions drawn from the meta-analysis are reflective of the variability present across the included studies. The P-value less than .05 indicates that the heterogeneity noted is statistically significant.

Ethical Approval and Informed Consent

Ethical approval was obtained from the Health Research Ethics Committee of the university of study for all studies on ARF with IRB number ESUTHP/C-MAC/RA/034/158. Since this work is a meta-analysis and systematic review, informed consent is not necessary as this study analyzes existing research data and does not involve new data collection from human subjects. In addition, all methods were performed by the relevant guidelines and regulations or declaration of Helsinki.

Results

The age of the children with ARF ranges from 1 year to 19 years. The number of males with ARF was 302 while that of females was 366 with a female-to-male ratio of 1:1.2. The sample size of the selected study ranges from 38 to 324 676. The pooled total sample size was 386, 875 and the mean (SD) of the pooled sample size was 15 475 ± 24,765.988 at 95% intervals. The mean pooled prevalence rate is 15.5604 ± 4.962 cases per 1000.

A PRISMA flow diagram according to the guidelines was used in the search strategy. About 8940 citations were identified in the search database and 2 articles were identified via registries. About 8790 duplicate records and studies on ARF among adults were removed. A total of 150 studies were included after screening for the prevalence of ARF in Africa. A total of 116 studies were further excluded from the 150 entries after further screening for incomplete data, review articles and other systematic reviews above the pediatric age range, incomplete data, and duplication of data. A total of 32 articles were retrieved after screening for analysis while 2 were not retrieved for further incomplete data. Two more reports were further excluded from the meta-analysis because they are case reports and case series.

A final total number of 30 studies that met our inclusion criteria were included as shown in the PRISMA flow diagram in Figure 1.

Figure 1.

PRISMA flow chart for included studies.

Heterogeneity Across Individual Study

The analysis revealed an estimated overall prevalence of acute rheumatic fever (ARF) in Africa of 1% under both the common and random effects models (Figure 2). However, while the common effect model produced a narrow 95% confidence interval [0.01, 0.01], the random effects model showed a wider interval [0.01, 0.02], reflecting variability between individual study estimates. Statistical measures confirmed considerable heterogeneity among the included studies. The between-study variance (τ²) was 2.5043, and the I² statistic—representing the percentage of total variability attributable to heterogeneity was extremely high at 98.2%. This suggests that nearly all variability in ARF prevalence across studies is due to real differences in study populations or methodologies rather than random error. Furthermore, Cochran’s Q test yielded a value of 1578.87 with a P-value <.0001, providing strong statistical evidence of heterogeneity.

Figure 2.

showed a sub-group analysis showing heterogeneity across each region in Africa.

Sub-Group Analysis of Each Region in Africa

To assess geographic variation in acute rheumatic fever (ARF) prevalence across the continent, subgroup analyses were conducted based on 5 African regions: Northern, Eastern, Western, Southern, and Central Africa (Figure 3). The estimated prevalence was highest in Northern and Southern Africa at 2%, although these estimates were accompanied by wide confidence intervals, suggesting considerable heterogeneity. Eastern and Western Africa both showed an estimated prevalence of 1%. However, Western Africa had a wider confidence interval compared to Eastern Africa, indicating more variation in study findings. For Central Africa, only one study was available, which limits the interpretability of the regional estimate. Within region, heterogeneity also varied. Northern and Western Africa exhibited greater between-study variance (τ²), implying inconsistent prevalence rates within those sub-regions. In contrast, Southern Africa showed relatively lower within-group heterogeneity, although still notable. Importantly, the statistical test for subgroup differences (Q = 3.23, P = .5197) revealed no significant difference in prevalence estimates across the 5 regions. This suggests that, despite apparent numerical variation, the prevalence of ARF does not significantly differ by African region based on currently available data.

Figure 3.

shows heterogeneity across each region in Africa.

Table 1 shows the countries of study, the age distribution, and the sample size of the included study. The majority of the studies were conducted in East Africa and North-Eastern Africa, with a paucity of studies from the central African continent. The minimum sample size was 38 and the maximum sample size was 324 676.

Table 1.

Socio-demographics of Included Studies.

Authors	Location of study	Age of subjects	Sample size
Ogunbi et al	Lagos	6-12 years	12 755
Kopecká et al	Libya	7-12	38
Anabwani et al	Morocco	11.1 ± 3.2 years	209
Tewodros et al	Ethiopia	4-12 years	816
Oli et al	Ethiopia	13.4 ± 3.5 years	9388
Longo-Mbenza et al	Congo	5-16 years	4848
Massoure et al	Djibouti	5.8 ± 4.9	156
Rossi et al	Eritrea	16.7 years	684
Engel et al	SA	9-15 years	2720
Rossi et al	Eritrea	16.7 years	684
Engel	Ethiopia	9-12 years	2000
Ngaïdé	Senegal	5-18 years	2019
Ploutz	Uganda	5-17 years	956
Gapu et al	Zimbabwe	1-12 years	2601
Mulatu et al	Ethiopia	8.86 ± 2.14 years.	1874
Sanyahumbi	Malawi	5-16 years	1450
Yadeta	Ethiopia	5-18 years	3238
Gemechu	Egypt	5-15 years	3062
Mucumbitsi et al	Rwanda	6-16 years	2501
Barakat et al	Nigeria	9.12 ± 2.75	324 676
Musuku	Zambia	10-24	2116
Sulafa et al	Sudan	3-18 years	818
Sadoh et al	Nigeria	10.9 ± 3.1 years	2742
Ekure	Nigeria	5-16 years	4107
Nkereuwem et al 2020	Nigeria	10-19 years	417

Table 2 shows the prevalence rates and the study area of the selected studies. The prevalence rates ranged from 0.6 to 48 cases per 10 000 and most of the studies were carried out in the primary and secondary schools. There is a slight female preponderance except in the study of Nkereuwem et al³⁴ where a male-to-female ratio of 3.5:1 was documented. The majority of children with ARF are from a low socio-economic class.

Table 2.

Prevalence Rates and Study Area of Selected Study.

Author	Prevalence rates/1000	Study area	Male:Female	Socio-economic class
Ogunbi et al	34 cases in 1000	School	1:1.6	Low
Kopecká et al	0.60 cases in 1000	School	NA	NA
Anabwani et al	25 cases in 1000	Comm. Survey	1:1	Low
Tewadros et al	48 cases in 1000	Hospital	1:4.1	NA
Oli et al	4.6 cases in 1000	School	NA	Low
Longo-Mbenza et al	14.03 cases in 1000	School	NA	Low
Massoure et al	12.8 cases in 1000	Hospital	1.1:1	Low
Rossi et al	40 cases in 1000	School	NA	Low
Engel et al	20.2 cases per 1000	School	NA	Low
Ngaïdé	4.96 cases per 1000	Schools	1.5:1	Low
Ploutz	12 cases in 1000	Schools	NA	Low
Gapu et al	11.9 cases in 1 000,	Hospital	1:1.1	Low
Mulatu et al	3.2 cases in 1000	Hospital	NA	Low
Sanyahumbi	7 cases in 1000	School	1:1	Low
Yadeta	19 cases per 1000	School	1:1.4	Low
Gemechu	19.6 cases per 1000	School	1:1	Low
Mucumbitsi et al	6.8 cases per 1000	School	1:1.1	Low
Barakat et al	1.1 cases per 10 000	School	1:1.1	Low
Musuku	11.8 cases per 1000	Hospital	1:1.6	Low
Sulafa et al	22 cases in 1000	School	NA	Low
Sadoh et al	15 cases in 1000	School	1:?	Low
Ekure	2.7 cases in 1000	School	1:1.2	Low
Nkereuwem et al	21.6 per 1000	School	3.5:1	Low

Discussion

The use of the common effect model to estimate the variation in the prevalence of ARF across each study showed that there may not be a significant variation in the prevalence. However, using the random effects model, a clear statistical difference in prevalence rates was noted in all studies. The variance of the random-effects model showed a substantial and very high degree of heterogeneity in individual studies.^10
-37 Further classification of the prevalence across subgroups such as Northern Africa, Eastern Africa, Western Africa, Southern Africa, and Central Africa revealed no significant subgroup differences in prevalence values; however, the heterogeneity within each group, particularly in Northern and Western Africa, suggests that prevalence rates may vary within these regions. Factors like study design, diagnostic criteria, and population differences noted in the meta-analysis could account for the heterogeneity in the Northern and Western African states.

This meta-analysis provides critical data on the pattern and trends of ARF in children of African descent. The distribution of the prevalence of ARF by individual studies showed heterogeneity, although the age range and sex distribution may remain the same. The findings of this systematic review provide information on the trends and patterns of ARF in developing countries. The articles included in the meta-analysis suggest a waxing and waning prevalence of ARF among school-age children in Africa. This may be due to harsh economic conditions, increasing poverty, and sociopolitical instability. This study showed that East Africa and Ethiopia in particular contributed to the highest number of studies on the prevalence of ARF.^{15,16,18,23,25} However, there are documented varying prevalence rates, even within the same country, as shown in studies in Ethiopia. Furthermore, Ethiopia, a country in Eastern Africa, had varying prevalence rates of 0.59 per 1000 and 4.6 in 1000 respectively in 1992, with an increased prevalence rate of 19 per 1000 reported in 2016.^13,14

The lowest prevalence in the meta-analysis was observed in Libya, where a prevalence rate of 0.6 per 1000 was obtained.¹² The fact that the study was also carried out in a section of the country, the children were not followed up when they had an antecedent history of sore throat, and the study being a cross-sectional study, coupled with the fact that the study was carried out within a short period, could explain this very low prevalence rate.¹² The highest prevalence rates documented in the meta-analysis were reported by Anabwani et al,¹² who reported prevalence rates as high as 48 per 1000 in Ethiopia. The long duration and the cohort nature of the study may explain the high prevalence rate.

The meta-analysis clearly showed that ARF occurred in the pediatric age group, with the lowest age of 3 years reported by Sulafa et al³⁰ in 2018 in Sudan. The early age was captured in Sudan because a highly sensitive method was used in the diagnosis of ARF. In addition, the best guidelines appraised and endorsed by the Federal Ministry of Health (FMOH) and the World Health Organization (WHO), as well as a simplified algorithm for microbiological diagnosis, were used.

The study by Ali et al³⁰ was corroborated by Rosenthal et al,³⁸ between 1939 and 1966, who identified 10 cases of ARF in children under the age of 3 years. This age group constituted approximately 0.5% of all cases of ARF among children. However, they noted that ARF in the first few years of life is rare but not uncommon. The youngest patient in their series was a 19-month-old child whose major symptoms were carditis and congestive heart failure with significant mitral regurgitation. The authors also documented a rising titer of antistreptolysin-O (ASO). However, the work of Rosenthal et al³⁸ was excluded from the meta-analysis because it was a case series.

The current study also showed a slight female predominance among the children with ARF. Female preponderance was also observed among children with valvular diseases in most studies. Female bias has also been reported in other survey studies and heart registries.^39
-43 Negi et al⁴⁵ showed that after 20 years of age, the prevalence of ARF increases by 200% in the female population. These findings are consistent with other registries.^42
-46 The female predominance after the age of 20 years could be due to increased diagnoses among females of reproductive age. Lawrence et al⁴⁶ in their report noted that although the prevalence of ARF was not significantly different between the sexes, however, the progression of the disease, especially with valvular involvement, was higher in females. The only study that showed a clear male preponderance was that of Nkereuwem et al.³⁴ In that particular study, more males were recruited in the study. Besides, the authors used a small sample size in their study.

The majority of children with ARF are from a low socio-economic class. It is important to note that ARF is often associated with poverty. Poverty creates the environment for ARF to flourish; this is worsened by the socio-political unrest, which creates a cascade that militates against the well-being of the children.⁴⁷ A study has documented that rising socio-economic status before the availability of penicillin was strongly linked with a continuous decline in mortality rates from ARF in developed countries.⁴⁸ It is crucial to note that despite a near eradication of ARF in high-resource countries, 40 million people still have the disease worldwide with 300 000 deaths every year.⁴⁹ Most of these high prevalence and mortality rates are reported in socio-economically disadvantaged, low-income and middle-income countries.⁵⁰ Currently, the WHO Global Rheumatic Heart Disease Resolution has prioritized eradication of the disease in areas of highest burden, with low socioeconomic status. Low SES has very far-reaching effects on children in sub-Saharan Africa.

Limitation

This study was limited by the heterogeneity in prevalence rates and sample size. Furthermore, some studies were excluded, especially those that combined pediatric and adult cases. One study from the People’s Republic of Congo was excluded because the authors used a sample frame of children with ARF and their parents. Furthermore, this meta-analysis did not include molecular genetics in children with ARF. This was due to the paucity of studies that met the inclusion criteria. In addition, we could not find studies from the African continent from 2024 to 2025 that fell in the inclusion criteria.

Conclusion

There was a clear statistical difference in the prevalence rates and a high degree of heterogeneity in individual studies. While the estimated prevalence varied slightly across regions, the differences observed were not significant. The heterogeneity within each region, particularly in Northern and Western Africa, suggests that the prevalence may still vary within these regions. These findings underscore the diverse epidemiological patterns of acute rheumatic fever across African regions and reinforce the necessity of a random effects model for appropriately summarizing prevalence estimates in this meta-analysis.

Supplemental Material

sj-docx-1-gph-10.1177_30502225251357145 – Supplemental material for The Prevalence Rates of Acute Rheumatic Fever in Africa: A Systematic Review and Meta-Analysis

Supplemental material, sj-docx-1-gph-10.1177_30502225251357145 for The Prevalence Rates of Acute Rheumatic Fever in Africa: A Systematic Review and Meta-Analysis by Josephat M. Chinawa, Awoere T. Chinawa, Francis N. Ogbuka, Philip C. Elobuike and Ogonna Nwankwo in Sage Open Pediatrics

Footnotes

Acknowledgements

We are grateful to the statistician who did the analysis.

ORCID iD

Josephat M. Chinawa

Ethical Considerations

Ethical approval was obtained from the Health Research Ethics Committee of the Enugu State University Teaching Hospital with IRB number ESUTHP/C-MAC/RA/034/158.

Consent to Participate

Since this work is a meta-analysis and systematic review, informed consent is not necessary since patients are not involved in the study. The study is based on collection and analysis of data derived from studies on ARF. In addition, all methods were performed by the relevant guidelines and regulations or Declaration of Helsinki.

Consent for Publication

Not applicable.

Author Contributions

Josephat M. Chinawa: Contributed to conception and design; Contributed to analysis; Drafted the manuscript; critically revised the manuscript; Gave final approval; Agrees to be accountable for all aspects of work ensuring integrity and accuracy. Awoere T. Chinawa: critically revised the manuscript; Gave final approval; Agrees to be accountable for all aspects of work ensuring integrity and accuracy. Francis N. Ogbuka: Contributed to conception and design; critically revised the manuscript; Gave final approval; Agrees to be accountable for all aspects of work ensuring integrity and accuracy. Philip C. Elobuike: critically revised the manuscript; Gave final approval; Agrees to be accountable for all aspects of work ensuring integrity and accuracy. Ogonna Nwankwo: Contributed to conception and design; critically revised the manuscript; Gave final approval; Agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data is provided within the manuscript or Supplemental Information files.

Clinical Trial Number

Not applicable.

Supplemental Material

Supplemental material for this article is available online.

References

Carapetis

Steer

Mulholland

Weber

The global burden of group A streptococcal diseases. Lancet Infect Dis. 2005;5(11):685-694. doi:10.1016/S1473-3099(05)70267-X

Lahiri

Sanyahumbi

Acute rheumatic fever. Pediatr Rev. 2021;42(5):221-232. doi:10.1542/pir.2019-0288

Okello

Beaton

Mondo

, et al. Rheumatic heart disease in Uganda: the association between MHC class II HLA DR alleles and disease: a case control study. BMC Cardiovasc Disord. 2014;14:28. doi:10.1186/1471-2261-14-28

Seckeler

Hoke

TR.

The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease. Clin Epidemiol. 2011;3:67-84. doi:10.2147/CLEP.S12977

Marijon

Celermajer

, et al. Prevalence of rheumatic heart disease detected by echocardiographic screening. N Engl J Med. 2007;357(5):470-476. doi:10.1056/NEJMoa065085

Beaton

Okello

Lwabi

Mondo

McCarter

Sable

Echocardiography screening for rheumatic heart disease in Ugandan schoolchildren. Circulation. 2012;125(25):3127-3132. doi:10.1161/CIR-CULATIONAHA.112.092312

Okello

Kakande

Sebatta

, et al. Socioeconomic and environmental risk factors among rheumatic heart disease patients in Uganda. PLoS One. 2012;7(8):e43917. doi:10.1371/journal.pone.0043917

Diao

Kane

Ndiaye

, et al. Pregnancy in women with heart disease in sub-Saharan Africa. Arch Cardiovasc Dis. 2011;104(6-7):370-374. doi:10.1016/j.acvd.2011.04.001

Gierisch

Beadles

Shapiro

, et al. Health Disparities in Quality Indicators of Healthcare Among Adults with Mental Illness [Internet]. Washington (DC): Department of Veterans Affairs (US); 2014.

10.

Ogunbi

Fadahunsi

Ahmed

, et al. An epidemiological study of rheumatic fever and rheumatic heart disease in Lagos. J Epidemiol Community Health. 1978;32:68-71. doi:10.1136/jech.32.1.68

11.

Kopecká

Kopecký

Rheumatic fever in a developing country in North Africa. Cesk Pediatr. 1989;44(9):533-535.

12.

Anabwani

Amoa

Muita

AK.

Epidemiology of rheumatic heart disease among primary school children in western Kenya. Int J Cardiol. 1989;23(2):249-252. doi:10.1016/0167-5273(89)90254-4

13.

Tewodros

Muhe

Daniel

Schalén

Kronvall

A one-year study of streptococcal infections and their complications among Ethiopian children. Epidemiol Infect. 1992;109(2):211-225. doi:10.1017/s0950268800050172

14.

Oli

Tekle-Haimanot

Forsgren

Ekstedt

Rheumatic heart disease prevalence among schoolchildren of an Ethiopian rural town. Cardiology. 1992;80(2):152-155. doi:10.1159/000174993

15.

Longo-Mbenza

Bayekula

Ngiyulu

, et al. Survey of rheumatic heart disease in school children of Kinshasa town. Int J Cardiol. 1998;63(3):287-294. doi:10.1016/s0167-5273(97)00311-2

16.

Massoure

Roche

Lamblin

Dehan

Kaiser

Fourcade

Cardiovascular disease in children in Djibouti: a single-centre study. Pan Afr Med J. 2013;14(14):141. doi:10.11604/pamj.2013.14.141.2016

17.

Rossi

Felici

Banteyrga

Subclinical rheumatic heart disease in an Eritrean high-school population, detected by echocardiography. J Heart Valve Dis. 2014;23(2):235-239.

18.

Engel

Haileamlak

Zühlke

, et al. Prevalence of rheumatic heart disease in 4720 asymptomatic scholars from South Africa and Ethiopia. Heart. 2015;101(17):1389-1394. doi:10.1136/heartjnl-2015-307444

19.

Beaton

Aliku

, et al. The utility of handheld echocardiography for early rheumatic heart disease diagnosis: a field study. Eur Heart J - Cardiovasc Imaging. 2015;16(5):475-482. doi:10.1093/ehjci/jeu296

20.

Ngaïdé

Mbaye

Kane

, et al. Prevalence of rheumatic heart disease in Senegalese school children: a clinical and echocardiographic screening. Heart Asia. 2015;7(2):40-45. doi:10.1136/heartasia-2015-010664

21.

Ploutz

Scheel

, et al. Handheld echocardiographic screening for rheumatic heart disease by non-experts. Heart. 2016;102(1):35-39. doi:10.1136/heartjnl-2015-308236

22.

Gapu

Bwakura-Dangarembizi

Kandawasvika

, et al. Rheumatic fever and rheumatic heart disease among children presenting to two referral hospitals in Harare, Zimbabwe. S Afr Med J. 2015;105(5):384-388. doi:10.7196/samj.7898

23.

Mulatu

Woldemichael

Aberra

Prevalence of rheumatic heart disease among primary school students in Mid-Eastern Ethiopia. J Biol Syst. 2015;05:149. doi:10.4172/2329-6577.1000149

24.

Sanyahumbi

Sable

Beaton

, et al. School and community screening shows Malawi, Africa, to have a high prevalence of latent rheumatic heart disease. Congenit Heart Dis. 2016;11(6):615-621. doi:10.1111/chd.12353

25.

Yadeta

Hailu

Haileamlak

, et al. Prevalence of rheumatic heart disease among school children in Ethiopia: a multisite echocardiography-based screening. Int J Cardiol. 2016;221:260-263. doi:10.1016/j.ijcard.2016.06.232

26.

Gemechu

Parry

EHO

Yacoub

Phillips

DIW

Kotit

Community-based prevalence of rheumatic heart disease in rural Ethiopia: five-year follow-up. PLoS Negl Trop Dis. 2021;15(10):e0009830. doi:10.1371/journal.pntd.0009830

27.

Mucumbitsi

Bulwer

Mutesa

, et al. Prevalence of rheumatic valvular heart disease in Rwandan school children: echocardiographic evaluation using the World Heart Federation criteria. Cardiovasc J Afr. 2017;28:285-292. doi:10.5830/CVJA-2017-007

28.

Animasahun

Madise Wobo

Itiola

Adekunle

Kusimo

Thomas

FB.

The burden of rheumatic heart disease among children in Lagos: how are we fairing?

Pan Afr Med J. 2018;29:150. doi:10.11604/pamj.2018.29.150.12603

29.

Musuku

Engel

Musonda

, et al. Prevalence of rheumatic heart disease in Zambian school children. BMC Cardiovasc Disord. 2018;18:135. doi:10.1186/s12872-018-0871-8

30.

Ali

Karadawi

Elhassan

, et al. Patterns, outcomes and trends in hospital visits of un-operated and operated children with rheumatic heart disease in Sudan. Cardiovasc Diagn Ther. 2019;9(2):165-172.

31.

Sadoh

Omuemu

Israel-aina

YT.

Prevalence of rheumatic heart disease among primary school pupils in mid-western Nigeria. East Afr Med J. 2013;90:28-32.

32.

Ekure

Amadi

Sokunbi

, et al. Echocardiographic screening of 4107 Nigerian school children for rheumatic heart disease. Trop Med Int Health. 2019;24(6):757-765. doi:10.1111/tmi.13235

33.

Sara

Bouchra

Angéla

Samira

Nehemie

Samir

Acute rheumatic fever in children: experience at the hospital Hassan II of Fez, Morocco. Clin Epidemiol Glob Health. 2020;8:1062-1066.

34.

Nkereuwem

Ige

Yilgwan

Jobe

Erhart

Bode-Thomas

Prevalence of rheumatic heart disease in north-central Nigeria: a school-based cross-sectional pilot study. Trop Med Int Health. 2020;25:1408-1415. doi:10.1111/tmi.13477

35.

Okello

Ndagire

Muhamed

, et al. Incidence of acute rheumatic fever in northern and western Uganda: a prospective, population-based study. Lancet Glob Health. 2021;9:e1423-e1430. doi:10.1016/S2214-109X(21)00288-6

36.

Kazahura

Mushi

Pallangyo

, et al. Prevalence and risk factors for subclinical rheumatic heart disease among primary school children in dar es Salaam, Tanzania: a community based cross-sectional study. BMC Cardiovasc Disord. 2021;21(1):610. doi:10.1186/s12872-021-02377-9

37.

Amade

Lichucha

Ossman

, et al. Leveraging School Health Programs in Africa: integrated screening for rheumatic heart disease and dental caries. Ann Global Health. 2023;89(1):81. doi:10.5334/aogh.4239

38.

Rosenthal

Czoniczer

Massell

BF.

Rheumatic fever under 3 years of age. A report of 10 cases. Pediatrics. 1968;41(3):612-619.

39.

Condon

Ralph

, et al. Long-term outcomes from acute rheumatic fever and rheumatic heart disease: a data-linkage and survival analysis approach. Circulation. 2016;134(3):222-232.

40.

Berry

JN.

Prevalence survey for chronic rheumatic heart disease and rheumatic fever in northern India. Br Heart J. 1972;34(2):143-149. doi:10.1136/hrt.34.2.143

41.

Talwar

Gupta

Predictors of mortality in chronic rheumatic heart disease. Indian J Med Res. 2016;144(3):311-313. doi:10.4103/0971-5916.198672

42.

Paar

Berrios

Rose

, et al. Prevalence of rheumatic heart disease in children and young adults in Nicaragua. Am J Cardiol. 2010;105(12):1809-1814. doi:10.1016/j.amjcard.2010.01.364

43.

Reeves

Kado

Brook

High prevalence of rheumatic heart disease in Fiji detected by echocardiography screening. J Paediatr Child Health. 2011;47(7):473-478. doi:10.1111/j.1440-1754.2010.01997.x

44.

Zühlke

Engel

Karthikeyan

, et al. Characteristics, complications, and gaps in evidence-based interventions in rheumatic heart disease: the Global Rheumatic Heart Disease Registry (the REMEDY study). Eur Heart J. 2015;36(18):1115-222a. doi:10.1093/eurheartj/ehu449

45.

Negi

Kandoria

Asotra

, et al. Gender differences in the epidemiology of rheumatic fever/rheumatic heart disease (RF/RHD) patient population of hill state of northern India; 9 years prospective hospital based, HP-RHD registry. Indian Heart J. 2020;72(6):552-556. doi:10.1016/j.ihj.2020.09.011

46.

Lawrence

Carapetis

Griffiths

Acute rheumatic fever and rheumatic heart disease: incidence and progression in the Northern Territory of Australia, 1997 to 2010. Circulation. 2013;128(5):492-501. doi:10.1161/CIRCULATIONAHA.113.001477

47.

Houweling

Karim-Kos

Kulik

, et al. Socioeconomic inequalities in neglected tropical diseases: a systematic review. PLoS Negl Trop Dis. 2016;10(5):e0004546.

48.

Nascimento

Beaton

AZ.

Rheumatic heart disease and socioeconomic development. Lancet Glob Health. 2019;7(10):e1297-e1299. doi:10.1016/S2214-109X(19)30369-9

49.

Watkins

Johnson

Colquhoun

, et al. Global, regional, and national burden of rheumatic heart disease, 1990-2015. N Engl J Med. 2017;377(8):713-722. doi:10.1056/NEJMoa1603693

50.

Ordunez

Martinez

Soliz

Giraldo

Mujica

Nordet

Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990-2017: a population-based study. Lancet Glob Health. 2019;7(10):e1388-e1397. doi:10.1016/S2214-109X(19)30360-2

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