https://orcid.org/0009-0000-9719-3934
Figures
Abstract
Introduction
Sickle Cell Anemia (SCA) is a significant genetic disorder in Africa; however, comprehensive data on its prevalence and geographic distribution remain limited. We aimed to estimate the pooled prevalence of SCA (HbSS) in African populations and examine regional, demographic, and temporal variations from 1994–2024.
Methods
We systematically searched PubMed, Scopus, Google Scholar, and BASE databases for studies reporting SCA prevalence in African populations. Screening and quality assessments were performed using JBI tools. A random-effects meta-analysis with logit transformation was performed, with subgroup analyses by region, age, sex, and study design. Meta-regression explored heterogeneity sources, including geographic region, age category, diagnostic method, study design, and publication year.
Results
From 115 studies with 1,203,839 participants and 17,458 confirmed HbSS cases, the pooled prevalence was 1.43% (95% CI: 1.08%–1.88%), with substantial heterogeneity (I2 = 99.1%) and a prediction interval of 0.21%–8.91%. Central Africa showed the highest prevalence (1.99%), and Southern Africa showed the lowest (0.59%). Children exhibited a higher prevalence (1.65%) than adults (0.45%), while sex differences were non-significant (males 2.71%, females 1.74%; p = 0.694). The prevalence has remained stable over three decades despite a six-fold increase in research output, although wide prediction intervals indicated substantial between-study variability. Electrophoretic techniques predominated (86.4% of cases). Diagnostic method (χ² = 16.73, p = 0.033) and age category (χ² = 33.66, p < 0.0001) significantly moderated the prevalence. The multivariable meta-regression was marginally significant (χ² = 29.01, p = 0.066), but substantial residual heterogeneity persisted (I2 = 98.6%). Leave-one-out sensitivity analysis showed that no single study significantly impacted the pooled estimates.
Conclusion
SCA represents a substantial and geographically variable public health challenge across Africa. These findings highlight the need for region-specific interventions, expanded newborn screening programs, improved diagnostic accessibility with quality assurance for point-of-care technologies, and continued surveillance to address geographic gaps.
Citation: Jonani B, Kasule EC, Herman BR, Arturo JF, Mugambwa MC, Stephen S, et al. (2025) Burden of sickle cell anemia in Africa: A systematic review and meta-analysis. PLoS One 20(11): e0337090. https://doi.org/10.1371/journal.pone.0337090
Editor: Yun Li, University of North Carolina at Chapel Hill, UNITED STATES OF AMERICA
Received: August 8, 2025; Accepted: November 4, 2025; Published: November 25, 2025
Copyright: © 2025 Jonani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data generated or analysed during this study are included in this published article and its linked supporting information files.
Funding: Sebbi Hospital did not provide specific funding for this work. However, Sebbi Hospital provided support in the form of salaries for authors BJ, ECK, BRH, JFA, MCM, SS, and JBM, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘Author Contributions’ section.
Competing interests: Authors BJ, ECK, BRH, JFA, MCM, SS, and JBM are employed by Sebbi Hospital, a commercial company. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors have declared that no other competing interests exist.
Abbreviations: SCA, sickle cell anemia; SCD, sickle cell disease; HBSS, hemoglobin SS (homozygous sickle cell genotype); HbS, hemoglobin S; HbF, fetal hemoglobin; β⁰, beta-zero thalassemia (absence of beta-globin production); β⁺, beta-plus thalassemia (reduced beta-globin production); HPLC, high-performance liquid chromatography; PCR, polymerase chain reaction; POCT, point-of-care testing
Introduction
Sickle cell anemia (SCA) is an autosomal recessive genetic disorder characterized by abnormal hemoglobin S in red blood cells, causing them to sickle when deoxygenated [1–3]. Severe forms include hemoglobin SS (sickle-cell anemia) from homozygous HbS inheritance and HbS/βo thalassemia from co-inheritance of HbS with a βo thalassemia mutation, where individuals produce no normal beta-globin [1,4,5]. Other variations involve co-inheritance of HbS with different β-globin gene mutations, such as hemoglobin C, hemoglobin D-Los Angeles/Punjab, or β+ thalassemia, which results in reduced beta-globin production [4,5].
Between 2000 and 2021, worldwide SCA diagnoses increased by 382 per 100,000 live births, rising from 453,000–515,000 [6]. The genotype distribution was as follows: 76.5% SS and Sβ° births, 19.6% SC births, and 3.9% Sβ + . Globally, SCA mortality has increased, with sub-Saharan Africa experiencing the highest rates [7]. In 2021, 29,400 individuals died from SCA in this region, a 30.1% increase from 2000, representing 2.2% of child mortality and the 11th leading cause of all-cause mortality [5,6].
Over the last three decades, research on SCA in Africa has advanced significantly. Newborn screening programs have expanded, enabling early diagnosis and interventions [8,9]. Point-of-care test kits have enhanced early detection [10–12], and treatments such as hydroxyurea have improved the quality of life of patients with SCA [13,14]. Genetic research has revealed the critical role of fetal hemoglobin (HbF) in reducing disease severity [15–17]. Public health initiatives have recognized SCA as a major concern leading to the establishement of research and healthcare networks to improve patient outcomes.
Despite these advancements, challenges remain. Many African countries have not consistently implemented neonatal screening or preventive measures for sickle cell anemia (SCA), which hinders early diagnosis and timely intervention. This is due to a lack of reliable data on the prevalence of SCA and the distribution of hemoglobin types across regions, making it difficult to accurately estimate the disease burden in the country. Child mortality rates in Africa remain among the highest globally, with approximately 50–90% of children dying before the age of five [18,19]. This is partly due to inadequate healthcare infrastructure, delayed diagnoses, cultural stigma, and limited awareness of SCA.
Although the burden of SCA in Africa is widely acknowledged [6,20], previous epidemiological estimates are fragmented or limited to specific regions, populations, and age groups [4,18]. Several country-level studies and regional summaries exist [9,21,22], but no recent systematic review has comprehensively synthesized prevalence data across all African regions, accounting for diagnostic methods, demographic subgroups and temporal trends. This lack of robust aggregated data impedes the development of evidence-based screening programs, resource allocation, and policy formulation. In light of the increased diagnostic capacity and research output in Africa since the 1990s, an updated systematic review and meta-analysis was needed to consolidate the burden of SCA on a continental scale and identify persistent gaps in surveillance and care delivery.
This review aimed to estimate the pooled prevalence of genetically confirmed sickle cell anemia (HbSS) in the African population between 1994 and 2024. We further sought to assess how prevalence varies by region, age group, sex, diagnostic method, and study design, and to evaluate temporal trends and methodological heterogeneity among studies.
Materials and methods
Study design
This quantitative systematic review was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (S1 Table). The protocol for this meta-analysis was registered with the Open Science Framework (OSF) on January 8, 2025 (https://doi.org/10.17605/OSF.IO/M8JXV) and published in PLOS ONE [23]. No amendments were made to the protocol after its registration.
Eligibility criteria
We included observational studies (cross-sectional and cohort) that reported the prevalence of sickle cell anemia (SCA) in African populations, specifically defined by the presence of the homozygous hemoglobin SS (HbSS) genotype. Studies were eligible if they were published between January 1, 1994, and December 31, 2024. Only studies using validated diagnostic methods, including hemoglobin electrophoresis, high-performance liquid chromatography (HPLC), isoelectric focusing, solubility tests, point-of-care tests (e.g., HemotypeSC™ or SickleScan), or DNA-based methods, were considered. We excluded case reports, reviews, editorials, conference abstracts lacking primary data, and interventional studies, such as randomized controlled trials, that did not report SCA prevalence. Studies that lacked confirmed diagnostic criteria or reported only sickle cell traits (HbAS) without distinction from SCA were also excluded. For subgroup analyses, studies were grouped by geographic region (North, West, Central, East, and Southern Africa), age category (newborns, children, adults, mixed), sex, study design, and diagnostic method.
Information sources
We searched four major databases: PubMed, Scopus, Google Scholar, and BASE (Bielefeld Academic Search Engine). We also manually screened the reference lists of all the included articles for additional studies. Grey literature (e.g., theses) was considered when full texts were accessible. No language restrictions were imposed. The last database search was conducted on January 15, 2025. These databases were selected for their broad and complementary coverage of biomedical and global academic literature, particularly in public health, hematology, and African studies fields.
Search strategy
A comprehensive search strategy was developed using Medical Subject Headings (MeSH) and free-text terms. Boolean operators (AND, OR) were used to combine terms related to sickle cell anemia, prevalence, and African countries. No date or language filters were applied to the search. The full search string used in PubMed was: “Sickle Cell Disease”[All Fields] OR “Sickle Cell Anaemia”[All Fields] OR “Sickle Cell”[All Fields] AND (“prevalence”[All Fields] OR “epidemiology”[All Fields] OR “burden”[All Fields]) AND (“Africa”[All Fields] OR “Uganda”[All Fields] OR “Nigeria”[All Fields] OR “Kenya”[All Fields] OR “Ghana”[All Fields] OR “South Africa”[All Fields] OR “Tanzania”[All Fields] OR “Ethiopia”[All Fields] OR “Democratic Republic of Congo”[All Fields] OR “Cameroon”[All Fields] OR “Ivory Coast”[All Fields] OR “Senegal”[All Fields] OR “Sudan”[All Fields] OR “Zambia”[All Fields] OR “Zimbabwe”[All Fields] OR “Malawi”[All Fields] OR “Mozambique”[All Fields] OR “Burkina Faso”[All Fields] OR “Mali”[All Fields] OR “Sierra Leone”[All Fields] OR “Benin”[All Fields] OR “Botswana”[All Fields] OR “Burundi”[All Fields] OR “Cape Verde”[All Fields] OR “Central African Republic”[All Fields] OR “Chad”[All Fields] OR “Comoros”[All Fields] OR “Djibouti”[All Fields] OR “Equatorial Guinea”[All Fields] OR “Eritrea”[All Fields] OR “Eswatini”[All Fields] OR “Gabon”[All Fields] OR “Gambia”[All Fields] OR “Guinea”[All Fields] OR “Guinea-Bissau”[All Fields] OR “Lesotho”[All Fields] OR “Liberia”[All Fields] OR “Madagascar”[All Fields] OR “Mauritania”[All Fields] OR “Mauritius”[All Fields] OR “Namibia”[All Fields] OR “Niger”[All Fields] OR “Rwanda”[All Fields] OR “Sao Tome and Principe”[All Fields] OR “Seychelles”[All Fields] OR “Somalia”[All Fields] OR “South Sudan”[All Fields] OR “Togo”[All Fields] OR “Western Sahara”[All Fields] OR “Algeria”[All Fields] OR “Angola”[All Fields] OR “Egypt”[All Fields] OR “Libya”[All Fields] OR “Morocco”[All Fields] OR “Tunisia”[All Fields]).
The search strategies for Scopus, Google Scholar, and BASE followed the same logic and used equivalent terms and Boolean combinations (S2 Table).
Selection process
Three reviewers (BJ, ECK, and HRB) independently screened all titles and abstracts for eligibility based on the inclusion and exclusion criteria. The full texts of potentially relevant studies were retrieved and assessed independently. Discrepancies were resolved through discussion or, if necessary, arbitration by a fourth reviewer (JBM and SS). Screening was performed manually, without the use of automation tools.
Data collection process
Data were extracted independently by two reviewers (JB and ECK) using a standardized extraction form that was piloted before use. The collected data included the author, publication year, country, study design, population characteristics (sample size and age category), diagnostic method, and number of confirmed HbSS cases. The study authors were not contacted for missing or unclear data due to feasibility constraints. No automation tools were employed.
Data items
Outcomes.
The primary outcome was the prevalence of sickle cell anemia (HbSS), defined as the proportion of individuals with confirmed HbSS in the study population. Where multiple measures were reported (e.g., by sex and age), all relevant results were extracted. If subgroup data were reported without an overall prevalence, we calculated an aggregate if the raw data were available.
Other variables.
Other extracted variables included the study setting (hospital vs. community-based), diagnostic method, age category (newborn, child, adult, mixed), region (North, East, West, Central, and Southern Africa), year of data collection, and study design. When classification was ambiguous (e.g., age categories spanning child and adult), categorization was based on the median age or dominant age group.
Risk of bias assessment
The quality of each included study was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Prevalence Studies. This tool evaluated nine domains related to methodological rigor and potential sources of bias. These included the appropriateness of the sample frame, suitability of the sampling method, adequacy of the sample size, clarity in describing the study population and setting, and coverage of a representative portion of the sample in the data analysis. The tool also assessed the use of valid and reliable diagnostic methods, consistency in measuring the condition across all participants, appropriate statistical analyses, and adequate management of response rates if they were low. Additionally, we explicitly evaluated whether the methods used to ascertain outcomes, specifically the diagnostic approaches for confirming SCA, were described clearly and consistently applied across participants. To assess potential reporting bias, we examined whether the studies presented multiple measurements or analyses of the same outcome, as this could indicate selective reporting of only favorable results. A study was considered to have a low risk of bias if it met at least eight of the nine JBI criteria and demonstrated valid diagnostic methods, consistent outcome measurements, and proper handling of response rates. A moderate-to-low risk of bias was assigned to studies that met at least six criteria and fulfilled key methodological expectations. Studies that failed to meet at least six criteria or had major flaws in outcome ascertainment, measurement consistency, or response rate handling were considered to have a high risk of bias. Only studies with a low or moderate risk of bias were included in the final meta-analysis to ensure the validity and reliability of findings.
Effect measures
The effect measure used was the pooled prevalence proportion, with corresponding 95% confidence intervals (CIs). Raw prevalence data were transformed using logit transformation to stabilize the variance before the meta-analysis. For studies reporting zero events (0% prevalence), we applied a continuity correction of 0.5 to both the numerator and denominator to enable logit transformation.
Synthesis methods
Study grouping.
The studies were grouped by age category, sex, region, diagnostic method, and study design to allow for subgroup and sensitivity analyses. The age categories included newborns (typically 0–28 days), children (generally encompassing individuals from 1 month to 17 years), and adults (≥ 18 years). When age distributions spanned more than one category or were reported as broad population groups, classification was based on the predominant age range or the median age of participants.
Data preparation.
All prevalence data were standardized to proportions. Subgroup data were aggregated where necessary. Geographic regions were standardized to the WHO African regions (North, West, Central, East, or Southern Africa). For the temporal analysis, a yearly dataset was generated by identifying all the unique years represented in the data. For each year, pooled prevalence estimates and corresponding confidence intervals were calculated. For studies reporting data collection across multiple years, we assigned a midpoint year. For retrospective studies spanning extended periods, we used the final year of the data collection. For prospective cohorts, we used the enrollment year. This approach balanced temporal precision with practical constraints, as most studies did not report exact monthly data collection periods. The results were merged with a complete set of years covering the study period to ensure continuity in the timeline, even when no data were available for specific years. Diagnostic methods for confirming Sickle Cell Anemia were grouped into five broader methodological categories to enhance consistency across studies: Rapid Sickling Tests (point-of-care or biochemical tests such as solubility or sickling assays), Chromatographic Methods (including High-Performance Liquid Chromatography), Electrophoretic Techniques (such as hemoglobin electrophoresis and isoelectric focusing), Molecular Methods (including DNA-based techniques),
Data presentation.
Summary characteristics were tabulated, and the results were displayed using forest plots, funnel plots, and PRISMA flow diagrams.
Meta-analysis and models.
We used the metaprop() function from the R Meta package to perform a random-effects meta-analysis. Statistical heterogeneity was assessed using Cochran’s Q and I2 statistics. Analyses were conducted in R version 4.5.1.
Exploration of heterogeneity.
To identify the sources of heterogeneity, we conducted subgroup analyses and meta-regressions using moderators, including region, diagnostic method, age category, and study design.
Sensitivity analyses.
We performed a leave-one-out sensitivity analysis to assess the impact of each individual study on the overall pooled prevalence by sequentially removing each study, recalculating the pooled estimate, and measuring the absolute difference and percentage change from the overall pooled effect. Studies were flagged as influential if their absolute differences exceeded a threshold of 0.002. The 75th percentile of the percentage change was calculated to identify studies with a larger impact on the meta-analysis results. A revised meta-analysis was subsequently conducted without the influential studies to compare the new pooled estimate with the original. The overlap of confidence intervals (CIs) from both analyses was examined, with substantial shifts indicated by non-overlapping CIs, suggesting a significant influence. A table was generated to visually represent the pooled prevalence of sickle cell anemia, showing the impact of each study’s removal.
Results
Search results
The search yielded 3,614 records screened. After removing duplicates, 1,115 records remained for screening purposes. Independent review of the titles and abstracts of these records by four reviewers resulted in Fleiss’ Kappa value of 0.546 (z = 44.3, p < .0001), prompting a joint screening of the records, which resulted in 186 studies being selected as eligible. The full texts of 15 studies were not available after multiple search efforts and attempts to request them from the authors. Following a detailed full-text review and quality appraisal, 56 studies were excluded based on one or more of the following criteria: insufficient or unclear diagnostic method, registry-based/no primary data, high risk of bias, and not being a sickle cell prevalence study (S3 Table). A total of 115 studies were included in the final analysis (Fig 1).
Synthesis overview
We included 115 studies with 1,203,839 participants and 17,458 confirmed HbSS cases. Of the 115 included studies, most were cross-sectional (n = 72), retrospective (n = 22), or prospective (n = 16), with a minority comprising quasi-experimental designs (n = 1), case-control (n = 2), controlled trial (n = 1), or mixed study design (n = 1) (Table 1). All studies used laboratory-confirmed diagnostic methods. The majority (n = 105, 91.3%) were rated as having a low risk of bias (S4 Table). These studies spanned 28 countries across Africa.
Distribution of included studies by country
Nigeria contributed the largest number of studies [24], followed by Kenya and Uganda (13 each), Ghana and Tanzania (10 each), Burkina Faso and the Democratic Republic of the Congo (6 each), and Benin [5] (Fig 2). Several other countries contributed one or two studies.
Map created by authors using shapefiles from GADM (Database of Global Administrative Areas, version 4.1, www.gadm.org).
Prevalence of sickle cell anaemia (HBSS) in Africa
The pooled prevalence of sickle cell anemia (SCA) in Africa due to HbSS hemoglobinopathies was 1.43% (95% CI: 1.08%–1.88%), based on 115 studies, including 1,203,839 participants and 17,458 HbSS cases. Heterogeneity was high (I2 = 99.1%, p < 0.001), with prevalence estimates ranging from 0% to 35.76% (Fig 3). The prediction interval was 0.21%–8.91%.
Regional variation in the prevalence of SCA (HBSS) across Africa
There was significant heterogeneity in the prevalence of HbSS across African regions (Fig 4). The highest pooled prevalence estimates were observed in Central Africa (1.99%, 95% CI: 0.81%–5.20%, p < 0.001) and North Africa (1.72%, 95% CI: 0.26%–6.64%, p < 0.001). East Africa showed a pooled prevalence of 1.40% (95% CI: 0.91%–2.32%), while West Africa had a prevalence of 1.13% (95% CI: 0.78%–1.77%). The lowest pooled prevalence was recorded in Southern Africa (0.59%, 95% CI: 0.05%–4.78%, p = 0.033). Heterogeneity was high across all regions (I2 > 95%), except in Southern Africa (I2 = 70.6%). West Africa contributed the largest number of studies (n = 53), whereas Southern Africa was underrepresented with only three studies.
Map created by authors using shapefiles from GADM (Database of Global Administrative Areas, version 4.1, www.gadm.org).
Effect of study designs
There was substantial variation in the prevalence of sickle cell anemia across the study designs (I2 > 96%). The pooled prevalence was 1.22% (95% CI: 0.89%–1.64%) among cross-sectional studies (n = 72), 1.92% (95% CI: 1.20%–3.07%) among retrospective studies (n = 40), and 1.40% (95% CI: 0.98%–2.00%) among prospective studies (n = 2). Two case–control studies and one quasi-experimental study were also included, with a limited contribution to the pooled estimates. Meta-regression indicated that study design did not significantly explain heterogeneity (QM = 4.10, p = 0.663).
Methods used to diagnose SCA across studies in Africa
Electrophoretic techniques, specifically hemoglobin electrophoresis and isoelectric focusing, have been the most commonly used methods for diagnosing HbSS. The pooled prevalence among studies using hemoglobin electrophoresis (n = 52) was 1.58% (95% CI: 1.01%–2.48%, I2 = 98.9%), while for isoelectric focusing (n = 21), it was 1.35% (95% CI: 0.95%–1.91%, I2 = 99.6%). Among studies using chromatographic methods (HPLC; n = 13), the pooled prevalence was 1.10% (95% CI: 0.65%–1.84%, I2 = 94.7). The molecular method (PCR; n = 15) showed a pooled prevalence of 0.97% (95% CI: 0.62%–1.51%, I2 = 93.7%). Rapid sickling tests (POCT), including HemotypeSC™ and Sickle scan (n = 6), had a pooled prevalence of 1.26% (95% CI: 0.92%–1.71%, I2 = 44.4%). Only one study used the biochemical method (sodium metabisulfite), conducted among anemic children at Mbeya Referral Hospital, Southern Tanzania (n = 50), which reported a prevalence of 18% (9/50) [79]. Meta-regression using Diagnostic Method as a moderator indicated that the type of diagnostic method significantly explained some of the variability in prevalence across studies (QM(df = 8) = 16.73, p = 0.0331), although substantial residual heterogeneity remained (tau2 = 0.944, I2 = 98.9%).
Temporal variation of SCA (HBSS) in Africa over the last 30 years (1994–2024)
The pooled prevalence of Sickle Cell Anemia (SCA) due to the hemoglobin variant HBSS has remained low and relatively stable across studies conducted in Africa between 1994 and 2024 (Fig 5). Annual pooled prevalence estimates were available from 2002 to 2024 and generally ranged between 0.1% and 3%, with most estimates falling between 1% and 2%. The number of studies increased from 1–3 per year between 1997 and 2003 to a peak of 14 in 2020, followed by a steady output of 6–9 studies annually thereafter. Despite the increase in research activity, annual pooled prevalence estimates showed overlapping 95% confidence intervals across years (ranging from approximately 0.03%–0.07% in the lower bounds to 2.8%–7.3% in the upper bounds), indicating no significant temporal change. This was further supported by meta-regression analysis including 115 studies, which showed no statistically significant association between the study year and prevalence (QM(22) = 20.82, p = 0.53). Residual heterogeneity remained high (tau2 = 1.21, I2 = 98.9%). The wide prediction interval (0.04%–28.48%) reflects the considerable between-study variability in the reported prevalence.
(blue line, left axis) back-transformed from the logarithmic scale and annual research output (grey bars, right axis) from 1994 to 2024. Numbers above the gray bars indicate the number of accessed studies published each year.
Age and sex variation
Sex-specific analyses showed a pooled prevalence of HBSS in males and females, with high heterogeneity across studies. The test for subgroup differences between the sexes yielded Cochran’s Q statistic Q(1) = 0.15, p = 0.694. Age-stratified analyses also showed substantial heterogeneity across children, adults, newborns, and mixed-age populations, with all subgroup heterogeneity tests being statistically significant (p < 0.001 for each) (Table 2).
Regression analysis
Meta-regression analyses showed that region, study design, and sample size did not significantly explain the heterogeneity (p > 0.19). Diagnostic method was a significant moderator (χ² = 16.73, df = 8, p = 0.033), and age category also influenced the HBSS prevalence (χ² = 33.66, df = 3, p < 0.0001). In the multivariate model, including all moderators, the overall effect was marginally significant (χ² = 29.01, df = 19, p = 0.066), accounting for some variability; however, substantial residual heterogeneity persisted (tau² = 1.014, I2 = 98.6%).
Sensitivity analysis: Leave-one-out approach
The leave-one-out sensitivity analysis revealed that no single study had a significant impact on the overall prevalence estimate of HBSS (S5 Table). Throughout the iterations, the prevalence remained consistent, with minimal absolute differences ranging from 0.0000 to 0.0017 and percentage changes between 0.00% and 2.82%. The exclusion of Makani et al. (2021) [22] resulted in the largest shift, causing a 2.84% change in the pooled prevalence estimate. The original pooled prevalence was 0.0130 (95% CI: 0.0094–0.0181), and the removal of individual studies produced estimates between 0.0130 and 0.0147. The median absolute difference was 0.0001, with a median percentage change of 0.38%.
Assessment of publication bias
Egger’s linear regression test did not indicate statistically significant evidence of publication bias (t = –0.32, df = 113, p = 0.752) (Fig 6). The bias estimate was –0.36 with a standard error of 1.13. The analysis showed a τ² value of 107.73, confirming substantial residual heterogeneity across studies.
Sources of heterogeneity
Regression analyses exploring potential sources of heterogeneity among studies showed that age category was a significant driver of the observed variation in the prevalence of HBSS ((χ²(df = 3) = 33.659, p < 0.0001).
Certainty of evidence
The methodological quality of the pooled estimates was assessed using the AMSTAR 2 tool. The review met 16 of the 17 AMSTAR 2 criteria, indicating a high methodological confidence (S1 Fig). However, the substantial heterogeneity across studies, especially in diagnostic methods and age groups, may limit the generalizability of the estimates.
Discussion
This systematic review and meta-analysis provides a comprehensive synthesis of sickle cell anemia (SCA) prevalence due to HbSS across Africa, analyzing 115 studies encompassing nearly 1.2 million participants. We estimated a pooled prevalence of 1.43% (95% CI: 1.08%–1.88%), though this summary estimate must be interpreted cautiously given the extreme heterogeneity (I2 = 99.1%) and wide prediction interval (0.21%–8.91%), indicating substantial variation across populations and settings.
The extraordinarily high heterogeneity reflects the genuine biological, geographical, and methodological diversity. Regional prevalence differences, from 2.0% in Central Africa to 0.6% in Southern Africa, align with evolutionary biology linking HbS allele frequency to historical malaria endemicity [133]. Regions with sustained malaria transmission exert stronger selective pressure favoring the protective HbAS heterozygous state, indirectly maintaining higher HbSS frequencies through Hardy-Weinberg dynamics [134–136]. Central Africa’s elevated prevalence supports the hypothesis that this region represents the origin point of the sickle mutation approximately 5,000–10,000 years ago [137,138], with subsequent migrations distributing the allele continentally.
Beyond geographic variation, sampling frame differences critically influence the estimates. Hospital-based convenience samples may overestimate the prevalence by enriching symptomatic individuals, while community newborn screening provides unbiased population estimates. Urban tertiary centers capture different demographics than rural ones. Unfortunately, inconsistent reporting prevented formal subgroup analysis by sampling frame, a significant limitation that requires future attention. The markedly lower adult prevalence (0.45%) compared to that in children (1.65%) and newborns (1.28%) provides compelling evidence of high early mortality. With 50–90% of affected children dying before the age of five in resource-limited settings [18,19], cross-sectional adult studies systematically underestimate birth prevalence. This survival bias explains why newborn screening provides the most accurate population-level estimate.
Although meta-regression identified the diagnostic method as a significant moderator (χ² = 16.73, p = 0.033), misclassification concerns remained. Electrophoretic techniques confirmed 86.4% of HbSS cases but vary in specificity. Standard alkaline hemoglobin electrophoresis reliably distinguishes HbSS from HbAA and HbAS, yet may not differentiate HbSS from rarer variants (HbSD, HbSO-Arab) without confirmatory testing [139]. Only 15 studies (13%) employed molecular methods representing the gold standard. One study using solely sodium metabisulfite testing [79], which cannot distinguish HbSS from HbAS, poses overestimation risk, though its small sample (n = 50) minimally impacted pooled estimates. However, subtle misclassification may persist in studies lacking confirmatory testing, particularly where compound heterozygotes are prevalent.
The statistical stability of pooled estimates across three decades (approximately 1.4–1.5% from 1994–2024) requires careful interpretation. While annual estimates showed overlapping confidence intervals, the wide prediction interval indicates substantial individual study variation. This paradox reflects that early epidemiological studies successfully captured broad continental patterns despite limited diagnostic capabilities, and meta-regression confirmed no significant temporal trend (QM = 20.82, p = 0.53). Yet persistent high heterogeneity shows that pooled estimate stability masks geographic and methodological variation. Six-fold increased research output (from 1–3 studies pre-2003–14 in 2020) expanded coverage but did not shift overall estimates, suggesting increased sampling reinforced rather than revised our understanding while revealing greater local variability. Critically, temporal stability should not suggest unchanging disease burden within populations. Birth prevalence may remain stable while mortality, severity, and life expectancy change substantially with improved healthcare access.
Southern Africa’s severe underrepresentation (only 3 studies) critically limits generalizability. This region exhibits distinct population genetics, lower historical malaria endemicity, and different demographic histories than West and Central Africa. The 0.59% estimate rests on insufficient evidence and warrants extreme caution. North Africa similarly contributed limited data (n = 3), and several nations had no representation. This geographic bias likely reflects publication patterns and research infrastructure concentration rather than SCA absence. Future research must prioritize underrepresented regions and systematically search African-specific databases beyond international platforms.
Egger’s test suggested no significant publication bias (t = –0.32, p = 0.752), though with high heterogeneity. Traditional assessments assume relatively homogeneous underlying effects, whereas our analysis encompasses genuinely diverse populations. The non-significant result may indicate that wide prevalence ranges (0%–35.76%) reflect authentic diversity rather than selective reporting. Nevertheless, subtle biases may persist, where studies reporting unexpectedly low prevalence may face publication barriers, while high-prevalence endemic-area studies receive preferential reporting. Language bias concerns persist given our English-language database focus, potentially missing French, Portuguese, or Arabic publications.
These findings carry actionable policy implications. Two- to three-fold regional variation necessitates tailored screening strategies rather than uniform continental policies, where high-burden regions require universal newborn screening while lower-prevalence areas might adopt risk-based targeting. Persistent electrophoretic method dominance (86.4% of diagnoses) suggests delayed point-of-care technology adoption that could expand rural screening access, though quality assurance must ensure accuracy. Absent temporal trends may reflect inadequate surveillance rather than stable burden, thus population-based registries with standardized protocols would enable meaningful epidemiological shift detection. Most critically, the observed childhood-to-adulthood prevalence decline underscores urgent need for comprehensive care programs addressing 50–90% under-five mortality. Evidence-based interventions including penicillin prophylaxis, pneumococcal vaccination, hydroxyurea therapy, and parent education demonstrate effectiveness but remain underutilized across Africa.
Limitations
Beyond acknowledged geographic underrepresentation and high heterogeneity, several limitations merit emphasis. The sensitivity analysis influence threshold (0.002 absolute prevalence difference) was pragmatically chosen but requires validation using alternative metrics (DFBETAS, Cook’s distance). Inconsistent reporting of urban/rural settings, facility levels, and sampling procedures prevented granular subgroup analyses better explaining heterogeneity. Lack of systematic African-specific database screening (AJOL, WHO African Index Medicus) represents a gap requiring future correction. Pre-specified analyses stratifying by sampling frame (community versus clinical), screening versus convenience samples, and malaria endemicity zones would provide more robust heterogeneity exploration. Finally, inaccessible full texts for 15 potentially eligible studies may introduce selection bias of unclear direction and magnitude.
Conclusion
SCA represents a substantial but geographically heterogeneous public health challenge across Africa, with 1.43% pooled prevalence masking variation from <1% to >3% across regions. Extreme heterogeneity reflects authentic diversity in genetic epidemiology, population structure, survival patterns, and methodological approaches rather than statistical artifact. Findings mandate region-specific interventions, expanded diagnostic access with quality assurance, systematic surveillance infrastructure, and continued research addressing geographic and methodological gaps. Future work must prioritize underrepresented regions, standardize diagnostic protocols, and establish prospective cohorts tracking mortality and morbidity trends alongside prevalence patterns.
Supporting information
S1 Table. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.
https://doi.org/10.1371/journal.pone.0337090.s001
(PDF)
S2 Table. Search tables for PubMed, Google scholar, Scopus and BASE databases from 1994–2024.
https://doi.org/10.1371/journal.pone.0337090.s002
(PDF)
S3 Table. Studies excluded with reasons for exclusion.
https://doi.org/10.1371/journal.pone.0337090.s003
(PDF)
S4 Table. Risk of bias analysis using JBI critical appraisal tool for prevalence studies.
https://doi.org/10.1371/journal.pone.0337090.s004
(PDF)
S5 Table. Results of the Leave-Out-One sensitivity analysis.
https://doi.org/10.1371/journal.pone.0337090.s005
(PDF)
S1 Fig. Certainty of evidence assessment using A Measurement Tool to Assess systematic Reviews (AMSTAR).
https://doi.org/10.1371/journal.pone.0337090.s006
(PDF)
Acknowledgments
Patricia Nakitende Peace, Namirembe Esther, Esther Nassinde, Namale Joanitah, Otema Ricky, Daniella Nabbaka, Nakibuka Gloria, Namutebi Proscovia, Kayizi Julius, Nasibo Shafinah, Bayiru Lillian
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