---
title: "Leukemia in Africa: Pooled Prevalence and Key Predictors from a Systematic Review and Meta-analysis"
id: "plos-one-20-prevalence-and-associated-factors-of-leukemia-in-africa-a-systematic-review-and"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-20-prevalence-and-associated-factors-of-leukemia-in-africa-a-systematic-review-and"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0354814"
published_at: "2026-08-03T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Leukemia in Africa: Pooled Prevalence and Key Predictors from a Systematic Review and Meta-analysis
## Provenance & Clinical Metadata
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- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0354814)
- **Published At:** 2026-08-03T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This systematic review and meta-analysis synthesized data on the **prevalence** of leukemia in Africa and identified associated factors using PRISMA methods. - The authors searched multiple databases (PubMed/MEDLINE, Scopus, ScienceDirect), Google Scholar and manual searches; STATA v11 with a random-effects model was used for pooled estimates. - From 17,546 retrieved records, 15 studies met inclusion criteria, contributing 42,884 participants to the meta-analysis. - The overall pooled prevalence of **leukemia** across included African studies was 5.09% (95% CI 4.02–6.17) with substantial heterogeneity (I2 = 97.1%). - Country-level subgroup results varied widely: Nigeria had the highest pooled prevalence (10.58%) and Zambia the lowest (0.58%). - Temporal subgrouping showed higher pooled prevalence in studies after 2016 (6.67%) than those before 2015 (4.16%). - Sample-size stratification found higher estimates in smaller studies (<384 participants: 7.99%) versus larger studies (≥385: 3.61%). - Population subgrouping yielded 3.38% in children and 7.15% in general-population samples. - Two factors were reported as significant predictors in the pooled analysis: **rural residency** (OR 56.98, 95% CI 9.97–326.70) and **older age** (OR 3.90, 95% CI 1.39–10.98). - Heterogeneity was explored with I2 statistics and meta-regression; publication bias was assessed via funnel plots and Egger’s test (p<0.05 indicates bias). - The authors conclude that leukemia is a notable public health concern in Africa given the pooled prevalence and identified predictors; data limitations and heterogeneity were acknowledged in the source.
## Clinical Analysis & Structured Key Points
Prevalence and associated factors of Leukemia in Africa: A systematic review and meta-analysis | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures Figures Abstract Background Leukemia is a group of diverse and biologically distinct blood cancers. While progress has been made in combating infectious diseases, chronic diseases like leukemia are increasingly prevalent. Current data on leukemia’s prevalence in Africa are inconsistent, necessitating a comprehensive systematic review. This study aims to determine the pooled prevalence and associated risk factors of leukemia across Africa through a systematic review and meta-analysis. Method This systematic review and meta-analysis were conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted across multiple databases, including PubMed/MEDLINE, Scopus, and Science Direct, supplemented by searches using the Google Scholar and manual searches to identify eligible studies. Statistical analysis was performed using STATA version 11, with a random-effects model employed to compute pooled estimates. Higgen’s I 2 test statistics and meta-regression was conducted to explore potential sources of heterogeneity. Publication bias was assessed visually by Funnel plots and statistically using Egger’s weighted regression test, a p-value of less than 0.05 indicates the presence of significant publication bias. Results A total of 17,546 articles were retrieved, of which fifteen studies, which recruited 42,884 individuals, were included in the meta-analysis. The pooled prevalence of leukemia was 5.09% (95% CI: 4.02, 6.17) with I 2 of 97.1%. Due to the heterogeneity, subgroup analyses by country showed that the highest prevalence was 10.58% in Nigeria, and the lowest was 0.58% in Zambia. Subgroup analysis based on publication year revealed 4.16% among studies conducted before 2015 and 6.67% among studies conducted after 2016. Additionally subgroup by sample size showed: smaller sample sizes (<384 participants) yielded a higher pooled estimate of 7.99%, conversely, studies with larger sample sizes (≥385 participants) showed 3.61%. Whereas subgroup analysis by population showed that 3.38% in children and 7.15% in the general population. Among the factors identified, rural residency (OR: 56.98, 95% CI: 9.97–326.70) and older age (OR: 3.90, 95% CI: 1.39–10.98) were significant predictors of leukemia in Africa. Conclusion This review reveals a pooled prevalence of 5.09%, with rural residency and older age identified as significant predictors of leukemia in Africa, underscoring a public health concern. Citation: Gedfie S, Reta MA, Gashaw M, Bazezew A, Jemal A, Getu N, et al. (2026) Prevalence and associated factors of Leukemia in Africa: A systematic review and meta-analysis. PLoS One 21(8): e0354814. https://doi.org/10.1371/journal.pone.0354814 Editor: Mehmet Baysal, Tekirdag Namik Kemal University: Tekirdag Namik Kemal Universitesi, TÜRKIYE Received: May 16, 2026; Accepted: July 13, 2026; Published: August 3, 2026 Copyright: © 2026 Gedfie 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 and analyzed during this study are included in this manuscript. Funding: The author(s) received no specific funding for this work. Competing interests: the authors declare no competing interest. Abbreviations: ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CI, confidence interval; CLL, chronic lymphocytic leukemia; CML, chronic myeloid leukemia; HL, Hodgkin lymphoma; NHL, non-Hodgkin lymphoma. Introduction Leukemia is a diverse group of blood cancers that originate in the bone marrow and other blood-forming tissues [ 1 ]. These cancers involve the unchecked growth of immature blood cells, interfering with normal blood cell production and potentially spreading to other organs [ 2 ]. Leukemia is classified according to both cell lineage and how quickly the disease progresses. The cell lineage determines whether it is lymphoid or myeloid leukemia, while the progression rate categorizes it as either acute or chronic [ 2 , 3 ]. This essential classification system informs how clinicians diagnose, treat, and predict outcomes for patients. When categorized clinically by disease progression (acute or chronic) and cell lineage (myeloid or lymphoid), this blood cancer exhibits unique biological characteristics across its various subtypes [ 4 ]. Leukemia represents a major global health challenge, affecting millions of people around the world. Its incidence, prevalence, and death rates differ considerably depending on geographic region and population group. Worldwide, the number of new leukemia diagnoses rose from 311,648 in 1990–461,423 in 2021, and projections indicate this figure will exceed half a million by 2050 [ 5 ]. Among the new cases recorded in 2021, 57.2% occurred in males, 70.8% involved individuals aged 45 or older (middle-aged and elderly), and acute myeloid leukemia (AML) remained the predominant subtype, accounting for 31.4% of cases [ 5 – 8 ]. Between 1990 and 2021, leukemia incidence fell notably from 6.9 to 5.6 per 100,000, mortality dropped from 5.6 to 3.9, and disability-adjusted life years (DALYs) decreased from 266.3 to 136.9 [ 5 ]. In Africa, especially Sub-Saharan Africa, the impact of leukemia poses distinct difficulties. Although accurate statistics are hard to obtain because of poor cancer registration and data collection systems, existing evidence points to a major gap in leukemia incidence, prevalence, and death rates when compared with high-income nations [ 6 , 9 ]. In 2021, East Africa experienced the highest leukemia burden, reporting 8,286 adult cases (age-standardized incidence rate [ASIR] of 4.5) and 2,954 pediatric cases (ASIR of 1.6). In contrast, West Africa had the lowest burden, with 3,612 adult cases (ASIR 2.3) and 790 pediatric cases (ASIR 0.47). Middle Africa and Southern Africa recorded moderate burdens: adults in Middle Africa accounted for 2,196 cases (ASIR 3.1), while those in Southern Africa accounted for 1,981 cases (ASIR 5.0) [ 10 ]. While global efforts have successfully lessened the toll of infectious diseases over the past several decades, chronic non-communicable conditions like leukemia are becoming an increasing public health threat, as their prevalence continues to rise steadily across the globe [ 11 ]. The demographic patterns of blood cancers in Africa are still not well defined, and it remains unclear whether they mirror those seen in high-income nations. Recent data from South Africa uncovered a notable disparity: White children had three times the incidence of pediatric hematologic malignancies compared to Black children. This observation is especially significant within the African setting, where population categories frequently align closely with social and economic gradients [ 12 ]. In developing countries, cancer plays a major role in both mortality and disability, as healthcare systems frequently lack the necessary resources to combat the disease effectively. Africa, in particular, is experiencing a rapid increase in its cancer burden. By 2030, projections suggest over one million new cancer cases and nearly 800,000 deaths, representing an 85% rise compared to 2008 levels [ 13 ]. The etiology of leukemia and its subtypes remains incompletely understood, partly because of the variety of abnormalities and the numerous potential risk factors. Genetic syndromes like Down syndrome, Li-Fraumeni syndrome, and neurofibromatosis have been connected to a predisposition for leukemia in young adults [ 14 ]. Benzene exposure is unequivocally associated with a heightened risk of acute myeloid leukemia (AML). Ionizing radiation has been linked to acute lymphoblastic leukemia (ALL), especially in children, as well as to AML and chronic myeloid leukemia (CML). Furthermore, viral infections have been implicated in lymphoproliferative disorders, with Epstein-Barr virus (EBV) and human T-cell lymphotropic virus type-1 (HTLV-1) playing well-documented roles in specific lymphoid malignancies [ 14 – 16 ]. The exact cause of leukemia is not fully understood, but it is believed to result from a combination of genetic predisposition and environmental influences [ 17 ]. Established risk factors include tobacco smoking, exposure to ionizing radiation, certain chemical agents (such as benzene), prior chemotherapy or radiation therapy, and genetic disorders like Down syndrome [ 18 ]. Additionally, individuals with a family history of leukemia or certain lifestyle factors may also face an increased risk of developing the disease [ 19 ]. Although progress has been made in understanding the molecular and genetic mechanisms underlying leukemia, data on its burden in Africa remain inconsistent. Consequently, this systematic review and meta-analysis aims to establish the pooled prevalence of leukemia in Africa and identify associated factors. Methods Design and protocol registration This systematic review and meta-analysis was conducted based on Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocols [ 20 ]. The protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251017790. Search strategy A comprehensive literature search was conducted across multiple electronic databases, including Scopus, PubMed/MEDLINE, Science Direct, and Google Scholar, along with relevant institutional repositories. The search was performed independently by two reviewers (SG and MAR). The search included all available publication dates and was restricted to English-language articles published up to March 30, 2025. A systematic search strategy was employed, utilizing both individual and combined search terms linked by Boolean operators (“AND,” “OR”). The primary search terms comprised “leukemia,” “hematological malignancy,” “blood cancer,” “prevalence,” “incidence,” “epidemiology,” and “Africa,” as well as related terms such as “magnitude” and “blood disorders.” The search string used in the PubMed was:-(((((((((((Prevalence) OR (Magnitude)) OR (Incidence)) AND (Predictors)) OR (factors)) OR (Risk-Factors)) OR (Associated factors)) AND (Leukemia)) OR (Hematological Malignancy)) OR (Cancer)) AND (Africa) by adding African search filter. Additional studies were searched by manual search and by looking into references in pertinent papers. The search strategy and number of articles retrieved from the searched databases and additional searches are depicted in the additional file (S1 Table in S1 File ). Eligibility criteria This systematic review incorporated peer-reviewed published journal articles, as well as studies available through institutional electronic repositories, that reported on leukemia prevalence and associated factors. Eligible study designs included case-control studies, cohort studies (both prospective and retrospective), and cross-sectional studies that measured the relevant outcome variables. Eligible studies were limited to English-language articles published up to March 30, 2025. The review excluded case reports, case series, narrative reviews, editorials, and studies available only as conference abstracts. Agreements on the inclusion and exclusion of the articles were held through the involvement of authors (MN, GK, ZT and MTA). Study selection and quality assessment To organize search results and remove duplicate studies, all retrieved articles were imported into EndNote X21 (Thomson Reuters, New York, USA). The literature search was conducted across electronic databases, trial registers, and Google Scholar by three reviewers (SG, WTK, and ZT). Title screening was performed independently by two reviewers (SG, AJ). Abstract screening was carried out independently by three reviewers (MN, AA, and BK). Full-text screening was conducted independently by four reviewers (SG, AB, BM, and MG). Any disagreements arising between reviewers at any stage were resolved through discussion and consultation with additional reviewers (WA, BBA, and AS). Additionally, the methodological quality of included studies was appraised using the Joanna Briggs Institute (JBI) critical appraisal tools, by reviewers (MAR, TM, GK, and YG) [ 21 ]. The quality appraisal guideline contains 10 evaluation domains or categories to evaluate the internal and external validity. The items are: 1. (a) representativeness for the population, (b) sampling frame, (c) Ways of study unit selection, (d) bias due to non-response, (e) data source (primary data), (f) acceptability of case definition, (g) reliability and validity of study tool, (h) mode of data collection, (i) appropriateness of numerator and denominator, and (j) summary. Each category was evaluated as low or high risk of bias. Unclear was considered as a high risk of bias. Eventually, the summary risk of bias was determined according to the number of the high risk of bias per study: that is defined as low (0–3), moderate [ 4 – 6 ], and high [ 7 – 9 ] depicted quality score (S2 Table in S1 File ). Data extraction Data extraction was accomplished by two reviewers (SG and MN) from studies that fulfill the eligibility criteria. The extracted data were summarized into an MS Excel spreadsheet. Disagreements were resolved through consensus and discourse with other reviewers (MAR, MN, EG and ET). The following data were systematically extracted from each included study for analysis: first author’s name, publication year, study design, and country where the research was conducted. Additionally, we collected data on study population characteristics, including total number of participants and their age distribution. Key outcome measures extracted were the number of confirmed leukemia cases and calculated prevalence rates. Additionally, to analyze factors associated with leukemia, we extracted factors that were reported as statistically significant in the primary studies. Factors that were not significant or not reported were not included in this analysis. We also documented all significant factors associated with leukemia that were reported in the studies. This comprehensive data extraction approach enabled thorough evaluation and synthesis of the available evidence on leukemia epidemiology across African populations ( Table 1 ). Download: PNG larger image TIFF original image Table 1. Characteristics of included studies in the meta-analysis of prevalence of leukemia in Africa. https://doi.org/10.1371/journal.pone.0354814.t001 Statistical analysis The data were checked for completeness in Microsoft Excel before being transferred to STATA version 11 for final analysis. The effect size was estimated using a random-effects model, presented with a 95% confidence interval (95% CI) [ 22 ]. Forest plots were generated to display the overall pooled prevalence, along with the relative weight assigned to each included study. The degree of heterogeneity was assessed using Higgins’ I 2 statistic, with I 2 values categorized as low (25%), moderate (50%), and high (75%) [ 23 ]. To explore potential sources of heterogeneity, sub-group analyses were performed based on the country where the primary study was conducted, year of publication, study population, and sample size. Furthermore, meta-regression analysis was conducted to investigate possible sources of heterogeneity. A sensitivity analysis was conducted by sequentially excluding individual studies to determine whether any single study significantly influenced the pooled estimate. To assess potential publication bias, funnel plots were visually inspected, and Egger’s weighted regression test was performed. In Egger’s test, a p-value of less than 0.05 was considered indicative of statistically significant publication bias [ 24 ]. Ethics approval and consent to participate As this study is a systematic review and meta-analysis of previously published data, it did not involve direct patient contact or primary data collection. Therefore, ethical approval and informed consent were not required. Results Searching results This systematic review and meta-analysis included published and unpublished articles on the prevalence and predictors of leukemia in Africa. The search strategy employed the following databases: PubMed/MEDLINE, ScienceDirect, Scopus, and Google Scholar. The initial search identified 17,546 studies. After removing 200 duplicate records, 17,296 studies were excluded during title and abstract screening. Following the screening of 50 full-text articles for eligibility, 35 were excluded because they did not report the relevant outcome variable. Consequently, 15 studies met all inclusion criteria and were included in the final meta-analysis ( Fig 1 ). Download: PNG larger image TIFF original image Fig 1. PRISMA flow diagram showing the results of the search and reasons for exclusion on systematic review and meta-analysis of leukemia in Africa [ 20 ]. https://doi.org/10.1371/journal.pone.0354814.g001 Characteristics of included studies A total of fifteen studies met the inclusion criteria for this systematic review and meta-analysis. Among the included studies: five were conducted in Ethiopia [ 25 – 29 ], three were in Nigeria [ 30 – 32 ] and the others were conducted in Libya [ 33 ], Tunisia [ 34 ], Egypt [ 35 ], Zambia [ 36 ], Rwanda [ 37 ], Sudan [ 38 ] and Eritrea [ 39 ]. The sample size ranges from 135 in Nigeria [ 30 ] to 21512 in Zambia [ 36 ]. The highest prevalence of leukemia (16.95%) was reported in Nigeria [ 40 ], whereas the lowest (0.585%) was found in Zambia [ 36 ] ( Table 1 ). Publication bias Publication bias occurs when studies with statistically significant or favorable results are more likely to be published than those with low prevalence findings. Included studies were assessed for potential publication bias visually by funnel plot and egger’s test statistics. The funnel plot ( Fig 2 ) is asymmetric, shows an evidence of publication bias. The egger’s weighted regression statistics showed that (p < 0.05) (in this case P = 0.001), also showed presence of publication bias ( Table 2 ). This suggests the necessity of performing a trim-and-fill analysis to assess and adjust for potential publication bias in the dataset. Download: PNG larger image TIFF original image Table 2. Egger’s test for prevalence of leukemia in Africa. https://doi.org/10.1371/journal.pone.0354814.t002 Download: PNG larger image TIFF original image Fig 2. Funnel plot on the prevalence of leukemia in Africa. https://doi.org/10.1371/journal.pone.0354814.g002 Trim and fill analysis of pooled estimate of leukemia in Africa Due to the presence of publication bias ( p-value = 0.001) and evidence of asymmetry from the funnel plot, so, nonparametric trim and fill analysis was performed. The trim-and-fill method imputed five additional studies to address potential publication bias. Based on the random-effects model, after trim and fill analyses the adjusted pooled prevalence of leukemia in Africa changed from 5.09% (95% CI: 4.02, 6.17) to 3.102% (95% CI: 2.104–4.099). This suggests that studies with smaller effects, which were either excluded or failed to be published, were incorporated into the model using the trim-and-fill method. As a result, the pooled estimate was lower compared to the original analysis conducted without trim-and-fill adjustment ( Fig 3 ). Download: PNG larger image TIFF original image Fig 3. Trim and fill analysis of the pooled prevalence of leukemia in Africa. https://doi.org/10.1371/journal.pone.035481
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