Purpose: Understanding the epidemiology of breast cancer (BC) in Africa, as well as regional variation is essential for planning future intervention. Our objective was to describe summary estimates of socio-demographic and clinical characteristics of BC in Africa, thus providing researchers and policymakers baseline data for planning diagnostic and treatment programs to improve BC outcomes in the future.
Method: We screened African publications on BC between 2010 and 2019 in PubMed, AJOL, Google, ScienceDirect, and ResearchGate to estimate the distribution of socio-demographic and clinical tumor characteristics. The meta-analysis used the random effect model.
Result: Eighty articles were eligible, including 33,199 total patients. Overall, 58% of patients were <50 years old. In East Africa, 38% (95% CI 31-45) were diagnosed before 40 years. Conversely, in Southern Africa, 37% were diagnosed after 60 years, with Caucasian-like age distribution. The overall prevalence of male BC was high (3%), with East Africa having the highest prevalence (5% (95% CI 5.0-6.0)). Only 2% (95% CI 1-2) of patients were diagnosed with carcinoma-in-situ. Invasive tumors were 7% stage I, 26% stage II, 50% stage III, and 17% stage IV. Seventy per-cent (95% CI 60-80) had clinical nodal involvement. The smallest tumors were in North Africa. The largest and most advanced tumors were in West Africa. Trend analysis showed decreasing age, an increasing population of unmarried BC patients, a relatively high proportion of uneducated BC patients, and a stable proportion of late-stage disease in the last decade.
Conclusion: Regional variation in the presentation of BC throughout Africa necessitates region/country-specific targets for improving BC control. 


According to 2018 global cancer statistics [1], breast cancer (BC) is one of the two most common adult cancers, accounting for nearly 25% of cancers in women worldwide. Africa has disproportionately high age-standardized mortality due to BC [2]. The World Health Organization (WHO) and other experts in the field [3] recommend early diagnosis combined with timely and effective treatment as cost-effective measures for improving BC outcomes in Africa. Understanding the epidemiology of BC in Africa, as well as regional variation, is essential for planning future interventions.

Prior researches have aggregated data to better understand BC in Africa, but there is a notable gap in the existing literature. Previous meta-analyses described BC incidence, stage at presentation [4], and biological characteristics in sub-Saharan Africa (SSA). However, none of the existing reviews adequately summarize patient demographics, clinical pattern, or regional variation of BC. These factors are of significant prognostic value and are critical determinants of resource allocation that can allow for tailored screening, early detection, diagnostic and treatment programs to be adapted for specific local or regional contexts.

This meta-analysis aims to describe summary estimates of patient demographics, tumor size, and BC stage in Africa. A secondary aim is to compare the clinical pattern between African regions and countries. Our ultimate goal is to provide researchers and policymakers a baseline and region-specific targets for planning future interventions.


This research aligned with the Preferred Reported Items for Systematic Reviews and Meta-analysis (PRISMA) recommendations [5]. The needs assessment and preliminary literature review [in PubMed, African Journal Online (AJOL), Cochrane library, and Prospero reference ID CRD42020153269] confirmed no similar meta-analysis was ongoing or previously conducted. The full literature search was performed in PubMed.gov between November 10, 2019, and December 31, 2019, using an iterative process with the search term “breast cancer AND country name” for each African country and only as “breast cancer” in AJOL. Hand-search was done on Google, Google Scholar, ScienceDirect, PubMed central, ResearchGate, and Academia. Snow-balling search was in the reference list of original articles and already published review articles. We sent an exclusive request email to authors for full articles not available online or to clarify data.

Article screening and data extraction

Full-text screening used predetermined Population, Intervention, Control, Outcome, Time, Study design (PICOTS) criteria (Table 1).

Participants/Population We included freely available publications of studies conducted in Africa and reporting on the total female breast cancer patients or both sexes or a representative sample. We excluded articles reporting on breast cancer patients’ subpopulations, such as early presentation alone, young women, older women, or treatment subgroups.
Intervention Not applicable
Control Not applicable
Outcomes The outcomes were: patient demographics (including age, sex, marital status, educational status, and menopausal status), and locoregional characteristic (including the primary tumor size, lymph node status, combined tumor staging, proportion of invasive and in-situ tumors, and tumor laterality).
  The sex distribution was extracted in studies where the proportion of both sexes were reported. Age distribution was extracted in the range <40, 40-49, 50-59, and ≥60 years and in the binary distribution≤30 years/ >30 years, and <50 years /≥50 years. Marital status was extracted into three categories: married, unmarried (separated, divorced, or widowed), and single (never married). Education was extracted into three categories: none/primary, secondary, and tertiary. Tumor laterality was extracted from articles that reported both unilateral and bilateral disease.
  The proportion of invasive disease and carcinoma in-situ were extracted using articles that reported the two. The primary tumor size was based on the American Joint Committee on Cancer (AJCC) classification for the articles reporting in T1-4 fashion, and staging was based on articles where all four stages could be distinctly identified.
  Nodal status was extracted as the presence or absence of nodal metastasis using the clinical or pathologic description according to the American Joint Committee on Cancer (AJCC) clinical staging criteria version 6 or 7.
Time Articles published between January 2010 and December 2019. Articles including data earlier than January 2000 were excluded.
Study design Study design was not a strict exclusion criterion because demographic characteristics are expected to be fundamental elements in the reporting of any study. Language was also not an exclusion criterion. We included any original article with a sample size of at least 30 subjects providing at least one data point or observation according to the outcomes list above. We excluded review articles. Original articles involving more than one country were included if the observation (s) could be extracted separately for each country.
Table 1. PICOTS Article Screening Criteria.

Author AO performed the article title and abstract screening while AO and AI performed the full article review independently. The same authors also conducted data extraction. The authors discussed to resolve any disagreement.

Quality assessment

Five quality assessment variables were designed using domains in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist [6]. The quality assessment classified the report into one of 5 levels, A-E, depending on the quality score, with A being the highest score of five and E being a score of one or zero. The quality score was not used in meta-analytical weighing.

Statistical analysis

The primary outcome was the summary estimate of each outcome variable defined in our PICOTS criteria. The meta-analytical procedure was conducted in MetaXL (www.epigear.com) add-in for Microsoft Excel. A random-effect model was implemented to obtain summary estimates using the double arcsine transformation to avoid overweighting studies with values close to 0 or 100%. I-squared [I2] values above 75% indicated high heterogeneity. Subgroup analysis was conducted based on the United Nations regional classification of African nations as Central Africa (CA), East Africa (EA), Northern Africa (NA), West Africa (WA), and Southern Africa (SNA). By-country analysis was also conducted to compare variables and explain the potential source of heterogeneity. We analyzed summary estimates of all variables where there were at least two observations for the continent, the region, or the country. The variables were analyzed as proportions of the total in each publication (n/N).

Tumor characteristics according to the clinical or pathologic AJCC, were analyzed separately. Results were presented in percentages with 95% confidence intervals (95% CI). The parent forest plots for all analyses are available in the supplementary file. Funding: No funding source.


Full electronic search returned 5661 articles; 80 articles were eligible after the article selection process (Figure 1, Table 2).

Figure 1. Article Screening Flow Chart.

Author Year Country UnitedNations Region Race Period Location of hospital Design N Study level age statistic. range/mean/ median
Adejumo, et al. [22] 2019 Nigeria WA NS 2015-2018 FMC Keffi pros 199 NS/NS/NS
Adebamiji, et al. [23] 2016 Nigeria WA NS 2003-2007 University of Ilorin Teaching Hospital Oke Oyi retro 203 21-99/49.2/NS
Agbo, et al. [24] 2014 Nigeria WA NS 2007-2011 Usman Dan Fodio Teaching Hospital Sokoto retro 816 NS/48.2/NS
Agodirin, et al. [25] 2018 Nigeria WA NS 2016-2018 Multicenter survey 100 26-80/50.5/NS
Akanbi, et al. [26] 2015 Nigeria WA NS 2012-2014 NS cross- 120 NS
Akinkuolie, et al. [27] 2016 Nigeria WA NS 2007-2013 Wesley Guild Hospital Ilesha cross 46 25-81/NS/NS
Anyanwu, et al. [28] 2011 Nigeria WA NS 2004-2008 Nnamdi Azikwe University Teaching Hospital Nnewi pros 275 18-80/45.2/NS
Ayoade, et al. [29] 2015 Nigeria WA NS 2011-2014 Olabisi Onabanjo University Teaching Hospital Sagamu survey 113 NS/47.8/NS
Balekouzou, et al. [12] 2016 Central African Republic CA NS 2003-2015 Tungji Med College & Bangui University retro 174 16-90/ 45.83/ NS
Balekouzou, et al. [11] 2018 Central African Republic CA NS 2003-2015 National Lab in Bangui and General and Gynecologic Service retro 174 16-90/NS/45.5
Bambara, et al. [30] 2017 Burkina faso WA NS 2015-2016 Yaldago Ouedraogo Teaching Hospital cross 80 28-80/48.2/NS
Bennis, et al. [31] 2012 Morocco NA NS 2007-2010 Hazzan University Hospital Fez retro 366 18-82/45/NS
Boder, et al. [32] 2010 Libya NA NS 2002-2006 African Oncology Institute database 234 NS/46/NS
Brinton, et al. [33] 2017 Ghana WA NS NS Korle Bu Teaching Hospital & Komfo Anoyle Kumasi & Peace and Love Kumasi case 1184 18-74/NS/NS
Burson, et al. [21] 2010 Tanzania EA NS 2007-2009 Ocean Road Cancer Institute dar es Salam retro 488 NS/43.4/NS
Cubasch, et al. [34] 2018 South Africa SNA blk (542)nblk (55) 2009-2011 CHBAH breast Clinic database 602  
Dagne, et al. [35] 2019 Ethiopia EA NS 2011-2012 Tikur Ambessa Specialized Hospital retro 303 N/42.1/NS
Dauda, et al. [36] 2011 Nigeria WA NS 2000-2007 FMC Gombe retro 172 21-80/43.9/NS
Dedey, et al. [37] 2016 Ghana WA NS 2013 National Center for Radiotherapy and Nuclear Medicine Korle Bu Teaching Hospital survey 265 NS/51.1/NS
Deressa, et al. [16] 2019 Ethiopia EA NS 2016-2017 University of GondorHospital Cancer Center cross 82 25-82/NS/45
Dickens, et al. [8] 2014 South Africa SNA blk (964)nblk (107) 2006-2016 CHBAH breast Clinic retro 1071 NS/55.4/NS
Effi, et al. [38] 2017 Ivory Coast WA NS 2013-2015 Central Laboratory Abidjan pros 302 24-84/48/NS
Engbang, et al. [14] 2015 Cameroon CA NS 2004-2013 Multicenter retro 3044 13-95/46/NS
Eniojukan, et al. [39] 2015 Nigeria WA NS 2008-2012 University College HospitalIbadan retro 583 NS/44.9/NS
Ermiah, et al. [40] 2012 Libya NA NS 2008-2009 National Oncology Institute Sabratha survey 200 22-75/45.4/NS
Errahhali, et al. [41] 2017 Morocco NA NS 2005-2012 Hassan II regional oncology center retro 2406 NS/48.7/NS
Fatiregun, et al. [42] 2016 Nigeria WA NS NS Lagos State University Teaching Hospital Ikeja survey 200 NS/49.6/NS
Fessahaye, et al. [43] 2017 Eritrea EA NS 2013-2014 National Health Laboratory Ministry of Health Amara retro 144 19-91/51.5/NS
Fitzpatrick, et al. [44] 2018 Senegal WA NS 2001-2016 Dantec Hospital retro 197 NS/47/NS
Gabremariam, et al. [45] 2019 Ethiopia EA NS 2017-2018 Multicenter cross 441 NS/44.4/NS
Galukande, et al. [46] 2014 Uganda EA NS 2008-2011 Mulago Hospital Kampala retro 201 22-87/46.5/45
Galukande, et al. [47] 2015 Uganda EA NS 2004-2012 National Institute Oncology Sabratha   200 22-75/NS/NS
Gross-frie, et al. [48] 2018 Mali WA NS 2016 University Hospital Bamako survey 64 NS/45/NS
Hussein, et al. [49] 2013 Egypt NA NS 2006-2011 Mansoura University Oncology center retro 263 NS/52/NS
Jedy-Agba, et al. [4] 2017 Nigeria WA NS 2014-2016 Multicenter pros 316 24-86/45.4/NS
Joffe, et al. [50] 2018 South Africa SNA NS 2015-2016 Chris Hani Baragwanath Academic Hospital Hospital Soweto survey 499 NS
Kene, et al. [51] 2010 Nigeria WA NS 2001-2005 ABUTH Zaria retro 103 NS/44.5/NS
Khaial, et al. [52] 2015 Libya NA NS 2007-2008 Al-Jamhouria Hospital pros 301 NS/49/NS
Kholer, et al.[53] 2015 Malawi EA NS 2011-2013 Kamuzu Central Hospital Lilongwe retro 198 12-89/NS/34
Kone, et al. [54] 2019 Mali WA NS 2014-2016 Bamako Radiotherapy center retro 134 18-88/47.1/N
Lopes, et al. [55] 2015 Angola CA NS 2006-2014 Angola Institute of Cancer Control Luanda retro 1843 16-87/NS/47
Mabula, et al. [56] 2012 Tanzania EA NS 2002-2011 Bugando Medical Centre, Mwanza retro 384 21-78/NS/NS
Medhin, et al. [57]   Eritrea EA            
Mensah, et al. [58] 2016 Ghana WA NS 2002-2008 Korle Bu Teaching Hospital & National center for radiology and Nuclear medicine pros 1022 20-92/47.9/NS
Miguel, et al. [59] 2017 Angola CA NS 2011-2014 Angola Institute of Cancer Control Luanda & clinica Sagrade esperanca pros 140 24-84/47/NS
Mokone- Fatunla, et al. [15] 2019 South Africa SNA blk (1461)others 25 2000-2016 Dr George Mukhari Academic Hospital retro 1482 21-96/54.9/NS
Moodley, et al. [60] 2018 South Africa SNA NS 2015-2016 Breast Clinic WesternProvince cross 201 NS/NS/54
Mousa, et al. [61] 2011 Egypt NA NS 2009-2010 Tanta Cancer CenterGharbiah Province survey 163 NS
Muchuweti, et al. [19] 2017 Zimbabwe EA NS 2010-2013 Parirenyatwa Group of Hospital Harare pros 73 NS
Murugan, et al. [9] 2014 South Africa SNA blk (964) 2006-2012 CHBAH Soweto database 1071 N/55/N
Mechita, et al. [62] 2016 Morocco NA NS 2005-2008 National Institute of Oncology Rabat database 626 NS/51.1/NS
Nasiru, et al. 2011 Nigeria WA NS 2006-2009 LASUTH Ikeja pros 350 23- 104/48.9/55.4
Nguefack, et al.[13] 2012 Cameroon CA NS 2006-2009 Duala General Hosptial pros 42 29-73/46/NS
Nwafor, et al. [63] 2012 Nigeria WA NS 2009-2013 MeCure Health Limited Lagos retro 48 29-78/49.5/NS
O neil, et al. [64] 2017 Rwanda EA NS 2012-2013 Butaro Cancer Center of Excellence retros 150 26-84/48.3/N
Oguntunde, et al. [20] 2016 Nigeria WA NS 2011-2016 University of Ilorin Teaching Hospital Oke Oyi database 300 20-96/49.7/NS
Ohene-yeboah, et al. [65] 2012 Ghana WA NS 2004-2009 Komfo Anokye Teaching Hospital Kumasi pros 330 N/49.1/N
Okoye, et al. [66] 2016 Nigeria WA NS 2012-2016 Multicentered retro 334 23-95/50.3/N
Omoniyi-esan, et al. [67] 2015 Nigeria WA NS 2007-2012 OAUTHC Ile-Ife retro 136 23-92/50.7/NS
Otieno, et al. [68] 2010 Kenya EA NS 2003-2006 Kenyatta National Hospital Pros 166 17-88/47/NS
Otieno, et al. [69] 2010 Kenya EA NS 2000-2004 Kenyatta National Hospital retro 389 17-99/44/NS
Pace, et al. [70] 2015 Rwanda EA NS 2012-2014 Butaro & RwnkwavuHosptial survey 144 NS/NS/49
Popoola, et al. [71] 2013 Nigeria WA NS NS Lagos State University Teaching Hospital Ikeja Pros 190 NS/32/NS
Popoola, et al. [18] 2012 Nigeria WA NS          
Quayson, et al. [72] 2014 Ghana WA NS 2000-2004 Korle Bu Teaching Hospital retro 821 14-98/48/NS
Rahman, et al. [73] 2014 Nigeria WA NS 2003-2008 University of Ilorin Teaching Hospital Oke Oyi [68] retro 82 29-75/48.9/NS
Rambau, et al. [74] 2014 Tanzania EA NS NS Bugando Medical Centre, Mwanza retro 52 NS/49/NS
Rayne, et al. [75] 2017 South Africa SNA blk (85)nblk (170) 2011-2013 Johannesburg survey 263 18-86/NS/52
Salih, et al. [76] 2016 Sudan NA NS 2014-2018 Bashaier University Hospital & Khartoum Center for Radiation and Isotopes pros 63 22-91/46.8/NS
Sayed, et al. [77] 2018 Kenya EA NS 2012-2015 Multicenter survey 846 NS/48/NS
Sengal, et al. [10] 2017 Sudan NA NS 2010-2015   retro 560 20-94/48.8/NS
Sengal, et al. [7] 2017 Eritrea EA NS 2011-2015 University of Gezira retro 562 NS/NS/NS
Sengal, et al. [7] 2017 Sudan NA NS 2011-2015 Orotta School of Medicine and Dentistry Amaru retro 116 NS/NS/NS
Ssemanda, et al. [78] 2018 Uganda EA NS 2005-2014 MaKCHS Lab Kampala retro 599 NS/NS/NS
Stapleton, et al. [79] 2011 Egypt NA NS 2007-2008 National Cancer Institute of Cairo University & Tanta Cancer Center Nile Delta cross 343 NS/NS/NS
Tazzite, et al. [80] 2013 Morocco NA NS 2009 Oncology Centre, Ibn Rochd University Hospital Casablanca retro 570 NS/47.07/NS
Tesfamariam, et al. [81] 2013 Eritrea EA NS 2007-2008 Multicenter retro 82 26-80/48.4/NS
Titiloye, et al. [82] 2013 Nigeria WA   2004-2006   retro 89  
Traore, et al. [83] 2015 Guinea WA NS 2007-2012 Donka Chu Conakry retro 278 20-85
Usman, et al.[84] 2019 Nigeria WA NS 2011-2015 Amino Kano Teaching Hospital Kano retro 478 20-80/46.9/N
Wondima -gagnehu, et al. [85] 2019 Ethiopia EA NS 2017-2018 Multicentered cross 428 NS/40/NS
Table 2. List of Eligible Articles and Their Characteristics.

Result of article request; Four authors contacted for full-text responded [42, 86-88] yielding one eligible article. One [54] of 20 authors [7, 12, 31, 32, 36, 40, 49, 54, 60-62, 64, 74, 75, 77, 89-93] contacted for data on age distribution responded. Three authors [30, 39, 61] could not be reached to clarify data on age distribution, two could not be reached for data on educational status [39, 94], and one could not be reached for data on time to presentation [94]. Five authors contacted for data on stage distribution did not respond [48, 58, 60, 64, 70], and one could not be reached [95]. Three authors could not be reached to clarify data on tumor stage [30, 39, 61]. Two authors contacted for data on tumor size did not respond. One [37] of four authors contacted for data on marital status [37, 40, 70, 75] responded with usable data and one could not be reached [31, 74, 95]. Regional representation and summary of design; Twenty-four countries were represented from all five regions of Africa; CA-6, EA-19, NA-13, SNA-7, and WA-35. Three publications were in french [14, 62, 83]; all others were in English. Abbreviations, CA- Central Africa; EA- East Africa; WA-West Africa; NA- North Africa; WA-West Africa; SNA- Southern Africa; udy, retro- retrospective study’ survey- questionnaire-based survey

Each article contributed data for one country except Sengal et al. [7], which provided data for Sudan and Eritrea in one article. Two articles from South Africa [8, 9], Sudan [7, 10], and Central African Republic [11, 12] shared the same population of subjects but provided different data points (Table 2). Attempts to clarify incomplete or obscured data via email communications with authors yielded variable results as detailed in Table 2 footnote.

Twenty-three countries from all five regions of Africa contributed articles. WA contributed the largest number of articles (35 articles), followed by EA-19, NA-13, SNA-7, and CA 6. Nigeria contributed 23 articles, the largest from a single country (Figure 2).

Figure 2. Map of Africa Showing the Distribution of Study Subjects from each Country. The deeper blue shading represents a higher contribution, and lighter blue shading represents a lower contribution. The gray shadings represent no contribution.

There were 33,199 subjects in total. The minimum number of subjects in a study was 42 [13], and the maximum was 3044 [14]. The maximum number of subjects from one country was 5425, contributed by Nigeria (Figure 2). The majority of studies (n=39) were in the B quality assessment category; the study rationale was well stated in 84%, the design was adequate in 85%, and the participants were adequately stated in all studies. The study outcomes were adequately described in 77%, but the ease of data extraction was present in only 36% (Supplementary File). There was marked heterogeneity (>75%) in the overall summary estimates of the continent- wide analysis. The heterogeneity was significantly reduced or eliminated in most by-region and by-country analyses (Supplementary File).

Sex distribution

Twenty-five articles, including 11,476 subjects, contributed to the analysis of sex distribution. Ninety-seven per-cent (95% CI 97-98, I2 77%) of patients were female and 3% (95% CI 2.0-4.0%, I2 77) were male. EA had prevalence of male BC at 5% (95% CI 2.0-476%), more than double the prevalence in WA (2% (95% CI 2.0-2.0, I2-80%). Regional analysis was not feasible for NA, CA and SNA. By-country analysis showed a similar distribution of 2-3% in Nigeria, Tanzania, Eritrea, and Ghana. Single studies reported 2% male prevalence in Cameroon [14] and South Africa [15]. A single study from Ethiopia recorded male BC prevalence of 18% [16]. Subgroup analysis showed a rising trend in male BC incidence in the last ten years compared to the decade before. (Supplementary file).

Age distribution

Thirty-three articles (14,545 subjects) contributed to age distribution analysis. Overall, more than half of patients (58%) were diagnosed before the age of 50. Twenty-eight per-cent of patients (95% CI 24-31) were diagnosed before the age of 40, and 6.0% (95% CI 5.0-8.0, I2=90%) were diagnosed at 30 years or younger. The youngest patients were in EA, where 8% (95%CI 6.0-11, I2=82%) were diagnosed under the age of 30, 38% (95% CI 31-45, I2=85%) were diagnosed under the age of 40, and 64% were diagnosed before the age of 50. Conversely, in SNA, over 60% were diagnosed at the age of 50 or above, and 37% (95% CI 35-39, I2=0%) were diagnosed at the age of 60 or above. The age distribution analysis was possible for NA only in the 50-year cutoff, showing that the majority were also younger than 50 years (58%, (95% CI 44-72, I2=94%). (Figure 3, Supplementary file)

Figure 3. Age of Breast Cancer Patients in Africa.

By-country analysis showed Ethiopia had the youngest patients with 10% (95% CI 8.0-13, I2=63%) being younger than 30 years and 73% (95% CI 61-80, I2=87%) younger than 50 years (Figure 3 and Supplementary File). Temporal analysis revealed declining age of BC patients over time. Sixty per-cent of patients from 2010-2019 were less than 50 years of age, compared to 55% in 2000-2010 period. Eight per-cent of patients were less than 30 years of age from 2010-2019 compared to 4% in the 2000-2010 period (Figure 2, Supplementary file). Overall, 57% (95% CI 54-61, I2 87%) of patients were premenopausal. This was similar throughout regions with available data (56-60%). By-country analysis showed that Ghana had the highest prevalence of premenopausal patients (64% (95 % CI 42-83, I2=92%)) (Supplementary file).

Educational and marital status

Eleven publications (3747 subjects) contributed to the marital status analysis. Overall, 61% were married and 39% were unmarried, including 20% single. One study from CA [12] reported the highest proportion (74%) of unmarried singles; sensitivity analysis excluding this study saw the proportion of singles overall drop to 15%. The regional distribution of single patients was 11% EA, 14% WA, and 24% SNA. The largest unmarried population was in South Africa (57%) while the smallest was in Nigeria (27%). The temporal trend showed a slight increase in unmarried women diagnosed with BC in the last decade (41%) compared to the period between 2000-2010 (35%) (Table 3). Twelve articles (3,103 subjects) contributed to the educational status analysis. Thirty-eight per-cent of patients had none or primary education, 36% completed secondary education, and 26% completed tertiary education overall. The proportion of patients with secondary or tertiary level education was highest in SNA (75%) than EA (62%) and WA (62%). The proportion of patients with secondary or tertiary level education in the last decade (64%) was slightly higher than the overall analysis (62%) (Table 3). Subgroup analysis for 2000-2010 was not feasible. However, a single study from Nigeria in the 2000-2010 period recorded 52% secondary and tertiary education [17], another study from Nigeria with data between 2010 and 2012 recorded 66%, [18], and a separate study in Uganda including data between 2010 and 2013 [19] recorded 62%. (Supplementary file).


Eight articles (2,947 subjects) contributed to the analysis of BC laterality in the continent. Most patients (97%) had unilateral BC, compared to 3% (95% CI 2.0-6.0 I2=80%) who had bilateral BC. The highest prevalence of bilateral BC was in Nigeria, 8% (95% CI 6.0-12 ) [20] and Tanzania (6%, 95%CI 4-8) [21]. A slightly higher proportion (51% (95% CI 46-55) were left-sided tumors (Table 3).

Sex Female % (95 % CI) Male % (95 % CI) I-squared (%)
Africa Overall 97 (96-98) 3 (2-4) 77
EA 96 (93-97) 5 (3-7) 76
WA 98 (96-99) 2 (1-4) 80
Eritrea 96 (93-96) 4 (2-7) 23
Ghana 98 (97-99) 2 (1-3) 23
Nigeria 97 (96-99) 3 (1-4) 85
Tanzania 97 (95-98) 3 (2-5) 16
Educational Status None/Primary % (95 % CI) Secondary % (95 % CI) Tertiary % (95 % CI)
Africa Overall 38 (29-47) 36 (28-41) 26 (19-34) 95
Africa (2011-2019) 35 (25-46) 39 (27-49) 26 (17-36) 90
EA 36 (19-56) 38 (18-56) 26 (9-43) 57
SNA 25 (21-29) 48 (43-53) 27 (23-31) 0
WA 38 (27-49) 33 (22-44) 29 (19-40) 52
Marital Status Married % (9 5% CI) Unmarried % (95 % CI) Single% (95 % CI)
Africa Overall 62 (50-69) 19 (11-26) 20 (12-27) 97
Africa (2011-2019) 59 (39-72) 19 (7-33) 22 (9-36) 98
Africa (2000-2010) 65 (47-77) 20 (8-33) 16 (6-28) 96
EA 62 (49-72) 27 (17-37) 11 (5-19) 91
SNA 43 (39-47) 33 (30-37) 24 (21-27) 0
WA 71 (64-78) 15 (9-20) 14 (9-20) 90
Ghana 67 (47-82) 17 (5.0- 32) 16 (4-32) 96
Nigeria 73 (59-84) 15 (6-26) 12 (4.0-22) 91
Laterality Right % (95 % CI) Left % (95 % CI) Bilateral% (95 % CI)
Africa Overall 46 (41-51) 51 (40-55) 3 (2-6) 80
WA 44 (32-56) 53 (40-64) 3 (0-9) 84
EA 52 (43-61) 45 (35-53) 3 (0-9) 85
Table 3. Demographic Characteristics and Time to the Presentation of African Breast Cancer Patients, Overall and by Region/country where Available.

Stage distribution

Fifteen articles (9,185) contributed to carcinoma-in- situ analysis. Prevalence of carcinoma in-situ was generally low in all regions (CA- 4%, EA-2%, and NA-1%, SNA, and WA-1%). The highest proportion of carcinoma-in-situ in individual publications was 6% reported in Central African Republic and 5% in Malawi.

Overall, 98% of BCs were invasive based on analysis of 30 articles (10,352 subjects). Advanced BC (AJCC stage III or IV) accounted for 67%. Overall, 7% (95% CI 4.0-9.0, I2= 98%) of patients were diagnosed stage I disease, ranging from 2-10% in each region. Twenty-six per-cent of disease was stage II, ranging from 21-35% in each region, 50% of disease was stage III, ranging from 39-74% in each region, and 17% of disease was stage IV, ranging from 3-21% in each region. The earliest tumors were in NA; 74%, and 81% were Stage II or III in SNA and NA, respectively, while 70% or above were Stage III or IV in other regions (Figure 4 and Supplementary File).

Figure 4. AAA.

Trend analysis showed a decreasing prevalence of stage I (from 8% to 4%) and stage IV (from 24% to 12%) disease, with an increasing prevalence of stage II and III disease in the last decade (Figure 4).

Two articles (577 subjects) contributed to the clinical T-stage analysis, and seven articles (2151 subjects) contributed to the pathologic T-stage analysis. The majority of tumors were clinical T3 or T4, whereas the majority were pathologic T2 or T3. The prevalence of pathologic or clinical nodal positivity was 70% (99% CI 60-80), based on the analysis of 21 articles (8,357 subjects). WA had the highest prevalence of nodal disease (84%, 95% CI 71-94, I2=99%). By-country analysis showed that Nigeria (91%, 95% CI 75-100, I2=98) had the highest prevalence of lymph node positivity, and Eritrea (34%, 95% CI 28-41, I2=21) had the lowest.

Treatment modalities

Post hoc analysis of the treatment modalities in the continent showed that 72% of BC patients overall underwent surgery. Overall mastectomy prevalence was 71% (95% CI 51-88, I2=99%) while prevalence of breast-conserving surgery was 1.0% (95% CI 0-2, I2=0%). Eighty-three per-cent (64-96 I2=98%) of patients received chemotherapy, 18% (95% CI 4-33, I2 =94%) received radiotherapy, and 77% (95% CI 42-100 I2= 99) received hormonal therapy. Three studies reported routine hormonal therapy in all patients (see Supplementary File). There was no data on targeted-therapy.


The African continent has a total population of approximately 1.34 billion inhabitants, accounting for 17% of the world’s population. The United Nations recognizes five African regions comprising 64 territories/ countries: EA (22 countries) 0.45 billion, WA (17 countries) 0.40 billion, NA (11 countries) 0.25 billion, CA (9 countries) 0.18 billion and SNA (5 countries) 0.07 billion. Together WA and EA account for more than 60% of Africa’s population, and South Africa (SA) alone accounts for 88% of the population of SNA.

We aggregated data from 80 articles published within the last decade, from 23 countries representing Africa’s regions. Our findings corroborated previous evidence that African BC patients are younger than those from Europe and the US. In this study, 6% of patients were <30 years of age compared to 0.43% in the UK, 28% were <40 years compared to 6.6% in the US [96, 97], and 58% were <50 years compared to 20% in Europe [98].

The age distribution of BC in South Africa showed a reverse pattern, mirroring the Caucasian age distribution seen in Europe. One explanation might be the proportion of Caucasian inhabitants in SA. Nonetheless, in four of the seven studies included from SA where the race was reported, 90% were Black patients (Table 1). However, it was not reported whether these patients might have been mixed-race Black patients. Even then, previous report suggests that black BC patients in SA are older than other races with BC in SA [99], though this may be partially- attributable to under-reporting.

The declining age of BC found in this study in the setting of the increasing age of Africa’s population overall [100] contradicts the view that the earlier age of BC onset can be entirely attributed to the younger population in Africa. Additionally, the elevated proportion of male breast cancer, 3% overall and 4% in the last decade, compared to approximately 1% reported globally [101, 102], and previously reported increased rates of triple negative disease raise questions regarding potential genetic predisposition and merit further investigation.

The early age of BC onset in Africa brings numerous challenges regarding screening, early diagnosis, and treatment compliance [103]. Young women may be less likely to complete the diagnostic process or treatment for BC due to social reasons, such as fertility issues and socio- cultural isolation. A report in Nigeria found that 31% of young women outrightly declined the diagnostic biopsy procedure, 60% of those who did not decline failed to return for the result of the biopsy, and only 45% of those offered mastectomy accepted treatment [103]. Future intervention should be directed toward improving early diagnosis and compliance with treatment in this patient population.

Even in high-income countries where screening is ubiquitous, it is recommended to begin after 40 years (or 50 according to some guidelines). A third of BC patients in Africa were <40 and would be missed by applying the same screening age guidelines as in the US. While population-based mammographic screening programs are not feasible in most African countries due to resource constraints, education of the general population, paired with clinical breast examination (CBE) has the ability to downstage clinically apparent disease, and age range recommendations should be based on available data.

Thirty-eight per-cent of breast cancer patients in this analysis had none or primary education, and education level varied widely by region. This underscores the importance of tailoring breast cancer education and breast health awareness for both patients and the general population to the local context, taking into account educational and cultural background.

Although unmarried BC patients’ population appears to be increasing in Africa, perhaps due to delaying marriage for education, the current predominantly married BC patient population still provides opportunities to include men as potential intervention targets. Reports suggest men are willing to support women in BC control [104, 105].

In this analysis, nearly 90% of tumors were greater than 2cm, half were greater than 5cm, and one-quarter had skin or chest wall involvement. This preponderance of clinically-detectable disease means that the vast majority of patients have the potential for earlier detection by CBE. Marked regional variations in tumor size and stage across regions, might be explained in part by differences in health systems. Coordinating and centralizing local resources provided affordable, comprehensive health financing, and helped to downstage BC in NA [106]. Increasing awareness and reducing the distance and bottlenecks between BC patients and specialists aided downstaging in SA [9]. Countries in SSA could benefit from the experience in NA and SA to attain the goals of downstaging invasive BC. The smaller pathologic T staging compared to the clinical T staging in our analysis might be linked to the widespread use of neoadjuvant chemotherapy or errors of clinical or pathologic measurement and requires further investigation.

The decreasing rate of stage IV disease over time (24% to 12% in the first decade vs. the second decade of this analysis) shows promise for a slow, but positive trend toward earlier diagnosis, which is one of the most important factors in improving outcomes. The generally low prevalence of carcinoma-in-situ, 2-4% in all regions, can be explained by the common lack of population- based screening. Population-based screening should not be considered until a health system has the resources and ability to effectively diagnose and treat clinically apparent disease. Nonetheless, it is important to note that the rise in the prevalence of small tumors (<1cm ) and carcinoma-in- situ (from 2% to >20% ) in developed countries was linked to screening [107]. In light of the emerging evidence supporting the use of ultrasound in detecting early BC in young women [96, 108, 109], when a health system is ready to consider targeted screening, ultrasound may be considered along with CBE.

The heterogeneity, narrow spread, and paucity of articles capturing some of the variables analyzed limit our findings. Notably, only SA contributed to the findings for SNA. Similarly, in a previous review [110], only SA contributed to the meta-analytical review of the stage at presentation in SNA because the literature on BC is scarce from other SNA countries. Also, the reporting of demographic variables was influenced by the region as articles from the same region reported similar data points in similar formats. The countries of WA and EA reported more information on patient demographics than in the other regions. Availability of data limited our ability to draw conclusions regarding certain critical variables of interest, such as immunohistochemistry or treatment modality by stage.

In conclusion, Africa has a common goal of downsizing and downstaging BC, which is achievable through the early diagnosis of clinically detectable disease. There is marked regional variation in the clinical pattern and patient demographics of BC in Africa, and the interventions developed should be tailored to the local context in each area, while allowing for countries and regions to benefit from shared knowledge and experiences.


Declaration Funding

The authors received no funding for this research

Conflict of interest/Competing interests

The authors declare no conflict of interest.

Availability of data and Materials:

All data used in this article are freely available

Authors Contribution

Agodirin Contributed to all aspect of the research, all authors contributed to approval. Aremu contributed to conception, data acquisition, extraction , and review. Rahman contributed to conception, data interpretation, drafting, review. Olatoke contributed to conception, data interpretation and review. Olaogun contributed to draft, review and data interpretation, Akande contributed to data interpretation, drafting and review. Romanoff contributed to data interpretation, drafting and review.



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