# Breast cancer characteristics in low- and middle-income countries: An umbrella review

Lisbeth Tolentino-Rodriguez, Mohamad Chkeir, Vanina Pofagi, Irénée

Ahindu, Jean Toniolo, Andrea Erazo, Pierre-Marie Preux, Véronique

Blanquet, Marion Vergonjeanne, Alexis Parenté

► To cite this version:

Lisbeth Tolentino-Rodriguez, Mohamad Chkeir, Vanina Pofagi, Irénée Ahindu, Jean Toniolo, et al. Breast cancer characteristics in low- and middle-income countries: An umbrella review. Cancer Epidemiology, 2025, 96, pp.102797. 10.1016/j.canep.2025.102797. hal-05021248

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# Breast cancer characteristics in low- and middle-income countries: An umbrella review

Lisbeth Tolentino-Rodriguez<sup>a, *,</sup>, Mohamad Chkeir<sup>a</sup>, Vanina Pofagi<sup>a, b</sup>, Irénée Ahindu<sup>a, b</sup>,
Jean Toniolo<sup>a</sup>, Andrea Erazo<sup>a</sup>, Pierre-Marie Preux<sup>a</sup>, Véronique Blanquet<sup>a</sup>,
Marion Vergonjeanne<sup>a, 1</sup>, Alexis Parenté<sup>a, b, 1</sup>

## ARTICLE INFO

### Keywords:

Breast cancer

Age

Stage

Phenotype

Molecular

LMIC

## ABSTRACT

Breast cancer presents significant challenges in low- and middle-income countries (LMICs) due to disparities in healthcare access and outcomes. This umbrella review synthesizes data on breast cancer characteristics—age at diagnosis, staging, and molecular subtypes—to guide targeted healthcare strategies in LMICs. Our umbrella review was conducted following PRISMA 2020 and JBI guidelines. Systematic reviews from 2009 to 2024 were sourced from PubMed, Google Scholar, and Cochrane. Reviews were assessed with AMSTAR 2, and only those rated moderate or higher were included. Data synthesis and meta-analyses were performed using R. From 1165 records, 35 systematic reviews met initial criteria; nine were included in the final synthesis, representing 305 primary studies (195 relevant to LMICs). Of those, 50% were hospital-based and 22% population-based, limiting the generalizability of the data and the importance of promoting more population-based studies. The overall quality of systematic reviews was variable, with only a few meeting high standards. Geographic analysis revealed a significant underrepresentation of high-quality reviews in sub-Saharan Africa and Latin America. Age at diagnosis varied: sub-Saharan Africa (45–52 years), Middle East (36–56 years), and Latin America (49–53 years). Advanced-stage diagnoses (stages III and IV) were common, worsening prognostic outcomes. Molecular subtype analysis indicated a predominance of luminal A but highlighted treatment challenges due to limited targeted therapy access. The results emphasize a pressing need to enhance the availability and quality of primary data, including both hospital-based and population-based studies, particularly in underrepresented regions like sub-Saharan Africa and Latin America. Addressing these gaps with rigorous, locally focused studies is essential for improving breast cancer prevention, diagnosis, and treatment. Enhancing methodological standards and expanding research in these areas will be crucial to bridging global breast cancer outcomes disparities.

## 1. Introduction

Breast cancer is defined by the abnormal proliferation of cells within the breast tissue, often originating in the ducts or lobules [1].

Breast cancer prognosis and treatment are significantly influenced by its molecular subtypes. Luminal A, typically ER and PR positive, has a favorable prognosis and responds well to hormone therapy. Conversely, despite being ER and PR positive, Luminal B has a higher proliferation rate and poorer prognosis [2]. The HER2-enriched subtype, known for HER2 overexpression, is aggressive but can be treated effectively with

targeted therapies like trastuzumab [2]. Triple Negative Breast Cancer (TNBC) is the most challenging subtype to treat due to the absence of ER, PR, and HER2, making it particularly aggressive with limited treatment options [3].

Breast cancer staging, ranging from Stage 1 to Stage 4, is crucial for determining prognosis and treatment plans. Stage 1 cancers are localized with survival rates typically over 90%. As the stage progresses, tumors grow larger and spread to nearby lymph nodes, leading to lower survival rates. Stage 4, or metastatic breast cancer, indicates that the cancer has spread beyond the breast and nearby lymph nodes to other

<footnote><sup>a</sup> Inserm U1094, IRD U270, Univ. Limoges, CHU Limoges, EpiMaCT - Epidemiology of Chronic Diseases in Tropical Zone, Institute of Epidemiology and Tropical Neurology, OmegaHealth, 2 rue du Dr Marcland, Limoges 87000, France</footnote>

<footnote><sup>b</sup> Laboratory of Epidemiology of Chronic and Neurological Diseases, LEMACEN, Champ de foire, Bernadin Gantin, Cotonou, Benin</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: listolen@gmail.com (L. Tolentino-Rodriguez).</footnote>

<footnote n="1">¹ Marion Vergonjeanne and Alexis Parenté are considered co-last authors and contributed equally to this work.</footnote>
parts of the body, such as bones, liver, or lungs, complicating treatment and reducing survival prospects [4–6].

Breast cancer presents a global challenge, affecting both high and low- to middle-income countries (LMICs) with approximately 1.7 million new cases diagnosed annually [7]. It is the most diagnosed cancer among women in 157 out of 185 countries and the fifth leading cause of death globally [8]. According to the World Health Organization (WHO), 2.3 million women were diagnosed with breast cancer in 2022, resulting in 685,000 deaths worldwide [1].

LMICs bear a significant burden of breast cancer cases, with a surge in late-stage presentations and limited access to treatment options leading to poorer outcomes compared to high-income countries [3]. For instance, Latin America reports a breast cancer incidence of 52 per 100, 000 and a mortality rate of 13.2 per 100,000, with Fiji leading the mortality rates worldwide [8]. In South Asia, there is a rapid increase in breast cancer incidence, with a high mortality/incidence ratio of 0.52 in 2020 [9].

Studies indicate that regions such as Central and Sub-Saharan Africa, along with South Asia, experience higher fatality rates due to limited diagnostic and treatment resources [10]. Sub-Saharan African countries like Nigeria (Ibadan, 1991–2010) and Malawi (Blantyre, 1994–2011) have reported the highest incidence rate increases of over 5 % per year during these periods from their population based cancer registries [11].

Addressing disparities in breast cancer characteristics, such as age at diagnosis, stage, and molecular subtypes across LMICs, is essential. This research aims to bridge the knowledge gap by providing insights that could inform the development of more effective, context-specific breast cancer treatment approaches for LMICs.

Our research question was: What are the demographic and clinical characteristics of breast cancer, including age at diagnosis, cancer stage, and molecular subtypes, in low- and middle-income countries? Numerous systematic reviews on breast cancer have been conducted in low- and middle-income countries (LMICs). However, what is their quality, and can we synthesize these findings into an umbrella review?

The general objective was to conduct an umbrella study to thor-
oughly synthesize and analyze breast cancer data in low- and middle-
income countries in Latin America, Africa, Asia, and Europe.

Our specific objectives were i) to detail the geographical distribution of breast cancer systematic reviews; ii) to identify the age of diagnosis in LMICs; iii) to identify stages of breast cancer in LMICs and iv) to identify breast cancer molecular phenotypes in LMICs.

This approach aims to consolidate existing knowledge, identify trends, regional disparities, molecular types, diagnostic stages, and the average age at diagnosis in these countries, ultimately enhancing prevention, diagnosis, and treatment strategies tailored to these regions' socioeconomic and cultural specificities.

**2. Materials and methods**

For this umbrella review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols (PRISMA 2020) statement and the Joanna Briggs Institute (JBI) methodology for Umbrella reviews were followed [12]. Following the guidelines, the systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the registered number CRD42024542044. Ethics approval was not required for this review as the analysis only comprised of published literature.

**2.1. Search strategy**

The research strategy included the databases of PubMed, Google Scholar (which included grey literature), African Journals Online, Cochrane Database for Systematic Reviews, and Scielo [13–15]. The research equation was constructed according to PICOTS terms[15]: (“Breast cancer”) AND (“age of diagnostics” OR “stage” OR “molecular subtype”) AND (“LMIC”). (SP 1). From Google Scholar 200 articles were

selected [14]. If the database had filtering options, we applied filters to
limit our search to reviews and systematic reviews.

All the results were imported into the Zotero library, where a manual
revision of doubles was performed. Then, the reviews were exported to
the platform Rayyan, where they were screened according to the
inclusion/non-inclusion criteria.

**2.2. Study selection**

Studies were included according to the following inclusion criteria in the PICOTS framework [15]: Systematic reviews: with or without meta-analysis and scoping reviews conducted according to PRISMA guidelines and subjected to peer review; ii) Breast cancer research in women, including mixed cancer studies done in English, Spanish, Portuguese, and/or French; iii) breast cancer age at diagnosis, stage, and molecular phenotype; iv) Secondary-type studies published between 2009 and 2024; and v) All LMICs in Latin America, Europe, Asia, and Africa, according to the World Bank's income classification 2023–2024.

Articles were excluded if they matched at least one of the following criteria: i) Breast cancer research focused on male participants and/or breast cancer in males; ii) Articles not validated by a peer-review committee or those that did not adhere to systematic review and meta-analysis guidelines (e.g., PRISMA, Cochrane); iii) Letters to the Editor; iv) Case studies; v) Primary studies such as interventional studies (clinical trials) and observational studies; vi) Protocols of systematic reviews. All reasons for exclusion were documented, and we followed the PRISMA Flow Chart.

**2.3. Selection process**

Two independent reviewers (MV, LT) worked in a first screening
process, which comprised reviewing the title and abstract to see if the
articles matched the inclusion or non-inclusion criteria; the second step
involved examining the full text to make the final selection. A third
reviewer (AP) resolved any reviewer disagreements through discussion
and consensus.

**2.4. Methodological quality assessment**

Two researchers (MV, LT) assessed the quality of the included studies with the AMSTAR 2 (Assessing the Methodological Quality of Systematic Reviews 2) tool. The choice of critical domains of AMSTAR 2 was “adapted” as mentioned by Kho et al. [16]. Considering the response in the critical domains, the articles were classified as follows i) high; ii) moderate; iii) low and iv) critically low.

Studies with moderate and above quality appraisal were considered for the analysis, (SP 3). Any disagreements between reviewers were resolved through discussion and consensus by a third reviewer (AP).

**2.5. Overlapping**

The Correct Cover Area (CCA) was used to manage the overlap of
many systematic reviews and meta-analyses analyzing similar out-
comes. The CCA was determined, prioritizing the most recent analysis in
cases where meta-analyses exhibit partial or complete overlap [17].
Current guidance for overviews recommends calculating overlap for all
primary studies included in the reviews constituting the overview (SP 1).

**2.6. Data extraction and statistical analysis**

Two independent reviewers (MV, LT) extracted data in two levels from the included studies. Results were compared and all conflicts were mediated using a third reviewer (AP). The data collected were: i) Characteristics of the systematic review, meta-analysis, and scoping study included: the date of publication, country, the aim of the review, number of studies reviewed, age of diagnosis of the population, stage of
diagnosis, and phenotype of breast cancer (including histological and molecular types) [18–21]; ii) Characteristics of the primary studies included: Authors, country, sample size, breast cancer cases, mean age, age at diagnostic, stage (TNM or Stage 0-IV or Extent of disease I-III), grade, ER, PR, HER2. Also, were collected the histological type (in situ), histological type (invasive), molecular type Luminal A (ER+, PR+, HER2-), molecular type Luminal B (ER+, PR+ HER2-), molecular type HER2 enriched, triple negative (basal-like), molecular type normal breast-like.

## 2.7. Statistical analysis

The results of studies with a moderate and low risk of bias were summarized qualitatively. For descriptive statistics, qualitative variables were expressed as numbers and percentages, and quantitative variables as mean ± standard deviation or median and interquartile

range, depending on their normality. QGIS was used to visualize the geographic distribution of studies on breast cancer in low- and middle-income countries and a meta-analysis was conducted using the "metafor" package in R (version 4.3.1, R Project for Statistical Computing) to synthesize the information concerning the age of diagnosis from the selected systematic reviews. A random-effects model was applied with the restricted maximum likelihood (REML) method to estimate the between-study variance (τ²). Heterogeneity among studies was quantified using the I² statistic, and the significance of heterogeneity was tested with Cochran's Q test. Subgroup analyses were performed based on the continent and the country's income, according to the World Bank classification. Publication bias was assessed graphically using a funnel plot, with statistical significance evaluated via Egger's test. Multiple meta-regression was conducted using a mixed-effects model with the Maximum Likelihood (ML) method to investigate possible causes of heterogeneity across studies. The covariates included were income,

<figure><img src="image_3.png" /><figcaption>Fig. 1. Prisma Flowchart.</figcaption></figure>
continent, study setting, study design, and publication year. Multi-collinearity was first checked for the eventual included factors, Using the Variance Inflation Factor (VIF). Afterward, the rma function in R was used to perform the multiple linear meta-regression.

A multimodel inference approach was used to model all possible predictor combinations. The model with the lowest value of Akaike Information Criterion corrected (AICc) was designated as the predictor combination that provides the best fit. The proportion of variance accounted for by the moderator variables was estimated using R². The Knapp-Hartung method was used to test the significance of predictors. The level of significance was set at 5 %.

# 3. Results

## 3.1. Study selection

A total of 1165 records were collected (Fig. 1). After eliminating 75 duplicate entries, 1090 systematic reviews remained. After screening titles and abstracts, 940 records were excluded (SP2), leaving 150 reviews for full-text evaluation. Of these, 35 reviews were included and then underwent methodological quality assessment. Five reviews (14 %) received a low-quality score, and 21 (60 %) received a critically low score. Only 3 (9 %) reviews received a high-quality score, and the remaining 6 (17 %) were moderate quality (SP3). Finally, these 9 reviews were ultimately included in the final synthesis, [22-30].

For calculating the overlap, 305 primary studies from 9 reviews were identified and the CCA was found to be 0 % (SP 4). Of the 305 primary studies, just 195 correspond to LMIC, which were included in our analysis.

## 3.2. Description of selected reviews

Three reviews came from China [25,27,28], making it the country with the highest contributions. Iran, Egypt, Brazil, and the United Kingdom each produced 1 systematic review [23,24,26,29]. The remaining 2 reviews were a multi-center study from Peru and the USA, and a collaborative effort involving Japan, the United Kingdom, and Pakistan [22,30]. The publication years ranged from 2014 to 2023, and with a peak in 2022. Of the 9 systematic reviews, only 1 did not conduct a meta-analysis [26]. The risk of bias in the primary studies included in

the systematic reviews was assessed using the Newcastle Ottawa Scale (NOS) for 8 [22-29], while 1 systematic review [30] used the Risk of Bias Assessment Tool for Non-randomized Studies (ROBANS) (SP 5) (Fig. 2).

In terms of regional distribution of primary studies, North and Latin America contributed 42.78 % (n = 83) of studies, the Middle East and North Africa contributed 28.35 % (n = 55), East Asia and Pacific 16.49 % (n = 32), Sub-Saharan Africa 9.79 % (n = 19), Europe and Central Asia 2.06 % (n = 4), and South Asia 0.52 % (n = 1). When ranking the origin of the primary studies, China led with 31 studies, followed closely by Egypt with 29 studies. Mexico and Colombia each contributed 14 primary studies, Brazil contributed 13, and Cuba contributed 10. Countries with at least one primary study included in the systematic reviews were Cameroon, Honduras, Haiti, India, Iran, Jamaica, Kenya, Libya, and Malaysia.

The sample sizes in the systematic reviews varied widely. The study by De Lemos et al. [29] had a total sample size of 221,255 participants; however, they did not report the percentage of breast cancer cases. Khoramdad et al. [23] reported the second-largest sample size, with 16, 454 participants, while the smallest was in the review by Martins et al. [26], with 598 participants. Of the 195 primary studies, 43 are from general population, 107 are hospital-based, and the remainder are mixed or unspecified.

Regarding the percentage of breast cancer cases reported by country, China reported a range between 46.5 % and 60 %, Iran reported between 8.9 % and 49.77 %, and Turkey reported 50 %.

A summary of the final 9 included studies is detailed in Table 1.

## 3.3. Clinical outcomes

The following description of the clinical outcomes comes from 195 primary studies conducted in LMIC which were selected from the 9 systematic reviews:

## 3.4. Age at diagnosis

All 9 systematic reviews reported the age of diagnosis in their studies, regarding the 195 primary articles a total of 82 reported the age. The mean age of breast cancer patients reported shows significant regional variation (SP 4). In Sub-Saharan Africa, only 3 countries

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Geographical distribution of systematic review and primary studies. The green circles represent the total number of systematic reviews conducted by country. The red color degradation represents the number of primary studies conducted by country, where the darkest red has the greatest number and the palest the least number.</figcaption></figure>
<table><caption>**Table 1**
Summary of the final included studies.</caption><thead><tr><th>Title of the article</th><th>Authors</th><th>Year of publication</th><th>Country of systematic review</th><th>Objectives</th><th>Quality Assessment (Tool)</th><th>Included studies</th><th>LMIC countries included</th><th>Synthesis of the results</th><th>Principal results</th></tr></thead><tbody><tr><td>Association between Insulin Resistance and Breast Carcinoma: A Systematic Review and Meta-Analysis</td><td>Hernandez, et al.</td><td>2014</td><td>Peru/USA</td><td>To evaluate the association between components defining insulin resistance and breast cancer in women</td><td>NOS</td><td>22*</td><td>China [1], Iran [1], Turkey [1]</td><td>Mixed</td><td>No significant association between insulin resistance components and breast cancer risk among women</td></tr><tr><td>Breast cancer risk factors in Iranian women: a systematic review and meta-analysis of matched case-control studies</td><td>Khoramdad, et al.</td><td>2022</td><td>Iran</td><td>To identify modifiable and non-modifiable risk factors of breast cancer in Iranian women</td><td>NOS</td><td>24**</td><td>Iran [24]</td><td>Mixed</td><td>Several risk factors were highlighted, including obesity, age at marriage, smoking, second-hand smoking, abortion history, oral contraceptive use, and non-modifiable risk factors like family history of breast cancer and age at menarche. Given that most of the above are modifiable risk factors, lifestyle changes can play an influential role in the primordial prevention of breast cancer.</td></tr><tr><td>Clinicopathologic Features of Breast Cancer in Egypt—Contemporary Profile and Future Needs: A Systematic Review and Meta-Analysis</td><td>Azim, et al.</td><td>2023</td><td>Egypt</td><td>To explore the pooled estimated proportions of different stages of breast cancer at presentation in Egypt and describe other clinicopathologic features including mean age, menopausal status, tumor (T) and lymph node (N) stages, histopathologic types, and biological subtypes</td><td>NOS</td><td>26*</td><td>Egypt [26]</td><td>Mixed</td><td>Dominance of advanced stage and young age at diagnosis among Egyptian women</td></tr><tr><td>Clinicopathological and Prognostic Significance of Cancer Antigen 15-3 and Carcinoembryonic Antigen in Breast Cancer: A Meta-Analysis including 12,993 Patients</td><td>Li, et al.</td><td>2018</td><td>China</td><td>To investigate the prognostic value of serum CA15-3 and CEA in breast cancer patients</td><td>NOS</td><td>36*</td><td>China [7], Egypt [2], Libya [1], Malaysia [1], South Africa [1], Turkey [2]</td><td>Mixed</td><td>Elevated serum CA15-3 or CEA was associated with poor DFS and OS in patients with breast cancer, and they should be tested anytime if possible elevated CA15-3 was associated with advanced histological grade and younger age.<br/>● Elevated CEA was related to non-triple-negative tumor types and older age.</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Title of the article</th><th>Authors</th><th>Year of publication</th><th>Country of systematic review</th><th>Objectives</th><th>Quality Assessment (Tool)</th><th>Included studies</th><th>LMIC countries included</th><th>Synthesis of the results</th><th>Principal results</th></tr></thead><tbody><tr><td>Prognostic significance of receptor expression discordance between primary and recurrent breast cancers: a meta-analysis</td><td>Shiino, et al.</td><td>2022</td><td>UK, Japan, Pakistan</td><td>To investigate whether discordance of receptors (ER, PR, and HER2) between primary and recurrent tumors affect the survival of patients with BC compared with receptor concordance using a meta-analytic approach</td><td>RoBANS</td><td>11**</td><td>China [2]</td><td>Mixed</td><td><ul><li>Loss of ER or PR in recurrent tumors was significantly associated with shorter overall survival (OS) compared to receptor-positive concordance. Specifically, ER loss resulted in a hazard ratio (HR) of 1.67 (95% CI: 1.37–2.04) and PR loss had an HR of 1.45 (95% CI: 1.21–1.75) for reduced OS.</li><li>Gain of ER was a significant predictor of longer post-recurrence survival (PRS) compared to receptor-negative concordance (HR, 0.76; 95% CI 0.59–0.97).</li><li>The study found that both loss and gain of HER2 could be related to poor outcomes, although specific hazard ratios for these outcomes were not detailed due to limited data</li></ul></td></tr><tr><td>Prognostic values of tumoral {MMP2} and {MMP9} overexpression in breast cancer: a systematic review and meta-analysis.</td><td>Jiang, et al.</td><td>2021</td><td>China</td><td>To investigate the associations between MMP2/ MMP9 overexpression in tumor cells with clinicopathologic features and survival outcomes in breast cancer patients</td><td>NOS</td><td>41**</td><td>Brazil [2], China [17], Egypt [1], Turkey [1]</td><td>Mixed</td><td><ul><li>MMP2 and MMP9 overexpression in tumor cells is associated with poor survival outcomes (OS and DFS) in breast cancer patients.</li><li>These markers are also associated with more aggressive disease features, including higher tumor stage and grade, lymph node metastasis, and potentially larger tumor sizes</li></ul></td></tr><tr><td>Routes to diagnosis of symptomatic cancer in sub-Saharan Africa: systematic review</td><td>Martins, et al.</td><td>2020</td><td>UK</td><td>To examine the evidence regarding the routes to cancer diagnosis in sub-Saharan Africa, using the model of pathways to treatment.</td><td>NOS - JBI</td><td>18*</td><td>Cameroon [1], Ethiopia [3], Ghana [2], Kenya [1], Nigeria [7], South Africa [2], Tanzania [1], Malawi [1]</td><td>Qualitative</td><td>The review highlighted the common use of complementary medicine prior to seeking conventional medical help, which may contribute to late-stage diagnosis and poorer survival rates</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Title of the article</th><th>Authors</th><th>Year of publication</th><th>Country of systematic review</th><th>Objectives</th><th>Quality Assessment (Tool)</th><th>Included studies</th><th>LMIC countries included</th><th>Synthesis of the results</th><th>Principal results</th></tr></thead><tbody><tr><td>Stage at diagnosis and stage-specific survival of breast cancer in Latin America and the Caribbean: A systematic review and meta-analysis</td><td>de Lemos, et al.</td><td>2019</td><td>Brasil</td><td>To systematically review the proportion of advanced stage (III-IV) at diagnosis and the five-year stage-specific survival estimates of breast cancer in Latin American and Caribbean (LAC) countries</td><td>Modified Elm et al., and Chubak et al., tools</td><td>95*</td><td>Argentina [9], Brasil [11], Colombia [14], Costa Rica [2], Cuba [10], Ecuador [3], Haiti [1], Honduras [1], Jamaica [1], Mexico [13], Paraguay [1], Peru [5], Suriname [1], Venezuela [8]</td><td>Mixed</td><td>Revealed that a significant proportion of women in LAC are diagnosed at an advanced stage of breast cancer, which correlates with poorer survival outcomes</td></tr><tr><td>The relationship between night shift work and breast cancer incidence: A systematic review and meta-analysis of observational studies</td><td>Hong, et al.</td><td>2022</td><td>China</td><td>To explore the relationship between night shift work and the risk of breast cancer incidence.</td><td>NOS</td><td>33*</td><td>China [4], India [1], Mexico [1]</td><td>Mixed</td><td>Night shift work is associated with an increased risk of breast cancer, particularly receptor-positive types. The relationship is influenced by factors such as the timing of the night shift work relative to menopausal status</td></tr></tbody></table>

reported the mean age: Ethiopia at 45.5 years, Ghana at 52.5 years, and Nigeria with a range between 45 and 51 years. In the Middle East and North Africa, Iran reported a mean age ranging from 36.1 to 50 years, while Egypt's range was between 48 and 56 years. In Latin America, Mexico reported a mean age between 49 and 53 years, and Brazil reported a mean age of 53 years. In East Asia and the Pacific, China's reported mean age ranged from 46.8 to 60.0 years. In Europe and Central Asia, Turkey reported a mean age between 40.7 and 51.0 years.

There is great variability in the mean age of diagnosis as represented in the forest plot in Fig. 3 and Fig. 4. A random effects model was used to account for the heterogeneity between the studies, which allowed us to summarize the information obtained from multiple studies. America presented a mean diagnostic age of 52.8 years [95 % IC 47.0–58.7] followed by the Middle East with 48.4 years [95 % IC 46.8 – 50.1]. Across Asia, the mean diagnostic age is approximately 50.7 years [95 % IC 47.6 – 53.9], and in Africa 47.8 years [95 % IC 44.9 – 50.7]. High levels of heterogeneity are present and in the test subgroup, the difference in age diagnostic is not statistically significant even though there is an observable difference (p = 0.26).

The meta-regression models selected primarily included only one variable. The most relevant model examines the impact of publication year (AICc = 460.3; R² = 1.43), followed by income level (AICc = 461.2; R² = 6.47), continent (AICc = 463.0; R² = 3.97), and study design (AICc = 464.0; R² = 5.95). None of the variables were significantly associated with variations in the age outcomes (moderator tests: p > 0.05), and the models explained very little of the heterogeneity between studies. In these models, residual heterogeneity remained high at approximately 99.7 % and was not accounted for by the model (Q test: p < 0.001).

### 3.5. Staging of breast cancer (TNM staging, stages I to IV)

We identified 3 systematic reviews that reported on cancer staging [24,25,29] and regarding the 195 primary studies just 36 reported the stage. Among these, the review by Jiang et al. [25] provided detailed information on TNM staging. This review included tumor staging data from 14 primary studies, while 15 primary articles reported on nodal staging, and three articles described the stage of metastasis. Additionally, the overall stage of breast cancer was documented in 15 primary articles.

The systematic review by Azim et al. [24] reported TNM staging data, including tumor (T) staging from 10 primary studies and nodal (N) involvement from 13 studies. Their findings indicated that stage T2 was the most frequently observed tumor stage, with a prevalence of 55 % (95 % CI: 48–61), while stage T3 was the least common, accounting for 8 % (95 % CI: 5–12). Regarding nodal staging, N1 was the most frequently reported stage at 29 % (95 % CI: 25–32), whereas N3 was the least reported at 18 % (95 % CI: 13–24).

Azim et al. [24] also reviewed 8 primary studies from Egypt that reported on breast cancer stages I to IV, with varying sample sizes across different stages. The pooled estimated proportions for overall breast cancer staging were as follows: stage I, 6 % (95 % CI: 4–8); stage II, 37 % (95 % CI: 31–43); stage III, 45 % (95 % CI: 42–49); and stage IV, 11 % (95 % CI: 9–15). Similarly, the review by De Lemos [29], indicated that in Latin American countries, the predominant stages at presentation were III and IV, with a pooled prevalence of 41 % according to their meta-analysis.

<footnote>*Population and hospital based.</footnote>

<footnote>**Hospital based</footnote>
<figure><img src="image_5.png" /></figure>

Heterogeneity: *I*² = 100%, *t*² = 35.6154, *p* = 0

Test for subgroup differences: *χ*² = 3.99, df = 3 (*p* = 0.26)

<figure><img src="image_6.png" /><figcaption>Fig. 3. Forest plots age of diagnostic by continent.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Forest plots age of diagnostic by country income classification.</figcaption></figure>
## 3.6. Molecular phenotypes

Among the 9 selected reviews, 3 reviews reported molecular phenotypes while of 195 primary articles just 24 included the molecular phenotypes. Azim et al. [24] was the only review to report pooled proportions of molecular subtypes at the time of breast cancer diagnosis. In their primary included studies, 10 articles reported the status of HER2, 7 articles reported the triple negative status and 11 articles reported the estrogen and progesterone status. Their analysis indicated that ER+ was the most prevalent subtype, accounting for 70 % (95 % CI: 61–77) of cases, followed by HER2-enriched at 21 % (95 % CI: 5–21) and triple-negative breast cancer at 10 % (95 % CI: 5–21). Notably, the review did not report the presence of the Luminal B subtype in the studied population.

The systematic review by Jiang et al. [25] included 11 primary studies that reported on estrogen and progesterone receptor status, while only six of these studies provided data on HER2 status.

The review by Shiino et al. [30], which included 2 primary studies, did not examine molecular subtypes at diagnosis. However, it highlighted the impact of receptor conversion in recurrent tumors, suggesting that changes in receptor status may influence patient survival compared to receptor concordance.

## 4. Discussion

The increasing global focus on breast cancer in LMIC highlights the importance of conducting an umbrella review to synthesize findings from multiple systematic reviews. A comprehensive assessment of breast cancer characteristics in LMICs is essential because of the rising number of scoping and systematic reviews from these regions, as well as the distinct population disparities and the urgent need to bridge gaps in knowledge and healthcare resources. This review provides a thorough examination of the age at diagnosis, staging, and molecular subtypes of breast cancer across LMICs, emphasizing both the observed patterns and the limitations of the selected reviews.

Our review initially included 1165 systematic reviews, of which 35 underwent quality assessment. Of these, only 3 reviews were rated as high quality, 6 as moderate, 5 as low, and 21 as critically low. Consequently, only 9 reviews were included in the final synthesis. These 9 reviews encompassed a total of 305 primary studies, with 195 studies specifically relevant to low- and middle-income countries (LMICs). Geographically, China contributed the largest number of reviews. The included publications spanned from 2014 to 2023, with a peak in 2022. Notably, one review did not conduct a meta-analysis, and various tools were used to assess the risk of bias [22–30].

The limited number of high-quality systematic reviews in LMICs reveals critical methodological deficiencies and emphasizes the need for improved rigor in research practices. Essential criteria, such as age at diagnosis and staging, were reported inconsistently across LMIC reviews, likely due to resource constraints and varying research priorities. Notably, high-quality systematic reviews were scarce in sub-Saharan Africa and Latin America, indicating significant geographic disparities that future primary studies should address to enhance representativeness. The methodological assessment revealed widespread deficits, with crucial elements—such as comprehensive discussions of heterogeneity, detailed descriptions of included studies, and clearly defined bias assessment—frequently absent. The high heterogeneity observed across studies further complicated synthesis efforts and underscores an urgent need for standardized methodological approaches to improve reliability and comparability in LMIC research. These findings are consistent with prior assessments of systematic review quality in LMICs, indicating a pressing need for enhanced training, robust infrastructure, and quality control mechanisms to elevate research standards[31]. Considering the substantial methodological limitations and restricted geographic representation, conducting an umbrella review may be premature in this context. Instead, prioritizing the development of systematic reviews and

primary studies that rigorously adhere to established quality standards and are tailored to local healthcare systems and populations would likely yield more impactful insights. Our analysis of 195 primary studies identified 43 as population-based, 107 as hospital-based, and 45 in mixed or unspecified settings. This distinction is important because hospital-based studies tend to report more late-stage diagnoses due to referral biases, while population-based studies give a broader national and **sub-national** view. Future research should improve data source transparency in systematic reviews for clearer results interpretation.

This review reveals marked regional variations in the age of breast cancer diagnosis, with patients in low- and middle-income countries (LMICs) often presenting at significantly younger ages than those in high-income countries. For instance, in sub-Saharan Africa, the mean diagnostic age ranges from 45 to 52 years, compared to a median diagnostic age of approximately 62 years in high-income regions such as the United States and Western Europe. Similarly, in the Middle East and North Africa, a broad diagnostic age range is observed (36–56 years), with particularly young ages noted in Iran.

Different prevalent risk factors, such as older age at first childbirth and lower parity in high-income countries, may delay disease onset. Distinct lifestyle, environmental, and genetic factors likely contribute to these observed disparities in diagnostic age across regions [23–27,32, 33–36].

Projections suggest that cancer rates in young adults in LMICs will double by 2050, indicating the necessity to reassess early detection strategies for high-risk groups. Despite traditional focus on older populations, the evidence supports the need for exploring targeted screening strategies for young, high-risk individuals in LMICs, considering both cost-effectiveness and practical implementation [32]. Rather than relying solely on mammographic screening, which may not be feasible in all settings, alternative cost-effective measures such as breast self-examination and educational programs should be promoted. These strategies facilitate early detection by empowering women and community health workers with the knowledge to identify early symptoms of breast cancer. Furthermore, beyond early screening, there is a significant value in implementing early diagnostic measures. Educational therapeutics aimed at teaching women, healthcare providers, and community volunteers about the symptoms of breast cancer can lead to timely and accurate diagnosis. This approach not only aids in recognizing the disease in its initial stages but also enhances the overall management and outcomes of breast cancer care in resource-limited settings.

Advanced-stage diagnoses are predominant across LMICs, with the majority of breast cancer cases identified at stages II or III, particularly in regions such as Latin America and sub-Saharan Africa. In Latin America, stage III and IV cases are especially prevalent, representing 30 % and 41 % of cases, respectively, with studies attributing this trend to limited access to early detection programs. These findings align with those of Cazap et al. [37], which reported advanced stages in 30–40 % of patients in LMICs, compared to only 10 % in Europe. Societal norms, stigma, and healthcare access barriers in LMICs contribute to delays in diagnosis, necessitating culturally sensitive outreach efforts to promote timely medical attention [38]. The late-stage diagnoses prevalent in LMICs significantly contribute to higher mortality rates, as delayed detection is often associated with poorer prognosis and reduced survival. In contrast, high-income countries benefit from extensive screening initiatives, resulting in higher rates of early-stage detection, typically at stage I. For instance, in the United States and Western Europe, early-stage detection rates have improved substantially due to routine mammography and public health initiatives aimed at increasing awareness [23–25,27,29,34,35,37,39]. However, the trends in LMICs remain stationary, with some countries even experiencing slight increases in late-stage diagnoses, reflecting both resource constraints and gaps in healthcare access. The lack of standardized and comprehensive cancer registry data in LMICs, with many African and Asian countries reporting more than 50 % missing stage information, further impedes
accurate monitoring and planning for breast cancer control [38].

The stark differences in diagnostic stages between LMICs and HICs highlight the critical role of healthcare infrastructure in determining cancer outcomes. Enhanced screening and early detection programs specific for high-risk groups in LMICs could shift the trend toward earlier diagnoses, improving prognosis and reducing mortality rates. Notably, disparities in stage at diagnosis are also strongly linked to socioeconomic factors. Women from lower socioeconomic groups in LMICs are significantly more likely to be diagnosed with distant metastatic disease than those from higher-income groups. This highlights the intersection of economic inequities, healthcare access barriers, and the need for more inclusive cancer awareness initiatives [38]. Investment in healthcare systems and public health education on breast cancer are essential to narrowing this disparity and achieving outcomes closer to those seen in HICs. These findings underscore the urgent need for tailored healthcare interventions to bridge the global gap in breast cancer outcomes, as supported by previous research that links early detection with improved survival.

Molecular phenotyping plays a pivotal role in treatment planning and prognosis, and our study reveals significant regional differences in subtype prevalence. In Egypt, there is a high prevalence of the luminal A subtype, representing 70 % of cases, while the HER2-enriched and triple-negative subtypes are less common. In contrast, East Asia, particularly China, shows a higher representation of the luminal B subtype, which is associated with a poorer prognosis. Although the literature suggests that triple-negative breast cancer is notably prevalent in African regions, the low quality of reviews available in our study limits our ability to present reliable, specific data on this subtype in Africa. In LMICs, limited access to targeted therapies for subtypes like luminal B exacerbates challenges in effective treatment, underscoring the disparity in treatment options across regions. This variability in molecular subtype distribution emphasizes the need for region-specific molecular profiling to guide resource allocation and therapy development in LMICs [40].

In high-income countries, the most prevalent molecular subtype of breast cancer is typically luminal A, accounting for approximately 50-60 % of cases. Other subtypes, such as luminal B, HER2-enriched, and triple-negative, are present but represent smaller proportions of cases. greater access to targeted therapies in these regions have significantly contributed to earlier detection and more effective treatment, leading to improved survival rates and outcomes [24,25,28,29,30,37,41].

The marked disparities in molecular subtype distribution between HICs and LMICs underscore the urgent need for region-specific molecular profiling to inform resource allocation and the development of appropriate therapeutic approaches in LMICs. Global health initiatives that focus on improving screening infrastructure, expanding access to targeted therapies, and enhancing molecular diagnostic capacities in LMICs are essential steps toward reducing these disparities and improving breast cancer outcomes worldwide.

Several limitations were identified in this research. A primary limitation was the exclusion of numerous studies due to low quality, as assessed by AMSTAR 2, which may have resulted in the loss of valuable information, especially from underrepresented regions like sub-Saharan Africa and South Asia, potentially skewing the findings. Time constraints also prevented the use of GRADE for evaluating primary studies, as recommended by JBI, which may have introduced bias in the result interpretation. The limited proportion of population-based studies (22 %) and inconsistent reporting of age at diagnosis (50 %) reduce the representativeness of findings across LMICs, impacting the generalizability of conclusions regarding regional breast cancer disparities. Furthermore, staging data were extracted exclusively from studies using the TNM classification system (I-IV) to maintain consistency. While this approach ensured comparability across studies, we did not find any research employing the Extent of Disease classification (localized, regional, metastatic). This methodological decision may have

introduced selection bias, particularly by limiting the inclusion of certain population-based cancer registries that do not utilize TNM staging, thereby further restricting the comprehensiveness of the available data.

Our extensive search of databases resulted in a significant volume of literature with minimal duplicates. Utilizing the most spoken languages in LMIC countries enriched our search and results, ultimately narrowing down the selection to nine high and moderate-quality articles. The rigorous methodological quality assessment of these pre-selected studies underscores the strengths of our research strategies and the detailed descriptions within the systematic reviews. However, it also highlights areas for improvement, particularly in assessing bias and ensuring transparency in the reviews. This comprehensive overview covered a wide range of regions, helping us understand the variations and socioeconomic impacts of breast cancer.

In conclusion, this umbrella review highlights substantial disparities in breast cancer profiles and diagnostic practices between low- and middle-income countries (LMICs) and high-income countries (HICs). Notable differences were observed in diagnostic age, stage at presentation, and molecular subtype distribution, with patients in LMICs frequently diagnosed at younger ages, at more advanced stages, and with a higher prevalence of aggressive molecular subtypes. These findings underscore the pressing need for tailored, region-specific screening protocols focused on symptomatic cases and molecular profiling strategies in LMICs. However, given that only 22 % of the primary studies included were population-based and that only 50 % reported age at diagnosis, the generalizability of findings is limited, emphasizing the urgent need for high-quality, population-based research. Additionally, the limited number of high-quality primary studies and systematic reviews, particularly from regions such as sub-Saharan Africa and Latin America, reveals significant gaps in the available data. To enhance the validity and impact of future reviews, there is a critical need for there is a critical need for methodologically rigorous primary studies that ensure comprehensive data collection, especially in underrepresented regions. Strengthening research infrastructure and prioritizing well-designed population-based studies will be essential to provide more representative data on breast cancer characteristics in LMICs, ultimately guiding evidence-based policy recommendations.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## CRediT authorship contribution statement

**Parenté Alexis:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Chkeir Mohamad:** Writing – review & editing, Writing – original draft, Visualization, Methodology. **Tolentino-Rodriguez Lisebeth:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Preux Pierre-Marie:** Writing – review & editing, Project administration. **Erazo Andrea:** Writing – review & editing. **Vergonjeanne Marion:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Blanquet Véronique:** Writing – review & editing, Project administration. **Pofagi Vanina:** Writing – review & editing. **Toniolo Jean:** Writing – review & editing. **Ahindu Irénée:** Writing – review & editing.

## Declaration of Competing Interest

None of the authors declare conflicts of interest during this research. No funding was provided to develop this study.
# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.canep.2025.102797.

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