Five-Year Survival Outcomes for Breast Cancer Patients Across Continental Africa: A Contemporary Review of Literature with Meta Analysis

Reviewer #1 (Public review):

Summary:

This meta-analysis synthesized data from 79 studies across 22 African countries, encompassing over 27,000 breast cancer patients, to estimate 5-year survival rates. The pooled survival rate was 48%, with substantial regional variation, ranging from 64% in Northern Africa to 32% in Western Africa. Survival outcomes were associated with socioeconomic indicators such as education level, Human Development Index (HDI), and Socio-demographic Index (SDI). Although no significant differences in survival were observed between sexes, non-Black Africans had better outcomes. Despite global advances in cancer care, breast cancer survival in Africa has largely stagnated since the early 2010s, underscoring the need for improved healthcare infrastructure, early detection, and equitable access to treatment.

Strengths:

The study has several strengths. It features a comprehensive literature search, adherence to the PRISMA reporting guideline, and prospective registration on PROSPERO, including documentation of protocol deviations. The authors employed rigorous meta-analytic techniques, including subgroup analyses and meta-regression, allowing for a nuanced investigation of potential effect modifiers.

Weaknesses:

Analyses of crude 5-year survival rates are inherently difficult to interpret, particularly in the absence of key clinical variables such as stage at diagnosis or whether cancers were detected through screening programs. This omission raises concerns about lead time bias, where earlier diagnosis (e.g., via screening) may falsely appear to improve survival without affecting actual mortality. The higher survival seen in North Africa, for example, may reflect earlier diagnosis rather than improved prognosis or care quality. In this context, the age of the study population is another important aspect.

Relatedly, the representativeness of the included study populations is unclear. The data sources for individual studies - whether from national cancer registries or single tertiary hospitals -are not systematically reported. This distinction is crucial, as survival outcomes differ significantly between population-based and hospital-based cohorts. Without this contextual information, the generalizability of the findings is limited.

The meta-regression analyses further raise concerns. The authors use study-level covariates (e.g., national HDI, average years of schooling) to explain variation in survival, yet they do not acknowledge the risk of ecological bias. Inferring individual-level effects from aggregated data is methodologically flawed, and the authors' causal interpretation of these associations is inappropriate, especially given the potential for confounding by unmeasured variables at both the individual and study levels.

The assessment of publication bias is similarly problematic. While funnel plot asymmetry and a significant Egger's test are interpreted as evidence of bias, such methods are unreliable in meta-analyses of observational studies. Smaller studies may differ meaningfully from larger ones, not due to selective reporting, but because they may recruit patients from specialized tertiary centers where outcomes are poorer. The observed relationship between study size and survival may therefore reflect true differences in patient populations, not publication bias.

Despite claiming to search for gray literature via Google Scholar, no such studies appear in the PRISMA flowchart. This is a missed opportunity. Gray literature - especially reports from cancer registries - could have enhanced the quality and completeness of the data. While cancer registration systems are not available in all African countries, several do exist, and the authors should have made greater efforts to incorporate routine surveillance data where available. Mortality data from vital statistics systems, available in some countries, could also have provided useful context or validation.

The study's approach to quality assessment is limited. The scoring tool, adapted from Ssentongo et al., conflates completeness of reporting with risk of bias and fails to address key domains such as study population representativeness, selection bias, and lead time bias. Rather than calculating an overall quality score, the authors should have used a structured tool that evaluates risk of bias across defined domains-such as ROBINS-I, ROBINS-E, or tools developed for prevalence studies (e.g., Tonia et al., BMJ Mental Health, 2023). Cochrane guidance and the textbook by Egger, Higgins, and Davey Smith (DOI:10.1002/9781119099369) provide valuable resources for this purpose.

The cumulative meta-analysis is not particularly informative, considering the massive heterogeneity in survival rates. It would be more meaningful to stratify the analysis by calendar period. In general, with such important heterogeneity, the calculation of an overall estimate does not add much.

The authors spend quite some time discussing differences in survival between men and women and between the current and the 2018 estimates, despite the fact that the survival rates are similar, with widely overlapping confidence intervals. The use of a Z-test in this context is inappropriate as it ignores the heterogeneity between studies.

Minor point:

The terms retrospective and prospective are not particularly helpful - every longitudinal study of survival is retrospective. What the authors mean is whether or not the data were collected within a study designed to address this question, or whether existing data were used that were collected for another purpose. See also DOI: 10.1136/bmj.302.6771.249.

Reviewer #2 (Public review):

Summary:

The study provides an updated literature review and meta-analysis for the 5-year survival estimates in breast cancer patients across continental Africa. The findings reveal substantial disparities between regions and other factors, highlighting the disadvantaged areas in Africa and the urgent need to address these inequities across the continent.

Strengths:

The main strengths of this study include:
(1) the thorough literature search with an increasing number of included studies that enhances result reliability;
(2) standard and appropriate statistical methods with clear reporting;
(3) a comprehensive discussion.

Overall, the paper is well-structured, clearly presented, and provides useful insights.

Weaknesses:

However, I have a few concerns that I would like the authors to address.

(1) The conclusion "A country-wise comparison with 2018 estimates suggests a declining survival tendency, with WHO AFRO countries reporting the poorest estimates among other WHO regions." appears to have been drawn from the comparisons across both different regions and different time periods, which is incorrect! As shown in Figure 8, survival in Africa has increased from below 30% (WHO AFRO 2017) to around 50% (AFRICA 2024, presumably the current study). Section 3.5 is confusing and headed in the wrong direction. The key message in Figure 8 is that the current survival estimate in Africa is still lower than that of other WHO regions from a few years ago, highlighting the urgent need to improve survival in Africa.

(2) The previous review by Ssentongo et al. classified countries into North Africa and sub-Saharan Africa (SSA), regions divided by the Sahara Desert. This classification is not only geographical-based, but also accounts for the significant differences in ethnicity, health system, and socioeconomic factors. North Africa (especially Egypt, Tunisia, Morocco) has better cancer registries, earlier detection, more treatment access, and therefore better survival outcomes (as shown in Figure 2). SSA tends to have worse outcomes, due to later-stage diagnosis, limited pathology, and access barriers. Given that the survival in women with breast cancer is among the lowest for several SSA countries, the study would benefit from an additional comparison between pooled estimates of North African and SSA, and comparisons with previous pooled estimates.

(3) The authors classified studies under the female group. Females constituted at least 80% of the sample population, and subgroup analysis revealed only a marginal discrepancy in survival rates between the two sexes. However, most of the breast cancer patients and related studies consist predominantly of females. Given the non-negligible differences in various aspects between females and males, sensitivity analyses restricted to studies among females (as in Figure 2-3) would be informative for future research in breast cancer patients.

(4) Stage at diagnosis and treatment are the strongest prognostic factors for breast cancer survival. Though data regarding these variables are not available for all studies, and it's complicated to compare or pool the results from different studies (as mentioned in the limitation), could the authors perform the regression analyses regarding early vs. late stages, and the percentage of treatment received? These two factors are too significant to overlook in studies on breast cancer survival.

(5) The authors reported that studies published before 2019 had a higher survival than those conducted from 2019 onwards, which could be misleading and requires further explanation. As the authors noted ─"the year of publication may not be a reliable measure of the effect in question"─ a better approach would be to use the year of inclusion, i.e., the year the studies were conducted.

(6) Northern and Western Africa both have the highest incidence of breast cancer in Africa, yet their 5-year survival estimates differ substantially. However, the authors have discussed the survival disparities without considering their similarly higher incidence rates. Could this disparity reflect different contributing factors, with the higher incidence rate in Northern Africa resulting from better screening programs (leading to more detections), while that in Western Africa stems from other epidemiological factors despite lower screening participation? Though the incidence rate is not the primary focus of this study, briefly exploring this dichotomy would enhance the discussion and provide valuable insights for readers.

Author response:

We thank the reviewing editors, senior editors, and reviewers for their time, efforts, and constructive feedback. We believe the points raised are addressable and we would like to proceed with a revised submission for further review. Specifically, we plan the following revisions:

Editor’s Comments

We will clarify study definitions to ensure the meaning of "5-year crude overall survival time" is explicit for readers.

Reviewer 1 Comments

- Clarify and supplement the work with detailed sources of study origin (cancer registries or single-center cohorts).

- Conduct a multi-level hierarchical meta-analysis to address concerns of ecological fallacy in interpreting results.

- Perform an ecological sensitivity analysis and clarify findings regarding small study effects.

- Expand the search base significantly by including African local databases; preliminary searches have identified over 50 potentially eligible doctoral theses, dissertations, local journal articles, and gray literature, potentially adding data from five or more additional countries.

Reviewer 2 Comments

- Conduct subgroup analyses by sex and assess the influence of the percentage of males in mixed cohorts.

- Enhance the limited meta-analysis and provide supplementary full forest plots for all analyses.

- Clarify phrasing in sections identified by the reviewer.

Additional Planned Clarifications and Analyses

- Elucidate the role of cumulative meta-analysis in mitigating lead-time bias.

- Include supplementary cumulative meta-analysis based on the year of investigation (instead of publication year).

- Perform subgroup analyses by clinical staging, TNM grading, and treatment modalities where data from ≥10 studies is available.

- Expand discussion on the merits of quality assessment versus risk of bias evaluation in large scale epidemiological and observational studies, in line with other studies of this scale.

- Condense the comparison with 2018 estimates, as per reviewer suggestions.

Clarification Regarding SSA vs. AU Classification

We do not intend to compare survival between "Sub-Saharan Africa" (SSA) and North Africa, as this binary classification is historically rooted and does not reflect current African Union (AU) administrative or policy groupings. Our regional analyses will adhere to the AU’s contemporary regional framework to better reflect political, cultural, and healthcare system realities.

On Registry Data

We will clarify that we will not extract raw registry data, as such data is typically unprocessed and does not provide 5-year overall survival metrics. As such extracting raw, individual-level data from registries or vital statistics systems falls outside the methodological scope of a meta-analysis. Meta-analyses are designed to synthesize published survival estimates or those available from reports where survival analyses have already been conducted. Utilizing raw surveillance data would require primary data processing and survival analysis — effectively creating new data, not synthesizing existing results. This would represent a distinct study design, such as a pooled analysis or original cohort study, rather than a meta-analysis. Where registry reports present summary survival estimates (e.g., 5-year overall survival) in a format compatible with meta-analysis, we will certainly include them.

All planned additional analyses will depend on data quality, consistency, and feasibility for pooling using state-of-the-art statistical techniques. Where pooling is not possible, we will transparently report limitations.