Global breast cancer survival estimates in 2017–2021 to advance the WHO Global Breast Cancer Initiative

Abstract The World Health Organization (WHO) Global Breast Cancer Initiative aims to attain meaningful global breast cancer mortality reductions by 2040—a target that hinges on improvements in patient outcomes and survival. So far, however, data on cancer survival remain limited in low- and middle-income countries. The WHO estimated population-based age-standardized 5-year net survival for women diagnosed with breast cancer between 2017 and 2021 across all 194 Member States, providing a global benchmark for monitoring breast cancer outcomes. Here we found that median 5-year net survival varied widely across WHO regions during 2017–2021: 39.1% (95% uncertainty interval 34.1–44.7%) in the African Region, 61.0% (51.4–69.8%) in the Eastern Mediterranean Region, 66.3% (57.7–73.7%) in the South-East Asia Region, 81.1% (78.6–83.5%) in the Western Pacific Region, 84.0% (82.8–85.1%) in the European Region and 88.5% (86.7–90.1%) in the Region of the Americas. Persisting disparities in survival reflect profound global inequities; sustained initiatives to narrow gaps in access to diagnosis and treatment for breast cancer are crucial to strengthening health systems. This will enable all countries to ensure optimal outcomes for their patients with breast cancer and achieve Global Breast Cancer Initiative and Sustainable Development Goals targets. Main Breast cancer is a major threat to women’s health worldwide as the most frequent type of cancer in women in almost 90% of the world’s countries1. In 2022, there were an estimated 2 million new diagnoses, 666,000 attributable deaths and 23 million disability-adjusted life years worldwide2,3. If current trends remain unchecked, the breast cancer burden is projected to increase to 1.04 million deaths by 2040. The projected increases in breast cancer incidence and mortality will impact all World Health Organization (WHO) regions with a greater relative impact on countries with the most limited resources4. Premature deaths from breast cancer can be reduced through early detection and improving access to high-quality services that provide surgery, radiotherapy and cancer medicines5,6,7,8,9,10. These domains are intertwined and can only translate into optimal cancer outcomes if health systems are effective and strengthened. Population-based survival is a metric that uniquely encompasses the whole cancer trajectory for individual patients and is fundamental to evaluating the effectiveness of cancer control strategies and health system performance11,12,13. While 5-year survival exceeds 90% in many high-income countries, it falls drastically to 50% or below in many low-and middle-income countries14,15. The WHO Global Breast Cancer Initiative (GBCI), launched in 2021, aims to achieve a reduction in global breast cancer mortality by 2.5% every year, potentially saving 2.5 million lives by 204016. GBCI builds its action based on three main operational pillars: (1) promoting early detection so that at least 60% of invasive breast cancers are diagnosed at stage I or II, (2) ensuring a timely diagnosis within 60 days of presentation at a health facility, and (3) completion of multimodality treatment for 80% or more of patients diagnosed with breast cancer. These three pillars have been proven to be cost-effective and are among the WHO’s Non-Communicable Diseases (NCD) expanded best buy interventions17. Yet, between 1990 and 2020, only 20 high-income countries met the breast cancer mortality reduction targets18,19. Observed survival data collected by cancer registries are largely available only in high- and middle-income settings. Even in countries with cancer registries, there can be a delay in publishing estimates, impeding timely measurement of progress in survival. In CONCORD-3, the largest study so far on international comparisons of breast cancer survival, only 21 of the 66 countries that contributed were classified as low- or middle-income15. While empirical data are essential for unbiased cancer survival monitoring, modeling can produce provisional survival estimates using indicators related to breast cancer survival20, providing a glimpse into global variation in breast cancer outcomes11. In this study, WHO produced population-based, worldwide 5-year breast cancer survival estimates for the period 2017–2021. These data will inform cancer control strategies in general and breast cancer programs specifically, and contribute to tracking progress of Sustainable Development Goals, particularly 3.4—a 30% reduction in premature mortality due to noncommunicable disease—through equitable breast cancer outcomes21. Table 1 summarizes the findings and the policy implications. Results Observed survival estimates availabilities We fitted the model with 223 non-overlapping, observed cancer registry 5-year net survival estimates, 131 of which (59.2%) were based on nationally representative data (Fig. 1). The criteria for survival estimates inclusion are specified in the Methods. Supplementary Table 1 provides a detailed description of the data sources by country. Observed cancer registry breast cancer survival estimates were available for 67 of 194 Member States (henceforth countries). For 14 countries, there was only one observation; 13 countries had two or three survival values, and four or more observed survival estimates were available for 40 countries. For four of these countries, survival was obtained only from hospital-based registries. Data availability varied widely according to country income level and across WHO regions (Fig. 1). At least one observed survival estimate was available for 1/25 low-income countries (4%), for 4/49 lower-middle-income countries (8.2%), 18/53 upper-middle-income countries (34%) and for 44/63 high-income countries (69.8%). Among the 37 fragile and conflict-affected countries, as defined by the World Bank22, only two—Nigeria and Libya—were able to contribute one observed survival estimate (Fig. 1). The African Region had the greatest gaps, with 40 of 47 countries (85.1%) lacking any survival data; hospital-based estimates were available for Namibia, Nigeria, Uganda and Zambia, while population-based observations were available for Algeria, Mauritius and South Africa. In the Eastern Mediterranean Region, 16 of 21 countries (76.2%) had no data, with single estimates available for Bahrain, the Islamic Republic of Iran, Libya and Qatar, and three estimates for Kuwait. Data were similarly sparse in the South-East Asia Region, where eight of ten countries (80.0%) had no observations, with estimates available only for India and Thailand. In the Western Pacific Region, 19 of 28 countries (67.9%) lacked data, while Australia, China, Indonesia, Japan, the Republic of Korea, Malaysia, Mongolia, New Zealand and Singapore each had one or more estimates. Data coverage was strongest in the European Region, where 33 of 53 countries (62.3%) had at least one survival observation. In the Region of the Americas, 24 of 35 countries (68.6%) lacked data, with estimates available for Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, Cuba, Ecuador, Guyana, Peru and the USA. For many of the countries, however, the most recent cancer registry estimates were representative only at the subnational level (Supplementary Tables 1 and 2). Net survival in 2017–2021 Population-based, 5-year, age-standardized net survival estimates (henceforth, net survival estimates) for breast cancer during 2017–2021 were examined by World Bank income groups (2024 classification) and across WHO regions23. Median net survival increased with income level. In the low-income group, median 5-year net survival during 2017–2021 was 41.9% (95% uncertainty interval (UI) 35.1–49.7%), with the lowest estimates concentrated among countries in sub-Saharan Africa. Median survival was higher in the lower-middle-income group, with an estimate of 60.1% (95% UI 54.3–65.4%). However, there was notable within-group variability. Approximately one-fifth of the countries, predominantly in sub-Saharan Africa, had predicted net survival below 40%. By contrast, several countries in the Americas, Southeast Asia and the Western Pacific Region exceeded 70%. In the upper-middle-income group, median 5-year net survival was 78.7% (95% UI 76.5–80.5%), yet heterogeneity persisted. A subset of countries had predicted net survival below 50%. For the high-income group, median survival was 87.3% (95% UI 86.3–88.1%). However, low survival estimates were predicted in several island nations including in the Western Pacific and Africa Regions (Fig. 2). By WHO regions, in the African Region, the median 5-year net survival during 2017–2021 was 39.1% (95% UI 34.1–44.7%), with more than half of countries not reaching 5-year net survival of 50%. Survival ranged between 20% and 29% in the Central African Republic, Lesotho and Nigeria. Survival was 30–39% in 18/47 countries, 40–49% in 16 countries and 50–59% in 7 countries: Cabo Verde, Equatorial Guinea, Mauritania, Namibia, Senegal, Seychelles and South Africa. Survival at 5 years after diagnosis was 60–69% in Botswana and Gabon, while it was highest in Mauritius at 84.5% (95% UI 75.2–91.0%) (Figs. 3 and 4 and Supplementary Table 2). In the Eastern Mediterranean Region, the median 5-year net survival between 2017 and 2021 was 61.0% (95% UI 51.4–69.8%), with survival values of 50% or more in 16/21 countries. Survival was lowest in Somalia at 29.9% (95% UI 15.4–48.9%) and 36% (95% UI 19.7–57.5%) in Djibouti. Survival was 40–49% in Afghanistan and Yemen, and 50–59% in Iran, Pakistan and Sudan. Survival ranged from 60% to 69% in Egypt, Iraq, Morocco, Libya and the Syrian Arab Republic. It was 70–79% in Bahrain, Jordan, Kuwait, Oman, Saudi Arabia and Tunisia. Survival at 5 years after diagnosis was highest at 80–89% in the United Arab Emirates and Qatar (Figs. 3 and 4 and Supplementary Table 2). In the South-East Asia Region, the median 5-year net survival during 2017–2021 was 66.3% (95% UI 57.7–73.7%). It ranged from 40% to 49% in Bhutan and Timor Leste, and from 60% to 69% in 5/10 countries: Bangladesh, the Democratic People’s Republic of Korea, India, Myanmar and Nepal. Survival at 5 years after diagnosis was highest at 70–79% in the Maldives, Sri Lanka and Thailand (Figs. 3 and 4 and Supplementary Table 2). In the Western Pacific Region, the median 5-year net survival between 2017 and 2021 was 81.1% (95% UI 78.6–83.5%), with 19/28 countries reaching a survival of 50% or more. Survival ranged from 40% to 49% in Micronesia, Fiji, Kiribati, the Marshall Islands, Nauru, the Solomon Islands, Tonga, Tuvalu and Vanuatu. It was 50–59% in Niue, Palau and Samoa; 60–69% in Cambodia, the Cook Islands, the Lao People’s Democratic Republic, Papua New Guinea and the Philippines; 70–79% in Indonesia, Malaysia, Mongolia and Viet Nam; 80–89% in Brunei Darussalam, China, Singapore, the Republic of Korea and New Zealand. Survival at 5 years after diagnosis exceeded 90% in Australia and Japan (Figs. 3 and 4 and Supplementary Table 2). In the European Region, the median 5-year net survival during 2017–2021 was 84.0% (95% UI 82.8–85.1%), with all countries exceeding 60%. Survival ranged between 60% and 69% in Kyrgyzstan, San Marino, Tajikistan and Uzbekistan. It was 70–79% in 13/53 countries, and 80–89% in 26 countries. Survival at 5 years after diagnosis was 90% or more in Andorra, Belgium, Cyprus, Denmark, Finland, Israel, Monaco, Norway, Portugal and Sweden (Figs. 3 and 4 and Supplementary Table 2). In the Region of the Americas, the median 5-year net survival between 2017 and 2021 was 88.5% (95% UI 86.7–90.1%), with nearly all countries being above 60%. Survival was 52.8% (95% UI 33.0–72.8%) in Haiti, while it ranged between 60% and 69% in Belize, Dominica, Grenada, Guyana, Saint Kitts and Nevis, Saint Lucia, and Saint Vincent and the Grenadines. Survival was 70–79% in Bolivia, Guatemala, Cuba, Honduras, Nicaragua and Paraguay, and exceeded 80% in 21/35 countries (Figs. 3 and 4 and Supplementary Table 2). Discussion We estimated population-based survival from breast cancer for all WHO Member States. The 2017 WHO Cancer Prevention and Control resolution24, adopted at the 70th World Health Assembly, provides a basis for this work as Member States committed to collect high-quality population-based data on cancer through population-based registries, household surveys and other health information systems to effectively guide national policies and plans. Produced exactly 5 years since the launch of the GBCI, these estimates also address a call from Member States to benchmark data to inform country-led actions for cancer control. We developed a model leveraging observed survival estimates, covariates related to cancer care and health systems effectiveness, and regional trends. These survival estimates went through a two-phase country consultation process, enabling focal points in each country to propose revisions to the methodology, submit comments and contribute to standardized primary, aggregate data. We found stark survival inequalities within and between WHO regions, and by World Bank income group, with 5-year net survival varying between less than 30% in some low-income sub-Saharan countries and more than 90% in the most affluent countries. Secondary prevention remains a critical driver of breast cancer survival, primarily through early detection. Mammographic screening has been shown to reduce breast cancer mortality by around 20–40% among women aged 50–69 years5,25. Evidence from low- and middle-income countries, however, indicates that organized population-based screening is often infeasible, especially in countries with weak health systems where most women are diagnosed in late stages. This underscores the importance of the use of early diagnosis strategies, including clinical breast examination. This, combined with strengthened referral and linkages to diagnostic pathways, can achieve downstaging of disease and is a prerequisite to establishing the more resource-intensive organized screening programs6,26. These findings support policy shifts from technology-centric models toward people-centered, integrated secondary prevention strategies embedded within primary health care. Early-stage diagnosis of breast cancer, linked with timely diagnostic work-up and comprehensive treatment, are among the expanded noncommunicable disease best buy interventions proven to be feasible for implementation in all settings, with a cost-effectiveness ratio of £100 or I$100 per healthy-life year gained in low- and middle-income countries17. Countries can improve access by investing in diagnostic services for confirmation of malignancy in women with suspicious breast cancer lesions, including imaging, tissue sampling, pathology and immunohistochemistry for treatment definition. The need to reduce the diagnostic interval to 60 days or less from the time of initial presentation at the facility is the basis for the second GBCI pillar, which countries must strive to achieve. In addition, ensuring availability and affordability of definitive multimodality treatment for breast cancer including surgery, chemotherapy and radiotherapy underpins the third GBCI pillar. Stage at diagnosis, a key determinant for breast cancer survival, encompasses the first pillar of GBCI, which aims at having 60% of breast cancer patients diagnosed in early stages (I or II). In previous studies, proportions of patients diagnosed at the metastatic stage were found to be 10% or less in most affluent countries, while they reached 30% in some low- and middle-income countries27,28. In a systematic review covering 21 countries in Asia, the proportion of patients diagnosed with breast cancer at an early stage was found to be positively correlated with survival at 5 years and with the universal health coverage service index, which is a mean of 14 tracer indicators of health service coverage29,30. We could not incorporate a granular stage distribution as the heterogeneity in staging systems across published studies allowed only a dichotomic stratification into nondistant or distant stage. Using stage information from available countries, we derived a model to estimate stage proportions using health access covariates as predictors for all countries. Here, the proportions of nondistant stage diagnoses varied widely across WHO regions and mirrored regional disparities in survival. The proportion of women diagnosed with nondistant stage was 84.8% in the African Region, 87.1% in the Eastern Mediterranean Region, 88.4% in the South-East Asia Region, 91.4% in the Western Pacific Region, 92.8% in the European Region and 92.7% in the Region of the Americas. During the country consultation, 12 countries submitted primary stage distributions, providing evidence that reporting of this fundamental indicator is feasible. Previous studies showed remarkable proportions of unstaged diagnoses in some countries. Accurate assessing of progress toward the GBCI pillar of early detection is possible only if high-quality stage data are available, prompting countries to include stage at diagnosis as a core indicator in their monitoring and evaluation frameworks. Human-resource constraints are among the most binding bottlenecks to achieving survival gains at scale. Shortages of cancer workforce—cancer surgeons, pathologists, oncologists and specialized nurses—limit countries’ ability to translate breast cancer early detection into prompt management and optimal cancer outcomes. The global distribution of healthcare workers is profoundly unfair, with countries and regions where specialized workers are non-existent31. Workforce optimization, rather than expanding the number of skilled specialists, is likely to yield the fastest returns. Interventions at country level included (1) shifting specific task—for instance, breast clinical examination—from physicians to mid-level health providers; and (2) empowering in-service professionals through knowledge sharing, including virtual tumor boards, twinning or training-of-trainers programs32,33. These interventions, facilitated by digital innovation, will improve timeliness, continuity and treatment completion across the cancer pathway. These approaches align with GBCI emphasis on practical and scalable solutions, capable of producing incremental gains. Surgery is the cornerstone of curative breast cancer treatment. However, only 25% of patients diagnosed with cancer worldwide have access to safe, affordable and timely surgery. In high-income countries, nearly all patients amenable of surgical treatment are treated appropriately, while the proportion dramatically falls to 5% in low- and middle-income countries7,8. Delays in surgical treatment in resource-limited settings may reflect financial hardship, health system fragmentation and centralization of services34,35. Travel distance and geographical barriers to treatment access impact both treatment choice and receipt of guideline-concordant surgical and adjuvant care. All these obstacles contribute directly to poorer survival outcomes, particularly when surgery is omitted or substantially postponed36,37,38. Even where surgery is available, quality gaps further compound survival outcomes. In a recent modeling study, the largest, projected mortality reductions between 2030 and 2050 were observed for cancer surgery capacity scaling-up scenarios, particularly in Africa39. We could not incorporate a covariate directly related to surgical workforce availability. The WHO Global Health Observatory presents absolute numbers of licensed surgeons by country40, but these estimates do not cover the 2017–2021 estimation period. Data on access to cancer surgery in the public sector are also available from the WHO Non-Communicable Disease Country Capacity Survey41, but the information is only qualitative and could not be used. The use of the Healthcare Access and Quality Index (HAQI) and total health expenditure (THE) for modeling four of five covariates, as detailed in the Methods, may explain in part the impact of workforce availability on survival. We endeavor to model more accurately this determinant of cancer outcome in future iterations of this work, if updated, standardized, country-specific information becomes available31,32,33. We included in the model radiotherapy unit density as a predictor of breast cancer survival. Radiotherapy is a key component in breast cancer management if breast-conserving surgery is indicated and feasible. However, the distribution of radiotherapy machines worldwide is inequitable: in high-income countries, one radiotherapy machine serves on average a population of 130,600 people, while the ratio grows to one machine for 15 million people in low-income countries. This disparity will lead to higher proportions of mastectomy as preferred surgery in low- and middle-income countries28. Radiotherapy availability may not be directly related to breast cancer survival, but the observed correlation in the model building may, in fact, reflect access to multimodality treatment and comprehensive cancer care, one of GBCI pillars and a key determinant of breast cancer survival. As part of the modeling process, we developed a continuous breast cancer medicine index, derived from categorical responses from a European Society for Medical Oncology survey42. Questionnaire-based data are inherently subject to bias. Furthermore, as only responses from the 2023 iteration were readily available, such an index may not accurately capture changes over time in availability and accessibility of cancer medicines. Standardized, quantitative data on global access to essential cancer medicines are not available, and, so far, cancer burden metrics have never accounted for this critical domain of health equity10. Such deficiency should prompt governments and relevant stakeholders to try to define robust indicators that will help inform cancer monitoring. In our analysis, survival did not exceed 50% in more than two-thirds of the fragile and conflict-affected countries. In crisis settings, management of traumas and communicable diseases is prioritized over cancer43,44. Conflicts can disrupt the whole cancer care continuum, from prevention to palliative care, through physical destruction of infrastructures, interruption of supply chains, and workforce shortages45. However, in some conflict-affected countries, locally driven adaptive models have helped to maintain continuity of cancer care46. These include telehealth for remote consultations, mobile oncology units and decentralization through community-based early detection. Multilateral collaboration has also enabled the establishment of safe, cross-border corridors for treatment of severely ill patients47. Some of these solutions align with GBCI, which underscores the need for pragmatic, community-led solutions19. The evidence on the quality of life among breast cancer survivors in low- and middle-income countries is scarce48,49. As the global cancer burden increasingly shifts toward low- and middle-income countries, there is growing recognition that effective cancer control must incorporate survivorship care models accounting for the lived experiences50. The acknowledgment of institutional barriers to getting back on track and the strengthening of social protection against financial discrimination in Europe are among the ongoing efforts to improve the quality of life of breast cancer survivors51. However, survivors in low- and middle-income countries face remarkable challenges that affect their quality of life, including limited access to follow-up care, fertility concerns for young survivors, financial hardship and gaps in psychosocial and rehabilitative services52. Studies from South-East Asia and other low- and middle-income settings showed that health-related quality of life and psychological distress are critical dimensions of survivorship, particularly where resources are constrained and support systems are limited53. Cancer registration and surveillance systems are critical components of cancer control strategies. This study highlighted a remarkable lack of observed survival estimates globally, available for only 67 out of 194 countries. Population-based cancer registries (PBCRs) are the gold standard to assess cancer burden, but only 21% of the world’s population is covered by PBCRs. Population coverage is often complete in Europe and North America, while it is as low as 2% in Africa and 13% in Asia20. In this work, we generated estimates for all 194 WHO Member States and conducted country consultations to assess and contextualize the findings, and to ensure country ownership. CONCORD-3, the largest comparison in cancer survival so far, included data from 71 countries and territories with available PBCRs. By extending coverage to countries where data availability is limited, our study provides a comprehensive global assessment. Modeling can help generate provisional evidence for countries with no or sparse data, but information from PBCRs is integral to the unbiased monitoring of cancer survival. Future, larger international comparisons of population-based breast cancer survival will be instrumental in validating our findings against real-world outcomes. This analysis highlights the critical need for timely, high-quality cancer surveillance data to inform policy decisions, guide resource allocation and target interventions. Countries should take steps to establish or strengthen PBCRs and equip them with human and financial resources to collect survival information, beyond incidence and mortality10,43,44,48,49,50,51,52,53. Some methodological limitations should be considered when interpreting these modeled survival estimates. CONCORD-3-observed survival estimates were the primary source, but CONCORD-2, SURVCAN, NORDCAN and ABC-DO data were also used to have the best possible geographical and temporal coverage. The different studies adopted a comparable methodology, but we used aggregate estimates rather than individual-level data, which may result in residual heterogeneity. Possible sources of heterogeneity include different geographical coverage (national versus subnational), estimation method (cohort versus period approach) and calendar period. For countries without published survival estimates, predictions relied exclusively on covariates. This may result in an ecological fallacy if the temporal changes in the distribution of covariates did not mirror trends in survival. For instance, in countries in transition, economic growth may rapidly translate into better health system indicator, but it may take a long time for survival metrics to reflect improvements in access to care. This also results in a high level of uncertainty, which requires caution when interpreting results. For five sub-Saharan countries, we used hospital-based survival estimates given the paucity of data in this region. Patients registered in a hospital setting are likely to present with a more advanced stage, leading to outcomes that may differ from those observed in an unselected population. We conducted a sensitivity analysis to quantify the influence of hospital-based estimates on regional and global survival patterns. After excluding the ABC-DO data, predicted 5-year net survival was higher in all five countries—by 10–20 percentage points—with substantially wider UIs. Estimated survival in the African Region rose from 39% to 51%, while the global estimate was essentially unchanged (78% to 79%) (Extended Data Table 1). These findings show that the hospital-based estimates substantially inform survival in this data-sparse region and require caution when interpreting survival estimates. They also reinforce once again the importance of collecting unbiased, population-based survival data. PBCRs were more common in high- and middle-income countries, which implies that data-rich countries primarily drive the estimated associations between survival and covariates. These associations may not fully reflect the conditions experienced in low-income or fragile settings where no registry data exist, and where the relationship between health system capacity and survival outcomes may differ structurally. Furthermore, uncertainty in the survival estimates for data-sparse countries is compounded by the fact that covariate uncertainty was not propagated into the survival model. In other words, the reported UIs for these countries are likely to underestimate the true range of plausible values. Caution should be taken when interpreting country-level estimates for settings without observed registry data, recognizing that they represent the model’s best inference from regional patterns and covariate relationships rather than absolute direct measurement. Only two-thirds of published survival estimates were nationally representative. We did not make model adjustments to extrapolate subnational predictions to the national level. This may lead to biased survival estimates, mainly in large countries with few regional PBCRs especially where coverage is low and not representative of the national level or if access to care is uneven. The COVID-19 pandemic caused sudden disturbances to services across the cancer care continuum that could affect survival estimates for the calendar period 2020–2021. However, because 5-year net survival integrates mortality risk across a 5-year follow-up window, disruptions concentrated in a single calendar year are more likely to be detectable using shorter diagnosis periods than the 5-year aggregates reported here. Consistent with this, observed survival data submitted by several countries during the country consultation for the 2017–2021 period remained closely aligned with our modeled estimates, with no pandemic-related disruption yet apparent. Nonetheless, updating these estimates as new registry data become available will be crucial to fully characterize the impact of COVID-19 disruptions on breast cancer survival trends and recovery patterns. Every woman diagnosed with breast cancer deserves to have access to the best possible outcome, regardless of where she lives. Our global analysis points to key areas of improvement for countries to be able to report in 5 years on implementation strategies around the GBCI key performance indicators, including cancer registration with high-quality and complete data, collection of information on stage at diagnosis, and definition of cancer workforce scaling-up plans to ensure timely and quality care. These survival estimates provide a common set of data against which countries can evaluate national readiness in cancer reporting and benchmark progress in breast cancer control. Breast cancer is the most common cancer and the leading cause of cancer-related death in most jurisdictions. Improvements in breast cancer outcomes will be a harbinger of improvements of the whole country’s health system. Investments to improve survival should be aligned with the three GBCI pillars and 60:60:80 targets, working toward an annual 2.5% reduction in mortality by 2040. Sustained reductions in premature deaths from breast cancer at this level will lead to survival improvements. This approach positions survival monitoring as a dynamic accountability tool, enabling early course correction to ensure that GBCI implementation remains on track to achieve its 2040 ambition and turns the tide on the growing global breast cancer burden. Methods To predict 5-year net survival for women diagnosed with breast cancer between 2017 and 2021 for all countries, a Bayesian hierarchical statistical model was used to combine information from PBCR estimates, covariates plausibly associated with breast cancer survival, and regional levels and trends. Cancer registry survival estimates were obtained from global and regional studies. Cancer registry estimates from 1995 onward were incorporated to maximize geographic coverage and gain insights into temporal trends. Covariate information, including socioeconomic, general healthcare access, and cancer-specific care and outcome indicators, were extracted from systematic reviews, surveys and publicly available datasets (Extended Data Tables 2 and 3). Data sources for survival CONCORD-3, the third iteration of the CONCORD study, collected individual-level data on more than 6 million breast cancer diagnoses from 2000 to 201415. Data for 41 countries covered 100% of the national population. In our study, survival predictions were primarily based on CONCORD-3 estimates, supplemented by CONCORD-2 data to either extend the time series between 1995 and 1999 for countries included in CONCORD-3, or provide data between 1995 and 2009 for countries not included in CONCORD-315,54. Additional estimates from the SURVCAN-3 and NORDCAN studies were also used to complement CONCORD survival estimates. SURVCAN-3 included around 200,000 breast cancer diagnoses during 2008–2012 in 32 countries across Africa, Central and South American, and Asia14. NORDCAN included over 700,000 women diagnosed with breast cancer during 1977–2023 in six Nordic countries55. All three studies provided net survival estimates; no patient-level data were used. Net survival measures the probability of surviving cancer itself, after removing the influence of other causes of death (background mortality)56. Rather than relying on cause-of-death information, which is not recorded consistently enough for reliable international comparisons57, net survival adjusts for background mortality using life tables. Survival estimates from CONCORD and SURVCAN-3, or NORDCAN, were considered comparable because they used similar methods to estimate survival. More specifically, in both SURVCAN-3 and NORDCAN, survival was calculated using the nonparametric Pohar Perme estimator and survival values were age-standardized using the International Cancer Survival Standards group 1 weights, for cancer types with a steeply increasing age distribution56,58. Only NORDCAN adopted adjusted age bands, but following the same age dependency of International Cancer Survival Standards cluster 1. CONCORD considered survival estimates less reliable if they were derived from cancer registries with 15% or more of patients subjected to any of the following criteria: (1) missing or incomplete dates, (2) identified only through a death certificate or autopsy, or (3) lost to follow-up or recorded as alive but censored within 5 years15. In this analysis, all survival estimates with possible reliability issues were excluded. The same cutoffs used by the CONCORD program were used for all data sources. Population-based survival estimates are the most reliable way to assess how effectively a healthcare system manages patients with cancer because they include all diagnosed cases within a country or territory. Conversely, hospital-based survival estimates can be valuable where PBCRs do not exist or where their survival estimates include a very high percentage of losses to follow-up. To maximize both the use of available survival data and geographical coverage, we incorporated hospital-based survival estimates from the African Breast Cancer-Disparities in Outcomes (ABC-DO) prospective study, but only for countries or calendar periods not covered by CONCORD. ABC-DO presented 5-year, net survival estimates for 2,228 women diagnosed with breast cancer across five sub-Saharan countries between 2014 and 201759. Only for ABC-DO did we use unstandardized survival estimates given the high proportions of young women with poor outcomes in these countries. In summary, the main data sources were the CONCORD-2 and CONCORD-3 studies (diagnoses 1995–2014), supplemented by SURVCAN-3 for Bahrain, Setif and Batna in Algeria, and Golestan in Iran. NORDCAN estimates for diagnoses from 1989 to 2023 were preferred over CONCORD estimates for six Nordic countries—Denmark, Finland, Iceland, Norway, Sweden, the Faroe Islands and Greenland—as survival time points were more granular in NORDCAN. Hospital-based ABC-DO estimates (2014–2017) were used for Namibia, Nigeria, Uganda, Zambia, and Eastern Cape in South Africa. Country consultation The preliminary WHO survival estimates were submitted to countries for consultation. This process aimed at ensuring transparency by enabling countries to review their own preliminary survival estimates, provide technical feedback based on available national data, and submit any additional information on survival or stage distribution. Any data submitted during the country consultation had to comply with strict methodological requirements specified in a data template, such as calendar periods of interest, data quality constraints and proper accounting of background mortality in the derivation of net survival (Supplementary Table 3). The Country Portal, the web interface supporting the consultation process, was open between 17 October 2025 and 4 February 2026. A revised set of survival estimates, incorporating the feedback received and any relevant additional data, was shared with countries to allow final endorsement. During the country consultation, 11 countries submitted primary, aggregate PBCR net survival estimates that were used to fit the statistical model: Belgium, Switzerland, Chile, China, Czechia, Guyana, Hungary, Ireland, Kazakhstan, Singapore and Slovenia. PBCR net survival estimates for the 2017–2021 time period were submitted by six countries: Belgium, Hungary, Ireland, Kazakhstan, Singapore and Slovenia. Countries were given detailed guidelines for submitting primary, aggregate survival data, to ensure that the same methods for age standardization and survival estimation were used. For Belgium, Hungary, Ireland, Kazakhstan, Singapore and Slovenia, observed survival estimates submitted by countries during the country consultation process were used as the final estimates, with no modeling applied. A vetting procedure was conducted to ensure that country-submitted estimates adhered to consistent case definitions and that there was strong alignment between observed estimates and the predicted values from our model. The largest absolute difference between observed survival estimates submitted by these countries and model predictions was 1.3%. Covariates In many countries, PBCRs do not exist or have not yet achieved sufficient robustness for inclusion of their data in this analysis. Therefore, we addressed data gaps using covariates to predict survival. Covariate selection for the model was guided by two main principles. First, each covariate needed to have sufficient data coverage across countries for the analysis period, including the prediction years from 2017 to 2021. Second, there had to be strong empirical evidence to justify its inclusion. We assessed this strength using scatter plots and correlation coefficients, and further applied shrinkage methods to penalize weak associations. A full list of assessed covariates is included in Extended Data Table 3. Five covariates were ultimately included in the survival model: proportion of breast cancer diagnoses classified as nondistant, a breast cancer medicines index, radiotherapy unit density, mammography unit density and female all-cause adult mortality rates retrieved from the 2024 Revision of World Population Prospects60. Radiotherapy and mammography unit density WHO Global Health Observatory releases data on radiotherapy unit density per 1 million population61, and mammography unit density per million females aged 50–69 (ref. 62). In total, 15 countries reported no data on radiotherapy unit density, and 59 countries reported no data on mammography unit density. For countries with data, the unit density was only available for 2010, 2014, 2019 and 2021, at most. In total, 39 countries reported at least one year with zero radiotherapy units, and 11 countries reported at least one year with zero mammography units. To account for the presence of country-years with zero-unit density, we used a Bayesian hierarchical hurdle model. The hurdle component addressed the surplus of countries with zero units by separately modeling: (1) the probability of having zero units, and (2) the expected unit density when it is greater than zero. We separately modeled the radiotherapy and mammography unit density using a similar model structure for both covariates. For both, the unit density \({\widehat{{\rm{y}}}}_{i}\) from data point i was modeled as Bernoulli distributed with probability \({\theta }_{c[i],t[i]}\) if \({\widehat{{\rm{y}}}}_{i}\) is zero. \({\theta }_{c,t}\) represents the probability of zero units in the country c and year t. If \({\widehat{{\rm{y}}}}_{i}\) is greater than zero, then it was modeled as lognormally distributed with mean \({\mu }_{c[i],t[i]}\), with variance equal to σ2. where ~ denotes ‘is distributed as’. Separate process models were defined for \({\theta }_{c,t}\) and \({\mu }_{c,t}\). For radiotherapy unit density, \({\theta }_{c,t}\) was modeled as a combination of a global intercept αθ, nested country–region random intercepts \({\gamma }_{c,\theta }\), and three covariates \({\mathbf{\upbeta} }_{\theta }{\mathbf{X}}_{c,t}\) including log transformation of the total population, log transformation of the THE per person, and the HAQI. THE and HAQI were obtained from the Institute for Health Metrics and Evaluation (IHME)63,64. With respect to the nested country–region random intercepts, countries were organized into 21 regions (r) according to the Global Burden of Disease (GBD) Study regional definition. The model for the mean of nonzero radiotherapy unit density \({\mu }_{c,t}\) followed a similar structure, but with separately estimated parameters and an additional time component. Here, the model was defined as a function of four components: a global intercept αμ, nested country–region random intercepts \({\gamma }_{c,\mu }\), the same three covariates, and a global time slope \({\beta }_{\mu }^{\text{t}}\) with nested country–region random slopes \({\eta }_{c,\mu }\). For mammography unit density, the probability of zero units (\({\theta }_{c,t}\)) was modeled as a combination of the same three components, but the log transformation of the total population was not used as a covariate. The model for the mean of nonzero mammography unit density (\({\mu }_{c,t}\)) was identical to the radiotherapy unit density model, except that the time component was excluded due to having fewer time points for most countries. In total, 135 countries had at least 3 years of reported radiotherapy unit density data, but only 33 countries had at least 3 years of reported mammography unit density data. Predictions for all country–years used the complete covariate time series. Breast cancer stage at diagnosis Stage data were primarily obtained from the International Agency for Research on Cancer, which conducted a systematic review, the largest so far, assessing the stage distribution of breast cancer at the population level worldwide. Here, diagnoses using different staging systems (that is, tumor, node, metastasis (TNM); Surveillance, Epidemiology, and End Results Program (SEER) Summary Stage) were standardized and decoded to two groups: nondistant and distant disease. In total, 81 countries were included with at least one reported data point. Countries where the proportion of unknown stage was 50% or more were excluded27. In addition, primary stage data from the VENUSCANCER study were used when available. The VENUSCANCER is a high-resolution study on patterns of care for women’s cancers spanning 40 countries and more than 200,000 women diagnosed with breast cancer between 2015 and 2018 ref. 28. Primary stage data submitted during the country consultation were also considered. In total, 70 non-overlapping stage data points from the systematic review were included, 27 from the VENUSCANCER project, and 12 data points newly submitted by countries. A Bayesian hierarchical model was used to estimate the percentage of breast cancer diagnoses that were classified as nondistant for all countries. \({\widehat{\text{y}}}_{i}\) is the reported percentage of breast cancer diagnoses classified as nondistant from each data point i. We assumed \({\widehat{\text{y}}}_{i}\) was beta distributed with mean \({\mu }_{c[i],t[i]}\) and precision φ. The process model for \({\mu }_{c,t}\) was assumed to be a function of a global intercept α, country intercepts γc nested within GBD super-region intercepts γsr, and two health system covariates (THE per person and HAQI) included in the covariate matrix Xc,t. Predictions of the percentage of nondistant breast cancer diagnoses were then made for all countries using the estimated model parameters and complete time series of covariates. Breast cancer medicine index The 2023 update to the European Society for Medical Oncology Global Consortium Study on the Availability, Out-of-Pocket Costs, and Accessibility of Cancer Medicines covered 126 countries across all WHO regions. While only 40% of countries in the African region participated, at least 50% participation rates were achieved in all other regions. In total 21 breast cancer medicines were included in the survey, 20 for metastatic breast cancer treatment and 9 for adjuvant treatment. For each cancer medicine, survey respondents reported categorical responses for both the availability/cost of a medicine and the actual availability of a medicine (that is, accessibility with a valid prescription). Hereafter, actual availability is defined as accessibility. To summarize accessibility and availability/cost, we assigned each categorical response a numeric score from 0 (‘never’ or ‘full cost’) to 4 (‘always’ or ‘free’); missing responses scored 0 (Extended Data Tables 4 and 5). For each country, the total score across both dimensions (accessibility and availability/cost) and across all breast cancer medicines was summed together and divided by the maximum possible score (116). The resulting percentage was used as an index of access to breast cancer medicines42. With breast cancer medicine indices calculated for the 126 countries included in the survey, we then used a Bayesian hierarchical model to predict plausible index values for the remaining countries. To account for the two countries with 0% index and the three countries with 100% index values, we used a zero- and one-inflated beta distribution. \({\widehat{\text{y}}}_{i}\) is the calculated breast cancer medicine index in each country. If \({\widehat{\text{y}}}_{i}\) is zero, we assumed it was Bernoulli distributed with probability equal to \({\theta }_{c\left[i\right]t[i]}^{0}\); likewise, if \({\widehat{\text{y}}}_{i}\) was equal to 1, we assumed it was Bernoulli distributed with probability equal to \({\theta }_{c\left[i\right]t[i]}^{1}\). \({\theta }_{c\left[i\right]t[i]}^{0}\) and \({\theta }_{c\left[i\right]t[i]}^{1}\) represent the probability of the index being exactly equal to zero and 1, respectively. If \({\widehat{\text{y}}}_{i}\) is not equal to zero or 1, we assume \({\widehat{\text{y}}}_{i}\) is beta distributed with mean \({\mu }_{c[i],t[i]}\) and precision φ. The process models for both \({\theta }_{c\left[i\right]t[i]}^{0}\) and \({\theta }_{c\left[i\right]t[i]}^{1}\) were defined as the sum of a global intercept α and regional random intercepts γsr using the seven GBD super-regions (sr). The process model for the mean of nonzero and non-one indices was defined as a function of a global intercept, nested GBD region intercepts (r) within GBD super-regions (sr), and two health system covariates (THE per person and HAQI) included in the covariate matrix \({\mathbf{X}}_{c,t}\). Predictions were made for all countries not included in the survey using the estimated parameters and full time series for each covariate. Observed index values were used in each available country, while predicted median values were used in unobserved countries. The estimated index value was held constant over the entire period of analysis for input to the breast cancer survival model. Statistical modeling to predict 5-year net survival Predictions for age-standardized 5-year net survival for women diagnosed with breast cancer were based on three principles. First, in countries with high-quality cancer registry data, the model was designed to closely match observed survival estimates, while accounting for uncertainty and extrapolating predictions to recent years. Second, in countries without cancer registry data, the model leveraged the observed relationship between survival data and predictive covariates to estimate plausible survival values. Third, in countries where survival data were available but highly uncertain, the model balanced the use of observed data with covariate-based estimates to produce more reliable predictions. Data model (likelihood) Let the logit-transformed 5-year net survival probability be denoted as \(\mathrm{logit}\left({\hat{p}}_{i}\right)\), where \({\hat{p}}_{i}\) is the cancer-registry reported 5-year net survival probability for the data point i, the country c[i], and the year t[i]. \(\text{Logit}\left({\hat{p}}_{i}\right)\) was assumed to be normally distributed with mean \({\phi }_{c\left[i\right],t[i]}\) and variance \({\hat{\zeta }}_{{\rm{c}}[{\rm{i}}],{\rm{t}}[{\rm{i}}]}^{2}+{\sigma }^{2}\). \({\hat{\zeta }}_{{\rm{c}}[{\rm{i}}],{\rm{t}}[{\rm{i}}]}^{2}\) refers to the variability inherited in the specific cancer registry estimates, whereas σ2 refers to the random stochastic variations. Calculation of standard error for logit-transformed estimates Deriving \({\hat{\zeta }}_{{\rm{c}}[{\rm{i}}],{\rm{t}}[{\rm{i}}]}^{2}\) requires first obtaining the standard errors of the survival estimates from registry data. Published cancer registry survival estimates typically include mean survival estimates alongside its 95% confidence intervals (CI) calculated with the Greenwood method. The lower \(L_i^{95\%\ \mathrm{CI}}\) and upper \(U_i^{95\%\ \mathrm{CI}}\) limits are estimated as where \({\hat{\sigma }}_{i}\) is the standard error of estimates. If \(L_i^{95\%\ \mathrm{CI}}\) or upper \(U_i^{95\%\ \mathrm{CI}}\) exceed the expected limit of 0 and 1, the values are truncated to the nearest boundary of 0 or 1. Based on the formula above, the standard error, \({\hat{\sigma }}_{i}\), can be backcalculated as follows: Because \({\hat{\zeta }}_{{\rm{c}}[{\rm{i}}],{\rm{t}}[{\rm{i}}]}^{2}\) is the standard error of \(\text{logit}\left({\hat{p}}_{i}\right)\), which is expressed in logit space, a transformation of \({\hat{\sigma }}_{i}\) is necessary. One option is the delta method; however, its performance becomes suboptimal when \({\hat{p}}_{i}\) are close to 0 or 1. As an alternative, one-dimensional optimization was used to estimate \({\hat{\zeta }}_{{\rm{i}}}^{2}\) by minimizing the absolute difference between the backtransformed CI bounds and those originally reported in the registry estimate. Specifically, the optimization searched over candidate values of \({\hat{\zeta }}_{{\rm{i}}}^{2}\), computed the 2.5th and 97.5th quantiles of a normal distribution with mean \(\text{logit}\left({\hat{p}}_{i}\right)\) and standard deviation \({\hat{\zeta }}_{{\rm{i}}}^{2}\), and then applied the inverse logit function to transform these quantiles back to probability space. The objective function minimized the sum of absolute differences between these backtransformed bounds and the reported CI limits. To ascertain the performance of this approach, we conducted simulations comparing CIs generated using the optimization-derived parameters against those reported in the cancer registries. Extended Data Fig. 1 shows the close agreement between the reported CI bounds and the simulations across most of the survival range, with discrepancies near zero for the lower bound and near one for the upper bound. These discrepancies are partly attributable to truncation in the registry-reported CIs, which were derived using the Greenwood method. Process model The mean logit 5-year net survival, \({\phi }_{c,t}\), was modeled as follows: \({\alpha }_{2000}\) represents the global intercept in the baseline year 2000. Country-level random intercepts, γc, nested within region intercepts, γsr, were used to account for differences in the baseline survival level across regions and countries. γc was assumed to be normally distributed around the corresponding regional mean, \({\gamma }_{{sr}[c]}\), with variance \({\sigma }_{{\gamma }_{c}}^{2}\). \({\eta }_{c}^{\text{t}}\) represents country-level random slopes for year, to capture country-specific temporal changes in survival that were not captured by covariates. \({\eta }_{c}^{\text{t}}\) was assumed to be normally distributed around 0 with variance \({\sigma }_{{\eta }_{c}^{t}}^{2}\). \({\mathbf{X}}_{c,t}\) is the covariate matrix that includes values from the list of covariates presented in Extended Data Table 3, namely the estimated percentage of breast cancer diagnoses classified as nondistant (logit transformed), the estimated breast cancer medicine index (logit transformed), the modeled radiotherapy unit density (log transformed), the modeled mammography unit density (log transformed) and the female adult mortality rate (logit transformed). The nested country–region random intercepts were organized into seven regions according to the GBD study super-region (sr) definition. GBD super-regions were chosen over other regional groupings because they are constructed to reflect shared sociodemographic and epidemiological characteristics rather than mainly geographic proximity, making them more relevant shrinkage targets for data-sparse countries. The normal distribution used for the random intercepts is standard in Bayesian models as the least-informative (maximum-entropy) choice given a mean and variance. The degree of shrinkage of country estimates toward their super-region mean is influenced jointly by the estimated between-country variance and the observed data, so that countries with data are informed primarily by their own estimates, while data-sparse countries borrow strength from epidemiologically similar countries. Sensitivity to this assumption is examined under a Student-t alternative (see ‘Model sensitivity analyses’ section). Parameter specifications and estimation Survival and covariate models were fitted using the R package brms65,66 and the underlying tool Stan67. The default brms priors were applied for all parameters except the survival model covariate β parameters where the horseshoe prior, with degrees of freedom (d.f.) set to 1, was used for regularization to prevent overfitting and shrink parameter estimates for correlated covariates. All covariates were standardized and scaled before model fitting. Four chains and 4,000 iterations for each chain (2,000 warmup iterations) were used to fit each model with a thinning rate of 4. A complete set of covariate values for all countries from 1995 to 2021 was used with the estimated parameters to generate draw-level predictions of the age-standardized 5-year net survival (%) for women diagnosed with breast cancer for each country and year. Each covariate’s central estimate was used as a fixed input to the model; uncertainty in the covariates was not propagated. This may lead to underestimation of breast cancer survival uncertainty, especially for countries with sparse cancer registry and covariate data availability. For each country and year, we generated 2,000 posterior draws of 5-year net survival. Global, WHO regional and World Bank Income Group estimates were obtained by averaging the country-level values within each draw, weighted by the IHME GBD 2023 annual estimates of breast cancer incident cases. Annual incidence from the IHME GBD 2023 study was used instead of GLOBOCAN because of the availability of incidence estimates for the entire analysis time period from 1995 to 2021. Finally, estimates for each location were summarized as the median and 95% UI across these draws. The median was reported in preference to the posterior mean because it is less sensitive to extreme draws where the posterior is skewed near the survival boundaries (0% and 100%); in practice, the two were very similar. Examples of fitting results in different scenarios Extended Data Fig. 2 illustrates the model fitting results in three example scenarios: (1) countries with high-certainty nationally representative CONCORD data, (2) countries with mixed subnational cancer registry data from multiple sources and (3) countries with sparse or no cancer registry data. Model validation To assess the breast cancer survival model performance, we examined in-sample fit and conducted three complementary out-of-sample validation exercises, each designed to evaluate a distinct aspect of predictive behavior. All three used tenfold cross-validation: the data were partitioned into ten folds, the model was refit ten times holding out one fold at a time, and agreement between held-out observations and out-of-sample predictions was quantified using the mean error, mean absolute error, coverage of the 95% UI, and the continuous ranked probability score. All performance metrics were computed on the original survival scale (0–100%) rather than the logit scale on which the model was fitted, so that performance statistics are expressed in percentage points of 5-year net survival and are directly interpretable. The three exercises differed in how folds were constructed to target different predictive challenges. First, to evaluate overall predictive performance, registry-based survival estimates were randomly partitioned regardless of country (random hold-out). Second, to assess the model’s ability to predict for countries with no observed survival data, partitions were applied at the country level, where observations from a given country were held completely (country hold-out). Third, to evaluate temporal extrapolation beyond the most recent available observation, partitions were applied to countries with at least two survival estimates, of which the most recent estimate from each was held out (forward hold-out). Results from all three exercises are presented in Extended Data Table 6. Model sensitivity analyses We conducted two sensitivity analyses to examine the influence of key modeling choices. First, we assessed the influence of the inclusion of hospital-based survival estimates. These estimates, from the ABC-DO study, were used for five African Region countries; for four of them (Namibia, Nigeria, Uganda and Zambia), they were the only survival data available. We refitted the model after excluding all hospital-based estimates, so that the four countries without other data had no observed registry survival estimates and predicted values were predominantly based on the estimated relationship with covariates. Excluding the hospital-based registry estimates, predicted 5-year net survival was higher in all five countries and the UIs were considerably wider. Estimated survival in the African Region rose from 39.1% (95% UI 34.1–44.7%) to 51.4% (95% UI 39.7–62.7%), whereas the global estimate was essentially unchanged from 77.8% (95% UI 76.4–79.2%) to 78.8% (95% UI 77.2–80.5%) (Extended Data Table 1). The hospital-based estimates therefore strongly influence survival for these countries and for the African Region, but not the global estimate. Second, we assessed the sensitivity of country-level predictions to the assumption that country random intercepts are normally distributed around their regional mean. We refitted the model with the country random intercept following a Student-t distribution rather than a normal distribution, with its degrees of freedom estimated from the data (weakly informative Gamma(2, 0.1) prior). The Student-t distribution has heavier tails than the normal distribution and allows individual countries to deviate further from their regional mean, which is most relevant for countries with sparse or no observed data. The estimated degrees of freedom had a posterior median of 15 (95% credible interval 3–54), indicating a mild departure from normality. Country-level predictions changed little: point estimates differed by less than 0.1 percentage points, including for countries with no observed data, and the 95% UIs for these countries were on average about 0.4 percentage points wider than under the normal model, while estimates for countries with observed data were essentially unchanged. These results indicate that the country-level predictions are robust to the assumed shape of the random-effects distribution. Ethics and inclusion This study presents breast cancer survival estimates for all WHO Member States, covering different geographical regions and income settings. The estimates were submitted to Member States through designated focal points during country consultations, to ensure transparency, engagement and ownership. The analysis was conducted at the WHO Headquarters, but the research methods were regularly reviewed by a diverse group of experts in the field from different WHO regions. Some of these experts contributed to the interpretation of the results and the preparation of this publication. We strived for an inclusive authorship, and roles and responsibilities were agreed upon by all authors. Statistics and reproducibility This is a modeling study based on aggregate, published or country-submitted survival estimates, and covariate data. No patient-level data were collected, and the analysis did not involve experimental groups. As the study is population-based, sample size calculations were not relevant to this analysis. All published or country-submitted survival estimates meeting the predefined inclusion criteria as specified in the Methods were used. All survival estimates were generated using the published code (see ‘Code availability’) and described input data (see ‘Data availability’). Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability Published survival estimates by country and WHO region are provided in a public code repository, available via Zenodo at https://doi.org/10.5281/zenodo.20539741 (ref. 68). The country comparable estimates will be published on the WHO website. Input data from CONCORD, SURVCAN-3, NORDCAN, ABC-DO, the WHO Global Health Observatory, UN DESA World Population Prospects 2024, IHME Global Burden of Disease Study 2023 and other cited sources are available from their original study or data repositories; download notes, formatting code and formatted data inputs are included in the public code repository. Survival estimates submitted by Member States during the WHO Country Consultation are not publicly redistributed. Modeled covariate intermediate estimates (radiotherapy unit density, mammography unit density, breast cancer stage at diagnosis, and breast cancer medicines availability) have not undergone Member State review and are not released for individual countries. Requests for access should be directed to the WHO Global Breast Cancer Initiative at gbci@who.int; access is subject to agreement by the contributing registry. All restricted-access data described above will be made available to the editors and to peer reviewers on request. Code availability All code used to compile and format inputs, fit the Bayesian hierarchical model, generate diagnostics, and produce the published estimates is openly available via Zenodo at https://doi.org/10.5281/zenodo.20539741 (ref. 68). References Ginsburg, O. et al. The global burden of women’s cancers: a grand challenge in global health. Lancet 389, 847–860 (2017). GBD 2023 Breast Cancer Collaborators. Global, regional, and national burden of breast cancer among females, 1990-2023, with forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2023. Lancet Oncol. 27, 302–326 (2026). Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229–263 (2024). Arnold, M. et al. Current and future burden of breast cancer: global statistics for 2020 and 2040. Breast 66, 15–23 (2022). Independent UK Panel on Breast Cancer Screening. The benefits and harms of breast cancer screening: an independent review. Lancet 380, 1778–1786 (2012). Mittra, I. et al. Effect of screening by clinical breast examination on breast cancer incidence and mortality after 20 years: prospective, cluster randomised controlled trial in Mumbai. BMJ 372, n256 (2021). Sullivan, R. et al. Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol. 16, 1193–1224 (2015). Are, C. et al. Global Cancer Surgery: pragmatic solutions to improve cancer surgery outcomes worldwide. Lancet Oncol. 24, e472–e518 (2023). Abdel-Wahab, M. et al. Radiotherapy and theranostics: a Lancet Oncology Commission. Lancet Oncol. 25, e545–e580 (2024). Jenei, K. & Sullivan, R. Cancer medicines remain absent from global access metrics. Lancet 407, 491–492 (2026). Coleman, M. P. Cancer survival: global surveillance will stimulate health policy and improve equity. Lancet 383, 564–573 (2014). Coleman, M. P. & Allemani, C. Cancer: the elephant in the room. Lancet 385, 1047–1048 (2015). Ellis, L. et al. Cancer incidence, survival and mortality: explaining the concepts. Int. J. Cancer 135, 1774–1782 (2014). Soerjomataram, I. et al. Cancer survival in Africa, Central and South America, and Asia (SURVCAN-3): a population-based benchmarking study in 32 countries. Lancet Oncol. 24, 22–32 (2023). Allemani, C. et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37,513,025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 391, 1023–1075 (2018). The Global Breast Cancer Initiative: empowering women, building capacity, providing care for all. World Health Organization https://www.who.int/initiatives/global-breast-cancer-initiative (accessed 30 April 2026). Tackling NCDs: Best Buys and Other Recommended Interventions for the Prevention and Control of Noncommunicable Diseases (World Health Organization, 2024). Anderson, B. O. et al. The Global Breast Cancer Initiative: a strategic collaboration to strengthen health care for non-communicable diseases. Lancet Oncol. 22, 578–581 (2021). Global Breast Cancer Initiative Implementation Framework: Assessing, Strengthening and Scaling Up Services for the Early Detection and Management of Breast Cancer. Executive Summary (World Health Organization, 2023). Cancer registries: the bedrock of global cancer care. Lancet 405, 353 (2025). Sustainable Development Goals. World Health Organization https://www.who.int/europe/about-us/our-work/sustainable-development-goals (accessed 30 April 2026). FY26 list of fragile and conflict-affected situations. World Bank https://thedocs.worldbank.org/en/doc/5c7e4e268baaafa6ef38d924be9279be-0090082025/original/FCSListFY26.pdf (accessed 30 April 2026). World Bank country classifications by income level for 2024–2025. World Bank https://blogs.worldbank.org/en/opendata/world-bank-country-classifications-by-income-level-for-2024-2025 (accessed 30 April 2026). Cancer prevention and control in the context of an integrated approach. (World Health Organization, accessed 30 April 2026); https://apps.who.int/gb/ebwha/pdf_files/wha70/a70_r12-en.pdf Broeders, M. et al. The impact of mammographic screening on breast cancer mortality in Europe: a review of observational studies. J. Med. Screen. 19, 14–25 (2012). Chanakira, E. Z., Thomas, C. V., Balen, J. & Mandrik, O. A systematic review of public health interventions to address breast cancer inequalities in low- and middle-income countries. Syst. Rev. 13, 195 (2024). Benitez Fuentes, J. D. et al. Global stage distribution of breast cancer at diagnosis: a systematic review and meta-analysis. JAMA Oncol. 10, 71–78 (2024). Allemani, C. et al. Global variation in patterns of care and time to initial treatment for breast, cervical, and ovarian cancer from 2015 to 2018 (VENUSCANCER): a secondary analysis of individual records for 275,792 women from 103 population-based cancer registries in 39 countries and territories. Lancet 406, 2325–2348 (2025). UHC Service Coverage Index (SDG 3.8.1). (The Global Health Observatory, accessed 30 April 2026); https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage Ong, S. K. et al. Feasibility of monitoring Global Breast Cancer Initiative Framework key performance indicators in 21 Asian National Cancer Centers Alliance member countries. EClinicalMedicine 67, 102365 (2024). Trapani, D. et al. Distribution of the workforce involved in cancer care: a systematic review of the literature. ESMO Open 6, 100292 (2021). Trapani, D. et al. Policy strategies for capacity building and scale up of the workforce for comprehensive cancer care: a systematic review. ESMO Open 9, 102946 (2024). Karim, S. et al. Oncology training and education initiatives in low and middle income countries: a scoping review. ecancermedicalscience 15, 1296 (2021). Brand, N. R., Qu, L. G., Chao, A. & Ilbawi, A. M. Delays and barriers to cancer care in low- and middle-income countries: a systematic review. Oncologist 24, e1371–e1380 (2019). Espina, C., McKenzie, F. & Dos-Santos-Silva, I. Delayed presentation and diagnosis of breast cancer in African women: a systematic review. Ann. Epidemiol. 27, 659–671(2017). Richards, M. A., Westcombe, A. M., Love, S. B., Littlejohns, P. & Ramirez, A. J. Influence of delay on survival in patients with breast cancer: a systematic review. Lancet 353, 1119–1926 (1999). Flytkjær Virgilsen, L., Møller, H. & Vedsted, P. Cancer diagnostic delays and travel distance to health services: a nationwide cohort study in Denmark. Cancer Epidemiol. 59, 115–122 (2019). Longacre, C. F., Neprash, H. T., Shippee, N. D., Tuttle, T. M. & Virnig, B. A. Travel, treatment choice, and survival among breast cancer patients: a population-based analysis. Womens Health Rep. 2, 1–10 (2021). Ward, Z. J. et al. Estimating the impact of scaling up workforce personnel on global cancer mortality from 2030 to 2050: a simulation-based analysis of 17 cancers and 18 personnel types. Lancet Oncol. 27, 569–579 (2026). Surgical workforce: surgeons, licensed qualified actively working. (The Global Health Observatory, accessed 30 April 2026); https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-licensed-qualified-surgeons-actively-working Cancer diagnosis and treatment services, availability. (The Global Health Observatory, accessed 30 April 2026); https://www.who.int/data/gho/data/indicators/indicator-details/GHO/cancer-diagnosis-and-treatment-services Cherny, N. I. et al. ESMO Global Consortium Study on the availability, out-of-pocket costs, and accessibility of cancer medicines: 2023 update. Ann. Oncol. 36, 247–262 (2025). Hejaz, H. A. Cross-sectional investigation of the effects of the war in Gaza on quality-of-life of breast cancer patients. East. Mediterr. Health J. 31, 101–108 (2025). eClinicalMedicine Breast cancer—the impact of conflict and displacement. EClinicalMedicine 76, 102906 (2024). Ghebreyesus, T. A. et al. A manifesto on improving cancer care in conflict-impacted populations. Lancet 404, 427 (2024). Ahmed, Y. Enhancing cancer care amid conflict: a proposal for optimizing oncology services during wartime. JCO Glob. Oncol. 9, e2300304 (2023). Hammad, N., Abdihamid, O., Elhassan, M. M. A. & Bague, A. H. Global disparities in cancer care for conflict-affected populations: Africa’s neglected crisis. Am. Soc. Clin. Oncol. Educ. Book 46, e521198 (2026). Vaz-Luis, I. et al. ESMO Expert Consensus Statements on Cancer Survivorship: promoting high-quality survivorship care and research in Europe. Ann. Oncol. 33, 1119–1133 (2022). Mollica, M. A. et al. Follow-up care for breast and colorectal cancer across the globe: survey findings from 27 countries. JCO Glob. Oncol. 6, 1394–1411 (2020). Cayrol, J. et al. The lived experience of people affected by cancer: a global cross-sectional survey protocol. PLoS ONE 19, e0294492 (2024). Meunier, F. Protecting cancer survivors from financial discrimination throughout the EU: a cross-European perspective. Eur. J. Cancer 209, 114238 (2024). Dos Anjos, G. R. et al. Cancer survivorship in low- and middle-income countries: challenges, needs, and emerging support strategies. Front. Public Health 13, 1601483 (2025). ACTION Study Group. Health-related quality of life and psychological distress among cancer survivors in Southeast Asia: results from a longitudinal study in eight low- and middle-income countries. BMC Med. 15, 10 (2017). Allemani, C. et al. Global surveillance of cancer survival 1995–2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet 385, 977–1010 (2015). Larønningen, S. et al. NORDCAN: cancer incidence, mortality, prevalence and survival in the Nordic countries, Version 9.5.1 (12.02.2026). (Association of the Nordic Cancer Registries, accessed 30 April 2026); https://nordcan.iarc.fr/ Pohar Perme, M., Stare, J. & Estève, J. On estimation in relative survival. Biometrics 68, 113–120 (2012). Laurenti, R., Coleman, M. P. & Aylin, P. in Death Certification and Mortality Statistics: An International Perspective (eds Coleman, M. P. & Aylin, M. P.) 1–9 (Office for National Statistics, 2000) Corazziari, I., Quinn, M. & Capocaccia, R. Standard cancer patient population for age standardising survival ratios. Eur. J. Cancer 40, 2307–2316 (2004). Mo, T. et al. Breast cancer overall survival, annual risks of death, and survival gap apportionment in sub-Saharan Africa (ABC-DO): 7-year follow-up of a prospective cohort study. Lancet Glob. Health 13, e1681–e1690 (2025). World Population Prospects 2024. (United Nations, Department of Economic and Social Affairs, Population Division, accessed 30 April 2026); https://population.un.org/wpp/ Medical devices: radiotherapy units (per million population), total density. (The Global Health Observatory, accessed 30 April 2026); https://www.who.int/data/gho/data/indicators/indicator-details/GHO/total-density-per-million-population-radiotherapy-units Medical devices: mammography units (per million females aged from 50 to 69 years old), total density. (The Global Health Observatory, (accessed 30 April 2026); https://www.who.int/data/gho/data/indicators/indicator-details/GHO/total-density-per-million-females-aged-from-50-to-69-years-old-mammography-units GBD 2023 Disease and Injury and Risk Factor Collaborators. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet 406, 1873–1922 (2025). WHO Global Spending on Health: Emerging from the Pandemic (World Health Organization, 2024). Bürkner, P.-C. Advanced Bayesian multilevel modeling with the R package brms. R J. 10, 395–411 (2018). Bürkner, P.-C. brms: an R package for Bayesian multilevel models using stan. J. Stat. Softw. 80, 1–28 (2017). Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017). Girardi, F. et al. Estimating global breast cancer survival, 2017–2021 Code and data accompanying 'Global breast cancer survival estimates in 2017–2021 to advance the WHO Global Breast Cancer Initiative' Zenodo https://doi.org/10.5281/zenodo.20539741 (2026). Acknowledgements We recognize the strategic guidance provided by Alarcos Cieza, Unit Head—Management of Noncommunicable Diseases, Department of Noncommunicable Diseases and Mental Health at WHO Headquarters—under whose leadership this work was conducted. M. Nyangasi, S.N., V.M., L.M., H.W. and A.M.I. are staff members of the World Health Organization. The authors alone are responsible for the views expressed in this Article, and they do not necessarily represent the decisions, policy or views of the World Health Organization. Funding This work was supported by the City Cancer Challenge Foundation, St. Jude Children’s Research Hospital (USA) and the Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Author information Authors and Affiliations Contributions F. Girardi and M. Nyangasi contributed equally to this work. H.W. and A.M.I. contributed equally to this work. F. Girardi led the study and wrote the original draft. M. Nyangasi provided overall supervision of the study and contributed to writing and reviewing the paper. C.C. conducted the formal analysis and contributed to writing and reviewing the paper. M. Ng supervised the analysis and contributed to writing and reviewing the paper. S.N. coordinated the study and contributed to reviewing and editing the paper. J.T., H.A., S.K.O., F. Gnangnon, V.M., W.S., R. and L.M. contributed to reviewing and editing the paper. H.W. and A.M.I. conceptualized the study and provided senior oversight. Corresponding author Ethics declarations Competing interests F. Girardi is a consultant for the Department of Noncommunicable Diseases and Mental Health at the World Health Organization, Geneva, Switzerland; Honorary Assistant Professor at the London School of Hygiene and Tropical Medicine, London, UK; Member of the CONCORD Working Group. M. Ng is an Affiliate Associate Professor at the Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. Peer review Peer review information Nature Medicine thanks Md. Mijan Rahman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team. Peer reviewer reports are available. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Extended data Extended Data Fig. 1 Validation of optimisation-derived confidence intervals. Comparison of CONCORD-reported CI bounds (x-axis) with corresponding bounds from 100,000 simulations using logit-space standard errors from the optimisation method (y-axis). Points falling along the identity line indicate agreement between the two approaches. Extended Data Fig. 2 Examples of estimated and predicted age-stadnardised 5-year net survival. This figure shows the age-standardised 5-year net survival (%) for women diagnosed with breast cancer as estimated by cancer registries, with WHO model prediction median and 95% uncertainty interval; each row of panels represents one example country; the left panel is shown from 0–100% survival, and the right-side panel is zoomed in to a country-specific scale. Supplementary information Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. About this article Cite this article Girardi, F., Nyangasi, M., Callender, C. et al. Global breast cancer survival estimates in 2017–2021 to advance the WHO Global Breast Cancer Initiative. Nat Med (2026). https://doi.org/10.1038/s41591-026-04531-2 Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41591-026-04531-2