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. 2026 Jun 25;19(1):2684148. doi: 10.1080/20523211.2026.2684148

Impact of the COVID-19 pandemic on systemic anti-cancer therapy in patients with breast cancer: a systematic review and meta-analysis

Dana Karasneh a, Mohammed Patel b, Syed Shahzad Hasan a,CONTACT, Barbara R Conway a, Ahmed A Sadeq a,c, Mamoon A Aldeyab a,d
PMCID: PMC13307381  PMID: 42370216

ABSTRACT

Background

The COVID-19 pandemic caused significant disruptions in oncology services worldwide. Breast cancer care depends on timely and coordinated treatment pathways. This review assessed changes in systemic anti-cancer therapy, including chemotherapy, endocrine therapy, targeted therapy, and immunotherapy. Secondary outcomes included treatment delays, short-term mortality, and hospital admissions.

Methods

We conducted a systematic review and meta-analysis following PRISMA guidelines. The protocol was registered with PROSPERO (CRD42024542702). We searched PubMed, Scopus, Web of Science, and CINAHL for studies published between March 2020 and August 2024. We included original studies comparing adult breast cancer patients treated before and during the pandemic. Patients of any stage and sex were eligible. We assessed risk of bias and used a random-effects model to present pooled proportions and odds ratios (ORs) with 95% confidence intervals (CIs).

Results

Forty-eight studies were included in the review, and 35 in the meta-analysis. Chemotherapy use increased from 35% before the pandemic to 42% during the pandemic. This rise was driven by an increase in neoadjuvant chemotherapy, from 18% to 22%, while adjuvant chemotherapy remained stable. Endocrine therapy increased from 35% to 42%. Neoadjuvant endocrine therapy rose from 2% to 10%, whereas adjuvant endocrine therapy declined from 46% to 43%. Targeted therapy and immunotherapy showed minimal change. The pooled OR for short-term mortality was 0.81 (95% CI: 0.56–1.15). Hospital admission estimates showed wide confidence intervals, reflecting heterogeneity.

Conclusion

The pandemic led to notable shifts in systemic treatment patterns, particularly an increase in the use of neoadjuvant strategies. Short-term mortality did not significantly differ between periods. The study findings should be interpreted with caution due to variability in studies. Health systems require robust pharmaceutical policies and resilient triage frameworks to ensure continuity of cancer treatment during future crises.

KEYWORDS: COVID-19, breast cancer, systemic anti-cancer therapy, treatment delay, endocrine therapy, pharmaceutical policy, access to medicines

Introduction

COVID-19 is a contagious illness caused by the SARS-CoV-2 virus (Coronavirus, n.d.) that has a global impact. This highly infectious virus led to the widespread use of counter measures such as quarantine and social distancing as the main response to minimise spread, prior to the availability of new treatments and vaccines (Peng et al., 2020). Additionally, it greatly affected the distribution of resources, prioritising the care of COVID-19 patients over other health conditions (Budiarta & Brennan, 2022).

Due to this pandemic, the medical community and all responsible authorities faced many challenges in maintaining a balance between responding to this unprecedented event and continuing to provide medical services and management as required (Budiarta & Brennan, 2022). Access to health care facilities was affected by lockdowns, and health resources were stretched thin. Concerns were raised about potential exposure to the virus during hospital visits, especially in patients with an underlying chronic disease or cancer diagnoses who mostly had their conditions managed in hospitals and required close monitoring. Due to this, access to diagnosis and treatment was significantly affected (Chen et al., 2016; Siegel et al., 2021)

Cancer patients are particularly vulnerable to severe risks associated with COVID-19 due to their compromised immune systems, either because of the cancer itself or because of the anticancer treatment (Ali et al., 2022). Considering the immunocompromised state of many cancer patients, there was fear among clinicians that a SARS-CoV-2 infection could lead to an even higher risk of morbidity and mortality in this group (Liang et al., 2020; Zhang et al., 2020).

One of the most commonly diagnosed types of cancer worldwide is breast cancer, with 2.3 million cases annually (Sung et al., 2020). Early detection and prompt treatment are crucial for enhancing survival rates and ensuring a better quality of life for breast cancer patients. However, due to COVID-19-related healthcare system strains, key diagnostic pathways such as general practitioner (GP) referrals, self-referrals, and national breast cancer screening programmes, including mammography, were either halted or operated at reduced capacity, leading to delays in early diagnosis and treatment. Such delays could result in patients being diagnosed with more advanced stages of breast cancer, thereby necessitating more aggressive treatments and potentially increasing mortality rates (Figueroa et al., 2021).

Given the uncertainty around the pandemic and associated factors such as its impact, prevalence and duration, along with differences in resource availability and patient factors such as age and comorbidities that can influence clinical decisions, it was unclear which guidelines clinicians should follow and to what degree they should be implemented (Tang et al., 2022) Similarly, as the virus evolved, treatment guidelines were updated to reflect the most effective therapies (Cascella et al., 2023).

The aim of this systematic review is to assess the impact of COVID-19 on systemic anti-cancer therapy (SACT) in breast cancer patients, including chemotherapy, endocrine therapy, targeted therapy, and immunotherapy, and to evaluate associated changes in treatment patterns, delays, and short-term clinical outcomes. Studies examining the impact of the pandemic on breast cancer treatment are an essential first step in accurately assessing how changes in treatment strategies affect recurrence risk and survival in breast cancer patients. These findings could offer important insights into improving treatment both in the present and during future scenarios with limited resources (Eijkelboom et al., 2023).

2. Methods

This review has been recorded in PROSPERO (The International Prospective Register of Systematic Reviews) under the code CRD42024542702.

PICO (Population, Intervention/Exposure, Comparison, and Outcome) was used to develop the structure of the research question of this study: ‘What is the impact of COVID-19 on the treatment of breast cancer patients?' The specific PICO parts were as follows: P, breast cancer patients; I, COVID-19; C, pre- and during-COVID-19 period; and O, systemic anti-cancer treatment management.

2.1. Information sources and search strategy

The authors conducted this systematic review and meta-analysis following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), which serves as an evidence-based reporting system for systematic reviews and meta-analyses (Page et al., 2021).

PubMed/MEDLINE, Web of Science, CINAHL, and Scopus databases were systematically searched for related original articles published from the start of the pandemic on 11 March 2020 to August 08, 2024.

All original articles investigating the impact of COVID-19 on treatment management among breast cancer patients were included for screening and review. The search strategy is detailed in Supplemental Material Table S1.

2.2. Eligibility criteria

The inclusion criteria for articles to be included in the review were: (i) observational studies that reported the management of breast cancer treatment during the COVID-19 pandemic; that included a comparison group from the pre-COVID period; and covered the study period from January 2020 to August 2024; (ii) studies reporting Systemic Anti-Cancer therapy (SACT), including chemotherapy, endocrine therapy, targeted therapy, and immunotherapy and including males or females aged 18 years or above diagnosed with breast cancer as the primary site regardless of stage; and (iii) original published peer-reviewed articles providing complete information in the English language. Treatment management was defined as any postponement, substitution of one treatment with another, modification of the treatment sequence, or omission of one or more standard therapies.

Exclusion criteria included: (i) articles that failed to document treatment changes due to the pandemic; (ii) studies that lacked a comparative analysis with the pre-pandemic period; (iii) studies reporting only surgical treatment; (iv) letters, review articles, commentaries, editorials, systematic reviews, abstracts only, opinion statements, practice guidelines, news articles, and case series or case reports; and (v) studies not published in English in full text.

2.3. Study selection

Two independent investigators (DAK and AAS) examined titles and abstracts identified from database searches to determine eligibility. Records were excluded at the title and abstract stage only when they clearly did not meet predefined eligibility criteria. Any disagreements between the investigators, such as the inclusion of different articles, were resolved through discussion and consensus. All inclusion and exclusion assessments were conducted independently by both reviewers. The PRISMA flowchart (Figure 1) illustrates the process for identifying, screening, and including studies.

Figure 1.

A flowchart diagram summarizing identification, screening, exclusion, and final inclusion of records and reports for a PRISMA review. The figure shows a Preferred Reporting Items for Systematic Reviews and Meta Analyses style flowchart diagram that traces study selection through identification, screening, and inclusion. At the top, a horizontal box states that studies are identified via databases and registers. A large box on the left lists records identified from databases with counts: PubMed 1898, Scopus 2768, Cumulative Index to Nursing and Allied Health Literature 374, Web of Science 3113, for a total of 8153 records. A box on the right lists records removed before screening: duplicate records removed based on title 3218 and duplicate records removed based on digital object identifier 1092, for a total of 4310 removed. An arrow leads down from the left identification box to a box labeled records screened with 3843 records, split into 2005 screened by title and 1838 screened by abstract. From this box an arrow leads down to reports sought for retrieval, 67 reports. To the right, another box records excluded with 3765 records. An arrow continues downward from reports sought to full text reports assessed for eligibility, again 67 reports. To the right, a box labeled reports excluded lists 19 exclusions with reasons and counts: full text not in English 4, focus on surgical treatments 3, lack of comparison 5, focus on diagnosis 1, focus on vaccines 1, and excluded study design 5. A note to the right of reports sought for retrieval states reports not retrieved 0. At the bottom, an arrow leads to a box labeled studies included in review with 48 studies, and within the same box, studies included in meta analysis with 35 studies. Side bars along the left margin label the vertical sections as identification, screening, and included.

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PRISMA flow diagram of study selection. A total of 8,153 records were identified from PubMed (1,898), Scopus (2,768), CINAHL (374), and Web of Science (3,113). After removal of 4,310 duplicate records, 3,843 records were screened. Sixty-seven reports were assessed for eligibility, with 19 excluded for predefined reasons. Forty-eight studies were included in the systematic review, of which 35 were included in the meta-analysis.

2.4. Data extraction

Extraction was carried out independently by DAK and AAS. Disagreements were resolved through discussion and consensus, or, when needed, by arbitration by a third author (MAA).

The following data were gathered from the identified studies: author name, year, study design, country, study setting, number of patients pre-COVID, number of patients COVID era, study period, age, sex, Tumour, Node, Metastasis (TNM)/ staging, number of chemotherapy treatments prior to and post COVID-19, number of hormonal treatments prior to and post COVID-19, number of immunotherapies prior to and post COVID-19, number of targeted therapies prior to and post COVID-19, mortality, hospital admission, treatment changes, treatment delays and patient outcomes.

2.5. Exposures and outcomes

Exposure: The primary exposure considered was the COVID-19 pandemic period; according to the World Health Organization (WHO), the COVID-19 pandemic officially began on 11 March 2020, when the disease was declared a global pandemic, and the public health emergency status ended on 5 May 2023, when COVID-19 was no longer classified as a Public Health Emergency of International Concern (PHEIC); although it remains a global health threat. For research purposes, the COVID-19 period is typically defined as March 11, 2020, to May 5, 2023; the pre-COVID period, defined individually by each study, generally refers to any time before March 2020. Where applicable, studies that include a post-COVID or recovery period refer to the period after May 5, 2023.

Even though the declaration of the pandemic period was based on the World Health Organisation's reference date of 11 March 2020, some specific timeframes included in the individual studies showed slight differences (e.g. calendar-year comparisons, pandemic waves, or institution-specific lockdown periods). The studies were included because they defined pre-pandemic and pandemic periods, although these were not necessarily March 2020 to May 2023. The data were used based on the comparison periods of each study. This heterogeneity in exposure definitions may have contributed to between-study heterogeneity.

Treatment options evaluated included adjuvant and neoadjuvant chemotherapy, adjuvant and neoadjuvant endocrine therapy, immunotherapy, and targeted therapy. These treatments were assessed in terms of frequency of use before and during the pandemic, modifications such as postponement or delay, substitution with alternative therapies, omission, and re-sequencing of treatment regimens.

Outcomes evaluated included the rate of treatment modifications, delays in therapy initiation or continuation, hospital admission rates, short-term patient mortality, and the overall impact on treatment access and continuity.

These variables were extracted and compared between pre-pandemic and COVID-19 era data, and where applicable, post-pandemic recovery trends.

2.6. Risk of bias assessment

The risk of bias was assessed using Risk of Bias in Non-randomised Studies of Exposure (ROBINS-E) Version 24 March 2024 (Higgins et al., 2024).

The overall risk of bias was assessed based on the scoring of the criteria; if the risk of bias across all domains was low, or if only Domain 1 had a high risk due to uncontrolled confounding, then the overall risk was low. If there were low or some concerns about risk of bias for all domains, then the risk was of some concern . If there was a high risk of bias in at least one domain, or several domains were of some concern, leading to an additive judgment of high risk of bias, the overall risk of judgment was high. The study was at very high risk of bias if at least one domain was at very high risk of bias or if several domains were at high risk of bias, leading to an additive judgement of very high risk of bias (Higgins et al., 2024). The risk of bias assessment was performed independently by two investigators (DAA and AAS), and any disagreements were resolved through discussion and consensus.

2.7. Statistical analysis

Meta-analyses were conducted to estimate pooled proportions of systemic anti-cancer therapies used (primary outcomes) and to calculate odds ratios (ORs) comparing pre- and during-pandemic periods for secondary outcomes, including short-term mortality and hospital admissions. Results were pooled using a random-effects model (Der Simonian and Laird method), which reflects variability within and between studies. To examine the changes before and after COVID-19, pooled proportions with 95% confidence intervals were generated. Although odds ratios were also reported, pooled proportions were preferred because they more accurately capture the actual treatment effects during these periods. For binary outcomes, odds ratios with corresponding 95% confidence intervals (CIs) were extracted from individual studies or calculated as needed.

Forest plots were employed to graphically display effect sizes and heterogeneity. I² statistic and Cochran’s Q test were utilised to calculate the degree of statistical heterogeneity, with 25%, 50%, and 75% I² representing low, moderate, and high heterogeneity. A p-value of <0.10 for Q was considered indicative of significant heterogeneity.

Meta-analyses of prevalence were performed using a logit transformation of proportions to stabilise variances. Back-transformed estimates were reported for interpretability. All statistical analyses were conducted using SPSS version 25 and Meta XL, version 5.3.

3. Results

3.1. Study selection

The initial database search yielded 8,153 records. After removing 4,310 duplicates, 3,843 titles and abstracts were screened. Of these, 67 full-text articles were assessed for eligibility, and 48 studies met the inclusion criteria for the systematic review. Thirty-five of these were eligible for meta-analysis.

Figure 1 illustrates the study selection process using a PRISMA flow diagram.

3.2. Study characteristics

Among the 48 included studies, 47 were retrospective cohort studies (Al-Hajeili et al., 2022; Baba et al., 2023; Barclay et al., 2024; Castillo et al., 2021; Di Cosimo et al., 2023; Do Nascimento et al., 2023; Duarte et al., 2022; Eijkelboom et al., 2021; Eijkelboom et al., 2023; Fu et al., 2022; Gao et al., 2020; Gosset et al., 2023; Guével et al., 2023; Habbous et al., 2022; Hacı Arak,haci arak, 2023; Hawrot et al., 2021; Howdle et al., 2024; Hui et al., 2023; Ibfelt et al., 2023; Iles et al., 2022; Ilgün & Özmen, 2022; Işıklar et al., 2021; Kapp et al., 2022; Lee et al., 2023; Li et al., 2020; Lou et al., 2022; Malmgren et al., 2023; Mason et al., 2022; Morais et al., 2022; Murris et al., 2021; Mühlmann et al., 2024; Navarro-Sabate et al., 2024; Neilson et al., 2023; Nogueira et al., 2024; Nowikiewicz et al., 2022; Parikh et al., 2024; Patt et al., 2020; Peacock et al., 2024; Rahimi et al., 2023; Ribeiro et al., 2024; Simão et al., 2021; Soyder et al., 2022; Tang et al., 2022; Tonneson et al., 2022; van Dam et al., 2022; Vanni, Pellicciaro, Combi, et al., 2021; Vanni, Pellicciaro, Materazzo, et al., 2021) with one cross-sectional study by Girardi et al. (2023). These studies were conducted across multiple countries, including Italy, France, Portugal, Turkey, and Brazil, with the greatest number originating from the United States. The included studies represented diverse healthcare systems across multiple countries. However, variability in reporting, outcome definitions, and the limited number of studies per country prevent a formal stratified meta-analysis by nation. Healthcare settings covered treatment periods from January 2020 to August 2024.

Details of the included studies are presented in Supplemental Material Table S2, including study design, country, sample size, treatment types, and outcome metrics. Table 1 summarises treatment changes, delays, and patient outcomes by treatment type. Also, Table 2 presents a summary of themes grouped by shared treatment changes, delays, and outcomes.

Table 1.

Summary of treatment changes, delays, and patient outcomes according to treatment type.

Treatment type Treatment changes (during COVID-19) Treatment delays (post/during COVID-19) Patient outcomes
Surgery Reduced surgery rates; shift to endocrine or neoadjuvant therapy; delayed reconstructions (haci arak 2023; Hawrot et al., 2021; Iles et al., 2022; Kapp et al., 2022; Morais et al., 2022; Navarro-Sabate et al., 2024; Nogueira et al., 2024; Peacock et al., 2024; Simão et al., 2021; Tang et al., 2022; van Dam et al., 2022) Postponed surgery; fewer surgeries within guideline windows; pre-diagnosis delays increased Increased advanced stage: stable outcomes with endocrine bridging in some cases
Chemotherapy Increased neoadjuvant chemotherapy; reduced adjuvant chemotherapy; regimen changes for toxicity or resource use (Habbous et al., 2022; Hawrot et al., 2021; Tang et al., 2022; Tonneson et al., 2022) Delays in initiation or interval extension between cycles Worse short-term outcomes in some; others maintained care using adjusted regimens
Endocrine Therapy Increased use as bridging therapy; switch from chemo to endocrine therapy in HR + patients (Barclay et al., 2024; Eijkelboom et al., 2023; Hawrot et al., 2021; Iles et al., 2022; Malmgren et al., 2023; Mason et al., 2022; Navarro-Sabate et al., 2024; Rahimi et al., 2023; Tang et al., 2022; Tonneson et al., 2022) Delays bridged using NET; adherence dropped in some cohorts Generally stable outcomes; some increased recurrence with nonadherence
Radiotherapy Reduction in use; shift to hypofractionation; treatment prioritisation (Barclay et al., 2024; Di Cosimo et al., 2023; Lee et al., 2023; Mason et al., 2022; Morais et al., 2022; Nogueira et al., 2024; van Dam et al., 2022) Radiotherapy is delayed 1–4 weeks in many centres Limited impact on outcomes if treatment timing was adjusted appropriately
Targeted Therapy / HER2+ Continued HER2-directed therapy; increased use in aggressive subtypes (Mason et al., 2022; Navarro-Sabate et al., 2024; Tang et al., 2022) Not typically delayed; managed using outpatient regimens Outcomes not clearly worse; some studies showed timely initiation
Palliative Treatment Maintained or adjusted regimens; increased use of G-CSF, oral regimens (Castillo et al., 2021; Di Cosimo et al., 2023; Duarte et al., 2022; Mühlmann et al., 2024) Limited delays; prioritised if urgent Higher mortality in metastatic patients during pandemic waves
Reconstruction/Plastic Surgery Reconstruction was delayed or cancelled in many centres (Guével et al., 2023; haci arak 2023; Morais et al., 2022; Murris et al., 2021; Tang et al., 2022; van Dam et al., 2022) Reconstructive procedures are often postponed No major adverse outcomes, but lower quality-of-life implications noted
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Table 2.

Summary of themes grouped by shared treatment changes, delays, and outcomes.

Grouped theme Treatment changes (during COVID-19) Treatment delays Patient outcomes
Increased use of neoadjuvant/endocrine therapy .Shift to NET or chemo as bridging; endocrine therapy substituted for surgery (Barclay et al., 2024; Eijkelboom et al., 2023; Habbous et al., 2022; Hawrot et al., 2021; Malmgren et al., 2023; Mason et al., 2022; Navarro-Sabate et al., 2024; Rahimi et al., 2023; Tang et al., 2022; Tonneson et al., 2022) Surgical or systemic delays managed with NET/endocrine therapy Stable outcomes or maintained care quality; some adherence concerns
Reduced surgical rates or delayed surgery Less surgery-first approach; mastectomies delayed or reduced (haci arak 2023; Iles et al., 2022; Kapp et al., 2022; Mason et al., 2022; Morais et al., 2022; Mühlmann et al., 2024; Nogueira et al., 2024; Simão et al., 2021; van Dam et al., 2022) Increased time to surgery, postponed procedures Increase in advanced stage or worse prognosis in several cases
Treatment pathway adaptation without outcome compromise Adjusted regimens, outpatient focus, NET increases (Baba et al., 2023; Castillo et al., 2021; Girardi et al., 2023; Guével et al., 2023; Lee et al., 2023; Lou et al., 2022; Murris et al., 2021; Peacock et al., 2024) No or minimal delays; some shortened intervals Stable survival, no major progression reported
Significant delays and worse outcomes Reduced or delayed systemic/surgical treatment (Di Cosimo et al., 2023; Duarte et al., 2022; Fu et al., 2022; Mühlmann et al., 2024; Navarro-Sabate et al., 2024; Patt et al., 2020; Simão et al., 2021) Substantial delays in diagnosis and initiation Higher mortality, advanced stage at presentation, and recurrence risk
Improved or maintained timelines More telehealth, faster consultation/treatment initiation (Eijkelboom et al., 2021; Guével et al., 2023; Lou et al., 2022; Peacock et al., 2024; Ribeiro et al., 2024; Tang et al., 2022; Tonneson et al., 2022) Shorter time to treatment or unchanged intervals Maintained survival, timely management
Decreased treatment adherence Switches in endocrine agents, patient-initiated discontinuation (Barclay et al., 2024; Navarro-Sabate et al., 2024; Rahimi et al., 2023) Indirect delays due to nonadherence Lower adherence linked to worse outcomes or risks
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3.3. Risk of bias assessment

The risk of bias assessment for the included studies is presented in Figure 2. All the 48 articles were non-randomised and were assessed using Risk of Bias in Non-randomised Studies-of Exposure (ROBINS-E). Out of those, 31 studies were at some concern for risk of bias (Baba et al., 2023; Barclay et al., 2024; Di Cosimo et al., 2023; Do Nascimento et al., 2023; Duarte et al., 2022; Eijkelboom et al., 2021; Eijkelboom et al., 2023; Fu et al., 2022; Gosset et al., 2023; Guével et al., 2023; Habbous et al., 2022; Hacı Arak 2023; Hawrot et al., 2021; Ibfelt et al., 2023; Ilgün & Özmen, 2022; Lee et al., 2023; Malmgren et al., 2023; Mason et al., 2022; Mühlmann et al., 2024; Navarro-Sabate et al., 2024; Nogueira et al., 2024; Parikh et al., 2024; Peacock et al., 2024; Rahimi et al., 2023; Ribeiro et al., 2024; Simão et al., 2021; Tang et al., 2022; Tonneson et al., 2022; van Dam et al., 2022; Girardi et al., 2023; Vanni, Pellicciaro, Combi, et al., 2021) and 17 were at high risk of bias (Al-Hajeili et al., 2022; Castillo et al., 2021; Gao et al., 2020; Howdle et al., 2024; Hui et al., 2023; Iles et al., 2022; Işıklar et al., 2021; Kapp et al., 2022; Li et al., 2020; Lou et al., 2022; Morais et al., 2022; Murris et al., 2021; Neilson et al., 2023; Nowikiewicz et al., 2022; Patt et al., 2020; Soyder et al., 2022; Vanni, Pellicciaro, Materazzo, et al., 2021). No article was considered to score low risk.

Figure 2.

A horizontal bar chart of ROBINS E bias domains showing most studies as moderate risk, with high risk greatest for confounding. The figure shows a horizontal stacked bar chart summarizing risk of bias across domains for studies assessed with Risk of Bias in Non randomised Studies of Exposure, also called ROBINS E. The vertical axis lists 7 bias domains from bottom to top: Confounding, Exposure Measurement, Selection into Study, Post Exposure Interventions, Missing Data, Outcome Measurement, and Selective Reporting. The horizontal axis is labeled Percentage of Studies with a numeric scale from 0 to 100 in intervals of 20. Each bar is divided into segments for high, moderate, and low risk levels. Confounding shows roughly 18 high risk, 82 moderate risk, and 0 low risk. Exposure Measurement shows 0 high risk, about 45 moderate risk, and 55 low risk. Selection into Study shows about 5 high risk, 70 moderate risk, and 25 low risk. Post Exposure Interventions shows 0 high risk, about 78 moderate risk, and 22 low risk. Missing Data shows around 3 high risk, 55 moderate risk, and 42 low risk. Outcome Measurement shows about 7 high risk, 38 moderate risk, and 55 low risk. Selective Reporting shows 0 high risk, roughly 72 moderate risk, and 28 low risk. A legend below labels the colors for high, moderate, and low risk. All data are approximate.

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Risk of bias assessment for the included studies.

3.4. Changes in treatment pre- and post-COVID-19

Figure 3 presents the pooled proportions (expressed as percentages) for systemic therapies before and during COVID-19, regardless of the stage. The use of chemotherapy increased from 35% pre-COVID (95% CI: 26%–44%) to 42% post-COVID (95% CI: 30%–54%); Neoadjuvant chemotherapy rose from 18% (95% CI: 13%−24%) to 22% (95% CI: 16%–28%), whereas adjuvant chemotherapy remained stable at 28%(pre-COVID-19, 95% CI: 21%–37%; post-COVID-19, 95% CI: 20%−37%). The use of hormonal therapy increased from 35% (95% CI: 26%−44%) to 42% (95% CI: 30%–54%). Neoadjuvant hormonal therapy rose from 2% (95% CI: 2%−3%) to 10% (95% CI: 6%–14%). However, the use of adjuvant hormonal therapy declined from 46% (95% CI: 37%−55%) to 43% (95% CI: 34%–53%). Immunotherapy use also increased from 5% (95% CI: 2−9%) to 7% (95% CI: 3%–13%). Targeted therapy use increased slightly, from 9% (95% CI: 3−17%) to 10% (95% CI: 4%–18%).

Figure 3.

A dot plot chart comparing pre-COVID-19 and post-COVID-19 systemic therapy proportions, with most treatments showing small increases. The figure shows a dot plot comparing pooled use of systemic therapy pre and post COVID-19. The horizontal axis is labeled Proportion percent sign with 95 percent sign confidence interval and runs from 0 to 60 in steps of 10. The vertical axis is labeled Treatment type and lists 7 rows from top to bottom as Chemotherapy, Neoadjuvant chemotherapy, Adjuvant chemotherapy, Immunotherapy, Targeted therapy, Hormonal therapy, Neoadjuvant hormonal therapy, and Adjuvant hormonal therapy. For each treatment type, there are 2 dots with horizontal error bars. The legend identifies blue dots as pre COVID 19 and red dots as post COVID 19. Chemotherapy dots cluster around 35 for pre COVID 19 and 40 for post COVID 19. Neoadjuvant chemotherapy appears near 18 for pre COVID 19 and just above 20 for post COVID 19. Adjuvant chemotherapy dots lie close to 28 for both time periods. Immunotherapy values are near 5 pre COVID 19 and near 7 post COVID 19. Targeted therapy dots are around 9 to 10 for both time periods, with the post COVID 19 value slightly higher. Hormonal therapy shows values near the mid 30s for pre COVID 19 and low 40s for post COVID 19. Neoadjuvant hormonal therapy lies near 2 for pre COVID 19 and near 10 for post COVID 19. Adjuvant hormonal therapy pre COVID 19 is close to the mid 40s, while post COVID 19 is slightly lower. All data are approximate.

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Pooled proportions of systemic therapy use before and during the COVID-19 pandemic. The plot compares pre-COVID-19 and post-COVID-19 proportions with 95% confidence intervals for chemotherapy, neoadjuvant chemotherapy, adjuvant chemotherapy, immunotherapy, targeted therapy, hormonal therapy, neoadjuvant hormonal therapy, and adjuvant hormonal therapy. Overall, the use of chemotherapy, neoadjuvant chemotherapy, hormonal therapy, and particularly neoadjuvant hormonal therapy increased during the pandemic, whereas adjuvant chemotherapy and targeted therapy remained relatively stable. Adjuvant hormonal therapy showed a slight decrease after the onset of COVID-19.

Forest plots of odds ratios for each therapy type are shown in the Supplemental Figures S1–S7. As shown in the proportion, the odds ratios indicated that neoadjuvant chemotherapy (OR 1.32, 95% CI: 0.63–1.10) or hormonal therapy (OR 3.70, 95% CI: 2.50-5.26) was significantly increased post-COVID-19.

3.5. Impact on mortality and hospital admissions

The pooled OR for short-term mortality comparing pre- and during-pandemic periods was 0.81 (95% CI: 0.56–1.15); see Figure 4. Substantial heterogeneity was observed (I² = 72%), indicating considerable variability between studies. The width of the confidence interval reflects between-study differences and limited event counts, which reduced the precision of the pooled estimate.

Figure 4.

A forest plot chart of mortality odds ratios on a 0 to 10 x axis, with 7 studies and an overall diamond near an odds ratio of 1.0. The figure shows a forest plot chart summarizing study specific and overall odds ratios for mortality. The x axis is labeled OR and ranges from 0 to 10 with tick marks at 0, 2, 4, 6, 8, and 10. There is no y axis scale, but study names appear in a vertical list at the left: Habbous 2022, Baba 2023, Guevel 2023, Cosimo 2023, Muhlmann 2024, Arak 2023, and Al Hajeili 2022, followed by the label Overall at the bottom. Each study name aligns with a square marker centered on its odds ratio and a horizontal line showing the confidence interval. The markers vary in size, reflecting different weights. A vertical reference line passes through an odds ratio of 1.0. To the right of the plot, a numeric table lists odds ratio and 95 percent confidence interval values and the percent weight for each study. At the bottom right, a summary row shows an overall odds ratio of 0.81 with a 95 percent confidence interval of 0.56 to 1.15 and a weight of 100.0. At the lower left, text reports Q equals 21.56, p equals 0.00, and I squared equals 72 percent. All data are approximate.

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Forest plot showing study-specific and pooled odds ratios (ORs) for mortality before and during the COVID-19 pandemic. Individual studies are represented by squares proportional to their statistical weight, with horizontal lines indicating 95% confidence intervals. The pooled analysis yielded an overall OR of 0.81 (95% CI: 0.56–1.15), indicating no statistically significant difference in mortality. Substantial heterogeneity was observed among studies (I² = 72%, Q = 21.56, p &lt; 0.001).

The pooled OR for hospital admissions was 0.58 (95% CI: 0.15–2.28); see Figure 5. Very high heterogeneity was identified (I² = 98%), suggesting marked variability across studies. This considerable heterogeneity, together with the limited number of contributing studies and differences in admission criteria and reporting practices, likely contributed to the wide confidence interval and limited interpretability of the pooled estimate.

Figure 5.

A forest plot chart of odds ratios for hospital admission around COVID-19 shows five studies and an overall pooled estimate with wide ranges. Fig 5. Forest plot showing study-specific and pooled odds ratios (ORs) for hospital admission before and during the COVID-19 pandemic. Individual studies are represented by squares proportional to their statistical weight, with horizontal lines indicating 95% confidence intervals. The pooled analysis yielded an overall OR of 0.58 (95% CI: 0.15–2.28), indicating no statistically significant difference in hospital admission rates between the pre-COVID-19 and post-COVID-19 periods. Considerable heterogeneity was observed among studies (I² = 98%, Q = 213.95, p < 0.001).

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Pooled odds ratios for hospital admission pre- versus post-COVID-19.

4. Discussion

This systematic review and meta-analysis of 48 studies (35 in meta-analysis) demonstrates that the COVID-19 pandemic significantly impacted the systemic treatment of breast cancer. Although overall chemotherapy and hormonal therapy use did not decrease, treatment patterns shifted: neoadjuvant hormonal therapy rose from 2% to 10% while adjuvant hormonal therapy declined significantly. Chemotherapy showed a non-significant overall increase (OR = 1.09), and mortality remained statistically unchanged. Variability in healthcare infrastructure, pandemic burden, and population responses across nations may have contributed to this difference. The available data, however, did not enable a formal country-level subgroup meta-analysis due to the number of studies per country and inconsistent reporting. Hence, the results have to be taken on a general scale but not as a country-specific impact. Importantly, the high heterogeneity reflects substantial variability in treatment modifications and healthcare delivery strategies.

These findings are consistent with several recent systematic reviews documenting the widespread impact of the COVID-19 pandemic on non-surgical breast cancer treatment. For instance, Budiarta and Brennan (2022) confirmed a global shift towards non-surgical treatment modalities, reporting an increased use of neoadjuvant endocrine therapy, reduced access to chemotherapy, and delayed initiation of systemic treatments, findings that align with our results. Similarly, Sun et al. (2021) focused on breast reconstruction but noted wide-ranging disruptions in postmastectomy planning and treatment sequences, reinforcing how resource strain deprioritised non-urgent care.

Meta-analysis by Johnson et al. (2020) demonstrated that treatment delays, even up to 12 weeks, were associated with decreased overall survival in breast cancer patients (HR 1.46), especially in early-stage disease. This quantitatively substantiates our finding that postponements in chemotherapy or endocrine therapy carry significant clinical risks. Li et al. (2023) further reported that screening disruptions during the pandemic led to delayed diagnoses, a shift towards more advanced-stage presentation, and downstream implications for systemic therapy choices.

In response to disruptions in care delivery, some institutions have adopted mHealth tools to manage chemotherapy-related symptoms. Shi et al. (2023) have discussed how it has been developed in some settings as a compensatory strategy for disrupted care delivery. Despite these digital innovations, they found wide variation in implementation and limited standardisation, echoing our finding of heterogeneity in access to non-surgical treatments across institutions.

Moreover, the systematic mapping by Garavand et al. (2023) reinforced the critical role of telemedicine in oncology during COVID-19, though they noted substantial gaps in access and consistency across cancer types and regions. This contextualises our observation that while non-surgical care delivery models adapted rapidly in some cases, these changes were uneven and often under-supported.

Although these reviews confirm our results, we must admit major methodological disparities. Some previous reviews focused on surgical delays or diagnostic interruptions rather than on systemic anticancer therapy (SACT) in particular, but our research quantitatively synthesised alterations in SACT. Also, not all reviews conducted meta-analyses or reported heterogeneity statistics, which limits quantitative interpretation. Conversely, our analysis used a random-effects meta-analysis and conducted a risk-of-bias assessment with ROBINS-E. Nevertheless, like the rest of the literature, our results are limited to observational study designs and differences in reporting across settings.

Collectively, these systematic reviews confirm our results and highlight the global, multifaceted nature of disruptions in non-surgical treatment in breast cancer care. They underscore the urgent need for resilient, adaptable oncology systems that can ensure continuity of systemic therapies, especially for hormone receptor-positive and high-risk patients during public health crises.

Although the focus of this review was not on surgery and radiotherapy, various studies noted that pandemic-related issues, such as delays, prioritisation changes, and modified protocols, impacted both fields (Morais et al., 2022; Murris et al., 2021; Nogueira et al., 2024; Peacock et al., 2024; van Dam et al., 2022). Scheduled operations for non-urgent conditions were suspended, leading to increased reliance on systemic bridging strategies, while radiotherapy was adapted to hypofractionated schedules or deferred entirely for low-risk patients. These systemic changes likely influenced the observed shifts in chemotherapy and endocrine therapy patterns.

With regard to chemotherapy, usage increased slightly post-pandemic (from 35% to 42%), with greater reliance on neoadjuvant chemotherapy (rising from 18% to 22%). Although ORs were not statistically significant, this suggests that chemotherapy remained a cornerstone of treatment, adapted through less immunosuppressive regimens and altered dosing intervals (Baba et al., 2023; Habbous et al., 2022). It is important to note that clinical practice typically involves either neoadjuvant or adjuvant chemotherapy, but rarely both, for the same patient. Habbous et al. (2022) found a significant increase in neoadjuvant chemo (OR 2.07) during the pandemic, while Duarte et al. (2022) reported a 25% drop in adjuvant chemo, indicating institutional prioritisation and resource constraints. Delays were reported in Japan and Brazil, but centres with proactive modifications (e.g. Nowikiewicz et al., 2022) maintained timely care in most cases.

Use of endocrine therapy increased significantly (OR = 1.14), particularly as a bridging strategy. Neoadjuvant hormonal therapy increased significantly, supporting findings from Habbous et al. (2022) and Barclay et al. (2024), who documented widespread adoption of aromatase inhibitors. Adherence declined, especially among long-term users (Rahimi et al., 2023). This poses potential risks for long-term disease control, despite relatively stable short-term results.

One possible reason the neoadjuvant systemic therapy increased could be a change in treatment sequence during the pandemic. In most centres, patients who would previously have received primary surgery followed by adjuvant systemic therapy were now receiving systemic therapy earlier, and surgery was postponed due to operating room availability and institutional policy (Eijkelboom et al., 2021; Habbous et al., 2022; van Dam et al., 2022). This flexibility in sequencing was appreciated in international consensus during the pandemic, especially in hormone receptor-positive disease, where neoadjuvant endocrine therapy can serve as a safe bridging strategy (Iles et al., 2022; van Dam et al., 2022). Currently available short-term data indicate that this method did not always affect immediate oncologic outcomes in properly selected patients (Di Cosimo et al., 2023; Habbous et al., 2022); however, long-term effects on recurrence and survival remain to be fully established.

In contrast, targeted and immunotherapy remained largely unchanged (ORs approximately 0.85–1.03), likely due to their critical role in treating high-risk patients and their relatively favourable safety profile, particularly their non-immunosuppressive mechanisms, which may have allowed patients with COVID-19 to retain a reasonable immune response. Most centres reported continuity of care through modified administration (e.g. subcutaneous formulations, extended intervals) as described by Kelemenic-Drazin et al. (2021), and in the ESMO Resilience Task Force survey by Banerjee et al. (2021), which documented rapid institutional responses to maintain systemic therapies despite resource constraints.

In the context of metastatic and palliative care, this subgroup remained a treatment priority. Studies (e.g. Lee et al., 2023) show 87% of metastatic patients stayed on therapy without interruption. However, others (e.g. Mühlmann et al., 2024) reported higher rates of distant recurrence and post-recurrence mortality, suggesting that even with adapted regimens, these patients remained vulnerable to system-wide delays and reduced access.

In terms of patient outcomes, overall mortality did not differ significantly between the pre- and post-pandemic periods (OR = 0.81), suggesting that adaptive treatment strategies helped preserve short-term outcomes. Hospital admissions also showed a non-significant decline. However, multiple studies (e.g. Malmgren et al., 2023; Tang et al., 2022; van Dam et al., 2022) reported stage migration and delayed recurrence detection. A significant drop in early-stage diagnoses, coupled with higher rates of node-positive or distant disease, raises concerns about long-term recurrence and survival. Mühlmann et al. (2024) found median post-recurrence survival dropped from 22 to 9 months, highlighting risks in delayed surveillance.

Nevertheless, the current body of evidence has notable limitations. All included studies were observational in design; 31 had ‘some concerns', and 17 were ‘high risk' per ROBINS-E. Common issues included confounding, exposure classification, and outcome reporting. Heterogeneity was high across most analyses, including substantial heterogeneity for mortality (I² = 72%) and very high heterogeneity for hospital admissions (I² = 98%), which limits the precision and interpretability of these pooled estimates. Variability in how individual studies defined the pandemic period (e.g. institutional lockdown phases versus calendar-based comparisons) may have further contributed to this heterogeneity. In addition, the wide 95% confidence intervals observed in some analyses further reflect imprecision related to variability in study populations, healthcare systems, and outcome definitions. Most included studies originated from high-income countries, limiting generalisability to lower-resource settings, and long-term oncologic outcomes were often not reported. Although surgery and radiotherapy were also substantially affected during the pandemic, this review was restricted to systemic anti-cancer therapies; therefore, changes in other treatment modalities may have influenced systemic therapy patterns, and these interdependencies were not formally analysed.

In addition, the literature search was completed in August 2024; therefore, studies published after this date were not included and may provide further evidence that could influence these findings.

Although a comprehensive and systematic search strategy was applied, the exclusion of many records during the title and abstract screening stage may have led to the omission of potentially relevant studies, particularly when abstracts lacked sufficient methodological detail.

Looking to the future, studies must evaluate long-term effects on recurrence and survival. Prospective, multicentre cohorts and health system-level policy assessments will be crucial. Telemedicine's role, treatment adherence tracking, and predictive models for triage should be investigated. There is also a need to focus on vulnerable subgroups (elderly, low-income, metastatic patients) who may face higher barriers to continuity of care.

6. Pharmaceutical policy & practice implications

The results of this review indicate several significant implications for pharmaceutical policy and pharmacy practice in the event of a public health emergency. To start with, the pandemic revealed the vulnerability of access to oncology medicine in the event of disruptions to hospital services. This greater dependency on neoadjuvant endocrine therapy and altered chemotherapy regimens indicates a change in the policy of drug use whereby the therapy shifted to regimens that impact fewer hospital visits, immunosuppression, and resource overload. Bringing oral anticancer therapies to the masses, defining access to remote prescribing, and supporting safe home-based delivery are then vital elements of future preparedness programmes.

Second, their prevalent treatment delays highlight the importance of effective triage mechanisms to categorise high-risk patients for timely systemic treatment. Pharmaceutical policies that normalise bridging policies, as in the case of endocrine therapy, could be used to facilitate continuity of care when surgical or infusion services are limited. Clinical pharmacists play a very important role in monitoring adherence, managing adverse effects remotely, and adjusting regimens to maximise patient safety.

Third, telemedicine was adopted more quickly during the pandemic as a treatment counselling tool, for toxicity monitoring, and for medication administration. These advances will require policy support for digital infrastructure and reimbursement systems, as well as pharmacist-led virtual clinics, to continue supporting them outside a crisis environment. These models improve access for vulnerable populations and enhance patient engagement in systemic therapy. Lastly, the treatment changes across nations are indicative of the need for harmonised global pharmaceutical policies, such as principles of SACT prioritisation, more flexible regulatory pathways to allow formulation or dosage schedule changes, and backup contingency plans in the event of supply chain failures. Enhancing interdisciplinary cooperation among oncology, pharmacy, and policymaker teams will be critical to safeguarding treatment continuity and minimising disparities in availability during future emergencies.

7. Conclusion

This systematic review and meta-analysis prove that despite the continuation of breast cancer treatment under the COVID-19 pandemic, significant changes had to be made to maintain access to systemic options. The rising use of neoadjuvant endocrine therapy and the changing trends in chemotherapy are indicative of extensive policy-directed changes to maintain continuity in treatment and minimise hospitalisation. These results highlight the importance of robust pharmaceutical guidelines, including transparent triage protocols, alternative treatment options, stronger supply chain measures, and increased telemedicine and pharmacist-led services. These strategies, incorporated into standard oncology practice, will help ensure that all patients have equitable access to essential cancer medicines and protect patient outcomes during future health emergencies.

Supplementary Material

Supplementary Data.docx
JPPP_A_2684148_SM5203.docx (254.1KB, docx)

Acknowledgement

The authors would like to thank Dr Mina Bakhit for his helpful input during the early development of this systematic review protocol.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

All data generated or analysed during this study are included in this published article and its Supplemental Material.

Ethical approval and consent to participate

Not applicable.

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/20523211.2026.2684148.

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