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The global impact of the COVID-19 pandemic on delays and disruptions in cancer care services: a systematic review and meta-analysis

Nature Cancer volume 6, pages 194–204 (2025)Cite this article

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Abstract

The coronavirus disease 2019 pandemic substantially impacted the delivery of cancer services and programs. Here we reviewed and synthesized the global scale and impact of pandemic-related delays and disruptions on cancer services, including diagnosis, diagnostic procedures, screening, treatment and supportive and palliative care. Based on data from 245 articles in 46 countries, we observed declines in the number of cancer screening participation (39.0%), diagnoses (23.0%), diagnostic procedures (24.0%) and treatment (28.0%), ranging from a 15.0% decline for radiotherapy to a 35.0% decline for systemic treatment during the pandemic compared to during the prepandemic period. Medium-human development index (HDI) category countries experienced greater reductions than high- and very-high-HDI countries. Missing data from low-HDI countries emphasize the need for increased investments in cancer surveillance and research in these settings. PROSPERO registration: CRD42022301816

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Fig. 1: Forest plot of pooled RRs for number of individuals screened for breast, cervical and colorectal cancers.
Fig. 2: Forest plot of pooled RRs for the number of individuals diagnosed with cancer.
Fig. 3: Forest plot of pooled RRs for individuals who underwent cancer diagnostic procedures.
Fig. 4: Forest plot of pooled RRs for the number of total cancer treatment deliveries, surgeries, radiotherapies and systemic therapies.
Fig. 5: Percent decrease in breast cancer care indicators for all countries combined and by HDI group based on results of two-sided random effects meta-analyses.

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Data availability

We declare that the data supporting the findings of this study are available within the paper and its supplementary information files. All the studies included in this study were obtained from the WHO COVID-19 global research database. Source data are provided with this paper.

Code availability

No new algorithms were developed for this paper. All code generated for the analysis is available from the authors upon request.

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Acknowledgements

This study was partly funded by the WHO (2021/1187438-0). The funder had no role in study design, data collection and analysis, decision to publish or preparation of this manuscript. R. Shah was funded by a fellowship from the International Agency for Research on Cancer during the duration of this study.

Author information

Authors and Affiliations

  1. Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France

    Richa Shah, Hanna Fink, Robabeh Ghodssighassemabadi, Jean Niyibaga, Oliver Langselius, Clara Frick, Jerome Vignat, Freddie Bray & Isabelle Soerjomataram

  2. Department of Surgery, Queen’s University, Kingston, Ontario, Canada

    Nader Mounir Hanna

  3. Centre for Clinical Epidemiology, Institute for Clinical Research, National Institutes of Health, Ministry of Health Malaysia, Selangor, Malaysia

    Ching Ee Loo

  4. The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia

    Michael David, Julia Steinberg, Suzanne Hughes, Sam Egger, Michael Caruana & Harriet Hui

  5. School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia

    Michael David

  6. Cancer Epidemiology and Prevention Team, Public Health Expertise, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg

    Allini Mafra

  7. Registre National du Cancer, Luxembourg Institute of Health, Strassen, Luxembourg

    Allini Mafra

  8. Queen’s University Belfast, Belfast, UK

    Ethna McFerran

  9. Cancer Screening Unit, Institut Català d’Oncologia (ICO), Early Detection of Cancer Group, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), L’Hospitalet de Llobregat, Spain

    Montse Garcia

  10. Assam Cancer Care Foundation, Assam, India

    Suryakanta Acharya

  11. PAY-W Clinic, Odisha, India

    Suryakanta Acharya

  12. Oncology Center, University of Nigeria Teaching Hospital, Enugu, Nigeria

    Nwamaka Lasebikan

  13. Queen’s University School of Nursing, Kingston, Ontario, Canada

    Colleen Elizabeth Kircher & Catherine Lindsay Goldie

  14. King’s College London, Institute of Cancer Policy, Guy’s Hospital, London, UK

    Richard Sullivan

  15. Center for Global Health, National Cancer Institute, Bethesda, MD, USA

    Ophira Ginsburg

  16. Department of Non-Communicable Diseases, World Health Organization, Geneva, Switzerland

    André Michel Ilbawi

  17. School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

    Karen Canfell

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  1. Richa Shah
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  2. Nader Mounir Hanna
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  28. Isabelle Soerjomataram
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Contributions

R. Shah, I.S., J.S., S.H., M.C., H.H., A.M.I. and K.C. conceptualized the study and developed the study protocol and methodology. R. Shah, N.M.H., C.E.L., A.M., H.F., E.M., M.G., R.G., S.A., J.N., O.L., C.F., N.L., C.E.K. and C.L.G. implemented the method, performed screenings, collected data and performed quality assessments. M.D., S.E., J.V. and R. Shah performed data analyses. R. Shah, N.M.H. and I.S. wrote the original draft. All authors, including R. Sullivan, F.B. and O.G., were involved in interpretation of the results, participated in the review and editing of the paper and approved the final version. K.C. helped with funding acquisition. Where authors are identified as personnel of the International Agency for Research on Cancer/WHO, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policies or views of the International Agency for Research on Cancer/WHO.

Corresponding author

Correspondence to Richa Shah.

Ethics declarations

Competing interests

K.C. is co-principal investigator of an investigator-initiated trial of cervical screening, ‘Compass’, run by the VCS Foundation Australia, which is a government-funded not-for-profit charity. She is also co-principal investigator on a major implementation program ‘Elimination of Cervical Cancer in the Western Pacific’, which will receive support from the Minderoo Foundation and the Frazer Family Foundation and equipment donations from Cepheid. M.C. is an investigator on an investigator-initiated trial of cytology and primary human papillomavirus screening in Australia (Compass; ACTRN12613001207707 and NCT02328872), which is also conducted and funded by the VCS Foundation. The VCS Foundation has received equipment and a funding contribution for the Compass trial from Roche Molecular Systems and Ventana. However, K.C., M.C. and their institution on their behalf (the Daffodil Centre, a joint venture between Cancer Council NSW and The University of Sydney) do not receive direct funding from the industry for these or any other research project. The other authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Timothy Hanna and Felicia Knaul for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 PRISMA flow diagram for selection of included studies.

PRISMA flowchart of the systematic search and selection process. From a total of 9,702 studies identified, we screened 9,458 studies for eligibility, removed 244 duplicates, and excluded 8,547 studies. We finally included a total of 245 eligible study for this systematic review and meta-analysis.

Extended Data Fig. 2 Forest plot of pooled rate ratios for number of individuals screened for breast, cervical, and colorectal cancers before and during the COVID-19 pandemic.

Forest plot of pooled rate ratios for individuals screened for breast, cervical, and colorectal cancers. Two-tailed Z-scores, following a natural logarithmic transformation were used to calculate p-values, with the DerSimonian and Laird method being used to pool effect estimates. The square box represents rate ratio (RR) and the size of the box represents study weight. Diamond represents pooled RR. Dotted vertical line represents line of overall effect. Horizontal bars indicate 95% CIs of two-sided random effects meta-analyses. Vertical solid black line represents the line of no effect. Pooled analysis of 48 studies with a combined cohort of approximately 35.2 million individuals pre-COVID-19 and 25.1 million individuals during COVID-19 showed that: a In the sub-group analysis comparing the HDI groups, the greatest reduction was seen in the number of individuals screened for breast cancer (50.0% in very high and 46.0% in high HDI countries), followed by cervical cancer (37.0% in very high and 39.0% in high HDI countries). b In the sub-group analysis comparing the continents, the highest relative decline in the number of individuals screened for breast cancer reported in studies was in the Americas (60.0%), followed by Oceania (51.0%) and Asia (43.0%). Similarly, the decline in cervical cancer screening participation was greatest in the Americas (34.0%) followed by Asia (22.0%). For colorectal cancer screening, the largest proportional decline was seen in Oceania (66.0%), followed by Asia (43.0%) and the Americas (29.0%). c The greatest reduction was seen in the number of individuals screened for breast cancer (49.0%).

Source data

Extended Data Fig. 3 Forest plot of pooled rate ratios for individuals diagnosed with cancer before and during the COVID-19.

Two-tailed Z-scores, following a natural logarithmic transformation were used to calculate p-values, with the DerSimonian and Laird method being used to pool effect estimates. The square box represents rate ratio (RR) and the size of the box represents study weight. Diamond represents pooled RR. Dotted vertical line represents line of overall effect. Horizontal bars indicate 95% CIs of two-sided random effects meta-analyses. Vertical solid black line represents the line of no effect. a Pooled analysis of 99 studies with a combined cohort of approximately 2 million individuals pre-COVID-19 and 1.2 million individuals during COVID-19 showed that the overall cancer diagnosis decreased by 23.0% (RR = 0.77; 95% CI: 0.74 to 0.80) with significant heterogeneity between studies (I2 = 99.4%, P < 0.01). Of all world regions with data included, the greatest reduction was reported across African countries (36.0%). b Pooled analysis of 92 studies with a combined cohort of approximately 1 million individuals pre-COVID-19 and 579,581 individuals during COVID-19 showed that overall cancer diagnosis decreased by 24.0% (RR = 0.76; 95% CI: 0.74 to 0.78) with significant heterogeneity between studies (I2 = 97.4%, P < 0.01). The reduction was highest for gynaecological (RR = 0.68; 95% CI: 0.60 to 0.77), skin (melanomas and non-melanomas) (Rate Ratio (RR) = 0.68; 95% CI: 0.57 to 0.81), and urogenital cancers (RR = 0.69, 95% CI: 0.65 to 0.74). c) Pooled analysis of 92 studies with a combined cohort of approximately 1 million individuals pre-COVID-19 and 580,937 individuals during COVID-19 showed that the overall cancer diagnostic procedures decreased by 24.0% (RR = 0.76; 95% CI: 0.75 to 0.78) with significant heterogeneity between studies (I2 = 96%, P < 0.01). For specific cancer types, large decreases were seen for cervical cancer (RR = 0.52, 95% CI: 0.41 to 0.66), vulva cancers (RR = 0.53;95% CI: 0.34 to 0.83), and lymphomas (RR = 0.56; 95% CI: 0.40 to 0.78).

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Extended Data Fig. 4 Forest plot of pooled rate ratios for individuals who underwent cancer diagnostic procedures before and during the COVID-19 pandemic.

Two-tailed Z-scores, following a natural logarithmic transformation were used to calculate p-values, with the DerSimonian and Laird method being used to pool effect estimates. The square box represents rate ratio (RR) and the size of the box represents study weight. Diamond represents pooled RR. Dotted vertical line represents line of overall effect. Horizontal bars indicate 95% CIs of two-sided random effects meta-analyses. Vertical solid black line represents the line of no effect. The results are pooled analyses of 55 studies with a combined cohort of approximately 10 million individuals pre-COVID-19 and 7 million individuals during COVID-19. a Overall cancer diagnostic procedures decreased by 24.0% (RR = 0.76; 95% CI: 0.75 to 0.78) with significant heterogeneity between studies (I2 = 96%, P < 0.01). The largest reduction in diagnostic procedures was for haematological cancers (RR = 0.31; 95% CI: 0.24 to 0.40) in medium HDI countries and gastrointestinal cancers in high HDI countries (RR = 0.71; 95% CI: 0.63 to 0.81), and gynaecological cancers in very high HDI countries (RR = 0.66, 95% CI: 0.59 to 0.74). b In Africa, the largest decline in diagnostic procedure was for urogenital cancers (RR = 0.42; 95% CI: 0.30 to 0.58), in the Americas it was for breast cancer (RR = 0.70; 95% CI: 0.62 to 0.80), in Asia, for haematological cancers (RR = 0.37; 95% CI: 0.28 to 0.49), while in Europe, gynaecological cancers exhibited the greatest decrease (RR = 0.53; 95% CI: 0.39 to 0.72). c In the sub-group analysis, the greatest reduction was seen in the number of individuals who underwent procedures for diagnosis of gynaecologic (RR = 0.68; 95% CI: 0.61 to 0.75) and haematological (RR = 0.69; 95% CI: 0.57 to 0.83) cancers.

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Extended Data Fig. 5 Forest plots of pooled rate ratios for number of cancer treatment deliveries before and during the COVID-19 pandemic.

Two-tailed Z-scores, following a natural logarithmic transformation were used to calculate p-values, with the DerSimonian and Laird method pooling effect estimates. The square box represents rate ratio (RR) and the size of the box represents study weight. Diamond represents pooled RR. Dotted vertical line represents overall effect. Horizontal bars indicate 95% CIs. Vertical solid black line represents no effect. a–c Analyses of 122 studies showed Asia had the highest reduction in overall cancer treatment (RR = 0.63; 95% CI: 0.57 to 0.70) (between 652,459 and 15,498,757 participants). The highest reductions were for musculoskeletal cancers (RR = 0.47; 95% CI: 0.36 to 0.61) (332,251 pre-COVID-19 and 285,261 during COVID-19), and for oral cavity cancers (RR = 0.13; 95% CI: 0.10 to 0.17) (319,916 pre-COVID-19 and 275,562 during COVID-19). d-f Analyses of 90 studies (616,101 participants) showed the highest decrease in surgical treatment in medium HDI countries (RR = 0.54; 95% CI: 0.47 to 0.63) and for head and neck cancers (RR = 0.45; 95% CI: 0.26 to 0.78). Africa had the highest reduction for head and neck cancers (RR = 0.21; 95% CI: 0.14 to 0.32). The highest reduction by cancer group was for musculoskeletal cancers (RR = 0.50; 95% CI: 0.38 to 0.66). g–i Analyses of 18 studies (33,994 participants) showed the greatest reduction in medium HDI countries and for head and neck cancers (RR = 0.13, 95% CI: 0.08 to 0.22). The largest reduction by site was for neurological cancers (RR = 0.78, 95% CI: 0.66 to 0.92). j-l Analyses of 25 studies (24,897 participants) showed the largest decrease for breast cancer in very high HDI (RR = 0.65; 95% CI: 0.49 to 0.87) and high HDI countries (RR = 0.38; 95% CI: 0.31 to 0.47), and for haematological cancers in medium HDI countries (RR = 0.16; 95% CI: 0.15 to 0.18). The highest decline was in Asia for musculoskeletal cancers (RR = 0.32; 95% CI: 0.16 to 0.64). m) The increase in time from diagnosis to treatment was not statistically significant (P = 0.13).

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Extended Data Fig. 6 Forest plot of pooled rate ratios for individuals who received supportive and palliative care before and during the COVID-19 pandemic.

Two-tailed Z-scores, following a natural logarithmic transformation were used to calculate p-values, with the DerSimonian and Laird method being used to pool effect estimates. The square box represents rate ratio (RR) and the size of the box represents study weight. Diamond represents pooled RR. Dotted vertical line represents line of overall effect. Horizontal bars indicate 95% CIs of two-sided random effects meta-analyses. Vertical solid black line represents the line of no effect. Analyses of 12 studies with a combined cohort of 44,414 individuals showed an overall decrease of 70.0% (RR = 0.30; 95% CI: 0.11 to 0.83) in supportive and palliative care during the pandemic with significant heterogeneity between studies (I2 = 99.5%, P < 0.01).

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Extended Data Fig. 7 Forest plot of pooled rate ratios for the stage at diagnosis of cancer before and during the COVID-19 pandemic.

Two-tailed Z-scores, following a natural logarithmic transformation were used to calculate p-values, with the DerSimonian and Laird method being used to pool effect estimates. The square box represents rate ratio (RR) and the size of the box represents study weight. Diamond represents pooled RR. Dotted vertical line represents line of overall effect. Horizontal bars indicate 95% CIs of two-sided random effects meta-analyses. Vertical solid black line represents the line of no effect. Results are from pooled analyses from 80 studies with a combined cohort of 174,343 individuals. a The results showed decline in patients diagnosed with cancer at any stage in all continents. This decrease was lowest in medium HDI countries (65.0% in non-metastatic cancers vs 67.0% in metastatic cancers) compared to high HDI (45.0% in non-metastatic cancers vs 25.0% in metastatic cancers) and very high HDI countries (12.0% in non-metastatic cancers vs 10.0% in metastatic cancers). b There was an overall decrease in the number of patients newly diagnosed with both non-metastatic and metastatic cancer (RR = 0.80; 95% CI: 0.77 to 0.83) with significant heterogeneity between studies (I2 = 86.9%, P < 0.01). In the sub-group analysis comparing between continents, significant reductions in newly diagnosed non-metastatic cancers were seen in Americas (RR = 0.75; 95% CI: 0.66 to 0.85), Asia (RR = 0.82; 95% CI: 0.72 to 0.93), and Europe (RR = 0.80; 95% CI: 0.77 to 0.84). The decrease in newly diagnosed metastatic cancers was significant only in Europe (RR = 0.86; 95% CI: 0.77 to 0.96). c The decrease in the number of patients newly diagnosed with non-metastatic cancer (RR = 0.79; 95% CI: 0.75 to 0.82) was higher than the number of patients newly diagnosed with metastatic cancer (RR = 0.86; 95% CI: 0.79 to 0.94) with significant heterogeneity between studies (I2 = 74.5%, P = 0.05).

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Extended Data Fig. 8 Funnel Plots for publication bias.

Funnel plots derived from Egger’s linear regression test for publication bias. Statistical test used was two-sided. All funnel plots showed evidence of publication bias as evidenced by substantial number of points outside each funnel and points not symmetrically distributed in each funnel. However, on Egger’s test, publication bias was present in eight of the meta-analyses (b–d,f–i,j,m), with effect sizes tending to decrease as standard errors increased. Publication bias was not present in figures a and e based on the results of Egger's test.

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Supplementary information

Reporting Summary

Supplementary Tables 1–8

Supplementary Table 1. List of countries, geographical regions and HDI level. Supplementary Table 2. Summary of included studies. Supplementary Table 3. PRISMA checklist. Supplementary Table 4. Search strategy. Supplementary Table 5. List of studies excluded at the full-text screening stage, with brief reasons. Supplementary Table 6. Quality assessment tool. Supplementary Table 7. Quality assessments of included studies. Supplementary Table 8. References.

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Shah, R., Hanna, N.M., Loo, C.E. et al. The global impact of the COVID-19 pandemic on delays and disruptions in cancer care services: a systematic review and meta-analysis. Nat Cancer 6, 194–204 (2025). https://doi.org/10.1038/s43018-024-00880-4

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