{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:31:55Z","timestamp":1787506315581,"version":"build-2736575974"},"reference-count":28,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"11","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Glob Oncol"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Cancer incidence is rising worldwide and estimated to double by 2040. A systematic method of allocating resources and prioritizing cancer control efforts is needed. We aimed to develop and test a simple metric to quantify disparities in cancer mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We extracted country-specific incidence and mortality rates for 33 cancers from 185 countries using data from Global Cancer Observatory (GLOBOCAN) 2020. Mortality-to-incidence ratios (MIRs) were calculated for each cancer in every country. Delta MIRs (dMIRs) were calculated as the difference between a country's MIR and the MIR of the highest performing country for each cancer. dMIR was validated against human development index (HDI), gender development index (GDI), and life expectancy index (LEI) using scatter plots, correlation coefficients, and linear regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Among 185 countries in the GLOBOCAN 2020 data set, mortality and incidence estimates were available for 54 high-income, 47 upper-middle\u2013income, 54 lower-middle\u2013income, and 27 low-income countries. The United States was the highest performing country for 10 of the 33 cancer subtypes, and South Korea was the highest performing country for eight cancer subtypes. Significant variation in dMIR was observed across the globe. The highest dMIRs were in sub-Saharan Africa and Southeast Asia, and the lowest dMIRs were in North America, Western Europe, and Australasia. dMIR showed strong correlations with HDI, GDI, and LEI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>In conclusion, dMIR is a novel and robust metric that can be used to track disparities in global cancer mortality and prioritize cancer control initiatives. We benchmarked cancer care performance for 33 cancers across 182 countries and provide country- and cancer-specific priority lists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/go-24-00336","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T15:59:16Z","timestamp":1737043156000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":7,"title":["Disparities in Cancer Mortality Worldwide: A Novel Metric for Measuring Global Disparities and Prioritizing Cancer Control Efforts"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-4579","authenticated-orcid":true,"given":"Thomas M.","family":"Diehl","sequence":"first","affiliation":[{"name":"Department of Surgery, School of Medicine and Public Health, University of Wisconsin, Madison, WI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6798-5076","authenticated-orcid":true,"given":"Kaleem S.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Surgery, School of Medicine and Public Health, University of Wisconsin, Madison, WI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7791-2668","authenticated-orcid":true,"given":"Sheida","family":"Pourdashti","sequence":"additional","affiliation":[{"name":"School of Medicine and Public Health, University of Wisconsin, Madison, WI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9226-1667","authenticated-orcid":true,"given":"Lily","family":"Stalter","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Wisconsin, School of Medicine and Public Health, Madison, WI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7936-8292","authenticated-orcid":true,"given":"Jessica","family":"Hellner","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Wisconsin, School of Medicine and Public Health, Madison, WI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewen M.","family":"Harrison","sequence":"additional","affiliation":[{"name":"Centre for Medical Informatics, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3843-9687","authenticated-orcid":true,"given":"Syed Nabeel","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Surgery, School of Medicine and Public Health, University of Wisconsin, Madison, WI"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_3_6_3_2","first-page":"1","article-title":"Deaths: Leading causes for 2019","volume":"70","author":"Heron M","year":"2021","unstructured":"Heron M: Deaths: Leading causes for 2019. 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