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Martinez-Cordero H, Fuentes-Lacouture C, von Glasenapp A, Peña C. The 5T's of health disparities in multiple myeloma in Latin America. Curr Opin Oncol 2024; 36:610-614. [PMID: 39246175 DOI: 10.1097/cco.0000000000001088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
PURPOSE OF REVIEW Health disparities or inequities, which are defined as differences in the quality of medical and healthcare between populations among racial, ethnic, and socioeconomic groups, have been validated in numerous studies as determinants of survival and quality of life in different diseases, including cancer.Compared to the improvement in overall survival in developed countries in relation to better diagnostic opportunity and novel therapeutic approaches, low and middle-income countries still have significant barriers in accessing these therapies.The potential impact of overcoming these barriers is immense and offers hope for better outcomes. RECENT FINDINGS There is great heterogeneity in the diagnostic and therapeutic approach to multiple myeloma among different latitudes. Latin America has been characterized by important limitations in using the best technologies currently available in developed countries. SUMMARY Overcoming health disparities in multiple myeloma in LMICs could help improve survival and quality of life outcomes. Likewise, it is necessary to increase the representation of the Latin population in clinical studies, primarily in our region.
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Affiliation(s)
- Humberto Martinez-Cordero
- Hospital Militar Central
- Universidad Militar Nueva Granada
- Instituto Nacional de Cancerología, Bogotá, Colombia
- Grupo de Estudio Latinoamericano en Mieloma Múltiple, GELAMM
- Latin-American Myeloma Network, International Myeloma Foundation
| | - Cynthia Fuentes-Lacouture
- Hospital Militar Central
- Universidad Militar Nueva Granada
- Grupo de Estudio Latinoamericano en Mieloma Múltiple, GELAMM
| | - Alana von Glasenapp
- Hospital Central Instituto de Previsión Social, Asunción, Paraguay
- Grupo de Estudio Latinoamericano en Mieloma Múltiple, GELAMM
- Latin-American Myeloma Network, International Myeloma Foundation
| | - Camila Peña
- Hospital Del Salvador, Santiago, Chile
- Grupo de Estudio Latinoamericano en Mieloma Múltiple, GELAMM
- Latin-American Myeloma Network, International Myeloma Foundation
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Casey M, Odhiambo L, Aggarwal N, Shoukier M, Islam KM, Cortes J. Global Trial Representation and Availability of Tyrosine Kinase Inhibitors for Treatment of Chronic Myeloid Leukemia. Cancers (Basel) 2024; 16:2838. [PMID: 39199609 PMCID: PMC11352545 DOI: 10.3390/cancers16162838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 09/01/2024] Open
Abstract
Background: Evaluating clinical trial representation for countries with different socio-demographic index (SDI) and tyrosine kinase inhibitor (TKI) availability for chronic myeloid leukemia (CML). Methods: CML incidence rates (IRs) and disability-adjusted life years (DALYs) (1999-2019) from the Institute of Health Metrics and Evaluation were analyzed. Trials investigating TKI use in CML were obtained from ClinicalTrials.gov. Site data for eligible trials (N = 30) and DALYs were analyzed. TKI approvals, DALYs, and IRs were summarized by SDI. Results: North America (NA) had significant decreases in annual percent change (APC) in DALYs and incidence rates from 1999 to 2004. IRs were highest in Europe and Central Asia (ECA) and NA, while DALYs were highest in South Asia (SAsia) and Sub-Saharan Africa (SSA). Countries in the high-SDI quintile were likely to have lower DALYs than lower-SDI quintiles. Differences in regional DALYs vs. sites in TKI trials were significant for SAsia, SSA, and ECA. High-SDI countries were included in all 30 trials, and TKI approvals were prominent in high-SDI (142) vs. low-SDI (14) countries. Conclusions: The inclusion of disproportionately affected countries during the design of and recruitment into clinical trials should occur, as should TKI availability. The lack of representation demonstrates healthcare disparities.
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Affiliation(s)
- Mycal Casey
- Division of Hematology-Oncology, MedStar Georgetown University Hospital, Washington, DC 20007, USA;
| | - Lorriane Odhiambo
- Department of Biostatistics, Data Science and Epidemiology, Augusta University, Augusta, GA 30912, USA
| | - Nidhi Aggarwal
- Department of Medicine, Medstar Georgetown University Hospital, Washington, DC 20007, USA
| | - Mahran Shoukier
- Georgia Cancer Center, Augusta University, Augusta, GA 30912, USA
| | - K. M. Islam
- Department of Biostatistics, Data Science and Epidemiology, Augusta University, Augusta, GA 30912, USA
- Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Jorge Cortes
- Georgia Cancer Center, Augusta University, Augusta, GA 30912, USA
- Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
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Geng J, Zhao J, Fan R, Zhu Z, Zhang Y, Zhu Y, Yang Y, Xu L, Lin X, Hu K, Rudan I, Song P, Li X, Wu X. Global, regional, and national burden and quality of care of multiple myeloma, 1990-2019. J Glob Health 2024; 14:04033. [PMID: 38299781 PMCID: PMC10832550 DOI: 10.7189/jogh.14.04033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Abstract
Background Multiple myeloma (MM) is the second most common haematologic malignancy, presenting a great disease burden on the general population; however, the quality of care of MM is overlooked. We therefore assessed gains and disparity in quality of care worldwide from 1990 to 2019 based on a novel summary indicator - the quality of care index (QCI) - and examined its potential for improvement. Methods Using the Global Burden of Disease 2019 data set, we calculated the QCI of MM for 195 countries and territories. We used the principal component analysis to extract the first principal component of ratios with the combinations of mortality to incidence, prevalence to incidence, disability-adjusted life years to prevalence, and years of life lost to years lived with disability as QCI. We also conducted a series of descriptive and comparative analyses of QCI disparities with age, gender, period, geographies, and sociodemographic development, and compared the QCI among countries with similar socio-demographic index (SDI) through frontier analysis. Results The age-standardised rates of MM were 1.92 (95% uncertainty interval (UI) = 1.68, 2.12) in incidence and 1.42 (95% UI = 1.24, 1.52) in deaths per 100 000 population in 2019, and were predicted to increase in the future. The global age-standardised QCI increased from 51.31 in 1990 to 64.28 in 2019. In 2019, New Zealand had the highest QCI at 99.29 and the Central African Republic had the lowest QCI at 10.74. The gender disparity of QCI was reduced over the years, with the largest being observed in the sub-Saharan region. Regarding age, QCI maintained a decreasing trend in patients aged >60 in SDI quintiles. Generally, QCI improved with the SDI increase. Results of frontier analysis suggested that there is a potential to improve the quality of care across all levels of development spectrum. Conclusions Quality of care of MM improved during the past three decades, yet disparities in MM care remain across different countries, age groups, and genders. It is crucial to establish local objectives aimed at enhancing MM care and closing the gap in health care inequality.
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Affiliation(s)
- Jiawei Geng
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Global Health, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianhui Zhao
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Rong Fan
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zecheng Zhu
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuchen Zhang
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingshuang Zhu
- Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yichi Yang
- Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
- Department of Social Medicine, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Liying Xu
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangjie Lin
- Department of Hematology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Hematologic Malignancies, Diagnosis and Treatment, Hangzhou, Zhejiang, China
| | - Kejia Hu
- Department of Hematology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Peige Song
- School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xue Li
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Xifeng Wu
- Department of Big Data in Health Science School of Public Health, Centre of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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