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Pitfalls of practicing cancer epidemiology in resource-limited settings: the case of survival and loss to follow-up after a diagnosis of Kaposi’s sarcoma in five countries across sub-Saharan Africa

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dc.contributor.author Freeman, Esther
dc.contributor.author Semeere, Aggrey
dc.contributor.author Wenger, Megan
dc.contributor.author Bwana, Mwebesa
dc.contributor.author Asirwa, F. Chite
dc.contributor.author Busakhala, Naftali
dc.contributor.author Oga, Emmanuel
dc.contributor.author Jedy-Agba, Elima
dc.contributor.author Kwaghe, Vivian
dc.contributor.author Iregbu, Kenneth
dc.contributor.author Jaquet, Antoine
dc.contributor.author Dabis, Francois
dc.contributor.author Yumo, Habakkuk Azinyui
dc.contributor.author Dusingize, Jean Claude
dc.contributor.author Bangsberg, David
dc.contributor.author Anastos, Kathryn
dc.contributor.author Phiri, Sam
dc.contributor.author Bohlius, Julia
dc.contributor.author Egger, Matthias
dc.contributor.author Yiannoutsos, Constantin
dc.contributor.author Wools-Kaloustian, Kara
dc.contributor.author Martin, Jeffrey
dc.date.accessioned 2024-03-05T08:57:58Z
dc.date.available 2024-03-05T08:57:58Z
dc.date.issued 2016-02
dc.identifier.uri http://ir.mu.ac.ke:8080/jspui/handle/123456789/8895
dc.description.abstract Background: Survival after diagnosis is a fundamental concern in cancer epidemiology. In resource-rich settings, ambient clinical databases, municipal data and cancer registries make survival estimation in real-world populations relatively straightforward. In resource-poor settings, given the deficiencies in a variety of health-related data systems, it is less clear how well we can determine cancer survival from ambient data. Methods: We addressed this issue in sub-Saharan Africa for Kaposi’s sarcoma (KS), a cancer for which incidence has exploded with the HIV epidemic but for which survival in the region may be changing with the recent advent of antiretroviral therapy (ART). From 33 primary care HIV Clinics in Kenya, Uganda, Malawi, Nigeria and Cameroon participating in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) Consortia in 2009–2012, we identified 1328 adults with newly diagnosed KS. Patients were evaluated from KS diagnosis until death, transfer to another facility or database closure. Results: Nominally, 22 % of patients were estimated to be dead by 2 years, but this estimate was clouded by 45 % cumulative lost to follow-up with unknown vital status by 2 years. After adjustment for site and CD4 count, age <30 years and male sex were independently associated with becoming lost. Conclusions: In this community-based sample of patients diagnosed with KS in sub-Saharan Africa, almost hal became lost to follow-up by 2 years. This precluded accurate estimation of survival. Until we either generally strengthen data systems or implement cancer-specific enhancements (e.g., tracking of the lost) in the region, insights from cancer epidemiology will be limited. en_US
dc.description.sponsorship (U01 AI069911, U01 AI096299, U01 AI069919, U01 AI069924, D43 CA153717, U54 CA190153, P30 AI027763 and T32 AR007098). en_US
dc.language.iso en en_US
dc.publisher BMC en_US
dc.subject Survival en_US
dc.subject Mortality en_US
dc.subject HIV/AIDS en_US
dc.subject Cancer en_US
dc.subject Resource-limited settings en_US
dc.subject Loss to follow-up en_US
dc.subject Cohort en_US
dc.subject Kaposi’s sarcoma en_US
dc.title Pitfalls of practicing cancer epidemiology in resource-limited settings: the case of survival and loss to follow-up after a diagnosis of Kaposi’s sarcoma in five countries across sub-Saharan Africa en_US
dc.type Article en_US


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