Please use this identifier to cite or link to this item: http://ir.mu.ac.ke:8080/jspui/handle/123456789/8895
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dc.contributor.authorFreeman, Esther-
dc.contributor.authorSemeere, Aggrey-
dc.contributor.authorWenger, Megan-
dc.contributor.authorBwana, Mwebesa-
dc.contributor.authorAsirwa, F. Chite-
dc.contributor.authorBusakhala, Naftali-
dc.contributor.authorOga, Emmanuel-
dc.contributor.authorJedy-Agba, Elima-
dc.contributor.authorKwaghe, Vivian-
dc.contributor.authorIregbu, Kenneth-
dc.contributor.authorJaquet, Antoine-
dc.contributor.authorDabis, Francois-
dc.contributor.authorYumo, Habakkuk Azinyui-
dc.contributor.authorDusingize, Jean Claude-
dc.contributor.authorBangsberg, David-
dc.contributor.authorAnastos, Kathryn-
dc.contributor.authorPhiri, Sam-
dc.contributor.authorBohlius, Julia-
dc.contributor.authorEgger, Matthias-
dc.contributor.authorYiannoutsos, Constantin-
dc.contributor.authorWools-Kaloustian, Kara-
dc.contributor.authorMartin, Jeffrey-
dc.date.accessioned2024-03-05T08:57:58Z-
dc.date.available2024-03-05T08:57:58Z-
dc.date.issued2016-02-
dc.identifier.urihttp://ir.mu.ac.ke:8080/jspui/handle/123456789/8895-
dc.description.abstractBackground: 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.isoenen_US
dc.publisherBMCen_US
dc.subjectSurvivalen_US
dc.subjectMortalityen_US
dc.subjectHIV/AIDSen_US
dc.subjectCanceren_US
dc.subjectResource-limited settingsen_US
dc.subjectLoss to follow-upen_US
dc.subjectCohorten_US
dc.subjectKaposi’s sarcomaen_US
dc.titlePitfalls 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 Africaen_US
dc.typeArticleen_US
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