Elvin H Geng1, Thomas A Odeny2, Rita E Lyamuya3, Alice Nakiwogga-Muwanga4, Lameck Diero5, Mwebesa Bwana6, Winnie Muyindike6, Paula Braitstein7, Geoffrey R Somi3, Andrew Kambugu4, Elizabeth A Bukusi2, Megan Wenger8, Kara K Wools-Kaloustian9, David V Glidden8, Constantin T Yiannoutsos10, Jeffrey N Martin11. 1. Division of HIV/AIDS at San Francisco General Hospital in the Department of Medicine, University of California, San Francisco, CA, USA; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. Electronic address: genge@php.ucsf.edu. 2. Kenya Medical Research Institute and the Family AIDS Care and Education Services Program, Kisumu, Kenya; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 3. National AIDS Control Program, Dar Es Salaam, Tanzania; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 4. Infectious Diseases Institute, Kampala, Uganda; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 5. College of Health Sciences, School of Medicine, Department of Medicine, Moi University, Eldoret, Kenya; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 6. Mbarara University of Science and Technology, Mbarara, Uganda; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 7. College of Health Sciences, School of Medicine, Department of Medicine, Moi University, Eldoret, Kenya; Department of Medicine, School of Medicine, Indiana University School of Public Health, Indiana University, Indianapolis, IN, USA; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 8. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 9. Department of Medicine, School of Medicine, Indiana University School of Public Health, Indiana University, Indianapolis, IN, USA; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 10. Department of Biostatistics, Indiana University School of Public Health, Indiana University, Indianapolis, IN, USA; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium. 11. Division of HIV/AIDS at San Francisco General Hospital in the Department of Medicine, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium.
Abstract
BACKGROUND: Mortality in HIV-infected people after initiation of antiretroviral treatment (ART) in resource-limited settings is an important measure of the effectiveness and comparative effectiveness of the global public health response. Substantial loss to follow-up precludes accurate accounting of deaths and limits our understanding of effectiveness. We aimed to provide a better understanding of mortality at scale and, by extension, the effectiveness and comparative effectiveness of public health ART treatment in east Africa. METHODS: In 14 clinics in five settings in Kenya, Uganda, and Tanzania, we intensively traced a sample of patients randomly selected using a random number generator, who were infected with HIV and on ART and who were lost to follow-up (>90 days late for last scheduled visit). We incorporated the vital status outcomes for these patients into analyses of the entire clinic population through probability-weighted survival analyses. FINDINGS: We followed 34 277 adults on ART from Mbarara and Kampala in Uganda, Eldoret, and Kisumu in Kenya, and Morogoro in Tanzania. The median age was 35 years (IQR 30-42), 11 628 (34%) were men, and median CD4 count count before therapy was 154 cells per μL (IQR 70-234). 5780 patients (17%) were lost to follow-up, 991 (17%) were selected for tracing between June 10, 2011, and Aug 27, 2012, and vital status was ascertained for 860 (87%). With incorporation of outcomes from the patients lost to follow-up, estimated 3 year mortality increased from 3·9% (95% CI 3·6-4·2) to 12·5% (11·8-13·3). The sample-corrected, unadjusted 3 year mortality across settings was lowest in Mbarara (7·2%) and highest in Morogoro (23·6%). After adjustment for age, sex, CD4 count before therapy, and WHO stage, the sample-corrected hazard ratio comparing the settings with highest and lowest mortalities was 2·2 (95% CI 1·5-3·4) and the risk difference for death at 3 years was 11% (95% CI 5·0-17·7). INTERPRETATION: A sampling-based approach is widely feasible and important to an understanding of mortality after initiation of ART. After adjustment for measured biological drivers, mortality differs substantially across settings despite delivery of a similar clinical package of treatment. Implementation research to understand the systems, community, and patients' behaviours driving these differences is urgently needed. FUNDING: The US National Institutes of Health and President's Emergency Fund for AIDS Relief.
BACKGROUND: Mortality in HIV-infected people after initiation of antiretroviral treatment (ART) in resource-limited settings is an important measure of the effectiveness and comparative effectiveness of the global public health response. Substantial loss to follow-up precludes accurate accounting of deaths and limits our understanding of effectiveness. We aimed to provide a better understanding of mortality at scale and, by extension, the effectiveness and comparative effectiveness of public health ART treatment in east Africa. METHODS: In 14 clinics in five settings in Kenya, Uganda, and Tanzania, we intensively traced a sample of patients randomly selected using a random number generator, who were infected with HIV and on ART and who were lost to follow-up (>90 days late for last scheduled visit). We incorporated the vital status outcomes for these patients into analyses of the entire clinic population through probability-weighted survival analyses. FINDINGS: We followed 34 277 adults on ART from Mbarara and Kampala in Uganda, Eldoret, and Kisumu in Kenya, and Morogoro in Tanzania. The median age was 35 years (IQR 30-42), 11 628 (34%) were men, and median CD4 count count before therapy was 154 cells per μL (IQR 70-234). 5780 patients (17%) were lost to follow-up, 991 (17%) were selected for tracing between June 10, 2011, and Aug 27, 2012, and vital status was ascertained for 860 (87%). With incorporation of outcomes from the patients lost to follow-up, estimated 3 year mortality increased from 3·9% (95% CI 3·6-4·2) to 12·5% (11·8-13·3). The sample-corrected, unadjusted 3 year mortality across settings was lowest in Mbarara (7·2%) and highest in Morogoro (23·6%). After adjustment for age, sex, CD4 count before therapy, and WHO stage, the sample-corrected hazard ratio comparing the settings with highest and lowest mortalities was 2·2 (95% CI 1·5-3·4) and the risk difference for death at 3 years was 11% (95% CI 5·0-17·7). INTERPRETATION: A sampling-based approach is widely feasible and important to an understanding of mortality after initiation of ART. After adjustment for measured biological drivers, mortality differs substantially across settings despite delivery of a similar clinical package of treatment. Implementation research to understand the systems, community, and patients' behaviours driving these differences is urgently needed. FUNDING: The US National Institutes of Health and President's Emergency Fund for AIDS Relief.
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