Retention in Care and Patient-Reported Reasons for Undocumented Transfer or Stopping Care among HIV-infected Patients on Antiretroviral Therapy in Eastern Africa

BACKGROUND:

Improving the implementation of the global response to HIV requires understanding retention after starting antiretroviral therapy, but loss to follow-up undermines assessment of the magnitude of and reasons for stopping care.

METHODS:

We evaluated adults starting ART over 2.5 years in 14 clinics Uganda, Tanzania and Kenya. We traced a random sample of patients lost to follow-up and incorporated updated information in weighted competing risks estimates of retention. Reasons for non-return were surveyed.

RESULTS:

Among 18,081 patients, 3150 (18%) were lost to follow-up and 579 (18%) were traced. Of 497 (86%) with ascertained vital status, 340 (69%) were alive and in 278 (82%) cases, updated care status was obtained. Among all patients initiating ART, weighted estimates incorporating tracing outcomes found that two years after ART, 69% were in care at original clinic, 14% transferred (4% official and 10% unofficial), 6% were alive but out of care, 6% died in care (< 60 days after last visit), and 6% died out of care (>= 60 days after last visit). Among lost patients found in care elsewhere, structural barriers (e.g., transportation) were most prevalent (65%), followed by clinic-based (e.g., waiting times) (33%) and psychosocial (e.g., stigma) (27%). Among patients not in care elsewhere, psychosocial barriers were most prevalent (76%) followed by structural (51%) and clinic-based (15%).

CONCLUSION:

Accounting for outcomes among the lost yields a more informative assessment of retention. Structural barriers contribute most to silent transfers whereas psychological and social barriers tend to result in longer-term care discontinuation.

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By:   Geng EH1, Odeny TA2, Lyamuya R3, Nakiwogga-Muwanga A4, Diero L5, Bwana M6, Braitstein P5, Somi G3, Kambugu A4, Bukusi E2, Wenger M7, Neilands TB,Glidden DV7, Wools-Kaloustian K8, Yiannoutsos C9, Martin J10.

• 1Department of Medicine, Division of HIV/AIDS, San Francisco General Hospital, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, San Francisco, USA.
• 2Kenya Medical Research Institute and the Family AIDS Care and Education Services Program, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Kenya.
• 3National AIDS Control Program in Dar Es Salaam, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Tanzania.
• 4Infectious Diseases Institute in Kampala, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Uganda.
• 5The USAID-AMPATH Program in Eldoret, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Kenya.
• 6Mbarara University of Science and Technology, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Mbarara, Uganda.
• 7Department of Epidemiology and Biostatistics, University of California, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, San Francisco, USA.
• 8Division of Infectious Diseases, Department of Medicine, Fairbanks School of Public Health, Indiana University, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Indianapolis, Indiana, USA.
• 9Department of Biostatistics, Fairbanks School of Public Health, Indiana University, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, Indianapolis, Indiana, USA.
• 10Department of Medicine, Division of HIV/AIDS, San Francisco General Hospital, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, San Francisco, USA Department of Epidemiology and Biostatistics, University of California, The East Africa International Epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium, San Francisco, USA. 

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