Estimating the need of second-line antiretroviral therapy in adults in sub-Saharan Africa up to 2030: a mathematical model

, Abstract Background— The number of patients in need of second-line antiretroviral drugs is increasing in sub-Saharan Africa. We aimed to project the need of second-line antiretroviral therapy (ART) in adults in sub-Saharan Africa up to 2030. Methods— We developed a simulation model for HIV and applied it to each sub-Saharan African country. We fitted the number of adult patients on ART to observed estimates, and predicted first-and second-line needs between 2015 and 2030. We present results for sub-Saharan Africa, and 8 selected countries. We present 18 scenarios, combining the availability of viral load monitoring (VLm), speed of ART scale-up, and rates of retention and switching to second-line. HIV transmission was not considered. Findings— Depending on the scenario, 8·7–25·6 million people are expected to receive ART in 2020, of whom 0·5–3·0 million (2·9%–15·6%) will be receiving second-line ART. The percentage was highest (15·6%) in the scenario with perfect retention and immediate switching, no further scale-up, and universal routine VLm. In 2030, the range of patients on ART remained constant, but the number (proportion) of patients on second-line ART increased to 0·8–4·6 million (6·6%– 19·6%). The need of second-line ART was 2–3 times higher if routine VLm was implemented throughout the region, compared with a scenario of no further VLm scale-up. For each monitoring strategy the future proportion of patients on second-line ART differed only minimally between countries. Interpretation— The demand for second-line ART will increase substantially in the future as countries increase access to routine VLm. Funding— World Health Organization

[1]  Jeffrey W. Eaton,et al.  Sustainable HIV treatment in Africa through viral-load-informed differentiated care , 2015, Nature.

[2]  M. Egger,et al.  Monitoring and switching of first-line antiretroviral therapy in adult treatment cohorts in sub-Saharan Africa: collaborative analysis. , 2015, The lancet. HIV.

[3]  M. Egger,et al.  The Cost-Effectiveness of Monitoring Strategies for Antiretroviral Therapy of HIV Infected Patients in Resource-Limited Settings: Software Tool , 2015, PloS one.

[4]  Nello Blaser,et al.  gems: An R Package for Simulating from Disease Progression Models. , 2015, Journal of statistical software.

[5]  M. Egger,et al.  Viral load versus CD4+ monitoring and 5-year outcomes of antiretroviral therapy in HIV-positive children in Southern Africa: a cohort-based modelling study , 2014, AIDS.

[6]  Nello Blaser,et al.  Tracing of Patients Lost to Follow-up and HIV Transmission: Mathematical Modeling Study Based on 2 Large ART Programs in Malawi , 2014, Journal of acquired immune deficiency syndromes.

[7]  G. Rutherford,et al.  Predicting treatment failure in adults and children on antiretroviral therapy: a systematic review of the performance characteristics of the 2010 WHO immunologic and clinical criteria for virologic failure , 2014, AIDS.

[8]  Nello Blaser,et al.  Cost-effectiveness of point-of-care viral load monitoring of antiretroviral therapy in resource-limited settings: mathematical modelling study , 2013, AIDS.

[9]  J. Stringer,et al.  Monitoring of Antiretroviral Therapy and Mortality in HIV Programmes in Malawi, South Africa and Zambia: Mathematical Modelling Study , 2013, PloS one.

[10]  Peter Mugyenyi,et al.  Antiretroviral drug resistance profiles and response to second-line therapy among HIV type 1-infected Ugandan children. , 2013, AIDS research and human retroviruses.

[11]  D. Pillay,et al.  Global trends in antiretroviral resistance in treatment-naive individuals with HIV after rollout of antiretroviral treatment in resource-limited settings: a global collaborative study and meta-regression analysis , 2012, The Lancet.

[12]  Thomas Gsponer,et al.  Viral load monitoring of antiretroviral therapy, cohort viral load and HIV transmission in Southern Africa: a mathematical modelling analysis , 2012, AIDS.

[13]  A. Wensing,et al.  Virological follow-up of adult patients in antiretroviral treatment programmes in sub-Saharan Africa: a systematic review. , 2010, The Lancet. Infectious diseases.

[14]  J. Hargrove,et al.  AIDS among older children and adolescents in Southern Africa: projecting the time course and magnitude of the epidemic , 2009, AIDS.

[15]  J. Overbaugh,et al.  Predictors of Early Mortality in a Cohort of Human Immunodeficiency Virus Type 1-Infected African Children , 2004, The Pediatric infectious disease journal.

[16]  Peter Piot,et al.  Joint United Nations Program on HIV/AIDS (UNAIDS) , 1997 .

[17]  C. Dolea,et al.  World Health Organization , 1949, International Organization.