To estimate the mortality impact of delay in antiretroviral therapy (ART) initiation from the time of entry into care.
A state–transition Markov process model. This technique allows for assessing mortality before and after ART initiation associated with delays in ART initiation among a general population of ART-eligible patients without conducting a randomized trial.
We used patient-level data from 3 South African cohorts to determine transition probabilities for pre-ART CD4 count changes and pre-ART and on-ART mortality. For each parameter, we generated probabilities and distributions for Monte Carlo simulations with 1-week cycles to estimate mortality 52 weeks from clinic entry.
We estimated an increase in mortality from 11.0% to 14.7% (relative increase of 34%) with a 10-week delay in ART for patients entering care with our pre-ART cohort CD4 distribution. When we examined low CD4 ranges, the relative increase in mortality delays remained similar; however, the absolute increase in mortality rose. For example, among patients entering with CD4 count 50–99 cells per cubic millimeter, 12-month mortality increased from 13.3% with no delay compared with 17.0% with a 10-week delay and 22.9% with a 6-month delay.
Delays in ART initiation, common in routine HIV programs, can lead to important increases in mortality. Prompt ART initiation for patients entering clinical care and eligible for ART, especially those with lower CD4 counts, could be a relatively low-cost approach with a potential marked impact on mortality.
*Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD;
†Aurum Institute, Johannesburg, South Africa;
Departments of ‡Infectious Disease Epidemiology, and §Clinical Research, London School for Hygiene and Tropical Medicine, London, UK;
‖Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD;
¶Perinatal HIV Research Unit, Johannesburg, South Africa; and
#School of Public Health, University of Witwatersrand, Johannesburg, South Africa.
Correspondence to: Christopher J. Hoffmann, MD, MPH, MSc, Division of Infectious Diseases, Johns Hopkins University School of Medicine, 1550 Orleans Street, CRB II Rm 1M-07, Baltimore, MD 21231 (e-mail: email@example.com).
Supported by National Institutes of Health AI083099 to C. J. Hoffmann; J. J. Lewis was fully funded and K. L. Fielding partially funded by the Bill and Melinda Gates Foundation, through the Biostatistics Core of CREATE; R. E. Chaisson by National Institutes of Health grants AI5535901, Johns Hopkins University Center for Applied Research P30-AI094189-01A1, and HL090312 and the Bill and Melinda Gates Foundation; A. D. Grant by Global Health Trials and the Bill and Melinda Gates Foundation.
The authors have no conflicts of interest to disclose.
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Received September 19, 2012
Accepted January 18, 2013