Abstract
BackgroundMonitoring and evaluation (M&E) of HIV care and treatment programs is impacted by losses to follow-up (LTFU) in the patient population. The severity of this effect is undeniable but its extent unknown. Tracing all lost patients addresses this but census methods are not feasible in programs involving rapid scale-up of HIV treatment in the developing world. Sampling-based approaches and statistical adjustment are the only scaleable methods permitting accurate estimation of M&E indices.Methodology/Principal FindingsIn a large antiretroviral therapy (ART) program in western Kenya, we assessed the impact of LTFU on estimating patient mortality among 8,977 adult clients of whom, 3,624 were LTFU. Overall, dropouts were more likely male (36.8% versus 33.7%; p = 0.003), and younger than non-dropouts (35.3 versus 35.7 years old; p = 0.020), with lower median CD4 count at enrollment (160 versus 189 cells/ml; p<0.001) and WHO stage 3–4 disease (47.5% versus 41.1%; p<0.001). Urban clinic clients were 75.0% of non-dropouts but 70.3% of dropouts (p<0.001). Of the 3,624 dropouts, 1,143 were sought and 621 had their vital status ascertained. Statistical techniques were used to adjust mortality estimates based on information obtained from located LTFU patients. Observed mortality estimates one year after enrollment were 1.7% (95% CI 1.3%–2.0%), revised to 2.8% (2.3%–3.1%) when deaths discovered through outreach were added and adjusted to 9.2% (7.8%–10.6%) and 9.9% (8.4%–11.5%) through statistical modeling depending on the method used. The estimates 12 months after ART initiation were 1.7% (1.3%–2.2%), 3.4% (2.9%–4.0%), 10.5% (8.7%–12.3%) and 10.7% (8.9%–12.6%) respectively.Conclusions/Significance Assessment of the impact of LTFU is critical in program M&E as estimated mortality based on passive monitoring may underestimate true mortality by up to 80%. This bias can be ameliorated by tracing a sample of dropouts and statistically adjust the mortality estimates to properly evaluate and guide large HIV care and treatment programs.
Highlights
In resource-rich settings such as North America and Europe, use of antiretroviral therapy (ART) has vastly improved the prognosis of persons living with HIV/AIDS [1,2,3,4]
The primary objective of this paper is to describe how the use of statistical sampling techniques, coupled with medical record infrastructure and a patient tracing program, can be used to a) enable more accurate estimation of mortality in large HIV treatment programs, and b) identify subsets of patients who are at higher risk of being lost to follow-up (LTFU) and who may benefit more from active patient tracing in order to improve their clinical care
We present data from AMPATH, a large President’s Emergency Plan for AIDS Relief (PEPFAR)-funded HIV clinical care program in western Kenya
Summary
In resource-rich settings such as North America and Europe, use of antiretroviral therapy (ART) has vastly improved the prognosis of persons living with HIV/AIDS [1,2,3,4]. Over the last five years, international response efforts, such as the Global Fund to fight AIDS, Tuberculosis and Malaria, World Health Organization’s (WHO) 3-by-5 program (three million patients under treatment by 2005) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR) [5,6,7], have made great strides in increasing the number of HIV infected individuals in resource-poor settings who have access to antiretroviral therapy. Accurate estimates of patient survival and other clinical outcomes have been difficult to obtain, as they are significantly impacted by patient loss to follow-up [12,13]. Monitoring and evaluation (M&E) of HIV care and treatment programs is impacted by losses to follow-up (LTFU) in the patient population. The severity of this effect is undeniable but its extent unknown. Sampling-based approaches and statistical adjustment are the only scaleable methods permitting accurate estimation of M&E indices
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