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Background: Although patient attrition is recognized as a threat to the long-term success of antiretroviral therapy programs worldwide, there is no universal definition for classifying patients as lost to follow-up (LTFU). We analyzed data from health facilities across Africa, Asia, and Latin America to empirically determine a standard LTFU definition. Methods and Findings: At a set "status classification" date, patients were categorized as either "active" or "LTFU" according to different intervals from time of last clinic encounter. For each threshold, we looked forward 365 d to assess the performance and accuracy of this initial classification. The best-performing definition for LTFU had the lowest proportion of patients misclassified as active or LTFU. Observational data from 111 health facilities-representing 180,718 patients from 19 countries-were included in this study. In the primary analysis, for which data from all facilities were pooled, an interval of 180 d (95% confidence interval [CI]: 173-181 d) since last patient encounter resulted in the fewest misclassifications (7.7%, 95% CI: 7.6%-7.8%). A secondary analysis that gave equal weight to cohorts and to regions generated a similar result (175 d); however, an alternate approach that used inverse weighting for cohorts based on variance and equal weighting for regions produced a slightly lower summary measure (150 d). When examined at the facility level, the best-performing definition varied from 58 to 383 d (mean = 150 d), but when a standard definition of 180 d was applied to each facility, only slight increases in misclassification (mean = 1.2%, 95% CI: 1.0%-1.5%) were observed. Using this definition, the proportion of patients classified as LTFU by facility ranged from 3.1% to 45.1% (mean = 19.9%, 95% CI: 19.1%-21.7%). Conclusions: Based on this evaluation, we recommend the adoption of $180 d since the last clinic visit as a standard LTFU definition. Such standardization is an important step to understanding the reasons that underlie patient attrition and establishing more reliable and comparable program evaluation worldwide. [ABSTRACT FROM AUTHOR]

Mentoring is a proven path to scientific progress, but it is not a common practice in low- and middle-income countries (LMICs). Existing mentoring approaches and guidelines are geared toward high-income country settings, without considering in detail the differences in resources, culture, and structure of research systems of LMICs. To address this gap, we conducted five Mentoring-the-Mentor workshops in Africa, South America, and Asia, which aimed at strengthening the capacity for evidence-based, LMIC-specific institutional mentoring programs globally. The outcomes of the workshops and two follow-up working meetings are presented in this special edition of the American Journal of Tropical Medicine and Hygiene . Seven articles offer recommendations on how to tailor mentoring to the context and culture of LMICs, and provide guidance on how to implement mentoring programs. This introductory article provides both a prelude and executive summary to the seven articles, describing the motivation, cultural context and relevant background, and presenting key findings, conclusions, and recommendations.

A growing number of low- and middle-income country (LMIC) institutions have developed and implemented formal programs to support mentorship. Although the individual-level benefits of mentorship are well established, such activities can also sustainably build institutional capacity, bridge inequities in health care, and catalyze scientific advancement. To date, however, evaluation of these programs remains limited, representing an important gap in our understanding about the impact of mentoring. Without rigorous and ongoing evaluation, there may be missed opportunities for identifying best practices, iteratively improving program activities, and demonstrating the returns on investment in mentorship. In this report, we propose a framework for evaluating mentorship programs in LMIC settings where resources may be constrained. We identify six domains: 1) mentor-mentee relationship, 2) career guidance, 3) academic productivity, 4) networking, 5) wellness, and 6) organizational capacity. Within each, we describe specific metrics and how they may be considered as part of evaluation plans. We emphasize the role of measurement and evaluation at the institutional level, so that programs may enhance their mentoring capacity and optimize the management of their resources. Although we advocate for a comprehensive approach to evaluation, we recognize that-depending on stage and relative maturity-some domains may be prioritized to address short- and medium-term program goals.

Background: HIV-1 RNA viral load (VL) testing is recommended to monitor antiretroviral therapy (ART) but not available in many resource-limited settings. We developed and validated CD4-based risk charts to guide targeted VL testing., Methods: We modeled the probability of virologic failure up to 5 years of ART based on current and baseline CD4 counts, developed decision rules for targeted VL testing of 10%, 20%, or 40% of patients in 7 cohorts of patients starting ART in South Africa, and plotted cutoffs for VL testing on colour-coded risk charts. We assessed the accuracy of risk chart-guided VL testing to detect virologic failure in validation cohorts from South Africa, Zambia, and the Asia-Pacific., Results: In total, 31,450 adult patients were included in the derivation and 25,294 patients in the validation cohorts. Positive predictive values increased with the percentage of patients tested: from 79% (10% tested) to 98% (40% tested) in the South African cohort, from 64% to 93% in the Zambian cohort, and from 73% to 96% in the Asia-Pacific cohort. Corresponding increases in sensitivity were from 35% to 68% in South Africa, from 55% to 82% in Zambia, and from 37% to 71% in Asia-Pacific. The area under the receiver operating curve increased from 0.75 to 0.91 in South Africa, from 0.76 to 0.91 in Zambia, and from 0.77 to 0.92 in Asia-Pacific., Conclusions: CD4-based risk charts with optimal cutoffs for targeted VL testing maybe useful to monitor ART in settings where VL capacity is limited.