A survey of paediatric HIV programmatic and clinical management practices in Asia and sub-Saharan Africa--the International epidemiologic Databases to Evaluate AIDS (IeDEA)

INTRODUCTION There are limited data on paediatric HIV care and treatment programmes in low-resource settings. METHODS A standardized survey was completed by International epidemiologic Databases to Evaluate AIDS paediatric cohort sites in the regions of Asia-Pacific (AP), Central Africa (CA), East Africa (EA), Southern Africa (SA) and West Africa (WA) to understand operational resource availability and paediatric management practices. Data were collected through January 2010 using a secure, web-based software program (REDCap). RESULTS A total of 64,552 children were under care at 63 clinics (AP, N=10; CA, N=4; EA, N=29; SA, N=10; WA, N=10). Most were in urban settings (N=41, 65%) and received funding from governments (N=51, 81%), PEPFAR (N=34, 54%), and/or the Global Fund (N=15, 24%). The majority were combined adult-paediatric clinics (N=36, 57%). Prevention of mother-to-child transmission was integrated at 35 (56%) sites; 89% (N=56) had access to DNA PCR for infant diagnosis. African (N=40/53) but not Asian sites recommended exclusive breastfeeding up until 4-6 months. Regular laboratory monitoring included CD4 (N=60, 95%), and viral load (N=24, 38%). Although 42 (67%) sites had the ability to conduct acid-fast bacilli (AFB) smears, 23 (37%) sites could conduct AFB cultures and 18 (29%) sites could conduct tuberculosis drug susceptibility testing. Loss to follow-up was defined as >3 months of lost contact for 25 (40%) sites, >6 months for 27 sites (43%) and >12 months for 6 sites (10%). Telephone calls (N=52, 83%) and outreach worker home visits to trace children lost to follow-up (N=45, 71%) were common. CONCLUSIONS In general, there was a high level of patient and laboratory monitoring within this multiregional paediatric cohort consortium that will facilitate detailed observational research studies. Practices will continue to be monitored as the WHO/UNAIDS Treatment 2.0 framework is implemented.

Beyond the tree of texts: Building an empirical model of scribal variation through graph analysis of texts and stemmata

Stemmatology, or the reconstruction of the transmission history of texts, is a field that stands particularly to gain from digital methods. Many scholars already take stemmatic approaches that rely heavily on computational analysis of the collated text (e.g. Robinson and O’Hara 1996; Salemans 2000; Heikkilä 2005; Windram et al. 2008 among many others). Although there is great value in computationally assisted stemmatology, providing as it does a reproducible result and allowing access to the relevant methodological process in related fields such as evolutionary biology, computational stemmatics is not without its critics. The current state-of-the-art effectively forces scholars to choose between a preconceived judgment of the significance of textual differences (the Lachmannian or neo-Lachmannian approach, and the weighted phylogenetic approach) or to make no judgment at all (the unweighted phylogenetic approach). Some basis for judgment of the significance of variation is sorely needed for medieval text criticism in particular. By this, we mean that there is a need for a statistical empirical profile of the text-genealogical significance of the different sorts of variation in different sorts of medieval texts. The rules that apply to copies of Greek and Latin classics may not apply to copies of medieval Dutch story collections; the practices of copying authoritative texts such as the Bible will most likely have been different from the practices of copying the Lives of local saints and other commonly adapted texts. It is nevertheless imperative that we have a consistent, flexible, and analytically tractable model for capturing these phenomena of transmission. In this article, we present a computational model that captures most of the phenomena of text variation, and a method for analysis of one or more stemma hypotheses against the variation model. We apply this method to three ‘artificial traditions’ (i.e. texts copied under laboratory conditions by scholars to study the properties of text variation) and four genuine medieval traditions whose transmission history is known or deduced in varying degrees. Although our findings are necessarily limited by the small number of texts at our disposal, we demonstrate here some of the wide variety of calculations that can be made using our model. Certain of our results call sharply into question the utility of excluding ‘trivial’ variation such as orthographic and spelling changes from stemmatic analysis.