data analysis

We describe a novel training programme, the tuberculosis Data Fellowship, designed to build capacity in low- and middle-income countries for tuberculosis data analytics. The programme was piloted in six countries (Bangladesh, Ethiopia, Ghana, Malawi, Mozambique) in July 2018 and Nigeria in September 2018; 20 participants completed the training. A number of key outputs have been achieved, such as improved instrument utilisation rates, decreased error rates, and improved instrument management. The training programme empowers local tuberculosis programme staff to discover and fix critical inefficiencies, provides high-level technical and operational support to the tuberculosis programme, and provides a platform for continued sharing of insights and best practices between countries. It supports the notion that connectivity can increase efficiencies and clinical benefits with better data for decision making, if coupled with commensurate capacity building in data analysis and interpretation.

Background: Mother-to-child transmission of HIV (MTCT) remains the most prevalent source of pediatric HIV infection. Most PMTCT (prevention of mother-to-child transmission of HIV) programs have concentrated monitoring and evaluation efforts on process rather than on outcome indicators. In this paper, we review service data from 28,320 children born to HIV-positive mothers to estimate MTCT rates.

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