Exploring a Preliminary Set of Indicators to Measure Adolescent Health: Results From a 12-Country Feasibility Study.

PURPOSE: To explore data availability, perceived relevance, acceptability and feasibility of implementing 52 draft indicators for adolescent health measurement in different countries globally. METHODS: A mixed-methods, sequential explanatory study was conducted in 12 countries. An online spreadsheet was used to assess data availability and a stakeholder survey to assess perceived relevance, acceptability, and feasibility of implementing each draft indicator proposed by the Global Action for Measurement of Adolescent health (GAMA). The assessments were discussed in virtual meetings with all countries and in deep dives with three countries. Findings were synthesized using descriptive statistics and qualitative thematic analysis. RESULTS: Data availability varied across the 52 draft GAMA indicators and across countries. Nine countries reported measuring over half of the indicators. Most indicators were rated relevant by stakeholders, while some were considered less acceptable and feasible. The ten lowest-ranking indicators were related to mental health, sexual health and substance use; the highest-ranking indicators centered on broader adolescent health issues, like use of health services. Indicators with higher data availability and alignment with national priorities were generally considered most relevant, acceptable and feasible. Barriers to measurement included legal, ethical and sensitivity issues, challenges with multi-sectoral coordination and data systems flexibility. DISCUSSION: Most of the draft GAMA indicators were deemed relevant and feasible, but contextual priorities and perceived acceptability influenced their implementation in countries. To increase their use for a more comprehensive understanding of adolescent health, better multi-sectoral coordination and tailored capacity building to accommodate the diverse data systems in countries will be required.

Introduction of Mobile Health Tools to Support Ebola Surveillance and Contact Tracing in Guinea.

Challenges in data availability and quality have contributed to the longest and deadliest Ebola epidemic in history that began in December 2013. Accurate surveillance data, in particular, has been difficult to access, as it is often collected in remote communities. We describe the design, implementation, and challenges of implementing a smartphone-based contact tracing system that is linked to analytics and data visualization software as part of the Ebola response in Guinea. The system, built on the mobile application CommCare and business intelligence software Tableau, allows for real-time identification of contacts who have not been visited and strong accountability of contact tracers through timestamps and collection of GPS points with their surveillance data. Deployment of this system began in November 2014 in Conakry, Guinea, and was expanded to a total of 5 prefectures by April 2015. To date, the mobile system has not replaced the paper-based system in the 5 prefectures where the program is active. However, as of April 30, 2015, 210 contact tracers in the 5 prefectures were actively using the mobile system to collectively monitor 9,162 contacts. With proper training, some investment in technical hardware, and adequate managerial oversight, there is opportunity to improve access to surveillance data from difficult-to-reach communities in order to inform epidemic control strategies while strengthening health systems to reduce risk of future disease outbreaks.

Diagnosis of trypanosomatid infections: targeting the spliced leader RNA.

Trypanosomatids transcribe their genes in large polycistronic clusters that are further processed into mature mRNA molecules by trans-splicing. During this maturation process, a conserved spliced leader RNA (SL-RNA) sequence of 39 bp is physically linked to the 5' end of the pre-mRNA molecules. Trypanosomatid infections cause a series of devastating diseases in man (sleeping sickness, leishmaniasis, Chagas disease) and animals (nagana, surra, dourine). Here, we investigated the SL-RNA molecule for its diagnostic potential using reverse transcription followed by real-time PCR. As a model, we used Trypanosoma brucei gambiense, which causes sleeping sickness in west and central Africa. We showed that the copy number of the SL-RNA molecule in one single parasitic cell is at least 8600. We observed a lower detection limit of the SL-RNA assay in spiked blood samples of 100 trypanosomes per milliliter of blood. We also proved that we can detect the trypanosome's SL-RNA in the blood of sleeping sickness patients with a sensitivity of 92% (95% CI, 78%-97%) and a specificity of 96% (95% CI, 86%-99%). The SL-RNA is thus an attractive new molecular target for next-generation diagnostics in diseases caused by trypanosomatids.

Population genetic structure of Guinea Trypanosoma brucei gambiense isolates according to host factors.

Human African trypanosomiasis (HAT) or sleeping sickness is a major public health problem in sub-Saharan Africa and is due to the kinetoplastid parasite Trypanosoma brucei gambiense in West and Central Africa. The exact role of multiple infections, the basis of clinical diversity observed in patients and the determinism that leads trypanosomes into different body fluids of the host remain opened questions to date. In this paper we investigate, in three Guinean foci, whether strains found in blood, lymph or cerebrospinal fluid (CSF) or in patients at different phase of HAT (phase 1, early phase 2 and late phase 2) are representative of the focus they belong to. Amplifications of parasites directly from body fluids led to substantial amounts of allelic drop outs, especially so for blood and CSF samples, which required data recoding of all homozygous sites into missing data. While controlling for geography, date of sampling and patient's phase of the disease, we found no effect of body fluids in the genetic structure of T. b. gambiense despite the presence of mixed infections. On the contrary, we found that the strains found in patients in different phase of the disease differed genetically, with early phase patients being more likely to be infected with more recent strains than patients at a more advanced phase of the disease. Thus, the combination of date of sampling and patient's status represents a parameter to be controlled for in population genetic structure analyses. Additional studies will also be required to explore further the phenomenon of mixed infections and its consequences.