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Background: Human African trypanosomiasis (HAT), also known as sleeping sickness, persists as a public health problem in several sub-Saharan countries. Evidence-based, spatially explicit estimates of population at risk are needed to inform planning and implementation of field interventions, monitor disease trends, raise awareness and support advocacy. Comprehensive, geo-referenced epidemiological records from HAT-affected countries were combined with human population layers to map five categories of risk, ranging from ''very high'' to ''very low,'' and to estimate the corresponding at-risk population. Results: Approximately 70 million people distributed over a surface of 1.55 million km2 are estimated to be at different levels of risk of contracting HAT. Trypanosoma brucei gambiense accounts for 82.2% of the population at risk, the remaining 17.8% being at risk of infection from T. b. rhodesiense. Twenty-one million people live in areas classified as moderate to very high risk, where more than 1 HAT case per 10,000 inhabitants per annum is reported. Discussion: Updated estimates of the population at risk of sleeping sickness were made, based on quantitative information on the reported cases and the geographic distribution of human population. Due to substantial methodological differences, it is not possible to make direct comparisons with previous figures for at-risk population. By contrast, it will be possible to explore trends in the future. The presented maps of different HAT risk levels will help to develop site-specific strategies for control and surveillance, and to monitor progress achieved by ongoing efforts aimed at the elimination of sleeping sickness. [ABSTRACT FROM AUTHOR]

Background. Human African trypanosomiasis (HAT) can affect travelers to sub-Saharan Africa, as well as migrants from disease endemic countries (DECs), posing diagnosis challenges to travel health services in non-disease endemic countries (non-DECs). Methods. Cases reported in journals have been collected through a bibliographic research and complemented by cases reported to the World Health Organization (WHO) during the process to obtain anti-trypanosome drugs. These drugs are distributed to DECs solely by WHO. Drugs are also provided to non-DECs when an HAT case is diagnosed. However, in non-DEC pentamidine can also be purchased in the market due to its indication to treat Pneumocystis and Leishmania infections. Any request for drugs from non-DECs should be accompanied by epidemiological and clinical data on the patient. Results. During the period 2000 to 2010, 94 cases of HAT were reported in 19 non-DECs. Seventy-two percent of them corresponded to the Rhodesiense form, whereas 28% corresponded to the Gambiense. Cases of Rhodesiense HAT were mainly diagnosed in tourists after short visits to DECs, usually within a few days of return. The majority of them were in first stage. Initial misdiagnosis with malaria or tick-borne diseases was frequent. Cases of Gambiense HAT were usually diagnosed several months after initial examination and subsequent to a variety of misdiagnoses. The majority were in second stage. Patients affected were expatriates living in DECs for extended periods and refugees or economic migrants from DECs. Conclusions. The risk of HAT in travelers and migrants, albeit low, cannot be overlooked. In non-DECs, rarity, nonspecific symptoms, and lack of knowledge and awareness in health staff make diagnosis difficult. Misdiagnosis is frequent, thus leading to invasive diagnosis methods, unnecessary treatments, and increased risk of fatality. Centralized distribution of drugs for HAT by WHO enables an HAT surveillance system for non-DECs to be maintained. This system provides valuable information on disease transmission and complements data collected in DECs. [ABSTRACT FROM AUTHOR]

Background: Updated, accurate and comprehensive information on the distribution of human African trypanosomiasis (HAT), also known as sleeping sickness, is critically important to plan and monitor control activities. We describe input data, methodology, preliminary results and future prospects of the HAT Atlas initiative, which will allow major improvements in the understanding of the spatial distribution of the disease. Methods: Up-to-date as well as historical data collected by national sleeping sickness control programmes, nongovernmental organizations and research institutes have been collated over many years by the HAT Control and Surveillance Programme of the World Health Organization. This body of information, unpublished for the most part, is now being screened, harmonized, and analysed by means of database management systems and geographical information systems (GIS). The number of new HAT cases and the number of people screened within a defined geographical entity were chosen as the key variables to map disease distribution in sub-Saharan Africa. Results: At the time of writing, over 600 epidemiological reports and files from seventeen countries were collated and included in the data repository. The reports contain information on approximately 20,000 HAT cases, associated to over 7,000 different geographical entities. The oldest epidemiological records considered so far date back to 1985, the most recent having been gathered in 2008. Data from Cameroon, Central African Republic, Chad, Congo, Equatorial Guinea and Gabon from the year 2000 onwards were fully processed and the preliminary regional map of HAT distribution is presented. Conclusion: The use of GIS tools and geo-referenced, village-level epidemiological data allow the production of maps that substantially improve on the spatial quality of previous cartographic products of similar scope. The significant differences between our preliminary outputs and earlier maps of HAT transmission areas demonstrate the strong need for this systematic approach to mapping sleeping sickness and point to the inaccuracy of any calculation of population at risk based on previous maps of HAT transmission areas. The Atlas of HAT will lay the basis for novel, evidence-based methodologies to estimate the population at risk and the burden of disease, ultimately leading to more efficient targeting of interventions. Also, the Atlas will help streamline future field data collection in those parts of Africa that still require it. [ABSTRACT FROM AUTHOR]