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      An updated atlas of human helminth infections: the example of East Africa

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          Abstract

          Background

          Reliable and updated maps of helminth (worm) infection distributions are essential to target control strategies to those populations in greatest need. Although many surveys have been conducted in endemic countries, the data are rarely available in a form that is accessible to policy makers and the managers of public health programmes. This is especially true in sub-Saharan Africa, where empirical data are seldom in the public domain. In an attempt to address the paucity of geographical information on helminth risk, this article describes the development of an updated global atlas of human helminth infection, showing the example of East Africa.

          Methods

          Empirical, cross-sectional estimates of infection prevalence conducted since 1980 were identified using electronic and manual search strategies of published and unpublished sources. A number of inclusion criteria were imposed for identified information, which was extracted into a standardized database. Details of survey population, diagnostic methods, sample size and numbers infected with schistosomes and soil-transmitted helminths were recorded. A unique identifier linked each record to an electronic copy of the source document, in portable document format. An attempt was made to identify the geographical location of each record using standardized geolocation procedures and the assembled data were incorporated into a geographical information system.

          Results

          At the time of writing, over 2,748 prevalence surveys were identified through multiple search strategies. Of these, 2,612 were able to be geolocated and mapped. More than half (58%) of included surveys were from grey literature or unpublished sources, underlining the importance of reviewing in-country sources. 66% of all surveys were conducted since 2000. Comprehensive, countrywide data are available for Burundi, Rwanda and Uganda. In contrast, information for Kenya and Tanzania is typically clustered in specific regions of the country, with few records from areas with very low population density and/or environmental conditions which are unfavourable for helminth transmission. Information is presented on the prevalence and geographical distribution for the major helminth species.

          Conclusion

          For all five countries, the information assembled in the current atlas provides the most reliable, up-to-date and comprehensive source of data on the distribution of common helminth infections to guide the rational implementation of control efforts.

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          Most cited references57

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          Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania.

          To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania. Bayesian geostatistical models were developed using parasitological data from 143 schools. In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation. Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.
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            Epidemiology and geography of Schistosoma mansoni in Uganda: implications for planning control.

            Intestinal schistosomiasis caused by infection with Schistosoma mansoni is a widespread public health problem in Uganda. Although long known to be endemic, its current distribution within the country requires updating of parasitological data to help guide planned control. We report such data collected between 1998 and 2002 from 201 schools and 68 communities across Uganda. In accordance with epidemiological expectation, prevalence and intensity increased with age, peaking at 10-20 years and thereafter declined moderately with age, whereas intensity declined more rapidly with age, and the prevalence of infection in a school was non-linearly related to the mean intensity of infection. We used geographical information systems to map the distribution of infection and to overlay parasitological data with interpolated environmental surfaces. The derived maps indicate both a widespread occurrence of infection and a marked variability in infection prevalence, with prevalence typically highest near the lakeshore and along large rivers. No transmission occurred at altitudes >1400 m or where total annual rainfall was <900 mm; limits which can help estimate the population at risk of schistosomiasis. The results are discussed in reference to the ecology of infection and provide an epidemiological framework for the design and implementation of control efforts underway in Uganda.
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              High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa

              Background Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. Methodology/Principal Findings We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. Conclusions We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.
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                Author and article information

                Journal
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central
                1476-072X
                2009
                9 July 2009
                : 8
                : 42
                Affiliations
                [1 ]Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, UK
                [2 ]Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Nairobi, Kenya
                [3 ]Vector Control Division, Uganda Ministry of Health, Kampala, Uganda
                [4 ]Neglected Tropical Disease Control Programme, Access Project, Kigali, Rwanda
                [5 ]Division of Vector Borne Diseases, Kenya Ministry of Health, Nairobi, Kenya
                [6 ]Projet Maladies Tropicales Négligées, Bujumbura, Burundi
                [7 ]National Institute for Medical Research, Mwanza, United Republic of Tanzania
                [8 ]Eastern and Southern Africa Centre of International Parasite Control, KEMRI, Nairobi, Kenya
                [9 ]School of Population Health, University of Queensland, Australia
                [10 ]Human Development Network, The World Bank, Washington DC, USA
                [11 ]Centre for Tropical Medicine, University of Oxford, UK
                Article
                1476-072X-8-42
                10.1186/1476-072X-8-42
                2714505
                19589144
                008a6244-78b4-407d-ba0d-35b6365c5328
                Copyright © 2009 Brooker et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 May 2009
                : 9 July 2009
                Categories
                Research

                Public health
                Public health

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