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      Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia

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          Abstract

          Background

          Malaria is the leading cause of morbidity and mortality in Ethiopia, accounting for over five million cases and thousands of deaths annually. The risks of morbidity and mortality associated with malaria are characterized by spatial and temporal variation across the country. This study examines the spatial and temporal patterns of malaria transmission at the local level and implements a risk mapping tool to aid in monitoring and disease control activities.

          Methods

          In this study, we examine the global and local patterns of malaria distribution in 543 villages in East Shoa, central Ethiopia using individual-level morbidity data collected from six laboratory and treatment centers between September 2002 and August 2006.

          Results

          Statistical analysis of malaria incidence by sex, age, and village through time reveal the presence of significant spatio-temporal variations. Poisson regression analysis shows a decrease in malaria incidence with increasing age. A significant difference in the malaria incidence density ratio (IDRs) is detected in males but not in females. A significant decrease in the malaria IDRs with increasing age is captured by a quadratic model. Local spatial statistics reveals clustering or hot spots within a 5 and 10 km distance of most villages in the study area. In addition, there are temporal variations in malaria incidence.

          Conclusion

          Malaria incidence varies according to gender and age, with males age 5 and above showing a statistically higher incidence. Significant local clustering of malaria incidence occurs between pairs of villages within 1–10 km distance lags. Malaria incidence was higher in 2002–2003 than in other periods of observation. Malaria hot spots are displayed as risk maps that are useful for monitoring and spatial targeting of prevention and control measures against the disease.

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          Most cited references 46

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          The global distribution of clinical episodes of Plasmodium falciparum malaria.

          Interest in mapping the global distribution of malaria is motivated by a need to define populations at risk for appropriate resource allocation and to provide a robust framework for evaluating its global economic impact. Comparison of older and more recent malaria maps shows how the disease has been geographically restricted, but it remains entrenched in poor areas of the world with climates suitable for transmission. Here we provide an empirical approach to estimating the number of clinical events caused by Plasmodium falciparum worldwide, by using a combination of epidemiological, geographical and demographic data. We estimate that there were 515 (range 300-660) million episodes of clinical P. falciparum malaria in 2002. These global estimates are up to 50% higher than those reported by the World Health Organization (WHO) and 200% higher for areas outside Africa, reflecting the WHO's reliance upon passive national reporting for these countries. Without an informed understanding of the cartography of malaria risk, the global extent of clinical disease caused by P. falciparum will continue to be underestimated.
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            Statistics for Spatial Data

            (1993)
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              A climate-based distribution model of malaria transmission in sub-Saharan Africa.

              Malaria remains the single largest threat to child survival in sub-Saharan Africa and warrants long-term investment for control. Previous malaria distribution maps have been vague and arbitrary. Marlies Craig, Bob Snow and David le Sueur here describe a simple numerical approach to defining distribution of malaria transmission, based upon biological constraints of climate on parasite and vector development. The model compared well with contemporary field data and historical 'expert opinion' maps, excepting small-scale ecological anomalies. The model provides a numerical basis for further refinement and prediction of the impact of climate change on transmission. Together with population, morbidity and mortality data, the model provides a fundamental tool for strategic control of malaria.
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                Author and article information

                Journal
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central
                1476-072X
                2009
                26 January 2009
                : 8
                : 5
                Affiliations
                [1 ]Boston University, Department of Geography and Environment, 675 Commonwealth Ave., Boston, MA 02215, USA
                [2 ]Federal Ministry of Health, PO Box: 1234, Addis Ababa, Ethiopia
                Article
                1476-072X-8-5
                10.1186/1476-072X-8-5
                2646707
                19171051
                Copyright © 2009 Yeshiwondim 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.

                Categories
                Research

                Public health

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