Diagnostic approaches to malaria in Zambia, 2009-2014

  • Victor M. Mukonka | vmukonka@gmail.com Department of Public Health, Copperbelt University, Ndola, Zambia.
  • Emmanuel Chanda Ministry of Health, National Malaria Control Centre, Lusaka, Zambia.
  • Mulakwa Kamuliwo Ministry of Health, National Malaria Control Centre, Lusaka, Zambia.
  • Maha A. Elbadry Department of Environmental and Global Health, University of Florida, Gainesville; Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States.
  • Pauline K. Wamulume Ministry of Health, National Malaria Control Centre, Lusaka, Zambia.
  • Mercy Mwanza-Ingwe Ministry of Health, National Malaria Control Centre, Lusaka, Zambia.
  • Jailos Lubinda Macha Research Trust, Macha, Zambia.
  • Lindsey A. Laytner Department of Environmental and Global Health, University of Florida, Gainesville; Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States.
  • Wenyi Zhang Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, China.
  • Gabriel Mushinge Zambian Ministry of Finance, General Statistics Office, Lusaka, Zambia.
  • Ubydul Haque Emerging Pathogens Institute, University of Florida, Gainesville; Department of Geography, University of Florida, Gainesville, FL, United States.

Abstract

Malaria is an important health burden in Zambia with proper diagnosis remaining as one of the biggest challenges. The need for reliable diagnostics is being addressed through the introduction of rapid diagnostic tests (RDTs). However, without sufficient laboratory amenities in many parts of the country, diagnosis often still relies on non-specific, clinical symptoms. In this study, geographical information systems were used to both visualize and analyze the spatial distribution and the risk factors related to the diagnosis of malaria. The monthly reported, district-level number of malaria cases from January 2009 to December 2014 were collected from the National Malaria Control Center (NMCC). Spatial statistics were used to reveal cluster tendencies that were subsequently linked to possible risk factors, using a non-spatial regression model. Significant, spatio-temporal clusters of malaria were spotted while the introduction of RDTs made the number of clinically diagnosed malaria cases decrease by 33% from 2009 to 2014. The limited access to road network(s) was found to be associated with higher levels of malaria, which can be traced by the expansion of health promotion interventions by the NMCC, indicating enhanced diagnostic capability. The capacity of health facilities has been strengthened with the increased availability of proper diagnostic tools and through retraining of community health workers. To further enhance spatial decision support systems, a multifaceted approach is required to ensure mobilization and availability of human, infrastructural and technological resources. Surveillance based on standardized geospatial or other analytical methods should be used by program managers to design, target, monitor and assess the spatio-temporal dynamics of malaria diagnostic resources country-wide.

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Published
2015-06-03
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Original Articles
Keywords:
Clinical malaria, Diagnosis, GIS, Zambia
Statistics
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How to Cite
Mukonka, V. M., Chanda, E., Kamuliwo, M., Elbadry, M. A., Wamulume, P. K., Mwanza-Ingwe, M., Lubinda, J., Laytner, L. A., Zhang, W., Mushinge, G., & Haque, U. (2015). Diagnostic approaches to malaria in Zambia, 2009-2014. Geospatial Health, 10(1). https://doi.org/10.4081/gh.2015.330