Geographical Reconnaissance of Household in Northern Nigeria towards Optimizing Indoor Residual Spraying Method for Malaria Elimination
- 1 Department of Geography and Ecosystem Analysis, Lund University, Lund, Sweden
- 2 Research and Data Solutions, Abuja, Nigeria
- 3 Stuttgart University, Stuttgart, Germany
Abstract
As a part of an effort to roll back malaria in Nigeria, exploring the use of geographically related tools triggered the use of modern approaches of knowing the spatial distribution of target populations to attain significant malaria elimination intervention. GIS tool was used for geographical reconnaissance (GR), providing demographic data on respondents’ household and spatial information on the distribution of households in the selected location. A cross-sectional study design was used to collect spatial data in the two locations, while a quantitative questionnaire was used to collect the household data. The analysis of field data indicated that 49,500 unique households were enumerated and thus included in the Indoor Residual Spraying to prevent malaria infection, covering 424 towns in the two Local Government Areas (LGAs). 383,301 persons were recorded during the GR exercise in Doma and Nassarawa Eggon LGAs out of which 79,339 were children of agesless than five years, with 13,526 pregnant women. Further data analysis revealed that the average number of persons per household in both LGAs was approximately eight. The spatial information from the GR provides a foundation for an updateable database for any future survey for developmental activities in Nigeria. The use of modern GR approach has proved to be accurate, reliable and more cost effective and less cumbersome than the traditional approach in the collection and geo-positioning of household data. Use of Garmin e-Trex GPS handheld instruments to collect household data in the designated areas removed the constraints of expensive Personal Digital Assistants and reduced errors of wrong location coordinates. Several African countries which did not use GR or applied the use of Geospatial tool appropriately had setbacks. The previous study in other countries showed limitations which was characterized by substantial inherent logistical and technical challenges culminating in missed targets. This setback was addressed in our study.
- WHO (1999) World Health Organization Malaria RBM. http://www.who.int/whr/1999/en/whr99_ch4_en.pdf
- Hay, S.I., Guerra, C.A., Gething, P.W., Patil, A.P., Tatem, A.J., Noor, A.M., Kabaria, C.W., Manh, B.H., Elyazar, I.R., Brooker, S., Smith, D.L., Moyeed, R.A. and Snow, R.W. (2009) A World Malaria Map: Plasmodium Falciparum Endemicity in 2007. PLOS Medicine, 6, 0286-0302. https://doi.org/10.1371/annotation/a7ab5bb8-c3bb-4f01-aa34-65cc53af065d
- Tanner, M. and de Savigny, D. (2008) Malaria Eradication Back on the Table. Bulletin of the World Health Organization, 86, 82. http://doi.org/10.2471/BLT.07.050633
- ABT Associates Inc. (2014) USAID—Africa Indoor Residual Spraying, Initiatives. Bethseda publishing, Maryland.
- World Health Organization (1965) Geographical Reconnaissance for Malaria Eradication Programme. Geneva.
- Huang, H. (1980) Field Manual for Geographical Reconnaissance and Spraying Operations. Malaria Control Programme, Department of Public Health, Papua New Guinea.
- Nigeria News Portal (2016) Historical Development of Nassarawa State Came into Existence.
- Government of Zambia (2009) General Guidelines for Zambia’s Indoor Residual Spraying. GOZ Press, Lusaka.
- National Population Commission (2006) Census Population Data.
- Yadav, K., Nath, M.J., Talukdar, P.K., Saikia, P.K., Baruah, I. and Singh, L. (2012) Malaria Risk Areas of Udalguri District of Assam, India: A GIS-Based Study. International Journal of Geographical Information Science, 26, 123-131. https://doi.org/10.1080/13658816.2011.576678
- Kelly, G.C., Hii, J., Batarii, W., Donald, W., Hale, E., Nausien, J., Pontifex, S., Vallely, A., Tanner, M. and Clements, A. (2010) Modern Geographical Reconnaissance of Target Populations in Malaria Elimination Zones. Malaria Journal, 9, 78-89.
- Gerald, C. (2013) A Spatial Decision Support System for Malaria Elimination. Ph.D Dissertation, Population Health Institution, The University of Queensland, Brisbane.
- Kelly, G.C., et al. (2010) Modern Geographical Reconnaissance of Target Populations in Malaria Elimination Zones. Malaria Journal, 9, 289. http://www.malariajournal.com/content/9/1/289 https://doi.org/10.1186/1475-2875-9-289
- International Institute of Tropical Agriculture, Geospatial Lab (2012) Landcover Raster and Vector Shapefiles. Ibadan.