https://doi.org/10.4081/gh.2024.1243

Nigeria’s malaria prevalence in 2015: a geospatial, exploratory district-level approach

Vol. 19 No. 2 (2024)
Submitted: 9 October 2023
Accepted: 9 March 2024
Published: 25 November 2024
Malaria, spatial, Geographic Information Systems (GIS), Nigeria
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Authors

Mina Whyte
mailmina@gmail.com
https://orcid.org/0000-0003-1410-5009
Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Kennedy Mwai Wambui
https://orcid.org/0000-0002-7757-7516
Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Kenya.
Eustasius Musenge
Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

This study used data from the second Nigeria Malaria Indicator Survey (NMIS) conducted in 2015 to investigate the spatial distribution of malaria prevalence in the country and identify its associated factors. Nigeria is divided into 36 states with 109 senatorial districts, most of which are affected by malaria, a major cause of morbidity and mortality in children under five years of age. We carried out an ecological study with analysis at the senatorial district level. A malaria prevalence map was produced combining geographic information systems data from the Nigeria Malaria Indicator Survey (NMIS) of 2015 with shape files from an open data-sharing platform. Spatial autoregressive models were fitted using a set of key covariates. Malaria prevalence in children under-five was highest in Kebbi South senatorial district (70.6%). It was found that poorest wealth index (β = 0.10 (95% CI: 0.01, 0.20), p = 0.04), mothers having only secondary level of education (β = 0.78 (95% CI: 0.05, 1.51), p = 0.04) and households without mosquito bed nets (β = 0.21 (95% CI: 0.02, 0.39), p = 0.03) were all significantly associated with higher malaria prevalence. Moran’s I (54.81, p<0.001) showed spatial dependence of malaria prevalence across contiguous districts and spatial autoregressive modelling demonstrated significant spill-over effect of malaria prevalence. Maps produced in this study provide a useful graphical representation of the spatial distribution of malaria prevalence based on NMIS-2015 data. Clustering of malaria prevalence in certain areas further highlights the need for sustained malaria elimination interventions across affected regions in order to break the chain of transmission.

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Crossref
Scopus
Google Scholar
Europe PMC
Adigun AB, Gajere EN, Oresanya O, Vounatsou P, 2015. Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data. Malar J 14:156.
Atieli HE, Zhou G, AfraneY, Lee M-C, Mwanzo I, Githeko AK, Yan G, 2011. Insecticide-treated net (ITN) ownership, usage, and malaria transmission in the highlands of western Kenya. Parasit Vectors 4:113.
Bamiselu OF, Ajayi I, Fawole O, Dairo D, Ajumobi O, Oladimeji A, Steven Y, 2016. Adherence to malaria diagnosis and treatment guidelines among healthcare workers in Ogun State, Nigeria. BMC Public Health16:1-10.
Bassey SE, Izah SC, 2017. Some Determinant Factors of Malaria Prevalence in Nigeria. J Mosq Res 7:48-58.
Bousema T, Griffin JT, Sauerwein RW, Smith DL, Churcher TS, Takken W, Ghani A, Drakeley C, Gosling R, 2012. Hitting Hotspots: Spatial Targeting of Malaria for Control and Elimination. PLoS Med 9:e1001165.
Dawaki S, Al-Mekhlafi HM, Ithoi I, Ibrahim J, Atroosh WM, Abdulsalam AM, Sady H, Elyana FN, Adamu AU, Yelwa SI, Ahmed A, Al-Areeqi MA, Subramaniam LR, Nasr NA, Lau YL, 2016. Is Nigeria winning the battle against malaria? Prevalence, risk factors and KAP assessment among Hausa communities in Kano State. Malar J 15:351.
Fahrmeir L, Kneib T, Lang S, Marx B, 2013. Regression: models, methods and applications. Berlin: Springer. p. 663.
Guthmann JP, Llanos-Cuentas A, Palacios A, Hall AJ, 2002. Environmental factors as determinants of malaria risk. A descriptive study on the northern coast of Peru. Trop Med Int Health 7:518-25.
Hoffman TD and Kedron P, 2023. Spatial Autoregressive Models. The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2023 Edition). John P. Wilson (Ed.).
Koram KA, Bennett S, Adiamah JH, Greenwood BM, 1995. Socio-economic risk factors for malaria in a peri-urban area of The Gambia. Trans R Soc Trop Med Hyg 89:146-50.
Kunihya I, Samaila A, Pukuma M, Qadeer M, 2016. Prevalence of malaria infection and malaria Anaemia among children attending federal medical centre Yola, Adamawa State, Nigeria. Int J Eng Sci 5:8-14.
National Malaria Elimination Programme (NMEP), 2020. National Malaria Strategic Plan 2021 - 2025. Available from: https://nmcp.gov.ng/
National Malaria Elimination Programme (NMEP), NPopC, NBS and ICF International, 2016. Nigeria Malaria Indicator Survey 2015. Abuja, Nigeria, and Rockville, Maryland, USA. Available from: http://dhsprogram.com/pubs/pdf/MIS20/MIS20.pdf
National Malaria Elimination Programme (NMEP), NPC and ICF, 2022. Nigeria Malaria Indicator Survey 2021. Abuja, Nigeria, and Rockville, Maryland, USA. Available from: https://dhsprogram.com/methodology/survey/survey-display-576.cfm
Okunlola OA, Oyeyemi OT, 2019. Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria. Sci Rep 9:17500.
Oluwafemi OA, Babatimehin OI, Oluwadare TS, Mahmud UM, 2013. Mapping malaria case event and factors of vulnerability to malaria in Ile-Ife, Southwestern Nigeria: using GIS. Ethiop J Environ Stud Manag 6(4):365-75.
Pfeiffer DU, Robinson TP, Stevenson M, Stevens KB, Rogers DJ, Clements AC, 2008. Spatial analysis in epidemiology. Oxford University Press.
Pond BS, 2013. Malaria indicator surveys demonstrate a markedly lower prevalence of malaria in large cities of sub-Saharan Africa. Malar J 12:313.
Pourtois JD, Tallam K, Jones I, Hyde E, Chamberlin AJ, Evans MV, Ihantamalala FA, Cordier LF, Razafinjato BR, Rakotonanahary RJL, Tsirinomen'ny Aina A, Soloniaina P, Raholiarimanana SH, Razafinjato C, Bonds MH, De Leo GA, Sokolow SH, Garchitorena A, 2023. Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar. PLOS Glob Public Health 3:e0001607.
Protopopoff N, Van Bortel W, Speybroeck N, Van Geertruyden J-P, Baza D, D'Alessandro U, Coosemans M, 2009. Ranking Malaria Risk Factors to Guide Malaria Control Efforts in African Highlands. PLoS One 4:e8022.
Shittu O, Opeyemi OA, Omotesho O, Fulani SF, Olaleye AD, Adebo PO, Ibrahim AO, Bamgboye M, Fakayode O, Akor SJ, 2018. Malaria prevalence, blood profile and risk factors for transmission in a peri-urban community, North-central, Nigeria. Niger J Parasitol 39:119.
Snow RW, Sartorius B, Kyalo D, Maina J, Amratia P, Mundia CW, Bejon P, Noor AM, 2017. The prevalence of Plasmodium falciparum in sub-Saharan Africa since 1900. Nature 550:515-8.
Sowunmi FA, Akinyosoye VO, Okoruwa VO, Omonona BT, 2012. The landscape of poverty in nigeria: a spatial analysis using senatorial districts-level data. Am J Econ 2:61-74.
Tsai P, Perng C, 2011.Spatial autocorrelation analysis of 13 leading malignant neoplasms in Taiwan: a comparison between the 1995-1998 and 2005-2008 periods. Health 3:712-31.
United Nations Children's Fund, 2019. Malaria. Available from: https://data.unicef.org/topic/child-health/malaria/
United Nations Office for the Coordination of Humanitarian Affairs, 2019. Nigeria - Datasets. Humanitarian Data Exchange. Available from: https://data.humdata.org/group/nga
US President’s Malaria Initiative, 2022. U.S. President’s Malaria Initiative Nigeria Malaria Operational Plan FY 2022. Available fom: https://www.pmi.gov/where-we-work/nigeria/
White MT, Conteh L, Cibulskis R, Ghani AC, 2011. Costs and cost-effectiveness of malaria control interventions - a systematic review. Malar J 10:337.
World Bank, 2021. Database for Nigeria. Available from: https://data.worldbank.org/country/nigeria
World Health Organisation, 2022. World malaria report 2022. Available from: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022
Xu J-W, Xu Q-Z, Liu H, Zeng Y-R, 2012. Malaria treatment-seeking behaviour and related factors of Wa ethnic minority in Myanmar: a cross-sectional study. Malar J 11:417.

Supporting Agencies

The lead author was funded by TDR, the Special Programme for Research and Training in Tropical Diseases for her Master's degree when this work was initiated.

How to Cite

Whyte, M., Wambui, K. M., & Musenge, E. (2024). Nigeria’s malaria prevalence in 2015: a geospatial, exploratory district-level approach. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1243
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