Geostatistical evaluation of the impact of climate variability on malaria incidence In the South-West of Nigeria

Year 2024, Issue: 53, 281 - 297, 25.09.2024

Olayinka Otusanya , Alabi Soneye Mayowa Fasona Amidu Ayeni Akinlabi Akintuyi Adebola Daramola

https://doi.org/10.32003/igge.1462298

Abstract

Malaria remains a significant health concern in Nigeria, particularly in the South-West region. This study assesses the impact of temperature and rainfall on malaria incidence and prevalence in South-West Nigeria using remotely sensed and modelled data sourced from the Malaria Atlas Project and NASA's POWER database covering 2000 to 2020. The study adopts the Geographically Weighted Regression geostatistical model to establish the relationship between malaria and rainfall and temperature in the study area. The result shows a rising oscillating annual mean temperature trend of 0.0088oC/yr-1 from 2000 to 2020. The malaria incidence exceeds 8 million cases annually, peaking in 2020 at almost 10 million cases. The rising trend of malaria incidence highlights the inadequacy of the malaria intervention programmes to meet their goal of reducing malaria incidence by 40% by 2020. The study highlights the spatial variations, with high incidence in urban centres like Lagos and Ibadan metropolises, their satellite towns, as well as other prominent and capital towns including Oshogbo, Ilesa, Akure, Ijebu-Ode and Abeokuta. Contrary to this, the greater malaria prevalence was recorded in less densely populated areas of Oyo state, Imeko-Afon, Odeda, Yewa and Ijebu-Waterside areas in Ogun state as well as Ose and Idanre in Ondo state. The Geographically Weighted Regression equation model shows a strong positive correlation between malaria prevalence and temperature at a significance of 0.76 compared to rainfall which exhibits no association indicating the relevance of temperature as an explanatory indicator of malaria. With the continuous endemicity of malaria in the South-West, malaria management and control efforts should be focused on high-incidence areas in the South-West and Nigeria in general to fulfil the Sustainable Development Goal of Good health and well-being and the eradication of malaria by 2030.

Keywords

Climate Variability, Geostatistics, Malaria Incidence, Malaria Prevalence, Rainfall, South-West, Temperature

References

• Abiodun B, Lawal K, Salami A. & Abatan A, (2013). Potential Influences of Global Warming on Future Climate and Extreme Events in Nigeria. Reg. Environ Change. 13(3), 477-491, https://doi.org/10.1007/s10113-012-0381-
• Ajayi I., Ughasoro M., Ogunwale A., Odeyinka O., Babalola O., Sharafadeen S., Adamu A., Ajumobi O., Orimogunje T., & Nguku P (2017) A qualitative exploration of malaria operational research situation in Nigeria. PLoS ONE 12(11): e0188128. https://doi.org/10.1371/journal.pone.0188128
• Akinbobola A., & Hamisu S. (2022). Malaria and Climate Variability in Two Northern Stations of Nigeria, American Journal of Climate Change, 11(2), ??-??. https://doi.org/10.4236/ajcc.2022.112004
• Alemu A., Tsegaye W., Golassa L. & Abebe G (2011). Urban malaria and associated risk factors in Jimma town, South-West Ethiopia, Malaria Journal. 10, ??-??.
• Amoah B., Giorgi E., Heyes D., Burren S., & Diggle P. (2018). Geostatistical modelling of the association between malaria and child growth in Africa. International Journal of Health Geographics, 17, ??-??, https://doi.org/10.1186/s12942-018-0127-y
• Arab A., Jackson M., & Kongoli C. (2014). Modelling the effects of weather and climate on malaria distributions in West Africa, Malaria Journal, 13, ??-??. https://doi.org/10.1186/1475-2875-13-126
• Black, N. C. (2014). An ecological approach to understanding adult obesity prevalence in the United States: A county-level analysis using geographically weighted regression. Applied Spatial Analysis & Policy, 7, 283- 299.
• Caminade C., Kovats S., Rocklov J., Tompkins A., Morse A., Colon-Gonzalez F., Stenlund H., Martens P., & Lloyd S. (2014). Impact of climate change on global malaria distribution, PNAS, 111(9), http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1302089111/-/DCSupplemental
• Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: the PLM package. Journal of Statistical Software, 27(2), 1–43. https://doi.org/10.18637/jss.v027.i02
• Dale P. & Knight J. (2008). Wetlands and mosquitoes: a review. Wetland Ecol Manage, 16, 255-276, https://doi.org/10.1007/s11273-008-9098-2
• Davey, C. A., & Pielke Sr, R. A. (2005). Microclimate exposures of surface-based weather stations: Implications for the assessment of long-term temperature trends. Bulletin of the American Meteorological Society, 86(4), 497-504.
• DHS (2018). The DHS Program. Demographic and health surveys 2018. https://dhsprogram.com.
• Efe S., & Ojoh C. (2013). Climate variability and malaria prevalence in Warri Metropolis, Atmospheric and Climate Sciences, 3, 132-140, http://dx.doi.org/10.4236/acs.2013.31015
• Ekpa D., Salubi E., Olusola J., & Akintade D. (2023). Spatio-temporal analysis of environmental and climatic factors impacts on malaria morbidity in Ondo State, Nigeria, Heliyon, 9: e14005, https://doi.org/10.1016/j.heliyon.2023.e14005
• Escobar L., Romero-Alvarez D., Leon R., Lepe-Lopez M., Craft M, Borbor-Cordova, M. & Svenning J (2016). Declining Prevalence of Disease Vectors Under Climate Change, Scientific Report, 6:39150, https://doi.org/10.1038/srep39150
• Faleyimu O., Adeja B., & Akinyemi O., (2013). State of forest regeneration in Southwest Nigeria, African Journal of Agricultural Research, 8(26), 3381-3383, https://doi.org/10.5897/AJAR09.035
• Fasona M., Adedoyin B. and Sobanke I. (2020a). Status and drivers of spatial change of forest reserves and protected areas in selected states of southwest Nigeria: A case study of Ogun, Osun, and Oyo State, Nigeria, Osun Geographical Review, 3, 54-69, https://ir.unilag.edu.ng/handle/123456789/12069
• Fasona M., Akintuyi A., Aseonipekun P., Akoso T., Udofia S., Agboola O., Ogunsanwo G., Ariori A., Omojola A., Soneye A., & Ogundipe, O. (2020b). Recent trends in land-use and cover change and deforestation in south- west Nigeria, GeoJournal, https://doi.org/10.1007/s10708-020-10318-w
• Fasona M., Muyiolu S., Soneye A., Ogundipe O., Otusanya O., Adekanmbi O., Adeonipekun P., & Onuminya, T. (2019). Temporal analysis of the present and future climate of the Lagos Coastal Environment. Unilag Journal of Medicine, Science and Technology (UJMST), 7(1): 113-128.
• Fene F., Rios-Blancas M., Lachaud J., Razo C., Lamadrid-Figueroa H., Liu M., Michel J., Thermidor R., & Lazano, R. (2020). Life expectancy, death, and disability in Haiti, 19902017: a systematic analysis from the Global Burden of Disease Study 2017, Rev Panam Salud Publica. 44: e136. https://doi.org/10.26633/RPSP.2020.136
• Ge Y., Song Y., Wang J., Liu W., Ren Z., Peng J., & Lu, B. (2017). Geographically weighted regression-based determinants of malaria incidences in northern China, Transactions in GIS, 21, 934-953.
• Ge, Y., Song, Y., Wang, J., Liu, W., Ren, Z., Peng, J., & Lu, B. (2017). Geographically weighted regression‐based determinants of malaria incidences in northern China. Transactions in GIS, 21(5), 934-953.
• Githeko, A., Lindsay, S., Confalonieri, U. & Patz, J. (2000). Climate change and vector-borne diseases: A regional analysis. Bull World Heal Org, 78, 1136-1147.
• Golding N., Burstein R., Longbottom J., Browne A., Fullman N., Osgood-Zimmerman A., et al. (2017). Mapping under-5 and neonatal mortality in Africa, 2000–15: a baseline analysis for the Sustainable Development
• Goals. Lancet, 390, 2171–2182. http://dx.doi.org/10.1016/S0140-6736(17)31758-0
• Hausman, J., & Taylor, W. (1981). Panel data and unobservable individual effects. Journal of Econometrics, 16(1), ???-155. https://doi.org/10.1016/0304-4076(81)90085-3
• IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [ Core Writing Team, Pachauri R.K. and Meyer L.A. eds]. Geneva, Switzerland.
• IPCC (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, http://dx.doi.org/10.1017/9781009157896.001
• Jasim I., FIleeh M., Ebrahhem M., AL-Maliki L., AL-Mamoori S., & Al-Ansari N. (2022). Geographically weighted regression model for physical, social, and economic factors affecting the COVID‑19 pandemic spreading, Environmental Science and Pollution Research, 29, 51507–51520, https://doi.org/10.1007/s11356-022-18564-w
• Kabaria C., Gilbert M., Noor A., Snow R., & Linard, C. (2017). The Impact of Urbanization and Population Density on Childhood Plasmodium Falciparum Parasite Prevalence Rates in Africa, Malaria Journal, 16:49, https://doi.org/10.1186/s12936-017-1694-2
• Kim, Y., Park, J., & Cheong, H., (2012). Estimated effect of climatic variables on the transmission of plasmodium vivax malaria in the Republic of Korea. Environ. Health Perspect. 120(9), ????-1315. Korenromp E., Hamilton M., Sanders R., Mahiané G., Briët O., Smith T., Winfrey W., Walker N., & Stover J. (2017). Impact of malaria interventions on child mortality in endemic African settings: comparison and alignment between LiST and Spectrum-Malaria model. BMC Public Health. 17(4), ???-781, https://doi.org/10.1186/s12889-017-4739-0
• Kottek, M., Grieser, J., Beck C., Rudolf B., & Ru, F. (2006). World Map of the Koppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263.
• Kumar D, Andimuthu R., Rajan R., & Venkatesan, M. (2014). Spatial trend, environmental and socioeconomic factors associated with malaria prevalence in Chennai. Malar J. 13, ??-??. https://doi.org/10.1186/1475-2875- 13-14
• Liu T., Yang S., Peng R., and Huang D. (2021) A Geographically Weighted Regression Model for Health Improvement: Insights from the Extension of Life Expectancy in China, Applied Sciences, 11(5): https://doi.org/10.3390/app11052022
• Lubinda J., Haque U., Bi Y., Hamainza B., & Moore, A. (2021). Near-term climate change impacts on sub-national malaria transmission, Science Reports, 11(751), ??-??. https://doi.org/10.1038/s41598-020-80432-9
• Malaria Indicator Survey (2018) Malaria indicator surveys 2018. http://www.malariasurveys.org.
• Mohammadkhani M., Khanjani N., Bakhtiari B., & Sheikhzadeh, K., (2016). The relation between climatic factors and malaria incidence in Kerman, South East of Iran, Parasite Epidemiology and Control, 1, 205-210, http://dx.doi.org/10.1016/j.parepi.2016.06.001
• Mordecai E., Paaijmans K., Johnson L., Balzer C., Ben-Horin T., de Moor E, McNally A., Pawar S., Ryan S., Thomas R., Kevin S., & Lafferty K. (2013). Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol Lett. 16(1), 22-30. https://doi.org/10.1111/ele.12015
• Moyes C., Temperley W., Henry A., Burgert C., & Hay S. (2013) Providing open access data online to advance malaria research and control. Malaria Journal, 12, ??-161. https://doi.org/10.1186/1475-2875-12-161
• Ndiath M., Cisse B., Ndiaye J., Gomis J., Bathiery O., Dia A., Gaye O., & Faye, B. (2015). Application of geographically‑weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site, Malaria Journal, 14, ??-463, https://doi.org/10.1186/s12936-015-0976-9
• Oguntunde P., Abiodun B., & Lischeid, G. (2011). Rainfall trends in Nigeria, 1901-2000, Journal of Hydrology, 411(3-4), 207-218, https://doi.org/10.1016/j.jhydrol.2011.09.037
• Oheneba-Dornyo T., Amuzu S., Maccangnan A., & Taylor, T. (2022). Estimating the Impact of Temperature and Rainfall on Malaria Incidence in Ghana from 2012 to 2017, Environmental Modelling & Assessment, 27, 473–489, https://doi.org/10.1007/s10666-022-09817-6
• Ojo O., Ojo K., & Oni, F. (2001). Fundamentals of physical and dynamic climatology, SEDEC Publishers (O.O. Ojo & Co.) Maryland, Lagos, Nigeria
• Okunlola O., & Oyeyemi, O. (2019). Spatio-temporal analysis of the association between the incidence of malaria and environmental predictors of malaria transmission in Nigeria, Scientific Reports, 9, ??-17500, https://doi.org/10.1038/s41598-019-53814-x
• Oluwatimileyiin I., Akerele J., Oladeji T., Omogbehin M., & Atai, G. (2022). Assessment of the impact of climate change on the occurrences of malaria, pneumonia, meningitis, and cholera in Lokoja City, Nigeria, Regional Sustainability, 3(4), 309-318, https://doi.org/10.1016/j.regsus.2022.11.007
• Omogunloye O., Abiodun O., Olunlade O., Epuh E., Asikolo I., & Odumosu, J. (2018). Modelling malaria prevalence rate in Lagos state using multivariate environmental variations, Geoinformatics FCE CTU, 17(1), 61-86. https://doi.org/1010.14311/gi.17.1.5
• Omotosoho, J. & Abiodun, B. (2007). A numerical study of moisture buildup and rainfall over West Africa. Meteorological Applications: A Journal of Forecasting, Practical Applications, Training Techniques and Modelling, 14(3), 209-225.
• Opoku A., & Ansa-Asare, O. (2009). The occurrences and habitat characteristics of mosquitoes in Accra, Ghana. West African Journal of Applied Ecology, 11(1), ??-??. https://doi.org/10.4314/wajae.v11i1.45730
• Pasculli, A., Palermi, S., Sarra, A., Piacentini, T. & Miccadei, E. (2014). A modelling methodology for the analysis of radon potential based on environmental geology and geographically weighted regression. Environmental Modelling & Software, 54, 165-181.
• Pfeffer D., Lucas T., May D., Harris J., Rozier J., Twohig K., Dalrymple U., Guerra G., Moyes C., Thorn M., Nguyen M., Bhatt S., Cameron E., Weiss D., Howes R., Battle K., Gibson H., & Gething, P. (2018). Malaria Atlas: an R interface to global malariometric data hosted by the Malaria Atlas Project, Malaria Journal, 17, ??-352, https://doi.org/10.1186/s12936-018-2500-5
• Piel F., Howes R., Nyangiri O., Moyes C., Williams T., Weatherall D., & Hay, S. (2013). Online biomedical resources for malaria-related red cell disorders. Human Mutation. 34, 937–944. https://doi.org/10.1002/humu.22330
• Santos-Vega M., Bouma M., Kohli V., & Pascual, M. (2016). Population Density, Climate Variables and Poverty Synergistically Structure Spatial Risk in Urban Malaria in India. PLOS Neglected Tropical Diseases, 10(12), 1-18, e0005155. https://doi.org/10.1371/journal. and.0005155
• Schober, P., Bossers, S., & Schwarte, L. (2018). Statistical significance versus clinical importance of observed effect sizes: what do P values and confidence intervals represent? Anaesthesia and analgesia, 126(3), ??? -1068.
• Segun O., Shohaimi S., Nallapan M., Lamidi-Sarumoh A., & Salari N. (2020). Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria. Int J Environ Res Public Health. 17(10), ???-3474. https://doi.org/10.3390%2Fijerph17103474
• Su, S. L., Xiao, R., & Zhang, Y. (2012). Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression. Applied Geography, 32, 360-375.
• Talapko J., Skrlec I., Alebic T, Jukic M. & Vcev, A. (2019). The past and the present, Microorganisms, 7, ??-179, https://doi.org/10.3390/microorganisms7060179
• Tatem A, Guerra C, Kabaria C, Noor A & Hay, S. (2008). Human population, urban Settlement Patterns and their Impact on Plasmodium Falciparum Malaria Endemicity, Malaria Journal, 7, ???-218. https://doi.org/10.1186/1475-2875-7-218
• Tesfamicheal S., Shiferaw Y., & Phiri, M. (2022). Monthly geographically weighted regression between climate and vegetation in the Eastern Cape Province of South Africa: Clustering pattern shifts and biome-dependent accuracies, Scientific African, 18: e01423, https://doi.org/10.1016/j.sciaf.2022.e01423
• Tewara M., Yunxia L., Mbah-Fongkimeh P., Zhaolei Z., Binang H., Xinhui L., Miao Z., Liu Z., & Xue, F. (2019). Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon, Research Square, https://doi.org/10.21203/rs.2.9820/v1
• Torres-reyna, O. (2010). Getting started in fixed / random effects models using R. Online Training Section-DSS at Princeton University. http://dss.princeton.edu/training/
• Weiss D., Bhatt S., Mappin B., A Boeckel T., Smith D., Kay S., & Gething P. (2014). Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction. Malaria Journal. 13, ??-171, https://doi.org/10.1186/1475-2875-13-171
• Weiss D., Lucas T., Nguyen M., Nandi A., Bisanzio D., Battle K., Cameron E., Twohig K., Pfeffer D., Rozier J., Gibson H., Rao P., Casey D., Bertozzi-Villa A., Collins E., Dalrymple U., Gray N., Harris J., Howes R., Kang S., Keddie S., May D., Rumisha S., Thorn M., Barber R., Fullman N., Huynh C., Kulikoff X., Kutz M., Lopez A., Mokdad A., Naghavi M., Nguyen G., Shackelford K., Vos T., Wang H., Smith D., Lim S., Murray C., Bhatt S., Hay S., & Gething, P. (2019). Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study, Lancet, 394, 322-331, http://dx.doi.org/10.1016/S0140-6736(19)31097-9
• White J., Hoogenboom G., Wilkens P., Stackhouse P., & Hoel, J. (2011) Evaluation of satellite-based, modelled- derived daily solar radiation data for the continental United States, Agronomy Journal, 103(4), 1242-1251
• WHO (2015). Achieving the malaria MDG target: reversing the incidence of malaria 2000–2015. Geneva: World Health Organization.
• WHO (2017). World Malaria Report 2017. Geneva: World Health Organization.
• WHO (6 April 2022). Malaria: Q&A, World Health Organisation, https://www.who.int/news-room/questions- and-answers/item/malaria? gclid=Cj0KCQjw8qmhBhClARIsANAtbocFx5tOFisAZd3Kg23GPoJZ8ORnEBiEErpMpL5sTjOGDk7EW3Z_N1saAmsSE ALw_wcB
• Wickremasinghe, R., Wickremasinghe, A., and Fernando, S. (2012). Climate change and malaria have a complex relationship. UN Chronicle, 47(2), 21-25.
• World Health Organisation (2019). World Malaria Report 2019. World Health Organization
• World Health Organisation (2020). World Malaria Report 2020: 20 years of global progress and challenges. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO.
• World Health Organisation (2021). World Malaria Report 2021. Geneva, World Health Organization. License: CC BY-NC-SA 3.0 IGO.
• Wu Y., Qiao Z., Wang N., Yu H., Feng Z., Li X., & Zhao, X. (2017). Describing interaction effect between lagged rainfalls on malaria: an epidemiological study in south-west China. Malaria Journal, 16. https://doi.org/10.1186/s12936-017-1706-2
• Yamana T., & Eltahir, E. (2013). Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasites & Vectors, 6: 1. https://doi.org/10.1186/1756-3305-6-235