Spatiotemporal Variation of Malaria Transmission in Different Altitudes of Lower Lake Victoria Basin, Kenya

Authors

  • Samwel Odhiambo Olela
  • Prof. George L. Makokha
  • Dr. Kennedy Obiero

DOI:

https://doi.org/10.47672/ejhs.1277

Keywords:

Spatiotemporal variation, malaria transmission, different altitudes, Lower Lake Victoria Basin

Abstract

Purpose: Malaria transmission is one of the consequences of climate variability and change. The burden is greatest in the developing countries of the tropics especially Africa south of the Sahara. In Kenya, particularly the Lower Lake Victoria Basin (LLVB), it is blamed on the historical high rainfall, temperature and relative humidity. This study sought to determine spatiotemporal variation of malaria transmission in different altitudes of the LLVB, Kenya.

Methodology: The study relied on data from routine malaria case transmission records archived by the Health Information System for ten years. Data for Kakamega, Kisumu and Migori Counties were obtained from the Kenya Health Information System (KHIS) through Sub-County Health Facilities. Pearson's Product Moment Correlation Coefficient was used to correlate the suspected and confirmed transmission cases. ANOVA was used in testing the variability of transmission, Time Series to determine transmission characteristics while malaria case transmission tables and Tukeys Honest Significance Difference (HSD) were for testing the significance of the distribution of malaria.

Findings: The suspected cases were found to have been overstated in Migori and Kakamega Counties. Malaria transmission varied by altitude, space and time during the study period. Trends increased in Kisumu and Kakamega Counties while it decreased in Migori County. Transmission depicted both endemic and epidictic characteristics in the study area.

Unique Contribution to Theory, Policy and Practices: Health facilities in the LLVB, Kenya should be equipped with more modern laboratory equipment to improve confirmation of transmission so as to reduce suspicions. Since most of the observations confirmed the varying nature of malaria transmission in relation to altitude, the aspect of blanket assumption concerning malaria transmission in the LLVB, Kenya should be stopped. LLVB, Kenya should be zoned by altitudes for effective mitigation and eradication strategies. The reduction of malaria case transmission in Migori County was an indication of possible future eradication. Its cause should be investigated and inferred to other counties. More research is necessary to establish situations elsewhere for effective management and control of transmissions. The revelations and recommendations were expected to enhance malaria eradication in the LLVB, Kenya and subsequently promote the realization of Kenya's vision 2030.

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Author Biographies

Samwel Odhiambo Olela

Post Graduate Student: School of Humanities and Social sciences, Kenyatta University, Kenya

Prof. George L. Makokha

Senior Lecturer, School of Humanities and Social sciences, Kenyatta University, Kenya

Dr. Kennedy Obiero

Lecturer, School of Humanities and Social sciences, Kenyatta University, Kenya

References

Alonso, P., & Noor, A. M. (2017). The global fight against malaria is at crossroads. The Lancet, 390(10112), 2532-2534.

Bashir, I. M., Nyakoe, N., & van der Sande, M. (2019). Targeting remaining pockets of malaria transmission in Kenya to hasten progress towards national elimination goals: an assessment of prevalence and risk factors in children from the Lake endemic region. Malaria Journal, 18(1), 1-10.

Bhattacharya, S., Sharma, C., Dhiman, R. C., &Mitra, A. P. (2006). Climate change and malaria in India. Current science, 90(3), 369-375.

Davies, P., Tanner, M., Jemari, B., & Henderson-Howat, S. (2020). Cast the net wide: Assessing the root causes of malaria transmission through the knowledge, attitude and practice of treated inpatients in rural western Uganda. Access Microbiology, 2(2), 150.

De Alban, J., Connette, G., Oswald, P., & Webb, E. (2018). Combined Landsat and L-band SAR data improves land cover classification and change detection in dynamic tropical landscapes. Remote Sensing, 10(2), 306.

Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8.

Hamel, M. J., Adazu, K., Obor, D., Sewe, M., Vulule, J., Williamson, J. M., ...&Laserson, K. F. (2011). A reversal in reductions of child mortality in western Kenya, 2003-2009.The American journal of tropical medicine and hygiene, 85(4), 597-605.

Hemingway, J., Ranson, H., Magill, A., Kolaczinski, J., Fornadel, C., Gimnig, J., ...& Pickett, J. (2018). Averting a malaria disaster: will insecticide resistance derail malaria control?.The Lancet, 387(10029), 1785-1788.

Kapesa, A., Kweka, E. J., Zhou, G., Atieli, H. E., Kamugisha, E., Mazigo, H. D., ... & Yan, G. (2018). Utility of passive malaria surveillance in hospitals as a surrogate to community infection transmission dynamics in western Kenya.Archives of Public Health, 76(1), 39.

Kenya Government. (1970). National Atlas of Kenya.

Kerandi, N. M., Laux, P., Arnault, J., &Kunstmann, H. (2017). Performance of the WRF model to simulate the seasonal and interannual variability of hydrometeorological variables in East Africa: a case study for the Tana River basin in Kenya. Theoretical and Applied Climatology, 130(1-2), 401-418.

Kipruto, E. K., Ochieng, A. O., Anyona, D. N., Mbalanya, M., Mutua, E. N., Onguru, D., ...&Estambale, B. B. (2017). Effect of climatic variability on malaria trends in Baringo County, Kenya.Malaria journal, 16(1), 220.

Lake Victoria Basin Commission (LVBC). (2018-2023). Climate Change Adaptation Strategy and Action Plan.

Mwagiru, W. (1986). Planning for settlement in rural regions: the case of spontaneous settlements in Kenya,'.Spontaneous Settlement Formation in Rural Regions, Volume TWo: Case Sntdies, 3-12.

Okuneye, K., Abdelrazec, A., &Gumel, A. B. (2018). Mathematical analysis of a weather-driven model for the population ecology of mosquitoes. Mathematical Biosciences & Engineering, 15(1), 57-93.

Oxborough, R. M. (2016). Trends in US President's Malaria Initiative-funded indoor residual spray coverage and insecticide choice in sub-Saharan Africa (2008-2015): urgent need for affordable, long-lasting insecticides. Malaria journal, 15(1), 146.

Roser, M., Ritchie, H., &Dadonaite, B. (2020). Child & Infant Mortality. Our World in Data.

Ryan, S. J., Lippi, C. A., &Zermoglio, F. (2020). Shifting transmission risk for malaria in Africa with climate change: A framework for planning and intervention. Malaria Journal, 19, 1-14.

Teboh-Ewungkem, M. I., &Ngwa, G. A. (2020). COVID-19 in malaria-endemic regions: potential consequences for malaria intervention coverage, morbidity, and mortality. The Lancet Infectious Diseases.

U.S. President's Malaria Initiative Kenya Malaria Operational Plan FY 2022. Retrieved from

www.pmi.go

World Health Organization. (2017). Design of epidemiological trials for vector control products: report of a WHO expert advisory group, Château de Penthes, Geneva, 24-25 April 2017 (No. WHO/HTM/NTD/VEM/2017.04). World Health Organization.

World Health Organization. (2021). WHO malaria policy advisory group (MPAG) meeting: meeting report, April 2021.

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Published

2022-11-11

How to Cite

Olela, S. O. ., Makokha, G. L., & Obiero, K. . (2022). Spatiotemporal Variation of Malaria Transmission in Different Altitudes of Lower Lake Victoria Basin, Kenya. European Journal of Health Sciences, 7(6), 1–24. https://doi.org/10.47672/ejhs.1277