Effect of Rainfall on Seasonality of Malaria Transmission Dynamics and Percentage Occurrence of mRDT Positives Around the Five Rural Health Facilities in Vihiga County, Kenya

Abstract

Background: The negative impact and concern of malaria is felt world over with 247 million cases reported in 2021. The cases were reported in 84 malaria endemic countries with high number of casualties experienced in Africa. Normally, malaria infection is influenced by climatic factors while its treatment and resilience to drugs is controlled by health interventions measures both at the hospital level and in the community. Malaria Rapid Diagnostic Tests (mRDTs) are recommended by WHO for malaria testing and are expansively used in rural set-ups. However, their success in performance is controlled by many factors. This study aimed at correlating the effect of rainfall patterns to malaria positive case as tested by mRDT in Vihiga County, Kenya. Methodology: This study focused on the participation of 500 patients from a population of 18201 patients within a two (2) Kilometre radius of the five rural health facilities in Vihiga County. Facilities had poor infrastructure in terms of malaria testing and mostly relied on mRDT for malaria testing and surveillance. Data was collected from the five health facilities at the end of every month for a period of twelve months. The rain gauge reading was also collected daily by Vihiga metrological department from which the average monthly rainfall was computed. This was run from April 2022 and March 2023. Care start TM Rapid Diagnostic Tests were used to detect HRP2/3 proteins/antigens which are specifically responsive to Plasmodium falciparum antibodies. Results and Data Analysis: Average rainfall for the period between April 2022 and March 2023 was 185mm; the average percentage prevalence of malaria during the same period in this region was 21.3%. This region experienced one peak of rainfall recorded at 360mm in the month of September; 2022.The month of April recorded the highest malaria prevalence of 38.4% with an average rainfall amount of 311.7mm while February recorded the lowest malaria prevalence of 7.16% with an average rainfall amount of 0.2mm. The data showed that there were more patients in the middle age category of between five (5) and eighteen (18) years. More women came to the facilities than men; hence more women were malaria positive than men. Linear regression analysis was performed which emerged that the average rainfall accounts for 28.37% variations in prevalence (R2=0.2837). The model coefficient showed that average rainfall has a positive significant effect on prevalence (β=0.0591, p<0.05). This implies that malaria prevalence is determined by rainfall amount. Although the infection rate is on the downward trend, there was a constant of 10 from the model equation which is apparent that malaria infection is not dependent on rainfall alone; there could be other factors as well. Conclusion: This study reported fluctuating average rainfall throughout the year. There was fluctuating average malaria prevalence as well. When the average rainfall was high, average malaria prevalence was also high and the converse was true. Therefore, rainfall significantly caused the malaria infection in this region. High rainfall is a significant determinant of malaria prevalence.