Modeling the Trend of Malaria Reported Cases in Kisumu County, Kenya

Abstract

Background: Although there has been an extensive scale up of Malaria interventions in Kenya, Malaria infections persist at unacceptably high levels in some of the regions. Even with renewed calls to eradicate the disease through increased international donor assistance and country specific government involvement, Malaria is still a cause of worry in the endemic regions. Study objective: To determine the factors associated with the incidence of Malaria in Kisumu County over time. Methodology: The study was a repeated cross-sectional survey involving secondary data analysis of routinely reported Malaria cases. The population of interest were patients confirmed to have Malaria by laboratory test. A sample size of 384 was randomly selected from all laboratory-confirmed Malaria cases as reported by health facilities in Kisumu County from January 2014 to December 2017. The analysis involved checking for completeness, consistency and accuracy of the data. This included descriptive, trend analysis and time series analysis (ARIMA).Negative binomial regression model was used to measure the effect of each of the selected predictor variables on incidence of Malaria and the Incidence Rate Ratio (IRR), was reported. Frequency distribution of each variable and each category for each of the categorical variables was calculated and presented using version 3.5.1 R statistical software. Results: The overall pattern of the reported malaria cases had variations in seasons for weekly cases. Whereas, the best fitted time series model developed for predicting the number of weekly reported cases of malaria was ARIMA (2, 0, 1). Negative Binomial was essentially the best model which fit the data since the dispersion parameter given by Poisson Regression Model had been reduced from 70.292 to 1.103. Kisumu East, Seme, Nyando, Kisumu Central localities, the year 2017 and the total number of patients who underwent a laboratory confirmation test for malaria were statistically associated to the incidence of Malaria in Kisumu County over time (P< 0.001). Conclusion: The findings provide better insight of environmental and socio-economic impacts malaria incidences at the same time providing and important information for the prediction of the disease. Nonetheless, there is need to encourage health professionals to regularly review and report cases of malaria in their facilities. This is because, reporting frequencies, completeness and the consistency of malaria reported cases remain extremely low.