Modeling the trend of reported malaria 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 endemic regions. The objective of this study was to determine the factors associated with the incidence of malaria in Kisumu County over time. Methods: The study conducted secondary analysis of data from a cross-sectional survey 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 descriptive, trend analysis and time series analysis (ARIMA). A 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, was reported. Frequency distribution of each of the categorical variables was calculated. Results: The overall pattern of the reported malaria cases had seasonal variations for weekly cases. The best-fitting time series model developed for predicting the number of weekly reported cases of malaria was ARIMA (2, 0, 1). It was observed that the negative binomial was actually the best model to fit the incidences of malaria because the dispersion parameter given by Poisson regression model had been reduced from 70.292 to 1.103. Conclusion: There is a need to encourage health professionals to regularly review and report cases of malaria in their facilities. This is because reporting rates, completeness and the consistency of malaria reported cases remain extremely low.