Ten Years Trend Analysis of Malaria Prevalence and its Correlation with Climatic Variables in Sibu Sire District, East Wollega Zone, Oromia Regional state, Western Ethiopia: A Retrospective Study

Abstract

Malaria is one of the most devastating diseases in the World and caused by a protozoan parasite of the genus Plasmodium. The disease remains one of the most important causes of human morbidity and mortality with enormous medical, economic and emotional impact in the world, and in most African countries including Ethiopia. The complexity of the disease control process, expensiveness of the control program, resistance of the parasite to anti-malarial drugs and vectors to insecticides are some of the challenges. The aim of the study was to assess the ten years trend analysis of malaria prevalence and its association with climatic variables in the Sibu Sire district, Western Ethiopia. Ten years (2004-2013) malaria clinical and epidemiological data were collected from health facilities and climatic variables data from Ethiopian Meteorological agency. The data were analyzed using SPSS software package 16.0. Pearson’s correlation analysis was conducted to see the correlation between plasmodium species and climatic variables. Within the last decade (2004–2013) a total of 30,070 blood films were examined for malaria in Sire health center and of this 6036 (20.07%) microscopically confirmed malaria cases were reported in the health center and P. falciparum becoming a predominant species. The result showed that maximum temperature, mean temperature and average relative humidity showed significant association with malaria (P<0.01).But minimum temperature (P=0.094) and rainfall (P=0.729), were not significant. In addition, regression analysis suggested that minimum temperature, rainfall, and average relative humidity (P<0.001) were statistically significant but the mean temperature (P=0.706) was insignificant. In conclusion the trend of malaria in the study area had a reducing but a fluctuating pattern and some of the metrological variables such as minimum temperature, rainfall, and average relative humidity were statistically significant.