ASSESSING THE IMPACTS OF CLIMATE VARIABILITY ON DISEASE INCIDENCE ACROSS MAJOR METROPOLITAN AREAS IN OGUN STATE, NIGERIA

Abstract

ABSTRACT Weather parameters play a role in the phenomenon of disease pandemics all over the world. This research studied climate variability impacts on the incidence of diseases in the three main metropolitan areas (Egba, Yewa and Ijebu) of Ogun state, Nigeria. This study determines the climate variability impacts on the incidence of diseases in Ogun state. Hospital data such as uncomplicated malaria, severe malaria, typhoid, cholera, measles, meningitis, pneumonia, dengue, plague and climate data of maximum and minimum temperature, rainfall, relative humidity, sunshine and evaporation, over a period of ten years record between 2024 and 2023 were collected from the Ministry of Health Management Board, Ogun state and the Nigerian Meteorological Agency respectively. Regression analysis was used to assess the relationship between the selected independent variables and the selected dependent variables using SPSS window version 23 statistics. The trend of weather parameters in the state revealed that temperature, rainfall, relative humidity, sunshine and evaporation have a positive trend pattern from year to year particularly, in the years 2015, 2017 and 2019. The regression equations were: Y = 0.999x – 0.915 and Y = 0.999x – 0.915,Y = 0.998x – 0.1, Y = 0.998x – 0.45, Y = 0.999x – 0.216, Y = 0.999x –– 0.283. Hence, the regression model detected that Uncomplicated malaria was highly predictable with an accuracy of 99.9 % from the linear combination of the climatic parameters. Therefore, this showed a predictive error of 4.2 %. In Egba area of Ogun state, the variability impacts on the incidence of diseases was statistically localized. Meanwhile, the regression model for Yewa area detected Measles were highly predictable with an accuracy of 99.8 % from the linear combinatioin of the climatic parameters indicating an error of 3.3 %. In Yewa area of Ogun state, the variability impacts on the incidence of diseases were also statistically different. Furthermore, the regression model for Ijebu area revealed predictable accuracy (99.8 %) for Pneumonia and climatic parameters showing predictive error of 8.9 %. Furthermore, the Ijebu area of Ogun state showed the variability impacts on the incidence of diseases with statistical variation. The regression analysis in terms of sex and age vulnerability revealed that Ijebu, Egba and Yewa had high rates of the diseases; depicting more prevalence in male than female and more in children than adults. The regression analysis clearly proved that there were cases of incidence of diseases triggered by climate variability. The incidence of uncomplicated malaria was extremely high in Yewa. The implication of this implied that indigenes in Ogun state were susceptible to been vulnerable to climate variability. Although, the degree of impacts varied based on geographical locations. Conclusively, this study found that there were climate variability impacts on the incidence of diseases in the studied areas.