A BINARY LOGISTIC REGRESSION MODEL FOR ASSESSING THE PREDISPOSING FACTORS OF MALARIA FEVER AMONG FARMERS IN BENUE STATE, NIGERIA - Idikwu, Joseph Sunday; Kuhe, David Adugh; Nyitar, Jacob Aondongu (FUDMA Journal of Sciences, 2025)
Despite sustained control efforts, the burden of malaria fever persists due to socio-demographic, environmental, and behavioural factors. This study employed a binary logistic regression model to assess the predisposing factors of malaria fever among farmers in Benue State, Nigeria. A cross-sectional survey was conducted among 750 respondents, predominantly farmers, selected from malaria-endemic communities in Ado, Katsina-Ala, Logo, Makurdi, and Ukum LGAs. Data were collected using structured questionnaires covering socio-demographic characteristics, environmental conditions, and malaria prevention practices, with malaria status as the binary dependent variable. The analysis revealed a malaria prevalence of 77.1%, indicating a high disease burden. The full model was statistically significant (Omnibus test χ² = 33.749, p = 0.009) with strong explanatory power (Nagelkerke R² = 0.747) and acceptable goodness-of-fit (Hosmer and Lemeshow χ² = 8.142, p = 0.420). Key predictors increasing malaria risk included occupation (OR = 2.066), previous household malaria (OR = 2.199), poor housing (OR = 2.713), residence near stagnant water (OR = 2.968), and travel to endemic areas (OR = 2.921), all significant at p < 0.001. Conversely, higher education (OR = 0.438), use of treated nets/insecticides (OR = 0.760), timely treatment (OR = 0.585), malaria prevention training (OR = 0.720), community programmes (OR = 0.737), and access to healthcare (OR = 0.863) significantly reduced infection odds. The study recommends that policymakers and public health stakeholders implement community-specific interventions prioritizing vulnerable groups, expanding healthcare access, promoting behavioural change, and addressing structural inequalities to reduce malaria burden in Benue State, Nigeria.