LOGISTIC REGRESSION ANALYSIS OF THE IMPACT OF MALARIA AND OTHER FACTORS ON MISCARRIAGES IN GOMBE STATE

Abstract

Malaria remains a significant public health challenge in Nigeria, particularly in regions like Gombe State, where the disease is endemic. Pregnant women are especially vulnerable to malaria, which can lead to adverse pregnancy outcomes, including miscarriage. This study aims to investigate the impact of malaria on miscarriage among pregnant women in Gombe State, Nigeria, using binary logistic regression. Data were retrospectively collected from medical records of 272 pregnant women admitted to the Gombe Specialist Hospital from August to December 2024. The study examined the association between malaria infection and miscarriage in, while controlling for demographic factors such as maternal age, coexisting health conditions, gestational age at diagnosis of malaria, level of education, and previous pregnancy history Using SPSS and MS Excel packages. Results at 5% significance level revealed that malaria infection was a significant predictor of miscarriage (p < 0.001), with an extremely high odds ratio of 107.932 at 95% confidence interval. Other factors, such as maternal age, gestational age at diagnosis of malaria and coexisting health conditions, were not statistically significant. Previous pregnancy history was also significant (p = 0.035), suggesting that women with a history of previous pregnancies were less likely to experience miscarriage compared to those without such a history. The study concluded that malaria is a major risk factor for miscarriage and emphasized the need for targeted interventions by the government and other non-governmental organizations towards prevention