Risk factors analysis of febrile diseases in LMICs: a case of southern Nigeria

Febrile diseases such as Malaria, Typhoid fever, HIV/AIDS, Tuberculosis, respiratory tract infections, and urinary tract infections remain major public health concerns in low-to-middle-income countries (LMICs). Their prevalence is driven by interacting socioeconomic, environmental, behavioral, and biological factors such as vector exposure, poor sanitation, overcrowding, and high-risk behaviors including intravenous drug use and smoking. This study investigates the key risk factors influencing the prevalence and diagnosis of febrile diseases in Southern Nigeria. A cross-sectional quantitative research design was adopted, with data collected between May 2021 and December 2021 from four states in southern Nigeria. A total of 4,868 valid responses were obtained, with distribution across states as follows: Cross River accounted for 31% (n = 1,531), Rivers 25% (n = 1,232), Akwa Ibom 25% (n = 1,223), and Imo 18% (n = 882). Participants were aged < 19 years (40%, n = 1,934), 19–24 years (9%, n = 424), 25–44 years (32%, n = 1,557), 45–60 years (12%, n = 600), and > 60 years (7%, n = 353). The sample comprised 55% females (n = 2,693) and 45% males (n = 2,175). Among female participants, 409 were pregnant, distributed as 0–3 months (34%), 4–6 months (45%), and 7–9 months (21%), while 153 were nursing mothers, with the largest proportion (41%) breastfeeding for over 9 months. Statistical analyses included Pearson correlation and multiple linear regression to determine the relationships between identified risk factors and confirmed diagnoses of febrile diseases. The analysis showed that several risk factors were significantly associated with disease diagnoses, while others had minimal influence. The strongest predictor observed was mosquito bites on confirmed Malaria diagnosis (t = 41.68, p < 0.01). Direct contact with infected persons significantly influenced diagnoses of Tuberculosis (t = 18.54, p < 0.01) and HIV/AIDS (t = 17.39, p < 0.01). Other notable factors included travel to endemic areas for malaria, overcrowding and smoking for tuberculosis, underlying chronic illness for HIV/AIDS, poor personal hygiene for upper urinary tract infections, and smoking exposure for lower respiratory tract infections. Malaria recorded the highest model significance (F = 166.91, p < 0.01) and the highest coefficient of determination (R² = 0.37), indicating that 37% of the variance in confirmed malaria diagnoses was explained by the studied risk factors. This was followed by tuberculosis (R² = 0.25, F(17) = 94.83, p < 0.01) and HIV/AIDS (R² = 0.18, F(17) = 63.41, p < 0.01), while yellow fever showed the lowest explained variance (R² = 0.01, F(17) = 2.17). The findings also revealed differences between urban and rural settings due to variations in healthcare access, environmental conditions, and population density. The study demonstrates that environmental exposure, behavioral practices, and healthcare access significantly shape the patterns of febrile diseases in southern Nigeria. Effective control strategies should integrate vector control, sanitation improvement, behavioral health interventions, and strengthened healthcare access, particularly in rural communities. The study also recommends the implementation of Medical Decision Support Systems (MDSS) to improve diagnostic accuracy in resource-limited settings, thereby supporting targeted public health policies and reducing the socio-economic burden of febrile diseases in LMICs.