Infection Prevention and Control Practices Among Healthcare Workers at a National Referral Hospital in Tanzania

Abstract

Background: Healthcare-associated infections (HAIs) pose a significant threat to patient safety, particularly in low- and middle-income countries where resource constraints complicate infection prevention and control (IPC). The “know-do gap,” the discrepancy between IPC knowledge and practice, remains a critical challenge in these settings. This study assessed the knowledge, attitude, and practice (KAP) of standard IPC measures among healthcare workers at Muhimbili National Hospital (MNH), Tanzania, and identified key predictors of IPC practice. Methods: A facility-based cross-sectional study was conducted from January to March 2025 among 153 healthcare workers selected via stratified random sampling. Data were collected using a validated questionnaire assessing KAP and contextual factors. Statistical analyses included descriptive statistics, Pearson’s correlation, ANOVA, and multiple linear regression. Results: Participants demonstrated high mean scores (scale 1–5) for knowledge (4.64 ± 0.49), attitude (4.47 ± 0.52), and self-reported practice (4.49 ± 0.54). All KAP domains were positively correlated (p < 0.001). Multiple regression revealed that the availability of materials (β = 0.488, p < 0.001) and knowledge level (β = 0.340, p < 0.001) were significant predictors of practice; attitude was not. Work overload (62.1%) and material shortages (29.4%) were the most cited barriers. Significant differences in KAP scores existed across departments and professional cadres (p < 0.05). Conclusion: Healthcare workers at MNH possess strong IPC knowledge and positive attitudes, but their ability to translate these into consistent self-reported practice appears to be associated primarily with systemic factors, specifically material availability and workload pressures. Sustainable IPC improvement requires a shift from educational interventions alone toward strengthening system-level enablers, including supply chain reliability and workload management.