Evaluation of Multilevel Regression Analysis in Urban Primary Care Networks in Uganda

Urban primary care networks in Uganda are facing challenges in delivering consistent quality health services across various socioeconomic settings. A mixed-methods approach was employed to collect data from multiple sources including structured questionnaires, administrative records, and observational studies. Multilevel regression analyses were conducted using Stata software to assess the impact of network structure and resource allocation on outcomes. Multilevel regression analysis revealed significant effects of network size (p < 0.05) and healthcare provider training (OR = 1.34, CI: 1.20-1.50) on patient satisfaction scores, indicating that larger networks with more trained providers correlate positively with improved service quality. The multilevel regression model provided robust insights into the determinants of clinical outcomes in urban primary care settings, offering a framework for policy makers to enhance resource allocation and intervention strategies. Urban health authorities should prioritise training programmes for healthcare providers within networks and consider network expansion to improve overall service quality and patient satisfaction. Uganda, Urban Primary Care Networks, Multilevel Regression Analysis, Clinical Outcomes