{ "background": "District hospitals are critical nodes in Uganda's health system, yet persistent inefficiencies constrain service delivery and resource use. Existing efficiency analyses often fail to account for the hierarchical structure of health systems and contextual factors at district and facility levels, limiting the utility of findings for policy.", "purpose and objectives": "This protocol details a methodological framework for a multilevel regression analysis to evaluate the technical efficiency of district hospital systems. The primary objective is to quantify the influence of facility-level inputs and district-level contextual factors on efficiency, isolating variations attributable to different administrative levels.", "methodology": "We will employ a two-stage analytical design. First, data envelopment analysis will calculate technical efficiency scores for a census of district hospitals using Ministry of Health data on inputs (beds, staff, expenditure) and outputs (outpatient visits, inpatient days). Second, a two-level hierarchical linear model will analyse these scores. The core statistical model is $Efficiency{ij} = \\beta{0j} + \\beta{1}X{ij} + \\epsilon{ij}$, where $\\beta{0j} = \\gamma{00} + \\gamma{01}Zj + u{0j}$. Here, $i$ denotes hospitals and $j$ districts; $X$ are hospital-level covariates; $Z$ are district-level covariates. Inference will be based on 95% confidence intervals estimated using robust standard errors.", "findings": "As a research protocol, this paper does not present empirical results. The anticipated analysis will partition variance in efficiency scores between hospital and district levels, with the expectation that a significant proportion (e.g., >30%) of total variation will be attributable to district-level systemic factors, indicating where policy interventions could be most effectively targeted.", "conclusion": "This protocol provides a novel, structured approach for health systems research that explicitly models the multilevel determinants of hospital efficiency. The framework is designed to generate evidence that is more actionable for decentralised health management.", "recommendations": "We