{ "background": "District hospitals are critical nodes in healthcare delivery, yet robust methodologies for evaluating their systemic performance and impact on clinical outcomes in resource-limited settings are underdeveloped.", "purpose and objectives": "This intervention study aimed to methodologically evaluate district hospital systems and quantify their influence on a key clinical outcome—surgical site infection (SSI) rates—using a multilevel modelling framework.", "methodology": "We conducted a longitudinal, quasi-experimental intervention across a stratified random sample of district hospitals. The intervention comprised a standardised surgical protocol bundle. Data were collected at patient, ward, and hospital levels. The primary analysis used a three-level mixed-effects logistic regression model: $\\logit(p{ijk}) = \\beta0 + \\beta X{ijk} + u{jk} + vk$, where $p{ijk}$ is the probability of SSI for patient $i$ in ward $j$ and hospital $k$, $X$ represents covariates, and $u{jk}$, $vk$ are random intercepts. Inference was based on 95% confidence intervals derived from robust standard errors.", "findings": "The intervention was associated with a significant reduction in SSI rates. The adjusted odds ratio was 0.62 (95% CI: 0.51 to 0.75), indicating a 38% reduction in the odds of infection. Hospital-level systemic factors, particularly supply chain functionality for sterile materials, accounted for 22% of the residual variance in outcomes post-intervention.", "conclusion": "The methodological approach successfully isolated the effect of the hospital-level intervention from nested patient and ward-level factors, demonstrating that systemic strengthening at the district level directly improves clinical outcomes.", "recommendations": "Health system evaluations should adopt multilevel frameworks to attribute outcomes accurately. Policy should prioritise investments in hospital-wide supply chain systems to amplify the impact of clinical care bundles.", "key words": "health systems research, multilevel modelling, surgical outcomes, quality improvement, implementation science