{ "background": "Public health surveillance systems are critical for disease control, yet their methodological reliability in low-resource settings is often unquantified. In Ethiopia, disparate data sources and infrastructural challenges necessitate a robust evaluation of these systems' consistency and validity to inform policy.", "purpose and objectives": "This protocol details a study to methodologically evaluate the reliability of public health surveillance systems in Ethiopia. The primary objective is to quantify the consistency of reported incidence data for selected notifiable diseases across different reporting tiers and over time.", "methodology": "A quasi-experimental design employing a difference-in-differences (DiD) model will be used. The analysis will utilise longitudinal, district-level surveillance data for three priority infectious diseases. The core statistical model is $Y{dt} = \\beta0 + \\beta1 \\text{Post}{t} + \\beta2 \\text{Intervention}{d} + \\delta (\\text{Post}{t} \\times \\text{Intervention}{d}) + \\epsilon_{dt}$, where $\\delta$ captures the differential change in reporting completeness. Inference will rely on cluster-robust standard errors at the regional level.", "findings": "As a protocol, this paper does not present empirical results. The anticipated findings will include the direction and magnitude of the DiD estimator ($\\delta$), which will indicate whether a recent system strengthening intervention improved reporting completeness, hypothesised to be by at least 15 percentage points.", "conclusion": "The study will conclude by determining the statistical and practical significance of the observed changes in system reliability, directly assessing the impact of the implemented intervention on data quality.", "recommendations": "Recommendations will be formulated for the Ethiopian Public Health Institute on optimising surveillance architecture and data flow, based on the identified sources of variance and bias in the DiD analysis.", "key words": "surveillance evaluation, reliability, difference-in-differences, health systems, data quality, Ethiopia", "cont