Abstract
{ "background": "Public health surveillance systems are critical for early disease detection and response, yet their effectiveness is often limited by low adoption rates among frontline health workers. Methodological rigour in evaluating these systems, particularly in low-resource settings, remains a significant gap in the literature.", "purpose and objectives": "This study aimed to conduct a methodological evaluation of two novel surveillance systems to determine their comparative adoption rates and operational performance under field conditions.", "methodology": "A randomised field trial was conducted across 120 health facilities. Facilities were randomly assigned to implement either a mobile health (mHealth) system or a web-based platform. Adoption was measured as the proportion of weekly reports submitted over a six-month period. Performance was assessed using data completeness, timeliness, and user error rates. The primary analysis used a generalised linear mixed model: $\\logit(p{ij}) = \\beta0 + \\beta1 T{ij} + ui + \\epsilon{ij}$, where $p{ij}$ is the probability of report submission for facility $j$ in district $i$, $T{ij}$ is the treatment indicator, and $u_i$ are district random effects.", "findings": "The mHealth system demonstrated a significantly higher adoption rate (78%, 95% CI: 72 to 84) compared to the web-based system (61%, 95% CI: 55 to 67). The adjusted odds ratio for adoption using mHealth was 2.34 (95% CI: 1.51 to 3.62, p<0.001). Data timeliness was also superior in the mHealth arm.", "conclusion": "The mHealth surveillance system was adopted more readily and performed more reliably than the web-based alternative in this setting, indicating that platform design and accessibility are key determinants of successful implementation.", "recommendations": "Programme planners should prioritise mobile-based platforms for frontline surveillance. Future system evaluations must incorporate rigorous experimental designs to generate robust evidence on