Abstract
{ "background": "Regional agricultural health monitoring networks are critical for early detection and management of crop and livestock diseases, yet their methodological rigour in generating reliable diagnostic data for clinical outcomes is not systematically appraised.", "purpose and objectives": "This systematic review evaluates the methodological rigour and diagnostic reliability of regional agricultural health monitoring networks in Tanzania, focusing on their quasi-experimental designs for measuring clinical outcomes in crops and livestock.", "methodology": "A systematic search of multiple electronic databases was conducted. Eligible studies were those describing the design, implementation, or evaluation of regional monitoring networks for agricultural health in Tanzania. Studies were critically appraised using a modified QUADAS-2 framework for diagnostic accuracy studies and a checklist for quasi-experimental design quality. A meta-analysis was precluded by heterogeneity; findings were synthesised narratively. The relationship between network design features and outcome accuracy was modelled using a generalised linear mixed model: $\\logit(p{ij}) = \\beta0 + \\beta1 X{ij} + ui + e{ij}$, where $p_{ij}$ is the probability of a correct diagnostic outcome.", "findings": "Findings reveal significant heterogeneity in methodological quality. A key theme was the frequent absence of validated reference standards for confirming field diagnoses, compromising reliability. Specifically, fewer than 30% of reviewed network protocols incorporated blinded verification of field assessments against laboratory confirmation. Inference from the statistical model indicated that networks employing standardised case definitions and routine audit cycles had significantly higher diagnostic accuracy (p < 0.05, with robust standard errors).", "conclusion": "The diagnostic reliability of evidence generated by Tanzania's regional agricultural health monitoring networks is substantially constrained by inconsistent methodological rigour, particularly in case validation and data quality assurance procedures.", "recommendations": "Future network designs must integrate validated reference standards and implement routine data audit frameworks. Capacity building should focus on standardised diagnostic procedures and analytical training for network personnel to improve the validity of collected data.", "key words": "agricultural health