Data Quality and Performance Classification in Multisectoral Nutrition Monitoring: A Composite Framework and Empirical Application

Background The consequences of using unfit data are substantial: misallocated resources, false signals of progress, delayed corrective action, and wasted investments. Objective This study developed and validated a composite traffic-light framework for jointly classifying data quality and performance in multisectoral indicator systems, and assessed the quality of 98 indicators from the Seqota Declaration scorecard in Addis Ababa, Ethiopia. Methods A retrospective audit examined 98 indicators across 7 sectors, 4 sub-cities, and 4 years (2014-2017 EC). The framework assessed: (1) completeness (≥75%), (2) temporal variation (Coefficient of Variation > 0%), (3) sub-city disaggregation (duplication detection with ±0.05 percentage point tolerance), and (4) performance classification (GREEN ≥85%, YELLOW 55-84%, RED <55%). Statistical validation employed non-parametric tests (Kruskal-Wallis, Wilcoxon, Jonckheere-Terpstra) on 11 priority indicators. A Monte Carlo simulation (1,000 iterations) compared parametric and non-parametric tests across sample sizes (n=4, 10, 20, 30, 50, 100) and distributions (normal, skewed, heavy-tailed). Sensitivity analyses tested alternative thresholds. Quality assurance followed the WHO Data Quality Review Framework. Results Of 98 indicators, 13.3% were GREEN, 46.9% YELLOW, 8.2% RED, and 31.6% EXCLUDED. Critically, 40.8% of indicators were duplicated across ≥3 sub-cities in ≥2 years. Women's Affairs (83.3%), Governance (80.0%), and Social Protection (66.7%) had the highest duplication rates. The disaggregation dimension reclassified 9 indicators from GREEN to YELLOW. Sensitivity analysis confirmed robustness. Statistical validation showed significant differences across categories (Kruskal-Wallis: χ 2 =29.381, p<0.001; Jonckheere-Terpstra: z=5.29, p<0.001). Monte Carlo simulation confirmed that for n=4, non-parametric tests maintain appropriate Type I error rates (∼4.9%) while parametric tests show inflated Type I error (6.2-7.1%) with non-normal data. Conclusions This is a data-quality-dominant dataset, and systematic duplication across sub-cities challenges the assumption of genuine disaggregation. The composite framework provides a replicable, transparent approach to classifying indicators before performance analysis. For small-sample longitudinal data (n=4), non-parametric tests are appropriate.