BACKGROUND: The Integrated Disease Surveillance and Response (IDSR) strategy implemented by the World Health Organization (WHO) in Africa has produced a large amount of data on participating countries, and in particular on the Democratic Republic of Congo (DRC). These data are increasingly considered as unevaluable and, therefore, as requiring a rigorous process of validation before they can be used for research or public health purposes. The aim of this study was to propose a method to assess the level of adequacy of IDSR morbidity data in reflecting actual morbidity. METHODS: A systematic search of English- and French-language articles was performed in Scopus, Medline, Science Direct, Springer Link, Cochrane, Cairn, Persée, and Erudit databases. Other types of documents were identified through manual searches. Selected articles focused on the determinants of the discrepancies (differences) between reported morbidity and actual morbidity. An adequacy score was constructed using some of the identified determinants. This score was applied to the 15 weekly reported diseases monitored by IDSR surveillance in the DRC. A classification was established using the Jenks method and a sensitivity analysis was performed. Twenty-three classes of determinants were identified in 35 IDSR technical guides and reports of outbreak investigations and in 71 out of 2254 researched articles. For each of the 15 weekly reported diseases, the SIA was composed of 12 items grouped in 6 dimensions. RESULTS: The SIA classified the 15 weekly reported diseases into 3 categories or types: high score or good adequacy (value > = 14), moderate score or fair adequacy (value > = 8 and < 14), and low score or low or non-adequacy (value < 8). Regardless of the criteria used in the sensitivity analysis, there was no notable variation in SIA values or categories for any of the 15 weekly reported diseases. CONCLUSION: In a context of sparse health information in low- and middle-income countries, this study developed a score to help classify IDSR morbidity data as usable, usable after adjustment, or unusable. This score can serve to prioritize, optimize, and interpret data analyses for epidemiological research or public health purposes.