Abstract Due to Meningococcal’s Disease (MD) severity and impact in public health, in Portugal it is mandatory to notify the suspicious cases to implement effective control measures. MD cases are notified in the National Surveillance System (SINAVE), which triggers epidemiological investigation, risk assessment and community intervention. The quality of SINAVE is defined by various attributes, being their evaluation crucial to improve and guarantee quality standards. Our goal is to evaluate the external and internal completeness of SINAVE for MD in northern Portugal from 2020 to 2022. We have conducted a cross-sectional study, using MD cases from 2020 to 2022. External Completeness was estimated using a two-source capture-recapture method, with data from SINAVE and Diagnostic-Related Group (DRG) cases. Hospital readmissions’, duplicates and cases from other regions were excluded. Internal completeness of SINAVE was accessed for each case and for each non-mandatory variable. For external completeness (EC) analysis, 21 of the 23 DRG cases were eligible for matching. Out of 31 SINAVE confirmed cases, 20 were eligible for matching. EC of SINAVE was 66,7%. For internal completeness (IC) analysis, we excluded 7 cases that did not meet the criteria for inclusion. The mean IC was 73,5% (min 38,5%, max 85,7%) when analysing by case. When assessing each of the 30 non-mandatory variables of the reports, 7 (23,3%) had an IC of 100% and 13 (43,3%) had an IC of 0%, the mean value was 49,7%. SINAVE assessment, as a routine, is crucial to identify the need for improvement. Completeness is fundamental to ensure an effective public health response. Our results confirm under-reporting and incomplete report of MD. There is a need to strengthen the improvement of data quality of SINAVE. Future studies may be important to estimate true incidence of MD and compare the results for other diseases in order to improve public health actions. Key messages • Meningococcal disease requires timely public health intervention. • valuation of the surveillance system is crucial, with completeness playing an important role for data quality and prompt response.