Reports of substandard medicines: a lexicographic analysis of the Brazilian Health Surveillance Report System - Mayra Bruna Fedalto; Antonio Eduardo Matoso Mendes; Fernanda Stumpf Tonin; Fernando Fernandez-Llimos; Roberto Pontarolo (São Paulo State University (UNESP), 2021)
Regulatory agencies are responsible for collecting and evaluating spontaneous reports of suspected problems related to medications, including those from substandard medicines (SM). Objectives: The aim was to evaluate the profile of SM reports submitted to the Brazilian Health Surveillance Notification System (Notivisa) and classify these reports objectively by means of lexicographic analysis. Methods: Was extracted all SM reports available in Notivisa during the period 1 January 2007 to 31 December 2017. Descriptive statistics were performed and the reasons for SM reporting were standardized (using OpenRefine and Microsoft Excel). The following analyses were performed using IRAMuTeQ 0.7 alpha2: lexicographic analysis to obtain the frequency of active words; descending hierarchical classification (DHC) to categorize the active words into lexical classes; factorial correspondence analysis (FCA) to obtain graphs of the classes. Approved by the Ethics Committee of the Hospital do Trabalhador/SES/PR CAAE 81873417.3.0000.5225 (protocol number: 2.506.594). Results: A total of 61,775 reports were analyzed, most of them reported by hospitals (46%). The DHC of the reasons for SM produced four classes visualized in the FCA: (i) packaging problems (16%) mainly leakages/opening issues; (ii) inadequate drug identification (22%), such as illegible label information; (iii) stability and contamination issues (11%) such as presence of particles; (iv) damaged tablets/blisters (23%) mainly broken tablets. Most SM (52%) were solutions for parenteral use; sodium chloride (9%), glucose and dipyrone (3%) were the products with most complaints. Conclusions: The reasons for SM reporting can be objectively classified into classes that represent the main problems submitted to Notivisa. This classification could guide the standardization of SM reporting and contribute to improving surveillance reporting systems worldwide.