ABSTRACT
Objectives: To examine drug overdose records in Brazil from 2000 to 2020, analyzing trends over time in overdoses and overall sociodemographic characteristics of the deceased. Methods: Using data from the Brazilian Mortality Information System (Sistema de Informações sobre Mortalidade), we identified records from 2000-2020 in which the underlying cause-of-death was one of the following codes: X40-X45 (accidental poisoning), X60-X65 (intentional poisoning), or Y10-Y15 (undetermined intentionality poisoning). The Brazilian dataset included 21,410 deaths. We used joinpoint regression analysis to assess changes in trends over time. Results: People who died of drug overdoses in Brazil between 2000 and 2020 had a mean age of 38.91 years; 38.45% were women, and 44.01% were identified as White. Of the overdose deaths, 44.70% were classified as intentional and 32.12% were classified as unintentional. Among the identified drugs, stimulants were the most common class. However, most records did not report which drug was responsible for death. Conclusion: Sociodemographic trends in overdose deaths in Brazil must guide country-specific policies. Nevertheless, data collection protocols must be improved, particularly regarding the drug used in overdoses.
ABSTRACT
OBJECTIVES: To examine drug overdose records in Brazil from 2000 to 2020, analyzing trends over time in overdoses and overall sociodemographic characteristics of the deceased. METHODS: Using data from the Brazilian Mortality Information System (Sistema de Informações sobre Mortalidade), we identified records from 2000-2020 in which the underlying cause-of-death was one of the following codes: X40-X45 (accidental poisoning), X60-X65 (intentional poisoning), or Y10-Y15 (undetermined intentionality poisoning). The Brazilian dataset included 21,410 deaths. We used joinpoint regression analysis to assess changes in trends over time. RESULTS: People who died of drug overdoses in Brazil between 2000 and 2020 had a mean age of 38.91 years; 38.45% were women, and 44.01% were identified as White. Of the overdose deaths, 44.70% were classified as intentional and 32.12% were classified as unintentional. Among the identified drugs, stimulants were the most common class. However, most records did not report which drug was responsible for death. CONCLUSION: Sociodemographic trends in overdose deaths in Brazil must guide country-specific policies. Nevertheless, data collection protocols must be improved, particularly regarding the drug used in overdoses.
Subject(s)
Central Nervous System Stimulants , Drug Overdose , Female , Humans , Adult , Male , Brazil/epidemiologyABSTRACT
The representation of texts by term vectors with element values calculated by a TFIDF method yields to significant results in text similarity problems, such as finding related documents in bibliographic or full-text databases and identifying MeSH concepts from medical texts by lexical approach and also harmonizing journal citation in ISI/SciELO references and normalizing author's affiliation in MEDLINE. Our work considered "trigrams" as the terms (elements) of a term vector representing a text, according to the Trigram Phrase Matching published by the NLM's Indexing Initiative and its logarithmic Term Frequency-Inverse Document Frequency method for term weighting. Trigrams are overlapping 3-char strings from a text, extracted by a couple of rules, and a trigram matching method may improve the probability of identifying synonym phrases or similar texts. The matching process was implemented as a simple algorithm, and requires a certain amount of computer resources. An efficiency-focused C-programming was adopted. In addition, some heuristic rules improved the efficiency of the method and made it feasible a regular "find your scientific production in SciELO collection" information service. We describe an implementation of the Trigram Matching method, the software tool we developed and a set of experimental parameters for the above results.
