دورية أكاديمية
أعرض في EDS

Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil.

التفاصيل البيبلوغرافية
العنوان: Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil.
المؤلفون: Valentim RAM; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., Caldeira-Silva GJP; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., da Silva RD; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., Albuquerque GA; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., de Andrade IGM; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.; Public Health School of Rio Grande do Norte, Natal, Brazil., Sales-Moioli AIL; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., Pinto TKB; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., Miranda AE; Postgraduate Program in Infectious Diseases, Federal University of Espírito Santo, Vitória, Brazil., Galvão-Lima LJ; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., Cruz AS; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil., Barros DMS; Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil. daniele.barros@lais.huol.ufrn.br., Rodrigues AGCDR; Digital Metrópole Institute, Federal University of Rio Grande do Norte, Natal, Brazil.
المصدر: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2022 Feb 15; Vol. 22 (1), pp. 40. Date of Electronic Publication: 2022 Feb 15.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : BioMed Central, [2001-
مواضيع طبية MeSH: Syphilis*/diagnosis , Syphilis*/epidemiology , Syphilis, Congenital*/diagnosis , Syphilis, Congenital*/epidemiology, Brazil/epidemiology ; Female ; Humans ; Information Systems ; Pregnancy ; Prenatal Care
مستخلص: Introduction: Syphilis is a sexually transmitted disease (STD) caused by Treponema pallidum subspecies pallidum. In 2016, it was declared an epidemic in Brazil due to its high morbidity and mortality rates, mainly in cases of maternal syphilis (MS) and congenital syphilis (CS) with unfavorable outcomes. This paper aimed to mathematically describe the relationship between MS and CS cases reported in Brazil over the interval from 2010 to 2020, considering the likelihood of diagnosis and effective and timely maternal treatment during prenatal care, thus supporting the decision-making and coordination of syphilis response efforts.
Methods: The model used in this paper was based on stochastic Petri net (SPN) theory. Three different regressions, including linear, polynomial, and logistic regression, were used to obtain the weights of an SPN model. To validate the model, we ran 100 independent simulations for each probability of an untreated MS case leading to CS case (PUMLC) and performed a statistical t-test to reinforce the results reported herein.
Results: According to our analysis, the model for predicting congenital syphilis cases consistently achieved an average accuracy of 93% or more for all tested probabilities of an untreated MS case leading to CS case.
Conclusions: The SPN approach proved to be suitable for explaining the Notifiable Diseases Information System (SINAN) dataset using the range of 75-95% for the probability of an untreated MS case leading to a CS case (PUMLC). In addition, the model's predictive power can help plan actions to fight against the disease.
(© 2022. The Author(s).)
التعليقات: Erratum in: BMC Med Inform Decis Mak. 2022 Mar 25;22(1):74. (PMID: 35337313)
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فهرسة مساهمة: Keywords: Congenital syphilis; Maternal syphilis; Stochastic Petri net
تواريخ الأحداث: Date Created: 20220216 Date Completed: 20220321 Latest Revision: 20220716
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC8845404
DOI: 10.1186/s12911-022-01773-1
PMID: 35168629
قاعدة البيانات: MEDLINE
الوصف
تدمد:1472-6947
DOI:10.1186/s12911-022-01773-1