Risk factors for carriage of meningococcus in third-level students in Ireland: an unsupervised machine learning approach
| Autor: | Richard J. Drew, Desirée Bennett, Sinéad O’Donnell, Robert Mulhall, Robert Cunney |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Human Vaccines & Immunotherapeutics, Vol 17, Iss 10, Pp 3702-3709 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2164-5515 2164-554X 21645515 |
| DOI: | 10.1080/21645515.2021.1940651 |
| Popis: | The aim of this study was to examine the risk factors for pharyngeal carriage of meningococci in third-level students using an unsupervised machine learning approach. Data were gathered as part of meningococcal prevalence studies conducted by the Irish Meningitis and Sepsis Reference Laboratory (IMSRL). Pharyngeal swab cultures for meningococcal carriage were taken from each student once they had completed a single-page anonymous questionnaire addressing basic demographics, social behaviors, living arrangements, vaccination, and antibiotic history. Data were analyzed using multiple correspondence analysis through a machine learning approach. In total, 16,285 students who had a pharyngeal throat swab taken returned a fully completed questionnaire. Overall, meningococcal carriage rate was 20.6%, and the carriage of MenW was 1.9% (n = 323). Young Irish adults aged under 20 years and immunized with the meningococcal C vaccine had a higher MenW colonization rate (n = 171/1260, 13.5%) compared with non-Irish adults aged 20 years or older without the MenC vaccine (n = 5/81, 6%, chi-square = 3.6, p = .05). Unsupervised machine learning provides a useful technique to explore meningococcal carriage risk factors. The issue is very complex, and asked risk factors only explain a small proportion of the carriage. This technique could be used on other conditions to explore reasons for carriage. |
| Databáze: | Directory of Open Access Journals |
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