Cargando…
Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points
OBJECTIVES: This tutorial describes and illustrates statistical methods to detect time trends possibly including abrupt changes (referred to as change-points) in the consumption of antibiotics in the community. METHODS: For the period 1997–2017, data on consumption of antibacterials for systemic use...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314099/ https://www.ncbi.nlm.nih.gov/pubmed/34312655 http://dx.doi.org/10.1093/jac/dkab180 |
_version_ | 1783729479240122368 |
---|---|
author | Bruyndonckx, Robin Coenen, Samuel Adriaenssens, Niels Versporten, Ann Monnet, Dominique L Goossens, Herman Molenberghs, Geert Weist, Klaus Hens, Niel |
author_facet | Bruyndonckx, Robin Coenen, Samuel Adriaenssens, Niels Versporten, Ann Monnet, Dominique L Goossens, Herman Molenberghs, Geert Weist, Klaus Hens, Niel |
author_sort | Bruyndonckx, Robin |
collection | PubMed |
description | OBJECTIVES: This tutorial describes and illustrates statistical methods to detect time trends possibly including abrupt changes (referred to as change-points) in the consumption of antibiotics in the community. METHODS: For the period 1997–2017, data on consumption of antibacterials for systemic use (ATC group J01) in the community, aggregated at the level of the active substance, were collected using the WHO ATC/DDD methodology and expressed in DDD (ATC/DDD index 2019) per 1000 inhabitants per day. Trends over time and presence of common change-points were studied through a set of non-linear mixed models. RESULTS: After a thorough description of the set of models used to assess the time trend and presence of common change-points herein, the methodology was applied to the consumption of antibacterials for systemic use (ATC J01) in 25 EU/European Economic Area (EEA) countries. The best fit was obtained for a model including two change-points: one in the first quarter of 2004 and one in the last quarter of 2008. CONCLUSIONS: Allowing for the inclusion of common change-points improved model fit. Individual countries investigating changes in their antibiotic consumption pattern can use this tutorial to analyse their country data. |
format | Online Article Text |
id | pubmed-8314099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83140992021-07-27 Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points Bruyndonckx, Robin Coenen, Samuel Adriaenssens, Niels Versporten, Ann Monnet, Dominique L Goossens, Herman Molenberghs, Geert Weist, Klaus Hens, Niel J Antimicrob Chemother Supplement Papers OBJECTIVES: This tutorial describes and illustrates statistical methods to detect time trends possibly including abrupt changes (referred to as change-points) in the consumption of antibiotics in the community. METHODS: For the period 1997–2017, data on consumption of antibacterials for systemic use (ATC group J01) in the community, aggregated at the level of the active substance, were collected using the WHO ATC/DDD methodology and expressed in DDD (ATC/DDD index 2019) per 1000 inhabitants per day. Trends over time and presence of common change-points were studied through a set of non-linear mixed models. RESULTS: After a thorough description of the set of models used to assess the time trend and presence of common change-points herein, the methodology was applied to the consumption of antibacterials for systemic use (ATC J01) in 25 EU/European Economic Area (EEA) countries. The best fit was obtained for a model including two change-points: one in the first quarter of 2004 and one in the last quarter of 2008. CONCLUSIONS: Allowing for the inclusion of common change-points improved model fit. Individual countries investigating changes in their antibiotic consumption pattern can use this tutorial to analyse their country data. Oxford University Press 2021-08-01 /pmc/articles/PMC8314099/ /pubmed/34312655 http://dx.doi.org/10.1093/jac/dkab180 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Supplement Papers Bruyndonckx, Robin Coenen, Samuel Adriaenssens, Niels Versporten, Ann Monnet, Dominique L Goossens, Herman Molenberghs, Geert Weist, Klaus Hens, Niel Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
title | Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
title_full | Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
title_fullStr | Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
title_full_unstemmed | Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
title_short | Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
title_sort | analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points |
topic | Supplement Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314099/ https://www.ncbi.nlm.nih.gov/pubmed/34312655 http://dx.doi.org/10.1093/jac/dkab180 |
work_keys_str_mv | AT bruyndonckxrobin analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT coenensamuel analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT adriaenssensniels analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT versportenann analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT monnetdominiquel analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT goossensherman analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT molenberghsgeert analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT weistklaus analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT hensniel analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints AT analysingthetrendovertimeofantibioticconsumptioninthecommunityatutorialonthedetectionofcommonchangepoints |