Cargando…

Characteristics and limitations of national antimicrobial surveillance according to sales and claims data

PURPOSE: Antimicrobial use (AMU) is estimated at the national level by using sales data (S-AMU) or insurance claims data (C-AMU). However, these data might be biased by generic drugs that are not sold through wholesalers (direct sales) and therefore not recorded in sales databases, or by claims that...

Descripción completa

Detalles Bibliográficos
Autores principales: Kusama, Yoshiki, Muraki, Yuichi, Tanaka, Chika, Koizumi, Ryuji, Ishikane, Masahiro, Yamasaki, Daisuke, Tanabe, Masaki, Ohmagari, Norio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112693/
https://www.ncbi.nlm.nih.gov/pubmed/33974635
http://dx.doi.org/10.1371/journal.pone.0251299
_version_ 1783690721285373952
author Kusama, Yoshiki
Muraki, Yuichi
Tanaka, Chika
Koizumi, Ryuji
Ishikane, Masahiro
Yamasaki, Daisuke
Tanabe, Masaki
Ohmagari, Norio
author_facet Kusama, Yoshiki
Muraki, Yuichi
Tanaka, Chika
Koizumi, Ryuji
Ishikane, Masahiro
Yamasaki, Daisuke
Tanabe, Masaki
Ohmagari, Norio
author_sort Kusama, Yoshiki
collection PubMed
description PURPOSE: Antimicrobial use (AMU) is estimated at the national level by using sales data (S-AMU) or insurance claims data (C-AMU). However, these data might be biased by generic drugs that are not sold through wholesalers (direct sales) and therefore not recorded in sales databases, or by claims that are not submitted electronically and therefore not stored in claims databases. We evaluated these effects by comparing S-AMU and C-AMU to ascertain the characteristics and limitations of each kind of data. We also evaluated the interchangeability of these data by assessing their relationship. METHODS: We calculated monthly defined daily doses per 1,000 inhabitants per day (DID) using sales and claims data from 2013 to 2017. To assess the effects of non-electronic claim submissions on C-AMU, we evaluated trends in the S-AMU/C-AMU ratio (SCR). To assess the effects of direct sales of S-AMU, we divided AMU into generic and branded drugs and evaluated each SCR in terms of oral versus parenteral drugs. To assess the relationship between S-AMU and C-AMU, we created a linear regression and evaluated its coefficient. RESULTS: Median annual SCRs from 2013 to 2017 were 1.046, 0.993, 0.980, 0.987, and 0.967, respectively. SCRs dropped from 2013 to 2015, and then stabilized. Differences in SCRs between branded and generic drugs were significant for oral drugs (0.820 vs 1.079) but not parenteral drugs (1.200 vs 1.165), suggesting that direct sales of oral generic drugs were omitted in S-AMU. Coefficients of DID between S-AMU and C-AMU were high (generic, 0.90; branded, 0.84) in oral drugs but relatively low (generic, 0.32; branded, 0.52) in parenteral drugs. CONCLUSIONS: The omission of direct sales information and non-electronically submitted claims have influenced S-AMU and C-AMU information, respectively. However, these data were well-correlated, and it is considered that both kinds of data are useful depending on the situation.
format Online
Article
Text
id pubmed-8112693
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-81126932021-05-24 Characteristics and limitations of national antimicrobial surveillance according to sales and claims data Kusama, Yoshiki Muraki, Yuichi Tanaka, Chika Koizumi, Ryuji Ishikane, Masahiro Yamasaki, Daisuke Tanabe, Masaki Ohmagari, Norio PLoS One Research Article PURPOSE: Antimicrobial use (AMU) is estimated at the national level by using sales data (S-AMU) or insurance claims data (C-AMU). However, these data might be biased by generic drugs that are not sold through wholesalers (direct sales) and therefore not recorded in sales databases, or by claims that are not submitted electronically and therefore not stored in claims databases. We evaluated these effects by comparing S-AMU and C-AMU to ascertain the characteristics and limitations of each kind of data. We also evaluated the interchangeability of these data by assessing their relationship. METHODS: We calculated monthly defined daily doses per 1,000 inhabitants per day (DID) using sales and claims data from 2013 to 2017. To assess the effects of non-electronic claim submissions on C-AMU, we evaluated trends in the S-AMU/C-AMU ratio (SCR). To assess the effects of direct sales of S-AMU, we divided AMU into generic and branded drugs and evaluated each SCR in terms of oral versus parenteral drugs. To assess the relationship between S-AMU and C-AMU, we created a linear regression and evaluated its coefficient. RESULTS: Median annual SCRs from 2013 to 2017 were 1.046, 0.993, 0.980, 0.987, and 0.967, respectively. SCRs dropped from 2013 to 2015, and then stabilized. Differences in SCRs between branded and generic drugs were significant for oral drugs (0.820 vs 1.079) but not parenteral drugs (1.200 vs 1.165), suggesting that direct sales of oral generic drugs were omitted in S-AMU. Coefficients of DID between S-AMU and C-AMU were high (generic, 0.90; branded, 0.84) in oral drugs but relatively low (generic, 0.32; branded, 0.52) in parenteral drugs. CONCLUSIONS: The omission of direct sales information and non-electronically submitted claims have influenced S-AMU and C-AMU information, respectively. However, these data were well-correlated, and it is considered that both kinds of data are useful depending on the situation. Public Library of Science 2021-05-11 /pmc/articles/PMC8112693/ /pubmed/33974635 http://dx.doi.org/10.1371/journal.pone.0251299 Text en © 2021 Kusama et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kusama, Yoshiki
Muraki, Yuichi
Tanaka, Chika
Koizumi, Ryuji
Ishikane, Masahiro
Yamasaki, Daisuke
Tanabe, Masaki
Ohmagari, Norio
Characteristics and limitations of national antimicrobial surveillance according to sales and claims data
title Characteristics and limitations of national antimicrobial surveillance according to sales and claims data
title_full Characteristics and limitations of national antimicrobial surveillance according to sales and claims data
title_fullStr Characteristics and limitations of national antimicrobial surveillance according to sales and claims data
title_full_unstemmed Characteristics and limitations of national antimicrobial surveillance according to sales and claims data
title_short Characteristics and limitations of national antimicrobial surveillance according to sales and claims data
title_sort characteristics and limitations of national antimicrobial surveillance according to sales and claims data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112693/
https://www.ncbi.nlm.nih.gov/pubmed/33974635
http://dx.doi.org/10.1371/journal.pone.0251299
work_keys_str_mv AT kusamayoshiki characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT murakiyuichi characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT tanakachika characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT koizumiryuji characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT ishikanemasahiro characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT yamasakidaisuke characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT tanabemasaki characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata
AT ohmagarinorio characteristicsandlimitationsofnationalantimicrobialsurveillanceaccordingtosalesandclaimsdata