Yosuke Mita1, Ryo Inose2, Ryota Goto3, Yoshiki Kusama4, Ryuji Koizumi5, Daisuke Yamasaki6, Masahiro Ishikane7, Masaki Tanabe8, Norio Ohmagari9, Yuichi Muraki10. 1. Department of Clinical Pharmacoepidemiology, Kyoto Pharmaceutical University, 5 Misasagi-Nakauchi-cho, Yamashina-ku, Kyoto-shi, Kyoto, 607-8414, Japan. Electronic address: ky15329@ms.kyoto-phu.ac.jp. 2. Department of Clinical Pharmacoepidemiology, Kyoto Pharmaceutical University, 5 Misasagi-Nakauchi-cho, Yamashina-ku, Kyoto-shi, Kyoto, 607-8414, Japan. Electronic address: inose2019@mb.kyoto-phu.ac.jp. 3. Department of Clinical Pharmacoepidemiology, Kyoto Pharmaceutical University, 5 Misasagi-Nakauchi-cho, Yamashina-ku, Kyoto-shi, Kyoto, 607-8414, Japan. Electronic address: ky15144@ms.kyoto-phu.ac.jp. 4. AMR Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan, 1-21-1 Toyama Shinjuku-ku, Tokyo, 162-8655, Japan. Electronic address: yokusama@hosp.ncgm.go.jp. 5. AMR Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan, 1-21-1 Toyama Shinjuku-ku, Tokyo, 162-8655, Japan. Electronic address: rykoizumi@hosp.ncgm.go.jp. 6. Department of Infection Control and Prevention, Mie University Hospital, Mie, Japan, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan. Electronic address: yamadai@clin.medic.mie-u.ac.jp. 7. AMR Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan, 1-21-1 Toyama Shinjuku-ku, Tokyo, 162-8655, Japan. Electronic address: mishikane@hosp.ncgm.go.jp. 8. Department of Infection Control and Prevention, Mie University Hospital, Mie, Japan, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan. Electronic address: m-tanabe@clin.medic.mie-u.ac.jp. 9. AMR Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan, 1-21-1 Toyama Shinjuku-ku, Tokyo, 162-8655, Japan. Electronic address: nohmagari@hosp.ncgm.go.jp. 10. Department of Clinical Pharmacoepidemiology, Kyoto Pharmaceutical University, 5 Misasagi-Nakauchi-cho, Yamashina-ku, Kyoto-shi, Kyoto, 607-8414, Japan. Electronic address: y-muraki@mb.kyoto-phu.ac.jp.
Abstract
INTRODUCTION: Anti-methicillin-resistant Staphylococcus aureus (MRSA) agents have different doses and administration periods. Thus, it is difficult to evaluate antimicrobial use (AMU) of anti-MRSA agents using defined daily doses per 1000 inhabitants per day (DID) or days of therapy per 1000 inhabitants per day (DOTID). This study aimed to evaluate the relationship between anti-MRSA agent use and resistant bacteria using the number of patients per 1000 inhabitants per day (PID) as an alternative index of AMU. METHODS: AMU data for anti-MRSA agents were collected from the National Database of Health Insurance Claims and Specific Health Checkups (NDB) in 2016. The relationship between PID and DID or DOTID was evaluated. The number of patients with MRSA isolated was obtained from Japan Nosocomial Infections Surveillance, and their correlation with PID was analyzed. The rate of anti-MRSA agent use in each prefecture was investigated. RESULTS: PID showed a significant linear relationship with both DID and DOTID (all p < 0.0001). PID was significantly correlated with the number of patients with MRSA isolated. Additionally, the rate of anti-MRSA agent use was markedly different in each region. CONCLUSIONS: PID is not affected by doses and administration periods, and thus may be an alternative index for the selective pressure of antibiotics. Evaluating AMU using PID based on NDB data will help in the development of effective antimicrobial resistance measures.
INTRODUCTION: Anti-methicillin-resistant Staphylococcus aureus (MRSA) agents have different doses and administration periods. Thus, it is difficult to evaluate antimicrobial use (AMU) of anti-MRSA agents using defined daily doses per 1000 inhabitants per day (DID) or days of therapy per 1000 inhabitants per day (DOTID). This study aimed to evaluate the relationship between anti-MRSA agent use and resistant bacteria using the number of patients per 1000 inhabitants per day (PID) as an alternative index of AMU. METHODS:AMU data for anti-MRSA agents were collected from the National Database of Health Insurance Claims and Specific Health Checkups (NDB) in 2016. The relationship between PID and DID or DOTID was evaluated. The number of patients with MRSA isolated was obtained from Japan Nosocomial Infections Surveillance, and their correlation with PID was analyzed. The rate of anti-MRSA agent use in each prefecture was investigated. RESULTS: PID showed a significant linear relationship with both DID and DOTID (all p < 0.0001). PID was significantly correlated with the number of patients with MRSA isolated. Additionally, the rate of anti-MRSA agent use was markedly different in each region. CONCLUSIONS: PID is not affected by doses and administration periods, and thus may be an alternative index for the selective pressure of antibiotics. Evaluating AMU using PID based on NDB data will help in the development of effective antimicrobial resistance measures.
Keywords:
Anti-Methicillin-resistant Staphylococcus aureus agent; Antimicrobial resistance; Antimicrobial use; Methicillin-resistant Staphylococcus aureus; National database of health insurance claims and specific health checkups