Indications and classes of outpatient antibiotic prescriptions in Japan: A descriptive study using the national database of electronic health insurance claims, 2012–2015

Journal Pre-proof Indications and classes of outpatient antibiotic prescriptions in Japan: A descriptive study using the national database of electronic health insurance claims, 2012–2015 Hideki Hashimoto, Makoto Saito, Jumpei Sato, Kazuo Goda, Naohiro Mitsutake, Masaru Kitsuregawa, Ryozo Nagai, Shuji Hatakeyama

PII:

S1201-9712(19)30448-5

DOI:

https://doi.org/10.1016/j.ijid.2019.11.009

Reference:

IJID 3830

To appear in:

International Journal of Infectious Diseases

Received Date:

10 August 2019

Revised Date:

6 November 2019

Accepted Date:

6 November 2019

Please cite this article as: Hashimoto H, Saito M, Sato J, Goda K, Mitsutake N, Kitsuregawa M, Nagai R, Hatakeyama S, Indications and classes of outpatient antibiotic prescriptions in Japan: A descriptive study using the national database of electronic health insurance claims, 2012–2015, International Journal of Infectious Diseases (2019), doi: https://doi.org/10.1016/j.ijid.2019.11.009

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Indications and classes of outpatient antibiotic prescriptions in Japan: A descriptive study

using the national database of electronic health insurance claims, 2012–2015

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Hideki Hashimoto, M.D.1,2; Makoto Saito, M.D.3; Jumpei Sato, Ph.D.4; Kazuo Goda, Ph.D.4;

Naohiro Mitsutake, Ph.D.5; Masaru Kitsuregawa, Ph.D.4; Ryozo Nagai, M.D.6; Shuji

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Hatakeyama, M.D.1,7*

1

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Division of General Internal Medicine, Jichi Medical University Hospital, 3311-1 Yakushiji,

Shimotsuke-shi, Tochigi 329-0498, Japan.

2

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Department of Infectious Diseases, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-

ku, Tokyo 113-8655, Japan.

3

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Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University

of Oxford. NDM Research Building, Old Road Campus, Roosevelt Drive, Oxford, OX3 7FZ,

UK.

1

4

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo

153-8505, Japan.

5

Institute for Health Economics and Policy, 1-5-11, Nishi-Shimbashi, Minato-ku, Tokyo 105-

0003, Japan

6

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President, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke-shi, Tochigi 329-0498,

Japan.

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Division of Infectious Diseases, Jichi Medical University Hospital, 3311-1 Yakushiji,

*Corresponding author.

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Shuji Hatakeyama

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Shimotsuke-shi, Tochigi 329-0498, Japan.

Division of General Internal Medicine/Division of Infectious Diseases, Jichi Medical University

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Hospital, 3311-1 Yakushiji, Shimotsuke-shi, Tochigi, 329-0498 Japan.

Phone: +81-285-58-7394

Fax: +81-285-44-0628

E-mail: [email protected]

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Word count for the main texts: 4,360 words

Running title: Outpatient antibiotic prescription in Japan

Highlights The rate of oral antibiotic prescriptions linked to infectious disease diagnoses in Japan was

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

704 per 1000 population per year in 2012–2015.

A total of 70% of antibiotic prescriptions were for acute respiratory infections and

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gastrointestinal infections, and 56% were for infections where antibiotics are rarely

indicated.

For pharyngitis and sinusitis, first-line antibiotics were rarely (< 10%) prescribed.



Children, adult women, and people living in western Japan received more antibiotics.

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

Abstract

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Objectives: To evaluate condition-specific antibiotic prescription rates and the appropriateness

of antibiotic use in outpatient settings in Japan.

Methods: Using Japan’s national administrative claims database, all outpatient visits with

infectious disease diagnoses were linked to reimbursed oral antibiotic prescriptions. Prescription

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rates stratified by age, sex, prefecture, and antibiotic category were determined for each

infectious disease diagnosis. The proportions of any antibiotic prescription to all infectious

disease visits and the proportions of first-line antibiotic prescriptions to all antibiotic

prescriptions were calculated for each infectious disease diagnosis.

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Results: Of the 659 million infectious disease visits between April 2012 and March 2015,

antibiotics were prescribed in 266 million visits (704 prescriptions per 1000 population per

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year). Third-generation cephalosporins, macrolides, and quinolones accounted for 85.9% of all

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antibiotic prescriptions. Fifty-six percent of antibiotic prescriptions were directed toward

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infections for which antibiotics are generally not indicated. The diagnoses with frequent

antibiotic prescription were bronchitis (184 prescriptions per 1000 population per year), viral

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upper respiratory infections (166), pharyngitis (104), sinusitis (52), and gastrointestinal

infection (41), for which 58.3%, 40.6%, 58.9%, 53.9%, and 26.1% of visits antibiotics were

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prescribed, respectively. First-line antibiotics were rarely prescribed for pharyngitis (8.8%) and

sinusitis (9.8%). More antibiotics were prescribed for children aged 0–9 years, adult women,

and patients living in western Japan.

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Conclusions: Antibiotic prescription rates are high in Japan. Acute respiratory or

gastrointestinal infections, which received the majority of the antibiotics generally not indicated,

should be the main targets of antimicrobial stewardship intervention.

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Keywords: National administrative claims database; Big data; antibiotic; antimicrobial

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stewardship

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Introduction

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Antimicrobial resistance (AMR) is a global issue transcending national borders. An

estimated 700,000 deaths are attributable to antimicrobial resistant infections every year, and

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AMR is projected to increase without effective measures (The Review on Antimicrobial

Resistance, 2016). The World Health Organization (WHO) has launched a global action plan on

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AMR encouraging the optimization of antibiotic use (World Health Organization, 2015). The

Japanese national action plan was launched in 2016 (The Government of Japan, 2016) and its

main outcome is to reduce the total antimicrobial use (defined daily doses [DDD] per 1000

inhabitants per day) to two-thirds of the use in 2013, and the use of oral cephalosporins,

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quinolones, and macrolides to one-half by 2020. To address this issue, the government of Japan

is promoting appropriate antimicrobial use by developing antimicrobial stewardship guidelines

and launching an advertising campaign to educate patients. National guidelines on the proper

use of antibiotics were promulgated in 2017 and monetary incentives for antimicrobial

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stewardship intervention in inpatients and pediatric outpatients started in 2018. To reduce

antibiotic use, identifying targets of antimicrobial stewardship by understanding the antibiotic

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prescription pattern is essential.

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In Japan, recent surveys based on antimicrobial sales data (Tsutsui et al., 2018) and regional

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claims data (Hashimoto et al., 2019) report that broad-spectrum antibiotics (i.e., third-

generation cephalosporins, macrolides, and quinolones) accounted for 77–88% of oral antibiotic

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prescriptions. Considering that more than 90% of the antibiotic consumption in Japan is of oral

antibiotics (Tsutsui et al., 2018), more than half of the antibiotic use in Japan might be

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inappropriate (i.e., the spectrum of prescribed antibiotics is unnecessarily broad). However, the

appropriateness of the use could not be estimated, as the details of antibiotic prescription

patterns were limited. Only a few studies have described antibiotic prescription patterns in

Japan, and they are restricted to acute respiratory infections and gastroenteritis (Okubo et al.,

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2019, Yoshida et al., 2018). In addition, antibiotic prescription rates per population, frequently

used as a measurement of antibiotic use in many countries (Dolk et al., 2018, King et al., 2019),

have heretofore been unavailable in Japan. Antibiotic prescription rates are useful especially in

the case of pediatric patients and patients with impaired renal function, because antibiotic use

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measured by DDD methods will be underestimated for these patients (Polk et al., 2007).

Japan provides a universal health insurance system that covers almost all inhabitants of

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Japan, a nation with a population of 127 million (Statistics Bureau, Ministry of Internal Affairs

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and Communications, 2019). In this study, we describe the outpatient antibiotic prescription

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pattern and estimate the antibiotic prescription rate and proportion of first-line use of antibiotics

using the national database, which includes nearly 95% of health insurance claims data in Japan.

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This study is intended to fill the knowledge gap about the effective targets of the current global

antimicrobial stewardship campaigns, in light of the fact that Japan is the country consuming

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the fifth greatest amount of antibiotics among 65 countries participating in the global survey by

WHO (World Health Organization, 2018).

Methods

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Universal health coverage in Japan

In Japan, almost all legal residents are enrolled in a universal health coverage insurance

program provided by either the Employees’ Health Insurance System (for employed workers

and their dependents) or the National Health Insurance System (for self-employed or

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unemployed people). The health insurance pays 70–90% of the medical costs depending on age,

and the patients pay the rest as co-payment at their visits. There is no option for private

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insurance to subsidize the co-payment. For children, the co-payment is further subsidized by

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municipalities providing access to medical care almost free of charge (Sakamoto et al., 2018).

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Patients can choose to attend any clinics (either specialized or general internal medicine) or the

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outpatient department at any general hospital (without referral).

Study design and data source

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We conducted a retrospective descriptive analysis using the National Database of Health

Insurance Claims and Specific Health Checkups of Japan (NDB). The NDB is a nationwide

administrative claims database created by the Ministry of Health, Labour and Welfare of Japan.

The NDB comprises all electronically recorded health insurance claims data from April 2009.

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Almost all citizens in Japan are included in the NDB except for patients covered by publicly-

funded healthcare and patients on public assistance (two million people, 1.6% of Japanese

nationals) (Ikegami et al., 2011): People on public assistance are de-enrolled from the National

Health Insurance system and their health-care benefits are fully financed by the government.

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Costs associated with publicly-funded healthcare (e.g., medical care for line-of-duty injuries of

soldiers) are not paid by the National or Employees’ Health Insurance System but by the

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government (Sakamoto et al., 2018). Also, a few claims data in non-electronic form (< 5%)

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were not included in the database.

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The NDB comprises medical and pharmacy claims. It includes information about patients’

sex, age, identification number, diagnostic codes with date of diagnoses, medical procedural

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codes, drug codes and date of prescription, and the prefecture where the medical facility is

located. Identification numbers are generated by the NDB system by combining sex, birthdate,

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and the insurance identification number for the sole purpose of linking the medical and

pharmacy claims. This ID number is untraceable. The dataset used for analysis had been further

de-identified. Diagnoses are recorded by medical doctors at each medical facility and coded

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according to the International Classification of Diseases and Related Health Problems, 10th

Revision (ICD-10).

Some diagnoses that included details beyond the ICD-10 coding (e.g., diagnoses with

anatomical sites) were regarded as uncoded. These uncoded diagnoses were unavailable in this

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study due to the restrictions for research purposes. The prevalence of uncoded diagnoses was

estimated to be approximately 9% in 2010 (Tanihara, 2014). Since diagnostic codes were

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recorded only at the first visit in one illness episode, information on the exact dates of follow-up

Data preparation

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visits were not captured in this study.

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To analyze the claims data of each patient, the medical and pharmacy claims on the database

were combined using the patient identification numbers. From the combined claims data, we

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identified all outpatients who were newly diagnosed with any infectious diseases between April

2012 and March 2015. Infectious diseases diagnoses were classified into 20 diagnosis

categories, and then further categorized into three groups depending on the indication of

antibiotics (Supplementary Table 1), according to the study by Fleming-Dutra et al. (2016).

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To assess the appropriateness of the antibiotic prescriptions by calculating the proportion of

visits with antibiotics prescribed, we first comprehensively extracted the claims of the patients

with infectious disease diagnosis code(s), then antibiotic prescription fills were identified and

linked to the diagnosis if the date of diagnosis and the date of prescription were the same. In the

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Japanese health insurance system, the cost of antibiotics cannot be reimbursed without

corresponding infectious disease diagnoses. Therefore, it is unlikely that antibiotics are

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prescribed without any infectious disease diagnoses. Only oral antibiotics were included.

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Antibiotics were categorized according to the Anatomical Therapeutic Chemical (ATC)

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classification system (http://www.whocc.no/atcddd/) into: tetracyclines (J01A), penicillins

(J01C), first- and second-generation cephalosporins (J01DB and J01DC), third-generation

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cephalosporins (J01DD), penems (J01DH, J01DI), sulphonamides and trimethoprim (J01E),

macrolides (J01FA), lincosamides (J01FF), quinolones (J01M), and others (J01B, J01G, and

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J01X). We divided cephalosporins into first/second-generation and third generation because

third-generation cephalosporins accounted for most of cephalosporins used in Japan (Hashimoto

et al., 2019, Tsutsui et al., 2018). We grouped third-generation cephalosporins, quinolones, and

macrolides as “broad-spectrum antibiotics” in this study.

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If multiple antibiotics were prescribed on the same day, different antibiotics were counted

separately to calculate the proportions of each antibiotic, and were counted collectively to

calculate the proportions of any antibiotic prescriptions. If multiple infectious diagnoses were

made on the same day, a proportional weight divided by the number of infectious disease

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diagnoses was assigned to each diagnosis as the number of prescriptions (e.g., if one antibiotic

was prescribed and two diagnoses were made in a day, 0.5 was assigned to each diagnosis as the

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number of prescriptions).

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Estimating the frequency of antibiotic prescription

The number and mean annual rates of antibiotic prescription stratified by age group (0–9,

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10–19, 20–64, ≥ 65 years), sex, and antibiotic categories were calculated. Population estimates

on October 1, 2012–2014 by the Statistics Bureau, Ministry of Internal Affairs and

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Communications, were used as the population denominators (Statistics Bureau, Ministry of

Internal Affairs and Communications, 2019). The mean annual prescription rates per 1000

population and the proportion of infectious disease outpatient visits with antibiotic prescriptions

were calculated by diagnosis and antibiotic categories. For acute respiratory infections (viral

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upper respiratory infections [URI], pharyngitis, bronchitis, and sinusitis) and gastrointestinal

infections, mean annual prescription rates per 1000 population and proportions of infectious

disease visits with any antibiotic prescription were calculated by age group and sex. To clarify

the sex differences in outpatient visit rates and antibiotic prescription rates, the confidence

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intervals (CI) of the incidence rate ratio (IRR) by the exact method using the Poisson

distribution were calculated using Stata MP 15.1 (StataCorp, TX, USA). To describe the

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geographical variation, mean annual rates per 1000 population of antibiotic prescriptions in each

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prefecture were standardized by age group as defined above and by sex, taking account of the

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different population structures. The age and sex distribution of the population of Japan in

October 2013 was used as the standard population (Statistics Bureau, Ministry of Internal

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Affairs and Communications, 2019).

Clinical guidelines recommend first-line antibiotic treatment for pharyngitis, sinusitis, otitis

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media, urinary tract infections, gastrointestinal infections, and skin and soft tissue infections

(Supplementary Table 2). The proportions of use of first-line antibiotics were calculated for

these diagnoses.

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Results

In total 268,733,953 antibiotic prescriptions were filled at 266,470,173 visits out of the

659,333,605 outpatient care visits with diagnoses of infectious diseases between April 1, 2012,

and March 31, 2015. An average of 89.6 million antibiotics were prescribed for infectious

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disease visits every year, with an average annual antibiotic prescription rate of 704 per 1000

population (Table 1). Female patients received more antibiotic prescriptions than male patients

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(755 vs 650 prescriptions per 1000 population per year). The most frequently prescribed

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antibiotic was third-generation cephalosporins (260 prescriptions per 1000 population per year,

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36.9% of all antibiotics prescribed), followed by macrolides (203, 28.8%) and quinolones (143,

20.3%). The prescriptions of penicillins comprised only 7.2%.

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Among different age groups, the average annual rate of antibiotic prescriptions was the

highest in children (0–9 years, 2238 prescriptions per 1000 population), followed by adolescents

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(10–19 years, 783 prescriptions per 1000 population), people aged 20–64 years (564

prescriptions per 1000 population), and the elderly (≥ 65 years, 484 prescriptions per 1000

population). Third-generation cephalosporins, macrolides, and quinolones were the most

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prescribed antibiotics regardless of age group except in children, for whom the prescription of

quinolones is not recommended (Table 1).

The pattern of antibiotic prescriptions stratified by antibiotic class and infectious disease

diagnosis is shown in Table 2. The annual rate of visits with any antibiotic prescription was 698

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per 1000 population per year. Antibiotic prescriptions were most frequently issued for Group 3

infections, for which antibiotics are rarely indicated (391 prescriptions per 1000 population per

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year), followed by Group 2 infections, for which antibiotics are potentially indicated (248

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prescriptions per 1000 population), and Group 1 infections, for which antibiotics are rarely

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indicated (59 prescriptions per 1000 population). The most frequent diagnosis with antibiotic

prescription was bronchitis (184 prescriptions per 1000 population per year), followed by viral

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URI (166 prescriptions), pharyngitis (104 prescriptions), sinusitis (52 prescriptions),

gastrointestinal infections (41 prescriptions), urinary tract infections (33 prescriptions), and

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skin, cutaneous, and mucosal infections (31 prescriptions). The proportions of visits with any

antibiotics prescribed were 58.2% (bronchitis), 40.5% (viral URI), 58.8% (pharyngitis), 54.1%

(sinusitis), 25.8% (gastrointestinal infections), 68.8% (urinary tract infections), and 51.7%

(skin, cutaneous and mucosal infections), respectively. Among those visits with any antibiotic

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prescribed, first-line antibiotics were prescribed for 8.8% of pharyngitis, 9.8% of sinusitis,

23.0% of suppurative otitis media, 44.9% of gastrointestinal infections, 61.1% (adults) and

66.7% (children) of urinary tract infections, and 16.5% of skin, cutaneous, and mucosal

infections (Figure 1). In total, first-line antibiotics were prescribed for only 24% of these

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conditions.

The pattern of antibiotic prescription among different age groups was tabulated by sex and

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the diagnosis of common infectious diseases (acute respiratory infections and gastrointestinal

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infections) (Table 3). Children aged 0–9 years received more antibiotics in number and had

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more visits to medical facilities than other age groups: The rate of visits with any infection was

5892 per 1000 population per year. In contrast, the proportion of any antibiotic prescription was

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higher among those 10–64 years old for Group 2 and Group 3 infections than those 0–9 and ≥

65 years old. A similar tendency was observed for each common infectious disease diagnosis

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(bronchitis, viral URI, pharyngitis, sinusitis, and gastrointestinal infection). The rates of overall

outpatient visits and any antibiotic prescription were not clinically different (< 10%) between

males and females in younger age groups less than 20 years old. In adults (20–64 years old),

however, women had more outpatient visits (IRR 1.55; 95% CI, 1.55–1.55) and received more

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antibiotics (IRR 1.45; 95% CI, 1.45–1.46). In the elderly, a similar difference existed but to a

lesser extent: IRR 1.24 (95% CI, 1.24–1.24) for overall outpatient visits and IRR 1.17 (95% CI,

1.17–1.17) for antibiotic prescriptions (Supplementary Table 3). In contrast, the proportions of

antibiotic prescription per visit were not clinically different (< 3%) between males and females

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in all age groups (Table 3).

There was considerable antibiotic prescribing variation across prefectures (Supplementary

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Table 4, Figure 2). Antibiotics were prescribed more frequently in western Japan. The top three

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prefectures with the highest rates of antibiotic prescription standardized by age and sex were

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Tokushima (885 prescriptions per 1000 population per year), Kumamoto (852 prescriptions),

and Gifu (840 prescriptions). The three prefectures with the lowest rates of antibiotic

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prescription were Hokkaido (557 prescriptions per 1000 population per year), Iwate (584

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prescriptions), and Saitama (624 prescriptions).

Discussion

This study describes Japanese nationwide antibiotic prescription patterns linked to infectious

disease diagnoses. The annual rate of prescription of oral antibiotics linked to infectious

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diagnostic code (704 per 1000 population) was higher than that found in a US study based on

the national survey data of ambulatory care settings (506 per 1000 population, Fleming et al.,

2016). In total, 56% of oral antibiotic prescriptions were directed (filled) for Group 3 infections,

for which antibiotics are rarely indicated; in other words, oral antibiotics was prescribed

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approximately 6.6 times more frequently for Group 3 infections than for Group 1 infections, for

which antibiotics are usually indicated. In addition, the majority (86%) of oral antibiotics

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prescribed were broad-spectrum (i.e., third-generation cephalosporins, macrolides, or

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quinolones).

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Previous studies in high-income countries (Dolk et al., 2018, Hicks et al., 2015, Kim et al.,

2018) showed smaller proportions of broad-spectrum antibiotics. A US study (Hicks et al.,

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2015) analyzing oral antibiotic prescriptions dispensed during 2011 revealed 842 antibiotic

prescriptions per 1000 persons in a year. The most prescribed antibiotics were penicillins

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(23%), followed by macrolides (23%) and cephalosporins (14%). Another study analyzing a

primary care electronic database in the United Kingdom (UK) revealed an antibiotic prescribing

rate with a median of 626 per 1000 population per year during 2013–2015 (Dolk et al., 2018).

In the UK study, penicillins were prescribed most frequently (50%), followed by macrolides

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(13%) and tetracyclines (12%). A similar trend was observed in a study in Asia (Kim et al.,

2018); the most prescribed antibiotics, defined by DDD per 1000 inhabitants per day, were

penicillins (38%), followed by cephalosporins (26%) and macrolides (12%).

In the present study, 56% of antibiotics were prescribed for conditions where antibiotic was

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generally not indicated. This is greater than found in a US study (Chua et al., 2019) that

analyzed claims database in 2016 to show that 23% of antibiotics were associated only with

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diagnosis codes classified into diagnoses for which antibiotics are never justified and are judged

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inappropriate. Of these, most antibiotics were prescribed for acute respiratory infections in

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Japan. Bronchitis (184 prescriptions per 1000 population per year, 23 million prescriptions per

year in number) and viral URI (166 prescriptions per 1000 population per year, 21 million

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prescriptions per year in number) were the top two diagnoses with frequent oral antibiotic

prescription. These antibiotic prescription rates were higher than those found in a US study:

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bronchitis or bronchiolitis (25 prescriptions per 1000 population per year), and viral URI (26

prescriptions per 1000 population per year) (Fleming-Dutra et al., 2016). Because viral URI and

most cases of bronchitis are caused by viral pathogens, these antibiotics are unnecessary and

potentially harmful (Harris et al., 2016).

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Antibiotics were also frequently prescribed for pharyngitis, sinusitis, gastrointestinal

infections, urinary tract infections, and skin, cutaneous, and mucosal infections. For these

infections, antibiotics are usually or potentially indicated, but first-line antibiotics were

prescribed for them in only 24% of the cases in Japan. In particular, the first-line antibiotics

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were prescribed for less than 10% of pharyngitis and sinusitis cases. These rates are much lower

than in a US study that found that first-line antibiotics accounted for approximately half of the

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antibiotics prescribed for pharyngitis and sinusitis (Hersh et al., 2016). Taken together, in Japan,

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most antibiotics are prescribed either unnecessarily or inappropriately (i.e., > 50% were

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unnecessary and > 70% for common infections were inappropriately broad).

Our study also demonstrated considerable variations in visit rates per population associated

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with infectious diseases with and without antibiotic prescription, according to age, sex, and

geographical region. For age, young patients (especially children aged 0–9 years) visited

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medical facilities more frequently (5892 visits per 1000 population per year) and received more

antibiotics (2226 prescriptions per 1000 population per year) than other age groups. This trend

is consistent with the US study, but Japanese children had more frequent annual visit rates and

approximately twice the number of antibiotic prescriptions than US children per capita: In the

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2005–2006 US ambulatory care setting, annual overall visit rates (including visits for non-

infectious disease) and antibiotic prescription rates per 1000 population for children aged less

than 5 years were 5174 and 1128, respectively (Grijalva et al., 2009). This is consistent with the

findings of a previous study reporting that Japanese children had 2.5-fold more physician’s

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clinic visits and 11-fold more hospital-based clinic visits than US children (Ishida et al., 2012).

Our study showed additionally that Japanese children received around twice as many antibiotic

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prescriptions as US children per capita (Grijalva et al., 2009). Some of these differences

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between Japanese and US children may be explained by the differences in the healthcare

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system. All Japanese inhabitants are covered by public health insurance and have free access to

medical care services at a low cost; moreover, they can choose and consult doctors freely and on

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the same day as their visit. In addition, children can consult a doctor and receive treatment

almost free of charge because out-of-pocket medical expenses for children are subsidized by

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municipalities in Japan (Sakamoto et al., 2018). Children whose out-of-pocket expenses were

subsidized by the municipality were reported to have more outpatient visits associated with

acute URI than children not subsidized (Miyawaki et al., 2017). Unexpectedly, antibiotic

prescription rates for the elderly were the lowest among the four age groups. This is not

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consistent with a previous Japanese study that analyzed the amount of antibiotic use using NDB

and revealed that the elderly received more antibiotics (daily doses defined per 1000 inhabitants

per day) than did middle-aged patients in the outpatient settings (Yamasaki et al., 2018).

Antibiotic prescriptions for the elderly may be lacking in this study because they are more likely

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to receive drugs by reusing the diagnosis codes that had been registered before, which is a

limitation of this study that should be taken into consideration. However, this hypothesis needs

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to be confirmed in another study.

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As for sex differences, female patients received 16% more antibiotics than male patients. In

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the 20–64 years age group especially, female patients received antibiotic prescriptions 45%

more often than male patients. This is in accordance with a recent meta-analysis, which found

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that female patients were 27% more likely to receive antibiotics than male patients over their

lifetimes (in particular, in the 16–54-year-old age group, 36 to 40% higher amounts of

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antibiotics were prescribed for women) (Schroder et al., 2016). A UK study indicated that sex

differences in antibiotic prescribing might be due to different consultation behaviors: Female

patients (especially adults) consulted a doctor more frequently than male patients, although

there was little sex difference in the proportion of visits receiving antibiotic prescriptions when

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consulting (Smith et al., 2018). Indeed, in our study, adult female patients had more frequent

visits than male patients, but the proportions of visits receiving antibiotic prescriptions were

similar between male and female patients. Another possible explanation for this difference is

that the incidence rates of some infections (such as urinary tract infections) might be higher in

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women, although this is not likely to explain the difference in this study for the following

reasons: The overall incidence of urinary tract infection was much lower than that of other

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infections, and the visit rates of adult female patients were higher than male patients for not only

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urinary tract infections but also other infections including bronchitis, viral URI, pharyngitis,

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sinusitis, and gastrointestinal infections (data not shown).

There is a considerable geographical variation in age- and sex-standardized antibiotic

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prescription rates. The standardized antibiotic prescription rate in the prefecture with the most

frequent prescriptions (Tokushima) was 1.6-fold higher than that in the prefecture with the least

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frequent prescriptions (Hokkaido). A recent US study showed geographical variation in

antibiotic prescriptions and identified several socio-economic factors associated with the

geographical differences: low education and income levels; high prevalence rates of obesity;

and more providers per population (Hicks et al., 2015). In Japan, there is a trend for more

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doctors per population to be observed in western Japan (Ministry of Health, Labour and

Welfare, 2016), which might be related to the higher antibiotic prescription rate. Additional

studies are needed to clarify patient demographic factors determining health seeking behavior

and provider characteristics associated with higher antibiotic prescriptions.

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This study revealed high prescribing rates of broad-spectrum oral antibiotics in Japan, while

there were no guidelines on antimicrobial stewardship during the study period, and presumably

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the concept of the proper use of antibiotics was not widely prevalent among medical doctors in

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Japan around that time. Since national guidelines on proper antibiotic use were instituted in

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2017 (The Government of Japan, Ministry of Health, Labour and Welfare, 2017) and the

monetary incentives for antimicrobial stewardship intervention started in 2018, the quality of

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antibiotic prescribing is expected to have improved.

The strength of this study is its comprehensiveness. In Japan, oral antibiotics are not available

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over the counter, but are prescribed by doctors and reimbursed by medical insurance. Nurses or

pharmacists cannot prescribe medication, and nurse practitioners do not exist in Japan.

Therefore, information on almost all oral antibiotics is included in the NDB. Moreover, this

study is unique in that almost all citizens were included, which allows a precise description of

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antibiotic prescription patterns across the entire country. This study gives a reliable and

comprehensive summary before the current national campaign, which will be necessary to

assess its outcomes.

Our study has several limitations. First, a certain number of prescriptions were uncaptured in

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this study. As described in the methods, these include prescriptions for publicly-funded

healthcare and people on public assistance-associated healthcare, and prescriptions linked to

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uncoded diagnoses. Another possible reason for uncaptured prescriptions might be the reuse of

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the diagnosis codes that had been registered before, although in principle the date must be

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updated at the same time that the diagnosis is reused. This might be more common in the

elderly, who are assumed to be more likely to visit the same clinic for different episodes of the

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same diagnosis. In addition, in our study antibiotic prescriptions could be captured only when

antibiotics were prescribed on the same day, at the first visit of the illness episode: Antibiotics

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prescribed at any re-visits with the same diagnosis were not included. Also, antibiotics

prescribed by dentists were not included in this study. In total, this study would miss ~30% of

all oral antibiotic prescriptions, based on our previous study analyzing the administrative claims

database in one prefecture of Japan (Hashimoto et al., 2019). Therefore, the antibiotic

25

prescription rates in our study may be underestimated but are less likely to be overestimated.

Our findings of over-prescription in Japan will thus remain unchanged. Second, due to the

nature of the administrative claims database, the accuracy of the diagnosis (coding) may not be

fully reliable. In the US, in a single-center study (Livorsi et al., 2018), ICD-10 codes for cystitis

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and pneumonia have been reported to have limited sensitivity (66% and 56%, respectively) and

positive predictive value (74% and 53%, respectively), although this is not directly applicable to

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our settings, as the health insurance system is totally different. The reliability of coding in the

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NDB should be validated in another study.

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Third, since we used the claims data for 2012—2015, the results of this study may need to be

updated using more recent data collected after the national antimicrobial stewardship campaign

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started in 2016.

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Conclusions

In conclusion, based on data from the NDB during 2012–2015, there was an estimated

average annual antibiotic prescription rate of 704 per 1000 population associated with infectious

diagnosis codes. Prescriptions of oral antibiotics should be reduced at least 50% based on our

26

data, showing that > 50% of them (391 per 1000 population) were prescribed for conditions

where antibiotics are generally not indicated. Broad-spectrum antibiotics were too frequently

prescribed and most of them were prescribed for acute respiratory infections, which should be

the main targets of antimicrobial stewardship intervention. The quality of antibiotic prescribing

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should be improved. More antibiotics were prescribed for young patients, female patients, and

patients living in western Japan. Further studies of the factors determining health seeking

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behavior and antibiotic prescriptions are warranted. The results of this study will be useful as a

re

benchmark to assess the impact of Japan’s AMR campaigns on antibiotic prescribing launched

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Author’s contributions

lP

in 2016.

HH and SH had full access to all of the data in the study and take responsibility for the integrity

Jo

of the data and the accuracy of the data analysis. HH and SH conceived the study, interpreted

the data and results, and drafted the manuscript. HH and JS, KG, and MK collected and

organized the data. JS, KG, and MK developed the system for managing and analyzing the data.

HH analyzed and interpreted the data. MS interpreted the analysis results and drafted and

27

revised the manuscript. NM and RN conceived the study and collected and interpreted the data.

All authors critically revised the manuscript for intellectual content. All authors read and

approved the final manuscript.

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Funding

This work was supported in part by the Japan Agency for Medical Research and Development

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(Grant nos. 16lk1010017h000, 18lk1010033h0001, and grant name, ICT Infrastructure

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Establishment and Implementation of Artificial Intelligence for Clinical and Medical Research);

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Ministry of Health, Labour and Welfare, Japan (Grant name, Health Labour Science Research

Grant); Cabinet Office, Government of Japan (Grant names, Funding Program for World-

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Leading Innovative R&D on Science and Technology, Impulsing Paradigm Change through

Disruptive Technologies Program); and MEXT KAKENHI (Grant Number, 16K09254). The

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funders had no role in the study design, data collection, data analysis, data interpretation,

writing of the manuscript, or decision to submit the manuscript for publication.

Ethical approval

28

This study was reviewed and approved by the Institutional Review Board of University of

Tokyo (18-113). Informed consent was waived because the used claims data were fully

anonymized and not traceable.

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Conflict of interest statement

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None.

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Acknowledgements

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Not applicable.

29

References

1.

Chua KP, Fischer MA, Linder JA. Appropriateness of outpatient antibiotic prescribing

among privately insured US patients: ICD-10-CM based cross sectional study. BMJ.

2019; 364: k5092.

Dolk FCK, Pouwels KB, Smith DRM, Robotham JV, Smieszek T. Antibiotics in primary

ro of

2.

care in England: which antibiotics are prescribed and for which conditions? J Antimicrob

Fleming-Dutra KE, Hersh AL, Shapiro DJ, Bartoces M, Enns EA, File TM Jr, et al.

re

3.

-p

Chemother 2018; 73: ii2–10.

lP

Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits,

2010-2011. JAMA 2016; 315: 1864–73.

Grijalva CG, Nuorti JP, Griffin MR. Antibiotic prescription rates for acute respiratory

ur na

4.

tract infections in US ambulatory settings. JAMA 2009; 302: 758–66.

Harris AM, Hicks LA, Qaseem A. Appropriate Antibiotic Use for Acute Respiratory

Jo

5.

Tract Infection in Adults: Advice for High-Value Care From the American College of

Physicians and the Centers for Disease Control and Prevention. Ann Intern Med 2016;

164: 425–34.

30

6.

Hashimoto H, Matsui H, Sasabuchi Y, Yasunaga H, Kotani K, Nagai R, et al. Antibiotic

prescription among outpatients in a prefecture of Japan, 2012–2013: A retrospective

claims database study. BMJ open 2019; 9: e026251.

7.

Hersh AL, Fleming-Dutra KE, Shapiro DJ, Hyun DY, Hicks LA; Outpatient Antibiotic

ro of

Use Target-Setting Workgroup. Frequency of First-line Antibiotic Selection Among US

Ambulatory Care Visits for Otitis Media, Sinusitis, and Pharyngitis. JAMA Intern Med

Hicks LA, Bartoces MG, Roberts RM, Suda KJ, Hunkler RJ, Taylor TH Jr, et al. US

re

8.

-p

2016; 176: 1870–2.

lP

outpatient antibiotic prescribing variation according to geography, patient population, and

provider specialty in 2011. Clin Infect Dis 2015; 60: 1308–16.

Ikegami N, Yoo BK, Hashimoto H, Matsumoto M, Ogata H, Babazono A, et al. Japanese

ur na

9.

universal health coverage: evolution, achievements, and challenges. Lancet 2011; 378:

Jo

1106–15.

10.

Ishida Y, Ohde S, Takahashi O, Deshpande GA, Shimbo T, Hinohara S, et al. Factors

affecting health care utilization for children in Japan. Pediatrics 2012; 129: e113–9.

11.

Kim YA, Park YS, Youk T, Lee H, Lee K. Changes in Antimicrobial Usage Patterns in

31

Korea: 12-Year Analysis Based on Database of the National Health Insurance Service-

National Sample Cohort. Sci Rep. 2018; 8: 12210.

12.

King LM, Bartoces M, Fleming-Dutra KE, Roberts RM, Hicks LA. Changes in US

ciz225. doi: 10.1093/cid/ciz225. [Epub ahead of print]

13.

ro of

Outpatient Antibiotic Prescriptions from 2011-2016. Clin Infect Dis. 2019 Mar 16. pii:

Livorsi DJ, Linn CM, Alexander B, Heintz BH, Tubbs TA, Perencevich EN. The

-p

Value of Electronically Extracted Data for Auditing Outpatient Antimicrobial Pres

Ministry of Health, Labour and Welfare. Survey of Physicians, Dentists and Phar

lP

14.

re

cribing. Infect Control Hosp Epidemiol. 2018; 39: 64–70.

ur na

macists 2016. https://www.mhlw.go.jp/english/database/db-hss/spdp.html (Accessed:

November 5, 2019).

15.

Miyawaki A, Noguchi H, Kobayashi Y. Impact of medical subsidy disqualification on

Jo

children's healthcare utilization: A difference-in-differences analysis from Japan. Soc Sci

Med 2017; 191: 89–98.

16.

Okubo Y, Miyairi I, Michihata N, Morisaki N, Kinoshita N, Urayama KY, et al. Recent

Prescription Patterns for Children with Acute Infectious Diarrhea. J Pediatr

32

Gastroenterol Nutr 2019; 68: 13–6.

17.

Polk RE, Fox C, Mahoney A, Letcavage J, MacDougall C. Measurement of adult

antibacterial drug use in 130 US hospitals: comparison of defined daily dose and days of

therapy. Clin Infect Dis. 2007; 44: 664–70.

Sakamoto H, Rahman M, Nomura S, Okamoto E, Koike S, Yasunaga H, et al. Japan

ro of

18.

Health System Review. 2018. World Health Organization, Regional Office for South-East

Schroder W, Sommer H, Gladstone BP, Foschi F, Hellman J, Evengard B, et al. Gender

re

19.

-p

Asia. http://www.who.int/iris/handle/10665/259941 (Accessed: November 5, 2019).

lP

differences in antibiotic prescribing in the community: a systematic review and meta-

analysis. J Antimicrob Chemother 2016; 71: 1800–6.

Smith DRM, Dolk FCK, Smieszek T, Robotham JV, Pouwels KB. Understanding the

ur na

20.

gender gap in antibiotic prescribing: a cross-sectional analysis of English primary care.

Jo

BMJ open 2018; 8: e020203.

21.

Statistics Bureau, Ministry of Internal Affairs and Communications. Population Es

timates (Monthly Report). https://www.e-stat.go.jp/en/stat-search/files?page=1&layou

t=datalist&tstat=000000090001&cycle=1&tclass1=000001011678&second2=1 (Acces

33

sed: November 5, 2019).

22.

Tanihara S. The Proportion of Uncoded Diagnoses in Computerized Health Insurance

Claims in Japan in May 2010 According to ICD-10 Disease Categories. J Epidemiol

2014; 24: 392–6.

The Government of Japan. National Action Plan on Antimicrobial Resistance (A

ro of

23.

MR) 2016-2020. 2016. https://www.mhlw.go.jp/file/06-Seisakujouhou-10900000-Ken

The Government of Japan, Ministry of Health, Labour and Welfare. Manual of A

re

24.

-p

koukyoku/0000138942.pdf (Accessed: November 5, 2019).

lP

ntimicrobial Stewardship (1st Edition). 2017. http://www.mhlw.go.jp/file/06-Seisakuj

25.

ur na

ouhou-10900000-Kenkoukyoku/0000193504.pdf (Accessed: November 5, 2019).

The Review on Antimicrobial Resistance. Tackling Drug-Resistant Infections Glob

ally: final report and recommendations. 2016. https://amr-review.org/sites/default/fil

Jo

es/160518_Final%20paper_with%20cover.pdf (Accessed: November 5, 2019).

26.

Tsutsui A, Yahara K, Shibayama K. Trends and patterns of national antimicrobial

consumption in Japan from 2004 to 2016. J Infect Chemother 2018; 24: 414–2

1.

34

27.

World Health Organization. Global action plan on antimicrobial resistance. 2015.

http://apps.who.int/iris/bitstream/10665/193736/1/9789241509763_engpdf?ua=1 (Acce

ssed: November 5, 2019).

28.

World Health Organization. WHO Report on Surveillance of Antibiotic Consumpti

ro of

on 2016 - 2018 Early implementation. 2018. https://www.who.int/medicines/areas/r

ational_use/oms-amr-amc-report-2016-2018/en/ (Accessed: November 5, 2019).

Yamasaki D, Tanabe M, Muraki Y, Kato G, Ohmagari N, Yagi T. The first report of

-p

29.

re

Japanese antimicrobial use measured by national database based on health insurance

lP

claims data (2011-2013): comparison with sales data, and trend analysis stratified by

30.

ur na

antimicrobial category and age group. Infection 2018; 46: 207–14.

Yoshida S, Takeuchi M, Kawakami K. Prescription of antibiotics to pre-school c

hildren from 2005 to 2014 in Japan: a retrospective claims database study. J Pu

Jo

blic Health (Oxf) 2018; 40: 397–403.

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Figure Legends

Figure 1. Proportion of prescription rate with first-line and non-first-line antibiotics for

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common infections in Japan (2012–2015).

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Abbreviations: 1st/2nd cephem, first/second generation cephalosporins; 3rd cephem, third

generation cephalosporins; SUL/TMP, sulphonamides and trimethoprim; GI infections,

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gastrointestinal infections; UTI, urinary tract infections.

(a) Respiratory infections. Antibiotics were categorized according to Anatomical Therapeutic

Chemical (ATC) codes: Penicillins, J01C; Third generation cephalosporins, J01DD;

Macrolides, J01FA; Quinolones, J01M; Other antibiotics, J01A, J01B, J01DB, J01DC, J01DH,

36

J01DI, J01E, J01FF, J01G, and J01X. (b) Non-respiratory infections. Antibiotics were

categorized according to ATC codes: Penicillins, J01C; First generation cephalosporins, J01DB;

Second generation cephalosporins, J01DC; Third generation cephalosporins, J01DD;

Macrolides, J01FA; Quinolones, J01M; Sulphonamides and trimethoprim, J01E; Tetracyclines,

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J01A; Lincosamides, J01FF; Other antibiotics, J01B, J01DH, J01DI, J01G, and J01X.

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Figure 2. Age and sex-standardized antibiotic prescriptions per 1000 population per year

by prefecture of Japan (2012–2015).

37

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Table 1. Annual antibiotic prescription by antibiotic category and sex by age (2012–2015) All ages Ratec

10–19 years

Nb

Ratec

Nb

≥ 65 years

20–64 years Ratec

Nb

Ratec

Nb

Ratec

704

23.7

2238

9.3

783

41.1

564

15.5

484

Male

40.2

650

12.5

2308

4.7

783

16.9

460

6.1

442

Female

49.3

755

11.2

2164

4.5

783

24.2

669

9.4

517

3rd cephem

33.1

260

10.5

990

3.8

325

13.7

187

5.1

159

Macrolides

25.8

203

6.8

643

3.3

277

11.7

160

4.1

128

Quinolones

18.2

143

1.2

111

0.9

77

11.2

154

4.9

152

Penicillins

6.4

51

3.5

327

0.5

40

1.9

26

0.6

18

1.5

12

0.2

14

0.3

26

0.8

11

0.2

6

1.4

11

0.4

36

0.1

10

0.6

9

0.3

9

1.1

9

0.7

62

0.1

7

0.3

4

0.1

3

SUL-TMP

0.2

1

0.0

1

0.0

0

0.1

1

0.1

2

Lincosamides

0.1

0

0.0

0

0.0

1

0.0

1

0.0

0

Other

1.8

14

0.6

54

0.2

21

0.8

11

0.2

6

Overall

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89.6

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Nb

0–9 years

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Annual antibiotic prescriptiona

Characteristic

Sex

Tetracyclines

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Penems

ur n

1st/2nd cephem

al P

Antibiotic categoryc

1st/2nd cephem, first/second generation cephalosporins; 3rd cephem, third generation cephalosporins; SUL-TMP, sulphonamides and trimethoprim. a

Different antibiotics were counted separately if multiple antibiotics were prescribed on the same day.

b

Number of visits with antibiotic prescriptions per year (in millions).

c

Rate of visits with antibiotic prescriptions per 1000 population per year.

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c

Antibiotics were categorized according to Anatomical Therapeutic Chemical (ATC) codes: Penicillins, J01C; First generation cephalosporins,

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J01DB; Second generation cephalosporins, J01DC; Third generation cephalosporins, J01DD; Penems, J01DH and J01DI; Macrolides, J01FA;

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ur n

al P

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Lincosamides, J01FF; Quinolones, J01M; Sulphonamides and trimethoprim, J01E; Tetracyclines, J01A; Other antibiotics, J01B, J01G, and J01X.

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Table 2. Annual outpatient care visits and antibiotic prescription per 1000 population by diagnosis and antibiotic category (2012–2015) (per 1000

Any antibiotic

population

(%)a

Penicillins

per year

Group 1c

59 (57.0%)

Urinary tract infections

48

33 (68.8%)

Pneumonia

16

9 (56.2%)

Abdominal infections

4

STD

1st/2nd

3rd

Macrolide

Quinolone

cephem

cephem

s

s

SUL-TMP

Tetracycline

Other

s

antibiotics

5

1

17

7

27

1

1

2

1

1

10

1

19

0

1

1

0

0

1

2

5

0

0

0

2 (50.0%)

0

0

1

0

1

0

0

0

6

2 (33.3%)

0

0

0

1

0

0

0

1

30

13 (43.3%)

4

0

5

3

1

0

0

0

533

248 (46.5%)

21

4

97

59

46

0

7

16

177

104 (58.8%)

9

2

48

26

18

0

1

1

96

52 (54.1%)

5

0

17

20

8

0

0

1

159

41 (25.8%)

3

0

9

5

13

0

0

11

60

31 (51.7%)

1

1

18

3

3

0

2

2

Suppurative otitis media

20

13 (64.3%)

3

0

5

1

4

0

0

1

Acnes

22

8 (35.2%)

0

0

1

3

0

0

3

0

1090

391 (35.8%)

25

6

146

137

69

0

4

5

Bronchitis

316

184 (58.2%)

9

2

54

79

37

0

2

2

Viral URI

410

166 (40.5%)

14

2

71

50

27

0

1

2

80

14 (17.5%)

1

1

10

1

1

0

0

0

infection Group 2c Sinusitis GI infections

Group 3

c

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Skin infections

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Pharyngitis

Trauma and burn

al P

Miscellaneous bacterial

re

103

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Annual rate of outpatient visits with antibiotic prescription (per 1000 population per year) b

All visits

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Diagnosis

1

1

Influenza

50

6 (11.3%)

0

0

Fever

14

4 (29.4%)

0

Nonsuppurative otitis

10

3 (34.2%)

0

3

0.2 (6.7%)

0.1

0.01 (10.0%)

infections Viral pneumonia

0 0

7

2

2

0

0

0

2

3

1

0

0

0

0

1

1

1

0

0

0

0

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

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Nonbacterial GI

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media

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13 (6.2%)

ro

208

Eye infection

1st/2nd cephem, first/second generation cephalosporins; 3rd cephem, third generation cephalosporins; SUL-TMP, sulphonamides and trimethoprim;

al P

URI, upper respiratory infections; GI infections, gastrointestinal infections; Skin infections, skin, cutaneous, and mucosal infections; Eye infections, infections of the eye and adnexa; STD, sexually transmitted diseases. a

Annual rate of visits with any antibiotic prescription (per 1000 population per year) with the proportion to the all visits by disease categories.

b

Antibiotics were categorized according to Anatomical Therapeutic Chemical (ATC) codes: Penicillins, J01C; First generation cephalosporins,

J01DB; Second generation cephalosporins, J01DC; Third generation cephalosporins, J01DD; Macrolides, J01FA; Quinolones, J01M; c

ur n

Sulphonamides and trimethoprim, J01E; Tetracyclines, J01A; Other antibiotics, J01B, J01DH, J01DI, J01FF, J01G, and J01X. Group 1, infections for which antibiotics are usually indicated; Group 2, infections for which antibiotics are potentially indicated; Group 3,

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infections for which antibiotics are rarely indicated. Corresponding ICD-10 codes are shown in Supplementary Table 1.

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Table 3. Annual outpatient visits and antibiotic prescription per 1000 population by age group, sex, and common infectious diagnosis (2012–2015) 0–9 years

10–19 years

Visits with antibiotic

All

Visits with antibiotic

All

Visits with antibiotic

All

Visits with antibiotic

visits

prescriptions (%)

visits

prescriptions (%)

visits

prescriptions (%)

visits

prescriptions (%)

1805

Male

6081

2295 (37.7%)

1813

Female

5694

2153 (37.8%)

Visits by sex

1499

478 (31.9%)

779 (43.0%)

950

456 (48.0%)

1317

435 (33.0%)

1470

663 (45.1%)

1635

511 (31.2%)

42

27 (64.4%)

85

48 (56.6%)

157

83 (53.0%)

887 (44.5%)

643

318 (49.4%)

384

199 (51.8%)

349

123 (35.1%)

1242 (33.0%)

1118

434 (38.8%)

737

311 (42.2%)

987

272 (27.6%)

615 (45.2%)

278

190 (68.5%)

200

140 (70.0%)

249

140 (56.1%)

505 (32.5%)

361

190 (52.6%)

278

141 (51.0%)

350

102 (29.1%)

613

328 (53.5%)

184

127 (69.1%)

131

88 (67.1%)

133

57 (43.5%)

374

204 (54.4%)

127

71 (55.7%)

69

41 (58.9%)

53

19 (35.8%)

655

152 (23.2%)

175

50 (28.4%)

111

33 (30.1%)

97

20 (20.5%)

Group 2

1992

Group 3

3760

Visits by diagnosis 1360

Viral URI

1554

96 (69.7%)

ur n

Bronchitis

a

al P

138

GI infections

559 (46.2%)

779 (43.4%)

Group 1

Sinusitis

1208

1796

Visits by tier classification of infection

Pharyngitis

779 (43.2%)

-p

2226 (37.8%)

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All 5892

Overall

≥ 65 years

20–64 years

ro

Characteristic

a

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URI, upper respiratory infections; GI infections, gastrointestinal infections. Group 1, infections for which antibiotics are usually indicated; Group 2, infections for which antibiotics are potentially indicated; Group 3,

infections for which antibiotics are rarely indicated. Corresponding ICD-10 codes are shown in Supplementary Table 1.