DOI: 10.5061/dryad.nk98sf7wb
title: Dataset for: Social dilemma in the excess use of antimicrobials incurring antimicrobial resistance

description: This is the dataset for the study of "Social dilemma in the excess
 use of antimicrobials incurring antimicrobial resistance". 
 The emergence of antimicrobial resistance (AMR) caused by the excess use
 of antimicrobials has come to be recognized as a global threat to public
 health. There is a ‘tragedy of the commons’ type social dilemma behind
 this excessive use of antimicrobials, which should be recognized by all
 stakeholders. To address this global threat, we thus surveyed eight
 countries/areas to determine whether people recognize this dilemma and
 showed that although more than half of the population pays little, if any,
 attention to it, almost 20% recognize this social dilemma, and 15–30% of
 those have a positive attitude toward solving that dilemma. We suspect
 that increasing individual awareness of this social dilemma contributes to
 decreasing the frequency of AMR emergencies.
description: We designed a questionnaire to observe a social dilemma in the
 excess use of antimicrobials incurring antimicrobial resistance by placing
 two types of imaginary artificial-intelligence (AI) physicians who perform
 medical practice from either an individual or societal
 perspective. We assume two AI medical diagnosis
 systems: “Individual precedence AI” (abbreviated Individual-AI) and “World
 precedence AI” (abbreviated World-AI). Both AIs diagnose and prescribe
 medicine automatically. The Individual-AI system diagnoses patients and
 prescribes medicine to prevent infections based on an individual
 perspective, including all prophylactic prescriptions against rare
 accidental infections (not yet present and unlikely to occur). It does not
 consider the global risk of AMR in the decision. The World-AI system,
 instead, takes into account the global mortality rate of AMR, aiming to
 reduce the total number of all AMR-related deaths. Because of this, this
 AI system does not prescribe antimicrobials against rare and
 not-yet-present infections.  This questionnaire design
 allows us to observe the social dilemma. For example, it shows a typical
 social dilemma caused by preferring the use of Individual-AI for
 diagnosing oneself but preferring the use of World-AI for diagnosing
 strangers.    The survey entitled
 “Survey on Medical Advancement” was administered to 8 countries/areas. The
 survey was conducted 4 times. For the two surveys in
 Japan, an internet survey company, Cross Marketing Inc.
 (https://www.cross-m.co.jp/en/), created the questionnaire webpages based
 on our study design. The company also collected the data. As of April
 2020, Cross Marketing Inc. has 4.79 million people in an active panel
 (survey participants who registered in advance). Here, the definition of
 an active panel is a survey respondent who has been active within the last
 year. For the panels, the questionnaire and response column were displayed
 on the website through which the respondents could complete and submit
 their responses. We extracted 500 submissions for each gender and each age
 group by random sampling from all samples collected during the survey
 periods. The surveys in the 7 countries/areas (i.e., the United States,
 the United Kingdom, Sweden, Taiwan, Australia, Brazil, and Russia) are
 conducted by Cint (https://www.cint.com/). Cint is the world’s largest
 consumer network for digital survey-based research. The headquarters of
 the company is in Sweden. Cint maintains a survey platform that contained
 more than 100 million consumer monitors in over 80 countries as of May
 2020. For surveys in the US, UK, Sweden, Taiwan, Australia, Brazil, and
 Russia, Cint Japan (https://jp.cint.com/), which is the Japanese
 distributor of Cint, created translated questionnaire webpages based on
 our study design. The company also collected the data. We extracted at
 least 500 (US, UK, SWE, BRA, RUS) or 250 (TWN, AUS) submissions for each
 gender (male and female) and each age group (20 s, 30 s, 40 s, 50 s, and
 60 s) by random sampling from all samples collected between survey
 periods. Note that both companies eliminated
 inconsistent or apathetic respondents. For example, respondents with
 inconsistent responses (e.g., the registered age of the respondent
 differed from the reported age at the time of the survey.) were eliminated
 before reaching the authors. In addition, respondents with significantly
 short response times (i.e., shorter than 1 min) were eliminated because
 they may not have read the questions carefully.