| Literature DB >> 34688313 |
Qiuyan Liao1, Jiehu Yuan2, Meihong Dong2, Pauline Paterson3, Wendy Wing Tak Lam2.
Abstract
BACKGROUND: How antimicrobial resistance (AMR) risk is communicated in news media can shape public understanding and the engagement of different sectors with AMR. This study examined online news media attention for AMR risk and analyzed how AMR risk was communicated using a global sample of English and Chinese news articles.Entities:
Keywords: Antimicrobial resistance; Risk communication; Risk representation
Mesh:
Year: 2021 PMID: 34688313 PMCID: PMC8542296 DOI: 10.1186/s13756-021-01015-5
Source DB: PubMed Journal: Antimicrob Resist Infect Control ISSN: 2047-2994 Impact factor: 4.887
Searching key words for Chinese and English news
| #1 Headlines contain: | Drug-resistan*; multidrug-resistan*; post-antibiotic*; MDR-TB; carbapenmen-resistan*; artemisinin-resistan*; superbug*; resistant-micro*; methicillin-resistan*; vancomycin-resistan*; colistin-resistan*; fluoroquinolone-resistan*; cephalosporin-resistan*; penicillin-resistan*; drug resistance; antibiotic resistance; antimicrobial resistance |
| #2 Contents contain: | Drug-resistan*; multidrug-resistan*; post-antibiotic*; MDR-TB; carbapenmen-resistan*; artemisinin-resistan*; superbug*; resistant-micro*; methicillin-resistan*; vancomycin-resistan*; colistin-resistan*; fluoroquinolone-resistan*; cephalosporin-resistan*; penicillin-resistan*; drug resistance; antibiotic resistance; antimicrobial resistance |
| #3 Headlines contain: | Bacteria*; virus; viruses; viral; microbial; microbe; microbes; microorganism*; protozoa*; parasite*; fungal; fungi; clostridium difficile; Staphlylococcus aureus |
| #4 Headlines contain: | Antibiotic*; antivir*; antiretroviral; antifungal*; antimalarial*; antiprotozoal*; anthelmintic; antimicrobial*; anti-micro*; multi-drug; carbapenmem; methicillin; vancomycin; oxacillin; fluoroquinolone*; cephalosporin*; colistin; penicillin* |
| #5 Headlines contain: | Pneumoniae; gonorrhoea; E. coli; tuberculosis; malaria; MRSA; ART |
Article searching strategies were: #1 OR (#2 AND (#3 OR #4 OR #5))
Inclusion and exclusion criteria for screening the eligibility of the news articles
| Details of the criteria | |
|---|---|
| Inclusion criteria | News articles reported any of aspects of AMR risk: what AMR is, who or what causes it, what the consequence is, whose responsibility, whether AMR can be controlled and how |
| Exclusion criteria | 1. Full texts were not public-accessible |
| 2. AMR is mentioned as a minor topic rather than the main topic in the article | |
| 3. Research articles or project reports for which the target audience are professional rather than the general public | |
| 4. Advertisements e.g., new drugs, conference advertisements, advertisement for recruiting subjects for an AMR study | |
| 5. Articles is about other drug resistance (e.g., cancer drug resistance) |
Coding scheme for content analysis on the representations of AMR risk
| Description | |
|---|---|
| How AMR or AMR risk was labelled or interpreted in the headlines | |
| Medical terms | The general medical terms such as antibiotic resistance, drug-resistant bacteria, or more specific medical term such as MSAR, multidrug-resistant |
| Superbug | Superbug, superbugs or bacteria- or disease-specific superbugs such as super-gonorrhea was used to labelled AMR |
| Doomsday | The way AMR risk was labelled aimed to give alarming that AMR risk could lead to antibiotic apocalypse or post-antibiotic era when antibiotic of last resort became ineffective |
| Military term | AMR risk was labelled as a battle or war for which weapons were needed |
| Catastrophic | AMR risk was labelled as a crisis, disaster, global threat or something that was out of control |
| What cause AMR and who/what should be blamed for the problem of AMR | |
| Misuse or overuse of antimicrobials | Misuse or overuse of antimicrobials particularly antibiotics including misuse or overuse of antimicrobials. The social actors for these can be sectors/individuals who were responsible for animal farming or health care, and the general consumers |
| Microbial evolution | The change of microbials themselves as a consequence of natural evolution was mentioned as the cause of AMR |
| The consequences caused by AMR | |
| Health consequences | Health consequences in humans due to AMR. This included sickness, infections becoming difficult to treat and death |
| Economic consequences | The economic loss due to AMR |
| Victims | Who was affected by AMR (whose risk is relevant). Two subcategories were found for this including vulnerable groups/individuals (patients, children or older people) or the general public (e.g. AMR affects AMR) |
| Whether AMR can be controlled or not | |
| Positive | The articles used a positive tone about the controllability of AMR such as words of “successfully harness”, “turning point”, and “hope” (of new solutions) |
| Negative | The articles presented a pessimistic tone about the controllability of AMR using words such as “fail” to treat, “untreatable”, “difficult to control” and “uncontrollable” |
| Technoscientific solutions | This includes discovery of new antibiotics, new treatments for resistant bacterial infections, and new technologies such as those that can be used to detect and kill drug-resistant bacteria in the environment |
| Appropriate antimicrobial use (AMU) | This emphasizes the importance of appropriate use of antimicrobials in humans and animals. It also includes those mentioned the importance of health education to raise people’s awareness of AMR risk and change their behaviours of antibiotic use |
| Political/organizational solutions | This emphasizes the responsibility of government or organization in the control of AMR. Solutions can be making policies to regulate antimicrobial use in humans and animals, strengthening surveillance of AMR and infection control, increasing funding for AMR research or control of AMR, or establishing new organizations for the control of AMR |
| Personal hygiene | This includes those emphasizing the importance of personal hygiene behaviours such as frequent handwashing, disinfection and avoiding close contact with animals |
| Others (e.g. vaccination, breastfeeding) | Other measures not included in the above such as vaccination and breastfeeding to promote personal immunity |
Fig. 1Flow chart of news article selection and screening procedure
Comparisons of daily AMR news counts by November and non-November months and by year, stratified by media language
| Statistics | English | Chinese |
|---|---|---|
| Mean daily counts | 13.24 | 5.70 |
| Standard deviation | 18.24 | 8.83 |
| Median daily counts | 8 | 3 |
| Range of daily counts | 0–335 | 0–81 |
| Difference in daily counts. (z-score based on Wilcoxon–Mann–Whitney test) by year in non-Nov. versus: | ||
| Nov. 2015 | − 0.26 | − 2.49* |
| Nov. 2016 | − 2.02* | − 4.86*** |
| Nov. 2017 | − 0.65 | − 0.57 |
| Nov. 2018 | − 2.19* | − 0.22 |
| Differences in daily counts among years (Kruskal Wallis test)a | 11.24* | 70.84*** |
| Post-hoc Dunn’s pairwise comparison between years (z-score) | ||
| 2015 versus 2016 | − 2.61* | − 4.53*** |
| 2015 versus 2017 | − 3.13* | − 7.49*** |
| 2015 versus 2018 | − 1.88 | − 7.05*** |
| 2016 versus 2017 | − 0.52 | − 2.96** |
| 2016 versus 2018 | 0.73 | − 2.53* |
| 2017 versus 2018 | 1.24 | 0.44 |
*p < 0.05; **p < 0.01; ***p < 0.001
aChi-square value of Kruskal Wallis test with three degrees of freedom
Comparisons of daily AMR news counts by November and non-November months and by year, stratified by country or territory
| Statistics | US | UK | CA | AU | SA | IN | MC | HK | TW |
|---|---|---|---|---|---|---|---|---|---|
| Mean daily counts | 6.04 | 2.40 | 0.70 | 0.71 | 0.27 | 1.15 | 4.83 | 0.47 | 0.33 |
| Standard deviation | 11.56 | 5.56 | 1.68 | 1.49 | 1.05 | 1.97 | 8.09 | 1.28 | 1.00 |
| Median daily counts | 3 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| Range of daily counts | 0–236 | 0–71 | 0–20 | 0–23 | 0–22 | 0–22 | 0–73 | 0–15 | 0–13 |
| Difference in daily counts. (z-score based on Wilcoxon–Mann–Whitney test) by year in non-Nov. versus | |||||||||
| Nov. 2015 | 0.38 | − 1.80 | − 0.98 | 0.15 | − 2.66** | − 1.73 | − 2.97** | − 0.60 | − 0.90 |
| Nov. 2016 | − 1.28 | 1.00 | − 1.14 | − 2.65** | 0.96 | − 2.95** | − 5.17*** | − 0.67 | 1.09 |
| Nov. 2017 | 0.16 | 1.11 | 0.58 | − 1.50 | 0.01 | 0.05 | 0.22 | − 0.78 | − 0.82 |
| Nov. 2018 | 0.02 | − 1.06 | − 0.43 | 0.08 | − 4.10*** | − 2.22* | − 0.32 | 0.72 | − 1.60 |
| Differences in daily counts among years (Kruskal Wallis test)a | 10.18* | 2.88 | 2.67 | 14.88** | 12.29** | 36.66*** | 61.79*** | 7.06 | 18.42*** |
| Post-hoc Dunn’s pairwise comparison between years (z-score)b | |||||||||
| 2015 versus 2016 | − 1.67 | – | – | − 3.74*** | − 2.10 | 0.11 | − 4.57*** | – | 1.32 |
| 2015 versus 2017 | − 0.31 | – | – | − 2.70* | − 2.44* | − 5.06*** | − 6.52*** | – | − 2.84 |
| 2015 versus 2018 | 1.52 | – | – | − 2.12 | − 3.40** | − 2.72* | − 7.06*** | – | − 0.09 |
| 2016 versus 2017 | 1.35 | – | – | 1.03 | − 0.34 | − 5.18*** | − 1.95 | – | − 4.17*** |
| 2016 versus 2018 | 3.19** | – | – | 1.61 | − 1.30 | − 2.83* | − 2.49* | – | − 1.41 |
| 2017 versus 2018 | 1.84 | – | – | 0.58 | 0.96 | 2.34 | − 0.54 | – | 2.75* |
CA Canada, AU Australia, SA South Africa, IN India, MC Mainland China, HK Hong Kong, TW Taiwan
*p < 0.05; **p < 0.01; ***p < 0.001
aChi-square value of Kruskal Wallis test with three degrees of freedom
bPost-hoc Dunn’s pairwise comparison was only conducted when Kruskal Wallis test indicates that there are significant differences in daily news counts among years
Categories of events that triggered peak media attention to AMR by media language, 2015–2018
| Categories of events that triggered peak media attentions | English media (N = 46) | Chinese media (N = 66) | Both Eng. & Chi. Media (N = 22) |
|---|---|---|---|
| Official reports assessing AMR risk (magnitude and consequence) | 11 (23.9%) | 10 (15.2%) | 5 (22.7%) |
| Reports of AMR human infections or outbreaks | 9 (19.6%) | 12 (18.2%) | 4 (18.2%) |
| Reports of new AMR solutions | 8 (17.4%) | 12 (18.2%) | 2 (9.1%) |
| Discovery of new AMR genes/strains | 7 (15.2%) | 9 (13.6%) | 6 (27.3%) |
| Official/organizational actions on tackling AMR | 4 (8.7%) | 11 (16.7%) | 2 (9.1%) |
| Reports of new sources of AMR infections | 3 (6.5%) | 5 (7.6%) | 1 (4.5%) |
| Reports of antibiotic misuse/overuse | 2 (4.3%) | 2 (3.0%) | 1 (4.5%) |
| World Antimicrobial Awareness Week | 1 (2.2%) | 2 (3.0%) | 1 (4.5%) |
| Individual experts’ talks on AMR risk | 1 (2.2%) | 3 (4.5%) | 0 (0.0%) |
Fig. 2Main events that triggered peak media attention to AMR, 2015–2018
Frequency of content codes across media language based on the analysis of the 788 news articles selected using constructed-week sampling
| English news (N = 527) (%) | Chinese news (N = 261) (%) | Total (N = 788) (%) | |
|---|---|---|---|
| 84.2 | 78.9 | 82.5 | |
| Medical terms | 44.2** | 34.5 | 41.0 |
| Superbug | 38.3 | 43.3 | 40.0 |
| Doomsday (e.g., antibiotic apocalypse, post-antibiotic era) | 4.7 | 6.5 | 5.3 |
| Military term (e.g., battle, war) | 6.3* | 2.3 | 4.9 |
| Catastrophic (e.g., crisis, disaster) | 1.5 | 1.1 | 1.4 |
| 49.5*** | 63.2 | 54.1 | |
| Inappropriate AMU | 33.8* | 42.5 | 36.7 |
| AMU in animals | 11.9 | 15.3 | 13.1 |
| AMU in the health sector | 12.9 | 10.0 | 11.9 |
| AMU in the general consumers | 5.9*** | 14.2 | 8.6 |
| Microbial evolution | 19.9** | 30.6 | 23.5 |
| 58.6* | 67.0 | 61.4 | |
| Health consequences | 57.1** | 67.0 | 60.4 |
| Economic consequences | 7.6 | 6.9 | 7.4 |
| 35.1*** | 52.1 | 40.7 | |
| Vulnerable individuals | 26.6* | 34.5 | 29.2 |
| General public | 9.5*** | 21.8 | 13.4 |
| 21.2*** | 39.5 | 27.3 | |
| Positive | 11.4*** | 26.4 | 16.4 |
| Negative | 9.8 | 13.0 | 10.9 |
| 78.9 | 81.2 | 79.7 | |
| Technoscientific solutions | 40.4 | 34.1 | 38.3 |
| Appropriate antimicrobial use (AMU) | 26.0 | 29.1 | 27.0 |
| Political/organizational solutions | 23.5 | 28.7 | 25.2 |
| Personal hygiene | 21.1 | 26.8 | 23.0 |
| Others (e.g. vaccination, breastfeeding) | 4.5 | 2.7 | 3.9 |
*p < 0.05; **p < 0.01; ***p < 0.001
All p values were calculated based on Pearson chi-square differences in the frequency of the code between the English and Chinese new articles
Fig. 3Proportions of the respective representations of AMR risk by country or territory, 2015–2018. AMU antimicrobials use, US The United States, UK The United Kingdom, IN India, AU Australia, CA Canada, SA South Africa, MC Mainland China, HK Hong Kong, TW Taiwan