Ignoring non‐English‐language studies may bias ecological meta‐analyses

Konno, Ko; Akasaka, Munemitsu; Koshida, Chieko; Katayama, Naoki; Osada, Noriyuki; Spake, Rebecca; Amano, Tatsuya

doi:10.1002/ece3.6368

Cited by 159 publications

(137 citation statements)

References 30 publications

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“…Database materials are available at DOI 10.5281/zenodo.3964895 (see Data availability) 10 . Field names and descriptions from the database (filename `events-db.csv`) are detailed in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…208 ProMEDmail texts passed this screening, contributing to a total of 1,791 total articles from PubMed, Embase, and ProMEDmail selected for review. Further details on the reproducible screening workflow are available in the data repository (see Data availability) 10 .…”

Section: Abstract and Report Screeningmentioning

confidence: 99%

“…Zenodo: Extracting novel antimicrobial emergence events from scientific literature and medical reports. https://doi.org/10.5281/ zenodo.3964895 10 .…”

Section: Data Availabilitymentioning

confidence: 99%

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Extracting novel antimicrobial emergence events from scientific literature and medical reports

et al. 2020

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Despite considerable global surveillance of antimicrobial resistance (AMR), data on the global emergence of new resistance genotypes in bacteria has not been systematically compiled. We conducted a study of English-language scientific literature (2006-2017) and disease surveillance reports (1994-2017) to identify global events of novel AMR emergence (first clinical reports of unique drug-bacteria resistance combinations). We screened 24,966 abstracts and reports, ultimately identifying 1,773 novel AMR emergence events from 294 articles. Events were reported in 66 countries, with most events in the United States (152), India (129), and China (128). The most common bacteria demonstrating new resistance were Klebsiella pneumoniae (352) and Escherichia coli (218). Resistance was most common against antibiotic drugs imipenem (89 events), ciprofloxacin (85) and ceftazidime (82). We provide an open-access database of emergence events with standardized fields for bacterial species, drugs, location, and date, and we discuss guidelines and caveats for data analysis. This database may be broadly useful for understanding rates and patterns of AMR evolution, identifying global drivers and correlates, and targeting surveillance and interventions.

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Section: Resultsmentioning

confidence: 99%

Section: Abstract and Report Screeningmentioning

confidence: 99%

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Extracting novel antimicrobial emergence events from scientific literature and medical reports

et al. 2020

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“…For example, it is widely recognized that exclusion of grey literature and unpublished data can impact on meta‐analytical estimates due to publication bias 8,9 . Also, it has been shown that if relevant non‐English‐language literature is omitted during literature searches, different meta‐analytical inferences could be drawn due to language bias 10‐12 . Such empirical evidence suggests that if meta‐analytical reviewers intentionally or unintentionally sample primary studies in a selective way during literature searches (eg, collect only studies from commercially published articles or those published in English‐language), meta‐analyses are expected to provide potentially biased estimates of overall effects.…”

Section: Introductionmentioning

confidence: 99%

Assessing the risk of bias in choice of search sources for environmental meta‐analyses

Konno

Pullin

2020

Research Synthesis Methods

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Results of meta‐analyses are potentially valuable for informing environmental policy and practice decisions. However, selective sampling of primary studies through searches exclusively using widely used bibliographic platform(s) could bias estimates of effect sizes. Such search strategies are common in environmental evidence reviews, and if risk of bias can be detected, this would provide the first empirical evidence that comprehensiveness of searches needs to be improved. We compare the impact of using single and multiple bibliographic platform(s) searches vs more comprehensive searches on estimates of mean effect sizes. We used 137 published meta‐analyses, based on multiple source searches, analyzing 9388 studies: 8095 sourced from commercially published articles; and 1293 from grey literature and unpublished data. Single‐platform and multiple‐platform searches missed studies in 100 and 80 of the meta‐analyses, respectively: 52 and 46 meta‐analyses provided larger‐effect estimates; 32 and 28 meta‐analyses provided smaller‐effect estimates; eight and four meta‐analyses provided opposite direction of estimates; and two each were unable to estimate effects due to missing all studies. Further, we found significant positive log‐linear relationships between proportions of studies missed and the deviations of mean effect sizes, suggesting that as the number of studies missed increases, deviation of mean effect size is likely to expand. We also found significant differences in mean effect sizes between indexed and non‐indexed studies for 35% of meta‐analyses, indicating high risk of bias when the searches were restricted. We conclude that the restricted searches are likely to lead to unrepresentative samples of studies and biased estimates of true effects.

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“…The copyright holder for this preprint this version posted August 15, 2020. ; https://doi.org/10.1101/2020.08.13.20165852 doi: medRxiv preprint systematically missing from the database for countries that are not English speaking and/or have less health care monitoring and reporting capacity, including those with lower GDP. Therefore, it is imperative that any analysis of the data account for the effects of reporting bias 21 .…”

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confidence: 99%

Extracting novel antimicrobial emergence events from scientific literature and medical reports

Mendelsohn

Ross

White

et al. 2020

Preprint

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Despite considerable global surveillance of antimicrobial resistance (AMR), data on the global emergence of new resistance genotypes in bacteria has not been systematically compiled. We conducted a study of English-language scientific literature (2006-2017) and disease surveillance reports (1994-2017) to identify novel global emergence events (first clinical reports of unique drug-bacteria resistance combinations). We screened 24,966 abstracts and reports, ultimately identifying 1,773 novel emergence events from 294 articles. Events were reported in 66 countries, with most events in the United States (152), India (129), and China (128). The most common bacteria demonstrating new resistance were Klebsiella pneumoniae (352) and Escherichia coli (218). Resistance was most common against antibiotic drugs Imipenem (89 events), Ciprofloxacin (85) and Ceftazidime (82). We provide an open-access database of emergence events with standardized fields for bacterial species, drugs, location, and date, and we discuss guidelines and caveats for data analysis. This database may be broadly useful for understanding rates and patterns of AMR evolution, identifying global drivers and correlates, and targeting key surveillance and interventions.

show abstract

Ignoring non‐English‐language studies may bias ecological meta‐analyses

Cited by 159 publications

References 30 publications

Extracting novel antimicrobial emergence events from scientific literature and medical reports

Extracting novel antimicrobial emergence events from scientific literature and medical reports

Assessing the risk of bias in choice of search sources for environmental meta‐analyses

Extracting novel antimicrobial emergence events from scientific literature and medical reports

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