Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account?
Bibliographic record
Abstract
The objective of this study was to assess potential biases that may influence the validity of contemporary antimicrobial‐resistant (AMR) pathogen surveillance systems. Although surveillance data have been widely published and used by researchers and decision makers, little attention has been devoted to the assessment of their validity. A Medline search was used to identify reports, in 2008, of laboratory‐based AMR surveillance systems. Identified surveillance systems were appraised for six different types of bias. Scores were assigned as ‘2’ (good), ‘1’ (fair) and ‘0’ (poor) for each bias. The results of this assessment indicate that there are several potential biases that can influence the validity of AMR surveillance information and, therefore, the potential for bias should be considered in the interpretation and use of AMR surveillance data. BACKGROUND: The validity of surveillance systems has rarely been a topic of investigation. OBJECTIVE: To assess potential biases that may influence the validity of contemporary antimicrobial‐resistant (AMR) pathogen surveillance systems. METHODS: In 2008, reports of laboratory‐based AMR surveillance systems were identified by searching Medline. Surveillance systems were appraised for six different types of bias. Scores were assigned as ‘2’ (good), ‘1’ (fair) and ‘0’ (poor) for each bias. RESULTS: A total of 22 surveillance systems were included. All studies used appropriate denominator data and case definitions (score of 2). Most (n=18) studies adequately protected against case ascertainment bias (score = 2), with three studies and one study scoring 1 and 0, respectively. Only four studies were deemed to be free of significant sampling bias (score = 2), with 17 studies classified as fair, and one as poor. Eight studies had explicitly removed duplicates (score = 2). Seven studies removed duplicates, but lacked adequate definitions (score = 1). Seven studies did not report duplicate removal (score = 0). Eighteen of the studies were considered to have good laboratory methodology, three had some concerns (score = 1), and one was considered to be poor (score = 0). CONCLUSION: Contemporary AMR surveillance systems commonly have methodological limitations with respect to sampling and multiple counting and, to a lesser degree, case ascertainment and laboratory practices. The potential for bias should be considered in the interpretation of surveillance data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.439 | 0.759 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".