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Record W3156184641 · doi:10.1093/annweh/wxab013

Spatial and Temporal Variability in Antineoplastic Drug Surface Contamination in Cancer Care Centers in Alberta and Minnesota

2021· article· en· W3156184641 on OpenAlexaffabout
Matthew Jeronimo, Susan Arnold, George Astrakianakis, Grace R. Lyden, Quinn Stewart, Ashley Petersen, Carole Chambers, Darcy Malard Johnson, Emily Zimdars, Hannah Kaup, Hugh Davies

Bibliographic record

VenueAnnals of Work Exposures and Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsAlberta Health ServicesUniversity of British Columbia
Fundersnot available
KeywordsContaminationEnvironmental healthMedicineAntineoplastic DrugsEnvironmental sciencePharmacology

Abstract

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The health risks of exposure to antineoplastic drugs (ADs) are well established, and healthcare professionals can be exposed while caring for cancer patients receiving AD therapy. Studies conducted worldwide over the past two decades indicate continuing widespread surface contamination by ADs. No occupational exposure limits have been established for ADs, but concerns over exposures have led to the development of guidelines, such as United States Pharmacopeia (USP) General Chapter <800> Hazardous Drugs-Handling in Healthcare. While recommending regular surveillance for surface contamination by ADs these guidelines do not provide guidance on sampling strategies. Better characterization of spatial and temporal variability of multidrug contamination would help to inform such strategies. We conducted surface-wipe monitoring of nine cancer care centers in Alberta, Canada and Minnesota, USA, with each center sampled eight times over a 12-month period. Twenty surfaces from within pharmacy and drug administration areas were sampled, and 11 drugs were analyzed from each wipe sample. Exposure data were highly left-censored which restricted data analysis; we examined prevalence of samples above limit of detection (LOD), and used the 90th percentile of the exposure distribution as a measure of level of contamination. We collected 1984 wipe samples over a total of 75 sampling days resulting in 21 824 observations. Forty-five percent of wipe samples detected at least one drug above the LOD, but only three of the drugs had more than 10% of observations above the LOD: gemcitabine (GEM) (24%), cyclophosphamide (CP) (16%), and paclitaxel (13%). Of 741 wipe samples with at least one drug above LOD, 60% had a single drug above LOD, 19% had two drugs, and 21% had three drugs or more; the maximum number of drugs found above LOD on one wipe was 8. Surfaces in the compounding area of the pharmacy and in the patient area showed the highest prevalence of samples above the LOD, including the compounding work surface, drug fridge handle, clean room cart, passthrough tray, and hazardous drug room temperature storage, the IV pump keypad, patient washroom toilet handle, patient washroom door handle, nurses' storage shelf/tray, and patient side table. Over the course of the study, both 90th percentiles and prevalence above LOD varied without clear temporal patterns, although some centers appeared to show decreasing levels with time. Within centers, the degree of variability was high, with some centers showing changes of two to three orders of magnitude in the 90th percentile of drug concentrations month to month. A clear difference was observed between the six centers located in Alberta and the three in Minnesota, with Minnesota centers having substantially higher percentages of samples above the LOD for CP and GEM. Other factors that were associated with significant variability in exposures were drug compounding volume, size of center, number of patients seen, and age of the center. We hope that demonstrating variability associated with drug, surface, clinic-factors, and time will aid in a better understanding of the nature of AD contamination, and inform improved sampling strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.397
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2021
Admission routes2
Has abstractyes

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