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Record W4295297510 · doi:10.1016/j.amsu.2022.104635

COVID vaccine wastage: Double trouble in growing pandemic

2022· editorial· en· W4295297510 on OpenAlexaboutno aff
Amrit Bhusal, Silan Bhandari, N. Kumari, Rajesh Prasad Sah

Bibliographic record

VenueAnnals of Medicine and Surgery · 2022
Typeeditorial
Languageen
FieldEngineering
TopicIntravenous Infusion Technology and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVialVaccinationCoronavirus disease 2019 (COVID-19)Cold chainSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Environmental healthVirologyInternal medicineFood science

Abstract

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Sir Vaccine wastage is said to occur in a vaccination program if the product (i.e. vaccine) is lost before the administration to the general public [1]. There are many reasons for vaccine wastage, the major ones being wastage during transportation, expiration, storage errors or contamination after opening vial [2]. Vaccine hesitancy contribute to wastage in both low and high income countries [3,4]. In Australia the main reason for vaccine wastage was cold chain failure for vaccines requiring refrigeration [3,4]. However in Canada, dropped vials and syringes, expired doses and inadequate management of doses contributed to vaccine wastage [5]. A committee formed by the Government of Nepal to find the missing doses reported that as many as 56,908 doses of Covid-19 vaccines supplied to districts and the local level were wasted. The committee did not find any doses missing but found that thousands of doses of vaccines either expired or were damaged due to negligence or lack of proper handling. According to the report, 25,562 doses of Moderna vaccine were found to have been ruined. Of them 14,020 doses were ruined under the watch of local governments and the remaining 11,542 wasted doses were under the watch of district authorities. Likewise, 19,049 doses of Covishield vaccine doses were ruined and of them 7369 were found ruined at the local level and 11,690 doses at the district level. Besides, 7023 doses of Vero Cell vaccine, 5195 doses of Janssen vaccine, and 79 doses of Pfizer-BioNTech vaccine doses were ruined. According to The Health Ministry, Nepal have received 47,882,800 doses of COVID-19 vaccines out of which 35,575,714 doses were used to vaccinate its population [6]. There are many measures that could be applied to minimize the burden of vaccine wastage. First of all, there must be a sound government policy for proper vaccine delivery and strict inspection policy for the adequate delivery of the vaccine to concerned place and people. There must be the provision of regular reporting of vaccine delivery to the targeted population by the local bodies. For this, use of data reporting system software from the local bodies could be of great help. Vaccine wastage due to unsuitable temperature can be prevented through continuous temperature monitoring and providing results from comprehensive stability studies to vaccine distributors [7]. This will inform the distributors when vaccines can be used in instances when recommended storage temperatures are exceeded. To reduce wastage due to expiry, vaccines should be ordered only for a 1–2-week supply, and old stock should be used before the new [8]. Preloading syringes at the pharmacy with vaccines is not recommended, and new vials should be opened as close to administration as possible [9]. New vials of vaccine are to be opened as close to administration as possible. Pargaien et al. proposed the role of IOT(Internet of Things) for monitoring the wastage of vaccines due to poor cold chain management [10]. Gorfinkel proposed the use of bar codes to create a robust national vaccine registry [11]. With 51% of world's population being fully vaccinated, the number of vaccines going to waste should be carefully monitored and kept within an acceptable range [12]. For a poor country like Nepal, every COVID vaccine does matter; every does means saving one life. So, there is a need on the part of authorities to ensure that not even a single dose is wasted. Co-ordinated effort of central and local government along with proper monitoring and surveillance of cold chain system and proper registration system both from the sending and receiving end must be taken into consideration. Then only we can even think of bringing this non-ending wave of Corona virus under control. Ethical approval Ethical approval is not required for letter to editorial. Sources of funding Since we are medical students under supervision, we don't have any financial support for our research. Author contributions First Amrit Bhusal literature review, writing the manuscript, and final approval of the manuscript, Second Dr. Silan Bhandari literature review, writing the manuscript, and final approval of the manuscript, Third Dr. Neelam Kumari literature review, writing the manuscript, and final approval of the manuscript, Fourth Dr. Rajesh Prasad Sah literature review and final approval of the manuscript. Registration of research studies Name of the registry: none Unique Identifying number or registration ID: none Hyperlink to your specific registration (must be publicly accessible and will be checked): Guarantor Amrit Bhusal is the Guarantor. Provenance and peer review Not commissioned, externally peer reviewed. Consent Even where consent has been given, identifying details should be omitted if they are not essential. If identifying characteristics are altered to protect anonymity, such as in genetic pedigrees, authors should provide assurance that alterations do not distort scientific meaning and editors should so note. Declaration of competing interest There are no conflicts of interest.

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)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.008
Open science0.0010.004
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0190.006

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 head0.037
GPT teacher head0.293
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreEditorial

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".

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Citations4
Published2022
Admission routes1
Has abstractyes

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