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Record W3189039592 · doi:10.37901/jcphp20-00023

Ethical Considerations of Hydroxychloroquine During the COVID-19 Pandemic

2021· article· en· W3189039592 on OpenAlexaboutno aff
Amy L. Reese

Bibliographic record

VenueJournal of Contemporary Pharmacy Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHydroxychloroquineMedicinePandemicDistributive justiceMedical prescriptionPharmacyCoronavirus disease 2019 (COVID-19)Economic JusticeFamily medicineIntensive care medicineLawPharmacologyPolitical scienceInternal medicine

Abstract

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The SARS-CoV-2 pandemic has greatly affected almost every aspect of our lives. Like many community pharmacists, I struggled with the shortage of hydroxychloroquine shortly after the FDA issued an Emergency Use Authorization (EUA) in March of 2020 to be used to treat COVID-19. Pharmacists who already dispensed hydroxychloroquine to lupus and rheumatoid arthritis patients scrambled to find enough drug to dispense to the expected influx of COVID-19 patients. Some chose to fill only 30-day supplies for any patient, no matter their diagnosis and some declined new patients. Long-term patients reportedly were unable to fill their prescriptions for hydroxychloroquine. Due to the dire situation many pharmacists faced, an ethical question arose: Is off-label prescribing of a medication ethically permissible during a drug shortage?It is ethically permissible to use medications to treat the off-label indications. The risks to the patients who regularly receive the medication versus the benefits to the new group of patients receiving the medication must be weighed and distributive justice must be considered. Distributive justice is equitable, fair, and appropriate distribution that is justified by structural norms of social cooperation.(1) Distributive justice is essential to ethically distributing a scarce resource such as hydroxychloroquine because it determines who receives the medication and how much. The normal global demand for hydroxychloroquine is 300 metric tons(2); if only 2% of COVID-19 patients were treated with hydroxychloroquine, the demand would increase more than three-fold to 1,000 metric tons.(2) The increase in demand without the ability to increase supply as quickly puts a strain on the healthcare system. The American Society of Health-System Pharmacists (ASHP) published on the Drug Shortages reference website stating the drug shortage indicated many generic-manufacturers could not keep up with the demand.(3) When dealing with a drug shortage, how does a pharmacist ethically allocate a medication that is in short supply in order to provide for all patients of the pharmacy?There are four fundamentals of allocation: maximize the benefits produced by scarce resources, treat people equally, promote and reward instrumental value, and give priority to the worst off.(4) Maximizing the benefits of the scarce resource can be translated to mean make the supply last as long as possible: instead of giving one patient a 90-day supply of hydroxychloroquine (180 tablets), give three patients 30-day supplies (60 tablets for each patient). The fundamental of treating people equally is explicated through the triage process. The triage process can determine who ought to receive care based on the patient’s prognosis: a first come, first serve method can be used in triage or a random selection of patients who have similar prognosis.(4) A first come, first served method of triage should not be used due to the disparities it could create. (4) People who have the means to arrive at the pharmacy faster and easier receive treatment before people who lack those means. A random selection of patients who have similar prognosis would be the most ethical because it would give all patients in the same severity a chance at receiving the medication. Often times, a first come, first serve basis is used to distribute medication in the pharmacy despite it not being the most ethical allocation of medication. Promoting and rewarding instrumental value can be performed either retrospectively or prospectively: a retrospective system prioritizes people who have contributed to the efforts against the COVID-19 such as researchers and healthcare workers while a prospective system prioritizes people who will directly contribute to efforts of mitigating the pandemic such as healthcare professionals.(4) In terms of allocation of scarce drugs, people who have positively impacted the COVID-19 pandemic such as research or healthcare professions would receive medication therapy before other patients. Giving priority to the worst off maximizes utility by treating the sickest first(4); the patients who are the sickest but would still benefit from the treatment would receive it over patient who is not as ill. Priority to the worst off also provides treatment to younger patients before older patients because they would usually fare better long term. In this manner, pharmacists would analyze the severity of each patient’s illness and age to dispense the medication to the most ill and the youngest.The issue of distributive justice is not only an ethical issue of proper allocation. Hydroxychloroquine was being used off-label to treat COVID-19 while supply of the medication was being taken from patients who rely on the drug to stave off their autoimmune diseases (rheumatoid arthritis and lupus erythematosus). Clinical trials indicated that hydroxychloroquine effectively treats disease manifestations in rheumatoid arthritis such as joint pain and rashes(5); withdrawal of the medication can put the patient at risk of flares which could be life-threatening such as lupus nephritis.(5) The drug shortage caused concern for patients, rheumatologists, and pharmacists due to this evidence.(5)Researchers around the world conducted clinical research to determine whether the off-label use of hydroxychloroquine was appropriate for treatment and prophylaxis of COVID-19. Many of the studies could not determine whether hydroxychloroquine was effective in treating or preventing COVID-19. A study published in the New England Journal of Medicine from Brazil by Cavalcanti et. al. stated that hydroxychloroquine did not improve mild-to-moderate hospitalized patients with or without azithromycin.(6) Another study performed throughout the United States and Canada by Boulware et. al. randomized patients to determine whether hydroxychloroquine was effective in staving off COVID-19 through postexposure prophylaxis.(7) Essentially, a patient population who have been shown to benefit from hydroxychloroquine faced devastating shortages due to experimentation with the drug which ended up inconclusive. Is this an ethical utilization of scarce medications? If hydroxychloroquine was proven effective, then it would be ethically permissible to deny patients of a chronic illness for a few months to treat patients with COVID-19 because it is a more critical and life-threatening disease. Depriving a patient maintenance medication would be justified if that medication was proven to save the lives of other patients who are fighting for life (life-or-death situation). But because hydroxychloroquine could not be demonstrated to be effective in preventing or treating COVID-19, it is unethical to deny patients of a chronic illness their medication which alleviates their symptoms and mitigates their autoimmune disease.The FDA rescinded the EUA for hydroxychloroquine on June 15, 2020(8), and the shortage resolved quickly to the benefit of patients suffering from autoimmune diseases. The ethical questions remain. As pharmacists, we must act ethically in all we do–even pertaining to the distribution of medication from the pharmacy. Pharmacists must allot essential medication according to the four fundamentals of allocation when faced with a paucity. We must also analyze the data to determine whether the data support depriving life-sustaining medication from one patient population to distribute it to another patient population. We can take the lessons we have learned from the unprecedented situations we faced during this COVID-19 pandemic and use them for the future. Hopefully we will not experience such a devastating pandemic in the future, but it is best to be prepared for possibilities than to face another situation like the one we have seen without pondering the ethical implications of our actions.

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)0.108
metaresearch head score (Gemma)0.133
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.108
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1080.133
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0160.037
Scholarly communication0.0140.009
Open science0.0030.010
Research integrity0.0350.039
Insufficient payload (model declined to judge)0.0040.001

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 head0.330
GPT teacher head0.498
Teacher spread0.168 · 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
GenreCommentary

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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Citations0
Published2021
Admission routes1
Has abstractyes

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