Alcohol for COVID-19
Serious outcome risk
Outcome (studies)
Improvement
Relative risk · 95% CI
Alcohol for COVID-19: real-time meta-analysis of 3 studies
Abstract
Significantly lower risk is seen for cases. 2 studies from 2 independent teams in 2 countries show significant
benefit.
Meta-analysis using the most serious outcome reported shows
24% [-13‑49%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies.
Alcohol for COVID-19
Serious outcome risk
Outcome (studies)
Improvement
Relative risk · 95% CI
Studies to date are from only 3 different groups.
Studies analyze alcohol consumption before infection. Results are expressed as the effect of lower or no alcohol consumption compared to higher consumption.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
All data and sources to reproduce this analysis are in the appendix.
Alcohol for COVID-19
Highlights- Alcohol reduces risk with low confidence for cases and very low confidence for pooled analysis.
- Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
A
Alcohol COVID-19 studies
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Mohsin28%0.72 · 0.59–0.87severe case302/928298/572
Gilley50%0.50 · 0.28–0.89cases12/391165/1,600
Paul−28%1.28 · 0.70–2.13PASC1,811 (all patients)LONG COVID
Prophylaxis24%0.76 · 0.51–1.13314/1,319463/2,17224% lower risk
Tau² = 0.08, I² = 63.1%, p = 0.18
All studies24%0.76 · 0.51–1.13314/1,319463/2,17224% lower risk
Tau² = 0.08, I² = 63.1%, p = 0.1800.511.52+
B
Fig. 1. A. Random-effects meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in alcohol studies.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury2-18 and
cognitive deficits5,10, cardiovascular
complications19-25, DNA
damage26-29, organ failure, and death.
Even mild untreated infections may result in persistent cognitive
deficits30—the spike protein binds to fibrin leading to
fibrinolysis-resistant blood clots, thromboinflammation, and
neuropathology.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 500+
host and viral proteins and other factorsA,31-38 , providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 12,000 compounds may
reduce COVID-19 risk39, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
studies reporting COVID-19 outcomes as a function of alcohol consumption and providing adjusted results.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random-effects
meta-analysis results for all studies, individual outcomes, and peer-reviewed studies.
Table 1 summarizes the results for all studies and for peer-reviewed studies.
Fig. 3, 4, 5, and 6
show forest plots for random-effects meta-analysis of
all studies with pooled effects, cases, peer reviewed studies, and long COVID.
Alcohol for COVID-19: meta-analysis results
November 2026
Relative Risk · CI
All studies3 studies · 5,302 patients
All stages0.76 0.51–1.1324% lower risk
Peer-reviewed2 studies · 3,491 patients
All stages0.67** 0.51–0.8933% lower risk
Random-effects meta-analysis. Relative risk with 95% CI. * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 1. Random-effects meta-analysis results for alcohol. All studies are for prophylaxis. Results show the relative risk and 95% confidence interval.
Alcohol COVID-19 studies
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Mohsin28%0.72 · 0.59–0.87severe case302/928298/572
Gilley50%0.50 · 0.28–0.89cases12/391165/1,600
Paul−28%1.28 · 0.70–2.13PASC1,811 (all patients)LONG COVID
Prophylaxis24%0.76 · 0.51–1.13314/1,319463/2,17224% lower risk
Tau² = 0.08, I² = 63.1%, p = 0.18
All studies24%0.76 · 0.51–1.13314/1,319463/2,17224% lower risk
Tau² = 0.08, I² = 63.1%, p = 0.1800.511.52+
Fig. 3. Random-effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Alcohol COVID-19 case results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Prophylaxis50%0.50 · 0.28–0.8912/391165/1,60050% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.018
All studies50%0.50 · 0.28–0.8912/391165/1,60050% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.01800.511.52+
Fig. 4. Random-effects meta-analysis for cases.
Alcohol COVID-19 peer reviewed studies
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Prophylaxis33%0.67 · 0.51–0.89314/1,319463/2,17233% lower risk
Tau² = 0.02, I² = 32.4%, p = 0.0059
All studies33%0.67 · 0.51–0.89314/1,319463/2,17233% lower risk
Tau² = 0.02, I² = 32.4%, p = 0.005900.511.52+
Fig. 5. Random-effects meta-analysis for peer reviewed studies.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta-analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta-analyses including
114 trials.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Alcohol COVID-19 long COVID results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Prophylaxis−28%1.28 · 0.72–2.2728% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.4
All studies−28%1.28 · 0.72–2.2728% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.400.511.52+
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 227
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Fig. 7 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.0000000001).
Similarly, Fig. 8 shows that improved recovery is very strongly associated
with lower mortality (p < 0.0000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Fig. 9 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.
Fig. 7. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Fig. 8. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Fig. 7. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months.
Fig. 10 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Fig. 10. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as differences in treatment delay are more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta-analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results50-53.
For alcohol, there is currently not
enough data to evaluate publication bias with high confidence.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Alcohol for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 alcohol trials have been run by
physicians on the front lines with the primary goal of finding the best
methods to save human lives and minimize the collateral damage caused by
COVID-19. While pharmaceutical companies are careful to run trials under
optimal conditions (for example, restricting patients to those most likely to
benefit, only including patients that can be treated soon after onset when
necessary, and ensuring accurate dosing), not all alcohol trials
represent the optimal conditions for efficacy.
Summary statistics from
meta-analysis necessarily lose information. As with all meta-analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone54-78.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex
interplay of 500+ host and viral proteins and other
factors31-38, providing many therapeutic
targets.
Over 12,000 compounds have been predicted to reduce COVID-19
risk39, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Fig. 11 shows an overview of the results for alcohol
in the context of multiple COVID-19 treatments, and Fig. 12 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Fig. 12. Efficacy vs. cost for COVID-19 treatments.
Significantly lower risk is seen for cases. 2 studies from 2 independent teams in 2 countries show significant
benefit.
Meta-analysis using the most serious outcome reported shows
24% [-13‑49%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies.
Studies to date are from only 3 different groups.
Studies analyze alcohol consumption before infection. Results are expressed as the effect of lower or no alcohol consumption compared to higher consumption.
Contact.
Contact us on X at @CovidAnalysis.
Funding.
We have received no funding or
compensation in any form, and do not accept donations. This is entirely volunteer work.
Conflicts of interest.
We have no conflicts of interest.
We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.AI.
We use AI models (Gemini, Grok, Claude, and
ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest
improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.Updates.
Our COVID-19 meta-analyses involve the extraction of over 229,000
datapoints from thousands of papers for 227 treatments. We thank the
thousands of scientists, physicians, and other contributors that have provided updates,
suggestions, feedback, and corrections. These are all welcome and can be submitted
at https://c19early.org/acmeta.html.
Dedication.
This work is dedicated to top
evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce
mortality and morbidity during the pandemic. In alphabetical order:
Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty,
Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr.
Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch,
Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.
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Alcohol for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Alcohol
reduces riskAlcohol
increases risk →
reduces riskAlcohol
increases risk →
Fewer cases with alcohol
p = 0.046
Retrospective survey of 1,997 college students in the USA, showing higher risk of COVID-19 cases with alcohol consumption.
Alcohol for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Alcohol
reduces riskAlcohol
increases risk →
reduces riskAlcohol
increases risk →
Lower severe cases with alcohol
p = 0.0011
Retrospective 1,500 COVID+ patients in Bangladesh, showing higher risk of severe cases with alcohol/smokeless tobacco/substance abuse.
Alcohol for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Alcohol
reduces riskAlcohol
increases risk →
reduces riskAlcohol
increases risk →
Higher long COVID with alcohol
Not statistically significant · p = 0.4
Retrospective 1,811 COVID-19 patients in the UK, showing no significant difference in self-reported long COVID based on having ≤14 vs. >14 alcoholic drinks per week in the month before infection.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane
Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the
reference lists of other studies and meta-analyses, and submissions to the site c19early.org, which regularly receives notification of studies upon
publication.
Search terms are alcohol AND COVID-19. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of alcohol for COVID-19 that report a comparison with a
control group are included in the main analysis.
Studies with major unexplained data issues, for example major outcome data that is
impossible to be correct with no response from the authors, are excluded.
Fig. 13.
Mid-recovery results can more accurately reflect efficacy when almost all patients
recover. Mateja et al. confirm that intermediate viral load results more accurately
reflect hospitalization/death.
We extracted effect sizes and associated data from all studies. If studies report multiple
kinds of effects then the most serious outcome is used in pooled analysis, while
other outcomes are included in the outcome-specific analyses. For example, if effects for
mortality and cases are reported then they are both used in specific outcome analyses,
while mortality is used for pooled analysis.
If symptomatic results are reported at multiple times, we use the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28 days have
preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious outcome with
one or more events is used. For example, in low-risk populations with no mortality, a
reduction in mortality with treatment is not possible, however a reduction in
hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral outcomes.
When basically all patients recover in both treatment and control groups, preference for
viral clearance and recovery is given to results mid-recovery where available. After most
or all patients have recovered there is little or no room for an effective treatment to do
better, however faster recovery is valuable.
An IPD meta-analysis confirms that intermediate viral load reduction is more closely
associated with hospitalization/death than later viral load reduction80.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
Forest plots are computed using PythonMeta81 with the DerSimonian and
Laird random-effects model (the fixed effect assumption is not plausible in this case) and
inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
When results provide an odds ratio, we compute the relative risk when possible, or convert
to a relative risk according to Zhang et al.
Reported confidence intervals and p-values are used when available, and adjusted
values are used when provided. If multiple types of adjustments are reported propensity
score matching and multivariable regression has preference over propensity score matching
or weighting, which has preference over multivariable regression. Adjusted results have
preference over unadjusted results for a more serious outcome when the adjustments
significantly alter results.
When needed, conversion between reported p-values and confidence intervals followed
Altman, Altman (B), and Fisher's exact test was used to calculate
p-values for event data. If continuity correction for zero values is required, we
use the reciprocal of the opposite arm with the sum of the correction factors equal to
185.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative
outcome when applicable (for example, the risk of death rather than the risk of survival).
If studies only report relative continuous values such as relative times, the ratio of the
time for the treatment group versus the time for the control group is used. Calculations
are done in Python (3.15.2) with
scipy (1.18.1), pythonmeta (1.26), numpy (2.5.4), statsmodels (0.15.0), and plotly (6.9.0).
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically
significant.
Grobid 0.8.2 is used to parse PDF documents.
When evaluating potential effect modification across groups, we use an
interaction test as described by Altman (C) et al. We compared the log-transformed
relative risks using a z-test, deriving the standard error of the difference from
the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was
considered a statistically significant difference in treatment effect between the
groups.
Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of
providing standardized rules that can be applied with minimal understanding of the domain
and study. However, the rules do not account for many real-world issues, often
overemphasize or underemphasize others, and studies show low inter-rater
reliability93.
Certain domains are more applicable for these tools, however the time-sensitive nature of
a pandemic, with significant mortality for every day of delay in evidence assessment, and
the characteristics of COVID-19 make them inappropriate for this domain.
This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match
reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low
risk of bias, however this is the opposite of reality—the trial not only has very
high risk of bias, but has very high actual known bias, refusing to release data despite
pledging to, reporting multiple impossible numbers, having blinding and randomization
failure, and many other issues95.
Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however
this is the opposite of reality—the very late treatment and excessive dosage used
produces results with no relevance to recommended usage.
HCQ shows poor results with
late treatment and excessive dosage, and the combination shows harmC.
Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having
the lowest risk of bias91,92 are far from the most informative. Both
involve very late treatment, providing no information on recommended usage, and ROBINS-I
does a very poor job of accounting for the impact of confounding factorsD.
Our quality evaluation focuses on known issues and bias, and the potential
impact on outcomes, rather than just the risk of bias.
The estimated potential impact of each confounding factor, and the direction of the impact
is considered. For example, consider a study that shows significantly lower risk, the
value of the study varies significantly if confounding points to an underestimate or an
overestimate of efficacy. In one case, the real effect may be null, while the other case
provides stronger evidence of efficacy (which may be greater than the study shows).
Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical
or technical issues that have no or minimal impact on outcomes. Analysis also depends on
the outcome, for example certain issues are less relevant for objective outcomes such as
mortality.
Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major
issues affecting outcomes, increases in significance during a pandemic when immediate
recognition of new evidence is critical, and when considering all global studies, as
required during a pandemic. Investigators in other countries may have different customs
for design, analysis, and reporting, and different English language skills, however they
may not be less diligent or have greater bias.
Investigators in lower-pharmaceutical-profit countries may have lower bias towards
profitable interventions.
We have classified studies as
early treatment if most patients are not already at a severe stage at the time of
treatment (for example based on oxygen status or lung involvement), and treatment started
within 5 days of the onset of symptoms. If studies contain a mix of early treatment and
late treatment patients, we consider the treatment time of patients contributing most to
the events (for example, consider a study where most patients are treated early but late
treatment patients are included, and all mortality events were observed with late
treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only considered
effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for
oseltamivir, with longer delays not being effective99,100.
This is a living analysis and is updated regularly.
We received no funding, this research is done in our spare time.
We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/acmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Gilley, 2/10/2022, retrospective, USA, peer-reviewed, survey, 21 authors, study period September 2020 - December 2020, trial NCT04766788 (history). | risk of case, 49.9% lower, RR 0.50, p = 0.046, treatment 12 of 391 (3.1%), control 165 of 1,600 (10.3%), adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable. |
| Mohsin, 9/30/2021, retrospective, Bangladesh, peer-reviewed, survey, 10 authors, study period November 2020 - April 2021. | risk of severe case, 27.8% lower, RR 0.72, p = 0.001, treatment 302 of 928 (32.5%), control 298 of 572 (52.1%), NNT 5.1, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, alcohol/smokeless tobacco/substance abuse, multivariable. |
| Paul, 4/13/2022, retrospective, United Kingdom, preprint, survey, 2 authors. | risk of long COVID, 28.2% higher, RR 1.28, p = 0.40, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, ≤14 vs. >14 alcoholic drinks per week, multivariable, model 4, control prevalance approximated with overall prevalence. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
Monoclonal antibodies were
previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib
were recommended for late stage hospitalized patients.
When administered late in infection, HCQ may enhance viral egress by further increasing
lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting
lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of
SARS-CoV-2 particles into the extracellular environment87,88.
Research also suggests potential cardioprotective effects at lower doses, but
cardiotoxicity with excessive dosage89. Bobrowski et al. also
indicate negative effects if HCQ and remdesivir are combined.
Peters et al. is subject to confounding by calendar-time (SOC evolved rapidly early in
the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital
specific effects), hospital type (non-treatment hospitals were tertiary university
centers), confounding by indication (4/7 hospitals initiated treatment on deterioration),
immortal-time bias for as-treated (exposure assigned after baseline), significant
differences for other experimental treatments, potential overadjustment from collider bias
(steroid use and indication bias), limited baseline severity information, differences in
hospice referral propensity across hospitals, unadjusted difference in time from onset to
admission, difference in PCR positivity, and other factors.
Mahévas et al. is subject to confounding by hospital (treatment highly dependent on
the hospital, different SOC/ICU transfer practices, not included in PS), immortal time
(only partly addressed in sensitivity analysis), co-treatment differences, calendar-time
(SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep
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