Vitamin A for COVID-19: real-time meta analysis of 21 studies (15 treatment studies and 6 sufficiency studies)
Abstract
Significantly lower risk is seen for recovery and cases. 8 studies from 7 independent teams in 4 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
31% [11‑47%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are consistent with early treatment being more effective than late treatment.
6 sufficiency studies analyze outcomes based on serum levels,
showing 80% [52‑92%] lower risk for patients with higher vitamin A levels.
Results are robust — in exclusion sensitivity analysis 5 of 15
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
The European Food Safety Authority has found evidence for a causal relationship between the intake of vitamin A and optimal immune system function1,2.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
Dietary sources may be preferred. The quality of non-prescription supplements varies widely3-5.
All data and sources to reproduce this analysis are in the appendix.
Vitamin A for COVID-19 — Highlights
Vitamin A reduces risk with very high confidence for recovery, cases, and in pooled analysis, low confidence for hospitalization, and very low confidence for progression.
45th treatment shown effective in June 2023, now with p = 0.0052 from 15 studies.
Real-time updates and corrections with a consistent protocol for 135 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
Figure 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 vitamin A studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome. Efficacy based on specific outcomes was delayed by 9.9 months, compared to using pooled outcomes.
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 injury6-18 and
cognitive deficits9,14, cardiovascular
complications19-23, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 100+
host and viral proteins and other factorsA,24-31, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk32, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Vitamin A has been identified by the European Food Safety Authority (EFSA) as having sufficient evidence for a causal relationship between intake and optimal immune system function1,2,33.
Vitamin A has potent antiviral activity against SARS-CoV-2 in both human cell lines and human organoids of the lower respiratory tract (active metabolite all-trans retinoic acid, ATRA)34, is predicted to bind critical host and viral proteins for SARS-CoV-2 and may compensate for gene expression changes related to SARS-CoV-235-37, may be beneficial for COVID-19 via antiviral, anti-inflammatory, and immunomodulatory effects according to network pharmacology analysis38, reduces barrier compromise caused by TNF-α in Calu-3 cells39, inhibits mouse coronavirus replication40, may stimulate innate immunity by activating interferon responses in an IRF3-dependent manner (ATRA)40, may reduce excessive inflammation induced by SARS-CoV-237, shows SARS-CoV-2 antiviral activity In Vitro37,41,42, is effective against multiple SARS-CoV-2 variants in Calu-3 cells42, and inhibits the entry and replication of SARS-CoV-2 via binding to ACE2 / 3CLpro / RdRp / helicase / 3′-to-5′ exonuclease37.
We analyze all significant
controlled studies of
vitamin A
for COVID-19.
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, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Vitamin A has potent antiviral activity against SARS-CoV-2 in both human cell lines and human organoids of the lower respiratory tract (active metabolite all-trans retinoic acid, ATRA)34, is predicted to bind critical host and viral proteins for SARS-CoV-2 and may compensate for gene expression changes related to SARS-CoV-235-37, may be beneficial for COVID-19 via antiviral, anti-inflammatory, and immunomodulatory effects according to network pharmacology analysis38, reduces barrier compromise caused by TNF-α in Calu-3 cells39, inhibits mouse coronavirus replication40, may stimulate innate immunity by activating interferon responses in an IRF3-dependent manner (ATRA)40, may reduce excessive inflammation induced by SARS-CoV-237, shows SARS-CoV-2 antiviral activity In Vitro37,41,42, is effective against multiple SARS-CoV-2 variants in Calu-3 cells42, and inhibits the entry and replication of SARS-CoV-2 via binding to ACE2 / 3CLpro / RdRp / helicase / 3′-to-5′ exonuclease37.
An In Vivo animal study supports the efficacy of vitamin A40.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes.
Table 2 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, sufficiency studies, peer reviewed studies, and long COVID.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 31% [11‑47%] ** | 15 | 22,297 | 128 |
| After exclusions | 33% [16‑46%] *** | 11 | 6,759 | 81 |
| Peer-reviewed studiesPeer-reviewed | 29% [9‑44%] ** | 11 | 21,961 | 97 |
| Randomized Controlled TrialsRCTs | 40% [-6‑67%] | 5 | 347 | 39 |
| Mortality | 30% [-210‑84%] | 5 | 401 | 26 |
| HospitalizationHosp. | 11% [-2‑21%] | 6 | 6,373 | 34 |
| Recovery | 44% [22‑59%] *** | 3 | 300 | 18 |
| Cases | 29% [12‑44%] ** | 5 | 19,391 | 69 |
| RCT mortality | 46% [-552‑96%] | 2 | 90 | 13 |
| RCT hospitalizationRCT hosp. | -3% [-27‑16%] | 3 | 270 | 19 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 62% [-3‑86%] | 20% [-126‑72%] | 31% [12‑45%] ** |
| After exclusions | 38% [-31‑71%] | 49% [-19‑78%] | 31% [11‑46%] ** |
| Peer-reviewed studiesPeer-reviewed | 64% [-80‑93%] | 46% [-6‑72%] | 18% [4‑31%] * |
| Randomized Controlled TrialsRCTs | 26% [-76‑69%] | 49% [-19‑78%] | |
| Mortality | 86% [39‑97%] ** | -26% [-354‑65%] | |
| HospitalizationHosp. | 26% [-76‑69%] | 3% [-53‑39%] | 18% [4‑30%] * |
| Recovery | 32% [-133‑80%] | 45% [22‑60%] *** | |
| Cases | 29% [12‑44%] ** |
||
| RCT mortality | 46% [-552‑96%] | ||
| RCT hospitalizationRCT hosp. | 26% [-76‑69%] | 3% [-53‑39%] |
Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
Figure 4. 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.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for ICU admission.
Figure 8. Random effects meta-analysis for hospitalization.
Figure 9. Random effects meta-analysis for progression.
Figure 10. Random effects meta-analysis for recovery.
Figure 11. Random effects meta-analysis for cases.
Figure 12. Random effects meta-analysis for sufficiency studies.
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.
Figure 13. Random effects meta-analysis for peer reviewed studies.
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.
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.
Figure 14. Random effects meta-analysis for long COVID.
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.
Figure 15 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
40% improvement,
compared to 29% for other studies.
Figure 16, 17, and 18
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCT results are included in Table 1 and Table 2.
Figure 15. Results for RCTs and non-RCT studies.
Figure 16. Random effects meta-analysis for all Randomized Controlled Trials.
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.
Figure 17. Random effects meta-analysis for RCT mortality results.
Figure 18. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases46, and
analysis of double-blind RCTs has identified extreme levels of bias47.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 135 treatments we have analyzed,
66% of RCTs involve very late treatment 5+ days after
onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of
early treatments. They may more accurately represent results for treatments
that require visiting a medical facility, e.g., those requiring intravenous
administration.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.93‑1.08]
across 135 treatments49.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 135 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.93‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.93‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.93 [0.83‑1.04].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see53,54.
Currently, 52 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.7 months (66% with 8.8 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
We need to evaluate each trial on its own merits. RCTs for a
given medication and disease may be more reliable, however they may also be
less reliable. For off-patent medications, very high conflict of interest
trials may be more likely to be RCTs, and more likely to be large trials that
dominate meta analyses.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 19 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Al-Sumiadai, minimal details of groups provided.
Doocy, unadjusted results with no group details.
Holt, significant unadjusted confounding possible.
Sarohan, unadjusted results with no group details, comments suggest significant group differences and confounding.
Figure 19. Random effects meta-analysis for all studies after exclusions.
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.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours59,60. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases61 |
| <24 hours | -33 hours symptoms62 |
| 24-48 hours | -13 hours symptoms62 |
| Inpatients | -2.5 hours to improvement63 |
Figure 20 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 135 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 20. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 135 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants65, for example the Gamma variant shows significantly
different characteristics66-69. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants70,71.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Non-prescription supplements may show very wide variations in quality3,4.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for vitamin A as of April 2024. Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 9.9 months compared to using pooled outcomes.
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 135
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 21 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 22 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
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.
Figure 23 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 = 0.00000011 to p = 0.0000000048.
Figure 21. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 22. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 21. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 52 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.3 months.
Figure 24 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 24. 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 difference in treatment delay is 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 results92-95.
For vitamin A, there is currently not
enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
Figure 25 shows a scatter plot of
results for prospective and retrospective treatment studies.
67% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
44% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 20% improvement,
compared to 56% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
Figure 25. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 26 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.0596-103.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 26. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Vitamin A for COVID-19
lacks this because it is an inexpensive and widely available supplement.
In contrast, most COVID-19 vitamin A 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 vitamin A 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.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
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 alone74-90.
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.
1 of 15 studies
combine treatments. The results of
vitamin A
alone may differ.
1 of 5 RCTs use combined treatment.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors24-31, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk32, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 27 shows an overview of the results for vitamin A
in the context of multiple COVID-19 treatments, and Figure 28 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 27.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy125.
Figure 28. Efficacy vs. cost for COVID-19 treatments.
Vitamin A is
an effective treatment for COVID-19.
Significantly lower risk is seen for recovery and cases. 8 studies from 7 independent teams in 4 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
31% [11‑47%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are consistent with early treatment being more effective than late treatment.
6 sufficiency studies analyze outcomes based on serum levels,
showing 80% [52‑92%] lower risk for patients with higher vitamin A levels.
Results are robust — in exclusion sensitivity analysis 5 of 15
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
Treatment and prophylaxis studies of vitamin A in Iraq.
The prophylaxis study contained 209 contacts of COVID-19 patients, 97 treated with vitamin A, showing significantly lower cases with treatment, and shorter duration of symptoms.
The treatment study is listed separately110.
The prophylaxis study contained 209 contacts of COVID-19 patients, 97 treated with vitamin A, showing significantly lower cases with treatment, and shorter duration of symptoms.
The treatment study is listed separately110.
Treatment and prophylaxis studies of vitamin A in Iraq.
The treatment study contained 100 patients, 50 treated with 200,000IU vitamin A for two days, showing lower progression to severe disease, and shorter duration of symptoms.
The prophylaxis study is listed separately110.
The treatment study contained 100 patients, 50 treated with 200,000IU vitamin A for two days, showing lower progression to severe disease, and shorter duration of symptoms.
The prophylaxis study is listed separately110.
Retrospective 70 severe condition patients treated with vitamin A (200,000IU for two days), salbutamol, and budesonide, and 70 patients not treated with vitamin A, showing significantly lower mortality with the addition of vitamin A.
Small RCT 60 ICU patients in Iran, 30 treated with vitamins A, B, C, D, and E, showing significant improvement in SOFA score and several inflammatory markers at day 7 with treatment.
5,000 IU vitamin A daily, 600,000 IU vitamin D once, 300 IU of vitamin E twice a day, 500 mg vitamin C four times a day, and one ampule daily of B vitamins [thiamine nitrate 3.1 mg, sodium riboflavin phosphate 4.9 mg (corresponding to vitamin B2 3.6 mg), nicotinamide 40 mg, pyridoxine hydrochloride 4.9 mg (corresponding to vitamin B6 4.0 mg), sodium pantothenate 16.5 mg (corresponding to pantothenic acid 15 mg), sodium ascorbate 113 mg (corresponding to vitamin C 100 mg), biotin 60 μg, folic acid 400 μg, and cyanocobalamin 5 μg].
5,000 IU vitamin A daily, 600,000 IU vitamin D once, 300 IU of vitamin E twice a day, 500 mg vitamin C four times a day, and one ampule daily of B vitamins [thiamine nitrate 3.1 mg, sodium riboflavin phosphate 4.9 mg (corresponding to vitamin B2 3.6 mg), nicotinamide 40 mg, pyridoxine hydrochloride 4.9 mg (corresponding to vitamin B6 4.0 mg), sodium pantothenate 16.5 mg (corresponding to pantothenic acid 15 mg), sodium ascorbate 113 mg (corresponding to vitamin C 100 mg), biotin 60 μg, folic acid 400 μg, and cyanocobalamin 5 μg].
RCT 24 patients with olfactory dysfunction post-COVID-19 in Hong Kong, showing significantly improved recovery with the addition of vitamin A to aerosolised diffuser olfactory training. 25,000IU vitamin A for 14 days.
Prospective study of 144 hospitalized COVID-19 patients in the DRC and South Sudan, showing no significant difference with vitamin A treatment in unadjusted results with only 8 patients receiving vitamin A.
Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency.
Case control study with 30 ICU COVID-19 patients, 30 hospitalized non-ICU patients, and 30 matched healthy controls, showing vitamin A levels associated with COVID-19 and severity, with ICU patient levels < hospitalized patients < healthy controls. Authors also show significantly lower risk of ARDS with vitamin A levels above 0.65µg/ml.
Retrospective 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with vitamin A prophylaxis.
Retrospective 112 pediatric COVID-19 patients and 23 healthy controls showing lower levels of vitamins A and D associated with more severe disease. Patients with moderate and severe COVID-19 had significantly lower vitamin A, vitamin D, and retinol-binding protein 4 (RBP4) levels compared to those with mild disease and healthy controls. Lower vitamin A and D levels were associated with higher levels of inflammatory markers such as CRP, leukocytes, and ESR.
RCT 91 vitamin A and 91 control patients in Iran, showing improved recovery with treatment. All patients received HCQ. 25,000IU/day oral vitamin A for 10 days.
Prospective pilot study of 20 critically ill COVID-19 ICU patients showing high deficiency rates of 50-100% for vitamins A, B6, and D; zinc; and selenium at admission. Deficiencies of vitamins B6 and D, and low iron status, persisted after 3 weeks. Plasma levels of vitamins A and E, zinc, and selenium increased over time as inflammation resolved, suggesting redistribution may explain some observed deficiencies. All patients received daily micronutrient administration. Additional intravenous and oral micronutrient administration for 10 patients did not significantly impact micronutrient levels or deficiency rates, however authors note that the administered doses may be too low. The form of vitamin D is not specified but may have been cholecalciferol which is expected to have a very long onset of action compared to more appropriate forms such as calcifediol or calcitriol.
Retrospective 27 severe COVID-19 patients and 23 non-COVID-19 patients, showing significantly lower vitamin A levels in COVID-19 patients (0.37mg/L vs. 0.52 mg/L, p<0.001). 10 of 27 COVID-19 patients received vitamin A, with higher mortality. Group details are not provided but authors note that 8 of 10 had comorbidities.
RCT 30 hospitalized patients in Iran, showing no significant difference with vitamin A treatment. All patients received HCQ. 50,000 IU/day intramuscular vitamin A for up to 2 weeks.
RCT 90 outpatients with post-COVID-19 anosmia showing significant improvements in smell alterations with olfactory training after 3 and 12 months. Adding oral vitamin A to olfactory training resulted in higher rates of improvement, but the difference was not statistically significant.
Prospective analysis of 40 hospitalized patients and 47 age-matched convalescent patients, showing significantly lower vitamin A levels in critical patients, and significantly lower vitamin A levels in hospitalized patients vs. controls. Low vitamin A levels were significantly associated with ARDS and mortality in hospitalized patients.
Retrospective 120 hospitalized patients in Spain showing vitamin A deficiency associated with higher ICU admission.
Analysis of nutrient intake and COVID-19 outcomes for 3,996 people in Iran, showing lower risk of COVID-19 hospitalization with sufficient vitamin A, vitamin C, and selenium intake, with statistical significance for vitamin A and selenium.
Prospective study of 57 consecutive hospitalized COVID-19 patients in Switzerland, showing higher risk of mortality/ICU admission with vitamin A, vitamin D, and zinc deficiency, with statistical significance only for vitamin A and zinc. Adjustments only considered age.
Computational drug repurposing study integrating genetically regulated gene expression (GReX) and pharmaceutical databases to identify 7 FDA-approved compounds that may reverse COVID-19-related gene expression.
Analysis of 755,346 people in the Veterans Health Administration cohort showed that retinol and azathioprine were associated with reduced incidence of COVID-19.
In Vitro analysis showed nelfinavir and saquinavir inhibited SARS-CoV-2 replication by ~95% and ~65% respectively.
Analysis of 755,346 people in the Veterans Health Administration cohort showed that retinol and azathioprine were associated with reduced incidence of COVID-19.
In Vitro analysis showed nelfinavir and saquinavir inhibited SARS-CoV-2 replication by ~95% and ~65% respectively.
Mendelian randomization study suggesting a causal association between retinol and related proteins (RBP4, RDH16, CRABP1) and COVID-19. The study found that genetically-predicted higher retinol levels were associated with lower COVID-19 susceptibility. There was a lower risk of hospitalization and severity without statistical significance. Authors conclude that the results support a potential protective effect of vitamin A treatment for COVID-19. Given the lack of clear evidence for pleiotropy, and the lower precision of the MR-Egger estimates, the IVW estimates are likely more reliable in this case when the IVW and MR-Egger estimates differ.
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.
Search terms are vitamin A and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of vitamin A for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
This is a living analysis and is updated regularly.
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
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used 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 test status. 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.
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.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to126.
Reported confidence intervals and p-values were used when available,
using adjusted values 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 1129.
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
(3.13.3) with
scipy (1.15.2), pythonmeta (1.26), numpy (2.2.5), statsmodels (0.14.4), and plotly (6.0.1).
Forest plots are computed using PythonMeta130
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.
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 0.8.0 is used to parse PDF documents.
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 effective59,60.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/vameta.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.
| Al-Sumiadai (B), 1/31/2021, prospective, Iraq, preprint, 3 authors. | risk of progression, 66.7% lower, RR 0.33, p = 0.27, treatment 2 of 50 (4.0%), control 6 of 50 (12.0%), NNT 13, progression to severe disease. |
| Al-Sumiadai, 12/31/2020, retrospective, Iraq, peer-reviewed, 3 authors, excluded in exclusion analyses: minimal details of groups provided. | risk of death, 85.7% lower, RR 0.14, p = 0.002, treatment 2 of 70 (2.9%), control 14 of 70 (20.0%), NNT 5.8. |
| Rohani, 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 39.4, 6 authors, study period 1 May, 2020 - 1 September, 2020, trial IRCT46974. | risk of hospitalization, 25.6% lower, RR 0.74, p = 0.63, treatment 8 of 89 (9.0%), control 11 of 91 (12.1%), NNT 32. |
| risk of no recovery, 31.8% lower, RR 0.68, p = 0.53, treatment 4 of 89 (4.5%), control 6 of 91 (6.6%), NNT 48, dyspnea. | |
| risk of no recovery, 79.6% lower, RR 0.20, p = 0.03, treatment 2 of 89 (2.2%), control 10 of 91 (11.0%), NNT 11, fever. | |
| risk of no recovery, 87.2% lower, RR 0.13, p = 0.01, treatment 1 of 89 (1.1%), control 8 of 91 (8.8%), NNT 13, body ache. | |
| risk of no recovery, 48.9% lower, RR 0.51, p = 0.32, treatment 3 of 89 (3.4%), control 6 of 91 (6.6%), NNT 31, headache. | |
| risk of no recovery, 62.8% lower, RR 0.37, p = 0.05, treatment 4 of 89 (4.5%), control 11 of 91 (12.1%), NNT 13, weakness and fatigue. | |
| risk of no recovery, 20.5% lower, RR 0.80, p = 0.63, treatment 7 of 89 (7.9%), control 9 of 91 (9.9%), NNT 49, chest pain. | |
| risk of no recovery, 40.4% lower, RR 0.60, p = 0.24, treatment 7 of 89 (7.9%), control 12 of 91 (13.2%), NNT 19, cough. |
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.
| Beigmohammadi, 11/14/2021, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 6 authors, study period April 2020 - July 2020, this trial uses multiple treatments in the treatment arm (combined with vitamins B, C, D, E) - results of individual treatments may vary, trial IRCT20200319046819N1. | risk of death, 88.9% lower, RR 0.11, p = 0.11, treatment 0 of 30 (0.0%), control 4 of 30 (13.3%), NNT 7.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization >7 days, 41.0% lower, RR 0.59, p = 0.25, treatment 4 of 30 (13.3%), control 16 of 30 (53.3%), NNT 2.5, adjusted per study, odds ratio converted to relative risk. | |
| relative SOFA score @day 7, 45.5% better, RR 0.55, p < 0.001, treatment 30, control 30. | |
| Chung, 6/30/2023, Randomized Controlled Trial, China, peer-reviewed, 14 authors, study period 14 August, 2020 - 11 June, 2021, trial NCT04900415 (history). | relative BTT improvement, 75.1% better, RR 0.25, p = 0.048, treatment mean 3.01 (±2.52) n=9, control mean 0.75 (±1.67) n=8, vitamin A + OT vs. OT. |
| anosmia, 68.0% lower, RR 0.32, p = 0.47, treatment 0 of 9 (0.0%), control 1 of 8 (12.5%), NNT 8.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), vitamin A + OT vs. OT. | |
| severe microsmia, 70.4% lower, RR 0.30, p = 0.29, treatment 1 of 9 (11.1%), control 3 of 8 (37.5%), NNT 3.8, vitamin A + OT vs. OT. | |
| moderate microsmia, 74.6% lower, RR 0.25, p = 0.02, treatment 2 of 9 (22.2%), control 7 of 8 (87.5%), NNT 1.5, vitamin A + OT vs. OT. | |
| Doocy, 10/19/2022, prospective, multiple countries, peer-reviewed, 6 authors, study period December 2020 - June 2021, average treatment delay 7.8 days, trial NCT04568499 (history), excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 26.1% lower, RR 0.74, p = 1.00, treatment 1 of 8 (12.5%), control 23 of 136 (16.9%), NNT 23, unadjusted. |
| Sarohan, 2/1/2021, retrospective, Turkey, preprint, 4 authors, excluded in exclusion analyses: unadjusted results with no group details, comments suggest significant group differences and confounding. | risk of death, 282.5% higher, RR 3.83, p = 0.001, treatment 9 of 10 (90.0%), control 4 of 17 (23.5%). |
| Somi, 10/7/2022, Randomized Controlled Trial, Iran, peer-reviewed, mean age 60.2, 7 authors, study period April 2020 - July 2020, trial IRCT20170117032004N3. | risk of death, 50.0% higher, RR 1.50, p = 1.00, treatment 3 of 15 (20.0%), control 2 of 15 (13.3%). |
| risk of mechanical ventilation, no change, RR 1.00, p = 1.00, treatment 3 of 15 (20.0%), control 3 of 15 (20.0%). | |
| risk of ICU admission, 25.0% lower, RR 0.75, p = 1.00, treatment 3 of 15 (20.0%), control 4 of 15 (26.7%), NNT 15. | |
| time to clinical response, 76.0% higher, HR 1.76, p = 0.21, treatment 15, control 15, Kaplan–Meier. | |
| hospitalization time, 8.1% higher, relative time 1.08, p = 0.49, treatment mean 7.33 (±2.31) n=15, control mean 6.78 (±1.84) n=15. | |
| Taheri, 6/3/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 6 authors, study period March 2020 - March 2021, post-COVID anosmia. | risk of no recovery, 37.5% lower, RR 0.62, p = 0.53, treatment 5 of 30 (16.7%), control 8 of 30 (26.7%), NNT 10.0, 12 months. |
| risk of no recovery, 42.9% lower, RR 0.57, p = 0.51, treatment 4 of 30 (13.3%), control 7 of 30 (23.3%), NNT 10.0, 3 months. | |
| risk of anosmia, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 30 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 12 months. | |
| risk of anosmia, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 30 (0.0%), control 3 of 30 (10.0%), NNT 10.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 3 months. | |
| risk of anosmia/severe hyposmia, 88.9% lower, RR 0.11, p = 0.11, treatment 0 of 30 (0.0%), control 4 of 30 (13.3%), NNT 7.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 12 months. | |
| risk of anosmia/severe hyposmia, 88.9% lower, RR 0.11, p = 0.11, treatment 0 of 30 (0.0%), control 4 of 30 (13.3%), NNT 7.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 3 months. |
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.
| Al-Sumiadai (C), 1/31/2021, prospective, Iraq, preprint, 3 authors. | risk of case, 64.5% lower, RR 0.36, p < 0.001, treatment 20 of 97 (20.6%), control 65 of 112 (58.0%), NNT 2.7. |
| Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK), excluded in exclusion analyses: significant unadjusted confounding possible. | risk of case, 56.3% lower, RR 0.44, p = 0.41, treatment 1 of 91 (1.1%), control 445 of 15,136 (2.9%), NNT 54, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated. |
| Nimer, 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021. | risk of hospitalization, 21.2% lower, RR 0.79, p = 0.40, treatment 15 of 144 (10.4%), control 204 of 2,004 (10.2%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of severe case, 20.8% lower, RR 0.79, p = 0.36, treatment 17 of 144 (11.8%), control 243 of 2,004 (12.1%), adjusted per study, odds ratio converted to relative risk, multivariable. | |
| Vaisi, 5/11/2023, retrospective, Iran, peer-reviewed, 5 authors. | risk of hospitalization, 16.7% lower, HR 0.83, p = 0.04, treatment 1,140, control 2,815, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards. |
| risk of symptomatic case, 10.6% lower, HR 0.89, p = 0.03, treatment 1,140, control 2,815, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards. | |
| Voloudakis, 1/14/2025, retrospective, USA, preprint, 21 authors. | risk of case, 19.0% lower, OR 0.81, p < 0.001, adjusted per study, multivariable, RR approximated with OR. |
| Wang (B), 4/26/2024, retrospective, multiple countries, peer-reviewed, 6 authors, Mendelian - per SD. | risk of severe case, 35.0% lower, OR 0.65, p = 0.28, IVW, RR approximated with OR. |
| risk of hospitalization, 24.0% lower, OR 0.76, p = 0.29, IVW, RR approximated with OR. | |
| risk of case, 31.0% lower, OR 0.69, p = 0.006, IVW, RR approximated with OR. |
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.
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