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Vitamin A for COVID-19: real-time meta analysis of 18 studies (12 treatment studies and 6 sufficiency studies)

@CovidAnalysis, April 2024, Version 27V27
 
0 0.5 1 1.5+ All studies 36% 12 22,237 Improvement, Studies, Patients Relative Risk Mortality 30% 5 401 Ventilation 0% 1 30 ICU admission 25% 1 30 Hospitalization 10% 5 6,373 Recovery 44% 2 240 Cases 44% 3 19,391 RCTs 45% 4 287 RCT mortality 46% 2 90 Peer-reviewed 32% 9 21,901 Sufficiency 80% 6 389 Prophylaxis 39% 4 21,539 Early 62% 3 420 Late 19% 5 278 Vitamin A for COVID-19 c19early.org April 2024 after exclusions Favorsvitamin A Favorscontrol
Abstract
Statistically significant lower risk is seen for recovery. 6 studies from 5 independent teams in 3 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 36% [1‑59%] 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.
In exclusion sensitivity analysis, statistical significance is lost after excluding only one of 12 studies 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 function Galmés, Galmés (B).
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. The quality of non-prescription supplements can vary widely Crawford, Crighton.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Vitamin A p=0.045 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org April 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Vitamin A reduces risk for COVID-19 with high confidence for pooled analysis, low confidence for recovery, and very low confidence for hospitalization, progression, and cases.
39th treatment shown effective with ≥3 clinical studies in June 2023, now with p = 0.045 from 12 studies.
We show outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor for COVID-19.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 69 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Al-Sumiadai 86% 0.14 [0.03-0.61] death 2/70 14/70 Improvement, RR [CI] Treatment Control Al-Sumiadai 67% 0.33 [0.07-1.57] progression 2/50 6/50 Rohani (DB RCT) 26% 0.74 [0.31-1.76] hosp. 8/89 11/91 Tau​2 = 0.39, I​2 = 48.3%, p = 0.058 Early treatment 62% 0.38 [0.14-1.03] 12/209 31/211 62% lower risk Sarohan -282% 3.83 [1.58-9.24] death 9/10 4/17 Improvement, RR [CI] Treatment Control Beigm.. (SB RCT) 89% 0.11 [0.01-1.98] death 0/30 4/30 ICU patients CT​1 Somi (RCT) -50% 1.50 [0.29-7.73] death 3/15 2/15 Doocy 26% 0.74 [0.11-4.80] death 1/8 23/136 Chung (RCT) 75% 0.25 [0.06-0.99] PASC 9 (n) 8 (n) LONG COVID Tau​2 = 1.55, I​2 = 72.2%, p = 0.77 Late treatment 19% 0.81 [0.22-3.07] 13/72 33/206 19% lower risk Al-Sumiadai 64% 0.36 [0.23-0.54] cases 20/97 65/112 Improvement, RR [CI] Treatment Control COVIDENCE UK Holt 56% 0.44 [0.06-2.96] cases 1/91 445/15,136 Nimer 21% 0.79 [0.45-1.35] hosp. 15/144 204/2,004 Vaisi 17% 0.83 [0.48-1.00] hosp. 1,140 (n) 2,815 (n) Tau​2 = 0.17, I​2 = 78.2%, p = 0.054 Prophylaxis 39% 0.61 [0.37-1.01] 36/1,472 714/20,067 39% lower risk All studies 36% 0.64 [0.41-0.99] 61/1,753 778/20,484 36% lower risk 12 vitamin A COVID-19 studies c19early.org April 2024 Tau​2 = 0.29, I​2 = 70.9%, p = 0.045 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors vitamin A Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Al-Sumiadai 86% death Improvement Relative Risk [CI] Al-Sumiadai 67% progression Rohani (DB RCT) 26% hospitalization Tau​2 = 0.39, I​2 = 48.3%, p = 0.058 Early treatment 62% 62% lower risk Sarohan -282% death Beigm.. (SB RCT) 89% death ICU patients CT​1 Somi (RCT) -50% death Doocy 26% death Chung (RCT) 75% PASC LONG COVID Tau​2 = 1.55, I​2 = 72.2%, p = 0.77 Late treatment 19% 19% lower risk Al-Sumiadai 64% case COVIDENCE UK Holt 56% case Nimer 21% hospitalization Vaisi 17% hospitalization Tau​2 = 0.17, I​2 = 78.2%, p = 0.054 Prophylaxis 39% 39% lower risk All studies 36% 36% lower risk 12 vitamin A C19 studies c19early.org April 2024 Tau​2 = 0.29, I​2 = 70.9%, p = 0.045 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors vitamin A Favors control
B
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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 date indicates the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for 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 issues Duloquin, Hampshire, Scardua-Silva, Sodagar, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, Lv, Lui, Niarakis, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk c19early.org, 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 function EFSA, Galmés, Galmés (B). 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) Tong, is predicted to bind critical host and viral proteins for SARS-CoV-2 and may compensate for gene expression changes related to SARS-CoV-2 Chakraborty, Huang, Pandya, may be beneficial for COVID-19 via antiviral, anti-inflammatory, and immunomodulatory effects according to network pharmacology analysis Li, reduces barrier compromise caused by TNF-α in Calu-3 cells DiGuilio, inhibits mouse coronavirus replication Franco, may stimulate innate immunity by activating interferon responses in an IRF3-dependent manner (ATRA) Franco, may reduce excessive inflammation induced by SARS-CoV-2 Huang, shows SARS-CoV-2 antiviral activity In Vitro Huang, Moatasim, Morita, is effective against multiple SARS-CoV-2 variants in Calu-3 cells Morita, and inhibits the entry and replication of SARS-CoV-2 via binding to ACE2 / 3CLpro / RdRp / helicase / 3′-to-5′ exonuclease Huang.
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) Tong, is predicted to bind critical host and viral proteins for SARS-CoV-2 and may compensate for gene expression changes related to SARS-CoV-2 Chakraborty, Huang, Pandya, may be beneficial for COVID-19 via antiviral, anti-inflammatory, and immunomodulatory effects according to network pharmacology analysis Li, reduces barrier compromise caused by TNF-α in Calu-3 cells DiGuilio, inhibits mouse coronavirus replication Franco, may stimulate innate immunity by activating interferon responses in an IRF3-dependent manner (ATRA) Franco, may reduce excessive inflammation induced by SARS-CoV-2 Huang, shows SARS-CoV-2 antiviral activity In Vitro Huang, Moatasim, Morita, is effective against multiple SARS-CoV-2 variants in Calu-3 cells Morita, and inhibits the entry and replication of SARS-CoV-2 via binding to ACE2 / 3CLpro / RdRp / helicase / 3′-to-5′ exonuclease Huang.
4 In Silico studies support the efficacy of vitamin A Chakraborty, Huang, Li, Pandya.
5 In Vitro studies support the efficacy of vitamin A DiGuilio, Huang, Moatasim, Morita, Tong.
An In Vivo animal study supports the efficacy of vitamin A Franco.
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, and 13 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, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01.
Improvement Studies Patients Authors
All studies36% [1‑59%]
*
12 22,237 95
After exclusions41% [12‑61%]
*
8 6,699 48
Peer-reviewed studiesPeer-reviewed32% [5‑52%]
*
9 21,901 85
Randomized Controlled TrialsRCTs45% [-31‑77%]4 287 33
Mortality30% [-210‑84%]5 401 26
HospitalizationHosp.10% [-5‑23%]5 6,373 28
Recovery44% [22‑61%]
***
2 240 12
Cases44% [-26‑75%]3 19,391 42
RCT mortality46% [-552‑96%]2 90 13
RCT hospitalizationRCT hosp.-3% [-27‑16%]3 270 19
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01.
Early treatment Late treatment Prophylaxis
All studies62% [-3‑86%]19% [-207‑78%]39% [-1‑63%]
After exclusions38% [-31‑71%]58% [-77‑90%]38% [-7‑63%]
Peer-reviewed studiesPeer-reviewed64% [-80‑93%]51% [-38‑82%]18% [3‑30%]
*
Randomized Controlled TrialsRCTs26% [-76‑69%]58% [-77‑90%]
Mortality86% [39‑97%]
**
-26% [-354‑65%]
HospitalizationHosp.26% [-76‑69%]3% [-53‑39%]17% [2‑30%]
*
Recovery32% [-133‑80%]45% [22‑62%]
**
Cases44% [-26‑75%]
RCT mortality46% [-552‑96%]
RCT hospitalizationRCT hosp.26% [-76‑69%]3% [-53‑39%]
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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.
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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.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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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.
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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 shows a comparison of results for RCTs and non-RCT studies. Random effects meta analysis of RCTs shows 45% improvement, compared to 34% for other studies. Figure 15, 16, and 17 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.
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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. 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.
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Figure 16. Random effects meta-analysis for RCT mortality results.
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Figure 17. 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 biases Jadad, and analysis of double-blind RCTs has identified extreme levels of bias Gøtzsche. 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 69 treatments we have analyzed, 63% 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.
Evidence shows that non-RCT 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. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. 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 Internet 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 see Deaton, Nichol.
Currently, 44 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, 28 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 23 have been confirmed with a delay of 8.4 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
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 18 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.
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Figure 18. 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 hours McLean, Treanor. Baloxavir 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.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 19 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 69 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 69 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 variants Korves, for example the Gamma variant shows significantly different characteristics Faria, Karita, Nonaka, Zavascki. 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 variants Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan, therefore efficacy may depend strongly on combined treatments.
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 quality Crawford, Crighton.
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.
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.
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.
Another way to view pooled analysis is that we are using more of the available information. Logically we should, and do, use additional information. For example dose-response and treatment delay-response relationships provide significant additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
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 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 69 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 20 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 21 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 22 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 = 0.0000031 to p = 0.0000000067.
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Figure 20. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 21. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 44 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 4.7 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 5.5 months. Figure 23 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 23. 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 results Boulware, Meeus, Meneguesso, twitter.com. 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 24 shows a scatter plot of results for prospective and retrospective treatment studies. 50% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 50% of prospective studies, showing no difference. The median effect size for retrospective studies is 19% improvement, compared to 60% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 24. 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 25 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 < 0.05 Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley. 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 25. 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 alone Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan. 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 12 studies combine treatments. The results of vitamin A alone may differ. 1 of 4 RCTs use combined treatment.
Multiple reviews cover vitamin A for COVID-19, presenting additional background on mechanisms and related results, including Andrade, DiGuilio (B), Midha, Stephensen.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors Lui, Lv, Malone, Murigneux, Niarakis, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk c19early.org, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 26 shows an overview of the results for vitamin A in the context of multiple COVID-19 treatments, and Figure 27 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Figure 26. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random effects meta-analysis. 0.6% of 7,000+ proposed treatments show efficacy c19early.org (B).
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Figure 27. Efficacy vs. cost for COVID-19 treatments.
Statistically significant lower risk is seen for recovery. 6 studies from 5 independent teams in 3 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 36% [1‑59%] 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. In exclusion sensitivity analysis, statistical significance is lost after excluding only one of 12 studies 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 function Galmés, Galmés (B).
Al-Sumiadai (C): 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 contained 209 contacts of COVID-19 patients, 97 treated with vitamin A, showing significantly lower cases with treatment, and shorter duration of symptoms.
0 0.5 1 1.5 2+ Progression 67% Improvement Relative Risk Recovery time 38% no CI Vitamin A  Al-Sumiadai et al.  EARLY TREATMENT Is early treatment with vitamin A beneficial for COVID-19? Prospective study of 100 patients in Iraq Lower progression with vitamin A (not stat. sig., p=0.27) c19early.org Al-Sumiadai et al., Systematic Reviews.., Jan 2021 Favors vitamin A Favors control
Al-Sumiadai (B): 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 contained 209 contacts of COVID-19 patients, 97 treated with vitamin A, showing significantly lower cases with treatment, and shorter duration of symptoms.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Vitamin A  Al-Sumiadai et al.  EARLY TREATMENT Is early treatment with vitamin A beneficial for COVID-19? Retrospective 140 patients in Iraq Lower mortality with vitamin A (p=0.0024) c19early.org Al-Sumiadai et al., EurAsian J. Biosci.., Dec 2020 Favors vitamin A Favors control
Al-Sumiadai: 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.
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Hospitalization >7 days 41% SOFA score @day 7 45% Vitamin A  Beigmohammadi et al.  ICU PATIENTS  RCT Is very late treatment with vitamin A + vitamins B, C, D, E beneficial for COVID-19? RCT 60 patients in Iran (April - July 2020) Improved recovery with vitamin A + vitamins B, C, D, E (p=0.001) c19early.org Beigmohammadi et al., Trials, November 2021 Favors vitamin A Favors control
Beigmohammadi: 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]. irct.ir.
0 0.5 1 1.5 2+ BTT improvement 75% Improvement Relative Risk Anosmia 68% Severe microsmia 70% Moderate microsmia 75% Vitamin A  Chung et al.  LATE TREATMENT  RCT  LONG COVID Does vitamin A reduce the risk of Long COVID (PASC)? RCT 24 patients in China (August 2020 - June 2021) Lower PASC with vitamin A (p=0.048) c19early.org Chung et al., Brain Sciences, June 2023 Favors vitamin A Favors control
Chung: 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.
0 0.5 1 1.5 2+ Mortality 26% unadjusted Improvement Relative Risk Vitamin A for COVID-19  Doocy et al.  LATE TREATMENT Is late treatment with vitamin A beneficial for COVID-19? Prospective study of 144 patients in multiple countries (Dec 2020 - Jun 2021) Study underpowered to detect differences c19early.org Doocy et al., PLOS Global Public Health, Oct 2022 Favors vitamin A Favors control
Doocy: 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.
0 0.5 1 1.5 2+ Case 56% Improvement Relative Risk Vitamin A for COVID-19  COVIDENCE UK  Prophylaxis Does vitamin A reduce COVID-19 infections? Prospective study of 15,227 patients in the United Kingdom (May 2020 - Feb 2021) Fewer cases with vitamin A (not stat. sig., p=0.41) c19early.org Holt et al., Thorax, March 2021 Favors vitamin A Favors control
Holt: 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. NCT04330599. COVIDENCE UK.
0 0.5 1 1.5 2+ ARDS 100% Improvement Relative Risk Vitamin A for COVID-19  Mandour et al.  Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Prospective study of 60 patients in Egypt (September 2021 - April 2022) Lower ARDS with higher vitamin A levels (p=0.001) c19early.org Mandour et al., The Egyptian J. Bronch.., Jul 2023 Favors vitamin A Favors control
Mandour: 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.
0 0.5 1 1.5 2+ Hospitalization 21% Improvement Relative Risk Severe case 21% Vitamin A for COVID-19  Nimer et al.  Prophylaxis Is prophylaxis with vitamin A beneficial for COVID-19? Retrospective 2,148 patients in Jordan (March - July 2021) Lower hospitalization (p=0.4) and severe cases (p=0.36), not sig. c19early.org Nimer et al., Bosnian J. Basic Medical.., Feb 2022 Favors vitamin A Favors control
Nimer: Retrospective 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with vitamin A prophylaxis.
0 0.5 1 1.5 2+ Severe case 61% Improvement Relative Risk Case 84% Vitamin A for COVID-19  Pavlyshyn et al.  Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Retrospective 135 patients in Ukraine Lower severe cases (p=0.14) and fewer cases (p=0.2), not sig. c19early.org Pavlyshyn et al., Неонатологія, хірург.., Apr 2024 Favors vitamin A Favors control
Pavlyshyn: 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.
0 0.5 1 1.5 2+ Hospitalization 26% Improvement Relative Risk Recovery, dyspnea 32% Recovery, fever 80% Recovery, body ache 87% Recovery, headache 49% Recovery, weakness and f.. 63% Recovery, chest pain 20% Recovery, cough 40% Vitamin A  Rohani et al.  EARLY TREATMENT  DB RCT Is early treatment with vitamin A beneficial for COVID-19? Double-blind RCT 180 patients in Iran (May - September 2020) Trial underpowered to detect differences in serious outcomes c19early.org Rohani et al., Eastern Mediterranean H.., Aug 2022 Favors vitamin A Favors control
Rohani: 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.
0 0.5 1 1.5 2+ ICU admission 100% Improvement Relative Risk Vitamin A for COVID-19  Rozemeijer et al.  Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Prospective study of 25 patients in Netherlands Lower ICU admission with higher vitamin A levels (p=0.011) c19early.org Rozemeijer et al., Nutrients, January 2024 Favors vitamin A Favors control
Rozemeijer: 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.
0 0.5 1 1.5 2+ Mortality -282% Improvement Relative Risk Vitamin A for COVID-19  Sarohan et al.  LATE TREATMENT Is late treatment with vitamin A beneficial for COVID-19? Retrospective 27 patients in Turkey Higher mortality with vitamin A, but no group details c19early.org Sarohan et al., medRxiv, February 2021 Favors vitamin A Favors control
Sarohan: 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.
0 0.5 1 1.5 2+ Mortality -50% Improvement Relative Risk Ventilation 0% ICU admission 25% Time to clinical response -76% Hospitalization time -8% Vitamin A  Somi et al.  LATE TREATMENT  RCT Is late treatment with vitamin A beneficial for COVID-19? RCT 30 patients in Iran (April - July 2020) Trial underpowered for serious outcomes c19early.org Somi et al., Nutrition and Health, Oct 2022 Favors vitamin A Favors control
Somi: 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.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk Progression 45% Vitamin A for COVID-19  Tepasse et al.  Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Prospective study of 40 patients in Germany Lower mortality (p=0.042) and progression (p=0.048) c19early.org Tepasse et al., Nutrients, June 2021 Favors vitamin A Favors control
Tepasse: 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.
0 0.5 1 1.5 2+ Ventilation 71% Improvement Relative Risk ICU admission 61% Vitamin A  Tomasa-Irriguible et al.  Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Retrospective 120 patients in Spain (March - May 2020) Lower ventilation (p=0.001) and ICU admission (p=0.004) c19early.org Tomasa-Irriguible et al., Metabolites, Oct 2020 Favors vitamin A Favors control
Tomasa-Irriguible (B): Retrospective 120 hospitalized patients in Spain showing vitamin A deficiency associated with higher ICU admission.
Tomasa-Irriguible: Estimated 300 patient vitamin A early treatment RCT with results expected soon (estimated completion over 4 months ago).
0 0.5 1 1.5 2+ Hospitalization 17% Improvement Relative Risk Symp. case 11% Vitamin A for COVID-19  Vaisi et al.  Prophylaxis Is prophylaxis with vitamin A beneficial for COVID-19? Retrospective 3,955 patients in Iran Lower hospitalization (p=0.043) and fewer symptomatic cases (p=0.033) c19early.org Vaisi et al., The Clinical Respiratory.., May 2023 Favors vitamin A Favors control
Vaisi: 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.
0 0.5 1 1.5 2+ Death/ICU 76% Improvement Relative Risk Vitamin A for COVID-19  Voelkle et al.  Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Prospective study of 57 patients in Switzerland (Mar - Apr 2020) Lower death/ICU with higher vitamin A levels (p=0.004) c19early.org Voelkle et al., Nutrients, April 2022 Favors vitamin A Favors control
Voelkle: 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.
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 to Zhang. 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 1 Sweeting. 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 (3.12.3) with scipy (1.13.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.21.0).
Forest plots are computed using PythonMeta Deng 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 (4.1.2) using the metafor (3.0-2) and rms (6.2-0) 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 effective McLean, Treanor.
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 the bottom of this page.
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.
Tomasa-Irriguible, 11/30/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, trial NCT04751669 (history) (CoVIT). Estimated 300 patient RCT with results unknown and over 4 months late.
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, 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.
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.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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