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Vitamin A for COVID-19: real-time meta analysis of 14 studies
Covid Analysis, December 2022
https://c19early.org/vameta.html
 
0 0.5 1 1.5+ All studies 40% 11 18,305 Improvement, Studies, Patients Relative Risk Mortality 42% 6 441 Ventilation 0% 1 30 ICU admission 48% 2 70 Hospitalization 1% 4 2,418 Recovery 37% 3 280 Cases 64% 2 15,436 Viral clearance 44% 1 40 RCTs 36% 4 310 RCT mortality 59% 3 130 Peer-reviewed 38% 7 17,929 Sufficiency 73% 3 217 Prophylaxis 49% 3 17,584 Early 62% 3 420 Late 9% 5 301 Vitamin A for COVID-19 c19early.org/va Dec 2022 Favorsvitamin A Favorscontrol after exclusions
Statistically significant improvements are seen for recovery, cases, and viral clearance. 5 studies from 4 independent teams in 3 different countries show statistically significant improvements in isolation (2 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 40% [-10‑67%] improvement, without reaching statistical significance. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Results are consistent with early treatment being more effective than late treatment.
Sufficiency studies, analyzing outcomes based on serum levels, show 73% [51‑85%] improvement for patients with higher vitamin A levels (3 studies).
0 0.5 1 1.5+ All studies 40% 11 18,305 Improvement, Studies, Patients Relative Risk Mortality 42% 6 441 Ventilation 0% 1 30 ICU admission 48% 2 70 Hospitalization 1% 4 2,418 Recovery 37% 3 280 Cases 64% 2 15,436 Viral clearance 44% 1 40 RCTs 36% 4 310 RCT mortality 59% 3 130 Peer-reviewed 38% 7 17,929 Sufficiency 73% 3 217 Prophylaxis 49% 3 17,584 Early 62% 3 420 Late 9% 5 301 Vitamin A for COVID-19 c19early.org/va Dec 2022 Favorsvitamin A Favorscontrol after exclusions
No treatment, vaccine, or intervention is 100% effective and available. 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. Only 18% of vitamin A studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix.
Highlights
Vitamin A reduces risk for COVID-19 with very high confidence for recovery, low confidence for cases, viral clearance, and in pooled analysis, and very low confidence for ICU admission and progression.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 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% improvement 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 Elkazzaz (RCT) 86% 0.14 [0.01-2.60] death 0/20 3/20 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 Tau​2 = 1.24, I​2 = 61.0%, p = 0.89 Late treatment 9% 0.91 [0.25-3.35] 13/83 36/218 9% improvement Al-Sumiadai 64% 0.36 [0.23-0.54] cases 20/97 65/112 Improvement, RR [CI] Treatment Control 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 Tau​2 = 0.19, I​2 = 65.4%, p = 0.046 Prophylaxis 49% 0.51 [0.27-0.99] 36/332 714/17,252 49% improvement All studies 40% 0.60 [0.33-1.10] 61/624 781/17,681 40% improvement 11 vitamin A COVID-19 studies c19early.org/va Dec 2022 Tau​2 = 0.54, I​2 = 69.0%, p = 0.096 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 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% improvement Sarohan -282% death Beigm.. (SB RCT) 89% death ICU patients CT​1 Elkazzaz (RCT) 86% death Somi (RCT) -50% death Doocy 26% death Tau​2 = 1.24, I​2 = 61.0%, p = 0.89 Late treatment 9% 9% improvement Al-Sumiadai 64% case Holt 56% case Nimer 21% hospitalization Tau​2 = 0.19, I​2 = 65.4%, p = 0.046 Prophylaxis 49% 49% improvement All studies 40% 40% improvement 11 vitamin A COVID-19 studies c19early.org/va Dec 2022 Tau​2 = 0.54, I​2 = 69.0%, p = 0.096 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors vitamin A Favors control
B
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C
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in vitamin A studies.
We analyze all significant studies concerning the use 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
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.
3 In Silico studies support the efficacy of vitamin A [Chakraborty, Li, Pandya].
2 In Vitro studies support the efficacy of vitamin A [Morita, Tong].
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, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 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, viral clearance, sufficiency studies, and peer reviewed studies.
Improvement Studies Patients Authors
All studies40% [-10‑67%]11 18,305 80
After exclusions46% [13‑67%]7 2,767 33
Peer-reviewed studiesPeer-reviewed38% [-3‑62%]7 17,929 66
Randomized Controlled TrialsRCTs36% [-56‑73%]4 310 23
Mortality42% [-133‑86%]6 441 30
ICU admissionICU48% [-40‑80%]2 70 11
HospitalizationHosp.1% [-20‑18%]4 2,418 23
Cases64% [46‑76%]2 15,436 37
RCT mortality59% [-153‑93%]3 130 17
RCT hospitalizationRCT hosp.-3% [-27‑16%]3 270 19
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Early treatment Late treatment Prophylaxis
All studies62% [-3‑86%] 39% [-235‑75%] 549% [1‑73%] 3
After exclusions38% [-31‑71%] 259% [-153‑93%] 348% [-14‑76%] 2
Peer-reviewed studiesPeer-reviewed64% [-80‑93%] 227% [-158‑79%] 324% [-23‑53%] 2
Randomized Controlled TrialsRCTs26% [-76‑69%] 159% [-153‑93%] 3-
Mortality86% [39‑97%] 19% [-235‑75%] 5-
ICU admissionICU-48% [-40‑80%] 2-
HospitalizationHosp.26% [-76‑69%] 13% [-53‑39%] 221% [-35‑55%] 1
Cases--64% [46‑76%] 2
RCT mortality-59% [-153‑93%] 3-
RCT hospitalizationRCT hosp.26% [-76‑69%] 13% [-53‑39%] 2-
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.
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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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.
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Figure 13. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 14 shows a comparison of results for RCTs and non-RCT 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.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for vitamin A are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows 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 could 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].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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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, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction 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.
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 may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
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.
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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, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction 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] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] report only 2.5 hours improvement for inpatient treatment.
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]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 19 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 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 (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. 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 viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] 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].
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 20. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 20. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
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]. 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.
67% of retrospective studies report positive effects, compared to 88% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 21% improvement, compared to 60% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 21 shows a scatter plot of results for prospective and retrospective treatment studies.
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Figure 21. Prospective vs. retrospective studies.
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 22 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 22. 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.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that 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.
1 of 11 studies combine treatments. The results of vitamin A alone may differ. 1 of 4 RCTs use combined treatment.
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 by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests 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, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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, vaccine, 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.
Statistically significant improvements are seen for recovery, cases, and viral clearance. 5 studies from 4 independent teams in 3 different countries show statistically significant improvements in isolation (2 for the most serious outcome). Meta analysis using the most serious outcome reported shows 40% [-10‑67%] improvement, without reaching statistical significance. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Results are consistent with early treatment being more effective than late treatment. Sufficiency studies, analyzing outcomes based on serum levels, show 73% [51‑85%] improvement for patients with higher vitamin A levels (3 studies).
[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 c19early.org/va Al-Sumiadai et al. Vitamin A for COVID-19 EARLY 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 c19early.org/va Al-Sumiadai et al. Vitamin A for COVID-19 EARLY 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% c19early.org/va Beigmohammadi et al. Vitamin A for COVID-19 RCT ICU 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]. IRCT20200319046819N [irct.ir].
0 0.5 1 1.5 2+ Mortality 26% unadjusted Improvement Relative Risk c19early.org/va Doocy et al. NCT04568499 Vitamin A LATE TREATMENT 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+ Mortality 86% Improvement Relative Risk ICU admission 67% Recovery time 35% Time to viral- 44% c19early.org/va Elkazzaz et al. NCT04353180 Vitamin A RCT LATE TREATMENT Favors vitamin A Favors control
[Elkazzaz] RCT with 20 13-cis-retinoic acid patients and 20 control patients, showing faster recovery and viral clearance with treatment. Aerosolized 13-cis-retinoic acid with increasing dose from 0.2 mg/kg/day to 4 mg/kg/day for 14 days, plus oral 13-cis-retinoic acid 20 mg/day. 13-cis retinoic acid is a synthetic vitamin A derivative, and is teratogenic. NCT04353180.
0 0.5 1 1.5 2+ Case 56% Improvement Relative Risk c19early.org/va Holt et al. NCT04330599 COVIDENCE UK Vitamin A Prophylaxis 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+ Hospitalization 21% Improvement Relative Risk Severe case 21% c19early.org/va Nimer et al. Vitamin A for COVID-19 Prophylaxis Favors vitamin A Favors control
[Nimer] Retrospective survey based analysis of 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+ Hospitalization 26% Improvement Relative Risk Recovery, dyspnea 32% Recovery, fever 80% Recovery, body ache 87% Recovery, headache 49% Recovery, weakness and.. 63% Recovery, chest pain 20% Recovery, cough 40% c19early.org/va Rohani et al. IRCT46974 Vitamin A RCT EARLY TREATMENT 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+ Mortality -282% Improvement Relative Risk c19early.org/va Sarohan et al. Vitamin A for COVID-19 LATE TREATMENT Favors vitamin A Favors control
[Sarohan] Retrospective 27 severe COVID-19 patients and 23 non-COVID-19 patients, showing signifcantly 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% Improvement -76% Hospitalization time -8% c19early.org/va Somi et al. Vitamin A for COVID-19 RCT LATE TREATMENT 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% c19early.org/va Tepasse et al. Vitamin A for COVID-19 Sufficiency 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% c19early.org/va Tomasa-Irriguible et al. Vitamin A Sufficiency Favors vitamin A Favors control
[Tomasa-Irriguible] Retrospective 120 hospitalized patients in Spain showing vitamin A deficiency associated with higher ICU admission.
0 0.5 1 1.5 2+ Death/ICU 76% Improvement Relative Risk c19early.org/va Voelkle et al. Vitamin A for COVID-19 Sufficiency 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 performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were vitamin A, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing 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 no room for an effective treatment to do better). 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 computed the relative risk when possible, or converted 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 including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome 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 (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
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. 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.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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].
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.
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, this trial uses multiple treatments in the treatment arm (combined with vitamins B, C, D, E) - results of individual treatments may vary. 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.
[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.
[Elkazzaz], 3/8/2022, Randomized Controlled Trial, Egypt, preprint, 4 authors, study period June 2020 - August 2020, trial NCT04353180 (history). risk of death, 85.7% lower, RR 0.14, p = 0.23, treatment 0 of 20 (0.0%), control 3 of 20 (15.0%), NNT 6.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 66.7% lower, RR 0.33, p = 0.24, treatment 2 of 20 (10.0%), control 6 of 20 (30.0%), NNT 5.0.
recovery time, 35.4% lower, relative time 0.65, p < 0.001, treatment mean 16.3 (±4.5) n=20, control mean 25.23 (±4.72) n=20.
time to viral-, 44.0% lower, relative time 0.56, p < 0.001, treatment mean 13.36 (±1.49) n=20, control mean 23.85 (±4.0) n=20.
[Sarohan], 2/1/2021, retrospective, Turkey, preprint, 4 authors, excluded in exclusion analyses: unadjusted results with no group details. 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. 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.
risk of no improvement, 76.0% higher, HR 1.76, p = 0.21, treatment 15, control 15, time to clinical response, 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.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, 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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