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Vitamin C for COVID-19: real-time meta analysis of 56 studies
Covid Analysis, December 2022
https://c19early.org/cmeta.html
 
0 0.5 1 1.5+ All studies 22% 56 58,331 Improvement, Studies, Patients Relative Risk Mortality 27% 33 35,482 Ventilation 19% 5 530 ICU admission 15% 5 654 Hospitalization 19% 10 4,294 Recovery 27% 8 2,046 Cases -8% 4 15,830 Viral clearance 10% 3 256 RCTs 19% 14 1,129 RCT mortality 17% 8 648 Peer-reviewed 24% 49 38,859 High dose IV 18% 17 1,769 Symptomatic 26% 51 42,375 Prophylaxis 14% 12 36,530 Early 24% 5 571 Late 27% 39 21,230 Vitamin C for COVID-19 c19early.org/c Dec 2022 Favorsvitamin C Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 19 studies from 19 independent teams in 12 different countries show statistically significant improvements in isolation (13 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 22% [14‑30%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral.
Results are robust — in exclusion sensitivity analysis 17 of 56 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 22% 56 58,331 Improvement, Studies, Patients Relative Risk Mortality 27% 33 35,482 Ventilation 19% 5 530 ICU admission 15% 5 654 Hospitalization 19% 10 4,294 Recovery 27% 8 2,046 Cases -8% 4 15,830 Viral clearance 10% 3 256 RCTs 19% 14 1,129 RCT mortality 17% 8 648 Peer-reviewed 24% 49 38,859 High dose IV 18% 17 1,769 Symptomatic 26% 51 42,375 Prophylaxis 14% 12 36,530 Early 24% 5 571 Late 27% 39 21,230 Vitamin C for COVID-19 c19early.org/c Dec 2022 Favorsvitamin C Favorscontrol after exclusions
The treatment regimen varies widely across studies and may be high-dose IV vitamin C.
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 are more effective. Only 2% of vitamin C 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. Other meta analyses for vitamin C can be found in [Bhowmik, Olczak-Pruc], showing significant improvements for mortality and severity.
Highlights
Vitamin C reduces risk for COVID-19 with very high confidence for mortality, recovery, and in pooled analysis, high confidence for ICU admission and hospitalization, and very low confidence for ventilation 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+ Su -135% 2.35 [0.67-8.27] progression n/a n/a Improvement, RR [CI] Treatment Control Thomas (RCT) -204% 3.04 [0.13-72.9] death 1/48 0/50 Zhao (PSM) 72% 0.28 [0.08-0.93] progression 4/55 12/55 Ried (RCT) 31% 0.69 [0.54-0.89] no recov. 69/162 46/75 Usanma Koban 33% 0.67 [0.07-5.38] viral+ 31 (n) 95 (n) Tau​2 = 0.25, I​2 = 44.8%, p = 0.43 Early treatment 24% 0.76 [0.38-1.50] 74/296 58/275 24% improvement Krishnan 31% 0.69 [0.47-0.92] death 40/79 52/73 Improvement, RR [CI] Treatment Control Zhang (RCT) 50% 0.50 [0.20-1.50] death 6/27 11/29 ICU patients Yüksel (ICU) 19% 0.81 [0.66-0.99] death 31/42 40/44 ICU patients Patel 29% 0.71 [0.43-1.14] death 22/96 26/80 Kumari (RCT) 36% 0.64 [0.26-1.55] death 7/75 11/75 Darban (RCT) 33% 0.67 [0.14-3.17] progression 2/10 3/10 ICU patients CT​2 Jang 51% 0.49 [0.23-1.01] no recov. 5/12 6/7 ECMO patients JamaliMo.. (RCT) 0% 1.00 [0.22-4.56] death 3/30 3/30 Gao 86% 0.14 [0.03-0.72] death 1/46 5/30 Hamidi-A.. (RCT) 44% 0.56 [0.20-1.51] death 5/40 9/40 CT​2 Al Sulaiman (PSM) 15% 0.85 [0.61-1.12] death 46/142 59/142 Mulhem -32% 1.32 [1.07-1.62] death 157/794 359/2,425 Gadhiya -1% 1.01 [0.48-1.91] death 19/55 36/226 Hakamifard (RCT) 46% 0.54 [0.14-2.08] ICU 3/38 5/34 CT​2 Elhadi (ICU) -12% 1.12 [0.96-1.31] death 175/277 106/188 ICU patients Suna 21% 0.79 [0.44-1.41] death 17/153 24/170 Pourhoseingholi 13% 0.87 [0.63-1.19] death 54/199 285/2,269 Li (ICU) -11% 1.11 [0.79-1.54] death 7/8 19/24 ICU patients Vishnuram 54% 0.46 [0.24-0.86] death 164/8,634 10/241 Özgünay (ICU) 9% 0.91 [0.63-1.30] death 17/32 75/128 ICU patients Tan 25% 0.75 [0.10-2.98] death/int. 1/46 14/115 CT​2 Zheng (PSM) -157% 2.57 [0.39-16.8] death 12/70 7/327 Simsek 44% 0.56 [0.23-1.35] death 6/58 15/81 Shousha 94% 0.06 [0.01-0.37] death 22/340 31/207 Tehrani (RCT) 87% 0.13 [0.01-2.25] death 0/18 4/26 Majidi (DB RCT) 14% 0.86 [0.74-1.01] death 26/31 67/69 ICU patients Baguma -48% 1.48 [0.41-4.70] death 385 (n) 96 (n) Tu 83% 0.17 [0.08-0.35] death 8/116 26/64 Yang (RCT) 15% 0.85 [0.68-1.06] recov. time 10 (n) 10 (n) LD​3 Gavrielatou (ICU) 58% 0.42 [0.12-1.48] death 2/10 49/103 ICU patients Salehi (ICU) 10% 0.90 [0.65-1.25] death 22/40 52/85 ICU patients Coppock (RCT) 5% 0.95 [0.16-7.84] progression 4/44 2/22 Hess (PSW) 20% 0.80 [0.40-1.60] death 10/25 37/75 Zangeneh (ICU) 4% 0.96 [0.64-1.45] death n/a n/a ICU patients Izzo 41% 0.59 [0.50-0.69] recovery 869 (n) 521 (n) LONG COVID OT​1 CT​2 Fogleman (DB RCT) 4% 0.96 [0.65-1.40] recovery 32 (n) 34 (n) Kumar (DB RCT) 23% 0.77 [0.40-1.47] death 10/30 13/30 ICU patients Özgülteki (ICU) -5% 1.05 [0.81-1.36] death 18/21 18/22 ICU patients Doocy 63% 0.37 [0.08-1.82] death 2/64 22/80 Tau​2 = 0.15, I​2 = 82.3%, p < 0.0001 Late treatment 27% 0.73 [0.62-0.85] 924/12,998 1,501/8,232 27% improvement Behera 18% 0.82 [0.45-1.57] cases case control Improvement, RR [CI] Treatment Control Louca 0% 1.00 [0.97-1.04] cases Mahto -26% 1.26 [0.63-2.28] IgG+ 34/140 59/549 Holt -3% 1.03 [0.77-1.39] cases 49/1,580 397/13,647 Abdulateef 19% 0.81 [0.37-1.78] hosp. 8/132 22/295 Aldwihi 36% 0.64 [0.45-0.86] hosp. 142/505 95/233 Mohseni -44% 1.44 [1.22-1.71] cases 34/43 307/560 Nimer 25% 0.75 [0.54-1.04] hosp. 52/651 167/1,497 Shehab 4% 0.96 [0.46-1.99] severe case 14/139 12/114 Loucera 28% 0.72 [0.58-0.88] death 840 (n) 15,128 (n) Guldemir 31% 0.69 [0.48-0.99] hosp. 33/173 84/304 Sharif 46% 0.54 [0.01-0.92] severe case n/a n/a Tau​2 = 0.06, I​2 = 83.3%, p = 0.08 Prophylaxis 14% 0.86 [0.73-1.02] 366/4,203 1,143/32,327 14% improvement All studies 22% 0.78 [0.70-0.86] 1,364/17,497 2,702/40,834 22% improvement 56 vitamin C COVID-19 studies c19early.org/c Dec 2022 Tau​2 = 0.09, I​2 = 82.6%, p < 0.0001 Effect extraction pre-specified, see appendix 1 OT: comparison with other treatment3 LD: comparison with low dose treatment 2 CT: study uses combined treatment Favors vitamin C Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Su -135% progression Relative Risk [CI] Thomas (RCT) -204% death Zhao (PSM) 72% progression Ried (RCT) 31% recovery Usanma Koban 33% viral- Tau​2 = 0.25, I​2 = 44.8%, p = 0.43 Early treatment 24% 24% improvement Krishnan 31% death Zhang (RCT) 50% death ICU patients Yüksel (ICU) 19% death ICU patients Patel 29% death Kumari (RCT) 36% death Darban (RCT) 33% progression ICU patients CT​2 Jang 51% recovery ECMO patients JamaliM.. (RCT) 0% death Gao 86% death Hamidi-.. (RCT) 44% death CT​2 Al Sulai.. (PSM) 15% death Mulhem -32% death Gadhiya -1% death Hakamifard (RCT) 46% ICU admission CT​2 Elhadi (ICU) -12% death ICU patients Suna 21% death Pourhoseingholi 13% death Li (ICU) -11% death ICU patients Vishnuram 54% death Özgünay (ICU) 9% death ICU patients Tan 25% death/intubation CT​2 Zheng (PSM) -157% death Simsek 44% death Shousha 94% death Tehrani (RCT) 87% death Majidi (DB RCT) 14% death ICU patients Baguma -48% death Tu 83% death Yang (RCT) 15% recovery LD​3 Gavrielatou (ICU) 58% death ICU patients Salehi (ICU) 10% death ICU patients Coppock (RCT) 5% progression Hess (PSW) 20% death Zangeneh (ICU) 4% death ICU patients Izzo 41% recovery LONG COVID OT​1 CT​2 Fogleman (DB RCT) 4% recovery Kumar (DB RCT) 23% death ICU patients Özgülteki (ICU) -5% death ICU patients Doocy 63% death Tau​2 = 0.15, I​2 = 82.3%, p < 0.0001 Late treatment 27% 27% improvement Behera 18% case Louca 0% case Mahto -26% IgG positive Holt -3% case Abdulateef 19% hospitalization Aldwihi 36% hospitalization Mohseni -44% case Nimer 25% hospitalization Shehab 4% severe case Loucera 28% death Guldemir 31% hospitalization Sharif 46% severe case Tau​2 = 0.06, I​2 = 83.3%, p = 0.08 Prophylaxis 14% 14% improvement All studies 22% 22% improvement 56 vitamin C COVID-19 studies c19early.org/c Dec 2022 Tau​2 = 0.09, I​2 = 82.6%, p < 0.0001 Effect extraction pre-specifiedRotate device for footnotes/details Favors vitamin C Favors control
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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 C studies.
We analyze all significant studies concerning the use of vitamin C 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 C [Kumar, Malla, Pandya].
2 In Vitro studies support the efficacy of vitamin C [Goc, Hajdrik].
An In Vivo animal study supports the efficacy of vitamin C [Zuo].
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, 13, and 14 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, high dose IV studies, peer reviewed studies, and non-symptomatic vs. symptomatic results.
Improvement Studies Patients Authors
All studies22% [14‑30%]56 58,331 595
After exclusions28% [18‑37%]39 27,025 429
Peer-reviewed studiesPeer-reviewed24% [14‑33%]49 38,859 507
Randomized Controlled TrialsRCTs19% [10‑27%]14 1,129 160
Mortality27% [13‑38%]33 35,482 353
VentilationVent.19% [-8‑39%]5 530 54
ICU admissionICU15% [0‑28%]5 654 46
HospitalizationHosp.19% [5‑31%]10 4,294 101
Cases-8% [-35‑14%]4 15,830 77
Viral10% [-13‑29%]3 256 29
RCT mortality17% [4‑29%]8 648 109
RCT hospitalizationRCT hosp.12% [-8‑28%]6 504 79
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 studies24% [-50‑62%] 527% [15‑38%] 3914% [-2‑27%] 12
After exclusions3% [-95‑52%] 434% [19‑46%] 2821% [2‑36%] 7
Peer-reviewed studiesPeer-reviewed24% [-50‑62%] 531% [17‑43%] 3312% [-5‑26%] 11
Randomized Controlled TrialsRCTs30% [10‑46%] 216% [6‑25%] 12-
Mortality-204% [-7189‑87%] 127% [13‑39%] 3128% [12‑42%] 1
VentilationVent.-19% [-8‑39%] 5-
ICU admissionICU-15% [0‑28%] 5-
HospitalizationHosp.31% [-298‑88%] 112% [-9‑28%] 532% [21‑41%] 4
Cases---8% [-35‑14%] 4
Viral-5% [-73‑36%] 214% [-11‑34%] 1-
RCT mortality-204% [-7189‑87%] 117% [4‑29%] 7-
RCT hospitalizationRCT hosp.31% [-298‑88%] 112% [-9‑28%] 5-
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 high dose IV 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.
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Figure 14. Random effects meta-analysis for non-symptomatic vs. symptomatic results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 15 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 27% improvement, compared to 19% for other studies. Figure 16, 17, and 18 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
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Figure 15. Results for RCTs and non-RCT studies.
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Figure 16. 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 17. Random effects meta-analysis for RCT mortality results.
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Figure 18. 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 19 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Abdulateef], unadjusted results with no group details.
[Elhadi], unadjusted results with no group details.
[Gadhiya], substantial unadjusted confounding by indication likely.
[Guldemir], unadjusted results with no group details.
[Holt], significant unadjusted confounding possible.
[Krishnan], unadjusted results with no group details.
[Li], very late stage, ICU patients.
[Mohseni], unadjusted results with no group details.
[Mulhem], substantial unadjusted confounding by indication likely; substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Salehi], unadjusted results with no group details.
[Shehab], unadjusted results with no group details.
[Suna], substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Tu], unadjusted results with no group details.
[Vishnuram], unadjusted results with no group details; minimal details of groups provided.
[Zhao], substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Zheng], substantial unadjusted confounding by indication likely; immortal time bias may significantly affect results; treatment start times unknown, treatment may not have started at baseline.
[Özgünay], substantial unadjusted confounding by indication likely.
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Figure 19. 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 (B)] 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 (B)]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 20 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 20. 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 21. 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 21. 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].
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.
35% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 32% of prospective studies, showing similar results. The median effect size for retrospective studies is 20% improvement, compared to 23% for prospective studies, showing similar results. Figure 22 shows a scatter plot of results for prospective and retrospective studies.
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Figure 22. 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 23 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 23. 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 C for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 vitamin C 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 C 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 the 56 studies compare against other treatments, which may reduce the effect seen. 5 of 56 studies combine treatments. The results of vitamin C alone may differ. 3 of 14 RCTs use combined treatment. Other meta analyses for vitamin C can be found in [Bhowmik, Olczak-Pruc], showing significant improvements for mortality and severity.
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.
Vitamin C is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 19 studies from 19 independent teams in 12 different countries show statistically significant improvements in isolation (13 for the most serious outcome). Meta analysis using the most serious outcome reported shows 22% [14‑30%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral. Results are robust — in exclusion sensitivity analysis 17 of 56 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
The treatment regimen varies widely across studies and may be high-dose IV vitamin C.
0 0.5 1 1.5 2+ Hospitalization 19% Improvement Relative Risk c19early.org/c Abdulateef et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Abdulateef] Survey of 428 recovered COVID-19 patients in Iraq, showing fewer hospital visits for patients on prophylactic vitamin C or D. Hospitalization was lower for those on vitamin C, D, or zinc, without statistical significance.
0 0.5 1 1.5 2+ Mortality 15% Improvement Relative Risk c19early.org/c Al Sulaiman et al. Vitamin C for COVID-19 LATE Favors vitamin C Favors control
[Al Sulaiman] Retrospective 158 critically ill patients receiving vitamin C and propensity matched controls, showing mortality OR 0.77 [0.48-1.23], and statistically significantly lower thrombosis, OR 0.42 [0.18-0.94]. 1000mg of vitamin C was given daily.
0 0.5 1 1.5 2+ Hospitalization 36% Improvement Relative Risk c19early.org/c Aldwihi et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Aldwihi] Retrospective survey-based analysis of 738 COVID-19 patients in Saudi Arabia, showing lower hospitalization with vitamin C, turmeric, zinc, and nigella sativa, and higher hospitalization with vitamin D. For vitamin D, most patients continued prophylactic use. For vitamin C, the majority of patients continued prophylactic use. For nigella sativa, the majority of patients started use during infection. Authors do not specify the fraction of prophylactic use for turmeric and zinc.
0 0.5 1 1.5 2+ Mortality -48% Improvement Relative Risk c19early.org/c Baguma et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Baguma] Retrospective COVID+ hospitalized patients in Uganda, 385 patients receiving vitamin C treatment, showing higher mortality with treatment, without statistical significance.
0 0.5 1 1.5 2+ Case 18% Improvement Relative Risk Case (b) 29% c19early.org/c Behera et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Behera] Retrospective matched case-control prophylaxis study for HCQ, ivermectin, and vitamin C with 372 healthcare workers, showing lower COVID-19 incidence for all treatments, with statistical significance reached for ivermectin.

HCQ OR 0.56, p = 0.29
Ivermectin OR 0.27, p < 0.001
Vitamin C OR 0.82, p = 0.58
0 0.5 1 1.5 2+ Progression 5% Improvement Relative Risk Improvement 50% Discharge 22% c19early.org/c Coppock et al. Vitamin C for COVID-19 RCT LATE TREATMENT Favors vitamin C Favors control
[Coppock] RCT with 66 very late stage (8 days from symptom onset) hospitalized patients, 44 treated with vitamin C and 22 control patients, showing no significant differences with treatment.
0 0.5 1 1.5 2+ Progression 33% Improvement Relative Risk ICU time 6% c19early.org/c Darban et al. Vitamin C for COVID-19 RCT ICU PATIENTS Favors vitamin C Favors control
[Darban] Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences. IRCT20151228025732N52.
0 0.5 1 1.5 2+ Mortality 63% Improvement Relative Risk c19early.org/c Doocy et al. NCT04568499 Vitamin C LATE TREATMENT Favors vitamin C Favors control
[Doocy] Prospective study of 144 hospitalized COVID-19 patients in the DRC and South Sudan, showing lower mortality with vitamin C treatment.
0 0.5 1 1.5 2+ Mortality -12% Improvement Relative Risk c19early.org/c Elhadi et al. Vitamin C for COVID-19 ICU PATIENTS Favors vitamin C Favors control
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
0 0.5 1 1.5 2+ Recovery 4% Improvement Relative Risk c19early.org/c Fogleman et al. NCT04530539 Vitamin C RCT LATE TREATMENT Favors vitamin C Favors control
[Fogleman] Early terminated low-risk patient RCT with 32 low-dose vitamin C, 32 melatonin, and 34 placebo patients, showing faster resolution of symptoms with melatonin in spline regression analysis, and no significant difference for vitamin C. All patients recovered with no serious outcomes reported. Baseline symptoms scores were higher in the melatonin and vitamin C arms (median 27 and 24 vs. 18 for placebo).
0 0.5 1 1.5 2+ Mortality -1% Improvement Relative Risk c19early.org/c Gadhiya et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Gadhiya] Retrospective 283 patients in the USA showing higher mortality with all treatments (not statistically significant). Confounding by indication is likely. In the supplementary appendix, authors note that the treatments were usually given for patients that required oxygen therapy. Oxygen therapy and ICU admission (possibly, the paper includes ICU admission for model 2 in some places but not others) were the only variables indicating severity used in adjustments.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk c19early.org/c Gao et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Gao] Retrospective 76 COVID-19 patients, 46 treated with intravenous high-dose vitamin C, showing lower mortality and improved oxygen requirements with treatment. Dosage was 6g intravenous infusion per 12hr on the first day, and 6g once for the following 4 days.
0 0.5 1 1.5 2+ Mortality 58% Improvement Relative Risk c19early.org/c Gavrielatou et al. Vitamin C for COVID-19 ICU Favors vitamin C Favors control
[Gavrielatou] Retrospective 113 consecutive mechanically ventilated COVID+ ICU patients in Greece, 10 receiving high dose IV vitamin C, showing lower mortality with treatment, without statistical significance (p=0.11).

The associated meta analysis includes only 11 studies, while there are currently 56 studies, 33 with mortality results. Authors only include critical patients, however not all studies with critical patients are included, for example [Hamidi-Alamdari, Majidi, Yüksel, Özgünay]. The meta analysis also uses unadjusted results, while PSM, Cox proportional hazards, or KM results are reported by [Al Sulaiman, Gao, Zhang (B), Zheng]. For [Zhang (B)] authors use 28 day mortality, while the study reports longer term in-hospital mortality.
0 0.5 1 1.5 2+ Hospitalization 31% Improvement Relative Risk c19early.org/c Guldemir et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Guldemir] Retrospective 477 COVID+ public transportation workers in Turkey, showing lower risk of hospitalization with vitamin C use in unadjusted results.
0 0.5 1 1.5 2+ ICU admission 46% Improvement Relative Risk Hospitalization time 1% c19early.org/c Hakamifard et al. Vitamin C for COVID-19 RCT LATE Favors vitamin C Favors control
[Hakamifard] RCT with 38 patients treated with vitamin C and vitamin E, and 34 control patients, showing lower ICU admission with treatment, but not statistically significant.
0 0.5 1 1.5 2+ Mortality 44% Improvement Relative Risk Hospitalization time 38% c19early.org/c Hamidi-Alamdari et al. NCT04370288 Vitamin C RCT LATE Favors vitamin C Favors control
[Hamidi-Alamdari] RCT 80 hospitalized patients with severe COVID-19, 40 treated with methylene blue + vitamin C + N-acetylcysteine, showing lower mortality, shorter hospitalization, and significantly improved SpO2 and respiratory distress with treatment. NCT04370288.
0 0.5 1 1.5 2+ Mortality 20% Improvement Relative Risk Ventilation 40% Ventilation (b) 50% ICU admission 27% ICU admission (b) 30% c19early.org/c Hess et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Hess] Retrospective 100 severe condition hospitalized patients in the USA, 25 treated with high dose IV vitamin C, showing lower mechanical ventilation and cardiac arrest, and increased length of survival with treatment. 3g IV vitamin C every 6h for 7 days.
0 0.5 1 1.5 2+ Case -3% Improvement Relative Risk c19early.org/c Holt et al. NCT04330599 COVIDENCE UK Vitamin C Prophylaxis Favors vitamin C 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+ Recovery 41% Improvement Relative Risk Recovery (b) 68% c19early.org/c Izzo et al. Vitamin C for COVID-19 LONG COVID Favors vitamin C Favors Vitamin B1, ..
[Izzo] Long COVID trial comparing L-arginine + vitamin C with multivitamin treatment (vitamin B1, B2, B6, B12, nicotinamide, folic acid, pantothenic acid), showing significant improvement in symptoms with L-arginine + vitamin C treatment.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Ventilation -25% Hospitalization time -31% c19early.org/c JamaliMoghadamSiahkali et al. Vitamin C RCT LATE Favors vitamin C Favors control
[JamaliMoghadamSiahkali] Small late stage RCT for the addition of vitamin C to HCQ and lopinavir/ritonavir, with 30 treatment and 30 control patients, finding a significant reduction in temperature and a significant improvement in oxygenation after 3 days in the vitamin C group. However, hospitalization time was longer and there was no significant difference in mortality.
0 0.5 1 1.5 2+ Recovery 51% Improvement Relative Risk c19early.org/c Jang et al. Vitamin C for COVID-19 ECMO PATIENTS Favors vitamin C Favors control
[Jang] Retrospective 19 COVID-19 ECMO patients in South Korea, showing a higher rate of weaning from ECMO with vitamin C treatment, without statistical significance. Authors perform multivariate analysis but do not provide full results, only reporting p > 0.05.
0 0.5 1 1.5 2+ Mortality 31% Improvement Relative Risk c19early.org/c Krishnan et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Krishnan] Retrospective 152 mechanically ventilated patients in the USA showing unadjusted lower mortality with vitamin C, vitamin D, HCQ, and zinc treatment, statistically significant only for vitamin C.
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk Ventilation 21% c19early.org/c Kumar et al. CTRI/2020/11/029230 Vitamin C RCT ICU Favors vitamin C Favors control
[Kumar (C)] RCT 60 ICU patients in India, showing no significant difference in outcomes with vitamin C. Mortality was lower in the vitamin C arm despite having more severe cases at baseline (87% vs. 67%). 1 gram intravenous vitamin C 8 hourly for four days.
0 0.5 1 1.5 2+ Mortality 36% Improvement Relative Risk Ventilation 20% Recovery time 26% Hospitalization time 24% c19early.org/c Kumari et al. Vitamin C for COVID-19 RCT LATE TREATMENT Favors vitamin C Favors control
[Kumari] RCT 150 hospitalized patients in Pakistan showing 26% faster recovery, p < 0.0001. 36% lower mortality, not statistically significant due to the small number of events. Dosage was 50 mg/kg/day of intravenous vitamin C.
0 0.5 1 1.5 2+ Mortality -11% Improvement Relative Risk c19early.org/c Li et al. Vitamin C for COVID-19 ICU PATIENTS Favors vitamin C Favors control
[Li] PSM retrospective 8 ICU patients treated with vitamin C and 24 matched controls, showing no significant difference. Authors note that "it is possible for the delayed timing of IV vitamin C to have blunted the beneficial effects as these patients may have already progressed to the late fibroproliferative phase or ARDS". IV vitamin C 1.5 grams every 6 hours.
0 0.5 1 1.5 2+ Case 0% Improvement Relative Risk c19early.org/c Louca et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Louca] Survey analysis of dietary supplements showing no significant difference in PCR+ cases with vitamin C usage in the UK, however significant reductions were found in the US and Sweden. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app.
0 0.5 1 1.5 2+ Mortality 28% Improvement Relative Risk c19early.org/c Loucera et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, aspirin, vitamin D, vitamin C, and budesonide.
0 0.5 1 1.5 2+ IgG positive -26% Improvement Relative Risk c19early.org/c Mahto et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Mahto] Retrospective 689 healthcare workers in India, showing no significant difference in IgG positivity with vitamin C prophylaxis.
0 0.5 1 1.5 2+ Mortality 14% Improvement Relative Risk c19early.org/c Majidi et al. Vitamin C for COVID-19 RCT ICU PATIENTS Favors vitamin C Favors control
[Majidi] RCT 100 ICU patients in Iran, 31 treated with vitamin C, showing lower mortality with treatment. IRCT20151226025699N5.
0 0.5 1 1.5 2+ Case -44% Improvement Relative Risk c19early.org/c Mohseni et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Mohseni] Retrospective 603 patients in Iran, 34 taking vitamin C supplements, showing increased risk of COVID-19 cases in unadjusted results. IR.SHOUSHTAR.REC.1399.015.
0 0.5 1 1.5 2+ Mortality -32% Improvement Relative Risk c19early.org/c Mulhem et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Mulhem] Retrospective database analysis of 3,219 hospitalized patients in the USA. Very different results in the time period analysis (Table S2), and results significantly different to other studies for the same medications (e.g., heparin OR 3.06 [2.44-3.83]) suggest significant confounding by indication and confounding by time.
0 0.5 1 1.5 2+ Hospitalization 25% Improvement Relative Risk Severe case 17% c19early.org/c Nimer et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Nimer] Retrospective survey based analysis of 2,148 COVID-19 recovered patients in Jordan, showing lower risk of severity and hospitalization with vitamin C prophylaxis, without statistical significance.
0 0.5 1 1.5 2+ Mortality 29% Improvement Relative Risk Mortality (b) 16% c19early.org/c Patel et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Patel] Retrospective 176 hospitalized patients, 96 treated with oral vitamin C (from 500mg to 1500mg daily), showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 13% Improvement Relative Risk c19early.org/c Pourhoseingholi et al. Vitamin C for COVID-19 LATE Favors vitamin C Favors control
[Pourhoseingholi] Prospective study of 2,468 hospitalized COVID-19 patients in Iran, showing no significant difference with vitamin C treatment. IR.MUQ.REC.1399.013.
0 0.5 1 1.5 2+ Recovery 31% Improvement Relative Risk c19early.org/c Ried et al. Vitamin C for COVID-19 RCT EARLY TREATMENT Favors vitamin C Favors control
[Ried] RCT 237 patients in Turkey, 162 treated with IV vitamin C in addition to HCQ/AZ/zinc/vitamin D used for all patients, showing significantly faster recovery with the addition of IV vitamin C.

97% of patients were vitamin D deficient, and lower vitamin D levels were associated with ICU admission and longer hospital stay.

Only 1 of 237 hospitalized patients died (average age 63, range 22-99) - a 70-year-old patient with heart and lung disease and severely deficient vitamin D levels (6 nmol/L). IV vitamin C (sodium ascorbate) was given as 50 mg/kg every six hours on day 1, followed by 100 mg/kg every six hours (four times daily, 400 mg/kg/day) for seven days. ACTRN12620000557932.
0 0.5 1 1.5 2+ Mortality 10% Improvement Relative Risk c19early.org/c Salehi et al. Vitamin C for COVID-19 ICU PATIENTS Favors vitamin C Favors control
[Salehi] Retrospective 125 mechanically ventilated ICU patients in Iran, showing no significant difference with vitamin C treatment in unadjusted results.
0 0.5 1 1.5 2+ Severe case, C 46%