Top
Introduction
Preclinical
Results
RCTs
Exclusions
Heterogeneity
Discussion
Conclusion
Revisions
Methods and Data
References

Treatment studies
Treatment RCTs
Sufficiency studies
Cholecalciferol studies
Calcifediol/calcitriol studies
Treatment with exclusions
Treatment peer-reviewed
Treatment mortality
Treatment ventilation
Treatment ICU admission
Treatment hospitalization
Treatment cases

Feedback
Home
Show Outline
Top   Introduction   Preclinical   Results   RCT   Exclusions   Heterogeneity   Discussion   Conclusion   Appendix   References
Home   COVID-19 treatment studies for Vitamin D  COVID-19 treatment studies for Vitamin D  C19 studies: Vitamin D  Vitamin D   Select treatmentSelect treatmentTreatmentsTreatments
Alkalinization Meta Lactoferrin Meta
Melatonin Meta
Bromhexine Meta Metformin Meta
Budesonide Meta Molnupiravir Meta
Cannabidiol Meta
Colchicine Meta Nigella Sativa Meta
Conv. Plasma Meta Nitazoxanide Meta
Curcumin Meta Nitric Oxide Meta
Ensovibep Meta Paxlovid Meta
Famotidine Meta Peg.. Lambda Meta
Favipiravir Meta Povidone-Iod.. Meta
Fluvoxamine Meta Quercetin Meta
Hydroxychlor.. Meta Remdesivir Meta
Iota-carragee.. Meta
Ivermectin Meta Zinc Meta

Other Treatments Global Adoption
Loading...
Vitamin D for COVID-19: real-time meta analysis of 264 studies (107 treatment studies and 157 sufficiency studies)
https://c19early.org/dmeta.html
 
0 0.5 1 1.5+ All studies 37% 107 182,747 Improvement, Studies, Patients Relative Risk Mortality 36% 60 62,080 Ventilation 26% 17 7,852 ICU admission 50% 25 40,098 Hospitalization 20% 21 85,626 Cases 15% 25 134,291 RCTs 31% 26 41,836 RCT mortality 31% 15 1,949 Sufficiency 53% 157 182,257 Cholecalciferol 36% 96 173,856 Calcifediol 49% 11 8,891 Prophylaxis 31% 54 130,219 Early 60% 11 43,587 Late 46% 42 8,941 Vitamin D for COVID-19 c19early.org/d Mar 2023 Favorsvitamin D Favorscontrol after exclusions
Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 56 studies from 52 independent teams in 20 different countries show statistically significant improvements in isolation (40 for the most serious outcome).
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 60% [40‑74%] and 37% [31‑42%] improvement for early treatment and for all studies. Results are similar after restriction to 101 peer-reviewed studies: 57% [36‑71%] and 37% [31‑42%], and for the 60 mortality results: 68% [39‑84%] and 36% [28‑44%].
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 41% [27‑52%].
0 0.5 1 1.5+ All studies 37% 107 182,747 Improvement, Studies, Patients Relative Risk Mortality 36% 60 62,080 Ventilation 26% 17 7,852 ICU admission 50% 25 40,098 Hospitalization 20% 21 85,626 Cases 15% 25 134,291 RCTs 31% 26 41,836 RCT mortality 31% 15 1,949 Sufficiency 53% 157 182,257 Cholecalciferol 36% 96 173,856 Calcifediol 49% 11 8,891 Prophylaxis 31% 54 130,219 Early 60% 11 43,587 Late 46% 42 8,941 Vitamin D for COVID-19 c19early.org/d Mar 2023 Favorsvitamin D Favorscontrol after exclusions
Sufficiency studies show a strong association between vitamin D sufficiency and outcomes. Meta analysis of the 157 studies using the most serious outcome reported shows 53% [49‑57%] improvement.
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 13% of vitamin D studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data and sources to reproduce this paper are in the appendix. Other meta analyses for vitamin D treatment can be found in [Argano, D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu, Xie], showing significant improvements for mortality, mechanical ventilation, ICU admission, hospitalization, severity, and cases.
Percentage improvement with vitamin D treatment (more)
Early treatment Late treatment All studies Studies Patients Authors
All studies60% [40‑74%]
****
46% [33‑56%]
****
37% [31‑42%]
****
107 182,747 1,057
Randomized Controlled TrialsRCTs32% [8‑50%]
*
34% [13‑50%]
**
31% [17‑42%]
****
26 41,836 299
Calcifediol/calcitriolCalcifediol-73% [57‑83%]
****
49% [26‑65%]
***
11 8,891 135
Mortality68% [39‑84%]
***
45% [31‑57%]
****
36% [28‑44%]
****
60 62,080 561
HospitalizationHosp.90% [-453‑100%]22% [6‑35%]
**
20% [8‑31%]
**
21 85,626 200
RCT mortality-31% [6‑50%]
*
31% [6‑50%]
*
15 1,949 174
Highlights
Vitamin D reduces risk for COVID-19 with very high confidence for mortality, ICU admission, hospitalization, recovery, cases, viral clearance, and in pooled analysis, low confidence for ventilation, and very low confidence for 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 50 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Annweiler 89% 0.11 [0.03-0.48] 80,000IU death 10/57 5/9 Improvement, RR [CI] Dose (5d) Treatment Control Annweiler 63% 0.37 [0.06-2.21] 80,000IU death 3/16 10/32 Burahee 93% 0.07 [0.01-0.54] 400,000IU death 0/12 2/2 Asimi 97% 0.03 [0.00-0.44] 10,000IU ventilation 0/270 9/86 CT​1 Sánchez-Zuno (RCT) 89% 0.11 [0.01-0.91] 50,000IU severe case 0/22 4/20 Efird 49% 0.51 [0.23-1.17] varies death 11/544 413/15,794 Valecha 87% 0.13 [0.01-2.43] ICU 0/30 3/25 CT​1 Khan (RCT) 33% 0.67 [0.37-1.19] 1,800IU no recov. 10/25 15/25 CT​1 Hunt 47% 0.53 [0.37-0.77] n/a death 43/1,019 1,569/25,489 Said (RCT) 42% 0.58 [0.09-3.47] 10,000IU recovery 30 (n) 30 (n) Din Ujjan (RCT) 29% 0.71 [0.50-1.03] 1,800IU no recov. 15/25 21/25 CT​1 Tau​2 = 0.21, I​2 = 62.3%, p < 0.0001 Early treatment 60% 0.40 [0.26-0.60] 92/2,050 2,051/41,537 60% improvement Tan 80% 0.20 [0.04-0.93] 5,000IU oxygen 3/17 16/26 CT​1 Improvement, RR [CI] Dose (5d) Treatment Control Krishnan 19% 0.81 [0.49-1.34] n/a death 8/16 84/136 Castillo (RCT) 85% 0.15 [0.01-2.93] 0.8mg (c) death 0/50 2/26 SHADE Rastogi (RCT) 53% 0.47 [0.24-0.92] 300,000IU viral+ 6/16 19/24 Murai (DB RCT) -49% 1.49 [0.55-4.05] 200,000IU death 9/119 6/118 Ling 80% 0.20 [0.08-0.48] 40,000IU death 73 (n) 253 (n) Jevalikar 82% 0.18 [0.02-1.69] 60,000IU death 1/128 3/69 Giannini 37% 0.63 [0.35-1.09] 400,000IU death/ICU 14/36 29/55 Nogués (QR) 79% 0.21 [0.10-0.43] 0.8mg (c) death 21/447 62/391 Lohia 11% 0.89 [0.32-1.89] n/a death 26 (n) 69 (n) Mazziotti 19% 0.81 [0.45-1.47] varies death 116 (n) 232 (n) Elhadi (ICU) 23% 0.77 [0.44-1.32] n/a death 7/15 274/450 ICU patients Alcala-Diaz 81% 0.19 [0.04-0.83] 0.8mg (c) death 4/79 90/458 Güven (ICU) 25% 0.75 [0.37-1.24] 300,000IU death 43/113 30/62 ICU patients Assiri (ICU) -66% 1.66 [0.25-7.87] n/a death 12/90 2/28 ICU patients Soliman (RCT) 63% 0.37 [0.09-2.78] 200,000IU death 7/40 3/16 Elamir (RCT) 86% 0.14 [0.01-2.63] 2.5μg (t) death 0/25 3/25 Yildiz 81% 0.19 [0.04-0.91] 300,000IU death 1/37 24/170 Maghbooli (DB RCT) 40% 0.60 [0.15-2.38] 125μg (c) death 3/53 5/53 Leal-Martínez (RCT) 86% 0.14 [0.03-0.80] 20,000IU death 1/40 7/40 CT​1 Beigm.. (SB RCT) 89% 0.11 [0.01-1.98] 600,000IU death 0/30 4/30 ICU patients CT​1 Baguma 97% 0.03 [0.00-0.54] n/a death 23 (n) 458 (n) Mahmood 30% 0.70 [0.47-1.04] varies death 45/238 31/114 REsCue Bishop (DB RCT) 34% 0.66 [0.23-1.92] 1020μg (c) no recov. 5/65 8/69 COVID-VIT-D Cannata-An.. (RCT) -44% 1.44 [0.76-2.72] 100,000IU death 22/274 15/269 Zangeneh (ICU) -26% 1.26 [0.73-2.16] n/a death n/a n/a ICU patients Fiore 93% 0.07 [0.07-0.63] 200,000IU death 3/58 11/58 CARED Mariani (DB RCT) -124% 2.24 [0.44-11.3] 500,000IU death 5/115 2/103 Baykal 22% 0.78 [0.41-1.47] 300,000IU death 7/18 28/56 Shade-S Singh (DB RCT) 45% 0.55 [0.31-0.99] 600,000IU death 11/45 20/45 Shahid 38% 0.62 [0.47-0.82] n/a death 705 (n) 773 (n) Karonova (RCT) 86% 0.14 [0.01-2.66] 50,000IU ICU 0/56 3/54 Zurita-C.. (SB RCT) 79% 0.21 [0.03-1.59] 10,000IU death 1/20 6/25 De Niet (DB RCT) 65% 0.35 [0.04-3.10] 100,000IU death 1/21 3/22 Fairfield -9% 1.09 [1.04-1.12] n/a death population-based cohort Lakkireddy (RCT) 61% 0.39 [0.08-1.91] 300,000IU death 2/44 5/43 see notes Hafez 94% 0.06 [0.00-1.29] 150,000IU death 0/7 12/30 Sharif-Askari (ICU) 36% 0.64 [0.46-0.90] 50,000IU ICU 20 (n) 25 (n) ICU patients Karimpour-Razke.. 79% 0.21 [0.10-0.45] n/a death 10/124 93/329 Hafezi (ICU) 63% 0.37 [0.14-0.94] 50,000IU death 8/43 12/37 ICU patients COVID-VIT Bychinin (DB RCT) 27% 0.73 [0.47-1.14] 80,000IU death 19/52 27/54 ICU patients Domazet B.. (RCT) 21% 0.79 [0.55-1.13] 50,000IU death 30/75 39/77 ICU patients Tau​2 = 0.26, I​2 = 81.6%, p < 0.0001 Late treatment 46% 0.54 [0.44-0.67] 309/3,569 978/5,372 46% improvement Blanch-Rubió 8% 0.92 [0.63-1.36] n/a cases 62/1,303 47/799 Improvement, RR [CI] Dose (1m) Treatment Control Sainz-Amo 33% 0.67 [0.27-1.67] n/a severe case case control Hernández -4% 1.04 [0.26-4.10] varies death 2/19 20/197 Annweiler 93% 0.07 [0.01-0.61] 50,000IU death 2/29 10/32 Cereda -73% 1.73 [0.81-2.74] varies death 7/18 40/152 Louca 8% 0.92 [0.88-0.97] n/a cases population-based cohort Cangiano 70% 0.30 [0.10-0.87] 50,000IU death 3/20 39/78 Vasheghani 30% 0.70 [0.33-1.49] n/a death 7/88 48/420 Ma 30% 0.70 [0.50-0.97] n/a cases 49/363 1,329/7,934 Sulli 76% 0.24 [0.17-0.36] n/a cases case control Ullah -42% 1.42 [0.74-2.37] n/a death 21/64 26/135 Meltzer 36% 0.64 [0.29-1.41] n/a cases 6/131 239/3,338 COVIDENCE UK Holt 7% 0.93 [0.76-1.15] n/a cases 141/5,640 305/9,587 Ünsal 71% 0.29 [0.11-0.76] varies pneumonia 4/28 14/28 Oristrell 43% 0.57 [0.41-0.80] 7.4μg (t) death 2,296 (n) 3,407 (n) Abdulateef 41% 0.59 [0.25-1.41] varies hosp. 6/127 24/300 Loucera (PSM) 33% 0.67 [0.50-0.91] varies (c) death 374 (n) 374 (n) Levitus 31% 0.69 [0.37-1.24] varies severe case 65 (n) 64 (n) Aldwihi -49% 1.49 [1.13-1.87] n/a hosp. 94/259 143/479 Dudley 22% 0.78 [0.23-2.61] 22,400IU symp. case 15/58 2/6 Fasano 42% 0.58 [0.34-0.99] n/a cases 13/329 92/1,157 Campi 88% 0.12 [0.09-0.15] n/a severe case case control Oristrell -1% 1.01 [0.93-1.09] varies (c) death population-based cohort Jimenez 50% 0.50 [0.28-0.90] 3.7μg (p) death 16/94 65/191 Israel 13% 0.87 [0.79-0.95] n/a hosp. case control Mohseni 12% 0.88 [0.75-1.03] n/a cases 99/192 242/411 Sinaci 90% 0.10 [0.01-1.70] n/a severe case 0/36 7/123 Golabi -25% 1.25 [0.86-1.84] n/a cases case control Pecina -70% 1.70 [0.36-8.20] n/a death 29 (n) 63 (n) Bagheri 71% 0.29 [0.10-0.83] n/a severe case 131 (n) 379 (n) Lázaro 27% 0.73 [0.07-7.96] n/a cases 1/97 2/142 Arroyo-Díaz -12% 1.12 [0.73-1.66] n/a death 50/189 167/1,078 Ahmed 10% 0.90 [0.72-1.07] n/a death n/a n/a Ma 49% 0.51 [0.29-0.91] varies hosp. 26,605 (n) 12,710 (n) Mahmood 9% 0.91 [0.60-1.38] varies death 34/138 31/114 Tylicki 14% 0.86 [0.40-1.38] n/a death 28/85 25/48 Subramanian 27% 0.73 [0.47-1.09] n/a death 31/131 80/336 Levy 30% 0.70 [0.49-1.00] n/a death/hosp. 39/208 168/641 Junior 22% 0.78 [0.30-1.99] n/a death 8/113 8/88 Nimer 33% 0.67 [0.48-0.90] n/a hosp. 66/796 153/1,352 Shehab 46% 0.54 [0.23-1.30] n/a severe case 6/90 20/163 CORONAVIT Jolliffe (RCT) -95% 1.95 [0.12-31.1] 89,600IU ventilation 1/1,515 1/2,949 Parant 50% 0.50 [0.20-1.17] varies death 7/66 28/162 Villasis.. (DB RCT) 67% 0.33 [0.01-8.15] 112,000IU hosp. 0/150 1/152 Jabeen 89% 0.11 [0.01-1.94] 200,000IU symp. case 0/20 4/20 PROTECT Hosseini (DB RCT) 82% 0.18 [0.01-3.50] 140,000IU cases 0/19 2/15 Brunvoll (DB RCT) -0% 1.00 [0.25-4.01] 11,200IU ICU 4/17,278 4/17,323 CT​1 van Helmond 98% 0.02 [0.00-1.35] 140,000IU cases 0/255 36/2,827 Gibbons (PSM) 33% 0.67 [0.59-0.75] varies death population-based cohort Guldemir 5% 0.95 [0.62-1.46] n/a hosp. 19/81 98/396 Sharif 28% 0.72 [0.30-0.98] 56,000IU severe case n/a n/a De Nicolò 88% 0.12 [0.05-0.52] n/a IgG+ 43 (n) 63 (n) Şengül 69% 0.31 [0.15-0.64] n/a cases case control Bhat 34% 0.66 [0.48-0.90] 1400μg (c) symp. case 59/262 52/152 Tau​2 = 0.08, I​2 = 90.1%, p < 0.0001 Prophylaxis 31% 0.69 [0.62-0.77] 900/59,834 3,572/70,385 31% improvement All studies 37% 0.63 [0.58-0.69] 1,301/65,453 6,601/117,294 37% improvement All 107 vitamin D COVID-19 treatment studies c19early.org/d Mar 2023 Tau​2 = 0.09, I​2 = 89.2%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors vitamin D Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Annweiler 89% death Relative Risk [CI] Annweiler 63% death Burahee 93% death Asimi 97% ventilation CT​1 Sánchez-Zuno (RCT) 89% severe case Efird 49% death Valecha 87% ICU admission CT​1 Khan (RCT) 33% recovery CT​1 Hunt 47% death Said (RCT) 42% recovery Din Ujjan (RCT) 29% recovery CT​1 Tau​2 = 0.21, I​2 = 62.3%, p < 0.0001 Early treatment 60% 60% improvement Tan 80% oxygen therapy CT​1 Krishnan 19% death Castillo (RCT) 85% death SHADE Rastogi (RCT) 53% viral- Murai (DB RCT) -49% death Ling 80% death Jevalikar 82% death Giannini 37% death/ICU Nogués (QR) 79% death Lohia 11% death Mazziotti 19% death Elhadi (ICU) 23% death ICU patients Alcala-Diaz 81% death Güven (ICU) 25% death ICU patients Assiri (ICU) -66% death ICU patients Soliman (RCT) 63% death Elamir (RCT) 86% death Yildiz 81% death Maghbooli (DB RCT) 40% death Leal-Martí.. (RCT) 86% death CT​1 Beigm.. (SB RCT) 89% death ICU patients CT​1 Baguma 97% death Mahmood 30% death REsCue Bishop (DB RCT) 34% recovery COVID-VIT-D Cannata-A.. (RCT) -44% death Zangeneh (ICU) -26% death ICU patients Fiore 93% death CARED Mariani (DB RCT) -124% death Baykal 22% death Shade-S Singh (DB RCT) 45% death Shahid 38% death Karonova (RCT) 86% ICU admission Zurita-.. (SB RCT) 79% death De Niet (DB RCT) 65% death Fairfield -9% death Lakkireddy (RCT) 61% death see notes Hafez 94% death Sharif-Ask.. (ICU) 36% ICU admission ICU patients Karimpour-Razk.. 79% death Hafezi (ICU) 63% death ICU patients COVID-VIT Bychinin (DB RCT) 27% death ICU patients Domazet .. (RCT) 21% death ICU patients Tau​2 = 0.26, I​2 = 81.6%, p < 0.0001 Late treatment 46% 46% improvement Blanch-Rubió 8% case Sainz-Amo 33% severe case Hernández -4% death Annweiler 93% death Cereda -73% death Louca 8% case Cangiano 70% death Vasheghani 30% death Ma 30% case Sulli 76% case Ullah -42% death Meltzer 36% case COVIDENCE UK Holt 7% case Ünsal 71% pneumonia Oristrell 43% death Abdulateef 41% hospitalization Loucera (PSM) 33% death Levitus 31% severe case Aldwihi -49% hospitalization Dudley 22% symp. case Fasano 42% case Campi 88% severe case Oristrell -1% death Jimenez 50% death Israel 13% hospitalization Mohseni 12% case Sinaci 90% severe case Golabi -25% case Pecina -70% death Bagheri 71% severe case Lázaro 27% case Arroyo-Díaz -12% death Ahmed 10% death Ma 49% hospitalization Mahmood 9% death Tylicki 14% death Subramanian 27% death Levy 30% death/hosp. Junior 22% death Nimer 33% hospitalization Shehab 46% severe case CORONAVIT Jolliffe (RCT) -95% ventilation Parant 50% death Villasi.. (DB RCT) 67% hospitalization Jabeen 89% symp. case PROTECT Hosseini (DB RCT) 82% case Brunvoll (DB RCT) -0% ICU admission CT​1 van Helmond 98% case Gibbons (PSM) 33% death Guldemir 5% hospitalization Sharif 28% severe case De Nicolò 88% IgG positive Şengül 69% case Bhat 34% symp. case Tau​2 = 0.08, I​2 = 90.1%, p < 0.0001 Prophylaxis 31% 31% improvement All studies 37% 37% improvement All 107 vitamin D COVID-19 treatment studies c19early.org/d Mar 2023 Tau​2 = 0.09, I​2 = 89.2%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors vitamin D Favors control
B
Loading..
C
Loading..
D
Loading..
Figure 1. A. Random effects meta-analysis of treatment studies. This plot shows pooled effects, analysis for individual outcomes is below, and more details on pooled effects can be found in the heterogeneity section. Effect extraction is pre-specified, using the most serious outcome reported. Simplified dosages are shown for comparison, these are the total dose in the first five days for treatment, and the monthly dose for prophylaxis. Calcifediol, calcitriol, and paricalcitol treatment are indicated with (c), (t), and (p). For details of effect extraction and full dosage information see the appendix. B. Scatter plot showing the distribution of effects reported in sufficiency studies and treatment studies. Diamonds show the results of random effects meta-analysis. C. Scatter plot showing the most serious outcome in all studies in the context of multiple COVID-19 treatments. Diamonds show the results of random effects meta-analysis for each treatment. D. Timeline of results in vitamin D treatment studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 10.8 months, compared to using all studies. Efficacy based on specific outcomes in RCTs was delayed by 2.2 months, compared to using pooled outcomes in RCTs.
Introduction
We analyze all significant controlled studies regarding vitamin D and 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 perform random-effects meta analysis for all treatment studies, Randomized Controlled Trials, peer-reviewed studies, studies using cholecalciferol, studies using calcifediol/calcitriol, and for specific outcomes: mortality, mechanical ventilation, ICU admission, hospitalization, and case results. Results are presented for prophylaxis, early treatment, and late treatment. Separately, we perform random-effects meta analysis for studies that analyze outcomes based on vitamin D sufficiency (non-treatment studies).
Vitamin D undergoes two conversion steps before reaching the biologically active form as shown in Figure 2. The first step is conversion to calcidiol, or 25(OH)D, in the liver. The second is conversion to calcitriol, or 1,25(OH)2D, which occurs in the kidneys, the immune system, and elsewhere. Calcitriol is the active, steroid-hormone form of vitamin D, which binds with vitamin D receptors found in most cells in the body. Vitamin D was first identified in relation to bone health, but is now known to have multiple functions, including an important role in the immune system [Carlberg, Martens]. For example, [Quraishi] show a strong association between pre-operative vitamin D levels and hospital-acquired infections, as shown in Figure 3. There is a significant delay involved in the conversion from cholecalciferol, therefore calcifediol (calcidiol) or calcitriol may be preferable for treatment.
Figure 2. Simplified view of vitamin D sources and conversion.
Figure 3. Risk of hospital-acquired infections as a function of pre-operative vitamin D levels, from [Quraishi].
Many vitamin D studies analyze outcomes based on serum vitamin D levels which may be maintained via sun exposure, diet, or supplementation. We refer to these studies as sufficiency studies, as they typically present outcomes based on vitamin D sufficiency. These studies do not establish a causal link between vitamin D and outcomes. In general, low vitamin D levels are correlated with many other factors that may influence COVID-19 susceptibility and severity. Therefore, beneficial effects found in these studies may be due to factors other than vitamin D. On the other hand, if vitamin D is causally linked to the observed benefits, it is possible that adjustments for correlated factors could obscure this relationship. COVID-19 disease may also affect vitamin D levels [Silva], suggesting additional caution in interpreting results for studies where the vitamin D levels are measured during the disease. For these reasons, we analyze sufficiency studies separately from treatment studies. We include all sufficiency studies that provide a comparison between two groups with low and high levels. Some studies only provide results as a function of change in vitamin D levels [Butler-Laporte, Gupta, Raisi-Estabragh], which may not be indicative of results for deficiency/insufficiency versus sufficiency (increasing already sufficient levels may be less useful for example). Some studies show the average vitamin D level for patients in different groups [Al-Daghri, Alarslan, Azadeh, Chodick, D'Avolio, Desai, Ersöz, Hosseini (B), Jabbar, Kerget, Latifi-Pupovci, Mansour, Mardani, Morad, Nicolescu, Qu, Ranjbar, Rathod, Saeed, Schmitt, Shannak, Sinnberg, Soltani-Zangbar, Takase, Vassiliou], most of which show lower D levels for worse outcomes. Other studies analyze vitamin D status and outcomes in geographic regions [Bakaloudi, Jayawardena, Marik, Papadimitriou, Rhodes, Sooriyaarachchi, Walrand, Yadav], all finding worse outcomes to be more likely with lower D levels.
Sufficiency studies vary widely in terms of when vitamin D levels were measured, the cutoff level used, and the population analyzed (for example studies with hospitalized patients exclude the effect of vitamin D on the risk of hospitalization). We do not analyze sufficiency studies in more detail because there are many controlled treatment studies that provide better information on the use of vitamin D as a treatment for COVID-19. A more detailed analysis of sufficiency studies can be found in [Chiodini]. [Mishra] present a systematic review and meta analysis showing that vitamin D levels are significantly associated with COVID-19 cases.
For studies regarding treatment with vitamin D, we distinguish three stages as shown in Figure 4. Prophylaxis refers to regularly taking vitamin D before being infected in order to minimize the severity of infection. Due to the mechanism of action, vitamin D is unlikely to completely prevent infection, although it may prevent infection from reaching a level detectable by PCR. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 4. Treatment stages.
Preclinical Research
6 In Silico studies support the efficacy of vitamin D [Al-Mazaideh, Chellasamy, Mansouri, Pandya, Qayyum, Song].
2 In Vitro studies support the efficacy of vitamin D [Mok, Pickard].
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.
Results
Table 1 summarizes the results for all stages combined, with different exclusions, for specific outcomes, and for sufficiency (non-treatment) studies. Table 2 shows results by treatment stage. Figure 5 plots individual results by treatment stage. Figure 6, 7, 8, 9, 10, 11, 12, 13, and 14 show forest plots for treatment studies with pooled effects, peer-reviewed studies, cholecalciferol studies, calcifediol/calcitriol studies, and for studies reporting mortality, mechanical ventilation, ICU admission, hospitalization, and case results only. Figure 15 shows a forest plot for random effects meta-analysis of sufficiency (non-treatment) studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, for specific outcomes, and for sufficiency (non-treatment) studies. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies37% [31‑42%] p < 0.0001
****
107 182,747 1,057
After exclusions39% [33‑45%] p < 0.0001
****
80 160,512 814
Peer-reviewed studiesPeer-reviewed37% [31‑42%] p < 0.0001
****
101 181,129 1,009
Randomized Controlled TrialsRCTs31% [17‑42%] p < 0.0001
****
26 41,836 299
RCTs after exclusionsRCTs w/exc.34% [17‑47%] p = 0.0003
***
19 40,741 223
Cholecalciferol36% [29‑41%] p < 0.0001
****
96 173,856 922
Calcifediol/calcitriolCalcifediol49% [26‑65%] p = 0.00036
***
11 8,891 135
Mortality36% [28‑44%] p < 0.0001
****
60 62,080 561
VentilationVent.26% [-2‑46%] p = 0.06817 7,852 184
ICU admissionICU50% [33‑62%] p < 0.0001
****
25 40,098 274
HospitalizationHosp.20% [8‑31%] p = 0.0022
**
21 85,626 200
Recovery38% [23‑50%] p < 0.0001
****
9 545 75
Cases15% [6‑23%] p = 0.0015
**
25 134,291 284
Viral52% [30‑67%] p = 0.00014
***
4 200 26
RCT mortality31% [6‑50%] p = 0.02
*
15 1,949 174
RCT hospitalizationRCT hosp.21% [-4‑40%] p = 0.0878 39,713 107
Sufficiency53% [49‑57%] p < 0.0001
****
157 182,257 1,349
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies60% [40‑74%]
****
46% [33‑56%]
****
31% [23‑38%]
****
After exclusions68% [45‑82%]
****
58% [45‑69%]
****
28% [20‑35%]
****
Peer-reviewed studiesPeer-reviewed57% [36‑71%]
****
45% [32‑56%]
****
31% [23‑38%]
****
Randomized Controlled TrialsRCTs32% [8‑50%]
*
34% [13‑50%]
**
22% [-129‑73%]
RCTs after exclusionsRCTs w/exc.65% [-65‑92%]34% [16‑47%]
***
22% [-129‑73%]
Cholecalciferol60% [40‑74%]
****
41% [27‑52%]
****
31% [22‑38%]
****
Calcifediol/calcitriolCalcifediol-73% [57‑83%]
****
32% [6‑50%]
*
Mortality68% [39‑84%]
***
45% [31‑57%]
****
21% [6‑33%]
**
VentilationVent.97% [56‑100%]
*
17% [-14‑40%]38% [-3‑63%]
ICU admissionICU87% [-143‑99%]52% [30‑67%]
***
46% [22‑63%]
**
HospitalizationHosp.90% [-453‑100%]22% [6‑35%]
**
13% [-4‑27%]
Recovery31% [7‑49%]
*
43% [24‑58%]
***
-
Cases--15% [6‑23%]
**
Viral52% [24‑70%]
**
53% [8‑76%]
*
-
RCT mortality-31% [6‑50%]
*
-
RCT hospitalizationRCT hosp.-29% [10‑44%]
**
-26% [-92‑17%]
Loading..
Figure 5. Results by treatment stage.
Loading..
Loading..
Figure 6. Random effects meta-analysis for treatment studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Loading..
Figure 7. Random effects meta-analysis for peer-reviewed treatment studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Loading..
Figure 8. Random effects meta-analysis for cholecalciferol treatment studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Loading..
Figure 9. Random effects meta-analysis for calcifediol/calcitriol treatment studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Loading..
Loading..
Figure 10. Random effects meta-analysis for treatment mortality results only.
Loading..
Figure 11. Random effects meta-analysis for treatment mechanical ventilation results only.
Loading..
Figure 12. Random effects meta-analysis for treatment ICU admission results only.
Loading..
Figure 13. Random effects meta-analysis for treatment hospitalization results only.
Loading..
Figure 14. Random effects meta-analysis for treatment COVID-19 case results only.
Loading..
Figure 15. Random effects meta-analysis for sufficiency studies. This plot pools studies with different effects, different vitamin D cutoff levels and measurement times, and studies may be within hospitalized patients, excluding the risk of hospitalization. However, the prevalence of positive effects is notable.
Randomized Controlled Trials (RCTs)
Results restricted to Randomized Controlled Trials (RCTs), after exclusions, and for specific outcomes are shown in Figure 16, 17, 18, and 19.
RCTs help to make study groups more similar and can provide a higher level of evidence. However they are subject to many biases [Jadad]. For example, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 50 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
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. RCTs for vitamin D 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, and may be greater when the risk of a serious outcome is overstated. 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].
Currently, 37 of 50 treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 37 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 7 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
Loading..
Figure 16. Random effects meta-analysis for Randomized Controlled Trials only. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Loading..
Figure 17. Random effects meta-analysis for RCTs after exclusions. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Loading..
Figure 18. Random effects meta-analysis for RCT mortality results.
Loading..
Figure 19. Random effects meta-analysis for RCT hospitalization results.
Exclusions
To avoid bias in the selection of studies, we include all studies in the main analysis, with the exception of [Espitia-Hernandez]. This study uses a combined protocol with another medication that shows high effectiveness when used alone. Authors report on viral clearance, showing 100% clearance with treatment and 0% for the control group. Based on the known mechanisms of action, the combined medication is likely to contribute more to the improvement.
Here we show the results after excluding studies with critical issues.
[Murai] is a very late stage study (mean 10 days from symptom onset, with 90% on oxygen at baseline), with poorly matched arms in terms of gender, ethnicity, hypertension, diabetes, and baseline ventilation, all of which favor the control group. Further, this study uses cholecalciferol, which may be especially poorly suited for such a late stage. [Cannata-Andía, Mariani] are also very late stage studies using cholecalciferol.
The studies excluded are as follows, and the resulting forest plot is shown in Figure 20.
[Abdulateef], unadjusted results with no group details.
[Asimi], excessive unadjusted differences between groups.
[Assiri], unadjusted results with no group details.
[Baykal], unadjusted results with no group details; significant confounding by time possible due to separation of groups in different time periods.
[Campi], significant unadjusted differences between groups.
[Cannata-Andía], very late stage study using cholecalciferol instead of calcifediol or calcitriol.
[Din Ujjan], based on dosages and previous research, combined treatments may contribute more to the effect seen.
[Elhadi], unadjusted results with no group details.
[Fairfield], substantial unadjusted confounding by indication likely.
[Guldemir], unadjusted results with no group details.
[Güven], very late stage, ICU patients.
[Holt], significant unadjusted confounding possible.
[Junior], unadjusted results with no group details.
[Khan], based on dosages and previous research, combined treatments may contribute more to the effect seen.
[Krishnan], unadjusted results with no group details.
[Leal-Martínez], combined treatments may contribute more to the effect seen.
[Lázaro], very few events; unadjusted results with no group details; minimal details provided.
[Mahmood], unadjusted results with no group details; substantial unadjusted confounding by indication likely.
[Mahmood], unadjusted results with no group details; substantial unadjusted confounding by indication likely.
[Mohseni], unadjusted results with no group details.
[Murai], very late stage, >50% on oxygen/ventilation at baseline; very late stage study using cholecalciferol instead of calcifediol or calcitriol.
[Pecina], unadjusted results with no group details.
[Shahid], minimal details provided.
[Shehab], unadjusted results with no group details.
[Singh], minimal details provided.
[Ullah], significant unadjusted confounding possible.
[Zurita-Cruz], randomization resulted in significant baseline differences that were not adjusted for.
Loading..
Figure 20. Random effects meta-analysis excluding studies with significant issues. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Heterogeneity
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.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 21 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 50 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 21. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 50 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, treatment regimen, and the form of vitamin D used (cholecalciferol, calcifediol, or calcitriol).
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 22. 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.
Currently, 37 of 50 treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 100% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.0 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
Loading..
Loading..
Figure 22. 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 with a specific form and dosage of vitamin D. 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.
Vitamin D studies vary widely in all the factors above, which makes the consistently positive results even more remarkable. A failure to detect an association after combining heterogeneous studies does not mean the treatment is not effective (it may only work in certain cases), however the reverse is not true — an identified association is valid, although the magnitude of the effect may be larger for more optimal cases, and lower for less optimal cases. While we present results for all studies in this paper, the individual outcome, form of vitamin D, and treatment time analyses are more relevant for specific use cases.
Discussion
For sufficiency studies, different studies use different levels as the threshold of sufficiency, vitamin D levels were measured at different times, and some studies measure risk only within hospitalized patients, which excludes the risk of a serious enough case to be hospitalized. However, 147 of 157 studies present positive effects.
Sufficiency studies show a strong correlation between low vitamin D levels and worse COVID-19 outcomes, however they do not provide information on vitamin D treatment. Studies with vitamin D levels measured after admission may show lower levels because COVID-19 infection reduces vitamin D levels. Studies with levels measured before infection also show signficant benefit, however the cause could be one or more correlated factors. For example, sunlight exposure increases vitamin D levels, but also increases intracellular melatonin [Zimmerman], and melatonin shows significant benefit for COVID-19 [c19melatonin.com]. Sun exposure is also correlated with physical exercise, which also shows benefit for COVID-19 [c19early.org].
91 of 107 treatment studies report positive effects. Studies vary significantly in terms of treatment delay, treatment regimen, patients characteristics, and (for the pooled effects analysis) outcomes, as reflected in the high degree of heterogeneity. However treatment consistently shows a significant benefit. The treatment studies not showing positive effects are mostly prophylaxis studies with unknown dosages. The only non-prophylaxis studies reporting negative effects are a small unadjusted retrospective [Assiri], [Zangeneh] with no details of treatment, and [Cannata-Andía, Mariani, Murai] which are very late stage studies using cholecalciferol. For [Murai], the result also has very low statistical significance due to the small number of events, and the other reported outcomes of ventilation and ICU admission, which have slightly more events and higher confidence, show benefits for vitamin D. Calcifediol or calcitriol, which avoids several days delay in conversion, may be more successful, especially with very late stage usage.
Acute treatment shows greater efficacy than chronic prophylaxis for mortality (and in pooled analysis). One hypothesis is that long-term supplementation may affect normal biological processing. A key component of vitamin D processing is regulation via the enzyme CYP24A1, which breaks down active vitamin D. Long-term supplementation may lead to upregulation of CYP24A1, and potentially lower availability of active vitamin D where needed during infection. The prophylaxis RCTs to date [Jolliffe, Villasis-Keever] are consistent with this possibility, with the shorter-term supplementation in [Villasis-Keever] showing better results compared to the longer-term high adherence daily supplementation in [Jolliffe]. Specific forms and administration of vitamin D may minimize upregulation of CYP24A1 [Petkovich]. [Bader] performed an RCT showing high-dose cholecalciferol (50,000 IU/week) significantly increased IL-6, however other studies have shown no significant difference in IL-6 [El Hajj, Mousa] (30,000IU/wk and 100,000IU bolus + 4,000IU/day).
Other factors may be responsible for the observed lower efficacy in prophylaxis studies. For example, analysis of hospitalized patients is subject to selection bias because long-term accurate-dosage supplementing individuals may be significantly less likely to be hospitalized. Studies spanning higher-UV months are subject to confounding. Note that prophylaxis studies include case results, whereas we may expect vitamin D to be more effective against serious outcomes. Comparison of acute treatment versus long-term supplementation should use the specific outcome analyses rather than the pooled outcome analyses.
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.
Figure 23 shows a scatter plot of results for prospective and retrospective treatment studies. Prospective studies show 51% [34‑64%] improvement in meta analysis, compared to 32% [25‑38%] for retrospective studies, suggesting possible negative publication bias, with a non-significant trend towards retrospective studies reporting lower efficacy. This gives us further confidence in the significant efficacy seen in all studies.
Loading..
Figure 23. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 24 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 24. 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 D for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 vitamin D 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 D trials represent the optimal conditions for efficacy.
Other meta analyses for vitamin D treatment can be found in [Argano, D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu, Xie], showing significant improvements for mortality, mechanical ventilation, ICU admission, hospitalization, severity, and cases.
The first version of [Lakkireddy] was censored based on incorrect claims from an anti-treatment researcher. For example, the author claims that the gender difference between arms (7/44 vs. 15/43 female) indicates randomization failure, however by simulation, using the group sizes and overall gender ratio, the difference between the number of female patients in each arm is expected to be ≥8 6.4% of the time (2.7% with ≥8 in the control arm, and 3.7% with ≥8 in the treatment arm).
Author claims that the difference in CRP would only happen about one in a billion times. This is incorrect. CRP is not normally distributed, and the observed values could be due to a very small number of outliers with very large CRP in one group.
A response from the study authors can be found at [c19vitamind.com]. The study was republished.
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.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials 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.
Table 4 shows the reported results of physicians that use early treatments for COVID-19, compared to the results for a non-treating physician. The treatments used vary. Physicians typically use a combination of treatments, with almost all reporting use of ivermectin and/or HCQ, and most using additional treatments, including vitamin D. These results are subject to selection and ascertainment bias and more accurate analysis requires details of the patient populations and followup, however results are consistently better across many teams, and consistent with the extensive controlled trial evidence that shows a significant reduction in risk with many early treatments, and improved results with the use of multiple treatments in combination.
Table 4. Physician results with early treatment protocols compared to no early treatment. (*) Dr. Uip reportedly prescribed early treatment for himself, but not for patients [medicospelavidacovid19.com.br].
LATE TREATMENT
Physician / TeamLocationPatients HospitalizationHosp. MortalityDeath
Dr. David Uip (*) Brazil 2,200 38.6% (850) Ref. 2.5% (54) Ref.
EARLY TREATMENT - 38 physicians/teams
Physician / TeamLocationPatients HospitalizationHosp. ImprovementImp. MortalityDeath ImprovementImp.
Dr. Roberto Alfonso Accinelli
0/360 deaths for treatment within 3 days
Peru 1,265 0.6% (7) 77.5%
Dr. Mohammed Tarek Alam
patients up to 84 years old
Bangladesh 100 0.0% (0) 100.0%
Dr. Oluwagbenga Alonge Nigeria 310 0.0% (0) 100.0%
Dr. Raja Bhattacharya
up to 88yo, 81% comorbidities
India 148 1.4% (2) 44.9%
Dr. Flavio Cadegiani Brazil 3,450 0.1% (4) 99.7% 0.0% (0) 100.0%
Dr. Alessandro Capucci Italy 350 4.6% (16) 88.2%
Dr. Shankara Chetty South Africa 8,000 0.0% (0) 100.0%
Dr. Deborah Chisholm USA 100 0.0% (0) 100.0%
Dr. Ryan Cole USA 400 0.0% (0) 100.0% 0.0% (0) 100.0%
Dr. Marco Cosentino
vs. 3-3.8% mortality during period; earlier treatment better
Italy 392 6.4% (25) 83.5% 0.3% (1) 89.6%
Dr. Jeff Davis USA 6,000 0.0% (0) 100.0%
Dr. Dhanajay India 500 0.0% (0) 100.0%
Dr. Bryan Tyson & Dr. George Fareed USA 20,000 0.0% (6) 99.9% 0.0% (4) 99.2%
Dr. Raphael Furtado Brazil 170 0.6% (1) 98.5% 0.0% (0) 100.0%
Dr. Heather Gessling USA 1,500 0.1% (1) 97.3%
Dr. Ellen Guimarães Brazil 500 1.6% (8) 95.9% 0.4% (2) 83.7%
Dr. Syed Haider USA 4,000 0.1% (5) 99.7% 0.0% (0) 100.0%
Dr. Mark Hancock USA 24 0.0% (0) 100.0%
Dr. Sabine Hazan USA 1,000 0.0% (0) 100.0%
Dr. Mollie James USA 3,500 1.1% (40) 97.0% 0.0% (1) 98.8%
Dr. Roberta Lacerda Brazil 550 1.5% (8) 96.2% 0.4% (2) 85.2%
Dr. Katarina Lindley USA 100 5.0% (5) 87.1% 0.0% (0) 100.0%
Dr. Ben Marble USA 150,000 0.0% (4) 99.9%
Dr. Edimilson Migowski Brazil 2,000 0.3% (7) 99.1% 0.1% (2) 95.9%
Dr. Abdulrahman Mohana Saudi Arabia 2,733 0.0% (0) 100.0%
Dr. Carlos Nigro Brazil 5,000 0.9% (45) 97.7% 0.5% (23) 81.3%
Dr. Benoit Ochs Luxembourg 800 0.0% (0) 100.0%
Dr. Ortore Italy 240 1.2% (3) 96.8% 0.0% (0) 100.0%
Dr. Valerio Pascua
one death for a patient presenting on the 5th day in need of supplemental oxygen
Honduras 415 6.3% (26) 83.8% 0.2% (1) 90.2%
Dr. Sebastian Pop Romania 300 0.0% (0) 100.0%
Dr. Brian Proctor USA 869 2.3% (20) 94.0% 0.2% (2) 90.6%
Dr. Anastacio Queiroz Brazil 700 0.0% (0) 100.0%
Dr. Didier Raoult France 8,315 2.6% (214) 93.3% 0.1% (5) 97.6%
Dr. Karin Ried
up to 99yo, 73% comorbidities, av. age 63
Turkey 237 0.4% (1) 82.8%
Dr. Roman Rozencwaig
patients up to 86 years old
Canada 80 0.0% (0) 100.0%
Dr. Vipul Shah India 8,000 0.1% (5) 97.5%
Dr. Silvestre Sobrinho Brazil 116 8.6% (10) 77.7% 0.0% (0) 100.0%
Dr. Vladimir Zelenko USA 2,200 0.5% (12) 98.6% 0.1% (2) 96.3%
Mean improvement with early treatment protocols 236,564 HospitalizationHosp. 94.0% MortalityDeath 94.8%
Conclusion
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 60% [40‑74%] and 37% [31‑42%] improvement for early treatment and for all studies. Results are similar after restriction to 101 peer-reviewed studies: 57% [36‑71%] and 37% [31‑42%], and for the 60 mortality results: 68% [39‑84%] and 36% [28‑44%].
Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 56 studies from 52 independent teams in 20 different countries show statistically significant improvements in isolation (40 for the most serious outcome).
Acute treatment (early 60% [40‑74%], late 46% [33‑56%]) shows greater efficacy than chronic prophylaxis (31% [23‑38%]).
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 41% [27‑52%].
This paper is data driven, all graphs and numbers are dynamically generated. We will update the paper as new studies are released or with any corrections. Please submit updates and corrections at the bottom of this page.Please submit updates and corrections at https://c19early.org/dmeta.html.
3/28: We added [Schmidt].
3/28: We added [Huang, Nasiri].
3/23: We added [Davran].
3/15: We added [Bucurica].
3/15: We added [Topan].
3/14: We added [Domazet Bugarin, Siuka].
3/4: We added [Şengül].
3/4: We added [Chen].
3/2: We added [Tan].
2/18: We added [Ortatatli].
2/8: We added [Arabi].
1/28: We added [Batur].
1/20: We added [Mostafa].
1/19: We added [Din Ujjan].
1/17: We added [Valecha].
1/8: We updated [van Helmond] to the journal version.
1/7/2023: We updated discussion of acute treatment vs. long-term supplementation.
12/31: We added [De Nicolò].
12/20: We added [Abdrabbo AlYafei].
12/20: We updated the discussion of heterogeneity and RCTs.
12/12: We added [Vásquez-Procopio].
12/3: We added [Tallon].
11/27: We added [Guldemir (B)].
11/26: We added [Sharif].
11/13: We added [Gibbons].
11/8: We added [Said].
11/4: We added [Bychinin (B)].
10/28: We added [Álvarez].
10/26: We added [Hafezi].
10/15: We added [Charla].
10/8: We added [Karimpour-Razkenari].
10/1: We added [Singh].
9/20: We added [Shahid].
9/19: We added [van Helmond].
9/15: We added [Brunvoll].
9/11: We added [Zeidan].
8/25: We added [Hafez].
8/24: We added [Aldwihi, Sharif-Askari].
8/23: We added [Doğan].
8/21: We added [Reyes Pérez].
8/19: We added [Kalichuran].
8/16: We updated [Lakkireddy] to the new version (post censorship of the previous version).
8/12: We added [Dana, Zurita-Cruz].
8/10: We added [Barrett].
8/5: We added [Bogliolo].
8/3: We added [Alzahrani].
7/27: We added [De Niet].
7/26: We added [Neves].
7/24: We added [Gholi].
7/19: We added [Baykal].
7/2: We added [Hunt].
6/24: We added [Karonova (D)].
5/28: We added [Mariani].
5/25: We added [Kazemi, Zangeneh].
5/24: We added [Ghanei].
5/23: We added [Fiore].
5/20: We added [Hosseini (C)].
5/19: We added [Jabeen].
5/19: We added [Ozturk].
5/8: We added [Charkowick].
5/5: We added [Nguyen].
5/1: We added [Khan].
4/30: We added [Voelkle].
4/24: We added [Davoudi].
4/22: We added discussion of [Lakkireddy].
4/18: We added [Villasis-Keever].
4/17: We added a section on preclinical research.
4/15: We added [Parant].
4/12: We added [Martínez-Rodríguez].
4/5: We added preprint discussion based on [Zeraatkar].
4/2: We added [Ferrer-Sánchez].
3/31: We added [Ramos].
3/27: We added [Pande].
3/25: We added [Elhadi].
3/23: We added [Jolliffe].
3/20: We added [Bushnaq].
3/19: We added [Shehab].
3/7: We added [Rodríguez-Vidales].
3/5: We added [Reis].
3/4: We added [Nimer].
3/3: We added [Karonova].
2/24: We added [Zidrou].
2/20: We added [Sanson].
2/19: We added [Cannata-Andía].
2/18: We added [González-Estevez, Junior].
2/17: We added [Mahmood].
2/15: We updated [Vanegas-Cedillo] to the journal version.
2/11: We added [Bychinin].
2/8: We added [Subramanian].
2/8: We added [Ranjbar].
2/7: We added [Tylicki, Ullah].
2/6: We added [Bishop].
2/4: We added [Ahmed].
2/4: We updated [Dror] to the journal version.
1/30: We updated [Leal-Martínez] to the journal version.
1/29: We added [Ansari].
1/28: We added [Anjum].
1/25: We added [Saponaro].
1/23: We added [Juraj].
1/14: We added [Baguma (B)].
1/13: We updated [Israel] to the journal version.
1/8: We added [Seal].
1/5: We added [Pepkowitz].
1/3/2022: We added [Efird].
12/26: We added [Abdulateef].
12/21: We added [Beigmohammadi, Sainz-Amo].
12/20: We added [Galaznik].
12/17: We added [Seven].
12/16: We added [Parra-Ortega].
12/14: We added [Putra].
12/9: We added analysis of the number of independent research groups reporting statistically significant positive results.
12/7: We added [Ma].
12/5: We added [Asgari].
12/3: We updated [Loucera] to the journal version.
12/3: We added [Fatemi].
12/3: We added [Kaur].
11/22: Added discussion related to sufficiency studies.
11/14: We added [Gönen].
11/12: We added [Asghar].
11/7: We added [Holt].
11/3: We added [Atanasovska].
11/2: We added [Al-Salman, Eden].
11/1: We updated [Golabi] to the journal version.
10/31: We added [Assiri, Bianconi, Leal-Martínez].
10/30: We added [Campi, Gaudio].
10/27: We added [Hurst, Lázaro].
10/19: We added [Jimenez].
10/19: We added [Sinaci, Zelzer].
10/18: We added [Mohseni].
10/18: We added [Basaran, Dudley].
10/16: We added a summary plot for all results.
10/15: We added [Ramirez-Sandoval].
10/15: We added [Maghbooli (B)].
10/14: We added [Arroyo-Díaz, Burahee] and analysis of treatment mechanical ventilation, ICU admission, and hospitalization results.
9/28: We added [Yildiz].
9/27: We added [Derakhshanian].
9/22: We added [Bagheri].
9/14: We added [Ribeiro].
9/14: We updated [Vasheghani (B)] to the journal version of the article.
9/14: We added [Elamir].
9/10: We added [Tomasa-Irriguible].
9/7: We added [Karonova (B), Pecina].
9/6: We added [Soliman].
9/1: We added [Golabi].
8/23: We corrected [Jain] to include the mortality outcome.
8/15: We added [Nimavat].
8/13: We added [di Filippo] and updated [Louca] to the journal version of the article.
8/12: We added [Alpcan].
8/10: We added discussion of the immune system and vitamin D.
8/2: We added [Matin].
8/1: We added [Pimental].
7/28: We added [Israel (B)].
7/27: We added [Cozier].
7/26: We added [Güven].
7/25: We added [Asimi].
7/24: We added [Orchard].
7/21: We added [Savitri].
7/19: We added [Oristrell].
7/11: We added [Krishnan].
6/25: We added [Cereda (B)].
6/19: We added [Jude].
6/16: We added [Campi].
6/12: We added [Levitus].
6/11: We updated [Oristrell (B)] to the journal version.
6/9: We added [Fasano].
6/8: We updated [Nogués] to the journal version.
6/7: We added [Diaz-Curiel, Dror].
5/29: We added [Sánchez-Zuno (B)].
5/22: We added analysis restricted to cholecalciferol studies.
5/21: We added [Alcala-Diaz, Li].
5/20: We updated [Lakkireddy] to the journal version.
5/19: We added [AlSafar].
5/10: We added additional information in the abstract.
5/9: We clarified terminology for prophylaxis and added discussion of heterogeneity.
5/8: We added analysis for treatment studies restricted to peer-reviewed articles.
4/30: We added [Loucera].
4/29: We corrected the treatment group counts for the early treatment group in [Annweiler] (there was no change in the relative risk).
4/24: We added analysis restricted to RCT studies and to calcifediol/calcitriol studies. We have excluded [Espitia-Hernandez] in the treatment analysis because they use a combined protocol with another medication that shows high effectiveness when used alone.
4/14: We added [Blanch-Rubió].
4/13: We added [Lohia, Oristrell (B)].
4/12: We added [Barassi].
4/10: We added [Szeto].
4/9: We added [Ünsal].
4/5: We added [Bayramoğlu, Livingston].
4/4: We added event counts to the forest plots.
3/31: We added [Mendy].
3/30: We added [Macaya].
3/29: We added [Im].
3/28: We added [Freitas].
3/22: We added [Meltzer].
3/15: We added [Vanegas-Cedillo].
3/14: We added [Cereda].
3/12: We added [Charoenngam].
3/10: We added [Mazziotti].
3/6: We added [Ricci].
2/26: We added [Lakkireddy].
2/25: We added [Sulli (B)].
2/20: We added [Gavioli].
2/20: We added [Infante].
2/18: [Murai] was updated to the journal version of the paper.
2/17: We corrected an error in the effect extraction for [Angelidi], and we added treatment case and viral clearance forest plots.
2/16: We added [Susianti].
2/10: We added [