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Vitamin D for COVID-19: real-time meta analysis of 286 studies (111 treatment studies and 175 sufficiency studies)
https://c19early.org/dmeta.html
 
0 0.5 1 1.5+ All studies 37% 111 183,150 Improvement, Studies, Patients Relative Risk Mortality 37% 62 62,281 Ventilation 26% 17 7,852 ICU admission 50% 25 40,098 Hospitalization 20% 21 85,626 Cases 16% 27 134,493 RCTs 30% 27 42,038 RCT mortality 31% 15 1,949 Sufficiency 54% 175 228,791 Cholecalciferol 35% 99 174,207 Calcifediol 51% 12 8,943 Prophylaxis 31% 58 130,622 Early 60% 11 43,587 Late 46% 42 8,941 Vitamin D for COVID-19 c19early.org/d Jun 2023 Favorsvitamin D Favorscontrol after exclusions
Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 57 studies from 53 independent teams in 20 different countries show statistically significant improvements in isolation (41 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 104 peer-reviewed studies: 57% [36‑71%] and 37% [31‑42%], and for the 62 mortality results: 68% [39‑84%] and 37% [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% 111 183,150 Improvement, Studies, Patients Relative Risk Mortality 37% 62 62,281 Ventilation 26% 17 7,852 ICU admission 50% 25 40,098 Hospitalization 20% 21 85,626 Cases 16% 27 134,493 RCTs 30% 27 42,038 RCT mortality 31% 15 1,949 Sufficiency 54% 175 228,791 Cholecalciferol 35% 99 174,207 Calcifediol 51% 12 8,943 Prophylaxis 31% 58 130,622 Early 60% 11 43,587 Late 46% 42 8,941 Vitamin D for COVID-19 c19early.org/d Jun 2023 Favorsvitamin D Favorscontrol after exclusions
Sufficiency studies show a strong association between vitamin D sufficiency and outcomes. Meta analysis of the 175 studies using the most serious outcome reported shows 54% [50‑58%] 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.
Evolution of COVID-19 clinical evidence Vitamin D p<0.0000000001 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
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%]
****
111 183,150 1,124
Randomized Controlled TrialsRCTs32% [8‑50%]
*
34% [13‑50%]
**
30% [17‑40%]
****
27 42,038 322
Calcifediol/calcitriolCalcifediol-73% [57‑83%]
****
51% [30‑66%]
***
12 8,943 150
Mortality68% [39‑84%]
***
45% [31‑57%]
****
37% [28‑44%]
****
62 62,281 595
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, and 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 51 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 Regalia 33% 0.67 [0.45-1.01] varies cases case control 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 Wang (RCT) 23% 0.77 [0.52-1.15] 400,000IU progression 99 (n) 103 (n) Aweimer 21% 0.79 [0.49-1.29] death 7/12 101/137 Intubated patients Baralić 67% 0.33 [0.13-0.86] n/a death 7/31 11/21 Tau​2 = 0.08, I​2 = 89.4%, p < 0.0001 Prophylaxis 31% 0.69 [0.62-0.77] 914/59,976 3,684/70,646 31% improvement All studies 37% 0.63 [0.58-0.69] 1,315/65,595 6,713/117,555 37% improvement All 111 vitamin D COVID-19 treatment studies c19early.org/d Jun 2023 Tau​2 = 0.09, I​2 = 88.9%, 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 Regalia 33% case 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 Wang (RCT) 23% progression Aweimer 21% death Intubated patients Baralić 67% death Tau​2 = 0.08, I​2 = 89.4%, p < 0.0001 Prophylaxis 31% 31% improvement All studies 37% 37% improvement All 111 vitamin D COVID-19 treatment studies c19early.org/d Jun 2023 Tau​2 = 0.09, I​2 = 88.9%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors vitamin D Favors control
B
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C
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D
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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 is a steroid hormone that helps regulate the immune system by binding to specific receptors and activating genes involved in immune defense. It increases the production of antimicrobial proteins, like cathelicidin and defensins, which fight a variety of pathogens, including bacteria, viruses, and fungi. Vitamin D supports the immune system by boosting our natural defenses and promoting healthy cell connections. It helps clear respiratory pathogens through processes like apoptosis and autophagy and regulates toll-like receptors, which play a key role in immunity. Vitamin D also aids in immune cell maturation, balances inflammation, and reduces the production of proinflammatory cytokines. Vitamin D has been shown to downregulate angiotensin-converting enzyme-2 (ACE-2) receptors, which play a role in COVID-19 infection. By suppressing the production of ACE-2 and related molecules, vitamin D increases antioxidant and anti-inflammatory effects, enhances antimicrobial defenses, reduces cytokine storms, and promotes a protective immune response, all of which help decrease the severity of the disease.
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, Beheshti, Chodick, D'Avolio, Desai, di Filippo, Ersöz, Hosseini (B), Jabbar, Kerget, Kumar, Latifi-Pupovci, Mansour, Mardani, Morad, Nicolescu, Pop-Kostova, Qu, Ranjbar, Rathod, Saeed, Saeed (B), 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].
4 In Vitro studies support the efficacy of vitamin D [DiGuilio, Mok, Pickard, Rybakovsky].
An In Vivo animal study supports the efficacy of vitamin D [Fernandes de Souza].
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
****
111 183,150 1,124
After exclusions39% [33‑44%] p < 0.0001
****
83 160,766 862
Peer-reviewed studiesPeer-reviewed37% [31‑42%] p < 0.0001
****
104 181,330 1,053
Randomized Controlled TrialsRCTs30% [17‑40%] p < 0.0001
****
27 42,038 322
RCTs after exclusionsRCTs w/exc.31% [17‑43%] p = 0.00016
***
20 40,943 246
Cholecalciferol35% [29‑41%] p < 0.0001
****
99 174,207 974
Calcifediol/calcitriolCalcifediol51% [30‑66%] p = 0.00011
***
12 8,943 150
Mortality37% [28‑44%] p < 0.0001
****
62 62,281 595
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
Cases16% [7‑23%] p = 0.0007
***
27 134,493 317
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
Sufficiency54% [50‑58%] p < 0.0001
****
175 228,791 1,521
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%]
****
29% [21‑36%]
****
Peer-reviewed studiesPeer-reviewed57% [36‑71%]
****
45% [32‑56%]
****
32% [24‑39%]
****
Randomized Controlled TrialsRCTs32% [8‑50%]
*
34% [13‑50%]
**
23% [-12‑47%]
RCTs after exclusionsRCTs w/exc.65% [-65‑92%]34% [16‑47%]
***
23% [-12‑47%]
Cholecalciferol60% [40‑74%]
****
41% [27‑52%]
****
30% [22‑38%]
****
Calcifediol/calcitriolCalcifediol-73% [57‑83%]
****
36% [13‑54%]
**
Mortality68% [39‑84%]
***
45% [31‑57%]
****
23% [9‑34%]
**
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--16% [7‑23%]
***
Viral52% [24‑70%]
**
53% [8‑76%]
*
-
RCT mortality-31% [6‑50%]
*
-
RCT hospitalizationRCT hosp.-29% [10‑44%]
**
-26% [-92‑17%]
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Figure 5. Results by treatment stage.
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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.
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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.
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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.
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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.
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Figure 10. Random effects meta-analysis for treatment mortality results only.
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Figure 11. Random effects meta-analysis for treatment mechanical ventilation results only.
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Figure 12. Random effects meta-analysis for treatment ICU admission results only.
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Figure 13. Random effects meta-analysis for treatment hospitalization results only.
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Figure 14. Random effects meta-analysis for treatment COVID-19 case results only.
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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.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, 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 51 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, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is 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.
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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.
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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.
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Figure 18. Random effects meta-analysis for RCT mortality results.
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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.
[Aweimer], 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.