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Vitamin D for COVID-19: real-time meta analysis of 101 treatment and 141 sufficiency studies
https://c19early.org/dmeta.html
 
0 0.5 1 1.5+ All studies 36% 101 181,970 Improvement, Studies, Patients Relative Risk Mortality 37% 59 61,928 Ventilation 26% 17 7,852 ICU admission 49% 24 40,043 Hospitalization 18% 20 85,571 Cases 12% 23 133,877 RCTs 36% 24 41,634 Peer-reviewed 36% 94 177,270 Sufficiency 53% 141 166,860 Cholecalciferol 35% 91 173,493 Calcifediol 52% 10 8,477 Prophylaxis 29% 51 129,699 Early 65% 9 43,482 Late 47% 41 8,789 Vitamin D for COVID-19 c19early.org/d Dec 2022 Favorsvitamin D Favorscontrol after exclusions
Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 50 studies from 47 independent teams in 20 different countries show statistically significant improvements in isolation (36 for the most serious outcome).
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 65% [43‑79%] and 36% [30‑42%] improvement for early treatment and for all studies. Results are similar after restriction to 94 peer-reviewed studies: 62% [39‑76%] and 36% [30‑42%], and for the 59 mortality results: 68% [39‑84%] and 37% [28‑44%].
Acute treatment (early 65% [43‑79%], late 47% [34‑58%]) shows greater efficacy than chronic prophylaxis (29% [21‑36%]).
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 42% [28‑53%].
0 0.5 1 1.5+ All studies 36% 101 181,970 Improvement, Studies, Patients Relative Risk Mortality 37% 59 61,928 Ventilation 26% 17 7,852 ICU admission 49% 24 40,043 Hospitalization 18% 20 85,571 Cases 12% 23 133,877 RCTs 36% 24 41,634 Peer-reviewed 36% 94 177,270 Sufficiency 53% 141 166,860 Cholecalciferol 35% 91 173,493 Calcifediol 52% 10 8,477 Prophylaxis 29% 51 129,699 Early 65% 9 43,482 Late 47% 41 8,789 Vitamin D for COVID-19 c19early.org/d Dec 2022 Favorsvitamin D Favorscontrol after exclusions
Sufficiency studies show a strong association between vitamin D sufficiency and outcomes. Meta analysis of the 141 studies using the most serious outcome reported shows 53% [49‑58%] 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 [D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu], showing significant improvements for cases, severity, mortality, mechanical ventilation, and ICU admission.
ImprovementStudies AuthorsPatients
Treatment RCTs 36% [17‑50%] 24 284 41,634
Treatment studies 36% [30‑42%] 101 1,013 181,970
Cholecalciferol treatment 35% [28‑41%] 91 891 173,493
Calcifediol/calcitriol treatment 52% [26‑69%] 10 122 8,477
Treatment mortality 37% [28‑44%] 59 552 61,928
Sufficiency studies 53% [49‑58%] 141 1,245 166,860
Highlights
Vitamin D reduces risk for COVID-19 with very high confidence for mortality, ICU admission, hospitalization, recovery, viral clearance, and in pooled analysis, high confidence for cases, 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 47 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 [0.00-1.06] 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 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) Tau​2 = 0.28, I​2 = 62.9%, p < 0.0001 Early treatment 65% 0.35 [0.21-0.57] 77/1,995 2,027/41,487 65% 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 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 Bishop (DB RCT) 34% 0.66 [0.23-1.92] 1020μg (c) no recov. 5/65 8/69 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 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 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 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 Bychinin (DB RCT) 27% 0.73 [0.47-1.14] 80,000IU death 19/52 27/54 ICU patients Tau​2 = 0.28, I​2 = 81.9%, p < 0.0001 Late treatment 47% 0.53 [0.42-0.66] 279/3,494 939/5,295 47% 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 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 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 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 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 Tau​2 = 0.08, I​2 = 90.3%, p < 0.0001 Prophylaxis 29% 0.71 [0.64-0.79] 841/59,529 3,520/70,170 29% improvement All studies 36% 0.64 [0.58-0.70] 1,197/65,018 6,486/116,952 36% improvement All 101 vitamin D COVID-19 treatment studies c19early.org/d Dec 2022 Tau​2 = 0.09, I​2 = 89.6%, 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 Khan (RCT) 33% recovery CT​1 Hunt 47% death Said (RCT) 42% recovery Tau​2 = 0.28, I​2 = 62.9%, p < 0.0001 Early treatment 65% 65% improvement Tan 80% oxygen therapy CT​1 Krishnan 19% death Castillo (RCT) 85% death 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 Bishop (DB RCT) 34% recovery Cannata-A.. (RCT) -44% death Zangeneh (ICU) -26% death ICU patients Fiore 93% death Mariani (DB RCT) -124% death Baykal 22% death 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 Bychinin (DB RCT) 27% death ICU patients Tau​2 = 0.28, I​2 = 81.9%, p < 0.0001 Late treatment 47% 47% 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 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 Jolliffe (RCT) -95% ventilation Parant 50% death Villasi.. (DB RCT) 67% hospitalization Jabeen 89% symp. case 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 Tau​2 = 0.08, I​2 = 90.3%, p < 0.0001 Prophylaxis 29% 29% improvement All studies 36% 36% improvement All 101 vitamin D COVID-19 treatment studies c19early.org/d Dec 2022 Tau​2 = 0.09, I​2 = 89.6%, 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.
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). A few studies show the average vitamin D level for patients in different groups [Al-Daghri, Alarslan, Azadeh, Chodick, D'Avolio, Desai, Ersöz, Jabbar, Kerget, Latifi-Pupovci, Mansour, Mardani, Nicolescu, Ranjbar, 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
5 In Silico studies support the efficacy of vitamin D [Al-Mazaideh, Chellasamy, 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.
Improvement Studies Patients Authors
All studies36% [30‑42%] p < 0.0001101 181,970 1,013
After exclusions38% [32‑44%] p < 0.000176 159,835 783
Peer-reviewed studiesPeer-reviewed36% [30‑42%] p < 0.000194 177,270 951
Randomized Controlled TrialsRCTs36% [17‑50%] p = 0.0005624 41,634 284
RCTs after exclusionsRCTs w/exc.40% [22‑53%] p = 0.0001319 40,639 221
Mortality37% [28‑44%] p < 0.000159 61,928 552
VentilationVent.26% [-2‑46%] p = 0.06817 7,852 184
ICU admissionICU49% [32‑62%] p < 0.000124 40,043 273
HospitalizationHosp.18% [5‑28%] p = 0.007220 85,571 199
Recovery42% [24‑55%] p < 0.00018 495 69
Cases12% [3‑21%] p = 0.01123 133,877 267
Viral51% [28‑66%] p = 0.000283 150 20
RCT mortality36% [5‑57%] p = 0.02714 1,797 165
RCT hospitalizationRCT hosp.21% [-4‑40%] p = 0.0878 39,713 107
Sufficiency53% [49‑58%] p < 0.0001141 166,860 1,245
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.
Early treatment Late treatment Prophylaxis
All studies65% [43‑79%] 947% [34‑58%] 4129% [21‑36%] 51
After exclusions62% [39‑76%] 860% [46‑71%] 2826% [18‑34%] 40
Peer-reviewed studiesPeer-reviewed62% [39‑76%] 846% [33‑57%] 4029% [20‑37%] 46
Randomized Controlled TrialsRCTs38% [-6‑64%] 338% [14‑55%] 1722% [-129‑73%] 4
RCTs after exclusionsRCTs w/exc.38% [-6‑64%] 341% [21‑57%] 1222% [-129‑73%] 4
Mortality68% [39‑84%] 547% [32‑59%] 3521% [6‑33%] 19
VentilationVent.97% [56‑100%] 117% [-14‑40%] 1238% [-3‑63%] 4
ICU admissionICU-52% [30‑67%] 1846% [22‑63%] 6
HospitalizationHosp.99% [84‑100%] 122% [6‑35%] 613% [-4‑27%] 13
Recovery37% [-9‑63%] 343% [24‑58%] 5-
Cases--12% [3‑21%] 23
Viral50% [20‑68%] 253% [8‑76%] 1-
RCT mortality-36% [5‑57%] 14-
RCT hospitalizationRCT hosp.-29% [10‑44%] 5-26% [-92‑17%] 3
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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 peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 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.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for vitamin 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 well-known treatments such as vitamin D. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
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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.
[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.
[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.
[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.
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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 47 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 47 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage, 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.
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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, 132 of 141 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].
85 of 101 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 (early 65% [43‑79%], late 47% [34‑58%]) shows greater efficacy than chronic prophylaxis (29% [21‑36%]). 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. If correct, this may suggest more judicious use of supplementation. 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].
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.
53% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 42% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 33% improvement, compared to 61% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 23 shows a scatter plot of results for prospective and retrospective treatment studies.
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Figure 23. Prospective vs. retrospective studies.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 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 [D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu], showing significant improvements for cases, severity, mortality, mechanical ventilation, and ICU admission.
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.
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 - 35 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 4,375 0.2% (9) 99.5% 0.1% (3) 97.2%
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. 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. Vladimir Zelenko USA 2,200 0.5% (12) 98.6% 0.1% (2) 96.3%
Mean improvement with early treatment protocols 219,653 HospitalizationHosp. 94.7% MortalityDeath 94.3%
Conclusion
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 65% [43‑79%] and 36% [30‑42%] improvement for early treatment and for all studies. Results are similar after restriction to 94 peer-reviewed studies: 62% [39‑76%] and 36% [30‑42%], and for the 59 mortality results: 68% [39‑84%] and 37% [28‑44%].
Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 50 studies from 47 independent teams in 20 different countries show statistically significant improvements in isolation (36 for the most serious outcome).
Acute treatment (early 65% [43‑79%], late 47% [34‑58%]) shows greater efficacy than chronic prophylaxis (29% [21‑36%]).
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 42% [28‑53%].
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.
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 (B)].
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 [Nogués].
2/10: We added [Karonova (C)].
2/9: We added [Karahan].
2/7: We added [Li (B)].
2/5: We added [Yılmaz].
1/31: We added [Demir].
1/30: We added [Ma (B)].
1/22: We added [Giannini].
1/21: We added [Bennouar].
1/19: We added [Amin].
1/18: We added [Vasheghani (B)].
1/16: We moved the analysis with exclusions to the main text, and added additional commentary.
1/15: We added the effect measured for each study in the forest plots.
1/10: We added [Angelidi].
1/7: We added direct links to the study details in the chronological plots.
1/5: We added direct links to the study details in the forest plots.
1/2/2021: We added dosage information and we added the number of patients to the forest plots.
12/31: We added additional details about the studies in the appendix.
12/28: We added [Jevalikar].
12/27: We added the total number of authors and patients.
12/23: We added [Cangiano].
12/17/2020: Initial revision.
Appendix 1. Methods and Data
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19vitamind.com. Search terms were vitamin D, cholecalciferol, and calcitriol, and COVID-19 or SARS-CoV-2. Automated searches are performed every hour with notification of new matches. All studies that report a result for vitamin D treatment of COVID-19 patients compared to a control group, and all studies comparing COVID-19 outcomes in groups of patients with low and high vitamin D levels are included. A few studies only provide results as a function of change in vitamin D levels, which may not be indicative of results for deficiency/insufficiency versus sufficiency (if levels are already sufficient then further increase may be less useful). This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome. Forest plots show simplified dosages 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 full dosage details see below.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients).
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/dmeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Abdollahi], 12/12/2020, retrospective, Iran, peer-reviewed, 7 authors. risk of case, 53.9% lower, RR 0.46, p = 0.001, high D levels 108, low D levels 294, >30ng/ml.
[Abrishami], 10/30/2020, retrospective, Iran, peer-reviewed, mean age 55.2, 7 authors. risk of death, 75.9% lower, RR 0.24, p = 0.04, high D levels (≥25ng/mL) 3 of 47 (6.4%), low D levels (<25ng/mL) 9 of 26 (34.6%), NNT 3.5, adjusted per study, inverted to make RR<1 favor high D levels (≥25ng/mL), Cox model 2.
[Afaghi], 10/12/2021, retrospective, Iran, peer-reviewed, 7 authors. risk of death, 55.0% lower, RR 0.45, p = 0.002, high D levels 97 of 537 (18.1%), low D levels 51 of 109 (46.8%), NNT 3.5, adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >20ng/mL, multivariate.
risk of mechanical ventilation, 55.9% lower, RR 0.44, p < 0.001, high D levels 89 of 537 (16.6%), low D levels 41 of 109 (37.6%), NNT 4.8, >20ng/mL, unadjusted.
risk of ICU admission, 34.1% lower, RR 0.66, p < 0.001, high D levels 211 of 537 (39.3%), low D levels 65 of 109 (59.6%), NNT 4.9, >20ng/mL, unadjusted.
[Al-Salman], 7/29/2021, retrospective, Bahrain, peer-reviewed, 5 authors. risk of ICU admission, 44.4% lower, OR 0.56, p = 0.03, high D levels (≥50nmol/L) 113, low D levels (<50nmol/L) 337, inverted to make OR<1 favor high D levels (≥50nmol/L), multinomial regression, RR approximated with OR.
[Alguwaihes], 12/5/2020, retrospective, Saudi Arabia, peer-reviewed, 10 authors. risk of death, 85.7% lower, RR 0.14, p = 0.007, high D levels 111, low D levels 328, inverted to make RR<1 favor high D levels, >12.5 nmol/L.
[Alpcan], 8/10/2021, retrospective, Turkey, peer-reviewed, 3 authors. risk of case, 73.0% lower, OR 0.27, p < 0.001, high D levels 42 of 75 (56.0%) cases, 66 of 80 (82.5%) controls, NNT 3.2, case control OR, >20ng/mL.
[AlSafar], 5/19/2021, retrospective, United Arab Emirates, peer-reviewed, 8 authors. risk of death, 59.3% lower, RR 0.41, p = 0.048, high D levels 16 of 337 (4.7%), low D levels 10 of 127 (7.9%), adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >=12ng/mL.
risk of severe case, 33.2% lower, RR 0.67, p = 0.005, high D levels 337, low D levels 127, adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >=12ng/mL.
[Alzahrani], 6/23/2022, retrospective, Saudi Arabia, peer-reviewed, mean age 54.3, 9 authors, study period March 2020 - July 2021. risk of death, 42.5% lower, OR 0.57, p = 0.46, high D levels (≥25ng/mL) 179, low D levels (<25ng/mL) 78, adjusted per study, inverted to make OR<1 favor high D levels (≥25ng/mL), multivariable, RR approximated with OR.
risk of ICU admission, 7.4% lower, OR 0.93, p = 0.80, high D levels (≥25ng/mL) 179, low D levels (<25ng/mL) 78, adjusted per study, inverted to make OR<1 favor high D levels (≥25ng/mL), multivariable, RR approximated with OR.
[Amin], 1/7/2021, retrospective, population-based cohort, United Kingdom, peer-reviewed, 2 authors. COVID-19 severity, 32.3% higher, RR 1.32, p = 0.20, high D levels 140,898, low D levels 35,079, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >=50nmol/L vs. <25nmol/L, MR Egger, baseline risk approximated with overall risk.
risk of case, 7.6% higher, RR 1.08, p = 0.14, high D levels 140,898, low D levels 35,079, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >=50nmol/L vs. <25nmol/L, MR Egger, baseline risk approximated with overall risk.
[Angelidi], 1/9/2021, retrospective, USA, peer-reviewed, 8 authors. risk of death, 88.0% lower, RR 0.12, p = 0.01, high D levels 6 of 65 (9.2%), low D levels 20 of 79 (25.3%), NNT 6.2, adjusted per study, >30ng/mL, supplementary table 2, multivariable logistic regression model 5.
[Anjum], 7/31/2020, prospective, Pakistan, peer-reviewed, 6 authors, study period March 2020 - June 2020, excluded in exclusion analyses: unadjusted results with no group details. risk of death, 62.5% lower, RR 0.38, p = 0.02, high D levels (≥25nmol/L) 8 of 80 (10.0%), low D levels (<25nmol/L) 16 of 60 (26.7%), NNT 6.0.
[Ansari], 12/31/2020, prospective, Pakistan, peer-reviewed, 6 authors, study period 1 March, 2020 - 31 August, 2020, excluded in exclusion analyses: unadjusted results with no group details. risk of death, 86.0% lower, RR 0.14, p = 0.02, high D levels (≥25nmol/L) 2 of 68 (2.9%), low D levels (<25nmol/L) 12 of 57 (21.1%), NNT 5.5.
[Asgari], 11/21/2021, retrospective, Iran, peer-reviewed, 6 authors, study period 21 May, 2020 - 4 September, 2020. risk of death, 72.5% lower, OR 0.27, p = 0.03, cutoff 25ng/mL, adjusted per study, inverted to make OR<1 favor high D levels (≥25ng/mL), RR approximated with OR.
risk of progression, 65.6% lower, OR 0.34, p = 0.02, cutoff 25ng/mL, adjusted per study, inverted to make OR<1 favor high D levels (≥25ng/mL), RR approximated with OR.
[Asghar], 11/10/2021, retrospective, Pakistan, peer-reviewed, 8 authors. risk of death, 53.1% lower, HR 0.47, p = 0.046, high D levels (≥10ng/mL) 73, low D levels (<10ng/mL) 18, inverted to make HR<1 favor high D levels (≥10ng/mL), multivariate Cox regression.
risk of mechanical ventilation, 19.4% lower, HR 0.81, p = 0.32, high D levels (≥10ng/mL) 5 of 73 (6.8%), low D levels (<10ng/mL) 6 of 18 (33.3%), NNT 3.8, adjusted per study, inverted to make HR<1 favor high D levels (≥10ng/mL), multivariate Cox regression.
risk of ICU admission, 32.9% lower, HR 0.67, p = 0.54, high D levels (≥10ng/mL) 73, low D levels (<10ng/mL) 18, inverted to make HR<1 favor high D levels (≥10ng/mL), multivariate Cox regression.
[Atanasovska], 11/2/2021, retrospective, North Macedonia, peer-reviewed, 8 authors. risk of death, 40.7% lower, RR 0.59, p = 0.68, high D levels (≥30ng/mL) 2 of 9 (22.2%), low D levels (<30ng/mL) 9 of 24 (37.5%), NNT 6.5.
risk of severe case, 59.0% lower, RR 0.41, p = 0.13, high D levels (≥30ng/mL) 2 of 9 (22.2%), low D levels (<30ng/mL) 13 of 24 (54.2%), NNT 3.1.
[Baktash], 8/27/2020, prospective, United Kingdom, peer-reviewed, 8 authors. risk of death, 28.6% lower, RR 0.71, p = 0.50, high D levels 4 of 31 (12.9%), low D levels 6 of 39 (15.4%), adjusted per study, inverted to make RR<1 favor high D levels, >30nmol/L.
[Barassi], 1/25/2021, retrospective, Italy, peer-reviewed, 8 authors. risk of death, 64.9% lower, RR 0.35, p = 0.44, high D levels 1 of 31 (3.2%), low D levels 8 of 87 (9.2%), NNT 17, >20ng/mL.
risk of mechanical ventilation, 64.9% lower, RR 0.35, p = 0.15, high D levels 2 of 31 (6.5%), low D levels 16 of 87 (18.4%), NNT 8.4, >20ng/mL.
[Barrett], 8/9/2022, prospective, Ireland, peer-reviewed, mean age 56.0, 19 authors, study period March 2020 - April 2021. risk of death, 78.4% lower, OR 0.22, p = 0.006, high D levels (≥30nmol/L) 144, low D levels (<30nmol/L) 88, adjusted per study, inverted to make OR<1 favor high D levels (≥30nmol/L), multivariable, RR approximated with OR.
risk of ICU admission, 15.3% lower, OR 0.85, p = 0.63, high D levels (≥30nmol/L) 144, low D levels (<30nmol/L) 88, adjusted per study, inverted to make OR<1 favor high D levels (≥30nmol/L), multivariable, RR approximated with OR.
risk of progression, 52.6% lower, OR 0.47, p = 0.12, high D levels (≥30nmol/L) 144, low D levels (<30nmol/L) 88, adjusted per study, inverted to make OR<1 favor high D levels (≥30nmol/L), extended oxygen requirement, multivariable, RR approximated with OR.
[Basaran], 2/12/2021, retrospective, Turkey, peer-reviewed, 6 authors. risk of severe case, 68.6% lower, RR 0.31, p = 0.005, high D levels 82 of 119 (68.9%), low D levels 80 of 85 (94.1%), NNT 4.0, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >10μg/L, per standard deviation increase in levels.
[Baykal], 5/30/2022, retrospective, Turkey, peer-reviewed, 2 authors, study period 1 April, 2020 - 1 March, 2021, dosage 300,000IU single dose. risk of death, 8.0% higher, RR 1.08, p = 0.80, high D levels (≥20ng/mL) 11 of 20 (55.0%), low D levels (<20ng/mL) 28 of 55 (50.9%), outcome based on serum levels.
risk of ICU admission, 4.8% lower, RR 0.95, p = 1.00, high D levels (≥20ng/mL) 9 of 20 (45.0%), low D levels (<20ng/mL) 26 of 55 (47.3%), NNT 44, outcome based on serum levels.
risk of progression, 6.1% lower, RR 0.94, p = 0.77, high D levels (≥20ng/mL) 14 of 20 (70.0%), low D levels (<20ng/mL) 41 of 55 (74.5%), NNT 22, severe/critical, outcome based on serum levels.
[Bayramoğlu], 3/31/2021, retrospective, Turkey, peer-reviewed, 7 authors. risk of moderate/severe case, 69.5% lower, RR 0.30, p = 0.03, high D levels 10 of 60 (16.7%), low D levels 24 of 43 (55.8%), NNT 2.6, adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >12 ng/mL, multivariate logistic regression.
[Bennouar], 1/12/2021, prospective, Algeria, peer-reviewed, 4 authors. risk of death, 85.5% lower, RR 0.14, p = 0.002, high D levels 4 of 30 (13.3%), low D levels 15 of 32 (46.9%), NNT 3.0, adjusted per study, inverted to make RR<1 favor high D levels, >30μg/l vs. <10μg/l, proportional Cox regression.
risk of death, 63.0% lower, RR 0.37, p = 0.10, high D levels 4 of 30 (13.3%), low D levels 14 of 35 (40.0%), NNT 3.7, adjusted per study, inverted to make RR<1 favor high D levels, >30μg/l vs. 10-19μg/l, proportional Cox regression.
risk of death, 23.1% lower, RR 0.77, p = 0.73, high D levels 4 of 30 (13.3%), low D levels 4 of 23 (17.4%), NNT 25, adjusted per study, inverted to make RR<1 favor high D levels, >30μg/l vs. 20-29μg/l, proportional Cox regression.
[Bianconi], 7/1/2021, prospective, Italy, peer-reviewed, 12 authors. risk of death, 17.5% lower, HR 0.82, p = 0.58, high D levels (≥12ng/ml) 94, low D levels (<12ng/ml) 106, model 3, Table S2, Cox proportional hazards.
risk of death, 13.9% lower, HR 0.86, p = 0.73, high D levels (≥20ng/ml) 40, low D levels (<20ng/ml) 160, model 3, Table S2, Cox proportional hazards.
risk of death/ICU, 15.9% lower, HR 0.84, p = 0.53, high D levels (≥12ng/ml) 94, low D levels (<12ng/ml) 106, model 3, Cox proportional hazards.
risk of death/ICU, 10.9% lower, HR 0.89, p = 0.73, high D levels (≥20ng/ml) 40, low D levels (<20ng/ml) 160, model 3, Cox proportional hazards.
[Bogliolo], 7/5/2022, prospective, Italy, peer-reviewed, median age 73.0, 16 authors, study period March 2020 - August 2020. risk of death, 15.3% lower, HR 0.85, p = 0.29, cutoff 20ng/mL, inverted to make HR<1 favor high D levels (≥20ng/mL).
[Bushnaq], 2/8/2022, retrospective, Saudi Arabia, peer-reviewed, 7 authors, excluded in exclusion analyses: unadjusted results with no group details. risk of mechanical ventilation, 32.1% lower, RR 0.68, p = 0.27, high D levels (≥20ng/mL) 10 of 53 (18.9%), low D levels (<20ng/mL) 40 of 144 (27.8%), NNT 11, unadjusted.
risk of ICU admission, 3.9% lower, RR 0.96, p = 0.87, high D levels (≥20ng/mL) 23 of 53 (43.4%), low D levels (<20ng/mL) 65 of 144 (45.1%), NNT 57, unadjusted.
[Bychinin], 5/7/2021, retrospective, Russia, peer-reviewed, 5 authors, excluded in exclusion analyses: excessive unadjusted differences between groups. risk of death, 36.2% lower, RR 0.64, p = 0.03, high D levels (≥10ng/mL) 16 of 38 (42.1%), low D levels (<10ng/mL) 31 of 47 (66.0%), NNT 4.2.
[Campi], 6/14/2021, prospective, Italy, peer-reviewed, 21 authors, dosage not specified. risk of death for severe patients, 24.3% lower, RR 0.76, p = 0.53, high D levels (≥20ng/ml) 6 of 39 (15.4%), low D levels (<20ng/ml) 13 of 64 (20.3%), NNT 20, hospitalized patients, outcome based on serum levels.
risk of ICU for severe patients, 53.1% lower, RR 0.47, p < 0.001, high D levels (≥20ng/ml) 12 of 39 (30.8%), low D levels (<20ng/ml) 42 of 64 (65.6%), NNT 2.9, hospitalized patients, outcome based on serum levels.
[Cannata-Andía], 2/18/2022, prospective, multiple countries, peer-reviewed, median age 59.0, 22 authors, dosage 100,000IU single dose, trial NCT04552951 (history), excluded in exclusion analyses: very late stage study using cholecalciferol instead of calcifediol or calcitriol. risk of death, 117.0% higher, RR 2.17, p = 0.20, high D levels 87, low D levels 96, >25 vs. ≤10 ng/mL, adjusted by demographics, comorbidities, and laboratory parameters, outcome based on serum levels.
risk of ICU admission, 65.0% lower, RR 0.35, p = 0.04, high D levels 87, low D levels 96, >25 vs. ≤10 ng/mL, adjusted by demographics, comorbidities, and laboratory parameters, outcome based on serum levels.
risk of progression, 79.0% lower, RR 0.21, p = 0.003, high D levels 87, low D levels 96, pulmonary involvment at admission, >25 vs. ≤10 ng/mL, adjusted by demographics, comorbidities, and laboratory parameters, outcome based on serum levels.
[Carpagnano], 8/9/2020, retrospective, Italy, peer-reviewed, 10 authors. risk of death at day 26, 70.6% lower, RR 0.29, p = 0.0499, high D levels 5 of 34 (14.7%), low D levels 4 of 8 (50.0%), NNT 2.8, >30 ng/mL.
risk of death at day 10, 90.0% lower, RR 0.10, p = 0.02, high D levels 2 of 34 (5.9%), low D levels 4 of 8 (50.0%), NNT 2.3, adjusted per study, >30 ng/mL.
[Cereda], 11/1/2020, prospective, Italy, peer-reviewed, 13 authors. risk of death, 120.0% higher, RR 2.20, p = 0.04, high D levels 10 of 30 (33.3%), low D levels 24 of 99 (24.2%), inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >20ng/mL.
risk of ICU admission, 86.7% lower, RR 0.13, p = 0.59, high D levels 0 of 30 (0.0%), low D levels 5 of 99 (5.1%), NNT 20, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
[Charkowick], 5/5/2022, retrospective, USA, peer-reviewed, 10 authors, study period 1 January, 2020 - 5 February, 2021. risk of death, 73.4% lower, OR 0.27, p = 0.02, high D levels 140, low D levels 68, adjusted per study, inverted to make OR<1 favor high D levels, multivariable, RR approximated with OR.
risk of ICU admission, 67.2% lower, OR 0.33, p = 0.001, high D levels 140, low D levels 68, adjusted per study, inverted to make OR<1 favor high D levels, multivariable, RR approximated with OR.
[Charla], 7/13/2022, retrospective, India, preprint, 8 authors, study period 1 April, 2020 - 30 April, 2021, excluded in exclusion analyses: excessive unadjusted differences between groups. risk of death, 10.7% lower, RR 0.89, p = 0.74, high D levels (≥20ng/ml) 24 of 91 (26.4%), low D levels (<20ng/ml) 26 of 88 (29.5%), NNT 32.
[Charoenngam], 3/8/2021, retrospective, USA, peer-reviewed, 6 authors. risk of death, 34.1% lower, RR 0.66, p = 0.26, high D levels 12 of 100 (12.0%), low D levels 29 of 187 (15.5%), adjusted per study, odds ratio converted to relative risk, >=30ng/mL.
risk of mechanical ventilation, 37.2% lower, RR 0.63, p = 0.17, high D levels 14 of 100 (14.0%), low D levels 34 of 187 (18.2%), adjusted per study, odds ratio converted to relative risk, >=30ng/mL.
risk of ICU admission, 23.1% lower, RR 0.77, p = 0.28, high D levels 25 of 100 (25.0%), low D levels 56 of 187 (29.9%), NNT 20, adjusted per study, odds ratio converted to relative risk, >=30ng/mL.
risk of death, 58.1% lower, RR 0.42, p = 0.05, high D levels 7 of 57 (12.3%), low D levels 25 of 79 (31.6%), NNT 5.2, adjusted per study, odds ratio converted to relative risk, >65 years old, >=30ng/mL.
[Cozier], 7/27/2021, prospective, USA, peer-reviewed, 6 authors. risk of case, 38.6% lower, RR 0.61, p = 0.04, high D levels 94 of 1,601 (5.9%), low D levels 33 of 373 (8.8%), NNT 34, adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >20ng/mL, multivariable.
[Dana], 8/11/2022, retrospective, Iran, peer-reviewed, 16 authors, study period March 2020 - November 2020. risk of death, 33.1% lower, RR 0.67, p = 0.29, high D levels (≥10ng/mL) 49 of 376 (13.0%), low D levels (<10ng/mL) 8 of 46 (17.4%), NNT 23, adjusted per study, inverted to make RR<1 favor high D levels (≥10ng/mL), odds ratio converted to relative risk, sufficiency vs. severe deficiency, multivariable.
risk of death, 15.7% lower, RR 0.84, p = 0.44, high D levels (≥20ng/mL) 49 of 376 (13.0%), low D levels (<20ng/mL) 30 of 197 (15.2%), NNT 46, adjusted per study, inverted to make RR<1 favor high D levels (≥20ng/mL), odds ratio converted to relative risk, sufficiency vs. deficiency, multivariable.
risk of severe case, no change, RR 1.00, p = 1.00, high D levels (≥10ng/mL) 59 of 376 (15.7%), low D levels (<10ng/mL) 7 of 46 (15.2%), adjusted per study, inverted to make RR<1 favor high D levels (≥10ng/mL), odds ratio converted to relative risk, sufficiency vs. severe deficiency, multivariable.
risk of severe case, 11.6% lower, RR 0.88, p = 0.45, high D levels (≥20ng/mL) 59 of 376 (15.7%), low D levels (<20ng/mL) 35 of 197 (17.8%), NNT 48, adjusted per study, inverted to make RR<1 favor high D levels (≥20ng/mL), odds ratio converted to relative risk, sufficiency vs. deficiency, multivariable.
[Davoudi], 5/18/2021, retrospective, Iran, peer-reviewed, 11 authors, study period February 2020 - March 2020, excluded in exclusion analyses: excessive unadjusted differences between groups. risk of death, 12.3% higher, RR 1.12, p = 1.00, high D levels (≥30ng/mL) 2 of 57 (3.5%), low D levels (<30ng/mL) 3 of 96 (3.1%).
risk of mechanical ventilation, 15.8% lower, RR 0.84, p = 1.00, high D levels (≥30ng/mL) 1 of 57 (1.8%), low D levels (<30ng/mL) 2 of 96 (2.1%), NNT 304.
risk of ICU admission, 27.8% lower, RR 0.72, p = 0.74, high D levels (≥30ng/mL) 3 of 57 (5.3%), low D levels (<30ng/mL) 7 of 96 (7.3%), NNT 49.
risk of severe case, 68.4% higher, RR 1.68, p = 0.30, high D levels (≥30ng/mL) 9 of 57 (15.8%), low D levels (<30ng/mL) 9 of 96 (9.4%).
[De Smet], 11/25/2020, retrospective, Belgium, peer-reviewed, 5 authors. risk of death, 70.1% lower, RR 0.30, p = 0.02, high D levels 7 of 77 (9.1%), low D levels 20 of 109 (18.3%), adjusted per study, odds ratio converted to relative risk, >20ng/mL.
[Demir], 1/29/2021, retrospective, Turkey, peer-reviewed, 3 authors. risk of severe case, 89.3% lower, RR 0.11, p < 0.001, high D levels 13, low D levels 99, ratio of the mean number of affected lung segments, >30ng/ml vs. <=10ng/mL.
hospitalization time, 87.1% lower, relative time 0.13, p < 0.001, high D levels 13, low D levels 99, >30ng/ml vs. <=10ng/mL.
risk of case, 24.2% lower, RR 0.76, p = 0.18, high D levels 13 of 31 (41.9%), low D levels 99 of 179 (55.3%), NNT 7.5, >30ng/ml vs. <=10ng/mL.
[Derakhshanian], 9/19/2021, retrospective, Iran, peer-reviewed, 11 authors. risk of death, 44.8% lower, RR 0.55, p = 0.046, high D levels 148, low D levels 142, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, control prevalance approximated with overall prevalence.
risk of mechanical ventilation, 41.7% lower, RR 0.58, p = 0.09, high D levels 148, low D levels 142, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, control prevalance approximated with overall prevalence.
risk of ICU admission, 37.3% lower, RR 0.63, p = 0.04, high D levels 148, low D levels 142, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, control prevalance approximated with overall prevalence.
[di Filippo], 8/12/2021, retrospective, Italy, peer-reviewed, 8 authors. risk of death, 10.7% lower, RR 0.89, p = 1.00, high D levels 5 of 28 (17.9%), low D levels 12 of 60 (20.0%), NNT 47, >20ng/mL.
risk of ICU admission, 41.6% lower, RR 0.58, p = 0.22, high D levels 6 of 28 (21.4%), low D levels 22 of 60 (36.7%), NNT 6.6, >20ng/mL.
risk of severe case, 39.6% lower, RR 0.60, p = 0.04, high D levels 11 of 28 (39.3%), low D levels 39 of 60 (65.0%), NNT 3.9, >20ng/mL.
[Diaz-Curiel], 6/6/2021, retrospective, Spain, peer-reviewed, 8 authors. risk of ICU admission, 73.2% lower, RR 0.27, p = 0.02, high D levels 3 of 214 (1.4%), low D levels 91 of 1,017 (8.9%), odds ratio converted to relative risk, >30ng/mL vs. <20ng/mL.
[Doğan], 8/4/2022, prospective, Turkey, peer-reviewed, 5 authors, study period 1 July, 2021 - 30 October, 2021. risk of case, 63.7% lower, OR 0.36, p = 0.003, high D levels (≥10ng/ml) 53 of 88 (60.2%) cases, 71 of 88 (80.7%) controls, NNT 4.1, case control OR.
[Dror], 6/7/2021, retrospective, Israel, peer-reviewed, 18 authors. risk of severe or critical case, 84.8% lower, RR 0.15, p = 0.001, high D levels 109 of 120 (90.8%), low D levels 76 of 133 (57.1%), adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >40ng/mL vs. <20ng/mL, multivariable.
[Eden], 8/5/2021, retrospective, United Kingdom, peer-reviewed, 5 authors. risk of death, 63.9% lower, RR 0.36, p = 0.10, high D levels (≥25nmol/L) 3 of 26 (11.5%), low D levels (<25nmol/L) 8 of 25 (32.0%), NNT 4.9.
risk of death, 92.9% lower, RR 0.07, p = 0.18, high D levels (≥50nmol/L) 0 of 8 (0.0%), low D levels (<50nmol/L) 11 of 43 (25.6%), NNT 3.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
[Faniyi], 10/6/2020, prospective, United Kingdom, preprint, 10 authors. risk of seropositive, 28.8% lower, RR 0.71, p = 0.003, high D levels 170 of 331 (51.4%), low D levels 44 of 61 (72.1%), NNT 4.8, >30nmol/L.
[Fatemi], 11/30/2021, prospective, Iran, peer-reviewed, 5 authors, study period 1 October, 2020 - 31 May, 2021. risk of death, 42.0% lower, RR 0.58, p = 0.07, high D levels 18 of 139 (12.9%), low D levels 25 of 109 (22.9%), NNT 10, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, vitamin D measured prior to COVID-19, multivariate.
risk of death, 51.1% lower, RR 0.49, p = 0.02, high D levels 13 of 115 (11.3%), low D levels 30 of 133 (22.6%), NNT 8.9, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, vitamin D measured on admission, multivariate.
risk of severe case, 37.9% lower, RR 0.62, p = 0.007, high D levels 38 of 139 (27.3%), low D levels 48 of 109 (44.0%), NNT 6.0, vitamin D measured prior to COVID-19.
risk of severe case, 34.8% lower, RR 0.65, p = 0.02, high D levels 31 of 115 (27.0%), low D levels 55 of 133 (41.4%), NNT 6.9, vitamin D measured on admission.
[Faul], 6/30/2020, retrospective, Ireland, peer-reviewed, 9 authors. risk of mechanical ventilation, 69.0% lower, RR 0.31, p = 0.03, high D levels 4 of 21 (19.0%), low D levels 8 of 12 (66.7%), NNT 2.1, adjusted per study, >30nmol/L.
[Ferrer-Sánchez], 3/26/2022, retrospective, Spain, peer-reviewed, 7 authors. risk of ICU admission, 81.8% lower, RR 0.18, p = 1.00, high D levels (≥20ng/mL) 0 of 9 (0.0%), low D levels (<20ng/mL) 4 of 73 (5.5%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), excluded in exclusion analyses: unadjusted results with no group details.
risk of moderate/severe case, 88.7% lower, RR 0.11, p = 1.00, high D levels (≥20ng/mL) 0 of 9 (0.0%), low D levels (<20ng/mL) 7 of 73 (9.6%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), excluded in exclusion analyses: unadjusted results with no group details.
risk of case, 62.7% lower, OR 0.37, p = 0.01, cutoff 20ng/mL, adjusted per study, inverted to make OR<1 favor high D levels (≥20ng/mL), multivariable, RR approximated with OR.
[Freitas], 3/27/2021, retrospective, Portugal, preprint, 36 authors. risk of death, 41.2% lower, RR 0.59, p = 0.02, high D levels 23 of 179 (12.8%), low D levels 68 of 311 (21.9%), NNT 11, >20ng/mL.
[Galaznik], 5/28/2021, retrospective, USA, preprint, 6 authors. risk of case, 35.1% lower, OR 0.65, p = 0.01, high D levels 13,903, low D levels 2,384, adjusted per study, inverted to make OR<1 favor high D levels, breast cancer patients, logistic regression, RR approximated with OR.
risk of case, 32.4% lower, OR 0.68, p = 0.045, high D levels 13,601, low D levels 1,318, adjusted per study, inverted to make OR<1 favor high D levels, prostate cancer patients, logistic regression, RR approximated with OR.
[Gaudio], 3/27/2021, retrospective, Italy, peer-reviewed, 6 authors. risk of case, 79.3% lower, OR 0.21, p < 0.001, high D levels 27 of 50 (54.0%) cases, 85 of 100 (85.0%) controls, NNT 2.7, case control OR.
[Gavioli], 2/19/2021, retrospective, USA, peer-reviewed, 4 authors. risk of death, 4.7% higher, RR 1.05, p = 0.83, high D levels 80 of 260 (30.8%), low D levels 52 of 177 (29.4%), >20ng/ml.
risk of death, 44.8% lower, RR 0.55, p < 0.001, high D levels 102 of 376 (27.1%), low D levels 30 of 61 (49.2%), NNT 4.5, >10ng/ml.
risk of oxygen therapy, 55.2% lower, RR 0.45, p < 0.001, high D levels 127 of 260 (48.8%), low D levels 116 of 177 (65.5%), NNT 6.0, adjusted per study, inverted to make RR<1 favor high D levels, >20ng/ml, multivariate.
risk of hospitalization, 3.6% lower, RR 0.96, p = 0.41, high D levels 218 of 260 (83.8%), low D levels 154 of 177 (87.0%), NNT 32, >20ng/ml.
[Ghanei], 3/23/2022, prospective, Iran, peer-reviewed, 6 authors, study period 20 March, 2020 - 20 January, 2021. risk of case, 42.1% lower, OR 0.58, p = 0.09, high D levels (≥20ng/ml) 58 of 90 (64.4%) cases, 72 of 95 (75.8%) controls, NNT 7.4, case control OR.
[Gholi], 7/19/2022, prospective, Iran, peer-reviewed, 4 authors. risk of death, 74.7% lower, HR 0.25, p < 0.001, high D levels 157, low D levels 38, inverted to make HR<1 favor high D levels, >30ng/mL vs. <20ng/mL, model 2, day 45.
risk of death, 39.8% lower, HR 0.60, p = 0.05, high D levels 157, low D levels 38, inverted to make HR<1 favor high D levels, >30ng/mL vs. <20ng/mL, ICU mortality, model 2.
risk of mechanical ventilation, 44.9% higher, HR 1.45, p = 0.27, high D levels 157, low D levels 38, inverted to make HR<1 favor high D levels, >30ng/mL vs. <20ng/mL, model 2, day 45.
[Golabi], 8/26/2021, retrospective, Iran, peer-reviewed, 10 authors. odds of symptoms, 90.0% lower, OR 0.10, p < 0.001, high D levels 34, low D levels 10, >30ng/mL vs. <20ng/mL, GEE regression, RR approximated with OR.
odds of symptoms, 81.0% lower, OR 0.19, p = 0.006, high D levels 34, low D levels 9, 20-30ng/mL vs. <20ng/mL, GEE regression, RR approximated with OR.
risk of case, 71.7% lower, OR 0.28, p = 0.07, high D levels 34 of 44 (77.3%) cases, 36 of 39 (92.3%) controls, NNT 3.5, case control OR, >30ng/mL vs. <20ng/mL.
[González-Estevez], 7/7/2021, retrospective, Mexico, peer-reviewed, 6 authors. risk of symptomatic case, 25.0% lower, RR 0.75, p = 0.04, high D levels (≥30ng/mL) 6 of 8 (75.0%), low D levels (<30ng/mL) 32 of 32 (100.0%), NNT 4.0.
[Green], 11/7/2022, retrospective, Israel, peer-reviewed, 9 authors, study period 1 February, 2020 - 31 December, 2020. risk of case, 18.7% lower, OR 0.81, p < 0.001, cutoff 30ng/mL, adjusted per study, inverted to make OR<1 favor high D levels (≥30ng/mL), multivariable, RR approximated with OR.
[Gönen], 11/12/2021, retrospective, Turkey, peer-reviewed, 20 authors, dosage varies. risk of death, 65.8% lower, RR 0.34, p = 0.62, high D levels (≥12ng/mL) 1 of 80 (1.2%), low D levels (<12ng/mL) 3 of 82 (3.7%), NNT 42, retrospective study.
risk of ICU admission, 16.9% lower, RR 0.83, p = 1.00, high D levels (≥12ng/mL) 4 of 77 (5.2%), low D levels (<12ng/mL) 5 of 80 (6.2%), NNT 95, retrospective study.
hospital stay >8 days, 21.1% lower, RR 0.79, p = 0.11, high D levels (≥12ng/mL) 40 of 78 (51.3%), low D levels (<12ng/mL) 52 of 80 (65.0%), NNT 7.3, retrospective study.
[Hastie], 8/26/2020, retrospective, population-based cohort, database analysis, United Kingdom, peer-reviewed, 14 authors. risk of death, 17.4% lower, RR 0.83, p = 0.31, cutoff 25nmol/L, adjusted per study, inverted to make RR<1 favor high D levels (≥25nmol/L), multivariable Cox.
risk of hospitalization, 9.1% lower, RR 0.91, p = 0.40, cutoff 25nmol/L, adjusted per study, inverted to make RR<1 favor high D levels (≥25nmol/L), multivariable Cox.
[Hernández], 10/27/2020, retrospective, Spain, peer-reviewed, mean age 60.9, 12 authors. risk of combined death/ICU/ventilation, 83.0% lower, RR 0.17, p < 0.001, high D levels 35, low D levels 162, >= 20ng/mL risk of hospitalization * risk of death/ICU/ventilation | hospitalization.
risk of combined death/ICU/ventilation if hospitalized, 12.0% lower, RR 0.88, p = 0.86, high D levels 35, low D levels 162, >= 20ng/mL risk of death/ICU/ventilation | hospitalization.
risk of hospitalization, 80.6% lower, RR 0.19, p < 0.001, >= 20ng/mL.
[Hurst], 10/22/2021, prospective, United Kingdom, peer-reviewed, 23 authors. risk of death, 68.4% lower, RR 0.32, p = 0.005, high D levels 68, low D levels 191, odds ratio converted to relative risk, >50nmol/l, multivariable, Supplementary Table 2, control prevalance approximated with overall prevalence.
risk of mechanical ventilation, 66.0% lower, RR 0.34, p = 0.004, high D levels 6 of 68 (8.8%), low D levels 61 of 191 (31.9%), NNT 4.3, odds ratio converted to relative risk, >50nmol/l, multivariable, Supplementary Table 2.
[Im], 8/11/2020, retrospective, South Korea, peer-reviewed, 6 authors. risk of case, 73.1% lower, OR 0.27, p < 0.001, high D levels 13 of 50 (26.0%) cases, 85 of 150 (56.7%) controls, NNT 4.3, case control OR.
[Infante], 2/18/2021, retrospective, Italy, peer-reviewed, 11 authors. risk of death, 54.8% lower, RR 0.45, p = 0.046, high D levels 4 of 19 (21.1%), low D levels 55 of 118 (46.6%), NNT 3.9, >20ng/mL.
[Israel], 9/10/2020, retrospective, population-based cohort, Israel, peer-reviewed, 9 authors, study period 1 March, 2020 - 31 October, 2020. risk of severe case, 33.9% lower, OR 0.66, p < 0.001, high D levels 423 of 1,036 (40.8%) cases, 509 of 934 (54.5%) controls, NNT 7.3, adjusted per study, inverted to make OR<1 favor high D levels, case control OR, >75 nmol/L vs. <30 nmol/L, multivariable.
risk of case, 19.7% lower, OR 0.80, p < 0.001, high D levels 6,152 of 15,892 (38.7%) cases, 73,810 of 159,193 (46.4%) controls, NNT 39, adjusted per study, inverted to make OR<1 favor high D levels, case control OR, >75 nmol/L vs. <30 nmol/L, among COVID+ cases, multivariable.
[Jain], 11/19/2020, prospective, India, peer-reviewed, 6 authors. risk of death, 85.2% lower, RR 0.15, p = 0.001, high D levels 2 of 64 (3.1%), low D levels 19 of 90 (21.1%), NNT 5.6, >20ng/mL.
risk of ICU admission, 95.4% lower, RR 0.05, p < 0.001, high D levels 2 of 64 (3.1%), low D levels 61 of 90 (67.8%), NNT 1.5, >20ng/mL.
[Jimenez], 7/26/2021, retrospective, Spain, peer-reviewed, 21 authors, study period 12 March, 2020 - 21 May, 2020, dosage paricalcitol 0.9μg weekly, excluded in exclusion analyses: many patients received vitamin D treatment. risk of death, 7.7% higher, OR 1.08, p = 0.81, high D levels 50, low D levels 110, >30 vs. <20ng/ml, RR approximated with OR, outcome based on serum levels.
risk of mechanical ventilation, 47.5% lower, OR 0.53, p = 0.56, high D levels 50, low D levels 110, >30 vs. <20ng/ml, RR approximated with OR, outcome based on serum levels.
risk of ICU admission, 12.2% lower, OR 0.88, p = 0.87, high D levels 50, low D levels 110, >30 vs. <20ng/ml, RR approximated with OR, outcome based on serum levels.
risk of hospitalization, 0.8% lower, OR 0.99, p = 0.98, high D levels 50, low D levels 110, >30 vs. <20ng/ml, RR approximated with OR, outcome based on serum levels.
[Jude], 6/17/2021, retrospective, United Kingdom, peer-reviewed, 5 authors. risk of hospitalization, 71.6% lower, RR 0.28, p < 0.001, adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >25 nmol/L, control prevalence approximated with overall prevalence.
risk of hospitalization, 57.9% lower, RR 0.42, p < 0.001, adjusted per study, inverted to make RR<1 favor high D levels, odds ratio converted to relative risk, >50 nmol/L, control prevalence approximated with overall prevalence.
[Junior], 2/17/2022, prospective, Brazil, peer-reviewed, 6 authors, dosage not specified. risk of mechanical ventilation, 84.4% lower, OR 0.16, p = 0.03, cutoff 40ng/dl, inverted to make OR<1 favor high D levels (≥40ng/dl), risk of mechanical ventilation for vitamin D levels >40ng/ml, RR approximated with OR, outcome based on serum levels.
[Juraj], 1/22/2022, retrospective, Slovakia, peer-reviewed, 13 authors, study period 1 November, 2020 - 30 April, 2021. risk of death, 19.0% lower, RR 0.81, p = 0.05, high D levels (≥12ng/mL) 127 of 283 (44.9%), low D levels (<12ng/mL) 41 of 74 (55.4%), NNT 9.5.
[Kalichuran], 4/26/2022, prospective, South Africa, peer-reviewed, survey, 4 authors, study period September 2020 - February 2021. risk of symptomatic case, 60.0% lower, RR 0.40, p < 0.001, high D levels (≥20ng/mL) 56, low D levels (<20ng/mL) 44, inverted to make RR<1 favor high D levels (≥20ng/mL).
risk of symptomatic case, 58.2% lower, RR 0.42, p = 0.004, inverted to make RR<1 favor high D levels, higher sunlight exposure vs. lower sunlight exposure.
[