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Vitamin D for COVID-19: real-time meta analysis of 160 studies
https://vdmeta.com/
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 44% 59 123,354 Improvement, Studies, Patients Relative Risk With exclusions 42% 47 105,648 Mortality 48% 34 29,840 Ventilation 35% 11 2,903 ICU admission 54% 14 4,019 Hospitalization 19% 13 63,657 Cases 15% 15 76,505 RCTs 55% 10 834 RCTs w/exc. 60% 8 517 RCT death w/exc. 65% 6 435 Peer-reviewed 43% 54 121,690 Sufficiency 56% 101 125,650 Cholecalciferol 43% 50 115,011 Calcifediol 53% 9 8,343 Prophylaxis 35% 31 102,090 Early 81% 6 16,864 Late 56% 22 4,400 Vitamin D for COVID-19 vdmeta.com Jan 26, 2022 Favors vitamin D Favors control
Statistically significant improvements are seen in treatment studies for mortality, ventilation, ICU admission, hospitalization, and cases. 31 studies from 28 independent teams in 13 different countries show statistically significant improvements in isolation (23 for the most serious outcome).
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 81% [53‑92%] and 44% [36‑51%] improvement for early treatment and for all studies. Results are similar after restriction to 54 peer-reviewed studies: 77% [45‑90%] and 43% [34‑50%], and for the 34 mortality results: 76% [37‑91%] and 48% [34‑60%].
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 78% [67‑85%] vs. 46% [26‑60%].
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 44% 59 123,354 Improvement, Studies, Patients Relative Risk With exclusions 42% 47 105,648 Mortality 48% 34 29,840 Ventilation 35% 11 2,903 ICU admission 54% 14 4,019 Hospitalization 19% 13 63,657 Cases 15% 15 76,505 RCTs 55% 10 834 RCTs w/exc. 60% 8 517 RCT death w/exc. 65% 6 435 Peer-reviewed 43% 54 121,690 Sufficiency 56% 101 125,650 Cholecalciferol 43% 50 115,011 Calcifediol 53% 9 8,343 Prophylaxis 35% 31 102,090 Early 81% 6 16,864 Late 56% 22 4,400 Vitamin D for COVID-19 vdmeta.com Jan 26, 2022 Favors vitamin D Favors control
Sufficiency studies show a strong association between vitamin D sufficiency and outcomes. Meta analysis of the 101 studies using the most serious outcome reported shows 56% [50‑61%] improvement.
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 12% of vitamin D treatment studies show zero events in the treatment arm.
Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. All practical, effective, and safe means should be used. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and the sources are in the appendix.
ImprovementStudies AuthorsPatients
Treatment RCTs 55% [26‑72%] 10 90 834
Treatment studies 44% [36‑51%] 59 600 123,354
Cholecalciferol treatment 43% [33‑51%] 50 489 115,011
Calcifediol/calcitriol treatment 53% [26‑71%] 9 111 8,343
Treatment mortality 48% [34‑60%] 34 315 29,840
Sufficiency studies 56% [50‑61%] 101 840 125,650
    
  
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 [0.01-1.86] 50,000IU severe case 0/22 4/20 Efird 49% 0.51 [0.23-1.17] varies death 11/544 413/15,794 Tau​2 = 0.63, I​2 = 62.8%, p = 0.00032 Early treatment 81% 0.19 [0.08-0.47] 24/921 443/15,943 81% improvement Tan 80% 0.20 [0.04-0.93] 5,000IU oxygen 3/17 16/26 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 Lakkireddy (RCT) 61% 0.39 [0.08-1.91] 300,000IU death 2/44 5/43 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) Alcala-Diaz 81% 0.19 [0.04-0.83] 0.8mg (c) death 4/79 90/458 Güven 25% 0.75 [0.37-1.24] 300,000IU death 43/113 30/62 Assiri -66% 1.66 [0.25-7.87] n/a death 12/90 2/28 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 [0.03-1.37] 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 (RCT) 86% 0.14 [0.03-0.80] 10,000IU death 1/40 7/40 CT​1 Beigmoha.. (SB RCT) 89% 0.11 [0.01-1.98] 600,000IU death 0/30 4/30 CT​1 Baguma 97% 0.03 [0.00-0.54] n/a death 23 (n) 458 (n) Tau​2 = 0.28, I​2 = 61.4%, p < 0.0001 Late treatment 56% 0.44 [0.32-0.62] 135/1,618 394/2,782 56% 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] 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 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) 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 9% 0.91 [0.85-0.97] n/a hosp. 737/2,406 6,216/18,453 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 progression 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 Ma 49% 0.51 [0.29-0.91] varies hosp. 26,605 (n) 12,710 (n) Tau​2 = 0.10, I​2 = 90.9%, p < 0.0001 Prophylaxis 35% 0.65 [0.56-0.76] 1,220/40,667 8,908/61,423 35% improvement All studies 44% 0.56 [0.49-0.64] 1,379/43,206 9,745/80,148 44% improvement All 59 vitamin D COVID-19 treatment studies vdmeta.com Jan 26, 2022 Tau​2 = 0.13, I​2 = 87.4%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors vitamin D Favors control
    
  
B
    
  
C
    
  
D
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 serum level analysis (sufficiency) studies and treatment studies (the vertical lines and shaded boxes show the median and interquartile range). C and D. Chronological history of all reported effects for treatment studies and sufficiency studies.
Introduction
We analyze all significant 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 present random-effects meta-analysis results for studies analyzing outcomes based on sufficiency, for all treatment studies, for mortality results only, and for treatment studies within each treatment stage.
Vitamin D.
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].
Sufficiency.
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. A few studies only provide results as a function of change in vitamin D levels [Butler-Laporte, 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, Chodick, D'Avolio, Desai, Ersöz, Kerget, Mardani, Vassiliou], all of which show lower D levels for worse outcomes. Other studies analyze vitamin D status and outcomes in geographic regions [Jayawardena, Marik, Papadimitriou, Rafailia, 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].
Treatment.
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.
Results
Figure 1 shows the effects reported in sufficiency studies and treatment studies. Figure 5 and 6 show results by treatment stage. Figure 7 shows a forest plot for random effects meta-analysis of sufficiency studies, while Figure 8, 9, 10, 11, 12, 13, 14, 15, and 16 show forest plots for all treatment studies with pooled effects, cholecalciferol studies, calcifediol/calcitriol studies, for studies reporting mortality, mechanical ventilation, ICU admission, hospitalization, and case results only. Table 1 summarizes the results.
Study typeNumber of studies reporting positive results Total number of studiesPercentage of studies reporting positive results Random effects meta-analysis results
Analysis of outcomes based on sufficiency 95 101 94.1% 56% improvement
RR 0.44 [0.39‑0.50]
p < 0.0001
Early treatment 6 6 100% 81% improvement
RR 0.19 [0.08‑0.47]
p = 0.00032
Late treatment 20 22 90.9% 56% improvement
RR 0.44 [0.32‑0.62]
p < 0.0001
Prophylaxis 25 31 80.6% 35% improvement
RR 0.65 [0.56‑0.76]
p < 0.0001
All treatment studies 51 59 86.4% 44% improvement
RR 0.56 [0.49‑0.64]
p < 0.0001
Table 1. Results.
    
  
Figure 5. Results by treatment stage.
    
  
Figure 6. Results by treatment stage.
    
  
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lau 45% 0.55 [0.18-1.68] ICU 2/5 11/15 Improvement, RR [CI] Treatment Control Mendy 7% 0.93 [0.33-2.47] death 21/600 5/89 Panagiotou 52% 0.48 [0.24-0.95] ICU 8/44 34/90 Faul 69% 0.31 [0.10-0.95] ventilation 4/21 8/12 Merzon 46% 0.54 [0.23-1.02] hosp. 79 (n) 703 (n) Carpagnano 71% 0.29 [0.10-0.85] death 5/34 4/8 Im 73% 0.27 [0.15-0.47] cases case control Hastie 17% 0.83 [0.57-1.20] death population-based cohort Baktash 29% 0.71 [0.18-2.78] death 4/31 6/39 Meltzer 44% 0.56 [0.36-0.89] cases 39/317 32/172 Israel 34% 0.66 [0.54-0.81] severe case case control Radujkovic 93% 0.07 [0.01-0.34] death 144 (n) 12 (n) Kaufman 53% 0.47 [0.30-0.74] cases 12,321 (n) 39,190 (n) Maghbooli 52% 0.48 [0.22-1.05] death 7/72 27/134 Pepkowitz 56% 0.44 [0.25-0.78] ICU 9/24 11/13 Karahan 83% 0.17 [0.08-0.41] death 5/46 64/103 Yılmaz 73% 0.27 [0.01-5.14] severe case 0/11 2/29 Faniyi 29% 0.71 [0.59-0.86] seropositive 170/331 44/61 Ye 93% 0.07 [0.01-0.81] hosp. 2/36 8/26 Macaya 55% 0.45 [0.15-1.06] severe case 11/35 20/45 Tomasa-Irriguible 35% 0.65 [0.31-1.24] ventilation 15/27 18/78 Hernández 83% 0.17 [0.07-0.41] death/ICU 35 (n) 162 (n) Abrishami 76% 0.24 [0.06-0.93] death 3/47 9/26 Cereda -120% 2.20 [1.01-3.22] death 10/30 24/99 Walk -0% 1.00 [0.60-1.67] death/int. 48/110 10/23 Luo 63% 0.37 [0.17-0.81] progression 335 (n) 560 (n) Jain 85% 0.15 [0.04-0.61] death 2/64 19/90 De Smet 70% 0.30 [0.10-0.80] death 7/77 20/109 Katz 78% 0.22 [0.17-0.27] cases population-based cohort Alguwaihes 86% 0.14 [0.04-0.59] death 111 (n) 328 (n) Vassiliou 91% 0.09 [0.01-1.51] death 0/15 5/15 Abdollahi 54% 0.46 [0.29-0.73] cases 108 (n) 294 (n) Szeto -6% 1.06 [0.49-2.26] death 14/58 8/35 Karonova 79% 0.21 [0.03-1.28] death 1/23 12/57 Amin -32% 1.32 [0.88-1.89] progression population-based cohort Angelidi 88% 0.12 [0.02-0.60] death 6/65 20/79 Li 36% 0.64 [0.53-0.78] severe case population-based cohort Bennouar 86% 0.14 [0.04-0.50] death 4/30 15/32 Vasheghani 64% 0.36 [0.20-0.65] ICU 13/185 53/323 Orchard 59% 0.41 [0.22-0.76] ICU 9/40 41/75 Barassi 65% 0.35 [0.05-2.69] death 1/31 8/87 Tehrani 48% 0.52 [0.29-0.96] death 34/180 9/25 Demir 89% 0.11 [0.03-0.40] severe case 13 (n) 99 (n) Susianti 91% 0.09 [0.01-1.34] death 0/8 9/42 Basaran 69% 0.31 [0.03-0.90] severe case 82/119 80/85 Infante 55% 0.45 [0.19-1.10] death 4/19 55/118 Gavioli -5% 1.05 [0.78-1.40] death 80/260 52/177 Sulli 79% 0.21 [0.15-0.29] cases case control Ricci 88% 0.12 [0.01-2.28] death 0/30 3/22 Lohia 15% 0.85 [0.47-1.41] death 88 (n) 95 (n) Mazziotti 2% 0.98 [0.61-1.48] death 187 (n) 161 (n) Charoenngam 34% 0.66 [0.32-1.33] death 12/100 29/187 Vanegas-Cedillo 53% 0.47 [0.28-0.81] death 95/494 21/57 Meltzer 35% 0.65 [0.39-1.10] cases 61/1,097 118/1,251 Freitas 41% 0.59 [0.38-0.91] death 23/179 68/311 Gaudio 79% 0.21 [0.14-0.31] cases case control Bayramoğlu 70% 0.30 [0.09-0.77] severe case 10/60 24/43 Livingston 51% 0.49 [0.25-0.83] cases 16/52 31/52 Ünsal 81% 0.19 [0.01-3.87] death 0/29 2/27 Savitri 88% 0.12 [0.05-0.32] symp. case 3/25 17/17 Li 9% 0.91 [0.79-1.06] cases 610/13,650 290/4,498 AlSafar 59% 0.41 [0.16-0.99] death 16/337 10/127 Galaznik 35% 0.65 [0.47-0.91] cases 13,903 (n) 2,384 (n) Sánchez-Zuno 6% 0.94 [0.44-2.02] severe case 4/8 18/34 Pimental 29% 0.71 [0.15-3.43] death 3/17 2/8 Diaz-Curiel 73% 0.27 [0.07-0.67] ICU 3/214 91/1,017 Dror 85% 0.15 [0.04-0.44] severe case 109/120 76/133 Campi 24% 0.76 [0.31-1.83] death 6/39 13/64 Jude 72% 0.28 [0.25-0.32] hosp. n/a n/a Zelzer 46% 0.54 [0.29-0.98] death 24/121 10/27 Bianconi 18% 0.82 [0.41-1.65] death 94 (n) 106 (n) Jimenez -8% 1.08 [0.59-1.98] death 50 (n) 110 (n) Cozier 39% 0.61 [0.39-0.96] cases 94/1,601 33/373 Al-Salman 44% 0.56 [0.33-0.95] ICU 113 (n) 337 (n) Matin 66% 0.34 [0.21-0.56] cases case control Nimavat 50% 0.50 [0.19-1.27] death 13/131 5/25 Ribeiro 50% 0.50 [0.28-0.87] cases n/a n/a Eden 64% 0.36 [0.11-1.21] death 3/26 8/25 Alpcan 73% 0.27 [0.20-0.36] cases case control Sinaci 79% 0.21 [0.10-0.43] m/s case 8/100 23/59 di Filippo 11% 0.89 [0.35-2.29] death 5/28 12/60 Parra-Ortega 99% 0.01 [0.00-0.20] death 0/15 63/79 Golabi 90% 0.10 [0.04-0.24] symp. 34 (n) 10 (n) Pecina 36% 0.64 [0.04-6.25] death 6/77 1/15 Karonova 78% 0.22 [0.07-0.67] death 8/96 10/37 Derakhshanian 45% 0.55 [0.30-0.98] death 148 (n) 142 (n) Afaghi 55% 0.45 [0.34-0.59] death 97/537 51/109 Ramirez-Sandoval 32% 0.68 [0.57-0.83] death 2,337 (n) 571 (n) Hurst 68% 0.32 [0.13-0.73] death 68 (n) 191 (n) Atanasovska 41% 0.59 [0.16-2.23] death 2/9 9/24 Asghar 53% 0.47 [0.22-0.99] death 73 (n) 18 (n) Gönen 66% 0.34 [0.04-3.22] death 1/80 3/82 Asgari 73% 0.27 [0.09-0.86] death n/a n/a Seven 47% 0.53 [0.34-0.84] severe case n/a n/a Kaur 90% 0.10 [0.04-0.25] death 5/64 13/17 Fatemi 42% 0.58 [0.30-1.05] death 18/139 25/109 Ma 67% 0.33 [0.08-1.30] hosp. 7,893 (n) 7,823 (n) Putra 26% 0.74 [0.42-1.31] hosp. case control Seal 45% 0.55 [0.38-0.79] death n/a n/a Juraj 19% 0.81 [0.64-1.03] death 127/283 41/74 Saponaro 36% 0.64 [0.25-1.59] ARDS 5/32 15/61 All studies 56% 0.44 [0.39-0.50] 2,007/60,919 1,878/64,731 56% improvement All 101 vitamin D COVID-19 sufficiency studies vdmeta.com Jan 26, 2022 Tau​2 = 0.29, I​2 = 88.2%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) Favors vitamin D Favors control
Figure 7. 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.
    
  
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 [0.01-1.86] 50,000IU severe case 0/22 4/20 Efird 49% 0.51 [0.23-1.17] varies death 11/544 413/15,794 Tau​2 = 0.63, I​2 = 62.8%, p = 0.00032 Early treatment 81% 0.19 [0.08-0.47] 24/921 443/15,943 81% improvement Tan 80% 0.20 [0.04-0.93] 5,000IU oxygen 3/17 16/26 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 Lakkireddy (RCT) 61% 0.39 [0.08-1.91] 300,000IU death 2/44 5/43 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) Alcala-Diaz 81% 0.19 [0.04-0.83] 0.8mg (c) death 4/79 90/458 Güven 25% 0.75 [0.37-1.24] 300,000IU death 43/113 30/62 Assiri -66% 1.66 [0.25-7.87] n/a death 12/90 2/28 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%