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Vitamin D for COVID-19: real-time meta analysis of 204 studies
https://vdmeta.com/
0 0.5 1 1.5+ All studies 39% 79 134,583 Improvement, Studies, Patients Relative Risk Mortality 39% 45 32,796 Ventilation 34% 13 7,483 ICU admission 50% 17 4,906 Hospitalization 19% 16 70,571 Cases 12% 20 96,193 RCTs 39% 16 6,360 Peer-reviewed 39% 71 128,368 Sufficiency 55% 125 128,650 Cholecalciferol 38% 69 126,106 Calcifediol 52% 10 8,477 Prophylaxis 31% 45 111,659 Early 74% 7 16,914 Late 52% 27 6,010 Vitamin D for COVID-19 vdmeta.com May 2022 Favorsvitamin D Favorscontrol after exclusions
Statistically significant improvements are seen in treatment studies for mortality, ventilation, ICU admission, hospitalization, and cases. 37 studies from 34 independent teams in 16 different countries show statistically significant improvements in isolation (26 for the most serious outcome).
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 74% [45‑88%] and 39% [32‑46%] improvement for early treatment and for all studies. Results are similar after restriction to 71 peer-reviewed studies: 70% [37‑85%] and 39% [32‑46%], and for the 45 mortality results: 76% [37‑91%] and 39% [28‑49%].
Acute treatment (early 74% [45‑88%], late 52% [36‑64%]) shows greater efficacy than chronic prophylaxis (31% [22‑39%]), suggesting that long-term supplementation may not be ideal.
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 45% [26‑58%].
0 0.5 1 1.5+ All studies 39% 79 134,583 Improvement, Studies, Patients Relative Risk Mortality 39% 45 32,796 Ventilation 34% 13 7,483 ICU admission 50% 17 4,906 Hospitalization 19% 16 70,571 Cases 12% 20 96,193 RCTs 39% 16 6,360 Peer-reviewed 39% 71 128,368 Sufficiency 55% 125 128,650 Cholecalciferol 38% 69 126,106 Calcifediol 52% 10 8,477 Prophylaxis 31% 45 111,659 Early 74% 7 16,914 Late 52% 27 6,010 Vitamin D for COVID-19 vdmeta.com May 2022 Favorsvitamin D Favorscontrol after exclusions
Sufficiency studies show a strong association between vitamin D sufficiency and outcomes. Meta analysis of the 125 studies using the most serious outcome reported shows 55% [49‑60%] improvement.
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 13% of vitamin D treatment studies show zero events in the treatment arm.
No treatment, vaccine, or intervention is 100% available and effective for all 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 39% [12‑58%] 16 171 6,360
Treatment studies 39% [32‑46%] 79 800 134,583
Cholecalciferol treatment 38% [30‑45%] 69 678 126,106
Calcifediol/calcitriol treatment 52% [26‑69%] 10 122 8,477
Treatment mortality 39% [28‑49%] 45 428 32,796
Sufficiency studies 55% [49‑60%] 125 1,081 128,650
Highlights
Vitamin D reduces risk for COVID-19 with very high confidence for mortality, ICU admission, hospitalization, progression, recovery, viral clearance, and in pooled analysis, and high confidence for ventilation and cases.
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 42 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 [0.01-1.86] 50,000IU severe case 0/22 4/20 Efird 49% 0.51 [0.23-1.17] varies death 11/544 413/15,794 Khan (RCT) 33% 0.67 [0.37-1.19] no recov. 10/25 15/25 CT​1 Tau​2 = 0.56, I​2 = 68.9%, p = 0.00051 Early treatment 74% 0.26 [0.12-0.55] 34/946 458/15,968 74% 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 Lakkireddy (RCT) 61% 0.39 [0.08-1.91] 300,000IU death 2/44 5/43 see retraction notes 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 [0.03-1.37] 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-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 Fiore 93% 0.07 [0.07-0.63] death 3/58 11/58 Tau​2 = 0.30, I​2 = 66.1%, p < 0.0001 Late treatment 52% 0.48 [0.36-0.64] 217/2,268 733/3,742 52% 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) 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 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) 69% 0.31 [0.01-7.15] 140,000IU cases 0/18 1/15 Tau​2 = 0.09, I​2 = 87.3%, p < 0.0001 Prophylaxis 31% 0.69 [0.61-0.78] 1,461/44,061 9,454/67,598 31% improvement All studies 39% 0.61 [0.54-0.68] 1,712/47,275 10,645/87,308 39% improvement All 79 vitamin D COVID-19 treatment studies vdmeta.com May 2022 Tau​2 = 0.12, I​2 = 84.3%, 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, Azadeh, Chodick, D'Avolio, Desai, Ersöz, Jabbar, Kerget, Latifi-Pupovci, Mardani, Ranjbar, Saeed, Schmitt, Soltani-Zangbar, Takase, Vassiliou], all 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].
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.
Preclinical Research
4 In Silico studies support the efficacy of vitamin D [Al-Mazaideh, 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
Figure 5 shows a visual overview of the results. Figure 1 shows a forest plot for all treatment studies, and the effects reported in sufficiency studies and treatment studies. Figure 6 and 7 show results by treatment stage. Figure 8 shows a forest plot for random effects meta-analysis of sufficiency studies, while Figure 9, 10, 11, 12, 13, 14, 15, 16, and 17 show forest plots for all treatment studies with pooled effects, cholecalciferol studies, calcifediol/calcitriol studies, and for studies reporting mortality, mechanical ventilation, ICU admission, hospitalization, and case results only. Table 1 summarizes the results.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION HOSPITALIZATION CASES RANDOMIZED CONTROLLED TRIALS PEER-REVIEWED SUFFICIENCY CHOLECALCIFEROL CALCIFEDIOL After Exclusions ALL STUDIES RANDOMIZED CONTROLLED TRIALS All Prophylaxis Early Late Vitamin D for COVID-19 VDMETA.COM MAY 2022
Figure 5. Overview of 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 117 125 93.6% 55% improvement
RR 0.45 [0.40‑0.51]
p < 0.0001
Early treatment 7 7 100% 74% improvement
RR 0.26 [0.12‑0.55]
p = 0.00051
Late treatment 24 27 88.9% 52% improvement
RR 0.48 [0.36‑0.64]
p < 0.0001
Prophylaxis 37 45 82.2% 31% improvement
RR 0.69 [0.61‑0.78]
p < 0.0001
All treatment studies 68 79 86.1% 39% improvement
RR 0.61 [0.54‑0.68]
p < 0.0001
Table 1. Results.
    
  
Figure 6. Results by treatment stage.
    
  
Figure 7. 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] High Levels Low Levels 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) Anjum 62% 0.38 [0.17-0.82] death 8/80 16/60 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 (ICU) 91% 0.09 [0.01-1.51] death 0/15 5/15 ICU patients 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 Ansari 86% 0.14 [0.03-0.60] death 2/68 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 Bychinin (ICU) 36% 0.64 [0.42-0.98] death 16/38 31/47 ICU patients Savitri 88% 0.12 [0.05-0.32] symp. case 3/25 17/17 Davoudi -12% 1.12 [0.19-6.52] death 2/57 3/96 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 Reis 23% 0.77 [0.08-7.38] death 198 (n) 16 (n) 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 (ICU) 29% 0.71 [0.15-3.43] death 3/17 2/8 ICU patients 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) González-Estevez 25% 0.75 [0.50-1.13] symp. case 6/8 32/32 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 (ICU) 64% 0.36 [0.11-1.21] death 3/26 8/25 ICU patients 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 Ramos 46% 0.54 [0.25-1.19] cases 4/9 9/11 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 Ranjbar 42% 0.58 [0.32-1.04] death 16/163 26/154 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