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Zinc for COVID-19: real-time meta analysis of 53 studies (41 treatment studies and 12 sufficiency studies)
Covid Analysis, June 2023
https://c19early.org/zmeta.html
 
0 0.5 1 1.5+ All studies 29% 41 45,885 Improvement, Studies, Patients Relative Risk Mortality 29% 20 13,290 Ventilation 47% 6 3,638 ICU admission 26% 7 3,745 Hospitalization 29% 14 6,454 Progression 77% 3 2,149 Recovery 22% 3 769 Cases 22% 6 25,221 Viral clearance 21% 1 115 RCTs 39% 8 2,220 RCT mortality 24% 3 694 Peer-reviewed 26% 37 40,339 Exc. combined 26% 34 41,353 Sufficiency 73% 12 1,216 Prophylaxis 32% 16 29,739 Early 41% 6 4,218 Late 26% 19 11,928 Zinc for COVID-19 c19early.org/z Jun 2023 Favorszinc Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ventilation, hospitalization, progression, recovery, and viral clearance. 17 studies from 17 independent teams in 9 different countries show statistically significant improvements in isolation (11 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 29% [18‑38%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment.
Sufficiency studies, analyzing outcomes based on serum levels, show 73% [63‑81%] improvement for patients with higher zinc levels (12 studies).
Results are robust — in exclusion sensitivity analysis 17 of 41 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 29% 41 45,885 Improvement, Studies, Patients Relative Risk Mortality 29% 20 13,290 Ventilation 47% 6 3,638 ICU admission 26% 7 3,745 Hospitalization 29% 14 6,454 Progression 77% 3 2,149 Recovery 22% 3 769 Cases 22% 6 25,221 Viral clearance 21% 1 115 RCTs 39% 8 2,220 RCT mortality 24% 3 694 Peer-reviewed 26% 37 40,339 Exc. combined 26% 34 41,353 Sufficiency 73% 12 1,216 Prophylaxis 32% 16 29,739 Early 41% 6 4,218 Late 26% 19 11,928 Zinc for COVID-19 c19early.org/z Jun 2023 Favorszinc Favorscontrol after exclusions
7 studies use combined treatments. When excluding those studies, the pooled improvement is 26% [15‑35%] compared to 29% [18‑38%].
Over-supplementation may be detrimental [karger.com].
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 are more effective. Only 10% of zinc studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix. Other meta analyses for zinc can be found in [Abuhelwa, Fan, Olczak-Pruc, Tabatabaeizadeh, Xie], showing significant improvements for mortality, severity, and cases.
Evolution of COVID-19 clinical evidence Zinc p=0.000004 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with zinc (more)
Early treatment Prophylaxis All studies Studies Patients Authors
All studies41% [8‑61%]
*
32% [9‑49%]
*
29% [18‑38%]
****
41 45,885 435
Randomized Controlled TrialsRCTs21% [-41‑55%]50% [26‑67%]
***
39% [17‑55%]
**
8 2,220 114
Mortality50% [33‑63%]
****
30% [-137‑79%]29% [10‑44%]
**
20 13,290 199
HospitalizationHosp.66% [-4‑89%]81% [-3‑96%]29% [7‑45%]
*
14 6,454 123
Cases-22% [-10‑45%]22% [-10‑45%] 6 25,221 105
RCT mortality30% [-31‑64%]-24% [-29‑55%] 3 694 46
Highlights
Zinc reduces risk for COVID-19 with very high confidence for mortality, progression, recovery, and in pooled analysis, high confidence for ventilation and hospitalization, low confidence for viral clearance, and very low confidence for ICU admission and cases. Over-supplementation may be detrimental.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Derwand 79% 0.21 [0.03-1.47] death 1/141 13/377 CT​2 Improvement, RR [CI] Treatment Control Thomas (RCT) -44% 1.44 [0.36-5.71] hosp. 5/58 3/50 Aldwihi 24% 0.76 [0.51-1.08] hosp. 53/199 184/539 Asimi 97% 0.03 [0.00-0.44] ventilation 0/270 9/86 CT​2 Mayberry 53% 0.47 [0.33-0.65] death 938 (n) 1,090 (n) VIZIR Abdallah (DB RCT) 30% 0.70 [0.36-1.31] death 15/231 22/239 Tau​2 = 0.13, I​2 = 60.6%, p = 0.018 Early treatment 41% 0.59 [0.39-0.92] 74/1,837 231/2,381 41% improvement Carlucci 38% 0.62 [0.46-0.84] death/HPC 54/411 119/521 Improvement, RR [CI] Treatment Control Krishnan 18% 0.82 [0.62-1.09] death 31/58 61/94 Yao 34% 0.66 [0.41-1.07] death 73/196 21/46 Frontera (PSM) 37% 0.63 [0.44-0.91] death 121/1,006 424/2,467 CT​2 Abd-Elsalam (RCT) 1% 0.99 [0.30-3.31] death 5/96 5/95 data issues, see notes Rosenthal -16% 1.16 [1.05-1.28] death n/a n/a Darban (RCT) 33% 0.67 [0.14-3.17] progression 2/10 3/10 ICU patients CT​2 Patel (DB RCT) 20% 0.80 [0.15-4.18] death 2/15 3/18 Mulhem 46% 0.54 [0.43-0.68] death 256/1,596 260/1,623 Gadhiya -41% 1.41 [0.69-2.57] death 21/54 34/229 Al Sulaiman (ICU) 36% 0.64 [0.37-1.10] death 23/82 32/82 ICU patients Elavarasi 65% 0.35 [0.24-0.56] death 486 (n) 1,201 (n) Assiri (ICU) -81% 1.81 [0.41-6.97] death 10/60 4/58 ICU patients Kaplan (RCT) -14% 1.14 [0.08-16.6] ventilation 1/14 1/16 CT​2 Zangeneh (ICU) -21% 1.21 [0.51-2.90] death n/a n/a ICU patients Alahmari 30% 0.70 [0.63-0.78] hosp. time 130 (n) 847 (n) Doocy 41% 0.59 [0.19-1.85] death 3/28 21/116 Ibrahim Alhajjaji 88% 0.12 [0.01-2.24] death 0/44 4/57 Kyagambiddwa 25% 0.75 [0.44-1.25] death 20/89 22/73 Tau​2 = 0.11, I​2 = 85.2%, p = 0.003 Late treatment 26% 0.74 [0.60-0.90] 622/4,375 1,014/7,553 26% improvement Louca 1% 0.99 [0.93-1.06] cases population-based cohort Improvement, RR [CI] Treatment Control Mahto 37% 0.63 [0.22-1.49] IgG+ 10/38 83/651 COVIDENCE UK Holt 7% 0.93 [0.59-1.44] cases 21/750 425/14,477 Abdulateef 13% 0.87 [0.38-1.97] hosp. 7/111 23/317 Seet (CLUS. RCT) 50% 0.50 [0.34-0.75] symp. case 33/634 64/619 OT​1 Israel 100% 0.00 [0.00-0.89] hosp. case control CT​2 Bagheri 60% 0.40 [0.04-3.53] severe case 33 (n) 477 (n) Gordon 68% 0.32 [0.01-7.87] death 0/104 1/96 Kumar 20% 0.80 [0.21-2.99] death 6/75 3/30 Nimer -25% 1.25 [0.87-1.77] hosp. 41/326 178/1,822 Shehab 47% 0.53 [0.19-1.47] severe case 4/65 22/188 Citu 18% 0.82 [0.12-5.68] severe case 2/74 2/61 CT​2 Stambouli (DB RCT) 68% 0.32 [0.03-2.95] symp. case 1/59 3/56 Adrean -12% 1.12 [0.74-1.70] cases 30/2,111 80/6,315 Sharif 40% 0.60 [0.46-0.77] severe case n/a n/a Asoudeh 57% 0.43 [0.21-0.90] severe case 250 (all patients) Tau​2 = 0.19, I​2 = 81.3%, p = 0.01 Prophylaxis 32% 0.68 [0.51-0.91] 155/4,380 884/25,109 32% improvement All studies 29% 0.71 [0.62-0.82] 851/10,592 2,129/35,043 29% improvement 41 zinc COVID-19 studies c19early.org/z Jun 2023 Tau​2 = 0.10, I​2 = 83.2%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment2 CT: study uses combined treatment Favors zinc Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Derwand 79% death CT​2 Relative Risk [CI] Thomas (RCT) -44% hospitalization Aldwihi 24% hospitalization Asimi 97% ventilation CT​2 Mayberry 53% death VIZIR Abdallah (DB RCT) 30% death Tau​2 = 0.13, I​2 = 60.6%, p = 0.018 Early treatment 41% 41% improvement Carlucci 38% death/hospice Krishnan 18% death Yao 34% death Frontera (PSM) 37% death CT​2 Abd-Elsalam (RCT) 1% death data issues Rosenthal -16% death Darban (RCT) 33% progression ICU patients CT​2 Patel (DB RCT) 20% death Mulhem 46% death Gadhiya -41% death Al Sulaiman (ICU) 36% death ICU patients Elavarasi 65% death Assiri (ICU) -81% death ICU patients Kaplan (RCT) -14% ventilation CT​2 Zangeneh (ICU) -21% death ICU patients Alahmari 30% hospitalization Doocy 41% death Ibrahim Alhajj.. 88% death Kyagambiddwa 25% death Tau​2 = 0.11, I​2 = 85.2%, p = 0.003 Late treatment 26% 26% improvement Louca 1% case Mahto 37% IgG positive COVIDENCE UK Holt 7% case Abdulateef 13% hospitalization Seet (CLUS. RCT) 50% symp. case OT​1 Israel 100% hospitalization CT​2 Bagheri 60% severe case Gordon 68% death Kumar 20% death Nimer -25% hospitalization Shehab 47% severe case Citu 18% severe case CT​2 Stambo.. (DB RCT) 68% symp. case Adrean -12% case Sharif 40% severe case Asoudeh 57% severe case Tau​2 = 0.19, I​2 = 81.3%, p = 0.01 Prophylaxis 32% 32% improvement All studies 29% 29% improvement 41 zinc COVID-19 studies c19early.org/z Jun 2023 Tau​2 = 0.10, I​2 = 83.2%, p < 0.0001 Effect extraction pre-specifiedRotate device for footnotes/details Favors zinc Favors control
B
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C
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,946 proposed treatments show efficacy [c19early.org]. D. Timeline of results in zinc studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 8.8 months, compared to using all studies.
We analyze all significant studies concerning the use of zinc for 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 all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
An In Silico study supports the efficacy of zinc [Pormohammad].
2 In Vitro studies support the efficacy of zinc [Hajdrik, Panchariya].
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.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, sufficiency studies, peer reviewed studies, and all studies excluding combined treatment studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies29% [18‑38%]
****
41 45,885 435
After exclusions32% [20‑41%]
****
26 25,146 292
Peer-reviewed studiesPeer-reviewed26% [14‑36%]
****
37 40,339 380
Excluding combined treatmentExc. combined26% [15‑35%]
****
34 41,353 371
Randomized Controlled TrialsRCTs39% [17‑55%]
**
8 2,220 114
Mortality29% [10‑44%]
**
20 13,290 199
VentilationVent.47% [4‑71%]
*
6 3,638 53
ICU admissionICU26% [-9‑49%]7 3,745 82
HospitalizationHosp.29% [7‑45%]
*
14 6,454 123
Recovery22% [8‑34%]
**
3 769 45
Cases22% [-10‑45%]6 25,221 105
RCT mortality24% [-29‑55%]3 694 46
RCT hospitalizationRCT hosp.4% [-8‑14%]4 514 57
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies41% [8‑61%]
*
26% [10‑40%]
**
32% [9‑49%]
*
After exclusions37% [10‑55%]
*
36% [27‑44%]
****
24% [2‑41%]
*
Peer-reviewed studiesPeer-reviewed37% [10‑55%]
*
21% [1‑36%]
*
32% [9‑49%]
*
Excluding combined treatmentExc. combined34% [7‑54%]
*
25% [6‑40%]
*
22% [4‑37%]
*
Randomized Controlled TrialsRCTs21% [-41‑55%]14% [-90‑61%]50% [26‑67%]
***
Mortality50% [33‑63%]
****
24% [2‑41%]
*
30% [-137‑79%]
VentilationVent.86% [-66‑99%]20% [-16‑45%]-
ICU admissionICU59% [48‑68%]
****
6% [-5‑15%]-
HospitalizationHosp.66% [-4‑89%]15% [-5‑31%]81% [-3‑96%]
Recovery23% [4‑37%]
*
6% [-62‑45%]-
Cases--22% [-10‑45%]
RCT mortality30% [-31‑64%]8% [-144‑65%]-
RCT hospitalizationRCT hosp.16% [-254‑80%]4% [-8‑14%]-
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for sufficiency studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 13. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 14. Random effects meta-analysis for all studies excluding combined treatment studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 15 shows a comparison of results for RCTs and non-RCT studies. Figure 16, 17, and 18 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1 and Table 2.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 51 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for zinc are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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Figure 15. Results for RCTs and non-RCT studies.
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Figure 16. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 17. Random effects meta-analysis for RCT mortality results.
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Figure 18. Random effects meta-analysis for RCT hospitalization results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 19 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Abd-Elsalam], multiple potential data reliability issues.
[Abdulateef], unadjusted results with no group details.
[Asimi], excessive unadjusted differences between groups.
[Assiri], unadjusted results with no group details.
[Doocy], unadjusted results with no group details.
[Gadhiya], substantial unadjusted confounding by indication likely.
[Holt], significant unadjusted confounding possible.
[Ibrahim Alhajjaji], excessive unadjusted differences between groups.
[Israel], treatment or control group size extremely small.
[Krishnan], unadjusted results with no group details.
[Kumar], unadjusted results with no group details.
[Kyagambiddwa], unadjusted results with no group details.
[Mulhem], substantial unadjusted confounding by indication likely; substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Rosenthal], confounding by indication is likely and adjustments do not consider COVID-19 severity at baseline.
[Shehab], unadjusted results with no group details.
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Figure 19. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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 (B)] 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 (B)]
Figure 20 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 20. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 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 and treatment regimen.
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 21. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 97% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 21. 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. 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.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso].
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
Figure 22 shows a scatter plot of results for prospective and retrospective treatment studies. Prospective studies show 31% [12‑45%] improvement in meta analysis, compared to 29% [17‑39%] for retrospective studies, showing no significant difference, with results to date favoring a possible negative publication bias.
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Figure 22. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 23 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 23. 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. Zinc for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 zinc 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 zinc trials represent the optimal conditions for efficacy.
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 zinc. 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 - 39 physicians/teams
Physician / TeamLocationPatients HospitalizationHosp. ImprovementImp. MortalityDeath ImprovementImp.
Dr. Roberto Alfonso Accinelli
0/360 deaths for treatment within 3 days
Peru 1,265 0.6% (7) 77.5%
Dr. Mohammed Tarek Alam
patients up to 84 years old
Bangladesh 100 0.0% (0) 100.0%
Dr. Oluwagbenga Alonge Nigeria 310 0.0% (0) 100.0%
Dr. Raja Bhattacharya
up to 88yo, 81% comorbidities
India 148 1.4% (2) 44.9%
Dr. Flavio Cadegiani Brazil 3,450 0.1% (4) 99.7% 0.0% (0) 100.0%
Dr. Alessandro Capucci Italy 350 4.6% (16) 88.2%
Dr. Shankara Chetty South Africa 8,000 0.0% (0) 100.0%
Dr. Deborah Chisholm USA 100 0.0% (0) 100.0%
Dr. Ryan Cole USA 400 0.0% (0) 100.0% 0.0% (0) 100.0%
Dr. Marco Cosentino
vs. 3-3.8% mortality during period; earlier treatment better
Italy 392 6.4% (25) 83.5% 0.3% (1) 89.6%
Dr. Jeff Davis USA 6,000 0.0% (0) 100.0%
Dr. Dhanajay India 500 0.0% (0) 100.0%
Dr. Bryan Tyson & Dr. George Fareed USA 20,000 0.0% (6) 99.9% 0.0% (4) 99.2%
Dr. Raphael Furtado Brazil 170 0.6% (1) 98.5% 0.0% (0) 100.0%
Dr. Heather Gessling USA 1,500 0.1% (1) 97.3%
Dr. Ellen Guimarães Brazil 500 1.6% (8) 95.9% 0.4% (2) 83.7%
Dr. Syed Haider USA 4,000 0.1% (5) 99.7% 0.0% (0) 100.0%
Dr. Mark Hancock USA 24 0.0% (0) 100.0%
Dr. Sabine Hazan USA 1,000 0.0% (0) 100.0%
Dr. Mollie James USA 3,500 1.1% (40) 97.0% 0.0% (1) 98.8%
Dr. Roberta Lacerda Brazil 550 1.5% (8) 96.2% 0.4% (2) 85.2%
Dr. Katarina Lindley USA 100 5.0% (5) 87.1% 0.0% (0) 100.0%
Dr. Ben Marble USA 150,000 0.0% (4) 99.9%
Dr. Edimilson Migowski Brazil 2,000 0.3% (7) 99.1% 0.1% (2) 95.9%
Dr. Abdulrahman Mohana Saudi Arabia 2,733 0.0% (0) 100.0%
Dr. Carlos Nigro Brazil 5,000 0.9% (45) 97.7% 0.5% (23) 81.3%
Dr. Benoit Ochs Luxembourg 800 0.0% (0) 100.0%
Dr. Ortore Italy 240 1.2% (3) 96.8% 0.0% (0) 100.0%
Dr. Valerio Pascua
one death for a patient presenting on the 5th day in need of supplemental oxygen
Honduras 415 6.3% (26) 83.8% 0.2% (1) 90.2%
Dr. Sebastian Pop Romania 300 0.0% (0) 100.0%
Dr. Brian Proctor USA 869 2.3% (20) 94.0% 0.2% (2) 90.6%
Dr. Anastacio Queiroz Brazil 700 0.0% (0) 100.0%
Dr. Didier Raoult France 8,315 2.6% (214) 93.3% 0.1% (5) 97.6%
Dr. Karin Ried
up to 99yo, 73% comorbidities, av. age 63
Turkey 237 0.4% (1) 82.8%
Dr. Roman Rozencwaig
patients up to 86 years old
Canada 80 0.0% (0) 100.0%
Dr. Vipul Shah India 8,000 0.1% (5) 97.5%
Dr. Silvestre Sobrinho Brazil 116 8.6% (10) 77.7% 0.0% (0) 100.0%
Dr. Unknown Brazil 957 1.7% (16) 95.7% 0.2% (2) 91.5%
Dr. Vladimir Zelenko USA 2,200 0.5% (12) 98.6% 0.1% (2) 96.3%
Mean improvement with early treatment protocols 237,521 HospitalizationHosp. 94.1% MortalityDeath 94.7%
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
1 of the 41 studies compare against other treatments, which may reduce the effect seen. 7 of 41 studies combine treatments. The results of zinc alone may differ. 2 of 8 RCTs use combined treatment. Other meta analyses for zinc can be found in [Abuhelwa, Fan, Olczak-Pruc, Tabatabaeizadeh, Xie], showing significant improvements for one or more of mortality, severity, and cases.
Zinc is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ventilation, hospitalization, progression, recovery, and viral clearance. 17 studies from 17 independent teams in 9 different countries show statistically significant improvements in isolation (11 for the most serious outcome). Meta analysis using the most serious outcome reported shows 29% [18‑38%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment. Sufficiency studies, analyzing outcomes based on serum levels, show 73% [63‑81%] improvement for patients with higher zinc levels (12 studies). Results are robust — in exclusion sensitivity analysis 17 of 41 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
7 studies use combined treatments. When excluding those studies, the pooled improvement is 26% [15‑35%] compared to 29% [18‑38%].
Over-supplementation may be detrimental [karger.com].
0 0.5 1 1.5 2+ Mortality 1% Improvement Relative Risk Ventilation 34% Recovery 6% Hospitalization time 4% c19early.org/z Abd-Elsalam et al. Zinc for COVID-19 RCT LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? RCT 191 patients in Egypt Trial underpowered for serious outcomes Abd-Elsalam et al., Biological Trace Element Res.., doi:10.1007/s12011-020-02512-1 Favors zinc Favors control
[Abd-Elsalam] 191 patient RCT in Egypt comparing the addition of zinc to HCQ, not showing a significant difference. No information on baseline zinc values was recorded. Egypt has a low rate of zinc deficiency so supplementation may be less likely to be helpful [ncbi.nlm.nih.gov, ncbi.nlm.nih.gov (B)]. For several issues with this trial, see [osf.io].
0 0.5 1 1.5 2+ Mortality 30% Improvement Relative Risk Death/ICU 38% ICU admission 54% Oxygen therapy, day 30 42% Oxygen therapy, day 15 23% Recovery, day 30 29% Recovery, day 15 14% Hospitalization, outpatients 69% Hospitalization time, inp.. 33% Recovery time, outpatients 25% c19early.org/z Abdallah et al. NCT05212480 VIZIR Zinc RCT EARLY TREATMENT Is early treatment with zinc beneficial for COVID-19? Double-blind RCT 470 patients in Tunisia (February - May 2022) Lower death/ICU (p=0.04) and ICU admission (p=0.01) Abdallah et al., Clinical Infectious Diseases, doi:10.1093/cid/ciac807 Favors zinc Favors control
[Abdallah] RCT 470 patients with symptoms ≤7 days, showing significantly lower ICU admission and combined mortality/ICU admission with zinc treatment. Greater benefit was seen for patients treated within 3 days. 25mg elemental zinc bid for 15 days.
0 0.5 1 1.5 2+ Hospitalization 13% Improvement Relative Risk c19early.org/z Abdulateef et al. Zinc for COVID-19 Prophylaxis Is prophylaxis with zinc beneficial for COVID-19? Retrospective 428 patients in Iraq (July - August 2020) Study underpowered to detect differences Abdulateef et al., Open Medicine, doi:10.1515/med-2021-0273 Favors zinc Favors control
[Abdulateef] Survey of 428 recovered COVID-19 patients in Iraq, showing fewer hospital visits for patients on prophylactic vitamin C or D. Hospitalization was lower for those on vitamin C, D, or zinc, without statistical significance.
0 0.5 1 1.5 2+ Case -12% Improvement Relative Risk c19early.org/z Adrean et al. Zinc for COVID-19 Prophylaxis Does zinc reduce COVID-19 infections? Retrospective 8,426 patients in the USA (April 2020 - April 2021) No significant difference in cases Adrean et al., Cureus, doi:10.7759/cureus.30881 Favors zinc Favors control
[Adrean] Retrospective 8,426 patients in the USA, showing no significant difference in cases with zinc prophylaxis. Severity results were not reported due to the small number of events.
0 0.5 1 1.5 2+ Mortality 36% Improvement Relative Risk Mortality (b) 48% ICU time -25% Hospitalization time -6% c19early.org/z Al Sulaiman et al. Zinc for COVID-19 ICU PATIENTS Is very late treatment with zinc beneficial for COVID-19? PSM retrospective 164 patients in Saudi Arabia (Mar 2020 - Mar 2021) Lower mortality (p=0.11) and longer ICU admission (p=0.28), not stat. sig. Al Sulaiman et al., Critical Care, doi:10.1186/s13054-021-03785-1 Favors zinc Favors control
[Al Sulaiman] Retrospective 266 ICU patients showing lower mortality with zinc treatment (very close to statistical significance), and higher odds of acute kidney injury. NRC21R/287/07.
0 0.5 1 1.5 2+ Hospitalization time 30% Improvement Relative Risk c19early.org/z Alahmari et al. Zinc for COVID-19 LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? Retrospective 977 patients in Saudi Arabia (May - July 2020) Shorter hospitalization with zinc (p<0.000001) Alahmari et al., Healthcare, doi:10.3390/healthcare10071201 Favors zinc Favors control
[Alahmari] Retrospective 977 hospitalized patients in Saudi Arabia, showing significantly shorter hospitalization with zinc treatment.
0 0.5 1 1.5 2+ Hospitalization 24% Improvement Relative Risk c19early.org/z Aldwihi et al. Zinc for COVID-19 EARLY TREATMENT Is early treatment with zinc beneficial for COVID-19? Retrospective 738 patients in Saudi Arabia (August - October 2020) Lower hospitalization with zinc (not stat. sig., p=0.16) Aldwihi et al., Int. J. Environmental Research a.., doi:10.3390/ijerph18105086 Favors zinc Favors control
[Aldwihi] Retrospective survey-based analysis of 738 COVID-19 patients in Saudi Arabia, showing lower hospitalization with vitamin C, turmeric, zinc, and nigella sativa, and higher hospitalization with vitamin D. For vitamin D, most patients continued prophylactic use. For vitamin C, the majority of patients continued prophylactic use. For nigella sativa, the majority of patients started use during infection. Authors do not specify the fraction of prophylactic use for turmeric and zinc.
0 0.5 1 1.5 2+ Ventilation 97% Improvement Relative Risk Hospitalization 99% Severe case 100% c19early.org/z Asimi et al. Zinc for COVID-19 EARLY TREATMENT Is early treatment with zinc+vitamin D and selenium beneficial for COVID-19? Retrospective 356 patients in Bosnia and Herzegovina Lower ventilation (p<0.0001) and hospitalization (p<0.0001) Asimi et al., Endocrine Abstracts, doi:10.1530/endoabs.73.PEP14.2 Favors zinc Favors control
[Asimi] Retrospective 356 Hashimoto's thyroiditis outpatients, 270 taking vitamin D, zinc, and selenium, showing significantly lower hospitalization with treatment. Authors adjust for age, gender, BMI, and smoking status, reporting statistically significant associations with p<0.001 for hospitalization and mechanical ventilation, however they do not report the adjusted risks.
0 0.5 1 1.5 2+ Severe case 57% Improvement Relative Risk c19early.org/z Asoudeh et al. Zinc for COVID-19 Prophylaxis Is prophylaxis with zinc beneficial for COVID-19? Retrospective 250 patients in Iran (June - September 2021) Lower severe cases with zinc (p=0.03) Asoudeh et al., Clinical Nutrition ESPEN, doi:10.1016/j.clnesp.2023.03.013 Favors zinc Favors control
[Asoudeh] Retrospective 250 recovered COVID-19 patients, showing lower risk of severe cases with higher zinc intake.
0 0.5 1 1.5 2+ Mortality -81% Improvement Relative Risk c19early.org/z Assiri et al. Zinc for COVID-19 ICU PATIENTS Is very late treatment with zinc beneficial for COVID-19? Retrospective 118 patients in Saudi Arabia Higher mortality with zinc (not stat. sig., p=0.44) Assiri et al., J. Infection and Public Health, doi:10.1016/j.jiph.2021.08.030 Favors zinc Favors control
[Assiri] Retrospective 118 ICU patients in Saudi Arabia showing no significant differences in unadjusted results with zinc, vitamin D, and favipiravir treatment.
0 0.5 1 1.5 2+ Severe case 60% Improvement Relative Risk Hospitalization 41% c19early.org/z Bagheri et al. Zinc for COVID-19 Prophylaxis Is prophylaxis with zinc beneficial for COVID-19? Retrospective 510 patients in Iran Lower severe cases (p=0.41) and hospitalization (p=0.37), not stat. sig. Bagheri et al., J. Family & Reproductive Health, doi:10.18502/jfrh.v14i3.4668 Favors zinc Favors control
[Bagheri] Retrospective 510 patients in Iran, showing lower risk of severity with vitamin D (statistically significant) and zinc (not statistically significant) supplementation. IR.TUMS.VCR.REC.1398.1063.
0 0.5 1 1.5 2+ Death/hospice 38% Improvement Relative Risk Ventilation 18% ICU admission 23% c19early.org/z Carlucci et al. Zinc for COVID-19 LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? Retrospective 932 patients in the USA Lower death/hospice with zinc (p=0.002) Carlucci et al., J. Med. Microbiol., Sep 15, 2020, doi: 10.1099/jmm.0.001250 Favors zinc Favors control
[Carlucci] Retrospective 932 patients showing that the addition of zinc to HCQ+AZ reduced mortality / transfer to hospice, ICU admission, and the need for ventilation.
0 0.5 1 1.5 2+ Severe case 18% Improvement Relative Risk c19early.org/z Citu et al. Zinc for COVID-19 Prophylaxis Is prophylaxis with zinc+calcium beneficial for COVID-19? Retrospective 135 patients in Romania (April 2020 - February 2022) Study underpowered to detect differences Citu et al., Nutrients, doi:10.3390/nu14071445 Favors zinc Favors control
[Citu] Retrospective 448 pregnant women with COVID-19. Patients with calcium, zinc, and magnesium supplementation, or magnesium only, had a significantly higher titer of SARS-CoV-2 anti-RBD antibodies. There was no statistically significant difference in severe cases based on supplementation.
0 0.5 1 1.5 2+ Progression 33% Improvement Relative Risk ICU time 6% c19early.org/z Darban et al. Zinc for COVID-19 RCT ICU PATIENTS Is very late treatment with zinc+melatonin and vitamin C beneficial for COVID-19? RCT 20 patients in Iran Trial underpowered to detect differences Darban et al., J. Cellular & Molecular Anesthesia, doi:10.22037/jcma.v6i2.32182 Favors zinc Favors control
[Darban] Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences. IRCT20151228025732N52.
0 0.5 1 1.5 2+ Mortality 79% Improvement Relative Risk Hospitalization 82% c19early.org/z Derwand et al. Zinc for COVID-19 EARLY TREATMENT Is early treatment with zinc+HCQ and azithromycin beneficial for COVID-19? Retrospective 518 patients in the USA Lower hospitalization with zinc+HCQ and azithromycin (p=0.001) Derwand et al., Int. J. Antimicrobial Agents, doi:10.1016/j.ijantimicag.2020.106214 Favors zinc Favors control
[Derwand] 79% lower mortality and 82% lower hospitalization with early HCQ+AZ+Z. Retrospective 518 patients (141 treated, 377 control).
0 0.5 1 1.5 2+ Mortality 41% unadjusted Improvement Relative Risk c19early.org/z Doocy et al. NCT04568499 Zinc LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? Prospective study of 144 patients in multiple countries (Dec 2020 - Jun 2021) Lower mortality with zinc (not stat. sig., p=0.41) Doocy et al., PLOS Global Public Health, doi:10.1371/journal.pgph.0000924 Favors zinc Favors control
[Doocy] Prospective study of 144 hospitalized COVID-19 patients in the DRC and South Sudan, showing lower mortality with zinc treatment, without statistical significance.
0 0.5 1 1.5 2+ Case 77% Improvement Relative Risk c19early.org/z Doğan et al. Zinc for COVID-19 Sufficiency Are zinc levels associated with COVID-19 outcomes? Prospective study of 176 patients in Turkey (Jul - Oct 2021) Fewer cases with higher zinc levels (p=0.0031) Doğan et al., J. Tropical Pediatrics, doi:10.1093/tropej/fmac072 Favors zinc Favors control
[Doğan] Prospective study of 88 pediatric COVID-19 patients and 88 healthy controls, showing significantly lower zinc and vitamin D levels in COVID-19 patients.
0 0.5 1 1.5 2+ Mortality 79% Improvement Relative Risk Mortality (b) 78% c19early.org/z Du Laing et al. Zinc for COVID-19 Sufficiency Are zinc levels associated with COVID-19 outcomes? Retrospective 73 patients in Belgium Lower mortality with higher zinc levels (p=0.012) Du Laing et al., Nutrients, doi:10.3390/nu13103304 Favors zinc Favors control
[Du Laing] Retrospective 73 hospitalized COVID-19 patients in Belgium, showing higher risk of mortality with selenium deficiency and zinc deficiency.
0 0.5 1 1.5 2+ Hospitalization 75% Improvement Relative Risk c19early.org/z Ekemen Keleş et al. Zinc for COVID-19 Sufficiency Are zinc levels associated with COVID-19 outcomes? Prospective study of 100 patients in Turkey (Aug - Nov 2020) Lower hospitalization with higher zinc levels (p=0.011) Ekemen Keleş et al., European J. Pediatrics, doi:10.1007/s00431-021-04348-w Favors zinc Favors control
[Ekemen Keleş] Prospective study of 100 COVID+ pediatric patients in Turkey, showing significantly increased risk of hospitalization for patients with zinc deficiency.
0 0.5 1 1.5 2+ Mortality 65% Improvement Relative Risk c19early.org/z Elavarasi et al. Zinc for COVID-19 LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? Retrospective 1,687 patients in India Lower mortality with zinc (p=0.0000016) Elavarasi et al., medRxiv, doi:10.1101/2021.08.10.21261855 Favors zinc Favors control
[Elavarasi] Retrospective 2017 hospitalized patients in India, showing lower mortality with zinc treatment.
0 0.5 1 1.5 2+ Hospitalization 89% Improvement Relative Risk Case 28% c19early.org/z Fromonot et al. Zinc for COVID-19 Sufficiency Are zinc levels associated with COVID-19 outcomes? Prospective study of 240 patients in France Lower hospitalization (p=0.002) and fewer cases (p=0.003) Fromonot et al., Clinical Nutrition, doi:10.1016/j.clnu.2021.04.042 Favors zinc Favors control
[Fromonot] Analysis of 240 consecutive patients in France, showing significantly higher zinc deficiency in COVID-19 patients, and significantly greater risk of hospitalization for COVID-19 patients with zinc deficiency. 2020PI087.
0 0.5 1 1.5 2+ Mortality 37% Improvement Relative Risk Mortality (b) 24% c19early.org/z Frontera et al. Zinc for COVID-19 LATE TREATMENT Is late treatment with zinc+HCQ beneficial for COVID-19? PSM retrospective 3,473 patients in the USA Lower mortality with zinc+HCQ (p=0.015) Frontera et al., Research Square, doi:10.21203/rs.3.rs-94509/v1 Favors zinc Favors control
[Frontera] Retrospective 3,473 hospitalized patients showing 37% lower mortality with HCQ+zinc.

PSM aHR 0.63, p=0.015
regression aHR 0.76, p = 0.023
0 0.5 1 1.5 2+ Mortality -41% Improvement Relative Risk c19early.org/z Gadhiya et al. Zinc for COVID-19 LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? Retrospective 283 patients in the USA Higher mortality with zinc (not stat. sig., p=0.33) Gadhiya et al., BMJ Open, doi:10.1136/bmjopen-2020-042549 Favors zinc Favors control
[Gadhiya] Retrospective 283 patients in the USA showing higher mortality with all treatments (not statistically significant). Confounding by indication is likely. In the supplementary appendix, authors note that the treatments were usually given for patients that required oxygen therapy. Oxygen therapy and ICU admission (possibly, the paper includes ICU admission for model 2 in some places but not others) were the only variables indicating severity used in adjustments.
0 0.5 1 1.5 2+ Severe case 82% Improvement Relative Risk c19early.org/z Gonçalves et al. Zinc for COVID-19 ICU PATIENTS Sufficiency Are zinc levels associated with COVID-19 outcomes? Retrospective 269 patients in Brazil Lower severe cases with higher zinc levels (p=0.001) Gonçalves et al., Nutrition in Clinical Practice, doi:10.1002/ncp.10612 Favors zinc Favors control
[Gonçalves] Retrospective 169 ICU patients in Brazil, 214 with low zinc levels, showing an association between low zinc levels and severe ARDS. CAAE 30608,020.9.0000.8114.
0 0.5 1 1.5 2+ Mortality 68% Improvement Relative Risk Symptomatic case 85% c19early.org/z Gordon et al. Zinc for COVID-19 Prophylaxis Is prophylaxis with zinc beneficial for COVID-19? Prospective study of 200 patients in the USA Fewer symptomatic cases with zinc (p=0.022) Gordon et al., Frontiers in Medicine, doi:10.3389/fmed.2021.756707 Favors zinc Favors control
[Gordon] Prospective study of zinc supplementation with 104 patients randomized to receive 10mg, 25mg, or 50mg of zinc picolinate daily, and a matched sample of 96 control patients from the adjacent clinic that did not routinely recommend/use zinc, showing significantly lower symptomatic COVID-19 with treatment.
0 0.5 1 1.5 2+ Case 7% Improvement Relative Risk c19early.org/z Holt et al. NCT04330599 COVIDENCE UK Zinc Prophylaxis Does zinc reduce COVID-19 infections? Prospective study of 15,227 patients in the United Kingdom (May 2020 - Feb 2021) No significant difference in cases Holt et al., Thorax, doi:10.1136/thoraxjnl-2021-217487 Favors zinc Favors control
[Holt] Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. NCT04330599. COVIDENCE UK.
0 0.5 1 1.5 2+ Mortality 88% Improvement Relative Risk Ventilation 26% ICU admission 3% Respiratory failure 73% Hospitalization time 29% c19early.org/z Ibrahim Alhajjaji et al. Zinc LATE TREATMENT Is late treatment with zinc beneficial for COVID-19? Retrospective 101 patients in Saudi Arabia (March 2020 - December 2021) Lower progression (p=0.0042) and shorter hospitalization (p=0.017) Ibrahim Alhajjaji et al., Saudi Pharmaceutical J., doi:10.1016/j.jsps.2023.02.011 Favors zinc Favors control
[Ibrahim Alhajjaji] Retrospective 101 hospitalized pediatric patients in Saudi Arabia, showing zinc treatment associated with lower respiratory failure and shorter hospitalization in unadjusted results. Patients receiving zinc were older. Authors note elevated serum creatinine and the possibility of kidney injury.