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Zinc
Zinc for COVID-19: real-time meta analysis of 49 studies
Covid Analysis, December 2022
https://c19early.org/zmeta.html
 
0 0.5 1 1.5+ All studies 28% 38 45,372 Improvement, Studies, Patients Relative Risk Mortality 28% 18 13,027 Ventilation 51% 5 3,537 ICU admission 28% 6 3,644 Hospitalization 30% 13 6,353 Progression 74% 2 2,048 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 24% 34 39,826 Exc. combined 24% 31 40,840 Sufficiency 74% 11 1,116 Prophylaxis 30% 15 29,489 Early 41% 6 4,218 Late 26% 17 11,665 Zinc for COVID-19 c19early.org/z Dec 2022 Favorszinc Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ventilation, hospitalization, recovery, and viral clearance. 15 studies from 15 independent teams in 8 different countries show statistically significant improvements in isolation (10 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 28% [16‑38%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment. Early treatment is more effective than late treatment.
Sufficiency studies, analyzing outcomes based on serum levels, show 74% [64‑82%] improvement for patients with higher zinc levels (11 studies).
Results are robust — in exclusion sensitivity analysis 14 of 38 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 28% 38 45,372 Improvement, Studies, Patients Relative Risk Mortality 28% 18 13,027 Ventilation 51% 5 3,537 ICU admission 28% 6 3,644 Hospitalization 30% 13 6,353 Progression 74% 2 2,048 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 24% 34 39,826 Exc. combined 24% 31 40,840 Sufficiency 74% 11 1,116 Prophylaxis 30% 15 29,489 Early 41% 6 4,218 Late 26% 17 11,665 Zinc for COVID-19 c19early.org/z Dec 2022 Favorszinc Favorscontrol after exclusions
7 studies use combined treatments. When excluding those studies, the pooled improvement is 24% [13‑34%] compared to 28% [16‑38%].
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 8% 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 [Olczak-Pruc, Tabatabaeizadeh], showing significant improvement for mortality.
Highlights
Zinc reduces risk for COVID-19 with very high confidence for mortality, recovery, and in pooled analysis, high confidence for ventilation and hospitalization, low confidence for ICU admission, progression, and viral clearance, and very low confidence for 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 47 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) 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 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 Tau​2 = 0.11, I​2 = 86.7%, p = 0.0056 Late treatment 26% 0.74 [0.60-0.92] 602/4,242 988/7,423 26% improvement Louca 1% 0.99 [0.93-1.06] cases Improvement, RR [CI] Treatment Control Mahto 37% 0.63 [0.22-1.49] IgG+ 10/38 83/651 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 Tau​2 = 0.18, I​2 = 81.6%, p = 0.023 Prophylaxis 30% 0.70 [0.52-0.95] 155/4,380 884/25,109 30% improvement All studies 28% 0.72 [0.62-0.84] 831/10,459 2,103/34,913 28% improvement 38 zinc COVID-19 studies c19early.org/z Dec 2022 Tau​2 = 0.10, I​2 = 84.1%, 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 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 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 Tau​2 = 0.11, I​2 = 86.7%, p = 0.0056 Late treatment 26% 26% improvement Louca 1% case Mahto 37% IgG positive 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 Tau​2 = 0.18, I​2 = 81.6%, p = 0.023 Prophylaxis 30% 30% improvement All studies 28% 28% improvement 38 zinc COVID-19 studies c19early.org/z Dec 2022 Tau​2 = 0.10, I​2 = 84.1%, 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, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. 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, along with the result of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in zinc 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for 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 by treatment stage and with different exclusions. 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.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 3841% [8‑61%]26% [8‑40%]30% [5‑48%] 45,372 402
After exclusions 2637% [10‑55%]36% [27‑43%]20% [-3‑38%] 25,087 292
Peer-reviewed 3437% [10‑55%]19% [-1‑36%]30% [5‑48%] 39,826 347
Randomized Controlled TrialsRCTs 821% [-41‑55%]14% [-90‑61%]50% [26‑67%] 2,220 114
Table 1. Random effects meta-analysis results by treatment stage.
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. 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 peer-reviewed studies are more trustworthy. They also show extremely slow review times during a 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 and 17 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for 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. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, 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 example, consider trials for an off-patent medication, 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, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. 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.
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 18 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[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.
[Israel], treatment or control group size extremely small.
[Krishnan], unadjusted results with no group details.
[Kumar], 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 18. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. 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.
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)]
Table 2. Early treatment is more effective for baloxavir and influenza.
Figure 19 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 19. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments. Early treatment is critical.
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 20. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 20. 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.
41% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 36% of prospective studies, showing similar results. The median effect size for retrospective studies is 34% improvement, compared to 30% for prospective studies, showing similar results. Figure 21 shows a scatter plot of results for prospective and retrospective treatment studies.
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Figure 21. Prospective vs. retrospective studies.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 22 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 22. 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.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that 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.
1 of the 38 studies compare against other treatments, which may reduce the effect seen. 7 of 38 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 [Olczak-Pruc, Tabatabaeizadeh], showing significant improvement for mortality.
Table 3 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.
LATE TREATMENT
Physician / TeamLocationPatients HospitalizationHosp. MortalityDeath
Dr. David Uip (*) Brazil 2,200 38.6% (850) Ref. 2.5% (54) Ref.
EARLY TREATMENT - 36 physicians/teams
Physician / TeamLocationPatients HospitalizationHosp. ImprovementImp. MortalityDeath ImprovementImp.
Dr. Roberto Alfonso Accinelli
0/360 deaths for treatment within 3 days
Peru 1,265 0.6% (7) 77.5%
Dr. Mohammed Tarek Alam
patients up to 84 years old
Bangladesh 100 0.0% (0) 100.0%
Dr. Oluwagbenga Alonge Nigeria 310 0.0% (0) 100.0%
Dr. Raja Bhattacharya
up to 88yo, 81% comorbidities
India 148 1.4% (2) 44.9%
Dr. Flavio Cadegiani Brazil 3,450 0.1% (4) 99.7% 0.0% (0) 100.0%
Dr. Alessandro Capucci Italy 350 4.6% (16) 88.2%
Dr. Shankara Chetty South Africa 8,000 0.0% (0) 100.0%
Dr. Deborah Chisholm USA 100 0.0% (0) 100.0%
Dr. Ryan Cole USA 400 0.0% (0) 100.0% 0.0% (0) 100.0%
Dr. Marco Cosentino
vs. 3-3.8% mortality during period; earlier treatment better
Italy 392 6.4% (25) 83.5% 0.3% (1) 89.6%
Dr. Jeff Davis USA 6,000 0.0% (0) 100.0%
Dr. Dhanajay India 500 0.0% (0) 100.0%
Dr. Bryan Tyson & Dr. George Fareed USA 4,375 0.2% (9) 99.5% 0.1% (3) 97.2%
Dr. Heather Gessling USA 1,500 0.1% (1) 97.3%
Dr. Ellen Guimarães Brazil 500 1.6% (8) 95.9% 0.4% (2) 83.7%
Dr. Syed Haider USA 4,000 0.1% (5) 99.7% 0.0% (0) 100.0%
Dr. Mark Hancock USA 24 0.0% (0) 100.0%
IppocrateOrg Italy 392 6.4% (25) 83.5% 0.3% (1) 89.6%
Dr. Mollie James USA 3,500 1.1% (40) 97.0% 0.0% (1) 98.8%
Dr. Roberta Lacerda Brazil 550 1.5% (8) 96.2% 0.4% (2) 85.2%
Dr. Katarina Lindley USA 100 5.0% (5) 87.1% 0.0% (0) 100.0%
Dr. Ben Marble USA 150,000 0.0% (4) 99.9%
Dr. Edimilson Migowski Brazil 2,000 0.3% (7) 99.1% 0.1% (2) 95.9%
Dr. Abdulrahman Mohana Saudi Arabia 2,733 0.0% (0) 100.0%
Dr. Carlos Nigro Brazil 5,000 0.9% (45) 97.7% 0.5% (23) 81.3%
Dr. Benoit Ochs Luxembourg 800 0.0% (0) 100.0%
Dr. Ortore Italy 240 1.2% (3) 96.8% 0.0% (0) 100.0%
Dr. Valerio Pascua
one death for a patient presenting on the 5th day in need of supplemental oxygen
Honduras 415 6.3% (26) 83.8% 0.2% (1) 90.2%
Dr. Sebastian Pop Romania 300 0.0% (0) 100.0%
Dr. Brian Proctor USA 869 2.3% (20) 94.0% 0.2% (2) 90.6%
Dr. Anastacio Queiroz Brazil 700 0.0% (0) 100.0%
Dr. Didier Raoult France 8,315 2.6% (214) 93.3% 0.1% (5) 97.6%
Dr. Karin Ried
up to 99yo, 73% comorbidities, av. age 63
Turkey 237 0.4% (1) 82.8%
Dr. Roman Rozencwaig
patients up to 86 years old
Canada 80 0.0% (0) 100.0%
Dr. Vipul Shah India 8,000 0.1% (5) 97.5%
Dr. Vladimir Zelenko USA 2,200 0.5% (12) 98.6% 0.1% (2) 96.3%
Mean improvement with early treatment protocols 220,045 HospitalizationHosp. 94.1% MortalityDeath 94.2%
Table 3. 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].
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
Zinc is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ventilation, hospitalization, recovery, and viral clearance. 15 studies from 15 independent teams in 8 different countries show statistically significant improvements in isolation (10 for the most serious outcome). Meta analysis using the most serious outcome reported shows 28% [16‑38%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment. Early treatment is more effective than late treatment. Sufficiency studies, analyzing outcomes based on serum levels, show 74% [64‑82%] improvement for patients with higher zinc levels (11 studies). Results are robust — in exclusion sensitivity analysis 14 of 38 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 24% [13‑34%] compared to 28% [16‑38%].
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 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. We note that Egypt has a low rate of zinc deficiency so supplementation is less likely to be helpful in Egypt [ncbi.nlm.nih.gov, ncbi.nlm.nih.gov (B)].
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 Zinc RCT EARLY TREATMENT 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 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 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 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 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 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 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+ Mortality -81% Improvement Relative Risk c19early.org/z Assiri et al. Zinc for COVID-19 ICU PATIENTS 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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+ Hospitalization 100% Improvement Relative Risk c19early.org/z Israel et al. Zinc for COVID-19 Prophylaxis Favors zinc Favors control
[Israel] Case control study examining medication usage with a healthcare database in Israel, showing lower risk of hospitalization with calcium + zinc supplements (defined as being picked up within 35 days prior to PCR+), however only 10 patients took the supplements. Other patients may have acquired supplements outside of the healthcare system.
0 0.5 1 1.5 2+ Mortality 90% Improvement Relative Risk ICU admission 92% c19early.org/z Jothimani et al. Zinc for COVID-19 Sufficiency Favors zinc Favors control
[Jothimani] Prospective study of zinc levels in 47 hospitalized COVID-19 patients and 45 healthy controls. COVID-19 patients had significantly lower zinc levels (74.5 vs. 105.8 median μg/dl, p < 0.001). 57.4% of COVID-19 patients were zinc deficient, and they had higher rates of complications, ARDS, prolonged hospital stay, and increased mortality.
0 0.5 1 1.5 2+ Ventilation -14% Improvement Relative Risk ICU admission -14% Hospitalization -14% c19early.org/z Kaplan et al. Zinc for COVID-19 RCT LATE TREATMENT Favors zinc Favors control
[Kaplan] Small RCT of zinc plus resveratrol in COVID-19+ outpatients, showing no significant differences in viral clearance or symptoms. Although the treatment group was older (46.3 vs. 38.5) and had more severe baseline symptoms, they had similar symptomatic recovery by the second week.
0 0.5 1 1.5 2+ Mortality 18% Improvement Relative Risk c19early.org/z Krishnan et al. Zinc for COVID-19 LATE TREATMENT Favors zinc Favors control
[Krishnan] Retrospective 152 mechanically ventilated patients in the USA showing unadjusted lower mortality with vitamin C, vitamin D, HCQ, and zinc treatment, statistically significant only for vitamin C.
0 0.5 1 1.5 2+ Mortality 20% unadjusted Improvement Relative Risk c19early.org/z Kumar et al. Zinc for COVID-19 Prophylaxis Favors zinc Favors control
[Kumar] Case control study of 105 COVID-19 patients in India, 55 with mucormycosis and 50 without, showing zinc prophylaxis and diabetes both associated with mucormycosis in unadjusted results. This is likely confounded because zinc supplementation is commonly used with diabetes [academic.oup.com], and Arora et al. show lower risk of mucormycosis with zinc prophylaxis, aOR 0.05 [0.01–0.19] [Arora]. There was no significant difference in mortality based on zinc prophylaxis in unadjusted results.
0 0.5 1 1.5 2+ Case 1% Improvement Relative Risk c19early.org/z Louca et al. Zinc for COVID-19 Prophylaxis Favors zinc Favors control
[Louca] Survey analysis of dietary supplements showing no significant difference in PCR+ cases with zinc usage. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app.
0 0.5 1 1.5 2+ IgG positive 37% Improvement Relative Risk c19early.org/z Mahto et al. Zinc for COVID-19 Prophylaxis Favors zinc Favors control
[Mahto] Retrospective 689 healthcare workers in India, showing no significant difference in IgG positivity with zinc prophylaxis.
0 0.5 1 1.5 2+ Mortality 53% Improvement Relative Risk Ventilation 64% ICU admission 60% Death/ventilation/ICU 58% primary Progression to ARDS 85% c19early.org/z Mayberry et al. Zinc for COVID-19 EARLY TREATMENT Favors zinc Favors control
[Mayberry] Retrospective 2,028 COVID patients in the USA, showing significantly lower mortality, ventilation, ICU admission, and progression to ARDS with zinc use, defined as at least one dose from one week prior to admission to 48 hours after admission.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk c19early.org/z Mulhem et al. Zinc for COVID-19 LATE TREATMENT Favors zinc Favors control
[Mulhem] Retrospective database analysis of 3,219 hospitalized patients in the USA. Very different results in the time period analysis (Table S2), and results significantly different to other studies for the same medications (e.g., heparin OR 3.06 [2.44-3.83]) suggest significant confounding by indication and confounding by time.
0 0.5 1 1.5 2+ Hospitalization -25% Improvement Relative Risk Severe case -13% c19early.org/z Nimer et al. Zinc for COVID-19 Prophylaxis Favors zinc Favors control
[Nimer] Retrospective survey based analysis of 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with zinc prophylaxis.
0 0.5 1 1.5 2+ Mortality 20% Improvement Relative Risk c19early.org/z Patel et al. Zinc for COVID-19 RCT LATE TREATMENT Favors zinc Favors control
[Patel] Small early terminated RCT with 33 hospitalized patients in Australia, 15 treated with zinc, showing no significant difference in clinical outcomes. Treatment increased zinc levels above the deficiency cutoff. Intravenous zinc 0.5mg/kg/day (elemental zinc concentration 0.24mg/kg/day) for up to 7 days. ACTRN12620000454976.
0 0.5 1 1.5 2+ Case 24% Improvement Relative Risk c19early.org/z Ramos et al. Zinc for COVID-19 Sufficiency Favors zinc Favors control
[Ramos] Retrospective 13 COVID-19 patients and 7 controls in Brazil, showing no significant difference in zinc deficiency.
0 0.5 1 1.5 2+ Mortality -16% Improvement Relative Risk c19early.org/z Rosenthal et al. Zinc for COVID-19 LATE TREATMENT Favors zinc Favors control
[Rosenthal] Retrospective database analysis of 64,781 hospitalized patients in the USA, showing lower mortality with vitamin C or vitamin D (authors do not distinguish between the two), and higher mortality with zinc and HCQ, statistically significant for zinc. Authors excluded hospital-based outpatient visits, without explanation. Confounding by indication is likely, adjustments do not appear to include any information on COVID-19 severity at baseline.
0 0.5 1 1.5 2+ Symptomatic case 50% Improvement