Zinc for COVID-19: real-time meta analysis of 57 studies (43 treatment studies and 14 sufficiency studies)
@CovidAnalysis, September 2023
https://c19early.org/zmeta.html
•Statistically significant lower risk is seen for mortality, ventilation, hospitalization, progression, recovery, and viral clearance. 17 studies from 17 independent teams in 9 countries show statistically significant
improvements.
•Meta analysis using the most serious outcome reported shows
29% [18‑38%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, peer-reviewed studies, and after excluding studies using combined treatment.
•14 sufficiency studies analyze outcomes based on serum levels,
showing 72% [63‑79%] lower risk for patients with higher zinc levels.
•Results are robust — in exclusion sensitivity analysis 18 of 43
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
•8 studies use combined treatments.
After exclusion the risk reduction is 25% [15‑35%] compared
to 29% [18‑38%].
•2 RCTs
with 255 patients have not reported results (up to 1.5 years late).
•The European Food Safety Authority has found evidence for a causal relationship between the intake of zinc and optimal immune system function Galmés, Galmés (B). Over-supplementation may be detrimental karger.com. Bioaccessibility of supplements varies widely Ośko.
•No treatment or intervention
is 100% effective. 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.
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 show significant improvements with zinc for mortality Abuhelwa, Olczak-Pruc, Rheingold, Tabatabaeizadeh, Xie, severity Fan, and cases Fan.
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 ICU admission and viral clearance, and very low confidence for 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 56
treatments.
B
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C
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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.
0.7% of 5,019 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.
3 In Vitro studies support the efficacy of zinc Hajdrik, Panchariya, te Velthuis.
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.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 29% [18‑38%] **** | 43 | 55,200 | 451 |
| After exclusions | 32% [21‑41%] **** | 28 | 34,461 | 308 |
| Peer-reviewed studiesPeer-reviewed | 26% [14‑36%] **** | 39 | 49,654 | 396 |
| Excluding combined treatmentExc. combined | 25% [15‑35%] **** | 35 | 50,582 | 377 |
| Randomized Controlled TrialsRCTs | 39% [18‑55%] ** | 9 | 2,306 | 124 |
| Mortality | 29% [10‑44%] ** | 20 | 13,290 | 199 |
| VentilationVent. | 44% [4‑68%] * | 7 | 12,867 | 59 |
| ICU admissionICU | 26% [-7‑49%] | 8 | 13,012 | 88 |
| HospitalizationHosp. | 29% [7‑45%] * | 14 | 6,454 | 123 |
| Recovery | 20% [8‑31%] ** | 4 | 827 | 55 |
| Cases | 22% [-10‑45%] | 6 | 25,221 | 105 |
| RCT mortality | 24% [-29‑55%] | 3 | 694 | 46 |
| RCT hospitalizationRCT hosp. | 4% [-8‑14%] | 4 | 514 | 57 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 41% [8‑61%] * | 27% [10‑40%] ** | 31% [9‑49%] * |
| After exclusions | 37% [10‑55%] * | 36% [28‑43%] **** | 23% [2‑40%] * |
| Peer-reviewed studiesPeer-reviewed | 37% [10‑55%] * | 21% [2‑36%] * | 31% [9‑49%] * |
| Excluding combined treatmentExc. combined | 34% [7‑54%] * | 25% [6‑40%] * | 22% [4‑36%] * |
| Randomized Controlled TrialsRCTs | 21% [-41‑55%] | 21% [-60‑61%] | 50% [26‑67%] *** |
| Mortality | 50% [33‑63%] **** | 24% [2‑41%] * | 30% [-137‑79%] |
| VentilationVent. | 86% [-66‑99%] | 20% [-16‑45%] | 18% [-213‑78%] |
| ICU admissionICU | 59% [48‑68%] **** | 6% [-5‑15%] | 30% [-154‑81%] |
| HospitalizationHosp. | 66% [-4‑89%] | 15% [-5‑31%] | 81% [-3‑96%] |
| Recovery | 23% [4‑37%] * | 11% [-19‑34%] | |
| Cases | 22% [-10‑45%] | ||
| RCT mortality | 30% [-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 56 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, 38 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 38 treatments with statistically significant efficacy/harm, 24 have been confirmed in RCTs, with a mean delay of 5.7 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and 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.
| Treatment delay | Result |
| Post exposure prophylaxis | 86% 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 56 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 56 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, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.9 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% [12‑46%] 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.
Studies for zinc were primarily late treatment studies, in
contrast with typical patented treatments that were tested with early
treatment as recommended.
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Figure 23. Patented treatments received mostly early treatment studies, while low cost treatments were typically tested for late treatment.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 24 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.05
Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 24. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. 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.
| LATE TREATMENT | ||||||
| Physician / Team | Location | Patients | HospitalizationHosp. | MortalityDeath | ||
| Dr. David Uip (*) | Brazil | 2,200 | 38.6% (850) | Ref. | 2.5% (54) | Ref. |
| EARLY TREATMENT - 39 physicians/teams | ||||||
| Physician / Team | Location | Patients | 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 43
studies compare against other treatments, which may reduce the effect
seen.
8 of 43 studies
combine treatments. The results of
zinc
alone may differ.
3 of 9 RCTs use combined treatment.
Other meta analyses show significant improvements with zinc for mortality Abuhelwa, Olczak-Pruc, Rheingold, Tabatabaeizadeh, Xie, severity Fan, and cases Fan.
NIH provides an analysis of zinc for
COVID-19 covid19treatmentguidelines.nih.gov, concluding that there is insufficient evidence to
recommend for or against use. However, they appear not to have looked at the
majority of the evidence. For example, considering RCTs providing clinical
results for COVID-19 and zinc, they reference only
Abd-Elsalam, Abdallah, Thomas, and appear not to know about 6 other RCTs Darban, Kaplan, Patel, Seely, Seet, Stambouli
as shown in Figure 25.
Notably, the NIH selection is not based on quality, for example
including Abd-Elsalam, with data reliability issues, and not
including Seet, a much larger and higher quality trial.
Loading..
Figure 25. Analysis by NIH is missing 6 RCTs.
Zinc is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, ventilation, hospitalization, progression, recovery, and viral clearance. 17 studies from 17 independent teams in 9 countries show statistically significant
improvements.
Meta analysis using the most serious outcome reported shows
29% [18‑38%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, peer-reviewed studies, and after excluding studies using combined treatment.
14 sufficiency studies analyze outcomes based on serum levels,
showing 72% [63‑79%] lower risk for patients with higher zinc levels.
Results are robust — in exclusion sensitivity analysis 18 of 43
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
8 studies use combined treatments.
After exclusion the risk reduction is 25% [15‑35%] compared
to 29% [18‑38%].
The European Food Safety Authority has found evidence for a causal relationship between the intake of zinc and optimal immune system function Galmés, Galmés (B). Over-supplementation may be detrimental karger.com. Bioaccessibility of supplements varies widely Ośko.
Other meta analyses show significant improvements with zinc for mortality Abuhelwa, Olczak-Pruc, Rheingold, Tabatabaeizadeh, Xie, severity Fan, and cases Fan.
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. See also link.springer.com. The primary outcome was changed from viral clearance to "improvement or mortality" in the last month of the trial. The pre-specified outcome was not reported.
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.
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.
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.
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.
Alahmari:
Retrospective 977 hospitalized patients in Saudi Arabia, showing significantly shorter hospitalization with zinc treatment.
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.
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.
Asoudeh:
Retrospective 250 recovered COVID-19 patients, showing lower risk of severe cases with higher zinc intake.
Assiri:
Retrospective 118 ICU patients in Saudi Arabia showing no significant differences in unadjusted results with zinc, vitamin D, and favipiravir treatment.
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.
Bejan:
Retrospective 9,748 COVID-19 patients in the USA showing lower ventilation and ICU admission with zinc prophylaxis, without statistical significance.
Boukef:
150 patient zinc early treatment RCT with results not reported over 6 months after completion.
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.
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.
Correa:
105 patient zinc late treatment RCT with results not reported over 1.5 years after completion.
Darban:
Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences.
Derwand:
79% lower mortality and 82% lower hospitalization with early HCQ+AZ+Z. Retrospective 518 patients (141 treated, 377 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.
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.
Du Laing:
Retrospective 73 hospitalized COVID-19 patients in Belgium, showing higher risk of mortality with selenium deficiency and zinc deficiency.
Ekemen Keleş:
Prospective study of 100 COVID+ pediatric patients in Turkey, showing significantly increased risk of hospitalization for patients with zinc deficiency.
Elavarasi:
Retrospective 2017 hospitalized patients in India, showing lower mortality with zinc treatment.
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.
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
PSM aHR 0.63, p=0.015
regression aHR 0.76, p = 0.023
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.
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.
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.
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.
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.
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.
Jiménez:
Prospective analysis of 100 hospitalized COVID-19 patients in Spain, showing higher risk of death/mechanical ventilation/ICU admission with zinc levels <79µg/dL, without statistical significance.
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.
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.
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.
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.
Kyagambiddwa:
Retrospective 246 severe COVID-19 patients in Uganda, showing lower mortality with zinc treatment in unadjusted results, without statistical significance.
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.
Mahto:
Retrospective 689 healthcare workers in India, showing no significant difference in IgG positivity with zinc prophylaxis.
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.
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.
Nimer:
Retrospective 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with zinc prophylaxis.
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.
Ramos:
Retrospective 13 COVID-19 patients and 7 controls in Brazil, showing no significant difference in zinc deficiency.
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.
Seely:
Early terminated low-risk population (no hospitalization) very late treatment (mean 8 days) RCT with 44 patients treated with vitamin C, D, K, and zinc, and 46 control patients, showing no significant differences.
Authors acknowledge that the very late treatment is a major limitation, noting that in an ideal setting, "patients would begin taking therapeutic interventions immediately after noticing symptoms". Authors note that patients already had a low symptom burden at baseline and that "it is likely that the majority of the participants had almost fully recovered before starting treatment."
Authors note that most participants were young, had few comorbidities and had excellent self-rated health at baseline, leaving less room for improvement.
There was low compliance with completing surveys. Data from only 64% of patients was in the main analysis.
Authors claim "high internal validity", but the loss of data was statistically significantly different between arms, without analysis or mention. Since the study involves widely available treatments, one possibility is that patients in the control arm who feel sick may be more likely to independently take the treatments (via supplementation or food/sun exposure), believing that they are in the control arm or that additional dosing is safe, and they may then feel it's inappropriate to continue submitting the surveys.
Discussion is biased, stating that "evidence for the use of these products in people with COVID-19 is limited", however there were 219 controlled studies at the time, including 8, 27, and 16 RCTs for vitamin C, D, and zinc. Authors claim high similarity between arms however there was 60% vs. 41% male patients, and 88% vs. 68% of patients that received a third dose.
Authors claim that treatment "showed no beneficial effects for overall health or symptom burden". However 48% lower ER visits is beneficial, and most outcomes show a benefit. The only statistically significant effect was the loss of data, however significant clinical effects are not expected based on the small sample, very late treatment, event rates, and outcomes.
Authors acknowledge that the very late treatment is a major limitation, noting that in an ideal setting, "patients would begin taking therapeutic interventions immediately after noticing symptoms". Authors note that patients already had a low symptom burden at baseline and that "it is likely that the majority of the participants had almost fully recovered before starting treatment."
Authors note that most participants were young, had few comorbidities and had excellent self-rated health at baseline, leaving less room for improvement.
There was low compliance with completing surveys. Data from only 64% of patients was in the main analysis.
Authors claim "high internal validity", but the loss of data was statistically significantly different between arms, without analysis or mention. Since the study involves widely available treatments, one possibility is that patients in the control arm who feel sick may be more likely to independently take the treatments (via supplementation or food/sun exposure), believing that they are in the control arm or that additional dosing is safe, and they may then feel it's inappropriate to continue submitting the surveys.
Discussion is biased, stating that "evidence for the use of these products in people with COVID-19 is limited", however there were 219 controlled studies at the time, including 8, 27, and 16 RCTs for vitamin C, D, and zinc. Authors claim high similarity between arms however there was 60% vs. 41% male patients, and 88% vs. 68% of patients that received a third dose.
Authors claim that treatment "showed no beneficial effects for overall health or symptom burden". However 48% lower ER visits is beneficial, and most outcomes show a benefit. The only statistically significant effect was the loss of data, however significant clinical effects are not expected based on the small sample, very late treatment, event rates, and outcomes.
Seet:
Prophylaxis RCT in Singapore with 3,037 low risk patients, showing lower serious cases, lower symptomatic cases, and lower confirmed cases of COVID-19 with all treatments (ivermectin, HCQ, PVP-I, and Zinc + vitamin C) compared to vitamin C.
Meta-analysis of vitamin C in 6 previous trials shows a benefit of 16%, so the actual benefit of ivermectin, HCQ, and PVP-I may be higher. Cluster RCT with 40 clusters.
There were no hospitalizations and no deaths. NCT04446104.
Meta-analysis of vitamin C in 6 previous trials shows a benefit of 16%, so the actual benefit of ivermectin, HCQ, and PVP-I may be higher. Cluster RCT with 40 clusters.
There were no hospitalizations and no deaths. NCT04446104.
Sharif:
Retrospective 962 COVID-19 patients in Bangladesh, showing significantly lower severity with vitamin C, vitamin D, and zinc supplementation, and improved results from the combination of all three.
Shehab:
Retrospective survey-based analysis of 349 COVID-19 patients, showing a lower risk of severe cases with vitamin D, zinc, turmeric, and honey prophylaxis in unadjusted analysis, without statistical significance. REC/UG/2020/03.
Stambouli:
Prophylaxis RCT with 59 zinc + doxycycline, 56 doxycycline, and 57 placebo healthcare workers, showing lower symptomatic cases and significantly improved Ct values with the addition of zinc to doxycycline treatment. Doxycycline 100mg/day and zinc 15 mg/day.
Thomas:
Small 214 low-risk outpatient RCT showing non-statistically significant faster recovery with zinc and with vitamin C. Study performed in the USA where zinc deficiency is relatively uncommon. The zinc dosage is relatively low, 50mg zinc gluconate (7mg elemental zinc), one tenth of that shown to reduce the duration of colds in other studies patrickholford.com.
Tomasa-Irriguible:
Retrospective 120 hospitalized patients in Spain showing zinc deficiency associated with higher ICU admission.
Voelkle:
Prospective study of 57 consecutive hospitalized COVID-19 patients in Switzerland, showing higher risk of mortality/ICU admission with vitamin A, vitamin D, and zinc deficiency, with statistical significance only for vitamin A and zinc. Adjustments only considered age.
Vogel-González:
Retrospective 249 PCR+ hospitalized patients in Spain, 58 with zinc levels on admission <50 μg/dL, showing higher mortality and ICU admission, and slower recovery with low zinc levels.
Wozniak:
Retrospective 345 COVID-19 patients in Switzerland, showing significantly different zinc levels with ICU patients < hospitalized patients < outpatients.
For ICU patients, there was higher mortality, septic shock, and mechanical ventilation days with lower zinc levels, without statistical significance.
For ICU patients, there was higher mortality, septic shock, and mechanical ventilation days with lower zinc levels, without statistical significance.
Wu:
TriNetX PSM retrospective 10,935 COVID-19 patients, showing higher mortality with zinc deficiency.
Yao:
Retrospective 242 hospitalized patients in the USA showing adjusted hazard ratio for zinc treatment, aHR 0.66 [0.41-1.07]. ncbi.nlm.nih.gov (C) notes that the study would be more informative if baseline serum zinc levels were known.
Yasui:
Retrospective 62 hospitalized patients, 29 with serum zinc data, showing significantly lower serum zinc levels for severe COVID-19 cases (intubation) compared with mild and moderate cases, p = 0.005. Authors recommend zinc supplementation.
Zangeneh:
Retrospective 193 ICU patients in Iran, showing no significant difference with zinc treatment.
We performed ongoing searches of PubMed, medRxiv,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms were zinc, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed
every few hours with notification of new matches.
All studies regarding the use of zinc for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days are used. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms were not used (the next most serious
outcome is used — no studies were excluded). For example, in low-risk
populations with no mortality, a reduction in mortality with treatment is not
possible, however a reduction in hospitalization, for example, is still
valuable.
Clinical outcome is considered more important than PCR testing status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available (after most or all patients have recovered there is no room
for an effective treatment to do better).
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we computed the relative risk when
possible, or converted to a relative risk according to Zhang.
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported including propensity score matching (PSM), the PSM results are used.
Adjusted primary outcome results have preference over unadjusted results for a more
serious outcome when the adjustments significantly alter results.
When needed, conversion between reported p-values and confidence
intervals followed Altman, Altman (B), and Fisher's exact test was
used to calculate p-values for event data. If continuity correction for
zero values is required, we use the reciprocal of the opposite arm with the
sum of the correction factors equal to 1 Sweeting.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.11.4) with
scipy (1.11.1), pythonmeta (1.26), numpy (1.25.0), statsmodels (0.14.0), and plotly (5.15.0).
Forest plots are computed using PythonMeta Deng
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor
(3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective
McLean, Treanor.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/zmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Abdallah, 11/4/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Tunisia, peer-reviewed, mean age 54.2, 24 authors, study period 15 February, 2022 - 4 May, 2022, average treatment delay 4.6 days, trial NCT05212480 (history) (VIZIR). | risk of death, 29.9% lower, RR 0.70, p = 0.27, treatment 15 of 231 (6.5%), control 22 of 239 (9.2%), NNT 37, odds ratio converted to relative risk, day 30. |
| risk of death/ICU, 37.6% lower, RR 0.62, p = 0.04, treatment 24 of 231 (10.4%), control 40 of 239 (16.7%), NNT 16, odds ratio converted to relative risk, day 30. | |
| risk of ICU admission, 54.0% lower, RR 0.46, p = 0.01, treatment 12 of 231 (5.2%), control 27 of 239 (11.3%), NNT 16, odds ratio converted to relative risk, day 30. | |
| risk of oxygen therapy, 41.7% lower, RR 0.58, p = 0.009, treatment 31 of 231 (13.4%), control 55 of 239 (23.0%), NNT 10, grade III, day 30, Figure 3. | |
| risk of oxygen therapy, 22.9% lower, RR 0.77, p = 0.003, treatment 108 of 231 (46.8%), control 145 of 239 (60.7%), NNT 7.2, grade III, day 15, Figure 3. | |
| risk of no recovery, 29.3% lower, RR 0.71, p = 0.002, treatment 82 of 231 (35.5%), control 120 of 239 (50.2%), NNT 6.8, grade II/III, day 30. | |
| risk of no recovery, 13.8% lower, RR 0.86, p < 0.001, treatment 180 of 231 (77.9%), control 216 of 239 (90.4%), NNT 8.0, grade II/III, day 15. | |
| risk of hospitalization, 69.1% lower, RR 0.31, p = 0.30, treatment 1 of 85 (1.2%), control 4 of 100 (4.0%), NNT 35, odds ratio converted to relative risk, outpatients. | |
| hospitalization time, 33.0% lower, relative time 0.67, p < 0.001, treatment mean 7.1 (±3.4) n=146, control mean 10.6 (±2.8) n=134, inpatients. | |
| recovery time, 25.0% lower, relative time 0.75, p < 0.001, treatment mean 9.6 (±4.1) n=85, control mean 12.8 (±6.7) n=100, outpatients. | |
| Aldwihi, 5/11/2021, retrospective, Saudi Arabia, peer-reviewed, survey, mean age 36.5, 8 authors, study period August 2020 - October 2020. | risk of hospitalization, 23.7% lower, RR 0.76, p = 0.16, treatment 53 of 199 (26.6%), control 184 of 539 (34.1%), NNT 13, adjusted per study, odds ratio converted to relative risk, multivariable. |
| Asimi, 5/22/2021, retrospective, Bosnia and Herzegovina, preprint, 3 authors, this trial uses multiple treatments in the treatment arm (combined with vitamin D and selenium) - results of individual treatments may vary, excluded in exclusion analyses: excessive unadjusted differences between groups. | risk of mechanical ventilation, 97.4% lower, RR 0.03, p < 0.001, treatment 0 of 270 (0.0%), control 9 of 86 (10.5%), NNT 9.6, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), unadjusted. |
| risk of hospitalization, 99.0% lower, RR 0.010, p < 0.001, treatment 0 of 270 (0.0%), control 24 of 86 (27.9%), NNT 3.6, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), unadjusted. | |
| risk of severe case, 99.5% lower, RR 0.005, p < 0.001, treatment 0 of 270 (0.0%), control 51 of 86 (59.3%), NNT 1.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), unadjusted. | Boukef, 2/28/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Tunisia, trial NCT05670444 (history). | 150 patient RCT with results missing over 6 months. |
| Derwand, 7/3/2020, retrospective, USA, peer-reviewed, 3 authors, this trial uses multiple treatments in the treatment arm (combined with HCQ and azithromycin) - results of individual treatments may vary. | risk of death, 79.4% lower, RR 0.21, p = 0.12, treatment 1 of 141 (0.7%), control 13 of 377 (3.4%), NNT 37, odds ratio converted to relative risk. |
| risk of hospitalization, 81.6% lower, RR 0.18, p < 0.001, treatment 4 of 141 (2.8%), control 58 of 377 (15.4%), NNT 8.0, odds ratio converted to relative risk. | |
| Mayberry, 12/16/2021, retrospective, USA, peer-reviewed, 14 authors, study period March 2020 - April 2021. | risk of death, 53.5% lower, OR 0.47, p < 0.001, treatment 938, control 1,090, adjusted per study, multivariable, RR approximated with OR. |
| risk of mechanical ventilation, 64.2% lower, OR 0.36, p < 0.001, treatment 938, control 1,090, adjusted per study, multivariable, RR approximated with OR. | |
| risk of ICU admission, 60.0% lower, OR 0.40, p < 0.001, treatment 938, control 1,090, adjusted per study, multivariable, RR approximated with OR. | |
| death/ventilation/ICU, 57.8% lower, OR 0.42, p < 0.001, treatment 938, control 1,090, adjusted per study, multivariable, primary outcome, RR approximated with OR. | |
| progression to ARDS, 85.4% lower, OR 0.15, p < 0.001, treatment 938, control 1,090, adjusted per study, multivariable, RR approximated with OR. | |
| Thomas, 2/12/2021, Randomized Controlled Trial, USA, peer-reviewed, 11 authors, study period 8 April, 2020 - 11 February, 2021, trial NCT04342728 (history) (COVIDAtoZ). | risk of hospitalization, 43.7% higher, RR 1.44, p = 0.72, treatment 5 of 58 (8.6%), control 3 of 50 (6.0%). |
| recovery time, 11.9% lower, relative time 0.88, p = 0.38, treatment mean 5.9 (±4.9) n=58, control mean 6.7 (±4.4) n=50, mean time to a 50% reduction in symptoms, primary outcome. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Abd-Elsalam, 11/29/2020, Randomized Controlled Trial, Egypt, peer-reviewed, 10 authors, study period 23 June, 2020 - 23 August, 2020, data issues, see notes, trial NCT04447534 (history), excluded in exclusion analyses: multiple potential data reliability issues. | risk of death, 1.0% lower, RR 0.99, p = 0.99, treatment 5 of 96 (5.2%), control 5 of 95 (5.3%), NNT 1824. |
| risk of mechanical ventilation, 34.0% lower, RR 0.66, p = 0.54, treatment 4 of 96 (4.2%), control 6 of 95 (6.3%), NNT 47. | |
| risk of no recovery, 5.8% lower, RR 0.94, p = 0.97, treatment 20 of 96 (20.8%), control 21 of 95 (22.1%), NNT 79. | |
| hospitalization time, 3.6% lower, relative time 0.96, p = 0.55, treatment 96, control 95. | |
| Al Sulaiman, 6/7/2021, retrospective, propensity score matching, Saudi Arabia, peer-reviewed, 10 authors, study period 1 March, 2020 - 31 March, 2021. | risk of death, 36.0% lower, HR 0.64, p = 0.11, treatment 23 of 82 (28.0%), control 32 of 82 (39.0%), NNT 9.1, adjusted per study, in-hospital, PSM, multivariable Cox proportional hazards. |
| risk of death, 48.0% lower, HR 0.52, p = 0.03, treatment 19 of 82 (23.2%), control 31 of 82 (37.8%), NNT 6.8, adjusted per study, 30 day, PSM, multivariable Cox proportional hazards. | |
| ICU time, 25.0% higher, relative time 1.25, p = 0.28, treatment 82, control 82. | |
| hospitalization time, 6.2% higher, relative time 1.06, p = 0.61, treatment 82, control 82. | |
| Alahmari, 6/27/2022, retrospective, Saudi Arabia, peer-reviewed, 7 authors, study period 1 May, 2020 - 30 July, 2020. | hospitalization time, 30.2% lower, relative time 0.70, p < 0.001, treatment mean 6.39 (±0.76) n=130, control mean 9.15 (±0.27) n=847. |
| Assiri, 8/28/2021, retrospective, Saudi Arabia, peer-reviewed, 8 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 80.8% higher, RR 1.81, p = 0.44, treatment 10 of 60 (16.7%), control 4 of 58 (6.9%), inverted to make RR<1 favor treatment, odds ratio converted to relative risk. |
| Carlucci, 5/8/2020, retrospective, USA, peer-reviewed, 6 authors. | risk of death/hospice, 37.7% lower, RR 0.62, p = 0.002, treatment 54 of 411 (13.1%), control 119 of 521 (22.8%), NNT 10, adjusted per study, odds ratio converted to relative risk, multivariate logistic regression. |
| risk of mechanical ventilation, 18.0% lower, RR 0.82, p = 0.40, treatment 29 of 411 (7.1%), control 62 of 521 (11.9%), adjusted per study, odds ratio converted to relative risk, multivariate logistic regression. | |
| risk of ICU admission, 23.5% lower, RR 0.77, p = 0.17, treatment 38 of 411 (9.2%), control 82 of 521 (15.7%), adjusted per study, odds ratio converted to relative risk, multivariate logistic regression. | Correa, 10/30/2021, Double Blind Randomized Controlled Trial, trial NCT04902976 (history). | 105 patient RCT with results missing over 1.5 years. |
| Darban, 12/15/2020, Randomized Controlled Trial, Iran, peer-reviewed, 8 authors, study period 7 April, 2020 - 8 June, 2020, this trial uses multiple treatments in the treatment arm (combined with melatonin and vitamin C) - results of individual treatments may vary, trial IRCT20151228025732N52. | risk of progression, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 10 (20.0%), control 3 of 10 (30.0%), NNT 10. |
| ICU time, 6.0% lower, relative time 0.94, p = 0.30, treatment 10, control 10. | |
| Doocy, 10/19/2022, prospective, multiple countries, peer-reviewed, 6 authors, study period December 2020 - June 2021, trial NCT04568499 (history), excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 40.8% lower, RR 0.59, p = 0.41, treatment 3 of 28 (10.7%), control 21 of 116 (18.1%), NNT 14, unadjusted. |
| Elavarasi, 8/12/2021, retrospective, India, preprint, 26 authors. | risk of death, 65.1% lower, RR 0.35, p < 0.001, treatment 486, control 1,201, adjusted per study, odds ratio converted to relative risk, model 4, multivariate logistic regression, control prevalence approximated with overall prevalence. |
| Frontera, 10/26/2020, retrospective, propensity score matching, USA, preprint, median age 64.0, 14 authors, this trial uses multiple treatments in the treatment arm (combined with HCQ) - results of individual treatments may vary. | risk of death, 37.0% lower, HR 0.63, p = 0.01, treatment 121 of 1,006 (12.0%), control 424 of 2,467 (17.2%), NNT 19, adjusted per study, PSM. |
| risk of death, 24.0% lower, HR 0.76, p = 0.02, treatment 121 of 1,006 (12.0%), control 424 of 2,467 (17.2%), NNT 19, adjusted per study, regression. | |
| Gadhiya, 4/8/2021, retrospective, USA, peer-reviewed, 4 authors, excluded in exclusion analyses: substantial unadjusted confounding by indication likely. | risk of death, 40.9% higher, RR 1.41, p = 0.33, treatment 21 of 54 (38.9%), control 34 of 229 (14.8%), adjusted per study, odds ratio converted to relative risk, multivariate logistic regression. |
| Ibrahim Alhajjaji, 3/4/2023, retrospective, Saudi Arabia, peer-reviewed, 8 authors, study period 1 March, 2020 - 31 December, 2021, excluded in exclusion analyses: excessive unadjusted differences between groups. | risk of death, 87.6% lower, RR 0.12, p = 0.13, treatment 0 of 44 (0.0%), control 4 of 57 (7.0%), NNT 14, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of mechanical ventilation, 26.0% lower, RR 0.74, p = 0.75, treatment 4 of 44 (9.1%), control 7 of 57 (12.3%), NNT 31. | |
| risk of ICU admission, 2.8% lower, RR 0.97, p = 1.00, treatment 9 of 44 (20.5%), control 12 of 57 (21.1%), NNT 167. | |
| respiratory failure, 72.7% lower, RR 0.27, p = 0.004, treatment 4 of 44 (9.1%), control 19 of 57 (33.3%), NNT 4.1. | |
| hospitalization time, 28.6% lower, relative time 0.71, p = 0.02, treatment 44, control 57. | |
| Kaplan, 10/1/2021, Randomized Controlled Trial, USA, preprint, 12 authors, study period 21 September, 2020 - 22 January, 2021, average treatment delay 5.9 days, this trial uses multiple treatments in the treatment arm (combined with resveratrol) - results of individual treatments may vary, trial NCT04542993 (history) (Reszinate). | risk of mechanical ventilation, 14.3% higher, RR 1.14, p = 1.00, treatment 1 of 14 (7.1%), control 1 of 16 (6.2%). |
| risk of ICU admission, 14.3% higher, RR 1.14, p = 1.00, treatment 1 of 14 (7.1%), control 1 of 16 (6.2%). | |
| risk of hospitalization, 14.3% higher, RR 1.14, p = 1.00, treatment 1 of 14 (7.1%), control 1 of 16 (6.2%). | |
| Krishnan, 7/20/2020, retrospective, USA, peer-reviewed, 13 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 17.6% lower, RR 0.82, p = 0.18, treatment 31 of 58 (53.4%), control 61 of 94 (64.9%), NNT 8.7. |
| Kyagambiddwa, 5/11/2023, retrospective, Uganda, peer-reviewed, mean age 39.0, 15 authors, study period May 2020 - August 2022, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 25.4% lower, RR 0.75, p = 0.28, treatment 20 of 89 (22.5%), control 22 of 73 (30.1%), NNT 13. |
| Mulhem, 4/7/2021, retrospective, database analysis, USA, peer-reviewed, 3 authors, excluded in exclusion analyses: 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. | risk of death, 45.6% lower, RR 0.54, p < 0.001, treatment 256 of 1,596 (16.0%), control 260 of 1,623 (16.0%), adjusted per study, odds ratio converted to relative risk, logistic regression. |
| Patel, 2/25/2021, Double Blind Randomized Controlled Trial, Australia, peer-reviewed, 12 authors. | risk of death, 20.0% lower, RR 0.80, p = 1.00, treatment 2 of 15 (13.3%), control 3 of 18 (16.7%), NNT 30. |
| Rosenthal, 12/10/2020, retrospective, database analysis, USA, peer-reviewed, 5 authors, excluded in exclusion analyses: confounding by indication is likely and adjustments do not consider COVID-19 severity at baseline. | risk of death, 16.0% higher, OR 1.16, p = 0.003, adjusted per study, multivariable, RR approximated with OR. |
| Seely, 9/22/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Canada, peer-reviewed, mean age 39.9, 10 authors, study period September 2021 - April 2022, this trial uses multiple treatments in the treatment arm (combined with vitamin C, D, K2, and zinc) - results of individual treatments may vary, trial NCT04780061 (history). | ER visit, 47.6% lower, RR 0.52, p = 0.68, treatment 2 of 42 (4.8%), control 4 of 44 (9.1%), NNT 23. |
| relative mean cumulative symptom score, 13.8% better, RR 0.86, p = 0.41, treatment mean 166.3 (±92.3) n=34, control mean 192.9 (±153.6) n=24. | |
| EQ-VAS average score <80, 29.4% lower, RR 0.71, p = 0.54, treatment 7 of 34 (20.6%), control 7 of 24 (29.2%), NNT 12, average daily EQ-VAS score <80. | |
| relative EQ5D improvement, 28.6% better, RR 0.71, p = 0.44, treatment 32, control 31, relative improvement in EQ5D, week 1. | |
| relative EQ5D improvement, 14.3% better, RR 0.86, p = 0.73, treatment 33, control 30, relative improvement in EQ5D, week 2. | |
| relative EQ5D improvement, 50.0% better, RR 0.50, p = 0.17, treatment 32, control 33, relative improvement in EQ5D, week 3. | |
| relative EQ5D improvement, 12.5% worse, RR 1.12, p = 0.47, treatment 30, control 25, relative improvement in EQ5D, week 4. | |
| recovery time, 4.0% higher, relative time 1.04, p = 0.81, treatment 34, control 24. | |
| risk of PASC, 12.1% lower, RR 0.88, p = 1.00, treatment 3 of 33 (9.1%), control 3 of 29 (10.3%), NNT 80, 12 weeks. | |
| risk of PASC, 35.7% lower, RR 0.64, p = 0.69, treatment 3 of 35 (8.6%), control 4 of 30 (13.3%), NNT 21, 8 weeks. | |
| risk of PASC, 0.6% lower, RR 0.99, p = 1.00, treatment 6 of 35 (17.1%), control 5 of 29 (17.2%), NNT 1015, 4 weeks. | |
| Yao, 7/22/2020, retrospective, USA, peer-reviewed, 9 authors. | risk of death, 34.0% lower, RR 0.66, p = 0.09, treatment 73 of 196 (37.2%), control 21 of 46 (45.7%), adjusted per study, multivariate Cox regression. |
| Zangeneh, 5/13/2022, retrospective, Iran, peer-reviewed, 3 authors. | risk of death, 21.0% higher, HR 1.21, p = 0.66, Cox proportional hazards. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Abdulateef, 4/8/2021, retrospective, Iraq, peer-reviewed, 7 authors, study period July 2020 - August 2020, excluded in exclusion analyses: unadjusted results with no group details. | risk of hospitalization, 13.1% lower, RR 0.87, p = 0.83, treatment 7 of 111 (6.3%), control 23 of 317 (7.3%), NNT 105, unadjusted. |
| Adrean, 10/30/2022, retrospective, USA, peer-reviewed, survey, 6 authors, study period 1 April, 2020 - 9 April, 2021. | risk of case, 12.2% higher, RR 1.12, p = 0.58, treatment 30 of 2,111 (1.4%), control 80 of 6,315 (1.3%). |
| Asoudeh, 3/21/2023, retrospective, Iran, peer-reviewed, 10 authors, study period June 2021 - September 2021. | risk of severe case, 57.0% lower, OR 0.43, p = 0.03, adjusted per study, T3 vs. T1, multivariable, model 3, RR approximated with OR. |
| Bagheri, 9/1/2021, retrospective, Iran, peer-reviewed, 6 authors. | risk of severe case, 60.4% lower, OR 0.40, p = 0.41, treatment 33, control 477, adjusted per study, multinomial logistic regression, RR approximated with OR. |
| risk of hospitalization, 41.0% lower, RR 0.59, p = 0.37, treatment 4 of 33 (12.1%), control 167 of 477 (35.0%), NNT 4.4, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, binary logistic regression. | |
| Bejan, 2/28/2021, retrospective, USA, peer-reviewed, mean age 42.0, 6 authors. | risk of mechanical ventilation, 18.0% lower, OR 0.82, p = 0.78, treatment 155, control 9,074, adjusted per study, RR approximated with OR. |
| risk of ICU admission, 30.0% lower, OR 0.70, p = 0.60, treatment 155, control 9,112, adjusted per study, RR approximated with OR. | |
| Citu, 3/30/2022, retrospective, Romania, peer-reviewed, survey, 14 authors, study period 14 April, 2020 - 14 February, 2022, this trial uses multiple treatments in the treatment arm (combined with calcium) - results of individual treatments may vary. | risk of severe case, 17.6% lower, RR 0.82, p = 1.00, treatment 2 of 74 (2.7%), control 2 of 61 (3.3%), NNT 174, Ca+Mg+Zn vs. Mg. |
| Gordon, 12/13/2021, prospective, USA, peer-reviewed, 2 authors. | risk of death, 67.6% lower, RR 0.32, p = 0.48, treatment 0 of 104 (0.0%), control 1 of 96 (1.0%), NNT 96, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of symptomatic case, 85.3% lower, RR 0.15, p = 0.02, treatment 2 of 104 (1.9%), control 9 of 96 (9.4%), NNT 13, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk. | |
| Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK), excluded in exclusion analyses: significant unadjusted confounding possible. | risk of case, 6.8% lower, RR 0.93, p = 0.77, treatment 21 of 750 (2.8%), control 425 of 14,477 (2.9%), NNT 737, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated. |
| Israel, 7/27/2021, retrospective, Israel, peer-reviewed, 10 authors, this trial uses multiple treatments in the treatment arm (combined with calcium) - results of individual treatments may vary, excluded in exclusion analyses: treatment or control group size extremely small. | risk of hospitalization, >99.99% lower, OR < 0.001, p = 0.04, treatment 0 of 6,953 (0.0%) cases, 10 of 13,906 (0.1%) controls, NNT 3.0, case control OR, PCR+, cohort 2. |
| Kumar, 2/23/2022, retrospective, India, peer-reviewed, 10 authors, study period June 2021 - August 2021, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 20.0% lower, RR 0.80, p = 0.71, treatment 6 of 75 (8.0%), control 3 of 30 (10.0%), NNT 50, unadjusted. |
| Louca, 11/30/2020, retrospective, United Kingdom, peer-reviewed, 26 authors. | risk of case, 0.9% lower, RR 0.99, p = 0.80, odds ratio converted to relative risk, United Kingdom, all adjustment model. |
| Mahto, 2/15/2021, retrospective, India, peer-reviewed, 6 authors. | risk of IgG positive, 36.8% lower, RR 0.63, p = 0.35, treatment 10 of 38 (26.3%), control 83 of 651 (12.7%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| Nimer, 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021. | risk of hospitalization, 25.4% higher, RR 1.25, p = 0.21, treatment 41 of 326 (12.6%), control 178 of 1,822 (9.8%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of severe case, 13.0% higher, RR 1.13, p = 0.46, treatment 46 of 326 (14.1%), control 214 of 1,822 (11.7%), adjusted per study, odds ratio converted to relative risk, multivariable. | |
| Seet, 4/14/2021, Cluster Randomized Controlled Trial, Singapore, peer-reviewed, 15 authors, study period 13 May, 2020 - 31 August, 2020, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04446104 (history). | risk of symptomatic case, 49.7% lower, RR 0.50, p < 0.001, treatment 33 of 634 (5.2%), control 64 of 619 (10.3%), NNT 19. |
| risk of case, 26.9% lower, RR 0.73, p = 0.03, treatment 300 of 634 (47.3%), control 433 of 619 (70.0%), NNT 4.4, adjusted per study, odds ratio converted to relative risk, model 6. | |
| Sharif, 11/26/2022, retrospective, Bangladesh, peer-reviewed, 14 authors, study period 13 December, 2020 - 4 February, 2021. | risk of severe case, 40.0% lower, OR 0.60, p < 0.001, adjusted per study, multivariable, RR approximated with OR. |
| risk of severe case, 97.0% lower, OR 0.03, p = 0.005, adjusted per study, combined use of vitamin C, vitamin D, and zinc, multivariable, RR approximated with OR. | |
| Shehab, 2/28/2022, retrospective, multiple countries, peer-reviewed, survey, 7 authors, study period September 2020 - March 2021, excluded in exclusion analyses: unadjusted results with no group details. | risk of severe case, 47.4% lower, RR 0.53, p = 0.24, treatment 4 of 65 (6.2%), control 22 of 188 (11.7%), NNT 18, unadjusted, severe vs. mild cases. |
| Stambouli, 6/17/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Tunisia, peer-reviewed, 22 authors, study period 12 November, 2020 - 10 February, 2021, trial NCT04584567 (history). | risk of symptomatic case, 68.4% lower, RR 0.32, p = 0.36, treatment 1 of 59 (1.7%), control 3 of 56 (5.4%), NNT 27, zinc + doxycycline vs. doxycycline. |
| risk of case, 5.1% lower, RR 0.95, p = 1.00, treatment 5 of 59 (8.5%), control 5 of 56 (8.9%), NNT 220, zinc + doxycycline vs. doxycycline. | |
| relative Ct values, 21.4% better, RR 0.79, p < 0.001, treatment mean 29.0 (±1.3) n=59, control mean 22.8 (±4.0) n=56, zinc + doxycycline vs. doxycycline. |
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