Zinc for COVID-19: real-time meta analysis of 53 studies (41 treatment studies and 12 sufficiency studies)
Covid Analysis, June 2023
•Statistically significant improvements are seen for mortality, ventilation, hospitalization, progression, recovery, and viral clearance. 17 studies from 17 independent teams in 9 different countries show statistically significant improvements in isolation (11 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 29% [18‑38%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment.
•Sufficiency studies, analyzing outcomes based on serum levels, show 73% [63‑81%] improvement for patients with higher zinc levels (12 studies).
•Results are robust — in exclusion sensitivity analysis 17 of 41 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
•7 studies use combined treatments. When excluding those studies, the pooled improvement is 26% [15‑35%] compared to 29% [18‑38%].
•Over-supplementation may be detrimental [karger.com].
•No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. Only 10% of zinc studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
•All data to reproduce this paper and sources are in the appendix. Other meta analyses for zinc can be found in [Abuhelwa, Fan, Olczak-Pruc, Tabatabaeizadeh, Xie], showing significant improvements for mortality, severity, and cases.
|Early treatment||Prophylaxis||All studies||Studies||Patients||Authors|
|All studies||41% [8‑61%]|
|Randomized Controlled TrialsRCTs||21% [-41‑55%]||50% [26‑67%]|
|30% [-137‑79%]||29% [10‑44%]
|HospitalizationHosp.||66% [-4‑89%]||81% [-3‑96%]||29% [7‑45%]
|Cases||-||22% [-10‑45%]||22% [-10‑45%]||6||25,221||105|
|RCT mortality||30% [-31‑64%]||-||24% [-29‑55%]||3||694||46|
Zinc reduces risk for COVID-19 with very high confidence for mortality, progression, recovery, and in pooled analysis, high confidence for ventilation and hospitalization, low confidence for viral clearance, and very low confidence for ICU admission and cases. Over-supplementation may be detrimental.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 51 treatments.
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.
An In Silico study supports the efficacy of zinc [Pormohammad].
2 In Vitro studies support the efficacy of zinc [Hajdrik, Panchariya].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, sufficiency studies, peer reviewed studies, and all studies excluding combined treatment studies.
|All studies||29% [18‑38%]|
|After exclusions||32% [20‑41%]|
|Peer-reviewed studiesPeer-reviewed||26% [14‑36%]|
|Excluding combined treatmentExc. combined||26% [15‑35%]|
|Randomized Controlled TrialsRCTs||39% [17‑55%]|
|ICU admissionICU||26% [-9‑49%]||7||3,745||82|
|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%]|
|After exclusions||37% [10‑55%]|
|Peer-reviewed studiesPeer-reviewed||37% [10‑55%]|
|Excluding combined treatmentExc. combined||34% [7‑54%]|
|Randomized Controlled TrialsRCTs||21% [-41‑55%]||14% [-90‑61%]||50% [26‑67%]
|VentilationVent.||86% [-66‑99%]||20% [-16‑45%]||-|
|ICU admissionICU||59% [48‑68%]|
|HospitalizationHosp.||66% [-4‑89%]||15% [-5‑31%]||81% [-3‑96%]|
|RCT mortality||30% [-31‑64%]||8% [-144‑65%]||-|
|RCT hospitalizationRCT hosp.||16% [-254‑80%]||4% [-8‑14%]||-|
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.
[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.
[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].
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.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|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 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 20. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 treatments.
[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].
[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].
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.
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.
[Boulware, Meeus, Meneguesso].
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
Figure 22 shows a scatter plot of results for prospective and retrospective treatment studies. Prospective studies show 31% [12‑45%] improvement in meta analysis, compared to 29% [17‑39%] for retrospective studies, showing no significant difference, with results to date favoring a possible negative publication bias.
Figure 23 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
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.
|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
|Dr. Mohammed Tarek Alam
patients up to 84 years old
|Dr. Oluwagbenga Alonge||Nigeria||310||0.0% (0)||100.0%|
|Dr. Raja Bhattacharya
up to 88yo, 81% comorbidities
|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
|Dr. Roman Rozencwaig
patients up to 86 years old
|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%|
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.
[Abuhelwa, Fan, Olczak-Pruc, Tabatabaeizadeh, Xie], showing significant improvements for one or more of mortality, severity, and cases.
Zinc is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ventilation, hospitalization, progression, recovery, and viral clearance. 17 studies from 17 independent teams in 9 different countries show statistically significant improvements in isolation (11 for the most serious outcome). Meta analysis using the most serious outcome reported shows 29% [18‑38%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment. Sufficiency studies, analyzing outcomes based on serum levels, show 73% [63‑81%] improvement for patients with higher zinc levels (12 studies). Results are robust — in exclusion sensitivity analysis 17 of 41 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
7 studies use combined treatments. When excluding those studies, the pooled improvement is 26% [15‑35%] compared to 29% [18‑38%].
Over-supplementation may be detrimental [karger.com].
[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].
[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.
[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.
[Darban] Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences. IRCT20151228025732N52.
[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.