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Selenium for COVID-19: real-time meta analysis of 11 studies (4 treatment studies and 7 sufficiency studies)

@CovidAnalysis, February 2024, Version 3V3
 
0 0.5 1 1.5+ All studies 34% 4 21,452 Improvement, Studies, Patients Relative Risk Mortality 35% 1 122 Hospitalization 22% 2 6,103 Cases 41% 2 19,182 RCTs 35% 1 122 Sufficiency 60% 7 463 Prophylaxis 36% 3 21,330 Late 35% 1 122 Selenium for COVID-19 c19early.org February 2024 after exclusions Favorsselenium Favorscontrol
Abstract
Meta analysis using the most serious outcome reported shows 34% [-40‑69%] lower risk, without reaching statistical significance. Results are similar for Randomized Controlled Trials and slightly worse for higher quality studies.
One study shows statistically significant improvement.
7 sufficiency studies analyze outcomes based on serum levels, showing 60% [33‑76%] lower risk for patients with higher selenium levels.
The European Food Safety Authority has found evidence for a causal relationship between the intake of selenium and optimal immune system function Galmés, Galmés (B). Sufficiency studies show COVID-19 associated with low selenium levels, however there is very limited and conflicting results for clinical outcomes with selenium treatment.
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 may be more effective. There has been no early treatment studies to date.
All data to reproduce this paper and sources are in the appendix. Fan present another meta analysis for selenium, showing significant improvement for cases.
Evolution of COVID-19 clinical evidence Selenium p=0.28 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org February 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
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 66 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hafizi (DB RCT) 35% 0.65 [0.11-3.73] death 2/62 3/60 CT​1 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.64 Late treatment 35% 0.65 [0.11-3.73] 2/62 3/60 35% lower risk COVIDENCE UK Holt 80% 0.20 [0.03-1.44] cases 1/167 445/15,060 Improvement, RR [CI] Treatment Control Nimer -26% 1.26 [0.64-2.32] hosp. 12/57 207/2,091 Vaisi 53% 0.47 [0.32-0.81] hosp. 3,853 (n) 102 (n) Tau​2 = 0.43, I​2 = 73.7%, p = 0.34 Prophylaxis 36% 0.64 [0.26-1.59] 13/4,077 652/17,253 36% lower risk All studies 34% 0.66 [0.31-1.40] 15/4,139 655/17,313 34% lower risk 4 selenium COVID-19 studies c19early.org February 2024 Tau​2 = 0.31, I​2 = 60.8%, p = 0.28 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors selenium Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hafizi (DB RCT) 35% death CT​1 Improvement Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.64 Late treatment 35% 35% lower risk COVIDENCE UK Holt 80% case Nimer -26% hospitalization Vaisi 53% hospitalization Tau​2 = 0.43, I​2 = 73.7%, p = 0.34 Prophylaxis 36% 36% lower risk All studies 34% 34% lower risk 4 selenium C19 studies c19early.org February 2024 Tau​2 = 0.31, I​2 = 60.8%, p = 0.28 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors selenium Favors control
B
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Prophylaxis Efficacy in COVID-19 selenium studies (pooled effects) Favors selenium Favors control c19early.org February 2024
C
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D
-100% -50% 0% 50% 100% Timeline of COVID-19 selenium studies (pooled effects) 2020 2021 2022 2023 Favorsselenium Favorscontrol c19early.org February 2024
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. 0.6% of 6,686 proposed treatments show efficacy c19early.org. D. Timeline of results in selenium studies.
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological issues Scardua-Silva, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, Lv, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 6,000 compounds may reduce COVID-19 risk c19early.org (B), either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies of selenium 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, Randomized Controlled Trials (RCTs), and higher quality studies.
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.
2 In Vitro studies support the efficacy of selenium Hajdrik, Sinha.
An In Vivo animal study supports the efficacy of selenium Zhou.
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, and 7 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, cases, and sufficiency studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Improvement Studies Patients Authors
All studies34% [-40‑69%]4 21,452 60
After exclusions24% [-66‑65%]3 6,225 26
Randomized Controlled TrialsRCTs35% [-273‑89%]1 122 17
HospitalizationHosp.22% [-106‑70%]2 6,103 9
Cases41% [-98‑82%]2 19,182 39
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
Late treatment Prophylaxis
All studies35% [-273‑89%]36% [-59‑74%]
After exclusions35% [-273‑89%]22% [-106‑70%]
Randomized Controlled TrialsRCTs35% [-273‑89%]
HospitalizationHosp.22% [-106‑70%]
Cases41% [-98‑82%]
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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 hospitalization.
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Figure 6. Random effects meta-analysis for cases.
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Figure 7. Random effects meta-analysis for sufficiency studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 8 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2. Currently there is only one RCT.
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.
RCTs are expensive and many RCTs are funded by pharmaceutical companies or interests closely aligned with pharmaceutical companies. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
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 66 treatments we have analyzed, 63% 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 selenium 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 et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee et al. showed 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, 44 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 44 treatments with statistically significant efficacy/harm, 28 have been confirmed in RCTs, with a mean delay of 5.7 months. When considering only low cost treatments, 23 have been confirmed with a delay of 6.9 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 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 8. 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.
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), and can be easily influenced by potential bias.
The studies excluded are as below. Figure 9 shows a forest plot for random effects meta-analysis of all studies after exclusions.
Holt, significant unadjusted confounding possible.
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Figure 9. 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 report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 10 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 66 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 10. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 66 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.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 11. 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, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.6 months. When restricting to RCTs only, 50% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.1 months.
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Figure 11. 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. For selenium, there is currently not enough data to evaluate publication bias with high confidence.
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 12 shows a scatter plot of results for prospective and retrospective treatment studies. The median effect size for retrospective studies is 13% improvement, compared to 57% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 12. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 13 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 13. 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. Selenium for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 selenium 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 selenium trials represent the optimal conditions for efficacy.
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 4 studies combine treatments. The results of selenium alone may differ. 1 of 1 RCTs use combined treatment. Currently all studies are peer-reviewed. Fan present another meta analysis for selenium, showing significant improvement for cases.
Multiple reviews cover selenium for COVID-19, presenting additional background on mechanisms and related results, including Foshati, Maia, Yuan.
Meta analysis using the most serious outcome reported shows 34% [-40‑69%] lower risk, without reaching statistical significance. Results are similar for Randomized Controlled Trials and slightly worse for higher quality studies. One study shows statistically significant improvement. 7 sufficiency studies analyze outcomes based on serum levels, showing 60% [33‑76%] lower risk for patients with higher selenium levels.
The European Food Safety Authority has found evidence for a causal relationship between the intake of selenium and optimal immune system function Galmés, Galmés (B). Sufficiency studies show COVID-19 associated with low selenium levels, however there is very limited and conflicting results for clinical outcomes with selenium treatment.
Fan present another meta analysis for selenium, showing significant improvement for cases.
0 0.5 1 1.5 2+ Mortality, 55.2µg/L 92% Improvement Relative Risk Mortality, 660µg/L 94% Selenium for COVID-19  Du Laing et al.  Sufficiency Are selenium levels associated with COVID-19 outcomes? Retrospective 73 patients in Belgium Lower mortality with higher selenium levels (p=0.0014) c19early.org Du Laing et al., Nutrients, September 2021 Favors selenium Favors control
Du Laing: Retrospective 73 hospitalized COVID-19 patients in Belgium, showing higher risk of mortality with selenium deficiency and zinc deficiency.
0 0.5 1 1.5 2+ Mortality 35% Improvement Relative Risk Improvement in oxygen s.. 81% no CI Improvement in fever score 22% no CI Improvement in cough s.. 67% no CI Selenium  Hafizi et al.  LATE TREATMENT  DB RCT Is late treatment with selenium + BCc1 beneficial for COVID-19? Double-blind RCT 122 patients in Iran (October 2020 - March 2021) Trial underpowered to detect differences c19early.org Hafizi et al., Trials, November 2023 Favors selenium Favors control
Hafizi: Randomized, double-blind, placebo-controlled trial of 122 moderate hospitalized COVID-19 patients in Iran, evaluating the addition of BCc1 iron chelator and Hep-S selenium nanomedicines to standard treatment. The nanomedicine group showed a significant 77% reduction in IL-6 levels by day 28 compared to an 18% increase in the placebo group, along with improvements in TNF-alpha and clinical scores for cough, fatigue, and oxygen need, without statistical significance.
0 0.5 1 1.5 2+ Case 80% Improvement Relative Risk Selenium for COVID-19  COVIDENCE UK  Prophylaxis Does selenium reduce COVID-19 infections? Prospective study of 15,227 patients in the United Kingdom (May 2020 - Feb 2021) Fewer cases with selenium (not stat. sig., p=0.11) c19early.org Holt et al., Thorax, March 2021 Favors selenium Favors control
Holt: Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. NCT04330599. COVIDENCE UK.
0 0.5 1 1.5 2+ Mortality 88% Improvement Relative Risk Ventilation 90% Progression -4% Selenium for COVID-19  Im et al.  Sufficiency Are selenium levels associated with COVID-19 outcomes? Retrospective 49 patients in South Korea Lower ventilation with higher selenium levels (p=0.028) c19early.org Im et al., Int. J. Infect. Dis., August 2020 Favors selenium Favors control
Im: Analysis of 50 hospitalized COVID-19 patients in South Korea showing 42% of patients with selenium deficiency, and lower mechanical ventilation with selenium sufficiency.
0 0.5 1 1.5 2+ Case 67% Improvement Relative Risk Selenium for COVID-19  Majeed et al.  Sufficiency Are selenium levels associated with COVID-19 outcomes? Prospective study of 60 patients in India Fewer cases with higher selenium levels (not stat. sig., p=0.057) c19early.org Majeed et al., Nutrition, February 2021 Favors selenium Favors control
Majeed: Analysis of 30 COVID-19 patients and 30 healthy controls in India, showing significantly lower selenium levels in COVID-19 patients. 43.3% of COVID-19 patients had selenium levels <70 ng/mL compared to 20% of controls.
0 0.5 1 1.5 2+ Mortality 56% Improvement Relative Risk Selenium for COVID-19  Moghaddam et al.  Sufficiency Are selenium levels associated with COVID-19 outcomes? Retrospective 166 patients in Germany Lower mortality with higher selenium levels (p=0.011) c19early.org Moghaddam et al., Nutrients, July 2020 Favors selenium Favors control
Moghaddam: Analysis of 33 COVID-19 patients showing selenium levels significantly lower than reference levels, and significantly lower levels in non-survivors compared with survivors.
0 0.5 1 1.5 2+ Hospitalization -26% Improvement Relative Risk Severe case -9% Selenium for COVID-19  Nimer et al.  Prophylaxis Is prophylaxis with selenium beneficial for COVID-19? Retrospective 2,148 patients in Jordan (March - July 2021) Higher hospitalization with selenium (not stat. sig., p=0.48) c19early.org Nimer et al., Bosnian J. Basic Medical.., Feb 2022 Favors selenium Favors control
Nimer: Retrospective 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with selenium prophylaxis.
0 0.5 1 1.5 2+ ICU admission 92% Improvement Relative Risk Selenium for COVID-19  Rozemeijer et al.  Sufficiency Are selenium levels associated with COVID-19 outcomes? Prospective study of 25 patients in Netherlands Lower ICU admission with higher selenium levels (not stat. sig., p=0.093) c19early.org Rozemeijer et al., Nutrients, January 2024 Favors selenium Favors control
Rozemeijer: Prospective pilot study of 20 critically ill COVID-19 ICU patients showing high deficiency rates of 50-100% for vitamins A, B6, and D; zinc; and selenium at admission. Deficiencies of vitamins B6 and D, and low iron status, persisted after 3 weeks. Plasma levels of vitamins A and E, zinc, and selenium increased over time as inflammation resolved, suggesting redistribution may explain some observed deficiencies. All patients received daily micronutrient administration. Additional intravenous and oral micronutrient administration for 10 patients did not significantly impact micronutrient levels or deficiency rates, however authors note that the administered doses may be too low. The form of vitamin D is not specified but may have been cholecalciferol which is expected to have a very long onset of action compared to more appropriate forms such as calcifediol or calcitriol.
Tomasa-Irriguible: Estimated 300 patient selenium early treatment RCT with results expected soon (estimated completion over 2 months ago).
0 0.5 1 1.5 2+ Hospitalization 53% Improvement Relative Risk Symp. case 15% Selenium for COVID-19  Vaisi et al.  Prophylaxis Is prophylaxis with selenium beneficial for COVID-19? Retrospective 3,955 patients in Iran Lower hospitalization (p=0.018) and fewer symptomatic cases (p=0.042) c19early.org Vaisi et al., The Clinical Respiratory.., May 2023 Favors selenium Favors control
Vaisi: Analysis of nutrient intake and COVID-19 outcomes for 3,996 people in Iran, showing lower risk of COVID-19 hospitalization with sufficient vitamin A, vitamin C, and selenium intake, with statistical significance for vitamin A and selenium.
0 0.5 1 1.5 2+ Death/ICU 12% Improvement Relative Risk Selenium for COVID-19  Voelkle et al.  Sufficiency Are selenium levels associated with COVID-19 outcomes? Prospective study of 57 patients in Switzerland (Mar - Apr 2020) Study underpowered to detect differences c19early.org Voelkle et al., Nutrients, April 2022 Favors selenium Favors control
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.
0 0.5 1 1.5 2+ Mortality 62% Improvement Relative Risk Septic shock 47% Selenium for COVID-19  Wozniak et al.  ICU PATIENTS Are selenium levels associated with COVID-19 outcomes? Retrospective 118 patients in Switzerland (March - May 2020) Lower mortality (p=0.1) and progression (p=0.2), not sig. c19early.org Wozniak et al., Nutrients, July 2023 Favors selenium Favors control
Wozniak: Retrospective 345 COVID-19 patients in Switzerland, showing significantly different selenium levels with ICU patients < hospitalized patients < outpatients.

For ICU patients, there was higher mortality, septic shock, and mechanical ventilation days with lower selenium levels, with statistical significance only for ventilation.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms are selenium and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of selenium 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 have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. 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 outcomes are considered more important than viral test 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 little or no room for an effective treatment to do better, however faster recovery is valuable. 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 compute the relative risk when possible, or convert 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 propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted 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 (3.12.2) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.19.0).
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. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. 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. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
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.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/semeta.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.
Tomasa-Irriguible, 11/30/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, trial NCT04751669 (history) (CoVIT). Estimated 300 patient RCT with results unknown and over 2 months late.
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.
Hafizi, 11/11/2023, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 17 authors, study period 2 October, 2020 - 20 March, 2021, this trial uses multiple treatments in the treatment arm (combined with BCc1) - results of individual treatments may vary, trial IRCT20170731035423N2. risk of death, 35.5% lower, RR 0.65, p = 0.68, treatment 2 of 62 (3.2%), control 3 of 60 (5.0%), NNT 56.
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.
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, 79.5% lower, RR 0.20, p = 0.11, treatment 1 of 167 (0.6%), control 445 of 15,060 (3.0%), NNT 42, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated.
Nimer, 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021. risk of hospitalization, 26.3% higher, RR 1.26, p = 0.48, treatment 12 of 57 (21.1%), control 207 of 2,091 (9.9%), adjusted per study, odds ratio converted to relative risk, multivariable.
risk of severe case, 8.7% higher, RR 1.09, p = 0.80, treatment 12 of 57 (21.1%), control 248 of 2,091 (11.9%), adjusted per study, odds ratio converted to relative risk, multivariable.
Vaisi, 5/11/2023, retrospective, Iran, peer-reviewed, 5 authors. risk of hospitalization, 53.1% lower, HR 0.47, p = 0.02, treatment 3,853, control 102, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards.
risk of symptomatic case, 15.3% lower, HR 0.85, p = 0.04, treatment 3,853, control 102, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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