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Nigella Sativa for COVID-19: real-time meta analysis of 14 studies

@CovidAnalysis, October 2024, Version 23V23
 
0 0.5 1 1.5+ All studies 43% 14 3,333 Improvement, Studies, Patients Relative Risk Mortality 57% 5 1,342 Ventilation 62% 1 150 ICU admission 40% 2 471 Hospitalization 34% 5 1,410 Recovery 64% 5 862 Cases 51% 3 654 Viral clearance 62% 4 345 RCTs 41% 10 1,915 RCT mortality 57% 5 1,342 Prophylaxis 46% 4 907 Early 46% 7 1,816 Late 9% 3 610 Nigella Sativa for COVID-19 c19early.org October 2024 after exclusions Favorsnigella sativa Favorscontrol
Abstract
Statistically significant lower risk is seen for ventilation, hospitalization, recovery, cases, and viral clearance. 11 studies from 10 independent teams in 8 countries show significant improvements.
Meta analysis using the most serious outcome reported shows 43% [24‑57%] lower risk. Results are similar for Randomized Controlled Trials and higher quality studies. Early treatment is more effective than late treatment.
Results are very robust — in exclusion sensitivity analysis 11 of 14 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 43% 14 3,333 Improvement, Studies, Patients Relative Risk Mortality 57% 5 1,342 Ventilation 62% 1 150 ICU admission 40% 2 471 Hospitalization 34% 5 1,410 Recovery 64% 5 862 Cases 51% 3 654 Viral clearance 62% 4 345 RCTs 41% 10 1,915 RCT mortality 57% 5 1,342 Prophylaxis 46% 4 907 Early 46% 7 1,816 Late 9% 3 610 Nigella Sativa for COVID-19 c19early.org October 2024 after exclusions Favorsnigella sativa Favorscontrol
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. The quality of non-prescription supplements can vary widely1,2.
All data to reproduce this paper and sources are in the appendix. Other meta analyses show significant improvements with nigella sativa for mortality3,4 and viral clearance4.
Evolution of COVID-19 clinical evidence Meta analysis results over time Nigella Sativa p=0.00016 Acetaminophen p=0.00000029 2020 2021 2022 2023 2024 Lowerrisk Higherrisk c19early.org October 2024 100% 50% 0% -50%
Nigella Sativa for COVID-19 — Highlights
Studies to date suggest that Nigella Sativa reduces risk with very high confidence for hospitalization, recovery, cases, and in pooled analysis, high confidence for viral clearance, and low confidence for mortality and ventilation.
12th treatment shown effective with ≥3 clinical studies in January 2021, now with p = 0.00016 from 14 studies.
Outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 98 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ HNS-COVID-PK Ashraf (RCT) 82% 0.18 [0.04-0.80] death 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Aldwihi 24% 0.76 [0.54-1.03] hosp. 85/345 152/393 Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 BOSS-001 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Said (RCT) 77% 0.23 [0.04-1.23] recovery 30 (n) 30 (n) Idris 39% 0.61 [0.44-0.84] recov. time 26 (n) 25 (n) Tau​2 = 0.08, I​2 = 44.1%, p = 0.0028 Early treatment 46% 0.54 [0.36-0.81] 88/838 182/978 46% lower risk Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Faruq (RCT) 6% 0.94 [0.63-1.38] death 29/75 31/75 ICU patients Tau​2 = 0.00, I​2 = 0.0%, p = 0.63 Late treatment 9% 0.91 [0.62-1.33] 32/305 36/305 9% lower risk Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Shehab 0% 1.00 [0.36-2.74] severe case 4/39 22/214 Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Daneshfard (RCT) 34% 0.66 [0.49-0.89] symp. case 37/89 53/84 CT​1 Tau​2 = 0.12, I​2 = 75.0%, p = 0.008 Prophylaxis 46% 0.54 [0.35-0.85] 113/368 263/539 46% lower risk All studies 43% 0.57 [0.43-0.76] 233/1,511 481/1,822 43% lower risk 14 nigella sativa COVID-19 studies c19early.org October 2024 Tau​2 = 0.12, I​2 = 67.5%, p = 0.00016 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ HNS-COVID-PK Ashraf (RCT) 82% death CT​1 Improvement Relative Risk [CI] Al-Haidari (RCT) 96% death Aldwihi 24% hospitalization Koshak (RCT) 75% hospitalization BOSS-001 Bencheq.. (DB RCT) 69% hospitalization Said (RCT) 77% recovery Idris 39% recovery Tau​2 = 0.08, I​2 = 44.1%, p = 0.0028 Early treatment 46% 46% lower risk Karimi (RCT) 51% death CT​1 Setayesh (RCT) -8% death CT​1 Faruq (RCT) 6% death ICU patients Tau​2 = 0.00, I​2 = 0.0%, p = 0.63 Late treatment 9% 9% lower risk Al-Haidari 62% symp. case Shehab 0% severe case Chandra (RCT) 49% case CT​1 Daneshfard (RCT) 34% symp. case CT​1 Tau​2 = 0.12, I​2 = 75.0%, p = 0.008 Prophylaxis 46% 46% lower risk All studies 43% 43% lower risk 14 nigella sativa C19 studies c19early.org October 2024 Tau​2 = 0.12, I​2 = 67.5%, p = 0.00016 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors nigella sativa Favors control
B
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in nigella sativa 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, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 6.4 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 15.1 months, compared to using pooled outcomes. Efficacy based on specific outcomes in RCTs was delayed by 8.7 months, compared to using pooled outcomes in RCTs.
Introduction
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 injury5-14 and cognitive deficits7,12, cardiovascular complications15-17, 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 factorsA,18-22, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 8,000 compounds may reduce COVID-19 risk23, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies of nigella sativa 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.
Preclinical Research
15 In Silico studies support the efficacy of nigella sativa24-38.
5 In Vitro studies support the efficacy of nigella sativa29,31,39-41.
An In Vivo animal study supports the efficacy of nigella sativa36.
Thomas investigate a novel formulation of nigella sativa that may be more effective for COVID-19.
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.
Results
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3 plots individual results by treatment stage. Figure 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance.
Table 1. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001.
Improvement Studies Patients Authors
All studies43% [24‑57%]
***
14 3,333 174
After exclusions49% [30‑62%]
****
12 2,930 163
Randomized Controlled TrialsRCTs41% [15‑60%]
**
10 1,915 148
Mortality57% [-20‑85%]5 1,342 80
ICU admissionICU40% [-61‑78%]2 471 41
HospitalizationHosp.34% [16‑47%]
***
5 1,410 87
Recovery64% [34‑80%]
**
5 862 89
Cases51% [21‑69%]
**
3 654 31
Viral62% [16‑82%]
*
4 345 67
RCT mortality57% [-20‑85%]5 1,342 80
RCT hospitalizationRCT hosp.53% [8‑76%]
*
4 672 79
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001.
Early treatment Late treatment Prophylaxis
All studies46% [19‑64%]
**
9% [-33‑38%]46% [15‑65%]
**
After exclusions46% [19‑64%]
**
39% [-157‑85%]51% [21‑69%]
**
Randomized Controlled TrialsRCTs82% [55‑93%]
***
9% [-33‑38%]35% [14‑51%]
**
Mortality87% [51‑96%]
**
9% [-33‑38%]
ICU admissionICU40% [-61‑78%]
HospitalizationHosp.25% [7‑40%]
**
50% [-15‑78%]
Recovery64% [24‑82%]
**
67% [35‑83%]
**
Cases51% [21‑69%]
**
Viral62% [16‑82%]
*
RCT mortality87% [51‑96%]
**
9% [-33‑38%]
RCT hospitalizationRCT hosp.73% [-61‑96%]50% [-15‑78%]
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Figure 3. 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.
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Figure 4. Random effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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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.
Randomized Controlled Trials (RCTs)
Figure 12 shows a comparison of results for RCTs and non-RCT studies. Random effects meta analysis of RCTs shows 41% improvement, compared to 40% for other studies. Figure 13, 14, and 15 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.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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Figure 15. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases43, and analysis of double-blind RCTs has identified extreme levels of bias44. 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, reporting, 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 98 treatments we have analyzed, 65% 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.
Evidence shows that non-RCT studies 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 may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see49,50.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 29 have been confirmed in RCTs, with a mean delay of 7.1 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.2 months. For the 19 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 15 are all consistent with the overall results (benefit or harm), with 13 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is sotrovimab.
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.
Exclusions
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 can be easily influenced by potential bias, may ignore or underemphasize serious issues not captured in the checklists, and may overemphasize issues unlikely to alter outcomes in specific cases (for example certain specifics of randomization with a very large effect size and well-matched baseline characteristics).
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
Faruq, potential data issue.
Shehab, unadjusted results with no group details.
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Figure 16. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
Heterogeneity
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 hours53,54. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. 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 et al. report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases55
<24 hours-33 hours symptoms56
24-48 hours-13 hours symptoms56
Inpatients-2.5 hours to improvement57
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 98 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 98 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, for example as in López-Medina et al.
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants59, for example the Gamma variant shows significantly different characteristics60-63. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants64,65.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic66-76, therefore efficacy may depend strongly on combined treatments.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. 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 quality1,2.
Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.
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. 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.
Pooled Effects
This section validates the use of pooled effects for COVID-19, which enables earlier detection of efficacy, however note that pooled effects are no longer required for nigella sativa as of May 2022. Efficacy is now known for nigella sativa based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes was delayed by 15.1 months, compared to using pooled outcomes. Efficacy based on specific outcomes in RCTs was delayed by 8.7 months, compared to using pooled outcomes in RCTs.
For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results.
We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.
Another way to view pooled analysis is that we are using more of the available information. Logically we should, and do, use additional information. For example dose-response and treatment delay-response relationships provide significant additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from all 98 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 18 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 19 shows that improved recovery is very strongly associated with lower mortality (p < 0.000000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Figure 20 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.00000053 to p = 0.000000028.
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Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.1 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months. Figure 21 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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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.
Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta analyses, it is important to review the different studies included. We also present 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 results80-83. For nigella sativa, 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 22 shows a scatter plot of results for prospective and retrospective studies. 0% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 92% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 12% improvement, compared to 57% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 22. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 23 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.0584-91. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 23. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Nigella Sativa for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 nigella sativa 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 nigella sativa 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 for 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 with conflicts of interest 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 alone66-76. 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 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.
5 of 14 studies combine treatments. The results of nigella sativa alone may differ. 5 of 10 RCTs use combined treatment. Currently all studies are peer-reviewed. Other meta analyses show significant improvements with nigella sativa for mortality3,4 and viral clearance4.
Many reviews cover nigella sativa for COVID-19, presenting additional background on mechanisms and related results, including92-97.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors18-22, providing many therapeutic targets. Over 8,000 compounds have been predicted to reduce COVID-19 risk23, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 24 shows an overview of the results for nigella sativa in the context of multiple COVID-19 treatments, and Figure 25 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Figure 24. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random effects meta-analysis. 0.6% of 8,000+ proposed treatments show efficacy98.
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Figure 25. Efficacy vs. cost for COVID-19 treatments.
Studies to date suggest that nigella sativa is an effective treatment for COVID-19. Statistically significant lower risk is seen for ventilation, hospitalization, recovery, cases, and viral clearance. 11 studies from 10 independent teams in 8 countries show significant improvements. Meta analysis using the most serious outcome reported shows 43% [24‑57%] lower risk. Results are similar for Randomized Controlled Trials and higher quality studies. Early treatment is more effective than late treatment. Results are very robust — in exclusion sensitivity analysis 11 of 14 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Other meta analyses show significant improvements with nigella sativa for mortality3,4 and viral clearance4.
Symp. case 62% Improvement Relative Risk Nigella Sativa  Al-Haidari et al.  Prophylaxis Is prophylaxis with nigella sativa beneficial for COVID-19? Prospective study of 376 patients in Iraq Fewer symptomatic cases with nigella sativa (p<0.000001) c19early.org Al-Haidari et al., Pakistan J. Medical.., Jan 2021 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Al-Haidari: Prophylaxis study with 376 mostly high-risk patients, 188 treated with nigella sativa, showing significantly lower cases with treatment. Black seeds 40mg/kg orally once daily.
Mortality 96% Improvement Relative Risk Severe case 93% Nigella Sativa  Al-Haidari et al.  EARLY TREATMENT  RCT Is early treatment with nigella sativa beneficial for COVID-19? RCT 419 patients in Iraq (September - November 2020) Lower mortality (p=0.0013) and severe cases (p<0.0001) c19early.org Al-Haidari et al., Indian J. Forensic .., Jan 2021 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Al-Haidari (B): Open-label RCT with 419 patients in Iraq, 160 treated with Nigella Sativa, showing lower mortality and severe cases with treatment. Black seeds 40mg/kg orally once daily for 14 days.
Hospitalization 24% Improvement Relative Risk Nigella Sativa  Aldwihi et al.  EARLY TREATMENT Is early treatment with nigella sativa beneficial for COVID-19? Retrospective 738 patients in Saudi Arabia (August - October 2020) Lower hospitalization with nigella sativa (not stat. sig., p=0.094) c19early.org Aldwihi et al., Int. J. Environmental .., May 2021 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 82% Improvement Relative Risk Mortality (b) 67% Mortality (c) 79% Recovery 84% Recovery (b) 75% Viral clearance 82% Viral clearance (b) 77% Nigella Sativa  HNS-COVID-PK  EARLY TREATMENT  RCT Is early treatment with nigella sativa + honey beneficial for COVID-19? RCT 313 patients in Pakistan (April - July 2020) Lower mortality (p=0.011) and improved recovery (p<0.0001) c19early.org Ashraf et al., Phytotherapy Research, Nov 2020 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Ashraf: RCT with 157 patients treated with honey and nigella sativa, and 156 control patients, showing significantly faster recovery and viral clearance.

Honey (1gm/kg/day) plus encapsulated nigella sativa seeds (80mg/kg/day) orally in 2-3 divided doses daily for up to 13 days.
Hospitalization 69% Improvement Relative Risk Time to sustained clinical.. 9% Time to sustained clin.. (b) 35% Viral clearance 43% Nigella Sativa  BOSS-001  EARLY TREATMENT  DB RCT Is early treatment with nigella sativa beneficial for COVID-19? Double-blind RCT 52 patients in the USA (May - September 2021) Lower hospitalization (p=0.44) and improved viral clearance (p=0.31), not sig. c19early.org Bencheqroun et al., Pathogens, May 2022 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Bencheqroun: 52 patient RCT in the USA with nigella sativa component thymoquinone, showing improved recovery with treatment. There was a significantly faster decline in the total symptom burden, and a significant increase in CD8+ and helper CD4+ central memory T lymphocytes. The treatment group contained 5 more vaccinated patients and 7 more overweight patients. Authors also present in vitro results showing an inhibitory effect with five SARS-CoV-2 variants including omicron.
Case 49% Improvement Relative Risk Case (b) 87% Case (c) 74% Nigella Sativa  Chandra et al.  Prophylaxis  RCT Does nigella sativa + combined treatments reduce COVID-19 infections? RCT 105 patients in India (September 2020 - May 2021) Fewer cases with nigella sativa + combined treatments (not stat. sig., p=0.36) c19early.org Chandra et al., Phytotherapy Research, Jul 2022 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Chandra: RCT 251 high-risk individuals in India, mostly with direct contact with COVID-19 positive patients, testing polyherbal formulations Infuza, which includes nigella sativa, and Kulzam. Both formulations showed lower risk, without statisical significance, while the best results were from the combination of both.
Symp. case, any symptom 34% Improvement Relative Risk Symp. case, fever 97% Symp. case, chest pain 66% Symp. case, loss of taste/.. 62% Symp. case, muscle ache 26% Symp. case, chills -6% Symp. case, cough 79% Symp. case, headache 76% Symp. case, vomiting 98% Nigella Sativa  Daneshfard et al.  Prophylaxis  RCT Is prophylaxis with nigella sativa + olea europaea oil beneficial for COVID-19? RCT 173 patients in Iran (June 2021 - May 2022) Fewer symptomatic cases with nigella sativa + olea europaea oil (p=0.0061) c19early.org Daneshfard et al., Phytotherapy Research, Jul 2023 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Daneshfard: RCT 173 family members of COVID-19 patients, showing lower incidence of COVID-19 symptoms with nasal drops containing nigella sativa oil and olea europaea oil. One drop in each nostril twice daily for 7 days.
Mortality 6% Improvement Relative Risk Ventilation, day 14 62% Ventilation, day 7 83% ICU stay, >28 days 23% ICU stay, >21 days 28% ICU stay, >14 days 34% ICU stay, >7 days 7% Nigella Sativa  Faruq et al.  ICU PATIENTS  RCT Is very late treatment with nigella sativa beneficial for COVID-19? RCT 150 patients in Bangladesh Lower ventilation with nigella sativa (p=0.012) c19early.org Faruq et al., Bangladesh Critical Care.., Sep 2023 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Faruq: Open label randomized trial of 150 ICU patients in Bangladesh, showing shorter ICU stay and lower requirements for increased oxygen support including mechanical ventilation with nigella sativa treatment, but no significant difference in mortality.

The large baseline difference in convalescent plasma usage suggests an error or randomization problem.
Recovery time 39% Improvement Relative Risk Viral clearance, day 5 15% Viral clearance, day 2 40% Nigella Sativa  Idris et al.  EARLY TREATMENT Is early treatment with nigella sativa beneficial for COVID-19? Prospective study of 51 patients in Nigeria (Oct 2020 - May 2021) Faster recovery with nigella sativa (p=0.003) c19early.org Idris et al., The Nigerian Health J., Jan 2024 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Idris: Prospective study of 51 mild COVID-19 cases in Nigeria, showing faster recovery and improved viral clearance with nigella sativa oil (NSO) treatment. NSO patients received 5mL twice daily in addition to usual care (zinc, vitamin C and a multivitamin).
Mortality 51% Improvement Relative Risk ICU admission 61% Hospitalization time 70% primary Fever 67% Dyspnea 14% Nigella Sativa  Karimi et al.  LATE TREATMENT  RCT Is late treatment with nigella sativa + several herbal medicines beneficial for COVID-19? RCT 381 patients in Iran (March - July 2020) Shorter hospitalization (p=0.001) and improved recovery (p=0.0013) c19early.org Karimi et al., Phytotherapy Research, Oct 2021 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Karimi: RCT 358 hospitalized patients in Iran, 184 receiving treatment with a combination of nigella sativa and several other herbal medicines, showing shorter hospitalization time and improved recovery with treatment. IR.TUMS.VCR.REC.1399.024.
Hospitalization 75% Improvement Relative Risk Recovery 43% Nigella Sativa  Koshak et al.  EARLY TREATMENT  RCT Is early treatment with nigella sativa beneficial for COVID-19? RCT 183 patients in Saudi Arabia (May - September 2020) Improved recovery with nigella sativa (p=0.00021) c19early.org Koshak et al., Complementary Therapies.., Aug 2021 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Koshak: RCT 183 mild COVID-19 outpatients in Saudi Arabia, 91 treated with Nigella Sativa, showing lower hospitalization and faster recovery with treatment. 500mg Nigella Sativa oil (MARNYS Cuminmar) twice daily for 10 days. NCT04401202.
Recovery, dyspnea 77% Improvement Relative Risk Recovery, NS+D, dyspnea 89% Recovery, cough 80% Recovery, NS+D, cough 77% Recovery, fatigue 85% Recovery, NS+D, fatigue 90% Recovery, smell 85% Recovery, NS+D, smell 67% Recovery, taste 58% Recovery, NS+D, taste 58% Recovery, sore throat 82% Recovery, NS+S 86% Recovery, headache 27% Recovery, NS+D, headache 56% Recovery, diarrhea 80% Recovery, NS+D, diarrhea 90% Viral clearance, day 14 61% Viral clearance, day 7 85% Viral clearance, NS+D, day.. 91% Viral clearance, NS+D, day 7 87% Nigella Sativa  Said et al.  EARLY TREATMENT  RCT Is early treatment with nigella sativa beneficial for COVID-19? RCT 60 patients in Egypt (July - December 2021) Improved recovery (p=0.092) and viral clearance (p=0.081), not sig. c19early.org Said et al., Frontiers in Pharmacology, Nov 2022 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Said (B): 120 patient RCT comparing vitamin D, nigella sativa, and combined vitamin D+nigella sativa, showing improved symptom recovery and viral clearance with both vitamin D and nigella sativa, and further improvements with the combination of both. All patients received vitamin C, zinc, and lactoferrin.
Mortality -8% Improvement Relative Risk Oxygen time 27% Hospitalization time 29% Nigella Sativa  Setayesh et al.  LATE TREATMENT  RCT Is late treatment with nigella sativa + combined treatments beneficial for COVID-19? RCT 79 patients in Iran (June - September 2020) Lower need for oxygen therapy (p=0.007) and shorter hospitalization (p<0.0001) c19early.org Setayesh et al., Integrative Medicine .., Jun 2022 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Setayesh: Small RCT 41 patients treated with nigella sativa, glycyrrhiza glabra, punica granatum, and rheum palmatum, and 41 control patients, showing shorter hospitalization with treatment.
Severe case 0% unadjusted Improvement Relative Risk Nigella Sativa  Shehab et al.  Prophylaxis Is prophylaxis with nigella sativa beneficial for COVID-19? Retrospective 253 patients in multiple countries (Sep 2020 - Mar 2021) Study underpowered to detect differences c19early.org Shehab et al., Tropical J. Pharmaceuti.., Feb 2022 Favorsnigella sativa Favorscontrol 0 0.5 1 1.5 2+
Shehab: Retrospective survey-based analysis of 349 COVID-19 patients, showing no significant difference with nigella sativa prophylaxis in unadjusted analysis. REC/UG/2020/03.
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 nigella sativa 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 nigella sativa 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 to111. 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 1114. 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 (3.12.7) with scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta115 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 (4.4.0) using the metafor (4.6-0) and rms (6.8-0) 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 effective53,54.
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/nsmeta.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.
Al-Haidari (B), 1/31/2021, Randomized Controlled Trial, Iraq, peer-reviewed, 3 authors, study period 5 September, 2020 - 15 November, 2020. risk of death, 95.8% lower, RR 0.04, p = 0.001, treatment 0 of 160 (0.0%), control 14 of 259 (5.4%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of severe case, 92.6% lower, RR 0.07, p < 0.001, treatment 2 of 160 (1.2%), control 44 of 259 (17.0%), NNT 6.4.
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, 24.0% lower, RR 0.76, p = 0.09, treatment 85 of 345 (24.6%), control 152 of 393 (38.7%), NNT 7.1, adjusted per study, odds ratio converted to relative risk, multivariable.
Ashraf, 11/3/2020, Randomized Controlled Trial, placebo-controlled, Pakistan, peer-reviewed, 29 authors, study period 30 April, 2020 - 29 July, 2020, this trial uses multiple treatments in the treatment arm (combined with honey) - results of individual treatments may vary, trial NCT04347382 (history) (HNS-COVID-PK). risk of death, 81.9% lower, RR 0.18, p = 0.01, treatment 2 of 157 (1.3%), control 11 of 156 (7.1%), NNT 17, all cases.
risk of death, 67.1% lower, RR 0.33, p = 0.49, treatment 0 of 107 (0.0%), control 1 of 103 (1.0%), NNT 103, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), moderate cases.
risk of death, 78.8% lower, RR 0.21, p = 0.03, treatment 2 of 50 (4.0%), control 10 of 53 (18.9%), NNT 6.7, severe cases.
risk of no recovery, 83.6% lower, HR 0.16, p < 0.001, treatment 107, control 103, inverted to make HR<1 favor treatment, moderate cases.
risk of no recovery, 75.2% lower, HR 0.25, p < 0.001, treatment 50, control 53, inverted to make HR<1 favor treatment, severe cases.
risk of no viral clearance, 81.9% lower, HR 0.18, p < 0.001, treatment 107, control 103, inverted to make HR<1 favor treatment, moderate cases.
risk of no viral clearance, 76.9% lower, HR 0.23, p < 0.001, treatment 50, control 53, inverted to make HR<1 favor treatment, severe cases.
Bencheqroun, 5/7/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 45.0, 25 authors, study period 27 May, 2021 - 27 September, 2021, trial NCT04914377 (history) (BOSS-001). risk of hospitalization, 69.3% lower, RR 0.31, p = 0.44, treatment 0 of 29 (0.0%), control 1 of 23 (4.3%), NNT 23, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
time to sustained clinical response, 9.1% lower, HR 0.91, p = 0.78, treatment 28, control 23, inverted to make HR<1 favor treatment, Kaplan–Meier.
time to sustained clinical response, 35.5% lower, HR 0.65, p = 0.25, treatment 28, control 23, inverted to make HR<1 favor treatment, Kaplan–Meier, high-risk patients.
risk of no viral clearance, 43.5% lower, RR 0.57, p = 0.31, treatment 5 of 21 (23.8%), control 8 of 19 (42.1%), NNT 5.5, day 14.
Idris, 1/15/2024, prospective, Nigeria, peer-reviewed, mean age 30.8, 8 authors, study period 27 October, 2020 - 20 May, 2021. recovery time, 39.0% lower, relative time 0.61, p = 0.003, treatment mean 4.5 (±1.51) n=26, control mean 7.38 (±2.2) n=25.
risk of no viral clearance, 15.4% lower, RR 0.85, p = 1.00, treatment 3 of 13 (23.1%), control 6 of 22 (27.3%), NNT 24, day 5.
risk of no viral clearance, 40.5% lower, RR 0.60, p = 0.02, treatment 13 of 26 (50.0%), control 21 of 25 (84.0%), NNT 2.9, day 2.
Koshak, 8/15/2021, Randomized Controlled Trial, Saudi Arabia, peer-reviewed, 10 authors, study period 1 May, 2020 - 30 September, 2020, trial NCT04401202 (history). risk of hospitalization, 74.7% lower, RR 0.25, p = 0.37, treatment 1 of 91 (1.1%), control 4 of 92 (4.3%), NNT 31.
risk of no recovery, 42.7% lower, RR 0.57, p < 0.001, treatment 34 of 91 (37.4%), control 60 of 92 (65.2%), NNT 3.6.
Said (B), 11/8/2022, Randomized Controlled Trial, Egypt, peer-reviewed, 5 authors, study period 21 July, 2021 - 30 December, 2021, trial NCT04981743 (history). risk of no recovery, 77.0% lower, OR 0.23, p = 0.09, treatment 30, control 30, adjusted per study, multivariable, dyspnea, RR approximated with OR.
risk of no recovery, 89.0% lower, OR 0.11, p = 0.01, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, dyspnea, RR approximated with OR.
risk of no recovery, 80.0% lower, OR 0.20, p = 0.003, treatment 30, control 30, adjusted per study, multivariable, cough, RR approximated with OR.
risk of no recovery, 77.0% lower, OR 0.23, p = 0.01, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, cough, RR approximated with OR.
risk of no recovery, 85.0% lower, OR 0.15, p = 0.003, treatment 30, control 30, adjusted per study, multivariable, fatigue, RR approximated with OR.
risk of no recovery, 90.0% lower, OR 0.10, p < 0.001, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, fatigue, RR approximated with OR.
risk of no recovery, 85.0% lower, OR 0.15, p = 0.04, treatment 30, control 30, adjusted per study, multivariable, smell, RR approximated with OR.
risk of no recovery, 67.0% lower, OR 0.33, p = 0.23, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, smell, RR approximated with OR.
risk of no recovery, 58.0% lower, OR 0.42, p = 0.28, treatment 30, control 30, adjusted per study, multivariable, taste, RR approximated with OR.
risk of no recovery, 58.0% lower, OR 0.42, p = 0.28, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, taste, RR approximated with OR.
risk of no recovery, 82.0% lower, OR 0.18, p = 0.05, treatment 30, control 30, sore throat, RR approximated with OR.
risk of no recovery, 86.0% lower, OR 0.14, p = 0.03, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, sore throat, RR approximated with OR.
risk of no recovery, 27.0% lower, OR 0.73, p = 0.62, treatment 30, control 30, headache, RR approximated with OR.
risk of no recovery, 56.0% lower, OR 0.44, p = 0.21, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, headache, RR approximated with OR.
risk of no recovery, 80.0% lower, OR 0.20, p = 0.05, treatment 30, control 30, diarrhea, RR approximated with OR.
risk of no recovery, 90.0% lower, OR 0.10, p = 0.03, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, diarrhea, RR approximated with OR.
risk of no viral clearance, 61.0% lower, OR 0.39, p = 0.08, treatment 30, control 30, day 14, RR approximated with OR.
risk of no viral clearance, 85.0% lower, OR 0.15, p = 0.004, treatment 30, control 30, day 7, RR approximated with OR.
risk of no viral clearance, 91.0% lower, OR 0.09, p < 0.001, treatment 30, control 30, vitamin D and nigella sativa, day 14, RR approximated with OR.
risk of no viral clearance, 87.0% lower, OR 0.13, p = 0.003, treatment 30, control 30, vitamin D and nigella sativa, day 7, RR approximated with OR.
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.
Faruq, 9/1/2023, Randomized Controlled Trial, Bangladesh, peer-reviewed, 4 authors, excluded in exclusion analyses: potential data issue. risk of death, 6.5% lower, RR 0.94, p = 0.87, treatment 29 of 75 (38.7%), control 31 of 75 (41.3%), NNT 37.
risk of mechanical ventilation, 61.9% lower, RR 0.38, p = 0.01, treatment 8 of 75 (10.7%), control 21 of 75 (28.0%), NNT 5.8, day 14.
risk of mechanical ventilation, 83.3% lower, RR 0.17, p < 0.001, treatment 3 of 75 (4.0%), control 18 of 75 (24.0%), NNT 5.0, day 7.
ICU stay, 23.5% lower, RR 0.77, p = 0.74, treatment 4 of 46 (8.7%), control 5 of 44 (11.4%), NNT 37, >28 days.
ICU stay, 28.3% lower, RR 0.72, p = 0.46, treatment 9 of 46 (19.6%), control 12 of 44 (27.3%), NNT 13, >21 days.
ICU stay, 33.6% lower, RR 0.66, p = 0.007, treatment 25 of 46 (54.3%), control 36 of 44 (81.8%), NNT 3.6, >14 days.
ICU stay, 6.6% lower, RR 0.93, p = 0.43, treatment 41 of 46 (89.1%), control 42 of 44 (95.5%), NNT 16, >7 days.
Karimi, 10/4/2021, Randomized Controlled Trial, Iran, peer-reviewed, 37 authors, study period March 2020 - July 2020, this trial uses multiple treatments in the treatment arm (combined with several herbal medicines) - results of individual treatments may vary. risk of death, 50.8% lower, RR 0.49, p = 0.45, treatment 2 of 192 (1.0%), control 4 of 189 (2.1%), NNT 93.
risk of ICU admission, 60.6% lower, RR 0.39, p = 0.28, treatment 2 of 192 (1.0%), control 5 of 189 (2.6%), NNT 62.
hospitalization time, 70.0% lower, HR 0.30, p < 0.001, treatment 184, control 174, Cox proportional hazards, primary outcome.
fever, 66.5% lower, OR 0.33, p = 0.001, treatment 184, control 174, inverted to make OR<1 favor treatment, RR approximated with OR.
dyspnea, 13.7% lower, OR 0.86, p < 0.001, treatment 184, control 174, inverted to make OR<1 favor treatment, RR approximated with OR.
Setayesh, 6/3/2022, Randomized Controlled Trial, Iran, peer-reviewed, mean age 59.1, 7 authors, study period June 2020 - September 2020, this trial uses multiple treatments in the treatment arm (combined with glycyrrhiza glabra, punica granatum, and rheum palmatum) - results of individual treatments may vary, trial IRCT20200330046899N1. risk of death, 7.9% higher, RR 1.08, p = 1.00, treatment 1 of 38 (2.6%), control 1 of 41 (2.4%).
oxygen time, 26.8% lower, relative time 0.73, p = 0.007, treatment mean 3.0 (±1.6) n=38, control mean 4.1 (±1.9) n=41.
hospitalization time, 28.7% lower, relative time 0.71, p < 0.001, treatment mean 5.7 (±1.9) n=38, control mean 8.0 (±1.8) n=41.
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.
Al-Haidari, 1/31/2021, prospective, Iraq, peer-reviewed, 3 authors. risk of symptomatic case, 62.2% lower, RR 0.38, p < 0.001, treatment 68 of 188 (36.2%), control 180 of 188 (95.7%), NNT 1.7.
Chandra, 7/5/2022, Randomized Controlled Trial, India, peer-reviewed, 12 authors, study period 18 September, 2020 - 21 May, 2021, this trial uses multiple treatments in the treatment arm (combined with Infuza polyherbal formulation) - results of individual treatments may vary, trial CTRI/2020/08/027222. risk of case, 49.0% lower, RR 0.51, p = 0.36, treatment 4 of 52 (7.7%), control 8 of 53 (15.1%), NNT 14, Infuza.
risk of case, 87.0% lower, RR 0.13, p = 0.03, treatment 1 of 51 (2.0%), control 8 of 53 (15.1%), NNT 7.6, Infuza and Kulzam.
risk of case, 74.0% lower, RR 0.26, p = 0.09, treatment 2 of 51 (3.9%), control 8 of 53 (15.1%), NNT 9.0, Kulzam.
Daneshfard, 7/16/2023, Randomized Controlled Trial, Iran, peer-reviewed, mean age 39.5 (treatment) 34.0 (control), 16 authors, study period 16 June, 2021 - 22 May, 2022, this trial uses multiple treatments in the treatment arm (combined with olea europaea oil) - results of individual treatments may vary, trial IRCT20210515051305N1. risk of symptomatic case, 34.1% lower, RR 0.66, p = 0.006, treatment 37 of 89 (41.6%), control 53 of 84 (63.1%), NNT 4.6, any symptom.
risk of symptomatic case, 97.3% lower, RR 0.03, p < 0.001, treatment 1 of 89 (1.1%), control 35 of 84 (41.7%), NNT 2.5, fever.
risk of symptomatic case, 65.7% lower, RR 0.34, p = 0.06, treatment 4 of 89 (4.5%), control 11 of 84 (13.1%), NNT 12, chest pain.
risk of symptomatic case, 62.2% lower, RR 0.38, p = 0.10, treatment 4 of 89 (4.5%), control 10 of 84 (11.9%), NNT 13, loss of taste/smell.
risk of symptomatic case, 26.0% lower, RR 0.74, p = 0.16, treatment 29 of 89 (32.6%), control 37 of 84 (44.0%), NNT 8.7, muscle ache.
risk of symptomatic case, 6.2% higher, RR 1.06, p = 1.00, treatment 9 of 89 (10.1%), control 8 of 84 (9.5%), chills.
risk of symptomatic case, 79.0% lower, RR 0.21, p = 0.001, treatment 4 of 89 (4.5%), control 18 of 84 (21.4%), NNT 5.9, cough.
risk of symptomatic case, 76.4% lower, RR 0.24, p < 0.001, treatment 5 of 89 (5.6%), control 20 of 84 (23.8%), NNT 5.5, headache.
risk of symptomatic case, 98.1% lower, RR 0.02, p < 0.001, treatment 0 of 89 (0.0%), control 25 of 84 (29.8%), NNT 3.4, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), vomiting.
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, 0.2% lower, RR 1.00, p = 1.00, treatment 4 of 39 (10.3%), control 22 of 214 (10.3%), NNT 4173, unadjusted, severe vs. mild cases.
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