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Curcumin for COVID-19: real-time meta analysis of 21 studies
Covid Analysis, December 2022
https://c19early.org/tmeta.html
 
0 0.5 1 1.5+ All studies 39% 21 4,804 Improvement, Studies, Patients Relative Risk Mortality 63% 7 665 Ventilation 77% 3 386 ICU admission 67% 1 120 Hospitalization 26% 9 3,639 Progression 76% 2 101 Recovery 38% 11 713 Viral clearance 35% 5 339 RCTs 41% 16 1,378 RCT mortality 63% 7 665 Peer-reviewed 39% 20 4,558 Prophylaxis 32% 2 2,401 Early 37% 9 1,509 Late 50% 10 894 Curcumin for COVID-19 c19early.org/t Dec 2022 Favorscurcumin Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, hospitalization, recovery, and viral clearance. 11 studies from 10 independent teams in 6 different countries show statistically significant improvements in isolation (7 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 39% [31‑46%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Results are robust — in exclusion sensitivity analysis 17 of 21 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 39% 21 4,804 Improvement, Studies, Patients Relative Risk Mortality 63% 7 665 Ventilation 77% 3 386 ICU admission 67% 1 120 Hospitalization 26% 9 3,639 Progression 76% 2 101 Recovery 38% 11 713 Viral clearance 35% 5 339 RCTs 41% 16 1,378 RCT mortality 63% 7 665 Peer-reviewed 39% 20 4,558 Prophylaxis 32% 2 2,401 Early 37% 9 1,509 Late 50% 10 894 Curcumin for COVID-19 c19early.org/t Dec 2022 Favorscurcumin Favorscontrol after exclusions
Studies typically use advanced formulations for greatly improved bioavailability.
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. Only 24% of curcumin studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix. Other meta analyses for curcumin can be found in [Kow, Vahedian-Azimi], showing significant improvements for mortality, hospitalization, and symptoms.
Highlights
Curcumin reduces risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for viral clearance, low confidence for ventilation, and very low confidence for progression. Studies typically use advanced formulations for greatly improved bioavailability.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Dound (RCT) 33% 0.67 [0.54-0.82] 6 pt. scale 100 (n) 100 (n) CT​1 Improvement, RR [CI] Treatment Control Saber-Moghaddam 94% 0.06 [0.00-0.93] progression 0/21 8/20 Aldwihi 31% 0.69 [0.43-1.04] hosp. 30/144 207/594 Pawar (DB RCT) 82% 0.18 [0.04-0.79] death 2/70 11/70 Ahmadi (DB RCT) 86% 0.14 [0.01-2.65] hosp. 0/30 3/30 Sankhe (RCT) 89% 0.11 [0.01-2.03] death 0/87 4/87 CT​1 Majeed (DB RCT) 66% 0.34 [0.01-8.09] ventilation 0/45 1/47 CT​1 Khan (RCT) 33% 0.67 [0.37-1.19] no recov. 10/25 15/25 CT​1 Askari (DB RCT) -125% 2.25 [0.30-16.6] no recov. 3/8 1/6 Tau​2 = 0.03, I​2 = 20.5%, p = 0.0005 Early treatment 37% 0.63 [0.48-0.81] 45/530 250/979 37% improvement Valizadeh (DB RCT) 50% 0.50 [0.18-1.40] death 4/20 8/20 Improvement, RR [CI] Treatment Control Tahmasebi (DB RCT) 83% 0.17 [0.02-1.32] death 1/40 6/40 Hassania.. (DB RCT) -46% 1.46 [0.01-329] SpO2 imp. 20 (n) 20 (n) Asadirad (RCT) 26% 0.74 [0.26-2.12] death 5/27 6/24 Kartika 41% 0.59 [0.35-1.00] hosp. time 139 (n) 107 (n) Hartono (RCT) 53% 0.47 [0.32-0.68] viral+ 14/30 30/30 CT​1 Thomas (DB RCT) 44% 0.56 [0.34-0.91] improv. 74 (n) 73 (n) LONG COVID CT​1 Sankhe (SB RCT) 86% 0.14 [0.01-2.71] death 0/60 3/60 CT​1 Hellou (DB RCT) 77% 0.23 [0.06-0.95] NEWS2 33 (n) 17 (n) CT​1 Abbaspour-A.. (RCT) 71% 0.29 [0.06-1.26] death 2/30 7/30 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Late treatment 50% 0.50 [0.39-0.63] 26/473 60/421 50% improvement Shehab 42% 0.58 [0.14-2.32] severe case 2/32 24/221 Improvement, RR [CI] Treatment Control Nimer 31% 0.69 [0.45-1.04] hosp. 29/329 179/1,819 Tau​2 = 0.00, I​2 = 0.0%, p = 0.039 Prophylaxis 32% 0.68 [0.48-0.98] 31/361 203/2,040 32% improvement All studies 39% 0.61 [0.54-0.69] 102/1,364 513/3,440 39% improvement 21 curcumin COVID-19 studies c19early.org/t Dec 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors curcumin Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Dound (RCT) 33% 6 pt. scale CT​1 Relative Risk [CI] Saber-Moghaddam 94% progression Aldwihi 31% hospitalization Pawar (DB RCT) 82% death Ahmadi (DB RCT) 86% hospitalization Sankhe (RCT) 89% death CT​1 Majeed (DB RCT) 66% ventilation CT​1 Khan (RCT) 33% recovery CT​1 Askari (DB RCT) -125% recovery Tau​2 = 0.03, I​2 = 20.5%, p = 0.0005 Early treatment 37% 37% improvement Valizadeh (DB RCT) 50% death Tahmas.. (DB RCT) 83% death Hassani.. (DB RCT) -46% SpO2 imp. Asadirad (RCT) 26% death Kartika 41% hospitalization Hartono (RCT) 53% viral- CT​1 Thomas (DB RCT) 44% improv. LONG COVID CT​1 Sankhe (SB RCT) 86% death CT​1 Hellou (DB RCT) 77% NEWS2 CT​1 Abbaspour-.. (RCT) 71% death Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Late treatment 50% 50% improvement Shehab 42% severe case Nimer 31% hospitalization Tau​2 = 0.00, I​2 = 0.0%, p = 0.039 Prophylaxis 32% 32% improvement All studies 39% 39% improvement 21 curcumin COVID-19 studies c19early.org/t Dec 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors curcumin 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, 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. D. Timeline of results in curcumin studies.
We analyze all significant studies concerning the use of curcumin for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
3CLpro inhibitorCurcumin inhibits SARS-CoV-2 3CLpro [Bahun, Guijarro-Real, Rehman].
RdRp inhibitorSARS-CoV-2 RNA‐dependent RNA polymerase (RdRp) inhibition [Singh].
ACE2 inhibitorCurcumin inhibits ACE2 activity. SARS-CoV-2 viral entry requires host cell surface proteins ACE2 and TMPRSS2 [Jena, Patel].
TMPRSS2 downregulationCurcumin downregulates transmembrane serine protease 2 (TMPRSS2). SARS-CoV-2 viral entry requires host cell surface proteins ACE2 and TMPRSS2 [Goc].
Cathepsin L inhibitorCurcumin inhibits cathepsin L activity. Cathepsin L plays a key role in viral entry [Goc].
Anti‑inflammatoryCurcumin shows anti-inflammatory effects [Daily, Derosa, Gupta, Marín-Palma, Rattis, Sahebkar].
Inhibition in Vero E6 cells demonstratedIn Vitro research shows curcumin inhibits SARS-CoV-2 in Vero E6 cells [Bormann, Marín-Palma].
Inhibition in Calu-3 cells demonstratedIn Vitro research shows curcumin inhibits SARS-CoV-2 in Calu-3 cells [Bormann].
Table 1. Curcumin mechanisms of action. Submit updates.
5 In Silico studies support the efficacy of curcumin [Kandeil, Nag, Rampogu, Sekiou, Singh].
7 In Vitro studies support the efficacy of curcumin [Bahun, Bormann, Goc, Goc (B), Guijarro-Real, Kandeil, Leka].
[Panda] present a phase I clinical study investigating a novel formulation of curcumin 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.
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 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, progression, recovery, viral clearance, and peer reviewed studies.
Improvement Studies Patients Authors
All studies39% [31‑46%]21 4,804 185
After exclusions40% [28‑50%]18 4,291 160
Peer-reviewed studiesPeer-reviewed39% [31‑47%]20 4,558 179
Randomized Controlled TrialsRCTs41% [32‑50%]16 1,378 151
Mortality63% [35‑79%]7 665 75
VentilationVent.77% [-6‑95%]3 386 22
HospitalizationHosp.26% [13‑37%]9 3,639 66
Viral35% [7‑55%]5 339 40
RCT mortality63% [35‑79%]7 665 75
RCT hospitalizationRCT hosp.11% [-5‑25%]5 466 39
Table 2. 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.
Early treatment Late treatment Prophylaxis
All studies37% [19‑52%] 950% [37‑61%] 1032% [2‑52%] 2
After exclusions47% [12‑68%] 848% [30‑62%] 931% [-4‑55%] 1
Peer-reviewed studiesPeer-reviewed37% [19‑52%] 952% [38‑63%] 932% [2‑52%] 2
Randomized Controlled TrialsRCTs39% [14‑57%] 752% [38‑63%] 9-
Mortality84% [39‑96%] 255% [17‑76%] 5-
VentilationVent.72% [-65‑95%] 286% [-171‑99%] 1-
HospitalizationHosp.30% [7‑48%] 517% [-5‑34%] 331% [-4‑55%] 1
Viral28% [-33‑61%] 243% [22‑58%] 3-
RCT mortality84% [39‑96%] 255% [17‑76%] 5-
RCT hospitalizationRCT hosp.32% [-69‑73%] 310% [-14‑29%] 2-
Table 3. 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.
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for viral clearance.
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Figure 11. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 60% improvement, compared to 41% 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.
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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, 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 14. Random effects meta-analysis for RCT mortality results.
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Figure 15. Random effects meta-analysis for RCT hospitalization results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Dound], potential randomization failure.
[Hartono], randomization resulted in significant baseline differences that were not adjusted for.
[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, 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.
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]
Table 4. Early treatment is more effective for baloxavir and influenza.
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer. Non-prescription supplements may show very wide variations in quality [Crawford, Crighton].
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 18. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 18. 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 curcumin, 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.
25% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 59% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 36% improvement, compared to 66% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
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Figure 19. Prospective vs. retrospective studies.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 20 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 20. 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. Curcumin for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 curcumin 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 curcumin trials represent the optimal conditions for efficacy.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
8 of 21 studies combine treatments. The results of curcumin alone may differ. 8 of 16 RCTs use combined treatment. Other meta analyses for curcumin can be found in [Kow, Vahedian-Azimi], showing significant improvements for mortality, hospitalization, and symptoms.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
Curcumin is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, hospitalization, recovery, and viral clearance. 11 studies from 10 independent teams in 6 different countries show statistically significant improvements in isolation (7 for the most serious outcome). Meta analysis using the most serious outcome reported shows 39% [31‑46%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Results are robust — in exclusion sensitivity analysis 17 of 21 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Studies typically use advanced formulations for greatly improved bioavailability.
0 0.5 1 1.5 2+ Mortality 71% Improvement Relative Risk Recovery, dyspnea 86% Recovery, fever >39.0 90% Recovery, bilateral chest.. 38% Recovery, cough 59% Recovery, headache 82% c19early.org/t Abbaspour-Aghdam et al. IRCT20200324046851N1 Curcumin RCT LATE Favors curcumin Favors control
[Abbaspour-Aghdam] RCT with 30 nanocurcumin and 30 control patients in Iran, showing lower mortality and improved recovery, without statistical significance, and improved NK cell function. 160mg nanocurcumin for 21 days.
0 0.5 1 1.5 2+ Hospitalization 86% Improvement Relative Risk Recovery time 21% c19early.org/t Ahmadi et al. Curcumin for COVID-19 RCT EARLY TREATMENT Favors curcumin Favors control
[Ahmadi] RCT 60 outpatients in Iran, 30 treated with nano-curcumin showing lower hospitalization and faster recovery with treatment.
0 0.5 1 1.5 2+ Hospitalization 31% Improvement Relative Risk c19early.org/t Aldwihi et al. Curcumin for COVID-19 EARLY TREATMENT Favors curcumin Favors control
[Aldwihi] Retrospective survey-based analysis of 738 COVID-19 patients in Saudi Arabia, showing lower hospitalization with vitamin C, turmeric, zinc, and nigella sativa, and higher hospitalization with vitamin D. For vitamin D, most patients continued prophylactic use. For vitamin C, the majority of patients continued prophylactic use. For nigella sativa, the majority of patients started use during infection. Authors do not specify the fraction of prophylactic use for turmeric and zinc.
0 0.5 1 1.5 2+ Mortality 26% Improvement Relative Risk Progression 50% Unresolved fever 45% Unresolved dyspnea 29% Unresolved cough 41% O2 <92% 37% O2 <97% 20% c19early.org/t Asadirad et al. Curcumin for COVID-19 RCT LATE TREATMENT Favors curcumin Favors control
[Asadirad] RCT 60 hospitalized patients in Iran, 30 treated with nano-curcumin, showing significant improvements in inflammatory cytokines, and improvements in clinical outcomes without statistical significance. 240 mg/day nano-curcumin for 7 days.
0 0.5 1 1.5 2+ Recovery, dyspnea -125% Improvement Relative Risk Recovery, ague -433% Recovery, weakness 73% Recovery, muscular pain 40% Recovery, headache 38% Recovery, sore throat -71% Recovery, sputum cough 12% Recovery, dry cough 0% c19early.org/t Askari et al. IRCT20121216011763N46 Curcumin RCT EARLY Favors curcumin Favors control
[Askari] Small RCT 46 outpatients in Iran, 23 treated with curcimin-piperine, showing no significant differences in recovery. 1000mg curcumin and 10mg piperine/day for 14 days.
0 0.5 1 1.5 2+ Improvement on 6-point.. 33% Improvement Relative Risk c19early.org/t Dound et al. Curcumin for COVID-19 RCT EARLY TREATMENT Favors curcumin Favors control
[Dound] RCT 200 COVID-19 positive patients in India, 100 treated with Curcumin, Vitamin C, Vitamin K2-7, and L-Selenomethionine, showing faster recovery with treatment.
0 0.5 1 1.5 2+ Viral clearance, day 10 53% Improvement Relative Risk Viral clearance, day 14 75% Viral clearance, day 21 67% c19early.org/t Hartono et al. Curcumin for COVID-19 RCT LATE TREATMENT Favors curcumin Favors control
[Hartono] RCT with 30 patients treated with curcumin and virgin coconut oil (VCO), and 30 SOC patients in Indonesia, showing faster viral clearance with treatment. Treatment also reduced IL-1β, IL-2, IL-6, IL-18, and IFN-β levels. VCO improves the bioavailability of curcumin. There were large unadjusted differences in baseline severity and age, for example 20% vs. 47% of patients >50. VCO 30ml and curcumin 1g tid for 21 days. 066/UN27.06.6.1/KEPK/EC/2020.
0 0.5 1 1.5 2+ Improvement in SpO2 -46% Improvement Relative Risk c19early.org/t Hassaniazad et al. Curcumin for COVID-19 RCT LATE Favors curcumin Favors control
[Hassaniazad] Small RCT with 40 low risk patients in Iran, 20 treated with nano-curcumin, showing no significant difference in outcomes with treatment. Authors note that treatment can improve peripheral blood inflammatory indices and modulate immune response by decreasing Th1 and Th17 responses, increasing T regulatory responses, further reducing IL-17 and IFN-γ, and increasing suppressive cytokines TGF-β and IL-4.
0 0.5 1 1.5 2+ NEWS2 score 77% Improvement Relative Risk Oxygen therapy 92% Oxygen time 70% Hospitalization time 13% Viral clearance 10% c19early.org/t Hellou et al. NCT04382040 Curcumin RCT LATE TREATMENT Favors curcumin Favors control
[Hellou] RCT 50 hospitalized patients in Israel, 33 treated with curcumin, vitamin C, artemisinin, and frankincense oral spray, showing improved recovery with treatment.
0 0.5 1 1.5 2+ Hospitalization time 41% Improvement Relative Risk c19early.org/t Kartika et al. Curcumin for COVID-19 LATE TREATMENT Favors curcumin Favors control
[Kartika] Retrospective 246 hospitalized patients in Indonesia, 136 treated with curcumin, showing shorter hospitalization time with treatment. All patients received vitamin C, D, and zinc.
0 0.5 1 1.5 2+ Recovery 33% Improvement Relative Risk CRP reduction 39% Viral clearance 50% c19early.org/t Khan et al. NCT05130671 Curcumin RCT EARLY TREATMENT Favors curcumin Favors control
[Khan] RCT 50 COVID+ outpatients in Pakistan, 25 treated with curcumin, quercetin, and vitamin D, showing significantly faster viral clearance, significantly improved CRP, and faster resolution of acute symptoms (p=0.154). 168mg curcumin, 260mg quercetin and 360IU cholecalciferol.
0 0.5 1 1.5 2+ Ventilation 66% Improvement Relative Risk Hospitalization 80% Ordinal scale 43% Time to improve one unit.. 30% no CI Recovery 25% Time to viral- 6% c19early.org/t Majeed et al. Curcumin for COVID-19 RCT EARLY TREATMENT Favors curcumin Favors control
[Majeed] RCT 100 patients in India, 50 treated with ImmuActive (curcumin, andrographolides, resveratrol, zinc, selenium, and piperine), showing improved recovery with treatment.
0 0.5 1 1.5 2+ Hospitalization 31% Improvement Relative Risk Severe case 13% c19early.org/t Nimer et al. Curcumin for COVID-19 Prophylaxis Favors curcumin Favors control
[Nimer] Survey 2,148 COVID-19 recovered patients in Jordan, showing lower hospitalization with turmeric prophylaxis, not reaching statistical significance.
0 0.5 1 1.5 2+ Mortality 82% Improvement Relative Risk Mortality (b) 60% Mortality (c) 91% Mortality (d) 67% c19early.org/t Pawar et al. Curcumin for COVID-19 RCT EARLY TREATMENT Favors curcumin Favors control
[Pawar] RCT 140 patients, 70 treated with curcumin and piperine (for absorption), showing faster recovery, lower progression, and lower mortality with treatment. Control group partients also received probiotics. CTRI/2020/05/025482.
0 0.5 1 1.5 2+ Progression 94% Improvement Relative Risk Recovery 38% Hospitalization time 45% c19early.org/t Saber-Moghaddam et al. Curcumin for COVID-19 EARLY Favors curcumin Favors control
[Saber-Moghaddam] Small prospective nonrandomized trial with 41 patients, 21 treated with curcumin, showing lower disease progression and faster recovery with treatment. IRCT20200408046990N1.
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Ventilation 75% 2-point improvement 46% Hospitalization time 10% c19early.org/t Sankhe et al. Curcumin for COVID-19 RCT EARLY TREATMENT Favors curcumin Favors control
[Sankhe] RCT 174 patients in India, 87 treated with AyurCoro-3 (turmeric, gomutra, potassium alum, khadisakhar, bos indicus milk, ghee), showing faster recovery with treatment. EC/NEW/INST/2019/245.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Ventilation 86% ICU admission 67% Hospitalization time 10% Hospitalization time (b) 17% Recovery time, fever 32% Recovery time, dyspnea 36% Recovery time, fever (b) 4% Recovery time, dyspnea (b) -5% Ct increase 44% c19early.org/t Sankhe et al. Curcumin for COVID-19 RCT LATE TREATMENT Favors curcumin Favors control
[Sankhe (B)] RCT with 60 hospitalized patients treated with Ayurcov and 60 control patients in India, showing improved viral clearance and faster symptom resolution in the mild/moderate group, but no significant differences in the severe group. Ayurcov contains curcuma longa, go ark, sphatika (alum), sita (rock candy), godugdham (bos indicus) milk, and goghritam (bos indicus ghee).
0 0.5 1 1.5 2+ Severe case 42% unadjusted Improvement Relative Risk c19early.org/t Shehab et al. Curcumin for COVID-19 Prophylaxis Favors curcumin Favors control
[Shehab] Retrospective survey-based analysis of 349 COVID-19 patients, showing a lower risk of severe cases with vitamin D, zinc, turmeric, and honey prophylaxis in unadjusted analysis, without statistical significance. REC/UG/2020/03.
0 0.5 1 1.5 2+ Mortality 83% Improvement Relative Risk Mortality (b) 67% Mortality (c) 80% c19early.org/t Tahmasebi et al. Curcumin for COVID-19 RCT LATE TREATMENT Favors curcumin Favors control
[Tahmasebi] RCT 40 hospitalized, 40 ICU, and 40 control patients in Iran, showing lower mortality and improved regulatory T cell responses with nanocurcumin treatment (SinaCurcumin).
0 0.5 1 1.5 2+ Improvement, CFS 44% Improvement Relative Risk Improvement, SWS 82% Improvement, CSS 64% c19early.org/t Thomas et al. Phyto-V Curcumin RCT LONG COVID Favors curcumin Favors control
[Thomas] RCT 147 long COVID patients in the UK, 56 treated with a phytochemical-rich concentrated food capsule, showing improved recovery with treatment. Treatment included curcumin, bioflavonoids, chamomile, ellagic acid, and resveratrol.
0 0.5 1 1.5 2+ Mortality 50% Improvement Relative Risk c19early.org/t Valizadeh et al. Curcumin for COVID-19 RCT LATE TREATMENT Favors curcumin Favors control
[Valizadeh] Small RCT with 40 nano-curcumin patients and 40 control patients showing lower mortality with treatment. Authors conclude that nano-curcumin may be able to modulate the increased rate of inflammatory cytokines especially IL-1β and IL-6 mRNA expression and cytokine secretion in COVID-19 patients, which may improve clinical outcomes.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were curcumin, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of curcumin for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/tmeta.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.
[Ahmadi], 6/19/2021, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 11 authors. risk of hospitalization, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 30 (0.0%), control 3 of 30 (10.0%), NNT 10.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
recovery time, 20.6% lower, relative time 0.79, p = 0.37, treatment 30, control 30.
[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, 31.2% lower, RR 0.69, p = 0.10, treatment 30 of 144 (20.8%), control 207 of 594 (34.8%), NNT 7.1, adjusted per study, odds ratio converted to relative risk, multivariable.
[Askari], 6/6/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 11 authors, study period November 2020 - April 2021, trial IRCT20121216011763N46. risk of no recovery, 125.0% higher, RR 2.25, p = 0.58, treatment 3 of 8 (37.5%), control 1 of 6 (16.7%), dyspnea.
risk of no recovery, 433.3% higher, RR 5.33, p = 0.19, treatment 2 of 6 (33.3%), control 0 of 7 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), ague.
risk of no recovery, 72.9% lower, RR 0.27, p = 0.04, treatment 2 of 12 (16.7%), control 8 of 13 (61.5%), NNT 2.2, weakness.
risk of no recovery, 40.0% lower, RR 0.60, p = 0.42, treatment 3 of 10 (30.0%), control 7 of 14 (50.0%), NNT 5.0, muscular pain.
risk of no recovery, 38.5% lower, RR 0.62, p = 0.65, treatment 4 of 13 (30.8%), control 4 of 8 (50.0%), NNT 5.2, headache.
risk of no recovery, 71.4% higher, RR 1.71, p = 1.00, treatment 2 of 7 (28.6%), control 1 of 6 (16.7%), sore throat.
risk of no recovery, 12.5% lower, RR 0.88, p = 1.00, treatment 1 of 8 (12.5%), control 1 of 7 (14.3%), NNT 56, sputum cough.
risk of no recovery, no change, RR 1.00, p = 1.00, treatment 3 of 13 (23.1%), control 3 of 13 (23.1%), dry cough.
[Dound], 11/16/2020, Randomized Controlled Trial, India, peer-reviewed, 5 authors, this trial uses multiple treatments in the treatment arm (combined with vitamin C, vitamin K2-7, and l-selenomethionine) - results of individual treatments may vary, excluded in exclusion analyses: potential randomization failure. relative improvement on 6-point scale, 33.3% better, RR 0.67, p < 0.001, treatment 100, control 100.
[Khan], 5/1/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, 7 authors, study period 2 September, 2021 - 28 November, 2021, this trial uses multiple treatments in the treatment arm (combined with quercetin and vitamin D) - results of individual treatments may vary, trial NCT05130671 (history). risk of no recovery, 33.3% lower, RR 0.67, p = 0.15, treatment 10 of 25 (40.0%), control 15 of 25 (60.0%), NNT 5.0.
relative CRP reduction, 39.1% better, RR 0.61, p = 0.006, treatment 25, control 25.
risk of no viral clearance, 50.0% lower, RR 0.50, p = 0.009, treatment 10 of 25 (40.0%), control 20 of 25 (80.0%), NNT 2.5.
[Majeed], 10/11/2021, Double Blind Randomized Controlled Trial, India, peer-reviewed, 4 authors, this trial uses multiple treatments in the treatment arm (combined with andrographolides, resveratrol, zinc, selenium, and piperine) - results of individual treatments may vary. risk of mechanical ventilation, 66.2% lower, RR 0.34, p = 1.00, treatment 0 of 45 (0.0%), control 1 of 47 (2.1%), NNT 47, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 79.7% lower, RR 0.20, p = 0.49, treatment 0 of 45 (0.0%), control 2 of 47 (4.3%), NNT 24, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
relative ordinal scale, 43.0% better, RR 0.57, p = 0.004, treatment 45, control 47, day 28.
risk of no recovery, 24.6% lower, RR 0.75, p = 0.08, treatment 26 of 45 (57.8%), control 36 of 47 (76.6%), NNT 5.3, day 28.
time to viral-, 5.8% lower, relative time 0.94, p = 0.47, treatment 45, control 47.
[Pawar], 5/28/2021, Double Blind Randomized Controlled Trial, India, peer-reviewed, 8 authors. risk of death, 81.8% lower, RR 0.18, p = 0.02, treatment 2 of 70 (2.9%), control 11 of 70 (15.7%), NNT 7.8.
risk of death, 60.0% lower, RR 0.40, p = 0.39, treatment 2 of 15 (13.3%), control 5 of 15 (33.3%), NNT 5.0, severe group.
risk of death, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 25 (0.0%), control 5 of 25 (20.0%), NNT 5.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), moderate group.
risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 30 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), mild group.
[Saber-Moghaddam], 1/3/2021, prospective, Iran, peer-reviewed, 9 authors. risk of progression, 94.3% lower, RR 0.06, p = 0.001, treatment 0 of 21 (0.0%), control 8 of 20 (40.0%), NNT 2.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no recovery, 38.4% lower, RR 0.62, p = 0.04, treatment 11 of 21 (52.4%), control 17 of 20 (85.0%), NNT 3.1.
hospitalization time, 44.8% lower, relative time 0.55, p < 0.001, treatment 21, control 20.
[Sankhe], 8/10/2021, Randomized Controlled Trial, India, peer-reviewed, 8 authors, this trial uses multiple treatments in the treatment arm (combined with gomutra, potassium alum, khadisakhar, bos indicus milk, ghee) - results of individual treatments may vary. risk of death, 88.9% lower, RR 0.11, p = 0.12, treatment 0 of 87 (0.0%), control 4 of 87 (4.6%), NNT 22, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 75.0% lower, RR 0.25, p = 0.37, treatment 1 of 87 (1.1%), control 4 of 87 (4.6%), NNT 29.
risk of no 2-point improvement, 46.5% lower, RR 0.54, p = 0.002, treatment 29 of 87 (33.3%), control 60 of 87 (69.0%), NNT 2.8, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, day 7 mid-recovery.
hospitalization time, 10.0% lower, relative time 0.90, p = 0.40, treatment 87, control 87.
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.
[Abbaspour-Aghdam], 9/17/2022, Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 16 authors, trial IRCT20200324046851N1. risk of death, 71.4% lower, RR 0.29, p = 0.15, treatment 2 of 30 (6.7%), control 7 of 30 (23.3%), NNT 6.0.
risk of no recovery, 86.3% lower, RR 0.14, p = 0.04, treatment 1 of 28 (3.6%), control 6 of 23 (26.1%), NNT 4.4, dyspnea.
risk of no recovery, 89.9% lower, RR 0.10, p = 0.04, treatment 0 of 28 (0.0%), control 4 of 23 (17.4%), NNT 5.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), fever >39.0.
risk of no recovery, 38.4% lower, RR 0.62, p = 0.17, treatment 9 of 28 (32.1%), control 12 of 23 (52.2%), NNT 5.0, bilateral chest radiograph involvement.
risk of no recovery, 58.9% lower, RR 0.41, p = 0.27, treatment 3 of 28 (10.7%), control 6 of 23 (26.1%), NNT 6.5, cough.
risk of no recovery, 81.6% lower, RR 0.18, p = 0.20, treatment 0 of 28 (0.0%), control 2 of 23 (8.7%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), headache.
[Asadirad], 1/17/2022, Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 7 authors. risk of death, 25.9% lower, RR 0.74, p = 0.74, treatment 5 of 27 (18.5%), control 6 of 24 (25.0%), NNT 15, excluding patients that stopped treatment due to progression - 3 for curcumin and 6 for control.
risk of progression, 50.0% lower, RR 0.50, p = 0.47, treatment 3 of 30 (10.0%), control 6 of 30 (20.0%), NNT 10.0.
risk of unresolved fever, 45.3% lower, RR 0.55, p = 0.09, treatment 8 of 27 (29.6%), control 13 of 24 (54.2%), NNT 4.1.
risk of unresolved dyspnea, 28.9% lower, RR 0.71, p = 0.72, treatment 4 of 27 (14.8%), control 5 of 24 (20.8%), NNT 17.
risk of unresolved cough, 40.7% lower, RR 0.59, p = 0.36, treatment 6 of 27 (22.2%), control 9 of 24 (37.5%), NNT 6.5.
risk of O2 <92%, 36.5% lower, RR 0.63, p = 0.51, treatment 5 of 27 (18.5%), control 7 of 24 (29.2%), NNT 9.4.
risk of O2 <97%, 20.0% lower, RR 0.80, p = 0.21, treatment 18 of 27 (66.7%), control 20 of 24 (83.3%), NNT 6.0.
[Hartono], 2/22/2022, Randomized Controlled Trial, Indonesia, peer-reviewed, 13 authors, this trial uses multiple treatments in the treatment arm (combined with virgin coconut oil) - results of individual treatments may vary, excluded in exclusion analyses: randomization resulted in significant baseline differences that were not adjusted for. risk of no viral clearance, 53.3% lower, RR 0.47, p < 0.001, treatment 14 of 30 (46.7%), control 30 of 30 (100.0%), NNT 1.9, day 10.
risk of no viral clearance, 75.0% lower, RR 0.25, p = 0.002, treatment 4 of 30 (13.3%), control 16 of 30 (53.3%), NNT 2.5, day 14.
risk of no viral clearance, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 30 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 21.
[Hassaniazad], 9/19/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 12 authors. relative improvement in SpO2, 45.7% worse, RR 1.46, p = 0.90, treatment 20, control 20.
[Hellou], 5/19/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Israel, peer-reviewed, 6 authors, this trial uses multiple treatments in the treatment arm (combined with vitamin C, artemisinin, and frankincense) - results of individual treatments may vary, trial NCT04382040 (history). relative NEWS2 score, 76.7% better, RR 0.23, p = 0.04, treatment mean 0.52 (±0.67) n=33, control mean 2.23 (±3.2) n=17, day 15.
risk of oxygen therapy, 92.2% lower, RR 0.08, p = 0.01, treatment 0 of 33 (0.0%), control 4 of 17 (23.5%), NNT 4.2, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 15.
oxygen time, 69.7% lower, relative time 0.30, p = 0.17, treatment mean 2.3 (±1.4) n=33, control mean 7.6 (±4.6) n=17.
hospitalization time, 13.3% lower, relative time 0.87, p = 0.92, treatment mean 7.8 (±7.3) n=33, control mean 9.0 (±8.0) n=17.
risk of no viral clearance, 9.8% lower, RR 0.90, p = 0.77, treatment 14 of 33 (42.4%), control 8 of 17 (47.1%), NNT 22, day 15.
[Kartika], 1/28/2022, retrospective, Indonesia, preprint, 6 authors, study period January 2021 - June 2021. hospitalization time, 41.0% lower, relative time 0.59, p = 0.048, treatment 139, control 107.
[Sankhe (B)], 3/25/2022, Single Blind Randomized Controlled Trial, India, peer-reviewed, 10 authors, this trial uses multiple treatments in the treatment arm (combined with gomutra, potassium alum, khadisakhar, bos indicus milk, ghee) - results of individual treatments may vary. risk of death, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 60 (0.0%), control 3 of 60 (5.0%), NNT 20, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 60 (0.0%), control 3 of 60 (5.0%), NNT 20, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 66.7% lower, RR 0.33, p = 0.62, treatment 1 of 60 (1.7%), control 3 of 60 (5.0%), NNT 30.
hospitalization time, 10.0% lower, relative time 0.90, p = 0.40, treatment 45, control 45, moderate group.
hospitalization time, 16.7% lower, relative time 0.83, p = 0.20, treatment 15, control 15, severe group.
recovery time, 31.9% lower, relative time 0.68, p < 0.001, treatment 45, control 45, moderate group, fever.
recovery time, 36.1% lower, relative time 0.64, p < 0.001, treatment 45, control 45, moderate group, dyspnea.
recovery time, 4.3% lower, relative time 0.96, p = 0.74, treatment 15, control 15, severe group, fever.
recovery time, 4.8% higher, relative time 1.05, p = 0.10, treatment 15, control 15, severe group, dyspnea.
relative Ct increase, 44.4% better, RR 0.56, p = 0.003, treatment mean 9.98 (±6.39) n=44, control mean 5.55 (±6.91) n=43, moderate group.
[Tahmasebi], 3/28/2021, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 14 authors. risk of death, 83.3% lower, RR 0.17, p = 0.11, treatment 1 of 40 (2.5%), control 6 of 40 (15.0%), NNT 8.0.
risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 20 (0.0%), control 1 of 20 (5.0%), NNT 20, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), non-ICU patients.
risk of death, 80.0% lower, RR 0.20, p = 0.18, treatment 1 of 20 (5.0%), control 5 of 20 (25.0%), NNT 5.0, ICU patients.
[Thomas], 3/22/2022, Double Blind Randomized Controlled Trial, placebo-controlled, United Kingdom, peer-reviewed, 7 authors, study period May 2020 - May 2021, this trial uses multiple treatments in the treatment arm (combined with bioflavonoids, chamomile, ellagic acid, resveratrol) - results of individual treatments may vary, Phyto-V trial. relative improvement, 44.3% better, RR 0.56, p = 0.02, treatment mean 6.1 (±7.5) n=74, control mean 3.4 (±6.1) n=73, CFS.
relative improvement, 81.8% better, RR 0.18, p < 0.001, treatment mean 6.6 (±10.5) n=74, control mean 1.2 (±7.4) n=73, SWS.
relative improvement, 63.6% better, RR 0.36, p = 0.02, treatment mean 1.1 (±2.0) n=74, control mean 0.4 (±1.5) n=73, CSS.
[Valizadeh], 10/20/2020, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 12 authors. risk of death, 50.0% lower, RR 0.50, p = 0.30, treatment 4 of 20 (20.0%), control 8 of 20 (40.0%), NNT 5.0.
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.
[Nimer], 6/10/2022, retrospective, Jordan, peer-reviewed, survey, mean age 40.2, 4 authors, study period March 2021 - July 2021. risk of hospitalization, 30.8% lower, RR 0.69, p = 0.08, treatment 29 of 329 (8.8%), control 179 of 1,819 (9.8%), adjusted per study, odds ratio converted to relative risk, multivariable.
risk of severe case, 12.6% lower, RR 0.87, p = 0.47, treatment 40 of 329 (12.2%), control 211 of 1,819 (11.6%), adjusted per study, odds ratio converted to relative risk, multivariable.
[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, 42.4% lower, RR 0.58, p = 0.55, treatment 2 of 32 (6.2%), control 24 of 221 (10.9%), NNT 22, unadjusted, severe vs. mild cases.
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