Vitamin D for COVID-19: real-time meta analysis of 264 studies (107 treatment studies and 157 sufficiency studies)
•Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 56 studies from 52 independent teams in 20 different countries show statistically significant improvements in isolation (40 for the most serious outcome).
•Random effects meta-analysis with pooled effects using the most serious outcome reported shows 60% [40‑74%] and 37% [31‑42%] improvement for early treatment and for all studies. Results are similar after restriction to 101 peer-reviewed studies: 57% [36‑71%] and 37% [31‑42%], and for the 60 mortality results: 68% [39‑84%] and 36% [28‑44%].
•Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 41% [27‑52%].
•Sufficiency studies show a strong association between vitamin D sufficiency and outcomes. Meta analysis of the 157 studies using the most serious outcome reported shows 53% [49‑57%] improvement.
•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 13% of vitamin D studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
•All data and sources to reproduce this paper are in the appendix. Other meta analyses for vitamin D treatment can be found in [Argano, D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu, Xie], showing significant improvements for mortality, mechanical ventilation, ICU admission, hospitalization, severity, and cases.
|Early treatment||Late treatment||All studies||Studies||Patients||Authors|
|All studies||60% [40‑74%]|
|Randomized Controlled TrialsRCTs||32% [8‑50%]|
|HospitalizationHosp.||90% [-453‑100%]||22% [6‑35%]|
|RCT mortality||-||31% [6‑50%]|
Vitamin D reduces risk for COVID-19 with very high confidence for mortality, ICU admission, hospitalization, recovery, cases, viral clearance, and in pooled analysis, low confidence for ventilation, and very low confidence for progression.
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 50 treatments.
We analyze all significant controlled studies regarding vitamin D and 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 perform random-effects meta analysis for all treatment studies, Randomized Controlled Trials, peer-reviewed studies, studies using cholecalciferol, studies using calcifediol/calcitriol, and for specific outcomes: mortality, mechanical ventilation, ICU admission, hospitalization, and case results. Results are presented for prophylaxis, early treatment, and late treatment. Separately, we perform random-effects meta analysis for studies that analyze outcomes based on vitamin D sufficiency (non-treatment studies).
Figure 2. The first step is conversion to calcidiol, or 25(OH)D, in the liver. The second is conversion to calcitriol, or 1,25(OH)2D, which occurs in the kidneys, the immune system, and elsewhere. Calcitriol is the active, steroid-hormone form of vitamin D, which binds with vitamin D receptors found in most cells in the body. Vitamin D was first identified in relation to bone health, but is now known to have multiple functions, including an important role in the immune system [Carlberg, Martens]. For example, [Quraishi] show a strong association between pre-operative vitamin D levels and hospital-acquired infections, as shown in Figure 3. There is a significant delay involved in the conversion from cholecalciferol, therefore calcifediol (calcidiol) or calcitriol may be preferable for treatment.
[Silva], suggesting additional caution in interpreting results for studies where the vitamin D levels are measured during the disease. For these reasons, we analyze sufficiency studies separately from treatment studies. We include all sufficiency studies that provide a comparison between two groups with low and high levels. Some studies only provide results as a function of change in vitamin D levels [Butler-Laporte, Gupta, Raisi-Estabragh], which may not be indicative of results for deficiency/insufficiency versus sufficiency (increasing already sufficient levels may be less useful for example). Some studies show the average vitamin D level for patients in different groups [Al-Daghri, Alarslan, Azadeh, Chodick, D'Avolio, Desai, Ersöz, Hosseini (B), Jabbar, Kerget, Latifi-Pupovci, Mansour, Mardani, Morad, Nicolescu, Qu, Ranjbar, Rathod, Saeed, Schmitt, Shannak, Sinnberg, Soltani-Zangbar, Takase, Vassiliou], most of which show lower D levels for worse outcomes. Other studies analyze vitamin D status and outcomes in geographic regions [Bakaloudi, Jayawardena, Marik, Papadimitriou, Rhodes, Sooriyaarachchi, Walrand, Yadav], all finding worse outcomes to be more likely with lower D levels.
Sufficiency studies vary widely in terms of when vitamin D levels were measured, the cutoff level used, and the population analyzed (for example studies with hospitalized patients exclude the effect of vitamin D on the risk of hospitalization). We do not analyze sufficiency studies in more detail because there are many controlled treatment studies that provide better information on the use of vitamin D as a treatment for COVID-19. A more detailed analysis of sufficiency studies can be found in [Chiodini]. [Mishra] present a systematic review and meta analysis showing that vitamin D levels are significantly associated with COVID-19 cases.
Figure 4. Prophylaxis refers to regularly taking vitamin D before being infected in order to minimize the severity of infection. Due to the mechanism of action, vitamin D is unlikely to completely prevent infection, although it may prevent infection from reaching a level detectable by PCR. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
6 In Silico studies support the efficacy of vitamin D [Al-Mazaideh, Chellasamy, Mansouri, Pandya, Qayyum, Song].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, for specific outcomes, and for sufficiency (non-treatment) studies. Table 2 shows results by treatment stage. Figure 5 plots individual results by treatment stage. Figure 6, 7, 8, 9, 10, 11, 12, 13, and 14 show forest plots for treatment studies with pooled effects, peer-reviewed studies, cholecalciferol studies, calcifediol/calcitriol studies, and for studies reporting mortality, mechanical ventilation, ICU admission, hospitalization, and case results only. Figure 15 shows a forest plot for random effects meta-analysis of sufficiency (non-treatment) studies.
|All studies||37% [31‑42%] p < 0.0001|
|After exclusions||39% [33‑45%] p < 0.0001|
|Peer-reviewed studiesPeer-reviewed||37% [31‑42%] p < 0.0001|
|Randomized Controlled TrialsRCTs||31% [17‑42%] p < 0.0001|
|RCTs after exclusionsRCTs w/exc.||34% [17‑47%] p = 0.0003|
|Cholecalciferol||36% [29‑41%] p < 0.0001|
|Calcifediol/calcitriolCalcifediol||49% [26‑65%] p = 0.00036|
|Mortality||36% [28‑44%] p < 0.0001|
|VentilationVent.||26% [-2‑46%] p = 0.068||17||7,852||184|
|ICU admissionICU||50% [33‑62%] p < 0.0001|
|HospitalizationHosp.||20% [8‑31%] p = 0.0022|
|Recovery||38% [23‑50%] p < 0.0001|
|Cases||15% [6‑23%] p = 0.0015|
|Viral||52% [30‑67%] p = 0.00014|
|RCT mortality||31% [6‑50%] p = 0.02|
|RCT hospitalizationRCT hosp.||21% [-4‑40%] p = 0.087||8||39,713||107|
|Sufficiency||53% [49‑57%] p < 0.0001|
|Early treatment||Late treatment||Prophylaxis|
|All studies||60% [40‑74%]|
|After exclusions||68% [45‑82%]|
|Peer-reviewed studiesPeer-reviewed||57% [36‑71%]|
|Randomized Controlled TrialsRCTs||32% [8‑50%]|
|RCTs after exclusionsRCTs w/exc.||65% [-65‑92%]||34% [16‑47%]|
|17% [-14‑40%]||38% [-3‑63%]|
|ICU admissionICU||87% [-143‑99%]||52% [30‑67%]|
|HospitalizationHosp.||90% [-453‑100%]||22% [6‑35%]|
|RCT mortality||-||31% [6‑50%]|
|RCT hospitalizationRCT hosp.||-||29% [10‑44%]|
Results restricted to Randomized Controlled Trials (RCTs), after exclusions, and for specific outcomes are shown in Figure 16, 17, 18, and 19.
[Jadad]. For example, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
[Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
To avoid bias in the selection of studies, we include all studies in the main analysis, with the exception of [Espitia-Hernandez]. This study uses a combined protocol with another medication that shows high effectiveness when used alone. Authors report on viral clearance, showing 100% clearance with treatment and 0% for the control group. Based on the known mechanisms of action, the combined medication is likely to contribute more to the improvement.
Here we show the results after excluding studies with critical issues.
[Murai] is a very late stage study (mean 10 days from symptom onset, with 90% on oxygen at baseline), with poorly matched arms in terms of gender, ethnicity, hypertension, diabetes, and baseline ventilation, all of which favor the control group. Further, this study uses cholecalciferol, which may be especially poorly suited for such a late stage. [Cannata-Andía, Mariani] are also very late stage studies using cholecalciferol.
The studies excluded are as follows, and the resulting forest plot is shown in Figure 20.
[Abdulateef], unadjusted results with no group details.
[Asimi], excessive unadjusted differences between groups.
[Assiri], unadjusted results with no group details.
[Baykal], unadjusted results with no group details; significant confounding by time possible due to separation of groups in different time periods.
[Campi], significant unadjusted differences between groups.
[Cannata-Andía], very late stage study using cholecalciferol instead of calcifediol or calcitriol.
[Din Ujjan], based on dosages and previous research, combined treatments may contribute more to the effect seen.
[Elhadi], unadjusted results with no group details.
[Fairfield], substantial unadjusted confounding by indication likely.
[Guldemir], unadjusted results with no group details.
[Güven], very late stage, ICU patients.
[Holt], significant unadjusted confounding possible.
[Junior], unadjusted results with no group details.
[Khan], based on dosages and previous research, combined treatments may contribute more to the effect seen.
[Krishnan], unadjusted results with no group details.
[Leal-Martínez], combined treatments may contribute more to the effect seen.
[Lázaro], very few events; unadjusted results with no group details; minimal details provided.
[Mahmood], unadjusted results with no group details; substantial unadjusted confounding by indication likely.
[Mahmood], unadjusted results with no group details; substantial unadjusted confounding by indication likely.
[Mohseni], unadjusted results with no group details.
[Murai], very late stage, >50% on oxygen/ventilation at baseline; very late stage study using cholecalciferol instead of calcifediol or calcitriol.
[Pecina], unadjusted results with no group details.
[Shahid], minimal details provided.
[Shehab], unadjusted results with no group details.
[Singh], minimal details provided.
[Ullah], significant unadjusted confounding possible.
[Zurita-Cruz], randomization resulted in significant baseline differences that were not adjusted for.
Heterogeneity in COVID-19 studies arises from many factors including:
[McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
|Post exposure prophylaxis||86% fewer cases [Ikematsu]|
|<24 hours||-33 hours symptoms [Hayden]|
|24-48 hours||-13 hours symptoms [Hayden]|
|Inpatients||-2.5 hours to improvement [Kumar]|
Figure 21 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 50 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 21. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 50 treatments.
[Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
[Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer. Non-prescription supplements may show very wide variations in quality [Crawford, Crighton].
Figure 22. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations with a specific form and dosage of vitamin D. 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.
Vitamin D studies vary widely in all the factors above, which makes the consistently positive results even more remarkable. A failure to detect an association after combining heterogeneous studies does not mean the treatment is not effective (it may only work in certain cases), however the reverse is not true — an identified association is valid, although the magnitude of the effect may be larger for more optimal cases, and lower for less optimal cases. While we present results for all studies in this paper, the individual outcome, form of vitamin D, and treatment time analyses are more relevant for specific use cases.
Sufficiency studies show a strong correlation between low vitamin D levels and worse COVID-19 outcomes, however they do not provide information on vitamin D treatment. Studies with vitamin D levels measured after admission may show lower levels because COVID-19 infection reduces vitamin D levels. Studies with levels measured before infection also show signficant benefit, however the cause could be one or more correlated factors. For example, sunlight exposure increases vitamin D levels, but also increases intracellular melatonin [Zimmerman], and melatonin shows significant benefit for COVID-19 [c19melatonin.com]. Sun exposure is also correlated with physical exercise, which also shows benefit for COVID-19 [c19early.org].
[Assiri], [Zangeneh] with no details of treatment, and [Cannata-Andía, Mariani, Murai] which are very late stage studies using cholecalciferol. For [Murai], the result also has very low statistical significance due to the small number of events, and the other reported outcomes of ventilation and ICU admission, which have slightly more events and higher confidence, show benefits for vitamin D. Calcifediol or calcitriol, which avoids several days delay in conversion, may be more successful, especially with very late stage usage.
[Jolliffe, Villasis-Keever] are consistent with this possibility, with the shorter-term supplementation in [Villasis-Keever] showing better results compared to the longer-term high adherence daily supplementation in [Jolliffe]. Specific forms and administration of vitamin D may minimize upregulation of CYP24A1 [Petkovich]. [Bader] performed an RCT showing high-dose cholecalciferol (50,000 IU/week) significantly increased IL-6, however other studies have shown no significant difference in IL-6 [El Hajj, Mousa] (30,000IU/wk and 100,000IU bolus + 4,000IU/day).
Other factors may be responsible for the observed lower efficacy in prophylaxis studies. For example, analysis of hospitalized patients is subject to selection bias because long-term accurate-dosage supplementing individuals may be significantly less likely to be hospitalized. Studies spanning higher-UV months are subject to confounding. Note that prophylaxis studies include case results, whereas we may expect vitamin D to be more effective against serious outcomes. Comparison of acute treatment versus long-term supplementation should use the specific outcome analyses rather than the pooled outcome analyses.
[Boulware, Meeus, Meneguesso].
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
Figure 23 shows a scatter plot of results for prospective and retrospective treatment studies. Prospective studies show 51% [34‑64%] improvement in meta analysis, compared to 32% [25‑38%] for retrospective studies, suggesting possible negative publication bias, with a non-significant trend towards retrospective studies reporting lower efficacy. This gives us further confidence in the significant efficacy seen in all studies.
Figure 24 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
[Argano, D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu, Xie], showing significant improvements for mortality, mechanical ventilation, ICU admission, hospitalization, severity, and cases.
[Lakkireddy] was censored based on incorrect claims from an anti-treatment researcher. For example, the author claims that the gender difference between arms (7/44 vs. 15/43 female) indicates randomization failure, however by simulation, using the group sizes and overall gender ratio, the difference between the number of female patients in each arm is expected to be ≥8 6.4% of the time (2.7% with ≥8 in the control arm, and 3.7% with ≥8 in the treatment arm).
Author claims that the difference in CRP would only happen about one in a billion times. This is incorrect. CRP is not normally distributed, and the observed values could be due to a very small number of outliers with very large CRP in one group.
A response from the study authors can be found at [c19vitamind.com]. The study was republished.
heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
Table 4 shows the reported results of physicians that use early treatments for COVID-19, compared to the results for a non-treating physician. The treatments used vary. Physicians typically use a combination of treatments, with almost all reporting use of ivermectin and/or HCQ, and most using additional treatments, including vitamin D. These results are subject to selection and ascertainment bias and more accurate analysis requires details of the patient populations and followup, however results are consistently better across many teams, and consistent with the extensive controlled trial evidence that shows a significant reduction in risk with many early treatments, and improved results with the use of multiple treatments in combination.
|Physician / Team||Location||Patients||HospitalizationHosp.||MortalityDeath|
|Dr. David Uip (*)||Brazil||2,200||38.6% (850)||Ref.||2.5% (54)||Ref.|
|EARLY TREATMENT - 38 physicians/teams|
|Physician / Team||Location||Patients||HospitalizationHosp.||ImprovementImp.||MortalityDeath||ImprovementImp.|
|Dr. Roberto Alfonso Accinelli
0/360 deaths for treatment within 3 days
|Dr. Mohammed Tarek Alam
patients up to 84 years old
|Dr. Oluwagbenga Alonge||Nigeria||310||0.0% (0)||100.0%|
|Dr. Raja Bhattacharya
up to 88yo, 81% comorbidities
|Dr. Flavio Cadegiani||Brazil||3,450||0.1% (4)||99.7%||0.0% (0)||100.0%|
|Dr. Alessandro Capucci||Italy||350||4.6% (16)||88.2%|
|Dr. Shankara Chetty||South Africa||8,000||0.0% (0)||100.0%|
|Dr. Deborah Chisholm||USA||100||0.0% (0)||100.0%|
|Dr. Ryan Cole||USA||400||0.0% (0)||100.0%||0.0% (0)||100.0%|
|Dr. Marco Cosentino
vs. 3-3.8% mortality during period; earlier treatment better
|Italy||392||6.4% (25)||83.5%||0.3% (1)||89.6%|
|Dr. Jeff Davis||USA||6,000||0.0% (0)||100.0%|
|Dr. Dhanajay||India||500||0.0% (0)||100.0%|
|Dr. Bryan Tyson & Dr. George Fareed||USA||20,000||0.0% (6)||99.9%||0.0% (4)||99.2%|
|Dr. Raphael Furtado||Brazil||170||0.6% (1)||98.5%||0.0% (0)||100.0%|
|Dr. Heather Gessling||USA||1,500||0.1% (1)||97.3%|
|Dr. Ellen Guimarães||Brazil||500||1.6% (8)||95.9%||0.4% (2)||83.7%|
|Dr. Syed Haider||USA||4,000||0.1% (5)||99.7%||0.0% (0)||100.0%|
|Dr. Mark Hancock||USA||24||0.0% (0)||100.0%|
|Dr. Sabine Hazan||USA||1,000||0.0% (0)||100.0%|
|Dr. Mollie James||USA||3,500||1.1% (40)||97.0%||0.0% (1)||98.8%|
|Dr. Roberta Lacerda||Brazil||550||1.5% (8)||96.2%||0.4% (2)||85.2%|
|Dr. Katarina Lindley||USA||100||5.0% (5)||87.1%||0.0% (0)||100.0%|
|Dr. Ben Marble||USA||150,000||0.0% (4)||99.9%|
|Dr. Edimilson Migowski||Brazil||2,000||0.3% (7)||99.1%||0.1% (2)||95.9%|
|Dr. Abdulrahman Mohana||Saudi Arabia||2,733||0.0% (0)||100.0%|
|Dr. Carlos Nigro||Brazil||5,000||0.9% (45)||97.7%||0.5% (23)||81.3%|
|Dr. Benoit Ochs||Luxembourg||800||0.0% (0)||100.0%|
|Dr. Ortore||Italy||240||1.2% (3)||96.8%||0.0% (0)||100.0%|
|Dr. Valerio Pascua
one death for a patient presenting on the 5th day in need of supplemental oxygen
|Honduras||415||6.3% (26)||83.8%||0.2% (1)||90.2%|
|Dr. Sebastian Pop||Romania||300||0.0% (0)||100.0%|
|Dr. Brian Proctor||USA||869||2.3% (20)||94.0%||0.2% (2)||90.6%|
|Dr. Anastacio Queiroz||Brazil||700||0.0% (0)||100.0%|
|Dr. Didier Raoult||France||8,315||2.6% (214)||93.3%||0.1% (5)||97.6%|
|Dr. Karin Ried
up to 99yo, 73% comorbidities, av. age 63
|Dr. Roman Rozencwaig
patients up to 86 years old
|Dr. Vipul Shah||India||8,000||0.1% (5)||97.5%|
|Dr. Silvestre Sobrinho||Brazil||116||8.6% (10)||77.7%||0.0% (0)||100.0%|
|Dr. Vladimir Zelenko||USA||2,200||0.5% (12)||98.6%||0.1% (2)||96.3%|
|Mean improvement with early treatment protocols||236,564||HospitalizationHosp.||94.0%||MortalityDeath||94.8%|
Random effects meta-analysis with pooled effects using the most serious outcome reported shows 60% [40‑74%] and 37% [31‑42%] improvement for early treatment and for all studies. Results are similar after restriction to 101 peer-reviewed studies: 57% [36‑71%] and 37% [31‑42%], and for the 60 mortality results: 68% [39‑84%] and 36% [28‑44%].
Statistically significant improvements are seen in treatment studies for mortality, ICU admission, hospitalization, and cases. 56 studies from 52 independent teams in 20 different countries show statistically significant improvements in isolation (40 for the most serious outcome).
Acute treatment (early 60% [40‑74%], late 46% [33‑56%]) shows greater efficacy than chronic prophylaxis (31% [23‑38%]).
Late stage treatment with calcifediol/calcitriol shows greater improvement compared to cholecalciferol: 73% [57‑83%] vs. 41% [27‑52%].
This paper is data driven, all graphs and numbers are dynamically generated. We will update the paper as new studies are released or with any corrections. Please submit updates and corrections at https://c19early.org/dmeta.html.
3/28: We added [Schmidt].
3/23: We added [Davran].
3/15: We added [Bucurica].
3/15: We added [Topan].
3/14: We added [Domazet Bugarin, Siuka].
3/4: We added [Şengül].
3/4: We added [Chen].
3/2: We added [Tan].
2/18: We added [Ortatatli].
2/8: We added [Arabi].
1/28: We added [Batur].
1/20: We added [Mostafa].
1/19: We added [Din Ujjan].
1/17: We added [Valecha].
1/8: We updated [van Helmond] to the journal version.
1/7/2023: We updated discussion of acute treatment vs. long-term supplementation.
12/31: We added [De Nicolò].
12/20: We added [Abdrabbo AlYafei].
12/20: We updated the discussion of heterogeneity and RCTs.
12/12: We added [Vásquez-Procopio].
12/3: We added [Tallon].
11/27: We added [Guldemir (B)].
11/26: We added [Sharif].
11/13: We added [Gibbons].
11/8: We added [Said].
11/4: We added [Bychinin (B)].
10/28: We added [Álvarez].
10/26: We added [Hafezi].
10/15: We added [Charla].
10/8: We added [Karimpour-Razkenari].
10/1: We added [Singh].
9/20: We added [Shahid].
9/19: We added [van Helmond].
9/15: We added [Brunvoll].
9/11: We added [Zeidan].
8/25: We added [Hafez].
8/24: We added [Aldwihi, Sharif-Askari].
8/23: We added [Doğan].
8/21: We added [Reyes Pérez].
8/19: We added [Kalichuran].
8/16: We updated [Lakkireddy] to the new version (post censorship of the previous version).
8/12: We added [Dana, Zurita-Cruz].
8/10: We added [Barrett].
8/5: We added [Bogliolo].
8/3: We added [Alzahrani].
7/27: We added [De Niet].
7/26: We added [Neves].
7/24: We added [Gholi].
7/19: We added [Baykal].
7/2: We added [Hunt].
6/24: We added [Karonova (D)].
5/28: We added [Mariani].
5/24: We added [Ghanei].
5/23: We added [Fiore].
5/20: We added [Hosseini (C)].
5/19: We added [Jabeen].
5/19: We added [Ozturk].
5/8: We added [Charkowick].
5/5: We added [Nguyen].
5/1: We added [Khan].
4/30: We added [Voelkle].
4/24: We added [Davoudi].
4/22: We added discussion of [Lakkireddy].
4/18: We added [Villasis-Keever].
4/17: We added a section on preclinical research.
4/15: We added [Parant].
4/12: We added [Martínez-Rodríguez].
4/5: We added preprint discussion based on [Zeraatkar].
4/2: We added [Ferrer-Sánchez].
3/31: We added [Ramos].
3/27: We added [Pande].
3/25: We added [Elhadi].
3/23: We added [Jolliffe].
3/20: We added [Bushnaq].
3/19: We added [Shehab].
3/7: We added [Rodríguez-Vidales].
3/5: We added [Reis].
3/4: We added [Nimer].
3/3: We added [Karonova].
2/24: We added [Zidrou].
2/20: We added [Sanson].
2/19: We added [Cannata-Andía].
2/18: We added [González-Estevez, Junior].
2/17: We added [Mahmood].
2/15: We updated [Vanegas-Cedillo] to the journal version.
2/11: We added [Bychinin].
2/8: We added [Subramanian].
2/8: We added [Ranjbar].
2/6: We added [Bishop].
2/4: We added [Ahmed].
2/4: We updated [Dror] to the journal version.
1/30: We updated [Leal-Martínez] to the journal version.
1/29: We added [Ansari].
1/28: We added [Anjum].
1/25: We added [Saponaro].
1/23: We added [Juraj].
1/14: We added [Baguma (B)].
1/13: We updated [Israel] to the journal version.
1/8: We added [Seal].
1/5: We added [Pepkowitz].
1/3/2022: We added [Efird].
12/26: We added [Abdulateef].
12/21: We added [Beigmohammadi, Sainz-Amo].
12/20: We added [Galaznik].
12/17: We added [Seven].
12/16: We added [Parra-Ortega].
12/14: We added [Putra].
12/9: We added analysis of the number of independent research groups reporting statistically significant positive results.
12/7: We added [Ma].
12/5: We added [Asgari].
12/3: We updated [Loucera] to the journal version.
12/3: We added [Fatemi].
12/3: We added [Kaur].
11/22: Added discussion related to sufficiency studies.
11/14: We added [Gönen].
11/12: We added [Asghar].
11/7: We added [Holt].
11/3: We added [Atanasovska].
11/1: We updated [Golabi] to the journal version.
10/31: We added [Assiri, Bianconi, Leal-Martínez].
10/19: We added [Jimenez].
10/18: We added [Mohseni].
10/16: We added a summary plot for all results.
10/15: We added [Ramirez-Sandoval].
10/15: We added [Maghbooli (B)].
10/14: We added [Arroyo-Díaz, Burahee] and analysis of treatment mechanical ventilation, ICU admission, and hospitalization results.
9/28: We added [Yildiz].
9/27: We added [Derakhshanian].
9/22: We added [Bagheri].
9/14: We added [Ribeiro].
9/14: We updated [Vasheghani (B)] to the journal version of the article.
9/14: We added [Elamir].
9/10: We added [Tomasa-Irriguible].
9/7: We added [Karonova (B), Pecina].
9/6: We added [Soliman].
9/1: We added [Golabi].
8/23: We corrected [Jain] to include the mortality outcome.
8/15: We added [Nimavat].
8/13: We added [di Filippo] and updated [Louca] to the journal version of the article.
8/12: We added [Alpcan].
8/10: We added discussion of the immune system and vitamin D.
8/2: We added [Matin].
8/1: We added [Pimental].
7/28: We added [Israel (B)].
7/27: We added [Cozier].
7/26: We added [Güven].
7/25: We added [Asimi].
7/24: We added [Orchard].
7/21: We added [Savitri].
7/19: We added [Oristrell].
7/11: We added [Krishnan].
6/25: We added [Cereda (B)].
6/19: We added [Jude].
6/16: We added [Campi].
6/12: We added [Levitus].
6/11: We updated [Oristrell (B)] to the journal version.
6/9: We added [Fasano].
6/8: We updated [Nogués] to the journal version.
6/7: We added [Diaz-Curiel, Dror].
5/29: We added [Sánchez-Zuno (B)].
5/22: We added analysis restricted to cholecalciferol studies.
5/21: We added [Alcala-Diaz, Li].
5/20: We updated [Lakkireddy] to the journal version.
5/19: We added [AlSafar].
5/10: We added additional information in the abstract.
5/9: We clarified terminology for prophylaxis and added discussion of heterogeneity.
5/8: We added analysis for treatment studies restricted to peer-reviewed articles.
4/30: We added [Loucera].
4/29: We corrected the treatment group counts for the early treatment group in [Annweiler] (there was no change in the relative risk).
4/24: We added analysis restricted to RCT studies and to calcifediol/calcitriol studies. We have excluded [Espitia-Hernandez] in the treatment analysis because they use a combined protocol with another medication that shows high effectiveness when used alone.
4/14: We added [Blanch-Rubió].
4/13: We added [Lohia, Oristrell (B)].
4/12: We added [Barassi].
4/10: We added [Szeto].
4/9: We added [Ünsal].
4/5: We added [Bayramoğlu, Livingston].
4/4: We added event counts to the forest plots.
3/31: We added [Mendy].
3/30: We added [Macaya].
3/29: We added [Im].
3/28: We added [Freitas].
3/22: We added [Meltzer].
3/15: We added [Vanegas-Cedillo].
3/14: We added [Cereda].
3/12: We added [Charoenngam].
3/10: We added [Mazziotti].
3/6: We added [Ricci].
2/26: We added [Lakkireddy].
2/25: We added [Sulli (B)].
2/20: We added [Gavioli].
2/20: We added [Infante].
2/18: [Murai] was updated to the journal version of the paper.
2/17: We corrected an error in the effect extraction for [Angelidi], and we added treatment case and viral clearance forest plots.
2/16: We added [Susianti].