Vitamin D for COVID-19: real-time meta analysis of 305 studies (118 treatment studies and 187 sufficiency studies)

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).

Vitamin D is a steroid hormone that helps regulate the immune system by binding to specific receptors and activating genes involved in immune defense. It increases the production of antimicrobial proteins, like cathelicidin and defensins, which fight a variety of pathogens, including bacteria, viruses, and fungi. Vitamin D supports the immune system by boosting our natural defenses and promoting healthy cell connections. It helps clear respiratory pathogens through processes like apoptosis and autophagy and regulates toll-like receptors, which play a key role in immunity. Vitamin D also aids in immune cell maturation, balances inflammation, and reduces the production of proinflammatory cytokines. Vitamin D has been shown to downregulate angiotensin-converting enzyme-2 (ACE-2) receptors, which play a role in COVID-19 infection. By suppressing the production of ACE-2 and related molecules, vitamin D increases antioxidant and anti-inflammatory effects, enhances antimicrobial defenses, reduces cytokine storms, and promotes a protective immune response, all of which help decrease the severity of the disease. The European Food Safety Authority has found evidence for a causal relationship between the intake of vitamin D and optimal immune system function Galmés, Galmés (B).

Vitamin D undergoes two conversion steps before reaching the biologically active form as shown in 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.

Many vitamin D studies analyze outcomes based on serum vitamin D levels which may be maintained via sun exposure, diet, or supplementation. We refer to these studies as sufficiency studies, as they typically present outcomes based on vitamin D sufficiency. These studies do not establish a causal link between vitamin D and outcomes. In general, low vitamin D levels are correlated with many other factors that may influence COVID-19 susceptibility and severity. Therefore, beneficial effects found in these studies may be due to factors other than vitamin D. On the other hand, if vitamin D is causally linked to the observed benefits, it is possible that adjustments for correlated factors could obscure this relationship. COVID-19 disease may also affect vitamin D levels 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, Abdulameer, Aci, Al-Daghri, Alarslan, Azadeh, Beheshti, Chodick, D'Avolio, Desai, di Filippo, Ersöz, Hosseini (B), Jabbar, Jain, Kerget, Kumar, Latifi-Pupovci, Mansour, Mardani, Morad, Nicolescu, Pop-Kostova, Qu, Ranjbar, Rathod, Saeed, Saeed (B), 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.

For studies regarding treatment with vitamin D, we distinguish three stages as shown in 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.

7 In Vitro studies support the efficacy of vitamin D DiGuilio, Moatasim, Mok, Pickard, Rybakovsky, Sposito, Vargas-Castro.

An In Vivo animal study supports the efficacy of vitamin D Fernandes de Souza.

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.

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 21.

Abdulateef, unadjusted results with no group details.

Al Sulaiman, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

Asimi, excessive unadjusted differences between groups.

Assiri, unadjusted results with no group details.

Aweimer, 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.

Beigmohammadi, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

Bychinin, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

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.

Domazet Bugarin, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

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.

Hafezi, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

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.

Saheb Sharif-Askari, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

Shahid, minimal details provided.

Shamsi, unadjusted results with no group details.

Shehab, unadjusted results with no group details.

Singh, minimal details provided.

Ullah, significant unadjusted confounding possible.

Zangeneh, very late stage study using cholecalciferol instead of calcifediol or calcitriol.

Zurita-Cruz, randomization resulted in significant baseline differences that were not adjusted for.

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 (B) report only 2.5 hours improvement for inpatient treatment.

Figure 22 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 62 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

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, treatment regimen, and the form of vitamin D used (cholecalciferol, calcifediol, or calcitriol).

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 23. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.

Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.

Currently, 41 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 52% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months.

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 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.

For sufficiency studies, different studies use different levels as the threshold of sufficiency, vitamin D levels were measured at different times, and some studies measure risk only within hospitalized patients, which excludes the risk of a serious enough case to be hospitalized. However, 174 of 187 studies present positive effects.

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 c19early.org. Sun exposure is also correlated with physical exercise, which also shows benefit for COVID-19 c19early.org (B).

101 of 118 treatment studies report positive effects. Studies vary significantly in terms of treatment delay, treatment regimen, patients characteristics, and (for the pooled effects analysis) outcomes, as reflected in the high degree of heterogeneity. However treatment consistently shows a significant benefit. The treatment studies not showing positive effects are mostly prophylaxis studies with unknown dosages. The only non-prophylaxis studies reporting negative effects are a small unadjusted retrospective 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.

Acute treatment shows greater efficacy than chronic prophylaxis for mortality (and in pooled analysis). One hypothesis is that long-term supplementation may affect normal biological processing. A key component of vitamin D processing is regulation via the enzyme CYP24A1, which breaks down active vitamin D. Long-term supplementation may lead to upregulation of CYP24A1, and potentially lower availability of active vitamin D where needed during infection. The prophylaxis RCTs to date 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.

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.

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 24 shows a scatter plot of results for prospective and retrospective treatment studies. Prospective studies show 51% [35‑63%] improvement in meta analysis, compared to 31% [24‑37%] 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.

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 25 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.

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Vitamin D for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 vitamin D 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 vitamin D trials represent the optimal conditions for efficacy.

Other meta analyses show significant improvements with vitamin D treatment for mortality Argano, D’Ecclesiis, Hariyanto, Hosseini, Nikniaz, Shah, Xie, mechanical ventilation Hariyanto, Meng, Shah, Xie, ICU admission Hariyanto, Hosseini, Meng, Shah, Tentolouris, Xie, hospitalization Argano, severity D’Ecclesiis, Nikniaz, Varikasuvu, Xie, and cases Varikasuvu.

The first version of 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 c19early.org (C). The study was republished.

Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.

Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.

Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.

Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.

In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.

Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.

This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.

No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.

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.

NIH provides an analysis of vitamin D for COVID-19 covid19treatmentguidelines.nih.gov, concluding that there is insufficient evidence to recommend for or against use. However, they appear not to have looked at the majority of the evidence. For example, considering RCTs providing clinical results for COVID-19 and vitamin D, they reference only Elamir, Mariani, Murai, Villasis-Keever, and appear not to know about 25 other RCTs Beigmohammadi, Bishop, Brunvoll, Bychinin, Cannata-Andía, Castillo, De Niet, Din Ujjan, Domazet Bugarin, Hosseini (C), Jolliffe, Karonova, Khan, Lakkireddy, Leal-Martínez, Maghbooli, Rastogi, Said, Salman, Seely, Singh, Soliman, Sánchez-Zuno, Wang, Zurita-Cruz as shown in Figure 26. Notably, the NIH selection does not correspond to the most relevant and highest quality studies, for example including Murai, which studies very late treatment (10 days from symptom onset, with 90% on oxygen at baseline) using cholecalciferol. Calcifediol or calcitriol, which avoids several days delay in conversion, may be more appropriate, especially with this very late stage usage. They include none of the early treatment RCTs.

Random effects meta-analysis with pooled effects using the most serious outcome reported shows 60% [40‑74%] and 36% [30‑41%] lower risk for early treatment and for all studies. Results are similar for higher quality studies, peer-reviewed studies, and mortality: early treatment - 68% [45‑82%], 57% [36‑71%], 68% [39‑84%]; all - 37% [31‑42%], 40% [34‑45%], 36% [28‑43%].

118 treatment studies show statistically significant lower risk for mortality, ICU admission, hospitalization, and cases. 60 studies from 56 independent teams in 21 countries show statistically significant lower risk.

Acute treatment (early 60% [40‑74%], late 44% [32‑54%]) shows greater efficacy than chronic prophylaxis (31% [23‑37%]).

Late stage treatment with calcifediol or calcitriol is more effective than cholecalciferol: 65% [41‑79%] vs. 39% [26‑49%].

Other meta analyses show significant improvements with vitamin D treatment for mortality Argano, D’Ecclesiis, Hariyanto, Hosseini, Nikniaz, Shah, Xie, mechanical ventilation Hariyanto, Meng, Shah, Xie, ICU admission Hariyanto, Hosseini, Meng, Shah, Tentolouris, Xie, hospitalization Argano, severity D’Ecclesiis, Nikniaz, Varikasuvu, Xie, and cases Varikasuvu.

This paper is data driven, all graphs and numbers are dynamically generated. Please submit updates and corrections at the bottom of this page.Please submit updates and corrections at https://c19early.org/dmeta.html.

11/27: We added Renieris.

11/9: We added Akbar.

10/2: We added Bogomaz.

9/24: We added Seely (B).

9/8: We added Ogasawara.

8/29: We added Shamsi.

8/24: We added Al Sulaiman.

8/21: We added Mayurathan.

8/13: We added Mingiano.

8/10: We added Connolly.

8/5: We added analysis for RCT ICU outcomes.

6/24: We added Frish.

6/4: We added Manojlovic.

6/4: We added Jalavu.

6/4: We added Wani.

5/8: We added Hogarth.

5/6: We added Ritsinger.

5/5: We added Regalia.

5/2: We added Sanamandra.

4/25: We added AlKhafaji, Baralić, Hafez.

4/20: We added Allami.

4/19: We added Cetin Ozbek, Zafar.

4/16: We added Rachman.

4/12: We added Basińska-Lewandowska.

4/7: We added Hermawan, Protas.

4/6: We added Bayrak.

4/5: We added Aweimer, Khalil, Wang.

4/2: We added Gonzalez.

4/1: We added Arabadzhiyska.

3/28: We added Schmidt.

3/28: We added Huang, Nasiri.

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.

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 (B).

8/24: We added Aldwihi, Saheb 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.

5/28: We added Mariani.

5/25: We added Kazemi, Zangeneh.

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 (B).

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 (B).

2/8: We added Subramanian.

2/8: We added Ranjbar.

2/7: We added Tylicki, Ullah.

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.

11/2: We added Al-Salman, Eden.

11/1: We updated Golabi to the journal version.

10/31: We added Assiri, Bianconi, Leal-Martínez.

10/30: We added Campi, Gaudio.

10/27: We added Hurst, Lázaro.

10/19: We added Jimenez.

10/19: We added Sinaci, Zelzer.

10/18: We added Mohseni.

10/18: We added Basaran, Dudley.

10/16: We added a summary plot for all results.

10/15: We added Ramirez-Sandoval.

10/15: We added Maghbooli.

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.