Vitamin D reduces COVID-19 risk: real-time meta analysis of 333 studies (122 treatment studies and 211 sufficiency studies)

SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury22-33 and cognitive deficits25,30, cardiovascular complications34-37, organ failure, and death. Minimizing replication as early as possible is recommended.

SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factorsA,38-44, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 8,000 compounds may reduce COVID-19 risk45, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.

We analyze all significant controlled studies of vitamin D 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 perform random-effects meta analysis for all treatment studies, Randomized Controlled Trials, peer-reviewed studies, studies using cholecalciferol vs. calcifediol/calcitriol and analogs, studies using large bolus doses vs. ongoing treatment, higher quality studies, 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 has been identified by the European Food Safety Authority (EFSA) as having sufficient evidence for a causal relationship between intake and optimal immune system function46-49. Vitamin D inhibits SARS-CoV-2 replication in vitro50,51, mitigates lung inflammation, damage, and lethality in mice with an MHV-3 model for β-CoV respiratory infections50,51, reduces SARS-CoV-2 replication in nasal epithelial cells via increased type I interferon expression52, downregulates proinflammatory cytokines IL-1β and TNF-α in SARS-CoV-2 spike protein-stimulated cells53, attenuates nucleocapsid protein-induced hyperinflammation by inactivating the NLRP3 inflammasome through the VDR-BRCC3 signaling pathway54, may be neuroprotective by protecting the blood-brain barrier, reducing neuroinflammation, and via immunomodulatory effects55, downregulates ACE2 and TMPRSS2 in human trophoblasts and minimizes spike protein-induced inflammation56, minimizes platelet aggregation mediated by SARS-CoV-2 spike protein via inhibiting integrin αIIbβ3 outside-in signaling57, and improves regulatory immune cell levels and control of proinflammatory cytokines in severe COVID-1958. Calcifediol inhibits SARS-CoV-2 papain-like protease (PLpro), a critical enzyme for viral replication59. Symptomatic COVID-19 is associated with a lower frequency of natural killer (NK) cells and vitamin D has been shown to improve NK cell activity60,61.

Studies have shown efficacy with vitamin D for influenza62, RSV62, and acute respiratory tract infections63-65.

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. 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 system66,67. For example, Quraishi et al. show a strong association between pre-operative vitamin D levels and hospital-acquired infections, as shown in Figure 2.

Vitamin D undergoes two conversion steps before reaching the biologically active form as shown in Figure 3. 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. 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 levels69, 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 levels70-72, which may not be indicative of results for deficiency/insufficiency versus sufficiency (increasing already sufficient levels may be less useful for example). Some studies only show the average vitamin D level for patients in different groups73-113, most of which show lower D levels for worse outcomes. Other studies analyze vitamin D status and outcomes in geographic regions114-121, 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 et al. Mishra et al. 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.

RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases139, and analysis of double-blind RCTs has identified extreme levels of bias140. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.

RCTs are expensive and many RCTs are funded by pharmaceutical companies or interests closely aligned with pharmaceutical companies. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.

High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 112 treatments we have analyzed, 66% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.

RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for vitamin D are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.

Evidence shows that observational studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. We performed a similar analysis across the 112 treatments we cover, showing no significant difference in the results of RCTs compared to observational studies, RR 1.00 [0.92‑1.08]. Similar results are found for all low-cost treatments, RR 1.02 [0.92‑1.12]. High-cost treatments show a non-significant trend towards RCTs showing greater efficacy, RR 0.92 [0.82‑1.03]. Details can be found in the supplementary data. Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or remote survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see146,147.

Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.

We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.

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 hours208,209. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar (B) et al. report only 2.5 hours improvement for inpatient treatment.

Figure 25 shows a mixed-effects meta-regression of efficacy as a function of treatment delay in COVID-19 vitamin D studies, with group estimates for different stages when a specific value is not provided. For comparison, Figure 26 shows a meta-regression for all studies providing specific values across 112 treatments. 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, for example as in López-Medina et al.

Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants214, for example the Gamma variant shows significantly different characteristics215-218. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants219,220.

Effectiveness may depend strongly on the dosage, treatment regimen, and the form of vitamin D used (cholecalciferol, calcifediol, or calcitriol).

The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer. Non-prescription supplements may show very wide variations in quality1,2.

The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic170,223-233, therefore efficacy may depend strongly on combined treatments.

Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.

The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations 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.

This section validates the use of pooled effects for COVID-19, which enables earlier detection of efficacy, however pooled effects are no longer required for vitamin D as of October 2020. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes in RCTs was delayed by 10.6 months compared to using pooled outcomes in RCTs.

For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results. Pooling the results of studies reporting different outcomes allows us to use more of the available information. Logically we should, and do, use additional information when evaluating treatments—for example dose-response and treatment delay-response relationships provide additional evidence of efficacy that is considered when reviewing the evidence for a treatment.

We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.

Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster and safer collection of evidence.

For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.

Analysis of the the association between different outcomes across studies from all 112 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 27 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 28 shows that improved recovery is very strongly associated with lower mortality (p < 0.000000000001). Considering the extremes, Singh (B) et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Figure 29 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh (B) et al., with higher confidence due to the larger number of studies. As with Singh (B) et al., the confidence increases when excluding the outlier treatment, from p = 0.00000032 to p = 0.000000011.

Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months. Figure 30 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as difference in treatment delay is more likely to hide efficacy.

Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta analyses, it is important to review the different studies included. We also present individual outcome analyses, which may be more informative for specific use cases.

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, 198 of 211 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 melatonin235, and melatonin shows significant benefit for COVID-19236. Sun exposure is also correlated with physical exercise, which also shows benefit for COVID-19237.

105 of 122 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 Murai, Mariani, Cannata-Andía 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 Villasis-Keever, Jolliffe 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. For acute respiratory infections, Wang (C) et al. show greater efficacy for prophylaxis trials with shorter durations. Specific forms and administration of vitamin D may minimize upregulation of CYP24A1238. 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-6240,241 (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 results242-245.

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 31 shows a scatter plot of results for prospective and retrospective treatment studies. Prospective studies show 51% [36‑63%] improvement in meta analysis, compared to 32% [25‑37%] 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 32 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 < 0.05246-253. 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.

19 other meta analyses show significant improvements with vitamin D treatment for mortality3-16, mechanical ventilation3,8,9,14,17,18, ICU admission3,5,8,9,12,14,16-20, hospitalization7,14, severity4,6,8,13,21, and cases10,20,21.

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 (E). The study was republished.

Genetic variants have been shown to affect COVID-19 infection, severity, and mortality risk255. Patients with certain vitamin D receptor gene variants may potentially benefit more from vitamin D treatment78,79,255-269.

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

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

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

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

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

Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone170,223-233. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.

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

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

Many reviews cover vitamin D for COVID-19, presenting additional background on mechanisms and related results, including48,49,55,62,179,270-299.

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.

SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors38-44, providing many therapeutic targets. Over 8,000 compounds have been predicted to reduce COVID-19 risk45, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 33 shows an overview of the results for vitamin D in the context of multiple COVID-19 treatments, and Figure 34 shows a plot of efficacy vs. cost for COVID-19 treatments.