Vitamin D Can Likely End the COVID-19 Pandemic

A Probabilistic Analysis of Recent Findings

On August 29, 2020, for the first time ever, a randomized controlled trial showed a significant and dramatic reduction in COVID-19 severity. 

Study participants that received a large dose of vitamin D (as calcifediol) experienced a 50-fold reduction in the odds of admissions to intensive care, which likely translates to a similar reduction in death rates (see further analysis). If these findings are accurate, the end of the pandemic is near. The study has multiple limitations that would normally warrant waiting for studies, but given the circumstances, it is important to dig deeper and accurately assess its implications. We will estimate the probability that the finding is true, and analyze the risks of adopting the treatment now vs. waiting for further studies.

Is the finding true?

A randomized controlled trial, where patients are randomly selected to receive the treatment or serve as control, makes it possible to isolate whether some clinical finding results from the treatment or from another factor. So far, there have been many studies on vitamin D and COVID-19, which demonstrated a strong link between the two, but causality had not been established. For example, people with poor health may have low vitamin D levels due to low sun exposure, creating a correlation with COVID-19 severity that is not causal.

We now have the results of the first randomized controlled trial on the effect of vitamin D on Covid-19 patients. If it was properly conducted, causality has finally been established, and an effective treatment was found. Unfortunately, the study has several limitations that may distort its result. 

Let’s review these possible problems, and their significance. A more mathematically rigorous analysis may be found in the appendix below.

1. The sample size is small, so the findings may be due to chance 

It is always possible that the patients that were randomly assigned to the treatment group suffered less deterioration by mere chance. This possibility is calculated using the p-value, which measures the probability of obtaining the study result (or stronger) by chance. The authors disclose it only as less than 1 in 1,000, but the actual number is less than 1 in 1,000,000 (can be verified here, using the study results of 13:13 vs 49:1).

It is important to understand that once a p-value has been obtained, the sample size no longer matters. The goal of a large sample is to reduce the random differences between the two groups, thus making the difference in treatment a larger factor in the final result. The p-value improves both with study size, and with effectiveness of the treatment.

In this case, the effect was so strong that the relatively small sample (76 people) turned out to be much larger than required.

It can be said with certainty that if the experimental results are incorrect, it is not because of the sample size or chance.

2. The control group included more people with risk factors

The control group happened to have significantly more people with hypertension, so it is expected they would have more admissions to intensive care. The researchers identified this issue and performed another analysis (logistic regression) that accounted for it, and the findings were only mildly weakened, from a 50-fold to a 30-fold reduction, with 95% confidence that the result is between 4-fold and 300-fold. We will use 12-fold as a conservative estimate.

We performed another analysis, which assumed that only those with high blood pressure could deteriorate (i.e. removing patients without hypertension from the sample), and the findings still remained very significant, with a p-value of 1 in 5,000, far better than the standard threshold of 1 in 20, or 0.05.

Another issue to evaluate is whether this imbalance indicates a deeper problem with randomization or reporting. The reported p-value of the difference in hypertension is 0.0023, meaning a 1:435 chance it would happen in a random assignment. However, this is just one of at least 10 parameters that could affect the study, and the p-value also accounts for an opposite effect (2-sided instead of 1-sided), so the probability that one of them would happen is only around 1:21, meaning 1 in 21 such studies would have such an imbalance by mere chance – hardly remarkable. Given that randomization was done electronically upon patient admission, such a mistake is unlikely, and as fraud it won’t make much sense (especially as it is later reported and corrected for).

This clearly seems like a chance occurrence, and we see no reason to reduce the estimate beyond what the investigators already did.

3. Patients in both groups were also treated with hydroxychloroquine and azithromycin

Patients in both groups received the standard treatment, which at the time was hydroxychloroquine and azithromycin, a treatment that has since fallen out of favor. Could it be that the findings result from vitamin D neutralizing negative effects of those drugs? This option is unlikely. Trials have shown differing results for hydroxychloroquine and azithromycin, with a few pointing only to a mild risk.

It is also possible that vitamin D only works in combination with the other treatments. Given these mechanisms of action, this is highly unlikely.

We estimate that, at most, this possibility reduces the effect from 12-fold to 8-fold (i.e. vitamin D may have neutralized a 50% increase in severity caused by the other drugs).

4. The experiment was not double-blind placebo-controlled

To prevent distortion of the results by the trial participants or the researchers (even unconsciously), it is preferable that neither know which patients were randomized to the control group and which to the treatment group. This was not the case in this experiment.

This is certainly a weakness of this study. It was mitigated by delegating the decision regarding transfer to intensive care to a committee of experts that included members of the hospital’s ethics committee, who were not aware which group the patient was assigned to, and reached decisions based on a structured protocol.

We have reached out to the investigators to learn more about the procedures, and learnt that this was a result of logistic problems in placebo manufacturing. We got the impression that an honest effort was made to mask the data as much as possible, and the two groups were not otherwise treated differently.

We still need to account for the possibility of outright fraud enabled by this weakness, in which case the findings are false. Since there are no commercial interests around vitamin D, and the fraud would be exposed in later studies, we assign this a probability of 10% at most.

5. There may be another, yet unidentified, factor 

Of course, there may be another source for the dramatic difference between the two groups, which has not yet been identified. This would usually be the responsibility of the publishing journal to expose. In this case, the publication has been peer-reviewed and published in a small journal specializing in vitamin D. The publisher is Elsevier, which also publishes the Lancet and Cell.

Such a major finding should ideally be published in a world-leading journal, but given the limitations above, and the likely urgency to publish, it is not unreasonable to choose a smaller journal.

Given the relative simplicity of the trial, we do not see unknown factors as a major risk, at most accounting for a further reduction from 8-fold to 6-fold, and a 10% probability of it invalidating the results.

6. Is the prior probability of the study findings low?

Equally important is the likelihood that vitamin D could cure Covid-19, based on the information known before the article was published. For example, if a study finds that five minutes of neck massage cures lung cancer, it is very likely that there is some error in the study, even if its statistical significance was high.

In this case, the opposite is true:

However, so far the indication has been for a weaker effect – about a 50% reduction in severity, not 30-fold, so the new finding indicating a near cure is initially surprising. But on further examination, there may not be any contradiction between the studies. A re-examination of a study that published detailed data shows that the rate of infection drops to nearly zero with high levels of vitamin D in the blood (above 50 ng/ml). That is, it is possible that the effectiveness increases with dose, and in the very high doses, as used in the study, near healing is achieved. 

It should also be noted that the earlier observational studies used vitamin D levels that were measured a significant time before infection. By the time patients got sick their levels may have changed, which would cause a possible strong correlation to appear weaker.

Another possibility is that the use of short term high dose calcifediol is more effective than the long term supplementation of vitamin D3.

Additionally, the investigators have informed us that the protocol has been used on patients after the trial completed, with similar results.

Overall, according to the prior knowledge, a mild effect is more likely than a strong or no effect, reducing our conservative estimate from 6-fold to 5-fold.

Summary – The findings are true

None of the possibilities mentioned invalidates the significant finding that emerges from the experiment. It is very possible that some minor biases occurred that exaggerated the effect, but it is unlikely that vitamin D had no positive effect.

Summarizing the numbers above, we estimate:

  • 20% probability that vitamin D has no significant effect on COVID-19 severity
  • 80% probability that it reduces severity and death, probably around 5-fold, and possibly much more.

Risk Management

In order to make a treatment policy decision, one must consider not only the likelihood that the finding is true, but also the potential harm and benefits of each possible course of action.

Alternative 1 – Wait

The easy decision is to wait for further studies to verify the new finding. This is what medical experts would normally decide after a first publication of a successful trial.

If the treatment is ineffective, there are no costs and risks to this decision.

If the treatment is effective, then based on the analysis above, the results in the study’s control group, and typical outcomes for hospitalized patients, the harm to a typical hospitalized patient, can be estimated as:

  • Additional 20% chance of suffering severe disease, with likely long-term implications.
  • Additional 5% chance of death

Alternative 2 – Adopt treatment

The second alternative is to immediately adopt the protocol for hospitalized patients. In this case the harm to patients is from the large vitamin D dose (whether or not the treatment is effective).

As vitamin D is already a popular treatment, there is abundant information on its risks.

  • The dose used in the study is about 10 times the maximum recommended dose for prolonged use.

However, the treatment protocol in the study is relatively short – until release of the patient or transfer to intensive care. Previous studies on short treatments at similar doses found them to be safe.

  • The risk in vitamin D is with increased use that maintains very high levels in the blood over a long period of time.

Even then, the risks are relatively limited, and can be corrected by a low-calcium diet and steroids. For hospitalized patients that can be monitored closely the risk is likely further reduced.

  • Covid-19 specific risk: vitamin D increases the expression of ACE2 in cells, which acts as a receptor for the coronavirus. Therefore, until now, there has been apprehension about its use. Since the new trial focused on COVID-19 patients and doesn’t show such negative effects, the concern seems to have been alleviated. There is still some low likelihood that the study results were completely wrong, either intentionally or due to a catastrophic mistake  that hid a worse outcome in the treatment group.

Based on existing knowledge, the risks in the proposed protocol appear to be low.

The risk can be further reduced by monitoring vitamin D levels in the patients’ blood, and keeping them in a high yet safe range, for example 80 ng/ml.

It is safe to assume the risks of the protocol are much lower than:

  • 5% chance of severe complications.
  • 1% chance of death


Given that both:

  • The likelihood that the treatment is very effective is greater than 50%;
  • The benefit of the treatment, if effective, is far higher than twice the risk of the treatment;

it is obvious that the right decision is immediate adoption of the treatment protocol.

Hospitals deciding to wait for further studies should have very strong reasoning that outweighs the apparent harm to patients by delaying treatment.

Global Implications on the COVID-19 pandemic

This analysis shows that if the protocol is widely adopted, COVID-19 severity can likely be reduced to that of the seasonal flu, allowing alleviation of certain limitations, which could bring a major improvement in the economy and social health.

A further conclusion, although with lower confidence, is that vitamin D could be effective at earlier stages of the disease. Previous studies have shown a correlation between high vitamin D levels and lower infection rates. The new study establishes a causal connection at late stages, increasing the likelihood that the correlation at earlier stages is also causal. This would mean that widespread vitamin D therapy (e.g. bringing blood levels to a healthy 30-40 ng/ml) could reduce R0. If that reduction is as significant as indicated by the studies, R0 could drop below 1, and stop the pandemic. 

Since vitamin D deficiency is already common, and risks are negligible at this dose, governments should immediately encourage and subsidize vitamin D tests and supplementation for the general population.

UPDATE: Following this analysis, Rootclaim is offering a $100,000 bet that vitamin D cures COVID-19 in order to show that the reluctance to immediately implement a vitamin D protocol is irrational.

Update 12 November 2020: An MIT study that went deeper into the statistical aspects of the trial has reached a similar conclusion that the results of the Cordoba trial are reliable, and that vitamin D treatment is the likely explanation for the dramatic reduction in ICU admissions of hospitalized Covid-19 patients.

Update 24 November 2020: Two additional double-blind, randomized, placebo-controlled trials have reached opposing conclusions regarding vitamin D and COVID-19. One study (printed in The BMJ) showed that those receiving a high dose of vitamin D were dramatically more likely to test negative for the virus within 21 days (21% vs 62%). The other study (preprint on medrxiv) reported that vitamin D supplementation did not significantly reduce hospital length of stay for patients with COVID-19.

Our analysis of the second study shows it does not significantly change the picture, and we maintain our contention that vitamin D should be immediately adopted as a treatment and prophylaxis for COVID-19.

The main flaws are that the study was designed in a way that had a low probability of achieving any of its endpoints, and that the treatment protocol itself was not suitable to demonstrate the efficacy of vitamin D.

  1. The study assumed that, if vitamin D were effective, it would reduce hospital stay from a mean of 7 to 3.5 days. That is an unreasonable expectation, especially given that bolus vitamin D takes a day or two to be mostly converted to 25(OH)D (calcifediol), and may take even longer for 1,25(OH)D. Previous trials were based on longer periods, or used calcifediol directly.
  2. Due to low mortality rates in the study, the study was incapable of detecting a significant effect. Even if there were zero deaths in the treatment group, the result would still be considered not significant (i.e. p>0.05), The study had only a marginally better chance of detecting an effect in ICU admissions or mechanical ventilation, and indeed there it showed some effect, although it is still not significant (as expected).
  3. The primary measure was length of hospital stay, which can be misleading. A short hospital stay could signify either a positive outcome due to improved healing or a negative outcome as with a quick death.
  4. Patients were recruited 10 days after symptoms began, and 90% were already on oxygen. It is possible that vitamin D’s effects are not very relevant at that point, so the failure to improve patient outcomes at a late stage does not reflect the efficacy at earlier stages.
  5. The effects of the vitamin D treatment could have been counteracted by the administration of steroids (to 62% of patients in the study), which weaken immune system activity.

While the study cannot demonstrate vitamin D is ineffective, due to its flawed design, it does suggest that at a late stage, alongside steroids, vitamin D is not immensely effective, i.e. it does not reduce the odds of severe outcomes by 5x or more. Our analysis at Rootclaim suggests that a 5x reduction is a reasonable outcome when vitamin D is administered correctly.

Update 24 May: A retrospective study on hospitalized patients with COVID-19 who received calcifediol showed a drop in mortality rate from 20% to 5%.Appendix – Bayesian Analysis

For those with a background in probability, following is a more rigorous analysis using Bayesian inference. By explicitly stating prior probabilities of hypotheses, and calculating the conditional probabilities of the study results under each hypothesis, a more accurate and robust result is achieved, removing the need to analyze sample sizes, p-values, or confidence intervals.


We will define five hypotheses to be considered:

  • Damage – Vitamin D worsens COVID-19.
  • Nothing – No effect
  • 2-fold – Vitamin D reduces the odds for severe COVID-19 by around 2.
  • 5-fold – Vitamin D reduces the odds for severe COVID-19 by around 5.
  • 20-fold – Vitamin D reduces the odds for severe COVID-19 by around 20.


First we shall estimate the probability of each hypothesis based on what was known before the new study. As a baseline, few drugs are effective for any specific disease, but as described above, there are multiple studies showing correlation between vitamin D and COVID-19, and several proposed mechanisms of actions. On the flip side, there is the aforementioned risk that vitamin D could actually exacerbate COVID-19 by increasing ACE2.

We will represent these facts with the following prior probabilities:

  • Damage – 10%
  • Nothing – 67%
  • 2-fold – 15%
  • 5-fold – 5%
  • 20-fold – 3%

Adjustments to Study

Given the limitations discussed above, we will make the following adjustments to the study results:

  • Move 2 cases from ICU to non-ICU in the control group, which we attribute to the higher hypertension cases there.
  • Move 2 cases from non-ICU to ICU within the treatment group, and do the opposite in the control group, due to unknown weaknesses not yet identified.
  • Estimate at 20%, as above, the probability that the study was grossly mismanaged, and should be ignored.

So instead of the reported matrix of:

Vitamin DControl
Admitted to ICU113
Not admitted to ICU4913

We will use:

Vitamin DControl
Admitted to ICU39
Not admitted to ICU4717

Conditional Probabilities

Next we estimate the probability of getting the adjusted study results, under each of the five hypotheses. To do that, we will use the odds of the control group (9:17 = 9/26 = 34.6%), and adjust by the hypothesis factor, to receive the expected odds in the treatment group. For example, the expected odds in the 2-fold hypotheses would be 9:17*2 = 9:34, or a probability of 20.9%. We then use a binomial distribution formula to estimate the conditional probability of getting the exact study result (3 out of 50 trials) given those expected odds. This is then normalized to sum 100%. Lastly we average with the prior probabilities at a weight of 20%:80%, accounting for the 20% possibility that the study is meaningless.

The full calculation:

Prior probability10%67%15%5%3%
Odds ratio (OR)0.712520
Convert odds
to probability =
Conditional probability
from binomial formula
Posterior =
Prior * Conditional
normalized to 100%
Account for 20%
failed study possibility
(final result)


This more rigorous analysis reaches a very similar conclusion of around 80% likelihood that vitamin D is effective against COVID-19, with a 5-fold reduction being the most probable.


  1. So what is the daily recommended dose?

    • Hi Joe,
      We do not provide medical advice, and the study was on hospitalized patients. You may research the recommended and maximum daily doses online.
      Good luck!

  2. You say:

    > A further conclusion, although with lower confidence, is that vitamin D could be effective at earlier stages of the disease. … This would mean that widespread use of vitamin D (e.g. at the upper limit of 4000 IU/d) could reduce R0, and possibly stop the pandemic.

    > Since vitamin D deficiency is already common, and risks are negligible at this dose, governments should immediately encourage and subsidize vitamin D supplementation by the general population.

    There is at least a plausible reason to believe that supplementing vitamin D at high ish doses could be counterproductive for preventative use against COVID 19, or in the treatment of early stage mild cases of COVID 19. See this video, from 42 minutes in:

    The gist is that vitamin D has a very complicated set of impacts on the body, and some of these could be immunosuppressive. That is not counter to the findings of the study you’ve looked at here – the reduction in hospitalised patients needing intensive care could actually be explained by immunosuppression, if it’s something like the body’s immune system overreacting that tends to cause much of the problems.

    • Thank you Jim. That’s a very interesting perspective, and indeed our early stage recommendation is given with lower confidence.
      Still, I think this view doesn’t coincide well with other findings:
      1. Studies showing correlation between high blood levels and resistance to COVID-19 infection.
      2. The known protective effect vitamin D supplementation has on other respiratory viruses.
      3. The very strong effect shown in the new study, which would be quite remarkable if vitamin D’s sole function was as an immunosuppressant.

      • Vitamin D is an immune-modulator, it will prevent cytokine storms which are triggered by an over-active immune system but it won’t suppress the immune system at the early stages of the disease- on the contrary, it will enable T-helper cells to do their job properly.

        • Thanks both. I should have acknowledged in my comment that I have approximately zero expertise in this area – I had just viewed the video I linked to and found the description of vitamin D as having complex effects to be compelling.

          My sense from reading your responses and some other odds and ends is that this study provides little evidence either way on the questions of vitamin D for pre exposure prophylaxis, or as treatment in early/mild stages. If its beneficial effect in hospitalised patients is through mechanisms like immuno modulation to prevent cytokine storms, that says nothing about what it might do — for good or for ill — for people whose immune systems aren’t overreacting in that way.

          Hence, lower confidence or not, I remain uncomfortable about the presentation of ‘potential for early stage use’ as a conclusion of this.

          From what I recall (can’t remember the reference) I think the effect on other respiratory illness tends to be reasonably small. It’s entirely plausible to me that vitamin D might end up being a wildly effective treatment in serious cases and also have (say) only smell ish effects in early stages, and achieve both of those through very different mechanisms.

          I do think both merit further study, but just would like to see caution over conflating them.

    Robert Heaney, M.D., of Creighton University wrote: “We call for the NAS-IOM and all public health authorities concerned with transmitting accurate nutritional information to the public to designate, as the RDA, a value of approximately 7,000 IU/day from all sources.”

    “This intake is well below the upper level intake specified by IOM as safe for teens and adults, 10,000 IU/day,” Garland said. Other authors were C. Baggerly and C. French, of GrassrootsHealth, a voluntary organization in San Diego CA, and E.D. Gorham, Ph.D., of UC San Diego.

  4. It was my view during that meeting (as you can see watching the video), that Amy’s worries about potential immune suppression from D were already more than countered by the evidence to date at the time of that meeting. The new Kaufman et al study of Quest patients (n=190,000+) that came out since that meeting significantly boost the idea that vitamin D is protective early.

  5. This article makes some good points, but the following quote is sheer nonsense:

    “It is important to understand that once a p-value has been obtained, the sample size no longer matters. The goal of a large sample is to reduce the random differences between the two groups, thus making the difference in treatment a larger factor in the final result. The p-value improves both with study size, and with effectiveness of the treatment.”

    This is simply not true. Small sample sizes are much more likely to yield false positive effects than large sample sizes. P values only “improve with study size” if there is actually a genuine effect. But since we are trying to determine whether there is a genuine effect, we cannot make this assumption beforehand.

    • You are right that this is irrelevant beforehand. What we explain is that once you get a certain p-value (or confidence interval), it is not important what was the sample size that generated it.

      • I think Rebecca meant to say “…is relevant beforehand.”

      • I know what is meant by: “once a p-value has been obtained, the sample size no longer matters” but I think it will often be misinterpreted. That statement is true after you condition on things like “there is no file-drawer/publicity effect in response to the p-value, and the p-value/power of the test has no influence on which tests the researchers chose to do.” Indeed to really make me feel 100% comfortable with the original statement, I’d like to believe that the statistical model is THE TRUE MODEL, which it never is, of course. In reality I think that after conditioning on the p-value, a larger sample size may, because power is higher, encourage the authors to use a more realistic statistical model and not p-hack as much. A larger sample may also reduce likelihood or magnitude of various forms of non-sampling error through its effect on study design.

        • Disagreeing with “once a p-value has been obtained, the sample size no longer matters” is exactly equivalent to saying that if two studies have the same p-value then the sample size still matters (and in particular, the larger one is better). However, if they have the same p-value then the larger one has smaller effect size. The power depends on both the sample size and the effect size, so there is no reason to believe that the larger study with smaller effect size has more power than the smaller one with larger effect size. Nor is there any less reason to p-hack with a larger study with small effect size than a small study with a large effect size. Regarding file drawer and publicity, you assert that they are connected to the p-value, in which case they will be the same for the two studies. So, yes, the p-value captures it all.

  6. On September 24th I hosted a Zoom conference in which 4 experts discussed this

    One of the studies used Causal Inference to examine 1.6 million data points from 240 locations worldwide and answer 16 questions. This gave a clear answer to the questions

    One of the experts reviewed the Bradford-Hill criteria and also gave a clear answer to the questions

    Learn more about this at:
    – or:

  7. Incidentally, the fatality rate in Iceland is very low (<0.39%), is this because vitamin D abundance in fish oil?

  8. Pessimisticly HCQ and Vit D interact. And Vit D status is just an indicator of your severity of C19 infection.
    Copying comment from statmodeling:

    There is no good evidence that vitamin D is an effective treatment for covid-19. Unfortunately, I do not think this study showed that vitamin D is an effective treatment for covid-19.

    There is a pretty big confounder in the clinical trial above: all patients received hydroxychloroquine. Some quick Google scholar searches suggest that use of HCQ is often associated with lowered vitamin D levels and that supplementing with high-dose vitamin D can be very beneficial. For example, two case studies here:

    It’s apparently also known that HCQ inhibits vitamin D production in vitro and in certain patient subpopulations:

    HCQ is basically ineffective for covid (, and it’s potentially dangerous due to these side effects (and more!). Seems more like the conclusion should be “if you’re going to use HCQ in covid treatment, you better supplement your patients with vitamin D.”

    Now it’s certainly possible that HCQ+vitamin D is a good treatment, but this study didn’t test that.”

    • If that is correct, HCQ causes immense damage that vitamin D then corrects. But we know from large trials that HCQ’s damage is at around 10% at most. So that could not explain the results of the study.

      • You are trying to compare different patients, in different hospitals, in different countries in different studies. That is super risky and i wouldnt bet my money on that kind of comparison.

        • We are not claiming 100% confidence, but rather ~80%, and are betting it’s above 50%. You are very welcome to take the bet.

          • but still, because vit d and hcq interact, you have to rely on comparisons to different patients in different studies. Effectivly you dont have any control for your assertion. Your assertion is that “vit d might cure covid” this study only is able to say that “vitd+hcq have better outcomes than only hcq”. We know that HCQ lowers vitd. because both substances interact you dont have a control for your assertion. in fact you dont have a test for your assertion at all. we KNOW that hcq lowers vit d and interact with vit d. your confidence is WAY too high.

          • It’s not only “better outcomes”, it’s 30x better odds. How can you get that without vitamin D being very effective, considering HCQ only worsens outcomes by 1.1x?

          • replying to your last comment:
            maybe hcq works only with sufficient vit d and doesnt work when vit d gets depleted? maybe your patient distribution isnt gaussian so that your significance test is worthless maybe maybe maybe maybe isnt enough for your 80/20 odds.

          • Sounds like you can make a big profit betting against us. Looking forward to your application.

          • Lets say i disagree with you on the confidence. Id say its not 20/80 but only 40/60. Following the kelly criterium i should only bet a small size of my bank roll. I dont have 100k to bet on anyway. But i would bet.
   is a place to make a bet in your scenario.

          • Our objective is to promote rational thinking and help fight the pandemic, not to negotiate bets that will profit an expected value of a few hundred dollars.

            The bet is just a way to expose irrational decision making. If you think there’s a 40% chance vitamin D is a highly effective treatment, we’re on the same side of believing it should be given to all Covid patients.

            You are of course still welcome to accept the original bet. Feel free to bring an investor or crowdfund it.

    • You raise an important point. In our analysis of the Córdoba study ( we pointed out that the study results might not generalize to patients who were not receiving the same background treatment including HCQ.

      However, the reference you cite showing that HCQ inhibits vitamin D production states in the abstract: “The results demonstrate the capacity of hydroxychloroquine to inhibit the conversion of 25-hydroxyvitamin D to 1,25-dihydroxyvitamin D”. But 25-hydroxyvitamin D is calcifediol, the very treatment that was given to the patients in Córdoba, so how could calcifediol treatment undo the harm from HCQ? Also, I am doubtful about the time scale of the effect. Your study shows that HCQ lowers vitamin D levels over time by inhibiting its production. But you don’t need new vitamin D to be produced in the time-frame of a few days (the treated patients were hospitalized for an average of only 9 days).

      So, yes, it is possible that the calcifediol would be less (or not) effective with a different treatment background, but this particular known effect of HCQ on vitamin D metabolism does not show that.

  9. please open a bet on i will bet against it.

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