https://c19early.org/dmeta.html
•Statistically significant improvements are seen in treatment studies for
mortality,
ICU admission,
hospitalization, and
cases.
57 studies from 53 independent teams in 20 different countries show statistically significant
improvements in isolation (41 for the most serious outcome).
•Random effects meta-analysis with pooled effects using the most serious
outcome reported shows 60% [40‑74%] and 37% [31‑42%] improvement for early treatment
and for all studies. Results are similar after restriction to
104 peer-reviewed
studies: 57% [36‑71%] and 37% [31‑42%], and for the 62 mortality results: 68% [39‑84%] and
37% [28‑44%].
•Late stage treatment with calcifediol/calcitriol shows greater
improvement compared to cholecalciferol:
73% [57‑83%] vs.
41% [27‑52%].
•Sufficiency studies show a strong association between
vitamin D sufficiency and outcomes. Meta analysis of the
175 studies using the most serious outcome reported
shows 54% [50‑58%] improvement.
•No treatment, vaccine, or
intervention is 100% effective and available. All practical, effective, and
safe means should be used based on risk/benefit analysis.
Multiple treatments are typically used
in combination, and other treatments
may be more effective.
Only 13% of vitamin D
studies show zero events with treatment.
The quality of non-prescription supplements can vary widely [Crawford, Crighton].
•All data and sources to reproduce this paper are in the appendix.
Other meta analyses for vitamin D treatment can be found in
[Argano, D’Ecclesiis, Hosseini, Nikniaz, Shah, Tentolouris, Varikasuvu, Xie], showing significant improvements for mortality, mechanical ventilation, ICU admission, hospitalization, severity, and cases.
| Early treatment | Late treatment | All studies | Studies | Patients | Authors | |
|---|---|---|---|---|---|---|
| All studies | 60% [40‑74%] **** | 46% [33‑56%] **** | 37% [31‑42%] **** |
111 | 183,150 | 1,124 |
| Randomized Controlled TrialsRCTs | 32% [8‑50%] * | 34% [13‑50%] ** | 30% [17‑40%] **** |
27 | 42,038 | 322 |
| Calcifediol/calcitriolCalcifediol | - | 73% [57‑83%] **** | 51% [30‑66%] *** |
12 | 8,943 | 150 |
| Mortality | 68% [39‑84%] *** | 45% [31‑57%] **** | 37% [28‑44%] **** |
62 | 62,281 | 595 |
| HospitalizationHosp. | 90% [-453‑100%] | 22% [6‑35%] ** | 20% [8‑31%] ** |
21 | 85,626 | 200 |
| RCT mortality | - | 31% [6‑50%] * | 31% [6‑50%] * |
15 | 1,949 | 174 |
Highlights
Vitamin D reduces
risk for COVID-19 with very high confidence for mortality, ICU admission, hospitalization, recovery, cases, viral clearance, and in pooled analysis, and low confidence for ventilation and progression.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 51
treatments.
B
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C
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Figure 1.
A. Random effects
meta-analysis of treatment studies. This plot shows pooled effects, analysis for individual outcomes is below, and
more details on pooled effects can be found in the heterogeneity section.
Effect extraction is pre-specified, using the most serious outcome reported.
Simplified dosages are shown for comparison, these are the total dose in the
first five days for treatment, and the monthly dose for prophylaxis.
Calcifediol, calcitriol, and paricalcitol treatment are indicated with (c), (t), and (p).
For details of effect extraction and full dosage information see the appendix.
B. Scatter plot
showing the distribution of effects reported in sufficiency studies and treatment studies. Diamonds show the
results of random effects meta-analysis.
C. Scatter plot showing the most serious outcome in all studies in the
context of multiple COVID-19 treatments. Diamonds show the
results of random effects meta-analysis for each treatment.
D. Timeline of results in vitamin D treatment studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 10.8 months, compared to using all studies. Efficacy based on specific outcomes in RCTs was delayed by 2.2 months, compared to using pooled outcomes in RCTs.
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.
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.
Figure 2. Simplified view of vitamin D sources and conversion.
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
[Al-Daghri, Alarslan, Azadeh, Beheshti, Chodick, D'Avolio, Desai, di Filippo, Ersöz, Hosseini (B), Jabbar, 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.
Figure 4. Treatment stages.
6 In Silico studies support the efficacy of vitamin D [Al-Mazaideh, Chellasamy, Mansouri, Pandya, Qayyum, Song].
4 In Vitro studies support the efficacy of vitamin D [DiGuilio, Mok, Pickard, Rybakovsky].
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.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 37% [31‑42%] p < 0.0001 **** | 111 | 183,150 | 1,124 |
| After exclusions | 39% [33‑44%] p < 0.0001 **** | 83 | 160,766 | 862 |
| Peer-reviewed studiesPeer-reviewed | 37% [31‑42%] p < 0.0001 **** | 104 | 181,330 | 1,053 |
| Randomized Controlled TrialsRCTs | 30% [17‑40%] p < 0.0001 **** | 27 | 42,038 | 322 |
| RCTs after exclusionsRCTs w/exc. | 31% [17‑43%] p = 0.00016 *** | 20 | 40,943 | 246 |
| Cholecalciferol | 35% [29‑41%] p < 0.0001 **** | 99 | 174,207 | 974 |
| Calcifediol/calcitriolCalcifediol | 51% [30‑66%] p = 0.00011 *** | 12 | 8,943 | 150 |
| Mortality | 37% [28‑44%] p < 0.0001 **** | 62 | 62,281 | 595 |
| VentilationVent. | 26% [-2‑46%] p = 0.068 | 17 | 7,852 | 184 |
| ICU admissionICU | 50% [33‑62%] p < 0.0001 **** | 25 | 40,098 | 274 |
| HospitalizationHosp. | 20% [8‑31%] p = 0.0022 ** | 21 | 85,626 | 200 |
| Recovery | 38% [23‑50%] p < 0.0001 **** | 9 | 545 | 75 |
| Cases | 16% [7‑23%] p = 0.0007 *** | 27 | 134,493 | 317 |
| Viral | 52% [30‑67%] p = 0.00014 *** | 4 | 200 | 26 |
| RCT mortality | 31% [6‑50%] p = 0.02 * | 15 | 1,949 | 174 |
| RCT hospitalizationRCT hosp. | 21% [-4‑40%] p = 0.087 | 8 | 39,713 | 107 |
| Sufficiency | 54% [50‑58%] p < 0.0001 **** | 175 | 228,791 | 1,521 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 60% [40‑74%] **** | 46% [33‑56%] **** | 31% [23‑38%] **** |
| After exclusions | 68% [45‑82%] **** | 58% [45‑69%] **** | 29% [21‑36%] **** |
| Peer-reviewed studiesPeer-reviewed | 57% [36‑71%] **** | 45% [32‑56%] **** | 32% [24‑39%] **** |
| Randomized Controlled TrialsRCTs | 32% [8‑50%] * | 34% [13‑50%] ** | 23% [-12‑47%] |
| RCTs after exclusionsRCTs w/exc. | 65% [-65‑92%] | 34% [16‑47%] *** | 23% [-12‑47%] |
| Cholecalciferol | 60% [40‑74%] **** | 41% [27‑52%] **** | 30% [22‑38%] **** |
| Calcifediol/calcitriolCalcifediol | - | 73% [57‑83%] **** | 36% [13‑54%] ** |
| Mortality | 68% [39‑84%] *** | 45% [31‑57%] **** | 23% [9‑34%] ** |
| VentilationVent. | 97% [56‑100%] * | 17% [-14‑40%] | 38% [-3‑63%] |
| ICU admissionICU | 87% [-143‑99%] | 52% [30‑67%] *** | 46% [22‑63%] ** |
| HospitalizationHosp. | 90% [-453‑100%] | 22% [6‑35%] ** | 13% [-4‑27%] |
| Recovery | 31% [7‑49%] * | 43% [24‑58%] *** | - |
| Cases | - | - | 16% [7‑23%] *** |
| Viral | 52% [24‑70%] ** | 53% [8‑76%] * | - |
| RCT mortality | - | 31% [6‑50%] * | - |
| RCT hospitalizationRCT hosp. | - | 29% [10‑44%] ** | -26% [-92‑17%] |
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Figure 5. Results by treatment stage.
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Figure 7. Random effects meta-analysis for peer-reviewed
treatment studies.
[Zeraatkar] analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
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Figure 10. Random effects meta-analysis for
treatment mortality results only.
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Figure 11. Random effects meta-analysis for
treatment mechanical ventilation results only.
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Figure 12. Random effects meta-analysis for
treatment ICU admission results only.
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Figure 13. Random effects meta-analysis for
treatment hospitalization results only.
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Figure 14. Random effects meta-analysis for
treatment COVID-19 case results only.
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Figure 15. Random effects meta-analysis for
sufficiency studies. This plot pools studies with different effects,
different vitamin D cutoff levels and measurement times, and studies may be
within hospitalized patients, excluding the risk of hospitalization. However,
the prevalence of positive effects is notable.
Results restricted to Randomized Controlled Trials (RCTs),
after exclusions, and for specific outcomes are shown in
Figure 16, 17, 18, and 19.
Bias in clinical research may be defined as something that tends to make
conclusions differ systematically from the truth. RCTs help to make study
groups more similar and can provide a higher level of evidence, however they
are subject to many biases [Jadad], and analysis of double-blind RCTs
has identified extreme levels of bias [Gøtzsche].
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, 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.
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 51 treatments we have analyzed,
64% 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 non-RCT trials can also
provide reliable results. [Concato] find that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. [Anglemyer] summarized reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates. [Lee] shows that only
14% of the guidelines of the Infectious Diseases Society of America were based
on RCTs. Evaluation of studies relies on an understanding of the study and
potential biases. Limitations in an RCT can outweigh the benefits, for example
excessive dosages, excessive treatment delays, or Internet survey bias could
have a greater effect on results. Ethical issues may also prevent running RCTs
for known effective treatments. For more on issues with RCTs see
[Deaton, Nichol].
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is aspirin.
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.
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Figure 16. Random effects meta-analysis for
Randomized Controlled Trials only. Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
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Figure 17. Random effects meta-analysis for
RCTs after exclusions. Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
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Figure 18. Random effects meta-analysis for
RCT mortality results.
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Figure 19. Random effects meta-analysis for
RCT hospitalization results.
To avoid bias in the selection of studies, we include all
studies in the main analysis, with the exception of
[Espitia-Hernandez]. This study uses a combined protocol with another
medication that shows high effectiveness when used alone. Authors report on
viral clearance, showing 100% clearance with treatment and 0% for the control
group. Based on the known mechanisms of action, the combined medication is
likely to contribute more to the improvement.
Here we show the results after excluding studies with critical
issues.
[Murai] is a very late stage study (mean 10 days from
symptom onset, with 90% on oxygen at baseline), with poorly matched arms in
terms of gender, ethnicity, hypertension, diabetes, and baseline ventilation,
all of which favor the control group. Further, this study uses
cholecalciferol, which may be especially poorly suited for such a late stage.
[Cannata-Andía, Mariani] are also very late stage studies using
cholecalciferol.
The studies excluded are as follows, and the resulting forest
plot is shown in Figure 20.
[Abdulateef], unadjusted results with no group details.
[Asimi], excessive unadjusted differences between groups.
[Assiri], unadjusted results with no group details.
[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.
[Campi], significant unadjusted differences between groups.
[Cannata-Andía], very late stage study using cholecalciferol instead of calcifediol or calcitriol.
[Din Ujjan], based on dosages and previous research, combined treatments may contribute more to the effect seen.
[Elhadi], unadjusted results with no group details.
[Fairfield], substantial unadjusted confounding by indication likely.