Diet for COVID-19
Serious outcome risk
Outcome (studies)
Improvement
Relative risk · 95% CI
Healthier diets reduce COVID-19 risk: real-time meta-analysis of 30 studies
Media coverage of diet for COVID‑19
November 2026
Media covered only 4% of studies showing significantly lower COVID-19 risk
Only 1 positive diet study was covered
2021
2022
2023
2024
2025
2021
2022
2023
2024
2025
4%
Abstract
Significantly lower risk is seen for ICU admission, hospitalization, progression, recovery, cases, and viral clearance. 26 studies from 25 independent teams in 10 countries show significant
benefit.
Meta-analysis using the most serious outcome reported shows
51% [42‑58%] lower risk. Results are similar for higher quality studies.
Results are very robust—in worst case exclusion sensitivity analysis 28 of
30 studies must be excluded before statistical significance is lost.
Diet for COVID-19
Serious outcome risk
Outcome (studies)
Improvement
Relative risk · 95% CI
Studies analyze diet quality before infection, and use different definitions of diet quality.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
All data and sources to reproduce this analysis are in the appendix.
Diet for COVID-19
Highlights- A healthier diet reduces risk with very high confidence for hospitalization, cases, and in pooled analysis, low confidence for ICU admission, progression, recovery, and viral clearance, and very low confidence for mortality.
- 26th treatment shown effective in June 2021, now with p < 0.0000000001 from 30 studies, recognized in 4 countries.
- Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
A
Diet COVID-19 studies
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Mahto20%0.80 · 0.49–1.21IgG+23/20670/483
Naushin40%0.60 · 0.50–0.71seropositiven/an/a
Kim72%0.28 · 0.10–0.82m/s case41 (n)527 (n)
Merino41%0.59 · 0.47–0.74severe case148,142 (n)148,143 (n)
Moludi92%0.08 · 0.05–0.19casesn/an/a
Ahmadi−3%1.03 · 0.77–1.39death185/206,28662/75,264
Nguyen15%0.85 · 0.75–0.96symp. case345/1,054433/1,082
Yamamoto66%0.34 · 0.13–0.85cases4/2019/32
Magaña53%0.47 · 0.22–0.99death58 (n)31 (n)
Jagielski82%0.18 · 0.04–0.65cases4/409/20
Perez-Araluce78%0.22 · 0.03–1.77severe case1/1,10310/3,300
Firoozi65%0.35 · 0.28–0.43casescase-control studyper unit E-DII change
Hou72%0.28 · 0.04–1.95severe case1/2278/487
Zargarzadeh77%0.23 · 0.11–0.50severe case89 (n)80 (n)
Yue19%0.81 · 0.69–0.94casesn/an/a
Zhou16%0.84 · 0.78–0.91cases1,321/10,2541,935/10,253
Ebrahimzadeh69%0.31 · 0.14–0.68severe casen/an/a
Tadbir Vajargah67%0.33 · 0.16–0.69severe case83 (n)83 (n)
Reis75%0.25 · 0.12–0.52hosp.17/38021/166
Zhao24%0.76 · 0.53–1.09death39,230 (n)39,231 (n)
Mohajeri25%0.75 · 0.63–0.88progression62/105392/495
Wang9%0.91 · 0.72–1.14PASC124/318218/480LONG COVID
Zamanian81%0.19 · 0.07–0.55hosp.case-control study
Barania Adabi99%0.01 · 0.00–0.21ICU0/12537/125
Aghajani88%0.12 · 0.04–0.29severe casecase-control study
Wang45%0.55 · 0.29–1.03severe case81 (n)67 (n)
Micek70%0.30 · 0.07–1.13cases32 (n)21 (n)
Pavlidou55%0.45 · 0.41–0.51cases2,609 (n)2,588 (n)
Darand37%0.63 · 0.42–0.95cases113 (n)155 (n)
Hawryłkowicz77%0.23 · 0.09–0.56hosp.183 (n)183 (n)
Prophylaxis51%0.49 · 0.42–0.582,087/410K3,284/283K51% lower risk
Tau² = 0.12, I² = 87.0%, p < 0.0001
All studies51%0.49 · 0.42–0.582,087/410K3,284/283K51% lower risk
Tau² = 0.12, I² = 87.0%, p < 0.000100.511.52+
B
Loading..
Fig. 1. A. Random-effects meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in diet studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome. Efficacy based on specific outcomes was delayed by 3.4 months, compared to using pooled outcomes.
Improved diets contain nutrients shown to be beneficial, enhance immune system function, support a healthy gut microbiome, help regulate energy levels and metabolism, and reduce the risk of chronic diseases.
We analyze all significant
studies reporting COVID-19 outcomes as a function of diet quality and providing adjusted results.
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 present random-effects
meta-analysis results for all studies, individual outcomes, and higher quality studies.
Table 1 summarizes the results for all studies, after exclusions, and for specific outcomes.
Fig. 2, 3, 4, 5, 6, 7, 8, 9, and 10
show forest plots for random-effects meta-analysis of
all studies with pooled effects, mortality results, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and long COVID.
Diet for COVID-19: meta-analysis results
November 2026
Relative Risk · CI
All studies30 studies · 694K patients
All stages0.49*** 0.42–0.5851% lower risk
After exclusions26 studies · 692K patients
All stages0.47*** 0.39–0.5653% lower risk
Mortality3 studies · 360K patients
All stages0.80 0.56–1.1520% lower risk
Hospitalization4 studies · 912 patients
All stages0.27*** 0.18–0.4073% lower risk
Cases14 studies · 408K patients
All stages0.65*** 0.57–0.7435% lower risk
Random-effects meta-analysis. Relative risk with 95% CI. * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 1. Random-effects meta-analysis results for diet. All studies are for prophylaxis. Results show the relative risk and 95% confidence interval.
Diet COVID-19 studies
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Mahto20%0.80 · 0.49–1.21IgG+23/20670/483
Naushin40%0.60 · 0.50–0.71seropositiven/an/a
Kim72%0.28 · 0.10–0.82m/s case41 (n)527 (n)
Merino41%0.59 · 0.47–0.74severe case148,142 (n)148,143 (n)
Moludi92%0.08 · 0.05–0.19casesn/an/a
Ahmadi−3%1.03 · 0.77–1.39death185/206,28662/75,264
Nguyen15%0.85 · 0.75–0.96symp. case345/1,054433/1,082
Yamamoto66%0.34 · 0.13–0.85cases4/2019/32
Magaña53%0.47 · 0.22–0.99death58 (n)31 (n)
Jagielski82%0.18 · 0.04–0.65cases4/409/20
Perez-Araluce78%0.22 · 0.03–1.77severe case1/1,10310/3,300
Firoozi65%0.35 · 0.28–0.43casescase-control studyper unit E-DII change
Hou72%0.28 · 0.04–1.95severe case1/2278/487
Zargarzadeh77%0.23 · 0.11–0.50severe case89 (n)80 (n)
Yue19%0.81 · 0.69–0.94casesn/an/a
Zhou16%0.84 · 0.78–0.91cases1,321/10,2541,935/10,253
Ebrahimzadeh69%0.31 · 0.14–0.68severe casen/an/a
Tadbir Vajargah67%0.33 · 0.16–0.69severe case83 (n)83 (n)
Reis75%0.25 · 0.12–0.52hosp.17/38021/166
Zhao24%0.76 · 0.53–1.09death39,230 (n)39,231 (n)
Mohajeri25%0.75 · 0.63–0.88progression62/105392/495
Wang9%0.91 · 0.72–1.14PASC124/318218/480LONG COVID
Zamanian81%0.19 · 0.07–0.55hosp.case-control study
Barania Adabi99%0.01 · 0.00–0.21ICU0/12537/125
Aghajani88%0.12 · 0.04–0.29severe casecase-control study
Wang45%0.55 · 0.29–1.03severe case81 (n)67 (n)
Micek70%0.30 · 0.07–1.13cases32 (n)21 (n)
Pavlidou55%0.45 · 0.41–0.51cases2,609 (n)2,588 (n)
Darand37%0.63 · 0.42–0.95cases113 (n)155 (n)
Hawryłkowicz77%0.23 · 0.09–0.56hosp.183 (n)183 (n)
Prophylaxis51%0.49 · 0.42–0.582,087/410K3,284/283K51% lower risk
Tau² = 0.12, I² = 87.0%, p < 0.0001
All studies51%0.49 · 0.42–0.582,087/410K3,284/283K51% lower risk
Tau² = 0.12, I² = 87.0%, p < 0.000100.511.52+
Fig. 2. Random-effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Diet COVID-19 mortality results
November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
Ahmadi−3%1.03 · 0.77–1.39185/206,28662/75,264
Magaña53%0.47 · 0.22–0.9958 (n)31 (n)
Zhao24%0.76 · 0.53–1.0939,230 (n)39,231 (n)
Prophylaxis20%0.80 · 0.56–1.15185/245K62/114K20% lower risk
Tau² = 0.05, I² = 55.2%, p = 0.23
All studies20%0.80 · 0.56–1.15185/245K62/114K20% lower risk
Tau² = 0.05, I² = 55.2%, p = 0.2300.511.52+
Fig. 3. Random-effects meta-analysis for mortality results.
Diet COVID-19 ICU results
November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
Prophylaxis99%0.01 · 0.00–0.210/12537/12599% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.0024
All studies99%0.01 · 0.00–0.210/12537/12599% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.002400.511.52+
Fig. 4. Random-effects meta-analysis for ICU admission.
Diet COVID-19 hospitalization results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Ebrahimzadeh56%0.44 · 0.18–1.06hosp.n/an/a
Reis75%0.25 · 0.12–0.52hosp.17/38021/166
Zamanian81%0.19 · 0.07–0.55hosp.case-control study
Hawryłkowicz77%0.23 · 0.09–0.56hosp.183 (n)183 (n)
Prophylaxis73%0.27 · 0.18–0.4017/56321/34973% lower risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
All studies73%0.27 · 0.18–0.4017/56321/34973% lower risk
Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+
Fig. 5. Random-effects meta-analysis for hospitalization.
Diet COVID-19 progression results
November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
Prophylaxis25%0.75 · 0.63–0.8862/105392/49525% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.00055
All studies25%0.75 · 0.63–0.8862/105392/49525% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.0005500.511.52+
Fig. 6. Random-effects meta-analysis for progression.
Diet COVID-19 recovery results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Prophylaxis68%0.32 · 0.15–0.6868% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.0032
All studies68%0.32 · 0.15–0.6868% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.003200.511.52+
Fig. 7. Random-effects meta-analysis for recovery.
Diet COVID-19 case results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Kim19%0.81 · 0.56–1.15cases41 (n)527 (n)
Merino18%0.82 · 0.78–0.86cases148,142 (n)148,143 (n)
Moludi92%0.08 · 0.05–0.19casesn/an/a
Nguyen15%0.85 · 0.75–0.96symp. case345/1,054433/1,082
Yamamoto66%0.34 · 0.13–0.85cases4/2019/32
Jagielski82%0.18 · 0.04–0.65cases4/409/20
Perez-Araluce15%0.85 · 0.62–1.15symp. case52/1,103214/3,300
Firoozi65%0.35 · 0.28–0.43casescase-control studyper unit E-DII change
Yue19%0.81 · 0.69–0.94casesn/an/a
Zhou16%0.84 · 0.78–0.91cases1,321/10,2541,935/10,253
Zhao15%0.85 · 0.81–0.91cases39,230 (n)39,231 (n)
Micek70%0.30 · 0.07–1.13cases32 (n)21 (n)
Pavlidou55%0.45 · 0.41–0.51cases2,609 (n)2,588 (n)
Darand37%0.63 · 0.42–0.95cases113 (n)155 (n)
Prophylaxis35%0.65 · 0.57–0.741,726/202K2,610/205K35% lower risk
Tau² = 0.04, I² = 90.3%, p < 0.0001
All studies35%0.65 · 0.57–0.741,726/202K2,610/205K35% lower risk
Tau² = 0.04, I² = 90.3%, p < 0.000100.511.52+
Fig. 8. Random-effects meta-analysis for cases.
Diet COVID-19 viral clearance results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Prophylaxis32%0.68 · 0.48–0.9781 (n)67 (n)32% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.032
All studies32%0.68 · 0.48–0.9781 (n)67 (n)32% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.03200.511.52+
Fig. 9. Random-effects meta-analysis for viral clearance.
Diet COVID-19 long COVID results
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Prophylaxis9%0.91 · 0.77–1.08124/318218/4809% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.28
All studies9%0.91 · 0.77–1.08124/318218/4809% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.2800.511.52+
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Fig. 11 shows a forest plot for random-effects
meta-analysis of all studies after exclusions.
Hou, unadjusted results with no group details. Excluded results: severe case, moderate/severe case.
Magaña, unadjusted results with no group details.
Mahto, unadjusted results with no group details.
Mohajeri, unadjusted results with no group details.
Yamamoto, unadjusted results with no group details.
Diet COVID-19 studies after exclusions
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Naushin40%0.60 · 0.50–0.71seropositiven/an/a
Kim72%0.28 · 0.10–0.82m/s case41 (n)527 (n)
Merino41%0.59 · 0.47–0.74severe case148,142 (n)148,143 (n)
Moludi92%0.08 · 0.05–0.19casesn/an/a
Ahmadi−3%1.03 · 0.77–1.39death185/206,28662/75,264
Nguyen15%0.85 · 0.75–0.96symp. case345/1,054433/1,082
Jagielski82%0.18 · 0.04–0.65cases4/409/20
Perez-Araluce78%0.22 · 0.03–1.77severe case1/1,10310/3,300
Firoozi65%0.35 · 0.28–0.43casescase-control studyper unit E-DII change
Hou74%0.26 · 0.02–3.97severe case0/947/127
Zargarzadeh77%0.23 · 0.11–0.50severe case89 (n)80 (n)
Yue19%0.81 · 0.69–0.94casesn/an/a
Zhou16%0.84 · 0.78–0.91cases1,321/10,2541,935/10,253
Ebrahimzadeh69%0.31 · 0.14–0.68severe casen/an/a
Tadbir Vajargah67%0.33 · 0.16–0.69severe case83 (n)83 (n)
Reis75%0.25 · 0.12–0.52hosp.17/38021/166
Zhao24%0.76 · 0.53–1.09death39,230 (n)39,231 (n)
Wang9%0.91 · 0.72–1.14PASC124/318218/480LONG COVID
Zamanian81%0.19 · 0.07–0.55hosp.case-control study
Barania Adabi99%0.01 · 0.00–0.21ICU0/12537/125
Aghajani88%0.12 · 0.04–0.29severe casecase-control study
Wang45%0.55 · 0.29–1.03severe case81 (n)67 (n)
Micek70%0.30 · 0.07–1.13cases32 (n)21 (n)
Pavlidou55%0.45 · 0.41–0.51cases2,609 (n)2,588 (n)
Darand37%0.63 · 0.42–0.95cases113 (n)155 (n)
Hawryłkowicz77%0.23 · 0.09–0.56hosp.183 (n)183 (n)
Prophylaxis53%0.47 · 0.39–0.561,997/410K2,772/281K53% lower risk
Tau² = 0.13, I² = 88.6%, p < 0.0001
All studies53%0.47 · 0.39–0.561,997/410K2,772/281K53% lower risk
Tau² = 0.13, I² = 88.6%, p < 0.000100.511.52+
Fig. 11. Random-effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Low-cost treatments were subject to bias and censorship during the pandemic.
Scientific bias is seen in the design, analysis, presentation, and selective
reporting of studies, which often favored negative results. A similar bias is seen in the media
coverage for low-cost treatments.
While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."3.
We analyze media coverage for the 227 treatments we
cover using Altmetric16 and PlumX
Metrics17. Altmetric covers ~14,000
news outlets18 and PlumX covers
~55,000 news sources19.
The two services are complementary with coverage of different news sources and different
methods for identifying articles not containing a DOI. We consider studies to have
received significant media coverage if they were covered by at least 100 articles in
either Altmetric of PlumX19,20. The most widespread study was covered
by 1,047 outlets. Studies covered by major
syndicated sources shows hundreds of news articles, while studies covered by few outlets
typically appear in niche medical news sources or press releases.
Fig. 12 shows that the mainstream media covered only a small fraction of the positive studies for diet.
Fig. 13 and 14 analyze coverage for all low-cost treatments and all high-profit treatments, showing significant bias.
For low-cost treatments media coverage favored negative results 5.4× more than positive results, whereas for high-profit treatments coverage favored positive results 3.8× more than negative results.
The media bias may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit
treatments and treatment equity. High-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.
Media coverage of diet for COVID‑19
November 2026
Media covered only 4% of studies showing significantly lower COVID-19 risk
Only 1 positive diet study was covered
2021
2022
2023
2024
2025
2021
2022
2023
2024
2025
4%
Fig. 12. Mainstream media covered only a small fraction of the positive studies for diet.
Media bias for low-cost COVID‑19 treatments
November 2026
Media covered only 0.8% of studies showing significantly lower COVID-19 risk
Only 5 positive studies were covered
2020
2021
2022
2023
2020
2021
2022
2023
0.8%
one each: budesonide, cannabidiol, fluvoxamine, HCQ, vitamin D
34 negative studies were covered
2020
2021
2022
2023
2020
2021
2022
2023
4%
HCQ 12, ivermectin 6, lopinavir/ritonavir 4, azithromycin 3, vitamin D 3, fluvoxamine 2, one each: ibuprofen, metformin, vitamin C, zinc
5.4×
Negative bias for low-cost treatments
negative studies were 5.4 times more likely to be coveredFig. 13. Mainstream media coverage was biased against positive results for low-cost treatments. When restricting to RCTs, the bias was even larger, with negative RCTs 6.9× more likely to be covered.
Media was 3.8× more likely to cover positive studies
19 positive studies were covered
2020
2021
2022
2023
2020
2021
2022
2023
11%
paxlovid 5, remdesivir 3, casirivimab/imdevimab 2, convalescent plasma 2, molnupiravir 2, one each: antiandrogens, peginterferon lambda, sargramostim, tixagevimab/cilgavimab, tocilizumab
Only 6 negative studies were covered
2020
2021
2022
2023
2020
2021
2022
2023
3%
remdesivir 3, one each: bamlanivimab/etesevimab, bebtelovimab, sotrovimab
3.8×
Positive bias for high-profit treatments
positive studies were 3.8 times more likely to be coveredFig. 14. For high-profit treatments, mainstream media coverage was biased towards positive results. When restricting to RCTs, the bias was even larger, with positive RCTs 12.0× more likely to be covered.
A combination of factors may have led to the very low media coverage of low-cost treatments:
•
Politicization
led to a media environment where coverage was often framed to support a political
narrative rather than to provide objective scientific information. As Scott Alexander
said: "if you say anything in favor of ivermectin you will be cast out
of civilization and thrown into the circle of social hell reserved for Klan members and
1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!'
at you and compare you to Josef Mengele."
There was strong social pressure to
discredit low-cost treatments.•
Censorship
of
information conflicting with selected authorities. For example, individuals and
organizations presenting conflicting science were often shadowbanned or completely banned
on Twitter and YouTube if the science they were reporting conflicted with the opinions of
specific authorities selected by those companies, including when the authorities had
not even reviewed the scientific results in question.•
FDA requires "no
adequate, approved, and available alternatives"
in order to grant an EUA for
novel high-profit interventions, creating an incentive for authorities to ignore or
downplay existing low-cost treatments.•
Regulatory
capture
biases authorities towards high-profit interventions.•
Authorities ignored
most evidence for low-cost treatments
, for example the NIH references only 2% of
studies in delayed, rarely-updated, biased commentaries with no quantitive analysis.•
Media coverage of
science is often not very accurate
, e.g., misunderstanding confounding issues. For
example the media widely considered the RECOVERY HCQ RCT to be conclusive on efficacy, but
very late treatment of late-stage patients (mostly on oxygen already) with an excessive
toxic dose (shown dangerous in a dose comparison RCT) provides no information on the
recommended early/prophylactic treatment. With difficulting in understanding basic
confounders like treatment delay and dose, the media may favor deferring to authorities.
Many studies for low-cost treatments require greater expertise to analyze.
•
Substantial funding
from pharmaceutical advertising
biases editorial decisions towards high-profit
interventions.•
PR power
-
companies/teams with strong PR presence are favored in the media, which correlates with
high-profit and high conflict of interest studies.•
The media was very
negative in general
, inflating risk, and elevating fear and anxiety. A negative bias may
improve ratings and revenue, increasing motivation to continue watching coverage.
26 low-cost treatments were approved in one
or more countries, yet many countries approved no low-cost treatments.
The countries that did adopt low-cost treatments analyzed the evidence early and made
timely approvals. With few exceptions, authorities did not change their initial views,
regardless of how much evidence accumulated showing either efficacy or harm. Why?
The harms of smoking here hidden for 25 yearsB. Authorities did not analyze the data in real-time, failing to act when
harm was known.
Widespread acknowledgement of harm came only after attempts by two new surgeon generals,
along with pressure from health advocates and a new president, and a review of 7,000 studies.
Similarly for COVID-19, most authorities and experts did not proactively
analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by
which time moral, legal, career, and reputational liabilities strongly disincentivize any
admission of error. Claims of no efficacy (for effective treatments) or safety (for
harmful treatments) were often made prior to strong data being available. Correction
would require admitting to errors that increased mortality, which is unlikely with the
same generation of officials.
Analysis of potential treatments was rarely done, and when done these were
typically minimal efforts.
For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments,
reference only 2% of studies for the treatments covered, and include no
quantitative analysis.
They appear as rarely updated side projects from
external panels implicitly tasked with justifying prior failures.
As with smoking, the thousands of studies could (and should)
have been analyzed and acted on in real-time.
A key structural improvement, applicable to all current and future
diseases, is for authorities to implement real-time proactive analysis of clinical
evidence. This does not remove all bias, but does make it possible to act on evidence,
whereas delayed action may be unlikely due to moral, legal, career, and reputational
liabilities.
c19early.org
Delayed public health acknowledgments
Official acknowledgment of efficacy or harm is often delayed—legal, career, and status risks disincentivize admission of error.
| Evidence | Official Acknowledgment | Approx. Delay | |
|---|---|---|---|
| Citrus Fruit (vitamin C) for Scurvy (effectiveness) | 1747: James Lind conducted one of the first-ever controlled clinical trials, proving that oranges and lemons cured scurvy in sailors. | 1795: The British Royal Navy finally made a daily ration of lemon juice a standard issue for all its sailors, effectively eliminating the disease. | 48 years |
| Handwashing (lower mortality) | 1847: Dr. Ignaz Semmelweis provided conclusive proof that having doctors wash their hands with a chlorine solution before delivering babies reduced maternal mortality rates from over 18% to around 1%. | ~1870s: Semmelweis's findings were rejected and he was ridiculed. His work was only validated decades later (after his death). | ~20+ years |
| Helicobacter pylori (bacteria causes ulcers) | 1982-1984: Marshall and Warren discovered that Helicobacter pylori bacteria causes ulcers, confirmed via direct exposure. Officials maintained that ulcers were caused by stress and spicy food. | 1994: The US NIH released a consensus statement officially recommending antibiotics as the standard treatment for peptic ulcers, overturning decades of acid-suppression therapy. | ~12 years |
| Asbestos (causes asbestosis & cancer) | 1924: The British Medical Journal published the first case study of a death from "asbestosis." By 1918, U.S. insurance companies had stopped selling life insurance to asbestos workers. | 1971 (US): The Occupational Safety and Health Administration (OSHA) was formed and began regulating asbestos as a carcinogen, setting the first federal workplace safety standards for it. | ~47 years |
| Leaded Gasoline (neurotoxicity) | ~1924: Dangers of low-level lead exposure were known. Experts like Alice Hamilton warned the U.S. Surgeon General that adding lead to gasoline would cause widespread public poisoning. | 1973 (US): The Environmental Protection Agency (EPA) ordered the first phasedown of lead in gasoline, following the Clean Air Act of 1970. A full ban for on-road vehicles took effect in 1996. | ~49 years |
| Harms of Smoking (causes lung cancer) | 1939: Franz Müller (Germany) published the first case-control epidemiological study strongly linking tobacco smoking to lung cancer. This was followed by major U.S. & U.K. studies in the 1950s. | 1964 (US): The U.S. Surgeon General's report, "Smoking and Health," was released. It was the first U.S. government report to definitively link smoking to lung cancer and heart disease. | 25 years |
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 diet as of September 2021. Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 3.4 months compared to using pooled outcomes.
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 227
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Fig. 15 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.0000000001).
Similarly, Fig. 16 shows that improved recovery is very strongly associated
with lower mortality (p < 0.0000000001).
Considering the extremes, Singh 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.
Fig. 17 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.
Fig. 15. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Fig. 16. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Fig. 15. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months.
Fig. 18 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Fig. 18. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
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 differences in treatment delay are 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.
SARS-CoV-2 infection and replication involves a complex
interplay of 500+ host and viral proteins and other
factors28-35, providing many therapeutic
targets.
Over 12,000 compounds have been predicted to reduce COVID-19
risk9, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Improved diets contain nutrients shown to be beneficial, enhance immune system function, support a healthy gut microbiome, help regulate energy levels and metabolism, and reduce the risk of chronic diseases.
Fig. 19 shows an overview of the results for a healthy diet
in the context of multiple COVID-19 treatments, and Fig. 20 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Fig. 20. Efficacy vs. cost for COVID-19 treatments.
Improved diets contain nutrients shown to be beneficial, enhance immune system function, support a healthy gut microbiome, help regulate energy levels and metabolism, and reduce the risk of chronic diseases.
People with healthier diets have reduced risk for COVID-19.
Significantly lower risk is seen for ICU admission, hospitalization, progression, recovery, cases, and viral clearance. 26 studies from 25 independent teams in 10 countries show significant
benefit.
Meta-analysis using the most serious outcome reported shows
51% [42‑58%] lower risk. Results are similar for higher quality studies.
Results are very robust—in worst case exclusion sensitivity analysis 28 of
30 studies must be excluded before statistical significance is lost.
Studies analyze diet quality before infection, and use different definitions of diet quality.
Other meta-analyses show significant improvements with diet for hospitalization1, severity2, and cases1,2.
Contact.
Contact us on X at @CovidAnalysis.
Funding.
We have received no funding or
compensation in any form, and do not accept donations. This is entirely volunteer work.
Conflicts of interest.
We have no conflicts of interest.
We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.AI.
We use AI models (Gemini, Grok, Claude, and
ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest
improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.Updates.
Our COVID-19 meta-analyses involve the extraction of over 229,000
datapoints from thousands of papers for 227 treatments. We thank the
thousands of scientists, physicians, and other contributors that have provided updates,
suggestions, feedback, and corrections. These are all welcome and can be submitted
at https://c19early.org/dtmeta.html.
Dedication.
This work is dedicated to top
evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce
mortality and morbidity during the pandemic. In alphabetical order:
Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty,
Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr.
Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch,
Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.
Public domain.
This is a public domain work
distributed in accordance with the Creative Commons CC0 1.0 Universal license, which
dedicates the work to the public domain by waiving all rights worldwide under copyright law.
You can distribute, remix, adapt, and build upon this work in any medium or format,
including for commercial purposes, without asking permission. Referenced material and
third-party images retain any original copyrights or restrictions.
See: https://creativecommons.org/publicdomain/zero/1.0/.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases with healthier diets
p = 0.000033
Case control study of 295 COVID-19 patients in Iran, showing lower risk of severe cases with higher dietary antioxidant quality scores, and with higher intake of vitamin D.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
No significant difference in mortality
Retrospective 468,569 adults in the UK, showing no significant difference in COVID-19 mortality based on diet quality, however significantly lower mortality was seen with higher diet quality for pneumonia and infectious diseases.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower ICU admission with healthier diets
p < 0.000001
Retrospective 500 COVID-19 patients, showing dietary inflammatory index (DII) and energy-adjusted dietary inflammatory index (E-DII) associated with COVID-19 severity.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p = 0.027
Analysis of 8,157 adults showing significantly higher risk of COVID-19 with higher adherence to an unhealthy diet, characterized by higher intake of less healthy foods such as fruit juices, refined grains, potatoes, and sugar-sweetened beverages. The association was independent of socio-demographic status and BMI.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases and improved recovery
p = 0.0037 (severe cases) and p = 0.0032 (recovery)
Retrospective 250 recovered COVID-19 patients, showing lower risk of severe cases and shorter recovery and hospitalization times with a healthy diet.
Notably, all individual symptoms show lower incidence with a healthy diet with the exception of fever and chills. Fever and chills help the immune system fight infections (shivering helps to raise the body temperature).
Notably, all individual symptoms show lower incidence with a healthy diet with the exception of fever and chills. Fever and chills help the immune system fight infections (shivering helps to raise the body temperature).
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
per unit E-DII change
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p < 0.000001
Retrospective 133 COVID-19 patients and 322 controls, showing higher risk of COVID-19 for diets that have a higher inflammatory index (E-DII).
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower hospitalization with healthier diets
p = 0.0014
Retrospective 550 COVID-19 patients in Poland showing that higher adherence to a processed food dietary pattern was associated with 4.4 times higher odds of hospitalization.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases with healthier diets
Not statistically significant · p = 0.23
Retrospective 509 COVID-19 patients in Taiwan, showing higher risk of critical COVID-19 cases with non-vegetarian diets.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p = 0.005
Retrospective 95 people in Poland, showing significantly lower risk of COVID-19 with higher consumption of fruits, vegetables, and nuts. Diets with higher consumption of fruits, vegetables, and nuts had a significantly lower dietary inflammatory index.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer moderate/severe cases with healthier diets
p = 0.02
Retrospective healthcare workers in six countries with exposure to COVID-19 patients, showing lower risk of moderate/severe COVID-19 with plant-based diets.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower mortality with healthier diets
p = 0.049
Retrospective 89 COVID-19 patients in Spain, showing lower mortality with adherence to the Mediterranean diet.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
unadjusted
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower IgG positivity with healthier diets
Not statistically significant · p = 0.32
Retrospective 689 healthcare workers in India, showing non-statistically significant lower risk of IgG positivity with a vegetarian diet in unadjusted results.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases and fewer cases
p < 0.0001 (severe cases) and p < 0.0001 (fewer cases)
Retrospective 592,571 participants in the UK and USA with 31,815 COVID-19 cases, showing lower risk or COVID-19 cases and severity for higher healthful plant-based diet scores. Notably, the assocation was less evident with higher levels of physical activity.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
Not statistically significant · p = 0.089
Dietary analysis of 95 adults in Poland, showing lower risk of COVID-19 with higher intake of polyphenols, lignans, and phytosterols. Results were statistically significant for total phytosterols, secoisolariciresinol, β-sitosterol, matairesinol, and stigmasterol. Authors suggest that beneficial effects on gut microbiota and immune function may contribute to the lower risk.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower progression with healthier diets
p = 0.00003
Retrospective 600 COVID-19 patients in Iran with moderate/severe CT scans, showing lower prevalence of dyspnea, fever, taste/smell abnormalities, and cough with high adherence to the Mediterranean diet in unadjusted results.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p < 0.000001
Retrospective 60 COVID-19 hospitalized patients and 60 controls in Iran, showing pro-inflammatory diets associated with COVID-19 cases and severity. IR.KUMS.REC.1399·444, IR.TBZMED.REC.1399·225.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower seropositivity with healthier diets
p < 0.000001
Retrospective 10,427 volunteers in India, 1,058 anti-nucleocapsid antibody positive, showing lower risk of seropositivity with a vegetarian diet.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer symptomatic cases with healthier diets
p = 0.006
Analysis of 3,947 participants in Vietnam, showing significantly lower risk of COVID-19-like symptoms with physical activity and with a healthy diet. The combination of being physically active and eating healthy reduced risk further compared to either alone. The analyzed period was Feb 14 to Mar 2, 2020, which may have been before testing was widely available.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p = 0.0009
Retrospective 5,197 Greek adults over 65. After adjustment for confounders, COVID-19 infection was independently associated with poor sleep, low physical activity, low Mediterranean diet adherence, living in urban areas, smoking, obesity, depression, anxiety, stress, and poor health-related quality of life.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases and fewer symptomatic cases
Not statistically significant · p = 0.15 (severe cases) and p = 0.31 (fewer symptomatic cases)
Retrospective 5,194 participants in Spain with 382 COVID-19 cases, showing lower risk of COVID-19 with high adherence to a Mediterranean diet, with statistical significance only when excluding healthcare professionals.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower hospitalization with healthier diets
p = 0.00025
Retrospective 546 COVID+ patients in the USA, showing lower risk of hospitalization with higher consumption of vegetables.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases with healthier diets
p = 0.003
Retrospective 250 hospitalized patients in Iran, showing higher consumption of fruits, vegetables, and fiber associated with lower COVID-19 severity.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Improved viral clearance with healthier diets
p = 0.032
Retrospective 148 hospitalized COVID-19 patients in China, showing lower severity and faster viral clearance with improved nutrition.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
No significant difference in long COVID
Prospective analysis of 32,249 women from the Nurses’ Health Study II in the USA, showing lower risk of PASC with a healthy lifestyle, and in a dose-dependent manner. Participants with 5 or 6 healthy lifestyle factors had significantly lower COVID-19 hospitalization and PASC. BMI and sleep were independently associated with risk of PASC.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p = 0.0092
Retrospective 84 flight attendants, 52 reporting COVID-19 status and diet quality, showing higher risk of COVID-19 with lower self-reported diet quality.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p = 0.0076
Analysis of 42,935 participants showing lower risk of COVID-19 with healthier diets. Risk of severe cases was also lower with healthier diets, while not reaching statistical significance. Severity results are only provided with diet indices as a continuous variable.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower hospitalization with healthier diets
p = 0.0016
Case control study with 53 inpatients and 88 outpatients in Iran, showing lower risk of hospitalization with increased adherence to the DASH (Dietary Approach to Stop Hypertension) diet. Increased intake of fruits, vegetables and low-fat dairy products, and lower intake of sodium and processed/red meat were significantly associated with reduced risk of hospitalization due to COVID-19.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases with healthier diets
p = 0.00016
Retrospective 250 COVID-19 patients in Iran, showing lower risk of severe disease with greater adherence to a Mediterranean diet.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Lower severe cases and fewer cases
p = 0.00034 (severe cases) and p < 0.0001 (fewer cases)
UK Biobank retrospective 196,154 participants with 11,288 COVID-19 cases, showing lower COVID-19 mortality, severity, and incidence for lower dietary inflammatory scores.
Diet for COVID-19
Outcome
Improvement
Relative Risk · 95% CI
00.511.52+
← Healthier diets
reduce riskHealthier diets
increase risk →
reduce riskHealthier diets
increase risk →
Fewer cases with healthier diets
p = 0.000011
Prospective study of 41,012 UK Biobank participants, showing higher risk of COVID-19 cases with ultra-processed food consumption.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane
Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the
reference lists of other studies and meta-analyses, and submissions to the site c19early.org, which regularly receives notification of studies upon
publication.
Search terms are diet AND COVID-19. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of diet for COVID-19 that report a comparison with a
control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues, epidemiological
studies, and studies with minimal available information.
Studies with major unexplained data issues, for example major outcome data that is
impossible to be correct with no response from the authors, are excluded.
Fig. 21.
Mid-recovery results can more accurately reflect efficacy when almost all patients
recover. Mateja et al. confirm that intermediate viral load results more accurately
reflect hospitalization/death.
We extracted effect sizes and associated data from all studies. If studies report multiple
kinds of effects then the most serious outcome is used in pooled analysis, while
other outcomes are included in the outcome-specific analyses. For example, if effects for
mortality and cases are reported then they are both used in specific outcome analyses,
while mortality is used for pooled analysis.
If symptomatic results are reported at multiple times, we use the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28 days have
preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious outcome with
one or more events is used. For example, in low-risk populations with no mortality, a
reduction in mortality with treatment is not possible, however a reduction in
hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral outcomes.
When basically all patients recover in both treatment and control groups, preference for
viral clearance and recovery is given to results mid-recovery where available. After most
or all patients have recovered there is little or no room for an effective treatment to do
better, however faster recovery is valuable.
An IPD meta-analysis confirms that intermediate viral load reduction is more closely
associated with hospitalization/death than later viral load reduction37.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
Forest plots are computed using PythonMeta38 with the DerSimonian and
Laird random-effects model (the fixed effect assumption is not plausible in this case) and
inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
When results provide an odds ratio, we compute the relative risk when possible, or convert
to a relative risk according to Zhang et al.
Reported confidence intervals and p-values are used when available, and adjusted
values are used when provided. If multiple types of adjustments are reported propensity
score matching and multivariable regression has preference over propensity score matching
or weighting, which has preference over multivariable regression. Adjusted results have
preference over unadjusted results for a more serious outcome when the adjustments
significantly alter results.
When needed, conversion between reported p-values and confidence intervals followed
Altman, Altman (B), and Fisher's exact test was used to calculate
p-values for event data. If continuity correction for zero values is required, we
use the reciprocal of the opposite arm with the sum of the correction factors equal to
142.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative
outcome when applicable (for example, the risk of death rather than the risk of survival).
If studies only report relative continuous values such as relative times, the ratio of the
time for the treatment group versus the time for the control group is used. Calculations
are done in Python (3.15.2) with
scipy (1.18.1), pythonmeta (1.26), numpy (2.5.4), statsmodels (0.15.0), and plotly (6.9.0).
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically
significant.
Grobid 0.8.2 is used to parse PDF documents.
When evaluating potential effect modification across groups, we use an
interaction test as described by Altman (C) et al. We compared the log-transformed
relative risks using a z-test, deriving the standard error of the difference from
the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was
considered a statistically significant difference in treatment effect between the
groups.
Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of
providing standardized rules that can be applied with minimal understanding of the domain
and study. However, the rules do not account for many real-world issues, often
overemphasize or underemphasize others, and studies show low inter-rater
reliability50.
Certain domains are more applicable for these tools, however the time-sensitive nature of
a pandemic, with significant mortality for every day of delay in evidence assessment, and
the characteristics of COVID-19 make them inappropriate for this domain.
This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match
reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low
risk of bias, however this is the opposite of reality—the trial not only has very
high risk of bias, but has very high actual known bias, refusing to release data despite
pledging to, reporting multiple impossible numbers, having blinding and randomization
failure, and many other issues52.
Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however
this is the opposite of reality—the very late treatment and excessive dosage used
produces results with no relevance to recommended usage.
HCQ shows poor results with
late treatment and excessive dosage, and the combination shows harmC.
Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having
the lowest risk of bias48,49 are far from the most informative. Both
involve very late treatment, providing no information on recommended usage, and ROBINS-I
does a very poor job of accounting for the impact of confounding factorsD.
Our quality evaluation focuses on known issues and bias, and the potential
impact on outcomes, rather than just the risk of bias.
The estimated potential impact of each confounding factor, and the direction of the impact
is considered. For example, consider a study that shows significantly lower risk, the
value of the study varies significantly if confounding points to an underestimate or an
overestimate of efficacy. In one case, the real effect may be null, while the other case
provides stronger evidence of efficacy (which may be greater than the study shows).
Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical
or technical issues that have no or minimal impact on outcomes. Analysis also depends on
the outcome, for example certain issues are less relevant for objective outcomes such as
mortality.
Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major
issues affecting outcomes, increases in significance during a pandemic when immediate
recognition of new evidence is critical, and when considering all global studies, as
required during a pandemic. Investigators in other countries may have different customs
for design, analysis, and reporting, and different English language skills, however they
may not be less diligent or have greater bias.
Investigators in lower-pharmaceutical-profit countries may have lower bias towards
profitable interventions.
We have classified studies as
early treatment if most patients are not already at a severe stage at the time of
treatment (for example based on oxygen status or lung involvement), and treatment started
within 5 days of the onset of symptoms. If studies contain a mix of early treatment and
late treatment patients, we consider the treatment time of patients contributing most to
the events (for example, consider a study where most patients are treated early but late
treatment patients are included, and all mortality events were observed with late
treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only considered
effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for
oseltamivir, with longer delays not being effective56,57.
This is a living analysis and is updated regularly.
We received no funding, this research is done in our spare time.
We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/dtmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Aghajani, 7/6/2023, retrospective, Iran, peer-reviewed, 4 authors, study period April 2022 - August 2022. | risk of severe case, 88.0% lower, OR 0.12, p < 0.001, higher quality diet 96, lower quality diet 85, adjusted per study, case control OR, DAQS tertile 3 vs. tertile 1, multivariable, model 3. |
| Ahmadi, 8/31/2021, retrospective, United Kingdom, peer-reviewed, 5 authors. | risk of death, 3.0% higher, RR 1.03, p = 0.85, adjusted per study, good vs. poor, model 2, multivariable. |
| Barania Adabi, 3/31/2023, retrospective, Iran, peer-reviewed, survey, mean age 40.3, 5 authors, study period March 2021 - September 2021. | risk of ICU admission, 98.7% lower, RR 0.01, p < 0.001, higher quality diet 0 of 125 (0.0%), lower quality diet 37 of 125 (29.6%), NNT 3.4, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), DII, quartile I vs. quartile IV. |
| risk of ICU admission, 98.1% lower, RR 0.02, p < 0.001, higher quality diet 0 of 125 (0.0%), lower quality diet 26 of 125 (20.8%), NNT 4.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), E-DII, quartile I vs. quartile IV. | |
| Darand, 11/5/2024, retrospective, Iran, peer-reviewed, 8 authors. | risk of case, 36.7% lower, OR 0.63, p = 0.03, higher quality diet 113, lower quality diet 155, inverted to make OR<1 favor higher quality diet, Q1 vs. Q5, RR approximated with OR. |
| Ebrahimzadeh, 8/19/2022, retrospective, Iran, peer-reviewed, survey, 3 authors, study period June 2021 - September 2021. | risk of severe case, 69.0% lower, OR 0.31, p = 0.004, healthy diet, T3 vs. T1, model 3, RR approximated with OR. |
| risk of hospitalization, 56.0% lower, OR 0.44, p = 0.07, hospitalization time, healthy diet, T3 vs. T1, model 3, RR approximated with OR. | |
| risk of no recovery, 68.0% lower, OR 0.32, p = 0.003, recovery duration, healthy diet, T3 vs. T1, model 3, RR approximated with OR. | |
| Firoozi, 3/29/2022, retrospective, Iran, peer-reviewed, survey, 8 authors, study period March 2020 - June 2020. | risk of case, 65.0% lower, OR 0.35, p < 0.001, adjusted per study, inverted to make OR<1 favor higher quality diet, case control OR, multivariable, per unit E-DII change. |
| Hawryłkowicz, 2/26/2025, retrospective, Poland, peer-reviewed, mean age 41.2, 7 authors, study period December 2021 - June 2022. | risk of hospitalization, 77.3% lower, OR 0.23, p = 0.001, higher quality diet 183, lower quality diet 183, inverted to make OR<1 favor higher quality diet, lower vs. upper tertile processed high fat, sugar, salt, meat, dairy, and potatoes, RR approximated with OR. |
| Hou, 4/29/2022, retrospective, Taiwan, peer-reviewed, survey, 3 authors, study period May 2021 - August 2021. | risk of critical case, 71.6% lower, RR 0.28, p = 0.23, higher quality diet 1 of 22 (4.5%), lower quality diet 78 of 487 (16.0%), NNT 8.7, excluded in exclusion analyses: unadjusted results with no group details. |
| risk of moderate to critical case, 10.8% lower, RR 0.89, p = 0.66, higher quality diet 11 of 22 (50.0%), lower quality diet 273 of 487 (56.1%), NNT 17, excluded in exclusion analyses: unadjusted results with no group details. | |
| risk of critical case, 73.6% lower, RR 0.26, p = 0.005, higher quality diet 0 of 9 (0.0%), lower quality diet 47 of 127 (37.0%), NNT 2.7, adjusted per study, inverted to make RR<1 favor higher quality diet, odds ratio converted to relative risk, multivariable, age >65. | |
| risk of moderate to critical case, 34.7% lower, RR 0.65, p = 0.04, higher quality diet 5 of 9 (55.6%), lower quality diet 108 of 127 (85.0%), NNT 3.4, age >65, excluded in exclusion analyses: unadjusted results with no group details. | |
| Jagielski, 1/14/2022, retrospective, Poland, peer-reviewed, 7 authors. | risk of case, 81.5% lower, RR 0.18, p = 0.005, higher quality diet 4 of 40 (10.0%), lower quality diet 9 of 20 (45.0%), NNT 2.9, adjusted per study, inverted to make RR<1 favor higher quality diet, odds ratio converted to relative risk, model 2, FV ≥ 500g and nuts ≥ 10g vs. FV < 500g and nuts < 10g, multivariable. |
| Kim, 6/7/2021, retrospective, multiple countries, peer-reviewed, survey, 8 authors, study period 17 July, 2020 - 25 September, 2020. | risk of moderate/severe case, 72.0% lower, OR 0.28, p = 0.02, higher quality diet 41, lower quality diet 527, adjusted per study, plant-based diets, multivariable, RR approximated with OR. |
| risk of moderate/severe case, 59.0% lower, OR 0.41, p = 0.05, higher quality diet 46, lower quality diet 522, adjusted per study, plant-based or pescatarian diets, multivariable, RR approximated with OR. | |
| risk of case, 19.0% lower, OR 0.81, p = 0.24, higher quality diet 41, lower quality diet 527, adjusted per study, plant-based diets, multivariable, RR approximated with OR. | |
| risk of case, 23.0% lower, OR 0.77, p = 0.14, higher quality diet 46, lower quality diet 522, adjusted per study, plant-based or pescatarian diets, multivariable, RR approximated with OR. | |
| Magaña, 12/31/2021, retrospective, Spain, peer-reviewed, 6 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 53.0% lower, HR 0.47, p = 0.049, higher quality diet 58, lower quality diet 31. |
| Mahto, 2/15/2021, retrospective, India, peer-reviewed, 6 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of IgG positive, 20.4% lower, RR 0.80, p = 0.32, higher quality diet 23 of 206 (11.2%), lower quality diet 70 of 483 (14.5%), NNT 30, unadjusted, inverted to make RR<1 favor higher quality diet, odds ratio converted to relative risk. |
| Merino, 6/25/2021, retrospective, multiple countries, peer-reviewed, survey, 30 authors, study period 24 March, 2020 - 2 December, 2020. | risk of severe case, 41.0% lower, HR 0.59, p < 0.001, higher quality diet 148,142, lower quality diet 148,143, adjusted per study, model 3, high vs. low hPDI, multivariable, Cox proportional hazards. |
| risk of case, 18.0% lower, HR 0.82, p < 0.001, higher quality diet 148,142, lower quality diet 148,143, adjusted per study, model 3, high vs. low hPDI, PCR+, multivariable, Cox proportional hazards. | |
| risk of case, 9.0% lower, HR 0.91, p < 0.001, higher quality diet 148,142, lower quality diet 148,143, adjusted per study, model 3, high vs. low hPDI, multivariable, Cox proportional hazards. | |
| Micek, 8/3/2023, retrospective, Poland, peer-reviewed, survey, 8 authors, study period July 2020 - December 2020. | risk of case, 70.0% lower, OR 0.30, p = 0.09, higher quality diet 32, lower quality diet 21, adjusted per study, total polyphenols, T3 vs. T1, multivariable, RR approximated with OR. |
| Mohajeri, 1/26/2023, retrospective, Iran, peer-reviewed, survey, 3 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of progression, 25.4% lower, RR 0.75, p < 0.001, higher quality diet 62 of 105 (59.0%), lower quality diet 392 of 495 (79.2%), NNT 5.0, dyspnea. |
| risk of progression, 51.1% lower, RR 0.49, p < 0.001, higher quality diet 50 of 105 (47.6%), lower quality diet 482 of 495 (97.4%), NNT 2.0, fever. | |
| risk of progression, 70.3% lower, RR 0.30, p < 0.001, higher quality diet 23 of 105 (21.9%), lower quality diet 365 of 495 (73.7%), NNT 1.9, taste/smell. | |
| risk of progression, 9.7% higher, RR 1.10, p = 0.03, higher quality diet 98 of 105 (93.3%), lower quality diet 421 of 495 (85.1%), fatigue. | |
| risk of progression, 52.9% lower, RR 0.47, p < 0.001, higher quality diet 38 of 105 (36.2%), lower quality diet 380 of 495 (76.8%), NNT 2.5, cough. | |
| risk of progression, 25.9% lower, RR 0.74, p = 0.007, higher quality diet 44 of 105 (41.9%), lower quality diet 280 of 495 (56.6%), NNT 6.8, diarrhea. | |
| Moludi, 8/23/2021, retrospective, Iran, peer-reviewed, 7 authors, study period June 2020 - July 2020. | risk of case, 91.6% lower, OR 0.08, p < 0.001, inverted to make OR<1 favor higher quality diet, case control OR, model 3, E-DII tertile 1 vs. tertile 3. |
| Naushin, 4/20/2021, retrospective, India, peer-reviewed, survey, 136 authors. | risk of seropositive, 40.1% lower, OR 0.60, p < 0.001, inverted to make OR<1 favor higher quality diet, RR approximated with OR. |
| Nguyen, 9/18/2021, retrospective, Vietnam, peer-reviewed, survey, 17 authors, study period 14 February, 2020 - 2 March, 2020. | risk of symptomatic case, 15.2% lower, RR 0.85, p = 0.006, higher quality diet 345 of 1,054 (32.7%), lower quality diet 433 of 1,082 (40.0%), NNT 14, adjusted per study, odds ratio converted to relative risk, high vs. low HES, COVID-19-like symptoms, multivariable. |
| Pavlidou, 11/9/2023, retrospective, Greece, peer-reviewed, 14 authors. | risk of case, 55.0% lower, OR 0.45, p < 0.001, higher quality diet 2,609, lower quality diet 2,588, adjusted per study, inverted to make OR<1 favor higher quality diet, moderate/high vs. very low/low Mediterranean diet adherence, multivariable, RR approximated with OR. |
| Perez-Araluce, 1/24/2022, retrospective, Spain, peer-reviewed, survey, 4 authors, study period March 2020 - December 2020. | risk of severe case, 77.9% lower, RR 0.22, p = 0.15, higher quality diet 1 of 1,103 (0.1%), lower quality diet 10 of 3,300 (0.3%), NNT 471, odds ratio converted to relative risk, high vs. low adherence. |
| risk of symptomatic case, 15.1% lower, RR 0.85, p = 0.31, higher quality diet 52 of 1,103 (4.7%), lower quality diet 214 of 3,300 (6.5%), odds ratio converted to relative risk, high vs. low adherence. | |
| risk of case, 19.7% lower, RR 0.80, p = 0.14, higher quality diet 58 of 1,103 (5.3%), lower quality diet 248 of 3,300 (7.5%), odds ratio converted to relative risk, high vs. low adherence. | |
| Reis (B), 10/24/2022, retrospective, USA, peer-reviewed, survey, 6 authors, study period December 2020 - February 2021. | risk of hospitalization, 74.8% lower, RR 0.25, p < 0.001, higher quality diet 17 of 380 (4.5%), lower quality diet 21 of 166 (12.7%), adjusted per study, inverted to make RR<1 favor higher quality diet, odds ratio converted to relative risk, 3+ vegetable servings/day vs. <3, multivariable. |
| Tadbir Vajargah, 9/29/2022, prospective, Iran, peer-reviewed, survey, mean age 44.2, 11 authors, study period June 2021 - September 2021. | risk of severe case, 67.0% lower, OR 0.33, p = 0.003, higher quality diet 83, lower quality diet 83, vegetables, highest vs. lowest tertile, RR approximated with OR. |
| risk of severe case, 72.0% lower, OR 0.28, p < 0.001, higher quality diet 83, lower quality diet 83, fruit, highest vs. lowest tertile, RR approximated with OR. | |
| risk of severe case, 75.0% lower, OR 0.25, p < 0.001, higher quality diet 83, lower quality diet 83, fiber, highest vs. lowest tertile, RR approximated with OR. | |
| Wang, 7/31/2023, retrospective, China, peer-reviewed, 9 authors, study period April 2022 - June 2022. | risk of severe case, 45.0% lower, OR 0.55, p = 0.06, higher quality diet 81, lower quality diet 67, adjusted per study, MNA-SF >11 vs. ≤11, multivariable, RR approximated with OR. |
| risk of no viral clearance, 31.5% lower, HR 0.68, p = 0.03, higher quality diet 81, lower quality diet 67, inverted to make HR<1 favor higher quality diet, MNA-SF >11 vs. ≤11, Cox proportional hazards. | |
| Wang (B), 2/6/2023, prospective, USA, peer-reviewed, survey, mean age 64.7, 8 authors, study period April 2020 - November 2021. | risk of long COVID, 9.0% lower, RR 0.91, p = 0.43, higher quality diet 124 of 318 (39.0%), lower quality diet 218 of 480 (45.4%), NNT 16, adjusted per study, Q5 vs. Q1, multivariable, model 2. |
| risk of long COVID, 49.0% lower, RR 0.51, p = 0.002, higher quality diet 188, lower quality diet 66, 5 or 6 healthy lifestyle factors vs. 0. | |
| Yamamoto, 12/30/2021, retrospective, USA, peer-reviewed, survey, mean age 35.0, 3 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of case, 66.3% lower, RR 0.34, p = 0.009, higher quality diet 4 of 20 (20.0%), lower quality diet 19 of 32 (59.4%), NNT 2.5, good, very good, excellent vs. fair, poor. |
| Yue, 8/9/2022, retrospective, multiple countries, peer-reviewed, survey, 11 authors. | risk of case, 19.0% lower, OR 0.81, p = 0.008, Q4 vs. Q1, model 3 + IPW, AHEI, RR approximated with OR. |
| risk of case, 21.0% lower, OR 0.79, p = 0.006, Q4 vs. Q1, model 3 + IPW, AMED, RR approximated with OR. | |
| risk of case, 28.6% lower, OR 0.71, p < 0.001, inverted to make OR<1 favor higher quality diet, Q1 vs. Q4, model 3 + IPW, EDIH, RR approximated with OR. | |
| risk of case, 11.5% lower, OR 0.88, p = 0.10, inverted to make OR<1 favor higher quality diet, Q1 vs. Q4, model 3 + IPW, EDIP, RR approximated with OR. | |
| Zamanian, 3/3/2023, retrospective, Iran, peer-reviewed, mean age 46.2, 10 authors. | risk of hospitalization, 81.0% lower, OR 0.19, p = 0.002, higher quality diet 41, lower quality diet 53, adjusted per study, case control OR, DASH ≥27 vs. ≤22, multivariable, model 3. |
| Zargarzadeh, 7/19/2022, retrospective, Iran, peer-reviewed, mean age 44.1, 11 authors, study period June 2021 - September 2021. | risk of severe case, 77.0% lower, OR 0.23, p < 0.001, higher quality diet 89, lower quality diet 80, adjusted per study, top tertile vs. lowest tertile, MD score, model 3, multivariable, RR approximated with OR. |
| Zhao, 12/14/2022, retrospective, United Kingdom, peer-reviewed, survey, 9 authors, study period January 2020 - March 2021. | risk of death, 24.2% lower, RR 0.76, p = 0.13, higher quality diet 39,230, lower quality diet 39,231, adjusted per study, inverted to make RR<1 favor higher quality diet, E-DII, quintile 1 vs. quintile 5, multivariable, model 4. |
| risk of death, 30.1% lower, RR 0.70, p = 0.04, higher quality diet 39,230, lower quality diet 39,231, adjusted per study, inverted to make RR<1 favor higher quality diet, DII, quintile 1 vs. quintile 5, multivariable, model 4. | |
| risk of severe case, 28.1% lower, RR 0.72, p < 0.001, higher quality diet 39,230, lower quality diet 39,231, adjusted per study, inverted to make RR<1 favor higher quality diet, E-DII, quintile 1 vs. quintile 5, multivariable, model 4. | |
| risk of severe case, 28.6% lower, RR 0.71, p < 0.001, higher quality diet 39,230, lower quality diet 39,231, adjusted per study, inverted to make RR<1 favor higher quality diet, DII, quintile 1 vs. quintile 5, multivariable, model 4. | |
| risk of case, 14.5% lower, RR 0.85, p < 0.001, higher quality diet 39,230, lower quality diet 39,231, adjusted per study, inverted to make RR<1 favor higher quality diet, E-DII, quintile 1 vs. quintile 5, multivariable, model 4. | |
| risk of case, 9.1% lower, RR 0.91, p = 0.002, higher quality diet 39,230, lower quality diet 39,231, adjusted per study, inverted to make RR<1 favor higher quality diet, DII, quintile 1 vs. quintile 5, multivariable, model 4. | |
| Zhou, 8/16/2022, prospective, United Kingdom, peer-reviewed, 6 authors. | risk of case, 15.7% lower, RR 0.84, p < 0.001, higher quality diet 1,321 of 10,254 (12.9%), lower quality diet 1,935 of 10,253 (18.9%), inverted to make RR<1 favor higher quality diet, odds ratio converted to relative risk, Q4 vs. Q1, model 3 (before healthy diet score adjustment). |
Monoclonal antibodies were
previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib
were recommended for late stage hospitalized patients.
Smoking was
known to cause lung cancer since at least 1939, but this was not widely recognized in the
US until 1964, 25 years later. Surgeon general Leroy Burney tried publicizing the danger
starting in 1957, with limited success. Surgeon general Luther Terry, appointed in 1961,
prompted by President Kennedy in 1962 amid pressure from health advocates, finally got
recognition in 1964. The 1964 report reviewed 7,000+ studies, but these could (and should)
have been reviewed and acted upon in real-time as they were published.
Historians attribute the 25 year delay to an industry campaign to manufacture doubt and
controversy, through tactics like funding biased and fraudulent research from
"independent" organizations, attacking scientists, and political lobbying.
The success of the industry campaign is only possible because officials did not analyze
the research in detail. Fraudulent industry research supported prior failures, but would
have been called out as fraudulent by officials that analyzed and understood the research
in real-time.
When administered late in infection, HCQ may enhance viral egress by further increasing
lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting
lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of
SARS-CoV-2 particles into the extracellular environment44,45.
Research also suggests potential cardioprotective effects at lower doses, but
cardiotoxicity with excessive dosage46. Bobrowski et al. also
indicate negative effects if HCQ and remdesivir are combined.
Peters et al. is subject to confounding by calendar-time (SOC evolved rapidly early in
the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital
specific effects), hospital type (non-treatment hospitals were tertiary university
centers), confounding by indication (4/7 hospitals initiated treatment on deterioration),
immortal-time bias for as-treated (exposure assigned after baseline), significant
differences for other experimental treatments, potential overadjustment from collider bias
(steroid use and indication bias), limited baseline severity information, differences in
hospice referral propensity across hospitals, unadjusted difference in time from onset to
admission, difference in PCR positivity, and other factors.
Mahévas et al. is subject to confounding by hospital (treatment highly dependent on
the hospital, different SOC/ICU transfer practices, not included in PS), immortal time
(only partly addressed in sensitivity analysis), co-treatment differences, calendar-time
(SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep
age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome
dependent on hospital triage/capacity, and other factors.
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