Improved sleep reduces COVID-19 risk: real-time meta-analysis of 17 studies

@CovidAnalysis, November 2026, Version 20V20 (V1 2022)

Media coverage of sleep for COVID‑19

c19early.org
November 2026

Media covered none of the 16 studies showing significantly lower risk

No positive sleep studies were covered

0%
Abstract
Significantly lower risk is seen for mortality, hospitalization, and cases. 14 studies from 14 independent teams in 7 countries show significant benefit.
Meta-analysis using the most serious outcome reported shows 31% [23‑38%] lower risk. Results are similar for peer-reviewed studies.
Results are very robust—in worst case exclusion sensitivity analysis 15 of 17 studies must be excluded before statistical significance is lost.
Studies analyze sleep quality before infection, and use different definitions of sleep 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.
Zhou et al. present another meta-analysis for sleep quality, showing significant improvements for mortality, hospitalization, cases, and long COVID with improved sleep.
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Evolution of COVID-19 clinical evidence Meta-analysis results over time Sleep p=0.00000000015 Acetaminophen p=0.00000021 2020 2021 2022 2023 2024 2025 2026 Lowerrisk Higherrisk c19early.org November 2026 50% 0% -50%

Sleep for COVID-19

Highlights
A

Sleep COVID-19 studies

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Cloosterman32%0.68 · 0.43–1.07symp. case31/201222/2,385 Gao36%0.64 · 0.42–0.97casescase-control study Kim17%0.83 · 0.70–0.99m/s case2,884 (all patients) Holt12%0.88 · 0.61–1.27cases15,227 (all patients)COVIDENCE UK Marcus16%0.84 · 0.76–0.93symp. case14,335 (all patients) Li43%0.57 · 0.35–0.90death46,535 (all patients) Ahmadi3%0.97 · 0.59–1.61death189/252,78817/14,520 Mohsin38%0.62 · 0.49–0.77severe case327/948273/552 Huang81%0.19 · 0.05–0.66severe case12/1274/9 Paul67%0.33 · 0.19–0.55PASC1,811 (all patients)LONG COVID Jones39%0.61 · 0.45–0.82deathn/an/a Pływaczewska-J..17%0.83 · 0.68–1.01m/s case1,225 (n)622 (n) Wang36%0.64 · 0.50–0.82PASC559 (n)180 (n)LONG COVID Pavlidou40%0.60 · 0.50–0.75cases3,345 (n)1,852 (n) Wang19%0.81 · 0.72–0.92death50,777 (n)18,119 (n) Atceken67%0.33 · 0.13–0.79severe case39 (n)182 (n) Papantoniou29%0.71 · 0.53–0.95PASC783 (n)174 (n)
Prophylaxis31%0.69 · 0.62–0.77559/310K516/38,59531% lower risk
Tau² = 0.03, I² = 66.6%, p < 0.0001
All studies31%0.69 · 0.62–0.77559/310K516/38,59531% lower risk
Tau² = 0.03, I² = 66.6%, p < 0.000100.511.52+
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← Improved sleep
reduces riskImproved sleep
increases risk →
B
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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 sleep 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.
Introduction
Sleep
Improved sleep aids the absorption, metabolism, and utilization of nutrients, reduces chronic inflammation, improves cardiovascular health, improves comorbidities, and reduces stress. Sleep is crucial for the proper functioning of the immune system. During sleep, the body produces and releases cytokines that help fight infections and reduce inflammation. Sleep also enhances T cell function and their ability to form immune memory.
Analysis
We analyze all significant studies reporting COVID-19 outcomes as a function of sleep 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 peer-reviewed studies.
Evaluation Notes
Results
Table 1 summarizes the results for all studies, for peer-reviewed studies, and for specific outcomes. Fig. 2, 3, 4, 5, 6, and 7 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, cases, peer reviewed studies, and long COVID.

Sleep for COVID-19: meta-analysis results

c19early.org
November 2026
Relative Risk · CI
All studies17 studies · 430K patients
All stages0.69*** 0.62–0.7731% lower risk
Peer-reviewed16 studies · 428K patients
All stages0.71*** 0.65–0.7929% lower risk
Mortality4 studies · 383K patients
All stages0.73** 0.60–0.9027% lower risk
Hospitalization3 studies · 115K patients
All stages0.75** 0.61–0.9125% lower risk
Cases7 studies · 40K patients
All stages0.86*** 0.80–0.9314% 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 sleep. All studies are for prophylaxis. Results show the relative risk and 95% confidence interval.

Sleep COVID-19 studies

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Cloosterman32%0.68 · 0.43–1.07symp. case31/201222/2,385 Gao36%0.64 · 0.42–0.97casescase-control study Kim17%0.83 · 0.70–0.99m/s case2,884 (all patients) Holt12%0.88 · 0.61–1.27cases15,227 (all patients)COVIDENCE UK Marcus16%0.84 · 0.76–0.93symp. case14,335 (all patients) Li43%0.57 · 0.35–0.90death46,535 (all patients) Ahmadi3%0.97 · 0.59–1.61death189/252,78817/14,520 Mohsin38%0.62 · 0.49–0.77severe case327/948273/552 Huang81%0.19 · 0.05–0.66severe case12/1274/9 Paul67%0.33 · 0.19–0.55PASC1,811 (all patients)LONG COVID Jones39%0.61 · 0.45–0.82deathn/an/a Pływaczewska-J..17%0.83 · 0.68–1.01m/s case1,225 (n)622 (n) Wang36%0.64 · 0.50–0.82PASC559 (n)180 (n)LONG COVID Pavlidou40%0.60 · 0.50–0.75cases3,345 (n)1,852 (n) Wang19%0.81 · 0.72–0.92death50,777 (n)18,119 (n) Atceken67%0.33 · 0.13–0.79severe case39 (n)182 (n) Papantoniou29%0.71 · 0.53–0.95PASC783 (n)174 (n)
Prophylaxis31%0.69 · 0.62–0.77559/310K516/38,59531% lower risk
Tau² = 0.03, I² = 66.6%, p < 0.0001
All studies31%0.69 · 0.62–0.77559/310K516/38,59531% lower risk
Tau² = 0.03, I² = 66.6%, p < 0.000100.511.52+
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← Improved sleep
reduces riskImproved sleep
increases risk →
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.

Sleep COVID-19 mortality results

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November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
Li43%0.57 · 0.35–0.9046,535 (all patients) Ahmadi3%0.97 · 0.59–1.61189/252,78817/14,520 Jones39%0.61 · 0.45–0.82n/an/a Wang19%0.81 · 0.72–0.9250,777 (n)18,119 (n)
Prophylaxis27%0.73 · 0.60–0.90189/303K17/32,63927% lower risk
Tau² = 0.02, I² = 45.9%, p = 0.0029
All studies27%0.73 · 0.60–0.90189/303K17/32,63927% lower risk
Tau² = 0.02, I² = 45.9%, p = 0.002900.511.52+
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← Improved sleep
reduces riskImproved sleep
increases risk →
Fig. 3. Random-effects meta-analysis for mortality results.

Sleep COVID-19 hospitalization results

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November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Li36%0.64 · 0.46–0.89hosp.46,535 (all patients) Jones32%0.68 · 0.56–0.82hosp.n/an/a Wang15%0.85 · 0.80–0.89hosp.50,777 (n)18,119 (n)
Prophylaxis25%0.75 · 0.61–0.9150,777 (n)18,119 (n)25% lower risk
Tau² = 0.02, I² = 73.7%, p = 0.0031
All studies25%0.75 · 0.61–0.9150,777 (n)18,119 (n)25% lower risk
Tau² = 0.02, I² = 73.7%, p = 0.003100.511.52+
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← Improved sleep
reduces riskImproved sleep
increases risk →
Fig. 4. Random-effects meta-analysis for hospitalization.

Sleep COVID-19 case results

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Cloosterman32%0.68 · 0.43–1.07symp. case31/201222/2,385 Gao36%0.64 · 0.42–0.97casescase-control study Kim11%0.89 · 0.81–0.97cases2,884 (all patients) Holt12%0.88 · 0.61–1.27cases15,227 (all patients)COVIDENCE UK Marcus16%0.84 · 0.76–0.93symp. case14,335 (all patients) Jones7%0.93 · 0.86–1.00casesn/an/a Pavlidou40%0.60 · 0.50–0.75cases3,345 (n)1,852 (n)
Prophylaxis14%0.86 · 0.80–0.9331/3,546222/4,23714% lower risk
Tau² = 0.00, I² = 38.2%, p < 0.0001
All studies14%0.86 · 0.80–0.9331/3,546222/4,23714% lower risk
Tau² = 0.00, I² = 38.2%, p < 0.000100.511.52+
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reduces riskImproved sleep
increases risk →
Fig. 5. Random-effects meta-analysis for cases.

Sleep COVID-19 peer reviewed studies

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Cloosterman32%0.68 · 0.43–1.07symp. case31/201222/2,385 Gao36%0.64 · 0.42–0.97casescase-control study Kim17%0.83 · 0.70–0.99m/s case2,884 (all patients) Holt12%0.88 · 0.61–1.27cases15,227 (all patients)COVIDENCE UK Marcus16%0.84 · 0.76–0.93symp. case14,335 (all patients) Li43%0.57 · 0.35–0.90death46,535 (all patients) Ahmadi3%0.97 · 0.59–1.61death189/252,78817/14,520 Mohsin38%0.62 · 0.49–0.77severe case327/948273/552 Huang81%0.19 · 0.05–0.66severe case12/1274/9 Jones39%0.61 · 0.45–0.82deathn/an/a Pływaczewska-J..17%0.83 · 0.68–1.01m/s case1,225 (n)622 (n) Wang36%0.64 · 0.50–0.82PASC559 (n)180 (n)LONG COVID Pavlidou40%0.60 · 0.50–0.75cases3,345 (n)1,852 (n) Wang19%0.81 · 0.72–0.92death50,777 (n)18,119 (n) Atceken67%0.33 · 0.13–0.79severe case39 (n)182 (n) Papantoniou29%0.71 · 0.53–0.95PASC783 (n)174 (n)
Prophylaxis29%0.71 · 0.65–0.79559/310K516/38,59529% lower risk
Tau² = 0.02, I² = 61.0%, p < 0.0001
All studies29%0.71 · 0.65–0.79559/310K516/38,59529% lower risk
Tau² = 0.02, I² = 61.0%, p < 0.000100.511.52+
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← Improved sleep
reduces riskImproved sleep
increases risk →
Fig. 6. Random-effects meta-analysis for peer reviewed studies. Zeraatkar et al. 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. Davidson et al. also showed no important difference between meta-analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta-analyses including 114 trials. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.

Sleep COVID-19 long COVID results

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November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Paul67%0.33 · 0.19–0.55PASC1,811 (all patients) Pływaczewska-J..7%0.93 · 0.73–1.16PASC1,015 (n)502 (n) Wang36%0.64 · 0.50–0.82PASC559 (n)180 (n) Wang23%0.77 · 0.73–0.82PASC50,777 (n)18,119 (n) Papantoniou29%0.71 · 0.53–0.95PASC783 (n)174 (n)
Prophylaxis30%0.70 · 0.58–0.8553,134 (n)18,975 (n)30% lower risk
Tau² = 0.03, I² = 73.4%, p = 0.00034
All studies30%0.70 · 0.58–0.8553,134 (n)18,975 (n)30% lower risk
Tau² = 0.03, I² = 73.4%, p = 0.0003400.511.52+
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← Improved sleep
reduces riskImproved sleep
increases risk →
Fig. 7. Random-effects meta-analysis for long COVID. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.
Media Bias
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."2.
We analyze media coverage for the 227 treatments we cover using Altmetric12 and PlumX Metrics13. Altmetric covers ~14,000 news outlets14 and PlumX covers ~55,000 news sources15. 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 PlumX15,16. 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. 8 shows that the mainstream media did not cover any of the positive studies for sleep. Fig. 9 and 10 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 sleep for COVID‑19

c19early.org
November 2026

Media covered none of the 16 studies showing significantly lower risk

No positive sleep studies were covered

0%

Data from Altmetric and PlumX: studies receiving significant mainstream media coverage from 17 studies for sleep treatment, defined as ≥100 news outlets in either Altmetric or PlumX, which cover ~14,000 and ~55,000 outlets respectively.

Fig. 8. Mainstream media did not cover any of the studies for sleep.

Media bias for low-cost COVID‑19 treatments

c19early.org
November 2026

Media covered only 0.8% of studies showing significantly lower COVID-19 risk

Only 5 positive studies were covered

0.8%

one each: budesonide, cannabidiol, fluvoxamine, HCQ, vitamin D

34 negative studies were covered

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 covered

Data from Altmetric and PlumX: studies receiving significant mainstream media coverage from 6,000+ studies for 227 treatments, defined as ≥100 news outlets in either Altmetric or PlumX, which cover ~14,000 and ~55,000 outlets respectively. Media coverage was significantly higher for negative studies, p < 0.001 (Fisher's exact test). Studies during the pandemic (until May 5, 2023).

Fig. 9. 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 bias for high-profit COVID‑19 treatments

c19early.org
November 2026

Media was 3.8× more likely to cover positive studies

19 positive studies were covered

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

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 covered

Data from Altmetric and PlumX: studies receiving significant mainstream media coverage from 6,000+ studies for 227 treatments, defined as ≥100 news outlets in either Altmetric or PlumX, which cover ~14,000 and ~55,000 outlets respectively. Media coverage was significantly higher for positive studies, p = 0.002 (Fisher's exact test). Studies during the pandemic (until May 5, 2023).

Fig. 10. 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.
Authority Delay
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
Pooled Effects
Pooled effects are no longer required to show efficacy as of March 2021
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 sleep as of March 2021. Efficacy is now known based on specific outcomes.
Combining studies is required
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.
Specific outcome and pooled analyses
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.
Ethical and practical issues limit high-risk trials
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.
Validating pooled outcome analysis for COVID-19
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. 11 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 12 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. 13 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.
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Fig. 11. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 12. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 11. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)
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. 14 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Fig. 14. 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.
Limitations
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.
Summary
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.
Discussion
Notes
Zhou et al. present another meta-analysis for sleep quality, showing significant improvements for mortality, hospitalization, cases, and long COVID with improved sleep.
Reviews
Larenas-Linnemann et al. present a review covering sleep for COVID-19.
Perspective
Results compared with other treatments
SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors19-26, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk8, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Improved sleep aids the absorption, metabolism, and utilization of nutrients, reduces chronic inflammation, improves cardiovascular health, improves comorbidities, and reduces stress. Sleep is crucial for the proper functioning of the immune system. During sleep, the body produces and releases cytokines that help fight infections and reduce inflammation. Sleep also enhances T cell function and their ability to form immune memory. Fig. 15 shows an overview of the results for improved sleep in the context of multiple COVID-19 treatments, and Fig. 16 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Fig. 15. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy27.
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Fig. 16. Efficacy vs. cost for COVID-19 treatments.
Conclusion
Improved sleep aids the absorption, metabolism, and utilization of nutrients, reduces chronic inflammation, improves cardiovascular health, improves comorbidities, and reduces stress. Sleep is crucial for the proper functioning of the immune system. During sleep, the body produces and releases cytokines that help fight infections and reduce inflammation. Sleep also enhances T cell function and their ability to form immune memory.
Improved sleep reduces risk for COVID-19. Significantly lower risk is seen for mortality, hospitalization, and cases. 14 studies from 14 independent teams in 7 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 31% [23‑38%] lower risk. Results are similar for peer-reviewed studies.
Results are very robust—in worst case exclusion sensitivity analysis 15 of 17 studies must be excluded before statistical significance is lost.
Studies analyze sleep quality before infection, and use different definitions of sleep quality.
Zhou et al. present another meta-analysis for sleep quality, showing significant improvements for mortality, hospitalization, cases, and long COVID with improved sleep.
 
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/slmeta.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/.
Study Notes
Retrospective 468,569 adults in the UK, showing no significant difference in COVID-19 mortality based on sleep quality. Submit Corrections or Updates.
Retrospective 221 COVID-19 patients showing an association between high-risk obstructive sleep apnea and COVID-19 severity. Submit Corrections or Updates.
Analysis of 2,586 participants of a running injury prevention RCT in the Netherlands, showing higher risk of COVID-19 symptoms with sleep disturbance. Submit Corrections or Updates.
Case control study in China with 105 cases and 210 matched controls, showing COVID-19 cases associated with lack of sleep. Submit Corrections or Updates.
ProphylaxisProspective · 15,227 patients · UK · May 2020 – Feb 2021
Sleep for COVID-19
COVIDENCE UK Holt et al. Thorax · March 2021 · NCT04330599
Outcome
Improvement
Relative Risk · 95% CI
Case
12%0.88 · 0.61–1.27
Case (b)
12%0.88 · 0.68–1.14
Case (c)
22%0.78 · 0.60–0.99
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Fewer cases with improved sleep
Not statistically significant · p = 0.5
Prospective survey-based study with 15,227 people in the UK, showing reduced risk of COVID-19 cases with 8 hours sleep, with statistical significance when compared with ≥9 hours. Submit Corrections or Updates.
Retrospective 164 COVID-19 patients and 188 controls in China, showing the risk of severe cases associated with lack of sleep. Submit Corrections or Updates.
ProphylaxisRetrospective · multinational
Sleep for COVID-19
Jones et al. Sleep Medicine · July 2022
Outcome
Improvement
Relative Risk · 95% CI
Mortality
39%0.61 · 0.45–0.82
Hospitalization
32%0.68 · 0.56–0.82
Case
7%0.93 · 0.86–1.00
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Lower mortality and hospitalization
p = 0.001 (mortality) and p < 0.0001 (hospitalization)
FinnGen Mendelian randomization study showing higher risk of COVID-19 mortality, hospitalization, and infection with insomnia. Submit Corrections or Updates.
ProphylaxisRetrospective · 2,884 patients · multinational · Jul – Sep 2020
Sleep for COVID-19
Kim et al. BMJ Nutrition, Prevention & Health · Mar 2021
Outcome
Improvement
Relative Risk · 95% CI
Moderate/severe case
17%0.83 · 0.70–0.99
Case
11%0.89 · 0.81–0.97
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Fewer moderate/severe cases and cases
p = 0.035 (Fewer moderate/severe cases) and p = 0.003 (cases)
Retrospective 2,884 high-risk healthcare workers in France, Germany, Italy, Spain, UK, and the USA, showing shorter sleep duration associated with increased risk of COVID-19 cases and severity. Submit Corrections or Updates.
ProphylaxisRetrospective · 46,535 patients · USA · Mar – Dec 2020
Sleep for COVID-19
Li et al. Sleep · June 2021
Outcome
Improvement
Relative Risk · 95% CI
Mortality
43%0.57 · 0.35–0.90
Hospitalization
36%0.64 · 0.46–0.89
Hospitalization (b)
21%0.79 · 0.64–0.96
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Lower mortality and hospitalization
p = 0.017 (mortality) and p = 0.008 (hospitalization)
UK Biobank retrospective, 46,535 participants with sleep behavior assessed between 2006 and 2010, showing higher risk of hospitalization and mortality with poor sleep. Submit Corrections or Updates.
Prospective survey based study with 14,335 participants, showing risk of viral symptoms associated with shorter sleep duration. Submit Corrections or Updates.
Retrospective 1,500 COVID+ patients in Bangladesh, showing lower risk of severe cases with good sleep. Submit Corrections or Updates.
ProphylaxisProspective · 1,939 patients · Spain
Sleep for COVID-19
Papantoniou et al. Scandinavian J. Work, Environment &amp; Health
Lower long COVID with improved sleep
p = 0.022
Prospective cohort study of 1,899 SARS-CoV-2-infected participants in Spain showing that night shift work and poor sleep were associated with higher risk of long COVID. Submit Corrections or Updates.
Retrospective 1,811 COVID-19 patients in the UK, showing lower risk of self-reported long COVID with good sleep quality in the month before infection. Submit Corrections or Updates.
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. Submit Corrections or Updates.
ProphylaxisRetrospective · 1,847 patients · Poland · May 2020 – Jan 2022
Sleep for COVID-19
Pływaczewska-Jakubowska et al. Frontiers in Medicine · October 2022
Outcome
Improvement
Relative Risk · 95% CI
Moderate/severe case
17%0.83 · 0.68–1.01
Long COVID
7%0.93 · 0.73–1.16
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Fewer moderate/severe cases with improved sleep
Not statistically significant · p = 0.063
Retrospective 1,847 COVID+ patients in Poland, showing lower moderate/severe cases with improved sleep, without statistical significance. Hospitalized patients were excluded. Submit Corrections or Updates.
ProphylaxisProspective · 68,896 patients · UK
Sleep for COVID-19
Wang et al. Nature Communications · January 2024
Outcome
Improvement
Relative Risk · 95% CI
Mortality
19%0.81 · 0.72–0.92
Hospitalization
15%0.85 · 0.80–0.89
Long COVID
23%0.77 · 0.73–0.82
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Lower mortality and hospitalization
p = 0.00079 (mortality) and p < 0.0001 (hospitalization)
Prospective study of 68,896 UK Biobank participants with COVID-19 showing adherence to a healthy lifestyle prior to infection, characterized by 10 factors including adequate physical activity and sleep, not smoking, and a healthy BMI, was associated with a significantly lower risk of mortality, hospitalization, and post-COVID multisystem sequelae. Risk decreased monotonically for increasing numbers of healthy lifestyle factors from 5-10. Reduced risks were evident across cardiovascular, metabolic, neurologic, respiratory, and other disorders over 210 days following infection, during both acute and post-acute phases, regardless of age, sex, ethnicity, test setting, vaccination status, or SARS-CoV-2 variant. Submit Corrections or Updates.
ProphylaxisRetrospective · 1,979 patients · USA
Sleep for COVID-19
Wang et al. JAMA Network Open · May 2023
Outcome
Improvement
Relative Risk · 95% CI
Long COVID, before and during
36%0.64 · 0.50–0.82
Long COVID, during pandemic
18%0.82 · 0.69–0.99
Long COVID, before pandemic
30%0.70 · 0.52–0.94
00.511.52+
← Improved sleep
reduces riskImproved sleep
increases risk →
Lower long COVID with improved sleep
p = 0.00044
Retrospective 1,979 nurses in the USA, showing lower risk of long COVID with better sleep quality. Submit Corrections or Updates.
Appendix 1. Methods and Data
Search methods
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 sleep AND COVID-19. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of sleep for COVID-19 that report a comparison with a control group are included in the main analysis. 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. 17. 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.
Effect extraction
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 reduction28. 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.
Statistical methods
Forest plots are computed using PythonMeta29 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 133. 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.
Quality evaluation
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 reliability41. 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 issues43. 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 bias39,40 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.
Treatment time
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 effective47,48.
Living analysis
This is a living analysis and is updated regularly. Submit updates or corrections with the form below. 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 the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/slmeta.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.
Ahmadi, 8/31/2021, retrospective, United Kingdom, peer-reviewed, 5 authors.
Submit Corrections or Updates.
 risk of death, 3.0% lower, RR 0.97, p = 0.91, adjusted per study, good vs. poor, model 2, multivariable.
Atceken, 10/26/2024, retrospective, Germany, peer-reviewed, 4 authors.
Submit Corrections or Updates.
 risk of severe case, 67.4% lower, OR 0.33, p = 0.01, improved sleep 39, worse sleep 182, inverted to make OR<1 favor improved sleep, RR approximated with OR.
Cloosterman, 10/21/2020, retrospective, Netherlands, peer-reviewed, 4 authors.
Submit Corrections or Updates.
 risk of symptomatic case, 31.6% lower, RR 0.68, p = 0.09, improved sleep 31 of 201 (15.4%), worse sleep 222 of 2,385 (9.3%), inverted to make RR<1 favor improved sleep, odds ratio converted to relative risk.
Gao, 11/5/2020, retrospective, China, peer-reviewed, survey, median age 55.0, 11 authors, study period 10 February, 2020 - 1 March, 2020.
Submit Corrections or Updates.
 risk of case, 35.9% lower, HR 0.64, p = 0.04, improved sleep 73 of 105 (69.5%) cases, 179 of 210 (85.2%) controls, NNT 4.6, inverted to make HR<1 favor improved sleep, case control OR, Cox proportional hazards.
Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK).
Submit Corrections or Updates.
 risk of case, 12.3% lower, OR 0.88, p = 0.50, adjusted per study, inverted to make OR<1 favor improved sleep, fully adjusted, 8 hours vs. ≤6 hours, RR approximated with OR.
risk of case, 12.3% lower, OR 0.88, p = 0.33, adjusted per study, inverted to make OR<1 favor improved sleep, fully adjusted, 8 hours vs. 7 hours, RR approximated with OR.
risk of case, 22.5% lower, OR 0.78, p = 0.04, adjusted per study, inverted to make OR<1 favor improved sleep, fully adjusted, 8 hours vs. ≥9 hours, RR approximated with OR.
Huang, 11/30/2021, retrospective, China, peer-reviewed, survey, 5 authors, study period 10 February, 2020 - 28 March, 2020.
Submit Corrections or Updates.
 risk of severe case, 80.9% lower, RR 0.19, p = 0.02, improved sleep 12 of 127 (9.4%), worse sleep 4 of 9 (44.4%), NNT 2.9, adjusted per study, inverted to make RR<1 favor improved sleep, odds ratio converted to relative risk, recommended vs. lack of sleep, multivariable.
Jones, 7/21/2022, retrospective, multiple countries, peer-reviewed, 12 authors.
Submit Corrections or Updates.
 risk of death, 39.0% lower, OR 0.61, p = 0.001, inverted to make OR<1 favor improved sleep, RR approximated with OR.
risk of hospitalization, 32.0% lower, OR 0.68, p < 0.001, inverted to make OR<1 favor improved sleep, RR approximated with OR.
risk of case, 7.4% lower, OR 0.93, p = 0.04, inverted to make OR<1 favor improved sleep, RR approximated with OR.
Kim, 3/22/2021, retrospective, multiple countries, peer-reviewed, survey, mean age 48.0, 8 authors, study period 17 July, 2020 - 25 September, 2020.
Submit Corrections or Updates.
 risk of moderate/severe case, 17.0% lower, OR 0.83, p = 0.03, per extra hour of sleep, RR approximated with OR.
risk of case, 11.0% lower, OR 0.89, p = 0.003, per extra hour of sleep, model 3, RR approximated with OR.
Li, 6/18/2021, retrospective, USA, peer-reviewed, mean age 69.4, 8 authors, study period March 2020 - December 2020.
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 risk of death, 43.2% lower, OR 0.57, p = 0.02, inverted to make OR<1 favor improved sleep, fully adjusted model C, significant poor sleep burden, RR approximated with OR.
risk of hospitalization, 35.9% lower, OR 0.64, p = 0.008, inverted to make OR<1 favor improved sleep, fully adjusted model C, significant poor sleep burden, RR approximated with OR.
risk of hospitalization, 21.3% lower, OR 0.79, p = 0.02, inverted to make OR<1 favor improved sleep, fully adjusted model C, moderate poor sleep burden, RR approximated with OR.
Marcus, 6/17/2021, prospective, multiple countries, peer-reviewed, survey, 12 authors, study period 26 March, 2020 - 3 May, 2020.
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 risk of symptomatic case, 16.0% lower, OR 0.84, p < 0.001, adjusted per study, per extra hour sleep, multivariable, RR approximated with OR.
Mohsin, 9/30/2021, retrospective, Bangladesh, peer-reviewed, survey, 10 authors, study period November 2020 - April 2021.
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 risk of severe case, 37.9% lower, RR 0.62, p < 0.001, improved sleep 327 of 948 (34.5%), worse sleep 273 of 552 (49.5%), NNT 6.7, adjusted per study, inverted to make RR<1 favor improved sleep, odds ratio converted to relative risk, sleep disturbance, multivariable.
Papantoniou, 8/4/2026, prospective, Spain, peer-reviewed, mean age 65.0, 16 authors.
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 risk of long COVID, 28.9% lower, RR 0.71, p = 0.02, improved sleep 783, worse sleep 174, adjusted per study, <6 hours or ≥9 hours.
risk of long COVID, 25.9% lower, RR 0.74, p = 0.08, improved sleep 107 of 783 (13.7%), worse sleep 36 of 174 (20.7%), NNT 14, adjusted per study, inverted to make RR<1 favor improved sleep, <6 hours, multivariable.
risk of long COVID, 37.9% lower, RR 0.62, p = 0.12, improved sleep 107 of 783 (13.7%), worse sleep 10 of 41 (24.4%), NNT 9.3, adjusted per study, inverted to make RR<1 favor improved sleep, ≥9 hours, multivariable.
risk of long COVID, 29.6% lower, RR 0.70, p = 0.003, improved sleep 212 of 1,561 (13.6%), worse sleep 81 of 378 (21.4%), NNT 13, adjusted per study, inverted to make RR<1 favor improved sleep, chronic insomia, multivariable.
risk of long COVID, 37.1% lower, RR 0.63, p < 0.001, improved sleep 139 of 1,164 (11.9%), worse sleep 81 of 378 (21.4%), NNT 11, adjusted per study, inverted to make RR<1 favor improved sleep, sleep problems ≥3 nights/week, multivariable.
risk of long COVID, 30.6% lower, RR 0.69, p = 0.006, improved sleep 139 of 1,164 (11.9%), worse sleep 73 of 397 (18.4%), NNT 16, adjusted per study, inverted to make RR<1 favor improved sleep, sleep problems <3 nights/week, multivariable.
Paul, 4/13/2022, retrospective, United Kingdom, preprint, survey, 2 authors.
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 risk of long COVID, 67.3% lower, RR 0.33, p < 0.001, adjusted per study, inverted to make RR<1 favor improved sleep, odds ratio converted to relative risk, very good/good vs. not good/very poor, multivariable, model 4, control prevalance approximated with overall prevalence.
risk of long COVID, 54.0% lower, RR 0.46, p = 0.002, adjusted per study, inverted to make RR<1 favor improved sleep, odds ratio converted to relative risk, very good/good vs. average, multivariable, model 4, control prevalance approximated with overall prevalence.
Pavlidou, 11/9/2023, retrospective, Greece, peer-reviewed, 14 authors.
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 risk of case, 40.5% lower, OR 0.60, p = 0.01, improved sleep 3,345, worse sleep 1,852, adjusted per study, inverted to make OR<1 favor improved sleep, adequate vs. inadequate sleep, multivariable, RR approximated with OR.
Pływaczewska-Jakubowska, 10/24/2022, retrospective, Poland, peer-reviewed, median age 51.0, 5 authors, study period May 2020 - January 2022.
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 risk of moderate/severe case, 17.4% lower, OR 0.83, p = 0.06, improved sleep 1,225, worse sleep 622, adjusted per study, inverted to make OR<1 favor improved sleep, higher quality sleep vs. insomnia or falling asleep after midnight or nightshifts, multivariable, model 3, RR approximated with OR.
risk of long COVID, 7.4% lower, OR 0.93, p = 0.51, improved sleep 1,015, worse sleep 502, adjusted per study, inverted to make OR<1 favor improved sleep, higher quality sleep vs. insomnia or falling asleep after midnight or nightshifts, multivariable, model 3, RR approximated with OR.
Wang, 1/31/2024, prospective, United Kingdom, peer-reviewed, 10 authors.
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 risk of death, 19.0% lower, HR 0.81, p < 0.001, improved sleep 50,777, worse sleep 18,119, adjusted per study, 7-9 hrs vs. <7 or >9, multivariable.
risk of hospitalization, 15.0% lower, HR 0.85, p < 0.001, improved sleep 50,777, worse sleep 18,119, adjusted per study, 7-9 hrs vs. <7 or >9, multivariable.
risk of long COVID, 23.0% lower, HR 0.77, p < 0.001, improved sleep 50,777, worse sleep 18,119, adjusted per study, 7-9 hrs vs. <7 or >9, multivariable.
Wang (B), 5/30/2023, retrospective, USA, peer-reviewed, 6 authors.
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 risk of long COVID, 36.0% lower, RR 0.64, p < 0.001, improved sleep 559, worse sleep 180, adjusted per study, healthy sleep before and during the pandemic, multivariable.
risk of long COVID, 18.0% lower, RR 0.82, p = 0.03, adjusted per study, healthy sleep during the pandemic, multivariable.
risk of long COVID, 30.0% lower, RR 0.70, p = 0.02, improved sleep 238, worse sleep 166, adjusted per study, healthy sleep before the pandemic, sleep score 5 vs. score 0 or 1, multivariable.
Footnotes
a.
Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.
b.
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.
c.
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 environment35,36. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage37. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.
d.
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.
References