Covid Analysis, August 14, 2022, DRAFT
https://c19early.com/slmeta.html
•Statistically significant improvements are seen for mortality, hospitalization, recovery, and cases. 7 studies from 7 independent teams in 4 different countries show statistically significant
improvements in isolation (6 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
35% [20‑48%] improvement. Results are similar for peer-reviewed studies.
•Studies analyze sleep quality before infection.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•All data to reproduce this paper and
sources are in the appendix.
Highlights
Good quality sleep reduces
risk for COVID-19 with very high confidence for hospitalization, recovery, cases, and in pooled analysis, and high confidence for mortality.
We show traditional outcome specific analyses and combined
evidence from all studies.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 43
treatments.
Figure 1. A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
Introduction
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, for studies within each treatment stage, for individual outcomes, for
peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after
exclusions.
Results
Figure 2 shows a visual overview of the results, with details in
Table 1 and Table 2.
Figure 3, 4, 5, 6, 7, and 8
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, hospitalization, recovery, cases, and peer reviewed studies.
Figure 2. Overview of results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Prophylaxis | 7 | 7 | 100% |
35% improvement RR 0.65 [0.52‑0.80] p < 0.0001 |
| All studies | 7 | 7 | 100% |
35% improvement RR 0.65 [0.52‑0.80] p < 0.0001 |
Table 1. Results by treatment stage.
| Studies | Prophylaxis | Patients | Authors | |
| All studies | 7 | 35% [20‑48%] | 1,636 | 79 |
| Peer-reviewed | 6 | 29% [14‑42%] | 1,636 | 77 |
Table 2. Results by treatment stage for all studies and with different exclusions.
Figure 3. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 4. Random effects meta-analysis for mortality results.
Figure 5. Random effects meta-analysis for hospitalization.
Figure 6. Random effects meta-analysis for recovery.
Figure 7. Random effects meta-analysis for cases.
Figure 8. Random effects meta-analysis for peer reviewed studies.
[Zeraatkar] analyze 356 COVID-19 trials, finding no
significant evidence that peer-reviewed studies are more trustworthy.
They also show extremely slow review times during a pandemic. Authors
recommend using preprint evidence, with appropriate checks for potential
falsified data, which provides higher certainty much earlier.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Conclusion
Better sleep reduces risk for COVID-19.
Statistically significant improvements are seen for mortality, hospitalization, recovery, and cases. 7 studies from 7 independent teams in 4 different countries show statistically significant
improvements in isolation (6 for the most serious outcome).
Meta analysis using the most serious outcome reported shows
35% [20‑48%] improvement. Results are similar for peer-reviewed studies.
Studies analyze sleep quality before infection.
Study Notes
[Holt]
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. NCT04330599. COVIDENCE UK.
[Huang]
Retrospective 164 COVID-19 patients and 188 controls in China, showing the risk of severe cases associated with lack of sleep.
[Kim]
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.
[Li]
UK Biobank retrospective, 46,535 participants with sleep behavior assessed between 2006 and 2010, showing higher risk of hospitalization and mortality with poor sleep.
[Marcus]
Prospective survey based study with 14,335 participants, showing risk of viral symptoms associated with shorter sleep duration.
[Mohsin]
Retrospective 1,500 COVID+ patients in Bangladesh, showing lower risk of severe cases with good sleep.
[Paul]
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.
We performed ongoing searches of PubMed, medRxiv,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.com. Search terms were sleep AND COVID-19. Automated searches are performed
every few hours with notification of new matches.
All studies regarding the use of sleep for COVID-19 that report
a comparison with a control group are included in the main analysis.
This is a living analysis and is updated regularly.
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
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days are used. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms were not used (the next most serious
outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing status. 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 no room
for an effective treatment to do better).
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.
When results provide an odds ratio, we computed the relative risk when
possible, or converted to a relative risk according to [Zhang].
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported including propensity score matching (PSM), the PSM results are used.
Adjusted primary outcome 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 1 [Sweeting].
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
(3.9.13) with
scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
Forest plots are computed using PythonMeta [Deng]
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor
(3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
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 effective
[McLean, Treanor].
A summary of study results is below. Please submit
updates and corrections at https://c19early.com/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.
| [Holt], 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK). | risk of case, 12.3% lower, OR 0.88, p = 0.50, adjusted per study, 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, 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, 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. | risk of severe case, 80.9% lower, RR 0.19, p = 0.02, higher quality sleep 12 of 127 (9.4%), lower quality sleep 4 of 9 (44.4%), NNT 2.9, adjusted per study, odds ratio converted to relative risk, recommended vs. lack of sleep, multivariable. |
| [Kim], 3/22/2022, retrospective, multiple countries, peer-reviewed, survey, mean age 48.0, 8 authors, study period 17 July, 2020 - 25 September, 2020. | 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. | risk of death, 43.2% lower, OR 0.57, p = 0.02, fully adjusted model C, significant poor sleep burden, RR approximated with OR. |
| risk of hospitalization, 35.9% lower, OR 0.64, p = 0.008, fully adjusted model C, significant poor sleep burden, RR approximated with OR. | |
| risk of hospitalization, 21.3% lower, OR 0.79, p = 0.02, 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. | 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. | risk of severe case, 37.9% lower, RR 0.62, p < 0.001, higher quality sleep 327 of 948 (34.5%), lower quality sleep 273 of 552 (49.5%), NNT 6.7, adjusted per study, odds ratio converted to relative risk, sleep disturbance, multivariable. |
| [Paul], 4/13/2022, retrospective, United Kingdom, preprint, survey, 2 authors. | risk of long COVID, 67.3% lower, RR 0.33, p < 0.001, adjusted per study, 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, odds ratio converted to relative risk, very good/good vs. average, multivariable, model 4, control prevalance approximated with overall prevalence. |
Supplementary Data
References
Altman, D., BMJ, doi:10.1136/bmj.d2304,
How to obtain the P value from a confidence interval,
https://www.bmj.com/content/343/bmj.d2304.
Altman (B) et al., BMJ, doi:10.1136/bmj.d2090,
How to obtain the confidence interval from a P value,
https://www.bmj.com/content/343/bmj.d2090.
Holt et al., Thorax, doi:10.1136/thoraxjnl-2021-217487,
Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK),
https://thorax.bmj.com/content/early/2021/11/02/thoraxjnl-2021-217487.
Huang et al., Nature and Science of Sleep, doi:10.2147/NSS.S263488,
Reduced Sleep in the Week Prior to Diagnosis of COVID-19 is Associated with the Severity of COVID-19,
https://www.dovepress.com/reduced-..sociated-peer-reviewed-article-NSS.
Kim et al., BMJ Nutrition, Prevention & Health, doi:10.1136/bmjnph-2021-000228,
COVID-19 illness in relation to sleep and burnout,
https://nutrition.bmj.com/content/4/1/132.info.
Li et al., Sleep, doi:10.1093/sleep/zsab138,
Poor sleep behavior burden and risk of COVID-19 mortality and hospitalization,
https://academic.oup.com/sleep/art../doi/10.1093/sleep/zsab138/6304657.
Marcus et al., PLOS ONE, doi:10.1371/journal.pone.0253120,
Predictors of incident viral symptoms ascertained in the era of COVID-19,
https://journals.plos.org/plosone/..le?id=10.1371/journal.pone.0253120.
McLean et al., Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100,
Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525010/.
Mohsin et al., Infection and Drug Resistance, doi:10.2147/IDR.S331470,
Lifestyle and Comorbidity-Related Risk Factors of Severe and Critical COVID-19 Infection: A Comparative Study Among Survived COVID-19 Patients in Bangladesh,
https://www.dovepress.com/lifestyl..peer-reviewed-fulltext-article-IDR.
Paul et al., medRxiv, doi:10.1101/2022.04.12.22273792,
Health behaviours the month prior to COVID-19 infection and the development of self-reported long COVID and specific long COVID symptoms: A longitudinal analysis of 1,811 UK adults,
https://www.medrxiv.org/content/10.1101/2022.04.12.22273792.
Sweeting et al., Statistics in Medicine, doi:10.1002/sim.1761,
What to add to nothing? Use and avoidance of continuity corrections in meta‐analysis of sparse data,
https://onlinelibrary.wiley.com/doi/10.1002/sim.1761.
Treanor et al., JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016,
Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial,
https://jamanetwork.com/journals/jama/fullarticle/192425.
Please send us corrections, updates, or comments. Vaccines and
treatments are both valuable and complementary. All practical, effective, and
safe means should be used. No treatment, vaccine, or intervention is 100%
available and effective for all current and future variants. Denying the
efficacy of any method increases mortality, morbidity, collateral damage, and
the risk of endemic status. We do not provide medical advice. Before taking
any medication, consult a qualified physician who can provide personalized
advice and details of risks and benefits based on your medical history and
situation. FLCCC and WCH
provide treatment protocols.