Covid Analysis, May 2023
•Statistically significant improvements are seen for mortality and cases. 4 studies from 4 independent teams in 3 different countries show statistically significant improvements in isolation.
•Meta analysis using the most serious outcome reported shows 45% [19‑62%] improvement.
•No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. None of the sunlight studies show zero events with treatment. There has been no early treatment studies to date.
•All data to reproduce this paper and sources are in the appendix.
|All studies||45% [19‑62%]
Sunlight reduces risk for COVID-19 with very high confidence for pooled analysis, high confidence for cases, and low confidence for mortality.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 51 treatments.
We analyze all significant studies reporting COVID-19 outcomes as a function of sunlight exposure. 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 and individual outcomes.
An In Vitro study supports the efficacy of sunlight [Ratnesar-Shumate].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all studies and for specific outcomes. Figure 2, 3, and 4 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, and cases.
|All studies||45% [19‑62%]|
Heterogeneity in COVID-19 studies arises from many factors including:
[Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Figure 5. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Increased sun exposure reduces risk for COVID-19. Statistically significant improvements are seen for mortality and cases. 4 studies from 4 independent teams in 3 different countries show statistically significant improvements in isolation. Meta analysis using the most serious outcome reported shows 45% [19‑62%] improvement.
[Cherrie] Analysis of UVA exposure and COVID-19 mortality in the USA, England, and Italy, showing increase UVA exposure associated with lower mortality.
[Jabbar] Analysis of 120 COVID-19 and 120 control patients in Iraq, showing lower risk of cases with regular sunlight exposure (3 times/week).
[Kalichuran] Prospective study of 100 COVID-19 patients in South Africa, 50 with COVID-19 pneumonia and 50 asymptomatic, showing higher risk of symptomatic COVID-19 with lower exposure to sunlight, and with vitamin D deficiency. Sunlight exposure may be correlated with physical activity and may have additional benefits independent of vitamin D [sciencedirect.com].
[Ma] Analysis of 39,915 patients with 1,768 COVID+ cases based on surveys in the Nurses' Health Study II, showing higher UVA/UVB exposure associated with lower risk of COVID-19 cases.
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.org. Search terms were sunlight, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of sunlight 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 (3.11.3) with scipy (1.10.1), pythonmeta (1.26), numpy (1.24.3), statsmodels (0.14.0), and plotly (5.14.1).
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.org/sunmeta.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.
|[Cherrie], 4/8/2021, retrospective, multiple countries, peer-reviewed, 7 authors, study period 22 January, 2020 - 30 April, 2020, per 100kJ m–2 increase.||risk of death, 32.0% lower, RR 0.68, p = 0.004, USA, England, Italy combined.|
|risk of death, 29.0% lower, RR 0.71, p < 0.001, USA.|
|risk of death, 19.0% lower, RR 0.81, p = 0.002, Italy.|
|risk of death, 49.0% lower, RR 0.51, p < 0.001, England.|
|[Jabbar], 12/31/2021, retrospective, Iraq, peer-reviewed, 4 authors.||risk of case, 62.8% lower, OR 0.37, p < 0.001, higher sunlight exposure 43 of 120 (35.8%) cases, 72 of 120 (60.0%) controls, NNT 4.1, case control OR.|
|[Kalichuran], 4/26/2022, prospective, South Africa, peer-reviewed, survey, 4 authors, study period September 2020 - February 2021.||risk of symptomatic case, 58.2% lower, RR 0.42, p = 0.004, higher sunlight exposure 21, lower sunlight exposure 79, inverted to make RR<1 favor higher sunlight exposure, higher sunlight exposure vs. lower sunlight exposure.|
|[Ma], 12/3/2021, retrospective, USA, peer-reviewed, 16 authors, study period May 2020 - March 2021.||risk of case, 23.0% lower, RR 0.77, p < 0.001, higher sunlight exposure 411 of 10,393 (4.0%), lower sunlight exposure 495 of 9,142 (5.4%), NNT 68, adjusted per study, odds ratio converted to relative risk, UVB, highest quartile vs. lowest quartile, model 3, table 3, multivariable.|
|risk of case, 23.1% lower, RR 0.77, p < 0.001, higher sunlight exposure 325 of 9,325 (3.5%), lower sunlight exposure 436 of 9,079 (4.8%), NNT 76, adjusted per study, odds ratio converted to relative risk, UVA, highest quartile vs. lowest quartile, model 3, table 3, multivariable.|
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.
Cherrie et al., British Journal of Dermatology, doi:10.1111/bjd.20093, Ultraviolet A radiation and COVID-19 deaths in the USA with replication studies in England and Italy*, https://onlinelibrary.wiley.com/doi/10.1111/bjd.20093.
Faria et al., Science, doi:10.1126/science.abh2644, Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, https://www.science.org/lookup/doi/10.1126/science.abh2644.
Jabbar et al., Nat. Volatiles & Essent. Oils, 8:4, Vitamin D Serum Levels and Its Association With COVID 19 Infection In Babylon Governorate, Iraq, https://www.nveo.org/index.php/journal/article/view/1046.
Kalichuran et al., Southern African Journal of Infectious Diseases, doi:10.4102/sajid.v37i1.359, Vitamin D status and COVID-19 severity, https://sajid.co.za/index.php/sajid/article/view/359.
Karita et al., medRxiv, doi:10.1101/2021.08.27.21262754, Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, https://www.medrxiv.org/content/10.1101/2021.08.27.21262754v1.
López-Medina et al., JAMA, doi:10.1001/jama.2021.3071, Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, https://jamanetwork.com/journals/jama/fullarticle/2777389.
Ma et al., The American Journal of Clinical Nutrition, doi:10.1093/ajcn/nqab389, Associations between predicted vitamin D status, vitamin D intake, and risk of SARS-CoV-2 infection and Coronavirus Disease 2019 severity, https://academic.oup.com/ajcn/adva..e/doi/10.1093/ajcn/nqab389/6448988.
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/.
Nonaka et al., International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003, SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, https://www.sciencedirect.com/science/article/pii/S1201971221006354.
Peacock et al., bioRxiv, doi:10.1101/2021.12.31.474653, The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, https://www.biorxiv.org/content/10.1101/2021.12.31.474653.
Ratnesar-Shumate et al., The Journal of Infectious Diseases, doi:10.1093/infdis/jiaa274, Simulated Sunlight Rapidly Inactivates SARS-CoV-2 on Surfaces, https://academic.oup.com/jid/article/222/2/214/5841129.
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.
Willett et al., medRxiv, doi:10.1101/2022.01.03.21268111, The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, https://www.medrxiv.org/content/10.1101/2022.01.03.21268111.
Zavascki et al., Research Square, doi:10.21203/rs.3.rs-910467/v1, Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, https://www.researchsquare.com/article/rs-910467/v1.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. 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.