Antihistamine H1RAs reduce COVID-19 risk: real-time meta-analysis of 17 studies

@CovidAnalysis, October 2026, Version 16V16 (V1 2024)

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (17)37%

Mortality (7)38%

Hospitalization (1)87%

Recovery (4)46%

Cases (3)35%

Viral clearance (2)7%

RCTs (4)46%

Peer-reviewed (14)30%

Prophylaxis (10)35%

Early (4)56%

Late (3)21%

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Abstract

Significantly lower risk is seen for mortality, recovery, cases, and viral clearance. 10 studies from 8 independent teams in 6 countries show significant benefit.

Meta-analysis using the most serious outcome reported shows 37% [22‑50%] lower risk. Results are similar for Randomized Controlled Trials and peer-reviewed studies. Early treatment is more effective than late treatment.

Results are very robust—in worst case exclusion sensitivity analysis 9 of 17 studies must be excluded before statistical significance is lost. Emergent results for meta-regression of efficacy vs. treatment delay (p = 0.00074) that match biological mechanisms confirm efficacy.

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (17)37%

Mortality (7)38%

Hospitalization (1)87%

Recovery (4)46%

Cases (3)35%

Viral clearance (2)7%

RCTs (4)46%

Peer-reviewed (14)30%

Prophylaxis (10)35%

Early (4)56%

Late (3)21%

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No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Other treatments are more effective. All data and sources to reproduce this analysis are in the appendix.

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Antihistamine H1RAs for COVID-19 — Highlights

Antihistamine H1RAs reduce risk with very high confidence for mortality, recovery, cases, and in pooled analysis, low confidence for viral clearance, and very low confidence for hospitalization.

Emergent results for meta-regression of efficacy vs. treatment delay (p = 0.00074) that match biological mechanisms confirm efficacy.

11th treatment shown effective in December 2020, now with p = 0.000052 from 17 studies.

Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.

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October 2026

Antihistamine H1RA COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Valerio-.. (DB RCT)61%0.39 · 0.24–0.63no recov.61 (n)40 (n)ACCROS-Ichlorpheniramine Valerio-Pascua54%0.46 · 0.36–0.58recov. time330 (n)330 (n)ACCROS-IIchlorpheniramine Sanchez-.. (DB RCT)87%0.13 · 0.01–2.46hosp.0/322/13chlorpheniramine Klussmann (DB RCT)66%0.34 · 0.01–7.95no recov.0/291/30CARVINazelastine

Early treatment56%0.44 · 0.36–0.540/4523/41356% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Mura (PSM)25%0.75 · 0.39–1.46death88 (n)88 (n) Salvucci29%0.71 · 0.51–1.01PASC10/1413/13LONG COVID Meiser (DB RCT)12%0.88 · 0.64–1.20recovery122 (n)129 (n)CARVIN-IIazelastine

Late treatment21%0.79 · 0.64–0.9910/22413/23021% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.038

Vila-Corcoles61%0.39 · 0.12–1.21cases Hoertel58%0.42 · 0.25–0.71death138 (n)7,207 (n)hydroxyzine Vila-Córcoles53%0.47 · 0.22–1.01cases Reznikov34%0.66 · 0.58–0.75casesn/an/a McKeigue−30%1.30 · 1.13–1.51severe casecase-control study Sánchez-Rico46%0.54 · 0.31–0.89death18/1641,571/14,939hydroxyzine Monserrat .. (PSM)80%0.20 · 0.02–0.93deathn/an/aloratadine Hunt43%0.57 · 0.49–0.66death260/7,6001,352/18,908 Loucera40%0.60 · 0.43–0.84death251 (n)15,717 (n) Hoertel−7%1.07 · 0.88–1.31death962 (n)4,810 (n)desloratadine/hydroxyzine

Prophylaxis35%0.65 · 0.49–0.86278/9,1152,923/61,58135% lower risk

Tau² = 0.15, I² = 91.9%, p = 0.0032

All studies37%0.63 · 0.50–0.78288/9,7912,939/62,22437% lower risk

Tau² = 0.14, I² = 88.5%, p < 0.000100.511.52+

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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 antihistamine H1RA studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome. Efficacy based on specific outcomes was delayed by 1.7 months, compared to using pooled outcomes.

Introduction

Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from1.

Immediate treatment recommended

SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury2-18 and cognitive deficits5,10, cardiovascular complications19-25, DNA damage26-29, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits30—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.

Many treatments are expected to modulate infection

SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,31-38, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk39, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.

Analysis

We analyze all significant controlled studies of Antihistamine H1RAs for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random-effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, and Randomized Controlled Trials (RCTs).

Treatment timing

Fig. 3 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early treatment refers to treatment immediately or soon after symptoms appear, while late treatment refers to more delayed treatment.

Fig. 3. Treatment stages.

Evaluation Notes

Analyzing the data from first principles is always important, and may be especially important for COVID-19. Bias in the design, analysis, and reporting of clinical studies has long been reported, and may be increased for COVID-19 due to politicization. For example, 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."40 Social pressure to report politically acceptable results was very strong. There may therefore be unusually strong bias in design, analysis, and reporting. For example, Prof. Greenland reports that Hayward et al. applied an unjustified and undisclosed extremely tight, null-centered Bayesian prior that biases towards more politically acceptable results42. While the politicization focused on certain treatments, it was not limited to them. For example, media coverage was highly biased against positive results for low-cost treatments43 across the 227 treatments we cover. Studies should be evaluated from first principles, incorporating the treatment delay, treatment regimen, patient population, and other confounding factors. For COVID-19, there is no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]44—in both cases bias varies from minimal to extreme, and all studies must be evaluated individually.

US authorities claim only three high-profit drugs from companies with strong US lobbying are beneficial for early treatment (2 repurposed drugs - remdesivir and molnupiravir, and one novel drug - nirmatrelvir)B. COVID-19 involves the interplay of many viral and host proteins and factors, providing over 500 therapeutic targets45. Given the 12,000+ proposed treatments39, existing results for many treatments with overlapping secondary complications and therapeutic targets, and the combination of known safety and pharmocokinetics for certain treatments along with strong mechanistic evidence, the probability of only 3 high-profit drugs from top lobbying companies being beneficial is extremely low. This likely reflects a bias toward evaluation of drugs with strong financial resources and efforts devoted to passing the US approval process, and very limited or non-existing evaluation of low-cost treatments. Indeed, official recommendations varied widely between countries46. Some countries evaluated limited low-cost treatments early and 25 low-cost treatments were approved in one or more countries46.

Contrary to claims found online, we analyze both all studies and higher-quality studies (with evaluation focusing on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias), we analyze specific outcomes and pooled outcomes (with extensive analysis and validation of pooled outcomes), and we do not include preclinical studies or retracted studies in meta-analysis. Pooled outcomes can detect efficacy earlier but do not bias towards efficacy—the inclusion of less appropriate designs such as very late treatment may hide efficacy for appopriate use.

Preclinical Research

4 in silico studies support the efficacy of antihistamine H1RAs47-50.

7 in vitro studies support the efficacy of antihistamine H1RAs48,50-55.

2 in vivo animal studies support the efficacy of antihistamine H1RAs51,56.

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.

Results

Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes. Table 2 shows results by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, recovery, cases, viral clearance, peer reviewed studies, and long COVID.

Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. * p<0.05 ** p<0.01 **** p<0.0001.
	Relative Risk	Studies	Patients

All studies	0.63 [0.50‑0.78]****	17	70K
Peer-reviewedPeer-reviewed	0.70 [0.55‑0.89]**	14	60K
RCTsRCTs	0.54 [0.26‑1.09]	4	456

Mortality	0.62 [0.46‑0.84]**	7	70K
Recovery	0.54 [0.34‑0.86]**	4	1,071
Cases	0.65 [0.57‑0.74]****	3	0
Viral	0.93 [0.91‑0.96]****	2	310

Table 2. Random-effects meta-analysis results by treatment stage. Results show the relative risk with treatment and the 95% confidence interval.treatment and the 95% confidence interval. * p<0.05 ** p<0.01 **** p<0.0001.
	Early treatment	Late treatment	Prophylaxis

All studies	0.44 [0.36‑0.54]**0.44** [0.36‑0.54]	0.79 [0.64‑0.99]0.79 [0.64‑0.99]	0.65 [0.49‑0.86]0.65 [0.49‑0.86]
Peer-reviewedPeer-reviewed	0.20 [0.02‑1.74]0.20 [0.02‑1.74]	0.79 [0.64‑0.99]0.79 [0.64‑0.99]	0.68 [0.50‑0.92]0.68 [0.50‑0.92]
RCTsRCTs	0.37 [0.23‑0.60]**0.37** [0.23‑0.60]	0.88 [0.64‑1.20]0.88 [0.64‑1.20]

Mortality		0.75 [0.39‑1.46]0.75 [0.39‑1.46]	0.60 [0.43‑0.84]0.60 [0.43‑0.84]
Recovery	0.44 [0.36‑0.55]**0.44** [0.36‑0.55]	0.88 [0.64‑1.20]0.88 [0.64‑1.20]
Cases			0.65 [0.57‑0.74]**0.65** [0.57‑0.74]
Viral	0.86 [0.67‑1.09]0.86 [0.67‑1.09]	0.94 [0.91‑0.96]**0.94** [0.91‑0.96]

Fig. 4. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random-effects meta-analysis.

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October 2026

Antihistamine H1RA COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Early treatment56%0.44 · 0.36–0.540/4523/41356% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Mura (PSM)25%0.75 · 0.39–1.46death88 (n)88 (n) Salvucci29%0.71 · 0.51–1.01PASC10/1413/13LONG COVID Meiser (DB RCT)12%0.88 · 0.64–1.20recovery122 (n)129 (n)CARVIN-IIazelastine

Late treatment21%0.79 · 0.64–0.9910/22413/23021% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.038

Prophylaxis35%0.65 · 0.49–0.86278/9,1152,923/61,58135% lower risk

Tau² = 0.15, I² = 91.9%, p = 0.0032

All studies37%0.63 · 0.50–0.78288/9,7912,939/62,22437% lower risk

Tau² = 0.14, I² = 88.5%, p < 0.000100.511.52+

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Fig. 5. 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.

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October 2026

Antihistamine H1RA COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Mura (PSM)25%0.75 · 0.39–1.4688 (n)88 (n)

Late treatment25%0.75 · 0.39–1.4588 (n)88 (n)25% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.4

Hoertel58%0.42 · 0.25–0.71138 (n)7,207 (n)hydroxyzine Sánchez-Rico46%0.54 · 0.31–0.8918/1641,571/14,939hydroxyzine Monserrat .. (PSM)80%0.20 · 0.02–0.93n/an/aloratadine Hunt43%0.57 · 0.49–0.66260/7,6001,352/18,908 Loucera40%0.60 · 0.43–0.84251 (n)15,717 (n) Hoertel−7%1.07 · 0.88–1.31962 (n)4,810 (n)desloratadine/hydroxyzine

Prophylaxis40%0.60 · 0.43–0.84278/9,1152,923/61,58140% lower risk

Tau² = 0.12, I² = 85.3%, p = 0.0031

All studies38%0.62 · 0.46–0.84278/9,2032,923/61,66938% lower risk

Tau² = 0.11, I² = 82.5%, p = 0.002200.511.52+

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Fig. 6. Random-effects meta-analysis for mortality results.

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October 2026

Antihistamine H1RA COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Sanchez-.. (DB RCT)87%0.13 · 0.01–2.46hosp.0/322/13chlorpheniramine

Early treatment87%0.13 · 0.01–2.460/322/1387% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.17

All studies87%0.13 · 0.01–2.460/322/1387% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.1700.511.52+

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Fig. 7. Random-effects meta-analysis for hospitalization.

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October 2026

Antihistamine H1RA COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Valerio-.. (DB RCT)61%0.39 · 0.24–0.63no recov.61 (n)40 (n)ACCROS-Ichlorpheniramine Valerio-Pascua54%0.46 · 0.36–0.58recov. time330 (n)330 (n)ACCROS-IIchlorpheniramine Klussmann (DB RCT)66%0.34 · 0.01–7.95no recov.0/291/30CARVINazelastine

Early treatment56%0.44 · 0.36–0.550/4201/40056% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Meiser (DB RCT)12%0.88 · 0.64–1.20recovery122 (n)129 (n)CARVIN-IIazelastine

Late treatment12%0.88 · 0.64–1.20122 (n)129 (n)12% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.42

All studies46%0.54 · 0.34–0.860/5421/52946% lower risk

Tau² = 0.14, I² = 76.4%, p = 0.008600.511.52+

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Fig. 8. Random-effects meta-analysis for recovery.

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October 2026

Antihistamine H1RA COVID-19 case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Vila-Corcoles61%0.39 · 0.12–1.21cases Vila-Córcoles53%0.47 · 0.22–1.01cases Reznikov34%0.66 · 0.58–0.75casesn/an/a

Prophylaxis35%0.65 · 0.57–0.7435% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies35%0.65 · 0.57–0.7435% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+

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Fig. 9. Random-effects meta-analysis for cases.

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October 2026

Antihistamine H1RA COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Klussmann (DB RCT)14%0.86 · 0.67–1.09viral load29 (n)30 (n)CARVINazelastine

Early treatment14%0.86 · 0.67–1.0929 (n)30 (n)14% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.22

Meiser (DB RCT)6%0.94 · 0.91–0.96viral load122 (n)129 (n)CARVIN-IIazelastine

Late treatment6%0.94 · 0.91–0.96122 (n)129 (n)6% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies7%0.93 · 0.91–0.96151 (n)159 (n)7% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+

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Fig. 10. Random-effects meta-analysis for viral clearance.

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October 2026

Antihistamine H1RA COVID-19 peer reviewed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Sanchez-.. (DB RCT)87%0.13 · 0.01–2.46hosp.0/322/13chlorpheniramine Klussmann (DB RCT)66%0.34 · 0.01–7.95no recov.0/291/30CARVINazelastine

Early treatment80%0.20 · 0.02–1.740/613/4380% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.15

Mura (PSM)25%0.75 · 0.39–1.46death88 (n)88 (n) Salvucci29%0.71 · 0.51–1.01PASC10/1413/13LONG COVID Meiser (DB RCT)12%0.88 · 0.64–1.20recovery122 (n)129 (n)CARVIN-IIazelastine

Late treatment21%0.79 · 0.64–0.9910/22413/23021% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.038

Vila-Corcoles61%0.39 · 0.12–1.21cases Vila-Córcoles53%0.47 · 0.22–1.01cases Reznikov34%0.66 · 0.58–0.75casesn/an/a McKeigue−30%1.30 · 1.13–1.51severe casecase-control study Sánchez-Rico46%0.54 · 0.31–0.89death18/1641,571/14,939hydroxyzine Monserrat .. (PSM)80%0.20 · 0.02–0.93deathn/an/aloratadine Hunt43%0.57 · 0.49–0.66death260/7,6001,352/18,908 Loucera40%0.60 · 0.43–0.84death251 (n)15,717 (n) Hoertel−7%1.07 · 0.88–1.31death962 (n)4,810 (n)desloratadine/hydroxyzine

Prophylaxis32%0.68 · 0.50–0.92278/8,9772,923/54,37432% lower risk

Tau² = 0.15, I² = 92.4%, p = 0.012

All studies30%0.70 · 0.55–0.89288/9,2622,939/54,64730% lower risk

Tau² = 0.13, I² = 87.9%, p = 0.003600.511.52+

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Fig. 11. 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.

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October 2026

Antihistamine H1RA COVID-19 long COVID results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Valerio-.. (DB RCT)74%0.26 · 0.12–0.58PASC55 (n)46 (n)ACCROS-Ichlorpheniramine

Early treatment74%0.26 · 0.12–0.5855 (n)46 (n)74% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.001

Salvucci29%0.71 · 0.51–1.01PASC10/1413/13

Late treatment29%0.71 · 0.51–1.0110/1413/1329% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.056

All studies54%0.46 · 0.18–1.2210/6913/5954% lower risk

Tau² = 0.40, I² = 80.4%, p = 0.1200.511.52+

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Fig. 12. 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.

Randomized Controlled Trials (RCTs)

Fig. 13 shows a comparison of results for RCTs and observational studies. Random-effects meta-analysis of RCTs shows 46% improvement, compared to 36% for other studies. Fig. 14 shows a forest plot for random-effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2.

Fig. 13. Results for RCTs and observational studies.

RCTs have many potential biases

RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases59, and analysis of double-blind RCTs has identified extreme levels of bias60. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.

Conflicts of interest for COVID-19 RCTs

RCTs are expensive and many RCTs are funded by pharmaceutical companies or other organizations with conflicts of interest, for example governments that previously denied treatment with the study drug. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. Bekelman et al. and Lundh et al. show that industry-sponsored studies are more likely to be favorable. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.

RCTs for novel acute diseases requiring rapid treatment

High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 227 treatments we have analyzed, 67% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.

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October 2026

COVID-19 RCT vs. observational results from 6,000+ studies

RR · CI

Low-cost0.98 · 0.90–1.06 High-profit0.93 · 0.85–1.02

All treatments0.97 · 0.91–1.03

00.511.52

Rotate screen for more detailsIncrease width for more details

RCTs show
higher efficacyRCTs show
lower efficacy

Fig. 15. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments64.

Observational studies have been shown to be reliable

Evidence shows that observational studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates.

We performed a similar analysis across the 227 treatments we cover, showing no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]44. Similar results are found for all low-cost treatments, RR 0.98 [0.90‑1.06]. High-cost treatments show a non-significant trend towards RCTs showing greater efficacy, RR 0.93 [0.85‑1.02]. Details can be found in the supplementary data.

Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or remote survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see68,69.

RCTs may be less reliable

Concato et al. report a paradoxical finding—RCT results had higher variability, and only RCTs were found to sometimes report significant results the opposite of the overall result. The same trend is seen for the most popular (most politicized) COVID-19 treatments—considering all statistically significant results reported in studies, RCTs are slightly more likely to report a result in the opposite direction. In other words, for these COVID-19 treatments and for the topics covered by Concato et al., assuming causality from a single study is more likely to result in an incorrect conclusion for RCTs.

Increased risk of inconsistent results for RCTs suggests higher prevalence of bias, which may arise due to many issues including design bias, conflicts of interest, treatment differences by physicians aware of allocation, attrition bias, ascertainment bias, randomization failures, errors, or fraud.

Using all studies identifies efficacy 8+ months faster (9+ months for low-cost treatments)

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. Of these, 54% have been confirmed in RCTs, with a mean delay of 7.8 months (62% with 8.7 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.

All studies must be carefully analyzed

Neither observational studies nor RCTs prove causation—any study can be flawed or fraudulent. We need much more, for example a combination of results from many independent teams, detailed understanding of each study, knowledge of conflicts/team reliability, dose-response relationships, delay-response relationships, logical results across outcomes, or details consistent with preclinical expectations.

All studies must be evaluated individually. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta-analyses.

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October 2026

Antihistamine H1RA COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Valerio-.. (DB RCT)61%0.39 · 0.24–0.63no recov.61 (n)40 (n)ACCROS-Ichlorpheniramine Sanchez-.. (DB RCT)87%0.13 · 0.01–2.46hosp.0/322/13chlorpheniramine Klussmann (DB RCT)66%0.34 · 0.01–7.95no recov.0/291/30CARVINazelastine

Early treatment63%0.37 · 0.23–0.600/1223/8363% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Meiser (DB RCT)12%0.88 · 0.64–1.20recovery122 (n)129 (n)CARVIN-IIazelastine

Late treatment12%0.88 · 0.64–1.20122 (n)129 (n)12% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.42

All studies46%0.54 · 0.26–1.090/2443/21246% lower risk

Tau² = 0.25, I² = 66.2%, p = 0.08600.511.52+

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← Antihistamine
H1RAs reduce
riskAntihistamine
H1RAs increase
risk →

Fig. 14. Random-effects meta-analysis for all Randomized Controlled Trials. 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.

Efficacy by Treatment Delay

Fig. 16 shows a mixed-effects meta-regression of efficacy as a function of treatment delay in COVID-19 antihistamine H1RA studies, with group estimates for different stages when a specific value is not provided, showing that efficacy declines significantly with treatment delay (p = 0.00074). Early treatment is critical. This significant efficacy gradient reinforces the reliability of the overall finding that antihistamine H1RA reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect to find a significant gradient in efficacy as a function of treatment delay that matches the biological mechanisms.

Fig. 16. Early treatment is more effective (p = 0.00074). Meta-regression showing efficacy as a function of treatment delay in COVID-19 antihistamine H1RA studies.

For comparison, Fig. 17 shows a meta-regression for all studies providing specific values across 227 treatments. Efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

Fig. 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.

Interaction Matching Biological Mechanisms

With 17 studies for antihistamine H1RAs, we can analyze trends across studies. A significant interaction that matches the biological mechanisms provides further confirmation of efficacy. Meta-regression of efficacy vs. treatment delay shows a significant association, with p = 0.00074.

This reinforces the reliability of the overall finding that antihistamine H1RAs reduce risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect analysis across studies to find this significant interaction that matches the biological mechanisms.

Media Censorship

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."40.

We analyze media coverage for the 227 treatments we cover using Altmetric70, which reports the number of ~12,000 tracked news outlets that covered each study71. Studies are considered to have received significant media coverage if they were covered by at least 0.5% of the tracked news outlets. Fig. 18, 19, and 20 show the bias toward negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments. This 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, and high-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

The mainstream media did not cover any of the positive studies for antihistamine H1RAs.

Fig. 18. Media was more likely to cover negative or inconclusive results for low-cost treatments, and positive results for high-profit treatments.

Fig. 19. Mainstream media was biased against positive results for low-cost treatments.

Fig. 20. In contrast to the results for low-cost treatments, mainstream media was biased towards positive results for high-cost treatments.

A combination of factors may have led to the media's suppression 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 banned on Twitter and YouTube.

•

FDA requires "no adequate, approved, and available alternatives"

in order to grant an EUA for novel high-profit interventions, creating a strong 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. Relatively few journalists have a strong ability to analyze clinical trials and are outnumbered by the rest.

•

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, fear, and anxieties. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage. A combination of low-cost treatments greatly reducing risk conflicts with the negative narrative.

Authority Delay

25 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 yearsC. 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

Heterogeneity

Heterogeneity in COVID-19 studies arises from many factors including:

Treatment delay

The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours72,73. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar et al. report only 2.5 hours improvement for inpatient treatment.

Table 3. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delay	Result
Post-exposure prophylaxis	86% fewer cases74
<24 hours	-33 hours symptoms75
24-48 hours	-13 hours symptoms75
Inpatients	-2.5 hours to improvement76

As shown in the treatment delay section, the efficacy of COVID-19 treatment declines as a function of the treatment delay. Early treatment is critical for COVID-19.

Patient demographics

Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results, for example as in López-Medina et al.

SARS-CoV-2 variants

Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants78, for example the Gamma variant shows significantly different characteristics79-82. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants83,84.

Treatment regimen

Effectiveness may depend strongly on the dosage and treatment regimen.

Medication quality

The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.

Other treatments

The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic87-111, therefore efficacy may depend strongly on combined treatments.

Effect measured

Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.

Meta-analysis

The distribution of studies will alter the outcome of a meta-analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. 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.

Pooled Effects

Pooled effects are no longer required to show efficacy as of January 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 antihistamine H1RAs as of January 2021. Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 1.7 months compared to using pooled 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. 21 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 22 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. 23 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.

Fig. 21. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.

Fig. 22. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.

Fig. 21. 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. 24 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Fig. 24. 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

Publication bias

Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results113-116. For antihistamine H1RA, there is currently not enough data to evaluate publication bias with high confidence.

One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.

Fig. 25 shows a scatter plot of results for prospective and retrospective studies. 54% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 75% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 43% improvement, compared to 64% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.

Fig. 25. Prospective vs. retrospective studies. The diamonds show the results of random-effects meta-analysis.

Funnel plot analysis

Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Fig. 26 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05117-124. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.

Fig. 26. Example funnel plot analysis for simulated perfect trials.

Conflicts of interest

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Antihistamine H1RA for COVID-19 lack this because they are generally inexpensive and widely available. In contrast, most COVID-19 antihistamine H1RA trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all antihistamine H1RA trials represent the optimal conditions for efficacy.

Limitations

Summary statistics from meta-analysis necessarily lose information. As with all meta-analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses for specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.

Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.

Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.

Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials with conflicts of interest may use designs better suited to the preferred outcome.

In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.

Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone87-111. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.

This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.

No treatment or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.

Perspective

Results compared with other treatments

SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors31-38, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk39, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 27 shows an overview of the results for antihistamine H1RAs in the context of multiple COVID-19 treatments, and Fig. 28 shows a plot of efficacy vs. cost for COVID-19 treatments.

Fig. 27. 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 efficacy125.

Fig. 28. Efficacy vs. cost for COVID-19 treatments.

Conclusion

Antihistamine H1RAs are an effective treatment for COVID-19. Significantly lower risk is seen for mortality, recovery, cases, and viral clearance. 10 studies from 8 independent teams in 6 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 37% [22‑50%] lower risk. Results are similar for Randomized Controlled Trials and peer-reviewed studies. Early treatment is more effective than late treatment.

Contact.

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 228,000 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/h1meta.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.

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Study Notes

Hoertel

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

No significant difference in mortality

Retrospective 72,105 COVID+ hospitalized patients in France, showing no significant difference in mortality with antihistamine H1RAs desloratadine and hydroxyzine. Submit Corrections or Updates.

Hoertel

Outcome

Improvement

Relative Risk · 95% CI

Mortality

58%0.42 · 0.25–0.71

00.511.52+

← Hydroxyzine
reduces riskHydroxyzine
increases risk →

Lower mortality with hydroxyzine

p = 0.0012

Retrospective 7,345 hospitalized COVID-19 patients in France showing lower mortality with hydroxyzine use, with a significant dose-response relationship. Hydroxyzine was also associated with a faster decrease in inflammatory markers. Submit Corrections or Updates.

Hunt

Outcome

Improvement

Relative Risk · 95% CI

Mortality

43%0.57 · 0.49–0.66

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Lower mortality with antihistamine H1RAs

p < 0.000001

Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including antihistamines. Treatment was defined as drugs administered ≥50% of the time within 2 weeks post-COVID+, and may be a continuation of prophylactic treatment. Further reduction in mortality was seen with combinations of treatments. Submit Corrections or Updates.

Klussmann

Outcome

Improvement

Relative Risk · 95% CI

Viral clearance, AUC, 0.1%

22%0.78 · 0.62–0.99

Viral clearance, AUC, 0.02%

6%0.94 · 0.77–1.15

00.511.52+

← Azelastine
reduces riskAzelastine
increases risk →

Improved viral clearance with azelastine

Not statistically significant · p = 0.22

RCT 90 outpatients showing potential benefit of azelastine nasal spray for reducing viral load. Patients were randomized to receive placebo, 0.02%, or 0.1% azelastine nasal spray for 11 days. The 0.1% azelastine group showed greater viral load reduction compared to placebo (p=0.007) and achieved PCR negativity earlier (48.2% vs 23.1% negative by day 11). Symptoms improved across all groups, with the 0.1% group showing slightly greater improvement (12.74 point reduction vs 11.12 for placebo) and significantly better improvement in shortness of breath on days 3-4. No significant differences were found in quality of life measures or WHO clinical progression scores. Submit Corrections or Updates.

Loucera

Outcome

Improvement

Relative Risk · 95% CI

Mortality, combined

40%0.60 · 0.43–0.84

Mortality, loratadine

30%

Mortality, cetirizine

51%

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Lower mortality with antihistamine H1RAs

p = 0.0028

Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with antihistamine H1RAs, without statistical significance. Since only hospitalized patients are included, results do not reflect different probabilities of hospitalization across treatments. Submit Corrections or Updates.

McKeigue

Outcome

Improvement

Relative Risk · 95% CI

Severe case

-30%1.30 · 1.13–1.51

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Higher severe cases with antihistamine H1RAs

p = 0.00042

Retrospective 4,251 severe COVID-19 cases and 36,738 matched controls in Scotland showing increased risk of severe COVID-19 with PPI use and antihistamine H1RA use. Adjusted results are only provided for the patients not in care homes (2,357 cases and 33,803 controls). Submit Corrections or Updates.

Meiser

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Azelastine
reduces riskAzelastine
increases risk →

Improved viral clearance with azelastine

p < 0.000001

RCT 294 low-risk subjects with mild COVID-19 showing significantly greater reduction in viral load with azelastine 0.1% nasal spray vs. placebo. There was no COVID-19 related hospitalization in either group. The reduction in viral load was significantly higher in the azelastine group at day 3, 6 and 11.

Authors report that treatment was associated with significant improvement in fever, weakness, and hypoxia compared to placebo, however detailed results are not provided for these and other symptoms.

Treatment delay from symptom onset is not specified, however the baseline viral load, lack of progression, and recovery profile are consistent with relatively late treatment among low-risk patients.

Time to recovery results are not clear, with an inconsistent HR and 95% CI reported 0.32 [0.83-2.32]. Submit Corrections or Updates.

Monserrat Villatoro

Outcome

Improvement

Relative Risk · 95% CI

Mortality, loratadine

80%0.20 · 0.02–0.93

00.511.52+

← Loratadine
reduces riskLoratadine
increases risk →

Lower mortality with loratadine

p = 0.05

PSM retrospective 3,712 hospitalized patients in Spain, showing lower mortality with existing use of loratadine. Submit Corrections or Updates.

Mura

Outcome

Improvement

Relative Risk · 95% CI

Mortality

25%0.75 · 0.39–1.46

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Lower mortality with antihistamine H1RAs

Not statistically significant · p = 0.4

PSM retrospective TriNetX database analysis of 1,379 severe COVID-19 patients requiring respiratory support, showing lower mortality with H1RAs+H2RAs versus famotidine alone, without statistical significance. Submit Corrections or Updates.

Reznikov

Outcome

Improvement

Relative Risk · 95% CI

Case, all medications and age groups...

Case, cetirizine, 61+

52%

Case, cetirizine...

43%

Case, fexofenadine...

62%

Case, fexofenadine...

-33%

Case, loratadine, 61+

Case, levocetirizine...

74%

Case, levocetirizine...

Case, azelastine, 61+

59%

Case, azelastine...

29%

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Fewer cases with antihistamine H1RAs

p < 0.000001

Retrospective 219,000 patients showing lower risk of COVID-19 with antihistamine H1RA use.

In vitro study showing these drugs exhibit direct antiviral activity against SARS-CoV-2. Molecular docking suggests hydroxyzine and azelastine may exert antiviral effects by binding ACE2 and the sigma-1 receptor. Submit Corrections or Updates.

Salvucci

Outcome

Improvement

Relative Risk · 95% CI

Long COVID

29%0.71 · 0.48–1.06

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Lower long COVID with antihistamine H1RAs

Not statistically significant · p = 0.098

Retrospective 14 patients with long-COVID symptoms attributed to mast cell activation treated with H1 and H2 antihistamines compared to 13 control patients, showing significant improvements in several symptoms in the treatment group compared to controls after 20 days. 29% of treated patients had complete resolution of long-COVID symptoms, compared with none in the control group. Submit Corrections or Updates.

Sanchez-Gonzalez

Outcome

Improvement

Relative Risk · 95% CI

Hospitalization

87%0.13 · 0.01–2.46

00.511.52+

← Chlorpheniramine
reduces riskChlorpheniramine
increases risk →

Lower hospitalization with treatment

Not statistically significant · p = 0.079

Small RCT showing significantly improved recovery with intranasal chlorpheniramine maleate. Authors also perform an in vitro study showing efficacy with a highly differentiated three-dimensional model of normal, human-derived tracheal/bronchial epithelial cells. Submit Corrections or Updates.

Sánchez-Rico

Outcome

Improvement

Relative Risk · 95% CI

Mortality

46%0.54 · 0.31–0.89

00.511.52+

← Hydroxyzine
reduces riskHydroxyzine
increases risk →

Lower mortality with hydroxyzine

p = 0.016

Retrospective 15,103 hospitalized COVID-19 patients in France showing lower mortality with hydroxyzine use. Submit Corrections or Updates.

Valerio-Pascua

Outcome

Improvement

Relative Risk · 95% CI

Recovery time

54%0.46 · 0.36–0.58

00.511.52+

← Chlorpheniramine
reduces riskChlorpheniramine
increases risk →

Faster recovery with chlorpheniramine

p < 0.000001

RCT and retrospective study of chlorpheniramine nasal spray for COVID-19. The retrospective study included 660 outpatients showing fewer days with general COVID-19 symptoms, cough, anosmia, and ageusia compared to standard of care alone. The RCT results are listed separately126. Submit Corrections or Updates.

Valerio-Pascua

Outcome

Improvement

Relative Risk · 95% CI

Recovery, all symptoms combined

00.511.52+

← Chlorpheniramine
reduces riskChlorpheniramine
increases risk →

Improved recovery and lower long COVID

p = 0.00018 (recovery) and p = 0.001 (long COVID)

RCT and retrospective study of chlorpheniramine nasal spray for COVID-19. The RCT included 101 outpatients showing significantly faster recovery with treatment. The retrospective study results are listed separately127. Long COVID results are from Valerio-Pascua (C) et al.. Submit Corrections or Updates.

Vila-Corcoles

Outcome

Improvement

Relative Risk · 95% CI

Case

61%0.39 · 0.12–1.21

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Fewer cases with antihistamine H1RAs

Not statistically significant · p = 0.1

Retrospective 34,936 hypertensive outpatients in Spain showing no significant difference in COVID-19 cases with PPIs and antihistamine H1RAs. Submit Corrections or Updates.

Vila-Córcoles

Outcome

Improvement

Relative Risk · 95% CI

Case

53%0.47 · 0.22–1.01

00.511.52+

← Antihistamine
H1RAs reduce riskAntihistamine H1RAs
increase risk →

Fewer cases with antihistamine H1RAs

Not statistically significant · p = 0.052

Retrospective 79,083 adults aged ≥50 years in Spain showing lower with of PCR-confirmed COVID-19 with antihistamine use, close to statistical significance. Submit Corrections or Updates.

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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 antihistamine H1RA and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of antihistamine H1RA 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. 29. 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 reduction129. 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 PythonMeta130 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 1134. 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 (3.14.8) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.3), 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 reliability142. 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 issues144. 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 harmD. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias140,141 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 factorsE.

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 effective72,73.

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/h1meta.html.

Early treatment

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.

Klussmann, 4/26/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Germany, peer-reviewed, 27 authors, CARVIN trial. Submit Corrections or Updates.	risk of no recovery, 66.3% lower, RR 0.34, p = 1.00, treatment 0 of 29 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 0.1%.
	risk of no recovery, 67.0% lower, RR 0.33, p = 0.49, treatment 0 of 31 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 0.02%.
	symptoms, 12.7% lower, RR 0.87, p = 0.54, treatment mean 12.74 (±10.74) n=29, control mean 11.12 (±9.45) n=30, relative change in symptom score, 0.1%.
	symptoms, 32.7% higher, RR 1.33, p = 0.26, treatment mean 8.38 (±9.42) n=31, control mean 11.12 (±9.45) n=30, relative change in symptom score, 0.02%.
	viral load, 14.2% lower, relative load 0.86, p = 0.22, treatment mean 4.45 (±2.25) n=29, control mean 3.82 (±1.61) n=30, 0.1%.
	viral load, 7.3% lower, relative load 0.93, p = 0.52, treatment mean 4.12 (±2.01) n=31, control mean 3.82 (±1.61) n=30, 0.02%.
	risk of no viral clearance, 21.7% lower, RR 0.78, p = 0.04, treatment mean 24.14 (±13.12) n=29, control mean 18.89 (±4.7) n=30, relative AUC, 0.1%.
	risk of no viral clearance, 6.2% lower, RR 0.94, p = 0.54, treatment mean 24.14 (±12.56) n=31, control mean 22.64 (±4.7) n=30, relative AUC, 0.02%.
Sanchez-Gonzalez, 12/31/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 44.5, 5 authors. Submit Corrections or Updates.	risk of hospitalization, 87.4% lower, RR 0.13, p = 0.08, treatment 0 of 32 (0.0%), control 2 of 13 (15.4%), NNT 6.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Valerio-Pascua (B), 10/18/2022, retrospective, Honduras, preprint, 16 authors, study period June 2021 - July 2022, trial NCT05520944 (history) (ACCROS-II). Submit Corrections or Updates.	recovery time, 54.3% lower, relative time 0.46, p < 0.001, treatment mean 4.97 (±3.32) n=330, control mean 10.88 (±6.64) n=330.
Valerio-Pascua, 10/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Honduras, preprint, 16 authors, study period June 2021 - July 2022, trial NCT05449405 (history) (ACCROS-I). Submit Corrections or Updates.	risk of no recovery, 61.4% lower, RR 0.39, p < 0.001, treatment 61, control 40, all symptoms combined.
	risk of no recovery, 67.2% lower, RR 0.33, p = 0.15, treatment 3 of 61 (4.9%), control 6 of 40 (15.0%), NNT 9.9, day 7, anosmia.
	risk of no recovery, 89.1% lower, RR 0.11, p = 0.01, treatment 1 of 61 (1.6%), control 6 of 40 (15.0%), NNT 7.5, day 7, ageusia.
	risk of no recovery, 53.2% lower, RR 0.47, p = 0.05, treatment 10 of 61 (16.4%), control 14 of 40 (35.0%), NNT 5.4, day 7, cough.
	risk of no recovery, 67.2% lower, RR 0.33, p = 0.21, treatment 2 of 61 (3.3%), control 4 of 40 (10.0%), NNT 15, day 7, fatigue.
	risk of no recovery, 59.0% lower, RR 0.41, p = 0.13, treatment 5 of 61 (8.2%), control 8 of 40 (20.0%), NNT 8.5, day 7, nasal congestion.
	relative long COVID score, 73.5% better, RR 0.26, p < 0.001, treatment 55, control 46, relative average composite long COVID score.

Late treatment

Meiser, 10/31/2024, Double Blind Randomized Controlled Trial, placebo-controlled, India, peer-reviewed, 8 authors, study period 27 September, 2022 - 16 May, 2023, trial CTRI/2022/09 (CARVIN-II). Submit Corrections or Updates.	risk of no recovery, 12.5% lower, RR 0.87, p = 0.42, treatment mean 0.8 (±0.99) n=122, control mean 0.7 (±0.96) n=129, WHO scale improvement, mid-recovery, day 3.
	viral load, 6.4% lower, relative load 0.94, p < 0.001, treatment mean 2.8 (±0.27) n=122, control mean 2.62 (±0.26) n=129, relative viral load reduction, mid-recovery, day 3.
	viral load, 2.2% lower, relative load 0.98, p = 0.001, treatment mean 5.05 (±0.29) n=122, control mean 4.94 (±0.25) n=129, relative viral load reduction, mid-recovery, day 6.
	viral load, 1.3% lower, relative load 0.99, p = 0.003, treatment mean 5.93 (±0.22) n=122, control mean 5.85 (±0.2) n=129, relative viral load reduction, mid-recovery, day 11.
	risk of no viral clearance, 3.2% lower, RR 0.97, p < 0.001, treatment mean 1014.96 (±35.25) n=122, control mean 982.98 (±31.68) n=129, relative AUC.
Mura, 3/31/2021, retrospective, database analysis, multiple countries, peer-reviewed, 6 authors. Submit Corrections or Updates.	risk of death, 25.0% lower, OR 0.75, p = 0.40, treatment 88, control 88, H1+H2 vs. famotidine, propensity score matching, RR approximated with OR.
Salvucci, 7/17/2023, retrospective, Italy, peer-reviewed, 9 authors. Submit Corrections or Updates.	risk of long COVID, 28.6% lower, RR 0.71, p = 0.10, treatment 10 of 14 (71.4%), control 13 of 13 (100.0%), NNT 3.5.

Prophylaxis

Hoertel, 8/4/2023, retrospective, France, peer-reviewed, 14 authors, study period 2 May, 2020 - 31 August, 2022. Submit Corrections or Updates.	risk of death, 7.2% higher, HR 1.07, p = 0.50, treatment 962, control 4,810, adjusted per study, combined.
	risk of death, 8.0% lower, HR 0.92, p = 0.81, treatment 11 of 94 (11.7%), control 62 of 470 (13.2%), NNT 67, adjusted per study, desloratadine, multivariable, Cox proportional hazards, day 28.
	risk of death, 9.0% higher, HR 1.09, p = 0.40, treatment 104 of 962 (10.8%), control 591 of 4,810 (12.3%), adjusted per study, hydroxyzine, multivariable, Cox proportional hazards, day 28.
Hoertel (B), 10/27/2020, retrospective, France, preprint, 18 authors, study period 24 January, 2020 - 1 April, 2020. Submit Corrections or Updates.	risk of death, 58.0% lower, HR 0.42, p = 0.001, treatment 138, control 7,207, adjusted per study, propensity score weighting, multivariable.
Hunt, 6/29/2022, retrospective, USA, peer-reviewed, 8 authors, study period 1 March, 2020 - 10 September, 2020. Submit Corrections or Updates.	risk of death, 43.0% lower, RR 0.57, p < 0.001, treatment 260 of 7,600 (3.4%), control 1,352 of 18,908 (7.2%), NNT 27, adjusted per study, day 30.
Loucera, 8/16/2022, retrospective, Spain, peer-reviewed, 8 authors, study period January 2020 - November 2020. Submit Corrections or Updates.	risk of death, 39.8% lower, HR 0.60, p = 0.003, treatment 251, control 15,717, combined.
	risk of death, 30.4% lower, HR 0.70, p = 0.05, treatment 251, control 15,717, loratadine, Cox proportional hazards, day 30.
	risk of death, 50.6% lower, HR 0.49, p = 0.002, treatment 233, control 15,735, cetirizine, Cox proportional hazards, day 30.
McKeigue, 2/22/2021, retrospective, Scotland, peer-reviewed, 18 authors, trial EUPAS35558. Submit Corrections or Updates.	risk of severe case, 30.0% higher, OR 1.30, p < 0.001, treatment 263 of 2,357 (11.2%) cases, 2,556 of 33,803 (7.6%) controls, adjusted per study, case control OR.
Monserrat Villatoro, 1/8/2022, retrospective, propensity score matching, Spain, peer-reviewed, 18 authors. Submit Corrections or Updates.	risk of death, 80.0% lower, OR 0.20, p = 0.05, loratadine, RR approximated with OR.
Reznikov, 1/31/2021, retrospective, USA, peer-reviewed, 9 authors. Submit Corrections or Updates.	risk of case, 34.0% lower, RR 0.66, p < 0.001, adjusted per study, all medications and age groups combined.
	risk of case, 36.7% lower, OR 0.63, p = 0.01, adjusted per study, inverted to make OR<1 favor treatment, hydroxyzine, 61+, multivariable, RR approximated with OR.
	risk of case, 16.7% lower, OR 0.83, p = 0.24, adjusted per study, inverted to make OR<1 favor treatment, hydroxyzine, 31-60, multivariable, RR approximated with OR.
	risk of case, 47.9% lower, OR 0.52, p = 0.27, adjusted per study, inverted to make OR<1 favor treatment, brompheniramine , 31-60, multivariable, RR approximated with OR.
	risk of case, 52.2% lower, OR 0.48, p < 0.001, adjusted per study, inverted to make OR<1 favor treatment, cetirizine, 61+, multivariable, RR approximated with OR.
	risk of case, 42.9% lower, OR 0.57, p < 0.001, adjusted per study, inverted to make OR<1 favor treatment, cetirizine, 31-60, multivariable, RR approximated with OR.
	risk of case, 62.3% lower, OR 0.38, p = 0.13, adjusted per study, inverted to make OR<1 favor treatment, fexofenadine, 61+, multivariable, RR approximated with OR.
	risk of case, 33.3% higher, OR 1.33, p = 0.58, adjusted per study, inverted to make OR<1 favor treatment, fexofenadine, 31-60, multivariable, RR approximated with OR.
	risk of case, 34.2% lower, OR 0.66, p = 0.008, adjusted per study, inverted to make OR<1 favor treatment, loratadine, 61+, multivariable, RR approximated with OR.
	risk of case, 26.5% lower, OR 0.74, p = 0.04, adjusted per study, inverted to make OR<1 favor treatment, loratadine, 31-60, multivariable, RR approximated with OR.
	risk of case, 35.5% lower, OR 0.65, p < 0.001, adjusted per study, inverted to make OR<1 favor treatment, diphenhydramine, 61+, multivariable, RR approximated with OR.
	risk of case, 13.8% lower, OR 0.86, p = 0.13, adjusted per study, inverted to make OR<1 favor treatment, diphenhydramine, 31-60, multivariable, RR approximated with OR.
	risk of case, 73.6% lower, OR 0.26, p = 0.05, adjusted per study, inverted to make OR<1 favor treatment, levocetirizine, 61+, multivariable, RR approximated with OR.
	risk of case, 78.3% lower, OR 0.22, p = 0.0496, adjusted per study, inverted to make OR<1 favor treatment, levocetirizine, 31-60, multivariable, RR approximated with OR.
	risk of case, 36.3% lower, OR 0.64, p = 0.19, adjusted per study, inverted to make OR<1 favor treatment, chlorpheniramine, 61+, multivariable, RR approximated with OR.
	risk of case, 8.3% lower, OR 0.92, p = 0.81, adjusted per study, inverted to make OR<1 favor treatment, chlorpheniramine, 31-60, multivariable, RR approximated with OR.
	risk of case, 58.8% lower, OR 0.41, p < 0.001, adjusted per study, inverted to make OR<1 favor treatment, azelastine, 61+, multivariable, RR approximated with OR.
	risk of case, 28.6% lower, OR 0.71, p = 0.17, adjusted per study, inverted to make OR<1 favor treatment, azelastine, 31-60, multivariable, RR approximated with OR.
Sánchez-Rico, 12/15/2021, retrospective, France, peer-reviewed, 20 authors, study period 24 January, 2020 - 1 May, 2020. Submit Corrections or Updates.	risk of death, 46.2% lower, RR 0.54, p = 0.02, treatment 18 of 164 (11.0%), control 1,571 of 14,939 (10.5%), adjusted per study, odds ratio converted to relative risk, multivariable.
Vila-Corcoles, 7/25/2020, retrospective, Spain, peer-reviewed, mean age 70.9, 11 authors, study period 1 March, 2020 - 30 April, 2020. Submit Corrections or Updates.	risk of case, 61.0% lower, HR 0.39, p = 0.10, treatment 1,579, control 33,357, adjusted per study, multivariable, Cox proportional hazards.
Vila-Córcoles, 12/10/2020, retrospective, Spain, peer-reviewed, 10 authors, study period 1 March, 2020 - 23 May, 2020. Submit Corrections or Updates.	risk of case, 53.0% lower, HR 0.47, p = 0.05, treatment 3,264, control 75,819, adjusted per study, multivariable, Cox proportional hazards, RR approximated with OR.

Supplementary Data

Footnotes

Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.

Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.

Smoking was known to cause lung cancer since at least 1939, but this was not widely recognized in the US until 1964, 25 years later. Surgeon general Leroy Burney tried publicizing the danger starting in 1957, with limited success. Surgeon general Luther Terry, appointed in 1961, prompted by President Kennedy in 1962 amid pressure from health advocates, finally got recognition in 1964. The 1964 report reviewed 7,000+ studies, but these could (and should) have been reviewed and acted upon in real-time as they were published. Historians attribute the 25 year delay to an industry campaign to manufacture doubt and controversy, through tactics like funding biased and fraudulent research from "independent" organizations, attacking scientists, and political lobbying. The success of the industry campaign is only possible because officials did not analyze the research in detail. Fraudulent industry research supported prior failures, but would have been called out as fraudulent by officials that analyzed and understood the research in real-time.

When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment136,137. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage138. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.

Peters (B) et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.

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