•Statistically significant improvements are seen for mortality, hospitalization, cases, and viral clearance. 7 studies from 7 independent teams in 5 different countries show statistically significant
improvements in isolation (5 for the most serious outcome).
•Results are robust — in exclusion sensitivity analysis 13 of 16
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
•Some studies only test short term viral load after a single application. Excessive use of PVP-I could affect thyroid function.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 19% of povidone-iodine
studies show zero events in the treatment arm.
Multiple treatments are typically used
in combination, and other treatments
may be more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•All data to reproduce this paper and
sources are in the appendix.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
Figure 1.A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
We analyze all significant studies
concerning the use of
povidone-iodine
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, for studies within each treatment stage, for individual outcomes, for
peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after
exclusions.
Figure 2 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.
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.
Figure 3 shows a visual overview of the results, with details in
Table 1 and Table 2.
Figure 4, 5, 6, 7, 8, 9, and 10
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, hospitalization, recovery, cases, viral clearance, and peer reviewed studies.
Figure 4. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 10. Random effects meta-analysis for peer reviewed studies.
[Zeraatkar] analyze 356 COVID-19 trials, finding no
significant evidence that peer-reviewed studies are more trustworthy.
They also show extremely slow review times during a pandemic. Authors
recommend using preprint evidence, with appropriate checks for potential
falsified data, which provides higher certainty much earlier.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding studies with
major issues likely to alter results, non-standard studies, and studies where
very minimal detail is currently available. Our bias evaluation is based on
analysis of each study and identifying when there is a significant chance that
limitations will substantially change the outcome of the study. We believe
this can be more valuable than checklist-based approaches such as Cochrane
GRADE, which may underemphasize serious issues not captured in the checklists,
overemphasize issues unlikely to alter outcomes in specific cases (for
example, lack of blinding for an objective mortality outcome, or certain
specifics of randomization with a very large effect size), or be easily
influenced by potential bias. However, they can also be very high
quality.
The studies excluded are as below.
Figure 11 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
[Pablo-Marcos], unadjusted results with no group details.
Figure 11. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 12 shows the distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
The median effect size for
RCTs is 70% improvement,
compared to 43% for other studies.
Figure 13 and 14
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 3 summarizes the results.
