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Hydroxychloroquine works extremely well: 79% less chance of dying.

According to the law, an EMERGENCY LAW may only be introduced if it meets certain requirements.

One of those requirements is that there must be no working alternative.

Three major studies have been conducted into the effect of Hydroxychloroquine (HCQ)

This research provides proof of this.

In addition, a combination of anti-virus effect Bromhexine and anti-virus replication effect HCQ is even more effective.

Bromhexine is an old medicine and is used against cough complaints, it is cheap and safe.

A randomized trial of bromhexine in patients admitted to hospital has been conducted in Iran. The people were divided into standard therapy (200 mg HCQ low dose) with and without bromhexine. In the bromhexine group, fewer people died (0 against 5), fewer people were admitted to the IC (2 against 11) and fewer people were intubated (1 against 9).

However, there was also a significant difference in the secondary outcomes. Improvement of cough and shortness of breath within two weeks between the two groups: Shortness of breath: 3.4% in the bromhexine group vs 48.3% in the control group P≤0.001. Cough (6.9% vs. 40.0% P = 0.003). Fatigue (6.9% vs 34.5%, P = 0.010). LDH (363.2 ± 83.6 vs. 445.3 ± 115.2; P = 0.056), NLR [1.7 (1.0) vs. 3.0 (6.3); P = 0.052] and CRP [0% vs. 81.8%; P.

The drug turned out to be very effective and this molecule inhibits the action of a certain enzyme called TMPRSS2 (see picture). As a result, the virus can no longer enter the cell and the infection is extinguished. (source)

We hereby ask the experts to submit this evidence to the correct persons and to suspend the proposed emergency law of 20 August next.

We very much feel that hydroxychloroquine has been trained to get vaccines (probably for other purposes).


All additional details, references, literature, hundreds of contributing experts and attachments can be found in the official study here:

Incidentally, Frontline Doctors America has offered a reward of $ 200.000 for those who can demonstrate that HCQ does NOT work. The reward will NEVER be collected. It has been proven to work.





Many countries have adopted or rejected early treatment with HCQ, effectively forming a large trial with 2,0 billion people in the treatment group and 663 million in the control group. As of August 16, 2020, an average of 45,0 / million died in the treatment group, and 454,4 / million in the control group, relative risk 0,099. After adjustments, deaths from treatment and control become 93,7 / million and 659,9 / million, relative risk 0,14. The probability of an equal or lower relative risk for random group assignments is 0,003. Taking into account predicted changes in the spread, we estimate a relative risk of 0,22. The treated group has a 78,2% lower mortality rate.Confounding factors influence this estimate. We examined diabetes, obesity, hypertension, life expectancy, population density, urbanization, test level and intervention level, which do not take into account the observed effect.



We are investigating early or prophylactic treatment for COVID-19 with hydroxychloroquine (HCQ), which has been adopted or denied in several countries. Since the severity of COVID-19 varies widely based on age and co-morbidity, treatment was generally initiated only in those at higher risk. The primary endpoint was death.

Treatment groups.

Entire countries have made different decisions about HCQ treatment based on the same information, leading their residents to be pre-assigned to the treatment or control group. Since the assignment is made without regard to individual information such as medical status, the assignment of individuals is arbitrary for the purposes of this study.

We focus here on countries that have chosen and maintained a clear assignment to one of the groups for most of the duration of their outbreak, either widespread use or very limited use. Some countries have very mixed use, and some countries have joined or left the treatment group during their outbreak. We searched government websites, Twitter and Google, with the help of several experts in HCQ use, to confirm the assignment to the treatment or control group, finding a total of 198 relevant references, shown in Annex 12. We have excluded countries with <1 million inhabitants and countries with <0,5% of people over 80. COVID-19 disproportionately affects older people and the age-based adjustments are less reliable when there are very few people in the high-risk age groups. We have also excluded countries that are rapidly adopting aggressive intervention and isolation strategies and thus have very little spread of the virus to date. This exclusion, based on analysis by Leffler ] , is in favor of the control group and is discussed in detail below.

Collectively, the countries we identified with stable and relatively clear missions represent 34,7% of the world's population (2,7 billion or 7,8 billion). Details of the groups and the evidence, including countries identified as using HCQ mixed, can be found in annex 12 .


We analyze deaths per capita with data from Our World in Data ] . To determine the effectiveness of the treatment, we could compare the mortality rates for the entire population in the treatment and control groups, but we use the average of the individual country figures in each group to minimize effects due to differences between countries. Because randomization was done at a coarse country level, we correct for differences between countries and analyze confounding factors.

Case statistics.

We analyze deaths rather than cases because the number of cases is highly dependent on the degree of testing effort, testing criteria, the accuracy and availability of tests, accuracy of reporting, and because there is very wide variability in the severity of the case, including a high percentage of asymptomatic cases.


As of August 16, 2020, an average of 45,0 / million died in the treatment group, and 454,4 / million in the control group, relative risk 0,099. After adjustments, deaths from treatment and control become 93,7 / million and 659,9 / million, relative risk 0,14. We have performed a simulation to calculate the probability of an equal or lower relative risk occurring by chance. We randomly assigned the same number of countries to the treatment and control groups 1.000.000 times, from all countries reporting deaths to OWID. The probability of an equal or lower relative risk occurring is 0,003

Taking into account predicted changes in spread (detailed below), we estimate a relative risk of 0,22. The treated group has a 78,2% lower mortality rate. We examined diabetes, obesity, hypertension, life expectancy, population density, urbanization, test level and intervention level, which do not take into account the observed effect.

Figure 1 shows the cumulative, demographically adjusted mortality rates by country and trial group. Adjustments are detailed in the next section. Some analyzes adjust charts for the date since a specific milestone was reached, such as 0,1 deaths per million. We are not doing this because effective treatment will change the time such a milestone is reached.

For comparison, if we use the median mortality rates by country in each group instead of the mean, the relative risk is 0,16 (before prediction of future distribution). If we combine all countries into single treatment and control groups, the relative risk is 0,14. Since the sample size is very large, p <0,0001 (for single combined treatment and control groups, for the other cases less clearly defined). While the difference in mortality rates is very statistically significant, other factors influencing the results are more important, which we analyze in the next section.



A number of confounding factors influence the results, which we examine here. For reference, the results before adjustments are shown in Figure 2 .



The COVID-19 IFR varies about four orders of magnitude, depending on age. Since the proportion of older adults varies significantly from country to country, this is likely to have a significant effect on the results Leffler ] . We estimate the relative risk based on age using the death rates for infections given in Verity ] and shown in Figure 3 . Due to the distribution, simple adjustment based on median age, the proportion of people over 65 or similar may not be very accurate. We have obtained age demographics from United Nations ] which gives a breakdown within the age groups of 5 years. Using the 9 age groups provided by Verity] , we calculated an age-adaptation factor for each country to normalize observed deaths to predicted deaths if the age distribution of the country matched that of the country with the oldest population. The age distributions and calculated age factors are given in annex 1 . These adjustments are relatively significant as in Leffler ] .



Risk differs significantly based on gender Gebhard ] , so we normalized for this in a similar way as well. The data comes from United Nations ] and using the hazard ratio of 1,78 of Williamson ] turn into the resulting correction factors are displayed in annex 1 . These adjustments are relatively minor, as in Leffler ] . After correction for age and gender, we obtain the results in figure 4 . The adjusted mean deaths from treatment and control are 93,7 / million and 659,9 / million, relative risk 0,14.



Early isolation and masks.

Many countries have opted for isolation, quickly and aggressively isolating themselves from the world and preventing any spread. With a very small and unknown fraction of the population infected, we cannot easily analyze these countries. Many of these countries have also not taken a strong position on HCQ use. Mask usage was analyzed in Leffler ] , which found 29 countries that adopted masks widely and quickly, as shown in annex 11. These countries have generally taken swift action with interventions and travel restrictions to prevent spread and so far have a significantly lower spread of the virus. We have excluded countries from this list, this excludes South Korea, Czech Republic and Venezuela, which were tentatively identified as using early HCQ. This is to the advantage of the control group. If we do not exclude these countries, the treatment group-adjusted mean mortality is 79,3 / million and the relative risk decreases to 0,12.

Health of the population.

Health problems such as diabetes, obesity, and hypertension significantly increase the risk of death with COVID-19 Gao , Williamson ] . This can affect the results because the prevalence of these conditions varies from country to country. These conditions often occur together. Iglay ] found, for example, that the most common comorbid conditions for diabetes were hypertension (82%) and obesity (78%), which complicates a combined country-level adaptation, but we can first analyze the conditions separately. We examined the relationship between the levels of diabetes, obesity and hypertension with the adjusted number of deaths per million for the countries in our study, using data from [International Diabetes Federation ] , [ CIA ] en Mills ] . Appendix 2 , Appendix 3 en Appendix 4 show scatter charts, and the data can be found in Appendix 1 . There was no significant correlation for diabetes, 2 = 0,01. There is a small correlation with obesity, r 2 = 0,10. Excluding countries with an obesity prevalence of less than 10% (2 countries) reduces the correlation, 2= 0,04. Performing the analysis again in this case produces a relative risk of 0,14. The correlation for hypertension, 2 = 0,02, is very low. Based on this, we do not expect that adjustments will significantly affect the results. We repeated the analysis with adjustment for each of these factors separately (HR estimates: diabetes 1,63 Williamson ] , obesity 1,4 Williamson ] , hypertension 2,12 Gao (B) ] ), resulting in a relative risk of 0,143, 0,140, 0.143 for diabetes, obesity and hypertension, respectively. We also examined life expectancy with data from Our World in Data (B) ] . Annex 5shows a scatter plot and the data can be found in annex 1 . The correlation, 2 = 0,07, is relatively low and is moving towards a higher life expectancy resulting in higher deaths. Therefore, we find no indications that differences in health at country level have a significant effect on the results.

To test.

Countries with more comprehensive testing could potentially be more successful in minimizing deaths. We examined the relationship between testing per capita and adjusted deaths, with data from Our World in Data (C) ] . Annex 10 shows a scatter plot, and the data can be found in annex 1 . The correlation 2 = 0,03, is very low, and is also in the opposite direction of the expected potential correlation (we find more testing correlated with higher deaths). Therefore, differences in tests do not appear to significantly affect results.

Concomitant Treatments.

There are several theories as to why HCQ is effective Andrean , Brufsky , Clementi , de Wilde , Derendorf , Devaux , Hoffmann , Hu , Keyaerts , Kono , Liu , Pagliano , Savarino , Savarino (B) , Scherrmann , Sheaffi , Vincent , Wang , Wang (B) ], some of which are associated with the concomitant administration of other medications or supplements. The most commonly used are zinc Derwand , shittu ] and Azithromycin (AZ) Guerin ] . In vitro experiments report a synergistic effect of HCQ and AZ on antiviral activity Andrean ] at concentrations obtained in the human lung, and in vivo results are consistent with this Gautret ] . Zinc reduces vitro SARS-CoV RNA Dependent RNA Polymerase Activity in Velthuis ]however, it is difficult to obtain significant intracellular concentrations with zinc alone March ] . The combination with a zinc ionophore such as HCQ increases uptake into the cell, making it more likely that effective intracellular concentrations are achieved Xue ] . Zinc deficiency varies and zinc absorption may be more or less important based on a person's existing zinc content. Zinc consumption varies greatly depending on the diet NIH ] . To the extent that the concomitant administration of zinc, azithromycin, or other medications or supplements is important, we can underestimate the effectiveness of HCQ as not all countries and locations use the optimal combination.

Population density and urbanization.

We have the effect of population density Our World in Data (D) , Our World in Data (E) ] and urbanization World Bank ] tested, with scatter charts in annex 9 en annex 6 and data in annex 1 . The correlation for population density 2 = 0,00. A small correlation was found for urbanization, 2 = 0,12. Excluding countries with an urbanization of less than 75% (7 countries) reduces the correlation, 2 = 0,02. In this case, re-performing the analysis results in a relative risk of 0,14. Therefore, differences in population density and urbanization do not appear to significantly affect the results.

Treatment regimen.

There are differences in treatment regimens between and within countries. Details of timing, risk assessment and dosages differ. Since not all sites use the optimal regimen, this can reduce the observed effect.

Counterfeit medication.

It has been reported counterfeit HCQ [ Covid19 Crusher ] . This can reduce the observed effect.


Seasonality can affect the results, though Jamil ] shows that there is currently little evidence of a high temperature dependence. We also note that the pandemic has already spanned more than one season and is likely to span all seasons over time.

Accuracy of death statistics.

The accuracy of reported mortality rates varies between and within countries. Death statistics can be used in the future as they become available to more countries, but it can be difficult to separate deaths from COVID-19 and changes in other causes of death related to interventions.

Degree of distribution.

The virus has spread across countries at different rates, due to differences in the initial number of infected persons arriving in the country, differences in treatments, population dynamics, cultural differences and interventions, including masks, social distance, lockdowns, quarantine and border restrictions . This factor is likely to be significant, but will diminish over time. Since the virus is unlikely to be eliminated any time soon, we expect more and more similar percentages of people to be exposed over time, and we will update this analysis periodically to reflect the latest data. While interventions can temporarily slow the spread of the virus, it is unlikely that high intervention levels can be sustained indefinitely. Some countries, such as New Zealand, have so far kept the virus under high control, essentially by quickly isolating themselves from the world with travel restrictions and strictly enforced quarantine regulations. These countries can prevent significant spread while remaining isolated, but all countries in the treatment and control groups here have experienced significant spread of the virus.

We tested the effect of interventions using the mean stringency index for interventions University of Oxford ] over the period analyzed, as provided by Our World in Data (E) , Our World in Data (F) ] . Annex 13 shows a scatter plot, the correlation 2 = 0,10, suggesting a possibly small effect where countries with higher interventions have lower deaths so far (note that we excluded countries that quickly adopted masks, the effect may be greater if they are included). Excluding the stringency outliers (3 countries) results in a correlation 2= 0,00. Running the analysis again in this case results in a relative risk of 0,15, suggesting that the differences in non-medical interventions currently have a relatively small impact on the results.

In general, the countries of the treatment group show a significantly slower increase in mortality, which may be due to treatment, interventions, cultural differences or the initial rate of infections entering the country. We expect that over time more and more similar percentages of people will have been exposed as the virus is unlikely to be eradicated anytime soon.

To account for future dispersion, we estimated the future adjusted deaths per million for each country, 90 days in the future, based on a second-degree polynomial fit according to the most recent 30 days, enforcing the requirement that deaths are not decreasing, and assuming a gradually decreasing maximum increase over time. The UK changed its counting method around August 13, requiring a special case where the data was only reviewed after the change. Figure 5 shows the results, which predict a future relative risk of 0,22, ie the treated group has a 78,2% lower risk of death.





CQ and HCQ are synthetic alternatives to 4-aminoquinoline to quinine, a naturally occurring compound found in cinchona bark, which has long been used for respiratory infections and other conditions. The costs of HCQ are according to Centers for Medicare and Medicaid Services ] about $ 0,28 per dose. CQ, HCQ and quinine are on the World Health Organization's list of essential medications, the safest and most effective medications needed in a health system World Health Organization ] .

HCQ is effective against SARS-CoV-2 and related viruses vitro Keyaerts , Savarino , Savarino (B) , Vincent , Wang ] , plasma concentrations that have been shown to be vitro are effective , can be reached safely Keyaerts , Savarino , Vincent , Wang ] , and it has decades of use and a well-established safety profile CDC ] .

Theory and vitro Results.

Various vitro onderzoeken Andrean , Clementi , de Wilde , Hoffmann , Keyaerts , Kono , Liu , Savarino , Sheaffi , Vincent , Wang , Wang (B) ] show that CQ inhibits related viruses and SARS-CoV-2, supported by several related theory articles Brufsky , Derendorf , Devaux , Hu , Pagliano , Savarino (B) , Scherrmann] . Mechanism of action theories include HCQ / CQ protonation and endosome accumulation, preventing the acidification required for genome delivery Fitch ] ; acting as an ionophore to transport zinc ions to infected cells, where they inhibit the viral RNA replicase enzyme Xue ] ; dampening excessive immune responses causing the hyperactive immune response sometimes associated with COVID-19 Schrezenmeier ], is reduced ; and inhibiting oxidative phosphorylation in mitochondria, likely by sequestering protons required to drive ATP synthase Sheaffi ] . Savarino (B, 2003) ]discusses the antiviral effects of CQ and notes that CQ inhibits replication of several viruses, including members of the flaviviruses, retroviruses, and coronaviruses. They note that CQ has immunomodulatory effects and suppresses the production / release of tumor necrosis factor α and interleukin 6, which mediate the inflammatory complications of various viral diseases; Keyaerts (2004) ] show that the IC50 of CQ for inhibition of SARS-CoV vitro approaches the plasma concentrations of CQ achieved during the treatment of acute malaria, suggesting that CQ may be considered for immediate use in the prevention and treatment of SARS-CoV; Vincent (2005) ]show that CQ has strong antiviral effects on SARS CoV infection when cells are treated before or after exposure, suggesting prophylactic and treatment use and describing three mechanisms by which the drug might work; Savarino (2006) ] in an update to their 2003 paper discusses the vitro confirmation of CQ inhibiting SARS replication through two studies, and note that CQ affects an early phase of SARS replication; Kono (2008) ] showed that CQ inhibits the viral replication of HCoV-229E at concentrations lower than clinical use; the Wild (2014) ] show that CQ inhibits replication of SARS-CoV, MERS-CoV and HCoV-229E-GFP in the low micromolar range;Wang (B, 2/4/20) ] showed that CQ (EC50 = 1,13 μM; CC50> 100 μM, SI> 88,50) potently blocked virus infection at a low micromolar concentration and high selectivity vitro showed; Devaux (3/12/20) ] discusses mechanisms of CQ interference with the SARS-CoV-2 replication cycle; Liu (18/03/20) ] show that HCQ vitro effective and less toxic than CQ. They note that, in addition to its direct antiviral activity, HCQ is a safe and successful anti-inflammatory agent widely used in autoimmune diseases and can significantly reduce the production of cytokines and, in particular, pro-inflammatory factors. Therefore, HCQ may also contribute to attenuating the inflammatory response in COVID-19 patients.

They note that, based on the selectivity index, careful design of clinical studies is important to achieve efficient and safe control of the infection; Hu (23/03/20) ] note that CQ is known in nanomedicine research for the investigation of nanoparticle uptake into cells, and may have potential for the treatment of COVID-19; Pagliano (24/03/20) ]notes that CQ and HCQ inhibit replication in the early stages of infection, that no comparable effect is reported for other drugs that can only interfere after cell infection, and that there is a large amount of existing safety data; Clementi (31/03/20) ] show greater inhibition for combined pre- and post-exposure treatment with Vero E6 and Caco-2 cells; Brufsky (15/4/20) ]to present a theory on the effectiveness of HCQ with COVID-19, in which HCQ blocks the polarization of macrophages to an M1 inflammatory subtype and is expected to interfere with glycosylation of a number of proteins involved in the humoral immune response, possibly including the macrophage FcR gamma IgG receptor and other immunomodulating proteins, possibly through inhibition of UDP-N-acetylglucosamine 2 epimerase. In combination with possible other immune modulating effects, this could attenuate the progression of COVID-19 pneumonia up to ARDS; Andreani (4/25/20) ] show that HCQ and AZ vitro have a synergistic effect on SARS-CoV-2 at concentrations compatible with those obtained in the human lung; Derendorf (7/5/20)] discuss the pharmacokinetic properties of HCQ + AZ as a possible underlying mechanism of the observed antiviral effects; Scherrmann (6/12/20) ] propose a new mechanism that supports the synergistic interaction between HCQ + AZ; Wang (22/6/20) ] show that CQ and HCQ inhibit the entry of COVID-19 spike pseudotype virus using ACE2 highly expressed HEK293T cells; and Sheaff (8/2/20) ]to present a new theory on SARS-CoV-2 infection and why HCQ / CQ is beneficial, possibly also explaining the observed relationships with smoking, diabetes, obesity, age and treatment delay, and confirming the importance of accurate dosing. Metabolic analysis revealed that HCQ / CQ inhibits oxidative phosphorylation in mitochondria (likely by sequestering protons required to drive ATP synthase), inhibiting infection and / or retarding replication.

Hoffmann ] performs one vitro investigated CQ and HCQ inhibition of SARS-CoV-2 in Vero (kidney), Vero-TMPRSS2 and Calu-3 (derived from human lung carcinoma) cells. They suggest a lack of effectiveness, but three unsupported steps appear to have been taken to arrive at the conclusions in this paper. First, the authors conclude that CQ does not block infection of Calu-3 when the results show statistically significant inhibition at higher concentrations. Second, the authors of analysis of a specific type of lung adenocarcinoma cells consider that vitro resemble serous gland cells to a common claim that the lung cells are not inhibited. Third, they ignore existing theories of CQ / HCQ effectiveness to conclude a general lack of effectiveness.

Calu-3 is one of several cell lines derived from human lung carcinomas Shen ] . Calu-3 cells resemble serous gland cells (they do not express 15-lipoxygenase, an enzyme specifically located in the surface epithelium, but they do express secretory component, secretory leukocyte protease inhibitor, lysozyme and lactoferrin, all markers of serous gland cells). Shen ] notes that the absence of systemic inflammation, circulatory factors and other paracrine systemic influences is a potential limitation of the isolated cell system.

Hoffmann ] FIG. 1b @ 100 µM shows CQ results in a ~ 4,5 fold decrease (note that a log scale is used) in extracellular virus, p = 0,05, at 24 hours (estimated from the graph). We note that the paper marks this as not significant because the value is 0,517, but the p-value is unlikely to be accurate at this level. In addition, authors use Dunnett's test, while other tests may have higher power Sauder ]. We further note that the 95% significance level is only an agreement and the results do not magically go from non-significant at p = 0,051 to significant at p = 0,049. Results do not apply until 24 hours later and we expect a further decline over time. FIG. 1a shows ~ 45-50% input inhibition @ 100 µM for HCQ / CQ (p = 0,0005 / 0,0045), ~ 10-30% @ 10 µM (p = 0,13 / 0,99). Inhibition is significantly better with Vero cells.

There are several theories of how HCQ can help with COVID-19, and we note that authors do not consider one of the most common theories in which HCQ functions as a zinc ionophore, allowing for significant intracellular concentrations of zinc. Zinc is known to inhibit the RNA-dependent RNA polymerase activity of SARS-CoV and is widely believed to be important for the effectiveness of SARS-CoV-2 shittu ] .

Animal-like in vivo studies.

Keyaerts (B, 2009) ] showed that CQ inhibits HCoV-OC43 replication in HRT-18 cells in a mouse study. Lethal HCoV-OC43 infection in newborn C57BL / 6 mice was treated with CQ transplacental or breast milk, with the highest survival rate (98,6%) found when mother mice were treated daily at a concentration of 15 mg CQ per kg body weight. Survival rates decreased in a dose-dependent manner, with 88% survival on treatment with 5 mg / kg CQ and 13% survival on treatment with 1 mg / kg CQ. They conclude that CQ can be very effective against HCoV-OC43 infection in newborn mice and can be considered as a future drug against HCoVs; Yan (2012) ]show that CQ can efficiently improve acute lung injury and dramatically improve the survival rate in mice infected with live avian influenza A H5N1 virus; and Maisonnasse (5/6/20) ]study treatment in monkeys. They report no effect, but the data shows varying signs of effectiveness despite the very small sample size and 100% recovery of all treated and control monkeys. Lung lesion data on the last day shows that 63% of control monkeys have lesions, while only 26% of treated monkeys do, p = 0,095 (missing data for 7 monkeys predicted from day 5 results and the trend of similar monkeys). After one week, 74% of the treated monkeys recovered with <= 4 log copies / ml viral load, compared to 10% of the control monkeys, p = 38. 0,095% of the control monkeys also have a higher peak viral load than 38% of the 100 treated monkeys after treatment. The group with the lowest peak viral load is the PrEP group. All animals in this study were infected with the same initial viral load,

Human in vivo to research.

We found 71 studies linked to human use of HCQ in vivo for the treatment of COVID-19 Ahmad , Alberics , An , Arshad , Ashraf , Ayerbe , Barbosa , Bernaola , Bhattacharya , Borba , Boulware , Carlucci , Cavalcanti , Chamieh , Chatterjee , Chen , Chen (B) , Chen (C) , colson , D'Arminio Monforte ,Davido , Davido (B) , Esper , Ferreira , Gao (B) , gao (C) , Gautret , Gautret (B) , Gelris , Gendelman , Giacomelli , Guerin , Hong , Horby , Huang , Huang (B) , Ip , Izoulet , Jiang , Kamran , khurana , Kim , lagier , Lee , Maciasmagagnolia , Mahevas , Mehra , Membrillo de Novales , Meo , Mikami , Million , Mitchell , Medium , Mitja (B) , Molina , okour , Ota , Paccoud , Pirnay , Rosenberg , Saleemi , sbidian , Scholz , Singh , Skipper , Pliers , Xue (B) , Yu ,Yu (B) , Zhong ] . 43 of these present positive results (to varying degrees and with confidence) Ahmad , Alberics , Arshad , Ayerbe , Bernaola , Bhattacharya , Boulware , Chamieh , Chatterjee , Chen , Chen (B) , colson , D'Arminio Monforte , Davido , Esper , Ferreira , Gao (B) , gao (C) , Gautret (B) ,Guerin , Hong , Huang , Huang (B) , Izoulet , Jiang , khurana , Kim , lagier , Lee , Membrillo de Novales , Meo , Mikami , Million , Mitchell , okour , Ota , Pirnay , sbidian , Scholz , Xue (B) , Yu , Yu (B) , Zhong ], 15 present negative results (also to varying degrees and with confidence) An , Barbosa , Borba , Cavalcanti , Chen (C) , Giacomelli , Horby , Ip , magagnolia , Mahevas , Molina , Rosenberg , Saleemi , Singh , Pliers ] , while the rest is inconclusive or has been withdrawn. Table 1 shows a distribution of exams based on treatment time.



Investigation of late treatment.

Most studies have focused on late treatment of hospital patients and the results are very mixed. We found that 24 of the studies reported positive effectiveness while 15 reported negative effectiveness, both with varying effect and confidence. We will not go into the late treatment studies here, as we are concerned with early treatment, except to note that these studies suggest that HCQ may be potentially beneficial in a hospital setting if used very quickly and in patients who are not yet advanced. stage. of the disease; and it may be of limited or negative value in later stage disease. Three studies consider higher doses than are commonly used Borba , Horby , World Health Organization (B) ], and the results suggest that these dosages may be harmful in late stage patients.

Pre-Exposure Prophylaxis (PrEP) Studies.

We found 8 PrEP studies Bhattacharya , Chatterjee , Ferreira , Gendelman , khurana , Macias , Mitchell , Zhong ] . khurana ] presents a study of hospital personnel showing that HCQ prophylaxis significantly reduces COVID-19, OR 0,30, p = 0,02. 94 positive health professionals with a matched sample of 87 who tested negative. The true benefit of HCQ may be greater because it does not take into account the severity of the symptoms; Zhong ]analyzed 6.228 patients with autoimmune rheumatic diseases with 55 COVID-positive members of families exposed to COVID-19, which showed that patients on HCQ had a lower risk of COVID-19 than those on other disease-modifying anti-rheumatic drugs with OR 0,09 (0,01-0,94), p = 0,044; Ferreira ] analyzed 26.815 patients who showed that chronic HCQ treatment (77 patients) protects against COVID-19, odds ratio 0,51 (0,37-0,70); Bhattacharya ] shows that PrEP reduced HCQ cases from 38% to 7% in 106 people; Chatterjee ]shows that PrEP HCQ of 4+ doses was associated with a significant decrease in the chance of becoming infected, along with a dose-response relationship, based on 378 treatment and 373 control cases; Mitchell ]analyze COVID-19 among 2,4 billion people, showing a wide counterintuitive disparity between well-developed and less developed countries, with more affluent countries about a hundred times more likely to be infected and die from COVID -19. They find the effect is most evident when compared to countries with the highest rates of endemic malaria. Since travelers to malaria-endemic countries are likely to use antimalarial prophylaxis, authors find the data to be highly supportive of the hypothesis that prophylactic antimalarial use by incoming visitors significantly reduces a country's COVID-19 death rate. Although authors do not correct for age differences, those adjustments can only explain a small part of the observed difference; Gendelman ]presents a small study of rheumatic disease / autoimmune disease patients showing no significant difference, but with only 3 chronic HCQ patient cases. Considering the significantly increased susceptibility and incidence of infections to SLE, RA and other autoimmune diseases, the results could potentially show a significant benefit to HCQ, but it is inconclusive with such a small sample size; and Macias ]analyzes the incidence in patients with rheumatic diseases, but with only 3 confirmed cases, and not corrected for significant differences between groups and the expected infection rates based on the patient's condition, we consider this study inconclusive. See bouza , bultink , Herrinton , Iliopoulos , Kim (B) , Li , Listing ] for details on the significantly increased susceptibility and incidence of infections to SLE, RA and other autoimmune diseases. The more recent studies Ferreira , Zhong ] with rheumatic disease / autoimmune disease give patients more confidence.

Post-Exposure Prophylaxis (PEP) studies.

We found 3 PEP studies Boulware , Lee , Medium ] . Lee ] studies post-exposure prophylaxis of 211 high-risk individuals in a long-term care hospital after a major exposure, with no positive cases after 14 days.

Boulware ]reports a lack of efficacy due to the failure to reach statistical significance, but multiple secondary analyzes show statistically significant and positive results. Because of this difference, we provide a detailed explanation. The paper shows a 17% reduction in cases, p = 0,35 due to the small sample size - we can say this is inconclusive, but not negative (it is positive rather than negative). Authors initially thought that 3 days after exposure was the maximum enrollment delay, but there was a mid-trial change that extended this to allow for an additional day delay. With the original trial specification, they show a 30% reduction in pre-treatment cases, p = 0,13. If the study was not terminated early and if the observed trend continued, p = 0,05 would have been reached in a total of ~ 840 patients (the original study specification was 1,

In the Supplementary Appendix, we can see that COVID-19 cases are reduced by [49%, 29%, 16%], respectively, when ingested within ~ [70, 94, 118] hours of exposure (including shipping delay), as shown in Figure 6 . To be a priori the most important cases to consider are the relationship between delay and response to treatment and the shortest delay to treatment (mean ~ 70 hours in this case). The shortest delay in treatment is significant @ 94% compared to all placebos. By simulation, assuming that cases occur randomly according to the observed frequency, we found that the probability that the results follow the observed favorable delay response relationship is 0,2% CovidAnalysis ] . Since we ran 2 tests, the conservative Bonferroni adjustment is [Jafari ] gives us p = 0,004. The efficacy of the treatment has also been demonstrated in another secondary analysis Watanabe ] .

A priori we expect an effective treatment here to be more effective when administered earlier Cohen ] . Extrapolation of the trend of treatment delay suggests a 93% reduction in immediate treatment cases, of course we have little confidence in this prediction, but it would be consistent with the data and cannot be ruled out.

The effectiveness found is all the more remarkable given the limitations of the research. Treatment was relatively late, with enrollment up to 4 days after exposure and an unspecified send delay. Although the paper does not contain shipping information, the study protocol provides some information. While not clear, it indicates there will be no weekend shipping and a possible 12 noon cut off for same-day issue and shipping, from which we estimate the treatment delay to be ~ 70 to 140 hours post-exposure on average for the 1-4 days since enrollment specified in the paper (we will update this when authors respond to our request for details). There was only 75% adherence, of which 16% did not take the medication at all, so the actual effectiveness is probably higher. The study is based on internet surveys,

The editorial accompanying this article also notes that in a small animal model of SARS-CoV-2 Sheahan ] , prevention of infection or more severe disease was observed only when the antiviral agent was given before or shortly after exposure Cohen ] . Research also shows that the placebo used in the US (folic acid) may be protective against COVID-19 Acosta-Elias ] . More details about this analysis can be found in CovidAnalysis ] .

[Mitjà] is conducting a highly delayed PEP treatment study that suggests efficacy, but lacks statistical significance due to the small number of cases. Mortality rates decreased from 0,6% to 0,4%, RR 0,71, not statistically significant due to the low incidence (8 control cases, 5 treatment cases).
Enrollment occurred up to 7 days after exposure and the delay in treatment in this study is unclear, with no details on the timing of the exposure events or the provision of medication. They seem to identify index cases based on the date of a positive test for a contact, which is likely to be much later than the actual exposure time. Due to quarantine at the time and likely cohabitation of a majority of contacts, it is likely that the actual exposure time was significantly earlier. 13,1% of patients already tested positive at baseline, which is consistent with the actual exposure time being significantly earlier. Nasopharyngeal viral load analysis is subject to testing for unreliability and temporospatial differences in viral shedding [Wang (C)]. PCR tests have a very high false negative rate in early stages (e.g. 100% on Day 1, 67% on Day 4 and 20% on Day 8 [Kucirka], therefore it is likely that a much higher rate was infected at an unknown time for registration.

Considering the enrollment delay, the PCR test delay, and the percentage of false negative PCRs in the early stages, the treatment delay in this study was generally very long and could exceed 2 weeks.
This study focuses on the existence of symptoms or PCR positive results, but the severity of the symptoms is more important. Research has shown that HCQ levels in the lungs can be much higher than in plasma [Browning], which can help minimize the occurrence of serious cases and death. The outcome analyzed here may not be very relevant to the goal of reducing mortality. For positive symptomatic cases they find RR = 0,89, which promotes treatment, but is not statistically significant. The RR for non-PCR positive at baseline is 0,74, which is consistent with previous treatment being more effective. A greater effect is seen for nursing home residents, RR = 0,49, possibly because exposure events in this context are identified more quickly than home exposure where source testing may be more delayed. There is a treatment delay response relationship consistent with effective treatment.

The paper does not mention zinc. Zinc deficiency in Spain has been reported in 83% [Olza], this can significantly reduce effectiveness to the extent that zinc is important to the success of HCQ treatment.
The definition of COVID-19 symptoms is very broad - only the existence of headache or muscle pain alone was considered COVID-19. Overall, there was a very low incidence of confirmed COVID-19 (138 cases in both arms). There were no serious adverse events assessed as related to treatment. Authors exclude those with symptoms within the past two weeks, but those with symptoms up to several months earlier can still test PCR positive even though there may not be a viable virus. There seems to be inaccurate data in the newspaper. Table 2, Secondary Outcomes, Control, Hospital / Vital Records, shows that 8 of 1042 is 9,7%.

In summary, this study appears positive in the context of very delayed treatment and the small number of cases.

Early Treatment Studies. We have 16 early treatment studies [Ahmad, Ashraf, Chen, Esper, Gautret, Gautret (B), Guérin, Hong, Izoulet, Lagier, Meo, Million, Mitjà (B), Otea, Scholz, Skipper] all of which show on some degree of effectiveness. [Hong] showed that 1-4 days after diagnosis HCQ was the only protective factor found against prolonged viral shedding, OR 0,111, p = 0,001. 57,1% viral clearance 1-4 day delay vs. 22,9% for 5+ days of deferred treatment. Authors report that early administration of HCQ significantly improves inflammatory cytokine secretion and that COVID-19 patients should receive HCQ as soon as possible. 42 patients with HCQ 1-4 days from diagnosis, 48 ​​with HCQ 5+ days from diagnosis; [Scholz] performs a retrospective analysis of 518 patients (141 treated, 377 control) showing that early treatment with HCQ + AZ + Z results in 84% less hospitalization and 80% less mortality - hospitalization OR 0,16 (p <0,001 ), death OR 0,2 (p = 0,16); [Lagier] analyzed 3.737 patients who showed that early treatment leads to a significantly better clinical outcome and faster viral load reduction with corresponding sample death HR 0,41 p = 0,048; [Chen] showed significantly faster clinical recovery and shorter time to RNA negative (from 7,0 days to 2,0 days (HCQ), p = 0,01 with 67 mild / moderate cases; [Otea] showed that HCQ + AZ appears to reduce serious complications and death with 80 patients [Guérin] conducted a small retrospective study with 88 patients and found that the mean recovery time was shortened from 26 days to 9 days with HCQ + AZ, p <0,0001 or to 13 days with AZ, including case-control analysis with matched patients; [Ahmad] treated 54 patients in long-term care settings with 6% death, compared to 22% based on a naive indirect comparison; [Million] showed that HCQ + AZ is safe and results in a low death rate with a retrospective analysis of 1061 patients, [Ashraf] concluded that HCQ improved the clinical outcome with a small, limited trial of 100 patients in Iran; n in the 10 days after the third death in countries that do and do not use [H] CQ.

They show dramatically lower mortality in [H] CQ countries, but try not to account for other differences between countries; [Esper] analyzed 636 patients who presented HCQ + AZ, reduced hospitalization by 79% when used within 7 days (65% in total); [Gautret (B)] presented a pilot study suggesting improvement with HCQ + AZ and recommending further study; and [Gautret] in an early and small study with significant limitations, showed that HCQ was associated with reduction of viral load and that this was enhanced with AZ. [Gautret] also conducted an early and small study showing that HCQ was associated with a reduction in viral load and that it was enhanced with AZ, but this study has significant limitations [Machiels, Rosendaal]. In addition, [Risch] presents an updated meta-analysis that includes several studies that are currently unpublished. 7 new studies of high-risk outpatients have been reported, for a total of 12 studies, all showing significant benefit. [Mitjà (B)] present a randomized study of 293 low-risk patients with no deaths, no serious side effects and no statistically significant improvements. There was a 25% reduction in hospitalization and a 16% reduction in the median time to symptom improvement for HCQ, with no statistical significance due to small sample sizes. However, this article contains inconsistent data - some of the values ​​listed in Table 2 and the summary correspond to 12 hospital admissions in the control group, while others correspond to 11 hospital admissions in the control group, therefore we are unsure of other data presented here. reported. This article also does not specify the delay in treatment, but only reports an enrollment delay of up to 120 hours after symptoms, plus an additional unspecified delay when medication was dispensed to patients during the first home visit. They do not abort the results by delaying treatment. The undetectable viral load was changed to 3 log10 copies / ml, possibly partially masking effectiveness. For viral load with nasopharyngeal swabs, we note that viral activity in the lung can be especially important for COVID-19, and that the HCQ concentration in the lungs can be significantly higher (for example, about 30 times the blood concentration in [Chhonker]). Nasopharyngeal viral load analysis is subject to testing for unreliability and temporo-spatial differences in viral shedding [Wang (C)]. Viral detection by PCR is not the same as viable virus [Academy of Medicine]. PCR tests do not distinguish between live virus and dead virus cell fragments, which can take months to remove [Bo-gyung]. [Schipper] to present an RCT with internet surveys among 423 patients.

They show that delayed treatment of ~ 70 to 140 hours with HCQ reduced combined hospitalization / death by 50%, p = 0,29 (5 HCQ cases, 10 control cases) and reduced hospitalization by 60%, p = 0,17 , 6000. There was one death in hospital check-up and one non-hospital death from HCQ. It is unclear why there was a non-hospital mortality, external factors such as a lack of standard care may play a role. Excluding that case results in one control death and zero HCQ deaths (not statistically significant, but noted if reducing mortality is the main outcome). Details about hospital admissions and deaths, such as adherence and treatment delay, may be informative but are not provided. The paper states that the endpoint has changed from hospitalization / death to symptom severity, as this would have required 95 participants. However, if the observed trend continues, they would be 725% significant for the reduction in hospitalization in ~ 95 patients and 1145% for the reduction in combined hospitalization / death in ~ 1.242 patients, both of which are less than the original plan of XNUMX patients . We hope this trial can be continued for statistical significance.

As in the accompanying PEP study, treatment in this study was relatively late, with an unspecified transmission delay, which we estimate to be ~ 70 to 140 hours after symptoms for enrollment days 1 to 4. We note that there is no overlap. has been used with the more typical delays such as 0 - 36 hours for oseltamivir. The article compares delayed treatment of 0 - 36 hours with oseltamivir (influenza) and delayed treatment of ~ 70 to 140 hours with HCQ (COVID-19), noting that oseltamivir appeared to be more effective. A more comparable study is [McLean] which showed that a 48 - 119 hour delayed treatment with oseltamivir has no effect. This suggests that HCQ is more effective than oseltamivir, and that HCQ may still have a significant effect during a certain delay after the delay in which oseltamivir is effective. It enrolled 6 people who enrolled with> 4d symptoms, although they did not meet the study's inclusion criteria. This reduces the perceived effectiveness. The patients in this study are relatively young and most recover unaided. This reduces the scope for a treatment to make improvements. Maximum improvement from an effective treatment would be expected before all patients approach recovery. For symptoms, authors focus on the end result where most recovered, but it is more informative to examine the curve and point of maximum effectiveness. Authors did not collect data for each day, but they did have intermediate results for days 3, 5, 10. Results are consistent with effective treatment and show statistically significant improvement, p = 0,05, at day 10 (other unreported days may increase effectiveness).

As with the accompanying PEP study, this study is based on internet surveys. Known bogus surveys were submitted to the PEP study, and there could be an unknown number of undetected bogus surveys in both studies. Research shows that the placebo used in the US may be protective against COVID-19 [Acosta-Elias], so the true effectiveness of HCQ could be higher than observed. Adherence was only 77%, which would likely increase the true effect of the treatment. Authors note that the results are not generalizable to the high-risk COVID population.


We originally used the term “land-randomized controlled trial” for this study - a drug is being tried, there is a control group, and a person in the study has randomized their group in advance, regardless of their medical status. Unlike a retrospective study, the control population is not related to the treatment decisions of the treatment population. People can't choose their group, and that's controlled by the countries (actually running the trial), unlike a natural experiment. This is perhaps a unique time in history when the world split over a treatment for a disease, with countries choosing to accept or decline treatment based on the same information, resulting in random selection for patients. We also note that a comparison can be made with cluster randomized controlled trials, and that the bar for “RCT” is relatively low. For example, Internet studies with unknown survey bias, unknown percentage of fake responses and low adherence are accepted as RCTs. However, it is possible that some people may have misinterpreted the nature of this study as a clinical trial if they did not read the paper, so we changed the name to avoid confusion.
All studies have some limitations, as limitations of HCQ studies may include confounding factors; sample sizes that are too small; suboptimal treatment regimens; dosing regimens that may be too low, too high, or do not sufficiently take into account the long half-life of HCQ; excessive delays in treatment; dependence on internet surveys; inclusion / exclusion criteria; the use of tests that may be inaccurate or poor measures of disease severity; and patient characteristics that differ greatly from the most at-risk population.

There are clear pros and cons to this trial, with several details discussed previously. Benefits include the very large scale, the absence of barriers to implementation and the absence of inclusion / exclusion criteria. The main drawback is the gross country-based randomization, which requires us to address differences between countries, and the main limitation at the moment is probably the varying degree of dispersion between countries. We assessed the available seroprevalance data [BBC, CDC (B), Eckerle, European University, Fontanet
, Fontanet (B), Havers, Ioannidis, Lewis, Public Health England, Salje, Skowronski, Slot, Swedish Public Health Agency, The Hindu, The Indian Express, The Irish Times, The Jerusalem Post, Valenti], but the scarce nature, different time periods and different geographic coverage prevent conclusions at this time. We expect that increased seroprevalence data will allow for improved analysis over time.
While this is not a double-blind study, it should not significantly affect the results. [Wood], based on an analysis of 1.346 studies, show that concealment and blinding of allocations are only important for subjective outcomes and should not have a significant effect on the objective outcome here.
Imperfect adherence, imperfect co-administration of treatments, imperfect dosage regimens, and adulterated HCQ may reduce the perceived effectiveness of the treatment.
In terms of early treatment, we consider this to be PrEP or PEP prophylaxis and treatment within approximately 48 hours of symptoms. Details of effectiveness based on treatment delay are currently not well known. In comparison, oseltamivir is generally considered to be effective within about 48 hours and is more likely to be considered better within that time. For example, [Nicholson, Treanor] finds the effectiveness of oseltamivir based on a delayed treatment of 0-36 hours, while [McLean] finds no effect for a delayed treatment of 48-119 hours.

The results here are consistent with the positive results from other early treatment studies, as discussed in the previous section. There are many other examples that are consistent with effectiveness, some of which in Brazil and Switzerland are discussed by [Rafaeli, Risch (B)]. We give a few more examples.
[Mitchell (B)] provide an extensive discussion of the differences between the death rates of New York City and Lagos, Nigeria, both of which were receiving infected travelers around the same time. NYC's high rate has been linked to population density, poverty, overpopulation, and ethnicity. Lagos is a busy urban center of about 22 million people with 30 families often sharing the same bathroom in one building, and none of the factors mentioned promote lower mortality rates in Lagos. Lagos also has a lower quality of medical care. Still, NYC had a death rate that was 600 times higher. The younger population can only explain a small part of this difference. Mitchell concludes that there is a crossover prophylactic effect of anti-malarial agents against COVID-19.
Early HCQ treatment is not widely used in France, but an exception is in Marseille. Table 2 shows the mortality rates up to the end of May for these two locations for 2020 and compared with the previous two years. Paris shows a big increase, while Marseille does not [Covid19Crusher (B)].


This study is regularly updated. The paper is fully data-driven - all charts and figures are dynamically generated from the latest data. As previously discussed, the constraint of varying degrees of dispersion should diminish over time, allowing for continuously improving analysis. Figures are subject to change as new statistics are released every day. OWID also periodically updates statistics for previous days, sometimes these changes are significant. The forecast for future spread will change based on the latest trend.
8/15: We noted that the UK changed their counting method around August 13th.
8/13: We have added references [Machiels, Mitchell, Rosendaal] and details on the definition of early treatment.
8/12: We've updated the title and associated discussion. We added an analysis of the probability of random assignment resulting in the observed difference or better. We have clarified the exclusion of countries that have widely and quickly adopted masks, which aims to exclude those countries that have taken an aggressive intervention and isolation approach and have very little spread of the virus.
8/10: We added a section to respond to frequently asked questions. This will expand over time. A numbering error in the appendix has been fixed for urbanization.
8/9: We have clarified the p-value for the full treatment and control groups. We have updated the Medication Cost Reference to point directly to the relevant data.
8/8: We clarified the mask-based exclusions in the earlier entry because feedback indicated that many people had not read and misinterpreted the confounders section. Feedback also indicated that many people missed the discussion of case statistics, so we moved that to a separate section.
7-8: We have updated and clarified the terminology related to the trial. We think it was originally clear from the title, with clear explanations of how the process came about, but some people reported a misunderstanding. We didn't think anyone would misinterpret the wording to think 2.7B was participating in a clinical trial, that's impossible. It seems obvious that countries should try this treatment (and we explain that in the first sentence of the summary). It's not clear how many people really misinterpreted this because of its combination with other baseless allegations. For example, it is claimed that this must be fake because it looks too professional. We appreciate the feedback on our basic design skills (hopefully clean and easy to navigate), but we're not following the logic. In any case, we want to be as clear as possible.
Why is country x not included? Our goal is to identify countries that have made strong decisions about treatment. Countries without clear decisions are much more difficult to analyze - to achieve meaningful results, we need to know the share of use to some extent. An opportunity for further investigation would be to analyze prescription data, if available.
Countries such as Italy or Brazil have very mixed uses, with differences over large time periods of their outbreak and / or wide geographic differences. Analyzing these countries would be much more complex. Data broken by geography within the country is typically not available, and analysis before / after changes in treatment decisions is complicated by the different spread over time.
Analysis of countries that have prevented significant spread of the virus is difficult because we have very little ability to predict the ultimate death rate when the virus is widespread, and the virus may never become widespread in these countries, for example, if left long enough stay isolated. and a highly effective vaccine is becoming available. These countries also generally have not made a strong decision for or against treatment.
Israel should not fall into the widespread use category. We have received some reports that usage in Israel is not as high as is believed. We would like to receive confirmation of use. Removing Israel would not significantly change the observed effect (it would benefit the treatment group somewhat).

All Appendix (English):


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