Nirmatrelvir combined with ritonavir for preventing and treating COVID-19.

Oral nirmatrelvir/ritonavir (Paxlovid®) aims to avoid severe COVID-19 in asymptomatic people or those with mild symptoms, thereby decreasing hospitalization and death. Due to its novelty, there are currently few published study results. It remains to be evaluated for which indications and patient populations the drug is suitable.  OBJECTIVES: To assess the efficacy and safety of nirmatrelvir/ritonavir (Paxlovid®) plus standard of care compared to standard of care with or without placebo, or any other intervention for treating COVID-19 and for preventing SARS-CoV-2 infection. To explore equity aspects in subgroup analyses. To keep up to date with the evolving evidence base using a living systematic review (LSR) approach and make new relevant studies available to readers in-between publication of review updates. We searched the Cochrane COVID-19 Study Register, Scopus, and WHO COVID-19 Global literature on coronavirus disease database, identifying completed and ongoing studies without language restrictions and incorporating studies up to 11 July 2022.  This is a LSR. We conduct monthly update searches that are being made publicly available on the open science framework (OSF) platform. Studies were eligible if they were randomized controlled trials (RCTs) comparing nirmatrelvir/ritonavir plus standard of care with standard of care with or without placebo, or any other intervention for treatment of people with confirmed COVID-19 diagnosis, irrespective of disease severity or treatment setting, and for prevention of SARS-CoV-2 infection. We screened all studies for research integrity. Studies were ineligible if they had been retracted, or if they were not prospectively registered including appropriate ethics approval. We followed standard Cochrane methodology and used the Cochrane risk of bias 2 tool. We rated the certainty of evidence using the GRADE approach for the following outcomes: 1. to treat outpatients with mild COVID-19; 2. to treat inpatients with moderate-to-severe COVID-19: mortality, clinical worsening or improvement, quality of life, (serious) adverse events, and viral clearance; 3. to prevent SARS-CoV-2 infection in post-exposure prophylaxis (PEP); and 4. pre-exposure prophylaxis (PrEP) scenarios: SARS-CoV-2 infection, development of COVID-19 symptoms, mortality, admission to hospital, quality of life, and (serious) adverse events. We explored inequity by subgroup analysis for elderly people, socially-disadvantaged people with comorbidities, populations from LICs and LMICs, and people from different ethnic and racial backgrounds. As of 11 July 2022, we included one RCT with 2246 participants in outpatient settings with mild symptomatic COVID-19 comparing nirmatrelvir/ritonavir plus standard of care with standard of care plus placebo. Trial participants were unvaccinated, without previous confirmed SARS-CoV-2 infection, had a symptom onset of no more than five days before randomization, and were at high risk for progression to severe disease. Prohibited prior or concomitant therapies included medications highly dependent on CYP3A4 for clearance and CYP3A4 inducers.  We identified eight ongoing studies. Nirmatrelvir/ritonavir for treating COVID-19 in outpatient settings with asymptomatic or mild disease For the specific population of unvaccinated, high-risk patients nirmatrelvir/ritonavir plus standard of care compared to standard of care plus placebo may reduce all-cause mortality at 28 days (risk ratio (RR) 0.04, 95% confidence interval (CI) 0.00 to 0.68; 1 study, 2224 participants; estimated absolute effect: 11 deaths per 1000 people receiving placebo compared to 0 deaths per 1000 people receiving nirmatrelvir/ritonavir; low-certainty evidence, and admission to hospital or death within 28 days (RR 0.13, 95% CI 0.07 to 0.27; 1 study, 2224 participants; estimated absolute effect: 61 admissions or deaths per 1000 people receiving placebo compared to eight admissions or deaths per 1000 people receiving nirmatrelvir/ritonavir; low-certainty evidence). Nirmatrelvir/ritonavir plus standard of care may reduce serious adverse events during the study period compared to standard of care plus placebo (RR 0.24, 95% CI 0.15 to 0.41; 1 study, 2224 participants; low-certainty evidence). Nirmatrelvir/ritonavir plus standard of care probably has little or no effect on treatment-emergent adverse events (RR 0.95, 95% CI 0.82 to 1.10; 1 study, 2224 participants; moderate-certainty evidence), and probably increases treatment-related adverse events such as dysgeusia and diarrhoea during the study period compared to standard of care plus placebo (RR 2.06, 95% CI 1.44 to 2.95; 1 study, 2224 participants; moderate-certainty evidence). Nirmatrelvir/ritonavir plus standard of care probably decreases discontinuation of study drug due to adverse events compared to standard of care plus placebo (RR 0.49, 95% CI 0.30 to 0.80; 1 study, 2224 participants; moderate-certainty evidence). No study results were identified for improvement of clinical status, quality of life, and viral clearance.  Subgroup analyses for equity Most study participants were younger than 65 years (87.1% of the : modified intention to treat (mITT1) population with 2085 participants), of white ethnicity (71.5%), and were from UMICs or HICs (92.1% of study centres). Data on comorbidities were insufficient.  The outcome 'admission to hospital or death' was investigated for equity: age (< 65 years versus ≥ 65 years) and ethnicity (Asian versus Black versus White versus others). There was no difference between subgroups of age. The effects favoured treatment with nirmatrelvir/ritonavir for the White ethnic group. Estimated effects in the other ethnic groups included the line of no effect (RR = 1). No subgroups were reported for comorbidity status and World Bank country classification by income level. No subgroups were reported for other outcomes. Nirmatrelvir/ritonavir for treating COVID-19 in inpatient settings with moderate to severe disease No studies available. Nirmatrelvir/ritonavir for preventing SARS-CoV-2 infection (PrEP and PEP) No studies available. There is low-certainty evidence that nirmatrelvir/ritonavir reduces the risk of all-cause mortality and hospital admission or death based on one trial investigating unvaccinated COVID-19 participants without previous infection that were at high risk and with symptom onset of no more than five days. There is low- to moderate-certainty evidence that nirmatrelvir/ritonavir is safe in people without prior or concomitant therapies including medications highly dependent on CYP3A4. Regarding equity aspects, except for ethnicity, no differences in effect size and direction were identified. No evidence is available on nirmatrelvir/ritonavir to treat hospitalized people with COVID-19 and to prevent a SARS-CoV-2 infection. We will continually update our search and make search results available on OSF.

Reis S ，Metzendorf MI ，Kuehn R ，Popp M ，Gagyor I ，Kranke P ，Meybohm P ，Skoetz N ，Weibel S ... -  《Cochrane Database of Systematic Reviews》

Nirmatrelvir combined with ritonavir for preventing and treating COVID-19.

Oral nirmatrelvir/ritonavir (Paxlovid) aims to avoid severe COVID-19 in asymptomatic people or those with mild symptoms, thereby decreasing hospitalization and death. It remains to be evaluated for which indications and patient populations the drug is suitable. To assess the efficacy and safety of nirmatrelvir/ritonavir plus standard of care (SoC) compared to SoC with or without placebo, or any other intervention for treating COVID-19 or preventing SARS-CoV-2 infection. To explore equity aspects in subgroup analyses. To keep up to date with the evolving evidence base using a living systematic review (LSR) approach and make new relevant studies available to readers in-between publication of review updates. We searched the Cochrane COVID-19 Study Register, Scopus, and World Health Organization COVID-19 Research Database, identifying completed and ongoing studies without language restrictions and incorporating studies up to 15 May 2023. This is a LSR. We conduct update searches every two months and make them publicly available on the open science framework (OSF) platform. We included randomized controlled trials (RCTs) comparing nirmatrelvir/ritonavir plus SoC to SoC with or without placebo, or any other intervention for treatment of people with confirmed COVID-19 diagnosis, irrespective of disease severity or treatment setting, and for prevention of SARS-CoV-2 infection. We screened all studies for research integrity. Studies were ineligible if they had been retracted, or if they were not prospectively registered including appropriate ethics approval. We followed standard Cochrane methodology and used the Cochrane RoB 2 tool. We rated the certainty of evidence using the GRADE approach for the following outcomes: 1. to treat outpatients with mild COVID-19; 2. to treat inpatients with moderate to severe COVID-19: mortality, clinical worsening or improvement, quality of life, (serious) adverse events, and viral clearance; 3. to prevent SARS-CoV-2 infection in postexposure prophylaxis (PEP); and 4. pre-exposure prophylaxis (PrEP) scenarios: SARS-CoV-2 infection, development of COVID-19 symptoms, mortality, admission to hospital, quality of life, and (serious) adverse events. We explored inequity by subgroup analysis for elderly people, socially-disadvantaged people with comorbidities, populations from low-income countries and low- to middle-income countries, and people from different ethnic and racial backgrounds. As of 15 May 2023, we included two RCTs with 2510 participants with mild and mild to moderate symptomatic COVID-19 in outpatient and inpatient settings comparing nirmatrelvir/ritonavir plus SoC to SoC with or without placebo. All trial participants were without previous confirmed SARS-CoV-2 infection and at high risk for progression to severe disease. Randomization coincided with the Delta wave for outpatients and Omicron wave for inpatients. Outpatient trial participants and 73% of inpatients were unvaccinated. Symptom onset in outpatients was no more than five days before randomisation and prior or concomitant therapies including medications highly dependent on CYP3A4 were not allowed. We excluded two studies due to concerns with research integrity. We identified 13 ongoing studies. Three studies are currently awaiting classification. Nirmatrelvir/ritonavir for treating people with asymptomatic or mild COVID-19 in outpatient settings Nirmatrelvir/ritonavir plus SoC compared to SoC plus placebo may reduce all-cause mortality at 28 days (risk ratio (RR) 0.04, 95% confidence interval (CI) 0.00 to 0.68; 1 study, 2224 participants; low-certainty evidence) and admission to hospital or death within 28 days (RR 0.13, 95% CI 0.07 to 0.27; 1 study, 2224 participants; low-certainty evidence). Nirmatrelvir/ritonavir plus SoC may reduce serious adverse events during the study period compared to SoC plus placebo (RR 0.24, 95% CI 0.15 to 0.41; 1 study, 2224 participants; low-certainty evidence). Nirmatrelvir/ritonavir plus SoC probably has little or no effect on treatment-emergent adverse events (RR 0.95, 95% CI 0.82 to 1.10; 1 study, 2224 participants; moderate-certainty evidence), and probably increases treatment-related adverse events such as dysgeusia and diarrhoea during the study period compared to SoC plus placebo (RR 2.06, 95% CI 1.44 to 2.95; 1 study, 2224 participants; moderate-certainty evidence). Nirmatrelvir/ritonavir plus SoC probably decreases discontinuation of study drug due to adverse events compared to SoC plus placebo (RR 0.49, 95% CI 0.30 to 0.80; 1 study, 2224 participants; moderate-certainty evidence). No studies reported improvement of clinical status, quality of life, or viral clearance. Nirmatrelvir/ritonavir for treating people with moderate to severe COVID-19 in inpatient settings We are uncertain whether nirmatrelvir/ritonavir plus SoC compared to SoC reduces all-cause mortality at 28 days (RR 0.63, 95% CI 0.21 to 1.86; 1 study, 264 participants; very low-certainty evidence), or increases viral clearance at seven days (RR 1.06, 95% CI 0.71 to 1.58; 1 study, 264 participants; very low-certainty evidence) and 14 days (RR 1.05, 95% CI 0.92 to 1.20; 1 study, 264 participants; very low-certainty evidence). No studies reported improvement or worsening of clinical status and quality of life. We did not include data for safety outcomes due to insufficient and inconsistent information. Subgroup analyses for equity For outpatients, the outcome 'admission to hospital or death' was investigated for equity regarding age (less than 65 years versus 65 years or greater) and ethnicity. There were no subgroup differences for age or ethnicity. For inpatients, the outcome 'all-cause mortality' was investigated for equity regarding age (65 years or less versus greater than 65 years). There was no difference between subgroups of age. No further equity-related subgroups were reported, and no subgroups were reported for other outcomes. Nirmatrelvir/ritonavir for preventing SARS-CoV-2 infection (PrEP and PEP) No studies available. Low-certainty evidence suggests nirmatrelvir/ritonavir reduces the risk of all-cause mortality and hospital admission or death in high-risk, unvaccinated COVID-19 outpatients infected with the Delta variant of SARS-CoV-2. There is low- to moderate-certainty evidence of the safety of nirmatrelvir/ritonavir. Very low-certainty evidence exists regarding the effects of nirmatrelvir/ritonavir on all-cause mortality and viral clearance in mildly to moderately affected, mostly unvaccinated COVID-19 inpatients infected with the Omicron variant of SARS-CoV-2. Insufficient and inconsistent information prevents the assessment of safety outcomes. No reliable differences in effect size and direction were found regarding equity aspects. There is no available evidence supporting the use of nirmatrelvir/ritonavir for preventing SARS-CoV-2 infection. We are continually updating our search and making search results available on the OSF platform.

Reis S ，Metzendorf MI ，Kuehn R ，Popp M ，Gagyor I ，Kranke P ，Meybohm P ，Skoetz N ，Weibel S ... -  《Cochrane Database of Systematic Reviews》

Ivermectin for preventing and treating COVID-19.

Ivermectin, an antiparasitic agent, inhibits the replication of viruses in vitro. The molecular hypothesis of ivermectin's antiviral mode of action suggests an inhibitory effect on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication in early stages of infection. Currently, evidence on ivermectin for prevention of SARS-CoV-2 infection and COVID-19 treatment is conflicting. To assess the efficacy and safety of ivermectin plus standard of care compared to standard of care plus/minus placebo, or any other proven intervention for people with COVID-19 receiving treatment as inpatients or outpatients, and for prevention of an infection with SARS-CoV-2 (postexposure prophylaxis). We searched the Cochrane COVID-19 Study Register, Web of Science (Emerging Citation Index and Science Citation Index), WHO COVID-19 Global literature on coronavirus disease, and HTA database weekly to identify completed and ongoing trials without language restrictions to 16 December 2021. Additionally, we included trials with > 1000 participants up to April 2022. We included randomized controlled trials (RCTs) comparing ivermectin to standard of care, placebo, or another proven intervention for treatment of people with confirmed COVID-19 diagnosis, irrespective of disease severity or treatment setting, and for prevention of SARS-CoV-2 infection. Co-interventions had to be the same in both study arms.  For this review update, we reappraised eligible trials for research integrity: only RCTs prospectively registered in a trial registry according to WHO guidelines for clinical trial registration were eligible for inclusion. We assessed RCTs for bias, using the Cochrane RoB 2 tool. We used GRADE to rate the certainty of evidence for outcomes in the following settings and populations: 1) to treat inpatients with moderate-to-severe COVID-19, 2) to treat outpatients with mild COVID-19 (outcomes: mortality, clinical worsening or improvement, (serious) adverse events, quality of life, and viral clearance), and 3) to prevent SARS-CoV-2 infection (outcomes: SARS-CoV-2 infection, development of COVID-19 symptoms, admission to hospital, mortality, adverse events and quality of life). We excluded seven of the 14 trials included in the previous review version; six were not prospectively registered and one was non-randomized. This updated review includes 11 trials with 3409 participants investigating ivermectin plus standard of care compared to standard of care plus/minus placebo. No trial investigated ivermectin for prevention of infection or compared ivermectin to an intervention with proven efficacy. Five trials treated participants with moderate COVID-19 (inpatient settings); six treated mild COVID-19 (outpatient settings). Eight trials were double-blind and placebo-controlled, and three were open-label. We assessed around 50% of the trial results as low risk of bias. We identified 31 ongoing trials. In addition, there are 28 potentially eligible trials without publication of results, or with disparities in the reporting of the methods and results, held in 'awaiting classification' until the trial authors clarify questions upon request. Ivermectin for treating COVID-19 in inpatient settings with moderate-to-severe disease We are uncertain whether ivermectin plus standard of care compared to standard of care plus/minus placebo reduces or increases all-cause mortality at 28 days (risk ratio (RR) 0.60, 95% confidence interval (CI) 0.14 to 2.51; 3 trials, 230 participants; very low-certainty evidence); or clinical worsening, assessed by participants with new need for invasive mechanical ventilation or death at day 28 (RR 0.82, 95% CI 0.33 to 2.04; 2 trials, 118 participants; very low-certainty evidence); or serious adverse events during the trial period (RR 1.55, 95% CI 0.07 to 35.89; 2 trials, 197 participants; very low-certainty evidence). Ivermectin plus standard of care compared to standard of care plus placebo may have little or no effect on clinical improvement, assessed by the number of participants discharged alive at day 28 (RR 1.03, 95% CI 0.78 to 1.35; 1 trial, 73 participants; low-certainty evidence); on any adverse events during the trial period (RR 1.04, 95% CI 0.61 to 1.79; 3 trials, 228 participants; low-certainty evidence); and on viral clearance at 7 days (RR 1.12, 95% CI 0.80 to 1.58; 3 trials, 231 participants; low-certainty evidence). No trial investigated quality of life at any time point. Ivermectin for treating COVID-19 in outpatient settings with asymptomatic or mild disease Ivermectin plus standard of care compared to standard of care plus/minus placebo probably has little or no effect on all-cause mortality at day 28 (RR 0.77, 95% CI 0.47 to 1.25; 6 trials, 2860 participants; moderate-certainty evidence) and little or no effect on quality of life, measured with the PROMIS Global-10 scale (physical component mean difference (MD) 0.00, 95% CI -0.98 to 0.98; and mental component MD 0.00, 95% CI -1.08 to 1.08; 1358 participants; high-certainty evidence). Ivermectin may have little or no effect on clinical worsening, assessed by admission to hospital or death within 28 days (RR 1.09, 95% CI 0.20 to 6.02; 2 trials, 590 participants; low-certainty evidence); on clinical improvement, assessed by the number of participants with all initial symptoms resolved up to 14 days (RR 0.90, 95% CI 0.60 to 1.36; 2 trials, 478 participants; low-certainty evidence); on serious adverse events (RR 2.27, 95% CI 0.62 to 8.31; 5 trials, 1502 participants; low-certainty evidence); on any adverse events during the trial period (RR 1.24, 95% CI 0.87 to 1.76; 5 trials, 1502 participants; low-certainty evidence); and on viral clearance at day 7 compared to placebo (RR 1.01, 95% CI 0.69 to 1.48; 2 trials, 331 participants; low-certainty evidence). None of the trials reporting duration of symptoms were eligible for meta-analysis. For outpatients, there is currently low- to high-certainty evidence that ivermectin has no beneficial effect for people with COVID-19. Based on the very low-certainty evidence for inpatients, we are still uncertain whether ivermectin prevents death or clinical worsening or increases serious adverse events, while there is low-certainty evidence that it has no beneficial effect regarding clinical improvement, viral clearance and adverse events. No evidence is available on ivermectin to prevent SARS-CoV-2 infection. In this update, certainty of evidence increased through higher quality trials including more participants. According to this review's living approach, we will continually update our search.

Popp M ，Reis S ，Schießer S ，Hausinger RI ，Stegemann M ，Metzendorf MI ，Kranke P ，Meybohm P ，Skoetz N ，Weibel S ... -  《Cochrane Database of Systematic Reviews》

Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.

Description of the condition Malaria, an infectious disease transmitted by the bite of female mosquitoes from several Anopheles species, occurs in 87 countries with ongoing transmission (WHO 2020). The World Health Organization (WHO) estimated that, in 2019, approximately 229 million cases of malaria occurred worldwide, with 94% occurring in the WHO's African region (WHO 2020). Of these malaria cases, an estimated 409,000 deaths occurred globally, with 67% occurring in children under five years of age (WHO 2020). Malaria also negatively impacts the health of women during pregnancy, childbirth, and the postnatal period (WHO 2020). Sulfadoxine/pyrimethamine (SP), an antifolate antimalarial, has been widely used across sub-Saharan Africa as the first-line treatment for uncomplicated malaria since it was first introduced in Malawi in 1993 (Filler 2006). Due to increasing resistance to SP, in 2000 the WHO recommended that one of several artemisinin-based combination therapies (ACTs) be used instead of SP for the treatment of uncomplicated malaria caused by Plasmodium falciparum (Global Partnership to Roll Back Malaria 2001). However, despite these recommendations, SP continues to be advised for intermittent preventive treatment in pregnancy (IPTp) and intermittent preventive treatment in infants (IPTi), whether the person has malaria or not (WHO 2013). Description of the intervention Folate (vitamin B9) includes both naturally occurring folates and folic acid, the fully oxidized monoglutamic form of the vitamin, used in dietary supplements and fortified food. Folate deficiency (e.g. red blood cell (RBC) folate concentrations of less than 305 nanomoles per litre (nmol/L); serum or plasma concentrations of less than 7 nmol/L) is common in many parts of the world and often presents as megaloblastic anaemia, resulting from inadequate intake, increased requirements, reduced absorption, or abnormal metabolism of folate (Bailey 2015; WHO 2015a). Pregnant women have greater folate requirements; inadequate folate intake (evidenced by RBC folate concentrations of less than 400 nanograms per millilitre (ng/mL), or 906 nmol/L) prior to and during the first month of pregnancy increases the risk of neural tube defects, preterm delivery, low birthweight, and fetal growth restriction (Bourassa 2019). The WHO recommends that all women who are trying to conceive consume 400 micrograms (µg) of folic acid daily from the time they begin trying to conceive through to 12 weeks of gestation (WHO 2017). In 2015, the WHO added the dosage of 0.4 mg of folic acid to the essential drug list (WHO 2015c). Alongside daily oral iron (30 mg to 60 mg elemental iron), folic acid supplementation is recommended for pregnant women to prevent neural tube defects, maternal anaemia, puerperal sepsis, low birthweight, and preterm birth in settings where anaemia in pregnant women is a severe public health problem (i.e. where at least 40% of pregnant women have a blood haemoglobin (Hb) concentration of less than 110 g/L). How the intervention might work Potential interactions between folate status and malaria infection The malaria parasite requires folate for survival and growth; this has led to the hypothesis that folate status may influence malaria risk and severity. In rhesus monkeys, folate deficiency has been found to be protective against Plasmodium cynomolgi malaria infection, compared to folate-replete animals (Metz 2007). Alternatively, malaria may induce or exacerbate folate deficiency due to increased folate utilization from haemolysis and fever. Further, folate status measured via RBC folate is not an appropriate biomarker of folate status in malaria-infected individuals since RBC folate values in these individuals are indicative of both the person's stores and the parasite's folate synthesis. A study in Nigeria found that children with malaria infection had significantly higher RBC folate concentrations compared to children without malaria infection, but plasma folate levels were similar (Bradley-Moore 1985). Why it is important to do this review The malaria parasite needs folate for survival and growth in humans. For individuals, adequate folate levels are critical for health and well-being, and for the prevention of anaemia and neural tube defects. Many countries rely on folic acid supplementation to ensure adequate folate status in at-risk populations. Different formulations for folic acid supplements are available in many international settings, with dosages ranging from 400 µg to 5 mg. Evaluating folic acid dosage levels used in supplementation efforts may increase public health understanding of its potential impacts on malaria risk and severity and on treatment failures. Examining folic acid interactions with antifolate antimalarial medications and with malaria disease progression may help countries in malaria-endemic areas determine what are the most appropriate lower dose folic acid formulations for at-risk populations. The WHO has highlighted the limited evidence available and has indicated the need for further research on biomarkers of folate status, particularly interactions between RBC folate concentrations and tuberculosis, human immunodeficiency virus (HIV), and antifolate antimalarial drugs (WHO 2015b). An earlier Cochrane Review assessed the effects and safety of iron supplementation, with or without folic acid, in children living in hyperendemic or holoendemic malaria areas; it demonstrated that iron supplementation did not increase the risk of malaria, as indicated by fever and the presence of parasites in the blood (Neuberger 2016). Further, this review stated that folic acid may interfere with the efficacy of SP; however, the efficacy and safety of folic acid supplementation on these outcomes has not been established. This review will provide evidence on the effectiveness of daily folic acid supplementation in healthy and malaria-infected individuals living in malaria-endemic areas. Additionally, it will contribute to achieving both the WHO Global Technical Strategy for Malaria 2016-2030 (WHO 2015d), and United Nations Sustainable Development Goal 3 (to ensure healthy lives and to promote well-being for all of all ages) (United Nations 2021), and evaluating whether the potential effects of folic acid supplementation, at different doses (e.g. 0.4 mg, 1 mg, 5 mg daily), interferes with the effect of drugs used for prevention or treatment of malaria. To examine the effects of folic acid supplementation, at various doses, on malaria susceptibility (risk of infection) and severity among people living in areas with various degrees of malaria endemicity. We will examine the interaction between folic acid supplements and antifolate antimalarial drugs. Specifically, we will aim to answer the following. Among uninfected people living in malaria endemic areas, who are taking or not taking antifolate antimalarials for malaria prophylaxis, does taking a folic acid-containing supplement increase susceptibility to or severity of malaria infection? Among people with malaria infection who are being treated with antifolate antimalarials, does folic acid supplementation increase the risk of treatment failure? Criteria for considering studies for this review Types of studies Inclusion criteria Randomized controlled trials (RCTs) Quasi-RCTs with randomization at the individual or cluster level conducted in malaria-endemic areas (areas with ongoing, local malaria transmission, including areas approaching elimination, as listed in the World Malaria Report 2020) (WHO 2020) Exclusion criteria Ecological studies Observational studies In vivo/in vitro studies Economic studies Systematic literature reviews and meta-analyses (relevant systematic literature reviews and meta-analyses will be excluded but flagged for grey literature screening) Types of participants Inclusion criteria Individuals of any age or gender, living in a malaria endemic area, who are taking antifolate antimalarial medications (including but not limited to sulfadoxine/pyrimethamine (SP), pyrimethamine-dapsone, pyrimethamine, chloroquine and proguanil, cotrimoxazole) for the prevention or treatment of malaria (studies will be included if more than 70% of the participants live in malaria-endemic regions) Studies assessing participants with or without anaemia and with or without malaria parasitaemia at baseline will be included Exclusion criteria Individuals not taking antifolate antimalarial medications for prevention or treatment of malaria Individuals living in non-malaria endemic areas Types of interventions Inclusion criteria Folic acid supplementation Form: in tablet, capsule, dispersible tablet at any dose, during administration, or periodically Timing: during, before, or after (within a period of four to six weeks) administration of antifolate antimalarials Iron-folic acid supplementation Folic acid supplementation in combination with co-interventions that are identical between the intervention and control groups. Co-interventions include: anthelminthic treatment; multivitamin or multiple micronutrient supplementation; 5-methyltetrahydrofolate supplementation. Exclusion criteria Folate through folate-fortified water Folic acid administered through large-scale fortification of rice, wheat, or maize Comparators Placebo No treatment No folic acid/different doses of folic acid Iron Types of outcome measures Primary outcomes Uncomplicated malaria (defined as a history of fever with parasitological confirmation; acceptable parasitological confirmation will include rapid diagnostic tests (RDTs), malaria smears, or nucleic acid detection (i.e. polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), etc.)) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Severe malaria (defined as any case with cerebral malaria or acute P. falciparum malaria, with signs of severity or evidence of vital organ dysfunction, or both) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Parasite clearance (any Plasmodium species), defined as the time it takes for a patient who tests positive at enrolment and is treated to become smear-negative or PCR negative. This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Treatment failure (defined as the inability to clear malaria parasitaemia or prevent recrudescence after administration of antimalarial medicine, regardless of whether clinical symptoms are resolved) (WHO 2019). This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Secondary outcomes Duration of parasitaemia Parasite density Haemoglobin (Hb) concentrations (g/L) Anaemia: severe anaemia (defined as Hb less than 70 g/L in pregnant women and children aged six to 59 months; and Hb less than 80 g/L in other populations); moderate anaemia (defined as Hb less than 100 g/L in pregnant women and children aged six to 59 months; and less than 110 g/L in others) Death from any cause Among pregnant women: stillbirth (at less than 28 weeks gestation); low birthweight (less than 2500 g); active placental malaria (defined as Plasmodium detected in placental blood by smear or PCR, or by Plasmodium detected on impression smear or placental histology). Search methods for identification of studies A search will be conducted to identify completed and ongoing studies, without date or language restrictions. Electronic searches A search strategy will be designed to include the appropriate subject headings and text word terms related to each intervention of interest and study design of interest (see Appendix 1). Searches will be broken down by these two criteria (intervention of interest and study design of interest) to allow for ease of prioritization, if necessary. The study design filters recommended by the Scottish Intercollegiate Guidelines Network (SIGN), and those designed by Cochrane for identifying clinical trials for MEDLINE and Embase, will be used (SIGN 2020). There will be no date or language restrictions. Non-English articles identified for inclusion will be translated into English. If translations are not possible, advice will be requested from the Cochrane Infectious Diseases Group and the record will be stored in the "Awaiting assessment" section of the review until a translation is available. The following electronic databases will be searched for primary studies. Cochrane Central Register of Controlled Trials. Cumulative Index to Nursing and Allied Health Literature (CINAHL). Embase. MEDLINE. Scopus. Web of Science (both the Social Science Citation Index and the Science Citation Index). We will conduct manual searches of ClinicalTrials.gov, the International Clinical Trials Registry Platform (ICTRP), and the United Nations Children's Fund (UNICEF) Evaluation and Research Database (ERD), in order to identify relevant ongoing or planned trials, abstracts, and full-text reports of evaluations, studies, and surveys related to programmes on folic acid supplementation in malaria-endemic areas. Additionally, manual searches of grey literature to identify RCTs that have not yet been published but are potentially eligible for inclusion will be conducted in the following sources. Global Index Medicus (GIM). African Index Medicus (AIM). Index Medicus for the Eastern Mediterranean Region (IMEMR). Latin American & Caribbean Health Sciences Literature (LILACS). Pan American Health Organization (PAHO). Western Pacific Region Index Medicus (WPRO). Index Medicus for the South-East Asian Region (IMSEAR). The Spanish Bibliographic Index in Health Sciences (IBECS) (ibecs.isciii.es/). Indian Journal of Medical Research (IJMR) (journals.lww.com/ijmr/pages/default.aspx). Native Health Database (nativehealthdatabase.net/). Scielo (www.scielo.br/). Searching other resources Handsearches of the five journals with the highest number of included studies in the last 12 months will be conducted to capture any relevant articles that may not have been indexed in the databases at the time of the search. We will contact the authors of included studies and will check reference lists of included papers for the identification of additional records. For assistance in identifying ongoing or unpublished studies, we will contact the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and the Division of Parasitic Diseases and Malaria (DPDM) of the CDC, the United Nations World Food Programme (WFP), Nutrition International (NI), Global Alliance for Improved Nutrition (GAIN), and Hellen Keller International (HKI). Data collection and analysis Selection of studies Two review authors will independently screen the titles and abstracts of articles retrieved by each search to assess eligibility, as determined by the inclusion and exclusion criteria. Studies deemed eligible for inclusion by both review authors in the abstract screening phase will advance to the full-text screening phase, and full-text copies of all eligible papers will be retrieved. If full articles cannot be obtained, we will attempt to contact the authors to obtain further details of the studies. If such information is not obtained, we will classify the study as "awaiting assessment" until further information is published or made available to us. The same two review authors will independently assess the eligibility of full-text articles for inclusion in the systematic review. If any discrepancies occur between the studies selected by the two review authors, a third review author will provide arbitration. Each trial will be scrutinized to identify multiple publications from the same data set, and the justification for excluded trials will be documented. A PRISMA flow diagram of the study selection process will be presented to provide information on the number of records identified in the literature searches, the number of studies included and excluded, and the reasons for exclusion (Moher 2009). The list of excluded studies, along with their reasons for exclusion at the full-text screening phase, will also be created. Data extraction and management Two review authors will independently extract data for the final list of included studies using a standardized data specification form. Discrepancies observed between the data extracted by the two authors will be resolved by involving a third review author and reaching a consensus. Information will be extracted on study design components, baseline participant characteristics, intervention characteristics, and outcomes. For individually randomized trials, we will record the number of participants experiencing the event and the number analyzed in each treatment group or the effect estimate reported (e.g. risk ratio (RR)) for dichotomous outcome measures. For count data, we will record the number of events and the number of person-months of follow-up in each group. If the number of person-months is not reported, the product of the duration of follow-up and the number of children evaluated will be used to estimate this figure. We will calculate the rate ratio and standard error (SE) for each study. Zero events will be replaced by 0.5. We will extract both adjusted and unadjusted covariate incidence rate ratios if they are reported in the original studies. For continuous data, we will extract means (arithmetic or geometric) and a measure of variance (standard deviation (SD), SE, or confidence interval (CI)), percentage or mean change from baseline, and the numbers analyzed in each group. SDs will be computed from SEs or 95% CIs, assuming a normal distribution of the values. Haemoglobin values in g/dL will be calculated by multiplying haematocrit or packed cell volume values by 0.34, and studies reporting haemoglobin values in g/dL will be converted to g/L. In cluster-randomized trials, we will record the unit of randomization (e.g. household, compound, sector, or village), the number of clusters in the trial, and the average cluster size. The statistical methods used to analyze the trials will be documented, along with details describing whether these methods adjusted for clustering or other covariates. We plan to extract estimates of the intra-cluster correlation coefficient (ICC) for each outcome. Where results are adjusted for clustering, we will extract the treatment effect estimate and the SD or CI. If the results are not adjusted for clustering, we will extract the data reported. Assessment of risk of bias in included studies Two review authors (KSC, LFY) will independently assess the risk of bias for each included trial using the Cochrane 'Risk of bias 2' tool (RoB 2) for randomized studies (Sterne 2019). Judgements about the risk of bias of included studies will be made according to the recommendations outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Disagreements will be resolved by discussion, or by involving a third review author. The interest of our review will be to assess the effect of assignment to the interventions at baseline. We will evaluate each primary outcome using the RoB2 tool. The five domains of the Cochrane RoB2 tool include the following. Bias arising from the randomization process. Bias due to deviations from intended interventions. Bias due to missing outcome data. Bias in measurement of the outcome. Bias in selection of the reported result. Each domain of the RoB2 tool comprises the following. A series of 'signalling' questions. A judgement about the risk of bias for the domain, facilitated by an algorithm that maps responses to the signalling questions to a proposed judgement. Free-text boxes to justify responses to the signalling questions and 'Risk of bias' judgements. An option to predict (and explain) the likely direction of bias. Responses to signalling questions elicit information relevant to an assessment of the risk of bias. These response options are as follows. Yes (may indicate either low or high risk of bias, depending on the most natural way to ask the question). Probably yes. Probably no. No. No information (may indicate no evidence of that problem or an absence of information leading to concerns about there being a problem). Based on the answer to the signalling question, a 'Risk of bias' judgement is assigned to each domain. These judgements include one of the following. High risk of bias Low risk of bias Some concerns To generate the risk of bias judgement for each domain in the randomized studies, we will use the Excel template, available at www.riskofbias.info/welcome/rob-2-0-tool/current-version-of-rob-2. This file will be stored on a scientific data website, available to readers. Risk of bias in cluster randomized controlled trials For the cluster randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 1b (bias arising from the timing of identification or recruitment of participants) and its related signalling questions. To generate the risk of bias judgement for each domain in the cluster RCTs, we will use the Excel template available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-cluster-randomized-trials. This file will be stored on a scientific data website, available to readers. Risk of bias in cross-over randomized controlled trials For cross-over randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 2 (bias due to deviations from intended interventions), and Domain 3 (bias due to missing outcome data), and their respective signalling questions. To generate the risk of bias judgement for each domain in the cross-over RCTs, we will use the Excel template, available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-crossover-trials, for each risk of bias judgement of cross-over randomized studies. This file will be stored on a scientific data website, available to readers. Overall risk of bias The overall 'Risk of bias' judgement for each specific trial being assessed will be based on each domain-level judgement. The overall judgements include the following. Low risk of bias (the trial is judged to be at low risk of bias for all domains). Some concerns (the trial is judged to raise some concerns in at least one domain but is not judged to be at high risk of bias for any domain). High risk of bias (the trial is judged to be at high risk of bias in at least one domain, or is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result). The 'risk of bias' assessments will inform our GRADE evaluations of the certainty of evidence for our primary outcomes presented in the 'Summary of findings' tables and will also be used to inform the sensitivity analyses; (see Sensitivity analysis). If there is insufficient information in study reports to enable an assessment of the risk of bias, studies will be classified as "awaiting assessment" until further information is published or made available to us. Measures of treatment effect Dichotomous data For dichotomous data, we will present proportions and, for two-group comparisons, results as average RR or odds ratio (OR) with 95% CIs. Ordered categorical data Continuous data We will report results for continuous outcomes as the mean difference (MD) with 95% CIs, if outcomes are measured in the same way between trials. Where some studies have reported endpoint data and others have reported change-from-baseline data (with errors), we will combine these in the meta-analysis, if the outcomes were reported using the same scale. We will use the standardized mean difference (SMD), with 95% CIs, to combine trials that measured the same outcome but used different methods. If we do not find three or more studies for a pooled analysis, we will summarize the results in a narrative form. Unit of analysis issues Cluster-randomized trials We plan to combine results from both cluster-randomized and individually randomized studies, providing there is little heterogeneity between the studies. If the authors of cluster-randomized trials conducted their analyses at a different level from that of allocation, and they have not appropriately accounted for the cluster design in their analyses, we will calculate the trials' effective sample sizes to account for the effect of clustering in data. When one or more cluster-RCT reports RRs adjusted for clustering, we will compute cluster-adjusted SEs for the other trials. When none of the cluster-RCTs provide cluster-adjusted RRs, we will adjust the sample size for clustering. We will divide, by the estimated design effects (DE), the number of events and number evaluated for dichotomous outcomes and the number evaluated for continuous outcomes, where DE = 1 + ((average cluster size 1) * ICC). The derivation of the estimated ICCs and DEs will be reported. We will utilize the intra-cluster correlation coefficient (ICC), derived from the trial (if available), or from another source (e.g., using the ICCs derived from other, similar trials) and then calculate the design effect with the formula provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). If this approach is used, we will report it and undertake sensitivity analysis to investigate the effect of variations in ICC. Studies with more than two treatment groups If we identify studies with more than two intervention groups (multi-arm studies), where possible we will combine groups to create a single pair-wise comparison or use the methods set out in the Cochrane Handbook to avoid double counting study participants (Higgins 2021). For the subgroup analyses, when the control group was shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double counting the participants. Trials with several study arms can be included more than once for different comparisons. Cross-over trials From cross-over trials, we will consider the first period of measurement only and will analyze the results together with parallel-group studies. Multiple outcome events In several outcomes, a participant might experience more than one outcome event during the trial period. For all outcomes, we will extract the number of participants with at least one event. Dealing with missing data We will contact the trial authors if the available data are unclear, missing, or reported in a format that is different from the format needed. We aim to perform a 'per protocol' or 'as observed' analysis; otherwise, we will perform a complete case analysis. This means that for treatment failure, we will base the analyses on the participants who received treatment and the number of participants for which there was an inability to clear malarial parasitaemia or prevent recrudescence after administration of an antimalarial medicine reported in the studies. Assessment of heterogeneity Heterogeneity in the results of the trials will be assessed by visually examining the forest plot to detect non-overlapping CIs, using the Chi2 test of heterogeneity (where a P value of less than 0.1 indicates statistical significance) and the I2 statistic of inconsistency (with a value of greater than 50% denoting moderate levels of heterogeneity). When statistical heterogeneity is present, we will investigate the reasons for it, using subgroup analysis. Assessment of reporting biases We will construct a funnel plot to assess the effect of small studies for the main outcome (when including more than 10 trials). Data synthesis The primary analysis will include all eligible studies that provide data regardless of the overall risk of bias as assessed by the RoB2 tool. Analyses will be conducted using Review Manager 5.4 (Review Manager 2020). Cluster-RCTs will be included in the main analysis after adjustment for clustering (see the previous section on cluster-RCTs). The meta-analysis will be performed using the Mantel-Haenszel random-effects model or the generic inverse variance method (when adjustment for clustering is performed by adjusting SEs), as appropriate. Subgroup analysis and investigation of heterogeneity The overall risk of bias will not be used as the basis in conducting our subgroup analyses. However, where data are available, we plan to conduct the following subgroup analyses, independent of heterogeneity. Dose of folic acid supplementation: higher doses (4 mg or more, daily) versus lower doses (less than 4 mg, daily). Moderate-severe anaemia at baseline (mean haemoglobin of participants in a trial at baseline below 100 g/L for pregnant women and children aged six to 59 months, and below 110 g/L for other populations) versus normal at baseline (mean haemoglobin above 100 g/L for pregnant women and children aged six to 59 months, and above 110 g/L for other populations). Antimalarial drug resistance to parasite: known resistance versus no resistance versus unknown/mixed/unreported parasite resistance. Folate status at baseline: Deficient (e.g. RBC folate concentration of less than 305 nmol/L, or serum folate concentration of less than 7nmol/L) and Insufficient (e.g. RBC folate concentration from 305 to less than 906 nmol/L, or serum folate concentration from 7 to less than 25 nmol/L) versus Sufficient (e.g. RBC folate concentration above 906 nmol/L, or serum folate concentration above 25 nmol/L). Presence of anaemia at baseline: yes versus no. Mandatory fortification status: yes, versus no (voluntary or none). We will only use the primary outcomes in any subgroup analyses, and we will limit subgroup analyses to those outcomes for which three or more trials contributed data. Comparisons between subgroups will be performed using Review Manager 5.4 (Review Manager 2020). Sensitivity analysis We will perform a sensitivity analysis, using the risk of bias as a variable to explore the robustness of the findings in our primary outcomes. We will verify the behaviour of our estimators by adding and removing studies with a high risk of bias overall from the analysis. That is, studies with a low risk of bias versus studies with a high risk of bias. Summary of findings and assessment of the certainty of the evidence For the assessment across studies, we will use the GRADE approach, as outlined in (Schünemann 2021). We will use the five GRADE considerations (study limitations based on RoB2 judgements, consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of the body of evidence as it relates to the studies which contribute data to the meta-analyses for the primary outcomes. The GRADEpro Guideline Development Tool (GRADEpro) will be used to import data from Review Manager 5.4 (Review Manager 2020) to create 'Summary of Findings' tables. The primary outcomes for the main comparison will be listed with estimates of relative effects, along with the number of participants and studies contributing data for those outcomes. These tables will provide outcome-specific information concerning the overall certainty of evidence from studies included in the comparison, the magnitude of the effect of the interventions examined, and the sum of available data on the outcomes we considered. We will include only primary outcomes in the summary of findings tables. For each individual outcome, two review authors (KSC, LFY) will independently assess the certainty of the evidence using the GRADE approach (Balshem 2011). For assessments of the overall certainty of evidence for each outcome that includes pooled data from included trials, we will downgrade the evidence from 'high certainty' by one level for serious (or by two for very serious) study limitations (risk of bias, indirectness of evidence, serious inconsistency, imprecision of effect estimates, or potential publication bias).

Crider K ，Williams J ，Qi YP ，Gutman J ，Yeung L ，Mai C ，Finkelstain J ，Mehta S ，Pons-Duran C ，Menéndez C ，Moraleda C ，Rogers L ，Daniels K ，Green P ... -  《Cochrane Database of Systematic Reviews》

Ivermectin for preventing and treating COVID-19.

Ivermectin, an antiparasitic agent used to treat parasitic infestations, inhibits the replication of viruses in vitro. The molecular hypothesis of ivermectin's antiviral mode of action suggests an inhibitory effect on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication in the early stages of infection. Currently, evidence on efficacy and safety of ivermectin for prevention of SARS-CoV-2 infection and COVID-19 treatment is conflicting. To assess the efficacy and safety of ivermectin compared to no treatment, standard of care, placebo, or any other proven intervention for people with COVID-19 receiving treatment as inpatients or outpatients, and for prevention of an infection with SARS-CoV-2 (postexposure prophylaxis). We searched the Cochrane COVID-19 Study Register, Web of Science (Emerging Citation Index and Science Citation Index), medRxiv, and Research Square, identifying completed and ongoing studies without language restrictions to 26 May 2021. We included randomized controlled trials (RCTs) comparing ivermectin to no treatment, standard of care, placebo, or another proven intervention for treatment of people with confirmed COVID-19 diagnosis, irrespective of disease severity, treated in inpatient or outpatient settings, and for prevention of SARS-CoV-2 infection. Co-interventions had to be the same in both study arms.  We excluded studies comparing ivermectin to other pharmacological interventions with unproven efficacy. We assessed RCTs for bias, using the Cochrane risk of bias 2 tool. The primary analysis excluded studies with high risk of bias. We used GRADE to rate the certainty of evidence for the following outcomes 1. to treat inpatients with moderate-to-severe COVID-19: mortality, clinical worsening or improvement, adverse events, quality of life, duration of hospitalization, and viral clearance; 2. to treat outpatients with mild COVID-19: mortality, clinical worsening or improvement, admission to hospital, adverse events, quality of life, and viral clearance; (3) to prevent SARS-CoV-2 infection: SARS-CoV-2 infection, development of COVID-19 symptoms, adverse events, mortality, admission to hospital, and quality of life. We found 14 studies with 1678 participants investigating ivermectin compared to no treatment, placebo, or standard of care. No study compared ivermectin to an intervention with proven efficacy. There were nine studies treating participants with moderate COVID-19 in inpatient settings and four treating mild COVID-19 cases in outpatient settings. One study investigated ivermectin for prevention of SARS-CoV-2 infection. Eight studies had an open-label design, six were double-blind and placebo-controlled. Of the 41 study results contributed by included studies, about one third were at overall high risk of bias.  Ivermectin doses and treatment duration varied among included studies.  We identified 31 ongoing and 18 studies awaiting classification until publication of results or clarification of inconsistencies. Ivermectin compared to placebo or standard of care for inpatient COVID-19 treatment We are uncertain whether ivermectin compared to placebo or standard of care reduces or increases mortality (risk ratio (RR) 0.60, 95% confidence interval (CI) 0.14 to 2.51; 2 studies, 185 participants; very low-certainty evidence) and clinical worsening up to day 28 assessed as need for invasive mechanical ventilation (IMV) (RR 0.55, 95% CI 0.11 to 2.59; 2 studies, 185 participants; very low-certainty evidence) or need for supplemental oxygen (0 participants required supplemental oxygen; 1 study, 45 participants; very low-certainty evidence), adverse events within 28 days (RR 1.21, 95% CI 0.50 to 2.97; 1 study, 152 participants; very low-certainty evidence), and viral clearance at day seven (RR 1.82, 95% CI 0.51 to 6.48; 2 studies, 159 participants; very low-certainty evidence). Ivermectin may have little or no effect compared to placebo or standard of care on clinical improvement up to 28 days (RR 1.03, 95% CI 0.78 to 1.35; 1 study; 73 participants; low-certainty evidence) and duration of hospitalization (mean difference (MD) -0.10 days, 95% CI -2.43 to 2.23; 1 study; 45 participants; low-certainty evidence). No study reported quality of life up to 28 days. Ivermectin compared to placebo or standard of care for outpatient COVID-19 treatment We are uncertain whether ivermectin compared to placebo or standard of care reduces or increases mortality up to 28 days (RR 0.33, 95% CI 0.01 to 8.05; 2 studies, 422 participants; very low-certainty evidence) and clinical worsening up to 14 days assessed as need for IMV (RR 2.97, 95% CI 0.12 to 72.47; 1 study, 398 participants; very low-certainty evidence) or non-IMV or high flow oxygen requirement (0 participants required non-IMV or high flow; 1 study, 398 participants; very low-certainty evidence). We are uncertain whether ivermectin compared to placebo reduces or increases viral clearance at seven days (RR 3.00, 95% CI 0.13 to 67.06; 1 study, 24 participants; low-certainty evidence). Ivermectin may have little or no effect compared to placebo or standard of care on the number of participants with symptoms resolved up to 14 days (RR 1.04, 95% CI 0.89 to 1.21; 1 study, 398 participants; low-certainty evidence) and adverse events within 28 days (RR 0.95, 95% CI 0.86 to 1.05; 2 studies, 422 participants; low-certainty evidence). None of the studies reporting duration of symptoms were eligible for primary analysis. No study reported hospital admission or quality of life up to 14 days. Ivermectin compared to no treatment for prevention of SARS-CoV-2 infection We found one study. Mortality up to 28 days was the only outcome eligible for primary analysis. We are uncertain whether ivermectin reduces or increases mortality compared to no treatment (0 participants died; 1 study, 304 participants; very low-certainty evidence). The study reported results for development of COVID-19 symptoms and adverse events up to 14 days that were included in a secondary analysis due to high risk of bias. No study reported SARS-CoV-2 infection, hospital admission, and quality of life up to 14 days. Based on the current very low- to low-certainty evidence, we are uncertain about the efficacy and safety of ivermectin used to treat or prevent COVID-19. The completed studies are small and few are considered high quality. Several studies are underway that may produce clearer answers in review updates. Overall, the reliable evidence available does not support the use ivermectin for treatment or prevention of COVID-19 outside of well-designed randomized trials.

Popp M ，Stegemann M ，Metzendorf MI ，Gould S ，Kranke P ，Meybohm P ，Skoetz N ，Weibel S ... -  《Cochrane Database of Systematic Reviews》