Mauna Loa November-December 2022 Eruption Livestream

Life expectancy dropped much more with the Spanish flu in 1918 than COVID-19 so far.

Dammed lies and statistics

Mark Twain wrote: “There are three kinds of lies: lies, damned lies, and statistics.”

Statistics continue to mislead the unwary in the current milieu of rapid online publication of articles and preprints. A case in point is the study published last month in the fast-tracking online medical journal Cureus. This study, entitled Regular Use of Ivermectin as Prophylaxis for COVID-19 Led Up to a 92% Reduction in COVID-19 Mortality Rate in a Dose-Response Manner: Results of a Prospective Observational Study of a Strictly Controlled Population of 88,012 Subjects, purported to show that prolonged, regular use of ivermectin lessened the incidence and mortality of COVID-19 as measured by total dose taken in the period before infection occurred.

Note that though the study title states it was prospective, planning a study in advance is not sufficient for a study to be prospective. Because subjects were only placed into their treatment groups after the study outcomes were known, this was a retrospective study.

The problem lies in the study's design: total dosage of ivermectin over the entire period before COVID infection was the regression variable, versus mortality in those infected. However, because intention-to-treat analysis was thus bypassed, the study was actually measuring the mortality in the group which took ivermectin for many months and either never got COVID-19 or got COVID-19 only after that period, versus the mortality in those who either never took ivermectin or who took ivermectin for only a short time. This means that during the early months of this study, almost all of the patients who developed COVID-19 were placed in the non-ivermectin or the "irregular user" group even if they would have been placed in the ivermectin group had they NOT come down with COVID (and thus have more time to take doses of ivermectin, as many ultimately did). In other words, the fact that some who got COVID-19 before taking ivermectin might well have taken ivermectin later in any given study period had they not gotten COVID-19 first was ignored.

Was this study "strictly controlled"? Yes, in a manner that biases it beyond repair, unless they had ALSO given intent-to-treat data! Only the authors know why their design was designed the way it was.

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REFERENCE: Kerr L, Baldi F, Lobo R, et al. (August 31, 2022) Regular Use of Ivermectin as Prophylaxis for COVID-19 Led Up to a 92% Reduction in COVID-19 Mortality Rate in a Dose-Response Manner: Results of a Prospective Observational Study of a Strictly Controlled Population of 88,012 Subjects. Cureus 14(8): e28624.

DOI: 10.7759/cureus.28624

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Here is a computer simulation (written in Julia) which demonstates the study bias mentioned above. In the program, the treatment is set to have no actual effect on the infection rate. The highly significant results are purely from the bias of excluding those who become infected from later entering a treatment arm.

using HypothesisTests

@enum TreatmentClass Untreated Irregular Excluded Regular

mutable struct Subject
    cum_dose::Float64
    treatment_class::TreatmentClass
    had_covid::Bool
    last_dose_day::Int
end

function update!(subjects::Vector{Subject}, day, pcovid = 0.001, pstart = 0.0075, pdosing = 0.25, dosage = 35)
    for subj in subjects
        if subj.had_covid
            continue
        elseif rand() < pcovid
            subj.had_covid = true
        elseif (subj.cum_dose > 0 && rand() <= pdosing  && (day > subj.last_dose_day + 14 || day == subj.last_dose_day + 1)) ||
           (subj.cum_dose == 0 && rand() < pstart)
            subj.cum_dose += dosage
            subj.last_dose_day = day
            subj.treatment_class =
               subj.cum_dose == 0 ? Untreated : subj.cum_dose >= 180 ? Regular : subj.cum_dose <= 60 ? Irregular : Excluded
        end
    end
end

function run_study(N = 10_000, duration = 150)
    population = [Subject(0.0, Untreated, false, 0) for _ in 1:N]
    unt, unt_covid, irr, irr_covid, reg, reg_covid, excluded = 0, 0, 0, 0, 0, 0, 0
    println("Population size $N, daily infection risk 0.1%")
    for day in 1:duration
        update!(population, day)
        if day % 30 == 0
            println("\nDay $day:")
            unt = count(s -> s.treatment_class == Untreated, population)
            unt_covid = count(s -> (s.treatment_class == Untreated) && s.had_covid, population)
            println("Untreated: N = $unt, with infection = $unt_covid")
            irr = count(s -> s.treatment_class == Irregular, population)
            irr_covid = count(s -> (s.treatment_class == Irregular) && s.had_covid, population)
            println("Irregular Use: N = $irr, with infection = $irr_covid")
            reg = count(s -> s.treatment_class == Regular, population)
            reg_covid = count(s -> (s.treatment_class == Regular) && s.had_covid, population)
            println("Regular Use: N = $reg, with infection = $reg_covid")
            exc = count(s -> (s.treatment_class == Excluded) && s.had_covid, population)
            println("Excluded: N = $exc")
        end
        if day == 75
            println("\nAt midpoint, Infection case percentages are:")
            println("  Untreated : ", Float16(100 * unt_covid / unt))
            println("  Irregulars: ", Float16(100 * irr_covid / irr))
            println("  Regulars  : ", Float16(100 * reg_covid / reg))
        end
    end
    println("\nAt study end, Infection case percentages are:")
    println("  Untreated : ", Float16(100 * unt_covid / unt), " of group size of $unt")
    println("  Irregulars: ", Float16(100 * irr_covid / irr), " of group size of $irr")
    println("  Regulars  : ", Float16(100 * reg_covid / reg), " of group size of $reg")
    untreated = [s.had_covid for s in population if s.treatment_class == Untreated]
    irregular = [s.had_covid for s in population if s.treatment_class == Irregular]
    regular = [s.had_covid for s in population if s.treatment_class == Regular]
    excluded = [s.had_covid for s in population if s.treatment_class == Excluded]
    println("\n\n   Final statistics:\n")
    @show KruskalWallisTest(untreated, irregular, regular, excluded)
end

run_study()

Treating late-presenting strokes from large vessel occlusion

Only a small minority of large vessel strokes which might be eligible for TPA thrombolysis of clot extraction removal of blockage are so treated. WOne reason is that such strokes often have a stuttering course which causes them to present to the ER only well after they have begun, or to present with changes on CT from previously begun tissue infaction even when deficits are small.

Unfortunately, many such persons with stroke worsen in the hospital. The study below offers hope for late treatment of at least some of these patients to reduce their stroke burden.

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ABSTRACT

Identifying Severe Stroke Patients Likely to Benefit From Thrombectomy Despite Delays of up to a Day

R. Gilberto González, Gisele Sampaio Silva, Julian He, Saloomeh Sadaghiani, Ona Wu, Aneesh B. Singhal

Scientific Reports, August 2022

doi: 10.1038/s41598-020-60933-3 0000005

Abstract

Selected patients with large vessel occlusions (LVO) can benefit from thrombectomy up to 24 hours after onset. Identifying patients who might benefit from late intervention after transfer from community hospitals to thrombectomy-capable centers would be valuable. We searched for presentation biomarkers to identify such patients. Frequent MR imaging over 2 days of 38 untreated LVO patients revealed logarithmic growth of the ischemic infarct core. In 24 patients with terminal internal carotid artery or the proximal middle cerebral artery occlusions we found that an infarct core growth rate (IGR) <4.1 ml/hr and initial infarct core volumes (ICV) <19.9 ml had accuracies >89% for identifying patients who would still have a core of <50ml 24 hours after stroke onset, a core size that should predict favorable outcomes with thrombectomy. Published reports indicate that up to half of all LVO stroke patients have an IGR<4.1 ml/hr. Other potentially useful biomarkers include the NIHSS and the perfusion measurements MTT and Tmax. We conclude that many LVO patients have a stroke physiology that is favorable for late intervention, and that there are biomarkers that can accurately identify them at early time points as suitable for transfer for intervention.

Aerobic exercise speeds recovery in adolescents with concussion.

Here.

ABSTRACT

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Early targeted heart rate aerobic exercise versus placebo stretching for sport-related concussion in adolescents: a randomised controlled trial

Authors: Prof John J Leddy, MD, Prof Christina L Master, MD, Rebekah Mannix, MD, Douglas J Wiebe, PhD, Matthew F Grady, MD, William P Meehan, MD, et al.

Published:September 30, 2021

DOI:https://doi.org/10.1016/S2352-4642(21)00267-4

Summary

Background

Sport-related concussion is a public health problem, particularly in adolescents. Quality of life is reduced in adolescents with persistent post-concussive symptoms (symptoms >28 days). We replicated a previous randomised controlled trial to validate the safety, efficacy, and generalisability of, and objective adherence to, prescribed early targeted heart rate subsymptom threshold aerobic exercise compared with placebo-like stretching exercise for adolescent recovery from sport-related concussion and for reducing the risk of persistent post-concussive symptoms.

Methods

This randomised controlled trial was done at three community and hospital-affiliated sports medicine concussion centres in the USA. Male and female adolescent athletes (aged 13–18 years) presenting within 10 days of sport-related concussion were randomly assigned to individualised subsymptom threshold aerobic or stretching exercise at least 20 min daily, for up to 4 weeks after injury. Exercise adherence and intensity were measured by heart rate monitors. The primary outcome was clinical recovery (ie, return to baseline symptoms, normal exercise tolerance, and a normal physical examination) within the 4-week intervention period, and development of persistent post-concussive symptoms beyond 28 days after injury. This study is registered with ClinicalTrials.gov, NCT02959216.

Findings

Between Aug 1, 2018, and March 31, 2020, 118 adolescents were recruited (61 were randomly assigned to the aerobic exercise group and 57 to the stretching exercise group) and included in the intention-to-treat analysis. On survival analysis, controlling for sex, site, and mean daily exercise time, patients assigned to aerobic exercise were more likely to recover within 4 weeks after injury compared with those assigned to stretching exercise, with a 48% reduced risk of persistent post-concussive symptoms (hazard ratio for stretching vs aerobic exercise of 0·52 [95% CI 0·28–0·97], p=0·039). No adverse events were reported.

Interpretation

This multicentre study found that early treatment with subsymptom threshold aerobic exercise safely speeds recovery from sport-related concussion and reduces the risk for persistent post-concussive symptoms, an important result given the impact of delayed recovery on adolescent quality of life. Adherence was good and there were no adverse events from this non-pharmacological treatment. These results suggest that physicians should not only permit, but consider prescribing, early subsymptom threshold physical activity to adolescents as treatment for sport-related concussion and to reduce the risk of persistent post-concussive symptoms.

COVID-19 on the Big Island after vaccine availability

At Hilo Medical Center, there have been 0 Covid related hospitalizations for vaccinated individuals in recent weeks! This means that 100% of our hospitalized COVID-19 patients are unvaccinated, suggesting that here we see that vaccinations against COVID-19 are effective in preventing severe illness, hospitalizations and utilization of our limited resources and staff time.
There have been a few ED visits for flulike and related vaccine reactions, none serious.
As of 2 July 2021, we have 4 unvaccinated, COVID-positive patients in our hospital, 3 in our 11-bed ICU with 1 patient on a ventilator and 1 in our Obstetric Unit. 98% of hospitalized COVID-positive patients in our state are unvaccinated, and these still can get very very sick. Clearly the risk benefit favors vaccines at any adult age!
After all, currently we have about a 50 50 ratio of fully vaccinated to not people on island, and it's clear who the losers in that gamble are...

World distribution of mammalian coronaviruses: One reason the pandemic started in Wuhan

It's currently fashionable in many US circles to speculate that Covid-19 started in a Wuhan lab. But according to this 2017 paper, more coronaviruses are to be found in the regions around Wuhan than much of the rest of the world. The next SARS will also likely start in Asia, or perhaps Africa. That could have happened with or without help of a lab, and without a lab seems more likely at the moment.

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ABSTRACT

Global patterns in coronavirus diversity

Simon J. Anthony, Christine K. Johnson, Denise J. Greig, Sarah Kramer, Xiaoyu Che, Heather Wells, Allison L. Hicks, Damien O. Joly, Nathan D. Wolfe, Peter Daszak

Virus Evolution, Volume 3, Issue 1, January 2017, vex012, https://doi.org/10.1093/ve/vex012

Published: 12 June 2017

Since the emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrom Coronavirus (MERS-CoV) it has become increasingly clear that bats are important reservoirs of CoVs. Despite this, only 6% of all CoV sequences in GenBank are from bats. The remaining 94% largely consist of known pathogens of public health or agricultural significance, indicating that current research effort is heavily biased towards describing known diseases rather than the ‘pre-emergent’ diversity in bats. Our study addresses this critical gap, and focuses on resource poor countries where the risk of zoonotic emergence is believed to be highest. We surveyed the diversity of CoVs in multiple host taxa from twenty countries to explore the factors driving viral diversity at a global scale. We identified sequences representing 100 discrete phylogenetic clusters, ninety-one of which were found in bats, and used ecological and epidemiologic analyses to show that patterns of CoV diversity correlate with those of bat diversity. This cements bats as the major evolutionary reservoirs and ecological drivers of CoV diversity. Co-phylogenetic reconciliation analysis was also used to show that host switching has contributed to CoV evolution, and a preliminary analysis suggests that regional variation exists in the dynamics of this process. Overall our study represents a model for exploring global viral diversity and advances our fundamental understanding of CoV biodiversity and the potential risk factors associated with zoonotic emergence.