Categories
Biden Pandemic COVID Opinion Politics Reprints from others.

Pfizer’s New 80,000-Page Data Dump Is A Nightmare.

Pfizer tested their COVID vaccine on rats and then let pregnant women take it

You probably didn’t know that Pfizer dumped 80,000 pages of documents this week.

That’s because the American corporate media refused to cover it — and that’s because almost all of them took money from the Biden regime to promote the experimental vaccines and kill any critical coverage of them.

Anyway, it turns out that Pfizer’s COVID vaccine was not 95% effective: the data shows it has a 12% efficacy rate.

Let me repeat: 12%. That’s a “1” followed by a “2.”

But wait: it gets worse.

There were no human clinical trials to determine if the experimental COVID vaccines were safe for pregnant women. They were excluded from all the trials.

None. Zero. Zilch. Nada.

Instead, they tested it on 44 rats.

Twitter avatar for @seancondevSean Conway – UAP 🇦🇺 ACT Bean Candidate @seancondev

What was the basis for Pfizer and the FDA to declare the mRNA vaccine ‘safe and effective’ for pregnant and breastfeeding women? Just 44 rats.

Pfizer deliberately cut off the clinical data trial before the bad news could be collected. We already know that Pfizer vaccine’s RNA is reverse-transcribing itself into your DNA. We already know that the vaccines increased the risk of getting COVID in children, the CDC intentionally withheld clinical data from the public, and a Moderna gene sequence patented in 2017 was found in the COVID virus spike protein.

Twitter avatar for @CramerSezCramerSez @CramerSez

#PfizerDump #Pfizer #BREAKING #BreakingNews PFIZER DATA DUMP PROVES THEY KNEW DRUG WAS ONLY 12% EFFECTIVE, AT BEST. They also knew it could cause harm to the unborn.

Pregnant women in the U.S. military who were coerced into taking the jab have suffered horrific side effects and “congenital malformations” in their babies. There were more than 18,900 babies born with abnormalities in 2021.

We know this because a few brave whistleblowers got their hands on the Deparment of Defense’s medical database.

Twitter avatar for @seancondevSean Conway – UAP 🇦🇺 ACT Bean Candidate @seancondev

What was the basis for Pfizer and the FDA to declare the mRNA vaccine ‘safe and effective’ for pregnant and breastfeeding women? Just 44 rats.

More than 18,900 babies. Just think about that.

There’s much more news to come out about the COVID vaccines — and all of it is bad. For example, doctors around the world are starting to notice an explosion in the cancer rates among the vaccinated.

Like I said: lots of doctors are noticing that cancers are increasing dramatically. Here’s a chart with data pulled from VAERS that will make your heart sink.

Let me finish with this thought: perhaps it wasn’t a good idea to re-program the DNA of half the world to produce spike proteins to “fight” a virus with a 99% survival rate?


Categories
Biden Pandemic COVID Faked news MSM Uncategorized

The Un-Vaccinated are not the enemy. Stop treating them like one.

In case you haven’t noticed, the Left, MSM, Tony the fauch, CDC, and the FDA HAVE DECLARED American citizens as enemies of the state. What’s really sad is they declared open war on the first responders. The same folks who saved hundreds of thousands from dying because of the Obama- Biden pandemic.

Causing many to have to give up their livelihood, some to commit suicide, and many asking why? What was their crime? Who did they kill? Wait they saved the lives of those who would call for their heads.

Categories
Corruption COVID How sick is this? Reprints from others.

OMG! US Military Used Contact Tracing During COVID Then Sent Families of the Possibly Infected to Filthy Barracks for Days

During the COVID crisis, the US military used contact tracing to identify individuals with a high risk of catching the virus and sent them and their families, with little warning to filthy barracks for 10 days.

A report coming out of Korea discussed the military’s tactics there to combat COVID.  The result was a trampling of soldiers’ freedoms and privacy.   A post at trmlx.com discussed what happened in the military when COVID hit.

A lot of things took place across the broad spectrum of the DOD during 2020 and 2021 which have been unethical at best and flat out illegal at worst…

…A system of surveillance rose up in South Korea during 2020 in a supposed effort to tackle the high volume of Covid positive individuals that were popping up all over the peninsula. Right out of the gate I’ll declare that it was illegal and runs afoul of a number of laws we have in place to protect American citizens from abuses at the hands of their own government.

Under the leadership of General Robert Abrams, a method of contact tracing involving the use of CCTV (closed circuit television, or security footage) was used to track the movement of personnel on Camp Humphreys, collect and store their information, then remove them from their homes and put them in isolation/quarantine facilities.

COL Tremblay (pictured on right and Garrison Commander of Camp Humphreys at the time) says, “we’re gonna find out who you are because we have other ways of finding you. Whether it’s through CCTV or a number of our other capabilities…”

The process for identifying and tracking personnel worked like this:

  • When people entered the PX (post exchange or mini mall) they would scan in with their CAC (common access card or ID) and sign their name along with the time.

  • If someone tested positive for Covid, they would use those sheets and CCTV footage to identify anyone who may have had close contact with the individual while out shopping.

  • Screenshots would be taken from the saved CCTV footage and posted to the Camp Humphrey’s official facebook page and pushed out for all to see.

The Covid tracking team was called the Covid Surveillance Cell. After personnel were identified and tracked based on the collected and stored information, a contact clean team, along with a team to collect the individual and/or that person’s family to send them into isolation, were sent out from the command.

TRMLX then discusses why this was illegal:

Executive Order 12333 dictates very clearly what is and isn’t allowed within the context of United States Intelligence Activities. This order is well known by commanders at every echelon due to the ramifications it holds if it’s broken.

Elements of the Intelligence Community are authorized to collect, retain, or disseminate information concerning United States persons only in accordance with procedures established by the head of the Intelligence Community element concerned or by the head of a department containing such element and approved by the Attorney General, consistent with the authorities provided by Part 1 of this Order, after consultation with the Director.

A document pertaining to Intelligence Oversight from Marine Corps Headquarters even states,

Generally you may not intentionally target, collect, retain, and disseminate information on U.S.
persons whether CONUS or OCONUS.

It goes on to clarify that law enforcement has the authority to retain information on U.S. persons for up to 90 days who pose a threat to DOD personnel, resources or activities, but I don’t think you could find a sober attorney alive who would argue that shopping at the PX poses a criminal threat to the DOD.

After individuals and their families were identified and teams came to collect them, it was often a hurried process to get them out of their domicile and into isolation.

Original Here:

 

Categories
Biden Pandemic COVID Reprints from others. Science

You make the call. Vaccinated Up to 15X MORE LIKELY Than Unvaxxed to Develop Heart Inflammation Requiring Hospitalization: Peer Reviewed Study That’s what happens when you listen to a guy who hasn’t practiced medicine since the 80’s

You can find the links and the original articles here.

The whole article can be found here.

From the *peer-reviewed study, which was published by the Journal of the American Medical Association (JAMA):

Question  Is SARS-CoV-2 messenger RNA (mRNA) vaccination associated with risk of myocarditis?

Findings  In a cohort study of 23.1 million residents across 4 Nordic countries, risk of myocarditis after the first and second doses of SARS-CoV-2 mRNA vaccines was highest in young males aged 16 to 24 years after the second dose. For young males receiving 2 doses of the same vaccine, data were compatible with between 4 and 7 excess events in 28 days per 100 000 vaccinees after second-dose BNT162b2, and between 9 and 28 per 100 000 vaccinees after second-dose mRNA-1273.

Meaning  The risk of myocarditis in this large cohort study was highest in young males after the second SARS-CoV-2 vaccine dose, and this risk should be balanced against the benefits of protecting against severe COVID-19 disease.

Abstract

Importance  Reports of myocarditis after SARS-CoV-2 messenger RNA (mRNA) vaccination have emerged.

Objective  To evaluate the risks of myocarditis and pericarditis following SARS-CoV-2 vaccination by vaccine product, vaccination dose number, sex, and age.

Design, Setting, and Participants  Four cohort studies were conducted according to a common protocol, and the results were combined using meta-analysis. Participants were 23 122 522 residents aged 12 years or older. They were followed up from December 27, 2020, until incident myocarditis or pericarditis, censoring, or study end (October 5, 2021). Data on SARS-CoV-2 vaccinations, hospital diagnoses of myocarditis or pericarditis, and covariates for the participants were obtained from linked nationwide health registers in Denmark, Finland, Norway, and Sweden.

Exposures  The 28-day risk periods after administration date of the first and second doses of a SARS-CoV-2 vaccine, including BNT162b2, mRNA-1273, and AZD1222 or combinations thereof. A homologous schedule was defined as receiving the same vaccine type for doses 1 and 2.

Main Outcomes and Measures  Incident outcome events were defined as the date of first inpatient hospital admission based on primary or secondary discharge diagnosis for myocarditis or pericarditis from December 27, 2020, onward. Secondary outcome was myocarditis or pericarditis combined from either inpatient or outpatient hospital care. Poisson regression yielded adjusted incidence rate ratios (IRRs) and excess rates with 95% CIs, comparing rates of myocarditis or pericarditis in the 28-day period following vaccination with rates among unvaccinated individuals.

Results  Among 23 122 522 Nordic residents (81% vaccinated by study end; 50.2% female), 1077 incident myocarditis events and 1149 incident pericarditis events were identified. Within the 28-day period, for males and females 12 years or older combined who received a homologous schedule, the second dose was associated with higher risk of myocarditis, with adjusted IRRs of 1.75 (95% CI, 1.43-2.14) for BNT162b2 and 6.57 (95% CI, 4.64-9.28) for mRNA-1273. Among males 16 to 24 years of age, adjusted IRRs were 5.31 (95% CI, 3.68-7.68) for a second dose of BNT162b2 and 13.83 (95% CI, 8.08-23.68) for a second dose of mRNA-1273, and numbers of excess events were 5.55 (95% CI, 3.70-7.39) events per 100 000 vaccinees after the second dose of BNT162b2 and 18.39 (9.05-27.72) events per 100 000 vaccinees after the second dose of mRNA-1273. Estimates for pericarditis were similar.

Conclusions and Relevance  Results of this large cohort study indicated that both first and second doses of mRNA vaccines were associated with increased risk of myocarditis and pericarditis. For individuals receiving 2 doses of the same vaccine, risk of myocarditis was highest among young males (aged 16-24 years) after the second dose. These findings are compatible with between 4 and 7 excess events in 28 days per 100 000 vaccinees after BNT162b2, and between 9 and 28 excess events per 100 000 vaccinees after mRNA-1273. This risk should be balanced against the benefits of protecting against severe COVID-19 disease.

 

Categories
Biden Pandemic COVID Reprints from others. Uncategorized

“Defeat the Mandates”

Defeat the Mandates is a reprint from the FLCCC Alliance.

Nearly 25,000 people gathered on Sunday, April 10 at the “Defeat the Mandates” rally in Los Angeles. While some mandates are dropping across the country, there are vaccine mandates that remain in schools, colleges, businesses, hospitals, and corporations across the country. The concerns over these mandates are over immoral restrictions on the way doctors treat their patients with COVID, persistent scientific censorship by Big Tech, the medically unnecessary COVID-19 vaccination of children, the silencing of scientific debate, and the extension of the Emergency Powers Act beyond March 1st for the coronavirus pandemic.

FLCCC physicians Drs. Pierre Kory, Paul Marik and Flavio Cadegiani took part in the rally, exhorting attendees and online viewers to rise up and #LetDoctorsBeDoctors.

Written highlights of our physicians’ speeches are HERE.

Video clips of the speeches are HERE.

This week, Del Bigtree breaks down the Defeat The Mandates rally on The Highwire.

“It’s no business of the federal government or agencies to tell doctors how to practice medicine.” —Dr. Paul Marik

“The world has gone mad… It’s from unrelenting propaganda and censorship of good information.” —Dr. Pierre Kory

“This goes beyond political parties, political orientations, or anything else. This is a time for us to be one — fighting for the truth.” —Dr. Flavio Cadegiani


 

All the big forces and all the flawed men couldn’t put Humpty TOGETHER again.

 

“When it comes to the TOGETHER trial however, there has been a distinct signal of concern. According to one site cataloguing the online effort to understand the trial, there are currently 43 distinct concerns that have been raised about the trial, most of them with real validity…” —Substack author Alexandros Marinos

Read this article in its entirety. The issues in the trial that have been exposed, says Marinos, are “deeply related to a central failure of the protocol of the TOGETHER trial.”

 

Categories
Corruption COVID Drugs How sick is this?

FOIA Request Unearths that Pfizer Planned to Hire 1,800 Employees to Deal with Reporting on Adverse Effects from COVID Vaccine

Pfizer hired 600 employees with a plan to hire a total of 1,800 employees when side effects from its COVID vaccine started showing up.  The employees were hired to address the flood in adverse effects reporting. 

Posted by Jim Holt for The Gateway Pundit April 10, 2022 at 4:00pm

Zerohedge shared a report Authored by Zachary Stieber via The Epoch Times

Pfizer hired 600 employees in the months after its COVID-19 vaccine was authorized in the United States due to the “large increase” of reports of side effects linked to the vaccine, according to a document prepared by the company.

Pfizer has “taken a multiple actions to help alleviate the large increase of adverse event reports,” according to the document. “This includes significant technology enhancements, and process and workflow solutions, as well as increasing the number of data entry and case processing colleagues.”

At the time when the document—from the first quarter of 2021—was sent to the U.S. Food and Drug Administration (FDA), Pfizer had onboarded about 600 extra full-time workers to deal with the jump.

“More are joining each month with an expected total of more than 1,800 additional resources by the end of June 2021,” Pfizer said.

Pfizer tried to hide the information

In addition, Zerohedge reported:

The analysis of adverse event reports was previously disclosed to the health transparency group, but certain portions were redacted (pdf), including the number of workers Pfizer onboarded to deal with the jump in adverse event reports.

“We asked that the redactions on page 6 of this report be lifted and the FDA agreed without providing an explanation,” Aaron Siri, a lawyer representing the plaintiffs, told The Epoch Times in an email.

After the document was produced, the FDA determined that the three redactions on that page “could be lifted,” an FDA spokesperson told The Epoch Times via email.

The redactions had been made under (b) (4) of the Freedom of Information Act, which lets agencies “withhold trade secrets and commercial or financial information obtained from a person which is privileged or confidential.”

The unredacted version of the document also now shows that approximately 126 million doses of Pfizer were shipped around the world since the company received the first clearance, from U.S. regulators, on Dec. 1, 2020. The shipments took place through Feb. 28, 2021.

It was unclear how many of those doses had been administered as of that date.

As TGP reported previously, after the courts ordered Pfizer to release data on its COVID vaccine, documents showed over 1,200 vaccine deaths in the first 90 days after taking the vaccine.

TGP has reported many additional reports of deaths or injuries caused by the Pfizer vaccine.  The information to date does not look good for the Pfizer vaccine.

Categories
Child Abuse COVID Drugs Science

Protect your kids: Persistent Cardiac MRI Findings in a Cohort of Adolescents with post COVID-19 mRNA vaccine myopericarditis —Actual science

By:Jenna Schauer, MD  Sujatha Buddhe, MD, MS  Avanti Gulhane, MD, DNB, FSCMR Sathish Mallenahalli Chikkabyrappa, MD Yuk Law, MD Michael A. Portman, MD et al for The Journal of Pediatrics

Published:March 25, 2022 DOI:https://doi.org/10.1016/j.jpeds.2022.03.032
Abbreviations:

Late gadolinium enhancement (LGE), Coronavirus disease of 2019 (COVID-19), Nonsteroidal anti-inflammatory drugs (NSAIDs), Intravenous immunoglobulin (IVIG), Left ventricle (LV), Left ventricular ejection fraction (LVEF), Global Longitudinal Strain (GLS)

Myopericarditis, , has emerged as an important adverse event following COVID-19 mRNA vaccination, particularly in adolescents

Patients typically exhibit chest pain and an elevated serum troponin level in the days following the COVID-19 mRNA vaccine. They usually are hemodynamically stable, and symptoms and cardiac biomarkers normalize within a few days cardiac magnetic resonance studies, when performed early, frequently demonstrate abnormalities such as edema and late gadolinium enhancement (LGE), meeting Lake Louise Criteria for diagnosing myocarditis noninvasively ,

In classical myocarditis LGE can be predictive of a poor outcome

Little is known about the prognostic value or expected evolution of these CMR abnormalities associated with post-COVID-19 mRNA vaccine myopericarditis. In this case series we report the evolution of CMR imaging compared with initial, acute phase, CMR in our cohort of patients with myopericarditis post COVID-19 mRNA vaccine.

Methods

This case review includes patients younger than 18 years of age presenting to Seattle Children’s Hospital with chest pain and elevated serum troponin level from April 1, 2021 to January 7, 2022 within one week of receiving the second dose of the Pfizer COVID-19 mRNA vaccine. Institutional Review Board approval was obtained. All patients were evaluated by a pediatric cardiologist, underwent ECG and echocardiogram, and were admitted for observation with telemetry, serial troponin measurements, and repeat cardiac testing as needed. All patients underwent CMR within 1 week of initial presentation and had repeat CMR imaging at 3-8 months follow up. CMR was performed on a 1.5 T Siemens scanner. CMR analysis was performed using CVI42 (version 5.11.4, Circle Cardiovascular Imaging Inc., Alberta Canada). Patients were excluded if they did not undergo CMR or did not have a follow up CMR. Initial and follow up CMR data for each patient were reviewed and compared using paired Student t-test. Statistical significance was defined as a p < 0.05. Statistical analysis was performed using SPSS 27 (SPSS Inc., Chicago, IL).

Results

A total of 35 patients with the diagnosis of myopericarditis associated with Pfizer COVID-19 mRNA vaccine are followed at our institution. Twelve patients were excluded as they never had CMR due to delayed presentation after initial symptoms resolved or admission to other centers. Six patients were excluded as they did not have a follow up CMR, either because they followed up out of state or a study is still pending. One patient was excluded as initial CMR was performed 3 weeks after presentation. Sixteen patients who had both acute phase and follow-up CMR available for review comprised the final cohort. This group had a median age of 15 years (range, 12-17), were mostly male (n=15, 94%), white and non-Hispanic (n= 14, 88%). One patient was Asian and one patient was American Indian. Median time to presentation from the second dose of the Pfizer COVID-19 mRNA vaccine was 3 days (range 2-4 days). All patients had chest pain. The most common other presenting symptoms were fever (n=6, 37.5%) and shortness of breath (n=6, 37.5%). All patients had elevated serum troponin levels (median 9.15 ng/mL, range 0.65-18.5, normal < 0.05 ng/mL). Twelve patients had c- reactive protein (CRP) measured with median value 3.45 mg/dL, range 0-6.5 mg/dL, normal < 0.08 mg/dL.
Ten (62.5%) patients had an abnormal electrocardiogram (ECG), with the most common finding being diffuse ST segment elevation. All patients had an echocardiogram on admission; 14/16 patients had normal left ventricular (LV) systolic function; two patients demonstrated mildly reduced LV systolic function with no dilation. Left ventricular ejection fraction (LVEF) for these two patients was 45% and 53% (normal > 55%). Median left LVEF was 59% (range 45-69%). No patients had pericardial effusion.

The initial CMRs were performed within 1 week of presentation (median 2, range 0-7 days). All were abnormal; all showed evidence of edema by T2 imaging and 15/16 had LGE in a patchy subepicardial to transmural pattern with predilection for the inferior LV free wall. Distribution of LGE can be seen in Figure 1. LV regional wall motion abnormalities were noted in 2 patients. CMR median LVEF% was 54%, range 46-63%. CMR LVEF was mildly decreased in 7 patients. CMR global longitudinal strain (GLS%) measurements were abnormal in 12 patients (median -16.1%, range -13.2% to -18.1%, normal <-18%).

Figure thumbnail gr1

Figure 1Distribution of Late Gadolinium Enhancement (LGE) in American Heart Association Myocardial Segments Figure shows segment with number of patients and percent of cohort.

All patients were treated with nonsteroidal anti-inflammatory drugs (NSAIDs): 75% (n=12) received scheduled dosing (mostly, 10 mg/kg ibuprofen every 8 hours) with the remaining 4 receiving NSAIDs as needed for pain. The median time from vaccination to NSAID initiation was 2.5 days (range 0-4 days) and from symptom onset to NSAID initiation was 1 day (range 0-4 days). The two patients who presented with echocardiographic LV dysfunction were treated with intravenous immunoglobulin (IVIG) plus a corticosteroid per our institutional pathway for treatment of myocarditis

One additional patient received IVIG without corticosteroids. Median hospital length of stay was 2 days (range 1-4 days) with no ICU admission and no significant morbidity or mortality. All patients had resolution of chest pain and down-trending serum troponin level prior to discharge.

All patients underwent follow up CMR at 3-8 months after their initial study (median 3.7 months, range 2.8-8.1 months). The results are compared in Table I. Follow up CMR LVEF (57.7 ±2.8%) was significantly improved from initial (54.5 ± 5.5%, p < 0.05), and none of the patients had regional wall motion abnormalities. LVEF by echocardiogram was normal for all patients at the time of follow up. Eleven patients (68.8%) had persistent LGE, although there was a significant decrease in the quantifiable LGE% (8.16± 5.74%) from the initial study (13.77± 8.53%, p <0.05). Cardiac edema resolved in all but one patient. GLS% remained abnormal in most patients (75%, mean -16.4 ± 2.1%) at follow up without significant change from the initial study (-16.0 ± 1.7, p = 0.6). Examples of initial and follow up CMR images are shown in Figure 2. The patient who received IVIG alone and one patient who received IVIG plus corticosteroid had resolution of LGE, and the other had persistence of LGE.

Table 1Covid Vaccine-Associated Myopericarditis Findings in 16 patients
Initial (Mean±SD)Follow up (Mean±SD)P value
Echocardiographic LVEF %59.4±6.062.6±2.8<0.05
Electrocardiogram

Abnormal

Normal

10 (62.5%)

6 (37.5%)

Peak Serum Troponin (ng/mL)9.0± 5.2
CMR LVEF %54.5 ± 5.557.7 ±2.7<0.05
CMR LGE % (n=15*)13.5± 8.37.7 ± 5.7<0.001
CMR global longitudinal strain % (n=15*)-16.0 ± 1.7-16.4 ± 2.10.5
*Initial source images were not available for reanalysis for one patient.
LVEF% = LV ejection fraction
LGE %= percentage of late gadolinium enhancem
ent
CMR = Cardiac MRI
Figure thumbnail gr2
Figure 2CMR images from 3 days after admission of a 16-year-old male who presented to emergency room with chest pain and elevated troponin 3 days after receiving Pfizer COVID-19 mRNA vaccine. Initial CMR. 1a and 1b. subepicardial to midmyocardial LGE in inferior and inferolateral LV wall from base to apex (arrows). 1c shows T2 hyper-intensity in similar segments, consistent with edema. 1d, 1e and 1f. Follow up CMR 4.4 months later. LGE still persistent but decreased from 26% to 19.84% (arrows), LVEF remained stable at 58%. There is improved T2 hyperintensity.
Eight patients (5 of whom had persistent LGE) underwent 24-hour cardiac rhythm monitoring, all of which studies were normal. Six patients, all with persistent LGE, underwent exercise tests, all of which were normal. Four patients complained of intermittent chest pain at follow up with no identifiable abnormality on evaluation; no therapy or intervention was required. No patient received heart failure medication.

DISCUSSION

We previously reported 15 patients with clinically suspected SARS-CoV-2 mRNA vaccine induced myopericarditis. All patients had an abnormal CMR, with edema and or LGE in addition to clinical symptoms and troponin elevation, and some had abnormal ECG or echocardiogram

We have since established a clinical protocol for serial CMR performance in these patients consistent with the 2021 American Heart Association (AHA) statement that stressed the risk of sudden cardiac death, particularly with exercise, while active inflammation is present.

our patients were restricted from exercise on discharge. Repeat CMR was performed within 3-6 months to guide next clinical decision-making steps; timing was modified in some individuals based on scanner accessibility and safety precautions during the COVID-19 pandemic. Although symptoms were transient and most patients appeared to respond to treatment (soley with NSAIDS), we demonstrated persistence of abnormal findings on CMR at follow up in most patients, albeit with improvement in extent of LGE.
CMR has increasingly been identified as an important diagnostic tool for myocarditis given its ability to identify subclinical injury and fibrosis by markers of LGE and edema. CMR also has been utilized in longitudinal follow up of patients with myocarditis to help therapeutic management, although exact screening protocols remain controversial
The presence of LGE is an indicator of cardiac injury and fibrosis and has been strongly associated with worse prognosis in patients with classical acute myocarditis. In a meta-analysis including 8 studies, Yang et al found that presence of LGE is a predictor of all cause death, cardiovascular death, cardiac transplant, rehospitalization, recurrent acute myocarditis and requirement for mechanical circulatory support]Similarly, Georgiopoulos et al found presence and extent of LGE to be a significant predictor of adverse cardiac outcomes in an 11 study meta-analysis
The persistence of LGE over time and its prognostic value is less well established. Malek et al found that in a cohort of 18 patients with myocarditis, nearly 70% had persistent CMR changes at a median follow-up time of 7 months Dubey et al found similar findings in their cohort of 12 pediatric patients, with persistence of LGE in all patients despite resolution of edema.
Prognostic meaning of LGE in vaccine associated myopericarditis requires further study.
Strain analysis by CMR also has been shown to have prognostic utility in myocarditis even in the setting of normal LV functionStrain testing can be performed without use of contrast material and can be particularly useful in situations where contrast administration is challenging or contraindicated. Notably, in our cohort, though there was significant reduction in LGE at follow up, abnormal strain persisted for the majority of patients at follow up.
This study has certain limitations. Patients who did not seek medical attention during acute illness or did not present with significant symptoms and require hospitalization were not captured, and their disease course may be different. Incomplete CMR data on other patients precludes extrapolation of our CMR findings to all who experienced mRNA vaccine-related myopericarditis. In addition, follow-up CMR timeframes varied from patient to patient making it difficult to predict the timing of CMR changes over time. the total number of patients reported is small, limiting ability to draw conclusions about the effect of treatment modalities or to generalize regarding outcomes of vaccine-associated myopericarditis.
In a cohort of adolescents with COVID-19 mRNA vaccine-related myopericarditis, a large portion have persistent LGE abnormalities, raising concerns for potential longer-term effects. Despite these persistent abnormalities, all patients had rapid clinical improvement and normalization of echocardiographic measures of systolic function. For patients with short acute illness, no dysfunction demonstrated by echocardiogram at presentation and resolution of symptoms at follow up, return to sports was guided by normalization of CMR alone. In patients with persistent CMR abnormalities we performed exercise stress testing prior to sports clearance per myocarditis recommendations We plan to repeat CMR at 1 year post-vaccine for our cohort to assess for resolution or continued CMR changes.
The CDC notes that even though the absolute risk for myopericarditis following mRNA COVID-19 vaccine is small, the relative risk is higher for particular groups, including males 12-39 years of age.

Some studies have suggested that increasing the interval between the first and second dose may reduce the incidence of myopericarditis in this population

These data led to an extension in CDC recommended dosing interval between dose 1 and dose 2 to 8 weeks. Further follow up assessment and larger multicenter studies are needed to determine the ultimate clinical significance of persistent CMR abnormalities in patients with post COVID-19 vaccine myopericarditis

Uncited reference

REFERENCES

  1. Gargano JW, Wallace M, Hadler SC, Langley G, Su JR, Oster ME, et al. Morbidity and Mortality Weekly Report Use of mRNA COVID-19 Vaccine After Reports of Myocarditis Among Vaccine Recipients: Update from the Advisory Committee on Immunization Practices-United States, June 2021 2021;70. https://doi.org/10.1161/CIR.0000000000000239?url_.
  2. mRNA Coronavirus-19 Vaccine–Associated Myopericarditis in Adolescents: A Survey Study.

    The Journal of Pediatrics. 2021; https://doi.org/10.1016/j.jpeds.2021.12.025

  3. Clinically Suspected Myocarditis Temporally Related to COVID-19 Vaccination in Adolescents and Young Adults.

    Circulation. 2021; https://doi.org/10.1161/circulationaha.121.056583

  4. Myopericarditis After the Pfizer Messenger Ribonucleic Acid Coronavirus Disease Vaccine in Adolescents.

    Journal of Pediatrics. 2021; 238: 317-320https://doi.org/10.1016/j.jpeds.2021.06.083

  5. The prognostic value of late gadolinium enhancement in myocarditis and clinically suspected myocarditis: systematic review and meta-analysis.

    European Radiology. 2020; 30: 2616-2626https://doi.org/10.1007/s00330-019-06643-5

  6. Diagnosis and Management of Myocarditis in Children: A Scientific Statement from the American Heart Association.

    Circulation. 2021; (E123–35)https://doi.org/10.1161/CIR.0000000000001001

  7. Prognostic Impact of Late Gadolinium Enhancement by Cardiovascular Magnetic Resonance in Myocarditis: A Systematic Review and Meta-Analysis.

    Circulation: Cardiovascular Imaging. 2021; : 55-65https://doi.org/10.1161/CIRCIMAGING.120.011492

  8. Children With Acute Myocarditis Often Have Persistent Subclinical Changes as Revealed by Cardiac Magnetic Resonance.

    Journal of Magnetic Resonance Imaging. 2020; 52: 488-496https://doi.org/10.1002/jmri.27036

  9. Persistence of Late Gadolinium Enhancement on Follow-Up CMR Imaging in Children with Acute Myocarditis.

    Pediatric Cardiology. 2020; 41: 1777-1782https://doi.org/10.1007/s00246-020-02445-5

  10. Diagnostic and Prognostic Value of Cardiac Magnetic Resonance Strain in Suspected Myocarditis With Preserved LV-EF: A Comparison Between Patients With Negative and Positive Late Gadolinium Enhancement Findings.

    Journal of Magnetic Resonance Imaging. 2021; https://doi.org/10.1002/jmri.27873

  11. Moulia D. Myocarditis and COVID-19 vaccine intervals: international data and policies. n.d.
  12. Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart.

    Circulation. 2002; 105: 539-542https://doi.org/10.1161/hc0402.102975

Footnotes

No funding was received for this research

The authors declare no conflicts of interest.

Abstract

We describe the evolution of Cardiac MRI (CMR) findings in 16 patients, 12-17 years of age, with myopericarditis after the second dose of the Pfizer mRNA COVID-19 vaccine. Although all patients showed rapid clinical improvement, many had persistent CMR findings at 3-8 month follow up.

Figures

  • Figure thumbnail gr1
    Figure 1Distribution of Late Gadolinium Enhancement (LGE) in American Heart Association Myocardial Segments

    . Figure shows segment with number of patients and percent of cohort.

          • Figure thumbnail gr2
            Figure 2CMR images from 3 days after admission

Categories
COVID Drugs Politics

Kansas Senate Passes Bill to Authorize the Prescriptions of Ivermectin and Hydroxychloroquine and Child Vaccine Exemptions

Kansas state senators passed a bill early Thursday that would authorize the prescriptions of off-label drugs for Covid-19 treatment, such as Ivermectin and hydroxychloroquine. The bill also exempts children from being vaccinated if “such immunizations would violate sincerely held religious beliefs.”

Senate Sub. for HB 2280, as amended, concerns prescribing and dispensing of drugs for off-label use and religious exemptions for childhood vaccines, the bill stated.

The bill was passed with 21 voted yes, and 16 voted no.

Capital-Journal reported:

The Senate worked on a host of bills into the early morning hours in a marathon session. The off-label drug bill, HB 2280, passed 21-16 shortly before 1:30 a.m.

“Thousands of Kansans and hundreds of thousands of Americans have died because of this propaganda that shut down early treatment,” said Sen. Mark Steffen, R-Hutchinson. “I fully believe that this passage of this bill through the Senate will gain national attention and help be a very important part of getting the care to the people who need it.”

The bill would allow doctors to prescribe ivermectin, hydroxychloroquine and any other FDA-approved drug that isn’t a controlled substance for an off-label use to prevent or treat COVID-19.

It further requires pharmacists to fill the prescriptions, removing their professional discretion to refuse to fill a prescription, unless they find a reason other than the connection to COVID-19.

“With this provision, a doctor can write a prescription for abortion medication under the guise of COVID, and the pharmacist must fill it,” said Cindy Holscher, D-Overland Park, who opposed the bill.

Another piece protects doctors from board of healing arts investigations connected to the pandemic, prohibiting any “recommendation, prescription, use or opinion” on COVID-19 treatments from being considered unprofessional conduct.

The bill also expands existing religious exemptions for childhood wellness vaccines at schools and day cares. It effectively creates a new exemption where any parent can claim a moral or ethical exemption to any youth vaccinations.

 

Categories
Biden Pandemic COVID

What Could Explain the Lower COVID-19 Burden in Africa despite Considerable Circulation of the SARS-CoV-2 Virus? No Tony the Fauch.

1
Department of Cultures, Societies, and Global Studies, Northeastern University, 201 Renaissance Park, 360 Huntington Ave., Boston, MA 02115, USA
2
Department of Public Health, Institute of Tropical Medicine, B-2000 Antwerp, Belgium
3
DUNDEX (Deployable U.N.-Experienced Development Experts), FX68 Belturbet, Ireland
4
School of Public Health, University of Illinois at Chicago, Chicago, IL 60607, USA
5
School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada
6
Researcher Africa Institute for Health Policy Foundation, Nairobi 020, Kenya
7
T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Anthony R. Mawson and Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(16), 8638; https://doi.org/10.3390/ijerph18168638
Received: 7 July 2021 / Revised: 13 August 2021 / Accepted: 13 August 2021 / Published: 16 August 2021
(This article belongs to the Collection Outbreak of a Novel Coronavirus: A Global Health Threat)

Abstract

The differential spread and impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing Coronavirus Disease 2019 (COVID-19), across regions is a major focus for researchers and policy makers. Africa has attracted tremendous attention, due to predictions of catastrophic impacts that have not yet materialized. Early in the pandemic, the seemingly low African case count was largely attributed to low testing and case reporting. However, there is reason to consider that many African countries attenuated the spread and impacts early on. Factors explaining low spread include early government community-wide actions, population distribution, social contacts, and ecology of human habitation. While recent data from seroprevalence studies posit more extensive circulation of the virus, continuing low COVID-19 burden may be explained by the demographic pyramid, prevalence of pre-existing conditions, trained immunity, genetics, and broader sociocultural dynamics. Though all these prongs contribute to the observed profile of COVID-19 in Africa, some provide stronger evidence than others. This review is important to expand what is known about the differential impacts of pandemics, enhancing scientific understanding and gearing appropriate public health responses. Furthermore, it highlights potential lessons to draw from Africa for global health on assumptions regarding deadly viral pandemics, given its long experience with infectious diseases.

1. Background

As of 11 August 2021, approximately 7.1 million confirmed COVID-19 cases were reported in Africa continentwide [1]. Now, a year and a half since the first infection was reported in Egypt on 14 February 2020, Africa has accounted for just 3.5% of 204.2 million lab-confirmed cases [2], despite containing 12.5% of the global population [3]. Africa’s share of deaths is just 4.1% of the 4.3 million reported globally [2]. These numbers significantly defy early predictions of mass COVID-19 catastrophe [4,5,6,7]. The doom and gloom predictions were based on what was known about how the disease is transmitted, and how socially deprived settings, unsanitary living conditions, and weak health systems, which are common throughout the continent, could exacerbate spread and subsequent disease burden [6,8,9]. To date, mass infection spread, high rates of severe disease, and excess mortality due to COVID-19 on the continent have not been reported [10]. One important exception is South Africa, which carries a substantial 35% of the confirmed cases and 42% of total deaths among the 55 countries on the continent [1]. This leads to questioning the differences in reporting between African countries, as South Africa makes up only 4.8% of the 1.2 billion people living in Africa and has conducted a disproportionate 24% of the 50.6 million tests administered, as of 17 June 2021 [1].
Because of low testing capacities, Africa has conducted the least number of tests of all global regions given its population size [1,11], but has exceeded the Africa Centers for Disease Control and Prevention (Africa CDC) targets of 8000 tests per million [1]. This should be seriously considered as having contributed to an underestimation of cases [12]. Several seroprevalence studies offer insights on the extent of spread in the continent. For example, a cross-sectional household study in Zambia reported much higher infections than reported via the limited normative testing, which showed only one confirmed case reported for every 92 community infections [13]. Small antibody studies among healthcare workers in hospitals have reported up to a 36% prevalence in Kinshasa, DRC [14], 45.1% in Ibadan, Nigeria [15], and 12.3% in Blantyre, Malawi [16], during the period of May to June 2020. Studies among blood donors in Kenya and South Africa from April to June 2020 report anti-SARS-CoV-2-IgG seroprevalence of, respectively, 4.5% nationally [17] and 60% among South African black populations, seven times that of in-country white populations [18]. Two studies reported SARS-CoV-2-IgG positivity of 23.7% in workers of low socio-economic status in Cape Town, South Africa [19], and 25.1% in gold mine workers and administrative staff in Ivory Coast [20]. These studies collected blood samples between April and October 2020 during the first wave of the COVID-19 spread throughout the continent, with the second wave largely peaking around December 2020 [21]. While these studies offer insights, the results are variable. A true picture requires more and larger antibody studies in other geographies and populations over time [22]. Additionally, these data should be interpreted with caution, as none of these tests have been validated in African-specific contexts where it is possible that cross-reactivity with other prevalent viruses, micro-organisms, and hypergammaglobulinemia due to malaria exposure may influence the sensitivity and/or specificity of these tests, potentially leading to either an underestimation or overestimation of seroprevalence [14,23,24,25,26,27].
The integrity of reporting case and death data in Africa has been repeatedly called into question. For instance, in January 2021, The New York Times published an article titled “A Continent Where the Dead Are Not Counted” [28]. However, while only 34.6% of countries globally have complete death registration data in the Civil Registration and Vital Statistics (CRVS), most African countries have a system in place [29], and there is no evidence that COVID-19 mortality data is less accurately reported in Africa than elsewhere. Only Tanzania ceased reporting COVID-19 cases or deaths since May 2020 [1]. The World Mortality Dataset reports the undercounting of COVID-19 deaths from many of the 77 countries included in the dataset, such as the U.S., U.K., and Russia, with South Africa, Egypt, and Mauritius being the only African countries listed [30]. While there are some reports of excess deaths from the continent [30,31], these may well be more due to the adverse indirect effects of pandemic prevention measures, such as lockdowns, causing a wide range of difficulties such as food shortages, and a near unilateral focus on COVID-19, diverting resources from treating other diseases and health conditions [32]. Despite having several limitations [33], data from an autopsy study in a small sample of deaths from a teaching hospital in Zambia indicated the underestimation of COVID-19 mortality is a problem in Africa [34]. Nevertheless, rapid mortality assessments for COVID-19 that is underway in some countries [35,36,37,38] could reveal a more complete picture. Expectedly, due to weak health systems, recent data show that Africa has a higher mortality in those with critical COVID-19 illness than elsewhere, with a mortality rate of 48.2% compared with a global average of 31.5% [39].
While various studies have postulated that demographic profile, early actions such as lockdowns, community factors, and possibly population-specific innate immune factors that are yet undetermined [9,12,40] have played a role in the apparently lower COVID-19 burden, the data and speculation still leave many questions unanswered [41,42]. Several articles in the popular media [43,44] postulate hypotheses, but to our knowledge there has not yet been a complete scholarly review. We are beginning to understand that context and history matter a great deal [45,46,47]. The first modelling of the pandemic for nearly all countries in Africa, based on multiple context-specific covariates, has more closely predicted what has been observed [48]. Here, a set of hypotheses to explore observations regarding SARS-CoV-2 spread and a comparatively low COVID-19 disease burden in the African region are examined. An analysis of factors underlying the spread and burden is important because of the potential for valuable global public health lessons, expanding on what is known regarding deadly viral pandemics, population and systems-level preparedness, and subsequent response.

2. On SARS-CoV-2 Spread

To understand the full picture of COVID-19 in Africa, we must first examine how spread patterns emerged, and what variables could have influenced these patterns. While recent data have shown extensive SARS-CoV-2 spread in the continent, this was not always thought to be the case, and the extent of the spread compared with other global regions is still somewhat debated. Below, we present three main areas of interest regarding viral propagation in an Africa-specific context.

2.1. Early Government Measures and Messaging

Many governments in Africa enacted early response measures to the pandemic [49,50,51,52]. On 5 February 2020, even before there was a single case reported in the continent, the Africa CDC had established the Africa Taskforce for Coronavirus (AFCOR), and on 22 April 2020, the WHO highlighted examples of how Africa was leading the global response. By 15 April 2020, 96% of the 50 African countries examined had in place at least five ‘stringent public health and social measures’ to prepare for the emerging pandemic [21]. Less international connectivity, early border closures, and lockdowns to prevent viral importation from international flight arrivals, especially from China [8], were associated with a lower case load [53]. Modelling studies also found reduced connectivity/travel at regional, national, and international levels as having an important early impact on slowing the spread [48,54].
Destructive epidemics are not new phenomena for Africa. The continent is constantly dealing with abundant infectious disease (e.g., malaria, yellow fever, tuberculosis, Ebola, polio) [55]. Due to their familiarity with these epidemics, many governments have developed effective public health programs with messaging aimed at unifying the community and highlighting the need for preventative action among individuals [56,57]; for example, the case of the response to Ebola in Western Africa [58,59,60] and to the HIV/AIDS epidemic in Uganda [61]. A similar unity around public health messaging has emerged around COVID-19 in many countries, including outside Africa in Vietnam (e.g., “Fighting the epidemic is like fighting against the enemy”) [12]. It is very possible that a certain baseline individual and community preparedness, awareness, and adherence to government public health recommendations on non-pharmaceutical interventions, and a readiness to adapt to a new epidemic had significant implications in stunting disease spread in the community.
Recent studies point to cultural adherence to government recommendations as being important for mitigating SARS-CoV-2 spread [62,63]. But are Africans, on average, more willing than other populations to respect in-country public health guidelines? Several surveys on adherence to masking, social distancing, and hand hygiene have been conducted in multiple countries, some of which are nationally representative [64]. Studies from Nigeria, Malawi, Ethiopia, Ghana, Kenya, and the DRC showed that although most participants had a good knowledge about COVID-19 transmission modes and prevention mechanisms that were consistent over place and time, there were gaps in the practices that prevent COVID-19 [65,66,67,68,69]. Studies further determined that individuals’ age, sex, educational status, occupation, and income level were associated with COVID-19 related practices [65,67,70,71]. From these studies, it can be concluded that awareness and (non)adherence to NPIs does not explain low reported cases.

2.2. Population Distribution and Structure of Social Networks

Population structure and spatial distribution strongly predict the patterns of SARS-CoV-2 transmission in communities [72,73]. Analysis of spatial and temporal clustering of populations shows a correlation between density/crowding and viral reproduction number [72]. Africa is the least urbanized global region, with 55% of the continent’s population living in rural areas with wide variations across countries [74,75]. Modelling shows greater reproduction rates in urban areas [48,76], and epidemiological data are skewed towards higher cases in urban areas across all countries [54,77]. A similar pattern was observed for Ebola [78]. South Africa, clearly the COVID-19 exception in the continent, is a rapidly urbanizing outlier, as only 30% of its population live in rural areas [75]. Nigeria, on the other hand, models the trends seen in the continent as a whole, with approximately a 50% rural population [75] and a low COVID-19 burden [1]. Limited research in Africa also shows significantly more intergenerational contacts in rural as compared to urban areas [79]. Researchers posit this distribution, household size, and patterns of age-structured social contacts modify the spread of epidemics [48,76,79,80]. Furthermore, a recent study depicts the effect that the nature of social networks and mobility have on COVID-19 transmission [81]. Communities with increased social capital tend to see worse disease outbreaks overall [81,82], although this is not always the case [83]. An increased feeling of integration and connection to society is beneficial in terms of social support, which could potentially benefit individual health outcomes, but may generally have negative consequences for containing infectious disease spread due to increased human contact [80].
The limited research conducted on social contact in Sub-Saharan Africa (SSA) shows that enhancing social distance mitigation strategies, particularly for elderly populations, would result in mortality decreases, but not to the extent that these changes would have in higher income settings, which tend to have increased proportions of elderly cohorts in the population [9]. The small slice of the African population who are older (only 3% of the African population is 65+) [3] live overwhelmingly at home, often with extended families spanning multiple generations. This alone explains a huge discrepancy in cases, as roughly one third to one half of deaths in wealthy countries, such as the U.S., have resulted from superspreading events in elderly nursing homes and assisted living facilities, providing the rationale for prioritizing the inoculation of these older individuals [84,85]. While multiple family homes generally have more people in a shared space than the typical single-family homes of Western countries, this slightly increased risk of within-household spread is offset by the significantly decreased risk of large-scale superspreading events in the community, often caused by congregate nursing home settings [86]. Despite an increasing trend of elders being cared for in long-term care facilities in Africa [87], especially in South Africa [88], this is still far less commonly practiced than in Western countries, Asia [89], or Latin America [90,91].

2.3. A Largely Outdoor Existence

Because infected persons transmit the virus through coughing, sneezing, talking, singing, and breathing [92], living environments matter. Further, viability and infectivity are influenced by environmental conditions [46,93]. Studies show that coronavirus transmission, while also possible outdoors [94], is concentrated in indoor settings where it is estimated to be about 19 times higher [95]. While most likely only minimally contributing to viral spread, built environments requiring ventilation, air-conditioning/heating, wastewater, and sewer systems have been shown to carry the virus that may escape through aerosolization [96,97,98]. These systems are generally in urban areas and are almost entirely lacking in rural Africa where most people live. In contrast, respondents in the U.S. National Human Activity Pattern Survey reported spending over 90% of time in enclosed environments, either in buildings or in their cars [96,99].
African livelihoods that are largely dependent on agriculture and pastoralism favor dawn-to-dusk outdoor lifestyles, with shelters used mostly for sleeping. Research shows people in rural areas spend far more time outdoors as compared to urban areas [100]. Even in the case of sleeping, these homes are often well ventilated with outside air, significantly reducing the chance of viral transmission when compared to tightly enclosed indoor spaces in developed countries. Additionally, higher temperatures and UV light intensity have been shown to predict SARS-CoV-2 spread [101,102], although the evidence is inconsistent [103]. Prolonged, year-round outdoor living with direct exposure to UV light in mostly warm and tropical climates could partially explain reduced transmission [46], perhaps due to endogenously produced vitamin D, which is suggested in some studies, including a systematic review and meta-analysis, to attenuate COVID-19 symptom severity [104,105]. Vitamin D supplements are under several ongoing clinical investigations [106].
As a final note regarding the extent of transmission, even if the SARS-CoV-2 virus is more widespread than reported as seroprevalence data suggest, there still has been much less morbidity and mortality observed. The proposed factors reviewed above provide some insight into how African-specific transmission patterns have emerged and evolved over time.

3. Factors Mitigating COVID-19 Burden in Africa

Even though case and death reporting has certainly been less reliable in Africa, there has been very little evidence of increased overall mortality or widespread COVID-19 disease, with the exceptions of South Africa and northern African countries. We now examine what could possibly explain this somewhat perplexing situation.

3.1. Demographic Pyramid

It is beyond doubt that the demographic pyramid is significantly related to decreased COVID-19 burden. It is well documented that COVID-19 burden is heavily skewed towards older populations [107,108], as demonstrated by a study of 17 million COVID-19 cases [109]. Compared with a reference demographic group of 5–17 years, the demographic of 65–74 years is 35 times more likely to become hospitalized from SARS-CoV-2 infection, and 1100 times more likely to die from COVID-19, with these risks increasing significantly in even higher age groups [108,109]. Africa has the youngest population among all global regions, with a median age of 19.7 years [25,51]. Conversely, the median ages among the hardest hit countries are much higher: 26.8 years in India [110], 31.4 years in Brazil [111], 38.5 years in the U.S. [112], and 40.5 years in the U.K. [113]. Modelling clearly shows that the COVID-19 mortality for Africa tracks this similar age pattern [48,54], and this is confirmed by actual current mortality data [114]. Through conducting a simple linear regression to show between-region differences, regressing cumulative mortality per 1 million population on the ratio of population aged 65+ vs. aged 15–64, the R2 = 0.283 is found, meaning that the variance in mortality accounted for by age structure is 28.3% (Figure 1) [2,75,115]; the cumulative mortality figures used here are for the period since the beginning of the pandemic through 17 June 2021, as reported on oneworldindata.org (accessed on 17 June 2021), which utilizes Johns Hopkins University Centers for Systems Science and Engineering COVID-19 data [2]. These data provide evidence that despite the considerable spread of infection, COVID-19 disease and mortality burden in younger African populations is comparatively absent. However, South Africa shows a much higher mortality than many countries with a similar age structure, including India and Egypt, meaning that other factors are also at play.
Figure 1. This analysis is based on data extracted from ourworldindata.org accessed on 17 June 2021, retaining data on countries with populations of at least 1 million, for which complete data were available for the analyses done. Among these countries, as one would expect, COVID-19 mortality is strongly correlated with age structure. Note the concentration of purple dots in the bottom left, indicating comparatively low COVID-19 mortality and young age structure among most African countries. The Pearson’s R2 for cumulative COVID-19 mortality and the ratio of persons aged 65+ to those aged 15–64 is 0.283; for example, 28.3% of the between-country variance in cumulative COVID-19 mortality can be accounted for by age structure alone.

3.2. Pre-Existing Conditions

It is well known that people with pre-existing conditions, such as diabetes, chronic respiratory diseases, obesity, and hypertension have a greatly increased risk of moderate to severe complications from COVID-19 infection [53,116,117]. Broadly, these conditions are considerably less prevalent in low income and lower middle income countries (LICs and LMICs) when compared to higher income countries (HICs) [9,118], providing an additional possible explanation for why COVID-19 burden is more reduced in the African continent. Indeed, African countries have a low prevalence of NCDs (only accounting for 29.8% of total burden of disease in SSA, with the majority of burden coming from infectious disease) [119], compared to 88% in the US and 74% in Brazil [120], which align with the impact of pre-existing conditions on increasingly severe complications and death from COVID-19 [9,116]. South Africa, which accounts for nearly 40% of all reported COVID-19 cases and deaths in the continent [2], reports an exceptionally high burden of NCDs [119,121]. However, some research suggests that the prevalence of infectious disease can similarly exacerbate COVID-19 burden and may actually indicate that regions with high infectious disease and low NCD prevalence (such as in Africa) are not advantaged [9,25]. For instance, a recent cohort study in South Africa suggested that HIV was associated with a doubling of mortality risk of COVID-19 [122]. This is potentially significant to consider in explaining why South Africa has a disproportionate COVID-19 burden in the continent, given that it also has the greatest number of people living with HIV/AIDS in the world [123]. More studies are needed, however, before this potential association can be determined.

3.3. Trained Immunity

The phenomenon of trained immunity may be tempering the COVID-19 burden in the continent. Here, we focus on four elements underlying this hypothesis: (i) BCG vaccinations, (ii) exposure to varied commensal microorganisms, or the “hygiene hypothesis”, (iii) prevalence of infectious diseases, and (iv) historical use of herbal plants and remedies.
(i) Live vaccines activate innate immune systems, conferring protection against future infections from other pathogens [124,125,126,127,128,129], which researchers believe may have the potential to also attenuate consequences of infection with SARS-CoV-2 [130]. Recent data suggest that regions with mandated BCG vaccinations have had lower COVID-19 disease burden [131], which may speak to an association between BCG vaccination rate and population COVID-19 burden. Children vaccinated with BCG could have a lower infection risk with SARS-CoV-2 [132], continuing well into adulthood. Interestingly, and applicable to COVID-19, BCG vaccination was shown to be especially protective against complications of other respiratory viral infections, supported by studies in Guinea-Bissau and South Africa [133,134]. Additionally, in rodent models, BCG reduces viral load from infection by influenza A and herpes simplex virus type 2 (HSV2) [135,136], with a mediation by a boosted innate, nonspecific immune defense via increased cytokine production and macrophage action. It is not known if BCG immunity confers such protection in older populations [132], but this has been suggested by some research [137]. This is hypothesized in part from observations in countries that lagged behind other efforts to disseminate BCG, such as Iran and Somalia, which have incurred a significant death toll from COVID-19. Still, it is possible that countries with earlier BCG administration campaigns have contributed to the protection of older populations from heavy COVID-19 burden, through childhood inoculation for tuberculosis [137]. Because COVID-19 complications are often a result of significant systemic inflammation [138,139], the fact that inoculation with BCG boosts an innate immunity that subsequently lowers the extent of inflammation [132] indicates that it could be a pathway by which BCG attenuates infection of SARS-CoV-2. Of course, controlled clinical trials are needed to verify this hypothesis. While this varies, most African countries have high BCG coverage [140], with the notable exception of Somalia, due to the long-standing civil wars interfering with child vaccination programs. As noted in one study, countries without universal policies for BCG vaccination (such as Italy, the U.K., Spain, and the U.S.) have experienced much more severe disease burden compared to countries with universal programs (including most African countries and Japan, for example) [137]. However, this association may be primarily due to other factors; for example, countries that have a BCG vaccine mandate may tend to have stricter public health measures in place that could indirectly affect population COVID-19 burden, instead of the effect being driven primarily by BCG vaccination. Furthermore, if this BCG hypothesis turns out to have some merit, countries in Africa with similar levels of BCG coverage and population structure should show comparable COVID-19 burden.
(ii) The so-called “hygiene hypothesis” posits that some environments advantage populations against certain forms of infection and disease, due to chronic exposure to a multi-microbial environment, potentially producing protective immune effects when encountering new pathogens [130,141]. There has been some concern regarding regions that use ultra-hygienic practices, exemplified by the overuse of hand sanitizer and other disinfection practices in many countries, as inadvertently creating a disadvantage for confronting new immune challenges such as SARS-CoV-2 [130]. Accordingly, non-specific immunity would be weakened and may have implications for disrupting the adaptive composition of commensal organisms on the skin, gastrointestinal tract, and other organ systems [130]. Because COVID-19 is a relatively new viral problem, it may take a while before conclusive statements can be made about the role of the microbial environment on infection susceptibility, but researchers agree that this hypothesis is plausible [130], in part indicated by the aforementioned dichotomy of burden between richer and poorer countries [142,143]. Higher income countries (with a few exceptions) have suffered much greater COVID-19 burdens (specifically, hospitalization and death rates) than the poorest countries, on average [2]. Such data raise the consideration that richer countries could be maladaptively over-sanitizing.
(iii) Given that 22 of the 25 most vulnerable countries to infectious disease epidemics are in Africa (the other three being Afghanistan, Haiti, and Yemen) [144], the continent carries the heaviest burden of infectious diseases, including the impoverishing neglected tropical diseases (NTDs) [145,146]. During 2018 alone, SSA faced 96 disease outbreaks in 36 of 47 countries [55]. This pathogenic environment precipitates the wide use of antibiotics, antimalarials, and other drugs to treat NTDs, such as azithromycin and ivermectin often distributed through mass drug administrations [147,148,149,150,151], which might counteract to mitigate COVID-19 morbidity. In particular, used widely over several decades in SSA, ivermectin has been spotlighted as a potential treatment for COVID-19 [152,153], including by the NIH [154,155]. Researchers have postulated that “circulating viruses or parasites in the African subcontinent” could explain high SARS-CoV-2 antibody seropositivity [14]. For instance, of 228 million cases of malaria worldwide in 2018, 93% were in SSA [156]. Notably, South Africa is not generally endemic for malaria and other NTDs [157]. Intense malaria exposure (which is frequent in many rural areas in SSA and much less so in urban areas, and not at all in South Africa or in northern Africa countries) has a strong influence on the immune system and could contribute to a better trained immunity [158,159]. It is possible that infection by malaria alone may overstimulate the immune system and confer an immune advantage when compared to nonexposed populations. To further investigate this potential role, as very few to no communities outside of Africa are holo-endemic for the disease [160], mechanistic studies would be needed to determine if there is cross-immunity between malaria and SARS-CoV-2 exposure.
(iv) Yet to be measured, the historical use of natural medicine for primary care [161,162,163] and widespread belief of self-medication with these for COVID-19 in Africa has triggered a WHO-AFRO expert panel in September 2020 to endorse a protocol for the clinical investigation of herbal medicine for COVID-19 [164]. During 30 March–1 April 2021, the Third Regional Consultation with Experts and Researchers on the Contribution of Traditional Medicine to COVID-19 Response in the African Region was held, with contributions from scores of scientists and countries. The case for exploring natural medicine in the fight against COVID-19 is justified [165]. Although African countries such as Madagascar have endorsed the wide use of a traditional therapeutic agent to fight COVID-19, there are no published scientific data that would lend support to this claim. Clinicaltrials.gov (accessed on 7 July 2021) reports that several studies are underway, including Chinese traditional herbal medicines [166]. The establishment in Africa of a Regional Expert Advisory Committee on Traditional Medicine for COVID-19 compromising 25 members speaks to the widespread use of and belief in herbal medicine as possible means of prevention or cure of COVID-19.

3.4. Genetics

Recent work suggests that there is a role for genetics in COVID-19 trajectory, and in differentially affecting separate populations [167,168]. Some genetic immunological factors could possibly be playing a role in shielding Africa from the brunt of the pandemic [41]. For example, SARS-CoV-2 infects human cells largely through its interactions with the ACE2 receptor, involved in regulating blood pressure dynamics [6,117,169]. Populations varying in the expression of the ACE2 protein may have different baseline ‘openness’ for infection. African people have been shown to respond less effectively to ACE inhibitors for treatment of blood pressure, and have less expression of ACE2; therefore, there is potential for a more difficult route that the virus must maneuver to infect cells in this population [6,169]. There also may be genetic susceptibility via the 3p21.31 gene cluster, as one GWAS study showed ABO blood group A as having the highest risk of COVID-19-associated respiratory failure, with group O having the lowest risk [170]. Studies have shown that African populations have a particularly high proportion of O-positivity at nearly 50%, which is higher than in White and Asian populations [171,172,173]. It is possible that this increased O prevalence could be conferring a greater protective effect in African populations compared with other groups with less O prevalence; however, no studies have concluded this. While this hypothesis is somewhat challenged by the particularly heavy COVID-19 burden facing African Americans in the U.S. [174], who would likely share some or most of these genetic advantages [175,176], elevated levels of NCDs are observed more in African Americans than in continental African populations [119,177,178], which could help explain this discrepancy along with other adverse socioeconomic and cultural factors.

3.5. Broader Sociocultural Implications

Importantly, inequality of distribution of income (standardized by the GINI co-efficient) appears to be at least partially correlated with increasing disease burden of COVID-19, as South Africa (with a value of 63.1, among the highest in the world) and Brazil (54.7) [179] have been hit hard by COVID-19. One study of the top 50 countries with the highest numbers of cases suggests that increased income inequality was associated with increased severe cases and mortality [53]. Across the US, Brazil, South Africa, and Europe, increased mortality has been reported among minority groups such as Africans and Asians [180,181]. Three countries (South Africa, Brazil, the U.S.) seem to have similar risk profiles: long historical cleavages of institutional racism and inequality [182,183] that exacerbate COVID-19 vulnerabilities among large Black populations. The role in which inequality and poverty play totally depends on the behavior and the biological/immunological factors that they influence. For example, in New York and in other U.S. cities, poor people have difficulty isolating themselves, as they commonly utilize public transportation, share living spaces with more people, work in crowded environments, and overall, have lower social mobility [184]. In contrast, in many African cities, the social and political elites are the ones who can afford to live and work in airconditioned closed spaces, increasing susceptibility to infection through close, indoor contact with others [185]. However, this should not be generalized as the case in every African country, as work has also pointed to the similar theme of poorer populations facing a higher burden, such as in South Africa, with the elites largely shielded from the virus [186].

4. Conclusions

Relatively low severity and death due to COVID-19 in Africa presents somewhat of a paradox [41,42,187]. Despite early ‘doomsday’ predictions for Africa, the continent succeeded in stemming the first wave of SARS-CoV-2 spread [188], although the second wave was more severe [21]. On the whole, the factors discussed here have contributed to modulating disease spread and severity; however, the strength of evidence of each varies. A third wave that is currently underway in many countries appears to bring more consequential morbidity and mortality concerns, and possible impacts [189], especially as early government measures have been relaxed in many countries [21]. Driven by more virulent and potentially lethal variants, such as Delta, this third wave may prove far more challenging for weaker health systems to cope with, leading to increased hospitalizations and deaths [190]. Future waves are also likely. Alongside facing the lowest quality of health systems [9,191], the continent will be significantly challenged if it faces an excessive COVID-19 disease burden [192]. Estimates already show that many excess deaths, especially in the SSA region, will result not from COVID-19 but from disruptions in programs addressing malnutrition [193], HIV/AIDS [194], malaria [195], and maternal and child health deaths [32], along with interruptions in the implementation of immunization programs [196]. Therefore, a range of policy options taking this into account, as well as considering economic and socio-cultural characteristics including the expected benefits and harms of control measures (e.g., adverse effects on education and livelihoods) [7,42], need to be implemented.
It is likely that SARS-CoV-2 has already been widely disseminated through Africa, yet evidently without having had the severe consequences of COVID-19 burden, such as the significant uptick in hospitalizations and deaths that many other regions have experienced [25]. If so, widespread infection is likely to also result in widespread natural immunity [197]. While the true picture of infections and mortality in the continent has yet to fully emerge, the quality of data for other diseases, such as HIV/AIDS, indicates that Africa has the capacity to collect and report valid disease surveillance data [198], which should give a degree of confidence in the existing COVID-19 data and the ability for the continent to do better. Nevertheless, improving completeness of data collection and reporting is an ongoing mission for Africa and elsewhere [30,199]. Strengthening lab capacities, validating current rapid tests in the context of other infectious diseases, and standardizing data and survey reporting will also expand the true picture [42]. Additionally, before the COVID-19 pandemic in 2019, researchers and policy makers from Africa called for a new model of public health in the 21st Century, precisely to prepare for this kind of pandemic [200]. Now, as the pandemic evolves, the vaccine rollout needs to be especially accelerated to reach more people, as the continent lags with less than 5% of its population having received at least one shot as of August 2021 [201]. We also urge the continuation of measures for which there is clear evidence of effectiveness [202]. While what has been observed is a comparatively low morbidity and mortality from COVID-19 in Africa, the continent faces a significant threat with the current progression of the pandemic that may change what has been seen thus far [203]. Still, as our assessment here shows, the unique experience of African countries may offer salient lessons for the rest of the world, given their long experience with infectious diseases and outbreak readiness [56,57

Author Contributions

Conceptualization, W.V.D., D.A., R.C.B., S.H. and R.G.W.; writing—original draft preparation, R.G.W. and J.L.H.; writing—review and editing, R.G.W., J.L.H., W.V.D., D.A., R.C.B., S.H., U.A. and M.A.; visualization, S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank Daniel Halperin, Mead Over, Norman Hearst, and Alan Whiteside for their contributions to the development of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Coronavirus Disease 2019 (COVID-19) Dashboard. Africa Centres for Disease Control and Prevention. 2021. Available online: https://africacdc.org/covid-19/ (accessed on 4 August 2021).
  2. Johns Hopkins Coronavirus Resource Center. Global Map. 2021. Available online: https://coronavirus.jhu.edu/map.html (accessed on 6 October 2020).
  3. United Nations Population Division. World Population Prospects. 2019. Available online: https://population.un.org/wpp/ (accessed on 1 May 2021).
  4. El-Sadr, W.M.; Justman, J. Africa in the path of Covid-19. N. Engl. J. Med. 2020, 383, e11. [Google Scholar] [CrossRef]
  5. Pearson, A.C.; Van Schalkwyk, C.; Foss, A.M.; O’Reilly, K.M.; SACEMA Modelling and Analysis Response Team; CMMID COVID-19 Working Group; Pulliam, J.R. Projected early spread of COVID-19 in Africa through 1 June 2020. Eurosurveillance 2020, 25, 2000543. [Google Scholar] [CrossRef]
  6. Quaresima, V.; Naldini, M.M.; Cirillo, D.M. The prospects for the SARS -CoV-2 pandemic in Africa. EMBO Mol. Med. 2020, 12, e12488. [Google Scholar] [CrossRef]
  7. Mueller, V.; Sheriff, G.; Keeler, C.; Jehn, M. COVID-19 policy modeling in sub-Saharan Africa. Appl. Econ. Perspect. Policy 2020, 43, 24–38. [Google Scholar] [CrossRef]
  8. Gilbert, M.; Pullano, G.; Pinotti, F.; Valdano, E.; Poletto, C.; Boëlle, P.-Y.; D’Ortenzio, E.; Yazdanpanah, Y.; Eholie, S.P.; Altmann, M.; et al. Preparedness and vulnerability of African countries against importations of COVID-19: A modelling study. Lancet 2020, 395, 871–877. [Google Scholar] [CrossRef]
  9. Walker, P.G.T.; Whittaker, C.; Watson, O.J.; Baguelin, M.; Winskill, P.; Hamlet, A.; Djafaara, B.A.; Cucunubá, Z.; Mesa, D.O.; Green, W.; et al. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020, 369, 413–422. [Google Scholar] [CrossRef]
  10. Fokoua-Maxime, D.C.; Amor-Ndjabo, M.; Ankobil, A.; Victor-Kiyung, M.; Franck-Metomb, S.; Choukem, S.P. Does sub-Saharan Africa truly defy the forecasts of the COVID-19 pandemic? Response from population data. medRxiv 2020. [Google Scholar] [CrossRef]
  11. Dzinamarira, T.; Dzobo, M.; Chitungo, I. COVID-19: A perspective on Africa’s capacity and response. J. Med. Virol. 2020, 92, 2465–2472. [Google Scholar] [CrossRef] [PubMed]
  12. Ogunleye, O.O.; Basu, D.; Mueller, D.; Sneddon, J.; Seaton, R.A.; Yinka-Ogunleye, A.F.; Wamboga, J.; Miljković, N.; Mwita, J.C.; Rwegerera, G.M.; et al. Response to the Novel Corona Virus (COVID-19) Pandemic Across Africa: Successes, Challenges, and Implications for the future. Front. Pharmacol. 2020, 11. [Google Scholar] [CrossRef] [PubMed]
  13. Mulenga, L.B.; Hines, J.Z.; Fwoloshi, S.; Chirwa, L.; Siwingwa, M.; Yingst, S.; Wolkon, A.; Barradas, D.T.; Favaloro, J.; Zulu, J.E.; et al. Prevalence of SARS-CoV-2 in six districts in Zambia in July 2020: A cross-sectional cluster sample survey. Lancet Glob. Health 2021, 9, e773–e781. [Google Scholar] [CrossRef]
  14. Ndaye, A.N.; Hoxha, A.; Madinga, J.; Mariën, J.; Peeters, M.; Leendertz, F.H.; Mundeke, S.A.; Ariën, K.K.; Tanfumu, J.-J.M.; Kingebeni, P.M.; et al. Challenges in interpreting SARS-CoV-2 serological results in African countries. Lancet Glob. Health 2021, 9, e588–e589. [Google Scholar] [CrossRef]
  15. Olayanju, O.; Bamidele, O.; Edem, F.; Eseile, B.; Amoo, A.; Nwaokenye, J.; Udeh, C.; Oluwole, G.; Odok, G.; Awah, N. SARS-CoV-2 seropositivity in asymptomatic frontline health workers in Ibadan, Nigeria. Am. J. Trop. Med. Hyg. 2021, 104, 91–94. [Google Scholar] [CrossRef] [PubMed]
  16. Chibwana, M.G.; Jere, K.C.; Kamn’gona, R.; Mandolo, J.; Katunga-Phiri, V.; Tembo, D.; Mitole, N.; Musasa, S.; Sichone, S.; Lakudzala, A.; et al. High SARS-CoV-2 seroprevalence in health care workers but relatively low numbers of deaths in urban Malawi. medRxiv 2020. [Google Scholar] [CrossRef]
  17. Uyoga, S.; Adetifa, I.M.O.; Karanja, H.K.; Nyagwange, J.; Tuju, J.; Wanjiku, P.; Aman, R.; Mwangangi, M.; Amoth, P.; Kasera, K.; et al. Seroprevalence of anti–SARS-CoV-2 IgG antibodies in Kenyan blood donors. Science 2020, 371, 79–82. [Google Scholar] [CrossRef]
  18. Sykes, W.; Mhlanga, L.; Swanevelder, R.; Glatt, T.N.; Grebe, E.; Coleman, C.; Pieterson, N.; Cable, R.; Welte, A.; van der Berg, K.; et al. Prevalence of anti-SARS-CoV-2 antibodies among blood donors in Northern Cape, KwaZulu-Natal, Eastern Cape, and Free State provinces of south Africa in January 2021. Res. Sq. 2021. [Google Scholar] [CrossRef]
  19. Shaw, J.A.; Meiring, M.; Cummins, T.; Chegou, N.N.; Claassen, C.; Du Plessis, N.; Flinn, M.; Hiemstra, A.; Kleynhans, L.; Leukes, V.; et al. Higher SARS-CoV-2 seroprevalence in workers with lower socioeconomic status in Cape Town, South Africa. PLoS ONE 2021, 16, e0247852. [Google Scholar] [CrossRef] [PubMed]
  20. Milleliri, J.M.; Coulibaly, D.; Nyobe, B.; Rey, J.-L.; Lamontagne, F.; Hocqueloux, L.; Giaché, S.; Valery, A.; Prazuck, T. SARS-CoV-2 infection in Ivory Coast: A serosurveillance survey among gold mine workers. Am. J. Trop. Med. Hyg. 2021, 104, 1709–1712. [Google Scholar] [CrossRef]
  21. Salyer, S.J.; Maeda, J.; Sembuche, S.; Kebede, Y.; Tshangela, A.; Moussif, M.; Ihekweazu, C.; Mayet, N.; Abate, E.; Ouma, A.O.; et al. The first and second waves of the COVID-19 pandemic in Africa: A cross-sectional study. Lancet 2021, 397, 1265–1275. [Google Scholar] [CrossRef]
  22. Usuf, E.; Roca, A. Seroprevalence surveys in sub-Saharan Africa: What do they tell us? Lancet Glob. Health 2021, 9, e724–e725. [Google Scholar] [CrossRef]
  23. Tso, F.Y.; Lidenge, S.J.; Peña, P.B.; Clegg, A.A.; Ngowi, J.R.; Mwaiselage, J.; Ngalamika, O.; Julius, P.; West, J.T.; Wood, C. High prevalence of pre-existing serological cross-reactivity against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in sub-Saharan Africa. Int. J. Infect. Dis. 2020, 102, 577–583. [Google Scholar] [CrossRef]
  24. Yadouleton, A.; Sander, A.-L.; Moreira-Soto, A.; Tchibozo, C.; Hounkanrin, G.; Badou, Y.; Fischer, C.; Krause, N.; Akogbeto, P.; Filho, E.F.D.O.; et al. Limited specificity of serologic tests for SARS-CoV-2 antibody detection, Benin. Emerg. Infect. Dis. 2021, 27, 233–237. [Google Scholar] [CrossRef] [PubMed]
  25. Wang, L.; Dong, S.; Zhao, Y.; Gao, Y.; Wang, J.; Yu, M.; Xu, F.; Chai, Y. Epidemic characteristics of COVID-19 in Africa. Front. Phys. 2020, 8. [Google Scholar] [CrossRef]
  26. Chanda-Kapata, P.; Kapata, N.; Zumla, A. COVID-19 and malaria: A symptom screening challenge for malaria endemic countries. Int. J. Infect. Dis. 2020, 94, 151–153. [Google Scholar] [CrossRef]
  27. Jacobs, J.; Kühne, V.; Lunguya, O.; Affolabi, D.; Hardy, L.; Vandenberg, O. Implementing COVID-19 (SARS-CoV-2) rapid diagnostic tests in sub-Saharan Africa: A review. Front. Med. 2020, 7, 557797. [Google Scholar] [CrossRef]
  28. Maclean, R. A continent where the dead are not counted. The New York Times, 1 February 2021. [Google Scholar]
  29. United Nations. Report on the Status of Civil Registration and Vital Statistics in Africa; Economic Commission for Africa: Addis Abeba, Ethiopia, 2017.
  30. Karlinsky, A.; Kobak, D. The world mortality dataset: Tracking excess mortality across countries during the COVID-19 pandemic. medRxiv 2021. [Google Scholar] [CrossRef]
  31. Cardoso, K. Measuring Africa’s Data Gap: The Cost of Not Counting the Dead. 2021. Available online: https://www.bbc.com/news/world-africa-55674139 (accessed on 22 February 2021).
  32. Roberton, T.; Carter, E.; Chou, V.B.; Stegmuller, A.R.; Jackson, B.D.; Tam, Y.; Sawadogo-Lewis, T.; Walker, N. Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: A modelling study. Lancet Glob. Health 2020, 8, e901–e908. [Google Scholar] [CrossRef]
  33. Tembo, J.; Maluzi, K.; Egbe, F.; Bates, M. Covid-19 in Africa. BMJ 2021, 372, 457. [Google Scholar] [CrossRef] [PubMed]
  34. Mwananyanda, L.; Gill, C.J.; MacLeod, W.; Kwenda, G.; Pieciak, R.; Mupila, Z.; Lapidot, R.; Mupeta, F.; Forman, L.; Ziko, L.; et al. Covid-19 deaths in Africa: Prospective systematic postmortem surveillance study. BMJ 2021, 372. [Google Scholar] [CrossRef]
  35. Africa Centres for Disease Control and Prevention. Revealing the Toll of COVID-19: A Technical Package for Member States. Available online: https://africacdc.org/download/revealing-the-toll-of-covid-19-a-technical-package-for-rapid-mortality-surveillance-and-epidemic-response/ (accessed on 22 June 2020).
  36. Setel, P.; AbouZahr, C.; Atuheire, E.B.; Bratschi, M.; Cercone, E.; Chinganya, O.; Clapham, B.; Clark, S.J.; Congdon, C.; De Savigny, D.; et al. Mortality surveillance during the COVID-19 pandemic. Bull. World Health Organ. 2020, 98, 374. [Google Scholar] [CrossRef] [PubMed]
  37. Post, L.A.; Argaw, S.T.; Jones, C.; Moss, C.B.; Resnick, D.; Singh, L.N.; Murphy, R.L.; Achenbach, C.J.; White, J.; Issa, T.Z.; et al. A SARS-CoV-2 surveillance system in sub-Saharan Africa: Modeling study for persistence and transmission to inform policy. J. Med. Internet Res. 2020, 22, e24248. [Google Scholar] [CrossRef]
  38. Rapid Mortality Surveillance for COVID-19 in West Africa. African Field Epidemiology Network (AFENET). Available online: http://www.afenet.net/index.php/news/news/849-rapid-mortality-surveillance-for-covid-19-in-west-africa (accessed on 8 April 2021).
  39. Biccard, B.M.; Gopalan, P.D.; Miller, M.; Michell, W.L.; Thomson, D.; Ademuyiwa, A.; Aniteye, E.; Calligaro, G.; Chaibou, M.S.; Dhufera, H.T.; et al. Patient care and clinical outcomes for patients with COVID-19 infection admitted to African high-care or intensive care units (ACCCOS): A multicentre, prospective, observational cohort study. Lancet 2021, 397, 1885–1894. [Google Scholar] [CrossRef]
  40. Mehtar, S.; Preiser, W.; Lakhe, N.A.; Bousso, A.; TamFum, J.-J.M.; Kallay, O.; Seydi, M.; Zumla, P.S.A.; Nachega, J.B. Limiting the spread of COVID-19 in Africa: One size mitigation strategies do not fit all countries. Lancet Glob. Health 2020, 8, e881–e883. [Google Scholar] [CrossRef]
  41. Ghosh, D.; Bernstein, J.A.; Mersha, T.B. COVID-19 pandemic: The African paradox. J. Glob. Health 2020, 10, 020348. [Google Scholar] [CrossRef] [PubMed]
  42. Maeda, J.M.; Nkengasong, J.N. The puzzle of the COVID-19 pandemic in Africa. Science 2020, 371, 27–28. [Google Scholar] [CrossRef] [PubMed]
  43. Mukherjee, S. Why Does the Pandemic Seem to Be Hitting Some Countries Harder Than Others. 2021. Available online: https://www.newyorker.com/magazine/2021/03/01/why-does-the-pandemic-seem-to-be-hitting-some-countries-harder-than-others (accessed on 22 February 2021).
  44. Leonhardt, D. A Covid mystery. The New York Times, 8 March 2021. [Google Scholar]
  45. Van Damme, W.; Dahake, R.; Delamou, A.; Ingelbeen, B.; Wouters, E.; Vanham, G.; Van De Pas, R.; Dossou, J.-P.; Ir, P.; Abimbola, S.; et al. The COVID-19 pandemic: Diverse contexts; different epidemics—How and why? BMJ Glob. Health 2020, 5, e003098. [Google Scholar] [CrossRef]
  46. Van Damme, W.; Dahake, R.; van de Pas, R.; Vanham, G.; Assefa, Y. COVID-19: Does the infectious inoculum dose-response relationship contribute to understanding heterogeneity in disease severity and transmission dynamics? Med. Hypotheses 2020, 146, 110431. [Google Scholar] [CrossRef]
  47. Cabore, J.W.; Karamagi, H.C.; Kipruto, H.; Asamani, J.A.; Droti, B.; Seydi, A.B.W.; Titi-Ofei, R.; Impouma, B.; Yao, M.; Yoti, Z.; et al. The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African region: A predictive model. BMJ Glob. Health 2020, 5, e002647. [Google Scholar] [CrossRef]
  48. Achoki, T.; Alam, U.; Were, L.; Gebremedhin, T.; Senkubuge, F.; Lesego, A.; Liu, S.; Wamai, R.; Kinfu, Y. COVID-19 pandemic in the African continent: Forecasts of cumulative cases, new infections, and mortality. medRxiv 2020. [Google Scholar] [CrossRef]
  49. Kuguyo, O.; Kengne, A.P.; Dandara, C. Singapore COVID-19 pandemic response as a successful model framework for low-resource health care settings in Africa? OMICS J. Integr. Biol. 2020, 24, 470–478. [Google Scholar] [CrossRef]
  50. Lone, S.A.; Ahmad, A. COVID-19 pandemic—An African perspective. Emerg. Microbes Infect. 2020, 9, 1300–1308. [Google Scholar] [CrossRef]
  51. Gaye, B.; Khoury, S.; Cene, C.W.; Kingue, S.; N’Guetta, R.; Lassale, C.; Baldé, D.; Diop, I.B.; Dowd, J.B.; Mills, M.C.; et al. Socio-demographic and epidemiological consideration of Africa’s COVID-19 response: What is the possible pandemic course? Nat. Med. 2020, 26, 996–999. [Google Scholar] [CrossRef] [PubMed]
  52. Moore, J. What African nations are teaching the west about fighting the coronavirus. The New Yorker, 15 May 2020. [Google Scholar]
  53. Chaudhry, R.; Dranitsaris, G.; Mubashir, T.; Bartoszko, J.; Riazi, S. A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortality and related health outcomes. EClinicalMedicine 2020, 25, 100464. [Google Scholar] [CrossRef]
  54. Rice, B.L.; Annapragada, A.; Baker, R.E.; Bruijning, M.; Dotse-Gborgbortsi, W.; Mensah, K.; Miller, I.F.; Motaze, N.V.; Raherinandrasana, A.; Rajeev, M.; et al. Variation in SARS-CoV-2 outbreaks across sub-Saharan Africa. Nat. Med. 2021, 27, 447–453. [Google Scholar] [CrossRef] [PubMed]
  55. Mboussou, F.; Ndumbi, P.; Ngom, R.; Kassamali, Z.; Ogundiran, O.; Van Beek, J.; Williams, G.; Okot, C.; Hamblion, E.L.; Impouma, B. Infectious disease outbreaks in the African region: Overview of events reported to the World Health Organization in 2018. Epidemiol. Infect. 2019, 147, e307. [Google Scholar] [CrossRef]
  56. Alam, U.; Nabyonga-Orem, J.; Mohammed, A.; Malac, D.R.; Nkengasong, J.N.; Moeti, M.R. Redesigning health systems for global heath security. Lancet Glob. Health 2021, 9, e393–e394. [Google Scholar] [CrossRef]
  57. Holst, C.; Sukums, F.; Radovanovic, D.; Ngowi, B.; Noll, J.; Winkler, A.S. Sub-Saharan Africa—The new breeding ground for global digital health. Lancet Digit. Health 2020, 2, e160–e162. [Google Scholar] [CrossRef]
  58. Wilkinson, A.; Parker, M.; Martineau, F.; Leach, M. Engaging “communities”: Anthropological insights from the west African Ebola epidemic. Philos. Trans. R. Soc. B Biol. Sci. 2017, 372, 20160305. [Google Scholar] [CrossRef]
  59. Laverack, G.; Manoncourt, E. Key experiences of community engagement and social mobilization in the Ebola response. Glob. Health Promot. 2015, 23, 79–82. [Google Scholar] [CrossRef]
  60. Shuaib, F.; Gunnala, R.; Musa, E.O.; Mahoney, F.J.; Oguntimehin, O.; Nguku, P.M.; Nyanti, S.B.; Knight, N.; Gwarzo, N.S.; Idigbe, O.; et al. Ebola virus disease outbreak—Nigeria, July–September 2014. MMWR Morb. Mortal. Wkly. Rep. 2014, 63, 867–872. [Google Scholar]
  61. Slutkin, G.; Okware, S.; Naamara, W.; Sutherland, D.; Flanagan, D.; Carael, M.; Blas, E.; DeLay, P.; Tarantola, D. How Uganda reversed its HIV epidemic. AIDS Behav. 2006, 10, 351–360. [Google Scholar] [CrossRef] [PubMed]
  62. Al-Hasan, A.; Yim, D.; Khuntia, J. Citizens’ adherence to COVID-19 mitigation recommendations by the government: A 3-country comparative evaluation using web-based cross-sectional survey data. J. Med. Internet Res. 2020, 22, e20634. [Google Scholar] [CrossRef] [PubMed]
  63. Margraf, J.; Brailovskaia, J.; Schneider, S. Behavioral measures to fight COVID-19: An 8-country study of perceived usefulness, adherence and their predictors. PLoS ONE 2020, 15, e0243523. [Google Scholar] [CrossRef]
  64. SSRN. LSMS-Supported High-Frequency Phone Surveys on COVID-19. 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3675884 (accessed on 8 April 2021).
  65. Isah, M.B.; Abdulsalam, M.; Bello, A.; Ibrahim, M.I.; Usman, A.; Nasir, A.; Abdulkadir, B.; Ibrahim, K.M.; Sani, A.; Aliu, M.; et al. Coronavirus disease 2019 (COVID-19): A cross-sectional survey of the knowledge, attitudes, practices (KAP) and misconceptions in the general population of Katsina State, Nigeria. UMYU J. Microbiol. Res. 2021, 6, 24–37. [Google Scholar] [CrossRef]
  66. Banda, J.; Dube, A.; Brumfield, S.; Amoah, A.; Crampin, A.; Reniers, G.; Helleringer, S. Knowledge, risk perceptions, and behaviors related to the COVID-19 pandemic in Malawi. Demogr. Res. 2021, 44, 459–480. [Google Scholar] [CrossRef]
  67. Defar, A.; Molla, G.; Abdella, S.; Tessema, M.; Ahmed, M.; Tadele, A.; Getachew, F.; Hailegiorgis, B.; Tigabu, E.; Ababor, S.; et al. Knowledge, practice and associated factors towards the prevention of COVID-19 among high-risk groups: A cross-sectional study in Addis Ababa, Ethiopia. PLoS ONE 2021, 16, e0248420. [Google Scholar] [CrossRef]
  68. Serwaa, D.; Lamptey, E.; Appiah, A.B.; Senkyire, E.K.; Ameyaw, J.K. Knowledge, risk perception and preparedness towards coronavirus disease-2019 (COVID-19) outbreak among Ghanaians: A quick online cross-sectional survey. Pan Afr. Med. J. 2020, 35. [Google Scholar] [CrossRef]
  69. Ditekemena, J.D.; Nkamba, D.M.; Muhindo, H.M.; Siewe, J.N.F.; Luhata, C.; Bergh, R.V.D.; Kitoto, A.T.; Van Damme, W.; Muyembe, J.J.; Colebunders, R. Factors associated with adherence to COVID-19 prevention measures in the Democratic Republic of the Congo (DRC): Results of an online survey. BMJ Open 2021, 11, e043356. [Google Scholar] [CrossRef] [PubMed]
  70. Hedima, E.W.; Michael, S.A.; David, E.A. Knowledge and risk perception of the novel coronavirus disease 2019 among adult Nigerians: A cross-sectional study. medRxiv 2020. [Google Scholar] [CrossRef]
  71. Olum, R.; Chekwech, G.; Wekha, G.; Nassozi, D.R.; Bongomin, F. Coronavirus disease-2019: Knowledge, attitude, and practices of health care workers at Makerere University Teaching Hospitals, Uganda. Front. Public Health 2020, 8, 181. [Google Scholar] [CrossRef]
  72. Rader, B.; Scarpino, S.V.; Nande, A.; Hill, A.L.; Adlam, B.; Reiner, R.C.; Pigott, D.M.; Gutierrez, B.; Zarebski, A.E.; Shrestha, M.; et al. Crowding and the shape of COVID-19 epidemics. Nat. Med. 2020, 26, 1829–1834. [Google Scholar] [CrossRef]
  73. Nadini, M.; Zino, L.; Rizzo, A.; Porfiri, M. A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment. Appl. Netw. Sci. 2020, 5, 1–30. [Google Scholar] [CrossRef] [PubMed]
  74. Nkalu, C.N.; Edeme, R.K.; Nchege, J.; Arazu, O.W. Rural-urban population growth, economic growth and urban agglomeration in sub-Saharan Africa: What does Williamson-Kuznets hypothesis say? J. Asian Afr. Stud. 2019, 54, 1247–1261. [Google Scholar] [CrossRef]
  75. United Nations Department of Economic and Social Affairs. World Urbanization Prospects. 2018. Available online: https://population.un.org/wup/Country-Profiles/ (accessed on 8 April 2021).
  76. Diop, B.Z.; Ngom, M.; Biyong, C.P.; Biyong, J.N.P. The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: A modelling study. BMJ Glob. Health 2020, 5, e002699. [Google Scholar] [CrossRef] [PubMed]
  77. Chirisa, I.; Mutambisi, T.; Chivenge, M.; Mabaso, E.; Matamanda, A.R.; Ncube, R. The urban penalty of COVID-19 lockdowns across the globe: Manifestations and lessons for anglophone sub-Saharan Africa. GeoJournal 2020, 1–14. [Google Scholar] [CrossRef] [PubMed]
  78. Yang, W.; Zhang, W.; Kargbo, D.; Yang, R.; Chen, Y.; Chen, Z.; Kamara, A.; Kargbo, B.; Kandula, S.; Karspeck, A.; et al. Transmission network of the 2014–2015 Ebola epidemic in Sierra Leone. J. R. Soc. Interface 2015, 12, 20150536. [Google Scholar] [CrossRef]
  79. Kiti, M.C.; Kinyanjui, T.; Koech, D.; Munywoki, P.K.; Medley, G.; Nokes, D.J. Quantifying age-related rates of social contact using diaries in a rural coastal population of Kenya. PLoS ONE 2014, 9, e104786. [Google Scholar] [CrossRef]
  80. Mossong, J.; Hens, N.; Jit, M.; Beutels, P.; Auranen, K.; Mikolajczyk, R.; Massari, M.; Salmaso, S.; Tomba, G.S.; Wallinga, J.; et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 2008, 5, e74. [Google Scholar] [CrossRef]
  81. Fraser, T.; Aldrich, D.P. The dual effect of social ties on COVID-19 spread in Japan. Sci. Rep. 2021, 11, 1–12. [Google Scholar] [CrossRef]
  82. Fraser, T. Japanese social capital and social vulnerability indices: Measuring drivers of community resilience 2000–2017. Int. J. Disaster Risk Reduct. 2020, 52, 101965. [Google Scholar] [CrossRef]
  83. Arachchi, J.I.; Managi, S. The role of social capital in COVID-19 deaths. BMC Public Health 2021, 21, 1–9. [Google Scholar] [CrossRef]
  84. Cash, R.; Patel, V. Has COVID-19 subverted global health? Lancet 2020, 395, 1687–1688. [Google Scholar] [CrossRef]
  85. Sugg, M.M.; Spaulding, T.J.; Lane, S.J.; Runkle, J.D.; Harden, S.R.; Hege, A.; Iyer, L.S. Mapping community-level determinants of COVID-19 transmission in nursing homes: A multi-scale approach. Sci. Total Environ. 2020, 752, 141946. [Google Scholar] [CrossRef]
  86. Otieno, S. COVID-19: Africa’s Eldery May Benefit from Social Structures. 2021. Available online: https://www.scidev.net/sub-saharan-africa/news/covid-19-africa-s-elderly-may-benefit-from-social-structures/ (accessed on 8 April 2021).
  87. Lloyd-Sherlock, P.; Ebrahim, S.; Geffen, L.; McKee, M. Bearing the brunt of Covid-19: Older people in low and middle income countries. BMJ 2020, 368, 1052. [Google Scholar] [CrossRef]
  88. Cowper, B.; Jassat, W.; Pretorius, P.; Geffen, L.; Legodu, C.; Singh, S.; Blumberg, L. COVID-19 in long-term care facilities in South Africa: No time for complacency. S. Afr. Med. J. 2020, 110, 962. [Google Scholar] [CrossRef]
  89. Adamek, M.E.; Balaswamy, S. Long term care for elders in developing countries in Asia and Africa: A systematic review. Gerontology 2016, 56, 413. [Google Scholar] [CrossRef]
  90. Cafagna, G.A.; Aranco, N.; Ibarrarán, P.; Oliveri, M.L.; Medellín, N.; Stampini, M. Age with Care: Long-Term Care in Latin America and the Caribbean. 2019. Available online: https://publications.iadb.org/en/age-care-long-term-care-latin-america-and-caribbean (accessed on 2 May 2021).
  91. Caruso, M.; Galiani, S.; Ibarrarán, P. Long-Term Care in Latin America and the Caribbean? Theory Policy Considerations. 2017. Available online: https://www.nber.org/papers/w23797 (accessed on 2 May 2021).
  92. World Health Organization. Modes of Transmission of Virus Causing COVID-19: Implications for IPC Precaution Recommendations. Available online: https://www.who.int/news-room/commentaries/detail/modes-of-transmission-of-virus-causing-covid-19-implications-for-ipc-precaution-recommendations (accessed on 8 April 2021).
  93. Dietz, L.; Horve, P.F.; Coil, D.A.; Fretz, M.; Eisen, J.A.; Wymelenberg, K.V.D. 2019 novel Coronavirus (COVID-19) pandemic: Built environment considerations to reduce transmission. mSystems 2020, 5. [Google Scholar] [CrossRef] [PubMed]
  94. Setti, L.; Passarini, F.; De Gennaro, G.; Barbieri, P.; Perrone, M.G.; Borelli, M.; Palmisani, J.; Di Gilio, A.; Torboli, V.; Fontana, F.; et al. SARS-Cov-2RNA found on particulate matter of Bergamo in northern Italy: First evidence. Environ. Res. 2020, 188, 109754. [Google Scholar] [CrossRef] [PubMed]
  95. Bulfone, T.C.; Malekinejad, M.; Rutherford, G.W.; Razani, N. Outdoor transmission of SARS-CoV-2 and other respiratory viruses: A systematic review. J. Infect. Dis. 2020, 223, 550–561. [Google Scholar] [CrossRef]
  96. Senatore, V.; Zarra, T.; Buonerba, A.; Choo, K.-H.; Hasan, S.W.; Korshin, G.; Li, C.-W.; Ksibi, M.; Belgiorno, V.; Naddeo, V. Indoor versus outdoor transmission of SARS-COV-2: Environmental factors in virus spread and underestimated sources of risk. Euro Mediterr. J. Environ. Integr. 2021, 6, 1–9. [Google Scholar] [CrossRef]
  97. Rowe, B.; Canosa, A.; Drouffe, J.; Mitchell, J. Simple quantitative assessment of the outdoor versus indoor airborne transmission of viruses and COVID-19. Environ. Res. 2021, 198, 111189. [Google Scholar] [CrossRef] [PubMed]
  98. Naddeo, V.; Liu, H. Editorial perspectives: 2019 novel coronavirus (SARS-CoV-2): What is its fate in urban water cycle and how can the water research community respond? Environ. Sci. Water Res. Technol. 2020, 6, 1213–1216. [Google Scholar] [CrossRef]
  99. Klepeis, N.E.; Nelson, W.C.; Ott, W.R.; Robinson, J.P.; Tsang, A.M.; Switzer, P.; Behar, J.V.; Hern, S.C.; Engelmann, W.H. The national human activity pattern survey (NHAPS): A resource for assessing exposure to environmental pollutants. J. Expo. Sci. Environ. Epidemiol. 2001, 11, 231–252. [Google Scholar] [CrossRef]
  100. Matz, C.J.; Stieb, D.M.; Brion, O. Urban-rural differences in daily time-activity patterns, occupational activity and housing characteristics. Environ. Health 2015, 14, 1–11. [Google Scholar] [CrossRef]
  101. Merow, C.; Urban, M.C. Seasonality and uncertainty in global COVID-19 growth rates. Proc. Natl. Acad. Sci. USA 2020, 117, 27456–27464. [Google Scholar] [CrossRef] [PubMed]
  102. Wang, J.; Tang, K.; Feng, K.; Lin, X.; Lv, W.; Chen, K.; Wang, F. Impact of temperature and relative humidity on the transmission of COVID-19: A modelling study in China and the United States. BMJ Open 2021, 11, e043863. [Google Scholar] [CrossRef]
  103. Kerr, G.H.; Badr, H.S.; Gardner, L.M.; Perez-Saez, J.; Zaitchik, B.F. Associations between meteorology and COVID-19 in early studies: Inconsistencies, uncertainties, and recommendations. One Health 2021, 12, 100225. [Google Scholar] [CrossRef] [PubMed]
  104. Rhodes, J.M.; Subramanian, S.; Laird, E.; Kenny, R.A. Letter: Low population mortality from COVID-19 in countries south of latitude 35° North supports vitamin D as a factor determining severity. Authors’ reply. Aliment. Pharmacol. Ther. 2020, 52, 412–413. [Google Scholar] [CrossRef]
  105. Pereira, M.; Damascena, A.D.; Azevedo, L.M.G.; Oliveira, T.D.A.; Santana, J.D.M. Vitamin D deficiency aggravates COVID-19: Systematic review and meta-analysis. Crit. Rev. Food Sci. Nutr. 2020, 1–9. [Google Scholar] [CrossRef]
  106. ClinicalTrials.gov. 2021. Available online: https://clinicaltrials.gov/ct2/results?recrs=&cond=Vitamin+D&term=Covid-19&cntry=&state=&city=&dist= (accessed on 8 April 2021).
  107. Levin, A.T.; Hanage, W.P.; Owusu-Boaitey, N.; Cochran, K.B.; Walsh, S.P.; Meyerowitz-Katz, G. Assessing the age specificity of infection fatality rates for COVID-19: Systematic review, meta-analysis, and public policy implications. Eur. J. Epidemiol. 2020, 35, 1123–1138. [Google Scholar] [CrossRef]
  108. Centers for Disease Control and Prevention (CDC). Risk for COVID-19 Infection, Hospitalization, and Death by Age Group. 2019. Available online: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-age.html (accessed on 8 April 2021).
  109. Williamson, E.J.; Walker, A.J.; Bhaskaran, K.; Bacon, S.; Bates, C.; Morton, C.E.; Curtis, H.J.; Mehrkar, A.; Evans, D.; Inglesby, P.; et al. Factors associated with COVID-19-related death using OpenSAFELY. Nat. Cell Biol. 2020, 584, 430–436. [Google Scholar] [CrossRef]
  110. Census of India. Office of the Registrar General and Census Commissioner, India. 2021. Available online: https://censusindia.gov.in (accessed on 8 April 2021).
  111. Brazil Institute of Geography and Statistics. Projections and Estimates of the Population of Brazil. 2021. Available online: https://www.ibge.gov.br/apps/populacao/projecao/index.html (accessed on 8 April 2021).
  112. United States Census Bureau. People and Population Data—United States of America. Available online: https://data.census.gov/cedsci/profile?g=0100000US (accessed on 8 April 2021).
  113. United Kingdom Office for National Statistics. United Kingdom 2011 Census Data. 2011. Available online: https://www.ons.gov.uk/census/2011census/2011censusdata (accessed on 8 April 2021).
  114. Lawal, Y. Africa’s low COVID-19 mortality rate: A paradox? Int. J. Infect. Dis. 2020, 102, 118–122. [Google Scholar] [CrossRef]
  115. COVID-19 Data Explorer. Oxford. 2020. Available online: https://ourworldindata.org/coronavirus (accessed on 19 February 2021).
  116. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef]
  117. Chakafana, G.; Mutithu, D.; Hoevelmann, J.; Ntusi, N.; Sliwa, K. Interplay of COVID-19 and cardiovascular diseases in Africa: An observational snapshot. Clin. Res. Cardiol. 2020, 109, 1460–1468. [Google Scholar] [CrossRef]
  118. Hughes, G.D.; Mbamalu, O.N.; Okonji, C.O.; Puoane, T.R. The impact of health disparities on COVID-19 outcomes: Early findings from a high-income country and two middle-income countries. J. Racial Ethn. Health Disparities 2021, 1–8. [Google Scholar] [CrossRef]
  119. Gouda, H.N.; Charlson, F.; Sorsdahl, K.; Ahmadzada, S.; Ferrari, A.; Erskine, H.; Leung, J.; Santamauro, D.; Lund, C.; Aminde, L.N.; et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990–2017: Results from the Global Burden of Disease Study 2017. Lancet Glob. Health 2019, 7, e1375–e1387. [Google Scholar] [CrossRef]
  120. World Health Organization. Noncommunicable Diseases Country Profile 2018; World Health Organization: Geneva, Switzerland, 2018. [Google Scholar]
  121. Osetinsky, B.; Hontelez, J.A.C.; Lurie, M.N.; McGarvey, S.T.; Bloomfield, G.S.; Pastakia, S.D.; Wamai, R.; Bärnighausen, T.; de Vlas, S.J.; Galárraga, O. Epidemiological and health systems implications of evolving HIV and hypertension in south Africa and Kenya. Health Aff. 2019, 38, 1173–1181. [Google Scholar] [CrossRef] [PubMed]
  122. Davies, M.-A. HIV and risk of COVID-19 death: A population cohort study from the Western Cape province, south Africa. medRxiv 2020. [Google Scholar] [CrossRef]
  123. UNAIDS. HIV and AIDS Estimates. South Africa 2019 Country Factsheet. 2019. Available online: https://www.unaids.org/en/regionscountries/countries/southafrica (accessed on 8 April 2021).
  124. Lee, Y.J.; Jang, Y.H.; Seo, S.-U.; Chang, J.; Seong, B.L. Non-specific effect of vaccines: Immediate protection against respiratory syncytial virus infection by a live attenuated influenza vaccine. Front. Microbiol. 2018, 9, 83. [Google Scholar] [CrossRef] [PubMed]
  125. Uthayakumar, D.; Paris, S.; Chapat, L.; Freyburger, L.; Poulet, H.; De Luca, K. Non-specific effects of vaccines illustrated through the BCG example: From observations to demonstrations. Front. Immunol. 2018, 9, 2869. [Google Scholar] [CrossRef] [PubMed]
  126. Benn, C.S.; Netea, M.G.; Selin, L.; Aaby, P. A small jab—A big effect: Nonspecific immunomodulation by vaccines. Trends Immunol. 2013, 34, 431–439. [Google Scholar] [CrossRef] [PubMed]
  127. Parmar, K.; Siddiqui, A.; Nugent, K. Bacillus Calmette-Guerin vaccine and nonspecific immunity. Am. J. Med. Sci. 2021, 361, 683–689. [Google Scholar] [CrossRef]
  128. Blok, B.A.; Arts, R.J.W.; Van Crevel, R.; Benn, C.S.; Netea, M.G. Trained innate immunity as underlying mechanism for the long-term, nonspecific effects of vaccines. J. Leukoc. Biol. 2015, 98, 347–356. [Google Scholar] [CrossRef]
  129. Clem, A.S. Fundamentals of vaccine immunology. J. Glob. Infect. Dis. 2011, 3, 73–78. [Google Scholar] [CrossRef] [PubMed]
  130. Sehrawat, S.; Rouse, B.T. Does the hygiene hypothesis apply to COVID-19 susceptibility? Microbes Infect. 2020, 22, 400–402. [Google Scholar] [CrossRef]
  131. Gursel, M.; Gursel, I. Is global BCG vaccination-induced trained immunity relevant to the progression of SARS-CoV-2 pandemic? Allergy 2020, 75, 1815–1819. [Google Scholar] [CrossRef]
  132. O’Neill, L.A.J.; Netea, M.G. BCG-induced trained immunity: Can it offer protection against COVID-19? Nat. Rev. Immunol. 2020, 20, 335–337. [Google Scholar] [CrossRef] [PubMed]
  133. Stensballe, L.G.; Nante, E.; Jensen, I.P.; Kofoed, P.-E.; Poulsen, A.; Jensen, H.; Newport, M.; Marchant, A.; Aaby, P. Acute lower respiratory tract infections and respiratory syncytial virus in infants in Guinea-Bissau: A beneficial effect of BCG vaccination for girls: Community based case-control study. Vaccine 2005, 23, 1251–1257. [Google Scholar] [CrossRef]
  134. Nemes, E.; Geldenhuys, H.; Rozot, V.; Rutkowski, K.T.; Ratangee, F.; Bilek, N.; Mabwe, S.; Makhethe, L.; Erasmus, M.; Toefy, A.; et al. Prevention of M. tuberculosis infection with H4:IC31 vaccine or BCG revaccination. N. Engl. J. Med. 2018, 379, 138–149. [Google Scholar] [CrossRef]
  135. Spencer, J.C.; Ganguly, R.; Waldman, R.H. Nonspecific protection of mice against influenza virus infection by local or systemic immunization with Bacille Calmette-Guerin. J. Infect. Dis. 1977, 136, 171–175. [Google Scholar] [CrossRef]
  136. Starr, S.E.; Visintine, A.M.; Tomeh, M.O.; Nahmias, A.J. Effects of immunostimulants on resistance of newborn mice to herpes simplex type 2 infection. Exp. Biol. Med. 1976, 152, 57–60. [Google Scholar] [CrossRef] [PubMed]
  137. Miller, A.; Reandelar, M.J.; Fasciglione, K.; Roumenova, V.; Li, Y.; Otazu, G.H. Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19: An epidemiological study. medRxiv 2020. [Google Scholar] [CrossRef]
  138. Manjili, R.H.; Zarei, M.; Habibi, M.; Manjili, M.H. COVID-19 as an acute inflammatory disease. J. Immunol. 2020, 205, 12–19. [Google Scholar] [CrossRef]
  139. Yeo, W.S.; Ng, Q.X. Distinguishing between typical Kawasaki disease and multisystem inflammatory syndrome in children (MIS-C) associated with SARS-CoV-2. Med. Hypotheses 2020, 144, 110263. [Google Scholar] [CrossRef]
  140. World Health Organization. Reported Estimates of BCG Coverage. Available online: https://apps.who.int/immunization_monitoring/globalsummary/timeseries/tscoveragebcg.html (accessed on 8 April 2021).
  141. Hadley, C. Should auld acquaintance be forgot…The “hygiene hypothesis” is less about cleanliness, and more about the changes that humans have made to their lifestyle. EMBO Rep. 2004, 5, 1122–1124. [Google Scholar] [CrossRef] [PubMed]
  142. Chatterjee, B.; Karandikar, R.L.; Mande, S.C. Paradoxical case fatality rate dichotomy of Covid-19 among rich and poor nations points to the “hygiene hypothesis”. medRxiv 2020. [Google Scholar] [CrossRef]
  143. Chatterjee, B.; Karandikar, R.L.; Mande, S.C. The mortality due to COVID-19 in different nations is associated with the demographic character of nations and the prevalence of autoimmunity. medRxiv 2020. [Google Scholar] [CrossRef]
  144. Moore, M.; Gelfeld, B.; Okunogbe, A.; Paul, C. Identifying future disease hot spots: Infectious disease vulnerability index. Rand Health Q. 2016, 6. [Google Scholar] [CrossRef]
  145. Hotez, P.J.; Kamath, A. Neglected tropical diseases in sub-Saharan Africa: Review of their prevalence, distribution, and disease burden. PLoS Negl. Trop. Dis. 2009, 3, e412. [Google Scholar] [CrossRef] [PubMed]
  146. Rweyemamu, M.; Otim-Nape, W.; Serwadda, D. Foresight. Infectious Diseases: Preparing for the Future. Africa; Office of Science and Innovation: London, UK, 2006. [Google Scholar]
  147. Elton, L.; Thomason, M.J.; Tembo, J.; Velavan, T.P.; Pallerla, S.R.; Arruda, L.B.; Vairo, F.; Montaldo., C.; Ntoumi, F.; Hamid, M.M.A.; et al. Antimicrobial resistance preparedness in sub-Saharan African countries. Antimicrob. Resist. Infect. Control 2020, 9, 1–11. [Google Scholar] [CrossRef] [PubMed]
  148. Eisele, T.P. Mass drug administration can be a valuable addition to the malaria elimination toolbox. Malar. J. 2019, 18, 1–5. [Google Scholar] [CrossRef] [PubMed]
  149. Romani, L.; Marks, M.; Sokana, O.; Nasi, T.; Kamoriki, B.; Wand, H.; Whitfeld, M.J.; Engelman, D.; Solomon, A.; Steer, A.C.; et al. Feasibility and safety of mass drug coadministration with azithromycin and ivermectin for the control of neglected tropical diseases: A single-arm intervention trial. Lancet Glob. Health 2018, 6, e1132–e1138. [Google Scholar] [CrossRef]
  150. Macfarlane, C.; Dean, L.; Thomson, R.; Garner, P. Community drug distributors for mass drug administration in neglected tropical disease programmes: Systematic review and analysis of policy documents. J. Glob. Health 2019, 9, 020414. [Google Scholar] [CrossRef]
  151. Echeverría-Esnal, D.; Martin-Ontiyuelo, C.; Navarrete-Rouco, M.E.; Cuscó, M.D.-A.; Ferrández, O.; Horcajada, J.P.; Grau, S. Azithromycin in the treatment of COVID-19: A review. Expert Rev. Anti-Infect. Ther. 2020, 19, 147–163. [Google Scholar] [CrossRef]
  152. Wamae, C.N. Mass drug administration and worms experience in Africa: Envisage repurposing ivermectin for SARS-COV-2. Am. J. Trop. Med. Hyg. 2020, 103, 10–11. [Google Scholar] [CrossRef] [PubMed]
  153. Hellwig, M.D.; Maia, A. A COVID-19 prophylaxis? Lower incidence associated with prophylactic administration of ivermectin. Int. J. Antimicrob. Agents 2020, 57, 106248. [Google Scholar] [CrossRef] [PubMed]
  154. Caly, L.; Druce, J.D.; Catton, M.G.; Jans, D.A.; Wagstaff, K.M. The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitro. Antivir. Res. 2020, 178, 104787. [Google Scholar] [CrossRef] [PubMed]
  155. National Institutes of Health (NIH). COVID-19 Treatment Guidelines: Ivermectin. 2021. Available online: https://www.covid19treatmentguidelines.nih.gov/antiviral-therapy/ivermectin/ (accessed on 8 April 2021).
  156. World Health Organization. World Malaria Report 2019; World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
  157. Olesen, O.F.; Parker, M.I. Health research in Africa: Getting priorities right. Trop. Med. Int. Health 2012, 17, 1048–1052. [Google Scholar] [CrossRef] [PubMed]
  158. Long, C.A.; Zavala, F. Immune responses in malaria. Cold Spring Harb. Perspect. Med. 2017, 7, a025577. [Google Scholar] [CrossRef]
  159. Artavanis-Tsakonas, K.; Tongren, J.E.; Riley, E.M. The war between the malaria parasite and the immune system: Immunity, immunoregulation and immunopathology. Clin. Exp. Immunol. 2003, 133, 145–152. [Google Scholar] [CrossRef]
  160. Centers for Disease Control and Prevention (CDC). Where Malaria Occurs. 2020. Available online: https://www.cdc.gov/malaria/about/distribution.html (accessed on 8 April 2021).
  161. Oyebode, O.; Kandala, N.-B.; Chilton, P.J.; Lilford, R.J. Use of traditional medicine in middle-income countries: A WHO-SAGE study. Health Policy Plan. 2016, 31, 984–991. [Google Scholar] [CrossRef]
  162. Mahomoodally, M.F. Traditional medicines in Africa: An appraisal of ten potent African medicinal plants. Evid. Based Complement. Altern. Med. 2013, 2013, 617459. [Google Scholar] [CrossRef]
  163. World Health Organisation. WHO Global Report on Traditional and Complementary Medicine 2019; World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
  164. World Health Organization. Expert Panel Endorses Protocol for COVID-19 Herbal Medicine Clinical Trials. 2020. Available online: https://www.afro.who.int/news/expert-panel-endorses-protocol-covid-19-herbal-medicine-clinical-trials (accessed on 9 April 2021).
  165. Akindele, A.J.; Agunbiade, F.O.; Sofidiya, M.O.; Awodele, O.; Sowemimo, A.; Ade-Ademilua, O.; Akinleye, M.O.; Ishola, I.O.; Orabueze, I.; Salu, O.B.; et al. COVID-19 pandemic: A case for phytomedicines. Nat. Prod. Commun. 2020, 15. [Google Scholar] [CrossRef]
  166. Clinicaltrials.gov. Available online: https://clinicaltrials.gov/ct2/results?recrs=&cond=herbal+medicine&term=COVID-19&cntry=&state=&city=&dist= (accessed on 9 April 2021).
  167. Pairo-Castineira, E.; Clohisey, S.; Klaric, L.; Bretherick, A.D.; Rawlik, K.; Pasko, D.; Walker, S.; Parkinson, N.; Fourman, M.H.; Russell, C.D.; et al. Genetic mechanisms of critical illness in COVID-19. Nat. Cell Biol. 2020, 591, 92–98. [Google Scholar] [CrossRef]
  168. Zeberg, H.; Pääbo, S. The major genetic risk factor for severe COVID-19 is inherited from Neanderthals. Nat. Cell Biol. 2020, 587, 610–612. [Google Scholar] [CrossRef]
  169. Zhao, Y.; Zhao, Z.; Wang, Y.; Zhou, Y.; Ma, Y.; Zuo, W. Single-cell RNA expression profiling of ACE2, the receptor of SARS-CoV-2. Am. J. Respir. Crit. Care Med. 2020, 202, 756–759. [Google Scholar] [CrossRef]
  170. Ellinghaus, D.; Degenhardt, F.; Bujanda, L.; Buti, M.; Albillos, A.; Invernizzi, P.; Fernández, J.; Prati, D.; Baselli, G.; Asselta, R.; et al. The severe Covid-19 GWAS group genomewide association study of severe Covid-19 with respiratory failure. N. Engl. J. Med. 2020, 383, 1522–1534. [Google Scholar] [CrossRef] [PubMed]
  171. Mwangi, J. Blood group distribution in an urban population of patient targeted blood donors. East. Afr. Med. J. 1999, 76. [Google Scholar]
  172. Cheng, Y.; Mohammed, S.; Okoh, A.; Lee, K.; Raczek, C.; Krushna, A.; Cohen, A.J.; Nagarakanti, S. Association of Blood type on clinical outcomes in Black/African Americans hospitalized for COVID-19 infection. Blood 2020, 136, 14. [Google Scholar] [CrossRef]
  173. Rettner, R. What’s the Rarest Blood Type? Live Science. 2019. Available online: https://www.livescience.com/36559-common-blood-type-donation.html (accessed on 9 April 2021).
  174. Millett, G.A.; Jones, A.T.; Benkeser, D.; Baral, S.; Mercer, L.; Beyrer, C.; Honermann, B.; Lankiewicz, E.; Mena, L.; Crowley, J.S.; et al. Assessing differential impacts of COVID-19 on black communities. Ann. Epidemiol. 2020, 47, 37–44. [Google Scholar] [CrossRef]
  175. Tishkoff, S.A.; Reed, F.A.; Friedlaender, F.R.; Ehret, C.; Ranciaro, A.; Froment, A.; Hirbo, J.B.; Awomoyi, A.A.; Bodo, J.-M.; Doumbo, O.; et al. The genetic structure and history of Africans and African Americans. Science 2009, 324, 1035–1044. [Google Scholar] [CrossRef] [PubMed]
  176. Bryc, K.; Auton, A.; Nelson, M.R.; Oksenberg, J.R.; Hauser, S.L.; Williams, S.; Froment, A.; Bodo, J.-M.; Wambebe, C.; Tishkoff, S.A.; et al. Genome-wide patterns of population structure and admixture in west Africans and African Americans. Proc. Natl. Acad. Sci. USA 2009, 107, 786–791. [Google Scholar] [CrossRef]
  177. Cooper, R.; Rotimi, C.; Ataman, S.; McGee, D.; Osotimehin, B.; Kadiri, S.; Muna, W.; Kingue, S.; Fraser, H.; Forrester, T.; et al. The prevalence of hypertension in seven populations of west African origin. Am. J. Public Health 1997, 87, 160–168. [Google Scholar] [CrossRef] [PubMed]
  178. Carnethon, M.; Pu, J.; Howard, G.; Albert, M.A.; Anderson, C.A.; Bertoni, A.G.; Mujahid, M.S.; Palaniappan, L.; Taylor, H.A.; Willis, M.; et al. Cardiovascular health in African Americans: A scientific statement from the American heart association. Circulation 2017, 136, e393–e423. [Google Scholar] [CrossRef] [PubMed]
  179. United Nations Development Programme (UNDP). Income Gini Coefficient. 2020. Available online: http://hdr.undp.org/en/content/income-gini-coefficient (accessed on 9 April 2021).
  180. Yaya, S.; Yeboah, H.; Charles, C.H.; Otu, A.; LaBonte, R. Ethnic and racial disparities in COVID-19-related deaths: Counting the trees, hiding the forest. BMJ Glob. Health 2020, 5, e002913. [Google Scholar] [CrossRef]
  181. Sze, S.; Pan, D.; Nevill, C.R.; Gray, L.; Martin, C.A.; Nazareth, J.; Minhas, J.; Divall, P.; Khunti, K.; Abrams, K.R.; et al. Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis. EClinicalMedicine 2020, 29-30, 100630. [Google Scholar] [CrossRef] [PubMed]
  182. Andrews, G.R. Racial Inequality in Brazil and the United States: A statistical comparison. J. Soc. Hist. 1992, 26, 229–263. [Google Scholar] [CrossRef]
  183. Hamilton, C.V.; Huntley, L.; Alexander, N.; Guimardes, A.S.A.; James, W. Beyond Racism: Race and Inequality in Brazil, South Africa, and the United States; Lynne Rienner Publishers: Boulder, CO, USA, 2001. [Google Scholar]
  184. Finch, W.H.; Finch, M.E.H. Poverty and Covid-19: Rates of incidence and deaths in the United States during the first 10 weeks of the pandemic. Front. Sociol. 2020, 5, 47. [Google Scholar] [CrossRef]
  185. Viens, A.M.; Eyawo, O. COVID-19: The rude awakening for the political elite in low- and middle-income countries. BMJ Glob. Health 2020, 5, e002807. [Google Scholar] [CrossRef] [PubMed]
  186. Nwosu, C.O.; Oyenubi, A. Income-related health inequalities associated with the coronavirus pandemic in south Africa: A decomposition analysis. Int. J. Equity Health 2021, 20, 1–12. [Google Scholar] [CrossRef]
  187. Musa, H.H.; Musa, T.H.; Musa, I.H.; Musa, I.H.; Ranciaro, A.; Campbell, M.C. Addressing Africa’s pandemic puzzle: Perspectives on COVID-19 transmission and mortality in sub-Saharan Africa. Int. J. Infect. Dis. 2020, 102, 483–488. [Google Scholar] [CrossRef]
  188. Kuehn, B.M. Africa succeeded against COVID-19′s first wave, but the second wave brings new challenges. JAMA 2021, 325, 327. [Google Scholar] [CrossRef] [PubMed]
  189. World Health Organization. Africa Faces Steepest COVID-19 Surge Yet. 2021. Available online: https://www.afro.who.int/news/africa-faces-steepest-covid-19-surge-yet (accessed on 29 June 2021).
  190. Callaway, E. Delta coronavirus variant: Scientists brace for impact. Nat. Cell Biol. 2021, 595, 17–18. [Google Scholar] [CrossRef]
  191. Fullman, N.; Yearwood, J.; Abay, S.M.; Abbafati, C.; Abd-Allah, F.; Abdela, J.; Abdelalim, A.; Abebe, Z.; Abebo, T.A.; Aboyans, V.; et al. Measuring performance on the healthcare access and quality index for 195 countries and territories and selected subnational locations: A systematic analysis from the Global burden of disease study 2016. Lancet 2018, 391, 2236–2271. [Google Scholar] [CrossRef]
  192. Dente, M.G.; Resti, C.V.; Declich, S.; Putoto, G. The reported few cases and deaths of Covid-19 epidemic in Africa are still data too questionable to reassure about the future of this continent. Front. Public Health 2021, 9, 613484. [Google Scholar] [CrossRef]
  193. Headey, D.; Heidkamp, R.; Osendarp, S.; Ruel, M.; Scott, N.; Black, R.; Shekar, M.; Bouis, H.; Flory, A.; Haddad, L.; et al. Impacts of COVID-19 on childhood malnutrition and nutrition-related mortality. Lancet 2020, 396, 519–521. [Google Scholar] [CrossRef]
  194. Jewell, B.L.; Mudimu, E.; Stover, J.; Brink, D.T.; Phillips, A.N.; Smith, J.A.; Martin-Hughes, R.; Teng, Y.; Glaubius, R.; Mahiane, S.G.; et al. Potential effects of disruption to HIV programmes in sub-Saharan Africa caused by COVID-19: Results from multiple mathematical models. Lancet HIV 2020, 7, e629–e640. [Google Scholar] [CrossRef]
  195. Goalkeepers Report. COVID-19 A Global Perspective. 2020. Available online: https://www.gatesfoundation.org/goalkeepers/report/2020-report/#GlobalPerspective (accessed on 14 April 2021).
  196. World Health Organization Immunization, Vaccines and Biologicals Website. More than 117 Million Children at Risk of Missing Out on Measles Vaccines, as COVID-19 Surges. 2020. Available online: https://www.who.int/immunization/diseases/measles/statement_missing_measles_vaccines_covid-19/en/ (accessed on 14 April 2021).
  197. World Health Organization. Coronavirus Disease (COVID-19): Herd Immunity, Lockdowns and COVID-19. 2020. Available online: https://www.who.int/news-room/q-a-detail/herd-immunity-lockdowns-and-covid-19?gclid=CjwKCAjwr_uCBhAFEiwAX8YJgVKPC4VjiRuciYR8yx-ynYd_hzAOgVyRLNlUNTwEgnwPofTqdvR1wRoCxOgQAvD_BwE (accessed on 9 April 2021).
  198. World Health Organization Regional Office for Africa. HIV/AIDS. 2021. Available online: https://www.afro.who.int/health-topics/hivaids (accessed on 9 April 2021).
  199. Sankoh, O.; Dickson, K.E.; Faniran, S.; Lahai, J.I.; Forna, F.; Liyosi, E.; Kamara, M.K.; Jabbi, S.-M.B.-B.; Johnny, A.B.; Conteh-Khali, N.; et al. Births and deaths must be registered in Africa. Lancet Glob. Health 2020, 8, e33–e34. [Google Scholar] [CrossRef]
  200. Bedford, J.; Farrar, J.; Ihekweazu, C.; Kang, G.; Koopmans, M.; Nkengasong, J. A new twenty-first century science for effective epidemic response. Nat. Cell Biol. 2019, 575, 130–136. [Google Scholar] [CrossRef]
  201. Africa Union CDC. COVID-19 Vaccination. 2021. Available online: https://africacdc.org/covid-19-vaccination/ (accessed on 11 August 2021).
  202. Halperin, D.T.; Hodgins, S.; Bailey, R.C.; Klausner, J.D.; Jackson, H.; Wamai, R.; Ladapo, J.A.; Over, M.; Baral, S.; Escandón, K.; et al. Revisiting COVID-19 Policies: 10 Evidence-Based Recommendations for Where to Go from Here. Available online: https://osf.io/nrvtf/ (accessed on 6 July 2021).
  203. Njenga, M.K.; Dawa, J.; Nanyingi, M.; Gachohi, J.; Ngere, I.; Letko, M.; Otieno, C.F.; Gunn, B.M.; Osoro, E. Why is there low morbidity and mortality of COVID-19 in Africa? Am. J. Trop. Med. Hyg. 2020, 103, 564–569. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Categories
COVID Drugs Politics

N.H. House Approves Bill for Ivermectin ‘Standing Order’ in Pharmacies

 

The New Hampshire’s state House approved a bill making ivermectin available by a medical prescribers’ “standing order,” meaning pharmacists will be able to dispense the medication without individual prescriptions.

Narrowly approved

The Republican dominated House in Concord voted 183-159 to approve the bill.

Republicans had argued that the drug is already over the counter in several countries and had been used specifically for COVID-19.

Supporters of the bill claimed the legislation would allow the medication to be safely dispensed by health care providers rather than patients buying and using the drug in its veterinary formula.

Democrats had criticized the legislation in the past.

“The committee’s endorsement of boosting access to ivermectin came over the criticism of Democrats on the committee. ‘I don’t think the legislature should be practicing medicine, which is basically what this is,’ said Rep. Gary Woods of Bow, a retired doctor and former president of the New Hampshire Medical Society.”

TrialSite has followed ivermectin research and intrigue since April 2020 when an Australian lab found that the drug attacks SARS-CoV-2 in a cell culture.

While a few key studies didn’t show any results many more have which makes the matter just more confusing for many.

According to a website that tracks 81 ivermectin studies worldwide the vast majority show promising results.

Fifty-three of these studies from 48 independent teams in 22 countries show statistically significant improvements in isolation (39 primary outcome and 36 most serious outcome) while meta-analysis using the most serious outcomes reveal 63% and 83% improvements for early treatment and prophylaxis with similar results post exclusion based sensitivity analysis for primary outcomes in peer-reviewed studies and for randomized controlled studies.

Yet the medical establishment in not only the United States but also most other developed places ignore much of this data declaring it afflicted with one problem or another, from design flow to too small a sample size.

The controversial bill made it to the floor of the New Hampshire House on Wednesday with the Republicans majority voting yay. In a 183 to 159 vote the House approved the proposal allowing pharmacists to make ivermectin available via standing order reports Adam Sexton of local WMUR.

State Representative Leah Cushman, a key Republican proponent declared for the local press:

“A standing order is a prescriptive protocol written by a physician or nurse practitioner that allows a pharmacist to dispense medication without an individual prescription.”

Reporting for WMUR Sexton wrote Advocates for the standing order legislation said any benefits of ivermectin might have been obscured by the political debate over the drug.

Cushman followed “Because of this politicization, doctors are afraid to prescribe, and pharmacies are afraid to dispense,” Cushman said.

Dr. Paul Marik has been actively involved with the proposed legislation. An ivermectin and early treatment advocate, Marik is a co-founder of the Front Line COVID-19 Critical Care Alliance or “FLCCC.”

Marik has been recognized in New Hampshire and elsewhere for his commitment to the effort. Marik’s accomplishments, awards and credentials can be found here.