Local History: Willow Point's behemoth – the Jennie R. Dubois
On Feb. 11, 1902, the largest schooner ever built on the Mystic River was launched at the Holmes Shipbuilding Company (now Mystic Shipyard). Built to Lloyds of London specifications at a cost of $100,000 (over $3 million today), she was a five-masted, 249-foot behemoth, 46 feet wide with a 20-foot draft, 5-inch planking, 108-foot masts and 56-foot topmasts, and a 50-foot bowsprit and 76-foot jibboom. She was named the Jennie R. Dubois.
She was said at the time to be the "best vessel ever built in the state," designed to carry freight – mostly coal and lumber. Schools and local stores were closed for the gala occasion, trains stopped at the nearby station, and over 6,000 showed up for the christening and launch, with Jennie Dubois herself (wife of a Rhode Island Supreme Court justice) performing the honors with champagne and an awful poem she had penned herself.
Perhaps this was an omen – Jennie was married, and vessels christened by married women were considered unlucky (Jennie herself faded into obscurity). In any case, gaily bedecked with colorful pennants, she slid off the rails at her launching so smoothly she "wouldn't have cracked an egg."
She drenched the spectators with seawater as she entered the river, and promptly became firmly moored in the Mystic mud. It took the stiff winds of a blustery snowstorm before she could be floated off.
Thereafter, she began her life as a cargo ship routinely enough (notwithstanding getting stuck in shallow waters regularly) – hauling thousands of pounds of coal and up to 2 million feet of timber up and down the northeast seaboard.
Then, on the night of Sept. 5, 1903, the Jennie Dubois was sailing along in the fog on its way into Boston from Philadelphia, carrying a load of coal. Just southeast of Block Island, she was suddenly rammed by a German tramp steamer lately having made a long transit from Calcutta, and coming now out of Boston.
The tramp steamer sheared the bow off the Dubois just forward of the anchor locker. The bow sank like a stone to the bottom of the sea with her anchors and foremast, which was snapped off. The hull continued to drift NNE for a quarter of a mile before the remainder of this magnificent schooner sank. As the hull drifted, the anchor chain was pulled out of its locker and is now stretched out in a perfectly straight line on the bottom for 600 feet (this points to her final resting place).
The next morning was a dense foggy one, and the Southeast Lighthouse keeper on Block Island sent out a rescue crew, having heard whistles during the night. As day broke, he saw four masts protruding in the waters. They found only a black cat surviving, presumably clinging to one of those masts.
Unbeknownst to the lighthouse rescue crew, that same German steamer who had rammed the Dubois had lowered her lifesaving boats and picked up the 11 shipmates "in nothing but what they stood in." They were being transported at that very moment to New York City where they were "deposited without a nickel between them."
Two days later the New London Day reported the sinking of an unidentified vessel, with no trace of crew or wreckage.
Slowly the story came out. In a scene not unlike something out of Russell Crowe's "Master and Commander," Captain Smeed aboard the Jennie Dubois had seen the German steamer coming in and out of the fog all night long. Although the schooner had sounded her foghorns continually, she had not altered course – sailing ships having the right-of-way then as now.
Every sail was set on the ship. Capt. Smeed had maintained his course (he would have been held responsible for the collision if he hadn't) ... but as ill luck would have it, the German steamer didn't alter its course either, and when it rammed the Jennie Dubois, it sliced her in two.
It was during a change of watch, however, and this accounted for the miraculous survival of all crew members.
Later, the sunken wreck's towering masts were deemed a 'hazard to navigation' by the Army Corps of Engineers – being directly in the track of north and southbound ship traffic to and from New York City. And so, the Navy blasted them to smithereens with guncotton.
Block Islanders remembered it as "like an earthquake." Later, River Pilot "Tal" Dodge went down on the wreck to see if he could salvage anything of value – sails, masts, etc.
From a newspaper article at the time: "The pilot has secured 100 lbs of dynamite, and will use it, if necessary, to start the big spars out of the wreck… Unless the cabin has been burst open by the action of the water, there is a good chance of getting the valuable nautical instruments, and possibly a large amount of money which Captain Smeed was unable to save before the schooner took her final dive to Davy Jones' locker."
The Jennie Dubois was reported to have a luxurious Captain's Cabin — thick Brussels carpets, steam heating (in the wheelhouse and forecastle, too), a modern bathroom, and gleaming wood. (To get a sense of what the Captain's Quarters might have looked like, visit the Benjamin Packard exhibit at Mystic Seaport.).
No word on whether Dodge ever reached the cash, or the nautical instruments before he lit off his dynamite.
For over 100 years, the remains of the largest ship ever built here lay hidden and lost. Then, in 2007, a local consortium (Sound Underwater Survey and Baccala Wreck Divers) got busy using towed side-scan sonar aboard the 42-foot Baccala. The team searched for five years using data from old newspapers, the National Archives, the Southeast Light's notes, and even old "hang" sites – sites of notorious fishermen's net losses.
All told, for five years they scanned over 17 square miles. In 2007, they decided to dive on an area that looked 'odd' on the side-scan sonar. And when they did …
There she was … they had found her – the largest ship ever built on the Mystic River, and built right here on Willow Point.
What the divers subsequently found and identified were her anchors, lengths of her anchor chain (pointing along the bottom to the hull), portions of her lead-lined scuppers, pieces of the keelson, hoistings engine, bilge pump, and hull sections and ribs. Except for the dynamiting, and the fact that she was stripped of as much as could be salvaged soon after the wreck, more might have been found.
The discovery was duly announced in 2008, and today the deep wreck site is popular with serious divers, and the discoverers also regularly dive on it. Mark Munro of Sound Underwater Survey has found a brass drifting pin that he now intends to mount and give to Mystic Shipyard.
What we know as Willow Point's Mystic Shipyard today had many iterations throughout the 19th and 20th centuries. Starting out as the Forsyth Wharf in 1843, it began a heyday of shipbuilding under the firm Maxson, Fish & Co., starting in 1853 and continuing during the Civil War. It was also called at this time the Oldfields Shipyard, after the original name given to what we now call Willow Point – probably from the fact that the Pequots had used this land for growing corn during the summer months.
(William Ellery Maxson built the large yellow house that stands today at the corner of School Street and the end of West Mystic Avenue, and he eventually bought the house built by another of the firm's owners, William Barber, standing today at the north corner of Maxson and Essex Streets).
During the Civil War, the Maxson, Fish & Co. shipyard here built many ships, including 14 steam vessels – the most famous being the ironclad screw steamer Galena, which was ordered by the government along with Ironsides and Monitor, and saw action against the Confederate Navy. They also built the beautiful Seminole, which made many trips 'round Cape Horn, and whose striking figurehead is now on display at Mystic Seaport.
Maxson, Fish & Co., went into receivership in the late 1860s, and was reorganized by Maxson and Alexander Irving. Eventually the yard became the Holmes Shipbuilding Company, sometimes seen on maps identified as the Holmes Engine Company. William K. Holmes was from Wall Street.
Only a few of the homes now on Willow Point date from this era, as so many were swept away or otherwise destroyed in the Hurricane of '38. The Holmes Shipyard and the Jennie Dubois were part of the revival of shipbuilding that saw many very large ships built here around the turn of the last century.
Photos of the Jennie Dubois' construction line the walls in the shipyard's offices today – I'm sure they won't mind if you go in to have a look.
The master-builder had been Willard Avery Hodgkins of Bath, Maine. As she was designed to haul mostly coal, coastal schooners like the Jennie Dubois were the "oil tankers" of their day, used to fuel homes and industry, and were relied upon as much as we rely on oil tankers today.
Thanks go to Mark Munro of Sound Underwater Surveys for his help with this article.
G.S. Casale is president of the Willow Point Association in Groton.
Wednesday, August 25, 2021
The Day - Local History: Willow Point’s behemoth – the Jennie R. Dubois - News from southeastern Connecticut
We Made a Big Mistake
Researcher: 'We Made a Big Mistake' on COVID-19 Vaccine
June 15, 2021
By Dr. Joseph Mercola
- Canadian immunologist and vaccine researcher Byram Bridle, Ph.D., has gained access to Pfizer's biodistribution study from the Japanese regulatory agency. The research, previously unseen, demonstrates a huge problem with all COVID-19 vaccines
- The assumption that vaccine developers have been working with is that the mRNA in the vaccines would primarily remain in and around the vaccination site. Pfizer's data, however, show the mRNA and subsequent spike protein are widely distributed in the body within hours
- This is a serious problem, as the spike protein is a toxin shown to cause cardiovascular and neurological damage. It also has reproductive toxicity, and Pfizer's biodistribution data show it accumulates in women's ovaries
- Once in your blood circulation, the spike protein binds to platelet receptors and the cells that line your blood vessels. When that happens, it can cause platelets to clump together, resulting in blood clots, and/or cause abnormal bleeding
- Pfizer documents submitted to the European Medicines Agency also show the company failed to follow industry-standard quality management practices during preclinical toxicology studies and that key studies did not meet good laboratory practice standards
The more we learn about the COVID-19 vaccines, the worse they look. In a recent interview with Alex Pierson (above), Canadian immunologist and vaccine researcher Byram Bridle, Ph.D., dropped a shocking truth bomb that immediately went viral, despite being censored by Google.
It also was featured in a "fact" check by The Poynter Institute's Politifact,2 which pronounced Bridle's findings as "false" after interviewing Dr. Drew Weissman,3 a UPenn scientist who is credited with helping to create the technology that enables the COVID mRNA vaccines to work. But, as you can see below, unlike Bridle, Politifact neglected to go beyond interviewing someone with such a huge stake in the vaccine's success.
In 2020, Bridle was awarded a $230,000 government grant for research on COVID vaccine development. As part of that research, he and a team of international scientists requested a Freedom of Information Act (FOIA) access to Pfizer's biodistribution study from the Japanese regulatory agency. The research,4,5 previously unseen, demonstrates a huge problem with all COVID-19 vaccines.
"We made a big mistake," Bridle says. "We thought the spike protein was a great target antigen; we never knew the spike protein itself was a toxin and was a pathogenic protein. So, by vaccinating people we are inadvertently inoculating them with a toxin."
This toxin, Bridle notes, can cause cardiovascular damage and infertility — a claim echoed by researchers such as Stephanie Seneff, Ph.D., and Judy Mikovits, Ph.D., whom I've interviewed about these issues.
Pfizer Omitted Industry-Standard Safety Studies
What's more, TrialSite News reports6 that Pfizer documents submitted to the European Medicines Agency [EMA] reveal the company "did not follow industry-standard quality management practices during preclinical toxicology studies … as key studies did not meet good laboratory practice (GLP)."
Neither reproductive toxicity nor genotoxicity (DNA mutation) studies were performed, both of which are considered critical when developing a new drug or vaccine for human use. The problems now surfacing matter greatly, as they significantly alter the risk-benefit analysis underlying the vaccines' emergency use authorization. As reported by TrialSite News:7
"Recently, there has been speculation regarding potential safety signals associated with COVID-19 mRNA vaccines. Many different unusual, prolonged, or delayed reactions have been reported, and often these are more pronounced after the second shot.
Women have reported changes in menstruation after taking mRNA vaccines. Problems with blood clotting (coagulation) — which are also common during COVID-19 disease — are also reported. In the case of the Pfizer COVID mRNA vaccine, these newly revealed documents raise additional questions about both the genotoxicity and reproductive toxicity risks of this product.
Standard studies designed to assess these risks were not performed in compliance with accepted empirical research standards. Furthermore, in key studies designed to test whether the vaccine remains near the injection site or travels throughout the body, Pfizer did not even use the commercial vaccine (BNT162b2) but instead relied on a 'surrogate' mRNA producing the luciferase protein.
These new disclosures seem to indicate that the U.S. and other governments are conducting a massive vaccination program with an incompletely characterized experimental vaccine.
It is certainly understandable why the vaccine was rushed into use as an experimental product under emergency use authority, but these new findings suggest that routine quality testing issues were overlooked in the rush to authorize use.
People are now receiving injections with an mRNA gene therapy-based vaccine, which produces the SARS-CoV-2 spike protein in their cells, and the vaccine may be also delivering the mRNA and producing spike protein in unintended organs and tissues (which may include ovaries)."
Toxic Spike Protein Enters Blood Circulation
The assumption that vaccine developers have been working with is that the mRNA in the vaccines (or DNA in the case of Johnson & Johnson and AstraZeneca's vaccines) would primarily remain in and around the vaccination site, i.e., your deltoid muscle, with a small amount draining into local lymph nodes.8
Pfizer's data, however, show this isn't the case at all. Using mRNA programmed to produce luciferase protein, as well as mRNA tagged with a radioactive label, Pfizer showed that the majority of the mRNA initially remain near the injection site, but within hours become widely distributed within the body.9We have known for a long time that the spike protein is a pathogenic protein. It is a toxin. It can cause damage in our body if it gets into circulation. ~ Dr. Byram Bridle
The mRNA enters your bloodstream and accumulates in a variety of organs, primarily your spleen, bone marrow, liver, adrenal glands and, in women, the ovaries. The spike protein also travel to your heart, brain and lungs, where bleeding and or blood clots can occur as a result, and is expelled in breast milk.
This is a problem, because rather than instructing your muscle cells to produce the spike protein (the antigen that triggers antibody production), spike protein is actually being produced inside your blood vessel walls and various organs, where it can do a great deal of damage.
"It's the first time ever scientists have been privy to seeing where these messenger RNA [mRNA] vaccines go after vaccination," Bridle told Pierson.10
"Is it a safe assumption that it stays in the shoulder muscle? The short answer is: absolutely not. It's very disconcerting … We have known for a long time that the spike protein is a pathogenic protein.
It is a toxin. It can cause damage in our body if it gets into circulation … The spike protein on its own is almost entirely responsible for the damage to the cardiovascular system, if it gets into circulation."
The Spike Protein Is the Problem
Indeed, for many months, we've known that the worst symptoms of severe COVID-19, blood clotting problems in particular, are caused by the spike protein of the virus. As such, it seemed really risky to instruct the body's cells to produce the very thing that causes severe problems.
Bridle cites research showing that laboratory animals injected with purified spike protein from SARS-CoV-2 straight into their bloodstream developed cardiovascular problems and brain damage.
Assuming that the spike protein would not enter into the circulatory system was a "grave mistake," according to Bridle, who calls the Japanese data "clear-cut evidence" that the vaccine, and the spike protein produced by it, enters your bloodstream and accumulates in vital organs. Bridle also cites recent research showing the spike protein remained in the bloodstream of humans for 29 days.
Once in your blood circulation, the spike protein binds to platelet receptors and the cells that line your blood vessels. As explained by Bridle, when that happens, one of several things can occur:
- It can cause platelets to clump together — Platelets, aka thrombocytes, are specialized cells in your blood that stop bleeding. When there's blood vessel damage, they clump together to form a blood clot. This is why we've been seeing clotting disorders associated with both COVID-19 and the vaccines
- It can cause abnormal bleeding
- In your heart, it can cause heart problems
- In your brain, it can cause neurological damage
Importantly, people who have been vaccinated against COVID-19 absolutely should not donate blood, seeing how the vaccine and the spike protein are both transferred. In fragile patients receiving the blood, the damage could be lethal.
Breastfeeding women also need to know that both the vaccine and the spike protein are being expelled in breast milk, and this could be lethal for their babies. You are not transferring antibodies. You are transferring the vaccine itself, as well as the spike protein, which could result in bleeding and/or blood clots in your child. All of this also suggests that for individuals who are at low risk for COVID-19, children and teens in particular, the risks of these vaccines far outweigh the benefits.
The Spike Protein and Blood Clotting
In related news, Dr. Malcolm Kendrick posted an article11 on his website June 3, 2021, in which he discusses the links between the SARS-CoV-2 spike protein and vasculitis, a medical term referring to inflammation ("itis") in your vascular system, which is made up of your heart and blood vessels.
There are many different types of vasculitis, including Kawasaki's disease, antiphospholipid syndrome, rheumatoid arthritis, scleroderma and Sjogren's disease. According to Kendrick, all of them have two things in common:12
1.Your body for some reason starts to attack the lining of your blood vessels, thereby causing damage and inflammation — The "why" can differ from one case to another, but in all cases, your immune system identifies something foreign in the lining of the blood vessel, causing it to attack. The attack causes damage to the lining, which results in inflammation.
Blood clots are a common result, and can occur either because the platelets clump together in response to the vessel wall damage, or because your anticlotting mechanism has been compromised. Your most powerful anticlotting system is your glycocalyx, the protective layer of glycoproteins that lines your blood vessels.
Among many other things, the glycocalyx contains a wide variety of anticoagulant factors, including tissue factor inhibitor, protein C, nitric oxide and antithrombin. It also modulates the adhesion of platelets to the endothelium. When blood clots completely block a blood vessel, you end up with a stroke or a heart attack.
A reduction in platelet count, known as thrombocytopenia, is a reliable sign that blood clots are forming in your system, as the platelets are being used up in the process. Thrombocytopenia is a commonly-reported side effect of COVID-19 vaccines, as are blood clots, strokes and lethal heart attacks — all of which are pointing toward spike proteins causing vascular damage.
2.They significantly increase your risk of death, in some cases raising mortality by 50 times compared to people who do not have these conditions.
The take-home message Kendrick delivers is that "If you damage the lining of blood vessel walls, blood clots are far more likely to form. Very often, the damage is caused by the immune system going on the attack, damaging blood vessel walls, and removing several of the anti-clotting mechanisms." The end result can be lethal, and this chain of events is exactly what these COVID-19 vaccines are setting into motion.
SARS-CoV-2 Spike Protein May Damage Mitochondrial Function
Other research suggests the SARS-CoV-2 spike protein can have a serious impact on your mitochondrial function, which is imperative for good health, innate immunity and disease prevention of all kinds.
When the spike protein interacts with the ACE2 receptor, it can disrupt mitochondrial signaling, thereby inducing the production of reactive oxygen species and oxidative stress. If the damage is serious enough, uncontrolled cell death can occur, which in turn leaks mitochondrial DNA (mtDNA) into your bloodstream.13
Aside from being detected in cases involving acute tissue injury, heart attack and sepsis, freely circulating mtDNA has also been shown to contribute to a number of chronic diseases, including systemic inflammatory response syndrome or SIRS, heart disease, liver failure, HIV infection, rheumatoid arthritis and certain cancers.14 As explained in "COVID-19: A Mitochondrial Perspective":15
"Apart from its role in energy production, mitochondria are crucial for … innate immunity, reactive oxygen species (ROS) generation, and apoptosis; all of these are important in COVID-19 pathogenesis. Dysfunctional mitochondria predispose to oxidative stress and loss of cellular function and vitality. In addition, mitochondrial damage leads to … inappropriate and persistent inflammation.
SARS coronavirus 2 (SARS-CoV-2) … enters cell by attaching to angiotensin converting enzyme 2 (ACE2) receptors on cell surface … Following infection, there is internalization and downregulation of ACE2 receptors.
At vascular endothelium, ACE2 performs conversion of angiotensin II to angiotensin (1–7). Thus, a low ACE2 activity subsequent to SARS-CoV-2 infection leads to imbalance in renin-angiotensin system with relative excess of angiotensin II.
Angiotensin II through binding to its type 1 receptors exerts pro-inflammatory, vasoconstrictive, and prothrombotic effects, while angiotensin (1–7) has opposing effects … In addition, angiotensin II increases cytoplasmic and mitochondrial ROS generation leading to oxidative stress.
Increased oxidative stress may lead to endothelial dysfunction and aggravate systemic and local inflammation, thus contributing to acute lung injury, cytokine storm, and thrombosis seen in severe COVID-19 illness …
A recent algorithm showed that majority of SARS-CoV-2 genomic and structural RNAs are targeted for mitochondrial matrix. Thus it appears that SARS-CoV-2 hijacks mitochondrial machinery for its own benefit, including DMV biogenesis. Manipulation of mitochondria by virus may lead to mitochondrial dysfunction and increased oxidative stress ultimately leading to loss of mitochondrial integrity and cell death …
Mitochondrial fission enables removal of the damaged portion of a mitochondrion to be cleared by mitophagy (a special form of autophagy). Metabolomic studies suggest that SARS-CoV-2 inhibits mitophagy. Thus, there is accumulation of damaged and dysfunctional mitochondria. This not only leads to impaired MAVS [mitochondrial antiviral signaling] response but also aggravates inflammation and cell death."
The author, Pankaj Prasun, points out that the virus' impact on mitochondria helps explain why COVID-19 is so much deadlier for older people, the obese, and those with diabetes, high blood pressure and heart disease.
All of these risk factors have something in common: They're all associated with mitochondrial dysfunction. If your mitochondria are already dysfunctional, the SARS-CoV-2 virus can more easily knock out more mitochondria, resulting in severe illness and death.
The Spike Protein Is a Bioweapon
In my interview with Seneff and Mikovits (see earlier hyperlink), they both stressed that the key danger — both in COVID-19 and with the vaccines — is the spike protein itself. However, while the spike protein found in the virus is bad, the spike protein your body produces in response to the vaccine is far worse. Why?
Because the synthetic mRNA in the vaccine has been programmed to instruct your cells to produce an unnatural, genetically engineered spike protein. Specific alterations make it far more toxic than that found on the virus itself. Mikovits goes so far as to call the spike protein a bioweapon, as it is a disease-causing agent that demolishes innate immunity and exhausts your natural killer (NK) cells' ability to determine which cells are infected and which aren't.
In short, when you get the COVID-19 vaccine, you are being injected with an agent that instructs your body to produce the bioweapon in its own cells. This is about as diabolical as it gets.
In her paper, "Worse Than The Disease: Reviewing Some Possible Unintended Consequences of mRNA Vaccines Against COVID-19," published in the International Journal of Vaccine Theory, Practice and Research in collaboration with Dr. Greg Nigh,16 Seneff explains why the unnatural spike protein is so problematic.
In summary, normally, the spike protein on a virus will collapse on itself and fall into the cell once it attaches to the ACE2 receptor. The vaccine-induced spike protein does not do this. Instead it stays open and remains attached to the ACE2 receptor, thereby disabling it and causing a host of problems that lead to heart, lung and immune impairment.
What's more, because the RNA code has been enriched with extra guanines (Gs) and cytosines (Cs), and configured as if it's a human messenger RNA molecule ready to make protein by adding a polyA tail, the spike protein's RNA sequence in the vaccine looks as if it is part bacteria,17 part human18 and part viral at the same time.
There's also evidence suggesting the SARS-CoV-2 spike protein may be a prion, which is yet another piece of really bad news, particularly as it pertains to vaccine-induced spike protein. Prions are membrane proteins and when they misfold, they form crystals in the cytoplasm resulting in prion disease.
Since the mRNA in the vaccines has been modified to spew out very high amounts of spike protein (far greater than that of the actual virus), the risk of excessive buildup in the cytoplasm is high. And, since the spike protein doesn't enter into the membrane of the cell, there's a high risk that it can become problematic if indeed it works like a prion.
Remember, the research cited by Bridle at the beginning of this article found the spike protein accumulates in the spleen, among other places. Parkinson's disease is a prion disease that has been traced back to prions originating in the spleen, that then travel up to the brain via the vagus nerve. In the same way, it's quite possible COVID-19 vaccines may promote Parkinson's and other human prion diseases such as Alzheimer's.
What Are the Solutions?
While all of this is highly problematic, there is help. As noted by Mikovits, remedies to the maladies that might develop post-vaccination include:
| Hydroxychloroquine and ivermectin treatments. Ivermectin appears particularly promising as it actually binds to the spike protein. Please listen to the interview that Brett Weinstein did with Dr. Pierre Kory,19 one of Dr. Paul Marik's collaborators |
| Low-dose antiretroviral therapy to reeducate your immune system |
| Low-dose interferons such as Paximune, developed by interferon researcher Dr. Joe Cummins, to stimulate your immune system |
| Peptide T (an HIV entry inhibitor derived from the HIV envelope protein gp120; it blocks binding and infection of viruses that use the CCR5 receptor to infect cells) |
| Cannabis, to strengthen Type I interferon pathways |
| Dimethylglycine or betaine (trimethylglycine) to enhance methylation, thereby suppressing latent viruses |
| Silymarin or milk thistle to help cleanse your liver |
From my perspective, I believe the best thing you can do is to build your innate immune system. To do that, you need to become metabolically flexible and optimize your diet. You'll also want to make sure your vitamin D level is optimized to between 60 ng/mL and 80 ng/mL (100 nmol/L to 150 nmol/L), ideally through sensible sun exposure. Sunlight also has other benefits besides making vitamin D.
Use time-restricted eating and eat all your meals for the day within a six- to eight-hour window. Avoid all vegetable oils and processed foods. Focus on certified-organic foods to minimize your glyphosate exposure, and include plenty of sulfur-rich foods to keep your mitochondria and lysosomes healthy. Both are important for the clearing of cellular debris, including these spike proteins. You can also boost your sulfate by taking Epsom salt baths.
To combat the toxicity of the spike protein, you'll want to optimize autophagy, which may help digest and remove the spike proteins. Time-restricted eating will upregulate autophagy, while sauna therapy, which upregulates heat shock proteins, will help refold misfolded proteins and also tag damaged proteins and target them for removal. It is important that your sauna is hot enough (around 170 degrees Fahrenheit) and does not have high magnetic or electric fields.
The National Vaccine Information Center (NVIC) recently posted more than 50 video presentations from the pay-for-view Fifth International Public Conference on Vaccination held online October 16 to 18, 2020, and made them available to everyone for free.
The conference's theme was "Protecting Health and Autonomy in the 21st Century" and it featured physicians, scientists and other health professionals, human rights activists, faith community leaders, constitutional and civil rights attorneys, authors and parents of vaccine injured children talking about vaccine science, policy, law and ethics and infectious diseases, including coronavirus and COVID-19 vaccines.
In December 2020, a U.K. company published false and misleading information about NVIC and its conference, which prompted NVIC to open up the whole conference for free viewing. The conference has everything you need to educate yourself and protect your personal freedoms and liberties with respect to your health.
Don't miss out on this incredible opportunity. I was a speaker at this empowering conference and urge you to watch these video presentations before they're censored and taken away by the technocratic elite.
_______________________________
I couldn't find the conference transcript but I got some Polulation DATA:
Critical!!!!! US POPULATION AS OF TODAY /us-population/
- The current population of the United States of America is 333,225,477 as of Wednesday, August 25, 2021, based on Worldometer elaboration of the latest United Nations data.
- The United States 2020 population is estimated at 331,002,651 people at mid year according to UN data.
- The United States population is equivalent to 4.25% of the total world population.
- The U.S.A. ranks number 3 in the list of countries (and dependencies) by population.
- The population density in the United States is 36 per Km2 (94 people per mi2).
- The total land area is 9,147,420 Km2 (3,531,837 sq. miles)
- 82.8 % of the population is urban (273,975,139 people in 2020)
- The median age in the United States is 38.3 years.
www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm
Excess Deaths Associated with COVID-19
Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19. Excess deaths are typically defined as the difference between the observed numbers of deaths in specific time periods and expected numbers of deaths in the same time periods. This visualization provides weekly estimates of excess deaths by the jurisdiction in which the death occurred. Weekly counts of deaths are compared with historical trends to determine whether the number of deaths is significantly higher than expected.
Counts of deaths from all causes of death, including COVID-19, are presented. As some deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not diagnosed or not mentioned on the death certificate), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. Additionally, deaths from all causes excluding COVID-19 were also estimated. Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are reported as due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to the COVID-19 pandemic (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems).
As of June 3, 2020, additional information on weekly counts of deaths by cause of death has been added to this release. Similar to all causes of death, these weekly counts can be compared to values from the same weeks in prior years to determine whether recent increases have occurred for specific causes of death. The causes shown here were chosen based on analyses of the most prevalent comorbid conditions reported on death certificates where COVID-19 was listed as a cause of death (see https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm#Comorbidities). Cause of death counts are based on the underlying cause of death, and presented for Respiratory diseases, Circulatory diseases, Malignant neoplasms, and Alzheimer disease and dementia. Deaths due to external causes (i.e. injuries) or unknown causes are excluded. For more detail, see the Technical Notes. Weekly counts of deaths were also added by age for all causes.
Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). A range of values for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound of the 95% prediction interval), by week and jurisdiction.
Provisional death counts are weighted to account for incomplete data. However, data for the most recent week(s) are still likely to be incomplete. Weights are based on completeness of provisional data in prior years, but the timeliness of data may have changed in 2020 relative to prior years, so the resulting weighted estimates may be too high in some jurisdictions and too low in others. As more information about the accuracy of the weighted estimates is obtained, further refinements to the weights may be made, which will impact the estimates. Any changes to the methods or weighting algorithm will be noted in the Technical Notes when they occur. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.
This visualization includes several different estimates:
- Number of excess deaths: A range of estimates for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound threshold), by week and jurisdiction. Negative values, where the observed count fell below the threshold, were set to zero.
- Percent excess: The percent excess was defined as the number of excess deaths divided by the threshold.
- Total number of excess deaths:The total number of excess deaths in each jurisdiction was calculated by summing the excess deaths in each week, from February 1, 2020 to present. Similarly, the total number of excess deaths for the US overall was computed as a sum of jurisdiction-specific numbers of excess deaths (with negative values set to zero), and not directly estimated using the Farrington surveillance algorithms.
Select a dashboard from the menu, then click on "Update Dashboard" to navigate through the different graphics.
- The first dashboard shows the weekly predicted counts of deaths from all causes, and the threshold for the expected number of deaths. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.
- The second dashboard shows the weekly predicted counts of deaths from all causes and the weekly count of deaths from all causes excluding COVID-19. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.
- The third dashboard shows the weekly counts of deaths from all causes. Predicted counts (weighted) are shown, along with reported (unweighted) counts, to illustrate the impact of underreporting. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.
- The fourth dashboard shows the total number of excess deaths since early February, 2020. Jurisdictions with one or more excess deaths are shown. Use the radio button to select all-cause mortality, or all-cause excluding COVID-19. Use the drop-down menu to select certain jurisdictions.
- The fifth dashboard shows the percent by which the observed counts exceed the threshold (i.e. percent excess) by week and jurisdiction. Use the radio button to select all-cause mortality, or all-cause excluding COVID-19. Use the drop-down menu to select certain jurisdictions.
- The sixth dashboard shows weekly counts of death by age group. Use the drop-down menu to select certain jurisdictions.
- The seventh dashboard shows weekly counts of death by race and Hispanic origin. Use the drop-down menus to select certain jurisdictions and mortality outcomes (e.g., all-cause mortality, all-cause excluding COVID-19, and COVID-19 deaths).
- The eighth dashboard shows the change in the weekly number of deaths in 2020 relative to 2015-2019, by race and Hispanic origin. Use the drop-down menu to select certain jurisdictions.
- The ninth dashboard shows weekly counts of death due to select cause of death groups (Respiratory diseases, Circulatory diseases, Malignant neoplasms, and Alzheimer disease and dementia). Use the drop-down menu to select a jurisdiction.
- The tenth dashboard shows weekly counts of death for more detailed causes of death within three of the larger groups: Respiratory diseases and Circulatory diseases. Use the drop-down menus to select causes of death and certain jurisdictions.
- The eleventh dashboard shows the change in the weekly number of deaths in 2020 relative to 2015-2019, by cause of death. Use the drop-down menu to select certain jurisdictions.
- The twelfth dashboard shows the total number of deaths above the average count since early February, 2020, by cause of death. Use the drop-down menu to select certain jurisdictions.
- The thirteenth dashboard shows the total number of deaths above the average count since early February, 2020, by jurisdiction and cause of death. Use the drop-down menu to select certain jurisdictions.
Download datasets in CSV format by clicking on the link for the desired dataset under "CSV Format" link. Additional file formats are available for download for each dataset at Data.CDC.Gov.
For several of the dashboards showing estimates of the percent change over previous years, adjustments had to be made to account for the 53rd week that occurred in 2020. The weekly data are tabulated based on MMWR weeks, which begin on a Sunday and contain at least four days in the calendar year (i.e., the week of Dec. 28, 2020-Jan. 2, 2021 is the 53rd week of 2020, even though it contains days from 2021). Prior years shown in the data visualizations included only 52 weeks. To compare the previous years to week 53, 2020, the average numbers of deaths in weeks 52 and 1 of those previous years were used. For example, when comparing the number of deaths occurring in week 53, 2020 to the same week in previous years, the average number of deaths in week 52, 2019 and week 1, 2020 may be used.
For weeks in 2021, data from 2020 are not included in any comparisons with the average numbers of deaths from the same weeks in previous years, to maintain appropriate comparisons with data prior to the COVID-19 pandemic.
Figure Notes:
Number of deaths reported on this page are the total number of deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. See https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm for more information. Data for New York excludes New York City. Data on all deaths excluding COVID-19 exclude deaths with U07.1 as an underlying or multiple cause of death. Death counts were derived from the National Vital Statistics System database that provides the timeliest access to the vital statistics mortality data and may differ slightly from other sources due to differences in completeness, COVID-19 definitions used, data processing, and imputation of missing dates. Weighted estimates may be too high or too low in certain jurisdictions where the timeliness of provisional data has changed in recent weeks relative to prior years. Data for jurisdictions where counts are between 1 and 9 are suppressed.
Technical Notes
Methods
Counts of deaths in the most recent weeks were compared with historical trends (from 2013 to present) to determine whether the number of deaths in recent weeks was significantly higher than expected, using Farrington surveillance algorithms (1). The 'surveillance' package in R (2) was used to implement the Farrington algorithms, which use overdispersed Poisson generalized linear models with spline terms to model trends in counts, accounting for seasonality. For each jurisdiction, a model is used to generate a set of expected counts, and an upper bound threshold based on a one-sided 95% prediction interval of these expected counts is used to determine whether a significant increase in deaths has occurred. Estimates of excess deaths are provided based on the observed number of deaths relative to two different thresholds. The lower end of the excess death estimate range is generated by comparing the observed counts to the upper bound threshold, and a higher end of the excess death estimate range is generated by comparing the observed count to the average expected number of deaths. Reported counts were weighted to account for potential underreporting in the most recent weeks.
This method is useful in detecting when jurisdictions may have higher than expected numbers of deaths, but cannot be used to determine whether a given jurisdiction has fewer deaths than expected given that the data are provisional. Provisional counts of deaths are known to be incomplete, and the degree of completeness varies considerably by jurisdiction and time. Incomplete data in recent weeks can contribute to observed counts below the threshold. Thus, the estimates of excess deaths – the numbers of deaths falling above the threshold – may be underestimated. While reported counts are weighted to account for potential underreporting in the most recent weeks, the true magnitude of underreporting is unknown. Therefore, weighted counts of deaths may over- or underestimate the true number of deaths in a given jurisdiction.
A range of estimates of excess deaths is provided based on comparing the observed numbers of deaths to two different thresholds, by week and jurisdiction: 1) the average expected number of deaths, and 2) the upper bound of the 95% prediction interval of the expected number of deaths. Negative values, where the observed count fell below the thresholds, were set to zero. The percent excess was defined as the number of excess deaths divided by the threshold. The total number of excess deaths in each state was calculated by summing the excess deaths in each week, from February 1, 2020 to present. Similarly, the total number of excess deaths in the US was calculated by summing the total numbers of excess deaths across the jurisdictions.
Estimates of excess deaths for the US overall were computed as a sum of jurisdiction-specific numbers of excess deaths (with negative values set to zero), and not directly estimated using the Farrington surveillance algorithms. Summation (rather than estimation) was chosen to account for the possibility that some jurisdictions may have substantially incomplete data while other jurisdictions report may more deaths than expected, these negative and positive values will cancel each other out when estimating excess deaths for the US directly using the Farrington surveillance algorithms. Until data are finalized (typically 12 months after the close of the data year), it is not possible to determine whether observed decreases in mortality using provisional data are due to true declines or to incomplete reporting. Thus, when computing excess deaths directly for the US, negative values due to incomplete reporting in some jurisdictions will offset excess deaths observed in other jurisdictions. For example, the total number of excess deaths in the US computed directly for the US using the Farrington algorithms was approximately 25% lower than the number calculated by summing across the jurisdictions with excess deaths. This difference is likely due to several jurisdictions reporting lower than expected numbers of deaths – which could be a function of underreporting, true declines in mortality in certain areas, or a combination of these factors. In addition, potential discrepancies between the number of excess deaths in the US when estimated directly compared with the sum of jurisdiction-specific estimates could be related to different estimated thresholds for the expected number of deaths in the US and across the jurisdictions.
Different definitions of excess deaths result in different estimates. For example, defining excess deaths as the difference between the observed counts and the expected (not the upper bound estimate) results in larger estimates of excess deaths. The upper bound more readily identifies areas experiencing statisticallysignificantly higher than normal mortality. Using the expected count, by contrast, would indicate which areas are experiencing higher than average mortality. Expected counts are now provided so that users can evaluate excess deaths relative to different thresholds.
Finally, the estimates of excess deaths reported here may not be due to COVID-19, either directly or indirectly. The pandemic may have changed mortality patterns for other causes of death. Upward trends in other causes of death (e.g., suicide, drug overdose, heart disease) may contribute to excess deaths in some jurisdictions. Future analyses of cause-specific excess mortality may provide additional information about these patterns.
As more information about the accuracy of the weighted estimates is obtained, further refinements may be made and changes to the weighting methods will impact the estimates. Any changes to the methods or weighting algorithm will be noted in the Technical Notes when they occur.
Completeness
Methods to address reporting lags (i.e. underreporting) were updated as of September 9, 2020. Generally, these updates resulted in estimates of the total number of excess deaths that were approximately 5% smaller than the previous method, as weights in some jurisdictions with improved timeliness were reduced. While these adjustments likely reduce potential overestimation for those jurisdictions with improved timeliness, estimates for the most recent weeks for the US overall are likely underestimated to a larger extent than in previous releases. Some jurisdictions have little to no provisional data available in the most recent week(s) (CT, NC, WV); together, these jurisdictions represent approximately 5% of US deaths. In previous releases, some of the underestimation or lack of provisional data from certain jurisdictions was offset by the overestimation in other jurisdictions with improved timeliness when considering trends for the US overall. Because the updated weighting methods mitigate the impact of the previous overestimation for some jurisdictions with improved timeliness but provide no additional adjustments for underestimation or a lack of recent provisional data in other jurisdictions, the excess death estimates for the US overall are expected to result in a larger degree of underestimation than in previous releases.
To account for potential underreporting in the most recent weeks, counts were weighted by the inverse of completeness. Completeness was estimated as follows. Using provisional data from 2018-2019, weekly provisional counts were compared to final data (with final data for 2019 approximated by the data available as of April, 9, 2020), at various lag times (e.g., 1 week following the death, 2 weeks, 3 weeks, up to 26 weeks) by reporting jurisdiction. Completeness by week, lag, and jurisdiction was modeled using zero-inflated binomial hierarchical Bayesian models with state-level and temporal random effects. Temporal random effects were included for both the time trend in the provisional counts, and the lag or reporting delay. These random effects were specified using a type-I random walk distribution, where counts in a given time period depend on the value for the prior time period, plus an error term. These models were implemented using R-INLA (3). Posterior predicted median values of completeness by jurisdiction and lag time were obtained from the models, and the weekly estimates for 2019 were averaged to provide the most recent possible estimates of completeness by jurisdiction, at given lag times. The inverse of these completeness values were applied as weights to adjust for incomplete reporting of provisional mortality data. For example, if provisional mortality data in 2019 for a given jurisdiction was 50% complete within 1 week of death and 75% complete within 2 weeks of death, then the weights for that jurisdiction would be 2 for data presented with a 1 week lag and 1.3 for data presented with a 2 week lag. Of note, these estimates of completeness differ from the estimates provided elsewhere (https://www.cdc.gov/nchs/nvss/vsrr/covid19/), which rely on the current counts of deaths relative to the expected number (i.e., percent over expected).
Weights in the first few weeks following the date of death were highly inflated and variable for some jurisdictions with relatively small numbers of deaths and where completeness of provisional data is typically very low (0–2%) in the first few weeks following the date of death. These jurisdictions include: Alaska, Connecticut, Louisiana, North Carolina, Ohio, Puerto Rico, Rhode Island, and West Virginia. To avoid highly inflated estimates in these jurisdictions, weights were trimmed at the 90th percentile for weeks reported with shorter lag times (e.g., 1–6 weeks). Additionally, as of September 9, weights for several jurisdictions were adjusted downward based on preliminary analyses of the timeliness of provisional data for deaths occurring in April through May of 2020. These analyses have suggested that timeliness has improved at shorter lags in Alaska, Mississippi, New York (excluding New York City), Ohio, Pennsylvania, South Carolina, Texas, Vermont, Virginia, West Virginia, and Puerto Rico. Weights for these jurisdictions were adjusted downward accordingly to improve the accuracy of the predicted counts.
Unweighted estimates are shown in one of the dashboards so that readers can examine the impact of weighting on estimates of excess deaths. For some jurisdictions, improvements in timeliness in 2020 relative to prior years will lead to weighted estimates that are too large. For other jurisdictions, the weighting may be insufficient to address reporting lags, particularly for data reported with shorter lag times (e.g., within 4–6 weeks). As an additional step to guard against underreporting, the weighted counts of deaths by week and jurisdiction were compared with control counts of deaths based on available demographic information from the death certificate. Demographic data are typically available prior to the cause of death data, which can take 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. For weeks and jurisdictions where the weighted count of deaths was less than the control count based on the demographic data, the weighted values were replaced with the control count. For example, if the weighted count for a given jurisdiction and week was 400, while the control count for that same jurisdiction and week was 800, this indicates that the weights are not fully accounting for incomplete data. In this case, the value of 800 would be used, as it represents a more complete estimate of the total number of deaths occurring in that jurisdiction and week.
Data for jurisdictions where counts are between 1 and 9 are suppressed. Additionally, data for weeks where the counts are less than 50% of the expected number are also suppressed, as these provisional counts are highly incomplete and potentially misleading. This change resulted in showing estimates with a lag of 1 week for most jurisdictions and the US. For some jurisdictions (Connecticut, North Carolina, Puerto Rico), lags may be greater. Declines in the observed numbers of deaths in recent weeks should not be interpreted to mean that the numbers of deaths are decreasing, as these declines are expected when relying on provisional data that are generally less complete in recent weeks. While the weighting method is intended to mitigate the impact of underreporting, it may not be sufficient to eliminate the problem of underreporting entirely. Therefore, it is not yet possible to determine whether decreases in the number of deaths is due to underreporting or to true declines until more complete data is obtained.
Mortality Outcomes
Weekly counts of deaths from all causes were examined, including deaths due to COVID-19. As many deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not mentioned on the death certificate as a suspected cause of death), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. These estimates can also provide information about deaths that may be indirectly related to COVID-19. For example, if deaths due to other causes may increase as a result of health care shortages due to COVID-19. Additionally, deaths from all causes excluding COVID-19 were also estimated. These counts excluded deaths with U07.1 as an underlying or multiple cause of death.
Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19. Additionally, death certificates are often initially submitted without a cause of death, and then updated when cause of death information becomes available. It may be the case that some excess deaths that are not attributed directly to COVID-19 will be updated in coming weeks with cause-of-death information that includes COVID-19. These analyses will be updated periodically, and the numbers presented will change as more data are received.
Cause of Death
As of June 3, 2020, weekly counts of deaths due to select causes of death are presented. These causes were selected based on analyses of comorbid conditions reported on death certificates where COVID-19 was listed as a cause of death (see https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm#Comorbidities). Some causes with insufficient numbers of deaths by week and jurisdiction were combined with other categories, and one cause was added to the Alzheimer disease and dementia category (ICD–10 code G31). These estimates are based on the underlying cause of death, and include: Respiratory diseases, Circulatory diseases, Malignant neoplasms, and Alzheimer disease and dementia. ICD–10 codes were used to classify deaths according to the following causes:
- Respiratory diseases
- Influenza and pneumonia (J09–J18)
- Chronic lower respiratory diseases (J40–J47)
- Other diseases of the respiratory system (J00–J06, J20–J39, J60–J70, J80–J86, J90–J96, J97–J99, R09.2, U04)
- Circulatory diseases
- Hypertensive diseases (I10–I15)
- Ischemic heart disease (I20–I25)
- Heart failure (I50)
- Cerebrovascular diseases (I60–I69)
- Other disease of the circulatory system (I00–I09, I26–I49, I51, I52, I70–I99)
- Malignant neoplasms (C00–C97)
- Alzheimer disease and dementia (G30, G31, F01, F03)
- Other select causes of death
- Diabetes (E10–E14)
- Renal failure (N17–N19)
- Sepsis (A40–A41)
Estimated numbers of deaths due to these other causes of death could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19 (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems). Deaths with an underlying cause of death of COVID-19 are not included in these estimates of deaths due to other causes, but deaths where COVID-19 appeared on the death certificate as a multiple cause of death may be included in the cause-specific estimates. For example, in some cases, COVID-19 may have contributed to the death, but the underlying cause of death was another cause, such as terminal cancer. For the majority of deaths where COVID-19 is reported on the death certificate (approximately 95%), COVID-19 is selected as the underlying cause of death.
Deaths due to all other natural causes were excluded (ICD-10 codes: A00–A39, A42–B99, D00–E07, E15–E68, E70–E90, F00, F02, F04–G26, G31–H95, K00–K93, L00–M99, N00–N16, N20–N98, O00–O99, P00–P96, Q00–Q99). External causes of death (i.e. injuries) were excluded, as the reporting lag is substantially longer for external causes of death (4). Additionally, causes of death where the underlying cause was unknown or ill-specified (i.e. R-codes) were excluded (except for R09.2, which is included under the Respiratory diseases category). Counts of deaths with unknown cause are typically substantially higher in provisional data, as many records are initially submitted without a specific cause of death and are then updated when more information becomes available (4). For deaths due to external causes of death or unknown cause, provisional data are highly unreliable and inaccurate in recent weeks, and it can take six to nine months to ensure sufficiently accurate estimates. Counts by cause provided here will not sum to the total number of deaths, given that some causes are excluded.
Estimates by cause of death and age at death are weighted, using the methods described above. The total count of deaths above average levels are shown for select causes of death. These totals are calculated by summing the number of deaths above average levels (based on weekly counts from 2015–2019) since 2/1/2020. Negative values were set to zero and therefore excluded from these sums. Because not all causes of death are shown and due to differences in how the average expected numbers of deaths are estimated, the total numbers of deaths across all the selected causes will not match the numbers of excess deaths from all causes excluding COVID-19.
Estimates by race and Hispanic origin are weighted using the methods described above. Weekly counts are shown for deaths due to all causes, all causes excluding COVID-19, and COVID-19. Because estimates are weighted to account for incomplete reporting in recent weeks, counts of death due to COVID-19 will not match other data sources. For data years 2018 – 2020, race and Hispanic-origin categories are based on the 1997 Office of Management and Budget (OMB) standards, allowing for the presentation of data by single race and Hispanic origin. These race and Hispanic-origin groups—non-Hispanic single-race white, non-Hispanic single-race black or African American, non-Hispanic single-race American Indian or Alaska Native (AIAN), and non-Hispanic single-race Asian—differ from the bridged-race categories used in previous data years when not all jurisdictions reported race and Hispanic origin using the 1997 OMB standards. Numbers may therefore differ from previous reports and other sources of data on mortality by race and Hispanic origin.
Limitations
These estimates are based on provisional data, which are incomplete. The weighting method applied may not fully account for reporting lags if there are longer delays at present than in past years. For example, in Pennsylvania, reporting lags are currently much longer than they have been in past years, and death counts for 2020 are therefore underestimated. Conversely, the weighting method may over-adjust for underreporting, given improvements in data timeliness in certain jurisdictions. Unweighted estimates are provided, so that users can see the impact of weighting the provisional counts. However, these unweighted provisional counts are incomplete, and the extent to which they may underestimate the true count of deaths is unknown. Some jurisdictions exhibit recent increases in deaths when using weighted estimates, but not the unweighted. The estimates presented may be an early indication of excess mortality related to COVID-19, but should be interpreted with caution, until confirmed by other data sources such as state or local health departments. It is possible that recent improvements in the timeliness of data could also contribute to the pattern where a jurisdiction exhibits recent increases with the weighted data, but not the unweighted. Conversely, recent increases may be missed in jurisdictions with historically low levels of completeness (e.g., Connecticut, North Carolina) either due to the lack of provisional data or insufficient weighting to address incomplete data.
The completeness of provisional data varies by cause of death and by age group. However, the weights applied do not account for this variability. It is unknown whether completeness varies by race and Hispanic origin. Therefore, the predicted numbers of deaths may be too low for some age groups, race/ethnicity groups, and causes of death. For example, provisional data on deaths among younger age groups is typically less complete than among older age groups. Predicted counts may therefore be too low among the younger age groups. Since the weights were based on the completeness of all-cause mortality data in past years, the weighted estimates for specific causes of death are likely too low, as reporting lags are typically larger for specific causes of death than for all-cause mortality. To minimize the degree of underreporting, cause-specific estimates are presented with a two-week lag.
References
- Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine 2012;32(7):1206-1222.
- Salmon M, Schumacher D, Hohle M. Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance. Journal of Statistical Software 2016;70(10):1-35.
- Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society Series B2009;71(2):319-392.
- Spencer MR, Ahmad F. Timeliness of death certificate data for mortality surveillance and provisional estimates. National Center for Health Statistics. 2016. http://www.cdc.gov/nchs/data/vsrr/report001.pdf.