Daily Archives: February 7, 2024

WASHINGTON As social media sites were flooded with misleading posts about vaccine safety, mask effectiveness, Covid-19s origins and federal shutdowns at the height of the pandemic, Biden officials urged platforms to pull down posts, delete accounts, and amplify correct information.

Now the Supreme Court could decide whether the government violated Americans First Amendment rights with those actions and dictate a new era for what role, if any, officials can play in combating misinformation on social media.

The Supreme Court is set to hear arguments next month in a case that could have sweeping ramifications for federal health agencies communications in particular. Murthy v. Missouri alleges that federal officials coerced social media and search giants like Facebook, Twitter, YouTube, and Google to remove or downgrade posts that questioned vaccine safety, Covids origins, or shutdown measures. Biden lawyers argue that officials made requests but never forced companies.

Government defenders say that if the Court limits the governments power, it could hamstring agencies scrambling to achieve higher vaccination rates and other critical public health initiatives. Critics argue that federal public health officials already in the throes of national distrust and apathy never should have tried to remove misleading posts in the first place.

The best way is to have a very vigorous offensive social media strategy, which we didnt have, said Paul Mango, a Trump deputy chief of staff for the Health and Human Services Department who worked closely on Operation Warp Speed, the effort to speed Covid-19 vaccines and treatments to market. Rather than trying to keep bad information off by suppression, why dont we have a strategy that really is very aggressive at propagating accurate information?

Though the Association of State and Territorial Health Officials is not taking a stance on the case or the governments argument that it can ask sites to take social media down, its chief medical officer Marcus Plescia also said the best use of federal public health resources is counter-messaging.

We really are limited to the extent that we can control misinformation, said Plescia. The number one [request from state officials] is we need good messaging thats been tested, and thats shown to be effective.

For their part, social media executives like Meta CEO Mark Zuckerberg have said in the past that they made and altered their content moderation policies on their own. But the tech executives are unlikely to weigh in now, considering they are in the midst of two other firestorms over moderation. One is a suit against a Florida law that would effectively diminish platforms abilities to moderate false and misleading posts. Another is last weeks very public battering by senators demanding more content moderation to protect childrens safety on their platforms.

The recent hearing before the Senate Judiciary Committee, which also called TikTok, Snap and Discord executives to testify, stands in stark contrast to the coronavirus misinformation lawsuit, as it conversely suggests tech companies arent doing enough to police their platforms. At one point, Sen. Josh Hawley (R-Mo.) urged Zuckerberg to stand up and apologize to families in the hearing room for damage caused by Facebook and Instagram use.

Senators from both parties seemed open to peeling back a federal protection of tech companies that host problematic or false content.

It is now time to make sure that the people who are holding up the signs can sue on behalf of their loved ones. Nothing will change until the courtroom door is open to victims of social media, South Carolina Republican Lindsay Graham said.

Bidens lawyers are set to argue that he, and his officials, can make the same type of demands.

A lower courts in this case ruled that the federal government cant put any pressure on social media platforms to censor their content. Under that ruling, even public statements by the president about the teen mental health crisis could be construed as undue pressure, Solicitor General Elizabeth Prelogar argued in a legal filing.

For instance, under that ruling, a White House statement condemning the role social media plays in teens mental health and calling for potential legislative reform might be viewed as coercion or significant encouragement under the Fifth Circuits novel understanding of those concepts, she wrote.

But this case didnt start with mental health, and much of it will likely rest on private rather than public comments from federal officials.

The lawsuit, started by then-Missouri Attorney General Eric Schmitt, reflects a growing trend of state attorneys general mounting politically divisive cases against the federal government. Another state, Louisiana, joined the suit along with three doctors who co-signed a paper on herd immunity, an anti-lockdown activist in Louisiana, and a conservative news site, The Gateway Pundit.

Federal officials began communicating with the social platforms in early 2021, according to court documents. Those communications included White House messages to one site saying to take a post down ASAP and keep an eye out for tweets that fall in the same genre or instructions to another platform to remove [an] account immediately. CDC officials also regularly flagged posts to the companies and in one instance asked what [was] being done on the amplification-side to promote official messaging on coronavirus information.

Later, according to court documents, government officials began asking Facebook and others for data and the details of their moderation policies and standards. They held regular meetings, suggested changes and at least one company created a portal for government requests to be prioritized. After a Washington Post article detailing Facebooks moderation struggle, an official wrote to the company that they felt Facebook was not trying to solve the problem and the White House was [i]nternally considering our options on what to do about it.

In July 2021, federal officials took their frustrations to the public. Surgeon General Vivek Murthy said in a press briefing that modern technology companies have enabled misinformation to poison our information environment, with little accountability to their users.

He added, Were asking them to operate with greater transparency and accountability. Were asking them to monitor misinformation more closely. The same day, he issued his first formal advisory as surgeon general on confronting health misinformation.

Despite a lower court ruling that those statements could be inappropriate pressure, experts who spoke to STAT said its hard to imagine the Supreme Court going that far.

The government does, and should, have the ability to communicate with private entities about the dangers that exist, said Clay Calvert, a senior fellow on technology policy at the American Enterprise Institute. Why this case is so controversial is the inherently political divisiveness of the content in question that divided Republicans and Democrats on matters like mask mandates and Covid vaccines.

The overarching question before the court is whether these actions count as government coercion of a private company, which would be an overstep of its authority. Justice Department lawyers argue that while officials frequently suggested removal or downgrade of posts, they didnt force companies nor did companies always oblige.

An appeals court deemed some officials actions particularly those of the White House potentially coercive, but vastly whittled down a district courts broad prohibition of government officials correspondence with social media companies. In doing so, they laid the groundwork for some communications particularly the CDCs alerts on changing recommendations and explainers on true vs. false information as valid dispatches.

But that does not mean the CDC is in the clear when the Supreme Court considers the case in March. Justice Samuel Alito already signaled some of his apprehension when he dissented from other justices on lifting the ban before they hear arguments.

At this time in the history of our country, what the Court has done, I fear, will be seen by some as giving the Government a green light to use heavy-handed tactics to skew the presentation of views on the medium that increasingly dominates the dissemination of news, Alito wrote.

Even if the court rejects broader controls on federal communications with social media sites, the case could have extensive implications for effective messaging from federal health officials, legal experts say.

It will have a chilling effect on the government especially for the CDC, said Dorit Reiss, a professor at UC Law San Francisco. Because the line is fuzzy and because they dont want to be accused of coercion, theyre not going to be sure when they can talk to social media.

Correction: A previous version of this article misstated Marcus Plescias title.

Follow this link:

Supreme Court to weigh whether Covid misinformation is protected speech - STAT

TULSA, Okla. Oklahoma leads the country in long Covid, having the highest rate of adults suffering from it.

That's according to data from the latest U.S. Census Bureau Household Pulse Survey.

Dr. Jason Lepak from Ascension Medical Group St. John defined it as, Long Covid is, [a] long time after you've had the illness and you've recovered from the acute things and immediate things, and you're developing many long-term complications and consequences from Covid.

Researchers have gradually learned more about it. Some things, however, they don't know yet. Even still, long Covid is hard to diagnose.

Lepak told 2 News Oklahoma, "It can be chronic lung conditions, weakness, fatigue, vascular disease many different problems, even a longer brain fog. So, it's a whole constellation of different symptoms that is different by individual, but clearly is associated with having the Covid illness previously."

"It can manifest differently in different people, and some people never get it, and they fully recover," Lepak also said. "But we've clearly seen this group of individuals who had Covid and then they've had symptoms of a variety of other problems that have existed well beyond when they should have normally recovered."

Most people who come down with Covid-19 symptoms are back on their feet within a week or two. However, the Household Pulse Survey in November found nearly a quarter (24.4%) of American adults who got Covid-19 reported symptoms lasting three months or longer.

That's about one third (34.1%) in Oklahoma, the highest rate of any state.

The survey demonstrates long Covid having a real-world impact.

A guide from HelpAdvisor about the survey cites one 2022 study showing long Covid possibly keeping up to 3.7 million people out of work. It references another 2022 study that found long Covid cost the U.S. economy nearly four trillion dollars, 17% of the 2019 U.S. gross domestic product.

The survey also found long Covid is also impacting the day-to-day life of these people, saying 31.1% percent of U.S. adults who reported having it said their symptoms lowered "their ability to carry out daily activities."

At 29.5%, the Sooner State fell slightly below the national average, putting it 35th place.

If you or anybody you know suffers from long Covid or symptoms of it, Lepak said, Dont just suffer at home. Go ahead and seek attention first by your primary care physician, and if they can't take care of you long term for that, they may refer you out either to a sub-specialist or to a couple of physicians in the community who deal specifically with patients who suffer with long Covid.

Stay in touch with us anytime, anywhere --

Read more:

Oklahoma leads country in long Covid - 2 News Oklahoma KJRH Tulsa

Dataset and study design

This section contains an overview of how the dataset was collected, its characteristics and its underlying study design. More in-depth descriptions are provided in two accompanying papers: Budd and co-workers23 report a detailed description of the full dataset, whereas Pigoli et al.30 present the rationale for and full details of the statistical design of our study.

Our main sources of recruitment were the REACT study and the NHS T+T system. REACT is a prevalence survey of SARS-CoV-2 that is based on repeated cross-sectional samples from a representative subpopulation defined via (stratified) random sampling from Englands NHS patient register31. The NHS T+T service was a key part of the UK governments COVID-19 recovery strategy for England. It ensured that anyone developing COVID-19 symptoms could be swab tested, followed by the tracing of recent close contacts of any individuals testing positive for SARS-CoV-2 (ref. 25).

Enrolment for both the REACT and NHS T+T recruitment channels was performed on an opt-in basis. Individuals participating in the REACT study were presented with the option to volunteer for this study. For the NHS T+T recruitment channel, individuals receiving a PCR test from the NHS T+T pillar 2 scheme were invited to take part in research (pillar 1 tests refer to all swab tests performed in Public Health England laboratories and NHS hospitals for those with a clinical need, and health and care workers, whereas pillar 2 comprises swab testing for the wider population25). The guidance provided to potential participants was that they should be at least 18 years old, had taken a recent swab test (initially no more than 48h, changing to 72h on 14 May 2021), agree to our data privacy statement and have their PCR barcode identifier available, which was then internally validated.

Participants were directed to the Speak up and help beat coronavirus web page24. Here, after agreeing to the privacy statement and completing the survey questions, participants were asked to record four audio clips. The first involved the participant reading out the sentence: I love nothing more than an afternoon cream tea, which was designed to contain a range of different vowel and nasal sounds. This was followed by three successive sharp exhalations, taking the form of a ha sound. The final two recordings involved the participant performing volitional/forced coughs, once, and then three times in succession. Recordings were saved in .wav format. Smart phones, tablets, laptops and desktops were all permitted. The audio recording protocol was homogenized across platforms to reduce the risk of bias due to device types.

Existing metadata such as age, gender, ethnicity and location were transferred from linked T+T/REACT records. Participants were not asked to repeat this information to avoid survey fatigue. An additional set of attributeshypothesized to pose the most utility for evaluating the possibility for COVID-19 detection from audiowas collected in the digital survey. This was in line with General Data Protection Regulation requirements that only the personal data necessary to the task should be collected and processed. This set included the symptoms currently on display (the full set of which are detailed in Fig. 1e,f), and long-term respiratory conditions such as asthma. The participants first language was also collected to control for different dialects/accents, and complement location and ethnicity. Finally, the test centre at which the PCR was conducted was recorded. This enabled the removal of submissions when cases were linked to faulty test centre results. A full set of the dataset attributes can be found in Budd and colleagues23.

The final dataset is downstream of a quality control filter (see Fig. 1g), in which a total of 5,157 records were removed, each with one or more of the following characteristics: (1) missing response data (missing a PCR test); (2) missing predictor data (any missing audio files or missing demographic/symptoms metadata); (3) audio submission delays exceeding ten days post test result; (4) self-inconsistent symptoms data; (5) a PCR testing laboratory under investigation for unreliable results; (6) a participant age of under 18; and (7) sensitive personal information detected in the audio signal (see Fig. 3d of ref. 23). Pigoli et al.30 present these implemented filters in full, and the rationale behind each one. The final collected dataset, after data filtration, comprised 23,514 COVID+ and 44,328 COVID individuals recruited between March 2021 and March 2022. Please note that the sample size here differs to that in our accompanying papers, in which Budd et al.23 reported numbers before the data quality filter was applied, whereas our statistical study design considerations, detailed in a work by Pigoli and colleagues30, focused on data from the restricted date range spanning March to November 2021. We note the step-like profile of the COVID count is due to the six REACT rounds, where a higher proportion of COVID participants were recruited than in the T+T channel. As detailed in the geo-plots in Fig. 1a,b, the dataset achieves a good coverage across England, with some areas yielding more recruited individuals than others. We are pleased to see no major correlation between geographical location and COVID-19 status, (Fig. 1c), with Cornwall displaying the highest level of COVID-19 imbalance, with a 0.8% difference in percentage proportion of COVID+ and COVID cases.

In our pre-specified analysis plan, we defined three training sets and five test sets to define a range of analyses in which we investigate, characterize and control for the effects of enrolment bias in our data:

Randomized train and test sets. A participant-disjoint train and test set was randomly created from the whole dataset, similar to methods in previous works.

Standard train and test set. Designed to be a challenging, out-of-distribution evaluation procedure. Carefully selected attributes such as geographical location, ethnicity and first language are held out for the test set. The standard test set was also engineered to over represent sparse combinations of categories such as older COVID+ participants30. The samples included in this split exclusively consist of recordings made prior to 29 November 2021.

Matched train and test sets. The numbers of COVID and COVID+ participants are balanced within each of several key strata. Each stratum is defined by a unique combination of measured confounders, including binned age, gender and a number of binary symptoms (for example, cough, sore throat, shortness of breath; see Methods for a full description). The samples included in this split exclusively consist of recordings made prior to 29 November 2021.

Longitudinal test set. To examine how classifiers generalized out-of-sample over time, the longitudinal test set was constructed only from participants joining the study after 29 November 2021.

Matched longitudinal test set. Within the longitudinal test set, the numbers of COVID and COVID+ participants are balanced within each of several key strata, similarly as in the matched test set above.

The supports for each of these splits are detailed in Fig. 1h.

Three separate models were implemented for the task of COVID-19 detection from audio, each representing an independent machine learning pipeline. These three models collectively span the machine learning research space thoroughlyranging from the established baseline to the current state of the art in audio classification technologiesand are visually represented in Extended Data Fig. 7. We also fitted an RF classifier to predict COVID-19 status from self-reported symptoms and demographic data. The outcome used to train and test each of the prediction models was a participants SARS-CoV-2 PCR test result. Each models inputs and predictors, and the details on how they are handled, can be found below. Wherever applicable, we have reported our studys findings in accordance with TRIPOD statement guidelines32. The following measures were used to assess model performance: ROCAUC, area under the precisionrecall curve (PRAUC), and UAR (also known as balanced accuracy). Confidence intervals for ROCAUC, PRAUC and UAR are based on the normal approximation method33, unless otherwise stated to be calculated by the DeLong method34.

We defaulted to the widely used openSMILESVM approach35 for our baseline model. Here, 6,373 handcrafted features (the ComParE 2016 set)including the zero-crossing rate and shimmer, which have been shown to represent human paralinguistics wellare extracted from the raw audio form. These features are then concatenated to form a 6,373-dimensional vector, fopenSMILE(w)v, where the raw waveform, ({{{bf{w}}}}in {{mathbb{R}}}^{n}) (n=clip duration in secondssample rate) is transformed to ({{{bf{v}}}}in {{mathbb{R}}}^{6,373}); v is then normalized prior to training and inference. A linear SVM is fitted to this space and tasked with binary classification. We select the optimal SVM configuration on the basis of the validation set before then retraining on the combined trainvalidation set.

Bayesian neural networks provide estimates of uncertainty, alongside strong supervised classification performance, which is desirable for real-world use cases, especially those involving clinical use. Bayesian neural networks are naturally suited to Bayesian decision theory, which benefits decision-making applications with different costs on error types (for example, assigning unequal weighting to errors in different COVID-19 outcome classifications)36,37. We thus supply a ResNet-50 (ref. 38) BNN model. The base ResNet-50 model showed initial strong promise for ABCS5, further motivating its inclusion in this comparison. We achieve estimates of uncertainty through Monte-Carlo Dropout to achieve approximate Bayesian inference over the posterior, as in ref. 39. We opt to use the pre-trained model for a warm start to the weight approximations, and allow full retraining of layers.

The features used to create an intermediate representation, as input to the convolutional layers, are Mel filterbank features with default configuration from the VGGish GitHub (ref. 40): ({{{{bf{X}}}}}_{i}in {{mathbb{R}}}^{96times 64}), 64 log-mel spectrogram coefficients using 96 feature frames of 10ms duration, taken from a resampled signal at 16kHz. Each input signal was divided into these two-dimensional windows, such that a 2,880ms clip would produce three training examples with the label assigned to each clip (COVID+ or COVID). Incomplete frames at edges were discarded. As with the openSMILESVM, silence was not removed. For evaluation, the mean prediction over feature windows was taken per audio recording, to produce a single decision per participant. To make use of the available uncertainty metrics, Supplementary Note 3 details an uncertainty analysis over all audio modalities for a range of traintest partitions.

In recent years, transformers41 have started to perform well in high-dimensional settings such as audio42,43. This is particularly the case when models are first trained in a self-supervised manner on unlabelled audio data. We adopt the SSAST44, which is on a par with the current state of the art for audio event classification. Raw audio is first resampled to 16kHz and normalized before being transformed into Mel filter banks. Strided convolutional neural layers are used to project the Mel filter bank to a series of patch level representations. During self-supervised pretraining, random patches are masked before all of the patches are passed to a transformer encoder. The model is trained to jointly reconstruct the masked audio and to classify the order of which the masked audio occurs. The transformer is made up of 12 multihead attention blocks. The model is trained end to end, with gradients being passed all of the way back to the convolutional feature extractors. The model is pre-trained on a combined set of AudioSet-2M (ref. 45) and Librispeech46, representing over two million audio clips for a total of ten epochs. The model is then fine-tuned in a supervised manner on the task of COVID-19 detection from audio. Silent sections of audio recordings are removed before then being resampled to 16kHz and normalized. Clips are cut/zero-padded to a fixed length of 5.12s, which corresponds to approximately the mean length of the audio clip. For cases in which the signal length exceeds 5.12s (after silence is removed), the first 5.12s are taken. At the training time, the signal is augmented through applying SpecAugment47 along with the addition of Gaussian noise. The output representations are mean pooled before being fed through a linear projection head. No layers are frozen and again the model is trained end-to-end. The model is fine-tuned for a total of 20 epochs. The model is evaluated on the validation set at the end of each epoch and its weights are saved. At the end of training the best performing model, over all epochs, is chosen.

To predict SARS-CoV-2 infection status from self-reported symptoms and demographic data, we applied an RF classifier with default settings (having self-reported symptoms and demographic data as inputs). In our dataset, predictor variables for the symptoms RF classifier on our dataset comprised: cough; sore throat; asthma; shortness of breath; runny/blocked nose; a new continuous cough; Chronic obstructive pulmonary disease (COPD) or emphysema; another respiratory condition; age; gender; smoker status; and ethnicity. In Han and colleagues dataset18, predictor variables for the symptoms RF classifier comprised: tightness of chest; dry cough; wet cough; runny/blocked nose; chills; smell/taste loss; muscle ache; headache; sore throat; short breath; dizziness; fever; runny/blocked nose; age; gender; smoker status; language; and location. Prior to training, categorical attributes were one-hot encoded. No hyperparameter tuning was performed, and models were trained on the combined Standard train and validation sets. For the hybrid symptoms+audio RF classifier, the outputted predicted COVID+ probability from an audio-trained SSAST is appended as an additional input variable to the self-reported symptoms and demographic variables listed above.

The matched test set was constructed by exactly balancing the numbers of individuals with COVID+ and COVID in each stratum where, to be in the same stratum, individuals must be matched on all of (recruitment channel)(10-year-wide age bins)(gender)(all of six binary symptoms covariates). The six binary symptoms matched on in the matched test set were: cough; sore throat; asthma; shortness of breath; runny/blocked nose; and at least one symptom.

Our matching algorithm proceeds as follows. First, each participant is mapped to exactly one stratum. Second, the following matching procedure is applied separately in each stratum: in stratum s (of a total of S strata) let ns,+ and ns, denote the number of individuals with COVID+ and COVID, respectively, and let ({{{{mathscr{A}}}}}_{s,+}) and ({{{{mathscr{A}}}}}_{s,-}) be the corresponding sets of individuals. Use ({{{{mathscr{M}}}}}_{s,+}) and ({{{{mathscr{M}}}}}_{s,-}) to denote random samples without replacement of size (min {{n}_{s,+},{n}_{s,-}}) from ({{{{mathscr{A}}}}}_{s,+}) and ({{{{mathscr{A}}}}}_{s,-}) respectively. Finally we combine matched individuals across all strata into the matched dataset ({{{mathscr{M}}}}) defined as:

$${{{mathscr{M}}}}:= {cup }_{s = 1}^{S}({{{{mathscr{M}}}}}_{s,+}cup {{{{mathscr{M}}}}}_{s,-}).$$

The resulting matched test set comprised 907 participants who were COVID positive and 907 who were COVID negative. The matched training set was constructed similarly to the matched test set, though with slightly different strata, so as to increase available sample size. For the matched training set, individuals were matched on all of: (10-year-wide age bins)(gender)(all of seven binary covariates). The seven binary covariates used for the matched training set were: cough; sore throat; asthma; shortness of breath; runny/blocked nose; COPD or emphysema; and smoker status. The resulting matched training set comprised 2,599 participants who were COVID positive and 2,599 who were COVID negative.

We consider the action of applying a particular testing protocol to an individual randomly selected from a population. The four possible outcomes ({O}_{hat{y},y}) are

$${O}_{hat{y},y}:= [,{{mbox{Predict COVID{{mbox{-}19}} status as}}},,hat{y}],{{{rm{AND}}}},[,{{mbox{True COVID{{mbox{-}19}} status is}}},,y]$$

(2)

for predicted COVID-19 status (hat{y}in {0,1}) and true COVID-19 status y{0,1}. We denote the probability of outcome ({O}_{hat{y},y}) by

$${p}_{hat{y},y}:= {mathbb{P}}({O}_{hat{y},y})$$

(3)

and use ({u}_{hat{y},y}) to denote the combined utility of the consequences of outcome ({O}_{hat{y},y}). For a particular population prevalence proportion, , the ({p}_{hat{y},y}) are subject to the constraints

$${p}_{0,1}+{p}_{1,1}=uppi$$

(4)

$${p}_{0,0}+{p}_{1,0}=1-uppi ,$$

(5)

leading to the following relationships, valid for (0,1), involving the sensitivity and specificity of the testing protocol:

$${{{rm{sensitivity}}}}equiv frac{{p}_{1,1}}{{p}_{1,1}+{p}_{0,1}}=frac{{p}_{1,1}}{uppi }$$

(6)

$${{{rm{specificity}}}}equiv frac{{p}_{0,0}}{{p}_{0,0}+{p}_{1,0}}=frac{{p}_{0,0}}{1-uppi }.$$

(7)

The expected utility is:

$${{{rm{EU}}}}=mathop{sum}limits_{hat{y}in {0,1}}mathop{sum}limits_{yin {0,1}}{u}_{hat{y},y}{p}_{hat{y},y}$$

(8)

$$={u}_{1,1}{p}_{1,1}+{u}_{0,1}(uppi -{p}_{1,1})+{u}_{0,0}{p}_{0,0}+{u}_{1,0}(1-uppi -{p}_{0,0})$$

(9)

$$begin{array}{l}=uppi [({u}_{1,1}-{u}_{0,1})times {{{rm{sensitivity}}}}+{u}_{0,1}]\+(1-uppi )[({u}_{0,0}-{u}_{1,0})times {{{rm{specificity}}}}+{u}_{1,0}],end{array}$$

(10)

where equations (4) and (5) are substituted into equation (8) to obtain equation (9), and equations (6) and (7) are substituted into equation (9) to obtain equation (10).

To provide researchers easy access to running the code, we have created a demonstration notebook where the participant is invited to record their own sentence, cough, three cough or exhalation sounds and evaluate our COVID-19 detection machine learning models on it. The model outputs a COVID-19 prediction, along with some explainable AI analysis, for example, enabling the user to listen back to the parts of the signal which the model allocated the most attention to. In the demonstration, we detail that this is not a clinical diagnostic test for COVID-19, but that it is instead for research purposes and does not provide any medical recommendation, nor should any action be taken following its use. The demonstration file is detailed on the main repository page and can be accessed at https://colab.research.google.com/drive/1Hdy2H6lrfEocUBfz3LoC5EDJrJr2GXpu?usp=sharing.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Excerpt from:

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers - Nature.com

A new study led by the Boston University School of Public Health (BUSPH) challenges prevailing narratives about the causes of excess mortality during the COVID-19 pandemic.

The research has produced compelling evidence that many deaths previously attributed to natural causes were, in fact, uncounted fatalities from COVID-19.

Official COVID-19 mortality statistics have not fully captured deaths attributable to SARS-CoV-2 infection in the United States, wrote the researchers.

While some excess deaths were likely related to pandemic health care interruptions and socioeconomic disruptions, temporal correlations between reported COVID-19 deaths and excess deaths reported to non-COVID-19 natural causes suggest that many of those excess deaths were unrecognized COVID-19 deaths.

The investigation represents a significant stride in understanding the true toll of the pandemic.

Kristin Urquiza co-founded Marked By COVID, a justice and remembrance movement, after losing her father to COVID. This study documents the deadliness of COVID-19 and the effectiveness of public health interventions, said Urquiza. The least we can do to honor those who died is to accurately account for what happened.

The official count of COVID-19 deaths in the United States stands at nearly 1.17 million, according to federal data. However, this figure is believed to be an underestimation, as suggested by multiple excess mortality studies.

Excess mortality refers to the number of deaths during a given time period that surpasses the number expected under normal circumstances.

Until now, the challenge has been to determine whether these additional deaths were directly due to COVID-19 or resulted from indirect consequences of the pandemic, such as healthcare disruptions or socioeconomic factors.

In collaboration with researchers at the University of Pennsylvania, the BUSPH team provides the first definitive evidence linking a significant portion of excess deaths during the pandemic directly to COVID-19, rather than to non-COVID natural causes like chronic illnesses.

By analyzing monthly data on natural-cause deaths and reported COVID-19 fatalities across 3,127 U.S. counties from March 2020 to August 2022, the team discovered that spikes in non-COVID natural cause deaths coincided with or preceded surges in COVID-19 deaths in most regions. This pattern suggests that many deaths were misclassified and should have been attributed to COVID-19.

Our findings show that many COVID-19 deaths went uncounted during the pandemic. Surprisingly, these undercounts persisted well beyond the initial phase of the pandemic, said study co-author Dr. Andrew Stokes, who has led numerous studies analyzing excess mortality patterns and drivers during the pandemic.

According to Dr. Stokes, the temporal correlation between reported COVID-19 deaths and excess deaths reported to non-COVID-19 natural causes offers insight into the causes of these deaths.

We observed peaks in non-COVID-19 excess deaths in the same or prior month as COVID-19 deaths, a pattern consistent with these being unrecognized COVID-19 deaths that were missed due to low community awareness and a lack of COVID-19 testing.

Study lead author Eugenio Paglino, a PhD student at UPenn, noted that if the primary explanation for these deaths were healthcare interruptions and delays in care, the non-COVID excess deaths would likely occur after a peak in reported COVID-19 deaths and subsequent interruptions in care. However, this pattern was not observed nationally or in any of the geographic subregions we assessed, said Paglino.

The study also disproves any claims that mortality during the pandemic can be attributed to COVID-19 vaccinations or shelter-in-place policies.

Dr. Nahid Bhadelia, founding director of the Boston University Center for Emerging Infectious Diseases Policy and Research, said that the research is important because our ability to detect and correctly assign deaths during an epidemic goes to the heart of our understanding of the disease and how we organize our response.

The study is published in the journal Proceedings of the National Academy of Sciences.

Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

-

Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

More:

Hidden death toll of COVID-19 pandemic revealed - Earth.com

An OHSU-led study finds a substantial increase in antibody response to two-dose vaccinations when the vaccine was administered into each arm instead of both vaccines administered intoone arm. (OHSU/Christine Torres Hicks)

New research reveals as much as a four-fold increase in immune response when people alternate from one arm to the other when given a multi-dose vaccine.

The laboratory study led by researchers at Oregon Health & Science University measured the antibody response in the blood of 947 people who received two-dose vaccinations against COVID-19 early in the pandemic. Participants included OHSU employees who agreed to enroll in research while getting vaccinated against the SARS-CoV-2 virus, and were randomized to get the second dose in either the same or the opposite arm as the first dose.

The study waspublishedrecently in The Journal of Clinical Investigation.

Historically, clinicians thought that arm choice didnt matter.

The new study tested serum samples collected at various times after vaccination. They found a substantial increase in the magnitude and breadth of the antibody response among people who had contralateral or a shot in each arm boosting compared with those who did not.

The improved response clearly materialized three weeks after the second booster and persisted beyond 13 months after boosting. Investigators found heightened immunity to the original SARS-CoV-2 strain, and an even stronger immune response to the omicron variant that emerged roughly a year after arm alternation.

Marcel Curlin, M.D. (OHSU)

Researchers arent sure why this happens, but they speculate that giving a shot in each arm activates new immune responses in different lymph nodes in each arm.

By switching arms, you basically have memory formation in two locations instead of one, said senior author Marcel Curlin, M.D., associate professor of medicine (infectious diseases) in the OHSU School of Medicine and medical director of OHSU Occupational Health.

OHSU had the opportunity to examine the question as part of a series of laboratory studies using blood drawn from willing employees beginning early in the COVID-19 pandemic. That line of research has produced a series of published studies related to the durability, breadth and potency of immune response following vaccination and breakthrough infections.

After vaccines became available in late 2020, some participants wondered whether it made a difference if they alternated arms in the two-dose regimen.

This question hasnt really been extensively studied, so we decided to check it out, Curlin said. It turned out to be one of the more significant things weve found, and its probably not limited to just COVID vaccines. We may be seeing an important immunologic function.

Among the people in the study who agreed to switch arms, researchers matched 54 pairs for age, gender and relevant time intervals between vaccination and exposure half getting the two doses in one arm and half in both.

Two weeks after the second dose, researchers didnt see much of a difference in immune response. After three weeks, however, researchers measured significantly greater numbers of antibodies capable of binding and neutralizing the SARS-CoV-2 virus in blood samples. The rates progressively increased over four weeks from 1.3-fold to as much as a 4-fold increase against the omicron variant of the virus.

Any incremental improvement might save a lot of lives, Curlin said.

At this point, most people have long since been exposed to the SARS-CoV-2 virus multiple times either through vaccination, infection or both.

Although the new study focused on vaccination against COVID-19, researchers say they expect the improved immune response could be similar for other multidose vaccinations. They call for further research to determine whether contralateral vaccination improves immune response for other vaccines, and especially among children.

Several prime-boost vaccine regimens are essential components of pediatric care, and immune responses may differ in children, they write.

Curlin said further study is needed and it is too soon to make clinical recommendations based on the results of this study. If and when a new virus emerges requiring a new two-dose vaccine, Curlin said he wont hesitate.

Im going to switch up my arms, he said.

In addition to Curlin, co-authors include Sedigheh Fazli, Archana Thomas, Abram Estrada, David Xthona Lee, Steven Kazmierczak, Ph.D., Mark Slifka, Ph.D., and Bill Messer, M.D., Ph.D., of OHSU; Hiro Ross, a former OHSU medical student now doing residency at the University of California, Los Angeles; and David Montefiori, Ph.D., of Duke University.

The study was supported by the M.J. Murdock Charitable Trust; the OHSU Foundation; and the National Institutes of Health award R01AI145835 and P51OD011092. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Continue reading here:

Switching arms improves effectiveness of two-dose vaccinations, OHSU study suggests - OHSU News

Tributes have been paid to a senior Irish official who played a central role in developing European Union health policies and managing the Covid-19 crisis who has died shortly after retirement.

John Ryan (65) moved into the European Commission from the Irish civil service and went on to hold a series of senior roles, particularly in public health, becoming the deputy director general of the bodys health department DG Sante.

His death in recent days drew heartfelt tributes from various public health organisations and EU officials who remembered him as a kind man with a brilliant intellect.

The European commissioner for Health and Food Safety Stella Kyriakides said he had been central to the EUs Covid-19 response, as well as to the blocs policy on countering cancer.

His memory and his contribution will be cherished, she said, adding that she had been deeply saddened by the news.

The World Health Organisations (WHO) regional director for Europe Hans Kluge said that Mr Ryan had left an indelible mark on public health and a rich legacy. His contribution was instrumental in creating EU-wide responses to health challenges, according to the WHO.

Several civil society organisations issued statements in memory of his contribution. He was recalled as one of our communitys major champions by the rare diseases network Eurordis, while Mental Health Europe called him a powerful voice and advocate. The alliance of civil society health organisations, EU4health, described him as a true public health champion who had made an an indelible mark on our collective pursuit of better health for all over decades of service.

Excerpt from:

Tributes paid to Irish health official 'central' to EU Covid-19 response - The Irish Times