Daily Archives: August 2, 2022

Mike Stucka USA TODAY NETWORK| Salina Journal

Kansas reported 7,490 new cases of coronavirus in the week ending Sunday, from 7,635 the week before of the virus that causes COVID-19.

Kansas ranked 19th among the states where coronavirus was spreading the fastest on a per-person basis, a USA TODAY Network analysis of Johns Hopkins University data shows. In the latest week coronavirus cases in the United States increased 7.4% from the week before, with 906,593 cases reported. With 0.88% of the country's population, Kansas had 0.83% of the country's cases in the last week. Across the country, 28 states had more cases in the latest week than they did in the week before.

Saline County reported 165 cases and minus one death in the latest week. A week earlier, it had reported 160 cases and zero deaths. Throughout the pandemic it has reported 15,084 cases and 251 deaths.

Within Kansas, the worst weekly outbreaks on a per-person basis were in Hamilton County with 906 cases per 100,000 per week; Woodson County with 574; and Grant County with 573. The Centers for Disease Control says high levels of community transmission begin at 100 cases per 100,000 per week.

Adding the most new cases overall were Sedgwick County, with 1,393 cases; Johnson County, with 1,309 cases; and Shawnee County, with 644. Weekly case counts rose in 55 counties from the previous week. The worst increases from the prior week's pace were in Cowley, Montgomery and Geary counties.

>> See how your community has fared with recent coronavirus cases

Across Kansas, cases fell in 48 counties, with the best declines in Reno County, with 149 cases from 230 a week earlier; in Finney County, with 131 cases from 191; and in Riley County, with 116 cases from 162.

In Kansas, five people were reported dead of COVID-19 in the week ending Sunday. In the week before that, nine people were reported dead.

A total of 835,500 people in Kansas have tested positive for the coronavirus since the pandemic began, and 8,971 people have died from the disease, Johns Hopkins University data shows. In the United States 91,316,648 people have tested positive and 1,029,926 people have died.

>> Track coronavirus cases across the United States

USA TODAY analyzed federal hospital data as of Sunday, July 31. Likely COVID patients admitted in the state:

Likely COVID patients admitted in the nation:

Hospitals in 24 states reported more COVID-19 patients than a week earlier, while hospitals in 21 states had more COVID-19 patients in intensive-care beds. Hospitals in 30 states admitted more COVID-19 patients in the latest week than a week prior, the USA TODAY analysis of U.S. Health and Human Services data shows.

The USA TODAY Network is publishing localized versions of this story on its news sites across the country, generated with data from Johns Hopkins University and the Centers for Disease Control. If you have questions about the data or the story, contact Mike Stucka at mstucka@gannett.com.

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Saline County reported 165 additional COVID-19 cases this week - Salina Journal

Dear Campus Community,

Beginning August 1, our on-campus COVID-19 testing sites have augmented their services and resources available to all UC Riverside faculty, staff, and students.

Employees who have not yet uploaded their COVID-19 vaccination/booster record into the MyChart system and would prefer to show proof of vaccination in person or virtually to the COVID-19 Management Team, can do so at either of our testing sites or by scheduling an appointment via Zoom.

In-Person VerificationNo appointment is necessary. You can walk-in and show proof of vaccination.

ZoomSchedule a time by either e-mailing covid19@ucr.edu or calling 844-827-6827 and selecting option no. 1. A Zoom link will be sent to you for your appointment time.

Please note that you will need to provide your photo ID or UCR ID and your COVID-19 vaccination card* for in person verification. We will not make a copy of the employees vaccine card.

*Acceptable Proof of COVID-19 Vaccination COVID-19 Vaccination Record Card (issued by the Centers for Disease Control & Prevention or WHO), which includes name of person vaccinated, type of vaccine provided, and date doses administered A photo of a vaccination card as a separate document A photo of a vaccine card stored on a phone or electronic device Documentation of vaccination from a healthcare provider Digital record that includes a QR code that when scanned by a SMART Health Card reader displays to the reader name, date of birth, vaccination dates, and vaccine type.

COVID-19 TestingFree PCR testing continues at both inside the Student Success Center and at Glen Mor. Hours have been slightly modified for the summer. No appointment is necessary.

Glen Mor Building C Room 001 Monday-Thursday: 6:30 a.m.-2:45 p.m. Friday: 6:30 a.m.-2:00 p.m.

Student Success Center Monday-Friday: 8 a.m.-2 p.m.

ResourcesAdditional items available for pick up include at the Glen Mor testing location include: Surgical and N95 masks Free antigen test kits (request form) Hand sanitizer bottles

You may also request free test kits to be mailed to your home address by visiting http://www.covid.gov/tests.

Face CoveringsAs a reminder, UCR strongly encourages the use of masks for everyone while indoors. Masks continue to be required in clinical settings including Student Health Services.

Individuals who have medical or religious exemptions to the vaccine policy must continue to mask while on campus, test weekly, and report their testing completion via the COVID Screening Check.

Here is the original post:

COVID-19 vaccine verification assistance and testing site updates - University of California, Riverside

Background: Hyperglycemia is commonly seen in critically ill patients. This disorder was also seen in coronavirus disease 2019 (COVID-19) patients and was associated with a worse prognosis. The current study determined the prevalence, risk factors, and prognostic implications of hyperglycemia in COVID-19 patients.

Method: This was a retrospective observational study performed in an intensive care unit for COVID-19 patients. Electronic data of COVID-19 patients admitted to the intensive care unit from August 2nd to October 15th, 2021, were collected. Patients were divided into non-hyperglycemia, hyperglycemia in diabetic patients, and hyperglycemia in non-diabetic patients. Primary outcomes were 28-day and in-hospital mortalities. Multinomial logistic regression and multivariable Cox regression models were used to determine the risk factors for hyperglycemia and mortality, respectively.

Results: Hyperglycemia was documented in 65.6% of patients: diabetic patients (44.8%) and new-onset hyperglycemia (20.8%). In-hospital and 28-day mortality rates were 30.2% and 26.1%, respectively. Respiratory failure, corticosteroid therapy, and a higher level of procalcitonin were risk factors for hyperglycemia in diabetic patients, whereas cardiovascular diseases, respiratory failure, and higher aspartate aminotransferase/glutamate aminotransferase ratio were risk factors for hyperglycemia in non-diabetic patients. The risk of the 28-day mortality rate was highest in the new-onset hyperglycemia (hazard ratio [HR] 3.535, 95% confidence interval [CI] 1.338-9.338, p=0.011), which was higher than hyperglycemia in type 2 diabetes mellitus patients (HR 1.408, 95% CI 0.513-3.862, p=0.506).

Conclusion: Hyperglycemia was common in COVID-19 patients in the intensive care unit. Hyperglycemia reflected the disease severity but was also secondary to therapeutic intervention. New-onset hyperglycemia was associated with poorer outcomes than that in diabetic patients.

Since December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has spread rapidly across the globe and claimed more than 511 million infected cases and 6.22 million deaths, making it one of the deadliest plagues in the human history [1].

Chronic comorbidities are common in severe and critical COVID-19 patients [2]. Type 2 diabetes mellitus (T2DM) was the second most frequent chronic disease in COVID-19 patients and was a risk factor for severity and mortality in this group of patients [3-5]. Compared to baseline hemoglobin A1C (HbA1C), acute hyperglycemia in diabetes is a stronger predictor of death in critically ill diabetic patients admitted to intensive care units (ICU) [6,7]. Hyperglycemiais also common among non-diabetic patients in ICU, and the more severe hyperglycemia is, the higher the risk of respiratory failure, infection, and mortality [8,9]. Interestingly, acute hyperglycemia is more harmful to non-diabetic patients than to diabetic patients in the ICU [10]. It was demonstrated that diabetes and hyperglycemia were the risk factors for a poorer prognosis of the previous severe acute respiratory syndrome (SARS) disease [11]. Early in the COVID-19 pandemic, it was recognized that pre-existing T2DM, newly diagnosed T2DM, and new-onset hyperglycemia were associated with a worse prognosis in COVID-19 patients [12,13].

Hyperglycemia in critically ill patients is caused by multiple pathological conditions, including inflammatory reactions. Cytokine storm plays a central role in the pathology of COVID-19 [14]. It is not surprising that hyperglycemia occurs in COVID-19 patients [5]. However, different from other critical diseases and also different from those in the early phase of the COVID-pandemic, the wide adoption of corticosteroid therapy (CST), especially mini pulse CST in the second half of the year 2021, might affect the epidemiology and the outcome of acute hyperglycemia in COVID-19 patients. This study was performed in this clinical context to investigate the incidence, the possible risk factors of hyperglycemia, and the effect of hyperglycemia in 28-day in-hospital mortality in COVID-19 patients.

This retrospective observational study recruited all COVID-19 patients, confirmed with positive reverse transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2, admitted to our COVID-19 intensive care center from August 2nd to October 15th, 2021. The diagnosis of COVID-19 was based on a positive RT-PCR for SARS-CoV-2 following the World Health Organization (WHO) interim guidance [15]. We excluded patients with type 1 diabetes mellitus, pregnancy, patients under 18 years of age, patients in whom a presence of a T2DM was not confirmed or excluded with certainty, and patients whose electronic medical record was not well documented. This center, a part of a field hospital, was a tertiary referral center established during the peak of the COVID-19 pandemic that ravaged the city of 10 million inhabitants.

The relevant data were extracted from the electronic medical records. Data of each patient were collected by two investigators. Any collected information that required further clarification was reviewed by the most senior investigatorsor by the whole team.

We mostly used capillary blood glucose (BG) values in clinical practice. The venous BG from the central laboratory was issued on a daily checkup or on demand, usually in combination with other hematological, biochemical, and immunological tests. Should the venous BG report an abnormal result, we used the capillary BG for closer monitoring. This study used the capillary BG values for analysis. All patients had their capillary BG values measured on admission to the ICU.

Hyperglycemia was defined when a patient had at least two random BG values >180 mg/dL in 24 hours. Based on the BG level during the hospital stay and history of T2DM, we divided the patients into three groups as follows:

Non-hyperglycemia: non-diabetic or T2DM patients who did not meet the criteria of hyperglycemia during their hospital stay;

Hyperglycemia with T2DM: at least two random BG values >180 mg/dL in 24 hours and the patient had been previously diagnosed with T2DM; and

New-onset hyperglycemia: at least two random BG values >180 mg/dL in 24 hours and the T2DM was excluded.

The primary outcome was 28-day and in-hospital mortality in the three groups mentioned above.

The statistical analysis was performed using the SPSS Statistics, version 28.0.1.0 (IBM, Armonk, NY, USA). Data are expressed as frequency (percentage) for categorical variables; mean standard deviation, and median (interquartile range) for continuous parameters. The one-way analysis of variance (ANOVA) was used to compare three groups. In univariate analysis, the chi-square test was used for categorical parameters and the Wilcoxon rank-sum test for continuous parameters to compare the survival and death groups. The variables that had significance in the univariate analysis were included in multivariate Cox (proportional-hazards) regression to identify the independent risk factors of in-hospital mortality. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant.

The present study was approved by the Institutional Review Board of University Medical Center, Ho Chi Minh City, Vietnam (approval number: 08022022/HDDD-BVDHYD). The informed consent for participation was obtained from the patients or their family members. All methods were performed in accordance with the Declaration of Helsinki.

After screening, 517 patients who met the inclusion criteria were recruited for the study. The demographic and clinical characteristics of the three groups on admission are presented in Table 1. The incidence of hyperglycemia was 65.6%. The non-hyperglycemia group was younger than the two groups with hyperglycemia. The two groups with hyperglycemia were comparable in age and body mass index (BMI), but the T2DM hyperglycemia group had a significantly higher percentage of female patients (62.9% vs. 52.3%). Comorbidities were common in three groups, with arterial hypertension more dominant in the T2DM hyperglycemia group. On admission, clinical parameters were not significantly different between the two groups with hyperglycemia. The respiratory failure in the non-hyperglycemia was less severe than in the rest. In the non-hyperglycemia group, there were 24 diabetic patients (13.5%). The new-onset hyperglycemia incidence in non-diabetic patients was 41.0% (107/261 patients).

As well recognized as a factor inducing hyperglycemia, CST was also investigated in the current study. Briefly, the new-onset hyperglycemia group seemed to receive slightly higher doses of corticosteroids than the two other groups.

The laboratory results on the admission of the three groups of patients are presented in Table 2. Both T2DM and new-onset hyperglycemia groups demonstrated higher levels of inflammatory markers, namely white blood cell counts (WBC), C-reactive protein (CRP), D-dimer, and interleukin-6, except for the plasma fibrinogen concentration. The procalcitonin concentrations of the three groups were statistically different in absolute values as well as in terms of the proportions of patients with procalcitonin levels equal to or above 0.5 ng/mL. The renal function of T2DM hyperglycemia and new-onset hyperglycemia groups was more depressed than the non-hyperglycemia group. Liver function tests in three groups showed a mild degree of hepatic damage with a higher aspartate aminotransferase/glutamate aminotransferase (AST/ALT) ratio in the new-onset hyperglycemia group.

We further investigated the independent risk factors of T2DM hyperglycemia and new-onset hyperglycemia. The analysis results are presented in Table 3. The invasive mechanical ventilation on admission, procalcitonin level of more than 0.5 ng/mL, and CST were the risk factors for hyperglycemia in T2DM patients. In non-diabetic patients, invasive mechanical ventilation on admission was still an important independent risk factor for hyperglycemia. In addition, the presence of cardiovascular diseases and the high AST/ALT ratio were also the risk factors for hyperglycemia in non-diabetic COVID-19 patients.

Out of 517 patients enrolled in the study, 156 (30.2%) patients died in the hospital. The 28-day mortality was 3.9% (7/178), 34.5% (80/232), and 44.9% (48/107) in the non-hyperglycemia, T2DM hyperglycemia, and new-onset hyperglycemia group, respectively.

The hazard ratio (HR) for death with adjustment for other risk factors was significantly increased among patients with hyperglycemia with or without T2DM compared to those of the non-hyperglycemia group (Figure 1).

We also noticed that the percentage of patients requiring invasive mechanical ventilation was significantly higher (70%) in the new-onset hyperglycemia groupthan in the T2DM hyperglycemia group (51%) and in patients without hyperglycemia (9%).

We also performed a multivariable Cox regression model to identify the risk factors of 28-day in-hospital mortality. The results are presented in Table 4.Out of 13 parameters investigated, higher age, increased mean glucose level, D-dimer, new-onset hyperglycemia, PaO2/FiO2 ratio on admission, PCT 0.5 ng/mL, and average daily corticosteroid dose were independently associated with increased risk of mortality. Interestingly, the new-onset hyperglycemia was associated with an important increase in in-hospital mortality (HR 3.535, 95% confidence interval [CI] 1.338-9.338, p = 0.011).

It is well established that diabetes significantly increases the risk of developing and dying from infectious diseases [16,17]. Hyperglycemia is a common manifestation directly correlated with increased mortality or morbidity in critically ill patients [18-20]. Early in the COVID-19 pandemic, the first studies found that diabetes was one of the most common comorbidities in COVID-19 patients [4,21]. In COVID-19 patients, hyperglycemia may be further accentuated due to the intense cytokine storm [22]. Little evidence existed on whether hyperglycemia in COVID-19 patients bears any significant prognostic implication. The current study investigated the incidence of hyperglycemia, the risk factors of hyperglycemia in diabetic and non-diabetic COVID-19 patients, and the impact of hyperglycemia on mortality and morbidity.

The incidence of hyperglycemia was noticeably high (65.6%) in the studied population. Hyperglycemia-associated mortality in critically ill patients and the beneficial effects of glycemic control have been intensively studied since the breakthrough work by Van den Berghe et al. [8]. These studies have shed light on the mechanism of hyperglycemia and its harmful consequence in this group of patients. The mechanisms of hyperglycemia in COVID-19 patients are likely multifactorial and were discussed in depth elsewhere [22]. The incidence of hyperglycemia in our study was higher than that in a study by Bode et al., including COVID-19 patients from 88 hospitals in the United States, where hyperglycemia was documented in 40% of patients [23]. Several possible reasons for the higher incidence of hyperglycemia in the current study should be mentioned. Our study was conducted in a field hospital built when the COVID-19 pandemic reached its peak, causing overwhelming in the healthcare facility and human resources. These factors might reduce the required adhesion to protocolized management, including glycemic control in critically ill patients. Furthermore, the current study was performed after nearly two months of strict travel restrictions and city lockdown. The consequent lifestyle change, reduced physical activity, poorly controlled diets, and difficulty in medical access, especially the diabetic medications, might partially explain the high incidence of hyperglycemia [22].

The new-onset hyperglycemia was documented in 20.8% of all patients and 41.0% of non-diabetic COVID-19 patients. This incidence was lower than that (28.4%) in the retrospective study by Li et al. [13]. More importantly, the widespread CST therapy, especially the mini-pulse dose, was obviously responsible for hyperglycemia, as shown in Table 3. Previous studies showed that 53-70% of non-diabetic individuals developed steroid-induced hyperglycemia after being treated with high-dose corticosteroids [24,25].

The 28-day and the in-hospital mortality rates were 26.1% and 30.2%, respectively. Our center was the tertiary referral hospital receiving the most severe COVID-19 patients during the zenith of the pandemic. This might explain the higher mortality rate compared to previous studies from China and United States. Besides a higher 28-day mortality, the new-onset hyperglycemia was also related to an increased need for mechanical ventilation during the hospitalization (70%) compared to the T2DM hyperglycemia group (51%). The current study confirmed once again the findings in previous works [3,26]: the new-onset hyperglycemia was associated with poorer outcomes in COVID-19 patients. Compared to the non-hyperglycemia group, the risk of the 28-day mortality rate was highest in the new-onset hyperglycemia (HR 3.535, 95% CI 1.338-9.338, p = 0.011), which was higher than T2DM hyperglycemia (HR 1.408, 95% CI 0.513-3.862, p = 0.506). The clinical and laboratory findings in T2DM hyperglycemia and new-onset hyperglycemia in the current study indicated the more severe manifestations in these two groups compared to the rest. At the first glance, the severity in the two hyperglycemia groups was not significant. However, the levels of CRP (72.7 mg/L vs. 57.9 mg/L), D-dimer (1540 ng/mL vs. 1139 ng/mL), AST/ALT ratio (1.5 vs. 1.2), and the proportion of patients requiring invasive mechanical ventilation (30.8% vs. 23.3%) on admission were higher in the new-onset than in the T2DM hyperglycemia group. This difference in severity may explain why the new-onset hyperglycemia group had poorer outcomes than the T2DM hyperglycemia group. It has been demonstrated that the relationship between hyperglycemia and COVID-19 is a complex and bidirectional interaction: hormone dysregulations with insulin resistance and the intense cytokine storm in COVID-19 induce hyperglycemia. Hyperglycemia, in turn, adversely affects the host immune response [27], increases inflammatory cytokines [28], and facilitates SARS-CoV-2 replication [29]. More importantly, hyperglycemia worsened the progression of respiratory failure [26]. T2DM has been well proven as one of the most common chronic comorbidities and an important risk factor for poorer outcomes and higher mortality in COVID-19 patients. Our study reinforced the evidence in a new demographic, therapeutic, and socio-economic population. The important finding was that the new-onset hyperglycemia patients were associated with higher mortality and other secondary outcomes compared to T2DM patients who developed hyperglycemia during their hospitalization due to COVID-19. The new-onset hyperglycemia mirrored the severity of COVD-19 disease and adversely affected the clinical course of these patients. Therefore, new-onset hyperglycemia is a strong predictor of severity in COVID-19 patients.

Hyperglycemia was documented in approximately two-thirds of severe and critical COVID-19 patients admitted to ICU. Hyperglycemia in patients with T2DM was more frequently seen and two-foldhigher than the new-onset hyperglycemia. The new-onset hyperglycemia group had the highest 28-day and in-hospital mortality rates, followed by the T2DM hyperglycemia group. The lowest mortality rates were documented in the non-hyperglycemia group. Similarly, the percentage of patients requiring invasive mechanical ventilation was significantly higher in the new-onset hyperglycemia group than in the T2DM hyperglycemia group and in patients without hyperglycemia. CST was the strongest independent risk factor for hyperglycemia in T2DM patients, whereas respiratory failure, reflected by the proportion of patients requiring invasive mechanical ventilation upon admission, was the strongest independent risk factor of hyperglycemia in non-diabetic patients. New-onset hyperglycemia was the most important risk factor for death in COVID-19 patients. The present study suggests that BG levels should be actively monitored in severe and critical COVID-19 patients. The occurrence of hyperglycemia in these patients, especially those without a previous diagnosis of T2DM, should be considered a marker of severity and worse outcomes. More studies are required to elucidate the likely multifactorial mechanisms of new-onset hyperglycemia in COVID-19 and the bidirectional interaction between new-onset hyperglycemia and severity in COVID-19 disease. We also need a prospective study investigating the impact of glycemic control in this group of patients and other similar diseases.

Continued here:

Hyperglycemia in Severe and Critical COVID-19 Patients: Risk Factors and Outcomes - Cureus

Covering COVID-19 is a daily Poynter briefing of story ideas about the coronavirus and other timely topics for journalists, written by senior faculty Al Tompkins. Sign up here to have it delivered to your inbox every weekday morning.

The Walgreens drug store chain posts the results of the COVID-19 tests it oversees, and the picture is grim. More than a third of the tests are positive and some states have seen a spike of 25% in the last week. Click on the map, which is linked to an interactive for each state.

Screnshots, Walgreens COVID-19 Index

The answer is yes and no. In all of the 60 or so years that monkeypox has infected people, it has not been spread by sexual contact. But that is one way that it is spreading now. The World Health Organization last week advised men to consider reducing their sexual partners for the moment. The WHO says 98% of the monkeypox cases detected since May have been among gay, bisexual and other men who have sex with men.

But the WHOs warning also shows that monkeypox is not only transmitted through sexual contact, so to call it an STD might lead people to believe there is no danger if they are not having sex with an infected person. The WHO says among the ways the virus can be transmitted are:

In recent days you have heard about the vaccine being used to fight the spread of monkeypox being the same vaccine used to fight smallpox. That might be disconcerting considering that smallpox killed one in three people it infected. Smallpox was extremely infectious.

Monkeypox is different from smallpox. It is rarely deadly. But the smallpox vaccine protected people against a related orthopoxvirus infection that we call monkeypox. Four decades ago, most countries that had a smallpox problem stopped delivering smallpox vaccines as the infection was eradicated. That means that now, monkeypox has an opportunity to make a comeback, as viruses sometimes do.

Vox explains that there are smallpox vaccines in U.S. storage called ACAM2000 that are not likely to be used unless monkeypox presents a much larger problem:

Public health leaders are weighing significant trade-offs: While using the USs stockpiles of smallpox vaccines might seem like an easy fix to this scary situation, the decision is much thornier than it appears. ACAM2000s potentially concerning side effects, the complex way it has to be administered, and limits on who can safely receive the vaccine seriously complicate the risk-benefit calculation around using it.

Health officials arent likely to make ACAM2000 widely available unless something big about the monkeypox outbreak changes. Heres why.

ACAM2000s best feature right now is its availability: 100 million-odd doses of the vaccine are currently sitting on the shelves at the US Strategic National Stockpile, largely untouched.

But it comes with a long list of contingencies, among them its unwieldy administration.

The FDA explains the not very comforting way that smallpox vaccines are administered:

ACAM2000 cannot cause smallpox; it does not contain the smallpox virus, but rather the live vaccinia virus not dead virus like many other vaccines. For this reason, attentively caring for the vaccination site is important to prevent the virus from spreading from the vaccination site to other parts of the body, or to other people.

ACAM2000 is administered differently than the typical shot associated with most vaccinations. A two-pronged stainless steel (or bifurcated) needle is dipped into the vaccine solution and the skin is pricked several times in the upper arm with a droplet of the vaccine. The virus begins growing at the injection site causing a localized infection or pock to form.

A red, itchy sore spot at the site of the vaccination within 3-4 days is an indicator that the vaccination was successful; that is, there is a take. A blister develops at the vaccination site and then dries up forming a scab that falls off in the third week, leaving a small scar. The vaccine stimulates a persons immune system to develop antibodies and cells in the blood and elsewhere that can then help the body fight off a real smallpox infection if exposure to smallpox ever occurs.

ACAM2000 is also not safe for immunocompromised people. And sometimes the vaccination does not take on the first try.

You might ask why the U.S. government keeps 100 million doses of vaccine on hand for a virus that was eradicated decades ago. The answer, the FDA says, is as a defense against smallpox being used as a biological weapon.

The Wall Street Journal zeroed in on the way Americans are shopping for bargains:

One way they are doing so is by relying more ondollar and discount stores for groceries. Average spending on grocery products at discount chains increased 71% from October 2021 to June 2022, according to analytics firm InMarket. Over that time period, spending on the same items in grocery stores decreased by 5%. Many large consumer brandsincluding WalmartandUnileverattest that theirprices arent going down anytime soon.

Other households are buying in bulk or making do without items they never used to think twice about spending money on. Sams Club membership income was up 10.5% year-over-year, according to parent company Walmarts May earnings call.

Consumer-products giantProcter & Gamble Co. just posted its largest sales gain in 16 years. Still, the companyis predicting its slowest sales growthin years as consumers cut back on household staples like the companys Tide detergent and Pampers diapers.

The WSJ story profiled some families that are cutting back on how much shampoo their kids use, shutting off nightlights to save electricity, wearing less makeup and using some products past their expiration dates.

These are the kinds of stories that make me want to listen to my mother tell me how her family made it through the Great Depression. I bet we could learn a lot from hearing those stories today.

There may soon be a day when before you go in for your colonoscopy you will not be drinking a gallon of awful-tasting liquid, but instead you will take a handful of pills and drink water.

The New York Times tells us:

In what experts believe could end the dread that keeps many people from this important screening the Food and Drug Administration approved a regimen of pills, Sutab, that studies showworks just as well as the liquid solutions without the vile flavor. Its a 24-tablet regimen: 12 pills the day before and 12 the next day, several hours before the procedure.

Patients still must drink lots of water, a total of 48 ounces the first day and another 48 ounces the next day. But at least plain water is tasteless.

The thing that is great about Sutab is that it takes the issue of the taste away, says Douglas K. Rex, distinguished professor emeritus of medicine at the Indiana University School of Medicine.

Youre still going to have to sit on the toilet, but not having to drink something that tastes awful is a big advantage.

Besides skin cancers, colon cancer is the third most common kind of cancer in the U.S., with 106,180 new cases expected this year. Anything that cuts down on the excuses people have not to get tested is a good thing.

The New York Times observed:

Owning an ice cream truck used to be a lucrative proposition, but for some, the expenses have become untenable: The diesel that powers the trucks has topped $7 a gallon, vanilla ice cream costs $13 a gallon and a 25-pound box of sprinkles now goes for about $60, double what it cost a year ago.

Though no organization appears to have hard figures on just how many ice-cream trucks are currently working the streets of New York City, some owners said they would likely leave the business in the next few years. Its a sentiment that is felt nationwide, where mobile ice-cream vendors face higher costs for city permits and registration, and hefty competition from other ice cream businesses, said Steve Christensen, the executive director of theNorth American Ice Cream Association.

The ice cream truck, he said, is unfortunately becoming a thing of the past.

New delivery methods, through third-party apps or ghost kitchens, are proliferating. Brick-and-mortar scoop shops are focusing on offering a fun experience, he said, and serve dozens more flavors than a traditional ice cream truck can, driving lines away from these vehicles.

New York City store clerks say it is not uncommon to see four shoplifting episodes per evening shift in one store as people steal food and other supplies.

(Twitter)

Well be back tomorrow with a new edition of Covering COVID-19. Sign up here to get it delivered right to your inbox.

Al Tompkins is senior faculty at Poynter. He can be reached at atompkins@poynter.org or on Twitter, @atompkins.

Originally posted here:

More than a third of COVID-19 tests done at Walgreens are positive - Poynter

The Centers for Medicare & Medicaid Services (CMS) recently issued the Medicare Physician Fee Schedule (PFS) proposed rule for calendar year (CY) 2023,[1] which clarified the timeline for increased COVID-19 vaccine administration fees and coverage of monoclonal antibody (mAb) products for the remainder of the public health emergency (PHE) and into the future.

The CY 2023 PFS proposed rule will become effective in 2023, and the deadline for public comment is September 6, 2022.

More broadly, CMS has reiterated that its goal for CY 2023 includes program expansions that create a more equitable health care system by providing better accessibility, quality, affordability, and innovation.[2] CMSs CY 2023 PFS proposed rule highlights the federal agencys goals to promote broad and timely access to both COVID-19 vaccines and mAb products.[3]

This Insight highlights four Medicare coverage and payment changes related to COVID-19 vaccines and mAb products in the CY 2023 PFS proposed rule and provides key takeaways for the commercialization of preventive vaccines and mAb products in the near future.

Under the Coronavirus Aid, Relief, and Economic Security (CARES) Act, COVID-19 was added to the vaccine coverage benefit under Part B of the Medicare program.[4] In its CY 2022 PFS final rule,[5] CMS established payments under Part B for COVID-19 vaccine administration at $40 per dose while existing preventive vaccines, such as pneumococcal, influenza, and Hepatitis B (HBV), remained at a payment rate of $30.[6]

CMS continues its efforts to establish payment for vaccine administration for the long-term in the CY 2023 PFS proposed rule. Through 2021, vaccine administration payment rates for pneumococcal, influenza, or HBV vaccines were established using a crosswalk for similar services paid under the PFS. In its CY 2022 PFS final rule, CMS responded to commenters concerns that the codes were improperly linked and did not reflect the unique costs of administering vaccines. Therefore, CMS finalized administration fees for non-COVID-19 vaccines (pneumococcal, influenza, and HBV) at $30, while establishing a $40 administration fee for COVID-19 vaccines.

CMS now proposes to update the payment for Part B vaccine administration by making adjustments to reflect cost differences for the geographic locality.[7]

CMS also proposes to continue the $40 administration fee for COVID-19. The agency anticipates vaccinating providers will continue to experience rising costs associated with staffing, scheduling, and reporting requirements as the number of patients increases, especially as boosters remain an important tool in the COVID-19 response. While CMS previously intended to maintain increased payment for COVID-19 vaccine administration through the end of the PHE, it has determined this transition will occur on January 1 in the year following the termination of the March 27, 2020, Emergency Use Authorization Declaration (EUA Declaration). The payment rate for COVID-19 vaccine administration will then be set at a rate that aligns with other Part B preventive vaccine administration payment rates, which are currently at $30.[8]

In June 2021, CMS announced an add-on payment with a national rate of $35 for COVID-19 vaccine in-home (at-home) administration, bringing the national average for at-home COVID-19 vaccine administration payments to $75 per dose ($40 for the COVID-19 vaccine administration and an additional $35 for administration in the home). By August 2021, CMS had expanded the circumstance for when the add-on payment was available, allowing providers and suppliers who administered the COVID-19 vaccine at a patients home to bill Medicare. These policies were established to ensure beneficiaries received access to COVID-19 vaccines during the PHE.

After hearing multiple requests from commenters to extend the add-on payment past the PHE, CMS acknowledged in the CY 2022 PFS final rule that the costs of at-home COVID-19 vaccine administration would not diminish immediately after the PHE, thereby stating that it would allow the $35 add-on payment to continue until the end of the calendar year of the PHE. CMSs CY 2023 PFS proposed rule suggests the continuation of the additional $35 payment for at-home vaccination beyond the PHE, which allows the agency to maintain expanded COVID-19 vaccine access for vulnerable housebound beneficiaries. The CY 2023 PFS proposed rule also underscores the agencys need to better understand COVID-19 vaccine inaccessibility barriers in the Medicare population.[9]

While the CY 2023 PFS proposed rule provides a continuation of the additional $35 payment for at-home COVID-19 vaccine administration, it does not include other preventive vaccines. CMS requests comments related to the inclusion of other Part B preventive vaccines (such as pneumococcal, influenza, and HBV).

Once COVID-19 mAb products for treatment and post-exposure prophylaxis were granted EUAs, CMSs CY 2022 PFS final rule finalized coverage and payment for COVID-19 mAb products under the Part B vaccine benefit. Notably, this determination absolved beneficiaries of cost-sharing responsibility for both the mAb product and its administration.

The payment for administration of mAb products for treatment or post-exposure prophylaxis under Part B ranges between $150.50 and $750.00. CMS intends to continue coverage under the Part B vaccine benefit at these reimbursement levels until the EUA Declaration is terminated. In the year following termination of the EUA Declaration, CMS intends to then transition coverage of these products to ordinary system coverage for complex biological products under Part B.

Following the CY 2022 PFS final rule, a mAb product was granted an EUA for use as pre-exposure prophylaxis prevention of COVID-19. Although the CMS policies regarding coverage of COVID-19 mAb products did not address mAb products used for prevention of COVID-19, the agency covered and paid for them without subjecting patients to out-of-pocket costs.

CMS proposes to continue coverage of preventive mAb products under the Part B vaccine benefit beyond the termination of the EUA Declaration, so long as a product has market authorization. CMS also proposes to maintain the current payment amounts for administration for pre-exposure prophylaxis mAb products under Part B of either $150.50 or $250.50, depending on a products administration setting.

While the Biden administration has yet to detail plans for a full market transition for COVID-19 vaccines and therapeutics,[10] CMSs articulation of timelines for coverage of vaccines and monoclonals provides a degree of future payment clarity for these products under Medicare.

CMSs decision to cover mAb products the same as vaccines under the Part B benefit is potentially precedent setting as future prophylactic monoclonals are licensed.[11] The agencys decision to shift coverage of mAb products for treatment to ordinary biological product coverage under Part B means that patients will begin to bear out-of-pocket costs for these products once the EUA Declaration is terminated.

CMSs continued consideration of its methods for setting vaccine administration fees and intent to extend at-home administration add-ons provides opportunities for commenters to encourage policies that promote better access to vaccines for Medicare beneficiaries.

Nija Chappel, a Summer Associate (not admitted to the practice of law) in the firms Washington, DC, office, contributed to the preparation of this Insight.

[1] CMS Proposed Rule, CY 2023 Payment Policies under the Physician Fee Schedule and Other Changes to Part B Payment Policies, 87 Fed. Reg. 45860 (July 29, 2022), available at https://www.govinfo.gov/content/pkg/FR-2022-07-29/pdf/2022-14562.pdf (hereinafter CMS CY 2023 PFS Proposed Rule).

[2] CMS Fact Sheet, Calendar Year (CY) 2023 Medicare Physician Fee Schedule Proposed Rule (July 7, 2022), available at https://www.cms.gov/newsroom/fact-sheets/calendar-year-cy-2023-medicare-physician-fee-schedule-proposed-rule.

[3] CMS CY 2023 PFS Proposed Rule, supra note 1, at 46225-46226.

[4] Id. at 46218.

[5] CMS Final Rule, Medicare Program; CY 2022 Payment Policies Under the Physician Fee Schedule and Other Changes to Part B Payment Policies (Nov. 19, 2021), available at https://www.federalregister.gov/documents/2021/11/19/2021-23972/medicare-program-cy-2022-payment-policies-under-the-physician-fee-schedule-and-other-changes-to-part.

[6] CMS CY 2023 PFS Proposed Rule, supra note 1, at 46219.

[7] Id. at 46222.

[8] Id.; CMS uses this example: [I]f the COVID-19 PHE ends in CY 2022, the payment amount for COVID-19 vaccine administration would change from $40 to $30 effective January 1, 2023, and would apply the proposed geographic adjustments and the proposed annual update as proposed for the other preventive vaccine administration services . . . .

[9] Inaccessibility barriers include patients having a condition due to illness or injury that limits their ability to leave home without a device or help from a caregiver, a condition that makes the patient more likely to contract COVID-19, or the patient is generally unable to leave the home and if they do, they consider it a considerable and taxing effort. CMS CY 2023 PFS Proposed Rule, supra note 1, at 46223.

[10] See Richard Hughes, As Congress, Biden administration squabble over COVID-19 funds, an ongoing pandemic response posture strains public health, HealthCareDive (July 14, 2022), available at https://www.healthcaredive.com/news/biden-administration-covid-19-funds-oped/627105/.

[11] Sara Rosenbaum, A Twenty-First Century Vaccines for Children Program, Health Affairs (July 12, 2022), available at https://www.healthaffairs.org/content/forefront/twenty-first-century-vaccines-children-program.

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COVID-19 Vaccine and Monoclonal Coverage: A Focus of the 2023 Medicare Physician Fee Schedule Proposed Rule - JD Supra

CHEROKEE COUNTY, S.C. (WBTW) Both urban and rural counties in South Carolina top the list for the areas with the highest rate of new COVID-19 cases, according to information updated Monday by the Centers for Disease Control and Prevention.

At the top of the list is Cherokee County, with an average of 534.03 new cases, per 100,000 people, within the last week. Following is Richland County, at 525.3 per 100,000 people.

There is an extremely large divide between the areas with the highest and lowest rates, according to the data, with the top county having more than twice the rate of the region with the least.

In South Carolina, five counties are considered to have a medium spread rate, and four are in the low range, according to the most recent data from the CDC. Every county within News13s coverage area Darlington, Dillon, Florence, Horry, Marion and Marlboro is considered to have a high case rate.

Under CDC guidance, people who live in areas rated with a medium level and who are considered at-risk are urged to wear a mask. In areas with high levels,masking is recommendedfor all people regardless of vaccination status in schools and workplaces.

CDC definitionsfor the three categories are based on new case counts within the last week, new COVID-19 hospitalizations and how many hospital beds are currently occupied by those with the virus.

The top five counties with the lowest COVID-19 case rates, per 100,000 people, are:

42. Oconee 204.91

43. McCormick 179.65

44. Greenwood 117.94

45. Abbeville 171.24

46. Saluda 117.23

The top five counties with the highest COVID-19 case rates, per 100,000 people, are:

5. Lexington 480.71

4. Chester 480.36

3. Kershaw 494.36

2. Richland 525.3

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What South Carolina counties have the highest COVID-19 case rates within the last week? - WSPA 7News

The study was conducted as a systematic review of published literature followed by a data synthesis6,7. For this purpose, searches were carried out for scientific publications (scientifically reviewed before publication), preprints (i.e. articles of a scientific nature that are published openly without prior review) and the gray literature (i.e. reports and documents published by organizations and authorities). The study protocol is registered in the database for structured literature syntheses and meta-analyzes PROSPERO (International prospective register of systematic reviews) no. CRD42021229514 (see Supplement S1).

The literature searches were based on the search triangle model6. Systematic searches were conducted between 22 January 2021 and 29 January 2021 of databases (PubMed, Cochrane Library, Embase, Love platform/Epistemikos), containing peer-reviewed scientific publications and systematic reviews in areas relevant to the review issue, exploratory searches were performed in preprint archives, while look-up searches were performed in the gray literature. The literature searches were reported according to the PRISMA-S protocol (see Supplement S2).

The systematic search (keywords: prediction, nowcast, forecast, simulation model, model, modeling, estimation, scenario, surveillance, Epidemiology, COVID-19, SARS-cov-2, swed*) of the collegially assessed scientific literature had the goal to identify all relevant publications (within the criteria of the study) in a transparent and reproducible manner.

The explorative searches in the preprint archives were initiated by asking a preliminary question via a tool specifically designed for searches in these archives (search.biopreprint) and then reviewing the recovered records. Thereafter, the searches were repeated iteratively until adjustments no longer led to significant changes in the set of identified preprints. A separate supplementary search was performed against the two largest preprint databases bioRxiv (which also includes preprints from medRxiv) and arXiv. Finally, a search (directed search) of the gray literature was performed. The searchalso called search for known documentswas carried out with the aim of obtaining documents from the websites of relevant Swedish and international authorities active in the area: PHAS, the National Board of Health and Welfare, the Swedish Civil Contingencies Agency and the European Center for Disease Prevention and Control (ECDC). Local and regionally produced forecast data in different healthcare regions are not included in this report. These are regarded as internal working material since they are not published and not publicly available.

scientific articles that report epidemiological results regarding actual or scenario-based predictions of morbidity, mortality, or healthcare burden caused by COVID-19 in Sweden or parts of Sweden in 2020.

reports of COVID-19 modelling published by the PHAS.

non-original analyzes (e.g. reviews, perspective articles, editorials, recommendations and guidelines).

duplicate studies.

in silico studies (pure simulations without comparison with data).

descriptive epidemiological publications (e.g. description of case incidences and geographical distributions).

models that only examine the effect of interventions (rather than predicting risk or disease burden).

articles or reports that present new mathematical models or software tools, unless an explicit central purpose of the study is to predict COVID-19 phenomena.

articles or reports from which predictions could not be extracted as a time series.

articles or reports that present predictions that are adjacent to or fall completely outside of 2020.

The systematic searches in the peer-reviewed scientific literature, the exploratory searches of preprint archives and the look-up searches in the gray literature resulted in document material being examined prior to data extraction. In this inclusion-confirming step, titles and summaries of the documents obtained were reviewed against the study criteria (inclusion/exclusion) by two independent reviewers. Documents that both reviewers considered to be included were included and those that both excluded were excluded from further analysis. In case of disagreement, the articles were downloaded in full text and a new assessment was made. If the disagreement persisted, this was resolved through discussions between the reviewers and, if necessary, with the research group. For data extraction from the final set of documents, a tool for retrieving data from each article in full text was developed. The tool included data on the authors' country of origin, study design, forecast methodology (type of model), study population, data sources, forecast period, forecast results, measures of prediction accuracy/performance (if applicable) and model documentation. One reviewer initially extracted data from each included article and then two other reviewers checked the data obtained. The data extracted from the articles were documented in a spreadsheet.

All models were assessed for systematic sources of error (bias). In articles that addressed several models, each model was assessed separately. For the assessment, a form, ROBOT (Risk of Bias Opinion Tool), was developed, based on previous guidelines for evaluations of forecast studies8,22. In summary, the following topics were examined at model level: relevance and quality of data, time frame for prediction, assumptions, and model development methods (verification and validation). The assessment of assumptions included reproduction rates, latency period, incubation period, serial interval, infectious period, population immunity, and impact of interventions during the prediction period. Model validation was classified as one out of three: retrospective/internal validation, external validation, or no validation.

The assessment of systematic sources of error was performed by two independent assessors, where another assessor assisted in case of disagreement. Each sub-aspect was given a score rating in an assessment form, ROBOT, (see Supplement S3). The partial assessments were added up to a total score for each model. To qualify for further result synthesis, a total score below a heuristically defined limit value was required (ROBOT<4). Given the impact of predictions made by PHAS these were included in the result synthesis even if they failed the ROBOT cut-off.

A secondary validation of model performance was made, where reported predictions were compared with factual outcome data. The data on the forecasting variables were retrieved from published figures using WebPlotDigitizer (v. 4.4, https://apps.automeris.io/wpd/). The models in the final set addressed the total incidence of COVID-19 cases, ICU-occupancy, and incidence of COVID-19 deaths. A simultaneous evaluation of prediction accuracy that included all models was not feasible due to differences in study populations, modeled outcome, and time period. The secondary validation was therefore broken down into subgroups based on the reported outcome variables. Data on the actual outcomes on deaths and ICU-occupancy were obtained from PHAS. Regarding the total case incidence, no source for reliable outcome data was available due to the variable testing strategy employed in Sweden during 2020. When possible, the model performance was quantified by measuring the Mean Absolute Percentage Error (MAPE) between model predictions and the outcome for the entire time period covered by each separate model. We classified the performance according to the following scheme: 0%MAPE10%excellent, 10%30%poor. Based on experiences from public health practitioners during the pandemic, as well as the fact that Sweden already before the pandemic lacked healthcare resources (for instance, at average 103 patients share 100 available hospital beds9), these limits was considered reasonable. The dates when the predictions were made (models finally calibrated) were retrieved from the articles. We acknowledge that measures have been developed that avoid some of the drawbacks of MAPE (e.g. divergence for outcomes close to zero)23, but for clarity and interpretability we opted for MAPE. To determine if difference in prediction errors had statistical significance, we employed the Diedbold-Mariano test. This test requires that the predictions are made for the exact same time period, and we therefore applied the test to the intersection of all prediction dates.

Not all predictions of the total number of cases did include entire Sweden, but all included the Stockholm region. The evaluation was therefore restricted to forecasting the pandemic development in this region (population 2.3 million). In order to be able to compare predictions of the total incidence of COVID-19 cases from PHAS, we had to adjust the predictions from PHAS, which are in terms of the number of reported cases. In the reports from PHAS (e.g. 35 in Table 1), the proportion of unconfirmed cases was estimated to be 98.7%, which made it possible to rescale the predictions of reported cases by dividing those predictions by (10.987), and thus obtaining the total number of cases.

All predictions of ICU-occupancy did not include the entire country but did include the Stockholm region. Also, this evaluation was therefore restricted to the Stockholm region. While acknowledging that assumptions regarding epidemiological homogeneity introduce uncertainty, we multiplied the predictions by the proportion of the total Swedish population that lived in the Stockholm region to allow comparisons with the entire country.

We compared predictions of the number of deaths in COVID-19 during the spring of 2020. In relation to this, we also analysed how much historical data was used to calibrate the models in relation to the length of the prediction by calculating the ratio of the number of days of data used in the calibration and the length of the prediction (in days).

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Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation...