Category: Corona Virus Vaccine

This retrospective study was approved by the institutional review boards of eight hospitals (Kobe University Hospital, St. Luke's International Hospital, Nishinomiya Watanabe Hospital, Kobe City Medical Center General Hospital, Kobe City Nishi-Kobe Medical Center, Hyogo Prefectural Kakogawa Medical Center, Kita Harima Medical Center, and Hyogo Prefectural Awaji Medical Center); the requirement for acquiring informed consent was waived by the institutional review boards of these eight hospitals owing to the retrospective nature of the study. This study complied with the Declaration of Helsinki and Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan (https://www.mhlw.go.jp/file/06-Seisakujouhou-10600000-Daijinkanboukouseikagakuka/0000080278.pdf).

The CXR datasets used for developing and evaluating our DL model contain CXRs for the following three categories: normal CXR (NORMAL), non-COVID-19 pneumonia CXR (PNEUMONIA), and COVID-19 pneumonia CXR (COVID). Our DL model was developed using two public (COVIDx and COVIDBIMCV) and one private (COVIDprivate) datasets. One public dataset (COVIDx) was built to accelerate the development of highly accurate and practical deep learning model for detecting COVID-19 cases (https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md)15. The other public dataset (COVIDBIMCV) was constructed from two public datasets: the PadChest dataset (https://github.com/auriml/Rx-thorax-automatic-captioning)16 and BIMCV-COVID19+dataset (https://github.com/BIMCV-CSUSP/BIMCV-COVID-19)17. COVIDprivate was based on the dataset collected from six hospitals previously, and the two public datasets (COVIDx and COVIDBIMCV) were the same as those in previous studies18,19. The details of these datasets are described in the Supplementary material. Compared with the previous study, CXRs were added for COVIDprivate in the current study. The additional CXRs included 37, 7, and 31 cases of NORMAL, PNEUMONIA, and COVID, respectively. COVIDprivate contained 530 CXRs (176 NORMAL, 146 PNEUMONIA, and 208 COVID).

In addition to COVIDprivate, CXRs were collected from two other medical institutions. In total, 168 CXRs (80 NORMAL, 37 PNEUMONIA, and 51 COVID) collected from one medical institution (Hospital A) were used for the internal validation of the DL model (as a part of validation set) and for radiologists reading practice conducted before the observer study. Moreover, as unseen test set, 180 CXR cases (60 NORMAL, 60 PNEUMONIA, and 60 COVID) collected from another medical institution (Hospital B) were used for the external validation of the DL model and observer study of radiologists.

In the Hospital B, COVID was limited to those diagnosed with COVID-19 pneumonia using RT-PCR, and CXR was obtained after symptom onset. The time of COVID-19 diagnosis was between January 24, 2020, and May 5, 2020. PNEUMONIA was defined as patients clinically diagnosed with bacterial pneumonia that improved with appropriate treatment. Patients who showed no pneumonia on CT or had lung metastasis of malignancy and acute exacerbation of interstitial pneumonia were excluded from PNEUMONIA. NORMAL was defined as the absence of abnormalities in the lung, mediastinum, thoracic cavity, or chest wall on CXR and CT. NORMAL and PNEUMONIA were limited to cases before the summer of 2019 (before the COVID-19 pandemic). The details of the unseen test set collected from the Hospital B are described in the Supplementary material. The inclusion criteria of CXRs in the COVIDprivate and the Hospital A were the same as the previous study19.

Table 1 lists the details of each CXR dataset. The 180 cases (as the unseen test set) used for the external validation and reading sessions were adults aged 20years or older. In the 180 cases, NORMAL included 39 men and 21 women aged 58.127.9years. PNEUMONIA included 43 men and 17 women aged 76.220.8years. The COVID group included 46 men and 14 women aged 53.438.6years.

Our EfficientNet-based DL model was constructed in the same manner as described in previous papers18,19. Figure1 shows a schematic of the construction of the DL model. There are two major differences in the DL model construction between the present study and previous studies; one is that the 168 CXRs collected from Hospital A were used for internal validation as a part of the validation set, and the other is that the 180 CXRs collected from Hospital B were used for external validation as the unseen test set. The DL model development set included two public datasets, COVIDprivate, and 168 CXRs collected from Hospital A. Five different random divisions of the training and validation sets were created from the development set. In the division, 300, 300, and 90 images were randomly selected as the validation set from COVIDx, COVIDBIMCV, and COVIDprivate, respectively. The remaining images of COVIDx, COVIDBIMCV, and COVIDprivate were used as the train set. In addition, all the 168 CXRs collected from Hospital A were used for the validation set. Model training and internal validation of diagnostic performance were performed for the training set and validation set, respectively. The training of our DL model is also described in the Supplementary material.

Schematic illustration of dataset splitting and model training for our DL model. Abbreviation: DL, deep learning; COVIDx, public dataset used for COVID-Net; COVIDBIMCV, public dataset obtained from the PadChest and BIMCV-COVID19+datasets; COVIDprivate, private dataset collected from six hospitals; Hospital A, dataset collected for internal validation and radiologists practice before the observer study; Hospital B, dataset collected for external validation.

The inference results of the DL model were calculated using an ensemble of five trained models. For the 180 CXRs of the external validation, an average of the probabilities obtained from the five trained models was calculated as the inference results of the DL model to evaluate the diagnostic performance of the DL model and to provide supporting information for radiologists during the observer study.

The DL model calculated the probability of NORMAL, PNEUMONIA, or COVID for each CXR, with a total of 100%. We also created images using Grad-CAM and Grad-CAM++as explainable artificial intelligence, which visualized the reasoning for the diagnosis of the DL model20,21. Grad-CAM and Grad-CAM++images were used for the observer study. Minmax normalization with a linear transformation was performed on the original Grad-CAM and Grad-CAM++images.

Eight radiologists (with 520years of experience in diagnostic radiology) performed the observer study at two medical facilities. For the 180 CXRs collected from Hospital B, each radiologist performed two reading sessions over a period of more than 1month. One reading session was performed with reference to CXRs only, and the other was performed with reference to both CXRs and the results of the DL model. The order of the two sessions was randomly selected to reduce bias. The eight radiologists scored the probabilities of NORMAL, PNEUMONIA, and COVID on a 100% scale. In the reading session with the DL model, the radiologists referred to the probabilities of NORMAL, PNEUMONIA, and COVID calculated using the DL model. If there was any uncertainty regarding the probabilities of the DL model, the results of Grad-CAM and Grad-CAM++were available. Images of the 168 CXRs collected from Hospital A were also processed with Grad-CAM and Grad-CAM++, and the diagnosis of the DL model and images of Grad-CAM and Grad-CAM++of the 168 CXRs were presented to the radiologists for practice sessions before each reading session. Eight radiologists were taught how to interpret the Grad-CAM and Grad-CAM++images before the observer study. There was no time limit for reading and practice sessions. Prior to the reading sessions, only the approximate frequencies of the three categories were presented to the radiologists and no other clinical information was provided. Our novelties in this study were to investigate whether radiologists changed their diagnosis by referring to our DL model of CXR and whether the diagnostic performance of radiologists was significantly improved.

After the observer study, one senior radiologist visually evaluated the 180 Grad-CAM++images in the test set. The visual evaluation of the Grad-CAM++images was performed on the images that were accurately diagnosed by the DL. The radiologist visually examined the CXR and Grad-CAM++images and determined whether the Grad-CAM++images were typical or understandable. The typical Grad-CAM++images were described in Supplementary material. If abnormal findings on CXR images were highlighted on Grad-CAM++images, the cases were considered understandable by the radiologist. In addition, for COVID, the radiologist counted the number of Grad-CAM++images with highlighted regions outside the lung area.

We evaluated the diagnostic performance of the DL model alone and compared the results between reading sessions with and without the DL model. The evaluation metrics were accuracy, sensitivity, specificity, and area under the curve (AUC) in the receiver operating characteristics. Because three-category classification was performed, these metrics were calculated class-wise (one-vs-rest), except for accuracy. For the AUC, multi-reader multi-case statistical analysis was used to statistically analyze the results of the eight radiologists. MRMCaov was used for the statistical analyses22. Although MRMCaov is a statistical method designed for binary classification of two categories, this study was designed to diagnose three categories: NORMAL, PNEUMONIA, and COVID. Therefore, the three-category classification was divided into three binary classifications (one-vs-rest): (1) NORMAL versus PNEUMONIA or COVID, (2) PNEUMONIA versus NORMAL or COVID, and (3) COVID versus NORMAL or PNEUMONIA. We then compared the class-wise AUC of the eight radiologists between reading sessions with and without the DL model. The difference in the AUC was statistically tested using MRMCaov. Because it was necessary to integrate the results from the eight radiologists, the class-wise MRMCaov was used in the present study. To control the family-wise error rate, Bonferroni correction was used; a p value less than 0.01666 was considered statistically significant. R (version 4.1.2) was used for the statistical analysis.

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Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in ... - Nature.com

Sen. Laphonza Butler (D-Calif.) has tested positive for COVID-19 and will be absent from Washington this week, she said Monday.

“After a busy 1st [week] on the job, I have tested positive for COVID-19 & am experiencing mild symptoms,” Butler said on X, the platform formerly known as Twitter. “Per CDC guidelines I will be isolating while the Senate is in session and working remotely.”

Senators are set to return later Monday from their weeklong recess for work on fiscal 2024 funding packages and supplemental proposals to give aid to Israel and Ukraine. 

California Gov. Gavin Newsom (D) appointed Butler to the seat earlier this month after Sen. Dianne Feinstein (D-Calif.) passed away Sept. 29. 

Butler took over the post after serving as the head of EMILY’s List, being a union organizer and a Democratic strategist.

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Laphonza Butler tests positive for COVID-19 - The Hill

Introduction

Severe acute respiratory syndrome type 2 coronavirus (SARS-CoV-2) has caused a serious global pandemic and brought major public health problems.1,2 Multi-national government has taken a variety of measures to fight against COVID-19, such as converting large public utility spaces into makeshift healthcare centers, increase in-hospital COVID-19 beds and ICU capacities and so on.3 The virus has undergone several mutations that will promote the evolution of the virus, which may affect the transmission and pathogenicity of the virus, as well as immune escape and resistance to therapeutic drugs.1

Since the Delta variant strain of SARS-CoV-2 was first detected in India in October 2020, it had been identified as the variant of concern (VOC) by the World Health Organization (WHO) and affected more than 80 countries.4 Since Delta VOC is more virulent, the patients infected have about twice the risk of hospital admission compared with those infected with Alpha VOC.5 The Delta VOC also shows a prolonged viral shedding, compared with the wild-type.6

SARS-CoV-2 is transmitted via respiratory droplets,7 close contact,8 and touching the contaminated object surface.9 Studies have shown that SARS-CoV-2 is widely distributed in the air and on object surfaces in the hospital.10,11 Positive specimens in the environment may appear on almost all the frequently touched surfaces in the isolation ward,12 included mobile phones,13 shelves in the toilet,13 bedside handrails,14 bedside tables, pillows, bed sheets, air exhaust outlets,15 and even the shoes.16 However, one study has indicated that the SARS-CoV-2 contamination on environmental surfaces in the hospital is limited, although it may persist for a longer time on surfaces under controlled laboratory conditions.17 Another study also indicates that the rate of environmental contamination by COVID-19 patient with prolonged viral carriage is low.18

Patients infected with Delta VOC have a longer course of disease. To the best of our knowledge, few studies have focused on the environmental contamination associated with Delta VOC-infected patients with the duration of more than two weeks. On 30 July, one official of Nanjing Center for Disease Control and Prevention reported the results of virus gene sequencing in 52 related cases in the outbreak of the COVID-19.19 The virus genome sequences were highly homologous, which indicates the same transmission chain. The early cases (Lukou international airport cleaners) have been confirmed to share the same RNA sequence as the Delta VOC, which was consistent with the genetic sequence of one imported patient on Flight CA910 from Russia on 10 July 2021.

Therefore, we detected the contamination of air, surfaces, and patients personalitems in the isolation wards among patients with Delta VOC infection with duration of more than two weeks in Nanjing. In addition, the study of environmental contamination of SARS-CoV-2 Delta VOC is of great significance to guide the prevention of COVID-19 infected by other mutants in the future, because there is no difference in there pollution to the environment.20 Moreover, hospital risk control needs to have a better understanding of different modes of transmission.

This study was conducted in four non-negative pressure general wards randomly selected from The Second Hospital of Nanjing. Biological, environmental samples from COVID-19 patients with duration of more than two weeks were collected (Figure 1). On August 21, 23, 27 and 30, 2021, the environmental samples were collected from four wards (Ward A, B, C, and D), where 144 COVID-19 cases were hospitalized in the 81 rooms, including 22 single-bed rooms, 55 two-bed rooms, and 4 three-bed rooms. General environmental sampling was performed in Ward A, B and C, including the patients rooms and public areas, while enhanced environmental sampling was conducted in Ward D, with additional sampling of air, personal items, masks (inside and outside) apart from the general sampling.

Figure 1 Flowchart of selecting study cases.

The patients epidemiological data were collected, including basic demographic information (such as age, gender, marital status, place of residence, education background), date of symptom onset, severity of disease, first symptoms (such as fever, dry cough, expectoration, fatigue, myalgia, diarrhea), and date of environmental sample collection.

The corridors and nurse station of the ward were cleaned and disinfected twice daily by nurses. The floor of the corridors and nurse station was disinfected with 1000 mg/liter chlorine solution and cleaned with the mop. The other surfaces were wiped with chlorine-containing disinfectant wipes. The inside and outside of the garbage bin and the contents inside the bin were disinfected with 2000 mg/liter chlorine solution before garbage collection. The surfaces and floors in the patients rooms, including the toilet bowls, were also disinfected. The air of wards (including the corridors, patients rooms, and nurse station) was disinfected with UV lamps twice a day for 1 hour each time.

Throat swabs were collected from the patients in the morning. The environmental samples were collected from bedside tables, bedrails, garbage bins (both bedside and toilet), and toilet seats before the first cleaning of the day. In ward D, samples from patients personal items, including masks (both inside and outside) and mobile phones, were collected. Samples from public area were collected from garbage bins, mobile treatmentcarts, armrests and electrocardiographs in the corridor, mobile phones of physicians and nurses, keyboards, mice, telephones, and desktop in the nurse station. For larger surfaces such as the bedside table, samples were collected from a minimum area of 100 cm2, and for smaller surfaces, such as mobile phones, samples should be collected in an area as large as possible. All samples were collected using sterile swabs (Yocon, Beijing, China) and kept in virus preservation solution (Yocon, Beijing, China).

We sampled the air in Ward D (8 rooms) where stayed 15 cases with positive throat swab on August 22 before disinfection using the Aerosol Particle Liquid Concentrator (model WA-400II, Beijing Dingblue Technology Co., Ltd.) at 400 L/min for 20 minutes. Each air sample was collected and stored in 3 mL of the virus sampling liquid as previously mentioned.21,22

The samples were stored in virus medium. Viral RNA was extracted within 2 hours of collection using the Nucleic Acid Isolation Kit (Jiangsu Bioperfectus Technologies Co., Ltd, China) according to the manufacturers instructions. RT-PCR was conducted with primers and probes targeting at the N, ORF1a/b genes and a positive reference gene by using the RNA Detection Kit for SARS-CoV-2 (Jiangsu Bioperfectus Technologies Co., Ltd, China).6 The reaction system and amplification conditions followed the manufacturers specification (Bioperfectus Technologies Co., Ltd). The detection limit of cycle threshold (Ct) was set to be 40 (500 copies/mL). Samples were consideredpositive when N orORF1a/b genes were detected with Ct values 40. All tests were performed under strict biosafety conditions following the standard operating procedures.

All participants were divided into the positive and negative groups based on the environmental surface sampling results. Categorical variables were expressed as numbers and percentages. Continuous variables were presented as medians and interquartile ranges (IQRs). SPSS software version 26.0 (IBM Co. LTD, Chicago, IL, USA) was used for all statistical analyses.

Our study protocol was approved by the Ethics Committee of The Second Hospital of Nanjing (2021-ls-ky030). In addition, we confirm that this study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from the patients before the questionnaire survey. Personal information was not involved in this study.

Table 1 shows the characteristics of 144 COVID-19 cases. Among these patients, 69 (47.9%) were male; 22 (15.3%) were under the age of 18 years, 100 (69.4%) were aged 1859 years, and 22 (15.3%) were aged 60 years and greater; 80 (55.6%) lived in the city, while 64(44.4%) lived in the countryside; 100(69.4%) cases had a high school or lower educational background; 52(36.1%) received 2 doses of vaccination, 34(23.6%) received 1 dose, and 58 (40.3%) were unvaccinated. In terms of clinical severity, 30 (20.8%) were mild, 114 (79.2%) were moderate, and all were cured and discharged. The common symptoms at onset were cough (including dry cough) (81, 56.3%), fever (57, 39.6%), throat discomfort (24, 16.7%), fatigue (19, 13.2%), hyposmia (4, 2.8%), and CT lung abnormalities 114(79.2%). The lowest PCR Ct value on gene N was 22 (IQR, 18 to 25.8), and lowest PCR Ct value on gene ORF1a/b was 23.5 (IQR, 19 to 28).

Table 1 Baseline Characteristics of 144 COVID-19 Cases

The time from symptom onset to surface sampling was 25 days (IQR, 21 to 33 days). Positive throat swabs were detected in 52(36.1%) patients, of which 30 (20.8%) had both N and ORF1a/b genes Ct value 40, and 22(15.3%) had only N genes Ct value 40. PCR Ct value on gene N was 37 (IQR, 36 to 38), and PCR Ct value on gene ORF1a/b was 38 (IQR, 36 to 40). Only 8(5.6%) patients had N or ORF1a/b genes Ct value <35.

A total of 692 environmental surfaces and air specimens were collected among 144 COVID-19 cases, and 3 specimens (3/692, 0.4%) related to 5 cases (3.5%, 5/144) were detected positive on RT-PCR (Table 2). Overall, bedside tables (2/144, 1.4%) were most likely to be contaminated, followed by toilet seats (1/81, 1.2%). By contrast, specimens from the garbage bins (at bedside and in the toilet), masks (both inside and outside), the patients mobile phones, public areas (including the corridor and nurse stations) were all tested negative on RT-PCR. All air samples (from bedside and toilet) were negative in patients rooms (Table 3).

Table 2 Information of 3 Positive Environmental Specimens Related to 5 COVID-19 Cases

Table 3 Distribution of Environmental Specimens of 144 COVID-19 Cases

In the present study, we assessed the environmental contamination of SARS-CoV-2 among 692 environmental surfaces, personal items, and air specimens related to 144 Delta VOC-infected cases with duration of more than two weeks in a hospital. Our study evidenced that the SARS-CoV-2 Delta VOC contamination on object surfaces or in the air might be limited, though a small number of positive samples were found from bedside tables and toilets.All personal item and air specimens were negative.

Delta VOC was the culprit of the infection among all these patients. Our study showed that the median lowest Ct value of throat swabs was 22 (gene N), indicating a high viral load. Previous studies suggested that the viral load of Delta VOC was about 1000 times higher than the wild-type strain, with a prolongedshedding time.6 Another study showed that compared with the wild-type, the Delta VOC was associated with a longer duration of Ct value 30 (median duration 18 days for Delta VOC, and 13 days for wild-type).8

Our study showed that in the hospital, the contamination of SARS-CoV-2 Delta VOC from COVID-19 patients with duration of more than two weeks was limited, which may be explained by the following factors. First, in the later stages of COVID-19, the patients release fewer viruses through the respiratory tract. Previous studies showed that the virus was readily isolated during the first week of symptom onset, and no isolates were obtained from the samples on day 8 despite a persistent high viral load.23 The evidence suggests that the viral load of SARS-CoV-2 from upper respiratory tract samples peaks around the time of symptom onset or a few days after and would not be detectable within about two weeks24 One study showed that patients with Ct value (gene E) above 3334 were not contagious.25 Our data demonstrated that the positive rate of pharyngeal swabs on the environmental sampling day was 36.1% (52/144), the median Ct value was 37 (IQR, 36 to 38) and the time from symptom onset to surface sampling was 25 days (IQR, 21 to 33 days). Second, the floor, surfaces, and the air were disinfected twice a day, which possibly played an important role. Third, the sampling was performed in early morning. In the wards, there was no treatment or care and patients were all asleep at night, which may reduce the possibility of environmental contamination.

Our study found that the Delta VOC was detected positive on bedside tables and toilet seats, which is consistent with some previous reports.21,26,27 A previous study showed a longer duration of virus in fecal samples than in the respiratory samples.28 That means that SARS-CoV-2 could be released through the stool despite the negative result of the throat swab. We also found that samples from the garbage bins (at bedside and in the toilet) and the air were all negative. According to one study22 the air samples from non-negative pressure general wards of four hospitals were detected positive (3 of 44, 6.8%). Surprisingly, another study discovered that all the air samples from negative-pressure wards were negative.18 Our study found that the air samples were negative, which was likely due to the prolonged course and the regular disinfection. While there was debate about the presence of SARS-CoV-2 in the air, some experts believe there was consistent, strong evidence that SARS-CoV-2 spreads by airborne transmission.29 Samples from masks (inside and outside) were detected negative. One study showed a 41.9% positivity rate of SARS-CoV-2 from the mask samples collected within 36 hours of their confirmed diagnosis.30 However, our mask sampling is more than two weeks after diagnosis.

This study has some limitations. First, we only tested viral nucleic acid and did not perform viral culture to demonstrate the viability. Second, only the general wards hospitalized with mild or moderate COVID-19 cases were detected. Third, we did not test the environmental samples at the early stage of admission. Despite these limitations, we believe that our findings can be applied in clinical practice to prevent and control the spread of SARS-CoV-2.

Our study revealed that the environmental contamination of SARS-CoV-2 Delta VOC-infected cases with duration of more than two weeks may be limited, which may be similar to that of the current Omicron. Hopefully, our findings can provide guidance on infection control for both COVID-19 patients and healthcare workers.

We thank KEY Translation Studio (Nanjing, China) for the critical revision of the English language and grammar in this manuscript.

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

This study was funded by Taizhou Clinical Medical School of Nanjing Medical University (Taizhou Peoples Hospital) (TZKY20220305) and Taizhou Peoples Hospital Medical Innovation Team Foundation (CXTDA201901).

All the authors declared no conflicts of interest.

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20. Glinert I, Ben-Shmuel A, Szwartcwort-Cohen M, et al. Revisiting SARS-CoV-2 environmental contamination by patients with COVID-19: the Omicron variant does not differ from previous strains. Int J Infect Dis. 2022;118:211213. doi:10.1016/j.ijid.2022.03.001

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25. La Scola B, Le Bideau M, Andreani J, et al. Viral RNA load as determined by cell culture as a management tool for discharge of SARS-CoV-2 patients from infectious disease wards. Eur J Clin Microbiol Infect Dis. 2020;39(6):10591061. doi:10.1007/s10096-020-03913-9

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Environmental contamination of SARS-CoV-2 | RMHP - Dove Medical Press

The Centers for Disease Control (CDC) has approved a new vaccine called Nirsevimab (Beyfortus) to protect newborns and infants under 8 months against Respiratory Syncytial Virus (RSV). Heres what you need to know.

Respiratory syncytial virus (RSV), usually seen in the fall and winter, causes infections of the airways and lungs. RSV symptoms can include runny nose, cough, fever, fussiness, feeding less and other breathing problems. In babies, RSV can cause pneumonia and/or bronchiolitis, which causes wheezing and breathing problems. In fact, RSV is a major reason why young children need to go to hospital or emergency room.

As always, washing your hands, cleaning surfaces, staying away from sick people and covering coughs and sneezes are great ways to lower your chances of catching colds and other similar infections. This year, we are very excited to have a new medicine that will help infants and young children stay safer from RSV. This new medicine is Nirsevimab!

Nirsevimab (Beyfortus), is a medicine given by injection that fights RSV infection. It contains a long acting antibody that helps the bodys immune system find and get rid of the RSV virus. Nirsevimab was approved by the U.S. Food and Drug Administration (FDA) and Centers for Disease Control (CDC) and is recommended by the American Academy of Pediatrics.

All infants under 8 months of age during RSV season should get one dose of Nirsevimab. Certain babies 8 to 19 months may also get Nirsevimab if they have a condition that makes RSV infection more dangerous for them. These conditions include prematurity (less than 29 weeks), weak immune system, problems swallowing or clearing secretions or heart or lung disease that has been treated with lung medicines, steroids, diuretics or oxygen in the past 6 months.

Nirsevimab should be given as soon as the first week of life for infants born during RSV season, which is usually October through March. Nirsevimab is long acting, so it usually only needs to be given once. For babies at higher risk, one more dose of Nirsevimab may be given during their second RSV season. Also, Nirsevimab can be given with the flu, COVID-19 and all other routine vaccines.

Yes and yes! Studies show that Nirsevimab is not only very safe, but it works really well too. Nirsevimab can prevent about 75-80% severe infections, ER visits and hospital stays caused by RSV. This is great news!

No, Nirsevimab is only for those at highest risk of severe RSV infection. This means that healthy toddlers and infants 8 months and older should not get Nirsevimab at this time. Also, otherwise healthy kids with asthma do not need Nirsevimab.

In most cases, Nirsevimab is not needed if mom got the RSV vaccine while pregnant, but talk to your provider to be sure.

If you have any questions or concerns about Nirsevimab or protecting your child from RSV, please talk to your childs pediatrician or primary care provider to learn more.

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Erie, PA - Pennsylvania Department of Human Services (DHS) Secretary Dr. Val ArkooshandPennsylvania Department of Aging Secretary Jason Kavulichtoday met with the Pennsylvania Association of Area Agencies on Aging (P4A) to discuss how the Shapiro Administration and local partners are working together to support Pennsylvanians through federal changes to Medicaid and CHIP renewal requirements with the goal of keeping them covered.

Pennsylvanians at every age and every stage of life deserve the dignity and peace of mind in knowing that they can go to the doctor or fill a prescription when they need to. DHS has been proud to work with community partners across the state to spread the word about Medicaid renewals so that Pennsylvanians can keep their coverage or be referred to other options, saidSecretary Arkoosh. It is so important that anyone who receives their health coverage through the state updates their contact information with DHS and they complete their Medicaid renewal on time so they dont risk a gap or loss in coverage.

The federal public health emergency in response to the COVID-19 pandemic allowed for individuals to remain enrolled in Medicaid even if they became ineligible, except in certain circumstances. This is also known as the Medicaid continuous coverage requirement. A federal law ended the continuous coverage requirement on March 31, 2023. Now, all Pennsylvanians receiving Medicaid or CHIP must once again complete their annual renewal when it is due to determine if they are still eligible for coverage.No one will lose Medicaid or CHIP coverage without first having an opportunity to renew their coverage or update their information.

The Department of Aging and the Area Agencies on Aging are committed to helping older adults who are no longer eligible for Medicaid coverage learn about other coverage options and assistance, such as PACE, our Pharmaceutical Assistance program, or calling PA MEDI for help enrolling in Medicare, saidSecretary Kavulich. With the federal changes also impacting insurance for children, we also want to ensure those grandparents raising grandchildren and other kinship caregivers review the important information for those children so they also continue receiving coverage. By taking this easy step, older adults will maintain coverage for themselves and the young people in their lives without risking a lack or loss of coverage.

Tomake sure they are getting updates about their renewal and benefits, Pennsylvanians should make sure their contact information is up to date with DHS. They can do this and report changes in their personal circumstances and check their renewal date:

Online atwww.dhs.pa.gov/COMPASS

Via the free myCOMPASS PA mobile app

By calling 215-560-7226 (or 1-877-395-8930 if outside Philadelphia)

Renewals will be completed over 12 months through April 2024, usually at a persons normal time of renewal. If a person is found ineligible for coverage or does not complete their renewal on time, their Medicaid coverage will end. Pennsylvanians who believe their coverage was ended incorrectly can appeal the termination or return their packet within 90 days of the deadline to have their renewal reconsidered. Medicaid recipients also may provide their renewal information to their localCounty Assistance Officeor by calling 1-866-550-4355.

Pennsylvanians who are no longer eligible for Medicaid will be referred to other sources of affordable medical coverage, like CHIP and Pennie, so they have no lapse in coverage.

Pennie is Pennsylvanias official health insurance marketplace and the only place to get financial assistance to help lower the cost of high-quality coverage and care. Those who are no longer eligible for Medicaid coverage can apply for coverage throughpennie.com,while some individuals will have their information securely transferred from Medicaid or CHIP for an easier enrollment process. Customers can simplycall Pennie Customer Service at 1-844-844-8040 or find Pennie-certified pros atpennie.com/connect.

P4A represents the 52 Area Agencies on Aging (AAAs) that serve all 67 counties. AAAs serve as the front door for aging services in the community and provide information about services while also assisting in obtaining access to and coordinating those services. The primary goal of AAAs is to help older Pennsylvanians remain in their homes and communities for as long as possible.

The health and wellbeing of all of us as we age is a top priority for P4A and the AAA network. We are appreciative of Secretaries Kavulich and Arkoosh for reinforcing the crucial action that older adults and kinship caregivers should take to prevent a gap or loss of health care coverage for themselves and the young people whom they are caring for. The AAAs are on the ground serving older adults in their community, and we urge any older adult to reach out to their local AAA for more information on health-related and other supports for which they may be eligible, saidP4A Executive Director Rebecca May-Cole.

To learn more about Medicaid and CHIP renewals and to access educational resources, visitwww.dhs.pa.gov/staycovered.

For more information on health insurance options available to Pennsylvanians, visitwww.pa.gov/healthcare.

MEDIA CONTACT: Brandon Cwalina - ra-pwdhspressoffice@pa.gov

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