Search

Your search keyword '"Hill, Elaine"' showing total 336 results
Start Over Author "Hill, Elaine" Search Limiters Full Text
336 results on '"Hill, Elaine"'

2. Generalisable long COVID subtypes: Findings from the NIH N3C and RECOVER programmes

Author

Reese, Justin T, Blau, Hannah, Casiraghi, Elena, Bergquist, Timothy, Loomba, Johanna J, Callahan, Tiffany J, Laraway, Bryan, Antonescu, Corneliu, Coleman, Ben, Gargano, Michael, Wilkins, Kenneth J, Cappelletti, Luca, Fontana, Tommaso, Ammar, Nariman, Antony, Blessy, Murali, TM, Caufield, J Harry, Karlebach, Guy, McMurry, Julie A, Williams, Andrew, Moffitt, Richard, Banerjee, Jineta, Solomonides, Anthony E, Davis, Hannah, Kostka, Kristin, Valentini, Giorgio, Sahner, David, Chute, Christopher G, Madlock-Brown, Charisse, Haendel, Melissa A, Robinson, Peter N, Consortium, N3C, Spratt, Heidi, Visweswaran, Shyam, Flack, Joseph Eugene, Yoo, Yun Jae, Gabriel, Davera, Alexander, G Caleb, Mehta, Hemalkumar B, Liu, Feifan, Miller, Robert T, Wong, Rachel, Hill, Elaine L, Consortium, RECOVER, Thorpe, Lorna E, and Divers, Jasmin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Emerging Infectious Diseases, Infectious Diseases, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Precision Medicine, Coronaviruses, Good Health and Well Being, Humans, COVID-19, Disease Progression, Post-Acute COVID-19 Syndrome, SARS-CoV-2, N3C Consortium, RECOVER Consortium, Human Phenotype Ontology, Long COVID, Machine learning, Precision medicine, Semantic similarity, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundStratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.MethodsWe present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.FindingsWe found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.InterpretationSemantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.FundingNIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Published
2023

4. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

Author

Aggarwal, Ataes, Agor, Joseph, Al-Amery, Amera, Aminu, Oluwatobiloba, Anand, Adit, Antonescu, Corneliu, Arora, Mehak, Asaduzzaman, Sayed, Asmussen, Tanner, Baghbanzadeh, Mahdi, Baker, Frazier, Bangert, Bridget, Bekhet, Laila, Bhattacharya, Biplab, Blase, Jenny, Butzin-Dozier, Zachary, Caffo, Brian, Chang, Hao, Chen, Zeyuan, Chen, Jiandong, Chiang, Jeffrey, Cho, Peter, Cockrell, Robert, Combs, Parker, Coyle, Jeremy, Crosby, Ciara, Dai, Zongyu, Dai, Ran, Danesharasteh, Anseh, Yildirim, Elif, Delong, Grant, Demilt, Ryan, Deng, Kaiwen, Dey, Sanjoy, Dhamdhere, Rohan, Dickson, Andrew, Dijour, Phoebe, Dinh, Dong, Dixon, Richard, Domi, Albi, Dutta, Souradeep, Elizondo, Mirna, Ertem, Zeynep, Feuerwerker, Solomon, Fliss, Danica, Fowler, Jennifer, Fu, Sunyang, Gardner, Kelly, Getty, Neil, Ghalwash, Mohamed, Gloster, Logan, Greer, Phil, Guan, Yuanfang, Ham, Colby, Hanoudi, Samer, Harper, Jeremy, Hendrix, Nathaniel, Hershkovich, Leeor, Hightower, Jake, Hu, Junjie, Huang, Jiani, Huang, Yu, Huang, Tongtong, Hur, Junguk, Isgut, Monica, Ismail, Hamid, Izmirlian, Grant, Jang, Kuk, Jemiyo, Christianah, Jeong, Hayoung, Ji, Yunwen, Ji, Xiayan, Jiang, Ming, Jiang, Sihang, Jiang, Xiaoqian, Jiang, Yuye, Johnson, Akin, Analyst, Zach, Kapse, Saarthak, Kartoun, Uri, KC, Dukka, Fard, Zahra, Kosfeld, Tim, Krichevsky, Spencer, Kuo, Mike, Larie, Dale, Lederer, Lauren, Leng, Shan, Li, Ziyang, Li, Hongyang, Li, Haodong, Li, Jianfu, Li, Tiantian, Liang, Xinwen, Liang, Hengyue, Liu, Feifan, Liu, Daniel, Luo, Gang, Munia, Tamanna, Madduri, Ravi, Madhira, Vithal, Mani, Shivali, Mansourifard, Farzaneh, Matson, Robert, Mertens, Andrew, Metsis, Vangelis, Meyer, Pablo, Mikhailova, Catherine, Miller, Dante, Milo, Christopher, Mitchell, Elliot, Modanwal, Gourav, Moore, Ronald, Morgenthaler, David, Musal, Rasim, Naik, Mayur, Nalawade, Vinit, Narain, Rohan, Narendrula, Saideep, Obiri, Alena, Okawa, Satoshi, Okechukwu, Chima, Olorunnisola, Toluwanimi, Ossowski, Tim, Parekh, Harsh, Park, Jean, Patel, Saaya, Patterson, Jason, Paul, Chetan, Peng, Le, Perkins, Diana, Pokharel, Suresh, Poplavskiy, Dmytro, Pryor, Zach, Pungitore, Sarah, Qin, Hong, Rababa, Salahaldeen, Rahman, Mahbubur, Rahmani, Elior, Rahnavard, Gholamali, Raihan, Md, Rajendran, Suraj, Ravichandran, Sarangan, Reddy, Chandan, Reyes, Abel, Roghanizad, Ali, Rouffa, Sean, Ruan, Xiaoyang, Saha, Arpita, Sawant, Sahil, Schiaffino, Melody, Seira, Diego, Sengupta, Saurav, Shalaev, Ruslan, Shetty, Gaurav, Shi, Seraphina, Shinguyen, Linh, Singh, Karnika, Sinha, Soumya, Socia, Damien, Stalians, Halen, Stavropoulos, Charalambos, Strube, Jan, Subramanian, Devika, Sun, Jiehuan, Sun, Ju, Sun, Chengkun, Sundararajan, Prathic, Talebi, Salmonn, Tawiah, Edward, Tesic, Jelena, Thiess, Mikaela, Tian, Raymond, Torre-Healy; Ming-Tse Tsai, Luke, Tyus, David, Vardhan, Madhurima, Velingker, Neelay, Walzer, Benjamin, Walzer, Jacob, Wang, Junda, Wang, Lu, Wang, Will, Wang, Jonathan, Wang, Yisen, Weatherly, Chad, Wu, Fanyou, Wu, Yifeng, Wu, Yinjun, Xia, Fangfang, Yan, Hao, Yang, Zhichao, Ye, Biao, Yin, Rui, Yin, Changyu, Yoo, Yun, You, Albert, Yu, June, Zanaj, Martin, Zaiman, Zachary, Zhang, Kai, Zhang, Xiaoyi, Zhang, Tianmai, Zhao, Zixuan, Zhi, Degui, Zhong, Yishan, Zhou, Huixue, Zhou, Andrea, Zhu, Yuanda, Zhu, Yitan, Zhu, Sophie, Adams, Meredith, Alexander, Caleb, Amor, Benjamin, Anzalone, Alfred, Bates, Benjamin, Beasley, Will, Bennett, Tellen, Bissell, Mark, Boudreau, Eilis, Bozzette, Samuel, Bradwell, Katie, Bramante, Carolyn, Brown, Don, Burgoon, Penny, Buse, John, Callahan, Tiffany, Cato, Kenrick, Chapman, Scott, Chute, Christopher, Clark, Jaylyn, Clark, Marshall, Cooper, Will, Cottrell, Lesley, Crowley, Karen, Deacy, Mariam, Dillon, Christopher, Eichmann, David, Emmett, Mary, Erwin-Cohen, Rebecca, Francis, Patricia, French, Evan, Fuentes, Rafael, Gabriel, Davera, Gagnier, Joel, Garbarini, Nicole, Ge, Jin, Gersing, Kenneth, Girvin, Andrew, Gordon, Valery, Graves, Alexis, Guinney, Justin, Haendel, Melissa, Hayanga, J.W., Hendricks, Brian, Hernandez, Wenndy, Hill, Elaine, Hillegass, William, Hong, Stephanie, Housman, Dan, Hurley, Robert, Islam, Jessica, Jawa, Randeep, Johnson, Steve, Kamaleswaran, Rishi, Kibbe, Warren, Koraishy, Farrukh, Kostka, Kristin, Kurilla, Michael, Lee, Adam, Lehmann, Harold, Liu, Hongfang, Loomba, Johanna, Madlock-Brown; Sandeep Mallipattu, Charisse, Manna, Amin, Mariona, Federico, Marti, Emily, Martin, Greg, Mathew, Jomol, Mazzotti, Diego, McMurry, Julie, Mehta, Hemalkumar, Michael, Sam, Miller, Robert, Misquitta, Leonie, Moffitt, Richard, Morris, Michele, Murray, Kimberly, Northington, Lavance, O’Neil, Shawn, Olex, Amy, Palchuk, Matvey, Patel, Brijesh, Patel, Rena, Payne, Philip, Pfaff, Emily, Pincavitch, Jami, Portilla, Lili, Prior, Fred, Pyarajan, Saiju, Pyles, Lee, Qureshi, Nabeel, Robinson, Peter, Rutter, Joni, Sadan, Ofer, Safdar, Nasia, Saha, Amit, Saltz, Joel, Saltz, Mary, Schmitt, Clare, Setoguchi, Soko, Sharafeldin, Noha, Sharathkumar, Anjali, Sheikh, Usman, Sidky, Hythem, Sokos, George, Southerland, Andrew, Spratt, Heidi, Starren, Justin, Subbian, Vignesh, Suver, Christine, Takemoto, Cliff, Temple-O'Connor, Meredith, Topaloglu, Umit, Vedula, Satyanarayana, Walden, Anita, Walters, Kellie, Ward-Caviness, Cavin, Wilcox, Adam, Wilkins, Ken, Williams, Andrew, Wu, Chunlei, Zampino, Elizabeth, Zhang, Xiaohan, Zhu, Richard, Bergquist, Timothy, Tariq, Adbul, Philips, Rachael V., Pirracchio, Romain, van der Laan, Mark, Colford, John M., Jr., Hubbard, Alan, Gao, Jifan, Chen, Guanhua, Stein, Adam, Long, Qi, Holmes, John, Mowery, Danielle, Wong, Eric, Parekh, Ravi, Getzen, Emily, and Blase, Jennifer

Published
2024
Full Text
View/download PDF

6. Multiple Dimensions of Environmental Justice and Oil and Gas Development in Pennsylvania

Author

Lieberman-Cribbin, Wil, Fang, Xin, Morello-Frosch, Rachel, Gonzalez, David JX, Hill, Elaine, Deziel, Nicole C, Buonocore, Jonathan J, and Casey, Joan A

Subjects
Pediatric Research Initiative, Pediatric, Peace, Justice and Strong Institutions, environmental justice, social factors, hydraulic fracturing, natural gas, proximity
Abstract

Background: Community socioeconomic deprivation (CSD) may be related to higher oil and natural gas development (OGD) exposure. We tested for distributive and benefit-sharing environmental injustice in Pennsylvania's Marcellus Shale by examining (1) whether OGD and waste disposal occurred disproportionately in more deprived communities and (2) discordance between the location of land leased for OGD and where oil and gas rights owners resided. Materials and Methods: Analyses took place at the county subdivision level and considered OGD wells, waste disposal, and land lease agreement locations from 2005 to 2019. Using 2005-2009 American Community Survey data, we created a CSD index relevant to community vulnerability in suburban/rural areas. Results: In adjusted regression models accounting for spatial dependence, we observed no association between the CSD index and conventional or unconventional drilled well presence. However, a higher CSD index was linearly associated with odds of a subdivision having an OGD waste disposal site and receiving a larger volume of waste. A higher percentage of oil and gas rights owners lived in the same county subdivision as leased land when the community was least versus most deprived (66% vs. 56% in same county subdivision), suggesting that individuals in more deprived communities were less likely to financially benefit from OGD exposure. Discussion and Conclusions: We observed distributive environmental injustice with respect to well waste disposal and benefit-sharing environmental injustice related to oil and rights owner's residential locations across Pennsylvania's Marcellus Shale. These results add evidence of a disparity between exposure and benefits resulting from OGD.

Published
2022

7. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study

Author

Hill, Elaine L., Mehta, Hemalkumar B., Sharma, Suchetha, Mane, Klint, Singh, Sharad Kumar, Xie, Catherine, Cathey, Emily, Loomba, Johanna, Russell, Seth, Spratt, Heidi, DeWitt, Peter E., Ammar, Nariman, Madlock-Brown, Charisse, Brown, Donald, McMurry, Julie A., Chute, Christopher G., Haendel, Melissa A., Moffitt, Richard, Pfaff, Emily R., and Bennett, Tellen D.

Published
2023
Full Text
View/download PDF

9. Decreasing Case Fatality Rates for Patients With Cirrhosis Infected With SARS-CoV-2: A National COVID Cohort Collaborative Study

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Far, Aryana T., Digitale, Jean C., Pletcher, Mark J., and Lai, Jennifer C.

Published
2024
Full Text
View/download PDF

10. COVID-19 outcomes in persons with hemophilia: results from a US-based national COVID-19 surveillance registry

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J. W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Sharathkumar, Anjali, Wendt, Linder, Ortman, Chris, Srinivasan, Ragha, Chute, Christopher G., Chrischilles, Elizabeth, and Takemoto, Clifford M.

Published
2024
Full Text
View/download PDF

13. Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysis

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Grimsley, Emily A., Torikashvili, Johnathan V., Janjua, Haroon M., Read, Meagan D., Kothari, Anai N., Verhagen, Nate B., Pietrobon, Ricardo, Kuo, Paul C., and Rogers, Michael P.

Published
2023
Full Text
View/download PDF

14. Hormone replacement therapy and COVID-19 outcomes in solid organ transplant recipients compared with the general population

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Vinson, Amanda J., Anzalone, Alfred, Schissel, Makayla, Dai, Ran, French, Evan T., Olex, Amy L., and Mannon, Roslyn B.

Published
2023
Full Text
View/download PDF

15. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

Author

Spratt, Heidi, Visweswaran, Shyam, Flack, Joseph Eugene, IV, Yoo, Yun Jae, Gabriel, Davera, Alexander, G. Caleb, Mehta, Hemalkumar B., Liu, Feifan, Miller, Robert T., Wong, Rachel, Hill, Elaine L., Thorpe, Lorna E., Divers, Jasmin, Reese, Justin T., Blau, Hannah, Casiraghi, Elena, Bergquist, Timothy, Loomba, Johanna J., Callahan, Tiffany J., Laraway, Bryan, Antonescu, Corneliu, Coleman, Ben, Gargano, Michael, Wilkins, Kenneth J., Cappelletti, Luca, Fontana, Tommaso, Ammar, Nariman, Antony, Blessy, Murali, T.M., Caufield, J. Harry, Karlebach, Guy, McMurry, Julie A., Williams, Andrew, Moffitt, Richard, Banerjee, Jineta, Solomonides, Anthony E., Davis, Hannah, Kostka, Kristin, Valentini, Giorgio, Sahner, David, Chute, Christopher G., Madlock-Brown, Charisse, Haendel, Melissa A., and Robinson, Peter N.

Published
2023
Full Text
View/download PDF

16. Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C.

Author

Bhatia, Abhishek, Preiss, Alexander J., Xiao, Xuya, Brannock, M. Daniel, Alexander, G. Caleb, Chew, Robert F., Davis, Hannah, Fitzgerald, Megan, Hill, Elaine, Kelly, Elizabeth P., Mehta, Hemalkumar B., Madlock-Brown, Charisse, Wilkins, Kenneth J., Chute, Christopher G., Haendel, Melissa, Moffitt, Richard, and Pfaff, Emily R.

Subjects
SARS-CoV-2, COVID-19, COVID-19 treatment, MEDICAL care use, ELECTRONIC health records
Abstract

Background: Nirmatrelvir with ritonavir (Paxlovid) is indicated for patients with Coronavirus Disease 2019 (COVID-19) who are at risk for progression to severe disease due to the presence of one or more risk factors. Millions of treatment courses have been prescribed in the United States alone. Paxlovid was highly effective at preventing hospitalization and death in clinical trials. Several studies have found a protective association in real-world data, but they variously used less recent study periods, correlational methods, and small, local cohorts. Their estimates also varied widely. The real-world effectiveness of Paxlovid remains uncertain, and it is unknown whether its effect is homogeneous across demographic strata. This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing severe COVID-19 outcomes. Methods and findings: This target trial emulation used a cohort of 703,647 patients with COVID-19 seen at 34 clinical sites across the United States between April 1, 2022 and August 28, 2023. Treatment was defined as receipt of a Paxlovid prescription within 5 days of the patient's COVID-19 index date (positive test or diagnosis). To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. The primary outcome was hospitalization; death was a secondary outcome. We estimated that Paxlovid reduced the risk of hospitalization by 39% (95% confidence interval (CI) [36%, 41%]; p < 0.001), with an absolute risk reduction of 0.9 percentage points (95% CI [0.9, 1.0]; p < 0.001), and reduced the risk of death by 61% (95% CI [55%, 67%]; p < 0.001), with an absolute risk reduction of 0.2 percentage points (95% CI [0.1, 0.2]; p < 0.001). We also conducted stratified analyses by vaccination status and age group. Absolute risk reduction for hospitalization was similar among patients that were vaccinated and unvaccinate, but was much greater among patients aged 65+ years than among younger patients. We observed disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. This study's main limitation is that it estimates causal effects using observational data and could be biased by unmeasured confounding. Conclusions: In this study of Paxlovid's real-world effectiveness, we observed that Paxlovid is effective at preventing hospitalization and death, including among vaccinated patients, and particularly among older patients. This remains true in the era of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron subvariants. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed. Abhishek Bhatia and team estimate the real-world effectiveness of Paxlovid and examine treatment disparities using target trial emulation on a cohort of 703,647 patients from N3C's electronic health record database. Author summary: Why was this study done?: Paxlovid is widely used to prevent severe Coronavirus Disease 2019 (COVID-19), but its real-world effectiveness is uncertain. Prior studies differed in their findings, and none of them used a large, nationally sampled, and recent cohort. It is unknown whether Paxlovid may be more or less effective in certain demographic groups. What did the researchers do and find?: We used a cohort of 703,647 patients from the National Covid Cohort Collaborative's (N3C's) electronic health record database to estimate Paxlovid's real-world effectiveness and investigate disparities in treatment. We used the target trial emulation technique to estimate the causal effect of Paxlovid treatment using observational data. We found that Paxlovid treatment reduced the risk of hospitalization by 39% and the risk of death by 61%. We found that Paxlovid reduced the risk of hospitalization by a similar absolute amount regardless of whether the patient was vaccinated prior to getting COVID-19. We found that Paxlovid reduced risk more among patients aged 65+ years than among younger patients. We found disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. What do these findings mean?: Paxlovid is effective at preventing severe COVID-19 and is effective against current variants. Fully vaccinated people may have a further reduced risk of severe outcomes by receiving Paxlovid. People aged 65 years or older may benefit the most from Paxlovid treatment. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed. This study's main limitation is that it estimates causal effects, but it is not randomized like a clinical trial. Instead, we control for other variables which could bias the estimate, but if we missed some important variables, the estimates could be incorrect. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

17. Educational Interventions to Improve Handover in Health Care

Author

Gordon, Morris, Hill, Elaine, Stojan, Jennifer N, and Daniel, Michelle

Subjects
Health Services and Systems, Nursing, Health Sciences, Patient Safety, Management of diseases and conditions, 7.3 Management and decision making, Generic health relevance, Quality Education, Education, Medical, Continuing, Humans, Interprofessional Relations, Patient Handoff, Quality of Health Care, Clinical Sciences, Curriculum and Pedagogy, General & Internal Medicine, Curriculum and pedagogy, Health services and systems
Abstract

PurposeEffective handovers (handoffs) are vital to patient safety. Medical educators investigated educational interventions to improve handovers in a 2011 systematic review. The number of publications on handover education has increased since then, so authors undertook this updated review.MethodThe authors considered studies involving educational interventions to improve handover amongst undergraduate or postgraduate health professionals in acute care settings. In September 2016, two authors independently conducted a standardized search of online databases and completed a data extraction and quality assessment of the articles included. They conducted a content analysis of and extracted key themes from the interventions described.ResultsEighteen reports met the inclusion criteria. All but two were based in the United States. Interventions most commonly involved single-patient exercises based on simulation and role-play. Many studies mentioned multiprofessional education or practice, but interventions occurred largely in single-professional contexts. Analysis of interventions revealed three major themes: facilitating information management, reducing the potential for errors, and improving confidence. The majority of studies assessed Kirkpatrick's outcomes of satisfaction and knowledge/skill improvement (Levels 1 and 2). The strength of conclusions was generally weak.ConclusionsDespite increased interest in and publications on handover, the quality of published research remains poor. Inadequate reporting of interventions, especially as they relate to educational theory, pedagogy, curricula, and resource requirements, continues to impede replication. Weaknesses in methodologies, length of follow-up, and scope of outcomes evaluation (Kirkpatrick levels) persist. Future work to address these issues, and to consider the role of multiprofessional and multiple-patient handovers, is vital.

Published
2018

19. Long‐Term Mortality Following SARS‐CoV‐2 Infection in Rural Versus Urban Dwellers With Autoimmune or Inflammatory Rheumatic Disease: A Retrospective Cohort Analysis From the National COVIDCohort Collaborative

Author

Anzalone, A. Jerrod, Jackson, Lesley E., Singh, Namrata, Danila, Maria I., Reisher, Elizabeth, Patel, Rena C., Singh, Jasvinder A., Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward‐Caviness, Cavin, Madlock‐Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M, Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple‐O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin‐Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, and Zhang, Xiaohan Tanner

Abstract

Autoimmune or inflammatory rheumatic diseases (AIRDs) increase the risk for poor COVID‐19 outcomes. Although rurality is associated with higher post–COVID‐19 mortality in the general population, whether rurality elevates this risk among people with AIRD is unknown. We assessed associations between rurality and post–COVID‐19 all‐cause mortality, up to two years post infection, among people with AIRD using a large nationally sampled US cohort. This retrospective study used the National COVID Cohort Collaborative, a medical records repository containing COVID‐19 patient data. We included adults with two or more AIRD diagnostic codes and a COVID‐19 diagnosis documented between April 2020 and March 2023. Rural residency was categorized using patient residential zip codes. We adjusted for AIRD medications and glucocorticoid prescription, age, sex, race and ethnicity, tobacco or substance use, comorbid burden, and SARS‐CoV‐2 variant‐dominant periods. Multivariable Cox proportional hazards with inverse probability treatment weighting assessed associations between rurality and two‐year all‐cause mortality. Among the 86,467 SARS‐CoV‐2–infected persons with AIRD, we observed a higher risk for two‐year post–COVID‐19 mortality in rural versus urban dwellers. Rural‐residing persons with AIRD had higher two‐year all‐cause mortality risk (adjusted hazard ratio 1.24, 95% confidence interval 1.19–1.29). Glucocorticoid, immunosuppressive, and rituximab prescriptions were associated with a higher risk for two‐year post–COVID‐19 mortality, whereas risk with nonbiologic or biologic disease‐modifying antirheumatic drugs was lower. Rural residence in people with AIRD was independently associated with higher two‐year post–COVID‐19 mortality in a large US cohort after adjusting for background risk factors. Policymakers and health care providers should consider these findings when designing interventions to improve outcomes in people with AIRD following SARS‐CoV‐2 infection, especially among high‐risk rural residents.

Published
2025
Full Text
View/download PDF

21. Associations among Historical Neighborhood Disinvestment, Hazardous Air Pollutants, and Current Adult Asthma Prevalence

Author

Campbell, Erin J., Sims, Kendra D., Hill, Elaine L., and Willis, Mary D.

Subjects
Risk factors, Demographic aspects, Environmental aspects, Health aspects, Air pollution -- Health aspects, Asthma -- Risk factors -- Environmental aspects, Disinvestment -- Health aspects -- Environmental aspects, Health care disparities -- Demographic aspects -- Environmental aspects
Abstract

Introduction Structural racism manifests through systematic neighborhood disinvestment in local infrastructure where racial and ethnic minorities reside, (1) leading to a disproportionate burden of environmental pollution and associated respiratory health [...]

Published
2023
Full Text
View/download PDF

22. Assessing associations between individual-level social determinants of health and COVID-19 hospitalizations: investigating racial/ethnic disparities among people living with HIV in the U.S. National COVID Cohort Collaborative (N3C)

Author

Vaidya, Dimple, primary, Wilkins, Kenneth J., additional, Hurwitz, Eric, additional, Islam, Jessica Y., additional, Li, Dongmei, additional, Sun, Jing, additional, Safo, Sandra E., additional, Ross, Jennifer M., additional, Hassan, Shukri, additional, Hill, Elaine, additional, Nosyk, Bohdan, additional, Varley, Cara D., additional, Fadul, Nada, additional, Camacho-Rivera, Marlene, additional, Madlock-Brown, Charisse, additional, and Patel, Rena C., additional

Published
2024
Full Text
View/download PDF

25. The Multicultural E-Mail of High School-College Collaboration.

Author

Hill, Elaine N. and Whitaker, Elaine E.

Abstract

A collaboration between a university English professor and a high-school English teacher was successful in building community, yet rather unsuccessful in manipulating the Internet. Their collaboration worked, as far as it did, because of an absence of hierarchy. To create a basis for comparison with other studies, analysis of the collaboration can be anchored by a list compiled by Paul Wangemann in his dissertation concerning successful collaboration. Cultural differences in the institutions made for day-to-day interference in collaborations: the calendars of high schools and universities do not match; the nature of instructional contact does not match; and accountability differs at the high school and college level. Particularly in the case of collaboration involving the Internet, sources of authority tend to lie outside the classroom, and classroom leaders now more frequently model the role of the active learner. (Contains 18 references and an outline of the criteria for successful collaboration developed by Paul Wangemann.) (RS)

Published
1994

26. DISC1 and PDE4B Are Interacting Genetic Factors in Schizophrenia That Regulate cAMP Signaling

Author

Millar, J. Kirsty, Pickard, Benjamin S., Mackie, Shaun, James, Rachel, Christie, Sheila, Buchanan, Sebastienne R., Malloy, M. Pat, Chubb, Jennifer E., Huston, Elaine, Baillie, George S., Thomson, Pippa A., Hill, Elaine V., Brandon, Nicholas J., Rain, Jean-Christophe, Camargo, L. Miguel, Whiting, Paul J., Houslay, Miles D., Muir, Walter J., and Porteous, David J.

Published
2005

29. Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysis

Author

Grimsley, Emily A., primary, Torikashvili, Johnathan V., additional, Janjua, Haroon M., additional, Read, Meagan D., additional, Kothari, Anai N., additional, Verhagen, Nate B., additional, Pietrobon, Ricardo, additional, Kuo, Paul C., additional, Rogers, Michael P., additional, Wilcox, Adam B., additional, Lee, Adam M., additional, Graves, Alexis, additional, Anzalone, Alfred (Jerrod), additional, Manna, Amin, additional, Saha, Amit, additional, Olex, Amy, additional, Zhou, Andrea, additional, Williams, Andrew E., additional, Southerland, Andrew, additional, Girvin, Andrew T., additional, Walden, Anita, additional, Sharathkumar, Anjali A., additional, Amor, Benjamin, additional, Bates, Benjamin, additional, Hendricks, Brian, additional, Patel, Brijesh, additional, Alexander, Caleb, additional, Bramante, Carolyn, additional, Ward-Caviness, Cavin, additional, Madlock-Brown, Charisse, additional, Suver, Christine, additional, Chute, Christopher, additional, Dillon, Christopher, additional, Wu, Chunlei, additional, Schmitt, Clare, additional, Takemoto, Cliff, additional, Housman, Dan, additional, Gabriel, Davera, additional, Eichmann, David A., additional, Mazzotti, Diego, additional, Brown, Don, additional, Boudreau, Eilis, additional, Hill, Elaine, additional, Zampino, Elizabeth, additional, Marti, Emily Carlson, additional, Pfaff, Emily R., additional, French, Evan, additional, Koraishy, Farrukh M., additional, Mariona, Federico, additional, Prior, Fred, additional, Sokos, George, additional, Martin, Greg, additional, Lehmann, Harold, additional, Spratt, Heidi, additional, Mehta, Hemalkumar, additional, Liu, Hongfang, additional, Sidky, Hythem, additional, Hayanga, J.W. Awori, additional, Pincavitch, Jami, additional, Clark, Jaylyn, additional, Harper, Jeremy Richard, additional, Islam, Jessica, additional, Ge, Jin, additional, Gagnier, Joel, additional, Saltz, Joel H., additional, Saltz, Joel, additional, Loomba, Johanna, additional, Buse, John, additional, Mathew, Jomol, additional, Rutter, Joni L., additional, McMurry, Julie A., additional, Guinney, Justin, additional, Starren, Justin, additional, Crowley, Karen, additional, Bradwell, Katie Rebecca, additional, Walters, Kellie M., additional, Wilkins, Ken, additional, Gersing, Kenneth R., additional, Cato, Kenrick Dwain, additional, Murray, Kimberly, additional, Kostka, Kristin, additional, Northington, Lavance, additional, Pyles, Lee Allan, additional, Misquitta, Leonie, additional, Cottrell, Lesley, additional, Portilla, Lili, additional, Deacy, Mariam, additional, Bissell, Mark M., additional, Clark, Marshall, additional, Emmett, Mary, additional, Saltz, Mary Morrison, additional, Palchuk, Matvey B., additional, Haendel, Melissa A., additional, Adams, Meredith, additional, Temple-O'Connor, Meredith, additional, Kurilla, Michael G., additional, Morris, Michele, additional, Qureshi, Nabeel, additional, Safdar, Nasia, additional, Garbarini, Nicole, additional, Sharafeldin, Noha, additional, Sadan, Ofer, additional, Francis, Patricia A., additional, Burgoon, Penny Wung, additional, Robinson, Peter, additional, Payne, Philip R.O., additional, Fuentes, Rafael, additional, Jawa, Randeep, additional, Erwin-Cohen, Rebecca, additional, Patel, Rena, additional, Moffitt, Richard A., additional, Zhu, Richard L., additional, Kamaleswaran, Rishi, additional, Hurley, Robert, additional, Miller, Robert T., additional, Pyarajan, Saiju, additional, Michael, Sam G., additional, Bozzette, Samuel, additional, Mallipattu, Sandeep, additional, Vedula, Satyanarayana, additional, Chapman, Scott, additional, O'Neil, Shawn T., additional, Setoguchi, Soko, additional, Hong, Stephanie S., additional, Johnson, Steve, additional, Bennett, Tellen D., additional, Callahan, Tiffany, additional, Topaloglu, Umit, additional, Sheikh, Usman, additional, Gordon, Valery, additional, Subbian, Vignesh, additional, Kibbe, Warren A., additional, Hernandez, Wenndy, additional, Beasley, Will, additional, Cooper, Will, additional, Hillegass, William, additional, and Zhang, Xiaohan Tanner, additional

Published
2023
Full Text
View/download PDF

31. Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

Author

Faherty, Emily A. G., Wilkins, Kenneth J., Jones, Sara, Challa, Anup, Qin, Qiuyuan, Chan, Lauren E., Olson-Chen, Courtney, Tarleton, Jessica L., Liebman, Michael N., Mariona, Federico, Hill, Elaine L., and Patel, Rena C.

Subjects
PREGNANT women, PREGNANCY outcomes, COVID-19 vaccines, VACCINE safety, SARS-CoV-2 Omicron variant, SICK people
Abstract

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

33. Associations between Residential Proximity to Oil and Gas Drilling and Term Birth Weight and Small-for-Gestational-Age Infants in Texas: A Difference-in-Differences Analysis

Author

Willis, Mary D., Hill, Elaine L., Boslett, Andrew, Kile, Molly L., Carozza, Susan E., and Hystad, Perry

Subjects
Physiological aspects, Health aspects, Environmental aspects, Fetal development -- Health aspects -- Physiological aspects -- Environmental aspects, Natural gas gathering -- Health aspects, Fetus -- Growth, Gas extraction -- Health aspects
Abstract

Background Since the early 2000s, shale gas extraction has increased exponentially and now accounts for more than three-quarters of U.S. natural gas production (U.S. Energy Information Administration 2017b). Texas, which [...], Background: Oil and natural gas extraction may produce environmental pollution at levels that affect reproductive health of nearby populations. Available studies have primarily focused on unconventional gas drilling and have not accounted for local population changes that can coincide with drilling activity. OBJECTIVE: Our study sought to examine associations between residential proximity to oil and gas drilling and adverse term birth outcomes using a difference-in-differences study design. Methods: We created a retrospective population-based term birth cohort in Texas between 1996 and 2009 composed of mother-infant dyads (n = 2,598,025) living Results: The adjusted mean difference in term birth weight for mothers living 0-1 vs. 3-10 km from a current or future drilling site was -7.3 g [95% confidence interval (CI): -11.6, -3.0] for births during active vs. future drilling. The corresponding adjusted odds ratio for SGA was 1.02 (95% CI: 0.98, 1.06). Negative associations with term birth weight were observed for the 1-2 and 2-3 km near groups, and no consistent differences were identified by type of drilling activity. Larger, though imprecise, adverse associations were found for infants born to Hispanic women, women with the lowest educational attainment, and women living in cities. Conclusions: Residing near oil and gas drilling sites during pregnancy was associated with a small reduction in term birth weight but not SGA, with some evidence of environmental injustices. Additional work is needed to investigate specific drilling-related exposures that might explain these associations. https://doi.org/10.1289/EHP7678

Published
2021
Full Text
View/download PDF

34. Developing health professional students’ handover skills through a virtual, interprofessional handover workshop

Author

Hill, Elaine Alais susannna, Gordon, Morris, Gurbutt, Dawne, Hill, Elaine Alais susannna, Gordon, Morris, and Gurbutt, Dawne

Abstract

Inter-professional education has been identified as a core theoretical pillar for improving learners’ non-technical skills (NTS) and consequently promoting safe patient handover in clinical practice. However, specific handover education, especially in an authentic interprofessional fashion, is rare. Contributing factors include pragmatic and logistical elements, which can potentially be addressed through virtual delivery. We developed a virtual inter-professional handover education programme for undergraduate healthcare students, based on a classroom-based version that we designed and piloted previously. The workshop used interactive large and small group activities to develop understanding of the reasons for poor handover, its implications for patient safety and the skills to undertake structured, focused handover. It specifically included exercises which emphasised the differences between single-profession and interprofessional handover and how miscommunication may arise and lead to errors. The workshop was delivered using Blackboard Learn, Microsoft Teams (version 12) and Vevox (November 2021 version) and based on materials which are readily available in the public domain. 37 students from four professions attended the workshop, which was evaluated using both in-workshop assessment and formal research elements. Handover knowledge and skills, attitudes towards inter-professional handover and confidence in giving and receiving handover increased following the workshop; in addition, students enjoyed taking part and were keen to apply their learning in practice. Content analysis of individual interviews also provides insights into how and why students learned and suggests that the underpinning SECTORS model is appropriate for explaining NTS acquisition in theoretical learning settings, as well as practice. Workshop delivery was both cost and resource effective.

Published
2023
Full Text
View/download PDF

35. Hormone Replacement Therapy and COVID-19 Outcomes in Solid Organ Transplant Recipients Compared with the General Population

Author

Vinson, Amanda J., primary, Anzalone, Alfred, additional, Schissel, Makayla, additional, Dai, Ran, additional, French, Evan T., additional, Olex, Amy L., additional, Mannon, Roslyn B., additional, Wilcox, Adam B., additional, Lee, Adam M., additional, Graves, Alexis, additional, Anzalone, Alfred Jerrod, additional, Manna, Amin, additional, Saha, Amit, additional, Olex, Amy, additional, Zhou, Andrea, additional, Williams, Andrew E., additional, Southerland, Andrew, additional, Girvin, Andrew T., additional, Walden, Anita, additional, Sharathkumar, Anjali A., additional, Amor, Benjamin, additional, Bates, Benjamin, additional, Hendricks, Brian, additional, Patel, Brijesh, additional, Alexander, Caleb, additional, Bramante, Carolyn, additional, Ward-Caviness, Cavin, additional, Madlock-Brown, Charisse, additional, Suver, Christine, additional, Chute, Christopher, additional, Dillon, Christopher, additional, Wu, Chunlei, additional, Schmitt, Clare, additional, Takemoto, Cliff, additional, Housman, Dan, additional, Gabriel, Davera, additional, Eichmann, David A., additional, Mazzotti, Diego, additional, Brown, Don, additional, Boudreau, Eilis, additional, Hill, Elaine, additional, Zampino, Elizabeth, additional, Marti, Emily Carlson, additional, Pfaff, Emily R., additional, French, Evan, additional, Koraishy, Farrukh M., additional, Mariona, Federico, additional, Prior, Fred, additional, Sokos, George, additional, Martin, Greg, additional, Lehmann, Harold, additional, Spratt, Heidi, additional, Mehta, Hemalkumar, additional, Liu, Hongfang, additional, Sidky, Hythem, additional, Hayanga, J.W. Awori, additional, Pincavitch, Jami, additional, Clark, Jaylyn, additional, Harper, Jeremy Richard, additional, Islam, Jessica, additional, Ge, Jin, additional, Gagnier, Joel, additional, Saltz, Joel H., additional, Saltz, Joel, additional, Loomba, Johanna, additional, Buse, John, additional, Mathew, Jomol, additional, Rutter, Joni L., additional, McMurry, Julie A., additional, Guinney, Justin, additional, Starren, Justin, additional, Crowley, Karen, additional, Bradwell, Katie Rebecca, additional, Walters, Kellie M., additional, Wilkins, Ken, additional, Gersing, Kenneth R., additional, Cato, Kenrick Dwain, additional, Murray, Kimberly, additional, Kostka, Kristin, additional, Northington, Lavance, additional, Pyles, Lee Allan, additional, Misquitta, Leonie, additional, Cottrell, Lesley, additional, Portilla, Lili, additional, Deacy, Mariam, additional, Bissell, Mark M., additional, Clark, Marshall, additional, Emmett, Mary, additional, Saltz, Mary Morrison, additional, Palchuk, Matvey B., additional, Haendel, Melissa A., additional, Adams, Meredith, additional, Temple-O'Connor, Meredith, additional, Kurilla, Michael G., additional, Morris, Michele, additional, Qureshi, Nabeel, additional, Safdar, Nasia, additional, Garbarini, Nicole, additional, Sharafeldin, Noha, additional, Sadan, Ofer, additional, Francis, Patricia A., additional, Burgoon, Penny Wung, additional, Robinson, Peter, additional, Payne, Philip R.O., additional, Fuentes, Rafael, additional, Jawa, Randeep, additional, Erwin-Cohen, Rebecca, additional, Patel, Rena, additional, Moffitt, Richard A., additional, Zhu, Richard L., additional, Kamaleswaran, Rishi, additional, Hurley, Robert, additional, Miller, Robert T., additional, Pyarajan, Saiju, additional, Michael, Sam G., additional, Bozzette, Samuel, additional, Mallipattu, Sandeep, additional, Vedula, Satyanarayana, additional, Chapman, Scott, additional, O'Neil, Shawn T., additional, Setoguchi, Soko, additional, Hong, Stephanie S., additional, Johnson, Steve, additional, Bennett, Tellen D., additional, Callahan, Tiffany, additional, Topaloglu, Umit, additional, Sheikh, Usman, additional, Gordon, Valery, additional, Subbian, Vignesh, additional, Kibbe, Warren A., additional, Hernandez, Wenndy, additional, Beasley, Will, additional, Cooper, Will, additional, Hillegass, William, additional, and Zhang, Xiaohan Tanner, additional

Published
2023
Full Text
View/download PDF

36. Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C

Author

Bhatia, Abhishek, Preiss, Alexander J., Xiao, Xuya, Brannock, M. Daniel, Alexander, G. Caleb, Chew, Robert F., Fitzgerald, Megan, Hill, Elaine, Kelly, Elizabeth P., Mehta, Hemalkumar B., Madlock-Brown, Charisse, Wilkins, Kenneth J., Chute, Christopher G., Haendel, Melissa, Moffitt, Richard, and Pfaff, Emily R.

Subjects
Article
Abstract

This study leverages electronic health record data in the National COVID Cohort Collaborative’s (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid’s real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

Published
2023

37. SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study

Author

Hadley, Emily, Yoo, Yun Jae, Patel, Saaya, Zhou, Andrea, Laraway, Bryan, Wong, Rachel, Preiss, Alexander, Chew, Rob, Davis, Hannah, Chute, Christopher G, Pfaff, Emily R, Loomba, Johanna, Haendel, Melissa, Hill, Elaine, and Moffitt, Richard

Subjects
Article
Abstract

Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer’s V: 0.18, DoF = 4).

Published
2023
Full Text
View/download PDF

38. Additional file 1 of Coding long COVID: characterizing a new disease through an ICD-10 lens

Author

Pfaff, Emily R., Madlock-Brown, Charisse, Baratta, John M., Bhatia, Abhishek, Davis, Hannah, Girvin, Andrew, Hill, Elaine, Kelly, Elizabeth, Kostka, Kristin, Loomba, Johanna, McMurry, Julie A., Wong, Rachel, Bennett, Tellen D., Moffitt, Richard, Chute, Christopher G., and Haendel, Melissa

Abstract

Additional file 1: Supplemental methods, supplemental table 1, supplemental figures 1-3. Supplemental Methods – Description of additional methods used, including: Community detection in diagnosis analysis. network stability, age-stratified condition co-occurrence networks, and standard N3C data quality checks. Supplemental Table 1. – Demographic breakdown of all COVID-positive patients across 34 N3C sites. Supplemental Figure 1. – Uptake of U09.9 and B94.8 ICD-10_CM codes across 34 N3C sites. Supplemental Figure 2. – Common medications among patients with a U09.9 code. Supplemental Figure 3. – Common conditions among patients with a U09.9 code

Published
2023
Full Text
View/download PDF

39. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

Author

Reese, Justin T., primary, Blau, Hannah, additional, Casiraghi, Elena, additional, Bergquist, Timothy, additional, Loomba, Johanna J., additional, Callahan, Tiffany J., additional, Laraway, Bryan, additional, Antonescu, Corneliu, additional, Coleman, Ben, additional, Gargano, Michael, additional, Wilkins, Kenneth J., additional, Cappelletti, Luca, additional, Fontana, Tommaso, additional, Ammar, Nariman, additional, Antony, Blessy, additional, Murali, T.M., additional, Caufield, J. Harry, additional, Karlebach, Guy, additional, McMurry, Julie A., additional, Williams, Andrew, additional, Moffitt, Richard, additional, Banerjee, Jineta, additional, Solomonides, Anthony E., additional, Davis, Hannah, additional, Kostka, Kristin, additional, Valentini, Giorgio, additional, Sahner, David, additional, Chute, Christopher G., additional, Madlock-Brown, Charisse, additional, Haendel, Melissa A., additional, Robinson, Peter N., additional, Spratt, Heidi, additional, Visweswaran, Shyam, additional, Flack, Joseph Eugene, additional, Yoo, Yun Jae, additional, Gabriel, Davera, additional, Alexander, G. Caleb, additional, Mehta, Hemalkumar B., additional, Liu, Feifan, additional, Miller, Robert T., additional, Wong, Rachel, additional, Hill, Elaine L., additional, Thorpe, Lorna E., additional, and Divers, Jasmin, additional

Published
2023
Full Text
View/download PDF

43. Factors which influence the effectiveness of clinical supervision for student nurses in Sri Lanka: A qualitative research study

Author

Hill, Elaine Alais susannna, Abhayasinghe, Kalpani, Hill, Elaine Alais susannna, and Abhayasinghe, Kalpani

Abstract

Background: Clinical placements are an essential part of student nurse education, but their effectiveness is influenced by the type and availability of supervision and existing resources. In Sri Lanka, the specific socio-political context in which nursing, and nurse education, operate may also be important. Objectives: To examine the impact of socio-political factors on Sri Lankan nurses’ supervisory practices and student nurses’ experiences of clinical supervision Design: Qualitative descriptive. Settings: Four teaching hospitals and four educational establishments in Colombo district, Sri Lanka. Participants: 217 student nurses in years 2-4 of their programmes. 205 qualified nurses (clinical and academic) with a minimum of two years’ supervisory experience. Methods: Written responses to three open questions, followed by reflexive thematic analysis with inductive, semantic coding. Results: Two themes were identified: 1. Personal and professional development 2. Tensions and conflicts. Sociocultural norms and governance structures, which limited nurses’ professional recognition and self-determination, negatively affected clinical supervision. Conclusions: The sociocultural changes necessary to raise the professional status of Sri Lankan nurses will take time to occur. Approval of a mentorship education programme for supervisors, recognition of their role and greater support for students are important first steps in this process.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results