1,997 results on '"Brown, Jamie"'
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152. The Effect of the First UK COVID-19 Lockdown on Users of the Drink Less App: Interrupted Time Series Analysis of Sociodemographic Characteristics, Engagement, and Alcohol Reduction
- Author
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Oldham, Melissa, primary, Perski, Olga, additional, Loebenberg, Gemma, additional, Brown, Jamie, additional, and Garnett, Claire, additional
- Published
- 2022
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153. Use of e-Cigarettes and Attendance at Stop Smoking Services: A Population Survey in England
- Author
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Hartwell, Greg, primary, Egan, Matt, additional, Brown, Jamie, additional, Pliakas, Triantafyllos, additional, and Petticrew, Mark, additional
- Published
- 2022
- Full Text
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154. Read All about It: Faculty Book Clubs in Action to Support Student Engagement
- Author
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Rouech, Kristina E., primary, VanDeusen, Betsy A., additional, Angera, Jeffrey J., additional, Arnekrans, Allison K., additional, Majorana, Jennifer C., additional, and Brown, Jamie L., additional
- Published
- 2022
- Full Text
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155. Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries
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Kahan, Brennan C., Koulenti, Desponia, Arvaniti, Kostoula, Beavis, Vanessa, Campbell, Douglas, Chan, Matthew, Moreno, Rui, Pearse, Rupert M., Pearse, Rupert M., Beattie, Scott, Clavien, Pierre-Alain, Demartines, Nicolas, Fleisher, Lee A., Grocott, Mike, Haddow, James, Hoeft, Andreas, Holt, Peter, Moreno, Rui, Pritchard, Naomi, Rhodes, Andrew, Wijeysundera, Duminda, Wilson, Matt, Ahmed, Tahania, Everingham, Kirsty, Hewson, Russell, Januszewska, Marta, Pearse, Rupert M., Phull, Mandeep-Kaur, Halliwell, Richard, Shulman, Mark, Myles, Paul, Schmid, Werner, Hiesmayr, Michael, Wouters, Patrick, de Hert, Stefan, Lobo, Suzana, Beattie, Scott, Wijeysundera, Duminda, Fang, Xiangming, Rasmussen, Lars, Futier, Emmanuel, Biais, Matthieu, Venara, Aurélien, Slim, Karem, Sander, Michael, Koulenti, Despoina, Arvaniti, Kostoula, Chan, Mathew, Kulkarni, Atul, Chandra, Susilo, Tantri, Aida, Geddoa, Emad, Abbas, Muntadhar, Della Rocca, Giorgio, Sivasakthi, Datin, Mansor, Marzida, Luna, Pastor, Bouwman, 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Drimala, Maria, Georgakakis, Georgios, Kiourtzieva, Ellada, Ntouma, Panagiota, Prionas, Apostolos, Xouplidis, Kyriakos, Dalampini, Eleftheria, Giannaki, Chrysavgi, Iasonidou, Christina, Ioannidis, Orestis, Lavrentieva, Athina, Lavrentieva, Athena, Papageorgiou, George, Kokkinoy, Maria, Stafylaraki, Maria, Gaitanakis, Stylianos, Karydakis, Periclis, Paltoglou, Josef, Ponireas, Panagiotis, Chaloulis, Panagiotis, Provatidis, Athanasios, Sousana, Anisoglou, Gardikou, Varvara Vanessa, Konstantivelli, Maria, Lataniotou, Olga, Lisari, Elisavet, Margaroni, Maria, Stamatiou, Konstantinos, Nikolaidis, Edouardos, Pnevmatikos, Ioannis, Sertaridou, Eleni, Andreou, Alexandros, Arkalaki, Eleni, Athanasakis, Elias, Chaniotaki, Fotini, Chatzimichali, Aikaterini, Christofaki, Maria, Dermitzaki, Despina, Fiorentza, Klara, Frantzeskos, Georgios, Geromarkaki, Elisavet, Kafkalaki, Kalliopi, Kalogridaki, Marina, Karydi, Konstyllia, Kokkini, Sofia, Kougentakis, Georgios, Lefaki, Tatiana, Lilitsis, Emmanouhl, 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Lam, Tracy, Chu, Susanna, Lai, Man Ling, Lam, Wing Yan, Wong, Kin Wai Kevin, Kwok, Dilys, Hung, Ching Yue Janice, Chan, Wai Kit Jacky, LamWong, Wing, Chung, Chun Kwong Eric, Lai, Man Ling, Ma, ShuKai, Kaushik, Shuchi, Shah, Bhagyesh, Shah, Dhiren, Shah, Sanjay, Ar, Praburaj, Muthuchellappan, Radhakrishnan, Agarwal, Vandana, Divatia, Jigeeshu, Kulkarni, Atul, Mishra, Sanghamitra, Nimje, Ganesh, Pande, Swati, Savarkar, Sukhada, Shrivastava, Aditi, Thomas, Martin, Yegnaram, Shashikant, Hidayatullah, Rahmat, Chandra, Susilo, Tantri, Aida, Puar, Nasman, Niman, Sumara, Indra, Imai, Hamzah, Zulkarnain, Yuliana, Annika, Abidin, Ucu Nurhadiat, Dursin, Ade Nurkacan, Kurnia, Andri, Susanti, Ade, Handayani, Dini, Aribawa, Mahaalit Alit, Arya, Aryabiantara, Senapathi, Tjokorda Gde Agung, Utara, Utara Hartawan, Wid, Widnyana Made, Wima, Semarawima, Wir, Wiryana Made, Jehosua, Brillyan, Kaunang, Jonathan, Lantang, Eka Yudha, Najoan, Rini, Waworuntu, Neil, Awad, Hadi, Fuad, Akram, Geddoa, Emad, Geddoa, Burair, Khalaf, Abdel Razzaq, Al hussaini, Sabah, Albaj, Safauldeensalem, Kenber, Maithem, Bettinelli, Alessandra, Spadaro, Savino, Volta, Carlo Alberto, Giancarlo, Luigi, Sottosanti, Vicari, Della Rocca, Giorgio, Spagnesi, Lorenzo, Toretti, Ilaria, Alloj, Chiara, Cardellino, Silvano, Carmino, Livio, Costanzo, Eleonora, Fanfani, Lucia Caterina, Novelli, Maria Teresa, Roasio, Agostino, Bellandi, Mattia, Beretta, Luigi, Bignami, Elena, Bocchino, Speranza, Cabrini, Luca, Corti, Daniele, Landoni, Giovanni, Meroni, Roberta, Moizo, Elena, Monti, Giacomo, Pintaudi, Margherita, Plumari, Valentina Paola, Taddeo, Daiana, Testa, Valentina, Winterton, Dario, Zangrillo, Alberto, Cloro, Luigi Maria, Colangelo, Chiara, Colangelo, Antonio, Rotunno, Giuseppe, Angel, MiguelPaludi, Maria, Cloro Paolo, Pata, Antonio, Parrini, Vieri, Gatta, Alessandro, Nastasi, Mauro, Tinti, Carla, Spagnesi, Lorenzo, Arrigo, Mario, Benevento, Angelo, Bottini, Corrado, Cannavo’, Maurizio, Gastaldi, Christian, Marchesi, Alessandro, Pascazio, Angelantonio, Pata, Francesco, Pozzi, Emilio, Premoli, Alberto, Tessera, Gaetano, Boschi, Luca, D’Andrea, Rocco, Ghignone, Federico, Poggioli, Gilberto, Sibilio, Andrea, Taffurelli, Mario, Ugolini, Giampaolo, Ab Majid, Mohd Azuan, Ab Rahman, Rusnah, Joseph, James, Pathan, Furquan, Shah, Mohammad Hafizshah Sybil, Yap, Huey Ling, Cheah, Seleen, Chin, Im Im, Looi, Ji Keon, Tan, Siew Ching, Visvalingam, Sheshendrasurian, Kwok, Fan Yin, Lee, Chew Kiok, Tan, Tse Siang, Wong, Sze Meng, Abdullah, Noor Hairiza, Liew, Chiat Fong, Luxuman, Lovenia, Mohd Zin, Nor Hafizah, Norddin, Muhamad Faiz, Alias, Raja Liza Raja, Wong, Juan Yong, Yong, Johnny, Bin Mustapha, Mohd Tarmimi, Chan, Weng Ken, Dzulkipli, Norizawati, Kuan, Pei Xuan, Lee, Yew Ching, Alias, Anita, Guok, Eng Ching, Jee, Chiun Chen, Ramon, Brian Rhadamantyne, Weng, Cheng Wong, Abd Ghafar, Fara Nur Idayu, Aziz, Faizal Zuhri, Hussain, Nabilah, Lee, Hooi Sean, Sukawi, Ismawaty, Woon, Yuan Liang, Abd Hadi, Husni Zaeem, Ahmad Azam, Ummi Azmira, Alias, Abdul Hafiz, Kesut, Saiful Aizar, Lee, Jun May, Ooi, Dar Vin, Sulaiman, Hetty Ayuni, Tengku Lih, Tengku Alini, Mansor, Marzida, Veerakumaran, Jeyaganesh, Luna, Pastor, Rojas, Eder, Resendiz, Gerardo Esteban Alvarez, Zapata, Darcy Danitza Mari, Aguilar López, Julio Cesar Jesús, Flores, Armando Adolfo Alvarez, Amador, Juan Carlosc Bravo, Avila, Erendira Jocelin Dominguez, Aquino, Laura Patricia González, Rodriguez, Ricardo Lopez, Landa, Mariana Torres, Urias, Emma, Hollmann, Markus, Hulst, Abraham, Kirzner, Osne, Preckel, Benedikt, Koopman-van Gemert, Ankie, Bouwman, Arthur, Buise, Marc, Tolenaar, Noortje, Weber, Eric, de Fretes, Jennifer, Houweling, Peter, Ormskerk, Patricia, Van Bommel, Jasper, Buhre, Wolfgang, Lance, Marcus, Smit-Fun, Valerie, van Zundert, Tom, Baas, Peter, de Boer, Hans Donald, Sprakel, Joost, Elferink-Vonk, Renske, Noordzij, Peter, van Zeggeren, Laura, Brand, Bastiaan, spanjersberg, Rob, ten Bokkel-Andela, Janneke, Numan, Janneke, van Klei, Wilton, van Zaane, Bas, Boer, Christa, van Duivenvoorde, Yoni, Hering, Jens Peter, Zonneveldt, Harry, Campbell, Doug, Hoare, Siobhan, Santa, Sahayam, Allen, Sara Jane, Beavis, Vanessa, Bell, Rachel, Choi, Hyun-min David, Drake, Matthew, Farrell, Helen, Higgie, Kushlin, Holmes, Kerry, Jenkins, Nicole, Kim, Chang Joon, Kim, Steven, Law, Kiew Chai, McAllister, Davina, Park, Karen, Pedersen, Karen, Pfeifer, Leesa, Salmond, Timothy, Steynor, Martin, Tan, Michael, Waymouth, Ellen, Ab Rahman, Ahmad Sufian, Armstrong, John, Dudson, Rosie, Jenkins, Nia, Nilakant, Jayashree, Richard, Seigne, Virdi, Pardeep, Dixon, Liane, Donohue, Roana, Farrow, Mehreen, Kennedy, Ross, Marissa, Henderson, McKellow, Margie, Nicola, Delany, Pascoe, Rebecca, Roberts, Stephen John, Rowell, George, Sumner, Matthew, Templer, Paul, Chandrasekharan, Shardha, Fulton, Graham, Jammer, Ib, Ali, Marlynn, More, Richard, Wilson, Leona, Chang, Yuan Hsuan, Chang, Julia, Fowler, Carolyn, Panckhurst, Jonathan, 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Ikemefuna, Onuora, Elias, Onyeka, Tonia, Ugwu, Innocent, Umeh, Friday, Alagbe-Briggs, Olubusola, Dodiyi-Manuel, Amabra, Echem, Richard, Obasuyi, Bright, Onajin-Obembe, Bisola, Bandeira, Maria Expedito, Martins, Alda, Tomé, Miguel, Martins Costa, Ana Cristina Miranda, Krystopchuk, Andriy, Branco, Teresa, Esteves, Simao, Melo, Marco António, Monte, Júlia, Rua, Fernando, Martins, Isabel, Pinho-Oliveira, Vítor Miguel, Rodrigues, Carla Maria, Cabral, Raquel, Marques, Sofia, Rêgo, Sara, Teixeira Jesus, Joana Sofia, Conceição Marques, Maria, Romao, Cristina, Dias, Sandra, Santos, Ana Margarida, Alves, Maria Joao, Salta, Cristina, Cruz, Salome, Duarte, Célia, Furtado Paiva, António Armando, do Nascimento Cabral, Tiago, Fariae Maia, Dionisio, Correia da Silva, Rui Freitas Mendonça, Langner, Anuschka, Oliveira Resendes, Hernâni, da Conceição Soares, Maria, Abrunhosa, Alexandra, Faria, Filomena, Miranda, Lina, Pereira, Helena, Serra, Sofia, Ionescu, Daniela, Margarit, Simona, Mitre, Calin, 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Jenna L., Torborg, Alexandra, Visser, Linda, Anderson, Philip, Conradie, Alae, de Swardt, Mathew, de Villiers, Martin, Eikman, Johan, Liebenberg, Riaan, Mouton, Johan, Paton, Abbey, van der Merwe, Louwrence, Wilscott-Davids, Candice, Barrett, Wendy Joan, Bester, Marlet, de Beer, Johan, Geldenhuys, Jacques, Gouws, Hanni, Potgieter, Jan-Hendrik, Strydom, Magdel, Wilberforce-Turton, Edwin, Chetty, Rubendraj R., Chirkut, Subash, Cronje, Larissa, de Vasconcellos, Kim, Dube, Nokukhanya Z., SibusisoGama, N., Green, Garyth M., Green-Thompson, Randolph, Kinoo, Suman Mewa, Kistnasami, Prenolin, Maharaj, Kapil, Moodley, Manogaran S., Mothae, Sibongile J., Naidoo, Ruvashni, Aslam, M., Noorbhai, F., Rughubar, Vivesh, Reddy, Jenendhiran, Singh, Avesh, Skinner, David L., Smith, Murray J., Singh, Bhagwan, Misra, Ravi, Naidoo, Maheshwar, Ramdharee, Pireshin, Selibea, Yvonne, Sewpersad, Selina, Sham, Shailendra, Wessels, Joseph D., Africander, Cucu, Bejia, Tarek, Blakemore, Stephen P., Botes, Marisa, Bunwarie, Bimalshakth, Hernandez, Carlos B., Jeeraz, Mohammud A., Legutko, Dagmara A., Lopez, Acela G., De Meyer, Jenine N., Muzenda, Tanaka, Naidoo, Noel, Patel, Maryam, Pentela, Rao, Junge, Marina, Mansoor, Naj, Rademan, Lana, Scislowski, Pawel, Seedat, Ismail, van den Berg, Bianca, van der Merwe, Doreen, van Wyk, Steyn, Govender, Komalan, Naicker, Darshan, Ramjee, Rajesh, Saley, Mueen, Kuhn, Warren Paul, Matos-Puig, Roel, Moolla, Zaheer, Lisi, Alberto, Perez, Gisela, Valle Beltran, Anna, Lozano, Angels, Delgado Navarro, Carlos, Duca, Alejandro, Pastor Martinez, Ernesto, Ferrando, Carlos, Fuentes, Isabel, García-Pérez, Maria Luisa, Gracia, Estefania, Izquierdo Palomares, Ana, Katime, Antonio, Miñana, Amanda, Incertis, Raul, Romero, Esther, Romero Garcia, Carolina Soledad, Rubio, Concepcion, Socorro Artiles, Tania, Soro, Marina, Valls, Paola, Laguarda, Gisela Alaman, Benavent, Pau, Cuenca, Vicente Chisbert, Cueva, Andreu, Lafuente, Matilde, Parra, Asuncion Marques, Rodrigo, Alejandra 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Daniel, Harvey, Lynch, Julie, Paton, Alison, Pyke, Shirley, Scholefield, John, Whitworth, Helen, Bottrill, Fiona, Ramalingam, Ganesh, Webb, Stephen, Akerman, Nik, Antill, Philip, Bourner, Lynsey, Buckley, Sarah, Castle, Gail, Charles, Rob, Eggleston, Christopher, Foster, Rebecca, Gill, Satwant, Lindley, Kate, Lklouk, Mohamed, Lowery, Tracey, Martin, Oliver, Milne, David, O’Connor, Patrick, Ratcliffe, Andrew, Rose, Alastair, Simeson, Karen, Smith, Annie, Varma, Sandeep, Ward, Jackie, Simeson, Karen, Barcraft-Barnes, Helena, Camsooksai, Julie, Colvin, Carolyn, Reschreiter, Henrik, Tbaily, Lee, Venner, Nicola, Hamilton, Caroline, Kelly, Lewis, Toth-Tarsoly, Piroska, Dodsworth, Kerry, Foord, Denise, Gordon, Paul, Hawes, Elizabeth, Lamb, Nikki, Mouland, Johanna, Nightingale, Jeremy, Rose, Steve, Schrieber, Joe, Al ‘Amri, Khalid, Aladin, Hafiz, Arshad, Mohammed Asif, Barraclough, James, Bentley, Conor, Bergin, Colin, Carrera, Ronald, Clarkson, Aisling, Collins, Michelle, Cooper, Lauren, Denham, Samuel, Griffiths, Ewen, Ip, Peter, Jeyanthan, Somasundaram, Joory, Kavita, Kaur, Satwant, Marriott, Paul, Mitchell, Natalie, Nagaiah, Sukumar, Nilsson, Annette, Parekh, Nilesh, Pope, Martin, Seager, Joseph, Serag, Hosam, Tameem, Alifia, Thomas, Anna, Thunder, Joanne, Torrance, Andrew, Vohra, Ravinder, whitehouse, Arlo, Wong, Tony, Blunt, Mark, Wong, Kate, Giles, Julian, Reed, Isabelle, Weller, Debbie, Bell, Gillian, Birch, Julie, Damant, Rose, Maiden, Jane, Mewies, Clare, Prince, Claire, Radford, Jane, Balain, Birender, Banerjee, Robin, Barnett, Andrew, Burston, Ben, Davies, Kirsty, Edwards, Jayne, Evans, Chris, Ford, David, Gallacher, Pete, Hill, Simon, Jaffray, David, Karlakki, Sudheer, Kelly, Cormac, Kennedy, Julia, Kiely, Nigel, Lewthwaite, Simon, Marquis, Chris, Ockendon, Matthew, Phillips, Stephen, Pickard, Simon, Richardson, James, Roach, Richard, Smith, Tony, Spencer-Jones, Richard, Steele, Niall, Steen, Julie, Van Liefland, Marck, White, Steve, Faulds, Matthew, Harris, Meredyth, Kelly, Carrie, Nicol, Scott, Pearson, Sally Anne, Chukkambotla, Srikanth, Andrew, Alyson, Attrill, Elizabeth, Campbell, Graham, Datson, Amanda, Fouracres, Anna, Graterol, Juan, Graves, Lynne, Hong, Bosun, Ishimaru, Alexander, Karthikeyan, Arvind, King, Helen, Lawson, Tom, Lee, Gregory, Lyons, Saoirse, Macalister Hall, Andrew, Mathoulin, Sophie, Mcintyre, Eilidh, Mclaughlin, Danny, Mulcahy, Kathleen, Ratcliffe, Anna, Robbins, James, Sung, Weilin, Tayo, Adeoluwa, Trembath, Lisa, Venugopal, Suneetha, Walker, Robert, Wigmore, Geoffrey, Boereboom, Catherine, Downes, Charlotte, Humphries, Ryan, Melbourne, Susan, Smith, Coral, Tou, Samson, Ullah, Shafa, Batchelor, Nick, Boxall, Leigh, Broomby, Rupert, Deen, Tariq, Hellewell, Alistair, Helliwell, Laurence, Hutchings, Melanie, Hutchins, David, Keenan, Samantha, Mackie, Donna, Donna, Alison, Smith, Frances, Stone, Lucy, Thorpe, Kevin, Wassall, Richard, Woodgate, Andrew, Baillie, Shelley, Campbell, Tara, James, Sarah, King, Chris, Marques de Araujo, Daniela, Martin, Daniel, Morkane, Clare, Neely, Julia, Rajendram, Rajkumar, Burton, Megan, James, Kathryn, Keevil, Edward, Minik, Orsolya, Morgan, Jenna, Musgrave, Anna, Rajanna, Harish, Roberts, Tracey, Szakmany, Tamas, Adamson, Michael, Jumbe, Sandra, Kendall, Jennie, Muthuswamy, Mohan Babu, Anderson, Charlotte, Cruikshanks, Andrew, Pothuneedi, Sailaja, Walker, Rachel, Wrench, Ian, Zeidan, Lisa, Ardern, Diane, Harris, Benjamin, Hellstrom, Johanna, Martin, Jane, Thomas, Richard, Varsani, Nimu, Wrey Brown, Caroline, Docherty, Philip, Gillies, Michael, McGregor, Euan, Usher, Helen, Craig, Jayne, Smith, Andrew, Ahmad, Tahania, Bodger, Phoebe, Creary, Thais, Everingham, Kirsty, Fowler, Alexander, Hewson, Russ, Ijuo, Eke, Januszewska, Marta, Jones, Timothy, Kantsedikas, Ilya, Lahiri, Sumitra, McLean, Aaron Lawson, Niebrzegowska, Edyta, Phull, Mandeep, Wang, Difei, Wickboldt, Nadine, Baldwin, Jacqueline, Doyle, Donna, Mcmullan, Sean, Oladapo, Michelle, Owen, Thomas, Tripathi, Shiva, Williams, Alexandra, Daniel, Hull, Gregory, Peter, Husain, Tauqeer, Kirk-Bayley, Justin, Mathers, Edward, Montague, Laura, White, Stuart, Avis, Joanne, Cook, Tim, Dali-Kemmery, Lola, Kerslake, Ian, Lambourne, Victoria, Pearson, Annabel, Boyd, Christine, Callaghan, Mark, Lawson, Cathy, McCrossan, Roopa, Nesbitt, Vanessa, O’connor, Laura, Scott, Julia, Sinclair, Rhona, Farid, Nahla, Morgese, Ciro, Bhatia, Kailash, Karmarkar, Swati, Vohra, Akbar, Ahmed, Jamil, Branagan, Graham, Hutton, Monica, Swain, Andrew, Brookes, Jamie, Cornell, Jonathan, Dolan, Rachael, Hulme, Jonathan, Jansen van Vuuren, Amanda, Jowitt, Tom, Kalashetty, Gunasheela, Lloyd, Fran, Patel, Kiran, Sherwood, Nicholas, Brown, Lynne, Chandler, Ben, Deighton, Kerry, Emma, Temlett, Haunch, Kirsty, Cheeseman, Michelle, Dent, Kathy, Garg, Sanjeev, Gray, Carol, Hood, Marion, Jones, Dawn, Juj, Joanne, Mitra, Atideb, Rao, Roshan, Walker, Tara, Al Anizi, Mashel, Cheah, Clarissa, Cheing, Yushio, Coutinho, Francisco, Gondo, Prisca, Hadebe, Bernard, Onie Hove, Mazvangu, khader, Ahamed, Krishnachetty, Bobby, Rhodes, Karen, Sokhi, Jagdish, Baker, Katie-Anne, Bertram, Wendy, Looseley, Alex, Mouton, Ronelle, Arnold, Glenn, Arya, Shobhit, Balfoussia, Danai, Baxter, Linden, Harris, James, Jones, Craig, Knaggs, Alison, Markar, Sheraz, Perera, Anisha, Scott, Alasdair, Shida, Asako, Sirha, Ravneet, Wright, Sally, Frost, Victoria, Gray, Catherine, MacGregor, Mark, Andrews, Emma, Arrandale, Lindsay, Barrett, Stephen, Bidd, Heena, Cifra, Elna, Cooper, Mariese, Dragnea, Dragos, Elna, Cifra, Maclean, Jennifer, Meier, Sonja, Milliken, Donald, Munns, Christopher, Ratanshi, Nadir, Salvana, Abegail, Watson, Anthony, Ali, Hani, Campbell, Gill, Critchley, Rebecca, Hicks, Catherine, Liddle, Alison, Pass, Marc, Ritchie, Charlotte, Thomas, Charlotte, Too, Lingxi, Welsh, Sarah, Gill, Talvinder, Johnson, Joanne, Reed, Joanne, Davis, Edward, Papadopoullos, Sam, Attwood, Clare, Biffen, Andrew, Boulton, Kerenza, Gray, Sophie, Hay, David, Mills, Sarah, Montgomery, Jane, Riddell, Rory, Simpson, James, Bhardwaj, Neeraj, Paul, Elaine, Uwubamwen, Nosakhare, Vohra, Akbar, Berntsen, Emer, Nolan, Tracey, Turner, Angus, Alexander, Maini, Arrich, James, Arumugam, Swarna, Blackwood, Douglas, Boggiano, Victoria, Brown, Robyn, Lam Chan, Yik, Chatterjee, Devnandan, Chhabra, Ashok, Christian, Rachel, Costelloe, Hannah, CoxwellMatthewman, Madeline, Dalton, Emma, Darko, Julia, Davari, Maria, Dave, Tejal, Deacon, Matthew, Deepak, Shantal, Edmond, Holly, Ellis, Jessica, El-Sayed, Ahmed, Eneje, Philip, English, Rose, Ewe, Renee, Foers, William, Franklin, John, Gallego, Laura, Garrett, Emily, Goldberg, Olivia, Goss, Harry, Greaves, Rosanna, Harris, Rudy, Hennings, Charles, Jones, Eleanor, Kamali, Nelson, Kokkinos, Naomi, Lewis, Carys, Lignos, Leda, Malgapo, Evaleen Victoria, Malik, Rizwana, Milne, Andrew, Mulligan, John-Patrick, Nicklin, Philippa, Palipane, Natasha, Parsons, Thomas, Piper, Rebecca, Prakash, Rohan, Ramesh, Byron, Rasip, Sarah, Reading, Jacob, Rela, Mariam, Reyes, Anna, Robert, Stephens, Rooms, Martin, Shah, Karishma, Simons, Henry, Solanki, Shalil, Spowart, Emma, Stevens, Amy, Thomas, Christopher, Waggett, Helena, Yassaee, Arrash, Kennedy, Anthony, Scott, Sara, Somanath, Sameer, Berg, Andrew, Hernandez, Miguel, Nanda, Rajesh, Tank, Ghanshyambhai, Wilson, Natalie, Wilson, Debbie, Al-Soudaine, Yassr, Baldwin, Matthew, Cornish, Julie, Davies, Zoe, Davies, Leigh, Edwards, Marc, Frewer, Natasha, Gallard, Sian, Glasbey, James, Harries, Rhiannon, Hopkins, Luke, Kim, Taeyang, Koompirochana, Vilavan, Lawson, Simon, Lewis, Megan, Makzal, Zaid, Scourfield, Sarah, Ahmad, Yousra, Bates, Sarah, Blackwell, Clare, Bryant, Helen, Coulter, Suzanne, Cruickshank, Ross, Daniel, Sonya, Daubeny, Thomas, Edwards, Mark, Golder, Kim, Hawkins, Lesley, Helen, Bryant, Hinxman, Honor, Levett, Denny, Skinner, Ben, Walsgrove, Joseph, Bradburn, Mike, Dickson, Jane, Constantin, Kathryn, Karen, Markwell, O’Brien, Peter, O’Donohoe, Lynn, Payne, Hannah, Sundayi, Saul, Walker, Elaine, Brooke, Jenny, Cardy, Jon, Humphreys, Sally, Kessack, Laura, Kubitzek, Christiane, Kumar, Suhas, Cotterill, Donna, Hodzovic, Emil, Hosdurga, Gurunath, Miles, Edward, Saunders, Glenn, Campbell, Marta, Chan, Peter, Jemmett, Kim, Raj, Ashok, Naik, Aditi, Ramamoorthy, Rajarajan, Shah, Nimesh, Sylvan, Axel, Blyth, Katharine, Burtenshaw, Andrew, Freeman, David, Johnson, Emily, Lo, Philip, Martin, Terry, Plunkett, Emma, Wollaston, Julie, Allison, Joanna, Carroll, Christine, Craw, Nicholas, Craw, Sarah, Pitt-Kerby, Tressy, Rowland-Axe, Rebecca, Spurdle, Katie, McDonald, Andrew, Simon, Davies, Sinha, Vivek, Smith, Thomas, Banner-Goodspeed, Valerie, Boone, Myles, Campbell, Kathleen, Lu, Fengxin, Scannell, Joseph, Sobol, Julia, Balajonda, Naraida, Clemmons, Karen, Conde, Carlos, Funk, Bonita, Hall, Roger, Hopkins, Thomas, Olaleye, Omowunmi, Omer, Omer, Pender, Michelle, Porto, Angelo, Stevens, Alice, Waweru, Peter, Yeh, Erlinda, Bodansky, Daniella, Evans, Adam, Kleopoulos, Steven, Maril, Robert, Mathney, Edward, Sanchez, Angela, Tinuoye, Elizabeth, Bateman, Brian, Eng, Kristen, Jiang, Ning, Ladha, Karim, Needleman, Joseph, Chen, Lee-lynn, Chen, Lee-lynn, Lane, Rondall, Robinowitz, David, Ghushe, Neil, Irshad, Mariam, Patel, Samir, Takemoto, Steven, Wallace, Art, Mazzeffi, Michael, Rock, Peter, Wallace, Karin, Zhu, Xiaomao, Chua, Pandora, Fleisher, Lee, Mattera, Matthew, Sharar, Rebecca, Thilen, Stephan, Treggiari, Miriam, Morgan, Angela, Sofjan, Iwan, Subramaniam, Kathirvel, Avidan, Michael, Maybrier, Hannah, Muench, Maxwell, Wildes, Troy, and The International Surgical Outcomes Study (ISOS) group
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- 2017
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156. User characteristics of a smartphone app to reduce alcohol consumption
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Garnett, Claire, Crane, David, West, Robert, Michie, Susan, Brown, Jamie, and Winstock, Adam
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- 2017
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157. 'Run out of puff': Survey of techniques to quit nicotine vaping
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Walker, Natalie, Bullen, Christopher, Lang, Bodo, Parag, Varsha, Nosa, Vili, Laking, George, Kemper, Joya, Brown, Jamie, Shahab, Lion, Palmer, Amanda, and Rahimi, Mehdi
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Medicine and Health Sciences ,Public Health - Abstract
The New Zealand (NZ) government quickly identified the role e-cigarettes (ECs) have as a reduced-harm product to help smokers switch away from tobacco. ECs are included as an option in NZs smoking cessation treatment guidelines, and clear messaging regarding their use is promoted by NZ health authorities (https://vapingfacts.health.nz). The first ECs delivered nicotine at similar levels to nicotine replacement therapy (NRT) products, but with rapid product innovation, new pod-based devices using nicotine salts deliver nicotine at levels comparable to that of smoked tobacco. Given the long-term health effects of EC use remain uncertain and that not vaping (use of an EC) or smoking is ideal from a public health perspective, it’s important to offer support to people who wish to switch from ECs to no product use. However, little evidence exists on the best way to support people to move away from ECs, and no data exists for NZ. We have therefore designed a survey to collect information from vapers and ex-vapers about enablers and barriers to becoming vape-free.
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- 2022
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158. Value of a Pharmacy-Adjudicated Community Care Prior Authorization Drug Request Service
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Jennings, Andrew J., Brown, Jamie N., Britt, Rachel B., McNaughton, Leigh A., Durkee, Melissa, and Hashem, Mohamed G.
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Features - Abstract
BACKGROUND: Many veterans are eligible to receive prescriptions from community-based pharmacies. Swift and accurate review of prior authorization drug requests by the US Department of Veterans Affairs (VA) pharmacy is necessary to mitigate treatment delays, medication misuse, adverse drug events, medication errors, and unnecessary cost to the health care system. METHODS: We performed a retrospective review of community care prior authorization drug requests to assess the direct cost savings achieved through a centralized process and to characterize submitted requests. RESULTS: The centralized community care pharmacy team demonstrated a cost savings of $515,872.31 over 6 months and increased patient safety. Community care prior authorization drug requests had a 46.2% approval rate. Coordination of care took an average of 8 days. CONCLUSIONS: Use of a centralized community care pharmacy team could result in significant annual cost savings for the VA. Considering the approval rate seen in this study, VA could allocate resources to educate community-based prescribers about its formulary to increase the approval rate and reduce administrative burden for VA pharmacies and prescribers.
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- 2022
159. Alcohol reduction attempts and their success among increasing and higher risk drinkers in England
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Garnett, Claire, oldham, melissa, Perski, Olga, Loebenberg, Gemma, Kimergård, Andreas, and Brown, Jamie
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Epidemiology ,Medicine and Health Sciences ,Public Health - Abstract
Paper aiming to characterise alcohol reduction attempts and their success among increasing and higher risk drinkers using a population survey in England.
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- 2022
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160. A positive view on e-cigarettes
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Brown, Jamie
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- 2015
161. Development of SmokeFree Baby: a smoking cessation smartphone app for pregnant smokers
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Tombor, Ildiko, Shahab, Lion, Brown, Jamie, Crane, David, Michie, Susan, and West, Robert
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- 2016
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162. Prospective Cohort Study of the Effectiveness of Smoking Cessation Treatments Used in the “Real World”
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Kotz, Daniel, Brown, Jamie, and West, Robert
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- 2014
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163. Patient beliefs and attitudes about alcohol prevention in health care in England – A cross sectional study
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O'Donnell, Amy, Nilsen, Per, Brown, Jamie, Karlsson, Nadine, Thomas, Kristin, Skagerstrom, Janna, and Roback, Kerstin
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- 2022
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164. Perceived addiction to smoking and associations with motivation to stop, quit attempts and quitting success
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Perski, Olga, Herd, Natalie, West, Robert, and Brown, Jamie
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behavior and behavior mechanisms - Abstract
Smokers who have previously made an unsuccessful quit attempt may infer that they must be addicted to smoking (i.e. ‘self-attribution of addiction’). Perceived addiction (PA) may in turn deter smokers from making a future quit attempt. To examine the impact of PA on the component parts of the quitting process, the present study aimed to assess the associations of PA with 1) past quit attempts; 2) current motivation to stop; 3) future quit attempts; and 4) quitting success.
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- 2022
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165. Characterising the patterns of and factors associated with increased alcohol consumption since COVID-19 in the UK
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Herbec, Aleksandra, Shahab, Lion, Brown, Jamie, Kale, Dimitra, oldham, melissa, Garnett, Claire, and Perski, Olga
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FOS: Psychology ,Social Statistics ,Drinking patterns ,Medicine and Health Sciences ,Psychology ,COVID-19 ,Public Health ,Social and Behavioral Sciences ,Alcohol - Abstract
Pre-registration (before conducting the data analysis) of the protocol for data analysis (v1.2 25/08/2020) for a planned manuscript entitled: Characterising the patterns of and factors associated with increased alcohol consumption since COVID-19 in the UK.
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- 2022
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166. The impact of electronic cigarette use on cigarette consumption in England: a time-series analysis
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Beard, Emma, West, Robert, Brown, Jamie, and Michie, Susan
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- 2022
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167. Paper: Brief interventions and health inequalities
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Brown, Jamie
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- 2022
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168. The prevalence and characteristics of long-term e-cigarette and NRT users in England
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Hill, Emily and Brown, Jamie
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- 2022
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169. Association of quarterly prevalence of e-cigarette use with ever smoking among young adults in England: a time series analysis between 2007 and 2017
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Beard, Emma, Brown, Jamie, and Shahab, Lion
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- 2022
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170. Protocol for the association of smoking status with hospitalisation for COVID-19 compared with other respiratory viruses a year previous: A case-control study at a single UK National Health Service trust
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Simons, David, Perski, Olga, Brown, Jamie, Shahab, Lion, and Bailey, Robin
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Epidemiology ,Smoking ,Medicine and Health Sciences ,COVID-19 ,Diseases ,Public Health ,Hardware_REGISTER-TRANSFER-LEVELIMPLEMENTATION - Abstract
Pre-registered study protocol and analysis plan
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- 2022
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171. Are population trends in high-risk alcohol consumption in smokers associated with trends in quit attempts and quit success?
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Beard, Emma, Michie, Susan, Brown, Jamie, and West, Robert
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- 2022
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172. A qualitative study exploring people’s perception of factors influencing the uptake and use of health and wellbeing smartphone apps
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Szinay, Dorothy, Naughton, Felix, Jones, Andy, Chadborn, Tim, and Brown, Jamie
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- 2022
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173. Have the associations between different measures of socioeconomic position and smoking changed in England between 2010 and 2019? A monthly population survey
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Cox, Sharon, Beard, Emma, Brown, Jamie, Kock, Loren, and Shahab, Lion
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Tobacco ,ridge regression ,health inequalities ,Social and Behavioral Sciences ,socio-economical positioning - Abstract
Overall smoking prevalence is at an all-time low in England, at 15%(1) but there remains substantial differences between subgroups of the population. Smoking is a major driver of health inequalities, significantly contributing to the differences in rates of morbidity and premature mortality between the most and least deprived (2–4). In England, smoking rates are almost twice as high in those in routine and manual employment (18.9%) compared with those in professional and managerial trades (10.9%; (1)) and the gap has stubbornly persisted over a decade of declines in overall prevalence (5). In addition to occupational social grade, other indicators of socioeconomic position (SEP), have been associated with smoking status and prevalence, such as income, wealth, education, home and car ownership (2,6). Recent analyses have shown housing status, and in particular living in social housing (7) (i.e., local government assisted housing), to be the strongest independent predictor of smoking status (6). Like other health conditions, it is unlikely that not owning a home per se leads to smoking, but rather it is a proxy measure for the economic, social, environmental, and commercial factors - the ‘wider determinants’ of health which also lead people to smoke (8,9). These are broad influences, liable to change, and so monitoring over time which measures of SEP best predict leading causes preventable illness and death (10) is essential. Understanding which factors are independently associated, that is, most strongly predict against competing measures, is helpful for targeting finite resources to support smoking cessation treatment and to help researchers know which measures are best for assessing tobacco-related inequalities. However, as has been argued by others, trying to find the best single SEP predictor of health outcomes is theoretically limiting as this is only useful for interpreting the behaviour within a particular time and context (10). For example, car ownership was once a good indicator of material wealth by income and in turn associated with health behaviours and outcomes (11). The rise in car ownership, partly through greater affordability (12), means ownership alone no longer clearly distinguishes between social grade or income (although it may be useful as part of a composite score). Using several different measures of SEP can usefully capture changing trends and is important for evaluating the full contribution of SEP factors that lead to poorer health outcomes. However, this can be difficult in traditional regression models, given the high collinearity between measures of SEP (13), requiring researchers to select measures at the exclusion of others. This may lead to false conclusions or missing other important factors related to the health condition being studied. In a previous study (6), we examined six different measures of SEP (home ownership, income, occupational social grade, working status, car ownership and education) and smoking status between 2013-2019 using ridge regression analyses, a machine learning technique that allows an assessment of the contribution of each independent variable while taking account of high collinearity (10,13). Results showed home ownership was the strongest predictor, with those who reported not owning their own home having twice the odds of smoking compared with those who did. Occupational social grade, education and income were also good predictors, with a reduced odds among those in more advantaged positions being smokers. However, associations between car ownership and working status were weaker. A composite score of the six measures of SEP showed a gradient effect, with greater levels of disadvantage resulting in higher odds of smoking. In this present study, we will again examine the independent associations between these six measures of SEP and smoking status in England. We will extend our previous study by examining whether there were trends in these associations over a ten-year period (between 2010-2019). We propose several hypotheses: we expect the association between housing and smoking status to have strengthened. In England housing has become less affordable for a greater number of people, and there is a housing shortage and a lack of adequate housing, making housing status a contemporary marker for large health inequalities (8,14). Education and income are related to each other, and we would expect an association for both with smoking status, and the strength of the association with income to have increased owing to the rising cost of living, rising levels of income-related poverty and austerity (14). Given the social patterning of smoking related health inequalities that persist, we expect occupation social grade to remain strongly associated with smoking. We also expect the association between car ownership and smoking status to have not changed since our earlier study (6). Similarly, there was a weak association between working status and smoking status in our previous work, and we expect this relationship to remain weak, reflecting a more diverse nature of employment status and opportunities across the social grade spectrum (e.g., gig economy). Lastly, we expect composite scores of measures of SEP that reflect greater disadvantage to be most strongly predictive of smoking status and to demonstrate a gradient effect. These hypothesised relationships are presented in a directed acyclic graph (DAG; Figure 1), highlighting the potentially causal role of these factors on smoking, though not firmly determined they provide an overview of the complexity of the factors contributing to the health behaviour. It is hoped the results of this study can inform researchers and policy makers on the changing trends in SEP and smoking status. Using monthly cross-sectional data from the Smoking Toolkit Study in England between 2010 and 2019, this study will examine: 1. Do time trends across a ten-year period moderate the univariate associations between six different measures of SEP (occupation social grade, housing tenure, income, car ownership, education and employment status) and a composite of these with smoking status? 2. Has there been a change in the strongest independent socioeconomic position predictor of smoking over time?
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- 2022
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174. Plan for the analysis of the primary outcome data from BupaQuit
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Herbec, Aleksandra, West, Robert, Brown, Jamie, and Beard, Emma
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primary outcome ,analysis plan - Abstract
Plan for the analysis of the primary outcome data from BupaQuit trial.
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- 2022
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175. Proposal
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Farrow, Emma, Shahab, Lion, and Brown, Jamie
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- 2022
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176. Association of psychological distress with smoking cessation, duration of abstinence from smoking, and use of other nicotine-containing products: A cross-sectional population survey in Great Britain
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Kock, Loren, Shahab, Lion, Brown, Jamie, Robson, Debbie, Brose, Leonie, Cox, Sharon, and McNeill, Ann
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Medicine and Health Sciences ,Public Health ,Social and Behavioral Sciences - Abstract
This study aims to compare mental health, assessed by past-month psychological distress, in adults by smoking status and continued use of nicotine-containing products among all adults who have stopped smoking.
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- 2022
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177. Perceived risk factors for Covid-19 and association with socio-demographic and health-related characteristics: Findings from the HEBECO study
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Herbec, Aleksandra, Shahab, Lion, Brown, Jamie, and Kale, Dimitra
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Medicine and Health Sciences ,beliefs ,survey ,HEBECO ,UK ,Social and Behavioral Sciences ,Covid-19 ,health behaviours ,risk - Abstract
Pre-registration (before conducting the data analysis) of the protocol for data analysis for a planned manuscript entitled: Perceived risk factors for Covid-19 and association with socio-demographic and health-related characteristics: Findings from the HEBECO study. The document provides the background for the project, research questions, outcomes and predictors of interest, and the planned analyses.
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- 2022
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178. BupaQuit Trial Supplementary Materials / Appendix
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Herbec, Aleksandra, West, Robert, Brown, Jamie, Michie, Susan, and Beard, Emma
- Abstract
BupaQuit Appendix Supplementary Materials
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- 2022
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179. Prior Selection
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Tattan-Birch, Harry, Brown, Jamie, Shahab, Lion, and Jackson, Sarah
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- 2022
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180. Eliciting adult smokers’ preferences for the uptake of smoking cessation apps: A Discrete Choice Experiment
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Szinay, Dorothy, Cameron, Rory, Jones, Andy, Whitty, Jennifer, Brown, Jamie, Chadborn, Tim, and Naughton, Felix
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- 2022
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181. Characterising changes in smoking behaviour among people with and without COVID-19 infection: Representative cross-sectional surveys in England
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Cox, Sharon, Tattan-Birch, Harry, Jackson, Sarah, Dawkins, Lynne, Brown, Jamie, and Shahab, Lion
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COVID-19 ,Smoking ,Tobacco ,Social and Behavioral Sciences - Abstract
Early in the COVID-19 pandemic, there was a strong a priori assumption that smoking was an increased risk factor for disease severity owing to a high incidence of respiratory illness and pre-existing poor lung function in long-term smokers (1,2). However, results of a living systematic meta-analysis indicate that current smokers appear to be at reduced risk of COVID-19 infection relative to non-smokers, and there is inconclusive evidence that smokers who are infected are at increased risk of more severe disease (3). Former smokers appear to be at increased risk of severe disease and mortality from COVID-19 (3). Nonetheless, the pandemic has triggered some smokers to consider improving their health; we found that rates of smoking cessation were higher post March 2020 lockdown compared with the pre-pandemic period in England (4). Evidence from the UK also shows that concern around infection is more common in current compared with never smokers, and smokers report being less able to enact personal protective behaviours (5). In the UK, several health agencies recognised this as a ‘tobacco teachable moment’, and there was a well a publicised ‘Quit for Covid’ public health campaign (6). Even within the context of the pandemic, smoking continues to be a public health priority. This is salient in England, where there is less than a decade to achieve the public health goal of being smoke free by 2030 (defined as a smoking prevalence of 10 cigarettes per day). Since the data presented above (5,9) were collected, there have been several important developments. Firstly, England has experienced two more lockdowns. Second, the absolute death and population infection rate has risen. These longer-term effects of the pandemic may have continued to impact people’s decisions to change their behaviour, especially modifying behaviours like smoking. Third, there has been an increase in access to and uptake of COVID-19 testing in England, and thus, the accuracy of whether people have had COVID-19 in self-reports is now likely to be greater than early in the history of the pandemic. Assessing the association of COVID-19 infection status on current and future changes to smoking with greater accuracy is now possible.
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- 2022
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182. Smoking and vaping prevalence and product use and mental health in Great Britain: A population survey
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Taylor, Eve, McNeill, Ann, Robson, Deborah, Kock, Loren, Brose, Leonie, and Brown, Jamie
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Statistics and Probability ,Social Statistics ,E-cigarette ,Mental Health ,Vaping ,Smoking ,Physical Sciences and Mathematics ,Social and Behavioral Sciences - Abstract
This study aims to investigate differences in the prevalence and characteristics of smoking and vaping among people with and without serious past month psychological distress, and with and without an ever MHC diagnosis in England; using pooled monthly cross-sectional data from the Smoking Toolkit Study collected between April 2020 and February 2021
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- 2022
- Full Text
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183. Associations between e-cigarette use and Covid-19. Cross-sectional findings from the HEBECO study
- Author
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Herbec, Aleksandra, Shahab, Lion, Brown, Jamie, and Kale, Dimitra
- Subjects
covid-19 ,motivation ,Medicine and Health Sciences ,vaping ,covid ,Social and Behavioral Sciences ,infection - Abstract
Pre-registration (before conducting the data analysis) of the protocol for data analysis for a planned manuscript entitled: Associations between e-cigarette use and Covid-19. Cross-sectional findings from the HEBECO study.
- Published
- 2022
- Full Text
- View/download PDF
184. Is the use of varenicline during an attempt to quit smoking associated with reduced alcohol consumption in heavy drinkers?
- Author
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Brown, Jamie
- Published
- 2022
- Full Text
- View/download PDF
185. Food consumption behaviour among UK adults during the Covid-19 pandemic: findings from the HEBECO study
- Author
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Herbec, Aleksandra, Shahab, Lion, Brown, Jamie, and Kale, Dimitra
- Subjects
covid-19 ,sugar ,fat ,food consumption ,salt ,hebeco ,Social and Behavioral Sciences ,diet - Abstract
Pre-registration (before conducting the data analysis) of the protocol for data analysis for a planned manuscript entitled: "Food consumption behaviour among UK adults during the Covid-19 pandemic: findings from the HEBECO study". The document provides the background for the project, research questions, outcomes and predictors of interest, and the planned analyses.
- Published
- 2022
- Full Text
- View/download PDF
186. Beliefs and attitudes about alcohol prevention in health care in England – A cross sectional study
- Author
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O'Donnell, Amy, Nilsen, Per, Brown, Jamie, Karlsson, Nadine, Thomas, Kristin, Skagerstrom, Janna, and Roback, Kerstin
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attitudes ,alcohol ,beliefs ,drinking ,survey ,health care - Abstract
This study uses a population survey to investigate beliefs and attitudes concerning when it is appropriate to address alcohol consumption in health care and to identify the characteristics of those who are most and least supportive, respectively, of addressing alcohol consumption in health care.
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- 2022
- Full Text
- View/download PDF
187. An assessment of inequalities in smoking and quitting behaviour among adults with and without children in the household in England 2013-2019
- Author
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Kock, Loren, Cox, Sharon, Brown, Jamie, Shahab, Lion, and Tattan-Birch, Harry
- Subjects
Other Social and Behavioral Sciences ,Social Statistics ,inequalities ,Smoking ,epidemiology ,Social and Behavioral Sciences - Abstract
Using a nationally representative repeat cross-sectional survey in England, this study aimed to i) assess inequalities in smoking and quitting behaviour among all adults in recent years by children in the household and ii) explore trends in smoking prevalence between 2013 and 2019 among adults with and without children in the household by SEP.
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- 2022
- Full Text
- View/download PDF
188. Longitudinal associations between e-cigarette use for harm reduction and quitting activity: a prospective cohort study
- Author
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Farrow, Emma, Shahab, Lion, and Brown, Jamie
- Published
- 2022
- Full Text
- View/download PDF
189. Associations between e-cigarette use and Covid-19. cross-sectional findings from the HEBECO study
- Author
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Kale, Dimitra, Herbec, Aleksandra, Shahab, Lion, Brown, Jamie, Perski, Olga, and Jackson, Sarah
- Subjects
FOS: Psychology ,Health Psychology ,Medicine and Health Sciences ,Life Sciences ,Psychology ,vaping ,e-cigarette ,Social and Behavioral Sciences ,Covid-19 - Abstract
Pre-registration (before conducting the data analysis) of the protocol for data analysis (v3. 06.09.2020) for a planned manuscript entitled: Associations between e-cigarette use and Covid-19: cross-sectional findings from the HEBECO study.
- Published
- 2022
- Full Text
- View/download PDF
190. Associations between smoking to relieve stress, motivation to stop and future quit attempts across the social spectrum: A population survey in England
- Author
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Perski, Olga, Theodoraki, Maria, Cox, Sharon, Kock, Loren, Shahab, Lion, and Brown, Jamie
- Subjects
Social and Behavioral Sciences - Abstract
Smoking prevalence is declining in England but remains persistently high in ‘lower’ socioeconomic position (SEP) groups compared with more advantaged groups. Smoking is a significant contributor to health and social inequalities. There is therefore an urgent need to identify factors that underpin this disparity and use this information to inform public health policies and smoking cessation interventions tailored to the needs of lower SEP groups. Lower SEP is associated with multiple stressors, including financial insecurity, ill health and reduced social capital, with lower SEP smokers experiencing greater nicotine dependence and therefore greater negative affect from withdrawal. Although research indicates that smoking to relieve stress is a commonly endorsed motive overall, it is unclear whether this is more pronounced in lower (compared with higher) SEP groups and if so, whether this has a negative impact on motivation to stop and future quit attempts. We therefore aim to examine whether smoking to relieve stress is associated with motivation to stop and attempts to quit in the next 12 months, and whether these associations are moderated by SEP. This will be an observational study, with cross-sectional and prospective survey data from the nationally representative Smoking Toolkit Study in England.
- Published
- 2022
- Full Text
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191. Usage and purchase of e-cigarette device types and heated tobacco products in England
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Tattan-Birch, Harry, Brown, Jamie, Shahab, Lion, and Jackson, Sarah
- Subjects
Medicine and Health Sciences ,Public Health ,Social and Behavioral Sciences - Abstract
The research aims, analysis plan and priors for this study were selected before data analysis. Details of these are available in the pre-analysis protocol and prior selection document.
- Published
- 2022
- Full Text
- View/download PDF
192. Protocol for expansion of an existing national monthly survey of smoking behaviour and alcohol use in England to Scotland and Wales: The Smoking and Alcohol Toolkit Study
- Author
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Kock, Loren and Brown, Jamie
- Subjects
health services administration ,natural sciences ,Social and Behavioral Sciences ,human activities - Abstract
Methods paper regarding the smoking and alcohol toolkit study across constituent nations in Great Britain.
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- 2022
- Full Text
- View/download PDF
193. Dry January/Alcohol Change UK
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oldham, melissa, Brown, Jamie, Garnett, Claire, Kersbergen, Inge, Cox, Sharon, and Piper, Richard
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Medicine and Health Sciences ,Public Health - Abstract
In this paper, we use data from two sources, the Alcohol Toolkit13 (a representative population survey in England) and the Try Dry app which is freely available internationally (with users from 175 countries), although here we focus on data from England. We compare the proportion of and sociodemographic and drinking characteristics of individuals who report participating in Dry January and those who signed up to use the Try Dry app to do so, in 2021 relative to 2020.
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- 2022
- Full Text
- View/download PDF
194. The application of supervised machine learning to predict group- and individual-level lapse risk in smokers attempting to stop with a popular smartphone app: An exploratory study
- Author
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Perski, Olga, Crane, David, Li, Ken, Pontikos, Nikolas, Naughton, Felix, and Brown, Jamie
- Subjects
Medicine and Health Sciences - Abstract
In this exploratory study, we will use supervised machine learning algorithms (e.g. Random Forest, Support Vector Machine) to predict group- and individual-level lapse risk in smokers attempting to stop with a popular smartphone app.
- Published
- 2022
- Full Text
- View/download PDF
195. The role of desire over obligation in explaining the maintenance of quit success: a prospective study of recent ex-smokers in England
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Cox, Sharon, Brown, Jamie, Perski, Olga, and Shahab, Lion
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- 2022
- Full Text
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196. Updating evidence of effectiveness of Drink Less using Bayes factors to analyse additional outcome data
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Garnett, Claire, Michie, Susan, West, Robert, and Brown, Jamie
- Published
- 2022
- Full Text
- View/download PDF
197. The association between smoking, nicotine and SARS-CoV-2 infection in the UK ONS COVID-19 Infection Survey
- Author
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Perski, Olga, Simons, David, Tattan-Birch, Harry, Beard, Emma, Walker, Ann, Shahab, Lion, and Brown, Jamie
- Subjects
cross-sectional ,SARS-CoV-2 ,Medicine and Health Sciences ,COVID-19 ,tobacco smoking ,Social and Behavioral Sciences - Abstract
The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, is placing an unprecedented burden on healthcare and social systems in the UK and globally: with community transmission established in February 2020, COVID-19 has led to >9 million infections in the UK, with >140,000 premature deaths as of November 2021. At the start of the pandemic, many researchers and healthcare professionals hypothesised that tobacco smoking would be a risk factor for SARS-CoV-2 infection and subsequent disease progression. This was based on prior knowledge of biological, social, and behavioural factors that increase the risk of viral transmission. First, SARSCoV2 enters epithelial cells through the angiotensin-converting enzyme 2 (ACE2) receptor, with evidence suggesting that gene expression and subsequent receptor levels are elevated in smokers, thus potentially putting smokers at higher risk of contracting SARSCoV2 if exposed to the virus. However, reduced receptor levels in smokers have also been reported. Second, both current and former smoking increase the risk of other respiratory viral and bacterial infections. Third, behavioural factors involved in smoking (e.g. regular hand-to-mouth movements) may increase SARSCoV2 infection through increased exposure and onward transmission once infected. Fourth, individuals from lower (compared with higher) socioeconomic position (SEP) groups are more likely to be smokers and are also more likely to live in overcrowded housing and/or be employed in occupations with increased viral exposure (e.g., supermarkets, warehouses, public transport). If smoking confers an increased risk of infection, this would further emphasise the need for public health efforts to support smoking cessation, with campaigns potentially leveraging motivation to quit to protect oneself and others from COVID-19. We aim to examine, in the nationally representative UK Office for National Statistics (ONS) COVID-19 Infection Survey, whether 1) current vs. never smoking is associated with reduced risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 2) former vs. never smoking is associated with increased risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 3) among nicotine users, current cigarette smoking vs. no cigarette smoking is associated with equivocal risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 4) among former and never smokers, e-cigarette use vs. no e-cigarette is associated with reduced risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 5) for the above questions, whether associations vary over the course of the pandemic; and 6) in those testing positive for SARS-CoV-2, current smoking/nicotine use is associated with a) greater mean PCR cycle threshold values; and b) a lower number of self-reported symptoms.
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- 2022
- Full Text
- View/download PDF
198. Perceived harm of e-cigarettes compared with cigarettes following the US outbreak of vaping-associated lung injury
- Author
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Tattan-Birch, Harry, Brown, Jamie, Shahab, Lion, and Jackson, Sarah
- Subjects
harm perceptions ,ENDS ,EVALI ,vaping ,e-cigarettes ,lung injury ,cigarettes ,smoking - Published
- 2022
- Full Text
- View/download PDF
199. Refinement of Drink Less app
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Garnett, Claire, Michie, Susan, West, Robert, Brown, Jamie, Perski, Olga, Field, Matt, Kaner, Eileen, Munafo, Marcus, Greaves, Felix, Hickman, Matthew, and Burton, Robyn
- Subjects
mental disorders ,Social and Behavioral Sciences - Abstract
This project details the process of refining the content and design of the Drink Less app to improve its usability and effectiveness using mixed methods.
- Published
- 2022
- Full Text
- View/download PDF
200. Longitudinal changes in alcohol consumption during the COVID-19 pandemic (HEBECO study)
- Author
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Herbec, Aleksandra, Shahab, Lion, Brown, Jamie, and Kale, Dimitra
- Subjects
Life Sciences ,sense organs ,Social and Behavioral Sciences - Abstract
In this study, we use data from the baseline (recorded between April and June 2020) and 6-month follow-up survey of the HEBECO study (recorded between November 2020 and January 2021) to longitudinally examine the longer-term effects of COVID-19 on drinking behavior in the UK. As we know that there is divergence in changes to drinking in lockdown between men and women and differences in the way men and women have been affected by changes in circumstances under lockdown all analyses will be stratified by gender.
- Published
- 2022
- Full Text
- View/download PDF
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