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2. Global, regional, and national consumption of animal-source foods between 1990 and 2018: findings from the Global Dietary Database

Author

Abbott, Pamela, Abdollahi, Morteza, Abedi, Parvin, Abumweis, Suhad, Adair, Linda, Al Nsour, Mohannad, Al-Daghri, Nasser, Al-Hamad, Nawal, Al-Hooti, Suad, Al-Zenki, Sameer, Alam, Iftikhar, Ali, Jemal H, Alissa, Eman, Anderson, Simon, Anzid, Karim, Arambepola, Carukshi, Arici, Mustafa, Arsenault, Joanne, Asciak, Renzo, Barbieri, Helene E, Barengo, Noël, Barquera, Simon, Bas, Murat, Becker, Wulf, Beer-Borst, Sigrid, Bergman, Per, Biró, Lajos, Boindala, Sesikeran, Bovet, Pascal, Bradshaw, Debbie, Bukhary, Noriklil BI, Bundhamcharoen, Kanitta, Caballero, Mauricio, Calleja, Neville, Cao, Xia, Capanzana, Mario, Carmikle, Jan, Castetbon, Katia, Castro, Michelle, Cerdena, Corazon, Chang, Hsing-Yi, Charlton, Karen, Chen, Yu, Chen, Mei F, Chiplonkar, Shashi, Cho, Yoonsu, Chuah, Khun-Aik, Costanzo, Simona, Cowan, Melanie, Damasceno, Albertino, Dastgiri, Saeed, De Henauw, Stefaan, DeRidder, Karin, Ding, Eric, Dommarco, Rivera, Don, Rokiah, Duante, Charmaine, Duleva, Vesselka, Duran Aguero, Samuel, Ekbote, Veena, El Ati, Jalila, El Hamdouchi, Asmaa, El-kour, Tatyana, Eldridge, Alison, Elmadfa, Ibrahim, Esteghamati, Alireza, Etemad, Zohreh, Fadzil, Fariza, Farzadfar, Farshad, Fernandez, Anne, Fernando, Dulitha, Fisberg, Regina, Forsyth, Simon, Gamboa-Delgado, Edna, Garriguet, Didier, Gaspoz, Jean-Michel, Gauci, Dorothy, Geleijnse, Marianne, Ginnela, Brahmam, Grosso, Giuseppe, Guessous, Idris, Gulliford, Martin, Gunnarsdottir, Ingibjorg, Hadden, Wilbur, Hadziomeragic, Aida, Haerpfer, Christian, Hakeem, Rubina, Haque, Aminul, Hashemian, Maryam, Hemalatha, Rajkumar, Henjum, Sigrun, Hinkov, Hristo, Hjdaud, Zaiton, Hoffman, Daniel, Hopping, Beth, Houshiar-rad, Anahita, Hsieh, Yao-Te, Hung, Shu-Yi, Huybrechts, Inge, Hwalla, Nahla C, Ibrahim, Hajah M, Ikeda, Nayu, Illescas-Zarate, Daniel, Inoue, Manami, Janakiram, Chandrashekar, Jayawardena, Ranil, Jeewon, Rajesh, Jitnarin, Nattinee, Johansson, Lars, Jonsdottir, Olof, Jundishapur, Ahvaz, Kally, Ola, Kandiah, Mirnalini, Karupaiah, Tilakavati, Keinan-Boker, Lital, Kelishadi, Roya, Khadilkar, Anuradha, Kim, Cho-il, Koksal, Eda, Konig, Jurgen, Korkalo, Liisa, Koster, Jeremy, Kovalskys, Irina, Krishnan, Anand, Kruger, Herculina, Kuriyan-Raj, Rebecca, Kweon, Sanghui, Lachat, Carl, Lai, Yuen, Lanerolle, Pulani, Laxmaiah, Avula, Leclercq, Catherine, Lee, Meei-Shyuan, Lee, Hae-Jeung, Lemming, Eva W, Li, Yanping, Lindström, Jaana, Ling, Annie, Liputo, Nur IL, Lopez-Jaramillo, Patricio, Luke, Amy, Lukito, Widjaja, Lupotto, Elisabette, Ma, Yi, Mahdy, Zaleha A, Malekzadeh, Reza, Manan, Wan, Marchioni, Dirce, Marques, Lydia L, Marques-Vidal, Pedro, Martin-Prevel, Yves, Mathee, Angie, Matsumura, Yasuhiro, Mazumdar, Paramita, Memon, Anjum, Mensink, Gert, Meyer, Alexa, Mirmiran, Parvin, Mirzaei, Masoud, Misra, Puneet, Misra, Anoop, Mitchell, Claudette, Mohamed, Hamid JBJ, Mohammadi-Nasrabadi, Fatemeh, Mohammadifard, Noushin, Moy, Foong M, Musaiger, Abdulrahman, Mwaniki, Elizabeth, Myhre, Jannicke, Nagalla, Balakrishna, Naska, Androniki, Ng, Swee A, Ng, Shu W, Ngoan, Le TN, Noshad, Sina, Ochoa, Angelica, Ocke, Marga, Odenkirk, Jillian, Oh, Kyungwon, Oleas, Mariana, Olivares, Sonia, Orfanos, Philippos, Ortiz-Ulloa, Johana, Otero, Johanna, Ovaskainen, Marja-Leena, Pakseresht, Mohammadreza, Palacios, Cristina, Palmer, Pam, Pan, Wen-Harn, Panagiotakos, Demosthenes, Parajuli, Rajendra, Park, Myungsook, Pekcan, Gulden, Petrova, Stefka, Piaseu, Noppawan, Pitsavos, Christos, Polasa, Kalpagam, Posada, Luz, Pourfarzi, Farhad, Preston, Alan M, Rached, Ingrid, Rahbar, Ali R, Rehm, Colin, Richter, Almut, Riley, Leanne, Salanave, Benoit, Sánchez-Romero, Luz M, Sarrafzadegan, Nizal, Sawada, Norie, Sekiyama, Makiko, Selamat, Rusidah, Shamsuddin, Khadijah, Shariff, Zalilah M, Sharma, Sangita, Sibai, Abla M, Sinkko, Harri, Sioen, Isabelle, Sisa, Ivan, Skeaff, Sheila, Steingrimsdottir, Laufey, Strand, Tor, Suarez-Ortegon, Milton F, Swaminathan, Sumathi, Swan, Gillian, Sygnowska, Elzbieta, Szabo, Maria, Szponar, Lucjan, Tan-Khouw, Ilse, Tapanainen, Heli, Tayyem, Reema, Tedla, Bemnet, Tedstone, Alison, Templeton, Robert, Termote, Celine, Thanopoulou, Anastasia, Thorgeirsdottir, Holmfridur, Thorsdottir, Inga, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Tsugane, Shoichiro, Turrini, Aida, van Oosterhout, Coline, Vartiainen, Erkki, Veerman, J Lennert, Virtanen, Suvi, Vollenweider, Peter, Vossenaar, Marieke, Waidyatilaka, Indu, Waskiewicz, Anna, Waterham, Eveline, Wieler, Lothar, Wondwossen, Tizita, Wu, Suh, Yaakub, Roseyati, Yap, Mabel, Yusof, Safiah, Zaghloul, Sahar, Zajkás, Gábor, Zapata, Maria, Zarina, Khairul, Zohoori, Fatemeh V, Miller, Victoria, Reedy, Julia, Cudhea, Frederick, Zhang, Jianyi, Shi, Peilin, Erndt-Marino, Josh, Coates, Jennifer, Micha, Renata, Webb, Patrick, and Mozaffarian, Dariush

Published
2022
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4. Cigarette prices and community price comparisons in US military retail stores

Author

Poston, Walker SC, Haddock, Christopher K, Jahnke, Sara A, Smith, Elizabeth, Malone, Ruth E, and Jitnarin, Nattinee

Subjects
Health Sciences, Public Health, Commerce, Management, Tourism and Services, Tobacco Smoke and Health, Cancer, Tobacco, Good Health and Well Being, Commerce, Costs and Cost Analysis, Humans, Marketing, Military Personnel, Smoking, Tobacco Products, United States, Economics, Prevention, Price, Public policy
Abstract

BackgroundTobacco pricing impacts use, yet military retailers sell discounted cigarettes. No systematic research has examined how military retail stores use internal community comparisons to set prices. We analysed data obtained through a Freedom of Information Act request on community price comparisons used by military retail to set cigarette prices.MethodsData on cigarette prices were obtained directly from military retailers (exchanges) from January 2013 to March 2014. Complete pricing data were obtained from exchanges on 114 military installations.ResultsThe average price for a pack of Marlboro cigarettes in military exchanges was US$5.51, which was similar to the average lowest community price (US$5.45; mean difference=-0.06; p=0.104) and almost a US$1.00 lower than the average highest price (US$6.44). Military retail prices were 2.1%, 6.2% and 13.7% higher than the lowest, average and highest community comparisons, respectively, and 18.2% of exchange prices violated pricing instructions. There was a negative correlation (r=-0.21, p=0.02) between the number of community stores surveyed and exchange cigarette prices.ConclusionsThere was no significant difference between prices for cigarettes on military installations and the lowest average community comparison, and in some locations, the prices violated Department of Defense (DoD) policy. US Marine Corps exchanges had the lowest prices, which is of concern given that the Marines also have the highest rates of tobacco use in the DoD. Given the relationship between tobacco product prices and demand, a common minimum (or floor) shelf price for tobacco products should be set for all exchanges and discount coupon redemptions should be prohibited.

Published
2017

5. Perspectives of US military commanders on tobacco use and tobacco control policy

Author

Poston, Walker SC, Haddock, Christopher K, Jahnke, Sara A, Jitnarin, Nattinee, Malone, Ruth E, and Smith, Elizabeth A

Subjects
Health Services and Systems, Public Health, Health Sciences, Human Society, Policy and Administration, Drug Abuse (NIDA only), Clinical Research, Tobacco, Substance Misuse, Prevention, Tobacco Smoke and Health, 3.1 Primary prevention interventions to modify behaviours or promote wellbeing, Prevention of disease and conditions, and promotion of well-being, Cancer, Good Health and Well Being, Female, Health Policy, Humans, Interviews as Topic, Male, Military Personnel, Smoke-Free Policy, Smoking, Smoking Prevention, United States, Addiction, Priority/special populations, Public policy
Abstract

BackgroundTobacco use among members of the US military service is unacceptably high, resulting in substantial healthcare and personnel costs. Support of military command is critical to the success of tobacco control policies because line commanders are responsible for implementation and enforcement. This study is the first to examine US military line commanders' perspectives about current tobacco control policies and the impact of tobacco on readiness.MethodsWe conducted key-informant interviews with 20 officers at the US Army's Command and General Staff College about military tobacco use and tobacco control policy.ResultsParticipants identified the long-term impact of tobacco use on military members, but were unaware of proximal effects on health and readiness other than lost productivity due to smoke breaks. Officers also discussed nicotine addiction and the logistics of ensuring that an addicted population had access to tobacco. Regarding policy, most knew about regulations governing smoke-free areas and were open to stronger restrictions, but were unaware of current policies governing prevention, intervention and product sales.ConclusionsFindings suggest that strong policy that takes advantage of the hierarchical and disciplined nature of the military, supported by senior line and civilian leadership up to and including the secretaries of the services and the Secretary of Defense, will be critical to substantially diminishing tobacco use by military personnel.

Published
2017

7. Cigarette prices and community price comparisons in US military retail stores.

Author

Poston, Walker SC, Haddock, Christopher K, Jahnke, Sara A, Smith, Elizabeth, Malone, Ruth E, and Jitnarin, Nattinee

Subjects
Humans, Smoking, Commerce, Marketing, Military Personnel, Costs and Cost Analysis, United States, Tobacco Products, Economics, Prevention, Price, Public policy, Tobacco Smoke and Health, Cancer, Tobacco, Public Health
Abstract

BackgroundTobacco pricing impacts use, yet military retailers sell discounted cigarettes. No systematic research has examined how military retail stores use internal community comparisons to set prices. We analysed data obtained through a Freedom of Information Act request on community price comparisons used by military retail to set cigarette prices.MethodsData on cigarette prices were obtained directly from military retailers (exchanges) from January 2013 to March 2014. Complete pricing data were obtained from exchanges on 114 military installations.ResultsThe average price for a pack of Marlboro cigarettes in military exchanges was US$5.51, which was similar to the average lowest community price (US$5.45; mean difference=-0.06; p=0.104) and almost a US$1.00 lower than the average highest price (US$6.44). Military retail prices were 2.1%, 6.2% and 13.7% higher than the lowest, average and highest community comparisons, respectively, and 18.2% of exchange prices violated pricing instructions. There was a negative correlation (r=-0.21, p=0.02) between the number of community stores surveyed and exchange cigarette prices.ConclusionsThere was no significant difference between prices for cigarettes on military installations and the lowest average community comparison, and in some locations, the prices violated Department of Defense (DoD) policy. US Marine Corps exchanges had the lowest prices, which is of concern given that the Marines also have the highest rates of tobacco use in the DoD. Given the relationship between tobacco product prices and demand, a common minimum (or floor) shelf price for tobacco products should be set for all exchanges and discount coupon redemptions should be prohibited.

Published
2016

15. Epidemiology of Adult Obesity

Author

Sue, R., primary, Jitnarin, Nattinee, additional, Vidoni, Michelle L., additional, Kaipust, Christopher M., additional, and Brown, Austin L., additional

Published
2019
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18. Maternal and Child Health Among Female Firefighters in the U.S

Author

Jahnke, Sara A., Poston, Walker S. C., Jitnarin, Nattinee, and Haddock, Christopher K.

Subjects
Firefighters -- Health aspects -- Research, Occupational exposure -- Complications and side effects -- Research, Health care industry
Abstract

Objectives Despite increasing attention to the occupational impact of firefighting, little is known specific to the health of women firefighters. In particular, quantitative data is lacking on the impact firefighting has on maternal and child health for women who become pregnant while working as firefighters. Methods A total of 1821 women firefighters responded to requests to complete a self-report survey of questions about pregnancy. Women answered questions about their departments' policies and practices and their own experiences of pregnancy. Those participants who reported a pregnancy while serving as a firefighter were asked detailed questions about their pregnancy and outcomes. Results Female firefighters reported that nearly a quarter of their first pregnancies while in the fire service ended in miscarriage and that increased to a third of pregnancies by the fourth. Rates of pre-term delivery also were high among this population. Conclusions Findings have important implications for policy and practice among women who become pregnant while actively serving in the fire service., Author(s): Sara A. Jahnke [sup.1] , Walker S. C. Poston [sup.1] , Nattinee Jitnarin [sup.1] , Christopher K. Haddock [sup.1] Author Affiliations: (Aff1) 0000 0004 0442 0766, grid.276773.0, Center for [...]

Published
2018
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19. Children’s and adolescents’ rising animal-source food intakes in 1990–2018 were impacted by age, region, parental education and urbanicity

Author

Miller, Victoria, Webb, Patrick, Cudhea, Frederick, Zhang, Jianyi, Reedy, Julia, Shi, Peilin, Erndt-Marino, Josh, Coates, Jennifer, Micha, Renata, Mozaffarian, Dariush, Bas, Murat, Ali, Jemal Haidar, Abumweis, Suhad, Krishnan, Anand, Misra, Puneet, Hwalla, Nahla Chawkat, Janakiram, Chandrashekar, Liputo, Nur Indrawaty, Musaiger, Abdulrahman, Pourfarzi, Farhad, Alam, Iftikhar, DeRidder, Karin, Termote, Celine, Memon, Anjum, Turrini, Aida, Lupotto, Elisabetta, Piccinelli, Raffaela, Sette, Stefania, Anzid, Karim, Vossenaar, Marieke, Mazumdar, Paramita, Rached, Ingrid, Rovirosa, Alicia, Zapata, María Elisa, Asayehu, Tamene Taye, Oduor, Francis, Boedecker, Julia, Aluso, Lilian, Ortiz-Ulloa, Johana, Meenakshi, J.V., Castro, Michelle, Grosso, Giuseppe, Waskiewicz, Anna, Khan, Umber S., Thanopoulou, Anastasia, Malekzadeh, Reza, Calleja, Neville, Ocke, Marga, Etemad, Zohreh, Nsour, Mohannad Al, Waswa, Lydiah M., Nurk, Eha, Arsenault, Joanne, Lopez-Jaramillo, Patricio, Sibai, Abla Mehio, Damasceno, Albertino, Arambepola, Carukshi, Lopes, Carla, Severo, Milton, Lunet, Nuno, Torres, Duarte, Tapanainen, Heli, Lindstrom, Jaana, Virtanen, Suvi, Palacios, Cristina, Roos, Eva, Agdeppa, Imelda Angeles, Desnacido, Josie, Capanzana, Mario, Misra, Anoop, Khouw, Ilse, Ng, Swee Ai, Delgado, Edna Gamboa, Caballero, Mauricio, Otero, Johanna, Lee, Hae-Jeung, Koksal, Eda, Guessous, Idris, Lachat, Carl, De Henauw, Stefaan, Rahbar, Ali Reza, Tedstone, Alison, Naska, Androniki, Mathee, Angie, Ling, Annie, Tedla, Bemnet, Hopping, Beth, Ginnela, Brahmam, Leclercq, Catherine, Duante, Charmaine, Haerpfer, Christian, Hotz, Christine, Pitsavos, Christos, Rehm, Colin, van Oosterhout, Coline, Cerdena, Corazon, Bradshaw, Debbie, Trichopoulos, Dimitrios, Gauci, Dorothy, Fernando, Dulitha, Sygnowska, Elzbieta, Vartiainen, Erkki, Farzadfar, Farshad, Zajkas, Gabor, Swan, Gillian, Ma, Guansheng, Pekcan, Gulden, Ibrahim, Hajah Masni, Sinkko, Harri, Barbieri, Helene Enghardt, Sioen, Isabelle, Myhre, Jannicke, Gaspoz, Jean-Michel, Odenkirk, Jillian, Bundhamcharoen, Kanitta, Nelis, Keiu, Zarina, Khairul, Biro, Lajos, Johansson, Lars, Steingrimsdottir, Laufey, Riley, Leanne, Yap, Mabel, Inoue, Manami, Szabo, Maria, Ovaskainen, Marja-Leena, Lee, Meei-Shyuan, Chan, Mei Fen, Cowan, Melanie, Kandiah, Mirnalini, Kally, Ola, Jonsdottir, Olof, Palmer, Pam, Vollenweider, Peter, Orfanos, Philippos, Asciak, Renzo, Templeton, Robert, Don, Rokiah, Yaakub, Roseyati, Selamat, Rusidah, Yusof, Safiah, Al-Zenki, Sameer, Hung, Shu-Yi, Beer-Borst, Sigrid, Wu, Suh, Lukito, Widjaja, Hadden, Wilbur, Becker, Wulf, Cao, Xia, Ma, Yi, Lai, Yuen, Hjdaud, Zaiton, Ali, Jennifer, Gravel, Ron, Tao, Tina, Veerman, Jacob Lennert, Chiplonkar, Shashi, Arici, Mustafa, Ngoan, Le Tran, Panagiotakos, Demosthenes, Li, Yanping, Trichopoulou, Antonia, Barengo, Noel, Khadilkar, Anuradha, Ekbote, Veena, Mohammadifard, Noushin, Kovalskys, Irina, Laxmaiah, Avula, Rachakulla, Harikumar, Rajkumar, Hemalatha, Meshram, Indrapal, Avula, Laxmaiah, Arlappa, Nimmathota, Hemalatha, Rajkumar, lacoviello, Licia, Bonaccio, Marialaura, Costanzo, Simona, Martin-Prevel, Yves, Castetbon, Katia, Jitnarin, Nattinee, Hsieh, Yao-Te, Olivares, Sonia, Tejeda, Gabriela, Hadziomeragic, Aida, de Moura Souza, Amanda, Pan, Wen-Harn, Huybrechts, Inge, de Brauw, Alan, Moursi, Mourad, Maghroun, Maryam, Zeba, Augustin Nawidimbasba, Sarrafzadegan, Nizal, Keinan-Boker, Lital, Goldsmith, Rebecca, Shimony, Tal, Jordan, Irmgard, Mastiholi, Shivanand C., Mwangi, Moses, Kombe, Yeri, Bukania, Zipporah, Alissa, Eman, Al-Daghri, Nasser, Sabico, Shaun, Gulliford, Martin, Diba, Tshilenge S., Oh, Kyungwon, Kweon, Sanghui, Park, Sihyun, Cho, Yoonsu, Al-Hooti, Suad, Luangphaxay, Chanthaly, Douangvichit, Daovieng, Siengsounthone, Latsamy, Marques-Vidal, Pedro, Rybak, Constance, Luke, Amy, Piaseu, Noppawan, Rojroongwasinkul, Nipa, Sundram, Kalyana, Baykova, Donka, Abedi, Parvin, Sandjaja, Sandjaja, Fadzil, Fariza, Bukhary, Noriklil Bukhary Ismail, Bovet, Pascal, Chen, Yu, Sawada, Norie, Tsugane, Shoichiro, Rangelova, Lalka, Petrova, Stefka, Duleva, Vesselka, Lindroos, Anna Karin, Sipinen, Jessica Petrelius, Moraeus, Lotta, Bergman, Per, Siamusantu, Ward, Szponar, Lucjan, Chang, Hsing-Yi, Sekiyama, Makiko, Le Nguyen Bao, Khanh, Nagalla, Balakrishna, Polasa, Kalpagam, Boindala, Sesikeran, El Ati, Jalila, Silva, Ivonne Ramirez, Dommarco, Juan Rivera, Barquera, Simon, Ramírez, Sonia Rodríguez, Illescas-Zarate, Daniel, Sanchez-Romero, Luz Maria, Ikeda, Nayu, Zaghloul, Sahar, Houshiar-rad, Anahita, Mohammadi-Nasrabadi, Fatemeh, Abdollahi, Morteza, Chuah, Khun-Aik, Mahdy, Zaleha Abdullah, Eldridge, Alison, Ding, Eric L., Kruger, Herculina, Henjum, Sigrun, Fernandez, Anne, Suarez-Ortegon, Milton Fabian, Hamad, Nawal Al, Janská, Veronika, Tayyem, Reema, Mirmiran, Parvin, Kelishadi, Roya, Lemming, Eva Warensjo, Richter, Almut, Mensink, Gert, Wieler, Lothar, Hoffman, Daniel, Salanave, Benoit, Kim, Cho-il, Kuriyan-Raj, Rebecca, Swaminathan, Sumathi, Garriguet, Didier, Dastgiri, Saeed, Vaask, Sirje, Karupaiah, Tilakavati, Zohoori, Fatemeh Vida, Esteghamati, Alireza, Hashemian, Maryam, Noshad, Sina, Mwaniki, Elizabeth, Yakes-Jimenez, Elizabeth, Chileshe, Justin, Mwanza, Sydney, Marques, Lydia Lera, Preston, Alan Martin, Aguero, Samuel Duran, Oleas, Mariana, Posada, Luz, Ochoa, Angelica, Shamsuddin, Khadijah, Shariff, Zalilah Mohd, Jan Bin Jan Mohamed, Hamid, Manan, Wan, Nicolau, Anca, Tudorie, Cornelia, Poh, Bee Koon, Abbott, Pamela, Pakseresht, Mohammadreza, Sharma, Sangita, Strand, Tor Arne, Alexy, Ute, Nöthlings, Ute, Carmikle, Jan, Brown, Ken, Koster, Jeremy, Waidyatilaka, Indu, Lanerolle, Pulani, Jayawardena, Ranil, Long, Julie M., Hambidge, K. Michael, Krebs, Nancy F., Haque, Aminul, Keding, Gudrun B., Korkalo, Liisa, Erkkola, Maijaliisa, Freese, Riitta, Eleraky, Laila, Stuetz, Wolfgang, Thorsdottir, Inga, Gunnarsdottir, Ingibjorg, Serra-Majem, Lluis, Moy, Foong Ming, Anderson, Simon, Jeewon, Rajesh, Zugravu, Corina Aurelia, Adair, Linda, Ng, Shu Wen, Skeaff, Sheila, Marchioni, Dirce, Fisberg, Regina, Henry, Carol, Ersino, Getahun, Zello, Gordon, Meyer, Alexa, Elmadfa, Ibrahim, Mitchell, Claudette, Balfour, David, Geleijnse, Johanna M., Manary, Mark, El-kour, Tatyana, Nikiema, Laetitia, Mirzaei, Masoud, and Hakeem, Rubina

Abstract

Animal-source foods (ASF) provide nutrition for children and adolescents’ physical and cognitive development. Here, we use data from the Global Dietary Database and Bayesian hierarchical models to quantify global, regional and national ASF intakes between 1990 and 2018 by age group across 185 countries, representing 93% of the world’s child population. Mean ASF intake was 1.9 servings per day, representing 16% of children consuming at least three daily servings. Intake was similar between boys and girls, but higher among urban children with educated parents. Consumption varied by age from 0.6 at

Published
2023

20. Incident type 2 diabetes attributable to suboptimal diet in 184 countries

Author

O’Hearn, Meghan, Lara-Castor, Laura, Cudhea, Frederick, Miller, Victoria, Reedy, Julia, Shi, Peilin, Zhang, Jianyi, Wong, John B., Economos, Christina D., Micha, Renata, Mozaffarian, Dariush, Bas, Murat, Ali, Jemal Haidar, Abumweis, Suhad, Krishnan, Anand, Misra, Puneet, Hwalla, Nahla Chawkat, Janakiram, Chandrashekar, Liputo, Nur Indrawaty, Musaiger, Abdulrahman, Pourfarzi, Farhad, Alam, Iftikhar, DeRidder, Karin, Termote, Celine, Memon, Anjum, Turrini, Aida, Lupotto, Elisabetta, Piccinelli, Raffaela, Sette, Stefania, Anzid, Karim, Vossenaar, Marieke, Mazumdar, Paramita, Rached, Ingrid, Rovirosa, Alicia, Zapata, María Elisa, Asayehu, Tamene Taye, Oduor, Francis, Boedecker, Julia, Aluso, Lilian, Ortiz-Ulloa, Johana, Meenakshi, J.V., Castro, Michelle, Grosso, Giuseppe, Waskiewicz, Anna, Khan, Umber S., Thanopoulou, Anastasia, Malekzadeh, Reza, Calleja, Neville, Ocke, Marga, Etemad, Zohreh, Nsour, Mohannad Al, Waswa, Lydiah M., Nurk, Eha, Arsenault, Joanne, Lopez-Jaramillo, Patricio, Sibai, Abla Mehio, Damasceno, Albertino, Arambepola, Carukshi, Lopes, Carla, Severo, Milton, Lunet, Nuno, Torres, Duarte, Tapanainen, Heli, Lindstrom, Jaana, Virtanen, Suvi, Palacios, Cristina, Roos, Eva, Agdeppa, Imelda Angeles, Desnacido, Josie, Capanzana, Mario, Misra, Anoop, Khouw, Ilse, Ng, Swee Ai, Delgado, Edna Gamboa, Caballero, Mauricio, Otero, Johanna, Lee, Hae-Jeung, Koksal, Eda, Guessous, Idris, Lachat, Carl, De Henauw, Stefaan, Rahbar, Ali Reza, Tedstone, Alison, Naska, Androniki, Mathee, Angie, Ling, Annie, Tedla, Bemnet, Hopping, Beth, Ginnela, Brahmam, Leclercq, Catherine, Duante, Charmaine, Haerpfer, Christian, Hotz, Christine, Pitsavos, Christos, Rehm, Colin, van Oosterhout, Coline, Cerdena, Corazon, Bradshaw, Debbie, Trichopoulos, Dimitrios, Gauci, Dorothy, Fernando, Dulitha, Sygnowska, Elzbieta, Vartiainen, Erkki, Farzadfar, Farshad, Zajkas, Gabor, Swan, Gillian, Ma, Guansheng, Pekcan, Gulden, Ibrahim, Hajah Masni, Sinkko, Harri, Barbieri, Helene Enghardt, Sioen, Isabelle, Myhre, Jannicke, Gaspoz, Jean-Michel, Odenkirk, Jillian, Bundhamcharoen, Kanitta, Nelis, Keiu, Zarina, Khairul, Biro, Lajos, Johansson, Lars, Steingrimsdottir, Laufey, Riley, Leanne, Yap, Mabel, Inoue, Manami, Szabo, Maria, Ovaskainen, Marja-Leena, Lee, Meei-Shyuan, Chan, Mei Fen, Cowan, Melanie, Kandiah, Mirnalini, Kally, Ola, Jonsdottir, Olof, Palmer, Pam, Vollenweider, Peter, Orfanos, Philippos, Asciak, Renzo, Templeton, Robert, Don, Rokiah, Yaakub, Roseyati, Selamat, Rusidah, Yusof, Safiah, Al-Zenki, Sameer, Hung, Shu-Yi, Beer-Borst, Sigrid, Wu, Suh, Lukito, Widjaja, Hadden, Wilbur, Becker, Wulf, Cao, Xia, Ma, Yi, Lai, Yuen, Hjdaud, Zaiton, Ali, Jennifer, Gravel, Ron, Tao, Tina, Veerman, Jacob Lennert, Chiplonkar, Shashi, Arici, Mustafa, Ngoan, Le Tran, Panagiotakos, Demosthenes, Li, Yanping, Trichopoulou, Antonia, Barengo, Noel, Khadilkar, Anuradha, Ekbote, Veena, Mohammadifard, Noushin, Kovalskys, Irina, Laxmaiah, Avula, Rachakulla, Harikumar, Rajkumar, Hemalatha, Meshram, Indrapal, Avula, Laxmaiah, Arlappa, Nimmathota, Hemalatha, Rajkumar, lacoviello, Licia, Bonaccio, Marialaura, Costanzo, Simona, Martin-Prevel, Yves, Castetbon, Katia, Jitnarin, Nattinee, Hsieh, Yao-Te, Olivares, Sonia, Tejeda, Gabriela, Hadziomeragic, Aida, de Moura Souza, Amanda, Pan, Wen-Harn, Huybrechts, Inge, de Brauw, Alan, Moursi, Mourad, Maghroun, Maryam, Zeba, Augustin Nawidimbasba, Sarrafzadegan, Nizal, Keinan-Boker, Lital, Goldsmith, Rebecca, Shimony, Tal, Jordan, Irmgard, Mastiholi, Shivanand C., Mwangi, Moses, Kombe, Yeri, Bukania, Zipporah, Alissa, Eman, Al-Daghri, Nasser, Sabico, Shaun, Gulliford, Martin, Diba, Tshilenge S., Oh, Kyungwon, Kweon, Sanghui, Park, Sihyun, Cho, Yoonsu, Al-Hooti, Suad, Luangphaxay, Chanthaly, Douangvichit, Daovieng, Siengsounthone, Latsamy, Marques-Vidal, Pedro, Rybak, Constance, Luke, Amy, Piaseu, Noppawan, Rojroongwasinkul, Nipa, Sundram, Kalyana, Baykova, Donka, Abedi, Parvin, Sandjaja, Sandjaja, Fadzil, Fariza, Bukhary, Noriklil Bukhary Ismail, Bovet, Pascal, Chen, Yu, Sawada, Norie, Tsugane, Shoichiro, Rangelova, Lalka, Petrova, Stefka, Duleva, Vesselka, Lindroos, Anna Karin, Sipinen, Jessica Petrelius, Moraeus, Lotta, Bergman, Per, Siamusantu, Ward, Szponar, Lucjan, Chang, Hsing-Yi, Sekiyama, Makiko, Le Nguyen Bao, Khanh, Nagalla, Balakrishna, Polasa, Kalpagam, Boindala, Sesikeran, El Ati, Jalila, Silva, Ivonne Ramirez, Dommarco, Juan Rivera, Barquera, Simon, Rodríguez-Ramírez, Sonia, Illescas-Zarate, Daniel, Sanchez-Romero, Luz Maria, Ikeda, Nayu, Zaghloul, Sahar, Houshiar-rad, Anahita, Mohammadi-Nasrabadi, Fatemeh, Abdollahi, Morteza, Chuah, Khun-Aik, Mahdy, Zaleha Abdullah, Eldridge, Alison, Ding, Eric L., Kruger, Herculina, Henjum, Sigrun, Fernandez, Anne, Suarez-Ortegon, Milton Fabian, Al-Hamad, Nawal, Janská, Veronika, Tayyem, Reema, Mirmiran, Parvin, Kelishadi, Roya, Lemming, Eva Warensjo, Richter, Almut, Mensink, Gert, Wieler, Lothar, Hoffman, Daniel, Salanave, Benoit, Kim, Cho-il, Kuriyan-Raj, Rebecca, Swaminathan, Sumathi, Garriguet, Didier, Dastgiri, Saeed, Vaask, Sirje, Karupaiah, Tilakavati, Zohoori, Fatemeh Vida, Esteghamati, Alireza, Hashemian, Maryam, Noshad, Sina, Mwaniki, Elizabeth, Yakes-Jimenez, Elizabeth, Chileshe, Justin, Mwanza, Sydney, Marques, Lydia Lera, Preston, Alan Martin, Aguero, Samuel Duran, Oleas, Mariana, Posada, Luz, Ochoa, Angelica, Shamsuddin, Khadijah, Shariff, Zalilah Mohd, Jan Bin Jan Mohamed, Hamid, Manan, Wan, Nicolau, Anca, Tudorie, Cornelia, Poh, Bee Koon, Abbott, Pamela, Pakseresht, Mohammadreza, Sharma, Sangita, Strand, Tor Arne, Alexy, Ute, Nöthlings, Ute, Carmikle, Jan, Brown, Ken, Koster, Jeremy, Waidyatilaka, Indu, Lanerolle, Pulani, Jayawardena, Ranil, Long, Julie M., Hambidge, K. Michael, Krebs, Nancy F., Haque, Aminul, Keding, Gudrun B., Korkalo, Liisa, Erkkola, Maijaliisa, Freese, Riitta, Eleraky, Laila, Stuetz, Wolfgang, Thorsdottir, Inga, Gunnarsdottir, Ingibjorg, Serra-Majem, Lluis, Moy, Foong Ming, Anderson, Simon, Jeewon, Rajesh, Zugravu, Corina Aurelia, Adair, Linda, Ng, Shu Wen, Skeaff, Sheila, Marchioni, Dirce, Fisberg, Regina, Henry, Carol, Ersino, Getahun, Zello, Gordon, Meyer, Alexa, Elmadfa, Ibrahim, Mitchell, Claudette, Balfour, David, Geleijnse, Johanna M., Manary, Mark, El-kour, Tatyana, Nikiema, Laetitia, Mirzaei, Masoud, and Hakeem, Rubina

Abstract

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.8–14.4 million) incident T2D cases, representing 70.3% (68.8–71.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.0–27.1%)), excess refined rice and wheat intake (24.6% (22.3–27.2%)) and excess processed meat intake (20.3% (18.3–23.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.4–87.7%)) and Latin America and the Caribbean (81.8% (80.1–83.4%)); and lowest proportional burdens were in South Asia (55.4% (52.1–60.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally. publishedVersion

Published
2023

30. Global, regional, and national consumption of animal-source foods between 1990 and 2018: findings from the Global Dietary Database

Author

Miller, Victoria, Reedy, Julia, Cudhea, Frederick, Zhang, Jianyi, Shi, Peilin, Erndt-Marino, Josh, Coates, Jennifer, Micha, Renata, Webb, Patrick, Mozaffarian, Dariush, Abbott, Pamela, Abdollahi, Morteza, Abedi, Parvin, Abumweis, Suhad, Adair, Linda, Al Nsour, Mohannad, Al-Daghri, Nasser, Al-Hamad, Nawal, Al-Hooti, Suad, Al-Zenki, Sameer, Alam, Iftikhar, Ali, Jemal, Alissa, Eman, Anderson, Simon, Anzid, Karim, Arambepola, Carukshi, Arici, Mustafa, Arsenault, Joanne, Asciak, Renzo, Barbieri, Helene, Barengo, Noël, Barquera, Simon, Bas, Murat, Becker, Wulf, Beer-Borst, Sigrid, Bergman, Per, Biró, Lajos, Boindala, Sesikeran, Bovet, Pascal, Bradshaw, Debbie, Bukhary, Noriklil Bi, Bundhamcharoen, Kanitta, Caballero, Mauricio, Calleja, Neville, Cao, Xia, Capanzana, Mario, Carmikle, Jan, Castetbon, Katia, Castro, Michelle, Cerdena, Corazon, Chang, Hsing-Yi, Charlton, Karen, Chen, Yu, Chen, Mei, Chiplonkar, Shashi, Cho, Yoonsu, Chuah, Khun-Aik, Costanzo, Simona, Cowan, Melanie, Damasceno, Albertino, Dastgiri, Saeed, de Henauw, Stefaan, Deridder, Karin, Ding, Eric, Dommarco, Rivera, Don, Rokiah, Duante, Charmaine, Duleva, Vesselka, Duran Aguero, Samuel, Ekbote, Veena, El Ati, Jalila, El Hamdouchi, Asmaa, El-Kour, Tatyana, Eldridge, Alison, Elmadfa, Ibrahim, Esteghamati, Alireza, Etemad, Zohreh, Fadzil, Fariza, Farzadfar, Farshad, Fernandez, Anne, Fernando, Dulitha, Fisberg, Regina, Forsyth, Simon, Gamboa-Delgado, Edna, Garriguet, Didier, Gaspoz, Jean-Michel, Gauci, Dorothy, Geleijnse, Marianne, Ginnela, Brahmam, Grosso, Giuseppe, Guessous, Idris, Gulliford, Martin, Gunnarsdottir, Ingibjorg, Hadden, Wilbur, Hadziomeragic, Aida, Haerpfer, Christian, Hakeem, Rubina, Haque, Aminul, Hashemian, Maryam, Hemalatha, Rajkumar, Henjum, Sigrun, Hinkov, Hristo, Hjdaud, Zaiton, Hoffman, Daniel, Hopping, Beth, Houshiar-Rad, Anahita, Hsieh, Yao-Te, Hung, Shu-Yi, Huybrechts, Inge, Hwalla, Nahla, Ibrahim, Hajah, Ikeda, Nayu, Illescas-Zarate, Daniel, Inoue, Manami, Janakiram, Chandrashekar, Jayawardena, Ranil, Jeewon, Rajesh, Jitnarin, Nattinee, Johansson, Lars, Jonsdottir, Olof, Jundishapur, Ahvaz, Kally, Ola, Kandiah, Mirnalini, Karupaiah, Tilakavati, Keinan-Boker, Lital, Kelishadi, Roya, Khadilkar, Anuradha, Kim, Cho-Il, Koksal, Eda, Konig, Jurgen, Korkalo, Liisa, Koster, Jeremy, Kovalskys, Irina, Krishnan, Anand, Kruger, Herculina, Kuriyan-Raj, Rebecca, Kweon, Sanghui, Lachat, Carl, Lai, Yuen, Lanerolle, Pulani, Laxmaiah, Avula, Leclercq, Catherine, Lee, Meei-Shyuan, Lee, Hae-Jeung, Lemming, Eva, Li, Yanping, Lindström, Jaana, Ling, Annie, Liputo, Nur Il, Lopez-Jaramillo, Patricio, Luke, Amy, Lukito, Widjaja, Lupotto, Elisabette, Ma, Yi, Mahdy, Zaleha, Malekzadeh, Reza, Manan, Wan, Marchioni, Dirce, Marques, Lydia, Marques-Vidal, Pedro, Martin-Prével, Yves, Mathee, Angie, Matsumura, Yasuhiro, Mazumdar, Paramita, Memon, Anjum, Mensink, Gert, Meyer, Alexa, Mirmiran, Parvin, Mirzaei, Masoud, Misra, Puneet, Misra, Anoop, Mitchell, Claudette, Mohamed, Hamid Jbj, Mohammadi-Nasrabadi, Fatemeh, Mohammadifard, Noushin, Moy, Foong, Musaiger, Abdulrahman, Mwaniki, Elizabeth, Myhre, Jannicke, Nagalla, Balakrishna, Naska, Androniki, Ng, Swee, Ng, Shu, Ngoan, Le Tn, Noshad, Sina, Ochoa, Angelica, Ocke, Marga, Odenkirk, Jillian, Oh, Kyungwon, Oleas, Mariana, Olivares, Sonia, Orfanos, Philippos, Ortiz-Ulloa, Johana, Otero, Johanna, Ovaskainen, Marja-Leena, Pakseresht, Mohammadreza, Palacios, Cristina, Palmer, Pam, Pan, Wen-Harn, Panagiotakos, Demosthenes, Parajuli, Rajendra, Park, Myungsook, Pekcan, Gulden, Petrova, Stefka, Piaseu, Noppawan, Pitsavos, Christos, Polasa, Kalpagam, Posada, Luz, Pourfarzi, Farhad, Preston, Alan, Rached, Ingrid, Rahbar, Ali, Rehm, Colin, Richter, Almut, Riley, Leanne, Salanave, Benoit, Sánchez-Romero, Luz, Sarrafzadegan, Nizal, Sawada, Norie, Sekiyama, Makiko, Selamat, Rusidah, Shamsuddin, Khadijah, Shariff, Zalilah, Sharma, Sangita, Sibai, Abla, Sinkko, Harri, Sioen, Isabelle, Sisa, Ivan, Skeaff, Sheila, Steingrimsdottir, Laufey, Strand, Tor, Suarez-Ortegon, Milton, Swaminathan, Sumathi, Swan, Gillian, Sygnowska, Elzbieta, Szabo, Maria, Szponar, Lucjan, Tan-Khouw, Ilse, Tapanainen, Heli, Tayyem, Reema, Tedla, Bemnet, Tedstone, Alison, Templeton, Robert, Termote, Celine, Thanopoulou, Anastasia, Thorgeirsdottir, Holmfridur, Thorsdottir, Inga, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Tsugane, Shoichiro, Turrini, Aida, van Oosterhout, Coline, Vartiainen, Erkki, Veerman, J Lennert, Virtanen, Suvi, Vollenweider, Peter, Vossenaar, Marieke, Waidyatilaka, Indu, Waskiewicz, Anna, Waterham, Eveline, Wieler, Lothar, Wondwossen, Tizita, Wu, Suh, Yaakub, Roseyati, Yap, Mabel, Yusof, Safiah, Zaghloul, Sahar, Zajkás, Gábor, Zapata, Maria, Zarina, Khairul, Zohoori, Fatemeh, Phd, Micha, Phd, Webb, Friedman School of Nutrition Science and Policy [Boston, MA, USA], Tufts University School of Medicine [Boston], Novartis Institute for Tropical Diseases (NITD), Departments of Epidemiology and Nutrition, Harvard School of Public Health, Tufts University [Medford], Chair for Biomarkers of Chronic Diseases [Riyadh, Kingdom of Saudi Arabia] (Biochemistry Department), Qassim University [Kingdom of Saudi Arabia]-College of Science [Riyadh, Kingdom of Saudi Arabia], Kuwait Institute for Scientific Research (KISR), Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) (UMR MoISA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Bill & Melinda Gates Foundation and American Heart Association

Subjects
Health (social science), Meat, Health Policy, Eggs, Public Health, Environmental and Occupational Health, Human health, Medicine (miscellaneous), food and beverages, Dietary surveys, Bayes Theorem, Global Health, Diet, Animal-source foods, Animals, Humans, Dairy Products, Environmental sustainability, Dietary intakes, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

Background\ud Diet is a major modifiable risk factor for human health and overall consumption patterns affect planetary health. We aimed to quantify global, regional, and national consumption levels of animal-source foods (ASF) to inform intervention, surveillance, and policy priorities.\ud \ud Methods\ud Individual-level dietary surveys across 185 countries conducted between 1990 and 2018 were identified, obtained, standardised, and assessed among children and adults, jointly stratified by age, sex, education level, and rural versus urban residence. We included 499 discrete surveys (91·2% nationally or subnationally representative) with data for ASF (unprocessed red meat, processed meat, eggs, seafood, milk, cheese, and yoghurt), comprising 3·8 million individuals from 134 countries representing 95·2% of the world population in 2018. We used Bayesian hierarchical models to account for differences in survey methods and representativeness, time trends, and input data and modelling uncertainty, with five-fold cross-validation.\ud \ud Findings\ud In 2018, mean global intake per person of unprocessed red meat was 51 g/day (95% uncertainty interval [UI] 48–54; region-specific range 7–114 g/day); 17 countries (23·9% of the world's population) had mean intakes of at least one serving (100 g) per day. Global mean intake of processed meat was 17 g/day (95% UI 15–21 g/day; region-specific range 3–54 g/day); seafood, 28 g/day (27–30 g/day; 12–44 g/day); eggs, 21 g/day (18–24 g/day; 6–35 g/day); milk 88 g/day (84–93 g/day; 45–185 g/day); cheese, 8 g/day (8–10 g/day; 1–34 g/day); and yoghurt, 20 g/day (17–23 g/day; 7–84 g/day). Mean national intakes were at least one serving per day for processed meat (≥50 g/day) in countries representing 6·9% of the global population; for cheese (≥42 g/day) in 2·3%; for eggs (≥55 g/day) in 0·7%; for milk (≥245 g/day) in 0·3%; for seafood (≥100 g/day) in 0·8%; and for yoghurt (≥245 g/day) in less than 0·1%. Among the 25 most populous countries in 2018, total ASF intake was highest in Russia (5·8 servings per day), Germany (3·8 servings per day), and the UK (3·7 servings per day), and lowest in Tanzania (0·9 servings per day) and India (0·7 servings per day). Global and regional intakes of ASF were generally similar by sex. Compared with children, adults generally consumed more unprocessed red meat, seafood and cheese, and less milk; energy-adjusted intakes of other ASF were more similar. Globally, ASF intakes (servings per week) were higher among more-educated versus less-educated adults, with greatest global differences for milk (0·79), eggs (0·47), unprocessed red meat (0·42), cheese (0·28), seafood (0·28), yoghurt (0·22), and processed meat (0·21). This was also true for urban compared to rural areas, with largest global differences (servings per week) for unprocessed red meat (0·47), milk (0·38), and eggs (0·20). Between 1990 and 2018, global intakes (servings per week) increased for unprocessed red meat (1·20), eggs (1·18), milk (0·63), processed meat (0·50), seafood (0·44), and cheese (0·14).\ud \ud Interpretation\ud Our estimates of ASF consumption identify populations with both lower and higher than optimal intakes. These estimates can inform the targeting of intervention, surveillance, and policy priorities relevant to both human and planetary health.\ud \ud Funding\ud Bill & Melinda Gates Foundation and American Heart Association.

Published
2022

31. Double the Service, Double the Risk? Exploring Military Service as a Risk Factor for Health Outcomes in Firefighters.

Author

Koeppel, Maria D. H., Haddock, Christopher K., Kaipust, Christopher M., Jitnarin, Nattinee, Day, R. Sue, Carlos Poston, Walker S., and Jahnke, Sara A.

Subjects
VETERANS' health, MILITARY service, FIRE fighters, VETERANS, BINGE drinking, SMOKELESS tobacco
Abstract

This study used secondary data analysis from three national firefighter studies of almost 1,900 veteran and nonveteran firefighters to compare and explain the impact of six personal demographic factors, two occupational characteristics, and 11 health outcomes on each group. Univariate data analysis showed veteran and nonveteran firefighters in our sample shared a large number of similarities. They reported significant statistical differences in only three of the 19 demographic, occupational, and health outcome variables. Veteran firefighters were older than nonveterans and less likely to have a college or advanced degree. Firefighters with military service were also significantly more likely to have a history of divorce. The means or percentages were not statistically significant in the other 13 univariate comparisons. The similarities between veteran and nonveteran firefighters included three demographic characteristics (race, ethnicity, and marital status); both occupational characteristics (rank and years of service); and all 11 health outcomes (previous anxiety diagnosis, previous depression diagnosis, CESD-10, work stress, work stress interference with duties, cigarette smoking, smokeless tobacco use, alcohol use, binge drinking, body mass index, and waist circumference). A multivariate analysis did show, however, that when controlled for age and educational attainment, one health outcome—a previous anxiety diagnosis—became statistically significant. An analysis of 66 combat veterans found that veterans who had conducted combat patrols or performed other dangerous duties had 3.4 times the rate of diagnosed anxiety disorders than nonveteran firefighters. However, there was no significant difference in the rates of previously diagnosed anxiety disorders between nonveterans and veterans without combat experience. These last two findings carry important policy and training implications for the fire service. [ABSTRACT FROM AUTHOR]

Published
2023

48. "They're Just Never Told That They Can": Recruitment and Retention of Women in the Fire Service.

Author

Koeppel, Maria D. H., Bucala, Matt, Kelley, Hannah, Jitnarin, Nattinee, Poston, Walker S. Carlos, Haddock, Christopher K., and Jahnke, Sara A.

Subjects
WOMEN'S programs, FIRE prevention, FIRE fighters, TELEPHONE interviewing, FOCUS groups
Abstract

The percentage of females in the U.S. fire service is low. In 2018, only 8% of firefighters were women (National Fire Protection Association [NFPA], 2020). To better understand the reasons for this low participation rate, we analyzed responses from telephone interviews and focus groups conducted with 87 firefighters and fire service leaders in 2013. Analysis of these responses identified several reasons why it is so difficult to recruit and retain women firefighters. A better understanding of these reasons, including the unique challenges that women firefighters face, is essential to increasing and supporting their active participation in this profession. Tactics used to improve the recruitment and retention of women in the fire service are also discussed. [ABSTRACT FROM AUTHOR]

Published
2022

49. Assault in the Atmosphere: Workplace Violence in the Fire Service.

Author

Koeppel, Maria D. H., Hollerbach, Brittany S., von Thaden, Terry L., Khurram, Sajid, Kelley, Hannah, Jitnarin, Nattinee, Kaipust, Christopher M., Poston, Walker S. Carlos, Haddock, Christopher K., and Jahnke, Sara A.

Subjects
VIOLENCE in the workplace, WORK environment, JOB stress, SEXUAL assault, POISONS
Abstract

Workplace violence has become an increasingly popular topic of discussion and includes forms of aggression such as general harassment, mobbing, ostracism, bullying, hazing, stalking, and sexual assault. Firefighting is a high stress job in a unique work environment with many known adverse health impacts that can be exacerbated by toxic work environments, including workplace violence. This study consists of interviews with 31 firefighters and fire service leaders. Answers from participants provided insight into the types of workplace violence experienced by fire service personnel, obstacles to reporting incidents of sexual assault, and inequities in disciplinary action. Our systematic examination of these personal accounts can be used to increase awareness of workplace violence in the fire service, promote understanding, and create a healthier, safer, and more inclusive fire service community. [ABSTRACT FROM AUTHOR]

Published
2022

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