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2. Impacts of the COVID-19 Lockdown on Cooking Fuel Use Patterns, Income and Food Security in an Informal Settlement in Nairobi, Kenya
- Author
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Shupler, M., primary, Mwitari, J., additional, Gohole, A., additional, Anderson de Cuevas, R., additional, Puzzolo, E., additional, Cukic, I., additional, Nix, E., additional, and Pope, D., additional
- Published
- 2020
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3. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017
- Author
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Stanaway, J, Afshin, A, Gakidou, E, Lim, S, Abate, D, Abate, K, Abbafati, C, Abbasi, N, Abbastabar, H, Abd-Allah, F, Abdela, J, Abdelalim, A, Abdollahpour, I, Abdulkader, R, Abebe, M, Abebe, Z, Abera, S, Abil, O, Abraha, H, Abrham, A, Abu-Raddad, L, Abu-Rmeileh, N, Accrombessi, M, Acharya, D, Acharya, P, Adamu, A, Adane, A, Adebayo, O, Adedoyin, R, Adekanmbi, V, Ademi, Z, Adetokunboh, O, Adib, M, Admasie, A, Adsuar, J, Afanvi, K, Afarideh, M, Agarwal, G, Aggarwal, A, Aghayan, S, Agrawal, A, Agrawal, S, Ahmadi, A, Ahmadi, M, Ahmadieh, H, Ahmed, M, Aichour, A, Aichour, I, Aichour, M, Akbari, M, Akinyemiju, T, Akseer, N, Al-Aly, Z, Al-Eyadhy, A, Al-Mekhlafi, H, Alahdab, F, Alam, K, Alam, S, Alam, T, Alashi, A, Alavian, S, Alene, K, Ali, K, Ali, S, Alijanzadeh, M, Alizadeh-Navaei, R, Aljunid, S, Alkerwi, A, Alla, F, Alsharif, U, Altirkawi, K, Alvis-Guzman, N, Amare, A, Ammar, W, Anber, N, Anderson, J, Andrei, C, Androudi, S, Animut, M, Anjomshoa, M, Ansha, M, Antó, J, Antonio, C, Anwari, P, Appiah, L, Appiah, S, Arabloo, J, Aremu, O, Ärnlöv, J, Artaman, A, Aryal, K, Asayesh, H, Ataro, Z, Ausloos, M, Avokpaho, E, Awasthi, A, Ayala Quintanilla, B, Ayer, R, Ayuk, T, Azzopardi, P, Babazadeh, A, Badali, H, Badawi, A, Balakrishnan, K, Bali, A, Ball, K, Ballew, S, Banach, M, Banoub, J, Barac, A, Barker-Collo, S, Bärnighausen, T, Barrero, L, Basu, S, Baune, B, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Behzadifar, M, Béjot, Y, Bekele, B, Bekru, E, Belay, E, Belay, Y, Bell, M, Bello, A, Bennett, D, Bensenor, I, Bergeron, G, Berhane, A, Bernabe, E, Bernstein, R, Beuran, M, Beyranvand, T, Bhala, N, Bhalla, A, Bhattarai, S, Bhutta, Z, Biadgo, B, Bijani, A, Bikbov, B, Bilano, V, Bililign, N, Bin Sayeed, M, Bisanzio, D, Biswas, T, Bjørge, T, Blacker, B, Bleyer, A, Borschmann, R, Bou-Orm, I, Boufous, S, Bourne, R, Brady, O, Brauer, M, Brazinova, A, Breitborde, N, Brenner, H, Briko, A, Britton, G, Brugha, T, Buchbinder, R, Burnett, R, Busse, R, Butt, Z, Cahill, L, Cahuana-Hurtado, L, Campos-Nonato, I, Cárdenas, R, Carreras, G, Carrero, J, Carvalho, F, Castañeda-Orjuela, C, Castillo Rivas, J, Castro, F, Catalá-López, F, Causey, K, Cercy, K, Cerin, E, Chaiah, Y, Chang, H, Chang, J, Chang, K, Charlson, F, Chattopadhyay, A, Chattu, V, Chee, M, Cheng, C, Chew, A, Chiang, P, Chimed-Ochir, O, Chin, K, Chitheer, A, Choi, J, Chowdhury, R, Christensen, H, Christopher, D, Chung, S, Cicuttini, F, Cirillo, M, Cohen, A, Collado-Mateo, D, Cooper, C, Cooper, O, Coresh, J, Cornaby, L, Cortesi, P, Cortinovis, M, Costa, M, Cousin, E, Criqui, M, Cromwell, E, Cundiff, D, Daba, A, Dachew, B, Dadi, A, Damasceno, A, Dandona, L, Dandona, R, Darby, S, Dargan, P, Daryani, A, Das Gupta, R, Das Neves, J, Dasa, T, Dash, A, Davitoiu, D, Davletov, K, De la Cruz-Góngora, V, De La Hoz, F, De Leo, D, De Neve, J, Degenhardt, L, Deiparine, S, Dellavalle, R, Demoz, G, Denova-Gutiérrez, E, Deribe, K, Dervenis, N, Deshpande, A, Des Jarlais, D, Dessie, G, Deveber, G, Dey, S, Dharmaratne, S, Dhimal, M, Dinberu, M, Ding, E, Diro, H, Djalalinia, S, Do, H, Dokova, K, Doku, D, Doyle, K, Driscoll, T, Dubey, M, Dubljanin, E, Duken, E, Duncan, B, Duraes, A, Ebert, N, Ebrahimi, H, Ebrahimpour, S, Edvardsson, D, Effiong, A, Eggen, A, El Bcheraoui, C, El-Khatib, Z, Elyazar, I, Enayati, A, Endries, A, Er, B, Erskine, H, Eskandarieh, S, Esteghamati, A, Estep, K, Fakhim, H, Faramarzi, M, Fareed, M, Farid, T, Farinha, C, Farioli, A, Faro, A, Farvid, M, Farzaei, M, Fatima, B, Fay, K, Fazaeli, A, Feigin, V, Feigl, A, Fereshtehnejad, S, Fernandes, E, Fernandes, J, Ferrara, G, Ferrari, A, Ferreira, M, Filip, I, Finger, J, Fischer, F, Foigt, N, Foreman, K, Fukumoto, T, Fullman, N, Fürst, T, Furtado, J, Futran, N, Gall, S, Gallus, S, Gamkrelidze, A, Ganji, M, Garcia-Basteiro, A, Gardner, W, Gebre, A, Gebremedhin, A, Gebremichael, T, Gelano, T, Geleijnse, J, Geramo, Y, Gething, P, Gezae, K, Ghadimi, R, Ghadiri, K, Ghasemi Falavarjani, K, Ghasemi-Kasman, M, Ghimire, M, Ghosh, R, Ghoshal, A, Giampaoli, S, Gill, P, Gill, T, Gillum, R, Ginawi, I, Giussani, G, Gnedovskaya, E, Godwin, W, Goli, S, Gómez-Dantés, H, Gona, P, Gopalani, S, Goulart, A, Grada, A, Grams, M, Grosso, G, Gugnani, H, Guo, Y, Gupta, R, Gupta, T, Gutiérrez, R, Gutiérrez-Torres, D, Haagsma, J, Habtewold, T, Hachinski, V, Hafezi-Nejad, N, Hagos, T, Hailegiyorgis, T, Hailu, G, Haj-Mirzaian, A, Hamadeh, R, Hamidi, S, Handal, A, Hankey, G, Hao, Y, Harb, H, Harikrishnan, S, Haro, J, Hassankhani, H, Hassen, H, Havmoeller, R, Hawley, C, Hay, S, Hedayatizadeh-Omran, A, Heibati, B, Heidari, B, Heidari, M, Hendrie, D, Henok, A, Heredia-Pi, I, Herteliu, C, Heydarpour, F, Heydarpour, S, Hibstu, D, Higazi, T, Hilawe, E, Hoek, H, Hoffman, H, Hole, M, Homaie Rad, E, Hoogar, P, Hosgood, H, Hosseini, S, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hoy, D, Hsairi, M, Hsiao, T, Hu, G, Hu, H, Huang, J, Hussen, M, Huynh, C, Iburg, K, Ikeda, N, Ilesanmi, O, Iqbal, U, Irvani, S, Irvine, C, Islam, S, Islami, F, Jackson, M, Jacobsen, K, Jahangiry, L, Jahanmehr, N, Jain, S, Jakovljevic, M, James, S, Jassal, S, Jayatilleke, A, Jeemon, P, Jha, R, Jha, V, Ji, J, Jonas, J, Jonnagaddala, J, Jorjoran Shushtari, Z, Joshi, A, Jozwiak, J, Jürisson, M, Kabir, Z, Kahsay, A, Kalani, R, Kanchan, T, Kant, S, Kar, C, Karami, M, Karami Matin, B, Karch, A, Karema, C, Karimi, N, Karimi, S, Kasaeian, A, Kassa, D, Kassa, G, Kassa, T, Kassebaum, N, Katikireddi, S, Kaul, A, Kawakami, N, Kazemi, Z, Karyani, A, Kefale, A, Keiyoro, P, Kemp, G, Kengne, A, Keren, A, Kesavachandran, C, Khader, Y, Khafaei, B, Khafaie, M, Khajavi, A, Khalid, N, Khalil, I, Khan, G, Khan, M, Khang, Y, Khater, M, Khazaei, M, Khazaie, H, Khoja, A, Khosravi, A, Khosravi, M, Kiadaliri, A, Kiirithio, D, Kim, C, Kim, D, Kim, Y, Kimokoti, R, Kinfu, Y, Kisa, A, Kissimova-Skarbek, K, Kivimäki, M, Knibbs, L, Knudsen, A, Kochhar, S, Kokubo, Y, Kolola, T, Kopec, J, Kosen, S, Koul, P, Koyanagi, A, Kravchenko, M, Krishan, K, Krohn, K, Kromhout, H, Kuate Defo, B, Kucuk Bicer, B, Kumar, G, Kumar, M, Kuzin, I, Kyu, H, Lachat, C, Lad, D, Lad, S, Lafranconi, A, Lalloo, R, Lallukka, T, Lami, F, Lang, J, Lansingh, V, Larson, S, Latifi, A, Lazarus, J, Lee, P, Leigh, J, Leili, M, Leshargie, C, Leung, J, Levi, M, Lewycka, S, Li, S, Li, Y, Liang, J, Liang, X, Liao, Y, Liben, M, Lim, L, Linn, S, Liu, S, Lodha, R, Logroscino, G, Lopez, A, Lorkowski, S, Lotufo, P, Lozano, R, Lucas, T, Lunevicius, R, Ma, S, Macarayan, E, Machado, Í, Madotto, F, Mai, H, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malta, D, Mamun, A, Manda, A, Manguerra, H, Mansournia, M, Mantovani, L, Maravilla, J, Marcenes, W, Marks, A, Martin, R, Martins, S, Martins-Melo, F, März, W, Marzan, M, Massenburg, B, Mathur, M, Mathur, P, Matsushita, K, Maulik, P, Mazidi, M, Mcalinden, C, Mcgrath, J, Mckee, M, Mehrotra, R, Mehta, K, Mehta, V, Meier, T, Mekonnen, F, Melaku, Y, Melese, A, Melku, M, Memiah, P, Memish, Z, Mendoza, W, Mengistu, D, Mensah, G, Mensink, G, Mereta, S, Meretoja, A, Meretoja, T, Mestrovic, T, Mezgebe, H, Miazgowski, B, Miazgowski, T, Millear, A, Miller, T, Miller-Petrie, M, Mini, G, Mirarefin, M, Mirica, A, Mirrakhimov, E, Misganaw, A, Mitiku, H, Moazen, B, Mohajer, B, Mohammad, K, Mohammadi, M, Mohammadifard, N, Mohammadnia-Afrouzi, M, Mohammed, S, Mohebi, F, Mokdad, A, Molokhia, M, Momeniha, F, Monasta, L, Moodley, Y, Moradi, G, Moradi-Lakeh, M, Moradinazar, M, Moraga, P, Morawska, L, Morgado-Da-Costa, J, Morrison, S, Moschos, M, Mouodi, S, Mousavi, S, Mozaffarian, D, Mruts, K, Muche, A, Muchie, K, Mueller, U, Muhammed, O, Mukhopadhyay, S, Muller, K, Musa, K, Mustafa, G, Nabhan, A, Naghavi, M, Naheed, A, Nahvijou, A, Naik, G, Naik, N, Najafi, F, Nangia, V, Nansseu, J, Nascimento, B, Neal, B, Neamati, N, Negoi, I, Negoi, R, Neupane, S, Newton, C, Ngunjiri, J, Nguyen, A, Nguyen, G, Nguyen, H, Nguyen, M, Nguyen, N, Nichols, E, Nie, J, Ningrum, D, Nirayo, Y, Nishi, N, Nixon, M, Nojomi, M, Nomura, S, Norheim, O, Noroozi, M, Norrving, B, Noubiap, J, Nouri, H, Nourollahpour Shiadeh, M, Nowroozi, M, Nsoesie, E, Nyasulu, P, Obermeyer, C, Odell, C, Ofori-Asenso, R, Ogbo, F, Oh, I, Oladimeji, O, Olagunju, A, Olagunju, T, Olivares, P, Olsen, H, Olusanya, B, Olusanya, J, Ong, K, Ong, S, Oren, E, Orpana, H, Ortiz, A, Ota, E, Otstavnov, S, Øverland, S, Owolabi, M, P A, M, Pacella, R, Pakhare, A, Pakpour, A, Pana, A, Panda-Jonas, S, Park, E, Parry, C, Parsian, H, Patel, S, Pati, S, Patil, S, Patle, A, Patton, G, Paudel, D, Paulson, K, Paz Ballesteros, W, Pearce, N, Pereira, A, Pereira, D, Perico, N, Pesudovs, K, Petzold, M, Pham, H, Phillips, M, Pillay, J, Piradov, M, Pirsaheb, M, Pischon, T, Pishgar, F, Plana-Ripoll, O, Plass, D, Polinder, S, Polkinghorne, K, Postma, M, Poulton, R, Pourshams, A, Poustchi, H, Prabhakaran, D, Prakash, S, Prasad, N, Purcell, C, Purwar, M, Qorbani, M, Radfar, A, Rafay, A, Rafiei, A, Rahim, F, Rahimi, Z, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rai, R, Rajati, F, Rajsic, S, Raju, S, Ram, U, Ranabhat, C, Ranjan, P, Rath, G, Rawaf, D, Rawaf, S, Reddy, K, Rehm, C, Rehm, J, Reiner, R, Reitsma, M, Remuzzi, G, Renzaho, A, Resnikoff, S, Reynales-Shigematsu, L, Rezaei, S, Ribeiro, A, Rivera, J, Roba, K, Rodríguez-Ramírez, S, Roever, L, Román, Y, Ronfani, L, Roshandel, G, Rostami, A, Roth, G, Rothenbacher, D, Roy, A, Rubagotti, E, Rushton, L, Sabanayagam, C, Sachdev, P, Saddik, B, Sadeghi, E, Saeedi Moghaddam, S, Safari, H, Safari, Y, Safari-Faramani, R, Safdarian, M, Safi, S, Safiri, S, Sagar, R, Sahebkar, A, Sahraian, M, Sajadi, H, Salam, N, Salamati, P, Saleem, Z, Salimi, Y, Salimzadeh, H, Salomon, J, Salvi, D, Salz, I, Samy, A, Sanabria, J, Sanchez-Niño, M, Sánchez-Pimienta, T, Sanders, T, Sang, Y, Santomauro, D, Santos, I, Santos, J, Santric Milicevic, M, Sao Jose, B, Sardana, M, Sarker, A, Sarmiento-Suárez, R, Sarrafzadegan, N, Sartorius, B, Sarvi, S, Sathian, B, Satpathy, M, Sawant, A, Sawhney, M, Saylan, M, Sayyah, M, Schaeffner, E, Schmidt, M, Schneider, I, Schöttker, B, Schutte, A, Schwebel, D, Schwendicke, F, Scott, J, Seedat, S, Sekerija, M, Sepanlou, S, Serre, M, Serván-Mori, E, Seyedmousavi, S, Shabaninejad, H, Shaddick, G, Shafieesabet, A, Shahbazi, M, Shaheen, A, Shaikh, M, Shamah Levy, T, Shams-Beyranvand, M, Shamsi, M, Sharafi, H, Sharafi, K, Sharif, M, Sharif-Alhoseini, M, Sharifi, H, Sharma, J, Sharma, M, Sharma, R, She, J, Sheikh, A, Shi, P, Shibuya, K, Shiferaw, M, Shigematsu, M, Shin, M, Shiri, R, Shirkoohi, R, Shiue, I, Shokraneh, F, Shoman, H, Shrime, M, Shupler, M, Si, S, Siabani, S, Sibai, A, Siddiqi, T, Sigfusdottir, I, Sigurvinsdottir, R, Silva, D, Silva, J, Silveira, D, Singh, J, Singh, N, Singh, V, Sinha, D, Skiadaresi, E, Skirbekk, V, Smith, D, Smith, M, Sobaih, B, Sobhani, S, Somayaji, R, Soofi, M, Sorensen, R, Soriano, J, Soyiri, I, Spinelli, A, Sposato, L, Sreeramareddy, C, Srinivasan, V, Starodubov, V, Steckling, N, Stein, D, Stein, M, Stevanovic, G, Stockfelt, L, Stokes, M, Sturua, L, Subart, M, Sudaryanto, A, Sufiyan, M, Sulo, G, Sunguya, B, Sur, P, Sykes, B, Szoeke, C, Tabarés-Seisdedos, R, Tabuchi, T, Tadakamadla, S, Takahashi, K, Tandon, N, Tassew, S, Tavakkoli, M, Taveira, N, Tehrani-Banihashemi, A, Tekalign, T, Tekelemedhin, S, Tekle, M, Temesgen, H, Temsah, M, Temsah, O, Terkawi, A, Tessema, B, Teweldemedhin, M, Thankappan, K, Theis, A, Thirunavukkarasu, S, Thomas, H, Thomas, M, Thomas, N, Thurston, G, Tilahun, B, Tillmann, T, To, Q, Tobollik, M, Tonelli, M, Topor-Madry, R, Torre, A, Tortajada-Girbés, M, Touvier, M, Tovani-Palone, M, Towbin, J, Tran, B, Tran, K, Truelsen, T, Truong, N, Tsadik, A, Tudor Car, L, Tuzcu, E, Tymeson, H, Tyrovolas, S, Ukwaja, K, Ullah, I, Updike, R, Usman, M, Uthman, O, Vaduganathan, M, Vaezi, A, Valdez, P, Van Donkelaar, A, Varavikova, E, Varughese, S, Vasankari, T, Venkateswaran, V, Venketasubramanian, N, Villafaina, S, Violante, F, Vladimirov, S, Vlassov, V, Vollset, S, Vos, T, Vosoughi, K, Vu, G, Vujcic, I, Wagnew, F, Waheed, Y, Waller, S, Walson, J, Wang, Y, Weiderpass, E, Weintraub, R, Weldegebreal, F, Werdecker, A, Werkneh, A, West, J, Westerman, R, Whiteford, H, Widecka, J, Wijeratne, T, Winkler, A, Wiyeh, A, Wiysonge, C, Wolfe, C, Wong, T, Wu, S, Xavier, D, Xu, G, Yadgir, S, Yadollahpour, A, Yahyazadeh Jabbari, S, Yamada, T, Yan, L, Yano, Y, Yaseri, M, Yasin, Y, Yeshaneh, A, Yimer, E, Yip, P, Yisma, E, Yonemoto, N, Yoon, S, Yotebieng, M, Younis, M, Yousefifard, M, Yu, C, Zaidi, Z, Zaman, S, Zamani, M, Zavala-Arciniega, L, Zhang, A, Zhang, H, Zhang, K, Zhou, M, Zimsen, S, Zodpey, S, Murray, C, Stanaway, Jeffrey D, Afshin, Ashkan, Gakidou, Emmanuela, Lim, Stephen S, Abate, Degu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Molla, Abebe, Zegeye, Abera, Semaw F, Abil, Olifan Zewdie, Abraha, Haftom Niguse, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen ME, Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Adamu, Abdu A, Adane, Akilew Awoke, Adebayo, Oladimeji M, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Ademi, Zanfina, Adetokunboh, Olatunji O, Adib, Mina G, Admasie, Amha, Adsuar, Jose C, Afanvi, Kossivi Agbelenko, Afarideh, Mohsen, Agarwal, Gina, Aggarwal, Anju, Aghayan, Sargis Aghasi, Agrawal, Anurag, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmadieh, Hamid, Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akbari, Mohammad Esmaeil, Akinyemiju, Tomi, Akseer, Nadia, Al-Aly, Ziyad, Al-Eyadhy, Ayman, Al-Mekhlafi, Hesham M, Alahdab, Fares, Alam, Khurshid, Alam, Samiah, Alam, Tahiya, Alashi, Alaa, Alavian, Seyed Moayed, Alene, Kefyalew Addis, Ali, Komal, Ali, Syed Mustafa, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Anderson, Jason A, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Ansha, Mustafa Geleto, Antó, Josep M, Antonio, Carl Abelardo T, Anwari, Palwasha, Appiah, Lambert Tetteh, Appiah, Seth Christopher Yaw, Arabloo, Jalal, Aremu, Olatunde, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Ataro, Zerihun, Ausloos, Marcel, Avokpaho, Euripide F G A, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayer, Rakesh, Ayuk, Tambe B, Azzopardi, Peter S, Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Balakrishnan, Kalpana, Bali, Ayele Geleto, Ball, Kylie, Ballew, Shoshana H, Banach, Maciej, Banoub, Joseph Adel Mattar, Barac, Aleksandra, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H, Basu, Sanjay, Baune, Bernhard T, Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Bekele, Bayu Begashaw, Bekru, Eyasu Tamru, Belay, Ezra, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Bergeron, Gilles, Berhane, Adugnaw, Bernabe, Eduardo, Bernstein, Robert S, Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhalla, Ashish, Bhattarai, Suraj, Bhutta, Zulfiqar A, Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bilano, Ver, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Bisanzio, Donal, Biswas, Tuhin, Bjørge, Tone, Blacker, Brigette F, Bleyer, Archie, Borschmann, Rohan, Bou-Orm, Ibrahim R, Boufous, Soufiane, Bourne, Rupert, Brady, Oliver J, Brauer, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Briko, Andrey Nikolaevich, Britton, Gabrielle, Brugha, Traolach, Buchbinder, Rachelle, Burnett, Richard T, Busse, Reinhard, Butt, Zahid A, Cahill, Leah E, Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R, Cárdenas, Rosario, Carreras, Giulia, Carrero, Juan J, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Causey, Kate, Cercy, Kelly M, Cerin, Ester, Chaiah, Yazan, Chang, Hsing-Yi, Chang, Jung-Chen, Chang, Kai-Lan, Charlson, Fiona J, Chattopadhyay, Aparajita, Chattu, Vijay Kumar, Chee, Miao Li, Cheng, Ching-Yu, Chew, Adrienne, Chiang, Peggy Pei-Chia, Chimed-Ochir, Odgerel, Chin, Ken Lee, Chitheer, Abdulaal, Choi, Jee-Young J, Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam J, Chung, Sheng-Chia, Cicuttini, Flavia M, Cirillo, Massimo, Cohen, Aaron J, Collado-Mateo, Daniel, Cooper, Cyrus, Cooper, Owen R, Coresh, Josef, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cousin, Ewerton, Criqui, Michael H, Cromwell, Elizabeth A, Cundiff, David K, Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dadi, Abel Fekadu, Damasceno, Albertino Antonio Moura, Dandona, Lalit, Dandona, Rakhi, Darby, Sarah C, Dargan, Paul I, Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Davitoiu, Dragos Virgil, Davletov, Kairat, De la Cruz-Góngora, Vanessa, De La Hoz, Fernando Pio, De Leo, Diego, De Neve, Jan-Walter, 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- Abstract
Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk- outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes d
- Published
- 2018
4. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017
- Author
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Stanaway, Jeffrey D., Afshin, Ashkan, Gakidou, Emmanuela, Lim, Stephen S., Abate, Degu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Molla, Abebe, Zegeye, Abera, Semaw F., Abil, Olifan Zewdie, Abraha, Haftom Niguse, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen ME, Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Adamu, Abdu A., Adane, Akilew Awoke, Adebayo, Oladimeji M., Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Ademi, Zanfina, Adetokunboh, Olatunji O., Adib, Mina G., Admasie, Amha, Adsuar, Jose C., Afanvi, Kossivi Agbelenko, Afarideh, Mohsen, Agarwal, Gina, Aggarwal, Anju, Aghayan, Sargis Aghasi, Agrawal, Anurag, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmadieh, Hamid, Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akbari, Mohammad Esmaeil, Akinyemiju, Tomi, Akseer, Nadia, Al-Aly, Ziyad, Al-Eyadhy, Ayman, Al-Mekhlafi, Hesham M., Alahdab, Fares, Alam, Khurshid, Alam, Samiah, Alam, Tahiya, Alashi, Alaa, Alavian, Seyed Moayed, Alene, Kefyalew Addis, Ali, Komal, Ali, Syed Mustafa, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T., Ammar, Walid, Anber, Nahla Hamed, Anderson, Jason A., Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Ansha, Mustafa Geleto, Antó, Josep M., Antonio, Carl Abelardo T., Anwari, Palwasha, Appiah, Lambert Tetteh, Appiah, Seth Christopher Yaw, Arabloo, Jalal, Aremu, Olatunde, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K., Asayesh, Hamid, Ataro, Zerihun, Ausloos, Marcel, Avokpaho, Euripide F.G.A., Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayer, Rakesh, Ayuk, Tambe B., Azzopardi, Peter S., Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Balakrishnan, Kalpana, Bali, Ayele Geleto, Ball, Kylie, Ballew, Shoshana H., Banach, Maciej, Banoub, Joseph Adel Mattar, Barac, Aleksandra, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H., Basu, Sanjay, Baune, Bernhard T., Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Bekele, Bayu Begashaw, Bekru, Eyasu Tamru, Belay, Ezra, Belay, Yihalem Abebe, Bell, Michelle L., Bello, Aminu K., Bennett, Derrick A., Bensenor, Isabela M., Bergeron, Gilles, Berhane, Adugnaw, Bernabe, Eduardo, Bernstein, Robert S., Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhalla, Ashish, Bhattarai, Suraj, Bhutta, Zulfiqar A., Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bilano, Ver, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Bisanzio, Donal, Biswas, Tuhin, Bjørge, Tone, Blacker, Brigette F., Bleyer, Archie, Borschmann, Rohan, Bou-Orm, Ibrahim R., Boufous, Soufiane, Bourne, Rupert, Brady, Oliver J., Brauer, Michael, Brazinova, Alexandra, Breitborde, Nicholas J.K., Brenner, Hermann, Briko, Andrey Nikolaevich, Britton, Gabrielle, Brugha, Traolach, Buchbinder, Rachelle, Burnett, Richard T., Busse, Reinhard, Butt, Zahid A., Cahill, Leah E., Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R., Cárdenas, Rosario, Carreras, Giulia, Carrero, Juan J., Carvalho, Félix, Castañeda-Orjuela, Carlos A., Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Causey, Kate, Cercy, Kelly M., Cerin, Ester, Chaiah, Yazan, Chang, Hsing Yi, Chang, Jung Chen, Chang, Kai Lan, Charlson, Fiona J., Chattopadhyay, Aparajita, Chattu, Vijay Kumar, Chee, Miao Li, Cheng, Ching Yu, Chew, Adrienne, Chiang, Peggy Pei Chia, Chimed-Ochir, Odgerel, Chin, Ken Lee, Chitheer, Abdulaal, Choi, Jee Young J., Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam J., Chung, Sheng Chia, Cicuttini, Flavia M., Cirillo, Massimo, Cohen, Aaron J., Collado-Mateo, Daniel, Cooper, Cyrus, Cooper, Owen R., Coresh, Josef, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cousin, Ewerton, Criqui, Michael H., Cromwell, Elizabeth A., Cundiff, David K., Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dadi, Abel Fekadu, Damasceno, Albertino Antonio Moura, Dandona, Lalit, Dandona, Rakhi, Darby, Sarah C., Dargan, Paul I., Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Davitoiu, Dragos Virgil, Davletov, Kairat, De la Cruz-Góngora, Vanessa, De La Hoz, Fernando Pio, De Leo, Diego, De Neve, Jan Walter, Degenhardt, Louisa, Deiparine, Selina, Dellavalle, Robert P., Demoz, Gebre Teklemariam, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Deshpande, Aniruddha, Des Jarlais, Don C., Dessie, Getenet Ayalew, Deveber, Gabrielle Aline, Dey, Subhojit, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Dinberu, Mesfin Tadese, Ding, Eric L., Diro, Helen Derara, Djalalinia, Shirin, Do, Huyen Phuc, Dokova, Klara, Doku, David Teye, Doyle, Kerrie E., Driscoll, Tim R., Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duncan, Bruce B., Duraes, Andre R., Ebert, Natalie, Ebrahimi, Hedyeh, Ebrahimpour, Soheil, Edvardsson, David, Effiong, Andem, Eggen, Anne Elise, El Bcheraoui, Charbel, El-Khatib, Ziad, Elyazar, Iqbal Rf, Enayati, Ahmadali, Endries, Aman Yesuf, Er, Benjamin, Erskine, Holly E., Eskandarieh, Sharareh, Esteghamati, Alireza, Estep, Kara, Fakhim, Hamed, Faramarzi, Mahbobeh, Fareed, Mohammad, Farid, Talha A., Farinha, Carla Sofia E.sá, Farioli, Andrea, Faro, Andre, Farvid, Maryam S., Farzaei, Mohammad Hosein, Fatima, Batool, Fay, Kairsten A., Fazaeli, Ali Akbar, Feigin, Valery L., Feigl, Andrea B., Fereshtehnejad, Seyed Mohammad, Fernandes, Eduarda, Fernandes, Joao C., Ferrara, Giannina, Ferrari, Alize J., Ferreira, Manuela L., Filip, Irina, Finger, Jonas David, Fischer, Florian, Foigt, Nataliya A., Foreman, Kyle J., Fukumoto, Takeshi, Fullman, Nancy, Fürst, Thomas, Furtado, João M., Futran, Neal D., Gall, Seana, Gallus, Silvano, Gamkrelidze, Amiran, Ganji, Morsaleh, Garcia-Basteiro, Alberto L., Gardner, William M., Gebre, Abadi Kahsu, Gebremedhin, Amanuel Tesfay, Gebremichael, Teklu Gebrehiwo, Gelano, Tilayie Feto, Geleijnse, Johanna M., Geramo, Yilma Chisha Dea, Gething, Peter W., Gezae, Kebede Embaye, Ghadimi, Reza, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghimire, Mamata, Ghosh, Rakesh, Ghoshal, Aloke Gopal, Giampaoli, Simona, Gill, Paramjit Singh, Gill, Tiffany K., Gillum, Richard F., Ginawi, Ibrahim Abdelmageed, Giussani, Giorgia, Gnedovskaya, Elena V., Godwin, William W., Goli, Srinivas, Gómez-Dantés, Hector, Gona, Philimon N., Gopalani, Sameer Vali, Goulart, Alessandra C., Grada, Ayman, Grams, Morgan E., Grosso, Giuseppe, Gugnani, Harish Chander, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Gutiérrez, Reyna Alma, Gutiérrez-Torres, Daniela S., Haagsma, Juanita A., Habtewold, Tesfa Dejenie, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B., Hailegiyorgis, Tewodros Tesfa, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R., Hamidi, Samer, Handal, Alexis J., Hankey, Graeme J., Hao, Yuantao, Harb, Hilda L., Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmus, Hawley, Caitlin N., Hay, Simon I., Hedayatizadeh-Omran, Akbar, Heibati, Behzad, Heidari, Behnam, Heidari, Mohsen, Hendrie, Delia, Henok, Andualem, Heredia-Pi, Ileana, Herteliu, Claudiu, Heydarpour, Fatemeh, Heydarpour, Sousan, Hibstu, Desalegn T., Higazi, Tarig B., Hilawe, Esayas Haregot, Hoek, Hans W., Hoffman, Howard J., Hole, Michael K., Homaie Rad, Enayatollah, Hoogar, Praveen, Hosgood, H. Dean, Hosseini, Seyed Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hoy, Damian G., Hsairi, Mohamed, Hsiao, Thomas, Hu, Guoqing, Hu, Howard, Huang, John J., Hussen, Mamusha Aman, Huynh, Chantal K., Iburg, Kim Moesgaard, Ikeda, Nayu, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Irvine, Caleb Mackay Salpeter, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jackson, Maria D., Jacobsen, Kathryn H., Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, James, Spencer L., Jassal, Simerjot K., Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S., Jonas, Jost B., Jonnagaddala, Jitendra, Jorjoran Shushtari, Zahra, Joshi, Ankur, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kabir, Zubair, Kahsay, Amaha, Kalani, Rizwan, Kanchan, Tanuj, Kant, Surya, Kar, Chittaranjan, Karami, Manoochehr, Karami Matin, Behzad, Karch, André, Karema, Corine, Karimi, Narges, Karimi, Seyed M., Kasaeian, Amir, Kassa, Dessalegn H., Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J., Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Kazemi, Zhila, Karyani, Ali Kazemi, Kefale, Adane Teshome, Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khafaei, Behzad, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A., Khan, Gulfaraz, Khan, Muhammad Shahzeb, Khan, Muhammad Ali, Khang, Young Ho, Khater, Mona M., Khazaei, Mohammad, Khazaie, Habibolah, Khoja, Abdullah T., Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kiadaliri, Aliasghar A., Kiirithio, Daniel N., Kim, Cho Il, Kim, Daniel, Kim, Young Eun, Kim, Yun Jin, Kimokoti, Ruth W., Kinfu, Yohannes, Kisa, Adnan, Kissimova-Skarbek, Katarzyna, Kivimäki, Mika, Knibbs, Luke D., Knudsen, Ann Kristin Skrindo, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A., Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael A., Krishan, Kewal, Krohn, Kristopher J., Kromhout, Hans, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kumar, G. Anil, Kumar, Manasi, Kuzin, Igor, Kyu, Hmwe Hmwe, Lachat, Carl, Lad, Deepesh P., Lad, Sheetal D., Lafranconi, Alessandra, Lalloo, Ratilal, Lallukka, Tea, Lami, Faris Hasan, Lang, Justin J., Lansingh, Van C., Larson, Samantha Leigh, Latifi, Arman, Lazarus, Jeffrey V., Lee, Paul H., Leigh, James, Leili, Mostafa, Leshargie, Cheru Tesema, Leung, Janni, Levi, Miriam, Lewycka, Sonia, Li, Shanshan, Li, Yichong, Liang, Juan, Liang, Xiaofeng, Liao, Yu, Liben, Misgan Legesse, Lim, Lee Ling, Linn, Shai, Liu, Shiwei, Lodha, Rakesh, Logroscino, Giancarlo, Lopez, Alan D., Lorkowski, Stefan, Lotufo, Paulo A., Lozano, Rafael, Lucas, Tim C.D., Lunevicius, Raimundas, Ma, Stefan, Macarayan, Erlyn Rachelle King, Machado, Ísis Eloah, Madotto, Fabiana, Mai, Hue Thi, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A., Manda, Ana Laura, Manguerra, Helena, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Maravilla, Joemer C., Marcenes, Wagner, Marks, Ashley, Martin, Randall V., Martins, Sheila C.O., Martins-Melo, Francisco Rogerlândio, März, Winfried, Marzan, Melvin B., Massenburg, Benjamin Ballard, Mathur, Manu Raj, Mathur, Prashant, Matsushita, Kunihiro, Maulik, Pallab K., Mazidi, Mohsen, McAlinden, Colm, McGrath, John J., McKee, Martin, Mehrotra, Ravi, Mehta, Kala M., Mehta, Varshil, Meier, Toni, Mekonnen, Fantahun Ayenew, Melaku, Yohannes A., Melese, Addisu, Melku, Mulugeta, Memiah, Peter T.N., Memish, Ziad A., Mendoza, Walter, Mengistu, Desalegn Tadese, Mensah, George A., Mensink, Gert B.M., Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J., Mestrovic, Tomislav, Mezgebe, Haftay Berhane, Miazgowski, Bartosz, Miazgowski, Tomasz, Millear, Anoushka I., Miller, Ted R., Miller-Petrie, Molly Katherine, Mini, G. K., Mirarefin, Mojde, Mirica, Andreea, Mirrakhimov, Erkin M., Misganaw, Awoke Temesgen, Mitiku, Habtamu, Moazen, Babak, Mohajer, Bahram, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadnia-Afrouzi, Mousa, Mohammed, Shafiu, Mohebi, Farnam, Mokdad, Ali H., Molokhia, Mariam, Momeniha, Fatemeh, Monasta, Lorenzo, Moodley, Yoshan, Moradi, Ghobad, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Morgado-Da-Costa, Joana, Morrison, Shane Douglas, Moschos, Marilita M., Mouodi, Simin, Mousavi, Seyyed Meysam, Mozaffarian, Dariush, Mruts, Kalayu Brhane, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Muhammed, Oumer Sada, Mukhopadhyay, Satinath, Muller, Kate, Musa, Kamarul Imran, Mustafa, Ghulam, Nabhan, Ashraf F., Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Nitish, Najafi, Farid, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramos, Neal, Bruce, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Subas, Newton, Charles Richard James, Ngunjiri, Josephine W., Nguyen, Anh Quynh, Nguyen, Grant, Nguyen, Ha Thu, Nguyen, Huong Lan Thi, Nguyen, Huong Thanh, Nguyen, Minh, Nguyen, Nam Ba, Nichols, Emma, Nie, Jing, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nishi, Nobuo, Nixon, Molly R., Nojomi, Marzieh, Nomura, Shuhei, Norheim, Ole F., Noroozi, Mehdi, Norrving, Bo, Noubiap, Jean Jacques, Nouri, Hamid Reza, Nourollahpour Shiadeh, Malihe, Nowroozi, Mohammad Reza, Nsoesie, Elaine O., Nyasulu, Peter S., Obermeyer, Carla M., Odell, Christopher M., Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In Hwan, Oladimeji, Olanrewaju, Olagunju, Andrew T., Olagunju, Tinuke O., Olivares, Pedro R., Olsen, Helen Elizabeth, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Kanyin L., Ong, Sok King, Oren, Eyal, Orpana, Heather M., Ortiz, Alberto, Ota, Erika, Otstavnov, Stanislav S., Øverland, Simon, Owolabi, Mayowa Ojo, P A, Mahesh, Pacella, Rosana, Pakhare, Abhijit P., Pakpour, Amir H., Pana, Adrian, Panda-Jonas, Songhomitra, Park, Eun Kee, Parry, Charles D.H., Parsian, Hadi, Patel, Shanti, Pati, Sanghamitra, Patil, Snehal T., Patle, Ajay, Patton, George C., Paudel, Deepak, Paulson, Katherine R., Paz Ballesteros, Wayra Citlali, Pearce, Neil, Pereira, Alexandre, Pereira, David M., Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Pham, Hai Quang, Phillips, Michael R., Pillay, Julian David, Piradov, Michael A., Pirsaheb, Meghdad, Pischon, Tobias, Pishgar, Farhad, Plana-Ripoll, Oleguer, Plass, Dietrich, Polinder, Suzanne, Polkinghorne, Kevan R., Postma, Maarten J., Poulton, Richie, Pourshams, Akram, Poustchi, Hossein, Prabhakaran, Dorairaj, Prakash, Swayam, Prasad, Narayan, Purcell, Caroline A., Purwar, Manorama B., Qorbani, Mostafa, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi, Zohreh, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz ur, Rahman, Muhammad Aziz, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Raju, Sree Bhushan, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Rath, Goura Kishor, Rawaf, David Laith, Rawaf, Salman, Reddy, K. Srinath, Rehm, Colin D., Rehm, Jürgen, Reiner, Robert C., Reitsma, Marissa B., Remuzzi, Giuseppe, Renzaho, Andre M.N., Resnikoff, Serge, Reynales-Shigematsu, Luz Myriam, Rezaei, Satar, Ribeiro, Antonio Luiz P., Rivera, Juan A., Roba, Kedir Teji, Rodríguez-Ramírez, Sonia, Roever, Leonardo, Román, Yesenia, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Roth, Gregory A., Rothenbacher, Dietrich, Roy, Ambuj, Rubagotti, Enrico, Rushton, Lesley, Sabanayagam, Charumathi, Sachdev, Perminder S., Saddik, Basema, Sadeghi, Ehsan, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salam, Nasir, Salamati, Payman, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A., Salvi, Devashri Digvijay, Salz, Inbal, Samy, Abdallah M., Sanabria, Juan, Sanchez-Niño, Maria Dolores, Sánchez-Pimienta, Tania G., Sanders, Taren, Sang, Yingying, Santomauro, Damian Francesco, Santos, Itamar S., Santos, João Vasco, Santric Milicevic, Milena M., Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R., Sawhney, Monika, Saylan, Mete, Sayyah, Mehdi, Schaeffner, Elke, Schmidt, Maria Inês, Schneider, Ione J.C., Schöttker, Ben, Schutte, Aletta Elisabeth, Schwebel, David C., Schwendicke, Falk, Scott, James G., Seedat, Soraya, Sekerija, Mario, Sepanlou, Sadaf G., Serre, Marc L., Serván-Mori, Edson, Seyedmousavi, Seyedmojtaba, Shabaninejad, Hosein, Shaddick, Gavin, Shafieesabet, Azadeh, Shahbazi, Mehdi, Shaheen, Amira A., Shaikh, Masood Ali, Shamah Levy, Teresa, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Sharafi, Heidar, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharifi, Hamid, Sharma, Jayendra, Sharma, Meenakshi, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shiferaw, Mekonnen Sisay, Shigematsu, Mika, Shin, Min Jeong, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokraneh, Farhad, Shoman, Haitham, Shrime, Mark G., Shupler, Matthew S., Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J., Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silva, Diego Augusto Santos, Silva, João Pedro, Silveira, Dayane Gabriele Alves, Singh, Jasvinder A., Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Skiadaresi, Eirini, Skirbekk, Vegard, Smith, David L., Smith, Mari, Sobaih, Badr Hasan, Sobhani, Soheila, Somayaji, Ranjani, Soofi, Moslem, Sorensen, Reed J.D., Soriano, Joan B., Soyiri, Ireneous N., Spinelli, Angela, Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Srinivasan, Vinay, Starodubov, Vladimir I., Steckling, Nadine, Stein, Dan J., Stein, Murray B., Stevanovic, Goran, Stockfelt, Leo, Stokes, Mark A., Sturua, Lela, Subart, Michelle L., Sudaryanto, Agus, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F., Sur, Patrick John, Sykes, Bryan L., Szoeke, Cassandra E.I., Tabarés-Seisdedos, Rafael, Tabuchi, Takahiro, Tadakamadla, Santosh Kumar, Takahashi, Ken, Tandon, Nikhil, Tassew, Segen Gebremeskel, Tavakkoli, Mohammad, Taveira, Nuno, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekelemedhin, Shishay Wahdey, Tekle, Merhawi Gebremedhin, Temesgen, Habtamu, Temsah, Mohamad Hani, Temsah, Omar, Terkawi, Abdullah Sulieman, Tessema, Belay, Teweldemedhin, Mebrahtu, Thankappan, Kavumpurathu Raman, Theis, Andrew, Thirunavukkarasu, Sathish, Thomas, Hannah J., Thomas, Matthew Lloyd, Thomas, Nihal, Thurston, George D., Tilahun, Binyam, Tillmann, Taavi, To, Quyen G., Tobollik, Myriam, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E., Tortajada-Girbés, Miguel, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Towbin, Jeffrey A., Tran, Bach Xuan, Tran, Khanh Bao, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tudor Car, Lorainne, Tuzcu, E. Murat, Tymeson, Hayley D., Tyrovolas, Stefanos, Ukwaja, Kingsley N., Ullah, Irfan, Updike, Rachel L., Usman, Muhammad Shariq, Uthman, Olalekan A., Vaduganathan, Muthiah, Vaezi, Afsane, Valdez, Pascual R., Van Donkelaar, Aaron, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Venkateswaran, Vidhya, Venketasubramanian, Narayanaswamy, Villafaina, Santos, Violante, Francesco S., Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vu, Giang Thu, Vujcic, Isidora S., Wagnew, Fasil Shiferaw, Waheed, Yasir, Waller, Stephen G., Walson, Judd L., Wang, Yafeng, Wang, Yanping, Wang, Yuan Pang, Weiderpass, Elisabete, Weintraub, Robert G., Weldegebreal, Fitsum, Werdecker, Andrea, Werkneh, Adhena Ayaliew, West, J. 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Hsiao, Thoma, Hu, Guoqing, Hu, Howard, Huang, John J, Hussen, Mamusha Aman, Huynh, Chantal K, Iburg, Kim Moesgaard, Ikeda, Nayu, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Irvine, Caleb Mackay Salpeter, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jackson, Maria D, Jacobsen, Kathryn H, Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, James, Spencer L, Jassal, Simerjot K, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S, Jonas, Jost B, Jonnagaddala, Jitendra, Jorjoran Shushtari, Zahra, Joshi, Ankur, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kabir, Zubair, Kahsay, Amaha, Kalani, Rizwan, Kanchan, Tanuj, Kant, Surya, Kar, Chittaranjan, Karami, Manoochehr, Karami Matin, Behzad, Karch, André, Karema, Corine, Karimi, Narge, Karimi, Seyed M, Kasaeian, Amir, Kassa, Dessalegn H, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J, Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Kazemi, Zhila, Karyani, Ali Kazemi, Kefale, Adane Teshome, Keiyoro, Peter Njenga, Kemp, Grant Rodger, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khafaei, Behzad, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A, Khan, Gulfaraz, Khan, Muhammad Shahzeb, Khan, Muhammad Ali, Khang, Young-Ho, Khater, Mona M, Khazaei, Mohammad, Khazaie, Habibolah, Khoja, Abdullah T, Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kiadaliri, Aliasghar A, Kiirithio, Daniel N, Kim, Cho-Il, Kim, Daniel, Kim, Young-Eun, Kim, Yun Jin, Kimokoti, Ruth W, Kinfu, Yohanne, Kisa, Adnan, Kissimova-Skarbek, Katarzyna, Kivimäki, Mika, Knibbs, Luke D, Knudsen, Ann Kristin Skrindo, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A, Kosen, Soewarta, Koul, Parvaiz A, Koyanagi, Ai, Kravchenko, Michael A, Krishan, Kewal, Krohn, Kristopher J, Kromhout, Han, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kumar, G Anil, Kumar, Manasi, Kuzin, Igor, Kyu, Hmwe Hmwe, Lachat, Carl, Lad, Deepesh P, Lad, Sheetal D, Lafranconi, Alessandra, Lalloo, Ratilal, Lallukka, Tea, Lami, Faris Hasan, Lang, Justin J, Lansingh, Van C, Larson, Samantha Leigh, Latifi, Arman, Lazarus, Jeffrey V, Lee, Paul H, Leigh, Jame, Leili, Mostafa, Leshargie, Cheru Tesema, Leung, Janni, Levi, Miriam, Lewycka, Sonia, Li, Shanshan, Li, Yichong, Liang, Juan, Liang, Xiaofeng, Liao, Yu, Liben, Misgan Legesse, Lim, Lee-Ling, Linn, Shai, Liu, Shiwei, Lodha, Rakesh, Logroscino, Giancarlo, Lopez, Alan D, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lucas, Tim C D, Lunevicius, Raimunda, Ma, Stefan, Macarayan, Erlyn Rachelle King, Machado, Ísis Eloah, Madotto, Fabiana, Mai, Hue Thi, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A, Manda, Ana-Laura, Manguerra, Helena, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Maravilla, Joemer C, Marcenes, Wagner, Marks, Ashley, Martin, Randall V, Martins, Sheila C O, Martins-Melo, Francisco Rogerlândio, März, Winfried, Marzan, Melvin B, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Mathur, Prashant, Matsushita, Kunihiro, Maulik, Pallab K, Mazidi, Mohsen, McAlinden, Colm, McGrath, John J, McKee, Martin, Mehrotra, Ravi, Mehta, Kala M, Mehta, Varshil, Meier, Toni, Mekonnen, Fantahun Ayenew, Melaku, Yohannes A, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mendoza, Walter, Mengistu, Desalegn Tadese, Mensah, George A, Mensink, Gert B M, Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Mezgebe, Haftay Berhane, Miazgowski, Bartosz, Miazgowski, Tomasz, Millear, Anoushka I, Miller, Ted R, Miller-Petrie, Molly Katherine, Mini, G.K., Mirarefin, Mojde, Mirica, Andreea, Mirrakhimov, Erkin M, Misganaw, Awoke Temesgen, Mitiku, Habtamu, Moazen, Babak, Mohajer, Bahram, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadnia-Afrouzi, Mousa, Mohammed, Shafiu, Mohebi, Farnam, Mokdad, Ali H, Molokhia, Mariam, Momeniha, Fatemeh, Monasta, Lorenzo, Moodley, Yoshan, Moradi, Ghobad, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Morgado-Da-Costa, Joana, Morrison, Shane Dougla, Moschos, Marilita M, Mouodi, Simin, Mousavi, Seyyed Meysam, Mozaffarian, Dariush, Mruts, Kalayu Brhane, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Muhammed, Oumer Sada, Mukhopadhyay, Satinath, Muller, Kate, Musa, Kamarul Imran, Mustafa, Ghulam, Nabhan, Ashraf F, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Nitish, Najafi, Farid, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramo, Neal, Bruce, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Suba, Newton, Charles Richard Jame, Ngunjiri, Josephine W, Nguyen, Anh Quynh, Nguyen, Grant, Nguyen, Ha Thu, Nguyen, Huong Lan Thi, Nguyen, Huong Thanh, Nguyen, Minh, Nguyen, Nam Ba, Nichols, Emma, Nie, Jing, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nishi, Nobuo, Nixon, Molly R, Nojomi, Marzieh, Nomura, Shuhei, Norheim, Ole F, Noroozi, Mehdi, Norrving, Bo, Noubiap, Jean Jacque, Nouri, Hamid Reza, Nourollahpour Shiadeh, Malihe, Nowroozi, Mohammad Reza, Nsoesie, Elaine O, Nyasulu, Peter S, Obermeyer, Carla M, Odell, Christopher M, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olsen, Helen Elizabeth, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Kanyin L, Ong, Sok King, Oren, Eyal, Orpana, Heather M, Ortiz, Alberto, Ota, Erika, Otstavnov, Stanislav S, Øverland, Simon, Owolabi, Mayowa Ojo, P A, Mahesh, Pacella, Rosana, Pakhare, Abhijit P, Pakpour, Amir H, Pana, Adrian, Panda-Jonas, Songhomitra, Park, Eun-Kee, Parry, Charles D H, Parsian, Hadi, Patel, Shanti, Pati, Sanghamitra, Patil, Snehal T, Patle, Ajay, Patton, George C, Paudel, Deepak, Paulson, Katherine R, Paz Ballesteros, Wayra Citlali, Pearce, Neil, Pereira, Alexandre, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Pham, Hai Quang, Phillips, Michael R, Pillay, Julian David, Piradov, Michael A, Pirsaheb, Meghdad, Pischon, Tobia, Pishgar, Farhad, Plana-Ripoll, Oleguer, Plass, Dietrich, Polinder, Suzanne, Polkinghorne, Kevan R, Postma, Maarten J, Poulton, Richie, Pourshams, Akram, Poustchi, Hossein, Prabhakaran, Dorairaj, Prakash, Swayam, Prasad, Narayan, Purcell, Caroline A, Purwar, Manorama B, Qorbani, Mostafa, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi, Zohreh, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz ur, Rahman, Muhammad Aziz, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Raju, Sree Bhushan, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Rath, Goura Kishor, Rawaf, David Laith, Rawaf, Salman, Reddy, K Srinath, Rehm, Colin D, Rehm, Jürgen, Reiner, Robert C, Reitsma, Marissa B, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Reynales-Shigematsu, Luz Myriam, Rezaei, Satar, Ribeiro, Antonio Luiz P, Rivera, Juan A, Roba, Kedir Teji, Rodríguez-Ramírez, Sonia, Roever, Leonardo, Román, Yesenia, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Roth, Gregory A, Rothenbacher, Dietrich, Roy, Ambuj, Rubagotti, Enrico, Rushton, Lesley, Sabanayagam, Charumathi, Sachdev, Perminder S, Saddik, Basema, Sadeghi, Ehsan, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salam, Nasir, Salamati, Payman, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A, Salvi, Devashri Digvijay, Salz, Inbal, Samy, Abdallah M, Sanabria, Juan, Sanchez-Niño, Maria Dolore, Sánchez-Pimienta, Tania G, Sanders, Taren, Sang, Yingying, Santomauro, Damian Francesco, Santos, Itamar S, Santos, João Vasco, Santric Milicevic, Milena M, Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R, Sawhney, Monika, Saylan, Mete, Sayyah, Mehdi, Schaeffner, Elke, Schmidt, Maria Inê, Schneider, Ione J C, Schöttker, Ben, Schutte, Aletta Elisabeth, Schwebel, David C, Schwendicke, Falk, Scott, James G, Seedat, Soraya, Sekerija, Mario, Sepanlou, Sadaf G, Serre, Marc L, Serván-Mori, Edson, Seyedmousavi, Seyedmojtaba, Shabaninejad, Hosein, Shaddick, Gavin, Shafieesabet, Azadeh, Shahbazi, Mehdi, Shaheen, Amira A, Shaikh, Masood Ali, Shamah Levy, Teresa, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Sharafi, Heidar, Sharafi, Kiomar, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharifi, Hamid, Sharma, Jayendra, Sharma, Meenakshi, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shiferaw, Mekonnen Sisay, Shigematsu, Mika, Shin, Min-Jeong, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokraneh, Farhad, Shoman, Haitham, Shrime, Mark G, Shupler, Matthew S, Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silva, Diego Augusto Santo, Silva, João Pedro, Silveira, Dayane Gabriele Alve, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Skiadaresi, Eirini, Skirbekk, Vegard, Smith, David L, Smith, Mari, Sobaih, Badr Hasan, Sobhani, Soheila, Somayaji, Ranjani, Soofi, Moslem, Sorensen, Reed J D, Soriano, Joan B, Soyiri, Ireneous N, Spinelli, Angela, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Srinivasan, Vinay, Starodubov, Vladimir I, Steckling, Nadine, Stein, Dan J, Stein, Murray B, Stevanovic, Goran, Stockfelt, Leo, Stokes, Mark A, Sturua, Lela, Subart, Michelle L, Sudaryanto, Agu, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sykes, Bryan L, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabuchi, Takahiro, Tadakamadla, Santosh Kumar, Takahashi, Ken, Tandon, Nikhil, Tassew, Segen Gebremeskel, Tavakkoli, Mohammad, Taveira, Nuno, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekelemedhin, Shishay Wahdey, Tekle, Merhawi Gebremedhin, Temesgen, Habtamu, Temsah, Mohamad-Hani, Temsah, Omar, Terkawi, Abdullah Sulieman, Tessema, Belay, Teweldemedhin, Mebrahtu, Thankappan, Kavumpurathu Raman, Theis, Andrew, Thirunavukkarasu, Sathish, Thomas, Hannah J, Thomas, Matthew Lloyd, Thomas, Nihal, Thurston, George D, Tilahun, Binyam, Tillmann, Taavi, To, Quyen G, Tobollik, Myriam, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Towbin, Jeffrey A, Tran, Bach Xuan, Tran, Khanh Bao, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tudor Car, Lorainne, Tuzcu, E Murat, Tymeson, Hayley D, Tyrovolas, Stefano, Ukwaja, Kingsley N, Ullah, Irfan, Updike, Rachel L, Usman, Muhammad Shariq, Uthman, Olalekan A, Vaduganathan, Muthiah, Vaezi, Afsane, Valdez, Pascual R, Van Donkelaar, Aaron, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Venkateswaran, Vidhya, Venketasubramanian, Narayanaswamy, Villafaina, Santo, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vu, Giang Thu, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Waller, Stephen G, Walson, Judd L, Wang, Yafeng, Wang, Yanping, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weldegebreal, Fitsum, Werdecker, Andrea, Werkneh, Adhena Ayaliew, West, J Jason, Westerman, Ronny, Whiteford, Harvey A, Widecka, Justyna, Wijeratne, Tissa, Winkler, Andrea Sylvia, Wiyeh, Alison B, Wiysonge, Charles Shey, Wolfe, Charles D A, Wong, Tien Yin, Wu, Shouling, Xavier, Deni, Xu, Gelin, Yadgir, Simon, Yadollahpour, Ali, Yahyazadeh Jabbari, Seyed Hossein, Yamada, Tomohide, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zavala-Arciniega, Lui, Zhang, Anthony Lin, Zhang, Hao, Zhang, Kai, Zhou, Maigeng, Zimsen, Stephanie R M, Zodpey, Sanjay, and Murray, Christopher J L
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Science & Technology ,GBD, risks ,risk factors ,comparative risk assesment ,Dalys ,Medicine (all) ,CHOLESTEROL ,BLOOD-PRESSURE ,11 Medical And Health Sciences ,ILLNESS ,TRENDS ,humanities ,Medicine, General & Internal ,TOBACCO CONTROL ,General & Internal Medicine ,parasitic diseases ,Medicine and Health Sciences ,CARDIOVASCULAR-DISEASES ,TRIAL ,Human medicine ,Life Sciences & Biomedicine ,METAANALYSIS - Abstract
Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk– outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Lancet 2018; 392: 1923–94 *Collaborators listed at the end of the paper Correspondence to: Prof Christopher J L Murray, Institute for Health Metrics and Evalution, Seattle, WA 98121, USA cjlm@uw.edu Global Health Metrics 1924 www.thelancet.com Vol 392 November 10, 2018 Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
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5. The association of short-term increases in ambient PM2.5 and temperature exposures with stillbirth: racial/ethnic disparities among Medicaid recipients.
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Shupler M, Huybrechts K, Leung M, Wei Y, Schwartz J, Hernandez-Diaz S, and Papatheodorou S
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- Adult, Female, Humans, Pregnancy, Young Adult, Air Pollutants analysis, Air Pollution adverse effects, Cross-Over Studies, Environmental Exposure adverse effects, Ethnicity, Racial Groups, Socioeconomic Factors, Temperature, United States epidemiology, Health Status Disparities, Medicaid, Particulate Matter analysis, Stillbirth ethnology, Stillbirth epidemiology
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Racial/ethnic disparities in the association between short-term (eg, days, weeks), ambient fine particulate matter (PM2.5) and temperature exposures and stillbirth in the United States have been understudied. A time-stratified, case-crossover design using a distributed lag nonlinear model (0- to 6-day lag) was used to estimate stillbirth odds due to short-term increases in average daily PM2.5 and temperature exposures among 118 632 Medicaid recipients from 2000 to 2014. Disparities by maternal race/ethnicity (Black, White, Hispanic, Asian, American Indian) and zip code-level socioeconomic status (SES) were assessed. In the temperature-adjusted model, a 10 μg m-3 increase in PM2.5 concentration was marginally associated with increased stillbirth odds at lag 1 (0.68%; 95% CI, -0.04% to 1.40%) and lag 2 (0.52%; 95% CI, -0.03 to 1.06) but not lag 0-6 (2.80%; 95% CI, -0.81 to 6.45). An association between daily PM2.5 concentrations and stillbirth odds was found among Black individuals at the cumulative lag (0-6 days: 9.26% 95% CI, 3.12%-15.77%) but not among other races or ethnicities. A stronger association between PM2.5 concentrations and stillbirth odds existed among Black individuals living in zip codes with the lowest median household income (lag 0-6: 14.13%; 95% CI, 4.64%-25.79%). Short-term temperature increases were not associated with stillbirth risk among any race/ethnicity. Black Medicaid enrollees, and especially those living in lower SES areas, may be more vulnerable to stillbirth due to short-term increases in PM2.5 exposure. This article is part of a Special Collection on Environmental Epidemiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
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- 2024
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6. Burden of headaches, eye irritation and respiratory symptoms among females stacking LPG with polluting cooking fuels: Modelling from peri-urban Cameroon, Ghana & Kenya.
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Parvizi K, Menya D, Nix E, Mangeni J, Lorenzetti F, Sang E, Anderson de Cuevas R, Tawiah T, Baame M, Betang E, Ronzi S, Twumasi M, Amenga-Etego S, Quansah R, Mbatchou Ngahane BH, Puzzolo E, Asante KP, Pope D, and Shupler M
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Introduction: Liquefied petroleum gas (LPG) is a clean cooking fuel that emits less household air pollution (HAP) than polluting cooking fuels (e.g. charcoal, wood). While switching from polluting fuels to LPG can reduce HAP and improve health, the impact of 'stacking' (concurrent use of polluting fuels and LPG) on adverse health symptoms (e.g. headaches, eye irritation, cough) among female cooks is uncertain., Methods: Survey data from the CLEAN-Air(Africa) study was collected on cooking patterns and health symptoms over the last 12 months (cough, wheezing, chest tightness, shortness of breath, eye irritation, headaches) from approximately 400 female primary cooks in each of three peri‑urban communities in sub-Saharan Africa: Mbalmayo, Cameroon; Obuasi, Ghana; and Eldoret, Kenya. Random effects Poisson regression, adjusted for socioeconomic and health-related covariates, assessed the relationship between primary and secondary cooking fuel type and self-reported health symptoms., Results: Among 1,147 participants, 10 % ( n = 118) exclusively cooked with LPG, 45 % ( n = 509) stacked LPG and polluting fuels and 45 % ( n = 520) exclusively cooked with polluting fuels. Female cooks stacking LPG and polluting fuels had significantly higher odds of shortness of breath (OR 2.16, 95 %CI:1.04-4.48) compared with those exclusively using LPG. In two communities, headache prevalence was 30 % higher among women stacking LPG with polluting fuels (Mbalmayo:82 %; Eldoret:65 %) compared with those exclusively using LPG (Mbalmayo:53 %; Eldoret:33 %). Women stacking LPG and polluting fuels (OR 2.45, 95 %CI:1.29-4.67) had significantly higher odds of eye irritation than women cooking exclusively with LPG. Second-hand smoke exposure was significantly associated with higher odds of chest tightness (OR 1.92, 95 % CI:1.19-3.11), wheezing (OR 1.76, 95 % CI:1.06-2.91) and cough (OR 1.78, 95 %CI:1.13-2.80)., Conclusions: In peri‑urban sub-Saharan Africa, women exclusively cooking with LPG had lower odds of several health symptoms than those stacking LPG and polluting fuels. Promoting a complete transition to LPG in these communities may likely generate short-term health benefits for primary cooks., Competing Interests: The authors have no conflicts of interest to declare., (© 2024 The Author(s).)
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- 2024
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7. Association between density of food retailers and fitness centers and gestational diabetes mellitus in Eastern Massachusetts, USA: population-based study.
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Shupler M, Klompmaker JO, Leung M, Petimar J, Drouin-Chartier JP, Modest AM, Hacker M, Farid H, James P, Hernandez-Diaz S, and Papatheodorou S
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Background: Few studies have investigated the relationship between the food and physical activity environment and odds of gestational diabetes mellitus (GDM). This study quantifies the association between densities of several types of food establishments and fitness centers with the odds of having GDM., Methods: The density of supermarkets, fast-food restaurants, full-service restaurants, convenience stores and fitness centers at 500, 1000 and 1500 m (m) buffers was counted at residential addresses of 68,779 pregnant individuals from Eastern Massachusetts during 2000-2016. The 'healthy food index' assessed the relative availability of healthy (supermarkets) vs unhealthy (fast-food restaurants, convenience stores) food retailers. Multivariable logistic regression quantified the cross-sectional association between exposure variables and the odds of having GDM, adjusting for individual and area-level characteristics. Effect modification by area-level socioeconomic status (SES) was assessed., Findings: In fully adjusted models, pregnant individuals living in the highest density tertile of fast-food restaurants had higher GDM odds compared to those living in the lowest density tertile (500 m: odds ratio (OR):1.17 95% CI: [1.04, 1.31]; 1000 m: 1.33 95% CI: [1.15, 1.53]); 1500 m: 1.18 95% CI: [1.01, 1.38]). Greater residential density of supermarkets was associated with lower odds of GDM (1000 m: 0.86 95% CI: [0.74, 0.99]; 1500 m: 0.86 95% CI: [0.72, 1.01]). Similarly, living in the highest fitness center density tertile was associated with decreased GDM odds (500 m:0.87 95% CI: [0.76, 0.99]; 1500 m: 0.89 95% CI: [0.79, 1.01]). There was no evidence of effect modification by SES and no association found between the healthy food index and GDM odds., Interpretation: In Eastern Massachusetts, living near a greater density of fast-food establishments was associated with higher GDM odds. Greater residential access to supermarkets and fitness centers was associated with lower the odds of having GDM., Funding: NIH., Competing Interests: Jean-Philippe Drouin-Chartier received consulting fees from Dairy Farmers of Canada (unrelated to this manuscript)., (© 2024 Published by Elsevier Ltd.)
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- 2024
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8. Household concentrations and female and child exposures to air pollution in peri-urban sub-Saharan Africa: measurements from the CLEAN-Air(Africa) study.
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Shupler M, Tawiah T, Nix E, Baame M, Lorenzetti F, Betang E, Chartier R, Mangeni J, Upadhya A, Anderson de Cuevas R, Sang E, Piedrahita R, Johnson M, Wilson D, Amenga-Etego S, Twumasi M, Ronzi S, Menya D, Puzzolo E, Quansah R, Asante KP, Pope D, and Mbatchou Ngahane BH
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- Child, Humans, Female, Ghana, Kenya, Charcoal, Rural Population, Particulate Matter analysis, Air Pollution, Indoor analysis, Air Pollution analysis
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Background: Relatively clean cooking fuels such as liquefied petroleum gas (LPG) emit less fine particulate matter (PM
2·5 ) and carbon monoxide (CO) than polluting fuels (eg, wood, charcoal). Yet, some clean cooking interventions have not achieved substantial exposure reductions. This study evaluates determinants of between-community variability in exposures to household air pollution (HAP) across sub-Saharan Africa., Methods: In this measurement study, we recruited households cooking primarily with LPG or exclusively with wood or charcoal in peri-urban Cameroon, Ghana, and Kenya from previously surveyed households. In 2019-20, we conducted monitoring of 24 h PM2·5 and CO kitchen concentrations (n=256) and female cook (n=248) and child (n=124) exposures. PM2·5 measurements used gravimetric and light scattering methods. Stove use monitoring and surveys on cooking characteristics and ambient air pollution exposure (eg, walking time to main road) were also administered., Findings: The mean PM2·5 kitchen concentration was five times higher among households cooking with charcoal than those using LPG in the Kenyan community (297 μg/m3 , 95% CI 216-406, vs 61 μg/m3 , 49-76), but only 4 μg/m3 higher in the Ghanaian community (56 μg/m3 , 45-70, vs 52 μg/m3 , 40-68). The mean CO kitchen concentration in charcoal-using households was double the WHO guideline (6·11 parts per million [ppm]) in the Kenyan community (15·81 ppm, 95% CI 8·71-28·72), but below the guideline in the Ghanaian setting (1·77 ppm, 1·04-2·99). In all communities, mean PM2·5 cook exposures only met the WHO interim-1 target (35 μg/m3 ) among LPG users staying indoors and living more than 10 min walk from a road., Interpretation: Community-level variation in the relative difference in HAP exposures between LPG and polluting cooking fuel users in peri-urban sub-Saharan Africa might be attributed to differences in ambient air pollution levels. Thus, mitigation of indoor and outdoor PM2·5 sources will probably be critical for obtaining significant exposure reductions in rapidly urbanising settings of sub-Saharan Africa., Funding: UK National Institute for Health and Care Research., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)- Published
- 2024
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9. Short-Term Increases in NO 2 and O 3 Concentrations during Pregnancy and Stillbirth Risk in the U.S.: A Time-Stratified Case-Crossover Study.
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Shupler M, Huybrechts K, Leung M, Wei Y, Schwartz J, Li L, Koutrakis P, Hernández-Díaz S, and Papatheodorou S
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- Pregnancy, Female, Humans, Cross-Over Studies, Nitrogen Dioxide analysis, Particulate Matter analysis, Stillbirth epidemiology, Environmental Exposure analysis, Air Pollution analysis, Air Pollutants analysis, Ozone analysis
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Associations between gaseous pollutant exposure and stillbirth have focused on exposures averaged over trimesters or gestation. We investigated the association between short-term increases in nitrogen dioxide (NO
2 ) and ozone (O3 ) concentrations and stillbirth risk among a national sample of 116 788 Medicaid enrollees from 2000 to 2014. A time-stratified case-crossover design was used to estimate distributed (lag 0-lag 6) and cumulative lag effects, which were adjusted for PM2.5 concentration and temperature. Effect modification by race/ethnicity and proximity to hydraulic fracturing (fracking) wells was assessed. Short-term increases in the NO2 and O3 concentrations were not associated with stillbirth in the overall sample. Among American Indian individuals ( n = 1694), a 10 ppb increase in NO2 concentrations was associated with increased stillbirth odds at lag 0 (5.66%, 95%CI: [0.57%, 11.01%], p = 0.03) and lag 1 (4.08%, 95%CI: [0.22%, 8.09%], p = 0.04) but not lag 0-6 (7.12%, 95%CI: [-9.83%, 27.27%], p = 0.43). Among participants living in zip codes within 15 km of active fracking wells ( n = 9486), a 10 ppb increase in NO2 concentration was associated with increased stillbirth odds in single-day lags (2.42%, 95%CI: [0.37%, 4.52%], p = 0.02 for lag 0 and 1.83%, 95%CI: [0.25%, 3.43%], p = 0.03 for lag 1) but not the cumulative lag (lag 0-6) (4.62%, 95%CI: [-2.75%, 12.55%], p = 0.22). Odds ratios were close to the null in zip codes distant from fracking wells. Future studies should investigate the role of air pollutants emitted from fracking and potential racial disparities in the relationship between short-term increases in NO2 concentrations and stillbirth.- Published
- 2024
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10. Deepened socioeconomic inequality in clean cooking fuel use in India from 2005-2006 to 2015-2016.
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Islam S, Rana MJ, and Shupler M
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Uptake of clean cooking fuels (CCF), such as liquefied petroleum gas (LPG), in place of traditional cooking fuels such as wood, charcoal, and kerosene can improve public health by reducing household air pollution exposures. Though studies have cross-sectionally examined socioeconomic determinants of cooking fuel adoption, little is known about socioeconomic disparities in CCF use over time. Data from the third (2005-06) and fourth (2015-16) rounds of the National Family Health Survey covering 109,041 and 601,509 households, respectively, were used to examine inequities in CCF use in India. While CCF use in India increased nationally from 25% in 2005-06 to 44% in 2015-16, the adoption of CCF varied widely across states and socio-economic groups. Approximately 2% of households in the poorest wealth quintile gained access to LPG during the study period, compared with an increase of 10% or more among households in the middle or richer wealth quintiles; the LPG access gap between the low (0.2%) and middle class (19.2%) was 19% in 2005-06 and nearly doubled to 35% (2.5% vs. 37.4%, respectively) in 2015-16. At the state level, there was a four-fold difference in the uptake of CCF over the two survey periods. The use of CCF increased by less than 10% in Himachal Pradesh, Bihar, Assam, Manipur, Mizoram, and Meghalaya as compared to the increases of at least 30% in Tamil Nadu (42%), undivided Andhra Pradesh (34%), and Kerala (30%). Further, in wealthier states (Delhi, Goa, Punjab, Haryana, Tamil Nadu, Kerala, and undivided Andhra Pradesh), CCF use increased by more than 20% among the poorest individuals compared with less than 1% among the poorest families in lower income states (Tripura, Meghalaya, Madhya Pradesh, Jharkhand, Chhattisgarh, Bihar). To promote a more equitable clean energy transition, poorer and rural Indian households should be prioritized for CCF promotion programs., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)
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- 2023
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11. Multiple aspects of energy poverty are associated with lower mental health-related quality of life: A modelling study in three peri-urban African communities.
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Shupler M, Baame M, Nix E, Tawiah T, Lorenzetti F, Saah J, Anderson de Cuevas R, Sang E, Puzzolo E, Mangeni J, Betang E, Twumasi M, Amenga-Etego S, Quansah R, Mbatchou B, Menya D, Asante KP, and Pope D
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Objective: Over 900 million people in sub-Saharan Africa (SSA) live in energy poverty, relying on cooking polluting fuels (e.g. wood, charcoal). The association between energy poverty and mental/physical health-related quality of life (HRQoL) among women in SSA, who are primarily tasked with cooking, is unknown., Methods: Females (n = 1,150) from peri-urban Cameroon, Kenya and Ghana were surveyed on their household energy use and mental/physical health status using the standardized Short-Form 36 (SF-36) questionnaire. Random effects linear regression linked household energy factors to SF-36 mental (MCS) and physical component summary (PCS) scores. A binary outcome of 'likely depression' was derived based on participants' MCS score. Random effects Poisson regression with robust error variance assessed the relationship between household energy factors and odds of likely depression., Results: The prevalence of likely depression varied by a factor of four among communities (36%-Mbalmayo, Cameroon; 20%-Eldoret, Kenya; 9%-Obuasi, Ghana). In the Poisson model (coefficient of determination (R
2 ) = 0.28), females sustaining 2 or more cooking-related burns during the previous year had 2.7 (95%CI:[1.8,4.1]) times the odds of likely depression as those not burned. Females cooking primarily with charcoal and wood had 1.6 times (95%CI:[0.9,2.7]) and 1.5 times (95%CI:[0.8,3.0]) the odds of likely depression, respectively, as those primarily using liquefied petroleum gas. Women without electricity access had 1.4 (95%CI:[1.1,1.9]) times the odds of likely depression as those with access. In the MCS model (R2 = 0.23), longer time spent cooking was associated with a lower average MCS score in a monotonically increasing manner. In the PCS model (R2 = 0.32), women injured during cooking fuel collection had significantly lower (-4.8 95%CI:[-8.1,-1.4]) PCS scores., Conclusion: The burden of energy poverty in peri-urban communities in SSA extends beyond physical conditions. Experiencing cooking-related burns, using polluting fuels for cooking or lighting and spending more time cooking are potential risk factors for lower mental HRQoL among women., Competing Interests: The authors have no competing interest to declare., (© 2022 The Authors.)- Published
- 2022
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12. Complex dynamics in sustaining clean cooking and food access through a pandemic: A COVID-19 impact study in peri-urban Cameroon.
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Nix E, Betang E, Baame M, Abbott M, Saligari S, Shupler M, Čukić I, Puzzolo E, Pope D, Mbatchou B, and Anderson de Cuevas R
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Access to clean energy for cooking is central to achieving Sustainable Development Goal 7. Latest predictions suggest that this goal will not be met by 2030, with further setbacks due to the COVID-19 pandemic. We investigated the impacts of COVID-19 restrictions on household cooking fuel, practices and dietary behaviours in a peri-urban community in Central Cameroon. Using surveys (n = 333) and qualitative semi-structured interviews (n = 12), we found negative financial impacts and high levels of food insecurity, with 83 % and 56 % of households reporting reduced income and insufficient food, respectively. Households reduced food intake and cooking frequency and relied more heavily on local sources (e.g., farmland) to feed their families. Changes in primary cooking fuel were less pronounced and fuel choice was inherently linked to cooking behaviours, with some households utilising LPG more often for simple tasks, such as reheating food. Local systems were key in sustaining food and fuel access and households demonstrated resilience by employing numerous mechanisms to overcome challenges. Our findings underline the vulnerability of households in maintaining sufficient food intake and sustaining clean cooking, highlighting how policy needs to take a nuanced approach considering food-energy dynamics and strengthening local systems to ensure access to clean energy is resistant to system shocks., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)
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- 2022
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13. Personal and household PM 2.5 and black carbon exposure measures and respiratory symptoms in 8 low- and middle-income countries.
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Wang Y, Shupler M, Birch A, Chu YL, Jeronimo M, Rangarajan S, Mustaha M, Heenan L, Seron P, Saavedra N, Oliveros MJ, Lopez-Jaramillo P, Camacho PA, Otero J, Perez-Mayorga M, Yeates K, West N, Ncube T, Ncube B, Chifamba J, Yusuf R, Khan A, Liu Z, Cheng X, Wei L, Tse LA, Mohan D, Kumar P, Gupta R, Mohan I, Jayachitra KG, Mony PK, Rammohan K, Nair S, Lakshmi PVM, Sagar V, Khawaja R, Iqbal R, Kazmi K, Yusuf S, Brauer M, and Hystad P
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- Carbon, Cooking, Developing Countries, Environmental Exposure analysis, Female, Humans, Male, Particulate Matter analysis, Prospective Studies, Soot, Air Pollutants analysis, Air Pollution analysis, Air Pollution, Indoor adverse effects, Air Pollution, Indoor analysis
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Background: Household air pollution (HAP) from cooking with solid fuels has been associated with adverse respiratory effects, but most studies use surveys of fuel use to define HAP exposure, rather than on actual air pollution exposure measurements., Objective: To examine associations between household and personal fine particulate matter (PM
2.5 ) and black carbon (BC) measures and respiratory symptoms., Methods: As part of the Prospective Urban and Rural Epidemiology Air Pollution study, we analyzed 48-h household and personal PM2.5 and BC measurements for 870 individuals using different cooking fuels from 62 communities in 8 countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Self-reported respiratory symptoms were collected after monitoring. Associations between PM2.5 and BC exposures and respiratory symptoms were examined using logistic regression models, controlling for individual, household, and community covariates., Results: The median (interquartile range) of household and personal PM2.5 was 73.5 (119.1) and 65.3 (91.5) μg/m3 , and for household and personal BC was 3.4 (8.3) and 2.5 (4.9) x10-5 m-1 , respectively. We observed associations between household PM2.5 and wheeze (OR: 1.25; 95%CI: 1.07, 1.46), cough (OR: 1.22; 95%CI: 1.06, 1.39), and sputum (OR: 1.26; 95%CI: 1.10, 1.44), as well as exposure to household BC and wheeze (OR: 1.20; 95%CI: 1.03, 1.39) and sputum (OR: 1.20; 95%CI: 1.05, 1.36), per IQR increase. We observed associations between personal PM2.5 and wheeze (OR: 1.23; 95%CI: 1.00, 1.50) and sputum (OR: 1.19; 95%CI: 1.00, 1.41). For household PM2.5 and BC, associations were generally stronger for females compared to males. Models using an indicator variable of solid versus clean fuels resulted in larger OR estimates with less precision., Conclusions: We used measurements of household and personal air pollution for individuals using different cooking fuels and documented strong associations with respiratory symptoms., (Copyright © 2022. Published by Elsevier Inc.)- Published
- 2022
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14. Measuring and predicting personal and household Black Carbon levels from 88 communities in eight countries.
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Wang Y, Shupler M, Birch A, Chu YL, Jeronimo M, Rangarajan S, Mustaha M, Heenan L, Seron P, Lanas F, Salazar L, Saavedra N, Oliveros MJ, Lopez-Jaramillo P, Camacho PA, Otero J, Perez-Mayorga M, Yeates K, West N, Ncube T, Ncube B, Chifamba J, Yusuf R, Khan A, Liu Z, Bo H, Wei L, Tse LA, Mohan D, Kumar P, Gupta R, Mohan I, Jayachitra KG, Mony PK, Rammohan K, Nair S, Lakshmi PVM, Sagar V, Khawaja R, Iqbal R, Kazmi K, Yusuf S, Brauer M, and Hystad P
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- Carbon, Cooking, Environmental Exposure, Environmental Monitoring, Female, Humans, Male, Particulate Matter analysis, Prospective Studies, Rural Population, Air Pollutants analysis, Air Pollution, Indoor analysis
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Black Carbon (BC) is an important component of household air pollution (HAP) in low- and middle- income countries (LMICs), but levels and drivers of exposure are poorly understood. As part of the Prospective Urban and Rural Epidemiological (PURE) study, we analyzed 48-hour BC measurements for 1187 individual and 2242 household samples from 88 communities in 8 LMICs (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Light absorbance (10
-5 m-1 ) of collected PM2.5 filters, a proxy for BC concentrations, was calculated via an image-based reflectance method. Surveys of household/personal characteristics and behaviors were collected after monitoring. The geometric mean (GM) of personal and household BC measures was 2.4 (3.3) and 3.5 (3.9)·10-5 m-1 , respectively. The correlation between BC and PM2.5 was r = 0.76 for personal and r = 0.82 for household measures. A gradient of increasing BC concentrations was observed for cooking fuels: BC increased 53% (95%CI: 30, 79) for coal, 142% (95%CI: 117, 169) for wood, and 190% (95%CI: 149, 238) for other biomass, compared to gas. Each hour of cooking was associated with an increase in household (5%, 95%CI: 3, 7) and personal (5%, 95%CI: 2, 8) BC; having a window in the kitchen was associated with a decrease in household (-38%, 95%CI: -45, -30) and personal (-31%, 95%CI: -44, -15) BC; and cooking on a mud stove, compared to a clean stove, was associated with an increase in household (125%, 95%CI: 96, 160) and personal (117%, 95%CI: 71, 117) BC. Male participants only had slightly lower personal BC (-0.6%, 95%CI: -1, 0.0) compared to females. In multivariate models, we were able to explain 46-60% of household BC variation and 33-54% of personal BC variation. These data and models provide new information on exposure to BC in LMICs, which can be incorporated into future exposure assessments, health research, and policy surrounding HAP and BC., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2021. Published by Elsevier B.V.)- Published
- 2022
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15. Multinational prediction of household and personal exposure to fine particulate matter (PM 2.5 ) in the PURE cohort study.
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Shupler M, Hystad P, Birch A, Chu YL, Jeronimo M, Miller-Lionberg D, Gustafson P, Rangarajan S, Mustaha M, Heenan L, Seron P, Lanas F, Cazor F, Jose Oliveros M, Lopez-Jaramillo P, Camacho PA, Otero J, Perez M, Yeates K, West N, Ncube T, Ncube B, Chifamba J, Yusuf R, Khan A, Liu Z, Wu S, Wei L, Tse LA, Mohan D, Kumar P, Gupta R, Mohan I, Jayachitra KG, Mony PK, Rammohan K, Nair S, Lakshmi PVM, Sagar V, Khawaja R, Iqbal R, Kazmi K, Yusuf S, and Brauer M
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- Bayes Theorem, Cohort Studies, Cooking, Environmental Exposure analysis, Environmental Monitoring, Female, Humans, Male, Particulate Matter analysis, Prospective Studies, Rural Population, Air Pollutants analysis, Air Pollution, Indoor analysis
- Abstract
Introduction: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM
2.5 ). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM2.5 exposure models., Methods: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM2.5 kitchen concentrations (n = 2,365) and male and/or female PM2.5 exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM2.5 exposures., Results: The final models explained half (R2 = 54%) of the variation in kitchen PM2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R2 = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM2.5 kitchen concentrations. Average national PM2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m3 (Chile); 55 μg/m3 (China)) and 12-fold among households primarily cooking with wood (36 μg/m3 (Chile)); 427 μg/m3 (Pakistan)). Average PM2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile)., Conclusion: Using survey data to estimate PM2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)- Published
- 2022
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16. Energy poverty influences urban outdoor air pollution levels during COVID-19 lockdown in south-central Chile.
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Martinez-Soto A, Avendaño Vera CC, Boso A, Hofflinger A, and Shupler M
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The effect of COVID-19 lockdowns on ambient air pollution levels in urban south-central Chile, where outdoor air pollution primarily originates indoors from wood burning for heating, may differ from trends in cities where transportation and industrial emission sources dominate. This quasi-experimental study compared hourly fine (PM
2.5 ) and coarse (PM10 ) particulate matter measurements from six air monitors (three beta attenuation monitors; three low-cost sensors) in commercial and low/middle-income residential areas of Temuco, Chile between 2019 and 2020. The potential impact of varying annual meterological conditions on air quality was also assessed. During COVID-19 lockdown, average monthly ambient PM2.5 concentrations in a commercial and middle-income residential neighborhood of Temuco were up to 50% higher (from 12 to 18 μg/m3 ) and 59% higher (from 22 to 35 μg/m3 ) than 2019 levels, respectively. Conversely, PM2.5 levels decreased by up to 52% (from 43 to 21 μg/m3 ) in low-income areas. The fine fraction of PM10 in April 2020 was 48% higher than in April 2017-2019 (from 50% to 74%) in a commercial area. These changes did not appear to result from meterological differences between years. During COVID-19 lockdown, higher outdoor PM2.5 pollution from wood heating existed in more affluent areas of Temuco, while PM2.5 concentrations declined among poorer households refraining from wood heating. To reduce air pollution and energy poverty in south-central Chile, affordability of clean heating fuels (e.g. electricity) should be a policy priority., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier Ltd. All rights reserved.)- Published
- 2021
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17. Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya.
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Johnson M, Piedrahita R, Pillarisetti A, Shupler M, Menya D, Rossanese M, Delapeña S, Penumetcha N, Chartier R, Puzzolo E, and Pope D
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- Air Pollutants, Cooking, Environmental Monitoring, Family Characteristics, Household Articles, Humans, Kenya, Particulate Matter, Rural Population, Soot, Ventilation, Air Pollution, Indoor statistics & numerical data, Environmental Exposure statistics & numerical data, Models, Statistical
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This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM
2.5 , black carbon, CO); household air pollution (PM2.5 , CO); personal exposure (PM2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM2.5 (R2 = 0.45) and CO (R2 = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R2 = 0.76) and a root mean-squared error of 85 µg/m3 , and the survey-only-based regression model was able to predict PM2.5 exposures with an R2 of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R2 of 0.57 and RMSE of 98 µg/m3 ., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)- Published
- 2021
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18. COVID-19 impacts on household energy & food security in a Kenyan informal settlement: The need for integrated approaches to the SDGs.
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Shupler M, Mwitari J, Gohole A, Anderson de Cuevas R, Puzzolo E, Čukić I, Nix E, and Pope D
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This longitudinal study presents the joint effects of a COVID-19 community lockdown on household energy and food security in an informal settlement in Nairobi, Kenya. Randomly administered surveys were completed from December 2019-March 2020 before community lockdown (n = 474) and repeated in April 2020 during lockdown (n = 194). Nearly universal (95%) income decline occurred during the lockdown and led to 88% of households reporting food insecurity. During lockdown, a quarter of households (n = 17) using liquefied petroleum gas (LPG), a cleaner cooking fuel typically available in pre-set quantities (e.g. 6 kg cylinders), switched to polluting cooking fuels (kerosene, wood), which could be purchased in smaller amounts or gathered for free. Household size increases during lockdown also led to participants' altering their cooking fuel, and changing their cooking behaviors and foods consumed. Further, households more likely to switch away from LPG had lower consumption prior to lockdown and had suffered greater income loss, compared with households that continued to use LPG. Thus, inequities in clean cooking fuel access may have been exacerbated by COVID-19 lockdown. These findings demonstrate the complex relationship between household demographics, financial strain, diet and cooking patterns, and present the opportunity for a food-energy nexus approach to address multiple Sustainable Development Goals (SDGs): achieving zero hunger (SDG 2) and universal affordable, modern and clean energy access (SDG 7) by 2030. Ensuring that LPG is affordable, accessible and meets the dietary and cooking needs of families should be a policy priority for helping improve food and energy security among the urban poor., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)
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- 2021
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19. Pay-as-you-go liquefied petroleum gas supports sustainable clean cooking in Kenyan informal urban settlement during COVID-19 lockdown.
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Shupler M, O'Keefe M, Puzzolo E, Nix E, Anderson de Cuevas R, Mwitari J, Gohole A, Sang E, Čukić I, Menya D, and Pope D
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Approximately 2.8 billion people rely on polluting fuels (e.g. wood, kerosene) for cooking. With affordability being a key access barrier to clean cooking fuels, such as liquefied petroleum gas (LPG), pay-as-you-go (PAYG) LPG smart meter technology may help resource-poor households adopt LPG by allowing incremental fuel payments. To understand the potential for PAYG LPG to facilitate clean cooking, objective evaluations of customers' cooking and spending patterns are needed. This study uses novel smart meter data collected between January 2018-June 2020, spanning COVID-19 lockdown, from 426 PAYG LPG customers living in an informal settlement in Nairobi, Kenya to evaluate stove usage (e.g. cooking events/day, cooking event length). Seven semi-structured interviews were conducted in August 2020 to provide context for potential changes in cooking behaviours during lockdown. Using stove monitoring data, objective comparisons of cooking patterns are made with households using purchased 6 kg cylinder LPG in peri -urban Eldoret, Kenya. In Nairobi, 95% of study households continued using PAYG LPG during COVID-19 lockdown, with consumption increasing from 0.97 to 1.22 kg/capita/month. Daily cooking event frequency also increased by 60% (1.07 to 1.72 events/day). In contrast, average days/month using LPG declined by 75% during lockdown (17 to four days) among seven households purchasing 6 kg cylinder LPG in Eldoret. Interviewed customers reported benefits of PAYG LPG beyond fuel affordability, including safety, time savings and cylinder delivery. In the first study assessing PAYG LPG cooking patterns, LPG use was sustained despite a COVID-19 lockdown, illustrating how PAYG smart meter technology may help foster clean cooking access., Competing Interests: Mark O’Keefe is co-founder and Product Manager at PayGo Energy. His employment at PayGo Energy had no impact on interpretation of the data., (© 2021 The Authors.)
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- 2021
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20. Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study.
- Author
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Shupler M, Hystad P, Birch A, Miller-Lionberg D, Jeronimo M, Arku RE, Chu YL, Mushtaha M, Heenan L, Rangarajan S, Seron P, Lanas F, Cazor F, Lopez-Jaramillo P, Camacho PA, Perez M, Yeates K, West N, Ncube T, Ncube B, Chifamba J, Yusuf R, Khan A, Hu B, Liu X, Wei L, Tse LA, Mohan D, Kumar P, Gupta R, Mohan I, Jayachitra KG, Mony PK, Rammohan K, Nair S, Lakshmi PVM, Sagar V, Khawaja R, Iqbal R, Kazmi K, Yusuf S, and Brauer M
- Subjects
- Air Pollutants standards, Air Pollution, Indoor statistics & numerical data, Cooking methods, Cooking statistics & numerical data, Environmental Monitoring, Family Characteristics, Female, Humans, Inhalation Exposure standards, Male, Particulate Matter standards, Rural Population, Soot analysis, Soot standards, Air Pollutants analysis, Air Pollution, Indoor analysis, Inhalation Exposure analysis, Particulate Matter analysis
- Abstract
Background: Approximately 2·8 billion people are exposed to household air pollution from cooking with polluting fuels. Few monitoring studies have systematically measured health-damaging air pollutant (ie, fine particulate matter [PM
2·5 ] and black carbon) concentrations from a wide range of cooking fuels across diverse populations. This multinational study aimed to assess the magnitude of kitchen concentrations and personal exposures to PM2·5 and black carbon in rural communities with a wide range of cooking environments., Methods: As part of the Prospective Urban and Rural Epidemiological (PURE) cohort, the PURE-AIR study was done in 120 rural communities in eight countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Data were collected from 2541 households and from 998 individuals (442 men and 556 women). Gravimetric (or filter-based) 48 h kitchen and personal PM2·5 measurements were collected. Light absorbance (10-5 m-1 ) of the PM2·5 filters, a proxy for black carbon concentrations, was calculated via an image-based reflectance method. Surveys of household characteristics and cooking patterns were collected before and after the 48 h monitoring period., Findings: Monitoring of household air pollution for the PURE-AIR study was done from June, 2017, to September, 2019. A mean PM2·5 kitchen concentration gradient emerged across primary cooking fuels: gas (45 μg/m3 [95% CI 43-48]), electricity (53 μg/m3 [47-60]), coal (68 μg/m3 [61-77]), charcoal (92 μg/m3 [58-146]), agricultural or crop waste (106 μg/m3 [91-125]), wood (109 μg/m3 [102-118]), animal dung (224 μg/m3 [197-254]), and shrubs or grass (276 μg/m3 [223-342]). Among households cooking primarily with wood, average PM2·5 concentrations varied ten-fold (range: 40-380 μg/m3 ). Fuel stacking was prevalent (981 [39%] of 2541 households); using wood as a primary cooking fuel with clean secondary cooking fuels (eg, gas) was associated with 50% lower PM2·5 and black carbon concentrations than using only wood as a primary cooking fuel. Similar average PM2·5 personal exposures between women (67 μg/m3 [95% CI 62-72]) and men (62 [58-67]) were observed. Nearly equivalent average personal exposure to kitchen exposure ratios were observed for PM2·5 (0·79 [95% 0·71-0·88] for men and 0·82 [0·74-0·91] for women) and black carbon (0·64 [0·45-0·92] for men and 0·68 [0·46-1·02] for women)., Interpretation: Using clean primary fuels substantially lowers kitchen PM2·5 concentrations. Importantly, average kitchen and personal PM2·5 measurements for all primary fuel types exceeded WHO's Interim Target-1 (35 μg/m3 annual average), highlighting the need for comprehensive pollution mitigation strategies., Funding: Canadian Institutes for Health Research, National Institutes of Health., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)- Published
- 2020
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21. Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems.
- Author
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McLamore ES, Huffaker R, Shupler M, Ward K, Datta SPA, Katherine Banks M, Casaburi G, Babilonia J, and Foster JS
- Abstract
Technologies to treat wastewater in decentralized systems are critical for sustainable development. Bioreactors are suitable for low-energy removal of inorganic and organic compounds, particularly for non-potable applications where a small footprint is required. One of the main problems associated with bioreactor use is sporadic spikes of chemical toxins, including nanoparticles. Here, we describe the development of DIYBOT (Digital Proxy of a Bio-Reactor), which enables remote monitoring of bioreactors and uses the data to inform decisions related to systems management. To test DIYBOT, a household-scale membrane aerated bioreactor with real-time water quality sensors was used to treat household greywater simulant. After reaching steady-state, silver nanoparticles (AgNP) representative of the mixture found in laundry wastewater were injected into the system to represent a chemical contamination. Measurements of carbon metabolism, effluent water quality, biofilm sloughing rate, and microbial diversity were characterized after nanoparticle exposure. Real-time sensor data were analyzed to reconstruct phase-space dynamics and extrapolate a phenomenological digital proxy to evaluate system performance. The management implication of the stable-focus dynamics, reconstructed from observed data, is that the bioreactor self-corrects in response to contamination spikes at AgNP levels below 2.0 mg/L. DIYBOT may help reduce the frequency of human-in-the-loop corrective management actions for wastewater processing.
- Published
- 2020
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22. Health and Climate Impacts of Scaling Adoption of Liquefied Petroleum Gas (LPG) for Clean Household Cooking in Cameroon: A Modeling Study.
- Author
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Kypridemos C, Puzzolo E, Aamaas B, Hyseni L, Shupler M, Aunan K, and Pope D
- Subjects
- Cameroon, Humans, Models, Theoretical, Air Pollution, Indoor analysis, Climate Change, Cooking instrumentation, Environmental Health, Natural Gas
- Abstract
Background: The Cameroon government has set a target that, by 2030, 58% of the population will be using Liquefied Petroleum Gas (LPG) as a cooking fuel, in comparison with less than 20% in 2014. The National LPG Master Plan (Master Plan) was developed for scaling up the LPG sector to achieve this target., Objectives: This study aimed to estimate the potential impacts of this planned LPG expansion (the Master Plan) on population health and climate change mitigation, assuming primary, sustained use of LPG for daily cooking., Methods: We applied existing and developed new mathematical models to calculate the health and climate impacts of expanding LPG primary adoption for household cooking in Cameroon over two periods: a ) short-term (2017-2030): Comparing the Master Plan 58% target with a counterfactual LPG adoption of 32% in 2030, in line with current trends; and b ) long-term (2031-2100, climate modeling only), assuming Cameroon will become a mature and saturated LPG market by 2100 (73% adoption, based on Latin American countries). We compared this with a counterfactual adoption of 41% by 2100, in line with current trends., Results: By 2030, successful implementation of the Master Plan was estimated to avert about 28,000 ( minimum = 22,000 , maximum = 35,000 ) deaths and 770,000 ( minimum = 580,000 maximum = 1 million ) disability-adjusted life years. For the same period, we estimated reductions in pollutant emissions of more than a third in comparison with the counterfactual, leading to a global cooling of - 0.1 milli ° C in 2030. For 2100, a cooling impact from the Master Plan leading to market saturation (73%) was estimated to be - 0.70 milli ° C in comparison with to the counterfactual, with a range of - 0.64 to - 0.93 milli ° C based on different fractions of nonrenewable biomass., Discussion: Successful implementation of the Master Plan could have significant positive impacts on population health in Cameroon with no adverse impacts on climate. https://doi.org/10.1289/EHP4899.
- Published
- 2020
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23. Analysis of black carbon on filters by image-based reflectance.
- Author
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Jeronimo M, Stewart Q, Weakley AT, Giacomo J, Zhang X, Hyslop N, Dillner AM, Shupler M, and Brauer M
- Abstract
Black carbon (BC) is an important contributor to global particulate matter emissions. BC is associated with adverse health effects, and an important short-lived climate pollutant. Here, we describe a low cost method of analysis that utilizes images of PTFE filters taken with a digital camera to estimate BC content on filters. This method is compared with two existing optical methods for analyzing BC (Smokestain Reflectance and Hybrid Integrating Plate and Sphere System) as well as the standard chemical analysis method for determining elemental carbon (Thermal-Optical Reflectance). In comparisons of aerosol generated under controlled conditions (using an inverted diffusion flame burner to cover a range of mass loading and reflectance levels) (N=12) and in field samples collected from residential solid fuel combustion in China and India (N=50), the image-based method was found to correlate well (normalized RMSE <10% for all comparisons) with existing methods. A correlational analysis of field samples between the optical methods and Fourier-transform infrared spectroscopy indicated that the same functional groups were predominantly responsible for light attenuation in each optical method. This method offers reduced equipment cost, rapid analysis time, and is available at no cost, which may facilitate more measurement of BC where PM
2.5 mass concentrations are already measured, especially in low income countries or other sampling efforts with limited resources., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.- Published
- 2020
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24. Household, Community, Sub-National and Country-level Predictors of Primary Cooking Fuel Switching in Nine Countries from the PURE Study.
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Shupler M, Hystad P, Gustafson P, Rangarajan S, Mushtaha M, Jayachtria KG, Mony PK, Mohan D, Kumar P, Lakshmi P, Sagar V, Gupta R, Mohan I, Nair S, Varma RP, Li W, Hu B, You K, Ncube T, Ncube B, Chifamba J, West N, Yeates K, Iqbal R, Khawaja R, Yusuf R, Khan A, Seron P, Lanas F, Lopez-Jaramillo P, Camacho PA, Puoane T, Yusuf S, and Brauer M
- Abstract
Introduction: Switching from polluting (e.g. wood, crop waste, coal) to clean cooking fuels (e.g. gas, electricity) can reduce household air pollution (HAP) exposures and climate-forcing emissions. While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood., Methods: We examined longitudinal survey data from 24,172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology (PURE) study. We assessed household-level primary cooking fuel switching during a median of 10 years of follow up (~2005-2015). We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching., Results: One-half of study households (12,369) reported changing their primary cooking fuels between baseline and follow up surveys. Of these, 61% (7,582) switched from polluting (wood, dung, agricultural waste, charcoal, coal, kerosene) to clean (gas, electricity) fuels, 26% (3,109) switched between different polluting fuels, 10% (1,164) switched from clean to polluting fuels and 3% (522) switched between different clean fuels. Among the 17,830 households using polluting cooking fuels at baseline, household-level factors (e.g. larger household size, higher wealth, higher education level) were most strongly associated with switching from polluting to clean fuels in India; in all other countries, community-level factors (e.g. larger population density in 2010, larger increase in population density between 2005-2015) were the strongest predictors of polluting-to-clean fuel switching., Conclusions: The importance of community and sub-national factors relative to household characteristics in determining polluting-to-clean fuel switching varied dramatically across the nine countries examined. This highlights the potential importance of national and other contextual factors in shaping large-scale clean cooking transitions among rural communities in low- and middle-income countries., Competing Interests: The authors have no competing interests to declare.
- Published
- 2019
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25. Global estimation of exposure to fine particulate matter (PM 2.5 ) from household air pollution.
- Author
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Shupler M, Godwin W, Frostad J, Gustafson P, Arku RE, and Brauer M
- Subjects
- Bayes Theorem, Biomass, Child, Preschool, Coal, Family Characteristics, Female, Global Health, Humans, Male, Rural Population, Urban Population, Wood, Air Pollution analysis, Air Pollution, Indoor analysis, Cooking, Environmental Exposure analysis, Particulate Matter analysis
- Abstract
Background: Exposure to household air pollution (HAP) from cooking with dirty fuels is a leading health risk factor within Asia, Africa and Central/South America. The concentration of particulate matter of diameter ≤ 2.5 μm (PM
2.5 ) is an important metric to evaluate HAP risk, however epidemiological studies have demonstrated significant variation in HAP-PM2.5 concentrations at household, community and country levels. To quantify the global risk due to HAP exposure, novel estimation methods are needed, as financial and resource constraints render it difficult to monitor exposures in all relevant areas., Methods: A Bayesian, hierarchical HAP-PM2.5 global exposure model was developed using kitchen and female HAP-PM2.5 exposure data available in peer-reviewed studies from an updated World Health Organization Global HAP database. Cooking environment characteristics were selected using leave-one-out cross validation to predict quantitative HAP-PM2.5 measurements from 44 studies. Twenty-four hour HAP-PM2.5 kitchen concentrations and male, female and child exposures were estimated for 106 countries in Asia, Africa and Latin America., Results: A model incorporating fuel/stove type (traditional wood, improved biomass, coal, dung and gas/electric), urban/rural location, wet/dry season and socio-demographic index resulted in a Bayesian R2 of 0.57. Relative to rural kitchens using gas or electricity, the mean global 24-hour HAP-PM2.5 concentrations were 290 μg/m3 higher (range of regional averages: 110, 880) for traditional stoves, 150 μg/m3 higher (range of regional averages: 50, 290) for improved biomass stoves, 850 μg/m3 higher (range of regional averages: 310, 2600) for animal dung stoves, and 220 μg/m3 higher (range of regional averages: 80, 650) for coal stoves. The modeled global average female/kitchen exposure ratio was 0.40. Average modeled female exposures from cooking with traditional wood stoves were 160 μg/m3 in rural households and 170 μg/m3 in urban households. Average male and child rural area exposures from traditional wood stoves were 120 μg/m3 and 140 μg/m3 , respectively; average urban area exposures were identical to average rural exposures among both sub-groups., Conclusions: A Bayesian modeling approach was used to generate unique HAP-PM2.5 kitchen concentrations and personal exposure estimates for all countries, including those with little to no available quantitative HAP-PM2.5 exposure data. The global exposure model incorporating type of fuel-stove combinations can add specificity and reduce exposure misclassification to enable an improved global HAP risk assessment., (Copyright © 2018 Elsevier Ltd. All rights reserved.)- Published
- 2018
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26. Global household air pollution database: Kitchen concentrations and personal exposures of particulate matter and carbon monoxide.
- Author
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Shupler M, Balakrishnan K, Ghosh S, Thangavel G, Stroud-Drinkwater S, Adair-Rohani H, Lewis J, Mehta S, and Brauer M
- Abstract
The Global Household Air Pollution (HAP) Measurements database, commissioned by the World Health Organization, provides an organized summary of data reported in the literature describing HAP microenvironments, methods and measurements. As of June 2018, the database contains measurements from 43 countries obtained from 196 studies published through 2016. The database includes information useful for understanding the range of household and personal air pollution measurements that have been collected in a country, as well as characteristics of the cooking environment, including primary cooking fuel type, stove type, heating fuel type and kitchen location. Quantitative particulate matter (PM) of various size fractions and/or carbon monoxide (CO) exposure measurements included in the database can be aggregated and analyzed to generate summary statistics (e.g. average sub-national, national, regional and global HAP exposures) to assess temporal and spatial relationships. The quantitative PM exposure measurements in the database have been used in global predictive modeling of HAP-PM
2.5 exposures ("Global Estimation of Exposure to Fine Particulate Matter (PM2.5) from Household Air Pollution" (Shupler et al., 2018) [1]).- Published
- 2018
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27. Characterizing exposure to household air pollution within the Prospective Urban Rural Epidemiology (PURE) study.
- Author
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Arku RE, Birch A, Shupler M, Yusuf S, Hystad P, and Brauer M
- Subjects
- Air Pollutants analysis, Cooking statistics & numerical data, Humans, Particulate Matter analysis, Air Pollution, Indoor analysis, Environmental Exposure analysis, Health Surveys, Rural Population statistics & numerical data
- Abstract
Background: Household air pollution (HAP) from combustion of solid fuels is an important contributor to disease burden in low- and middle-income countries (LIC, and MIC). However, current HAP disease burden estimates are based on integrated exposure response curves that are not currently informed by quantitative HAP studies in LIC and MIC. While there is adequate evidence supporting causal relationships between HAP and respiratory disease, large cohort studies specifically examining relationships between quantitative measures of HAP exposure with cardiovascular disease are lacking., Objective: We aim to improve upon exposure proxies based on fuel type, and to reduce exposure misclassification by quantitatively measuring exposure across varying cooking fuel types and conditions in diverse geographies and socioeconomic settings. We leverage technology advancements to estimate household and personal PM
2.5 (particles below 2.5 μm in aerodynamic diameter) exposure within the large (N~250,000) multi-country (N~26) Prospective Urban and Rural Epidemiological (PURE) cohort study. Here, we detail the study protocol and the innovative methodologies being used to characterize HAP exposures, and their application in epidemiologic analyses., Methods/design: This study characterizes HAP PM2.5 exposures for participants in rural communities in ten PURE countries with >10% solid fuel use at baseline (Bangladesh, Brazil, Chile, China, Colombia, India, Pakistan, South Africa, Tanzania, and Zimbabwe). PM2.5 monitoring includes 48-h cooking area measurements in 4500 households and simultaneous personal monitoring of male and female pairs from 20% of the selected households. Repeat measurements occur in 20% of households to assess impacts of seasonality. Monitoring began in 2017, and will continue through 2019. The Ultrasonic Personal Aerosol Sampler (UPAS), a novel, robust, and inexpensive filter based monitor that is programmable through a dedicated mobile phone application is used for sampling. Pilot study field evaluation of cooking area measurements indicated high correlation between the UPAS and reference Harvard Impactors (r = 0.91; 95% CI: 0.84, 0.95; slope = 0.95). To facilitate tracking and to minimize contamination and analytical error, the samplers utilize barcoded filters and filter cartridges that are weighed pre- and post-sampling using a fully automated weighing system. Pump flow and pressure measurements, temperature and RH, GPS coordinates and semi-quantitative continuous particle mass concentrations based on filter differential pressure are uploaded to a central server automatically whenever the mobile phone is connected to the internet, with sampled data automatically screened for quality control parameters. A short survey is administered during the 48-h monitoring period. Post-weighed filters are further analyzed to estimate black carbon concentrations through a semi-automated, rapid, cost-effective image analysis approach. The measured PM2.5 data will then be combined with PURE survey information on household characteristics and behaviours collected at baseline and during follow-up to develop quantitative HAP models for PM2.5 exposures for all rural PURE participants (~50,000) and across different cooking fuel types within the 10 index countries. Both the measured (in the subset) and the modelled exposures will be used in separate longitudinal epidemiologic analyses to assess associations with cardiopulmonary mortality, and disease incidence., Discussion: The collected data and resulting characterization of cooking area and personal PM2.5 exposures in multiple rural communities from 10 countries will better inform exposure assessment as well as future epidemiologic analyses assessing the relationships between quantitative estimates of chronic HAP exposure with adult mortality and incident cardiovascular and respiratory disease. This will provide refined and more accurate exposure estimates in global CVD related exposure-response analyses., (Copyright © 2018 Elsevier Ltd. All rights reserved.)- Published
- 2018
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28. Socio-economic status and fruit juice consumption in Canada.
- Author
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Shupler M and Raine KD
- Subjects
- Adolescent, Adult, Aged, Canada, Child, Cross-Sectional Studies, Female, Health Surveys, Humans, Male, Middle Aged, Young Adult, Fruit and Vegetable Juices statistics & numerical data, Social Class
- Abstract
Objectives: The role of socio-economic status (SES) in fruit juice and fruit drink consumption is not well understood in a Canadian context. This study examines the relationship between SES and Canadian fruit juice and fruit drink consumption., Methods: The Canadian Community Health Survey (2011-2012), a cross-sectional survey that employs multistage cluster sampling, provided relevant data for a sample of 103 125 Canadians, aged 12 and older, living in the 10 provinces. Household income level decile, ranked at the health region level, was used as a surrogate measure of SES. Fruit juice and fruit drink consumption data were collected via self-report in telephone/in-person interviews. Multivariable gamma regression was used to model the relationship between SES and frequency of fruit juice and fruit drink consumption, adjusting for age, sex, diabetes status, daily fruit and vegetable consumption, education level, racial identity and physical activity., Results: A negative relationship was found, with a decreasing daily rate of fruit juice and fruit drink consumption associated with increasing SES. In the adjusted model, Canadians in the lowest SES category consumed fruit juice and fruit drinks at an average daily rate 1.18 times (95% CI: 1.14-1.23) that of Canadians in the highest SES category., Conclusion: The negative association between health region-adjusted SES and fruit juice and fruit drink consumption highlights the potentially important role of socio-economic factors at a local level. Canadian policy that aims to lower fruit juice and fruit drink consumption, and thus sugar intake, should target financial avenues (such as making fruit juice less financially attractive by lowering the cost of whole fruit and vegetables) in addition to communicating health benefits.
- Published
- 2017
- Full Text
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