Author: "Chandrasekar EK" - Searchworks@Jio Institute Digital Library Search Results

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1. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Naghavi, M, Ong, KL, Aali, A, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abebe, SS, Abedi, A, Abegaz, KH, Abhilash, ES, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abtahi, D, Abu Rumeileh, S, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Accrombessi, MMK, Adal, TG, Adamu, AA, Addo, IY, Addolorato, G, Adebiyi, AO, Adekanmbi, V, Adepoju, AV, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adeyinka, DA, Adeyomoye, OI, Admass, BAA, Adnani, QES, Adra, S, Afolabi, AA, Afzal, MS, Afzal, S, Agampodi, SB, Agasthi, P, Aggarwal, M, Aghamiri, S, Agide, FD, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadzade, AM, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MS, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Akara, EM, Akbarialiabad, H, Akinosoglou, K, Akinyemiju, T, Akkaif, MA, Akyirem, S, Al Hamad, H, Al Hasan, SM, Alahdab, F, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Alam, M, Alam, N, Al-amer, RM, Alanezi, FM, Alanzi, TM, Al-Azzam, S, Albakri, A, Albashtawy, M, AlBataineh, MT, Alcalde-Rabanal, JE, Aldawsari, KA, Aldhaleei, WA, Aldridge, RW, Alema, HB, Alemayohu, MA, Alemi, S, Alemu, YM, Al-Gheethi, AAS, Alhabib, KF, Alhalaiqa, FAN, Al-Hanawi, MK, Ali, A, Ali, L, Ali, MU, Ali, R, Ali, S, Ali, SSS, Alicandro, G, Alif, SM, Alikhani, R, Alimohamadi, Y, Aliyi, AA, Aljasir, MAM, Aljunid, SM, Alla, F, Allebeck, P, Al-Marwani, S, Al-Maweri, SAA, Almazan, JU, Al-Mekhlafi, HM, Almidani, L, Almidani, O, Alomari, MA, Al-Omari, B, Alonso, J, Alqahtani, JS, Alqalyoobi, S, Alqutaibi, AY, Al-Sabah, SK, Altaany, Z, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Alwafi, H, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Amani, R, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Amusa, GA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Angus, C, Anil, A, Anil, S, Anoushiravani, A, Ansari, H, Ansariadi, A, Ansari-Moghaddam, A, Antony, CM, Antriyandarti, E, Anvari, D, Anvari, S, Anwar, S, Anwar, SL, Anwer, R, Anyasodor, AE, Aqeel, M, Arab, JP, Arabloo, J, Arafat, M, Aravkin, AY, Areda, D, Aremu, A, Aremu, O, Ariffin, H, Arkew, M, Armocida, B, Arndt, MB, Ärnlöv, J, Arooj, M, Artamonov, AA, Arulappan, J, Aruleba, RT, Arumugam, A, Asaad, M, Asadi-Lari, M, Asgedom, AA, Asghariahmadabad, M, Asghari-Jafarabadi, M, Ashraf, M, Aslani, A, Astell-Burt, T, Athar, M, Athari, SS, Atinafu, BTT, Atlaw, HW, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awedew, AF, Aweke, AM, Ayala Quintanilla, BP, Ayatollahi, H, Ayuso-Mateos, JL, Ayyoubzadeh, SM, Azadnajafabad, S, Azevedo, RMS, Azzam, AY, B, DB, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bagherieh, S, Bah, S, Bahadorikhalili, S, Bahmanziari, N, Bai, R, Baig, AA, Baker, JL, Bako, AT, Bakshi, RK, Balakrishnan, S, Balasubramanian, M, Baltatu, OC, Bam, K, Banach, M, Bandyopadhyay, S, Banik, PC, Bansal, H, Bansal, K, Barbic, F, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barone-Adesi, F, Barqawi, HJ, Barrero, LH, Barrow, A, Barteit, S, Barua, L, Basharat, Z, Bashiri, A, Basiru, A, Baskaran, P, Basnyat, B, Bassat, Q, Basso, JD, Basting, AVL, Basu, S, Batra, K, Baune, BT, Bayati, M, Bayileyegn, NS, Beaney, T, Bedi, N, Beghi, M, Behboudi, E, Behera, P, Behnoush, AH, Behzadifar, M, Beiranvand, M, Bejarano Ramirez, DF, Béjot, Y, Belay, SA, Belete, CM, Bell, ML, Bello, MB, Bello, OO, Belo, L, Beloukas, A, Bender, RG, Bensenor, IM, Beran, A, Berezvai, Z, Berhie, AY, Berice, BN, Bernstein, RS, Bertolacci, GJ, Bettencourt, PJG, Beyene, KA, Bhagat, DS, Bhagavathula, AS, Bhala, N, Bhalla, A, Bhandari, D, Bhangdia, K, Bhardwaj, N, Bhardwaj, P, Bhardwaj, PV, Bhargava, A, Bhaskar, S, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Bisulli, F, Biswas, A, Biswas, B, Bitaraf, S, Bitew, BD, Bitra, VR, Bjørge, T, Boachie, MK, Boampong, MS, Bobirca, AV, Bodolica, V, Bodunrin, AO, Bogale, EK, Bogale, KA, Bohlouli, S, Bolarinwa, OA, Boloor, A, Bonakdar Hashemi, M, Bonny, A, Bora, K, Bora Basara, B, Borhany, H, Borzutzky, A, Bouaoud, S, Boustany, A, Boxe, C, Boyko, EJ, Brady, OJ, Braithwaite, D, Brant, LC, Brauer, M, Brazinova, A, Brazo-Sayavera, J, Breitborde, NJK, Breitner, S, Brenner, H, Briko, AN, Briko, NI, Britton, G, Brown, J, Brugha, T, Bulamu, NB, Bulto, LN, Buonsenso, D, Burns, RA, Busse, R, Bustanji, Y, Butt, NS, Butt, ZA, Caetano dos Santos, FL, Calina, D, Cámera, LA, Campos, LA, Campos-Nonato, IR, Cao, C, Cao, Y, Capodici, A, Cárdenas, R, Carr, S, Carreras, G, Carrero, JJ, Carugno, A, Carvalheiro, CG, Carvalho, F, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catalá-López, F, Catapano, AL, Cattaruzza, MS, Cederroth, CR, Cegolon, L, Cembranel, F, Cenderadewi, M, Cercy, KM, Cerin, E, Cevik, M, Chadwick, J, Chahine, Y, Chakraborty, C, Chakraborty, PA, Chan, JSK, Chan, RNC, Chandika, RM, Chandrasekar, EK, Chang, C-K, Chang, J-C, Chanie, GS, Charalampous, P, Chattu, VK, Chaturvedi, P, Chatzimavridou-Grigoriadou, V, Chaurasia, A, Chen, AW, Chen, A-T, Chen, CS, Chen, H, Chen, MX, Chen, S, Cheng, C-Y, Cheng, ETW, Cherbuin, N, Cheru, WA, Chien, J-H, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Chirinos-Caceres, JL, Chitheer, A, Cho, WCS, Chong, B, Chopra, H, Choudhari, SG, Chowdhury, R, Christopher, DJ, Chukwu, IS, Chung, E, Chung, S-C, Chutiyami, M, Cindi, Z, Cioffi, I, Claassens, MM, Claro, RM, Coberly, K, Cogen, RM, Columbus, A, Comfort, H, Conde, J, Cortese, S, Cortesi, PA, Costa, VM, Costanzo, S, Cousin, E, Couto, RAS, Cowden, RG, Cramer, KM, Criqui, MH, Cruz-Martins, N, Cuadra-Hernández, SM, Culbreth, GT, Cullen, P, Cunningham, M, Curado, MP, Dadana, S, Dadras, O, Dai, S, Dai, X, Dai, Z, Dalli, LL, Damiani, G, Darega Gela, J, Das, JK, Das, S, Dascalu, AM, Dash, NR, Dashti, M, Dastiridou, A, Davey, G, Dávila-Cervantes, CA, Davis Weaver, N, Davletov, K, De Leo, D, de Luca, K, Debele, AT, Debopadhaya, S, Degenhardt, L, Dehghan, A, Deitesfeld, L, Del Bo', C, Delgado-Enciso, I, Demessa, BH, Demetriades, AK, Deng, K, Deng, X, Denova-Gutiérrez, E, Deravi, N, Dereje, N, Dervenis, N, Dervišević, E, Des Jarlais, DC, Desai, HD, Desai, R, Devanbu, VGC, Dewan, SMR, Dhali, A, Dhama, K, Dhimal, M, Dhingra, S, Dhulipala, VR, Dias da Silva, D, Diaz, D, Diaz, MJ, Dima, A, Ding, DD, Ding, H, Dinis-Oliveira, RJ, Dirac, MA, Djalalinia, S, Do, THP, do Prado, CB, Doaei, S, Dodangeh, M, Dohare, S, Dokova, KG, Dolecek, C, Dominguez, R-MV, Dong, W, Dongarwar, D, D'Oria, M, Dorostkar, F, Dorsey, ER, dos Santos, WM, Doshi, R, Doshmangir, L, Dowou, RK, Driscoll, TR, Dsouza, HL, Dsouza, V, Du, M, Dube, J, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dwyer-Lindgren, L, Dzianach, PA, Dziedzic, AM, E'mar, AR, Eboreime, E, Ebrahimi, A, Echieh, CP, Edinur, HA, Edvardsson, D, Edvardsson, K, Efendi, D, Efendi, F, Effendi, DE, Eikemo, TA, Eini, E, Ekholuenetale, M, Ekundayo, TC, El Sayed, I, Elbarazi, I, Elema, TB, Elemam, NM, Elgar, FJ, Elgendy, IY, ElGohary, GMT, Elhabashy, HR, Elhadi, M, El-Huneidi, W, Elilo, LT, Elmeligy, OAA, Elmonem, MA, Elshaer, M, Elsohaby, I, Emeto, TI, Engelbert Bain, L, Erkhembayar, R, Esezobor, CI, Eshrati, B, Eskandarieh, S, Espinosa-Montero, J, Esubalew, H, Etaee, F, Fabin, N, Fadaka, AO, Fagbamigbe, AF, Fahim, A, Fahimi, S, Fakhri-Demeshghieh, A, Falzone, L, Fareed, M, Farinha, CSES, Faris, MEM, Faris, PS, Faro, A, Fasanmi, AO, Fatehizadeh, A, Fattahi, H, Fauk, NK, Fazeli, P, Feigin, VL, Feizkhah, A, Fekadu, G, Feng, X, Fereshtehnejad, S-M, Feroze, AH, Ferrante, D, Ferrari, AJ, Ferreira, N, Fetensa, G, Feyisa, BR, Filip, I, Fischer, F, Flavel, J, Flood, D, Florin, BT, Foigt, NA, Folayan, MO, Fomenkov, AA, Foroutan, B, Foroutan, M, Forthun, I, Fortuna, D, Foschi, M, Fowobaje, KR, Francis, KL, Franklin, RC, Freitas, A, Friedman, J, Friedman, SD, Fukumoto, T, Fuller, JE, Fux, B, Gaal, PA, Gadanya, MA, Gaidhane, AM, Gaihre, S, Gakidou, E, Galali, Y, Galles, NC, Gallus, S, Ganbat, M, Gandhi, AP, Ganesan, B, Ganiyani, MA, Garcia-Gordillo, MA, Gardner, WM, Garg, J, Garg, N, Gautam, RK, Gbadamosi, SO, Gebi, TG, Gebregergis, MW, Gebrehiwot, M, Gebremeskel, TG, Georgescu, SR, Getachew, T, Gething, PW, Getie, M, Ghadiri, K, Ghahramani, S, Ghailan, KY, Ghasemi, M-R, Ghasempour Dabaghi, G, Ghasemzadeh, A, Ghashghaee, A, Ghassemi, F, Ghazy, RM, Ghimire, A, Ghoba, S, Gholamalizadeh, M, Gholamian, A, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Ghorbani Vajargah, P, Ghoshal, AG, Gill, PS, Gill, TK, Gillum, RF, Ginindza, TG, Girmay, A, Glasbey, JC, Gnedovskaya, EV, Göbölös, L, Godinho, MA, Goel, A, Golchin, A, Goldust, M, Golechha, M, Goleij, P, Gomes, NGM, Gona, PN, Gopalani, SV, Gorini, G, Goudarzi, H, Goulart, AC, Goulart, BNG, Goyal, A, Grada, A, Graham, SM, Grivna, M, Grosso, G, Guan, S-Y, Guarducci, G, Gubari, MIM, Gudeta, MD, Guha, A, Guicciardi, S, Guimarães, RA, Gulati, S, Gunawardane, DA, Gunturu, S, Guo, C, Gupta, AK, Gupta, B, Gupta, MK, Gupta, M, Gupta, RD, Gupta, R, Gupta, S, Gupta, VB, Gupta, VK, Gurmessa, L, Gutiérrez, RA, Habibzadeh, F, Habibzadeh, P, Haddadi, R, Hadei, M, Hadi, NR, Haep, N, Hafezi-Nejad, N, Hailu, A, Haj-Mirzaian, A, Halboub, ES, Hall, BJ, Haller, S, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hamilton, EB, Han, C, Han, Q, Hanif, A, Hanifi, N, Hankey, GJ, Hanna, F, Hannan, MA, Haque, MN, Harapan, H, Hargono, A, Haro, JM, Hasaballah, AI, Hasan, I, Hasan, MT, Hasani, H, Hasanian, M, Hashi, A, Hasnain, MS, Hassan, I, Hassanipour, S, Hassankhani, H, Haubold, J, Havmoeller, RJ, Hay, SI, He, J, Hebert, JJ, Hegazi, OE, Heidari, G, Heidari, M, Heidari-Foroozan, M, Helfer, B, Hendrie, D, Herrera-Serna, BY, Herteliu, C, Hesami, H, Hezam, K, Hill, CL, Hiraike, Y, Holla, R, Horita, N, Hossain, MM, Hossain, S, Hosseini, M-S, Hosseinzadeh, H, Hosseinzadeh, M, Hosseinzadeh Adli, A, Hostiuc, M, Hostiuc, S, Hsairi, M, Hsieh, VC-R, Hsu, RL, Hu, C, Huang, J, Hultström, M, Humayun, A, Hundie, TG, Hussain, J, Hussain, MA, Hussein, NR, Hussien, FM, Huynh, H-H, Hwang, B-F, Ibitoye, SE, Ibrahim, KS, Iftikhar, PM, Ijo, D, Ikiroma, AI, Ikuta, KS, Ikwegbue, PC, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imam, MT, Immurana, M, Inamdar, S, Indriasih, E, Iqhrammullah, M, Iradukunda, A, Iregbu, KC, Islam, MR, Islam, SMS, Islami, F, Ismail, F, Ismail, NE, Iso, H, Isola, G, Iwagami, M, Iwu, CCD, Iyamu, IO, Iyer, M, J, LM, Jaafari, J, Jacob, L, Jacobsen, KH, Jadidi-Niaragh, F, Jafarinia, M, Jafarzadeh, A, Jaggi, K, Jahankhani, K, Jahanmehr, N, Jahrami, H, Jain, N, Jairoun, AA, Jaiswal, A, Jamshidi, E, Janko, MM, Jatau, AI, Javadov, S, Javaheri, T, Jayapal, SK, Jayaram, S, Jebai, R, Jee, SH, Jeganathan, J, Jha, AK, Jha, RP, Jiang, H, Jin, Y, Johnson, O, Jokar, M, Jonas, JB, Joo, T, Joseph, A, Joseph, N, Joshua, CE, Joshy, G, Jozwiak, JJ, Jürisson, M, K, V, Kaambwa, B, Kabir, A, Kabir, Z, Kadashetti, V, Kadir, DH, Kalani, R, Kalankesh, LR, Kaliyadan, F, Kalra, S, Kamal, VK, Kamarajah, SK, Kamath, R, Kamiab, Z, Kamyari, N, Kanagasabai, T, Kanchan, T, Kandel, H, Kanmanthareddy, AR, Kanmiki, EW, Kanmodi, KK, Kannan S, S, Kansal, SK, Kantar, RS, Kapoor, N, Karajizadeh, M, Karanth, SD, Karasneh, RA, Karaye, IM, Karch, A, Karim, A, Karimi, SE, Karimi Behnagh, A, Kashoo, FZ, Kasnazani, QHA, Kasraei, H, Kassebaum, NJ, Kassel, MB, Kauppila, JH, Kaur, N, Kawakami, N, Kayode, GA, Kazemi, F, Kazemian, S, Kazmi, TH, Kebebew, GM, Kebede, AD, Kebede, F, Keflie, TS, Keiyoro, PN, Keller, C, Kelly, JT, Kempen, JH, Kerr, JA, Kesse-Guyot, E, Khajuria, H, Khalaji, A, Khalid, N, Khalil, AA, Khalilian, A, Khamesipour, F, Khan, A, Khan, G, Khan, I, Khan, IA, Khan, MN, Khan, M, Khan, MJ, Khan, MAB, Khan, ZA, Khan suheb, MZ, Khanmohammadi, S, Khatab, K, Khatami, F, Khatatbeh, H, Khatatbeh, MM, Khavandegar, A, Khayat Kashani, HR, Khidri, FF, Khodadoust, E, Khorgamphar, M, Khormali, M, Khorrami, Z, Khosravi, A, Khosravi, MA, Kifle, ZD, Kim, G, Kim, J, Kim, K, Kim, MS, Kim, YJ, Kimokoti, RW, Kinzel, KE, Kisa, A, Kisa, S, Klu, D, Knudsen, AKS, Kocarnik, JM, Kochhar, S, Kocsis, T, Koh, DSQ, Kolahi, A-A, Kolves, K, Kompani, F, Koren, G, Kosen, S, Kostev, K, Koul, PA, Koulmane Laxminarayana, SL, Krishan, K, Krishna, H, Krishna, V, Krishnamoorthy, V, Krishnamoorthy, Y, Krohn, KJ, Kuate Defo, B, Kucuk Bicer, B, Kuddus, MA, Kuddus, M, Kuitunen, I, Kulimbet, M, Kulkarni, V, Kumar, A, Kumar, H, Kumar, M, Kumar, R, Kumari, M, Kumie, FT, Kundu, S, Kurmi, OP, Kusnali, A, Kusuma, D, Kwarteng, A, Kyriopoulos, I, Kyu, HH, La Vecchia, C, Lacey, B, Ladan, MA, Laflamme, L, Lagat, AK, Lager, ACJ, Lahmar, A, Lai, DTC, Lal, DK, Lalloo, R, Lallukka, T, Lam, H, Lám, J, Landrum, KR, Lanfranchi, F, Lang, JJ, Langguth, B, Lansingh, VC, Laplante-Lévesque, A, Larijani, B, Larsson, AO, Lasrado, S, Lassi, ZS, Latief, K, Latifinaibin, K, Lauriola, P, Le, NHH, Le, TTT, Le, TDT, Ledda, C, Ledesma, JR, Lee, M, Lee, PH, Lee, SW, Lee, SWH, Lee, W-C, Lee, YH, LeGrand, KE, Leigh, J, Leong, E, Lerango, TL, Li, M-C, Li, W, Li, X, Li, Y, Li, Z, Ligade, VS, Likaka, ATM, Lim, L-L, Lim, SS, Lindstrom, M, Linehan, C, Liu, C, Liu, G, Liu, J, Liu, R, Liu, S, Liu, X, Llanaj, E, Loftus, MJ, López-Bueno, R, Lopukhov, PD, Loreche, AM, Lorkowski, S, Lotufo, PA, Lozano, R, Lubinda, J, Lucchetti, G, Lugo, A, Lunevicius, R, Ma, ZF, Maass, KL, Machairas, N, Machoy, M, Madadizadeh, F, Madsen, C, Madureira-Carvalho, ÁM, Maghazachi, AA, Maharaj, SB, Mahjoub, S, Mahmoud, MA, Mahmoudi, A, Mahmoudi, E, Mahmoudi, R, Majeed, A, Makhdoom, IF, Malakan Rad, E, Maled, V, Malekzadeh, R, Malhotra, AK, Malhotra, K, Malik, AA, Malik, I, Malta, DC, Mamun, AA, Mansouri, P, Mansournia, MA, Mantovani, LG, Maqsood, S, Marasini, BP, Marateb, HR, Maravilla, JC, Marconi, AM, Mardi, P, Marino, M, Marjani, A, Martinez, G, Martinez-Guerra, BA, Martinez-Piedra, R, Martini, D, Martini, S, Martins-Melo, FR, Martorell, M, Marx, W, Maryam, S, Marzo, RR, Masaka, A, Masrie, A, Mathieson, S, Mathioudakis, AG, Mathur, MR, Mattumpuram, J, Matzopoulos, R, Maude, RJ, Maugeri, A, Maulik, PK, Mayeli, M, Mazaheri, M, Mazidi, M, McGrath, JJ, McKee, M, McKowen, ALW, McLaughlin, SA, McPhail, SM, Mechili, EA, Medina, JRC, Mediratta, RP, Meena, JK, Mehra, R, Mehrabani-Zeinabad, K, Mehrabi Nasab, E, Mekene Meto, T, Meles, GG, Mendez-Lopez, MAM, Mendoza, W, Menezes, RG, Mengist, B, Mentis, A-FA, Meo, SA, Meresa, HA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mesfin, BA, Mestrovic, T, Mettananda, KCD, Mettananda, S, Meylakhs, P, Mhlanga, A, Mhlanga, L, Mi, T, Miazgowski, T, Micha, G, Michalek, IM, Miller, TR, Mills, EJ, Minh, LHN, Mini, GK, Mir Mohammad Sadeghi, P, Mirica, A, Mirijello, A, Mirrakhimov, EM, Mirutse, MK, Mirzaei, M, Misganaw, A, Mishra, A, Misra, S, Mitchell, PB, Mithra, P, Mittal, C, Mobayen, M, Moberg, ME, Mohamadkhani, A, Mohamed, J, Mohamed, MFH, Mohamed, NS, Mohammad-Alizadeh-Charandabi, S, Mohammadi, S, Mohammadian-Hafshejani, A, Mohammadifard, N, Mohammed, H, Mohammed, M, Mohammed, S, Mohan, V, Mojiri-Forushani, H, Mokari, A, Mokdad, AH, Molinaro, S, Molokhia, M, Momtazmanesh, S, Monasta, L, Mondello, S, Moni, MA, Moodi Ghalibaf, A, Moradi, M, Moradi, Y, Moradi-Lakeh, M, Moradzadeh, M, Moraga, P, Morawska, L, Moreira, RS, Morovatdar, N, Morrison, SD, Morze, J, Mosser, JF, Motappa, R, Mougin, V, Mouodi, S, Mousavi, P, Mousavi, SE, Mousavi Khaneghah, A, Mpolya, EA, Mrejen, M, Mubarik, S, Muccioli, L, Mueller, UO, Mughal, F, Mukherjee, S, Mulita, F, Munjal, K, Murillo-Zamora, E, Musaigwa, F, Musallam, KM, Mustafa, A, Mustafa, G, Muthupandian, S, Muthusamy, R, Muzaffar, M, Myung, W, Nagarajan, AJ, Nagel, G, Naghavi, P, Naheed, A, Naik, 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Zhu, L, Ziafati, M, Zielińska, M, Zitoun, OA, Zoladl, M, Zou, Z, Zuhlke, LJ, Zumla, A, Zweck, E, Zyoud, SH, Wool, EE, and Murray, CJL
Published: 2024

2. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Ferrari, AJ, Santomauro, DF, Aali, A, Abate, YH, Abbafati, C, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdollahi, A, Abdullahi, A, Abegaz, KH, Abeldaño Zuñiga, RA, Aboagye, RG, Abolhassani, H, Abreu, LG, Abualruz, H, Abu-Gharbieh, E, Abu-Rmeileh, NME, Ackerman, IN, Addo, IY, Addolorato, G, Adebiyi, AO, Adepoju, AV, Adewuyi, HO, Afyouni, S, Afzal, S, Agodi, A, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, S, Ahmed, A, Ahmed, LA, Ahmed, MB, Ajami, M, Akinosoglou, K, Akkaif, MA, Al Hasan, SM, Alalalmeh, SO, Al-Aly, Z, Albashtawy, M, Aldridge, RW, Alemu, MD, Alemu, YM, Alene, KA, Al-Gheethi, AAS, Alharrasi, M, Alhassan, RK, Ali, MU, Ali, R, Ali, SSS, Alif, SM, Aljunid, SM, Al-Marwani, S, Almazan, JU, Alomari, MA, Al-Omari, B, Altaany, Z, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Alwafi, H, Al-Wardat, MS, Al-Worafi, YM, Aly, S, Alzoubi, KH, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Ansari, H, Antony, CM, Anwar, S, Anwar, SL, Anwer, R, Anyanwu, PE, Arab, JP, Arabloo, J, Arafat, M, Araki, DT, Aravkin, AY, Arkew, M, Armocida, B, Arndt, MB, Arooj, M, Artamonov, AA, Aruleba, RT, Arumugam, A, Ashbaugh, C, Ashemo, MY, Ashraf, M, Asika, MO, Askari, E, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awotidebe, AW, Awuviry-Newton, K, Ayala Quintanilla, BP, Ayuso-Mateos, JL, Azadnajafabad, S, Azevedo, RMS, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bah, S, Bai, R, Baker, JL, Bakkannavar, SM, Bako, AT, Balakrishnan, S, Bam, K, Banik, PC, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Barqawi, HJ, Barrow, A, Barteit, S, Barua, L, Bashiri Aliabadi, S, Basiru, A, Basu, S, Bathini, PP, Batra, K, Baune, BT, Bayileyegn, NS, Behnam, B, Behnoush, AH, Beiranvand, M, Bejarano Ramirez, DF, Bell, ML, Bello, OO, Beloukas, A, Bensenor, IM, Berezvai, Z, Bernabe, E, Bernstein, RS, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhandari, D, Bhargava, A, Bhaskar, S, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Bitra, VR, Bjørge, T, Bodolica, V, Bodunrin, AO, Bogale, EK, Bonakdar Hashemi, M, Bonny, A, Bora Basara, B, Borhany, H, Boxe, C, Brady, OJ, Bragazzi, NL, Braithwaite, D, Brant, LC, Brauer, M, Breitner, S, Brenner, H, Brown, J, Brugha, T, Bulamu, NB, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, ZA, Byun, J, Caetano dos Santos, FL, Calina, D, Cámera, LA, Campos-Nonato, IR, Cao, C, Capodici, A, Carr, S, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catapano, AL, Cattaruzza, MS, Caye, A, Cegolon, L, Cembranel, F, Cenderadewi, M, Cerin, E, Chakraborty, PA, Chan, JSK, Chan, RNC, Chandika, RM, Chandrasekar, EK, Charalampous, P, Chattu, VK, Chatzimavridou-Grigoriadou, V, Chen, AW, Chen, A-T, Chen, CS, Chen, H, Chen, NM, Cheng, ETW, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Cho, WCS, Choi, S, Chong, B, Chong, YY, Choudhari, SG, Chowdhury, R, Christensen, SWM, Chu, D-T, Chukwu, IS, Chung, E, Chutiyami, M, Claassens, MM, Cogen, RM, Columbus, A, Conde, J, Cortesi, PA, Cousin, E, Criqui, MH, Cruz-Martins, N, Dadras, O, Dai, S, Dai, X, Dai, Z, Dalaba, MA, Damiani, G, Das, JK, Das, S, Dashti, M, Dávila-Cervantes, CA, Davletov, K, De Leo, D, Debele, AT, Debopadhaya, S, DeCleene, NK, Deeba, F, Degenhardt, L, Del Bo', C, Delgado-Enciso, I, Demetriades, AK, Denova-Gutiérrez, E, Dervenis, N, Desai, HD, Desai, R, Deuba, K, Dhama, K, Dharmaratne, SD, Dhingra, S, Dias da Silva, D, Diaz, D, Diaz, LA, Diaz, MJ, Dima, A, Ding, DD, Dirac, MA, Do, THP, do Prado, CB, Dohare, S, Dominguez, R-MV, Dong, W, Dongarwar, D, D'Oria, M, Dorsey, ER, Doshmangir, L, Dowou, RK, Driscoll, TR, Dsouza, HL, Dsouza, V, Dube, J, Dumith, SC, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dzianach, PA, Dziedzic, AM, Eboreime, E, Ebrahimi, A, Edinur, HA, 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S, Hsu, JM, Huang, J, Hugo, FN, Hushmandi, K, Hussain, J, Hussein, NR, Huynh, CK, Huynh, H-H, Hwang, B-F, Iannucci, VC, Ihler, AL, Ikiroma, AI, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imam, MT, Immurana, M, Irham, LM, Islam, MR, Islam, SMS, Islami, F, Ismail, F, Ismail, NE, Isola, G, Iwagami, M, Iwu, CCD, Iyer, M, Jaafari, J, Jacobsen, KH, Jadidi-Niaragh, F, Jafarinia, M, Jaggi, K, Jahankhani, K, Jahanmehr, N, Jahrami, H, Jain, A, Jain, N, Jairoun, AA, Jaiswal, A, Jakovljevic, M, Jatau, AI, Javadov, S, Javaheri, T, Jayapal, SK, Jayaram, S, Jee, SH, Jeganathan, J, Jeyakumar, A, Jha, AK, Jiang, H, Jin, Y, Jonas, JB, Joo, T, Joseph, A, Joseph, N, Joshua, CE, Jozwiak, JJ, Jürisson, M, K, V, Kaambwa, B, Kabir, A, Kabir, Z, Kadashetti, V, Kalani, R, Kalankesh, LR, Kaliyadan, F, Kalra, S, Kamenov, K, Kamyari, N, Kanagasabai, T, Kandel, H, Kanmanthareddy, AR, Kanmodi, KK, Kantar, RS, Karaye, IM, Karim, A, Karimi, SE, Karimi, Y, Kasraei, H, Kassel, MB, Kauppila, JH, Kawakami, N, Kayode, 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Nguyen, DH, Nguyen, HQ, Niazi, RK, Nikolouzakis, TK, Nikoobar, A, Nikoomanesh, F, Nikpoor, AR, Nnaji, CA, Nnyanzi, LA, Noman, EA, Nomura, S, Norrving, B, Nri-Ezedi, CA, Ntaios, G, Ntsekhe, M, Nurrika, D, Nzoputam, CI, Nzoputam, OJ, Oancea, B, Odetokun, IA, O'Donnell, MJ, Oguntade, AS, Oguta, JO, Okati-Aliabad, H, Okeke, SR, Okekunle, AP, Okonji, OC, Olagunju, AT, Olasupo, OO, Olatubi, MI, Oliveira, GMM, Olufadewa, II, Olusanya, BO, Olusanya, JO, Omar, HA, Omer, GL, Omonisi, AEE, Onie, S, Onwujekwe, OE, Ordak, M, Orish, VN, Ortega-Altamirano, DV, Ortiz, A, Ortiz-Brizuela, E, Osman, WMS, Ostroff, SM, Osuagwu, UL, Otoiu, A, Otstavnov, N, Otstavnov, SS, Ouyahia, A, Ouyang, G, Owolabi, MO, P A, MP, Padron-Monedero, A, Padubidri, JR, Palicz, T, Palladino, C, Pan, F, Pandi-Perumal, SR, Pangaribuan, HU, Panos, GD, Panos, LD, Pantea Stoian, AM, Pardhan, S, Parikh, RR, Pashaei, A, Pasovic, M, Passera, R, Patel, J, Patel, SK, Patil, S, Patoulias, D, Patthipati, VS, Pawar, S, Pazoki Toroudi, H, 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Y, Yusuf, H, Zahid, MH, Zakham, F, Zaki, L, Zaki, N, Zaman, BA, Zamora, N, Zand, R, Zandieh, GGZ, Zar, HJ, Zarrintan, A, Zastrozhin, MS, Zhang, H, Zhang, N, Zhang, Y, Zhao, H, Zhong, C, Zhong, P, Zhou, J, Zhu, Z, Ziafati, M, Zielińska, M, Zimsen, SRM, Zoladl, M, Zumla, A, Zyoud, SH, Vos, T, and Murray, CJL
Published: 2024

3. Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019

Author: Peden, AE, Cullen, P, Francis, KL, Moeller, H, Peden, MM, Ye, P, Tian, M, Zou, Z, Sawyer, SM, Aali, A, Abbasi-Kangevari, Z, Abbasi-Kangevari, M, Abdelmasseh, M, Abdoun, M, Abd-Rabu, R, Abdulah, DM, Abdurehman, AM, Abebe, G, Abebe, AM, Abedi, A, Abidi, H, Aboagye, RG, Ali, HA, Abu-Gharbieh, E, Adane, DE, Adane, TD, Addo, IY, Adewole, OG, Adhikari, S, Adnan, M, Adnani, QES, Afolabi, AAB, Afzal, S, Afzal, MS, Aghdam, ZB, Ahinkorah, BO, Ahmad, AR, Ahmad, T, Ahmad, S, Ahmadi, A, Ahmed, H, Ahmed, MB, Ahmed, A, Ahmed, JQ, Rashid, TA, Aithala, JP, Aji, B, Akhlaghdoust, M, Alahdab, F, Alanezi, FM, Alemayehu, A, Al Hamad, H, Ali, SS, Ali, L, Alimohamadi, Y, Alipour, V, Aljunid, SM, Almidani, L, Almustanyir, S, Altirkawi, KA, Alvis-Zakzuk, NJ, Ameyaw, EK, Amin, TT, Amir-Behghadami, M, Amiri, S, Amiri, H, Anagaw, TF, Andrei, T, Andrei, CL, Anvari, D, Anwar, SL, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Arja, A, Arulappan, J, Arumugam, A, Aryannejad, A, Asgary, S, Ashraf, T, Athari, SS, Atreya, A, Attia, S, Aujayeb, A, Awedew, AF, Azadnajafabad, S, Azangou-Khyavy, M, Azari, S, Jafari, AA, Azizi, H, Azzam, AY, Badiye, AD, Baghcheghi, N, Bagherieh, S, Baig, AA, Bakkannavar, SM, Balta, AB, Banach, M, Banik, PC, Bansal, H, Bardhan, M, Barone-Adesi, F, Barrow, A, Bashiri, A, Baskaran, P, Basu, S, Bayileyegn, NS, Bekel, AA, Bekele, AB, Bendak, S, Bensenor, IM, Berhie, AY, Bhagat, DS, Bhagavathula, AS, Bhardwaj, P, Bhardwaj, N, Bhaskar, S, Bhat, AN, Bhattacharyya, K, Bhutta, ZA, Bibi, S, Bintoro, BS, Bitaraf, S, Bodicha, BBA, Boloor, A, Bouaoud, S, Brown, J, Burkart, K, Butt, NS, Butt, MH, Camera, LA, Rincon, JCC, Cao, C, Carvalho, AF, Carvalho, M, Chakraborty, PA, Chandrasekar, EK, Chang, J-C, Charalampous, P, Charan, J, Chattu, VK, Chekole, BM, Chitheer, A, Cho, DY, Chopra, H, Christopher, DJ, Chukwu, IS, Cruz-Martins, N, Dadras, O, Dahlawi, SMA, Dai, X, Damiani, G, Darmstadt, GL, Soltani, RDC, Darwesh, AM, Das, S, Dastiridou, A, Debela, SA, Dehghan, A, Demeke, GM, Demetriades, AK, Demissie, S, Dessalegn, FN, Desta, AA, Dianatinasab, M, Diao, N, da Silva, DD, Diaz, D, Digesa, LE, Diress, M, Djalalinia, S, Linh, PD, Dodangeh, M, Doku, PN, Dongarwar, D, Dsouza, HL, Eini, E, Ekholuenetale, M, Ekundayo, TC, Elagali, AEM, Elbahnasawy, MA, Elhabashy, HR, Elhadi, M, Zaki, MES, Enyew, DB, Erkhembayar, R, Eskandarieh, S, Etaee, F, Fagbamigbe, AF, Faris, PS, Farmany, A, Faro, A, Farzadfar, F, Fatehizadeh, A, Fereshtehnejad, S-M, Feroze, AH, Fetensa, G, Feyisa, BR, Filip, I, Fischer, F, Foroutan, B, Foroutan, M, Fowobaje, KR, Franklin, RC, Fukumoto, T, Gaal, PA, Gadanya, MA, Galali, Y, Galehdar, N, Ganesan, B, Garg, T, Gebrehiwot, MGD, Gebremariam, YH, Gela, YY, Gerema, U, Ghafourifard, M, Ghamari, S-H, Ghanbari, R, Nour, MG, Gholamalizadeh, M, Gholami, A, Gholamrezanezhad, A, Ghozy, S, Gilani, SA, Gill, TK, Gine-Vazquez, I, Girma, ZA, Glasbey, JC, Glozah, FN, Golechha, M, Goleij, P, Grivna, M, Guadie, HA, Gunawardane, DA, Guo, Y, Gupta, VB, Gupta, S, Gupta, B, Gupta, VK, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hanfore, LK, Hanif, A, Hargono, A, Harlianto, NI, Harorani, M, Hasaballah, AI, Hasan, SMM, Hassan, A, Hassanipour, S, Hassankhani, H, Havmoeller, RJ, Hay, SI, Heidari, M, Hendrie, D, Heyi, DZ, Hiraike, Y, Holla, R, Horita, N, Hossain, SJ, Hossain, MBH, Shabanan, SH, Hosseinzadeh, M, Hostiuc, S, Hoveidaei, AH, Hsiao, AK, Hussain, S, Hussein, A, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imani, B, Immurana, M, Inbaraj, LR, Islam, SMS, Islam, RM, Islam, MM, Ismail, NE, Merin, LJ, Jahrami, H, Jakovljevic, M, Janodia, MD, Javaheri, T, Jayapal, SK, Jayarajah, UU, Jayaraman, S, Jeganathan, J, Jemal, B, Jha, RP, Jonas, JB, Joo, T, Joseph, N, Jozwiak, JJ, Jurisson, M, Kabir, A, Kadashetti, V, Kadir, DH, Kalankesh, LR, Kalhor, R, Kamal, VK, Kamath, R, Kandel, H, Kantar, RS, Kapoor, N, Karami, H, Karaye, IM, Karkhah, S, Katoto, PDMC, Kauppila, JH, Kayode, GA, Keikavoosi-Arani, L, Keskin, C, Khader, YS, Khajuria, H, Khammarnia, M, Khan, EA, Khan, MN, Khan, M, Khan, YH, Khan, IA, Khan, A, Khan, MAB, Khanali, J, Khatatbeh, MM, Kashani, HRK, Khazaie, H, Khubchandani, J, Kifle, ZD, Kim, J, Kim, YJ, Kisa, S, Kisa, A, Kneib, CJ, Kompani, F, Koohestani, HR, Koul, PA, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Krishnamoorthy, V, Bicer, BK, Kumar, N, Kumar, M, Kurmi, OP, Laflamme, L, Lam, J, Landires, I, Larijani, B, Lasrado, S, Lauriola, P, La Vecchia, C, Lee, SWH, Lee, YH, Lee, S-W, Lee, W-C, Legesse, SM, Li, S, Lim, SS, Lorenzovici, L, Luke, AO, Madadizadeh, F, Madureira-Carvalho, AM, Abd El Razek, MM, Mahjoub, S, Mahmoodpoor, A, Mahmoudi, R, Mahmoudimanesh, M, Majeed, A, Makki, A, Rad, EM, Malekpour, M-R, Malik, AA, Mallhi, TH, Malta, DC, Mansouri, B, Mansournia, MA, Mathews, E, Maulud, SQ, Mazingi, D, Nasab, EM, Mendoza-Cano, O, Menezes, RG, Mengistu, DA, Mentis, A-FA, Meretoja, A, Mesregah, MK, Mestrovic, T, Gomide Nogueira de Sa, ACM, Miller, TR, Mirghaderi, SP, Mirica, A, Mirmoeeni, S, Mirrakhimov, EM, Mirza, M, Misra, S, Mithra, P, Mittal, C, Moberg, ME, Mohammadi, M, Mohammadi, S, Mohammadi, E, Mohammadpourhodki, R, Mohammed, S, Mohammed, TA, Mohseni, M, Mokdad, AH, Momtazmanesh, S, Monasta, L, Moni, MA, Moreira, RS, Morrison, SD, Mostafavi, E, Isfahani, HM, Mubarik, S, Muccioli, L, Mukherjee, S, Mulita, F, Mustafa, G, Nagarajan, AJ, Naimzada, MD, Nangia, V, Nassereldine, H, Natto, ZS, Nayak, BP, Negoi, I, Nejadghaderi, SA, Nepal, S, Kandel, SN, Noroozi, N, Nunez-Samudio, V, Nzoputam, OJ, Nzoputam, CI, Ochir, C, Odhiambo, JN, Odukoya, OO, Okati-Aliabad, H, Okonji, OC, Olagunju, AT, Bali, AO, Omer, E, Otoiu, A, Otstavnov, SS, Otstavnov, N, Oumer, B, Owolabi, MO, Mahesh, PA, Padron-Monedero, A, Padubidri, JR, Fallahy, MTP, Panda-Jonas, S, Pandi-Perumal, SR, Pardhan, S, Park, E-K, Patel, SK, Pathan, AR, Pati, S, Paudel, U, Pawar, S, Pedersini, P, Peres, MFP, Petcu, I-R, Phillips, MR, Pillay, JD, Piracha, ZZ, Poursadeqiyan, M, Pourtaheri, N, Qattea, I, Radfar, A, 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BM, Chitheer, A, Cho, DY, Chopra, H, Christopher, DJ, Chukwu, IS, Cruz-Martins, N, Dadras, O, Dahlawi, SMA, Dai, X, Damiani, G, Darmstadt, GL, Soltani, RDC, Darwesh, AM, Das, S, Dastiridou, A, Debela, SA, Dehghan, A, Demeke, GM, Demetriades, AK, Demissie, S, Dessalegn, FN, Desta, AA, Dianatinasab, M, Diao, N, da Silva, DD, Diaz, D, Digesa, LE, Diress, M, Djalalinia, S, Linh, PD, Dodangeh, M, Doku, PN, Dongarwar, D, Dsouza, HL, Eini, E, Ekholuenetale, M, Ekundayo, TC, Elagali, AEM, Elbahnasawy, MA, Elhabashy, HR, Elhadi, M, Zaki, MES, Enyew, DB, Erkhembayar, R, Eskandarieh, S, Etaee, F, Fagbamigbe, AF, Faris, PS, Farmany, A, Faro, A, Farzadfar, F, Fatehizadeh, A, Fereshtehnejad, S-M, Feroze, AH, Fetensa, G, Feyisa, BR, Filip, I, Fischer, F, Foroutan, B, Foroutan, M, Fowobaje, KR, Franklin, RC, Fukumoto, T, Gaal, PA, Gadanya, MA, Galali, Y, Galehdar, N, Ganesan, B, Garg, T, Gebrehiwot, MGD, Gebremariam, YH, Gela, YY, Gerema, U, Ghafourifard, M, Ghamari, S-H, Ghanbari, R, Nour, MG, Gholamalizadeh, M, Gholami, A, Gholamrezanezhad, A, Ghozy, S, Gilani, SA, Gill, TK, Gine-Vazquez, I, Girma, ZA, Glasbey, JC, Glozah, FN, Golechha, M, Goleij, P, Grivna, M, Guadie, HA, Gunawardane, DA, Guo, Y, Gupta, VB, Gupta, S, Gupta, B, Gupta, VK, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hanfore, LK, Hanif, A, Hargono, A, Harlianto, NI, Harorani, M, Hasaballah, AI, Hasan, SMM, Hassan, A, Hassanipour, S, Hassankhani, H, Havmoeller, RJ, Hay, SI, Heidari, M, Hendrie, D, Heyi, DZ, Hiraike, Y, Holla, R, Horita, N, Hossain, SJ, Hossain, MBH, Shabanan, SH, Hosseinzadeh, M, Hostiuc, S, Hoveidaei, AH, Hsiao, AK, Hussain, S, Hussein, A, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imani, B, Immurana, M, Inbaraj, LR, Islam, SMS, Islam, RM, Islam, MM, Ismail, NE, Merin, LJ, Jahrami, H, Jakovljevic, M, Janodia, MD, Javaheri, T, Jayapal, SK, Jayarajah, UU, Jayaraman, S, Jeganathan, J, Jemal, B, Jha, RP, Jonas, JB, Joo, T, Joseph, N, Jozwiak, JJ, Jurisson, M, Kabir, A, Kadashetti, V, Kadir, DH, Kalankesh, LR, Kalhor, R, Kamal, VK, Kamath, R, Kandel, H, Kantar, RS, Kapoor, N, Karami, H, Karaye, IM, Karkhah, S, Katoto, PDMC, Kauppila, JH, Kayode, GA, Keikavoosi-Arani, L, Keskin, C, Khader, YS, Khajuria, H, Khammarnia, M, Khan, EA, Khan, MN, Khan, M, Khan, YH, Khan, IA, Khan, A, Khan, MAB, Khanali, J, Khatatbeh, MM, Kashani, HRK, Khazaie, H, Khubchandani, J, Kifle, ZD, Kim, J, Kim, YJ, Kisa, S, Kisa, A, Kneib, CJ, Kompani, F, Koohestani, HR, Koul, PA, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Krishnamoorthy, V, Bicer, BK, Kumar, N, Kumar, M, Kurmi, OP, Laflamme, L, Lam, J, Landires, I, Larijani, B, Lasrado, S, Lauriola, P, La Vecchia, C, Lee, SWH, Lee, YH, Lee, S-W, Lee, W-C, Legesse, SM, Li, S, Lim, SS, Lorenzovici, L, Luke, AO, Madadizadeh, F, Madureira-Carvalho, AM, Abd El Razek, MM, Mahjoub, S, Mahmoodpoor, A, Mahmoudi, R, Mahmoudimanesh, M, Majeed, A, Makki, A, Rad, EM, Malekpour, M-R, Malik, AA, Mallhi, TH, Malta, DC, Mansouri, B, Mansournia, MA, Mathews, E, Maulud, SQ, Mazingi, D, Nasab, EM, Mendoza-Cano, O, Menezes, RG, Mengistu, DA, Mentis, A-FA, Meretoja, A, Mesregah, MK, Mestrovic, T, Gomide Nogueira de Sa, ACM, Miller, TR, Mirghaderi, SP, Mirica, A, Mirmoeeni, S, Mirrakhimov, EM, Mirza, M, Misra, S, Mithra, P, Mittal, C, Moberg, ME, Mohammadi, M, Mohammadi, S, Mohammadi, E, Mohammadpourhodki, R, Mohammed, S, Mohammed, TA, Mohseni, M, Mokdad, AH, Momtazmanesh, S, Monasta, L, Moni, MA, Moreira, RS, Morrison, SD, Mostafavi, E, Isfahani, HM, Mubarik, S, Muccioli, L, Mukherjee, S, Mulita, F, Mustafa, G, Nagarajan, AJ, Naimzada, MD, Nangia, V, Nassereldine, H, Natto, ZS, Nayak, BP, Negoi, I, Nejadghaderi, SA, Nepal, S, Kandel, SN, Noroozi, N, Nunez-Samudio, V, Nzoputam, OJ, Nzoputam, CI, Ochir, C, Odhiambo, JN, Odukoya, OO, Okati-Aliabad, H, Okonji, OC, Olagunju, AT, Bali, AO, Omer, E, Otoiu, A, Otstavnov, SS, Otstavnov, N, Oumer, B, Owolabi, MO, Mahesh, PA, Padron-Monedero, A, Padubidri, JR, Fallahy, MTP, Panda-Jonas, S, Pandi-Perumal, SR, Pardhan, S, Park, E-K, Patel, SK, Pathan, AR, Pati, S, Paudel, U, Pawar, S, Pedersini, P, Peres, MFP, Petcu, I-R, Phillips, MR, Pillay, JD, Piracha, ZZ, Poursadeqiyan, M, Pourtaheri, N, Qattea, I, Radfar, A, Rafiee, A, Raghav, PR, Rahim, F, Rahman, MA, Rahman, FS, Rahman, M, Rahmani, AM, Rahmani, S, Moolambally, SR, Ramazanu, S, Ramezanzadeh, K, Rana, J, Rana, SM, Rao, CR, Rao, SJ, Rashedi, V, Rashidi, M-M, Rastogi, P, Rasul, A, Rawaf, S, Rawaf, DL, Rawal, L, Rawassizadeh, R, Rezaei, N, Rezaeian, M, Rezapour, A, Riad, A, Riaz, M, Rickard, J, Buendia Rodriguez, JA, Roever, L, Ronfani, L, Roy, B, Manjula, S, Saad, AMA, Sabour, S, Sabzmakan, L, Saddik, B, Sadeghi, M, Saeb, MR, Saeed, U, Moghaddam, SS, Safi, SZ, Sahiledengle, B, Sahoo, H, Sahraian, MA, Saki, M, Salamati, P, Salehi, S, Salem, MR, Samy, AM, Sanabria, J, Santric-Milicevic, MM, Saqib, MAN, Sarikhani, Y, Sarveazad, A, Sathian, B, Satpathy, M, Saya, GK, Ceola Schneider, IJ, Schwebel, DC, Seddighi, H, Senthilkumaran, S, Seylani, A, Shabaninejad, H, Shafeghat, M, Shah, PA, Shahabi, S, Shahbandi, A, Shahraki-Sanavi, F, Shaikh, MA, Shaker, E, Shams-Beyranvand, M, Shanawaz, M, Shannawaz, M, Sharew, MMS, Sharma, N, Shashamo, BB, Shayan, M, Sheikhi, RA, Shen, J, Shetty, BSK, Shetty, PH, Shin, JI, Shitaye, NA, Shivakumar, KM, Shobeiri, P, Shorofi, SA, Shrestha, S, Siabani, S, Sidemo, NB, Simegn, W, Sinaei, E, Singh, P, Sinto, R, Siraj, MS, Skryabin, VY, Skryabina, AA, Sleet, DA, Chandan, SN, Socea, B, Solmi, M, Solomon, Y, Song, Y, Sousa, RARC, Soyiri, IN, Stokes, MA, Suleman, M, Abdulkader, RS, Sun, J, Tabares-Seisdedos, R, Tabatabaei, SM, Tabish, M, Taheri, E, Soodejani, MT, Tampa, M, Tan, K-K, Tarigan, IU, Tariqujjaman, M, Tat, NY, Tat, VY, Tavakoli, A, Tefera, BN, Tefera, YM, Temesgen, G, Temsah, M-H, Thangaraju, P, Thapar, R, Thomas, NK, Ticoalu, JHV, Tincho, MB, Tiyuri, A, Togtmol, M, Tovani-Palone, MR, Mai, TNT, Ullah, S, Ullah, I, Umakanthan, S, Unnikrishnan, B, Upadhyay, E, Tahbaz, SV, Valdez, PR, Vasankari, TJ, Vaziri, S, Veroux, M, Vervoort, D, Violante, FS, Vlassov, V, Linh, GV, Waheed, Y, Wang, Y, Wang, Y-P, Wang, C, Wiangkham, T, Wickramasinghe, ND, Woday, AT, Wu, A-M, Yahya, GAT, Jabbari, SHY, Yang, L, Yaya, S, Yigit, A, Yigit, V, Yisihak, E, Yonemoto, N, You, Y, Younis, MZ, Yu, C, Yunusa, I, Yusefi, H, Zahir, M, Bin Zaman, S, Zare, I, Zarea, K, Zastrozhin, MS, Zhang, Z-J, Zhang, Y, Ziapour, A, Zodpey, S, Zoladl, M, Patton, GC, and Ivers, RQ
Abstract: BACKGROUND: Globally, transport and unintentional injuries persist as leading preventable causes of mortality and morbidity for adolescents. We sought to report comprehensive trends in injury-related mortality and morbidity for adolescents aged 10-24 years during the past three decades. METHODS: Using the Global Burden of Disease, Injuries, and Risk Factors 2019 Study, we analysed mortality and disability-adjusted life-years (DALYs) attributed to transport and unintentional injuries for adolescents in 204 countries. Burden is reported in absolute numbers and age-standardised rates per 100 000 population by sex, age group (10-14, 15-19, and 20-24 years), and sociodemographic index (SDI) with 95% uncertainty intervals (UIs). We report percentage changes in deaths and DALYs between 1990 and 2019. FINDINGS: In 2019, 369 061 deaths (of which 214 337 [58%] were transport related) and 31·1 million DALYs (of which 16·2 million [52%] were transport related) among adolescents aged 10-24 years were caused by transport and unintentional injuries combined. If compared with other causes, transport and unintentional injuries combined accounted for 25% of deaths and 14% of DALYs in 2019, and showed little improvement from 1990 when such injuries accounted for 26% of adolescent deaths and 17% of adolescent DALYs. Throughout adolescence, transport and unintentional injury fatality rates increased by age group. The unintentional injury burden was higher among males than females for all injury types, except for injuries related to fire, heat, and hot substances, or to adverse effects of medical treatment. From 1990 to 2019, global mortality rates declined by 34·4% (from 17·5 to 11·5 per 100 000) for transport injuries, and by 47·7% (from 15·9 to 8·3 per 100 000) for unintentional injuries. However, in low-SDI nations the absolute number of deaths increased (by 80·5% to 42 774 for transport injuries and by 39·4% to 31 961 for unintentional injuries). In the high-SDI quintile in 2010-19
Published: 2022

4. Trends in depression by glycemic status: Serial cross-sectional analyses of the National Health and Nutrition Examination Surveys, 2005-2016.

Author: Chandrasekar EK, Ali MK, Wei J, Narayan KV, Owens-Gary MD, and Bullard KM
Subjects: Adult, Cross-Sectional Studies, Depression diagnosis, Depression epidemiology, Glycated Hemoglobin analysis, Health Surveys, Humans, Nutrition Surveys, Prevalence, Depressive Disorder, Major diagnosis, Depressive Disorder, Major epidemiology, Diabetes Mellitus diagnosis, Diabetes Mellitus epidemiology, Prediabetic State diagnosis, Prediabetic State epidemiology
Abstract: Aims: We examined changes in the prevalence of elevated depressive symptoms among US adults with diabetes, prediabetes, and normal glycemic status during 2005-2016., Methods: We analyzed data from 32,676 adults in the 2005-2016 National Health and Nutrition Examination Surveys. We defined diabetes as self-reporting a physician diagnosis of diabetes or A1C ≥ 6.5% [48 mmol/mol], and prediabetes as A1C 5.7-6.4% [39-46 mmol/mol]. We used the 9-item Patient Health Questionnaire (PHQ-9) score ≥ 10 or antidepressant use to define 'clinically significant depressive symptoms' (CSDS) and PHQ-9 score ≥ 12 as 'Major Depressive Disorder' (MDD). We calculated prevalence age-standardized to the 2000 US census and used logistic-regression to compute adjusted odds of CSDS and MDD for 2005-2008, 2009-2012, and 2015-2016. We analyzed the prevalence of A1C ≥ 9.0% [75 mmol/mol], systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, non-HDL cholesterol ≥ 130 mg/dL, and current smoking among adults with diagnosed diabetes by depressive status., Results: The prevalence of CSDS increased among individuals with normal glycemic status from 15.0% (13.5-16.2) to 17.3% (16.0-18.7) (p = 0.03) over 2005-2016. The prevalence of CSDS and MDD remained stable among adults with prediabetes (~ 16% and 1%, respectively) and diabetes (~ 26% and ~3%). After controlling for glycemic, sociodemographic, economic, and self-rated health variables, we found 2-fold greater odds of CSDS among unemployed individuals and 3-fold greater odds among those with fair/poor self-rated health across all survey periods. Cardiometabolic care targets for adults with diagnosed diabetes were stable from 2005 to 2016 and similar across depressive status., Conclusions: One-fourth of adults with diabetes have comorbid CSDS; this prevalence remained stable over 2005-2016 with no change in diabetes care. At the population level, depression does not appear to impact diabetes care, but further research could explore subgroups that may be more vulnerable and could benefit from integrated care that addresses both conditions., (Copyright © 2022. Published by Elsevier Ltd.)
Published: 2022
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5. Association Between the COVID-19 Pandemic and Disparities in Access to Major Surgery in the US.

Author: Glance LG, Chandrasekar EK, Shippey E, Stone PW, Dutton R, McCormick PJ, Shang J, Lustik SJ, Wu IY, Eaton MP, and Dick AW
Subjects: Adult, Aged, Cross-Sectional Studies, Female, Humans, Medicare, Pandemics, Retrospective Studies, SARS-CoV-2, United States epidemiology, COVID-19 epidemiology
Abstract: Importance: Racial minority groups account for 70% of excess deaths not related to COVID-19. Understanding the association of the Centers for Medicare & Medicaid Services' (CMS's) moratorium delaying nonessential operations with racial disparities will help shape future pandemic responses., Objective: To evaluate the association of the CMS's moratorium on elective operations during the first wave of the COVID-19 pandemic among Black individuals, Asian individuals, and individuals of other races compared with White individuals., Design, Setting, and Participants: This cross-sectional study assessed a 719-hospital retrospective cohort of 3 470 905 adult inpatient hospitalizations for major surgery between January 1, 2018, and October 31, 2020., Exposure: The first wave of COVID-19 infections between March 1, 2020, and May 31, 2020., Main Outcomes and Measures: The main outcome was the association between changes in monthly elective surgical case volumes and the first wave of COVID-19 infections as a function of patient race, evaluated using negative binomial regression analysis., Results: Among 3 470 905 adults (1 823 816 female [52.5%]) with inpatient hospitalizations for major surgery, 70 752 (2.0%) were Asian, 453 428 (13.1%) were Black, 2 696 929 (77.7%) were White, and 249 796 (7.2%) were individuals of other races. The number of monthly elective cases during the first wave was 49% (incident rate ratio [IRR], 0.49; 95% CI, 0.486-0.492; P < .001) compared with the baseline period. The relative reduction in unadjusted elective surgery cases for Black (unadjusted IRR, 0.99; 95% CI, 0.97-1.01; P = .36), Asian (unadjusted IRR, 1.08; 95% CI, 1.03-1.14; P = .001), and other race individuals (unadjusted IRR, 0.97; 95% CI, 0.95-1.00; P = .05) during the surge period compared with the baseline period was very close to the change in cases for White individuals. After adjustment for age, sex, comorbidities, and surgical procedure, there was still no evidence that the first wave of the pandemic was associated with disparities in access to elective surgery., Conclusions and Relevance: In this cross-sectional study, the CMS's moratorium on nonessential operations was associated with a 51% reduction in elective operations. It was not associated with greater reductions in operations for racial minority individuals than for White individuals. This evidence suggests that the early response to the pandemic did not increase disparities in access to surgical care.
Published: 2022
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6. The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review.

Author: Gunasekeran DV, Chew A, Chandrasekar EK, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, and Leong CK
Subjects: Artificial Intelligence, Humans, Pandemics prevention & control, Public Health methods, COVID-19 prevention & control, Social Media
Abstract: Background: Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions., Objective: This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature., Methods: We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health., Results: The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy., Conclusions: Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health., (©Dinesh Visva Gunasekeran, Alton Chew, Eeshwar K Chandrasekar, Priyanka Rajendram, Vasundhara Kandarpa, Mallika Rajendram, Audrey Chia, Helen Smith, Choon Kit Leong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2022.)
Published: 2022
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7. Sleep, sedentary activity, physical activity, and cognitive function among older adults: The National Health and Nutrition Examination Survey, 2011-2014.

Author: Wei J, Hou R, Xie L, Chandrasekar EK, Lu H, Wang T, Li C, and Xu H
Subjects: Aged, Cross-Sectional Studies, Female, Humans, Life Style, Male, Middle Aged, Nutrition Surveys, Self Report, Time Factors, Cognition physiology, Exercise psychology, Sedentary Behavior, Sleep physiology
Abstract: Objectives: We aimed to estimate the association of sleep, sedentary activity and physical activity with cognitive function among older adults, with consideration of the competing nature between variables of activity status., Design: Cross-sectional study., Methods: A total of 3086 older adults (60 years or older) in the 2011-2014 National Health and Nutrition Examination Survey were included. The Global Physical Activity Questionnaire was used to measure self-reported time for sedentary activity, walking/bicycling and moderate-to-vigorous physical activity (MVPA). Cognitive function was examined using the CERAD Word Learning subtest (memory), Digit Symbol Substitution Test (executive function/processing speed), and Animal Fluency Test (language). Sleep duration was obtained via interview. Isotemporal substitution models using multivariable linear regression were applied to examine the associations of replacing sleep, sedentary activity, walking/bicycling, MVPA with each other and cognitive function, stratified by sleep duration per night (≤7h, >7h)., Results: Among participants with sleep duration ≤7h/night, replacing 30min/day of sedentary activity with 30min/day of MVPA or 30min/day was associated with better cognition. Among participants with sleep duration >7h/night, replacing 30min/day of sleep with 30min/day of sedentary activity, walking/bicycling, or MVPA was associated with better cognition., Conclusions: Replacing sedentary activities with MVPA was associated with favorable cognitive function among older adults sleeping no longer than 7h/night, and replacing excessive sleep with sedentary or physical activities was associated with favorable cognition. Future research is expected to examine the associations of replacing different activity status on long-term cognitive outcomes in longitudinal studies., (Copyright © 2020 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)
Published: 2021
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8. Regionally Specific Economic Impact of Screening and Treating Retinopathy of Prematurity in Middle-Income Societies in the Philippines.

Author: Naguib MM, Soares RR, Anzures R, Kamel J, Chandrasekar EK, Rothschild M, Fernandes A, Paul Chan RV, and Olsen TW
Subjects: Adolescent, Adult, Child, Child, Preschool, Cost-Benefit Analysis, Female, Humans, Incidence, Infant, Infant, Newborn, Male, Philippines epidemiology, Retinopathy of Prematurity economics, Retinopathy of Prematurity therapy, Young Adult, Disease Management, Health Care Costs, Income, Mass Screening economics, Retinopathy of Prematurity epidemiology
Abstract: Purpose: To estimate the economic effects of implementing a universal screening and treatment program for retinopathy of prematurity (ROP) in the Philippines with the Economic Model for Retinopathy of Prematurity (EcROP)., Methods: The EcROP is a cost-effectiveness, cost-benefit, and cost-utility analysis. Fifty parents of legally blind individuals (aged 3 to 28 years) from three schools for the blind in the Philippines were interviewed to estimate the societal burden of raising a blind child. A decision tree analytic model, with deterministic and probabilistic sensitivity analysis, was used to calculate the incremental cost-effectiveness ratio (primary outcome) and the incremental monetary benefit (secondary outcome) for implementing an optimal national ROP program, compared to estimates of the current policy. Findings were extrapolated to estimate the national economic benefit of an ideal screening and treatment program., Results: The incremental cost-effectiveness ratio for a national program over the current policy was strongly favorable to the ideal program for the Philippines and represents an opportunity for substantial societal cost savings. The per-child incremental, annual monetary benefit of a national program over the current policy was $2,627. Extrapolating to the population of children at risk in 1 year showed that the national annual net benefit estimate would be $64,320,692, which is favorable to the current policy., Conclusions: The EcROP demonstrates that implementing a national ROP screening and treatment program is cost-saving and cost-effective, and would substantially decrease childhood blindness in the Philippines. [J Pediatr Ophthalmol Strabismus. 2019;56(6):388-396.]., (Copyright 2019, SLACK Incorporated.)
Published: 2019
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9. Protecting children's health in a calorie-surplus context: Household structure and child growth in the United States.

Author: Cunningham SA, Chandrasekar EK, Cartwright K, and Yount KM
Subjects: Adolescent, Child, Child Development, Child, Preschool, Energy Intake, Growth, Humans, Longitudinal Studies, Obesity etiology, Body Mass Index, Child Health trends, Family Characteristics
Abstract: Studies from the social and health sciences have tended to view the household as the locus of access to and distribution of care, resources, monitoring and modeling for children's wellbeing. Obesity may present a special case for the study of investments in children, being a component of health for which more of certain inputs may not lead to better outcomes. We expanded on common measures of household structure in the child health literature by considering co-residence and relatedness of parents, grandparents, other relatives, and other children. Data were from a longitudinal sample of 6,700 children participating in the Early Childhood Longitudinal Study Kindergarten Class of 1998-99 (ECLS-K), the largest U.S. national dataset with measures of child anthropometrics and household structure at seven time-points over nine years. We used lagged survey-adjusted regressions to estimate associations between household structure and subsequent changes in children's weight between ages 5 and 14 years in terms of BMI gain and incident obesity. Adjusting for household structure more thoroughly, children living in households with two parents rather than one parent did not experience advantages in terms of less excess weight gain or lower incidence of obesity during elementary and middle school. Children living with a grandmother gained more weight than children not living with a grandmother. Living with siblings and with non-related adults was associated with less weight gain. These findings corroborate a scenario in which, for health problems associated with caloric surplus, classic household factors have more complex associations with child wellbeing., Competing Interests: The authors have declared that no competing interests exist.
Published: 2019
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10. The association of late-life depression with all-cause and cardiovascular mortality among community-dwelling older adults: systematic review and meta-analysis.

Author: Wei J, Hou R, Zhang X, Xu H, Xie L, Chandrasekar EK, Ying M, and Goodman M
Subjects: Aged, Humans, Cardiovascular Diseases mortality, Depression epidemiology, Independent Living statistics & numerical data, Mortality
Abstract: Background: Late-life depression has become an important public health problem. Available evidence suggests that late-life depression is associated with all-cause and cardiovascular mortality among older adults living in the community, although the associations have not been comprehensively reviewed and quantified.AimTo estimate the pooled association of late-life depression with all-cause and cardiovascular mortality among community-dwelling older adults., Method: We conducted a systematic review and meta-analysis of prospective cohort studies that examine the associations of late-life depression with all-cause and cardiovascular mortality in community settings., Results: A total of 61 prospective cohort studies from 53 cohorts with 198 589 participants were included in the systematic review and meta-analysis. A total of 49 cohorts reported all-cause mortality and 15 cohorts reported cardiovascular mortality. Late-life depression was associated with increased risk of all-cause (risk ratio 1.34; 95% CI 1.27, 1.42) and cardiovascular mortality (risk ratio 1.31; 95% CI 1.20, 1.43). There was heterogeneity in results across studies and the magnitude of associations differed by age, gender, study location, follow-up duration and methods used to assess depression. The associations existed in different subgroups by age, gender, regions of studies, follow-up periods and assessment methods of late-life depression., Conclusion: Late-life depression is associated with higher risk of both all-cause and cardiovascular mortality among community-dwelling elderly people. Future studies need to test the effectiveness of preventing depression among older adults as a way of reducing mortality in this population. Optimal treatment of late-life depression and its impact on mortality require further investigation.Declaration of interestNone.
Published: 2019
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11. Healthy dietary patterns and risk and survival of breast cancer: a meta-analysis of cohort studies.

Author: Hou R, Wei J, Hu Y, Zhang X, Sun X, Chandrasekar EK, and Voruganti VS
Subjects: Breast Neoplasms prevention & control, Female, Humans, Prospective Studies, Risk Factors, Breast Neoplasms epidemiology, Diet
Abstract: Purposes: Dietary patterns have been found to be associated with the overall cancer risk and survival. However, the associations of healthy dietary patterns and breast cancer remain unclear. We aimed to conduct a meta-analysis of prospective cohort studies to estimate the pooled results of the association of healthy dietary patterns with breast cancer risk and survival., Methods: PubMed, EMBASE, and Web of Science were searched for literature published until June 24th, 2018 that examined the associations between healthy dietary patterns and breast cancer risk and survival. Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated by using a random-effects model for meta-analysis., Results: There were 32 articles retrieved for the meta-analysis, with 27 for breast cancer risk and five for breast cancer survival. There was a statistically significant lower risk of breast cancer associated with healthy dietary patterns (RR = 0.93, 95% CI: 0.88, 0.98). Subgroup analysis results suggested that there was an inverse association between breast cancer risk and posterori-derived healthy patterns, but no statistically significant associations were found in other stratified subgroups (a priori-derived diet, study region, menopausal status, or breast cancer subtypes). Healthy dietary patterns were associated inversely with all-cause mortality (RR = 0.76, 95% CI: 0.63, 0.92); however, no association was found for breast cancer-specific mortality., Conclusions: The results suggested that healthy dietary patterns might be associated with a reduced risk of breast cancer and all-cause mortality among breast cancer patients. It could be clinically relevant to promote healthy dietary patterns for breast cancer prevention and improve survival among breast cancer patients.
Published: 2019
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12. Late-life depression and cognitive function among older adults in the U.S.: The National Health and Nutrition Examination Survey, 2011-2014.

Author: Wei J, Ying M, Xie L, Chandrasekar EK, Lu H, Wang T, and Li C
Subjects: Aged, Aged, 80 and over, Cognitive Dysfunction etiology, Cross-Sectional Studies, Depression complications, Diabetes Complications, Female, Health Surveys, Humans, Male, Middle Aged, Nutrition Surveys, United States epidemiology, Aging, Cognitive Dysfunction epidemiology, Depression epidemiology, Depressive Disorder epidemiology, Diabetes Mellitus epidemiology
Abstract: Discrepancies exist on the associations of late-life depression with cognition, and synergistic effect of depression and diabetes on cognition among older adults was suggested in literature. We aimed to examine the associations of late-life depression with cognitive function in a representative sample of older adults in the U.S., and to examine the associations among individuals with diabetes. A total of 3101 adults aged 60 and above of the 2011-2014 National Health and Nutrition Examination Survey who completed measurements of depressive symptoms and diabetes were included in cross-sectional analyses. The 9-item Patient Health Questionnaire (PHQ-9) was used to measure depressive symptoms (including overall, somatic and cognitive). Clinically relevant depression (CRD) and clinically significant depression (CSD) were defined by cutoffs of PHQ-9. Domain-specific cognitive function was examined using Delayed Word Recall Test, Digit Symbol Substitution Test, and Animal Fluency Test for memory, executive function/processing speed, and language, respectively. Z scores were created for overall cognition and specific domains. Multivariable linear regression models were applied to examine the association of depressive symptoms and scale-defined depression with cognition z scores. The overall, somatic and cognitive depressive symptoms were associated with lower cognitive function among older adults. Both CRD (β = -0.20, 95% CI: -0.28, -0.12) and CSD (β = -0.56, 95% CI: -0.75, -0.37) were associated with lower cognition. A synergistic relationship was found between depression and diabetes on lower cognition. These results suggested that cognition among older adults may be modified by late-life depression, and older adults with both depression and diabetes may be particularly impacted on cognition., (Copyright © 2019 Elsevier Ltd. All rights reserved.)
Published: 2019
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13. Cost-effectiveness of a faith-based lifestyle intervention for diabetes prevention among African Americans: A within-trial analysis.

Author: Rhodes EC, Chandrasekar EK, Patel SA, Narayan KMV, Joshua TV, Williams LB, Marion L, and Ali MK
Subjects: Adult, Black or African American, Cost-Benefit Analysis, Female, Humans, Male, Middle Aged, Diabetes Mellitus economics, Diabetes Mellitus prevention & control, Life Style ethnology, Quality of Life psychology
Abstract: Aims: We assessed costs and cost-effectiveness of implementing Fit Body and Soul (FBAS), a church-based 18-session lifestyle education intervention for African Americans., Methods: We calculated incremental cost-effectiveness ratios (ICER) using data from a cluster randomized controlled trial comparing FBAS with health education (HE) among 604 overweight participants in 20 churches. The ICER was the adjusted difference in costs to deliver FBAS versus HE over the difference in weight change (kilograms [kg]) at one-year follow-up. Costs included those incurred for participant identification and program implementation. We fitted linear mixed-effects regression models, accounting for clustering of participants within churches and for age, sex, and educational attainment. We repeated these analyses for secondary outcomes (waist circumference [cm], physical activity [MET], glucose, blood pressure, and quality of life)., Results: Per-person intervention cost of FBAS was $50.39 more than HE ($442.22 vs. $391.83 per-person), and adjusted differences in weight change (1.9 kg [95% CI: 1.0 to 2.8]) and waist circumference (2.4 cm [95% CI: 1.3 to 3.4]) were both significant. FBAS did not result in statistically significant differences in physical activity, glucose, blood pressures, or quality of life. We estimated that compared to HE, FBAS costs an additional $26.52 per kg weight lost and $21.00 per cm reduction in waist circumference., Conclusions: For a modest increase in cost, FBAS led to greater weight and waist reductions among African Americans in a church setting. ClinicalTrials.gov Identifier NCT01730196., (Copyright © 2018 Elsevier B.V. All rights reserved.)
Published: 2018
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14. The association and dose-response relationship between dietary intake of α-linolenic acid and risk of CHD: a systematic review and meta-analysis of cohort studies.

Author: Wei J, Hou R, Xi Y, Kowalski A, Wang T, Yu Z, Hu Y, Chandrasekar EK, Sun H, and Ali MK
Subjects: Adult, Aged, Cohort Studies, Coronary Disease mortality, Dose-Response Relationship, Drug, Female, Humans, Linear Models, Male, Middle Aged, Odds Ratio, Prospective Studies, Randomized Controlled Trials as Topic, Risk Factors, Coronary Disease metabolism, Coronary Disease prevention & control, Diet, alpha-Linolenic Acid adverse effects
Abstract: Previous studies show inconsistent associations between α-linolenic acid (ALA) and risk of CHD. We aimed to examine an aggregate association between ALA intake and risk of CHD, and assess for any dose-response relationship. We searched the PubMed, EMBASE and Web of Science databases for prospective cohort studies examining associations between ALA intake and CHD, including composite CHD and fatal CHD. Data were pooled using random-effects meta-analysis models, comparing the highest category of ALA intake with the lowest across studies. Subgroup analysis was conducted based on study design, geographic region, age and sex. For dose-response analyses, we used two-stage random-effects dose-response models. In all, fourteen studies of thirteen cohorts were identified and included in the meta-analysis. The pooled results showed that higher ALA intake was associated with modest reduced risk of composite CHD (risk ratios (RR)=0·91; 95 % CI 0·85, 0·97) and fatal CHD (RR=0·85; 95 % CI 0·75, 0·96). The analysis showed a J-shaped relationship between ALA intake and relative risk of composite CHD (χ 2=21·95, P<0·001). Compared with people without ALA intake, only people with ALA intake <1·4 g/d showed reduced risk of composite CHD. ALA intake was linearly associated with fatal CHD - every 1 g/d increase in ALA intake was associated with a 12 % decrease in fatal CHD risk (95 % CI -0·21, -0·04). Though a higher dietary ALA intake was associated with reduced risk of composite and fatal CHD, the excess composite CHD risk at higher ALA intakes warrants further investigation, especially through randomised controlled trials.
Published: 2018
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15. Hearing Impairment, Mild Cognitive Impairment, and Dementia: A Meta-Analysis of Cohort Studies.

Author: Wei J, Hu Y, Zhang L, Hao Q, Yang R, Lu H, Zhang X, and Chandrasekar EK
Abstract: Background: To estimate a pooled association between hearing impairment and risk of mild cognitive impairment and dementia., Methods: PubMed, Embase, and Web of Science were searched for prospective cohort studies that examined the association between hearing impairment and risk of mild cognitive impairment and/or dementia. Random-effects models were fitted to estimate the summary risk ratios (RRs) and 95% confidence interval (CIs), which represents the pooled association between hearing impairment with risk of mild cognitive impairment and dementia, compared to subjects free of hearing impairment., Results: Four studies on hearing impairment with mild cognitive impairment and 7 studies on hearing impairment with dementia were included in the meta-analysis. A total of 15,521 subjects were studied with follow-up periods between 2 and 16.8 years. Hearing impairment was associated with a greater risk of mild cognitive impairment (RR = 1.30, 95% CI: 1.12, 1.51) and dementia (RR = 2.39, 95% CI: 1.58, 3.61)., Conclusions: The meta-analysis showed that hearing impairment is associated with a higher risk of mild cognitive impairment and dementia among older adults.
Published: 2017
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15 results on '"Chandrasekar EK"'

1. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

3. Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019

4. Trends in depression by glycemic status: Serial cross-sectional analyses of the National Health and Nutrition Examination Surveys, 2005-2016.

5. Association Between the COVID-19 Pandemic and Disparities in Access to Major Surgery in the US.

6. The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review.

7. Sleep, sedentary activity, physical activity, and cognitive function among older adults: The National Health and Nutrition Examination Survey, 2011-2014.

8. Regionally Specific Economic Impact of Screening and Treating Retinopathy of Prematurity in Middle-Income Societies in the Philippines.

9. Protecting children's health in a calorie-surplus context: Household structure and child growth in the United States.

10. The association of late-life depression with all-cause and cardiovascular mortality among community-dwelling older adults: systematic review and meta-analysis.

11. Healthy dietary patterns and risk and survival of breast cancer: a meta-analysis of cohort studies.

12. Late-life depression and cognitive function among older adults in the U.S.: The National Health and Nutrition Examination Survey, 2011-2014.

13. Cost-effectiveness of a faith-based lifestyle intervention for diabetes prevention among African Americans: A within-trial analysis.

14. The association and dose-response relationship between dietary intake of α-linolenic acid and risk of CHD: a systematic review and meta-analysis of cohort studies.

15. Hearing Impairment, Mild Cognitive Impairment, and Dementia: A Meta-Analysis of Cohort Studies.

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