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1. Agency cost and conflict of interests in health care delivery system

Author: Mohammadi SM and Poor Reza A
Subjects: agency cost, conflict of interests, delivery of health care, health insurance, moral hazard, Medicine, Medicine (General), R5-920
Abstract: Agency relationship, conflict of interests, and asymmetry of information have been examined and discussed in detail in more classic economic systems and corporate governance. In this paper we attempt to apply these concepts to healthcare delivery system to understand its nature and dynamics. Considering the vital role of health insurance, the nature of agency cost in various forms of insurance is examined. Finally, we suggest strategies for handling and containment of this phenomenon in health systems with any forms of insurance.
Published: 2005

2. The global burden of adolescent and young adult cancer in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Author: Alvarez, EM, Force, LM, Xu, R, Compton, K, Lu, D, Henrikson, HJ, Kocarnik, JM, Harvey, JD, Pennini, A, Dean, FE, Fu, W, Vargas, MT, Keegan, THM, Ariffin, H, Barr, RD, Erdomaeva, YA, Gunasekera, DS, John-Akinola, YO, Ketterl, TG, Kutluk, T, Malogolowkin, MH, Mathur, P, Radhakrishnan, V, Ries, LAG, Rodriguez-Galindo, C, Sagoyan, GB, Sultan, I, Abbasi, B, Abbasi-Kangevari, M, Abbasi-Kangevari, Z, Abbastabar, H, Abdelmasseh, M, Abd-Elsalam, S, Abdoli, A, Abebe, H, Abedi, A, Abidi, H, Abolhassani, H, Ali, HA, Abu-Gharbieh, E, Achappa, B, Acuna, JM, Adedeji, IA, Adegboye, OA, Adnani, QES, Advani, SM, Afzal, MS, Meybodi, MA, Ahadinezhad, B, Ahinkorah, BO, Ahmad, S, Ahmadi, S, Ahmed, MB, Rashid, TA, Salih, YA, Aiman, W, Akalu, GT, Al Hamad, H, Alahdab, F, AlAmodi, AA, Alanezi, FM, Alanzi, TM, Alem, AZ, Alem, DT, Alemayehu, Y, Alhalaiqa, FN, Alhassan, RK, Ali, S, Alicandro, G, Alipour, V, Aljunid, SM, Alkhayyat, M, Alluri, S, Almasri, NA, Al-Maweri, SA, Almustanyir, S, Al-Raddadi, RM, Alvis-Guzman, N, Ameyaw, EK, Amini, S, Amu, H, Ancuceanu, R, Andrei, CL, Andrei, T, Ansari, F, Ansari-Moghaddam, A, Anvari, D, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Argaw, AM, Arshad, M, Arulappan, J, Aryannejad, A, Asemi, Z, Jafarabadi, MA, Atashzar, MR, Atorkey, P, Atreya, A, Attia, S, Aujayeb, A, Ausloos, M, Avila-Burgos, L, Awedew, AF, Quintanilla, BPA, Ayele, AD, Ayen, SS, Azab, MA, Azadnajafabad, S, Azami, H, Azangou-Khyavy, M, Jafari, AA, Azarian, G, Azzam, AY, Bahadory, S, Bai, J, Baig, AA, Baker, JL, Banach, M, Barnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Basaleem, H, Batiha, A-MM, Behzadifar, M, Bekele, NC, Belete, R, Belgaumi, UI, Bell, AW, Berhie, AY, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Birara, S, Bjorge, T, Bolarinwa, OA, Bolla, SR, Boloor, A, Braithwaite, D, Brenner, H, Bulamu, NB, Burkart, K, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, dos Santos, FLC, Cao, C, Cao, Y, Carreras, G, Catala-Lopez, F, Cembranel, F, Cerin, E, Chakinala, RC, Chakraborty, PA, Chattu, VK, Chaturvedi, P, Chaurasia, A, Chavan, PP, Chimed-Ochir, O, Choi, J-YJ, Christopher, DJ, Chu, D-T, Chung, MT, Conde, J, Costa, VM, Daar, OB, Dadras, O, Dahlawi, SMA, Dai, X, Damiani, G, Amico, ED, Dandona, L, Dandona, R, Daneshpajouhnejad, P, Darwish, AH, Daryani, A, De la Hoz, FP, Debela, SA, Demie, TGG, Demissie, GD, Demissie, ZG, Denova-Gutierrez, E, Molla, MD, Desai, R, Desta, AA, Dhamnetiya, D, Dharmaratne, SD, Dhimal, ML, Dhimal, M, Dianatinasab, M, Didehdar, M, Diress, M, Djalalinia, S, Huyen, PD, Doaei, S, Dorostkar, F, dos Santos, WM, Drake, TM, Ekholuenetale, M, El Sayed, I, Zaki, MES, El Tantawi, M, El-Abid, H, Elbahnasawy, MA, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, S, Enyew, DB, Erkhembayar, R, Eshrati, B, Eskandarieh, S, Faisaluddin, M, Fares, J, Farooque, U, Fasanmi, AO, Fatima, W, Ferreira de Oliveira, JMP, Ferrero, S, Desideri, LF, Fetensa, G, Filip, I, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gaewkhiew, P, Gallus, S, Garg, T, Gemeda, BNB, Getachew, T, Ghafourifard, M, Ghamari, S-H, Ghashghaee, A, Ghassemi, F, Ghith, N, Gholami, A, Navashenaq, JG, Gilani, SA, Ginindza, TG, Gizaw, AT, Glasbey, JC, Goel, A, Golechha, M, Goleij, P, Golinelli, D, Gopalani, SV, Gorini, G, Goudarzi, H, Goulart, BNG, Grada, A, Gubari, MIM, Guerra, MR, Guha, A, Gupta, B, Gupta, S, Gupta, VB, Gupta, VK, Haddadi, R, Hafezi-Nejad, N, Hailu, A, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hambisa, MT, Hameed, S, Hamidi, S, Haque, S, Hariri, S, Haro, JM, Hasaballah, A, Hasan, SMM, Hashemi, SM, Hassan, TS, Hassanipour, S, Hay, S, Hayat, K, Hebo, SH, Heidari, G, Heidari, M, Herrera-Serna, BY, Herteliu, C, Heyi, DZ, Hezam, K, Hole, MK, Holla, R, Horita, N, Hossain, MM, Hossain, MB, Hosseini, M-S, Hosseini, M, Hosseinzadeh, A, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hussein, NR, Hwang, B-F, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Innos, K, Irham, LM, Islam, RM, Islam, SMS, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jadidi-Niaragh, F, Jain, V, Jakovljevic, M, Janghorban, R, Mamaghani, AJ, Jayaram, S, Jayawardena, R, Jazayeri, SB, Jebai, R, Jha, RP, Joo, T, Joseph, N, Joukar, F, Jurisson, M, Kaambwa, B, Kabir, A, Kalankesh, LR, Kaliyadan, F, Kamal, Z, Kamath, A, Kandel, H, Kar, SS, Karaye, IM, Karimi, A, Kassa, BG, Kauppila, JH, Bohan, PMK, Kengne, AP, Kerbo, AA, Keykhaei, M, Khader, YS, Khajuria, H, Khalili, N, Khan, EA, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khanali, J, Khayamzadeh, M, Khosravizadeh, O, Khubchandani, J, Khundkar, R, Kim, MS, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kolahi, A-A, Kopec, JA, Koteeswaran, R, Laxminarayana, SLK, Koyanagi, A, Kugbey, N, Kumar, GA, Kumar, N, Kwarteng, A, La Vecchia, C, Lan, Q, Landires, I, Lasrado, S, Lauriola, P, Ledda, C, Lee, S-W, Lee, W-C, Lee, YY, Lee, YH, Leigh, J, Leong, E, Li, B, Li, J, Li, M-C, Lim, SS, Liu, X, Lobo, SW, Loureiro, JA, Lugo, A, Lunevicius, R, Abd El Razek, HM, Razek, MMAE, Mahmoudi, M, Majeed, A, Makki, A, Male, S, Malekpour, M-R, Malekzadeh, R, Malik, AA, Mamun, MA, Manafi, N, Mansour-Ghanaei, F, Mansouri, B, Mansournia, MA, Martini, S, Masoumi, SZ, Matei, CN, Mathur, MR, McAlinden, C, Mehrotra, R, Mendoza, W, Menezes, RG, Mentis, A-FA, Meretoja, TJ, Mersha, AG, Mesregah, MK, Mestrovic, T, Jonasson, JM, Miazgowski, B, Michalek, IM, Miller, TR, Mingude, AB, Mirmoeeni, S, Mirzaei, H, Misra, S, Mithra, P, Mohammad, KA, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, A, Mohammed, S, Mohammed, TA, Moka, N, Mokdad, AH, Molokhia, M, Momtazmanesh, S, Monasta, L, Moni, MA, Moradi, G, Moradi, Y, Moradzadeh, M, Moradzadeh, R, Moraga, P, Morrison, SD, Mostafavi, E, Khaneghah, AM, Mpundu-Kaambwa, C, Mubarik, S, Mwanri, L, Nabhan, AF, Nagaraju, SP, Nagata, C, Naghavi, M, Naimzada, MD, Naldi, L, Nangia, V, Naqvi, AA, Swamy, SN, Narayana, AI, Nayak, BP, Nayak, VC, Nazari, J, Nduaguba, SO, Negoi, I, Negru, SM, Nejadghaderi, SA, Nepal, S, Kandel, SN, Nggada, HA, Nguyen, CT, Nnaji, CA, Nosrati, H, Nouraei, H, Nowroozi, A, Nunez-Samudio, V, Nwatah, VE, Nzoputam, CI, Oancea, B, Odukoya, OO, Oguntade, AS, Oh, I-H, Olagunju, AT, Olagunju, TO, Olakunde, BO, Oluwasanu, MM, Omar, E, Bali, AO, Ong, S, Onwujekwe, OE, Ortega-Altamirano, D, Otstavnov, N, Otstavnov, SS, Oumer, B, Owolabi, MO, Mahesh, PA, Padron-Monedero, A, Padubidri, JR, Pakshir, K, Pana, A, Pandey, A, Pardhan, S, Kan, FP, Pasovic, M, Patel, JR, Pati, S, Pattanshetty, SM, Paudel, U, Pereira, RB, Peres, MFP, Perianayagam, A, Postma, MJ, Pourjafar, H, Pourshams, A, Prashant, A, Pulakunta, T, Qadir, MMFF, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Rafiee, A, Rafiei, A, Rafiei, S, Rahim, F, Rahimzadeh, S, Rahman, M, Rahman, MA, Rahmani, AM, Rajesh, A, Ramezani-Doroh, V, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rashedi, S, Rashidi, M-M, Rath, GK, Rawaf, DL, Rawaf, S, Rawal, L, Rawassizadeh, R, Razeghinia, MS, Regasa, MT, Renzaho, AMN, Rezaei, M, Rezaei, N, Rezaeian, M, Rezapour, A, Rezazadeh-Khadem, S, Riad, A, Lopez, LER, Rodriguez, JAB, Ronfani, L, Roshandel, G, Rwegerera, GM, Saber-Ayad, MM, Sabour, S, Saddik, B, Sadeghi, E, Sadeghian, S, Saeed, U, Sahebkar, A, Saif-Ur-Rahman, KM, Sajadi, SM, Salahi, S, Salehi, S, Salem, MR, Salimzadeh, H, Samy, AM, Sanabria, J, Sanmarchi, F, Sarveazad, A, Sathian, B, Sawhney, M, Sawyer, SM, Saylan, M, Schneider, IJC, Seidu, A-A, Sekerija, M, Sendo, EG, Sepanlou, SG, Seylani, A, Seyoum, K, Sha, F, Shafaat, O, Shaikh, MA, Shamsoddin, E, Shannawaz, M, Sharma, R, Sheikhbahaei, S, Shetty, A, Shetty, BSK, Shetty, PH, Shin, JI, Shirkoohi, R, Shivakumar, KM, Shobeiri, P, Siabani, S, Sibhat, MM, Malleshappa, SKS, Sidemo, NB, Silva, DAS, Julian, GS, Singh, AD, Singh, JA, Singh, JK, Singh, S, Sinke, AH, Sintayehu, Y, Skryabin, VY, Skryabina, AA, Smith, L, Sofi-Mahmudi, A, Soltani-Zangbar, MS, Song, S, Spurlock, EE, Steiropoulos, P, Straif, K, Subedi, R, Sufiyan, MB, Abdulkader, RS, Sultana, S, Szerencses, V, Szocska, M, Tabaeian, SP, Tabaras-Seisdedos, R, Tabary, M, Tabuchi, T, Tadbiri, H, Taheri, M, Taherkhani, A, Takahashi, K, Tampa, M, Tan, K-K, Tat, VY, Tavakoli, A, Tbakhi, A, Tehrani-Banihashemi, A, Temsah, M-H, Tesfay, FH, Tesfaye, B, Thakur, JS, Thapar, R, Thavamani, A, Thiyagarajan, A, Thomas, N, Tobe-Gai, R, Togtmol, M, Tohidast, SA, Tohidinik, HR, Tolani, MA, Tollosa, DN, Touvier, M, Tovani-Palone, MR, Traini, E, Bach, XT, Mai, TNT, Tripathy, JP, Tusa, BS, Ukke, GG, Ullah, I, Ullah, S, Umapathi, KK, Unnikrishnan, B, Upadhyay, E, Ushula, TW, Vacante, M, Tahbaz, SV, Varthya, SB, Veroux, M, Villeneuve, PJ, Violante, FS, Vlassov, V, Giang, TV, Waheed, Y, Wang, N, Ward, P, Weldesenbet, AB, Wen, YF, Westerman, R, Winkler, AS, Wubishet, BL, Xu, S, Jabbari, SHY, Yang, L, Yaya, S, Yazdi-Feyzabadi, V, Yazie, TS, Yehualashet, SS, Yeshaneh, A, Yeshaw, Y, Yirdaw, BW, Yonemoto, N, Younis, MZ, Yousefi, Z, Yu, C, Yunusa, I, Zadnik, V, Zahir, M, Moghadam, TZ, Zamani, M, Zamanian, M, Zandian, H, Zare, F, Zastrozhin, MS, Zastrozhina, A, Zhang, J, Zhang, Z-J, Ziapour, A, Zoladl, M, Murray, CJL, Fitzmaurice, C, Bleyer, A, Bhakta, N, Gebremeskel, TG, Alvarez, EM, Force, LM, Xu, R, Compton, K, Lu, D, Henrikson, HJ, Kocarnik, JM, Harvey, JD, Pennini, A, Dean, FE, Fu, W, Vargas, MT, Keegan, THM, Ariffin, H, Barr, RD, Erdomaeva, YA, Gunasekera, DS, John-Akinola, YO, Ketterl, TG, Kutluk, T, Malogolowkin, MH, Mathur, P, Radhakrishnan, V, Ries, LAG, Rodriguez-Galindo, C, Sagoyan, GB, Sultan, I, Abbasi, B, Abbasi-Kangevari, M, Abbasi-Kangevari, Z, Abbastabar, H, Abdelmasseh, M, Abd-Elsalam, S, Abdoli, A, Abebe, H, Abedi, A, Abidi, H, Abolhassani, H, Ali, HA, Abu-Gharbieh, E, Achappa, B, Acuna, JM, Adedeji, IA, Adegboye, OA, Adnani, QES, Advani, SM, Afzal, MS, Meybodi, MA, Ahadinezhad, B, Ahinkorah, BO, Ahmad, S, Ahmadi, S, Ahmed, MB, Rashid, TA, Salih, YA, Aiman, W, Akalu, GT, Al Hamad, H, Alahdab, F, AlAmodi, AA, Alanezi, FM, Alanzi, TM, Alem, AZ, Alem, DT, Alemayehu, Y, Alhalaiqa, FN, Alhassan, RK, Ali, S, Alicandro, G, Alipour, V, Aljunid, SM, Alkhayyat, M, Alluri, S, Almasri, NA, Al-Maweri, SA, Almustanyir, S, Al-Raddadi, RM, Alvis-Guzman, N, Ameyaw, EK, Amini, S, Amu, H, Ancuceanu, R, Andrei, CL, Andrei, T, Ansari, F, Ansari-Moghaddam, A, Anvari, D, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Argaw, AM, Arshad, M, Arulappan, J, Aryannejad, A, Asemi, Z, Jafarabadi, MA, Atashzar, MR, Atorkey, P, Atreya, A, Attia, S, Aujayeb, A, Ausloos, M, Avila-Burgos, L, Awedew, AF, Quintanilla, BPA, Ayele, AD, Ayen, SS, Azab, MA, Azadnajafabad, S, Azami, H, Azangou-Khyavy, M, Jafari, AA, Azarian, G, Azzam, AY, Bahadory, S, Bai, J, Baig, AA, Baker, JL, Banach, M, Barnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Basaleem, H, Batiha, A-MM, Behzadifar, M, Bekele, NC, Belete, R, Belgaumi, UI, Bell, AW, Berhie, AY, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Birara, S, Bjorge, T, Bolarinwa, OA, Bolla, SR, Boloor, A, Braithwaite, D, Brenner, H, Bulamu, NB, Burkart, K, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, dos Santos, FLC, Cao, C, Cao, Y, Carreras, G, Catala-Lopez, F, Cembranel, F, Cerin, E, Chakinala, RC, Chakraborty, PA, Chattu, VK, Chaturvedi, P, Chaurasia, A, Chavan, PP, Chimed-Ochir, O, Choi, J-YJ, Christopher, DJ, Chu, D-T, Chung, MT, Conde, J, Costa, VM, Daar, OB, Dadras, O, Dahlawi, SMA, Dai, X, Damiani, G, Amico, ED, Dandona, L, Dandona, R, Daneshpajouhnejad, P, Darwish, AH, Daryani, A, De la Hoz, FP, Debela, SA, Demie, TGG, Demissie, GD, Demissie, ZG, Denova-Gutierrez, E, Molla, MD, Desai, R, Desta, AA, Dhamnetiya, D, Dharmaratne, SD, Dhimal, ML, Dhimal, M, Dianatinasab, M, Didehdar, M, Diress, M, Djalalinia, S, Huyen, PD, Doaei, S, Dorostkar, F, dos Santos, WM, Drake, TM, Ekholuenetale, M, El Sayed, I, Zaki, MES, El Tantawi, M, El-Abid, H, Elbahnasawy, MA, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, S, Enyew, DB, Erkhembayar, R, Eshrati, B, Eskandarieh, S, Faisaluddin, M, Fares, J, Farooque, U, Fasanmi, AO, Fatima, W, Ferreira de Oliveira, JMP, Ferrero, S, Desideri, LF, Fetensa, G, Filip, I, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gaewkhiew, P, Gallus, S, Garg, T, Gemeda, BNB, Getachew, T, Ghafourifard, M, Ghamari, S-H, Ghashghaee, A, Ghassemi, F, Ghith, N, Gholami, A, Navashenaq, JG, Gilani, SA, Ginindza, TG, Gizaw, AT, Glasbey, JC, Goel, A, Golechha, M, Goleij, P, Golinelli, D, Gopalani, SV, Gorini, G, Goudarzi, H, Goulart, BNG, Grada, A, Gubari, MIM, Guerra, MR, Guha, A, Gupta, B, Gupta, S, Gupta, VB, Gupta, VK, Haddadi, R, Hafezi-Nejad, N, Hailu, A, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hambisa, MT, Hameed, S, Hamidi, S, Haque, S, Hariri, S, Haro, JM, Hasaballah, A, Hasan, SMM, Hashemi, SM, Hassan, TS, Hassanipour, S, Hay, S, Hayat, K, Hebo, SH, Heidari, G, Heidari, M, Herrera-Serna, BY, Herteliu, C, Heyi, DZ, Hezam, K, Hole, MK, Holla, R, Horita, N, Hossain, MM, Hossain, MB, Hosseini, M-S, Hosseini, M, Hosseinzadeh, A, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hussein, NR, Hwang, B-F, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Innos, K, Irham, LM, Islam, RM, Islam, SMS, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jadidi-Niaragh, F, Jain, V, Jakovljevic, M, Janghorban, R, Mamaghani, AJ, Jayaram, S, Jayawardena, R, Jazayeri, SB, Jebai, R, Jha, RP, Joo, T, Joseph, N, Joukar, F, Jurisson, M, Kaambwa, B, Kabir, A, Kalankesh, LR, Kaliyadan, F, Kamal, Z, Kamath, A, Kandel, H, Kar, SS, Karaye, IM, Karimi, A, Kassa, BG, Kauppila, JH, Bohan, PMK, Kengne, AP, Kerbo, AA, Keykhaei, M, Khader, YS, Khajuria, H, Khalili, N, Khan, EA, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khanali, J, Khayamzadeh, M, Khosravizadeh, O, Khubchandani, J, Khundkar, R, Kim, MS, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kolahi, A-A, Kopec, JA, Koteeswaran, R, Laxminarayana, SLK, Koyanagi, A, Kugbey, N, Kumar, GA, Kumar, N, Kwarteng, A, La Vecchia, C, Lan, Q, Landires, I, Lasrado, S, Lauriola, P, Ledda, C, Lee, S-W, Lee, W-C, Lee, YY, Lee, YH, Leigh, J, Leong, E, Li, B, Li, J, Li, M-C, Lim, SS, Liu, X, Lobo, SW, Loureiro, JA, Lugo, A, Lunevicius, R, Abd El Razek, HM, Razek, MMAE, Mahmoudi, M, Majeed, A, Makki, A, Male, S, Malekpour, M-R, Malekzadeh, R, Malik, AA, Mamun, MA, Manafi, N, Mansour-Ghanaei, F, Mansouri, B, Mansournia, MA, Martini, S, Masoumi, SZ, Matei, CN, Mathur, MR, McAlinden, C, Mehrotra, R, Mendoza, W, Menezes, RG, Mentis, A-FA, Meretoja, TJ, Mersha, AG, Mesregah, MK, Mestrovic, T, Jonasson, JM, Miazgowski, B, Michalek, IM, Miller, TR, Mingude, AB, Mirmoeeni, S, Mirzaei, H, Misra, S, Mithra, P, Mohammad, KA, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, A, Mohammed, S, Mohammed, TA, Moka, N, Mokdad, AH, Molokhia, M, Momtazmanesh, S, Monasta, L, Moni, MA, Moradi, G, Moradi, Y, Moradzadeh, M, Moradzadeh, R, Moraga, P, Morrison, SD, Mostafavi, E, Khaneghah, AM, Mpundu-Kaambwa, C, Mubarik, S, Mwanri, L, Nabhan, AF, Nagaraju, SP, Nagata, C, Naghavi, M, Naimzada, MD, Naldi, L, Nangia, V, Naqvi, AA, Swamy, SN, Narayana, AI, Nayak, BP, Nayak, VC, Nazari, J, Nduaguba, SO, Negoi, I, Negru, SM, Nejadghaderi, SA, Nepal, S, Kandel, SN, Nggada, HA, Nguyen, CT, Nnaji, CA, Nosrati, H, Nouraei, H, Nowroozi, A, Nunez-Samudio, V, Nwatah, VE, Nzoputam, CI, Oancea, B, Odukoya, OO, Oguntade, AS, Oh, I-H, Olagunju, AT, Olagunju, TO, Olakunde, BO, Oluwasanu, MM, Omar, E, Bali, AO, Ong, S, Onwujekwe, OE, Ortega-Altamirano, D, Otstavnov, N, Otstavnov, SS, Oumer, B, Owolabi, MO, Mahesh, PA, Padron-Monedero, A, Padubidri, JR, Pakshir, K, Pana, A, Pandey, A, Pardhan, S, Kan, FP, Pasovic, M, Patel, JR, Pati, S, Pattanshetty, SM, Paudel, U, Pereira, RB, Peres, MFP, Perianayagam, A, Postma, MJ, Pourjafar, H, Pourshams, A, Prashant, A, Pulakunta, T, Qadir, MMFF, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Rafiee, A, Rafiei, A, Rafiei, S, Rahim, F, Rahimzadeh, S, Rahman, M, Rahman, MA, Rahmani, AM, Rajesh, A, Ramezani-Doroh, V, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rashedi, S, Rashidi, M-M, Rath, GK, Rawaf, DL, Rawaf, S, Rawal, L, Rawassizadeh, R, Razeghinia, MS, Regasa, MT, Renzaho, AMN, Rezaei, M, Rezaei, N, Rezaeian, M, Rezapour, A, Rezazadeh-Khadem, S, Riad, A, Lopez, LER, Rodriguez, JAB, Ronfani, L, Roshandel, G, Rwegerera, GM, Saber-Ayad, MM, Sabour, S, Saddik, B, Sadeghi, E, Sadeghian, S, Saeed, U, Sahebkar, A, Saif-Ur-Rahman, KM, Sajadi, SM, Salahi, S, Salehi, S, Salem, MR, Salimzadeh, H, Samy, AM, Sanabria, J, Sanmarchi, F, Sarveazad, A, Sathian, B, Sawhney, M, Sawyer, SM, Saylan, M, Schneider, IJC, Seidu, A-A, Sekerija, M, Sendo, EG, Sepanlou, SG, Seylani, A, Seyoum, K, Sha, F, Shafaat, O, Shaikh, MA, Shamsoddin, E, Shannawaz, M, Sharma, R, Sheikhbahaei, S, Shetty, A, Shetty, BSK, Shetty, PH, Shin, JI, Shirkoohi, R, Shivakumar, KM, 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Giang, TV, Waheed, Y, Wang, N, Ward, P, Weldesenbet, AB, Wen, YF, Westerman, R, Winkler, AS, Wubishet, BL, Xu, S, Jabbari, SHY, Yang, L, Yaya, S, Yazdi-Feyzabadi, V, Yazie, TS, Yehualashet, SS, Yeshaneh, A, Yeshaw, Y, Yirdaw, BW, Yonemoto, N, Younis, MZ, Yousefi, Z, Yu, C, Yunusa, I, Zadnik, V, Zahir, M, Moghadam, TZ, Zamani, M, Zamanian, M, Zandian, H, Zare, F, Zastrozhin, MS, Zastrozhina, A, Zhang, J, Zhang, Z-J, Ziapour, A, Zoladl, M, Murray, CJL, Fitzmaurice, C, Bleyer, A, Bhakta, N, and Gebremeskel, TG
Abstract: BACKGROUND: In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. METHODS: Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. FINDINGS: There were 1·19 million (95% UI 1·11-1·28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59·6 [54·5-65·7] per 100 000 person-years) and high-middle SDI countries (53·2 [48·8-57·9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14
Published: 2022

3. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.

Author: Global Burden of Disease 2019 Cancer Collaboration, Kocarnik, JM, Compton, K, Dean, FE, Fu, W, Gaw, BL, Harvey, JD, Henrikson, HJ, Lu, D, Pennini, A, Xu, R, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Bisignano, C, Bjørge, T, Bleyer, A, Blyuss, O, Bolarinwa, OA, Bolla, SR, Ababneh, E, Braithwaite, D, Brar, A, Brenner, H, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, Caetano Dos Santos, FL, Cao, Y, Carreras, G, Catalá-López, F, Abbasi-Kangevari, M, Cembranel, F, Cerin, E, Cernigliaro, A, Chakinala, RC, Chattu, SK, Chattu, VK, Chaturvedi, P, Chimed-Ochir, O, Cho, DY, Christopher, DJ, Abbastabar, H, Chu, D-T, Chung, MT, Conde, J, Cortés, S, Cortesi, PA, Costa, VM, Cunha, AR, Dadras, O, Dagnew, AB, Dahlawi, SMA, Abd-Elsalam, SM, Dai, X, Dandona, L, Dandona, R, Darwesh, AM, das Neves, J, De la Hoz, FP, Demis, AB, Denova-Gutiérrez, E, Dhamnetiya, D, Dhimal, ML, Abdoli, A, Dhimal, M, Dianatinasab, M, Diaz, D, Djalalinia, S, Do, HP, Doaei, S, Dorostkar, F, Dos Santos Figueiredo, FW, Driscoll, TR, Ebrahimi, H, Abedi, A, Eftekharzadeh, S, El Tantawi, M, El-Abid, H, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, SI, Eshrati, B, Eskandarieh, S, Esmaeilzadeh, F, Abidi, H, Etemadi, A, Ezzikouri, S, Faisaluddin, M, Faraon, EJA, Fares, J, Farzadfar, F, Feroze, AH, Ferrero, S, Ferro Desideri, L, Filip, I, Abolhassani, H, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gallus, S, Gaspar Fonseca, M, Getachew Obsa, A, Adedeji, IA, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gholamalizadeh, M, Gilani, SA, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Golechha, M, Goleij, P, Adnani, QES, Gomez, RS, Gopalani, SV, Gorini, G, Goudarzi, H, Grosso, G, Gubari, MIM, Guerra, MR, Guha, A, Gunasekera, DS, Gupta, B, Advani, SM, Gupta, VB, Gupta, VK, Gutiérrez, RA, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Afzal, MS, Hanif, A, Haque, S, Harlianto, NI, Haro, JM, Hasaballah, AI, Hassanipour, S, Hay, RJ, Hay, SI, Hayat, K, Heidari, G, Aghaali, M, Heidari, M, Herrera-Serna, BY, Herteliu, C, Hezam, K, Holla, R, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseini, M, Hosseinzadeh, M, Ahinkorah, BO, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hugo, FN, Hussain, R, Hussein, NR, Hwang, B-F, Iavicoli, I, Ahmad, S, Ibitoye, SE, Ida, F, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irham, LM, Islam, JY, Islam, RM, Islam, SMS, Ahmad, T, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jain, V, Jakovljevic, MB, Javaheri, T, Jayaram, S, Jazayeri, SB, Jha, RP, Ahmadi, A, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jürisson, M, Kabir, A, Kahrizi, D, Kalankesh, LR, Kalhor, R, Kaliyadan, F, Ahmadi, S, Kalkonde, Y, Kamath, A, Kameran Al-Salihi, N, Kandel, H, Kapoor, N, Karch, A, Kasa, AS, Katikireddi, SV, Kauppila, JH, Kavetskyy, T, Ahmed Rashid, T, Kebede, SA, Keshavarz, P, Keykhaei, M, Khader, YS, Khalilov, R, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khang, Y-H, Ahmed Salih, Y, Khater, AM, Khayamzadeh, M, Kim, GR, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kopec, JA, Koteeswaran, R, Koul, PA, Akalu, GT, Koulmane Laxminarayana, SL, Koyanagi, A, Kucuk Bicer, B, Kugbey, N, Kumar, GA, Kumar, N, Kurmi, OP, Kutluk, T, La Vecchia, C, Aklilu, A, Lami, FH, Landires, I, Lauriola, P, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YH, Leigh, J, Leong, E, Li, J, Akram, T, Li, M-C, Liu, X, Loureiro, JA, Lunevicius, R, Magdy Abd El Razek, M, Majeed, A, Makki, A, Male, S, Malik, AA, Mansournia, MA, Akunna, CJ, Martini, S, Masoumi, SZ, Mathur, P, McKee, M, Mehrotra, R, Mendoza, W, Menezes, RG, Mengesha, EW, Mesregah, MK, Mestrovic, T, Al Hamad, H, Miao Jonasson, J, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirzaei, H, Mirzaei, HR, Misra, S, Mithra, P, Moghadaszadeh, M, Alahdab, F, Mohammad, KA, Mohammad, Y, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Moka, N, Mokdad, AH, Molokhia, M, Monasta, L, Al-Aly, Z, Moni, MA, Moosavi, MA, Moradi, Y, Moraga, P, Morgado-da-Costa, 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Skryabin, VY, Skryabina, AA, Soeberg, MJ, Sofi-Mahmudi, A, Sotoudeh, H, Ansari, F, Steiropoulos, P, Straif, K, Subedi, R, Sufiyan, MB, Sultan, I, Sultana, S, Sur, D, Szerencsés, V, Szócska, M, Tabarés-Seisdedos, R, Ansari-Moghaddam, A, Tabuchi, T, Tadbiri, H, Taherkhani, A, Takahashi, K, Talaat, IM, Tan, K-K, Tat, VY, Tedla, BAA, Tefera, YG, Tehrani-Banihashemi, A, Antwi, MH, Temsah, M-H, Tesfay, FH, Tessema, GA, Thapar, R, Thavamani, A, Thoguluva Chandrasekar, V, Thomas, N, Tohidinik, HR, Touvier, M, Tovani-Palone, MR, Anvari, D, Traini, E, Tran, BX, Tran, KB, Tran, MTN, Tripathy, JP, Tusa, BS, Ullah, I, Ullah, S, Umapathi, KK, Unnikrishnan, B, Anyasodor, AE, Upadhyay, E, Vacante, M, Vaezi, M, Valadan Tahbaz, S, Velazquez, DZ, Veroux, M, Violante, FS, Vlassov, V, Vo, B, Volovici, V, Aqeel, M, Vu, GT, Waheed, Y, Wamai, RG, Ward, P, Wen, YF, Westerman, R, Winkler, AS, Yadav, L, Yahyazadeh Jabbari, SH, Yang, L, Arabloo, J, Yaya, S, Yazie, TSY, Yeshaw, Y, Yonemoto, N, Younis, MZ, Yousefi, Z, Yu, C, Yuce, D, Yunusa, I, Zadnik, V, Arab-Zozani, M, Zare, F, Zastrozhin, MS, Zastrozhina, A, Zhang, J, Zhong, C, Zhou, L, Zhu, C, Ziapour, A, Zimmermann, IR, Fitzmaurice, C, Aremu, O, Murray, CJL, Force, LM, Ariffin, H, Aripov, T, Arshad, M, Artaman, A, Arulappan, J, Asemi, Z, Asghari Jafarabadi, M, Ashraf, T, Atorkey, P, Aujayeb, A, Ausloos, M, Awedew, AF, Ayala Quintanilla, BP, Ayenew, T, Azab, MA, Azadnajafabad, S, Azari Jafari, A, Azarian, G, Azzam, AY, Badiye, AD, Bahadory, S, Baig, AA, Baker, JL, Balakrishnan, S, Banach, M, Bärnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Behzadifar, M, Belgaumi, UI, Bezabhe, WMM, Bezabih, YM, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattacharyya, K, Global Burden of Disease 2019 Cancer Collaboration, Kocarnik, JM, Compton, K, Dean, FE, Fu, W, Gaw, BL, Harvey, JD, Henrikson, HJ, Lu, D, Pennini, A, Xu, R, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Bisignano, C, Bjørge, T, Bleyer, A, Blyuss, O, Bolarinwa, OA, Bolla, SR, Ababneh, E, Braithwaite, D, Brar, A, Brenner, H, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, Caetano Dos Santos, FL, Cao, Y, Carreras, G, Catalá-López, F, Abbasi-Kangevari, M, Cembranel, F, Cerin, E, Cernigliaro, A, Chakinala, RC, Chattu, SK, Chattu, VK, Chaturvedi, P, Chimed-Ochir, O, Cho, DY, Christopher, DJ, Abbastabar, H, Chu, D-T, Chung, MT, Conde, J, Cortés, S, Cortesi, PA, Costa, VM, Cunha, AR, Dadras, O, Dagnew, AB, Dahlawi, SMA, Abd-Elsalam, SM, Dai, X, Dandona, L, Dandona, R, Darwesh, AM, das Neves, J, De la Hoz, FP, Demis, AB, Denova-Gutiérrez, E, Dhamnetiya, D, Dhimal, ML, Abdoli, A, Dhimal, M, Dianatinasab, M, Diaz, D, Djalalinia, S, Do, HP, Doaei, S, Dorostkar, F, Dos Santos Figueiredo, FW, Driscoll, TR, Ebrahimi, H, Abedi, A, Eftekharzadeh, S, El Tantawi, M, El-Abid, H, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, SI, Eshrati, B, Eskandarieh, S, Esmaeilzadeh, F, Abidi, H, Etemadi, A, Ezzikouri, S, Faisaluddin, M, Faraon, EJA, Fares, J, Farzadfar, F, Feroze, AH, Ferrero, S, Ferro Desideri, L, Filip, I, Abolhassani, H, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gallus, S, Gaspar Fonseca, M, Getachew Obsa, A, Adedeji, IA, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gholamalizadeh, M, Gilani, SA, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Golechha, M, Goleij, P, Adnani, QES, Gomez, RS, Gopalani, SV, Gorini, G, Goudarzi, H, Grosso, G, Gubari, MIM, Guerra, MR, Guha, A, Gunasekera, DS, Gupta, B, Advani, SM, Gupta, VB, Gupta, VK, Gutiérrez, RA, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Afzal, MS, Hanif, A, Haque, S, Harlianto, NI, Haro, JM, Hasaballah, AI, Hassanipour, S, Hay, RJ, Hay, SI, Hayat, K, Heidari, G, Aghaali, M, Heidari, M, Herrera-Serna, BY, Herteliu, C, Hezam, K, Holla, R, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseini, M, Hosseinzadeh, M, Ahinkorah, BO, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hugo, FN, Hussain, R, Hussein, NR, Hwang, B-F, Iavicoli, I, Ahmad, S, Ibitoye, SE, Ida, F, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irham, LM, Islam, JY, Islam, RM, Islam, SMS, Ahmad, T, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jain, V, Jakovljevic, MB, Javaheri, T, Jayaram, S, Jazayeri, SB, Jha, RP, Ahmadi, A, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jürisson, M, Kabir, A, Kahrizi, D, Kalankesh, LR, Kalhor, R, Kaliyadan, F, Ahmadi, S, Kalkonde, Y, Kamath, A, Kameran Al-Salihi, N, Kandel, H, Kapoor, N, Karch, A, Kasa, AS, Katikireddi, SV, Kauppila, JH, Kavetskyy, T, Ahmed Rashid, T, Kebede, SA, Keshavarz, P, Keykhaei, M, Khader, YS, Khalilov, R, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khang, Y-H, Ahmed Salih, Y, Khater, AM, Khayamzadeh, M, Kim, GR, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kopec, JA, Koteeswaran, R, Koul, PA, Akalu, GT, Koulmane Laxminarayana, SL, Koyanagi, A, Kucuk Bicer, B, Kugbey, N, Kumar, GA, Kumar, N, Kurmi, OP, Kutluk, T, La Vecchia, C, Aklilu, A, Lami, FH, Landires, I, Lauriola, P, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YH, Leigh, J, Leong, E, Li, J, Akram, T, Li, M-C, Liu, X, Loureiro, JA, Lunevicius, R, Magdy Abd El Razek, M, Majeed, A, Makki, A, Male, S, Malik, AA, Mansournia, MA, Akunna, CJ, Martini, S, Masoumi, SZ, Mathur, P, McKee, M, Mehrotra, R, Mendoza, W, Menezes, RG, Mengesha, EW, Mesregah, MK, Mestrovic, T, Al Hamad, H, Miao Jonasson, J, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirzaei, H, Mirzaei, HR, Misra, S, Mithra, P, Moghadaszadeh, M, Alahdab, F, Mohammad, KA, Mohammad, Y, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Moka, N, Mokdad, AH, Molokhia, M, Monasta, L, Al-Aly, Z, Moni, MA, Moosavi, MA, Moradi, Y, Moraga, P, Morgado-da-Costa, J, Morrison, SD, Mosapour, A, Mubarik, S, Mwanri, L, Nagarajan, AJ, Ali, S, Nagaraju, SP, Nagata, C, Naimzada, MD, Nangia, V, Naqvi, AA, Narasimha Swamy, S, Ndejjo, R, Nduaguba, SO, Negoi, I, Negru, SM, Alimohamadi, Y, Neupane Kandel, S, Nguyen, CT, Nguyen, HLT, Niazi, RK, Nnaji, CA, Noor, NM, Nuñez-Samudio, V, Nzoputam, CI, Oancea, B, Ochir, C, Alipour, V, Odukoya, OO, Ogbo, FA, Olagunju, AT, Olakunde, BO, Omar, E, Omar Bali, A, Omonisi, AEE, Ong, S, Onwujekwe, OE, Orru, H, Aljunid, SM, Ortega-Altamirano, DV, Otstavnov, N, Otstavnov, SS, Owolabi, MO, P A, M, Padubidri, JR, Pakshir, K, Pana, A, Panagiotakos, D, Panda-Jonas, S, Alkhayyat, M, Pardhan, S, Park, E-C, Park, E-K, Pashazadeh Kan, F, Patel, HK, Patel, JR, Pati, S, Pattanshetty, SM, Paudel, U, Pereira, DM, Almasi-Hashiani, A, Pereira, RB, Perianayagam, A, Pillay, JD, Pirouzpanah, S, Pishgar, F, Podder, I, Postma, MJ, Pourjafar, H, Prashant, A, Preotescu, L, Almasri, NA, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Radhakrishnan, V, Rafiee, A, Rahim, F, Rahimzadeh, S, Rahman, M, Rahman, MA, Al-Maweri, SAA, Rahmani, AM, Rajai, N, Rajesh, A, Rakovac, I, Ram, P, Ramezanzadeh, K, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Almustanyir, S, Rawassizadeh, R, Razeghinia, MS, Renzaho, AMN, Rezaei, N, Rezapour, A, Roberts, TJ, Rodriguez, JAB, Rohloff, P, Romoli, M, Alonso, N, Ronfani, L, Roshandel, G, Rwegerera, GM, S, M, Sabour, S, Saddik, B, Saeed, U, Sahebkar, A, Sahoo, H, Salehi, S, Alvis-Guzman, N, Salem, MR, Salimzadeh, H, Samaei, M, Samy, AM, Sanabria, J, Sankararaman, S, Santric-Milicevic, MM, Sardiwalla, Y, Sarveazad, A, Sathian, B, Amu, H, Sawhney, M, Saylan, M, Schneider, IJC, Sekerija, M, Seylani, A, Shafaat, O, Shaghaghi, Z, Shaikh, MA, Shamsoddin, E, Shannawaz, M, Anbesu, EW, Sharma, R, Sheikh, A, Sheikhbahaei, S, Shetty, A, Shetty, JK, Shetty, PH, Shibuya, K, Shirkoohi, R, Shivakumar, KM, Shivarov, V, Ancuceanu, R, Siabani, S, Siddappa Malleshappa, SK, Silva, DAS, Singh, JA, Sintayehu, Y, Skryabin, VY, Skryabina, AA, Soeberg, MJ, Sofi-Mahmudi, A, Sotoudeh, H, Ansari, F, Steiropoulos, P, Straif, K, Subedi, R, Sufiyan, MB, Sultan, I, Sultana, S, Sur, D, Szerencsés, V, Szócska, M, Tabarés-Seisdedos, R, Ansari-Moghaddam, A, Tabuchi, T, Tadbiri, H, Taherkhani, A, Takahashi, K, Talaat, IM, Tan, K-K, Tat, VY, Tedla, BAA, Tefera, YG, Tehrani-Banihashemi, A, Antwi, MH, Temsah, M-H, Tesfay, FH, Tessema, GA, Thapar, R, Thavamani, A, Thoguluva Chandrasekar, V, Thomas, N, Tohidinik, HR, Touvier, M, Tovani-Palone, MR, Anvari, D, Traini, E, Tran, BX, Tran, KB, Tran, MTN, Tripathy, JP, Tusa, BS, Ullah, I, Ullah, S, Umapathi, KK, Unnikrishnan, B, Anyasodor, AE, Upadhyay, E, Vacante, M, Vaezi, M, Valadan Tahbaz, S, Velazquez, DZ, Veroux, M, Violante, FS, Vlassov, V, Vo, B, Volovici, V, Aqeel, M, Vu, GT, Waheed, Y, Wamai, RG, Ward, P, Wen, YF, Westerman, R, Winkler, AS, Yadav, L, Yahyazadeh Jabbari, SH, Yang, L, Arabloo, J, Yaya, S, Yazie, TSY, Yeshaw, Y, Yonemoto, N, Younis, MZ, Yousefi, Z, Yu, C, Yuce, D, Yunusa, I, Zadnik, V, Arab-Zozani, M, Zare, F, Zastrozhin, MS, Zastrozhina, A, Zhang, J, Zhong, C, Zhou, L, Zhu, C, Ziapour, A, Zimmermann, IR, Fitzmaurice, C, Aremu, O, Murray, CJL, Force, LM, Ariffin, H, Aripov, T, Arshad, M, Artaman, A, Arulappan, J, Asemi, Z, Asghari Jafarabadi, M, Ashraf, T, Atorkey, P, Aujayeb, A, Ausloos, M, Awedew, AF, Ayala Quintanilla, BP, Ayenew, T, Azab, MA, Azadnajafabad, S, Azari Jafari, A, Azarian, G, Azzam, AY, Badiye, AD, Bahadory, S, Baig, AA, Baker, JL, Balakrishnan, S, Banach, M, Bärnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Behzadifar, M, Belgaumi, UI, Bezabhe, WMM, Bezabih, YM, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, and Bhattacharyya, K
Abstract: Importance: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. Objective: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. Evidence Review: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). Findings: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low
Published: 2022

4. The prevalence of onchocerciasis in Africa and Yemen, 2000-2018: a geospatial analysis

Author: Schmidt, CA, Cromwell, EA, Hill, E, Donkers, KM, Schipp, MF, Johnson, KB, Pigott, DM, Abbas, J, Adekanmbi, V, Adetokunboh, OO, Ahmed, MB, Alanezi, FM, Alanzi, TM, Alipour, V, Andrei, CL, Andrei, T, Anvari, D, Appiah, SCY, Aqeel, M, Arabloo, J, Jafarabadi, MA, Ausloos, M, Baig, AA, Banach, M, Bärnighausen, TW, Bhattacharyya, K, Bhutta, ZA, Bijani, A, Brady, OJ, Bragazzi, NL, Butt, ZA, Carvalho, F, Chattu, VK, Dahlawi, SMA, Damiani, G, Demeke, FM, Deribe, K, Dharmaratne, SD, Diaz, D, Didarloo, A, Earl, L, Zaki, MES, El Tantawi, M, Fattahi, N, Fernandes, E, Foigt, NA, Foroutan, M, Franklin, RC, Guo, Y, Haj-Mirzaian, A, Hamidi, S, Hassankhani, H, Herteliu, C, Higazi, TB, Hosseini, M, Hosseinzadeh, M, Househ, M, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irvani, SSN, Jha, RP, Ji, JS, Jonas, JB, Jozwiak, JJ, Kalankesh, LR, Kamyari, N, Matin, BK, Karimi, SE, Kayode, GA, Karyani, AK, Khan, EA, Khan, MN, Khatab, K, Khater, MM, Kianipour, N, Kim, YJ, Kosen, S, Kusuma, D, La Vecchia, C, Lansingh, VC, Lee, PH, Li, S, Maleki, S, Mansournia, MA, Martins-Melo, FR, McAlinden, C, Mendoza, W, Mestrovic, T, Moghadaszadeh, M, Mohammadian-Hafshejani, A, Mohammadi, SM, Mohammed, S, Moradzadeh, R, Moraga, P, Naderi, M, Nagarajan, AJ, Negoi, I, Nguyen, CT, Nguyen, HLT, Oancea, B, Olagunju, AT, Bali, AO, Onwujekwe, OE, Pana, A, Rahimi-Movaghar, V, Ramezanzadeh, K, Rawaf, DL, Rawaf, S, Rawassizadeh, R, Rezapour, A, Ribeiro, AI, Samy, AM, Shaikh, MA, Sharafi, K, Sheikh, A, Singh, JA, Skiadaresi, E, Soltani, S, Stolk, WA, Sufiyan, MB, Thomson, AJ, Tran, BX, Tran, KB, Unnikrishnan, B, Violante, FS, Vu, GT, Yamada, T, Yaya, S, Yip, P, Yonemoto, N, Yu, C, Yu, Y, Zamanian, M, Zhang, Y, Zhang, Z-J, Ziapour, A, and Hay, SI
Subjects: Onchocerciasis, Geospatial model, Neglected tropical diseases, Ivermectin, Yemen, Settore MED/06 - Oncologia Medica, Settore MED/42 - Igiene Generale e Applicata, Nigeria, Bayes Theorem, General Medicine, Ghana, Settore MED/01 - Statistica Medica, General & Internal Medicine, Prevalence, Humans, 11 Medical and Health Sciences
Abstract: Background Onchocerciasis is a disease caused by infection with Onchocerca volvulus, which is transmitted to humans via the bite of several species of black fly, and is responsible for permanent blindness or vision loss, as well as severe skin disease. Predominantly endemic in parts of Africa and Yemen, preventive chemotherapy with mass drug administration of ivermectin is the primary intervention recommended for the elimination of its transmission. Methods A dataset of 18,116 geo-referenced prevalence survey datapoints was used to model annual 2000–2018 infection prevalence in Africa and Yemen. Using Bayesian model-based geostatistics, we generated spatially continuous estimates of all-age 2000–2018 onchocerciasis infection prevalence at the 5 × 5-km resolution as well as aggregations to the national level, along with corresponding estimates of the uncertainty in these predictions. Results As of 2018, the prevalence of onchocerciasis infection continues to be concentrated across central and western Africa, with the highest mean estimates at the national level in Ghana (12.2%, 95% uncertainty interval [UI] 5.0–22.7). Mean estimates exceed 5% infection prevalence at the national level for Cameroon, Central African Republic, Democratic Republic of the Congo (DRC), Guinea-Bissau, Sierra Leone, and South Sudan. Conclusions Our analysis suggests that onchocerciasis infection has declined over the last two decades throughout western and central Africa. Focal areas of Angola, Cameroon, the Democratic Republic of the Congo, Ethiopia, Ghana, Guinea, Mali, Nigeria, South Sudan, and Uganda continue to have mean microfiladermia prevalence estimates exceeding 25%. At and above this level, the continuation or initiation of mass drug administration with ivermectin is supported. If national programs aim to eliminate onchocerciasis infection, additional surveillance or supervision of areas of predicted high prevalence would be warranted to ensure sufficiently high coverage of program interventions.
Published: 2022

5. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019:A Systematic Analysis for the Global Burden of Disease Study 2019

Author: Global Burden of Disease 2019 Cancer Collaboration, Kocarnik, JM, Compton, K, Dean, FE, Fu, W, Gaw, BL, Harvey, JD, Henrikson, HJ, Lu, D, Pennini, A, Xu, R, Ababneh, E, Abbasi-Kangevari, M, Abbastabar, H, Abd-Elsalam, SM, Abdoli, A, Abedi, A, Abidi, H, Abolhassani, H, Adedeji, IA, Adnani, QES, Advani, SM, Afzal, MS, Aghaali, M, Ahinkorah, BO, Ahmad, S, Ahmad, T, Ahmadi, A, Ahmadi, S, Ahmed Rashid, T, Ahmed Salih, Y, Akalu, GT, Aklilu, A, Akram, T, Akunna, CJ, Al Hamad, H, Alahdab, F, Al-Aly, Z, Ali, S, Alimohamadi, Y, Alipour, V, Aljunid, SM, Alkhayyat, M, Almasi-Hashiani, A, Almasri, NA, Al-Maweri, SAA, Almustanyir, S, Alonso, N, Alvis-Guzman, N, Amu, H, Anbesu, EW, Ancuceanu, R, Ansari, F, Ansari-Moghaddam, A, Antwi, MH, Anvari, D, Anyasodor, AE, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aremu, O, Ariffin, H, Aripov, T, Arshad, M, Artaman, A, Arulappan, J, Asemi, Z, Asghari Jafarabadi, M, Ashraf, T, Atorkey, P, Aujayeb, A, Ausloos, M, Awedew, AF, Ayala Quintanilla, BP, Ayenew, T, Azab, MA, Azadnajafabad, S, Azari Jafari, A, Azarian, G, Azzam, AY, Badiye, AD, Bahadory, S, Baig, AA, Baker, JL, Balakrishnan, S, Banach, M, Bärnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Behzadifar, M, Belgaumi, UI, Bezabhe, WMM, Bezabih, YM, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Bisignano, C, Bjørge, T, Bleyer, A, Blyuss, O, Bolarinwa, OA, Bolla, SR, Braithwaite, D, Brar, A, Brenner, H, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, Caetano Dos Santos, FL, Cao, Y, Carreras, G, Catalá-López, F, Cembranel, F, Cerin, E, Cernigliaro, A, Chakinala, RC, Chattu, SK, Chattu, VK, Chaturvedi, P, Chimed-Ochir, O, Cho, DY, Christopher, DJ, Chu, D-T, Chung, MT, Conde, J, Cortés, S, Cortesi, PA, Costa, VM, Cunha, AR, Dadras, O, Dagnew, AB, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Darwesh, AM, Das Neves, J, De la Hoz, FP, Demis, AB, Denova-Gutiérrez, E, Dhamnetiya, D, Dhimal, ML, Dhimal, M, Dianatinasab, M, Diaz, D, Djalalinia, S, Do, HP, Doaei, S, Dorostkar, F, Dos Santos Figueiredo, FW, Driscoll, TR, Ebrahimi, H, Eftekharzadeh, S, El Tantawi, M, El-Abid, H, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, SI, Eshrati, B, Eskandarieh, S, Esmaeilzadeh, F, Etemadi, A, Ezzikouri, S, Faisaluddin, M, Faraon, EJA, Fares, J, Farzadfar, F, Feroze, AH, Ferrero, S, Ferro Desideri, L, Filip, I, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gallus, S, Gaspar Fonseca, M, Getachew Obsa, A, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gholamalizadeh, M, Gilani, SA, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Golechha, M, Goleij, P, Gomez, RS, Gopalani, SV, Gorini, G, Goudarzi, H, Grosso, G, Gubari, MIM, Guerra, MR, Guha, A, Gunasekera, DS, Gupta, B, Gupta, VB, Gupta, VK, Gutiérrez, RA, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hanif, A, Haque, S, Harlianto, NI, Haro, JM, Hasaballah, AI, Hassanipour, S, Hay, RJ, Hay, SI, Hayat, K, Heidari, G, Heidari, M, Herrera-Serna, BY, Herteliu, C, Hezam, K, Holla, R, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hugo, FN, Hussain, R, Hussein, NR, Hwang, B-F, Iavicoli, I, Ibitoye, SE, Ida, F, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irham, LM, Islam, JY, Islam, RM, Islam, SMS, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jain, V, Jakovljevic, MB, Javaheri, T, Jayaram, S, Jazayeri, SB, Jha, RP, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jürisson, M, Kabir, A, Kahrizi, D, Kalankesh, LR, Kalhor, R, Kaliyadan, F, Kalkonde, Y, Kamath, A, Kameran Al-Salihi, N, Kandel, H, Kapoor, N, Karch, A, Kasa, AS, Katikireddi, SV, Kauppila, JH, Kavetskyy, T, Kebede, SA, Keshavarz, P, Keykhaei, M, Khader, YS, Khalilov, R, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khang, Y-H, Khater, AM, Khayamzadeh, M, Kim, GR, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kopec, JA, Koteeswaran, R, Koul, PA, Koulmane Laxminarayana, SL, Koyanagi, A, Kucuk Bicer, B, Kugbey, N, Kumar, GA, Kumar, N, Kurmi, OP, Kutluk, T, La Vecchia, C, Lami, FH, Landires, I, Lauriola, P, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YH, Leigh, J, Leong, E, Li, J, Li, M-C, Liu, X, Loureiro, JA, Lunevicius, R, Magdy Abd El Razek, M, Majeed, A, Makki, A, Male, S, Malik, AA, Mansournia, MA, Martini, S, Masoumi, SZ, Mathur, P, McKee, M, Mehrotra, R, Mendoza, W, Menezes, RG, Mengesha, EW, Mesregah, MK, Mestrovic, T, Miao Jonasson, J, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirzaei, H, Mirzaei, HR, Misra, S, Mithra, P, Moghadaszadeh, M, Mohammad, KA, Mohammad, Y, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Moka, N, Mokdad, AH, Molokhia, M, Monasta, L, Moni, MA, Moosavi, MA, Moradi, Y, Moraga, P, Morgado-da-Costa, J, Morrison, SD, Mosapour, A, Mubarik, S, Mwanri, L, Nagarajan, AJ, Nagaraju, SP, Nagata, C, Naimzada, MD, Nangia, V, Naqvi, AA, Narasimha Swamy, S, Ndejjo, R, Nduaguba, SO, Negoi, I, Negru, SM, Neupane Kandel, S, Nguyen, CT, Nguyen, HLT, Niazi, RK, Nnaji, CA, Noor, NM, Nuñez-Samudio, V, Nzoputam, CI, Oancea, B, Ochir, C, Odukoya, OO, Ogbo, FA, Olagunju, AT, Olakunde, BO, Omar, E, Omar Bali, A, Omonisi, AEE, Ong, S, Onwujekwe, OE, Orru, H, Ortega-Altamirano, DV, Otstavnov, N, Otstavnov, SS, Owolabi, MO, P A, M, Padubidri, JR, Pakshir, K, Pana, A, Panagiotakos, D, Panda-Jonas, S, Pardhan, S, Park, E-C, Park, E-K, Pashazadeh Kan, F, Patel, HK, Patel, JR, Pati, S, Pattanshetty, SM, Paudel, U, Pereira, DM, Pereira, RB, Perianayagam, A, Pillay, JD, Pirouzpanah, S, Pishgar, F, Podder, I, Postma, MJ, Pourjafar, H, Prashant, A, Preotescu, L, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Radhakrishnan, V, Rafiee, A, Rahim, F, Rahimzadeh, S, Rahman, M, Rahman, MA, Rahmani, AM, Rajai, N, Rajesh, A, Rakovac, I, Ram, P, Ramezanzadeh, K, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rawassizadeh, R, Razeghinia, MS, Renzaho, AMN, Rezaei, N, Rezapour, 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Y., Mohammadi M., Mohammadi S.M., Mohammadian-Hafshejani A., Mohammed S., Moka N., Mokdad A.H., Molokhia M., Monasta L., Moni M.A., Moosavi M.A., Moradi Y., Moraga P., Morgado-Da-Costa J., Morrison S.D., Mosapour A., Mubarik S., Mwanri L., Nagarajan A.J., Nagaraju S.P., Nagata C., Naimzada M.D., Nangia V., Naqvi A.A., Narasimha Swamy S., Ndejjo R., Nduaguba S.O., Negoi I., Negru S.M., Neupane Kandel S., Nguyen C.T., Nguyen H.L.T., Niazi R.K., Nnaji C.A., Noor N.M., Nunez-Samudio V., Nzoputam C.I., Oancea B., Ochir C., Odukoya O.O., Ogbo F.A., Olagunju A.T., Olakunde B.O., Omar E., Omar Bali A.O., Omonisi A.E.E., Ong S., Onwujekwe O.E., Orru H., Ortega-Altamirano D.V., Otstavnov N., Otstavnov S.S., Owolabi M.O., P A M., Padubidri J.R., Pakshir K., Pana A., Panagiotakos D., Panda-Jonas S., Pardhan S., Park E.-C., Park E.-K., Pashazadeh Kan F., Patel H.K., Patel J.R., Pati S., Pattanshetty S.M., Paudel U., Pereira D.M., Pereira R.B., Perianayagam A., Pillay J.D., Pirouzpanah S., Pishgar F., Podder I., Postma M.J., Pourjafar H., Prashant A., Preotescu L., Rabiee M., Rabiee N., Radfar A., Radhakrishnan R.A., Radhakrishnan V., Rafiee A., Rahim F., Rahimzadeh S., Rahman M., Rahman M.A., Rahmani A.M., Rajai N., Rajesh A., Rakovac I., Ram P., Ramezanzadeh K., Ranabhat K., Ranasinghe P., Rao C.R., Rao S.J., Rawassizadeh R., Razeghinia M.S., Renzaho A.M.N., Rezaei N., Rezapour A., Roberts T.J., Rodriguez J.A.B., Rohloff P., Romoli M., Ronfani L., Roshandel G., Rwegerera G.M., Manjula S., Sabour S., Saddik B., Saeed U., Sahebkar A., Sahoo H., Salehi S., Salem M.R., Salimzadeh H., Samaei M., Samy A.M., Sanabria J., Sankararaman S., Santric-Milicevic M.M., Sardiwalla Y., Sarveazad A., Sathian B., Sawhney M., Saylan M., Schneider I.J.J., Sekerija M., Seylani A., Shafaat O., Shaghaghi Z., Shaikh M.A., Shamsoddin E., Shannawaz M., Sharma R., Sheikh A., Sheikhbahaei S., Shetty A., Shetty J.K., Shetty P.H., Shibuya K., Shirkoohi R., Shivakumar K.M., Shivarov V., Siabani S., Siddappa Malleshappa S.K., Silva D.A.S., Singh J.A., Sintayehu Y., Skryabin V.Y., Skryabina A.A., Soeberg M.J., Sofi-Mahmudi A., Sotoudeh H., Steiropoulos P., Straif K., Subedi R., Sufiyan M.B., Sultan I., Sultana S., Sur D., Szerencses V., Szocska M., Tabares-Seisdedos R., Tabuchi T., Tadbiri H., Taherkhani A., Takahashi K., Talaat I.M., Tan K.-K., Tat V.Y., Tedla B.A.A., Tefera Y.G., Tehrani-Banihashemi A., Temsah M., Tesfay F.H., Tessema G.A., Thapar R., Thavamani A., Thoguluva Chandrasekar V., Thomas N., Tohidinik H.R., Touvier M., Tovani-Palone M.R., Traini E., Tran B.X., Tran K.B., Tran M.T.N., Tripathy J.P., Tusa B.S., Ullah I., Ullah S., Umapathi K.K., Unnikrishnan B., Upadhyay E., Vacante M., Vaezi M., Valadan Tahbaz S., Velazquez D.Z., Veroux M., Violante F.S., Vlassov V., Vo B., Volovici V., Vu G.T., Waheed Y., Wamai R.G., Ward P., Wen Y.F., Westerman R., Winkler A.S., Yadav L., Yahyazadeh Jabbari S.H., Yang L., Yaya S., Yazie T.S.Y., Yeshaw Y., Yonemoto N., Younis M.Z., Yousefi Z., Yu C., Yuce D., Yunusa I., Zadnik V., Zare F., Zastrozhin M.S., Zastrozhina A., Zhang J., Zhong C., Zhou L., Zhu C., Ziapour A., Zimmermann I.R., Fitzmaurice C., Murray C.J.L., Force L.M., Bill & Melinda Gates Foundation, American Lebanese Syrian Associated Charities, Kuwait University (Kuwait), National University of Malaysia (Malasia), Alexander von Humboldt Foundation, Federal Ministry of Education & Research (Alemania), NIH - National Cancer Institute (NCI) (Estados Unidos), Unión Europea. Comisión Europea. European Research Council (ERC), Unión Europea. Comisión Europea. H2020, Novo Nordisk Foundation, National Health and Medical Research Council (Australia), Jazan University (Arabia Saudí), Romanian National Authority for Scientific Research and Innovation, Ministry of Education (Brasil), National Heart Foundation of Australia, Ministry of Education, Science and Technological Development (Serbia), NHS - Research Scotland (Reino Unido), Medical Research Council (Reino Unido), Scottish Government (Reino Unido), Jatiya Kabi Kazi Nazrul Islam University (Bangladesh), Xiamen University (Malasia), Manipal Academy of Higher Education (India), Sistema Nacional de Investigación (Panamá), Secretaría Nacional de Ciencia, Tecnología e Innovación (Panamá), King College London, National Health Service (Reino Unido), Government of the Russian Federation, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), National Council for Scientific and Technological Development (Brasil), Cancer Prevention and Research Institute of Texas (Estados Unidos), Fundação para a Ciência e Tecnologia (Portugal), Neurosurgery, Kocarnik, J. M., Compton, K., Dean, F. E., Fu, W., Gaw, B. L., Harvey, J. D., Henrikson, H. J., Lu, D., Pennini, A., Xu, R., Ababneh, E., Abbasi-Kangevari, M., Abbastabar, H., Abd-Elsalam, S. M., Abdoli, A., Abedi, A., Abidi, H., Abolhassani, H., Adedeji, I. A., Adnani, Q. E. S., Advani, S. M., Afzal, M. S., Aghaali, M., Ahinkorah, B. O., Ahmad, S., Ahmad, T., Ahmadi, A., Ahmadi, S., Ahmed Rashid, T., Ahmed Salih, Y., Akalu, G. T., Aklilu, A., Akram, T., Akunna, C. J., Al Hamad, H., Alahdab, F., Al-Aly, Z., Ali, S., Alimohamadi, Y., Alipour, V., Aljunid, S. M., Alkhayyat, M., Almasi-Hashiani, A., Almasri, N. A., Al-Maweri, S. A. A., Almustanyir, S., Alonso, N., Alvis-Guzman, N., Amu, H., Anbesu, E. W., Ancuceanu, R., Ansari, F., Ansari-Moghaddam, A., Antwi, M. H., Anvari, D., Anyasodor, A. 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A., Narasimha Swamy, S., Ndejjo, R., Nduaguba, S. O., Negoi, I., Negru, S. M., Neupane Kandel, S., Nguyen, C. T., Nguyen, H. L. T., Niazi, R. K., Nnaji, C. A., Noor, N. M., Nunez-Samudio, V., Nzoputam, C. I., Oancea, B., Ochir, C., Odukoya, O. O., Ogbo, F. A., Olagunju, A. T., Olakunde, B. O., Omar, E., Omar Bali, A., Omonisi, A. E. E., Ong, S., Onwujekwe, O. E., Orru, H., Ortega-Altamirano, D. V., Otstavnov, N., Otstavnov, S. S., Owolabi, M. O., P A, M., Padubidri, J. R., Pakshir, K., Pana, A., Panagiotakos, D., Panda-Jonas, S., Pardhan, S., Park, E. -C., Park, E. -K., Pashazadeh Kan, F., Patel, H. K., Patel, J. R., Pati, S., Pattanshetty, S. M., Paudel, U., Pereira, D. M., Pereira, R. B., Perianayagam, A., Pillay, J. D., Pirouzpanah, S., Pishgar, F., Podder, I., Postma, M. J., Pourjafar, H., Prashant, A., Preotescu, L., Rabiee, M., Rabiee, N., Radfar, A., Radhakrishnan, R. A., Radhakrishnan, V., Rafiee, A., Rahim, F., Rahimzadeh, S., Rahman, M., Rahman, M. A., Rahmani, A. M., Rajai, N., Rajesh, A., Rakovac, I., Ram, P., Ramezanzadeh, K., Ranabhat, K., Ranasinghe, P., Rao, C. R., Rao, S. J., Rawassizadeh, R., Razeghinia, M. S., Renzaho, A. M. N., Rezaei, N., Rezapour, A., Roberts, T. J., Rodriguez, J. A. B., Rohloff, P., Romoli, M., Ronfani, L., Roshandel, G., Rwegerera, G. M., Manjula, S., Sabour, S., Saddik, B., Saeed, U., Sahebkar, A., Sahoo, H., Salehi, S., Salem, M. R., Salimzadeh, H., Samaei, M., Samy, A. M., Sanabria, J., Sankararaman, S., Santric-Milicevic, M. M., Sardiwalla, Y., Sarveazad, A., Sathian, B., Sawhney, M., Saylan, M., Schneider, I. J. C., Sekerija, M., Seylani, A., Shafaat, O., Shaghaghi, Z., Shaikh, M. A., Shamsoddin, E., Shannawaz, M., Sharma, R., Sheikh, A., Sheikhbahaei, S., Shetty, A., Shetty, J. K., Shetty, P. H., Shibuya, K., Shirkoohi, R., Shivakumar, K. M., Shivarov, V., Siabani, S., Siddappa Malleshappa, S. K., Silva, D. A. S., Singh, J. A., Sintayehu, Y., Skryabin, V. Y., Skryabina, A. A., Soeberg, M. J., Sofi-Mahmudi, A., Sotoudeh, H., Steiropoulos, P., Straif, K., Subedi, R., Sufiyan, M. B., Sultan, I., Sultana, S., Sur, D., Szerencses, V., Szocska, M., Tabares-Seisdedos, R., Tabuchi, T., Tadbiri, H., Taherkhani, A., Takahashi, K., Talaat, I. M., Tan, K. -K., Tat, V. Y., Tedla, B. A. A., Tefera, Y. G., Tehrani-Banihashemi, A., Temsah, M. -H., Tesfay, F. H., Tessema, G. A., Thapar, R., Thavamani, A., Thoguluva Chandrasekar, V., Thomas, N., Tohidinik, H. R., Touvier, M., Tovani-Palone, M. R., Traini, E., Tran, B. X., Tran, K. B., Tran, M. T. N., Tripathy, J. P., Tusa, B. S., Ullah, I., Ullah, S., Umapathi, K. K., Unnikrishnan, B., Upadhyay, E., Vacante, M., Vaezi, M., Valadan Tahbaz, S., Velazquez, D. Z., Veroux, M., Violante, F. S., Vlassov, V., Vo, B., Volovici, V., Vu, G. T., Waheed, Y., Wamai, R. G., Ward, P., Wen, Y. F., Westerman, R., Winkler, A. S., Yadav, L., Yahyazadeh Jabbari, S. H., Yang, L., Yaya, S., Yazie, T. S. Y., Yeshaw, Y., Yonemoto, N., Younis, M. Z., Yousefi, Z., Yu, C., Yuce, D., Yunusa, I., Zadnik, V., Zare, F., Zastrozhin, M. S., Zastrozhina, A., Zhang, J., Zhong, C., Zhou, L., Zhu, C., Ziapour, A., Zimmermann, I. R., Fitzmaurice, C., Murray, C. J. L., and Force, L. M.
Subjects: Cancer Research, GBD, 195 COUNTRIES, Global Health, 1117 Public Health and Health Services, Global Burden of Disease, SDG 3 - Good Health and Well-being, WORLDWIDE, Risk Factors, Neoplasms, SURVEILLANCE, SUPPORT, Global Burden of Disease 2019 Cancer Collaboration, Prevalence, Online First, cancer, Humans, 1112 Oncology and Carcinogenesis, public health, cancer, burden of diseases, PROGRESS, Original Investigation, Global burden, Science & Technology, CHALLENGES, Research, Incidence, Medisinske Fag: 700::Klinisk medisinske fag: 750::Onkologi: 762 [VDP], COVID-19, 1112 Oncology and Carcinogenesis, 1117 Public Health and Health Services, Disability-Adjusted Life Years, mortality, STATISTICS, Oncology, Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019 A Systematic Analysis for the Global Burden of Disease Study 2019, HEALTH-CARE, TERRITORIES, Quality-Adjusted Life Years, Life Sciences & Biomedicine, Comments
Abstract: Key Points Question What was the burden of cancer globally and across Sociodemographic Index (SDI) groupings in 2019, and how has incidence, morbidity, and mortality changed since 2010? Findings In this systematic analysis, there were 23.6 million new global cancer cases in 2019 (17.2 million when excluding those with nonmelanoma skin cancer), 10.0 million cancer deaths, and an estimated 250 million disability-adjusted life years estimated to be due to cancer; since 2010, these represent increases of 26.3%, 20.9%, and 16.0%, respectively. Absolute cancer burden increased in all SDI quintiles since 2010, but the largest percentage increases occurred in the low and low-middle SDI quintiles. Meanings The study results suggest that increased cancer prevention and control efforts are needed to equitably address the evolving and increasing burden of cancer across the SDI spectrum., Importance The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. Objective To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. Evidence Review The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). Findings In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. Conclusions and Relevance The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world., The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 examines cancer burden and trends globally for 204 countries and territories and by Socio-demographic Index quintiles from 2010 to 2019.
Published: 2022
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6. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.

Author: Global Burden of Disease 2019 Cancer Collaboration, Kocarnik, JM, Compton, K, Dean, FE, Fu, W, Gaw, BL, Harvey, JD, Henrikson, HJ, Lu, D, Pennini, A, Xu, R, Ababneh, E, Abbasi-Kangevari, M, Abbastabar, H, Abd-Elsalam, SM, Abdoli, A, Abedi, A, Abidi, H, Abolhassani, H, Adedeji, IA, Adnani, QES, Advani, SM, Afzal, MS, Aghaali, M, Ahinkorah, BO, Ahmad, S, Ahmad, T, Ahmadi, A, Ahmadi, S, Ahmed Rashid, T, Ahmed Salih, Y, Akalu, GT, Aklilu, A, Akram, T, Akunna, CJ, Al Hamad, H, Alahdab, F, Al-Aly, Z, Ali, S, Alimohamadi, Y, Alipour, V, Aljunid, SM, Alkhayyat, M, Almasi-Hashiani, A, Almasri, NA, Al-Maweri, SAA, Almustanyir, S, Alonso, N, Alvis-Guzman, N, Amu, H, Anbesu, EW, Ancuceanu, R, Ansari, F, Ansari-Moghaddam, A, Antwi, MH, Anvari, D, Anyasodor, AE, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aremu, O, Ariffin, H, Aripov, T, Arshad, M, Artaman, A, Arulappan, J, Asemi, Z, Asghari Jafarabadi, M, Ashraf, T, Atorkey, P, Aujayeb, A, Ausloos, M, Awedew, AF, Ayala Quintanilla, BP, Ayenew, T, Azab, MA, Azadnajafabad, S, Azari Jafari, A, Azarian, G, Azzam, AY, Badiye, AD, Bahadory, S, Baig, AA, Baker, JL, Balakrishnan, S, Banach, M, Bärnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Behzadifar, M, Belgaumi, UI, Bezabhe, WMM, Bezabih, YM, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Bisignano, C, Bjørge, T, Bleyer, A, Blyuss, O, Bolarinwa, OA, Bolla, SR, Braithwaite, D, Brar, A, Brenner, H, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, Caetano Dos Santos, FL, Cao, Y, Carreras, G, Catalá-López, F, Cembranel, F, Cerin, E, Cernigliaro, A, Chakinala, RC, Chattu, SK, Chattu, VK, Chaturvedi, P, Chimed-Ochir, O, Cho, DY, Christopher, DJ, Chu, D-T, Chung, MT, Conde, J, Cortés, S, Cortesi, PA, Costa, VM, Cunha, AR, Dadras, O, Dagnew, AB, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Darwesh, AM, das Neves, J, De la Hoz, FP, Demis, AB, Denova-Gutiérrez, E, Dhamnetiya, D, Dhimal, ML, Dhimal, M, Dianatinasab, M, Diaz, D, Djalalinia, S, Do, HP, Doaei, S, Dorostkar, F, Dos Santos Figueiredo, FW, Driscoll, TR, Ebrahimi, H, Eftekharzadeh, S, El Tantawi, M, El-Abid, H, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, SI, Eshrati, B, Eskandarieh, S, Esmaeilzadeh, F, Etemadi, A, Ezzikouri, S, Faisaluddin, M, Faraon, EJA, Fares, J, Farzadfar, F, Feroze, AH, Ferrero, S, Ferro Desideri, L, Filip, I, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gallus, S, Gaspar Fonseca, M, Getachew Obsa, A, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gholamalizadeh, M, Gilani, SA, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Golechha, M, Goleij, P, Gomez, RS, Gopalani, SV, Gorini, G, Goudarzi, H, Grosso, G, Gubari, MIM, Guerra, MR, Guha, A, Gunasekera, DS, Gupta, B, Gupta, VB, Gupta, VK, Gutiérrez, RA, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hanif, A, Haque, S, Harlianto, NI, Haro, JM, Hasaballah, AI, Hassanipour, S, Hay, RJ, Hay, SI, Hayat, K, Heidari, G, Heidari, M, Herrera-Serna, BY, Herteliu, C, Hezam, K, Holla, R, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hugo, FN, Hussain, R, Hussein, NR, Hwang, B-F, Iavicoli, I, Ibitoye, SE, Ida, F, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irham, LM, Islam, JY, Islam, RM, Islam, Shariful, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jain, V, Jakovljevic, MB, Javaheri, T, Jayaram, S, Jazayeri, SB, Jha, RP, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jürisson, M, Kabir, A, Kahrizi, D, Kalankesh, LR, Kalhor, R, Kaliyadan, F, Kalkonde, Y, Kamath, A, Kameran Al-Salihi, N, Kandel, H, Kapoor, N, Karch, A, Kasa, AS, Katikireddi, SV, Kauppila, JH, Kavetskyy, T, Kebede, SA, Keshavarz, P, Keykhaei, M, Khader, YS, Khalilov, R, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khang, Y-H, Khater, AM, Khayamzadeh, M, Kim, GR, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kopec, JA, Koteeswaran, R, Koul, PA, Koulmane Laxminarayana, SL, Koyanagi, A, Kucuk Bicer, B, Kugbey, N, Kumar, GA, Kumar, N, Kurmi, OP, Kutluk, T, La Vecchia, C, Lami, FH, Landires, I, Lauriola, P, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YH, Leigh, J, Leong, E, Li, J, Li, M-C, Liu, X, Loureiro, JA, Lunevicius, R, Magdy Abd El Razek, M, Majeed, A, Makki, A, Male, S, Malik, AA, Mansournia, MA, Martini, S, Masoumi, SZ, Mathur, P, McKee, M, Mehrotra, R, Mendoza, W, Menezes, RG, Mengesha, EW, Mesregah, MK, Mestrovic, T, Miao Jonasson, J, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirzaei, H, Mirzaei, HR, Misra, S, Mithra, P, Moghadaszadeh, M, Mohammad, KA, Mohammad, Y, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Moka, N, Mokdad, AH, Molokhia, M, Monasta, L, Moni, MA, Moosavi, MA, Moradi, Y, Moraga, P, Morgado-da-Costa, J, Morrison, SD, Mosapour, A, Mubarik, S, Mwanri, L, Nagarajan, AJ, Nagaraju, SP, Nagata, C, Naimzada, MD, Nangia, V, Naqvi, AA, Narasimha Swamy, S, Ndejjo, R, Nduaguba, SO, Negoi, I, Negru, SM, Neupane Kandel, S, Nguyen, CT, Nguyen, HLT, Niazi, RK, Nnaji, CA, Noor, NM, Nuñez-Samudio, V, Nzoputam, CI, Oancea, B, Ochir, C, Odukoya, OO, Ogbo, FA, Olagunju, AT, Olakunde, BO, Omar, E, Omar Bali, A, Omonisi, AEE, Ong, S, Onwujekwe, OE, Orru, H, Ortega-Altamirano, DV, Otstavnov, N, Otstavnov, SS, Owolabi, MO, P A, M, Padubidri, JR, Pakshir, K, Pana, A, Panagiotakos, D, Panda-Jonas, S, Pardhan, S, Park, E-C, Park, E-K, Pashazadeh Kan, F, Patel, HK, Patel, JR, Pati, S, Pattanshetty, SM, Paudel, U, Pereira, DM, Pereira, RB, Perianayagam, A, Pillay, JD, Pirouzpanah, S, Pishgar, F, Podder, I, Postma, MJ, Pourjafar, H, Prashant, A, Preotescu, L, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Radhakrishnan, V, Rafiee, A, Rahim, F, Rahimzadeh, S, Rahman, M, Rahman, MA, Rahmani, AM, Rajai, N, Rajesh, A, Rakovac, I, Ram, P, Ramezanzadeh, K, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rawassizadeh, R, Razeghinia, MS, Renzaho, AMN, Rezaei, N, Rezapour, A, Roberts, TJ, Rodriguez, JAB, Rohloff, P, Romoli, M, Ronfani, L, Roshandel, G, Rwegerera, GM, S, M, Sabour, S, Saddik, B, Saeed, U, Sahebkar, A, Sahoo, H, Salehi, S, Salem, MR, Salimzadeh, H, Samaei, M, Samy, AM, Sanabria, J, Sankararaman, S, Santric-Milicevic, MM, Sardiwalla, Y, Sarveazad, A, Sathian, B, Sawhney, M, Saylan, M, Schneider, IJC, Sekerija, M, Seylani, A, Shafaat, O, Shaghaghi, Z, Shaikh, MA, Shamsoddin, E, Shannawaz, M, Sharma, R, Sheikh, A, Sheikhbahaei, S, Shetty, A, Shetty, JK, Shetty, PH, Shibuya, K, Shirkoohi, R, Shivakumar, KM, Shivarov, V, Siabani, S, Siddappa Malleshappa, SK, Silva, DAS, Singh, JA, Sintayehu, Y, Skryabin, VY, Skryabina, AA, Soeberg, MJ, Sofi-Mahmudi, A, Sotoudeh, H, Steiropoulos, P, Straif, K, Subedi, R, Sufiyan, MB, Sultan, I, Sultana, S, Sur, D, Szerencsés, V, Szócska, M, Tabarés-Seisdedos, R, Tabuchi, T, Tadbiri, H, Taherkhani, A, Takahashi, K, Talaat, IM, Tan, K-K, Tat, VY, Tedla, BAA, Tefera, YG, Tehrani-Banihashemi, A, Temsah, M-H, Tesfay, FH, Tessema, GA, Thapar, R, Thavamani, A, Thoguluva Chandrasekar, V, Thomas, N, Tohidinik, HR, Touvier, M, Tovani-Palone, MR, Traini, E, Tran, BX, Tran, KB, Tran, MTN, Tripathy, JP, Tusa, BS, Ullah, I, Ullah, S, Umapathi, KK, Unnikrishnan, B, Upadhyay, E, Vacante, M, Vaezi, M, Valadan Tahbaz, S, Velazquez, DZ, Veroux, M, Violante, FS, Vlassov, V, Vo, B, Volovici, V, Vu, GT, Waheed, Y, Wamai, RG, Ward, P, Wen, YF, Westerman, R, Winkler, AS, Yadav, L, Yahyazadeh Jabbari, SH, Yang, L, Yaya, S, Yazie, TSY, Yeshaw, Y, Yonemoto, N, Younis, MZ, Yousefi, Z, Yu, C, Yuce, D, Yunusa, I, Zadnik, V, Zare, F, Zastrozhin, MS, Zastrozhina, A, Zhang, J, Zhong, C, Zhou, L, Zhu, C, Ziapour, A, Zimmermann, IR, Fitzmaurice, C, Murray, CJL, Force, LM, Global Burden of Disease 2019 Cancer Collaboration, Kocarnik, JM, Compton, K, Dean, FE, Fu, W, Gaw, BL, Harvey, JD, Henrikson, HJ, Lu, D, Pennini, A, Xu, R, Ababneh, E, Abbasi-Kangevari, M, Abbastabar, H, Abd-Elsalam, SM, Abdoli, A, Abedi, A, Abidi, H, Abolhassani, H, Adedeji, IA, Adnani, QES, Advani, SM, Afzal, MS, Aghaali, M, Ahinkorah, BO, Ahmad, S, Ahmad, T, Ahmadi, A, Ahmadi, S, Ahmed Rashid, T, Ahmed Salih, Y, Akalu, GT, Aklilu, A, Akram, T, Akunna, CJ, Al Hamad, H, Alahdab, F, Al-Aly, Z, Ali, S, Alimohamadi, Y, Alipour, V, Aljunid, SM, Alkhayyat, M, Almasi-Hashiani, A, Almasri, NA, Al-Maweri, SAA, Almustanyir, S, Alonso, N, Alvis-Guzman, N, Amu, H, Anbesu, EW, Ancuceanu, R, Ansari, F, Ansari-Moghaddam, A, Antwi, MH, Anvari, D, Anyasodor, AE, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aremu, O, Ariffin, H, Aripov, T, Arshad, M, Artaman, A, Arulappan, J, Asemi, Z, Asghari Jafarabadi, M, Ashraf, T, Atorkey, P, Aujayeb, A, Ausloos, M, Awedew, AF, Ayala Quintanilla, BP, Ayenew, T, Azab, MA, Azadnajafabad, S, Azari Jafari, A, Azarian, G, Azzam, AY, Badiye, AD, Bahadory, S, Baig, AA, Baker, JL, Balakrishnan, S, Banach, M, Bärnighausen, TW, Barone-Adesi, F, Barra, F, Barrow, A, Behzadifar, M, Belgaumi, UI, Bezabhe, WMM, Bezabih, YM, Bhagat, DS, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bibi, S, Bijani, A, Biondi, A, Bisignano, C, Bjørge, T, Bleyer, A, Blyuss, O, Bolarinwa, OA, Bolla, SR, Braithwaite, D, Brar, A, Brenner, H, Bustamante-Teixeira, MT, Butt, NS, Butt, ZA, Caetano Dos Santos, FL, Cao, Y, Carreras, G, Catalá-López, F, Cembranel, F, Cerin, E, Cernigliaro, A, Chakinala, RC, Chattu, SK, Chattu, VK, Chaturvedi, P, Chimed-Ochir, O, Cho, DY, Christopher, DJ, Chu, D-T, Chung, MT, Conde, J, Cortés, S, Cortesi, PA, Costa, VM, Cunha, AR, Dadras, O, Dagnew, AB, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Darwesh, AM, das Neves, J, De la Hoz, FP, Demis, AB, Denova-Gutiérrez, E, Dhamnetiya, D, Dhimal, ML, Dhimal, M, Dianatinasab, M, Diaz, D, Djalalinia, S, Do, HP, Doaei, S, Dorostkar, F, Dos Santos Figueiredo, FW, Driscoll, TR, Ebrahimi, H, Eftekharzadeh, S, El Tantawi, M, El-Abid, H, Elbarazi, I, Elhabashy, HR, Elhadi, M, El-Jaafary, SI, Eshrati, B, Eskandarieh, S, Esmaeilzadeh, F, Etemadi, A, Ezzikouri, S, Faisaluddin, M, Faraon, EJA, Fares, J, Farzadfar, F, Feroze, AH, Ferrero, S, Ferro Desideri, L, Filip, I, Fischer, F, Fisher, JL, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gallus, S, Gaspar Fonseca, M, Getachew Obsa, A, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gholamalizadeh, M, Gilani, SA, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Golechha, M, Goleij, P, Gomez, RS, Gopalani, SV, Gorini, G, Goudarzi, H, Grosso, G, Gubari, MIM, Guerra, MR, Guha, A, Gunasekera, DS, Gupta, B, Gupta, VB, Gupta, VK, Gutiérrez, RA, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hanif, A, Haque, S, Harlianto, NI, Haro, JM, Hasaballah, AI, Hassanipour, S, Hay, RJ, Hay, SI, Hayat, K, Heidari, G, Heidari, M, Herrera-Serna, BY, Herteliu, C, Hezam, K, Holla, R, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Huang, J, Hugo, FN, Hussain, R, Hussein, NR, Hwang, B-F, Iavicoli, I, Ibitoye, SE, Ida, F, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irham, LM, Islam, JY, Islam, RM, Islam, Shariful, Ismail, NE, Isola, G, Iwagami, M, Jacob, L, Jain, V, Jakovljevic, MB, Javaheri, T, Jayaram, S, Jazayeri, SB, Jha, RP, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jürisson, M, Kabir, A, Kahrizi, D, Kalankesh, LR, Kalhor, R, Kaliyadan, F, Kalkonde, Y, Kamath, A, Kameran Al-Salihi, N, Kandel, H, Kapoor, N, Karch, A, Kasa, AS, Katikireddi, SV, Kauppila, JH, Kavetskyy, T, Kebede, SA, Keshavarz, P, Keykhaei, M, Khader, YS, Khalilov, R, Khan, G, Khan, M, Khan, MN, Khan, MAB, Khang, Y-H, Khater, AM, Khayamzadeh, M, Kim, GR, Kim, YJ, Kisa, A, Kisa, S, Kissimova-Skarbek, K, Kopec, JA, Koteeswaran, R, Koul, PA, Koulmane Laxminarayana, SL, Koyanagi, A, Kucuk Bicer, B, Kugbey, N, Kumar, GA, Kumar, N, Kurmi, OP, Kutluk, T, La Vecchia, C, Lami, FH, Landires, I, Lauriola, P, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YH, Leigh, J, Leong, E, Li, J, Li, M-C, Liu, X, Loureiro, JA, Lunevicius, R, Magdy Abd El Razek, M, Majeed, A, Makki, A, Male, S, Malik, AA, Mansournia, MA, Martini, S, Masoumi, SZ, Mathur, P, McKee, M, Mehrotra, R, Mendoza, W, Menezes, RG, Mengesha, EW, Mesregah, MK, Mestrovic, T, Miao Jonasson, J, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirzaei, H, Mirzaei, HR, Misra, S, Mithra, P, Moghadaszadeh, M, Mohammad, KA, Mohammad, Y, Mohammadi, M, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Moka, N, Mokdad, AH, Molokhia, M, Monasta, L, Moni, MA, Moosavi, MA, Moradi, Y, Moraga, P, Morgado-da-Costa, J, Morrison, SD, Mosapour, A, Mubarik, S, Mwanri, L, Nagarajan, AJ, Nagaraju, SP, Nagata, C, Naimzada, MD, Nangia, V, Naqvi, AA, Narasimha Swamy, S, Ndejjo, R, Nduaguba, SO, Negoi, I, Negru, SM, Neupane Kandel, S, Nguyen, CT, Nguyen, HLT, Niazi, RK, Nnaji, CA, Noor, NM, Nuñez-Samudio, V, Nzoputam, CI, Oancea, B, Ochir, C, Odukoya, OO, Ogbo, FA, Olagunju, AT, Olakunde, BO, Omar, E, Omar Bali, A, Omonisi, AEE, Ong, S, Onwujekwe, OE, Orru, H, Ortega-Altamirano, DV, Otstavnov, N, Otstavnov, SS, Owolabi, MO, P A, M, Padubidri, JR, Pakshir, K, Pana, A, Panagiotakos, D, Panda-Jonas, S, Pardhan, S, Park, E-C, Park, E-K, Pashazadeh Kan, F, Patel, HK, Patel, JR, Pati, S, Pattanshetty, SM, Paudel, U, Pereira, DM, Pereira, RB, Perianayagam, A, Pillay, JD, Pirouzpanah, S, Pishgar, F, Podder, I, Postma, MJ, Pourjafar, H, Prashant, A, Preotescu, L, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Radhakrishnan, V, Rafiee, A, Rahim, F, Rahimzadeh, S, Rahman, M, Rahman, MA, Rahmani, AM, Rajai, N, Rajesh, A, Rakovac, I, Ram, P, Ramezanzadeh, K, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rawassizadeh, R, Razeghinia, MS, Renzaho, AMN, Rezaei, N, Rezapour, A, Roberts, TJ, Rodriguez, JAB, Rohloff, P, Romoli, M, Ronfani, L, Roshandel, G, Rwegerera, GM, S, M, Sabour, S, Saddik, B, Saeed, U, Sahebkar, A, Sahoo, H, Salehi, S, Salem, MR, Salimzadeh, H, Samaei, M, Samy, AM, Sanabria, J, Sankararaman, S, Santric-Milicevic, MM, Sardiwalla, Y, Sarveazad, A, Sathian, B, Sawhney, M, Saylan, M, Schneider, IJC, Sekerija, M, Seylani, A, Shafaat, O, Shaghaghi, Z, Shaikh, MA, Shamsoddin, E, Shannawaz, M, Sharma, R, Sheikh, A, Sheikhbahaei, S, Shetty, A, Shetty, JK, Shetty, PH, Shibuya, K, Shirkoohi, R, Shivakumar, KM, Shivarov, V, Siabani, S, Siddappa Malleshappa, SK, Silva, DAS, Singh, JA, Sintayehu, Y, Skryabin, VY, Skryabina, AA, Soeberg, MJ, Sofi-Mahmudi, A, Sotoudeh, H, Steiropoulos, P, Straif, K, Subedi, R, Sufiyan, MB, Sultan, I, Sultana, S, Sur, D, Szerencsés, V, Szócska, M, Tabarés-Seisdedos, R, Tabuchi, T, Tadbiri, H, Taherkhani, A, Takahashi, K, Talaat, IM, Tan, K-K, Tat, VY, Tedla, BAA, Tefera, YG, Tehrani-Banihashemi, A, Temsah, M-H, Tesfay, FH, Tessema, GA, Thapar, R, Thavamani, A, Thoguluva Chandrasekar, V, Thomas, N, Tohidinik, HR, Touvier, M, Tovani-Palone, MR, Traini, E, Tran, BX, Tran, KB, Tran, MTN, Tripathy, JP, Tusa, BS, Ullah, I, Ullah, S, Umapathi, KK, Unnikrishnan, B, Upadhyay, E, Vacante, M, Vaezi, M, Valadan Tahbaz, S, Velazquez, DZ, Veroux, M, Violante, FS, Vlassov, V, Vo, B, Volovici, V, Vu, GT, Waheed, Y, Wamai, RG, Ward, P, Wen, YF, Westerman, R, Winkler, AS, Yadav, L, Yahyazadeh Jabbari, SH, Yang, L, Yaya, S, Yazie, TSY, Yeshaw, Y, Yonemoto, N, Younis, MZ, Yousefi, Z, Yu, C, Yuce, D, Yunusa, I, Zadnik, V, Zare, F, Zastrozhin, MS, Zastrozhina, A, Zhang, J, Zhong, C, Zhou, L, Zhu, C, Ziapour, A, Zimmermann, IR, Fitzmaurice, C, Murray, CJL, and Force, LM
Abstract: Importance: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. Objective: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. Evidence Review: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). Findings: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low
Published: 2021

7. Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Author: Paulson, KR, Kamath, AM, Alam, T, Bienhoff, K, Abady, GG, Abbas, J, Abbasi-Kangevari, M, Abbastabar, H, Abd-Allah, F, Abd-Elsalam, SM, Abdoli, A, Abedi, A, Abolhassani, H, Abreu, LG, Abu-Gharbieh, E, Abu-Rmeileh, NME, Abushouk, A, Adamu, AL, Adebayo, OM, Adegbosin, AE, Adekanmbi, V, Adetokunboh, OO, Adeyinka, DA, Adsuar, JC, Afshari, K, Aghaali, M, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, T, Ahmadi, K, Ahmed, MB, Aji, B, Akalu, Y, Akinyemi, OO, Aklilu, A, Al-Aly, Z, Alam, K, Alanezi, FM, Alanzi, TM, Alcalde-Rabanal, JE, Al-Eyadhy, A, Ali, T, Alicandro, G, Alif, SM, Alipour, V, Alizade, H, Aljunid, SM, Almasi-Hashiani, A, Almasri, NA, Al-Mekhlafi, HM, Alonso, J, Al-Raddadi, RM, Altirkawi, KA, Alumran, AK, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Ameyaw, EK, Amini, S, Amini-Rarani, M, Amit, AML, Amugsi, DA, Ancuceanu, R, Anderlini, D, Andrei, CL, Ansari, F, Ansari-Moghaddam, A, Antonio, CAT, Antriyandarti, E, Anvari, D, Anwer, R, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aripov, T, Arnlov, J, Artanti, KD, Arzani, A, Asaad, M, Asadi-Aliabadi, M, Asadi-Pooya, AA, Jafarabadi, MA, Athari, SS, Athari, SM, Atnafu, DD, Atreya, A, Atteraya, MS, Ausloos, M, Awan, AT, Quintanilla, BPA, Ayano, G, Ayanore, MA, Aynalem, YA, Azari, S, Azarian, G, Azene, ZN, Darshan, BB, Babaee, E, Badiye, AD, Baig, AA, Banach, M, Banik, PC, Barker-Collo, SL, Barqawi, HJ, Bassat, Q, Basu, S, Baune, BT, Bayati, M, Bedi, N, Beghi, E, Beghi, M, Bell, ML, Bendak, S, Bennett, DA, Bensenor, IM, Berhe, K, Berman, AE, Bezabih, YM, Bhagavathula, AS, Bhandari, D, Bhardwaj, N, Bhardwaj, P, Bhattacharyya, K, Bhattarai, S, Bhutta, ZA, Bikbov, B, Biondi, A, Birihane, BM, Biswas, RK, Bohlouli, S, Bragazzi, NL, Breusov, A, Brunoni, AR, Burkart, K, Nagaraja, SB, Busse, R, Butt, ZA, dos Santos, FLC, Cahuana-Hurtado, L, Camargos, P, Camera, LA, Cardenas, R, Carreras, G, Carrero, JJ, Carvalho, F, Castaldelli-Maia, JM, Castaneda-Orjuela, CA, Castelpietra, G, Cerin, E, Chang, J-C, Chanie, WF, Charan, J, Chatterjee, S, Chattu, SK, Chattu, VK, Chaturvedi, S, Chen, S, Cho, DY, Choi, J-YJ, Chu, D-T, Ciobanu, LG, Cirillo, M, Conde, J, Costa, VM, Couto, RAS, Dachew, BA, Dahlawi, SMA, Dai, H, Dai, X, Dandona, L, Dandona, R, Daneshpajouhnejad, P, Darmstadt, GL, Das, JK, Davila-Cervantes, CA, Davis, AC, Davletov, K, De la Hoz, FP, De Leo, D, Deeba, F, Denova-Gutierrez, E, Dervenis, N, Desalew, A, Deuba, K, Dey, S, Dharmaratne, SD, Dhingra, S, Dhungana, GP, da Silva, DD, Diaz, D, Dorostkar, F, Doshmangir, L, Dubljanin, E, Duraes, AR, Eagan, AW, Edinur, HA, Efendi, F, Eftekharzadeh, S, El Sayed, I, El Tantawi, M, Elbarazi, I, Elgendy, IY, El-Jaafary, S, Emami, A, Enany, S, Eyawo, O, Ezzikouri, S, Faris, PS, Farzadfar, F, Fattahi, N, Fauk, NK, Fazlzadeh, M, Feigin, VL, Ferede, TY, Fereshtehnejad, S-M, Fernandes, E, Ferrara, P, Filip, I, Fischer, F, Fisher, JL, Foigt, NA, Folayan, MO, Foroutan, M, Franklin, RC, Freitas, M, Friedman, SD, Fukumoto, T, Gad, MM, Gaidhane, AM, Gaidhane, S, Gaihre, S, Gallus, S, Garcia-Basteiro, AL, Garcia-Gordillo, M, Gardner, WM, Fonseca, MG, Gebremedhin, KB, Getacher, L, Ghashghaee, A, Gholamian, A, Gilani, SA, Gill, TK, Giussani, G, Gnedovskaya, E, Godinho, MA, Goel, A, Golechha, M, Gona, PN, Gopalani, SV, Goudarzi, H, Grivna, M, Gugnani, HC, Guido, D, Guimaraes, RA, Das Gupta, R, Gupta, R, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Hamidi, S, Hanif, A, Hankey, GJ, Hargono, A, Hasaballah, A, Hasan, MM, Hasan, SS, Hassan, A, Hassanipour, S, Hassankhani, H, Havmoeller, RJ, Hayat, K, Heidari-Soureshjani, R, Henry, NJ, Herteliu, C, Hole, MK, Holla, R, Hossain, N, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Huang, J, Humayun, A, Hwang, B-F, Iavicoli, I, Ibitoye, SE, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Inamdar, S, Inbaraj, LR, Iqbal, K, Iqbal, U, Islam, MM, Shariful Islam, Sheikh, Iso, H, Iwagami, M, Iwu, CCD, Jaafari, J, Jacobsen, KH, Jagnoor, J, Jain, V, Janodia, MD, Javaheri, T, Javanmardi, F, Jayaram, S, Jayatilleke, AU, Jenabi, E, Jha, RP, Ji, JS, John, O, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jozwiak, JJ, Jurisson, M, Kabir, A, Kabir, Z, Kalankesh, LR, Kamyari, N, Kanchan, T, Kapoor, N, Matin, BK, Karch, A, Karimi, SE, Kassahun, G, Kayode, GA, Karyani, AK, Kemmer, L, Khalid, N, Khalilov, R, Khammarnia, M, Khan, EA, Khan, G, Khan, M, Khan, MN, Khang, Y-H, Khatab, K, Khater, AM, Khater, MM, Khayamzadeh, M, Khosravi, A, Kim, D, Kim, Y-E, Kim, YJ, Kimokoti, RW, Kisa, A, Kisa, S, Kissoon, N, Kopec, JA, Kosen, S, Koul, PA, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Krishnamoorthy, V, Defo, BK, Bicer, BK, Kulkarni, V, Kumar, GA, Kumar, M, Kumar, N, Kurmi, OP, Kusuma, D, La Vecchia, C, Lacey, B, Lalloo, R, Lami, FH, Landires, I, Larsson, AO, Lasrado, S, Lassi, ZS, Lauriola, P, Lee, PH, Lee, SWH, Lee, YH, Leigh, J, Leonardi, M, Lewycka, S, Li, B, Li, S, Liang, J, Lim, L-L, Limenih, MA, Lin, R-T, Liu, X, Lodha, R, Lopez, AD, Lozano, R, Lugo, A, Lunevicius, R, Mackay, MT, Kunjathur, SM, Magnani, FG, Prasad, DRM, Maheri, M, Mahmoudi, M, Majeed, A, Maled, V, Maleki, A, Maleki, S, Malekzadeh, R, Malik, AA, Malta, DC, Mamun, AA, Mansouri, B, Mansournia, MA, Martinez, G, Martini, S, Martins-Melo, FR, Masoumi, SZ, Maulik, PK, McAlinden, C, McGrath, JJ, Medina-Solis, CE, Nasab, EM, Mejia-Rodriguez, F, Memish, ZA, Mendoza, W, Menezes, RG, Mengesha, EW, Mensah, GA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mestrovic, T, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mini, GK, Miri, M, Mirica, A, Mirrakhimov, EM, Mirzaei, H, Mirzaei, M, Moazen, B, Moghadaszadeh, M, Mohajer, B, Mohamad, O, Mohammad, Y, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Mokdad, AH, Molokhia, M, Monasta, L, Mondello, S, Moni, MA, Moore, CE, Moradi, G, Moradi, M, Moradzadeh, R, Moraga, P, Morawska, L, Morrison, SD, Mosser, JF, Khaneghah, AM, Mustafa, G, Naderi, M, Nagarajan, AJ, Nagaraju, SP, Naghavi, M, Naghshtabrizi, B, Naimzada, MD, Nangia, V, Swamy, SN, Nascimento, BR, Naveed, M, Nazari, J, Ndejjo, R, Negoi, I, Negoi, RI, Nena, E, Nepal, S, Netsere, HB, Nguefack-Tsague, G, Ngunjiri, JW, Chi, TYN, Cuong, TN, Huong, LTN, Nigatu, YT, Nigussie, SN, Nixon, MR, Nnaji, CA, Nomura, S, Noor, NM, Noubiap, JJ, Nunez-Samudio, V, Nwatah, VE, Oancea, B, Odukoya, OO, Ogbo, FA, Olusanya, BO, Olusanya, JO, Bali, AO, Onwujekwe, OE, Ortiz, A, Otoiu, A, Otstavnov, N, Otstavnov, SS, Owolabi, MO, Mahesh, PA, Padubidri, JR, Pakhale, S, Pakshir, K, Pal, PK, Palladino, R, Pana, A, Panda-Jonas, S, Pandey, A, Pandi-Perumal, SR, Pangaribuan, HU, Pardo-Montano, AM, Park, E-K, Patel, SK, Patton, GC, Pawar, S, Toroudi, HP, Peden, AE, Pepito, VCF, Peprah, EK, Pereira, J, Perez-Gomez, J, Perico, N, Pesudovs, K, Pilgrim, T, Pinheiro, M, Piradov, MA, Pirsaheb, M, Platts-Mills, JA, Pokhrel, KN, Postma, MJ, Pourjafar, H, Prada, S, Prakash, S, Pupillo, E, Syed, ZQ, Rabiee, N, Radfar, A, Rafiee, A, Rafiei, A, Raggi, A, Rahimzadeh, S, Rahman, MHU, Rahmani, AM, Ramezanzadeh, K, Rana, J, Ranabhat, CL, Rao, SJ, Rasella, D, Rastogi, P, Rathi, P, Rawaf, DL, Rawaf, S, Rawasia, WF, Rawassizadeh, R, Jr, RCR, Remuzzi, G, Renzaho, AMN, Reshmi, B, Resnikoff, S, Rezaei, N, Rezapour, A, Riahi, SM, Ribeiro, D, Rickard, J, Roever, L, Ronfani, L, Rothenbacher, D, Rubagotti, E, Rumisha, SF, Ryan, PM, Saddik, B, Sadeghi, E, Moghaddam, SS, Sagar, R, Sahebkar, A, Salahshoor, MR, Salehi, S, Salem, MR, Salimzadeh, H, Salomon, JA, Samodra, YL, Samy, AM, Sanabria, J, Santric-Milicevic, MM, Saraswathy, SYI, Sarker, AR, Sarrafzadegan, N, Sarveazad, A, Sathian, B, Sathish, T, Sattin, D, Saxena, S, Saya, GK, Saylan, M, Schiavolin, S, Schlaich, MP, Schwebel, DC, Schwendicke, F, Senthilkumaran, S, Sepanlou, SG, Servan-Mori, E, Sha, F, Shafaat, O, Shahabi, S, Shahbaz, M, Shaheen, AA, Shahid, I, Shaikh, MA, Shakiba, S, Shalash, AS, Shams-Beyranvand, M, Shannawaz, M, Sharafi, K, Sheikh, A, Sheikhbahaei, S, Shiferaw, WS, Shigematsu, M, Shin, JI, Shiri, R, Shiue, I, Shuval, K, Siddiqi, TJ, Sidemo, NB, Sigfusdottir, ID, Sigurvinsdottir, R, Silva, JP, Silverberg, JIS, Simonetti, B, Singh, BB, Singh, JA, Singhal, D, Sinha, DN, Skiadaresi, E, Skryabin, VY, Skryabina, AA, Sleet, DA, Sobaih, BH, Sobhiyeh, MR, Soltani, S, Soriano, JB, Spurlock, EE, Sreeramareddy, CT, Steiropoulos, P, Stokes, Mark, Stortecky, S, Sufiyan, MB, Abdulkader, RS, Sulo, G, Swope, CB, Sykes, BL, Szeto, MD, Szocska, M, Tabares-Seisdedos, R, Tadesse, EG, Taherkhani, A, Tamiru, AT, Tareque, MI, Tehrani-Banihashemi, A, Temsah, M-H, Tesfay, FH, Tessema, GA, Tessema, ZT, Thankappan, KR, Thapar, R, Tolani, MA, Tovani-Palone, MR, Traini, E, Bach, XT, Tripathy, JP, Tsapparellas, G, Tsatsakis, A, Car, LT, Uddin, Riaz, Ullah, A, Umeokonkwo, CD, Unim, B, Unnikrishnan, B, Upadhyay, E, Usman, MS, Vacante, M, Vaezi, M, Tahbaz, SV, Valdez, PR, Vasankari, TJ, Venketasubramanian, N, Verma, M, Violante, FS, Vlassov, V, Vo, B, Giang, TV, Wado, YD, Waheed, Y, Wamai, RG, Wang, Y, Wang, Y-P, Ward, P, Werdecker, A, Westerman, R, Wickramasinghe, ND, Wilner, LB, Wiysonge, CS, Wu, A-M, Wu, C, Xie, Y, Jabbari, SHY, Yamagishi, K, Yandrapalli, S, Yaya, S, Yazdi-Feyzabadi, V, Yip, P, Yonemoto, N, Yoon, S-J, Younis, MZ, Yousefi, Z, Yousefinezhadi, T, Yu, C, Yusuf, SS, Zaidi, SS, Bin Zaman, S, Zamani, M, Zamanian, M, Zastrozhin, MS, Zastrozhina, A, Zhang, Y, Zhang, Z-J, Zhao, X-JG, Ziapour, A, Hay, S, Murray, CJL, Wang, H, Kassebaum, NJ, Paulson, KR, Kamath, AM, Alam, T, Bienhoff, K, Abady, GG, Abbas, J, Abbasi-Kangevari, M, Abbastabar, H, Abd-Allah, F, Abd-Elsalam, SM, Abdoli, A, Abedi, A, Abolhassani, H, Abreu, LG, Abu-Gharbieh, E, Abu-Rmeileh, NME, Abushouk, A, Adamu, AL, Adebayo, OM, Adegbosin, AE, Adekanmbi, V, Adetokunboh, OO, Adeyinka, DA, Adsuar, JC, Afshari, K, Aghaali, M, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, T, Ahmadi, K, Ahmed, MB, Aji, B, Akalu, Y, Akinyemi, OO, Aklilu, A, Al-Aly, Z, Alam, K, Alanezi, FM, Alanzi, TM, Alcalde-Rabanal, JE, Al-Eyadhy, A, Ali, T, Alicandro, G, Alif, SM, Alipour, V, Alizade, H, Aljunid, SM, Almasi-Hashiani, A, Almasri, NA, Al-Mekhlafi, HM, Alonso, J, Al-Raddadi, RM, Altirkawi, KA, Alumran, AK, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Ameyaw, EK, Amini, S, Amini-Rarani, M, Amit, AML, Amugsi, DA, Ancuceanu, R, Anderlini, D, Andrei, CL, Ansari, F, Ansari-Moghaddam, A, Antonio, CAT, Antriyandarti, E, Anvari, D, Anwer, R, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aripov, T, Arnlov, J, Artanti, KD, Arzani, A, Asaad, M, Asadi-Aliabadi, M, Asadi-Pooya, AA, Jafarabadi, MA, Athari, SS, Athari, SM, Atnafu, DD, Atreya, A, Atteraya, MS, Ausloos, M, Awan, AT, Quintanilla, BPA, Ayano, G, Ayanore, MA, Aynalem, YA, Azari, S, Azarian, G, Azene, ZN, Darshan, BB, Babaee, E, Badiye, AD, Baig, AA, Banach, M, Banik, PC, Barker-Collo, SL, Barqawi, HJ, Bassat, Q, Basu, S, Baune, BT, Bayati, M, Bedi, N, Beghi, E, Beghi, M, Bell, ML, Bendak, S, Bennett, DA, Bensenor, IM, Berhe, K, Berman, AE, Bezabih, YM, Bhagavathula, AS, Bhandari, D, Bhardwaj, N, Bhardwaj, P, Bhattacharyya, K, Bhattarai, S, Bhutta, ZA, Bikbov, B, Biondi, A, Birihane, BM, Biswas, RK, Bohlouli, S, Bragazzi, NL, Breusov, A, Brunoni, AR, Burkart, K, Nagaraja, SB, Busse, R, Butt, ZA, dos Santos, FLC, Cahuana-Hurtado, L, Camargos, P, Camera, LA, Cardenas, R, Carreras, G, Carrero, JJ, Carvalho, F, Castaldelli-Maia, JM, Castaneda-Orjuela, CA, Castelpietra, G, Cerin, E, Chang, J-C, Chanie, WF, Charan, J, Chatterjee, S, Chattu, SK, Chattu, VK, Chaturvedi, S, Chen, S, Cho, DY, Choi, J-YJ, Chu, D-T, Ciobanu, LG, Cirillo, M, Conde, J, Costa, VM, Couto, RAS, Dachew, BA, Dahlawi, SMA, Dai, H, Dai, X, Dandona, L, Dandona, R, Daneshpajouhnejad, P, Darmstadt, GL, Das, JK, Davila-Cervantes, CA, Davis, AC, Davletov, K, De la Hoz, FP, De Leo, D, Deeba, F, Denova-Gutierrez, E, Dervenis, N, Desalew, A, Deuba, K, Dey, S, Dharmaratne, SD, Dhingra, S, Dhungana, GP, da Silva, DD, Diaz, D, Dorostkar, F, Doshmangir, L, Dubljanin, E, Duraes, AR, Eagan, AW, Edinur, HA, Efendi, F, Eftekharzadeh, S, El Sayed, I, El Tantawi, M, Elbarazi, I, Elgendy, IY, El-Jaafary, S, Emami, A, Enany, S, Eyawo, O, Ezzikouri, S, Faris, PS, Farzadfar, F, Fattahi, N, Fauk, NK, Fazlzadeh, M, Feigin, VL, Ferede, TY, Fereshtehnejad, S-M, Fernandes, E, Ferrara, P, Filip, I, Fischer, F, Fisher, JL, Foigt, NA, Folayan, MO, Foroutan, M, Franklin, RC, Freitas, M, Friedman, SD, Fukumoto, T, Gad, MM, Gaidhane, AM, Gaidhane, S, Gaihre, S, Gallus, S, Garcia-Basteiro, AL, Garcia-Gordillo, M, Gardner, WM, Fonseca, MG, Gebremedhin, KB, Getacher, L, Ghashghaee, A, Gholamian, A, Gilani, SA, Gill, TK, Giussani, G, Gnedovskaya, E, Godinho, MA, Goel, A, Golechha, M, Gona, PN, Gopalani, SV, Goudarzi, H, Grivna, M, Gugnani, HC, Guido, D, Guimaraes, RA, Das Gupta, R, Gupta, R, Hafezi-Nejad, N, Haider, MR, Haj-Mirzaian, A, Hamidi, S, Hanif, A, Hankey, GJ, Hargono, A, Hasaballah, A, Hasan, MM, Hasan, SS, Hassan, A, Hassanipour, S, Hassankhani, H, Havmoeller, RJ, Hayat, K, Heidari-Soureshjani, R, Henry, NJ, Herteliu, C, Hole, MK, Holla, R, Hossain, N, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Huang, J, Humayun, A, Hwang, B-F, Iavicoli, I, Ibitoye, SE, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Inamdar, S, Inbaraj, LR, Iqbal, K, Iqbal, U, Islam, MM, Shariful Islam, Sheikh, Iso, H, Iwagami, M, Iwu, CCD, Jaafari, J, Jacobsen, KH, Jagnoor, J, Jain, V, Janodia, MD, Javaheri, T, Javanmardi, F, Jayaram, S, Jayatilleke, AU, Jenabi, E, Jha, RP, Ji, JS, John, O, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jozwiak, JJ, Jurisson, M, Kabir, A, Kabir, Z, Kalankesh, LR, Kamyari, N, Kanchan, T, Kapoor, N, Matin, BK, Karch, A, Karimi, SE, Kassahun, G, Kayode, GA, Karyani, AK, Kemmer, L, Khalid, N, Khalilov, R, Khammarnia, M, Khan, EA, Khan, G, Khan, M, Khan, MN, Khang, Y-H, Khatab, K, Khater, AM, Khater, MM, Khayamzadeh, M, Khosravi, A, Kim, D, Kim, Y-E, Kim, YJ, Kimokoti, RW, Kisa, A, Kisa, S, Kissoon, N, Kopec, JA, Kosen, S, Koul, PA, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Krishnamoorthy, V, Defo, BK, Bicer, BK, Kulkarni, V, Kumar, GA, Kumar, M, Kumar, N, Kurmi, OP, Kusuma, D, La Vecchia, C, Lacey, B, Lalloo, R, Lami, FH, Landires, I, Larsson, AO, Lasrado, S, Lassi, ZS, Lauriola, P, Lee, PH, Lee, SWH, Lee, YH, Leigh, J, Leonardi, M, Lewycka, S, Li, B, Li, S, Liang, J, Lim, L-L, Limenih, MA, Lin, R-T, Liu, X, Lodha, R, Lopez, AD, Lozano, R, Lugo, A, Lunevicius, R, Mackay, MT, Kunjathur, SM, Magnani, FG, Prasad, DRM, Maheri, M, Mahmoudi, M, Majeed, A, Maled, V, Maleki, A, Maleki, S, Malekzadeh, R, Malik, AA, Malta, DC, Mamun, AA, Mansouri, B, Mansournia, MA, Martinez, G, Martini, S, Martins-Melo, FR, Masoumi, SZ, Maulik, PK, McAlinden, C, McGrath, JJ, Medina-Solis, CE, Nasab, EM, Mejia-Rodriguez, F, Memish, ZA, Mendoza, W, Menezes, RG, Mengesha, EW, Mensah, GA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mestrovic, T, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mini, GK, Miri, M, Mirica, A, Mirrakhimov, EM, Mirzaei, H, Mirzaei, M, Moazen, B, Moghadaszadeh, M, Mohajer, B, Mohamad, O, Mohammad, Y, Mohammadi, SM, Mohammadian-Hafshejani, A, Mohammed, S, Mokdad, AH, Molokhia, M, Monasta, L, Mondello, S, Moni, MA, Moore, CE, Moradi, G, Moradi, M, Moradzadeh, R, Moraga, P, Morawska, L, Morrison, SD, Mosser, JF, Khaneghah, AM, Mustafa, G, Naderi, M, Nagarajan, AJ, Nagaraju, SP, Naghavi, M, Naghshtabrizi, B, Naimzada, MD, Nangia, V, Swamy, SN, Nascimento, BR, Naveed, M, Nazari, J, Ndejjo, R, Negoi, I, Negoi, RI, Nena, E, Nepal, S, Netsere, HB, Nguefack-Tsague, G, Ngunjiri, JW, Chi, TYN, Cuong, TN, Huong, LTN, Nigatu, YT, Nigussie, SN, Nixon, MR, Nnaji, CA, Nomura, S, Noor, NM, Noubiap, JJ, Nunez-Samudio, V, Nwatah, VE, Oancea, B, Odukoya, OO, Ogbo, FA, Olusanya, BO, Olusanya, JO, Bali, AO, Onwujekwe, OE, Ortiz, A, Otoiu, A, Otstavnov, N, Otstavnov, SS, Owolabi, MO, Mahesh, PA, Padubidri, JR, Pakhale, S, Pakshir, K, Pal, PK, Palladino, R, Pana, A, Panda-Jonas, S, Pandey, A, Pandi-Perumal, SR, Pangaribuan, HU, Pardo-Montano, AM, Park, E-K, Patel, SK, Patton, GC, Pawar, S, Toroudi, HP, Peden, AE, Pepito, VCF, Peprah, EK, Pereira, J, Perez-Gomez, J, Perico, N, Pesudovs, K, Pilgrim, T, Pinheiro, M, Piradov, MA, Pirsaheb, M, Platts-Mills, JA, Pokhrel, KN, Postma, MJ, Pourjafar, H, Prada, S, Prakash, S, Pupillo, E, Syed, ZQ, Rabiee, N, Radfar, A, Rafiee, A, Rafiei, A, Raggi, A, Rahimzadeh, S, Rahman, MHU, Rahmani, AM, Ramezanzadeh, K, Rana, J, Ranabhat, CL, Rao, SJ, Rasella, D, Rastogi, P, Rathi, P, Rawaf, DL, Rawaf, S, Rawasia, WF, Rawassizadeh, R, Jr, RCR, Remuzzi, G, Renzaho, AMN, Reshmi, B, Resnikoff, S, Rezaei, N, Rezapour, A, Riahi, SM, Ribeiro, D, Rickard, J, Roever, L, Ronfani, L, Rothenbacher, D, Rubagotti, E, Rumisha, SF, Ryan, PM, Saddik, B, Sadeghi, E, Moghaddam, SS, Sagar, R, Sahebkar, A, Salahshoor, MR, Salehi, S, Salem, MR, Salimzadeh, H, Salomon, JA, Samodra, YL, Samy, AM, Sanabria, J, Santric-Milicevic, MM, Saraswathy, SYI, Sarker, AR, Sarrafzadegan, N, Sarveazad, A, Sathian, B, Sathish, T, Sattin, D, Saxena, S, Saya, GK, Saylan, M, Schiavolin, S, Schlaich, MP, Schwebel, DC, Schwendicke, F, Senthilkumaran, S, Sepanlou, SG, Servan-Mori, E, Sha, F, Shafaat, O, Shahabi, S, Shahbaz, M, Shaheen, AA, Shahid, I, Shaikh, MA, Shakiba, S, Shalash, AS, Shams-Beyranvand, M, Shannawaz, M, Sharafi, K, Sheikh, A, Sheikhbahaei, S, Shiferaw, WS, Shigematsu, M, Shin, JI, Shiri, R, Shiue, I, Shuval, K, Siddiqi, TJ, Sidemo, NB, Sigfusdottir, ID, Sigurvinsdottir, R, Silva, JP, Silverberg, JIS, Simonetti, B, Singh, BB, Singh, JA, Singhal, D, Sinha, DN, Skiadaresi, E, Skryabin, VY, Skryabina, AA, Sleet, DA, Sobaih, BH, Sobhiyeh, MR, Soltani, S, Soriano, JB, Spurlock, EE, Sreeramareddy, CT, Steiropoulos, P, Stokes, Mark, Stortecky, S, Sufiyan, MB, Abdulkader, RS, Sulo, G, Swope, CB, Sykes, BL, Szeto, MD, Szocska, M, Tabares-Seisdedos, R, Tadesse, EG, Taherkhani, A, Tamiru, AT, Tareque, MI, Tehrani-Banihashemi, A, Temsah, M-H, Tesfay, FH, Tessema, GA, Tessema, ZT, Thankappan, KR, Thapar, R, Tolani, MA, Tovani-Palone, MR, Traini, E, Bach, XT, Tripathy, JP, Tsapparellas, G, Tsatsakis, A, Car, LT, Uddin, Riaz, Ullah, A, Umeokonkwo, CD, Unim, B, Unnikrishnan, B, Upadhyay, E, Usman, MS, Vacante, M, Vaezi, M, Tahbaz, SV, Valdez, PR, Vasankari, TJ, Venketasubramanian, N, Verma, M, Violante, FS, Vlassov, V, Vo, B, Giang, TV, Wado, YD, Waheed, Y, Wamai, RG, Wang, Y, Wang, Y-P, Ward, P, Werdecker, A, Westerman, R, Wickramasinghe, ND, Wilner, LB, Wiysonge, CS, Wu, A-M, Wu, C, Xie, Y, Jabbari, SHY, Yamagishi, K, Yandrapalli, S, Yaya, S, Yazdi-Feyzabadi, V, Yip, P, Yonemoto, N, Yoon, S-J, Younis, MZ, Yousefi, Z, Yousefinezhadi, T, Yu, C, Yusuf, SS, Zaidi, SS, Bin Zaman, S, Zamani, M, Zamanian, M, Zastrozhin, MS, Zastrozhina, A, Zhang, Y, Zhang, Z-J, Zhao, X-JG, Ziapour, A, Hay, S, Murray, CJL, Wang, H, and Kassebaum, NJ
Published: 2021

8. Effect of Transdermal Nitroglycerin on Insulin Resistance in Healthy Young Men

Author: Sultani Mh, Babaei A, Ghanizadah Ma, and Mohammadi Sm
Subjects: medicine.medical_specialty, business.industry, Insulin, medicine.medical_treatment, Venous blood, Pharmacology, medicine.disease, No donors, Blood pressure, Endocrinology, Insulin resistance, Internal medicine, Diabetes mellitus, cardiovascular system, medicine, Transdermal nitroglycerin, business, circulatory and respiratory physiology, Transdermal
Abstract: Objective: There are evidences indicating transdermal nitroglycerin changes the sensitivity of peripheral tissues to the hypoglycemic effect of insulin. In this study we determined effect of continuous application of transdermal nitroglycerin patches in healthy volunteers on development of tolerance to the hypotensive effect of nitroglycerin and hypoglycemic effect of insulin. Materials and methods: The effect of transdermal application of nitroglycerin, as NO donor was studied during 24 hours on blood insulin and glucose level and on blood pressure in healthy, young volunteers. Patches of 0.2 mg/ hour nitroglycerine were administered to young healthy volunteers along 24 hours and Venous blood samples were taken early before and 24 hours after the nitroglycerin. Serum was separated and free zed (-80°C) for insulin and glucose determination. Blood pressure also was determined in 6 hours intervals. Results: Before nitroglycerin patches application mean serum insulin level determined level 9.53 ± 4.37 and after nitroglycerin were 9.18 ± μU/ml which is statistically significant (P>0.05). Fasting blood glucose levels were increased by 6.63 ± 1.9 mg/dl determined 24 hours after nitroglycerine application in comparison with before treatment. The changes observed in blood glucose levels also were statistically significant (P
Published: 2015
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9. Energy and exergy analysis of a two-stage cascade refrigeration system

Author: Mohammadi, SM Hojjat, primary and Ameri, Mehran, additional
Published: 2015
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10. Effect of Transdermal Nitroglycerin on Insulin Resistance in Healthy Young Men

Author: Mohammadi SM, Babaei A, primary
Published: 2015
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11. Malnutrition and developmental defects of enamel in 2- to 6-year-old Saudi boys.

Author: Rugg-Gunn AJ, Al-Mohammadi SM, Butler TJ, Rugg-Gunn, A J, Al-Mohammadi, S M, and Butler, T J
Abstract: Three hundred and ninety boys aged 2, 4 or 6 years from Riyadh, Saudi Arabia, took part in a survey in 1993/94. The main aims of the study were first, to identify factors related to malnutrition in young children since a study of older children from the same area 1 year before had shown malnutrition to be strongly related to prevalence of developmental defects of enamel (DDE) of permanent teeth and, second, to identify factors related to the prevalence of developmental defects of primary teeth. Enamel defects were recorded by clinical examination of the buccal surfaces of all primary teeth by 1 examiner using the DDE index. A questionnaire to parents provided information on socio-economic status, illness in the mother and child, infant feeding, trauma to teeth and toothbrushing. A 24-hour dietary record, to estimate water and milk intake, and a 24-hour urine collection were obtained for each child twice. Nutritional status was calculated from height for age using WHO methods. Multiple regression analyses revealed four variables related (p<0.05) to malnourished status: low birth-weight, low volume of water drunk, child stopped breast- and bottle-feeding before 1 year of age, and low class urban or rural area of residence. Birth-weight was itself related to area of residence (p = 0.02), parental education (p = 0.02) and maternal illness during pregnancy (p = 0.06). Malnutrition (p<0.001), low birth-weight (p<0.001), childhood illness (p<0.001), brushing of child's teeth (p = 0.003) and swallowing toothpaste (p<0.001) were related to the prevalence of developmental defects of primary teeth. This study indicated several independent variables which may be related to the prevalence of enamel defects in primary and permanent teeth, but longitudinal studies are required to determine which are causes and which are markers of these developmental defects. [ABSTRACT FROM AUTHOR]
Published: 1998
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12. Effects of fluoride level in drinking water, nutritional status, and socio-economic status on the prevalence of developmental defects of dental enamel in permanent teeth in Saudi 14-year-old boys.

Author: Rugg-Gunn AJ, Al-Mohammadi SM, Butler TJ, Rugg-Gunn, A J, al-Mohammadi, S M, and Butler, T J
Published: 1997
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13. Feasibility, reliability and validity of the Iranian version of the Diabetes Quality of Life Brief Clinical Inventory (IDQOL-BCI)

Author: Mirfeizi M, Jafarabadi MA, Toorzani ZM, Mohammadi SM, Azad MD, Mohammadi AV, and Teimori Z
Abstract: AIMS: To validate and culturally adapt the Diabetes-specific Quality of Life Brief Clinical Inventory (DQOL-BCI) for the Iranian population. METHODS: After translation - back translation, content validity was assessed utilizing a panel of six experts. Based on a sample of 180 diabetic patients referred to two Diabetics Clinic Centers from September to May 2011 in Karaj, Iran, construct validity via detecting the factor structure, and convergent and discriminant validity were evaluated by scale-item correlations and known group analyses. Internal consistency and test-retest reliability were assessed in sample of 30 patients by Cronbach's and intraclass correlation coefficient (ICC). RESULTS: The IDQOL-BCI showed good content validity (CVI values>0.75 and CVR values>0.99), internal consistency ([alpha]=0.75) and test-retest reliability (ICC=0.81). A 3-factor solution was found. In addition, high values of item-scale correlations confirmed the convergence validity, and some subscales and total scores differentiate between groups defined by sex, disease duration, income levels, drug using status and physical activity demonstrated the discriminant validity. CONCLUSIONS: Our findings demonstrate the initial feasibility, reliability and validity of the Iranian version of the IDQOL-BCI as a measure of diabetic-specific QOL measure in Iranian patients. [ABSTRACT FROM AUTHOR]
Published: 2012
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14. Role of phosphate-modified cellulose into the scaffold based on poly (glycerol azelaic acid)-co-poly(ε-caprolactone) for using bone regenerative medicine.

Author: Shabestari SM, Jafari SH, Benisi SZ, Khoeini R, Shojaei S, Ghorbani M, and Goodarzi V
Abstract: Advancements in bone tissue engineering critically depend on the development of specialized scaffolds that promote effective bone regeneration. This study introduces innovative scaffolds composed of poly (glycerol azelaic acid) (PGAz) and poly (caprolactone) (PCL), enhanced with varying concentrations of phosphate modified cellulose (PMC) nanofibers. Utilizing citric acid as a green cross-linker enhances the eco-friendliness and biocompatibility of these scaffolds. The molecular structures were verified using Hydrogen 1 H NMR and Carbon 13 CNMR spectroscopy. Fourier Transform Infrared (FTIR) Spectroscopy confirmed successful integration of vital phosphate groups for biological functionality. Morphological and compositional analysis through Field Emission Scanning Electron Microscopy (FESEM) and Energy-Dispersive X-ray (EDX) mapping revealed homogeneous phosphate distribution and increased porosity across the scaffolds. Thermogravimetric Analysis (TGA) demonstrated that higher PMC concentrations improve thermal stability, enhancing scaffold resilience. Mechanical testing showed that scaffolds with 5 % PMC provide an optimal balance of compressive strength, compressive modulus, and compressive strain, suitable for bone tissue engineering. Dynamic water contact angle studies highlighted significant hydrophilic enhancements, crucial for cell attachment and proliferation. Hydrolytic degradation tests indicated that increased PMC accelerates degradation rates, aligning with tissue regeneration timelines. Hematoxylin and Eosin (H&E) staining and MTT assays, along with FESEM imaging of L929 mouse fibroblast cells cultured on the scaffolds, confirmed biocompatibility and cellular proliferation, particularly with 5 % PMC. These findings underscore the potential of PGAz-co-PCL scaffolds in advancing bone tissue regeneration through superior mechanical, thermal, and biological properties., Competing Interests: Declaration of competing interest There is no conflict and interest for this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)
Published: 2025
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15. Pioneering pain management with botulinum toxin type A: From anti-inflammation to regenerative therapies.

Author: Rahmatipour H, Shabestari SM, Benisi SZ, and Samadikhah H
Abstract: In the present paper, a comprehensive review was conducted to evaluate the performance of botulinum toxin type A (BTX-A) in managing various types of pain, including myofascial, muscular temporomandibular joint pain, orofacial pain, chronic migraines, and more. Firstly, the mechanism of action and anti-inflammatory effects of BTX-A was introduced. Following this, recent advancements in BTX-A applications were discussed, with an emphasis on emerging combination therapies, regenerative medicine, and personalized treatment strategies. Unlike previous reviews, this study explored a broader spectrum of pain conditions and highlighted BTX-A's versatility and potential as a long-term, minimally invasive pain management option. Additionally, the importance of tailoring BTX-A treatment was emphasized through the integration of biomarkers, genetic factors, and optimized dosing regimens to enhance efficacy and minimize side effects. Novel combinations with regenerative therapies, such as stem cells and tissue engineering, were identified as promising avenues for joint and nerve repair, providing both symptomatic relief and tissue regeneration. Furthermore, digital health tools and artificial intelligence were suggested as innovative approaches to monitor treatment responses and optimize dosing protocols in real-time, advancing personalized pain management. Overall, this review underscores BTX-A's potential in comprehensive and patient-centered pain management and offers recommendations to guide future studies in optimizing BTX-A therapy., 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., (© 2025 The Authors.)
Published: 2025
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16. Exploring the Influence of Feature Selection Methods on a Random Forest Model for Gait Time Series Prediction Using Inertial Measurement Units.

Author: Moghadam SM and Choisne J
Subjects: Humans, Child, Adolescent, Female, Male, Biomechanical Phenomena, Time Factors, Machine Learning, Mechanical Phenomena, Joints physiology, Models, Biological, Gait Analysis methods, Random Forest, Gait physiology
Abstract: Despite the increasing use of inertial measurement units (IMUs) and machine learning techniques for gait analysis, there remains a gap in which feature selection methods are best tailored for gait time series prediction. This study explores the impact of using various feature selection methods on the performance of a random forest (RF) model in predicting lower limb joints kinematics from two IMUs. The primary objectives of this study are as follows: (1) Comparing eight feature selection methods based on their ability to identify more robust feature sets, time efficiency, and impact on RF models' performance, and (2) assessing the performance of RF models using generalized feature sets on a new dataset. Twenty-three typically developed (TD) children (ages 6-15) participated in data collection involving optical motion capture (OMC) and IMUs. Joint kinematics were computed using opensim. By employing eight feature selection methods (four filter and four embedded methods), the study identified 30 important features for each target. These selected features were used to develop personalized and generalized RF models to predict lower limbs joints kinematics during gait. This study reveals that various feature selection methods have a minimal impact on the performance of personalized and generalized RF models. However, the RF and mutual information (MI) methods provided slightly lower errors and outliers. MI demonstrated remarkable robustness by consistently identifying the most common features across different participants. ElasticNet emerged as the fastest method. Overall, the study illuminated the robustness of RF models in predicting joint kinematics during gait in children, showcasing consistent performance across various feature selection methods., (Copyright © 2025 by ASME; reuse license CC-BY 4.0.)
Published: 2025
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17. The Impact of Telenursing on the Self-management of Gastrointestinal Symptoms in Adolescent Cancer Patients Receiving Chemotherapy.

Author: Shahri MA, Farahani AS, Rassouli M, Khabazkhoob M, and Aghbelagh SM
Abstract: Background: Chemotherapy is one of the cancer treatments among adolescents, after which nursing care at home is required due to developing side effects such as constipation, nausea, vomiting, and diarrhea. One solution to deliver nursing care is to provide remote self-management training., Objective: The aim of this study is to investigate the impact of telenursing on the self-management of gastrointestinal (GI) symptoms among adolescents undergoing chemotherapy., Methods: In this intervention study, 66 adolescents 12 to 18 years of age who were referred to teaching hospitals for receiving chemotherapy were selected through randomized block sampling. The data were collected through demographic and clinical questionnaires, the researcher-made form for GI symptoms and conditions, and the researcher-made questionnaire for the self-management of GI symptoms among adolescents. Data analysis was done using SPSS version 20., Results: The findings show that there was no significant statistical difference between the control group and the intervention group in terms of demographic characteristics. According to the independent-samples t test and repeated-measures analysis of variance, using an educational website had a significant positive impact on the scores of GI symptoms self-management, 1 week and 1 month after the intervention (P < .001)., Conclusions: Given that the intervention group patients could better manage their GI symptoms on their own by visiting the educational website Cancer Information, it can be concluded that telenursing can affect the self-management of GI symptoms among adolescent patients with cancer who receive chemotherapy., Implications for Practice: The website Cancerinformation.ir can be used in the self-management of GI symptoms in cancer patients., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
Published: 2024
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18. The Role of Osteocalcin in Patients with Osteoporosis: A Systematic Review.

Author: Mohammadi SM, Saniee N, Mousaviasl S, Radmanesh E, and Doustimotlagh AH
Abstract: Background: Osteoporosis is the most common systemic skeletal disease worldwide. We aimed to review the latest studies related to osteocalcin and osteoporosis to clarify this relationship more precisely., Methods: A systematic literature search was performed to review studies on the effects of osteocalcin on osteoporosis, on studied published between January 2013 and January 2023. We systematically reviewed Web of Science, PubMed, ProQuest, Scopus, and Google Scholar., Results: The search yielded 4903 records, including 1063 from PubMed, 2307 from Scopus, 1084 from Web of Science, 408 from ProQuest, and 41 from Google Scholar, and twelve articles were included for data extraction and quality assessment. A significant increase in the serum level of osteocalcin was observed in postmenopausal women with osteoporosis ( P <0.05), and there was a negative correlation between bone mineral density and the serum level of osteocalcin., Conclusion: Osteocalcin could be a promising marker for the diagnosis and screening of patients with osteoporosis., (Copyright© 2024 Mohammadi et al. Published by Tehran University of Medical Sciences.)
Published: 2024
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19. Efficacy of Intraarticular Vancomycin in Preventing Infection in Patients Undergoing Hip Hemiarthroplasty.

Author: Mohammadhoseini P, Mohammadi SM, Aghaei Aghdam A, and Asgari M
Abstract: Background: Hip fractures are among the top ten causes of disability in adults worldwide. Patients with hip fracture are at significant risk of mortality and morbidity and reduced quality of life. The use of intra-wound vancomycin has been reported to be effective in reducing the incidence of infection in orthopedic surgeries. This study was conducted with the aim of investigating the effect of intra-articular vancomycin in preventing infection in patients undergoing hip hemiarthroplasty., Materials and Methods: This double-blind controlled clinical trial study was conducted on 48 patients with femoral neck fracture candidates for hip hemiarthroplasty hemiarthroplasty in Orthopedic clinic of Golestan and Imam Khomeini Hospital, Ahvaz, Iran between June and November 2023. Eligible patients were divided into two equal groups. The intervention group received 1gram vancomycin intra-articularly during the operation before closure of fascia, and the control group did not receive vancomycin. The patients were followed up for 6 months after the operation, and the rate of superficial infection, periprosthetic joint infection (PJI) and wound complications were compared in two groups. The obtained data were statistically analyzed with IBM SPSS Statistics 21.0 for Windows., Results: The vancomycin group and the control group had no significant difference in the incidence of overall infection. The PJI in vancomycin and control groups were 4.16% and 8.33%, respectively. This difference was not statistically considerable (P=0.55). The results showed the incidence of superficial estimated 8.33% in vancomycin group and 4.16% in control group with no considerable difference in infection (P=0.52). Moreover, there was no meaningful difference in side effects between the two groups (P=0.63). There was no significant difference in wound complications between the two groups (P=0.3). After the intervention, it was found that the ESR value in the control group and vancomycin group was 32.79±9.94, 31.83±9.78 mm/hr, respectively (P=0.73)., Conclusion: Intra-articular injection of 1gram of vancomycin suspension did not reduce the overall, superficial and deep infection after surgery. It is suggested that more clinical trial studies with higher sample size be conducted in order to determine the effect of intra-articular vancomycin in preventing infection in patients undergoing hip hemiarthroplasty., Competing Interests: The authors declare that they have no competing interests., (Copyright© 2024, Galen Medical Journal.)
Published: 2024
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20. Inhibitor development upon switching from plasma-derived to recombinant factor VIII in previously untreated patients with severe hemophilia A: the PUP-SWITCH study.

Author: Miri S, Rosendaal FR, Kavakli K, Eshghi P, Moghaddam SM, Scardo S, Habibpanah B, Elalfy M, Halimeh S, Nicolò G, Gökçebay D, Özbek N, Celkan T, Mohammadi A, Karimi M, Shahsavani A, Yılmaz B, Albayrak C, Gunes B, Kaya Z, Ay Y, Akbayram S, Sarper N, Mannucci PM, and Peyvandi F
Abstract: Background: The SIPPET randomized clinical trial showed that in previously untreated patients (PUPs) with severe hemophilia A, treatment with plasma-derived factor (F)VIII (pdFVIII) within the first 50 exposure days (EDs) was associated with a lower cumulative incidence of inhibitors than with recombinant FVIII (rFVIII). Switching to rFVIII beyond 50 EDs with pdFVIII is a treatment often implemented by many centers. The question is whether or not this switch may induce a risk of inhibitor development., Objectives: We investigated if in PUPs with severe hemophilia A switched after 50 EDs from pdFVIII to rFVIII, a novel inhibitor peak appears., Methods: The PUP-SWITCH observational retrospective study was designed to investigate the cumulative incidence of novel inhibitors after switching PUPs to rFVIII after 50 and before 150 EDs. Hemophilia centers that routinely switched PUPs from pdFVIII to rFVIII within this exposure time frame were invited to participate. Patients were followed up for at least 50 EDs after the switch., Results: Ninety-seven patients were evaluated, and 87 were included according to eligibility criteria between 2020 and 2022. Only one of them developed an inhibitor 20 EDs after switching, so the cumulative incidence was 1.15% (95% CI, 0.03%-6.24%)., Conclusion: PUP-SWITCH, a study focusing on PUPs undergoing a product class switch from pdFVIII to rFVIII after 50 EDs, showed that switching appears to be safe pertaining to the risk of development of new inhibitors., (© 2024 The Author(s).)
Published: 2024
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21. In Silico Design of a Trans-Amplifying RNA-Based Vaccine against SARS-CoV-2 Structural Proteins.

Author: Nafian F, Soleymani G, Pourmanouchehri Z, Kiyanjam M, Nafian S, Mohammadi SM, Jeyroudi H, Berenji Jalaei S, and Sabzpoushan F
Abstract: Nucleic acid-based vaccines allow scalable, rapid, and cell-free vaccine production in response to an emerging disease such as the current COVID-19 pandemic. Here, we objected to the design of a multiepitope mRNA vaccine against the structural proteins of SARS-CoV-2. Through an immunoinformatic approach, promising epitopes were predicted for the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. Fragments rich in overlapping epitopes were selected based on binding affinities with HLA classes I and II for the specific presentation to B and T lymphocytes. Two constructs were designed by fusing the fragments in different arrangements via GG linkers. Construct 1 showed better structural properties and interactions with toll-like receptor 2 (TLR-2), TLR-3, and TLR-4 during molecular docking and dynamic simulation. A 50S ribosomal L7/L12 adjuvant was added to its N-terminus to improve stability and immunogenicity. The final RNA sequence was used to design a trans-amplifying RNA (taRNA) vaccine in a split-vector system. It consists of two molecules: a nonreplicating RNA encoding a trans-acting replicase to amplify the second one, a trans-replicon (TR) RNA encoding the vaccine protein. Overall, the immune response simulation detected that activated B and T lymphocytes and increased memory cell formation. Macrophages and dendritic cells proliferated continuously, and IFN- γ and cytokines like IL-2 were released highly., Competing Interests: All authors declare that they have no conflicts of interest regarding the present study., (Copyright © 2024 Fatemeh Nafian et al.)
Published: 2024
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22. The Effect of Plantago major Hydroalcoholic Extract on the Healing of Diabetic Foot and Pressure Ulcers: A Randomized Open-Label Controlled Clinical Trial.

Author: Ghanadian M, Soltani R, Homayouni A, Khorvash F, Jouabadi SM, and Abdollahzadeh M
Subjects: Humans, Male, Female, Middle Aged, Treatment Outcome, Phytotherapy methods, Aged, Plant Leaves, Diabetic Foot drug therapy, Wound Healing drug effects, Plantago, Plant Extracts pharmacology, Plant Extracts administration & dosage, Pressure Ulcer drug therapy, Pressure Ulcer etiology
Abstract: Aims: Diabetic foot ulcer (DFU) and pressure ulcer (PU) both are common types of ulcers worldwide. The wound healing effect of Plantago major leaves has been shown in a few animal studies. This study aimed to evaluate the clinical efficacy of P. major hydroalcoholic extract on DFU and PU healing. Methods: In this clinical trial, patients with DFU or PU who met the inclusion criteria were randomly assigned to drug ( P. major ) or control groups. For patients in the drug group, Plantago extract 10% topical gel was applied on the wound once daily concurrent with dressing and routine wound care for two weeks, while for the control group, an appropriate novel dressing was used along with routine wound care for the same duration. The percentage of wound size reduction at the end of the seventh and 14th days of intervention was recorded and compared between the groups. Results: Fifty and 44 patients in drug and control groups, respectively, completed the interventions. Plantago extract gel significantly resulted in more reduction in the wound size compared to control at the end of the first (64.90 ± 29.75% vs. 33.11 ± 26.55%; P < 0.001) and second week (86.85 ± 24.34% vs. 52.87 ± 32.41%; P < 0.001). Furthermore, the number of patients with complete wound healing in the drug group (n = 32, 64%) was significantly more than the control group (n = 9, 20.45%; OR: 3.129, 95% CI: 1.685-5.809, P < 0.001). Conclusion: The use of 10% topical gel of P. major leaf extract results in the acceleration of DFU and PU healing. Key points: Application of P. major topical gel results in the acceleration of diabetic foot ulcer and pressure ulcer healing. - P. major extract helps reducing the wound's erythema.- P. major leaf extract assists decreasing the wound size.- The number of patients completing wound healing process is higher among whom undergoing P. major dressing., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Published: 2024
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23. Utilizing Slope Entropy as an Effective Index for Wearable EEG-Based Depth of Anesthesia Monitoring.

Author: Shahbakhti M, Beiramvand M, Far SM, Sole-Casals J, Lipping T, and Augustyniak P
Subjects: Humans, Adult, Male, Signal Processing, Computer-Assisted, Female, Electroencephalography methods, Electroencephalography instrumentation, Entropy, Anesthesia methods, Wearable Electronic Devices
Abstract: Based on prior research indicating a decrease in the spectral slope of electroencephalogram (EEG) during anesthesia induction and an increase during recovery, we propose Slope Entropy (SlopEn), which uniquely emphasizes variations in signal slope, as a new index for monitoring the depth of anesthesia (DoA). The performance of SlopEn is investigated on just a single frontal EEG channel and is compared against other well-known entropy metrics utilized in the field. After filtering the EEG signal, four types of entropy, including SlopEn, are derived from all EEG sub-bands and separately inputted to a regressor for estimating DoA index values. Comparing the results obtained using SlopEn with those from the Sample entropy demonstrates the superiority of the former, achieving a higher correlation coefficient (0.75 vs. 0.63) and a lower median absolute error (4.2 vs. 6.2) between the estimated and reference DoA index values. These findings establish that the SlopEn has the potential to become a valuable index for DoA monitoring using single frontal channel EEG systems.
Published: 2024
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24. Combination of Empirical Mode Decomposition and Hjorth Parameters for Prediction of Preterm Labor using Electrohysterogram Signals.

Author: Far SM, Beiramvand M, Shahbakhti M, and Augustyniak P
Subjects: Humans, Female, Pregnancy, Signal Processing, Computer-Assisted, Obstetric Labor, Premature physiopathology, Obstetric Labor, Premature diagnosis, Algorithms
Abstract: Timely predicting preterm labor is vital for increasing the chance of neonatal survival rates and promoting maternal well-being. In this study, we explore the potential of Hjorth parameters for predicting preterm labor using the electrohysterogram (EHG) signals. Our proposed algorithm begins by decomposing the EHG signals into four modes through empirical mode decomposition. Subsequently, for each mode, we extract Hjorth parameters-activity, mobility, and complexity- and input them into a random forest classifier for discrimination between term and preterm labor. The comparative analysis of these three parameters highlights the superiority of complexity over activity and mobility, resulting in higher accuracy (85% compared to 73% and 77%). These findings underscore the effectiveness of Hjorth parameters in predicting preterm labor using EHG signals.
Published: 2024
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25. Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study.

Author: Gaba F, Mohammadi SM, Krivonosov MI, and Blyuss O
Abstract: The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models ( p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.
Published: 2024
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26. The inhibition of ADAM17 in cord blood stem cell-derived CD16 + NK cells to enhance their cytotoxicity against acute lymphoblastic leukemia cells.

Author: Valipour B, Mohammadi SM, Abedelahi A, and Charoudeh HN
Subjects: Humans, Cell Line, Tumor, Cytotoxicity, Immunologic, GPI-Linked Proteins metabolism, Coculture Techniques, Apoptosis, Antibody-Dependent Cell Cytotoxicity, Interferon-gamma metabolism, CD47 Antigen, Killer Cells, Natural immunology, Precursor Cell Lymphoblastic Leukemia-Lymphoma immunology, ADAM17 Protein metabolism, ADAM17 Protein antagonists & inhibitors, Receptors, IgG metabolism, Fetal Blood cytology
Abstract: Fortunately, ample efforts are being made to find the best strategy to improve the anti-leukemia capacity of NK cells for treating different types of cancer. Despite the favorable ADCC capacity of functional CD16 + NK cells for immunotherapy, when NK cells face leukemia cells, the CD16 receptor is cleaved during the process mediated by a disintegrin and metalloproteinase-17(ADAM17). Reduced CD16 expression on NK cells weakens their cytotoxicity against leukemia cells. In addition, the expression of the CD47 receptor is high in acute lymphoblastic leukemia (ALL) compared to normal cells and can be correlated with poor prognosis. In the present study, ADAM17 was inhibited in cord blood-derived CD16 + NK cells, and their activity against ALL cell lines was evaluated following blockage with anti-CD47 antibody. As the results showed, the CD16 expression was reduced in the NK cells co-cultured with ALL cell lines. However, the ADAM17 inhibition increased the CD16 expression on the NK cells. This enhanced the cytotoxicity of those cells as well as cytokine production was evaluated by measuring expression of CD107-a expression, and IFN-γ production. Moreover, the presence of the ADAM17 inhibitor increased the apoptosis effect of the generated NK cells in response to ALL cells. Therefore, the inhibition of ADAM17 is useful for the activity of CD16 + NK cells against cancer cells., Competing Interests: Declaration of competing interest 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., (Copyright © 2024 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.)
Published: 2024
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27. Unveiling the lead exposure attributed burden in Iran from 1990 to 2019 through the lens of the Global Burden of Disease study 2019.

Author: Karimi H, Mahdavi S, Moghaddam SS, Abbasi-Kangevari M, Soleimani Z, Esfahani Z, Masinaei M, Fateh SM, Golestani A, Dilmaghani-Marand A, Kompani F, Rezaei N, Ghasemi E, Larijani B, and Farzadfar F
Subjects: Male, Female, Animals, Humans, Global Burden of Disease, Quality-Adjusted Life Years, Lead, Iran epidemiology, Global Health, Risk Factors, Life Expectancy, Unionidae
Abstract: This study aimed to investigate the estimated burden attributed to lead exposure (LE), at the national and subnational levels from 1990 to 2019 in Iran. The burden attributed to LE was determined through the estimation of deaths, disability-adjusted life years (DALYs), years of life lost (YLLs) and years lived with disability (YLDs) using the comparative risk assessment method of Global Burden of Disease (GBD) study presenting as age-standardized per 100,000 person year (PY) with 95% uncertainty intervals (95% UI). Furthermore, the burden of each disease were recorded independently. Eventually, the age-standardized YLLs, DALYs, deaths and YLDs rates attributed to LE demonstrated a decrease of 50.7%, 48.9%, 38.0%, and 36.4%, respectively, from 1990 to 2019. The most important causes of LE burden are divided into two acute and chronic categories: acute, mainly causes mental disorders (DALYs rate of 36.0 in 2019), and chronic, results in cardiovascular diseases (CVDs) (DALYs rate of 391.8) and chronic kidney diseases (CKDs) (DALYs rate of 26.6), with CVDs bearing the most significant burden. At the sub-national level, a decrease in burden was evident in most provinces; moreover, low and low-middle SDI provinces born the highest burden. The burden increased mainly by ageing and was higher in males than females. It was concluded that although the overall decrease in the burden; still it is high, especially in low and low-middle SDI provinces, in advanced ages and in males. Among IDID, CKDs and CVDs that are the most important causes of LE-attributed burden in Iran; CVDs bear the highest burden., (© 2024. The Author(s).)
Published: 2024
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28. "Anterior Chamber Diaphragm": A Temporary Device to Make Corneal Endothelial Cell Therapy Safe!

Author: Yousefpour-Marzbali M, Asadigandomani H, Soleimanifar M, Bahmanpour A, Mohammadi SM, and Mohammadi SF
Abstract: Competing Interests: There are no conflicts of interest.
Published: 2024
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29. Circulatory resistin levels in inflammatory bowel disease: a systematic review and meta-analysis.

Author: Behnoush AH, Maroufi SP, Reshadmanesh T, Mohtasham Kia Y, Norouzi M, Mohammadi SM, Klisic A, and Khalaji A
Subjects: Humans, Resistin, Inflammatory Bowel Diseases, Colitis, Ulcerative, Crohn Disease
Abstract: Background: Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), is a chronic relapsing-remitting systemic disease of the gastrointestinal tract with rising incidence. Studies have shown that adipocytes play a crucial role in patients with IBD by actively participating in systemic immune responses. The present study was designed to investigate the correlation between the circulatory levels of resistin, as an adipokine, and active and remission phases of IBD in comparison with healthy controls., Methods: Relevant articles were retrieved from PubMed, Embase, the Web of Science, and Scopus from inception until June 2023. Estimation of the standardized mean difference (SMD) and 95% confidence interval (CI) for comparison of plasma/serum resistin levels between IBD patients, patients in remission, and healthy controls were conducted through random-effect meta-analysis., Results: A total of 19 studies were included, assessing 1836 cases. Meta-analysis indicated that generally, serum/plasma resistin levels were higher in IBD patients in comparison with healthy controls (SMD 1.33, 95% CI 0.58 to 2.08, p-value < 0.01). This was true for each of the UC and CD separate analyses, as well. Moreover, it was shown that higher serum/plasma resistin levels were detected in the active phase of IBD than in the remission phase (SMD 1.04, 95% CI 0.65 to 1.42, p-value = 0.01). Finally, higher serum/plasma resistin levels were found in the remission phase compared to healthy controls (SMD 0.60, 95% CI 0.15 to 1.06, p-value < 0.01)., Conclusion: The results of this systematic review and meta-analysis support the conclusion that circulating resistin levels are increased in IBD (both UC and CD). Also, higher resistin levels were recorded in the remission phase of IBD in comparison with healthy controls. This indicates that further studies may provide valuable insights into the role of resistin in the pathogenesis of IBD., (© 2024. The Author(s).)
Published: 2024
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30. Healing of diabetic foot ulcer with topical and oral administrations of herbal products: A systematic review and meta-analysis of randomized controlled trials.

Author: Zamanifard M, Nasiri M, Yarahmadi F, Zonoori S, Razani O, Salajegheh Z, Imanipour M, Mohammadi SM, Jomehzadeh N, and Asadi M
Subjects: Humans, Randomized Controlled Trials as Topic, Ulcer drug therapy, Bandages, Administration, Oral, Diabetic Foot drug therapy, Diabetes Mellitus drug therapy
Abstract: This systematic review aimed to qualitatively synthesize recent randomized controlled trials (RCTs) regarding the effect of topical application and oral intake of herbal products on the healing of diabetic foot ulcer (DFU). Also, we sought to pool the obtained findings in a meta-analysis using a random-effects model, if RCTs were relatively comparable and homogenous. A comprehensive search was performed on five electronic data sources from their inception through 23 January 2024. The RCTs, without restriction on the country of origin, were included if they compared the effect of administering standard treatments and/or placebo (i.e. control condition) to applying standard treatments and/or herbal products in topical or oral routes (i.e. experimental condition). Out of 1166 retrieved records, 28 RCTs were included. Studies used different poly and single herbal formulations. Based on the meta-analysis, administration of standard care plus daily dressing of the ulcer site with olive oil for 28 days significantly increased the total ulcer healing score (3 RCTs; weighted mean difference [WMD] = 89.30; p < 0.001), raised frequency of complete ulcer healing (2 RCTs; risk ratio [RR] = 12.44; p = 0.039) and declined ulcer degree (3 RCTs; WMD = -22.28; p = 0.002). Also, daily use of the bitter melon leaf extract in oral form for 28 days significantly increased the total ulcer healing score (2 RCTs; WMD = 0.40; p = 0.001). Additionally, based on qualitative synthesis, the adjuvant use of herbal agents seems an intriguing choice to manage DFU. Nonetheless, considering the undesirable methodological quality of most studies and the high heterogeneity in administered herbal formulations, more robust trials are required to build a solid conclusion regarding the use of herbal products for healing DFU., (© 2024 The Authors. International Wound Journal published by Medicalhelplines.com Inc and John Wiley & Sons Ltd.)
Published: 2024
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31. Association between ELMO1 gene polymorphisms and diabetic kidney disease: A systematic review and meta-analysis.

Author: Azarboo A, Hosseinkhani S, Ghaseminejad-Raeini A, Aazami H, Mohammadi SM, Zeidi S, Razi F, and Bandarian F
Subjects: Humans, Genetic Predisposition to Disease, Polymorphism, Genetic, Adaptor Proteins, Signal Transducing genetics, Diabetic Nephropathies genetics, Diabetic Nephropathies complications, Diabetes Mellitus, Type 2 complications, Renal Insufficiency, Chronic complications
Abstract: Background: Previous research has suggested that the ELMO1 gene may play a role in the development of diabetic kidney disease. Diabetic kidney disease (DKD) is a serious complication of diabetes and the leading cause of chronic kidney disease and end-stage renal disease (ESRD)., Objective and Rationale: This study aim was to systematically review and explore the association between ELMO1 gene polymorphisms and diabetic kidney disease. A comprehensive systematic review provides a clear conclusion and high-level evidence for the association between ELMO1 gene and DKD for future application in personalized medicine., Methods: A comprehensive search of electronic databases, per PRISMA instructions, was conducted in Scopus, EMBASE, Web of Science, and PubMed databases from 1980 to January 2023. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using appropriate models. Subgroup and sensitivity analyses were performed to explore potential sources of heterogeneity and assess the robustness of the findings., Results: A total of 5794 diabetes patients with DKD, 4886 diabetes patients without DKD, and 2023 healthy controls were included in the 17 studies that made up this systematic review. In the investigation of DM (Diabetes Mellitus) with DKD vs. DM without DKD, the susceptibility for DKD for the EMLO1 rs741301 polymorphism indicated a significant difference under the dominant, homozygote, and recessive genetic models. The susceptibility for DKD for the EMLO1 rs1345365, rs10255208, and rs7782979 polymorphisms demonstrated a significant difference under the allele genetic models in the analysis of DM with DKD vs. DM without DKD groups. There was a considerable increase in DKD risk in the Middle East when the population was stratified by the region., Conclusion: The findings of the meta-analysis show that there are a significant connection between the EMLO1 rs741301 polymorphism and DKD susceptibility in overall analyses; as well as rs1345365, rs10255208, and rs7782979 polymorphisms; especially in the Middle East region., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Azarboo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Published: 2024
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32. Global, regional, and national quality of care index of cervical and ovarian cancer: a systematic analysis for the global burden of disease study 1990-2019.

Author: Azangou-Khyavy M, Ghasemi E, Rezaei N, Khanali J, Kolahi AA, Malekpour MR, Heidari-Foroozan M, Nasserinejad M, Mohammadi E, Abbasi-Kangevari M, Ghamari SH, Ebrahimi N, Koolaji S, Khosravifar M, Fateh SM, Larijani B, and Farzadfar F
Subjects: Humans, Female, Global Burden of Disease, Health Status, Incidence, Uterine Cervical Neoplasms epidemiology, Persons with Disabilities, Ovarian Neoplasms epidemiology
Abstract: Background and Objective: Cervical cancer is the most preventable and ovarian cancer is the most lethal gynecological cancer. However, in the world, there are disparities in health care performances resulting in differences in the burden of these cancers. The objective of this study was to compare the health-system quality of care and inequities for these cancers using the Quality of Care Index (QCI)., Material and Methods: The 1990-2019 data of the Global Burden of Disease (GBD) was analyzed to extract rates of incidence, prevalence, mortality, Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLL), and Years of healthy life lost due to disability (YLD) of cervical and ovarian cancer. Four indices were developed as a proxy for the quality of care using the above-mentioned rates. Thereafter, a Principal Components Analysis (PCA) was applied to construct the Quality of Care Index (QCI) as a summary measure of the developed indices., Results: The incidence of cervical cancer decreased from 1990 to 2019, whereas the incidence of ovarian cancer increased between these years. However, the mortality rate of both cancers decreased in this interval. The global age-standardized QCI for cervical cancer and ovarian cancer were 43.1 and 48.5 in 1990 and increased to 58.5 and 58.4 in 2019, respectively. QCI for cervical cancer and ovarian cancer generally decreased with aging, and different age groups had inequitable QCIs. Higher-income countries generally had higher QCIs for both cancers, but exceptions were also observed., Conclusions: Uncovering disparities in cervical and ovarian cancer care across locations, Socio-Demographic Index levels, and age groups necessitate urgent improvements in healthcare systems for equitable care. These findings underscore the need for targeted interventions and prompt future research to explore root causes and effective strategies for narrowing these gaps., (© 2024. The Author(s).)
Published: 2024
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33. The global, regional, and national burden and quality of care index of kidney cancer; a global burden of disease systematic analysis 1990-2019.

Author: Khadembashiri MM, Ghasemi E, Khadembashiri MA, Azadnajafabad S, Moghaddam SS, Eslami M, Rashidi MM, Naderian M, Esfahani Z, Ahmadi N, Rezaei N, Fateh SM, Kompani F, Larijani B, and Farzadfar F
Subjects: Male, Female, Humans, Global Burden of Disease, Prevalence, Incidence, Global Health, Quality-Adjusted Life Years, Persons with Disabilities, Kidney Neoplasms
Abstract: Kidney cancer (KC) is a prevalent cancer worldwide. The incidence and mortality rates of KC have risen in recent decades. The quality of care provided to KC patients is a concern for public health. Considering the importance of KC, in this study, we aim to assess the burden of the disease, gender and age disparities globally, regionally, and nationally to evaluate the quality and inequities of KC care. The 2019 Global Burden of Disease study provides data on the burden of the KC. The secondary indices, including mortality-to-incidence ratio, disability-adjusted life years -to-prevalence ratio, prevalence-to-incidence ratio, and years of life lost-to-years lived with disability ratio, were utilized. These four newly merged indices were converted to the quality-of-care index (QCI) as a summary measure using principal component analysis. QCI ranged between 0 and 100, and higher amounts of QCI indicate higher quality of care. Gender disparity ratio was calculated by dividing QCI for females by males to show gender inequity. The global age-standardized incidence and mortality rates of KC increased by 29.1% (95% uncertainty interval 18.7-40.7) and 11.6% (4.6-20.0) between 1990 and 2019, respectively. Globally, the QCI score for KC increased by 14.6% during 30 years, from 71.3 to 81.6. From 1990 to 2019, the QCI score has increased in all socio-demographic index (SDI) quintiles. By 2019, the highest QCI score was in regions with a high SDI (93.0), and the lowest was in low SDI quintiles (38.2). Based on the World Health Organization regions, the QCI score was highest in the region of America, with Canada having the highest score (99.6) and the lowest in the African Region, where the Central African Republic scored the lowest (17.2). In 1990, the gender disparity ratio was 0.98, and in 2019, it was 0.97 showing an almost similar QCI score for females and males. Although the quality of care for KC has improved from 1990 to 2019, there is a significant gap between nations and different socioeconomic levels. This study provides clinicians and health authorities with a global perspective on the quality of care for KC and identifies the existing disparities., (© The Author(s) 2024. Published by Oxford University Press on behalf of International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Published: 2024
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34. Osteoporosis and Leptin: A Systematic Review.

Author: Mohammadi SM, Saniee N, Borzoo T, and Radmanesh E
Abstract: Background: Leptin has a great effect on bone through direct or indirect involvement in bone remodeling. Considering the ambiguities that exist regarding the effect of leptin on bone and bone-related diseases including osteoporosis, in this study, we aimed to conduct a systematic review of various studies on the effect of leptin on osteoporosis, which may find an answer to the existing ambiguities., Methods: The search was performed to review studies on the effects of leptin on osteoporosis by using several databases including Scopus, PubMed, Web of Science, and Google Scholar. Electronic searches were conducted on 5 Jan 2023. There was no limit on the publication date of the articles. The risk of bias for the animal study was assessed with the CAMARADES checklist, and the study quality assessment was also assessed based on the guidelines for in vivo experiments (ARRIVE). In this study, the risk of bias (quality) of human studies was assessed using the quality assessment checklists by NHLBI., Results: Overall, 34 articles were included for data extraction and quality assessment. Overall, 27 human studies and seven animal studies were included in the article. The results of most of the studies conducted in this study showed that leptin has a physiological role in maintaining bone mass and better bone quality and reduces bone marrow adipogenesis and increases bone mineral density (BMD). As plasma leptin levels increased, BMD values or bone formation biomarkers increased., Conclusion: Leptin has an inhibitory role against bone resorption and increasing osteoprotegerin (OPG) levels, which, as a result, maintains bone density and reduces osteoclast activity, and has a positive relationship with increasing osteocalcin., (Copyright© 2024 Mohammadi et al. Published by Tehran University of Medical Sciences.)
Published: 2024
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35. Short-Term and Long-Term Therapeutic Results of Deep Flexor Tendon Repair in Zone II in Patients Referred to Imam Khomeini Hospital, Ahvaz, Southern Iran.

Author: Mohammadhoseini P, Mohammadi SM, and Mousavi Nia N
Abstract: Background: Hand injury as an important concern for the surgeon and the patient requires proper and timely treatment to prevent complications such as infection and adhesions, and with a proper rehabilitation program, the patient returns to maximum function as soon as possible. We aimed to investigate the short-term and long-term treatment results of deep flexor tendon repair in in zone II., Methods: This retrospective study was performed on 34 patients with 45 injured fingers in the zone II referred to Ahvaz Imam Khomeini Hospital, Ahvaz, Iran during 2017-2019. The results of deep flexor tendons repair in two groups, immediate and delayed primary repair were assessed., Results: The mean age of the patients was 27.76 years. There was no significant remarkable between male and female in the incidence of complications such as infection, tendon rupture and adhesions. 29.4% (n=10) had poor outcome, 8.8% (n=3) had fair outcome, 29.4% (n=10) had good outcome and 32.4% (n=11) had excellent outcomes. 26.5% had adhesion and infection rate was 11.8%., Conclusion: Among surgeons, there is consensus for the primary repair of tendon injury, but there was no significant difference between the results of immediate and delayed primary repair. Although physiotherapy has been suggested as an effective factor in improving hand function, its positive effect on the range of motion of the fingers has not been proven., Competing Interests: There is no conflict of interests.
Published: 2024
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36. Clinical Impact and Mechanisms of Nonatherosclerotic Vascular Aging: The New Kid to Be Blocked.

Author: Jouabadi SM, Ataabadi EA, Golshiri K, Bos D, Stricker BHC, Danser AHJ, Mattace-Raso F, and Roks AJM
Subjects: Humans, Aged, Aging physiology, Inflammation, Signal Transduction, Cellular Senescence, Cardiovascular Diseases, Stroke
Abstract: Ischemic cardiovascular disease and stroke remain the leading cause of global morbidity and mortality. During aging, protective mechanisms in the body gradually deteriorate, resulting in functional, structural, and morphologic changes that affect the vascular system. Because atherosclerotic plaques are not always present along with these alterations, we refer to this kind of vascular aging as nonatherosclerotic vascular aging (NAVA). To maintain proper vascular function during NAVA, it is important to preserve intracellular signalling, prevent inflammation, and block the development of senescent cells. Pharmacologic interventions targeting these components are potential therapeutic approaches for NAVA, with a particular emphasis on inflammation and senescence. This review provides an overview of the pathophysiology of vascular aging and explores potential pharmacotherapies that can improve the function of aged vasculature, focusing on NAVA., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2023
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37. Statin Use and Coronary Artery Calcification: a Systematic Review and Meta-analysis of Observational Studies and Randomized Controlled Trials.

Author: Shahraki MN, Jouabadi SM, Bos D, Stricker BH, and Ahmadizar F
Subjects: Humans, Coronary Vessels diagnostic imaging, Risk Factors, Observational Studies as Topic, Coronary Artery Disease complications, Hydroxymethylglutaryl-CoA Reductase Inhibitors therapeutic use, Vascular Calcification diagnostic imaging, Vascular Calcification drug therapy
Abstract: Purpose of Review: This review aimed to determine the association between statin use and coronary artery calcification (CAC), as detected by computed tomography in the general population, in previously published observational studies (OSs) and randomized controlled trials (RCTs)., Recent Findings: A systematic search until February 2022 identified 41 relevant studies, comprising 29 OSs and 12 RCTs. We employed six meta-analysis models, stratifying studies based on design and effect metrics. For cohort studies, the pooled β of the association with CAC quantified by the Agatston score was 0.11 (95% CI = 0.05; 0.16), with an average follow-up time per person (AFTP) of 3.68 years. Cross-sectional studies indicated a pooled odds ratio of 2.11 (95% CI = 1.61; 2.78) for the presence of CAC. In RCTs, the pooled standardized mean differences (SMDs) for CAC, quantified by Agatston score or volume, over and AFTP of 1.25 years were not statistically significant (SMD = - 0.06, 95% CI = - 0.19; 0.06 and SMD = 0.26, 95% CI = - 0.66; 1.19), but significantly different (p-value = 0.04). Meta-regression and subgroup analyses did not show any significant differences in pooled estimates across covariates. The effect of statins on CAC differs across study designs. OSs demonstrate associations between statin use and higher CAC scores and presence while being prone to confounding by indication. Effects from RCTs do not reach statistical significance and vary depending on the quantification method, hampering drawing conclusions. Further investigations are required to address the limitations inherent in each approach., (© 2023. The Author(s).)
Published: 2023
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38. miRNAs as key modulators between normal cells and tumor microenvironment interactions.

Author: Nour SM, Abbasi N, Sadi S, Ravan N, Alipourian A, Yarizadeh M, Soofi A, Ataei A, and Tehrany PM
Subjects: Humans, Endothelial Cells metabolism, Tumor Microenvironment genetics, Signal Transduction, MicroRNAs genetics, MicroRNAs metabolism, Neoplasms pathology
Abstract: The tumor microenvironment (TME) is well-defined target for understanding tumor progression and various cell types. Major elements of the tumor microenvironment are the followings: endothelial cells, fibroblasts, signaling molecules, extracellular matrix, and infiltrating immune cells. MicroRNAs (miRNAs) are a group of small noncoding RNAs with major functions in the gene expression regulation at post-transcriptional level that have also appeared to exerts key functions in the cancer initiation/progression in diverse biological processes and the tumor microenvironment. This study summarized various roles of miRNAs in the complex interactions between the tumor and normal cells in their microenvironment., (© 2023 John Wiley & Sons Ltd.)
Published: 2023
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39. A Computed Tomography-based Radiomics Analysis of Low-energy Proximal Femur Fractures in the Elderly Patients.

Author: Mohammadi SM, Moniri S, Mohammadhoseini P, Hanafi MG, Farasat M, and Cheki M
Subjects: Humans, Aged, Aged, 80 and over, Radiopharmaceuticals, Tomography, X-Ray Computed methods, Femur diagnostic imaging, Retrospective Studies, Proximal Femoral Fractures
Abstract: Introduction: Low-energy proximal femur fractures in elderly patients result from factors, like osteoporosis and falls. These fractures impose high rates of economic and social costs. In this study, we aimed to build predictive models by applying machine learning (ML) methods on radiomics features to predict low-energy proximal femur fractures., Methods: Computed tomography scans of 40 patients (mean ± standard deviation of age = 71 ± 6) with low-energy proximal femur fractures (before a fracture occurs) and 40 individuals (mean ± standard deviation of age = 73 ± 7) as a control group were included. The regions of interest, including neck, trochanteric, and intertrochanteric, were drawn manually. The combinations of 25 classification methods and 8 feature selection methods were applied to radiomics features extracted from ROIs. Accuracy and the area under the receiver operator characteristic curve (AUC) were used to assess ML models' performance., Results: AUC and accuracy values ranged from 0.408 to 1 and 0.697 to 1, respectively. Three classification methods, including multilayer perceptron (MLP), sequential minimal optimization (SMO), and stochastic gradient descent (SGD), in combination with the feature selection method, SVM attribute evaluation (SAE), exhibited the highest performance in the neck (AUC = 0.999, 0.971 and 0.971, respectively; accuracy = 0.988, 0.988, and 0.988, respectively) and the trochanteric (AUC = 1, 1 and 1, respectively; accuracy = 1, 1 and 1, respectively) regions. The same methods demonstrated the highest performance for the combination of the 3 ROIs' features (AUC = 1, 1 and 1, respectively; accuracy =1, 1 and 1, respectively). In the intertrochanteric region, the combination methods, MLP + SAE, SMO + SAE, and SGD + SAE, as well as the combination of the SAE method and logistic regression (LR) classification method exhibited the highest performance (AUC = 1, 1, 1 and 1, respectively; accuracy= 1, 1, 1 and 1, respectively)., Conclusion: Applying machine learning methods to radiomics features is a powerful tool to predict low-energy proximal femur fractures. The results of this study can be verified by conducting more research on bigger datasets., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)
Published: 2023
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40. 'Inequalities in prevalence of hypertension, prehypertension, anti-hypertensive coverage, awareness, and effective treatment in 429 districts of Iran; a population-based STEPS 2016 small area spatial estimation model'.

Author: Mohammadi E, Yoosefi M, Shaker E, Shahmohamadi E, Ghasemi E, Ahmadi N, Azadnajafabad S, Rashidi MM, Rezaei N, Koolaji S, Dilmaghani-Marand A, Fateh SM, Kazemi A, Haghshenas R, and Rezaei N
Abstract: Purpose: While many studies have reported hypertension (HTN) and pre-hypertension (PHTN) in large geographic locations of Iran, information regarding district levels is missing. We aimed to examine inequalities in the prevalence of hypertension, prehypertension, anti-hypertensive coverage, awareness, and effective treatment of adults in districts of Iran., Methods: We used 27,165 participants' data from the STEPS 2016 study in Iran. A small area estimation model was carried out to predict HTN in the 429 districts of Iran. HTN and PHTN were defined based on the American Heart Association Guideline. Awareness of being hypertensive, treatment coverage, and effective treatment were also estimated., Results: HTN's crude prevalence was estimated to be in the range of 11.5-42.2% in districts. About PHTN, it was estimated to be 19.9-56.1%. Moreover, for awareness, treatment coverage, and effective treatment crude estimates ranged from 24.3 to 79.9%, 9.1 - 64.6%, and 19.5 - 68.3%, respectively, indicating inequalities in the distribution of aforementioned variables in 429 districts of Iran. Overall, better conditions were detected in central geographical locations and in females., Conclusion: The inequality of increased blood pressure disorder and related measures are high in districts of Iran and pave the way for policymakers and local health organizers to use the findings of this study to address the inequity of existing resources and improve HTN control., Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01186-5., Competing Interests: Competing interestAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript., (© The Author(s), under exclusive licence to Tehran University of Medical Sciences 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
Published: 2023
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41. The Effectiveness of Silymarin in the Prevention of Anti-tuberculosis Drug-induced Hepatotoxicity: A Randomized Controlled Clinical Trial.

Author: Talebi A, Soltani R, Khorvash F, and Jouabadi SM
Abstract: Background: Several animal studies have shown the protective effect of silymarin (the extract of Silybum marianum seeds) against anti-tuberculosis drug-induced hepatotoxicity (ATDH). However, the knowledge of ATDH of silymarin in humans is scarce. In this study, we aimed to clinically evaluate it., Methods: During this randomized controlled clinical trial, 36 new cases of tuberculosis (TB) were enrolled to receive either silymarin 150 mg twice daily for two weeks along with a standard anti-TB therapeutic regimen (experimental group; n = 16) or standard anti-TB therapeutic regimen alone (control group; n = 21). Liver function tests (serum AST, ALT, ALP, and total bilirubin) at the end of weeks 1 and 2 as well as the rate of ATDH during the study were determined and compared between the groups., Results: No significant differences between the experimental and control groups were observed at the end of the first week regarding liver function tests; However, at the end of the second week, the mean serum levels of AST ( P = 0.03) and ALP ( P = 0.04) were significantly lower in the experimental group. ALT ( P = 0.016) and ALP ( P = 0.027) levels in the experimental group significantly decreased during the study, while the changes in the control group were not significant. Two patients in the control group (9.5%) developed ATDH, while no one in the experimental group manifested this adverse effect., Conclusions: Our study suggests that silymarin use has the potential for the reduction of anti-TB drug-induced hepatotoxicity., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 International Journal of Preventive Medicine.)
Published: 2023
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42. A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.

Author: Moghadam SM, Yeung T, and Choisne J
Subjects: Humans, Biomechanical Phenomena, Machine Learning, Muscles, Gait physiology, Wearable Electronic Devices
Abstract: A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors' data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets' prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors' data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis., (© 2023. The Author(s).)
Published: 2023
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43. National, subnational and risk attributed burden of chronic respiratory diseases in Iran from 1990 to 2019.

Author: Heidari-Foroozan M, Aryan A, Esfahani Z, Shahrbaf MA, Moghaddam SS, Keykhaei M, Ghasemi E, Rashidi MM, Rezaei N, Ghamari SH, Abbasi-Kangevari M, Fateh SM, Farzi Y, Rezaei N, and Larijani B
Subjects: Male, Female, Humans, Aged, Iran, Quality-Adjusted Life Years, Risk Factors, Global Health, Respiration Disorders, Asthma epidemiology
Abstract: Introduction: Data on the distribution of the burden of diseases is vital for policymakers for the appropriate allocation of resources. In this study, we report the geographical and time trends of chronic respiratory diseases (CRDs) in Iran from 1990 to 2019 based on the Global burden of the Disease (GBD) study 2019., Methods: Data were extracted from the GBD 2019 study to report the burden of CRDs through disability-adjusted life years (DALYs), mortality, incidence, prevalence, Years of Life lost (YLL), and Years Lost to Disability (YLD). Moreover, we reported the burden attributed to the risk factors with evidence of causation at national and subnational levels. We also performed a decomposition analysis to determine the roots of incidence changes. All data were measured as counts and age-standardized rates (ASR) divided by sex and age group., Results: In 2019, the ASR of deaths, incidence, prevalence, and DALYs attributed to CRDs in Iran were 26.9 (23.2 to 29.1), 932.1 (799.7 to 1091.5), 5155.4 (4567.2 to 5859.6) and 587,911 (521,418 to 661,392) respectively. All burden measures were higher in males than females, but in older age groups, CRDs were more incident in females than males. While all crude numbers increased, all ASRs except for YLDs decreased over the studied period. Population growth was the main contributor to the changes in incidence at a national and subnational levels. The ASR of mortality in the province (Kerman) with the highest death rate (58.54 (29.42 to 68.73) was four times more than the province (Tehran) with the lowest death rate (14.52 (11.94 to 17.64)). The risk factors which imposed the most DALYs were smoking (216 (189.9 to 240.8)), ambient particulate matter pollution (117.9 (88.1 to 149.4)), and high body mass index (BMI) (57 (36.3 to 81.8)). Smoking was also the main risk factor in all provinces., Conclusion: Despite the overall decrease in ASR of burden measures, the crude counts are rising. Moreover, the ASIR of all CRDs except asthma is increasing. This suggests that the overall incidence of CRDs will continue to grow in the future, which calls for immediate action to reduce exposure to the known risk factors. Therefore, expanded national plans by policymakers are essential to prevent the economic and human burden of CRDs., (© 2023. The Author(s).)
Published: 2023
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44. The impact of livestock activities and geochemical processes on groundwater quality of fractured volcanic rock aquifer: Lake Çıldır watershed (NE Turkey).

Author: Govarchin SM, Yolcubal İ, Şener A, Sanğu E, Güneş K, and Beşiktaş M
Subjects: Animals, Ecosystem, Lakes, Nitrates, Turkey, Environmental Monitoring, Livestock, Groundwater
Abstract: This paper presents the impact of livestock activities and geochemical processes on the water quality of a fractured volcanic rock aquifer in the Lake Çıldır watershed, located at the northeastern part of Turkey. The existence of a high livestock population and animal grazing activities in meadow and pasturelands of the watershed during the short summer period poses serious stress on both surface and groundwater resources being the only drinking water supply for the local communities. Therefore, understanding the effect of grazing and livestock breeding activities occurring in the recharge areas of the fractured volcanic rock aquifer is vital to take precautions in order to protect limited water supplies at the watershed and vulnerable lake ecosystem as well. The mean nitrate content of the groundwater was measured at 6.4 ± 6.6 (std. dev) mg/L in the wet (before grazing) period and 7.1 ± 5.9 mg/L in the dry (after grazing) period. Despite low nitrate concentration levels of groundwater, microbial contamination was observed in the spring waters at alarming levels especially after the animal grazing activities. 56%, 26%, and 11% of the groundwater samples showed bacterial contamination in terms of total coliform, fecal coliform, and fecal streptococci contents, respectively, prior to grazing activity, while in pursuit of intense livestock grazing at highland, these microbial indicators have been increased to 92%, 85%, and 77% in the dry period. A significant increase observed in fecal contamination indicates the negative impact of livestock activities on groundwater quality. Al (200-638 µg/L) and Fe (66-218 µg/L) enrichments locally observed in groundwater were related to advanced argillic alteration (kaolinization) and hematization zones in pyroclastic rocks., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Published: 2023
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45. Targeted co-delivery of paclitaxel and anti P-gp shRNA by low molecular weight PEI decorated with L-3,4-dihydroxyphenylalanine.

Author: Kazemi M, Parhizkar E, Samani SM, Firuzi O, Sadeghpour H, Ahmadi F, and Dehshahri A
Subjects: Humans, RNA, Small Interfering genetics, Molecular Weight, Plasmids, Polyethyleneimine chemistry, Cell Line, Tumor, Paclitaxel pharmacology, Levodopa pharmacology, Levodopa genetics
Abstract: Co-delivery of small chemotherapeutic molecules and nucleic acid materials via targeted carriers has attracted great attention for treatment of resistant tumors and reducing adverse effects. In this study, a targeted carrier for co-delivery was prepared based on low-molecular weight polyethylenimine (LMW PEI). Paclitaxel (PTX) was covalently conjugated onto PEI via a succinate linker. The PEI conjugate was decorated with L-DOPA in order to target large neutral amino acid transporter-1 (LAT-1) that is over-expressed on various cancer cells. This PEI conjugate was complexed with human ABCB1 shRNA plasmid to down-regulate the expression of P-glycoprotein, as one of the major efflux pumps inducing resistance against chemotherapeutics. The formation of PEI conjugate enhanced the solubility of PTX and resulted in the condensation and protection of plasmid DNA in nanosized polyplexes. The results of targeted delivery into the cells demonstrated that PEI conjugate transferred the payloads to the cells over-expressing LAT-1 transporter, while the biological effects on the cells lacking the transporter was negligible. Also, shRNA-mediated down-regulation of P-gp led to the increase of toxic effects on the cells over-expressing P-gp. This study suggests a promising approach for co-delivery of small molecules and nucleic acid materials in a targeted manner for cancer therapy., (© 2022 American Institute of Chemical Engineers.)
Published: 2023
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46. Survival and prognostic factors among hospitalized pancreatic cancer patients in northwestern Iran.

Author: Vahedi L, Asvadi Kermani T, Asghari-Jafarabadi M, Asghari E, Mohammadi SM, and Khameneh A
Abstract: Background: Pancreatic cancer (PC) is associated with a poor prognosis, with various modifiable risk factors affecting the survival of patients. Our aim was to evaluate the survival rate and the prognostic factors influencing survival in PC patients in northwestern Iran., Materials and Methods: All the PC patients admitted to the Imam Reza Hospital of Tabriz, Iran, from 2016 to 2020, were enrolled in this study. The survival rate and time were calculated, and the risk factors related to survival were evaluated by Cox regressions. The data were analyzed using the Cox proportional hazards model using STATA software., Results: Of 110 patients, 12-, 24-, 36-, and 48-month survival rates were 29.1%, 19.8%, 14.1%, and 8.5%, respectively, with the median survival time of seven months. The mean age was 65.5 years. The results showed that a higher age (hazard ratio [HR] [95% confidence interval (CI)] = 2.04 [1.20-3.46]), lower education (1.72 [1.03-2.89]), delayed diagnosis (1.03 [1.02-1.05]), hypertension (1.53 [1.01-2.31]), concomitant heart disorders (2.67 [1.50-4.74]), COPD (4.23 [1.01-17.69]), consanguineous marriage (1.59 [1.01-2.50]), and the presence of icterus complications (adjusted HR = 3.64 [1.56-8.49]) were directly associated with a worse survival. On the contrary, radiotherapy (0.10 [0.01-0.85]), chemotherapy (0.57 [0.36-0.89]), and surgical therapy (AHR = 0.48 [0.23-0.99]) were directly related to a good prognosis., Conclusion: Surgery, chemotherapy, and radiotherapy were the best predictors of survival in PC patients. Moreover, it seems that resolving jaundice can improve survival in these patients. It seems that increasing social awareness, treating underlying diseases, and employing an appropriate therapeutic method may promise a better outlook, improve the survival rate of patients, and reduce PC risk., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Journal of Research in Medical Sciences.)
Published: 2023
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47. Effects of statins on the incidence and outcomes of acute kidney injury in critically ill patients: a systematic review and meta-analysis.

Author: Vahedian-Azimi A, Beni FH, Fras Z, Banach M, Mohammadi SM, Jamialahmadi T, and Sahebkar A
Abstract: Introduction: In critically ill patients, acute kidney injury (AKI) is a common complication with very high mortality rates. Several studies indicated that statin therapy, primarily due to its so-called pleiotropic effects, may beneficially affect the course of the disease, otherwise leading to significant clinical complications. However, both the original research as well as available meta-analyses on these associations report equivocal results. This leaves open a question whether pre- and perioperative statins might prevent AKI and improve overall prognosis in patients undergoing surgery., Material and Methods: Following a systematic search of the literature, we performed a meta-analysis of selected clinical studies investigating the impact of statin treatment on the development and the clinical outcomes of AKI among subjects undergoing surgeries. The pooled odds ratios (OR) with 95% confidence intervals (CI) for the development of AKI and AKI-associated mortality, as well as the pooled mean differences (MD) and 95% CI for mean intensive care unit (ICU) stay and overall hospital length of stay were calculated for statin users compared to non-users., Results: Our results showed a highly significant association between statin use and the decrease in mortality of patients with AKI (OR = 0.73, 95% CI: 0.69-0.77; p<0.001). The development of AKI (OR = 0.92, 95% CI: 0.63-1.33; p = 0.659) as well as the ICU stay (MD = -0.02, 95% CI: -0.06 - 0.02; p = 0.321) were not significantly affected, while the overall hospital length of stay (MD = -0.49, 95% CI: -0.91 -0.07; p = 0.020) was reduced. Subgroup analysis showed that both pre- and postoperative statin use were not associated with the risk of AKI., Conclusions: Our analysis showed a significant association between statin therapy and overall mortality of critically ill surgical patients diagnosed with AKI, while at the same time the use of statins did not affect the length of their stay in ICU., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2023 Termedia & Banach.)
Published: 2023
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48. A year of experience with COVID-19 in patients with cancer: A nationwide study.

Author: Khosravifar M, Koolaji S, Rezaei N, Ghanbari A, Hashemi SM, Ghasemi E, Bitaraf A, Tabatabaei-Malazy O, Rezaei N, Fateh SM, Dilmaghani-Marand A, Haghshenas R, Kazemi A, Pakatchian E, Kompani F, and Djalalinia S
Subjects: Humans, Male, SARS-CoV-2, Retrospective Studies, Comorbidity, COVID-19 epidemiology, Neoplasms epidemiology
Abstract: Background: Cancer is a major public health problem and comorbidity associated with COVID-19 infection. According to previous studies, a higher mortality rate of COVID-19 in cancer patients has been reported., Aims: This study was undertaken to determine associated risk factors and epidemiological characteristics of hospitalized COVID-19 patients with cancer using a nationwide COVID-19 hospital data registry in Iran for the first time., Methods: In this retrospective study, we used a national data registry of hospitalized patients with Severe Acute Respiratory Syndrome (SARS) symptoms and patients with confirmed positive COVID-19 PCR between 18 February 2020 and 18 November 2020. The patients were classified into two groups patients with/without malignancy. Logistic regression model was utilized to analyze demographic factors, clinical features, comorbidities, and their associations with the disease outcomes., Results: In this study, 11 068 and 645 186 in-patients with SARS symptoms with and without malignancy were included, respectively. About 1.11% of our RT-PCR-positive patients had cancer. In patients with malignancy and COVID-19, older ages than 60 (OR: 1.88, 95% CI: 1.29-2.74, p-value: .001), male gender (OR: 1.43, 95% CI: 1.16-1.77, p-value: .001), concomitant chronic pulmonary diseases (CPD) (OR: 1.75, 95% CI: 1.14-2.68, p-value: .009), and presence of dyspnea (OR; 2.00, 95% CI: 1.60-2.48, p-value: <.001) were associated with increased mortality rate., Conclusion: Given the immunocompromised state of patients with malignancy and their vulnerability to Covid-19 complications, collecting data on the comorbidities and their effects on the disease outcome can build on a better clinical view and help clinicians make decisions to manage these cases better; for example, determining special clinical care, especially in the shortage of health services., (© 2022 The Authors. Cancer Reports published by Wiley Periodicals LLC.)
Published: 2023
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49. Fusion of EEG and Eye Blink Analysis for Detection of Driver Fatigue.

Author: Shahbakhti M, Beiramvand M, Nasiri E, Far SM, Chen W, Sole-Casals J, Wierzchon M, Broniec-Wojcik A, Augustyniak P, and Marozas V
Subjects: Humans, Reproducibility of Results, Algorithms, Databases, Factual, Electroencephalography methods, Wavelet Analysis
Abstract: Objective: The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel., Methods: First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters' tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters., Main Results: The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection., Significance: Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios.
Published: 2023
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50. The effect of different doses of vitamin D on the prognosis of patients undergoing carpal tunnel syndrome surgery.

Author: Noorizadeh K, Mohammadi SM, Seifi M, Moradi MS, and Sadoni H
Abstract: Background and Objective: Vitamin D accelerates myelin repair and recovery after nerve damage. This study aimed to evaluate the effect of vitamin D on the prognosis of patients with carpal tunnel syndrome (CTS)., Methods: A randomized clinical trial was conducted in the orthopedic ward of Golestan and Imam Khomeini hospitals in Ahvaz for 2 years (from October 2018 to October 2020). Patients were divided into three groups: the first group received 1,000 units of vitamin D daily, the second group received 4,000 units of vitamin D per week for the first 4-6 weeks and then 2,000 units per month, and the third group received no vitamin D supplementation. The results were compared before and after 6 months between the study groups., Results: A total of 105 patients were included in the study, who were divided into three groups. The mean age of the patients was 39.24 ± 7.01 years (25-52 years). The mean level of the vitamin D in the control group was 25.40 ± 8.37 ng/mL, the group receiving 1,000 units/day was 26.71 ± 8.70 ng/mL, and the group receiving 50,000 units per week was 26.17 ± 8.63 ng/mL. The mean values of preoperative pain intensity, symptom severity, and functional status were almost the same in the three groups. These values were reduced after surgery in the two groups receiving the drug compared to the control group., Conclusions: The findings of the study showed that the administration of vitamin D supplementation in patients with CTS can significantly improve the postoperative symptoms of patients who underwent tendon release surgery and further improve the severity of symptoms and dysfunction of patients., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Journal of Family Medicine and Primary Care.)
Published: 2022
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