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7. Global, regional, and national mortality due to unintentional carbon monoxide poisoning, 2000–2021: results from the Global Burden of Disease Study 2021

Author

Moberg, Madeline E, Hamilton, Erin B, Zeng, Scott M, Bryazka, Dana, Zhao, Jeff T, Feldman, Rachel, Abate, Yohannes Habtegiorgis, Abbasi-Kangevari, Mohsen, Abdurehman, Ame Mehadi, Abedi, Aidin, Abu-Gharbieh, Eman, Addo, Isaac Yeboah, Adepoju, Abiola Victor, Adnani, Qorinah Estiningtyas Sakilah, Afzal, Saira, Ahinkorah, Bright Opoku, Ahmad, Sajjad, Ahmed, Danial, Ahmed, Haroon, Alem, Dejene Tsegaye, Al-Gheethi, Adel Ali Saeed, Alimohamadi, Yousef, Ameyaw, Edward Kwabena, Amrollahi-Sharifabadi, Mohammad, Anagaw, Tadele Fentabil, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Aravkin, Aleksandr Y, Athari, Seyyed Shamsadin, Atreya, Alok, Azari Jafari, Amirhossein, Badiye, Ashish D, Baghcheghi, Nayereh, Bagherieh, Sara, Bansal, Hansi, Barrow, Amadou, Bashiri, Azadeh, Bayileyegn, Nebiyou Simegnew, Berhie, Alemshet Yirga, Bhagavathula, Akshaya Srikanth, Bhardwaj, Pankaj, Boloor, Archith, Cámera, Luis Alberto, Carvalho, Felix, Carvalho, Márcia, Chandrasekar, Eeshwar K, Chang, Jung-Chen, Chattu, Vijay Kumar, Chu, Dinh-Toi, Coberly, Kaleb, Cruz-Martins, Natália, Dadras, Omid, Dai, Xiaochen, Darvishi Cheshmeh Soltani, Reza, Das, Saswati, Das, Subasish, Debela, Sisay Abebe, Demessa, Berecha Hundessa, Deng, Xinlei, Desta, Abebaw Alemayehu, Desye, Belay, Dhimal, Meghnath, Dibas, Mahmoud, Dsouza, Haneil Larson, Ekholuenetale, Michael, El Sayed, Iman, El-Huneidi, Waseem, Enyew, Daniel Berhanie, Fagbamigbe, Adeniyi Francis, Fatehizadeh, Ali, Fatima, Syeda Anum Fatima, Fischer, Florian, Franklin, Richard Charles, Garg, Tushar, Gebi, Tilaye Gebru, Gerema, Urge, Getachew, Melaku, Getachew, Motuma Erena, Ghamari, Farhad, Golechha, Mahaveer, Goleij, Pouya, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Harorani, Mehdi, Hasani, Hamidreza, Hassan, Abbas M, Hassanian-Moghaddam, Hossein, Hassen, Mohammed Bheser, Hay, Simon I, Hayat, Khezar, Heidari, Mohammad, Heidari-Foroozan, Mahsa, Heyi, Demisu Zenbaba, Holla, Ramesh, Hoogar, Praveen, Hossain, Md Shakhaoat, Hosseini, Mohammad-Salar, Hostiuc, Sorin, Hoveidamanesh, Soodabeh, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Immurana, Mustapha, Iwu, Chidozie C D, Jayarajah, Umesh, Joseph, Nitin, Joshua, Charity Ehimwenma, Kadashetti, Vidya, Kanchan, Tanuj, Kandel, Himal, Kantar, Rami S, Kapoor, Neeti, Karaye, Ibraheem M, Katoto, Patrick DMC, Khajuria, Himanshu, Khan, Ejaz Ahmad, Khateri, Sorour, Khodamoradi, Farzad, Khormali, Moein, Khubchandani, Jagdish, Kim, Grace, Kisa, Adnan, Koohestani, Hamid Reza, Krishan, Kewal, Kumar, Naveen, Laflamme, Lucie, Landires, Iván, Larijani, Bagher, Lauriola, Paolo, Le, Thao Thi Thu, Ledda, Caterina, Lee, Seung Won, Lim, Stephen S, Lobo, Stany W, Lunevicius, Raimundas, Maharaj, Sandeep B, Menezes, Ritesh G, Mentis, Alexios-Fotios A, Mestrovic, Tomislav, Miller, Ted R, Mirmoeeni, Seyyedmohammadsadeq, Misganaw, Awoke, Mishra, Manish, Misra, Sanjeev, Mittal, Chaitanya, Mohammadi, Esmaeil, Mokdad, Ali H, Moni, Mohammad Ali, Mostafavi, Ebrahim, Mubarik, Sumaira, Mulita, Francesk, Mulualem, Jember Azanaw, Mulugeta, Temesgen, Murray, Christopher J L, Myers, Isabella, Nayak, Biswa Prakash, Nayak, Vinod C, Nejadghaderi, Seyed Aria, Nguyen, Huong Lan Thi, Nguyen, Van Thanh, Nouraei, Hasti, Nzoputam, Ogochukwu Janet, Okati-Aliabad, Hassan, Olufadewa, Isaac Iyinoluwa, Ordak, Michal, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pandey, Ashok, Pant, Suman, Parekh, Utsav, Pawar, Shrikant, Peden, Amy E, Petcu, Ionela-Roxana, Piel, Frédéric B, Piracha, Zahra Zahid, Pourali, Ghazaleh, Qattea, Ibrahim, Qureshi, Maryam Faiz, Raghav, Pankaja Raghav, Rahman, Mosiur, Rahmani, Shayan, Ramasubramani, Premkumar, Ramazanu, Sheena, Rawaf, Salman, Rezaei, Nazila, Rezaei, Negar, Rezaeian, Mohsen, Saddik, Basema, Sadeghi, Malihe, Sadeghian, Farideh, Saeed, Umar, Sahebkar, Amirhossein, Saif, Zahra, Sakshaug, Joseph W, Salahi, Saina, Salamati, Payman, Samy, Abdallah M, Sarmiento-Suárez, Rodrigo, Schwebel, David C, Senthilkumaran, Subramanian, Seylani, Allen, Shaikh, Masood Ali, Sham, Sunder, Shashamo, Bereket Beyene, Sheikhi, Rahim Ali, Shetty, B Suresh Kumar, Shetty, Pavanchand H, Sibhat, Migbar Mekonnen, Singh, Harpreet, Singh, Paramdeep, Sisay, Eskinder Ayalew, Solomon, Yonatan, Taheri, Majid, Ullah, Irfan, Ullah, Sana, Violante, Francesco S, Vu, Linh Gia, Wickramasinghe, Nuwan Darshana, Yigit, Arzu, Yonemoto, Naohiro, Yousefi, Zabihollah, Zaman, Muhammad, Zastrozhin, Mikhail Sergeevich, Zhang, Zhi-Jiang, Zheng, Peng, Zoladl, Mohammad, Steinmetz, Jaimie D, Vos, Theo, Naghavi, Mohsen, and Ong, Kanyin Liane

Published
2023
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8. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Author

Ong, Kanyin Liane, Stafford, Lauryn K, McLaughlin, Susan A, Boyko, Edward J, Vollset, Stein Emil, Smith, Amanda E, Dalton, Bronte E, Duprey, Joe, Cruz, Jessica A, Hagins, Hailey, Lindstedt, Paulina A, Aali, Amirali, Abate, Yohannes Habtegiorgis, Abate, Melsew Dagne, Abbasian, Mohammadreza, Abbasi-Kangevari, Zeinab, Abbasi-Kangevari, Mohsen, Abd ElHafeez, Samar, Abd-Rabu, Rami, Abdulah, Deldar Morad, Abdullah, Abu Yousuf Md, Abedi, Vida, Abidi, Hassan, Aboagye, Richard Gyan, Abolhassani, Hassan, Abu-Gharbieh, Eman, Abu-Zaid, Ahmed, Adane, Tigist Demssew, Adane, Denberu Eshetie, Addo, Isaac Yeboah, Adegboye, Oyelola A, Adekanmbi, Victor, Adepoju, Abiola Victor, Adnani, Qorinah Estiningtyas Sakilah, Afolabi, Rotimi Felix, Agarwal, Gina, Aghdam, Zahra Babaei, Agudelo-Botero, Marcela, Aguilera Arriagada, Constanza Elizabeth, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Danish, Ahmad, Rizwan, Ahmad, Sajjad, Ahmad, Aqeel, Ahmadi, Ali, Ahmadi, Keivan, Ahmed, Ayman, Ahmed, Ali, Ahmed, Luai A, Ahmed, Syed Anees, Ajami, Marjan, Akinyemi, Rufus Olusola, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, AL-Ahdal, Tareq Mohammed Ali, Alalwan, Tariq A, Al-Aly, Ziyad, AlBataineh, Mohammad T, Alcalde-Rabanal, Jacqueline Elizabeth, Alemi, Sharifullah, Ali, Hassam, Alinia, Tahereh, Aljunid, Syed Mohamed, Almustanyir, Sami, Al-Raddadi, Rajaa M, Alvis-Guzman, Nelson, Amare, Firehiwot, Ameyaw, Edward Kwabena, Amiri, Sohrab, Amusa, Ganiyu Adeniyi, Andrei, Catalina Liliana, Anjana, Ranjit Mohan, Ansar, Adnan, Ansari, Golnoosh, Ansari-Moghaddam, Alireza, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Aravkin, Aleksandr Y, Areda, Demelash, Arifin, Hidayat, Arkew, Mesay, Armocida, Benedetta, Ärnlöv, Johan, Artamonov, Anton A, Arulappan, Judie, Aruleba, Raphael Taiwo, Arumugam, Ashokan, Aryan, Zahra, Asemu, Mulu Tiruneh, Asghari-Jafarabadi, Mohammad, Askari, Elaheh, Asmelash, Daniel, Astell-Burt, Thomas, Athar, Mohammad, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Avila-Burgos, Leticia, Awaisu, Ahmed, Azadnajafabad, Sina, B, Darshan B, Babamohamadi, Hassan, Badar, Muhammad, Badawi, Alaa, Badiye, Ashish D, Baghcheghi, Nayereh, Bagheri, Nasser, Bagherieh, Sara, Bah, Sulaiman, Bahadory, Saeed, Bai, Ruhai, Baig, Atif Amin, Baltatu, Ovidiu Constantin, Baradaran, Hamid Reza, Barchitta, Martina, Bardhan, Mainak, Barengo, Noel C, Bärnighausen, Till Winfried, Barone, Mark Thomaz Ugliara, Barone-Adesi, Francesco, Barrow, Amadou, Bashiri, Hamideh, Basiru, Afisu, Basu, Sanjay, Basu, Saurav, Batiha, Abdul-Monim Mohammad, Batra, Kavita, Bayih, Mulat Tirfie, Bayileyegn, Nebiyou Simegnew, Behnoush, Amir Hossein, Bekele, Alehegn Bekele, Belete, Melaku Ashagrie, Belgaumi, Uzma Iqbal, Belo, Luis, Bennett, Derrick A, Bensenor, Isabela M, Berhe, Kidanemaryam, Berhie, Alemshet Yirga, Bhaskar, Sonu, Bhat, Ajay Nagesh, Bhatti, Jasvinder Singh, Bikbov, Boris, Bilal, Faiq, Bintoro, Bagas Suryo, Bitaraf, Saeid, Bitra, Veera R, Bjegovic-Mikanovic, Vesna, Bodolica, Virginia, Boloor, Archith, Brauer, Michael, Brazo-Sayavera, Javier, Brenner, Hermann, Butt, Zahid A, Calina, Daniela, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Yin, Cao, Chao, Car, Josip, Carvalho, Márcia, Castañeda-Orjuela, Carlos A, Catalá-López, Ferrán, Cerin, Ester, Chadwick, Joshua, Chandrasekar, Eeshwar K, Chanie, Gashaw Sisay, Charan, Jaykaran, Chattu, Vijay Kumar, Chauhan, Kirti, Cheema, Huzaifa Ahmad, Chekol Abebe, Endeshaw, Chen, Simiao, Cherbuin, Nicolas, Chichagi, Fatemeh, Chidambaram, Saravana Babu, Cho, William C S, Choudhari, Sonali Gajanan, Chowdhury, Rajiv, Chowdhury, Enayet Karim, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Sheng-Chia, Coberly, Kaleb, Columbus, Alyssa, Contreras, Daniela, Cousin, Ewerton, Criqui, Michael H, Cruz-Martins, Natália, Cuschieri, Sarah, Dabo, Bashir, Dadras, Omid, Dai, Xiaochen, Damasceno, Albertino Antonio Moura, Dandona, Rakhi, Dandona, Lalit, Das, Saswati, Dascalu, Ana Maria, Dash, Nihar Ranjan, Dashti, Mohsen, Dávila-Cervantes, Claudio Alberto, De la Cruz-Góngora, Vanessa, Debele, Gebiso Roba, Delpasand, Kourosh, Demisse, Fitsum Wolde, Demissie, Getu Debalkie, Deng, Xinlei, Denova-Gutiérrez, Edgar, Deo, Salil V, Dervišević, Emina, Desai, Hardik Dineshbhai, Desale, Aragaw Tesfaw, Dessie, Anteneh Mengist, Desta, Fikreab, Dewan, Syed Masudur Rahman, Dey, Sourav, Dhama, Kuldeep, Dhimal, Meghnath, Diao, Nancy, Diaz, Daniel, Dinu, Monica, Diress, Mengistie, Djalalinia, Shirin, Doan, Linh Phuong, Dongarwar, Deepa, dos Santos Figueiredo, Francisco Winter, Duncan, Bruce B, Dutta, Siddhartha, Dziedzic, Arkadiusz Marian, Edinur, Hisham Atan, Ekholuenetale, Michael, Ekundayo, Temitope Cyrus, Elgendy, Islam Y, Elhadi, Muhammed, El-Huneidi, Waseem, Elmeligy, Omar Abdelsadek Abdou, Elmonem, Mohamed A, Endeshaw, Destaw, Esayas, Hawi Leul, Eshetu, Habitu Birhan, Etaee, Farshid, Fadhil, Ibtihal, Fagbamigbe, Adeniyi Francis, Fahim, Ayesha, Falahi, Shahab, Faris, MoezAlIslam Ezzat Mahmoud, Farrokhpour, Hossein, Farzadfar, Farshad, Fatehizadeh, Ali, Fazli, Ghazal, Feng, Xiaoqi, Ferede, Tomas Y, Fischer, Florian, Flood, David, Forouhari, Ali, Foroumadi, Roham, Foroutan Koudehi, Masoumeh, Gaidhane, Abhay Motiramji, Gaihre, Santosh, Gaipov, Abduzhappar, Galali, Yaseen, Ganesan, Balasankar, Garcia-Gordillo, MA, Gautam, Rupesh K, Gebrehiwot, Mesfin, Gebrekidan, Kahsu Gebrekirstos, Gebremeskel, Teferi Gebru, Getacher, Lemma, Ghadirian, Fataneh, Ghamari, Seyyed-Hadi, Ghasemi Nour, Mohammad, Ghassemi, Fariba, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Gopalani, Sameer Vali, Guadie, Habtamu Alganeh, Guan, Shi-Yang, Gudayu, Temesgen Worku, Guimarães, Rafael Alves, Guled, Rashid Abdi, Gupta, Rajeev, Gupta, Kartik, Gupta, Veer Bala, Gupta, Vivek Kumar, Gyawali, Bishal, Haddadi, Rasool, Hadi, Najah R, Haile, Teklehaimanot Gereziher, Hajibeygi, Ramtin, Haj-Mirzaian, Arvin, Halwani, Rabih, Hamidi, Samer, Hankey, Graeme J, Hannan, Md Abdul, Haque, Shafiul, Harandi, Hamid, Harlianto, Netanja I, Hasan, S M Mahmudul, Hasan, Syed Shahzad, Hasani, Hamidreza, Hassanipour, Soheil, Hassen, Mohammed Bheser, Haubold, Johannes, Hayat, Khezar, Heidari, Golnaz, Heidari, Mohammad, Hessami, Kamran, Hiraike, Yuta, Holla, Ramesh, Hossain, Sahadat, Hossain, Md Shakhaoat, Hosseini, Mohammad-Salar, Hosseinzadeh, Mehdi, Hosseinzadeh, Hassan, Huang, Junjie, Huda, Md Nazmul, Hussain, Salman, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Ikeda, Nayu, Ilic, Irena M, Ilic, Milena D, Inbaraj, Leeberk Raja, Iqbal, Afrin, Islam, Sheikh Mohammed Shariful, Islam, Rakibul M, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Itumalla, Ramaiah, Iwagami, Masao, Iwu, Chidozie C D, Iyamu, Ihoghosa Osamuyi, Iyasu, Assefa N, Jacob, Louis, Jafarzadeh, Abdollah, Jahrami, Haitham, Jain, Rajesh, Jaja, Chinwe, Jamalpoor, Zahra, Jamshidi, Elham, Janakiraman, Balamurugan, Jayanna, Krishnamurthy, Jayapal, Sathish Kumar, Jayaram, Shubha, Jayawardena, Ranil, Jebai, Rime, Jeong, Wonjeong, Jin, Yinzi, Jokar, Mohammad, Jonas, Jost B, Joseph, Nitin, Joseph, Abel, Joshua, Charity Ehimwenma, Joukar, Farahnaz, Jozwiak, Jacek Jerzy, Kaambwa, Billingsley, Kabir, Ali, Kabthymer, Robel Hussen, Kadashetti, Vidya, Kahe, Farima, Kalhor, Rohollah, Kandel, Himal, Karanth, Shama D, Karaye, Ibraheem M, Karkhah, Samad, Katoto, Patrick DMC, Kaur, Navjot, Kazemian, Sina, Kebede, Sewnet Adem, Khader, Yousef Saleh, Khajuria, Himanshu, Khalaji, Amirmohammad, Khan, Moien AB, Khan, Maseer, Khan, Ajmal, Khanal, Saval, Khatatbeh, Moawiah Mohammad, Khater, Amir M, Khateri, Sorour, khorashadizadeh, Fatemeh, Khubchandani, Jagdish, Kibret, Biruk Getahun, Kim, Min Seo, Kimokoti, Ruth W, Kisa, Adnan, Kivimäki, Mika, Kolahi, Ali-Asghar, Komaki, Somayeh, Kompani, Farzad, Koohestani, Hamid Reza, Korzh, Oleksii, Kostev, Karel, Kothari, Nikhil, Koyanagi, Ai, Krishan, Kewal, Krishnamoorthy, Yuvaraj, Kuate Defo, Barthelemy, Kuddus, Mohammed, Kuddus, Md Abdul, Kumar, Rakesh, Kumar, Harish, Kundu, Satyajit, Kurniasari, Maria Dyah, Kuttikkattu, Ambily, La Vecchia, Carlo, Lallukka, Tea, Larijani, Bagher, Larsson, Anders O, Latief, Kamaluddin, Lawal, Basira Kankia, Le, Thao Thi Thu, Le, Trang Thi Bich, Lee, Shaun Wen Huey, Lee, Munjae, Lee, Wei-Chen, Lee, Paul H, Lee, Sang-woong, Lee, Seung Won, Legesse, Samson Mideksa, Lenzi, Jacopo, Li, Yongze, Li, Ming-Chieh, Lim, Stephen S, Lim, Lee-Ling, Liu, Xuefeng, Liu, Chaojie, Lo, Chun-Han, Lopes, Graciliana, Lorkowski, Stefan, Lozano, Rafael, Lucchetti, Giancarlo, Maghazachi, Azzam A, Mahasha, Phetole Walter, Mahjoub, Soleiman, Mahmoud, Mansour Adam, Mahmoudi, Razzagh, Mahmoudimanesh, Marzieh, Mai, Anh Tuan, Majeed, Azeem, Majma Sanaye, Pantea, Makris, Konstantinos Christos, Malhotra, Kashish, Malik, Ahmad Azam, Malik, Iram, Mallhi, Tauqeer Hussain, Malta, Deborah Carvalho, Mamun, Abdullah A, Mansouri, Borhan, Marateb, Hamid Reza, Mardi, Parham, Martini, Santi, Martorell, Miquel, Marzo, Roy Rillera, Masoudi, Reza, Masoudi, Sahar, Mathews, Elezebeth, Maugeri, Andrea, Mazzaglia, Giampiero, Mekonnen, Teferi, Meshkat, Mahboobeh, Mestrovic, Tomislav, Miao Jonasson, Junmei, Miazgowski, Tomasz, Michalek, Irmina Maria, Minh, Le Huu Nhat, Mini, GK, Miranda, J Jaime, Mirfakhraie, Reza, Mirrakhimov, Erkin M, Mirza-Aghazadeh-Attari, Mohammad, Misganaw, Awoke, Misgina, Kebede Haile, Mishra, Manish, Moazen, Babak, Mohamed, Nouh Saad, Mohammadi, Esmaeil, Mohammadi, Mohsen, Mohammadian-Hafshejani, Abdollah, Mohammadshahi, Marita, Mohseni, Alireza, Mojiri-forushani, Hoda, Mokdad, Ali H, Momtazmanesh, Sara, Monasta, Lorenzo, Moniruzzaman, Md, Mons, Ute, Montazeri, Fateme, Moodi Ghalibaf, AmirAli, Moradi, Yousef, Moradi, Maryam, Moradi Sarabi, Mostafa, Morovatdar, Negar, Morrison, Shane Douglas, Morze, Jakub, Mossialos, Elias, Mostafavi, Ebrahim, Mueller, Ulrich Otto, Mulita, Francesk, Mulita, Admir, Murillo-Zamora, Efrén, Musa, Kamarul Imran, Mwita, Julius C, Nagaraju, Shankar Prasad, Naghavi, Mohsen, Nainu, Firzan, Nair, Tapas Sadasivan, Najmuldeen, Hastyar Hama Rashid, Nangia, Vinay, Nargus, Shumaila, Naser, Abdallah Y, Nassereldine, Hasan, Natto, Zuhair S, Nauman, Javaid, Nayak, Biswa Prakash, Ndejjo, Rawlance, Negash, Hadush, Negoi, Ruxandra Irina, Nguyen, Hau Thi Hien, Nguyen, Dang H, Nguyen, Phat Tuan, Nguyen, Van Thanh, Nguyen, Hien Quang, Niazi, Robina Khan, Nigatu, Yeshambel T, Ningrum, Dina Nur Anggraini, Nizam, Muhammad A, Nnyanzi, Lawrence Achilles, Noreen, Mamoona, Noubiap, Jean Jacques, Nzoputam, Ogochukwu Janet, Nzoputam, Chimezie Igwegbe, Oancea, Bogdan, Odogwu, Nkechi Martina, Odukoya, Oluwakemi Ololade, Ojha, Vivek Anand, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Okonji, Osaretin Christabel, Okwute, Patrick Godwin, Olufadewa, Isaac Iyinoluwa, Onwujekwe, Obinna E, Ordak, Michal, Ortiz, Alberto, Osuagwu, Uchechukwu Levi, Oulhaj, Abderrahim, Owolabi, Mayowa O, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Palladino, Raffaele, Panagiotakos, Demosthenes, Panda-Jonas, Songhomitra, Pandey, Ashok, Pandey, Anamika, Pandi-Perumal, Seithikurippu R, Pantea Stoian, Anca Mihaela, Pardhan, Shahina, Parekh, Tarang, Parekh, Utsav, Pasovic, Maja, Patel, Jay, Patel, Jenil R, Paudel, Uttam, Pepito, Veincent Christian Filipino, Pereira, Marcos, Perico, Norberto, Perna, Simone, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Podder, Vivek, Postma, Maarten J, Pourali, Ghazaleh, Pourtaheri, Naeimeh, Prates, Elton Junio Sady, Qadir, Mirza Muhammad Fahd, Qattea, Ibrahim, Raee, Pourya, Rafique, Ibrar, Rahimi, Mehran, Rahimifard, Mahban, Rahimi-Movaghar, Vafa, Rahman, Md Obaidur, Rahman, Muhammad Aziz, Rahman, Mohammad Hifz Ur, Rahman, Mosiur, Rahman, Md Mosfequr, Rahmani, Mohamed, Rahmani, Shayan, Rahmanian, Vahid, Rahmawaty, Setyaningrum, Rahnavard, Niloufar, Rajbhandari, Bibek, Ram, Pradhum, Ramazanu, Sheena, Rana, Juwel, Rancic, Nemanja, Ranjha, Muhammad Modassar Ali Nawaz, Rao, Chythra R, Rapaka, Deepthi, Rasali, Drona Prakash, Rashedi, Sina, Rashedi, Vahid, Rashid, Ahmed Mustafa, Rashidi, Mohammad-Mahdi, Ratan, Zubair Ahmed, Rawaf, Salman, Rawal, Lal, Redwan, Elrashdy Moustafa Mohamed, Remuzzi, Giuseppe, Rengasamy, Kannan RR, Renzaho, Andre M N, Reyes, Luis Felipe, Rezaei, Nima, Rezaei, Nazila, Rezaeian, Mohsen, Rezazadeh, Hossein, Riahi, Seyed Mohammad, Rias, Yohanes Andy, Riaz, Muhammad, Ribeiro, Daniela, Rodrigues, Mónica, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Rohloff, Peter, Roshandel, Gholamreza, Roustazadeh, Abazar, Rwegerera, Godfrey M, Saad, Aly M A, Saber-Ayad, Maha Mohamed, Sabour, Siamak, Sabzmakan, Leila, Saddik, Basema, Sadeghi, Erfan, Saeed, Umar, Saeedi Moghaddam, Sahar, Safi, Sare, Safi, Sher Zaman, Saghazadeh, Amene, Saheb Sharif-Askari, Narjes, Saheb Sharif-Askari, Fatemeh, Sahebkar, Amirhossein, Sahoo, Soumya Swaroop, Sahoo, Harihar, Saif-Ur-Rahman, KM, Sajid, Mirza Rizwan, Salahi, Sarvenaz, Salahi, Saina, Saleh, Mohamed A, Salehi, Mohammad Amin, Salomon, Joshua A, Sanabria, Juan, Sanjeev, Rama Krishna, Sanmarchi, Francesco, Santric-Milicevic, Milena M, Sarasmita, Made Ary, Sargazi, Saman, Sathian, Brijesh, Sathish, Thirunavukkarasu, Sawhney, Monika, Schlaich, Markus P, Schmidt, Maria Inês, Schuermans, Art, Seidu, Abdul-Aziz, Senthil Kumar, Nachimuthu, Sepanlou, Sadaf G, Sethi, Yashendra, Seylani, Allen, Shabany, Maryam, Shafaghat, Tahereh, Shafeghat, Melika, Shafie, Mahan, Shah, Nilay S, Shahid, Samiah, Shaikh, Masood Ali, Shanawaz, Mohd, Shannawaz, Mohammed, Sharfaei, Sadaf, Shashamo, Bereket Beyene, Shiri, Rahman, Shittu, Aminu, Shivakumar, K M, Shivalli, Siddharudha, Shobeiri, Parnian, Shokri, Fereshteh, Shuval, Kerem, Sibhat, Migbar Mekonnen, Silva, Luís Manuel Lopes Rodrigues, Simpson, Colin R, Singh, Jasvinder A, Singh, Paramdeep, Singh, Surjit, Siraj, Md Shahjahan, Skryabina, Anna Aleksandrovna, Sohag, Abdullah Al Mamun, Soleimani, Hamidreza, Solikhah, Solikhah, Soltani-Zangbar, Mohammad Sadegh, Somayaji, Ranjani, Sorensen, Reed J D, Starodubova, Antonina V, Sujata, Sujata, Suleman, Muhammad, Sun, Jing, Sundström, Johan, Tabarés-Seisdedos, Rafael, Tabatabaei, Seyyed Mohammad, Tabatabaeizadeh, Seyed-Amir, Tabish, Mohammad, Taheri, Majid, Taheri, Ensiyeh, Taki, Elahe, Tamuzi, Jacques JL Lukenze, Tan, Ker-Kan, Tat, Nathan Y, Taye, Birhan Tsegaw, Temesgen, Worku Animaw, Temsah, Mohamad-Hani, Tesler, Riki, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapa, Rajshree, Tharwat, Samar, Thomas, Nihal, Ticoalu, Jansje Henny Vera, Tiyuri, Amir, Tonelli, Marcello, Tovani-Palone, Marcos Roberto, Trico, Domenico, Trihandini, Indang, Tripathy, Jaya Prasad, Tromans, Samuel Joseph, Tsegay, Guesh Mebrahtom, Tualeka, Abdul Rohim, Tufa, Derara Girma, Tyrovolas, Stefanos, Ullah, Sana, Upadhyay, Era, Vahabi, Seyed Mohammad, Vaithinathan, Asokan Govindaraj, Valizadeh, Rohollah, van Daalen, Kim Robin, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vaziri, Siavash, Verma, Madhur verma, Verras, Georgios-Ioannis, Vo, Danh Cao, Wagaye, Birhanu, Waheed, Yasir, Wang, Ziyue, Wang, Yanqing, Wang, Cong, Wang, Fang, Wassie, Gizachew Tadesse, Wei, Melissa Y Wei, Weldemariam, Abrha Hailay, Westerman, Ronny, Wickramasinghe, Nuwan Darshana, Wu, YiFan, Wulandari, Ratna DWI, Xia, Juan, Xiao, Hong, Xu, Suowen, Xu, Xiaoyue, Yada, Dereje Y, Yang, Lin, Yatsuya, Hiroshi, Yesiltepe, Metin, Yi, Siyan, Yohannis, Hunachew Kibret, Yonemoto, Naohiro, You, Yuyi, Zaman, Sojib Bin, Zamora, Nelson, Zare, Iman, Zarea, Kourosh, Zarrintan, Armin, Zastrozhin, Mikhail Sergeevich, Zeru, Naod Gebrekrstos, Zhang, Zhi-Jiang, Zhong, Chenwen, Zhou, Jingjing, Zielińska, Magdalena, Zikarg, Yossef Teshome, Zodpey, Sanjay, Zoladl, Mohammad, Zou, Zhiyong, Zumla, Alimuddin, Zuniga, Yves Miel H, Magliano, Dianna J, Murray, Christopher J L, Hay, Simon I, and Vos, Theo

Published
2023
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12. Prevalence and associated factors of cigarette smoking and substance use among university entrance test-taking students: A GIS-based study.

Author

Mamun, Mohammed A., Roy, Nitai, Gozal, David, Almerab, Moneerah Mohammad, Hossain, Md. Shakhaoat, and Al Mamun, Firoj

Subjects
TRANSITION to adulthood, SUBSTANCE abuse, CONVENIENCE sampling (Statistics), SUICIDAL behavior, MENTAL health education
Abstract

Background: Numerous studies have examined substance use and smoking behavior among adolescents and university students. However, little is known about these behaviors among students undergoing university entrance tests, a critical transition period from adolescence to adulthood. The entrance test can significantly affect students' mental health, potentially leading to substance use. This study aims to investigate the prevalence of cigarette smoking and substance use among students taking these exams and the associated factors. Methods: A cross-sectional survey was carried out on September 4th and 11th, 2022 to collect data from 1,480 university entrance test-taking students using a convenience sampling technique. Chi-square tests and logistic regression were conducted using SPSS software. Besides, GIS mapping was used to visualize the distribution of substance use and smoking behavior across districts via ArcGIS. Results: The study found a 10% prevalence of current tobacco smoking and 4% substance use. Females (OR = 1.98; 95% CI: 1.38–2.85), urban residence (OR = 2.03; 95% CI: 1.42–2.88), repeater (OR = 1.45; 95% CI: 1.02–2.06), anxiety (OR = 1.55, 95% CI: 1.10–2.19), burnout (OR = 1.51, 95% CI: 1.00–2.12), and suicidal behavior (OR = 1.57; 95% CI: 1.03–2.40) were the significant factors for cigarette use. Whereas the urban residence (OR = 1.91; 95% CI: 1.11–3.31), anxiety (OR = 2.47, 95% CI: 1.45–4.20), and suicidal behavior (OR = 2.76; 95% CI: 1.55–4.92) significantly increased the risk of substance use. GIS analysis revealed males varied in substance use and females in tobacco smoking by district. Repeat test-takers were associated with district variations in both smoking and substance use. Conclusions: Educational institutions, public health authorities, and policymakers must implement mental health support and substance use prevention programs for students. Integrating mental health education, providing resources, and enforcing regulations can promote healthier coping strategies and reduce substance use risks among students. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Investigation and detection of multiple antibiotic-resistant pathogenic bacteria in municipal wastewater of Dhaka city.

Author

Siddique, Abu Bakkar, Munni, Atia, Hasan, Maruf, Raj, Rayhan, Mutalib, Md. Abdul, Sikder, Md. Tajuddin, Okino, Tatsufumi, Ahmed, Ayesha, and Hossain, Md. Shakhaoat

Subjects
SEWAGE, PATHOGENIC bacteria, DRUG resistance in bacteria, COLIFORMS, ESCHERICHIA coli, FECAL contamination, PUBLIC health officers
Abstract

Background: Water pollution in densely populated urban areas, mainly from municipal wastewater, poses a significant threat. Pathogenic bacteria, such as Vibrio spp. and fecal coliform, endanger public health and the environment. Additionally, antibiotic-resistant bacteria in wastewater complicate treatment and heighten public health concerns. Methods: The study sampled municipal wastewater from ten Dhaka neighborhoods, selecting treatment plants, sewage outlets, and various collection points using meticulous techniques for representative samples. Bacteriological and biochemical analyses were conducted using standardized methods. Antimicrobial susceptibility testing (AST) was performed with the disk diffusion method against 13 widely used antibiotics. Results: All sampled areas exhibited positive results for Vibrio spp., fecal coliform, E. coli, and Salmonella spp. Varying bacterial concentrations were observed, with the highest concentration of TVC, total vibrio spp., and total fecal coliform, total E. coli count, and total Salmonella spp. were found in Uttara (1.9 × 104 CFU/ml), Bangshal (1.8 × 102 CFU/ml), and Lalbag (2.1 × 103 CFU/ml), Mirpur (3.70 × 102 CFU/ml), and Lalbag (6 × 102 CFU/ml) respectively. AST results revealed significant resistance among all bacterial species to various antibiotics. Specifically, Vibrio spp. showed 100% resistance to cefuroxime, fecal coliform exhibited 90% resistance to cephradine, E. coli demonstrated 60% resistance to cephradine, and Salmonella spp. displayed 90% resistance to ampicillin. Conclusion: The study highlights the existence of multiple antibiotic-resistant bacteria in Dhaka's wastewater. Addressing antibiotic resistance is essential to manage the risks of multiple antibiotic-resistant infections and maintain antibiotic effectiveness. These implications are critical for various stakeholders, including public health officials, policymakers, environmentalists, and urban planners. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Inter-and Intra-Individual Variability of Personal Health Risk of Combined Particle and Gaseous Pollutants across Selected Urban Microenvironments

Author

Hossain, Md Shakhaoat, Che, Wenwei, Lau, Alexis Kai Hon, Hossain, Md Shakhaoat, Che, Wenwei, and Lau, Alexis Kai Hon

Abstract

Exposure surrogates, such as air quality measured at a fixed-site monitor (FSM) or residence, are typically used for health estimates. However, people spend various amounts of time in different microenvironments, including the home, office, outdoors and in transit, where they are exposed to different magnitudes of particle and gaseous air pollutants. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as the toxicity of pollutants. We evaluated individual and combined added health risks (AR) of exposure to PM2.5, NO2, and O3 for 21 participants in their daily life based on real-world personal exposure measurements. Exposure errors from using surrogates were quantified. Inter-and intra-individual variability in health risks and key contributors in variations were investigated using linear mixed-effects models and correlation analysis, respectively. Substantial errors were found between personal exposure concentrations and ambient concentrations when using air quality measurements at either FSM or the residence location. The mean exposure errors based on the measurements taken at either the FSM or residence as exposure surrogates was higher for NO2 than PM2.5, because of the larger spatial variability in NO2 concentrations in urban areas. The daily time-integrated AR for the combined PM2.5, NO2, and O3 (TIARcombine) ranged by a factor of 2.5 among participants and by a factor up to 2.5 for a given person across measured days. Inter-and intra-individual variability in TIARcombine is almost equally important. Several factors were identified to be significantly correlated with daily TIARcombine, with the top five factors, including PM2.5, NO2 and O3 concentrations at ‘home indoor’, O3 concentrations at ‘office indoor’ and ambient PM2.5 concentrations. The results on the contributors of variability in the daily TIARcombine could help in targeting interventions to reduce daily heal

Published
2022

19. Procedure to Quantify Variability in Air Pollution Infiltration Factors for a Selected Pollutant and Selected Homes

Author

Eluri, Sailaja, Christopher, Frey H., Che, Wenwei, Hossain, Md Shakhaoat, Lau, Alexis Kai Hon, Eluri, Sailaja, Christopher, Frey H., Che, Wenwei, Hossain, Md Shakhaoat, and Lau, Alexis Kai Hon

Abstract

Exposure to air pollutants causes adverse human health effects. Indoor air pollution is caused by infiltration of ambient air pollution indoors and indoor generated sources (e.g., cooking). Infiltration of ambient air pollution into homes depends on type and age of home, pollutant, and type of ventilation. Stochastic exposure models quantify variability in air pollutant exposures for a microenvironment based on probabilistic inputs of pollutant infiltration factors. This work develops a procedure to quantify inter-home variability in air pollution infiltration factors. The procedure was developed and demonstrated for a selected pollutant and selected homes in a selected city, with a focus on PM2.5 and selected ventilation conditions. Infiltration factors were estimated based on simultaneous indoor and outdoor concentrations with different averaging times. Three scenarios were implemented: (1) linear regression for all homes combined; (2) linear regression for each home individually; and (3) use of a linear mixed effects model (LMM). The procedure developed in this work will help in quantification of air pollution infiltration factor distributions for indoor microenvironments for use in stochastic exposure models. © 2022 Air and Waste Management Association. All rights reserved.

Published
2022

20. Investigation of exposure variability of gaseous and particulate pollutants through field campaigns using next generation sensors

Author

Lau, Alexis Kai Hon, Fung, Jimmy Chi Hung, Hossain, Md Shakhaoat, Lau, Alexis Kai Hon, Fung, Jimmy Chi Hung, and Hossain, Md Shakhaoat

Abstract

Air pollution is a leading environmental risk factor for premature death globally. People are typically exposed to gaseous and particulate pollutants simultaneously in the real-world. People spend various time in different microenvironments, including home, office, transit, others and outdoor. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as toxicity of pollutants. Most of the existing individual exposure studies were conducted on particulate matter (PM). Limited information is available on personal exposure variability for gaseous pollutants (i.e., NO2 and O3) due to complexity and difficulty in measuring those pollutants. Using added health risk (AR) model, we evaluated short-term health risk of NO2, O3 and PM2.5 based on ambient concentrations in urban areas with dense traffic and less urbanized areas. Although PM2.5 has a significant long-term health risk, NO2 and O3 are more predominant in short-term health risk than PM2.5. Thus, with the recent technological advancement, we measured real-world personal exposure to both gaseous and particulate pollutants using next generation sensors across 21 participants in their daily life. We quantified health risk of combined exposure to NO2, O3 and PM2.5 using AR model. Inter-and intra-individual variability in health risks and sources of variations were investigated using linear mixed-effects models and correlation analysis, respectively. Daily time-integrated AR for combined NO2, O3 and PM2.5 (TIARcombine) ranged by a factor of 2.5 among participants and by a factor of 1.0 to 2.5 for a given person across measured days. Several factors were identified to be significantly correlated with daily TIARcombine, with the top 5 factors including N

Published
2021

21. Physical distancing implementation, ambient temperature and Covid-19 containment: An observational study in the United States

Author

Guo, Cui, Chan, Shin Heng Teresa, Lin, Changqing, Zeng, Yiqian, Bo, Yacong, Zhang, Yumiao, Hossain, Md Shakhaoat, Chan, Wai Man, Yeung, David W., Lau, Alexis Kai Hon, Lao, Xiang Qian, Guo, Cui, Chan, Shin Heng Teresa, Lin, Changqing, Zeng, Yiqian, Bo, Yacong, Zhang, Yumiao, Hossain, Md Shakhaoat, Chan, Wai Man, Yeung, David W., Lau, Alexis Kai Hon, and Lao, Xiang Qian

Abstract

Governments may relax physical distancing interventions for coronavirus disease 2019 (Covid-19) containment in warm seasons/areas to prevent economic contractions. However, it is not clear whether higher temperature may offset the transmission risk posed by this relaxation. This study aims to investigate the associations of the effective reproductive number (Rt) of Covid-19 with ambient temperature and the implementation of physical distancing interventions in the United States (US). This study included 50 states and one territory of the US with 4,532,650 confirmed cases between 29 January and 31 July 2020. We used an interrupted time-series model with a state-level random intercept for data analysis. An interaction term of ‘physical distancing×temperature’ was included to examine their interactions. Stratified analyses by temperature and physical distancing implementation were also performed to analyse the modifying effects. The overall median (interquartile range) Rt was 1.2 (1.0–2.3). The implementation of physical distancing was associated with a 12% decrease in the risk of Rt (relative risk [RR]: 0.88, 95% confident interval [CI]: 0.86–0.89), and each 5 °C increase in temperature was associated with a 2% decrease (RR: 0.98, 95%CI: 0.97–0.98). We observed a statistically significant interaction between temperature and physical distancing implementation, but all the RRs were small (close to one). The containing effects of high temperature were attenuated by 5.1% when physical distancing was implemented. The association of COVID-19 Rt with physical distancing implementation was more stable (0.88 vs. 0.89 in days when temperature was low and high, respectively). Increased temperature did not offset the risk of Covid-19 Rt posed by the relaxation of physical distancing implementation. Our study does not recommend relaxing the implementation of physical distancing interventions in warm seasons/areas. © 2021 Elsevier B.V.

Published
2021

22. Meteorological factors and COVID-19 incidence in 190 countries: an observational study

Author

Guo, Cui, Bo, Yacong, Lin, Changqing, Li, Hao Bi, Zeng, Yiqian, Zhang, Yumiao, Hossain, Md Shakhaoat, Chan, Wai Man, Yeung, David W., Kwok, Kin-On, Wong, Samuel Y.S., Lau, Alexis Kai Hon, Lao, Xiangqian, Guo, Cui, Bo, Yacong, Lin, Changqing, Li, Hao Bi, Zeng, Yiqian, Zhang, Yumiao, Hossain, Md Shakhaoat, Chan, Wai Man, Yeung, David W., Kwok, Kin-On, Wong, Samuel Y.S., Lau, Alexis Kai Hon, and Lao, Xiangqian

Abstract

Novel corona virus disease 2019 (COVID-19), which first emerged in December 2019, has become a pandemic. This study aimed to investigate the associations between meteorological factors and COVID-19 incidence and mortality worldwide. This study included 1,908,197 confirmed cases of and 119,257 deaths from COVID-19 from 190 countries between 23 January and 13 April, 2020. We used a distributed lag non-linear model with city-/country-level random intercept to investigate the associations between COVID19 incidence and daily temperature, relative humidity, and wind speed. A series of confounders were considered in the analysis including demographics, socioeconomics, geographic locations, and political strategies. Sensitivity analyses were performed to examine the robustness of the associations. The COVID-19 incidence showed a stronger association with temperature than with relative humidity or wind speed. An inverse association was identified between the COVID-19 incidence and temperature. The corresponding 14-day cumulative relative risk was 1.28 [95% confidence interval (CI), 1.20–1.36] at 5 °C, and 0.75 (95% CI, 0.65–0.86) at 22 °C with reference to the risk at 11 °C. An inverse J-shaped association was observed between relative humidity and the COVID-19 incidence, with the highest risk at 72%. A higher wind speed was associated with a generally lower incidence of COVID-19, although the associations were weak. Sensitivity analyses generally yielded similar results. The COVID-19 incidence decreased with the increase of temperature. Our study suggests that the spread of COVID-19 may slow during summer but may increase during winter.

Published
2021

23. Air Quality and Synergistic Health Effects of Ozone and Nitrogen Oxides in Response to China’s Integrated Air Quality Control Policies During 2015-2019

Author

Zhang, Xuguo, Fung, Jimmy Chi Hung, Lau, Alexis Kai Hon, Hossain, Md Shakhaoat, Louie, Kwok Keung Peter, Huang, Wei, Zhang, Xuguo, Fung, Jimmy Chi Hung, Lau, Alexis Kai Hon, Hossain, Md Shakhaoat, Louie, Kwok Keung Peter, and Huang, Wei

Abstract

O3 pollution had been worsening in mainland China in the past decade, posing significant human health challenges. The NOx control would trigger increasing O3 concentrations in response to a series of released China emission reduction policies. This study used sensitivity analysis methodology to explore the effectiveness of integrated sectoral emission control policies that have been expanded throughout China. Air quality and synergistic health effects of O3 and NO2 were investigated to obtain an in-depth understanding of the O3 control, especially under a VOC-limited regime. The findings demonstrated that although the NOx-titration effect triggered an increase in O3, the combined health effects of two pollutants tended to improve in most regions of China under a VOC-limited regime. The region-based annual average NO2 concentrations exhibited a larger reduction in Hong Kong (HK) than in the Pearl River Delta Economic Zone (PRD EZ). The short-term measures led to substantial health benefits for Shenzhen and HK. The sectoral emission controls demonstrated a considerable health improvement for the major PRD EZ cities. Joint national control efforts confined the domain-wide health risks below the safety line in China. National cooperative efforts in China could avoid more than 1.5–2% of the emergency hospital admissions for cardiovascular and respiratory diseases attributed to NO2 and O3 exposure. The observed O3 increases due to the NOx-titration effect for calculating the integral health effects of emission control on concentration reduction called for simultaneously strengthened controls on both NOx and VOC in areas subject to a VOC-limited regime

Published
2021

27. Combined effects of increased O3 and reduced NO>sub>2 concentrations on short-term air pollution health risks in Hong Kong

Author

Hossain, Md Shakhaoat, Frey, Henry Christopher, Louie, Peter K.K., Lau, Alexis Kai Hon, Hossain, Md Shakhaoat, Frey, Henry Christopher, Louie, Peter K.K., and Lau, Alexis Kai Hon

Abstract

The reduction of NOx emissions in a VOC-limited region can lead to an increase of the local O3 concentration. An evaluation of the net health effects of such pollutant changes is therefore important to ascertain whether the emission control measures effectively improve the overall protection of public health. In this study, we use a short-term health risk (added health risk or AR) model developed for the multi-pollutant air quality health index (AQHI) in Hong Kong to examine the overall health impacts of these pollutant changes. We first investigate AR changes associated with NO2 and O3 changes, followed by those associated with changes in all four AQHI pollutants (NO2, O3, SO2, and particulate matter (PM)). Our results show that for the combined health effects of NO2 and O3 changes, there is a significant reduction in AR in urban areas with dense traffic, but no statistically significant changes in other less urbanized areas. The increase in estimated AR for higher O3 concentrations is offset by a decrease in the estimated AR for lower NO2 concentrations. In areas with dense traffic, the reduction in AR as a result of decreased NO2 is substantially larger than the increase in AR associated with increased O3. When additionally accounting for the change in ambient SO2 and PM, we found a statistically significant reduction in total AR everywhere in Hong Kong. Our results show that the emission control measures resulting in NO2, SO2, and PM reductions over the past decade have effectively reduced the AR over Hong Kong, even though these control measures may have partially contributed to an increase in O3 concentrations. Hence, efforts to reduce NOx, SO2, and PM should be continued. © 2020 Elsevier Ltd

Published
2020

28. A Mechanism-Based Parameterisation Scheme to Investigate the Association Between Transmission Rate of COVID-19 and Meteorological Factors on Plains in China

Author

Lin, Changqing, Lau, Alexis Kai Hon, Fung, Jimmy Chi Hung, Guo, Cui, Chan, Wai Man, Yeung, David W., Zhang, Yumiao, Bo, Yacong, Hossain, Md Shakhaoat, Zeng, Yiqian, Lao, Xiang Qian, Lin, Changqing, Lau, Alexis Kai Hon, Fung, Jimmy Chi Hung, Guo, Cui, Chan, Wai Man, Yeung, David W., Zhang, Yumiao, Bo, Yacong, Hossain, Md Shakhaoat, Zeng, Yiqian, and Lao, Xiang Qian

Abstract

The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, −0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between −9.41 °C and −13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge.

Published
2020

29. PRAISE-HK: A Personalized Real-Time Air Quality Informatics System for Citizen Participation in Exposure and Health Risk Management

Author

Che, Wenwei, Frey, Henry Christopher, Fung, Jimmy Chi Hung, Ning, Zhi, Qu, Huamin, Lo, Hong Kam, Chen, Lei, Wong, Kit Man, Lee, Ophelia C.W., Chan, Wai Man, Yeung, David W., Fung, Yik Him, Zhang, Xuguo, Hohenberger, Tilman Leo, Leung, King Wai, Louie, Phillip Yan Kit, Li, Alison T.Y., Sun, Li, Wei, Peng, Li, Zhiyuan, Zhang, Yumiao, Wang, Meilan, Shen, Qiaomu, Huang, Wei, Lee, Enoch, Patwary, Ashraf Uz Zaman, Lei, Xiayu, Cheng, Steven, Hossain, Md Shakhaoat, Tang, Kimberly Tasha Jiayi, Leung, Chi Wan, Chan, Kwan Yung Denise, Lau, Alexis Kai Hon, Che, Wenwei, Frey, Henry Christopher, Fung, Jimmy Chi Hung, Ning, Zhi, Qu, Huamin, Lo, Hong Kam, Chen, Lei, Wong, Kit Man, Lee, Ophelia C.W., Chan, Wai Man, Yeung, David W., Fung, Yik Him, Zhang, Xuguo, Hohenberger, Tilman Leo, Leung, King Wai, Louie, Phillip Yan Kit, Li, Alison T.Y., Sun, Li, Wei, Peng, Li, Zhiyuan, Zhang, Yumiao, Wang, Meilan, Shen, Qiaomu, Huang, Wei, Lee, Enoch, Patwary, Ashraf Uz Zaman, Lei, Xiayu, Cheng, Steven, Hossain, Md Shakhaoat, Tang, Kimberly Tasha Jiayi, Leung, Chi Wan, Chan, Kwan Yung Denise, and Lau, Alexis Kai Hon

Abstract

Exposure to air pollutants causes a range of adverse health effects. These harmful effects occur whenever and wherever people come into direct contact with air pollution. Therefore, individual actions that reduce the frequency, duration, and severity of personal contact with air pollution can reduce health risks. We developed a system that empowers the public with personalized information on air quality and exposure health risk. This system, the Personalised Real-Time Air Quality Informatics System for Exposure – Hong Kong (PRAISE-HK, http://praise.ust.hk/), is embodied in an interactive mobile application. PRAISE-HK is based on real-time data on emissions, high resolution urban morphology, meteorology, physical and chemical processes affecting pollutant transport and transformations, extensive measurements of air pollution concentrations in typical locations such as homes, schools, offices, and transportation, and big data integration of sensor monitoring to accurately estimate current and short-term forecasted street-level air quality. The street-level air quality simulation has been validated against reference monitoring data. Ongoing and planned future enhancements to PRAISE-HK include prediction of personal exposure and health response. PRAISE-HK is an example of the use of collective intelligence in a smart city to engage citizens in learning about and managing their own exposure to air pollution.

Published
2020

30. Meteorological factors and COVID-19 incidence in 190 countries: An observational study

Author

Guo, Cui, primary, Bo, Yacong, additional, Lin, Changqing, additional, Li, Hao Bi, additional, Zeng, Yiqian, additional, Zhang, Yumiao, additional, Hossain, Md Shakhaoat, additional, Chan, Jimmy W.M., additional, Yeung, David W., additional, Kwok, Kin-on, additional, Wong, Samuel Y.S., additional, Lau, Alexis K.H., additional, and Lao, Xiang Qian, additional

Published
2021
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32. A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China

Author

Lin, Changqing, primary, Lau, Alexis K.H., additional, Fung, Jimmy C.H., additional, Guo, Cui, additional, Chan, Jimmy W.M., additional, Yeung, David W., additional, Zhang, Yumiao, additional, Bo, Yacong, additional, Hossain, Md Shakhaoat, additional, Zeng, Yiqian, additional, and Lao, Xiang Qian, additional

Published
2020
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33. Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools

Author

Che, Wenwei, primary, Li, Alison T. Y., additional, Frey, Henry Christopher, additional, Tang, Kimberly Tasha Jiayi, additional, Sun, Li, additional, Wei, Peng, additional, Hossain, Md Shakhaoat, additional, Hohenberger, Tilman Leo, additional, Leung, King Wai, additional, and Lau, Alexis K. H., additional

Published
2020
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35. Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools.

Author

Che, Wenwei, Li, Alison T. Y., Frey, Henry Christopher, Tang, Kimberly Tasha Jiayi, Sun, Li, Wei, Peng, Hossain, Md Shakhaoat, Hohenberger, Tilman Leo, Leung, King Wai, and Lau, Alexis K. H.

Subjects
AIR pollutants, MICROPOLLUTANTS, SECONDARY schools, PRIMARY schools, AIR pollution, INDOOR air pollution, PARTICULATE matter, INDOOR air quality
Abstract

School‐age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real‐time PM10, PM2.5, NO2, and O3 concentrations were measured in 76 classrooms and 23 non‐classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM2.5, PM10, O3, and NO2 microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM2.5, PM10, O3, and NO2 microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations. [ABSTRACT FROM AUTHOR]

Published
2021
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40. Exploring mental health literacy among prospective university students using GIS techniques in Bangladesh: an exploratory study.

Author

Mamun MA, Al-Mamun F, Ikram T, Trisha MK, Limon MH, Mostofa NB, Chowdhury TBK, Shanto NP, ALmerab MM, Apou AC, Roy N, Hossain MB, and Hossain MS

Abstract

Prospective university students experience substantial academic stressors and psychological vulnerabilities, yet their mental health literacy (MHL) remains inadequately explored. This study investigates four dimensions of MHL - help-seeking behaviors, stigma, knowledge about mental health and understanding of mental illnesses. Besides, Geographic Information System (GIS) techniques are employed to analyze spatial disparities in MHL, which is the first in the context of MHL research. A total of 1,485 students were assessed for sociodemographic characteristics, admission-related variables, health behaviors and family histories of mental health issues. Data were analyzed using SPSS and ArcGIS software. Multivariable linear regression analyses unveiled predictors of the MHL dimensions, with gender, family income, admission test performance, smoking, alcohol and drug use, physical and mental health history, current depression or anxiety and family history of mental health and suicide incidents emerging as common predictors. GIS analysis unraveled notable regional disparities in MHL, particularly in knowledge of mental health and mental illness, with northern and some southern districts displaying higher literacy levels. In conclusion, these findings accentuate significant gender and sociodemographic inequalities in MHL among prospective university students, highlighting the imperative for targeted interventions to enhance MHL and foster mental well-being in this cohort., Competing Interests: The authors declare none., (© The Author(s) 2024.)

Published
2024
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41. Combined effects of increased O 3 and reduced NO 2 concentrations on short-term air pollution health risks in Hong Kong.

Author

Hossain MS, Frey HC, Louie PKK, and Lau AKH

Subjects
Hong Kong, Nitrogen Dioxide analysis, Particulate Matter analysis, Air Pollutants analysis, Air Pollution analysis, Ozone analysis
Abstract

The reduction of NO x emissions in a VOC-limited region can lead to an increase of the local O 3 concentration. An evaluation of the net health effects of such pollutant changes is therefore important to ascertain whether the emission control measures effectively improve the overall protection of public health. In this study, we use a short-term health risk (added health risk or AR) model developed for the multi-pollutant air quality health index (AQHI) in Hong Kong to examine the overall health impacts of these pollutant changes. We first investigate AR changes associated with NO 2 and O 3 changes, followed by those associated with changes in all four AQHI pollutants (NO 2 , O 3 , SO 2 , and particulate matter (PM)). Our results show that for the combined health effects of NO 2 and O 3 changes, there is a significant reduction in AR in urban areas with dense traffic, but no statistically significant changes in other less urbanized areas. The increase in estimated AR for higher O 3 concentrations is offset by a decrease in the estimated AR for lower NO 2 concentrations. In areas with dense traffic, the reduction in AR as a result of decreased NO 2 is substantially larger than the increase in AR associated with increased O 3 . When additionally accounting for the change in ambient SO 2 and PM, we found a statistically significant reduction in total AR everywhere in Hong Kong. Our results show that the emission control measures resulting in NO 2 , SO 2 , and PM reductions over the past decade have effectively reduced the AR over Hong Kong, even though these control measures may have partially contributed to an increase in O 3 concentrations. Hence, efforts to reduce NOx, SO 2 , and PM should be continued., 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 © 2020 Elsevier Ltd. All rights reserved.)

Published
2021
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42. Retracted Human Health Risk of Chromium Intake From Consumption of Poultry Meat and Eggs in Dhaka, Bangladesh.

Author

Hossain MS, Roy P, Islam M, Chowdhury MAZ, Fardous Z, Rahman MA, Saifullah ASM, Hasan M, and Rahman MM

Abstract

Background: Heavy metals contamination of food is a serious threat. Long term exposure may lead to human health risks. Poultry and eggs are a major source of protein, but if contaminated by heavy metals, have the potential to lead to detrimental effects on human health., Objectives: The objective of this study is to determine chromium concentrations in poultry meat (flesh and liver) and eggs collected from poultry farms in Dhaka, Bangladesh, to calculate the daily intake of chromium from the consumption of poultry meat and eggs for adults, and to evaluate their potential health risk by calculating the target hazard quotients (THQ)., Methods: All samples of poultry feed, meat (flesh and liver) and eggs were analyzed by a graphite furnace atomic absorption spectrometer (AAS) (GFA- EX- 7i Shimadju, Japan)., Results: Chromium concentrations were recorded in the range of not detected (ND) to 1.3926±0.0010 mg kg -1 and 0.0678±0.0001 mg kg -1 to 1.3764±0.0009 mg kg -1 in the liver of broiler and layer chickens, respectively. Chromium concentrations were determined in the range of 0.069±1.0004 mgkg -1 to 2.0746±0.0021 mg kg -1 and 0.0362±0.0002 mg kg -1 to 1.2752±0.0014 mg kg -1 in the flesh of broiler and layer chicken, respectively. The mean concentration of chromium in eggs was 0.2174-1.08 mg kg. -1 The highest concentration of chromium 2.4196±0.0019 mg kg -1 was found in egg yolk. Target hazard quotients values in all poultry flesh, liver and eggs samples were less than one, indicating no potential health risks to consumers., Conclusions: The estimated daily intake values of chromium were below the threshold limit. Thus, our results indicate that no adverse health effects are expected as a resultof ingestion of chicken fed with tannery waste., Ethics Approval: This study was approved by the Biosafety, Biosecurity & Ethical Committee of Jahangirnagar University., Competing Interests: Competing Interests. The authors declare no competing financial interests

Published
2017
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43. Retraction: Adyel et al. Health Risk Assessment of Pesticide Residues via Dietary Intake of Market Vegetables from Dhaka, Bangladesh. Foods 2013, 2 , 64-75.

Author

Hossain MS, Hossain MA, Rahman MA, Islam MM, Rahman MA, and Adyel TM

Abstract

The following article [1], doi: 10.3390/foods2010064, website: http://www.mdpi.com/2304-8158/2/1/64, has been retracted by the authors because of some major errors in broad field of pesticide residues identification and concentrations. During random cross check retention time of pesticides by HPLC did not match with the standards of detected pesticides. As a result concentration of all detected pesticides, maximum residue limits (MRLs) and health risk assessments were changed. All these errors made the article [1] as a wrong one. All authors have confirmed that the reported results produced using quite inappropriate procedures. As first author herein, I take full responsibility for the retraction of our experiments and any other errors in its contents, and would like to offer my apologies on behalf of my co-authors to the readership of Foods for any inconveniences caused by this retraction.

Published
2013
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44. Health Risk Assessment of Pesticide Residues via Dietary Intake of Market Vegetables from Dhaka, Bangladesh.

Author

Hossain MS, Hossain MA, Rahman MA, Islam MM, Rahman MA, and Adyel TM

Abstract

The present study was designed to assess the health risk of pesticide residues via dietary intake of vegetables collected from four top agro-based markets of Dhaka, Bangladesh. High performance liquid chromatography with a photo diode array detector (HPLC-PDA) was used to determine six organophosphorus (chlorpyrifos, fenitrothion, parathion, ethion, acephate, fenthion), two carbamate (carbaryl and carbofuran) and one pyrethroid (cypermethrin) pesticide residues in twelve samples of three common vegetables (tomato, lady's finger and brinjal). Pesticide residues ranged from below detectable limit (<0.01) to 0.36 mg/kg. Acephate, chlorpyrifos, ethion, carbaryl and cypermethrin were detected in only one sample, while co-occurrence occurred twice for fenitrothion and parathion. Apart from chlorpyrifos in tomato and cypermethrin in brinjal, all pesticide residues exceeded the maximum residue limit (MRL). Hazard risk index (HRI) for ethion (10.12) and carbaryl (1.09) was found in lady's finger and tomato, respectively. Rest of the pesticide residues were classified as not a health risk. A continuous monitoring and strict regulation should be enforced regarding control of pesticide residues in vegetables and other food commodities.

Published
2013
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