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Cited by 125Vaccine Coverage and HesitancyVirology and Viral DiseasesGlobal Maternal and Child Health

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Mapping routine measles vaccination in low- and middle-income countries

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Local Burden of Disease Vaccine Coverage Collaborators, Sbarra, Alyssa N., Rolfe, Sam, Nguyen, Jason Q., Earl, Lucas, Galles, Natalie C., Marks, Ashley, Abbas, Kaja M., Abbasi-Kangevari, Mohsen, Abbastabar, Hedayat, Abd-Allah, Foad, Abdelalim, Ahmed, Abdollahi, Mohammad, Abegaz, Kedir Hussein, Abiy, Hailemariam Abiy Alemu, Abolhassani, Hassan, Abreu, Lucas Guimarães, Abrigo, Michael R. M., Abushouk, Abdelrahman I., Accrombessi, Manfred Mario Kokou, Adabi, Maryam, Adebayo, Oladimeji M., Adekanmbi, Victor, Adetokunboh, Olatunji O., Adham, Davoud, Afarideh, Mohsen, Aghaali, Mohammad, Ahmad, Tauseef, Ahmadi, Raman, Ahmadi, Keivan, Ahmed, Muktar Beshir, Alanezi, Fahad Mashhour, Alanzi, Turki M., Alcalde-Rabanal, Jacqueline Elizabeth, Alemnew, Birhan Tamene, Ali, Beriwan Abdulqadir, Ali, Muhammad, Alijanzadeh, Mehran, Alinia, Cyrus, Alipoor, Reza, Alipour, Vahid, Alizade, Hesam, Aljunid, Syed Mohamed, Almasi, Ali, Almasi-Hashiani, Amir, Al-Mekhlafi, Hesham M., Altirkawi, Khalid A., Amare, Bekalu, Amini, Saeed, Amini-Rarani, Mostafa, Amiri, Fatemeh, Amit, Arianna Maever L., Amugsi, Dickson A., Ancuceanu, Robert, Andrei, Catalina Liliana, Anjomshoa, Mina, Ansari, Fereshteh, Ansari-Moghaddam, Alireza, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T., Antriyandarti, Ernoiz, Anvari, Davood, Arabloo, Jalal, Arab-Zozani, Morteza, Aremu, Olatunde, Armoon, Bahram, Aryal, Krishna K., Arzani, Afsaneh, Asadi-Aliabadi, Mehran, Asgari, Samaneh, Atafar, Zahra, Ausloos, Marcel, Awoke, Nefsu, Quintanilla, Beatriz Paulina Ayala, Ayanore, Martin Amogre, Aynalem, Yared Asmare, Azadmehr, Abbas, Azari, Samad, Babaee, Ebrahim, Badawi, Alaa, Badiye, Ashish D., Bahrami, Mohammad Amin, Baig, Atif Amin, Bakhtiari, Ahad, Balakrishnan, Senthilkumar, Banach, Maciej, Banik, Palash Chandra, Barac, Aleksandra, Baradaran-Seyed, Zahra, Baraki, Adhanom Gebreegziabher, Basu, Sanjay, Bayati, Mohsen, Bayou, Yibeltal Tebekaw, Bedi, Neeraj, Behzadifar, Masoud, Bell, Michelle L., Berbada, Dessalegn Ajema, Berhe, Kidanemaryam, Bhattarai, Suraj, Bhutta, Zulfiqar A., Bijani, Ali, Birhanu, Minyichil, Bisanzio, Donal, Biswas, Atanu, Bohlouli, Somayeh, Bolla, Srinivasa Rao, Borzouei, Shiva, Brady, Oliver J., Bragazzi, Nicola Luigi, Briko, Andrey Nikolaevich, Briko, Nikolay Ivanovich, Nagaraja, Sharath Burugina, Butt, Zahid A., Cámera, Luis Alberto, Campos-Nonato, Ismael R., Car, Josip, Cárdenas, Rosario, Carvalho, Felix, Castaldelli-Maia, João Maurício, Castro, Franz, Chattu, Vijay Kumar, Chehrazi, Mohammad, Chin, Ken Lee, Chu, Dinh-Toi, Cook, Aubrey J., Cormier, Natalie Maria, Cunningham, Brandon, Dahlawi, Saad M. 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M., Gopalani, Sameer Vali, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Guimarães, Rafael Alves, Guo, Yuming, Gupta, Rahul, Hafezi-Nejad, Nima, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Handiso, Demelash Woldeyohannes, Hanif, Asif, Haririan, Hamidreza, Hasaballah, Ahmed I., Hasan, Md Mehedi, Hasanpoor, Edris, Hasanzadeh, Amir, Hassanipour, Soheil, Hassankhani, Hadi, Heidari-Soureshjani, Reza, Henry, Nathaniel J., Herteliu, Claudiu, Heydarpour, Fatemeh, Hollerich, Gillian I., Rad, Enayatollah Homaie, Hoogar, Praveen, Hossain, Naznin, Hosseini, Mostafa, Hosseinzadeh, Mehdi, Househ, Mowafa, Hu, Guoqing, Huda, Tanvir M., Humayun, Ayesha, Ibitoye, Segun Emmanuel, Ikilezi, Gloria, Ilesanmi, Olayinka Stephen, Ilic, Irena M., Ilic, Milena D., Imani-Nasab, Mohammad Hasan, Inbaraj, Leeberk Raja, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Islam, Sheikh Mohammed Shariful, Islam, M. Mofizul, Iwu, Chinwe Juliana, Iwu, Chidozie C. D., Jadidi-Niaragh, Farhad, Jafarinia, Morteza, Jahanmehr, Nader, Jakovljevic, Mihajlo, Jalali, Amir, Jalilian, Farzad, Javidnia, Javad, Jenabi, Ensiyeh, Jha, Vivekanand, Ji, John S., John, Oommen, Johnson, Kimberly B., Joukar, Farahnaz, Jozwiak, Jacek Jerzy, Kabir, Zubair, Kabir, Ali, Kalani, Hamed, Kalankesh, Leila R., Kalhor, Rohollah, Kamal, Zul, Kanchan, Tanuj, Kapoor, Neeti, Karami, Manoochehr, Matin, Behzad Karami, Karch, André, Karimi, Salah Eddin, Kayode, Gbenga A., Karyani, Ali Kazemi, Keiyoro, Peter Njenga, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khammarnia, Mohammad, Khan, Muhammad Shahzeb, Khan, Ejaz Ahmad, Khan, Junaid, Khan, Md Nuruzzaman, Khatab, Khaled, Khater, Mona M., Khatib, Mahalaqua Nazli, Khayamzadeh, Maryam, Khazaei, Mojtaba, Khazaei, Salman, Khosravi, Ardeshir, Khubchandani, Jagdish, Kianipour, Neda, Kim, Yun Jin, Kimokoti, Ruth W., Kinyoki, Damaris K., Kisa, Adnan, Kisa, Sezer, Kolola, Tufa, Komaki, Hamidreza, Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kraemer, Moritz U. G., Krishan, Kewal, Kuate Defo, Barthelemy, Kumar, Manasi, Kumar, Pushpendra, Kumar, G. Anil, Kusuma, Dian, La Vecchia, Carlo, Lacey, Ben, Lad, Sheetal D., Lal, Dharmesh Kumar, Lam, Felix, Lami, Faris Hasan, Lansingh, Van Charles, Larson, Heidi Jane, Lasrado, Savita, Lee, Shaun Wen Huey, Lee, Paul H., LeGrand, Kate E., Lenjebo, Tsegaye Lolaso, Li, Shanshan, Liang, Xiaofeng, Liu, Patrick Y., Lopukhov, Platon D., Machado, Daiane Borges, Mahasha, Phetole Walter, Mahdavi, Mokhtar Mahdavi, Maheri, Mina, Mahotra, Narayan B., Maled, Venkatesh, Maleki, Shokofeh, Malik, Manzoor Ahmad, Malta, Deborah Carvalho, Mansour-Ghanaei, Fariborz, Mansouri, Borhan, Mansourian, Morteza, Mansournia, Mohammad Ali, Martins-Melo, Francisco Rogerlândio, Masaka, Anthony, Mayala, Benjamin K., Mehndiratta, Man Mohan, Mehri, Fereshteh, Mehta, Kala M., Memiah, Peter T. N., Mendoza, Walter, Menezes, Ritesh G., Mengesha, Meresa Berwo, Mengesha, Endalkachew Worku, Mestrovic, Tomislav, Mihretie, Kebadnew Mulatu, Miller-Petrie, Molly K., Mills, Edward J., Milne, George J., Mirabi, Parvaneh, Mirrakhimov, Erkin M., Mirzaei, Roya, Mirzaei, Maryam, Mirzaei, Hamid Reza, Mirzaei, Hamed, Mirzaei-Alavijeh, Mehdi, Moazen, Babak, Moghadaszadeh, Masoud, Mohamadi, Efat, Mohammad, Dara K., Mohammad, Yousef, Mohammad, Karzan Abdulmuhsin, Mohammad Gholi Mezerji, Naser, Mohammadbeigi, Abolfazl, Mohammadian-Hafshejani, Abdollah, Mohammadpourhodki, Reza, Mohammed, Shafiu, Mohammed, Ammas Siraj, Mohammed, Hussen, Mohebi, Farnam, Mokdad, Ali H., Monasta, Lorenzo, Moosavi, Mohammad Amin, Moosazadeh, Mahmood, Moradi, Ghobad, Moradi, Masoud, Moradi-Joo, Mohammad, Moradi-Lakeh, Maziar, Moradzadeh, Rahmatollah, Moraga, Paula, Mosapour, Abbas, Mouodi, Simin, Mousavi, Seyyed Meysam, Khaneghah, Amin Mousavi, Mueller, Ulrich Otto, Muluneh, Atalay Goshu, Munro, Sandra B., Murray, Christopher J. L., Murthy, G. V. S., Muthupandian, Saravanan, Naderi, Mehdi, Nagarajan, Ahamarshan Jayaraman, Naghavi, Mohsen, Nangia, Vinay, Nansseu, Jobert Richie, Nayak, Vinod C., Nazari, Javad, Ndwandwe, Duduzile Edith, Negoi, Ionut, Ngunjiri, Josephine W., Nguyen, Huong Lan Thi, Nguyen, Chuc T. K., Nguyen, Trang Huyen, Nigatu, Yeshambel T., Nikbakhsh, Rajan, Nikfar, Shekoufeh, Nikpoor, Amin Reza, Ningrum, Dina Nur Anggraini, Nnaji, Chukwudi A., Oh, In-Hwan, Oladnabi, Morteza, Olagunju, Andrew T., Olusanya, Jacob Olusegun, Olusanya, Bolajoko Olubukunola, Bali, Ahmed Omar, Omer, Muktar Omer, Onwujekwe, Obinna E., Osgood-Zimmerman, Aaron E., Owolabi, Mayowa O., P A, Mahesh, Padubidri, Jagadish Rao, Pakshir, Keyvan, Pana, Adrian, Pandey, Anamika, Pando-Robles, Victoria, Pashaei, Tahereh, Pasupula, Deepak Kumar, Paternina-Caicedo, Angel J., Patton, George C., Pazoki Toroudi, Hamidreza, Pepito, Veincent Christian Filipino, Pescarini, Julia Moreira, Pigott, David M., Pilgrim, Thomas, Pirsaheb, Meghdad, Poljak, Mario, Postma, Maarten J., Pourjafar, Hadi, Pourmalek, Farshad, Pourmirza Kalhori, Reza, Prada, Sergio I., Prakash, Sanjay, Quazi Syed, Zahiruddin, Quintana, Hedley, Rabiee, Navid, Rabiee, Mohammad, Radfar, Amir, Rafiei, Alireza, Rahim, Fakher, Rajati, Fatemeh, Rameto, Muhammed Ahmed, Ramezanzadeh, Kiana, Ranabhat, Chhabi Lal, Rao, Sowmya J., Rasella, Davide, Rastogi, Prateek, Rathi, Priya, Rawaf, Salman, Rawaf, David Laith, Rawal, Lal, Rawassizadeh, Reza, Rawat, Ramu, Renjith, Vishnu, Renzaho, Andre M. 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S., Simonetti, Biagio, Singh, Jasvinder A., Sinha, Dhirendra Narain, Sinke, Abiy H., Soheili, Amin, Sokhan, Anton, Soltani, Shahin, Soofi, Moslem, Sorrie, Muluken Bekele, Soyiri, Ireneous N., Spotin, Adel, Spurlock, Emma Elizabeth, Sreeramareddy, Chandrashekhar T., Sudaryanto, Agus, Sufiyan, Mu’awiyyah Babale, Suleria, Hafiz Ansar Rasul, Abdulkader, Rizwan Suliankatchi, Taherkhani, Amir, Tapak, Leili, Taveira, Nuno, Taymoori, Parvaneh, Tefera, Yonatal Mesfin, Tehrani-Banihashemi, Arash, Teklehaimanot, Berhane Fseha, Tekulu, Gebretsadkan Hintsa, Tesfay, Berhe Etsay, Tessema, Zemenu Tadesse, Tessema, Belay, Thankappan, Kavumpurathu Raman, Tohidinik, Hamid Reza, Topor-Madry, Roman, Tovani-Palone, Marcos Roberto, Tran, Bach Xuan, Uddin, Riaz, Ullah, Irfan, Umeokonkwo, Chukwuma David, Unnikrishnan, Bhaskaran, Upadhyay, Era, Usman, Muhammad Shariq, Vaezi, Maryam, Valadan Tahbaz, Sahel, Valdez, Pascual R., Vasseghian, Yasser, Veisani, Yousef, Violante, Francesco S., Vollmer, Sebastian, Waheed, Yasir, Wakefield, Jon, Wang, Yafeng, Wang, Yuan-Pang, Weldesamuel, Girmay Teklay, Werdecker, Andrea, Westerman, Ronny, Wiangkham, Taweewat, Wiens, Kirsten E., Wiysonge, Charles Shey, Woldu, Gebremariam, Wondafrash, Dawit Zewdu, Wonde, Tewodros Eshete, Wu, Ai-Min, Yadollahpour, Ali, Jabbari, Seyed Hossein Yahyazadeh, Yamada, Tomohide, Yaya, Sanni, Yazdi-Feyzabadi, Vahid, Yeheyis, Tomas Y., Yeshaw, Yigizie, Yilgwan, Christopher Sabo, Yip, Paul, Yonemoto, Naohiro, Younis, Mustafa Z., Yousefi, Zabihollah, Yousefifard, Mahmoud, Yousefinezhadi, Taraneh, Yu, Chuanhua, Yusefzadeh, Hasan, Zadey, Siddhesh, Zahirian Moghadam, Telma, Zaki, Leila, Zaman, Sojib Bin, Zamani, Mohammad, Zamanian, Maryam, Zandian, Hamed, Zangeneh, Alireza, Zarei, Fatemeh, Zerfu, Taddese Alemu, Zhang, Yunquan, Zhang, Zhi-Jiang, Zhao, Xiu-Ju George, Zhou, Maigeng, Ziapour, Arash, Hay, Simon I., Lim, Stephen S., Mosser, Jonathan F.·2021·Nature
DOI 10.1038/s41586-020-03043-4PMC PMC7739806
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Abstract

The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs) 1–4 . Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19) 5–8 . Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km 2 pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.

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Figures

Fig. 1 Estimated MCV1 coverage among districts in 101 LMICs, 2000–2019. a–c, MCV1 coverage among target population in districts in 2000 (a), 2010 (b) and 2019 (c). Countries excluded from the analysis and pixels classified as ‘barren or sparsely vegetated’ based on European Space Agency Climate Change Initiative (ESA-CCI) satellite data or with fewer than 10 people per 1 × 1-km2 pixel based on WorldPop estimates are masked in grey30,50.
Fig. 1 Estimated MCV1 coverage among districts in 101 LMICs, 2000–2019. a–c, MCV1 coverage among target population in districts in 2000 (a), 2010 (b) and 2019 (c). Countries excluded from the analysis and pixels classified as ‘barren or sparsely vegetated’ based on European Space Agency Climate Change Initiative (ESA-CCI) satellite data or with fewer than 10 people per 1 × 1-km2 pixel based on WorldPop estimates are masked in grey30,50.CC BY · PubMed Central
Fig. 2 Estimated absolute changes in MCV1 coverage in the early (2000–2010) and late (2010–2019) study periods. a, b, Mean district-level absolute differences in MCV1 coverage from 2000 to 2010 (a) and from 2010 to 2019 (b). Countries excluded from the analysis and pixels classified as ‘barren or sparsely vegetated’ based on ESA-CCI satellite data or with fewer than 10 people per 1 × 1-km2 pixel based on WorldPop estimates are masked in grey30,50.
Fig. 2 Estimated absolute changes in MCV1 coverage in the early (2000–2010) and late (2010–2019) study periods. a, b, Mean district-level absolute differences in MCV1 coverage from 2000 to 2010 (a) and from 2010 to 2019 (b). Countries excluded from the analysis and pixels classified as ‘barren or sparsely vegetated’ based on ESA-CCI satellite data or with fewer than 10 people per 1 × 1-km2 pixel based on WorldPop estimates are masked in grey30,50.CC BY · PubMed Central
Fig. 3 Absolute geographical inequality of MCV1 coverage in 2000 and 2019. We compared the change in geographical absolute inequality to change in national-level coverage from 2000 to 2019. Points are sized by under-5 population size.
Fig. 3 Absolute geographical inequality of MCV1 coverage in 2000 and 2019. We compared the change in geographical absolute inequality to change in national-level coverage from 2000 to 2019. Points are sized by under-5 population size.CC BY · PubMed Central
Fig. 4 Vaccination status in 2019 and geographical remoteness. Cumulative proportion of unvaccinated children living within the spectrum of the travel time (in hours) to a major city or settlement per region (left) and coverage among children living within the spectrum of travel time to a major city or settlement per region (right). Vertical dashed grey line shows thresholds for ‘urban’ and ‘remote rural’, living within 30 min and at least 3 h from a major city or settlement, respectively.
Fig. 4 Vaccination status in 2019 and geographical remoteness. Cumulative proportion of unvaccinated children living within the spectrum of the travel time (in hours) to a major city or settlement per region (left) and coverage among children living within the spectrum of travel time to a major city or settlement per region (right). Vertical dashed grey line shows thresholds for ‘urban’ and ‘remote rural’, living within 30 min and at least 3 h from a major city or settlement, respectively.CC BY · PubMed Central
Extended Data Fig. 1 Data processing and geospatial modelling flowchart. Survey data and the suite of covariates used in modelling are first compiled and processed (orange and grey). The modelling process (purple) consists of data being used in a stacked generalization ensemble modelling process via boosted regression tree, lasso and generalized additive models, fitting the second-stage spatiotemporal model using integrated nested Laplace approximation, and finally calibration to GBD estimates (blue). Steps in dark green and outputs in yellow indicate the post-estimation process when the full posterior distribution of predications is transformed to both 5 × 5-km2 and first and second administrative-unit-level maps and their various final results. Intermediate outputs throughout the process are shown in blue and overall processes are shown in light green.
Extended Data Fig. 1 Data processing and geospatial modelling flowchart. Survey data and the suite of covariates used in modelling are first compiled and processed (orange and grey). The modelling process (purple) consists of data being used in a stacked generalization ensemble modelling process via boosted regression tree, lasso and generalized additive models, fitting the second-stage spatiotemporal model using integrated nested Laplace approximation, and finally calibration to GBD estimates (blue). Steps in dark green and outputs in yellow indicate the post-estimation process when the full posterior distribution of predications is transformed to both 5 × 5-km2 and first and second administrative-unit-level maps and their various final results. Intermediate outputs throughout the process are shown in blue and overall processes are shown in light green.CC BY · PubMed Central
Extended Data Fig. 2 Regions of countries used in modelling. Analyses were divided into 13 regions based on the GBD super-regions to allow for locations similar in data availability and patterns of vaccine coverage to be analysed using similar covariate and modelling relationships. Each colour represents a different region, as described in the legend.
Extended Data Fig. 2 Regions of countries used in modelling. Analyses were divided into 13 regions based on the GBD super-regions to allow for locations similar in data availability and patterns of vaccine coverage to be analysed using similar covariate and modelling relationships. Each colour represents a different region, as described in the legend.CC BY · PubMed Central
Extended Data Fig. 3 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2000. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.
Extended Data Fig. 3 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2000. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.CC BY · PubMed Central
Extended Data Fig. 4 National, first- and second-administrative level, and pixel-level MCV1 coverage, 2005. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.
Extended Data Fig. 4 National, first- and second-administrative level, and pixel-level MCV1 coverage, 2005. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.CC BY · PubMed Central
Extended Data Fig. 5 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2010. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.
Extended Data Fig. 5 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2010. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.CC BY · PubMed Central
Extended Data Fig. 6 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2015. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.
Extended Data Fig. 6 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2015. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.CC BY · PubMed Central
Extended Data Fig. 7 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2019. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.
Extended Data Fig. 7 National, first- and second-administrative-unit level, and pixel-level MCV1 coverage, 2019. a–d, Posterior means are represented at the national (a), first-administrative-unit (b), second-administrative-unit (c) and 5 × 5-km2 pixel (d) levels. Pixels that are grey in colour are either not included in the analysis, or have been classified as being ‘barren or sparsely vegetated’ or had fewer than 10 people per 1 × 1-km2 pixel30,50.CC BY · PubMed Central
Extended Data Fig. 8 Probability of increased or decreased coverage from 2000 to 2010 and 2010 to 2019. a–d, Probability of an increase in coverage in each district (a, b) and probability of decrease in coverage in each district (c, d) from 2000 to 2010 (a, c) and 2010 to 2019 (b, d).
Extended Data Fig. 8 Probability of increased or decreased coverage from 2000 to 2010 and 2010 to 2019. a–d, Probability of an increase in coverage in each district (a, b) and probability of decrease in coverage in each district (c, d) from 2000 to 2010 (a, c) and 2010 to 2019 (b, d).CC BY · PubMed Central
Extended Data Fig. 9 Estimated district-level probabilities of reaching MCV1 coverage targets in 2019. a, b, Probability of districts having achieved 80% GVAP and Measles Rubella Initiative targets (a) and 95% critical proportion to immunize coverage targets to reach herd immunity (b). Countries excluded from the analysis and pixels classified as ‘barren or sparsely vegetated’ based on ESA-CCI satellite data or with fewer than 10 people per 1 × 1-km2 pixel based on WorldPop estimates are masked in grey30,50.
Extended Data Fig. 9 Estimated district-level probabilities of reaching MCV1 coverage targets in 2019. a, b, Probability of districts having achieved 80% GVAP and Measles Rubella Initiative targets (a) and 95% critical proportion to immunize coverage targets to reach herd immunity (b). Countries excluded from the analysis and pixels classified as ‘barren or sparsely vegetated’ based on ESA-CCI satellite data or with fewer than 10 people per 1 × 1-km2 pixel based on WorldPop estimates are masked in grey30,50.CC BY · PubMed Central
Extended Data Fig. 10 Country examples of concentration curves. Concentration curves of the cumulative proportion of unvaccinated children as a function of travel time (in hours) in Chad (a), Madagascar (b), India (c) and Mexico (d). Curves for both the indicated countries (blue) and all LMICs (grey) are shown.
Extended Data Fig. 10 Country examples of concentration curves. Concentration curves of the cumulative proportion of unvaccinated children as a function of travel time (in hours) in Chad (a), Madagascar (b), India (c) and Mexico (d). Curves for both the indicated countries (blue) and all LMICs (grey) are shown.CC BY · PubMed Central

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  1. Roberts, L. Polio, measles, other diseases set to surge as COVID-19 forces suspension of vaccination campaigns. Science https://www.sciencemag.org/news/2020/04/polio-measles-other-diseases-set-surge-covid-19-forces-suspension-vaccination-campaigns (9 April 2020).
  2. Plotkin, S., Orenstein, W., Offit, P. & Edwards, K. M. Plotkin’s Vaccines (Elsevier, 2017).
  3. Takahashi, S., Metcalf, C. J. E., Ferrari, M. J., Tatem, A. J. & Lessler, J. The geography of measles vaccination in the African Great Lakes region. Nat. Commun. 8, 15585 (2017).
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Collaborators, L. B. O. D. V. C., Sbarra, A. N., Rolfe, S., Nguyen, J. Q., Earl, L., Galles, N. C., Marks, A., Abbas, K. M., Abbasi-Kangevari, M., Abbastabar, H., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abegaz, K. H., Abiy, H. A. A., Abolhassani, H., Abreu, L. G., Abrigo, M. R. M., Abushouk, A. I., Accrombessi, M. M. K., Adabi, M., Adebayo, O. M., Adekanmbi, V., Adetokunboh, O. O., Adham, D., Afarideh, M., Aghaali, M., Ahmad, T., Ahmadi, R., Ahmadi, K., Ahmed, M. B., Alanezi, F. M., Alanzi, T. M., Alcalde-Rabanal, J. E., Alemnew, B. T., Ali, B. A., Ali, M., Alijanzadeh, M., Alinia, C., Alipoor, R., Alipour, V., Alizade, H., Aljunid, S. M., Almasi, A., Almasi-Hashiani, A., Al-Mekhlafi, H. M., Altirkawi, K. A., Amare, B., Amini, S., Amini-Rarani, M., Amiri, F., Amit, A. M. L., Amugsi, D. A., Ancuceanu, R., Andrei, C. L., Anjomshoa, M., Ansari, F., Ansari-Moghaddam, A., Ansha, M. G., Antonio, C. A. T., Antriyandarti, E., Anvari, D., Arabloo, J., Arab-Zozani, M., Aremu, O., Armoon, B., Aryal, K. K., Arzani, A., Asadi-Aliabadi, M., Asgari, S., Atafar, Z., Ausloos, M., Awoke, N., Quintanilla, B. P. A., Ayanore, M. A., Aynalem, Y. A., Azadmehr, A., Azari, S., Babaee, E., Badawi, A., Badiye, A. D., Bahrami, M. A., Baig, A. A., Bakhtiari, A., Balakrishnan, S., Banach, M., Banik, P. C., Barac, A., Baradaran-Seyed, Z., Baraki, A. G., Basu, S., Bayati, M., Bayou, Y. T., Bedi, N., Behzadifar, M., Bell, M. L., Berbada, D. A., Berhe, K., Bhattarai, S., Bhutta, Z. A., Bijani, A., Birhanu, M., Bisanzio, D., Biswas, A., Bohlouli, S., Bolla, S. R., Borzouei, S., Brady, O. J., Bragazzi, N. L., Briko, A. N., Briko, N. I., Nagaraja, S. B., Butt, Z. A., Cámera, L. A., Campos-Nonato, I. R., Car, J., Cárdenas, R., Carvalho, F., Castaldelli-Maia, J. M., Castro, F., Chattu, V. K., Chehrazi, M., Chin, K. L., Chu, D.-T., Cook, A. J., Cormier, N. M., Cunningham, B., Dahlawi, S. M. A., Damiani, G., Dandona, R., Dandona, L., Danovaro, M. C., Dansereau, E., Daoud, F., Darwesh, A. M., Darwish, A. H., Das, J. K., Weaver, N. D., De Neve, J.-W., Demeke, F. M., Demis, A. B., Denova-Gutiérrez, E., Desalew, A., Deshpande, A., Desta, D. M., Dharmaratne, S. D., Dhungana, G. P., Dianatinasab, M., Diaz, D., Dipeolu, I. O., Djalalinia, S., Do, H. T., Dorostkar, F., Doshmangir, L., Doyle, K. E., Dunachie, S. J., Duraes, A. R., Kalan, M. E., Leylabadlo, H. E., Edinur, H. A., Effiong, A., Eftekhari, A., El Sayed, I., El Sayed Zaki, M., Elema, T. B., Elhabashy, H. R., El-Jaafary, S. I., Elsharkawy, A., Emamian, M. H., Enany, S., Eshrati, B., Eskandari, K., Eskandarieh, S., Esmaeilnejad, S., Esmaeilzadeh, F., Esteghamati, A., Etisso, A. E., Farahmand, M., Faraon, E. J. A., Fareed, M., Faridnia, R., Farioli, A., Farzadfar, F., Fattahi, N., Fazlzadeh, M., Fereshtehnejad, S.-M., Fernandes, E., Filip, I., Fischer, F., Foigt, N. A., Folayan, M. O., Foroutan, M., Fukumoto, T., Fullman, N., Gad, M. M., Geberemariyam, B. S., Gebrehiwot, T. T., Gebrehiwot, A. M., Gebremariam, K. T., Gebremedhin, K. B., Gebremeskel, G. G., Gebreslassie, A. A., Gedefaw, G. A., Gezae, K. E., Ghadiri, K., Ghaffari, R., Ghaffarifar, F., Ghajarzadeh, M., Gheshlagh, R. G., Ghashghaee, A., Ghiasvand, H., Gholamian, A., Gilani, S. A., Gill, P. S., Girmay, A., Gomes, N. G. M., Gopalani, S. V., Goulart, B. N. G., Grada, A., Guimarães, R. A., Guo, Y., Gupta, R., Hafezi-Nejad, N., Haj-Mirzaian, A., Haj-Mirzaian, A., Handiso, D. W., Hanif, A., Haririan, H., Hasaballah, A. I., Hasan, M. M., Hasanpoor, E., Hasanzadeh, A., Hassanipour, S., Hassankhani, H., Heidari-Soureshjani, R., Henry, N. J., Herteliu, C., Heydarpour, F., Hollerich, G. I., Rad, E. H., Hoogar, P., Hossain, N., Hosseini, M., Hosseinzadeh, M., Househ, M., Hu, G., Huda, T. M., Humayun, A., Ibitoye, S. E., Ikilezi, G., Ilesanmi, O. S., Ilic, I. M., Ilic, M. D., Imani-Nasab, M. H., Inbaraj, L. R., Iqbal, U., Irvani, S. S. N., Islam, S. M. S., Islam, M. M., Iwu, C. J., Iwu, C. C. D., Jadidi-Niaragh, F., Jafarinia, M., Jahanmehr, N., Jakovljevic, M., Jalali, A., Jalilian, F., Javidnia, J., Jenabi, E., Jha, V., Ji, J. S., John, O., Johnson, K. B., Joukar, F., Jozwiak, J. J., Kabir, Z., Kabir, A., Kalani, H., Kalankesh, L. R., Kalhor, R., Kamal, Z., Kanchan, T., Kapoor, N., Karami, M., Matin, B. K., Karch, A., Karimi, S. E., Kayode, G. A., Karyani, A. K., Keiyoro, P. N., Khader, Y. S., Khafaie, M. A., Khammarnia, M., Khan, M. S., Khan, E. A., Khan, J., Khan, M. N., Khatab, K., Khater, M. M., Khatib, M. N., Khayamzadeh, M., Khazaei, M., Khazaei, S., Khosravi, A., Khubchandani, J., Kianipour, N., Kim, Y. J., Kimokoti, R. W., Kinyoki, D. K., Kisa, A., Kisa, S., Kolola, T., Komaki, H., Kosen, S., Koul, P. A., Koyanagi, A., Kraemer, M. U. G., Krishan, K., Kuate Defo, B., Kumar, M., Kumar, P., Kumar, G. A., Kusuma, D., La Vecchia, C., Lacey, B., Lad, S. D., Lal, D. K., Lam, F., Lami, F. H., Lansingh, V. C., Larson, H. J., Lasrado, S., Lee, S. W. H., Lee, P. H., LeGrand, K. E., Lenjebo, T. L., Li, S., Liang, X., Liu, P. Y., Lopukhov, P. D., Machado, D. B., Mahasha, P. W., Mahdavi, M. M., Maheri, M., Mahotra, N. B., Maled, V., Maleki, S., Malik, M. A., Malta, D. C., Mansour-Ghanaei, F., Mansouri, B., Mansourian, M., Mansournia, M. A., Martins-Melo, F. R., Masaka, A., Mayala, B. K., Mehndiratta, M. M., Mehri, F., Mehta, K. M., Memiah, P. T. N., Mendoza, W., Menezes, R. G., Mengesha, M. B., Mengesha, E. W., Mestrovic, T., Mihretie, K. M., Miller-Petrie, M. K., Mills, E. J., Milne, G. J., Mirabi, P., Mirrakhimov, E. M., Mirzaei, R., Mirzaei, M., Mirzaei, H. R., Mirzaei, H., Mirzaei-Alavijeh, M., Moazen, B., Moghadaszadeh, M., Mohamadi, E., Mohammad, D. K., Mohammad, Y., Mohammad, K. A., Mohammad Gholi Mezerji, N., Mohammadbeigi, A., Mohammadian-Hafshejani, A., Mohammadpourhodki, R., Mohammed, S., Mohammed, A. S., Mohammed, H., Mohebi, F., Mokdad, A. 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I., Sarmiento-Suárez, R., Saroshe, S., Sartorius, B., Sarveazad, A., Sathian, B., Sathish, T., Schaeffer, L. E., Schwebel, D. C., Senthilkumaran, S., Shabaninejad, H., Shahabi, S., Shaheen, A. A., Shaikh, M. A., Shalash, A. S., Shams-Beyranvand, M., Shamsi, M., Shamsizadeh, M., Sharafi, K., Sharifi, H., Sheikh, A., Sheikhtaheri, A., Shetty, R. S., Shiferaw, W. S., Shigematsu, M., Shin, J. I., Shirkoohi, R., Siabani, S., Siddiqi, T. J., Silverberg, J. I. S., Simonetti, B., Singh, J. A., Sinha, D. N., Sinke, A. H., Soheili, A., Sokhan, A., Soltani, S., Soofi, M., Sorrie, M. B., Soyiri, I. N., Spotin, A., Spurlock, E. E., Sreeramareddy, C. T., Sudaryanto, A., Sufiyan, M. B., Suleria, H. A. R., Abdulkader, R. S., Taherkhani, A., Tapak, L., Taveira, N., Taymoori, P., Tefera, Y. M., Tehrani-Banihashemi, A., Teklehaimanot, B. F., Tekulu, G. H., Tesfay, B. E., Tessema, Z. T., Tessema, B., Thankappan, K. R., Tohidinik, H. R., Topor-Madry, R., Tovani-Palone, M. R., Tran, B. X., Uddin, R., Ullah, I., Umeokonkwo, C. D., Unnikrishnan, B., Upadhyay, E., Usman, M. S., Vaezi, M., Valadan Tahbaz, S., Valdez, P. R., Vasseghian, Y., Veisani, Y., Violante, F. S., Vollmer, S., Waheed, Y., Wakefield, J., Wang, Y., Wang, Y.-P., Weldesamuel, G. T., Werdecker, A., Westerman, R., Wiangkham, T., Wiens, K. E., Wiysonge, C. S., Woldu, G., Wondafrash, D. Z., Wonde, T. E., Wu, A.-M., Yadollahpour, A., Jabbari, S. H. Y., Yamada, T., Yaya, S., Yazdi-Feyzabadi, V., Yeheyis, T. Y., Yeshaw, Y., Yilgwan, C. S., Yip, P., Yonemoto, N., Younis, M. Z., Yousefi, Z., Yousefifard, M., Yousefinezhadi, T., Yu, C., Yusefzadeh, H., Zadey, S., Zahirian Moghadam, T., Zaki, L., Zaman, S. B., Zamani, M., Zamanian, M., Zandian, H., Zangeneh, A., Zarei, F., Zerfu, T. A., Zhang, Y., Zhang, Z.-J., Zhao, X.-J. G., Zhou, M., Ziapour, A., Hay, S. I., Lim, S. S., & Mosser, J. F.
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  • Iranian Academy of Medical Sciences (IR)
  • Ministry of Health and Medical Education (IR)
  • New Mexico State University (US)
  • Xiamen University Malaysia (MY)
  • Simmons University (US)
  • OsloMet – Oslo Metropolitan University (NO)
  • Ambo University (ET)
  • Sher-i-Kashmir Institute of Medical Sciences (IN)
  • Institució Catalana de Recerca i Estudis Avançats (ES)
  • Panjab University (IN)
  • Public Health Foundation of India (IN)
  • University of Indonesia (ID)
  • Post Graduate Institute of Medical Education and Research (IN)
  • Clinton Health Access Initiative (US)
  • University of Baghdad (IQ)
  • Instituto Mexicano de Oftalmología IAP (MX)
  • Father Muller Medical College Hospital (IN)
  • Monash University Malaysia (MY)
  • Monash University (AU)
  • University of California, Los Angeles (US)
  • Saveh University of Medical Sciences (IR)
  • Tribhuvan University (NP)
  • Rajiv Gandhi University of Health Sciences (IN)
  • Indian Institute of Technology Roorkee (IN)
  • Botho University (BW)
  • University of California, San Francisco (US)
  • University of Maryland, Baltimore (US)
  • United Nations Population Fund (US)
  • Adigrat University (ET)
  • McMaster University (CA)
  • The University of Western Australia (AU)
  • Kyrgyz State Medical Academy (KG)
  • Kashan University of Medical Sciences (IR)
  • Heidelberg University (DE)
  • Health Equity Research Center (IR)
  • Karolinska Institutet (SE)
  • Salahaddin University-Erbil (IQ)
  • Shahrekord University of Medical Sciences (IR)
  • Mashhad University of Medical Sciences (IR)
  • Ahmadu Bello University (NG)
  • Dire Dawa University (ET)
  • IRCCS Materno Infantile Burlo Garofolo (IT)
  • National Institute of Genetic Engineering and Biotechnology (IR)
  • King Abdullah University of Science and Technology (SA)
  • Universidade Estadual de Campinas (UNICAMP) (BR)
  • Federal Institute for Population Research (DE)
  • Suraj Eye Institute (IN)
  • Université de Yaoundé I (CM)
  • University of Embu (KE)
  • Trường ĐH Nguyễn Tất Thành (VN)
  • Centre for Addiction and Mental Health (CA)
  • State University of Semarang (ID)
  • Kyung Hee University (KR)
  • University of Lagos (NG)
  • Centre for Healthy Start Initiative (NG)
  • Jigjiga University (ET)
  • University of Nigeria (NG)
  • University of Pittsburgh Medical Center (US)
  • Ateneo de Manila University (PH)
  • University of Bern (CH)
  • University of Ljubljana (SI)
  • University Medical Center Groningen (NL)
  • Alborz University of Medical Sciences (IR)
  • University of British Columbia (CA)
  • Fundación Valle del Lili (CO)
  • Sharif University of Technology (IR)
  • Amirkabir University of Technology (IR)
  • University of Central Florida (US)
  • Government of Nepal (NP)
  • Universidade Federal da Bahia (BR)
  • Public Health England (GB)
  • University College Hospital (GB)
  • Royal College of Surgeons in Ireland - Bahrain (BH)
  • Universal Scientific Education and Research Network (IR)
  • Birjand University of Medical Sciences (IR)
  • University of Minnesota (US)
  • Instituto Nacional de Salud Pública (MX)
  • Universidade Federal de Uberlândia (BR)
  • Gonabad University of Medical Sciences (IR)
  • University of Sassari (IT)
  • University of Botswana (BW)
  • Cleveland Clinic (US)
  • All India Institute of Medical Sciences (IN)
  • Cihan University-Erbil (IQ)
  • Central University of Punjab (IN)
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  • Foundation for Advancement of International Medical Education and Research (US)
  • Instituto de Salud Carlos III (ES)
  • Mahatma Gandhi Memorial Medical College (IN)
  • Hamad Medical Corporation (QA)
  • Population Health Research Institute (CA)
  • University of Alabama at Birmingham (US)
  • Shiraz University (IR)
  • An-Najah National University (PS)
  • University of Borås (SE)
  • National Institute of Infectious Diseases (JP)
  • Yonsei University (KR)
  • University of Technology Sydney (AU)
  • Dow University of Health Sciences (PK)
  • George Washington University (US)
  • University of Sannio (IT)
  • United States Department of Veterans Affairs (US)
  • Patna University (IN)
  • Pathfinder International (US)
  • Semnan University of Medical Sciences (IR)
  • Kharkiv National Medical University (UA)
  • University of Hull (GB)
  • IMU University (MY)
  • Muhammadiyah University of Surakarta (ID)
  • University of Lisbon (PT)
  • Wollo University (ET)
  • Central University of Kerala (IN)
  • Hanoi Medical University (VN)
  • Deakin University (AU)
  • Iqra National University (PK)
  • Federal Teaching Hospital Abakaliki (NG)
  • Amity University Jaipur (IN)
  • Medical University of Ilam (IR)
  • IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
  • Foundation University Islamabad (PK)
  • Naresuan University (TH)
  • Wenzhou Medical University (CN)
  • University of Sheffield (GB)
  • Milad Hospital (IR)
  • The University of Tokyo (JP)
  • University of Jos (NG)
  • University of Hong Kong (HK)
  • Juntendo University (JP)
  • Jackson State University (US)
  • Wuhan University (CN)
  • International Centre for Diarrhoeal Disease Research (BD)
  • Dilla University (ET)
  • Wuhan University of Science and Technology (CN)
  • Wuhan Polytechnic University (CN)
  • Chinese Center For Disease Control and Prevention (CN)
Corresponding author
  • Hay, Simon I.
  • Lim, Stephen S.
  • Mosser, Jonathan F.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2020-12-16
Volume
589
Issue
7842
Pages
415-419
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-020-03043-4
PMC
PMC7739806
OpenAlex
W3118415022
Cited by
125
References
49
Citations by year
2026: 12 · 2025: 27 · 2024: 18 · 2023: 19 · 2022: 24 · 2021: 23 · 2020: 2
FWCI
9.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DR-NTU (Nanyang Technological University) (publishedVersion, cc-by)
  • Research Portal (King's College London) (publishedVersion, other-oa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
  • Lincoln Repository (University of Lincoln) (acceptedVersion)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • City Research Online (City University London) (acceptedVersion)
  • Research Open (London South Bank University) (acceptedVersion, cc-by)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) (acceptedVersion)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
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  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • Työväentutkimus Vuosikirja (submittedVersion)
  • Repository@Hull (Worktribe) (University of Hull) (submittedVersion, cc-by)
  • YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
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  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, cc-by)
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  • The Sydney eScholarship Repository (The University of Sydney) (submittedVersion)
  • UniSA Research Outputs Repository (University of South Australia) (submittedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • Bill and Melinda Gates Foundation (#OPP1132415, OPP1106023, OPP1119467)
  • World Health Organization (OPP1132415)
  • Alexander von Humboldt-Stiftung
  • Florida International University
  • Johns Hopkins University
  • Birmingham City University
  • Wellcome Trust (206471/Z/17/Z, 109949/Z/15/Z)
  • Iran University of Medical Sciences
  • Australian Government
  • National Authority for Scientific Research and Innovation
  • Rafsanjan University of Medical Sciences
  • Maragheh University of Medical Sciences
  • National Institute for Health and Care Research (NIHR300791)
  • British Heart Foundation
  • University of Leicester
  • University of Oxford
  • Department of Science and Technology, Ministry of Science and Technology, India
  • RMIT University
  • Mazandaran University of Medical Sciences
  • Tabriz University of Medical Sciences
  • Tehran University of Medical Sciences and Health Services
  • Universiti Sains Malaysia
  • Katolicki Uniwersytet Lubelski Jana Pawla II
  • Zahedan University of Medical Sciences
  • Ahvaz Jundishapur University of Medical Sciences
  • Birjand University of Medical Sciences
  • Kermanshah University of Medical Sciences
  • BHF Centre of Research Excellence, Oxford
  • Babol University of Medical Sciences
  • Autoritatea Natională pentru Cercetare Stiintifică
  • Shahid Beheshti University of Medical Sciences
  • National Institute for Genetic Engineering and Biotechnology
  • Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (PN-III-P4-ID-PCCF-2016-0084, PN-III-P4-ID-PCCF-2016)
  • Yasuj University of Medical Sciences
  • University of the Philippines
  • Universitas Sebelas Maret
  • Universitatea de Medicină şi Farmacie "Carol Davila" Bucureşti
  • University of Tabriz
  • University of Gondar
  • Xiamen University
  • African Population and Health Research Center
  • Alexandria University
  • National Institute for Medical Research Development
  • Razi Vaccine and Serum Research Institute
  • Universidad Autónoma de Sinaloa
  • Western Sydney University
  • NIHR Oxford Biomedical Research Centre
  • Fogarty International Center (K43TW010716)
  • Health Data Research UK
Topics
  • Social Sciences › Health › Vaccine Coverage and Hesitancy
  • Health Sciences › Medicine › Epidemiology › Virology and Viral Diseases
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Global Maternal and Child Health
Keywords
measles vaccination, routine childhood immunization, low- and middle-income countries, Global Vaccine Action Plan, urban-rural differences, measles mortality
MeSH terms
Vaccination Refusal, Child, Child, Preschool, Humans, Measles, Rural Health, Urban Health, Vaccination, Developed Countries, Uncertainty, Internationality, Healthcare Disparities, Geographic Mapping

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