6 research outputs found
Scaling study of Si and strained Si n-MOSFETs with different high-k gate stacks
- Publication venue
- 'Institute of Electrical and Electronics Engineers (IEEE)'
- Publication date
- 01/01/2004
- Field of study
Using ensemble Monte Carlo device simulations, this paper studies the impact of interface roughness and soft-optical phonon scattering on the performance of sub-100nm Si and strained Si MOSFETs with different high-k gate stacks. Devices with gate lengths down to 25nm have been investigated
Use of anticoagulants and antiplatelet agents in stable outpatients with coronary artery disease and atrial fibrillation. International CLARIFY registry
- Author
- A. L. R. Ng
- Abardia Oliva F. J.
- Abdel Malak S.
- Abdel Wahab H.
- Abdul Kareem B. B.
- Abdul Manap H.
- Abdul Rahim A. A.
- Abdulkader F.
- Abdulla A. H. K.
- AbdulWahab M. Z.
- Abduvalieva V.
- Abele S.
- Aboyans V.
- Abreu P. E.
- Adam-Blanpain M.
- Adamaszek K.
- Adamczyk-Kot D.
- Adamkiewicz-Piejko A.
- Adda M.
- Aftab A.
- Agrawal A.
- Aguiar C.
- Ahuad Guerrero A.
- Ahuja R.
- Ai F.
- Aimouch N.
- Aiyegbayo O.
- Ajani A.
- Akbar M.
- Aksyutina N.
- Al Dhanki M.
- Al Ghool S.
- Al Kandari F.
- Al Lawati A.
- Al Mahmeed W.
- Al Radaideh G.
- Al Rashdan I.
- Al Sousi A.
- Al Suwaidi J.
- Al Tamimi F.
- Al Wahshi Y.
- Al Yazeedi J.
- Al Zaibag M.
- Al-Baker H.
- Al-Faleh H.
- Al-Hersi A.
- Al-Khalidi B.
- Al-Shamiri M.
- Al-Zaibag M.
- Alam A.
- Alanazy M.
- Alanbaei M.
- Albero Martinez V.
- Alberto Ramirez Reyes H.
- Albuquerque A.
- Alcaravela J.
- Alcocer Gamba M.
- Alegret Colomer J. M.
- Alegria Ezquerra E.
- Alekseenko V.
- Alekseeva T.
- Alexanderson E.
- Alexopoulos I.
- Alford K.
- Alitto F.
- Alizon F.
- Allcock A.
- Almeida Fernandez C. A.
- Altevogt B. -M.
- Altmann U.
- Alvarenga Recalde N.
- Alvarez Aunon A.
- Alvarez Garcia P.
- Alvarez Gil J.
- Alvarez Ramires F. J.
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- Publication venue
- 'Public Library of Science (PLoS)'
- Publication date
- 01/01/2015
- Field of study
Country-Level Aid Coordination at the United Nations: Taking the Resident Coordinator System Forward
- Author
- / Un Doco
- A / S Bigsten
- A / S Liese
- A Yoder
- Amanda Grittner
- Axel Berger
- B Reinalda
- Berger
- Berger
- Berichte Und Gutachten
- Brecht / Nicola Lein
- C Messina
- C Murphy
- C Weaver
- Carla / Carmen Richerzhagen / Katharina Brandi
- Clara Brandi
- D / J Messner
- D Booth
- D Dijkzeul
- D J Ross
- D Lindores
- E B Haas
- E C Luck
- Erik Lundsgaarde
- F Hill
- Frank Vollmer
- Frederick Boltz
- Frederick Boltz
- G Ashoff
- G Bhatta
- G Rosenthal
- G Torsvik
- Georgeta Vidican
- Guido Ashoff
- Guido Ashoff
- Guido Ashoff
- Guido Ashoff
- Guido Ashoff
- Guido Ashoff
- H Aufricht
- H Sahlmann
- H Volger
- H W Singer
- Hartmut Brandt
- I Dante
- I Venzke
- Inge Kaul
- J / J Ryan
- J E Krasno
- J Leininger
- J Mueller
- J Ostereich
- Jonas Hein
- J�rg / J�rn Faust
- K Huefner
- Klaus Liebig
- L Metcalfe
- M / M Barnett
- M Vatterodt
- Mark / Heiner Janus / Stephan Furness
- Markus Loewe
- Markus Loewe
- Martin St�rmer
- Matthias Krause
- Melody Garcia
- Niels / Adam Moe Keijzer
- O Stokke
- Oliver Schlumberger
- P Balogun
- P De Renzio
- Qhu5/$file/Dp%2014
- R
- R / L Jolly
- R / T G Thakur
- R Jolly
- R Longhurst
- R Russell
- Rasmus / Axel Lema
- Regine Qualmann
- Roger / Daniela Peltzer
- S / A Knack
- S B. / J Gareis
- S Bauer
- S Bauer
- S Browne
- S Klingebiel
- Schiller
- Stefan Leiderer
- Stephan Klingebiel
- Stephan Klingebiel
- Svea Koch
- T Fues
- T G Weiss
- T Mahn
- Thomas / Liu Fues
- Timo C. Mahn
- U N Nordic
- Ulrich Volz
- Unaids Ifad
- Van De Sand
- Vidyadhar Mallik
- W Easterly
- Zulueta-F�lscher
- Publication venue
- 'Elsevier BV'
- Publication date
- 01/01/2013
- Field of study
DCD Liver Transplant: a Meta-review of the Evidence and Current Optimization Strategies
- Author
- A Blasi
- A Casavilla
- A Casavilla
- A Franchello
- AI Skaro
- AK Mathur
- B Beck-Schimmer
- B Dominguez-Gil
- B Koneru
- B Nemes
- B Nemes
- C Jay
- C Kubal
- CB Taner
- D Azoulay
- D Azoulay
- D Monbaliu
- D Monbaliu
- D Routh
- DJ Firl
- DJ Reich
- DJ Reich
- DJ Reich
- DP Foley
- DP Foley
- E Savier
- EA Eren
- ES Orman
- F Dodson
- GL Lema Zuluaga
- HM Noujaim
- HP Grewal
- I Kristo
- J Dubbeld
- J Terblanche
- J Wind
- JB Seal
- JC Hong
- JJ Fung
- JL Bernat
- JP Deroose
- JV Guarrera
- JV Guarrera
- K Hashimoto
- KP Croome
- KW Lee
- M Bral
- M Kalisvaart
- M Sainz-Barriga
- MB Doyle
- ME Sutton
- ME Vera de
- MH Meine
- MM Mourad
- MS Noormohamed
- MT Gulsen
- N Gilbo
- ND Parikh
- P Dutkowski
- PG Northup
- PL Abt
- R Adam
- R Adam
- R Angelico
- R Lang
- R Mateo
- R Ravikumar
- R Steinbrook
- R Thuret
- RH Tolba
- RM Merion
- RW Busuttil
- S Dries Op den
- S O’Neill
- SD Halpern
- SE Khorsandi
- SM Brunner
- SW Biggins
- T Asano
- TC Wong
- TJ Chirichella
- TM Fishbein
- W Xia
- WA Rogers
- Y Cao
- Publication venue
- 'Springer Science and Business Media LLC'
- Publication date
- Field of study
Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
- Author
- Aali Amirali
- Abate Yohannes Habtegiorgis
- Abbasgholizadeh Rouzbeh
- Abbasi-Kangevari Mohsen
- Abbasian Mohammadreza
- Abbastabar Hedayat
- Abd ElHafeez Samar
- Abd-Elsalam Sherief
- Abdollahi Mohammad
- Abdollahifar Mohammad-Amin
- Abdoun Meriem
- Abdullahi Auwal
- Abebe Mesfin
- Abebe Samrawit Shawel
- Abiodun Olumide
- Abolhassani Hassan
- Abolmaali Meysam
- Abouzid Mohamed
- Aboye Girma Beressa
- Abreu Lucas Guimarães
- Abrha Woldu Aberhe
- Abrigo Michael R M
- Abtahi Dariush
- Abu-Gharbieh Eman
- Abu-Rmeileh Niveen ME
- Abualruz Hasan
- Abubakar Bilyaminu
- Adal Tadele Girum Girum
- Adane Mesafint Molla
- Adeagbo Oluwafemi Atanda Adeagbo
- Adedoyin Rufus Adesoji
- Adekanmbi Victor
- Aden Bashir
- Adepoju Abiola Victor
- Adetokunboh Olatunji O
- Adetunji Juliana Bunmi
- Adeyinka Daniel Adedayo
- Adeyomoye Olorunsola Israel
- Adnani Qorinah Estiningtyas Sakilah
- Adra Saryia
- Afolabi Rotimi Felix
- Afyouni Shadi
- Afzal Muhammad Sohail
- Afzal Saira
- Aghamiri Shahin
- Agodi Antonella
- Agyemang-Duah Williams
- Ahinkorah Bright Opoku
- Ahlstrom Austin J
- Ahmad Aqeel
- Ahmad Danish
- Ahmad Firdos
- Ahmad Muayyad M
- Ahmad Sajjad
- Ahmad Tauseef
- Ahmed Ali
- Ahmed Ayman
- Ahmed Haroon
- Ahmed Luai A
- Ahmed Meqdad Saleh
- Ahmed Syed Anees
- Ajami Marjan
- Aji Budi
- Akalu Gizachew Taddesse
- Akbarialiabad Hossein
- Akinyemi Rufus Olusola
- Akkaif Mohammed Ahmed
- Akkala Sreelatha
- Al Hamad Hanadi
- Al Hasan Syed Mahfuz
- Al Qadire Mohammad
- AL-Ahdal Tareq Mohammed Ali
- Al-Aly Ziyad
- Al-amer Rasmieh Mustafa
- Al-Eyadhy Ayman
- Al-Gheethi Adel Ali Saeed
- Al-Marwani Sabah
- Al-Mekhlafi Hesham M
- Al-Omari Basem
- Al-Raddadi Rajaa M
- Al-Sabah Salman Khalifah
- Al-Tawfiq Jaffar A
- Al-Worafi Yaser Mohammed
- Alalalmeh Samer O
- Alalwan Tariq A
- Alam Khurshid
- Alanezi Fahad Mashhour
- Alanzi Turki M
- Albakri Almaza
- Albashtawy Mohammed
- AlBataineh Mohammad T
- Alemi Hediyeh
- Alemi Sharifullah
- Alemu Yihun Mulugeta
- Alhabib Khalid F
- Alhajri Noora
- Alhalaiqa Fadwa Alhalaiqa Naji
- Alhassan Robert Kaba
- Ali Abid
- Ali Beriwan Abdulqadir
- Ali Liaqat
- Ali Mohammed Usman
- Ali Rafat
- Ali Syed Shujait Shujait
- Alif Sheikh Mohammad
- Aligol Mohammad
- Alijanzadeh Mehran
- Aljasir Mohammad A M
- Aljunid Syed Mohamed
- Almazan Joseph Uy
- Almidani Omar
- Alomari Mahmoud A
- Alqahtani Jaber S
- Alqutaibi Ahmed Yaseen
- Altaf Awais
- Altirkawi Khalid A
- Aluh Deborah Oyine
- Alvi Farrukh Jawad
- Alvis-Guzman Nelson
- Alwafi Hassan
- Aly Hany
- Aly Safwat
- Alzoubi Karem H
- Ameyaw Edward Kwabena
- Amin Tarek Tawfik
- Amindarolzarbi Alireza
- Amini-Rarani Mostafa
- Amiri Sohrab
- Ampomah Irene Gyamfuah
- Amugsi Dickson A
- Amusa Ganiyu Adeniyi
- Ancuceanu Robert
- Anderlini Deanna
- Andrade Pedro Prata
- Andrei Catalina Liliana
- Andrei Tudorel
- Anil Abhishek
- Anil Sneha
- Ansar Adnan
- Ansari-Moghaddam Alireza
- Antony Catherine M
- Antriyandarti Ernoiz
- Anvari Saeid
- ANWAR SALEHA
- Anwer Razique
- Anyasodor Anayochukwu Edward
- Arabloo Jalal
- Arabzadeh Bahri Razman
- Arafa Elshaimaa A
- Arafat Mosab
- Aravkin Aleksandr Y
- Araújo Ana Margarida
- Aremu Abdulfatai
- Aripov Timur
- Arkew Mesay
- Armocida Benedetta
- Arooj Mahwish
- Artamonov Anton A
- Arulappan Judie
- Aruleba Raphael Taiwo
- Arumugam Ashokan
- Asadi-Lari Mohsen
- Asemi Zatollah
- Asgary Saeed
- Asghari-Jafarabadi Mohammad
- Asghariahmadabad Mona
- Ashemo Mubarek Yesse
- Ashraf Muhammad
- Ashraf Tahira
- Asika Marvellous O
- Athari Seyyed Shamsadin
- Atout Maha Moh'd Wahbi
- Atreya Alok
- Aujayeb Avinash
- Ausloos Marcel
- Avan Abolfazl
- Aweke Amlaku Mulat
- Ayele Getnet Melaku
- Ayyoubzadeh Seyed Mohammad
- Azadnajafabad Sina
- Azevedo Rui M S
- Azzam Ahmed Y
- Badar Muhammad
- Badiye Ashish D
- Baghdadi Soroush
- Bagheri Nasser
- Bagherieh Sara
- Bahmanziari Najmeh
- Bai Ruhai
- Baig Atif Amin
- Baker Jennifer L
- Bako Abdulaziz T
- Bakshi Ravleen Kaur
- Balasubramanian Madhan
- Baltatu Ovidiu Constantin
- Bam Kiran
- Banach Maciej
- Bandyopadhyay Soham
- Banik Biswajit
- Banik Palash Chandra
- Bansal Hansi
- Baran Mehmet Firat
- Barchitta Martina
- Bardhan Mainak
- Bardideh Erfan
- Barker-Collo Suzanne Lyn
- Barone-Adesi Francesco
- Barqawi Hiba Jawdat
- Barrow Amadou
- Barteit Sandra
- Basharat Zarrin
- Bashir Asma'u I J
- Bashiru Hameed Akande
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- Salamati Payman
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- Salimi Sohrab
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- Saqib Haaris
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- Silva Luís Manuel Lopes Rodrigues
- Silva Soraia
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- Singh Balbir Bagicha
- Singh Jasvinder A
- Siraj Md Shahjahan
- Smith Amanda E
- Smith Georgia
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- Solomon Yonatan
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- Spartalis Michael
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- Srivastava Vijay Kumar
- Stanikzai Muhammad Haroon
- Starodubov Vladimir I
- Starodubova Antonina V
- Stefan Simona Cătălina
- Steiropoulos Paschalis
- Stokes Mark A
- Subramaniyan Vetriselvan
- Suleman Muhammad
- Suliankatchi Abdulkader Rizwan
- Sultana Abida
- Sun Jing
- Swain Chandan Kumar
- Sykes Bryan L
- Szarpak Lukasz
- Szeto Mindy D
- Szócska Miklós
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- Tabatabaeizadeh Seyed-Amir
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- Tabish Mohammad
- Taheri Soodejani Moslem
- Taiba Jabeen
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- Talaat Iman M
- Talukder Ashis
- Tampa Mircea
- Tamuzi Jacques Lukenze
- Tan Ker-Kan
- Tang Haosu
- Tareke Derbie Alemu DA
- Tariku Mengistie Kassahun
- Tat Vivian Y
- Tavangar Seyed Mohammad
- Teimoori Mojtaba
- Temsah Mohamad-Hani
- Temsah Reem Mohamad Hani
- Teramoto Masayuki
- Terefa Dufera Rikitu
- Tesler Riki
- Teye-Kwadjo Enoch
- Thakur Ramna
- Thangaraju Pugazhenthan
- Thankappan Kavumpurathu Raman
- Thapar Rekha
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- Thayakaran Rasiah
- Thomas Nihal
- Tichopad Ales
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- Tiruye Tenaw Yimer
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- Tonelli Marcello
- Tovani-Palone Marcos Roberto
- Traini Eugenio
- Tran Jasmine T
- Tran Nghia Minh
- Trihandini Indang
- Tromans Samuel Joseph
- Truyen Thien Tan Tri Tai
- Tsatsakis Aristidis
- Tsermpini Evangelia Eirini
- Tumurkhuu Munkhtuya
- Tyrovolas Stefanos
- Uddin Sayed Mohammad Nazim
- Udoakang Aniefiok John
- Udoh Arit
- Ullah Atta
- Ullah Saeed
- Ullah Sana
- Umakanthan Srikanth
- Umeokonkwo Chukwuma David
- Unim Brigid
- Unnikrishnan Bhaskaran
- Upadhyay Era
- Usman Jibrin Sammani
- Vacante Marco
- Vahabi Seyed Mohammad
- Vaithinathan Asokan Govindaraj
- Valizadeh Rohollah
- Van den Eynde Jef
- Varavikova Elena
- Varga Orsolya
- Vart Priya
- Varthya Shoban Babu
- Vasankari Tommi Juhani
- Vellingiri Balachandar
- Venugopal Deneshkumar
- Verghese Nicholas Alexander
- Verma Madhur
- Veroux Massimiliano
- Verras Georgios-Ioannis
- Vervoort Dominique
- Villafañe Jorge Hugo
- Vinayak Manish
- Violante Francesco S
- Vishwakarma Mukesh
- Vladimirov Sergey Konstantinovitch
- Vlassov Vasily
- Vo Bay
- Vollset Stein Emil
- Volovat Simona Ruxandra
- Vos Theo
- Vujcic Isidora S
- Wafa Hatem A
- Waheed Yasir
- Wakwoya Elias Bekele
- Wang Cong
- Wang Denny
- Wang Fang
- Wang Shu
- Wang Yanzhong
- Wang Yuan-Pang
- Ward Paul
- Wassie Emebet Gashaw
- Watson Stefanie
- Weaver Marcia R
- Weerakoon Kosala Gayan
- Weiss Daniel J
- Wells Katherine M
- Wen Yi Feng
- Westerman Ronny
- Wiangkham Taweewat
- Wickramasinghe Dakshitha Praneeth
- Wickramasinghe Nuwan Darshana
- Willeit Peter
- Wondimagegene Yohannes Addisu
- Wu Felicia
- Xia Juan
- Xiao Hong
- Xu Gelin
- Xu Suowen
- Xu Xiaoyue
- Yadollahpour Ali
- Yaghoobpoor Shirin
- Yaghoobpour Tina
- Yaghoubi Sajad
- Yahaya Zwanden Sule
- Yang Danting
- Yang Lin
- Yano Yuichiro
- Yaribeygi Habib
- Ye Pengpeng
- Yesodharan Renjulal
- Yesuf Subah Abderehim
- Yezli Saber
- Yigezu Amanuel
- Yip Paul
- Yon Dong Keon
- Yonemoto Naohiro
- You Yuyi
- Younis Mustafa Z
- Yousefi Zabihollah
- Yu Chuanhua
- Yu Yong
- Yuan Chun-Wei
- Zafari Nima
- Zakham Fathiah
- Zaki Nazar
- Zamagni Giulia
- Zandi Milad
- Zandieh Ghazal G Z
- Zangiabadian Moein
- Zastrozhin Mikhail Sergeevich
- Zhang Haijun
- Zhang Meixin
- Zhang Yunquan
- Zhong Chenwen
- Zhou Juexiao
- Zhu Bin
- Zhu Lei
- Zielińska Magdalena
- Zou Zhiyong
- Zyoud Samer H
- Ärnlöv Johan
- Publication venue
- Elsevier
- Publication date
- 20/03/2024
- Field of study
BackgroundAccurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. MethodsTo estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. FindingsDuring the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. InterpretationFertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. FundingBill & Melinda Gates Foundation
Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
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- Aadam Olalekan Bodunrin (18346572)
- Abanoub Riad (12229703)
- Abbas Mosapour (9154247)
- Abdalla Z Mohamed (18346983)
- Abdallah M Samy (8383428)
- Abdallah Y Naser (18347016)
- Abdoljalal Marjani (18346929)
- Abdollah Mohammadian-Hafshejani (9714923)
- Abdul-Monim Mohammad Batiha (12229466)
- Abdulaziz T Bako (18346515)
- Abdulfatai Aremu (9536881)
- Abdulkareem Kabir (18346809)
- Abdullah A Mamun (8383290)
- Abdullah Hamid Feroze (11553706)
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- Abhishek Shankar (18207631)
- Abid Ali (3123210)
- Abida Sultana (8147670)
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- Abolfazl Avan (631885)
- Abu Sayeed (195793)
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- Zwanden Sule Yahaya (18347382)
- Áurea M Madureira-Carvalho (18346908)
- Publication venue
- Publication date
- 16/05/2024
- Field of study
BackgroundAccurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios.MethodsTo estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline.FindingsDuring the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction.InterpretationFertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world.</p