Abstract
Background: Accurate 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. Methods: To 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. Findings: During 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. Interpretation: Fertility 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. Funding: Bill & Melinda Gates Foundation.
| Original language | English |
|---|---|
| Pages (from-to) | 2057-2099 |
| Number of pages | 43 |
| Journal | The Lancet |
| Volume | 403 |
| Issue number | 10440 |
| Early online date | May 2024 |
| DOIs | |
| Publication status | Published - 18 May 2024 |
Keywords
- Reproductive health
- Economic-growth
- Education
- Impact
- Empowerment
- Transition
- Policies
- Outcomes
- Africa
- Leave
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In: The Lancet, Vol. 403, No. 10440, 18.05.2024, p. 2057-2099.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100
T2 - a comprehensive demographic analysis for the Global Burden of Disease Study 2021
AU - GBD 2021 Fertility and Forecasting Collaborators
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AU - Schumacher, Austin E.
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AU - Horita, Nobuyuki
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AU - Hossain, Mohammad Bellal Hossain
AU - Hosseinzadeh, Hassan
AU - Hosseinzadeh, Mehdi
AU - Hostiuc, Mihaela
AU - Hostiuc, Sorin
AU - Hsairi, Mohamed
AU - Hsieh, Vivian Chia-rong
AU - Hu, Chengxi
AU - Huang, Junjie
AU - Huda, M. Mamun
AU - Humayun, Ayesha
AU - Hussain, Javid
AU - Hussein, Nawfal R.
AU - Huynh, Hong-Han
AU - Hwang, Bing-Fang
AU - Ibitoye, Segun Emmanuel
AU - Iftikhar, Pulwasha Maria
AU - Ilesanmi, Olayinka Stephen
AU - Ilic, Irena M.
AU - Ilic, Milena D.
AU - Immurana, Mustapha
AU - Inbaraj, Leeberk Raja
AU - Iqbal, Afrin
AU - Islam, Md Rabiul
AU - Ismail, Nahlah Elkudssiah
AU - Iso, Hiroyasu
AU - Isola, Gaetano
AU - Iwagami, Masao
AU - Iyer, Mahalaxmi
AU - J., Linda Merin
AU - Jaafari, Jalil
AU - Jacob, Louis
AU - Jadidi-Niaragh, Farhad
AU - Jaggi, Khushleen
AU - Jahankhani, Kasra
AU - Jahanmehr, Nader
AU - Jahrami, Haitham
AU - Jain, Akhil
AU - Jain, Nityanand
AU - Jairoun, Ammar Abdulrahman
AU - Jakovljevic, Mihajlo
AU - Jamshidi, Elham
AU - Javadov, Sabzali
AU - Javaheri, Tahereh
AU - Jayapal, Sathish Kumar
AU - Jayaram, Shubha
AU - Jee, Sun Ha
AU - Jeganathan, Jayakumar
AU - Jha, Anil K.
AU - Jha, Ravi Prakash
AU - Jiang, Heng
AU - Jokar, Mohammad
AU - Jonas, Jost B.
AU - Joo, Tamas
AU - Joseph, Nitin
AU - Joshua, Charity Ehimwenma
AU - Joukar, Farahnaz
AU - Jozwiak, Jacek Jerzy
AU - Jürisson, Mikk
AU - K., Vaishali
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AU - Kabir, Abdulkareem
AU - Kabir, Ali
AU - Kabir, Hannaneh
AU - Kabir, Zubair
AU - Kalani, Rizwan
AU - Kalankesh, Laleh R.
AU - Kaliyadan, Feroze
AU - Kalra, Sanjay
AU - Kamath, Rajesh
AU - Kamath, Sagarika
AU - Kanchan, Tanuj
AU - Kanmiki, Edmund Wedam
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AU - Kantar, Rami S.
AU - Kapoor, Neeti
AU - Karajizadeh, Mehrdad
AU - Karami, Manoochehr
AU - Karaye, Ibraheem M.
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AU - Kassel, Molly B.
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AU - Kazeminia, Sara
AU - Kempen, John H.
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AU - Keykhaei, Mohammad
AU - Khajuria, Himanshu
AU - Khalaji, Amirmohammad
AU - Khalid, Nauman
AU - Khalil, Anees Ahmed
AU - Khalilian, Alireza
AU - Khamesipour, Faham
AU - Khan, Ajmal
AU - Khan, Asaduzzaman
AU - Khan, Ikramullah
AU - Khan, M. Nuruzzaman
AU - Khan, Maseer
AU - Khan, Mohammad Jobair
AU - Khan, Moien A. B.
AU - Khang, Young-Ho
AU - Khanmohammadi, Shaghayegh
AU - Khatab, Khaled
AU - Khavandegar, Armin
AU - Kashani, Hamid Reza Khayat
AU - Khidri, Feriha Fatima
AU - Khormali, Moein
AU - Khosravi, Mohammad Ali
AU - Khosrowjerdi, Mahmood
AU - Kidane, Wondwosen Teklesilasie
AU - Kifle, Zemene Demelash
AU - Kim, Julie Sojin
AU - Kim, Min Seo
AU - Kimokoti, Ruth W.
AU - Kinzel, Kasey E.
AU - Kiross, Girmay Tsegay
AU - Kisa, Adnan
AU - Kisa, Sezer
AU - Kolahi, Ali-Asghar
AU - Kompani, Farzad
AU - Koren, Gerbrand
AU - Korzh, Oleksii
AU - Kosen, Soewarta
AU - Koulmane Laxminarayana, Sindhura Lakshmi
AU - Krishan, Kewal
AU - Krishna, Varun
AU - Krishnamoorthy, Vijay
AU - Kuate Defo, Barthelemy
AU - Kubeisy, Connor M.
AU - Kucuk Bicer, Burcu
AU - Kuddus, Md Abdul
AU - Kuddus, Mohammed
AU - Kuitunen, Ilari
AU - Kulimbet, Mukhtar
AU - Kumar, Harish
AU - Kundu, Satyajit
AU - Kunle, Kunle Rotimi
AU - Kurmi, Om P.
AU - Kusnali, Asep
AU - Kusuma, Dian
AU - Kyei, Evans F.
AU - Kyriopoulos, Ilias
AU - La Vecchia, Carlo
AU - Lacey, Ben
AU - Ladan, Muhammad Awwal
AU - Laflamme, Lucie
AU - Lahariya, Chandrakant
AU - Lai, Daphne Teck Ching
AU - Lal, Dharmesh Kumar
AU - Lalloo, Ratilal
AU - Lám, Judit
AU - Lamnisos, Demetris
AU - Landires, Iván
AU - Lanfranchi, Francesco
AU - Langguth, Berthold
AU - Laplante-Lévesque, Ariane
AU - Larson, Heidi Jane
AU - Larsson, Anders O.
AU - Lasrado, Savita
AU - Latief, Kamaluddin
AU - Latifinaibin, Kaveh
AU - Le, Long Khanh Dao
AU - Le, Nhi Huu Hanh
AU - Le, Trang Diep Thanh
AU - Ledda, Caterina
AU - Lee, Munjae
AU - Lee, Paul H.
AU - Lee, Seung Won
AU - Lee, Yo Han
AU - Lema, Gebretsadik Kiros
AU - Leong, Elvynna
AU - Lerango, Temesgen L.
AU - Li, An
AU - Li, Ming-Chieh
AU - Li, Shanshan
AU - Li, Wei
AU - Li, Xiaopan
AU - Ligade, Virendra S.
AU - Lim, Stephen S.
AU - Lin, Ro-Ting
AU - Lindstedt, Paulina A.
AU - Listl, Stefan
AU - Liu, Gang
AU - Liu, Jue
AU - Liu, Xiaofeng
AU - Liu, Xuefeng
AU - Liu, Yuewei
AU - Llanaj, Erand
AU - López-Bueno, Rubén
AU - Lopukhov, Platon D.
AU - Lorenzovici, László
AU - Lotufo, Paulo A.
AU - Lubinda, Jailos
AU - Lucchetti, Giancarlo
AU - Lugo, Alessandra
AU - Lunevicius, Raimundas
AU - Lv, Hengliang
AU - Ma, Zheng Feei
AU - Maass, Kelsey Lynn
AU - Machoy, Monika
AU - Madureira-Carvalho, Aurea M.
AU - Magdy Abd El Razek, Muhammed
AU - Maghazachi, Azzam A.
AU - Mahjoub, Soleiman
AU - Mahmoud, Mansour Adam
AU - Majeed, Azeem
AU - Malagón-Rojas, Jeadran N.
AU - Malakan Rad, Elaheh
AU - Malhotra, Kashish
AU - Malik, Ahmad Azam
AU - Malik, Iram
AU - Malta, Deborah Carvalho
AU - Mamun, Abdullah A.
AU - Manla, Yosef
AU - Mansoori, Yasaman
AU - Mansour, Ali
AU - Mansouri, Borhan
AU - Mansouri, Zeinab
AU - Mansournia, Mohammad Ali
AU - Maravilla, Joemer C.
AU - Marino, Mirko
AU - Marjani, Abdoljalal
AU - Martinez, Gabriel
AU - Martinez-Piedra, Ramon
AU - Martins-Melo, Francisco Rogerlândio
AU - Martorell, Miquel
AU - Maryam, Sharmeen
AU - Marzo, Roy Rillera
AU - Masoudi, Alireza
AU - Mattumpuram, Jishanth
AU - Maude, Richard James
AU - Maugeri, Andrea
AU - May, Erin A.
AU - Mayeli, Mahsa
AU - Mazaheri, Maryam
AU - McGrath, John J.
AU - McKee, Martin
AU - McKowen, Anna Laura Wensel
AU - McLaughlin, Susan A.
AU - McPhail, Steven M.
AU - Mehra, Rahul
AU - Mehrabani-Zeinabad, Kamran
AU - Mehrabi Nasab, Entezar
AU - Mekene Meto, Tesfahun
AU - Mendez-Lopez, Max Alberto Mendez
AU - Mendoza, Walter
AU - Menezes, Ritesh G.
AU - Mensah, George A.
AU - Mentis, Alexios-Fotios A.
AU - Meo, Sultan Ayoub
AU - Merati, Mohsen
AU - Meretoja, Atte
AU - Meretoja, Tuomo J.
AU - Mersha, Abera M.
AU - Mestrovic, Tomislav
AU - Metanat, Pouya
AU - Mettananda, Kukulege Chamila Dinushi
AU - Mettananda, Sachith
AU - Mhlanga, Adquate
AU - Mhlanga, Laurette
AU - Mi, Tianyue
AU - Miazgowski, Tomasz
AU - Micha, Georgia
AU - Michalek, Irmina Maria
AU - Miller, Ted R.
AU - Minh, Le Huu Nhat
AU - Mirghafourvand, Mojgan
AU - Mirrakhimov, Erkin M.
AU - Mirutse, Mizan Kiros
AU - Mirza, Moonis
AU - Mirzaei, Roya
AU - Mishra, Ashim
AU - Misra, Sanjeev
AU - Mitchell, Philip B.
AU - Mittal, Chaitanya
AU - Moazen, Babak
AU - Mohamed, Abdalla Z.
AU - Mohamed, Ahmed Ismail
AU - Mohamed, Jama
AU - Mohamed, Mouhand F. H.
AU - Mohamed, Nouh Saad
AU - Mohammad-Alizadeh-Charandabi,, Sakineh
AU - Mohammadi, Soheil
AU - Mohammadian-Hafshejani, Abdollah
AU - Mohammed, Mustapha
AU - Mohammed, Salahuddin
AU - Mohammed, Shafiu
AU - Mokdad, Ali H.
AU - Mokhtarzadehazar, Peyman
AU - Molavi Vardanjani, Hossein
AU - Molinaro, Sabrina
AU - Monasta, Lorenzo
AU - Moni, Mohammad Ali
AU - Moradi, Maryam
AU - Moradi, Yousef
AU - Moraga, Paula
AU - Moreira, Rafael Silveira
AU - Morovatdar, Negar
AU - Morrison, Shane Douglas
AU - Morze, Jakub
AU - Mosapour, Abbas
AU - Mossialos, Elias
AU - Motappa, Rohith
AU - Mousavi, Parsa
AU - Mousavi Khaneghah, Amin
AU - Mpundu-Kaambwa, Christine
AU - Mubarik, Sumaira
AU - Muccioli, Lorenzo
AU - Mulita, Francesk
AU - Munjal, Kavita
AU - Murillo-Zamora, Efrén
AU - Musa, Jonah
AU - Musaigwa, Fungai
AU - Musina, Ana-Maria
AU - Muthu, Sathish
AU - Muthupandian, Saravanan
AU - Muzaffar, Muhammad
AU - Myung, Woojae
AU - Nagarajan, Ahamarshan Jayaraman
AU - Nagel, Gabriele
AU - Naghavi, Pirouz
AU - Naik, Ganesh R.
AU - Naik, Gurudatta
AU - Naimzada, Mukhammad David
AU - Nainu, Firzan
AU - Nangia, Vinay
AU - Narasimha Swamy, Sreenivas
AU - Nascimento, Bruno Ramos
AU - Nascimento, Gustavo G.
AU - Naser, Abdallah Y.
AU - Nasiri, Mohammad Javad
AU - Natto, Zuhair S.
AU - Nauman, Javaid
AU - Naveed, Muhammad
AU - Nayak, Biswa Prakash
AU - Nayak, Vinod C.
AU - Ndejjo, Rawlance
AU - Nduaguba, Sabina Onyinye
AU - Negash, Hadush
AU - Negesse, Chernet Tafere
AU - Negoi, Ionut
AU - Negoi, Ruxandra Irina
AU - Nejadghaderi, Seyed Aria
AU - Nejjari, Chakib
AU - Nepal, Samata
AU - Netsere, Henok Biresaw
AU - Nguefack-Tsague, Georges
AU - Ngunjiri, Josephine W.
AU - Nguyen, Dang H.
AU - Nguyen, Hau Thi Hien
AU - Nguyen, Phuong The
AU - Nguyen, QuynhAnh P.
AU - Nguyen, Van Thanh
AU - Niazi, Robina Khan
AU - Nigatu, Yeshambel T.
AU - Nikolouzakis, Taxiarchis Konstantinos
AU - Nikoobar, Ali
AU - Nikpoor, Amin Reza
AU - Nnaji, Chukwudi A.
AU - Nnyanzi, Lawrence Achilles
AU - Noman, Efaq Ali
AU - Nomura, Shuhei
AU - Noreen, Mamoona
AU - Noroozi, Nafise
AU - Nri-Ezedi, Chisom Adaobi
AU - Nunemo, Mengistu H.
AU - Nuñez-Samudio, Virginia
AU - Nurrika, Dieta
AU - Nutor, Jerry John
AU - Oancea, Bogdan
AU - Obamiro, Kehinde O.
AU - Odetokun, Ismail A.
AU - Odogwu, Nkechi Martina
AU - O'Donnell, Martin James
AU - Odukoya, Oluwakemi Ololade
AU - Oguntade, Ayodipupo Sikiru
AU - Oguta, James Odhiambo
AU - Oh, In-Hwan
AU - Okeke, Sylvester Reuben
AU - Okekunle, Akinkunmi Paul
AU - Okonji, Osaretin Christabel
AU - Okwute, Patrick Godwin
AU - Olagunju, Andrew T.
AU - Olasupo, Omotola O.
AU - Olatubi, Matthew Idowu
AU - Oliveira, Gláucia Maria Moraes
AU - Olusanya, Bolajoko Olubukunola
AU - Olusanya, Jacob Olusegun
AU - Oluwatunase, Gideon Olamilekan
AU - Omar, Hany A.
AU - Omer, Goran Latif
AU - Onwujekwe, Obinna E.
AU - Ordak, Michal
AU - Orisakwe, Orish Ebere
AU - Orish, Verner N.
AU - Ortega-Altamirano, Doris V.
AU - Ortiz, Alberto
AU - Ortiz-Prado, Esteban
AU - Osman, Wael M. S.
AU - Osuagwu, Uchechukwu Levi
AU - Osuolale, Olayinka
AU - Otoiu, Adrian
AU - Otstavnov, Stanislav S.
AU - Ouyahia, Amel
AU - Ouyang, Guoqing
AU - Owolabi, Mayowa O.
AU - Ozten, Yaz
AU - P. A., Mahesh Padukudru
AU - Pahlevan Fallahy, Mohammad Taha
AU - Pan, Feng
AU - Pan, Hai-Feng
AU - Pana, Adrian
AU - Panda, Paramjot
AU - Panda-Jonas, Songhomitra
AU - Pangaribuan, Helena Ullyartha
AU - Panos, Georgios D.
AU - Panos, Leonidas D.
AU - Pantazopoulos, Ioannis
AU - Pantea Stoian, Anca Mihaela
AU - Parikh, Romil R.
AU - Park, Seoyeon
AU - Parthasarathi, Ashwaghosha
AU - Pashaei, Ava
AU - Passera, Roberto
AU - Patel, Hemal H.
AU - Patel, Jay
AU - Patil, Shankargouda
AU - Patoulias, Dimitrios
AU - Patthipati, Venkata Suresh
AU - Paudel, Uttam
AU - Paun, Mihaela
AU - Pazoki Toroudi, Hamidreza
AU - Pease, Spencer A.
AU - Peden, Amy E.
AU - Pedersini, Paolo
AU - Peng, Minjin
AU - Pensato, Umberto
AU - Pepito, Veincent Christian Filipino
AU - Peprah, Prince
AU - Pereira, Gavin
AU - Peres, Mario F. P.
AU - Perianayagam, Arokiasamy
AU - Perico, Norberto
AU - Perna, Simone
AU - Pestell, Richard G.
AU - Petermann-Rocha, Fanny Emily
AU - Pham, Hoang Tran
AU - Philip, Anil K.
AU - Pierannunzio, Daniela
AU - Pigeolet, Manon
AU - Pigott, David M.
AU - Plotnikov, Evgenii
AU - Poddighe, Dimitri
AU - Pollner, Peter
AU - Poluru, Ramesh
AU - Postma, Maarten J.
AU - Pourali, Ghazaleh
AU - Pourshams, Akram
AU - Pourtaheri, Naeimeh
AU - Prabhu, Disha
AU - Prada, Sergio I.
AU - Pradhan, Pranil Man Singh
AU - Prasad, Manya
AU - Prashant, Akila
AU - Purohit, Bharathi M.
AU - Puvvula, Jagadeesh
AU - Qasim, Nameer Hashim
AU - Qattea, Ibrahim
AU - R., Deepthi
AU - Rad, Mehrdad Rabiee
AU - Radfar, Amir
AU - Radhakrishnan, Venkatraman
AU - Raee, Pourya
AU - Raeisi Shahraki, Hadi
AU - Rafiei, Alireza
AU - Rafiei Alavi, Seyedeh Niloufar
AU - Raggi, Cat
AU - Raghav, Pankaja Raghav
AU - Rahim, Fakher
AU - Rahim, Md Jillur
AU - Rahman, Md. Mosfequr
AU - Rahman, Mohammad Hifz Ur
AU - Rahman, Mosiur
AU - Rahman, Muhammad Aziz
AU - Rahmanian, Vahid
AU - Rahmati, Masoud
AU - Rahnavard, Niloufar
AU - Rai, Pramila
AU - Raimondo, Diego
AU - Rajabpour-Sanati, Ali
AU - Rajput, Prashant
AU - Ram, Prasanna
AU - Ramasamy, Shakthi Kumaran
AU - Rana, Juwel
AU - Rana, Kritika
AU - Rana, Shailendra Singh
AU - Ranabhat, Chhabi Lal
AU - Rancic, Nemanja
AU - Rane, Amey
AU - Ranjan, Shubham
AU - Rao, Chythra R.
AU - Rao, Indu Ramachandra
AU - Rapaka, Deepthi
AU - Rasella, Davide
AU - Rashedi, Sina
AU - Rashedi, Vahid
AU - Rashidi, Mohammad-Mahdi
AU - Rasul, Azad
AU - Ratan, Zubair Ahmed
AU - Babu, Giridhara Rathnaiah
AU - Rauniyar, Santosh Kumar
AU - Ravikumar, Nakul
AU - Rawaf, David Laith
AU - Rawaf, Salman
AU - Rawassizadeh, Reza
AU - Rawlley, Bharat
AU - Reddy, Murali Mohan Rama Krishna
AU - Redwan, Elrashdy Moustafa Mohamed
AU - Remuzzi, Giuseppe
AU - Reshmi, Bhageerathy
AU - Rezaei, Nazila
AU - Rezaei Nejad, Aida
AU - Rezaeian, Mohsen
AU - Riad, Abanoub
AU - Riaz, Mavra A.
AU - Rickard, Jennifer
AU - Rikhtegar, Reza
AU - Robinson-Oden, Hannah Elizabeth
AU - Fortuna Rodrigues, Celia
AU - Rodriguez, Jefferson Antonio Buendia
AU - Rohilla, Ravi
AU - Romadlon, Debby Syahru
AU - Ronfani, Luca
AU - Rout, Himanshu Sekhar
AU - Roy, Bedanta
AU - Roy, Nitai
AU - Roy, Priyanka
AU - Rubagotti, Enrico
AU - Ruela, Guilherme de Andrade
AU - Rumisha, Susan Fred
AU - Runghien, Tilleye
AU - S., Manjula
AU - S. N., Chandan
AU - Saad, Aly M. A.
AU - Saadatian, Zahra
AU - Saber-Ayad, Maha Mohamed
AU - SaberiKamarposhti, Morteza
AU - Sabour, Siamak
AU - Sada, Fatos
AU - Saddik, Basema
AU - Sadee, Bashdar Abuzed
AU - Sadeghi, Ehsan
AU - Sadeghi, Erfan
AU - Saeb, Mohammad Reza
AU - Saeed, Umar
AU - Safi, Sher Zaman
AU - Sagoe, Dominic
AU - Saha, Manika
AU - Sahebkar, Amirhossein
AU - Sahoo, Soumya Swaroop
AU - Sahu, Monalisha
AU - Saif, Zahra
AU - Sakshaug, Joseph W.
AU - Salamati, Payman
AU - Salami, Afeez Abolarinwa
AU - Saleh, Mohamed A.
AU - Salem, Marwa Rashad
AU - Salem, Mohammed Z. Y.
AU - Salimi, Sohrab
AU - Samadzadeh, Sara
AU - Samodra, Yoseph Leonardo
AU - Samuel, Vijaya Paul
AU - Samy, Abdallah M.
AU - Sanabria, Juan
AU - Sanadgol, Nima
AU - Sanna, Francesca
AU - Santric-Milicevic, Milena M.
AU - Saqib, Haaris
AU - Saraswathy, Sivan Yegnanarayana Iyer
AU - Saravanan, Aswini
AU - Saravi, Babak
AU - Sarikhani, Yaser
AU - Sarkar, Tanmay
AU - Sarmiento-Suárez, Rodrigo
AU - Sarode, Gargi Sachin
AU - Sarode, Sachin C.
AU - Sarveazad, Arash
AU - Sathian, Brijesh
AU - Sathish, Thirunavukkarasu
AU - Sathyanarayan, Anudeep
AU - Sayeed, Abu
AU - Sayeed, Md Abu
AU - Scarmeas, Nikolaos
AU - Schlee, Winfried
AU - Schuermans, Art
AU - Schwebel, David C.
AU - Schwendicke, Falk
AU - Selvaraj, Siddharthan
AU - Sengupta, Pallav
AU - Senthilkumaran, Subramanian
AU - Sepanlou, Sadaf G.
AU - Serban, Dragos
AU - Serván-Mori, Edson
AU - Sethi, Yashendra
AU - SeyedAlinaghi, Seyed Ahmad
AU - Seyedi, Seyed Arsalan
AU - Seylani, Allen
AU - Shafie, Mahan
AU - Shah, Jaffer
AU - Shah, Pritik A.
AU - Shahbandi, Ataollah
AU - Shahid, Samiah
AU - Shahwan, Moyad Jamal
AU - Shaikh, Ahmed
AU - Shaikh, Masood Ali
AU - Shamim, Muhammad Aaqib
AU - Shams-Beyranvand, Mehran
AU - Shamsi, Mohammad Anas
AU - Shanawaz, Mohd
AU - Shankar, Abhishek
AU - Shannawaz, Mohammed
AU - Sharath, Medha
AU - Sharfaei, Sadaf
AU - Sharifan, Amin
AU - Sharifi-Rad, Javad
AU - Sharma, Manoj
AU - Sharma, Rajesh
AU - Sharma, Ujjawal
AU - Sharma, Vishal
AU - Shastry, Rajesh P.
AU - Shavandi, Amin
AU - Shaw, David H.
AU - Shayan, Amir Mehdi
AU - Shayan, Maryam
AU - Shehabeldine, Amr Mohamed Elsayed
AU - Sheikh, Aziz
AU - Sheikhi, Rahim Ali
AU - Shenoy, Manjunath Mala
AU - Shetty, Pavanchand H.
AU - Shi, Peilin
AU - Shiferaw, Desalegn
AU - Shigematsu, Mika
AU - Shiri, Rahman
AU - Shirkoohi, Reza
AU - Shittu, Aminu
AU - Shivarov, Velizar
AU - Shokraneh, Farhad
AU - Shool, Sina
AU - Shorofi, Seyed Afshin
AU - Shuja, Kanwar Hamza
AU - Shuval, Kerem
AU - Siddig, Emmanuel Edwar
AU - Silva, João Pedro
AU - Silva, Luis Manuel Lopes Rodrigues
AU - Silva, Soraia
AU - Simonetti, Biagio
AU - Singal, Anjali
AU - Singh, Abhinav
AU - Singh, Balbir Bagicha
AU - Singh, Jasvinder A.
AU - Siraj, Md Shahjahan
AU - Smith, Georgia
AU - Socea, Bogdan
AU - Sokhan, Anton
AU - Solanki, Ranjan
AU - Solanki, Shipra
AU - Soleimani, Hamidreza
AU - Soliman, Sameh S. M.
AU - Solomon, Yonatan
AU - Song, Yimeng
AU - Sorensen, Reed J. D.
AU - Spartalis, Michael
AU - Sreeramareddy, Chandrashekhar T.
AU - Srivastava, Vijay Kumar
AU - Stanikzai, Muhammad Haroon
AU - Starodubov, Vladimir I.
AU - Starodubova, Antonina V.
AU - Ștefan, Simona Cătălina
AU - Steiropoulos, Paschalis
AU - Stokes, Mark A.
AU - Subramaniyan, Vetriselvan
AU - Suleman, Muhammad
AU - Suliankatchi Abdulkader, Rizwan
AU - Sultana, Abida
AU - Sun, Jing
AU - Swain, Chandan Kumar
AU - Sykes, Bryan L.
AU - Szarpak, Lukasz
AU - Szeto, Mindy D.
AU - Szócska, Miklós
AU - Tabaee Damavandi, Payam
AU - Tabarés-Seisdedos, Rafael
AU - Tabatabaei Malazy, Ozra
AU - Tabatabaeizadeh, Seyed-Amir
AU - Tabatabai, Shima
AU - Tabb, Karen M.
AU - Tabish, Mohammad
AU - Taheri Soodejani, Moslem
AU - Taiba, Jabeen
AU - Tajbakhsh, Ardeshir
AU - Talaat, Iman M.
AU - Talukder, Ashis
AU - Tampa, Mircea
AU - Tamuzi, Jacques Lukenze
AU - Tan, Ker-Kan
AU - Tang, Haosu
AU - Tareke, Derbie Alemu D. A.
AU - Tariku, Mengistie Kassahun
AU - Tat, Vivian Y.
AU - Tavangar, Seyed Mohammad
AU - Teimoori, Mojtaba
AU - Temsah, Mohamad-Hani
AU - Temsah, Reem Mohamad Hani
AU - Teramoto, Masayuki
AU - Terefa, Dufera Rikitu
AU - Tesler, Riki
AU - Teye-Kwadjo, Enoch
AU - Thakur, Ramna
AU - Thangaraju, Pugazhenthan
AU - Thankappan, Kavumpurathu Raman
AU - Thapar, Rekha
AU - Tharwat, Samar
AU - Thayakaran, Rasiah
AU - Thomas, Nihal
AU - Tichopad, Ales
AU - Ticoalu, Jansje Henny Vera
AU - Tiruye, Tenaw Yimer
AU - Titova, Mariya Vladimirovna
AU - Tonelli, Marcello
AU - Tovani-Palone, Marcos Roberto
AU - Traini, Eugenio
AU - Tran, Jasmine T.
AU - Tran, Nghia Minh
AU - Trihandini, Indang
AU - Tromans, Samuel Joseph
AU - Truyen, Thien Tan Tri Tai
AU - Tsatsakis, Aristidis
AU - Tsermpini, Evangelia Eirini
AU - Tumurkhuu, Munkhtuya
AU - Tyrovolas, Stefanos
AU - Uddin, Sayed Mohammad Nazim
AU - Udoakang, Aniefiok John
AU - Udoh, Arit
AU - Ullah, Atta
AU - Ullah, Saeed
AU - Ullah, Sana
AU - Umakanthan, Srikanth
AU - Umeokonkwo, Chukwuma David
AU - Unim, Brigid
AU - Unnikrishnan, Bhaskaran
AU - Upadhyay, Era
AU - Usman, Jibrin Sammani
AU - Vacante, Marco
AU - Vahabi, Seyed Mohammad
AU - Vaithinathan, Asokan Govindaraj
AU - Valizadeh, Rohollah
AU - Van den Eynde, Jef
AU - Varavikova, Elena
AU - Varga, Orsolya
AU - Vart, Priya
AU - Varthya, Shoban Babu
AU - Vasankari, Tommi Juhani
AU - Vellingiri, Balachandar
AU - Venugopal, Deneshkumar
AU - Verghese, Nicholas Alexander
AU - Verma, Madhur
AU - Veroux, Massimiliano
AU - Verras, Georgios-Ioannis
AU - Vervoort, Dominique
AU - Villafane, Jorge Hugo
AU - Vinayak, Manish
AU - Violante, Francesco S.
AU - Vishwakarma, Mukesh
AU - Vladimirov, Sergey Konstantinovitch
AU - Vlassov, Vasily
AU - Vo, Bay
AU - Volovat, Simona Ruxandra
AU - Vos, Theo
AU - Vujcic, Isidora S.
AU - Wafa, Hatem A.
AU - Waheed, Yasir
AU - Wakwoya, Elias Bekele
AU - Wang, Cong
AU - Wang, Denny
AU - Wang, Fang
AU - Wang, Shu
AU - Wang, Yanzhong
AU - Wang, Yuan-Pang
AU - Ward, Paul
AU - Wassie, Emebet Gashaw
AU - Watson, Stefanie
AU - Weaver, Marcia R.
AU - Weerakoon, Kosala Gayan
AU - Weiss, Daniel J.
AU - Wells, Katherine M.
AU - Wen, Yi Feng
AU - Westerman, Ronny
AU - Wiangkham, Taweewat
AU - Wickramasinghe, Dakshitha Praneeth
AU - Wickramasinghe, Nuwan Darshana
AU - Willeit, Peter
AU - Wondimkun, Yohannes Addisu
AU - Wu, Felicia
AU - Xia, Juan
AU - Xiao, Hong
AU - Xu, Gelin
AU - Xu, Suowen
AU - Xu, Xiaoyue
AU - Yadollahpour, Ali
AU - Yaghoobpoor, Shirin
AU - Yaghoobpoor, Tina
AU - Yaghoubi, Sajad
AU - Yahaya, Zwanden Sule
AU - Yang, Danting
AU - Yang, Lin
AU - Yano, Yuichiro
AU - Yaribeygi, Habib
AU - Ye, Pengpeng
AU - Yesodharan, Renjulal
AU - Yesuf, Subah Abderehim
AU - Yezli, Saber
AU - Yigezu, Amanuel
AU - Yip, Paul
AU - Yon, Dong Keon
AU - Yonemoto, Naohiro
AU - You, Yuyi
PY - 2024/5/18
Y1 - 2024/5/18
N2 - Background: Accurate 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. Methods: To 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. Findings: During 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. Interpretation: Fertility 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. Funding: Bill & Melinda Gates Foundation.
AB - Background: Accurate 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. Methods: To 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. Findings: During 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. Interpretation: Fertility 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. Funding: Bill & Melinda Gates Foundation.
KW - Reproductive health
KW - Economic-growth
KW - Education
KW - Impact
KW - Empowerment
KW - Transition
KW - Policies
KW - Outcomes
KW - Africa
KW - Leave
UR - https://www.scopus.com/pages/publications/85188450830
U2 - 10.1016/S0140-6736(24)00550-6
DO - 10.1016/S0140-6736(24)00550-6
M3 - Article
C2 - 38521087
AN - SCOPUS:85188450830
SN - 0140-6736
VL - 403
SP - 2057
EP - 2099
JO - The Lancet
JF - The Lancet
IS - 10440
ER -