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
AU - Bhattacharjee, Natalia V.
AU - Schumacher, Austin E.
AU - Aali, Amirali
AU - Abate, Yohannes Habtegiorgis
AU - Abbasgholizadeh, Rouzbeh
AU - Abbasian, Mohammadreza
AU - Abbasi-Kangevari, Mohsen
AU - Abbastabar, Hedayat
AU - Abd-Elsalam, Sherief
AU - Abd-Elsalam, Sherief
AU - Abdollahi, Mohammad
AU - Abdollahifar, Mohammad Amin
AU - Abdoun, Meriem
AU - Abdullahi, Auwal
AU - Abebe, Mesfin
AU - Abebe, Samrawit Shawel
AU - Abiodun, Olumide
AU - Abolhassani, Hassan
AU - Abolmaali, Meysam
AU - Abouzid, Mohamed
AU - Aboye, Girma Beressa
AU - Abreu, Lucas Guimarães
AU - Abrha, Woldu Aberhe
AU - Abrigo, Michael R.M.
AU - Abtahi, Dariush
AU - Abualruz, Hasan
AU - Abubakar, Bilyaminu
AU - Abu-Gharbieh, Eman
AU - Abu-Rmeileh, Niveen ME
AU - Adal, Tadele Girum Girum
AU - Adane, Mesafint Molla
AU - Adeagbo, Oluwafemi Atanda Adeagbo
AU - Adedoyin, Rufus Adesoji
AU - Adekanmbi, Victor
AU - Aden, Bashir
AU - Adepoju, Abiola Victor
AU - Adetokunboh, Olatunji O.
AU - Adetunji, Juliana Bunmi
AU - Adeyinka, Daniel Adedayo
AU - Adeyomoye, Olorunsola Israel
AU - Adnani, Qorinah Estiningtyas Sakilah
AU - Adra, Saryia
AU - Afolabi, Rotimi Felix
AU - Afyouni, Shadi
AU - Afzal, Saira
AU - Afzal, Saira
AU - Aghamiri, Shahin
AU - Agodi, Antonella
AU - Agyemang-Duah, Williams
AU - Ahinkorah, Bright Opoku
AU - Ahlstrom, Austin J.
AU - Ahmad, Aqeel
AU - Ahmad, Danish
AU - Ahmad, Firdos
AU - Ahmad, Muayyad M.
AU - Ahmad, Sajjad
AU - Ahmad, Tauseef
AU - Ahmed, Luai A.
AU - Ahmed, Luai A.
AU - Ahmed, Haroon
AU - Ahmed, Luai A.
AU - Ahmed, Meqdad Saleh
AU - Ahmed, Syed Anees
AU - Ajami, Marjan
AU - Aji, Budi
AU - Akalu, Gizachew Taddesse
AU - Akbarialiabad, Hossein
AU - Akinyemi, Rufus Olusola
AU - Akkaif, Mohammed Ahmed
AU - Akkala, Sreelatha
AU - Al Hamad, Hanadi
AU - Al Hasan, Syed Mahfuz
AU - Al Qadire, Mohammad
AU - AL-Ahdal, Tareq Mohammed Ali
AU - Alalalmeh, Samer O.
AU - Alalwan, Tariq A.
AU - Al-Aly, Ziyad
AU - Alam, Khurshid
AU - Al-amer, Rasmieh Mustafa
AU - Alanezi, Fahad Mashhour
AU - Alanzi, Turki M.
AU - Albakri, Almaza
AU - Albashtawy, Mohammed
AU - AlBataineh, Mohammad T.
AU - Alemi, Hediyeh
AU - Alemi, Sharifullah
AU - Alemu, Yihun Mulugeta
AU - Al-Eyadhy, Ayman
AU - Al-Gheethi, Adel Ali Saeed
AU - Alhabib, Khalid F.
AU - Alhajri, Noora
AU - Alhalaiqa, Fadwa Alhalaiqa Naji
AU - Alhassan, Robert Kaba
AU - Ali, Beriwan Abdulqadir
AU - Ali, Beriwan Abdulqadir
AU - Ali, Liaqat
AU - Ali, Mohammed Usman
AU - Ali, Rafat
AU - Ali, Syed Shujait Shujait
AU - Alif, Sheikh Mohammad
AU - Aligol, Mohammad
AU - Alijanzadeh, Mehran
AU - Aljasir, Mohammad A.M.
AU - Aljunid, Syed Mohamed
AU - Al-Marwani, Sabah
AU - Almazan, Joseph Uy
AU - Al-Mekhlafi, Hesham M.
AU - Almidani, Omar
AU - Alomari, Mahmoud A.
AU - Al-Omari, Basem
AU - Alqahtani, Jaber S.
AU - Alqutaibi, Ahmed Yaseen
AU - Al-Raddadi, Rajaa M.
AU - Al-Sabah, Salman Khalifah
AU - Altaf, Awais
AU - Al-Tawfiq, Jaffar A.
AU - Altirkawi, Khalid A.
AU - Aluh, Deborah Oyine
AU - Alvi, Farrukh Jawad
AU - Alvis-Guzman, Nelson
AU - Alwafi, Hassan
AU - Al-Worafi, Yaser Mohammed
AU - Aly, Hany
AU - Aly, Safwat
AU - Alzoubi, Karem H.
AU - Ameyaw, Edward Kwabena
AU - Amin, Tarek Tawfik
AU - Amindarolzarbi, Alireza
AU - Amini-Rarani, Mostafa
AU - Amiri, Sohrab
AU - Ampomah, Irene Gyamfuah
AU - Amugsi, Dickson A.
AU - Amusa, Ganiyu Adeniyi
AU - Ancuceanu, Robert
AU - Anderlini, Deanna
AU - Andrade, Pedro Prata
AU - Andrei, Catalina Liliana
AU - Andrei, Tudorel
AU - Anil, Abhishek
AU - Anil, Sneha
AU - Ansar, Adnan
AU - Ansari-Moghaddam, Alireza
AU - Antony, Catherine M.
AU - Antriyandarti, Ernoiz
AU - Anvari, Saeid
AU - ANWAR, SALEHA
AU - Anwer, Razique
AU - Anyasodor, Anayochukwu Edward
AU - Arabloo, Jalal
AU - Arabzadeh Bahri, Razman
AU - Arafa, Elshaimaa A.
AU - Arafat, Mosab
AU - Araújo, Ana Margarida
AU - Aravkin, Aleksandr Y.
AU - Aremu, Abdulfatai
AU - Aripov, Timur
AU - Arkew, Mesay
AU - Armocida, Benedetta
AU - Ärnlöv, Johan
AU - Arooj, Mahwish
AU - Artamonov, Anton A.
AU - Arulappan, Judie
AU - Aruleba, Raphael Taiwo
AU - Arumugam, Ashokan
AU - Asadi-Lari, Mohsen
AU - Asemi, Zatollah
AU - Asgary, Saeed
AU - Asghariahmadabad, Mona
AU - Asghari-Jafarabadi, Mohammad
AU - Ashemo, Mubarek Yesse
AU - Ashraf, Muhammad
AU - Ashraf, Tahira
AU - Asika, Marvellous O.
AU - Athari, Seyyed Shamsadin
AU - Atout, Maha Moh d.Wahbi
AU - Atreya, Alok
AU - Aujayeb, Avinash
AU - Ausloos, Marcel
AU - Avan, Abolfazl
AU - Aweke, Amlaku Mulat
AU - Ayele, Getnet Melaku
AU - Ayyoubzadeh, Seyed Mohammad
AU - Azadnajafabad, Sina
AU - Azevedo, Rui M.S.
AU - Azzam, Ahmed Y.
AU - Badar, Muhammad
AU - Badiye, Ashish D.
AU - Baghdadi, Soroush
AU - Bagheri, Nasser
AU - Bagherieh, Sara
AU - Bahmanziari, Najmeh
AU - Bai, Ruhai
AU - Baig, Atif Amin
AU - Baker, Jennifer L.
AU - Bako, Abdulaziz T.
AU - Bakshi, Ravleen Kaur
AU - Balasubramanian, Madhan
AU - Baltatu, Ovidiu Constantin
AU - Bam, Kiran
AU - Banach, Maciej
AU - Bandyopadhyay, Soham
AU - Banik, Biswajit
AU - Banik, Palash Chandra
AU - Bansal, Hansi
AU - Baran, Mehmet Firat
AU - Barchitta, Martina
AU - Bardhan, Mainak
AU - Bardideh, Erfan
AU - Barker-Collo, Suzanne Lyn
AU - Bärnighausen, Till Winfried
AU - Barone-Adesi, Francesco
AU - Barqawi, Hiba Jawdat
AU - Barrow, Amadou
AU - Barteit, Sandra
AU - Basharat, Zarrin
AU - Bashir, Asma'u I.J.
AU - Bashiru, Hameed Akande
AU - Basiru, Afisu
AU - Basso, João Diogo
AU - Basu, Sanjay
AU - Batiha, Abdul Monim Mohammad
AU - Batra, Kavita
AU - Baune, Bernhard T.
AU - Bayati, Mohsen
AU - Begum, Tahmina
AU - Behboudi, Emad
AU - Behnoush, Amir Hossein
AU - Beiranvand, Maryam
AU - Bejarano Ramirez, Diana Fernanda
AU - Bekele, Alehegn
AU - Belay, Sefealem Assefa
AU - Belgaumi, Uzma Iqbal
AU - Bell, Michelle L.
AU - Bello, Olorunjuwon Omolaja
AU - Beloukas, Apostolos
AU - Bensenor, Isabela M.
AU - Berezvai, Zombor
AU - Berhie, Alemshet Yirga
AU - Bermudez, Amiel Nazer C.
AU - Bettencourt, Paulo J.G.
AU - Bhagavathula, Akshaya Srikanth
AU - Bhardwaj, Nikha
AU - Bhardwaj, Prarthna V.
AU - Bhardwaj, Prarthna V.
AU - Islam, Md Rabiul
AU - McGrath, John J.
AU - Nguyen, Dang H.
AU - Pham, Hoang Tran
AU - Rahman, Mohammad Hifz Ur
AU - Stokes, Mark A.
N1 - Funding Information:
Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the UK Department of Health and Social Care, the Norwegian Institute of Public Health, and the New Zealand Ministry of Health. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with licence number SLN2019-8-64, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. This analysis uses data or information from the LASI Pilot micro data and documentation. The development and release of the LASI Pilot Study was funded by the National Institute on Ageing, NIH (R21AG032572, R03AG043052, and R01 AG030153), the Russia Longitudinal Monitoring survey, RLMS-HSE, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS. We would like to thank Statistics Botswana and the Directorate of National Statistics, Somalia, for data presented in this publication.
Funding Information:
Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the UK Department of Health and Social Care, the Norwegian Institute of Public Health, and the New Zealand Ministry of Health. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with licence number SLN2019-8-64, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. This analysis uses data or information from the LASI Pilot micro data and documentation. The development and release of the LASI Pilot Study was funded by the National Institute on Ageing, NIH (R21AG032572, R03AG043052, and R01 AG030153), the Russia Longitudinal Monitoring survey, RLMS-HSE, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS. We would like to thank Statistics Botswana and the Directorate of National Statistics, Somalia, for data presented in this publication. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps, tables, and institutional affiliations.
Publisher Copyright:
© 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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.
UR - http://www.scopus.com/inward/record.url?scp=85188450830&partnerID=8YFLogxK
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 -