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

Data de publicação: Data Ahead of Print:

Autores da FMUP

  • José Alberto Da Silva Freitas

    Autor

Participantes de fora da FMUP

  • Muthupandian, S
  • Muzaffar, M
  • Myung, W
  • Nagarajan, AJ
  • Nagel, G
  • Naghavi, P
  • Naik, GR
  • Naik, G
  • Naimzada, MD
  • Nainu, F
  • Nangia, V
  • Narasimha Swamy, S
  • Nascimento, BR
  • Nascimento, GG
  • Naser, AY
  • Nasiri, MJ
  • Natto, ZS
  • Nauman, J
  • Naveed, M
  • Nayak, BP
  • Nayak, VC
  • Ndejjo, R
  • Nduaguba, SO
  • Negash, H
  • Negesse, CT
  • Negoi, I
  • Negoi, RI
  • Nejadghaderi, SA
  • Nejjari, C
  • Nepal, S
  • Netsere, HB
  • Nguefack Tsague, G
  • Ngunjiri, JW
  • Nguyen, DH
  • Nguyen, HTH
  • Nguyen, PT
  • Nguyen, QP
  • Nguyen, VT
  • Niazi, RK
  • Nigatu, YT
  • Nikolouzakis, TK
  • Nikoobar, A
  • Nikpoor, AR
  • Nnaji, CA
  • Nnyanzi, LA
  • Noman, EA
  • Nomura, S
  • Noreen, M
  • Noroozi, N
  • Nri Ezedi, CA
  • Nunemo, MH
  • Nu?ez Samudio, V
  • Nurrika, D
  • Nutor, JJ
  • Oancea, B
  • Obamiro, KO
  • Odetokun, IA
  • Odogwu, NM
  • O'Donnell, MJ
  • Odukoya, OO
  • Oguntade, AS
  • Oguta, JO
  • Oh, I
  • Okeke, SR
  • Okekunle, AP
  • Okonji, OC
  • Okwute, PG
  • Olagunju, AT
  • Olasupo, OO
  • Olatubi, MI
  • Oliveira, GMM
  • Olusanya, BO
  • Olusanya, JO
  • Oluwatunase, GO
  • Omar, HA
  • Omer, GL
  • Onwujekwe, OE
  • Ordak, M
  • Orisakwe, OE
  • Orish, VN
  • Ortega Altamirano, DV
  • Ortiz, A
  • Ortiz Prado, E
  • Osman, WMS
  • Osuagwu, UL
  • Osuolale, O
  • Otoiu, A
  • Otstavnov, SS
  • Ouyahia, A
  • Ouyang, G
  • Owolabi, MO
  • Ozten, Y
  • P A, MP
  • Pahlevan Fallahy, MT
  • Pan, F
  • Pan, H
  • Pana, A
  • Panda, P
  • Panda Jonas, S
  • Pangaribuan, HU
  • Panos, GD
  • Panos, LD
  • Pantazopoulos, I
  • Pantea Stoian, AM
  • Parikh, RR
  • Park, S
  • Parthasarathi, A
  • Pashaei, A
  • Passera, R
  • Patel, HM
  • Patel, J
  • Patil, S
  • Patoulias, D
  • Patthipati, VS
  • Paudel, U
  • Paun, M
  • Pazoki Toroudi, H
  • Pease, SA
  • Peden, AE
  • Pedersini, P
  • Peng, M
  • Pensato, U
  • Pepito, VCF
  • Peprah, P
  • Pereira, G
  • Peres, MFP
  • Perianayagam, A
  • Perico, N
  • Perna, S
  • Pestell, RG
  • Petermann Rocha, FE
  • Pham, HT
  • Philip, AK
  • Pierannunzio, D
  • Pigeolet, M
  • Pigott, DM
  • Plotnikov, E
  • Poddighe, D
  • Pollner, P
  • Poluru, R
  • Postma, MJ
  • Pourali, G
  • Pourshams, A
  • Pourtaheri, N
  • Prabhu, D
  • Prada, SI
  • Pradhan, PMS
  • Prasad, M
  • Prashant, A
  • Purohit, BM
  • Puvvula, J
  • Qasim, NH
  • Qattea, I
  • R, D
  • Rabiee Rad, M
  • Radfar, A
  • Radhakrishnan, V
  • Raee, P
  • Raeisi Shahraki, H
  • Rafiei, A
  • Rafiei Alavi, SN
  • Raggi, C
  • Raghav, PR
  • Rahim, F
  • Rahim, MJ
  • Rahman, MM
  • Rahman, MHU
  • Rahman, M
  • Rahman, MA
  • Rahmanian, V
  • Rahmati, M
  • Rahnavard, N
  • Rai, P
  • Raimondo, D
  • Rajabpour Sanati, A
  • Rajput, P
  • Ram, P
  • Ramasamy, SK
  • Rana, J
  • Rana, K
  • Rana, SS
  • Ranabhat, CL
  • Rancic, N
  • Rane, A
  • Ranjan, S
  • Rao, CR
  • Rao, IR
  • Rapaka, D
  • Rasella, D
  • Rashedi, S
  • Rashedi, V
  • Rashidi, M
  • Rasul, A
  • Ratan, ZA
  • Rathnaiah Babu, G
  • Rauniyar, SK
  • Ravikumar, N
  • Rawaf, DL
  • Rawaf, S
  • Rawassizadeh, R
  • Rawlley, B
  • Reddy, MMRK
  • Redwan, EMM
  • Remuzzi, G
  • Reshmi, B
  • Rezaei, N
  • Rezaei Nejad, A
  • Rezaeian, M
  • Riad, A
  • Riaz, MA
  • Rickard, J
  • Rikhtegar, R
  • Robinson Oden, HE
  • Rodrigues, CF
  • Rodriguez, JAB
  • Rohilla, R
  • Romadlon, DS
  • Ronfani, L
  • Rout, HS
  • Roy, B
  • Roy, N
  • Roy, P
  • Rubagotti, E
  • Ruela, GdA
  • Rumisha, SF
  • Runghien, T
  • S, M
  • S N, C
  • Saad, AMA
  • Saadatian, Z
  • Saber Ayad, MM
  • SaberiKamarposhti, M
  • Sabour, S
  • Sada, F
  • Saddik, B
  • Sadee, BA
  • Sadeghi, E
  • Saeb, MR
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  • Safi, SZ
  • Sagoe, D
  • Saha, M
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  • Sahoo, SS
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  • Samodra, YL
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  • Samy, AM
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  • Santric Milicevic, MM
  • Saqib, H
  • Saraswathy, SYI
  • Saravanan, A
  • Saravi, B
  • Sarikhani, Y
  • Sarkar, T
  • Sarmiento Su?rez, R
  • Sarode, GS
  • Sarode, SC
  • Sarveazad, A
  • Sathian, B
  • Sathish, T
  • Sathyanarayan, A
  • Sayeed, A
  • Sayeed, MA
  • Scarmeas, N
  • Schlee, W
  • Schuermans, A
  • Schwebel, DC
  • Schwendicke, F
  • Selvaraj, S
  • Sengupta, P
  • Senthilkumaran, S
  • Sepanlou, SG
  • Serban, D
  • Serv?n Mori, E
  • Sethi, Y
  • SeyedAlinaghi, S
  • Seyedi, SA
  • Seylani, A
  • Shafie, M
  • Shah, J
  • Shah, PA
  • Shahbandi, A
  • Shahid, S
  • Shahwan, MJ
  • Shaikh, A
  • Shaikh, MA
  • Shamim, MA
  • Shams Beyranvand, M
  • Shamsi, MA
  • Shanawaz, M
  • Shankar, A
  • Shannawaz, M
  • Sharath, M
  • Sharfaei, S
  • Sharifan, A
  • Sharifi Rad, J
  • Sharma, M
  • Sharma, R
  • Sharma, U
  • Sharma, V
  • Shastry, RP
  • Shavandi, A
  • Shaw, DH
  • Shayan, AM
  • Shayan, M
  • Shehabeldine, AME
  • Sheikh, A
  • Sheikhi, RA
  • Shenoy, MM
  • Shetty, PH
  • Shi, P
  • Shiferaw, D
  • Shigematsu, M
  • Shiri, R
  • Shirkoohi, R
  • Shittu, A
  • Shivarov, V
  • Shokraneh, F
  • Shool, S
  • Shorofi, SA
  • Shuja, KH
  • Shuval, K
  • Siddig, EE
  • Silva, JP
  • Silva, LMLR
  • Silva, S
  • Simonetti, B
  • Singal, A
  • Singh, A
  • Singh, BB
  • Singh, JA
  • Siraj, MS
  • Smith, G
  • Socea, B
  • Sokhan, A
  • Solanki, R
  • Solanki, S
  • Soleimani, H
  • Soliman, SSM
  • Solomon, Y
  • Song, Y
  • Sorensen, RJD
  • Spartalis, M
  • Sreeramareddy, CT
  • Srivastava, VK
  • Stanikzai, MH
  • Starodubov, VI
  • Starodubova, AV
  • Stefan, SC
  • Steiropoulos, P
  • Stokes, MA
  • Subramaniyan, V
  • Suleman, M
  • Suliankatchi Abdulkader, R
  • Sultana, A
  • Sun, J
  • Swain, CK
  • Sykes, BL
  • Szarpak, L
  • Szeto, MD
  • Sz?cska, M
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  • Tabar?s Seisdedos, R
  • Tabatabaei Malazy, O
  • Tabatabaeizadeh, S
  • Tabatabai, S
  • Tabb, KM
  • Tabish, M
  • Taheri Soodejani, M
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  • Tajbakhsh, A
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  • Talukder, A
  • Tampa, M
  • Tamuzi, JL
  • Tan, K
  • Tang, H
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  • Tariku, MK
  • Tat, VY
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  • Temsah, M
  • Temsah, RMH
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  • Terefa, DR
  • Tesler, R
  • Teye Kwadjo, E
  • Thakur, R
  • Thangaraju, P
  • Thankappan, KR
  • Thapar, R
  • Tharwat, S
  • Thayakaran, R
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  • Tichopad, A
  • Ticoalu, JHV
  • Tiruye, TY
  • Titova, MV
  • Tonelli, M
  • Tovani Palone, MR
  • Traini, E
  • Tran, JT
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  • Trihandini, I
  • Tromans, SJ
  • Truyen, TTTT
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  • Tsermpini, EE
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Unidades de investigação

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 worlds 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 im

Dados da publicação

ISSN/ISSNe:
0140-6736, 1474-547X

LANCET  Elsevier Ltd.

Tipo:
Article
Páginas:
2057-2099
Link para outro recurso:
www.scopus.com

Citações Recebidas na Web of Science: 59

Citações Recebidas na Scopus: 76

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Keywords

  • REPRODUCTIVE HEALTH; ECONOMIC-GROWTH; EDUCATION; IMPACT; EMPOWERMENT; TRANSITION; POLICIES; OUTCOMES; AFRICA; LEAVE

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