Lancet. 2024 May 18;403(10440):2057-2099. doi: 10.1016/S0140-6736(24)00550-6. Epub 2024 Mar 20.
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.
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.
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.
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.
Bill & Melinda Gates Foundation.
准确评估当前和未来的生育率——包括国家和地区整体趋势以及不断变化的人口年龄结构——对于帮助规划这些变化带来的深远的社会、经济、环境和地缘政治挑战至关重要。生育率的估计和预测对于涉及资源和医疗保健需求、劳动力供应、教育、性别平等以及计划生育和支持的政策非常必要。全球疾病、伤害和风险因素研究(GBD)2021 年根据参考情景和关键政策相关的替代情景,提供了全球、区域和国家层面的关键生育率指标的最新和全面的人口统计评估,以及到 2100 年的生育率预测。
为了估计 1950 年至 2021 年的生育率指标,我们使用混合效应回归模型和时空高斯过程回归,综合了来自 8709 个国家/地区的 1455 项调查和人口普查以及 150 个其他来源的 1950 年至 2021 年的 9429 年生命登记和抽样登记数据,生成了 5 岁年龄组的特定年龄生育率(ASFR)。我们将特定年龄生育率相加得出总生育率(TFR)。通过将特定年龄生育率乘以特定年龄的女性人口数,再将总和乘以 10-54 岁年龄组,计算出活产数。为了预测到 2100 年的未来生育率,我们的研究所使用的预测模型基于 50 岁时完成的队列生育率(CCF50;指定出生队列的女性一生中平均生育的子女数)进行预测,该模型比直接对 TFR 进行建模产生了更稳定和准确的生育率衡量标准。通过使用两个、三个和四个协变量(女性教育程度、避孕需求、可居住地区的人口密度和 5 岁以下儿童死亡率)的集成方法来建模 CCF50,其中每个子模型的权重相等,并使用元回归-贝叶斯、正则化、修剪(MR-BRT)工具进行分析。为了捕捉 CCF50 中无法由这些协变量解释的时间序列趋势,我们在残差项上使用一阶自回归模型。我们使用具有固定效应协变量(女性教育程度和避孕需求)和地理区域随机截距的线性混合效应模型,预测每个 5 岁 ASFR 比例的 CCF50。然后,我们将每年的预测 TFR 计算为各年龄组单一年龄 ASFR 的总和。参考预测是我们根据模型、过去的生育率、预测的协变量以及协变量和生育率之间的历史关系,对最有可能的未来生育率进行的估计。我们还为每个地点的多个替代情景进行了预测:可持续发展目标(SDG)中关于教育的目标在 2030 年实现;避孕需求的 SDG 在 2030 年实现;制定支持生育的政策以创造有利于生育的环境;以及之前三个情景的组合。通过对过去和现在的生育率估计值进行 1000 次抽样,以及对未来的预测值进行 500 次抽样,从每个指标的估计分布中得出不确定性,95%置信区间(UI)为抽取的 2.5%和 97.5%的分位数。为了评估我们的模型和其他模型的预测性能,我们计算了预测准确性的技能值——这是一个衡量预测准确性提高的指标——通过比较过去 15 年(2007-21 年)的预测与观测到的 ASFR。正值表示评估的模型表现优于基线模型(这里,一个在未来保持 2007 年数值不变的简化模型),负值表示评估的模型表现不如基线。
1950 年至 2021 年期间,全球总和生育率下降了一半以上,从 4.84(95%UI 4.63-5.06)降至 2.23(2.09-2.38)。全球每年的活产数在 2016 年达到峰值,为 1.42 亿(95%UI 1.37-1.47),到 2021 年降至 1.29 亿(1.21-1.38)。自 1950 年以来,所有国家和地区的生育率都有所下降,2021 年仍有 94 个(46.1%)国家和地区的总和生育率高于 2.1-通常被认为是替代生育率。这包括撒哈拉以南非洲的 44 个国家,撒哈拉以南非洲是 2021 年活产数最大的超区域,占 29.2%(28.7-29.6)。在 1950 年至 2021 年期间生育率最低的 47 个国家和地区中,有一个或多个年份的生育率高于更替水平;只有三个地点的生育率反弹到了更替水平之上。未来的生育率预计将继续下降,到 2050 年,全球总和生育率预计将降至 1.83(1.59-2.08),到 2100 年将降至 1.59(1.25-1.96)。预计到 2050 年,生育率仍保持在更替水平以上的国家和地区数量将下降到 49 个(24.0%),到 2100 年将下降到 6 个(2.9%),其中三个位于世界银行定义的低收入国家,全部位于 GBD 超区域撒哈拉以南非洲。预计到 2100 年,撒哈拉以南非洲的活产数比例将增至全球活产数的一半以上,到 2050 年为 41.3%(39.6-43.1),到 2100 年为 54.3%(47.1-59.5)。预计在大多数其他六个超区域中,包括南亚(从 2021 年的 24.8%降至 2050 年的 16.7%和 2100 年的 7.1%)在内,活产数的比例将在 2021 年至 2100 年期间下降,但在北非和中东以及高收入超区域,活产数的比例预计将适度增加。对替代综合情景的预测估计表明,实现教育和避孕需求的可持续发展目标以及实施支持生育的政策,将导致全球总和生育率在 2050 年降至 1.65(1.40-1.92),在 2100 年降至 1.62(1.35-1.95)。我们的 IHME 模型的预测技能值在所有年龄组中均为正值,表明该模型的预测优于常数预测。
生育率正在全球范围内下降,2021 年超过一半的国家和地区的总和生育率低于更替水平。自 2000 年以来的趋势表明,下降的幅度存在很大差异,只有少数几个国家在经历了最低生育率之后出现了生育率的轻微反弹,没有一个国家的生育率反弹到更替水平,而且生育率的反弹幅度很小。此外,全球活产数的分布正在发生变化,越来越多的活产数发生在最低收入国家。未来的生育率将继续下降,即使成功实施支持生育的政策也是如此。这些变化将产生深远的经济和社会影响,因为在高收入国家,人口老龄化和劳动力减少,再加上世界上最贫穷地区的活产数不断增加,这将导致人口结构发生变化。