Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study

Сценарии рождаемости, смертности, миграции и численности населения для 195 стран и территорий в 2017–2100 годах: прогнозный анализ для исследования «Глобальное бремя болезней»
Christopher J L Murray, Catherine Bisignano, Simon I Hay, Ali H. Mokdad, Dan J. Stein, Rafael Lozano, Amanda Smith, Han Yong Wunrow, Tahvi Frank, Kai Fukutaki, Emily Goren, Chun-Wei Yuan, Jackie Cao, Thomas Hsiao, Gulrez Shah Azhar, Emma Castro, Julian Chalek, Andrew J. Dolgert, Vishnu Nandakumar, Maxwell Pierce, Martin A Pletcher, Toshana Robalik, Krista M. Steuben, Bianca S Zlavog, Stein Emil Vollset
2020-07-14

Fertility forecastingGlobal Burden of Disease StudyGlobal population projectionsMigration modelingMortality forecasting
BACKGROUND: Understanding potential patterns in future population levels is crucial for anticipating and planning for changing age structures, resource and health-care needs, and environmental and economic landscapes. Future fertility patterns are a key input to estimation of future population size, but they are surrounded by substantial uncertainty and diverging methodologies of estimation and forecasting, leading to important differences in global population projections. Changing population size and age structure might have profound economic, social, and geopolitical impacts in many countries. In this study, we developed novel methods for forecasting mortality, fertility, migration, and population. We also assessed potential economic and geopolitical effects of future demographic shifts. METHODS: We modelled future population in reference and alternative scenarios as a function of fertility, migration, and mortality rates. We developed statistical models for completed cohort fertility at age 50 years (CCF50). Completed cohort fertility is much more stable over time than the period measure of the total fertility rate (TFR). We modelled CCF50 as a time-series random walk function of educational attainment and contraceptive met need. Age-specific fertility rates were modelled as a function of CCF50 and covariates. We modelled age-specific mortality to 2100 using underlying mortality, a risk factor scalar, and an autoregressive integrated moving average (ARIMA) model. Net migration was modelled as a function of the Socio-demographic Index, crude population growth rate, and deaths from war and natural disasters; and use of an ARIMA model. The model framework was used to develop a reference scenario and alternative scenarios based on the pace of change in educational attainment and contraceptive met need. We estimated the size of gross domestic product for each country and territory in the reference scenario. Forecast uncertainty intervals (UIs) incorporated uncertainty propagated from past data inputs, model estimation, and forecast data distributions. FINDINGS: The global TFR in the reference scenario was forecasted to be 1·66 (95% UI 1·33-2·08) in 2100. In the reference scenario, the global population was projected to peak in 2064 at 9·73 billion (8·84-10·9) people and decline to 8·79 billion (6·83-11·8) in 2100. The reference projections for the five largest countries in 2100 were India (1·09 billion [0·72-1·71], Nigeria (791 million [594-1056]), China (732 million [456-1499]), the USA (336 million [248-456]), and Pakistan (248 million [151-427]). Findings also suggest a shifting age structure in many parts of the world, with 2·37 billion (1·91-2·87) individuals older than 65 years and 1·70 billion (1·11-2·81) individuals younger than 20 years, forecasted globally in 2100. By 2050, 151 countries were forecasted to have a TFR lower than the replacement level (TFR <2·1), and 183 were forecasted to have a TFR lower than replacement by 2100. 23 countries in the reference scenario, including Japan, Thailand, and Spain, were forecasted to have population declines greater than 50% from 2017 to 2100; China's population was forecasted to decline by 48·0% (-6·1 to 68·4). China was forecasted to become the largest economy by 2035 but in the reference scenario, the USA was forecasted to once again become the largest economy in 2098. Our alternative scenarios suggest that meeting the Sustainable Development Goals targets for education and contraceptive met need would result in a global population of 6·29 billion (4·82-8·73) in 2100 and a population of 6·88 billion (5·27-9·51) when assuming 99th percentile rates of change in these drivers. INTERPRETATION: Our findings suggest that continued trends in female educational attainment and access to contraception will hasten declines in fertility and slow population growth. A sustained TFR lower than the replacement level in many countries, including China and India, would have economic, social, environmental, and geopolitical consequences. Policy options to adapt to continued low fertility, while sustaining and enhancing female reproductive health, will be crucial in the years to come. FUNDING: Bill & Melinda Gates Foundation.
1
Age-specific mortality, fertility, and net migration were projected with covariate-based statistical models and ARIMA time-series components.
2
Completed cohort fertility at age 50 was modelled using education and contraceptive met need, offering a more stable basis than period total fertility rates.
3
Reference and alternative population scenarios were generated according to different trajectories of educational attainment and contraceptive met need.
4
The framework assessed projected demographic shifts alongside their potential economic and geopolitical effects, including country-level GDP estimates in the reference scenario.
5
The study developed integrated forecasting methods for fertility, mortality, migration, and population across 195 countries and territories through 2100.

Fertility, mortality, migration, and population in 195 countries and territories (2017–2100)

Forecasted fertility, mortality, migration, population size and age structure, and their potential economic and geopolitical effects under reference and alternative scenarios

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2020-07-14
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Authors
Christopher J L Murray
Catherine Bisignano
Simon I Hay
Ali H. Mokdad
Dan J. Stein
Rafael Lozano
Amanda Smith
Han Yong Wunrow
Tahvi Frank
Kai Fukutaki
Emily Goren
Chun-Wei Yuan
Jackie Cao
Thomas Hsiao
Gulrez Shah Azhar
Emma Castro
Julian Chalek
Andrew J. Dolgert
Vishnu Nandakumar
Maxwell Pierce
Martin A Pletcher
Toshana Robalik
Krista M. Steuben
Bianca S Zlavog
Stein Emil Vollset
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