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.

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.

Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories.

BACKGROUND: Understanding potential trajectories in health and drivers of health is crucial to guiding long-term investments and policy implementation. Past work on forecasting has provided an incomplete landscape of future health scenarios, highlighting a need for a more robust modelling platform from which policy options and potential health trajectories can be assessed. This study provides a novel approach to modelling life expectancy, all-cause mortality and cause of death forecasts -and alternative future scenarios-for 250 causes of death from 2016 to 2040 in 195 countries and territories. METHODS: We modelled 250 causes and cause groups organised by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) hierarchical cause structure, using GBD 2016 estimates from 1990-2016, to generate predictions for 2017-40. Our modelling framework used data from the GBD 2016 study to systematically account for the relationships between risk factors and health outcomes for 79 independent drivers of health. We developed a three-component model of cause-specific mortality: a component due to changes in risk factors and select interventions; the underlying mortality rate for each cause that is a function of income per capita, educational attainment, and total fertility rate under 25 years and time; and an autoregressive integrated moving average model for unexplained changes correlated with time. We assessed the performance by fitting models with data from 1990-2006 and using these to forecast for 2007-16. Our final model used for generating forecasts and alternative scenarios was fitted to data from 1990-2016. We used this model for 195 countries and territories to generate a reference scenario or forecast through 2040 for each measure by location. Additionally, we generated better health and worse health scenarios based on the 85th and 15th percentiles, respectively, of annualised rates of change across location-years for all the GBD risk factors, income per person, educational attainment, select intervention coverage, and total fertility rate under 25 years in the past. We used the model to generate all-cause age-sex specific mortality, life expectancy, and years of life lost (YLLs) for 250 causes. Scenarios for fertility were also generated and used in a cohort component model to generate population scenarios. For each reference forecast, better health, and worse health scenarios, we generated estimates of mortality and YLLs attributable to each risk factor in the future. FINDINGS: Globally, most independent drivers of health were forecast to improve by 2040, but 36 were forecast to worsen. As shown by the better health scenarios, greater progress might be possible, yet for some drivers such as high body-mass index (BMI), their toll will rise in the absence of intervention. We forecasted global life expectancy to increase by 4·4 years (95% UI 2·2 to 6·4) for men and 4·4 years (2·1 to 6·4) for women by 2040, but based on better and worse health scenarios, trajectories could range from a gain of 7·8 years (5·9 to 9·8) to a non-significant loss of 0·4 years (-2·8 to 2·2) for men, and an increase of 7·2 years (5·3 to 9·1) to essentially no change (0·1 years [-2·7 to 2·5]) for women. In 2040, Japan, Singapore, Spain, and Switzerland had a forecasted life expectancy exceeding 85 years for both sexes, and 59 countries including China were projected to surpass a life expectancy of 80 years by 2040. At the same time, Central African Republic, Lesotho, Somalia, and Zimbabwe had projected life expectancies below 65 years in 2040, indicating global disparities in survival are likely to persist if current trends hold. Forecasted YLLs showed a rising toll from several non-communicable diseases (NCDs), partly driven by population growth and ageing. Differences between the reference forecast and alternative scenarios were most striking for HIV/AIDS, for which a potential increase of 120·2% (95% UI 67·2-190·3) in YLLs (nearly 118 million) was projected globally from 2016-40 under the worse health scenario. Compared with 2016, NCDs were forecast to account for a greater proportion of YLLs in all GBD regions by 2040 (67·3% of YLLs [95% UI 61·9-72·3] globally); nonetheless, in many lower-income countries, communicable, maternal, neonatal, and nutritional (CMNN) diseases still accounted for a large share of YLLs in 2040 (eg, 53·5% of YLLs [95% UI 48·3-58·5] in Sub-Saharan Africa). There were large gaps for many health risks between the reference forecast and better health scenario for attributable YLLs. In most countries, metabolic risks amenable to health care (eg, high blood pressure and high plasma fasting glucose) and risks best targeted by population-level or intersectoral interventions (eg, tobacco, high BMI, and ambient particulate matter pollution) had some of the largest differences between reference and better health scenarios. The main exception was sub-Saharan Africa, where many risks associated with poverty and lower levels of development (eg, unsafe water and sanitation, household air pollution, and child malnutrition) were projected to still account for substantive disparities between reference and better health scenarios in 2040. INTERPRETATION: With the present study, we provide a robust, flexible forecasting platform from which reference forecasts and alternative health scenarios can be explored in relation to a wide range of independent drivers of health. Our reference forecast points to overall improvements through 2040 in most countries, yet the range found across better and worse health scenarios renders a precarious vision of the future-a world with accelerating progress from technical innovation but with the potential for worsening health outcomes in the absence of deliberate policy action. For some causes of YLLs, large differences between the reference forecast and alternative scenarios reflect the opportunity to accelerate gains if countries move their trajectories toward better health scenarios-or alarming challenges if countries fall behind their reference forecasts. Generally, decision makers should plan for the likely continued shift toward NCDs and target resources toward the modifiable risks that drive substantial premature mortality. If such modifiable risks are prioritised today, there is opportunity to reduce avoidable mortality in the future. However, CMNN causes and related risks will remain the predominant health priority among lower-income countries. Based on our 2040 worse health scenario, there is a real risk of HIV mortality rebounding if countries lose momentum against the HIV epidemic, jeopardising decades of progress against the disease. Continued technical innovation and increased health spending, including development assistance for health targeted to the world's poorest people, are likely to remain vital components to charting a future where all populations can live full, healthy lives. FUNDING: Bill & Melinda Gates Foundation.

Variation in Health Care Access and Quality Among US States and High-Income Countries With Universal Health Insurance Coverage.

Importance: Based on mortality estimates for 32 causes of death that are amenable to health care, the US health care system did not perform as well as other high-income countries, scoring 88.7 out of 100 on the 2016 age-standardized Healthcare Access and Quality (HAQ) index. Objective: To compare US age-specific HAQ scores with those of high-income countries with universal health insurance coverage and compare scores among US states with varying insurance coverage. Design, Setting, and Participants: This cross-sectional study used 2016 Global Burden of Diseases, Injuries, and Risk Factor study results for cause-specific mortality with adjustments for behavioral and environmental risks to estimate the age-specific HAQ indices. The US national age-specific HAQ scores were compared with high-income peers (Canada, western Europe, high-income Asia Pacific countries, and Australasia) in 1990, 2000, 2010, and 2016, and the 2016 scores among US states were also analyzed. The Public Use Microdata Sample of the American Community Survey was used to estimate insurance coverage and the median income per person by age and state. Age-specific HAQ scores for each state in 2010 and 2016 were regressed based on models with age fixed effects and age interaction with insurance coverage, median income, and year. Data were analyzed from April to July 2018 and July to September 2020. Main Outcomes and Measures: The age-specific HAQ indices were the outcome measures. Results: In 1990, US age-specific HAQ scores were similar to peers but increased less from 1990 to 2016 than peer locations for ages 15 years or older. For example, for ages 50 to 54 years, US scores increased from 77.1 to 82.1 while high-income Asia Pacific scores increased from 71.6 to 88.2. In 2016, several states had scores comparable with peers, with large differences in performance across states. For ages 15 years or older, the age-specific HAQ scores were 85 or greater for all ages in 3 states (Connecticut, Massachusetts, and Minnesota) and 75 or less for at least 1 age category in 6 states. In regression analysis estimates with state-fixed effects, insurance coverage coefficients for ages 20 to 24 years were 0.059 (99% CI, 0.006-0.111); 45 to 49 years, 0.088 (99% CI, 0.009-0.167); and 50 to 54 years, 0.101 (99% CI, 0.013-0.189). A 10% increase in insurance coverage was associated with point increases in HAQ scores among the ages of 20 to 24 years (0.59 [99% CI, 0.06-1.11]), 45 to 49 years (0.88 [99% CI, 0.09-1.67]), and 50 to 54 years (1.01 [99% CI, 0.13-1.89]). Conclusions and Relevance: In this cross-sectional study, the US age-specific HAQ scores for ages 15 to 64 years were low relative to high-income peer locations with universal health insurance coverage. Among US states, insurance coverage was associated with higher HAQ scores for some ages. Further research with causal models and additional explanations is warranted.

Funding and services needed to achieve universal health coverage: applications of global, regional, and national estimates of utilisation of outpatient visits and inpatient admissions from 1990 to 2016, and unit costs from 1995 to 2016.

BACKGROUND: To inform plans to achieve universal health coverage (UHC), we estimated utilisation and unit cost of outpatient visits and inpatient admissions, did a decomposition analysis of utilisation, and estimated additional services and funds needed to meet a UHC standard for utilisation. METHODS: We collated 1175 country-years of outpatient data on utilisation from 130 countries and 2068 country-years of inpatient data from 128 countries. We did meta-regression analyses of annual visits and admissions per capita by sex, age, location, and year with DisMod-MR, a Bayesian meta-regression tool. We decomposed changes in total number of services from 1990 to 2016. We used data from 795 National Health Accounts to estimate shares of outpatient and inpatient services in total health expenditure by location and year and estimated unit costs as expenditure divided by utilisation. We identified standards of utilisation per disability-adjusted life-year and estimated additional services and funds needed. FINDINGS: In 2016, the global age-standardised outpatient utilisation rate was 5·42 visits (95% uncertainty interval [UI] 4·88-5·99) per capita and the inpatient utilisation rate was 0·10 admissions (0·09-0·11) per capita. Globally, 39·35 billion (95% UI 35·38-43·58) visits and 0·71 billion (0·65-0·77) admissions were provided in 2016. Of the 58·65% increase in visits since 1990, population growth accounted for 42·95%, population ageing for 8·09%, and higher utilisation rates for 7·63%; results for the 67·96% increase in admissions were 44·33% from population growth, 9·99% from population ageing, and 13·55% from increases in utilisation rates. 2016 unit cost estimates (in 2017 international dollars [I$]) ranged from I$2 to I$478 for visits and from I$87 to I$22 543 for admissions. The annual cost of 8·20 billion (6·24-9·95) additional visits and 0·28 billion (0·25-0·30) admissions in low-income and lower-middle income countries in 2016 was I$503·12 billion (404·35-605·98) or US$158·10 billion (126·58-189·67). INTERPRETATION: UHC plans can be based on utilisation and unit costs of current health systems and guided by standards of utilisation of outpatient visits and inpatient admissions that achieve the highest coverage of personal health services at the lowest cost. FUNDING: Bill & Melinda Gates Foundation.