Mortality Projections Research Articles

Projections of cancer mortality by 2025 in central China: A modeling study of global burden of disease 2019

BackgroundIn China, there are few studies that have reported future estimations for cancer mortality. Therefore, this study aimed to assess cancer mortality in China and identify priorities for future cancer control strategies. MethodsBased on the Global Burden of Disease 2019 study, we extracted data on cancer-related deaths from 1990 to 2019 in Hunan Province, China. Under the current trends evaluated using a joinpoint regression model, we fitted a linear regression model for cancer mortality projections by 2025. ResultsThe age-standardized mortality rate of total cancer in Hunan, China, declined slowly and is projected to be 140.80 (95% confidence interval [CI]: 140.12–141.48) by 2025, with the mortality rate in men approximately twice that in women. In 2025, the top five causes of cancer-related deaths in males are projected to be lung, liver, colorectal, stomach, and esophageal cancers, with the corresponding causes in females being lung, breast, colorectal, liver, and cervical cancers. Between 2019 and 2025, male mortality rates due to liver and pancreatic cancer are expected to increase, while those due to the six leading female cancers will increase. Excess male deaths were associated with liver and esophageal cancers, while all main cancers in females will have excess mortality, except for colorectal cancer. ConclusionA comprehensive cancer spectrum characteristic of both developing and developed countries will remain in Hunan, China. Lung cancer remains the most common cause of cancer-related deaths, and tobacco control efforts are urgently required. Additional efforts should be made to promote universal screening, improve access to cancer healthcare services, optimize medical payment models, and enhance access to valuable anticancer drugs.

The global syndemic of metabolic diseases in the young adult population: A consortium of trends and projections from the Global Burden of Disease 2000–2019

BackgroundA significant proportion of premature deaths globally are related to metabolic diseases in young adults. We examined the global trends and mortality of metabolic diseases in individuals aged below 40 years using data from the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) 2019. MethodsFrom 2000 to 2019, global estimates of deaths and disability-adjusted life years (DALYs) were described for metabolic diseases (type 2 diabetes mellitus [T2DM], hyperlipidemia, hypertension, obesity, non-alcoholic fatty liver disease [NAFLD]). Subgroup analyses were performed based on sex, geographical regions and Socio-Demographic Index (SDI). Age-standardised death and DALYs were presented per 100,000 population with 95 % uncertainty intervals (UI). Projections of mortality and DALYs were estimated using regression models based on the GBD 2019 data and combining them with Institute for Health Metrics and Evaluation projection counts for years up to 2050. ResultsIn 2019, the highest age-standardised death rates were observed in hypertension (133·88 [121·25–155·73]), followed by obesity (62·59 [39·92–89·13]), hyperlipidemia (56·51 [41·83–73·62]), T2DM (18·49 [17·18–19·66]) and NAFLD (2·09 [1·61–2·60]). Similarly, obesity (1932·54 [1276·61–2639·74]) had the highest age-standardised DALYs, followed by hypertension (2885·57 [2580·75–3201·05]), hyperlipidemia (1207·15 [975·07–1461·11]), T2DM (801·55 [670·58–954·43]) and NAFLD (53·33 [40·73–68·29]). Mortality rates decreased over time in hyperlipidemia (−0·6 %), hypertension (−0·47 %), NAFLD (−0·31 %) and T2DM (−0·20 %), but not in obesity (1·07 % increase). The highest metabolic-related mortality was observed in Eastern Mediterranean and low SDI countries. By 2050, obesity is projected to contribute to the largest number of deaths (102·8 % increase from 2019), followed by hypertension (61·4 % increase), hyperlipidemia (60·8 % increase), T2DM (158·6 % increase) and NAFLD (158·4 % increase), with males continuing to bear the greatest burden across all metabolic diseases. ConclusionThe growing burden of metabolic diseases, increasing obesity-related mortality trends, and the sex-regional-socioeconomic disparities evident in young adulthood, underlie the concerning growing global burden of metabolic diseases now and in future.

Long-term projections of cancer incidence and mortality in Canada: The OncoSim All Cancers Model

Using the OncoSim All Cancers Model, we estimated the annual cancer incidence, mortality and cancer management costs in Canada from 2020 to 2040. Incidence for each cancer type was estimated from logistic regression analyses of the Canadian Cancer Registry (1992–2017), with province/territory, sex, five-year age groups and year as covariates. Deaths were estimated by sex and tumour site for cancers diagnosed between 2000 and 2017 (deaths to the end of 2017). The total cost of a cancer type was the sum of costs for individuals across four phases of cancer care. The projections presented in this study were generated based on a simulation of 32 million cases. The OncoSim All Cancers Model projects a 40% increase in the overall number of incident cancer cases from 2020 to 2040. The number of the four most commonly diagnosed cancers in Canada (breast, colorectal, lung, and prostate) are projected to increase annually. The overall number of cancer deaths is projected to increase by 44% from 2020 to 2040. More cancer deaths are projected in males than in females. The age-standardized mortality rate is expected to remain relatively stable over time. Overall cancer management costs are projected to increase from $20.6B in 2020 to $31.4B in 2040. Due mainly to an aging population and population growth in Canada, we estimate that cancer incidence, mortality and cancer management costs will increase considerably between 2020 and 2040. These results highlight the importance of planning for increasing investment and capacity in cancer control.

Long-Term Projections of Cancer Incidence and Mortality in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020-2054: An Empirical Validation Approach.

The aim of this study was to project new cancer cases/deaths forward to 2054, and decompose changes in cancer cases/deaths to assess the impact of demographic transitions on cancer burden. We collected data on cancer cases/deaths up to 2019, empirically validated the projection performance of multiple statistical models, and selected optimal models by applying time series cross-validation. We showed an increasing number of new cancer cases but decreasing number of cancer deaths in both genders, with a large burden attributed to population aging. We observed the increasing incidence rates in most cancer sites but reducing rates in some infection-associated cancers, including stomach and liver cancers. Colorectal and lung cancers were projected to remain as leading cancer burdens of both incidence and mortality in Japan over 2020-2054, while prostate and female breast cancers would be the leading incidence burdens among men and women, respectively. Findings from decomposition analysis require more supportive interventions for reducing mortality and improving the quality of life of Japanese elders. We emphasize the important role of governments and policymakers in reforming policies for controlling cancer risk factors, including oncogenic infections. The rapid increase and continued presence of those cancer burdens associated with modifiable risk factors warrant greater efforts in cancer control programs, specifically in enhancing cancer screening and controlling cancer risk factors in Japan.

Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model

This paper proposes a two-step LASSO based vector autoregressive (2-LVAR) model to forecast mortality rates. Within the VAR framework, recent studies have developed a spatial–temporal autoregressive (STAR) model, in which age-specific mortality rates are related to their own historical values (temporality) and the rates of the neighboring cohorts (spatiality). Despite its desirable age coherence property and the improved forecasting accuracy over the widely used Lee–Carter (LC) model, STAR employs a rather restrictive structure that only allows for non-zero cohort effects of the same cohorts and the neighboring cohorts. To address this limitation, the proposed 2-LVAR model adopts a data-driven principle, as in a sparse VAR (SVAR) model, to offer more flexibility in the parametric structure. A two-step estimation strategy is developed accordingly to resolve the challenging objective function of 2-LVAR, which consists of non-standard L2 and LASSO-type penalties with constraints. Using empirical data from Australia, the United Kingdom, France, and Switzerland, we show that the 2-LVAR model outperforms the LC, STAR, and SVAR models in most of our forecasting results. Further simulation studies confirm this outperformance, and analyses based on life expectancy at birth empirically support the existence of age coherence. The results of this paper will help researchers understand the mortality projections in the long run and improve the reserving/ratemaking accuracy for life insurers.

Projections of future coronary heart disease and stroke mortality in Japan until 2040: a Bayesian age-period-cohort analysis

We aimed to estimate the future burden of coronary heart disease (CHD) and stroke mortalities by sex and all 47 prefectures of Japan until 2040 while accounting for effects of age, period, and cohort and integrating them to be at the national level to account for regional differences among prefectures. We estimated future CHD and stroke mortality projections, developing Bayesian age-period-cohort (BAPC) models in population and the number of CHD and stroke by age, sex, and all 47 prefectures observed from 1995 to 2019; then applying these to official future population estimates until 2040. The present participants were all men and women aged over 30 years and were residents of Japan. In the BAPC models, the predicted number of national-level cardiovascular deaths from 2020 to 2040 would decrease (39,600 [95% credible interval: 32,200-47,900] to 36,200 [21,500-58,900] CHD deaths in men, and 27,400 [22,000-34,000] to 23,600 [12,700-43,800] in women; and 50,400 [41,900-60,200] to 40,800 [25,200-67,800] stroke deaths in men, and 52,200 [43,100-62,800] to 47,400 [26,800-87,200] in women). After adjusting these factors, future CHD and stroke deaths will decline until 2040at the national level and in most prefectures. This research was supported by the Intramural Research Fund of Cardiovascular Diseases of the National Cerebral and Cardiovascular Center (21-1-6, 21-6-8), JSPS KAKENHI Grant Number JP22K17821, and the Ministry of Health, Labour and Welfare Comprehensive Research on Life-Style Related (Diseases Cardiovascular Diseases and Diabetes Mellitus Program), Grant Number 22FA1015.

Updated projections of UK heat-related mortality using policy-relevant global warming levels and socio-economic scenarios

High temperatures and heatwaves are associated with significant impacts on human health. With continued global temperature increases, extreme thresholds relevant to health will be exceeded more frequently. This study provides an updated spatial analysis of heat-related mortality for the UK, using the UK Climate Projections (UKCP18) at 1.5 °C–4 °C global warming levels, and embedding population and demographic data from the recently released UK Shared Socioeconomic Pathways (UK-SSPs). Climate change will lead to an increase in heat-related mortality in the future, exacerbated by increased exposure due to increasing population. We find an increase from ∼1400 average annual deaths in the near-past (1990–2019) (95% CI: 1299–1485), to ∼2500 (2304–2794), ∼3700 (3280–4214), ∼8200 (7376–9072) and >18 000 (16 690–20 394) average annual deaths at 1.5 °C, 2 °C, 3 °C and 4 °C respectively (assuming no adaptation). This is considered a high-end estimate due to the assumption of high population growth (UK-SSP5). Older populations are shown to be most vulnerable. A large proportion of heat-related deaths (76% (74%–79%) with 1.5 °C global warming) are attributed to more moderate (1 °C–5 °C) increases above regional temperature thresholds as opposed to extremes. Our results provide a timely update that can serve as a first step to supporting future UK climate policy and risk assessments. Future research considering nonlinearity in the health response to heat exposure is vital.

An analysis of lactate dehydrogenase (LDH) levels in advanced stage IV melanoma of the skin: prognostic capabilities and demographic variability.

Despite its limitations, in part due to decreased specificity in advanced disease, serum lactate dehydrogenase (LDH) is one of few serum factors used in cancer staging. This study quantifies the predictive capabilities of LDH in stage IV melanoma of the skin and explores the validity of suggested demographic discrepancies which may exist in its use. The 1975-2017 Surveillance Epidemiology and End Results (SEER) database was queried for stage IV cutaneous melanoma cases. Demographic characteristics were compared between LDH groups using chi-square and t tests. Subsequent Cox multivariable regression was performed to assess survival differences. 334 cases of stage IV cutaneous melanoma (average age: 63.0years) with measured serum LDH levels were identified. Of these patients, 150 (44.9%) had normal LDH, 112 (33.5%) had LDH < 1.5 × upper limit of normal (ULN), 57 (17.1%) had LDH 1.5-10 × ULN, and 15 (4.5%) had LDH > 10 × ULN. Lower incomes were associated with higher LDH; individuals with incomes < $50,000 had the greatest proportion of LDH 10 × ULN (19.2%; p = 0.0031). LDH > 10 × ULN also had the lowest proportion of White patients (p = 0.04). On Cox multivariable survival analysis, increasing LDH levels showed increased risk of death (LDH < 1.5 × ULN: HR = 2.05, p = 0.01; LDH 1.5-10 × ULN: HR = 1.46, p < 0.001; LDH > 10 × ULN: HR = 5.91, p < 0.001). This study reaffirms the utility of LDH as a significant predictor of mortality with incremental severity, suggesting possible use for mortality projections. We note that Black patients and those with lower incomes may be more likely to have an elevated LDH. Older age groups and presence of ulceration among patients with stage IV melanoma were also associated with a greater risk of mortality.

Projection of Cancer Incidence and Mortality From 2020 to 2035 in the Korean Population Aged 20 Years and Older.

This study aimed to identify the current patterns of cancer incidence and estimate the projected cancer incidence and mortality between 2020 and 2035 in Korea. Data on cancer incidence cases were extracted from the Korean Statistical Information Service from 2000 to 2017, and data on cancer-related deaths were extracted from the National Cancer Center from 2000 to 2018. Cancer cases and deaths were classified according to the International Classification of Diseases, 10th edition. For the current patterns of cancer incidence, age-standardized incidence rates (ASIRs) and age-standardized mortality rates were investigated using the 2000 mid-year estimated population aged over 20 years and older. A joinpoint regression model was used to determine the 2020 to 2035 trends in cancer. Overall, cancer cases were predicted to increase from 265 299 in 2020 to 474 085 in 2035 (growth rate: 1.8%). The greatest increase in the ASIR was projected for prostate cancer among male (7.84 vs. 189.53 per 100 000 people) and breast cancer among female (34.17 vs. 238.45 per 100 000 people) from 2000 to 2035. Overall cancer deaths were projected to increase from 81 717 in 2020 to 95 845 in 2035 (average annual growth rate: 1.2%). Although most cancer mortality rates were projected to decrease, those of breast, pancreatic, and ovarian cancer among female were projected to increase until 2035. These up-to-date projections of cancer incidence and mortality in the Korean population may be a significant resource for implementing cancer-related regulations or developing cancer treatments.

Estimated projection of oral cavity and oropharyngeal cancer deaths in Spain to 2044

BackgroundOral cavity cancer (OCC) and oropharyngeal cancer (OPC) are two common malignancies whose mortality is worryingly increasing worldwide. However, few studies have estimated the mortality trends for these cancers in the coming years. This study analysed the mortality rates for OCC and OPC observed between 1980 and 2019 to generate a predictive model for the next 25 years in Spain.MethodsMid-year population data and death certificates for the period 1980–2019 were obtained from the Spanish National Institute of Statistics. The Nordpred program (Norwegian Cancer Registry, Oslo, Norway) was used to calculate adjusted mortality rates as well as estimated mortality projections with an age-period-cohort model for the period 2020–2044.ResultsThe specific mortality rate per 100,000 inhabitants for OCC decreased from 2.36 (1980–1984) to 2.17 (2015–2019) and is expected to decline to 1.68 (2040–2044), particularly in males. For OPC, mortality rates rose from 0.67 (1980–1984) to 1.23 (2015–2019) and are projected to drop to 0.71 (2040–2044). In the group of females > 65 years predictions showed rising mortality rates for both OCC and OPC. The predictive model projects more deaths in females than in males for OCC in the period 2040–2044, while deaths for OPC will decrease in males and gradually increase in females.ConclusionsAlthough OCC mortality rates have been found to decrease in males in the last observed decades, there is still room to improve them in females > 65 years in the future by promoting campaigns against smoking and alcohol consumption. OPC mortality will become a growing health problem. Vaccination campaigns for the prevention of human papillomavirus-associated cancers may have a long-term impact on the mortality of these cancers, which should be evaluated in upcoming studies.Clinical relevanceOur findings highlighted the importance of closely monitoring OCC and OPC mortality rates in the coming years by age group and sex, and the need to continue preventive measures against the main known risk factors, such as tobacco, alcohol, and human papillomavirus infection.

The importance of internal climate variability in climate impact projections

Uncertainty in climate projections is driven by three components: scenario uncertainty, intermodel uncertainty, and internal variability. Although socioeconomic climate impact studies increasingly take into account the first two components, little attention has been paid to the role of internal variability, although underestimating this uncertainty may lead to underestimating the socioeconomic costs of climate change. Using large ensembles from seven coupled general circulation models with a total of 414 model runs, we partition the climate uncertainty in classic dose-response models relating county-level corn yield, mortality, and per-capita gross domestic product to temperature in the continental United States. The partitioning of uncertainty depends on the time frame of projection, the impact model, and the geographic region. Internal variability represents more than 50% of the total climate uncertainty in certain projections, including mortality projections for the early 21st century, although its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early-century and midcentury projections, projections in regions with high internal variability such as the Upper Midwest United States, and impacts driven by nonlinear relationships.

Progress towards achieving child survival goals in Kenya after devolution: Geospatial analysis with scenario-based projections, 2015-2025.

Subnational projections of under-5 mortality (U5M) have increasingly become an essential planning tool to support Sustainable Development Goals (SDGs) agenda and strategies for improving child survival. To support child health policy, planning, and tracking child development goals in Kenya, we projected U5M at units of health decision making. County-specific annual U5M were estimated using a multivariable Bayesian space-time hierarchical model based on intervention coverage from four alternate intervention scale-up scenarios assuming 1) the highest subnational intervention coverage in 2014, 2) projected coverage based on the fastest county-specific rate of change observed in the period between 2003-2014 for each intervention, 3) the projected national coverage based on 2003-2014 trends and 4) the country-specific targets of intervention coverage relative to business as usual (BAU) scenario. We compared the percentage change in U5M based on the four scale-up scenarios relative to BAU and examined the likelihood of reaching SDG 3.2 target of at least 25 deaths/1,000 livebirths by 2022 and 2025. Projections based on 10 factors assuming BAU, showed marginal reductions in U5M across counties with all the counties except Mandera county not achieving the SDG 3.2 target by 2025. Further, substantial reductions in U5M would be achieved based on the various intervention scale-up scenarios, with 63.8% (30), 74.5% (35), 46.8% (22) and 61.7% (29) counties achieving SDG target for scenarios 1,2,3 and 4 respectively by 2025. Scenario 2 yielded the highest reductions of U5M with individual scale-up of access to improved water, recommended treatment of fever and accelerated HIV prevalence reduction showing considerable impact on U5M reduction (≥ 20%) relative to BAU. Our results indicate that sustaining an ambitious intervention scale-up strategy matching the fastest rate observed between 2003-2014 would substantially reduce U5M in Kenya. However, despite this ambitious scale-up scenario, 25% (12 of 47) of the Kenya's counties would still not achieve SDG 3.2 target by 2025.

Trends of Cirrhosis-related Mortality in the USA during the COVID-19 Pandemic.

Immunocompromised status and interrupted routine care may render patients with cirrhosis vulnerable to the coronavirus disease 2019 (COVID-19) pandemic. A nationwide dataset that includes more than 99% of the decedents in the U.S. between April 2012 and September 2021 was used. Projected age-standardized mortality during the pandemic were estimated according to prepandemic mortality rates, stratified by season. Excess deaths were determined by estimating the difference between observed and projected mortality rates. A temporal trend analysis of observed mortality rates was also performed in 0.83 million decedents with cirrhosis between April 2012 and September 2021 was included. Following an increasing trend of cirrhosis-related mortality before the pandemic, with a semiannual percentage change (SAPC) of 0.54% [95% confidence interval (CI): (0.0-1.0%), p=0.036], a precipitous increase with seasonal variation occurred during the pandemic (SAPC 5.35, 95% CI: 1.9-8.9, p=0.005). Significantly increased mortality rates were observed in those with alcohol-associated liver disease (ALD), with a SAPC of 8.44 (95% CI: 4.3-12.8, p=0.001) during the pandemic. All-cause mortality of nonalcoholic fatty liver disease rose steadily across the entire study period with a SAPC of 6.79 (95% CI: 6.3-7.3, p<0.001). The decreasing trend of HCV-related mortality was reversed during the pandemic, while there was no significant change in HBV-related deaths. While there was significant increase in COVID-19-related deaths, more than 55% of the excess deaths were the indirect impact of the pandemic. We observed an alarming increase in cirrhosis-related deaths during the pandemic especially for ALD, with evidence in both direct and indirect impact. Our findings have implications on formulating policies for patients with cirrhosis.

Backtesting stochastic mortality models by prediction interval-based metrics

Human lifespan increments represent one of the main current risks for governments and pension and health benefits providers. Longevity societies imply financial sustainability challenges to guarantee adequate socioeconomic conditions for all individuals for a longer period. Consequently, modelling population dynamics and projecting future longevity scenarios are vital tasks for policymakers. As an answer, the demographic and the actuarial literature have been introduced and compared to several stochastic mortality models, although few studies have thoroughly tested the uncertainty concerning mortality projections. Forecasting mortality uncertainty levels have a central role since they reveal the potential, unexpected longevity rise and the related economic impact. Therefore, the present study poses a methodological framework to backtest uncertainty in mortality projections by exploiting uncertainty metrics not yet adopted in mortality literature. Using the data from the Human Mortality Database of the male and female populations of five countries, we present some numerical applications to illustrate how the proposed criterion works. The results show that there is no mortality model overperforming the others in all cases, and the best model choice depends on the data considered.

Evaluating the Comparative Accuracy of COVID-19 Mortality Forecasts: An Analysis of the First-Wave Mortality Forecasts in the United States

The sudden onset of the COVID-19 pandemic posed significant challenges for forecasting professionals worldwide. This article examines the early forecasts of COVID-19 transmission, using the context of the United States, one of the early epicenters of the crisis. The article compares the relative accuracy of selected models from two forecasters who informed government policy in the first three months of the pandemic, the Institute of Health Metrics and Evaluation (IHME) and Columbia University. Furthermore, we examine whether the forecasts improved as more data became available in the subsequent months of the pandemic, using the forecasts from Los Alamos National Laboratory and the University of Texas, Austin. The analysis focuses on mortality estimates and compares forecasts using epidemiological and curve-fitting models during the first wave of the pandemic from March 2020 to October 2020. As health agencies worldwide struggled with uncertainty in models and projections of COVID-19 caseload and mortality, this article provides important insights that can be useful for crafting policy responses to the ongoing pandemic and future outbreaks.

Climate change and migrant workers: Projections of temperatures and mortality in Kuwait

Climate change and migrant workers: Projections of temperatures and mortality in Kuwait

Future mortality burden attributable to non-optimal temperatures under the dual threats from climate change and population aging

Background and Aim: Elderly population subgroups are amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with progressive global warming, the influence of population aging on these trends remains unclear. Here we quantify the contribution of population aging to future temperature-related mortality in 729 locations across 42 countries at 1.5 &amp;#xb0;C, 2 &amp;#xb0;C, and 3 &amp;#xb0;C of warming using climate models of the latest Coupled Model Inter-comparison Project (CMIP6). Methods: We first estimated the location-age-specific temperature-mortality associations in a two-stage time-series analysis using quasi-Poisson regression with distributed lag nonlinear models and multivariate dose-response meta-regression using data from the Multi-Country Multi-City (MCC) Collaborative Research Network. We then combined the association estimates with future temperature series (18 models from CMIP6) to derive excess temperature-mortality projections at three warming levels. We derived age group-specific population and baseline mortality projections according to the Shared Socio-economic Pathway 5-8.5 to derive the corresponding excess mortality. Finally, we quantified the impact of population aging as the difference in the change in temperature-related mortality fractions (warming target minus historical period) between &amp;#x22;climate-population&amp;#x22; and &amp;#x22;climate-only&amp;#x22; scenarios (i.e., either accounting or not accounting for changes in population demographics). Results: Future heat-related mortality will increase by 0.8%-7.0% at 1.5-3.0 &amp;#xb0;C warming, among which 1 in 2 to 5 in 7 deaths would be attributable to population aging. Population aging would mostly offset the decrease of future cold-related mortality driven by climate only, leading to a net increase of 1.5%-5.3% at 1.5-3 ºC warming. Countries in Middle-East Asia, East-Asia, South-East Asia, and South America that have larger increases in population aging generally will face substantially elevated temperature-related mortality. Conclusions: Our findings suggest that population aging would constitute a crucial driver for a larger impact of non-optimal temperatures under a warming climate.

Projections of heatwave-attributable mortality under climate change and future population scenarios in China

SummaryBackgroundIn China, most previous projections of heat-related mortality have been based on modeling studies using global climate models (GCMs), which can help to elucidate the risks of extreme heat events in a changing climate. However, spatiotemporal changes in the health effects of climate change considering specific regional characteristics remain poorly understood. We aimed to use credible climate and population projections to estimate future heatwave-attributable deaths under different emission scenarios and to explore the drivers underlying these patterns of changes.MethodsWe derived climate data from a regional climate model driven by three CMIP5 GCM models and calculated future heatwaves in China under Representative Concentration Pathway (RCP) 2.6, RCP4.5, and RCP8.5. The future gridded population data were based on Shared Socioeconomic Pathway 2 assumption with different fertility rates. By applying climate zone-specific exposure-response functions to mortality during heatwave events, we projected the scale of heatwave-attributable deaths under each RCP scenario. We further analyzed the factors driving changes in heatwave-related deaths and main sources of uncertainty using a decomposition method. We compared differences in death burden under the 1.5°C target, which is closely related to achieving carbon neutrality by mid-century.FindingsThe number of heatwave-related deaths will increase continuously to the mid-century even under RCP2.6 and RCP4.5 scenarios, and will continue increasing throughout the century under RCP8.5. There will be 20,303 deaths caused by heatwaves in 2090 under RCP2.6, 35,025 under RCP4.5, and 72,260 under RCP8.5, with half of all heatwave-related deaths in any scenario concentrated in east and central China. Climate effects are the main driver for the increase in attributable deaths in the near future till 2060, explaining 78% of the total change. Subsequent population decline cannot offset the losses caused by higher incidence of heatwaves and an aging population under RCP8.5. Although health loss under the 1.5°C warming scenario is 1.6-fold higher than the baseline period 1986–2005, limiting the temperature rise to 1.5°C can reduce the annual mortality burden in China by 3,534 deaths in 2090 compared with RCP2.6 scenarios.InterpretationWith accelerating climate change and population aging, the effects of future heatwaves on human health in China are likely to increase continuously even under a low emission scenario. Significant health benefits are expected if the optimistic 1.5°C goal is achieved, suggesting that carbon neutrality by mid-century is a critical target for China's sustainable development. Policymakers need to tighten climate mitigation policies tailored to local conditions while enhancing climate resilience technically and infrastructurally, especially for vulnerable elderly people.FundingNational Key R&D Program of China (2018YFA0606200), Wellcome Trust (209734/Z/17/Z), Natural Science Foundation of China (41790471), and Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004).

On the evolution of the gender gap in life expectancy at normal retirement age for OECD countries

Population aging is evolving at different rates across countries and over time, and it represents a long-term challenge for both the sustainability of pension schemes and for the realization of public intergenerational transfers. In this context, this work focuses on gender differences in survival at older ages. Specifically, we implement a comparative analysis of OECD countries to assess the adequacy of the corresponding gender-specific normal retirement age when faced with growing life expectancy. The analysis hinges on several graphical representations and is motivated by recent findings on Italian longevity to determine optimal retirement age shifts necessary to match growing life expectancy at older ages while accounting for model risk for mortality projections. Our analysis determines—at the country level—the extent to which adjustments to the normal retirement age are advisable for the sustainability of the intergenerational paradigm for pensions. The study considers males and females separately because most of these countries are characterized by aging societies where men and women have different crucial characteristics, including life expectancy. It is therefore important that policymakers have information on the future evolution of the longevity gender gap so they will be able to apply policies that preserve the principles of equality and solidarity and reduce the pension gender gap. We find groups of countries where gender gap in life expectancy follows the same dynamics.

A Model Stacking Approach for Forecasting Mortality

This article adopts a machine learning method called stacked generalization for forecasting mortality. The main idea is to combine the forecasts from different projection models or algorithms in a certain way in order to increase the prediction accuracy. In particular, the article considers not just the traditionally used mortality projection models, such as the Lee–Carter and CBD models and their extensions, but also some learning algorithms called feedforward and recurrent neural networks that are starting to gain attention in the actuarial literature. For blending the different forecasts, the article examines a number of choices, including simple averaging, weighted averaging, linear regression, and neural network. Using U.S. mortality data, it is found that the proposed stacking approach often outperforms the cases where a projection model or algorithm is applied individually, and that neural networks tend to generate better results than many of the traditional models.