Mortality Projections Research Articles

Bark water vapour conductance is associated with drought performance in tropical trees

Bark water vapour conductance (gbark) is a rarely considered functional trait. However, for the few tree species measured to date, it appears high enough to create stem water deficits associated with mortality during droughts, when access to water is limited. I tested whether gbark correlates with stem water deficit during drought conditions in two datasets of tropical trees: one of saplings in forest understories during an annual dry season and one of potted saplings in a shadehouse during extreme drought conditions. Among all 14 populations of eight species measured, gbark varied more than 10-fold (0.86-12.98 mmol m-2 s-1). In the forest understories, gbark was highly correlated with stem water deficit among four deciduous species, but not among evergreen species that likely maintained access to soil water. In the shadehouse, gbark was positively correlated with stem water deficit and mortality among all six species. Overall, tree species with higher gbark suffer higher stem water deficit when soil water is unavailable. Incorporating gbark into soil-plant-atmosphere hydrodynamic models may improve projections of plant mortality under drought conditions.

Health Information: Applications and challenges in the COVID-19 pandemic

Global health systems are under immense pressure with the exponential growth and spread of COVID-19. Public health and health system responses to the pandemic have relied on health information reporting, visualisation, and projections of incidence, morbidity, and mortality. This commentary aims to explore how health information has been used to inform the public, manage risk, understand capacity, prepare the health system and to plan public health strategy. We also aim to share the health information challenges and our insights to inform future debate and strategic investment. This paper will be relevant to health service and health information managers wanting to understand vulnerabilities and focus for future health information initiatives.

80 will be the new 70: Old‐age mortality postponement in the United States and its likely effect on the finances of the OASI program

AbstractUsing a Bayesian cohort‐based mortality model, we identify strong evidence of mortality postponement at older ages in the United States. We use the results of the model to project mortality rates 75 years ahead, and show that this will likely raise the U. S. old‐age dependency ratio to ~55% by 2090 (~30% larger than the Social Security Administration [SSA] intermediate estimates). We estimate how this will affect the finances of the Old‐Age and Survivors Insurance program as modeled by the SSA. In our median estimate, using the SSA's intermediate‐cost assumptions, mortality postponement will raise the open‐system unfunded liability over a 75‐year period by around $6.8trn over SSA estimates, worsening the actuarial balance of the program by an extra 1.30% of taxable payroll. Mortality postponement will also have significant implications for other government programs, notably Medicare, and for private‐sector financial intermediaries, such as pension funds and insurance companies, as well as private households.

On the management of retirement age indexed to life expectancy: a scenario analysis of the Italian longevity experience

PurposeThis paper aims to measure the financial impact on social security system of a recently proposed indexation mechanism for retirement age by considering the Italian longevity experience. The analysis is motivated by the progressive increase in life expectancy at advanced age, which is rapidly bringing to the fore noticeable socio-economic consequences in most industrialized countries. Among those, the impact on National Social Security systems is particularly relevant if people live longer than expected; this will lead to greater financial exposure for pension providers.Design/methodology/approachReferring to the Italian population for illustrative purposes, the authors contemplate different scenarios for mortality projection methods and for the implementation of pension age shift while accounting for gender and cohort gaps and model risk. Synthetic indicators to measure the impact of the indexation mechanism on social security system are introduced on the basis of pension cash flows.FindingsAn indexation policy that manages gender gap while adjusting retirement age for varying life expectancy is proposed. As a result, sustainability of public retirement expenditure is improved.Originality/valueThe paper is a concise scenario analysis of the reduction of costs and risks that pension providers would have if the system resorted to link retirement age to life expectancy. The ideas fostered by the paper follow a recent proposal of the Authors on a flexible retirement scheme that deals with model risk for mortality projection and accounts for gender gap in mortality rates.

Estimation of Uncertainty in Mortality Projections Using State-Space Lee-Carter Model

The study develops alternatives of the classical Lee-Carter stochastic mortality model in assessment of uncertainty of mortality rates forecasts. We use the Lee-Carter model expressed as linear Gaussian state-space model or state-space model with Markovian regime-switching to derive coherent estimates of parameters and to introduce additional flexibility required to capture change in trend and non-Gaussian volatility of mortality improvements. For model-fitting, we use a Bayesian Gibbs sampler. We illustrate the application of the models by deriving the confidence intervals of mortality projections using Lithuanian and Swedish data. The results show that state-space model with Markovian regime-switching adequately captures the effect of pandemic, which is present in the Swedish data. However, it is less suitable to model less sharp but more prolonged fluctuations of mortality trends in Lithuania.

Projections of temperature-related cause-specific mortality under climate change scenarios in a coastal city of China

BackgroundNumerous studies have been conducted to project temperature-related mortality under climate change scenarios. However, most of the previous evidence has been limited to the total or non-accidental mortality, resulting in insufficient knowledge on the influence of climate change on different types of disease. ObjectivesWe aimed to project future temperature impact on mortality from 16 causes under multiple climate change models in a coastal city of China. MethodsWe first estimated the baseline exposure-response relationships between daily average temperature and cause-specific mortality during 2009–2018. Then, we acquired downscaled future temperature projections from 28 general circulation models (GCMs) under two Representative Concentration Pathway (RCP4.5 and RCP8.5). Finally, we combined these exposure-response associations with projected temperature to estimate the change in the temperature-related death burden in different future decades in comparison to the 2010 s, assuming no demographic changes and population acclimatization. ResultsWe found a consistently decreasing trend in cold-related mortality but a steep rise in heat-related mortality among 16 causes under climate change scenarios. Compared with the 2010 s, the net change in the fraction of total mortality attributable to temperature are projected to −0.54% (95% eCI: −1.69% to 0.71%) and −0.38% (95% eCI: −2.73% to 2.12%) at the end of the 21st century under RCP4.5 and RCP8.5, respectively. However, the magnitude of future cold and heat effects varied by different causes of death. A net reduction of future temperature-related death burden was observed among 10 out of 15 causes, with estimates ranging from −5.02% (95% eCI: −17.42% to 2.50%) in mental disorders to −1.01% (95% eCI: −5.56% to 3.28%) in chronic lower respiratory disease. Conversely, the rest diseases are projected to experience a potential net increase of temperature-related death burden, with estimates ranging from 0.44% (95% eCI: −4.40% to 6.02%) in ischemic heart disease and 4.80% (95% eCI: −0.04% to 9.84%) in external causes. ConclusionsOur study indicates that the mortality burden of climate change varied greatly by the mortality categories. Further investigations are warranted to comprehensively understand the impacts of climate change on different types of disease across various regions.

FORECASTING MORTALITY FOR KAZAKHSTAN AND ASSESSING IT’S IMPACT ON THE STATE PENSION SPENDING

This paper uses the mortality projection methods of Knykova A. and Sapin A., based on the original Lee-Carter model, in order to investigate mortality rates for Kazakhstan for the time period from 2018 to 2025 and assess their impact on the state pension spending in Kazakhstan. The results show that improvements in mortality rates have been increasing the number of years that people spend in retirement in Kazakhstan, which directly affects the public pension spending. The overall state pension spending is expected to increase in Kazakhstan from 2019 to 2025, reflecting the gradually increasing effect of mortality improvements, but the state pension spending’s share of GDP is projected to decline from 2021 to 2024. However, extra expenditures of the government on pensions due to future mortality improvements will increase gradually as a share of GDP over time and reach 0,02% in 2024

Potential impacts of post-Brexit agricultural policy on fruit and vegetable intake and cardiovascular disease in England: a modelling study

BackgroundCurrent proposals for post-Brexit agricultural policy do not explicitly incorporate public health goals. The revised agricultural policy may be an opportunity to improve population health by supporting domestic production and...

Cancer Surgery During COVID-19: How We Move Forward.

Cancer Surgery During COVID-19: How We Move Forward.

Calculation of changes in life expectancy based on proportional hazards model of an intervention

Mortality projections are of great interest to the pension and insurance industry and with an ageing population, the projections need to cover a longer period. A significant question is how to incorporate in mortality projections the longevity risk due to medical advances and uptake of health interventions. We show how hazard ratios obtained from medical studies in combination with the baseline hazards described by Gompertz or Weibull survival distributions, can be translated into changes in individual and population period life expectancy. The impact of medical advances and health interventions can differ among groups of people, such as by sex, age, and deprivation. Changes in life expectancy depend on the composition of the population and these attributes. These calculations are illustrated by a case study on statins, a drug that can significantly improve life expectancy. An R program implementing our methodology is provided in the Appendix.

Projections of Excessive Mortality Related to Diurnal Temperature Range Under Climate Change Scenarios: A Multi-Country Study

Background: Various retrospective studies report that a higher diurnal temperature range (DTR) increases mortality risk. This study projects the impact of DTR on future mortality across 445 communities in 20 countries. Methods: We first estimated the DTR-related mortality risk based on the historical daily time-series of mortality and of weather factors from 1985 to 2015. Next, we projected the excess deaths attributable to the DTR during the current (1985–2015) and future (2020–2099) periods using daily DTR series under four climate-change scenarios. We then calculated the future excess deaths based on two assumptions: warmer temperatures affect or do not affect the DTR-mortality risk. Findings: DTR was associated with excess mortality in our time-series analyses. Under the unmitigated scenario, the future average DTR is projected to increase in most countries (-0·4–1·6 °C), and the increasing pattern was more evident in the United States, south-central Europe, Mexico, and South Africa. The excess deaths currently attributable to DTR were estimated to be 0·2–7·4%. Furthermore, the DTR-related mortality risk increased with an increasing average temperature. Based on the positive interactive effect between DTR and temperature, the DTR-related excess deaths are projected to increase in all countries (1·4–10·3%) in 2090–2099. Interpretation: This study suggests that the DTR-related excess mortality may increase globally under climate change, and this increasing pattern may vary between countries. Our findings can contribute to both international and regional public health interventions to reduce the DTR impacts on health under climate change. Funding Statement: Ho Kim is supported by the Korea Ministry of Environment via the “Climate Change Correspondence Program” (2014001310007). Declaration of Interests: All authors report no financial relationships with commercial interests.

Trends and Projections in Breast Cancer Mortality among four Asian countries (1990\u20132017): Evidence from five Stochastic Mortality Models

The current study aimed to explore some important insights into the breast cancer mortality (BCM) trends and projections among four Asian countries by using five advanced stochastic mortality models. BCM data over 28 years from 1990–2017 with ages 20–84 were retrieved from the Global Burden of Disease (GBD) Study 2017 for four Asian countries, namely, China, India, Pakistan, and Thailand. Five stochastic mortality models with the family of generalized age-period-cohort were implemented to find the present and future BCM trends in these four Asian countries. Based on Cairns-Blake-Dowd (CBD) model and Lee-Carter model (LCM), overall, results revealed that BCM increased with the passage of time. Aging factor was the most influential factor of elevated BCM in each Asian country under consideration. Projection of BCM showed that mortality rates might continue to grow with time, especially in older ages in each Asian country under study. The highest forecasted BCM rates were observed in Pakistan as compared to other countries. The obvious increase in BCM suggested that earlier tactics should be implemented to reduce the subsequent morbidity and mortality due to breast cancer. The last but not least, some additional tactics to mitigate the BCM in older ages must be adopted.

A modified common factor model for modelling mortality jointly for both sexes

There is an increasing attention on the joint modelling of multiple populations. Populations are related in several ways, such as neighbouring countries, females and males, and socioeconomic subgroups within a population. They are associated due to certain common driving forces, and mortality projection models should be constructed to allow for such underlying relationships. One such example is the Poisson common factor model. In this paper, we consider some extensions of the original Poisson common factor model. The first is to use a different number of additional factors for each sex. With this, the potential trend differences between the two sexes can be captured. The second is to incorporate a common age sensitivity effect into the additional factors. It may help improve parameter parsimony. A hybrid version between these two extensions is also considered. Overall, the modified versions deliver better fitting and projection results than the original model, using mortality data from eight developed countries. Qualitatively speaking, the new models provide much more flexibility in modelling populations with different mortality patterns. Empirically, they are shown to be able to produce improved performance in fitting and projection. The models are selected as the optimal choices based on information criteria statistics, and they tend to produce more accurate forecasts of the male-to-female ratios of death rates.

ESTIMATING AND FORECASTING THE SMOKING-ATTRIBUTABLE MORTALITY FRACTION FOR BOTH GENDERS JOINTLY IN OVER 60 COUNTRIES.

Smoking is one of the leading preventable threats to human health and a major risk factor for lung cancer, upper aero-digestive cancer, and chronic obstructive pulmonary disease. Estimating and forecasting the smoking attributable fraction (SAF) of mortality can yield insights into smoking epidemics and also provide a basis for more accurate mortality and life expectancy projection. Peto et al. (1992) proposed a method to estimate the SAF using the lung cancer mortality rate as an indicator of exposure to smoking in the population of interest. Here we use the same method to estimate the all-age SAF (ASAF) for both genders for over 60 countries. We document a strong and cross-nationally consistent pattern of the evolution of the SAF over time. We use this as the basis for a new Bayesian hierarchical model to project future male and female ASAF from over 60 countries simultaneously. This gives forecasts as well as predictive distributions that can be used to find uncertainty intervals for any quantity of interest. We assess the model using out-of-sample predictive validation, and find that it provides good forecasts and well calibrated forecast intervals, comparing favorably with other methods.

A Bayesian Approach to Modeling and Projecting Cohort Effects

One of the key motivations in the construction of ever more sophisticated mortality models was the realization of the importance of “cohort effects” in the historical data. However, these are often difficult to estimate robustly, due to the identifiability issues present in age/period/cohort mortality models, and exhibit spurious features for the most recent years of birth, for which we have little data. These can cause problems when we project the model into the future. In this study, we show how to ensure that projected mortality rates from the model are independent of the arbitrary identifiability constraints needed to identify the cohort parameters. We then go on to develop a Bayesian approach for projecting the cohort parameters that allows fully for uncertainty in the recent parameters due to the lack of information for these years of birth, which leads to more reasonable projections of mortality rates in future.

Mortality Projections using Generalized Additive Models with applications to annuity values for the Irish population – CORRIGENDUM

Mortality Projections using Generalized Additive Models with applications to annuity values for the Irish population – CORRIGENDUM

Trends and projections of pleural mesothelioma incidence and mortality in the national priority contaminated sites of Sicily (Southern Italy).

to evaluate short-medium term incidence and mortality projections of pleural malignant mesothelioma (PMM) in Sicily Region (Southern Italy) and in its four National Priority Contaminated Sites (NPCSs). population-based prediction study. PMM cases from 1998 to 2016 registered by the Regional Operations Centre of the Sicilian Region. incidence and mortality trends of PMM were estimated for the period 1998-2016 from the relationships among mortality, incidence, and survival. Projections of incidence and mortality rates were obtained up to 2026. age-standardized incidence rates of PMM in Sicily were estimated to increase in men from 1.4 (x100,000) in 1998 to 2.29 in 2021 and to slightly decrease down to 2.2 in 2026. Women age-standardized rates in the same period are estimated to decrease from 0.52 to 0.27. In Biancavilla, age-standardized incidence rates were estimated to remain stable between 8.1 and 8.0 in men, while crude rates increased from 8.3 in 1998 to 10.7 in 2026. For women, the estimated age-standardized incidence rates are increasing from 3.08 to 6.75. In the three pooled NPCSs of Augusta-Priolo, Gela, and Milazzo, the estimates of age-standardized incidence rates show an initial trend to growth followed by a decreasing trend, both in men and women, down to predicted values in 2016 of 3.0 in men and 0.77 in women. Estimated age-standardized and crude mortality rates show, for both sexes and all areas, similar patterns as those estimated for incidence. in Sicily and in the three NPCSs of Gela, Milazzo, and Priolo, incidence and mortality projections are downward in both sexes. In Biancavilla, mesothelioma occurrence is estimated to increase up to 2026, slightly in males and more significantly in females.

A Multi-population Approach to Forecasting All-Cause Mortality Using Cause-of-Death Mortality Data

All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-of-death mortality allows one to understand the drivers of the recent changes in all-cause mortality. However, the existing literature has argued that all-cause mortality projections based on cause-specific mortality experience have a number of serious drawbacks, including the inferior cause-of-death mortality data and the complex dependence structure between causes of death. In this article, we use the recent World Health Organization causes-of-death data to address this issue in a multipopulation context. We construct a new model in the spirit of N. Li and Lee (2005) but in terms of cause-specific mortality. A new two-step beta convergence test is used to capture the cause-specific mortality dynamics between different countries and between different causes. We show that the all-cause mortality estimations produced by the new model perform in the sample similarly to the estimations by the Lee-Carter and Li-Lee all-cause mortality models. However, in contrast to results from earlier studies, we find that the all-cause mortality projections of the new model have better out-of-sample performance in a long forecast horizon. Moreover, for the case of The Netherlands, an approximately 1-year higher remaining life expectancy projection for a 67-year-old Dutch male in a 30-year forecast horizon is obtained by this new model, compared to the all-cause Li-Lee mortality model.

Incorporating hierarchical credibility theory into modelling of multi-country mortality rates

A hierarchical credibility model is a generalization of the Bühlmann credibility model and the Bühlmann–Straub credibility model with a tree structure of four or more levels. This paper aims to incorporate hierarchical credibility theory, which is used in property and casualty insurance, to model multi-country mortality rates. The forecasting performances of the three/four/five-level hierarchical credibility models are compared with those of the classical Lee–Carter model and its three extensions for multiple populations (the joint- k, the co-integrated, and the augmented common factor Lee–Carter models). Numerical illustrations based on mortality data from the Human Mortality Database for both genders of the US, the UK and Japan with a series of fitting year spans and three forecasting periods show that the hierarchical credibility approach contributes to more accurate forecasts measured by the AMAPE (average of mean absolute percentage errors). Finally, a stochastic version of the proposed hierarchical credibility mortality model is also proposed, which can be used to construct predictive intervals on the projected mortality rates and to conduct stochastic simulations for applications.

Multi-population Mortality Projection: The Augmented Common Factor Model with Structural Breaks

Multi-population mortality forecasting has become an increasingly important area in actuarial science and demography, as a means to avoid long-run divergence in mortality projection. This paper aims to establish a unified state-space Bayesian framework to model, estimate and forecast mortality rates in a multi-population context. In this regard, we reformulate the augmented common factor model to account for structural breaks in the mortality indexes. Further, we conduct a Bayesian analysis to make inferences and generate forecasts so that process, parameter and model uncertainties can be considered simultaneously and appropriately. The square-root-form of the Kalman Filter is exploited to improve robustness when sampling latent states. We illustrate the efficiency of our methodology through two distinctive case studies. The first uses Australian two-gender mortality data. The second projects mortality for a list of selected Eurozone countries, where the hierarchical clustering approach on principal components is utilised to group countries with similar mortality characteristics together. Both point and probabilistic forecast evaluations are considered in the empirical analysis. The derived results support the fact that the incorporation of stochastic drifts mitigates the impact of the structural change in the time indexes on mortality projection.