scholarly journals Common Factor Cause-Specific Mortality Model

Risks ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 221
Author(s):  
Geert Zittersteyn ◽  
Jennifer Alonso-García
Keyword(s):  
Life Expectancy ◽  
Common Factor ◽  
Trivial Extension ◽  
Mortality Data ◽  
Mortality Model ◽  
Mortality Models ◽  
Specific Mortality ◽  
Accurate Forecast ◽  
Mortality Forecasts ◽  
Population Mortality

Recent pension reforms in Europe have implemented a link between retirement age and life expectancy. The accurate forecast of life tables and life expectancy is hence paramount for governmental policy and financial institutions. We developed a multi-population mortality model which includes a cause-specific environment using Archimedean copulae to model dependence between various groups of causes of death. For this, Dutch data on cause-of-death mortality and cause-specific mortality data from 14 comparable European countries were used. We find that the inclusion of a common factor to a cause-specific mortality context increases the robustness of the forecast and we underline that cause-specific mortality forecasts foresee a more pessimistic mortality future than general mortality models. Overall, we find that this non-trivial extension is robust to the copula specification for commonly chosen dependence parameters.

scholarly journals Identifying Structural Breaks in Stochastic Mortality Models

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Colin O’Hare ◽  
Youwei Li
Keyword(s):  
Life Expectancy ◽  
Structural Breaks ◽  
Structural Break ◽  
Mortality Rates ◽  
Insurance Companies ◽  
Mortality Data ◽  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Mortality Forecasts

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

scholarly journals A Two-Population Mortality Model to Assess Longevity Basis Risk

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 44
Author(s):  
Selin Özen ◽  
Şule Şahin
Keyword(s):  
Risk Reduction ◽  
Significant Risk ◽  
Reference Population ◽  
Mortality Data ◽  
Basis Risk ◽  
Mortality Model ◽  
Proposed Model ◽  
Population Mortality ◽  
Mortality Jumps ◽  
The Impact

Index-based hedging solutions are used to transfer the longevity risk to the capital markets. However, mismatches between the liability of the hedger and the hedging instrument cause longevity basis risk. Therefore, an appropriate two-population model to measure and assess longevity basis risk is required. In this paper, we aim to construct a two-population mortality model to provide an effective hedge against the basis risk. The reference population is modelled by using the Lee–Carter model with the renewal process and exponential jumps, and the dynamics of the book population are specified. The analysis based on the U.K. mortality data indicate that the proposed model for the reference population and the common age effect model for the book population provide a better fit compared to the other models considered in the paper. Different two-population models are used to investigate the impact of sampling risk on the index-based hedge, as well as to analyse the risk reduction regarding hedge effectiveness. The results show that the proposed model provides a significant risk reduction when mortality jumps and sampling risk are taken into account.

MORTALITY IN PARKINSON'S DISEASE AND ATYPICAL PARKINSONIAN DISORDERS

2015 ◽  
Vol 86 (11) ◽  
pp. e4.88-e4
Author(s):  
Angus Macleod ◽  
Carl Counsell
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Life Expectancy ◽  
Median Survival ◽  
Lewy Body Dementia ◽  
Mortality Data ◽  
Kaplan Meier ◽  
Central Register ◽  
Population Mortality ◽  
Incident Cohort

BackgroundWe evaluated the mortality associated with Parkinson's disease (PD), Lewy body dementia (LBD), progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and vascular parkinsonism (VP) using a community-based incident cohort.MethodsAll incident parkinsonism cases identified over 4.5 years (2002-4, 2006-9) were tagged to the NHS central register for regular death notifications. Kaplan-Meier survival probabilities were plotted. Standardised mortality ratios (SMRs) and life expectancy, adjusted for age, sex and calendar year, were calculated using regional mortality data.ResultsUntil June 2014, 90 deaths occurred in 198 PD patients, and 107 deaths in 117 patients with other syndromes. Median survival in PD, LBD, PSP, MSA, and VP was 7.8 (6.7–9.4), 3.3 (2.3–4.1), 2.6 (1.1–3.8), 5.1 (1.3–NA), 2.1 (1.5–3.4) years, respectively. SMRs were 1.5 (1.2–1.9), 4.2 (3.0–5.9), 3.8 (2.6–5.5), 1.8 (0.9–3.4), 4.2 (3.0–6.0) respectively. In PD, median survival was lower than life expectancy, but more so in under 65s.ConclusionsMortality in PD was increased by 50% over expected population mortality. Younger patients have proportionally more to lose. Survival was much poorer in other syndromes.

scholarly journals Coherent Mortality Model in A State-Space Approach

2021 ◽  
Vol 50 (4) ◽  
pp. 1101-1111
Author(s):  
Siti Rohani Mohd Nor ◽  
Fadhilah Yusof ◽  
Siti Mariam Norrulashikin
Keyword(s):  
State Space ◽  
Prediction Interval ◽  
Forecast Accuracy ◽  
Age Effect ◽  
Joint Estimation ◽  
Mortality Model ◽  
Proposed Model ◽  
Mortality Forecasts ◽  
Population Mortality ◽  
Life Annuities

Mortality improvements that have recently become apparent in most developing countries have significantly shaped queries on forecast divergent between populations in recent years. Therefore, to ensure a more coherent way of forecasting, previous researchers have proposed multi-population mortality model in the form of independent estimation procedures. However, similar to single-population mortality model, such independent approaches might lead to inaccurate prediction interval. As a result of this inaccurate mortality forecasts, the life expectancies and the life annuities that the mortality model aims to generate is underestimated. In this study, we propose another new extension of the multi-population mortality model in a joint estimation approach by recasting the model into a state-space framework. A combination of augmented Li-Lee and O’Hare-Li methods are employed, before we transform the proposed model into a state-space formulation. In addition, this study incorporates the quadratic age effect parameter to the proposed model to better capture the younger ages mortality. We apply the method to gender and age-specific data for Malaysia. The results show that our latter framework brings a significant contribution to the multi-population mortality model due to the incorporation of joint-estimate and quadratic age effect parameters into the model’s structure. Consequently, the proposed model improves the mortality forecast accuracy.

Mortality models incorporating long memory for life table estimation: a comprehensive analysis

2021 ◽  
pp. 1-38
Author(s):  
Hongxuan Yan ◽  
Gareth W. Peters ◽  
Jennifer Chan
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Long Memory ◽  
Age Groups ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Life Tables ◽  
Mortality Data ◽  
Model Parameters ◽  
Mortality Models

Abstract Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.

scholarly journals Mortality rates in people with intellectual disabilities

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Rachael Williams ◽  
Jessie Oyinlola ◽  
Pauline Heslop ◽  
Gyles Glover
Keyword(s):  
Life Expectancy ◽  
Mortality Rates ◽  
Practice Research ◽  
Mortality Data ◽  
Clinical Practice Research Datalink ◽  
Crude Mortality ◽  
Specific Mortality ◽  
Standardised Mortality Ratios ◽  
People With Intellectual Disabilities ◽  
Read Codes

ABSTRACTObjectivesA growing body of evidence highlights a disparity in mortality rates for people with intellectual disability (ID) compared with the general population. However, national data for England is lacking. The objective of this study was to provide evidence on mortality rates in people with ID. ApproachPatients registered for at least a day during 01/04/10-31/03/14 at a GP practice contributing to the Clinical Practice Research Datalink (CPRD) and consenting to linkage were included. Patients with ID were identified via Read codes. Date and cause of death were identified using linked Office of National Statistics mortality data. Crude mortality rates, life expectancy and indirectly age/sex standardised mortality ratios (SMR) were calculated with 95% confidence intervals (CI), overall, by ICD10 chapter, for frequently occurring causes, and those classified as avoidable. Results11 million person-years were included (0.5% for patients with ID) and 98,035 deaths occurred (0.7% in patients with ID). The mortality rate for patients with ID was 11.2 per 1,000 population, 1.3 times the rate for those without ID, with an associated SMR of 3.2 (95% CI 2.93.4). Life expectancy was 65.5 years (95% CI 61.969.2)for patients with ID and 85.3 years for those without (95% CI 85.285.4). Mortality rates were higher in patients with ID in all age/sex groups, with larger differences for younger ages. Patients with ID had higher cause-specific mortality rates across all ICD10 chapters, with highest SMRs for congenital malformations (72.9, 95% CI 55.194.7), nervous system diseases (9.8, 95% CI 7.812.1) and mental disorders (5.4, 95% CI 3.97.3). Circulatory deaths were the most frequent, with ischaemic heart disease (SMR 2.2, 95% CI 1.62.8) and cerebrovascular disease (SMR 3.3, 95% CI 2.34.5) most prominent. A higher proportion of deaths were classified as avoidable for patients with ID (44.7%, 95% CI 41.048.5%) compared to those without (21.0%, 95% CI 20.721.3). ConclusionNational English data confirm that patients with ID have higher mortality rates than those without. Mortality rates for patients with ID were higher across all age/sex groups and causes, with almost half of deaths classified as avoidable.

scholarly journals Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

2019 ◽  
Author(s):  
Ikhan Kim ◽  
Hwa-Kyung Lim ◽  
Hee-Yeon Kang ◽  
Young-Ho Khang
Keyword(s):  
Population Structure ◽  
Life Expectancy ◽  
Rural Areas ◽  
Urban Areas ◽  
Small Area ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Mortality Figure ◽  
Specific Mortality ◽  
Mortality Ratio

Abstract Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.Methods: We utilized the National Health Information Database to obtain annual age-specific numbers of population and deaths for all neighborhood-level areas in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all neighborhoods. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all neighborhoods, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in neighborhoods in rural areas, particularly in those with small populations, than in metropolitan and urban areas.Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each neighborhood.

scholarly journals Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 203
Author(s):  
Qian Lu ◽  
Katja Hanewald ◽  
Xiaojun Wang
Keyword(s):  
United States ◽  
Life Expectancy ◽  
National Level ◽  
Common Factors ◽  
The United States ◽  
Mortality Data ◽  
Bayesian Hierarchical ◽  
New Model ◽  
Mortality Modelling ◽  
Mortality Forecasts

We propose a new model in a Bayesian hierarchical framework to project mortality at both national and subnational levels based on sparse or missing data. The new model, which has a country–region–province structure, uses common factors to pool information at the national level and within regions consisting of several provinces or states. We illustrate the model’s use by drawing on a new database containing provincial-level mortality data for China from four censuses conducted during the period 1982–2010. The new model provides good estimates and reasonable forecasts at both the country and provincial levels. The model’s forecast intervals reflect provincial- and regional-level uncertainty. Using subnational data for the period 1999–2018 from the Centers for Disease Control and Prevention (CDC), we also apply the model to the United States. We use mortality forecasts to compute and compare national and subnational life expectancies for China and the United States. The model predicts that, in 2030, China will have a similar national life expectancy at age 60 and a similar heterogeneity in subnational life expectancy as the United States.

scholarly journals Burden of occupational injuries occurred in Italy, 1900-2017

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Ariani ◽  
A Baldasseroni ◽  
B Rondinone ◽  
P Ferrante ◽  
M Levi ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Occupational Injuries ◽  
Industrial Worker ◽  
Economic Changes ◽  
Mean Values ◽  
Mortality Model ◽  
Mortality Models ◽  
Long Time ◽  
Injury Burden

Abstract Background The GBD study allows comparison of health conditions among different societies and cultures. This permits also to estimate health variations over a long time in a single nation, overcoming the difficulties deriving from social and economic changes. INAIL, the National Institute for Insurance against Accidents at Work, provides since 1884, detailed data about the number of events and their consequences, age and sex of injured workers, and the total number of insured workers. Such data allow us to estimate DALYs in terms of incidence and prevalence, and under different mortality models. Methods 1.8 mln. individual injury records occurred in 1990-2015 were transcoded into GBD injury categories. YLLs and YLDs were calculated considering life expectancy, DWs and duration, then distributed by compensation category. The YLLs and YLDs of permanent disabilities have been assessed with reference both to the life expectancy of the GBD 2017 and, in a competitive mortality model, to mean values at the time of the accident. Estimated DALYs were assessed both in terms of incidence and prevalence. Results Around 1900, an industrial worker suffered on average 0.087 incident DALY/year for occupational injuries, or 0.058 in a competitive mortality model. These values remained almost stationary until WWII when they showed a peak around 0.11, then declined to about a third in the 1970s, and to about a twentieth in 2017. The YLL / DALY ratio was 0.82 around 1900, then slowly decreased to less than 0.5 in the late 1930s, rose to a new peak around 0.8 in WWII, then diminished again to 0.32 in 2017. Considering prevalence, variations are much slower, due to the expected average durations of permanent disabilities, between 32.5 and 52 years. Risk breakdown by main industry sectors is ongoing. Conclusions DALY rate and YLL/DALY injuries declined largely along time. Nevertheless, such events continue to leave a very long-term legacy of disabilities. Key messages INAIL data permit to estimate burden for occupational injuries occurred in Italy along over a century. Injury burden declined over time, but continues to leave a legacy of long-term disabilities.

scholarly journals Evaluation of the Kou-Modified Lee-Carter Model in Mortality Forecasting: Evidence from French Male Mortality Data

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 123 ◽  
Author(s):  
Marie Angèle Cathleen Alijean ◽  
Jason Narsoo
Keyword(s):  
Mortality Data ◽  
Male Mortality ◽  
Forecasting Accuracy ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Double Exponential ◽  
Jump Diffusion Model ◽  
Mortality Forecasts ◽  
Death Data ◽  
Carter Model

Mortality forecasting has always been a target of study by academics and practitioners. Since the introduction and rising significance of securitization of risk in mortality and longevity, more in-depth studies regarding mortality have been carried out to enable the fair pricing of such derivatives. In this article, a comparative analysis is performed on the mortality forecasting accuracy of four mortality models. The methodology employs the Age-Period-Cohort model, the Cairns-Blake-Dowd model, the classical Lee-Carter model and the Kou-Modified Lee-Carter model. The Kou-Modified Lee-Carter model combines the classical Lee-Carter with the Double Exponential Jump Diffusion model. This paper is the first study to employ the Kou model to forecast French mortality data. The dataset comprises death data of French males from age 0 to age 90, available for the years 1900–2015. The paper differentiates between two periods: the 1900–1960 period where extreme mortality events occurred for French males and the 1961–2015 period where no significant jump is observed. The Kou-modified Lee-Carter model turns out to give the best mortality forecasts based on the RMSE, MAE, MPE and MAPE metrics for the period 1900–1960 during which the two World Wars occurred. This confirms that the consideration of jumps and leptokurtic features conveys important information for mortality forecasting.

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