1,721,004 research outputs found
Longevity and concentration in survival times: the log-scale-location family of failure time models
No full textEvidence suggests that the increasing life expectancy levels at birth wit- nessed over the past centuries are associated with a decreasing concentration of the survival times. The purpose of this work is to study the relationships that exist be- tween longevity and concentration measures for some regression models for the evo- lution of survival. In particular, we study a family of survival models that can be used to capture the observed trends in longevity and concentration over time. The para- metric family of log-scale-location models is shown to allow for modeling different trends of expected value and concentration of survival times. An extension towards mixture models is also described in order to take into account scenarios where a fraction of the population experiences short term survival. Some results are also pre- sented for such framework. The use of both the log-scale-location family and the mixture model is illustrated through an application to period life tables from the Hu- man Mortality Database
The average uneven mortality index: building on the "e-dagger" measure of lifespan inequality
No full textIn recent years, lifespan inequality has become an important indicator of population health. Uncovering the statistical properties of lifespan inequality measures can provide novel in- sights on the study of mortality.
We introduce the “Average Uneven Mortality” (AUM) index, a novel mortality indicator for the study of mortality patterns and lifespan inequality. We prove some new properties of interest, as well as relationships with the “e-dagger” and entropy measures of lifespan inequality.
The use of the AUM index is illustrated through an application to observed period and cohort death rates from the Human Mortality Database. We explore the behavior of the index across age and over time, and we study its relationship with life expectancy. The AUM index at birth declined over time until the 1950s, when it reverted its trend; also, the index generally increases with age.
The AUM index is a normalized version of Vaupel and Canudas-Romo’s e-dagger measure that can be meaningfully compared across countries and over time. Additionally, we derive an upper bound for both e-dagger and the lifetable entropy measure, which are novel formal results. Finally, we develop novel routines to compute e-dagger and the standard deviation of lifetimes from death rates, which are possibly more precise than available software, particularly for calculations involving older ages
Epilocal: a real-time tool for local epidemic monitoring
Full text linkWe describe Epilocal, a simple R program designed to automatically download the most recent data on reported infected SARS-CoV-2 cases for all Italian provinces and regions, and to provide a simple descriptive analysis. For each province the cumulative number of reported infected cases is available each day. In addition, the current numbers of hospitalized patients (separately for intensive care or not) and the cumulative number of deceased individuals are available at the region level. The data are analyzed through Poisson generalized linear models with logarithmic link function and polynomial regression on time. For cumulative data, we also consider a logistic parameterisation of the hazard function. Automatic model selection is performed to choose among the different model specifications, based on the statistical significance of the corresponding estimated parameters and on goodness-of-fit assessment. The chosen model is used to produce up-to-today estimates of the growth rate of the counts. Results are plotted on a map of the country to allow for a visual assessment of the geographic distribution of the areas with differential prevalence and rates of growth
Evaluation of age-specific causes of death in the context of the Italian longevity transition
Full text linkAbstract In many low-mortality countries, life expectancy at birth increased steadily over the last century. In particular, both Italian females and males benefited from faster improvements in mortality compared to other high-income countries, especially from the 1960s, leading to an exceptional increase in life expectancy. However, Italy has not become the leader in longevity. Here, we investigate life expectancy trends in Italy during the period 1960–2015 for both sexes. Additionally, we contribute to the existing literature by complementing life expectancy with an indicator of dispersion in ages at death, also known as lifespan inequality. Lifespan inequality underlies heterogeneity over age in populating health improvements and is a marker of uncertainty in the timing of death. We further quantify the contributions of different age groups and causes of death to recent trends in life expectancy and lifespan inequality. Our findings highlight the contributions of cardiovascular diseases and neoplasms to the recent increase in life expectancy but not necessarily to the decrease in lifespan inequality. Our results also uncover a more recent challenge across Italy: worsening mortality from infectious diseases and mortality at older age
The Gini concentration index for the study of survival
No full textHere, we review some developments that have occurred mostly over the past 20 years in the use of the Gini concentration index to study survival distributions. We first describe methods to estimate the concentration index from incomplete data, both within the parametric and the nonparametric setting. We then move to illustrate work in the demographic domain, where survival distributions are typically estimated from life tables. Lastly, we consider some recent developments that have focused on the study of a class of survival distributions for which the measures of life expectancy at birth and of concentration can move in the same or opposite directions across groups (typically, birth cohorts) as a result of changes in mortality over time. For all cases we also refer to the software packages that can be used to compute the Gini index and implement the different approaches
Do senescent declines in elite tennis players differ across the sexes?
No full textAging is characterized by rising mortality, declining fertility and declines in physiological function with age (functional senescence). Sex differences in the tempo and severity of survival and fertility declines are widespread, but it is less clear how often and how much trajectories of functional senescence diverge between the sexes. We tested how physiological function changed with age in male and female elite tennis players using first-serve speed (power) and first-serve accuracy as performance measures. We found absolute differences between the sexes with men serving faster, but less accurately than women. Both power and accuracy showed senescent declines but these began earlier for power. There were signals of trait-compensation, where players with pronounced power declines showed relative increases in accuracy, which might partially buffer against power deterioration. However, there were no sex differences in how either trait changed with age, contrasting with other sports. Sex differences in functional senescence are probably shaped by interactions between natural and sexual selection, the proximate costs of trait expression and a trait’s genetic architecture, and so are highly trait-specific. We discuss the strengths and potential pitfalls of using data from elite athletes to disentangle these complex interactions
New Approaches in Mortality Modelling and Forecasting
Full text linkDødelighedsmodellering og -prognoser er dybt forankret i demografiske og aktuariske videnskaber. Modeller til at beskrive dødelighedsmønstre over alder og tid er længe blevet brugt og udviklet, siden John Graunt (1662) introducerede en af de første modeller for dødelighed, dødelighedstavlen. Prognoser for dødelighed er blevet udarbejdet igennem mange år: De første eksempler kan spores tilbage til begyndelsen af det tyvende århundrede, hvor engelske aktuarer begyndte at måle den økonomiske byrde ved uventede forbedringer af levetiden på forsikringsog pensionsudbyderes reserver.I dag indtager analysen af dødelighed for mennesker en central rolle i demografiske og aktuariske analyser. Det meste af opmærksomheden, som dette forskningsområde får, kommer fra to presserende udfordringer det moderne samfund står over for: aldring af befolkningen og levetidsrisiko. I henhold til de seneste verdensbefolkningsprognoser vil praktisk talt alle lande i verden opleve vækst i antallet og andelen af ældre, som følge af kontinuerlige stigninger i den forventede levealder og forringet fertilitet (United Nations, 2019). Den demografiske transition har indflydelse på både offentlige og private pensionssystemer i udviklede økonomier, hvis pensionsforpligtelser, på grund af uventede forbedringer i dødeligheden, ligger mellem 60 og 80 billioner dollars (Michaelson and Mulholland, 2014). Finansiering af offentlige aktiviteter og pensionsprodukter til ældre bliver stadig vanskeligere, når befolkningsandelen i den erhvervsaktive alder falder og forsøgerkvoten stiger over hele verden.De enorme uventede offentlige og private pensionsforpligtelser er resultatet af for konservative prognoser for dødelighed i de seneste årtier. På trods af mange store fremskridt inden for dødelighedsprognoser, herunder skiftet fra en deterministisk til en stokastisk tilgang, har nuværende og bredt anvendte metoder gentagne gange undladt at forudse de konstante forbedringer i dødeligheden, som er observeret i mange lande med lav dødelighed. Behovet for nye modeller, der kan forudsige forbedringer af levetiden mere nøjagtigt end etablerede metoder er indlysende og påkrævet. Derfor har denne afhandling til formål at bringe ny viden til analysen og forudsigelsen af menneskelig dødelighed ved at introducere nye statistiske metoder, der tilbyder forskellige perspektiver på dødelighedsudviklingen.Denne afhandling består af seks kapitler, hvoraf fem er analyser, er udført til at nå dette mål. Hver analyse har form af et forskningsmanuskript, der er blevet offentliggjort eller sendt til videnskabelige tidsskrifter; endvidere er metoderne til at replicere resultaterne, der er præsenteret i afhandlingen, gjort offentligt tilgængelige. Det første kapitel introducerer de grundlæggende forestillinger og mål, der er anvendt i studiet af menneskelig dødelighed, gennemgår de vigtigste bidrag i historien om modellering og forudsigelse af dødelighed og giver en kort oversigt over de fem studier, der er udført i afhandlingen. I kapitel 2 illustrerer vi en generel ramme for modellering af dødelighed for voksne, der forener velkendte dødelighedslove i en enkelt fleksibel familie. Re-parametrisering af dødelighedsmodeller med hensyn til den foreslåede location-scale familie har to vigtige fordele: Modellens parametre har en direkte demografisk fortolkning, og deres estimering er mere præcis på grund af deres lavere korrelation. Fra tredje til femte kapitel flyttes opmærksomheden fra dødelighed til fordelingen af dødsfald som en alternativ, men alligevel informativ (og forsømt) funktion til modellering og forudsigelse af menneskelig dødelighed. Kapitel 3 foreslår en relationel tilgang til at modellere og forudsige dødelighed blandt voksne ved at omdanne aldersaksen for en standardfordeling af dødsfald. Den foreslåede STAD-model modellerer succesfuldt dødelighedsudviklingen over alder og tid, og dens fremskrivninger er mere nøjagtige og optimistiske end dem, der opnås med den banebrydende Lee-Carter (LC) model (Lee and Carter, 1992) og dennes udvidelser. STAD-modellen anvendes også og generaliseres i de følgende to kapitler. I kapitel 4 udvides modellen til at inkluderer alle aldre. Aldersmønsteret på dødelighed er først dekomponeret i tre glatte og uafhængige komponenter, der opererer med barndom, middel-alder og alderdom (oprindeligt foreslået af Thiele, 1871). De tre komponenter modelleres og fremskrives derefter med specialiserede versioner af STAD-modellen. Prognoser opnået med denne STAD-modellen viser sig at være mere nøjagtige og optimistiske end traditionelle og veletablerede modeller. Kapitel 5 præsenterer en generalisering og anvendelse af STAD-metodologien til modellering og prognoser af kohorte dødelighedsdata. Modeller, der er udviklet til at forudsige kohortedata, er meget få i litteraturen, og vores foreslåede fremgangsmåde giver os mulighed for præcist at fuldføre livsforløbet af delvist observerede kohorter. Endelig foreslår kapitel 6 en ny udvidelse af den indflydelsesrige LC-model, der afhjælper nogle af dens kendte problemer. Med Penalized Composite Link model nedbryder vi dødelighedsmønsteret i tre uafhængige komponenter, der er modelleret, estimeret og fremskrevet inden for en glat LC-struktur. Estimerede og fremskrevne dødelighedsprofiler viser ikke den skarphed, der typisk opstår af LC-modellen; endvidere kan dødelighedsændringer variere mere fleksibelt over alder og tid, da de er resultatet af en kombination af tre komponentspecifikke skemaer for mortalitetsforbedringer.Mortality modelling and forecasting are deeply rooted in demographic and actuarial sciences. Models to describe mortality patterns over age and time have long been used and developed since John Graunt (1662) introduced one of the first models of mortality, the life table. Forecasts of mortality have also been produced for many years: the first examples trace back to the beginning of the twentieth century, when English actuaries started to measure the financial burden of unanticipated longevity improvements on insurance and pension providers’ reserves.Today, the study of human mortality still occupies a central role in demographic and actuarial analyses. Most of the attention received by this area of research has been stimulated by two pressing challenges faced by modern societies: population ageing and longevity risk. According to the latest World Population Prospects, virtually every country of the world is experiencing growth in the number and proportion of older persons, resulting from continuous mortality and fertility declines (United Nations, 2019). Furthermore, the demographic transition has been impacting both public and private pension systems, whose retirement liabilities lie between 80 trillions in developed economies due to unexpected mortality improvements (Michaelson and Mulholland, 2014). Funding public policies and retirement products for the elderly becomes increasingly difficult as working-age populations shrink and dependency ratios increase worldwide.The enormous size of unexpected public and private retirement liabilities is the result of overly conservative forecasts of mortality during most recent decades. Despite the great advances in the field of mortality forecasting, including the shift from deterministic to stochastic approaches, currently and widely used methods have repeatedly failed to anticipate the sustained rate of mortality improvements observed in many low-mortality countries. The need for novel models that can predict longevity improvements more accurately than established methodologies is evident and timely. Therefore, this dissertation aims to bring new insights to the analysis and forecasting of human mortality by introducing novel statistical methods that offer different perspectives on mortality developments.This dissertation comprises six chapters, five of which are studies that have been devised to address this goal. Each study takes the form of a research manuscript, which has been published or submitted to scientific journals; furthermore, routines for reproducing the results presented in the thesis have been made publicly available. The first chapter introduces the basic notions and measures employed in the study of human mortality, reviews the main contributions in the history of modelling and forecasting mortality, and provides a short overview of the five studies developed in the thesis. In Chapter 2, we illustrate a general framework for modelling adult mortality that reconciles the well-known laws of mortality into a single flexible family. Re-parameterizing mortality models in terms of the proposed location–scale family has two important advantages: the model’s parameters have a direct demographic interpretation, and their estimation is more precise due to their lower correlation.From the third to the fifth chapters, the attention is shifted from mortality rates to age-atdeath distributions as an alternative, yet informative (and neglected), function for modelling and forecasting human mortality. Chapter 3 proposes a relational approach to model and forecast adult mortality by transforming the age-axis of a standard distribution of deaths. The proposed Segmented Transformation Age-at-death Distributions (STAD) model successfully captures mortality developments over age and time, and its forecasts are more accurate and optimistic than those obtained with the seminal Lee-Carter (LC) model (Lee and Carter, 1992) and its extensions. The STAD model is further employed and generalized in the following two chapters. In Chapter 4, the methodology is extended to the entire age-range. The age-pattern of mortality is first smoothly decomposed into three independent components that operate upon childhood, middle and old ages (as originally proposed by Thiele, 1871). The three components are then modelled and forecast with specialized versions of the STAD model. The resulting forecasts are shown to be more accurate and optimistic than those of traditional and well-established models. Chapter 5 presents a generalization and application of the STAD methodology for modelling and forecasting cohort mortality data. Models developed to forecast cohort data are very scarce in the literature, and our proposed approach allows us to precisely complete the mortality experience of partially observed cohorts. Finally, Chapter 6 proposes a new extension of the influential LC model that overcomes some of its known drawbacks. Working in a penalized composite link framework, we simultaneously smooth and decompose the mortality pattern into three independent components, which are modelled, estimated and forecast within an LC smooth framework. Fitted and forecast mortality profiles do not show the jaggedness typically displayed by the LC model; furthermore, mortality rates can vary more flexibly across age and time, as they result from a combination of three component-specific schedules of mortality changes.
New Approaches in Mortality Modelling and Forecasting
No full textMortality modelling and forecasting are deeply rooted in demographic and actuarial sciences. Models to describe mortality patterns over age and time have long been used and developed since John Graunt (1662) introduced one of the first models of mortality, the life table. Forecasts of mortality have also been produced for many years: the first examples trace back to the beginning of the twentieth century, when English actuaries started to measure the financial burden of unanticipated longevity improvements on insurance and pension providers’ reserves. Today, the study of human mortality still occupies a central role in demographic and actuarial analyses. Most of the attention received by this area of research has been stimulated by two pressing challenges faced by modern societies: population ageing and longevity risk. According to the latest World Population Prospects, virtually every country of the world is experiencing growth in the number and proportion of older persons, resulting from continuous mortality and fertility declines (United Nations, 2019). Furthermore, the demographic transition has been impacting both public and private pension systems, whose retirement liabilities lie between 80 trillions in developed economies due to unexpected mortality improvements (Michaelson and Mulholland, 2014). Funding public policies and retirement products for the elderly becomes increasingly difficult as working-age populations shrink and dependency ratios increase worldwide. The enormous size of unexpected public and private retirement liabilities is the result of overly conservative forecasts of mortality during most recent decades. Despite the great advances in the field of mortality forecasting, including the shift from deterministic to stochastic approaches, currently and widely used methods have repeatedly failed to anticipate the sustained rate of mortality improvements observed in many low-mortality countries. The need for novel models that can predict longevity improvements more accurately than established methodologies is evident and timely. Therefore, this dissertation aims to bring new insights to the analysis and forecasting of human mortality by introducing novel statistical methods that offer different perspectives on mortality developments. This dissertation comprises six chapters, five of which are studies that have been devised to address this goal. Each study takes the form of a research manuscript, which has been published or submitted to scientific journals; furthermore, routines for reproducing the results presented in the thesis have been made publicly available.
The first chapter introduces the basic notions and measuresemployedinthestudyofhumanmortality, reviewsthemaincontributionsinthehistory ofmodellingandforecastingmortality, and provides a short overview of the five studies developed in the thesis.
In Chapter 2, we illustrate a general framework for modelling adult mortality that reconciles the well-known laws of mortality into a single flexible family. Re-parameterizing mortality models in terms of the proposed location–scale family has two important advantages: the model’s parameters have a direct demographic interpretation, and their estimation is more precise due to their lower correlation.
From the third to the fifth chapters, the attention is shifted from mortality rates to age-atdeath distributions as an alternative, yet informative (and neglected), function for modelling and forecasting human mortality. Chapter 3 proposes a relational approach to model and forecast adult mortality by transforming the age-axis of a standard distribution of deaths. The proposed Segmented Transformation Age-at-death Distributions (STAD) model successfully captures mortality developments over age and time, and its forecasts are more accurate and optimistic than those obtained with the seminal Lee-Carter (LC) model (Lee and Carter, 1992) and its extensions. The STAD model is further employed and generalized in the following two chapters.
In Chapter 4, the methodology is extended to the entire age-range. The age-pattern of mortality is first smoothly decomposed into three independent components that operate upon childhood, middle and old ages (as originally proposed by Thiele, 1871). The three components are then modelled and forecast with specialized versions of the STAD model. The resulting forecasts are shown to be more accurate and optimistic than those of traditional and well-established models.
Chapter 5 presents a generalization and application of the STAD methodology for modelling and forecasting cohort mortality data. Models developed to forecast cohort data are very scarce in the literature, and our proposed approach allows us to precisely complete the mortality experience of partially observed cohorts. Finally, Chapter 6 proposes a new extension of the influential LC model that overcomes some of its known drawbacks. Working in a penalized composite link framework, we simultaneously smooth and decompose the mortality pattern into three independent components, which are modelled, estimated and forecast within an LC smooth framework. Fitted and forecast mortality profiles do not show the jaggedness typically displayed by the LC model; furthermore, mortality rates can vary more flexibly across age and time, as they result from a combination of three component-specific schedules of mortality changes
The Average Uneven Mortality index: Building on the ‘e-dagger’ measure of lifespan inequality
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