arXiv subjects

Christos H Skiadas

Publications and source records attributed to Christos H Skiadas.

10 recordsLinked to original sources

arXiv2019

Direct Healthy Life Expectancy Estimates from Life Tables with a Sullivan Extension. Bridging the Gap Between HALE and Eurostat Estimates

The analytic derivation of a more general model of survival-mortality and the estimation of a parameter bx related to the Healthy Life Years Lost (HLYL) is followed with the formulation of a computer program providing results similar to those of the World Health Organization for the Healthy Life Expectancy (HALE) and the corresponding HLYL estimates. This program is an extension of the classical life table including more columns to estimate the cumulative mortality, the average mortality, the person life years lost and finally the HLYL parameter bx. Evenmore, a further extension of the Excel program based on the Sullivan method provides estimates of the Healthy Life Expectancy at every year of the lifespan for five different types of estimates that are the Direct, WHO, Eurostat, Equal and Other. Estimates for several countries are presented. It is also presented a methodology and a program to bridge the gap between the World Health Organization (HALE) and Eurostat (HLE) healthy life expectancy estimates. The latest version of this program (SKI-6) appear in the Demographics2020 website.

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arXiv2019

Relation of the Weibull Shape Parameter with the Healthy Life Years Lost Estimates: Analytic Derivation and Estimation from an Extended Life Table

Matsushita et al (1992) have done an interesting finding. They observed that the shape parameter of the Weibull model presented systematic changes over time and age when applied to mortality data for males and females in Japan. They have also estimated that this parameter was smaller in the 1891-1898 data in Japan compared to the 1980 mortality data and they presented an illustrative figure for females where the values of the shape parameter are illustrated on the diagram close to the corresponding survival curves. However, they have not provided an analytical explanation of this behavior of the shape parameter of the Weibull model. The cumulative hazard of this model can express the additive process of applying a force in a material for enough time before cracking. To pass to the human data, the Weibull model and the cumulative hazard can express the additive process which disabilities and diseases cause the human organism during the life span leading to healthy life years lost. In this paper we further analytically derive a more general model of survival-mortality in which we estimate a parameter related to the Healthy Life Years Lost (HLYL) and leading to the Weibull model and the corresponding shape parameter as a specific case. We have also demonstrated that the results found for the general HLYL parameter we have proposed provides results similar to those provided by the World Health Organization for the Healthy Life Expectancy (HALE) and the corresponding HLYL estimates. An analytic derivation of the mathematical formulas is presented along with an easy to apply Excel program. This program is an extension of the classical life table including four more columns to estimate the cumulative mortality, the average mortality, the person life years lost and finally the HLYL parameter bx. The latest versions of this program appear in the Demographics2019 website

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arXiv2019

Modeling the Health Expenditure in Japan, 2011. A Healthy Life Years Lost Methodology

The Healthy Life Years Lost Methodology (HLYL) is introduced to model and estimate the Health Expenditure in Japan in 2011. The HLYL theory and estimation methods are presented in our books in the Springer Series on Demographic Methods and Population Analysis vol. 45 and 46 titled: Exploring the Health State of a Population by Dynamic Modeling Methods and Demography and Health Issues: Population Aging, Mortality and Data Analysis. Special applications appear in Chapters of these books as in The Health-Mortality Approach in Estimating the Healthy Life Years Lost Compared to the Global Burden of Disease Studies and Applications in World, USA and Japan and in Estimation of the Healthy Life Expectancy in Italy Through a Simple Model Based on Mortality Rate by Skiadas and Arezzo. Here further to present the main part of the methodology with more details and illustrations, we develop and extend a life table important to estimate the healthy life years lost along with the fitting to the health expenditure in the related case. The application results are quite promising and important to support decision makers and health agencies with a powerful tool to improve the health expenditure allocation and the future predictions.

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arXiv2017

The Health Status of a Population estimated: The History of Health State Curves

Following the recent publication of our book on Exploring the Health State of a Population by Dynamic Modeling Methods in The Springer Series on Demographic Methods and Population Analysis (DOI 10.1007/978-3-319-65142-2) we provide this brief presentation of the main findings and improvements regarding the Health State of a Population. (See at: http://www.springer.com/gp/book/9783319651415). Here the brief history of the Health State or Health Status curves for individuals and populations is presented including the main references and important figures along with an illustrated Poster (see Figure 13 and http://www.smtda.net/demographics2018.html). Although the Survival Curve is known as long as the life tables have introduced, the Health State Curve was calculated after the introduction of the advanced stochastic theory of the first exit time. The health state curve is illustrated in several graphs either as a fit curve to data or produced after a large number of stochastic realizations. The Health State, the Life Expectancy and the age at mean zero health state are also estimated. Keywords: Health State and Survival Curves, Health status of a population, First exit time stochastic theory, stochastic simulations of health state, Age at Maximum Curvature, Healthy Life Expectancy and HALE, Standard Deviation, Health State Curves, Maximum human lifespan and other.

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arXiv2016

The Health Status of a Population: Health State and Survival Curves, and HALE Estimates

In this paper we explore the very important case of finding a health measure in the lines of the survival curve but independent of the standard deviation parameter. This is done by estimating the health state curve and calculating the total area under the curve. An interesting comparison with the survival curves is done. The health status measure (HSM or HS) resulting is of the form of the life expectancy (LE) as it is expressed in terms of years of age. The HS is independent of the standard deviation. When a perfect rectangularization of the survival curve appear, the LE is equal to the age where the health state curve approaches zero. The provided HS form gives rise to an interesting classification of various countries. The results are close to those obtained by the LE estimator with an interesting reorganization of country ranking. We also provide illustrations of our estimated Health Status along with comparative presentations of the Healthy Life Expectancy and the HALE measures in several countries. Theoretical issues are provided and important stochastic simulations are done along with reproduction of the death probability density forms.

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arXiv2016

The Health-Mortality Approach in Estimating the Healthy Life Years Lost Compared to the Global Burden of Disease Studies and Applications

We propose a series of methods and models in order to explore the Global Burden of Disease Study and the provided healthy life expectancy HALE estimates from the World Health Organization WHO based on the mortality mx of a population provided in a classical life table and a mortality diagram. Our estimates are compared with the HALE estimates for the World territories and the WHO regions along with providing comparative results with to findings of Chang, Molla, Truman et al. (2015) on the Differences in healthy life expectancy for the US population by sex, race or ethnicity and geographic region in 2008 and from Yong and Saito (2009) regarding Trends in healthy life expectancy in Japan. From the mortality point of view we have developed a simple model for the estimation of a characteristic parameter b related to the healthy life years lost to disability and providing full application details along with characteristic parameter selection and stability of the coefficients. We also provide a direct estimation method of the parameter b from the life tables. We straighten the importance of our methodology by proposing and applying estimates of the parameter b by using the Gompertz and the Weibull models. From the Health State point of view we summarize the main points of the first exit time theory to life table data and present the basic models starting from the first related model published by Janssen and Skiadas (1995). Even more we develop the simpler 2-parameter health state model and an extension of a model expressing the infant mortality to a 4-parameter model which is the simpler model providing very good fitting on the logarithm of the force of mortality. More important is the use of the Health State Function and the relative impact on mortality to find an estimate for the healthy life years lost to disability.

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arXiv2015

Verifying the HALE measures of the Global Burden of Disease Study: Quantitative Methods Proposed

To verify the Global Burden of Disease Study and the provided healthy life expectancy (HALE) estimates from the World Health Organization (WHO) we propose a very simple model based on the mortality μx of a population provided in a classical life table and a mortality diagram. We use the abridged life tables provided by WHO. Our estimates are compared with the HALE estimates for the World territories and the WHO countries. Even more we have developed the related simple program in Excel which provides immediately the Life Expectancy, the Loss of Healthy Life Years and the Healthy Life Expectancy estimate. We also apply the health state function theory to have more estimates and comparisons. The results suggest improved WHO estimates in recent years for the majority of the cases. Keywords: Health state function, Healthy life expectancy, Mortality Diagram, Loss of healthy years, LHLY, HALE, DALE, World Health Organization, WHO, Global burden of Disease, Health status.

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arXiv2013

A Quantitative Method for Estimating the Human Development Stages by Based on the Health State Function Theory and the Resulting Deterioration Process

The Health State Function theory is applied to find a quantitative estimate of the Human Development Stages by defining and calculating the specific age groups and subgroups. Early and late adolescence stages, first, second and third stages of adult development are estimated along with the early, middle and old age groups and subgroups. We briefly present the first exit time theory used to find the health state function of a population and then we give the details of the new theoretical approach with the appropriate applications to support and validate the theoretical assumptions. Our approach is useful for people working in several scientific fields and especially in medicine, biology, anthropology, psychology, gerontology, probability and statistics. The results are connected with the speed and acceleration of the deterioration of the human organism during age as a consequence of the changes in the first, second and third differences of the Health State Function and of the Deterioration Function. Keywords: Human development stages, Deterioration, Deterioration function, Human Mortality Database, HMD, World Health Organization, WHO, Quantitative methods, Health State Function, Erikson's stages of psychosocial development, Piaget method, Sullivan method, Disability stages, Light disability, Moderate disability, Severe disability stage, Old ages, Critical ages.

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arXiv2011

A Life Expectancy Study based on the Deterioration Function and an Application to Halley's Breslau Data

Further to the proposal and application of a stochastic methodology and the resulting first exit time distribution function to life table data we introduce a theoretical framework for the estimation of the maximum deterioration age and to explore on how "vitality," according to Halley and Strehler and Mildvan, changes during the human lifetime. The mortality deceleration or mortality leveling-off is also explored. The effect of the deterioration over time is estimated as the expectation that an individual will survive from the deterioration caused in his organism by the deterioration mechanism. A method is proposed and the appropriate software was developed for the estimation of life expectancy. Several applications follow. The method was applied to the Halley life table data of Breslau. Extrapolations are done showing a gradual improvement of vitality mechanisms during last centuries.

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arXiv2011

Properties of a Stochastic Model for Life Table Data: Exploring Life Expectancy Limits

In this paper we explore the life expectancy limits by based on the stochastic modeling of mortality and applying the first exit or hitting time theory of a stochastic process. The main assumption is that the health state or the "vitality", according to Strehler and Mildvan, of an individual is a stochastic variable and thus it was introduced and applied a first exit time density function to mortality data. The model is used to estimate the development of mortality rates in the late stages of the human life span, to make better fitting to population mortality data including the infant mortality, to compare it with the classical Gompertz curve, and to make comparisons between the Carey med-fly data and the population mortality data estimating the health state or "vitality" functions. Furthermore, we apply the model to the life table data of Italy, France, USA, Canada, Sweden, Norway and Japan, and we analyze the characteristic parameters of the model and make forecasts.

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