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E-grāmata: Exploring the Health State of a Population by Dynamic Modeling Methods

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Exploring the Health State of a Population by Dynamic Modeling MethodsPalielināt
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This book introduces and applies the stochastic modeling techniques and the first exit time theory in demography through describing the theory related to the health state of a population and the introduced health state function. The book provides the derivation and classification of the human development stages. The data fitting techniques and related programs are also presented. Many new and old terms are explored and quantitatively estimated, especially the health state or “vitality” of a population, the deterioration and related functions, as well as healthy life expectancy. The book provides the appropriate comparative applications and statistics as connecting tools accompanied by the existing literature, and as such it will be a valuable source to demographers, health scientists, statisticians, economists and sociologists.
1 Life Expectancy, Deterioration Function and Application to Halley Breslau Data
1(20)
1.1 Introduction
1(1)
1.2 The Deterioration Function: Further Analysis
2(5)
1.3 Stability of the Deterioration Function Characteristics
7(4)
1.4 Estimation of the Life Expectancy by the DTR System
11(2)
1.5 The Halley Life Table
13(5)
1.6 The Program
18(1)
1.7 Summary and Conclusions
18(3)
References
19(2)
2 A Quantitative Method for Estimating the Human Development Stages Based on the Health State Function Theory and the Resulting Deterioration Process
21(22)
2.1 Introduction: The Health State Function Theory
21(4)
2.2 Human Development Stages Based on the Health State Function
25(3)
2.3 Human Development Stages Based on the Death Probabilities
28(1)
2.4 An Application to Disease Data in USA
29(2)
2.5 Human Development Age Groups Based on the Deterioration Function
31(4)
2.6 Further Analysis and Quantification
35(2)
2.7 Final Classification Groups
37(3)
2.8 Conclusions
40(3)
References
42(1)
3 Estimating the Healthy Life Expectancy from the Health State Function of a Population in Connection to the Life Expectancy at Birth
43(24)
3.1 Introduction
43(3)
3.2 Related Theory
46(1)
3.3 Application to WHO Data
47(8)
3.4 Conclusions
55(12)
References
64(3)
4 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
67(58)
4.1 Introduction
67(2)
4.1.1 Further Details
68(1)
4.2 The Mortality Approach
69(7)
4.2.1 The Simplest Model
69(4)
4.2.2 Estimation Without a Model (Direct Estimation)
73(2)
4.2.3 More Details: The Gompertz and the Weibull Distributions
75(1)
4.3 The Health State Models
76(8)
4.3.1 The Health State Distribution
76(2)
4.3.2 An Important Extension: The Simplest IM-Model
78(4)
4.3.3 The Health State Function and the Relative Impact on Mortality
82(2)
4.4 Applications
84(38)
4.4.1 Comparative Application for the World and World Regions
84(2)
4.4.2 Application to USA 2008
86(2)
4.4.3 Application in Japan 1986--2004
88(2)
4.4.4 Application in the World
90(32)
4.5 Discussion and Conclusions
122(3)
References
122(3)
5 The Health State Status of the US States for the Period 1989--1991 (Decennial Life Tables)
125(16)
5.1 The Health State Theory: Mortality Versus Health State
125(4)
5.2 Application to United States states
129(9)
5.3 Summary and Conclusions
138(3)
References
138(3)
6 Life Expectancy at Birth, Estimates and Forecasts in the Netherlands (Females)
141(16)
6.1 Introduction
141(1)
6.2 General Theory on Stochastic Modeling of Health State
142(2)
6.3 Modeling and Applications in the Netherlands (Females): First Method of Forecasts
144(5)
6.4 Parameter Analysis of the IM-Model: Second Method of Forecasts (Classical)
149(4)
6.5 Conclusions
153(4)
References
154(3)
7 Remarks and Findings on "Evidence for a Limit to Human Lifespan"
157(18)
7.1 The Mortality Probability Density Function and Related Modeling
157(3)
7.2 Maximum Death Age
160(5)
7.3 Data Description
165(1)
7.4 Data Transformation, Fit and Projections
166(3)
7.5 Use of Life Table Data
169(2)
7.6 Model Application Method
171(1)
7.7 Fit and Simulation
171(1)
7.8 The Methodology
172(2)
7.9 Summary and Conclusions
174(1)
References
174(1)
8 Stages of Human Development: The Life-Span Approach and Related Applications and Comparisons
175(30)
8.1 Introduction
175(1)
8.2 The Health State Approach and Applications
176(13)
8.3 Human Development Age Groups Based on the Deterioration Function
189(2)
8.4 Applications
191(1)
8.5 Discussion
192(13)
Appendix
195(9)
References
204(1)
9 Derivation and Validation of the Health State Function Form of a Population
205(10)
9.1 Derivation of the Final Health State Form
205(2)
9.2 First Application
207(1)
9.3 Second Application
208(3)
9.4 Final Health State Form
211(1)
9.5 Validation of the Final Health State Form by Stochastic Simulations
211(2)
9.6 Summary and Conclusions
213(2)
References
214(1)
10 The Health Status of a Population: Health State and Survival Curves and HALE Estimates
215(18)
10.1 Introduction
215(4)
10.1.1 The Related Theory
215(3)
10.1.2 The General Case
218(1)
10.2 The Weitz and Fraser Paper Revisited
219(2)
10.2.1 Deterministic and Stochastic Case
219(2)
10.2.2 The Main Points in Estimating the Health State of a Population
221(1)
10.3 The More General Case
221(2)
10.4 Connection of the Health State Curve to the Survival Curve
223(2)
10.5 Comparing the Health State or Health Status and Life Expectancy
225(1)
10.5.1 The Time Development of the Standard Deviation σ
225(1)
10.6 Health State, Life Expectancy and HALE Estimates
226(3)
10.7 Further Discussion
229(1)
10.8 Conclusion
230(3)
References
231(2)
11 Theoretical Approach to Health State Modeling
233(6)
11.1 The Stochastic Model
233(1)
11.2 General Solution
234(1)
11.3 Specific Solution
235(1)
11.4 A First Approximation Form
236(1)
11.5 A Second Approximation Form
236(1)
11.6 Summary and Conclusions
237(2)
References
237(2)
Index 239
Christos H. Skiadas, PhD, was the founder and director of the Data Analysis and Forecasting Laboratory at the Technical University of Crete. He is chair of the Demographics Workshop series, the Applied Stochastic Models and Data Analysis Conference series and the Chaotic Modeling and Simulation Conference series. He has published more than 80 papers, three monographs, and 18 books, including probability, statistics, data analysis and forecasting. His research interests include innovation diffusion modeling and forecasting, life table data modeling, healthy life expectancy estimates, and deterministic, stochastic, and chaotic modeling. Charilaos Skiadas, PhD, is an associate professor in mathematics and computer science at Hanover College. His research interests encompass a wide array of mathematical and computing topics, ranging from algebraic geometry to statistics and programming languages to data science and health state modeling.
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