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Biodemography of Aging: Determinants of Healthy Life Span and Longevity 1st ed. 2016 [Hardback]

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  • Formāts: Hardback, 463 pages, height x width: 235x155 mm, weight: 8454 g, 59 Illustrations, color; 23 Illustrations, black and white; XVII, 463 p. 82 illus., 59 illus. in color., 1 Hardback
  • Sērija : The Springer Series on Demographic Methods and Population Analysis 40
  • Izdošanas datums: 30-Aug-2016
  • Izdevniecība: Springer
  • ISBN-10: 9401775850
  • ISBN-13: 9789401775854
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Biodemography of Aging: Determinants of Healthy Life Span and Longevity 1st ed. 2016Palielināt
  • Formāts: Hardback, 463 pages, height x width: 235x155 mm, weight: 8454 g, 59 Illustrations, color; 23 Illustrations, black and white; XVII, 463 p. 82 illus., 59 illus. in color., 1 Hardback
  • Sērija : The Springer Series on Demographic Methods and Population Analysis 40
  • Izdošanas datums: 30-Aug-2016
  • Izdevniecība: Springer
  • ISBN-10: 9401775850
  • ISBN-13: 9789401775854
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789401775854.html
Keywords:
This volume is a critical exposition of the data and analyses from a full decade of rigorous research into how age-related changes at the individual level, along with other factors, contribute to morbidity, disability and mortality risks at the broader population level. After summarizing the state of our knowledge in the field, individual chapters offer enlightening discussion on a range of key topics such as age trajectory analysis in select and general populations, incidence/age patterns of major chronic illnesses, and indices of cumulative deficits and their use in characterizing and understanding the detailed properties of individual aging.

The book features comprehensive statistical analyses of unique longitudinal data sets including the unique resource of the Framingham Heart Study, with its more than 60 years of follow-up. Culminating in penetrating conclusions about the insights gained from the work involved, this book adds much to our understanding of the links between aging and human health.


This summary of a full decade’s intensive research connecting individual age-related physiology to broader health trends features statistical analysis of unique longitudinal data sets and enlightening discussion of a wide range of key topics in the field.

Recenzijas

A key strength of the book is the serious endeavor to go beyond traditional demographic approaches and incorporate more sophisticated mathematical analyses in the quest to integrate models for health, biology, and lifespan. A reasonable job is done of highlighting useful stochastic models for longitudinal data. Another positive is the variety and quality of the data used. (Anthony Medford, Canadian Studies in Population, Vol. 46, 2019)



The book is an effort to conciliate the empirical evidence that indicates that aging is a multidimensional process that involves changesin many variables, such as biomarkers. Thus, the book contributes at integrating biological knowledge and methods with traditional demographic analyses. Population scientists interested in population and biological mechanisms of aging will find this book valuable since it provides an overview of the most recent developments in the field. (Vladimir Canudas-Romo and José Manuel Aburto, European Journal of Population, Vol. 33, 2017)

1 Introduction: The Biodemography of Complex Relationships Among Aging, Health, and Longevity
1(20)
1.1 Introduction
1(6)
1.1.1 Frailty Models
2(1)
1.1.2 Biodemographic Ideas in Genetic Analyses of Human Longevity
3(1)
1.1.3 Evolution of Aging, Health, and Mortality: Many Open Questions
4(1)
1.1.4 Strehler and Mildvan's Model of Aging and Mortality
5(1)
1.1.5 Historical Roots of the Stochastic Process Model
6(1)
1.2 Information on Aging, Health, and Longevity from Available Data: Part I
7(3)
1.3 Statistical Modeling and Other Advanced Methods of Analyzing Data on Aging, Health, and Longevity: Part II
10(3)
1.4 Conclusion
13(1)
References
14(7)
Part I Information on Aging, Health, and Longevity from Available Data
2 Age Trajectories of Physiological Indices: Which Factors Influence Them?
21(26)
2.1 Introduction
21(2)
2.2 Data: The Framingham Heart Study (FHS)
23(2)
2.3 Methods
25(1)
2.4 Results
25(17)
2.4.1 Average Age Trajectories of Physiological Variables
25(3)
2.4.2 Age Trajectories of Standard Deviations (SD) of Physiological Variables
28(1)
2.4.3 Age Patterns of Survival and Physiological Variables for Smokers and Non-smokers
29(2)
2.4.4 Effects of Education on Survival and Average Age Trajectories of Physiological Indices
31(2)
2.4.5 Age Trajectories of Long Lived (LL) and Short Lived (SL) Individuals
33(4)
2.4.6 Effects of Disease on Dynamic Properties of Physiological Indices
37(3)
2.4.7 Effects of Genetic Dose on Age Patterns of Physiological Indices
40(2)
2.5 Conclusion
42(2)
References
44(3)
3 Health Effects and Medicare Trajectories: Population-Based Analysis of Morbidity and Mortality Patterns
47(48)
3.1 Introduction
47(1)
3.2 Data and Methods
48(3)
3.2.1 Data: SEER-M and NLTCS-M
48(2)
3.2.2 Definitions of Dates of Disease Onset and Dates of Recovery/Remission
50(1)
3.3 Results
51(35)
3.3.1 Age Patterns of Age-Associated Disease Incidence
52(2)
3.3.2 Incidence Rates: Comparisons with Other Studies
54(9)
3.3.3 Age-Adjusted Rates: Gender Disparities, Time Trends, and Sensitivity Analysis
63(2)
3.3.4 Disability and Comorbidity Patterns of Incidence Rates
65(5)
3.3.5 Mortality Age Patterns and Medicare Data
70(2)
3.3.6 Recovery or Long-Term Remission
72(2)
3.3.7 Risk Factors for Disease Incidence
74(4)
3.3.8 Mutual Dependence in Disease Risks: Age-Patterns
78(1)
3.3.9 Comorbidity and Multimorbidity
79(2)
3.3.10 Predictive Population Models
81(5)
3.4 Conclusion
86(3)
References
89(6)
4 Evidence for Dependence Among Diseases
95(18)
4.1 Introduction
95(1)
4.2 Data and Methods
96(2)
4.3 Results
98(4)
4.3.1 Empirical Analyses Reveal Negative Correlations among Major Causes of Death
98(2)
4.3.2 A Dependent Competing Risk Model Capturing Negative Correlations Between Causes of Death
100(2)
4.4 Discussion
102(5)
4.4.1 Evidence of Trade-Offs Between Cancer and Aging
103(1)
4.4.2 Trade-Offs Between Cancer and Other Diseases
104(1)
4.4.3 Time Trends in Negative Correlations Between Cancer and Other Diseases
105(1)
4.4.4 Cancer and Anti-aging Interventions
106(1)
4.5 Conclusion
107(1)
References
107(6)
5 Factors That May Increase Vulnerability to Cancer and Longevity in Modern Human Populations
113(30)
5.1 Introduction: Economic Prosperity, Longevity, and Cancer Risk
113(6)
5.2 The Proportion of People Who Are More Susceptible to Cancer May Be Higher in the More Developed World
119(9)
5.2.1 Improved Survival of Frail Individuals
120(2)
5.2.2 Avoiding or Reducing Traditional Exposures
122(1)
5.2.3 Burden of Novel and Nontraditional Exposures
123(5)
5.3 Some of the Factors Associated with Economic Development and the Western Lifestyle May Antagonistically Influence Aging and Vulnerability to Cancer
128(3)
5.3.1 Cancer and Aging: A Trade-Off7
128(1)
5.3.2 Increased Exposure to Growth Factors
128(1)
5.3.3 Later Menopause
129(1)
5.3.4 Giving Birth at Later Age
130(1)
5.4 Conclusion
131(1)
References
132(11)
6 Medical Cost Trajectories and Onset of Age-Associated Diseases
143(20)
6.1 Introduction
143(2)
6.2 Data and Methods
145(2)
6.2.1 Data
145(1)
6.2.2 Date of Disease Onset Definitions
146(1)
6.2.3 Medical Cost Trajectories
147(1)
6.3 Results
147(9)
6.3.1 Medical Cost as Disease Severity
153(1)
6.3.2 Forecasting Models
154(2)
6.4 Discussion
156(4)
References
160(3)
7 Indices of Cumulative Deficits
163(24)
7.1 Introduction
163(1)
7.2 Conceptualization of the Deficits Index
164(1)
7.3 Cross-Sectional Age Patterns of the Deficits Index as Characteristics of Aging-Related Processes
164(1)
7.4 Deficits Indices and Age as Indicators of Aging-Related Processes
165(7)
7.4.1 Frequency Distributions
166(1)
7.4.2 Correlation of the DI and Age
167(1)
7.4.3 DI-Specific Age Patterns for Decedents and Survivors
167(1)
7.4.4 The DI and Age Patterns of Time to Death
168(1)
7.4.5 The DI and Age Specific Mortality Rates
169(1)
7.4.6 Relative Risks of Death
170(2)
7.5 Longitudinal Analyses: The DI as an Indicator and Predictor of Long Life
172(11)
7.5.1 Construction of Long- and Short-Life Phenotypes
173(1)
7.5.2 Longitudinal Changes of the Mean DI in the SL, LLD, and LLA Cohorts
173(1)
7.5.3 The DI as an Indicator of Frailty
174(2)
7.5.4 The Phenotypic Frailty Index (PH) and the DI
176(1)
7.5.5 The PH and DI as Predictors of Death
177(3)
7.5.6 Mid-to-Late Life DIs and Physiological Indices as Characteristics of Long-Term Survival
180(1)
7.5.7 The DI, Endophenotypes, and Long-Term Survival in the FHS
181(2)
7.6 Conclusion
183(1)
References
183(4)
8 Dynamic Characteristics of Aging-Related Changes as Predictors of Longevity and Healthy Lifespan
187(24)
8.1 Introduction
187(3)
8.2 Data and Methods
190(11)
8.2.1 Definitions and Evaluation of Dynamic Risk Factors
190(6)
8.2.2 Statistical Analyses
196(5)
8.3 Results
201(3)
8.3.1 Effects of Individual Dynamics of Physiological Indices at Ages 40-60 on Mortality Risk and Risk of Onset of "Unhealthy Life" at Ages 60+
201(1)
8.3.2 Effects of Dynamic Characteristics of Physiological Indices with Non-monotonic Age Trajectories on Mortality Risk and Risk of Onset of "Unhealthy Life"
201(1)
8.3.3 Effects of Dichotomized Dynamic Characteristics of Physiological Indices with Non-monotonic Age Trajectories
202(1)
8.3.4 Sensitivity Analyses
203(1)
8.4 Discussion
204(3)
8.5 Conclusion
207(1)
References
208(3)
9 The Complex Role of Genes in Diseases and Traits in Late Life: An Example of the Apolipoprotein E Polymorphism
211(20)
9.1 Genes and Diseases in Late Life
211(2)
9.2 The Antagonistic Role of the APOE Gene and Two Types of Sexually Dimorphic Tradeoffs: The Case of CVD and Cancer
213(9)
9.2.1 The FHSO: Tradeoffs in the Effects of the APOE Polymorphism on the Ages at Onset of CVD and Cancer
213(3)
9.2.2 The FHS: The Antagonistic Role of the APOE Polymorphism in CVD and Its Tradeoffs with Cancer
216(2)
9.2.3 The FHS and the FHSO: Aging-Related Heterogeneity in a Changing Environment
218(4)
9.3 Tradeoffs in the Effects of APOE on Risks of CVD and Cancer Influence Human Lifespan
222(6)
9.3.1 The FHS and FHSO: Survival
222(6)
9.4 Conclusion
228(1)
References
228(3)
10 Conclusions Regarding Empirical Patterns of Aging, Health, and Longevity
231(10)
Part II Statistical Modeling of Aging, Health, and Longevity
11 Approaches to Statistical Analysis of Longitudinal Data on Aging, Health, and Longevity: Biodemographic Perspectives
241(22)
11.1 Introduction
241(3)
11.2 Statistical Approaches to Joint Analysis of Longitudinal and Time-to-Event Outcomes
244(9)
11.2.1 Standard Joint Models and Their Extensions
244(6)
11.2.2 The Use of Stochastic Processes to Capture Biological Variation and Heterogeneity in Longitudinal Patterns in Joint Models
250(3)
11.3 Bringing Biology to Statistics: Biodemographic Models for Analysis of Longitudinal Data on Aging, Health, and Longevity
253(2)
References
255(8)
12 Stochastic Process Models of Mortality and Aging
263(22)
12.1 Introduction
263(3)
12.2 Models
266(9)
12.2.1 General Description
266(2)
12.2.2 Estimation Procedure
268(4)
12.2.3 Simulation Studies
272(3)
12.3 Discussion
275(10)
12.3.1 To What Extent Can Mortality Rates Characterize Aging?
275(1)
12.3.2 The Strehler and Mildvan Model
275(1)
12.3.3 Comparing Two Versions of the Stochastic Process Model
276(3)
12.3.4 Modeling Personalized Aging Changes
279(1)
References
279(6)
13 The Latent Class Stochastic Process Model for Evaluation of Hidden Heterogeneity in Longitudinal Data
285(18)
13.1 Introduction
285(1)
13.2 Approaches to the Incorporation of Hidden Heterogeneity in Analyses of Longitudinal and Time-to-Event Data
286(3)
13.3 The Latent Class Stochastic Process Model
289(5)
13.3.1 Specification of the Model
289(2)
13.3.2 Likelihood Estimation Procedure
291(3)
13.4 Simulation Studies
294(4)
13.4.1 Simulation Study for Latent Class Stochastic Process Model
294(1)
13.4.2 Simulation Study for Stochastic Process Model That Ignores Latent Classes
295(3)
13.5 Discussion and Conclusion
298(2)
References
300(3)
14 How Biodemographic Approaches Can Improve Statistical Power in Genetic Analyses of Longitudinal Data on Aging, Health, and Longevity
303(18)
14.1 Introduction
303(3)
14.2 Simulation Studies of the Longitudinal Genetic-Demographic Model
306(4)
14.3 Simulation Studies in Genetic Stochastic Process Model
310(4)
14.4 Discussion
314(4)
References
318(3)
15 Integrative Mortality Models with Parameters That Have Biological Interpretations
321(10)
15.1 Introduction
321(2)
15.2 Conditional Risk of Death and Demographic Mortality Rate
323(1)
15.3 Description of the Processes θt and Yt and Their Connections to t
324(1)
15.4 Evolution of the Conditional Distribution of θt and Yt Among Those Who Survived to Age t
325(2)
15.5 Gaussian Approximation
327(1)
15.6 Conclusion
328(2)
References
330(1)
16 Integrative Mortality Models for the Study of Aging, Health, and Longevity: Benefits of Combining Data
331(22)
16.1 Introduction
331(1)
16.2 Observational Plans and Combining Data
332(11)
16.2.1 Likelihood Function of Life Span Data
332(1)
16.2.2 Longitudinal Data on Physiological Variables: Health Changes Are Not Observed: Observational Plan #1
333(2)
16.2.3 Gaussian Approximation of the Model of Physiological Variables
335(2)
16.2.4 Data on Health Transitions Without Measurements of the Physiological State: Observational Plan #2
337(1)
16.2.5 The Likelihood of the Data on Health Transitions
338(1)
16.2.6 Gaussian Approximation of the Model with Health Transitions
338(1)
16.2.7 Discrete Time Observations of the Physiological State and Health Transitions: Observational Plan #3
339(2)
16.2.8 Gaussian Approximation of the Model of Longitudinal Data on Physiological Variables and Health Transitions
341(2)
16.3 A Simulation Study
343(7)
16.3.1 The Model with Repeated Measurements of a Physiological Variable and Changes in Health State: Observational Plan #3
343(4)
16.3.2 Combining Data with Observational Plans #1 and #2
347(3)
16.4 Discussion and Conclusion
350(1)
References
351(2)
17 Analysis of the Natural History of Dementia Using Longitudinal Grade of Membership Models
353(66)
17.1 Introduction
353(3)
17.2 Methods
356(19)
17.2.1 Model
356(5)
17.2.2 Likelihood
361(2)
17.2.3 Log-Likelihood
363(1)
17.2.4 Derivatives of Log-Likelihood
363(1)
17.2.5 Constrained Log-Likelihood
364(1)
17.2.6 Kuhn-Tucker Conditions
364(1)
17.2.7 Derivatives of Constrained Log-Likelihood
365(2)
17.2.8 Constrained Newton-Raphson Procedures
367(3)
17.2.9 Consistency and Asymptotic Normality
370(3)
17.2.10 Model Testing
373(2)
17.3 Data
375(5)
17.3.1 National Long Term Care Survey
375(2)
17.3.2 Sample Selection
377(3)
17.4 Results
380(27)
17.4.1 Model Selection
380(3)
17.4.2 Model Description
383(12)
17.4.3 Ancillary Analysis: Mortality
395(3)
17.4.4 Ancillary Analysis: Acute and Long-Term Care
398(9)
17.5 Discussion
407(3)
Appendix
410(1)
Synthesis of Known Results Regarding the Consistency of the General (Cross-Sectional) Empirical GoM Model
410(5)
References
415(4)
18 Linear Latent Structure Analysis: Modeling High-Dimensional Survey Data
419(26)
18.1 Introduction
419(1)
18.2 Linear Latent Structure Analysis
420(8)
18.2.1 Structure of Datasets and Population Characteristics
420(1)
18.2.2 LLS Task: Statistical, Geometrical, and Mixing Distribution Points of View
421(2)
18.2.3 Moment Matrix and the Main System of Equations
423(5)
18.3 Computational Algorithm for Estimating LLS Model
428(8)
18.3.1 Moment Matrix Calculation
428(1)
18.3.2 Computational Rank of the Frequency Matrix
429(1)
18.3.3 Finding the Supporting Plane
429(4)
18.3.4 Choice of a Basis
433(1)
18.3.5 Calculation of Individual Conditional Expectations
433(1)
18.3.6 Mixing Distribution
434(1)
18.3.7 Properties of LLS Estimator
434(1)
18.3.8 Clustering
435(1)
18.3.9 Missing Data
436(1)
18.4 Applications
436(5)
18.4.1 Simulation Studies
436(1)
18.4.2 LLS and Latent Class Models
437(1)
18.4.3 LLS and Grade of Membership (GoM) Models
437(2)
18.4.4 Application to the NLTCS Data
439(2)
18.5 Discussion
441(2)
References
443(2)
19 Conclusions Regarding Statistical Modeling of Aging, Health, and Longevity
445(8)
Part III Conclusions
20 Continuing the Search for Determinants of Healthy Life Span and Longevity
453
References
456