ABSTRACT
A better understanding of processes and mechanisms linking human aging with changes in health status and survival requires methods capable of analyzing new data that take into account knowledge about these processes accumulated in the field. In this paper, we describe an approach to analyses of longitudinal data based on the use of stochastic process models of human aging, health, and longevity which allows for incorporating state of the art advances in aging research into the model structure. In particular, the model incorporates the notions of resistance to stresses, adaptive capacity, and "optimal" (normal) physiological states. To capture the effects of exposure to persistent external disturbances, the notions of allostatic adaptation and allostatic load are introduced. These notions facilitate the description and explanation of deviations of individuals' physiological indices from their normal states, which increase the chances of disease development and death. The model provides a convenient conceptual framework for comprehensive systemic analyses of aging-related changes in humans using longitudinal data and linking these changes with genotyping profiles, morbidity, and mortality risks. The model is used for developing new statistical methods for analyzing longitudinal data on aging, health, and longevity.
Subject(s)
Aging , Health , Life Expectancy , Longevity , Longitudinal Studies/statistics & numerical data , Proportional Hazards Models , HumansABSTRACT
BACKGROUND: We employ an approach based on the elaborated frailty index (FI), which is capable of taking into account variables with mild effect on the aging, health and survival outcomes, and investigate the connections between the FI, chronological age and the aging-associated outcomes in the elderly. METHODS: Cross-sectional analysis of pooled data from the National Long Term Care Survey (NLTCS) assessing health and functioning of the U.S. elderly in 1982, 1984, 1989, 1994, and 1999. RESULTS: Distributions of frequency, residual life span, mortality rate, and relative risk of death are remarkably similar over age and FI. Coefficients of correlation between FI and age are low both for males (0.127, p<.01) and females (0.221, p<.01). The FI-specific age patterns show deceleration at advanced ages. The FI can provide order of magnitude better resolution in estimating mean remaining life span compared to age. Males have smaller FI than females while males' mortality risks are higher. For short-time horizons, the FI and age are largely independently associated with mortality risks. CONCLUSIONS: The FI: (i) can be considered as an adequate sex-specific indicator of the aging-associated processes in the elderly, (ii) can characterize these processes independently of age, and (iii) is a better characteristic of the aging phenotype than chronological age.
Subject(s)
Aging/physiology , Health Care Surveys , Health Status Indicators , Age Factors , Aged , Aged, 80 and over , Data Interpretation, Statistical , Female , Humans , Logistic Models , Male , Middle Aged , Mortality , Proportional Hazards Models , Risk , Sex Factors , Surveys and Questionnaires , United States/epidemiologyABSTRACT
Efforts to model the health effects of low-dose ionizing radiation (IR) have often focused on cancer. Meanwhile, significant evidence links IR and age-associated non-cancer diseases. Modeling of such complex processes, which are not currently well understood, is a challenging problem. In this paper we briefly overview recent successful attempts to model cancer on a population level and propose how those models may be adapted to include the impact of IR and to describe complex non-cancer diseases. We propose three classes of models which we believe are well suited for the analysis of the health effects in human populations exposed to low-dose IR. These models use biostatistical/epidemiological techniques and mathematical formulas describing the biological mechanisms of the impact of IR on human health. They can combine data from multiple sources and from distinct levels of biological/population organization. The proposed models are intrinsically multivariate and non-linear and capture the dynamic aspects of health change.
Subject(s)
Chronic Disease , Models, Biological , Neoplasms, Radiation-Induced/epidemiology , Radiation, Ionizing , Radioisotopes/adverse effects , Bayes Theorem , Biophysical Phenomena , Biophysics , Dose-Response Relationship, Radiation , Female , Genetics, Population , Humans , Male , Models, Genetic , Neoplasms, Radiation-Induced/etiology , Nonlinear Dynamics , Population , Stochastic ProcessesABSTRACT
In this paper we review recently-developed extension frailty, quadratic hazard, stochastic process, microsimulation, and linear latent structure models, which have the potential to describe the health effects of human populations exposed to ionizing radiation. We discuss the most common situations for which such models are appropriate. We also provide examples of how to estimate the parameters of these models from datasets of various designs. Carcinogenesis models are reviewed in context of application to epidemiologic data of population exposed to ionizing radiation. We also discuss the ways of how to generalize stochastic process and correlated frailty models for longitudinal and family analyses in radiation epidemiology.
Subject(s)
Health , Models, Theoretical , Population , Radiation, Ionizing , Family , Humans , Longitudinal Studies , Medicare , Neoplasms, Radiation-Induced/epidemiology , Proportional Hazards Models , Risk Factors , Stochastic Processes , United StatesABSTRACT
The health effects of ionizing radiation on human populations are often analyzed using epidemiological statistical methods. Because of the complexity of the health consequences of ionizing radiation and the prolonged period during which the consequences emerge, we propose to evaluate these health effects using mathematical models that are based on the best theoretical reasoning and prior biological evidence about disease mechanisms. We believe this will improve the ability of the model to identify health effects and reduce erroneous inferences.
Subject(s)
Demography , Radiation Injuries/epidemiology , Radiation, Ionizing , Dose-Response Relationship, Radiation , Humans , Likelihood Functions , Models, Biological , Models, Statistical , Models, Theoretical , Population , Power Plants , Radiation Dosage , Radiation Protection , Radiation Tolerance , Radioactive Hazard Release , Radiobiology , Risk , Risk Assessment , UkraineABSTRACT
Despite the wealth of longitudinal data on the health dynamics of human populations, information on covariates (risk factors) changes in those studies has not been systematically and fully exploited. In this work we use the 46-year follow-up of the Framingham Heart Study to analyze dynamics of these risk factors in survival models that go far beyond the standard linear dynamic formulation. We focus on improving the inferences about the physiology of human aging processes and its plasticity and on modeling state trajectories for individuals considering the effect of nonlinear interactions among covariates. We find that using standard statistical methods to construct models describing the age dependence of health status might give rise to surprising results with highly "diluted" dynamics, but with significantly improved statistical criteria. It is found that problems with the dynamics are a consequence of the intrinsic nonlinear nature of these models. We show that evolution of the risk factors measured in the Framingham study is more complicated for females than for males (i.e., female health status is more sensitive to nonlinear interactions among risk factors). We suggest that this is due to the rapid rate of decline of estrogen production after menopause.
