Biomedical subjects

K G Manton

Publications and source records attributed to K G Manton.

168 records · Page 10Linked to original sources

PubMedDate not supplied

Estimating hidden morbidity via its effect on mortality and disability.

The applicability of the theory of partially observed finite-state Markov processes to the study of disease, morbidity, and disability is explored. A method is developed for the continuous updating of parameter estimates over time in longitudinal studies analogous to Kalman filtering in continuous valued continuous time stochastic processes. It builds on a model of filtering of incompletely observed finite-state Markov processes subject to mortality due to Yashin et al. The method of estimation is based on maximum likelihood theory and the incompleteness in the observation of the process is dealt with by applying missing information principles in maximum likelihood estimation.

Aged↗

PubMedDate not supplied

A statistical analysis of the classification of depression in a mixed community and clinical sample.

Depressive symptoms in three samples are assessed using grade-of-membership analysis to clarify the distribution of depressive symptoms across traditional affective diagnoses. The technique is used to examine whether depressive symptoms and symptoms frequently associated with depressive disorders cluster into recognizable syndromes or pure types that parallel current operational diagnoses. Three hundred and ninety subjects were studied to address the question: among a mixed population with a range of depressive symptoms, will syndromes resembling endogenous depression and demoralization emerge from the range of presentation of depressive symptoms? A single pure type is nearly identical to the DSM-III classification of major depression with melancholia. No such pure type emerged that resembled demoralization.

Adult↗

PubMedDate not supplied

Markov Network Analysis: suggestions for innovations in covariance structure analysis.

Studies of aging offer special methodological challenges to the researcher in that he must often examine the change of multiple correlated variables over time. We present a set of procedures that are specifically designed to model change in such multivariate situations. These procedures, which we will call Markov Network Analysis, are directly applicable to modeling change from longitudinal or serial data. In such cases, the parameters of the model have dynamic interpretations, e.g., as coefficients in positive or negative feedback loops. In cross-sectional data, one cannot directly estimate the dynamic coefficients but the model does show how certain dynamic interpretations can be made. Statistically, maximum likelihood estimation procedures are developed and presented. In the development of the statistical model, it is shown how the bias of sequential hypothesis testing, a frequent occurrence in the estimation of complex covariance structure models, may be reduced.

Age Factors↗

PubMedDate not supplied

CVD mortality, 1968-1978: observations and implications.

U.S. multiple cause of death data were examined for the period 1968-1978. Specifically, the role of cerebrovascular disease mortality as an underlying and associated cause of death was studied. The number of deaths where cerebrovascular disease was cited as the underlying cause of death declined much faster than the number of deaths where cerebrovascular disease was cited as the associated cause of death. This trend is indicative of a possible change in the role of cerebrovascular disease as an associated cause of death. Cause elimination life tables were constructed for cerebrovascular disease as the underlying cause of death and as any cause of death. In the general population, eliminating stroke as a cause of death is projected to have less impact in 1978 than in 1968, for men than for women, and for whites than for nonwhites. Tables were also constructed to examine the life expectancy gains for the group of individuals who died of cerebrovascular disease. For these individuals, the gain in life expectancy at birth ranges from 9 years for white males to 18 years for nonwhite females.

Actuarial Analysis↗

PubMedDate not supplied

Gender differences in the cross-sectional and cohort age dependence of cause-specific mortality: the United States, 1962 to 1995.

OBJECTIVE: Gender- and age-specific mortality rates were calculated to determine the role of behavioral and biologically based differences in health. DESIGN: Changes in age-specific mortality rates were calculated for males and females across select time periods, in addition to gender- and cause-specific mortality age trajectories for birth cohorts for heart disease, stroke, and lung and breast cancer. Differences between cohort mortality age trajectories and the age pattern of mortality rates were related to socioeconomic differences between genders and to biological risk factors. SUBJECTS: U.S. males and females aged 30 to 95+ over the 34-year period 1962 to 1995. METHODS: Gender- and age-specific mortality rates were computed and displayed using the logarithmic transform of the rates for five-year age categories. RESULTS: Major gender cross-sectional mortality differences were found for heart disease, stroke, and lung cancer, with males being disadvantaged except for stroke. Less variation was found for lung cancer in recent male cohorts compared to large rates of change for recent female cohorts. Female breast cancer mortality showed modest variation, with most of the cohort differential in risk in postmenopausal breast cancer risk. Relative risk of lung cancer with age was greater for females. CONCLUSION: Differences in male and female cohort mortality rates underlie many of the period changes for heart disease, stroke, and lung cancer. Cohort differences were largest for lung cancer, although the cohort-specific smoking patterns these rates imply could also be responsible for some of the heart disease and mortality differences. The breast cancer mortality rates showed consistency with the concept of two distinct etiologies for breast cancer: premenopausal and postmenopausal. Large cohort increases in female lung cancer mortality may be alterable by behavioral measures and education.

Cause of Death↗

PubMedDate not supplied

Mortality model based on delays in progression of chronic diseases: alternative to cause elimination model.

For the analysis of the impact of major chronic diseases on a population, a life table model is proposed in which the age at death due to specific cause (chronic disease) is postponed. Even though many of the major causes of death related to intrinsic aging processes are impossible to eliminate, these causes might be significantly delayed or retarded. To illustrate the use of this model, the effects of a delay of 5, 10, and 15 years in deaths due to three chronic degenerative diseases (cancer, ischemic heart disease, and stroke) are calculated for specific race-sex components of the U.S. population in 1969. These calculations show that even moderate delays in the progression of major chronic diseases will yield a sizable portion of the total gain in longevity that would be available if the diseases were totally eliminated. Thus, they demonstrate that a life table model based on cause delay provides a more biomedically plausible representation of the health impact of a chronic disease on a population than does the cause elimination life table model. Additionally, the cause-delay model provides a mechanism for incorporating the likely effects of medical innovation on survival.

Actuarial Analysis↗