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Demography of Aging (1994) / Chapter Skim
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7 Medical Demography: Interaction of Disability Dynamics and Mortality
Pages 217-278

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From page 217...
... Moreover, death in the old is due mostly to chronic diseases, with long histories. The models that apply to younger people, therefore, are not suitable for the analysis of the old.
From page 218...
... But medical demography lost impetus as research into the epidemiology of chronic disease specialized. That work, however, had its limitations in its reliance on case-control studies or studies of occupational cohorts and on longitudinal studies of selected populations from which it was difficult to estimate national rates of health events.
From page 219...
... The need for medical-demographic analyses to describe health changes in the old has two aspects. First, to describe the effects of preventative and other public health interventions we need models that can both identify relevant inputs say, risk factors for chronic diseases and the provision of health services and accurately predict the influence of those inputs on health.
From page 220...
... Throughout, we discuss the use of primary data in estimation and of secondary data in determining model structure, because seldom will a single data set have the information necessary to estimate all relevant parameters for models of chronic disease and functional loss. This presentation assumes the reader has been exposed to the use of life tables, survival functions, hazard models, and common matrix and vector notation used in regression analysis.
From page 221...
... If continuous hazard functions with specific parameters are to be used to analyze health events, they must be substantively rationalized. The Weibull hazard function assumes that an event emerges after an individual takes m
From page 222...
... is the age-specific mortality rate; b is a scale parameter; t is age; and m is the shape parameter.
From page 223...
... , and by relating individual risks to a distribution of marginal risk factors. If the hazard is a convex function of risk factors, then the age trajectories of population and individual hazards are related by Jensen's inequality E(,uf xi~)
From page 224...
... Few longitudinal studies meet this requirement. Conversely, mortality data are insufficient to study survival at late ages when age reporting error may significantly affect the distribution of times of age at death (Kestenbaum, 19921.
From page 225...
... Second, a model's validity is based on its biological rationale. If there is no biological rationale to constrain model choice, a model fitting the data can always be found because of the inability to identify hazard functions in mortality data (Manton et al., 1994a)
From page 226...
... Strong selection can generate a rapid rise in mortality in "mid" age (e.g., age 30-80) , with mortality approaching a constant at late ages (Perks, 1932; Thatcher, 19921.
From page 227...
... Clinical studies suggest that some risk factors are significant to late ages; (in one example, treatment of isolated systolic hypertension reduced the risk of stroke; SHEP Cooperative Research Group, 19911. The terms in equation (1)
From page 228...
... A U- or J-shaped function describes the relation of mortality to many risk factors (Jacobs et al., 1992; Neaton et al., 19921. The curvature of the quadratic is a function of 0, so the rate of change of risk factor effects on mortality that is related to age reflects interactions with unobserved variables, Zi,.
From page 229...
... As age increases from 65-95, with ~ and xi fixed, mortality increases. Changes in ~ are illustrated in Table 7-1 for the 34-year follow-up of 10 risk factors from the Framingham Study: 7 dimensions identified from 27 functional and physical performance items for the 1982-1984 NLTCS and mortality for 1982-1986; and 7 dimensions from the 1982-1984-1989 NLTCS and mortality for 1982-1991.
From page 231...
... Thus, interactions of risk factors are constant within age strata. Since the inverse of equation (4)
From page 232...
... , x t are risk-factor means and v is the risk-factor covariance matrix at t. The vector of , at mortality-adjusted risk factor means is It = (Xt- Vt Bt It)
From page 233...
... Because ~ represents the average age-related effects of Zip, it reflects unmeasured differences between sources of population data just as it adjusts for unobserved variable effects when different risk factors, or measurement intervals, are used. Dependent risks can be identified using time-varying state variables measured before death (Tsiatis, 19754.
From page 234...
... For example, the answer to the question of whether shortfalls in Social Security trust funds are as important as, or more important than, excesses could reduce uncertainty about decisions on marginal tax rates. Thus, to deal with cohort effects in medical demography, one needs both macrolevel compartment models represented by differential equations with parameters specified from biomedical data, or microlevel state-space models estimated from longitudinal data in which measurements of multiple risk factors are made.
From page 235...
... SOURCE: Data are from the Framingham Heart Study.
From page 236...
... . Despite improvements in the quality of data for late ages, reliance on assumptions about the age trajectory of mortality is common.
From page 237...
... Below we briefly review the characteristics of several chronic conditions or syndromes important in elderly populations. Dementia Diseases that are prevalent at late ages include Alzheimer's and related dementias.
From page 238...
... Thus, when cholesterol rises postmenopausally for some women, risks could decline.6 Nutritionally Related Disorders: Systemic Factors A newly appreciated aspect of chronic disease is the effect of nutrition on antenatal development. The data suggest that maternal malnutrition, which produces low birth and placental weight, affects the fetal development of multiple organs and that these effects become manifest above age 65 as chronic diseases (Barker, 1990; Barker and Martyn, 1992; Barker et al., 1989, l991a,b, 1992a,b,c)
From page 239...
... Immunologically Related Diseases: Interactions of Viral and Nutritional Factors Nutrition affects the immune system. Some chronic diseases, are due to early viral infections; for example, cytomegalia virus is implicated in CVD (Mozar et al., 1990~.
From page 240...
... CVD is reduced by estrogen replacement, a result correlated with improvement in status on multiple risk factors (Nabulsi et al., 19934. However, when that therapy is combined with vitamin D, lean women showed no improvement in cholesterol (Mysrup et al., 1992~.
From page 241...
... Life-cycle effects differ by sex and race. Fertility may affect mortality from chronic diseases, like CVD and breast cancer (Beral, 1985; MacMahon, 1973~.
From page 242...
... . We estimated male and female life tables (for the 1982-1989 NLTCS and mortality for 1982-1991)
From page 243...
... At early ages, death is often due to the catastrophic failure of one system. At late ages, it reflects the overwhelming of homeostasis by the accumulation of loss of function in multiple organs.
From page 244...
... a' an o of An 4~ o ~ CQ to so Go _4 1 ~ ~ au .
From page 245...
... ~ ~ oo~ ~ ~Do ~ .
From page 246...
... _~ 85 Age Females 95 1 05 85 Age 1 05 FIGURE 7-4 Comparing discrete to fuzzy-state models. SOURCE: Data are from the 1982, 1984, and 1989 National Long-Term Care Surveys.
From page 247...
... 50 o cd ¢ o o .
From page 248...
... The 1982, 1984, and 1989 NLTCS and the 1982-1991 Medicare mortality data were used to construct cohort life tables based on disability scores. Cohort life expectancy at age 65 was 15.6 years for males and 20.9 years for females in 1989 increases from 14.2 years and 18.6 years, respectively, in 1982-1984.
From page 249...
... SOURCE: Data are from the 1982, 1984, and 1989 National LongTerm Care Surveys.
From page 250...
... Overall life expectancy and active life expectancy increased for women more than for men for each education group because of the rapid growth of high-education groups above 85. Between 1980 and 2015 the proportion of persons 85 to 89 with 8 or fewer years of education is projected to decline significantly (Preston, 1992~.
From page 251...
... The data and the model results are similar. In both, severe disability declines at late ages.
From page 252...
... Genetic influences are polygenic, and the expression of genetically determined traits depends on environment. To estimate the effects of genetics on population health
From page 253...
... Such model-based integration of data, and indirect inference, is often used in medical demography. Often, problems involving latent variables must be dealt with in genetic models: some are time-constant effects such as fixed genetic factors, and some are timevariable effects.
From page 254...
... I . 65 75 85 95 105 Age Females 65 75 Age 85 95 105 FIGURE 7-6 Comparing observed monthly mean scores to state-space model, pre dictions for the category "healthy" using combined survey data.
From page 255...
... , 65 75 2.00% 1.00% - _' 0.00% 65 75 I 85 95 105 Age Females _/ it' 85 95 105 Age FIGURE 7-7 Comparing observed monthly mean scores to state-space model, predictions for the category "frail" using combined survey data. SOURCE: Data are from the 1982, 1984, and 1989 National Long-Term Care Surveys.
From page 256...
... 0% 20. 0% 0.0% 1 80.0% 60.0% it, 40.0% a' /: 20.0% 0.0% 65 75 85 Age Females W : ~ Males it\ ~3 State Space Model · 1982 N LTCS - ~1984 NLTCS 0 1989 N LTCS 9 5 1 0 5 Age FIGURE 7-8 Comparisons of the observed smoothed mean monthly scores for the category "healthy," in the three surveys to the predicted smoothed scores in the state-space model.
From page 257...
... o.o~- . , 65 75 85 g5 1 05 Age 4 ~ / Sac Space In 0~ ~o^e~ FIGORE 7-9 Comparing the smoothed mean monthly scores for the category "beabby~ Tom the three surveys combined [a [be values predicted by the state-space model.
From page 258...
... 00% 65 75 6.00% 5. 00% 4.00% 3.00% _~< Males _' At, ~ 85 95 105 Age Females fit' $,: 65 7585 95 105 Age FIGURE 7-10 Comparing the smoothed mean monthly scores to the state-space model prediction for the category "frail" for all three surveys combined.
From page 259...
... 259 V: Cal Do o Do 4 o Cal .
From page 260...
... 260 ·_' 3 V: v Ed oo Cal o no an o .~ Cal no ¢ o Cal a' 4 .
From page 261...
... Modifications of the Elston-Shinant algorithm can represent covariates in these relations. A model based on the correlation of continuously distributed risk factors between a random pair of relatives and its effect on the probability of being affected by a risk factor, modeled as a logistic, is less
From page 262...
... Weiss (1990) suggests that, to analyze age effects, one must relate genotype to disease risk by defining a hazard with the distribution of risk factors conditional on genotype modeled as a separate factor, or, At I g)
From page 263...
... The identifiability of components of the state-space model depends on the length of follow-up and measurement density. In the hazard model, in addition to risk conditional on A, ~ represents the average age effects of unobserved factors on xi' with We)
From page 264...
... found that the concordance rate for CVD risk factors decreased with age. As discussed above, genetically determined breast cancer is expressed early.
From page 265...
... Medical demography is conceptually and procedurally distinct from the epidemiology of chronic disease. The epidemiologist attempts to discern causal relations between risk factors and disease endpoints, often using general statistical models to test the significance of relations.
From page 266...
... Thus, many biostatistical methods do not deal with the long-term age changes that are the focus of medical demography. Of greater importance to the medical demographer than to epidemiologists or biostatisticians is the effect of functional change in the population.
From page 267...
... Simmonds 1991a Fetal and placental size and risk of hypertension in adult life. British Medical Journal 301:259-262.
From page 268...
... Swaminathan 1992 Comparison of enalapril and nifedipine in treating non-insulin dependent diabetes associated with hypertension: One year analysis. British Medical Journal 2(305)
From page 269...
... Journal of Clinical Epidemiology 44:947-953. 1992 A longitudinal study on glucose tolerance and other cardiovascular risk factors: Associations within an elderly population.
From page 270...
... Raskind 1990 The association between head trauma and Alzheimer's disease. American Journal of Epidemiology 131:491 -501.
From page 271...
... Winter 1991 Fetal and infant growth and impaired glucose tolerance at age 64. British Medical Journal 303: 1019- 1022.
From page 272...
... Watson 1990 Editorial commentary: Early age at breast cancer onset A genetic and oncologic perspective. American Journal of Epidemiology 131 :984-986.
From page 273...
... Manton, K.G., and E Stallard 1988 Chronic Disease Modeling: Measurement and Evaluation of the Risks of Chronic Disease Processes.
From page 274...
... Wu, M Szklo 1993 Association of hormone-replacement therapy with various cardiovascular risk factors in postmenopausal women.
From page 275...
... Wentworth, and Multiple Risk Factor Intervention Trial Research Group 1992 Serum cholesterol level and mortality findings for men screened in the multiple risk factor intervention trial. Archives of Internal Medicine 152:1490-1500.
From page 276...
... Grim 1991 Concordance of ischemic heart disease in the NHLBI Twin Study after 14-18 years of follow-up. Journal of Clinical Epidemiology 44:797-805.
From page 277...
... Tosteson, and L Goldman 1991 Expected gains in life expectancy from various coronary heart disease risk factor modifications.
From page 278...
... Manton, and E Stallard 1981 Longitudinal models for chronic disease risk: An evaluation of logistic multiple regression and alternatives.


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