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Cancer is often reported as contributing to the risk of noncancer causes of death. The age variation of these reports was studied using U.S. data on all causes of death listed on death certificates for 1968 to 1986. The occurrence of cancer as a nonunderlying cause of death increased with age and was higher for treatable and slowly growing tumor types. These patterns persisted even if the cancer manifested changes in occurrence. Nonunderlying occurrences were highest in the 85 to 94 age group and were correlated with cancer survival. This suggests increased importance of cancer as a cause of death and a comorbid condition among oldest-old persons. The high rate of occurrence as an associated cause of death suggests that if life expectancy increases due to declines in circulatory and other chronic disease mortality, cancer could become the preeminent cause of death in the United States.

A new multivariate analytical technique for the analysis of medical classification, Grade of Membership analysis, is utilized to examine somatization disorder in a community population. The authors examine whether somatic symptoms will cluster into a clinical syndrome resembling somatization disorder, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-III), if no prior assumptions are made about the inter-relationship of somatic symptoms or their clustering into clinical syndromes. Using respondents in the US National Institute of Mental Health Epidemiologic Catchment Area project of the Piedmont Region of North Carolina, Grade of Membership analysis was applied to all respondents reporting 3 or more somatic symptoms from the somatization disorder section of the Diagnostic Interview Schedule. Seven 'pure' types, roughly analogous to clusters in cluster analysis emerged from the analysis. One 'pure' type in the analysis is nearly identical to DSM-III somatization disorder and is associated with demographic characteristics found among patients with DSM-III somatization disorder. The results indicate that symptoms associated with somatization disorder cluster in a highly predictable fashion and represent a strong validation of the natural occurrence of an entity resembling somatization disorder.

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.

Compartment model approaches have been proposed for the analysis of the age incidence of specific types of cancer. These models represented the age increases in incidence as the result of a compound hazard function where individual level risks were described by the Weibull hazard function and where the population level hazard rate is a continuous mixture of the Weibull hazards. These formulations assumed that the mixing function, which described differences in risk due to different exposure histories, was constant after the age at which the model was first applied. In this paper we show how the mixing distribution can be allowed to change with time reflecting changing exposures. The model is fitted to U.S. lung cancer mortality data where for recent male cohorts there appear to be changing patterns of exposure possibly related to recent declines in male smoking. The implications for future lung cancer mortality trends in the United States are discussed.

Interest has recently emerged in increases in suicide among US teenagers. Nonetheless suicide remains one of the major causes of death among the fastest growing components of the US population--the elderly. We examine the trends in US age-specific suicide rates for the period 1962 to 1981 for four elderly race and sex groups. The trends were analysed using a Poisson regression model which isolated the age, period, cohort effects for suicide mortality for demographic groups. Age, period and cohort effects all significantly contributed to these trends. The race and sex differences were largest for the age pattern of changes in suicide mortality. Of particular interest was the different contribution of each of these factors to recent increases in mortality at advanced ages for black males--a hitherto little recognized or studied trend.

Cross-temporal data on suicide for the period 1962 to 1981 from the National Center for Health Statistics were analyzed. These data were used to estimate period and cohort suicide rates for the four middle-aged and elderly groups (ages 45 to 64, 65 to 74, 75 to 84, and 85 and older) by sex and race. Statistical procedures included multiple decrement life table analyses and cause elimination life table analyses for each year 1968 to 1981. Results from an age, period, cohort analysis of cohort trends 1962 to 1981 also were presented. The analysis showed that suicide continues to be a serious problem in later life especially among the "oldest-old" (those aged 85 and over) and among nonwhite males. It also showed important differences in cohort risks that may strongly affect future suicide risks among elderly adults.

"The geographic mapping of age-standardized, cause-specific death rates is a powerful tool for identifying possible etiologic factors, because the spatial distribution of mortality risks can be examined for correlations with the spatial distribution of disease-specific risk factors. This article presents a two-stage empirical Bayes procedure for calculating age-standardized cancer death rates, for use in mapping, which are adjusted for the stochasticity of rates in small area populations. Using the adjusted rates helps isolate and identify spatial patterns in the rates. The model is applied to sex-specific data on U.S. county cancer mortality in the white population for 15 cancer sites for three decades: 1950-1959, 1960-1969, and 1970-1979. Selected results are presented as maps of county death rates for white males.", (excerpt)