This study was designed to demonstrate the feasibility of forecasting functional health for the elderly. Using life-table techniques, we analyzed the expected remaining years of functional well-being, in terms of the activities of daily living, for noninstitutionalized elderly people living in Massachusetts in 1974. The expected years, or active life expectancy, showed a decrease, from 10 years for those aged 65 to 70 years to 2.9 for those 85 or older. Active life expectancy was shorter for the poor than for others, and women had a longer average duration of expected dependence than men. The measure of active life expectancy provides important information about health at a given population level, in terms other than death. This information can be used for actuarial purposes in planning and policy making. It is also useful in identifying high-risk populations for which preventive health care and medical care can compress morbidity during the last years of life.

Continuity of contact between patients and physicians has become an important criterion of quality primary care. Using three measures of continuity that have appeared in the literature, this article examines, through the use of simulated data and through application to data from five primary care settings, the differences and utility of these approaches for measuring continuity. Further, these measures are applied to four selected diagnoses from each of the five sites, and the observed continuity scores afforded patients with these diagnoses are compared with those expected based on the population. Finally, the scores are correlated with the number of return visits prescribed and kept and with the rate at which laboratory studies are ordered. The findings indicate that site-specific differences in continuity prevail even after adjustments in the number of visits. Continuity based on selected diagnoses is greater, for the most part, than continuity afforded the patient population. Finally, continuity is related to the number of return visits prescribed but not to the number kept or the rate at which laboratory studies are ordered. The implications of continuity for other aspects of quality patient care are discussed.

Abstract. There are currently a number of streaming data analysis systems in research or commercial operation. These systems are generally large-scale distributed systems, but each system operates in isolation, under the control of one administrative authority. We are developing middleware that permits autonomous or semi-autonomous streaming analysis systems (called "sites") to interoperate, providing them opportunities for data access, performance improvements, and reliability far exceeding that available in a single system. Unique characteristics of our system include an architecture for the management of multiple cooperation paradigms depending on the degree of trust and dependencies among the participating sites; a multisite planner that converts user-specified declarative queries into specifications of distributed jobs; and a mechanism for automatic recovery of site failures by redispatching failed pieces of a distributed job. We evaluate our architecture via experiments on a running prototype, and the results demonstrate the advantages of multisite cooperation: collaborative jobs that share resources, even across only a few sites, can produce results 50% faster than independent execution, and jobs on failed sites can be recovered within a few seconds.

System S is a large-scale distributed streaming data analysis environment. Ultimately, we envision that there will be multiple sites running the System S software, each with their own administration and goals. However, cooperation between these sites can frequently be of mutual benefit. We are designing the framework to support numerous sites that can work both independently and in cooperative fashions, with a variety of interaction models such as peer-to-peer or federated. Depending on the degree of site autonomy and the relationships between any given pair of sites, the sites would be able to share data, perform processing on behalf of one another, or even take over tasks of a failed site. Interoperability is complicated by the degree of site autonomy as well as differences in execution environments and security policies. This paper surveys System S, describes its models for site interoperability, and discusses particular design issues such as site failover and heterogeneity.