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Objective: The performance of standard comorbidity scores to control confounding is poorly defined in health care utilization data across elderly populations. We sought to evaluate and rank the performance of comorbidity scores across selected U.S. and Canadian elderly populations using health care utilization databases.Design: Cross-population validation study.Participants: Study participants were residents age 65 years or older who had prescription drug coverage through state-funded programs selected from several large health care utilization databases available to the investigators: British Columbia, BC (N = 141,161), New Jersey, NJ (N = 235,881), and Pennsylvania, PA (N = 230,913).Measurements: We calculated 6 commonly used comorbidity scores for all subjects during the baseline year (1994 for NJ and PA, and 1995 for BC). These included scores based on diagnoses (Romano, Deyo, D'Hoore, Ghali) and prescription drugs (CDS-1, CDS-2). The study outcome was 1-year mortality. The performance of scores was measured by c-statistics derived from multivariate logistic regression that included age and gender.Main Results: Across these 4 large elderly populations, we found the same rank order of performance in predicting 1-year mortality after including age and gender in each model: Romano (c-statistic 0.754 to 0.771), Deyo (c-statistic 0.753 to 0.768), D'Hoore (c-statistic 0.745 to 0.760), Ghali (c-statistic 0.733 to 0.745), CDS-1 (c-statistic 0.689 to 0.738), CDS-2 (c-statistic 0.677 to 0.718), and age and gender alone (c-statistic 0.664 to 0.681). Performance was improved by an average of 6% by adding the number of different prescription drugs received during the past year.Conclusions: Performance ranking of 6 frequently used comorbidity scores was consistent across selected elderly populations. We recommend that investigators use these performance data as one important factor when selecting a comorbidity score for epidemiologic analyses of health care utilization data. [ABSTRACT FROM AUTHOR]

When subjects are measured twice, once at each of two symmetrical locations or times, stability of responses in the absence of treatment within subjects, together with comparability of untreated responses between subjects, is often viewed as supporting a conclusion that differences between treated and control responses reflect effects actually caused by the treatment. The degree to which this intuitive argument is formally correct is explored is several related models: a multivariate Normal model, a nonparametric model defined by symmetries, an analogues randomized experiment, and sensitivity analysis model for observational studies in which treatments are not randomly assigned to subjects, nor to locations within subjects. Card and Kreuger's study of the employment effects of the minimum wage is used to illustrate the methods. [ABSTRACT FROM AUTHOR]

Objective: To analyze hospitalization patterns of persons with AIDS (PWAs) in a multi-state/multi-episode continuous time duration framework.Data Sources: PWAs on Medicaid identified through a match between the state's AIDS Registry and Medicaid eligibility files; hospital admission and discharge dates identified through Medicaid claims.Study Design: Using a Weibull event history framework, we model the hazard of transition between hospitalized and community spells, incorporating the competing risk of death in each of these states. Simulations are used to translate these parameters into readily interpretable estimates of length of stay, the probability that a hospitalization will end in death, and the probability that a nonhospitalized person will be hospitalized within 90 days.Principal Findings: In multivariate analyses, participation in a Medicaid waiver program offering case management and home care was associated with hospital stays 1.3 days shorter than for nonparticipants. African American race and Hispanic ethnicity were associated with hospital stays 1.2 days and 1.0 day longer than for non-Hispanic whites; African Americans also experienced more frequent hospital admissions. Residents of the high-HIV-prevalence area of the state had more frequent admissions and stays two days longer than those residing elsewhere in the state. Older PWAs experienced less frequent hospital admissions but longer stays, with hospitalizations of 55-year-olds lasting 8.25 days longer than those of 25-year-olds.Conclusions: Much socioeconomic and geographic variability exists both in the incidence and in the duration of hospitalization among persons with AIDS in New Jersey. Event history analysis provides a useful statistical framework for analysis of these variations, deals appropriately with data in which duration of observation varies from individual to individual, and permits the competing risk of death to be incorporated into the model. Transition models of this type have broad applicability in modeling the risk and duration of hospitalization in chronic illnesses. [ABSTRACT FROM AUTHOR]

This study attempts to verify age reporting on the death certificate for the "extreme aged" population and to evaluate the accuracy of recent death rates for this group in light of the findings. In addition, methods used and problems encountered in carrying out a record linkage study, particularly a low match rate, are identified. A sample of more than three thousand death records was selected from those filed for decedents age 85 and over in Pennsylvania and New Jersey in the 1968 to 1972 period. Death certificates of 53 percent of whites and 30 percent of nonwhites were linked to the 1900 U.S. Census. A comparison of age on the death certificate with the age reported for the same individual in the census record showed a high level of agreement for whites, except at ages 100 and over; for nonwhites, however, age agreement levels were substantially lower. Within racial groups, there was little difference by sex in agreement on age. These results, corroborating those of earlier studies, make it clear that nonwhite mortality at the oldest ages has been consistently understated in official statistics. [ABSTRACT FROM AUTHOR]

Focuses on the statistics and science investigation program of the Highstown High School in New Jersey for all students. Application and experimentation of the learned principles of statistical analysis in analyzing a medical situation such as the relationship between fetal alcohol exposure and infant development; Curriculum of the program.