Advanced illness index: Predictive modeling to stratify elders using self-report data.

OBJECTIVES: Develop a prediction model to identify persons who have an increased risk of dying within the next 36 months, in order to focus additional resources and assessment in areas related to advanced care planning. DESIGN: Retrospective study with a 3-year observation period. SETTING: Integrated, not-for-profit managed care organization. PARTICIPANTS: Beneficiaries aged 65-105 responding to an annual survey (n = 4888). MEASUREMENTS: Survey instrument includes physical function, geriatric syndromes, health care utilization, special equipment use, self-care deficits, caregiving responsibilities, and general health problems. RESULTS: An 11-variable model changed the baseline chi2 from 315.71 (df = 1) to 742.511 (df = 11). The percent of subjects correctly classified was 74.3% and the negative predictive value was 92.2%. CONCLUSION: Advanced Illness Index (AII) model is stable. Characteristic variables used are not easily reversed: the 1997 cohort classified as at-risk consistently remained at risk or died in the subsequent years (1998, 92%; and 1999, 96%) and 92% of those not at-risk survived the next 36 months. Persons at high risk should at a minimum be made aware of the types of integrated home and community-based services available to them should it be needed. They also should be targeted for elicitation of treatment preferences, values, designation of health care proxy, planning, and advanced care directives.

Usefulness of a single item in a mail survey to identify persons with possible dementia: a new strategy for finding high-risk elders.

The objective of this study was to examine the characteristics of elderly persons who responded positively to a question about "severe memory problems" on a mailed health questionnaire yet were missed by the existing health risk algorithm to identify vulnerable elderly persons. A total of 324,471 respondents aged 65 and older completed a primary care health status questionnaire that gathered clinical information to quickly identify members with functional impairment, multiple chronic diseases, and higher medical care needs. The respondents were part of a large, integrated, not-for-profit managed care organization that implemented a model of care for elders using a uniform risk identification method across eight regions. Respondents with severe memory problems were compared to general respondents by morbidity, geriatric syndromes, functional impairments, service utilization, sensory impairments, sociodemographic characteristics, and activities of daily living. Of the respondents, 13,902 persons (4.3%) reported severe memory problems; the existing health risk algorithm missed 47.1% of these. When severe memory problems were included in the risk algorithm, identification increased from 11% to 13%, and risk prevalence by age groups ranged from 4.4% to 40.5%; one third had severe memory problems, a finding that was fairly consistent within age groups (28.4% to 36.5%). A question about severe memory problems should be incorporated into population risk-identification techniques. While false-negative rates are unknown, the false-positive rate of a self-report mail survey appears to be minimal. Persons reporting severe memory problems clearly have multiple comorbidities, higher prevalence of geriatric syndromes, and greater functional and sensory impairments.