Abstract
BACKGROUND: 5.8 M living Americans have experienced a stroke at some time in their lives, 780K had either their first or a recurrent stroke this year, and 150K died from strokes this year. Stroke costs about $66B annually in the US, and also results in serious, long-term disability. Therefore, it is prudent to identify all possible risk factors and their effects so that appropriate intervention points may be targeted. METHODS: Baseline (1993-1994) interview data from the nationally representative Survey on Assets and Health Dynamics among the Oldest Old (AHEAD) were linked to 1993-2005 Medicare claims. Participants were 5,511 self-respondents >or= 70 years old. Two ICD9-CM case-identification approaches were used. Two approaches to stroke case-identification based on ICD9-CM codes were used, one emphasized sensitivity and the other emphasized specificity. Participants were censored at death or enrollment into managed Medicare. Baseline risk factors included sociodemographic, socioeconomic, place of residence, health behavior, disease history, and functional and cognitive status measures. A time-dependent marker reflecting post-baseline non-stroke hospitalizations was included to reflect health shocks, and sensitivity analyses were conducted to identify its peak effect. Competing risk, proportional hazards regression was used. RESULTS: Post-baseline strokes occurred for 545 (9.9%; high sensitivity approach) and 374 (6.8%; high specificity approach) participants. The greatest static risks involved increased age, being widowed or never married, living in multi-story buildings, reporting a baseline history of diabetes, hypertension, or stroke, and reporting difficulty picking up a dime, refusing to answer the delayed word recall test, or having poor cognition. Risks were similar for both case-identification approaches and for recurrent and first-ever vs. only first-ever strokes. The time-dependent health shock (recent hospitalization) marker did not alter the static model effect estimates, but increased stroke risk by 200% or more. CONCLUSION: The effect of our health shock marker (a time-dependent recent hospitalization indicator) was large and did not mediate the effects of the traditional risk factors. This suggests an especially vulnerable post-hospital transition period from adverse effects associated with both their underlying health shock (the reasons for the recent hospital admission) and the consequences of their treatments.