
pmid: 8650714
Background and Purpose Although previous studies have shown that geographic variation in the decline of stroke mortality rates may be an important contributor to the changing geographic distribution of stroke mortality in the United States, some concern has been raised that this phenomenon may be model dependent. This study examines the geographic variation in the decline of stroke mortality rates in the United States with the use of both additive and multiplicative models. Methods National Center for Health Statistics and Bureau of the Census data were used to assess regional-level temporal trends of underlying-cause stroke mortality rates in the United States for 1979 through 1989. Both additive and multiplicative models were fit to the data. Results Underlying-cause stroke mortality rates have declined fairly steadily in all regions of the United States and for all race-sex groups, although there was significant regional variation in the rate of decline during the period 1979 through 1989. The South, which initially had the highest rates, had the most rapid decline for all race-sex groups when either additive or multiplicative models were used. Conclusions From 1979 through 1989 there was significant geographic variation in the rate of decline of stroke mortality rates, with the most rapid rates of decline in the South. As a result, there has been a decrease in interregional variation in stroke mortality rates.
Male, Models, Statistical, Northwestern United States, Age Factors, Black People, Southeastern United States, United States, White People, Midwestern United States, Black or African American, Cerebrovascular Disorders, Sex Factors, New England, Cause of Death, Linear Models, Humans, Regression Analysis, Female
Male, Models, Statistical, Northwestern United States, Age Factors, Black People, Southeastern United States, United States, White People, Midwestern United States, Black or African American, Cerebrovascular Disorders, Sex Factors, New England, Cause of Death, Linear Models, Humans, Regression Analysis, Female
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