Reappraisal of the epidemiology of giant cell arteritis in Olmsted County, Minnesota, over a fifty-year period

Arthritis Rheum. 2004 Apr 15;51(2):264-8. doi: 10.1002/art.20227.


Objective: To investigate time trends in the incidence and survival of giant cell arteritis (GCA) over a 50-year period in Olmsted County, Minnesota.

Methods: Using the unified record system at the Mayo Clinic, we identified all incident cases of GCA first diagnosed between 1950 and 1999. Incidence rates were estimated and adjusted to the 1980 United States white population for age and sex. The annual incidence rates were graphically illustrated using a 3-year centered moving average. Survival rates were computed and compared with the expected rates in the population.

Results: There were 173 incident cases of GCA during the 50-year study period. Of these, 79% were women and the mean age at diagnosis was 74.8 years. The overall age- and sex-adjusted incidence per 100,000 persons 50 years of age or older was 18.8 (95% confidence interval [95% CI] 15.9-21.6). Incidence was higher in women (24.4; 95% CI 20.3-28.6) than in men (10.3; 95% CI 6.9-13.6). Incidence rates increased significantly over the study period (P = 0.017); in particular, a progressive increase was observed from 1950 to 1979; subsequently, no substantial increases in incidence rates were observed. A cyclic pattern of annual incidence rates was apparent, with evidence of 6 peak periods. Survival among individuals with GCA was not significantly different from that expected in the population (P = 0.80).

Conclusions: The incidence of GCA increased over the first 3 decades of the study, then remained stable over the last 20 years. The previously observed cyclic pattern of annual incidence rates was still apparent over a 50-year period. Overall survival in GCA was similar to that in the population.

MeSH terms

  • Age Distribution
  • Aged
  • Aged, 80 and over
  • Female
  • Giant Cell Arteritis / mortality*
  • Humans
  • Incidence
  • Male
  • Middle Aged
  • Minnesota / epidemiology
  • Sex Distribution
  • Survival Analysis