Predictors of failure to attend scheduled mammography appointments at a public teaching hospital.
OBJECTIVE
To identify patient, institutional, and physician characteristics that predict failure to attend scheduled mammography appointments.


METHODS
Retrospective chart review.


METHODS
Medicine clinic at an urban public teaching hospital.


METHODS
All 907 women aged 40 years and more scheduled for mammography from March 1990 to June 1991.


RESULTS
The main outcome measure was whether a woman kept her scheduled mammography appointment. Potential predictor variables included patient age, race, marital status, and insurance status; waiting interval to obtain a mammography appointment; and physician gender, level of training, country of training, and native language. The rate of failed mammography appointments was 23%. Univariate analysis showed that appointment failure was associated with age (p = 0.03), with the lowest failure rates (19%) among women aged 60 years and more. Appointment keeping varied significantly by race (p = 0.01), largely because of the higher failure rate among Native American women (36%). Insured women had a failure rate of 22% vs 33% for uninsured women (p = 0.01). The rate of failed appointments varied significantly by waiting interval (p = 0.05), with a peak failure rate of 27% for appointments scheduled 14-27 days in advance. None of the physician variables was associated with appointment failure. Multivariate analysis confirmed these results.


CONCLUSIONS
Interventions to improve completion of breast cancer screening should include additional efforts targeted at groups with high rates of appointment failure, such as women under the age of 60, the uninsured, and Native Americans. Long waiting intervals to obtain mammography appointments may decrease compliance.
Margolis
K L
KL
Hennepin County Medical Center, Minneapolis, MN 55415.
Lurie
N
N
McGovern
P G
PG
Slater
J S
JS
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001071:Appointments and Schedules; D005260:Female; D005544:Forecasting; D006766:Hospitals, County / Q000706:statistics & numerical data; D006784:Hospitals, Teaching / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000706:statistics & numerical data*; D008875:Middle Aged; D008910:Minnesota; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012189:Retrospective Studies; D012959:Socioeconomic Factors; D014850:Waiting Lists