Congress Abstracts 2005

84

PREDICTORS OF COMPLEMENTARY AND ALTERNATIVE THERAPY USE BY CANCER PATIENTS. Judith Fouladbakhsh, MSN, APRN, BC, AHN-C, CHTP, Wayne State University, Detroit, MI; and Manfred Stommel, PhD, Barbara Given, PhD, RN, FAAN, and Charles Given, PhD, Michigan State University, East Lansing, MI.

Complementary and alternative therapies (CAT) are used by cancer patients along with “mainstream” medical treatments.

Estimates of CAT use by cancer patients range from 7%–64%. It is important for oncology nurses to understand factors that lead to use of CAT because of implications for nursing care.

This study sought to determine predictors of use of CAT by cancer patients.

Andersen’s Behavioral Model of Health Services Use was employed. The study focused on three key aspects of the model: predisposing, enabling and need-for-care factors, which are relevant to the prediction of CAT use.

A secondary analysis of two NIH federally funded panel studies was conducted. The sample included lung, breast,colon and prostate cancer patients (N = 968), interviewed in two panel waves (three months apart). Study participants were asked if they used any of the following complementary/alternative therapies: herbs/supplements, spiritual healing, relaxation, massage, acupuncture, energy healing, hypnosis, therapeutic spas, alternative diets, audio/videotapes, osteopathic, homeopathic or chiropractic treatment. Most of the sample (97%) received “mainstream” medical treatment: surgery (65%), chemotherapy (38.5%) radiation therapy (54%).

The dependent, dichotomous variable for this analysis was: use or no-use of any of the identified complementary/alternative therapies at time of interviews.

Independent variables analyzed in the model included:

Predisposing: gender, age, race, education, marital status;

Enabling: income, health insurance status, caregiver presence, geographic location;

Need-for-care: cancer stage, site, symptoms, treatment, perceived health need.

Binary Logistic Regression was the primary statistical model employed in the analysis, which focused on the between-subject differences in CAT use (disregarding changes in use over time). A stepwise procedure was followed in which potential predictor variables were excluded from the model if their p-value exceeded 0.10.

Statistically significant predictors of at least one-time CAT use were: gender (women’s odds of CAT use is 2.5 times larger than men’s), marital status (single and divorced persons are more likely to use CAT), income (the higher the income, the more likely to use CAT), and cancer treatment (patients who underwent surgery were more likely to use CAT). Implications for nursing practice will be discussed.