Preliminary Psychologic Survey of Orofacial Outpatients. Part 1: Predictors of Anxiety or Depression

Aims: To identify predictors for anxiety and depression in orofacial outpatients and to investigate the patients' compliance rate in taking a series of psychologic tests. Methods: Three thousand six hundred sixty-six patients completed a battery of questionnaires. These consisted of items inquiring about sex, age, past history of disease, presence of pain, the Hospital Anxiety and Depression Scale (HADS), the Eysenck Personality Questionnaire Short Form (S-EPQ), a Japanese dental version of the McGill Pain Questionnaire (JDMPQ), a visual analog scale (VAS) of pain, pain duration, and diagnosis. After univariate analyses had determined those variables with significant differences between an over-probable group (OPG, HADS scores >= 8) and an absent group (AG, HADS scores 8), we estimated the odds ratios of these variables for OPG as independent variables, and every variable was adjusted between the independent variables by multiple logistic regression models. Results: For anxiety, 3 variables were independently related to the OPG and considered to be meaningful: age 30 or older, neuroticism score on the S-EPQ, and selection of the JDMPQ pain expression term "sickening." For depression, 4 variables were independently related to the OPG and considered to be meaningful: age 30 or older, neuroticism and extroversion scores on the S-EPQ, and selection of the JDMPQ pain expression term "sickening." The compliance rate for the tests was under half of the patients (3,666 of 7,542 patients). Conclusion: Although the predictability for anxiety or depression by some baseline parameters is considered to be low, age, personality traits, and choice of certain pain expression terms are useful predictors of anxiety or depression. The improvement of the compliance rate for psychologic screening will be a future challenge for Japanese clinics managing orofacial patients. Parole chiave: questionnaires, anxiety, depression, predictor, logistic model, regression analysis