How Primary Care Doctors Diagnose Depression: A Decision Tree Analysis Of The Pathways Leading To The Diagnosis Of Depression

Abstract

Background: How primary care physicians (PCPs) decide if a patient has depression is poorly understood. Objectives: The aim of this study was to examine the criteria used by primary care doctors to diagnose depression and the factors influencing their decisions. Methods: 10,179 adult patients were consecutively recruited from the waiting rooms of 59 PCPs in Hong Kong. Patients completed a waiting room survey collecting data on socio-demographics and the Patient Health Questionnaire-9 (PHQ-9). Blinded doctors completed a case report form documenting whether they thought the patient had depression. Data was analyzed using multiple logistic regression and conditional inference decision tree modeling. Results: PCPs diagnosed 1,054 patients with depression. Logistic regression identified gender, age, employment status, past history of depression, family history of mental illness and recent doctor visit as factors associated with diagnosis. Tree analysis revealed different diagnostic pathways for patients with and without past depression. The PHQ-9 symptom tree model revealed low mood, sense of worthlessness, fatigue, sleep disturbance and functional impairment as early classifiers. The PHQ-9 total score tree model revealed cut-off scores of >12 and >15 were used to diagnose patients with and without past depression. Conclusions: The tree analyses demonstrated how PCPs use a hypothetical-deductive problem-solving approach incorporating pre-test probability, using different diagnostic criteria for patients with and without past depression. Diagnostic thresholds may be too low for patients with past depression and too high for patients with no past depression potentially leading to over and under diagnosis of depression. Suicide assessment and prevention may be suboptimal in our setting.