Unassisted detection of XXXX by GPs: who is most likely to be misclassified?

@entity1 @entity308
BACKGROUND: Meta-analyses indicate 50% of cases of @entity308 are not detected by GPs. It is important to examine @entity1 and GP characteristics associated with misclassification so that systems can be improved to increase accurate detection and optimal management for groups at risk of @entity308 . OBJECTIVE: To examine @entity1 and GP characteristics associated with GP misclassification of @entity308 for @entity1 classified by the @entity1 Health Questionnaire-9 as depressed. METHODS: A cross-sectional study within general practices in two states of Australia. GPs completed a one-page paper and pencil survey indicating whether they thought each @entity1 was clinically depressed. @entity1 completed a computer tablet survey while waiting for their appointment to provide demographic information and indicate @entity308 status. Chi-square analyses were used to determine whether @entity1 and GP characteristics were associated with a false-negative and false-positive result. The probability of misclassification was modelled using Generalized Estimating Equations to account for clustering of @entity1 . RESULTS: Fifty GPs from 12 practices participated. GPs completed surveys for 1880 @entity1 . Younger @entity1 aged 25-44, and those with a health care card were less likely to have a false-negative assessment. @entity1 with 0-3 GP visits in the past 12 months, and those with private health insurance were less likely to have a false-positive assessment. GPs who worked five sessions or fewer per week were more likely to make false-positive assessments.
