Case-finding for common mental disorders in primary care using routinely collected data: a systematic review

Abstract

Purpose: Case-finding for common mental disorders (CMD) in routine data unobtrusively identifies patients for mental health research. There is absence of a review of studies examining CMD-case-finding accuracy in routine primary care data. CMD-case definitions include diagnostic/prescription codes, signs/symptoms, and free text within electronic health records. This systematic review assesses evidence for case-finding accuracy of CMD-case definitions compared to reference standards. Methods: PRISMA-DTA checklist guided review. Eligibility criteria were outlined prior to study search; studies compared CMD-case definitions in routine primary care data to diagnostic interviews, screening instruments, or clinician judgement. Studies were quality assessed using QUADAS-2. Results: Fourteen studies were included, and most were at high risk of bias. Nine studies examined depressive disorders and seven utilised diagnostic interviews as reference standards. Receiver operating characteristic (ROC) planes illustrated overall variable case-finding accuracy across case definitions, quantified by Youden’s index. Forest plots demonstrated most case definitions provide high specificity. Conclusion: Case definitions effectively identify cases in a population with good accuracy and few false positives. For 100 anxiety cases, identified using diagnostic codes, between 12 and 20 will be false positives; 0–47 cases will be missed. Sensitivity is more variable and specificity is higher in depressive cases; for 100 cases identified using diagnostic codes, between 0 and 87 will be false positives; 4–18 cases will be missed. Incorporating context to case definitions may improve overall case-finding accuracy. Further research is required for meta-analysis and robust conclusions.