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

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This systematic review found that case definitions using routine primary care data effectively identify common mental disorders (CMD) with good accuracy and high specificity, though sensitivity varies. Further research is needed to improve case-finding accuracy.

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Area of Science:

  • Medical Informatics
  • Mental Health Research
  • Primary Care

Background:

  • Routine primary care data can unobtrusively identify patients for mental health research.
  • Common mental disorders (CMD) case definitions utilize diagnostic/prescription codes, signs/symptoms, and free text in electronic health records.
  • A systematic review assessing CMD case-finding accuracy in primary care data is absent.

Purpose of the Study:

  • To systematically review and assess the evidence for the case-finding accuracy of CMD case definitions.
  • To compare the accuracy of CMD case definitions against established reference standards.

Main Methods:

  • A systematic review guided by the PRISMA-DTA checklist.
  • Inclusion criteria focused on studies comparing CMD case definitions in routine primary care data to diagnostic interviews, screening instruments, or clinician judgment.
  • Study quality was assessed using the QUADAS-2 tool.

Main Results:

  • Fourteen studies were included, with most exhibiting a high risk of bias.
  • Nine studies focused on depressive disorders, and seven used diagnostic interviews as reference standards.
  • Receiver operating characteristic (ROC) planes showed variable case-finding accuracy, while forest plots indicated high specificity for most case definitions.

Conclusions:

  • CMD case definitions demonstrate good accuracy and low false positive rates in identifying cases within a population.
  • Sensitivity for case definitions is variable, with higher specificity observed for depressive disorders compared to anxiety disorders.
  • Incorporating contextual information into case definitions may enhance overall case-finding accuracy, warranting further research for meta-analysis.