Addressing the 'optimal cutoff' Bias in Primary Care Testing.

Area of Science:

• Psychometrics
• Clinical Decision Making
• Mental Health Assessment

Background:

• Ordinal tests are crucial for identifying mental disorders in primary care, typically validated against binary standards.
• Diagnostic accuracy is assessed using Sensitivity and Specificity at various cutoffs, with an 'optimal' cutoff maximizing population separation (e.g., Youden's statistic).
• This 'optimal' cutoff, while statistically sound for research, lacks clinical utility for individual patient screening and case-finding, hindering personalized post-test decision-making.

Purpose of the Study:

• To investigate the "optimality bias" in ordinal mental health tests.
• To demonstrate how this bias impacts clinical practice and patient-centered care.
• To propose a method for eliminating the optimality bias.

Main Methods:

• Empirical investigation of the optimality bias magnitude.
• Utilized DEPRESSD Individual Participant dataset-based meta-analyses.
• Included Edinburgh Postnatal Depression Scale and Patient Health Questionnaire 9.

Main Results:

• Developed an online Clinical Information Aid.
• The aid provides instant access to all test metric outputs across all cutoffs.
• Crucially, it offers metrics at the patient's precise score, addressing individual needs.

Conclusions:

• The "optimality bias" in ordinal test interpretation can be empirically established.
• This bias can be effectively eliminated through the use of comprehensive, individualized test metric outputs.
• Clinical tools can be developed to provide personalized results, supporting informed patient decision-making.