Resource allocation for depression management in general practice: A simple data-based filter model

Background This study aimed to illustrate the potential utility of a simple filter model in understanding the patient outcome and cost-effectiveness implications for depression interventions in primary care. Methods Modelling of hypothetical intervention scenarios during different stages of the treatment pathway was conducted. Results Three scenarios were developed for depression related to increasing detection, treatment response and treatment uptake. The incremental costs, incremental number of successes (i.e., depression remission) and the incremental costs-effectiveness ratio (ICER) were calculated. In the modelled scenarios, increasing provider treatment response resulted in the greatest number of incremental successes above baseline, however, it was also associated with the greatest ICER. Increasing detection rates was associated with the second greatest increase to incremental successes above baseline and had the lowest ICER. Conclusions The authors recommend utility of the filter model to guide the identification of areas where policy stakeholders and/or researchers should invest their efforts in depression management.

Automated summary

The study illustrated the potential utility of a simple filter model in understanding patient outcomes and cost-effectiveness implications for depression interventions in primary care. Modervated scenarios showed that increasing detection rates had the second greatest increase in successful depression remission above baseline and the lowest incremental cost-effectiveness ratio, while increasing provider treatment response resulted in the greatest number of incremental successes above baseline but also had the greatest cost-effectiveness ratio.