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Open Access Research article

Practice nurse involvement in primary care depression management: an observational cost-effectiveness analysis

Jodi Gray1*, Hossein Haji Ali Afzali1, Justin Beilby2, Christine Holton3, David Banham4 and Jonathan Karnon1

Author Affiliations

1 Discipline of Public Health, The University of Adelaide, Adelaide, South Australia

2 Faculty of Health Sciences, The University of Adelaide, Adelaide, South Australia

3 Discipline of General Practice, The University of Adelaide, Adelaide, South Australia

4 Office for Research Development, Health System Performance Division, SA Health, Adelaide, South Australia

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BMC Family Practice 2014, 15:10  doi:10.1186/1471-2296-15-10

Published: 14 January 2014

Abstract

Background

Most evidence on the effect of collaborative care for depression is derived in the selective environment of randomised controlled trials. In collaborative care, practice nurses may act as case managers. The Primary Care Services Improvement Project (PCSIP) aimed to assess the cost-effectiveness of alternative models of practice nurse involvement in a real world Australian setting. Previous analyses have demonstrated the value of high level practice nurse involvement in the management of diabetes and obesity. This paper reports on their value in the management of depression.

Methods

General practices were assigned to a low or high model of care based on observed levels of practice nurse involvement in clinical-based activities for the management of depression (i.e. percentage of depression patients seen, percentage of consultation time spent on clinical-based activities). Linked, routinely collected data was used to determine patient level depression outcomes (proportion of depression-free days) and health service usage costs. Standardised depression assessment tools were not routinely used, therefore a classification framework to determine the patient’s depressive state was developed using proxy measures (e.g. symptoms, medications, referrals, hospitalisations and suicide attempts). Regression analyses of costs and depression outcomes were conducted, using propensity weighting to control for potential confounders.

Results

Capacity to determine depressive state using the classification framework was dependent upon the level of detail provided in medical records. While antidepressant medication prescriptions were a strong indicator of depressive state, they could not be relied upon as the sole measure. Propensity score weighted analyses of total depression-related costs and depression outcomes, found that the high level model of care cost more (95% CI: -$314.76 to $584) and resulted in 5% less depression-free days (95% CI: -0.15 to 0.05), compared to the low level model. However, this result was highly uncertain, as shown by the confidence intervals.

Conclusions

Classification of patients’ depressive state was feasible, but time consuming, using the classification framework proposed. Further validation of the framework is required. Unlike the analyses of diabetes and obesity management, no significant differences in the proportion of depression-free days or health service costs were found between the alternative levels of practice nurse involvement.

Keywords:
Depression; Practice nurse; Primary care; Collaborative care; Cost-effectiveness; RAC-E analysis