Objective: Sub-Saharan Africa has the highest HIV prevalence worldwide and depression is highly prevalent among those infected. The negative impact of depression on HIV outcomes highlights the need to identify and treat it in this population. A model for doing this in lower-resourced settings involves task-shifting depression treatment to primary care; however, HIV-infected individuals are often treated in a parallel HIV specialty setting. We adapted a model of task-shifting, measurement-based care (MBC), for an HIV clinic setting and tested its feasibility in Tanzania. MBC involves measuring depressive symptoms at meaningful intervals and adjusting antidepressant medication treatment based on the measure of illness.
Method: Twenty adults presenting for care at an outpatient HIV clinic in Tanzania were enrolled and followed by a nurse care manager who measured depressive symptoms at baseline and every 4 weeks for 12 weeks. An algorithm-based decision-support tool was utilized by the care manager to recommend individualized antidepressant medication doses to participants' HIV providers at each visit.
Results: Retention was high and fidelity of the care manager to the MBC protocol was exceptional. Follow through of antidepressant prescription dosing recommendations by the prescriber was low. Limited availability of antidepressants was also noted. Despite challenges, baseline depression scores decreased over the 12-week period.
Conclusions: Overall, the model of algorithm-based nursing support of prescription decisions was feasible. Future studies should address implementation issues of medication supply and dosing. Further task-shifting to relatively more abundant and lower-skilled health workers, such as nurses' aides, warrants examination.