Abstract summary 

Integration of depression treatment into primary care could improve patient outcomes in low-resource settings. Losses along the depression care cascade limit integrated service effectiveness. This study identified patient-level factors that predicted detection of depressive symptoms by nurses, referral for depression treatment, and uptake of counseling, as part of integrated care in KwaZulu-Natal, South Africa.This was an analysis of baseline data from a prospective cohort. Participants were adult patients with at least moderate depressive symptoms at primary care facilities in Amajuba, KwaZulu-Natal, South Africa. Participants were screened for depressive symptoms prior to routine assessment by a nurse. Generalized linear mixed-effects models were used to estimate associations between patient characteristics and service delivery outcomes.Data from 412 participants were analyzed. Nurses successfully detected depressive symptoms in 208 [50.5%, 95% confidence interval (CI) 38.9-62.0] participants; of these, they referred 76 (36.5%, 95% CI 20.3-56.5) for depression treatment; of these, 18 (23.7%, 95% CI 10.7-44.6) attended at least one session of depression counseling. Depressive symptom severity, alcohol use severity, and perceived stress were associated with detection. Similar factors did not drive referral or counseling uptake.Nurses detected patients with depressive symptoms at rates comparable to primary care providers in high-resource settings, though gaps in referral and uptake persist. Nurses were more likely to detect symptoms among patients in more severe mental distress. Implementation strategies for integrated mental health care in low-resource settings should target improved rates of detection, referral, and uptake.

Authors & Co-authors:  Kemp Christopher G CG Mntambo Ntokozo N Bachmann Max M Bhana Arvin A Rao Deepa D Grant Merridy M Hughes James P JP Simoni Jane M JM Weiner Bryan J BJ Gigaba Sithabisile Gugulethu SG Luvuno Zamasomi Prudence Busisiwe ZPB Petersen Inge I

Study Outcome 

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