Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings

Version 1 : Received: 9 May 2022 / Approved: 10 May 2022 / Online: 10 May 2022 (15:40:48 CEST)

A peer-reviewed article of this Preprint also exists.

Waqas, A.; Sikander, S.; Malik, A.; Atif, N.; Karyotaki, E.; Rahman, A. Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings. J. Pers. Med. 2022, 12, 1046. Waqas, A.; Sikander, S.; Malik, A.; Atif, N.; Karyotaki, E.; Rahman, A. Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings. J. Pers. Med. 2022, 12, 1046.

Journal reference: J. Pers. Med. 2022, 12, 1046
DOI: 10.3390/jpm12071046

Abstract

Task sharing approaches are challenged by the barriers fundamental to the use of non-specialists who lack specialist mental health training required to triage the candidates who could benefit from task-shared treatments. However, these challenges could be offset by using standardized and easy-to-implement algorithmic devices (e.g., nomograms) to help with the targeted dissemination of interventions. Therefore, the present investigation posits a prognostic model and a nomogram to predict the prognosis of perinatal depression among women in rural Pakistan. This secondary analysis utilizes data based on 903 pregnant women with depression who participated in a cluster randomized controlled trial that tested the effectiveness of the Thinking Healthy Program in rural Rawalpindi, Pakistan. The participants were recruited from 40 union councils in two sub-districts of Rawalpindi and randomly assigned to intervention and enhanced usual care. Sixteen sessions of the THP intervention were delivered by trained community health workers to women with depression over pregnancy and the postnatal period. A trained assessment team used the Structured Clinical Interview for the DSM-4 current major depressive episode module to diagnose depression at the baseline and post-intervention. The intervention received by the participants emerged as the most significant predictor in the model. Among clinical factors, baseline severity of core-emotional symptoms emerged as an essential predictor, followed by atypical symptoms and insomnia. Higher severity of these symptoms was associated with a poorer prognosis. Other important predictors of a favorable prognosis included living with paternal and maternal grandmothers, financial empowerment, higher socioeconomic class, and living in a joint family system. This prognostic model yielded acceptable discrimination (c-statistic =0.75) and calibration to aid in personalized delivery of psychological treatment.

Keywords

Perinatal depression; prognosis; prognostic modeling; nomogram; Pakistan

Subject

MEDICINE & PHARMACOLOGY, Psychiatry & Mental Health studies

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