4.5 Article

Identification of clinical and psychosocial characteristics associated with perinatal depression in the south Indian population

Journal

GENERAL HOSPITAL PSYCHIATRY
Volume 66, Issue -, Pages 161-170

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.genhosppsych.2020.08.002

Keywords

Depression; Perinatal; Trajectories; EPDS; Latent class mixture modeling; Economically-weaker sections

Categories

Funding

  1. Tata Education and Development Trust [TEDT/MUM/HEA/SSSIoHL/2017-2018/0069-RM-db]

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Background: Longitudinal perinatal depression (PND) data is sparsely available in the Indian population. We have employed Edinburgh Postnatal Depression Scale (EPDS) to assess the prevalence and identify characteristics associated with PND in the south Indian population. PND was assessed longitudinally using EPDS scores with traditional cut-off approach as well as a novel method of latent class mixture modeling (LCMM). The LCMM method, to the best of our knowledge, has been used for the first time in the Indian population. Methods: Three hundred and forty seven women, predominantly from economically-weaker sections of rural and urban South India were longitudinally assessed for antenatal depression (AD) and postnatal depression (PD) using EPDS cutoff-scores >= 13 and >= 10, respectively. Uni/multivariable analyses were used to identify PND associated characteristics. LCMM was then implemented, followed by risk characteristics identification. Results: PND prevalence from traditional approach was 24.50 % (12.68 % AD; 18.16% PD). Characteristics associated with PND were urban-site and recent adverse life events. Irregular menstrual history and chronic health issues were associated with AD and PD, respectively. Three distinct PND trajectories were observed from LCMM-analysis: low-risk (76.08%), medium-risk (19.89%) and high-risk (4.04%). Urban-site, recent adverse life events, irregular menstrual history and pregnancy complications were associated with medium-risk/high-risk trajectories. Limitations: EPDS is a screening tool and not a diagnostic tool for depression. Since the study population included women from economically-weaker sections, the results need verification in other socio-economic groups. Conclusions: Both the traditional cut-off-based approach and LCMM provided very similar conclusions regarding the prevalence of PND and characteristics associated with it. Higher PND prevalence was observed in urban women compared to rural women. In low-income countries, identifying risk characteristics associated with PND is a critical component in designing prevention strategies for PND related conditions because of the limited access to mental health resources.

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