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

Badiya, Pradeep Kumar; Siddabattuni, Sasidhar; Dey, Debarshi; Javvaji, Sai Kiran; Nayak, Sai Prasad; Hiremath, Akkamahadevi C.; Upadhyaya, Rajani; Madras, Loukya; Nalam, Raj Lakshmi; Yendluri, Prabhakar; Vaitheswaran, Sridhar; Manjjuri, A.R.; Jk, Kiran Kumar; Subramaniyan, M.; Sarma, R. Raghunatha; Ramamurthy, Sai Sathish

doi:10.1016/j.genhosppsych.2020.08.002

Cited by 14 publications

(4 citation statements)

References 46 publications

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“…Studies have used LCMM to successfully identify meaningful latent subgroups using longitudinal data ( 25 ). For example, it has been used to categorize trajectories of depression and anxiety symptom changes ( 26 ) and shown to perform as accurately as the traditional cutoff score approach in identifying heterogeneous subgroups in a longitudinal study on perinatal depression ( 27 ). The LCMM provided strong robustness in current analysis given the lack of a traditional or standard method for categorizing longitudinal post-COVID-19 depression changes.…”

Section: Discussionmentioning

confidence: 99%

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis

Sung,

Kim,

Kim

et al. 2024

Front. Public Health

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BackgroundDuring the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population.ObjectiveThis study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial.MethodsA total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth’s correction to identify associations between acute COVID-19 symptoms and the subgroups.ResultsTwo latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30–165.42; chest pain: OR, 6.55, 95% CI, 1.15–34.61; brain fog: OR, 7.03, 95% CI 2.57–20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71–14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71–14.21).ConclusionThese findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

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Section: Discussionmentioning

confidence: 99%

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis

Sung,

Kim,

Kim

et al. 2024

Front. Public Health

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“…Badiya, et al 5 (2020) showed that urban women exhibited a higher prevalence of PND compared to their rural counterparts. In low-income nations, the identification of risk factors linked to PND is crucial for formulating preventive measures for conditions associated with PND due to the restricted availability of mental health services.…”

Section: Identification Of Studies Via Databases and Registersmentioning

confidence: 99%

Risk Factors of Perinatal Depression in Women: An Updated Systematic Review

Investari Nugraheni,

Amelia Rahmah,

Agus Prastyo

et al. 2024

JARMHS

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Background: Perinatal depression (PND) can result in adverse outcomes for both mother and child, including poorer pregnancy outcomes and long-term emotional, social, and cognitive difficulties in children. Furthermore, it correlates with high morbidity and mortality rates, posing significant burdens on affected individuals, families, and society. The objective of this study is to conduct an extensive systematic review evaluating the risk factors of perinatal depression in women based on literature published within the last five years. Methods: The systematic review followed PRISMA 2020 standards and examined full-text English literature published between 2019 and 2024. This review excluded editorials, review papers from the same journal, and submissions without a DOI. Literature was sourced from online platforms such as PubMed, SagePub, and SpringerLink. Result: A total of 1,161 articles were retrieved from online databases (PubMed, SagePub, and SpringerLink). After three rounds of screening, five articles directly relevant to the systematic review were selected for full-text reading and analysis. Conclusion: Perinatal depression (PND) affects numerous women during and after childbirth, influenced by socioeconomic, psychosocial, and environmental factors like poverty, social support, pregnancy loss, and urban living. Early detection and comprehensive interventions are crucial to support at-risk mothers, promoting better mental health outcomes for both them and their families.

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“…Despite such high prevalence, maternal mental health problems remain "under-recognized and under-treated" [4]. In India, recent studies estimated the prevalence of perinatal depression to be between 14 and 24% [5][6][7]. Researchers estimated in 2020 the magnitude of other pregnancy-related disorders such as pregnancy-related stress and anxiety to be 30.9% [8] and 23% [9], respectively.…”

Section: Introductionmentioning

confidence: 99%

Recommendations for maternal mental health policy in India

Priyadarshini¹,

Rao

Dash

2023

J Public Health Pol

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Mental illnesses are a serious concern in India where every seventh person suffers from mental health problems—with women more affected than men. While the burden of perinatal mental illnesses grows, India lacks exclusive policies to address it. The COVID-19 pandemic has had an impact on routine antenatal care and institutional deliveries and has also affected the mental health of pregnant women and mothers. We evaluated existing policies. Policy options were evaluated against criteria like cost–benefit analysis, administrative feasibility, human resources, and equity along with the intended and unintended consequences. We propose three policy options: (1) strengthening and focused implementation of the existing national mental health program (NMHP), (2) integrating mental health in the ongoing Reproductive, Maternal, Newborn, Child and Adolescent Health Program, and (3) including a ‘maternal’ component in NMHP. We offered policy recommendations to fill the gap in addressing the maternal mental health challenge in India.

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

Cited by 14 publications

References 46 publications

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis

Risk Factors of Perinatal Depression in Women: An Updated Systematic Review

Recommendations for maternal mental health policy in India

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