Profiles and characteristics of clinical subtypes of perinatal depressive symptoms: A latent class analysis

Sun, Jiwei; Cao, Dong-Yuan; Li, Jiahuan; Zhang, Xuan; Wang, Ying; Bai, Huayu; Lin, Ping-Zhen; Zhang, Huihui; Cao, Fenglin

doi:10.1111/jan.14136

Cited by 21 publications

(28 citation statements)

References 63 publications

(86 reference statements)

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“…These findings provided evidence for the external validity of the distinction between the MDD somatic and non-somatic factors. Third, the two-factor model could be better interpreted as different symptomatic representation and subtypes of MDD than the unidimensional model [50,51]. A number of empirical studies used machine learning, genetic, and biological approaches to yield strong evidence in favor of MDD consisting of somatic and affective/cognitive factors, rather than one "depression" factor [51][52][53].…”

Section: Discussionmentioning

confidence: 99%

The factor structure of major depressive symptoms in a sample of Chinese earthquake survivors

Wang

Cao

et al. 2021

BMC Psychiatry

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Background Experiencing natural disasters is associated with common mental disorders including major depressive disorder (MDD). However, the latent structure of MDD is widely debated, and few studies tested the MDD factor structure in Chinese natural disaster survivors. Therefore, the aim of the current study was to evaluate the factorial validity of the Patient Health Questionnaire-9 (PHQ-9) for DSM-5 major depressive disorder (MDD) symptoms in Chinese earthquake survivors. Method Participants were 1058 Chinese earthquake survivors. Self-reported measures included the PHQ-9 and the Short-Form Health Survey (SF-36). Confirmatory factor analysis (CFA) and structural equation modelling (SEM) was used to examine the latent structure of MDD and the associations between latent factors of MDD and different domains of health-related quality of life (HRQoL), respectively. Results In the current sample, the model consisted of somatic and cognitive/affective (non-somatic) factors demonstrated significantly better fit than the other competing MDD models (χ2 = 173.89, df = 26, CFI = 0.986, TLI = 0.981, RMSEA = 0.073, BIC = 18,091.13). Further SEM analyses indicated that the non-somatic factor was significantly related to both physical (β = − 0.362, p < .01) and psychosocial HRQoL (β = − 0.773, p < .01), while the somatic factor was a uniquely predictor of physical HRQoL (β = − 0.336, p < .01). Furthermore, we found the somatic factor partially mediated the relationship between the cognitive/affective factor and physical HRQoL (all ps < .05). Conclusions The MDD symptoms was best captured by a two-factor model comprised of somatic and cognitive/affective factors in Chinese natural disaster survivors. The two MDD factors were differentially associated with physical and psychosocial HRQoL, and the cognitive/affective factor associated physical HRQoL partially through the somatic factor. The current findings increase our understanding of latent structure of MDD symptoms, and carry implications for assessment and intervention of post-disaster mental health problems.

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Section: Discussionmentioning

confidence: 99%

The factor structure of major depressive symptoms in a sample of Chinese earthquake survivors

Wang

Cao

et al. 2021

BMC Psychiatry

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“…In high-income countries, the prevalence of antenatal and postnatal depression varies between 7 and 15% [ 2 ], and between 19 and 25% in low-income countries [ 3 ]. PND is considered one of the most common complications of pregnancy and has a significant impact on the mother, her offspring and even her entire family [ 4 , 5 ]. When not treated properly, PND has a negative impact on the outcome of childbirth [ 6 ], the child’s health, and their social and emotional development [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Distinct trajectories of perinatal depression in Chinese women: application of latent growth mixture modelling

Hong

Le²,

Lu³

et al. 2022

BMC Pregnancy Childbirth

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Background Current research on perinatal depression rarely pays attention to the continuity and volatility of depression symptoms over time, which is very important for the early prediction and prognostic evaluation of perinatal depression. This study investigated the trajectories of perinatal depression symptoms and aimed to explore the factors related to these trajectories. Methods The study recruited 550 women during late pregnancy (32 ± 4 weeks of gestation) and followed them up 1 and 6 weeks postpartum. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS). Latent growth mixture modelling (LGMM) was used to identify trajectories of depressive symptoms during pregnancy. Results Two trajectories of perinatal depressive symptoms were identified: “decreasing” (n = 524, 95.3%) and “increasing” (n = 26, 4.7%). History of smoking, alcohol use and gestational hypertension increased the chance of belonging to the increasing trajectories, and a high level of social support was a protective factor for maintaining a decreasing trajectory. Conclusions This study identified two trajectories of perinatal depression and the factors associated with each trajectory. Paying attention to these factors and providing necessary psychological support services during pregnancy would effectively reduce the incidence of perinatal depression and improve patient prognosis.

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“…Several authors have also found an anxiety subscale within the EPDS (e.g., Loyal et al, 2020), and indeed, comorbidity between anxiety and depressive symptoms during the postpartum period has been reported in the literature (Falah‐Hassani et al, 2016). Thus, heterogeneity in PPD symptoms should also be understood in light of both anhedonia and anxiety symptoms, in addition to depressive symptoms (Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Profiles of women with postpartum depressive symptoms: A latent profile analysis using the Edinburgh Postnatal Depression Scale factors

2022

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Objectives To examine the multidimensionality of symptoms assessed with the Edinburgh Postnatal Depression Scale (EPDS) and to identify and characterize the profiles of women with Postpartum Depression (PPD) symptoms. Methods A sample of 487 Portuguese postpartum women with clinically relevant depressive symptoms participated in this cross‐sectional online study. Confirmatory factor analysis was conducted to analyze the factor structure of the EPDS. A latent profile analysis based on the EPDS factors was conducted. Differences in sociodemographic characteristics and risk factors between profiles were explored. Results Three EPDS factors were confirmed: depression, anxiety, and anhedonia. Based on these dimensions, three profiles with different symptom severities were found: mild anxious‐depressive symptoms; severe PPD symptoms; and moderate anxious‐depressive symptoms. Conclusion Identifying profiles of women with PPD symptoms according to the EPDS factors allows to understand the distinct paths of development of PPD and can inform the development of tailored interventions.

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Profiles and characteristics of clinical subtypes of perinatal depressive symptoms: A latent class analysis

Cited by 21 publications

References 63 publications

The factor structure of major depressive symptoms in a sample of Chinese earthquake survivors

The factor structure of major depressive symptoms in a sample of Chinese earthquake survivors

Distinct trajectories of perinatal depression in Chinese women: application of latent growth mixture modelling

Profiles of women with postpartum depressive symptoms: A latent profile analysis using the Edinburgh Postnatal Depression Scale factors

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