Skip to main content
Download PDF

Risk factors for the development of postpartum depression in individuals who screened positive for antenatal depression

BMC Psychiatry volume 23, Article number: 557 (2023) Cite this article

Abstract

Background

Women with antenatal depression often have a higher risk of developing postpartum depression (PPD) after delivery. A number of factors associated with the PDD in those previously reporting antenatal depression have been suggested, but further research is needed. This study aimed to investigate factors associated with developing subsequent postnatal depression in women who had screened positive for antenatal depression.

Methods

This study was carried out in Hangzhou women’s Hospital. 578 women who experienced antenatal depression from this cohort were enrolled in this study. The sociodemographic and clinical characteristics of the participants were collected and tabulated against the incidence of postnatal depression. Binary logistic regression was used to estimate the effects of the principal underlying variables. The Chinese-version Edinburgh Postnatal Depression Scale (EPDS) was used to screen for PPD. Antenatal screening for depression was conducted at 28–34 weeks during pregnancy and postpartum depressive symptoms were assessed at 6 weeks after childbirth in the women. Path Analysis of Structural Equation Model (SEM) was employed to explore the direct, indirect, and total effects of risk factors of PPD.

Results

57.6% (n = 333) of the participants subsequently developed PPD in our study. The results of the logistic analysis indicated that ages ≤ 35 years old (OR = 1.852; 95%CI: 1.002–3.423), non-one-child families (OR = 1.518; 95%CI: 1.047-2.200), and rare care from partner during pregnancy (OR = 2.801; 95%CI: 1.038–7.562), the antenatal EPDS score (OR = 1.128; 95%CI: 1.052–1.209), pyrexia during pregnancy (OR = 2.43; 95%CI: 1.358–4.345), fairly good (OR = 1.836; 95%CI: 1.009–3.340), fairly bad (OR = 3.919; 95%CI:2.072–7.414) and very bad postpartum sleep quality (OR = 9.18; 95%CI: 2.335–36.241) were associated with increased risk of PPD (compared to very good postpartum sleep quality). In path analysis model, antenatal EPDS score (standardized total β = 0.173) and pyrexia during pregnancy (standardized total β = 0.132) had both direct and indirect effects (the impact on outcome variables needs to be determined through other variables) on PPD. Sleep quality after delivery (standardized β = 0.226) and one-child family (standardized β = 0.088) had direct effects only on PPD.

Conclusion

The results from our study indicated that more than 50% of the women who experienced antepartum depression would subsequently develop PPD. Depressive symptoms and pyrexia during pregnancy increase PPD scores, and these effects were in part mediated via poor sleep quality during the postpartum period.

Peer Review reports

Introduction

For most women, pregnancy is a momentous life event accompanied by marked psychological and physiological changes, which increase their vulnerability to the onset or recurrence of mental disorders [1]. Among these disorders, antenatal depression constitutes a major health challenge for pregnant women [2, 3]. Antenatal depression profoundly affects a woman’s sense of well-being, relationships, and quality of life, leading to adverse consequences such as difficulty performing common activities and failure to obtain prenatal care, the use of tobacco, alcohol, and other harmful substances, and the risk of self-harm or suicide [4, 5]. Antenatal depression may also affect fetal growth as well as infant temperament and later behavior in childhood [6, 7]. Moreover, antenatal depression is one of the strongest risk factors for postpartum depression (PPD), a condition linked to developmental problems in children [8]. Evidence from longitudinal studies indicated that, on average, 39% of women who experienced antenatal depression went on to have PPD, and 47% of those with PPD had also experienced antenatal depression [9]. The antenatal depression continuing to develop into postnatal depression not only lengthens the duration of depressive episodes in the mother, but significantly increases the risks of marital conflict and impaired infant-caregiver attachment, as well as the risks of impaired emotional, social, and cognitive development in the child [6, 10]. Therefore, developing strategies that prevent antenatal depression continuing to develop into PPD is of great importance for the health of both women and their offspring.

From the view of prevention, identifying modifiable risk factors is the first step toward primary prevention. Several studies have been conducted to investigate the associations between antenatal depression and PPD [11,12,13,14,15]. The results from these studies consistently revealed that antenatal depression was associated with significantly higher odds of PPD in women, with an odds ratio ranging from 1.50 [11] to 7.52 [12]. However, to the best of our knowledge, only Redshaw et al. investigated the risk factors for developing subsequent postnatal depression in mothers who experienced antenatal depression [16], and their results indicated that being left alone in labor and experiencing poor postnatal health was associated with increased risk of PPD in this population. Therefore, the factors that drive the continuous development of antenatal depression into PPD in women with established antenatal depression require further investigation. Moreover, as PPD is a complex disease that originates from the interactions between social, psychological, behavioral, and biological factors, the mechanisms underlying these interactions and their contributions to the continuous development of antenatal depression into PPD also require further assessment.

To address this issue, we conducted a nested case-control study to investigate the factors associated with the continuous development of antenatal depression into PPD in women with established antenatal depression. We enrolled women with new-onset antenatal depression and analyzed the characteristics of women with or without PPD at the end of the follow-up, trying to clarify the key factors that influence the outcomes of PPD who were screened positive for antenatal depression in this population. Potential risk factors were selected under the framework of stress process theory [17], which described the model of a health problem with three basic concepts: stressors, mediators, and outcomes. Previous studies have shown that sociodemographic characteristics of pregnant women can be important stressors of PPD, while their family support status and personal coping ability play mediating role during this process [18, 19]. Therefore, in this study, factors including socio-demographics, clinical features, family support, and individual coping ability were incorporated in the analysis. Furthermore, we use a path analysis of structural equation model to specify the underlying relationships among the factors that characterize the process through which women with antenatal depression continue to develop into PPD.

Methods

Participants and general procedure

Participants of this study were drawn from a cohort designed to explore the determinants of antenatal depression and postnatal depression in women, which was carried out in Hangzhou women’s Hospital. Pregnant women who met the inclusion-exclusion criteria were included in this study. Women were eligible for inclusion if they were between 28 and 34 weeks gestation. Women were excluded as follows: (1) women with 18 years old or older; (2) able to understand the content of the questionnaire; (3) without a history of anxiety and depression. This study was approved by the Ethics Committee of Hangzhou Women’s Hospital (No. 2020-01-06) and was carried out following the approved guidelines. Initially we contacted more than 6000 pregnant women during their hospital visit. However, many women refused to participate, either because they did not have enough time to listen to our presentations or complete our questionnaire, or had other concerns. Finally, a total of 2243 women were included in the cohort and 578 women with new-onset antenatal depression were enrolled in this study.

Measures

Demographic data of the participants were collected from the medical records by trained staff. Basic information collected was spilt into (1) general characteristics (age, education level, family income, gravidity, history of abortion, family support, health status etc.); (2) previous or current disease status, sleep quality, perinatal complications, delivery experiences and lifestyle habits.

In this study, maternal depressive symptoms during pregnancy were assessed by the Chinese version of Edinburgh Postnatal Depression Scale (EPDS) [20], which has been demonstrated to have excellent psychometric properties in Chinese population [21]. The EPDS scale contains 10 items and total scores ranged from 0 to 30, with higher scores indicating more severe depression. According to the information from the reliability and validity study of the Chinese version of the EPDS, 10 was taken as the cutoff point for the detection of risk for clinical depressive symptoms [20, 22]. Antenatal screening for depression was conducted at 28–34 weeks during pregnancy and postpartum depressive symptoms were assessed at 6 weeks after childbirth in the women. The flow chart of our implementation process was shown in Fig. 1.

Fig. 1
figure 1

The flowchart of participants, from recruitment to final follow-up.

Full size image

Statistical analysis

In the current study, nested case-control design was applied focusing on those women who had experienced antenatal depression in the cohort. Sociodemographic and clinical characteristics were described and compared between women with or without PPD. This was followed by multiple logistic regression analysis to examine all potential risk factors associated with PPD and the results were visualized with the “forestplot” package in R 4.0 (R Core Team, 2017).

Path analysis of structural equation model (SEM) was constructed to explore the possible links between sociodemographic characteristics, lifestyle habits, and clinical characteristics with PPD in women. Six parameters, including the Sartorra-Bentler Chi-squared test of fit (P > 0.05), CMIN (chi-square, χ2), cardinality of freedom ratio (normal chi-square, χ2/df, CMIN/DF＜3.00 is acceptable), comparative fit index (CFI ≥ 0.90 is acceptable, ≥ 0.95 is good); adjusted goodness-of-fit index (AGFI ≥ 0.90 is acceptable, ≥ 0.95 is good); root mean square error of approximation (RMSEA ≤ 0.08 is recommended) were used to judge the degree of fit between the model and the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were also calculated to evaluate the fit of the model, with lower values representing a better fit. Both direct and indirect effects of multiple interacting variables on PPD were presented. The Amos 24.0 (IBM SPSS Statistics 23) was used to build the SEMs.

Results

The sociodemographic and clinical characteristics of the participants

In our longitudinal study, a total of 2243 women had completed both rounds of depression screening, with 578 women diagnosed with antenatal depression. The sociodemographic and clinical characteristics of the participants were presented in Table 1. The age of the women ranged from 22 to 43 years (Mean: 30.2 years, SD: 3.3 years), and more than 90% of them were ≤ 35 years. 79.6% of the women had completed undergraduate degree, 13.0% had graduate degree, and the rest were high school educated or below. According to the information from the reliability and validity study of the Chinese version of the EPDS, 10 was taken as the cutoff point for the detection of risk for clinical depressive symptoms [20]. Based on this cutoff score, 57.6% of the original sample (n = 333) was found to have PDD. The results showed that there were significant differences in education level, one-child family, and the levels of spouse care during pregnancy between women with and without PPD.

Table 1 Sociodemographic and clinical characteristics of the participants
Full size table

The clinical characteristics of the participants, including antenatal EPDS score, history of abortion, nausea, and vomiting of pregnancy, pyrexia during pregnancy and postpartum, sleep quality during pregnancy and postpartum, and hypertension complicating pregnancy, were analyzed and compared between women with and without PPD. The results indicated that there were significant differences in antenatal EPDS score, pyrexia during pregnancy, and postpartum sleep quality between the participants from the two groups.

Multiple logistic regression analysis

The multiple logistic regression analysis model was employed to identify the predictors of PPD in the participants (Fig. 2). The results showed that age ≤ 35 years old (OR = 1.852; 95%CI: 1.002–3.423), non-one-child families (OR = 1.518; 95%CI: 1.047-2.200), and rare care from spouses during pregnancy (OR = 2.801; 95%CI: 1.038–7.562) were significantly associated with increased risk of PPD in the participants. For clinical characteristics, the antenatal EPDS score (OR = 1.128; 95%CI: 1.052–1.209), pyrexia during pregnancy (OR = 2.43; 95%CI: 1.358–4.345), very good (OR = 1.836; 95%CI: 1.009–3.340), fairly good (OR = 3.919; 95%CI: 2.072–7.414) and fairly poor postpartum sleep quality (compared with those with good postpartum sleep quality) were associated with increased risk of PPD.

Fig. 2
figure 2

Forest plot of factors associated with postpartum depression

Full size image
Fig. 3
figure 3

A generalized path analysis model of the risk factors for postnatal depression. All the 9 variables are observational variables: Postpartum depression as the outcome variable and the other 8 variables as independent variables. In the path analysis, the arrow direct point to postpartum depression indicated that the direct effect of doing, while the variables pointed to postpartum depression through other variables represented the indirect effects of doing. One-child family, partner support, EPDS score at visit 1 and Sleep quality after delivery also participated as media variables. In the path analysis model, two variables (sleep quality after delivery, one-child family) had a direct effect to postpartum depression, two variables (partner support, education level) only have indirect effects. On the contrary, the EPDS score at visit 1and pyrexia during pregnancy have both direct and indirect effects. The numbers on the lines indicate the correlation coefficients

Full size image

Path analysis model

The fitting results, including the estimated standardized path loadings and interrelationships between the observational variables for the path analysis model, are shown in Fig. 3. The overall model fit was excellent according to the various fit indices (CMIN = 26.740, CMIN/DF = 1.215, P = 0.221, RMSEA = 0.019, CFI = 0.962, AGIF = 0.979, ACI = 72.740, BIC = 173.010) (Table 2). Other fit indictors were in Supplementary Table 1. In line with the path analysis, several direct predictors and mediators related to participants’ risk of PPD were identified (Tables 3, 4 and 5). One-child family (p = 0.025), pyrexia during pregnancy (p = 0.003), sleep quality after delivery (p < 0.001), and EPDS score at visit 1 (antenatal EPDS score) (p < 0.001) were all identified as predictors significantly associated with PPD the participants. As shown in Table 4, sleep quality after delivery (standardized β = 0.226, p = 0.004), and one-child family (standardized β = 0.088, p = 0.015) only showed direct effects on the risk of PPD in the participants. In Table 5, Partner support (standardized β=-0.036, p = 0.002) and education level only showed indirect effects (standardized β=-0.011, p = 0.009) on the risk of PPD in the participants. On the contrary, the EPDS score at visit 1 (antenatal EPDS score) has both direct (standardized β = 0.137, p = 0.005) and indirect (standardized β = 0.037, p = 0.004) effects on the risk of PPD in the participants, i.e., EPDS score at visit 1 indirectly influenced PPD via sleep quality after delivery. Pyrexia during pregnancy may also have direct (standardized β = 0.116) and indirect (through sleep quality after delivery, standardized β = 0.017) effects on the risk of PPD, though the indirect effects were not significant (p = 0.051) (Table 5).

Table 2 Model fit indices for path analysis of testing a stress process model for postpartum depression
Full size table
Table 3 Path coefficients of path analysis for the impact factors of postpartum depression
Full size table
Table 4 Total and direct standardized effects identified via the path analysis
Full size table
Table 5 Indirect standardized effects identified via the path analysis
Full size table

Discussion

This study aimed to investigate the factors that drive the continuous development of antenatal depression into PPD in a group of women who had experienced antenatal depression in China. The findings from our study suggested that younger age, non-one-child families, rare care from spouses during pregnancy, higher antenatal EPDS score, and pyrexia during pregnancy were significantly associated with increased risk of PPD in the participants, with antenatal EPDS score being the most strongly associated. In particular, we utilized a path analysis model approach to explore the direct and indirect effects of these risk factors on the occurrence of PPD in women. Path analysis, a form of structural equation model, uses “observational variables” and focuses on the correlation mechanisms that may exist among independent variables [23]. It presents a path diagram to show the relationships among the variables, which is clearer than the equations [24]. By specifically decomposing direct, indirect, and total effects, path analysis can lead to a more comprehensive understanding of the relationships among variables. The quantitative expression of regulatory mechanism also makes the analysis more thorough and clear [25]. Therefore, path analysis has been applied to multiple scientific research fields, such as behavioral sciences, social sciences, economics, biology, agriculture, medicine, etc. [18, 26,27,28]. In addition, since our data were from a cohort, we have also verified the impact of prenatal depression on postpartum depression using longitudinal analyses. The results showed that after adjusting for multiple confounders, prenatal depression remains a strong independent risk factor for postpartum depression, which again demonstrates the important impact of prenatal depression on postpartum depression (Supplementary Table 2).

In this study, 57.6% of the women who had experienced antenatal depression were found to have PPD at 6 weeks after their delivery. Similar to our study, Faisal et al. reported that 49.8% of women with postpartum depression experienced prenatal depression [29]. However, the prevalence of our report was significantly higher than that reported in some previous studies [16, 30], which reported that about 21-25% of the women with antenatal depression would develop PPD at 3 months after childbirth. There are several possible explanations for this discrepancy. Firstly, the cut-off value used to define PPD in our study (EPDS score > 10) was lower than that used in these two studies (EPDS score > 12) [16, 30]. By this criterion, more women would be judged to have PPD in our study. Secondly, there was evidence that notable racial differences and ethnic disparities exist in the risk of PPD among women with different ethnic backgrounds. Green et al. found that, compared with non-Hispanic (NH) whites, Asian women were significantly more likely to report PPD symptoms [31]. Hayes et al. also found that, compared with white women, Chinese women had higher odds of self-reported PPD symptoms (adjusted odds ratio [AOR] = 2.0; 95% CI: 1.5–2.7) [32]. As the participants of our study were Chinese women, a higher prevalence of PPD can be expected. Moreover, the following-up time of our study (6 weeks) was shorter than that of the study conducted by Redshaw et al. (3 months) [16]. Aasheim et al. found that the prevalence of psychological distress in women after childbirth showed a U-shaped pattern over time up to 18 months, with the lowest point at 6 months [33]. Therefore, the prevalence of PPD may be higher among women at 6 weeks than that of women at 3 months after childbirth. Taken together, these results suggested that the prevalence of PPD in women after childbirth may be altered by the diagnostic criterion, race/ethnic, and following-up time.

The results from logistic regression analysis and structural equation model described the predictors of and their cause mechanisms on the occurrence of PPD. Maternal educational level, partner support, and one-child family may reflect the social support of the women, which were found to be significantly associated with the occurrence of PPD. This finding was consistent with those of the study conducted by Yamada et al., which found that mothers with low social support from partner or others had significantly higher odds to have PPD, in comparison with mothers who have high social support [34]. Desta et al. also found that mothers with inadequate social support (OR = 5.46,95%CI: 3.94, 7.56) or suffer from intimate partner violence (OR = 6.27,95%CI: 4.83, 8.13) had a significantly higher risk of PPD relative to those who had adequate social support. These findings highlight the importance of social support in the development of PPD. Studies such as Alessandra et al. believe that domestic violence is a risk factor for postpartum depression [35]. Nonetheless, studies regarding intimate partner violence (IPV) in China are far less than other countries [36]. A possible explanation is that due to traditional Chinese culture, family affairs are usually kept private and women are reluctant to report IPV. As a consequence, although there have been some studies on domestic violence in recent years in China, the rates seemed to be underreported. For example, in one study for risk factors of antenatal depression, only one domestic violence was reported [37]. Therefore, in our study, such information is lacking. In future research, we will try to incorporate such information and evaluate the impact of domestic violence on postpartum depression.

The clinical characteristics, including pyrexia during pregnancy, the antenatal EPDS score, and sleep quality after childbirth were found to be major contributors to the risk of PPD in the participants of our study. Pyrexia during pregnancy, an indicator of the infectious status [38], has been demonstrated to be an important risk factor for both the physical and mental health of pregnant women [39]. This was supported by findings from previous studies, which found that persistent fever or dengue fever during pregnancy were key determinants of depressive symptoms among postpartum mothers [39, 40]. The positive and significant association between antenatal EPDS score and risk of PPD in our study suggested the continuity and stability of depressive symptoms from the antenatal to the postpartum stage. As antenatal depression is one of the strongest predictors of PPD [41], this finding highlight the importance of the prevention and early intervention of depression in pregnant women [42, 43]. Sleep disturbance, which is caused by the factors such as alterations of the hormones, physical discomforts, the presence of restless legs syndrome (RLS), and feeding and caretaking behaviors [44], is a common problem for mothers during the postpartum period [45]. Our study found that sleep quality had dose-response effects on the odds of PPD, illustrating the central role of poor sleep quality in the development of PPD. These results were in line with those of previous studies, which found that sleep disturbance was significantly associated with an increase in the odds of PPD [46, 47]. However, we only found the impact of postpartum sleep quality on postnatal depression, but did not find the impact of prenatal sleep quality, which is inconsistent with previous studies [48]. We think that the impact of sleep during pregnancy on postpartum depression is not absolute, all these findings revealed that PPD was a complex disorder that results from an intricate interplay among various social, physical, and psychological factors.

The results from the structural equation model indicated that, besides their direct effects, pyrexia during pregnancy and antenatal depression had indirect effects on the occurrence of PPD through their impacts on the sleep quality after delivery. This finding was in agreement with those obtained by Wang et al., which found that poor sleep quality mediated the relationship between antepartum depressive symptoms and postpartum depressive symptoms [49]. Though lack of direct evidence, this result was supported by the data from adult patients with familial Mediterranean fever, which demonstrated that fever may increase inflammatory marker levels and lead to poor sleep quality and depression symptoms [50]. Similar associations were observed for these social support-related factors, including maternal age, partner support, and educational level, though some were not statistically significant. These findings seem to be consistent with other research which found social support had a direct impact on depressive symptoms and mediated the effect of stress on depressive symptoms during pregnancy [51]. These results described the causal mechanisms linking socio-demographic and clinical characteristics to PPD among women who experienced antenatal depression.

Strength, limitation, and public health implications

To our knowledge, this is the first study to explore the risk factors and their causal mechanisms driving the continuous development of antenatal depression into PPD in women from an urban region in China. As we used a nested sample from a cohort study and a path analysis frame to analyze the data, a conceptual framework on the relationship between risk factors for PPD can be developed with a relatively low attrition rate. This approach allowed us to simultaneously assess the direct and indirect associations between potential risk factors and PPD and describe the possible explore potential causal mechanisms.

This study has several limitations. Firstly, though a nested case-control design was employed and the associations between predictors and PPD were demonstrated in this study, the causality cannot be inferred. Secondly, we have only focused on the impact of family support on depression but without paying attention to the negative impact of domestic violence, this would be of value to research in future studies in China. Moreover, although we enrolled the participants from one of the major obstetrics and gynecology hospitals in Hangzhou, the findings from this study cannot be generalized nationally. Last but not least, the proportion of primipara in our population is relatively high. With the changes in fertility policies, we will focus on the impact of the number of pregnancies and births on postpartum depression in the next few years.

The study has implications for preventing and controlling PDD in Chinese women. The identified predictors played an important role in the risk of PPD in our participants. Prevention and early treatment of antepartum depressive symptoms, infection, and sleep disturbance in women may help to eliminate the risk of PPD in this population. Similarly, since social and partner support were found to have mediating and buffer effects on the risk of PPD, promoting activities that persevere such social capitals are needed to maintain the psychological and mental well-being of mothers.

Conclusion

In summary, the results from our study indicated that more than 50% of the women who experienced antepartum depression would subsequently develop PPD. Based on our findings, severe antenatal depressive symptoms, pyrexia during pregnancy and poor sleep quality after delivery are the main risk factors for PPD. Social and partner support was found to buffer or reduce the risk of PPD. The insights gained from this study may be of assistance in comprehensively understanding the etiology of PPD in women who experienced antenatal depression.

Data Availability

The datasets generated and/or analyzed during the current study are not publicly available due to ethic issues involving participant’s data and privacy but are available from the corresponding author on reasonable request.

References

  1. Yin X, Sun N, Jiang N, Xu X, Gan Y, Zhang J, Qiu L, Yang C, Shi X, Chang J, et al. Prevalence and associated factors of antenatal depression: systematic reviews and meta-analyses. Clin Psychol Rev. 2021;83:101932.

    Article  PubMed  Google Scholar 

  2. Vigod SN, Wilson CA, Howard LM. Depression in pregnancy. BMJ (Clinical research ed). 2016;352:i1547.

    PubMed  Google Scholar 

  3. Stewart DE. Clinical practice. Depression during pregnancy. N Engl J Med. 2011;365(17):1605–11.

    Article  CAS  PubMed  Google Scholar 

  4. Fuhr DC, Calvert C, Ronsmans C, Chandra PS, Sikander S, De Silva MJ, Patel V. Contribution of suicide and injuries to pregnancy-related mortality in low-income and middle-income countries: a systematic review and meta-analysis. The lancet Psychiatry. 2014;1(3):213–25.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Madeghe BA, Kogi-Makau W, Ngala S, Kumar M. Nutritional factors Associated with maternal depression among pregnant women in Urban Low-Income settlements in Nairobi, Kenya. FoodNutr Bull. 2021;42(3):334–46.

    Google Scholar 

  6. Stein A, Pearson RM, Goodman SH, Rapa E, Rahman A, McCallum M, Howard LM, Pariante CM. Effects of perinatal mental disorders on the fetus and child. Lancet (London England). 2014;384(9956):1800–19.

    Article  PubMed  Google Scholar 

  7. Kim Y, Bird A, Peterson E, Underwood L, Morton SMB, Grant CC. Maternal Antenatal Depression and Early Childhood Sleep: potential pathways through infant temperament. J Pediatr Psychol. 2020;45(2):203–17.

    Article  CAS  PubMed  Google Scholar 

  8. Wisner KL, Sit DK, McShea MC, Rizzo DM, Zoretich RA, Hughes CL, Eng HF, Luther JF, Wisniewski SR, Costantino ML, et al. Onset timing, thoughts of self-harm, and diagnoses in postpartum women with screen-positive depression findings. JAMA psychiatry. 2013;70(5):490–8.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Underwood L, Waldie K, D’Souza S, Peterson ER, Morton S. A review of longitudinal studies on antenatal and postnatal depression. Arch Women Ment Health. 2016;19(5):711–20.

    Article  Google Scholar 

  10. O’Hara MW, McCabe JE. Postpartum depression: current status and future directions. Ann Rev Clin Psychol. 2013;9:379–407.

    Article  Google Scholar 

  11. Underwood L, Waldie KE, D’Souza S, Peterson ER, Morton SM. A longitudinal study of pre-pregnancy and pregnancy risk factors Associated with Antenatal and postnatal symptoms of Depression: evidence from growing up in New Zealand. Matern Child Health J. 2017;21(4):915–31.

    Article  PubMed  Google Scholar 

  12. Milgrom J, Gemmill AW, Bilszta JL, Hayes B, Barnett B, Brooks J, Ericksen J, Ellwood D, Buist A. Antenatal risk factors for postnatal depression: a large prospective study. J Affect Disord. 2008;108(1–2):147–57.

    Article  PubMed  Google Scholar 

  13. Eastwood J, Ogbo FA, Hendry A, Noble J, Page A, Early Years Research G. The impact of Antenatal Depression on Perinatal Outcomes in Australian Women. PLoS ONE. 2017;12(1):e0169907.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Leigh B, Milgrom J. Risk factors for antenatal depression, postnatal depression and parenting stress. BMC Psychiatry. 2008;8:24.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mohammad KI, Gamble J, Creedy DK. Prevalence and factors associated with the development of antenatal and postnatal depression among jordanian women. Midwifery. 2011;27(6):e238–245.

    Article  CAS  PubMed  Google Scholar 

  16. Redshaw M, Henderson J. From antenatal to postnatal depression: associated factors and mitigating influences. J Womens Health (Larchmt). 2013;22(6):518–25.

    Article  PubMed  Google Scholar 

  17. Pearlin LI. The stress process revisited: reflections on concepts and their interrelationships. Handb Sociol mental health 1999:395–415.

  18. Li Y, Zeng Y, Zhu W, Cui Y, Li J. Path model of antenatal stress and depressive symptoms among chinese primipara in late pregnancy. BMC Pregnancy Childbirth. 2016;16(1):180.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dadi AF, Miller ER, Woodman R, Bisetegn TA, Mwanri L. Antenatal depression and its potential causal mechanisms among pregnant mothers in Gondar town: application of structural equation model. BMC Pregnancy Childbirth. 2020;20(1):168.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lee DT, Yip SK, Chiu HF, Leung TY, Chan KP, Chau IO, Leung HC, Chung TK. Detecting postnatal depression in chinese women. Validation of the chinese version of the Edinburgh postnatal depression scale. Br J psychiatry: J mental Sci. 1998;172:433–7.

    Article  CAS  Google Scholar 

  21. Zhao Y, Kane I, Wang J, Shen B, Luo J, Shi S. Combined use of the postpartum depression screening scale (PDSS) and Edinburgh postnatal depression scale (EPDS) to identify antenatal depression among chinese pregnant women with obstetric complications. Psychiatry Res. 2015;226(1):113–9.

    Article  PubMed  Google Scholar 

  22. Gan Y, Xiong R, Song J, Xiong X, Yu F, Gao W, Hu H, Zhang J, Tian Y, Gu X, et al. The effect of perceived social support during early pregnancy on depressive symptoms at 6 weeks postpartum: a prospective study. BMC Psychiatry. 2019;19(1):232.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Næs T, Romano R, Tomic O, Måge I, Smilde A, Liland KH. Sequential and orthogonalized PLS (SO-PLS) regression for path analysis: order of blocks and relations between effects. J Chemom. 2021;35(10):e3243.

    Article  Google Scholar 

  24. Stigler SM. Correlation and causation: a comment. Perspect Biol Med. 2005;48(1):88–S94.

    Article  Google Scholar 

  25. Finney JM. Indirect effects in path analysis. Sociol Methods Res. 1972;1(2):175–86.

    Article  Google Scholar 

  26. Duncan OD. Path analysis: sociological examples. Am J Sociol. 1966;72(1):1–16.

    Article  Google Scholar 

  27. Hwang HJ, Chun HY, Ok KH. The path analysis of parental divorce on children’s emotional and behavioral problems: through child-rearing Behaviors and Children’s Self-Esteem. J Korean Home Econ Association. 2010;48(7):99–110.

    Article  Google Scholar 

  28. Kang D. An path analysis of the elderly’s deprivation experience on the thinking of suicide. J Soc Sci. 2019;58(1):197–245.

    Article  Google Scholar 

  29. Faisal-Cury A, Menezes PR. Antenatal depression strongly predicts postnatal depression in primary health care. Revista brasileira de psiquiatria (Sao Paulo Brazil: 1999). 2012;34(4):446–50.

    Article  PubMed  Google Scholar 

  30. Matthey S, Barnett B, Ungerer J, Waters B. Paternal and maternal depressed mood during the transition to parenthood. J Affect Disord. 2000;60(2):75–85.

    Article  CAS  PubMed  Google Scholar 

  31. Green TL, Son YK, Simuzingili M, Mezuk B, Bodas M, Hagiwara N. Pregnancy-related weight and Postpartum depressive symptoms: do the Relationships Differ by Race/Ethnicity? J Womens Health (Larchmt). 2021;30(6):816–28.

    Article  PubMed  Google Scholar 

  32. Hayes DK, Ta VM, Hurwitz EL, Mitchell-Box KM, Fuddy LJ. Disparities in self-reported postpartum depression among asian, hawaiian, and Pacific Islander Women in Hawaii: pregnancy risk Assessment Monitoring System (PRAMS), 2004–2007. Matern Child Health J. 2010;14(5):765–73.

    Article  PubMed  Google Scholar 

  33. Aasheim V, Waldenström U, Hjelmstedt A, Rasmussen S, Pettersson H, Schytt E. Associations between advanced maternal age and psychological distress in primiparous women, from early pregnancy to 18 months postpartum. BJOG: an international journal of obstetrics and gynaecology. 2012;119(9):1108–16.

    Article  CAS  PubMed  Google Scholar 

  34. Yamada A, Isumi A, Fujiwara T. Association between lack of Social Support from Partner or others and Postpartum Depression among japanese mothers: a Population-Based cross-sectional study. Int J Environ Res Public Health 2020, 17(12).

  35. Biaggi A, Conroy S, Pawlby S, Pariante CM. Identifying the women at risk of antenatal anxiety and depression: a systematic review. J Affect Disord. 2016;191:62–77.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Yuan W, Hesketh T. Intimate Partner Violence and Depression in Women in China. J interpers Violence. 2021;36(21–22):Np12016–np12040.

    Article  PubMed  Google Scholar 

  37. Chen J, Cross WM, Plummer V, Lam L, Sun M, Qin C, Tang S. The risk factors of antenatal depression: a cross-sectional survey. J Clin Nurs. 2019;28(19–20):3599–609.

    Article  PubMed  Google Scholar 

  38. Le Gouez A, Benachi A, Mercier FJ. Fever and pregnancy. Anaesth Crit Care Pain Med. 2016;35(Suppl 1):5–s12.

    Article  Google Scholar 

  39. Liang P, Wang Y, Shi S, Liu Y, Xiong R. Prevalence and factors associated with postpartum depression during the COVID-19 pandemic among women in Guangzhou, China: a cross-sectional study. BMC Psychiatry. 2020;20(1):557.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Moraes EV, Toledo OR, David FL, Godoi BN, Monteiro KA, Deluqui TC, Teixeira TW, Carvalho AL, Avelino MM. Implications of the clinical gestational diagnosis of ZIKV infection in the manifestation of symptoms of postpartum depression: a case-control study. BMC Psychiatry. 2019;19(1):199.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Robertson E, Grace S, Wallington T, Stewart DE. Antenatal risk factors for postpartum depression: a synthesis of recent literature. Gen Hosp Psychiatry. 2004;26(4):289–95.

    Article  PubMed  Google Scholar 

  42. Sangsawang B, Wacharasin C, Sangsawang N. Interventions for the prevention of postpartum depression in adolescent mothers: a systematic review. Arch Women Ment Health. 2019;22(2):215–28.

    Article  Google Scholar 

  43. Yasuma N, Narita Z, Sasaki N, Obikane E, Sekiya J, Inagawa T, Nakajima A, Yamada Y, Yamazaki R, Matsunaga A, et al. Antenatal psychological intervention for universal prevention of antenatal and postnatal depression: a systematic review and meta-analysis. J Affect Disord. 2020;273:231–9.

    Article  PubMed  Google Scholar 

  44. Bei B, Coo S, Trinder J. Sleep and Mood during pregnancy and the Postpartum Period. Sleep Med Clin. 2015;10(1):25–33.

    Article  PubMed  Google Scholar 

  45. Montgomery-Downs HE, Insana SP, Clegg-Kraynok MM, Mancini LM. Normative longitudinal maternal sleep: the first 4 postpartum months. Am J Obstet Gynecol. 2010;203(5):465e461–467.

    Article  Google Scholar 

  46. Koutra K, Vassilaki M, Georgiou V, Koutis A, Bitsios P, Kogevinas M, Chatzi L. Pregnancy, perinatal and postpartum complications as determinants of postpartum depression: the Rhea mother-child cohort in Crete, Greece. Epidemiol psychiatric Sci. 2018;27(3):244–55.

    Article  CAS  Google Scholar 

  47. Yun BS, Shim SH, Cho HY, Heo SJ, Jung I, Jeon HJ, Han YJ, Kwak DW, Kim MH, Park HJ, et al. The impacts of insufficient sleep and its change during pregnancy on postpartum depression: a prospective cohort study of korean women. Int J Gynaecol Obstet. 2021;155(1):125–31.

    Article  PubMed  Google Scholar 

  48. MacLean JV, Faisal-Cury A, Chan YF, Menezes PR, Winters A, Joseph R, Huang H. The relationship between sleep disturbance in pregnancy and persistent common mental disorder in the perinatal period (sleep disturbance and persistent CMD). J Ment Health. 2015;24(6):375–8.

    Article  PubMed  Google Scholar 

  49. Wang Y, Liu H, Zhang C, Li C, Xu JJ, Duan CC, Chen L, Liu ZW, Jin L, Lin XH, et al. Antepartum sleep quality, mental status, and postpartum depressive symptoms: a mediation analysis. BMC Psychiatry. 2022;22(1):521.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Kucuksahin O, Omma A, Ozdemirel AE, Tecer D, Ulutas S, Maras Y, Balkarli A, Erten S. Incidence of sleep disturbances in patients with familial Mediterranean fever and the relation of sleep quality with disease activity. Int J Rheum Dis. 2018;21(10):1849–56.

    Article  CAS  PubMed  Google Scholar 

  51. Ngai FW, Chan SW. Learned resourcefulness, social support, and perinatal depression in chinese mothers. Nurs Res. 2012;61(2):78–85.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the participants for their contribution to the study.

Funding

This work was supported in part by grants from the National Natural Science Foundation of China (Nos. 32270186 and 31971138).

Author information

Author notes

  1. Jingjing Yu and Zhiyin Zhang these two authors contributed equally to this work.

Authors and Affiliations

  1. Department of Nutrition and Toxicology, Hangzhou Normal University School of Public Health, 2318 Yuhangtang Rd, Hangzhou, 311121, Zhejiang, China

    Jingjing Yu, Yuanyuan Deng, Lijun Zhang, Chuncao He, Xianrong Xu & Jun Yang

  2. Department of Obstetrics, The Affiliated Hangzhou Women’s Hospital, Hangzhou Normal University, Hangzhou, China

    Zhiyin Zhang

  3. Department of Epidemiology and Biostatistics, Hangzhou Normal University School of Public Health, Hangzhou, China

    Yinyin Wu

  4. Zhejiang Provincial Research Center for the Diagnosis and Treatment of Uterine Cancer, The Affiliated Women’s Hospital, Zhejiang University, Hangzhou, China

    Jun Yang

Authors
  1. Jingjing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiyin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanyuan Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chuncao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yinyin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xianrong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jingjing Yu and Zhiyin Zhang collected and analyzed the data, wrote the first draft of the manuscript. Yuanyuan Deng, Lijun Zhang, Chuncao He and Yinyin Wu contributed to study design, collected data and assisted with data analysis, critically reviewed and revised the manuscript. Xianrong Xu and Jun Yang contributed to study design, collected data, and critically reviewed and revised the manuscript.

Corresponding authors

Correspondence to Xianrong Xu or Jun Yang.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Hangzhou Women’s Hospital (No. 2020-01-06) and all procedures were performed in accordance with the Declaration of Helsinki. None of the participants was illiterate and the informed consent was obtained from all the participants.

Conflict of interest

None.

Consent to publish

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, J., Zhang, Z., Deng, Y. et al. Risk factors for the development of postpartum depression in individuals who screened positive for antenatal depression. BMC Psychiatry 23, 557 (2023). https://doi.org/10.1186/s12888-023-05030-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-023-05030-1

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us