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Risk psychosocial factors associated with postpartum depression trajectories from birth to six months

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Abstract

Purpose

The purpose of this study was to assess the trajectory of women's depressive symptoms during the first six months postpartum, identify risk factors (sociodemographic, obstetric and personality) associated with classes, and examine associations between classes and postpartum PTSD at two months and bonding at six months.

Methods

The final sample included 212 women who gave birth in the maternity wards of a large tertiary health center that were approached at 1–3 days, two months, and six months postpartum and completed a demographic questionnaire and measures of neuroticism (BFI) and postpartum depression (EPDS), postpartum PTSD (City Birth Trauma Scale) and bonding (PBQ). Obstetric data were taken from the medical files.

Results

Cluster analysis revealed three distinctive clusters: "stable-low" (64.2%), "transient-decreasing" (25.9%), and "stable-high" (9.9%). Neuroticism, general-related PTSD symptoms, and bonding were associated with differences between all trajectories. Birth-related PTSD symptoms were associated with differences between both stable-high and transient-decreasing trajectories and the stable-low trajectory. No obstetric or demographic variables were associated with differences between trajectories.

Conclusion

We suggest that screening women for vulnerabilities such as high levels of neuroticism and offering treatment can alleviate the possible deleterious effects of high-symptom depression trajectories that may be associated with their vulnerability.

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Data availability

Data is available upon request from the corresponding author.

References

  1. Hahn-Holbrook J, Cornwell-Hinrichs T, Anaya I (2017) Economic and health predictors of national postpartum depression prevalence: a systematic review, meta-analysis, and meta-regression of 291 studies from 56 Countries. Front Psychiatry 8:248. https://doi.org/10.3389/fpsyt.2017.00248

    Article  PubMed  Google Scholar 

  2. Shorey S, Chee CYI, Ng ED et al (2018) Prevalence and incidence of postpartum depression among healthy mothers: a systematic review and meta-analysis. J Psychiatr Res 104:235–248. https://doi.org/10.1016/j.jpsychires.2018.08.001

    Article  PubMed  Google Scholar 

  3. Garthus-Niegel S, Horsch A, Handtke E, et al (2018) The Impact of Postpartum Posttraumatic Stress and Depression Symptoms on Couples’ Relationship Satisfaction: A Population-Based Prospective Study . Front. Psychol. 9

  4. Netsi E, Pearson RM, Murray L et al (2018) Association of persistent and severe postnatal depression with child outcomes. JAMA Psychiat 75:247–253. https://doi.org/10.1001/jamapsychiatry.2017.4363

    Article  Google Scholar 

  5. Ahmed A, Bowen A, Feng CX, Muhajarine N (2019) Trajectories of maternal depressive and anxiety symptoms from pregnancy to five years postpartum and their prenatal predictors. BMC Pregnancy Childbirth 19:26. https://doi.org/10.1186/s12884-019-2177-y

    Article  PubMed  PubMed Central  Google Scholar 

  6. Drozd F, Haga SM, Valla L, Slinning K (2018) Latent trajectory classes of postpartum depressive symptoms: a regional population-based longitudinal study. J Affect Disord 241:29–36. https://doi.org/10.1016/j.jad.2018.07.081

    Article  PubMed  Google Scholar 

  7. Kiviruusu O, Pietikäinen JT, Kylliäinen A et al (2020) Trajectories of mothers’ and fathers’ depressive symptoms from pregnancy to 24 months postpartum. J Affect Disord 260:629–637

    Article  PubMed  Google Scholar 

  8. Nieh H-P, Chou L-T, Chang C-J (2022) Depressed mood trajectories in the first year postpartum among Taiwanese mothers: Associations with perceived support, financial stress, and marital satisfaction. J Health Psychol 27:2478–2488. https://doi.org/10.1177/13591053211049944

    Article  PubMed  Google Scholar 

  9. Rinne GR, Davis EP, Mahrer NE et al (2022) Maternal depressive symptom trajectories from preconception through postpartum: associations with offspring developmental outcomes in early childhood. J Affect Disord 309:105–114. https://doi.org/10.1016/j.jad.2022.04.116

    Article  PubMed  PubMed Central  Google Scholar 

  10. Fredriksen E, von Soest T, Smith L, Moe V (2017) Patterns of pregnancy and postpartum depressive symptoms: latent class trajectories and predictors. J Abnorm Psychol 126:173–183. https://doi.org/10.1037/abn0000246

    Article  PubMed  Google Scholar 

  11. PACT Consortium (2015) Heterogeneity of postpartum depression: a latent class analysis. The Lancet Psychiatry 2:59–67. https://doi.org/10.1016/S2215-0366(14)00055-8

    Article  Google Scholar 

  12. Baron E, Bass J, Murray SM et al (2017) A systematic review of growth curve mixture modelling literature investigating trajectories of perinatal depressive symptoms and associated risk factors. J Affect Disord 223:194–208. https://doi.org/10.1016/j.jad.2017.07.046

    Article  PubMed  PubMed Central  Google Scholar 

  13. Santos HJ, Tan X, Salomon R (2017) Heterogeneity in perinatal depression: how far have we come? A systematic review. Arch Womens Ment Health 20:11–23. https://doi.org/10.1007/s00737-016-0691-8

    Article  PubMed  Google Scholar 

  14. Vanwetswinkel F, Bruffaerts R, Arif U, Hompes T (2022) The longitudinal course of depressive symptoms during the perinatal period: a systematic review. J Affect Disord 315:213–223. https://doi.org/10.1016/j.jad.2022.06.087

    Article  PubMed  Google Scholar 

  15. Yim IS, Tanner Stapleton LR, Guardino CM et al (2015) Biological and psychosocial predictors of postpartum depression: systematic review and call for integration. Annu Rev Clin Psychol 11:99–137. https://doi.org/10.1146/annurev-clinpsy-101414-020426

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gastaldon C, Solmi M, Correll CU et al (2022) Risk factors of postpartum depression and depressive symptoms: umbrella review of current evidence from systematic reviews and meta-analyses of observational studies. Br J Psychiatry 221:591–602. https://doi.org/10.1192/bjp.2021.222

    Article  PubMed  Google Scholar 

  17. Hutchens BF, Kearney J (2020) Risk factors for postpartum depression: an umbrella review. J Midwifery Womens Health 65:96–108. https://doi.org/10.1111/jmwh.13067

  18. Sutter-Dallay AL, Cosnefroy O, Glatigny-Dallay E, et al (2012) Evolution of perinatal depressive symptoms from pregnancy to two years postpartum in a low-risk sample: The MATQUID cohort. J Affect Disord 139:23–29. https://doi.org/10.1016/j.jad.2011.08.018

  19. Puyané M, Subirà S, Torres A et al (2022) Personality traits as a risk factor for postpartum depression: A systematic review and meta-analysis. J Affect Disord 298:577–589. https://doi.org/10.1016/j.jad.2021.11.010

    Article  PubMed  Google Scholar 

  20. Costa PT, McCrae RR (1980) Influence of extraversion and neuroticism on subjective well-being: Happy and unhappy people. J Pers Soc Psychol 38:668–678

    Article  PubMed  Google Scholar 

  21. Iliadis SI, Koulouris P, Gingnell M et al (2015) Personality and risk for postpartum depressive symptoms. Arch Womens Ment Health 18:539–546. https://doi.org/10.1007/s00737-014-0478-8

    Article  PubMed  CAS  Google Scholar 

  22. Dekel S, Ein-Dor T, Dishy GA, Mayopoulos PA (2020) Beyond postpartum depression: posttraumatic stress-depressive response following childbirth. Arch Womens Ment Health 23:557–564. https://doi.org/10.1007/s00737-019-01006-x

    Article  PubMed  Google Scholar 

  23. Oh W, Muzik M, McGinnis EW et al (2016) Comorbid trajectories of postpartum depression and PTSD among mothers with childhood trauma history: Course, predictors, processes and child adjustment. J Affect Disord 200:133–141. https://doi.org/10.1016/j.jad.2016.04.037

    Article  PubMed  PubMed Central  Google Scholar 

  24. Radoš SN, Matijaš M, Anđelinović M et al (2020) The role of posttraumatic stress and depression symptoms in mother-infant bonding. J Affect Disord 268:134–140. https://doi.org/10.1016/j.jad.2020.03.006

    Article  PubMed  Google Scholar 

  25. Handelzalts JE, Hairston IS, Muzik M et al (2022) A paradoxical role of childbirth-related posttraumatic stress disorder (PTSD) symptoms in the association between personality factors and mother-infant bonding: A cross-sectional study. Psychol Trauma 14:1066–1072. https://doi.org/10.1037/tra0000521

    Article  PubMed  Google Scholar 

  26. Kasamatsu H, Tsuchida A, Matsumura K et al (2020) Understanding the relationship between postpartum depression one month and six months after delivery and mother-infant bonding failure one-year after birth: results from the Japan Environment and Children’s study (JECS). Psychol Med 50:161–169. https://doi.org/10.1017/S0033291719002101

    Article  PubMed  Google Scholar 

  27. Kerstis B, Aarts C, Tillman C et al (2016) Association between parental depressive symptoms and impaired bonding with the infant. Arch Womens Ment Health 19:87–94. https://doi.org/10.1007/s00737-015-0522-3

    Article  PubMed  Google Scholar 

  28. Nonnenmacher N, Noe D, Ehrenthal JC, Reck C (2016) Postpartum bonding: the impact of maternal depression and adult attachment style. Arch Womens Ment Health 19:927–935. https://doi.org/10.1007/s00737-016-0648-y

    Article  PubMed  CAS  Google Scholar 

  29. John OP, Donahue EM, Kentle RL (1991) Big five inventory. J Pers Soc Psychol

  30. Cox JL, Holden JM, Sagovsky R (1987) Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry 150:782–786. https://doi.org/10.1192/bjp.150.6.782

    Article  PubMed  CAS  Google Scholar 

  31. Levis B, Negeri Z, Sun Y et al (2020) Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data. BMJ 371:m4022. https://doi.org/10.1136/bmj.m4022

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ayers S, Wright DB, Thornton A (2018) Development of a Measure of Postpartum PTSD: The City Birth Trauma Scale . Front. Psychiatry 9

  33. American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th ed. American Psychiatric Association

  34. Handelzalts JE, Hairston IS, Matatyahu A (2018) Construct Validity and Psychometric Properties of the Hebrew Version of the City Birth Trauma Scale . Front. Psychol. 9

  35. Brockington IF, Oates J, George S et al (2001) A Screening Questionnaire for mother-infant bonding disorders. Arch Womens Ment Health 3:133–140. https://doi.org/10.1007/s007370170010

    Article  Google Scholar 

  36. Muzik M, Bocknek EL, Broderick A et al (2013) Mother-infant bonding impairment across the first 6 months postpartum: the primacy of psychopathology in women with childhood abuse and neglect histories. Arch Womens Ment Health 16:29–38. https://doi.org/10.1007/s00737-012-0312-0

    Article  PubMed  Google Scholar 

  37. R Core Team (2021) R: A language and environment for statistical computing

  38. IBM Corp. (2021) IBM SPSS Statistics for Windows, Version 28.0

  39. Charbonnier L, van Meer F, van der Laan LN et al (2016) Standardized food images: A photographing protocol and image database. Appetite 96:166–173. https://doi.org/10.1016/j.appet.2015.08.041

    Article  PubMed  Google Scholar 

  40. Ball G, Hall D (1965) ISODATA, a novel method of data analysis and pattern classification. Menlo Park

  41. Friedman HP, Rubin J (1967) On Some Invariant Criteria for Grouping Data. J Am Stat Assoc 62:1159–1178. https://doi.org/10.1080/01621459.1967.10500923

    Article  Google Scholar 

  42. Marriott FHC (1971) Practical Problems in a Method of Cluster Analysis. Biometrics 27:501–514. https://doi.org/10.2307/2528592

    Article  PubMed  CAS  Google Scholar 

  43. Scott AJ, Symons MJ (1971) Clustering Methods Based on Likelihood Ratio Criteria. Biometrics 27:387–397. https://doi.org/10.2307/2529003

    Article  Google Scholar 

  44. Duda R, Hart P (1973) Pattern classification and scene analysis. John Wiley & Sons, New York

    Google Scholar 

  45. Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3:1–27. https://doi.org/10.1080/03610927408827101

    Article  Google Scholar 

  46. Roelfsema JH, White SJ, Ariyürek Y et al (2005) Genetic Heterogeneity in Rubinstein-Taybi Syndrome: Mutations in Both the CBP and EP300 Genes Cause Disease. Am J Hum Genet 76:572–580. https://doi.org/10.1086/429130

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Dunn JC (1974) Well-Separated Clusters and Optimal Fuzzy Partitions. J Cybern 4:95–104. https://doi.org/10.1080/01969727408546059

    Article  Google Scholar 

  48. McClain JO, Rao VR (1975) CLUSTISZ: A Program to Test for the Quality of Clustering of a Set of Objects. J Mark Res 12:456–460

    Google Scholar 

  49. Hartigan JA (1975) Clustering Algorithms, 99th edn. John Wiley & Sons Inc, USA

    Google Scholar 

  50. Hubert L, Levin J (1976) A general statistical framework for assessing categorical clustering in free recall. Psychol Bull 83:1072–1080. https://doi.org/10.1037/0033-2909.83.6.1072

  51. Ratkowsky D, Lance G (1978) A criterion for determining the number of groups in a classification. Aust Comput J 10:115–117

    Google Scholar 

  52. Davies DL, Bouldin DW (1979) A Cluster Separation Measure. IEEE Trans Pattern Anal Mach Intell PAMI-1:224–227. https://doi.org/10.1109/TPAMI.1979.4766909

  53. Milligan GW (1980) An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 45:325–342. https://doi.org/10.1007/BF02293907

    Article  Google Scholar 

  54. Sarle W (1983) SAS Technical Report A-108. Cubic clustering criterion

  55. Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika 50:159–179. https://doi.org/10.1007/BF02294245

    Article  Google Scholar 

  56. Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7

  57. Krzanowski WJ, Lai YT (1988) A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering. Biometrics 44:23–34. https://doi.org/10.2307/2531893

    Article  Google Scholar 

  58. Halkidi M, Vazirgiannis M, Batistakis Y (2000) Quality scheme assessment in the clustering process. In: Zighed D, Komorowski J, Żytkow J (eds) Principles of data mining and knowledge discovery. Springer, Berlin, Heidelberg, pp 265–276

    Chapter  Google Scholar 

  59. Halkidi M, Vazirgiannis M (2001) Clustering validity assessment: finding the optimal partitioning of a data set. In: Proceedings 2001 IEEE international conference on data mining. IEEE, pp 187–194

  60. Beale E (1969) Cluster Analysis. Scientific Control Systems, London

    Google Scholar 

  61. Charrad M, Ghazzali N, Boiteau V, Niknafs A (2014) NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J Stat Softw 61:1–36. https://doi.org/10.18637/jss.v061.i06

  62. Atkinson-Clement C, Pigalle E (2021) What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown. Humanit Soc Sci Commun 8:81. https://doi.org/10.1057/s41599-021-00749-2

    Article  Google Scholar 

  63. Murphy M, Whitton AE, Deccy S et al (2020) Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder. Neuropsychopharmacology 45:2030–2037. https://doi.org/10.1038/s41386-020-0749-1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Khan M, Brandenburger T (2020) ROCit: Performance Assessment of Binary Classifier with Visualization

  65. Champely S (2020) pwr: Basic Functions for Power Analysis

  66. Williams AL, Craske MG, Mineka S, Zinbarg RE (2021) Neuroticism and the longitudinal trajectories of anxiety and depressive symptoms in older adolescents. J Abnorm Psychol 130:126–140. https://doi.org/10.1037/abn0000638

    Article  PubMed  Google Scholar 

  67. Ormel J, Jeronimus BF, Kotov R et al (2013) Neuroticism and common mental disorders: Meaning and utility of a complex relationship. Clin Psychol Rev 33:686–697. https://doi.org/10.1016/j.cpr.2013.04.003

    Article  PubMed  PubMed Central  Google Scholar 

  68. Nelemans SA, Hale WW, Branje SJT et al (2014) Heterogeneity in development of adolescent anxiety disorder symptoms in an 8-year longitudinal community study. Dev Psychopathol 26:181–202. https://doi.org/10.1017/S0954579413000503

    Article  PubMed  Google Scholar 

  69. Buttner MM, Brock RL, O’Hara MW (2015) Patterns of women’s mood after delivery: A growth curve analysis. J Affect Disord 174:201–208

    Article  PubMed  Google Scholar 

  70. Montoliu T, Hidalgo V, Salvador A (2020) Personality and Hypothalamic–Pituitary–Adrenal Axis in Older Men and Women . Front. Psychol. 11

  71. Soliemanifar O, Soleymanifar A, Afrisham R (2018) Relationship between Personality and Biological Reactivity to Stress: A Review. Psychiatry Investig 15:1100–1114. https://doi.org/10.30773/pi.2018.10.14.2

  72. Warfa N, Harper M, Nicolais G, Bhui K (2014) Adult attachment style as a risk factor for maternal postnatal depression: a systematic review. BMC Psychol 2:56. https://doi.org/10.1186/s40359-014-0056-x

    Article  PubMed  PubMed Central  Google Scholar 

  73. Axfors C, Sylvén S, Ramklint M, Skalkidou A (2017) Adult attachment’s unique contribution in the prediction of postpartum depressive symptoms, beyond personality traits. J Affect Disord 222:177–184. https://doi.org/10.1016/j.jad.2017.07.005

    Article  PubMed  Google Scholar 

  74. Robakis TK, Williams KE, Crowe S et al (2016) Maternal attachment insecurity is a potent predictor of depressive symptoms in the early postnatal period. J Affect Disord 190:623–631. https://doi.org/10.1016/j.jad.2015.09.067

    Article  PubMed  Google Scholar 

  75. Grekin R, O’Hara MW (2014) Prevalence and risk factors of postpartum posttraumatic stress disorder: a meta-analysis. Clin Psychol Rev 34:389–401. https://doi.org/10.1016/j.cpr.2014.05.003

    Article  PubMed  Google Scholar 

  76. Walker AL, de Rooij SR, Dimitrova MV et al (2021) Psychosocial and peripartum determinants of postpartum depression: Findings from a prospective population-based cohort. The ABCD study Compr Psychiatry 108:152239. https://doi.org/10.1016/j.comppsych.2021.152239

    Article  PubMed  Google Scholar 

  77. Vliegen N, Casalin S, Luyten P (2014) The course of postpartum depression: a review of longitudinal studies. Harv Rev Psychiatry 22:1–22. https://doi.org/10.1097/HRP.0000000000000013

    Article  PubMed  Google Scholar 

  78. Bleidorn W, Hopwood CJ, Lucas RE (2018) Life Events and Personality Trait Change. J Pers 86:83–96. https://doi.org/10.1111/jopy.12286

    Article  PubMed  Google Scholar 

  79. Stuart AL, Pasco JA, Jacka FN et al (2014) Comparison of self-report and structured clinical interview in the identification of depression. Compr Psychiatry 55:866–869. https://doi.org/10.1016/j.comppsych.2013.12.019

    Article  PubMed  Google Scholar 

  80. Fransson E, Sörensen F, Kunovac Kallak T et al (2020) Maternal perinatal depressive symptoms trajectories and impact on toddler behavior - the importance of symptom duration and maternal bonding. J Affect Disord 273:542–551. https://doi.org/10.1016/j.jad.2020.04.003

    Article  PubMed  Google Scholar 

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We have no known conflicts of interest to disclose.

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Author notes

  1. Jonathan E. Handelzalts and Shay Ohayon contributed equaly to this work.

Authors and Affiliations

  1. School of Behavioral Sciences, The Academic College of Tel-Aviv Yafo, 68114, Tel-Aviv, Israel

    Jonathan Eliahu Handelzalts & Sigal Levy

  2. Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA

    Jonathan Eliahu Handelzalts

  3. Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel

    Shay Ohayon

  4. Rabin Medical Center, The Helen Schneider Hospital for Women, Petach-Tikva, Israel

    Yoav Peled

  5. Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    Yoav Peled

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  1. Jonathan Eliahu Handelzalts
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  4. Yoav Peled
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Contributions

Conceptualization: [Jonathan E. Handelzalts]; Methodology: [Jonathan E. Handelzalts; Shay Ohayon]; Formal analysis and investigation: [Shay Ohayon, Sigal Levy]; Writing—original draft preparation: [Jonathan E. Handelzalts, Shay Ohayon, Yoav Peled]; Resources: [Yoav Peled]; Supervision: [Jonathan E. Handelzalts; Yoav Peled].

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Correspondence to Jonathan Eliahu Handelzalts.

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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the (removed for blind review) institutional review board.

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Informed consent was obtained from all individual participants included in the study.

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Handelzalts, J.E., Ohayon, S., Levy, S. et al. Risk psychosocial factors associated with postpartum depression trajectories from birth to six months. Soc Psychiatry Psychiatr Epidemiol (2024). https://doi.org/10.1007/s00127-023-02604-y

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