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Predictors of Postpartum Depression

An Update

Beck, Cheryl Tatano


Background Approximately 13% of women experience postpartum depression. Early recognition is one of the most difficult challenges with this mood disorder because of how covertly it is suffered.

Objectives The purpose of this meta-analysis was to update the findings of an earlier meta-analysis of postpartum depression predictors that had synthesized the results of studies conducted mostly in the 1980s.

Method A meta-analysis of 84 studies published in the decade of the 1990s was conducted to determine the magnitude of the relationships between postpartum depression and various risk factors. Using the software system Advanced Basic Meta-Analysis, effect sizes were calculated three ways: unweighted, weighted by sample size, and weighted by quality index score.

Results Thirteen significant predictors of postpartum depression were revealed. Ten of the 13 risk factors had moderate effect sizes while three predictors had small effect sizes. The mean effect size indicator ranges for each risk factor were as follows: prenatal depression (.44 to .46), self esteem (.45 to. 47), childcare stress (.45 to .46), prenatal anxiety (.41 to .45), life stress (.38 to .40), social support (.36 to .41), marital relationship (.38 to .39), history of previous depression (.38 to .39), infant temperament (.33 to .34), maternity blues (.25 to .31), marital status (.21 to .35), socioeconomic status (.19 to .22), and unplanned/unwanted pregnancy (.14 to .17).

Conclusions Results confirmed findings of an earlier meta-analysis and in addition revealed four new predictors of postpartum depression: self-esteem, marital status, socioeconomic status, and unplanned/unwanted pregnancy.

Cheryl Tatano Beck, DNSc, CNM, FAAN, is Professor, School of Nursing University of Connecticut, Storrs.

Accepted for publication June 1, 2001.

The author thanks Joann Gleeson-Kreig, MS, RN, an assistant professor at Plattsburgh State University of New York, for her assistance in achieving interrater agreement in the coding of variables included in the meta-analysis codebook, and Perry Wein, MS, RN, for his help in retrieving the studies included in the meta-analysis.

Address reprint requests to Cheryl Tatano Beck, DNSc, CNM, FAAN, School of Nursing, University of Connecticut, 231 Glenbrook Road, U-26, Storrs, CT 06269 (e-mail:

© 2001 Lippincott Williams & Wilkins, Inc.