Predictors of Postpartum Depression

April Peter

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Chamberlain College of Nursing

NR 439: Evidence Based Practice

December 2017

 

Predictors of Postpartum Depression

            Childbirth is supposed to be a
wonderful and joyful occasion for those involved. Mothers often hear stories of
how elated they will be when they see their newborn baby that they will
completely forget about the tremendous pain they just went through. Not all
mothers experience this feeling of excitement and elation. According to the
Centers of Disease Control, 1 out of 9 women will experience postpartum
depression (cdc.gov). There are some moms that will experience the “baby blues”
after childbirth, these symptoms can cause anxiety, mood swings, unexpected
crying spells, and insomnia, and can be expected due to the changes in
hormones. However, some other moms will experience a more intense form of the
“baby blues” that is longer lasting and this is known as postpartum depression.

            The purpose of this research article
review is to determine the predictors of postpartum depression.  The research was performed to see if different
factors such as medical and psychiatric illness, depression, psychosocial
stress, or pregnancy related illness, and birth outcomes increase the
likelihood of developing postpartum depression.

Research Question

            The research question aims to see if
health risk behaviors, history of depression, sociodemographic factors, medical
and psychiatric illnesses, pregnancy related illnesses, and birth outcomes are
risk factors for developing postpartum depression. There have not been many
studies completed, or enough research done to show all of the possible causes
and risk factors for postpartum depression. Some things that could have
affected the research question would be if the clinical staff was unable to get
a portion of the questionnaires completed, high-risk pregnancy trends due to
the nature of the clinics patient population and the point in pregnancy in
which patients were seeking care at this clinic.

Research Design

            The research design of this study was
predictive and retrospective. According to Houser, predictive research is used
to determine whether a certain risk factor will lead to a certain health
condition and a retrospective study will use data that has already been
collected as part of an event or studies that has taken place (Houser, 2018).  The patients in the study were all receiving prenatal
care and had completed surveys during their second and third trimesters and in
the postpartum period. Information from these surveys as well as all the
demographic information, smoking history, and health history were all obtained for
the study.

            A retrospective study relies on how
well documentation was taken.  An example
of this within the study would be that the office staff was unable to complete
all the questionnaires. This could be a disadvantage to the study. Some
advantages or strengths of this type of study would be shorter timeframes and
cost effectiveness.

The Research Sample and
Data Collection

            The participants in this study were
women receiving prenatal care at the university obstetrical clinic between
2004-2011 who delivered at the University of Washington Hospital (JOWH 2014).

All women receiving care and who completed surveys during the second and third
trimester were eligible for the study. 
Exclusions in the study were women who were unable to complete the
questionnaire because of language barriers and mental capacity, or women less
than 15 years of age at the time of delivery. From this screening sample of
3,039, 1423 women were included and compared to the 1,616 who were excluded
(JOWH, 2014).  The sample size and number
of patients in this study was an adequate one, however inclusion of all pregnancies
could have given a better representation to the population of women who suffer
from postpartum depression, not just the high-risk pregnancies from this
setting. Another gap recognized within this study was that not everyone from
the sample completed their postpartum survey, and others did not complete both
surveys at 4 and 8 months or did not provide data on birth outcomes.

            Data was collected by the clinical
staff, the tools used were questionnaires and the questions covered
sociodemographic, medical, and behavioral information. The questionnaires were
implemented as a QI initiative in January of 2004 and were completed by patients
in their second and third trimesters. They were then completed again at the
6-week postpartum check. The Patient Health Questionnaire was used to assess
depressive symptoms and a continuous severity measure was used as a variable. Information
regarding the use of antidepressants was obtained via self-reporting. Patients
diagnosed with gestational diabetes were selected based on their ICD-9 code.

The PPPS scale, Prenatal Psychosocial Profile Stress scale, is an eleven item
self-report scale that measures perceived currently hassles or stressors. The TACE
alcohol screen, abuse assessment screen, and CAGE questionnaire were obtained
through self-reporting surveys. Gestational age, birth weight, birth outcomes,
were all obtained by electronic medical record documentation. Ethical
considerations were not specifically recognized but exclusion criteria could be
considered an ethical gap.

Limitations

            There are limits to this study that
have an effect on results. Such limitations are that the sample population is a
representation of only one clinic in the United States. Studying a larger population
sample and providing more detail in the questionnaires about alcohol,
postpartum depression history, psychiatric assessments, and social support
could help overcome the limitations of the study. Knowing and recognizing
limitations is important to understanding the research results, and discussing
these limitations validates the research and puts them into a context that is
easy to understand.

Findings

            The findings in this study conclude
that predictors of postpartum depression include: pre-pregnancy depression,
patients taking antidepressants, young age, unemployed status, chronic illness,
and smoking (JOWH, 2014).         These
findings determined which factors would increase the likelihood of postpartum
depression. Credibility of the research should be assessed using the following
key issues by Houser:

1.     The
author has appropriate clinical and educational credentials

2.     Determine
if there is any conflict of interest that will being bias to the study

3.     Is
the journal unbiased?

4.     Is
the research peer reviewed?

5.     Is
the research published in a reasonable timeframe?

This
research study is credible because the author has appropriate credentials,
there is no conflict of interest, the journal is not biased, the research was
peer reviewed and it was published in a timely manner.

Conclusion

Knowing and understanding the predictors
that increase the likelihood of developing postpartum depression are important
in the treatment of postpartum depression. Being able to identify these
predictors helps practitioners use interventions that will result in best
possible outcomes for the patients. Examples of interventions include better
management of chronic illness during pregnancy, management of depression in
those that have it prior to pregnancy, offering assistance with smoking and
alcohol cessation, nutrition assistance, and promoting mental health wellness.

This research study identified predictors that can increase postpartum
depression risk. Interventions and their implementation will require
collaboration across all areas of the healthcare team.

 

 

References

 

Depression
Among Women. (2017, February 15). Retrieved December 03, 2017, from

 

https://www.cdc.gov/reproductivehealth/depression/index.htm

 

Houser, J. (2018). Nursing Research: Reading,
Using and Creating Evidence, 4th Edition. Bookshelf

Online. Retrieved
from https://online.vitalsource.com/#/books/9781284138887/

 

Katon, W., Russo, J., & Gavin,
A. (2014). Predictors of postpartum depression. Journal of Women’s Health
(15409996), 23(9), 753-759. doi:10.1089/jwh.2014.4824. (7 p.)

 

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