JOURNAL OF WOMEN’S HEALTH
Volume 23, Number 9, 2014
ª Mary Ann Liebert, Inc.
DOI: 10.1089/jwh.2014.4824
Predictors of Postpartum Depression
Wayne Katon, MD,1 Joan Russo, PhD,1 and Amelia Gavin, PhD 2
Abstract
Objective: To examine sociodemographic factors, pregnancy-associated psychosocial stress and depression,
health risk behaviors, prepregnancy medical and psychiatric illness, pregnancy-related illnesses, and birth
outcomes as risk factors for post-partum depression (PPD).
Methods: A prospective cohort study screened women at 4 and 8 months of pregnancy and used hierarchical
logistic regression analyses to examine predictors of PPD. The study sample include 1,423 pregnant women at a
university-based high risk obstetrics clinic. A score of ‡ 10 on the Patient Health Questionnaire-9 (PHQ-9)
indicated clinically significant depressive symptoms.
Results: Compared with women without significant postpartum depressive symptoms, women with PPD were
significantly younger ( p < 0.0001), more likely to be unemployed ( p = 0.04), had more pregnancy associated
depressive symptoms ( p < 0.0001) and psychosocial stress ( p < 0.0001), were more likely to be smokers
(p < 0.0001), were more likely to be taking antidepressants (ADs) during pregnancy ( p = 0.002), were less likely
to drink any alcohol during pregnancy ( p = 0.02), and were more likely to have prepregnancy medical illnesses,
including diabetes ( p = 0.02) and neurologic conditions ( p = 0.02).
Conclusion: Specific sociodemographic and clinical risk factors for PPD were identified that could help
physicians target depression case finding for pregnant women.
Introduction
T
he post-partum period is a high-risk time for development of major depressive episodes. Two systematic
reviews have found that 7%–13% of women will experience a
serious episode of postpartum depression (PPD).1,2 Women
who experience PPD have an increased risk of future depressive episodes and resulting functional impairment.3,4
PPD has been shown to adversely affect maternal functioning
and is a risk factor for poor mother–infant bonding, subsequent delayed child developmental milestones,5–7 and child
and adolescent mental health disorders.8,9
Several systematic reviews have examined risk factors for
development of PPD.1,2 Risk factors found to be associated
with moderate to high risk of PPD include depression or
anxiety during pregnancy, stressful life events, low levels of
social support, previous history of depression, and the personality factor of neuroticism.1,2 Pregnancy-related complications such as preeclampsia, premature labor, and other
labor-related complications were associated with significant
but lower level of risk in most studies.1 Markers of lower
socioeconomic status such as unemployment and lower educational attainment have also been associated with significant but lower risk of PPD.1,2
1
2
The systematic reviews found that a limitation of the literature was that few studies included the full wide range of
potential risk factors for PPD, such as sociodemographic
factors; prepregnancy medical illness; health risk behaviors
such as smoking, drug and alcohol use; depression history
prior to and during pregnancy; psychosocial stress; intimate
partner violence during pregnancy; pregnancy-related complications such as gestational diabetes and pregnancy-related
hypertension; and adverse birth outcomes such as preterm
birth, low birth weight, and fetal death.1,2
The purpose of this study was to examine a wide range of
socio-demographic factors, health risk behaviors, depression
history, prepregnancy medical illness, pregnancy-related
illnesses, and birth outcomes as risk factors for PPD.
Materials and Methods
Participants in this study were women receiving prenatal
care at the University Obstetrics Clinic between January 2004
and June 2011, who delivered at the University of Washington
Hospital. The university’s Obstetrics and Gynecology Clinic
and Obstetrics Inpatient Service have linked electronic records. Questionnaires assessing mood and other important
sociodemographic, medical, and behavioral information were
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington.
University of Washington School of Social Work, Seattle, Washington.
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introduced as a quality improvement initiative in January
2004.10 All women receiving obstetrical care and completing
at least one survey during their second or third trimester as well
as at 6-week postpartum follow-up were eligible for the study.
Women presented to this tertiary care clinic at different pregnancy trimesters, and therefore, some had only one questionnaire completed during pregnancy and others had two (i.e.,
4- and 8-month questionnaires). Clinic staff were responsible
for screening patients with the survey questionnaire and once
completed, obtaining written informed consent to link medical
records with survey results. Given the nature of a busy, urban
obstetrics clinic, staff were unable to get a small percentage of
questionnaires completed.
Exclusion criteria included being < 15 years of age at the
time of delivery or inability to complete the questionnaire due
to language difficulty or mental incapacity. All procedures
were approved by the University of Washington Human
Subjects Institutional Review Board.
Study variables and measures
The Patient Health Questionnaire-9 (PHQ-9) was used to
assess depressive symptoms during the second or third trimester and postpartum.11 A continuous PHQ-9 severity
measure was used as an independent variable. When questionnaires were filled out at both 4 and 8months, the mean
PHQ-9 score was used. A PHQ-9 of ‡ 10 was utilized as the
main outcome variable to define significant depressive
symptoms at the post-partum visit. A PHQ-9 of ‡ 10 has been
found in obstetrics and gynecology (Ob-Gyn) patients to have
the highest sensitivity (73%) and specificity (98%), compared to a structured psychiatric interview diagnosis of major
depression. Information about antidepressant (AD) use in
pregnancy was obtained from the self-report questionnaire.11
Self-report of AD use in pregnancy has been found to have
high concordance with pharmacy records.12
Sociodemographic information on age, marital status,
race/ethnicity, education, and employment, as well as general
health history, health risk behaviors, social history, and
psychosocial stressors were collected during either the second or third trimester. Chronic medical problems prior to
pregnancy were screened for with a standard list that included
hypertension, diabetes, asthma, thyroid disorders, migraines,
arthritis, seizure disorders, heart failure, cancer, and other
heart disease. Tobacco status was assessed using the SmokeFree Families prenatal screen that was developed to screen
for smoking during pregnancy.13 Women with any current
smoking were classified as smokers.
Diagnosis of pregnancy-induced/gestational hypertension
was based on outpatient and inpatient physician International
Classification of Diseases, Ninth Revision (ICD-9) diagnoses
of 642.3, 642.4, 642.5, 642.6, and 642.7, respectively.14 Diagnosis of gestational diabetes mellitus (GDM) was determined by a physician ICD-9 diagnosis of 648.8 in the
outpatient or inpatient medical record.15 GDM is clinically
defined as glucose intolerance with the first recognition or
onset in pregnancy;16 therefore, this diabetes category could
potentially include women with previously unrecognized
type 2 diabetes.
The Prenatal Psychosocial Profile Stress Scale is an 11item self-report scale that measures perceived current hassles
and stressors.17 Women indicate the extent to which each
KATON ET AL.
item is a current hassle or stressor on a 4-point Likert scale [1
(no stress) to 4 (severe stressor)] with a range of 11 to 44. It
has been shown to have high reliability and validity in
pregnant populations. The three-question Abuse Assessment
Screen has been validated in pregnant patients as a sensitive
and specific screen for intimate partner violence.18 Each item
is rated as ‘‘yes’’ or ‘‘no,’’ and the percentage of women with
at least one measure of intimate partner violence was described. The revised four item alcohol screening questionnaire,
the T-ACE (Take [number of drinks], Annoyed, Cut-down,
Eye-opener), was employed to assess use of alcohol during
pregnancy. The T-ACE modification of the alcohol screening
questionnaire, the CAGE (Cut-down, Annoyed, Guilt, Eyeopener), substitutes the guilt question with an alcohol tolerance
question.19 The T-ACE has been found to outperform obstetric
staff assessment of alcohol use by pregnant women.19 Women
with any use of alcohol during pregnancy were identified with
the T-ACE.
Offspring birth weight, gestational age at birth, and fetal
death were obtained from study participants’ computerized
medical records. Low birth weight was based on a gestational
weight threshold of 2,500 g. Pre-term birth was determined as
less than 37 weeks of completed gestation.
Statistical analyses
We selected the study sample from the entire screening
sample (N = 3,039). The 1,423 women whose data were included in the study were compared with the 1,616 women
who were excluded based on missing key data on baseline
demographic and clinical variables using Fischer exact tests
and t-tests. Due to significant inclusion group differences,
non-response propensity scores were created using baseline
variables (demographics, medical conditions, health risk
behaviors, pregnancy variables, and depression). The inverse
of the probability of not responding was used to weight our
regression analyses.
We compared baseline variables for the women with and
without PPD (having a postpartum PHQ-9 score ‡ 10) using
Fischer Exact tests and t-tests. Hierarchical logistic regression
analyses were used to predict the odds of PPD. We first examined the unadjusted association between prepregnancy
PHQ-9 scores and PPD. In the next model, we added demographic variables and reevaluated the odds for prepregnancy
depression. The third model contained the demographics and
medical conditions. The fourth model added health-related
behaviors to the model, and the fifth model added pregnancyrelated variables. Lastly, we calculated a final model predicting PPD, including all odds ratios and their 95% confidence
intervals for all the predictor variables.
Because an increase of five points on the PHQ-9 is associated with significant clinical change,20 we recalculated the
odds of PPD based on pregnancy PHQ-9 total scores formed
by creating groups with five-point intervals, adjusting for all
other study variables. Lastly, we conducted a sensitivity
analysis by examining the final model without the use of the
propensity weights.
Results
A total of 3,039 women were screened either at four
months or eight months (or at both time periods) of pregnancy. Of these, 1,515 women were excluded due to lack of a
PREDICTORS OF POSTPARTUM DEPRESSION
postpartum assessment (the vast majority of these women
attended post-partum visits at clinics closer to their homes
rather than the university high-risk obstetrics clinic); 84 were
excluded due to only filling out the 8-month questionnaire
which had no questions on medical history and 17 were excluded due to lack of data on birth outcomes (preterm labor or
low birth weight), leaving a study sample of 1,423.
Univariate analyses comparing the pregnancy data for
those women who were and were not included in this study
revealed significant group differences. Those women not
eligible for this study were slightly younger ( p < 0.001, although the difference in means was only 1.3 years), less
likely to be college educated ( p < 0.001), and more likely to
be single ( p < 0.001), non-white ( p < 0.001), and unemployed ( p < 0.001) than the women included in the study
sample. In addition, ineligible women reported being slightly
more depressed ( p < 0.003), 0.5 difference on PHQ-9), were
more likely to have a baby die ( p < 0.01), and less likely to
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have GDM ( p < 0.006) or preeclampsia ( p < 0.02) than those
women retained for analysis. Due to these differences, we
created nonresponse propensity weights utilizing the variables in Table 1, and have applied these weights to our regression analyses.
Table 1 displays the descriptive data for the groups with
and without PPD. A total of 6.7% of women had a PHQ-9
score of ‡ 10 during pregnancy, and 5.8% had a score of ‡ 10
at the postpartum check. Women with PPD reported significantly more depressive symptoms during pregnancy than
women without PPD. In addition, women with PPD were
significantly younger, less likely to be married, less educated,
and more likely to report being unemployed than women
without PPD. In terms of medical conditions, women with
PPD in comparison with women without PPD reported higher
rates of diabetes, migraines, and a trend ( p = 0.06) for hypertension and neurological conditions (0.07). Women with
PPD were more likely to report current smoking, taking an
Table 1. Descriptive Data for Women With and Without Post-Partum Depression (PPD)
Variablesa
mean (SD) or percent (n)
Pregnancy PHQ-9 Total Scoreb
Pregnancy PHQ-9 ( ‡ 10)c
Pregnancy minor depression PHQ-9 (5–9)c
Ageb
Racec
White
African American
Hispanic
Other
Marriedc
At least some collegec
Unemployedc
Total sample
N = 1,423
PHQ-9 (0–9)
no PPD
n = 1,340
Depression
3.4 (3.4)
3.1 (3.1)
6.7 (95)
4.9 (66)
4.4 (62)
4.1 (55)
Demographic variables
31.5 (5.9)
31.7 (5.9)
73.8 (1006)
74.1 (953)
5.6 (76)
5.4 (70)
4.6 (63)
44 (56)
16.0 (218)
16.1 (207)
89.4 (1257)
90.5 (1197)
85.5 (1200)
86.6 (1143)
40.1 (564)
38.7 (511)
Prepregnancy medical conditions
Asthmac
10.7 (152)
10.4 (139)
Diabetesc
7.3 (104)
6.9 (92)
7.2 (103)
7.0 (93)
GI disordersc
4.7 (67)
4.5 (60)
Heart conditionsc
Hypertensionc
6.6 (94)
6.3 (84)
Migrainec
14.1 (200)
13.2 (176)
2.0 (28)
1.8 (24)
Neurological conditionsc
7.0 (99)
6.8 (91)
Thyroid problemsc
Health-related behaviors
5.5 (78)
4.0 (53)
Current cigarette smokingc
Taking an antidepressantc
7.0 (100)
6.2 (83)
14.1 (201)
14.3 (190)
Drinking alcohol during pregnancyc
2.5 (36)
2.3 (30)
Intimate partner violence during the past yearc
Stress total scoreb
14.3 (3.4)
14.0 (3.1)
Pregnancy-related variables
GDMc
20.7 (294)
20.7 (278)
Preeclampsiac
21.2 (301)
20.6 (277)
Low birth weightc
11.5 (164)
10.8 (145)
Preterm birthc
15.0 (214)
14.2 (190)
Fetal deaths
0.35 (5)
0.37 (5)
a
PHQ-9 ( ‡ 10)
PPD
n = 83
p Value from t-test
or Fisher’s Exact test
7.8 (5.3)
34.9 (29)
8.4 (7)
0.0001
0.0001
0.09
28.5 (6.3)
0.0001
68.8
7.8
9.1
14.2
72.3
68.7
63.9
(53)
(6)
(7)
(11)
(60)
(57)
(53)
0.17
15.7
14.5
12.0
8.4
12.0
29.6
4.9
9.6
(13)
(12)
(10)
(7)
(10)
(24)
(4)
(8)
0.14
0.02
0.12
0.11
0.06
0.0001
0.07
0.37
30.5
20.5
11.6
7.2
18.3
(25)
(17)
(11)
(6)
(4.9)
0.0001
0.0001
0.54
0.02
0.0001
19.3
28.9
22.9
28.9
0
(16)
(24)
(19)
(24)
(0)
0.89
0.10
0.002
0.001
1.0
0.0001
0.0001
0.0001
Only a mean of 2.7% of the model variables were missing.
Mean (standard deviation [SD]).
Percent (n).
GI, gastrointestinal; GDM, gestational diabetes mellitus; PHQ-9, Patient Health Questionnaire-9; PPD, post-partum depression.
b
c
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KATON ET AL.
AD [women taking an AD had higher PHQ-9 scores than
women not treated with ADs: PHQ-9 = 6.1 (4.5) versus 3.1
(3.2), p < 0.001], being a victim of intimate partner violence
in the past year, and had higher stress scores than women
without PPD. The percentage of women with GDM and
preeclampsia during the pregnancy did not differ between the
depression groups. However, having an infant with low birth
weight or having a preterm birth were both significantly more
prevalent in the women with PPD. There were five fetal
deaths, all in women without PPD.
Table 2 presents the logistic regression results. The unadjusted odds ratio (OR) for PHQ-9 scores assessed during
pregnancy was 1.25 (95% confidence interval [95% CI] = 1.21–
1.29). The addition of the demographic variables and medical
conditions did little to change this result. The addition of the
health-related behaviors minimally reduced the odds to 1.10
(1.05–1.15). The addition of the pregnancy-related variables
did not change the odds ratio. Therefore, there is a 10% increase
in the odds of reporting PPD for every one-point increase in
PHQ-9 score assessed during pregnancy. A clinically significant five-point increase in PHQ-9 scores assessed during
pregnancy resulted in a 70% increase in the odds of PPD
[OR = 1.70, 95% CI = 1.34–2.16, p = .0001], after adjusting for
all the demographic, medical conditions, health-related behaviors, and pregnancy-related variables. The final model in
Table 2 shows the odds ratios and their 95% confidence intervals for all included variables. In addition to depression,
women with PPD were significantly more likely to be younger,
to be unemployed and to have prepregnancy diabetes and
neurological conditions, to be smokers; to report using Ads, and
Table 2. Risk Factors Associated with Post-Partum Depression
Model
Unadjusted total PHQ-9 score assessed during pregnancy
Total PHQ-9 Score assessed during pregnancy adjusted for demographic
variablesa
Total PHQ-9 Score assessed during pregnancy adjusted for demographic
variablesa and medical conditionsb
Total PHQ-9 Score assessed during pregnancy adjusted for demographic
variables,a medical conditions,b and health-related behaviorsc
Final multi-variable model
Total PHQ-9 Score assessed during pregnancy
Demographic variablesa
Age
White race
Married
Some college
Unemployment
Prepregnancy medical conditionsb
Asthma
Diabetes
GI disorders
Heart conditions
Hypertension
Migraines
Neurological conditions
Thyroid problems
Health-related behaviorsc
Current cigarette smoking
Taking an antidepressant
Drinking alcohol during pregnancy
Intimate partner violence within the past year
Stress total score
Pregnancy-related variablesd
GDM
Preeclampsia
Low birth weight
Preterm birth
Odds ratio (95% CI)
for total PHQ-9 Score (1 point) p Value
1.25 (1.21–1.29)
1.22 (1.18–1.26)
0.0001
0.0001
1.21 (1.17–1.25)
0.0001
1.10 (1.05–1.16)
0.0001
1.10 (1.05–1.15)
0.0001
0.94
1.01
1.14
1.18
1.50
(0.91–0.97)
(0.69–1.48)
(0.73–1.77)
(0.77–1.82)
(1.02–2.21)
0.0001
0.95
0.58
0.44
0.04
0.61
1.98
0.74
1.92
0.70
1.40
2.37
0.70
(0.37–1.00)
(1.12–3.52)
(0.42–1.32)
(0.98–3.79)
(0.39–1.25)
(0.93–2.11)
(1.12–5.02)
(0.34–1.46)
0.05
0.02
0.31
0.06
0.23
0.10
0.02
0.34
2.84
2.23
0.46
0.53
1.14
(1.80–4.48)
(1.35–3.68)
(0.24–0.90)
(0.24–1.13)
(1.09–1.19)
0.0001
0.002
0.02
0.10
0.0001
0.68
1.43
1.38
1.07
(0.40–1.13)
(0.95–2.17)
(0.79–2.43)
(0.64–1.81)
0.13
0.09
0.26
0.79
The above analyses are propensity weighted.
a
Demographic variables: age, race, marital status, education, unemployment.
b
Prepregnancy medical conditions: asthma, hypertension, diabetes, neurological condition, heart condition, GI condition, thyroid
problems, migraines.
c
Health-related behaviors: current cigarette smoking, taking an antidepressant, any domestic violence in the past year, drinking alcohol
during pregnancy, and stress total score.
d
Pregnancy-related variables: GDM, preeclampsia, low birth weight, and preterm birth.
95% CI, 95% confidence interval.
PREDICTORS OF POSTPARTUM DEPRESSION
to endorse more stress and less alcohol use during pregnancy in
comparison with the women without PPD.
For our sensitivity analysis, we ran the same complete model
without the propensity weights. The significance and odds ratios
for all the variables were similar for the propensity weighted and
unweighted models, except for four variables which became
nonsignificant: unemployment ( p = 0.37), asthma ( p = 0.41),
neurological problems ( p = 0.31), and alcohol consumption
during pregnancy ( p = 0.18). These variables were less robustly
associated with PPD in the weighted model, and all four of these
variables also occurred in higher rates in women who were
excluded from the study, which probably accounts for differences in the weighted versus unweighted samples.
Discussion
PPD is a very common disorder that causes significant
functional impairment and increases risk of poor mother–
infant bonding and delays in infant development. Therefore,
enhancing the understanding of vulnerability factors could
raise awareness for obstetricians and primary care physicians
about high-risk populations. Our data show that depressive
symptoms in pregnancy, AD use at the time of pregnancy
screening, younger age, unemployment, prepregnancy diabetes and neurologic disorders, smoking, less alcohol use
during pregnancy, and a high degree of psychosocial stressors
were independent predictors of risk of PPD. Of these, neither
smoking nor prepregnancy diabetes (or other medical disorders) were found to be associated with risk in the previous
meta-analyses1,2 but were likely not included as predictors in
many previous studies.
Our findings emphasize that one of the highest risk factors
for PPD, which is potentially modifiable, is depressive symptoms in pregnancy. We found that for every one-point change in
depressive symptoms there was an associated 10% increased
risk of PPD. A significant clinical increase of five-points on the
PHQ-9 was associated with an approximately 70% increased
risk of PPD. These data emphasize the importance of improving
case-finding of patients with depression using well-validated
tools such as the PHQ-9 coupled with development of effective
primary care or Ob-Gyn evidence-based depression interventions. Screening for depression and referral out-of-clinic to
mental health specialists is not likely to be effective due to data
showing that approximately half of primary care patients with
depression do not follow through with primary care referrals to
mental health specialists.21 Collaborative depression care interventions integrated into medical clinics which include a
physician-supervised care manager, longitudinal measurement of depressive symptoms, and increasing intensity of
care based on persistent symptoms have been shown to be
an effective way to improve quality of treatment of depression, and depressive and functional outcomes, in both
primary care22 and Ob-Gyn settings.23
The finding that younger, unemployed, psychosocially
stressed women with adverse health habits such as smoking,
and development of depressive disorders and medical disorders in early adulthood, are vulnerable to PPD is supported
by epidemiologic data showing that women growing up in
socially disadvantaged environments tend to have greater risk
for both medical and psychiatric disorders, which often develop at younger ages than women growing up in less vulnerable situations.24,25 Recent research has focused on the
757
effect of stressors over a woman’s life-course in adding to
risk of pregnancy-related complications, low birth weight,
preterm labor, and PPD.26 Prospective studies suggest that
maternal exposure to low socioeconomic status in childhood
and exposure to violence/mental health issues in childhood
was associated with low birth weight in offspring.27–30 An
emerging literature suggests that causal mechanisms link
maternal early-life risk and offspring birth weight. Specifically, through a causal pathway that includes adolescent
substance use and prenatal substance use,31,32 researchers
have shown that maternal exposure to maltreatment and
economic disadvantage during early childhood is associated
with offspring low birth weight.
Physicians strongly advise women to quit smoking during
pregnancy due to its association with risk of low birth
weight.33 Depression during pregnancy has been found to
increase risk of not being able to quit smoking.34 Many
pregnant women do quit smoking, and it is possible that those
in our sample who had not quit by the 4- or 8-month screening
had both stronger nicotine dependence and more psychological vulnerabilities that they cope with by smoking.34,35
We also found that prepregnancy diabetes was associated
with a higher risk of PPD. Lower socioeconomic status,36
childhood adversity,37 and depression38 have been found to
be associated with a higher risk of prepregnancy metabolic
abnormalities and diabetes, suggesting that development of
this disorder early in a woman’s life may reflect psychological and social vulnerabilities. Obesity and prepregnancy
diabetes are also linked to the risk for pregnancy-induced
hypertension and GMD, which may increase patient’s perception of stress during pregnancy and risk of PPD.39
AD use is likely a marker of depressive episodes that occurred prior to pregnancy. However, AD use in observational
studies of medical populations is usually an example of
confounding by severity, since only approximately half of
patients in primary care are accurately recognized by physicians as having depression, and only half of those diagnosed
are effectively treated.40 Thus, AD prescriptions may be
correlated with more severe and persistent episodes of depression, which are more likely to be recognized but are still
often inadequately treated. We examined whether there was
evidence of less than adequate treatment in patients in our
sample who were treated with ADs and found significantly
more depressive symptoms based on the PHQ-9 in these
patients compared to those not treated with ADs.
The strengths of this study include the large sample size,
using propensity weights to allow use of the full screening
sample, and inclusion of a full range of predictor variables.
Limitations include study of a population from one large university clinic in one geographical region of the United States,
lack of use of structured psychiatric interviews for diagnosis of
depression and history of prior depressive episodes, and not
assessing body mass index (BMI) or social support. However,
the PHQ-9 has been validated in a large Ob-Gyn study of 3,000
patients and found to have high sensitivity and specificity for
the diagnosis of major depression based on structured psychiatric interview,11 and we did assess prepregnancy diabetes,
hypertension, and gestational diabetes—all of which are associated with prepregnancy BMI. Moreover, the PHQ-9 is being
widely used in quality improvement efforts in many primary
care settings. We also found a lower rate of probable major
depression based on a PHQ-9 score of ‡ 10 during pregnancy
758
(6.7%) and postpartum periods (5.8%) than has been found in
many previous studies. The relatively high educational level
and the high percentage of married women in our sample may
have served as protective factors for development of affective
illness. The finding that less alcohol use during pregnancy was
associated with a high risk of PPD was surprising and may
reflect the limitations of the questionnaire used, which identified a minority of women with ‘‘any use’’ of alcohol but very
few with abuse or dependence problems. It also may reflect
underreporting of alcohol use. The single 6-week postpartum
screen for depression may have missed some women who
develop PPD within a three-month period. However, a recent
large study found that most post-partum episodes began within
the first postpartum month.41
Conclusion
In summary, we found that younger age, unemployment,
antenatal depressive symptoms, taking ADs, psychosocial
stressors, prepregnancy chronic physical illnesses (diabetes
and neurologic conditions), and smoking were independent
predictors of development of PPD. Attention to these risk
factors may help primary care and Ob-Gyn physicians focus
depression case-finding efforts.
Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Wayne Katon, MD
Department of Psychiatry and Behavioral Sciences
University of Washington School of Medicine
Campus Box 356560
1959 Northeast Pacific Street
Seattle, WA 98195
E-mail: wkaton@uw.edu
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ORIGINAL RESEARCH
Maternal and neonatal health outcomes following the implementation
of an innovative model of nurse practitioner-led care for diabetes in
pregnancy
Giuliana O. Murfet, Penny Allen & Tania J. Hingston
Accepted for publication 28 August 2013
Correspondence to P. Allen:
e-mail: penny.allen@utas.edu.au
Giuliana O. Murfet MSc MNurs (Nurse
Practitioner) RN CDE
Nursing Practitioner
Diabetes Centre, Tasmanian Health
Organisation – North West, Burnie,
Tasmania, Australia
Penny Allen BA (Hons) MPH PhD
Research Fellow
Rural Clinical School, University of
Tasmania, Burnie, Tasmania, Australia
Tania J. Hingston MBBS (Hons)
FRANZCOG
Consultant Obstetrician and Gynaecologist
Maternity Department, North West Private
Hospital, Burnie, Tasmania, Australia
M U R F E T G . O . , A L L E N P . & H I N G S T O N T . J . ( 2 0 1 4 ) Maternal and neonatal
health outcomes following the implementation of an innovative model of nurse
practitioner-led care for diabetes in pregnancy. Journal of Advanced Nursing 70
(5), 1150–1163. doi: 10.1111/jan.12277
Abstract
Aim. To investigate maternal and neonatal outcomes following implementation
of a nurse practitioner-led model of care for diabetes in pregnancy.
Background. Diabetes in pregnancy increases the risk of adverse health outcomes
in mothers and infants. The management of diabetes in pregnancy is crucial to
reduce poor outcomes.
Design. Uncontrolled before-after intervention study.
Methods. International Classification of Diseases codes were used to identify
pregnancies suspected of being complicated by diabetes. Demographic, health,
diabetes and maternity data were extracted from hospital records. Adverse
maternal and neonatal outcomes were compared pre- (2003–2006) and
postintervention (2010–2011). Adjusted relative risks (aRR) were calculated using
the glm command in Stata.
Results. A total of 261 pregnancies were included: 112 pre-intervention and 149
managed under the nurse practitioner-led model. There were 37 women with preexisting diabetes (26 T1DM, 11 T2DM) and 195 with gestational diabetes.
Referrals to dieticians and diabetes educators increased, while referrals to
physicians decreased. There was no decrease in the risk of adverse maternal
outcomes for all women with DIP or women with GDM. However, there was a
24% decrease in adverse neonatal outcomes overall and a 40% decrease among
infants of women with gestational diabetes.
Conclusion. The study demonstrated that nurse practitioner-led models of care
for diabetes in pregnancy are feasible. The findings suggest that the model
reduced adverse neonatal outcomes. By improving information provision, support
and care coordination, the model is particularly valuable in rural areas, where
access to medical specialists is often restricted.
Keywords: diabetes, model of care, nurse practitioner, nurses, nursing, pregnancy
1150
© 2013 John Wiley & Sons Ltd
JAN: ORIGINAL RESEARCH
Why is this research or review needed?
•
This research describes an innovative model of nurse practitioner-led care for the women with pregnancies complicated
Nurse practitioner-led care for diabetes in pregnancy
mentation of nurse practitioner-led models of care for DIP
in rural regions. This study evaluated a nurse practitionerled DIP clinic in the sparsely populated North West of the
island state of Tasmania, Australia.
by diabetes.
•
Published studies of nurse-led care models for the management of diabetes in pregnancy differ from the model presented in this paper.
•
Few studies have evaluated the effectiveness of nurse-led
models for reducing adverse maternal and neonatal health
outcomes in pregnancies complicated by diabetes.
What are the key findings?
•
The nurse practitioner-led model of care increased referrals to
•
The model resulted in decreased referrals to physicians for
•
There was a 24% reduction in the risk of adverse neonatal
dieticians and diabetes educators.
diabetes management.
outcomes among all women with diabetes in pregnancy in the
postintervention period and a 40% reduction in the risk of
adverse neonatal outcomes among women with gestational
diabetes.
•
The model may have played an important role in reducing
adverse neonatal outcomes.
How should the findings be used to influence policy/
practice/research/education?
•
The findings support the implementation of nurse practitioner-led care models for women with pregnancies complicated by diabetes.
Introduction
The prevalence of diabetes in pregnancy (DIP) has escalated
in developed and developing countries over the past
10–15 years (Dabelea et al. 2005, Hunt & Schuller 2007,
Baraban et al. 2008, Bell et al. 2008). This presents a challenge to health professionals and healthcare systems as DIP
increases the risk of adverse outcomes for both the mother
and child (Farrell et al. 2002, Temple et al. 2002, Dunne
et al. 2003, Clausen et al. 2005, Macintosh et al. 2006,
Hapo Study Cooperative Research Group et al. 2008, Peticca et al. 2009). Women with DIP living in rural Australia
are likely to be at greater risk of complications due to poor
levels of access to specialist care and barriers to accessing
diabetes self-management information (King & Wellard
2009). A potential solution to this problem is the imple© 2013 John Wiley & Sons Ltd
Background
DIP encompasses pregnancies in women with pre-existing
type 1 diabetes (T1DM), pre-existing type 2 diabetes
(T2DM) or gestational diabetes mellitus (GDM). During
the early stages of pregnancy, particularly in the first trimester when nausea and vomiting are common, the mother
with either T1DM or T2DM may find it difficult to maintain blood glucose levels to target, resulting in recurrent hyperglycaemic or hypoglycaemic events, which are associated
with pregnancy loss (Jovanovic et al. 2005). Pre-eclampsia
is a serious maternal complication found in approximately
7–13% of women with DIP (McIntyre et al. 2004, Temple
et al. 2006, Peticca et al. 2009), compared with approximately 3% in normoglycaemic pregnancies (McIntyre et al.
2004). As a consequence of pre-eclampsia, glycaemic
changes and/or foetal complications, the pregnancy may
not reach full term. Pre-term labour accounts for around
31–38% of pregnancies complicated by diabetes (Ferrara
et al. 2012). A further concern is the approximate 25–6
fold increased risk of perinatal and early neonatal mortality
compared with the general population (Dunne et al. 2003,
Macintosh et al. 2006). Pregnancies that continue to near
full term may be complicated by difficulties encountered
during birth. The large, macrosomic baby born vaginally
may result in perineal lacerations requiring repair (Berard
et al. 1998). On occasion, the macrosomic infant is simply
too large for a vaginal delivery. Consequently, women with
DIP typically have high rates of caesarean delivery with a
large Canadian study reporting caesarean rates of 52%
among women with T1DM and 38% among women with
T2DM or GDM (Peticca et al. 2009).
Infants born to women with DIP are at increased risk of
growth retardation, macrosomia, birth trauma, polycythaemia, cardiomyopathy, thrombosis, hypoglycaemia, hypomagnesaemia, jaundice, feeding difficulties and long-term
metabolic abnormalities (Inkster et al. 2006, Ponzo et al.
2006, Hawdon 2011). Respiratory distress syndrome
(RDS), which affects approximately 5% of infants born to
women with T1DM, (Persson et al. 2009) poses a serious
risk to the survival of the neonate. A major concern is the
risk of congenital abnormalities, which occur in 5–10% of
infants born to women with diabetes (Farrell et al. 2002,
Evers et al. 2004, McElduff et al. 2005, Macintosh et al.
2006). Congenital anomalies are seen more frequently in
1151
G.O. Murfet et al.
babies born to mothers with pre-existing T1DM (Peticca
et al. 2009) or T2DM, compared with women with GDM
(Macintosh et al. 2006).
Macrosomia is a common complication seen in babies of
women who have DIP. Although there are differing definitions for macrosomia, a generally accepted definition is
birthweight >4 kg and/or >90th percentile for gestational
age (Negrato et al. 2012). Macrosomia can lead to pre-term
delivery, stillbirth and early neonatal death (Zhang et al.
2008). Furthermore, the large baby can sustain damage as
it passes through the birth canal, resulting in shoulder dystocia, fractures to the clavicle and injury to the brachial
plexus and facial nerve (Berard et al. 1998).
The long-term effects of GDM in women include an
increased risk of recurrence in subsequent pregnancies,
impaired glucose tolerance and diabetes in the future
(Homko et al. 2001, Kim et al. 2007, Anderberg et al.
2011, Malinowska-Polubiec et al. 2012). Children born to
women with DIP are at increased risk of insulin resistance, impaired glucose tolerance, metabolic disorders,
increased BMI, changes in fat distribution, childhood
obesity and developing diabetes in childhood or teenage
years (Weintrob et al. 1996, Crume et al. 2011, Dabelea
& Crume 2011, Yessoufou & Moutairou 2011, Sparano
et al. 2013).
Australian guidelines for the management of diabetes in
pregnancy
The management of diabetes in pregnancy is crucial to
reduce adverse maternal and peri-natal outcomes. Models
consisting of dietary and blood glucose advice and insulin
therapy have been shown to improve glycaemic control and
reduce rates of macrosomia, premature delivery, shoulder
dystocia, caesarean delivery, stillbirth and neonatal mortality (Crowther et al. 2005, Temple et al. 2006, Landon
et al. 2009).
Given the strength of this evidence, the Australasian Diabetes in Pregnancy Society (ADIPS) guidelines recommend
fasting blood glucose levels to be targeted at 40–55 mmol/
L and 2-hour postprandial levels at 40 units of insulin at review.
The DNP liaised with the pharmacy departments of the
two local public hospitals to enable appropriate insulins to
be dispensed on the day of the clinic, as required. By
August 2009, the DIPC, incorporating a multidisciplinary
team [obstetrician, diabetes educator, dietician and antenatal nurse] and the use of a Management of Gestational Diabetes Protocol, was established at the public and private
hospital sites. Figure 1 outlines the DNP model of care for
pregnancies complicated by diabetes.
In women with T1DM or T2DM, a Continuous Glucose
Monitoring System (CGMS) was used to identify target
areas of hyperglycaemia, with women sent to the Diabetes
Centre for this monitoring. An after-hours DNE contact
number was provided to women for further information
and support regarding management of blood glucose levels
(BGLs). Dependant on glycaemic control, use of insulin and
obstetric reasons, women were reviewed on a 1–4 weekly
basis.
Women who screened positive for GDM were informed
of diagnosis, provided information and booked into the
next weekly DIPC. As per ADIPS guidelines at the time of
the study, screening could occur earlier, either at
12–16 weeks if the woman had a previous history of
GDM, or at presentation if the woman was symptomatic.
At the DIPC, women received an initial assessment with the
dietician and education with the CDE, including education
about home blood glucose monitoring. At future appointments, clients were seen by the dietician and CDE simultaneously, with review by the midwife and obstetrician as
necessary.
The study
Aim
The study aimed to investigate maternal and neonatal
health outcomes pre- and postimplementation of a nurse
practitioner-led model of care for pregnancies complicated
by diabetes in a rural locality.
Design
Uncontrolled before-after study of maternal and neonatal
outcomes following implementation of a nurse practitionerled model of care for DIP.
1154
Sample/participants
The pre-intervention audit included all pregnancies in
North West Tasmania that were complicated by diabetes
between July 2003–June 2006. Between late 2006–2009,
one local hospital underwent major organizational and
funding changes. Additionally, in 2008, a trial DIP screening programme was implemented for 12 months by one of
the antenatal services with support of the Diabetes Centre
as preliminary data highlighted lack of screening and management. It was, therefore, decided to exclude these periods
from the study to reduce bias and potential contamination
of the results from the effects of the DIPC pilot. While the
nurse-led DIPC was implemented in August 2009, it took
several months for the model to become embedded in local
maternity services. It was therefore decided to delay the
postintervention audit to cover the period from January
2010–December 2011.
The inclusion criteria for the pre- and postintervention
periods were: (1) Women with pre-existing diabetes who
attended maternity services at either of the two local public
hospitals or the one local private hospital during the study
period; (2) Women diagnosed with GDM who attended
maternity services at the same hospitals during the study
period. The DNP used International Classification of Diseases (ICD) codes (Table 1) to identify women with pregnancies suspected of being complicated by diabetes. This
was necessary as the pre-intervention audit found screening
at one hospital was extremely limited.
Data collection
Three CDEs recorded data from maternity services records
onto a data collection audit form for all pregnancies suspected of being complicated by diabetes. The lead DNP
reviewed each form for completeness and cross-checked a
random sample of 20% of forms with medical records for
data accuracy. The audit form included information on
demographics, GDM-specific screening, pre-existing diabetes-specific complication screening, referral to multidisciplinary team, monitoring of diabetes, treatments for
diabetes during pregnancy and both maternal and neonatal
health outcomes. Local experts in the field, including a diabetes paediatrician, diabetes nurse educator with a pregnancy portfolio and diabetes physician, reviewed the audit
tool for content validity prior to use. Furthermore, an
expert ‘diabetes in pregnancy’ endocrinologist (based interstate) validated the panel’s findings, benchmarking the form
against current best practice evidence. BMI was not available for the pre-intervention group as height and weight
© 2013 John Wiley & Sons Ltd
JAN: ORIGINAL RESEARCH
Nurse practitioner-led care for diabetes in pregnancy
Table 1 ICD codes used to identify study participants.
ICD codes
O240
0241
0242
0243
0244
0245
0249
0335
0366
0350
P700
P701
P704
Pre-Existing T1DM in pregnancy
Pre-Existing T2DM in pregnancy
Pre-Existing DM other specified in pregnancy
Pre-Existing DM unspecified in pregnancy
Gestational Diabetes Mellitus, diabetes arising at >24 weeks in pregnancy
Pre-existing impaired glucose regulation
Diabetes mellitus in pregnancy, unspecified onset
Maternal care for disproportion due to unusually large foetus (included if screening for GDM had not occurred & hypoglycaemia
evident)
Maternal care for excessive foetal growth (known or expected LGA)
Maternal care for (suspected) Central Nervous System malformation in foetus – anencephaly, spina bifida (included if the baby was
macrosomic and screening for GDM had not occurred
Syndrome of infant of a mother with gestational diabetes
Syndrome of infant of diabetic mother – maternal diabetes mellitus (pre-existing) affecting foetus or new born (with
hypoglycaemic)
Other neonatal hypoglycaemia (included if the baby was macrosomic and screening for GDM had not occurred)
were not routinely measured as part of antenatal care during this period. HbA1c using Diabetes Control and Complications Trial (DCCT)% units were converted to
International Federation of Clinical Chemistry (IFCC)
mmol/mol units. Socio-economic status (SES) was derived
from the Australian Bureau of Statistics (ABS) Socio-Economic Indexes for Areas (SEIFA) 2006, (Australian Bureau
of Statistics 2006) by matching postcodes to the Index of
Relative Socio-economic Advantage and Disadvantage (IRSAD), an area-level measure of socio-economic advantage
to disadvantage that includes data on income, education
level, unemployment, housing expenditure and assets. The
higher deciles of this measure indicate relative advantage in
an area.
Ethical considerations
Research Ethics Committee approval for the audit was
granted by the Patient Care Committee of the Quality and
Safety Executive Unit at the North West Regional Hospital,
Tasmania and from the Executive Board of the North West
Private Hospital. Patient anonymity was maintained by
assigning a unique study identifier to each pregnancy and
not recording patient identifiable data such as names and
addresses.
Data analysis
Macrosomia was defined as birthweight >90th percentile
for gestational age, while pre-term births were defined as
births up to 37 weeks gestation. Adverse maternal
© 2013 John Wiley & Sons Ltd
outcomes included loss of consciousness, threatened abortion requiring cervical sutures, hypoglycaemia, diabetic
ketoacidosis, metabolic complication, polyhydramnious,
placenta previa, pyelonephritis, emergency caesarean section, failure to progress in labour, 2–4° tear and postpartum haemorrhage. Adverse neonatal health outcomes were
defined as hypoglycaemia [BGL
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