Comparing Effectiveness of Treating Depression With & Without Comorbidity to Improve Fetal Health
Conditions: Depression, Preterm Delivery
Sponsor: Kaiser Permanente

COMPARING EFFECTIVENESS OF TREATING DEPRESSION 
WITH & WITHOUT COMORBIDITY TO IMPROVE FETAL 
HEALTH 
STUDY PROTOCOL  
12/1/2013 
 
CLINICAL TRIALS REGISTRATION NUMBER: NCT02371356 
 

 
STUDY PROTOCOL 
.  
Title: 
Comparing Effectiveness of Treating Depression With & Without 
Comorbidity to Improve Fetal Health 
 
Short Title: 
Maternal Depression and Its Treatment in Relation to Fetal Health 
 
Sponsor: 
  Patient Centered Outcomes Research Institute (PCORI) 
IRB Number: 
  CN-13-1650-H 
Protocol version: 
  1 
 
Study Principal Investigator: De-Kun Li, MD, PhD 
Senior Research Scientist 
Division of Research 
Kaiser Foundation Research Institute 
Kaiser Permanente 
2000 Broadway 
Oakland, CA 94612 
510-891-3755  
510-891-3761 (fax) 
De-Kun.Li@kp.org 
 
 

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 TABLE OF CONTENTS 
Table of Contents............................................................................................................ 1 
Abstract .......................................................................................................................... 3 
1 
BACKGROUND INFORMATION AND RATIONALE ....................................................... 4 
2 
STUDY OBJECTIVES ................................................................................................... 7 
3 
STUDY PROCEDURES ................................................................................................ 8 
3.1 
STUDY DESIGN ............................................................................................................... 8 
3.1.1 Overview of the Study Design and Approaches ..................................................... 8 
3.1.2 Choice of outcomes ............................................................................................... 9 
3.1.3 Data collection ....................................................................................................... 9 
4 
STUDY DURATION, ENROLLMENT AND SITES .......................................................... 12 
4.1 
STUDY POPULATION ...................................................................................................... 13 
4.1.1 Inclusion Criteria .................................................................................................. 13 
4.1.2 Exclusion Criteria ................................................................................................. 13 
4.1.3 Populations .......................................................................................................... 13 
5 
COMPARATORS ..................................................................................................... 14 
6 
ANALYTIC METHODS .............................................................................................. 16 
6.1 
GENERAL APPROACH TO ANALYSES .................................................................................. 16 
6.1.1 Propensity Score Analysis .................................................................................... 18 
6.1.2 Instrumental Variable Analysis ............................................................................ 19 
6.2 
AVOIDANCE OF BIAS ...................................................................................................... 19 
6.2.1 Study population ................................................................................................. 20 
6.2.2 Sample size and statistical power ....................................................................... 20 
7 
STUDY ADMINISTRATION ....................................................................................... 22 
7.1 
DATA COLLECTION AND MANAGEMENT ............................................................................ 22 
7.1.1 Data sources ........................................................................................................ 22 
7.2 
CONFIDENTIALITY ......................................................................................................... 22 
7.3 
RISKS AND BENEFITS ..................................................................................................... 22 
7.3.1 Potential and anticipated risks to research participants. ................................... 22 
7.3.2 Risk Minimization ................................................................................................ 23 
7.3.3 Benefits to participants and to society ................................................................ 23 
7.3.4 How potential risks are justified by potential benefits ........................................ 23 
7.4 
RECRUITMENT STRATEGY ............................................................................................... 23 
7.5 
INFORMED CONSENT AND HIPAA AUTHORIZATION ............................................................ 23 
7.5.1 Waiver of Consent for Stage-One Sample ........................................................... 23 
7.5.2 Waiver of Consent for Stage-Two Sample ........................................................... 24 
7.5.3 Waiver of HIPAA Authorization ........................................................................... 24 
7.5.4 Payment to Subjects ............................................................................................ 24 

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8 
DISSEMINATION ..................................................................................................... 24 
8.1 
DISSEMINATION TO PATIENTS AND ADVOCATES (STATEWIDE AND NATIONWIDE) ....................... 25 
8.2 
DISSEMINATION TO PROVIDERS WITHIN KPNC ................................................................... 25 
8.3 
MAKING STUDY RESULTS AVAILABLE TO STUDY PARTICIPANTS AFTER COMPLETION OF ANALYSES. .. 25 
9 
REFERENCES ........................................................................................................... 26 
 
 
 

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ABSTRACT 
Depression during pregnancy is prevalent (15-20%) and results in adverse fetal outcomes including 
preterm delivery (PTD) and low birthweight (LBW). Currently, significant confusion exists about 
unknown risk–benefit profiles of various treatments during pregnancy. We propose to conduct a 
two-stage prospective cohort study, using KPNC’s peripartum depression screening program, to 
determine if treating depression in pregnancy is effective in improving fetal outcomes, and which 
treatment is most effective: pharmacotherapy, psychotherapy or a combination. Risk-benefit of 
treatments will be examined separately for two depression types: those with depression only and 
those with psychiatric co-morbidities to evaluate differences in treatment effectiveness. Four 
cohorts with different treatment options will be formed within each depression type (with or 
without co-morbidity): (A) “Antidepressant only”: depressed and use only antidepressants; (B) 
“Psychotherapy only”: depressed and receive psychotherapy only; (C) “Combination therapy”: 
depressed and receive antidepressants and psychotherapy; (D)”Untreated depression”: depressed 
and receive no treatment. Eight cohorts will be formed. We will also form cohort (E) “No 
depression”: screen negative and receive no treatment, for baseline comparison. Information on 
depression treatment and PTD & LBW will be available for all 88,000 women in the stage-one 
sample. We will form stage-two sub-cohorts by randomly selecting 400 from each cohort (total 
3,600) and interview them to obtain detailed information on treatment compliance and 
confounders. Comparison of Cohort A, B & C to D, respectively, will determine if treating 
depression is effective.  Pair-wise comparisons among Cohorts A, B & C will determine comparative 
effectiveness of treatments. Comparison of Cohort D to E provides baseline fetal risks of untreated 
depression. Findings will provide answers to pressing questions of how to treat depression in 
pregnancy and which treatment is most effective with the best risk-benefit profile in improving 
fetal outcomes, thereby reducing PTD or LBW, infant mortality and morbidity, and medical costs. 
 
 

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Figure 1. Potential mechanistic pathways between depression 
and adverse pregnancy outcomes 
1 
BACKGROUND INFORMATION AND RATIONALE 
A.1. 
Impact of the Condition on the Health of Individuals and Populations  
A.a.1. Impact of maternal depression during pregnancy   
More than 20% of pregnant women may have a depressive disorder or symptoms during 
pregnancy 1-3.  However, it is well-recognized that depression during pregnancy is significantly 
underappreciated and under-recognized, thus under-diagnosed 1, 4-9.  While there have been no 
good data on the degree of under-diagnosis of depression during pregnancy, one of the studies 
conducted in the Kaiser Permanente Northern California (KPNC) population suggested a detection 
rate of only 37% for postpartum depression 10.  This detection rate is comparable to that reported 
in the literature11-13.   Various studies including a study conducted by the Principal Investigator 
among the KPNC population have shown that prenatal depression is likely to affect about 20% of 
pregnant women3 9, 14-16.  Therefore, depression during pregnancy is prevalent and has significant 
clinical and public health importance. Emerging evidence has shown that depression in pregnancy 
increases the risk of preterm delivery (PTD) and low birthweight (LBW).  
A.a.2. Impact of PTD and LBW    
PTD, defined as birth before 37 completed weeks of gestation, is the leading cause of 
perinatal mortality and morbidity.  It accounts for about 30% of early neonatal deaths worldwide 17, 
18. It is also the leading cause of congenital neurological disabilities including cerebral palsy, 
blindness and deafness and learning disability in the U.S. and most developed countries 19-22.  In 
addition, it is the major cause of admission to the neonatal intensive care unit and a significant 
contributor to medical expenditure for infants.  The costs associated with PTD amount to more 
than $26 billion each year 23-25.  Such impact on infant health and on staggering medical and 
economic costs makes PTD one of the most serious challenges for clinicians and biomedical 
researchers today and has become a global crisis 26, 27.  Thus, reducing PTD and LBW could lead to a 
significant reduction in medical care costs and to improved newborn health.  However, despite 
decades of research, the incidence of PTD has not been reduced; instead, it has been steadily 
increasing 20, 28-30.  In the US, about 12% of all births (>half a million births) are the result of PTD 
each year.   
A. a.3. Prenatal depression and the risk 
of PTD and LBW 
While the causes of PTD remain largely 
unknown, which explains the lack of progress in 
reducing the incidence of PTD, emerging 
evidence, including a study conducted by the PI, 
has linked depression during pregnancy to PTD 
risk3.  In addition, a growing body of literature 
has provided underlying mechanisms for the 
observed link between depression during 
pregnancy and PTD risk.  Through the direct 
effect of hypothalamic-pituitary-adrenal (HPA) 
axis and its indirect effect by the interaction 
between HPA and HPG (hypothalamic-pituitary-gonadal) axes, psychopathological factors could 

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have a profound impact on the female reproductive system and pregnancy outcomes including PTD 
(Figure 1) 31-35. 
A. a.4. Epidemiological evidence of fetal risk with untreated depression during pregnancy 
           Emerging epidemiological studies including one conducted by the PI (see Preliminary Studies) 
have shown that the presence of significant and severe depressive symptoms during pregnancy 
increased the risk of PTD 36-39.  Given that only a fraction of depressed pregnant women were 
clinically diagnosed 10, 40, all studies used population-based screening instruments for depression 
symptoms such as the Patient Health Questionnaire (PHQ-9) or Center for Epidemiological Studies 
Depression Scale (CES-D).  These instruments have been shown to be highly correlated with clinical 
diagnoses with 85-90% sensitivity and specificity 39, 41, 42.  Severe depressive symptoms have also 
been demonstrated to be a good indicator for clinical diagnosis of depression 42-44.  Women with 
significant depressive symptoms, usually ascertained in the second and third trimesters, had 
increased risk of PTD with a relative risk ranging from 1.9 to 6.8 36-38.  A similar association was also 
reported for LBW 45-47. 
A. a.5. Treatment of depression during pregnancy and its effect on PTD and LBW 
Given that depression during pregnancy is an important risk factor for both PTD and LBW, it 
would seem logical to treat depression during pregnancy to reduce these risks.  However, two 
obstacles have made this seemingly straightforward decision complicated, especially for 
pharmacological treatment of depression (i.e. antidepressants).  First, it is well-recognized that 
prenatal depression is significantly underappreciated and under-diagnosed 1, 4-9.  Without first 
identifying pregnant women with depression, evaluating treatment effectiveness would likely not 
be valid.  Second, in addition to treatment effect on depression, the safety concerns of 
antidepressant use during pregnancy has been raised.  Studies have shown that use of 
antidepressants during pregnancy itself may increase the risk of PTD and other adverse outcomes48-
51.  However, most of those studies compared users of antidepressants to non-users who were 
mostly pregnant women without underlying depression.  Due to a lack of universal screening for 
depression during pregnancy, those studies were not able to identify a relevant comparison group 
of pregnant women with untreated depression.  Thus, those findings were unable to disentangle 
the pharmacologic effect of antidepressant use during pregnancy from the effect of underlying 
depression, confounding by indication.  Currently, the risk-benefit balance of treating depression 
during pregnancy remains uncertain, and comparative effectiveness studies to examine the risk-
benefit profile of depression treatment during pregnancy in relation to fetal risk are urgently 
needed to provide evidence-based treatment decisions.    
 
A. a.5.1. Psychotherapy treatment and the risk of PTD 
While psychotherapy is not expected to increase the risk of PTD, evidence for a beneficial 
effect of   psychotherapy treatment of depression during pregnancy on reducing the risk of PTD has 
been absent largely due to a lack of examination of such an effect.   A recent review was able to 
identify only one small study (38 subjects) that examined the effect of psychotherapy on treatment 
of depression, but not its subsequent impact on the risk of PTD 52, 53.  Essentially, the effect of 
psychotherapy treatment of depression during pregnancy on the risk of PTD has not been 
examined.  The effect of psychotherapy for treatment of depression during pregnancy including 
timing and duration of such psychotherapies on the risk of PTD is currently unknown.  

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A. a.5.2.  Antidepressants and the risk of PTD and LBW 
 
A difficult challenge in deciding treatment options for depression during pregnancy is the 
potential adverse effects of antidepressant use on fetal risk including PTD and LBW.   While their 
potential benefit to mitigate depression during pregnancy is well-known 54, the potential adverse 
pharmacological effects of antidepressant use during pregnancy on fetal risk have been raised by a 
limited number of studies.   While research findings were not consistent, a few studies reported an 
increased risk of PTD and, to a lesser extent, LBW among women who used antidepressants, both 
SSRI (selective serotonin reuptake inhibitors) and non-SSRI antidepressants 55-62.  However, many of 
these studies had methodological problems that are common in studying pharmacological effects 1. 
Because the comparison groups were usually women with neither depression nor antidepressant 
use, it is difficult to disentangle whether the high risk of PTD among women who used 
antidepressants was due to the pharmacological effect of antidepressants or the effect of the 
underlying depression itself.  A more valid comparison would be a cohort of women with 
depression, but who did not use antidepressants (i.e., untreated controls).            
Some earlier studies did not observe an increased risk of PTD with antidepressant use 
(mainly SSRIs) 63 and one study even reported a decreased risk of PTD among women who used 
antidepressants 64.   One study reported an increased risk of PTD associated with SSRIs although the 
number of women who had both depression and used SSRIs was small (n=55)62.  Therefore, the 
effect of antidepressant use during pregnancy on the risk of PTD remains largely uncertain.  The 
crucial issue in studying the effect of antidepressants remains to identify a comparison group 
consisting of women with depression who do not use antidepressants (untreated controls).  Our 
proposed study will provide exactly such an appropriate comparison group thanks to the KPNC 
universal peripartum depression screening program. Furthermore, the proposed study will be able 
to examine comparative effectiveness of various treatment regimens among pregnant women with 
similar underlying depression severity thanks to universal depression screening.   
A.a.6.    Depression with and without comorbidity 
Depression with and without concurrent other psychiatric disorders may be different 
disease entities with different underlying pathophysiological pathways 65, 66.  Depression with 
psychiatric comorbidity (psychotic depression) has been associated with a greater HPA axis 
dysregulation than depression without comorbidity 66. Treatment for these two types of depression 
has been different 67-69. Therefore, their impact and treatment effectiveness on fetal risk are likely 
different. It would be clinically informative and valuable to examine these two types of depression 
and their treatment effectiveness separately.   
In summary, while depression during pregnancy is an important risk factor for adverse 
pregnancy outcomes, treating depression during pregnancy has proven to be a difficult decision for 
both clinicians and pregnant women to make due to the uncertain fetal risk of antidepressant use1, 
52, 70.  Both the American College of Obstetricians and Gynecologists (ACOG) and the American 
Psychiatric Association (APA) have called for more research to demonstrate the comparative 
effectiveness of treatment choices in reducing fetal risk and to better understand risk-benefit of 
various treatment therapies71-73.   Given the potential ethical problems in randomizing pregnant 
women into treatment regimens with potential fetal risk, the primary concern of pregnant women, 
carefully designed prospective studies to compare treatment effectiveness for various treatment 
choices for depression during pregnancy are likely the next best alternative approach.  The first 
step for conducting such a comparative effectiveness study is to have a universal depression 

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screening to identify women with depressive symptoms, especially those who do not seek diagnosis 
or treatment (those with untreated depression). ACOG issued a call for "Screening for Depression 
During and After Pregnancy”74, 75.   
KPNC has implemented a region-wide universal screening program for peripartum 
depression in over 40 clinical facilities including 16 delivery hospitals, covering racially/ethnically 
diverse populations.  This is likely the nation’s first large scale screening program for peripartum 
depression which screens more than 35,000 pregnant women annually.  Thus, KPNC provides a 
unique setting to conduct the proposed comparative effectiveness study of depression treatment 
during pregnancy to reduce fetal risk and improve pregnancy outcomes, including head-to-head 
comparison between pharmacotherapy and psychotherapy.  
B.3    
Benefit to patients and their clinicians 
As stated above, our proposed study will provide answers to important questions that are 
significant challenges faced by both pregnant women and their clinicians.  When making treatment 
decision during pregnancy, pregnant women and their physicians face the complexity of needing to 
consider “Treating for Two”.   The results of our study will provide important evidence to allow 
pregnant women and their physicians to make informed decisions based on the findings of (1) the 
risk of PTD and LBW if not treating depression during pregnancy and (2) the benefit of treating 
depression in reducing the risk of PTD and LBW.   Answers to those questions will be valuable to 
pregnant women and clinicians, and directly address their concerns when making treatment 
decisions.   
2 
STUDY OBJECTIVES 
Specific Aims:  Depression during pregnancy is prevalent with nearly one million pregnant women 
suffering from it in US annually. Depression, if UNTREATED, has been linked to adverse fetal 
outcomes including preterm delivery (PTD) and low birthweight (LBW). Currently, significant 
confusion exists about risks and benefits of treating depression during pregnancy.  Due to ethical 
concerns of randomizing pregnant women into treatment regimens with potential fetal risks, 
observational studies are likely the best alternative to RCTs, and have the advantage of providing 
real-life evidence of risks/benefits of depression treatment. Based on an universal depression 
screening program, we propose to conduct a comparative effectiveness study with a two-stage 
prospective cohort design to determine if treating depression in pregnancy is effective in improving 
fetal outcomes, and which treatment has the best risk-benefit profile: pharmacotherapy, 
psychotherapy or a combination. The questions will be examined separately for two depression 
types, depression only and depression with other psychiatric co-morbidities, to elucidate possible 
differences in treatment effectiveness between the two groups. Four cohorts will be formed within 
each depression type: (A) “Antidepressant only”: screen positive for depression and use only 
antidepressants during pregnancy; (B) “Psychotherapy only”: screen positive and receive 
psychotherapy only; (C) “Combination therapy”: screen positive and receive both antidepressants 
and psychotherapy; (D)”Untreated depression”: screen positive and receive no treatment. We will 
also form a cohort (E) “No depression”: screen negative and receive no treatment, for baseline 
comparison. Information on depression, treatment and PTD & LBW will be available for all 88,000 
women (stage-one sample). To control for confounding, we will randomly select 400 women from 
each cohort (total 3,600) for a telephone interview to obtain detailed information on confounding 
factors. Information from both stages will allow efficient and valid estimates while controlling for 
confounders. The aims are: 

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Aim 1.   Do women who receive treatment during pregnancy have a lower fetal risk than women 
with untreated depression? (Cohort A, B & C vs. Cohort D).  
Aim 2.   Which treatment is more effective in reducing fetal risk? (Pair-wise comparison among 
Cohort A, B & C, respectively)  
Aim 3.   Do women with untreated depression have a higher fetal risk than women without 
depression? (Cohort D vs. E). 
Aim 4.   Does the treatment effectiveness differ between women with depression only and women 
with depression and other comorbidity? 
Exploratory analyses will be conducted to examine whether treatment effectiveness (Aims 
1 & 2) is impacted by baseline depression severity. The aims will be refined with continued input 
from stakeholders. This will be the first large scale comparative effectiveness study to answer 
pressing clinical questions with an innovative and efficient two-stage design. 
3 
STUDY PROCEDURES 
3.1 
Study Design 
3.1.1 
Overview of the Study Design and Approaches  
 The proposed study will be a population-based prospective cohort study with an efficient two-
stage design. This study design is uniquely suited for the KPNC available infrastructure, an advanced 
and comprehensive EMR system and universal peripartum depression screening program. Given 
that recalling depression during pregnancy in the presence of adverse pregnancy outcomes (PTD or 
LBW) is likely biased (recall bias), retrospective study designs like case-control studies are not 
appropriate for the objectives of the proposed study. Thus, a prospective cohort study design 
obtaining information on depression and its treatment during pregnancy before pregnancy 
outcomes is the only appropriate study design for addressing the Specific Aims of the proposed 
study. In addition, KPNC’s unique infrastructure allows for an efficient two-stage prospective cohort 
study. The KPNC comprehensive EMR which captures the PHQ-9 scores for participants of the 
universal depression screening allows us to identify information on depression and its treatment 
status for 88,000 pregnant women expected to be screened.  KPNC EMR will also allow us to 
ascertain pregnancy outcomes, PTD and LBW; thus, we will have information on exposures 
(depression and its treatment) and outcomes (PTD and LBW) for all 88,000 pregnant women during 
the study period to form 4 comparison cohorts within each depression type (with or without 
comorbidity) and one control cohort (a total of nine cohorts). While it is not practical to interview 
88,000 women, contacting and interviewing 3,600 (400 from each cohort) in the stage-two sample 
will allow us to obtain information on confounders. Information on underlying depression severity 
at baseline allows us to control for confounding by indication and to examine comparative 
effectiveness of treatment options among women with similar severity of underlying depression. 
Other psychiatric disorders captured by KPNC EMR will allow us to examine treatment effectiveness 
separately for two different types of depression. Combining information from both stage-one and 
stage-two populations allow us to examine the Specific Aims using an appropriate study design 
(prospective cohort study) with an efficient approach (two-stage design). KPNC’s infrastructure 
makes this unique combination of study designs possible. 

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3.1.2 
Choice of outcomes 
PTD, defined as birth before 37 completed weeks of gestation, is the leading cause of 
perinatal mortality and morbidity as well as a leading cause of congenital neurological disabilities 
including cerebral palsy, blindness and deafness in the U.S. and most developed countries 19-22.  In 
addition, it is the major cause of admission to the neonatal intensive care unit and a significant 
contributor to medical expenditure for infants, amounting to more than $26 billion each year23-25.  
Such impact on infant health and on staggering medical and economic costs makes prematurity one 
of the most serious challenges for clinicians and researchers today and has become a global crisis 
according to WHO reports and March of Dimes White Paper 26, 27.  Patients with premature infants 
have severe physical, emotional, and financial burdens beginning at delivery.  Premature birth turns 
a happy event (having a new child) into a nightmare. Risk factors for prematurity are largely 
unknown, thus there has not been much progress in reducing premature births largely due to a lack 
of intervention strategy, which leaves pregnant women to face significant uncertainty throughout 
pregnancy. Given depression in pregnancy has emerged as an important and potentially modifiable 
risk factor for prematurity, identifying an effective treatment option for pregnant women with 
varying type and severity of underlying depression could prove a valuable intervention strategy to 
reduce prematurity, which could be a significant contribution and help for pregnant women and 
society in general.   
 
3.1.3 
Data collection 
Information on exposures (depression status, and treatment), outcome (PTD & LBW), and 
other psychiatric disorders (comorbidity) will be ascertained through KPNC EMR data sources for all 

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88,000 women who have been screened for peripartum depression.  Confounders not available 
from the KPNC EMR will be ascertained during interview of the stage-two subsample of 3,600 
women (Figure 2).  OBGYNS will inform all pregnant women who screen positive by PHQ-9 about 
our study during their prenatal visits.  Table 1 describes briefly the sources and categories of factors 
to be ascertained.  
Depression status and severity 
Depression status and its 
severity during pregnancy will be 
ascertained through PHQ-9 scores 
from the universal depression 
screening and clinical diagnosis.  
All pregnant women who come to 
the first prenatal visit will be 
screened for depression using the 
PHQ-9, which has been validated in 
many studies as an instrument for 
screening for depression with high 
sensitivity (> 88%) and specificity 
(> 88%) in obstetric patients 103-107.  
PHQ-9 was chosen as the screening 
instrument after consultation with 
many nationally recognized 
experts in the field of diagnosis 
and treatment of peripartum 
depression balancing out many 
factors including scientific validity 
and feasibility for a large scale 
population-based screening 
program.  As a currently accepted 
cutoff, PHQ-9 scores (≥10) in 
combination with evaluation by 
medical practitioners will be used 
to determine whether a pregnant 
woman screens positive for 
depression. A score above 10 will 
be used as a measure of the 
severity of depression. Women 
who screen positive are referred 
by clinicians, under the screening 
protocol, to departments of 
psychiatry, psychology, or behavioral modification. Information on PHQ-9 scores, referrals, 
patient's uptake (or decline) of prescribed treatments are also captured by our EMRs. Any clinical 
diagnosis and treatment of depression for those not captured by the screening will also be 
identified through EMRs. During the glucola visit (22-24 weeks of gestation), all women will be 
screened for depression a second time using the same screening process as the first prenatal visit. 
Table 1:  Sources of Exposures, Outcomes, Confounders and Modifiers 
(category only) 
 
Universal 
Screening 
EMR 
data 
Interview 
Depression 
and 
treatment 
Depression and its severity 
X 
X 
 
Psychotherapies 
X 
X 
 
Antidepressant use 
X 
X 
 
Combination therapy 
X 
X 
 
Other psychiatric disorders 
 
X 
 
Outcome 
Gestational Age/PTD, LBW 
 
X 
 
Potential 
confounders, 
mediators 
and effect 
modifiers 
Risk factors for PTD, LBW 
 
X 
X 
History of mental illness 
 
X 
X 
Reproductive history 
 
X 
X 
Preexisting diabetes or GDM 
 
X 
X 
BMI 
 
X 
X 
History of depression  
X 
X 
X 
Substance abuse 
 
X 
X 
Reproductive and pregnancy 
history 
 
X 
X 
Family and social support 
 
X 
X 
Demographic characteristics 
 
X 
X 
Family history of mental illness 
 
 
X 

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Women being treated for depression before the screening will be identified (<2%) and analyzed as 
a separate group.  
Depression severity will also be used for stratification (to control for confounding by 
indication) when conducting head-to-head comparison of treatment effect among various 
treatment options.   
Depression type (with or without other psychiatric comorbidity) will be determined by 
identifying concurrent psychiatric disorders from KPNC EMRs including organic psychotic 
conditions, other psychoses, neurotic and personality disorder and other mental disorders.  The 
expected number of women who have depression only and depression with comorbidity during the 
study period is listed in Figure 2.   
Antidepressant use during pregnancy.  Antidepressant use, including types, timing, and duration of 
use, will be ascertained both through the depression screening and EMR (i.e., pharmacy database). 
The KPNC pharmacy database captures all inpatient and outpatient prescriptions and 
dispensations.  Because of a large financial incentive to obtain medications at KPNC (in most cases, 
medications are covered with minimal co-payments), the KPNC pharmacy database has been 
shown to capture 93-98% of prescription medications of varying types. Compliance of taking 
antidepressants will also be ascertained during interview.   
Psychotherapy.  KPNC EMR records any psychotherapy patients receive for depression. The 
information includes the type of therapies (individual or group), sources of the treatment 
(psychologist, behavioral modification specialist, or psychologist/social worker) and number of 
sessions attended. 
Outcomes (PTD & LBW):  Gestational age at delivery is determined by obstetricians based 
on multiple sources including ultrasound dating, physical examination, and LMP.  Birthweight is 
measured by medical practitioners at delivery. This information is recorded in the KPNC EMR.  In 
several studies conducted by the PI, more than 99% of participants had reliable information on 
gestational age and birthweight at delivery in the KPNC EMR.  For the remaining <1% that may have 
delivered outside KPNC hospitals, we are also able to obtain information through searching various 
databases including outside payment service databases. In addition, KPNC has a well-established 
Neonatal Minimum Data Set which verifies all NICU admissions for diagnosis and treatments. This 
database allows ascertainment of information on gestational age and birthweight for deliveries 
with missing information. We will also ascertain other related information such as indications for 
PTD and other pregnancy or labor complications for further classification of PTD.  A sample of PTD 
and LBW records will be selected and verified by manual review of medical records for quality 
control.  
Potential confounders.  In the stage-two subsample, we will conduct a structured telephone 
interview to ascertain detailed information on potential confounders that may not be available 
from the KPNC EMR.  The focus will be known and suspected risk factors for PTD and LBW, personal 
and family history of mental illness, demographic, reproductive and psychosocial factors (e.g., 
social and family support that may be related to depression, PTD, and LBW), BMI, diseases during 
the index pregnancy (e.g., GDM), diet, lifetime and prenatal smoking. The PI and his research team 
have extensive experience ascertaining information on these variables for many of his previous 
NIH-funded studies.  Questionnaires with these questions have been used in many previous 

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studies.  Based on our experience conducting telephone interviews with KPNC pregnant women, a 
response rate of 80-85% is expected.  
4 
STUDY DURATION, ENROLLMENT AND SITES 
The proposed study will be completed within three years and will involve eligible patients 
from all Kaiser Permanente Northern California facilities. Start-up will occur during the first three 
months and will involve obtaining IRB approval, hiring staff, training, conducting focus group 
interviews, testing data collection materials, developing and revising interview instruments 
(including the questionnaire) for stage-two data collection of information on confounders, 
establishing mechanisms for identification of eligible subjects through the EMR.  Data collection 
(including data linkage, establishment of depression screening registry, recruitment and conducting 
interviews) will be ongoing from month 4 through month 27. Ascertainment of diagnosis and 
treatment of depression and pregnancy outcomes will continue from month 10 through month 31.  
Data cleaning and analysis as well as report writing will be carried out during the last 6 months. The 
timeline for activities involved in the study is as follows: 
 
Months 1-3 
Obtain IRB approval. 
     Hire staff. 
Train interviewers. 
Set-up tracking database. 
Develop algorithm for identifying eligible participants. 
Create interview instruments including questionnaire and instructions, 
operations manual, and pilot test questionnaire.  
Conduct focus group interviews. 
 
Months 4-27 
Identify and include eligible pregnant women for the stage-one sample. 
 
Conduct data linkage and build the peripartum depression screening registry. 
Classify 88,000 women from stage-one sample into treatment categories based 
on their depression status and treatment regimens. 
Select a random sample of 400 from each category to form the stage-two 
subsample. 
Conduct telephone interviews with stage-two subsamples. 
 
Months 10-31       Ascertain outcomes of interest. 
 
Months 30-35       Clean data, conduct and complete data analyses, and prepare final report. 
 
Months 35-36    Create materials for dissemination of the findings.  
   Dissemination of the findings, which will continue beyond month 36. 
 

13 
 
                         
 
 
 
4.1 
Study Population 
4.1.1 
Inclusion Criteria  
1) Females > age 18  
2) Kaiser members 
3) Pregnant beyond 20 weeks of gestation during the recruitment period  
4) Meeting the definition of one of the following nine cohorts: 
Cohort A (depression only): pregnant women who are depressed and use only antidepressants 
Cohort A (depression with comorbidity): pregnant women who are depressed and use only 
antidepressants 
Cohort B (depression only): pregnant women who are depressed and receive psychotherapy only  
Cohort B (depression with comorbidity): pregnant women who are depressed and receive 
psychotherapy only  
Cohort C (depression only):  pregnant women who are depressed and receive antidepressants and 
psychotherapy  
Cohort C (depression with comorbidity):  pregnant women who are depressed and receive 
antidepressants and psychotherapy  
Cohort D (depression only): pregnant women who are depressed and receive no treatment 
Cohort D (depression with comorbidity): pregnant women who are depressed and receive no 
treatment 
Cohort E: pregnant women who are not depressed (screen negative) and receive no treatment 
4.1.2 
 Exclusion Criteria 
1) <18 years old 
2) Not Kaiser Permanente members 
3) Not pregnant. 
4) Not meeting the definition for the nine cohorts (pregnancy and depression/treatment status). 
Subjects that do not meet all of the enrollment criteria may not be enrolled.   
4.1.3 Populations 
Stage I population        
All KPNC women aged 18 years (legally allowed to participate in these types of studies at 
KPNC) or older who are pregnant and who participate in the universal peripartum depression 
screening will be eligible for the study. We expect that about 88,000 pregnant women will be 

14 
 
                         
 
 
 
eligible during the study recruitment period. If a woman becomes pregnant a second time during 
the study period (an unlikely event given the short recruitment period of less than three years) and 
she was already in the study for her previous pregnancy, her second pregnancy will not be included 
in the study so as to avoid non-independent observations.   
All participants in the universal depression screening will be identified and information on 
screening results, referrals, treatments, pregnancy related conditions and outcomes will be 
collected through linkage to various data sources of our comprehensive EMR system.  
Stage II samples         
A sample of 400 pregnant women from each of the nine cohorts previously described will 
be randomly selected to form Stage II samples. The random selection will be spread evenly 
throughout the study period so that all of the estimated 88,000 participants in Stage I will have an 
equal chance of being selected. They will be selected after their inclusion in Stage I with known 
status of depression and treatment, mostly before the 3rd trimester to avoid interviewing after 
delivery to reduce participation and recall biases. Any change in exposure status (depression and 
treatment) after initial assignment will be rectified and finalized during analyses, as we have done 
in other similar studies with two-stage design (with different exposures in pregnancy).   
Participants in Stage II samples will be interviewed to obtain more detailed information on 
depression and its treatments. The Stage II samples will also allow for direct ascertainment of 
compliance with prescribed medications.  Studies based on pharmacy data alone usually have to 
make an assumption of 100% compliance with dispensed medications. In this study, we can directly 
examine the compliance and assess its potential impact on treatment effectiveness. This will be an 
important strength for this study.  
Through the interview, we will also directly ascertain compliance to prescribed medications 
for treating depression. We will make appropriate adjustment for compliance ratio in data analysis 
to more accurately estimate the treatment effectiveness after taking into account of non-
compliance. In addition, we will also ascertain additional confounders including multivitamin intake 
during pregnancy and physical activity.  
Informed consent will be obtained before the interview. All contact procedures will follow 
the standard contact and recruitment procedures as required by KPNC’s IRB. Based on our 
experience with similar studies among KPNC pregnant women, a participation rate of 70-75% 
among eligible pregnant women can be expected. The recruitment will continue until 400 subjects 
from each of the nine cohort groups have completed the interview. 
5 
  COMPARATORS 
Comparators will vary depending on the research questions to be examined in analyses.  
The overall objective is to use appropriate comparators so that correct results of comparative 
effectiveness can be obtained after disentangling treatment effect from the effect of underlying 
depression (confounding by indication), and treatment benefits after balancing treatment benefit 
against the simultaneous risk of side effect (e.g., use of antidepressants).  The following four 
cohorts (A-D) will be established within each depression type (depression only or depression with 
comorbidity) and various comparators will be used to answer the research questions in the Specific 

15 
 
                         
 
 
 
Aims.  We will also establish a control cohort (E) of women without depression for comparison of 
baseline risk. 
Cohort A: women who screened positive and used antidepressants only.   All women who 
screened positive for depression and were subsequently prescribed antidepressants during 
pregnancy will form Cohort A.  Comparison of this cohort to Cohort D, a comparator for Specific 
Aim 1, will demonstrate the treatment effect of antidepressant use in reducing the fetal risk after 
controlling for severity of depressive symptoms and other confounders.  Comparisons to Cohort B 
or C, comparators for Specific Aim 2, will determine which treatment option (antidepressant use, 
psychotherapy or combination therapy) is more effective in reducing fetal risk or has a better risk-
benefit profile (pair-wise comparisons).  Results from such comparisons will provide valuable 
information for clinicians as well as pregnant women to make an informed decision on the 
treatment options for depression during pregnancy based on research evidence.   
Cohort B: women who screened positive and received psychotherapy only.  This cohort 
consists of women who screened positive and subsequently received psychotherapy during 
pregnancy, but did not take any antidepressants.  Comparison to Cohort D will determine if 
mitigation of depression by psychotherapy reduces fetal risk, after controlling for depression 
severity.  Comparisons of this cohort to antidepressant use (Cohort A) or combination therapy 
(Cohort C), two separate comparators for Specific Aim 2, will provide head-to-head assessment of 
comparative effectiveness of treatment regimens.   
Cohort C: women who screened positive and used both antidepressants and 
psychotherapy.   This will be a cohort similar to Cohort A and B except that women in this cohort 
received combination therapies after screening positive.  We classify them in a separate cohort to 
make Cohorts A and B encompass more clearly defined exposures of antidepressants and 
psychotherapy (pure exposure groups).  In addition, this cohort may allow us to examine the 
potential benefit of combination therapy.  Like Cohorts A and B described above, they will be 
compared to untreated comparator (Cohort D) for Specific Aim 1 and other treatment comparators 
(Cohort A and B) for Specific Aim 2.  
Cohort D: women who screened positive for depression, and did not obtain treatment.  
Pregnant women whose PHQ-9 score is ≥10 at either of two screening visits (the first prenatal visit 
and glucola visit) or who were diagnosed as having depression by their medical providers, but did 
not receive any treatment for depression during pregnancy, will be classified into this cohort.  
Comparison of this cohort to Cohort E (control cohort), a comparator for Specific Aim 3, will allow 
us to assess whether untreated depression during pregnancy increases fetal risk (i.e., PTD or LBW) 
without the interference of treatment effect. We can also examine potential dose-response 
relationships based on the severity of depression (PHQ-9 scores) and duration of depression 
(persistence of the symptoms in both screening visits vs. at only one of the visits).   Importantly, this 
cohort can be used as a comparator for Cohorts A, B, and C to examine the effect of depression 
treatment after controlling for underlying depression and severity (untreated comparators).   
Cohort E (controls): women who screened negative for depression in both visits and did 
not receive treatments for depression.  This cohort consists of the largest number of pregnant 
women, those who have neither depression and concurrent psychiatric comorbidity, nor treatment 
during pregnancy. This cohort will serve as the comparator for determining the effect of untreated 
depression on fetal risk (i.e., PTD, LBW). It will also serve as a reference check for other 

16 
 
                         
 
 
 
comparisons. For example, does the benefit of a treatment totally eliminate the risk of PTD 
associated with depression during pregnancy?   
The categorization into each cohort will be finalized at the end of pregnancy based on 
whether a woman met the criteria throughout the pregnancy. Comparisons of Cohort A, B, C, vs. D 
respectively, will answer the question of whether treating depression during pregnancy improves 
fetal outcomes, reducing PTD or LBW associated with untreated depression after controlling for 
underlying depression severity.  Pairwise comparisons among Cohort A, B and C will answer the 
question of which treatment is most effective in improving fetal outcomes and has the best risk-
benefit profile.   
6 
ANALYTIC METHODS  
6.1 
General Approach to Analyses 
While all eligible pregnant women will be included in the registry after their pregnancies 
are identified, the follow-up time for all pregnancy outcomes of interest will start at 20 weeks of 
gestation because, by definition, PTD and LBW among live births occur after 20 weeks of gestation.  
All pregnancies ending before 37 completed weeks (259 days) will be considered as failure (PTD).  
As in our previous studies, we will use the Cox proportional hazards regression model, 
accommodating delayed entry into the cohort76, 77.  Birthweight for all newborns will be obtained 
from EMR and analyses for LBW will utilize logistic regression.  
The study design can be characterized as two-stage. Unlike typical applications of two-stage 
sampling in epidemiologic studies, where the stage-two sample is selected on the basis of both 
exposure (or exposure surrogate) and outcome (e.g. two-stage case-control sampling), only 
exposure status will be known at the time of stage-two sampling to keep the nature of a cohort 
study design. However, study outcomes of interest will eventually be measured on all 88,000 stage-
one units, and there is much to be gained by utilizing all available information in estimation of 
associations of interest (see Sample Size and Statistical Power section).   
Regression parameter estimates and associated standard errors will be obtained via 
semiparametric maximum likelihood 78, utilizing exposure and outcome measurements on all 
88,000 stage-one units in addition to confounder/effect modifier measurements on the subset 
sample at stage-two (N=3,600).  Scott and Wild presented a unified method for fitting arbitrary 
regression models to a large class of missing data and/or response selective sampling problems. 
These applications are generally characterized as: 1) a set of easily-obtainable variables is measured 
on a sample of N individuals; one or more of these variables are to be used as explanatory in the 
regression model (e.g. depression and treatment status), or are informative surrogates for 
“expensive” variables to be measured on a subsample;  2) the response variable is obtained for all 
N individuals (e.g. PTD, LBW);  and, 3) a set of “expensive” explanatory variables (e.g. potential 
confounders/effect modifiers) are measured on a  subsample. Software (R language) has been 
developed and available from the authors, with functions for various regression models including 
binary regression, linear regression for continuous response, and clustered binary data.  The 
associated software documentation provides details on key parts of the system enabling users to 
implement new regression models (e.g. Cox).  We will utilize logistic and Cox regression to address 
the study aims, as described below.  

17 
 
                         
 
 
 
Each set of analyses addressing Aims 1, 2 and 3, outlined below, will be conducted in 
samples of women with and without other psychiatric comorbidities (Aim 4).    
Aim 1.   Treatment effect compared to untreated depression (Cohort A, B & C vs. Cohort D) 
Point and interval estimates of the relative hazard of PTD  associated with each treatment 
modality (A, B, C), relative to no treatment (D) will be calculated via semiparametric maximum 
likelihood estimates of Cox proportional hazards regression parameters, with adjustment for 
potential confounders, as described above.   In determining potential confounders in our regression 
models, we will first examine risk factors that may be plausibly linked to depression and PTD.  
Inclusion of potential confounding factors in our final regression models will be evaluated based on 
comparison of adjusted and unadjusted relative hazard ratios 79.   Departures from model 
assumptions will be assessed via diagnostic plots of weighted residuals and tests for interaction 
between exposure and time (gestational age).   The assessment of confounding variables for 
inclusion in regression models and testing of model assumptions will be performed in the stage-two 
sample using standard software for fitting Cox regression models.  After determining the 
appropriate set of confounders and that there are no departures from model assumptions, the 
stage-one information on primary exposure and outcome will be incorporated using the 
semiparametric maximum likelihood estimator.  This process applies to analyses of the remaining 
aims.  
These comparisons will allow us to assess possible treatment effects on reducing PTD 
through both pharmacotherapy and psychotherapy.  When compared to untreated (Cohort D), the 
comparison will be made after controlling for underlying severity of depression as measured by 
PHQ-9 scores or clinical diagnosis of severity before treatment. Table 2 shows that depression 
severity is relatively comparable among comparison groups, allowing control for confounding by 
indication (underlying depression severity, a unique strength of the proposed study due to the 
availability of PHQ-9 scores through the universal depression screening).   
We will also examine the possible dose-response in the association between 
antidepressant use and PTD by examining dosage of use, duration of the use, and timing of effect 
(single vs. multiple trimesters), in the context of time-dependent covariates.  Similarly, we will also 
assess any dose-response relationship in the association between psychotherapy and PTD (the 
number of sessions received) and the effect of timing of treatment, also both as time-dependent 
covariates.  If sample sizes allow, we will also examine the effect of combination of dose and timing 
(e.g., dose-response during early and late pregnancy).  In addition, we will examine difference in 
the type of antidepressants (SSRI vs. non-SSRI). 
Analyses of LBW as the outcome will parallel those described above for PTD, but logistic 
regression techniques will be used rather than Cox regression.       
Aim 2.   Head-to-head comparison of treatment regimens among Cohorts A, B & C, respectively 
We will obtain point and interval estimates of relative hazards of PTD associated with the 
three pairwise contrasts among the three treatment groups.  These estimates, and associated tests 
of significance, will be obtained using the Cox proportional hazards regression model developed in 
addressing Aim 1, fully adjusted for potential confounders including depression severity, as 
described above, via transformed linear contrasts of treatment group associated regression 
parameters.   Based on the results of Aim 1 analyses of dose-response, we will obtain confounder 
adjusted relative hazards associated with PTD with levels of dose of a given treatment modality 

18 
 
                         
 
 
 
(e.g. antidepressants, <30 days, 30-120 days, > 120 days duration during pregnancy), vs. particular 
levels of dose of another treatment (e.g. psychotherapy, ≥4 visits that has shown to be effective).  
We would categorize the comparator treatment (e.g., psychotherapy), based on the Aim 1 findings 
that the effectiveness of the comparator treatment in reducing risk of PTD varied by dose (e.g. no 
effect for < 4 visits).                  
As a secondary (exploratory) analysis, we will evaluate the treatment effectiveness 
separately for those with more severe underlying depression before treatment vs. those with more 
mild depression.  Treating depression may be more beneficial for those with severe depression.   
Analyses of LBW as the outcome will parallel those described above for PTD, but logistic 
regression techniques will be used rather than Cox regression. 
Aim 3.   Risk of PTD & LBW for untreated depression (Cohort D vs. E) 
Cox proportional hazards regression will be used to obtain point and interval estimates of 
the hazard ratio for PTD associated with untreated depression, vs. no depression, with adjustment 
for potential confounders, as described above.   Parameter estimation, the approach to inclusion of 
potential confounders and model diagnostics are as described above for Aim 1 analyses.   
To evaluate potential dose-response relationships, we will examine the strength of the 
association between PTD and severity of depressive symptoms.  Severity will be based on results of 
the KPNC universal screening and clinical diagnosis of depression from the KPNC EMR. Those with a 
clinical diagnosis of depression will be considered to be the most severe cases followed by those 
who screened positive with PHQ-9 higher scores (e.g., > 20).  Those who screened positive, but with 
lower PHQ-9 scores (10-19) will be considered to have moderate depression.  The effect of severity 
will also be examined in terms of duration.  
Analyses of LBW as the outcome will parallel those described above for PTD, but logistic 
regression techniques will be used rather than the Cox model.    
Aim 4.   Difference in treatment effectiveness by depression type (with and without comorbidity) 
 
Analyses of Aims 1, 2 and 3, will be initially stratified by depression type, with or without 
other psychiatric comorbidity (yes/no).  Heterogeneity in treatment effectiveness by depression 
type will be assessed by inclusion of appropriate cross-product (interaction) terms in regression 
models outlined above, using a likelihood ratio test.  If there is evidence of heterogeneity, point and 
interval estimates of relative hazards/odds ratios of interest by comorbidity status (yes/no), 
adjusted for confounders, will be obtained via linear combination of regression parameter 
estimates (main effect and interaction terms, as appropriate). 
6.1.1 
Propensity Score Analysis 
In addition to the conventional analyses outlined above, we will apply a propensity score (PS) 
analysis method to reduce or correct for any selection bias into treatment groups in our 
conventional approach described above.  PS analysis uses information about all measured 
covariates to balance unobserved factors between treatment groups. It is not necessary to know 
the exact relationship of unobservable factors with measured covariates - as long as there is any 
type of relationship between unobservable and measured factors, propensity score analysis can 
reduce over 90% of selection bias associated with unobserved factors 80.   Given the study aims, 
which primarily focus on various pair-wise comparisons of treatment groups, PS analyses will be 

19 
 
                         
 
 
 
conducted in the cohort subsets consisting of each pair of groups of interest.  This will allow us to 
construct a multivariable logistic model for dichotomous outcomes to calculate a propensity score, 
i.e., the probability of receiving a given treatment, vs. another treatment (or no treatment), as a 
function of all known factors that might affect the treatment outcome.  This model will include the 
same variables used in our conventional Cox proportional hazards/logistic regression models 
described above.  The resulting probabilities (propensity scores) will be used to analyze the extent 
of overlap on all covariates, using graphical methods and frequency distributions. Areas of non-
overlap in score between treatment groups will be trimmed (removed) from analyses.  Stratified 
Cox proportional hazards models, with stratification on deciles of propensity score, will be used to 
compare the two treatment groups with respect to PTD risk 81, 82.  Logistic regression analyses of 
low birthweight will include the categorized score as a covariate.   We will compare results from our 
conventional regression models with results from the PS analysis.  We note that results with 
adjustment for confounding via propensity score techniques are not expected to appreciably differ 
from results using traditional regression adjustment81, 83.  If we find that results differ, we will 
report the PS analysis results as our main findings due to the correction for selection bias in our 
measured characteristics.  
6.1.2 
Instrumental Variable Analysis   
We will also use instrumental variable analysis (IVA) methods to assess the sensitivity of our results 
to unmeasured confounders. IVA is an alternative method for reducing selection bias that 
addresses both the effects of unobservable characteristics and the issue of dual causality between 
the choice of a treatment and the health outcome84-86. IVA methodology uses an indirect attribute 
that is closely associated with the type of treatment a patient receives, recreating randomized 
treatment assignment from a trial. This type of analysis requires identifying at least one factor that 
significantly affects treatment choice but is unrelated to the health outcome. IVA can be used to 
measure treatment effects independent of selection bias, which helps inform broad policy 
decisions, but it is limited in guiding clinical decisions for specific population subgroups 87.  We will 
initially consider physician specific prescribing preference, as measured in the KPNC pharmacy 
database, as the instrument 88. It is necessary to consider how the characteristics of patients vary 
with respect to the instrument used when interpreting results of an IVA 84.  Our access to a rich 
amount of data will permit a thorough evaluation of the validity of our candidate instruments. If 
our selected instrument appears to be valid, and we find that IVA yields different results compared 
with PS analysis, we will have confirmed the likelihood of unmeasured confounding in our PS 
analysis. Given the extensive control for confounding using our clinical data, and the large sample 
sizes, we anticipate PS analysis and IVA to yield similar results. However, if IVA yields results that 
vary from PS results, and our instruments are reasonably valid, it suggests the presence of 
unmeasured confounders in our treatment comparisons in the PS and traditional analyses.  Thus, it 
will inform in the interpretation of findings with respect to possible impact of unmeasured 
confounders.    
6.2 
Avoidance of bias   
The most important strength of the proposed study is the existence of the unique KPNC 
region-wide universal peripartum depression screening program. Such a universal screening 
provides a rare opportunity to conduct a large scale (N= 88,000) population-based comparative 
effectiveness study of depression treatment on reducing fetal risk, while avoiding potential 
selection bias. Without such a screening program, a large percentage of pregnant women with 
depression (>50%) would not have been identified, since depression during pregnancy is 

20 
 
                         
 
 
 
substantially under-recognized and under-diagnosed 5, 14, 45.   Thus, relying solely on clinical 
diagnosis of depression would likely lead to self-selection bias.   
Given that randomized clinical trials are not feasible for evaluating treatment effectiveness 
on reducing fetal risk due to ethical concerns about assigning women into a treatment group with 
potential fetal risk, the proposed two-stage prospective cohort study is likely one of the best 
alternatives to examine the comparative effectiveness of treating depression during pregnancy.  In 
addition to significantly reducing self-selection bias, the results of universal screening will provide 
information on depression severity measured by PHQ-9 score for participants.  This valuable 
information will allow us to control for not only depression (yes/no), but also depression severity (a 
continuous scale) at baseline. While the distribution of depression severity was comparable among 
treatment groups (Table 2), ability to control for confounding by indication in such a refined detail 
will substantially increase the validity of our findings. Such a refined control for baseline depression 
severity is not likely feasible in other settings without universal peripartum depression screening.   
We have also implemented several analytic methods to address the issue of possible 
selection into various treatment groups including propensity score analysis for balancing 
unobserved factors among treatment group and instrument variable analysis for possible 
unmeasured confounders (see Analytic Methods).  
Finally, the findings from this observational study based on real world clinical experience is 
more relevant and applicable to actual clinical practice compared to findings from much controlled 
settings with selected populations (RCTs).   
6.2.1 
Study population  
The proposed study will include all pregnant KPNC members (a population-based study), 
thus their racial/ethnic distribution will be similar as shown in Table 4 and will closely represent the 
underlying population (including Medicaid/MediCal) in the service region of Northern California. 
The study cohorts are described above (see C.b.2. Choice of comparators). 
6.2.2 
Sample size and statistical power  
We expect to measure primary exposures (depression and treatment), and outcomes of 
interest (PTD, LBW) in approximately 88,000 women (stage-one sample). Randomly sampling from 
the stage-one cohort, we expect to recruit 400 pregnant women from each cohort (see Figure 2). 
Given the lack of methods for power calculations for the semiparametric maximum likelihood 
estimator (SMLE) in our two-stage sampling scheme, we very conservatively present minimum 
detectable effect calculations assuming the second stage sample of 3,600, noting that the gains in 
precision and statistical power in utilizing the measurements in stage-one sample of 88,000 women 
will be significant. In a small simulation study described below, we examine efficiency gains of an 
ad-hoc two-stage relative hazard estimator currently under investigation, which is an alternative to 
the semiparametric maximum likelihood estimator, attractive in its simplicity. These results provide 
information on the efficiency gains that can be expected in our two-stage design with the maximum 
likelihood estimation; the maximum likelihood estimator will be more efficient than the ad-hoc. 
Given results of the preliminary simulation study, we also present below estimates of minimum 
detectable effects using the two-stage SMLE for most estimates (in brackets, assumptions 
described below).  Power calculations are based on the likelihood ratio test in the context of a Cox 
proportional hazards regression analysis and a logistic regression analyses, as appropriate to the 
outcome 89 90.    

21 
 
                         
 
 
 
Table 5.  Minimum detectable hazard ratios (HR) for preterm delivery and 
minimum detectable odds ratios (OR) for low birthweight in a pair-wise 
comparison of depression exposure/treatment categories (N=800); two-
sided test, significance level (α)  = .05, power (1-β) =.80. 
Preterm Delivery (HR) 
Low Birthweight 
Rate = 8% 
Rate = 11% 
Rate = 5% 
Rate = 10% 
Given previous experience 3, 91-93, we expect negligible 
loss to follow-up (< 1%).  Based on our experience with 
our study population 94, we expect a rate of PTD 
(gestational age < 37 weeks) ranging from 8% - 11% 
and LBW ranging from 5% to 10%.   
Each primary contrast of interest in analyses 
addressing Aims 1 through 3 can be characterized as 
a comparison of two groups of 400 women with 
respect to risk of study outcomes of interest (N=800).   
Minimum detectable hazard ratios for PTD range 
from 1.69 – 1.83 across the range in expected rate of 
PTD (Table 5).  Minimum detectable odds ratios for 
LBW range from 1.67 to 2.05 across the range in 
expected event rate.   
Relevant to Aim 4 analyses of heterogeneity 
in the association between treatment/depression 
and PTD by psychiatric comorbidity status (yes/no with 50% of total stage 2 sample in each of the 
two groups), the minimum detectable hazard ratio for effect modification (interaction) was 
calculated, assuming a pairwise comparison of treatment effect (e.g. A vs. D) and the midpoint of 
the range in expected PTD rate (9.5%) [N for analyses = 1600].  The hazard ratio for effect 
modification is interpreted as the factor by which exposure to the effect modifier (comorbidity 
status = yes) increases the hazard ratio associated with treatment, over and above that in those 
unexposed to the effect modifier (comorbidity status = no).  Using methods as outlined above, the 
minimum detectable hazard 
ratio for effect modification is 
1.50, and similarly for analyses 
of LBW, the minimum 
detectable odds ratio for 
effect modification is 1.62 
(two-sided test, α=.05, power 
=.80).   While this stratification 
analysis is an a priori aim of 
the study with relevant clinical implications, we will be conservative in interpretation of our 
findings due to increased number of comparisons from  stratification by depression type, though 
the need to adjust for multiple comparisons remains controversial 95-97.  
Efficiency increase resulting from two-stage study design:  A preliminary simulation study has 
been conducted providing information on the expected efficiency gains of the two-stage SMLE, 
relative to an analysis of the stage-two subsample only. For our preliminary simulation study, we 
made a few simplifying assumptions.  We assumed three depression status categories, none, 
depressed without treatment, depression with treatments, with population prevalence set at 70%, 
25% and 5%,  and outcome relative risks of 1.0 (ref), 2.0 and 3.0, respectively.  We assumed one 
binary confounder with varying prevalence across depression categories and strength of association 
with outcome.  The event incidence was assumed at 5% (among those unexposed to depression 
and confounder).  The stage-one sample size was 88,000, with random selection of 400 in each of 
the three exposure categories.  Across the scenarios considered relative efficiencies [i.e. Var(stage 2 
Table 4.  Comparison of Maternal Age and Race/Ethnicity 
for Newborns between KPNC and the General Population 
in the KPNC Service Regions 
Characteristics 
KPNC 
Births (%) 
All births in the KPNC 
Service Regions (%) 
Maternal age 
 
 
   < 20 
12 
10 
   20-29 
43 
52 
   30+ 
45 
38 
Maternal race/ethnicity 
 
 
   Asian 
15 
16 
   Black 
10 
10 

22 
 
                         
 
 
 
MLE) / Var(SMLE)] ranged from 4 to 82, demonstrating substantial gains by utilizing exposure and 
outcome information in the full sample of 88,000.    
 
As a very crude approximation to the impact of incorporating the exposure and outcome 
information on 88,000 women, we took an “effective sample size” approach, and assumed the 
worst case among the simulation scenarios considered, where the relative efficiency of the SMLE 
was approximately 4.0.  Thus, rather than basing power calculations on the stage-two sample size 
of 800, we based them on 3200 (i.e. 4.0 * 800; 1,600 in each cohort category).  Under these 
assumptions, the minimum detectable PTD hazard ratios are significantly decreased, ranging from 
1.32 to 1.38 (for PTD rate ranging from 8%-11%) in pair-wise comparisons of depression 
exposure/treatment categories of interest.  Similarly, the minimum detectable LBW odds ratios 
range from 1.32 to 1.45 (for LBW rate ranging from 5% to 10%).  In addition, taking the same 
approach to estimation of efficiency gains, the minimum detectable hazard ratio and odds ratio for 
effect modification by comorbidity status (yes vs. no) are 1.31 and 1.37 in analyses of PTD and LBW, 
respectively.   We acknowledge that these estimates of gains in efficiency are based on results from 
a simulation study which did not reflect the complexities in analyses that this study will encounter 
(e.g. multiple confounders, both multicategory and continuous).    
7 
STUDY ADMINISTRATION 
7.1 
Data Collection and Management 
7.1.1 
Data sources   
 
KPNC EMRs and other clinical databases will be used to identify all women who meet our 
recruitment criteria and to include them in the study.  Approximately 88,000 pregnant KPNC 
members who are 18 years or older, will be identified and recruited to the study.  All eligible 
pregnant women will be classified into one of the categories based on the diagnosis and treatment 
of their depression during pregnancy.  For each of these nine categories, 400 women (for a total of 
3,600) will be randomly selected and recruited to conduct telephone interviews to collect 
information on confounders and effect modifiers.   
Birth outcomes for all participants will be ascertained through the KPNC EMR and other 
clinical databases 
7.2 
Confidentiality 
 
All standard procedures following HIPAA requirements will be implemented for this study. 
Names and other identifying information on study subjects will be obtained for record-keeping 
purposes only, and no individuals will be identified in any reports from this study.  Only persons 
directly involved in the study will have access to data identifying individual subjects.  Records and 
forms with identifying information will be kept in locked drawers when not in use.  Access to 
computerized information will require simultaneous knowledge of the database, language, file 
names, and multiple passwords.  
7.3 
Risks and Benefits 
7.3.1 
Potential and anticipated risks to research participants.  
For participants in the telephone interview (stage-two study), the risk of participating in 
this study is minimal.  Women who had adverse pregnancy outcomes in the past may become 

23 
 
                         
 
 
 
upset when recalling these events. All interviewers will be trained on how to appropriately handle 
these situations in case they arise.  Our experience also indicates that some women find it 
therapeutic to discuss these events.  
 
7.3.2 
Risk Minimization   
 
 
No invasive procedures are involved in the study protocol, only interviews.   All interviewers 
have had experience interviewing pregnant women and are specifically trained for these types of 
studies. All of the women contacted for a telephone interview will have received a letter informing 
them about the study. 
 
7.3.3 
Benefits to participants and to society 
 
No direct benefit is expected to participants resulting from participation in the study.  
Findings from the study in a publishable format will be made available to all participants who 
request them.  The potential benefit to society is to enhance the understanding of the risk-benefit 
profiles of various treatment options depression during pregnancy to prevent preterm delivery and 
low birthweight.   Because of the prevalence of depresion among pregnant women, findings from 
the proposed study will have a significant public health impact. Correct treatment choices may lead 
to reduction of PTD and LBW which remain top public health challenges globally.   
 
7.3.4 
How potential risks are justified by potential benefits  
 
 
There is only minimal risk to participants.  Potential benefits to society described above can 
be significant. 
 
7.4 
Recruitment Strategy 
 
The chiefs and other obstetricians in the departments of Obstetrics and Gynecology at 
participating KPNC facilities will be informed of the nature of the study and permission to contact 
their patients (if selected) in stage-two will be obtained from those departments.  Informational 
posters will be sent to all OBGYN departments for display and OBGYNS will inform all pregnant 
women about our study during their prenatal visits.  For stage-two samples, after they are 
identified, a letter explaining the study and requesting their participation will be sent to women.    
A postage-paid and self-addressed refusal card will be included in the letter for participants who 
choose to refuse.  For those who do not return refusal postcards, interviewers will contact women 
to answer their questions and ascertain their willingness to participate in the study. 
7.5 
Informed Consent and HIPAA Authorization 
7.5.1 
Waiver of Consent for Stage-One Sample 
A waiver of consent has been granted for the stage-one sample, since the stage-one sample 
involves data only, there are no risks associated with recruiting subjects, although there is always a 
small chance of the loss of confidentiality during data analyses. Since there is no direct contact of 
patients, we do not anticipate an adverse effect on their rights or welfare. 
  

24 
 
                         
 
 
 
7.5.2 
Waiver of Consent for Stage-Two Sample 
A waiver of the requirement for participants to sign a written consent form has been 
granted for the stage-two telephone interviews. As has been used in other studies of telephone 
interviews, for those who will be contacted by telephone for an interview, the consent process will 
be conducted over the phone.  Verbal Informed consent will be obtained before each interview and 
no interviews will be conducted without consent for participation.   Interviewers who are 
employees of Division of Research and who have had extensive experience conducting such 
interviews will obtain the consent.  There is no more than minimal risk to participants and there are 
no other procedures involved beyond an interview. 
7.5.3 
Waiver of HIPAA Authorization 
A waiver of HIPAA Authorization has been granted for both stages of the study.  
The stage-one sample will be data only. There will be no direct contact with participants. For the 
stage-one sample of 88,000 women, we will need to access PHI to identify maternal pregnancy and 
depression status.  The medical record number will be used to link the mother-infant pairs and 
other datasets on pregnancy, maternal, and birth outcomes. Stage-two will involve telephone 
interviews for which we are requesting a waiver of written consent for participation (verbal consent 
will be obtained).  
 
 Data with identifiable information will be password protected by the programmer. An 
additional dataset will be created and used by the programmer which will not include any PHI. Data 
will not be shared outside of KPNC. 
 
 No PHI data will be disclosed to outside investigators. We will provide assurance of the 
confidentiality and proper procedures for protecting PHI as set forth by the KPNC IRB. 
  
 Personal identifiers will be destroyed at the end of study according to IRB requirements. 
 
7.5.4 
Payment to Subjects 
After the interview, a thank-you letter with a check for $40 will be sent to each participant 
for their time and out-of-pocket expenses. 
8 
DISSEMINATION 
A Stakeholder Advisory Board (SAB) will be closely involved in the dissemination of study 
findings and implementation assessment.  The Stakeholder Advisory Board (SAB) will be established 
to ensure that the study addresses the concerns of pregnant women and providers about prenatal 
depression and its treatment (see Patient and Stakeholder Engagement Plan for detail). This board 
will be comprised of six stakeholders: two patient partners, two women who were diagnosed with 
depression during pregnancy, and two health care providers. The two patient organizations (see 
below) that have enthusiastically supported the proposed study, were identified as appropriate 
partners for this study because, among other things, they have many years of first-hand experience 
disseminating scientific information in the areas of pregnancy and mental health to their members 
nationwide including providers and pregnant women. 
The SAB members are valuable facilitators to dissemination and implementation of study 
findings.  Our collaboration with Childbirth Connection and CMMHC will enable us to disseminate 
study findings to a broader audience through the use of their extensive statewide and national 

25 
 
                         
 
 
 
networks. Their constituents will be informed of our study findings.  For dissemination within KPNC, 
Drs. Flanagan and Turner together with co-investigators, Drs. Young and Hamilton, working with 
the OB/GYNs and mental health care providers at KPNC will greatly facilitate both the 
dissemination and implementation of the study findings as well as incorporation of the study 
findings into practice guidelines for treating depression among pregnant women. They will also be 
champions for disseminating study findings through KPNC existing member communication 
channels. 
8.1 
Dissemination to Patients and Advocates (statewide and nationwide) 
 
The California Maternal Mental Health Collaborative (CMMHC) consists of medical and 
mental health professionals (i.e., providers), educators, community advocates, and individuals who 
have themselves experienced mental health disorders (i.e., patients) and their mission is to 
increase and improve maternal mental health awareness, diagnosis and treatment. CMMHC utilize 
educational pamphlets, their website and social networking media outlets such as Facebook, 
twitter and blogs to disseminate study findings to their members and communities throughout the 
nation.  They will disseminate our study findings throughout their nationwide networks. 
 
 
Childbirth Connection has a history of providing national leadership in identifying, 
demonstrating and fostering innovative ways to improve maternity care for all women and their 
families using research, education and advocacy. Childbirth Connection will disseminate the 
findings from our study through their online evidence-based maternity care resource directory, 
among other methods nationwide. This resource directory provides a way to disseminate evidence 
based medicine information to those who want to plan, practice, and receive care within this 
essential framework.  Our findings will also be distributed through their Transforming Maternity 
Care Blog which features news, opinions and analyses from Childbirth Connection and prominent 
guest contributors. Other dissemination tools will include reports, fact sheets, pamphlets and other 
handouts which will also be used to disseminate findings from the study to pregnant women and 
women of child-bearing age.  
 
 
8.2 
Dissemination to Providers within KPNC 
 
Drs. Flanagan (OBGYN) and Mason (Mental & Behavioral Health), as leaders in their 
respective specialties at KPNC, will work with KPNC leadership to implement the findings from this 
study into practice throughout the Kaiser Permanente Northern California Region. They will also 
work with health care providers and educators to disseminate the findings among both providers 
and patients, and encourage providing depression treatment among pregnant women based on 
findings from this study. 
8.3 
Making study results available to study participants after completion of analyses.  
We will work with Childbirth Connection and CMMHC to develop a brief summary of the 
results from our study for the general public, including our participants. Both of these organizations 
have a long-history of developing educational materials for patients and the general public and we 
will use their expertise to guide development of the materials for dissemination to our study 
participants. The summary will then be sent to our participants according to their preference (US 
postal service, email, link to organizational websites, etc.)  
 
 

26 
 
                         
 
 
 
9 
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