Using Clinical Data to Study
Women’s Health
Deborah Ehrenthal, MD
Christiana Care Health Services
Using Clinical Data to Study
Women’s Health
Deborah Ehrenthal, MD
Christiana Care Health System
Using Clinical Data to Study Women’s Health
• Retrospective cohort
studies
• Studies to measure the
effectiveness of system
change
• Linking data to study the
life course
Women’s Health Across the Life Course
•
•
•
•
Demographic
Psychosocial
Behavioral
Medical
Reproductive
Years
Perinatal
Outcomes
• Mother
• Neonate
• Woman’s health
status
• Child’s health
status
Later years
Breadth of Clinical Data at CCHS
Inpatient
Outside
Sources
Laboratory
Pharmacy
Blood
Bank
Individual
Outpatient
Billing
Discharge
Limitations & Strengths


Limitations
You work with the data you
have, not the data you wish
you had.
Clinician determined
outcomes can lead to some
variation and difficulty
quantifying disease severity.

Data is collected for clinical
purposes at variable intervals.

Definitions can change over
time.

Challenging to pull data.

Strengths
Large cohort

Real world diversity

Real world setting

Lower cost

Shorter time-line
Rich Data Source for Reproductive Age Women:
CCHS Deliveries, 2008
Christiana
Hospital (7538)
55% of births in
Delaware
Women’s Health
Group (1395)
Healthy
Beginnings (533)
Medical co-morbidity and the risk of
prematurity in blacks
Does the higher
prevalence of medical
co-morbidities among
black women account
for their increased risk
of prematurity?
Preterm birth rates, US
Ehrenthal DB, Jurkovitz C, Hoffman M, Kroelinger C, Weintraub W. A population study of the contribution of
medical comorbidity to the risk of prematurity in blacks. Am J Obstet Gynecol. 2007 Oct;197(4):409 e1-6.
Retrospective Cohort Study Using Clinical Data:
Adjusted Odds Ratios
Maternal risk factor
< 32
weeks
aOR
(95% CI)
<37 weeks
aOR
(95% CI)
<1500 g
aOR
(95% CI)
<2500 g
aOR
(95% CI)
African American
2.5 (2.0-3.1)
1.5 (1.4-1.7)
2.9 (2.3-3.7)
2.1(1.9-2.4)
Hispanic
1.1 (0.7-1.7)
0.9 (0.8-1.1)
1.5 (1.0-2.3)
1.1 (0.9-1.3)
Asian
2.3 (0.7-7.4)
0.8 (0.6-1.0)
1.1 (0.5-2.2)
1.1 (0.9-1.5)
ORF=1
1.8 (1.4-2.2)
1.5 (1.3-1.6)
2.1 (1.6-2.6)
1.8 (1.6-2.1)
ORF=2 or more
3.5 (2.2-5.4)
3.2 (2.5-4.1)
3.7 (2.3-6.0)
3.8 (3.0-5.0)
Age < 20*
1.6 (1.2-2.2)
1.3 (1.1-1.5)
1.3 (0.9-1.9)
1.4 (1.2-1.7)
Gestational
hypertension
3.6 (2.8-4.6)
3.5 (3.1-4.0)
5.2 (4.1-6.7)
3.3 (2.9-3.8)
Gestational Diabetes
0.8 (0.5-1.3)
1.2 (1.0-1.5)
0.7 (0.4-1.2)
0.9 (0.7-1.1)
NS = not significant
ORF= Overall risk factor.
ORF=1: presence of one risk factor compared to no risk factor
ORF=2: presence of two risk factors or more compared to no risk factor
* The ORs associated with the other age categories (30-39 and ≥40) are not significant except for the outcome
Gestational Weeks <32 weeks where the OR associated with age≥40 is 1.8 (1.0-3.0)
Risk Factors for Cesarean Delivery, CCHS
Cesarean Delivery Rates, US
What are the risk factors at
CCHS?
Black race (aOR=1.4)
Age 35+ (aOR=1.7)
BMI 40+ (aOR=4.5)
Weight gain (aOR=1.4)
Gestational DM (aOR=1.4)
Gestational HTN (aOR=1.4)
Post-dates (aOR=1.6)
Labor induction (aOR=1.9)
Ehrenthal DB, Jiang X, Strobino DM. Labor induction and the risk of a cesarean delivery among nulliparous women
at term. Obstet Gynecol. 2010 Jul;116(1):35-42.
Trends in Cesarean Delivery, Anemia, and
Peripartum Transfusion, CCHS 2000-2008
40
16
35
14
30
12
25
10
20
8
15
6
10
4
5
2
0
0
2000-1
2002-3
2004-5
2006-7
Year
C-section
Hgb <10.5
Transfusion
2008
Transfusion rate per 1000 deliveries
Percentage Cesarean Delivery and
Hgb <10.5 g/dL
Trends in Cesarean Delivery, Anemia and Transfusion Rates,
2000-2008
Joint Effects of Anemia and Cesarean Delivery on the
Odds of Transfusion
Anemia
(Hgb<10.5)
Cesarean
Delivery
Number of
women (%)
Adjusted
Odds Ratio*
95% CI
No
No
35048
(63.6)
1
Reference
Yes
4133 (7.5)
2.98
2.36, 3.78
No
14185
(25.7)
3.52
2.56, 4.82
Yes
1746 (3.2)
17.08
13.15, 22.17
Yes
*Adjusted for all factors included in the full model.
Differences in the Prevalence of Anemia Contribute to
Disparities in Outcomes
25
Percentage Anemic
20
15
Hgb<10.5 g/dL
Hgb <9.5 g/dL
10
5
ul
l ip
1 aro
pr
io us
r
2 birt
or h
m
or
e
Bl
a
H ck
is
pa
ni
c
W
hi
te
As
ia
O n
th
er
s
>3
5
N
<
20
ye
a
20 rs
–
35
0
Demographic and Obstetrical Characteristics
Limiting Elective Early Term Delivery

Between 1990 and 2005 in the US:
• Preterm delivery increased from 10.6% to 12.7%
• Decrease in delivery at 40 and 41 or greater weeks
• Increase in term deliveries between 37-39 weeks
• Early term now defined: 37-38 weeks
The “Term” Group, 1990 and 2006, US
Source: Martin JA, Hamilton BE, Sutton PD, Ventura SJ, et al. Births: Final data for 2006. National vital statistics reports; vol 57
no 7. Hyattsville, MD: National Center for Health Statistics. 2009.
Effectively Decreasing Elective Early Term Delivery,
CCHS 2005-2009
Policy Change
Data Linkage Across Institutions:
The Delaware Birth Defects Registry
Antenatal
diagnosis
Diagnosis at birth
Postnatal
diagnosis
Delaware
Center MFM
CCHS
Bay Health
St.
Francis
Nemours:
Outpatient
Bayhealth MFM
Nanticoke
Birth Center
Beebe
MFM
Nemours:
Inpatient
Public Health: Fetal Death, Infant Death, Birth Records, Newborn
Screening
Linked Database
Understanding Determinants of Obesity




Fetal origins of adult disease
Influence of early factors, eg birthweight, breast
feeding, maternal medical problems
Role of social determinants
Role of health care
Maternal Perinatal
Risks
Maternal Medical/
Behavioral Risks
Demographic &
Social Factors
Moderating Factors
Neonatal
Characteristic
Childhood
Obesity
Mediating Factors
Adult
Obesity
Delaware Mother-Baby Cohort:
Linking CCHS and Nemours
Obstetrical
Other
Inpatient
Pharmacy
Laboratory
Mother+Baby
Mother
Laboratory
Pharmacy
Baby
Billing
Outpatient
Outpatient
Discharge
Billing
Discharge
It Takes a Village



My team
• Kristin Maiden, PhD
• Stephanie Rogers, RN
• Ashley Stewart, MS, CHES
• Amy Acheson, MA
• Kate Stomieroski
• Richard Butler
CCOR
• William Weintraub, MD
• Claudine Jurkovitz, MD, MPH
• Mark Jiang, MD, BS
• Paul Kolm, PhD
• James Bowen, MS
CCHS ObGyn
• Matthew Hoffman, MD, MPH
• Melanie Chichester, RN
• Suzanne Cole, MD
• Richard Derman, MD, MPH





CCHS Pediatrics
• Louis Bartoshesky, MD, MPH
• David Paul, MD
TJU/Nemours Pediatrics
• Judy Ross, MD
• David West, MD
• Sam Gidding, MD
University of Delaware
• Ben Carterette, PhD
• Michael Peterson, PhD
Johns Hopkins Bloomberg School
of Public Health
• Donna Strobino, PhD
CDC
• Charlan Kroelinger, PhD