1. Using Clinical Data to Study Women’s Health Deborah Ehrenthal, MD Christiana Care Health Services

2. Deborah Ehrenthal, MD Christiana Care Health System Using Clinical Data to Study Women’s Health

3. Retrospective cohort studies Studies to measure the effectiveness of system change Linking data to study the life course Using Clinical Data to Study Women’s Health

4. Women’s Health Across the Life Course Demographic Psychosocial Behavioral Medical Reproductive Years Mother Neonate Perinatal Outcomes Woman’s health status Child’s health status Later years

5. IndividualInpatientPharmacy Blood Bank BillingDischargeOutpatientLaboratory Outside Sources Breadth of Clinical Data at CCHS

6. 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 Limitations & Strengths

7. Christiana Hospital (7538) 55% of births in Delaware Women’s Health Group (1395) Healthy Beginnings (533) Rich Data Source for Reproductive Age Women: CCHS Deliveries, 2008

8. Does the higher prevalence of medical co-morbidities among black women account for their increased risk of prematurity? 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. Preterm birth rates, US Medical co-morbidity and the risk of prematurity in blacks

9. 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) 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)

10. 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) Cesarean Delivery Rates, US Risk Factors for Cesarean Delivery, CCHS 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.

11. Trends in Cesarean Delivery, Anemia, and Peripartum Transfusion, CCHS 2000-2008

12. 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 35048 (63.6) 1Reference Yes4133 (7.5)2.982.36, 3.78 YesNo 14185 (25.7) 3.522.56, 4.82 Yes1746 (3.2)17.0813.15, 22.17 *Adjusted for all factors included in the full model.

13. Differences in the Prevalence of Anemia Contribute to Disparities in Outcomes

14. 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

15. 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. The “Term” Group, 1990 and 2006, US

16. Effectively Decreasing Elective Early Term Delivery, CCHS 2005-2009 Policy Change

17. Data Linkage Across Institutions: The Delaware Birth Defects Registry Bayhealth MFM Delaware Center MFM CCHS Nanticoke Bay Health Birth Center St. Francis Beebe MFM Nemours: Outpatient Nemours: Inpatient Public Health: Fetal Death, Infant Death, Birth Records, Newborn Screening Linked Database Antenatal diagnosis Diagnosis at birthPostnatal diagnosis

18.  Fetal origins of adult disease  Influence of early factors, eg birthweight, breast feeding, maternal medical problems  Role of social determinants  Role of health care Mediating Factors Moderating Factors Childhood Obesity Adult Obesity Maternal Perinatal Risks Neonatal Characteristic Maternal Medical/ Behavioral Risks Demographic & Social Factors Understanding Determinants of Obesity

19. Mother ObstetricalPharmacyBilling Discharge OutpatientLaboratory Other Baby InpatientPharmacyBilling Discharge OutpatientLaboratory Mother+Baby Delaware Mother-Baby Cohort: Linking CCHS and Nemours

20.  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 It Takes a Village