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

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Presentation on theme: "Using Clinical Data to Study Women’s Health Deborah Ehrenthal, MD Christiana Care Health Services."— Presentation transcript:

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

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