Women's Cardiovascular Health Disparities
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Julie J Ramos, MD presents background and personal research on racial and gender disparities in cardiovascular health at Columbia University Medical Center
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Women's Cardiovascular Health Disparities
- 1. Disparities in Women's Cardiovascular Health Julie J Ramos, MD Center for Interventional Vascular Therapeutics Columbia University Medical Center Obstetrics/Gynecology Grand Rounds February 1, 2018 1
- 2. OUTLINE OF DISCUSSION • Overview of cardiovascular disease in women • Overview of women cardiovascular risk factors • Focus on CUMC existing disparities • How to decrease disparities • Elicit questions, conversation, and future collaboration 2
- 3. 3
- 4. Figure 1. Trends in perceived leading cause of death among women. Mosca L et al. Circulation 2006;113:525-534 Copyright © American Heart Association 4
- 5. Pregnancies Middle Age Population with complicated pregnancies Healthy population Threshold for vascular or metabolic disease Age Vascular Risk Factors Source: Sattar & Greer, 2002;. Adapted from Deborah Ehrenthal, MD, FACP Pregnancy: Is A Stress Test
- 6. Pregnancy Related Risk Factors of Cardiovascular Disease • Metabolic Syndrome • Diabetes • Gestational Diabetes • Preeclampsia • IUGR 6
- 7. 26.7% 22.9% 30.1% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% TOTAL MEN WOMEN Prevalence of Metabolic Syndrome in NYC TOTAL MEN WOMEN 7
- 8. Academic Research • Cardiovascular disease research disparities • Across the board cardiovascular subspecialties 8
- 9. 9
- 10. Academic Medical Care • Insurance Disparities • Racial Disparities 10
- 11. 11
- 12. 12
- 13. 13
- 14. Where is Intervention Needed? • Team approach • Cardiologists • Ob/Gyns • Focus on Missed Opportunities - PPV 14
- 15. How are We Doing in a High Risk Postpartum Population? 15
- 16. 16 Incident Coronary Heart Disease After Preeclampsia Journal of the American Heart Association. March 6, 2017
- 17. Adverse Pregnancy Outcomes • What is the incidence of non-attendance to the postpartum visit? • Identify predictors for postpartum visit non-attendance? • Associated co-morbidities and postpartum non-attendance 17
- 18. Study Design •Retrospective cohort study – Patients were identified using Clinical Looking Glass •Inclusion criteria – Live births occurring in 2013 •Outcomes – Attendance of a postpartum visit within 12 weeks 18
- 19. Women with adverse pregnancy outcomes have higher baseline comorbidities Rosser, Zhang, Ramos, 2015 P<0.001 APO N = 6,457 ; Normal Pregnancy N = 21,971 1.0% 19
- 20. Obesity Trends Among Women Delivered Each Year Rosser, Zhang, Ramos, 2015 P<0.001 12.7% 16.8% 21.3% 27.6% 30.7% 2010 2011 2012 2013 2014 p<0.001 20
- 21. Gestational Diabetes Trends Among Women Delivered Each Year Rosser, Zhang, Ramos, 2015 P<0.001 6.1% 7.9% 9.0% 10.2% 9.7% 2010 2011 2012 2013 2014 p<0.001 21
- 22. Pre-Eclampsia Trends Among Women Delivered Each Year Rosser, Zhang, Ramos, 2015 P<0.001 8.0% 6.5% 7.9% 7.7% 11.4% 2010 2011 2012 2013 2014 p<0.001 22
- 23. Preterm Birth Trends Among Women Delivered Each Year Rosser, Zhang, Ramos, 2015 P<0.001 6.0% 6.8% 6.3% 8.2% 8.6% 2010 2011 2012 2013 2014 p<0.001 23
- 24. Intrauterine Growth Restriction Trends Among Women Delivered Each Year Rosser, Zhang, Ramos, 2015 P<0.001 6.0% 6.8% 6.3% 8.2% 8.6% 2010 2011 2012 2013 2014 p<0.001 24
- 25. 2010-2014 Postpartum visit rates Rosser, Zhang, Ramos, 2015 APO N = 6,457 ; Normal Pregnancy N = 21,971 p<.00010 25
- 26. 45 Day Postpartum visit rates Rosser, Zhang, Ramos, 2015 P<0.001 APO N = 6,457 ; Normal Pregnancy N = 21,971 * * * p<0.0001 26
- 27. Results- Deliveries 12 month study period •6489 women delivered live births •The majority of women did not attended a PPV 27
- 28. Limitations Lack of access to data outside of present medical network – Attending Prenatal Visit or PPV elsewhere – Overestimation of non-attendance rate Exclusion of variables due to incomplete data – Gestational Age – Parity – Education 28
- 30. 30
- 31. How are We Doing at CUMC? • Internal CUMC cath lab data • Cath Lab Gender Disparities • Cath Lab Racial Disparities 31
- 32. CUMC CATH LAB DATA - 2016 Gender Cases % FEMALE 3,431 36% MALE 6,134 64% Total 9,565 Primary Language = Spanish 10% 32
- 33. 2177 1502 1023 923 0 500 1000 1500 2000 2500 WHITE MALE NON-WHITE MALE WHITE FEMALE NON-WHITE FEMALE #PROCEDURES OVERVIEW RACE & GENDER CATH PROCEDURES 33
- 34. 3200 56% 2425 44% 5625 0 1000 2000 3000 4000 5000 6000 WHITE PATIENTS NON-WHITE PATIENTS TOTAL PATIENTS WHITE VS NON-WHITE CATH PROCEDURES 34
- 35. 3679 65% 1946 35% 5625 0 1000 2000 3000 4000 5000 6000 MALE FEMALE TOTAL PATIENTS MALE VS FEMALE CATH PROCEDURES 35
- 37. Visits • PPV should be time to capture high risk women • Cardiovascular risk reduction should be addressed annually • Blood pressure monitoring • Body mass index calculation, and • Lifestyle modification involving exercise and dietary instruction • Lipid and glucose measurements should be measured every five years 37
- 38. Addressing Disparities • Educating healthcare professionals • Cross Specialty collaboration • Decrease healthcare cultural barriers –Clinical care –Research 38
- 39. Future Collaboration to Improve Women’s Cardiovascular Disease • Screen • Educate • Research • Collaborate 39
Editor's Notes
- Figure 1. Trends in perceived leading cause of death among women. Numbers are percentages. Shown are the 1997 survey (n=1000), 2000 survey (n=1004), 2003 survey (n=1024), and 2005 survey (n=1008).