Maternal morbidity and its impact on preterm births -Georgia’s experience-

1. Maternal morbidity and its impact on preterm births -Georgia’s experience- MCH Epidemiology Conference December 2003, Arizona

2. Violanda Grigorescu, MD, MSPHCDC/ORISE MCH epidemiologist fellow • Mohamed Qayad, MD, MPH, MSPH 2 Medical epidemiologist • Emily Kahn, PhD, MPH 1,2 Chief MCH Epidemiology Section • Debra Hersh, MPH 2 MCH epidemiologist 1 Centers for Disease Control and Prevention, Division of Reproductive Health 2 Georgia Division of Public Health/MCH Epidemiology Section Acknowledgements • William Sappenfield MD, MPH • William Callaghan, MD, MPH Centers for Disease Control and Prevention, Division of Reproductive Health

3. Why maternal morbidity? • Maternal mortality represents the definitive consequence of severe maternal morbidity • Maternal morbidity is not very well understood, less frequently measured and more difficult to track at the population level than maternal mortality • Maternal morbidity may relates to a pre-existing medical condition that affects or is exacerbated by pregnancy, or a pregnancy-related medical condition

4. Why maternal morbidity to women enrolled in Medicaid? • High risk population - poor socio-economic status may lead to poor health outcomes • State priority - Medicaid women made up 46.7% of the 2001 live births in GA

5. Objectives • To assess the health conditions of pregnant women enrolled in Medicaid and their impact on pregnancy outcomes • To disseminate the findings among Medicaid health care providers • To strengthen the collaboration with Medicaid and to develop better prevention strategies for women and their children • To develop mechanisms for tracking different health conditions, if necessary

6. Method • Data source: 2001 Medicaid delivery claims data (Inpatient file) linked with birth certificates data • Outcome: preterm (20 –36 wks.gestation) • Prevalence of health conditions and their association with preterm births • SAS used for cleaning, re-coding and data analysis

7. Method (cont.) • From Medicaid claims (inpatient) data: - Mothers’ age - ICD9 codes • From birth certificate data: - Preterm (gestational age) - Mothers’ race

8. Method (cont.) Mothers’ race - Medicaid data compared to birth certificates data 1,257 recorded as unknown race in Medicaid were coded as White in birth certificate file. 644 recorded as Black in Medicaid, were coded as White in birth certificate file. Based on previous knowledge and information, the race recorded on birth certificates considered more accurate and used for further analysis. Small number for other races not included in further analysis

9. Method (cont.) • 224,429 ICD9 codes in Medicaid claims data (Inpatient file): -Two diagnostic fields with one or more ICD9 codes for each client - All ICD9 codes considered - ICD9 codes grouped into 116 categories • ICD9 codes unrelated to maternal health conditions were not used for further analysis

10. Method (cont.) • Appropriate groupings were classified as: - Nonpregnancy-related (pre-existing) = health conditions diagnosed before or during pregnancy but not determined by the pregnancy status - Pregnancy-related = health conditions determined by the pregnancy status - Either nonpregnancy-related or pregnancy- related health conditions (anemia, nutritional deficiency, psychoses, genital infections) Not included in this analysis

11. Results

12. Demographic characteristics Age pattern Age distribution of Medicaid clients between 10 and 50 years old Mean = 23.56 Median = 22 Mode = 21 Std = 5.35

13. Demographic characteristics Black/White ratio by age groups

14. Prevalence of nonpregnancy-related (pre-existing) health conditions

15. Black/White ratio within each nonpregnancy-related (pre-existing) health conditions

16. Prevalence of pregnancy-related health conditions

17. Black/White ratio within each pregnancy-related health conditions

18. Prevalence of hypertension during pregnancy (by stage) 11.79% cases of hypertension during pregnancy had previous hypertension

19. Black/White ratio within different stages of hypertension during pregnancy

20. What is the association between the identified health conditions and preterm births?

21. Statistical analysis • Crude OR calculated to measure the strength of the association between preterm births and nonpregnancy (pre-existing) and pregnancy-related health conditions • Exploratory data analysis (CMH, Breslow-Day test and backward selection, logit-plots) • Logistic regression models developed to estimate the odds ratio (OR) for preterm births (<37 weeks) adjusting for maternal age and race

22. Health conditions significantly associated with preterm births

23. Discussion/Conclusion • Preterm birth is a very complex problem with no simple solution • Maternal health conditions have an impact on pregnancy outcomes and so on preterm births : to be considered in further analysis

24. Discussion/Conclusion (cont.) • These findings reflect Medicaid clients • Non Medicaid women/mothers may have different health problems • Maternal morbidity still has many unknowns and should be explored further : a continuing challenge for public health professionals • Further prevention strategies should be focused on improving women’s health not just during but beyond pregnancy

25. Public Health implications • Conduct further epidemiological analysis of the existing data (Medicaid, hospital discharge) to: - better understand the impact of morbidity on pregnancy outcomes - identify potential prevention strategies • Collaborate with health care providers to: - develop follow up protocols for women at risk (diagnosed with different health conditions) - develop a life span approach to women’s health

26. Limitations • Medicaid data collected for billing and not for epi/research purpose • Accurate cleaning required • Just Medicaid inpatient file used for this analysis • Underestimated prevalence of different health conditions • Utilization of services may differ by health insurance status

27. To be continued

28. Thank you for your attention!