Cristin Larder, MS 1 Violanda Grigorescu, MD, MSPH 1 Larry Hembroff, PhD 2

1. Access to Interconception Care in Michigan: Population Based Findings from the Michigan Health Outside Pregnancy Survey (HOPS) June 13, 2011 Cristin Larder, MS1 Violanda Grigorescu, MD, MSPH1 Larry Hembroff, PhD2 1Michigan Department of Community Health, Division of Genomics, Perinatal Health, and Chronic Disease Epidemiology 2Michigan State University, Office for Survey Research

2. Presentation Outline • Background on Preconception Health • Description of HOPS • Variable Measurement • Statistical Methods • Results • Demographics of Respondents • Prevalence of Not Being Able to See a Doctor • Barriers to Interconception Health Care • Predictors of Not Being Able to See a Doctor • Conclusions • Limitations • Public Health Implications • Acknowledgements • References

3. Background • Preconception health has been increasingly recognized as an important precursor for healthy pregnancies and infants • In 2006, the CDC/ATSDR Preconception Care Workgroup and the Select Panel on Preconception Care published Recommendations to Improve Preconception Health and Health Care – United States1 • The report contained 10 recommendations for reaching four health outcome goals

4. Recommendation 9: Research • Action steps • Prepare updated evidence-based systematic reviews… • Encourage and support evaluation of model programs… • Conduct quantitative and qualitative studies… • Design and conduct analyses of cost-benefit and cost effectiveness… • Conduct health services research… • Conduct studies to examine the factors that result in variations in individual use of preconception care (i.e., barriers and motivators that affect health care use)

5. Description of HOPS • The Health Outside Pregnancy Survey (HOPS) is a structured mail survey developed by the MDCH Division of Genomics, Perinatal Health, and Chronic Disease Epidemiology (Dr. Violanda Grigorescu) • The sampling frame was drawn from resident, occurrent Michigan live births in 2007-08 • Low birthweight (LBW) infants, maternal age <20, and black race were oversampled • Survey was sent to 4,202 sampled mothers in four batches, with one mailing per mother • Survey data were weighted by MSU’s Office for Survey Research (Dr. Larry Hembroff) to reflect the state’s entire population of women giving birth in the years 2007-08 • Median response was 18 months after delivery

6. Variable Measurement • Women were asked whether they had needed to see a doctor, but couldn’t, in the 12 months before the survey date • Those who answered yes were directed to a multiple choice question asking about specific barriers they may have experienced • Maternal age, race, and ethnicity, as well as low birth weight (LBW) status, were obtained from birth certificates • Survey questions determined other maternal demographic characteristics • Racism related stress was measured by an indicator variable representing a modified version of the Reactions to Race Module from the Behavioral Risk Factor Surveillance System (BRFSS)2

7. Statistical Methods • SUDAAN release 10.0.1 was used for all statistical analyses to account for the complex sampling design • Non-response analysis was conducted using Wald chi-square tests and multivariate logistic regression to determine the extent which responders and non-responders differed with respect to age, race, ethnicity, and LBW status • Frequencies were calculated for the overall prevalence of not being able to see a doctor when needed and for each barrier to seeing a doctor • Wald chi-square tests and a multivariate logistic regression model were used to determine the characteristics of women who were not able to see a doctor when needed, compared to women who either didn’t need to see a doctor or were able to

8. Demographics of Respondents Weighted Response Rate = 28.4% Bivariate Results: Characteristic x Responded

9. Significant at  = 0.05 Demographics of Respondents Weighted Response Rate = 28.4% Bivariate Results: Characteristic x Responded

10. Demographics of Respondents Multivariate Logistic Regression Results:

11. Significant at  = 0.05 Demographics of Respondents Multivariate Logistic Regression Results:

12. Demographics of Respondents Multivariate Logistic Regression Results: Final Model Responded = b0 + b1(age) + b2(race) If the non-responders in any of the groups above would have answered differently with respect to the variables of interest, then non-response bias exists in the survey

13. Prevalence of Not Being Able to See a Doctor • A weighted 21.5% of women responded that there was a time in the past 12 months when they needed to see a doctor but could not • 95% Confidence Interval: (17.5 – 26.1)

14. Barriers to Interconception Health Care

15. Barriers to Interconception Health Care Significant at  = 0.05

16. Predictors of Not Being Able to See a Doctor Bivariate Results: Characteristic x Not Able to See Doctor

17. Significant at  = 0.05 Predictors of Not Being Able to See a Doctor Bivariate Results: Characteristic x Not Able to See Doctor

18. Predictors of Not Being Able to See a Doctor Multivariate Logistic Regression Results:

19. Significant at  = 0.05 Predictors of Not Being Able to See a Doctor Multivariate Logistic Regression Results:

20. Predictors of Not Being Able to See a Doctor Multivariate Logistic Regression Results:

21. Significant at  = 0.05 Predictors of Not Being Able to See a Doctor Multivariate Logistic Regression Results:

22. Not Able to See Doctor = b0 + b1(Insurance) + b2(Marital) + b3(Racism Stress) Predictors of Not Being Able to See a Doctor Multivariate Logistic Regression Results: Final Model

23. Conclusions • Not having health insurance was by far the number one barrier to seeing a doctor when needed during the interconception period • This self-report was confirmed by logistic regression analysis • The findings mirrored the results of prior studies • Kaiser Women’s Health Study: 24% of all women and 55% of uninsured delayed health care because of cost3 • Central PA Women’s Health Study: Uninsured 44% less likely to visit OB/GYN and 55% less likely to receive routine screening than privately insured4

24. Limitations • Low response rate may have lead to bias if non-responders would have answered differently than responders • Only women who have had a live birth were surveyed • Survey was sent in only one mailing, with no telephone follow up

25. Public Health Implications • The lack of health care coverage outside pregnancy limits our ability to develop and implement prevention strategies targeted to women of reproductive age during the preconception and interconception periods • The advent of the Affordable Care Act may help mitigate this limitation

26. Acknowledgements • I’d like to thank my coauthors, Drs. Violanda Grigorescu and Larry Hembroff • A special thanks goes out to our sampling statistician at the MDCH Division for Vital Records and Health Statistics, Dr. Jose Saraiva

27. References • Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR). Preconception Care Work Group and the Select Panel on Preconception Care. Recommendations to Improve Preconception Health and Health Care – United States. Morbidity and Mortality Weekly Reports, 55(RR-6); April 21, 2006. • Behavioral Risk Factor Surveillance System. Reactions to Race Module. 2002 BRFSS questionnaire. http://apps.nccd.cdc.gov/BRFSSQuest/ListByYear.asp. Accessed 05/31/2011. • Ranji U, Salganicoff A. Women’s Health Care Chartbook – Key Findings from the Kaiser Women’s Health Survey. Menlo Park, CA: Kaiser Family Foundation; May 2011. • Hillemeier MM, Weisman CS, Chase GA, Dyer AM, Shaffer ML. Women’s Preconceptional Health and Use of Health Services: Implications for Preconception Care. Health Services Research 43(1):54-75; February 2008.