Linking County Infant Mortality Data and Disparities Policy

1. Linking County Infant Mortality Data and Disparities Policy 12th Annual Maternal and Child Health Epidemiology Conference December 7, 2006 Yvonne W. Fry-Johnson, MD Fellow, MSCR Program, Morehouse School of Medicine Diane L. Rowley, MD MPH Director, Research Center on Health Disparities at Morehouse College George Rust, MD, MPH & Robert Levine, MD National Center for Primary Care at Morehouse School of Medicine

2. Background • National and Regional Analyses of black: white infant mortality may obscure local patterns that provide insight into characteristics that may explain the disparities • Selection of communities based more on convenience than representation may underestimate disparities

3. Background • A relatively recent MMWR reported variability in the racial/ethnic infant mortality rates (IMR’s) in the 60 largest US Cities, 1995-1998 (MMWR, 2002) • Black IMR’s were 1.4-4.8 times higher than white IMR’s in the 49 cities where both were reported

4. Background and Significance • Disparities in Infant health are not inevitable… • Other researchers have begun looking at ecology and neighborhood analyses for determinants of health and SES status (Galea S, Ahen J.) • We propose using what may be perceived as anomalous counties, or outliers to direct our research in new ways

5. Question What is the difference between communities where: • The Black infant mortality rates, or • The Black: White infant mortality rate ratios are significantly higher or lower than predicted by multivariate models?

6. Methodology Data Sources: • Compressed Mortality Files (NCHS) 1999 -2003, CDC Wonder • Contains annual national-, state-, county-, age-, gender-, race-, and cause-specific mortality data. • 2000 US Census

7. Methodology-cont- • We identified a cohort of 329 US Counties with reliable Black Infant Mortality Rates 1999-2003 (NCHS definition=20+ Black deaths in the given county) • These 329 counties represented 84% of the black infant deaths; 54% of the white infant deaths in the US in the 5 year study period

8. Methodology(cont) Linear regression was usedto create an ecologic model Outcome Variable: Black Infant Mortality Predictor Variables for SES: • % Black women age 25yoa+, <High School Grad; • Black per capita income; • % Blacks living below poverty level

9. Methodology(cont) Contextual SES Index: • % Black women age 25 or greater <High School Grad; • Black per capita income; • % Blacks yearly income< poverty level All 3,141 counties in the US are ranked • identify % associated with each ranking (i.e. x %-ile for per capita income…) • (1-rank of per capita income) • add up total ranking and divide by 3 (Kilbourne, B)

10. Methodology(cont) Contextual SES Index: • % Blacks living below the poverty level • Higher the percentage, the lower the contextual SES • % Black female with low educational attainment • Higher the percentage, the lower the contextual SES • (1-Black per capita income) • Higher the value, the lower the income

11. Methodology(cont) Inequality Measure: • Black: White poverty white ratio Predictor Variables for Place: • Percent of population residing in urban area • Percent Blacks 18-64 years speaking English not well or not at all

12. Methodology-cont- Evaluation Residual Diagnostic Analysis (Jack-knife): used to identify counties whose mortality rates are significantly above or below those predicted by the multivariate model (defined as Anomalous Counties)

13. Methodology-cont- Evaluation • We will seek to identify Contextual Explanations for anomaly (e.g. factors which cause anomalous status to be lost)

14. Findings • Simple regression

19. Findings • Multiple Regression

20. Model 1. Outcome = black Infant Mortality 329 US Counties with Reliable Rates for Blacks and Whites. 1999-2003.

21. Findings: Anomalous Counties Model 1: SES Index+ % Urban Black IMR significantly lower than predicted: State, County Black IM • Mass, Essex 341 • Mass, Plymouth 561.3 • NY, Bronx 656.9 • NJ, Passaic 804.2 • NY, NY 812.3 • Fl, St. Lucie 970.6

22. Findings: Anomalous Counties Model 1: SES Index+ % Urban Black IMR significantly higher than predicted: State, County Black IM • TX, Wichita 2069 • IA, Scott 2195.5 • TX, Potter 2205.9 • NC, Anson 2214.8 • SC, Horry 2225.4 • GA, Muscogee 2301.2

23. Findings: Anomalous Counties Model 1: SES Index+ % Urban Black IMR significantly higher than predicted: State, County Black IM • NY, Oneida 2336 • MS, Copiah 2401.2 • AL, Tuscaloosa 2401.2 • GA, Baldwin 2430.3 • NC, Alamance 2571.1 • FL, Bay 2621.5

26. Model 2. Outcome = black Infant Mortality 329 US Counties with Reliable Rates for Blacks and Whites. 1999-2003.

27. Findings: Anomalous Counties Model 2: SES Index+ % Urban + Non-English speakers Black IMR significantly lower than predicted: State, County Black IM • MA, Essex 341 • MA, Plymouth 561.3 • NJ, Passaic 804.2

28. Findings: Anomalous Counties Model 2: SES Index+ % Urban + Non-English speakers Black IMR significantly higher than predicted: State, County Black IM • TX, Wichita 2069 • IA, Scott 2195.5 • SC, Horry 2225.4 • GA, Muscogee 2301.2

29. Findings: Anomalous Counties Model 2: SES Index+ % Urban + Non-English speakers Black IMR significantly higher than predicted: State, County Black IM • AL, Tuscaloosa 2401.2 • GA, Baldwin 2430.3 • NC, Alamance 2571.1 • FL, Bay 2621.5

33. Future Implications Follow study with prospective study in identified successful or unsuccessful communities to further delineate contributing factors to success or failure. Explain beyond SES what are the real issues affecting Black birth outcomes, especially infant mortality

34. Health Disparities in black: white Infant Mortality rates • Continued evidence of disproportionate IMRs by race • Despite decreases in IMRs over time, disparity both persists, and rate ratios increase • Disparities are disparate across geographic areas…and county data helps hone in more precisely

35. Health Disparities in black: white Infant Mortality rates • Both biological and socioeconomic factors may play a role, but geographical differences raise questions of their interactions • Communities exist with resistance or resiliency to disparities • Communities who remain disparate also exist

36. Current Theories of black: white Infant Mortality Disparities (NCHS Terminology) • Low Socioeconomic Status • Low Educational Status • Race • Differences in Quality of Medical Care • Prenatal care (content; onset; quality in terms of frequency and timeliness)

37. Improving the Theoretical Base Using Geographic Variations in Disparities Identify what Location Characteristics Influence Disparities After Accounting for Contextual Socio-economic Factors: • Urban-Rural • Educational Levels • Economic Status • English not as primary language Characteristics of Geographic Location Over Time

38. Strengths and Limitations of the Data • Strengths • Trans-geographic focus on communities with vulnerable black populations allows comparison with whites in those same geographic areas • Usual methods involve “representative national samples” that compare blacks who live in urban and rural areas, with whites who live in urban, rural and largely white suburban areas • Inclusion of communities with 84% of black infant deaths allows identification of unusually successful areas

39. Strengths and Limitations Cont’d • Limitations • Complete picture of contributing factors to the historical outcomes is not easily obtained; • Little collaboration between existing health agencies on local, state, regional, or national levels; • Communication regarding existing resources or innovative programs not well disseminated, even in the local geographic areas;

40. Strengths and Limitations Cont’d • Limitations • Successes not always identified in a timely manner, to be evaluated, replicated, and supported for on-going positive effects within given communities; • Information used less to inform policy and more to count outcomes

41. Future Implications Consider use of Area Resource Files (GIS) to add to this body of knowledge… • use information on the types of providers, • hospital resources, • level of services, etc. in the identified counties to further clarify protective factors and resilience, and to identify areas and issues of resistance.

42. Future Implications Expand research methodology to include use of Maternal Infant Linked Birth Death Records to obtain individual level information on: • Timing of onset of prenatal care; • Clearly specified maternal level of educational attainment; • Marital status; • Gestational age; • Etc.

43. Future Implications Use model (s) to further explain beyond SES what are the real issues affecting Black birth outcomes, especially infant mortality • It is not just being black, but also where one is black that is important; • Culture, ethnicity, and acculturation all impact actual experience of being black, and subsequent health outcomes.

44. Future Implications Follow study with prospective study in identified successful or unsuccessful communities to further delineate contributing factors to success or failure: • accessibility, acceptability, utilization, and quality of health care; • Replicate what has worked in the resilient or resistant communities, to transform the more disparate communities.