Estimation of US Life Tables for Minority Populations: Issues of Data Quality and Availability

1. Estimation of US Life Tables for Minority Populations: Issues of Data Quality and Availability Elizabeth Arias, Ph.D. Mortality Statistics Branch Division of Vital Statistics National Center for Health Statistics

2. Will it be possible to estimate life tables for groups other than the White and Black populations? • Data Limitations and Availability • Vital Statistics and Medicare Data • US Life Table Methodology • Example: Comparison of White and Hispanic US Life Tables for Decennial Period 1999-2001

3. Data Limitations and Availability • Race/Ethnic Misclassification on Death Certificate • Race/Ethnic Classification on Centers for Medicare and Medicaid Services (CMS) Medicare Data

4. Race/Ethnic Misclassification on Death Certificates • Results based on study • “The Validity of Race and Hispanic Origin Reporting on Death Certificates in the United States,” by Arias, et al. (2007) • Study used the National Longitudinal Mortality Study (NLMS) – linkage of Current Population Surveys (1973, 1978-1998) with NCHS Mortality Data (Mortality follow-up 1979-1998) • For Sample of Decedents identified in the NLMS, results show that reporting on the death certificate is excellent for White and Black populations; less than optimal for API and Hispanic populations; and, poor for AIAN population. • There is some improvement between 1980s and 1990s for most of these groups

5. Ratio of CPS (self) to Death Certificate Report for the Sample of NLMS Decedents

6. Application of Validity Study to the Estimation of Life Tables • Evaluation of misclassification was carried out by age, sex, region, rural/urban residence, and co-ethnic concentration • These results can be used to adjust observed death rates • These adjusted death rates can then be used to estimate life tables • Ideally, this would be all that is needed……But,

7. The Role of CMS Medicare Data in the Production of US Life Tables • NCHS has traditionally used Medicare data to estimate mortality at the oldest ages for its Decennial Life Tables (Annual Life Tables since 1997) • It is believed that Medicare coverage is better because age-reporting is verified with date of birth, whereas the denominators of Vital Statistics rates come from Census estimates, which are not verified for age reporting • The 1999-2001 US Decennial Life Table Method blends Vital and Medicare rates to estimate mortality for ages 65 – 100 • q(x) for ages 65-94 are blended with progressive weight given to Medicare Data • q(x) for ages 95-100 are derived exclusively from Medicare Data

8. CMS Medicare Race/Ethnic Classification • CMS Medicare Data derives its Race and Ethnic information from the Social Security Administration • Race and ethnic data is collected by SSA when individuals complete form SS-5 for SS Card. • Between 1936 and 1980 Race categories included in the SS-5 application were limited to: White, Negro or Other • As per OMB Directive No. 15 SSA revised the SS-5 (1980) by expanding the options to 5 categories, combining Race and Hispanic Origin : • White (Non-Hispanic) • Black (Non-Hispanic) • Asian or Pacific Islander • American Indian or Alaskan Native • Hispanic

9. CMS Medicare Race/Ethnic Classification • The result is that CMS Race/Ethnic Categories are a combination of pre-1980 and post-1980 SSA Race/Ethnic Categories: • 0=Unknown • 1=White (Non-Hispanic) • 2=Black (Non-Hispanic) • 3=Other • 4=Asian, Asian American or Pacific Islander • 5=Hispanic • 6=American Indian or Alaskan Native

10. Use of CMS Medicare for Groups Other than White or Black • How do we disentangle AIANs, APIs, and Hispanics from the combination of pre-1980 and post-1980 categories? • Experiment: Use NLMS – CMS linked Data. NLMS was recently linked to 1991-1995 CMS Medicare files • Compare CPS and CMS-Medicare Classification • CPS has provided respondents with full-range of race/ethnicity since 1977

11. Sensitivity: Percent of NLMS Respondents Correctly Identified by CMS – Medicare (1991-95)

12. Predictive Value Positive: Percent of Respondents Identified by CMS who Self-Identified in the same group in NLMS

13. Effects on Estimation of Life Tables for these Populations • NLMS-CMS link suggests the majority of Hispanics, AIANs, and APIs are not easily identifiable in CMS Medicare Data. • Does it Matter? Can we do without Medicare Data? • Exploration • Hispanic Mortality Compared to White Mortality 1999-2001 • Following 3 Graphs compare Vital q(x) between Hispanic (observed and adjusted for DC under-report) and White populations

17. What do Mortality Patterns Say? • Even after Adjustment for DC misclassification, Hispanic Mortality remains lower. • Lower Mortality for Hispanics is concentrated in the older ages, except for Hispanic Females who show advantage throughout full age range. • Next, Closer look at Mortality at ages 65 and above

21. Effects of Using Medicare Data on Life Expectancy • Previous 3 graphs show that for the white population CMS Medicare shows higher mortality at ages 65-100 than Vital Statistics • What impact does including CMS Medicare Data have on Life Expectancy Estimates? • Experiment: Quantify the Effect of Excluding Medicare Data and Closing the Life Table at age 85 for the White Population:

22. Comparison of Life Expectancy - Blending Vital and Medicare Data for Ages 65-100 vs. Vital Statistics Closed at Age 85 – White Population • Decennial Method Close at Age 85 Diff • Total White Population • Birth 77.22 77.58 0.36 • 65 17.63 18.06 0.43 • 85 5.99 6.38 0.39 • White Male • Birth 74.60 74.90 0.30 • 65 16.01 16.38 0.37 • 85 5.23 5.66 0.43 • White Female • Birth 79.74 80.16 0.42 • 65 18.95 19.43 0.48 • 85 6.38 6.73 0.35

23. Comparison of White and Hispanic Life Expectancy Using Estimates based Solely on Vital Statistics, Closing Table at Age 85 • 1999-2001 Decennial Period • Total Hispanic Total White Diff • Birth 79.53 77.58 1.95 • 65 19.76 18.06 1.70 • 85 7.88 6.38 1.50 • Hispanic Male White Male • Birth 76.69 74.90 1.79 • 65 18.10 16.38 1.72 • 85 7.33 5.66 1.67 • Hispanic Female White Female • Birth 82.31 80.16 2.15 • 65 21.10 19.43 1.67 • 85 8.23 6.73 1.50

24. How Do Vital Statistics Estimates Compare to NLMS Estimates of Hispanic Life Expectancy? • Vital Statistics NLMS • Total Hispanic Diff • Birth 79.53 80.12 0.59 • 65 19.76 20.15 0.39 • 85 7.88 8.03 0.15 • Hispanic Male • Birth 76.69 77.25 0.56 • 65 18.10 18.28 0.18 • 85 7.33 7.37 0.04 • Hispanic Female • Birth 82.31 83.35 1.04 • 65 21.10 21.86 0.76 • 85 8.23 8.50 0.27

25. Summary and Future Research and Exploration • Data Quality and Limitations Pose Challenges to the Production of Life Tables for Minority Populations. • Are they insurmountable? • Perhaps, Perhaps Not • On the plus side: We have been able to identify and quantify Race/Ethnic misclassification on DC and use this information to correct the resulting under-count of deaths for affected groups

26. Summary and Future Research and Exploration • On the negative side: we may not be able to use Medicare data for a very long time, if ever for minority populations • We may need to accept that if we want life tables for Hispanics, APIs, and AIANs we will need to rely solely on Vital Statistics • Can we accept this alternative? • For example, can we accept that Hispanic life expectancy is higher than that of NHWhites? • Previous studies using the NLMS, NHIS-NDI all show that Hispanics indeed have lower mortality than NHWhites. • One study attributes the advantage to the Salmon Bias Effect, but finds that this Effect applies only to Foreign Born Mexicans and Central/South Americans (Palloni and Arias, 2004) • Could the large gap in mortality at the oldest ages we observed be due to Salmon Bias?

27. Next Steps • Explore upcoming NLMS – CMS 1996-2000 Medicare linkage for possibility of re-classification of CMS categories for use in the 1999-2001 Decennial Life Tables. • Repeat comparative exercises for AIANs and APIs • Explore other statistical modeling techniques for estimates of old-age mortality for these populations.

28. Contact Information • Elizabeth Arias • MSB/DVS/NCHS • EArias@cdc.gov • 301-458-4727