© 2006 American Public Health Association DOI: 10.2105/AJPH.2005.075408
The authors are with the Department of Society, Human Development and Health, Harvard School of Public Health, Boston, Mass. Correspondence: Requests for reprints should be sent to David H. Rehkopf, ScD, Department of Society, Human Development, and Health, Harvard School of Public Health, 677 Huntington Ave, Kresge 7th Floor, Boston, MA 02115 (e-mail: drehkopf{at}hsph.harvard.edu).
We compared all-cause mortality rates stratified by individual-level education and by census tract areabased socioeconomic measures for Massachusetts (19992001). Among persons aged 25 and older, the age-adjusted relative index of inequality was slightly higher for the census tract than for the individual education measures (1.5 vs 1.2, respectively). Only the census tract socioeconomic measures could provide a relative index of inequality (23) for deaths before age 25 or detect expected socioeconomic disparities for deaths among persons 65 and older (relative index of inequality= approximately 1.2 vs 0.8 for census tract measures and individual education, respectively).
Population health data stratified by socioeconomic position are critical for monitoring and analyzing health disparities. When individual-level socioeconomic measures are not available, as is often the case with health surveillance data,14 an alternative approach is to use census tract areabased socioeconomic measures to characterize rates in relation to the socioeconomic position of the immediate areas in which people reside.1,36 Moreover, even when individual-level education data are available (e.g., for death certificates), the public-release census summary files before the 2000 US census did not provide data on educational level cross-tabulated by age, needed for denominators. In this study, we used the newly available 2000 census population counts for education level cross-tabulated by age to report and compare, for the first time, the socioeconomic inequalities in mortality detected with individual-level education data and census tract areabased socioeconomic measures.
We obtained mortality data, including years of individual education,7 from the state of Massachusetts for the years 1999 to 2001 (N = 165 217) and geocoded all deceased persons according to the address on the death certificate. We employed a commercial geocoding firm with known high accuracy8; thus, we were able to geocode 97% of the records with certainty to the census tract level. A priori determined categories for individual-level education and the 3 census tract areabased socioeconomic measures (percentage of persons below poverty, percentage of adults aged 25 and older with less than a high-school education, and percentage of adults aged 25 and older with a 4-year college education) are shown in Tables 1
To calculate age-standardized rates for the population aged 25 and older (Table 1
Table 1 65). The individual-level education and census tract areabased socioeconomic measures had a similar low proportion of missing data (typically less than 3%).
For the population aged 25 and older (Table 1
Our findings suggest that census tract areabased socioeconomic measures such as "percentage of persons below poverty" and individual-level education detect a similar magnitude of socioeconomic inequality for all-cause mortality in the state of Massachusetts for individuals between ages 45 and 64. Census tract areabased socioeconomic measures also uniquely provide evidence of socioeconomic inequality for (1) persons younger than 25 years, for whom education may not yet be completed; and (2) persons aged 65 and older, for whom individual-level education analyses indicated that mortality rates were higher among persons with 12 to 15 years of education than among those with both less than 12 and 16 or more years. However, for persons aged 25 to 44, the magnitude of the relative index of inequality for the census tract areabased socioeconomic measures, although still large (approximately 3.5), was less than that yielded by the individual-level education (6.8). Consistent with our results, previous empirical research has reported selective misclassification in education level on death certificates, chiefly because of individuals who did not graduate from high school being reported as having obtained a high-school diploma, especially among persons aged 65 and older.15,16 The net effect is to deflate the mortality rate among persons with fewer than 12 years of education and inflate it among persons with 12 to 15 years of education.15 For this reason, the National Center for Health Statistics report Socioeconomic Status and Health provided mortality rates by individual education only for individuals between ages 25 and 64.16 Importantly, studies with self-reported individual-level educational data document socioeconomic inequality in all-cause mortality analogous to that detected with this studys census tract areabased socioeconomic measures.17 Census tract areabased socioeconomic measures thus offer 2 advantages over individual-level education data for monitoring socioeconomic inequality in mortality. First, they provide an estimate of effect with decreased misclassification bias for persons aged 65 and older. Second, they can be used validly for persons younger than 25. Of note, our use of census tract areabased socioeconomic measures is unlikely to be substantially affected by ecological bias, given the similar direction of estimates for the individual and area-based socioeconomic measures and results that are of a comparable magnitude (except for older ages, for which individual data are likely misclassified). From an etiological standpoint, multilevel analyses assessing the relative contribution of individual- and area-level socioeconomic characteristics to social inequities in mortality would be useful.1821 Future research also should evaluate whether our findings vary by type of mortality,22 race/ethnicity, and gender.
This work was funded by the National Institutes of Health (grant 1 R01 HD3685-01) via the National Institute of Child Health and Human Development and the Office of Behavioral and Social Science Research (Principal Investigator, Nancy Krieger). S. V. Subramanian is supported by the National Institutes of Health Career Development Award (1 K25 HL081275 ) We thank Bruce Cohen (Division of Research and Epidemiology, Massachusetts Department of Public Health) for facilitating the conduct of this study with data from the Massachusetts Health Department and for providing helpful comments. We also thank Malena Orejuela Hood (Division of Research and Epidemiology, Massachusetts Department of Public Health) and Charlene Zion (Registry of Vital Records and Statistics, Massachusetts Department of Public Health) for assistance with data handling and preparation.
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Peer Reviewed
Contributors Accepted for publication November 15, 2005.
1. Krieger N. Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992;82:703710. 2. Krieger N, Chen JT, Ebel G. Can we monitor socioeconomic inequalities in health? A survey of US health departments data collection and reporting practices. Public Health Rep. 1997;112:481491.[Web of Science][Medline] 3. Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measuresthe public health disparities geocoding project. Am J Public Health. 2003; 93:16551671. 4. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. Am J Epidemiol. 2002;156: 471482. 5. Singh GK, Miller BA, Hankey BF, Edwards BK. Persistent area socioeconomic disparities in US incidence of cervical cancer, mortality, stage, and survival, 19752000. Cancer. 2004;101:10511057.[CrossRef][Web of Science][Medline] 6. The Health of Washington State 2004 Supplement. Olympia, Wash: Washington State Department of Health; 2004. Available at: http://www.doh.wa.gov/HWS/HWS2004supp.htm. Accessed September 13, 2005. 7. Tolson GC, Barnes JM, Gay GA, Kowaleski JL. The 1989 revision of the US Standard Certificates and Reports. Vital Health Stat 4. 1991;No. 28:134. 8. Krieger N, Waterman P, Lemieux K, Zierler S, Hogan JW. On the wrong side of the tracts? Evaluating the accuracy of geocoding in public health research. Am J Public Health. 2001;91:11141116.[Abstract] 9. Anderson RN, Rosenberg HM. Age standardization of death rates: implementation of the year 2000 standard. Natl Vital Stat Rep. 1998;47:116, 20.[Medline] 10. US Census Bureau. Census 2000 Summary File 3 Technical Documentation. Available at: http://www.census.gov/prod/cen2000/doc/sf3.pdf. Accessed June 13, 2005. 11. Wagstaff A, Paci P, van Doorslaer E. On the measurement of inequalities in health. Soc Sci Med. 1991; 33:545557.[CrossRef][Web of Science][Medline] 12. Pamuk ER. Social class inequality in mortality from 1921 to 1972 in England and Wales. Popul Stud (Camb). 1985;39:1731.[Medline] 13. Keppel K, Pamuk E, Lynch J, et al. Methodological issues in measuring health disparities. Vital Health Stat 2. 2005;141:116. 14. Makuc DM, Feldman JJ, Mussolino ME. Validity of education and age as reported on death certificates. In: American Statistical Association: 1996 Proceedings of the Social Science Statistics Section. Alexandria, Va: American Statistical Association; 1997: 102106. 15. Sorlie P, Johnson NJ. Validity of education information on the death certificate. Epidemiology. 1996;7: 437439.[Web of Science][Medline] 16. Pamuk E, Makuc D, Heck K, Reuben C, Lochner K. Socioeconomic Status and Health Chartbook: Health, United States, 1998. Hyattsville, Md: National Center for Health Statistics; 1998. 17. Sorlie PD, Backlund E, Keller JB. US mortality by economic, demographic, and social characteristics: the National Longitudinal Mortality Study. Am J Public Health. 1995;85:949956. 18. Subramanian SV, Chen JT, Rehkopf DH, Waterman P, Krieger N. Racial disparities in context: a multilevel analysis of neighborhood variation in poverty and excess mortality among black populations in Massachusetts. Am J Public Health. 2005;95: 260265. 19. Subramanian SV. The relevance of multilevel statistical methods for identifying causal neighborhood effects. Soc Sci Med. 2004;58:19611967.[CrossRef][Web of Science][Medline] 20. Robert SA, Strombom I, Trentham-Dietz A, et al. Socioeconomic risk factors for breast cancer: distinguishing individual- and community-level effects. Epidemiology. 2004;15:442450.[CrossRef][Web of Science][Medline] 21. Subramanian SV, Chen JT, Rehkopf DH, Waterman P, Krieger N. Comparing individual and area-based socioeconomic measures for the surveillance of health disparities: a multilevel analysis of Massachusetts (US) births, 19881992. Am J Epidemiol. In press. 22. Steenland K, Henley J, Calle E, Thun M. Individual- and area-level socioeconomic status variables as predictors of mortality in a cohort of 179 383 persons. Am J Epidemiol. 2004;159: 10471056. This article has been cited by other articles:
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