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RESEARCH AND PRACTICE |
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).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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We compared all-cause mortality rates stratified by individual-level education and by census tract area–based socioeconomic measures for Massachusetts (1999–2001). 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 (2–3) 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).
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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and 2.3,4
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), we used the US year 2000 standard million for ages 25 and older.9 We used the least deprived group as the comparison group to calculate incidence rate ratios for individual-level education and census tract area–based socioeconomic measures. We could not compute mortality rates by individual-level education for individuals younger than 25 because persons in this age group may not have completed their education, and the requisite person-year data for denominators were not available for persons younger than 18.10 On the basis of age-standardized rates, we calculated the relative index of inequality, which is a coefficient of linear slope that takes into account the effect estimate of each socioeconomic category weighted by the number of individuals in that category.11–13 This measure permits meaningful comparison of health inequalities across diverse socioeconomic measures, even if their proportionate allocation of persons across socioeconomic strata differs. |
RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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presents data on deaths, person-years, and age-standardized mortality rates for the population aged 25 and older, by individual-level education and by census tract area–based socioeconomic measure. Table 2 presents the same data for 4 age strata (0–24, 25–44, 45–64, 
65). The individual-level education and census tract area–based socioeconomic measures had a similar low proportion of missing data (typically less than 3%).
For the population aged 25 and older (Table 1), the degree of socioeconomic inequality in mortality detected with the census tract area–based socioeconomic measures was slightly greater than that detected by the individual-level education measure (relative index of inequality of approximately 1.5 vs 1.2). Additionally, as shown in Table 2, only the census tract area–based socioeconomic measures yielded estimates of socioeconomic inequality for persons younger than 25 (relative index of inequality between 2.3 and 3.0). For persons aged 25 to 44, the magnitude of the relative index of inequality was greater for the individual-level education measure (6.8) compared with the census tract area–based socioeconomic measures (range = 3.3–3.7) but was similar for persons aged 45 to 64 (range = 2.7–2.9). For persons aged 65 and older, the relative index of inequality was significantly below 1 for individual-level education (0.8) but ranged between 1.2 and 1.3 for the 3 census tract area–based socioeconomic measures.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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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 study’s census tract area–based socioeconomic measures.17
Census tract area–based 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 area–based 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.18–21 Future research also should evaluate whether our findings vary by type of mortality,22 race/ethnicity, and gender.
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Acknowledgments |
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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.
Human Participant Protection
Use of the data in this study was approved by all relevant institutional review boards and human subjects committees at the Harvard School of Public Health and the Massachusetts Department of Public Health.
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Footnotes |
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Contributors
D. H. Rehkopf led the data analysis and writing. L. T. Haughton assisted with data analysis and manuscript preparation. J. T. Chen developed the tools for data analysis and assisted with data analysis. P.D. Waterman assisted with data preparation. S. V. Subramanian assisted with data interpretation. N. Krieger originated the study and assisted with manuscript preparation. All authors helped to conceptualize ideas, interpret findings, and review drafts of the manuscripts.
Accepted for publication November 15, 2005.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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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;112:481–491.[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 measures—the public health disparities geocoding project. Am J Public Health. 2003; 93:1655–1671.
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: 471–482.
5. Singh GK, Miller BA, Hankey BF, Edwards BK. Persistent area socioeconomic disparities in US incidence of cervical cancer, mortality, stage, and survival, 1975–2000. Cancer. 2004;101:1051–1057.[CrossRef][Web of Science][Medline]
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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: 102–106.
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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;85:949–956.
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: 260–265.
19. Subramanian SV. The relevance of multilevel statistical methods for identifying causal neighborhood effects. Soc Sci Med. 2004;58:1961–1967.[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;15:442–450.[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, 1988–1992. 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: 1047–1056.
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