The Public Health Disparities
Geocoding Project Monograph

Geocoding and Monitoring US Socioeconomic Inequalities in Health:
An introduction to using area-based socioeconomic measures
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Executive Summary
Introduction
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Geocoding
Generating ABSMs
Analytic Methods
Multi-level Modeling
Visual Display
Case Example
U.S. Census Tract Poverty Data
Glossary

GENERATING ABSMs
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Generating ABSMs: concepts, methods, and measures

Generating area-based measures of socioeconomic position requires an explicit approach to understanding what socioeconomic inequality is and how to measure it, at multiple levels. In this section we briefly review our definitions of “social class” and “socioeconomic position,” and then delineate our approach to generating and appraising the validity and utility of our Project’s area-based socioeconomic measures (ABSMs).

Definitions: social class and socioeconomic position

Starting first with definitions, in the Public Health Disparities Geocoding Project we used the construct of “social class” to refer to social groups arising from interdependent economic relationships among people.1-2 Stated simply, broad classes--like the working class, business owners, and their managerial class--exist in relationship to and co-define each other. One cannot, for example, be an employee if one does not have an employer and this distinction--between employee and employer--is not about whether one has more or less of a particular attribute, but concerns one’s relationship to work and to others through a society’s economic structure. Also at issue is an asymmetry of economic relations, whereby owners of resources (e.g., capital) gain economically from the labor or effort of employees.

Class, as such, is therefore logically and materially prior to its manifest expression in what can be referred to as socioeconomic position, an aggregate concept that includes both resource-based and prestige-based measures, as determined by both childhood and adult social class position.1 Resource-based measures refer to material and social resources and assets, including income, wealth, and educational credentials; terms used to describe inadequate resources include “poverty” and “deprivation.” Prestige-based measures refer to individuals’ rank or status in a social hierarchy. The term “socioeconomic status” should accordingly be avoided both because it arbitrarily (if not intentionally) privileges “status” over material resources as a determinant of health and because it conflates pathways involving material resources with those involving psychosocial appraisals of relative status.


Measuring socioeconomic position: domains, levels, & lifecourse

Key domains of socioeconomic position relevant to understanding population health thus include:1-2

(1) Occupational class, which can affect health both directly and indirectly, via occupational hazards and via wages or income, relevant to standard of living;

(2) Educational attainment/credentials, usually reflective of childhood socioeconomic position and relevant to future economic prospects, and also relevant vis a vis knowledge & health literacy;

(3) Income & entitlements/subsidies, together affecting standard of living, noting that what “income” buys in a given society is related in part to what is provided by the social wage;

(4) Wealth, referring to accumulated assets, with important distinctions between what’s readily fungible or not (e.g., stocks vs equity in a home), plus also wealth’s converse, i.e., debt; and

(5) Relative social ranking, typically referring to “status” & “prestige.”

Second, each domain can be assessed at multiple levels, including: individual, household, and area or neighborhood, plus also regional, national and global.1-5

Likewise, relevant moments during the lifecourse for which one may want socioeconomic data include: in utero, infancy, childhood, plus early, middle, and late adulthood.1-2

From this vantage, we opted to create a variety of ABSMs, intended to capture diverse domains of SEP, for diverse outcomes that spanned the lifecourse, literally from birth until death.

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Formulating the ABSMs from census data

As described more fully in our Project’s manuscripts,6-9 we created 19 theoretically-justified ABSMs (11 single variable, 8 composite ABSMS), delineated in the ABSM Creation Table. Two criteria central to formulating these ABSMS for socioeconomic position (SEP) were that they: (a) meaningfully summarized important aspects of the specified area’s socioeconomic conditions, and (b) employed socioeconomic data that could legitimately be compared over time and across regions.1-9 Based on our a priori conceptual definitions of SEP and social class1 and US, UK, and other global evidence emphasizing detrimental effects of material deprivation on health,10-14 we developed ABSMs for 6 domains of SEP: occupational class, income, poverty, wealth, education, and crowding, premised on the understanding that social class, as a social relationship, fundamentally drives the distribution of these manifest aspects of SEP.1 Of note, one measure we included differs from the others: the Gini coefficient, which is a measure of within-area socioeconomic inequality rather than a measure of the average socioeconomic level of an area.15 We included it because of concerns expressed about its uncritical use at the BG and CT level, given realities of economic segregation.9,16

Operationally, we generated each ABSM at each level of geography for each state. Among the composite variables, two were US analogues of the UK Townsend4-5,17 and Carstairs4-5,18 deprivation indices, one used the algorithm for the US Center for Disease Control and Prevention’s “Index of Local Economic Resources,”19 and five were created exclusively for our study. To mirror the skewed population distribution of socioeconomic resources, “SEP1” and “SEP2” simultaneously combined categorical data on poverty, working class, and either wealth or high income. Finally, we produced an “SEP index” akin to the Townsend index, based on summation of standardized z scores of selected ABSM.

Lastly, our a priori criteria for evaluating the ABSMs pertained to: (1) external validity (did it detect the expected socioeconomic gradients for each outcome, i.e., positive, negative, or none at all?), (2) robustness (did the ABSM do so across multiple outcomes, as well as within diverse population groups?), (3) completeness (was the ABSM relatively unaffected by missing data?), and (4) user-friendliness (was it easy to understand and use?).7-9,22 Based on these criteria, and the key findings we summarize in the introduction to this monograph, in the case example developed for this monograph, we focus solely on the census tract poverty ABSM.

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Example:

Creating a single variable ABSM - % of persons below poverty
This section will describe how to create a single variable ABSM from census data, using the example of “percent of persons below poverty.” As indicated in the ABSM creation table, the data for creating this variable for 1990 is available from census table P117, available from the summary tape file 3 (STF3). P117 gives population counts of persons above and below poverty, stratified by age. As an example, the table below shows the counts for table P117 for all of Massachusetts.

 

P117. Poverty Status in 1989 by Age [24]
Universe: Persons for whom poverty status is determined

Income in 1989 above poverty level:      
Under 5 years P1170001 346803
5 years P1170002
67131
6 to 11 years P1170003 376888
12 to 17 years P1170004 366353
18 to 24 years P1170005 520219
25 to 34 years P1170006 1007694
35 to 44 years P1170007 859171
45 to 54 years P1170008 574743
55 to 59 years P1170009 237919
60 to 64 years P1170010 241233
65 to 74 years P1170011 421973
75 years and over P1170012 272949
Income in 1989 below poverty level:
Under 5 years P1170013 58986
5 years P1170014 11649
6 to 11 years P1170015
60386
12 to 17 years P1170016 45200
18 to 24 years P1170017 78749
25 to 34 years P1170018 83304
35 to 44 years P1170019 49958
45 to 54 years P1170020 27960
55 to 59 years P1170021 13489
60 to 64 years P1170022 17335
65 to 74 years P1170023 33139
75 years and over P1170024 39184

To calculate the proportion of persons below poverty for this region, we sum all categories P1170001 to P1170024 to get the denominator, and sum categories P1170013 to P1170024 to get the numerator, and then simply divide this numerator by the denominator:

(P1170013 + … + P1170024) / (P1170001 + … + P1170024)

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This economically depressed area in Boston's Chinatown, turned out to be characterized as a highly working class, poor, low income area with high unemployment and few expensive homes.
 
This one house in Beacon Hill looked like it was -- and turned out to be -- in a fairly affluent area: over 75% professionals, low poverty, high income, low unemployment, and lots of expensive homes.


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REFERENCES
1. Krieger N, Williams D, Moss N. Measuring social class in US public health research: concepts, methodologies and guidelines. Annu Rev Public Health 1997; 18:341-378.
2. Lynch J, Kaplan G. Socioeconomic position. In: Berkman L, Kawachi I (eds). Social Epidemiology. Oxford: Oxford University Press, 2000; 13-35.
3. Krieger N. Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health 1992; 82:703-710.
4. Lee P, Murie A, Gordon D. Area Measures of Deprivation: A Study of Current Methods and Best Practices in the Identification of Poor Areas in Great Britain. Birmingham, UK: Centre for Urban and Regional Studies, University of Birmingham, 1995.
5. Carstairs V. Socio-economic factors at areal level and their relationship with health. In: Elliott P, Wakefield J, Best N, Briggs D (eds). Spatial Epidemiology: Methods and Applications. Oxford: Oxford University Press, 2000; 51-67.
6. Krieger N, Zierler S, Hogan JW, Waterman P, Chen JT, Lemieux K, Gjelsvik A. Geocoding and measurement of neighborhood socioeconomic position: a U.S. perspective. In: Kawachi I, Berkman L (eds). Neighborhoods and Health. Oxford, UK: Oxford University Press, 2003; 147-178.
7. Krieger N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV, Carson R. Geocoding and monitoring US socioeconomic inequalities in mortality and cancer incidence: does choice of area-based measure and geographic level matter?—The Public Health Disparities Geocoding Project. Am J Epidemiol 2002;156:471-82.
8. Krieger N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV, Carson R. Choosing area-based socioeconomic measures to monitor social inequalities in low birthweight and childhood lead poisoning –The Public Health Disparities Geocoding Project (US). J Epidemiol Community Health 2003;57:186-99.
9. Krieger N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV. Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: geocoding and choice of area-based socioeconomic measures—The Public Health Disparities Geocoding Project (US). Public Health Reports 2003; 118:240-260.
10. National Center for Health Statistics. Health, United States, 1998 with Socioeconomic Status And Health Chartbook. Hyattsville, MD: US Dept of Health and Human Services, 1998.
11. Townsend P, Davidson N, Whitehead M. Inequalities in Health: The Black Report and The Health Divide. London: Penguin Books, 1990.
12. Shaw M, Dorling D, Gordon D, Davey Smith G. The Widening Gap: Health Inequalities And Policy In Britain. Bristol, UK: Policy Press, 1999.
13. Evans T, Whitehead M, Diderichsen F, Bhuiya A, Wirth M. Challenging Inequities In Health: From Ethics To Action. New York, NY: Oxford University Press, 2001.
14. Leon D, Walt G (eds). Poverty, Inequality, And Health: An International Perspective. Oxford: Oxford University Press, 2001.
15. Cowell FA. Measuring Inequality, 2nd ed. LSE Handbooks in Economics Series. London: Prentice Hall, 1995.
16. Soobader M, LeClere FB. Aggregation and the measurement of income inequality: effects on morbidity. Soc Sci Med 1999; 48:733-744.
17. Townsend P, Phillimore P, Beattie A. Health And Deprivation: Inequality And The North. London: Croom Helm, 1988.
18. Carstairs V, Morris R. Deprivation and mortality: an alternative to social class? Community Med 1989; 11:210-219.
19. Casper ML, Barnett E, Halverson JA, Elmer GA, Braham VE, Majeed ZA, Bloom AS, Stanley S. Women And Heart Disease: An Atlas Of Racial And Ethnic Disparities In Mortality. Office for Social Environment and Health Research, West Virginia University, Morgantown, WV, 1999.
20. Gorsuch RL. Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983
21. Bartholomew DJ. Latent Variable Models And Factor Analysis. London: Charles Griffin & Company, 1987.
22. Rossi RJ, Gilmartin KJ. The Handbook Of Social Indicators: Sources, Characteristics, And Analysis. New York, NY: Garland STPM Press; 1980.
23. Source: U.S. Bureau of the Census, Current Population Survey, Annual Demographic Supplements, Poverty and Health Statistics Branch/HHES Division .
24. Census of Population and Housing, 1990: Summary Tape File 3 Technical Documentation / prepared by the Bureau of the Census. - Washington: The Bureau, 1991.
25. U.S. Census Bureau, 2000 Census of Population and Housing, Summary File 3: Technical Documentation, 2002.
26. Census of Population and Housing, 1990: Summary Tape File 1 Technical Documentation / prepared by the Bureau of the Census. - Washington: The Bureau, 1991.
27. Krieger N, A Glossary for Social Epidemiology. Journal Epidemiol Community Health 2001; 55:693-700
28. Cowell, F.A., 1995, Measuring Inequality, 2nd edition, Harvester Wheatsheaf, Hemel Hempstead.
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This work was funded by the National Institutes of Health (1RO1HD36865-01) via the National Institute of Child Health & Human Development (NICHD) and the Office of Behavioral & Social Science Research (OBSSR).
Copyright © 2004 by the President and Fellows of Harvard College - The Public Health Disparities Geocoding Project.