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Using aggregate geographic data to proxy individual socioeconomic status: Does size matter?

Author

Listed:
  • Soobader, M.-J.
  • LeClere, F.B.
  • Hadden, W.
  • Maury, B.

Abstract

Objectives. This study assessed whether aggregate-level measures of socioeconomic status (SES) are less biased as proxies for individual-level measures if the unit of geographic aggregation is small in size and population. Methods. National Health Interview Survey and census data were used to replicate analyses that identified the degree to which aggregate proxies of individual SES bias interpretations of the effects of SES on health. Results. Ordinary least squares regressions on self-perceived health showed that the coefficients for income and education measured at the tract and block group levels were larger than those at the individual level but smaller than those estimated by Geronimus et al. at the zip code level. Conclusions. Researchers should be cautious about use of proxy measurement of individual SES even if proxies are calculated from small geographic units.

Suggested Citation

  • Soobader, M.-J. & LeClere, F.B. & Hadden, W. & Maury, B., 2001. "Using aggregate geographic data to proxy individual socioeconomic status: Does size matter?," American Journal of Public Health, American Public Health Association, vol. 91(4), pages 632-636.
    • Handle: RePEc:aph:ajpbhl:2001:91:4:632-636_9
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    Cited by:

    1. Chunzhu Wei & Pablo Cabrera-Barona & Thomas Blaschke, 2016. "Local Geographic Variation of Public Services Inequality: Does the Neighborhood Scale Matter?," IJERPH, MDPI, vol. 13(10), pages 1-20, October.
    2. Laura Kuznetsov & Werner Maier & Matthias Hunger & Martin Meyer & Andreas Mielck, 2012. "Regional deprivation in Bavaria, Germany: linking a new deprivation score with registry data for lung and colorectal cancer," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(5), pages 827-835, October.
    3. Alessio Petrelli & Roberta Picariello & Giuseppe Costa, 2010. "Toward a needs based mechanism for capitation purposes in Italy: the role of socioeconomic level in explaining differences in the use of health services," International Journal of Health Economics and Management, Springer, vol. 10(1), pages 29-42, March.
    4. Esposito, Tonino & Trocmé, Nico & Chabot, Martin & Shlonsky, Aron & Collin-Vézina, Delphine & Sinha, Vandna, 2013. "Placement of children in out-of-home care in Québec, Canada: When and for whom initial out-of-home placement is most likely to occur," Children and Youth Services Review, Elsevier, vol. 35(12), pages 2031-2039.
    5. Michael Adjemian & Jeffrey Williams, 2009. "Using census aggregates to proxy for household characteristics: an application to vehicle ownership," Transportation, Springer, vol. 36(2), pages 223-241, March.
    6. Masayoshi Oka, 2022. "Census-Tract-Level Median Household Income and Median Family Income Estimates: A Unidimensional Measure of Neighborhood Socioeconomic Status?," IJERPH, MDPI, vol. 20(1), pages 1-23, December.
    7. Jessica Y. Islam & Veeral Saraiya & Rebecca A. Previs & Tomi Akinyemiju, 2021. "Health Care Access Measures and Palliative Care Use by Race/Ethnicity among Metastatic Gynecological Cancer Patients in the United States," IJERPH, MDPI, vol. 18(11), pages 1-15, June.
    8. Nathaniel Bell & Nadine Schuurman, 2010. "GIS and Injury Prevention and Control: History, Challenges, and Opportunities," IJERPH, MDPI, vol. 7(3), pages 1-16, March.
    9. Tao Hu & Qingyun Du & Fu Ren & Shi Liang & Denan Lin & Jiajia Li & Yan Chen, 2014. "Spatial Analysis of the Home Addresses of Hospital Patients with Hepatitis B Infection or Hepatoma in Shenzhen, China from 2010 to 2012," IJERPH, MDPI, vol. 11(3), pages 1-13, March.

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