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Why Public Health Researchers Should Consider Using Disability Data from the American Community Survey

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Abstract

The United States (US) federal government allocates hundreds of billions of dollars to provide resources to Americans with disabilities, older adults, and the poor. The American Community Survey (ACS) influences the distribution of those resources. The specific aim of the project is to introduce health researchers to Public Use Microdata Sample file from 2009 to 2011. The overall goal of our paper is to promote the use of ACS data relevant to disability status. This study provides prevalence estimates of three disability related items for the population at or over the age of 15 years who reside in one of the continental states. When population weights are applied to the 7,198,221 individuals in the sample under analysis, they are said to represent 239,641,088 of their counterparts in the US population. Detailed tabulations by state (provided as Microsoft Excel® spreadsheets in ACS output) clearly show disability prevalence varies from state-to-state. Because analyses of the ACS data have the ability to influence resources aiding individuals with physical mobility challenges, its use should be promoted. Particular attention should be given to monetary allocations which will improve accessibility of the existing built environment for the individuals with mobility impairment.

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References

  1. United States Census Bureau. How disability data are collected from the American Community Survey. Retrieved Jan 11, 2018, from https://www.census.gov/topics/health/disability/guidance/data-collection-acs.html.

  2. Siordia, C. (2014). Number of people in the United States experiencing ambulatory and independent living difficulties. Journal of Social Work and Disability and Rehabilitation, 13(3), 261–267.

    Article  Google Scholar 

  3. Southwestern Pennsylvania Public Transit Human Services Coordinated Transportation Plan. Retrieved Jan 14, 2018, from http://www.spcregion.org/pdf/atwichs/FullFinalHSReport.pdf.

  4. Reamer, A. D. (2010). Surveying for dollars: The role of the American Community Survey in the geographic distribution of federal funds. Washington, D.C.: Metropolitan Policy Program at Brookings.

    Google Scholar 

  5. Herman, E. (2008). The American Community Survey: An introduction to the basics. Government Information Quarterly, 25(3), 504–519.

    Article  Google Scholar 

  6. Siordia, C. (2014). Detecting “real” population changes with American Community Survey data: The implicit assumption of treating between-year differences as “trends”. Journal of Sociological Research, 4(2), 494–509.

    Article  Google Scholar 

  7. Siordia, C. (2013). Are health insurance item allocations in the American Community Survey missing completely at random? Journal of Frailty and Aging, 2(4), 198–204.

    PubMed  CAS  Google Scholar 

  8. Siordia, C. (2015). Disability estimates between same-sex and different-sex couples: Data from the American Community Survey (2009-2011). Sexuality and Disability, 33(1), 107–121.

    Article  PubMed  Google Scholar 

  9. Siordia, C., & Young, R. L. (2013). Methodological note: Allocation of disability items in the American Community Survey. Disability and Health Journal, 6(2), 149–153.

    Article  PubMed  Google Scholar 

  10. Siordia, C., & Fox, A. (2013). Public use microdata area fragmentation: Research and policy implications of polygon discontiguity. Spatial Demography, 1(1), 42–56.

    Article  Google Scholar 

  11. United States Government Accountability Office. Legal authority for American Community Survey, B-289852, April 4, 2002. Retrieved Jan 14, 2018, from https://www.gao.gov/decisions/other/289852.htm.

  12. Siordia, C., & Le, V. D. (2013). Precision of disability estimates for southeast asians in the American Community Survey 2008-2010 microdata. Central Asian Journal of Global Health. https://doi.org/10.5195/cajgh.2012.2166-7403.

    Article  PubMed  PubMed Central  Google Scholar 

  13. United States Census Bureau. (2013). Workers with a disability less likely to be employed, more likely to hold jobs with lower earnings, Census Bureau reports. Retrieved Jan 14, 2018, from https://www.census.gov/newsroom/press-releases/2013/cb13-47.html.

  14. World Health Organization. (2001). The international classification of functioning, disability, and health. Geneva: World Health Organization.

    Google Scholar 

  15. Siordia, C., & Farias, R. A. (2013). A multilevel analysis on latino’s economic inequality: a test of the minority group threat theory. In R. Verdugo (Ed.), The economic status: Volume of the hispanic population (pp. 65–79). Charlotte, NC: Information Age Publishing.

    Google Scholar 

  16. Singh, G. K., & Lin, S. C. (2013). Marked ethnic, nativity, and socioeconomic disparities in disability and health insurance among US children and adults: The 2008-2010 American Community Survey. BioMed Research International, 2013, 17. https://doi.org/10.1155/2013/627412.

    Article  Google Scholar 

  17. Clarke, P., Ailshire, J. A., & Lantz, P. (2009). Urban built environments and trajectories of mobility disability: Findings from a national sample of community-dwelling American adults (1986-2001). Social Science & Medicine, 69, 964–970.

    Article  Google Scholar 

  18. Clarke, P., Ailshire, J. A., Bader, M., Morenoff, J. D., & House, J. S. (2008). Mobility disability and the built environment. American Journal of Epidemiology, 168(5), 506–513.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Botticello, A. L., Rohrbach, T., & Cobbold, N. (2015). Differences in the community built environment influence poor perceived health among persons with spinal cord injury. Archives of Physical Medicine and Rehabilitation, 96, 1583–1590.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sund, T., Iwarsson, S., Anttila, H., & Brandt, A. (2015). Effectiveness of powered mobility devices in enabling community mobility-related participation: A prospective study among people with mobility restrictions. Physical Medicine & Rehabilitation, 7, 859–870.

    Google Scholar 

  21. Hammel, J., Magasi, S., Heinemann, A., et al. (2015). Environmental barriers and supports to everyday participation: A qualitative insider perspective from people with disabilities. Archives of Physical Medicine and Rehabilitation, 96, 578–588.

    Article  PubMed  Google Scholar 

  22. Ripat, J. D., Brown, C. L., & Ethans, K. D. (2015). Barriers to wheelchair use in the winter. Archives of Physical Medicine and Rehabilitation, 96, 1117–1122.

    Article  PubMed  Google Scholar 

  23. Bazuin, J. T., & Fraser, J. C. (2013). How the ACS gets it wrong: The story of the American Community Survey and a small, inner city neighborhood. Applied Geography, 45, 292–302.

    Article  Google Scholar 

  24. Krahn, G. L., Fujiura, G., Drum, C. E., Cardinal, B. J., & Nosek, M. A. (2009). The dilemma of measuring perceived health status in the context of disability. Disability and Health Journal, 2(2), 49–56.

    Article  PubMed  Google Scholar 

  25. Tate, R. L. (2014). Measuring outcomes using the international classification of functioning, disability and health (ICF) model, with special reference to participation and environmental factors. In H. S. Levin, D. H. K. Shum & R. C. K. Chan (Eds.), Understanding traumatic brain injury: Current research and future directions (pp. 163–189). New York, NY: Oxford University Press.

    Google Scholar 

  26. McDermott, S., & Turk, M. A. (2011). The myth and reality of disability prevalence: measuring disability for research and service. Disability and Health Journal, 4(1), 1–5.

    Article  PubMed  Google Scholar 

  27. Norwich, B. (2008). Dilemmas of difference, inclusion and disability: international perspectives and future directions. New York, NY: Routledge.

    Google Scholar 

  28. Koutsogeorgou, E., Leonardi, M., Bickenbach, J. E., Cerniauskaite, M., Quintas, R., & Raggi, A. (2014). Social capital, disability, and usefulness of the international classification of functioning, disability and health for the development and monitoring of policy interventions. Disability & Society, 29(7), 1104–1116.

    Article  Google Scholar 

  29. Dorsey, R., & Graham, G. (2011). New HHS data standards for race, ethnicity, sex, primary language, and disability status. JAMA, 306(21), 2378–2379.

    Article  PubMed  CAS  Google Scholar 

  30. Office of Minority Health. (2017). Data collection standards for race, ethnicity, primary language, sex, and disability status. Retrieved Jan 14, 2018, from https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=2&lvlid=23.

  31. Gibney, K., Sinclair, M., O’Toole, J., et al. (2013). Using disability-adjusted life years to set health-based targets: A novel use of an established burden of disease metric. Journal of Public Health Policy, 34(3), 439–446.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Fairchild, A., Rosner, L., Colgrove, D., Bayer, J., Fried, R., L (2010). The EXODUS of public health what history can tell us about the future. American Journal of Public Health, 100(1), 54–63.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Walker Institute for the Study of Race and Ethnic Relations, Western Michigan University, Kalamazoo, MI, 49008, USA

    Carlos Siordia

  2. Environmental and Occupational Health Sciences, School of Public Health, State University of New York Downstate Medical Center, 450 Clarkson Avenue, MSC 43, Brooklyn, NY, 11203, USA

    Lori A. Hoepner

  3. College of Health Related Professions, State University of New York Downstate Medical Center, Brooklyn, NY, 11203, USA

    Allen N. Lewis

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  1. Carlos Siordia
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  2. Lori A. Hoepner
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  3. Allen N. Lewis
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Correspondence to Lori A. Hoepner.

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Siordia, C., Hoepner, L.A. & Lewis, A.N. Why Public Health Researchers Should Consider Using Disability Data from the American Community Survey. J Community Health 43, 738–745 (2018). https://doi.org/10.1007/s10900-018-0478-0

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  • DOI: https://doi.org/10.1007/s10900-018-0478-0

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