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Variation in the rate of difficulty in IADL and ADL among older Americans across communities

Variation in the rate of difficulty in IADL and ADL among older Americans across communities. Mitchell P. LaPlante, Ph.D. Professor, Dept. of Social & Behavioral Sciences & Institute for Health & Aging Co-PI, Center for Personal Assistance Services University of California, San Francisco

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Variation in the rate of difficulty in IADL and ADL among older Americans across communities

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  1. Variation in the rate of difficulty in IADL and ADL among older Americans across communities Mitchell P. LaPlante, Ph.D. Professor, Dept. of Social & Behavioral Sciences & Institute for Health & Aging Co-PI, Center for Personal Assistance Services University of California, San Francisco Presented at the 62nd Annual Scientific Meeting of the Gerontological Society of America, Atlanta, Georgia, November 19 (session #310). Funding from the National Institute on Disability & Rehabilitation Research (H133B080002)

  2. An ecological analysis • Explore the extent that disability—particularly difficulty performing IADL and ADL activities—varies across geographic areas • Explore what factors measured at the community level explain this variation

  3. Disability rates vary across geographies • Disability rates vary considerably across states, counties, cities, and places (McCoy, Davis & Hudson, 1994; Okoro, Balluz, Campbell, Holt, & Mokdad, 2005; Waldrop & Stern, 2003) with high rates in Appalachia and the Deep South for persons of all ages • Okoro et al. have looked at disability in ages 65+ using the BRFSS for states and metro areas • However, disability can be high in city neighborhoods, counties and places within states • Studying variation in disability rates has implications for social planning: formulas used for state funding and for service delivery • A handful of studies have found poor socioeconomic conditions predict higher rates of disability at the state level (Haber, 1987; Kaplan, 1994) for adults of working ages or adults of all ages • No studies have examined, for geographies smaller than states, predictors of disability in IADL or ADL for persons of older ages Haber, L.D. 1987. State disability prevalence rates: An ecological analysis of social and economic influences on disability. Final Report, National Institute on Disability and Rehabilitation Research Switzer Fellowship. Kaplan, G. A., Pamuk, E. R., Lynch, J. W., Cohen, R. D., & Balfour, J. L. (1996). Inequality in income and mortality in the United States: analysis of mortality and potential pathways. BMJ, 312(7037), 999-1003. McCoy, J. L., Davis, M., & Hudson, R. E. (1994). Geographic patterns of disability in the United States. Soc Secur Bull, 57(1), 25-26. Okoro, C. A., Balluz, L. S., Campbell, V. A., Holt, J. B., & Mokdad, A. H. (2005). State and metropolitan-area estimates of disability in the United States, 2001. Am J Public Health, 95(11), 1964-1969. Waldrop, J., & Stern, S. M. (2003). Disability status, 2000. Washington, D.C.: U.S. Dept. of Commerce Economics and Statistics Administration U.S. Census Bureau.

  4. American Community Survey • A 2.5% sample of households annually, 2005-2007 • 1.3 million records for ages 65+ • PUMAS—public use microdata areas • Areas with populations of ~100,000 • 2,069 PUMAS in 2005-2007 • PUMAs are defined in terms of counties, census tracts and/or places. Large urban counties are typically subdivided into multiple PUMAs with boundaries based on census tracts and/or places. In less populated rural areas PUMAs are typically comprised of smaller contiguous counties. • PUMAS do not cross state boundaries • Mean PUMA size for persons 65+ is 617, so statistics are fairly robust

  5. ADL difficulty

  6. IADL difficulty

  7. Other disability questions

  8. Percent of persons ages 65 and over with a disability by State, ACS 2005-2007 Source: U.S. Census Bureau, American Factfinder

  9. Disability rate by state, ages 65+

  10. Percent of persons ages 65 and over with a disability by PUMA (N=2,069), ACS 2005-2007 Source: U.S. Census Bureau, American Factfinder

  11. New York city detail

  12. San Francisco detail

  13. Disability ages 65+ • The variation in I/ADL disability shows a similar pattern as for overall disability • Correlation of overall disability rate, based on 5 questions, and the I/ADL rate is Pearson r=0.85 • However, it is the IADL and ADL difficulty questions that broadly define the population needing, or at risk of needing LTSS

  14. Persons ages 65+, 2005-2007 • Total population—37,244,326 • Household population—35,367,099 • With IADL or ADL difficulty: 13,394,366 • With ADL difficulty: 7,437,955 • Institutional population—1,600,211 • Non-institutional group quarters—277,016 • First, I will examine the household population then institutional population

  15. Rate of IADL/ADL disability among persons ages 65+ residing in households in 2,069 PUMAS Mean=0.191 Median=0.185 Smallest=0.072 (Kansas City) Highest=0.454 (Brooklyn)

  16. Rate of IADL and ADL disability among persons ages 65+ residing in households in 2,069 PUMAS Mean=0.176 Median=0.170 Smallest=0.068 (Kansas city) Highest=0.427 (Brooklyn) Mean=0.103 Median=0.097 Smallest=0.024 (Kansas city) Highest=0.346 (Brooklyn)

  17. Disability rates highly correlated • Difficulty going outside the home alone (IADL) with dressing, bathing (ADL) (Pearson r=0.895) • Any I/ADL with IADL (Pearson r=0.989) • Any I/ADL with ADL (Pearson r=0.919)

  18. Highest 10 PUMAS, I/ADL

  19. Lowest 10 PUMAS, I/ADL

  20. Correlations

  21. OLS regression • Disability rate = fn (mean age of PUMA, proportion male in PUMA, employment rate of PUMA, mean poverty index [up to 5x poverty], proportion white race and proportion Hispanic origin in PUMA)

  22. I/ADL regression results, ages 65+ Poverty rate is more significant than income (mean or median), earnings are not significant

  23. ADL regression results, ages 65+ Poverty rate is more significant than income (mean or median), earnings are not significant

  24. Regression results, ages 18-64

  25. Rate of institutionalization, ages 65+ Mean=0.042 Median=0.038 Smallest=0.0 (158 areas) Highest=0.234 (New jersey)

  26. Institutionalization regression results, ages 65+ Similar results for average earnings, poverty

  27. Conclusions • There is considerable variation in rates of I/ADL difficulty among older persons across areas: from 7% to 47% • Much of the variation—60 percent—is explained by socioeconomic conditions • Two kinds of aggregation: PUMAs and over time • PUMAs are less variable than individuals • With the 3-year 2005-2007 ACS, geographic characteristics are more stable statistically than single years

  28. Conclusions • The rate of difficulty in IADL and ADL is inversely related to economic well-being • The rate of difficulty in IADL or ADL is not related to population age; however, the rate of institutionalization is related to rate of oldest-old • Older Americans living in poor areas have high need for help with AT & HCBS

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