Cognitive Function Among Older Adults Over Time:

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• Cognitive Function Among Older Adults Over Time
• A Multi-level Analysis
• Dana Miller-Martinez

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Background Significance

• The association between individual educational
  attainment
• and cognitive function has been firmly
  established.
• The effect of broader social contexts on change
  in
• individual-level cognitive function over
  time has not been
• studied in older adults who spend more time
  in their
• homes and neighborhoods.

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Specific Aims Individual-level

• Describe temporal progression of cognitive
  performance among older adults over time, with
  special attention to race differences.
• Examine SES correlates of cognitive performance
  over time and the contribution of SES to racial
  variation in cognitive performance.
• Examine the contribution of comorbid health
  conditions to changes in cognitive function over
  time to racial variation in cognitive
  performance, net of SES covariates.

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Specific Aims Contextual-level

• Analyze the effect of social context on change in
  cognitive status net of other individual-level
  characteristics using a multi-level model.

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Multi-level Conceptual Framework
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ANALYTIC SAMPLE
ANALYTIC DELETIONS

• Multistage Area Probability Sample
• Medicare Enrollment Sample

6,047 Age-eligible (70) Persons 2,175 Spouses
8,222 Individuals

• 775 Age ineligible spouses
• 791 Proxy interviews
• Missing/invalid data
• 367 Hispanic or Other

5,757 Individuals 4,792 Households
965 in multi-person households
4,792 Individuals
1,609 non-urban
3,183 Urban
2,592 White
591 African American
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Analysis Plan

• All individual level analysis will be conducted
  with STATA 8.2.
• Focal Relationship Cognitive performance
• Cross-lagged OLS regression.
• Cog2 ß0 ß1Cog1 ß2Race ß3(Cog1Race)

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• Generalizability of urban, non-institutionalized
  sample.
• Comorbidity with other health conditions.
• Gross measure of neighborhood.
• Lack of measures of environmental buoys.
• Difficulty in assessing individuals with
  cognitive impairment
• Two time points.

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• Generalizability of a national sample.
• Racial differences.
• Longitudinal data.
• Multilevel analysis.
• Results will inform prevention programming and
  other public health endeavors.

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