May 2006
Volume 47, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2006
Examination of Associations Between Visual Impairment and Mortality Risk Using Structural Equation Modeling
Author Affiliations & Notes
  • B.L. Lam
    University of Miami Miller School of Medicine, Miami, FL
    Bascom Palmer Eye Institute,
  • S.L. Christ
    Odum Institute, University of North Carolina, Chapel Hill, NC
  • D.J. Lee
    University of Miami Miller School of Medicine, Miami, FL
    Department of Epidemiology,
  • D.D. Zheng
    University of Miami Miller School of Medicine, Miami, FL
    Department of Epidemiology,
  • A.J. Caban
    University of Miami Miller School of Medicine, Miami, FL
    Department of Epidemiology,
  • Footnotes
    Commercial Relationships  B.L. Lam, None; S.L. Christ, None; D.J. Lee, None; D.D. Zheng, None; A.J. Caban, None.
  • Footnotes
    Support  NIH Grant AG021627
Investigative Ophthalmology & Visual Science May 2006, Vol.47, 3487. doi:
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      B.L. Lam, S.L. Christ, D.J. Lee, D.D. Zheng, A.J. Caban; Examination of Associations Between Visual Impairment and Mortality Risk Using Structural Equation Modeling . Invest. Ophthalmol. Vis. Sci. 2006;47(13):3487.

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Abstract

Purpose: : Visual impairment (VI) is associated with increased risk of mortality in some studies; however potential mechanisms through which VI may impact mortality risk are poorly understood. The purpose of this study is to examine the association between VI and mortality using structural equation modeling (SEM).

Methods: : The National Health Interview Survey (NHIS) is a multistage area probability cross–sectional survey of the US civilian non–institutionalized population. Selected families are interviewed using a primary household respondent with all adults at home participating in the interview. A randomly selected group of NHIS participants was administered a chronic conditions list that included questions about VI. Mortality linkage with over 96% of participants from the 1986–1994 NHIS was performed by the National Center for Health Statistics through 1997. Complete data were available on 116,796 adults 18 years of age and older. SEM modeling was completed with adjustment for the complex sample survey design using the M–Plus statistical package.

Results: : Mortality linkage identified 8,949 deaths; the average follow–up was 7.0 years. After controlling for age, gender, self–rated health, and eye disease modeled as a latent variable, some VI and severe bilateral VI were significantly associated with risk of mortality (Hazard Ratios, HR= 1.44, 95% CI: [1.08–1.92]; 3.03, [1.48–6.20], respectively). There was also evidence that VI influenced mortality indirectly via its association with self–rated health.

Conclusions: : SEM is a powerful and flexible analytic tool for examining the indirect and direct effects of VI on survival. Additional research applying this analytic tool to more complex models including other important risk factors such as smoking and intervening variables such as functional health status is needed to better understand the role that VI plays in mortality risk.

Keywords: clinical (human) or epidemiologic studies: prevalence/incidence • clinical research methodology • clinical (human) or epidemiologic studies: risk factor assessment 
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