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Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout

Published online by Cambridge University Press:  18 April 2018

George O. Agogo Open the ORCID record for George O. Agogo [Opens in a new window] ,
Christine M. Ramsey ,
Danijela Gnjidic ,
Daniela C. Moga  and
Heather Allore
George O. Agogo*
Affiliation:
Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
Christine M. Ramsey
Affiliation:
Yale Center for Medical Informatics, Yale School of Medicine, New Haven, Connecticut, USA
Danijela Gnjidic
Affiliation:
Faculty of Pharmacy and Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
Daniela C. Moga
Affiliation:
Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, Kentucky, USA Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Heather Allore
Affiliation:
Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
*
Correspondence should be addressed to: Heather Allore, PhD, Department of Internal Medicine, Yale School of Medicine, 300 George Street suite 775, New Haven, Connecticut 06511, USA. Phone: +1 203 785 1800. Email: heather.allore@yale.edu.
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Abstract

Background:

Longitudinal studies of older adults are characterized by high dropout rates, multimorbid conditions, and multiple medication use, especially proximal to death. We studied the association between multiple medication use and incident dementia diagnoses including Alzheimer's disease (AD), vascular dementia (VD), and Lewy-body dementia (LBD), simultaneously accounting for dropout.

Methods:

Using the National Alzheimer's Coordinating Center data with three years of follow-up, a set of covariate-adjusted models that ignore dropout was fit to complete-case data, and to the whole-cohort data. Additionally, covariate-adjusted joint models with shared random effects accounting for dropout were fit to the whole-cohort data. Multiple medication use was defined as polypharmacy (⩾ five medications), hyperpolypharmacy (⩾ ten medications), and total number of medications.

Results:

Incident diagnoses were 2,032 for AD, 135 for VD, and 139 for LBD. Percentages of dropout at the end of follow-up were as follows: 71.8% for AD, 81.5% for VD, and 77.7% for LBD. The odds ratio (OR) estimate for hyperpolypharmacy among those with LBD versus AD was 2.19 (0.78, 6.15) when estimated using complete-case data and 3.00 (1.66, 5.40) using whole-cohort data. The OR reduced to 1.41 (0.76, 2.64) when estimated from the joint model accounting for dropout. The OR for polypharmacy using complete-case data differed from the estimates using whole-cohort data. The OR for dementia diagnoses on total number of medications was similar, but non-significant when estimated using complete-case data.

Conclusion:

Reasons for dropout should be investigated and appropriate statistical methods should be applied to reduce bias in longitudinal studies among high-risk dementia cohorts.

Keywords

Alzheimer's diseaseattritionjoint modelmissing datapolypharmacy
Type
Original Research Article
Information
International Psychogeriatrics , Volume 30 , Issue 10: Issue Theme: Mild Cognitive Impairment , October 2018 , pp. 1477 - 1487
Copyright
Copyright © International Psychogeriatric Association 2018 

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