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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: [email protected].
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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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References

Beach, T. G., Monsell, S. E., Phillips, L. E. and Kukull, W. (2012). Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010. Journal of Neuropathology & Experimental Neurology, 71, 266–273.Google Scholar
Beekly, D. L. et al. (2007). The National Alzheimer's Coordinating Center (NACC) database: the uniform data set. Alzheimer Disease and Associated Disorders, 21, 249–258.Google Scholar
Burton, A., Altman, D. G., Royston, P. and Holder, R. L. (2006). The design of simulation studies in medical statistics. Statistics in Medicine, 25, 4279–4292.Google Scholar
Chan, J. S. K. (2016). Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches. Biometrical Journal, 58, 549–569.Google Scholar
Chang, C. C. H., Yang, H. C., Tang, G. and Ganguli, M. (2009). Minimizing attrition bias: a longitudinal study of depressive symptoms in an elderly cohort. International Psychogeriatrics, 21, 869–878.Google Scholar
Eaker, E. D., Mickel, S. F., Chyou, P. H., Mueller-Rizner, N. J. and Slusser, J. P. (2002). Alzheimer's disease or other dementia and medical care utilization. Annals of Epidemiology, 12, 39–45.Google Scholar
Fried, T. R., O'Leary, J., Towle, V., Goldstein, M. K., Trentalange, M. and Martin, D. K. (2014). Health outcomes associated with polypharmacy in community-dwelling older adults: a systematic review. Journal of the American Geriatrics Society, 62, 2261–2272.Google Scholar
Gao, S. J. (2004). A shared random effect parameter approach for longitudinal dementia data with non-ignorable missing data. Statistics in Medicine, 23, 211–219.Google Scholar
Gavin, T. S. and Myers, A. M. (2003). Characteristics, enrollment, attendance, and dropout patterns of older adults in beginner Tai-Chi and line-dancing programs. Journal of Aging and Physical Activity, 11, 123–141.Google Scholar
Gnjidic, D. et al. (2012). Polypharmacy cutoff and outcomes: five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes. Journal of Clinical Epidemiology, 65, 989–995.Google Scholar
Hilmer, S. N. and Gnjidic, D. (2009). The effects of polypharmacy in older adults. Clinical Pharmacology & Therapeutics, 85, 86–88.Google Scholar
Imfeld, P., Pernus, Y. B. B., Jick, S. S. and Meier, C. R. (2013). Epidemiology, co-morbidities, and medication use of patients with Alzheimer's disease or vascular dementia in the UK. Journal of Alzheimer's Disease, 35, 565–573.Google Scholar
Lau, D. T., Mercaldo, N. D., Harris, A. T., Trittschuh, E., Shega, J. and Weintraub, S. (2010). Polypharmacy and potentially inappropriate medication use among community-dwelling elders with dementia. Alzheimer Disease & Associated Disorders, 24, 56–63.Google Scholar
Lavikainen, P., Leskinen, E., Hartikainen, S., Mottonen, J., Sulkava, R. and Korhonen, M. J. (2015). Impact of missing data mechanism on the estimate of change: a case study on cognitive function and polypharmacy among older persons. Clinical Epidemiology, 7, 169–180.Google Scholar
Lim, L. M. et al. (2017). Prevalence, risk factors and health outcomes associated with polypharmacy among urban community-dwelling older adults in multi-ethnic Malaysia. PLoS One, 12, e017346. doi: 10.1371/journal.pone.0173466.Google Scholar
Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112–1121.Google Scholar
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D. and Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology, 34, 939–944.Google Scholar
Mirra, S. S. et al. (1991). The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer's disease. Neurology, 41, 479–486.Google Scholar
Moreno-Betancur, M. and Chavance, M. (2016). Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: methodology and application in a clinical trial with drop-outs. Statistical Methods in Medical Research, 25, 1471–1489.Google Scholar
Morris, J. C. et al. (2006). The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer disease centers. Alzheimer Disease & Associated Disorders, 20, 210–216.Google Scholar
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–590.Google Scholar
Sganga, F. et al. (2015). Polypharmacy and health outcomes among older adults discharged from hospital: results from the CRIME study. Geriatrics & Gerontology International, 15, 141–146.Google Scholar
Shardell, M., Hicks, G. and Ferrucci, L. (2013). Causal inference in studies of older adults with dropout and death: vitamin D and gait speed in inchianti. Gerontologist, 53, 30.Google Scholar
Touloumi, G., Babiker, A. G., Pocock, S. J. and Darbyshire, J. H. (2001). Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study. Statistics in Medicine, 20, 3715–3728.Google Scholar
Van Beijsterveldt, C. E. M., van Boxtel, M. P. J., Bosma, H., Houx, P. J., Buntinx, F. and Jolles, J. (2002). Predictors of attrition in a longitudinal cognitive aging study: the Maastricht Aging Study (MAAS). Journal of Clinical Epidemiology, 55, 216–223.Google Scholar
Vonesh, E. F., Greene, T. and Schluchter, M. D. (2006). Shared parameter models for the joint analysis of longitudinal data and event times. Statistics in Medicine, 25, 143–163.Google Scholar
Weintraub, S. et al. (2009). The Alzheimer's Disease Centers' Uniform Data Set (UDS): the neuropsychologic test battery. Alzheimer Disease and Associated Disorders, 23, 91–101.Google Scholar
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