Jennifer Weuve, Rush University Medical Center
Over the past few decades, epidemiologists and their colleagues have contributed critical insights to understanding the exploding epidemic of cognitive decline and dementia in the population. Those contributions have been as fundamental as defining Alzheimer’s dementia and estimating how many people have it. The methods of dementia epidemiology largely emanate from the methods of chronic diseases such as heart disease and cancer, but dementia offers its own special brand of complications. In response, epidemiologists have developed and honed methods for evaluating and correcting biases from measurement error, differential selection and confounding that influence effect estimates in dementia research.
The papers below represent a mixture of seminal works (Evans et al, 1989; Glymour et al, 2005; Schneider et al, 2007) along with newer ones that inspire and provoke by: applying innovative methods to old problems (e.g., Nguyen et al, 2016); confronting the usefulness of big data for identifying dementia cases (Taylor et al, 2009); and addressing emerging concerns, such as selective participation in brain imaging studies (Ganguli et al, 2015). The last paper summarizes the methodologic challenges in the field and provides guidelines for transparently reporting on potential biases in individual studies.
Prevalence of Alzheimer's disease in a community population of older persons. Higher than previously reported.
Evans DA, Funkenstein HH, Albert MS, Scherr PA, Cook NR, Chown MJ, Hebert LE, Hennekens CH, Taylor JO. Prevalence of Alzheimer’s disease in a community population of older persons. Higher than previously reported. JAMA. 1989 Nov 10;262(18):2551-6. PubMed PMID: 2810583.
Who wants a free brain scan? Assessing and correcting for recruitment biases in a population-based sMRI pilot study.
Ganguli M, Lee CW, Hughes T, Snitz BE, Jakubcak J, Duara R, Chang CC. Who wants a free brain scan? Assessing and correcting for recruitment biases in a population-based sMRI pilot study. Brain Imaging Behav. 2015 Jun;9(2):204-12. doi: 10.1007/s11682-014-9297-9. PubMed PMID: 24573773; PubMed Central PMCID: PMC4147027.
When is baseline adjustment useful in analyses of change? An example with education and cognitive change.
Glymour MM, Weuve J, Berkman LF, Kawachi I, Robins JM. When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol. 2005 Aug 1;162(3):267-78. Epub 2005 Jun 29. PubMed PMID: 15987729.
Instrumental variable approaches to identifying the causal effect of educational attainment on dementia risk.
Nguyen TT, Tchetgen EJ, Kawachi I, Gilman SE, Walter S, Liu SY, Manly JJ, Glymour MM. Instrumental variable approaches to identifying the causal effect of educational attainment on dementia risk. Ann Epidemiol. 2016 Jan;26(1):71-76.e3. doi: 10.1016/j.annepidem.2015.10.006. Epub 2015 Oct 30. PubMed PMID: 26633592; PubMed Central PMCID: PMC4688127.
Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology. 2007 Dec 11;69(24):2197-204. Epub 2007 Jun 13. PubMed PMID: 17568013.
Taylor DH Jr, Østbye T, Langa KM, Weir D, Plassman BL. The accuracy of Medicare claims as an epidemiological tool: the case of dementia revisited. J Alzheimers Dis. 2009;17(4):807-15. doi: 10.3233/JAD-2009-1099. PubMed PMID: 19542620; PubMed Central PMCID: PMC3697480.
Guidelines for reporting methodological challenges and evaluating potential bias in dementia research.
Weuve J, Proust-Lima C, Power MC, Gross AL, Hofer SM, Thiébaut R, Chêne G, Glymour MM, Dufouil C; MELODEM Initiative. Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimers Dement. 2015 Sep;11(9):1098-109. doi: 10.1016/j.jalz.2015.06.1885. Review. PubMed PMID: 26397878; PubMed Central PMCID: PMC4655106.