Could differential under-reporting of loneliness between men and women bias the gender-specific association between loneliness duration and rate of memory decline? A probabilistic bias analysis of effect modification.

Journal: American journal of epidemiology

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Affiliated Institutions:  Center for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA. Department of Psychology, University of Michigan, Ann Arbor, MI, USA. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA. Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, MI, USA.

Abstract summary 

Gender is an observed effect modifier of the association between loneliness and memory aging. However, this effect modification may be a result of information bias due to differential loneliness under-reporting by gender. We applied probabilistic bias analyses to examine whether effect modification of the loneliness-memory decline relationship by gender is retained under three simulation scenarios with various magnitudes of differential loneliness under-reporting between men and women. Data were from biennial interviews with adults aged 50+ in the US Health and Retirement Study from 1996-2016 (5,646 women and 3,386 men). Loneliness status (yes vs. no) was measured from 1996-2004 using the CES-D loneliness item and memory was measured from 2004-2016. Simulated sensitivity and specificity of the loneliness measure were informed by a validation study using the UCLA Loneliness Scale as a gold standard. The likelihood of observing effect modification by gender was higher than 90% in all simulations, although the likelihood reduced with an increasing difference in magnitude of the loneliness under-reporting between men and women. The gender difference in loneliness under-reporting did not meaningfully affect the observed effect modification by gender in our simulations. Our simulation approach may be promising to quantify potential information bias in effect modification analyses.

Authors & Co-authors:  Yu Xuexin X Zahodne Laura B LB Gross Alden L AL Needham Belinda L BL Langa Kenneth M KM Cho Tsai-Chin TC Kobayashi Lindsay C LC

Study Outcome 

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Statistics
Citations : 
Authors :  7
Identifiers
Doi : kwae186
SSN : 1476-6256
Study Population
Men,Women
Mesh Terms
Other Terms
effect modification;gender difference;loneliness exposure misclassification;memory aging;probabilistic bias analysis
Study Design
Study Approach
Country of Study
Publication Country
United States