Evidence map›Paper›PMID 38973726›Full record

ArticleAmerican journal of epidemiology2025

Could differential underreporting 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.

Xuexin Yu, Laura B Zahodne, Alden L Gross, Belinda L Needham, Kenneth M Langa, Tsai-Chin Cho, Lindsay C Kobayashi

Abstract read
In one paragraph

Article in American journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xuexin YuCenter for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.ORCID 0000-0002-6484-6035
Laura B ZahodneDepartment of Psychology, University of Michigan, Ann Arbor, MI 48109, United States.
Alden L GrossDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States.
Belinda L NeedhamCenter for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.ORCID 0000-0001-5939-2027
Kenneth M LangaDepartment of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, MI 48109, United States.
Tsai-Chin ChoCenter for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Lindsay C KobayashiCenter for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.ORCID 0000-0003-2725-3107

Funding

HRS Yrs29-34: Y33 SSA CoFundingU01AG009740 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jessica Faul, KENNETH M LANGA · 1990 to 2026
$555.8M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
Research Education CoreP30AG024824 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Neil Alexander · 2004 to 2026
$29.2M
Socioeconomic, Cardiovascular, and Psychosocial Sources of Cross-National Variation in Cognitive Health Among Older AdultsR01AG070953 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alden L. Gross, Lindsay C Kobayashi · 2021 to 2026
$4.6M
Cumulative socioeconomic exposures, cash transfer interventions, and later-life cognitive decline and dementia risk in a low-income region of South AfricaR01AG069128 · NIA · TRUSTEES OF INDIANA UNIVERSITY · PI Lindsay C Kobayashi, Molly Sears Rosenberg · 2020 to 2026
$4.1M
National Institute on Aging at the National Institutes of Health R01AG070953NIA NIH HHS P30 AG024824NIA NIH HHS P30 AG066507NIA NIH HHS R01 AG069128NIA NIH HHS R01 AG070953NIA NIH HHS U01 AG009740NIA NIH HHS U01AG009740
6 · The paper itself

Abstract

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 underreporting 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 underreporting between men and women. Data were from biennial interviews with adults aged ≥ 50 years in the US Health and Retirement Study from 1996-2016 (5646 women and 3386 men). Loneliness status (yes vs no) was measured from 1996-2004 using the Center for Epidemiologic Studies Depression (CES-D) Scale 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 underreporting between men and women. The gender difference in loneliness underreporting 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.

Indexed as

LonelinessMemory DisordersAgedBiasFemaleHumansMaleMiddle AgedSex FactorsUnited Stateseffect modificationgender differenceloneliness exposure misclassificationmemory agingprobabilistic bias analysis

Identifiers

PMID38973726
PMCPMC11879530

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.