Evidence map›Paper›PMID 41398559›Full record

ArticleBMC geriatrics2025

Net effects of correlated determinants of health among U.S. older adults.

Haomiao Jia, Erica I Lubetkin

Abstract read
In one paragraph

Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Haomiao JiaDepartment of Biostatistics, Mailman School of Public Health and School of Nursing, Columbia University, 560 West 168th Street, New York, NY, 10032, USA. hj2198@columbia.edu.ORCID http://orcid.org/0000-0002-1391-8190
Erica I LubetkinDepartment of Community Health and Social Medicine, CUNY School of Medicine, New York, NY, USA.

Funding

Research Center in Minority Institutions (RCMI) at City CollegeU54MD017979 · NIMHD · CITY COLLEGE OF NEW YORK · PI M. Felice Marina GHILARDI · 2024 to 2026
$15.9M
NIMHD NIH HHS U54 MD017979
6 · The paper itself

Abstract

backgroundThe health effect produced by a contributing factor alone may be different from the effect when other interacting factors are present. The effects of a set of contributors, estimated by regression coefficients, may be invalid or inappropriate if some of the predictors are correlated or the model has interaction and nonlinear terms. The net effect (NE) of a given contributing factor is the effect produced by the factor alone, not by other factors due to their correlations with the factor of interest. This study estimated NE of selected determinants of health for U.S. older adults.

methodsParticipants, aged ≥ 65 years, were from the baseline 2020 Medicare Health Outcome Survey (N = 196,641). We examined the NE of each explanatory variable from five specified categories on four health outcomes: physical component summary scores (PCS), mental component summary scores (MCS), health utilities (SF-6D), and 2-year mortality. The five categories of explanatory variables included: six difficulties in activities of daily living (ADLs), three difficulties in instrumental activities of daily living (IADLs), five geriatric syndromes, five demographics, and fifteen chronic conditions. The NE for a given factor was calculated as the average of the factor's marginal effect over all possible values of other factors.

resultsAfter adjusting for age, race, and sex, difficulty in walking contributed the greatest decrement in PCS (NE= -7.4) among the 6 ADLs. Other ADL decrements were 4.5-points for bathing, 4.4-points for getting in/out of chairs, 2.7-points for dressing, 1.5-points for using the toilet, and 1.3-points for eating. For MCS, difficulty in eating contributed the greatest decrement (-4.9), followed by bathing (-3.9) and walking (-3.2). For the SF-6D, difficulty in walking had a NE of -0.078 while difficulty in bathing had the greatest increment for 2-year mortality (8.1%). The NEs for the explanatory variables in the other categories are presented.

conclusionsThis study introduced NE as a measure of effect produced by each determinant of health and demonstrated how to estimate the net effect for various types of data. Our results highlight how the net effect varies within a given category, and differs according to physical and mental health and mortality.

Indexed as

Activities of Daily LivingGeriatric AssessmentHealth StatusAgedAged, 80 and overFemaleHealth SurveysHumansMaleMedicareUnited StatesGeriatric syndromesHealth-related quality of life (HRQOL)MortalityMulticollinearityNet effect

Identifiers

PMID41398559
PMCPMC12910756

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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.