Evidence map›Paper›PMID 41437255›Full record

ArticlePopulation health metrics2025

Non-housing assets and all-cause mortality in middle-aged and older Chinese adults: a National cohort study.

Yuanyuan Qin, Biheng Feng, Qingjiang Cai, Liuyun Huang, Mingjie Xie, Ling Li, Debin Huang

Abstract read
In one paragraph

Article in Population health metrics, 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
–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

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

7 authors.

Yuanyuan QinDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Biheng FengDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Qingjiang CaiDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Liuyun HuangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Mingjie XieDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Ling LiDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Debin HuangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. hdeb@sina.com.

Funding

Integrting Information About Aging SurveysR01AG030153 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Sara Adar, Alden L. Gross · 2007 to 2026
$41.7M
Mega Meta Data Set of the Health and Retirement Surveys Around the WorldRC2AG036619 · NIA · RAND CORPORATION · PI KAPTEYN, ARIE · 2009 to 2010
$2.0M
Archiving & Creating User Friendly Data: the Longitudinal Aging Survey in IndiaR03AG043052 · NIA · RAND CORPORATION · PI LEE, JINKOOK · 2012 to 2012
$95k
General Program of Guangxi Natural Science Foundation 2025GXNSFAA069940Hospital Self-Set Scientific Research Cultivation Project YYZS2023018NIA NIH HHS R01 AG030153NIA NIH HHS R03 AG043052NIA NIH HHS RC2 AG036619
6 · The paper itself

Abstract

backgroundFinancial resources beyond housing may influence survival in later life. Given China's rapid population aging and high home ownership, focusing on non-housing assets can clarify wealth-health links. We therefore examined the association between total non-housing assets and all-cause mortality among Chinese middle-aged and older adults.

methodsA nationwide cohort of 12,670 adults (aged 45-85) was established using the harmonized CHARLS dataset (2011-2018). All-cause mortality was ascertained through 2020 by supplementing harmonized data with vital status information from the raw CHARLS 2020 wave. The main exposure was total non-housing assets. In addition, non-housing assets were combined with household consumption (median split) to create four joint groups: Group 1 (low assets/low consumption), Group 2 (low assets/high consumption), Group 3 (high assets/low consumption), and Group 4 (high assets/high consumption). All-cause mortality was tracked. Baseline characteristics and mortality were presented by asset quartile and asset consumption group. Survival curves, Cox models (adjusted for confounders), and restricted cubic splines assessed associations. Subgroup and interaction analyses, especially for marital status, were visualized using forest and stratified plots.

resultsDuring a 9-year follow-up, 2,418 deaths occurred. Higher total non-housing assets were associated with lower mortality: Q4 (highest) vs. Q1 (lowest), adjusted HR = 0.79 (95% CI 0.68-0.91). In fully adjusted models, we also observed a graded inverse association across asset-consumption groups (P for trend < 0.001); high-consumption categories remained protective (Group 2: HR = 0.85, 95% CI 0.74-0.97; Group 4: HR = 0.75, 95% CI 0.65-0.85). Marital status showed a significant interaction with asset level (P‑interaction < 0.001).

conclusionsGreater non-housing assets was associated with lower mortality. Marital status has a significant interacting effect on this association. Focus should be on vulnerable elderly groups with middle-low assets, low consumption, or those who are non-married.

Indexed as

MortalityAgedAged, 80 and overCause of DeathChinaCohort StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedSocioeconomic FactorsAging populationAll-cause mortalityTotal non-housing assetsWealth-health gradient

Identifiers

PMID41437255
PMCPMC12837438

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.