Evidence map›Paper›PMID 40018624›Full record

ArticleBMJ public health2024

Associations of serial negative income shock and all-cause mortality: a longitudinal study in China.

Nan Xiao, Ran Li, Shanshan Li, Yudan Yu, Fan Yang, Jianan Yang, Gordon G Liu, Beini Lyu

Abstract read
In one paragraph

Article in BMJ public health, 2024. 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

8 authors.

Nan XiaoPeking University, Beijing, China.
Ran LiPeking University, Beijing, China.
Shanshan LiPeking University, Beijing, China.
Yudan YuPeking University, Beijing, China.
Fan YangPeking University, Beijing, China.
Jianan YangPeking University, Beijing, China.
Gordon G LiuPeking University, Beijing, China.
Beini LyuPeking University, Beijing, China.ORCID 0000-0001-9219-468X

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Ian Michael Carroll · 1999 to 2026
$31.6M
MONITORING SOCIAL CHANGE: HEALTH, REPRODUCTION, AGINGR01HD030880 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 1994 to 2024
$27.0M
Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
Program Development (Pilot) CoreP30AG066615 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI TAYLOR HARGROVE · 2020 to 2026
$4.1M
Monitoring Social Change: Dynamics Of Aging And Cognitive FunctionR01AG065357 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 2020 to 2024
$3.6M
Emergence of Cardiometabolic Risk Across the Lifecycle in ChinaR01HL108427 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2012 to 2015
$2.7M
Transition to a Western diet and cardiometabolic risk: biomarkers derived from the microbiomeR01DK104371 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2015 to 2018
$2.5M
CHILD HEALTH EFFECTS OF RAPID SOCIO-DEMOGRAPHIC CHANGER01HD038700 · NICHD · UNIVERSITY OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2000 to 2004
$1.5M
Nutrition-Related NCD Prevention Training in ChinaD43TW009077 · FIC · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2011 to 2015
$1.1M
FIC NIH HHS D43 TW009077NHLBI NIH HHS R01 HL108427NIA NIH HHS P30 AG066615NIA NIH HHS R01 AG065357NICHD NIH HHS P2C HD050924NICHD NIH HHS R01 HD030880NICHD NIH HHS R01 HD038700NIDDK NIH HHS P30 DK056350NIDDK NIH HHS R01 DK104371
6 · The paper itself

Abstract

Introduction: With a precarious economic outlook and increasing income volatility in current times, understanding the association between negative income shock and health is crucial. However, few studies have examined such associations in developing countries. Using data from China, this study aimed to examine associations of both serial absolute income drops and relative income trajectory and mortality. Methods: We included 4757 participants from the China Health and Nutrition Survey, a large prospective cohort study. Data between 1989 and 1997 were used to define income drops and relative income trajectories. We defined income drop as a decrease of ≥50% between two consecutive interviews and defined relative income trajectory using a latent class model. All-cause mortality between 2000 and 2015 was ascertained by participants' family members. Results: A total of 2066 (43.43%) experienced 1 income drop and 477 (10.03%) experienced ≥2 income drops. A total of 535 deaths occurred (incidence rate 8.88 per 1000 person-years). Income drops were associated with a greater risk of mortality after adjusting for baseline income, comorbidities, sociodemographic and behavioural factors (HR 1.42 (95% CI 1.04 to 1.93) for ≥2 income drops vs no income drop). The downshift in relative income was also associated with increased mortality risk (HR 3.61 (95% CI 1.45 to 8.96) for always low; HR 3.36 (95% CI 1.36 to 8.32) for decreasing; HR 2.92 (95% CI 1.14 to 7.51) for increasing vs always high relative income). The associations between income drops and mortality were observed only among individuals with low wealth and low household income. Conclusion: In a large sample of the Chinese population with repeated income measurement and over 14 years of follow-up, both serial absolute income drops and a downward relative income trajectory were associated with higher risks of mortality in China. Priority should be given to policies aimed at enhancing resilience against serial income shocks and financial burdens.

Indexed as

education, medicalpublic healthsociodemographic factors

Identifiers

PMID40018624
PMCPMC11816213

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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.