Evidence map›Paper›PMID 40796723›Full record

ArticleInternational journal of behavioral medicine2025

Changing Dietary Patterns among Chinese Older Adults: A Rural-Urban Comparative Analysis (2008-2018).

Cai Xu, Yen-Han Lee, Shante Jeune, Mack Shelley

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of behavioral medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

4 authors.

Cai XuThe Ohio University, Athens, United States.
Yen-Han LeeThe University of Central Florida, Orlando, United States. yen-han.lee@ucf.edu.
Shante JeuneThe University of Central Florida, Orlando, United States.
Mack ShelleyThe Iowa State University, Ames, United States.

Funding

NIMHD NIH HHS 5U54MD007592
6 · The paper itself

Abstract

backgroundSince the 1990s, Chinese residents have experienced rapid dietary shifts, with potential disparities emerging between rural and urban populations. This study examined dietary patterns and changes across these areas, particularly among older adults.

methodsData from four waves (2008-2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used, covering 20,945 older adults aged 65 and above. Latent class analysis identified dietary patterns based on five core food items (fresh vegetable, fruit, meat, egg, dairy products). Multinomial logistic regression assessed correlates of class membership for rural and urban participants.

resultsThe study sample included 11,357 rural and 9,588 urban residents. Dietary trends showed greater fluctuations among urban residents than rural residents. Four distinct dietary classes were identified: vegetable-dominant (27.39% rural vs. 37.66% urban), low-frequency (63.42% rural vs. 24.10% urban), balanced (5.10% rural vs. 15.84% urban), and egg-dominant (4.09% rural vs. 22.39% urban). Among rural participants, the odds of following a balanced or egg-dominant diet were significantly higher compared to the low-frequency group (reference group; odds ratio [OR] > 1). Among urban participants, those with formal education, regular exercise, or better self-rated health were more likely to follow vegetable-dominant, balanced, or egg-dominant diets compared to the low-frequency group (OR > 1).

conclusionsTargeted interventions addressing dietary disparities and promoting balanced diets may reduce nutritional inequality and improve health outcomes, particularly for rural residents.

Indexed as

ChinaChinese Longitudinal Healthy Longevity SurveyDietary patternOlder adultsUrban–rural disparity

Identifiers

What OpenQuestion holds

Textmetadata
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.