Evidence map›Paper›PMID 38155810›Full record

ArticleJournal of nutritional science2023

Meat, vegetable, and fruit consumption among urban and rural elders aged 60+ years in regional China: a population-level nutritional study.

Guilin Zhang, Jian Kang, Shibao Jing, Yinhao Chen, Tianrui Deng, Huiqing Xu, Haidi Wu, Fei Xu

Abstract read
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Article in Journal of nutritional science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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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.

Guilin ZhangGeriatric Hospital of Nanjing Medical University, Nanjing, China.
Jian KangThe First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Shibao JingNanjing Liuhe District Center for Disease Control and Prevention, Nanjing, China.
Yinhao ChenWenzhou Center for Disease Control and Prevention, Wenzhou, China.
Tianrui DengNanjing Medical University School of Public Health, Nanjing, China.
Huiqing XuNanjing Medical University School of Public Health, Nanjing, China.
Haidi WuGeriatric Hospital of Nanjing Medical University, Nanjing, China.
Fei XuNanjing Municipal Center for Disease Control and Prevention Affiliated to Nanjing Medical University, Nanjing, China.ORCID 0000-0003-1785-3635

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim was to assess epidemiological characteristics of the most recent consumption patterns of meat, vegetable, and fruit among representative urban and rural residents aged 60+ years in regional China. In this cross-sectional survey conducted in mid-2018, participants aged 60+ years were randomly chosen from urban and rural communities in Nanjing municipality of China. Meat, vegetable, and fruit intake were assessed with a validated food frequency questionnaire. Multivariate logistic regression models were applied to compute odds ratio (OR) and 95 % confidence interval (CI) to investigate the association of socio-demographic characteristics with a likelihood of meeting intake recommendation. Among the 20 867 participants, 49⋅5 % were men and 45⋅0 % urban elders, and 6⋅5 % aged 80+ years. The mean values of consumption frequency of red meat, white meat, vegetable, and fruit were 2⋅99 ± 2⋅28, 1⋅37 ± 1⋅13, 5⋅24 ± 6⋅43, and 2⋅64 ± 2⋅91 times/week, respectively, among overall participants. Moreover, there were 14⋅9, 23⋅7, and 12⋅1 % of participants meeting intake recommendations of meat, vegetable, and fruit, separately, in this study. After adjustment for potential confounders, age, gender, residence area, and educational attainment each was associated with the likelihood of meeting intake recommendation of meat, vegetable, or fruit. The consumption frequency and proportion of participants meeting intake recommendations of meat, vegetable, or fruit were not high among elders in regional China. Socio-demographic characteristics were associated with intake recommendations of meat, vegetables, and fruit. It has public health implications that participants' socio-demographic attributes shall be considered for precision intervention on meat, vegetable, and fruit consumption in healthy eating campaigns among elders in China.

Indexed as

FruitVegetablesAgedCross-Sectional StudiesDietFemaleHumansMaleMeatRural PopulationEldersFruitIntakeRed meatVegetableWhite meat

Identifiers

PMID38155810
PMCPMC10753460

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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.