Evidence map›Paper›PMID 33953978›Full record

ArticleJournal of nutrition and metabolism2021

Malnutrition among the Elderly in Malaysia and Its Associated Factors: Findings from the National Health and Morbidity Survey 2018.

Mohamad Hasnan Ahmad, Ruhaya Salleh, Cheong Siew Man, Munawara Pardi, Norsyamlina Che Abdul Rahim, Norhafizah Shahril, Mohd Hatta Abdul Mutalib, Suzana Shahar, Noor Ani Ahmad

Open access · goldAbstract read
In one paragraph

Article in Journal of nutrition and metabolism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

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  8. Healthy Ageing in Malaysia by 2030: Needs, Challenges and Future Directions.The Malaysian journal of medical sciences : MJMS · 2024
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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

9 authors at 2 institutions in 1 country.

Mohamad Hasnan AhmadInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0003-2280-2293
Ruhaya SallehInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0001-6418-840X
Cheong Siew ManInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0001-7756-0215
Munawara PardiInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0002-5715-4196
Norsyamlina Che Abdul RahimInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0003-4045-6566
Norhafizah ShahrilInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0003-2325-5551
Mohd Hatta Abdul MutalibInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0002-2750-5792
Suzana ShaharCentre for Healthy Aging and Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, 50300 Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0002-7191-9212
Noor Ani AhmadInstitute for Public Health, National Institutes of Health, Ministry of Health Malaysia, 41070 Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0001-8136-5295
Ministry of Health · MYNational University of Malaysia · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalaysia is predicted to become an ageing population by 2035. Malnutrition among the elderly is one of growing concern. This study aims to identify the prevalence of malnutrition and its associated factors among the elderly in Malaysia.

methodsData from the National Health and Morbidity Survey (NHMS) 2018 was analysed. This survey applied a multistage stratified cluster sampling design to ensure national representativeness. Malnutrition was identified using a validated Mini Nutrition Assessment-Short Form (MNA-SF). Variables on sociodemographic, health status, and dietary practices were also obtained. The complex sampling analysis was used to determine the prevalence and associated factors of at-risk or malnutrition among the elderly.

resultA total of 3,977 elderly completed the MNA-SF. The prevalence of malnutrition and at-risk of malnutrition was 7.3% and 23.5%, respectively. Complex sample multiple logistic regression found that the elderly who lived in a rural area, with no formal or primary level of education, had depression, Instrumental Activity of Daily Living (IADL) dependency, and low quality of life (QoL), were underweight, and had food insecurity and inadequate plain water intake were at a significant risk of malnutrition (malnutrition and at-risk), while Chinese, Bumiputra Sarawak, and BMI more than 25 kgm

conclusionsCurrently, three out of ten elderly in Malaysia were at-risk or malnutrition. The elderly in a rural area, low education level, depression, IADL dependency, low QoL, underweight, food insecurity, and inadequate plain water intake were at risk of malnutrition in Malaysia. The multiagency approach is needed to tackle the issue of malnutrition among the elderly by considering all predictors identified from this study.

Identifiers

PMID33953978
PMCPMC8057910
OpenAlexW3154593524

What OpenQuestion holds

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LicenceCC BY
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.