ArticleBMC pediatrics2026
Nutritional disparities between urban poor and non-poor Malaysian children under five in the context of the COVID-19 pandemic: a cross-sectional study.
Article in BMC pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid urbanisation in Asia has widened intra-urban nutritional disparities, particularly among urban-poor children during periods of compromised food security.
methodsThis cross-sectional study aimed to investigate nutritional disparities among children under five in an urban locality during the COVID-19 pandemic. We recruited 717 children aged 6-59 months from July 2020 to June 2023 across three sites in Selangor, Malaysia. Preterm or small-for-gestational-age children and those with incomplete data were excluded. Dietary intake was assessed by 24-h recall adapted from WHO-IYCF and FAO-WDD tools. Anthropometric indices were weight-for-age, length/height-for-age, and weight-for-length/height z-scores. Malnutrition indicators were WHO-defined underweight, stunting, wasting, and overweight. Associations of sociodemographic factors and minimum dietary diversity with malnutrition indicators were analysed.
results357 urban-poor and 360 urban non-poor children were recruited. Urban-poor children had lower mean weight and height z-scores than urban non-poor children (-0.77 [SD 1.20] vs -0.46 [SD 1.17], p < 0.001; -0.73 [SD 1.06] vs -0.46 [SD 1.21], p = 0.002). Underweight prevalence was higher among urban-poor children (13.2% vs 7.0%, p = 0.009). Among children aged 6-23 months, dietary diversity scores were lower in the urban-poor group (mean DDS 5.02 vs 5.60, p < 0.001), with lower breastfeeding prevalence (47.5% vs 59.8%, p = 0.007). Intake of sugar-sweetened beverages and foods was higher in both urban-poor age groups (52.9% vs 30.0%, p < 0.001; 80.4% vs 59.7%, p = 0.003). In multivariable models, urban poverty increased the odds of underweight (adjusted OR 1.99 [95% CI 1.19, 3.32], p = 0.008). Higher birth weight z-scores were protective against underweight (adjusted OR 0.65 [95% CI 0.47, 0.91], p = 0.013) and wasting (adjusted OR 0.68 [95% CI 0.49, 0.94], p = 0.018), while higher birth length z-scores were protective against stunting (adjusted OR 0.65 [95% CI 0.49, 0.87], p = 0.004). Achieving minimum dietary diversity was protective against underweight (adjusted OR 0.46 [95% CI 0.23, 0.92], p = 0.029).
conclusionUrban-poor children had lower anthropometric z-scores, higher underweight prevalence, poorer early-childhood dietary diversity, and higher intake of sugar-sweetened beverages and foods. Targeted socioeconomic and nutrition support is needed for urban-poor families.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.