BMI categories across different living arrangements.

Abstract read

In one paragraph

Article in International journal of obesity (2005), 2023. 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

2 authors.

Mengmeng YanSchool of Healthcare and Technology, Chengdu Neusoft University, Chengdu, China. mengmyan@qq.com.ORCID 0000-0002-5504-9915

Yingying LiuDepartment of Health Management Center & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionResearch on the prevalence of body mass index (BMI) categories across different living arrangements remains limited. We aimed to quantify the prevalence of BMI categories among those living alone (LA) and those not living alone (NLA).

methodsIn this population-based cross-sectional study, we used individual-level data from the 2011-2021 Behavioral Risk Factor Surveillance System. Main outcomes were prevalence of BMI categories in LA and NLA, adjusted for age, gender, and race/ethnicity, and socioeconomic status, using logistic regression and model-predicted marginal prevalence to estimate BMI categories prevalence.

resultsBetween 2011 and 2021, we quantified BMI categories prevalence in 4,195,414 adults in the BRFSS, with 1,197,787 (28.5%) adults LA and 2,997,627 (71.5%) adults NLA. In comparison to NLA, LA consistently demonstrates lower adjusted obesity prevalence across genders and age groups, with the highest prevalence observed in the 45-64 age range, particularly within the 45-54 group (LA: 37.4%, 95% CI: 37.1-37.8%; NLA: 34.3%, 95% CI: 33.8-34.7%). Additionally, LA displays an overall lower adjusted prevalence of overweight compared to NLA, notably in the 18-34 and >64 age groups.

conclusionsHeterogeneity in BMI categories prevalence exists between LA and NLA. Future studies and public health efforts should consider this heterogeneity.

Indexed as

ObesityOverweightAdultBody Mass IndexCross-Sectional StudiesEthnicityFemaleHumansMalePrevalence

Identifiers

PMID37709861

DOI10.1038/s41366-023-01381-7

What OpenQuestion holds

Textmetadata

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Registered trials

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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.