Evidence map›Paper›PMID 38223959›Full record

ArticleOral health & preventive dentistry2024

Socioeconomic Inequalities in Oral Health among Adults in Guangxi, China.

Andi Li, Tingting Zhang, Qiulin Liu, Xueting Yu, Xiaojuan Zeng

Open access · greenAbstract read
In one paragraph

Article in Oral health & preventive dentistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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

5 authors.

Andi Li
Tingting Zhang
Qiulin Liu
Xueting Yu
Xiaojuan Zeng

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo examine the relationship between socioeconomic inequalities and oral health among adults in the Guangxi province of China. MATERIALS AND

methodsThe present work was designed as a cross-sectional study, and comprises a secondary analysis of the Fourth National Oral Health Survey from 2015-2016. A multistage cluster sampling method was adopted for this survey, conducted in three urban and three rural districts Guangxi province. Dental examinations were conducted to determine oral health indicators: decayed teeth (DT), clinical attachment loss (CAL) and missing teeth (MT). The outcome measures were DT, CAL and MT. A structured questionnaire was used to collect data on demographic characteristics and socioeconomic status (SES). Multiple logistic regression models were used to analyse the relationship between SES and oral health by adjusting covariates.

resultsThe sample consisted of 651 participants aged 35-74 years. Logisitic analysis showed a statistically significant association between SES and oral health indicators. In the fully adjusted model, participants with primary education were more likely to suffer more DT (OR = 2.67, 95% CI: 1.17-6.10), teeth with CAL ≥ 4 mm (OR = 2.15, 95% CI: 1.25-3.67) and MT (OR = 3.04, 95% CI: 1.65-5.60) compared to the higher education group. Participants with secondary education exhibited a higher likelihood of experiencing increased MT compared to those in the higher education group in the fully adjusted model (OR = 3.21, 95% CI: 1.78-5.76). Household income was associated with DT and MT in the unadjusted model only.

conclusionsThere was strong relationship between SES and oral health of adults. The survey suggested a relationship between low educational attainment and oral health.

Indexed as

Oral HealthTooth LossAdultChinaCross-Sectional StudiesHumansSocial ClassSocioeconomic Factorsoral healthsocioeconomic inequalitiessocioeconomic status

Identifiers

PMID38223959
PMCPMC11619902
OpenAlexW4390889579

What OpenQuestion holds

Textmetadata
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.