Evidence map›Paper›PMID 41588505›Full record

ArticleBMC public health2026

Disparities in early-stage cervical cancer detection: the critical role of educational attainment in 75,268 women in Hubei Province.

Wenli Li, Qin Yan, Da Zhou, Xiaoxv Yin, Wenfei Xia

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Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Wenli LiCollege of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Qin YanCollege of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Da ZhouCollege of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Xiaoxv YinCollege of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China. yxx@hust.edu.cn.
Wenfei XiaDepartment of Breast and Thyroid surgery, Division of General Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Qiaokou District, Wuhan City, 430030, Hubei Province, PR China. xiawenfei102038@tjh.tjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevention and treatment of cervical cancer is of great importance in the advancement of global public health. The aim of this study was to analyze the impact of women’s educational attainment on disparities in early diagnosis of cervical cancer and, as well as the resulting differences in length of hospital stay and associated costs.

methodA cross-sectional study involving 75,268 cervical cancer patients extracted from the Hubei Provincial Inpatient Medical Records Database from 2018 to 2023 in Hubei provinces of China was conducted. Logistic regression was used to explore associations between education attainment and early diagnosis. Generalized linear models (GLM) was employed to analyze the differences in hospital stay duration and costs between patients with early-diagnosed cervical cancer and those without early diagnosis.

results(1) Of the 75,268 cervical cancer patients, 44.75% were diagnosed with invasive cervical cancer. (2) Education level was an important influencing factor of early diagnosis of cervical cancer, with women with a college degree or higher are more likely to receive an early diagnosis of cervical cancer (OR = 1.58; 95% CI:1.46–1.70; p < 0.001). (3) Early diagnosis patients showed superior socioeconomic characteristics, a higher proportion of professional and managerial positions, and less reliance on government medical assistance. (4) Early-stage cervical cancer patients had 1.59 fewer hospital days (95% CI: -1.60, -1.58) and 1.55 lower costs (95% CI: -1.56, -1.54).

conclusionsWomen’s educational attainment plays an important role in early diagnosis of cervical cancer, thereby further influencing patients’ hospitalization costs and length of stay. The identified socioeconomic barriers (education, occupation, insurance) can be leveraged to design targeted health promotion strategies for vulnerable women. Such focused interventions could improve early diagnosis in resource-limited areas, potentially reducing cervical cancer costs and improving outcomes globally.

Indexed as

Early Detection of CancerEducational StatusHealthcare DisparitiesUterine Cervical NeoplasmsAdultAgedChinaCross-Sectional StudiesFemaleHumansLength of StayMiddle AgedSocioeconomic Disparities in HealthCervical cancer screening disparitiesEarly cancer detection factorsHealthcare access inequitiesHealth education impactPreventive screening uptake

Identifiers

PMID41588505
PMCPMC12918635

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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.