Evidence map›Paper›PMID 40809765›Full record

ArticleFrontiers in public health2025

The impact of long-term trends in continuity of care on the medical expenses of hypertensive patients: based on group-based trajectory model.

Yanqiu Du, Di Liang, Gaofeng Zhang, Yongsong Luo, Jiayan Huang, Yin Dong

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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.

  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

6 authors.

Yanqiu DuSchool of Public Health, Fudan University, Shanghai, China.
Di LiangSchool of Public Health, Fudan University, Shanghai, China.
Gaofeng ZhangDean's Office, The People's Hospital of Yuhuan, Taizhou, Zhejiang, China.
Yongsong LuoMedical Administration Department, Health Commission of Yuhuan, Taizhou, Zhejiang, China.
Jiayan HuangSchool of Public Health, Fudan University, Shanghai, China.
Yin DongDean's Office, The People's Hospital of Yuhuan, Taizhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to identify long-term trends in continuity of care (COC) among hypertensive patients using group-based trajectory modeling (GBTM) and evaluate their association with medical expenses, thereby providing evidence for chronic disease management. Methods: We analyzed 6-year (2016-2021) reimbursement data of the social health insurance from Yuhuan City, China, including 30,545 hypertensive adults. Continuity of Care Index (COCI) was calculated annually. GBTM was employed to classify patients into trajectory subgroups based on COCI trends, with the best-fitting model selection guided by Bayesian information criterion (BIC), average posterior probability (AvePP). Multiple linear regression assessed the relationship between trajectory groups and annual medical expenses, adjusting for age, gender, insurance type, and Charlson Comorbidity Index (CCI). Results: Four COCI trajectories were identified: low-level maintenance (52.06%), low-level increase (17.14%), high-level decrease (18.94%), and high-level maintenance (11.87%). Patients in the high-level maintenance group incurred the lowest annual medical expenses (mean range: ¥3,786-¥5,088), while the low-level maintenance group exhibited the highest (mean range: ¥6,450-¥10,321). After adjustment, the low-level maintenance group had significantly higher expenses than the high-level maintenance group (β = 3,049.44 CNY, Conclusion: Sustained high continuity of care correlates with reduced medical expenses in hypertensive patients. Long-term COC maintenance should be prioritized in chronic disease management to mitigate healthcare costs. Policymakers should incentivize care continuity through integrated health systems and targeted patient interventions.

Indexed as

Continuity of Patient CareHealth ExpendituresHypertensionAdultAgedChinaFemaleHumansMaleMiddle Agedcontinuity of care (COC)group-based trajectory modeling (GBTM)hypertensionlong-term trendsmedical expenses

Identifiers

PMID40809765
PMCPMC12343502

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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.