Evidence map›Paper›PMID 42082673›Full record

ArticleCommunications medicine2026

Data-driven identification of hub chains in disease accruals to reduce healthcare utilization and costs.

Shasha Han, Can Zhou, Sairan Li, Shuya Zhou, Ruitai Shao, Weizhong Yang, Chen Wang

Abstract read
In one paragraph

Article in Communications medicine, 2026. 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
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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

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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

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

7 authors.

Shasha Han *School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. hanshasha@pumc.edu.cn.ORCID http://orcid.org/0000-0001-7388-8125
Can Zhou *School of Statistics and Data Science, Nanjing Audit University, Nanjing, China.
Sairan Li *School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Shuya ZhouSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Ruitai ShaoSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Weizhong YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0009-0003-7240-0898
Chen WangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0000-0001-7857-5435

Funding

National Natural Science Foundation of China (National Science Foundation of China) No. 82304269
6 · The paper itself

Abstract

backgroundUnderstanding how diseases accumulate over time is crucial for managing their combined impact. We aim to identify these common pathways of multimorbidity and determine how they increase healthcare use and costs beyond the sum of single diseases.

methodsWe analyzed hospital records from over 600,000 patients in China between 2013 and 2021. We identified significant sequences of three diseases diagnosed over time and measured their synergistic effect by comparing the total healthcare use along a sequence to the added use of each disease occurring in isolation. Outcomes included hospital visits, length of stay, total costs, and out-of-pocket spending. We then grouped these sequences to identify common, high-impact connections between diseases, referred to as hub chains.

resultsHere we show that distinct disease sequences are common, with 83 pathways identified in women and 174 in men. The combined cost of a disease sequence often exceeds the sum of its parts, in 69.9% (58/83) of female and 81.0% (141/174) of male trajectories, demonstrating a synergistic effect. Pathways involving cancer-related care show the highest resource use for both sexes. We find that a small number of critical hub chains connect many sequences; analyses indicate that interrupting these hubs could reduce associated healthcare use and costs by approximately half.

conclusionsThis study demonstrates that specific sequences of diseases, particularly those linked by hub chains, drive disproportionate healthcare burdens. Focusing clinical interventions and policy on these high-impact pathways offers a promising strategy to alleviate system strain and improve care for patients with multimorbidity.

Identifiers

PMID42082673
PMCPMC13346480

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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.