Evidence map›Paper›PMID 39188662›Full record

ArticleRisk management and healthcare policy2024

Trajectories of Treatment Burden Among Primary Care Patients with Long-Term Conditions in Southern China: A Latent Class Growth Analysis.

Zhihui Jia, Zimin Niu, Jia Ji Wang, Jose Hernandez, Yu Ting Li, Harry H X Wang

Abstract read
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Article in Risk management and healthcare policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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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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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Measuring treatment burden with the treatment burden questionnaire (TBQ): a narrative literature review and implications for geriatric research and clinical practice.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Review
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zhihui JiaSchool of Public Health, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Zimin NiuSchool of Public Health, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Jia Ji WangSchool of Public Health, Guangzhou Medical University, Guangzhou, People's Republic of China.
Jose HernandezFaculty of Medicine and Health, EDU, Digital Education Holdings Ltd., Kalkara, Malta.
Yu Ting LiState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People's Republic of China.ORCID 0000-0002-6716-358X
Harry H X WangSchool of Public Health, Sun Yat-Sen University, Guangzhou, People's Republic of China.ORCID 0000-0002-0361-6527

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Treatment burden is a patient-centred, dynamic concept. However, longitudinal data on the changing pattern of treatment burden among patients with one or more long-term conditions (LTCs) are relatively scanty. We aimed to explore the longitudinal trajectories of treatment burden and associated risk factors in a large, patient population in primary care settings. Methods: We analysed data from 5573 primary care patients with long-term conditions (LTCs) recruited using a multistage sampling method in Shenzhen, southern China. The treatment burden was assessed by the Mandarin Chinese version of the Treatment Burden Questionnaire (TBQ). We used latent class growth mixture modelling (LCGMM) to determine trajectories of treatment burden across four time points, ie, at baseline, and at 6, 12, and 18 months. Predictors of trajectory classes were explored using multivariable logistic regression analysis. Results: The mean TBQ scores of patients with a single LTC (n = 2756), 2 LTCs (n = 1871), 3 LTCs (n = 699), and ≥4 LTCs (n = 247) were 18.17, 20.28, 21.32, and 26.10, respectively, at baseline. LCGMM identified three discrete classes of treatment burden trajectories over time, ie, a high-increasing class, a low-stable class, and a high-decreasing class. When controlling for individual-level factors including age, education, monthly household income per head, smoking, alcohol consumption, and attendance in health education, patients who had a clinical diagnosis of 3 LTCs (adjusted odds ratio [aOR] = 1.49, 95% CI = 1.21-1.86, Conclusion: Our study revealed the presence of discrete patterns of treatment burden over time in Chinese primary care patients with LTCs, providing directions for tailored interventions to optimise disease management. Patients with 3 or more LTCs should receive close attention in healthcare delivery as they tend to experience a greater treatment burden.

Indexed as

longitudinal trajectorieslong-term conditionsmultimorbidityrisk factorstreatment burden

Identifiers

PMID39188662
PMCPMC11346491

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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.