Evidence map›Paper›PMID 41908755›Full record

ArticleFrontiers in public health2026

Heterogeneity in longitudinal medication adherence patterns among patients with spinal tuberculosis: a latent class analysis using multi-timepoint follow-up data and associations with clinical outcomes.

Na Wang, Haijing Xiao, Yujuan Liu, Yawen Ma, Liping Wu, Qianqian Wang, Zheng Wang, Jine Chen, Xi Zhang

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Article in Frontiers in 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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5 · Who and what money

Authors and funding

9 authors.

Na Wang *The Second Department of Orthopaedic Trauma, General Hospital of Ningxia Medical University, Yinchuan, China.
Haijing Xiao *Outpatient Department of the People's Hospital of Ningxia Hui Autonomous Region, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China.
Yujuan LiuDepartment of Outpatient Services, General Hospital of Ningxia Medical University, Yinchuan, China.
Yawen MaThe Second Department of Orthopaedic Trauma, General Hospital of Ningxia Medical University, Yinchuan, China.
Liping WuThe Second Department of Orthopaedic Trauma, General Hospital of Ningxia Medical University, Yinchuan, China.
Qianqian WangDepartment of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, China.
Zheng WangThe Second Department of Orthopaedic Trauma, General Hospital of Ningxia Medical University, Yinchuan, China.
Jine ChenThe Second Department of Orthopaedic Trauma, General Hospital of Ningxia Medical University, Yinchuan, China.
Xi ZhangDepartment of Nursing, Cardio-Cerebrovascular Hospital, General Hospital of Ningxia Medical University, Yinchuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to explore the dynamic trajectory of medication adherence behavior and its influencing factors among patients with spinal tuberculosis within 6 months after discharge, providing a basis for developing individualized intervention strategies. Methods: A retrospective analysis was conducted using data from a longitudinal follow-up cohort, enrolling 117 spinal tuberculosis patients who underwent surgical treatment at the General Hospital of Ningxia Medical University between January 2024 and August 2025. Data were collected via telephone follow-up at 1 week, 1 month, 3 months, and 6 months after discharge, including general demographic information, scores from the Morisky Medication Adherence Questionnaire, Oswestry Disability Index (ODI), and laboratory indicators such as erythrocyte sedimentation rate and C-reactive protein levels. Group-based trajectory modeling (GBTM) was employed to identify subgroups of medication adherence trajectories, and multivariate logistic regression analysis was conducted to examine the influencing factors of these trajectory patterns. Results: Among 117 patients, group-based trajectory modeling identified three medication adherence trajectories: "persistently high adherence" (35.0%, Conclusions: Medication adherence behavior among spinal tuberculosis patients exhibits heterogeneous dynamic trajectories. Clinically, tailored interventions should be developed according to the characteristics and influencing factors of different trajectory subgroups, with particular attention to older patients, those with lower educational levels, those experiencing adverse drug reactions, and those lacking supervision, in order to improve medication adherence.

Indexed as

Antitubercular AgentsDrug MonitoringMedication AdherenceTuberculosis, SpinalAdultChinaFemaleFollow-Up StudiesHumansLongitudinal StudiesMaleMiddle AgedRetrospective StudiesAntitubercular Agentsdynamic trajectorygroup-based trajectory modelinfluencing factorsmedication adherencespinal tuberculosis

Identifiers

PMID41908755
PMCPMC13017939

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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.