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Zhihui Jia,1 Zimin Niu,1 Jia Ji Wang,2,3 Jose Hernandez,4,5 Yu Ting Li,6,7 Harry HX Wang1,8,9 1School of Public Health, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2School of Public Health, Guangzhou Medical University, Guangzhou, People’s Republic of China; 3Centre for General Practice, The Seventh Affiliated Hospital, Southern Medical University, Foshan, People’s Republic of China; 4Faculty of Medicine and Health, EDU, Digital Education Holdings Ltd., Kalkara, Malta; 5Green Templeton College, University of Oxford, Oxford, UK; 6State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 7Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 8Department of General Practice, The Second Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 9JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR, People’s Republic of ChinaCorrespondence: Yu Ting Li, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, People’s Republic of China, Email liyuting3@mail.sysu.edu.cn Harry HX Wang, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China, Email haoxiangwang@163.com; haoxiangwang@cuhk.edu.hkBackground: 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, P < 0.001) or ≥ 4 LTCs (aOR = 1.97, 95% CI = 1.44– 2.72, P < 0.001) were more likely to belong to the high-increasing class. Sensitivity analysis using propensity score methods obtained similar results.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.Keywords: longitudinal trajectories, treatment burden, long-term conditions, multimorbidity, risk factors |
