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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Geriatr . 2026 Mar 4;26:497. doi: 10.1186/s12877-026-07268-2 Search in PMC Search in PubMed View in NLM Catalog Add to search Dynamic evolution of multimorbidity patterns and association with medication adherence in Chinese older adults: a longitudinal analysis using latent transition analysis and semi-Markov models Qian Liu Qian Liu 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Qian Liu 1, 2 , Shuzhi Lin Shuzhi Lin 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Shuzhi Lin 1, 2 , Lin Yin Lin Yin 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Lin Yin 1, 2 , Xiaoying Zhu Xiaoying Zhu 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Xiaoying Zhu 1, 2 , Wei Liu Wei Liu 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Wei Liu 1, 2 , Yifang Shen Yifang Shen 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Yifang Shen 1, 2 , Zimeng Li Zimeng Li 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Zimeng Li 1, 2 , Bianling Feng Bianling Feng 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China Find articles by Bianling Feng 1, 2, ✉ Author information Article notes Copyright and License information 1 The Department of Pharmacy Administration, School of Pharmacy, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China 2 The Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710061 China ✉ Corresponding author. Received 2025 Aug 29; Accepted 2026 Feb 25; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13067648 PMID: 41776461 Abstract Background Multimorbidity is prevalent among the elderly, with long-term medication use posing increasing adherence challenges. This study examines the evolution of multimorbidity patterns and their dynamic association with medication adherence to generate empirical evidence for the design of personalized treatment strategies and targeted health interventions for older adults with multimorbidity. Methods Using data from 2,798 elderly individuals with multimorbidity in the 2011–2020 China Health and Retirement Longitudinal Study, this study employs latent transition analysis and a semi-Markov model to assess adherence transitions across four multimorbidity patterns. Relative Transition Rates (RTRs) were used to quantify differences in adherence improvement and deterioration probabilities. Results The cardiovascular disease group remained relatively stable, with other patterns shifting toward it over time. Between 2013 and 2018, the incidence of adherence improvement (60.60%) was more frequent than deterioration (39.40%), especially in the multi-system disorders (62.08%) and respiratory diseases groups (67.59%), while deterioration was higher in the gastrointestinal metabolism (40.75%) and cardiovascular disease groups (39.64%). Compared to the multi-system disorders group, the gastrointestinal metabolism group was less likely to improve from low to moderate adherence (RTR = 0.63, 95% CI: 0.41–0.98), while both the gastrointestinal metabolism (RTR = 4.45, 95% CI: 1.92–10.18) and cardiovascular disease groups (RTR = 2.57, 95% CI: 1.07–6.11) had higher risks of declining from high to moderate adherence. Conclusions Cardiovascular diseases appear increasingly central in elderly multimorbidity, with gastrointestinal metabolism and respiratory conditions potentially serving as early or coexisting risk factors. Early cardiovascular risk monitoring may be beneficial in these groups, while long-term adherence support remains important for gastrointestinal and cardiovascular patterns. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-026-07268-2. Keywords: Multimorbidity, Medication adherence, Aging population, Latent transition analysis, Semi-Markov model Introduction Population aging has become one of the most pressing global social challenges. By 2050, the number of people aged 60 years and above is projected to double to 2.1 billion, with 80% residing in low- and middle-income countries [ 1 ]. As one of the fastest-aging nations, China had 310 million elderly individuals (22% of the total population) at the end of 2024, far exceeding the global average [ 2 , 3 ]. This rapid aging not only increases the burden on public policy and social security but also poses unprecedented demands on healthcare resource allocation and elderly health management. Multimorbidity is a critical challenge in the context of population aging, referring to the coexistence of two or more chronic conditions [ 4 ]. It affects over half of the elderly population and is a major risk factor for high mortality, functional decline, and rising healthcare costs [ 5 , 6 ]. China has the largest elderly population and 76% of older adults in China have at least one chronic disease [ 7 ]. Since certain disease patterns occur more frequently than random disease combinations, identifying multimorbidity patterns can help clinicians to better assess multimorbidity risks and facilitate targeted prevention and intervention efforts [ 8 ]. Multimorbidity patterns vary across countries [ 9 ] and, in China, common multimorbidity types include cardiovascular, respiratory, and digestive system diseases [ 10 , 11 ]. These chronic conditions often interact with one another and exhibit dynamic changes over time, complicating treatment strategies and posing challenges for long-term health management [ 12 ]. The clinical complexity of multimorbidity often requires older adults to take multiple medications to manage each condition. This polypharmacy burden may reduce medication adherence, impair disease control, and ultimately a vicious cycle of worsening multimorbidity and health outcomes [ 13 , 14 ]. Improving adherence is therefore essential to breaking this cycle and enhancing chronic disease management. Although numerous studies have reported that more than half of older adults with multimorbidity fail to adhere to prescribed medications, most of this research has been conducted in high-income countries [ 15 , 16 ]. In contrast, evidence from China remains scarce, particularly concerning the relationship between multimorbidity and medication adherence [ 14 ]. Both multimorbidity patterns and medication adherence are dynamic. However, existing literature primarily focuses on cross-sectional effects of specific diseases or disease combinations, overlooking the long-term interplay between medication adherence and multimorbidity evolution [ 15 , 17 ]. Higher disease burden, including a greater number of disease types and greater severity, often correlates with lower medication adherence [ 18 ]. Poor medication adherence is also linked to higher hospitalization and mortality rates, especially among elderly patients with multimorbidity [ 19 ]. Recent evidence also suggests that different multimorbidity patterns may be associated with varying levels of medication adherence, possibly due to differences in treatment complexity, symptom profiles, and disease perception [ 20 ]. However, most existing studies focus on short-term medication adherence and pay limited attention to longitudinal changes—particularly how specific multimorbidity patterns are associated with evolving adherence behavior, especially in low- and middle-income countries [ 12 , 21 ]. Building on previous research [ 22 ], in this study, we utilize national longitudinal data to track the temporal evolution of multimorbidity patterns among the Chinese elderly using Latent Transition Analysis (LTA). Additionally, we utilize a Semi-Markov Multi-State Model (SMM) to reveal and quantify the dynamic association between medication adherence and multimorbidity transitions. By doing so, we aim to generate robust empirical evidence to inform the design of more personalized treatment strategies and targeted health interventions for older adults with multimorbidity—particularly in response to the growing challenges posed by rapid population aging. Methods Study design and data sources This study utilizes data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey targeting Chinese adults aged 45 years and older. CHARLS collects data on quality of life, socioeconomic status, and health through face-to-face interviews, structured questionnaires, and physical measurements [ 23 ]. CHARLS has tracked over 17,000 participants across five waves (2011, 2013, 2015, 2018, 2020). Peking University Biomedical Ethics Committee approved the study (IRB00001052-11015). All participants provided informed consent. The study included elderly participants (age ≥ 60 years) with at least two chronic diseases who participated in all five survey waves. Exclusion criteria were: (1) baseline age < 60 years; (2) missing follow-up data or deceased; (3) missing values in the chronic disease survey; (4) only one chronic disease across all survey waves. In the final analysis, 2,798 participants were included (see flowchart in Appendix 1). Measures Multimorbidity Consistent with previous studies [ 24 ], multimorbidity was measured using 14 self-reported diseases, including hypertension, dyslipidemia, diabetes, cancer, chronic lung diseases, liver diseases, heart diseases, stroke, kidney diseases, stomach diseases, emotional and mental disorders, memory-related diseases, arthritis, and asthma. Multimorbidity was defined as the presence of at least two of these conditions [ 10 ]. Multimorbidity status at each survey wave was assessed based on all conditions reported at that wave. Medication adherence Medication adherence refers to the extent to which individuals follow prescribed treatments, including medication use, diet, and lifestyle changes [ 25 ]. In this study, adherence was assessed using standardized self-reported questions from the CHARLS health survey. For each reported chronic condition, participants were asked whether they were taking prescribed medications or using other disease-specific treatment approaches to manage the condition. Each treatment option was explicitly described in the questionnaire to ensure consistent understanding across respondents. Responses were recorded in a binary format (yes = 1, no = 0). Each participant’s total adherence score was calculated by summing adherence scores across all reported diseases and standardized by dividing by the number of diseases (range: 0–1). To classify adherence levels, thresholds were defined based on the quartile distribution of standardized scores. Notably, the 75th percentile of standardized adherence scores was 1, indicating that participants above this threshold were almost uniformly highly adherent. To avoid creating a separate category for those with near-perfect scores, this threshold was incorporated as a natural extension of the high adherence group rather than used as an independent cutoff. The final adherence levels were : (1) low (standardized score ≤ 0.33); (2) moderate (standardized score 0.34 to 0.67 inclusive); (3) high (standardized score > 0.67). To ensure consistency in adherence measurement over time, we focused on survey waves with complete and comparable adherence information. Medication adherence data were largely missing in CHARLS 2011 and 2020; therefore, we analyzed adherence levels from 2013 to 2018, linking them with multimorbidity groups to compare adherence transitions across groups during the mid-phase of the study period. Additionally, due to missing adherence scores for asthma patients in the CHARLS dataset, we assumed that adherence behavior among individuals with asthma was highly consistent with that of patients with chronic lung diseases. This assumption was made to ensure data completeness and preserve analytical integrity. To validate the plausibility of this substitution, we first examined the correlation between the prevalence of asthma and chronic lung diseases, and found a significant and strong association between their prevalence rates (Appendix 2). Furthermore, existing literature suggests that asthma and chronic lung diseases belong to the respiratory diseases multimorbidity group and share similarities in clinical treatment, patient management, and adherence behavior [ 26 – 28 ]. Therefore, substituting asthma adherence scores with chronic lung disease adherence scores was considered scientifically reasonable. Associated factors Based on literature reviews and previous research, factors potentially associated with different multimorbidity groups were classified into five categories: demographic; lifestyle and health; medication-related; healthcare utilization; community service utilization [ 11 , 22 , 29 ]. Classifications are in Appendix 3. Statistical analysis Categorical variables were summarized using frequencies and percentages. Continuous variables were summarized as means ± standard deviations (SDs). Chi-square tests for categorical variables, and one-way ANOVAs for continuous variables, were conducted to compare baseline characteristics across multimorbidity groups. In our prior work, we applied latent class analysis to the CHARLS 2020 data using the same inclusion criteria and classification process, and identified four distinct multimorbidity patterns among 2,798 older adults: multi-system disorders ( N = 289, 10.33%); gastrointestinal metabolism ( N = 1,233, 44.07%); cardiovascular diseases ( N = 1,058, 37.81%); respiratory diseases ( N = 218, 7.79%) [ 22 ]. This classification was validated and consistently applied across all five CHARLS waves. Further details are provided in Appendices 4 and 5. To maintain consistency with our previous research, the 2020 CHARLS wave was used as the baseline for multimorbidity classification. Some missing variables were supplemented using the most recent available data to ensure analytic completeness and comparability. Building on this, the current study employed LTA to estimate transition probability matrices across successive time points and to model the longitudinal trajectories of multimorbidity patterns. In addition to identifying latent groups and their prevalence, LTA enables the assessment of individual-level transitions between multimorbidity patterns over time, offering a dynamic perspective on how health states evolve in older adults [ 30 ]. To ensure consistency in latent class structures across the five waves, we assumed measurement invariance (i.e., the construction and structure of latent classes remained equal over time) [ 31 ]. Multiple LCA models were tested across the five survey waves, and model fit indices such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were compared. The results supported the consistency of latent class structures over time, ensuring the validity of cross-wave comparisons in subsequent analyses. SMM was further applied to analyze the transition processes between multimorbidity patterns and adherence levels over time in the elderly population. Unlike traditional Markov models, SMMs use a “clock-reset” approach, which accounts for state-specific sojourn time distributions thus enhancing biological plausibility [ 32 ]. First, a multi-state medication adherence model allowing transitions was constructed (Fig. 1 ). Participants at different adherence levels in each wave could either remain in the same state or transition to another state in the next wave. Fig. 1. Open in a new tab Permitted transition multi-state model of medication adherence To capture the overall trends in adherence transitions from 2013 to 2018, we first calculated the number of adherence level changes within each multimorbidity group. Subsequently, taking the multi-system disorders group as the reference, model coefficients were exponentiated to derive Relative Transition Rates (RTR), which quantified both the strength and direction of each group’s influence on adherence changes. An RTR > 1 indicates that the probability of transitioning to a specific adherence level increases under that multimorbidity group. An RTR < 1 indicates a decrease. A P value < 0.05 indicates a significant effect. By examining the magnitude and statistical significance of the RTRs, we were able to identify which multimorbidity groups had a stronger influence on either the improvement or deterioration of medication adherence. R (Version 4.1.1) and IBM SPSS (Version 26.0) were used for analyses. A two-sided P < 0.05 was considered statistically significant. Results Participant characteristics Table 1 presents the baseline characteristics of the 2,798 participants. Most were women (53.22%), lived in rural areas (66.51%), and were aged 70–79 years (61.83%). The mean age was 75.31 years (SD = 5.48). Around half (46.96%) achieved high adherence, 8.93% reported difficulty in self-administering medication, and 69.26% could self-administer but required assistance from family or caregivers. Self-medication was common (66.83%), with an average expenditure of ¥194.74 (SD = 494.64). During the 2019 coronavirus pandemic, 10.36% had unmet medical needs and 2.93% faced difficulties obtaining medication. Additionally, 73.34% had not accessed any community-based elderly care services, and 94.50% had not enrolled in paid family doctor services. Table 1. Sample characteristics ( n = 2,798) Variables Total (%) Multi-system disorders group(10.33%) Gastrointestinal metabolism group(44.07%) Cardiovascular diseases group(37.81%) Respiratory diseases group (7.79%) P Sex, n (%) a < 0.001 Female 1489 (53.22%) 150 (51.90%) 660 (53.53%) 600 (56.71%) 79 (36.24%) Male 1309 (46.78%) 139 (48.10%) 573 (46.47%) 458 (43.29%) 139 (63.76%) Region, n (%) a < 0.001 Eastern 1032 (36.90%) 97 (33.56%) 417 (33.82%) 454 (42.91%) 64 (29.36%) Central 858 (30.70%) 88 (30.45%) 352 (28.55%) 337 (31.85%) 81 (37.16%) Western 908 (32.50%) 104 (35.99%) 464 (37.63%) 267 (25.24%) 73 (33.49%) Residence, n (%) a 0.001 Urban 937 (33.49%) 96 (33.22%) 368 (29.85%) 400 (37.81%) 73 (33.49%) Rural 1861 (66.51%) 193 (66.78%) 865 (70.15%) 658 (62.19%) 145 (66.51%) Age, n (%) a 0.010 60–69 593 (21.19%) 66 (22.84%) 287 (23.28%) 209 (19.75%) 31 (14.22%) 70–79 1730 (61.83%) 186 (64.36%) 725 (58.80%) 674 (63.71%) 145 (66.51%) ≥ 80 475 (16.98%) 37 (12.80%) 221 (17.92%) 175 (16.54%) 42 (19.27%) BMI (kg/m 2 ), n (%) a < 0.001 Underweight 252 (9.01%) 17 (5.88%) 143 (11.60%) 56 (5.29%) 36 (16.51%) Normal 1357 (48.50%) 131 (45.33%) 699 (56.69%) 406 (38.37%) 121 (55.50%) Overweight 830 (29.66%) 74 (25.61%) 294 (23.84%) 413 (39.04%) 49 (22.48%) Obese 359 (12.83%) 67 (23.18%) 97 (7.87%) 183 (17.30%) 12 (5.50%) Education level, n (%) a < 0.001 No education 941 (33.63%) 77 (26.64%) 465 (37.71%) 348 (32.89%) 51 (23.39%) Primary 1350 (48.25%) 142 (49.13%) 586 (47.53%) 500 (47.26%) 122 (55.96%) Secondary 399 (14.26%) 58 (20.07%) 140 (11.35%) 164 (15.50%) 37 (16.97%) Vocational 92 (3.29%) 11 (3.81%) 36 (2.92%) 39 (3.69%) 6 (2.75%) University and above 16 (0.57%) 1 (0.35%) 6 (0.49%) 7 (0.66%) 2 (0.92%) Marital status, n (%) a 0.948 Single 878 (31.38%) 94 (32.53%) 390 (31.63%) 327 (30.91%) 67 (30.73%) Married/cohabiting 1920 (68.62%) 195 (67.47%) 843 (68.37%) 731 (69.09%) 151 (69.27%) Occupation, n (%) a < 0.001 Farmer 1071 (38.28%) 92 (31.83%) 568 (46.07%) 350 (33.08%) 61 (27.98%) Non-farmer 83 (2.97%) 4 (1.38%) 47 (3.81%) 27 (2.55%) 5 (2.29%) Unemployed/retired 1644 (58.76%) 193 (66.78%) 618 (50.12%) 681 (64.37%) 152 (69.72%) Household income, n (%) a < 0.001 Low 697 (24.91%) 61 (21.11%) 327 (26.52%) 252 (23.82%) 57 (26.15%) Lower-middle 702 (25.09%) 62 (21.45%) 341 (27.66%) 232 (21.93%) 67 (30.73%) Upper-middle 700 (25.02%) 73 (25.26%) 302 (24.49%) 282 (26.65%) 43 (19.72%) High 699 (24.98%) 93 (32.18%) 263 (21.33%) 292 (27.60%) 51 (23.39%) Health insurance, n (%) a < 0.001 Employee insurance 387 (13.83%) 52 (17.99%) 123 (9.98%) 185 (17.49%) 27 (12.39%) Resident insurance 2317 (82.81%) 227 (78.55%) 1070 (86.78%) 835 (78.92%) 185 (84.86%) Other insurance 94 (3.36%) 10 (3.46%) 40 (3.24%) 38 (3.59%) 6 (2.75%) Medication adherence, n (%) a < 0.001 Low adherence 546 (19.51%) 22 (7.61%) 355 (28.79%) 137 (12.95%) 32 (14.68%) Moderate adherence 938 (33.52%) 92 (31.83%) 415 (33.66%) 382 (36.11%) 49 (22.48%) High adherence 1314 (46.96%) 175 (60.55%) 463 (37.55%) 539 (50.94%) 137 (62.84%) Difficulty in self-administering medication, n (%) a < 0.001 Yes 250 (8.93%) 32 (11.07%) 80 (6.49%) 123 (11.63%) 15 (6.88%) No 2548 (91.07%) 257 (88.93%) 1153 (93.51%) 935 (88.37%) 203 (93.12%) Assistance with medication intake, n (%) a 0.003 Yes 1938 (69.26%) 184 (63.67%) 831 (67.40%) 774 (73.16%) 149 (68.35%) No 860 (30.74%) 105 (36.33%) 402 (32.60%) 284 (26.84%) 69 (31.65%) Self-medication status, n (%) a < 0.001 Yes 1870 (66.83%) 232 (80.28%) 768 (62.29%) 715 (67.58%) 155 (71.10%) No 928 (33.17%) 57 (19.72%) 465 (37.71%) 343 (32.42%) 63 (28.90%) OOPE for self-medication (yuan), mean ± SD b 194.74 ± 494.64 310.85 ± 715.78 148.66 ± 332.92 213.10 ± 586.54 212.37 ± 369.66 < 0.001 Self-rated health status, n (%) a < 0.001 Good 400 (14.30%) 21 (7.27%) 189 (15.33%) 165 (15.60%) 25 (11.47%) Fair 1330 (47.53%) 111 (38.41%) 635 (51.50%) 500 (47.26%) 84 (38.53%) Poor 1068 (38.17%) 157 (54.33%) 409 (33.17%) 393 (37.15%) 109 (50.00%) Body pain, n (%) a < 0.001 None 960 (34.31%) 43 (14.88%) 416 (33.74%) 429 (40.55%) 72 (33.03%) Some 1074 (38.38%) 103 (35.64%) 513 (41.61%) 377 (35.63%) 81 (37.16%) Moderate 764 (27.31%) 143 (49.48%) 304 (24.66%) 252 (23.82%) 65 (29.82%) Sleep duration, n (%) a 0.001 < 4 h 760 (27.16%) 99 (34.26%) 353 (28.63%) 246 (23.25%) 62 (28.44%) 4–6 h 1004 (35.88%) 111 (38.41%) 431 (34.96%) 384 (36.29%) 78 (35.78%) 6–8 h 721 (25.77%) 61 (21.11%) 310 (25.14%) 301 (28.45%) 49 (22.48%) ≥ 8 h 313 (11.19%) 18 (6.23%) 139 (11.27%) 127 (12.00%) 29 (13.30%) Physical activity, n (%) a < 0.001 Vigorous activity 659 (23.55%) 66 (22.84%) 371 (30.09%) 184 (17.39%) 38 (17.43%) Moderate activity 722 (25.80%) 79 (27.34%) 324 (26.28%) 274 (25.90%) 45 (20.64%) Light activity 888 (31.74%) 98 (33.91%) 343 (27.82%) 363 (34.31%) 84 (38.53%) No activity 529 (18.91%) 46 (15.92%) 195 (15.82%) 237 (22.40%) 51 (23.39%) Social participation, n (%) a 0.664 Yes 1202 (42.96%) 134 (46.37%) 522 (42.34%) 453 (42.82%) 93 (42.66%) No 1596 (57.04%) 155 (53.63%) 711 (57.66%) 605 (57.18%) 125 (57.34%) Smoking status, n (%) a < 0.001 Yes 1255 (44.85%) 145 (50.17%) 557 (45.17%) 410 (38.75%) 143 (65.60%) No 1543 (55.15%) 144 (49.83%) 676 (54.83%) 648 (61.25%) 75 (34.40%) Alcohol consumption, n (%) a 0.005 Yes 813 (29.06%) 84 (29.07%) 397 (32.20%) 269 (25.43%) 63 (28.90%) No 1985 (70.94%) 205 (70.93%) 836 (67.80%) 789 (74.57%) 155 (71.10%) Functional status, n (%) a < 0.001 Normal 1705 (60.94%) 139 (48.10%) 806 (65.37%) 638 (60.30%) 122 (55.96%) Mildly impaired 704 (25.16%) 83 (28.72%) 299 (24.25%) 253 (23.91%) 69 (31.65%) Moderately impaired 226 (8.08%) 41 (14.19%) 82 (6.65%) 90 (8.51%) 13 (5.96%) Severely impaired 163 (5.83%) 26 (9.00%) 46 (3.73%) 77 (7.28%) 14 (6.42%) Depression, n (%) a < 0.001 Yes 1321 (47.21%) 180 (62.28%) 562 (45.58%) 474 (44.80%) 105 (48.17%) No 1477 (52.79%) 109 (37.72%) 671 (54.42%) 584 (55.20%) 113 (51.83%) Cognitive function, n (%) a 0.434 Normal cognition 1870 (66.83%) 181 (62.63%) 826 (66.99%) 717 (67.77%) 146 (66.97%) Cognitive impairment 928 (33.17%) 108 (37.37%) 407 (33.01%) 341 (32.23%) 72 (33.03%) Life satisfaction, n (%) a < 0.001 Satisfied 1054 (37.67%) 80 (27.68%) 445 (36.09%) 445 (42.06%) 84 (38.53%) Fair 1424 (50.89%) 154 (53.29%) 653 (52.96%) 504 (47.64%) 113 (51.83%) Dissatisfied 320 (11.44%) 55 (19.03%) 135 (10.95%) 109 (10.30%) 21 (9.63%) Outpatient service use, n (%) a < 0.001 Yes 517 (18.48%) 89 (30.80%) 210 (17.03%) 178 (16.82%) 40 (18.35%) No 2281 (81.52%) 200 (69.20%) 1023 (82.97%) 880 (83.18%) 178 (81.65%) Number of outpatient visits, mean ± SD b 0.41 ± 1.36 0.77 ± 1.73 0.34 ± 1.23 0.39 ± 1.44 0.37 ± 1.07 < 0.001 OOPE for outpatient (yuan), mean ± SD b 180.38 ± 1232.56 210.80 ± 690.44 210.33 ± 1621.60 155.86 ± 895.13 89.61 ± 315.21 0.475 OOPE for the most recent outpatient visit and medication purchase (yuan), mean ± SD b 135.42 ± 1063.22 146.70 ± 552.64 158.92 ± 1403.26 118.31 ± 770.56 70.60 ± 290.87 0.633 Inpatient service use, n (%) a < 0.001 Yes 699 (24.98%) 120 (41.52%) 238 (19.30%) 258 (24.39%) 83 (38.07%) No 2099 (75.02%) 169 (58.48%) 995 (80.70%) 800 (75.61%) 135 (61.93%) Number of inpatient visits, mean ± SD b 0.41 ± 0.92 0.84 ± 1.42 0.28 ± 0.70 0.40 ± 0.92 0.61 ± 0.99 < 0.001 OOPE for inpatient (yuan), mean ± SD b 2033.53 ± 9137.33 3919.32 ± 15974.44 1541.03 ± 7494.55 2035.39 ± 8420.53 2310.12 ± 8185.99 0.001 Unmet medical needs during the 2019 coronavirus pandemic, n (%) a < 0.001 Yes 290 (10.36%) 67 (23.18%) 95 (7.70%) 95 (8.98%) 33 (15.14%) No 2508 (89.64%) 222 (76.82%) 1138 (92.30%) 963 (91.02%) 185 (84.86%) Impact on medication access during the 2019 coronavirus pandemic, n (%) a < 0.001 Yes 82 (2.93%) 28 (9.69%) 25 (2.03%) 25 (2.36%) 4 (1.83%) No 2716 (97.07%) 261 (90.31%) 1208 (97.97%) 1033 (97.64%) 214 (98.17%) Community-based elderly care services, n (%) a Care centers 19 (0.68%) 2 (0.69%) 9 (0.73%) 7 (0.66%) 1 (0.46%) 0.976 Regular check-ups 646 (23.09%) 63 (21.80%) 273 (22.14%) 256 (24.20%) 54 (24.77%) 0.578 Home visits 124 (4.43%) 16 (5.54%) 52 (4.22%) 45 (4.25%) 11 (5.05%) 0.743 Home care beds 5 (0.18%) 1 (0.35%) 2 (0.16%) 2 (0.19%) 0 (0.00%) 0.833 Community nursing 19 (0.68%) 4 (1.38%) 4 (0.32%) 11 (1.04%) 0 (0.00%) 0.047 Health management 62 (2.22%) 8 (2.77%) 28 (2.27%) 23 (2.17%) 3 (1.38%) 0.767 Recreational activities 66 (2.36%) 7 (2.42%) 35 (2.84%) 22 (2.08%) 2 (0.92%) 0.313 None 2052 (73.34%) 213 (73.70%) 909 (73.72%) 774 (73.16%) 156 (71.56%) 0.923 Paid family doctor services, n (%) a 0.070 Yes 154 (5.50%) 16 (5.54%) 54 (4.38%) 73 (6.90%) 11 (5.05%) No 2644 (94.50%) 273 (94.46%) 1179 (95.62%) 985 (93.10%) 207 (94.95%) Satisfaction with local healthcare, n (%) a 0.009 Satisfied 1212 (43.32%) 116 (40.14%) 527 (42.74%) 476 (44.99%) 93 (42.66%) Fair 1125 (40.21%) 105 (36.33%) 526 (42.66%) 410 (38.75%) 84 (38.53%) Dissatisfied 461 (16.48%) 68 (23.53%) 180 (14.60%) 172 (16.26%) 41 (18.81%) Open in a new tab Note: Data in bold indicate a p -value < 0.05 a Categorical variables were tested using the Pearson chi-square test b Continuous variables were tested using one-way analysis of variance (ANOVA) Sample characteristics varied significantly across multimorbidity groups. The multi-system disorders group had the poorest health status, greater pain and depression rates, greater medical needs and healthcare utilization, and greater adherence. The gastrointestinal metabolism group had lower education and income levels, lowest adherence, greater alcohol consumption in some cases, and less reliance on healthcare resources. The cardiovascular diseases group had the highest proportion of women, along with a greater prevalence of medication-taking difficulties and higher reliance on family members or caregivers for assistance. The respiratory diseases group had a greater proportion of men and the greatest smoking rate. Latent transition model The transition probabilities represent the estimated percentage of participants who shift between, or remain in, multimorbidity groups over time (Appendix 6) [ 33 ]. From 2011 to 2013, the gastrointestinal metabolism group was the most stable (78.80%), while the cardiovascular diseases group primarily transitioned to the multi-system disorders group (41.20%; Fig. 2 ). The multi-system disorders group mainly shifted to cardiovascular diseases (34.40%) and gastrointestinal metabolism (27.20%) groups, while the respiratory diseases group was the least stable (9.80% retention), with most transitioning to cardiovascular diseases (39.70%). Between 2013 and 2015, stability decreased in both the multi-system disorders (13.90%) and gastrointestinal metabolism (27.30%) groups, with a substantial shift toward cardiovascular diseases (40.80% and 40.60%, respectively). The respiratory diseases group became much more stable between 2013 and 2015 (63.40% retention). Fig. 2. Open in a new tab The percentage in each of the four classes and transition probabilities from 2011 to 2020 From 2015 to 2018, the cardiovascular diseases group was the most stable (58.90%) and absorbed a large proportion of participants from other groups (multi-system disorders: 39.90%; gastrointestinal metabolism: 61.20%; respiratory diseases: 44.20%; Fig. 2 ). Between 2018 and 2020, the cardiovascular diseases group remained stable (54.00%), while the multi-system disorders group was the most dynamic, with only 8.90% retention. Most transitioned to the cardiovascular diseases (50.20%) or gastrointestinal metabolism (31.40%) groups. Gastrointestinal metabolism group stability improved (38.10%), though many moved to cardiovascular diseases (39.10%). The respiratory diseases group remained relatively stable, similar to the previous period. Semi-Markov model A total of 2,863 adherence transitions occurred between 2013 and 2018, with 1,735 (60.60%) indicating improvement and 1,128 (39.40%) indicating decline (Fig. 3 ). The multi-system disorders (62.08%) and respiratory diseases (67.59%) groups had the highest adherence improvement rates, similar to the cardiovascular diseases group (28.27%), where improvement mainly involved moderate to high adherence. In contrast, improvement in the gastrointestinal metabolism group was primarily driven by low to moderate adherence (21.94%) or high adherence (19.38%). The gastrointestinal metabolism (40.75%) and cardiovascular diseases (39.64%) groups exhibited a comparatively greater tendency toward adherence decline. Across all multimorbidity groups, the most common decline was from high to moderate adherence. Fig. 3. Open in a new tab Observed transitions in adherence levels across multimorbidity groups The percentages in the “total” column are calculated based on the total number of transitions ( N = 2,863) to provide an overall comparison across adherence levels. For each multimorbidity group, the percentages are calculated based on the total number of transitions within that specific group, ensuring a more accurate reflection of adherence changes within each group. Arrows represent the directional changes in adherence levels among different multimorbidity groups Using the multi-system disorders group as the reference, RTRs were calculated for adherence transitions across different multimorbidity groups (Fig. 4 ). For adherence improvement, the gastrointestinal metabolism group had a significantly lower probability of moving from low to moderate adherence (RTR = 0.63, 95% CI: 0.41–0.98). For adherence deterioration, both the gastrointestinal metabolism and cardiovascular diseases groups had a higher risk of decline, particularly from high-to-low adherence (gastrointestinal metabolism: RTR = 4.45, 95% CI: 1.92–10.18; cardiovascular disease: RTR = 2.57, 95% CI: 1.07–6.11). No significant differences were observed in the respiratory diseases group. Fig. 4. Open in a new tab Relative Transition Rates (RTR) of adherence levels across multimorbidity groups Bold values indicate statistically significant transitions compared to the reference group (multi-system disorders group). Discussion This is the first study to longitudinally and dynamically examine the evolution of multimorbidity groups and their interaction with medication adherence among Chinese older adults. This study reveals the evolving patterns of multimorbidity among older adults and also provides empirical support for optimizing adherence management strategies. Using five waves of CHARLS data from 2011 to 2020, we found that the cardiovascular diseases group was the most stable, with other groups tending to shift toward it over time. Overall, the proportion of adherence improvement exceeded that of deterioration, though the gastrointestinal metabolism and cardiovascular diseases groups had a significantly higher risk of adherence decline compared to the multi-system disorders group. Among 2,798 older adults, the multi-system disorders group had the highest disease burden and relatively high medication adherence. In contrast, the gastrointestinal metabolism group, who had lower average income and education levels, had the lowest medication adherence. This disparity may stem from multiple factors, including perceived disease severity, healthcare accessibility, economic burden, and health literacy. Patients with more complex conditions or severe symptoms tend to prioritize medication and healthcare services [ 20 ], but limited drug affordability and inadequate health education can reduce adherence [ 25 ]. Notably, 66.83% of participants reported self-medication. Inappropriate self-medication is common worldwide, particularly in developing countries where older adults often lack adequate medication literacy, increasing the risk of misuse and related harm [ 34 ]. Nearly 70% of participants required external assistance or supervision for medication adherence, with cardiovascular disease patients being the most affected. This dependence on caregiver support heightened vulnerability during the pandemic, when healthcare service disruptions further hindered chronic disease management. Additionally, only a small proportion of participants had access to community-based elderly care or family doctor services, highlighting insufficient coverage and the need to strengthen primary healthcare infrastructure—including home-based care, telemedicine, and regular follow-ups—to improve medication safety and chronic disease management among older adults [ 35 ]. Early transition probability results from 2011 to 2013 showed that the gastrointestinal metabolism group was relatively stable, possibly related to the slower progression of metabolic diseases [ 36 ]. Over time, the cardiovascular diseases group became increasingly stable and emerged as the most absorptive multimorbidity group. This trend may be explained by two key mechanisms. First, chronic respiratory conditions have been associated with systemic low-grade inflammation, endothelial dysfunction, and autonomic dysregulation, which may increase the risk of arterial sclerosis and subsequent cardiac impairment [ 37 ]. Second, oxidative stress, gut microbiota imbalance, and chronic inflammation, which are commonly observed in liver and kidney diseases, may impair vascular function and increase the risk of cardiovascular disease [ 38 ]. Therefore, early screening and intervention for respiratory and gastrointestinal metabolism-related diseases may help reduce cardiovascular diseases and mortality [ 39 ]. As multi-system disorder patients in our study aged, they often transitioned into more dominant cardiovascular disease trajectories. This is consistent with prior studies suggesting that gastrointestinal metabolism and respiratory diseases may co-occur with and increase cardiovascular risk [ 40 ]. Together, these findings underscore the importance of continued clinical attention to cardiovascular risk, especially when multiple organ dysfunctions and comorbidities co-occur in older adults. There was an improvement in adherence between 2013 and 2018, particularly among the multi-system disorders and respiratory diseases groups. However, patients in the gastrointestinal metabolism group were significantly less likely to improve from low to moderate adherence compared to those in the multi-system disorders group. Additionally, similar to the cardiovascular disease group, they faced high risk of declining from high to low adherence, suggesting that even those with initially good adherence may be more likely to experience decline in these two patterns. One possible explanation is that early-stage or post-event patients (e.g., after a heart attack) may prioritize treatment, but as their condition stabilizes, a false sense of control, treatment fatigue, or medication cost burden may develop, leading to reduced adherence and irregular follow-ups [ 41 , 42 ]. For example, statin adherence declines as the time since a cardiovascular event increases, with a 75% discontinuation rate after two years [ 43 ]. Similarly, diabetes patients who lack ongoing education and follow-up support may ease dietary and medication management once their blood glucose levels improve [ 44 ]. Patients in the gastrointestinal metabolism group with low adherence had greater difficulty with improving their medication adherence compared to those with multi-system disorders. This may be due to more entrenched barriers, such as difficulty in lifestyle changes, poor medication awareness, or the burden of multimorbidity management, which might persist without targeted interventions and can reduce medication adherence [ 45 ]. Therefore, clinicians and public health professionals may need to consider not only patients with severe symptoms but also those with long-term metabolic and cardiovascular risks, even if their conditions appear stable. Sustained health education, medication management, and regular monitoring (e.g., monthly phone calls or outpatient visits) may be beneficial for early detection and correction of adherence issues. This study addresses several important gaps in the existing literature. First, prior research on multimorbidity and medication adherence has largely relied on cross-sectional designs or static classifications, providing limited insight into how multimorbidity patterns and adherence behaviors evolve over time. Second, few studies have explicitly examined heterogeneity in adherence transitions across different multimorbidity profiles in older adults, particularly in low- and middle-income settings such as China. The key methodological novelty of this study lies in the integrated use of LTA and SMM to characterize longitudinal trajectories of multimorbidity patterns and to quantify differences in medication adherence transitions across these patterns. Unlike previous static approaches, this framework allows us to model dynamic health states and adherence changes simultaneously, thereby capturing the temporal complexity of multimorbidity and treatment behavior. From a clinical and scientific perspective, our findings demonstrate that adherence trajectories vary substantially across different multimorbidity patterns, suggesting that adherence behavior differs not only by the number of conditions but also by the structure of disease clustering. By using the multi-system disorders group as a reference, we further quantified relative differences in adherence improvement and decline across patterns, providing empirical support for identifying high-risk populations and designing targeted interventions. Leveraging nationally representative, multi-wave data from CHARLS enhances the robustness and population relevance of these findings, which may inform more targeted adherence monitoring and stratified intervention strategies for older adults with multimorbidity in China. Limitations should be noted. First, self-reported data may be subject to recall bias or social desirability effects, potentially affecting the accuracy of multimorbidity and adherence assessments. While adherence was based on a single binary item per condition, this simplified approach enabled standardized scoring across multiple diseases and facilitated comparison of adherence transitions across multimorbidity patterns over time, rather than capturing short-term dosing behavior. Similar simplified or aggregated adherence measures have been widely used in population-based aging studies using CHARLS and other nationally representative cohorts when the research focus is on population-level or pattern-level adherence differences rather than clinical dosing behavior [ 46 – 48 ]. Future studies could incorporate validated adherence scales or objective indicators to improve measurement precision and better account for differences in health literacy. Second, the study did not examine specific medication types, dosages, or regimen complexity and impact on adherence. Future research could incorporate electronic health records, insurance claims data, and psychological assessments to refine adherence-related factors. Third, the analytic sample was restricted to older adults who survived and continuously participated across survey waves. This cohort design may introduce survivor bias and participation-related bias, as healthier and more engaged individuals are more likely to remain in long-term follow-up. As a result, the generalizability of the findings to the broader older population may be limited. However, this restriction was necessary to ensure the validity of longitudinal trajectory and transition modeling. Future studies could address this limitation by applying weighting strategies, sensitivity analyses, or complementary study designs to assess the robustness of findings in more representative populations. Fourth, adherence analyses were limited to the years between 2013 and 2018, potentially restricting insights into long-term adherence trends. However, this period aligns with a relatively dynamic phase of multimorbidity transitions, during which substantial shifts between patterns were observed. Therefore, it provides a meaningful window to explore the differences in adherence across different multimorbidity patterns. Since the latent class structure of multimorbidity patterns was held constant and validated across all five waves, the shorter adherence analysis period does not compromise the consistency or comparability of the disease trajectory modeling. Future studies could apply predictive modeling or integrate other data sources to extend adherence tracking over a longer time horizon. At the same time, future studies could further explore the causal pathways between multimorbidity transitions and adherence changes, which would provide deeper insights into the evolution of adherence behaviors. Fifth, although the 2020 CHARLS wave was included to maintain longitudinal consistency in identifying multimorbidity patterns, it is important to note that the COVID-19 pandemic may have been one of the factors influencing the patterns observed in the final wave. However, the primary focus of this study was not on the impact of the pandemic. Future research could explore the effects of COVID-19 on the evolution of multimorbidity patterns in greater detail, as well as investigate the factors influencing transitions between patterns. Conclusions Overall, this study suggests that cardiovascular diseases are emerging as the core trajectory in older adults in China with multimorbidity, with gastrointestinal metabolism and respiratory diseases potentially serving as precursors or coexisting risk factors. Early risk monitoring and intervention in the gastrointestinal metabolism and respiratory groups may help prevent cardiovascular complications and progression to multi-system disorders. Long-term follow-up may help reduce adherence-related risks in gastrointestinal metabolism and cardiovascular groups, while continued attention to medication burden remains important for other patterns. Tailored strategies by multimorbidity type may support better chronic care in aging populations. Supplementary Information Supplementary Material 1. (802KB, docx) Acknowledgements We thank Katherine East, PhD, from Edanz (https://jp.edanz.com/ac) for editing drafts of this manuscript. Abbreviations LTA Latent Transition Analysis SMM Semi-Markov Multi-State Model CHARLS China Health And Retirement Longitudinal Study SD Standard Deviations AIC Akaike Information Criterion BIC Bayesian Information Criterion RTR Relative Transition Rate OOPE Out-of-Pocket Expenses ANOVA One-Way Analysis of Variance Authors’ contributions QL contributed to the conceptualization, data curation, formal analysis, investigation, methodology, software development, validation, visualization, and wrote the original draft. SL was involved in conceptualization, project administration, data curation, validation, and visualization. LY, XZ, WL, YS, and ZL contributed to data verification, results validation, preparation of figures and tables, and supported the interpretation and presentation of findings. BF contributed to conceptualization, funding acquisition, investigation, methodology, project administration, supervision, validation, visualization, and writing—review and editing. Funding None. Data availability The data used in this study are released data by CHARLS for public use. Permissions were acquired to access the data used in our research, which were granted by CHARLS team. The raw data is available on website (http://charls. pku.edu.cn/en). Declarations Ethics approval and consent to participate This study was approved by the Peking University Biomedical Ethics Committee (IRB00001052-11015) and all participants provided informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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