Abstract
Older people constitute a significant group of chronic disease patients. Chronic diseases place a significant financial burden on patients and cause both material and psychological stress. This stress tends to induce frequent depressive moods, which negatively affect the patient’s physical and mental health. Although there is a large number of studies that have explored depressive problems, chronic illness, and social support among older people, there is relatively little research related to the heterogeneity of depressive symptoms among older people with chronic disease. In addition, the relationship between different categories of depressive symptoms and social support is unclear. The purpose of this research was to explore the potential categories of depressive symptoms and their relationship with social support in older patients with chronic diseases. We used data from the 2020 China Health and Retirement Longitudinal Study (CHARLS) to answer this question. We applied latent profile analysis methods to explore the potential categories of depressive symptoms in older patients with chronic diseases and to analyze the relationship between different depressive symptom categories and social support. The results demonstrate that depressive symptoms in older adults with chronic diseases can be categorized into four distinct subgroups: mild depression - persistent low mood with diminished hopefulness type (57.58%), moderate depression - balanced type(7.95%), major depression - high fearfulness type (15.71%), and major depression - low fearfulness type (18.76%). Furthermore, the stress-buffering effect of social support on adverse psychological outcomes demonstrates significant subtype-specific variations among these clinical subgroups.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40359-025-03376-7.
Keywords: Older patients, Chronic diseases, Depression, Social support, Latent profiling
Background
The world is undergoing a demographic transition marked by a sustained increase in the proportion of elderly populations across nations. Projections indicate that by 2050, individuals aged 65 and above will account for 25% of East Asia’s total population [1]. With advancing age, physiological functions decline, immune systems weaken, and the incidence of chronic diseases rises significantly [2]. Chronic diseases - characterized by insidious onset, prolonged and recurrent courses, and lack of definitive infectious etiologies - represent a major global public health challenge with complex, incompletely understood pathogenic mechanisms. These conditions have emerged as significant socioeconomic burdens, with over 36 million annual deaths attributable to chronic diseases worldwide (accounting for > 60% of total mortality), a proportion projected to reach 75% by 2030 [3]. Older adults constitute the primary population affected by chronic diseases. The long disease duration, low reversibility, and persistent treatment requirements of chronic conditions not only exacerbate financial burdens but also predispose to depression through combined material and psychological stressors, thereby compromising mental health [4]. Numerous international studies have confirmed that chronic disease significantly increases the probability of depressive symptoms in older adults, with disease severity positively correlating with depression risk [5–7].
This depressive state not only impairs quality of life but also amplifies healthcare utilization: even after adjusting for chronic disease status, depressed older adults incur 47–51% higher total medical expenditures and 43–52% greater outpatient costs compared to non-depressed peers [8]. These effects impose substantial economic and psychological burdens on families while presenting formidable challenges to China’s healthcare and eldercare systems. Consequently, accurately identifying depression risk among elderly chronic disease patients and developing effective interventions has become an urgent public health priority.
Social support, as a crucial external resource for stress coping, offers a promising solution. This multidimensional concept encompasses both objective and subjective support - the various forms of assistance individuals receive from family, friends, and community when facing stressors [9]. As a fundamental manifestation of human sociality, social support serves protective functions against physiological and psychological stressors, with its beneficial health effects well-documented [10, 11]. For elderly chronic disease patients, social support may alleviate depression through pathways like financial stress reduction and loneliness mitigation, though its effectiveness may vary across different depression manifestations.
Current research on geriatric depression, chronic diseases, and social support, while abundant, has predominantly focused on their aggregate associations, with insufficient attention paid to the heterogeneity of depressive symptoms among elderly chronic disease patients and the subtype-specific relationships between social support and different depression categories. Emerging studies have begun exploring this heterogeneity: Pérez-Belmonte et al. [12] classified major depression in Spanish older adults into three subtypes (psychosomatic, melancholic, and anhedonic), providing valuable references for diagnosis and treatment, though their findings were geographically limited and exclusively focused on severe depression. Similarly, Zhou et al. [13] examined latent classifications of depressive and anxiety symptoms among Chinese older women but excluded male populations. Notably, neither study adequately investigated the differential associations between social support and distinct depression subtypes. Potential profile analysis is a classification based on probabilistic models from a person-centred perspective, which can categorize the heterogeneity of groups of people [14]. The research used latent profile analysis to identify the categories of depressive symptoms among older patients with chronic diseases in China. It explored the differences in social support among older patients with chronic diseases with different categories of depressive symptoms to provide a reference for the adoption of targeted interventions.
Methods
Design
This research is exploratory, quantitative, individual-centered cross-sectional research.
Participants
The study data were sourced from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS, https://charls.pku.edu.cn/), with publicly released data as of November 16, 2023. Approved by the Peking University Medical Ethics Committee (IRB00001052-11015), this national longitudinal survey project has been conducted by the National School of Development at Peking University since 2011. As China’s first nationally representative longitudinal study of middle-aged and older adults, it covers populations aged 45 years and above and their spouses across 28 provinces (autonomous regions and municipalities). The project systematically collects comprehensive data on health status, retirement policies, and economic conditions, with core variables including demographic characteristics, health information, social support, and economic status. This cohort provides valuable scientific evidence for research on population aging, health disparities, and the effectiveness of social security policies [15].
Instrument
Depression assessment
The CHARLS uses a shortened version of the 10-item Center for Epidemiological Studies depression scale (CES-D) [16] to assess the respondent’s depression in the past one week. The scale comprises 10 items, with differential scoring applied based on item valence. For the 8 items assessing negative affect, responses were coded on a 4-point Likert scale: 0 for “rarely (less than 1 day)”, 1 for “sometimes (1–2 days)”, 2 for “often (3–4 days)”, and 3 for “most of the time (5–7 days)”. Conversely, the remaining 2 items measuring positive affect (‘I felt hopeful about the future’ and ‘I was happy’) employed reverse scoring to maintain consistent psychological interpretation. The scores of the ten entries were summed to give a total score, with higher total scores representing more severe depression. A score of ≥ 10 is considered to be the presence of depressive symptoms [17].
Definition of covariates
In the CHARLS study, the Katz Index of Independence in Activities of Daily Living (Katz ADL scale) was used to assess functional independence in daily living. The Katz ADL scale is a widely recognized international tool for evaluating an individual’s ability to perform six basic activities of daily living, including bathing, dressing, toileting, transferring (moving between bed and chair), continence, and feeding. In this study, participants were classified as having a functional limitation (assigned a score of 1) if they reported difficulty but could still complete the activity, required assistance, or were unable to perform it.
Self-rated health was assessed based on the question: “How would you describe your health? Is it very good, good, fair, poor, or very poor?” Responses were categorized as very good, good, fair, poor, or very poor.
Pain interference was measured using the question: “How often are you troubled by pain?” Possible responses included not at all, a little, some, quite a bit, and very much.
Chronic disease status in CHARLS was determined based on the question: “Has a doctor ever told you that you have any of the following chronic conditions?” The listed conditions included hypertension, diabetes, dyslipidemia, cancer, chronic lung disease (emphysema), liver disease, heart disease, stroke, kidney disease, digestive disorders, psychiatric conditions, memory-related diseases, arthritis, asthma, and Parkinson’s disease.
Definition of social support
The core explanatory variable of this research is social support, and there is no unified consensus on the measurement of social support, so in combination with the previous section and with a comprehensive reference to previous studies, formal social support and informal social support are used for measurement [18, 19]. Formal social support variables [1]. Whether or not you are receiving an old-age pension. The questionnaire reads, “Are you receiving, expect to receive in the future, or are you currently contributing to governmental and institutional pension insurance (retirement pension) and basic pension insurance for employees, supplementary pension insurance (annuity), urban and rural residents’ pension insurance, new rural pension insurance, and land requisition pension insurance?”. The integration question was a dichotomous variable, whereby a person was considered to have pension insurance if they were insured or were receiving one of these types of insurance. A person who chose “no” for all four questions was considered to have no pension insurance. The option “yes” is assigned a value of 1, and the option “no” is assigned a value of 0 [2]. Whether or not you are insured. The question in the questionnaire is, “Are you currently enrolled in any of the following health insurance programs (multiple choice)?” was simplified to a dichotomous variable, assigning a value of 1 if you are enrolled in 1 of these programs, and a value of 0 if you are not enrolled in any of these programs [3]. Presence of community care support. Extract the question from the questionnaire, “Do you have access to the following home and community care services?” Simplify this question to a dichotomous variable, with a value of 1 assigned and without a value of 0. (Due to the absence of this option in the 2020 epidemic, the 2018 questionnaire was followed). Informal support variables [1]. Child financial support, titled “In the past year, how much financial support did you or your spouse receive from [child’s name] when [child’s name] did not live with you?” which was simplified into categorical variables based on amount [2]. Frequency of child contact, with the question “When you and [child’s name] do not live together, how often do you contact [child’s name] by phone, text, tweet, letter, or email?” a continuous variable that was reduced to a categorical variable based on data contact [3]. Frequency of meeting children, “How often do you see [child’s name] when you and [child’s name] do not live together?” The higher the option frequency, the higher the score assigned, and the option to set the same frequency of meeting the topic [4]. Whether or not you have participated in social activities. The question is “Have you engaged in any of the following social activities in the past month (multiple choices are allowed)?” and the cumulative choices are 1 point for each activity selected with the higher the score, the greater the diversity of social activities. Numerous studies have shown that an individual’s traits are also important factors in their health status [20]. Therefore, this study included gender, age, education level, marital status, whether they smoke or drink alcohol, life satisfaction, ADL Impairments, Pain Interference Level, Self-Rated Health and the number of chronic diseases as covariates in the model. The specific variable definitions and statistical information are shown in Table 1.
Table 1.
Variable assignment and demographic characteristics
| Variables | Assign a value | n (%) |
|---|---|---|
| Age (years) | ||
| 60–69 | 1 | 2784(55.2) |
| 70–79 | 2 | 1884(37.4) |
| ≥ 80 | 3 | 374(7.4) |
| Gender | ||
| Male | 1 | 2555(50.7) |
| Female | 0 | 2487(49.3) |
| Community-based care | ||
| Yes | 1 | 1072(21.26) |
| No | 0 | 3970(78.74) |
| Education | ||
| Illiteracy | 1 | 2464(48.9) |
| Secondary school | 2 | 1169(23.2) |
| Middle school | 3 | 882(17.5) |
| University and above | 4 | 527(10.5) |
| Marital status | ||
| Married | 1 | 4104(81.4) |
| Unmarried | 0 | 938(18.6) |
| Smoking status | ||
| Yes | 1 | 1275(25.3) |
| No | 0 | 3767(74.7) |
| Drinking status | ||
| Yes | 1 | 1704(33.8) |
| No | 0 | 3338(66.2) |
| Number of chronic diseases (category) | ||
| 1–2 | 1 | 2353(46.7) |
| 3–4 | 2 | 1657(32.9) |
| ≥ 5 | 3 | 1032(20.5) |
| Health insurance participation | ||
| Participation in at least one | 1 | 4830(95.8) |
| Non-participation | 0 | 212(4.2) |
| Pension insurance participation | ||
| Participation in at least one | 1 | 4449(88.2) |
| Non-participation | 0 | 593(11.8) |
| Socialization participation (items) | ||
| Non-participation | 1 | 2663(52.8) |
| 1–2 | 2 | 2122(42.1) |
| > 3 | 3 | 257(5.1) |
| Life satisfaction | ||
| Not at all satisfied | 1 | 142(2.8) |
| Slightly satisfied | 2 | 369(7.3) |
| Moderately satisfied | 3 | 2710(53.7) |
| Very satisfied | 4 | 1618(32.1) |
| Extremely satisfied | 5 | 203(4.0) |
| Rate of children meeting for evaluation (times) | ||
| 0–10 | 1 | 1990(39.5) |
| 11–20 | 2 | 1975(39.2) |
| 21–30 | 3 | 793(15.7) |
| 31–40 | 4 | 220(4.4) |
| > 40 | 5 | 64(1.3) |
| Children’s online communication (times) | ||
| 0–10 | 1 | 2450(48.6) |
| 11–20 | 2 | 1655(32.8) |
| 21–30 | 3 | 717(14.2) |
| 31–40 | 4 | 181(3.6) |
| > 40 | 5 | 39(0.8) |
| Financial support received from children (yuan) | ||
| 0-10000 | 1 | 4135(82) |
| 10,001–20,000 | 2 | 588(11.7) |
| 20,001–30,000 | 3 | 173(3.4) |
| 30,001–40,000 | 4 | 65(1.3) |
| > 40,000 | 5 | 81(1.6) |
| Self-rated health | ||
| Very Poor | 1 | 417(8.3) |
| Poor | 2 | 1152(22.9) |
| Fair | 3 | 2567(50.9) |
| Good | 4 | 515(10.2) |
| Very good | 5 | 391(7.8) |
| Pain interference level | ||
| None | 1 | 1841(36.5) |
| A little | 2 | 1500(29.8) |
| Somewhat | 3 | 558(11.0) |
| Quite a bit | 4 | 549(10.9) |
| Very | 5 | 594(11.8) |
| Activity of daily living (ADL) impairments | ||
| Without Impairments | 0 | 3593(71.3) |
| With Impairments | 1 | 1449(28.7) |
Data analysis
Potential profile analysis using Mplus 8.3. We hypothesize the existence of several latent, unobservable homogeneous subpopulations (i.e., distinct depression symptom profiles). Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample correction BIC (adjusted BIC, aBIC). Evaluate the model fitting effect; the smaller the value, the better the fitting effect. Entropy evaluates the classification quality of the model; when Entropy > 0.8 and closer to 1, it indicates that the model is more accurate in classification [21]. The Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) and Bootstrap Likelihood Ratio Test (BLRT) are used to compare the difference in fit between different category models, with a statistically significant difference indicating that the k-category model outperforms the k-1-category model [22]. When the category models favoured by each evaluation indicator were inconsistent, the best group was selected by integrating the results of each indicator, considering the clinical significance, and combining the principles of interpretability and simplicity. Consequently, we identify latent profiles with distinct depression symptom patterns among older adults with chronic diseases, and subsequently characterize the proportions and clinical features of each profile. All analyses were performed using R 4.5.1. Descriptive statistics were complemented by χ² tests and logistic regression analyses. When certain categories of categorical variables contained limited sample sizes, we employed the Kruskal-Wallis test as a robust non-parametric alternative. Multinomial logistic regression was employed to examine the association between social support and latent depression profiles, with the derived profiles serving as the dependent variable and multidimensional social support measures as independent variables. Demographic characteristics and disease status were included as covariates to control for potential confounding effects. The significance level was set at α = 0.05. Given the large sample size, Monte Carlo simulation was applied to estimate exact significance values.
Results
Descriptive statistics
A total of 19,816 CHARLS respondents were enrolled in FY2020, of whom 10,989 were ≥ 60 years of age, 1,521 were excluded without chronic disease, 2001 were excluded with missing entries for the core variable of depressive symptoms, and 2,425 were excluded with missing entries for other variables, resulting in a final inclusion sample of 5,042 respondents. The short version of the CES-D scale score for older patients with chronic diseases was 9.00 (4.00, 14.00), and the detection rate of depressive symptoms was 54.1% (2730/5042). Other variables are detailed in Table 1.
Latent profiling of depressive symptoms in older patients with chronic diseases
Potential profiles of depressive symptoms in older patients with chronic diseases were fitted using the 10-item scores of the CES-D scale as exogenous variables, and this research explored 1 to 6 potential profile groups and the fitting indexes of each group are shown in Table 2. As the number of profiles increased, AIC, BIC, and aBIC decreased gradually. LMRT and BLRT were statistically significant for each category (all P < 0.05), but entropy reached a maximum of 0.950 at four categories and had the best classification accuracy. At six categories, the existence probability category was 0.0484 (< 0.05), so it was excluded. Combining the indicators and considering the practical significance, interpretability and brevity of the final results, the best group selected for this research was the 4-category group.
Table 2.
Indicators of model fit
| Group | AIC | BIC | aBIC | Entropy | P(LMR) | P(BLRT) | Categorical probability(%) |
|---|---|---|---|---|---|---|---|
| 1 | 154052.255 | 154182.766 | 154119.213 | ||||
| 2 | 141876.547 | 142078.839 | 141980.331 | 0.900 | < 0.001 | < 0.001 | 68.22/31.78 |
| 3 | 136869.719 | 137143.793 | 137010.331 | 0.925 | < 0.001 | < 0.001 | 60.87/23.42/15.71 |
| 4 | 134445.793 | 134791.648 | 134623.232 | 0.950 | < 0.001 | < 0.001 | 57.58/7.95/15.71/18.76 |
| 5 | 131561.621 | 131979.257 | 131775.887 | 0.945 | < 0.001 | < 0.001 | 54.64/10.77/6.94/18.88/8.77 |
| 6 | 130463.446 | 130952.863 | 130714.538 | 0.945 | < 0.001 | < 0.001 | 4.84/19.26/54.26/6.94/5.93/8.77 |
Characterization and naming of categories for latent profile analysis
Figure 1 represents the depressive symptom scores for each sample identified in this research. Different categories of older patients with chronic diseases were identified. Group 1 had 2903 older patients with chronic diseases, representing 57.58% of the population. It had a composite score greater than ten but lower than the other three group scores, so it was labeled mildly depressed. Among the scores of the entries in group 1, the scores of the other entries were low. There were two peaks in the entries “I felt hopeful about the future” and “I was happy” with scores of about 2.5 or more, so it was named as the type of persistent low mood and diminished hopefulness. Group 2 had 401 older patients with chronic diseases, representing 7.95% of the total population. It has a combined score of about 22 and scores in the middle of the pack, so it is labeled as moderately depressed. In the scores of the entries, the scores are more balanced without large fluctuations, so they are named balanced. Group 3 had 792 older patients with chronic diseases, representing 15.71% of the population. It had a composite score of approximately 28.4. Group 4 had 946 older patients with chronic diseases, representing 18.76% of the population. It had a composite score of approximately 24.5. Both Group3 and Group 4 had higher depression scores and were therefore labeled as severely depressed. Group3, among the scores of the entries, the entry “I feel fearful” scored the highest, about 3.4, so it is named as high-fear type. Group 4, among the scores of the entries, “I feel fearful” scored the lowest, about 1 point, so it is named as low-fear type. In subsequent manuscripts, we will adopt simplified yet clinically precise labels for the identified depression subgroups to optimize clarity and readability while maintaining scientific rigor: Group 1(mild depression - persistent low mood with diminished hopefulness type) will be termed “mild depression,” Group 2(Moderate depression - balanced type) “Moderate depression ,” Group 3(major depression - high fearfulness type) “major depression (high fearfulness),” and Group 4(major depression - low fearfulness type) “major depression (low fearfulness).
Fig. 1.
The result of latent profile analysis. Note, * represents reverse scoring
One-way analysis of variance
Univariate analyses showed statistically significant differences in gender, age, education, marital status, community caregiving status, smoking status, drinking status, number of chronic diseases, health insurance participation, pension insurance, participation in social activities, life satisfaction, children’s online communication, self-rated health, pain interference level, ADL Impairments and children’s financial support (P < 0.05). For further details, please refer to Tables 3, 4, 5 and 6.
Table 3.
One-way analysis of variance
| Groups | n(%) | Genders | Age | Educational Background | Marital status | Smoking status | Drinking status | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Male | Female | 60–69 | 70–79 | ≥ 80 | Illiteracy | Secondary school | Middle school | University and above | Married | Unmarried | Yes | No | Yes | No | ||
| C1* | 2903 (57.58) | 1692 | 1241 | 1621 | 1053 | 229 | 1226 | 710 | 582 | 385 | 2442 | 461 | 813 | 2090 | 1094 | 1809 |
| C2* | 401 (7.95) | 181 | 220 | 230 | 142 | 29 | 213 | 93 | 72 | 23 | 338 | 63 | 94 | 307 | 147 | 254 |
| C3* | 792 (15.71) | 262 | 530 | 451 | 296 | 45 | 507 | 151 | 91 | 43 | 595 | 197 | 143 | 649 | 187 | 605 |
| C4* | 946 (18.76) | 420 | 526 | 482 | 393 | 71 | 518 | 215 | 137 | 76 | 729 | 217 | 225 | 721 | 276 | 670 |
| X2 | 185.171 | 13.95 | 166.423 | 48.739 | 35.131 | 66.839 | ||||||||||
| P(95%CI) | < 0.001(0.000) | 0.032 (0.028,0.035) | < 0.001(0.000) | < 0.001(0.000) | < 0.001(0.000) | < 0.001(0.000) | ||||||||||
Note, C1* Mild depression - persistent low mood with diminished hopefulness, C2* Moderate depression - balanced, C3* Major depression - high fearfulness, C4* Major depression - low fearfulness
Table 4.
Single factor analysis (continued from above table)
| Groups | n(%) | Number of chronic diseases (category) | Health insurance participation | Pension insurance participation | Socialization participation (items) | Life satisfaction | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1–2 | 3–4 | ≥ 5 | Participation in at least one | Non-participation | Participation in at least one | Non-participation | Non-participation | 1–2 | > 3 | 1 | 2 | 3 | 4 | 5 | ||
| C1* | 2930(57.58) | 1544 | 926 | 433 | 2793 | 110 | 2572 | 331 | 1493 | 1243 | 167 | 24 | 79 | 1564 | 1100 | 136 |
| C2* | 401 (7.95) | 192 | 115 | 94 | 390 | 11 | 336 | 35 | 198 | 173 | 30 | 5 | 39 | 230 | 112 | 15 |
| C3* | 792(15.71) | 270 | 264 | 258 | 753 | 39 | 698 | 94 | 448 | 313 | 31 | 62 | 124 | 393 | 184 | 29 |
| C4* | 946 (18.76) | 347 | 352 | 247 | 894 | 52 | 813 | 133 | 524 | 393 | 29 | 51 | 127 | 523 | 222 | 23 |
| X2 | 198.966 | 8.31 | 8.738 | 23.785 | 434.911 | |||||||||||
| P(95%CI) | < 0.001(0.000) | 0.044(0.040,0.048) | 0.036(0.032,0.039) | 0.001(0.000) | < 0.001(0.000) | |||||||||||
Table 5.
Single factor analysis (continued from above table)
| Groups | n(%) | Rate of children meeting for evaluation (times) | Children’s online communication (times) | Financial support received from children (yuan) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0–10 | 11–20 | 21–30 | 31–40 | > 40 | 0–10 | 11–20 | 21–30 | 31–40 | > 40 | 0-10000 | 10,001–20,000 | 20,001–30,000 | 30,001–40,000 | > 40,000 | |||
| C1* | 2930(57.58) | 1169 | 1115 | 451 | 136 | 32 | 1497 | 900 | 376 | 107 | 23 | 2345 | 355 | 111 | 34 | 58 | |
| C2* | 401 (7.95) | 159 | 164 | 50 | 20 | 8 | 185 | 137 | 59 | 17 | 3 | 341 | 36 | 13 | 8 | 3 | |
| C3* | 792(15.71) | 314 | 326 | 106 | 36 | 10 | 349 | 280 | 135 | 23 | 5 | 661 | 88 | 29 | 6 | 8 | |
| C4* | 946 (18.76) | 348 | 370 | 186 | 28 | 14 | 419 | 338 | 147 | 34 | 8 | 788 | 109 | 20 | 17 | 12 | |
| Kruskal-Wallis | 4.676 | 21.340 | 7.992 | ||||||||||||||
| P | 0.197 | 0.000 | 0.046 | ||||||||||||||
Table 6.
Single factor analysis (continued from above table)
| Groups | n(%) | Self-Rated Health | ADL Impairments | Pain Interference Level | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 0 | 1 | 1 | 2 | 3 | 4 | 5 | ||
| C1* | 2930(57.58) | 122 | 484 | 1587 | 402 | 308 | 2367 | 536 | 1366 | 896 | 258 | 212 | 171 |
| C2* | 401 (7.95) | 33 | 104 | 211 | 28 | 25 | 261 | 140 | 105 | 136 | 48 | 52 | 60 |
| C3* | 792(15.71) | 117 | 246 | 365 | 42 | 22 | 417 | 375 | 163 | 218 | 104 | 130 | 177 |
| C4* | 946 (18.76) | 145 | 318 | 404 | 43 | 36 | 548 | 398 | 207 | 250 | 148 | 155 | 186 |
| X2 | 467.575 | 373.171 | 550.907 | ||||||||||
| P(95%CI) | 0.000(0.000, 0.000) | 0.000(0.000, 0.000) | 0.000(0.000, 0.000) | ||||||||||
Multifactorial analysis
Potential profiles of depressive symptoms in older patients with chronic diseases were used as dependent variables, and variables statistically significant in the univariate analysis were included in logistic regression analysis as independent variables. The research results showed significance for gender, education, Age, marriage, alcohol consumption, number of chronic diseases, social participation, financial support from children, participation in pension insurance, ADL Impairments, pain interference level, self-rated health and life satisfaction.
Women were more likely to be biased toward being in the moderate depression, major depression, and major depression(low fearfulness) than the mild depression. Patients with fewer than five chronic diseases were more likely to exhibit mild depression levels compared to those with moderate depression or major depression(high fearfulness). Compared to individuals with mild depression and moderate Depression, those reporting ‘Not at all satisfied’,‘Slightly satisfied’ and ‘moderately satisfied’ with life were more likely to develop major depression - either high fear type or low fear type - than those reporting ‘Extremely satisfied’. Compared with the mild depression, those who received income from their children of 30,000 to 40,000 yuan were more likely to be in the moderate depression and the major depression(low fearfulness), which were 5.3 and 2.9 times greater than those who received more than 40,000 yuan in income from their children, respectively. Compared to mild depression, individuals with physical impairment, higher pain interference, and lower educational attainment showed increased likelihood of developing moderate depression, major depression(high fearfulness), and major depression(low fearfulness). Compared to mild depression, individuals with poorer self-rated health and unmarried status demonstrated significantly higher odds of developing both major depression(high fearfulness) and major depression(low fearfulness). Notably, patients aged < 80 years showed 1.5-fold greater likelihood of progressing to major depression(high fearfulness) than octogenarians. Compared to moderate depression cases, alcohol users demonstrated 1.3-fold increased odds of developing major depression(high fearfulness). Conversely, limited social engagement and lack of pension coverage were significantly associated with progression to severe depression with low fear subtype. Table 7 presents only those variables that demonstrated statistically significant associations with depression classification. The complete results, including all non-significant variables, is provided in the Supplementary Materials.
Table 7.
Risk factors for depression in older adults with chronic diseases: a logistic regression analysis
| Reference: Groups1 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | Groups2 | Groups3 | Groups4 | |||||||||
| B | P | OR | 95%CI | B | P | OR | 95%CI | B | P | OR | 95%CI | |
| Gender | ||||||||||||
| Female | 0.459 | 0.001 | 1.582 | (1.201,2.085) | 0.666 | 0.001 | 1.946 | (1.553,2.438) | 0.355 | 0.001 | 1.426 | (1.165 ,1.745) |
| Male | Reference | |||||||||||
| Education | ||||||||||||
| Illiteracy | 0.899 | 0.001 | 2.458 | (1.532,3.944) | 0.842 | 0.001 | 2.321 | (1.607,3.35) | 0.351 | 0.023 | 1.42 | (1.05,1.921) |
| Secondary school | 0.736 | 0.003 | 2.088 | (1.28,3.405) | 0.451 | 0.023 | 1.57 | (1.063,2.318) | 0.156 | 0.337 | 1.168 | (0.851,1.605) |
| Middle school | 0.727 | 0.004 | 2.07 | (1.259,3.402) | 0.298 | 0.152 | 1.348 | (0.896,2.028) | 0.108 | 0.521 | 1.115 | (0.8,1.552) |
| University and above | Reference | |||||||||||
| Marital status | ||||||||||||
| Married | Reference | |||||||||||
| Unmarried | -0.109 | 0.492 | 0.897 | (0.658,1.223) | 0.405 | 0.001 | 1.5 | (1.201,1.873) | 0.321 | 0.003 | 1.379 | (1.117,1.701) |
| Number of chronic diseases (category) | ||||||||||||
| 1–2 | -0.119 | 0.438 | 0.888 | (0.658,1.198) | -0.557 | 0.001 | 0.573 | (0.452,0.726) | -0.187 | 0.103 | 0.829 | (0.662,1.039) |
| 3–4 | -0.339 | 0.032 | 0.713 | (0.523,0.971) | -0.354 | 0.003 | 0.702 | (0.557,0.884) | 0.009 | 0.934 | 1.009 | (0.811,1.256) |
| ≥ 5 | Reference | |||||||||||
| Socialization participation (items) | ||||||||||||
| Non-participation | -0.583 | 0.01 | 0.558 | (0.359,0.867) | -0.062 | 0.779 | 0.94 | (0.608,1.451) | 0.261 | 0.237 | 1.299 | (0.842,2.002) |
| 1–2 | -0.499 | 0.026 | 0.607 | (0.391,0.942) | -0.143 | 0.522 | 0.867 | (0.561,1.341) | 0.242 | 0.274 | 1.273 | (0.826,1.963) |
| > 3 | Reference | |||||||||||
| Life satisfaction | ||||||||||||
| Not at all satisfied | 0.335 | 0.558 | 1.398 | (0.455,4.291) | 1.878 | 0.001 | 6.538 | (3.351,12.754) | 1.878 | 0.001 | 6.537 | (3.259,13.114) |
| Not at all | 1.242 | 0.001 | 3.463 | (1.758,6.822) | 1.593 | 0.001 | 4.919 | (2.895,8.358) | 1.816 | 0.001 | 6.149 | (3.534,10.701) |
| Quite satisfied | 0.26 | 0.369 | 1.297 | (0.735,2.286) | 0.18 | 0.433 | 1.198 | (0.763,1.88) | 0.64 | 0.009 | 1.897 | (1.175,3.063) |
| Very satisfied | -0.073 | 0.807 | 0.93 | (0.519,1.667) | -0.219 | 0.356 | 0.803 | (0.504,1.279) | 0.205 | 0.413 | 1.228 | (0.751,2.008) |
| Extremely satisfied | Reference | |||||||||||
| Financial support received from children (yuan) | ||||||||||||
| 0-10000 | 0.951 | 0.116 | 2.589 | (0.791,8.476) | 0.488 | 0.231 | 1.629 | (0.733,3.62) | 0.46 | 0.184 | 1.584 | (0.804,3.118) |
| 10,001–20,000 | 0.532 | 0.396 | 1.702 | (0.498,5.818) | 0.443 | 0.297 | 1.557 | (0.678,3.574) | 0.421 | 0.244 | 1.523 | (0.75,3.094) |
| 20,001–30,000 | 0.624 | 0.353 | 1.866 | (0.501,6.956) | 0.468 | 0.312 | 1.597 | (0.644,3.956) | -0.158 | 0.711 | 0.854 | (0.37,1.97) |
| 30,001–40,000 | 1.677 | 0.021 | 5.35 | (1.288,22.231) | 0.523 | 0.396 | 1.687 | (0.504,5.647) | 1.088 | 0.021 | 2.969 | (1.181,7.466) |
| > 40,000 | Reference | |||||||||||
| ADL impairments | ||||||||||||
| Without Impairments | -0.45 | 0.001 | 0.637 | (0.496,0.819) | -0.662 | 0.001 | 0.516 | (0.424,0.627) | -0.482 | 0.001 | 0.617 | (0.514,0.742) |
| With Impairments | Reference | |||||||||||
| Pain interference level | ||||||||||||
| None | -0.908 | 0.001 | 0.403 | (0.27,0.603) | -0.949 | 0.001 | 0.387 | (0.283,0.529) | -0.996 | 0.001 | 0.369 | (0.276,0.495) |
| A little | -0.464 | 0.016 | 0.629 | (0.431,0.918) | -0.623 | 0.001 | 0.536 | (0.4,0.719) | -0.659 | 0.001 | 0.517 | (0.391,0.684) |
| Somewhat | -0.366 | 0.11 | 0.694 | (0.443,1.086) | -0.343 | 0.049 | 0.71 | (0.504,0.999) | -0.124 | 0.442 | 0.884 | (0.645,1.211) |
| Quite a bit | -0.241 | 0.277 | 0.786 | (0.509,1.214) | -0.232 | 0.165 | 0.793 | (0.571,1.101) | -0.162 | 0.31 | 0.851 | (0.622,1.162) |
| Very | Reference | |||||||||||
| Self-rated health | ||||||||||||
| Very Poor | 0.419 | 0.18 | 1.521 | (0.824,2.806) | 1.182 | 0.001 | 3.262 | (1.877,5.669) | 1.139 | 0.001 | 3.124 | (1.965,4.968) |
| Poor | 0.449 | 0.078 | 1.566 | (0.951,2.58) | 1.028 | 0.001 | 2.795 | (1.702,4.588) | 0.911 | 0.001 | 2.487 | (1.661,3.725) |
| Fair | 0.241 | 0.297 | 1.273 | (0.809,2.001) | 0.788 | 0.001 | 2.199 | (1.373,3.522) | 0.387 | 0.046 | 1.473 | (1.007,2.155) |
| Good | -0.171 | 0.556 | 0.843 | (0.476,1.491) | 0.317 | 0.266 | 1.373 | (0.785,2.402) | -0.168 | 0.495 | 0.846 | (0.522,1.369) |
| Very good | Reference | |||||||||||
| Age (years) | ||||||||||||
| 60–69 | -0.025 | 0.915 | 0.975 | (0.611,1.555) | 0.427 | 0.036 | 1.533 | (1.029,2.283) | 0.118 | 0.497 | 1.125 | (0.801,1.582) |
| 70–79 | -0.089 | 0.702 | 0.915 | (0.579,1.444) | 0.306 | 0.123 | 1.358 | (0.92,2.002) | 0.203 | 0.228 | 1.225 | (0.881,1.703) |
| ≥ 80 | Reference | |||||||||||
Discussion
This research explored the heterogeneity of depressive symptoms in older patients with chronic diseases through the ten dimensions of the CES-D scale. An individual-centered approach was used in this research to identify subtypes of depressive symptoms in older patients with chronic diseases. Some of the entries in social support can have an impact on depressive symptoms in older patients with chronic diseases. The details are discussed below.
Latent profiling of depressive symptoms in older patients with chronic diseases
We used latent profile analysis to categorize depressive symptoms in older patients with chronic disease. The results showed that the depressive symptom scores of older patients with chronic diseases had distinct categorical features and could be categorized into four potential categories: mild depression, moderate depression, major depression(high fearfulness), and major depression(low fearfulness). The fit indices showed that the model fit well, indicating that there were differences between the potential categories of depressive symptoms in older patients with chronic diseases, reflecting the heterogeneity of depressive symptoms in this group. According to the scale scoring results, patients with mild depression exhibited elevated scores on the reverse-scored items ‘I felt hopeful about the future’ and ‘I was happy.’ This pattern aligns with their symptomatic presentation of chronic emotional distress and loss of hope in daily life. The moderately depressed-balanced type accounted for 7.95% of elderly patients with chronic diseases, representing a relatively low proportion. This subtype is characterized by comprehensive depressive manifestations, with relatively balanced severity across various depression-related symptoms (e.g., low mood, anhedonia, anxiety), without predominant emphasis on any specific emotional dimension. Whereas the major depression(high fearfulness), the major depression(low fearfulness) moved more consistently across all items, except for the entry ‘I feel fearful,’ where scores varied considerably. Fear is prevalent among older adults, particularly those with chronic conditions, and frequently manifests as fear of falling and fear of death [23, 24]. Such emotional states not only exacerbate patients’ psychological burden but may also adversely impact health status through behavioral feedback mechanisms. For instance, excessive fear of falling may lead to intentional reduction of daily activities [25], ultimately resulting in diminished quality of life and creating a vicious cycle of ‘fear - activity restriction - functional decline - increased fear’.
The refined classification and characteristic analysis of four latent depression subtypes among older adults with chronic diseases provides clinically actionable guidance for targeted health management. For patients with the mild depression, whose distinctive high scores on reverse-coded items like ‘I felt hopeful about the future’ and ‘I was happy’ were observed, clinical interventions should emphasize hope-building and positive affect enhancement through tailored psychological counseling to restore positive life expectations.Patients exhibiting the moderate depression require comprehensive intervention strategies addressing multiple dimensions including emotion regulation, interest stimulation, and anxiety reduction, necessitating integrated psychological approaches to holistically improve depressive symptoms. Regarding the two severe depression subtypes, their differential manifestation primarily in “I feel fearful” items warrants distinct fear-focused interventions: for the high-fear type, prioritized fear management (particularly addressing fall-related and death anxiety) through fall prevention training and death education could break the vicious cycle of “fear-activity restriction-functional decline-worsened fear”; whereas for the low-fear type, while maintaining standard severe depression interventions, clinicians should investigate potential underlying psychological issues not masked by fear responses to ensure intervention precision.
This subtype-specific intervention paradigm effectively addresses depression heterogeneity in older chronic disease patients, enhances psychological intervention efficacy, improves quality of life, and ultimately reduces health burdens. The approach demonstrates significant practical utility for clinical practice and health management.
The effect of social support on a subgroup of depressive symptoms in older adults with chronic disease
Social support is the recognition and assistance individuals feel originates from society and can buffer life’s stresses and promote healthy outcomes [26]. Social support is an essential protective factor for mental health status, i.e., the higher the level of support received from family, friends, and medical staff, the lower the level of psychological distress in older patients with chronic disease [27].
The present research showed that patients with a lower number of social engagement activities were more likely to be in the major depression group, which is consistent with previous studies [28–32]. For older people with chronic disease, the impact of social support through participation in group leisure activities on the quality of life increases as their chronic conditions worsen over time. This is because participation in social activities not only provides older patients with chronic diseases with a greater sense of social participation but also strengthens the immunity of older patients with chronic diseases against chronic diseases through physical exercise, thus providing a buffering effect and reducing the negative impact of chronic diseases on their quality of life [33]. The activity type may have different effects on people’s health and functioning, e.g., Hong S I suggests that engaging in more volunteering and healthy exercise can significantly affect longitudinal changes in depressive symptoms [34]. Such hypotheses are also supported by subsequent relevant studies [35, 36]. However, Simone Croezen [37] showed that participation in religious activities was the only form of social engagement associated with reduced depressive symptoms. Conversely, participation in political or community organizations was associated with increased depressive symptoms. However, Wanchai’s review of the relevant English language literature from 2006 to 2016 indicates that social participation significantly benefits older people’s health and encourages society to create a favourable environment conducive to older people’s participation in group and individual activities [38]. There is no uniformity between the results of different studies, and older people with chronic disease should be offered social activities of interest to them, depending on their specific situation.
This study revealed that compared to individuals with mild depression, those receiving financial support from their children in the range of 30,000 to 40,000 RMB exhibited a significantly higher probability of progressing to moderate depression and major depression(low fearfulness). This finding suggests that the significant association between financial support from children and depression classification exists only within a specific income range. Considering the limited existing research evidence, we tentatively propose the following interpretation: The relationship between financial support from children and depression among older adults is not driven by a single factor, but rather results from the complex interplay of multiple variables. Specifically, factors such as the elderly’s pre-existing financial reserves, cognitive biases arising from “mismatches between expectations and reality,” a potential “threshold effect” among middle-income groups, and the coverage level of healthcare policies may all play moderating roles [39, 40]. The combined effect of these factors may lead to a nonlinear relationship that demonstrates statistical significance only within specific ranges of financial support. Furthermore, these interacting factors may obscure or distort the underlying association pattern, resulting in only weak significance within particular intervals. Additionally, limitations in sample representativeness might have influenced the results, as the study sample did not adequately cover older adult populations from diverse socioeconomic, cultural, and regional backgrounds, which somewhat reduces the generalizability of the findings. Based on this analysis, future research should further optimize the study design, improve the variable measurement system, and expand the sample coverage to enable a more thorough and accurate investigation of the intrinsic relationship between financial support from children and depression among older adults.
Compared to the moderate depression, those who did not participate in pension insurance were more likely to favour the major depression(low fearfulness), which was 1.5 times more likely than those who participated in at least one type of pension insurance. This finding is consistent with previous research [41]. However, there is another argument that health insurance and pension insurance can, to a certain extent, increase the level of life satisfaction of the older [42], which, in turn, affects the psychological condition of the patients [43, 44]. The main purpose of pension insurance is to provide income in old age; alleviating depressive symptoms may only be a “by-product” of being a pensioner who can contribute to improving mental health through at least three channels: labour supply, family relationships, and consumer spending [41].
The stress-buffering effect of social support demonstrates significant differential characteristics across depression subtypes in elderly patients with chronic diseases. Research reveals that higher levels of social support are associated with marked reductions in negative emotions within this population, with this association being particularly pronounced among individuals exhibiting mild depressive symptoms—enhanced social support effectively alleviates their psychological distress, including low mood and anxiety.
It should be emphasized that certain social support variables showed no significant correlation with depressive symptoms. This finding should not be dismissed as a mere “negative result,” but rather highlights the complexity of psychological mechanisms in elderly chronic disease patients: the development and alleviation of depressive symptoms may involve interactive effects among multiple factors, including dimensions of social support, disease characteristics, and individual psychological traits. Relying solely on a single type of support or a uniform support model may fail to meet the needs of all subgroups.This discovery of differential associations carries important practical implications: it indicates that social support is a key modifiable factor in improving the mental health of elderly chronic disease patients [45]. Accordingly, community health workers should implement interventions from two perspectives. First, they should actively guide patients in establishing family support systems by encouraging family members to strengthen emotional companionship and address psychological needs, ensuring elderly patients consistently perceive care and support from their surroundings—particularly for those with mild depression, where timely and targeted support is crucial. Second, community health service centers should enhance complementary health services, such as chronic disease health education to improve patients’ disease awareness and coping skills, and optimize specialized health check-ups for the elderly to reduce anxiety stemming from insufficient health information. Through systematic assistance, these measures can effectively fortify mental health safeguards for elderly chronic disease patients [33].
Effects of other influences on depressive symptom subgroups in older adults with chronic diseases
The present research shows that females are more skewed towards moderate depression, major depression(high fearfulness) and major depression(low fearfulness). This phenomenon may be attributed to multiple factors: women typically shoulder greater responsibilities and burdens in both social and family domains; with advancing age, they gradually withdraw from the workforce while experiencing reduced social and recreational activities; furthermore, their emotional processing tends to be more nuanced and sensitive - all of which collectively contribute to women’s heightened emotional vulnerability compared to men [46, 47]. Additionally, Physiological stress response differences may also increase susceptibility [48], though these mechanisms remain debated.
The lower the number of chronic disease, the more likely they are to be in the mild depression. Older adults with chronic physical disease and the persistence of chronic disease can cause or exacerbate psychiatric symptoms to some extent [49]. Older adults with multimorbidity often experience functional decline and reduced daily activity capacity. Moreover, persistent disease not only compromises quality of life but also leads to sleep disturbances, impaired concentration, and social participation difficulties, potentially triggering anxiety and depression [33]. The chronicity of these conditions may foster fears of disease progression and uncontrolled deterioration, exacerbating psychological distress [33]. Effective management of physical illnesses in older adults could therefore alleviate both physical and mental health burdens.
Compared to the mild depression, unmarried people were more likely to be in the major depression(high fearfulness) and major depression(low fearfulness), which were 1.445 and 1.323 times more likely than married people, respectively. This research refers to unmarried, including widowed, divorced, separated, and never married. Spousal support may buffer against depression through mutual comfort during adverse events [50]. Without such support, unmarried older adults often experience loneliness, reduced life satisfaction, and increased anxiety/depression risk due to inadequate psychosocial support [51].
Individuals with lower educational attainment exhibit a higher likelihood of falling into the moderate-to-severe depression category. This may be related to education enhancing an individual’s cognitive abilities, including problem-solving skills, critical thinking, etc. The enhancement of these abilities helps individuals to better cope with life’s challenges and difficulties and reduces depression [52, 53]. This research also showed that the lower the life satisfaction, the more likely you are to be in the major depression group. This likely reflects cumulative negative impacts from multiple factors, where limited social engagement fails to adequately mitigate depressive symptoms [50].
The present research also showed that people who drank alcohol were more likely to favour the major depression(high fearfulness). Chronic heavy drinking may trigger mood changes, aggression, attention, and productivity deficits, including relationship, work, and financial instability, which in turn may trigger negative mood and depressive symptoms [54, 55]. Enzymes involved in the metabolism of folic acid may be affected by prolonged heavy drinking, which in turn may lead to depressive symptoms, and sleep patterns disrupted by alcohol consumption may also underlie the development of depression [56, 57].
Interestingly, our study revealed that compared to mild depression, elderly individuals aged below 80 years were more likely to develop major depression(high fearfulness) than those aged above 80 years. This difference can be reasonably explained by the relatively preserved neural structures (e.g., prefrontal cortex, amygdala) in younger elderly individuals, which maintain more sensitive emotional response mechanisms to negative stimuli. In contrast, older age groups exhibit elevated emotional regulation thresholds due to neurodegenerative changes [58]. Moreover, advanced-age elderly often present with multiple chronic conditions, where somatic suffering may replace or mask emotional fear. Conversely, younger elderly with relatively preserved physical function demonstrate more distinct fears about “potential future disability/illness,” which tend to compound with depressive symptoms [59, 60]. However, current evidence remains insufficient regarding optimal age stratification for these observations. Individuals with limitations in activities of daily living and poorer self-rated health demonstrated significantly higher susceptibility to moderate-to-severe depression compared to those with mild depressive symptoms, reflecting multifaceted underlying mechanisms. Restricted physical activity may disrupt normal neurotransmitter secretion, interfering with their synthesis and transmission, thereby contributing to depressed mood and increased depression risk [61]. In our current analysis, we dichotomized physical activity limitation as present or absent, which might obscure potential gradient effects of ADL disability severity on study outcomes. This factor warrants further stratified analysis in subsequent research. Those with poorer self-rated health likely experience greater chronic disease burden, exacerbating physical and psychological stress. Furthermore, this population often exhibits excessive health-related anxiety, and this persistent anxious state may impair normal psychological regulation processes.
Conclusion
In this research, a person-centered approach was used to categorize the depressive symptoms of older patients with chronic diseases into four groups: mild depression, moderate depression, major depression(high fearfulness), and major depression(low fearfulness). This study found a correlation between social support and depressive symptoms in older patients with chronic illness. Specifically, those with better financial support from their children, participation in pension insurance, and participation in more social activities were more likely to fall into the mild depression category. Although the results on the impact of social support on depressive symptoms are inconclusive, having better social support may play a role in light depressive symptoms. Future research could further explore how to improve the mental health of older patients by optimizing social support strategies. Other variables such as gender, education, marriage, alcohol consumption, number of chronic diseases, and life satisfaction have some correlation with depressive symptoms in older patients with chronic diseases. The above aspects can be enhanced in the future to focus on older patients with chronic diseases. In the progression of depressive states, the association between pain interference and depression severity reveals a seemingly paradoxical phenomenon: compared to mild depression, individuals reporting pain interference at “a little or below” are actually more likely to develop moderate or severe depression than those experiencing “very severe pain”. Intense pain often generates salient somatic signals, which may dominate the individual’s attentional focus, creating a “pain-priority” cognitive mode. This acute somatic stress response may, to some extent, mitigate the deepening of depressive affect [62].
Limitation
The present research only extracted data from cross-sectional research, which could not predict the longitudinal trajectory of depressive symptoms in older patients with chronic disease or derive causal associations between potential profiles of depressive symptoms and social support. In the future, a longitudinal research design could be used to clarify the impact of different categories of depressive symptoms on social support in older patients with chronic disease. Secondly this study only investigated the presence of chronic illnesses in older adults, ignoring the fact that different types of chronic illnesses may lead to different depressive symptom outcomes. The chronic category could be included in future studies. Thirdly, the social support variables in this study were determined by referring to previous studies and related concepts, which may affect the accuracy of the variables. In the future, relevant variables can be collected through already validated scales or other techniques to ensure the accuracy of the study. Finally, the relevant influences in this study are limited by publicly available databases, and in the future for us for us we can expand the study of relevant influences to enhance for us our understanding of the complexity of depressive symptoms in this population.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to express our sincere gratitude to Peking University for providing data of CHARLS and to those involved in data collection and management.
Author contributions
JZ and HZ were primarily responsible for the conception and design of the study. WMW and XXG were responsible for data cleaning and organization. LXL and YPZ performed the data analysis and interpretation. LXL and WMW wrote the first draft of the manuscript. LXL, WMW, XXG, YPZ, JZ, and HZ were involved in revising and completing this work. All authors contributed to and approved the final manuscript.
Funding
This research received no funding.
Data availability
The datasets supporting the conclusions of this article are available publicly, http://charls.pku.edu.cn.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Langxuan Liu and Wenmian Wang contributed equally to this manuscript.
Contributor Information
Jing Zeng, Email: zengjinger@163.com.
Hao Zhang, Email: zhanghaord@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets supporting the conclusions of this article are available publicly, http://charls.pku.edu.cn.