Unpacking the Multifaceted Burden: A Cross-sectional Analysis of Challenges Faced by Dementia Family Caregivers in Rural China

Unpacking the Multifaceted Burden: A Cross-sectional Analysis of Challenges Faced by Dementia Family Caregivers in Rural China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unpacking the Multifaceted Burden: A Cross-sectional Analysis of Challenges Faced by Dementia Family Caregivers in Rural China Jun Zhou, Lily Xiao, Xiajun Guo, Hui You, Yao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9374067/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background In rural China, family members serve as the primary caregivers for people with dementia (PWD), and their burden is a multidimensional concept that is particularly burdensome in the resource-limited rural context. However, the previous study investigated caregiver burden as a holistic concept, which limits the identification of different types of burdens and their contributing factors. This study aims to explore the specific domain structure and reliability of the Caregiver Burden Inventory (CBI) among dementia family caregivers in rural China, and to determine the levels and key determinants of these domains. Methods This study enrolled 145 PWD and their family caregivers from rural areas. Data collected included PWDs’ socio-demographic information and the severity of neuropsychiatric symptoms, as well as family caregivers’ socio-demographic information, caregiver burden, social support, and mental health. Exploratory graph analysis was used to define the CBI’s domains and the structure of each domain, and Cronbach’s alpha coefficients were calculated to assess reliability. Multiple linear regression was used to assess factors influencing caregiver burden domains. Results Exploratory graph analysis revealed a robust four-domain model, with Cronbach’s alphas between 0.690 and 0.846. Rural family caregivers reported high time-dependence and developmental burdens, while emotional and social burdens were lower. Multiple regression analyses identified PWD’s self-care ability, the presence of comorbidities, rural family caregivers’ depression levels, and weekly caregiving hours as significant predictors of these burdens. Conclusions This study validated the multidimensional structure of the CBI in rural Chinese family caregivers, demonstrating its utility in capturing diverse aspects of caregiver burden. These findings emphasize the need for targeted interventions to address the time management and personal development burdens faced by rural family caregivers. Dementia Family caregivers Caregiver burden Rural China Exploratory graph analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background China has the world’s largest population of people with dementia (PWD), contributing approximately 25.5% of global cases [ 1 ]. A large-scale study estimates the national prevalence of dementia among those aged 60 + at 6.0%, with rural areas exhibiting significantly higher rates than urban ones [ 2 ]. Influenced by national conditions and the traditional filial piety, about 80% of PWD receive home-based care from family caregivers [ 3 ], imposing substantial demands on these caregivers. Family caregivers are spouses, children, or other family members who provide unpaid care to patients [ 4 ]. They are responsible not only for providing daily care and health monitoring, but also for coping with the PWD’s functional deterioration and dementia-related symptoms, such as declines in activities of daily living (ADL) or instrumental activities of daily living (IADL), as well as symptoms like agitation and hallucinations [ 5 – 7 ]. These heavy and complex caregiving tasks place a substantial, multifaceted burden on caregivers, including physiological, psychological, social, and economic challenges, which can often be more severe in rural China [ 8 – 10 ]. People in rural areas are more likely to uphold traditional filial piety, emphasizing family responsibility toward elders, than urban dwellers influenced by Western independence values [ 11 , 12 ]. Moreover, lower dementia knowledge in rural areas can exacerbate stigma and caregiver isolation [ 13 ]. In addition, rural caregivers face unique obstacles, including lower levels of education, financial strain, and limited healthcare resources [ 14 ]. More seriously, the mass migration of younger generations further weakens traditional family support systems [ 15 ]. These cumulative factors contribute to an increased burden on rural caregivers. Dementia family care support programs can effectively enhance family caregivers’ ability to provide care and alleviate their burden [ 16 , 17 ]. Understanding caregiver burden is a prerequisite for implementing the support program, yet most assessments rely on a single global score [ 3 , 18 ]. Given the multidimensional nature of the burden, reliance on global scores may not provide a comprehensive or accurate assessment [ 19 ]. This oversimplification hinders a deeper understanding of distinct burdens, potentially leading to generalized interventions that fail to address caregivers’ specific needs. Therefore, identifying these distinct burden domains is essential for developing effective, targeted support programs for dementia family caregivers in rural China. The Caregiver Burden Inventory (CBI) is a widely used instrument for measuring multiple domains of caregiver burden, including time-dependence burden (stress resulting from time constraints), emotional burden (negative feelings toward the care recipient), social burden (feelings arising from role conflicts), physical burden (fatigue and health deterioration resulting from long-term caregiving), and developmental burden (a sense of “stagnation” in personal development compared to peers) [ 20 ]. While providing valuable insights into the patterns of burden experienced by caregivers, the CBI was developed in a Western cultural context and primarily validated in urban Chinese populations [ 21 , 22 ]. Given the sociocultural correlates of caregiver burden, the applicability of the CBI’s original factor structure to rural areas is uncertain. Previous studies have shown that, despite its internal validity and reliability, the CBI exhibits inconsistent factor structures in different economic and cultural contexts [ 20 , 22 , 23 ]. Thus, exploratory analysis of the CBI in rural China is warranted. Traditional factor analysis methods suffer from sample-size sensitivity in factor identification and involve subjectivity in rotation method selection and in the interpretation of factor loadings [ 24 ]. Furthermore, they also lack effective assessment of the stability and replicability of factor structure and item allocation across different samples [ 25 ]. To overcome these methodological limitations, this study introduces Exploratory Graph Analysis (EGA), an emerging network psychometric technique [ 24 ]. EGA constructs a variable network model, transforming variables and their relationships into nodes and edges to identify variable communities (network clusters of items with strong conditional dependencies). These communities represent underlying psychological structures, which are equivalent to “factors” in traditional factor analysis, that is, domains or dimensions [ 24 , 26 ]. In terms of technical implementation, EGA uses the Gaussian Graphical Model (GGM) combined with community detection algorithms (such as the “walktrap” algorithm) to construct these models and identify domains and their structures [ 24 ]. EGA demonstrates accuracy comparable to or superior to traditional methods while enabling automated factor estimation and visualization of results [ 27 , 28 ]. Additionally, to address potential biases from sampling variability, EGA is augmented with the bootstrap technique (bootEGA), which provides insights into the stability of item assignments and domain estimates [ 28 , 29 ]. This study pioneers the application of EGA to explore the domains of the CBI among rural dementia family caregivers, offering a novel methodological approach to burden assessment. In addition to identifying the domains of burden, it is necessary to investigate their determinants. Previous studies have divided the factors affecting caregiver burden into three categories: PWD-related variables (e.g., dementia duration, neuropsychiatric symptoms), caregiver-related factors (e.g., age, gender, mental health), and social factors (e.g., perceived social support, economic status) [ 21 ]. However, prior studies have predominantly focused on urban populations; the factors influencing caregiver burden among rural family caregivers remain under-investigated. For instance, behaviors and psychological symptoms of dementia (BPSD) have a 90% incidence rate [ 30 ]. Rural family caregivers typically demonstrate lower health literacy [ 31 ], which may increase the burden of managing these potentially disruptive symptoms. Additionally, these caregivers often face mental health issues such as depression and anxiety, which can further exacerbate their burden [ 32 ]. Although social support is known to alleviate caregiver burden, the most effective sources in rural China are still underexplored. A comprehensive understanding of these determinants helps in designing the dementia family care support program that precisely targets the most impactful factors. Given the insufficient validation of the CBI among family caregivers of PWD in rural China, and the lack of in-depth understanding of the domains, levels, and influencing factors of caregiver burden in this unique context, this study aims to: (1) evaluate the domains and associated structures and reliability of the CBI among family caregivers of PWD in rural China, and (2) identify the levels and key determinants of different burden domains. Methods Study design This cross-sectional study was conducted between April 2022 and February 2023, and its reporting adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Participants and sampling The required sample size was calculated using the cross-sectional sample size formul: n \(\:\:=\:\) ( \(\:{\frac{{Z}_{1-\alpha\:/2\:}\sigma\:}{d})}^{2}\) [ 33 ]. Here, \(\:{Z}_{1-\alpha\:/2}\) denotes the critical value of the standard normal distribution. With a significance level “ α ” of 0.05, \(\:{Z}_{0.975}\:\) = 1.96. The population standard deviation (σ) was estimated at 19.7, based on prior research [ 22 ]. The allowable error “ d” , which is usually set at 0.25 or 0.5 times the standard deviation, is chosen to be 0.25 times the standard deviation to balance sample size and precision, resulting in d = 0.25 × 19.7 = 4.925. The calculated theoretical sample size was 62. Accounting for a 20% invalid questionnaire rate, the final sample size was set at 75. Participants were recruited from rural community health centers and tertiary hospitals’ memory clinics in Hunan Province, China. These settings were carefully chosen to capture the diverse experiences of rural family caregivers of PWD, recognizing the significant differences in access to healthcare and support services between rural and urban environments. Participants were purposefully recruited from hospital and rural community health center patient lists. Inclusion criteria were: (1) permanent rural residents (having rural household registration and residing in a natural village for more than six months of the year); (2) care recipients with a confirmed diagnosis of dementia; (3) caregivers who assumed primary caregiving responsibilities (providing the majority of daily care for at least six months, including but not limited to meal assistance, personal hygiene, medication reminders, accompanying outings, and managing medical affairs); (4) caregivers with sufficient literacy and communication skills to participate in the study (i.e., ability to read, write, understand, and converse in Mandarin); (5) voluntary participation and able to give informed consent. Family-employed caregivers and those currently participating in similar research were excluded. Measures General Information Questionnaire A self-designed questionnaire was used to collect socio-demographic information about the family caregivers (e.g., age, gender, education, marital status, employment status, perceived health status, presence of acute or chronic diseases, monthly household income, relationship to the PWD, co-residence, weekly caregiving hours) and the PWD (e.g., age, gender, education, presence of acute or chronic diseases, prior role as primary income earner, type of medical insurance, level of self-care, duration of dementia). Caregiver Burden Inventory (CBI) The CBI was used to assess the level of burden among rural family caregivers. This 24-item inventory, originally developed by Novak and Guest [ 20 ], evaluates five domains of burden: time-dependence (5 items), emotional (5 items), social (5 items), physical (4 items), and developmental (5 items). Each item is rated on a scale from 0 (not descriptive) to 4 (highly descriptive), yielding a total score ranging from 0 to 96, with higher scores indicating greater burden. The Chinese version of the CBI, translated and validated by Chou et al. [ 22 ], was used in this study. Neuropsychiatric Inventory Questionnaire (NPI-Q) The NPI-Q developed by Kaufer et al. [ 34 ] was used to assess the severity of BPSD and related caregiver distress. The questionnaire consists of 12 items corresponding to various symptoms such as hallucinations, delusions, agitation, and anxiety. Family caregivers are required to assess whether the patient has experienced these symptoms over the past month, and only evaluate the severity (1–3 points) and caregiver distress (0–5 points) for symptoms that are present (rated as “yes”). This study primarily utilized the NPI-Q severity scores, with a total score ranging from 0 to 36. Higher scores indicate more severe symptoms. The Chinese version validated by Ma et al. [ 35 ] showed good reliability, with a Cronbach’s alpha of 0.85 for the NPI-Q. Multidimensional Scale of Perceived Social Support (MSPSS) Perceived social support among family caregivers was measured using the MSPSS [ 36 ]. The scale comprises three subscales, each containing four items, assessing support from family members, friends, and significant others, for a total of 12 items. Each item is rated on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with total scores ranging from 12 to 84. Higher scores reflect greater perceived social support. The total Cronbach’s alpha coefficient for the Chinese version of MSPSS was 0.90, with subscale coefficients of 0.87 (family), 0.82 (friends), and 0.90 (significant others), demonstrating satisfactory reliability [ 37 ]. Depression-Anxiety-Stress Scale (DASS-21) The mental health of rural family caregivers was evaluated with the DASS-21, a simplified version of the original 42-item DASS, which includes three subscales: depression, anxiety, and stress [ 38 ]. Each subscale consists of 7 items, scored from 0 (doesn’t apply to me at all) to 3 (applies to me a lot). Scores for each subscale are calculated by summing all item scores and multiplying by 2. Caregivers with scores above 9 for depression, above 7 for anxiety, and above 14 for stress are considered to exhibit mental health issues, with higher scores indicating greater severity of symptoms. The Cronbach’s alpha coefficient for the Chinese version of the DASS-21 total scale was 0.92, and the subscales were 0.80–0.83 [ 39 ]. Data collection and ethical considerations Data were collected through face-to-face structured interviews conducted by two trained researchers. Hospital nurses and community health center staff assisted the researchers by distributing the study information to potential participants. Rural family caregivers interested in the study were instructed to text the researcher via WeChat. The researcher contacted eligible caregivers by telephone to obtain initial consent and schedule face-to-face interviews. The interviews were conducted at outpatient departments of hospitals and community health centers. Before the interviews, participants were informed about the study’s purpose, procedures, rights, and potential risks. After obtaining signed informed consent, participants completed the questionnaire. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Xiangya School of Nursing, Central South University (Approval No.: E2021143). Statistical Analysis We utilized the EGAnet package in R 4.4.1 to explore the network structure. Employing the Glasso model and the walktrap community detection algorithm to identify the number of domains and the domain affiliations of the items. We then conducted bootEGA with 1000 iterations to evaluate the stability. The criteria for evaluation included the frequency of detecting the same number of domains, the frequency with which items were categorized into their respective domains (item replication index), and the frequency of structural consistency within each domain, each with an acceptable threshold of 0.75 [ 29 ]. Aiming for distinct yet cohesive burden domains, we retained items based on the item replication index to enhance internal consistency within the CBI. As suggested by Golino et al. [ 40 ], items with a replication index below 0.75 were excluded, and the analysis was rerun. After establishing the domain, we analyzed the data with SPSS 26.0. To ensure the maximum internal reliability of the CBI’s domains, we calculated Cronbach’s alpha coefficients for each subdomain. Data normality was assessed with the Kolmogorov-Smirnov test. For normally distributed data, we reported results as means and standard deviations (M ± SD), whereas for non-normally distributed data, we reported medians and interquartile ranges (M [IQR]). Categorical variables were reported as frequencies and percentages. Group comparisons were conducted using t-tests or ANOVA for normally distributed data, and Mann-Whitney U or Kruskal-Wallis H test for non-normally distributed data. Correlations were evaluated using either Pearson (for normally distributed data) or Spearman (for non-normally distributed data), and multiple linear regression was used to identify factors affecting each domain of caregiver burden. Statistical significance was set at p < 0.05. Results Socio-demographic characteristics A total of 150 questionnaires were collected, with 145 valid responses, yielding an effective response rate of 96.7%. The majority of rural family caregivers in the study were female (56.6%), married (89.0%), and adult children of PWD (60.0%), with an average age of 52.61 ± 13.84 years (Table 1 ). The average age of PWD was 71.74 ± 8.55 years, and most of them were female (54.5%). The median duration since dementia diagnosis was 3.0 (2.0–4.0) years. A significant percentage, 75.2%, suffered from comorbidities. Only 35.9% of PWD are fully capable of self-care and do not require assistance from others (Table 1 ). Table 1 Socio-demographic characteristics of the family caregivers and PWD (n = 145). Socio-demographic characteristics Family Caregivers PWD Gender, n (%) Male 63 (43.4) 66 (45.5) Female 82 (56.6) 79 (54.5) Age (years old), Mean ± SD 52.61 ± 13.84 71.74 ± 8.55 Age, n (%) ≤ 60 years old 103 (71.0) 18 (12.4) > 60 years old 42 (29.0) 127 (87.6) Marital status,n (%) Spousal 129 (89.0) — Non-spouse 16 (11.0) — Employment, n (%) Employed 74 (51.0) — Unemployed 71 (49.0) — Educational level, n (%) Illiteracy 4 (2.8) 22 (15.2) Primary school 34 (23.4) 62 (42.8) Middle school 40 (27.6) 46 (31.7) High school or junior college 40 (27.6) 9 (6.2) Professional training college 19 (13.1) 1 (0.7) Bachelor and above 8 (5.5) 5 (3.4) Perceived health status, n (%) Poor 45 (31.0) — General 72 (49.7) — Good 28 (19.3) — Comorbidity, n (%) None 73 (50.3) 36 (24.8) Have 72 (49.7) 109 (75.2) Monthly household income, n (%) 1999yuan 75 (51.7) — Relationship to PWD, n (%) Spouse 37 (25.5) — Adult child 87 (60.0) — Daughter/son-in-law 16 (11.0) — Table 1 Socio-demographic characteristics of the rural family caregivers and PWD (n = 145) (Continued). Socio-demographic characteristics Family Caregivers PWD Others 5 (3.4) — Co-residence or not Yes 107 (73.8) — No 38 (26.2) — Weekly caregiving hours (h), Median (IQR) 70.0 (46.5–144.0) — Weekly caregiving hours, n (%) 120 h 38 (26.2) — Duration of dementia (years), Median (IQR) — 3.0 (2.0–4.0) Duration of dementia, n (%) ≤ 5 years — 119 (82.1) > 5 years — 26 (17.9) Income was ever the primary source, n (%) Yes — 72 (49.7) No — 73 (50.3) Medical insurance, n (%) Provincial and municipal health insurance — 46 (31.7) Rural cooperative medical insurance — 69 (47.6) Self-financed — 21 (14.5) Other Insurance — 9 (6.2) Level of self-care, n (%) Fully self-care — 52 (35.9) Partially self-care — 52 (35.9) Completely unable to self-care — 41 (28.3) Note: PWD=People with Dementia, IQR=Interquartile Range, SD=Standard Deviation. Caregiver burden of rural family caregivers measured by the CBI The EGA identified a five-domain network (Fig. 1 ), in which the 24 scale items are represented as nodes, and line thickness and color denote the strength and nature of associations between items. Details of the items within each domain are in Supplementary Table S1 . As shown in Table 2 , bootEGA detected five domains with a frequency of 0.660 (i.e., in 1000 bootstrap samples, 660 samples identified five domains). Figure 3 shows that the item replication indexes for items C1 (“I’m not getting enough sleep”), C2 (“I’m physically tired”), C3 (“Caregiving has made me physically sick”), C4 (“My health has suffered”), C9 (“ I don’t do as good a job at work as I used to”), and C19 (“I don’t have a minute’s break from my caregiving chores”) are all significantly below 0.75, indicating poor stability of these items. The structural consistency frequencies for Domain 1 (developmental burden) and Domain 2 (physical burden) also fell below the 0.75 threshold (Table 3 ), suggesting that some items were misclassified into these domains. It is necessary to delete these low-stability items. After exclusion, a four-domain network structure with 18 items was obtained (Fig. 2 ), categorized into social burden, emotional burden, time-dependence burden, and developmental burden. Upon re-examining the EGA structure through bootstrap, the frequency of the four domains rose to 0.941, while that of the five domains decreased to 0.055 (Table 2 ). Figure 4 and Table 3 demonstrate that the replication index of items and the consistency of domain structures within the scale have reached satisfactory levels (> 0.75). The Cronbach’s alpha coefficients for each domain ranged from 0.690 to 0.846. The complete list of 18 items and Cronbach’s alpha coefficients are available in Supplementary Table S2. Scores across all four domains showed skewed distributions (Supplementary Table S2). Specifically, the median score for the time-dependence burden was 13.0 (11.0–15.0). Both the developmental and social burdens had median scores of 12.0 (10.0–14.0) and 7.0 (5.0-9.5), respectively. The emotional burden had the lowest median score, at 5.0 (4.0–8.0). Table 2 Frequency of the CBI domains across all bootstrap replicate samples. Number of domains Frequency EGA (24 items) EGA (18 items) 3 0.007 0.003 4 0.180 0.941 5 0.660 0.055 6 0.144 0.001 7 0.009 — Note: EGA=Exploratory Graph Analysis. Table 3 Frequency with consistent internal structure across all domains. Domains Frequency of structural consistency EGA(24 items) EGA(18 items) Time-dependence burden 0.997 1.000 Emotional burden 0.932 0.912 Social burden 0.824 0.847 Development burden 0.313 0.870 Physical burden 0.606 — Note: EGA = Exploratory Graph Analysis. Relationships between caregiver burden and socio-demographic characteristics A univariate analysis revealed that among the socio-demographic variables of rural family caregivers, significant differences in emotional and developmental burden were observed only for weekly caregiving hours ( p < 0.05). Time-dependence burden showed significant differences across age, employment status, and monthly household income ( p 0.05). Among the PWD’s socio-demographic variables, significant differences in the emotional burden of rural family caregivers were observed in PWD’s age and types of medical insurance ( p < 0.05). Significant differences in time-dependence burden were found in PWD’s age, presence of acute or chronic diseases, levels of self-care, and duration of dementia ( p 0.05). Specifics are presented in Table 4 . Correlation between caregiver burden and perceived social support, mental health, and BPSD Spearmen correlation analysis (Table 5 ) showed that social burden was negatively correlated with overall perceived social support ( r = -0.296, p < 0.001), support from family ( r = -0.320, p < 0.001), friends ( r = -0.268, p < 0.01), and significant others ( r = -0.264, p < 0.01), and positively correlated with depression ( r = 0.359, p < 0.01). Emotional ( r = 0.183, p < 0.05) and developmental burdens ( r = 0.268, p < 0.01) were each positively associated with depression, while time-dependence burden correlated positively with BPSD severity ( r = 0.326, p < 0.001). Multiple regression of factors influencing caregiver burden The four domains of burden were each examined as the dependent variable in a multiple linear regression analysis (Table 6 ). The significant variables from the univariate and Spearman correlation analyses served as the independent variables. The multiple linear regression analyses revealed that: (1) Social burden is associated with rural family caregivers’ depression (explaining 18.9% of the variance). (2) Emotional burden is associated with the rural family caregivers’ weekly caregiving hours (explaining 9.1% of the variance). (3) Higher time-dependence burden was associated with the presence of comorbidities and poorer self-care ability in PWD (explaining 28.3% of the variance). (4) Developmental burden is associated with depression and weekly caregiving time among family caregivers (explaining 9.3% of the variance). Table 4 Univariate analysis of caregiver burden (n = 145). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Rural family caregivers Gender Male 8.0 (5.0–10.0) 5.0 (3.0–7.0) 13.0 (12.0–15.0) 11.10 ± 3.04 Female 7.0 (5.0–9.3) 5.5 (5.0–8.0) 13.0 (11.0–15.0) 12.02 ± 2.97 t/F/Z/H -0.465 c -1.198 c -1.282 c -1.847 a p -value 0.642 0.231 0.200 0.067 Age ≤ 60 years old 7.0 (5.0–9.0) 5.0 (4.0–8.0) 13.0 (11.0–14.0) 12.0 (10.0–14.0) > 60 years old 8.0 (4.0–10.0) 5.0 (3.0–7.0) 13.5 (13.0–15.0) 11.0 (10.0–13.0) t/F/Z/H -0.353 c -0.015 c -2.613 c -1.351 c p -value 0.724 0.988 0.009** 0.177 Marital status Spousal 7.0 (5.0–9.0) 5.0 (4.0–8.0) 13.0 (11.0–15.0) 12.0 (10.0–13.5) Non-spousal 9.0 (5.5–10.0) 5.0 (0.0–7.8) 14.0 (11.0–15.0) 13.0 (10.3–15.0) t/F/Z/H -1.545 c -1.146 c -0.450 c 1.149 c p -value 0.122 0.252 0.653 0.251 Employment Employed 8.0 (5.0–10.0) 5.0 (4.0–8.0) 12.0 (10.0–14.0) 12.0 (10.0–15.0) Table 4 Univariate analysis of caregiver burden (n = 145) (Continued). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Unemployed 7.0 (4.0–9.0) 5.0 (3.0–7.0) 14.0 (12.0–15.0) 12.0 (10.0–13.0) t/F/Z/H -0.619 c -0.008 c -3.755 c -1.347 c p -value 0.536 0.994 < 0.001*** 0.178 Educational level Illiteracy 9.5 (7.5–10.8) 9.5 (5.0–14.8) 13.5 (12.3–15.5) 14.0 (10.8–17.3) Primary school 7.0 (4.0–9.0) 6.0 (5.0–7.0) 13.0 (12.0–15.3) 12.0 (10.0–13.0) Middle school 7.5 (4.0–10.0) 5.0 (3.0–8.8) 13.0 (12.3–14.0) 11.0 (10.0–13.0) High school or junior college 7.0 (5.0–8.8) 5.0 (4.0–6.0) 13.0 (11.0–15.0) 12.0 (10.0–14.8) Professional training college 7.0 (5.0–10.0) 5.0 (1.0–7.0) 13.0 (11.0–16.0) 11.0 (9.0–13.0) Bachelor and above 9.0 (5.3–10.5) 7.0 (3.5–8.8) 11.0 (10.0–12.8) 15.0 (10.0–15.8) t/F/Z/H 8.078 d 5.757 d 6.467 d 7.461 d p -value 0.152 0.331 0.263 0.189 Perceived health status Poor 8.0 (6.0–10.0) 7.0 (5.0–9.0) 13.0 (12.0–15.0) 12.0 (11.0–14.5) General 7.0 (5.0–9.0) 5.0 (3.0–7.0) 13.0 (11.0–14.0) 11.0 (9.3–14.0) Good 7.0 (4.0–8.0) 5.0 (3.0–6.8) 12.5 (10.0–15.0) 10.5 (8.3–13.8) t/F/Z/H 3.427 d 4.675 d 3.276 d 3.300 d p -value 0.180 0.097 0.194 0.192 Table 4 . Univariate analysis of caregiver burden (n = 145) (Continued). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Acute or chronic disease None 7.0 (5.0–9.0) 5.0 (4.0–7.5) 13.0 (11.0–15.0) 12.0 (9.0–14.0) Have 7.5 (4.3–10.0) 5.0 (3.3–8.0) 13.0 (12.0–15.0) 12.0 (10.0–13.0) t/F/Z/H 0.187 c -0.162 c 1.520 c 0.682 c p -value 0.852 0.871 0.128 0.495 Monthly household income 1999 yuan 7.0 (4.0–9.0) 5.0 (4.0–8.0) 13.0 (11.0–14.0) 12.0 (10.0–14.0) t/F/Z/H 0.617 d 0.174 d 7.142 d 1.095 d p-value 0.734 0.917 0.028* 0.578 Relationship to PWD Spouse 8.0 (4.5–10.0) 5.0 (3.0–7.0) 13.0 (13.0–15.5) 11.0 (9.5–13.0) Adult child 7.0 (5.0–10.0) 5.0 (4.0–8.0) 13.0 (11.0–15.0) 12.0 (9.0–14.0) Daughter/son-in-law 6.0 (4.0–8.0) 5.5 (4.3–8.5) 12.0 (11.0–14.5) 12.0 (11.0–14.0) Others 8.0 (7.0–8.5) 5.0 (4.0–7.0) 13.0 (10.5–15.0) 11.0 (10.5–16.5) t/F/Z/H 3.510 d 0.654 d 5.059 d 3.848 d p -value 0.319 0.884 0.168 0.278 Table 4 . Univariate analysis of caregiver burden (n = 145) (Continued). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Cohabitation or not Yes 8.0 (4.0–9.0) 5.0 (3.0–7.0) 13.0 (12.0–15.0) 11.0 (10.0–13.0) No 7.0 (6.0–10.0) 5.5 (5.0–8.3) 12.0 (10.0–14.3) 12.5 (11.0–15.0) t/F/Z/H -0.502 c -1.114 c -1.790 c -1.811 c p -value 0.616 0.265 0.073 0.070 Weekly caregiving hours < 40 h 8.0 (6.0–9.0) 6.0 (5.0–8.0) 13.0 (12.0–15.0) 12.0 (11.0–15.0) 40–79 h 7.0 (5.8–10.0) 6.0 (5.0–8.3) 13.0 (11.0–14.0) 12.0 (11.0–14.0) 80–120 h 8.0 (4.0–10.3) 6.0 (3.0–8.3) 14.0 (12.0–16.0) 10.5 (8.8–12.8) > 120 h 7.0 (4.0–9.0) 5.0 (2.8–5.3) 13.0 (11.0–15.3) 10.0 (8.8–13.0) t/F/Z/H 1.412 d 14.004 d 5.077 d 10.813 d p -value 0.703 0.003** 0.166 0.013* PWD Gender Male 8.0 (5.0–10.0) 5.0 (3.8–7.0) 13.0 (12.0–15.0) 11.76 ± 3.39 Female 7.0 (4.0–9.0) 5.0 (4.0–8.0) 13.0 (11.0–14.0) 11.51 ± 2.70 t/F/Z/H -1.484 c -0.735 c -1.871 c 0.496 a p -value 0.138 0.462 0.061 0.620 Table 4 . Univariate analysis of caregiver burden (n = 145) (Continued). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Age ≤ 60 years old 7.0 (3.0–10.3) 3.0 (1.0–5.5) 11.5 (8.0–13.0) 11.0 (9.0–14.3) > 60 years old 7.0 (5.0–9.0) 5.0 (5.0–8.0) 13.0 (12.0–15.0) 12.0 (10.0–13.0) t/F/Z/H -0.217 c -2.417 c -3.344 c -0.087 c p -value 0.828 0.016* 0.001** 0.930 Educational level Illiteracy 7.5 (6.0–9.3) 5.0 (4.8–7.0) 13.0 (11.8–14.3) 11.0 (10.8–15.0) Primary school 8.0 (5.0–10.0) 6.0 (5.0–8.0) 13.0 (12.0–15.0) 12.0 (10.0–13.0) Middle school 7.0 (4.0–9.0) 5.0 (3.0–8.3) 13.0 (10.8–15.0) 12.0 (10.0–15.0) High school and junior college 6.0 (4.0–11.0) 5.0 (0.0–6.5) 11.0 (6.5–16.0) 10.0 (7.0–12.5) Professional training college — — — — Bachelor and above 7.0 (4.5–10.0) 5.0 (3.0–9.0) 13.0 (11.0–15.5) 12.0 (6.5, 13.5) t/F/Z/H 2.568 d 5.962 d 2.491 d 4.957 d p -value 0.766 0.310 0.778 0.421 Comorbidity None 7.0 (5.0–10.8) 5.0 (1.0–6.8) 12.0 (10.0–14.0) 11.5 (9.3–14.0) Have 8.0 (5.0–9.0) 5.0 (5.0–8.0) 13.0 (12.0–15.0) 12.0 (10.0–14.0) t/F/Z/H 0.065 c -1.595 c 3.084 c 0.191 c p -value 0.798 0.111 0.002** 0.848 Table 4 . Univariate analysis of caregiver burden (n = 145) (Continued). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Duration of dementia ≤ 5 years 7.0 (5.0–9.0) 5.0 (4.0–8.0) 13.0 (11.0–14.0) 11.0 (10.0–13.0) > 5 years 7.5 (5.0–10.0) 5.0 (4.5–7.3) 15.0 (13.0–16.0) 12.0 (10.0–14.0) t/F/Z/H -0.345 c -0.245 c -3.479 c -0.811 c p -value 0.730 0.806 0.001** 0.417 Income was ever the primary source Yes 7.0 (5.0–10.0) 5.0 (3.3–7.8) 13.0 (11.0–15.0) 11.63 ± 3.30 No 8.0 (4.5–9.0) 5.0 (4.0–8.0) 13.0 (11.0–15.0) 11.62 ± 2.76 t/F/Z/H -0.350 c -0.656 c -0.264 c 0.017 a p -value 0.726 0.512 0.792 0.986 Medical insurance Provincial and municipal health insurance 7.0 (5.8–10.0) 6.0 (5.0–8.0) 13.0 (11.8–15.0) 11.0 (10.0–14.0) Rural cooperative medical insurance 8.0 (6.0–10.0) 5.0 (3.0–7.0) 14.0 (11.0–15.0) 12.0 (10.0–13.0) Self-financed 8.0 (4.0–9.0) 5.0 (3.5–9.0) 13.0 (10.0–13.5) 12.0 (10.0–14.0) Others 4.0 (1.5–7.5) 3.0 (0.0–4.5) 12.0 (12.0–15.0) 10.0 (8.0–12.5) t/F/Z/H 4.845 d 8.686 d 5.587 d 4.295 d Table 4 . Univariate analysis of caregiver burden (n = 145) (Continued). Socio-demographic characteristics Social burden Emotional burden Time-dependence burden Developmental burden Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD Median (IQR) / Mean ± SD p -value 0.183 0.034* 0.134 0.231 Level of self-care Fully self-care 7.0 (5.0–10.0) 5.0 (5.0–7.8) 12.0 (10.0–13.0) 12.00 ± 3.35 Partially self-care 7.5 (5.0–9.8) 6.0 (3.5–8.0) 13.0 (12.0–14.0) 11.69 ± 2.76 Completely unable to self-care 8.0 (5.0–9.0) 5.0 (2.0–7.0) 15.0 (14.0–16.0) 11.05 ± 2.91 t/F/Z/H 0.177 d 4.680 d 41.541 d 1.156 b p -value 0.915 0.096 < 0.001*** 0.318 Note: PWD = People with Dementia, IQR = Interquartile Range, SD = Standard Deviation. a = t-test, b = ANOVA, c = Mann-Whitney U test, d = Kruskal-Wallis H test. * p < 0.05, ** p < 0.01, *** p < 0.001. Table 5 Correlation between perceived social support, negative emotions, severity of BPSD, and caregiver burden (n = 145). Variables Median (IQR) Social burden Emotional burden Time-dependence burden Developmental burden MSPSS total score 53.0 (48.0–57.0) -0.296*** -0.024 0.075 -0.046 family 18.0 (17.0–19.0) -0.320*** -0.022 0.083 -0.031 friends 17.0 (16.0–19.0) -0.268** -0.006 0.073 -0.023 significant others 17.0 (15.0–19.0) -0.264** -0.068 0.046 -0.096 Depression 8.0 (4.0–10.0) 0.359*** 0.183* 0.046 0.268** Anxiety 8.0 (4.0–11.0) 0.012 -0.051 0.080 -0.087 Stress 12.0 (8.0–16.0) 0.118 -0.039 -0.033 0.032 NPI-S 13.0 (10.0–15.0) 0.116 -0.050 0.326*** -0.017 Note: * p < 0.05, ** p < 0.01, *** p < 0.001. Table 6 Multivariate linear regression related to caregiver burden (n = 145). Variables B SE 95%CI p-value Adjusted R 2 Dependent variable: Social burden Support from significant others -0.024 0.126 (-0.272, 0.224) 0.849 18.9% Support from friends -0.077 0.172 (-0.416, 0.262) 0.654 Support from family -0.318 0.182 (-0.677, 0.042) 0.083 Depression 0.191 0.053 (0.087, 0.296) < 0.001*** Dependent variable: Emotional burden Weekly caregiving hours (< 40 h = 1) -0.654 0.258 (-1.164, -0.145) 0.012* 9.1% PWD’s age (≤ 60 years old = 1) 1.433 0.818 (-0.184, 3.050) 0.082 Medical insurance (Provincial and municipal) 0.283 0.846 (-1.390, 1.956) 0.739 Medical insurance (Rural cooperative) -0.144 0.802 (-1.730, 1.443) 0.858 Medical insurance (others) -2.261 1.261 (-4.753, 0.232) 0.075 Depression 0.072 0.055 (-0.037, 0.182) 0.193 Dependent variable: Time-dependence burden Caregiver’s age (< 60 years old = 1) 0.024 0.578 (-1.107, 1.155) 0.967 28.3% Work status (employed = 1) 0.918 0.520 (-0.110, 1.947) 0.080 Monthly household income (< 1000 yuan = 1) 0.129 0.377 (-0.616, 0.874) 0.733 PWD’s age (≤ 60 years old = 1) 0.995 0.667 (-0.324, 2.314) 0.863 Comorbidity (none = 1) 1.261 0.504 (0.265, 2.257) 0.013* Duration of dementia (≤ 5 years = 1) 0.598 0.560 (-0.509, 1.705) 0.287 Level of self-care (fully self-care = 1) 1.234 0.301 (0.634, 1.824) < 0.001*** NPI-S total score 0.028 0.057 (-0.084, 0.140) 0.621 Table 6 Multivariate linear regression related to caregiver burden (n = 145) (Continued). Variables B SE 95%CI p-value Adjusted R 2 Dependent variable: Developmental burden Weekly caregiving hours (< 40 h = 1) -0.761 0.229 (-1.215, -0.307) 0.001** 9.3% Depression 0.120 0.051 (0.020, 0.219) 0.019* Note: SE = Standard Error, CI = Confidence Interval. * p < 0.05, ** p < 0.01, *** p < 0.001. Discussion Numerous comparative studies [ 41 – 43 ] have shown that caregivers of PWD experience a greater burden than those caring for individuals with other chronic diseases, such as cancer, multiple sclerosis, or mild cognitive impairment. The burden on caregivers related to dementia care is particularly acute in rural China, where access to resources and support services is limited. Consequently, family caregivers of PWD in rural China represent a vulnerable population who need formal support. A comprehensive assessment of the various burdens they face is essential for developing effective support services for this population. The CBI serves as a valuable tool that not only quantifies the overall burden but also assesses its various aspects [ 44 ]. In this study, we employed EGA to explore the domains and associated structures of the CBI among family caregivers of PWD in rural areas. The initial EGA results revealed five domains: developmental, physical, social, emotional, and time-dependence, consistent with the original English version [ 20 ] and the existing Chinese version [ 22 ]. However, in this study, items C1 (“I’m not getting enough sleep”) and C2 (“I’m physically tired”) were categorized under “developmental burden” rather than “physical burden”, and .items C9 (“I don’t do as good a job at work as I used to”) and C19 (“I don’t have a minute’s break from my caregiving chores”) were shifted to “developmental burden” from “emotional” and “time-dependence” burdens, respectively. These differences may be attributed to cultural factors. In Western countries and urban Chinese populations, sleep deprivation and physical fatigue are typically associated with physical burden. However, in rural China, family caregivers of PWD may view these issues as an inevitable cost of fulfilling family responsibilities, which can affect their productivity (e.g., farming or working) and self-improvement (e.g., learning or skill training), ultimately restricting personal development. Similarly, items C9 and C19 reflect emotional exhaustion due to decreased work performance and restricted time freedom in Western countries and urban Chinese populations. In the rural Chinese sample in this study, these two items more directly reflect the negative impact of caregiving responsibilities on career development and personal growth opportunities, as evidenced by decreased job performance and the occupation of personal time. Additionally, EGA constructs network models based on the co-occurrence relationships among items and clusters those that frequently co-occur [ 28 ]. The strength of the connections between items reflects their correlations. As shown in Fig. 1 , items C1, C2, C9, and C19 have denser connections with nodes representing developmental burden, as indicated by thicker edges, suggesting strong correlations with developmental burden and are thus classified as such. However, these items still have connections to the nodes of the original domains, but these connections are sparser and thinner, indicating weaker correlations. Besides, the only two items of physical burden — item C3 (“Caregiving has made me physically sick”) and item C4 (“My health has suffered”) — also connect with developmental, social, and emotional burden nodes, suggesting that these experiences are not solely physical but intertwined with multiple pressures. The cross-domain connections of these six items indicate instability and cross-loadings, supported by their low item replication indexes in bootEGA (below the 0.75 threshold). The frequent cross-domain occurrence of items leads to low structural consistency, which is why the structural consistency of developmental and physical burden is also below the threshold. After excluding the six unstable items, a subsequent EGA revealed that the remaining 18 items clustered into four burden domains: social, emotional, time-dependence, and developmental, confirming the multidimensionality of caregiver burden. The social burden emerged primarily as a conflict between caregiving duties and social roles, which aligns with previous findings [ 20 ]. Emotional burden refers to the negative emotions caregivers experience [ 20 ]. The time-dependence burden reflects significant time constraints imposed by dementia care, confirming the considerable commitment required from family caregivers [ 20 ]. The developmental burden, closely related to the time-dependence burden, mainly reflects caregivers’ feelings of being out of step with their peers in terms of personal growth [ 20 ]. Unlike the initial EGA, the second analysis did not retain the physical burden domain, as its only two items were excluded. The developmental burden domain, however, contained more items, and after excluding unstable items, the item replication indexes and structural consistency within the domain reached acceptable levels, allowing it to be retained. BootEGA indicated that the four dimensions appeared with a frequency of 0.941 in 1000 bootstrap replications, demonstrating high statistical stability and supporting the conclusion that the CBI presents a four-domain structure among rural family caregivers in China. Some may question why item C24 (“I expected that things would be different at this point in my life”) in the 18-item CBI is categorized under developmental burden, despite its weak connections (thin edges) with other items in that domain: item C20 (“I feel that I am missing out on life”), item C21 (“I wish I could escape from this situation”), item C22 (“My social life has suffered”), item C23 (“I feel emotionally drained due to caring for my care-receiver”).. Firstly, while it has weak connections with nodes in the developmental burden domain (as shown in Fig. 2 ), it links to four other items within this domain, significantly more than in other domains (emotional and time-dependence burdens), indicating domain specificity. Secondly, the item replication indexes in two bootEGA exceeded the threshold, demonstrating its consistent allocation to the developmental burden domain. Thirdly, the item captures the unique psychological experience of the discrepancy between caregivers’ reality and expectations, distinct from emotional and time-dependence burdens and aligning with the developmental burden’s theoretical significance. Lastly, its consistent classification in both the original English version [ 20 ] and the existing Chinese version [ 22 ] further supports its placement in this domain. Thus, both network analysis and theoretical considerations justify assigning item C24 to the developmental burden domain. We also evaluated the CBI’s internal consistency using Cronbach’s alpha. An alpha coefficient of 0.70 or higher for the total scale and 0.60 or higher for each domain suggests satisfactory reliability [ 45 ]. The Cronbach’s alpha coefficients obtained in our study further confirm that the CBI is a suitable instrument for assessing the burden of family caregivers of PWD in rural China. Next, our regression analysis showed that the characteristics of both rural PWD and their family caregivers significantly impact caregiver burden. Within the PWD-related variables, we found that the level of self-care and the presence of acute or chronic illnesses are associated with a time-dependence burden. The anticipated impact of PWDs’ self-care levels on this burden aligns with research in Gushan Town, Fuzhou [ 46 ]. A possible explanation is that dementia, as a neurodegenerative disease, progressively impairs PWD’s memory and cognition, leading to a decline in their self-care abilities [ 47 – 49 ]. This decline means that family caregivers must spend more time and energy assisting with ADL and IADL, such as dressing, eating, transportation, and housekeeping [ 50 ]. There is also no doubt that caring for PWD with comorbidities increases the time-dependence burden, as family caregivers often need to accompany PWD to medical appointments and manage their illnesses. To alleviate these burdens, we recommend a two-pronged approach: enhancing the self-care abilities of PWD through home-based multimodal exercise programs [ 51 , 52 ] and providing respite services, such as daycare centers and home care assistance, to give family caregivers much-needed breaks [ 53 ]. Further, rural family caregiver-related variables, including weekly caregiving hours and depression, were analyzed. Surprisingly, longer weekly caregiving hours correlated with lower emotional and developmental burdens. The median caregiving time in this study was 70.0 hours (46.5–144.0), with 26.2% of caregivers dedicating over 120 hours per week, surpassing the averages in individualistic countries with advanced dementia care systems [ 54 , 55 ]. This suggests that rural family caregivers who are more willing to devote time to caring for PWD are deeply influenced by filial piety. As a core ethical norm in Confucian culture, filial piety emphasizes the obligation of younger generations to support, care for, and respect their parents and elders, leading caregivers to prioritize the elderly’s health over their own well-being [ 56 ]. The internalization of this ethical norm motivates them to overcome negative emotions and limitations in personal development. Depression was significantly associated with developmental burden and was the sole factor explaining the social burden. While the causal relationship between depression and caregiver burden remains to be clarified, it is crucial to recognize depression as a prevalent psychological disorder that can adversely affect caregiver well-being. Healthcare providers and policymakers should improve mental health services in rural areas, screen caregivers for depression, and implement supportive measures such as mobile-based psychoeducation [ 57 ] or exergaming [ 58 ] to improve family caregivers’ mental health and reduce their burden. Surprisingly, contrary to previous studies [ 59 , 60 ], we found that perceived social support and the severity of BPSD did not influence caregiver burden. Although rural family caregivers received considerable support in this study, the general lack of dementia-related knowledge and caregiving skills in rural areas often results in support that falls short of meeting their actual needs [ 61 , 62 ]. In addition, cultural emphasis on family and filial piety in rural settings may lead family caregivers to view elderly care as an inherent duty [ 11 ], making their burden more dependent on their self-efficacy and self-satisfaction in their caregiver role [ 21 ] rather than on the amount of external support. Similarly, due to the limited knowledge of dementia among rural family caregivers, they may be more prone to misinterpret the BPSD as normal aspects of aging, rather than perceiving them as an additional burden. These reasons confirm the importance of the dementia care support program in rural areas. To construct and refine such a program, it is imperative to vigorously cultivate professional multidisciplinary care teams. Strengths and limitations To our knowledge, this is the first study to employ EGA to explore the structure of the CBI and to investigate the factors influencing various burden domains among rural Chinese dementia caregivers. It fills a critical gap in the literature and provides both theoretical support and practical guidance for designing a dementia care support program for rural China. While the study provides several valuable insights, it also has some limitations. First, recruiting participants exclusively from Hunan Province may introduce bias and limit the generalizability of the findings to other rural regions. Secondly, we did not collect specific indicators of participants’ residential rurality (e.g., village population size, distance to tertiary hospitals), which precluded a deeper understanding of how the rural environment affects caregiver burden. Moreover, the cross-sectional design restricts our ability to capture dynamic trends or causal relationships in caregiver burden. Finally, the overall multiple regression models explained relatively low variances across different types of burden, with a maximum of 28.3%, which may be attributed to the exploratory nature of the study. To address current limitations and improve future research quality, we propose the following recommendations. First, future studies should expand the sample to include multiple provinces to improve representativeness. Second, standardized rurality indicators should be incorporated to better characterize how rural contexts influence caregiver burden. Third, longitudinal research methods are needed to track changes in caregiver burden and establish causal relationships between caregiver burden and various factors. Lastly, other potential influencing factors should be considered to gain a deeper understanding of the burden’s origins. Conclusion In conclusion, this study indicates that caregiver burden among rural Chinese dementia family caregivers is composed of four distinct domains: social, emotional, time-dependence, and developmental burdens. It is worth noting that the time-dependence and development burdens of rural family caregivers are particularly heavy, revealing the impact of caregiving tasks on caregivers’ time investment and personal development. Additionally, the study identified key factors affecting each domain of burden, including PWD’s self-care abilities and health status, the time family caregivers spend on care, and their mental health. These findings highlight the urgent need for a family care support program in rural China. Such a program should involve a multidisciplinary team focused on enhancing PWD self-care capabilities, providing respite care, and improving family caregivers’ mental health through education. Recognizing the multidimensionality of caregiver burden will foster a more supportive environment, ultimately enhancing family caregiver well-being and the quality of care for PWD. Abbreviations PWD People with dementia ADL Activities of daily living IADL Instrumental activities of daily living CBI Caregiver burden inventory EGA Exploratory graph analysis GGM Gaussian graphical model BPSD Behaviors and psychological symptoms of dementia STROBE Strengthening the reporting of observational studies in epidemiology NPI-Q Neuropsychiatric inventory questionnaire MSPSS Multidimensional scale of perceived social support DASS-21 Depression-anxiety-stress scale IQR interquartile ranges ANOVA Analysis of variance Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Xiangya School of Nursing, Central South University (Approval No. : E2021143) and followed the principles of the Declaration of Helsinki. All participants provided informed written consent before their participation. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Key R&D Program of China [grant IDs 2023YFC3605200, 2023YFC3605204], the Open Competition Research Project of the China Medical Board [grant ID #19–344], and the Jiaxing Key Discipline of Medicine — Nursing [grant ID 2023-ZC-007]. Author Contribution JZ conceptualized the study, developed the methodology, performed the formal analysis, and was a major contributor in writing the original draft as well as reviewing and editing the manuscript. LX contributed to the conceptualization and methodology, reviewed and edited the manuscript, and supervised the study. XJG contributed to the conceptualization and methodology, and reviewed and edited the manuscript. HY contributed to the conceptualization, conducted the investigation, and curated the data. YW reviewed and edited the manuscript, supervised the study, curated the data, handled project administration, provided resources, and managed funding acquisition. All authors read and approved the final manuscript. Acknowledgement The authors greatly acknowledge all the healthcare professionals who assisted with data collection, as well as the family caregivers of rural people with dementia who willingly participated in the questionnaire survey. Data Availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. References Ren R, Qi J, Lin S, Liu X, Yin P, Wang Z, et al. The China Alzheimer Report 2022. Gen Psychiat. 2022;35(1):e100751. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661–71. Wang L, Zhou Y, Fang X, Qu G. Care burden on family caregivers of patients with dementia and affecting factors in China: a systematic review. Front Psychiatry. 2022;13:1004552. 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The influence of dementia beliefs and knowledge on perceived dementia worry: an empirical study among adults in rural China. Am J Alzheimers Dis. 2022;37:240471375. Ma M, Dorstyn D, Ward L, Prentice S. Alzheimers' disease and caregiving: a meta-analytic review comparing the mental health of primary carers to controls. Aging Ment Health. 2018;22(11):1395–405. Binu VS, Mayya SS, Dhar M. Some basic aspects of statistical methods and sample size determination in health science research. Ayu. 2014;35(2):119–23. Kaufer DI, Cummings JL, Ketchel P, Smith V, MacMillan A, Shelley T, et al. Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsych Clin N. 2000;12(2):233–9. Ma WX, Wang HL, L·Cummings J, Yu X. Reliability and validity of Chinese version of Neuropsychiatric Inventory-Questionnaire in patients with Alzheimer's disease. Chin Mental Health J. 2010;24(05):338–42. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30–41. Jiang QJ. Perceived Social Support Scale. Chin J Behav Sci. 2001;10(10):41–3. Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. 1995;33(3):335–43. Wang K, Shi HS, Geng FL, Zou LQ, Tan SP, Wang Y, et al. Cross-cultural validation of the Depression Anxiety Stress Scale-21 in China. Psychol Assess. 2016;28(5):e88–100. Golino H, Lillard AS, Becker I, Christensen AP. Investigating the structure of the Children's Concentration and Empathy Scale using exploratory graph analysis. Psychol Test Adapt Dev. 2021;2(1):35–49. Spatuzzi R, Vespa A, Fabbietti P, Ricciuti M, Rosati G, Guariniello L, et al. Elderly helping other elderly: a comparative study of family caregiver burden between patients with dementia or cancer at the end of life. J Soc Work End-Life. 2022;18(1):96–108. Tramonti F, Bonfiglio L, Bongioanni P, Belviso C, Fanciullacci C, Rossi B, et al. Caregiver burden and family functioning in different neurological diseases. Psychol Health Med. 2019;24(1):27–34. O'Caoimh R, Calnan M, Dhar A, Molloy DW. Prevalence and predictors of caregiver burden in a memory clinic population. J Alzheimers Dis Rep. 2021;5(1):739–47. Ghazawy ER, Mohammed ES, Mahfouz EM, Abdelrehim MG. Determinants of caregiver burden of persons with disabilities in a rural district in Egypt. BMC Public Health. 2020;20(1):1110–56. Zakariya YF. Cronbach's alpha in mathematics education research: Its appropriateness, overuse, and alternatives in estimating scale reliability. Front Psychol. 2022;13:1074430. Li H, Zhang H, Huang H, Wang Y, Huang H. Caring burden and associated factors of care providers for senile dementia patients in an urban-rural fringe of Fuzhou City, China. Aging Clin Exp Res. 2012;24(6):707–13. Cass SP. Alzheimer's disease and exercise: a literature review. Curr Sport Med Rep. 2017;16(1):19–22. Contreras ML, Mioshi E, Kishita N. Factors related to the quality of life in family Carers of people with dementia: a meta-analysis. J Geriatr Psych Neur. 2021;34(5):482–500. Ganapathy SS, Sooryanarayana R, Ahmad NA, Jamaluddin R, Abd RM, Tan MP, et al. Prevalence of dementia and quality of life of caregivers of people living with dementia in Malaysia. Geriatr Gerontol Int. 2020;20(Suppl 2):16–20. Mlinac ME, Feng MC. Assessment of activities of daily living, self-care, and independence. Arch Clin Neuropsych. 2016;31(6):506–16. Cezar N, Ansai JH, Oliveira M, Da SD, Gomes WL, Barreiros BA, et al. Feasibility of improving strength and functioning and decreasing the risk of falls in older adults with Alzheimer's dementia: a randomized controlled home-based exercise trial. Arch Gerontol Geriat. 2021;96:104476. Kang HS, Myung W, Na DL, Kim SY, Lee JH, Han SH, et al. Factors associated with caregiver burden in patients with Alzheimer's disease. Psychiat Invest. 2014;11(2):152–9. Aksin OZ, Bilgic B, Guner P, Gunes ED, Kuscu K, Ormeci EL, et al. Caregiver support and burden drive intention to engage in a peer-to-peer exchange of services among caregivers of dementia patients. Front Psychiatry. 2023;14:1208594. Yamaguchi Y, Greiner C, Nakamura M, Kabaya S. Caregiver burden and psychological status and their associations with sleep quality among family caregivers living with older people with dementia: a mixed method study. Geriatr Nurs. 2024;60:504–10. Grossberg G, Willey C, Houle C, Schein J, Bungay R, Cloutier M et al. Agitation in individuals with Alzheimer's disease: an assessment of behaviors using the cohen-mansfield agitation inventory in community-dwellers and impact on caregiver experience. Dementia-London. 2025:483471023. Song J, Wu H, Lan H, Xu D, Wang W. The influence of disease status on loneliness of the elderly: evidence from rural China. Int J Env Res Pub He. 2022;19(5):3023. Yuan Q, Lee YT, Samari E, Zhang Y, Goveas R, Ng LL et al. Evaluating the feasibility and potential effectiveness of a mobile-based intervention to promote the mental health of informal caregivers of persons with dementia in Singapore: results from a mixed-methods two-arm pilot randomized controlled trial. J Affect Disorders. 2025:121059. Cheung DSK, Tse HYJ, Wong DW, Chan CY, Wan WL, Chu KK, et al. The effects of exergaming on the depressive symptoms of people with dementia: a systematic review and meta-analysis. J Clin Nurs. 2025;34(5):1648–64. Jhang K, Chen C, Wang S, Wang W, Yen S, Wu H. Caregivers' burden analytics: combining variables from patients with dementia and their caregivers. Bmc Geriatr. 2025;25(1):620. Barakat Z, Sacre H, Khatib S, Hajj A, Bou Malham C, Haddad C, et al. Examining burden among caregivers of community-dwelling older adults in Lebanon. Sci Rep-Uk. 2025;15(1):22775. Innes A, Morgan D, Kosteniuk J. Dementia care in rural and remote settings: a systematic review of informal/family caregiving. Maturitas. 2011;68(1):34–46. Liu D, Cheng G, An L, Gan X, Wu Y, Zhang B, et al. Public knowledge about dementia in China: a national WeChat-based survey. Int J Env Res Pub He. 2019;16(21):4231. Additional Declarations No competing interests reported. Supplementary Files AppendixA.Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 04 May, 2026 Editor invited by journal 15 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 14 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9374067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641490449,"identity":"25c0e126-518b-47fb-a745-5b4af0731153","order_by":0,"name":"Jun Zhou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhou","suffix":""},{"id":641490451,"identity":"6f8ef54c-4a78-4723-8cfe-b4f6a10e3b08","order_by":1,"name":"Lily Xiao","email":"","orcid":"","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Lily","middleName":"","lastName":"Xiao","suffix":""},{"id":641490456,"identity":"d53e8acb-58cd-4f6b-854f-84fef99e436a","order_by":2,"name":"Xiajun Guo","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xiajun","middleName":"","lastName":"Guo","suffix":""},{"id":641490457,"identity":"b0710ef8-3f39-4f26-9cc3-61815cf0c56e","order_by":3,"name":"Hui You","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"You","suffix":""},{"id":641490458,"identity":"7f79b618-a2ef-4dc0-955f-d87cb46a38e9","order_by":4,"name":"Yao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBADHsb25oMPEipqiNcix9xzLNngwZljxGsxZp+RYyb5sIWZsFKD44ePSfzcUZvY25CWVpHYwMbA396dgF/LmbQ0yd4zxxNnNhw+diNxhwyDxJmzG/BrOZBjdoO37Vjixsa2tBuJZ9gYDCRyCWg5/8bs5l+glv2HecwKEtuYidByI8fsNm9bjTFjG48ZA1FaJG88S/8t23ZAjrGHLVki4cwxHoJ+4TuffNjwbVsdD+P8xwc//qiokeNv78WvReEAmDoMF+DBqxwE5BvAVB1BhaNgFIyCUTCCAQCzalO6UUetXwAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Yao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-10 03:38:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9374067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9374067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109490619,"identity":"54365c04-2877-4b60-9add-69ea7da8960f","added_by":"auto","created_at":"2026-05-18 17:39:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63418,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structure of Caregiver Burden Inventory (24 items).\u003c/p\u003e\n\u003cp\u003eNote: 1 = Developmental burden, 2 = Physical burden, 3 = Social burden, 4 = Emotional burden,\u003c/p\u003e\n\u003cp\u003e5 = Time-dependence burden.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9374067/v1/e399ed7c71ac917b5fd8af9f.jpeg"},{"id":109490620,"identity":"812b64c7-ce11-4583-832a-84269f56dd43","added_by":"auto","created_at":"2026-05-18 17:39:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49711,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structure of Caregiver Burden Inventory (18 items).\u003c/p\u003e\n\u003cp\u003eNote: 1 = Developmental burden, 2 = Time-dependence burden, 3 = Social burden, 4 = Emotional burden.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9374067/v1/2e1f44460831e83c2eea03cc.jpeg"},{"id":109490621,"identity":"d73d8418-1243-404d-8667-c7479e59a74a","added_by":"auto","created_at":"2026-05-18 17:39:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56652,"visible":true,"origin":"","legend":"\u003cp\u003eItem stability plot of the CBI based on bootstrapped EGA results (24 items).\u003c/p\u003e\n\u003cp\u003eNote: 1 = Developmental burden, 2 = Physical burden, 3 = Social burden, 4 = Emotional burden,\u003c/p\u003e\n\u003cp\u003e5 = Time-dependence burden.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9374067/v1/6a6ca8f610a86a5290bdac73.jpeg"},{"id":109490622,"identity":"cf80ff72-555e-4d33-8dd0-f351dbc356af","added_by":"auto","created_at":"2026-05-18 17:39:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49634,"visible":true,"origin":"","legend":"\u003cp\u003eItem stability plot of the CBI based on bootstrapped EGA results (18 items).\u003c/p\u003e\n\u003cp\u003eNote: 1 = Developmental burden, 2 = Time-dependence burden, 3 = Social burden, 4 = Emotional burden.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9374067/v1/d6077d8b541bc278a58f1702.jpeg"},{"id":109759801,"identity":"9492b158-5d62-4554-8b81-6b2e7dccfb44","added_by":"auto","created_at":"2026-05-22 07:27:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9374067/v1/f5cc7ddd-bd03-4470-8b35-4e965d358bb6.pdf"},{"id":109490618,"identity":"abd0b308-687c-476b-b9e9-19d873210763","added_by":"auto","created_at":"2026-05-18 17:39:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21021,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9374067/v1/7ccadbd447775694c5740724.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unpacking the Multifaceted Burden: A Cross-sectional Analysis of Challenges Faced by Dementia Family Caregivers in Rural China","fulltext":[{"header":"Background","content":"\u003cp\u003eChina has the world\u0026rsquo;s largest population of people with dementia (PWD), contributing approximately 25.5% of global cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A large-scale study estimates the national prevalence of dementia among those aged 60\u0026thinsp;+\u0026thinsp;at 6.0%, with rural areas exhibiting significantly higher rates than urban ones [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Influenced by national conditions and the traditional filial piety, about 80% of PWD receive home-based care from family caregivers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], imposing substantial demands on these caregivers.\u003c/p\u003e \u003cp\u003eFamily caregivers are spouses, children, or other family members who provide unpaid care to patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. They are responsible not only for providing daily care and health monitoring, but also for coping with the PWD\u0026rsquo;s functional deterioration and dementia-related symptoms, such as declines in activities of daily living (ADL) or instrumental activities of daily living (IADL), as well as symptoms like agitation and hallucinations [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These heavy and complex caregiving tasks place a substantial, multifaceted burden on caregivers, including physiological, psychological, social, and economic challenges, which can often be more severe in rural China [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. People in rural areas are more likely to uphold traditional filial piety, emphasizing family responsibility toward elders, than urban dwellers influenced by Western independence values [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, lower dementia knowledge in rural areas can exacerbate stigma and caregiver isolation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, rural caregivers face unique obstacles, including lower levels of education, financial strain, and limited healthcare resources [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. More seriously, the mass migration of younger generations further weakens traditional family support systems [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These cumulative factors contribute to an increased burden on rural caregivers.\u003c/p\u003e \u003cp\u003eDementia family care support programs can effectively enhance family caregivers\u0026rsquo; ability to provide care and alleviate their burden [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Understanding caregiver burden is a prerequisite for implementing the support program, yet most assessments rely on a single global score [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the multidimensional nature of the burden, reliance on global scores may not provide a comprehensive or accurate assessment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This oversimplification hinders a deeper understanding of distinct burdens, potentially leading to generalized interventions that fail to address caregivers\u0026rsquo; specific needs. Therefore, identifying these distinct burden domains is essential for developing effective, targeted support programs for dementia family caregivers in rural China.\u003c/p\u003e \u003cp\u003eThe Caregiver Burden Inventory (CBI) is a widely used instrument for measuring multiple domains of caregiver burden, including time-dependence burden (stress resulting from time constraints), emotional burden (negative feelings toward the care recipient), social burden (feelings arising from role conflicts), physical burden (fatigue and health deterioration resulting from long-term caregiving), and developmental burden (a sense of \u0026ldquo;stagnation\u0026rdquo; in personal development compared to peers) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While providing valuable insights into the patterns of burden experienced by caregivers, the CBI was developed in a Western cultural context and primarily validated in urban Chinese populations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Given the sociocultural correlates of caregiver burden, the applicability of the CBI\u0026rsquo;s original factor structure to rural areas is uncertain. Previous studies have shown that, despite its internal validity and reliability, the CBI exhibits inconsistent factor structures in different economic and cultural contexts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, exploratory analysis of the CBI in rural China is warranted.\u003c/p\u003e \u003cp\u003eTraditional factor analysis methods suffer from sample-size sensitivity in factor identification and involve subjectivity in rotation method selection and in the interpretation of factor loadings [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, they also lack effective assessment of the stability and replicability of factor structure and item allocation across different samples [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To overcome these methodological limitations, this study introduces Exploratory Graph Analysis (EGA), an emerging network psychometric technique [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. EGA constructs a variable network model, transforming variables and their relationships into nodes and edges to identify variable communities (network clusters of items with strong conditional dependencies). These communities represent underlying psychological structures, which are equivalent to \u0026ldquo;factors\u0026rdquo; in traditional factor analysis, that is, domains or dimensions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In terms of technical implementation, EGA uses the Gaussian Graphical Model (GGM) combined with community detection algorithms (such as the \u0026ldquo;walktrap\u0026rdquo; algorithm) to construct these models and identify domains and their structures [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. EGA demonstrates accuracy comparable to or superior to traditional methods while enabling automated factor estimation and visualization of results [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, to address potential biases from sampling variability, EGA is augmented with the bootstrap technique (bootEGA), which provides insights into the stability of item assignments and domain estimates [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study pioneers the application of EGA to explore the domains of the CBI among rural dementia family caregivers, offering a novel methodological approach to burden assessment.\u003c/p\u003e \u003cp\u003eIn addition to identifying the domains of burden, it is necessary to investigate their determinants. Previous studies have divided the factors affecting caregiver burden into three categories: PWD-related variables (e.g., dementia duration, neuropsychiatric symptoms), caregiver-related factors (e.g., age, gender, mental health), and social factors (e.g., perceived social support, economic status) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, prior studies have predominantly focused on urban populations; the factors influencing caregiver burden among rural family caregivers remain under-investigated. For instance, behaviors and psychological symptoms of dementia (BPSD) have a 90% incidence rate [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Rural family caregivers typically demonstrate lower health literacy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which may increase the burden of managing these potentially disruptive symptoms. Additionally, these caregivers often face mental health issues such as depression and anxiety, which can further exacerbate their burden [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although social support is known to alleviate caregiver burden, the most effective sources in rural China are still underexplored. A comprehensive understanding of these determinants helps in designing the dementia family care support program that precisely targets the most impactful factors.\u003c/p\u003e \u003cp\u003eGiven the insufficient validation of the CBI among family caregivers of PWD in rural China, and the lack of in-depth understanding of the domains, levels, and influencing factors of caregiver burden in this unique context, this study aims to: (1) evaluate the domains and associated structures and reliability of the CBI among family caregivers of PWD in rural China, and (2) identify the levels and key determinants of different burden domains.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003e This cross-sectional study was conducted between April 2022 and February 2023, and its reporting adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and sampling\u003c/h3\u003e\n\u003cp\u003eThe required sample size was calculated using the cross-sectional sample size formul: n\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:=\\:\\)\u003c/span\u003e\u003c/span\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\frac{{Z}_{1-\\alpha\\:/2\\:}\\sigma\\:}{d})}^{2}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{1-\\alpha\\:/2}\\)\u003c/span\u003e\u003c/span\u003e denotes the critical value of the standard normal distribution. With a significance level \u0026ldquo;\u003cem\u003eα\u003c/em\u003e\u0026rdquo; of 0.05, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{0.975}\\:\\)\u003c/span\u003e\u003c/span\u003e= 1.96. The population standard deviation (σ) was estimated at 19.7, based on prior research [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The allowable error \u0026ldquo;\u003cem\u003ed\u0026rdquo;\u003c/em\u003e, which is usually set at 0.25 or 0.5 times the standard deviation, is chosen to be 0.25 times the standard deviation to balance sample size and precision, resulting in \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25 \u0026times; 19.7 = 4.925. The calculated theoretical sample size was 62. Accounting for a 20% invalid questionnaire rate, the final sample size was set at 75.\u003c/p\u003e \u003cp\u003eParticipants were recruited from rural community health centers and tertiary hospitals\u0026rsquo; memory clinics in Hunan Province, China. These settings were carefully chosen to capture the diverse experiences of rural family caregivers of PWD, recognizing the significant differences in access to healthcare and support services between rural and urban environments.\u003c/p\u003e \u003cp\u003eParticipants were purposefully recruited from hospital and rural community health center patient lists. Inclusion criteria were: (1) permanent rural residents (having rural household registration and residing in a natural village for more than six months of the year); (2) care recipients with a confirmed diagnosis of dementia; (3) caregivers who assumed primary caregiving responsibilities (providing the majority of daily care for at least six months, including but not limited to meal assistance, personal hygiene, medication reminders, accompanying outings, and managing medical affairs); (4) caregivers with sufficient literacy and communication skills to participate in the study (i.e., ability to read, write, understand, and converse in Mandarin); (5) voluntary participation and able to give informed consent. Family-employed caregivers and those currently participating in similar research were excluded.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Information Questionnaire\u003c/h2\u003e \u003cp\u003eA self-designed questionnaire was used to collect socio-demographic information about the family caregivers (e.g., age, gender, education, marital status, employment status, perceived health status, presence of acute or chronic diseases, monthly household income, relationship to the PWD, co-residence, weekly caregiving hours) and the PWD (e.g., age, gender, education, presence of acute or chronic diseases, prior role as primary income earner, type of medical insurance, level of self-care, duration of dementia).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCaregiver Burden Inventory (CBI)\u003c/h3\u003e\n\u003cp\u003eThe CBI was used to assess the level of burden among rural family caregivers. This 24-item inventory, originally developed by Novak and Guest [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], evaluates five domains of burden: time-dependence (5 items), emotional (5 items), social (5 items), physical (4 items), and developmental (5 items). Each item is rated on a scale from 0 (not descriptive) to 4 (highly descriptive), yielding a total score ranging from 0 to 96, with higher scores indicating greater burden. The Chinese version of the CBI, translated and validated by Chou et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], was used in this study.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychiatric Inventory Questionnaire (NPI-Q)\u003c/h2\u003e \u003cp\u003eThe NPI-Q developed by Kaufer et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] was used to assess the severity of BPSD and related caregiver distress. The questionnaire consists of 12 items corresponding to various symptoms such as hallucinations, delusions, agitation, and anxiety. Family caregivers are required to assess whether the patient has experienced these symptoms over the past month, and only evaluate the severity (1\u0026ndash;3 points) and caregiver distress (0\u0026ndash;5 points) for symptoms that are present (rated as \u0026ldquo;yes\u0026rdquo;). This study primarily utilized the NPI-Q severity scores, with a total score ranging from 0 to 36. Higher scores indicate more severe symptoms. The Chinese version validated by Ma et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] showed good reliability, with a Cronbach\u0026rsquo;s alpha of 0.85 for the NPI-Q.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultidimensional Scale of Perceived Social Support (MSPSS)\u003c/h3\u003e\n\u003cp\u003ePerceived social support among family caregivers was measured using the MSPSS [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The scale comprises three subscales, each containing four items, assessing support from family members, friends, and significant others, for a total of 12 items. Each item is rated on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree), with total scores ranging from 12 to 84. Higher scores reflect greater perceived social support. The total Cronbach\u0026rsquo;s alpha coefficient for the Chinese version of MSPSS was 0.90, with subscale coefficients of 0.87 (family), 0.82 (friends), and 0.90 (significant others), demonstrating satisfactory reliability [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDepression-Anxiety-Stress Scale (DASS-21)\u003c/h3\u003e\n\u003cp\u003eThe mental health of rural family caregivers was evaluated with the DASS-21, a simplified version of the original 42-item DASS, which includes three subscales: depression, anxiety, and stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Each subscale consists of 7 items, scored from 0 (doesn\u0026rsquo;t apply to me at all) to 3 (applies to me a lot). Scores for each subscale are calculated by summing all item scores and multiplying by 2. Caregivers with scores above 9 for depression, above 7 for anxiety, and above 14 for stress are considered to exhibit mental health issues, with higher scores indicating greater severity of symptoms. The Cronbach\u0026rsquo;s alpha coefficient for the Chinese version of the DASS-21 total scale was 0.92, and the subscales were 0.80\u0026ndash;0.83 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData collection and ethical considerations\u003c/h2\u003e \u003cp\u003eData were collected through face-to-face structured interviews conducted by two trained researchers. Hospital nurses and community health center staff assisted the researchers by distributing the study information to potential participants. Rural family caregivers interested in the study were instructed to text the researcher via WeChat. The researcher contacted eligible caregivers by telephone to obtain initial consent and schedule face-to-face interviews. The interviews were conducted at outpatient departments of hospitals and community health centers. Before the interviews, participants were informed about the study\u0026rsquo;s purpose, procedures, rights, and potential risks. After obtaining signed informed consent, participants completed the questionnaire.\u003c/p\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Xiangya School of Nursing, Central South University (Approval No.: E2021143).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe utilized the EGAnet package in R 4.4.1 to explore the network structure. Employing the Glasso model and the walktrap community detection algorithm to identify the number of domains and the domain affiliations of the items. We then conducted bootEGA with 1000 iterations to evaluate the stability. The criteria for evaluation included the frequency of detecting the same number of domains, the frequency with which items were categorized into their respective domains (item replication index), and the frequency of structural consistency within each domain, each with an acceptable threshold of 0.75 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Aiming for distinct yet cohesive burden domains, we retained items based on the item replication index to enhance internal consistency within the CBI. As suggested by Golino et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], items with a replication index below 0.75 were excluded, and the analysis was rerun.\u003c/p\u003e \u003cp\u003eAfter establishing the domain, we analyzed the data with SPSS 26.0. To ensure the maximum internal reliability of the CBI\u0026rsquo;s domains, we calculated Cronbach\u0026rsquo;s alpha coefficients for each subdomain. Data normality was assessed with the Kolmogorov-Smirnov test. For normally distributed data, we reported results as means and standard deviations (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), whereas for non-normally distributed data, we reported medians and interquartile ranges (M [IQR]). Categorical variables were reported as frequencies and percentages. Group comparisons were conducted using t-tests or ANOVA for normally distributed data, and Mann-Whitney U or Kruskal-Wallis H test for non-normally distributed data. Correlations were evaluated using either Pearson (for normally distributed data) or Spearman (for non-normally distributed data), and multiple linear regression was used to identify factors affecting each domain of caregiver burden. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics\u003c/h2\u003e \u003cp\u003eA total of 150 questionnaires were collected, with 145 valid responses, yielding an effective response rate of 96.7%. The majority of rural family caregivers in the study were female (56.6%), married (89.0%), and adult children of PWD (60.0%), with an average age of 52.61\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe average age of PWD was 71.74\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55 years, and most of them were female (54.5%). The median duration since dementia diagnosis was 3.0 (2.0\u0026ndash;4.0) years. A significant percentage, 75.2%, suffered from comorbidities. Only 35.9% of PWD are fully capable of self-care and do not require assistance from others (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the family caregivers and PWD (n\u0026thinsp;=\u0026thinsp;145).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily Caregivers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePWD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (45.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (54.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years old), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.61\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.74\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (87.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status,n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (15.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (42.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (31.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or junior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional training college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived health status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (75.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;1999 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1999yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelationship to PWD, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaughter/son-in-law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the rural family caregivers and PWD (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily Caregivers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePWD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCo-residence or not\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeekly caregiving hours (h), Median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (46.5\u0026ndash;144.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeekly caregiving hours, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;79 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;120 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;120 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of dementia (years), Median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of dementia, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (82.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (17.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome was ever the primary source, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (49.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical insurance, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial and municipal health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (31.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural cooperative medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (47.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-financed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of self-care, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFully self-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartially self-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely unable to self-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (28.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: PWD=People with Dementia, IQR=Interquartile Range, SD=Standard Deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCaregiver burden of rural family caregivers measured by the CBI\u003c/h2\u003e \u003cp\u003eThe EGA identified a five-domain network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), in which the 24 scale items are represented as nodes, and line thickness and color denote the strength and nature of associations between items. Details of the items within each domain are in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, bootEGA detected five domains with a frequency of 0.660 (i.e., in 1000 bootstrap samples, 660 samples identified five domains). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the item replication indexes for items C1 (\u0026ldquo;I\u0026rsquo;m not getting enough sleep\u0026rdquo;), C2 (\u0026ldquo;I\u0026rsquo;m physically tired\u0026rdquo;), C3 (\u0026ldquo;Caregiving has made me physically sick\u0026rdquo;), C4 (\u0026ldquo;My health has suffered\u0026rdquo;), C9 (\u0026ldquo; I don\u0026rsquo;t do as good a job at work as I used to\u0026rdquo;), and C19 (\u0026ldquo;I don\u0026rsquo;t have a minute\u0026rsquo;s break from my caregiving chores\u0026rdquo;) are all significantly below 0.75, indicating poor stability of these items. The structural consistency frequencies for Domain 1 (developmental burden) and Domain 2 (physical burden) also fell below the 0.75 threshold (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that some items were misclassified into these domains. It is necessary to delete these low-stability items.\u003c/p\u003e \u003cp\u003eAfter exclusion, a four-domain network structure with 18 items was obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), categorized into social burden, emotional burden, time-dependence burden, and developmental burden. Upon re-examining the EGA structure through bootstrap, the frequency of the four domains rose to 0.941, while that of the five domains decreased to 0.055 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrate that the replication index of items and the consistency of domain structures within the scale have reached satisfactory levels (\u0026gt;\u0026thinsp;0.75). The Cronbach\u0026rsquo;s alpha coefficients for each domain ranged from 0.690 to 0.846. The complete list of 18 items and Cronbach\u0026rsquo;s alpha coefficients are available in Supplementary Table S2.\u003c/p\u003e \u003cp\u003eScores across all four domains showed skewed distributions (Supplementary Table S2). Specifically, the median score for the time-dependence burden was 13.0 (11.0\u0026ndash;15.0). Both the developmental and social burdens had median scores of 12.0 (10.0\u0026ndash;14.0) and 7.0 (5.0-9.5), respectively. The emotional burden had the lowest median score, at 5.0 (4.0\u0026ndash;8.0).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of the CBI domains across all bootstrap replicate samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of domains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGA (24 items)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEGA (18 items)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: EGA=Exploratory Graph Analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency with consistent internal structure across all domains.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDomains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFrequency of structural consistency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGA(24 items)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEGA(18 items)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevelopment burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: EGA\u0026thinsp;=\u0026thinsp;Exploratory Graph Analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between caregiver burden and socio-demographic characteristics\u003c/h2\u003e \u003cp\u003eA univariate analysis revealed that among the socio-demographic variables of rural family caregivers, significant differences in emotional and developmental burden were observed only for weekly caregiving hours (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Time-dependence burden showed significant differences across age, employment status, and monthly household income (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, social burden did not show statistically significant differences across any of the examined sociodemographic variables among family caregivers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong the PWD\u0026rsquo;s socio-demographic variables, significant differences in the emotional burden of rural family caregivers were observed in PWD\u0026rsquo;s age and types of medical insurance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Significant differences in time-dependence burden were found in PWD\u0026rsquo;s age, presence of acute or chronic diseases, levels of self-care, and duration of dementia (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The other two burdens (social burden and developmental burden) showed no significant differences across PWD\u0026rsquo;s socio-demographic characteristics (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Specifics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between caregiver burden and perceived social support, mental health, and BPSD\u003c/h2\u003e \u003cp\u003eSpearmen correlation analysis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that social burden was negatively correlated with overall perceived social support (\u003cem\u003er\u003c/em\u003e = -0.296, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), support from family (\u003cem\u003er\u003c/em\u003e = -0.320, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), friends (\u003cem\u003er\u003c/em\u003e = -0.268, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and significant others (\u003cem\u003er\u003c/em\u003e = -0.264, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and positively correlated with depression (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.359, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Emotional (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.183, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and developmental burdens (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.268, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were each positively associated with depression, while time-dependence burden correlated positively with BPSD severity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.326, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMultiple regression of factors influencing caregiver burden\u003c/h2\u003e \u003cp\u003eThe four domains of burden were each examined as the dependent variable in a multiple linear regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The significant variables from the univariate and Spearman correlation analyses served as the independent variables. The multiple linear regression analyses revealed that: (1) Social burden is associated with rural family caregivers\u0026rsquo; depression (explaining 18.9% of the variance). (2) Emotional burden is associated with the rural family caregivers\u0026rsquo; weekly caregiving hours (explaining 9.1% of the variance). (3) Higher time-dependence burden was associated with the presence of comorbidities and poorer self-care ability in PWD (explaining 28.3% of the variance). (4) Developmental burden is associated with depression and weekly caregiving time among family caregivers (explaining 9.3% of the variance).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRural family caregivers\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.465 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.198 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.282 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.847 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5 (13.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.353 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.015 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.613 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.351 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-spousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (5.5\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (0.0\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.0 (10.3\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.545 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.146 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.450 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.149 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.619 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.008 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.755 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.347 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (7.5\u0026ndash;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5 (5.0\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5 (12.3\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0 (10.8\u0026ndash;17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (4.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.3\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or junior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional training college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (1.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (9.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (5.3\u0026ndash;10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (3.5\u0026ndash;8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.0 (10.0\u0026ndash;15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.078 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.757 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.467 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.461 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived health status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (6.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (11.0\u0026ndash;14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (9.3\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5 (10.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5 (8.3\u0026ndash;13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.427 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.675 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.276 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.300 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Univariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute or chronic disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (9.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (4.3\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.3\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.187 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.162 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.520 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.682 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (3.0\u0026ndash;12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (1.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 (5.5\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;1999 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.5\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.5\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1999 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.617 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.142 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.095 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelationship to PWD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.5\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (13.0\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (9.5\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (9.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaughter/son-in-law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (4.3\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (11.0\u0026ndash;14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (7.0\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (10.5\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.5\u0026ndash;16.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.510 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.654 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.059 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.848 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Univariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohabitation or not\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (6.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (5.0\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.502 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.114 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.790 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.811 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeekly caregiving hours\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (6.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;79 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.8\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;120 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (12.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5 (8.8\u0026ndash;12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;120 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (2.8\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (8.8\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.412 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.004 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.077 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.813 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePWD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.8\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.484 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.735 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.871 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.496 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Univariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (3.0\u0026ndash;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (1.0\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5 (8.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (9.0\u0026ndash;14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.217 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.417 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.344 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.087 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (6.0\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.8\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.8\u0026ndash;14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.8\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (10.8\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school and junior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.0\u0026ndash;11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (0.0\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (6.5\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (7.0\u0026ndash;12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional training college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.5\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (6.5, 13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.568 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.962 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.491 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.957 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (1.0\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5 (9.3\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.595 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.084 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.191 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv class=\"DuplicateTablecaptionEnd\"\u003e\u003c/div\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Univariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of dementia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.5\u0026ndash;7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0 (13.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.345 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.245 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.479 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.811 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome was ever the primary source\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.3\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.5\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.350 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.656 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.264 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial and municipal health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.8\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (11.8\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural cooperative medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (6.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (11.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-financed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.5\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (10.0\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (1.5\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (0.0\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (8.0\u0026ndash;12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.845 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.686 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.587 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.295 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv class=\"DuplicateTablecaptionEnd\"\u003e\u003c/div\u003e \u003cdiv class=\"DuplicateTablecaptionEnd\"\u003e\u003c/div\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Univariate analysis of caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of self-care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFully self-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (5.0\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartially self-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (5.0\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.5\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (12.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely unable to self-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (2.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0 (14.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/F/Z/H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.177 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.680 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.541 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.156 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: PWD\u0026thinsp;=\u0026thinsp;People with Dementia, IQR\u0026thinsp;=\u0026thinsp;Interquartile Range, SD\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv class=\"DuplicateTablecaptionEnd\"\u003e\u003c/div\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;t-test, b\u0026thinsp;=\u0026thinsp;ANOVA, c\u0026thinsp;=\u0026thinsp;Mann-Whitney U test, d\u0026thinsp;=\u0026thinsp;Kruskal-Wallis H test.\u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between perceived social support, negative emotions, severity of BPSD, and caregiver burden (n\u0026thinsp;=\u0026thinsp;145).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmotional burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime-dependence burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDevelopmental burden\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMSPSS total score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.0 (48.0\u0026ndash;57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.296***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.0 (17.0\u0026ndash;19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.320***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efriends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.0 (16.0\u0026ndash;19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.268**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esignificant others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.0 (15.0\u0026ndash;19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.264**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.183*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.268**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStress\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.0 (8.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPI-S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.0 (10.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear regression related to caregiver burden (n\u0026thinsp;=\u0026thinsp;145).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable: Social burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport from significant others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.272, 0.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport from friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.416, 0.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport from family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.677, 0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.087, 0.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent variable: Emotional burden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly caregiving hours (\u0026lt;\u0026thinsp;40 h\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.164, -0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWD\u0026rsquo;s age (\u0026le;\u0026thinsp;60 years old\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.184, 3.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance (Provincial and municipal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.390, 1.956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance (Rural cooperative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.730, 1.443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance (others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.753, 0.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.037, 0.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent variable: Time-dependence burden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver\u0026rsquo;s age (\u0026lt;\u0026thinsp;60 years old\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.107, 1.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork status (employed\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.110, 1.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly household income (\u0026lt;\u0026thinsp;1000 yuan\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.616, 0.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWD\u0026rsquo;s age (\u0026le;\u0026thinsp;60 years old\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.324, 2.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity (none\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.265, 2.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of dementia (\u0026le;\u0026thinsp;5 years\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.509, 1.705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of self-care (fully self-care\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.634, 1.824)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPI-S total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.084, 0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear regression related to caregiver burden (n\u0026thinsp;=\u0026thinsp;145) (Continued).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable: Developmental burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly caregiving hours (\u0026lt;\u0026thinsp;40 h\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.215, -0.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.020, 0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: SE\u0026thinsp;=\u0026thinsp;Standard Error, CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNumerous comparative studies [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] have shown that caregivers of PWD experience a greater burden than those caring for individuals with other chronic diseases, such as cancer, multiple sclerosis, or mild cognitive impairment. The burden on caregivers related to dementia care is particularly acute in rural China, where access to resources and support services is limited. Consequently, family caregivers of PWD in rural China represent a vulnerable population who need formal support. A comprehensive assessment of the various burdens they face is essential for developing effective support services for this population. The CBI serves as a valuable tool that not only quantifies the overall burden but also assesses its various aspects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we employed EGA to explore the domains and associated structures of the CBI among family caregivers of PWD in rural areas. The initial EGA results revealed five domains: developmental, physical, social, emotional, and time-dependence, consistent with the original English version [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the existing Chinese version [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, in this study, items C1 (\u0026ldquo;I\u0026rsquo;m not getting enough sleep\u0026rdquo;) and C2 (\u0026ldquo;I\u0026rsquo;m physically tired\u0026rdquo;) were categorized under \u0026ldquo;developmental burden\u0026rdquo; rather than \u0026ldquo;physical burden\u0026rdquo;, and .items C9 (\u0026ldquo;I don\u0026rsquo;t do as good a job at work as I used to\u0026rdquo;) and C19 (\u0026ldquo;I don\u0026rsquo;t have a minute\u0026rsquo;s break from my caregiving chores\u0026rdquo;) were shifted to \u0026ldquo;developmental burden\u0026rdquo; from \u0026ldquo;emotional\u0026rdquo; and \u0026ldquo;time-dependence\u0026rdquo; burdens, respectively. These differences may be attributed to cultural factors. In Western countries and urban Chinese populations, sleep deprivation and physical fatigue are typically associated with physical burden. However, in rural China, family caregivers of PWD may view these issues as an inevitable cost of fulfilling family responsibilities, which can affect their productivity (e.g., farming or working) and self-improvement (e.g., learning or skill training), ultimately restricting personal development. Similarly, items C9 and C19 reflect emotional exhaustion due to decreased work performance and restricted time freedom in Western countries and urban Chinese populations. In the rural Chinese sample in this study, these two items more directly reflect the negative impact of caregiving responsibilities on career development and personal growth opportunities, as evidenced by decreased job performance and the occupation of personal time.\u003c/p\u003e \u003cp\u003eAdditionally, EGA constructs network models based on the co-occurrence relationships among items and clusters those that frequently co-occur [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The strength of the connections between items reflects their correlations. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, items C1, C2, C9, and C19 have denser connections with nodes representing developmental burden, as indicated by thicker edges, suggesting strong correlations with developmental burden and are thus classified as such. However, these items still have connections to the nodes of the original domains, but these connections are sparser and thinner, indicating weaker correlations. Besides, the only two items of physical burden \u0026mdash; item C3 (\u0026ldquo;Caregiving has made me physically sick\u0026rdquo;) and item C4 (\u0026ldquo;My health has suffered\u0026rdquo;) \u0026mdash; also connect with developmental, social, and emotional burden nodes, suggesting that these experiences are not solely physical but intertwined with multiple pressures. The cross-domain connections of these six items indicate instability and cross-loadings, supported by their low item replication indexes in bootEGA (below the 0.75 threshold). The frequent cross-domain occurrence of items leads to low structural consistency, which is why the structural consistency of developmental and physical burden is also below the threshold.\u003c/p\u003e \u003cp\u003eAfter excluding the six unstable items, a subsequent EGA revealed that the remaining 18 items clustered into four burden domains: social, emotional, time-dependence, and developmental, confirming the multidimensionality of caregiver burden. The social burden emerged primarily as a conflict between caregiving duties and social roles, which aligns with previous findings [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Emotional burden refers to the negative emotions caregivers experience [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The time-dependence burden reflects significant time constraints imposed by dementia care, confirming the considerable commitment required from family caregivers [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The developmental burden, closely related to the time-dependence burden, mainly reflects caregivers\u0026rsquo; feelings of being out of step with their peers in terms of personal growth [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Unlike the initial EGA, the second analysis did not retain the physical burden domain, as its only two items were excluded. The developmental burden domain, however, contained more items, and after excluding unstable items, the item replication indexes and structural consistency within the domain reached acceptable levels, allowing it to be retained. BootEGA indicated that the four dimensions appeared with a frequency of 0.941 in 1000 bootstrap replications, demonstrating high statistical stability and supporting the conclusion that the CBI presents a four-domain structure among rural family caregivers in China.\u003c/p\u003e \u003cp\u003eSome may question why item C24 (\u0026ldquo;I expected that things would be different at this point in my life\u0026rdquo;) in the 18-item CBI is categorized under developmental burden, despite its weak connections (thin edges) with other items in that domain: item C20 (\u0026ldquo;I feel that I am missing out on life\u0026rdquo;), item C21 (\u0026ldquo;I wish I could escape from this situation\u0026rdquo;), item C22 (\u0026ldquo;My social life has suffered\u0026rdquo;), item C23 (\u0026ldquo;I feel emotionally drained due to caring for my care-receiver\u0026rdquo;).. Firstly, while it has weak connections with nodes in the developmental burden domain (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), it links to four other items within this domain, significantly more than in other domains (emotional and time-dependence burdens), indicating domain specificity. Secondly, the item replication indexes in two bootEGA exceeded the threshold, demonstrating its consistent allocation to the developmental burden domain. Thirdly, the item captures the unique psychological experience of the discrepancy between caregivers\u0026rsquo; reality and expectations, distinct from emotional and time-dependence burdens and aligning with the developmental burden\u0026rsquo;s theoretical significance. Lastly, its consistent classification in both the original English version [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the existing Chinese version [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] further supports its placement in this domain. Thus, both network analysis and theoretical considerations justify assigning item C24 to the developmental burden domain.\u003c/p\u003e \u003cp\u003eWe also evaluated the CBI\u0026rsquo;s internal consistency using Cronbach\u0026rsquo;s alpha. An alpha coefficient of 0.70 or higher for the total scale and 0.60 or higher for each domain suggests satisfactory reliability [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The Cronbach\u0026rsquo;s alpha coefficients obtained in our study further confirm that the CBI is a suitable instrument for assessing the burden of family caregivers of PWD in rural China.\u003c/p\u003e \u003cp\u003e Next, our regression analysis showed that the characteristics of both rural PWD and their family caregivers significantly impact caregiver burden. Within the PWD-related variables, we found that the level of self-care and the presence of acute or chronic illnesses are associated with a time-dependence burden. The anticipated impact of PWDs\u0026rsquo; self-care levels on this burden aligns with research in Gushan Town, Fuzhou [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A possible explanation is that dementia, as a neurodegenerative disease, progressively impairs PWD\u0026rsquo;s memory and cognition, leading to a decline in their self-care abilities [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This decline means that family caregivers must spend more time and energy assisting with ADL and IADL, such as dressing, eating, transportation, and housekeeping [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. There is also no doubt that caring for PWD with comorbidities increases the time-dependence burden, as family caregivers often need to accompany PWD to medical appointments and manage their illnesses. To alleviate these burdens, we recommend a two-pronged approach: enhancing the self-care abilities of PWD through home-based multimodal exercise programs [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and providing respite services, such as daycare centers and home care assistance, to give family caregivers much-needed breaks [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurther, rural family caregiver-related variables, including weekly caregiving hours and depression, were analyzed. Surprisingly, longer weekly caregiving hours correlated with lower emotional and developmental burdens. The median caregiving time in this study was 70.0 hours (46.5\u0026ndash;144.0), with 26.2% of caregivers dedicating over 120 hours per week, surpassing the averages in individualistic countries with advanced dementia care systems [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This suggests that rural family caregivers who are more willing to devote time to caring for PWD are deeply influenced by filial piety. As a core ethical norm in Confucian culture, filial piety emphasizes the obligation of younger generations to support, care for, and respect their parents and elders, leading caregivers to prioritize the elderly\u0026rsquo;s health over their own well-being [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The internalization of this ethical norm motivates them to overcome negative emotions and limitations in personal development.\u003c/p\u003e \u003cp\u003eDepression was significantly associated with developmental burden and was the sole factor explaining the social burden. While the causal relationship between depression and caregiver burden remains to be clarified, it is crucial to recognize depression as a prevalent psychological disorder that can adversely affect caregiver well-being. Healthcare providers and policymakers should improve mental health services in rural areas, screen caregivers for depression, and implement supportive measures such as mobile-based psychoeducation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] or exergaming [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] to improve family caregivers\u0026rsquo; mental health and reduce their burden.\u003c/p\u003e \u003cp\u003eSurprisingly, contrary to previous studies [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], we found that perceived social support and the severity of BPSD did not influence caregiver burden. Although rural family caregivers received considerable support in this study, the general lack of dementia-related knowledge and caregiving skills in rural areas often results in support that falls short of meeting their actual needs [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In addition, cultural emphasis on family and filial piety in rural settings may lead family caregivers to view elderly care as an inherent duty [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], making their burden more dependent on their self-efficacy and self-satisfaction in their caregiver role [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] rather than on the amount of external support. Similarly, due to the limited knowledge of dementia among rural family caregivers, they may be more prone to misinterpret the BPSD as normal aspects of aging, rather than perceiving them as an additional burden. These reasons confirm the importance of the dementia care support program in rural areas. To construct and refine such a program, it is imperative to vigorously cultivate professional multidisciplinary care teams.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first study to employ EGA to explore the structure of the CBI and to investigate the factors influencing various burden domains among rural Chinese dementia caregivers. It fills a critical gap in the literature and provides both theoretical support and practical guidance for designing a dementia care support program for rural China. While the study provides several valuable insights, it also has some limitations. First, recruiting participants exclusively from Hunan Province may introduce bias and limit the generalizability of the findings to other rural regions. Secondly, we did not collect specific indicators of participants\u0026rsquo; residential rurality (e.g., village population size, distance to tertiary hospitals), which precluded a deeper understanding of how the rural environment affects caregiver burden. Moreover, the cross-sectional design restricts our ability to capture dynamic trends or causal relationships in caregiver burden. Finally, the overall multiple regression models explained relatively low variances across different types of burden, with a maximum of 28.3%, which may be attributed to the exploratory nature of the study.\u003c/p\u003e \u003cp\u003eTo address current limitations and improve future research quality, we propose the following recommendations. First, future studies should expand the sample to include multiple provinces to improve representativeness. Second, standardized rurality indicators should be incorporated to better characterize how rural contexts influence caregiver burden. Third, longitudinal research methods are needed to track changes in caregiver burden and establish causal relationships between caregiver burden and various factors. Lastly, other potential influencing factors should be considered to gain a deeper understanding of the burden\u0026rsquo;s origins.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study indicates that caregiver burden among rural Chinese dementia family caregivers is composed of four distinct domains: social, emotional, time-dependence, and developmental burdens. It is worth noting that the time-dependence and development burdens of rural family caregivers are particularly heavy, revealing the impact of caregiving tasks on caregivers\u0026rsquo; time investment and personal development. Additionally, the study identified key factors affecting each domain of burden, including PWD\u0026rsquo;s self-care abilities and health status, the time family caregivers spend on care, and their mental health.\u003c/p\u003e \u003cp\u003eThese findings highlight the urgent need for a family care support program in rural China. Such a program should involve a multidisciplinary team focused on enhancing PWD self-care capabilities, providing respite care, and improving family caregivers\u0026rsquo; mental health through education. Recognizing the multidimensionality of caregiver burden will foster a more supportive environment, ultimately enhancing family caregiver well-being and the quality of care for PWD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePWD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeople with dementia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActivities of daily living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrumental activities of daily living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCaregiver burden inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory graph analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGaussian graphical model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBPSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBehaviors and psychological symptoms of dementia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the reporting of observational studies in epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPI-Q\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeuropsychiatric inventory questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidimensional scale of perceived social support\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDASS-21\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepression-anxiety-stress scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile ranges\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was approved by the Ethics Committee of Xiangya School of Nursing, Central South University (Approval No. : E2021143) and followed the principles of the Declaration of Helsinki. All participants provided informed written consent before their participation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China [grant IDs 2023YFC3605200, 2023YFC3605204], the Open Competition Research Project of the China Medical Board [grant ID #19\u0026ndash;344], and the Jiaxing Key Discipline of Medicine \u0026mdash; Nursing [grant ID 2023-ZC-007].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJZ conceptualized the study, developed the methodology, performed the formal analysis, and was a major contributor in writing the original draft as well as reviewing and editing the manuscript. LX contributed to the conceptualization and methodology, reviewed and edited the manuscript, and supervised the study. XJG contributed to the conceptualization and methodology, and reviewed and edited the manuscript. HY contributed to the conceptualization, conducted the investigation, and curated the data. YW reviewed and edited the manuscript, supervised the study, curated the data, handled project administration, provided resources, and managed funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors greatly acknowledge all the healthcare professionals who assisted with data collection, as well as the family caregivers of rural people with dementia who willingly participated in the questionnaire survey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRen R, Qi J, Lin S, Liu X, Yin P, Wang Z, et al. The China Alzheimer Report 2022. Gen Psychiat. 2022;35(1):e100751.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. 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Int J Env Res Pub He. 2019;16(21):4231.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dementia, Family caregivers, Caregiver burden, Rural China; Exploratory graph analysis","lastPublishedDoi":"10.21203/rs.3.rs-9374067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9374067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn rural China, family members serve as the primary caregivers for people with dementia (PWD), and their burden is a multidimensional concept that is particularly burdensome in the resource-limited rural context. However, the previous study investigated caregiver burden as a holistic concept, which limits the identification of different types of burdens and their contributing factors. This study aims to explore the specific domain structure and reliability of the Caregiver Burden Inventory (CBI) among dementia family caregivers in rural China, and to determine the levels and key determinants of these domains.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study enrolled 145 PWD and their family caregivers from rural areas. Data collected included PWDs\u0026rsquo; socio-demographic information and the severity of neuropsychiatric symptoms, as well as family caregivers\u0026rsquo; socio-demographic information, caregiver burden, social support, and mental health. Exploratory graph analysis was used to define the CBI\u0026rsquo;s domains and the structure of each domain, and Cronbach\u0026rsquo;s alpha coefficients were calculated to assess reliability. Multiple linear regression was used to assess factors influencing caregiver burden domains.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eExploratory graph analysis revealed a robust four-domain model, with Cronbach\u0026rsquo;s alphas between 0.690 and 0.846. Rural family caregivers reported high time-dependence and developmental burdens, while emotional and social burdens were lower. Multiple regression analyses identified PWD\u0026rsquo;s self-care ability, the presence of comorbidities, rural family caregivers\u0026rsquo; depression levels, and weekly caregiving hours as significant predictors of these burdens.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study validated the multidimensional structure of the CBI in rural Chinese family caregivers, demonstrating its utility in capturing diverse aspects of caregiver burden. These findings emphasize the need for targeted interventions to address the time management and personal development burdens faced by rural family caregivers.\u003c/p\u003e","manuscriptTitle":"Unpacking the Multifaceted Burden: A Cross-sectional Analysis of Challenges Faced by Dementia Family Caregivers in Rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 17:39:21","doi":"10.21203/rs.3.rs-9374067/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"88527960173406351639793391848220349608","date":"2026-05-18T09:36:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147597917578244659938688131522802102195","date":"2026-05-14T21:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95121256254048970954464206637865318197","date":"2026-05-14T16:05:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T10:14:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T09:44:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T10:18:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T14:32:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-04-14T13:04:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bbe7775d-6648-4027-9846-2854fc556ff4","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"88527960173406351639793391848220349608","date":"2026-05-18T09:36:12+00:00","index":63,"fulltext":""},{"type":"reviewerAgreed","content":"147597917578244659938688131522802102195","date":"2026-05-14T21:11:31+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"95121256254048970954464206637865318197","date":"2026-05-14T16:05:12+00:00","index":59,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-07T10:14:36+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T17:39:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 17:39:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9374067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9374067","identity":"rs-9374067","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}