Organizational profiles and personal factors affecting Health-related quality of life among older persons diagnosed with depressive disorders: path analysis and GEE

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Thida Mulalint, 2. Acharaporn Seeherunwong, 3. Sasima Tongsai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108211/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Knowledge about mental health service use for depression, mediating organizational profiles, and personal factors on health-related quality of life (HRQoL) among older persons is critical to the health service system. Our study aimed to explore HRQoL six months through one year after persons received services for depression, and explains the effect of organizational profiles and personal factors on HRQoL, mediated through continued mental health service use. It also explains organizational profiles and personal factors affecting HRQoL. Method This study is a cross-sectional analytic study with information on 424 older persons (≥ 60 years) diagnosed with depressive disorder (DD)—medical record information provided data on personal and clinical factors. HRQoL and attitude toward depression and its treatment (ATDS) were obtained six months to one year after diagnosis with DD. HRQoL was measured using the EuroQol Group's EQ-5D Index and a visual analog scale (EQ-VAS). Organizational profiles were obtained from the authorized staff of 12 hospitals identified by latent class analysis into two classes. Descriptive statistics and path analysis tested mediated factors, and a marginal linear regression model using a generalized estimating equation (GEE) analyzed the final model. Results HRQoL at six months to one year was assessed as good and higher than in previous studies. Continuing mental health service use was not a mediated variable among organizational profiles and personal factors. HRQoL of older persons with DD is associated with personal factors, including age, sex, comorbidity, ATDS, perceived social support, and sufficient income. However, high and low-resource organizational profiles did not affect HRQoL. Conclusions These findings are crucial for reconsidering the quality of care and mental health services in general hospitals. Attitude toward depression Depressive disorders Health-related quality of life Health service delivery system Older persons Organizational factors Mental health service use Thailand Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Quality of life (QoL) is closely aligned with an indicator of achievement of Sustainable Development Goal item # 3 (SDG 3) in Thailand, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment, and promote mental health and well-being by 2030 [ 1 ]. The advanced age of those who have depressive disorders (DD) affects their QoL more than other physical factors[ 2 ]. As a result of WHO and Thai mental health policy addressing the accessibility and availability of mental health care services [ 3 ], access to mental health service use was quite properly improved [ 4 ]. In addition, the service delivery system is most likely to evaluate the symptoms of depression as a health outcome [ 3 ]. However, evidence has recommended that relief of symptoms is inadequate to expect older persons to live healthy lives. The QoL is recognized as a critical indicator of personal recovery from mental illness [ 5 – 6 ]. The World Health Organization (WHO) defines QoL as individuals' perception of their position in life in the context of the culture and value systems in which they live and that affect their goals, expectations, standards, and concerns [ 7 ]. However, the QoL is a comprehensive and multidimensional concept influenced by various aspects, both health service system determinants and personal determinants. Therefore, the current study uses health-related quality of life (HRQoL) to evaluate the outcome of receiving mental health services. This study explores and explains the factors affecting the HRQoL of older persons who receive health services for depression. Before treatment, less than 3% of patients with MDD reported normal QoL [ 8 ]. Improving HRQoL is an essential goal in the health service delivery system worldwide and reducing long-term disability. Factors impacting HRQoL in older persons with DD were ambiguous. Most evidence found that HRQoL was associated with socio-demographic, and clinical factors. A recent study found that older age, lower level of education, lower income, worse subjective perception of health, unemployment, obesity, and mental health struggles were significantly associated with low HRQoL in Korean older adults diagnosed with DD after adjustment for multiple covariates [ 9 ]. After a one-year follow-up of older persons with DD after hospitalization, improved HRQoL was significantly better in those with remission of depression and those with better baseline physical health. Moreover, HRQoL was equal to the reference group of older persons without depression when adjusting for differences in socio-demographics and health conditions [ 10 ]. In contrast, a report of a large longitudinal international cohort study of adults with Major Depressive Disorder (MDD) who were in routine clinical practice in five European countries showed HRQoL to be severely impaired at the time of initiating or undergoing the first switch of antidepressant monotherapy. It was not fully restored after the acute and short-term maintenance phases of treatment but continues to be impaired long after [ 11 ]. A study revealed that the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) treatment had a statistically significant positive impact on HRQoL in adults with MDD. Nevertheless, most patients continued to experience HRQoL deficits, and HRQoL scores declined after 12 months [ 8 ]. In addition, clinical factors, including the severity of depression and severity of cognitive symptoms were significantly associated with HRQoL impairment at baseline, and months 12, 18, and 24 of follow-up [ 11 ]. The Organizational Profile is a snapshot of the organization, the key influences on how it operates, and the competitive environment. It is composed of organizational context and organizational situation [ 12 ]. The organization profiles may reveal directions and opportunities for health system improvement. Previous studies in Thailand have focused on each characteristic of the hospital affecting health outcomes. The characteristics of the hospital include the level of hospital, number of health manpower, facilities of the hospital, etc. They were rarely associated with health outcomes as a result of little variation in hospital characteristics [ 13 , 14 ]. Also, the level of the hospitals including psychiatric hospitals, regional hospitals, general hospitals, and community hospitals did not show the effect of improving HRQoL differently in patients with DD who received mental service in the first three months [ 13 ]. According to Latent Class Analysis (LCA), a hospital-centered modeling approach, hospital profiles can classify heterogeneous characteristics into more meaningful groups [ 15 ]. Therefore, this present study conducted Latent Class Analysis (LCA), classifying organizational profiles composed of hospital levels, nurse competencies, nurse-patient ratio, and appointment reminders into 2 groups of low and high-resource organizations [ 16 ]. The high resource organizations most likely were university hospitals (77.4%), the patients-nurse ratio between 16.80–37.50, the total of their nurses had master’s degrees in Nursing Science, and the hospital had appointment reminders 77.4%. The low-resource organization had those profiles differently. A detail of characteristics of organizational profile analysis was provided by Mulalint et al. [ 16 ] According to the process of health service delivery, patients with DD require continuous treatment to prevent the relapse and recurrence of depression symptoms [ 17 ]. A meta-analysis reported that providing antidepressant therapy to the depressed elderly for 12 months could reduce the risk of relapse [ 18 ]; also, depressed elderly receiving adequate duration of treatment demonstrated a better outcome than those with shorter durations of treatment [ 19 ]. Therefore, depressed older persons must receive an entire course of depression treatment to ensure that they have reached the treatment goal and improved their HRQoL. However, some evidence supporting this point is limited to the effects of mental health service use on HRQoL and limited evidence examining the health outcome between the organizational factors and individual characteristics among older persons with DD. A study closely related to this solution indicates that continuing depression treatment could contribute to HRQoL in older persons with DD that found EQ-5D-5L was significantly higher than before receiving treatment [ 9 ]. Notably, no study examined the HRQoL among older persons with DD who are not continuing therapy in any way. In addition to insufficient evidence, health behavior is examined as a mediator effect on HRQoL concerning the mental health care system and individual factors. Regarding service delivery for older persons with DD, understanding factors promoting HRQoL among older persons with DD is vital in assessing the effectiveness of the mental health service delivery system. The Behavior Model of Service Use (BM) [ 20 ], the fifth phase is proper for understanding the health service use by focusing on contextual and individual determinants. Therefore, this study aims to explore the HRQoL of older persons with DD who received service in general hospitals and explain the effect of organizational profiles and personal factors on HRQoL mediated by mental health service use. The first hypothesis examined the effect of organizational profiles and personal factors on HRQoL via mental health service use. The personal factors consisted of age, sex, marital status, comorbidity, sufficient income, attitude toward depression and its treatment (ATDS), social support, and severity of depression. The second hypothesis was whether the organizational profiles and personal factors were significantly associated with HRQoL among older persons with DD while controlling within-subject correlation between HRQoL within the same hospital. Methods This study is part of a project on determinants of continuing mental health service use among older persons diagnosed with depressive disorders in general hospitals [ 16 ] that seeks to explain the effects of system factors and personal factors on HRQoL. Design We conducted an analytic cross-sectional study design with data from the project mentioned above from May 2018 through November 2020 before the first national restriction of the coronavirus pandemic in Thailand. All selected variables of this study were based on the BM [ 20 ]. The personal and organizational profiles came from 12 general hospitals in Thailand using multi-stratified sampling based on Thailand's health service system administration [ 16 ]. There are 3 clusters: community hospitals, advanced and standard hospitals, and university hospitals. To achieve an equal sample size in all groups, the number of participants in all clusters was 150. The hospitals in each grouping cluster were selected by purposive sampling. Detail of calculating sample size and research setting was provided by Mulalint et al. [ 16 ]. Participants and Data Collection Participants were patients receiving service in the psychiatric clinics of the general hospital and staff members providing care to them. Patients had to be at least 60 years old with a first or recurrent diagnosis of DD based on the 10th revision of the International Classification of Diseases [ 21 ] from at least six months to one year before collecting data. The exclusion criteria included patients who had been diagnosed with mania or bipolar conditions, had severe somatic disease, could not answer questionnaires, or had severe depression and suicidal thoughts. Data on the patients come from two sources: medical records and interviews. Demographic and clinical profiles were extracted from medical records. After that, the researcher interviewed them with translated HRQoL questionnaires - EQ-5D-5L and EQ-VAS, and other interviewing instruments, including Attitude toward Depression and Its treatment (ATDS). The patients who did not respond when scheduled to visit the clinic by appointment were later contacted by phone or visited at home to make a date and time for an appointment. The recruitment of patient participants and data collection procedure are presented in Fig. 1 . The total number of patient participants used for analysis was 424 cases representative of the population studied. Staff members were twelve nurses who had experienced care provision at an outpatient unit for at least five years or had experience caring for psychiatric patients for at least two years. They provided data on the mental health service delivery system through an interviewing questionnaire. Measurement The Health-related Quality of Life (HRQoL) The EuroQol Group self-report instruments were used to assess HRQoL as the health outcome of mental health service use among older persons with DD [ 22 ]. This instrument consisted of European QOL 5 dimension, five levels (EQ-5D-5L), and a Visual Analog Scale (EQ-VAS). The EQ-5D-5L comprised five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), each with five levels of problems (1 = none, 2 = slight, 3 = moderate, 4 = severe, 5 = extreme). These scores, including each scale, are transformed into a utility index developed for the Thai population and used as the standard QOL of Thai people[ 23 ]. The utility score ranges from − 0.283 to 1.00. A value score less than 1 implied worse than death, value 0 implied death, and value 1 implied the best health. It was determined by subtracting the coefficients of each of the five health dimensions. In this study, the internal consistency of the EQ-5D-5L was .84 (n = 424). EQ-VAS is a self-assessment tool for recording the respondent's self-rated health from 0 (the worst health) to 100 (the best health). In this study, the EQ-VAS had a moderate correlation with EQ-5D-5L (Pearson's correlation; r = 0.34, n = 424). The EQ-5D-5L was used as the first HRQoL outcome measure. Also, the EQ-VAS value was used for discussion combined with the result of the utility index. Mental Health Services Use Mental health services use refers to attending a mental healthcare appointment for older persons within six months after being diagnosed with DD. The response is categorized into non-continuing service use (score = 0) and continuing service use (score = 1). "Non-continuing service use" means that older persons have a non-attending mental health service period of 90 days or more since the diagnosis date. "Continuing service use" means that older persons attend mental health clinics continuously according to clinical appointments and never miss a clinical visit for depression treatment for more than 90 days. 2.3.3 Personal factors and measurement Personal demographic and clinical factors are described in Table 1 . 2.3.4 Organizational profiles Organizational profiles relevant to mental health service use for depression in the 12 hospitals were categorized into high and low-resource organizations. The two groups were obtained by conducting a latent class analysis of four variables: hospital level, nurse competency, nurse-patient ratio, and appointment reminders. The high-resource organization consisted of the university hospital and standard and advanced hospitals, the higher qualifications of nurses, higher patient-to-nurse ratio, and more success with appointment reminders than low-resource organizations. More detailed information on groups of organizations can be found elsewhere [ 16 ]. Table 1 Description of the variables No. variables Questionnaires/measurement Categorizations/ scores 1. Health-related quality of life (HRQoL) EQ-5D-5L and EQ-VAS The utility index ranged from − 0.283 to 1 EQ-VAS recorded participants 'self-rated health from 0-100 2. mental health service use Case record form 0 = not continuing mental health service use; 1 = continuing mental health service use 3. Age Case record form continuous score 4. Sex Case record form 1 = female, 2 = male 5. Marital status Case record form 1 single, divorced, separated, other 2 = Married 6. Perceived income Interview form 1 = insufficient, 2 = sufficient 7. Comorbidity The Charlson Comorbidity Index (CCI)[ 24 ] Possible total score from 0–42 8. The severity of depressive symptoms Case record form of depression diagnosis according to ICD-10 1 = Mild, 2 = Moderate, 3 = Severe, 4 = Not other specified 9. Attitude toward depression and its treatment (ATDS) Attitude toward depression and its treatment, Thai version [ 25 ] 25 items from the original 27 items Reliability .70 (n = 30) A Likert score of 1 strongly disagree to 5 strongly agree. 10. Perceived social support (MSPSS) Multidimensional Scale of Perceived Social Support [ 26 ] which was translated into Thai by Wongpakaran Tinakon and Wongpakaran Nahathai [ 27 ]. 12 items. Reliability .90 (n = 30) Likert scales of 1 (very strongly disagree), to 7 (very strongly agree), and overall positive opinion 11. Organization profiles Mental health service delivery factors (hospital level, nurse-patient ratio, nurse competency, and appointment reminders) were divided into two groups using latent class analysis [ 16 ]. Organization groups 1 = Low resource organization 2 = High resource organization Ethics This study was reviewed and approved by the institutional review board of the Research Ethics Committee of the Faculty of Medicine, Siriraj Hospital (COA No. Si527/2019), Medicine Ramathibodi Hospital, Mahidol University (COA No. MURA2019/1264), and the Committees of all selected hospitals under the Ministry of Public Health. Statistical analysis Percentage problems per domain of EQ-5D-5L of the entire participant and categorized by continuing mental health service use were demonstrated. Mean and standard deviation (SD) of EQ-5D index and EQ-VAS scores were reported for the entire participant and specific subgroups by the hospital level where they received services. Descriptive demographic data and covariate variables were presented in the previous article [ 16 ]. The analytical framework of this study was anchored in path analysis using the lavaan package in R, aimed at elucidating the relationships between various independent variables and their effects on mental health service utilization and health-related quality of life (HRQoL), operationalized through EQ-5D-5L and EQ-VAS scores. Initial analyses identified a suite of key predictors—encompassing demographic and personal characteristics (such as age, sex, marital status, and social support), economic factors (income sufficiency), health conditions (severity of depression and comorbidity), and organizational groups—to assess their direct impact on mental health service use and HRQoL. Further exploration involved assessing the indirect effects of organizational groups and personal characteristics on HRQoL, mediated by the utilization of mental health services. The choice of the robust weighted least squares (WLSMV) method was pivotal in addressing the challenges posed by non-normal and categorical outcomes, ensuring accuracy in parameter estimation. Model adequacy was rigorously evaluated using a comprehensive set of fit metrics, including the Chi-square (χ²) test, where a p-value greater than 0.05 was targeted for a satisfactory fit; RMSEA and SRMR values were expected to be ≤ 0.08, and CFI and TLI values were sought to be > 0.95, indicating a good fit. Additionally, the geepack package in R facilitated the running of a marginal linear regression model to account for within-subject correlations of patient outcomes within the same hospital, examining the association between organizational groups, individual characteristics, and the EQ-5D-5L and EQ-VAS measures. Data collection and analysis were carried out with the 2020 IBM Corp. release of IBM SPSS Statistics for Windows, Version 27.0, and R version 4.3.2. This methodological approach underscores our commitment to employing advanced statistical techniques to glean meaningful insights from complex healthcare data, contributing to the broader understanding of factors influencing mental health service utilization and HRQoL. Results HRQoL as the health outcome The proportion of participants who responded to the descriptive system of the EQ-5D-5L by dimension and classified by continuous mental health service use is shown in Figs. 2 & 3 . Of the overall participants who had DD after six months to one year, the pain and discomfort dimension was the central problem and most extreme problem compared to other dimensions (65.57%). For those who continued mental health service use, pain was also the most frequently reported problem; participants without continuous service use (69.2%) had a more significant problem than participants who had continuous service use (62.4%). The utility index and EQ-VAS score of the overall participants, categorized by the hospital level, are shown in Tables 2 & 3 . The actual range of the utility index of the overall participants was − 0.250 to 1; the mean was 0.632 (SD = 0.31). Regarding hospital levels, the university hospital had the highest utility score (-0.265 to 1), with a mean of 0.662 (SD = 0.31). Regarding the EQ-VAS score, the results demonstrated that the actual range of overall participants was 5-100; the mean was 73.81 (SD = 16.40). The EQ-VAS scores by kind of hospital were different. The university hospital produced the highest perceived health; the mean was 74.65 (SD = 15.38). Table 2 Possible range, actual range, mean, and standard deviation (SD) of participant’s utility index Hospital Possible range Actual range Mean SD University Hospital (n = 137) -0.283 to 1 -0.265 to 1 0.662 0.31 Advanced/standard hospital (n = 147) -0.283 to 1 -0.250 to 1 0.634 0.32 Community hospital (n = 140) -0.283 to 1 -0.236 to 1 0.600 0.31 Overall (n = 424) -0.283 to1 -0.250 to 1 0.632 0.31 Table 3 Possible range, actual range, mean, and standard deviation (SD) of the self-related health status using EQ-VAS score of participants categorized by hospital level. Hospital Possible range Actual range Mean SD University Hospital (n = 137) 0-100 5-100 74.65 15.38 Advanced/standard hospital (n = 147) 0-100 20–100 74.04 16.72 Community hospital (n = 140) 0-100 25–100 72.75 17.07 Overall (n = 424) 0-100 5-100 73.81 16.40 The effect of the mental health service delivery factors and individual factors on HRQoL via mental health service use Path analysis results of the participants' characteristics and organization groups on EQ-5D-5L/EQ-VAS via mental health service use are shown in Figs. 4 & 5 and Tables S1-S4 respectively. In assessing the assumptions for our path analysis, we observed low multicollinearity among independent variables, with correlation coefficients ranging from − 0.093 to 0.245. This indicates minimal dependence between variables, supporting the reliability of our model estimates. Both path analysis models showed some exciting results regarding model fit indices. Chi-square = 0.00, df = 0, p -value < 0.0001, the comparative fit index (CFI) = 1.00, the Tucker-Lewis fit index (TLI) = 1.00, the Standardized Root Mean Square Residual (SRMR) = 0.00, and the RMSEA = 0.00. Those values indicate an excellent fit between the model and the observed data. Based on the path analysis results, only the organization groups were associated directly with mental health service use. In contrast, five independent variables were associated with and directly affected the EQ-5D-5L, and three independent variables were associated with and directly affected the EQ-VAS. However, those independent variables did not indirectly affect the EQ-5D-5L and EQ-VAS. Therefore, the generalized estimating equation (GEE) was used for statistical analysis to account for the within-subject correlation between patient outcomes within the same hospital. Marginal linear regression models using GEE were performed to examine the association between factors and mental health service use and HRQoL (EQ-5D-5L and EQ-VAS), respectively. Organization and personal factors affecting HRQoL. As shown in Table 4 , the marginal linear regression model using GEE to model the within-subject correlation for each hospital was performed to assess the predictors of EQ-5D-5L among older persons with DD. In the univariate model, the organization group, sex, marital status, comorbidity, perceived income, ATDS, and perceived social support were statistically significantly associated with the EQ-5D-5L. As can be seen in the unadjusted association between the organization group and EQ-5D-5L, the high-resource hospital had a significantly higher coefficient index of EQ-5D-5L than the low-resource hospital (unadjusted coefficient = 0.063, 95% CI = 0.012,114, p -value = 0.016). However, after adjusting the other variables in the multivariable model, the organization group was not an independent factor associated with the EQ-5D-5L. Table 4 Factors associated with EQ-5D index in older persons with DD (n = 412) Factors Univariable Multivariable Unadjusted coefficient SE 95% CI p -value Adjusted coefficient SE 95% CI p -value Organization Group Low resource Reference Reference High resource 0.063 0.026 (0.012, 0.114) 0.016 0.041 0.029 (-0.017,0.098) 0.164 Age -0.009 0.002 (-0.012, -0.006) < 0.001 -0.006 0.002 (-0.009, -0.003) < 0.001 Sex Female Reference Reference Male 0.090 0.032 (0.027, 0.152) 0.005 0.082 0.028 (0.026, 0.137) 0.004 Marital status Other Reference Reference Married 0.043 0.021 (0.003, 0.084) 0.037 -0.015 0.020 (-0.053, 0.024) 0.452 CCI -0.031 0.008 (-0.047, -0.015) < 0.001 -0.022 0.007 (-0.036, -0.008) 0.002 Perceived income insufficient Reference Reference Sufficient 0.059 0.020 (0.020, 0.099) 0.004 0.049 0.023 (0.003, 0.095) 0.037 ATDS 0.008 0.001 (0.006, 0.011) < 0.001 0.007 0.001 (0.004, 0.010) < 0.001 MSPSS 0.005 0.002 (0.002, 0.008) 0.002 0.004 0.001 (0.001, 0.006) 0.003 The severity of depression symptom Mild Reference Reference Moderate -0.017 0.053 (-0.121, 0.086) 0.741 -0.018 0.047 (-0.110, 0.074) 0.698 Severe 0.059 0.032 (-0.002, 0.121) 0.060 0.040 0.035 (-0.028, 0.108) 0.253 F32.9/NOS -0.046 0.033 (-0.111, 0.018) 0.159 -0.005 0.046 (-0.096, 0.085) 0.908 SE = standard error; CCI = Charlson Comorbidity Index; ATDS = Attitude Toward Depression; MSPSS = Multidimensional Scale of Perceived Social Support. The independent variables with a p -value less than 0.10 in univariable analysis (i.e., organization class, age, sex, marital status, CCI, perceived income, ATDS, MSPSS, and severity of depression) were included in the multivariable analysis. As shown in Table 5 , the marginal linear regression model using GEE to model the within-subject correlation for each hospital was performed to assess EQ-VAS predictors among older persons with DD. Notably, the organization group was not associated with EQ-VAS in univariable and multivariable models. Nevertheless, individual characteristics, that is, age, perceived income, ATDS, and perceived social support, were statistically significantly associated with EQ-VAS in univariable and multivariable models. Table 5 Factors associated with EQ-VAS in older persons with DD (n = 412) Factors Univariable p -value Multivariable p -value Unadjusted coefficient SE 95% CI Adjusted coefficient SE 95% CI Organization Group Low resource Reference High resource 1.518 1.103 (-0.644, 3.680) 0.169 Age -0.374 0.089 (-0.548, -0.200) < 0.001 -0.177 0.074 (-0.323, -0.032) 0.017 Sex Female Reference Male 1.571 1.659 (-1.681, 4.822) 0.344 Marital status Other Reference Married 2.544 1.650 (-0.689, 5.788) 0.123 CCI -0.591 0.460 (-1.493, 0.311) 0.199 Perceived income Insufficient Reference Reference Sufficient 3.948 1.047 (1.895, 6.000) < 0.001 3.813 0.898 (2.053, 5.572) < 0.001 ATDS 0.318 0.074 (0.173, 0.462) < 0.001 0.237 0.069 (0.102, 0.373) 0.001 MSPSS 0.208 0.067 (0.076, 0.340) 0.002 0.154 0.062 (0.032, 0.277) 0.013 The severity of depression symptoms depression Reference Reference Moderate 0.322 1.737 (-3.073, 3.738) 0.848 0.525 2.642 (-4.654, 5.704) 0.843 Severe 2.245 0.814 (0.650, 3.841) 0.006 0.634 1.875 (-3.040, 4.309) 0.735 F32.9/NOS -1.273 0.944 (-3.124, 0.578) 0.178 0.800 1.972 (-3.065, 4.664) 0.685 SE = standard error; CCI = Charlson Comorbidity Index; ATDS = Attitude Toward Depression Scale; MSPSS = Multidimensional Scale of Perceived Social Support. The independent variables with a p -value less than 0.10 in univariable analysis (i.e., age, sex, marital status, CCI, perceived income, ATDS, MSPSS, and severity of depression) were included in the multivariable analysis. Discussion Our study is the first to include mental health service delivery and personal factors such as demographic, clinical, and health-related factors to predict HRQoL through mental health service use among older persons with DD. HRQoL is an essential long-term outcome of service use. In addition, we examined predictors of HRQoL while controlling for subject correlation of patient outcomes within the same hospital. First, this study reveals that the HRQoL of older persons diagnosed with DD who received service in a general hospital in the last six months to one year tended to be high. It might be inferred that older persons with DD had a state of health at a moderate level due to the utility index being in the possible range of -0.283 to 1, 81.37% with no problem in the self-care dimension, followed by the usual activities dimension (58.25%). In addition, the EQ-5D index was almost higher in all dimensions among older persons with continuous mental health services use than those with non-continuous service use. The lowest three dimensions of the perceived state of health include pain/discomfort, mobility, and anxiety/depression. It was supported by a report that older persons diagnosed with DD exhibited lower HRQoL concerning WHOQOL-BREF dimensions of physical health, psychological, social relationships, and global QoL [ 28 ]. In addition, [ 29 ] examined the EQ-5D index in eight patient groups of chronic diseases in six European countries (mean age = 51.9 years); participants with depression perceived no problem, most in the self-care dimension. Similarly, Pan, Cong [ 2 ] examined the HRQoL of older persons with chronic conditions. They found that older persons with depression had a utility index of 0.748 (SD = 0.18), while the utility index of older persons with no depression was 0.956 (SD = 0.08). Significant differences existed between the utility index score among older persons who had depression and those who had no depression. However, after remission, the HRQoL was not significantly different between those with DD and no DD. Regarding the EQ-VAS, the subjective rating of overall health from 0-100 score (the worst-the best health you can imagine), EQ-VAS from this study was higher than found in the previous survey in Asian countries, in Indonesia (38.6 +- 29.7), Vietnam (38.1 +- 18.1), Japan (51.5 +- 19.0) [ 30 ] and in older Chinese persons (60.2, SD = 12.9) [ 2 ]. It is possible that the participants in this study were diagnosed with DD and received some treatment, which is contrary to the participants in those mentioned above, their participants having depressive symptoms, dwelling in the community, and never receiving depression treatment or care. The finding supported the previous one-year follow-up study that HRQol of the older psychogeriatric patients was improved in all five dimensions of EQ-5D from the first diagnosis, but more improvement in those with remission from depression at follow-up than those without remission. Predictors of HRQoL among older persons with DD The findings reveal that mental health service use did not mediate organizational profiles and HRQol among older persons with DD. HRQol of those with DD was associated and affected by personal factors, including sex, age, comorbidity, ATDS, social support, and sufficient income, by both analyses with EQ-5D-5L and EQ-VAS. The finding of adjusted coefficient multivariable GEE analysis confirmed this finding. It is possible to explain that the HRQoL is a multidimensional and multilevel concept [ 31 ] covering various dimensions of life. The HRQoL in the current study comprised five dimensions: mobility, self-care, usual activity, pain/comfort, anxiety/depression, and evaluations of life. Mental health service use in the general hospital only provides drug treatment and psychosocial counseling related to depressive problems. As a result, mental health service use is not a mediator to the HRQoL. The results reveal that policy and the health service systems must improve intervention or expand the service to meet the long-term goal of HRQoL of older persons with DD. According to organization groups, the high-resource organization has independently affected the EQ-5D-5L significantly; after adjusting for other personal factors, it was not affected significantly. Similarly, using EQ-VAS as the dependent variable, the higher resource organization was not affected by EQ-VAS significantly. The resource of an organization, such as nurse-patient ratio, nurse competency, and appointment reminders, was recommended for improvement. Regarding personal factors, younger age, male sex, greater number of comorbidities, sufficient income, ATDS, and perceived social support were all statistical predictors of better EQ-5D index (p < .05). Only younger age, sufficient income, and ATDS affected health status measured with the EQ-VAS (p < .05). This finding is consistent with EQ-5D-5L utility and EQ-VAS scores reported for other countries indicating the fundamental different measurement of HRQoL [ 32 ]. On the other hand, the EQ-VAS is an adjectival rating scale that uses verbal descriptions of the respondents' health. The older persons had difficulty comparing scores between 100 (best imaginable health) and 0 (worst imaginable health). The result also accords with a recent systematic review and meta-regression analysis showing that test-retest reliability in EQ-VAS was inconsistent between those ≤ 50 years and ≥ 50 years [ 33 ]. Perceived sufficient income, ATDS, and social support had a higher HRQoL, measured by the EQ-5D-5L and EQ-VAS while controlling for subject correlation patient outcome within the same hospital in the multivariable model. This result is an explanation by the economy or income, which is essential for living in daily life, for spending to meet essential needs, or for transportation when persons have a visit for mental health service use at the hospital. This economic factor was consistent with the findings of a previous study that monthly income was significantly associated with EQ-5D-5L and EQ-VAS among older Chinese persons with depressive symptoms [ 2 ]. Regarding ATDS, Gabriel and Violato [ 34 ] concluded that knowledge of ATDS might significantly impact adherence to antidepressants. Also, a study of depressed patients who received service in a psychiatric hospital in Thailand revealed that knowledge of depression, the trustworthiness of antidepressant treatment, continued medication, and appointments were significantly associated with QoL (WHOQOL-BREF-THAI) [ 35 ]. Social support refers to information leading individuals to believe that they are cared for and loved, have self-esteem and are valued, belong to a community, and have mutual obligations [ 36 ]. The support can be emotional, informational, and instrumental for convenience [ 26 ]. In addition, social support also significantly protects the QoL regarding sensory ability, autonomy, ability, and intimacy of older persons [ 37 ]. Therefore, social support could promote the wellness and health of older persons with DD. Primarily, social support mediates the role of psychological resilience and its effect on the QoL among older people [ 38 ]. Consistent with previous studies, Kong, Zhang [ 38 ] found that social support and psychological resilience were positively associated with physical and mental HRQoL after adjusting for gender, education level, marital status, household registration, and chronic diseases among older migrant adults in China. Similarly, previous studies on older persons with depression in the community reinforced that social support improved HRQol [ 37 , 39 ]. This study found that older persons with younger age had a higher HRQoL than those with older age. This association may be because depression tends to increase in older persons due to developmental deterioration of physical and psychological aging and pathophysiological neurotransmitter disorders, especially in older persons [ 40 ], and changes in psychosocial factors such as stresses of life [ 41 ]. This finding is consistent with previous studies that poorer HRQoL by EQ-5D-5L and EQ-VAS were associated with higher age [ 9 ]. Older persons with more comorbidities had less HRQoL than those with less comorbidity, measured by the EQ-5D-5L except for EQ-VAS. A possible explanation for this result might be related to physical illness. Seniors' comorbidities may result from degenerative pathological deterioration in physical abilities that could impact their complicated illness, treatment, and care, making their health increasingly compromised. As a result of having comorbidities, older adults with depression feel more suffering from physical pain. Consistent with a previous study by[ 9 ], among older people with depression in one-year follow-up in Norway, those who had a higher number of comorbidities had lower HRQoL in EQ-5D-5L and EQ-VAS than those with less comorbidity. The marital status of older persons was associated with EQ-5D-5L, except EQ-VAS in univariable analysis controlling for within-subject correlation between patient outcomes within the same hospital; however, after coefficient adjustment, the marital status was not associated with EQ-5D-5L and EQ-VAS. Namely, older persons who were married had a greater EQ-5D-5L than those with another marital status, thereby dependent on the influence of organization group, age, sex, sufficiency income, ATDS, social support, and severity of depression. In comparison with a previous study, which agreed with this result [ 42 ], researchers found that the association of marital status with quality of life (SF-26, physical pain) was independent of the influence of sex and psychiatric history among older persons with depressive disorders who received mental health service in the university hospital in Thailand. The severity of depression at the first visit was not significantly associated with HRQoL after 6–12 months when using adjusted coefficient multivariable analysis measured by the EQ-5D-5L and EQ-VAS. This severity finding is in line with the findings of the HRQoL one year after hospitalization for treatment of depression in older persons who had experienced remission and had their HRQoL equal to the reference group of older persons without depression when adjusted for differences in socio-demographics and health conditions [ 9 ]. Typically, previous studies reported that the severity of depression at initial treatment is associated with poor HRQol in adults and older persons [ 2 , 11 , 32 ]. However, the HRQoL improved at six and twelve-month follow-ups and decreased with increasing depression severity. Besides, a cross-sectional study in older Thai persons with DD who received mental health services at the medical hospital for more than five years (57.8%) had HRQoL (WHOQOL-BREF-THAI) associated with the severity of depression, and it was dependent on the influence of education and perceived treatment outcomes [ 43 ]. Additionally, in a study of trajectories of quality of life in old age through a longitudinal approach, the finding showed that lower education and higher levels of depression and anxiety at baseline were associated with worse quality of life later; 42.1% of the variation of CASP-12 across age [ 44 ]. However, future studies must assess the quality of life between older persons who continued mental health service use from baseline and performed at the time of follow-up. Based on these results, it can be concluded that older, male sex, lower age, less comorbidity, higher ATDS, having social support, and receiving services in the same hospital had higher EQ-5D-5L than those without these characteristics. This study demonstrated the mechanism of organization groups that was a variable influence on individual characteristics. This principle underpins the Behavioral Model of health service uses (BM), improving in the fifth phase [ 45 ] that applies organizational groups as a contextual characteristic. This study emphasized only organization groups and ATDs that promote the use of mental health services. The organization groups encompass the number and distribution of health facilities and staff to provide the population services such as nurse-patient ratio, nursing competency, appointment reminders, and hospital level. The advantage of organization groups in this study was considered distinct from the subtype pattern (high-resource and low-resource organization) and shared characteristics in all hospitals. This organization group could be an independent variable affecting mental health service use. Therefore, as discussed above, this study demonstrated the mechanism and evidence of the Behavior model of health services for investigating factors predicting mental health and quality of life among older persons with DD. Strengths and limitations of the study Although this study has several strengths, we have discovered that the organization groups with different available resources can directly impact mental health service use among older persons with DD. In addition, mental health service use is inadequate in predicting HRQoL measured by EQ-5D-5L and EQ-VAS. Only personal factors such as demographics, ATDS, and perceived social support affected HRQoL directly. The use of WLSMV in SEM revealed strong insights into the complex interplay between organization groups and personal factors influencing HRQoL through mental health service use, providing a valuable framework for future clinical research in this area. However, the limitations of this study need to be addressed in four ways. First, the time to assess the HRQoL of the participants from six months to one year was too broad a range. Therefore, the score of HRQoL cannot identify the exact time and may vary. Second, research should follow up on the score of depressive symptoms when obtaining HRQoL to identify whether the good HRQoL was retrieved from older persons in remission. Third, this study did not assess information that can impact HRQoL, such as drug therapy and psychosocial intervention. Meta-analyses revealed that pharmacotherapy and psychotherapy effectively enhance QoL by improving depressive symptoms but produce mixed results [ 46 ]. Lastly, ideal PA model fit indices were observed (e.g., CFI = 1.00, TLI = 1.00, RMSEA = 0.00), which, while indicating a perfect fit, should be interpreted with caution due to the possibility of overfitting or the model's recent identification. Conclusion The HRQoL of older persons after being diagnosed with DD in the general hospital tends to be high, although some did not visit the mental health clinic continuously. However, those who attend the mental health clinic continuously demonstrate more HRQoL than those who attend intermittently. Continuous mental health service use was not a mediated variable among personal and organizational factors on HRQoL. The predictors of HRQoL, while controlling for subject correlation patient outcome within the same hospital, revealed that age, sufficient income, ATDS, and perceived social support were the predictors of both the EQ-5D-5L and EQ-VAS. Meanwhile, male sex and comorbidity are the only predictors of EQ-VAS. Neither high nor low resource organization status affected HRQoL. The lack of effect from the resource status of the hospital is crucial for reconsidering the general hospital's quality of care and mental health service. Abbreviations ATDS: Attitude toward depression and its treatment; BM: Behavioral model of health service uses DD Depressive disorders EQ-5D-5L European Quality of life, five Dimension, five Level EQ-VAS European Quality of Life, Visual Analog Scale GEE: Marginal logistic regression model using generalized estimating equation; MDD Major depressive disorders MSPSS: Multidimensional Scale of Perceived Social Support; HRQoL: Health-related quality of life; Declarations Acknowledgments The authors would like to express their gratitude to all participants, who agreed to participate in this study with both head nurses in twelve hospitals and older persons with depression. We also want to thank Mr Stephen Hamann for English language editing. Authors’ Contributions All authors made substantial contributions to the study's conception and design. TM collected data and did the initial analysis; AS and ST did the final analysis and interpretation of data. AS made a significant contribution to writing the manuscript. All authors participated in the feedback on the initial investigation. AS and ST were involved in developing and revising the manuscript. All authors read and approved the final manuscript before submission. Funding The authors did not receive any funding for this paper. Availability of Data and Materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Ethics Approval and consent to participate This protocol was approved by the Mahidol University Institutional Review Broad, Faculty of Medicine Siriraj Hospital; reference number COA. No. Si 527/2019 and faculty of medicine Ramathipbodi Hospital; reference number COA.MURA2019/1264. This study was approved by a Review broad committee in each hospital under the Ministry of Public Health in Thailand including, Supasit Hospital (CAcode021/2563:081/62S), Khon Kean Hospital (KEF62042), Patha lung hospital (2/2563), Lamphune hospital (Ethic LPN122/2562), Chaophraya Yommarat Hospital (YM009/2563). Permission participants were provided with information and written informed consent as well. The research conforms to the provisions of the Declaration of Helsinki, the Belmont Report, and CIOMS Guidelines. In addition, the committee for research of the hospital approved the research project before working with human subjects. Consent for publication Not applicable Competing Interests The authors declare that they have no competing interests. Author Details 1 D.N.S. Candidate, Faculty of Nursing, Mahidol University, Bangkok, Thailand. 2 Department of Mental Health and Psychiatric Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand. 3 Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. 4 Department of Surgical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand. References United Nations Thailand. Sustainable Development Goal 3 Good Health and Well-being [Internet]. [cited July,23]. Available from: https://thailand.un.org/en/sdgs/3 . Pan C-W, Cong X-L, Zhou H-J, Wang X-Z, Sun H-P, Xu Y, et al. Evaluating health-related quality of life impact of chronic conditions among older adults from a rural town in Suzhou, China. Arch Gerontol Geriatr. 2018;76:6–11. Department of Mental Health. 5-year government action plan (2023–2027) of the Department of Mental Health [Internet]. [cited 2023 June 23]. Available from: https://dmh.go.th/intranet/p2554/download/ . 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Measurement Properties of the EQ VAS Around the Globe: A Systematic Review and Meta-Regression Analysis. Value Health. 2021;24(8):1223–33. Gabriel A, Violato C. Knowledge of and attitudes towards depression and adherence to treatment: The Antidepressant Adherence Scale (AAS). J Affect Disord. 2010;126(3):388–94. Khunarak U. Factors affecting the quality of life in outpatients with depressive disorder at Somdet Chaopraya Institute of Psychiatry. J Psychiatr Nurs Ment Health. 2011;25(1):44–54. Cobb S. Social support as a moderator of life stress. Psychosom Med. 1976;38(5):300–14. Unalan D, Gocer S, Basturk M, Baydur H, Ozturk A. Coincidence of low social support and high depressive score on quality of life in elderly. Eur Geriatr Med. 2015;6(4):319–24. Kong LN, Zhang N, Yuan C, Yu ZY, Yuan W, Zhang GL. Relationship of social support and health-related quality of life among migrant older adults: The mediating role of psychological resilience. Geriatr Nurs. 2021;42(1):1–7. Lu S, Wu Y, Mao Z, Liang X. Association of Formal and Informal Social Support With Health-Related Quality of Life Among Chinese Rural Elders. Int J Environ Res Public Health. 2020;17(4). Rapp MA, Dahlman K, Sano M, Grossman HT, Haroutunian V, Gorman JM. Neuropsychological differences between late-onset and recurrent geriatric major depression. Am J Psychiatry. 2005;162(4):691–8. Steffens DC, McQuoid DR, Smoski MJ, Potter GG. Clinical outcomes of older depressed patients with and without comorbid neuroticism. Int Psychogeriatr. 2013;25(12):1985–90. Charoensak S, Sittironnarit G, Satra T, Muangpaisan W, Srinonprasert V. Prevalence of Psychiatric Disorders in Elderly Patients, Quality of Life of Patients and Caregivers, and Their Correlated Factors. J Psychiatr Assoc Thail. 2018;63(1):89–98. Sripetch S, Prasertchai R, Santhaweesuk K, Pimpitkanueng K, Thaipisuttiku P. Factors Associated Quality of life in Elderly with Major Depressive Disorder at Psychiatric Outpatient Unit, Ramathibodi Hospital. J Psychiatric Association Thail. 2019;64(1):89–98. Ribeiro O, Teixeira L, Araújo L, Rodríguez-Blázquez C, Calderón-Larrañaga A, Forjaz MJ. Anxiety, Depression and Quality of Life in Older Adults: Trajectories of Influence across Age. Int J Environ Res Public Health. 2020;17(23). Andersen RM. National health surveys and the behavioral model of health services use. Med Care. 2008;46(7):647–53. Hofmann SG, Curtiss J, Carpenter JK, Kind S. Effect of treatments for depression on quality of life: a meta-analysis. Cogn Behav Ther. 2017;46(4):265–86. Additional Declarations No competing interests reported. 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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-4108211","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280671829,"identity":"eb5aba88-ca48-4dc3-8ed9-8a3b494a178e","order_by":0,"name":"1.\tThida Mulalint","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"1.\tThida","middleName":"","lastName":"Mulalint","suffix":""},{"id":280671830,"identity":"d8e6e689-fb91-41b4-bf29-bec8b1882a4d","order_by":1,"name":"2.\tAcharaporn 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categorized by\u003cstrong\u003e \u003c/strong\u003econtinuing mental health service use.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4108211/v1/23b10fed6fb13912f940b2d9.png"},{"id":53119722,"identity":"95d6e739-ae18-4925-a120-399f9ac9452f","added_by":"auto","created_at":"2024-03-20 20:37:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140886,"visible":true,"origin":"","legend":"\u003cp\u003ePath analysis of mental health service use predictors for EQ-5D index in older persons with depressive disorders.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4108211/v1/cbf7a5e048e82ab043c0d51b.png"},{"id":53119724,"identity":"c1baf655-1a3d-4967-883f-6f1cd16bc9ba","added_by":"auto","created_at":"2024-03-20 20:37:10","extension":"png","order_by":5,"title":"Figure 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20:37:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20989,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileSupplementaryTableS1S4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4108211/v1/070ab9e6982fbcc4613c70b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":" Organizational profiles and personal factors affecting Health-related quality of life among older persons diagnosed with depressive disorders: path analysis and GEE","fulltext":[{"header":"Introduction","content":"\u003cp\u003eQuality of life (QoL) is closely aligned with an indicator of achievement of Sustainable Development Goal item # 3 (SDG 3) in Thailand, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment, and promote mental health and well-being by 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The advanced age of those who have depressive disorders (DD) affects their QoL more than other physical factors[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As a result of WHO and Thai mental health policy addressing the accessibility and availability of mental health care services [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], access to mental health service use was quite properly improved [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, the service delivery system is most likely to evaluate the symptoms of depression as a health outcome [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, evidence has recommended that relief of symptoms is inadequate to expect older persons to live healthy lives. The QoL is recognized as a critical indicator of personal recovery from mental illness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The World Health Organization (WHO) defines QoL as individuals' perception of their position in life in the context of the culture and value systems in which they live and that affect their goals, expectations, standards, and concerns [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the QoL is a comprehensive and multidimensional concept influenced by various aspects, both health service system determinants and personal determinants. Therefore, the current study uses health-related quality of life (HRQoL) to evaluate the outcome of receiving mental health services. This study explores and explains the factors affecting the HRQoL of older persons who receive health services for depression. Before treatment, less than 3% of patients with MDD reported normal QoL [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Improving HRQoL is an essential goal in the health service delivery system worldwide and reducing long-term disability. Factors impacting HRQoL in older persons with DD were ambiguous. Most evidence found that HRQoL was associated with socio-demographic, and clinical factors. A recent study found that older age, lower level of education, lower income, worse subjective perception of health, unemployment, obesity, and mental health struggles were significantly associated with low HRQoL in Korean older adults diagnosed with DD after adjustment for multiple covariates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. After a one-year follow-up of older persons with DD after hospitalization, improved HRQoL was significantly better in those with remission of depression and those with better baseline physical health. Moreover, HRQoL was equal to the reference group of older persons without depression when adjusting for differences in socio-demographics and health conditions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, a report of a large longitudinal international cohort study of adults with Major Depressive Disorder (MDD) who were in routine clinical practice in five European countries showed HRQoL to be severely impaired at the time of initiating or undergoing the first switch of antidepressant monotherapy. It was not fully restored after the acute and short-term maintenance phases of treatment but continues to be impaired long after [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study revealed that the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) treatment had a statistically significant positive impact on HRQoL in adults with MDD. Nevertheless, most patients continued to experience HRQoL deficits, and HRQoL scores declined after 12 months [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, clinical factors, including the severity of depression and severity of cognitive symptoms were significantly associated with HRQoL impairment at baseline, and months 12, 18, and 24 of follow-up [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Organizational Profile is a snapshot of the organization, the key influences on how it operates, and the competitive environment. It is composed of organizational context and organizational situation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The organization profiles may reveal directions and opportunities for health system improvement. Previous studies in Thailand have focused on each characteristic of the hospital affecting health outcomes. The characteristics of the hospital include the level of hospital, number of health manpower, facilities of the hospital, etc. They were rarely associated with health outcomes as a result of little variation in hospital characteristics [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Also, the level of the hospitals including psychiatric hospitals, regional hospitals, general hospitals, and community hospitals did not show the effect of improving HRQoL differently in patients with DD who received mental service in the first three months [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to Latent Class Analysis (LCA), a hospital-centered modeling approach, hospital profiles can classify heterogeneous characteristics into more meaningful groups [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, this present study conducted Latent Class Analysis (LCA), classifying organizational profiles composed of hospital levels, nurse competencies, nurse-patient ratio, and appointment reminders into 2 groups of low and high-resource organizations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The high resource organizations most likely were university hospitals (77.4%), the patients-nurse ratio between 16.80\u0026ndash;37.50, the total of their nurses had master\u0026rsquo;s degrees in Nursing Science, and the hospital had appointment reminders 77.4%. The low-resource organization had those profiles differently. A detail of characteristics of organizational profile analysis was provided by Mulalint et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAccording to the process of health service delivery, patients with DD require continuous treatment to prevent the relapse and recurrence of depression symptoms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A meta-analysis reported that providing antidepressant therapy to the depressed elderly for 12 months could reduce the risk of relapse [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; also, depressed elderly receiving adequate duration of treatment demonstrated a better outcome than those with shorter durations of treatment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, depressed older persons must receive an entire course of depression treatment to ensure that they have reached the treatment goal and improved their HRQoL. However, some evidence supporting this point is limited to the effects of mental health service use on HRQoL and limited evidence examining the health outcome between the organizational factors and individual characteristics among older persons with DD. A study closely related to this solution indicates that continuing depression treatment could contribute to HRQoL in older persons with DD that found EQ-5D-5L was significantly higher than before receiving treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, no study examined the HRQoL among older persons with DD who are not continuing therapy in any way. In addition to insufficient evidence, health behavior is examined as a mediator effect on HRQoL concerning the mental health care system and individual factors.\u003c/p\u003e \u003cp\u003eRegarding service delivery for older persons with DD, understanding factors promoting HRQoL among older persons with DD is vital in assessing the effectiveness of the mental health service delivery system. The Behavior Model of Service Use (BM) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the fifth phase is proper for understanding the health service use by focusing on contextual and individual determinants. Therefore, this study aims to explore the HRQoL of older persons with DD who received service in general hospitals and explain the effect of organizational profiles and personal factors on HRQoL mediated by mental health service use. The first hypothesis examined the effect of organizational profiles and personal factors on HRQoL via mental health service use. The personal factors consisted of age, sex, marital status, comorbidity, sufficient income, attitude toward depression and its treatment (ATDS), social support, and severity of depression. The second hypothesis was whether the organizational profiles and personal factors were significantly associated with HRQoL among older persons with DD while controlling within-subject correlation between HRQoL within the same hospital.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study is part of a project on determinants of continuing mental health service use among older persons diagnosed with depressive disorders in general hospitals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] that seeks to explain the effects of system factors and personal factors on HRQoL.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eWe conducted an analytic cross-sectional study design with data from the project mentioned above from May 2018 through November 2020 before the first national restriction of the coronavirus pandemic in Thailand. All selected variables of this study were based on the BM [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The personal and organizational profiles came from 12 general hospitals in Thailand using multi-stratified sampling based on Thailand's health service system administration [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. There are 3 clusters: community hospitals, advanced and standard hospitals, and university hospitals. To achieve an equal sample size in all groups, the number of participants in all clusters was 150. The hospitals in each grouping cluster were selected by purposive sampling. Detail of calculating sample size and research setting was provided by Mulalint et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Data Collection\u003c/h2\u003e \u003cp\u003e Participants were patients receiving service in the psychiatric clinics of the general hospital and staff members providing care to them. Patients had to be at least 60 years old with a first or recurrent diagnosis of DD based on the 10th revision of the International Classification of Diseases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] from at least six months to one year before collecting data. The exclusion criteria included patients who had been diagnosed with mania or bipolar conditions, had severe somatic disease, could not answer questionnaires, or had severe depression and suicidal thoughts. Data on the patients come from two sources: medical records and interviews. Demographic and clinical profiles were extracted from medical records. After that, the researcher interviewed them with translated HRQoL questionnaires - EQ-5D-5L and EQ-VAS, and other interviewing instruments, including Attitude toward Depression and Its treatment (ATDS). The patients who did not respond when scheduled to visit the clinic by appointment were later contacted by phone or visited at home to make a date and time for an appointment. The recruitment of patient participants and data collection procedure are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The total number of patient participants used for analysis was 424 cases representative of the population studied.\u003c/p\u003e \u003cp\u003eStaff members were twelve nurses who had experienced care provision at an outpatient unit for at least five years or had experience caring for psychiatric patients for at least two years. They provided data on the mental health service delivery system through an interviewing questionnaire.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eThe Health-related Quality of Life (HRQoL)\u003c/h2\u003e \u003cp\u003eThe EuroQol Group self-report instruments were used to assess HRQoL as the health outcome of mental health service use among older persons with DD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This instrument consisted of European QOL 5 dimension, five levels (EQ-5D-5L), and a Visual Analog Scale (EQ-VAS). The EQ-5D-5L comprised five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), each with five levels of problems (1\u0026thinsp;=\u0026thinsp;none, 2\u0026thinsp;=\u0026thinsp;slight, 3\u0026thinsp;=\u0026thinsp;moderate, 4\u0026thinsp;=\u0026thinsp;severe, 5\u0026thinsp;=\u0026thinsp;extreme). These scores, including each scale, are transformed into a utility index developed for the Thai population and used as the standard QOL of Thai people[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The utility score ranges from \u0026minus;\u0026thinsp;0.283 to 1.00. A value score less than 1 implied worse than death, value 0 implied death, and value 1 implied the best health. It was determined by subtracting the coefficients of each of the five health dimensions. In this study, the internal consistency of the EQ-5D-5L was .84 (n\u0026thinsp;=\u0026thinsp;424). EQ-VAS is a self-assessment tool for recording the respondent's self-rated health from 0 (the worst health) to 100 (the best health). In this study, the EQ-VAS had a moderate correlation with EQ-5D-5L (Pearson's correlation; \u003cb\u003er\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.34, n\u0026thinsp;=\u0026thinsp;424). The EQ-5D-5L was used as the first HRQoL outcome measure. Also, the EQ-VAS value was used for discussion combined with the result of the utility index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eMental Health Services Use\u003c/h2\u003e \u003cp\u003eMental health services use refers to attending a mental healthcare appointment for older persons within six months after being diagnosed with DD. The response is categorized into non-continuing service use (score\u0026thinsp;=\u0026thinsp;0) and continuing service use (score\u0026thinsp;=\u0026thinsp;1). \"Non-continuing service use\" means that older persons have a non-attending mental health service period of 90 days or more since the diagnosis date. \"Continuing service use\" means that older persons attend mental health clinics continuously according to clinical appointments and never miss a clinical visit for depression treatment for more than 90 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Personal factors and measurement\u003c/h2\u003e \u003cp\u003ePersonal demographic and clinical factors are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Organizational profiles\u003c/h2\u003e \u003cp\u003eOrganizational profiles relevant to mental health service use for depression in the 12 hospitals were categorized into high and low-resource organizations. The two groups were obtained by conducting a latent class analysis of four variables: hospital level, nurse competency, nurse-patient ratio, and appointment reminders. The high-resource organization consisted of the university hospital and standard and advanced hospitals, the higher qualifications of nurses, higher patient-to-nurse ratio, and more success with appointment reminders than low-resource organizations. More detailed information on groups of organizations can be found elsewhere [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\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\u003eDescription of the variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaires/measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorizations/\u003c/p\u003e \u003cp\u003escores\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth-related quality of life\u003c/p\u003e \u003cp\u003e(HRQoL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEQ-5D-5L and EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe utility index ranged from \u0026minus;\u0026thinsp;0.283 to 1\u003c/p\u003e \u003cp\u003eEQ-VAS recorded participants 'self-rated health from 0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emental health service use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase record form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;not continuing mental health service use; 1\u0026thinsp;=\u0026thinsp;continuing mental health service use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase record form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtinuous score\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase record form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;female, 2\u0026thinsp;=\u0026thinsp;male\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase record form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 single, divorced, separated, other\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;Married\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=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;insufficient, 2\u0026thinsp;=\u0026thinsp;sufficient\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe Charlson Comorbidity Index (CCI)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePossible total score from 0\u0026ndash;42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe severity of depressive symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase record form of depression diagnosis according to ICD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Mild, 2\u0026thinsp;=\u0026thinsp;Moderate, 3\u0026thinsp;=\u0026thinsp;Severe, 4\u0026thinsp;=\u0026thinsp;Not other specified\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude toward depression and its treatment\u003c/p\u003e \u003cp\u003e(ATDS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttitude toward depression and its treatment, Thai version [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e25 items from the original 27 items\u003c/p\u003e \u003cp\u003eReliability .70 (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA Likert score of 1 strongly disagree to 5 strongly agree.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived social support\u003c/p\u003e \u003cp\u003e(MSPSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultidimensional Scale of Perceived Social Support [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] which was translated into Thai by Wongpakaran Tinakon and Wongpakaran Nahathai [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e12 items. Reliability .90 (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLikert scales of 1 (very strongly disagree), to 7 (very strongly agree), and overall positive opinion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganization profiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMental health service delivery factors (hospital level, nurse-patient ratio, nurse competency, and appointment reminders) were divided into two groups using latent class analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrganization groups\u003c/p\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Low resource organization\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;High resource organization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003e This study was reviewed and approved by the institutional review board of the Research Ethics Committee of the Faculty of Medicine, Siriraj Hospital (COA No. Si527/2019), Medicine Ramathibodi Hospital, Mahidol University (COA No. MURA2019/1264), and the Committees of all selected hospitals under the Ministry of Public Health.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePercentage problems per domain of EQ-5D-5L of the entire participant and categorized by continuing mental health service use were demonstrated. Mean and standard deviation (SD) of EQ-5D index and EQ-VAS scores were reported for the entire participant and specific subgroups by the hospital level where they received services. Descriptive demographic data and covariate variables were presented in the previous article [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The analytical framework of this study was anchored in path analysis using the lavaan package in R, aimed at elucidating the relationships between various independent variables and their effects on mental health service utilization and health-related quality of life (HRQoL), operationalized through EQ-5D-5L and EQ-VAS scores. Initial analyses identified a suite of key predictors\u0026mdash;encompassing demographic and personal characteristics (such as age, sex, marital status, and social support), economic factors (income sufficiency), health conditions (severity of depression and comorbidity), and organizational groups\u0026mdash;to assess their direct impact on mental health service use and HRQoL. Further exploration involved assessing the indirect effects of organizational groups and personal characteristics on HRQoL, mediated by the utilization of mental health services. The choice of the robust weighted least squares (WLSMV) method was pivotal in addressing the challenges posed by non-normal and categorical outcomes, ensuring accuracy in parameter estimation. Model adequacy was rigorously evaluated using a comprehensive set of fit metrics, including the Chi-square (χ\u0026sup2;) test, where a p-value greater than 0.05 was targeted for a satisfactory fit; RMSEA and SRMR values were expected to be \u0026le;\u0026thinsp;0.08, and CFI and TLI values were sought to be \u0026gt;\u0026thinsp;0.95, indicating a good fit. Additionally, the geepack package in R facilitated the running of a marginal linear regression model to account for within-subject correlations of patient outcomes within the same hospital, examining the association between organizational groups, individual characteristics, and the EQ-5D-5L and EQ-VAS measures. Data collection and analysis were carried out with the 2020 IBM Corp. release of IBM SPSS Statistics for Windows, Version 27.0, and R version 4.3.2. This methodological approach underscores our commitment to employing advanced statistical techniques to glean meaningful insights from complex healthcare data, contributing to the broader understanding of factors influencing mental health service utilization and HRQoL.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHRQoL as the health outcome\u003c/h2\u003e \u003cp\u003eThe proportion of participants who responded to the descriptive system of the EQ-5D-5L by dimension and classified by continuous mental health service use is shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Of the overall participants who had DD after six months to one year, the pain and discomfort dimension was the central problem and most extreme problem compared to other dimensions (65.57%). For those who continued mental health service use, pain was also the most frequently reported problem; participants without continuous service use (69.2%) had a more significant problem than participants who had continuous service use (62.4%). The utility index and EQ-VAS score of the overall participants, categorized by the hospital level, are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The actual range of the utility index of the overall participants was \u0026minus;\u0026thinsp;0.250 to 1; the mean was 0.632 (SD\u0026thinsp;=\u0026thinsp;0.31). Regarding hospital levels, the university hospital had the highest utility score (-0.265 to 1), with a mean of 0.662 (SD\u0026thinsp;=\u0026thinsp;0.31).\u003c/p\u003e \u003cp\u003eRegarding the EQ-VAS score, the results demonstrated that the actual range of overall participants was 5-100; the mean was 73.81 (SD\u0026thinsp;=\u0026thinsp;16.40). The EQ-VAS scores by kind of hospital were different. The university hospital produced the highest perceived health; the mean was 74.65 (SD\u0026thinsp;=\u0026thinsp;15.38).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePossible range, actual range, mean, and standard deviation (SD) of participant\u0026rsquo;s utility index\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePossible range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActual range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity Hospital (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.283 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.265 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced/standard hospital (n\u0026thinsp;=\u0026thinsp;147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.283 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.250 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity hospital (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.283 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.236 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.283 to1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.250 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\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=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePossible range, actual range, mean, and standard deviation (SD) of the self-related health status using EQ-VAS score of participants categorized by hospital level.\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=\"char\" char=\"\u0026minus;\" 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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePossible\u003c/p\u003e \u003cp\u003erange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActual range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity Hospital (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced/standard hospital (n\u0026thinsp;=\u0026thinsp;147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity hospital (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.40\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 \u003cb\u003eThe effect of the mental health service delivery factors and individual factors on HRQoL via mental health service use\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePath analysis results of the participants' characteristics and organization groups on EQ-5D-5L/EQ-VAS via mental health service use are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Tables S1-S4 respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn assessing the assumptions for our path analysis, we observed low multicollinearity among independent variables, with correlation coefficients ranging from \u0026minus;\u0026thinsp;0.093 to 0.245. This indicates minimal dependence between variables, supporting the reliability of our model estimates.\u003c/p\u003e \u003cp\u003eBoth path analysis models showed some exciting results regarding model fit indices. Chi-square\u0026thinsp;=\u0026thinsp;0.00, df\u0026thinsp;=\u0026thinsp;0, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, the comparative fit index (CFI)\u0026thinsp;=\u0026thinsp;1.00, the Tucker-Lewis fit index (TLI)\u0026thinsp;=\u0026thinsp;1.00, the Standardized Root Mean Square Residual (SRMR)\u0026thinsp;=\u0026thinsp;0.00, and the RMSEA\u0026thinsp;=\u0026thinsp;0.00. Those values indicate an excellent fit between the model and the observed data.\u003c/p\u003e \u003cp\u003eBased on the path analysis results, only the organization groups were associated directly with mental health service use. In contrast, five independent variables were associated with and directly affected the EQ-5D-5L, and three independent variables were associated with and directly affected the EQ-VAS. However, those independent variables did not indirectly affect the EQ-5D-5L and EQ-VAS.\u003c/p\u003e \u003cp\u003eTherefore, the generalized estimating equation (GEE) was used for statistical analysis to account for the within-subject correlation between patient outcomes within the same hospital. Marginal linear regression models using GEE were performed to examine the association between factors and mental health service use and HRQoL (EQ-5D-5L and EQ-VAS), respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrganization and personal factors affecting HRQoL.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the marginal linear regression model using GEE to model the within-subject correlation for each hospital was performed to assess the predictors of EQ-5D-5L among older persons with DD. In the univariate model, the organization group, sex, marital status, comorbidity, perceived income, ATDS, and perceived social support were statistically significantly associated with the EQ-5D-5L. As can be seen in the unadjusted association between the organization group and EQ-5D-5L, the high-resource hospital had a significantly higher coefficient index of EQ-5D-5L than the low-resource hospital (unadjusted coefficient\u0026thinsp;=\u0026thinsp;0.063, 95% CI\u0026thinsp;=\u0026thinsp;0.012,114, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.016). However, after adjusting the other variables in the multivariable model, the organization group was not an independent factor associated with the EQ-5D-5L.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with EQ-5D index in older persons with DD (n\u0026thinsp;=\u0026thinsp;412)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003cp\u003ecoefficient\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\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003ecoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eOrganization Group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.012, 0.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.017,0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.012, -0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.009, -0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.027, 0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.026, 0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003, 0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.053, 0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.047, -0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.036, -0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003ePerceived income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.020, 0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.003, 0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006, 0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.004, 0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002, 0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.001, 0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eThe severity of depression symptom\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.121, 0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.110, 0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.002, 0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.028, 0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF32.9/NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.111, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.096, 0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.908\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\u003eSE\u0026thinsp;=\u0026thinsp;standard error; CCI\u0026thinsp;=\u0026thinsp;Charlson Comorbidity Index; ATDS\u0026thinsp;=\u0026thinsp;Attitude Toward Depression; MSPSS\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;Multidimensional Scale of Perceived Social Support. The independent variables with \u003cem\u003ea p\u003c/em\u003e-value less than 0.10 in univariable analysis (i.e., organization class, age, sex, marital status, CCI, perceived income, ATDS, MSPSS, and severity of depression) were included in the multivariable analysis.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the marginal linear regression model using GEE to model the within-subject correlation for each hospital was performed to assess EQ-VAS predictors among older persons with DD. Notably, the organization group was not associated with EQ-VAS in univariable and multivariable models. Nevertheless, individual characteristics, that is, age, perceived income, ATDS, and perceived social support, were statistically significantly associated with EQ-VAS in univariable and multivariable models.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with EQ-VAS in older persons with DD (n\u0026thinsp;=\u0026thinsp;412)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003cp\u003ecoefficient\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=\"c6\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003ecoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrganization Group\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.644, 3.680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.548, -0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.323, -0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003eReference\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e1.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.681, 4.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.689, 5.788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.493, 0.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived income\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.895, 6.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(2.053, 5.572)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.173, 0.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.102, 0.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.076, 0.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.032, 0.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eThe severity of depression symptoms\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.073, 3.738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-4.654, 5.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.650, 3.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-3.040, 4.309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF32.9/NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.124, 0.578)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-3.065, 4.664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.685\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\u003eSE\u0026thinsp;=\u0026thinsp;standard error; CCI\u0026thinsp;=\u0026thinsp;Charlson Comorbidity Index; ATDS\u0026thinsp;=\u0026thinsp;Attitude Toward Depression Scale; MSPSS\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;Multidimensional Scale of Perceived Social Support. The independent variables with \u003cem\u003ea p\u003c/em\u003e-value less than 0.10 in univariable analysis (i.e., age, sex, marital status, CCI, perceived income, ATDS, MSPSS, and severity of depression) were included in the multivariable analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is the first to include mental health service delivery and personal factors such as demographic, clinical, and health-related factors to predict HRQoL through mental health service use among older persons with DD. HRQoL is an essential long-term outcome of service use. In addition, we examined predictors of HRQoL while controlling for subject correlation of patient outcomes within the same hospital.\u003c/p\u003e \u003cp\u003eFirst, this study reveals that the HRQoL of older persons diagnosed with DD who received service in a general hospital in the last six months to one year tended to be high. It might be inferred that older persons with DD had a state of health at a moderate level due to the utility index being in the possible range of -0.283 to 1, 81.37% with no problem in the self-care dimension, followed by the usual activities dimension (58.25%). In addition, the EQ-5D index was almost higher in all dimensions among older persons with continuous mental health services use than those with non-continuous service use. The lowest three dimensions of the perceived state of health include pain/discomfort, mobility, and anxiety/depression. It was supported by a report that older persons diagnosed with DD exhibited lower HRQoL concerning WHOQOL-BREF dimensions of physical health, psychological, social relationships, and global QoL [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] examined the EQ-5D index in eight patient groups of chronic diseases in six European countries (mean age\u0026thinsp;=\u0026thinsp;51.9 years); participants with depression perceived no problem, most in the self-care dimension.\u003c/p\u003e \u003cp\u003eSimilarly, Pan, Cong [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] examined the HRQoL of older persons with chronic conditions. They found that older persons with depression had a utility index of 0.748 (SD\u0026thinsp;=\u0026thinsp;0.18), while the utility index of older persons with no depression was 0.956 (SD\u0026thinsp;=\u0026thinsp;0.08). Significant differences existed between the utility index score among older persons who had depression and those who had no depression. However, after remission, the HRQoL was not significantly different between those with DD and no DD. Regarding the EQ-VAS, the subjective rating of overall health from 0-100 score (the worst-the best health you can imagine), EQ-VAS from this study was higher than found in the previous survey in Asian countries, in Indonesia (38.6 +- 29.7), Vietnam (38.1 +- 18.1), Japan (51.5 +- 19.0) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and in older Chinese persons (60.2, SD\u0026thinsp;=\u0026thinsp;12.9) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is possible that the participants in this study were diagnosed with DD and received some treatment, which is contrary to the participants in those mentioned above, their participants having depressive symptoms, dwelling in the community, and never receiving depression treatment or care. The finding supported the previous one-year follow-up study that HRQol of the older psychogeriatric patients was improved in all five dimensions of EQ-5D from the first diagnosis, but more improvement in those with remission from depression at follow-up than those without remission.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of HRQoL among older persons with DD\u003c/h2\u003e \u003cp\u003eThe findings reveal that mental health service use did not mediate organizational profiles and HRQol among older persons with DD. HRQol of those with DD was associated and affected by personal factors, including sex, age, comorbidity, ATDS, social support, and sufficient income, by both analyses with EQ-5D-5L and EQ-VAS. The finding of adjusted coefficient multivariable GEE analysis confirmed this finding. It is possible to explain that the HRQoL is a multidimensional and multilevel concept [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] covering various dimensions of life. The HRQoL in the current study comprised five dimensions: mobility, self-care, usual activity, pain/comfort, anxiety/depression, and evaluations of life. Mental health service use in the general hospital only provides drug treatment and psychosocial counseling related to depressive problems. As a result, mental health service use is not a mediator to the HRQoL. The results reveal that policy and the health service systems must improve intervention or expand the service to meet the long-term goal of HRQoL of older persons with DD.\u003c/p\u003e \u003cp\u003eAccording to organization groups, the high-resource organization has independently affected the EQ-5D-5L significantly; after adjusting for other personal factors, it was not affected significantly. Similarly, using EQ-VAS as the dependent variable, the higher resource organization was not affected by EQ-VAS significantly. The resource of an organization, such as nurse-patient ratio, nurse competency, and appointment reminders, was recommended for improvement.\u003c/p\u003e \u003cp\u003eRegarding personal factors, younger age, male sex, greater number of comorbidities, sufficient income, ATDS, and perceived social support were all statistical predictors of better EQ-5D index (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). Only younger age, sufficient income, and ATDS affected health status measured with the EQ-VAS (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). This finding is consistent with EQ-5D-5L utility and EQ-VAS scores reported for other countries indicating the fundamental different measurement of HRQoL [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On the other hand, the EQ-VAS is an adjectival rating scale that uses verbal descriptions of the respondents' health. The older persons had difficulty comparing scores between 100 (best imaginable health) and 0 (worst imaginable health). The result also accords with a recent systematic review and meta-regression analysis showing that test-retest reliability in EQ-VAS was inconsistent between those\u0026thinsp;\u0026le;\u0026thinsp;50 years and \u0026ge;\u0026thinsp;50 years [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerceived sufficient income, ATDS, and social support had a higher HRQoL, measured by the EQ-5D-5L and EQ-VAS while controlling for subject correlation patient outcome within the same hospital in the multivariable model. This result is an explanation by the economy or income, which is essential for living in daily life, for spending to meet essential needs, or for transportation when persons have a visit for mental health service use at the hospital. This economic factor was consistent with the findings of a previous study that monthly income was significantly associated with EQ-5D-5L and EQ-VAS among older Chinese persons with depressive symptoms [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Regarding ATDS, Gabriel and Violato [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] concluded that knowledge of ATDS might significantly impact adherence to antidepressants. Also, a study of depressed patients who received service in a psychiatric hospital in Thailand revealed that knowledge of depression, the trustworthiness of antidepressant treatment, continued medication, and appointments were significantly associated with QoL (WHOQOL-BREF-THAI) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial support refers to information leading individuals to believe that they are cared for and loved, have self-esteem and are valued, belong to a community, and have mutual obligations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The support can be emotional, informational, and instrumental for convenience [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, social support also significantly protects the QoL regarding sensory ability, autonomy, ability, and intimacy of older persons [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, social support could promote the wellness and health of older persons with DD. Primarily, social support mediates the role of psychological resilience and its effect on the QoL among older people [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consistent with previous studies, Kong, Zhang [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] found that social support and psychological resilience were positively associated with physical and mental HRQoL after adjusting for gender, education level, marital status, household registration, and chronic diseases among older migrant adults in China.\u003c/p\u003e \u003cp\u003eSimilarly, previous studies on older persons with depression in the community reinforced that social support improved HRQol [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This study found that older persons with younger age had a higher HRQoL than those with older age. This association may be because depression tends to increase in older persons due to developmental deterioration of physical and psychological aging and pathophysiological neurotransmitter disorders, especially in older persons [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and changes in psychosocial factors such as stresses of life [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This finding is consistent with previous studies that poorer HRQoL by EQ-5D-5L and EQ-VAS were associated with higher age [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOlder persons with more comorbidities had less HRQoL than those with less comorbidity, measured by the EQ-5D-5L except for EQ-VAS. A possible explanation for this result might be related to physical illness. Seniors' comorbidities may result from degenerative pathological deterioration in physical abilities that could impact their complicated illness, treatment, and care, making their health increasingly compromised. As a result of having comorbidities, older adults with depression feel more suffering from physical pain. Consistent with a previous study by[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], among older people with depression in one-year follow-up in Norway, those who had a higher number of comorbidities had lower HRQoL in EQ-5D-5L and EQ-VAS than those with less comorbidity.\u003c/p\u003e \u003cp\u003eThe marital status of older persons was associated with EQ-5D-5L, except EQ-VAS in univariable analysis controlling for within-subject correlation between patient outcomes within the same hospital; however, after coefficient adjustment, the marital status was not associated with EQ-5D-5L and EQ-VAS. Namely, older persons who were married had a greater EQ-5D-5L than those with another marital status, thereby dependent on the influence of organization group, age, sex, sufficiency income, ATDS, social support, and severity of depression. In comparison with a previous study, which agreed with this result [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], researchers found that the association of marital status with quality of life (SF-26, physical pain) was independent of the influence of sex and psychiatric history among older persons with depressive disorders who received mental health service in the university hospital in Thailand.\u003c/p\u003e \u003cp\u003eThe severity of depression at the first visit was not significantly associated with HRQoL after 6\u0026ndash;12 months when using adjusted coefficient multivariable analysis measured by the EQ-5D-5L and EQ-VAS. This severity finding is in line with the findings of the HRQoL one year after hospitalization for treatment of depression in older persons who had experienced remission and had their HRQoL equal to the reference group of older persons without depression when adjusted for differences in socio-demographics and health conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Typically, previous studies reported that the severity of depression at initial treatment is associated with poor HRQol in adults and older persons [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the HRQoL improved at six and twelve-month follow-ups and decreased with increasing depression severity. Besides, a cross-sectional study in older Thai persons with DD who received mental health services at the medical hospital for more than five years (57.8%) had HRQoL (WHOQOL-BREF-THAI) associated with the severity of depression, and it was dependent on the influence of education and perceived treatment outcomes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, in a study of trajectories of quality of life in old age through a longitudinal approach, the finding showed that lower education and higher levels of depression and anxiety at baseline were associated with worse quality of life later; 42.1% of the variation of CASP-12 across age [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, future studies must assess the quality of life between older persons who continued mental health service use from baseline and performed at the time of follow-up.\u003c/p\u003e \u003cp\u003eBased on these results, it can be concluded that older, male sex, lower age, less comorbidity, higher ATDS, having social support, and receiving services in the same hospital had higher EQ-5D-5L than those without these characteristics. This study demonstrated the mechanism of organization groups that was a variable influence on individual characteristics. This principle underpins the Behavioral Model of health service uses (BM), improving in the fifth phase [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] that applies organizational groups as a contextual characteristic. This study emphasized only organization groups and ATDs that promote the use of mental health services. The organization groups encompass the number and distribution of health facilities and staff to provide the population services such as nurse-patient ratio, nursing competency, appointment reminders, and hospital level. The advantage of organization groups in this study was considered distinct from the subtype pattern (high-resource and low-resource organization) and shared characteristics in all hospitals. This organization group could be an independent variable affecting mental health service use. Therefore, as discussed above, this study demonstrated the mechanism and evidence of the Behavior model of health services for investigating factors predicting mental health and quality of life among older persons with DD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e \u003cp\u003eAlthough this study has several strengths, we have discovered that the organization groups with different available resources can directly impact mental health service use among older persons with DD. In addition, mental health service use is inadequate in predicting HRQoL measured by EQ-5D-5L and EQ-VAS. Only personal factors such as demographics, ATDS, and perceived social support affected HRQoL directly. The use of WLSMV in SEM revealed strong insights into the complex interplay between organization groups and personal factors influencing HRQoL through mental health service use, providing a valuable framework for future clinical research in this area.\u003c/p\u003e \u003cp\u003eHowever, the limitations of this study need to be addressed in four ways. First, the time to assess the HRQoL of the participants from six months to one year was too broad a range. Therefore, the score of HRQoL cannot identify the exact time and may vary. Second, research should follow up on the score of depressive symptoms when obtaining HRQoL to identify whether the good HRQoL was retrieved from older persons in remission. Third, this study did not assess information that can impact HRQoL, such as drug therapy and psychosocial intervention. Meta-analyses revealed that pharmacotherapy and psychotherapy effectively enhance QoL by improving depressive symptoms but produce mixed results [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Lastly, ideal PA model fit indices were observed (e.g., CFI\u0026thinsp;=\u0026thinsp;1.00, TLI\u0026thinsp;=\u0026thinsp;1.00, RMSEA\u0026thinsp;=\u0026thinsp;0.00), which, while indicating a perfect fit, should be interpreted with caution due to the possibility of overfitting or the model's recent identification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe HRQoL of older persons after being diagnosed with DD in the general hospital tends to be high, although some did not visit the mental health clinic continuously. However, those who attend the mental health clinic continuously demonstrate more HRQoL than those who attend intermittently. Continuous mental health service use was not a mediated variable among personal and organizational factors on HRQoL. The predictors of HRQoL, while controlling for subject correlation patient outcome within the same hospital, revealed that age, sufficient income, ATDS, and perceived social support were the predictors of both the EQ-5D-5L and EQ-VAS. Meanwhile, male sex and comorbidity are the only predictors of EQ-VAS. Neither high nor low resource organization status affected HRQoL. The lack of effect from the resource status of the hospital is crucial for reconsidering the general hospital's quality of care and mental health service.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eATDS:\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Attitude toward depression and its treatment;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBM:\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Behavioral model of health service uses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Depressive disorders\u003c/p\u003e\n\u003cp\u003eEQ-5D-5L\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;European Quality of life, five Dimension, five Level\u003c/p\u003e\n\u003cp\u003eEQ-VAS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;European Quality of Life, Visual Analog Scale\u003c/p\u003e\n\u003cp\u003eGEE:\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Marginal logistic regression model using generalized estimating equation;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Major depressive disorders\u003c/p\u003e\n\u003cp\u003eMSPSS:\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multidimensional Scale of Perceived Social Support;\u003c/p\u003e\n\u003cp\u003eHRQoL: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Health-related quality of life;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to all participants, who agreed to participate in this study with both head nurses in twelve hospitals and older persons with depression. We also want to thank Mr Stephen Hamann for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to the study\u0026apos;s conception and design. TM collected data and did the initial analysis; AS and ST did the final analysis and interpretation of data. AS made a significant contribution to writing the manuscript. All authors participated in the feedback on the initial investigation. AS and ST were involved in developing and revising the manuscript. All authors read and approved the final manuscript before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any funding for this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis protocol was approved by the Mahidol University Institutional Review Broad, Faculty of Medicine Siriraj Hospital; reference number COA. No. Si 527/2019 and faculty of medicine Ramathipbodi Hospital; reference number COA.MURA2019/1264. This study was approved by a Review broad committee in each hospital under the Ministry of Public Health in Thailand including, Supasit Hospital (CAcode021/2563:081/62S), Khon Kean Hospital (KEF62042), Patha lung hospital (2/2563), Lamphune hospital (Ethic LPN122/2562), Chaophraya Yommarat Hospital (YM009/2563). Permission participants were provided with information and written informed consent as well. The research conforms to the provisions of the Declaration of Helsinki, the Belmont Report, and CIOMS Guidelines. In addition, the committee for research of the hospital approved the research project before working with human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eD.N.S. Candidate, Faculty of Nursing, Mahidol University, Bangkok, Thailand. \u003csup\u003e2\u003c/sup\u003eDepartment of Mental Health and Psychiatric Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand. \u003csup\u003e3\u003c/sup\u003eOffice\u0026nbsp;for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.\u003csup\u003e\u0026nbsp;4\u003c/sup\u003eDepartment of Surgical Nursing, Faculty of Nursing, \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMahidol University, Bangkok, Thailand.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Thailand. Sustainable Development Goal 3 Good Health and Well-being [Internet]. [cited July,23]. 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Dement Geriatr Cogn Disord. 2015;40(5\u0026ndash;6):311\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCaffrey N, Kaambwa B, Currow DC, Ratcliffe J. Health-related quality of life measured using the EQ-5D\u0026ndash;5L: South Australian population norms. Health Qual Life Outcomes. 2016;14(1):133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng LJ, Tan RL, Luo N. Measurement Properties of the EQ VAS Around the Globe: A Systematic Review and Meta-Regression Analysis. Value Health. 2021;24(8):1223\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabriel A, Violato C. Knowledge of and attitudes towards depression and adherence to treatment: The Antidepressant Adherence Scale (AAS). J Affect Disord. 2010;126(3):388\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhunarak U. Factors affecting the quality of life in outpatients with depressive disorder at Somdet Chaopraya Institute of Psychiatry. J Psychiatr Nurs Ment Health. 2011;25(1):44\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCobb S. Social support as a moderator of life stress. Psychosom Med. 1976;38(5):300\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnalan D, Gocer S, Basturk M, Baydur H, Ozturk A. Coincidence of low social support and high depressive score on quality of life in elderly. Eur Geriatr Med. 2015;6(4):319\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong LN, Zhang N, Yuan C, Yu ZY, Yuan W, Zhang GL. Relationship of social support and health-related quality of life among migrant older adults: The mediating role of psychological resilience. Geriatr Nurs. 2021;42(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu S, Wu Y, Mao Z, Liang X. Association of Formal and Informal Social Support With Health-Related Quality of Life Among Chinese Rural Elders. Int J Environ Res Public Health. 2020;17(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRapp MA, Dahlman K, Sano M, Grossman HT, Haroutunian V, Gorman JM. Neuropsychological differences between late-onset and recurrent geriatric major depression. Am J Psychiatry. 2005;162(4):691\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteffens DC, McQuoid DR, Smoski MJ, Potter GG. Clinical outcomes of older depressed patients with and without comorbid neuroticism. Int Psychogeriatr. 2013;25(12):1985\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharoensak S, Sittironnarit G, Satra T, Muangpaisan W, Srinonprasert V. Prevalence of Psychiatric Disorders in Elderly Patients, Quality of Life of Patients and Caregivers, and Their Correlated Factors. J Psychiatr Assoc Thail. 2018;63(1):89\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSripetch S, Prasertchai R, Santhaweesuk K, Pimpitkanueng K, Thaipisuttiku P. Factors Associated Quality of life in Elderly with Major Depressive Disorder at Psychiatric Outpatient Unit, Ramathibodi Hospital. J Psychiatric Association Thail. 2019;64(1):89\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibeiro O, Teixeira L, Ara\u0026uacute;jo L, Rodr\u0026iacute;guez-Bl\u0026aacute;zquez C, Calder\u0026oacute;n-Larra\u0026ntilde;aga A, Forjaz MJ. Anxiety, Depression and Quality of Life in Older Adults: Trajectories of Influence across Age. Int J Environ Res Public Health. 2020;17(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen RM. National health surveys and the behavioral model of health services use. Med Care. 2008;46(7):647\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofmann SG, Curtiss J, Carpenter JK, Kind S. Effect of treatments for depression on quality of life: a meta-analysis. Cogn Behav Ther. 2017;46(4):265\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Attitude toward depression, Depressive disorders, Health-related quality of life, Health service delivery system, Older persons, Organizational factors, Mental health service use, Thailand","lastPublishedDoi":"10.21203/rs.3.rs-4108211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4108211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eKnowledge about mental health service use for depression, mediating organizational profiles, and personal factors on health-related quality of life (HRQoL) among older persons is critical to the health service system. Our study aimed to explore HRQoL six months through one year after persons received services for depression, and explains the effect of organizational profiles and personal factors on HRQoL, mediated through continued mental health service use. It also explains organizational profiles and personal factors affecting HRQoL.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional analytic study with information on 424 older persons (\u0026ge;\u0026thinsp;60 years) diagnosed with depressive disorder (DD)\u0026mdash;medical record information provided data on personal and clinical factors. HRQoL and attitude toward depression and its treatment (ATDS) were obtained six months to one year after diagnosis with DD. HRQoL was measured using the EuroQol Group's EQ-5D Index and a visual analog scale (EQ-VAS). Organizational profiles were obtained from the authorized staff of 12 hospitals identified by latent class analysis into two classes. Descriptive statistics and path analysis tested mediated factors, and a marginal linear regression model using a generalized estimating equation (GEE) analyzed the final model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHRQoL at six months to one year was assessed as good and higher than in previous studies. Continuing mental health service use was not a mediated variable among organizational profiles and personal factors. HRQoL of older persons with DD is associated with personal factors, including age, sex, comorbidity, ATDS, perceived social support, and sufficient income. However, high and low-resource organizational profiles did not affect HRQoL.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings are crucial for reconsidering the quality of care and mental health services in general hospitals.\u003c/p\u003e","manuscriptTitle":"Organizational profiles and personal factors affecting Health-related quality of life among older persons diagnosed with depressive disorders: path analysis and GEE","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 20:37:06","doi":"10.21203/rs.3.rs-4108211/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbb0d03f-80b8-4f4d-a8e9-b5550a283ad1","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T11:10:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 20:37:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4108211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4108211","identity":"rs-4108211","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}