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Keywords:

  • Caregiver Burden Scale;
  • chronic mental illness;
  • five-item Brief Symptom Rating Scale;
  • quality of life;
  • 12-item Short-Form Health Survey

Abstract

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Aim:  This study explored the associations of personal, disease, family, and social factors with quality of life (QoL) in patients with two common types of chronic mental illness (CMI) living in Kaohsiung City, Taiwan.

Methods:  Using a convenience sample and a cross-sectional design, 714 patients (50.1% male, 49.9% female) with CMI (72.1% schizophrenia and 27.9% affective disorder) and their caregivers were recruited. Demographic information was collected via the following questionnaires: 12-Item Short-Form Health Survey (SF-12), five-item Brief Symptom Rating Scale (BSRS-5), Caregiver Burden Scale, and Clinical Global Impressions (CGI-S) Scale. Pearson correlations and hierarchical regression analyses were used to predict QoL.

Results:  Disease factors accounted for 17–50% of the change in variance. Predictors of low mental subscale scores included the following: high psychological distress and high family burden as well as a history of suicide attempts, negative caregiver attitudes, and living away from home. Disease factors also explained the greatest variance in the physical subscales. Predictors of low physical subscale scores included the following: high psychological distress, age, unemployment, a history of suicide attempts, high family burden, and living alone.

Conclusions:  Disease factors were the most important predictors of QoL in patients with CMI. Family factors were more important than social factors on the mental subscales. Differential relationships were also found for the other two dimensions. Together, these results indicate that a wide range of factors improve the QoL in patients with CMI.

SINCE SHIFTING FROM a focus on improving symptoms to a holistic view of psychiatric illness, research on quality of life (QoL) issues in patients with chronic mental illness (CMI) has received increasing attention. QoL assessment in patients with poor mental health is particularly important because the QoL in psychiatric patients is consistently worse compared to patients with physical diseases and the general population.1,2

Marwaha et al. found that age and education both negatively affect QoL in outpatients with schizophrenia.3 Interestingly, employed outpatients reported higher QoL than those who were unemployed.3,4 Ruesch et al. also found that employed outpatients reported higher subjective QoL, even after controlling for illness characteristics such as diagnosis and psychopathology.5 Generally, certain demographic variables influence QoL in people with mental illness,2 although some studies produced contrasting results due to methodological differences (e.g. sampling and tools used).6

Studies on the impacts of clinical variables have shown that psychiatric symptoms have effects on overall QoL and daily living activities. These studies suggested that psychiatric symptoms (including both positive and negative symptoms) are negatively related to patient-rated QoL.3,7 When psychiatric symptoms are ameliorated, QoL improves.8 Depression symptoms are consistently and strongly related to self-rated QoL.9,10 Morcillo et al. found that the longer the illness duration of outpatients with schizophrenia, the worse they rated their QoL.11 Evidence suggests that substance abuse negatively affects both psychiatric symptom severity and QoL in people with severe mental illness.12 Substance abuse and psychiatric comorbidity are associated with lower QoL in participants in substance abuse treatment and those in psychiatric treatment.13

According to the Taiwan Ministry of the Interior, there were 100 045 people diagnosed with CMI, defined as illness duration of a minimum of 2 years, in Taiwan in 2008. Of these, 7381 were in Kaohsiung City, the second largest city in Taiwan (Ministry of the Interior, unpubl. data, 2008). The majority of Taiwanese patients with CMI are cared for at home by their family. This is consistent with Taiwanese culture wherein traditional Confucian ideas and ideals promote a particular way of caring for loved ones. Some researchers, however, have argued that the act of caregiving may negatively affect the caregiver.14 Family relationships are considered one of the major factors that influence QoL in psychiatric patients. The study by Halford et al. on QoL, family relationships, and attitudes towards care found that the positive expression of emotion in families significantly predicted fewer negative symptoms and a higher QoL for patients after discharge.15 Additionally, negative family relationships predicted rehospitalization.16

Patients with CMI typically report a lower QoL, and those who are homeless report even lower levels compared to those who have homes.17 In addition, Fitzgerald et al. showed that QoL was significantly and positively correlated with social function variables, such as living conditions.9 QoL scores are higher in communal patients compared to those living alone.18

According to the aforementioned literature, many factors affect QoL in patients with CMI, which makes comparing the results of different studies difficult. In addition, although de-institutionalization began in Taiwan many years ago, few studies have examined the QoL of these psychiatric patients.19 QoL is a culture-sensitive construct; therefore, conclusions drawn from Western studies20 cannot necessarily be generalized to Chinese populations. In the current study, we hypothesized that four dimensions or factors (e.g. personal/demographic, disease, family, and social) would affect the QoL of patients with CMI. This study explored the associations between these four dimensions and QoL as well as their ability to predict the QoL of patients with CMI living in Kaohsiung City, Taiwan.

METHODS

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Location

Kaohsiung City is situated on the southwest coast of Taiwan and has a geographic area of 153.6 km2 and, as of 2009, a population of 1.52 million. Kaohsiung City is the largest commercial harbor in Taiwan. This study was conducted at the Kai-Suan Psychiatric Hospital, the largest public and teaching psychiatric hospital in Kaohsiung City, with 1403 beds (one emergency bed, 378 acute-ward beds, 372 chronic-ward beds, 70 day-care beds, and 347 long-term institution beds). In addition, the hospital owns 235 person-capacities in four community rehabilitation institutes; thus, it serves >70% of the patients with CMI in Kaohsiung City.

Subjects

Using a cross-sectional design, we collected a convenience sample of 1308 patients with CMI and their caregivers who consecutively visited the Kai-Suan Psychiatric Hospital and who currently live in the community.

We defined caregivers as co-resident family members or, if not co-resident, those who had the most frequent contact and who assisted with most, if not all, of the patient's daily care needs for at least 6 months. To be included in this study, patients with CMI had to be 18–65 years old, speak Chinese, have schizophrenia or affective disorder according to their treating psychiatrist (using ICD-9-CM criteria) and possess a Catastrophic Illness Registration (CIR) card. In Taiwan, patients with major psychiatric disorders, including ICD-9-CM codes 290, 293–297, and 299, can apply for CIR cards given by the Bureau of National Health Insurance (BNIH). When a psychiatrist follows up with a patient diagnosed with schizophrenia and affective disorder for a period of time, which is usually more than 6 months as an outpatient or 1 month as an inpatient, the doctor is able to provide the patient with a certificate that the patient can use to apply for a CIR card from the BNIH. Almost all patients with schizophrenia and affective disorder have a CIR card in Taiwan.21 People with CIR cards do not need to make co-payments when they seek health care for their disease; thus, having a CIR card makes obtaining medical services relatively easy. We excluded those patients who had severe physical illnesses, were currently abusing drugs, had a history of brain injury, or were mentally retarded. We then provided a copy of the informed consent form to the 998 patients with CMI and their family who fulfilled the inclusion criteria. Of these patients, 799 (79.0%) and their families participated with completed informed consent. Due to missing data or unclear answers, we included 714 patients (71.5%) and their families in the final analyses.

Of the 714 patients, 50.1% were male and 49.9% were female (mean age, 44.65 ± 10.89 years). Of the patients, 38.4% had a high school education, 87.4% were unemployed, 70.3% were unmarried, 70.6% lived with their caregiver, and 69.7% lived in their own home. Furthermore, 72.1% and 27.9% of the patients were diagnosed with schizophrenia and affective disorder, respectively. Although 82.1% of the patients had previously been hospitalized, all patients were currently in stable or chronic phases of their illness. Most participants' Clinical Global Impressions Scale (CGI-S) scores were either mild (39.6%) or borderline (28.3%). More than half of the participants had been ill for either 11–20 years (35.0%) or >20 years (20.6%). The most common psychiatric treatment was via an outpatient clinic (76.2%) or a community rehabilitation program (23.8%). There was no significant difference between these treatment modalities with regard to demographic data. Some participants lived alone (15.5%), rented a house (12.5%) or had unstable housing (17.8%). Finally, 9.4% of the participants had a history of violence; 9.5% had attempted suicide, 8.7% had abused substances, and 5.5% had had legal issues.

Instruments

The CGI-S is a standardized assessment developed by Guy to rate the severity of psychiatric illnesses.22 It is widely used as an outcome in clinical psychopharmacology trials. The ratings of illness severity are 1, not at all; 2, borderline; 3, mild; 4, moderate; 5, markedly; 6, severe; and 7, extremely ill. In this study, the CGI-S score indicated the overall severity of the psychiatric illness.

The Caregiver Burden Scale was developed by Biegel et al. and translated into Chinese by Song for use with family caregivers of patients with mental illness in Taiwan.23 It measures caregivers' feelings towards their mentally ill family member/client and is composed of the following dimensions: family disruption, strain, feelings of guilt, client dependency and stigma. Respondents answered each item using a 5-point scale, ranging from 0 (never) to 4 (always). The internal consistency of the overall scale was α = 0.88, and the internal consistency of the subscales ranged from α = 0.65 to 0.90. The test–retest reliability of the overall scale was κ = 0.90, and the inter-item correlation within their respective dimensions was α = 0.45. The total scores from this 18-item scale ranged from 0 to 64; higher scores indicate more caregiver burden.

Lee et al. modified the original Symptom Checklist-90-R (SCL-90-R) to develop the five-item Brief Symptom Rating Scale (BSRS-5).24 This measure screens for psychiatric illnesses in non-psychiatric health settings. Because of time and resource limitations, Lee et al. shortened the rating scale to five items. The scale can be efficiently used in the community as well as in general medical and psychiatric settings. The BSRS-5 measures five symptoms: anxiety, depression, hostility, interpersonal sensitivity/inferiority and insomnia. The internal consistency (Cronbach's alpha) is typically in the range of 0.77–0.90, the test–retest reliability was α = 0.82, and the correct classification rate was 76.3%. In this study, the total BSRS-5 score indicated psychological distress.

The Medical Outcomes Study Short Form-12 (SF-12) is a shorter version of the popular Short Form 36 (SF-36). The SF-12 measures generic health concepts across age, disease and treatment groups. It is comprehensive, psychometrically sound, and reliable and as precise in its estimates as the SF-36.25 Rubenach et al. argued that the SF-36 should be used in studies with smaller sample sizes (i.e. n < 500) in clinical research.26 Salyers et al. used the SF-12 mental component summary (MCS) to study severe mental illness and concluded that the SF-12 is a psychometrically sound instrument for measuring health-related QoL in people with severe mental illness.27 SF-12 is suitable for cross-national studies because the scores have been standardized for each population. Although it has not been used in patients with CMI, the Chinese version of the SF-12 has been used in a QoL survey of a large sample of police officers and firefighters in the Kaohsiung area, yielding satisfactory reliability and validity to thus demonstrate that the Chinese version of the SF-12 is an appropriate instrument for the assessment of QoL in Taiwan.28,29

The SF-12 incorporates two dimensions: a physical component summary (PCS) and an MCS, to estimate health-related functioning along eight subscales. The subscales of the PCS are as follows: physical functioning (PF), role limitations caused by physical problems (RP), bodily pain (BP), and general health (GH). The subscales of the MCS are role limitations caused by emotional problems (RE), vitality (VT), social functioning (SF), and mental health (MH). All scores were transformed to a 0–100 scale, with 0 indicating poor QoL, and 100 indicating high QoL. In the current study, the SF-12 was used as an indicator of QoL.

Procedure

Before the study, we provided a training session to improve the counselors' interviewing skills. In total, 10 counselors participated in the study. The ratings by the counselors were tested for reliability before the study. Inter-rater reliability was established for CGI-S (κ = 0.85). After being informed of the study's objectives, each patient was privately interviewed for approximately 25–40 min by the counselors. The interviews were conducted at the patients' homes from October 2006 to December 2006. The Institutional Review Board (IRB) (KSPH-2007-20) of the Kai-Suan Psychiatric Hospital approved all study procedures.

We obtained information from patients with CMI regarding participant demographics including age, sex, educational level, marital status, employment status, and the number of people with whom they were currently living. Patients also completed the SF-12 and BSRS-5 scales. Caregivers completed the Caregiver Burden Scale and used a 4-point scale to rate the patient's social function (1, no disability; 4, severe disability) and medical compliance (1, >80% drug adherence; 4, drug free) and their attitude towards the patient (1, accepting attitude; 4, rejecting attitude). Patient psychiatric histories (e.g. diagnosis, duration of illness, comorbidity, history of suicide attempts, history of violence, and legal problems) were extracted from their medical records. Trained counselors assessed overall severity of illness using the CGI-S severity score.

Statistical analysis

We analyzed data using SPSS 10.0 for Windows (SPSS, Chicago, IL, USA). We classified all variables into four dimensions: personal, disease, family and social. Pearson's correlation coefficients examined the relationships between the continuous independent variables and the 10 variables of the SF-12. We inspected the correlations between independent variables for multicollinearity. Only the independent variables that were significantly correlated with the dependent variables were selected as predictors for regression analysis. We considered P ≤ 0.05 as statistically significant.

To predict the QoL of patients with CMI, all significantly correlated variables across the four dimensions were entered into a four-block regression analysis in the order of the amount of variance each was predicted to explain. First, we entered the variables we thought affected patients the earliest (e.g. age and sex as well as marital, education and work status). Second, we entered the disease variables (e.g. CGI-S and BSRS-5 scores, illness duration, diagnosis, previous admissions, as well as a previous history of substance abuse, violence and suicide attempts). Third, we entered the family variables (e.g. family burden, caregiver attitude, presence of caregiver, and living with caregiver). Finally, we entered in the fourth block the social variables (e.g. community/social function, living in own home, and living alone).

RESULTS

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Univariate relationships between variables

To decide which variables to include in the regression analysis, all independent variables were correlated with the dependent variables; Table 1 presents the correlation coefficients and levels of significance. QoL was positively correlated with education status, employment, schizophrenia and living in one's own home; the magnitudes of these correlations were modest. Most factors were negatively correlated with QoL.

Table 1. Correlations among independent variables and SF-12 dependent variables
 MCSMHRESFVTPCSBPGHPFRP
  1. *P < 0.05, **P < 0.01, ***P < 0.001. BP, bodily pain; BSRS-5, five-item Brief Symptom Rating Scale; CGI-S, Clinical Global Impressions Scale; GH, general health; MCS, mental component summary; MH, mental health; PCS, physical component summary; PF, physical functioning; RE, role limitations caused by emotional problems; RP, role limitations caused by physical problems; SF, social functioning; SF-12, 12-item Short-Form Health Survey; VT, vitality.

Sex0.070.09*0.060.040.030.040.12**0.010.060.01
Age−0.06−0.06−0.09*−0.10**−0.09*−0.20***−0.12**−0.17***−0.20***−0.09*
Education0.08*0.12**0.030.020.060.070.050.12**0.050.05
Employment0.09*0.07*0.15***0.070.050.12**0.060.13***0.13***0.10**
Marital status−0.07−0.10**−0.07−0.05−0.05−0.10**−0.08*−0.10**−0.11**−0.07
Previous admission0.070.09*0.050.050.030.050.040.040.08*0.05
CGI-S−0.23***−0.21***−0.18***−0.22***−0.18***−0.16***−0.18***−0.15***−0.12***−0.19***
Schizophrenia0.20***0.21***0.19***0.13***0.15***0.16***0.21***0.15***0.15***0.14***
Substance abuse history−0.03−0.040.000.000.05−0.01−0.01−0.07*0.03−0.03
Suicide history−0.27***−0.31***−0.21***−0.22***−0.21***−0.22***−0.22***−0.19***−0.23***−0.22***
Violence history−0.08*−0.06−0.07−0.05−0.06−0.02−0.05−0.03−0.01−0.05
Comorbidity−0.03−0.07−0.07−0.03−0.01−0.12**−0.10*−0.08*−0.11**−0.11**
Legal problems−0.07−0.07−0.08*0.00−0.020.01−0.02−0.010.00−0.05
BSRS-5−0.64***−0.67***−0.47***−0.47***−0.44***−0.37***−0.48***−0.44***−0.34***−0.44***
Medical compliance−0.02−0.01−0.02−0.070.01−0.020.020.01−0.02−0.07
Illness duration0.040.06−0.040.010.03−0.05−0.070.04−0.04−0.04
Family burden−0.33***−0.20**−0.26***−0.39***−0.25***−0.14*−0.17*−0.09−0.08−0.32***
Caregiver attitude−0.17***−0.12**−0.16***−0.17***−0.12**−0.11**−0.13**−0.08*−0.09*−0.16***
Has caregiver−0.030.010.01−0.05−0.040.050.050.020.070.00
Lives with caregiver−0.05−0.010.02−0.05−0.070.070.08*−0.010.08*0.03
Living in own home0.070.09*0.060.040.13**0.15**0.12**0.13**0.17**0.08*
Lives alone−0.05−0.010.010.00−0.10**0.040.02−0.030.08*0.03
Community/social functioning−0.15***−0.14***−0.15***−0.14***−0.12**−0.14***−0.09*−0.13**−0.15***−0.16***

All variables except the duration of illness, medical compliance, and the presence of a caregiver were significantly correlated with the 10 SF-12 dependent variables. Therefore, we omitted these three variables from the regression analysis.

Regression analysis

Tables 2,3 list the results of regression analysis.

Table 2. SF-12 subscale hierarchical regression analysis
 MCSMHRESFVT
  1. *P < 0.05; **P < 0.01; ***P < 0.001. We dummy coded categorical variables. BSRS-5, five-item Brief Symptom Rating Scale; CGI-S, Clinical Global Impressions Scale; MCS, mental component summary; MH, mental health; RE, role limitations caused by emotional problems; SF, social functioning; VT, vitality.

Level 1. Personal factors     
(constant)56.5489.7685.35119.4086.90
 Sex (M = 1/F = 0)     
 Age (years)     
 Education     
 Married (yes = 1/no = 0)     
 Employed (yes = 1/no = 0)     
  ΔR20.030.050.040.020.01
  ΔF1.172.171.700.670.43
Level 2. Disease factors     
 Previous admission (yes = 1/no = 0)     
 CGI-S     
 Schizophrenia (yes = 1/no = 0)     
 Substance history (yes = 1/no = 0)     
 Suicide history (yes = 1/no = 0) −10.78*   
 Violence history (yes = 1/no = 0)     
 Comorbidity (yes = 1/no = 0)     
 Legal problems (yes = 1/no = 0)     
 BSRS-5−1.44***−3.55***−2.64***−2.51***−1.96***
  ΔR20.400.500.240.240.17
  ΔF15.47***24.78***7.52***7.01***4.48***
Level 3. Family factors     
 Family burden−0.20***−0.19*−0.39***−1.09***−0.40**
 Caregiver attitude  5.04*  
 Lives with caregiver (yes = 1/no = 0)     
  ΔR20.050.020.040.100.05
  ΔF6.23***3.44*3.69*9.99***4.57***
Level 4. Social factors     
 Living in own home(yes = 1/no = 0)    9.16*
 Lives alone (yes = 1/no = 0)     
 Community/social functioning     
  ΔR20.010.010.010.010.03
  ΔF1.490.810.512.782.78*
Adjusted R20.440.540.260.290.19
Table 3. Hierarchical regression analysis of the SF-12 physical subscales
 PCSBPGHPFRP
  1. *P < 0.05; **P < 0.01; ***P < 0.001. We dummy coded categorical variables. BP, bodily pain; BSRS-5, five-item Brief Symptom Rating Scale; CGI-S, Clinical Global Impressions Scale; GH, general health; PCS, physical component summary; PF, physical functioning; RP, role limitations caused by physical problems.

Level 1. Personal factors     
(constant)59.9595.3293.89130.3184.13
 Sex (M = 1/F = 0)     
 Age (years)−0.13* −0.49**−0.37* 
 Education     
 Married (yes = 1/no = 0)     
 Employed (yes = 1/no = 0)  13.76**  
  ΔR20.030.040.070.030.02
  ΔF1.391.872.96*1.500.95
Level 2. Disease factors     
 Previous admission (yes = 1/no = 0)     
 CGI-S     
 Schizophrenia (yes = 1/no = 0)     
 Substance history (yes = 1/no = 0)     
 Suicide history (yes = 1/no = 0)−4.89*  −18.08*−13.98*
 Violence history (yes = 1/no = 0)    10.65*
 Comorbidity (yes = 1/no = 0)     
 Legal problems (yes = 1/no = 0)     
 BSRS-5−0.51***−2.16***−1.97***−2.15***−2.30***
  ΔR20.150.240.160.160.24
  ΔF4.15***7.44***4.64***4.31***7.23***
Level 3. Family factors     
 Family burden    −0.58***
 Caregiver attitude     
 Lives with caregiver (yes = 1/no = 0)     
  ΔR20.010.010.010.010.07
  ΔF0.681.240.800.406.47***
Level 4. Social factors     
 Living in own home(yes = 1/no = 0)     
 Lives alone (yes = 1/no = 0)  −18.69*  
 Community/social functioning     
  ΔR20.010.000.040.010.00
  ΔF1.080.363.13*1.150.42
Adjusted R20.130.230.200.130.26

This predetermined model predicted the SF-12 variables. Disease and family variables explained the most variance in the MCS and other mental subscales, whereas personal and social factors did not account for any significant variance, except within VT. Disease factors accounted for a 40% change in the variance of MCS (ΔF(9,200) = 15.47, P < 0.01) and family factors accounted for an additional 5% of the variance (ΔF(3,197) = 6.23, P < 0.01). The overall omnibus F-test indicated that these factors together accounted for 44% of the variance of MCS. Disease factors accounted for 17–50% of the variance in the other mental subscales, and family factors accounted for an additional 2–10% of this variance. Combined, these factors accounted for 19–54% of the variance in the other mental subscales. High BSRS-5 and family burden predicted low MCS and other mental subscales. Additionally, a history of suicide attempts predicted low MH, negative caregiver attitude predicted low RE, and lives in their own home predicted high VT (Table 2).

Disease factors also explained the most variance in PCS and the other physical subscales. Disease factors predicted PCS, accounting for a 15% change in its variance (ΔF(9,200) = 4.15, P < 0.01), and also accounted for 15–24% of the variance in the other physical subscales, specifically GH. Personal and social factors accounted for an additional 7% and 4% of the variance, respectively. High BSRS-5 scores predicted low PCS and other physical subscales. Furthermore, age predicted low PCS, GH and PF; employment predicted high GH; a history of suicide attempts predicted low PCS, PF and RP; high family burden predicted low RP; and living alone predicted low GH (Table 3).

DISCUSSION

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

The objective of the present study was to ascertain the extent to which personal, disease, family and social factors influence the QoL of patients with CMI in Kaohsiung City. The results show that the disease factor explained the most variance in patient QoL, accounting for 15–50% of its variance. Furthermore, the family factor accounted for a 2–10% variance change across all mental subscales as well as RP. The social factor accounted for 4% of the variance change in GH and 3% of the variance change in VT. The personal factor, which did not account for a significant amount of the variance in MCS or the other mental subscales, accounted for 7% of the variance change in GH.

Most of the variables used in this study had a modest correlation with patient QoL. After we entered all of the variables into the model in their specified order, only a few variables remained in the final model with a relationship to overall QoL. If patients with CMI had high psychological distress, had a history of suicide attempts, placed a high burden on their family or did not live in their own home, their mental QoL was low. Additionally, if patients with CMI were older, unemployed, lived alone, had a high amount of psychological distress, had attempted suicide or placed a high burden on their family, they rated their physical QoL as low.

Psychological distress, as measured by BSRS-5, was the strongest negative predictor of both mental and physical QoL subscales in patients with CMI. This result is consistent with the literature10 and supports the finding of Pukrop et al. that current mood influences self-assessed QoL.30 A history of suicide attempts also strongly predicts lower PCS subscale and MH scores. This finding supports the studies in which a history of suicide attempts was negatively correlated with QoL in patients with schizophrenia,30 despite the fact that suicide and QoL are both sensitive to culture effects.

Placing a high burden on their family predicted a lower QoL in patients with CMI on all mental subscales and RP. Caregiver burden was positively related to the family's care attitude. This result is in concordance with Halford et al.15 This result also suggests that not only do exclusive family care attitudes lower patient QoL, they also increase caregiver burden. Conversely, families who accept and demonstrate inclusive care attitudes have less caregiver burden and higher patient QoL.

Living in one's own home positively predicted mental subscale scores, whereas living alone negatively predicted physical subscale scores. As in other studies, housing stability is positively related to QoL.17 The present study also confirmed the Kilian et al. result that the QoL of patients (either medical or psychiatric) who live with others is higher compared to those living alone.18

Many studies have indicated that psychiatric symptoms significantly affect overall QoL and daily living activities.3,7 Some studies have suggested that psychiatric symptoms have weak or non-existent correlations with QoL ratings.31 The present study did not find that the overall severity of psychiatric illness (as measured by CGI-S scores) had a significant effect on patient QoL. The discrepancy between the literature and the present findings might be partly due to differences in the sources of the participants. Because the present patients lived in communities during a stable phase of their illness, their CGI-S scores were mostly mild or borderline. The present study used a simple method (CGI-S) to evaluate the severity of psychiatric illness, whereas specific instruments measured this construct in other studies. Thus, the present results are difficult to compare with others, which is a weakness of this study. Certain personal variables (e.g. education level,32 marital status,3 and sex2) influence QoL in people with mental illness, but we did not observe that these variables were relevant to QoL in the current results.

Conclusions

This study confirms that disease factors explain the most variance in the QoL of patients with CMI and highlights that family factors are more important than social factors with regard to mental subscale scores. In addition, the only positive predictor was living in one's own home. Age, a history of suicide attempts, family burden and living alone negatively predicted PCS and the other physical subscale scores, whereas employment and a history of violence positively predicted these variables.

Current Taiwanese government policy on mental welfare payments is still to focus on individual patients, and a focus on family-centered or family support services is lacking. Therefore, to improve QoL for patients with CMI in Taiwan, the government should consider changing its care policy to make welfare benefits or health insurance more family-centered. Providing a living allowance, providing services to relieve families of physical and mental burdens, promoting family education programs to strengthen the family's understanding of disease and their communication skills could improve the relationships and increase the family's willingness to take care of patients in their homes, in turn, improving the QoL among patients with CMI.

Limitations

This study is limited by its cross-sectional design, which does not allow the establishment of causal relationships between independent variables and QoL. Although medical compliance was assessed by the counselors, we did not include medication in variables, therefore medication-induced bias could not be ruled out. In addition, because we sampled patients from one specific city, the present results may not be able to be generalized to patients with CMI in other areas of Taiwan.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

The Department of Health of the Republic of China supported this study (DOH96-TD-M-113-042). The authors thank all colleagues, patients and families who participated in this study. There is no conflict of interest.

REFERENCES

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES
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