Background
Substance abuse, such as heroin addiction, may cause economic burdens for individual users and for the society as a whole [
1],[
2]. Substance abusers are likely to have comorbid infectious diseases, for example, the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV), because of needle sharing [
3]. Therefore, substance abusers may have physically and mentally multiple negative consequences as well as those applicable to their social relationships [
4], and they are reported to have a poorer quality of life (QoL) than those who are not substance-dependent [
5].
QoL is used as an important outcome measure for healthcare decision-making and for evaluating intervention effects [
6],[
7], such as the effect of medicine [
8], and studies e.g. [
9],[
10] have used QoL to examine the effect of maintenance treatment on substance abusers. Of the QoL instruments commonly used in maintenance treatment studies, the brief version of the World Health Organization Quality of Life assessment (WHOQOL-BREF) has been suggested to be the most suitable for assessing the global QoL in research on addiction behavior [
6],[
11]. The WHOQOL-BREF has been translated into various languages, including the traditional Chinese used in Taiwan, and the Taiwan version of WHOQOL-BREF has been suggested as valid and reliable (α = 0.70-0.77, comparative fit index [CFI] = 0.89) [
12]. Many studies have established the psychometric properties of the WHOQOL-BREF in different populations (e.g., community-dwelling older people [
13], people with schizophrenia [
14], and depressed people [
15]); however, almost no studies have examined substance abusers. To the best of our knowledge, only one recent study [
6] has used classical theory test (CTT) methods to evaluate the psychometric properties of the WHOQOL-BREF as applied to substance abusers.
However, using only CTT methods is insufficient for clinicians to understand the psychometric properties of the WHOQOL-BREF. Specifically, CTT methods treat raw scores and item responses to rating scales as interval data, and this may yield invalid scores. Therefore, there is a trend toward using Rasch analysis, a modern statistical method that can transform the ordinal scores of polytomous items into interval scores for the purpose of psychometric evaluation [
16]. Although Rasch models have the main weakness of being a complicated model theory in terms of mathematical equations that are hard for clinicians to understand [
17], it has the following strengths: (1) the validity of the items can be individually analyzed to determine any redundancy, which may not be detected by CTT; (2) item difficulty can be estimated; (3) an ordinal-to-interval conversion table can be produced that can help clinicians use the items to understand the latent traits of respondents [
18]-[
20]. In addition, a number of ordered polytomous Rasch models, such as the partial credit model (PCM) and the rating scale model (RSM), have been used in QoL instruments that are rated on a Likert scale [
18].
Previous psychometric evaluations for the WHOQOL-BREF on substance abusers have used mainly CTT methods. Because Rasch models can detect items that are out-of-concept or redundant and can precisely measure the latent QoL of a heroin user using an ordinal-to-interval conversion table, the purpose of this study was to use several Rasch models to examine the psychometric properties of the WHOQOL-BREF in a heroin-dependent sample in Taiwan.
Results
The mean (SD) age of the participants was 38.07 (7.44) years, and the mean for first use of heroin was at age 26.13 (6.32). Their mean duration of heroin use was 8.05 (5.85) years. Most participants (
n = 212, 89.8%) were male, and had 9.43 (2.35) years of formal education. In addition, 19.9% were HIV-positive; 16.5% were HBV-positive; and 94.5% were HCV-positive. Moreover, 155 participants had simultaneously used methamphetamine and heroin (Table
2).
Table 2
Demographic characteristics of participants (
N
= 236)
Demographic
| |
Age (years) | 38.07 (7.44) |
Male gender | 212 (89.8%) |
Living alone | 15 (6.4%)a |
Years of formal education | 9.43 (2.35) |
Had fixed employment | 125 (53.2%)b |
Family history of drug abuse
| 33 (14.0%) |
Medical history
| |
Age at first heroin use | 26.13 (6.32) |
Duration of heroin use | 8.05 (5.85)c |
Concurrently use methamphetamine | 155 (65.7%) |
HIV carrier positive | 47 (19.9%) |
HBV carrier positive | 39 (16.5%) |
HCV carrier positive | 223 (94.5%) |
The PA showed that only the first factor extracted from each domain had a higher observed eigenvalue as compared to the estimated eigenvalue at the 95th percentile. In addition, the second factor extracted from the Physical domain had the same value (1.15) as the mean eigenvalue from repeated sampling, and the second factor extracted from the other three domains had eigenvalues lower than those from repeated samplings (Psychological: 0.84 vs. 1.11; Social: 0.52 vs. 1.04; Environment: 1.06 vs. 1.20) (Table
3). Because no estimated values at 95% were higher than the observed values in all domains, the PA results suggested that each domain was unidimensional.
Table 3
Observed, resampled mean, and estimated eigenvalues of WHOQOL-BREF
Physical domain | | |
1 | 3.41 | 1.25 | 1.33 |
2 | 1.15 | 1.15 | 1.21 |
3 | 0.71 | 1.07 | 1.11 |
4 | 0.60 | 0.99 | 1.04 |
5 | 0.46 | 0.92 | 0.98 |
6 | 0.42 | 0.85 | 0.91 |
7 | 0.24 | 0.76 | 0.83 |
Psychological domain | | |
1 | 3.34 | 1.22 | 1.31 |
2 | 0.84 | 1.11 | 1.16 |
3 | 0.73 | 1.03 | 1.08 |
4 | 0.47 | 0.96 | 1.00 |
5 | 0.34 | 0.89 | 0.94 |
6 | 0.27 | 0.80 | 0.87 |
Social domain | | |
1 | 2.65 | 1.15 | 1.23 |
2 | 0.52 | 1.04 | 1.10 |
3 | 0.45 | 0.95 | 1.00 |
4 | 0.37 | 0.85 | 0.91 |
Environment domain | | |
1 | 4.17 | 1.30 | 1.38 |
2 | 1.06 | 1.20 | 1.26 |
3 | 0.86 | 1.13 | 1.17 |
4 | 0.75 | 1.05 | 1.09 |
5 | 0.64 | 0.99 | 1.03 |
6 | 0.46 | 0.93 | 0.97 |
7 | 0.42 | 0.87 | 0.91 |
8 | 0.34 | 0.80 | 0.86 |
9 | 0.31 | 0.72 | 0.78 |
Because each domain was found to be unidimensional, further Rasch analyses were appropriate. Four misfit items were found in the WHOQOL-BREF, and all other items demonstrated acceptable goodness-of-fit in regard to both the infit MnSq and the outfit MnSq. Of the four misfit items, 3 were in the Physical domain (Ph1 [Pain and discomfort]: infit MnSq = 1.77, outfit MnSq = 1.89; Ph2 [Medication]: infit MnSq = 1.56, outfit MnSq = 1.66; Ph6 [Activities of daily living]: infit MnSq = 0.47, outfit MnSq = 0.46) and 1 was in the Psychological domain (Ps6 [Negative feelings]: infit MnSq = 1.56, outfit MnSq = 1.63) (Table
1). The reliability values (item reliability = 0.94-0.98, person reliability = 0.79-0.87) of all four domain scores were acceptable, suggested good internal consistency in each domain. The separation indices (item separation = 4.05-7.65, person separation = 2.06-2.56) of all four domain scores were adequate, except for the person separation index in Physical domain , which was close to the criterion (1.93), indicating good discrimination and separation ability in each domain.
Our results showed that the category functioning of the WHOQOL-BREF followed monotonic increases in average and step measures in four domains, and only three fit indices (Infit MnSq of category 5 in Social domain: 1.41, and of category 5 in Environment domain: 1.46; Outfit MnSq of category 3 in Social domain: 0.59) slightly violated the recommended criterion (Table
4). Therefore, the thresholds of the 5 categories followed the expected order. In addition, the absolute correlations of every two item residuals were all ≤ 0.4, except for two values (Ph2 [Medication] and Ph6 [Activities of daily living] in Physical domain; S2 [Sexual activity] and S3 [Social support] in Social domain), and suggested acceptable local dependency (Table
5). Moreover, the person fit statistics demonstrated that about one fifth of our participants were misfit (Table
6).
Table 4
Threshold disordering tests for each domain on WHOQOL-BREF
Physical
| | | | |
1 | -1.43 | - | 1.10 | 1.17 |
2 | -0.65 | -2.96 | 1.12 | 1.27 |
3 | -0.03 | -0.86 | 0.87 | 0.86 |
4 | 1.21 | 0.21 | 0.87 | 0.89 |
5 | 2.53 | 3.62 | 1.04 | 0.98 |
Psychological
| | | | |
1 | -2.42 | - | 1.19 | 1.25 |
2 | -1.29 | -2.80 | 1.01 | 1.04 |
3 | -0.19 | -1.29 | 0.86 | 0.83 |
4 | 1.32 | 0.59 | 0.93 | 0.92 |
5 | 2.87 | 3.49 | 1.13 | 1.07 |
Social
| | | | |
1 | -3.57 | -- | 1.28 | 1.33 |
2 | -2.34 | -4.31 | 0.96 | 1.11 |
3 | -0.70 | -2.30 | 0.69 |
0.59
|
4 | 2.35 | 0.54 | 0.97 | 0.95 |
5 | 4.75 | 6.07 |
1.41
| 1.06 |
Environment
| | | | |
1 | -2.56 | -- | 1.02 | 1.09 |
2 | -1.35 | -2.98 | 0.98 | 1.00 |
3 | -0.25 | -1.43 | 0.85 | 0.82 |
4 | 1.34 | 0.29 | 0.92 | 0.93 |
5 | 2.84 | 4.12 |
1.46
| 1.18 |
Table 5
Correlation coefficients of residuals between items for Item dependency tests
Physical
| | |
Psychological
| | |
Environment
| | |
Environment
| | |
Ph1 | Ph2 | 0.02 | Ps1 | Ps2 | -0.20 | E1 | E2 | 0.12 | E4 | E5 | 0.11 |
| Ph3 | -0.31 | | Ps3 | -0.13 | | E3 | -0.26 | | E6 | -0.22 |
| Ph4 | -0.30 | | Ps4 | -0.32 | | E4 | -0.25 | | E7 | -0.24 |
| Ph5 | -0.28 | | Ps5 | -0.26 | | E5 | -0.10 | | E8 | -0.24 |
| Ph6 | -0.40 | | Ps6 | -0.25 | | E6 | -0.15 | | E9 | -0.15 |
| Ph7 | -0.34 | Ps2 | Ps3 | -0.004 | | E7 | -0.06 | E5 | E6 | -0.30 |
Ph2 | Ph3 | -0.20 | | Ps4 | -0.23 | | E8 | -0.23 | | E7 | -0.34 |
| Ph4 | -0.24 | | Ps5 | -0.11 | | E9 | -0.14 | | E8 | -0.16 |
| Ph5 | -0.32 | | Ps6 | -0.32 | E2 | E3 | -0.39 | | E9 | -0.15 |
| Ph6 |
-0.43
| Ps3 | Ps4 | -0.19 | | E4 | -0.13 | E6 | E7 | 0.04 |
| Ph7 | -0.23 | | Ps5 | -0.21 | | E5 | -0.13 | | E8 | 0.02 |
Ph3 | Ph4 | -0.10 | | Ps6 | -0.26 | | E6 | 0.17 | | E9 | -0.11 |
| Ph5 | 0.08 | Ps4 | Ps5 | 0.17 | | E7 | -0.11 | E7 | E8 | 0.13 |
| Ph6 | -0.04 | | Ps6 | -0.30 | | E8 | -0.25 | | E9 | -0.15 |
| Ph7 | -0.14 | Ps5 | Ps6 | -0.25 | | E9 | -0.24 | E8 | E9 | -0.10 |
Ph4 | Ph5 | -0.20 | | | | E3 | E4 | 0.09 | | | |
| Ph6 | 0.09 |
Social
| | | | E5 | -0.07 | | | |
| Ph7 | 0.06 | S1 | S2 | -0.35 | | E6 | -0.24 | | | |
Ph5 | Ph6 | 0.12 | | S3 | -0.31 | | E7 | -0.22 | | | |
| Ph7 | -0.24 | | S4 | -0.25 | | E8 | -0.06 | | | |
Ph6 | Ph7 | 0.23 | S2 | S3 |
-0.43
| | E9 | 0.02 | | | |
| | | | S4 | -0.30 | | | | | | |
| | | S3 | S4 | -0.34 | | | | | | |
Table 6
Summaries of person fit
Physical | | | |
Infit | 1.00 (0.73)a | 0.08-3.73a | 56 (23.7%) |
Outfit | 1.02 (0.76)a | 0.07-3.96a | 60 (25.4%) |
Psychological | | | |
Infit | 1.01 (0.91) | 0.05-7.37 | 50 (21.2%) |
Outfit | 1.00 (0.91) | 0.05-7.18 | 50 (21.2%) |
Socialb | | | |
Infit | 0.92 (1.18) | 0.03-6.62 | 42 (17.8%) |
Outfit | 0.93 (1.24) | 0.03-7.65 | 48 (20.3%) |
Environment | | | |
Infit | 0.98 (0.79)c | 0.10-5.60c | 50 (21.2%) |
Outfit | 0.98 (0.78)c | 0.08-5.43c | 51 (21.6%) |
No DIF items were detected on WHOQOL-BREF across either the HBV-positive and HBV-negative carriers or the HCV-positive and HCV-negative carriers. Items E9 (Eating) and S2 (Sexual activity) were found to be DIF items for educational level and gender, respectively. For the participants with the same QoL, those with a junior high educational level and below tended to score higher than those with a senior high educational level and above on item E9 (DIF contrast = 0.50), and females were prone to score lower than were males (DIF contrast = -0.86) on item S2. Five items were found to have a DIF between HIV-positive carriers versus HIV-negative carriers. For the participants with the same QoL, HIV-positive carriers tended to score higher on Ps3 (Think; DIF contrast = 0.63) and S2 (Sexual activity; DIF contrast = 0.64), and tended to score lower on Ps6 (Negative feelings; DIF contrast = -0.62), S1 (Personal relationship; DIF = -0.71), and E7 (Health service; DIF contrast = -0.57) than HIV-negative carriers (Table
7).
Table 7
Differential item functioning (DIF) items detected across different groups
Junior high and below | Senior high and above | E9 | Eating | 0.50 | 0.23 | 2.23 | 0.03 |
Female | Male | S2 | Sexual activity | -0.86 | 0.42 | 2.05 | 0.048 |
HIV-positive | HIV-negative | Ps3 | Think | 0.63 | 0.24 | 2.64 | 0.01 |
HIV-positive | HIV-negative | Ps6 |
Negative feelings
| -0.62 | 0.24 | 2.62 | 0.01 |
HIV-positive | HIV-negative | S1 | Personal relationship | -0.71 | 0.30 | 2.37 | 0.02 |
HIV-positive | HIV-negative | S2 | Sexual activity | 0.64 | 0.29 | 2.18 | 0.03 |
HIV-positive | HIV-negative | E7 | Health services | -0.57 | 0.26 | 2.20 | 0.03 |
Discussion
To the best of our knowledge, this is the first study using several Rasch models to examine the psychometric properties of the WHOQOL-BREF with a substance-addicted sample. Unidimensionality of the Social and Environment domains was evidenced using both PA and Rasch models; however, the Physical and Psychological domains had misfit items. Three items (Ph1, Ph2, and Ps6) were not embedded in their underlying domain, and item Ph6 was redundant. The WHOQOL-BREF was shown to have satisfactory reliability and separation indices (including person and item). No disordering was detected for 5 thresholds in the four domains, and item dependency was acceptable. However, about one-fifth of the subjects were found to be misfit, possibly indicating the unstable nature of heroin users.
The WHOQOL-BREF has been confirmed as an appropriate QoL instrument around the world e.g., [
6],[
13],[
29]. Our results of the satisfactory reliability (Physical: 0.96 and 0.79; Psychological: 0.98 and 0.94; Social: 0.94 and 0.82; Environment: 0.98 and 0.87) corroborate with the findings of Fu et al. [
6], who used CTT methods on a heroin-dependent sample (Physical: 0.79; Psychological: 0.78; Social: 0.76; Environment: 0.87); of Liang et al. [
13], who applied a Rasch model to community-dwelling older people (Physical: 0.76; Psychological: 0.77; Social: 0.68; Environment: 0.78); and of Wang et al. [
16], who used a Rasch model on a general Taiwanese population (Physical: 0.86; Psychological: 0.84; Social: 0.83; Environment: 0.82). In addition, we extended the satisfactory reliability to the acceptable separation index. That is, the WHOQOL-BREF has enough items and is sensitive enough to distinguish both high and low QoL participants, and our sample is large enough to verify the item difficulty hierarchy [
26].
However, we found that some misfit items in the Physical and Psychological domains contradicted previous Rasch model findings [
13],[
16],[
29]. One possible reason for this is the different populations used between our study and previous studies (substance abusers vs. a general population and community-dwelling elderly people). Substance abusers are often cognitively impaired [
30], and thus may have difficulty understanding some items that use indirect wordings (e.g., negatively worded items). Because three misfit items in this study were negatively worded, we tentatively concluded that substance abusers may not have sufficient intact cognitive function to interpret the three items as they were intended to be understood. Negatively worded items have a wording effect that biases the evaluation of the extracting constructs of QoL instruments [
31], especially in the case of people without sufficient cognitive ability. Therefore, the underlying constructs, such as the Physical and Psychological domains, may be affected by the negatively worded items [
32],[
33].
In order to strengthen our hypothesis (i.e., negatively worded items have a wording effect on substance abusers), we used another statistical method (confirmatory factor analysis, CFA) to justify the results. Two CFA models (M1: 4-QoL-factor model, and M2: correlated QoL traits and uncorrelated wording methods) were compared, and we hypothesized that M2 outperformed M1 because it includes the wording effect in the model. Our results showed that M2 substantially improved the data-model fit in X2 difference test (∆X2 = 149.81, ∆df = 23; P < 0.001), expected cross-validation index (ECVI; M1: 3.894, M2: 3.166), and Akaike information criterion (AIC; M1: 895.673, M2:728.120). The CFA results, therefore, somewhat confirmed our hypothesis. However, because no cognitive tests were done in this study, our hypothesis was only supported by indirect evidence (i.e., Rasch and CFA models). Therefore, future researchers may want to verify our hypothesis using direct investigations. For example, cognitive interviews can be conducted to clarify whether substance abusers have insufficient cognitive functioning by which to understand negatively worded items. In addition, DIF analyses among substance abusers and non-abusers on negatively worded items may also justify our hypothesis.
Our results suggested that the WHOQOL-BREF exhibited the expected threshold ordering among the five categories and low item dependence for a sample of heroin users. The major reasons for this may be a combination of following factors: excellent instruction documents originally provided by the WHOQOL team for the establishment of the version for Taiwan, sound leadership and cooperation among psychometricians, clinicians, and statisticians in Taiwan to form a focus group for the development, careful selection of descriptors for each item [
34], standard translation procedure (i.e., forward translation, backward translation, and reconciliation), as well as active participation from 17 hospitals/clinics throughout Taiwan [
12]. Future studies are warranted for corroboration of our findings in people with other mental illnesses.
A substantial percentage (17.8% to 25.4%) of person misfit was found in our heroin-dependent patients, which is much higher than 5%, as suggested by Fisher et al. [
28]. However, such high percentages might be largely explained by following reasons: First, all of the people in our sample had a diagnosis of opioid dependence, and they are frequently associated with mood problems and/or impaired cognition. Second, the less than 5% misfit was suggested for children without any mental health problems in cases where the objective ability to complete school tasks was being measured [
28]. Because we assessed subject reported outcomes from patients, which are frequently affected by emotion and cognition [
35],[
36], less than one-quarter of person misfit may indeed be acceptable. However, more studies are needed to corroborate our speculation.
Although the DIF analyses did not detect any item for HBV or HCV infection, we did find 5 DIF items for HIV infection. Namely, WHOQOL-BREF should be measured and interpreted in opioid users with and without HIV infection separately because 5 out of 26 items were DIF items. In addition, we found one DIF item for educational level and another one for gender. The participants with less than a junior high school education seemed to overestimate eating satisfaction, or they seemed to be more easily satisfied than those with higher education and thus to report a higher score on item E9. Furthermore, the reason that females report an underestimated sexual life QoL may be due to their embarrassment or higher expectations.
This study has three main limitations. First, all participants were recruited in the same MMT program in southern Taiwan, which prevents us from generalizing our results to the entire Taiwanese population. However, our results were comparable to those of another study [
6] testing psychometric properties of WHOQOL-BREF in substance abusers from northern Taiwan. Therefore, the generalizability issue may not be serious. Second, the participants were recruited from an MMT program; thus, our results may not be applicable to substance abusers who do not seek anti-addiction treatment. Third, all participants in this study used heroin, and only some used other substances (e.g., methamphetamine, ketamine). Therefore, our results may be more representative of the heroin-dependent population and less representative of populations dependent upon other substances.
In conclusion, the WHOQOL-BREF is suitable to use for evaluating the QoL of substance abusers. It can also be used as a treatment outcome measure to evaluate the effect of treatments for substance abusers. However, those with and without HIV infection should be interpreted after stratification, and the three negatively worded items should be used with caution because substance abusers may have cognitive problems that may preclude them from having a full understanding of the meanings. Future research may apply cognitive interviews to determine the cognitive functioning of substance abusers and their interpretation of negatively worded items.
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Competing interests
The authors declare that there are no conflicts of interests and that the agency that funded this research was not involved in the study design, data analysis, data interpretation, or writing of the report.
Authors’ contributions
K-CC conceptualized and designed the study, interpreted the data, and revised the manuscript. J-DW designed the study, interpreted the data, and revised the manuscript. H-PT and C-MC revised the manuscript. C-YL acquired and analyzed the data and drafted the manuscript. All of the authors revised the manuscript and approved the final manuscript.