All PLWHAs who referred to a voluntary counselling and testing (VCT) centre for preventive and medical care services, were invited to the study between February and June 2015 in Shiraz (Sothern Iran). During the study period, 150 individuals who provided informed consent form participated in the study. Patients who could read and write were included to the study; while those with neurocognitive impairment were excluded. We collected some socio-demographic characteristics such as age, gender, job status, and education. In order to assess depression and anxiety of the patients, the Persian version of the CESD-10 and BAI questionnaires were used, respectively.
Furthermore, the random sample of 500 healthy individuals were selected based on a two-stage cluster sampling technique across four educational districts located in diverse socioeconomic areas in the city of Shiraz. The data was collected from September to December 2015. In the first stage, a random sample of schools was selected from each educational district. Then, we selected one or two classes out of each school. A trained researcher distributed the CESD-10, BAI instruments, and the informed consent form to all students and asked them to give the questionnaires to their parents. Approximately 70% (500 out of 700) of parents filled out the questionnaires at home after signing the informed consent form. In a couple of days, the completed questionnaires were turned back to school by children.
Beck Anxiety Inventory (BAI): The Persian version of the BAI questionnaire was used to measure both PLWHAs’ and healthy individuals’ anxiety. The BAI is a self-report questionnaire that can reliably discriminate anxiety from depression. It comprises 21 anxiety symptoms that bothered the participants during their last week. Individuals responded to the items on a 4-point Likert scale (from 0= not at all to 3= severely, I could barely stand it). The total score is the sum of the individual scores of the items ranging from 0 to 63, with a higher score showing greater anxiety (38). The instrument was translated and validated previously in Persian (39). The main reasons for selecting the BAI are its simplicity, briefness and widely use in clinical research.
10-item Centre for Epidemiological Studies-Depression Scale (CESD-10): To measure the depression status of PLWHAs and healthy individuals, the Persian version of the CESD-10 which had been validated in Iran (40) was applied. According to previous research, this questionnaire is suitable to assess depression among PLWHAs. It is a short, easy to read and easy to score instrument which can reduce interview burden on the patients (33). Moreover, it has been widely used for assessing depression symptoms in general population. The items are scored on a 4-point Likert scale from 0 (not at all) to 3 (a lot). Total score is the sum of items score and the possible range is 0 to 30. The higher the total score, the greater is the degree of depressive symptoms.
The qualitative and quantitative variables were presented in frequency (percentage) and mean± standard deviation, respectively. In addition, two-sided independent sample t-test and Chi-square statistics were applied to investigate whether PLWHAs and healthy people differed significantly in terms of quantitative and qualitative demographic characteristics, respectively. P value<0.05 was considered as significance level.
The measurement invariance of a questionnaire is evaluated by differential item functioning (DIF) analysis. DIF occurs when people from different groups respond differently to a particular item given the same level of latent trait of interest. Two types of DIF can be identified, namely uniform and non-uniform DIF (35). Uniform DIF means that on the entire continuum of the latent trait, item response probabilities are higher (lower) in one group compared to the other one. In contrast, in non-uniform DIF, the direction of DIF is different in different levels of latent trait (35).
In the present study, the multi-group multiple-indicators multiple-causes model (MG-MIMIC) model, which is an extension of the MG-CFA model with covariates, was used to assess the measurement invariance (i.e., DIF) of the BAI and CESD-10 instruments across PLWHAs and healthy individuals. In this model, uniform DIF is detected when discrepancy is observed in the thresholds of a given item across the groups and non-uniform DIF is identified when the factor loading of an item differs between the groups. A distinguished advantage of this method is that the effect of confounding variables can be controlled while assessing DIF. Consequently, in this study, the effect of age, gender and education which differed significantly between PLWHAs and healthy individuals were taken into account while examining DIF. In the MG-MIMIC model, DIF detection process is iterative and also consists of serial tests of nested models. In the first step, the most constraint model in which the factor loadings, thresholds, residual variance, latent trait variance and scaling factor considered invariant in both groups fit as baseline model. If the model fits adequately, measurement invariance is established or there is no item with DIF; however, if the model does not fit well it may be an indication of DIF (41, 42). In this case, the values of modification indices specify which item can be a candidate for DIF. If the modification index associated with thresholds of an item is larger than the others, this item is a candidate for uniform DIF. If the modification index associated with factor loadings of an item is large, this may be an indication of non-uniform DIF item.
In the second step, equality constraint on the factor loading of the item with the largest magnitude of the modification index is relaxed. This new model is fitted and compared to the baseline model. Simultaneously, the equality constraint on the factor loading and thresholds of the item with the largest magnitude of modification index is relaxed and the model is fitted and compared to the baseline model. If relaxing of factor loading parameter leads to larger improvement of the model, non-uniform DIF is detected; while, if relaxing of thresholds parameter results in larger improvement, uniform DIF is detected. The resultant model is considered as the new baseline model with the values of modification indices being examined again and all the steps mentioned above are repeated until no significant model modification is identified. In order to assess the goodness of fit of the MG-MIMIC models several indices were used including Chi-square statistics, root mean square error of approximation (RMSEA), Tucker-Lewis index (TLI), and comparative fit index (CFI). Although non-significant values of Chi-square shows acceptable model fit, this index detects even trivial differences under large sample size. Hence, the other above-mentioned fit indices should also be considered for testing goodness of fit of the model. Values of CFI and TLI > 0.90, and RMSEA < 0.08 support that the model fit well (43). In the present study, the mean and variance-adjusted weighted least square (WLSMV) estimation procedure which has been introduced for ordinal indicators was applied to fit the MG-MIMIC model using Mplus 6.1 software.