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Positive and negative relationship between anxiety and depression of patients in pain: a bifactor model analysis.

Xie J, Bi Q, Li W, Shang W, Yan M, Yang Y, Miao D, Zhang H - PLoS ONE (2012)

Bottom Line: In addition, we tested this hierarchical model with model fit comparisons with unidimensional, bidimensional, and tridimensional models.Compared with the three first-order models, the bifactor hierarchical model had the best model fit.This finding has not been convincingly demonstrated in previous research.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Fourth Military Medical University, Xi'an, People's Republic of China.

ABSTRACT

Background: The relationship between anxiety and depression in pain patients has not been clarified comprehensively. Previous research has identified a common factor in anxiety and depression, which may explain why depression and anxiety are strongly correlated. However, the specific clinical features of anxiety and depression seem to pull in opposite directions.

Objective: The purpose of this study is to develop a statistical model of depression and anxiety, based on data from pain patients using Hospital Anxiety and Depression Scale (HADS). This model should account for the positive correlation between depression and anxiety in terms of a general factor and also demonstrate a latent negative correlation between the specific factors underlying depression and anxiety.

Methods: The anxiety and depression symptoms of pain patients were evaluated using the HADS and the severity of their pain was assessed with the visual analogue scale (VAS). We developed a hierarchical model of the data using an IRT method called bifactor analysis. In addition, we tested this hierarchical model with model fit comparisons with unidimensional, bidimensional, and tridimensional models. The correlations among anxiety, depression, and pain severity were compared, based on both the bidimensional model and our hierarchical model.

Results: The bidimensional model analysis found that there was a large positive correlation between anxiety and depression (r = 0.638), and both scores were significantly positively correlated with pain severity. After extracting general factor of distress using bifactor analysis, the specific factors underlying anxiety and depression were weakly but significantly negatively correlated (r = -0.245) and only the general factor was significantly correlated with pain severity. Compared with the three first-order models, the bifactor hierarchical model had the best model fit.

Conclusion: Our results support the hypothesis that apart from distress, anxiety and depression are inversely correlated. This finding has not been convincingly demonstrated in previous research.

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Related in: MedlinePlus

Structure of the hierarchical model of the HADS built using bifactor analysis.In the original scale, 14 items load on 2 subscales (anxiety and depression) respectively. In which, item 1, 3, 5, 7, 9, 11, and 13 belong to anxiety subscale. And item 2, 4, 6, 8, 10, 12, and 14 belong to depression subscale. In the bifactor analysis, all the items have loadings on both the general distress factor and one of the subscale specific factors.
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pone-0047577-g002: Structure of the hierarchical model of the HADS built using bifactor analysis.In the original scale, 14 items load on 2 subscales (anxiety and depression) respectively. In which, item 1, 3, 5, 7, 9, 11, and 13 belong to anxiety subscale. And item 2, 4, 6, 8, 10, 12, and 14 belong to depression subscale. In the bifactor analysis, all the items have loadings on both the general distress factor and one of the subscale specific factors.

Mentions: In addition to performing CTT bidimensional and IRT bifactor analyses of the HADS data, we wanted to prove the superiority of the hierarchical model created through the IRT bifactor analysis. To this end, we conducted several confirmatory factor analyses to compare the model fit of different models. Guided by the systematic review of the latent structure of HADS [5], we chose three representative competing models: unidimensional [6], bidimensional, and tridimensional [8]. The bidimensional model was in accordance with the original construct of the scale, while the tridimensional model followed Clark and Watson’s [20] suggestion for a non-hierarchical three-factor model. Our own tripartite hierarchical model, constructed using bifactor analysis, can be seen as a combination of the unidimensional and bidimensional models (see Figure 2).


Positive and negative relationship between anxiety and depression of patients in pain: a bifactor model analysis.

Xie J, Bi Q, Li W, Shang W, Yan M, Yang Y, Miao D, Zhang H - PLoS ONE (2012)

Structure of the hierarchical model of the HADS built using bifactor analysis.In the original scale, 14 items load on 2 subscales (anxiety and depression) respectively. In which, item 1, 3, 5, 7, 9, 11, and 13 belong to anxiety subscale. And item 2, 4, 6, 8, 10, 12, and 14 belong to depression subscale. In the bifactor analysis, all the items have loadings on both the general distress factor and one of the subscale specific factors.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3475698&req=5

pone-0047577-g002: Structure of the hierarchical model of the HADS built using bifactor analysis.In the original scale, 14 items load on 2 subscales (anxiety and depression) respectively. In which, item 1, 3, 5, 7, 9, 11, and 13 belong to anxiety subscale. And item 2, 4, 6, 8, 10, 12, and 14 belong to depression subscale. In the bifactor analysis, all the items have loadings on both the general distress factor and one of the subscale specific factors.
Mentions: In addition to performing CTT bidimensional and IRT bifactor analyses of the HADS data, we wanted to prove the superiority of the hierarchical model created through the IRT bifactor analysis. To this end, we conducted several confirmatory factor analyses to compare the model fit of different models. Guided by the systematic review of the latent structure of HADS [5], we chose three representative competing models: unidimensional [6], bidimensional, and tridimensional [8]. The bidimensional model was in accordance with the original construct of the scale, while the tridimensional model followed Clark and Watson’s [20] suggestion for a non-hierarchical three-factor model. Our own tripartite hierarchical model, constructed using bifactor analysis, can be seen as a combination of the unidimensional and bidimensional models (see Figure 2).

Bottom Line: In addition, we tested this hierarchical model with model fit comparisons with unidimensional, bidimensional, and tridimensional models.Compared with the three first-order models, the bifactor hierarchical model had the best model fit.This finding has not been convincingly demonstrated in previous research.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Fourth Military Medical University, Xi'an, People's Republic of China.

ABSTRACT

Background: The relationship between anxiety and depression in pain patients has not been clarified comprehensively. Previous research has identified a common factor in anxiety and depression, which may explain why depression and anxiety are strongly correlated. However, the specific clinical features of anxiety and depression seem to pull in opposite directions.

Objective: The purpose of this study is to develop a statistical model of depression and anxiety, based on data from pain patients using Hospital Anxiety and Depression Scale (HADS). This model should account for the positive correlation between depression and anxiety in terms of a general factor and also demonstrate a latent negative correlation between the specific factors underlying depression and anxiety.

Methods: The anxiety and depression symptoms of pain patients were evaluated using the HADS and the severity of their pain was assessed with the visual analogue scale (VAS). We developed a hierarchical model of the data using an IRT method called bifactor analysis. In addition, we tested this hierarchical model with model fit comparisons with unidimensional, bidimensional, and tridimensional models. The correlations among anxiety, depression, and pain severity were compared, based on both the bidimensional model and our hierarchical model.

Results: The bidimensional model analysis found that there was a large positive correlation between anxiety and depression (r = 0.638), and both scores were significantly positively correlated with pain severity. After extracting general factor of distress using bifactor analysis, the specific factors underlying anxiety and depression were weakly but significantly negatively correlated (r = -0.245) and only the general factor was significantly correlated with pain severity. Compared with the three first-order models, the bifactor hierarchical model had the best model fit.

Conclusion: Our results support the hypothesis that apart from distress, anxiety and depression are inversely correlated. This finding has not been convincingly demonstrated in previous research.

Show MeSH
Related in: MedlinePlus