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DAI-10 is as good as DAI-30 in schizophrenia
European Neuropsychopharmacology (2012) 22, 747–750
www.elsevier.com/locate/euroneuro
SHORT COMMUNICATION
DAI-10 is as good as DAI-30 in schizophrenia René Ernst Nielsen a, b,⁎, Eva Lindström c , Jimmi Nielsen a , Sten Levander d a
Unit for Psychiatric Research, Aalborg Psychiatric Hospital, Aarhus University Hospital, Aalborg, Denmark Regional Psychiatric Services West, Central Region Denmark, Herning, Denmark c Department of Neuroscience, Psychiatry, Uppsala University Hospital, Uppsala, Sweden d Faculty of Health & Society, Malmo University, Malmo, Sweden b
Received 6 January 2012; received in revised form 19 February 2012; accepted 23 February 2012
KEYWORDS Rating scales; Schizophrenia; Cognitive neuroscience; Insight; DAI
Abstract Drug attitude inventory (DAI-30) is considered to be the best predictor of poor adherence in firstepisode schizophrenia. We compared the short version (DAI-10) with DAI-30 in long-term schizophrenia, documented if DAI was associated with poor insight, PANSS and GAF and constructed DAI-10 percentiles. DAI-30 and DAI-10 were homogenous (r = 0.82 and 0.72, respectively) with good test–retest reliability (0.79). The correlation between the DAI versions was high (0.94). Percentile scores of DAI-10 were computed. DAI is an easy-to-use self-report instrument seemingly assessing a unique clinical dimension relevant to non-adherence. DAI-10 might be preferred for its simplicity and good psychometric properties. © 2012 Elsevier B.V. and ECNP. All rights reserved.
1. Introduction Treatment non-adherence and specifically drug discontinuation is a critical issue in the long-term management of schizophrenia (Lieberman et al., 2005). One scale used to assess patient attitude towards medication is the Drug Attitude Inventory with 30 dichotomous items (DAI-30) (Hogan et al., 1983). A 10-item version (DAI-10) consisting of a subset of the DAI-30 items is also available.
⁎ Corresponding author at: Unit for Psychiatric Research, Aalborg Psychiatric Hospital, Aarhus University Hospital, Mølleparkvej 10, 9000 Aalborg, Denmark. Tel.: +45 28 72 29 62. E-mail address: [email protected] (R.E. Nielsen).
DAI-30 was recently used in the EUFEST study of first episode patients (Gaebel et al., 2010). Among a large set of variables, DAI displayed the strongest association with discontinuation (Gaebel et al., 2010), although the association was weak (hazard ratio 0.96). Still it was possible to recommend that patients scoring lower than a specific threshold value should be considered for adherence-improving interventions. Although DAI is not an optimal instrument in predicting non-adherence, the easy use and the lack of alternatives for prediction of non-adherence have made DAI the scale of choice (Gaebel et al., 2010). The DAI-30 is composed of 30 statements of which half are positively phrased and half negatively, in order to eliminate aquiescence as a source of variance. Factor analyses suggest the presence of seven factors (Hogan et al., 1983), some of them intercorrelated, but a large portion of the
0924-977X/$ - see front matter © 2012 Elsevier B.V. and ECNP. All rights reserved. doi:10.1016/j.euroneuro.2012.02.008
748 total variance is not explained by these factors. To be considered a scale rather than a set of items, internal consistence has to be adequate, as assessed by Cronbach's alpha or an intra-class correlation coefficient (ricc). When comparing subscales it should be noted that the scale ricc (as well as Cronbach's alpha) increases by the number of items, in contrast to the single item ricc. The single item ricc is then a better statistic when comparing the internal consistency of scales (or subscales) with different numbers of items. Psychometrically, DAI-30 is not sophisticated — improvement of the scale should be possible but there is a risk that any changes will compromise its demonstrated predictive accuracy. DAI-10 is a short version, with items selected to be representative of the full version. If the correlation between DAI-30 and DAI-10 is very high, it can be assumed that the shorter instrument maintains the predictive accuracy of the full instrument. Unfortunately, there is a psychometric flaw (unbalance) in DAI-10; there are six positively phrased items and four negatively phrased items. Three of the negatively phrased items refer to side-effects whereas most of the positively phrased items refer to symptom reduction, generating a confounding problem as well as an aquiescence problem. In spite of the short-comings, DAI-30 was a better predictor of adherence than any other of the variables assessed at study entry in the EUFEST material, in itself and when added to other predictor candidate variables like Insight, Cognitive reduction or Symptom ratings. DAI seems to assess an important latent characteristic which we presently cannot assess in any other way. More detailed analyses of DAI data may allow us to understand more of this latent characteristic. One aim of our study was to investigate DAI-30 and DAI-10 in long-term patients with schizophrenia, assess the factor structure and internal consistency of the scales and subscales, the amount of shared variance, and the test–retest reliability. Another aim was to construct percentile tables and cut-off values corresponding to the EUFEST-based recommendations for adherence-improving interventions for DAI-10. Furthermore we wanted to explore and cross-validate the EUFEST findings on first episode patients that DAI is more or less independent of symptoms, cognition and social function, in our sample of long-term schizophrenia patients.
2. Experimental procedures The study population consisted of two groups. One group participated in the 5-year study (Lindstrom et al., 2007) where DAI-10 was administered once in the fifth year of the study to 52 men and 44 women with schizophrenia or a schizophrenia-related psychosis. The other group, 30 men and 20 women diagnosed with schizophrenia, was recruited for a drug study of sertindole augmentation to clozapine treatment (Nielsen et al., 2012a). The 30-item DAI version was administered three times (at baseline, after 6 weeks and after 12 weeks). The study population is representative of the diversity of long-term schizophrenia. Symptoms were assessed by the Positive and Negative Syndrome Scale PANSS (Kay et al., 1987). Function was assessed by the function subscale of Global Assessment of Function scale (Pedersen and Karterud, 2011). Neuropsychological tests were selected from the computerized EuroCog battery (Eberhard et al., 2009; Grawe and Levander, 2001). Non-verbal working memory was assessed by the Austin maze test; Motor speed by five tapping/alternation subtasks; Selective attention
R.E. Nielsen et al. by two variants of the K-test; and Reaction time was assessed in three subtasks of increasing complexity. A set of Executive indices was computed on the basis of problem solving strategies in the different tests.
2.1. Statistics In order to compensate for multiple comparisons, significance level was set at p b .01. Standard statistical methods were applied using SPSS18 — details are provided in the text. Missing values were replaced by estimates based on linear regressions with all available values as predictors.
3. Results 3.1. Sample characteristics Mean age for the two samples was 43 years. There was no difference on sex and age between the two study groups. Mean PANSS was 59 ± 23 in the 5-year and 79 ± 11 in the clozapine group. In the 5-year group, 11% were drug-free, 11% had received clozapine, 9% first and 70% second generation antipsychotics.
3.2. DAI-30 (clozapine group) There were 112 DAI-30 ratings available for analyses, with a mean score of 24.5 ± 4.62. Over the three sessions the test–retest reliability was 0.79. Preliminary analyses suggested that Item #24 had no meaningful associations with any other item or sums of items. Item #27 (a reversed item) correlated positively and significantly with the sum of the non-reversed items. Item #19 had no variance. The scale ricc (intra-class correlation, in this case identical to Cronbach's alpha) of the remaining 27 items was 0.84 (single item ricc = 0.16). A factor analysis suggested the presence of nine factors. The largest factor included items #1 (r), #6, #9, #15, #18, #20 (r), #21, #23 and #26 (r = reversed item). This subscale was homogenous (ricc = .86) and with a good single item ricc (0.41), but a substantial unbalance with respect to positively and negatively phrased items. The original DAI-30 was converted to DAI-10 scores, with a mean of 7.53 ± 2.01 and a test–retest reliability of 0.92. When excluding the three items described previously (#19, #24 and #27) the correlation to DAI-10 items was 0.94. The regression line joining DAI-30 and DAI-10 can be described by a simple algorithm (DAI-10 = 0.4 × DAI-30 − 2).
3.3. DAI-10 (5-year group) Ninety-five DAI-10 ratings were available for analyses. The DAI-10 mean was 7.60± 2.27.
3.4. DAI-10 combined The DAI-10 data for the two samples were combined yielding 207 ratings for analysis. There was no difference between the two patient groups in the DAI-10 sum, p = 0.81. The ricc was 0.71 (single item ricc = 0.20). A factor analysis extracted three factors explaining 54% of the variance. Factor I included the five nonreversed items (#1, #4, #7, #9, #10); Factor II included items #5 and #8 and Factor III items #2, #3 and #6. Item #1 crossed all factors and items #2 and #3 loaded in Factor II as well. Factors I and III were intercorrelated (Promax rotation, r = .40).
DAI-10 is as good as DAI-30 in schizophrenia
749
The analysis was non-convincing but suggested that a sumscore based on Factor I items would be meaningful (scale ricc = 0.73, single item ricc = 0.35), as well as the DAI-10 sum. Means and percentiles for the Factor I based sum of items and for DAI-10 are presented in Table 1.
3.5. Symptom ratings There was a trend that PANSS G12 (Insight) was correlated with DAI-30 (clozapine group) as well as DAI-10 (5-year group). In the combined group (DAI-10), this correlation was significant (r = −0.24, p b .01). Total DAI-10 was also correlated with PANSS positive symptoms (r = .31, p b .001), but not with negative or general symptoms, or with the Excitation/Aggression or Cognitive subscales of PANSS (van der Gaag et al., 2006). There was no significant correlation between GAF scores and DAI-30 or DAI-10.
3.6. Neuropsychological tests Both groups of patients performed poorly on all tests (Eberhard et al., 2009; Nielsen et al., 2012b). There were no significant differences in the neurocognitive measures between the clozapine patients and the 5-year study patients. In the combined material (clozapine-treated and the 5-year study patients), 101 patients had data on DAI-10 as well as the neurocognitive tests. No correlations between DAI-10 and the neuropsychological test indices were significant.
4. Discussion The DAI-30 data displayed good scale homogeneity (0.82) and test–retest reliability (0.79), but the single item ricc was rather poor (0.16). The homogeneity of DAI-10 scale was lower (0.71) as expected because of only ten versus 30 items, but the single item ricc (0.20) was better and the retest reliability (0.92) was much better. Thus, the participants, although quite ill with a mean sum of PANSS 79 ± 11, were able to respond in a consistent and meaningful way to the DAI-10 items. Self-ratings are probably not used to their potential in the current type of patients (Lindstrom et al., 2009). Table 1 and 10).
Percentiles of DAI-10 and Factor 1 (items 1, 4,7, 9
Value
DAI-10 percentiles
Factor 1 percentiles
0 1 2 3 4 5 6 7 8 9 10
0–1 1–2 2–3 3–7 7–11 11–15 15–24 24–37 37–55 55–85 85–100
0–4 4–10 10–19 19–33 33–54 54–100
DAI-30 and DAI-10 were strongly intercorrelated, (r=0.93) and thereby shared almost 90% of the total variance. This suggests that there is only a small risk that the predictive accuracy of DAI-30 for treatment adherence is lost when using DAI-10. What is measured by DAI-30 and DAI-10? A factor analysis of DAI-30 based on the clozapine sample was non-convincing and did not reproduce previous findings in the literature (Hogan et al., 1983). The factor analysis of DAI-10 produced one meaningful factor, but did not shed more light on what dimensions build the scale. We did find significant correlations between DAI-10 on one hand, and the PANSS Positive subscale and the Lack of Insight item, in line with previous studies. However, the correlations were low, implying less than five percent shared variance (Gaebel et al., 2010; Yang et al., 2012). Three theoretically interesting candidates failed — the PANSS Cognitive and the Excitation/Aggression factors and GAF. With respect to the neurocognitive measures, no significant correlations were obtained. Whatever is measured by the DAI instruments, it does not seem to be possible to reduce to domains of symptoms, to lack of insight (PANSS item G12), level of function as assessed by GAF or to neurocognitive deficits. In our study, the DAI-10 appears to be better than the complete instrument, DAI-30. However, resolution is a problem with DAI-10, which can be solved by increasing the number of response alternatives (Likert scale). DAI-10 is simple to fill in for patients and easy to interpret by clinicians. The information appears to be unique and cannot be obtained in any other way. The importance of the recent findings by Gaebel et al. (2010) makes it timely to consider the cumulated evidence since the invention of the scale over 40 years ago, in order to improve the instrument rather than constructing new ones (Thompson et al., 2000; Karow et al., 2007), which has not gained wide-spread acceptance. We also need more information on the predictive power of the different DAI versions for non-adherence in other patient groups than first episode psychosis, both as a single instrument or in combination with other kinds of information. As an aid in the clinic, we provide percentile scores, to measure Drug attitude along an interval scale, relative to the current combined material. In this way, change in attitude can be assessed in a more objective way. The use of percentiles is an alternative to the optimal cut-off values suggested by Gaebel et al. (2010). Using their values, a DAI-10 score of 8 or lower (percentile value = 55% in our material), should alert the clinician about the need for adherence-improving interventions. Depending on the costs for such interventions (the pay-off matrix), this number can be adjusted downwards in steps, reducing sensitivity and improving specificity until the pay-off matrix can be defended. The study has some limitations. Two materials were combined which makes generalization to a specified mother population somewhat uncertain. One group filled in DAI-30, one DAI-10. Responding to 30 items might have affected the response to the items common to DAI30 and DAI-10. However, the pattern of relations between DAI-30 and DAI-10 on one hand, and all the other variables was quite similar.
Role of the funding source The article is funded by the Unit for Psychiatric Research, and has no external funding.
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Contributors R. Nielsen has managed literature search, written the first draft and subsequent versions of the manuscript. E. Lindström has refined the manuscript and participated in the data analysis strategy. J. Nielsen was involved in preparing initial and subsequent versions of the manuscript, as well as initial idea for the manuscript. S. Levander has developed data analysis strategy, and performed most of the data analysis, as well as being involved in the preparation of the manuscript.
Conflict of interest R. E. Nielsen has received research grants from H. Lundbeck for clinical trials, received speaking fees from Bristol-Myers Squibb, Astra Zeneca, Janssen & Cilag, Lundbeck and has acted as advisor to Astra Zeneca. E. Lindstrom has received speaking fees from Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Pharmaceutica, and EliLilly. J. Nielsen has received research grants from H. Lundbeck, Pfizer and Chempaq for clinical trials and received speaking fees from Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Pharmaceutica, and Eli-Lilly. S. Levander has no conflicts of interest.
References Eberhard, J., Levander, S., Lindstrom, E., 2009. Remission in schizophrenia: analysis in a naturalistic setting. Compr. Psychiatry 50, 200–208. Gaebel, W., et al., 2010. Drug attitude as predictor for effectiveness in first-episode schizophrenia: results of an open randomized trial (EUFEST). Eur. Neuropsychopharmacol. 20, 310–316. Grawe, R.W., Levander, S., 2001. Neuropsychological impairments in patients with schizophrenia: stability and prediction of outcome. Acta Psychiatr. Scand. Suppl. 60–64. Hogan, T.P., Awad, A.G., Eastwood, R., 1983. A self-report scale predictive of drug compliance in schizophrenics: reliability and discriminative validity. Psychol. Med. 13, 177–183.
R.E. Nielsen et al. Karow, A., Czekalla, J., Dittmann, R.W., Schacht, A., Wagner, T., Lambert, M., et al., 2007. Association of subjective well-being, symptoms, and side effects with compliance after 12 months of treatment in schizophrenia. J. Clin. Psychiatry 68, 75–80. Kay, S.R., Fiszbein, A., Opler, L.A., 1987. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261–276. Lieberman, J.A., et al., 2005. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N. Engl. J. Med. 353, 1209–1223. Lindstrom, E., Eberhard, J., Levander, S., 2007. Five-year follow-up during antipsychotic treatment: efficacy, safety, functional and social outcome. Acta Psychiatr. Scand. Suppl. 5–16. Lindstrom, E., Jedenius, E., Levander, S., 2009. A symptom selfrating scale for schizophrenia (4S): psychometric properties, reliability and validity. Nord. J. Psychiatry 1–11. Nielsen, J., Emborg, C., Gydesen, S., Dybbro, J., Aagaard, J., Haderup, K., et al., 2012a. Augmenting clozapine with sertindole: a double blinded randomized placebo controlled study. J. Clin. Psychopharmacol. 32 (2), 173–178. Nielsen, R.E., Levander, S., Thode, D., Nielsen, J., 2012b. Effects of sertindole on cognition in clozapine-treated schizophrenia patients. Acta Psychiatr. Scand. doi:10.1111/j.1600-0447.2012.01840.x (Electronic publication ahead of print). Pedersen, G., Karterud, S., 2011. The symptom and function dimensions of the Global Assessment of Functioning (GAF) scale. Compr. Psychiatry. http://dx.doi.org/10.1016/j.comppsych. 2011.04.007 (Electronic publication ahead of print). Thompson, K., Kulkarni, J., Sergejew, A.A., 2000. Reliability and validity of a new Medication Adherence Rating Scale (MARS) for the psychoses. Schizophr. Res. 42, 241–247. van der Gaag, M., Hoffman, T., Remijsen, M., Hijman, R., de Haan, L., van Meijel, B., et al., 2006. The five-factor model of the Positive and Negative Syndrome Scale II: a ten-fold crossvalidation of a revised model. Schizophr. Res. 85, 280–287. Yang, J., Ko, Y.H., Paik, J.W., Lee, M.S., Han, C., Joe, S.H., et al., 2012. Symptom severity and attitudes toward medication: impacts on adherence in outpatients with schizophrenia. Schizophr. Res. 134, 226–231.
www.elsevier.com/locate/euroneuro
SHORT COMMUNICATION
DAI-10 is as good as DAI-30 in schizophrenia René Ernst Nielsen a, b,⁎, Eva Lindström c , Jimmi Nielsen a , Sten Levander d a
Unit for Psychiatric Research, Aalborg Psychiatric Hospital, Aarhus University Hospital, Aalborg, Denmark Regional Psychiatric Services West, Central Region Denmark, Herning, Denmark c Department of Neuroscience, Psychiatry, Uppsala University Hospital, Uppsala, Sweden d Faculty of Health & Society, Malmo University, Malmo, Sweden b
Received 6 January 2012; received in revised form 19 February 2012; accepted 23 February 2012
KEYWORDS Rating scales; Schizophrenia; Cognitive neuroscience; Insight; DAI
Abstract Drug attitude inventory (DAI-30) is considered to be the best predictor of poor adherence in firstepisode schizophrenia. We compared the short version (DAI-10) with DAI-30 in long-term schizophrenia, documented if DAI was associated with poor insight, PANSS and GAF and constructed DAI-10 percentiles. DAI-30 and DAI-10 were homogenous (r = 0.82 and 0.72, respectively) with good test–retest reliability (0.79). The correlation between the DAI versions was high (0.94). Percentile scores of DAI-10 were computed. DAI is an easy-to-use self-report instrument seemingly assessing a unique clinical dimension relevant to non-adherence. DAI-10 might be preferred for its simplicity and good psychometric properties. © 2012 Elsevier B.V. and ECNP. All rights reserved.
1. Introduction Treatment non-adherence and specifically drug discontinuation is a critical issue in the long-term management of schizophrenia (Lieberman et al., 2005). One scale used to assess patient attitude towards medication is the Drug Attitude Inventory with 30 dichotomous items (DAI-30) (Hogan et al., 1983). A 10-item version (DAI-10) consisting of a subset of the DAI-30 items is also available.
⁎ Corresponding author at: Unit for Psychiatric Research, Aalborg Psychiatric Hospital, Aarhus University Hospital, Mølleparkvej 10, 9000 Aalborg, Denmark. Tel.: +45 28 72 29 62. E-mail address: [email protected] (R.E. Nielsen).
DAI-30 was recently used in the EUFEST study of first episode patients (Gaebel et al., 2010). Among a large set of variables, DAI displayed the strongest association with discontinuation (Gaebel et al., 2010), although the association was weak (hazard ratio 0.96). Still it was possible to recommend that patients scoring lower than a specific threshold value should be considered for adherence-improving interventions. Although DAI is not an optimal instrument in predicting non-adherence, the easy use and the lack of alternatives for prediction of non-adherence have made DAI the scale of choice (Gaebel et al., 2010). The DAI-30 is composed of 30 statements of which half are positively phrased and half negatively, in order to eliminate aquiescence as a source of variance. Factor analyses suggest the presence of seven factors (Hogan et al., 1983), some of them intercorrelated, but a large portion of the
0924-977X/$ - see front matter © 2012 Elsevier B.V. and ECNP. All rights reserved. doi:10.1016/j.euroneuro.2012.02.008
748 total variance is not explained by these factors. To be considered a scale rather than a set of items, internal consistence has to be adequate, as assessed by Cronbach's alpha or an intra-class correlation coefficient (ricc). When comparing subscales it should be noted that the scale ricc (as well as Cronbach's alpha) increases by the number of items, in contrast to the single item ricc. The single item ricc is then a better statistic when comparing the internal consistency of scales (or subscales) with different numbers of items. Psychometrically, DAI-30 is not sophisticated — improvement of the scale should be possible but there is a risk that any changes will compromise its demonstrated predictive accuracy. DAI-10 is a short version, with items selected to be representative of the full version. If the correlation between DAI-30 and DAI-10 is very high, it can be assumed that the shorter instrument maintains the predictive accuracy of the full instrument. Unfortunately, there is a psychometric flaw (unbalance) in DAI-10; there are six positively phrased items and four negatively phrased items. Three of the negatively phrased items refer to side-effects whereas most of the positively phrased items refer to symptom reduction, generating a confounding problem as well as an aquiescence problem. In spite of the short-comings, DAI-30 was a better predictor of adherence than any other of the variables assessed at study entry in the EUFEST material, in itself and when added to other predictor candidate variables like Insight, Cognitive reduction or Symptom ratings. DAI seems to assess an important latent characteristic which we presently cannot assess in any other way. More detailed analyses of DAI data may allow us to understand more of this latent characteristic. One aim of our study was to investigate DAI-30 and DAI-10 in long-term patients with schizophrenia, assess the factor structure and internal consistency of the scales and subscales, the amount of shared variance, and the test–retest reliability. Another aim was to construct percentile tables and cut-off values corresponding to the EUFEST-based recommendations for adherence-improving interventions for DAI-10. Furthermore we wanted to explore and cross-validate the EUFEST findings on first episode patients that DAI is more or less independent of symptoms, cognition and social function, in our sample of long-term schizophrenia patients.
2. Experimental procedures The study population consisted of two groups. One group participated in the 5-year study (Lindstrom et al., 2007) where DAI-10 was administered once in the fifth year of the study to 52 men and 44 women with schizophrenia or a schizophrenia-related psychosis. The other group, 30 men and 20 women diagnosed with schizophrenia, was recruited for a drug study of sertindole augmentation to clozapine treatment (Nielsen et al., 2012a). The 30-item DAI version was administered three times (at baseline, after 6 weeks and after 12 weeks). The study population is representative of the diversity of long-term schizophrenia. Symptoms were assessed by the Positive and Negative Syndrome Scale PANSS (Kay et al., 1987). Function was assessed by the function subscale of Global Assessment of Function scale (Pedersen and Karterud, 2011). Neuropsychological tests were selected from the computerized EuroCog battery (Eberhard et al., 2009; Grawe and Levander, 2001). Non-verbal working memory was assessed by the Austin maze test; Motor speed by five tapping/alternation subtasks; Selective attention
R.E. Nielsen et al. by two variants of the K-test; and Reaction time was assessed in three subtasks of increasing complexity. A set of Executive indices was computed on the basis of problem solving strategies in the different tests.
2.1. Statistics In order to compensate for multiple comparisons, significance level was set at p b .01. Standard statistical methods were applied using SPSS18 — details are provided in the text. Missing values were replaced by estimates based on linear regressions with all available values as predictors.
3. Results 3.1. Sample characteristics Mean age for the two samples was 43 years. There was no difference on sex and age between the two study groups. Mean PANSS was 59 ± 23 in the 5-year and 79 ± 11 in the clozapine group. In the 5-year group, 11% were drug-free, 11% had received clozapine, 9% first and 70% second generation antipsychotics.
3.2. DAI-30 (clozapine group) There were 112 DAI-30 ratings available for analyses, with a mean score of 24.5 ± 4.62. Over the three sessions the test–retest reliability was 0.79. Preliminary analyses suggested that Item #24 had no meaningful associations with any other item or sums of items. Item #27 (a reversed item) correlated positively and significantly with the sum of the non-reversed items. Item #19 had no variance. The scale ricc (intra-class correlation, in this case identical to Cronbach's alpha) of the remaining 27 items was 0.84 (single item ricc = 0.16). A factor analysis suggested the presence of nine factors. The largest factor included items #1 (r), #6, #9, #15, #18, #20 (r), #21, #23 and #26 (r = reversed item). This subscale was homogenous (ricc = .86) and with a good single item ricc (0.41), but a substantial unbalance with respect to positively and negatively phrased items. The original DAI-30 was converted to DAI-10 scores, with a mean of 7.53 ± 2.01 and a test–retest reliability of 0.92. When excluding the three items described previously (#19, #24 and #27) the correlation to DAI-10 items was 0.94. The regression line joining DAI-30 and DAI-10 can be described by a simple algorithm (DAI-10 = 0.4 × DAI-30 − 2).
3.3. DAI-10 (5-year group) Ninety-five DAI-10 ratings were available for analyses. The DAI-10 mean was 7.60± 2.27.
3.4. DAI-10 combined The DAI-10 data for the two samples were combined yielding 207 ratings for analysis. There was no difference between the two patient groups in the DAI-10 sum, p = 0.81. The ricc was 0.71 (single item ricc = 0.20). A factor analysis extracted three factors explaining 54% of the variance. Factor I included the five nonreversed items (#1, #4, #7, #9, #10); Factor II included items #5 and #8 and Factor III items #2, #3 and #6. Item #1 crossed all factors and items #2 and #3 loaded in Factor II as well. Factors I and III were intercorrelated (Promax rotation, r = .40).
DAI-10 is as good as DAI-30 in schizophrenia
749
The analysis was non-convincing but suggested that a sumscore based on Factor I items would be meaningful (scale ricc = 0.73, single item ricc = 0.35), as well as the DAI-10 sum. Means and percentiles for the Factor I based sum of items and for DAI-10 are presented in Table 1.
3.5. Symptom ratings There was a trend that PANSS G12 (Insight) was correlated with DAI-30 (clozapine group) as well as DAI-10 (5-year group). In the combined group (DAI-10), this correlation was significant (r = −0.24, p b .01). Total DAI-10 was also correlated with PANSS positive symptoms (r = .31, p b .001), but not with negative or general symptoms, or with the Excitation/Aggression or Cognitive subscales of PANSS (van der Gaag et al., 2006). There was no significant correlation between GAF scores and DAI-30 or DAI-10.
3.6. Neuropsychological tests Both groups of patients performed poorly on all tests (Eberhard et al., 2009; Nielsen et al., 2012b). There were no significant differences in the neurocognitive measures between the clozapine patients and the 5-year study patients. In the combined material (clozapine-treated and the 5-year study patients), 101 patients had data on DAI-10 as well as the neurocognitive tests. No correlations between DAI-10 and the neuropsychological test indices were significant.
4. Discussion The DAI-30 data displayed good scale homogeneity (0.82) and test–retest reliability (0.79), but the single item ricc was rather poor (0.16). The homogeneity of DAI-10 scale was lower (0.71) as expected because of only ten versus 30 items, but the single item ricc (0.20) was better and the retest reliability (0.92) was much better. Thus, the participants, although quite ill with a mean sum of PANSS 79 ± 11, were able to respond in a consistent and meaningful way to the DAI-10 items. Self-ratings are probably not used to their potential in the current type of patients (Lindstrom et al., 2009). Table 1 and 10).
Percentiles of DAI-10 and Factor 1 (items 1, 4,7, 9
Value
DAI-10 percentiles
Factor 1 percentiles
0 1 2 3 4 5 6 7 8 9 10
0–1 1–2 2–3 3–7 7–11 11–15 15–24 24–37 37–55 55–85 85–100
0–4 4–10 10–19 19–33 33–54 54–100
DAI-30 and DAI-10 were strongly intercorrelated, (r=0.93) and thereby shared almost 90% of the total variance. This suggests that there is only a small risk that the predictive accuracy of DAI-30 for treatment adherence is lost when using DAI-10. What is measured by DAI-30 and DAI-10? A factor analysis of DAI-30 based on the clozapine sample was non-convincing and did not reproduce previous findings in the literature (Hogan et al., 1983). The factor analysis of DAI-10 produced one meaningful factor, but did not shed more light on what dimensions build the scale. We did find significant correlations between DAI-10 on one hand, and the PANSS Positive subscale and the Lack of Insight item, in line with previous studies. However, the correlations were low, implying less than five percent shared variance (Gaebel et al., 2010; Yang et al., 2012). Three theoretically interesting candidates failed — the PANSS Cognitive and the Excitation/Aggression factors and GAF. With respect to the neurocognitive measures, no significant correlations were obtained. Whatever is measured by the DAI instruments, it does not seem to be possible to reduce to domains of symptoms, to lack of insight (PANSS item G12), level of function as assessed by GAF or to neurocognitive deficits. In our study, the DAI-10 appears to be better than the complete instrument, DAI-30. However, resolution is a problem with DAI-10, which can be solved by increasing the number of response alternatives (Likert scale). DAI-10 is simple to fill in for patients and easy to interpret by clinicians. The information appears to be unique and cannot be obtained in any other way. The importance of the recent findings by Gaebel et al. (2010) makes it timely to consider the cumulated evidence since the invention of the scale over 40 years ago, in order to improve the instrument rather than constructing new ones (Thompson et al., 2000; Karow et al., 2007), which has not gained wide-spread acceptance. We also need more information on the predictive power of the different DAI versions for non-adherence in other patient groups than first episode psychosis, both as a single instrument or in combination with other kinds of information. As an aid in the clinic, we provide percentile scores, to measure Drug attitude along an interval scale, relative to the current combined material. In this way, change in attitude can be assessed in a more objective way. The use of percentiles is an alternative to the optimal cut-off values suggested by Gaebel et al. (2010). Using their values, a DAI-10 score of 8 or lower (percentile value = 55% in our material), should alert the clinician about the need for adherence-improving interventions. Depending on the costs for such interventions (the pay-off matrix), this number can be adjusted downwards in steps, reducing sensitivity and improving specificity until the pay-off matrix can be defended. The study has some limitations. Two materials were combined which makes generalization to a specified mother population somewhat uncertain. One group filled in DAI-30, one DAI-10. Responding to 30 items might have affected the response to the items common to DAI30 and DAI-10. However, the pattern of relations between DAI-30 and DAI-10 on one hand, and all the other variables was quite similar.
Role of the funding source The article is funded by the Unit for Psychiatric Research, and has no external funding.
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Contributors R. Nielsen has managed literature search, written the first draft and subsequent versions of the manuscript. E. Lindström has refined the manuscript and participated in the data analysis strategy. J. Nielsen was involved in preparing initial and subsequent versions of the manuscript, as well as initial idea for the manuscript. S. Levander has developed data analysis strategy, and performed most of the data analysis, as well as being involved in the preparation of the manuscript.
Conflict of interest R. E. Nielsen has received research grants from H. Lundbeck for clinical trials, received speaking fees from Bristol-Myers Squibb, Astra Zeneca, Janssen & Cilag, Lundbeck and has acted as advisor to Astra Zeneca. E. Lindstrom has received speaking fees from Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Pharmaceutica, and EliLilly. J. Nielsen has received research grants from H. Lundbeck, Pfizer and Chempaq for clinical trials and received speaking fees from Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Pharmaceutica, and Eli-Lilly. S. Levander has no conflicts of interest.
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