- Email: [email protected]
Do treatment and illness beliefs influence adherence to medication in patients with bipolar affective disorder? A preliminary cross-sectional study
European Psychiatry 25 (2010) 216–219
Short communication
Do treatment and illness beliefs influence adherence to medication in patients with bipolar affective disorder? A preliminary cross-sectional study R. Hou *, V. Cleak, R. Peveler Room 1. 80, Floor A, Mental Health Group, Division of Clinical Neurosciences, Department of Psychiatry, School of Medicine, University of Southampton, Royal South Hants Hospital, Southampton, SO14 0YG, United Kingdom Received 2 March 2009; received in revised form 6 September 2009; accepted 7 September 2009 Available online 14 December 2009
Abstract Adherence to medication is essential for achieving good outcomes for patients with bipolar affective disorder. This study tested whether treatment and illness beliefs are important predictors of adherence to medication. Results indicate that beliefs are predictive, and may be a suitable target for modification in efforts to change behaviour. # 2009 Elsevier Masson SAS. All rights reserved. Keywords: Bipolar affective disorder; Adherence; Treatment beliefs; Illness beliefs
1. Introduction
1.1. Aims of the study
Bipolar affective disorder (BPAD) is one of the most common, severe, and persistent mental illnesses with a lifetime prevalence of 1–3% [27,17]. Adherence to prescribed medication is increasingly recognized as a critical issue in treating BPAD patients [24]. A high incidence of medication nonadherence has been found in BPAD patients, ranging from 20 to 60% [4,9,20], which leads to negative outcomes including recurrence/relapse, hospitalization, functional impairment, and suicide [8,1,12]. There is a recognized need for research into defining, monitoring and enhancing adherence to medication [20,28]. Research based on health psychology theory has highlighted that, in patients with chronic diseases, treatment and illness beliefs may affect the way in which patients choose to cope with their illness [25,14,15]. However, the influence of these beliefs on medication adherence in BPAD patients has not yet been fully understood.
The study aimed to investigate the impact of treatment and illness beliefs on medication adherence in BPAD patients, specifically looking at the different dimensions of their beliefs, and to understand how these interact with demographic and clinical characteristics.
* Corresponding author. Tel.: +44 0 2380825537; fax: +44 0 23808234243. E-mail address: [email protected] (R. Hou).
2. Methods 2.1. Participants Participants were recruited from secondary care psychiatric services in Southampton. Patients aged between 18–60 years who met the International Classification of Disease-Tenth Revision diagnostic criteria for BPAD (Code F31) and were receiving psychiatric medications (mood stabilizers, and/or antidepressants, and/or antipsychotics) were included. Patients were excluded if they were subject to the Mental Health Act or unable to read written English. Thirty-five patients (10 male, 25 female; mean age 45 years, SD = 11) gave their written consent and completed all the measures. The study protocol was reviewed and approved by the Southampton NHS Research Ethics Committee.
0924-9338/$ – see front matter # 2009 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.eurpsy.2009.09.003
R. Hou et al. / European Psychiatry 25 (2010) 216–219
2.2. Measures The questionnaire booklet included items determining the demographic and clinical features and the following standardized self-report scales. 2.2.1. The Beliefs about Medicines Questionnaire (BMQ)[16] The BMQ is a validated questionnaire to measure patients’ beliefs about medication [16]. It comprises a Specific and a General scale. The BMQ-Specific scale comprises a Necessity and a Concern scale which measure beliefs about the necessity for taking medication and adverse effects. The BMQ-General scale comprises three components: whether medications are overprescribed, generally harmful or generally beneficial. 2.2.2. The Revised Illness Perception Questionnaire (IPQr)[23] The IPQ-r is a widely used quantitative measure of the key components of patients’ illness beliefs, including acute/chronic timeline, cyclical timeline, consequences, personal control, treatment control, illness coherence and emotional representation [23,10,19,13]. 2.2.3. Morisky Medication Adherence Scale (MMAS) [22] The MMAS is a validated, 4-item self-reported adherence measure of medication use patterns [22,26,6,30]. The original binary response option (no/yes) will be used and a score of MMAS 1 is taken to indicate a patient is probably nonadherent to medication, whereas MMAS = 0 is taken as probably adherent to medication. We have previously shown that MMAS scores proved a useful screening technique with a sensitivity of 72.2% and specificity of 74.1% for greater than or equal to 80% compliance in patients on antidepressants [11]. Although self-report measures can be biased by inaccurate patient recall or by social desirability, we have reported significant agreement between self-report of adherence and more objective adherence measures (e.g., micro-processor capped medication bottles, serum drug levels) [11,18]. 2.3. Procedures A cross-sectional design was employed. Participants were approached by the clinic nurse when they attended the clinic and returned their completed questionnaire booklet. Diagnosis and hospital admission data were verified from clinical notes. 2.4. Statistical analysis Data were analyzed using SPSS version 16. Cut-offs derived from the MMAS score were used to dichotomise patients into probable adherent and non-adherent groups. Demographic and clinical features were compared between groups using independent sample t tests, continuity corrected Chi-square tests (Yates’ correction) or Mann-Whitney U tests as appropriate. Variables which indicated possible association with non-adherence from these univariate analyses were
217
subjected to logistic regression modelling. Non-adherence was entered as the dependent variable, with each selected variable as a covariate. A stepwise backward logistic regression procedure was used to derive the model. The Likelihood Ratio Test was used to select predictor variables in the logistic regression model. Fit of the model was assessed by the HosmerLemeshow ‘‘goodness of fit statistic’’ for significance. 3. Results 3.1. Univariate analyses 3.1.1. Demographic and clinical characteristics Nineteen (54.3%) patients were categorized as being probably non-adherent, and 16 (45.7%) as probably adherent. Table 1 presents the demographic and clinical characteristics of the two groups. There were significant differences between groups with regards to age (P = 0.001) and number of medication items (P = 0.03), indicating that being younger and having more medication prescribed were associated with non-adherence to medication, although there was no difference in length of illness or of untreated illness. There were also no significant differences between groups on gender, education, marital status, employment, number of hospitalizations, length of illness (P > 0.05, in all cases). 3.1.2. Treatment beliefs and illness beliefs There were significant differences in the consequences (P = 0.02) and timeline (P = 0.02) subscale of IPQ-r, but not in any of the other five subscales of IPQ-r nor in any subscale of the BMQ (P > 0.05 in all cases) (Table 1). This indicates that of the two groups, the non-adherent group believed that their illness caused more negative effects on their life (consequences) and would have a longer-term impact (timeline). 3.1.3. Associations between age and treatment/illness beliefs The mean age 45 was used to dichotomise patients into young age group (age < 45) (mean age 37, SD = 6.26) and old age group (age 45) (mean age 57, SD = 4.68). A comparison between groups on treatment and illness beliefs was conducted using independent samples t-tests. There were significant differences in the BMQ harm subscale (P = 0.04) and IPQ-r personal control subscale (P = 0.007), but not in any of the other subscales of the IPQ-r or BMQ (P > 0.05, in all cases), indicating that different age groups have different beliefs about: (1) whether the medications they are given are harmful, and (2) extent of personal control over how best to manage their condition. The group difference on timeline subscale tends to be significant (P = 0.06) indicating that the beliefs about the timeline of the impact of their illness may also be different. 3.2. Logistic regression analysis After univariate analysis, the significant variables associated with non-adherence, including age, number of medications, the IPQ timeline and consequence subscales, and the three
218
R. Hou et al. / European Psychiatry 25 (2010) 216–219
Table 1 Comparison of demographic characteristics, clinical data, illness beliefs and treatment beliefs between adherent and non-adherent group.
Age (Mean SD) Gender – number of males (%) Education – up to A level (%) Marital status – married/living with partner (%) Employment – unemployed (%) Length of illness (Mean SD) Length of untreated illness – median (IQR) Number of hospitalizations per year – median (IQR) Number of psychiatric medications – median (IQR) BMQ total overuse (Mean SD) BMQ total benefit (Mean \SD) BMQ total Harm (Mean SD) BMQ total need (Mean SD) BMQ total concern (Mean SD) IPQ total timeline (Mean SD) IPQ total consequences (Mean SD) IPQ total personal control (Mean SD) IPQ total treatment control (Mean SD) IPQ total illness coherence (Mean SD) IPQ total cyclical timeline (Mean SD) IPQ total emotional representations (Mean SD)
Adherent (n = 16)
Non-adherent (n = 19)
P
52.80 9.84 6 (17.10) 11 (31.40) 5 (14.30) 8 (22.90) 23.80 12.00 2.5 (0, 14) 3.00 (2.00, 5.00) 2.00 (1.00, 3.00) 10.80 4.41 13.66 3.42 9.64 3.34 15.33 4.30 12.92 4.25 19.67 8.60 20.00 6.87 20.00 4.84 17.13 3.58 11.60 5.08 12.07 5.35 16.40 6.06
40.60 9.45 4 (11.40) 10 (28.60) 13 (17.10) 11 (31.40) 18.95 9.40 6 (0, 14) 4.00 (2.00, 3.00) 3.00 (2.00, 4.00) 12.65 2.92 13.75 2.20 8.30 3.57 15.630 4.30 13.77 3.77 24.80 4.09 24.65 4.82 22.75 5.10 17.95 2.67 9.55 3.80 13.75 3.81 18.15 4.21
0.001a* 0.87b 0.16b 0.87b 0.77b 0.40a 0.87c 0.90c 0.03c* 0.14a 0.93a 0.28a 0.83a 0.56a 0.02a* 0.02a* 0.12a 0.44a 0.22a 0.29a 0.32a
*p 0.05. a Independent samples t-test. b Yates’ correction test. c Mann-Whitney U-test.
variables closest to being significant, including education, the BMQ overuse subscale, and the IPQ personal control subscale, were subjected to logistic regression modelling. Of these age and the BMQ overuse subscale (beliefs about whether medications are overprescribed) were included in the final model. The regression coefficients indicated that age (odds ratio 1.216, 95% confidence interval 1.065 to 1.389, P = 0.004) and the overuse subscale of BMQ (odds ratio 0.681, 95% confidence interval 0.476 to 0.974, P = 0.035) were significant predictors of non-adherence to medication. This difference from the univariate analysis resulted from the fact that both the consequences and timeline subscale scores of the IPQ-r were strongly correlated with age (P < 0.01). 4. Discussion Younger age was found to be a significant predictor of nonadherence. This finding is consistent with recent studies on BPAD which found that older patients were more adherent to medications [29,3]. The current data suggest that younger patients have a more negative view of medicines, perceiving them to be more harmful, and seeing themselves as having more personal control over how best to manage their condition. Differences in drug metabolism and insight into long term impact of the illness may also affect younger patients’ decisionmaking priorities. Being prescribed, more medications was also found to be associated with non-adherence. Therefore, intervention strategies to improve medication adherence might be usefully directed to younger patients and those on multiple medications as high-risk groups for non-adherence.
Patients’ beliefs about whether medications are overprescribed by clinicians were found to be the best variable to add to age to predict non-adherence, which is in accordance with previous work emphasizing that treatment beliefs are ‘‘the hidden determinant of treatment outcome’’[14,26]. Addressing patients’ beliefs about medication could be an intervention target to facilitate adherence. Specific medication beliefs such as beliefs about the necessity of taking medication and concerns about adverse effects were not related to adherence in this study. This is not consistent with studies across long-term conditions [2,5] and a recent BPAD study where low adherence was predicted by greater doubts about personal need for treatment and stronger concerns about potential adverse effects [7], which might be due to insufficient variation in treatment beliefs within a relatively small sample and different approaches of measuring adherence. Previous work based on the health beliefs model approach indicates that patients’ illness beliefs may lead to changes in treatment planning and adherence behaviour [21]. In this study, in univariate analyses, there were significant group differences in illness beliefs about the consequences and timeline of illness. However, this was not shown in the logistic regression model which was due to a high level of shared variance between demographic variables and IPQ-r subscales as well as a small sample size – future replication using a large sample may allow further exploration. It should also be noted that the self-reported measure of adherence might not always provide accurate or reliable ascertainment of adherence status, and no account was taken of drug intolerance which is a potential factor for early non-adherence.
R. Hou et al. / European Psychiatry 25 (2010) 216–219
Despite the above-mentioned limitations, this preliminary study suggests that age and treatment beliefs are significant predictors of non-adherence to medication. This evidence can be used to develop new targets for intervention strategies to foster treatment adherence and improve clinical outcomes. Further work in a large sample is needed to verify these preliminary findings. Acknowledgements We would like to thank patients and staff who helped with this study, and in particular Karen Osola of the outpatient department, and Helen Crossley. We would also like to thank Louise Dent, medical statistician-NIHR Evaluation, Trials and Studies Coordinating Centre, who provided statistical advice. References [1] Adams J, Scott J. Predicting medication adherence in severe mental disorders. Acta Psychiatr Scand 2000;101(2):119–24. [2] Aikens JE, Nease Jr DE, Klinkman MS. Explaining patients’ beliefs about the necessity and harmfulness of antidepressants. Ann Fam Med 2008; 6(1):23–9. [3] Baldessarini RJ, Perry R, Pike J. Factors associated with treatment nonadherence among US bipolar disorder patients. Hum Psychopharmacol 2008;23(2):95–105. [4] Berk M, Berk L, Castle D. A collaborative approach to the treatment alliance in bipolar disorder. Bipolar Disord 2004;6(6):504–18. [5] Brown C, et al. Beliefs about antidepressant medications in primary care patients: relationship to self-reported adherence. Med Care 2005;43(12): 1203–7. [6] Clatworthy J, et al. The value of self-report assessment of adherence, rhinitis and smoking in relation to asthma control. Prim Care Respir J 2009. [7] Clatworthy J, et al. Understanding medication non-adherence in bipolar disorders using a Necessity-Concerns Framework. J Affect Disord 2009; 116(1–2):51–5. [8] Colom F, Vieta E. Improving the outcome of bipolar disorder through nonpharmacological strategies: the role of psychoeducation. Rev Bras Psiquiatr 2004;26(Suppl. 3):47–50. [9] Colom F, et al. Identifying and improving non-adherence in bipolar disorders. Bipolar Disord 2005;7(Suppl. 5):24–31. [10] Foster NE, et al. Illness perceptions of low back pain patients in primary care: What are they, do they change and are they associated with outcome? Pain 2008;136(1–2):177–87. [11] George CF, et al. Compliance with tricyclic antidepressants: the value of four different methods of assessment. Br J Clin Pharmacol 2000;50(2): 166–71.
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[12] Gonzalez-Pinto A, et al. Suicidal risk in bipolar I disorder patients and adherence to long-term lithium treatment. Bipolar Disord 2006;8(5 Pt 2): 618–24. [13] Hermele S, et al. Illness representations and psychological distress in patients undergoing coronary artery bypass graft surgery. Psychol Health Med 2007;12(5):580–91. [14] Horne R. Patients’ beliefs about treatment: the hidden determinant of treatment outcome? J Psychosom Res 1999;47(6):491–5. [15] Horne R, Weinman J. Patients’ beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness. J Psychosom Res 1999;47(6):555–67. [16] Horne R, Weinman J, Hankins M. The beliefs about medications questionnaire: the development of a new method for assessing the cognitive representation of medication. Psychol Health 1999;14:1–24. [17] Judd LL, Akiskal HS. The prevalence and disability of bipolar spectrum disorders in the US population: re-analysis of the ECA database taking into account subthreshold cases. J Affect Disord 2003;73(1–2): 123–31. [18] Lam DH, et al. A randomized controlled study of cognitive therapy for relapse prevention for bipolar affective disorder: outcome of the first year. Arch Gen Psychiatry 2003;60(2):145–52. [19] Lee BO, Chaboyer W, Wallis M. Predictors of health-related quality of life 3 months after traumatic injury. J Nurs Scholarsh 2008;40(1):83–90. [20] Lingam R, Scott J. Treatment non-adherence in affective disorders. Acta Psychiatr Scand 2002;105(3):164–72. [21] Makela EH, Griffith RK. Enhancing treatment of bipolar disorder using the patient’s belief system. Ann Pharmacother 2003;37(4):543–5. [22] Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986;24(1): 67–74. [23] Moss-Morris R, et al. The revised illness perception questionnaire (IPQR). Psychology and Health 2002;17:1–16. [24] Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005; 353(5):487–97. [25] Petrie KJ, et al. Role of patients’ view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. Bmj 1996;312(7040):1191–4. [26] Phatak HM, Thomas 3rd J. Relationships between beliefs about medications and nonadherence to prescribed chronic medications. Ann Pharmacother 2006;40(10):1737–42. [27] Regeer EJ, et al. Prevalence of bipolar disorder in the general population: a Reappraisal Study of the Netherlands Mental Health Survey and Incidence Study. Acta Psychiatr Scand 2004;110(5):374–82. [28] Sajatovic M, Davies M, Hrouda DR. Enhancement of treatment adherence among patients with bipolar disorder. Psychiatr Serv 2004;55(3):264–9. [29] Sajatovic M, et al. Age comparison of treatment adherence with antipsychotic medications among individuals with bipolar disorder. Int J Geriatr Psychiatry 2007;22(10):992–8. [30] Shalansky SJ, Levy AR, Ignaszewski AP. Self-reported Morisky score for identifying nonadherence with cardiovascular medications. Ann Pharmacother 2004;38(9):1363–8.
Short communication
Do treatment and illness beliefs influence adherence to medication in patients with bipolar affective disorder? A preliminary cross-sectional study R. Hou *, V. Cleak, R. Peveler Room 1. 80, Floor A, Mental Health Group, Division of Clinical Neurosciences, Department of Psychiatry, School of Medicine, University of Southampton, Royal South Hants Hospital, Southampton, SO14 0YG, United Kingdom Received 2 March 2009; received in revised form 6 September 2009; accepted 7 September 2009 Available online 14 December 2009
Abstract Adherence to medication is essential for achieving good outcomes for patients with bipolar affective disorder. This study tested whether treatment and illness beliefs are important predictors of adherence to medication. Results indicate that beliefs are predictive, and may be a suitable target for modification in efforts to change behaviour. # 2009 Elsevier Masson SAS. All rights reserved. Keywords: Bipolar affective disorder; Adherence; Treatment beliefs; Illness beliefs
1. Introduction
1.1. Aims of the study
Bipolar affective disorder (BPAD) is one of the most common, severe, and persistent mental illnesses with a lifetime prevalence of 1–3% [27,17]. Adherence to prescribed medication is increasingly recognized as a critical issue in treating BPAD patients [24]. A high incidence of medication nonadherence has been found in BPAD patients, ranging from 20 to 60% [4,9,20], which leads to negative outcomes including recurrence/relapse, hospitalization, functional impairment, and suicide [8,1,12]. There is a recognized need for research into defining, monitoring and enhancing adherence to medication [20,28]. Research based on health psychology theory has highlighted that, in patients with chronic diseases, treatment and illness beliefs may affect the way in which patients choose to cope with their illness [25,14,15]. However, the influence of these beliefs on medication adherence in BPAD patients has not yet been fully understood.
The study aimed to investigate the impact of treatment and illness beliefs on medication adherence in BPAD patients, specifically looking at the different dimensions of their beliefs, and to understand how these interact with demographic and clinical characteristics.
* Corresponding author. Tel.: +44 0 2380825537; fax: +44 0 23808234243. E-mail address: [email protected] (R. Hou).
2. Methods 2.1. Participants Participants were recruited from secondary care psychiatric services in Southampton. Patients aged between 18–60 years who met the International Classification of Disease-Tenth Revision diagnostic criteria for BPAD (Code F31) and were receiving psychiatric medications (mood stabilizers, and/or antidepressants, and/or antipsychotics) were included. Patients were excluded if they were subject to the Mental Health Act or unable to read written English. Thirty-five patients (10 male, 25 female; mean age 45 years, SD = 11) gave their written consent and completed all the measures. The study protocol was reviewed and approved by the Southampton NHS Research Ethics Committee.
0924-9338/$ – see front matter # 2009 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.eurpsy.2009.09.003
R. Hou et al. / European Psychiatry 25 (2010) 216–219
2.2. Measures The questionnaire booklet included items determining the demographic and clinical features and the following standardized self-report scales. 2.2.1. The Beliefs about Medicines Questionnaire (BMQ)[16] The BMQ is a validated questionnaire to measure patients’ beliefs about medication [16]. It comprises a Specific and a General scale. The BMQ-Specific scale comprises a Necessity and a Concern scale which measure beliefs about the necessity for taking medication and adverse effects. The BMQ-General scale comprises three components: whether medications are overprescribed, generally harmful or generally beneficial. 2.2.2. The Revised Illness Perception Questionnaire (IPQr)[23] The IPQ-r is a widely used quantitative measure of the key components of patients’ illness beliefs, including acute/chronic timeline, cyclical timeline, consequences, personal control, treatment control, illness coherence and emotional representation [23,10,19,13]. 2.2.3. Morisky Medication Adherence Scale (MMAS) [22] The MMAS is a validated, 4-item self-reported adherence measure of medication use patterns [22,26,6,30]. The original binary response option (no/yes) will be used and a score of MMAS 1 is taken to indicate a patient is probably nonadherent to medication, whereas MMAS = 0 is taken as probably adherent to medication. We have previously shown that MMAS scores proved a useful screening technique with a sensitivity of 72.2% and specificity of 74.1% for greater than or equal to 80% compliance in patients on antidepressants [11]. Although self-report measures can be biased by inaccurate patient recall or by social desirability, we have reported significant agreement between self-report of adherence and more objective adherence measures (e.g., micro-processor capped medication bottles, serum drug levels) [11,18]. 2.3. Procedures A cross-sectional design was employed. Participants were approached by the clinic nurse when they attended the clinic and returned their completed questionnaire booklet. Diagnosis and hospital admission data were verified from clinical notes. 2.4. Statistical analysis Data were analyzed using SPSS version 16. Cut-offs derived from the MMAS score were used to dichotomise patients into probable adherent and non-adherent groups. Demographic and clinical features were compared between groups using independent sample t tests, continuity corrected Chi-square tests (Yates’ correction) or Mann-Whitney U tests as appropriate. Variables which indicated possible association with non-adherence from these univariate analyses were
217
subjected to logistic regression modelling. Non-adherence was entered as the dependent variable, with each selected variable as a covariate. A stepwise backward logistic regression procedure was used to derive the model. The Likelihood Ratio Test was used to select predictor variables in the logistic regression model. Fit of the model was assessed by the HosmerLemeshow ‘‘goodness of fit statistic’’ for significance. 3. Results 3.1. Univariate analyses 3.1.1. Demographic and clinical characteristics Nineteen (54.3%) patients were categorized as being probably non-adherent, and 16 (45.7%) as probably adherent. Table 1 presents the demographic and clinical characteristics of the two groups. There were significant differences between groups with regards to age (P = 0.001) and number of medication items (P = 0.03), indicating that being younger and having more medication prescribed were associated with non-adherence to medication, although there was no difference in length of illness or of untreated illness. There were also no significant differences between groups on gender, education, marital status, employment, number of hospitalizations, length of illness (P > 0.05, in all cases). 3.1.2. Treatment beliefs and illness beliefs There were significant differences in the consequences (P = 0.02) and timeline (P = 0.02) subscale of IPQ-r, but not in any of the other five subscales of IPQ-r nor in any subscale of the BMQ (P > 0.05 in all cases) (Table 1). This indicates that of the two groups, the non-adherent group believed that their illness caused more negative effects on their life (consequences) and would have a longer-term impact (timeline). 3.1.3. Associations between age and treatment/illness beliefs The mean age 45 was used to dichotomise patients into young age group (age < 45) (mean age 37, SD = 6.26) and old age group (age 45) (mean age 57, SD = 4.68). A comparison between groups on treatment and illness beliefs was conducted using independent samples t-tests. There were significant differences in the BMQ harm subscale (P = 0.04) and IPQ-r personal control subscale (P = 0.007), but not in any of the other subscales of the IPQ-r or BMQ (P > 0.05, in all cases), indicating that different age groups have different beliefs about: (1) whether the medications they are given are harmful, and (2) extent of personal control over how best to manage their condition. The group difference on timeline subscale tends to be significant (P = 0.06) indicating that the beliefs about the timeline of the impact of their illness may also be different. 3.2. Logistic regression analysis After univariate analysis, the significant variables associated with non-adherence, including age, number of medications, the IPQ timeline and consequence subscales, and the three
218
R. Hou et al. / European Psychiatry 25 (2010) 216–219
Table 1 Comparison of demographic characteristics, clinical data, illness beliefs and treatment beliefs between adherent and non-adherent group.
Age (Mean SD) Gender – number of males (%) Education – up to A level (%) Marital status – married/living with partner (%) Employment – unemployed (%) Length of illness (Mean SD) Length of untreated illness – median (IQR) Number of hospitalizations per year – median (IQR) Number of psychiatric medications – median (IQR) BMQ total overuse (Mean SD) BMQ total benefit (Mean \SD) BMQ total Harm (Mean SD) BMQ total need (Mean SD) BMQ total concern (Mean SD) IPQ total timeline (Mean SD) IPQ total consequences (Mean SD) IPQ total personal control (Mean SD) IPQ total treatment control (Mean SD) IPQ total illness coherence (Mean SD) IPQ total cyclical timeline (Mean SD) IPQ total emotional representations (Mean SD)
Adherent (n = 16)
Non-adherent (n = 19)
P
52.80 9.84 6 (17.10) 11 (31.40) 5 (14.30) 8 (22.90) 23.80 12.00 2.5 (0, 14) 3.00 (2.00, 5.00) 2.00 (1.00, 3.00) 10.80 4.41 13.66 3.42 9.64 3.34 15.33 4.30 12.92 4.25 19.67 8.60 20.00 6.87 20.00 4.84 17.13 3.58 11.60 5.08 12.07 5.35 16.40 6.06
40.60 9.45 4 (11.40) 10 (28.60) 13 (17.10) 11 (31.40) 18.95 9.40 6 (0, 14) 4.00 (2.00, 3.00) 3.00 (2.00, 4.00) 12.65 2.92 13.75 2.20 8.30 3.57 15.630 4.30 13.77 3.77 24.80 4.09 24.65 4.82 22.75 5.10 17.95 2.67 9.55 3.80 13.75 3.81 18.15 4.21
0.001a* 0.87b 0.16b 0.87b 0.77b 0.40a 0.87c 0.90c 0.03c* 0.14a 0.93a 0.28a 0.83a 0.56a 0.02a* 0.02a* 0.12a 0.44a 0.22a 0.29a 0.32a
*p 0.05. a Independent samples t-test. b Yates’ correction test. c Mann-Whitney U-test.
variables closest to being significant, including education, the BMQ overuse subscale, and the IPQ personal control subscale, were subjected to logistic regression modelling. Of these age and the BMQ overuse subscale (beliefs about whether medications are overprescribed) were included in the final model. The regression coefficients indicated that age (odds ratio 1.216, 95% confidence interval 1.065 to 1.389, P = 0.004) and the overuse subscale of BMQ (odds ratio 0.681, 95% confidence interval 0.476 to 0.974, P = 0.035) were significant predictors of non-adherence to medication. This difference from the univariate analysis resulted from the fact that both the consequences and timeline subscale scores of the IPQ-r were strongly correlated with age (P < 0.01). 4. Discussion Younger age was found to be a significant predictor of nonadherence. This finding is consistent with recent studies on BPAD which found that older patients were more adherent to medications [29,3]. The current data suggest that younger patients have a more negative view of medicines, perceiving them to be more harmful, and seeing themselves as having more personal control over how best to manage their condition. Differences in drug metabolism and insight into long term impact of the illness may also affect younger patients’ decisionmaking priorities. Being prescribed, more medications was also found to be associated with non-adherence. Therefore, intervention strategies to improve medication adherence might be usefully directed to younger patients and those on multiple medications as high-risk groups for non-adherence.
Patients’ beliefs about whether medications are overprescribed by clinicians were found to be the best variable to add to age to predict non-adherence, which is in accordance with previous work emphasizing that treatment beliefs are ‘‘the hidden determinant of treatment outcome’’[14,26]. Addressing patients’ beliefs about medication could be an intervention target to facilitate adherence. Specific medication beliefs such as beliefs about the necessity of taking medication and concerns about adverse effects were not related to adherence in this study. This is not consistent with studies across long-term conditions [2,5] and a recent BPAD study where low adherence was predicted by greater doubts about personal need for treatment and stronger concerns about potential adverse effects [7], which might be due to insufficient variation in treatment beliefs within a relatively small sample and different approaches of measuring adherence. Previous work based on the health beliefs model approach indicates that patients’ illness beliefs may lead to changes in treatment planning and adherence behaviour [21]. In this study, in univariate analyses, there were significant group differences in illness beliefs about the consequences and timeline of illness. However, this was not shown in the logistic regression model which was due to a high level of shared variance between demographic variables and IPQ-r subscales as well as a small sample size – future replication using a large sample may allow further exploration. It should also be noted that the self-reported measure of adherence might not always provide accurate or reliable ascertainment of adherence status, and no account was taken of drug intolerance which is a potential factor for early non-adherence.
R. Hou et al. / European Psychiatry 25 (2010) 216–219
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