Predictors of medication adherence among patients with severe psychiatric disorders: findings from the baseline assessment of a randomized controlled trial (Tecla)

Predictors of medication adherence among patients with severe psychiatric disorders: findings... Background: Schizophrenia and bipolar disorder are characterized by a high disease burden. Antipsychotic medication is an essential part of the treatment. However, non-adherence is a major problem. Our aim was to examine potential determinants of non-adherence for patients with severe mental disorders. Methods: Baseline data of the study “Post stationary telemedical care of patients with severe psychiatric disorders” (Tecla) were used. Medication adherence was assessed with the Medication Adherence Report Scale German version (MARS-D). A logistic regression was calculated with age, sex, education, employment status, level of global functioning, social support and intake of typical and atypical antipsychotics as predictors. Results: N = 127 participants were included in the analysis (n = 73 men, mean age 42 years). The mean MARS-D Score was 23.4 (SD 2.5). The most common reason for non-adherence was forgetting to take the medicine. Significant positive determinants for adherence were older age (OR 1.02, 95% CI 1.011–1.024, p < 0.0001), being employed (OR 2.46, 95% CI 1.893–3.206, p < 0.0001), higher level of global functioning (overall measure of how patients are doing) (OR 1.02, 95% CI 1.012–1.028, p < 0.0001), having social support (OR 1.02, 95% CI 1.013–1.026, p < 0.0001), and intake of typical antipsychotics (OR 2.389, 95% CI 1.796–3.178, p < 0.0001). A negative determinant was (female) sex (OR 0.73, 95% CI 0. 625–0.859, p = 0.0001). Conclusions: Especially employment, functioning and social support could be promising targets to facilitate adherence in patients with schizophrenia or bipolar disorder. Trial registration: This study is retrospectively registered at the German Clinical Trials Register with the trial registration number DRKS00008548 at 21/05/2015. Keywords: Psychiatry, Mental health disorders, Schizophrenia, Psychotic disorders, Bipolar disorders, Adherence, Non- adherence, MARS-D * Correspondence: ulrike.stentzel@uni-greifswald.de Ulrike Stentzel and Neeltje van den Berg contributed equally to this work. Institute for Community Medicine, University Medicine Greifswald, Ellernholzstraße 1-2, 17487 Greifswald, Germany Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Stentzel et al. BMC Psychiatry (2018) 18:155 Page 2 of 8 Background project is to improve medication adherence for patients Schizophrenia as well as other psychotic disorders and bi- with severe psychiatric disorders on the basis of regular, polar disorders are serious mental diseases. In Germany, individualized telephone calls and short-text-messages. 19 new schizophrenia-cases per 100,000 people per year Tecla is designed as a prospective controlled randomized are diagnosed. In Germany, the 12-month-prevalence of intervention study. All participants receive computer schizophrenia and other psychotic disorders is estimated assisted baseline and follow-up interviews after 3 and 2.6% and of bipolar disorders 1.5% [1]. The disease burden 6 months. The participants are recruited from three psy- is high for mental disorders. The number of days with lim- chiatric departments in Western-Pomerania in the very itations is 3 times higher for people with mental disorders northeast of Germany. Participants were patients in compared to healthy persons [1] and schizophrenia is one day-care hospitals or in open or closed inpatient wards. of the ten diseases with the highest number of years of life The recruitment occurs shortly before discharge and is lived with disability (YLD) [2]. done by a study psychologist. Inclusion criteria are a med- Medication is an essential part of the treatment of ical diagnosis of any form of schizophrenia (ICD-10 F20), schizophrenia and bipolar disorders; both in acute epi- schizoaffective disorders (ICD-10 F25), or bipolar disor- sodes and in long-term management. Several studies ders (ICD-10 F31), and age ≥ 18 years. Exclusion criteria showed that the relapse rate is significantly lower with were scheduled inpatient treatments within the next drug therapy [2–5], provided that the patient is adherent. 6 months and missing accessibility by telephone. A com- Adherence is defined by the WHO as “the extent to which prehensive description of the study protocol for the Tecla aperson’sbehavior – taking medication, following a diet, study is published elsewhere [21]. and/or executing lifestyle changes – corresponds with Additionally, data from participants of the IMeS study agreed recommendations from a health care provider” [6]. (Approaches of individualized medicine in psychiatric Non-adherent behavior increases the risk of relapses and disorders) were included. The aim of this study is to rehospitalization [7–9]. However, non-adherence is one of identify biomarkers from genetic material, and to exam- the major problems in patients with schizophrenia and bi- ine metabolic processes and bodily protein in blood polar disorders [10]. The prevalence of non-adherence to samples. The recruitment of the patients, the inclusion antipsychotics ranges from 20 to 89% for patients with criteria and the baseline assessment of the IMeS-study schizophrenia or bipolar disorders [11–13]. Dolder et al. are identical with the Tecla study. Both samples were examined adherence in an outpatient setting using pre- collected at the same recruitment sites. scription fill rates. Their results showed an adherence of All participating patients gave their written informed 55% after 12 months among patients taking atypical anti- consent. The data assessment and documentation were psychotics (second generation) [14]. conducted based on eCRFs and an IT-supported docu- To reduce non-adherence in patients with severe men- mentation system [22]. tal disorders, it is necessary to know more about the rea- sons for non-adherence und to determine factors that Measures influence adherence positively or negatively. A few stud- Medication adherence was measured with the Medication ies have addressed specific factors determining adher- Adherence Report Scale, German version (MARS-D) that ence of patients with schizophrenia or with bipolar detects non-adherent behavior by self-report [23]. It is a disorders [15–20]. However, the results of these studies measure for non-adherence in general, not for mental dis- often differ [16, 19, 20] and non-adherence is considered orders in particular. The scale considers also the fre- a multi-causal phenomenon [16]. quency of non-adherent behavior. The questions are The aim of this analysis is to identify possible determi- formulated in a non-threatening and non-judgmental way nants for non-adherence of patients with schizophrenia, to minimize social desirability bias [24, 25]. The original other psychotic disorders and bipolar disorders, includ- Medication Adherence Report Scale (MARS-5, in English ing age, sex, education, the status of employment, the language) was developed because patients tend to overesti- level of functioning, presence of social support, adverse mate their adherence [26–28] or to conceal non-adherent drug effects. behavior [23]. The MARS-D has 5 items to assess how the drugs were taken. The 5 items are “Iforget totake my Methods medication”, “I change the dose of my medication”, “From Patient sample and data time to time I stop taking my medication for a while”, “I The data for this analysis were taken from the baseline sometimes decide to skip the medication” and “Itakeless assessment from an intervention study to evaluate tele- medication than I am instructed to.” The questions provide medical care for patients with severe psychiatric disor- 5 answer categories from “always” to “never” (scored 1 to ders (“Post stationary telemedical care of patients with 5) so that the total score is between 5 (no adherence) and severe psychiatric disorders” (Tecla)). The goal of this 25 (complete adherence) [29]. Stentzel et al. BMC Psychiatry (2018) 18:155 Page 3 of 8 The Global Assessment of Functioning (GAF) is an over- MARS-D score was dichotomized in “adherent” and all measure of how patients are doing from positive mental “non-adherent”. Following recent literature, the cut-off health up to severe psychopathology [30]. It is known, that was set at a MARS-D score of 24. Participants with a functioning is low in people with current mental health MARS-D score of 25 were seen as adherent (coded as disorders, so functioning can be used as an expression of 1), participants with a score < 25 were considered as theseverityof illness [31]. The GAF-questionnaire mea- non-adherent (coded as 0) [37, 38]. A multiple imput- sures the degree of mental illness by rating psychological, ation (based on the EM algorithm [39]) was performed social and occupational functioning [30]onanordinal to deal with missing data. Fifty-nine percent of the re- scale from 1 to 100 [32]. The scale is divided into 10-point cords where complete. Thirty percent of the records intervals. The lowest interval (score 1 to 10) represents se- missed one and 11% of the records missed two or more vere illness, the highest interval (score 91 to 100) repre- items. There were no missing items regarding the ques- sents the healthiest condition [30]. tionnaire of the primary endpoint. The data was missing Social support was assessed using the measure F-SozU at random. With the imputed data set a multivariate in- (Social support, short form with 14 items) [33]. The authors tension to treat analysis was performed. As independent defined social support as the result of cognitive-emotional variables age, sex, education, employment status, GAF, processing and assessment of current and past social inter- social support, the total number of strong and very actions. The concept is based on cognitive approaches and strong adverse drug effects, the NQ-aspect of social de- assesses the subjective conviction to get support from the sirability, and the intake of atypical and typical antipsy- subject’s social network if necessary. This 14-item short chotics were included in the model. Data processing and form is appropriate for the assessment of a more generally statistical calculations were performed with SAS 9.4 (© perceived social support [33]. The statements refer to the 2002–2012 by SAS Institute Inc., Cary, North Carolina, fields of emotional support (to be liked and accepted by USA.). others, to share feelings, to experience participation), to In a sensitivity analysis the MARS-D score was mod- provide practical assistance (practical help in everyday elled as a continuous variable. Due to its discrete distribu- problems, for example to borrow things, getting practical tion a Poisson regression was performed in a generalized advice, getting help with challenging tasks) and social inte- linear model (GLM). It was necessary to reverse the gration (belonging to a circle of friends, doing joint ven- MARS-D-variable for the Poisson regression because of tures, knowing people with similar interests) and are the left skewed distribution of the data. assessed using a 5 category Likert-scale from “does not apply” (scored 1) to “applies exactly” (scored 5) [33]. Adverse drug effects were assessed using a 5 category Results Likert-scale including “no side effects”, “little”, “moderate”, Of 135 participants recruited, 127 could be included in “strong” and “very strong” for each of the following side ef- the analyses (Fig. 1). fects: movement disorders, muscle stiffness, involuntary The participants had a mean age of 42 years (SD 12.9), shiver, motionlessness, muscle spasm, agonizing restless- 57% were men. Eighty-four percent were unemployed and ness/problems to sit still (can’t be suppressed at will), lack of 28% had an education of less than 10 years. One hundred sexual desire/loss of libido, increase in weight, increased ap- and-six participants had a diagnosis of schizophrenia, petite, heavy feeling of illness/chills/fever and milk flow [34]. schizotypal and delusional disorders (F20 – F29), thereof To adjust for social desirability (defined as the “ten- 72 paranoid schizophrenia (F20), 1 Persistent delusional dency to give overly positive self-descriptions” [35]), the disorders (F22), 8 acute and transient psychotic disorders Short Scale Social Desirability-Gamma (KSE-G) was (F 23) and 25 schizoaffective disorders (F25). Thirty par- used [36]. The KSE-G measures two aspects of social de- ticipants had a diagnosis of mood (affective) disorders sirability: the exaggeration of positive qualities (PQ +), (F30 – F39), thereof 27 bipolar affective disorder (F31) and the minimization of negative qualities (NQ-) [35]. and 3 depressive episode (F32). Nine participants had both Both aspects were assessed with three items each on a 5 a diagnosis of schizophrenia, schizotypal and delusional category Likert-scale. The categories range from “does disorders as well as mood (affective) disorders. Atyp- not apply” (score 0) to “fully applies” (score 4) [36]. ical antipsychotics were prescribed to 85 participants. The baseline assessment contained also a sociodemo- Typical antipsychotics were prescribed to 27 partici- graphic part to assess sex, age, education, and employment pants. Fifteen participants had no prescription for an- status. Patients’ diagnoses were extracted from medical files. tipsychotics but for drugs of other drug types. Table 1 shows the descriptive results for adherence, global Statistical analysis functioning, social support, the number of strong to To investigate determinants for medication adherence, a very strong adverse drug effects, and social desirabil- multivariate logistic regression approach was used. The ity. The adherence showed a left skewed distribution Stentzel et al. BMC Psychiatry (2018) 18:155 Page 4 of 8 Fig. 1 Number of patients included in the analysis (Fig. 2), 54% of the participants reported some kind that are not or just marginally employed and a lower level of non-adherence (MARS-D score < 25). of global functioning (GAF) are associated with lesser ad- Figure 3 shows the reasons for non-adherence. To forget herence. Having social support showed no significant im- to take the medicine is the most frequent reason for pact on medication adherence. The intake of atypical non-adherent behavior. Active deviation from the pre- antipsychotics is significantly associated with higher scribed medication scheme (change the dose, stop taking non-adherence whereas the intake of typical antipsy- medicines for a while, skip a dose, take less than instructed) chotics is associated with higher adherence. were each reported at prevalence of less than 20%. The results of the logistic regression are shown in Discussion Table 2. Higher age, being employed in full time, part The MARS-5 was designed to evaluate reasons and time or vocational training, a higher level of global func- prevalences for non-adherent behavior, [23, 40]rather tioning, having more social support and intake of typical than to measure exact values of the medication use antipsychotics have a significant positive influence on [25, 41, 42]. In the patient group with severe mental adherence. Being female is a negative determinant for disorders, both the primary and the sensitivity ana- adherence. The level of education, the number of strong lyses showed a positive influence of the global func- and very strong adverse drug effects and intake of atyp- tioning level, of having social support and being ical antipsychotics have no statistically significant effect employed on adherence. on adherence. Medication adherence of patients with severe psy- A Poisson regression model was performed and used as chiatric diseases is generally low. The patients in this a sensitivity analysis. The reversal of the MARS-D for the study were treated in hospitals or day clinics, data as- linear Poisson regression also leads to a reversal in the dir- sessment was performed shortly before their dis- ection of the results. The results (Table 3) are similar to charge. However, the proportion of non-adherent the findings of the primary analysis except for sex and em- patients was relatively high (54%). Stange et al. com- ployment status. Patients with lower education, patients pared the adherence of patients during the hospital stay with the situation 6 weeks after discharge and found that non-adherence was lower during hospital Table 1 Descriptive statistics of the measured variables (Tecla baseline assessment) stay (37.6% vs. 61.2% after 6 weeks) [37]. Hence long-term non-adherence is likely underestimated in Mean (SD) Median Range our analysis. Adherence (MARS-D) 23.4 (2.5) 24 13–25 Jonsdottir et al. examined medication adherence in pa- Global functioning (GAF) 54.8 (10.9) 55 30–85 tients with severe mental disorders in an ambulant set- Social support (score) 48.6 (12.9) 51 14–70 ting [8]. The MARS-5 mean score in this study (22.0) Social desirability was slightly lower than in our analysis. These authors positive qualities (PQ+) 2.7 (0.8) 2.7 0–4 also found a statistically significant correlation with pro- minimize negative qualities (NQ-) 1.1 (0.8) 1 0–4 vider rated medication adherence which supports the validity of the self-rated score used in our study. Number of strong to very strong 2.6 (1.5) 2 1–10 adverse drug effects In two studies (Mahler et al. [43] and Huther et al. SD standard deviation [38]) the adherence of chronically ill patients with Stentzel et al. BMC Psychiatry (2018) 18:155 Page 5 of 8 Fig. 2 Histogram of the MARS-D score (MARS-D score 25 means complete adherence, < 25 some kind of non-adherence, the lower the MARS-D score the higher is non-adherence) MARS-D in primary care settings in Germany was ex- Menckeberg et al. used the MARS-5 in a study amined. The average MARS-D scores were similar in both about inhaled corticosteroids (ICS) in asthma control studies (mean 23.6 (SD 2.17) [43] and mean 23.5 (SD 2.7) [41]. In their patient group the mean score value was [38]). Mahler et al. reported ‘forget the medication intake’ 19.4 (SD 4.4). Compared to our and others’ findings as the most common cause of non-adherence [43]. These this score is rather low. However, with 79% scoring findings correspond with our results. However, Huther et above the scale midpoint Menckeberg’s results showed al. found no significant determinants for medication ad- a skewed distribution too. The study showed that herence in a subsequent multivariate analysis [38]. many patients were skeptical about the benefits of Tommelein et al. investigated the accuracy of the ICS [41]. This might be one cause for the discrepancy MARS-5 for patients with chronic obstructive pulmon- between their results and results of other studies. ary disease (COPD) [40]. The mean adherence for In many cases, medication adherence is overestimated COPD patients was 23.5 (SD = 2.6). 52.9% of patients re- based on self-report questionnaires [23, 26–28]. Ose et corded complete adherence (MARS-5 sum score = 25). al. examined the concordance in rating medication ad- Further testing of the MARS-5 showed low sensitivity, herence among multimorbid patients and their general specificity, and positive predictive value (PPV). Hence practitioners (GPs) [44]. Patients often rated their adher- the authors assessed the MARS-5 as inaccurate in iden- ence higher than their GPs and only for 20% of the pa- tifying non-adherent users of inhalation medication in tients medication adherence was rated concordantly. An patients with COPD [40]. inherent limitation of self-report questionnaires is that Fig. 3 Relative frequencies for reasons of non-adherent behavior assessed with MARS-D Stentzel et al. BMC Psychiatry (2018) 18:155 Page 6 of 8 Table 2 Results of the multivariate logistic regression (dependent variable: dichotomized adherence (MARS-D), cut-off score = 24), (being adherent vs. being non-adherent) regression coefficient standard error p-value OR 95% CI (α 0.05) Age in years 0.0170 0.0033 < 0.0001 1.017 1.011–1.024 Sex (female vs male) −0.1557 0.0406 0.0001 0.732 0.625–0.859 Education (≥ 10 years of education vs. < 10 years of education) 0.0026 0.0448 0.9531 1.005 0.843–1.198 Employment status (being employed vs. not or marginally employed) 0.4507 0.0672 < 0.0001 2.463 1.893–3.206 Global assessment of functioning (GAF) 0.0198 0.0039 < 0.0001 1.02 1.012–1.028 Social desirability (NQ-) −0.6507 0.0562 < 0.0001 0.522 0.467–0.582 Social support 0.0193 0.0032 < 0.0001 1.02 1.013–1.026 Adverse drug effects 0.0382 0.0255 0.1341 1.039 0.988–1.092 Atypical antipsychotics (atypical drugs vs. other drug types) −0.1036 0.0627 0.0987 0.813 0.636–1.039 Typical antipsychotics (typical drugs vs. other drug types) 0.4355 0.0728 < 0.0001 2.389 1.796–3.178 OR odds Ratio, CI confidence interval full time, part time, vocational training unintentional non-adherence is commonly not assessed adherence [8]. The original MARS-5 in English as well [40, 42]. as the German version MARS-D are reliable and valid Besides self-reports, adherence can be measured by self-report measures of non-adherence [23, 46]. directly observing the patients while taking their medica- In the literature, predictors for adherence or tion, using pill counts, Medication Event Monitoring non-adherence differ. Sendt et al. gives a comprehensive Systems (MEMS), medical records, medication dispens- overview [19]. As possible predictors were indicated ing records, and pharmacological and biochemical marriage status, higher education, status of employment, markers [42, 45]. Some of these methods are costly and gender, higher subjective well-being, later stage of illness, require increased effort or can only be used for certain absence of cannabis use, lower rates of illicit substances drugs. Due to the importance of non-adherent behavior, and alcohol use, lower rates of medication refusal in a simple tool is needed that can easily be implemented early stages of treatment, better therapeutic alliance and in various study settings [8]. Self-reporting question- higher trust in the physicians [19]. In our findings, naires are more patient-friendly, less expensive and eas- higher education showed no significant results but being ier to conduct. Jonsdottir et al. validated self-report employed versus not or just marginally being employed measures with serum concentrations and found showed a strong influence on adherence. Inconsistent self-report questionnaires a valid method for measuring predictors were symptom severity, insight, positive Table 3 Results of the generalized linear Poisson regression. Dependent variable: MARS-D score Regression coefficient Standard error p-value beta estimate 95% CI (α 0.05) Age 0,0018 0,0012 0,1321 1,0018 0,9994–1,0042 sex (female vs male) −0,0873 0,0295 0,0031 0,9164 0,8649–0,9711 Education −0,2761 0,0321 < 0.0001 0,7587 0,7125–0,8079 (≥ 10 years of education vs. < 10 years of education) Employment status −0,2793 0,0523 < 0.0001 0,7563 0,6826–0,8380 (being employed vs. not or marginally employed) Global functioning (GAF) −0,0246 0,0014 < 0.0001 0,9757 0,9731–0,9784 Social desirability (NQ-) 0,3951 0,0181 < 0.0001 1,4846 1,4329–1,5381 social support 0,0018 0,0012 0,1336 1,0018 0,9995–1,0041 adverse drug effects 0,0152 0,0084 0,0721 1,0153 0,9986–1,0323 Atypical antipsychotics 0,2339 0,0245 < 0.0001 1,2635 1,2043–1,3257 (atypical drugs vs. other drug types) Typical antipsychotics −0,2533 0,0313 < 0.0001 0,7762 0,7300–0,8254 (typical drugs vs. other drug types) CI confidence interval full time, part time, vocational training Stentzel et al. BMC Psychiatry (2018) 18:155 Page 7 of 8 attitudes and social support [19]. An Israeli study Authors’ contributions NvdB, HJG, WH and HJF designed the study. LNS, HJG, JML and NvdB showed better adherence with having more social sup- participated in the coordination of the patient recruitment. US and LNS port [47]. A majority of studies did not found associa- coordinate the study. US conducted the statistical calculation with support tions between side effects and adherence [19, 48, 49]. from TS and FR. US and NvdB drafted the manuscript. All authors read and approved the final manuscript. This corresponds to our findings.Our results showed that participants taking typical antipsychotics had a sig- Ethics approval and consent to participate nificantly better adherence whereas the intake of atypical Tecla is approved by the Ethics Committee of the University Medicine psychotics was associated with lower adherence. That re- Greifswald (BB 122/14). The committee stated that the majority of the members of the committee concluded that there are no ethical and legal sult differs from other studies, where adherence was concerns against the implementation of the study, and therefore approves higher in patients taking atypical antipsychotics [14, 49, the proposal. Tecla is retrospectively registered at 2015\05\21 at the German 50]. Clinical Trials Register (DRKS00008548). IMeS is also approved by the Ethics Committee of the University Medicine Greifswald; BB 017/15. All patients had In summary, this suggests that adherence apparently is to sign an informed consent to participate. All appropriate legal guardians or a complex issue [49]. Further research that also con- representatives were informed about the participation. All guardians or siders longitudinal analysis is intended. representatives indicated that the patients were capable of providing ethical consent to participate. Conclusions Competing interests Medication adherence is a complex problem that is influ- The authors declare that they have no competing interests. enced by many different parameters [45]. An important finding of this study is that also parameters that are influ- Publisher’sNote enceable by interventions like the functioning level or the Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. degree of social support have an effect on adherence. These results can specifically be used for the development Author details of adherence-promoting interventions. For example, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstraße 1-2, 17487 Greifswald, Germany. Department of Psychiatry knowledge, understanding and support for drug treatment and Psychotherapy, University Medicine Greifswald, Ellernholzstraße 1-2, should be strengthened also in the patient’s social environ- 17487 Greifswald, Germany. Department of Vascular Medicine, University ment, among family members and caregivers. Heart Center Hamburg, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20246 Hamburg, Germany. Bethanien Hospital for Psychiatry, Psychosomatics and Psychotherapy, Gützkower Landstraße 69, Abbreviations 17489 Greifswald, Germany. HELIOS Hanseklinikum Stralsund, department CI: Confidence interval; COPD: Chronic obstructive pulmonary disease; for psychiatry and psychotherapy, Rostocker Chaussee 70, 18437 Stralsund, eCRF: Electronic Care Report Forms; F-SozU: Social support questionnaire; Germany. GAF : Global Assessment Functioning; GP: General practitioner; ICS: inhaled corticosteroids; IMeS: Study “Approaches of individualized medicine in Received: 9 January 2018 Accepted: 11 May 2018 psychiatric disorders”; KSE-G: Short scale Social desirability-gamma question- naire; MARS: Original Medication Adherence Report Scale in English language; MARS-D: Medication Adherence Report Scale, German version; MEMS: Medication Event Monitoring Systems; NQ-: Minimization of negative References qualities (social desirability); OR: Odds ratio; PPV: positive predictive value; PQ 1. Jacobi F, Höfler M, Strehle J, Mack S, Gerschler A, Scholl L, Busch MA, Maske +: Exaggeration of positive qualities (social desirability); SD: Standard U, Hapke U, Gaebel W, et al. 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Tecla: a telephone- and text-message based telemedical concept for patients with severe mental health disorders - study protocol for a controlled, randomized, study. BMC Psychiatry. 2015; 15(1):273. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Psychiatry Springer Journals

Predictors of medication adherence among patients with severe psychiatric disorders: findings from the baseline assessment of a randomized controlled trial (Tecla)

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Medicine & Public Health; Psychiatry; Psychotherapy
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Abstract

Background: Schizophrenia and bipolar disorder are characterized by a high disease burden. Antipsychotic medication is an essential part of the treatment. However, non-adherence is a major problem. Our aim was to examine potential determinants of non-adherence for patients with severe mental disorders. Methods: Baseline data of the study “Post stationary telemedical care of patients with severe psychiatric disorders” (Tecla) were used. Medication adherence was assessed with the Medication Adherence Report Scale German version (MARS-D). A logistic regression was calculated with age, sex, education, employment status, level of global functioning, social support and intake of typical and atypical antipsychotics as predictors. Results: N = 127 participants were included in the analysis (n = 73 men, mean age 42 years). The mean MARS-D Score was 23.4 (SD 2.5). The most common reason for non-adherence was forgetting to take the medicine. Significant positive determinants for adherence were older age (OR 1.02, 95% CI 1.011–1.024, p < 0.0001), being employed (OR 2.46, 95% CI 1.893–3.206, p < 0.0001), higher level of global functioning (overall measure of how patients are doing) (OR 1.02, 95% CI 1.012–1.028, p < 0.0001), having social support (OR 1.02, 95% CI 1.013–1.026, p < 0.0001), and intake of typical antipsychotics (OR 2.389, 95% CI 1.796–3.178, p < 0.0001). A negative determinant was (female) sex (OR 0.73, 95% CI 0. 625–0.859, p = 0.0001). Conclusions: Especially employment, functioning and social support could be promising targets to facilitate adherence in patients with schizophrenia or bipolar disorder. Trial registration: This study is retrospectively registered at the German Clinical Trials Register with the trial registration number DRKS00008548 at 21/05/2015. Keywords: Psychiatry, Mental health disorders, Schizophrenia, Psychotic disorders, Bipolar disorders, Adherence, Non- adherence, MARS-D * Correspondence: ulrike.stentzel@uni-greifswald.de Ulrike Stentzel and Neeltje van den Berg contributed equally to this work. Institute for Community Medicine, University Medicine Greifswald, Ellernholzstraße 1-2, 17487 Greifswald, Germany Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Stentzel et al. BMC Psychiatry (2018) 18:155 Page 2 of 8 Background project is to improve medication adherence for patients Schizophrenia as well as other psychotic disorders and bi- with severe psychiatric disorders on the basis of regular, polar disorders are serious mental diseases. In Germany, individualized telephone calls and short-text-messages. 19 new schizophrenia-cases per 100,000 people per year Tecla is designed as a prospective controlled randomized are diagnosed. In Germany, the 12-month-prevalence of intervention study. All participants receive computer schizophrenia and other psychotic disorders is estimated assisted baseline and follow-up interviews after 3 and 2.6% and of bipolar disorders 1.5% [1]. The disease burden 6 months. The participants are recruited from three psy- is high for mental disorders. The number of days with lim- chiatric departments in Western-Pomerania in the very itations is 3 times higher for people with mental disorders northeast of Germany. Participants were patients in compared to healthy persons [1] and schizophrenia is one day-care hospitals or in open or closed inpatient wards. of the ten diseases with the highest number of years of life The recruitment occurs shortly before discharge and is lived with disability (YLD) [2]. done by a study psychologist. Inclusion criteria are a med- Medication is an essential part of the treatment of ical diagnosis of any form of schizophrenia (ICD-10 F20), schizophrenia and bipolar disorders; both in acute epi- schizoaffective disorders (ICD-10 F25), or bipolar disor- sodes and in long-term management. Several studies ders (ICD-10 F31), and age ≥ 18 years. Exclusion criteria showed that the relapse rate is significantly lower with were scheduled inpatient treatments within the next drug therapy [2–5], provided that the patient is adherent. 6 months and missing accessibility by telephone. A com- Adherence is defined by the WHO as “the extent to which prehensive description of the study protocol for the Tecla aperson’sbehavior – taking medication, following a diet, study is published elsewhere [21]. and/or executing lifestyle changes – corresponds with Additionally, data from participants of the IMeS study agreed recommendations from a health care provider” [6]. (Approaches of individualized medicine in psychiatric Non-adherent behavior increases the risk of relapses and disorders) were included. The aim of this study is to rehospitalization [7–9]. However, non-adherence is one of identify biomarkers from genetic material, and to exam- the major problems in patients with schizophrenia and bi- ine metabolic processes and bodily protein in blood polar disorders [10]. The prevalence of non-adherence to samples. The recruitment of the patients, the inclusion antipsychotics ranges from 20 to 89% for patients with criteria and the baseline assessment of the IMeS-study schizophrenia or bipolar disorders [11–13]. Dolder et al. are identical with the Tecla study. Both samples were examined adherence in an outpatient setting using pre- collected at the same recruitment sites. scription fill rates. Their results showed an adherence of All participating patients gave their written informed 55% after 12 months among patients taking atypical anti- consent. The data assessment and documentation were psychotics (second generation) [14]. conducted based on eCRFs and an IT-supported docu- To reduce non-adherence in patients with severe men- mentation system [22]. tal disorders, it is necessary to know more about the rea- sons for non-adherence und to determine factors that Measures influence adherence positively or negatively. A few stud- Medication adherence was measured with the Medication ies have addressed specific factors determining adher- Adherence Report Scale, German version (MARS-D) that ence of patients with schizophrenia or with bipolar detects non-adherent behavior by self-report [23]. It is a disorders [15–20]. However, the results of these studies measure for non-adherence in general, not for mental dis- often differ [16, 19, 20] and non-adherence is considered orders in particular. The scale considers also the fre- a multi-causal phenomenon [16]. quency of non-adherent behavior. The questions are The aim of this analysis is to identify possible determi- formulated in a non-threatening and non-judgmental way nants for non-adherence of patients with schizophrenia, to minimize social desirability bias [24, 25]. The original other psychotic disorders and bipolar disorders, includ- Medication Adherence Report Scale (MARS-5, in English ing age, sex, education, the status of employment, the language) was developed because patients tend to overesti- level of functioning, presence of social support, adverse mate their adherence [26–28] or to conceal non-adherent drug effects. behavior [23]. The MARS-D has 5 items to assess how the drugs were taken. The 5 items are “Iforget totake my Methods medication”, “I change the dose of my medication”, “From Patient sample and data time to time I stop taking my medication for a while”, “I The data for this analysis were taken from the baseline sometimes decide to skip the medication” and “Itakeless assessment from an intervention study to evaluate tele- medication than I am instructed to.” The questions provide medical care for patients with severe psychiatric disor- 5 answer categories from “always” to “never” (scored 1 to ders (“Post stationary telemedical care of patients with 5) so that the total score is between 5 (no adherence) and severe psychiatric disorders” (Tecla)). The goal of this 25 (complete adherence) [29]. Stentzel et al. BMC Psychiatry (2018) 18:155 Page 3 of 8 The Global Assessment of Functioning (GAF) is an over- MARS-D score was dichotomized in “adherent” and all measure of how patients are doing from positive mental “non-adherent”. Following recent literature, the cut-off health up to severe psychopathology [30]. It is known, that was set at a MARS-D score of 24. Participants with a functioning is low in people with current mental health MARS-D score of 25 were seen as adherent (coded as disorders, so functioning can be used as an expression of 1), participants with a score < 25 were considered as theseverityof illness [31]. The GAF-questionnaire mea- non-adherent (coded as 0) [37, 38]. A multiple imput- sures the degree of mental illness by rating psychological, ation (based on the EM algorithm [39]) was performed social and occupational functioning [30]onanordinal to deal with missing data. Fifty-nine percent of the re- scale from 1 to 100 [32]. The scale is divided into 10-point cords where complete. Thirty percent of the records intervals. The lowest interval (score 1 to 10) represents se- missed one and 11% of the records missed two or more vere illness, the highest interval (score 91 to 100) repre- items. There were no missing items regarding the ques- sents the healthiest condition [30]. tionnaire of the primary endpoint. The data was missing Social support was assessed using the measure F-SozU at random. With the imputed data set a multivariate in- (Social support, short form with 14 items) [33]. The authors tension to treat analysis was performed. As independent defined social support as the result of cognitive-emotional variables age, sex, education, employment status, GAF, processing and assessment of current and past social inter- social support, the total number of strong and very actions. The concept is based on cognitive approaches and strong adverse drug effects, the NQ-aspect of social de- assesses the subjective conviction to get support from the sirability, and the intake of atypical and typical antipsy- subject’s social network if necessary. This 14-item short chotics were included in the model. Data processing and form is appropriate for the assessment of a more generally statistical calculations were performed with SAS 9.4 (© perceived social support [33]. The statements refer to the 2002–2012 by SAS Institute Inc., Cary, North Carolina, fields of emotional support (to be liked and accepted by USA.). others, to share feelings, to experience participation), to In a sensitivity analysis the MARS-D score was mod- provide practical assistance (practical help in everyday elled as a continuous variable. Due to its discrete distribu- problems, for example to borrow things, getting practical tion a Poisson regression was performed in a generalized advice, getting help with challenging tasks) and social inte- linear model (GLM). It was necessary to reverse the gration (belonging to a circle of friends, doing joint ven- MARS-D-variable for the Poisson regression because of tures, knowing people with similar interests) and are the left skewed distribution of the data. assessed using a 5 category Likert-scale from “does not apply” (scored 1) to “applies exactly” (scored 5) [33]. Adverse drug effects were assessed using a 5 category Results Likert-scale including “no side effects”, “little”, “moderate”, Of 135 participants recruited, 127 could be included in “strong” and “very strong” for each of the following side ef- the analyses (Fig. 1). fects: movement disorders, muscle stiffness, involuntary The participants had a mean age of 42 years (SD 12.9), shiver, motionlessness, muscle spasm, agonizing restless- 57% were men. Eighty-four percent were unemployed and ness/problems to sit still (can’t be suppressed at will), lack of 28% had an education of less than 10 years. One hundred sexual desire/loss of libido, increase in weight, increased ap- and-six participants had a diagnosis of schizophrenia, petite, heavy feeling of illness/chills/fever and milk flow [34]. schizotypal and delusional disorders (F20 – F29), thereof To adjust for social desirability (defined as the “ten- 72 paranoid schizophrenia (F20), 1 Persistent delusional dency to give overly positive self-descriptions” [35]), the disorders (F22), 8 acute and transient psychotic disorders Short Scale Social Desirability-Gamma (KSE-G) was (F 23) and 25 schizoaffective disorders (F25). Thirty par- used [36]. The KSE-G measures two aspects of social de- ticipants had a diagnosis of mood (affective) disorders sirability: the exaggeration of positive qualities (PQ +), (F30 – F39), thereof 27 bipolar affective disorder (F31) and the minimization of negative qualities (NQ-) [35]. and 3 depressive episode (F32). Nine participants had both Both aspects were assessed with three items each on a 5 a diagnosis of schizophrenia, schizotypal and delusional category Likert-scale. The categories range from “does disorders as well as mood (affective) disorders. Atyp- not apply” (score 0) to “fully applies” (score 4) [36]. ical antipsychotics were prescribed to 85 participants. The baseline assessment contained also a sociodemo- Typical antipsychotics were prescribed to 27 partici- graphic part to assess sex, age, education, and employment pants. Fifteen participants had no prescription for an- status. Patients’ diagnoses were extracted from medical files. tipsychotics but for drugs of other drug types. Table 1 shows the descriptive results for adherence, global Statistical analysis functioning, social support, the number of strong to To investigate determinants for medication adherence, a very strong adverse drug effects, and social desirabil- multivariate logistic regression approach was used. The ity. The adherence showed a left skewed distribution Stentzel et al. BMC Psychiatry (2018) 18:155 Page 4 of 8 Fig. 1 Number of patients included in the analysis (Fig. 2), 54% of the participants reported some kind that are not or just marginally employed and a lower level of non-adherence (MARS-D score < 25). of global functioning (GAF) are associated with lesser ad- Figure 3 shows the reasons for non-adherence. To forget herence. Having social support showed no significant im- to take the medicine is the most frequent reason for pact on medication adherence. The intake of atypical non-adherent behavior. Active deviation from the pre- antipsychotics is significantly associated with higher scribed medication scheme (change the dose, stop taking non-adherence whereas the intake of typical antipsy- medicines for a while, skip a dose, take less than instructed) chotics is associated with higher adherence. were each reported at prevalence of less than 20%. The results of the logistic regression are shown in Discussion Table 2. Higher age, being employed in full time, part The MARS-5 was designed to evaluate reasons and time or vocational training, a higher level of global func- prevalences for non-adherent behavior, [23, 40]rather tioning, having more social support and intake of typical than to measure exact values of the medication use antipsychotics have a significant positive influence on [25, 41, 42]. In the patient group with severe mental adherence. Being female is a negative determinant for disorders, both the primary and the sensitivity ana- adherence. The level of education, the number of strong lyses showed a positive influence of the global func- and very strong adverse drug effects and intake of atyp- tioning level, of having social support and being ical antipsychotics have no statistically significant effect employed on adherence. on adherence. Medication adherence of patients with severe psy- A Poisson regression model was performed and used as chiatric diseases is generally low. The patients in this a sensitivity analysis. The reversal of the MARS-D for the study were treated in hospitals or day clinics, data as- linear Poisson regression also leads to a reversal in the dir- sessment was performed shortly before their dis- ection of the results. The results (Table 3) are similar to charge. However, the proportion of non-adherent the findings of the primary analysis except for sex and em- patients was relatively high (54%). Stange et al. com- ployment status. Patients with lower education, patients pared the adherence of patients during the hospital stay with the situation 6 weeks after discharge and found that non-adherence was lower during hospital Table 1 Descriptive statistics of the measured variables (Tecla baseline assessment) stay (37.6% vs. 61.2% after 6 weeks) [37]. Hence long-term non-adherence is likely underestimated in Mean (SD) Median Range our analysis. Adherence (MARS-D) 23.4 (2.5) 24 13–25 Jonsdottir et al. examined medication adherence in pa- Global functioning (GAF) 54.8 (10.9) 55 30–85 tients with severe mental disorders in an ambulant set- Social support (score) 48.6 (12.9) 51 14–70 ting [8]. The MARS-5 mean score in this study (22.0) Social desirability was slightly lower than in our analysis. These authors positive qualities (PQ+) 2.7 (0.8) 2.7 0–4 also found a statistically significant correlation with pro- minimize negative qualities (NQ-) 1.1 (0.8) 1 0–4 vider rated medication adherence which supports the validity of the self-rated score used in our study. Number of strong to very strong 2.6 (1.5) 2 1–10 adverse drug effects In two studies (Mahler et al. [43] and Huther et al. SD standard deviation [38]) the adherence of chronically ill patients with Stentzel et al. BMC Psychiatry (2018) 18:155 Page 5 of 8 Fig. 2 Histogram of the MARS-D score (MARS-D score 25 means complete adherence, < 25 some kind of non-adherence, the lower the MARS-D score the higher is non-adherence) MARS-D in primary care settings in Germany was ex- Menckeberg et al. used the MARS-5 in a study amined. The average MARS-D scores were similar in both about inhaled corticosteroids (ICS) in asthma control studies (mean 23.6 (SD 2.17) [43] and mean 23.5 (SD 2.7) [41]. In their patient group the mean score value was [38]). Mahler et al. reported ‘forget the medication intake’ 19.4 (SD 4.4). Compared to our and others’ findings as the most common cause of non-adherence [43]. These this score is rather low. However, with 79% scoring findings correspond with our results. However, Huther et above the scale midpoint Menckeberg’s results showed al. found no significant determinants for medication ad- a skewed distribution too. The study showed that herence in a subsequent multivariate analysis [38]. many patients were skeptical about the benefits of Tommelein et al. investigated the accuracy of the ICS [41]. This might be one cause for the discrepancy MARS-5 for patients with chronic obstructive pulmon- between their results and results of other studies. ary disease (COPD) [40]. The mean adherence for In many cases, medication adherence is overestimated COPD patients was 23.5 (SD = 2.6). 52.9% of patients re- based on self-report questionnaires [23, 26–28]. Ose et corded complete adherence (MARS-5 sum score = 25). al. examined the concordance in rating medication ad- Further testing of the MARS-5 showed low sensitivity, herence among multimorbid patients and their general specificity, and positive predictive value (PPV). Hence practitioners (GPs) [44]. Patients often rated their adher- the authors assessed the MARS-5 as inaccurate in iden- ence higher than their GPs and only for 20% of the pa- tifying non-adherent users of inhalation medication in tients medication adherence was rated concordantly. An patients with COPD [40]. inherent limitation of self-report questionnaires is that Fig. 3 Relative frequencies for reasons of non-adherent behavior assessed with MARS-D Stentzel et al. BMC Psychiatry (2018) 18:155 Page 6 of 8 Table 2 Results of the multivariate logistic regression (dependent variable: dichotomized adherence (MARS-D), cut-off score = 24), (being adherent vs. being non-adherent) regression coefficient standard error p-value OR 95% CI (α 0.05) Age in years 0.0170 0.0033 < 0.0001 1.017 1.011–1.024 Sex (female vs male) −0.1557 0.0406 0.0001 0.732 0.625–0.859 Education (≥ 10 years of education vs. < 10 years of education) 0.0026 0.0448 0.9531 1.005 0.843–1.198 Employment status (being employed vs. not or marginally employed) 0.4507 0.0672 < 0.0001 2.463 1.893–3.206 Global assessment of functioning (GAF) 0.0198 0.0039 < 0.0001 1.02 1.012–1.028 Social desirability (NQ-) −0.6507 0.0562 < 0.0001 0.522 0.467–0.582 Social support 0.0193 0.0032 < 0.0001 1.02 1.013–1.026 Adverse drug effects 0.0382 0.0255 0.1341 1.039 0.988–1.092 Atypical antipsychotics (atypical drugs vs. other drug types) −0.1036 0.0627 0.0987 0.813 0.636–1.039 Typical antipsychotics (typical drugs vs. other drug types) 0.4355 0.0728 < 0.0001 2.389 1.796–3.178 OR odds Ratio, CI confidence interval full time, part time, vocational training unintentional non-adherence is commonly not assessed adherence [8]. The original MARS-5 in English as well [40, 42]. as the German version MARS-D are reliable and valid Besides self-reports, adherence can be measured by self-report measures of non-adherence [23, 46]. directly observing the patients while taking their medica- In the literature, predictors for adherence or tion, using pill counts, Medication Event Monitoring non-adherence differ. Sendt et al. gives a comprehensive Systems (MEMS), medical records, medication dispens- overview [19]. As possible predictors were indicated ing records, and pharmacological and biochemical marriage status, higher education, status of employment, markers [42, 45]. Some of these methods are costly and gender, higher subjective well-being, later stage of illness, require increased effort or can only be used for certain absence of cannabis use, lower rates of illicit substances drugs. Due to the importance of non-adherent behavior, and alcohol use, lower rates of medication refusal in a simple tool is needed that can easily be implemented early stages of treatment, better therapeutic alliance and in various study settings [8]. Self-reporting question- higher trust in the physicians [19]. In our findings, naires are more patient-friendly, less expensive and eas- higher education showed no significant results but being ier to conduct. Jonsdottir et al. validated self-report employed versus not or just marginally being employed measures with serum concentrations and found showed a strong influence on adherence. Inconsistent self-report questionnaires a valid method for measuring predictors were symptom severity, insight, positive Table 3 Results of the generalized linear Poisson regression. Dependent variable: MARS-D score Regression coefficient Standard error p-value beta estimate 95% CI (α 0.05) Age 0,0018 0,0012 0,1321 1,0018 0,9994–1,0042 sex (female vs male) −0,0873 0,0295 0,0031 0,9164 0,8649–0,9711 Education −0,2761 0,0321 < 0.0001 0,7587 0,7125–0,8079 (≥ 10 years of education vs. < 10 years of education) Employment status −0,2793 0,0523 < 0.0001 0,7563 0,6826–0,8380 (being employed vs. not or marginally employed) Global functioning (GAF) −0,0246 0,0014 < 0.0001 0,9757 0,9731–0,9784 Social desirability (NQ-) 0,3951 0,0181 < 0.0001 1,4846 1,4329–1,5381 social support 0,0018 0,0012 0,1336 1,0018 0,9995–1,0041 adverse drug effects 0,0152 0,0084 0,0721 1,0153 0,9986–1,0323 Atypical antipsychotics 0,2339 0,0245 < 0.0001 1,2635 1,2043–1,3257 (atypical drugs vs. other drug types) Typical antipsychotics −0,2533 0,0313 < 0.0001 0,7762 0,7300–0,8254 (typical drugs vs. other drug types) CI confidence interval full time, part time, vocational training Stentzel et al. BMC Psychiatry (2018) 18:155 Page 7 of 8 attitudes and social support [19]. An Israeli study Authors’ contributions NvdB, HJG, WH and HJF designed the study. LNS, HJG, JML and NvdB showed better adherence with having more social sup- participated in the coordination of the patient recruitment. US and LNS port [47]. A majority of studies did not found associa- coordinate the study. US conducted the statistical calculation with support tions between side effects and adherence [19, 48, 49]. from TS and FR. US and NvdB drafted the manuscript. All authors read and approved the final manuscript. This corresponds to our findings.Our results showed that participants taking typical antipsychotics had a sig- Ethics approval and consent to participate nificantly better adherence whereas the intake of atypical Tecla is approved by the Ethics Committee of the University Medicine psychotics was associated with lower adherence. That re- Greifswald (BB 122/14). The committee stated that the majority of the members of the committee concluded that there are no ethical and legal sult differs from other studies, where adherence was concerns against the implementation of the study, and therefore approves higher in patients taking atypical antipsychotics [14, 49, the proposal. Tecla is retrospectively registered at 2015\05\21 at the German 50]. Clinical Trials Register (DRKS00008548). IMeS is also approved by the Ethics Committee of the University Medicine Greifswald; BB 017/15. All patients had In summary, this suggests that adherence apparently is to sign an informed consent to participate. All appropriate legal guardians or a complex issue [49]. Further research that also con- representatives were informed about the participation. All guardians or siders longitudinal analysis is intended. representatives indicated that the patients were capable of providing ethical consent to participate. Conclusions Competing interests Medication adherence is a complex problem that is influ- The authors declare that they have no competing interests. enced by many different parameters [45]. An important finding of this study is that also parameters that are influ- Publisher’sNote enceable by interventions like the functioning level or the Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. degree of social support have an effect on adherence. These results can specifically be used for the development Author details of adherence-promoting interventions. For example, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstraße 1-2, 17487 Greifswald, Germany. Department of Psychiatry knowledge, understanding and support for drug treatment and Psychotherapy, University Medicine Greifswald, Ellernholzstraße 1-2, should be strengthened also in the patient’s social environ- 17487 Greifswald, Germany. Department of Vascular Medicine, University ment, among family members and caregivers. Heart Center Hamburg, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20246 Hamburg, Germany. Bethanien Hospital for Psychiatry, Psychosomatics and Psychotherapy, Gützkower Landstraße 69, Abbreviations 17489 Greifswald, Germany. HELIOS Hanseklinikum Stralsund, department CI: Confidence interval; COPD: Chronic obstructive pulmonary disease; for psychiatry and psychotherapy, Rostocker Chaussee 70, 18437 Stralsund, eCRF: Electronic Care Report Forms; F-SozU: Social support questionnaire; Germany. GAF : Global Assessment Functioning; GP: General practitioner; ICS: inhaled corticosteroids; IMeS: Study “Approaches of individualized medicine in Received: 9 January 2018 Accepted: 11 May 2018 psychiatric disorders”; KSE-G: Short scale Social desirability-gamma question- naire; MARS: Original Medication Adherence Report Scale in English language; MARS-D: Medication Adherence Report Scale, German version; MEMS: Medication Event Monitoring Systems; NQ-: Minimization of negative References qualities (social desirability); OR: Odds ratio; PPV: positive predictive value; PQ 1. Jacobi F, Höfler M, Strehle J, Mack S, Gerschler A, Scholl L, Busch MA, Maske +: Exaggeration of positive qualities (social desirability); SD: Standard U, Hapke U, Gaebel W, et al. 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BMC PsychiatrySpringer Journals

Published: May 29, 2018

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