Total White Cell Count 2001 - 2016

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Palliative care patients are often vulnerable to psychological distress and the development of depression [1]. However, it is challenging to distinguish between depression and grief or appropriate sadness in this group of patients. There are several approaches proposed in the making of diagnosis of major depressive disorder in palliative care patients. The standard approach is using Structured Clinical Interview for DSM – IV (SCID) [2]. This etiological approach gives a more exact view on the presence of depression as these interviews will only include symptoms of depression if it is not attributed to medical conditions. The disadvantage of this approach is that the it is unlikely to have adequate knowledge to be able to determine whether the symptoms arise from a medical condition or are due to depression instead [3]. Other approaches have been recommended to overcome this diagnostic problem; inclusive approach, substitutive approach and exclusive approach [3, 4]. The inclusive approach includes all symptoms regardless whether or not the symptoms may be attributable to medical illness. This approach has the tendency to over diagnose depression in patients who are medically ill [3, 4]. The substitutive approach replaces the somatic or physical symptoms (disturbed sleep, poor appetite or lose of weight, poor

depressed appearance, social withdrawal or decreased talkativeness, self-pity or pessimism and cannot be cheered up).

Endicott proposed such an approach to diagnose depression in cancer patients [3, 4].

The last approach is the exclusive approach (Cavanaugh Criteria), whereby the somatic symptoms of depression is removed and replaced with symptoms such as, not participating in medical care in spite of ability to do so, not progressing despite improving medical condition and/ or in functioning at a lower level than the medical condition warrants. To date, there is still lack of studies comparing the difference approaches and to determine the best diagnostic criteria to be used in palliative care patients.

References

[1] National Comprehensive Cancer Network (NCCN) (2013) Clinical Practice Guidelines in Oncology:

Distress Management, Version 2.

[2] Kelly B, McClement S, Chochinoc HM (2006) Measurement of psychological distress in palliative care. Palliative Medicine 20: 779- 789.

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Journal of the National Cancer Institute Monographs 32: 80-92.

[4] Akechi T, Ietsugu T, Sukigara M, et al. (2009) Symptoms indicator of

severity of depression in cancer patients: A comparison of the DSM IV Criteria with alternative diagnostic criteria. General Hospital Psychiatry 31: 225-232.

Corresponding Author

Assoc Prof. Dr. Ng Chong Guan, Editor-in-Chief

Malaysian Journal of Psychiatry Email: [email protected]

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ORIGINAL PAPER

Psychometric Properties of the Malay Version of Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-M)

Yee A¹, Salina M², Rusdi AR¹, Zahari MM¹, Sharmilla K¹

1Department of Psychological Medicine, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia

2Department of Psychological Medicine, Faculty of Medicine, UiTM, 50603 Kuala Lumpur, Malaysia

Abstract

Background: The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) developed by the World Health Organization is designed to identify individuals at risk for alcohol use problems and provide brief intervention. Objectives: To evaluate the validity and reliability of the ASSIST in Malaysia’s official language, the Bahasa Malay, among a group of alcohol users. The study also aimed to assess the impact of alcohol on the quality of life of patients. Methods: The final version of ASSIST-M was administered to 51 patients who were identified as alcohol users and attended the outpatient psychiatric clinic during a three-month period from September to November of 2012. Patients completed ASSIST, ASSIST-M, Mini-International Neuropsychiatric Interview (M.I.N.I.), Malay version of The Alcohol Use Disorders Identification Test (AUDIT-M), CAGE Assessment for Alcohol Abuse (CAGE), and the World Health Organization Quality of Life Scale-Brief-Malay version (WHOQoL-Brief-M). Statistical procedures were performed to assess the reliability and validity of the ASSIST-M. Results: Factor analysis of ASSIST-M yielded one factor similar to the factor structure of the English version. The Cronbach’s α coefficient of ASSIST-M was 0.80. The parallel reliability of the ASSIST-M was high with the intra-class correlation (ICC) of 0.96 (P< 0.01). The test–retest reliability of the ASSIST-M after the 1-week interval was high with the ICC of 0.94(P<

0.01). The ASSIST-M was positively correlated with the AUDIT-M (r =0.67, p <0 .01) and the CAGE (r=0. 57, p < 0.01). ASSIST-M score showed a negative and statistically significant relationships with the physical (r= -0 .28, p < 0.01) and social relationship (r= -0.37, p<0.01) subscales of the WHOQoL-Brief-M. The optimal cutoff point for ASSIST-M for alcohol use disorder was more than 14 with the with a sensitivity of 84.21 %, specificity of 84.38 %, positive predictive value (PPV) of 76.19% and negative predictive value (NPV) of 90%. Conclusions/Importance: The ASSIST-M displayed a

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Keywords: Alcohol Dependence, ASSIST, ASSIST-M, M.I.N.I., CAGE, WHOQoL

Introduction

According to the World Health Organization (WHO) estimation, the harmful use of alcohol results in 2.5 million deaths in alcohol or alcohol-related diseases each year.

Of those deaths, 9% are young adults aged between 15 to 29 years. Although the status of alcohol consumption in Malaysia is rated as “stable and moderate use” by the WHO, there are groups of people who use unquantifiable amounts of alcohol and traditional alcohol beverages such as tapai and tuak in conjunction with harvest celebrations and social gatherings [1]. It is well known that harmful use of alcohol not only causes harm to the physical and psychological health of the drinker but also harms the well-being and health of those around the drinker.

The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) is a screening instrument developed by the World Health Organization (WHO) and is used to identify problematic and risky behavior which may or may not occur with concomitant use of tobacco, alcohol, and other psychoactive drugs [2]. It was initially aimed for primary health care settings to identify patients with problems associated with substances, and to provide an intervention to at- risk patients. However, due to the simplicity of the scale, ASSIST can be also used by trained health care workers such as community nurses. Later, a brief intervention could be administered by the same interviewer. This would be an advantage in countries with limited resources such as Malaysia. Furthermore, the ASSIST can be used even when patients have low levels of literacy as it is a

clinician-administered assessment³. Therefore, primary health care professionals or health care workers can explain questions and ask probing questions to clarify inconsistent or incomplete responses.

AUDIT (alcohol use disorders identification test) is a similar tool but designed only to measure alcohol use. Hence the necessity for ASSIST which measures tobacco, alcohol, and other psychoactive drugs.

The ASSIST is brief screening tool which consists of eight items that investigates the lifetime use of substances, the frequency of using the substances, the urge of using the substances, the frequency of substance use that led to health, social, legal or financial problems, the frequency of failure to fulfill the usual role obligation due to substance use, whether anyone is concerned about their substance use and frequency of failed attempts to control, cut down or stop using the substance over the last 3 months. After that, a risk score is obtained and categorized into ‘low’, ‘moderate’ or ‘high’ which determines the type of intervention either

‘none’, ‘brief intervention’, ‘brief intervention plus referral [4].’ Analyses have shown that the ASSIST has excellent concurrent, construct, predictive and discriminative validity and is able to adequately screen for low, moderate and high risk substances [5, 6]. In this study, only alcohol use will be assessed. This study is part of a research which will be carried out in a remote area in 2015.

The ASSIST has been validated and shown to be effective in different languages and different ethnicities across the world.

Malaysia is a unique multi-ethnic country with the Malay language as the official

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national language. Most Malaysians especially those who live in remote areas can only understand Malay. Therefore, the aim of this study was to to evaluate the validity and reliability of the Malay version of the ASSIST (ASSIST-M) based on a group of patients who consume alcohol.

Hence, ASSIST-M would be a valuable tool for early detection and intervention for alcohol dependence or abuse among the natives of the remote area who spoke only the Malay language.

Methodology

The study was approved by the Medical Ethical Committee. Permission to use and translate the questionnaire was obtained from World Health Organization [7], ID:

103572. This study was conducted in three stages.

Stage 1

Translation from English (“Source language”) of the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) to Malay was done in parallel (“Target language”) by two doctors who were bi-lingual (Malay and English), whereas a bilingual language expert performed the back-translation.

Discrepancies between the original version and the back-translation were discussed and adjustments were made until the consensus was reached by an expert panel. Discussion composed of three psychiatrists, all of whom were qualified professionals regarding psychometric instruments and had clinical experience in alcohol use disorder. At the end of this process, we produced a draft of the ASSIST-M (ASSIST-M- draft).

Stage 2

The ASSIST-M- draft was then pilot tested

Medical Centre (UMMC), who are native speakers of the Malay language. Any flaws which were identified by these 10 respondents would be then corrected. The finalized version of ASSIST in the Malay language (ASSIST-M) was reviewed by a psychiatric consultant to ensure satisfactory face, semantic, criterion and conceptual equivalence.

Stage 3

The final version of ASSIST-M was administered to 51 patients who were identified as alcohol consumers and who were followed up at the outpatient psychiatric clinic of UMMC during a three month period from September to November of 2012. As a rule of thumb in determining a priori sample size, Costello and Osborne (2005) [8], state that it is based on subject to item ratios of only 5:1. In this regard, it seems statistically appropriate to recruit a minimum sample size of 40 participants per group. Patients were approached by the research team to take part in the targeted sample size was 40 patients (5 patients to 1 item in the ASSIST–M). Patients were approached by the research team to take part in the study and provided written informed consent for participation.

All of the patients who consumed alcohol who also fulfilled the inclusion criteria were approached for the study. The inclusion criteria were as follows: (1) Above 18 years of age (2) Alcohol consumers in the psychiatric clinic and/ or ward (3) Willing to participate and keen to be interviewed (4) Able to fluently read and converse in both English and Malay. Those with the following were excluded: (1) Acute medical conditions (such as acute liver failure) (2) Using other psychotropic medications other than alcohol (3) Participants must not be in

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Fifty-one patients, who met the selection criteria and had given consent to participate in the study completed the demographic questionnaire, the English and the Malay versions of ASSIST, the Malay version of The Alcohol Use Disorders Identification Test (AUDIT-M) the alcohol use disorder subscale of the Mini-International Neuropsychiatric Interview (M.I.N.I.), CAGE Assessment for Alcohol Abuse (CAGE), and the Malay version of the World Health Organization Quality of Life- Brief scale (WHOQoL-Brief-M). One week later, the same patients who participated would again complete the ASSIST-M but by another researcher would determine the inter-rater reliability of the ASSIST-M.

Instruments

Socio-demographic questionnaire

A brief questionnaire was used to record relevant background information including age, gender, ethnicity, citizenship, marital status, education level, religion, employment status, present occupation, and alcohol use.

Mini International Neuropsychiatric Interview (M.I.N.I), K component, Version 5.0.0

M.I.N.I is a short structured diagnostic interview designed to diagnose DSM-IV-TR lifetime and current. It is a relatively brief instrument that deals with 10 psychiatric disorders. The M.I.N.I has a good validity and reliability but can be administered in a much shorter period of time than SCID-P (Structured Clinical Interview for DSM-III- R Patient Edition) and the CIDI (Composite International Diagnostic Interview). In this study, only the subscale for alcohol use disorder (K component) was used to confirm the diagnosis among the participants.

Malay version of The Alcohol Use Disorders Identification Test (AUDIT-M)

The AUDIT is a screening instrument to detect excessive and harmful patterns of alcohol consumption. The AUDIT consists of a 10- tem scale which assesses recent alcohol use, alcohol dependence symptoms, and alcohol-related problems. The AUDIT typically takes 2 - 4 minutes to administer.

The validation and reliability of Malay version of AUDIT was established with the good internal consistency, Cronbach α coefficients of 0.823 and high test-retest reliability (Spearman’s rho=0.955, p< 0.01) [9].

CAGE Assessment for Alcohol Abuse (CAGE)

The CAGE is a screening instrument for the detection of alcoholism. It consists of 4 items only and takes less one minute to complete and is easy to understand. Two or more affirmative responses suggest that the patient has a drinking problem [10].

World Health Organization Quality of Life-Brief-Malay version (WHOQoL- Brief-M)

The WHOQOL-Brief is used to assess the individual's subjective perception of quality of life for the past 2 weeks. It consists of 26 questions and covers mainly four domains (physical, psychological, social relationship and environment). The total scores of the WHOQoL-Brief range from 0 to 100, with higher scores reflecting a higher quality of life. The WHOQoL-Brief has been validated in the Malay language (WHOQoL-Brief-M) [11].

Statistical Analyses

All analyses will be conducted by the Statistical Package for the Social Sciences version 21.0 (SPSS, Chicago, IL, USA).

Descriptive statistics will be computed for the baseline characteristics of the participants. The Cronbach’s alpha was used

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to assess the internal consistency of ASSIST-M. Homogeneity of scale items was addressed through correlation coefficients between items and total scores if item was deleted. Construct validity was determined by principal component analysis with factor loading of >0.30 to determine the items for each factor. The intraclass coefficient (ICC) was used to examine the parallel reliability between the ASSIST-M with the ASSIST, and test-retest reliability of the ASSIST-M. The Spearman correlation was used to assess the correlation between ASSIST-M and the AUDIT-M, CAGE, WHOQoL-Bref-M. Receiver Operating Characteristic (ROC) analyses was applied to compare the screening performance of the ASSIST-M by using the Mini International Neuropsychiatric Interview- K component (M.I.N.I.- K component) as the standard diagnostic test of alcohol use disorder. The

cut-off score of the ASSIST-M was determined by the co-ordinate points whereby the sensitivity and specificity are optimal in the ROC. The Area Under the Curve (AUC) of the ROC was determined.

Result

Characteristics of participants

A total of 51 male patients participated in this study. The mean age was 40.1 years, ranging from 25 years to 66 years old. As shown in Table 1, majority of the patients were married (n=27, 52.9%), Indian (n=24, 47.1%), and of Hindu religion (n=18, 27.5%). 54.9% (n=28) of participants completed secondary school and more than 70% of them were employed. More than half of them had family history of alcohol consumption.

Table 1. Socio-demographic characteristics of all participants

Variables Total, n=51 (%)

Age (mean ± SD) 40.1± 11.1

Marital Status

Single 20 (39.2)

Married 27 (52.9)

Divorced 4 (7.8)

Ethnicity

Malay 6 (11.8)

Chinese 16 (31.4)

Indian 24 (47.1)

Others 5 (9.8)

Religion

Islam 9 (17.6)

Christian 11 (21.6)

Buddha 9 (17.6)

Hindu 18 (27.5)

Others 3 (5.9)

Education Level

Primary 8 (15.7)

Secondary

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Employment Status

Employed 40 (78.4)

Unemployed 11 (21.6)

Family history of alcohol consumption 27 (52.9)

Factor Structure and Internal Consistency of ASSIST-M

The mean score of the respondents to the ASSIST-M was 11.19, standard deviation of 9.97. The Bartlett’s test of sphericity was significant (p <0.01) and the Kaiser-Mayer- Olkin measure of sampling adequacy for the ASSIST-M was 0.76 indicating middling [12] which indicated that factor analysis was appropriate. Only one factor was extracted with the Principle Component approach (eigenvalue >1.000) which accounted for 50.67% of total variance. (Table 2.) This result was consistent with the original ASSIST [13].

The ASSIST-M exhibited good internal consistency, with a Cronbach’s alpha coefficient of was 0.80. All items had corrected-item total correlations of more than 0.7. The deletion of any of the items would not increase the internal consistency of the total score. (Table 3.)The parallel reliability of the ASSIST-M was high as demonstrated by the intra-class correlation (ICC) of 0.96 (P< 0.01). The test–retest reliability of the ASSIST-M after the 1-week interval was high with the ICC of 0.94 (P<

0.01).

Table 2. Principal Component Analysis of ASSIST-M items

Factor 2. In the past 3 months, how often have you used the alcohol? 0.665

3. During the past three months, how often have you had a

strong desire or urge to use alcohol? 0.794

4. During the past three months, how often has your use of

alcohol led to health, social, legal or financial problems? 0.737 6. Has a friend or relative or anyone else ever expressed

concern about your use of alcohol? 0.529

7. Have you ever tried and failed to control, cut down or stop

using alcohol? 0.697

Cronbach alpha 0.80

Eigen value 3.040

Explained variance (%) 50.67

Loading below 0.30 is suppressed.

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Table 3. Corrected Item – Total correlations and Cronbach’s ɑ if Item deleted for the ASSIST-Malay version

Items Scale Mean

if Item Deleted

Scale

Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted Q2 In the past 3

months, how often have you ever used the alcohol ?

8.13 77.814 0.500 0.782

Q3. During the past 3 months, how often have you had a strong desire or urge to use alcohol?

9.88 67.431 0.648 0.747

Q4. During the past 3 months, how often has your use of alcohol that led to health, social, legal or financial problems?

9.35 68.829 0.594 0.761

Q5 During the past 3 months, how often have you failed to do what was normally expected of you

because of your use of alcohol?

10.52 80.851 0.381 0.805

Q6 Has a friend of relative or anyone else ever expressed concern about your use of alcohol?

8.63 68.324 0.537 0.777

Q6 Have you ever tried to control, cut down or stop using alcohol?

9.44 67.953 .692 .738

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Concurrent and discriminative Validity of ASSIST-M

Table 4 shows the Spearman’s correlations between the ASSIST-M and the participants’ respective scores on the AUDIT-M, CAGE, and WHOQoL-Brief-M.

The ASSIST-M was positively correlated with the AUDIT-M (r =0.67, p <0 .01) and the CAGE (r=0. 57, p < 0.01). ASSIST-M score showed a negative and statistically significant relationships with the physical (r= -0 .28, p < 0.01) and social relationship

(r= -0.37, p<0.01) subscales of the WHOQoL-Bref-M. The discriminatory ability of ASSIST-M based on the Mini International Neuropsychiatric Interview (M.I.N.I.) for alcohol use disorder was high with the Area Under Curve (AUC) of 0. 83 (p<0.01). The optimal cutoff point for ASSIST-M for alcohol use disorder was more than 14 with the with a sensitivity of 84.21 %, specificity of 84.38 %, positive predictive value (PPV) of 76.19% and negative predictive value (NPV) of 90%.

Table 4. Correlation (Spearman’s rho) between the ASSIST-M with AUDIT-M, CAGE, WHOQoL-Brief-M

ASSIST-M AUDIT-M CAGE

D1 D2 D3

AUDIT-M 0.670^**

CAGE 0.556^** .438^**

D1 -0.282^* -0.140 -0.201

D2 -0.255 -0.144 -0.219 0.442^**

D3 -0.376^** -0.253 -0.222 0.695^** 0.451^**

D4 -0.224 -0.200 -0.253 0.550^** 0.578^** 0.538^**

Note: D1=Domain1= Physical domain, D2=Domain2= Psychological, D3=Domain3= Social relationship, D4=Domain4= Environment

ASSIST-M= Alcohol, Smoking, and Substance Involvement Screening Test- Malay version, AUDIT-M= Malay version of The Alcohol Use Disorders Identification Test (AUDIT-M), ** p

< 0.01* p < 0.05 Discussion

This study validated the Malay version of ASSIST (ASSIST-M) for alcohol use disorders where current study found that the factor analysis of the ASSIST-M was consistent with the original ASSIST where only one factor was extracted with the Principle Component Analysis [6].

Furthermore, ASSIST-M demonstrated good internal consistency with Cronbach’s alpha of 0.80 which was consistent with the original ASSIST for alcohol use problem with high one week interval test-retest reliability of 0.94 which was similar to another study [14, 15]. The internal

consistency of the Spanish and Parsi version of ASSIST was 0.93 [16] and 0.87 [17]

respectively.

The validity of ASSIST-M was assessed by correlating ASSIST-M with scores of other instruments which have demonstrated high validity for measuring alcohol abuse. The ASSIST-M showed significant positive correlations with the Malay version of AUDIT (AUDIT-M) and CAGE indicating significant concurrent validity for alcohol.

The French version of ASSIST was compared with ASI (Addiction Severity Index) for alcohol and opioid scores, tobacco use with RTQ (Revised Fagerstrom

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Tolerance Questionnaire) scores, and AUDIT for the alcohol scores [13]. The total substance involvement score was compared with MINI plus in the French scale however the rest of the substances were not assessed in our scale.

Using Mini International Neuropsychiatric Interview (M.I.N.I) subscale for alcohol use disorder as the gold standard, the ASSIST- M showed a high sensitivity and specificity at the cut-off point of 14 which was higher that other studies in the West [14, 15]. This shows that the ASSIST –M has high discriminative validity and so is a good screening tool.

The cultural differences between the previous studies and in the local setting itself may have contributed to the differing cut-off point. Some alcoholic drinks in Malaysia are homemade such as “tuak” and this may not be construed as alcoholic beverages as it is made out of fermented rice.

In addition to that, the inexpensive and widely available coconut based alcoholic beverage such as “toddy” may also not be construed as alcoholic beverage by many in the Malaysian population [1]. Therefore, these factors may have contributed to the higher cut off scores in the ASSIST-M as drinking these substances are common and also acceptable in Malaysia than the conventional alcoholic beverages.

The findings from this current study showed that those with higher ASSIST-M scores had poorer quality of life in the WHOQoL-Brief- M especially in the physical and social domains indicating that for our sample, those who were at risk of alcohol use problems were more likely to be physically and socially impaired. Our study findings are consistent with studies reporting that

general population or with other chronic health conditions¹⁸ and the quality of life worsened with the presence of co-morbid psychiatric illnesses such as depression and anxiety [19].

This study has several limitations which included the small sample size, and the findings generalizability is limited as the patients were recruited from a University medical centre. Also predictive validity could not be assessed due to the cross sectional nature of the study. Concurrent validity for substances other than alcohol was not assessed and hence this tool cannot be used with substances.

However, the findings from this study demonstrated that the Malay version of ASSIST is a valid and reliable screening tool for alcohol use disorder and this will aid in detecting the at risk individuals and allow for early interventions to be carried out.

Declaration of Interests

There are no conflicts of interest for either author.

Acknowledgements

We would like to thank the University of Malaya Research Management Centre for providing UMRG Grant (RP014-2012A) to fund this study.

References

[1] WHO, Global status report on alcohol and health-20142014:

World Health Organization.

[2] WHO, The alcohol, smoking and substance involvement screening

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reliability and feasibility. Addiction, 2002. 97(9): p. 1183-1194.

[3] Humeniuk, R., et al., The Alcohol,

Smoking and Substance

involvement Screening Test (ASSIST): manual for use in primary care/prepared by R.

HumeniukƯ [et al]. 2010.

[4] WHO, The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST). Manual for Use in Primary Care. Geneva, Switzerland:

World Health Organization, 2010.

[5] Hides, L., et al., The reliability and validity of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) in first- episode psychosis. Addictive Behaviors, 2009. 34(10): p. 821-825.

[6] Humeniuk, R., et al., Validation of the alcohol, smoking and substance involvement screening test (ASSIST). Addiction, 2008. 103(6):

p. 1039-1047.

[7] Organisation, W.H. Global status report on alcohol and health 2014 2014 [cited 2014 July 2014];

http://www.who.int/substance_abus e/publications/global_alcohol_report /en/].

[8] Osborne, J.W. and A.B. Costello, Sample size and subject to item ratio in principal components analysis.

Practical assessment, research &

evaluation, 2004. 9(11): p. 8.

[9] Yee, A., et al., Validation of the alcohol use disorders identification test (AUDIT) – Bahasa Malaysia version among a group of alcohol

users. Journal of Substance Use.

0(0): p. 1-5.

[10] Ewing, J.A., Detecting alcoholism.

JAMA: The Journal of the American Medical Association, 1984. 252(14): p. 1905-1907.

[11] Hasanah, C., L. Naing, and A.

Rahman, World Health

Organization quality of life assessment: brief version in Bahasa Malaysia. Medical Journal of Malaysia, 2003. 58(1): p. 79-88.

[12] Kaiser, H.F., An index of factorial simplicity. Psychometrika, 1974.

39(1): p. 31-36.

[13] Humeniuk, R. and R. Ali, Validation of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) and pilot brief intervention [electronic resource]: a technical report of phase II findings of the WHO ASSIST Project/prepared by Rachel Humeniuk & Robert Ali, on behalf of the WHO ASSIST Phase II Study Group. 2006.

[14] Khan, R., et al., Validation of the French version of the alcohol, smoking and substance involvement screening test (ASSIST). European addiction research, 2011. 17(4): p.

190-197.

[15] Newcombe, D.A., R.E. Humeniuk, and R. Ali, Validation of the world health organization alcohol, smoking and substance involvement screening test (ASSIST): report of results from the Australian site.

Drug and alcohol review, 2005.

24(3): p. 217-226.

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[16] Valladolid, G.R., et al., Validation of the Spanish version of the alcohol, smoking and substance involvement screening test (ASSIST).

Psicothema, 2014. 26(2): p. 180-185.

[17] Tiburcio, S.M., et al., Validity and Reliability of the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) in University Students. Adicciones, 2015. 28(1): p. 19-27.

[18] Srivastava, S. and M.S. Bhatia, Quality of life as an outcome measure in the treatment of alcohol dependence. Industrial psychiatry journal, 2013. 22(1): p. 41.

[19] Saatcioglu, O., et al., Quality of life, depression and anxiety in alcohol dependence. Drug and alcohol review, 2008. 27(1): p. 83-90.

Corresponding Author Anne Yee

Department of Psychological Medicine,

University of Malya Center for Addiction Sciences (UMCAS), Faculty of Medicine Building, University of Malaya,

50603 Kuala Lumpur, Malaysia Tel: +60379493049

Fax: +60379556477

Email: [email protected]

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ORIGINAL PAPER

The Psychometric Properties of the Positive Emotion Rating Scale – Chinese Version (PERS-C)

Ng CG¹, Chong SH²

1Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

2Department of Psychology, UCSI University, Kuala Lumpur, Malaysia Abstract

Introduction: Positive emotions in depressed patients are always disregarded and overlooked due to the inadequacy of assessment tool. There is a newly developed self-report scale, yet has good psychometric properties named Positive Emotion Rating Scale (PERS). Objective: The aim of this study were to translate and examine the psychometric properties of the Chinese version PERS (PERS-C). Method: This is a cross-sectional study involved 52 depressed and 106 control subject. Both groups were assessed with PERS-C, original PERS, Malay version of Snaith-Hamilton Pleasure Scale (SHAPS-M) and Malay version of Center for Epidemiological Studies Depression (CESD- M). Results: PERS-C displayed good internal consistency (Cronbach’s α = 0.91) and parallel reliability with PERS (r = 0.95, p<0.01). It was negatively correlated with CES-D (r= - 0.43, p<0.01) and positively correlated with SHAPS-M (r = 0.73, p<0.01). The optimal cut-off value was 32, with sensitivity = 0.64 and specificity = 0.67, positive predictive value of 0.80 and negative predictive value of 0.48. The area under the curve (AUC) for receiver operating characteristic (ROC) was 0.71 (95% CI = 0.65 - 0.80).

Conclusion: The PERS-C is a simple and easy-to-use tool to measure positive emotion for Chinese speaking subjects, with demonstrable satisfactory psychometric properties.

Keywords: Depression, Instrument, Positive Emotion, Psychometric Properties

Introduction

Major depressive disorder (MDD), which is also referred as clinical depression is a common mental illness (World Health Organization, 2012). This disorder affects functioning of one’s life by impacting the mood and behaviours as well as the physical

functions such as sleep and appetite. People with MDD feel depressed nearly every day and have diminished interest or pleasure to the activities they once enjoyed, as defined in the diagnostic guideline of the DSM-5 (American Psychiatric Association, 2013).

They also having the other symptoms of depression included fatigue, low

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concentration, insomnia, felt worthlessness and have the recurrent thoughts of death (American Psychiatric Association, 2013).

MDD has become the leading cause of disabilities worldwide (Kessler et al., 2003;

World Health Organization, 2012).

The negative emotions are being focused mostly in clinical practice. The consequence has shown that the positive emotion was under-reviewed or under-recognized among depressed subjects (Burgdorf & Panksepp, 2006; Carl, Soskin, Kerns, & Barlow, 2013).

Positive emotions refers to optimum well- being or flourishing (Fredrickson, 2001) experience such as love, interest, joy and contentment. The contribution of positive emotion is important in expending ones thought and behavior tendencies when dealing with stress. (Fredrickson, 2001;

Fredrickson, 2004). When people with positive emotion are undergoing stressful event, they are prone to have high resiliency as they have big capacity to recover quickly from difficulties or toughness. The increment of psychological resilience and emotional regulation are being promoted by the flexibility of cognition (Tugade &

Fredrickson, 2007). Thus, positive emotion advocates ones well-being and able to enhance the quality of life (Cohn, Fredrickson, Brown, Mikels, & Conway, 2009).

The risk of having depression and anxiety is because of the deficiency in positive emotions (Brown & Barlow, 2009; Gruber, Kogan, Quoidbach, & Mauss, 2013).

Positive emotions broaden the thinking, attention and action. Hence, it is advantageous as a part of treatment of depression. Studies showed that symptoms of depression will decrease as well as prevent relapse when positive emotions

depressed patients, which frequent being neglected in the common practice. There are many assessment of emotion are developed to measure different type of emotional experiences as a result of people perceived different definitions of emotions (Lucas, Diener, & Larsen, 2009). Numerous studies on positive emotion have been done.

However, there is still inadequacy of valid instrument to measure positive emotion among the depression patients (Ng, et al., 2016). This shows the importance of creating a valid instrumentation to evaluate positive emotion. The original PERS was developed to assess positive emotion. The instrument consists of eight items with six domains of positive emotions which are interest, love, contentment, pride, active and gratification. PERS has adopted the core domains into the questionnaire in order to provide extensive assessment of positive emotion. Previous study demonstrated that the tool has good reliability and validity (Ng et al., 2016). Up to date, this questionnaire is only available in English language and Malay language for the measurement of positive emotion. Therefore, the aim of this study is to translate the Positive Emotion Rating Scale (PERS) to the Chinese language and to study the psychometric properties of the Chinese version of Positive Emotion Rating Scale (PERS-C).

Methods Study Design

This was a cross sectional study conducted at the University Malaya Medical Centre (UMMC), from January 2017 until December 2017. Ethical approval was obtained from Medical ethical Committee, UMMC.

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based on DSM-5 diagnosis of major depressive disorder (MDD) (American Psychiatric Association, 2013) without other major psychiatric illnesses or psychosis. The second group is healthy subjects, recruited from family members, care takers, students and visitors, without MDD and other psychiatric illnesses. All participants from both group were able to read and understand Malay or English languages, age 18 years and above, and consented to this study.

Both groups were explained about the study.

Those who agreed and gave consents, would be recruited into the study. They were assessed using the sociodemographic information questionnaire, PERS-C and following tools.

Positive Emotion Rating Scale (PERS) The PERS was developed to measure the positive emotion (Ng et al., 2016). It is a self-report questionnaire consisted of eight items. It is scored using a Likert scale ranges from one (never) to five (always). The cut- off score of 30 of the original scale demonstrated significant discriminant validity between depressed and non- depressed subjects, with sensitivity = 0.75 and specificity = 0.73. It has a good validity and reliability to measure positive emotion in depressed patients, with high internal consistency (Cronbach’s alpha 0.90) (Ng et al., 2016).

Malay version Center for Epidemiologic Studies Depression Scale (CESD-M)

The Malay version of CESD is a valid and reliable depression assessment scale (Sabki, Zainal, & Guan, 2014). It was developed by Radloff (1977). It is one of the most commonly used instrument in the study of psychiatry epidemiology to identify people at risk for clinical depression. The CESD consisted of 20-item self-report questionnaire, score ranging from 0 to 3.

Total score ranges from 0 to 60, with higher score signifies greater depressive symptoms, with the cut-off score of 16 and more. It has good sensitivity and good specificity with high internal consistency in identifying people at risk of clinical depression (Lewinsohn, Seeley, Roberts, & Allen, 1997).

Malay version Snaith Hamilton Pleasure Scale (SHAPS-M)

The SHAPS is a self-rated instrument to assess hedonic capacity (Snaith et al., 1995).

It consists 14 items with a set of four response categories: definitely agree, agree, disagree and definitely disagree. It will be scored as the sum of all 14 items. A higher total SHAPS score indicates higher level of anhedonia.

In this study, SHAPS-M was used, which had been validated and showed to be reliable to assess anhedonia among depressed patients (Ng et al., 2014). Unlike the original English version, the SHAPS-M applied a reverse scoring. The SHAPS-M is scored as sum of all 14 items, with total score range from 14 to 56. The lower score of SHAPS-M indicates higher level of anhedonia. The cut-off value of total score SHAPS-M of 42 to distinguish depressed from non-depressed subjects, with good sensitivity (0.79) and specificity (0.74) (Ng et al., 2014).

Procedure

The English version of Positive Emotion Rating Scale (PERS) was translated to Chinese language by two bilingual (English and Chinese) researchers who were a psychiatrist and a psychologist.

Subsequently, two different bilingual researchers back translated the Chinese version scale to English language. The translated version was pilot tested among 20

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staffs for face validity. The finalized version of PERS-C was reviewed by two different bilingual researachers for the content validity and to ensure satisfactory face criterion and conceptual equivalent.

All subjects consisted of depressed patients and healthy subjects who consented to the study were given the assessment questionnaires: sociodemographic and clinical background, PERS-C, PERS, CESD-M and SHAPS-M.

Statistical Analysis

The results were analysed with Statistical Package for Social Sciences (SPSS) version 23.0. The descriptive statistics were used to examine the baseline characteristics of the subjects. The internal consistency of PERS- C was analysed with Cronbach’s α coefficient. The parallel reliability between PERS-C and PERS was analysed with Spearman’s correlation test. Spearman’s correlation was also used to examine concurrent validity between the PERS-C

with CESD-M and SHAPS-M. Mann- Whitney test was used to study the PERS-C total and item score between depressed subjects and healthy subjects. The optimal cut-off score of PERS-C for depressed cases was determined from the co-ordinate points which optimal sensitivity and specificity in the Receiver Operating Characteristic (ROC) was determined. The Area Under the Curve (AUC) of the ROC was determined. All of the analysis were 2-tailed with alpha value of 0.05.

Results

A total of 52 depressed and 106 control subject consented for this study. The mean age was 36 years old, with the depressed group about eight years older than the control group. In both groups, there were more female and employed. Depressed group were mainly married (61.5%) while two third of the control group were single.

Additionally, most of the depressed and healthy subjects achieved at least secondary level of education.

Table 1. Sociodemographic background of participants Depressed Subjects

(N:52) Non-depressed

Subjects (N:106) Total (N: 158) Age (years), mean (sd) 41.41 (13.94) 33.70 (14.93) 36.20 (15.02) Gender, n (%)

Male 19 (36.5%) 52 (49.1%) 71 (44.9%)

Female 33 (63.5%) 54 (50.9%) 87 (55.1%)

Race, n (%)

Chinese 51 (98.1%) 106 (100%) 157 (99.4%)

Indian 1 (1.9%) 0 (0%) 1 (0.6%)

Religion, n (%)

Buddhist 37 (71.2%) 84 (79.2%) 121 (76.6%)

Christian 7 (13.5%) 16 (15.1%) 23 (14.6%)

Hindu 0 (0%) 1 (0.9%) 1 (0.6%)

Others 8 (15.4%) 5 (4.7%) 13 (8.2%)

Marital Status, n (%)

Single 19 (36.5%) 70 (66.0%) 89 (56.3%)

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Widow/Widower 0 (0%) 1 (0.9%) 1 (0.6%) Education level, n (%)

Primary 6 (11.5%) 6 (5.7%) 12 (7.6%)

Secondary 15(28.8%) 40 (37.7%) 55 (34.8%)

Tertiary 31 (59.6%) 60 (56.6%) 91 (57.6%)

Occupation, n (%)

Not employed 11 (21.2%) 4 (3.8%) 15 (9.5%)

Part-time 3 (5.8%) 6 (5.7%) 9 (5.7%)

Full-time 28 (53.8%) 36 (34.0%) 64 (40.5%)

Pensioner 1 (1.9%) 1 (0.9%) 2 (1.3%)

Student 2 (3.8%) 48 (45.3%) 50 (31.6%)

Housewife 7 (13.5%) 11 (10.4%) 18 (11.4%)

Table 2. Antidepressants used by the depressed subjects (N=52)

Frequency (N) Percent (%)

Monotherapy

SSRI 16 30.8

SNRI 3 5.8

NaSSA 3 5.8

Agomelatine 11 21.2

Vortioxetine 2 3.8

TCA 1 1.8

Combination therapy 16 30.8

SSRI = selective serotonin reuptake inhibitor, SNRI = serotonin-norepinephrine reuptake inhibitor, NaSSA = noradrenergic and specific serotonergic antidepressant, TCA = tricyclic antidepressant

Most of them treated with monotherapy of antidepressant (69.2%) with mostly on escitalopram followed by agomelatin. Less

than one third of the depressed patients were on combination therapy of antidepressant (30.8%).

Table 3. Comparison of PERS-C between depressed (N=52) and healthy subjects (N=106) PERS-C, mean (sd) Mean difference Mann-Whitney

test p value Depressed Non Depressed

Item 1 3.71 (1.14) 4.30 (1.02) 0.59 < 0.01

Item 2 3.35 (1.37) 3.89 (1.27) 0.54 0.02

Item 3 3.60 (1.30) 4.12 (1.14) 0.53 < 0.01

Item 4 3.50 (1.34) 4.11 (1.15) 0.61 < 0.01

Item 5 3.56 (1.16) 4.33 (0.93) 0.77 < 0.01

Item 6 3.92 (1.08) 4.30 (0.98) 0.38 0.02

Item 7 2.73 (1.09) 3.99 (1.02) 1.26 < 0.01

Item 8 3.06 (1.27) 3.96 (1.16) 0.90 < 0.01

Total 27.42 (6.77) 33.01 (6.92) 5.59 < 0.01 PERS-C = Chinese version of Positive Emotion Rating Scale

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The PERS-C scores for the depressed subjects (mean=27.42, SD = 6.77) were significantly lower than healthy subjects (mean=33.01, SD = 6.92). For each

individual item, the scores among the depressed group were significantly lower than the healthy subjects.

Table 4. Spearman’s Correlation (r) of the total score between PERS-C and original PERS, CES-D and SHAPS in the depressed subjects

CES-D PERS-C SHAPS PERS

CES-D 1.00 -0.43** -0.35* -0.40**

PERS-C -0.43** 1.00 0.73** 0.95**

SHAPS -0.35* 0.73** 1.00 0.71**

PERS -0.40** 0.95** 0.71** 1.00

* p < 0.05, ** p < 0.01

PERS-C = Chinese version of Positive Emotion Rating Scale, PERS = English version of Positive Emotion Rating Scale, CES-D = Centre for Epidemiological Studies, SHAPS = Snaith-Hamilton Pleasure Scale

The PERS-C exhibited good internal consistency, with Cronbach’s alpha coefficient of 0.91. Parallel reliability of PERS-C and PERS was good, as showed by Spearman’s Correlation of 0.95, (p<0.01).

The PERS-C was positively correlated SHAPS (r=0.73, p<0.01), and negatively correlated with CES-D (r=-0.43, p<0.01).

Table 5. Sensitivity and specificity of each coordinates for the ROC curve of PERS-C to determine depressed cases in the study subjects

PERS-C Score Sensitivity Specificity

29 0.745 0.500

30 0.726 0.577

31 0.679 0.635

32 0.642 0.673

33 0.575 0.769

34 0.547 0.788

PERS-C = Chinese version of Positive Emotion Rating Scale, ROC = receiver operating characteristic

The area under the receiver operating characteristic curve (AUC) was 0.71 (95%

CI = 0.65 - 0.80). The optimal cut-off score to differentiate depressed subjects from

healthy subjects was 32, with sensitivity 0.64, specificity 0.67, positive predictive value = 0.80 and negative predictive value = 0.48.

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Discussion

This study aimed to test the psychometric properties of the Chinese version of questionnaire. There are 52 depressed subjects are being compared with 106 healthy subjects. The result from this study has shown PERS-C with remarkable psychometric properties. Hence, it is applicable for measuring the positive emotion. The PERS-C shown had high reliability with internal consistency of Cronbach’s α of 0.91. There is significant correlation between PERS-C with CES-D and SHAPS. The score for depressed subjects of PERS-C (27.42) was significantly lower than the healthy subjects (33.01). From the results shown above, each of the items among the depressed subjects was significantly lower than the healthy subjects (p < 0.01). The optimum cut-off score to segregate depressed subjects was 32 with high sensitivity and high specificity.

This result is differ from the original English version which has the cut-off score of 30 (Ng et al., 2016), but it showed the same cut-off score from the Malay version of PERS which has the cut-off score of 32.

According to the list of the literature reviews, many studies were focusing on negative emotion. Furthermore, professions in clinical psychiatric practice were more concentrating on negative impacts on depressed subjects (Burgdorf & Panksepp, 2006; Carl, Soskin, Kerns, & Barlow, 2013).

Thus, positive emotion is frequent being neglected in common practice. It is challenging in providing an effective tool to evaluate the level of positive emotion in depression. Hence, it is important to develop an effective instrument in this aspect.

As Malaysia is a diverse country with different ethnic background, there is a need to establish an easy-to-use instrument that is

applicable for the local setting. Different languages of questionnaire showed the importance and need for subjects. There are different races in Malaysia and not all of them are multilingual or bilingual. Hence, developing more languages can assess more depressed subjects of their positive emotion.

Since there is an English version and also a Malay version, Chinese language should be included as Chinese is a common language used among the Chinese population in Malaysia.

Although good psychometric properties of PERS-C is shown, limitations were found in this study. Firstly, samples may have the risk of bias as this tool is a self-evaluation tool. Respondents may rate the answers which are socially acceptable. Second, samples were homogenous due to they were taken from the same setting. The results cannot be generalised as it was not represented the general population.

Conclusion

The PERS-C is short and compressed self- rating tool which is easy to be administered.

The current results of PERS-C demonstrated that the scale has good psychometric properties to evaluate positive emotion among adults who are proficient in in Chinese in Malaysia.

References

[1] American Psychiatric Association.

(2013). Diagnostic and statistical manual of mental disorder (5th Edition). Washington, DC.

[2] Brown, T. A., & Barlow, D. H.

(2009). A proposal for a dimensional classification system based on the shared features of the DSM-IV anxiety and mood

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disorders: implications for assessment and treatment.

Psychological assessment, 21(3), 256-271. doi:10.1037/a0016608.

[3] Burgdorf, J., & Panksepp, J. (2006).

The neurobiology of positive emotions. Neurosci Biobehav Rev,

30(2), 173-187.

doi:10.1016/j.neubiorev.2005.06.00 1.

[4] Carl, J. R., Soskin, D. P., Kerns, C.,

& Barlow, D. H. (2013). Positive emotion regulation in emotional disorders: a theoretical review. Clin Psychol Rev, 33(3), 343-360.

doi:10.1016/j.cpr.2013.01.003.

[5] Cohn, M. A., Fredrickson, B. L., Brown, S. L., Mikels, J. A., &

Conway, A. M. (2009). Happiness unpacked: positive emotions increase life satisfaction by building resilience. Emotion, 9(3), 361-368.

doi:10.1037/a0015952.

[6] Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. The American psychologist, 56(3), 218- 226.

[7] Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotions. Philos Trans R Soc Lond B Biol Sci, 359(1449), 1367-1378.

doi:10.1098/rstb.2004.1512

[8] Gruber, J., Kogan, A., Quoidbach, J.,

& Mauss, I. B. (2013). Happiness is best kept stable: positive emotion

variability is associated with poorer psychological health. Emotion, 13(1), 1-6. doi:10.1037/a0030262.

[9] Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., . . . National Comorbidity Survey, R. (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA, 289(23), 3095- 3105.

doi:10.1001/jama.289.23.3095.

[10] Lucas, R. E., Diener, E., & Larsen, R. J. (2009). Measuring positive emotions, In Diener E. Assessing Well-Being. Netherlands: Springer.

[11] Santos, V., Paes, F., Pereira, V., Arias-Carrión, O., Silva, A. C., Carta, M. G., . . . Machado, S.

(2013). The role of positive emotion and contributions of positive psychology in depression treatment:

systematic review. Clinical Practice and Epidemiology in Mental Health:

CP & EMH, 9, 221-237.

doi:10.2174/1745017901309010221.

[12]Tugade, M. M., & Fredrickson, B. L.

(2007). Regulation of positive emotions: Emotion regulation strategies that promote resilience.

Journal of Happiness Studies, 8(3), 311-333.

[13] World Health Organization (2012).

Depression A Global Public Health Concern. Geneva: World Health Organization.

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Corresponding Author Dr Ng Chong Guan

Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Email: [email protected]

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ORIGINAL PAPER

Comparison between Aripiprazole with Quetiapine in Patients with Bipolar Disorder: A Retrospective Outcomes Study

Ng CG¹, Seed HF², Thong KS³

1Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

2Hospital Sentosa, Kuching, Sarawak, Malaysia

3Department of Psychiatry and Mental Health, Sarawak General Hospital, Kuching, Sarawak, Malaysia

Introduction: Atypical antipsychotic drugs are effective in the treatment of bipolar disorder. Studies have shown that atypical antipsychotic drugs are more superior to typical antipsychotic in term of neurocognitive function, negative symptoms and extrapyramidal side effects. Both aripiprazole and quetiapine are atypical antipsychotic drugs that are effective and commonly used in all phases of bipolar disorder treatment. Objective: The aim of this study is to examine and compare the clinical outcomes of aripiprazole and quetiapine in bipolar disorder patients. Method: This was a retrospective cohort study among patients from psychiatric unit, University Malaya Medical Center. Prescription records dated between January 1, 2013 and December 31, 2014 for psychiatric unit were extracted. The data of the subjects with prescription of the two atypical antipsychotic, namely aripiprazole and quetiapine was extracted. The outcome measures were the co-prescription of antihypertensive drugs, antidiabetic drugs and lipid lowering drugs. Results: A total of 58 subjects were recruited, 11 were on aripiprazole and 47 were on quetiapine. Statistical analysis has shown that both aripiprazole and quetiapine do not have any association with compliance to the medication and also follow up. Study also revealed that there is no association between the aripiprazole and quetiapine group with the metabolic side effects that were measured such as systolic or diastolic blood pressure, waist circumference, weight, glucose level and body mass index. Conclusion: This study has shown that both aripiprazole and quetiapine were similar in terms of metabolic side effect, compliance to medications and follow up.

Keywords: Aripiprazole, Quetiapine, Bipolar Disorder, Atypical Antipsychotic

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Introduction

Atypical antipsychotic drugs (APD) are emerging as treatments with valid efficacy in all phases of bipolar disorder [1].

Quetiapine and aripiprazole are both APD.

Atypical antipsychotic drugs are generally described as drugs that produce minimal or no extrapyramidal side effects at clinically therapeutic dose [2]. Atypical APDs have cognitive benefits with superiority in some cognitive domain. Review of clinical studies on the effects of atypical antipsychotic among schizophrenia patients with cognitive impairment showed 30-50% significant improvement in specific cognitive domains or composite scores [3]. APDs such as olanzapine and risperidone demonstrated an overall superiority to haloperidol on a range of neurocognitive functions [4]. Besides, APDs such as clozapine, olanzapine and risperidone were better than typical antipsychotic such as haloperidol in improving negative symptoms[4]. However, all APDs will lead to significant weight gain in some patients[2]. The weight gain maybe mainly associated with H1 receptor blockage [3]. Other side effects of APDs include diabetes mellitus, hyperlipidaemia, sexual side effects and cataract[5]^.

Aripiprazole has been shown in clinical trials to have good efficacy, safety, and tolerability in treating Bipolar 1 Disorder patients with an acute manic or mixed episode and these improvements in manic symptoms were shown as early as day four⁶. Findings from this trial also suggested that aripiprazole may offer significant benefit in rapid-cycling patients, a subpopulation of patients at risk for treatment non-responsiveness [6,7]. Patients with bipolar disorder are at high risk for metabolic syndrome with the risk greater in those taking prescribed antipsychotic medication [8]. Aripiprazole is an

antipsychotic with a novel mechanism of action which differentiates it from the currently marketed typical and atypical antipsychotics. It is a dopamine D2 receptor partial agonist with partial agonist activity at serotonin 5HT1A receptors and antagonist activity at 5HT2A receptors[9].

This pharmacologically distinct antipsychotic leads to a low potential for weight gain, risk of developing diabetes or a deranged lipid profile [10]. Clinical trials have shown that aripiprazole was associated with minimal effects on cholesterol and glucose, comparable to effects with placebo [6]. Aripiprazole has a lower risk of EPS and hyperprolactinemia than other antipsychotics[11]^.

Quetiapine acts by blocking 5-HT2A receptors and D2 receptors. It also has affinity for different serotonergic and noradrenergic receptors. These actions explained the antipsychotic effects with lesser extrapyramidal symptoms, antidepressant and anxiolytic effects [12].

As quetiapine binds to H1 histamine receptors as well, it is associated with increased appetite and weight gain¹². Quetiapine has become a commonly used drug for bipolar patients with early response and sustained efficacy over 12 weeks[13].

Quetiapine is efficacious in treating acute mania, mixed and depressive episodes with good tolerability but has metabolic side effects such as obesity, dyslipidaemia and hyperglycaemia [1]. A head-to-head meta-analysis that looked into the metabolic side effects of second-generation antipsychotics showed that quetiapine had intermediate elevations in weight gain, cholesterol and glucose elevation amongst atypical antipsychotic [14]. On the other hand, CATIE study revealed that quetiapine has no more weight gain and metabolic changes associated with increased risk of

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diabetes than those on other antipsychotics [15].

Studies have shown that both aripiprazole and quetiapine are effective in the treating patients with Bipolar Disorder. Our aim of this study is to examine the clinical outcomes of both drugs in bipolar disorder patients.

Methods

Setting and Design

The study was conducted in University Malaya Medical Centre (UMMC), a referral and a teaching hospital located in Kuala Lumpur, Malaysia. This was a retrospective cohort study among patients from psychiatric unit, UMMC between January 1, 2013 and December 31, 2014. This study was approved by the Medical Ethics Committee, UMMC.

Data Sources

The pharmacy department in UMMC maintained all prescriptions records electronically in Medication Management and Use System Ascribe (Version 10.09) database. From this database, prescription records dated between January 1, 2013 and December 31, 2014 for psychiatric unit were

extracted using IBM’s Cognos Business Intelligence PowerPlay. The information extracted from the database includes patients’ gender, age, ethnicity and drug’s generic name for every individual. The prescription of coverage was 100% for the above-mentioned study period.

All drugs in the database were coded according to British National Formulary (BNF) classification. All subjects with at least one prescription of antipsychotic (ATC code = N05A) was selected. The data of the subjects with prescription of the two atypical antipsychotic, namely aripiprazole and quetiapine was extracted. The outcome measures were the co-prescription of antihypertensive drugs, antidiabetic drugs and lipid lowering drugs.

Statistical Analyses

Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS version 22, IBM Corporation). The odds of co-prescription of antihypertensive drugs, antidiabetic drugs and lipid lowering drugs were calculated and analyzed with using Chi-Square analysis. All statistical analyses were tested with alpha less than 5%

using two-tailed.

Results Table 1. Socio-demographic characteristic

Aripiprazole

(n=11) Quetiapine

(n=47) Age (years),

Mean (sd) 43.36 (14.12) 46.89 (15.69)

Total White Cell Count 2001 - 2016

CONTENTS