Author's Accepted Manuscript
Comorbid physical and mental illnesses among pathological gamblers: Results from a population based study in Singapore Mythily Subramaniam, Edimansyah Abdin, Janhavi Ajit Vaingankar, Kim Eng Wong, Siow Ann Chong
www.elsevier.com/locate/psychres
PII: DOI: Reference:
S0165-1781(15)00166-3 http://dx.doi.org/10.1016/j.psychres.2015.03.033 PSY8827
To appear in:
Psychiatry Research
Received date: 25 July 2014 Revised date: 9 March 2015 Accepted date: 26 March 2015 Cite this article as: Mythily Subramaniam, Edimansyah Abdin, Janhavi Ajit Vaingankar, Kim Eng Wong, Siow Ann Chong, Comorbid physical and mental illnesses among pathological gamblers: Results from a population based study in Singapore, Psychiatry Research, http://dx.doi.org/10.1016/j.psychres.2015.03.033 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Words: 4935
Comorbid Physical and Mental Illnesses among Pathological Gamblers: Results from a Population Based Study in Singapore. Running Head: Pathological Gamblers and Comorbidity Authors: Mythily Subramaniam*1a, Edimansyah Abdin a, Janhavi Ajit Vaingankar a, Kim Eng Wongb, Siow Ann Chong a a
Research Division, Institute of Mental Health, Singapore
b
National Addiction Management Service, Institute of Mental Health, Singapore
1
Corresponding Author: Tel: 65 63893633, Fax: 65 63437962, Email: [email protected]
1
Abstract The aim of the current study was to examine the comorbidity of pathological gambling with other mental and physical disorders as well as to examine health related quality of life perceived by those with pathological gambling using data from a community survey in Singapore. All respondents were administered the South Oaks Gambling Screen to screen for pathological gambling. The diagnosis of mental disorders was established using the Composite International Diagnostic Interview; while chronic physical conditions were established using a checklist. The weighted lifetime prevalence of pathological gambling was 2.7%. After multiple logistic regression, age 18-34 years (OR=5.3, 95% CI=1.6-17.4), male gender (OR=7.8, CI=3.8-16.2), widowhood (OR=4.2, 95% CI=1.02-17.5), and those with pre-primary (OR=17.1, CI=4.9-59.1), primary (OR=5.3, CI=1.7-16.6), and secondary education (OR=6, CI=2.5-14.7) had significantly higher odds of having pathological gambling. Those of Malay (OR=0.1, 95% CI=0.07-0.2) and Indian ethnicity (OR=-0.2, 95% CI=0.1-0.3) had significantly lower odds of having pathological gambling compared to those of Chinese ethnicity. Pathological gamblers had significantly higher odds of having comorbid mental and physical disorders than non-gamblers/non problem gamblers. The significant association of comorbid mental and physical disorders among those with pathological gambling indicates a need to screen for these disorders and for their subsequent treatment.
Key words: Pathological Gambling; Comorbidity; Health Related Quality of Life Singapore; Survey
2
1. Introduction Although gambling is a widespread socially sanctioned and legalized activity in some communities, it can lead to serious adverse consequences including pathological gambling. Pathological gambling is classified by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV-TR) as an impulse control disorder characterized by persistent and recurrent maladaptive gambling behavior with dire consequences (American Psychological Association (APA), 2000). However, the fifth edition of DSM (DSM-5) (APA, 2013) has included ‘Gambling Disorder’ under ‘Substance-Related and Addictive Disorders’ where it is defined as “persistent and recurrent problematic gambling behavior leading to clinically significant impairment or distress, as indicated by the individual exhibiting four (or more) symptoms as described in a checklist within a 12-month period”. A recent systematic review of problem gambling prevalence rates by Williams et al. (2012) established the standardized past year rate of problem gambling as ranging from 0.5% to 7.6%, with the average rate across all countries being 2.3%. The authors defined problem gambling as ‘difficulties in limiting money and/or time spent on gambling which leads to adverse consequences for the gambler, others, or for the community.’ The term problem gambling is assumed to encompass varying degrees of severity, and ‘pathological gambling’ was considered synonymous with severe problem gambling. Standardised prevalence rates were calculated by converting problem gambling prevalence rates in all jurisidictional studies to a problem and pathological gambling measure (PPGM) rate using standard conversion factors (Williams and Volberg, 2010). In general, based on national level data, the lowest standardized prevalence rates of problem gambling were in Europe (0.5 % in Denmark and Netherlands to 3.3% in North Ireland), with intermediate rates in North
3
America and Australia (2.1%), and the highest rates in Asia. The authors reported that the highest rates were observed in Singapore (3.8%), Macau (6.0%), Hong Kong (5.6%), and South Africa (6.4%) (Williams et al., 2012). Research has suggested that pathological gambling is associated with adverse consequences. At an individual level pathological gambling is associated with domestic violence, suicide, financial troubles and criminal behavior (Dobkin et al., 2002; Shaffer et al., 2004). Co-occurring psychiatric illnesses are common among those with pathological gambling - a national survey in the US found that 96.3% of respondents with lifetime pathological gambling also met lifetime criteria for one or more DSM-IV disorders (Kessler et al., 2008). Other community studies have similarly found high rates of psychiatric comorbidities in pathological gamblers (Petry et al., 2005; Park et al., 2010). Studies also suggest that pathological gambling is associated with medical problems such as hypertension, tachycardia, angina and liver disease (Morasco et al., 2006) while prospectively associated with elevated incidences of arteriosclerosis and any heart condition (Pilver and Potenza, 2013). Given this association of pathological gambling with adverse life events and comorbidity with psychiatric and physical illnesses, pathological gamblers have a significantly reduced health related quality of life (HRQoL) (Subramaniam et al., 2011).
According to the US National Gambling Impact Study Commission, the
annual cost of gambling problems in US from job loss, poor physical and mental health and their treatment is around 5 billion US dollars per year (National Gambling Impact Study Commission, 1999). Singapore is an island city-nation off the southern tip of the Malay Peninsula. In 2009, the population of Singapore was just under 4.99 million of which 3.73 million were Singapore residents. Of these residents, 74.2% are of Chinese descent, 13.4%
4
are Malays, and 9.2% are of Indian descent (Singapore Department of Statistics, 2013). Legalized gambling in Singapore includes various forms of lotteries known locally as Singapore Sweep, 4D and Toto; horse racing; and gaming in private clubs. ‘Sports betting’ is also allowed in the national football (soccer) league and more recently for Formula One races. In 2005, the Singapore Government lifted the ban on casinos and allowed two casinos to be established as part of what is termed the ‘Integrated Resort’ in Singapore. The two integrated resorts and their casinos started operating in February and April of 2010. Two
studies done in Singapore (using screening questions based on DSM-IV
criteria ) established the one-year prevalence of pathological gambling to be 1.2% and 1.4% respectively (Ministry of Community Development, Youth and Sports (MCDYS), 2008; National Council on Problem Gambling (NCPG), 2011). However, these studies were largely descriptive in nature. While both studies have described in detail the prevalence of various gambling activities in different socio-demographic groups, they have not examined the physical and psychiatric comorbidity among those with probable pathological gambling. The aim of the current study was thus to examine the comorbidity of pathological gambling with other psychiatric and physical disorders as well as to examine HRQoL of those with pathological gambling in Singapore. We hypothesized that pathological gamblers would have a higher prevalence of comorbid mental and physical disorders and a lower HRQoL as compared to non-gamblers and non-problem gamblers. 2.
Materials and Methods
2.1 Sample The Singapore Mental Health Study (SMHS) surveyed Singapore Residents (including Singapore Citizens and Permanent Residents) aged 18 years and above. They were randomly
5
selected from a database obtained from a national registry. A disproportionate stratified sampling was used where the 3 main ethnic groups (Chinese, Malays, and Indians) were sampled in equivalent proportion of about 30% each rather than in proportion to the ethnic distribution of the general population. The survey was conducted from December 2009 to December 2010 and a total of 6,616 respondents were interviewed giving a response rate of 75.9%. Approval for conducting the survey was given by the relevant ethics board (National Healthcare Group Domain Specific Review Board, Singapore). Informed consent from all respondents (and from parents for those aged 18-20 years) was taken prior to participation. A detailed description of the study design has been published elsewhere (Subramaniam et al., 2012). 2.2 Assessments All respondents were administered the South Oaks Gambling Screen (SOGS) (Lesieur and Blume, 1987) - a 20-item instrument used to screen for pathological gambling. There are 20 scoring items in the instrument, all equally weighted, requiring a ‘yes’ or ‘no’ answer. To score, each ‘yes’ answer attains one point, with a score of five or more indicating
that the respondent is a ‘probable pathological
gambler’. The non-scoring items identify type of gambling, amount of money gambled in a day, and relatives and friends with a gambling problem. For the purpose of this study, respondents scoring 5 or more were categorized as ‘pathological gamblers’, and those scoring 0 as ‘non problem gamblers’. Those who stated that they had never gambled in their lives were categorized as non-gamblers. The SOGS is based on the Diagnostic and Statistical Manual of Mental Disorders Third Edition (DSM-III) (APA, 1980) criteria for pathological gambling. SOGS can be completed as a self-report questionnaire or administered by professional or nonprofessional interviewers (this study used the self-administered version). Internal
6
consistency and test-retest reliability of this questionnaire has been established, and it shows good agreement with the DSM-IV criteria for pathological gambling (Stinchfield, 2002). The English version of SOGS has been validated in the Singapore population (Abdin et al., 2012). The value of the Cronbach’s alpha in the study of 0.84 was higher compared to those obtained in the Brazilian (Cronbach’s alpha = 0.75) (de Oliveira et al., 2009) and American (Cronbach’s alpha = 0.69) (Stinchfield, 2002) general population-based studies. However, it was slightly lower compared to those obtained in a sample of gamblers receiving treatment in the United States (Cronbach’s alpha = 0.86) (Stinchfield, 2002). An examination of the item-total correlations of the SOGS items also indicated that all questions were interrelated and were therefore important variables in the whole composite. The diagnosis of mental disorders was established using the Composite International Diagnostic Interview (CIDI) version 3.0 with diagnostic hierarchy rules (Wittchen, 1994). The CIDI 3.0 is a fully structured diagnostic instrument, which assesses lifetime and recent prevalence of disorders using the definitions and criteria of the DSM-IV and the International Classification of Disease 10th revision (ICD-10) Classification of Mental and Behavioral Disorders. For the survey, we included only mood disorder (major depressive disorder (MDD) and bipolar disorder), anxiety disorder (generalised anxiety disorder (GAD) and obsessive compulsive disorder) (OCD) and alcohol abuse and dependence. CIDI has been validated by comparing it with clinician-administered non-patient edition of the Structured Clinical Interview for DSM-IV (SCID) in probability subsamples of the World Mental Health surveys in France, Italy, Spain, and the US; and moderate to good individual-level CIDI-SCID concordance was found for lifetime prevalence estimates of most disorders (Haro et al., 2006). The SMHS did not include a clinical validation of the diagnoses generated
7
by CIDI. Given the low prevalence of substance use in Singapore, we did not include substance use/ dependence module of the CIDI (Subramaniam et al., 2010). Nicotine dependence was established using the 6-item Modified Fagerstrom test for Nicotine Dependence (FTND) (Heatherton et al., 1991). The number of response options varies for each item and response options are given a score of 0, 1, 2 or 3, which are then summed to calculate a total score ranging from 0 to 10. Scores of 4 or less are classified as ‘low dependence’ whilst scores of 8 to 10 equate to ‘very high dependence’. We categorized those with scores ≤ 4 as no-dependence; ≥ 5 as dependence as recommended by other studies (Huang et al., 2008; Manimunda et al., 2012). The FTND has been shown to have adequate validity and reliability across many studies (Heatherton et al., 1991; Weinberger et al., 2007). The construct validity and reliability of FTND has also been demonstrated in the Singapore population (Abdin et al., 2011). Relevant socio-demographic data such as age, gender, marital status, education, employment status and income was collected using a structured questionnaire. A modified version of the CIDI chronic medical conditions checklist was used to gather data on the prevalence of physical conditions in the population. The respondents were asked to report any of the disorders listed in the checklist. Respondents were read the following statement: ‘I’m going to read to you a list of health problems some people have. Has a doctor ever told you that you have any of the following…’Data was collected on 15 conditions, namely, asthma; diabetes; hypertension; arthritis or rheumatism; cancer; neurological conditions such as epilepsy, convulsions or Parkinson’s disease; stroke or major paralysis; heart attack, coronary artery disease, angina, congestive heart disease; back problems including disc or spine problems; stomach ulcer; chronic inflamed bowel disease, colitis, enteritis; thyroid disease;
8
kidney failure; migraine headaches; chronic lung disease such as chronic bronchitis and emphysema. The disorders chosen were those that are prevalent in Singapore as per local data. These were re-categorized into eight categories: (1) respiratory disorders (asthma, chronic lung disease such as chronic bronchitis or emphysema), (2) diabetes, (3) hypertension or high blood pressure, (4) chronic pain (arthritis or rheumatism, back problems including disk or spine, migraine headaches), (5) cancer, (6) neurological disorders (epilepsy, convulsion, Parkinson’s disease), (7) cardiovascular disorders (stroke or major paralysis, heart attack, coronary heart disease, angina, congestive heart failure or other heart disease), and, (8) ulcer and chronic inflamed bowel disease (stomach ulcer, chronic inflamed bowel, enteritis, or colitis). CIDI and other supplementary instruments were translated into Chinese and Bahasa Melayu which is the official language of the Malay ethnic group. HRQoL was measured using the EuroQol-5 dimension (EQ-5D) (EuroQol Group, 1990). EQ-5D is a standardized measure of health status developed by the EuroQol Group. It comprises a descriptive system and a VAS. The descriptive system assesses 5 domains (i.e., mobility, self-care, usual activities, pain/discomfort, anxiety/depression) and respondents are asked to rate their health on a three-point severity scale (no problem/moderate problem/extreme problem). These together define 243 health states (3 to the power of 5 gives the 243 possible combination). The utility-based EQ-5D index score ranges from −0.59 to 1.00, with negative values representing health states worse than being dead, 0 representing being dead, and 1.00 representing the state of full health. The EQ-VAS records the respondent’s selfrated health on a vertical, VAS where the endpoints are labeled ‘Best imaginable health state’ (100) and ‘Worst imaginable health state’ (0). We used the EQ-5D Index and EQ-5D Visual Analogue Scale (EQ-VAS) scores for the study. The EQ-5D has
9
been translated into Chinese, Malay, and Tamil languages and validated in several disease groups including Parkinson’s disease (Luo et al., 2009), cancer (Gao et al., 2009), and rheumatic diseases (Luo et al., 2003) in Singapore. The population norms for the EQ-5D index scores have been established using both UK and Singapore preference weights (Abdin et al., 2013; Abdin et al., 2014). 2.3 Statistical Analyses All estimates were weighted to adjust for over sampling and post-stratified for age and ethnicity distributions between the survey sample and the Singapore resident population in 2007. Mean and standard deviations or median (if normality was not satisfied) were calculated for continuous variables, and frequencies and percentages for categorical variables. The socio-demographic characteristics were compared among the groups and tested for significant differences using Chi-square tests. This was followed by a multiple logistic regression analysis to explore the sociodemographic correlates of pathological gambling. A series of multiple logistic regression analyses were also performed to examine the comorbidity of pathological gambling with other mental and physical disorders. First regression models were run separately for each mental and physical disorder with pathological gambling as the dependent variable adjusted for age and gender. Then, all mental and physical disorders were included in the same model (i.e. controlled for comorbid mental and physical condition for each disorder). In addition, possible interaction effects in the relationship between significant mental and physical disorders with pathological gambling were further tested. We further explored the variable that may be said to function as a mediator to the extent that it accounts for the relation between a predictor and an outcome (Baron and Kenny, 1986). This analysis was performed using structural equation modeling (SEM) framework (Muthén, 2011) and
10
implemented in MPLUS software (Muthén and Muthén, 2012), using weighted maximum likelihood estimation with robust standard error. We decided to use SEM primarily for exploratory analysis and hypothesis generation as there were a number of potential direct and indirect effects to be investigated (VanderWeele, 2012). In these SEM analyses, potential mediating variables (i.e. one or more disorders considered) were added into the model and the direct and indirect effects of each predictor and the mediator to the outcome variable was simultaneously estimated while controlling for remaining comorbid disorders. To explore significant mediation, we tested 25 logically possible combination of each significant variable (i.e., MDD, bipolar, alcohol abuse, alcohol dependence, and hypertension) in both directions. The standard errors of the direct and indirect causal effects were calculated using delta method (McKinnon et al., 2007; Muthen, 2011). Mean EQ-5D index and EQVAS scores were compared between four groups; those with:
pathological
gambling, any mental disorder, any chronic physical condition and controls (i.e. those without pathological gambling and other mental and/or physical disorders) using ANOVA test followed by multiple linear regression to adjust for age and gender. Standard errors (SE) and significance tests were estimated using the Taylor series linearisation method. Multivariate significance was evaluated using Wald x² tests based on design corrected coefficient variance–covariance matrices. Statistical significance was evaluated at the
Comorbid physical and mental illnesses among pathological gamblers: Results from a population based study in Singapore Mythily Subramaniam, Edimansyah Abdin, Janhavi Ajit Vaingankar, Kim Eng Wong, Siow Ann Chong
www.elsevier.com/locate/psychres
PII: DOI: Reference:
S0165-1781(15)00166-3 http://dx.doi.org/10.1016/j.psychres.2015.03.033 PSY8827
To appear in:
Psychiatry Research
Received date: 25 July 2014 Revised date: 9 March 2015 Accepted date: 26 March 2015 Cite this article as: Mythily Subramaniam, Edimansyah Abdin, Janhavi Ajit Vaingankar, Kim Eng Wong, Siow Ann Chong, Comorbid physical and mental illnesses among pathological gamblers: Results from a population based study in Singapore, Psychiatry Research, http://dx.doi.org/10.1016/j.psychres.2015.03.033 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Words: 4935
Comorbid Physical and Mental Illnesses among Pathological Gamblers: Results from a Population Based Study in Singapore. Running Head: Pathological Gamblers and Comorbidity Authors: Mythily Subramaniam*1a, Edimansyah Abdin a, Janhavi Ajit Vaingankar a, Kim Eng Wongb, Siow Ann Chong a a
Research Division, Institute of Mental Health, Singapore
b
National Addiction Management Service, Institute of Mental Health, Singapore
1
Corresponding Author: Tel: 65 63893633, Fax: 65 63437962, Email: [email protected]
1
Abstract The aim of the current study was to examine the comorbidity of pathological gambling with other mental and physical disorders as well as to examine health related quality of life perceived by those with pathological gambling using data from a community survey in Singapore. All respondents were administered the South Oaks Gambling Screen to screen for pathological gambling. The diagnosis of mental disorders was established using the Composite International Diagnostic Interview; while chronic physical conditions were established using a checklist. The weighted lifetime prevalence of pathological gambling was 2.7%. After multiple logistic regression, age 18-34 years (OR=5.3, 95% CI=1.6-17.4), male gender (OR=7.8, CI=3.8-16.2), widowhood (OR=4.2, 95% CI=1.02-17.5), and those with pre-primary (OR=17.1, CI=4.9-59.1), primary (OR=5.3, CI=1.7-16.6), and secondary education (OR=6, CI=2.5-14.7) had significantly higher odds of having pathological gambling. Those of Malay (OR=0.1, 95% CI=0.07-0.2) and Indian ethnicity (OR=-0.2, 95% CI=0.1-0.3) had significantly lower odds of having pathological gambling compared to those of Chinese ethnicity. Pathological gamblers had significantly higher odds of having comorbid mental and physical disorders than non-gamblers/non problem gamblers. The significant association of comorbid mental and physical disorders among those with pathological gambling indicates a need to screen for these disorders and for their subsequent treatment.
Key words: Pathological Gambling; Comorbidity; Health Related Quality of Life Singapore; Survey
2
1. Introduction Although gambling is a widespread socially sanctioned and legalized activity in some communities, it can lead to serious adverse consequences including pathological gambling. Pathological gambling is classified by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV-TR) as an impulse control disorder characterized by persistent and recurrent maladaptive gambling behavior with dire consequences (American Psychological Association (APA), 2000). However, the fifth edition of DSM (DSM-5) (APA, 2013) has included ‘Gambling Disorder’ under ‘Substance-Related and Addictive Disorders’ where it is defined as “persistent and recurrent problematic gambling behavior leading to clinically significant impairment or distress, as indicated by the individual exhibiting four (or more) symptoms as described in a checklist within a 12-month period”. A recent systematic review of problem gambling prevalence rates by Williams et al. (2012) established the standardized past year rate of problem gambling as ranging from 0.5% to 7.6%, with the average rate across all countries being 2.3%. The authors defined problem gambling as ‘difficulties in limiting money and/or time spent on gambling which leads to adverse consequences for the gambler, others, or for the community.’ The term problem gambling is assumed to encompass varying degrees of severity, and ‘pathological gambling’ was considered synonymous with severe problem gambling. Standardised prevalence rates were calculated by converting problem gambling prevalence rates in all jurisidictional studies to a problem and pathological gambling measure (PPGM) rate using standard conversion factors (Williams and Volberg, 2010). In general, based on national level data, the lowest standardized prevalence rates of problem gambling were in Europe (0.5 % in Denmark and Netherlands to 3.3% in North Ireland), with intermediate rates in North
3
America and Australia (2.1%), and the highest rates in Asia. The authors reported that the highest rates were observed in Singapore (3.8%), Macau (6.0%), Hong Kong (5.6%), and South Africa (6.4%) (Williams et al., 2012). Research has suggested that pathological gambling is associated with adverse consequences. At an individual level pathological gambling is associated with domestic violence, suicide, financial troubles and criminal behavior (Dobkin et al., 2002; Shaffer et al., 2004). Co-occurring psychiatric illnesses are common among those with pathological gambling - a national survey in the US found that 96.3% of respondents with lifetime pathological gambling also met lifetime criteria for one or more DSM-IV disorders (Kessler et al., 2008). Other community studies have similarly found high rates of psychiatric comorbidities in pathological gamblers (Petry et al., 2005; Park et al., 2010). Studies also suggest that pathological gambling is associated with medical problems such as hypertension, tachycardia, angina and liver disease (Morasco et al., 2006) while prospectively associated with elevated incidences of arteriosclerosis and any heart condition (Pilver and Potenza, 2013). Given this association of pathological gambling with adverse life events and comorbidity with psychiatric and physical illnesses, pathological gamblers have a significantly reduced health related quality of life (HRQoL) (Subramaniam et al., 2011).
According to the US National Gambling Impact Study Commission, the
annual cost of gambling problems in US from job loss, poor physical and mental health and their treatment is around 5 billion US dollars per year (National Gambling Impact Study Commission, 1999). Singapore is an island city-nation off the southern tip of the Malay Peninsula. In 2009, the population of Singapore was just under 4.99 million of which 3.73 million were Singapore residents. Of these residents, 74.2% are of Chinese descent, 13.4%
4
are Malays, and 9.2% are of Indian descent (Singapore Department of Statistics, 2013). Legalized gambling in Singapore includes various forms of lotteries known locally as Singapore Sweep, 4D and Toto; horse racing; and gaming in private clubs. ‘Sports betting’ is also allowed in the national football (soccer) league and more recently for Formula One races. In 2005, the Singapore Government lifted the ban on casinos and allowed two casinos to be established as part of what is termed the ‘Integrated Resort’ in Singapore. The two integrated resorts and their casinos started operating in February and April of 2010. Two
studies done in Singapore (using screening questions based on DSM-IV
criteria ) established the one-year prevalence of pathological gambling to be 1.2% and 1.4% respectively (Ministry of Community Development, Youth and Sports (MCDYS), 2008; National Council on Problem Gambling (NCPG), 2011). However, these studies were largely descriptive in nature. While both studies have described in detail the prevalence of various gambling activities in different socio-demographic groups, they have not examined the physical and psychiatric comorbidity among those with probable pathological gambling. The aim of the current study was thus to examine the comorbidity of pathological gambling with other psychiatric and physical disorders as well as to examine HRQoL of those with pathological gambling in Singapore. We hypothesized that pathological gamblers would have a higher prevalence of comorbid mental and physical disorders and a lower HRQoL as compared to non-gamblers and non-problem gamblers. 2.
Materials and Methods
2.1 Sample The Singapore Mental Health Study (SMHS) surveyed Singapore Residents (including Singapore Citizens and Permanent Residents) aged 18 years and above. They were randomly
5
selected from a database obtained from a national registry. A disproportionate stratified sampling was used where the 3 main ethnic groups (Chinese, Malays, and Indians) were sampled in equivalent proportion of about 30% each rather than in proportion to the ethnic distribution of the general population. The survey was conducted from December 2009 to December 2010 and a total of 6,616 respondents were interviewed giving a response rate of 75.9%. Approval for conducting the survey was given by the relevant ethics board (National Healthcare Group Domain Specific Review Board, Singapore). Informed consent from all respondents (and from parents for those aged 18-20 years) was taken prior to participation. A detailed description of the study design has been published elsewhere (Subramaniam et al., 2012). 2.2 Assessments All respondents were administered the South Oaks Gambling Screen (SOGS) (Lesieur and Blume, 1987) - a 20-item instrument used to screen for pathological gambling. There are 20 scoring items in the instrument, all equally weighted, requiring a ‘yes’ or ‘no’ answer. To score, each ‘yes’ answer attains one point, with a score of five or more indicating
that the respondent is a ‘probable pathological
gambler’. The non-scoring items identify type of gambling, amount of money gambled in a day, and relatives and friends with a gambling problem. For the purpose of this study, respondents scoring 5 or more were categorized as ‘pathological gamblers’, and those scoring 0 as ‘non problem gamblers’. Those who stated that they had never gambled in their lives were categorized as non-gamblers. The SOGS is based on the Diagnostic and Statistical Manual of Mental Disorders Third Edition (DSM-III) (APA, 1980) criteria for pathological gambling. SOGS can be completed as a self-report questionnaire or administered by professional or nonprofessional interviewers (this study used the self-administered version). Internal
6
consistency and test-retest reliability of this questionnaire has been established, and it shows good agreement with the DSM-IV criteria for pathological gambling (Stinchfield, 2002). The English version of SOGS has been validated in the Singapore population (Abdin et al., 2012). The value of the Cronbach’s alpha in the study of 0.84 was higher compared to those obtained in the Brazilian (Cronbach’s alpha = 0.75) (de Oliveira et al., 2009) and American (Cronbach’s alpha = 0.69) (Stinchfield, 2002) general population-based studies. However, it was slightly lower compared to those obtained in a sample of gamblers receiving treatment in the United States (Cronbach’s alpha = 0.86) (Stinchfield, 2002). An examination of the item-total correlations of the SOGS items also indicated that all questions were interrelated and were therefore important variables in the whole composite. The diagnosis of mental disorders was established using the Composite International Diagnostic Interview (CIDI) version 3.0 with diagnostic hierarchy rules (Wittchen, 1994). The CIDI 3.0 is a fully structured diagnostic instrument, which assesses lifetime and recent prevalence of disorders using the definitions and criteria of the DSM-IV and the International Classification of Disease 10th revision (ICD-10) Classification of Mental and Behavioral Disorders. For the survey, we included only mood disorder (major depressive disorder (MDD) and bipolar disorder), anxiety disorder (generalised anxiety disorder (GAD) and obsessive compulsive disorder) (OCD) and alcohol abuse and dependence. CIDI has been validated by comparing it with clinician-administered non-patient edition of the Structured Clinical Interview for DSM-IV (SCID) in probability subsamples of the World Mental Health surveys in France, Italy, Spain, and the US; and moderate to good individual-level CIDI-SCID concordance was found for lifetime prevalence estimates of most disorders (Haro et al., 2006). The SMHS did not include a clinical validation of the diagnoses generated
7
by CIDI. Given the low prevalence of substance use in Singapore, we did not include substance use/ dependence module of the CIDI (Subramaniam et al., 2010). Nicotine dependence was established using the 6-item Modified Fagerstrom test for Nicotine Dependence (FTND) (Heatherton et al., 1991). The number of response options varies for each item and response options are given a score of 0, 1, 2 or 3, which are then summed to calculate a total score ranging from 0 to 10. Scores of 4 or less are classified as ‘low dependence’ whilst scores of 8 to 10 equate to ‘very high dependence’. We categorized those with scores ≤ 4 as no-dependence; ≥ 5 as dependence as recommended by other studies (Huang et al., 2008; Manimunda et al., 2012). The FTND has been shown to have adequate validity and reliability across many studies (Heatherton et al., 1991; Weinberger et al., 2007). The construct validity and reliability of FTND has also been demonstrated in the Singapore population (Abdin et al., 2011). Relevant socio-demographic data such as age, gender, marital status, education, employment status and income was collected using a structured questionnaire. A modified version of the CIDI chronic medical conditions checklist was used to gather data on the prevalence of physical conditions in the population. The respondents were asked to report any of the disorders listed in the checklist. Respondents were read the following statement: ‘I’m going to read to you a list of health problems some people have. Has a doctor ever told you that you have any of the following…’Data was collected on 15 conditions, namely, asthma; diabetes; hypertension; arthritis or rheumatism; cancer; neurological conditions such as epilepsy, convulsions or Parkinson’s disease; stroke or major paralysis; heart attack, coronary artery disease, angina, congestive heart disease; back problems including disc or spine problems; stomach ulcer; chronic inflamed bowel disease, colitis, enteritis; thyroid disease;
8
kidney failure; migraine headaches; chronic lung disease such as chronic bronchitis and emphysema. The disorders chosen were those that are prevalent in Singapore as per local data. These were re-categorized into eight categories: (1) respiratory disorders (asthma, chronic lung disease such as chronic bronchitis or emphysema), (2) diabetes, (3) hypertension or high blood pressure, (4) chronic pain (arthritis or rheumatism, back problems including disk or spine, migraine headaches), (5) cancer, (6) neurological disorders (epilepsy, convulsion, Parkinson’s disease), (7) cardiovascular disorders (stroke or major paralysis, heart attack, coronary heart disease, angina, congestive heart failure or other heart disease), and, (8) ulcer and chronic inflamed bowel disease (stomach ulcer, chronic inflamed bowel, enteritis, or colitis). CIDI and other supplementary instruments were translated into Chinese and Bahasa Melayu which is the official language of the Malay ethnic group. HRQoL was measured using the EuroQol-5 dimension (EQ-5D) (EuroQol Group, 1990). EQ-5D is a standardized measure of health status developed by the EuroQol Group. It comprises a descriptive system and a VAS. The descriptive system assesses 5 domains (i.e., mobility, self-care, usual activities, pain/discomfort, anxiety/depression) and respondents are asked to rate their health on a three-point severity scale (no problem/moderate problem/extreme problem). These together define 243 health states (3 to the power of 5 gives the 243 possible combination). The utility-based EQ-5D index score ranges from −0.59 to 1.00, with negative values representing health states worse than being dead, 0 representing being dead, and 1.00 representing the state of full health. The EQ-VAS records the respondent’s selfrated health on a vertical, VAS where the endpoints are labeled ‘Best imaginable health state’ (100) and ‘Worst imaginable health state’ (0). We used the EQ-5D Index and EQ-5D Visual Analogue Scale (EQ-VAS) scores for the study. The EQ-5D has
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been translated into Chinese, Malay, and Tamil languages and validated in several disease groups including Parkinson’s disease (Luo et al., 2009), cancer (Gao et al., 2009), and rheumatic diseases (Luo et al., 2003) in Singapore. The population norms for the EQ-5D index scores have been established using both UK and Singapore preference weights (Abdin et al., 2013; Abdin et al., 2014). 2.3 Statistical Analyses All estimates were weighted to adjust for over sampling and post-stratified for age and ethnicity distributions between the survey sample and the Singapore resident population in 2007. Mean and standard deviations or median (if normality was not satisfied) were calculated for continuous variables, and frequencies and percentages for categorical variables. The socio-demographic characteristics were compared among the groups and tested for significant differences using Chi-square tests. This was followed by a multiple logistic regression analysis to explore the sociodemographic correlates of pathological gambling. A series of multiple logistic regression analyses were also performed to examine the comorbidity of pathological gambling with other mental and physical disorders. First regression models were run separately for each mental and physical disorder with pathological gambling as the dependent variable adjusted for age and gender. Then, all mental and physical disorders were included in the same model (i.e. controlled for comorbid mental and physical condition for each disorder). In addition, possible interaction effects in the relationship between significant mental and physical disorders with pathological gambling were further tested. We further explored the variable that may be said to function as a mediator to the extent that it accounts for the relation between a predictor and an outcome (Baron and Kenny, 1986). This analysis was performed using structural equation modeling (SEM) framework (Muthén, 2011) and
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implemented in MPLUS software (Muthén and Muthén, 2012), using weighted maximum likelihood estimation with robust standard error. We decided to use SEM primarily for exploratory analysis and hypothesis generation as there were a number of potential direct and indirect effects to be investigated (VanderWeele, 2012). In these SEM analyses, potential mediating variables (i.e. one or more disorders considered) were added into the model and the direct and indirect effects of each predictor and the mediator to the outcome variable was simultaneously estimated while controlling for remaining comorbid disorders. To explore significant mediation, we tested 25 logically possible combination of each significant variable (i.e., MDD, bipolar, alcohol abuse, alcohol dependence, and hypertension) in both directions. The standard errors of the direct and indirect causal effects were calculated using delta method (McKinnon et al., 2007; Muthen, 2011). Mean EQ-5D index and EQVAS scores were compared between four groups; those with:
pathological
gambling, any mental disorder, any chronic physical condition and controls (i.e. those without pathological gambling and other mental and/or physical disorders) using ANOVA test followed by multiple linear regression to adjust for age and gender. Standard errors (SE) and significance tests were estimated using the Taylor series linearisation method. Multivariate significance was evaluated using Wald x² tests based on design corrected coefficient variance–covariance matrices. Statistical significance was evaluated at the
