General Socioeconomic Gambling Data

2013

• When neighborhood disadvantage was high and individual socioeconomic status was low, the highest levels of problem gambling were observed (Barnes et al., 2013).
• Frequent gambling and problem gambling are highest in the lowest socioeconomic status (SES) group and the greatest neighborhood disadvantage group (Barnes et al., 2013).
  • (These same socioeconomic and neighborhood patterns do not apply to alcohol use and abuse rates which were highest among the highest SES and neighborhoods with advantageous conditions).
• All of the predictors of alcohol abuse are not the same as the predictors for problem gambling in the present study (Barnes et al., 2013).
• When individual socioeconomic status is low and neighborhood disadvantage is high, problem gambling is at the highest level (Barnes et al., 2013).

2011

• Gambling involvement tends to decline as SES rises (Welte, 2011).
• Frequent and problem gambling become more common as SES gets lower (Welte, 2011).
• Lower SES respondents have higher odds of being a frequent gambler than higher SES respondents (Welte, 2011).

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Socioeconomic Co-Occurring SUD Data

2021

• 94% of people with gambling problems will have at least one cooccurring mental health or addiction disorder (including alcohol and nicotine dependence, depression, anxiety, and obsessive-compulsive disorder) (Pricel et al., 2021).
• Gambling problems stem from complex and diverse social and economic factors, which may be complicated by the high rate of co-occurring health conditions (Pricel et al., 2021).

2013

• Gambling in the past year is more prevalent (75%) than drinking any alcohol in the past year (60%) (Barnes et al., 2013).
• 75% of respondents reported gambling in the past year (Barnes et al., 2013).
  • A higher rate than drinking any alcohol (60%) in the past year.

Socioeconomic College & Young ADULT Data

2017

• Higher socioeconomic status lowered the odds of problem gambling (Barnes, 2017).

Socioeconomic Education Data

2021

• Sports betting has a disproportionate appeal to those at the higher end of the socio-economic scale (by income and educational attainment) (NCPG, 2021).

Socioeconomic Employment Data

2021

• Gambling harms can take the form of financial insecurity, employment disruption, suicide, substance abuse, psychological disorders, and more (Pricel et al., 2021).

Socioeconomic Policy Data

2021

• The governance structure of legal gambling industries typically do not include the department of health (This will continue until the public views gambling harm as a public crisis that requires public health input) (Pricel et al., 2021).

Socioeconomic Suicide Data

2021

• Gambling harms can take the form of financial insecurity, employment disruption, suicide, substance abuse, psychological disorders, and more (Pricel et al., 2021).

Socioeconomic Treatment Data

2021

• The Problem Gambling Severity Index (PGSI) is useful for determining risk categories (though, missing data measuring harm as an outcome) (Pricel et al., 2021).
• The Problem and Pathological Gambling Measure (PPGM) is an instrument used by some researchers to identify the incidence of specific gambling harms concurrently with an assessment of problem (though it fails to capture the extent of harm being experienced by close relations) (Pricel et al., 2021).

Socioeconomic Veteran Data

2020

• Gambling Disorder is the second strongest predictor of homelessness among veterans, second to illicit drug use (Etuk et al., 2020).

2019

• Several factors such as level of exposure, emotional states, as well as organizational context and culture, may affect assessment and communication of risk (Breivik et al., 2019).
• Sociodemographic background, personality factors, and general mind-set seem to influence soldiers’ risk behavior and choice of career (Breivik et al., 2019).
• Men who had greater academic abilities were more likely to go to college. Thereby, they avoided military service and the possibility of serving in a combat occupation (Breivik et al., 2019).
• On the demand side, the armed forces were more likely to exclude men with lower academic abilities. But they were also more likely to assign such men to combat occupations when they had entered the military system. There was, thus, an overrepresentation of men with lower academic abilities among soldiers and especially in combat occupations (Breivik et al., 2019).

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Socioeconomic Risk Factors

• Those with an income below $30,000 (OASAS, 2020).
• Those with a HS diploma or less (OASAS, 2020).
• Low socioeconomic status (Barnes, 2017) (Barnes et al., 2013) (Welte, 2011)
• High neighborhood disadvantage (Barnes et al., 2013)

2023

Population Level

• Gender (Pricel et al., 2021).
• Age (Pricel et al., 2021).
• Ethnicity (Pricel et al., 2021).
• Employment status (Pricel et al., 2021).
• Having a family history of gambling (Pricel et al., 2021).
• Participating regularly in continuous forms of gambling (Pricel et al., 2021).

Veteran Risk Factors

• Being in receipt of benefits (Dighton, 2023).
• Living in supported accommodation (Dighton, 2023).
• Living in privately rented accommodation (Dighton, 2023).
• Unemployment (including those on a disability support pension) (Metcalf et al, 2022).

Socioeconomic Protective Factors

• Gambling research towards population impacts (Pricel et al., 2021).

Veteran Protective Factors

• Achieving a Doctorate as one’s highest qualification (Dighton, 2023).
• Living with non-family members (Dighton, 2023).
• In full or part-time paid work (Metcalf et al, 2022).
• Strong social support (Etuk et al., 2020).

Research Recommendations

Research

• More gambling-related harm data is needed to develop integrated systems for gambling policy, treatment and counselling interactions (Pricel et al., 2021).
• Encourage public health system involvement to leverage expertise and resources for preventive interventions, health promotional information, and data development and analysis for the field’s advancement (Pricel et al., 2021).
• To help advance knowledge and fill the research gap, cross-jurisdictional support can expand longitudinal research and present an opportunity to share data widely among the gambling and public health research communities (Pricel et al., 2021).
• Collect data of gambling harms experienced by gamblers and significant others through helpline calls to financial institutions to monitor gambling transactions, as well as other sectors such as intimate partner violence services (where gambling is listed as a cause), bankruptcy courts, and coroners’ reports where gambling is indicated as a cause of suicide (Pricel et al., 2021).

Policy

• Develop network of stakeholders to access and assess gambling related data to work towards a common public health goal to identify gambling problems at earlier stages and respond in a concerted manner to reduce or prevent harm from occurring disorders (Pricel et al., 2021).
• Form a basis to address population gambling-related health problems by developing integrated approaches to where gambling harm intersects with other public health issues such as substance abuse, mental illness, poverty, etc. (Pricel et al., 2021).