The Problem Gambling Severity Index (PGSI) is a widely used nine item scale for measuring the severity of gambling problems in the general population. Of the four gambler types defined by the PGSI, non-problem, low-risk, moderate-risk and problem gamblers, only the latter category underwent any validity testing during the scale's development, despite the fact that over 95% of gamblers fall into one of the remaining three categories. Using Canadian population data on over 25,000 gamblers, we conducted a comprehensive validity and reliability analysis of the four PGSI gambler types. The temporal stability of PGSI subtype over a 14-month interval was modest but adequate (intraclass correlation coefficient = 0.63). There was strong evidence for the validity of the non-problem and problem gambler categories however the low-risk and moderate-risk categories showed poor discriminant validity using the existing scoring rules. The validity of these categories was improved with a simple modification to the scoring system.

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Longitudinal data in Canada suggest low-risk gambling thresholds of eight times per month, $75CAN total per month and 1.7% of income spent on gambling, all of which are higher than previously derived limits from cross-sectional data. Gamblers who exceed any of the three low-risk limits are four times more likely to experience future harm than those who do not.

American Indian/Alaska Native (AI/AN) persons experienced disproportionate mortality during the 2009 influenza A(H1N1) pandemic (1,2). Concerns of a similar trend during the coronavirus disease 2019 (COVID-19) pandemic led to the formation of a workgroup* to assess the prevalence of COVID-19 deaths in the AI/AN population. As of December 2, 2020, CDC has reported 2,689 COVID-19associated deaths among non-Hispanic AI/AN persons in the United States. † A recent analysis found that the cumulative incidence of laboratory-confirmed COVID-19 cases among AI/AN persons was 3.5 times that among White persons (3). Among 14 participating states, the age-adjusted AI/AN COVID-19 mortality rate (55.8 deaths per 100,000; 95% confidence interval [CI] = 52.5-59.3) was 1.8 (95% CI = 1.7-2.0) times that among White persons (30.3 deaths per 100,000; 95% CI = 29.9-30.7). Although COVID-19 mortality rates increased with age among both AI/AN and White persons, the disparity was largest among those aged 20-49 years. Among persons aged 20-29 years, 30-39 years, and 40-49 years, the COVID-19 mortality rates among AI/AN were 10.5, 11.6, and 8.2 times, respectively, those among White persons. Evidence that AI/AN communities might be at increased risk for COVID-19 illness and death demonstrates the importance of documenting and understanding the reasons for these disparities while developing collaborative approaches with federal, state, municipal, and tribal agencies to minimize the impact of COVID-19 on AI/AN communities. Together, public health partners can plan for medical countermeasures and prevention activities for AI/AN communities.During July 22-September 3, 2020, data were collected on confirmed COVID-19-associated deaths that occurred during January 1-June 30, 2020, from 14 participating states. § These states represent approximately 46.5% of the AI/AN population * Representatives from 14 state health departments,

The Problem Gambling Severity Index (PGSI), a screening tool used to measure the severity of gambling problems in general population research, was subjected to confirmatory factor analysis and Rasch modelling to (a) confirm the one-factor structure; (b) assess how well the items measure the continuum of problem gambling severity; (c) identify sources of differential item functioning among relevant subpopulations of gamblers. Analyses were conducted on a nationally representative sample of over 25,000 gamblers compiled by merging data from the Canadian Community Health Survey and Canadian Problem Gambling Index (CPGI) integrated datasets. Results provided support for a one-factor model that was invariant across gender, age, income level, and gambler type. Rasch modelling revealed a well-fitting, unidimensional model with no miss-fitting items. The average severity assessed by the PGSI is consistent with moderately severe problem gambling. The PGSI is therefore weak in assessing low to moderate problem severity, a notable limitation of most brief gambling screens. Evidence of clinically significant differential item functioning was found with only one item, borrowing money to gamble, which behaved differently in gamblers who play electronic gaming machines or casino games compared to gamblers who avoid these games.

Longitudinal research on the determinants of gambling behavior is sparse. This article briefly reviews the previous seventeen longitudinally designed studies, focusing on the methodology for each study. This is followed by a description of our ongoing longitudinal study entitled the Leisure, Lifestyle, & Lifecycle Project (LLLP). Participants for the LLLP were recruited from four locations in Alberta, Canada, including both rural and urban populations. In the LLLP most participants were recruited using random digit dialing (RDD), with 1808 participants from 5 age cohorts at baseline: 13-15, 18-20, 23-25, 43-45, and 63-65. Individuals completed telephone, computer, and face-to-face surveys at baseline, with the data collection occurring between February and October, 2006. At baseline, a wide variety of constructs were measured, including gambling behavior, substance use, psychopathology, intelligence, family environment, and internalizing and externalizing problems. Finally, the conclusions that can be drawn thus far are discussed as well as the plans for three future data collections.

These data suggest a model in which higher-frequency gambling, particularly with electronic gambling machines, when combined with any type of emotional vulnerability increased the likelihood of gambling disorder.

An increase in the intensity of gambling behaviour is associated with greater likelihood of future gambling related harm in adults.