Blase Gambino

Perceived family history of problem gambling and scores on SOGS

A sample of 93 veterans (92.4% males), with a median age of 41, (Mean=43.5) attending clinics for problem drinking, drug abuse and other mental disorders was screened for problems associated with the diagnosis of pathological gambling. The diagnostic instrument employed was the South Oaks Gambling Screen developed by Lesieur and Blume. The data replicate earlier findings indicating a link between parental problem gambling and pathological gambling. The results extended this association to include grandparents thus firming the familial relationship. Several epidemiological measures were defined and illustrated. These included relative risk, the odds ratio, attributable risk percent and population attributable risk percent. The data were consistent with previous research that substance abusers are about six times as likely to be addicted to gambling as the general population.

The correction for bias in prevalence estimation with screening tests.

The concern that the South Oaks Gambling Screen (SOGS) and other screening tests have a relatively high rate of false positive errors which results in overestimation of the true prevalence in general population studies is shown to be unfounded. False positives are seen to be a necessary but not sufficient condition for overestimation. It is demonstrated that the proper research question is whether the sample prevalence estimator is biased, and, if so, in which direction. One solution to the problem of bias is shown to depend on the availability of estimates of the error rates of the test.

An Epidemiologic Note on Verification Bias: Implications for Estimation of Rates.

Two-stage designs offer several advantages for purposes of test validation and prevalence estimation. These include enhanced precision and increased cost-efficiency. Cost efficiency is obtained when the best available verification criterion is too expensive to employ in a large-scale epidemiologic study. The use of two-stage (or double-sampling) designs permit the same inferences from application to only a subset of those who were screened (with the less expensive criterion) during stage one. The retesting of only some of the first stage respondents introduces a bias, however, if these are sampled on the basis of first stage screening results. The form of this bias is described and solutions for correcting estimates are provided. These solutions are applied to the data reported by Abbott and Volberg (1996) in their study of the New Zealand general population. Corrected estimates of the sensitivity and specificity of the South Oaks Gambling Screen are obtained and used to adjust the reported lifetime and current prevalence estimates. The value of multi-stage designs for validity assessment and prevalence estimation are briefly described.

The validation of screening tests: meet the new screen same as the old screen?

The focus of this report is to examine the process of validation of new screening tests designed to detect the problem gambler in research and practice settings. A hierarchical or phases of evaluation model is presented as a conceptual framework to describe the basic features of the validation process and its implications for application and interpretation of test results. The report describes a number of threats to validity in the form of sources of unintended bias that when unrecognized may lead to incorrect interpretations of study results and the drawing of incorrect conclusions about the usefulness of the new screening tests. Examples drawn from the gambling literature on problem gambling are used to illustrate some of the more important concepts including spectrum bias and clinical variation in test accuracy. The concept of zones of severity and the bias inherent in selecting criterion thresholds are reviewed. A definition of reference or study gold standard is provided. The use of 2-stage designs to establish validity by efficiently using reference standards to determine indices of accuracy and prevalence is recommended.

Reflections on Accuracy

The difference between test accuracy and predictive accuracy is presented and defined. The failure to distinguish between these two types of measures is shown to have led to a misguided debate over the interpretation of prevalence estimates. The distinction between test accuracy defined as sensitivity and specificity, and predictive accuracy defined as positive and negative predictive value is shown to reflect the choice of the denominator used to calculate true positive, false positive, false negative, and true negative rates. It is further shown that any instrument will tend to overestimate prevalence in low base rate populations and underestimate it in those populations where prevalence is high. The implications of these observations are then discussed in terms of the need to define diagnostic thresholds that have clinical and policy relevance.

The epistemology of “addictive disease”: Gambling as predicament

This research examines the relative perception of “gambling” as a disease in the context of drug use/abuse, and other excessive behavior patterns, for example, workaholism. A sample of 144 adults provided subjective perceptions of disease status of 80 representative items drawn from the literature. A subset of these items represented the class of addictive diseases. These items were subjected to principal components analysis to reveal the presence of clusters or constellations of perceptually similar entities. Gambling was perceived to beless of a predicament (i.e., a potentially dangerous situation; Shaffer, 1987) than substance abuse or substance dependence, particularly if the substance was illicit, such as cocaine or heroin. Gambling was not perceived to be characterized by the underlying construct of dependence, a dimension associated with the abuse of illicit substances. Another analysis revealed that when gambling and substance abuse and dependence were perceived against ause of substances background, licit and illicit, gambling was more likely perceived as a permissible “activity” than a predicament which might lead to abuse and/or dependence. This same result characterizes the perceptions of alcohol use, cocaine use, chemical use, and heroin use. While individuals may recognize the potential danger of drug abuse, illicit drug use per se is not perceived to lead to drug abuse, dependence, or addiction as often portrayed by the government and media. Against a background of excessive behaviors, for example, workaholism, gambling emerged as more similar to these behaviors than abuse or use. The implications of these findings are discussed. The clinical implications of an epistemology of addictive “disease” were also discussed.

A comment on the utility of prevalence estimates of pathological gambling.

The case is presented that researchers interested in policy aimed at treating the pathological gambler need to shift focus to improving the utility of prevalence estimates. It is argued that researchers supplement prevalence estimates with practical and well-defined measures of severity and other predictors and correlates of help-seeking. The dimension of severity is emphasized as one means of providing estimates that are relevant to policy makers when placed in the context of additional measures that improve their meaning and utility. Estimates may then be partitioned along these dimensions to ascertain the proportion of gamblers most likely to need or seek treatment for gambling-related disorders. The recommendations provided are subject to a number of possible objections and are presented in the interest of stimulating further discussion such as the distinction between symptom assessment and the measurement of severity.

Setting Criterion Thresholds for Estimating Prevalence: What is Being Validated?

Much of the debate over how best to estimate the prevalence of problem gambling in the general population is driven by a number of misconceptions, misinterpretations, and questionable, sometimes erroneous assumptions. Among the latter is the failure to understand that what is being validated is not the test but the interpretation of test scores for a specific purpose. In addition there has been a lack of attention to defining the clinical and/or epidemiologic relevance of case definitions in terms of severity and other clinical attributes, a misunderstanding of how test values are interpreted when criterion thresholds or cut-off points are selected, and a failure to replicate the validation of criterion thresholds for defining cases of problem gambling. It is argued further that the distinction between dichotomy and continuum is a false choice, and any emphasis on overestimation is misdirected. Alternative methods for evaluating tests and estimating prevalence are described and a pragmatic empirical approach to the interpretation of prevalence estimates is recommended.

Estimating Confidence Intervals and Sampling Proportions in Two-Stage Prevalence Designs

In response to Abbott and Volbergs (in press) rejoinder to my epidemiologic note on verification bias and estimation of prevalence rates (Gambino, in press), I provide the formulas for computing confidence intervals for the results of second-stage verification. In addition, I provide the appropriate equation for determining confidence intervals when prevalence is near zero or one. Finally, we present formulas for determining the most efficient sample sizes needed to minimize second-stage variance estimates. These allow the investigator working under a fixed budget to determine the relative value of sampling negative screens to test for false negatives. We close with an observation on the interpretability of evidence.

Test Performance Variation Between Settings and Populations

The present paper is the third in a series on the evaluation of new tests designed to detect the disordered gambler. The present paper has two objectives. First, the observed variation in test performance between settings and populations is described in general terms and an explanation of the observed variation is presented in terms of what is referred to as spectrum effects. Second the expected variation in test performance between settings and populations is illustrated with several examples and the implications emphasized for the purpose of test evaluation.