Second, PG and MD correlate more substantially within monozygotic twins as compared with dizygotic twins. Third, genetic and unique environmental factors, but not shared environmental factors, contribute to the risk for PG and MD. Fourth, the factors contributing to the risk for PG correlate with those contributing to the risk for MD. All of the participants were members of the VET Registry, a national sample of male twins, both of whom served during the Vietnam era and were born between and , inclusively.
A detailed description of the VET Registry development and participants has been described.
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Of the twin pairs, were monozygotic and were dizygotic as determined by responses to questionnaires regarding similarity of physical appearance and supplemental blood typing. The mean SD age of the sample was A high school education was reported by Of those with more than a high school education, Of the entire sample, Symptoms of PG were only assessed in participants acknowledging having gambled 25 or more times in a year.
To examine the hypothesis that PG and MD correlate more substantially within monozygotic twins than in dizygotic twins, tetrachoric correlations were examined using PRELIS 2 17 as described previously. To examine the hypotheses that genetic and unique environmental factors, but not shared environmental factors, contribute to the lifetime co-occurrence of PG and MD and that these factors correlate across disorders, bivariate models fitting the association between PG and MD were examined as described elsewhere.
The correlation between the 2 diagnoses was similarly partitioned into components resulting from additive genetic influences, shared environmental influences, and unique environmental influences plus measurement error Figure. Using Mx software 18 Virginia Commonwealth University, Richmond , models were fit by the method of maximum likelihood. A series of nested submodels were each tested for their goodness of fit against a saturated model that placed no constraints on the elements of the estimated monozygotic and dizygotic twin correlation matrices.
The most parsimonious model was selected as best fitting. Lifetime criteria for PG were met by 1. High rates of lifetime co-occurrence were observed between PG and MD. In a logistic regression model adjusting for sociodemographic measures, the OR for MD was 4. This elevation persisted at an OR of 1.
Tetrachoric correlations Table 2 demonstrated greater within-diagnosis concordance for both PG and MD in monozygotic twins as compared with that in dizygotic twins. Within-twin, cross-diagnosis correlations were comparable for monozygotic and dizygotic twins, and cross-twin, cross-diagnosis correlations were more substantial in monozygotic twins than in dizygotic twins Table 2 , suggesting overlapping genetic contributions to PG and MD. The best-fitting bivariate model Table 3 for the relationship between PG and MD had the correlations for shared and unique environmental factors r C and r E , respectively set to 0 and estimated the correlation for additive genetic factors r A at 0.
Parameter estimates for the additive genetic, shared environmental, and unique environmental contributions to the individual disorders suggest that there are significant genetic and unique environmental contributions to each disorder Figure. All of the overlap between PG and MD was accounted for by genetic factors. Our hypothesis regarding the observation of high rates of lifetime co-occurrence between PG and MD was supported.
These ORs are comparable to that of 3. The VET sample largely comprises white, highly educated, middle-aged, male twins with military service whereas ECA participants were oversampled for African Americans and included approximately equal numbers of men and women from the St Louis community. Together, these data suggest that the high rates of lifetime co-occurrence observed in the present study generalize beyond the cohort of VET participants. Unfortunately, other ECA sites and large national surveys of psychiatric disorders 6 have generally not included gambling measures, contributing to a relative deficiency in our understanding of the relationship between PG and other psychiatric disorders.
The finding that the OR remains significantly elevated following adjustment for sociodemographic and other psychiatric measures suggests that a considerable portion of the risk for MD in association with PG is not accounted for by sociodemographics and commonly co-occurring psychiatric disorders. Our hypothesis regarding higher rates of lifetime co-occurrence of PG and MD in monozygotic twins as compared with the rates in dizygotic twins appears to be partially supported.
Higher rates of within-diagnosis concordance for both PG and MD within monozygotic vs dizygotic twins are consistent with prior reports from the VET data and heritable contributions to each disorder. The most significant finding from the present study was the identification of substantial genetic overlap between PG and MD. The findings from the best-fitting bivariate model for PG and MD were consistent with our hypothesis that PG and MD would exhibit overlapping genetic influences but not shared environmental influences.
Our hypothesis that the unique environmental factors for PG and MD would correlate was not supported by the best-fitting model. These findings suggest that the environmental factors influencing PG and MD differ. A surprising finding was the magnitude of the genetic overlap between PG and MD. The genetic correlation between PG and MD is as great as or greater than those reported for other disorders grouped together within the DSM-IV-TR , eg, those between different substance use disorders correlation range, 0.
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Polymyalgia rheumatica: clinical update. Are we there yet? Travel vaccinations for Australian children. Although this view has some merit Gambino, , it is insufficient to explain which of those gamblers who score in this range will make the predicted transition from less seriously to more seriously disordered. It further implies that the number of symptoms is a straightforward measure of severity, an implication that may not be true. A more literal interpretation of at-risk is that those individuals so labeled are not pathological gamblers at the time of testing but might become so in the future.
This interpretation that at-risk gamblers are not pathological gamblers fails to recognize that some gamblers among those labeled at-risk may be false negatives. The four outcomes of testing for the presence or absence of pathological gambling are true positives, true negatives, false negatives, and false positives. The terms positive and negative by convention refer to meeting or not meeting the criterion score for designating a gambler as pathological or not.
Not meeting criteria is not equivalent to not being a pathological gambler at the time of testing; some pathological gamblers will be missed by setting a cutoff criterion false negatives.
Conversely, meeting criteria is not equivalent to being a pathological gambler; some gamblers who are free of the disorder but score at or above the cutoff will be falsely identified as pathological false positives. The decision not to count those who do not meet some arbitrary cutoff score as cases merely represents an analytical choice of convenience Robins, and, in fact, raises the question of reporting these at all. One reason is the assumption that these individuals are at risk.
This raises a second question: why are they at risk, or, put another way, what has placed these gamblers at risk for progressing to more serious problems or to the status of pathological gambler? A reference to scores alone is insufficient to make the case; additional information is needed.
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This additional information requires the identification of those indicators of risk that predict movement between being pathological and not pathological. Finally, the use of the at-risk label has resulted in the misleading practice of labeling nongamblers as not-at-risk. One approach to clarifying the concept of at-risk is to adopt an epidemiologic framework.
From this perspective, everyone is at risk for becoming a pathological gambler over the course of a lifetime, including nongamblers. To understand this, recall that risk refers to future events and takes on meaning only in the context of an implied or specified time-line; for example, what is the 1-year, 5-year, … … lifetime risk of becoming a pathological gambler? Or, what is the risk of becoming a pathological gambler following the initiation of gambling? It might also be asked what the risk is of a nongambler beginning to gamble.
Drawing upon the epidemiologic literature, risk when applied to the onset of pathological gambling is defined as the average probability of becoming a pathological gambler during a specified interval of time: the period of risk Schlesselman, In this sense, risk is inherently a theoretical measure of incidence, where the latter may be defined as the rate of onset of pathological gambling among specified classes of individuals Miettinen, In the epidemiologic context, not-at-risk is equivalent to the statement that risk equals zero for this class of individuals Schlesselman, It is only in the sense that risk equals zero that the application of not-at-risk to nongamblers is meaningful, but this is rarely, if ever, spelled out.
At risk, on the other hand, is defined as a risk greater than zero and, when defined relative to a class of individuals with a low risk, it signifies being at higher risk. The current assertion that nongamblers are not at risk is not a valid statement in the absence of supportive evidence that relates this class of individuals to the determinants of pathological gambling and an associated time-line.
Although nongamblers may be at zero risk of becoming pathological gamblers at the time of testing, it cannot be assumed that they remain at zero risk for becoming pathological gamblers in the absence of a specified future time-line. For example, at least one study has found, using a retrospective measure that the risk of pathological gambling among a sample of nongamblers remained at zero after a period of 5 years British Columbia, Additional data of this form are necessary to firm up the relationship between being a nongambler and being at risk for a starting to gamble and b becoming a pathological gambler after the onset of gambling.
A second example illustrates the importance of the time-line. The goal of the researcher is to quantify risks for eligible populations, for example, classes of individuals who at the start of a study do not display any signs or symptoms of pathological gambling. The epidemiologic task is to assign a probability value that defines the likelihood of becoming a pathological gambler during the interval of time under study. To repeat, from the perspective of the epidemiologist, to state that individuals in a particular group are at-risk simply implies that the risk of becoming a pathological gambler is greater than zero Miettinen, Conversely, to state that a class of individuals such as nongamblers is not-at-risk is to imply that the individual risk among this class is zero.
The relevant issues associated with the use of the risk concept as applied to nongamblers can be illustrated with a common example. Smokers are at risk for developing a number of disorders including pathological gambling. This does not imply that nonsmokers are not at risk! It merely signifies that smokers are at higher risk than nonsmokers for those disorders for which there is an established empirical association with smoking. It also implies that if the nonsmoker nongambler takes up smoking gambling , then that individual's risk for developing a disorder will increase accordingly.
Similar notions apply to the situation where the smoker stops smoking, and by extension to the gambler who quits gambling. The risk associated with those individuals who quit smoking would then be adjusted downward on the basis of the relevant variables such as age at cessation, years of smoking, frequency of smoking, intensity of smoking inhale deeply, inhale lightly , and so on. It remains an open question whether the onset of gambling is a risk factor in the sense attributed to smoking. In fact, this is unlikely to be the case and highlights the distinction between the epidemiologic term risk factor , suggesting a causal connection, and the more general epidemiologic term risk indicator , which refers to any attribute associated with higher risk Miettinen, Alternatively, gambling certainly qualifies as a determinant of risk as this term is used by epidemiologists.
Clearly, in the absence of exposure to gambling, pathological gambling will not occur and risk will equal zero during the interval of time under observation. It is also not difficult to show that the application of the at-risk label on the basis of score levels is inappropriate if it is meant to denote those who are not pathological gamblers simply because these gamblers did not meet the criteria. The general practice is to assign the at-risk label to those gamblers who score between 1 and 4; this is often limited further to those who score 3 or 4.
To understand why this is inappropriate, it need only be recognized that it is possible to set a criterion of 1 to define cases of pathological gambling! Note that the selection of scores of 1 as the cutoff does not imply that these gamblers are pathological gamblers. This choice relative to conventional cutoff scores of, for example, 5 or higher, simply implies that the likelihood of false positives is enhanced while the likelihood of false negatives is decreased. It should also be noted that the at-risk assertion as generally used implies that conventional cutoff criteria have a degree of diagnostic certainty that is clearly undeserved Gambino, There are two major weaknesses in the use of cutoff scores in prevalence studies of the general population.
The first has been the failure to address the critical question of whether cutoff criteria based on current conventions are related to the clinical significance of the symptomology exhibited by those gamblers who meet the criteria Kessler, This reflects in large measure the lack of effort to define the concept of clinical significance Gambino, in the context of pathological gambling. There has been little effort to date to relate cutoff criteria to meaningful decisions such as to treat or not to treat; the referral of screening outcomes for more intensive testing; or the allocation of scarce resources for treatment, education, or prevention Gambino, ; Jenkins, One problem that deserves to be highlighted is the current practice in which gamblers with scores of 1 or 2 are generally lumped together with those who score 0.
Shaffer and Hall noted this problem in their analysis of adolescent prevalence rates.http://pansionat-kanaka.com.ua/includes/price-hydroxychloroquine-400mg-canadian-pharmacy.php
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These investigators argued properly that it is important to distinguish between symptom-free and symptomatic gamblers. Additionally, those who score 1 or 2 are often labeled as not-at-risk along with those who score 0 or those who report they have never gambled.
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These three groups are often placed in the same category. This represents a significant loss of information and in the case of those who score between 1 and 2 permits a demonstration of the misuse of the not-at-risk terminology. It is unclear at present whether such potent indicators of pathological gambling will occur frequently or at all among this class of gamblers.
This requires an evaluation of individual items and their distribution among those who score 1 or 2 on the instrument employed in any specific study. The argument that a score of 1 or 2 may reflect the presence of pathological gambling is not without empirical merit. A recent study has begun examining the distribution of clinical indicators among those who endorse one or more items and clearly demonstrates the importance of this task.
Toce-Gerstein et al. Analysis of these items by the present author using the likelihood ratio LR is revealing Gambino, Sensitivity the true positive rate of the test was estimated by the proportion of gamblers who scored 10 or higher and endorsed the specific item, while 1 — specificity the false positive rate was estimated by the proportion who scored 1 or 2 and endorsed the item. The LR for chasing was estimated at According to interpretative guidelines provided by Jaeschke, Guyatt, and Sackett , an LR that falls in the range of 2 to 5 represents a small, although sometimes important, association, whereas an LR greater than 10 is considered large and often conclusive.
These results emphasize that it is a mistake to assume that individuals who score 1 or 2 are equivalent to those who score 0. It should be noted that this method is equivalent to correlating test items with the total test score. The weakness in this assertion lies in the failure to clearly specify the determinants of risk associated with changes in scores over time, as Winters et al. Which indicators of risk are associated with increasing symptoms and which are associated with decreasing symptoms is an important issue that cannot be resolved on the basis of the evidence to date.
In fact, the establishment of validated risk and protective factors would help to clarify the current reliance on score levels to indicate individuals at risk. It should be apparent, for example, that if risk indicators are identified, then some proportion of those who score 0 must be at higher risk than the remaining gamblers in this class who lack the identified attributes of risk, and in theory at least could be at higher risk than some of those who score 1 or 2.
There is a lack of strong evidence and theoretical rationales for applying different labels: problem versus pathological, level 2 versus level 3, probable versus potential, subclinical versus clinical, or not-at-risk versus at-risk. The basis for these labels appears not to reflect relationships that are consistently supported but rather what is intuitively appealing or a historical uncritical acceptance of the terminology found in the literature.
For one thing, each of these labels implies incorrectly that these are qualitatively different individuals with respect to being or not being a pathological gambler. This is not a valid statement since, in the absence of additional evidence; it cannot be shown that, for example, a gambler who scored just above and a gambler who scored just below an arbitrary criterion score such as 5 are, in fact, different with respect to being or not being pathological gamblers Robins, This can be generalized to the selection of any cutoff score as the criterion for defining a case.
In technical terms, acceptance of the construct of pathological gambling implies the two gamblers described in the above illustration represent, respectively, one of four possible combinations of states. These are 1 true positive, false negative both pathological ; 2 false positive, true negative neither pathological ; 3 true positive, true negative the first pathological but not the second ; or 4 false positive, false negative the second pathological but not the first.
This description technically applies to the entire population, including nongamblers who may be less than honest in responding and those in treatment who may be misdiagnosed. The selection of a criterion cutoff then determines the possible labels; that is those at or above can only be true positives or false positives. Those below the criterion can only be true negatives or false negatives. This is an important issue since the screening of large numbers of the population is an expensive undertaking.
Further, the decision to take additional action such as referral for treatment or for more intensive assessment entails additional incurred costs associated with false positive results. A second question that needs to be answered is whether those at or above the criterion represent cases that are clinically significant Gambino, Clinical significance might be demonstrated by showing that those who meet or exceed criteria are more likely to seek help than those who do not Productivity Commission ; Tremayne et al.
Researchers need to identify those risk and protective factors that are associated with the onset or prediction of pathological gambling if the terminology of risk is to be meaningful, useful, and relevant. This process is only recently underway and remains predominantly in the conceptual stage of development Derevensky et al.
The best estimate of predicting the occurrence of pathological gambling, or the progression of the gambler to a more serious level, is to base it on the experiences of a large sample of people who are not pathological gamblers at the outset.
These individuals are then followed over a defined period of time, e. The group is referred to as a cohort and the measure of interest is the incidence or inception of some event of interest, such as the onset of pathological gambling or movement to a more severe level.
What are needed, but currently lacking, are case definitions that can be related to the utility of clinical decisions treat or not treat , their usefulness in testing research hypotheses who is at risk , and their value for applications to policy who will seek treatment , and that will, in the final analysis, serve to improve the health of those who suffer from gambling-related disorders. The latter is itself an unresolved question. Is there a single disorder that may be designated pathological gambling, or does the phenomenon encompass several distinct gambling disorders, for example, in the sense that different gaming venues e.