The single offspring characteristic that contributed to a son or daughter correctly reporting alcohol problems for their AUD father was a higher number of the DSM-IV AUD criterion for themselves.Our 3 original hypotheses focused on informant demographic characteristics that might be related to higher FHM sensitivities, but these predictions were not supported by the data. However, as shown in Table 3, the results indicated significant relationships to FHM sensitivity for several characteristics of the AUD father, including higher endorsement of 3 specific DSM-IV criterion items, that father’s FH of AUDs and the father’s identification with a religion. The current study is unique in evaluating FHM data from a longitudinal evaluation of members of 2 generations of the SDPS families using detailed validated and reliable semi-structured interviews with both probands and offspring regarding the roles of demography and other characteristics, including specific AUD criteria, in FHM sensitivity across generations. Regarding demography, informant higher education was implicated regarding FHM sensitivities in some prior studies but, like our negative findings, not in others . The older offspring were more accurate regarding a smoking FH in the Pape and colleagues large European study of diverse populations but Vandeleur et al.’s, 2008 study of the FH reports of AUDs, similar to ours, found little relationship of informant’s age to FHM sensitivity. There is similar disagreement regarding the relationship of sex to FH accuracy , with our data revealing little evidence of a relationship. Thus, overall, there is little to indicate that easy to identify demographic characteristics of informants might give useful information about the likely sensitivity of offspring reports about parental alcohol use and problems. These negative findings might reflect differences in the samples studied , or differences across the drugs evaluated,cannabis grow kit or the methods used . Or the variation in results across studies could occur if the impact of demography only applies to a small subset of families or if demography has too small an effect size to be identified across studies.
It is equally likely that both men and women, those with higher and lower education, as well as younger and older informants are all similar in the modest accuracy of their reports of positive AUD FHs. We favor the latter explanation. While not originally hypothesized and with relatively few studies of this phenomenon in the literature, Table 3 regression analyses identified 5 characteristics of the AUD father but only one offspring variable that were associated with higher FHM sensitivity. The father’s demography was not strongly related to FHM accuracy, but the probability of being correctly identified as having alcohol problems increased with the AUD father’s severity of alcohol involvement. However, it is worth noting that many of the father’s missed by the FHM had serious alcohol problems including an average of 16 maximum drinks per occasion, and an average endorsement of 5 of 11 DSM-IV criteria. The latter included 87% of Group 2 probands who drank more or longer than intended, 79% with hazardous use, 76% missing obligations, 62% with persistent problems decreasing alcohol use, and 46% spending much time involved with alcohol. Significant proband variables in Table 3 predicting correct offspring reports included endorsement of AUD items of spending a great deal of time related to alcohol, continued use of alcohol despite social or interpersonal problems, and continued use despite physical or psychological problems. This supports the conclusion that the presence of problems more easily observed by the informant is related to higher sensitivities in FHM protocols . The father’s FH of AUDs was also highlighted in Table 3 perhaps because knowledge of alcohol problems in a grandparent might raise awareness of the risk for similar problems in an offspring’s father . In addition, the regression analyses suggest that correct identification of the proband’s problems related to his identification with a religion, which, if replicated, might relate to a family’s emphasis on the need to recognize unacceptable problematic behaviors that might generalize to the recognition of alcohol problems.
The data in Table 1 suggest that several proband drug-related variables might contribute to offspring correctly identifying the father’s alcohol problems, especially having a SUD for illicit drugs other than cannabis. While the lack of statistical significance could be a product of relatively low statistical power, none of the drug-related items in Table 1 were significantly different across Groups 1 and 2, the pattern of differences operated in different directions across different drug related variables, and, most importantly, no drug-related item was significant in Table 3.Overall, the disappointing sensitivity of less than 30% in identifying a father’s alcohol problems was found despite inclusion of many of the study characteristics reported to be associated with relatively higher sensitivities in the FHM approach . This result is at the lower end of the studies of FHM sensitivity, and it is important to remember that Crews and Sher reported up to a 70% correlation for offspring and parent reports on the SMAST. The fact that 50% of the mothers with higher SMAST scores in that study would have been missed underscores the importance to recognizing the need to place FHM results into perspective. At the same time, the current results and the literature also indicate that even if sensitivities are relatively low, we know the direction of the bias is toward under reporting and that the families indicated as positive by offspring using the FHM are accurate over 90% of the time. The current results also offer reminders of the potentially limited generalizability of FHM findings to other FH-positive families and that FHM-based FH-negative families, despite their high specificity, are likely to contain some FHpositive family units that have been mislabeled. The latter adds heterogeneity to the FH-negative group which might make it harder to establish significant differences in characteristics that might exist between FH-positive and FH-negative individuals. The limited sensitivity of the FHM indicates that the approach is not likely to be adequate in epidemiological studies or for public health planning. However, despite the problems outlined above the FHM approach can be useful under some circumstances.
Families identified as positive for a disorder can offer useful data regarding at least a subset of individuals with that condition. For example, in 1978 the SDPS used one offspring informant per family to identify FH-positive and FH-negative drinking but not yet alcoholic young adult participants. This relatively quick and inexpensive application of the FHM helped identify a subset of families with AUDs carrying the low response to alcohol as a familial potential risk factor for future heavy drinking in the young adult probands themselves and a phenotype that turned out to be a good predictor of future alcohol problems . Establishing the validity of the original findings took decades of work that led to the development of a prevention approach that was successful in mitigating the impact of a low LR on heavy drinking and alcoholic blackouts in college students.Thus, the FHM approach was a useful first step in identifying a risk factor for alcohol problems.First and foremost, longitudinal in-depth studies of several generations of families offer useful information, but results might not generalize widely to other populations. Recognizing the relatively high education and socioeconomic status and overwhelmingly European American background of the SDPS families, it is possible that our FHM findings might not apply equally to other FHM studies. A related consideration is that the SDPS originally recruited only male probands in order to maximize the heavier drinking outcome that might be expected of men. However,cannabis grow supplies additional protocols from our laboratory have also studied female subjects . A second major caveat is that although 135 AUD proband pairings are considered in Table 1 and that the data were also analyzed in R where pairings were evaluated in a regression analysis with 1,000 bootstraps, only 73 AUD probands contributed to the analyses. This approach of using bootstrapping to include multiple offspring from each family runs a risk that our results are impacted by nonindependence of some proband/offspring pairs, but results were similar when data were tested on only 73 generational pairs. Third, the modest statistical power reflecting a modest sized sample might underestimate the importance of some variables that were not significant in the current analyses. Fourth, the reasons behind the low sensitivity of the FHM in identifying an alcohol problem in the fathers of these offspring are not clear and are likely to reflect a combination of the offspring’s ignorance of the problem and some offspring’s hesitation to report what they actually know. Fifth, we have no information on additional potentially important explanations for the low sensitivity of the FHM such as poor communication between father and off- spring.
Another possibility is that these offspring did not view their fathers as having an alcohol problem because his behavior did not fit the usual public stereotype of what people with AUDs looks like. In conclusion, this paper hypothesized that the sensitivity of the relatively quick and less expensive FHM approach to gathering a FH of alcohol problems could be improved by considering the demographic characteristics of the informants and/or the fathers on whom they were reporting. However, although none of the 3 demographic characteristics studied here consistently related to the sensitivity of the FHM regarding familial alcohol problems, multiple characteristics of the AUD fathers being reported upon were significantly related to FHM sensitivity, but for the offspring only their own higher number of alcohol problems related to the accuracy of their FH report. At the same time, the SDPS is an example of how the subset of correctly identified informants with a parental FH of an AUD can offer important preliminary information in the search for genetically related characteristics that increase the AUD risk in a subset of families.Some researchers and advocates have raised concerns that alternative nicotine delivery systems act as a gateway into cigarette smoking and promote nicotine dependency for youth.However, other researchers argue that ANDS are important for harm minimization because they may replace higher risk combustible tobacco products, ultimately supporting goals related to the cigarette smoking endgame.Despite these debates, we still know little about how youth make sense of their transitions between ANDS and cigarettes and justify their unique initiation pathways of use. Existing research on pathways of nicotine and tobacco use has primarily focused on examining whether youth initiation of vaping encourages progression to smoking initiation. A few studies suggest that compared to never vapers, youth who use ANDS are likely to progress to smoking and that adolescent smokers who then initiate vaping are likely to adopt dual use practices of smoking and vaping.For example, cross sectional studies have found that among never smoking adolescents, ever use of ecigarettes was associated with increased susceptibility to initiate smoking,and that e-cigarette use was not associated with intentions to quit smoking. Recent longitudinal studies suggest that youth ecigarette use was associated with future cigarette initiation and current cigarette use, suggesting that e-cigarette use is a risk factor for cigarette smoking. This body of evidence, however, has been criticized for not considering the potential counterfactual that, for reasons related to experimentation, the same youth who initiated e-cigarettes first may have been likely to try cigarettes had ANDS been unavailable, and that most e-cigarette-only youth vape infrequently and are not necessarily using devices containing nicotine.Few studies consider other pathways, most notably from cigarettes to ANDS, which is arguably a pathway of harm reduction should smoking be eventually reduced or stopped. Findings from a growing body of qualitative research suggest that the positioning of ANDS as a “gateway” into smoking cigarettes may not align with the reasons why some youth report vaping.For example, a study of 16 young adult vapers in New Zealand found that participants, who smoked and vaped, used ANDS to either recreate or replace rituals of smoking, and non-smoking vapers tended to dislike smoking and vaped to foster social connectedness. Another study of disadvantaged young adult smokers and ex-smokers in Scotland found that although most participants preferred smoking, the few who used e-cigarettes were motivated by health concerns and desires to quit smoking.Qualitative studies have also highlighted that vapers are not a homogenous group and that meanings of vaping vary across users,which suggests a need for a more nuanced understanding of the role of vaping for youth with different NT initiation pathways, particularly in light of ‘gateway’ concerns that early ANDS initiation leads to cigarette smoking initiation.