Impact of alcohol consumption on young people a systematic review of published reviews.
Bibliographic record
Abstract
This review of reviews relating to the impact of alcohol consumption on young people was undertaken between May and October 2008 by a research team based at the Institute of Health and Society at Newcastle University. The work was commissioned by the Department for Children, Schools and Families (DCSF) - Tender No: eor/sbu/2008/007. \nAims and Objectives: The aim of the study was to undertake a systematic review of the published review literature and summarise the evidence on the harms and benefits of alcohol consumption for children \nand young people. In this process we assessed the quality of the evidence in this field and its relevance to a UK population. We also identified gaps in the research which need to be addressed. \nMethods and Limitations: After a systematic search of the published evidence, 162 eligible reviews were found. These \nreviews were graded in terms of methodological quality and the strength of the conclusions that could be drawn from them. In the final narrative, 102 reviews were summarised to consider the impact of drinking on young people. The unused reviews consisted of duplicate material, older reviews and/or those of poorer methodological quality. This report takes the \nform of a narrative summary of the reviews’ findings. For ease of reading, references are \npresented using the Vancouver system in which each one is represented in the text by a unique number and then all are ordered numerically in a reference list at the end of the report. The evidence-base included many older reviews and there was a preponderance of research from the USA. In addition, the literature focused on adolescents and older teenagers but there was little information about prepubescent children. As a result, this body of work has \nlimited generalisability to contemporary drinking by young people in England, particularly in younger children. Moreover, many of the reviews were methodologically weak and so it is not possible to discount bias from their findings. Lastly, most of the reviews were based on cross-sectional research which is unable to determine a causal link between risk factors and \nalcohol misuse, or indeed, between alcohol misuse and specific health or social \nconsequences. It is not clear whether current adult guidance on low risk drinking is pertinent to young people or if specific recommendations are required for individuals who are in the midst of ongoing \nphysiological and emotional development. The review has confirmed that there is a lack of \ngood review evidence available about the impact of drinking on children and young people. However, an absence of evidence in this field does not mean that there is evidence of no impact of alcohol on such individuals. Despite the methodological weakness of research in this field, there is a large body of evidence which reports consistent trends between alcohol use and a range of adverse effects. This convergence allows us to draw credible conclusions \nabout the impact of drinking on young people (particularly around or following puberty). Concluding remarks: There are many adverse consequences of drinking alcohol during childhood and adolescence which would seem to outweigh the modest number of positive impacts. Overall, it seems that delaying the age of alcohol initiation and limiting the amount drunk by young people is likely to enhance their health and well-being. The review literature has significant evidence gaps in the area of alcohol use by, and its \nconsequences in, children and young people. A key gap is the lack of information about the precise amounts of alcohol that lead to adverse consequences. Also the majority of the evidence in this field relates to older youth (adolescents and college/university students). There is a real need for more research relating to younger children, since it is clear that alcohol drinking can and does occur prior to puberty in some UK children. This study has been constrained by the material that is currently available in published reviews. More progress is likely to be made by a systematic review of primary data in this field. However, there is also a clear need for more well-designed prospective studies in UK populations which measure alcohol use with precision and which characterise the range of possible adverse effects of drinking. There is also a requirement, in population studies, for \nmultivariate analysis of key predictor variables in such a way that can control for possible confounding factors. Lastly, there is a need for longitudinal studies that follow-up younger people who drink to identify impacts over the longer term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".