ABSTRACT
OBJETIVO: Valorar la influencia que la densidad de los puntos de venta y los de venta y consumo de alcohol ejercen sobre los patrones de consumo de los/las jóvenes preuniversitarios/as de Galicia. MÉTODOS: Se ha llevado a cabo un análisis transversal de la cohorte de estudiantes de la Universidad de Santiago de Compostela (Cohorte Compostela 2016). Se calcularon las prevalencias de consumo para cada uno de los municipios de procedencia de los/las estudiantes de primer ciclo durante el año anterior al ingreso. Se valoró la asociación del consumo de riesgo de alcohol (CRA) y consumo intensivo de alcohol (CIA) con un modelo logístico, considerando como variables independientes la población del municipio, la densidad de locales de venta, la densidad de locales de venta y consumo de alcohol, y la densidad de ambos tipos de locales en el municipio. RESULTADOS: La prevalencia de CRA fue del 60,5% (interval de confianza del 95% [IC95%]: 58,4-62,5) y la de CIA de 28,5% (IC95%: 26,7-30,2). Se observó una gran variabilidad según el municipio de procedencia. El modelo logístico multivariante mostró que los municipios con una densidad de 8,42-9,34 de ambos tipos de locales por mil habitantes presentaban mayor riesgo de CRA (odds ratio [OR]:1.39; IC95%: 1,09-1,78) y de CIA (OR= 1,29; IC95%: 1,01-1,66). CONCLUSIÓN: Estos datos sugieren la importancia de incluir la información del entorno al estudiar el consumo de alcohol. Conocer mejor el entorno podría ayudar a plantear políticas que fomenten en la población conductas más saludables
OBJECTIVE: To assess the influence that alcohol outlet density, off- and on-alcohol premises, and alcohol consumption wield on the consumption patterns of young pre-university students in Galicia (Spain). METHOD: A cross-sectional analysis of a cohort of students of the University of Santiago de Compostela (Compostela Cohort 2016) was carried out. Consumption prevalence were calculated for each of the municipalities from the first-cycle students' home residence during the year prior to admission. The association with risky alcohol consumption (RC) and binge-drinking (BD) was assessed with a logistic model considering as independent variables the municipality population, alcohol outlet density of off- premises, density of off- and on- premises and total density of both types of premises in the municipality. RESULTS: The prevalence of RC was 60.5% (95% confidence interval [95%CI]: 58.4-62.5) and the BD was 28.5% (95%CI: 26.7-30.2). A great variability was observed according to the municipality of provenance. The multivariate logistic model showed municipalities with a density of 8.42-9.34 of both types of premises per thousand inhabitants presented a higher risk of RC (odds ratio [OR]: 1,39; 95%CI: 1.09-1.78) and BD (OR: 1.29; 95%CI: 1.01-1.66). CONCLUSIONS: These data suggest the importance of including environmental information when studying alcohol consumption. Knowing our environment better could help plan policies that encourage healthier behaviour in the population
Subject(s)
Humans , Male , Female , Adolescent , Adolescent Behavior/classification , Underage Drinking/classification , Alcoholism/epidemiology , Dangerous Behavior , Risk-Taking , Health Risk Behaviors , Prevalence , Cross-Sectional Studies , SpainABSTRACT
BACKGROUND AND AIMS: Youth alcohol consumption has declined significantly during the past 15 years in many high-income countries, which may have significant public health benefits. However, if the reductions in drinking occur mainly among lighter drinkers who are at lower risk, then rates of alcohol-related harm among young people today and adults in future may not fall in line with consumption. There is conflicting evidence from Swedish school studies, with some suggesting that all young people are drinking less, while others suggest that alcohol consumption among heavier drinkers may be stable or rising while average consumption declines. This paper extends the geographical focus of previous research and examines whether the decline in youth drinking is consistent across the consumption distribution in England. DESIGN: Quantile regression of 15 waves of repeat cross-sectional survey data. SETTING: England, 2001-16. PARTICIPANTS: A total of 31 882 schoolchildren (50.7% male) aged 11-15 who responded to the Smoking Drinking and Drug Use among Young People surveys. MEASUREMENTS: Past-week alcohol consumption in UK units at each fifth percentile of the consumption distribution. FINDINGS: Reductions in alcohol consumption occurred at all percentiles of the consumption distribution analysed between 2001 and 2016, but the magnitude of the decline differed across percentiles. The decline in consumption at the 90th percentile [ß = -0.21, confidence interval (CI) = -0.24, -0.18] was significantly larger than among either lighter drinkers at the 50th percentile (ß = -0.02, CI = -0.02, -0.01) or heavier drinkers at the 95th percentile (ß = -0.16, CI = -0.18, -0.13). CONCLUSIONS: Alcohol consumption among young people in England appears to be declining across the consumption distribution, and peaks among heavy drinkers. The magnitude of this decline differs significantly between percentiles of the consumption distribution, with consumption falling proportionally less among the lightest, moderate and very heaviest youth drinkers.
Subject(s)
Underage Drinking/classification , Underage Drinking/trends , Adolescent , Child , Cross-Sectional Studies , England/epidemiology , Female , Humans , Male , Regression AnalysisABSTRACT
BACKGROUND: Despite concerns over measurement error, self-report continues to be the most common measure of adolescent alcohol use used by researchers. Objective measures of adolescent alcohol use continue to advance; however, they tend to be cost prohibitive for larger studies. By combining appropriate statistical techniques and validation subsamples, the benefits of objective alcohol measures can be made more accessible to a greater number of researchers. OBJECTIVES: To compare three easily implemented methods to correct for measurement error when objective measures of alcohol use are available for a subsample of participants, regression calibration, multiple imputation for measurement error (MIME), and probabilistic sensitivity analysis (PSA), and provide guidance regarding the use of each method in scenarios likely to occur in practice. METHODS: This simulation experiment compared the performance of each method across different sample sizes, both differential and non-differential error, and differing levels of sensitivity and specificity of the exposure measure. RESULTS: Failure to adjust for measurement error led to substantial bias across all simulated scenarios ranging from a 35% to 208% change in the log-odds. For non-differential misclassification, regression calibration reduced this bias to between a 1% and 23% change in the log-odds regardless of sample size. At higher sample sizes, MIME produced approximately unbiased (between a 0% and 9% change in the log-odds) and relatively efficient corrections for both non-differential and differential misclassification. PSA provided little utility for correcting misclassification due to the inefficiency of its estimates. CONCLUSION: Concern over measurement error resulting from self-reported adolescent alcohol use persists in research. Where appropriate, methods involving validity subsamples provide an efficient avenue for addressing these concerns.
