https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39525 Wed 27 Jul 2022 14:04:09 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47131 Wed 14 Dec 2022 15:13:34 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46524 Tue 05 Sep 2023 14:38:18 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43039 4 standard drinks on a single occasion), and (ii) the total number of alcoholic drinks consumed in the past year, adjusted for a range of potential child, parent, family, and peer covariates. Results: Fifty percent of adolescents reported alcohol use and 36% reported bingeing at wave 5 (mean age 16.9 years), and the mean age of initiation to alcohol use for drinkers was 15.1 years. Age of initiation was significantly associated with binge drinking and total quantity of alcohol consumed in unadjusted and adjusted models. Age of first drunkenness was associated with total quantity of alcohol consumed in unadjusted models but not adjusted models and was not associated with subsequent bingeing. Conclusions: Initiating alcohol use earlier in adolescence is associated with an increased risk of binge drinking and higher quantity of consumption in late secondary school, supporting an argument for delaying alcohol initiation for as long as possible to reduce the risk for problematic use in later adolescence and the alcohol-related harms that may accompany this use.]]> Thu 24 Aug 2023 09:26:02 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49317 Thu 11 May 2023 14:53:09 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23227 20 cig/day) compared to light-moderate smoking is unknown. The link between behavioural risk factors and future smoking for both ex and current smokers is also unknown. This study sought to examine these relationships. It is hypothesised that behavioural risk factors will be more strongly associated with heavy smoking. Method: Data from Wave 7 (2007) of the Household and Labour Dynamics in Australia (HILDA) survey was analysed using logistic regression to determine relationships between diet (fruit and vegetable consumption, and unhealthy diet choices), alcohol consumption, obesity and physical activity with light-moderate smoking and heavy smoking. The association between these risk factors and future smoking (2008) was assessed for current and ex-smokers (2007). Results: Obese respondents were less likely to be light/moderate smokers (RRR: 0.53; 95% CI: 0.43, 0.66) but not heavy smokers. Those who consume confectionary weekly were less likely to be light/moderate smokers (RRR: 0.73; 95% CI: 0.61, 0.87), but not heavy smokers. Smokers in 2007 were more likely to continue smoking in 2008 if they consumed 1-4 drinks per occasion (OR: 2.52; 95% CI: 1.13, 5.62). Ex-smokers in 2007 were less likely to relapse in 2008 if they consumed recommended levels of both fruit and vegetables (OR: 0.31; CI: 0.10, 0.91). Conclusion: The relationships between heavy smoking and behavioural risk factors differ from moderate-light smoking. Future primary care interventions would benefit from targeting multiple risk factors, particularly for heavy smokers.]]> Thu 04 Nov 2021 10:40:06 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35002 Thu 04 Nov 2021 10:40:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49210 Sun 07 May 2023 09:29:54 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51177  0.05). Average heavy consumption definitions of tolerance were most strongly associated with persistent AUD (OR = 6.66, P = 0.001; OR = 4.65, P = 0.004) but not associated with new-onset AUD (Ps > 0.05). Conclusions: Initial drink and percentage change thresholds appear to improve the efficacy of change-based tolerance as an indicator for new-onset alcohol use disorder diagnosis in self-report surveys of young adults. When predicting persistent alcohol use disorder, average heavy consumption-based indicators appear to be a better way to measure tolerance than self-reported change-based definitions.]]> Mon 22 Apr 2024 12:31:43 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46245 Mon 14 Nov 2022 13:21:59 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39579 Mon 08 Aug 2022 11:35:05 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49927 Mon 06 May 2024 15:16:01 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50072 Fri 30 Jun 2023 11:20:33 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48638 4 standard drinks), and alcohol-related harms. Results: At mean age of 12.9 years about one in ten children report parental supply of alcohol which increases to about four in ten children by 17.8 years. Mothers consistently more often supply their daughters with alcohol than their sons, [Wave 5 OR 1.77 (1.53,2.05)], while mothers less often supply sons than their daughters, [Wave 5 OR 0.82 (0.71,0.95)]. Mothers’ supply of alcohol to daughters predicts substantially increased odds of daughters binge drinking, [OR 1.67 (1.10,2.53)] and experiencing alcohol related harms, [OR 1.65 (1.10,2.48)]. Conclusion: There is a need to involve both mothers and fathers and to equally target female and male children in programs to reduce the harmful consequences of parental supply of alcohol to their children.]]> Fri 24 Mar 2023 13:23:51 AEDT ]]>