Patterns of alcohol consumption among people with major chronic diseases

Pham, Thi Thu Le; Callinan, Sarah; Livingston, Michael

doi:10.1071/py18075

Cited by 12 publications

(11 citation statements)

References 19 publications

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“…A Canadian study also did not find any association between excess alcohol consumption and MM [15]. Risky alcohol consumption is associated with a higher risk of many chronic conditions, and in people with existing chronic diseases it is related to a higher risk of medical conditions [19,20]. An explanation for our results might be that those with 1 chronic condition at baseline did not tend to use alcohol regularly.…”

Section: Discussionmentioning

confidence: 50%

Socio-demographic and lifestyle factors associated with multimorbidity in New Zealand

et al. 2019

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OBJECTIVES: The incidence of multimorbidity (MM) and its correlates among older adults remain poorly understood. This study aimed to examine the socio-demographic and lifestyle factors associated with MM in New Zealand.METHODS: People aged 55-70 years were invited to participate in a population-based cohort study, the Health Work and Retirement Study, in 2006. Those who accepted the invitation and completed the baseline questionnaire were followed up on a biennial basis. Data on socio-demographic factors, health and lifestyle behaviours, and diagnoses of chronic diseases were obtained from baseline and 6 waves of follow-up. Generalised estimating equations (GEE) adjusted for both time-constant and time-varying factors were used to model factors associated with the onset of MM.RESULTS: A total of 1,673 participants (with 0 or 1 chronic condition) contributed to an overall 8,616 person-years of observation. There were 590 new cases of MM over 10 years of follow-up, corresponding to an overall incidence of 68.5 per 1,000 person-years. The results of the age- and sex-adjusted GEE analysis showed that age, ethnicity, living alone, obesity, hypertension, and having 1 chronic condition at baseline were significant predictors of MM onset. Higher education, income, physical activity, and regular alcohol consumption were protective factors. In a fully adjusted model, marital status (odds ratio [OR], 1.18; 95% confidence interval [CI], 1.01 to 1.37; p=0.039), hypertension (OR, 1.23; 95% CI, 1.02 to 1.48; p=0.032) and having 1 chronic condition at baseline (OR, 2.92; 95% CI, 2.33 to 3.67; p<0.001) remained significant.CONCLUSIONS: The higher incidence of MM among Māori people, socioeconomically disadvantaged groups, those with low physical activity, and obese individuals highlights the importance of targeted prevention strategies.

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Section: Discussionmentioning

confidence: 50%

Socio-demographic and lifestyle factors associated with multimorbidity in New Zealand

et al. 2019

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“… 42 , 43 Another study among 22,684 Australian adults with chronic diseases found that 18% of the subjects experienced long-term risky drinking. 44 These pieces of evidence suggest that the relationship between multimorbidity and lifestyle could be reciprocal causation.…”

Section: Discussionmentioning

confidence: 99%

Relationship between multimorbidity and composite lifestyle status in Shenzhen, China

Yang

Molassiotis

et al. 2022

Journal of Multimorbidity and Comorbidity

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Objective The study aimed to understand multimorbidity among adults in a high-tech city in southern coastal China that has undergone rapid economic development and to investigate its relationship with lifestyle status. Methods A population-based survey was conducted among 24 community centers in the Nanshan District of Shenzhen from February to December of 2018. Participants were recruited using a stratified random sampling approach. A self-administered questionnaire on typical chronic diseases, lifestyle factors, body composition, and social demographics was used to collect data. Multimorbidity was defined as two or more chronic diseases coexisting in a single person. An algorithm on body mass index, physical activity, drinking, smoking, and sleep quality was used to calculate lifestyle scores (0–9), with higher scores predicting a healthier lifestyle. Results A total of 2,905 participants were included in the analysis, with men accounting for 52.4%, and single for 25%. The prevalence of multimorbidity was 4.8%, and the mean lifestyle score was 4.79 ± 1.55. People who were old, retired, married, and had less education were more likely to have multimorbidity (all P < .05). A higher prevalence of multimorbidity was found among those who were obese, less engaged in physical activity, consumed more alcohol, and had poorer sleep quality (all p < .05). After adjusting for age, employment, education, and marital status, one unit increase in lifestyle score was associated with 0.74 times lower to have multimorbidity (OR: 0.74; 95% CI: 0.63–0.87, p < .05). Conclusion The prevalence of multimorbidity was relatively low in economically developed Shenzhen. Keeping a healthy lifestyle was related to the lower possibility of suffering from multiple chronic diseases.

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“…As a result, it is one of the leading causes of mortality and disability [ 3 ]. It also leads to family disintegration and broader societal dysfunction by causing additional damages such as traffic and other accidents, domestic and public violence, and criminality [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Because of personal and social reasons [ 5 ], the questionnaire-based analysis is less accurate. The blood test is not only invasive and unpleasant, but also often unreliable [ 4 ]. In this scenario, the electroencephalogram (EEG)-based noninvasive approaches have been presented for alcoholism identification [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

Mukhtar

Qaisar

Zaguia

2021

Sensors

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Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

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Patterns of alcohol consumption among people with major chronic diseases

Cited by 12 publications

References 19 publications

Socio-demographic and lifestyle factors associated with multimorbidity in New Zealand

Socio-demographic and lifestyle factors associated with multimorbidity in New Zealand

Relationship between multimorbidity and composite lifestyle status in Shenzhen, China

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

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