Alcohol and Mortality: An Actuarial Issue

Gutterman, Sam

doi:10.1080/10920277.2021.1946660

Cited by 3 publications

(2 citation statements)

References 59 publications

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“…A common methodological issue in studies of alcohol consumption and health/mortality outcomes is the 'sick-quitter effect', whereby the inclusion of former drinkers in non-drinker reference groups can cause biased risk estimates due to many former drinkers quitting because of ill-health [6][7][8][9]. It has been suggested that lifetime abstainers or low-volume/occasional drinkers be used as the reference group, as these should mitigate bias from the sick-quitter effect [6,10].…”

Section: Introductionmentioning

confidence: 99%

The association between alcohol consumption and all‐cause mortality: An umbrella review of systematic reviews using lifetime abstainers or low‐volume drinkers as a reference group

Sarich,

Gao,

Zhu

et al. 2024

Addiction

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Background and aimsSystematic reviews of the relationship between alcohol consumption and all‐cause mortality have reported different relative risk (RR) curves, possibly due to the choice of reference group. Results have varied from ‘J‐shaped’ curves, where low‐volume consumption is associated with reduced risk, to monotonically increased risk with increasing consumption. We summarised the evidence on alcohol consumption and all‐cause mortality exclusively from systematic reviews using lifetime abstainers or low‐volume/occasional drinkers as the reference group.MethodsWe conducted a systematic umbrella review of systematic reviews of the relationship between alcohol consumption and all‐cause mortality in prospective cohort studies using a reference group of lifetime abstainers or low‐volume/occasional drinkers. Several databases (PubMed/Medline/Embase/PsycINFO/Cochrane Library) were searched to March 2022. Reviews were assessed for risk of bias, and those with reference groups containing former drinkers were excluded.ResultsFrom 2149 articles retrieved, 25 systematic reviews were identified, and five did not include former drinkers in the reference group. Four of the five included reviews had high risk of bias. Three reviews reported a J‐shaped relationship between alcohol consumption and all‐cause mortality with significant decreased risk for low‐volume drinking (RR range 0.84 to 0.95), while two reviews did not. The one review at low risk of bias reported monotonically increased risk with greater consumption (RRs = 1.02, 1.13, 1.33 and 1.52 for low‐, medium‐, high‐ and higher‐volume drinking, respectively, compared with occasional drinking). All five reviews reported significantly increased risk with higher levels of alcohol consumption (RR range 1.28 to 3.70). Sub‐group analyses were reported by sex and age; however, there were evidence gaps for many important factors. Conversely, 17 of 20 excluded systematic reviews reported decreased mortality risk for low‐volume drinking.ConclusionsOver 70% of systematic reviews and meta‐analyses published to March 2022 of all‐cause mortality risk associated with alcohol consumption did not exclude former drinkers from the reference group and may therefore be biased by the ‘sick‐quitter effect’.

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Section: Introductionmentioning

confidence: 99%

The association between alcohol consumption and all‐cause mortality: An umbrella review of systematic reviews using lifetime abstainers or low‐volume drinkers as a reference group

Sarich,

Gao,

Zhu

et al. 2024

Addiction

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“…Alcohol consumption is a widespread and significant public health concern with numerous negative consequences for individuals and society. It can also lead to road collisions and injuries [1]. Accurate identification of alcohol consumption is essential for early intervention and prevention of the adverse effects of excessive alcohol use.…”

Section: Introductionmentioning

confidence: 99%

Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Vital Terlapu,

Prasad Reddy Sadi

2024

IJCDS

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Alcohol consumption can lead to vocal health risks and long-term health issues for individuals. The paper introduces a novel dataset that analyzes vowel vocalizations to detect early alcohol consumption. This study examines hidden parameters in vowel sounds, such as frequency, jitters, shimmer, and harmonic ratio, which can identify individuals who consume alcohol. It aims to identify subtle vocal patterns that serve as markers for alcohol consumption. This study analyzed 509 vowel vocalizations from 290 records of 46 alcohol-consuming individuals and 219 non-drinkers aged 22-34. The study used intelligent machine learning models and Incremental Hidden Layer Neurons Artificial Neural Networks (IHLN-ANNs) with Back-propagation to identify patterns indicative of alcohol consumption. The Random Forest (RF) model achieved 95.3% accuracy, while the BP-ANNs model showed 99.4% accuracy with five neurons in a hidden layer. The findings could be applied to developing smartphone applications to provide timely alerts and cautionary measures for alcohol consumption, reducing accident risks. The study highlights voice analysis's potential as a non-invasive and cost-effective tool for identifying alcohol consumers, offering potential avenues for future public health initiatives.

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Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

Terlapu

2023

Preprint

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Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-term damage to the vocal cords. A new procedure to automatically detect alcohol drinkers using vowel vocalizations is an earlier and lower-cost method than other alcohol drinker-detecting models and equipment. The hidden parameters of vowel sounds (such as frequency, jitter, shimmer, harmonic ratio, etc.) are significant for recognizing individuals who drink or do not drink. In this research, we analyze 509 multiple vocalizations of the vowels (/a, /e, /i, /o, and /u) from 290 multiple records of 46 drinkers and 219 multiple records of 38 non-drinkers. The age group is 22 to 34 years. Apply the 10-fold cross-validation vowelized dataset on intelligent machine learning models and incremental hidden layer neurons of artificial neural networks (IHLN-ANNs) with backpropagation. The findings showed that experimental ML models such as Naïve Bayes (NB), Random Forest (RF), k-NN, SVM, and C4.5 (Tree) performed well. The RF model performed best, with 95.3% accuracy. We also applied the incremental hidden layer (HL) neurons BP-ANNs model (from 2 to 5). In this analysis, accuracy increased proportionally with the incremental neurons (2–5) in the HL of the ANN. At the moment of 5 neurons HL ANN, the model performed with a highly accurate 99.4% without an over-fit problem. It will implement smartphone apps for caution and alerts for alcohol consumers to avoid accidents. Voice analysis has been explored as a non-invasive and cost-effective means of identifying alcohol consumers.

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Alcohol and Mortality: An Actuarial Issue

Cited by 3 publications

References 59 publications

The association between alcohol consumption and all‐cause mortality: An umbrella review of systematic reviews using lifetime abstainers or low‐volume drinkers as a reference group

The association between alcohol consumption and all‐cause mortality: An umbrella review of systematic reviews using lifetime abstainers or low‐volume drinkers as a reference group

Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

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