Frequency Of Alcohol Drinking Research Articles

The Health4Life e-health intervention for modifying lifestyle risk behaviours of adolescents: secondary outcomes of a cluster randomised controlled trial.

To investigate the effectiveness of a school-based multiple health behaviour change e-health intervention for modifying risk factors for chronic disease (secondary outcomes). Cluster randomised controlled trial. Students (at baseline [2019]: year 7, 11-14 years old) at 71 Australian public, independent, and Catholic schools. Health4Life: an e-health school-based multiple health behaviour change intervention for reducing increases in the six major behavioural risk factors for chronic disease: physical inactivity, poor diet, excessive recreational screen time, poor sleep, and use of alcohol and tobacco. It comprises six online video modules during health education class and a smartphone app. Comparison of Health4Life and usual health education with respect to their impact on changes in twelve secondary outcomes related to the six behavioural risk factors, assessed in surveys at baseline, immediately after the intervention, and 12 and 24 months after the intervention: binge drinking, discretionary food consumption risk, inadequate fruit and vegetable intake, difficulty falling asleep, and light physical activity frequency (categorical); tobacco smoking frequency, alcohol drinking frequency, alcohol-related harm, daytime sleepiness, and time spent watching television and using electronic devices (continuous). A total of 6640 year 7 students completed the baseline survey (Health4Life: 3610; control: 3030); 6454 (97.2%) completed at least one follow-up survey, 5698 (85.8%) two or more follow-up surveys. Health4Life was not statistically more effective than usual school health education for influencing changes in any of the twelve outcomes over 24 months; for example: fruit intake inadequate: odds ratio [OR], 1.08 (95% confidence interval [CI], 0.57-2.05); vegetable intake inadequate: OR, 0.97 (95% CI, 0.64-1.47); increased light physical activity: OR, 1.00 (95% CI, 0.72-1.38); tobacco use frequency: relative difference, 0.03 (95% CI, -0.58 to 0.64) days per 30 days; alcohol use frequency: relative difference, -0.34 (95% CI, -1.16 to 0.49) days per 30 days; device use time: relative difference, -0.07 (95% CI, -0.29 to 0.16) hours per day. Health4Life was not more effective than usual school year 7 health education for modifying adolescent risk factors for chronic disease. Future e-health multiple health behaviour change intervention research should examine the timing and length of the intervention, as well as increasing the number of engagement strategies (eg, goal setting) during the intervention. Australian New Zealand Clinical Trials Registry: ACTRN12619000431123 (prospective).

Estimating the prevalence and clinical significance of chronic obstructive pulmonary disease-obstructive sleep apnea overlap in South Korea

IntroductionConcurrent obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) are referred to as COPD-OSA overlap. We investigated the prevalence and clinical significance of COPD-OSA overlap in the general population of South Korea. MethodsData were obtained from the 2019 Korea National Health and Nutrition Examination Survey. Participants ≥40 years of age with complete STOP-Bang questionnaire and spirometry data were included. OSA was presumed in individuals with a STOP-Bang score of ≥3. COPD was determined using forced expiratory volume at 1 s/forced vital capacity <0.7. Participants were classified on the basis of the STOP-Bang score and spirometry findings. Clinical characteristics, comorbidities, and quality of life (using the EuroQoL 5-dimension instrument) were compared between the overlap group and COPD-alone or OSA-alone groups. ResultsAmong the 3157 participants, 6.9 % demonstrated COPD-OSA overlap. Individuals with OSA alone and COPD alone were 31.8 % and 5.2 %, respectively. The overlap group included more males, ever smokers, and frequent alcohol drinkers than the COPD- or OSA-alone groups. The overlap group had more diagnoses of hypertension, diabetes, and stroke than the COPD-alone group. The risk of anxiety/depression was approximately 2.5 times higher in the overlap group than in the COPD-alone group. COPD-OSA overlap was a significant risk factor for anxiety/depression after adjusting for age, sex, household income, and education levels. ConclusionCOPD-OSA overlap is not rare within the general population of South Korea. Patients with overlap showed more comorbidities and higher levels of anxiety/depression than those in the COPD-alone group.

Air pollution, alcohol consumption, and the risk of elevated liver enzyme levels: a cross-sectional study in the UK Biobank.

Evidences on the association between exposure to air pollution and liver enzymes was scarce in low pollution area. We aimed to investigate the association between air pollution and liver enzyme levels and further explore whether alcohol intake influence this association. This cross-sectional study included 425,773 participants aged 37 to 73years from the UK Biobank. Land Use Regression was applied to assess levels of PM2.5, PM10, NO2, and NOx. Levels of liver enzymes including AST, ALT, GGT, and ALP were determined by enzymatic rate method. Long-term low-level exposure to PM2.5 (per 5-μg/m3 increase) was significantly associated with AST (0.596% increase, 95% CI, 0.414 to 0.778%), ALT (0.311% increase, 0.031 to 0.593%), and GGT (1.552% increase, 1.172 to 1.933%); The results were similar for PM10; NOX and NO2 were only significantly correlated with AST and GGT Significant modification effects by alcohol consumption were found (P-interaction < 0.05). The effects of pollutants on AST, ALT, and GGT levels gradually increased along with the weekly alcohol drinking frequency. In conclusion, long-term low-level air pollutants exposure was associated with elevated liver enzyme levels. And alcohol intake may exacerbate the effect of air pollution on liver enzymes.

Evaluating the interaction between 3'aQTL and alcohol consumption/smoking on anxiety and depression: 3'aQTL-by-environment interaction study in UK Biobank cohort

BackgroundSmoking and alcohol consumption were associated with the development of depression and anxiety. 3′UTR APA quantitative trait loci (3'aQTLs) have been associated with multiple health states and conditions. Our aim is to evaluate the interactive effects of 3'aQTLs-alcohol consumption/tobacco smoking on the risk of anxiety and depression. MethodsThe 3'aQTL data of 13 brain regions were extracted from the large-scale 3'aQTL atlas. The phenotype data (frequency of cigarette smoking and alcohol drinking, anxiety score, self-reported anxiety, depression score and self-reported depression) of 90,399–103,011 adults aged 40–69 years living in the UK and contributing to the UK Biobank during 2006–2010, were obtained from the UK Biobank cohort. The frequency of cigarette smoking and alcohol drinking of each subject were defined by the amount of smoking and alcohol drinking of self-reported, respectively. The continuous alcohol consumption/smoking terms were further categorized in tertiles. 3'aQTL-by-environmental interaction analysis was then performed to evaluate the associations of gene-smoking/alcohol consumption interactions with anxiety and depression using generalized linear model (GLM) of PLINK 2.0 with an additive mode of inheritance. Furthermore, GLM was also used to explore the relationship between alcohol consumption/smoking with hazard of anxiety/depression stratified by allele for the significant genotyped SNPs that modified the alcohol consumption/smoking-anxiety/depression association. ResultsThe interaction analysis identified several candidate 3'aQTLs-alcohol consumption interactions, such as rs7602638 located in PPP3R1 (β = 0.08, P = 6.50 × 10−6) for anxiety score; rs10925518 located in RYR2 (OR = 0.95, P = 3.06 × 10−5) for self-reported depression. Interestingly, we also observed that the interactions between TMOD1 (β = 0.18, P = 3.30 × 10−8 for anxiety score; β = 0.17, P = 1.42 × 10−6 for depression score), ZNF407 (β = 0.17, P = 2.11 × 10−6 for anxiety score; β = 0.15, P = 4.26 × 10−5 for depression score) and alcohol consumption was not only associated with anxiety, but related to depression. Besides, we found that relationship between alcohol consumption and hazard of anxiety/depression was significantly different for different SNPs genotypes, such as rs34505550 in TMOD1 (AA: OR = 1.03, P = 1.79 × 10−6; AG: OR = 1.00, P = 0.94; GG: OR = 1.00, P = 0.21) for self-reported anxiety. LimitationsThe identified 3'aQTLs-alcohol consumption/smoking interactions were associated with depression and anxiety, and its potential biological mechanisms need to be further revealed. ConclusionsOur study identified important interactions between candidate 3'aQTL and alcohol consumption/smoking on depression and anxiety, and found that the 3'aQTL may modify the associations between consumption/smoking with depression and anxiety. These findings may help to further explore the pathogenesis of depression and anxiety.

Development and validation of a model for measuring alcohol consumption from transdermal alcohol content data among college students.

Transdermal alcohol content (TAC) data collected by wearable alcohol monitors could potentially contribute to alcohol research, but raw data from the devices are challenging to interpret. We aimed to develop and validate a model using TAC data to detect alcohol drinking. We used a model development and validation study design. Indiana, USA PARTICIPANTS: In March to April 2021, we enrolled 84 college students who reported drinking at least once a week (median age = 20 years, 73% white, 70% female). We observed participants' alcohol drinking behavior for 1week. Participants wore BACtrack Skyn monitors (TAC data), provided self-reported drinking start times in real time (smartphone app) and completed daily surveys about their prior day of drinking. We developed a model using signal filtering, peak detection algorithm, regression and hyperparameter optimization. The input was TAC and outputs were alcohol drinking frequency, start time and magnitude. We validated the model using daily surveys (internal validation) and data collected from college students in 2019 (external validation). Participants (N = 84) self-reported 213 drinking events. Monitors collected 10915 hours of TAC. In internal validation, the model had a sensitivity of 70.9% (95% CI = 64.1%-77.0%) and a specificity of 73.9% (68.9%-78.5%) in detecting drinking events. The median absolute time difference between self-reported and model-detected drinking start times was 59 min. Mean absolute error (MAE) for the reported and detected number of drinks was 2.8 drinks. In an exploratory external validation among five participants, number of drinking events, sensitivity, specificity, median time difference and MAE were 15%, 67%, 100%, 45 minutes and 0.9 drinks, respectively. Our model's output was correlated with breath alcohol concentration data (Spearman's correlation [95% CI] = 0.88 [0.77, 0.94]). This study, the largest of its kind to date, developed and validated a model for detecting alcohol drinking using transdermal alcohol content data collected with a new generation of alcohol monitors. The model and its source code are available as Supporting Information (https://osf.io/xngbk).

MAIN PSYCHOLOGICAL AND CHARACTEROLOGICAL FEATURES OF FORCED MIGRANTS: THE THEORETICAL AND PRACTICAL EXPERIENCE

The article examines the psychological characteristics of young people who drink alcohol, investigates the reasons for it and intensity of alcohol consumption. The propensity of young people to various addictions (addictive behaviour) and its connection with people's impulsivity are analysed. Ukrainian youth who have experience in alcohol drinking usually tried it firstly in adolescence. At the same time, the respondents most often did not mention problems with alcohol in their families, but a third of them recorded such problems, and almost half of the respondents noted that they had experienced physical or psychological violence from a drunken relative. Among the reasons for alcohol drinking, the following were predominant: as a way to distract from problems, improve mood, achieve courage or solve internal problems. Among young people, the strongest addictions were addictions to a healthy lifestyle, television, food or work. Alcohol, romantic relations, medicals, smoking, interpersonal relations and computer usage (internet, social networks) as types of addictions were at an average level. The least pronounced were religious, gambling and drug addictions. There were gender differences in susceptibility to addictions (addictive behaviour). Boys, compared to girls, had higher propensity to romantic relations, drugs and interpersonal relationships as addictions and lower propensity to addiction to medical substances. Peculiarities of personal impulsivity, its links with alcohol consumption were studied. Impulse control disorder influenced on youth's propensity to drinking, increasing alcohol drinking frequency. People with higher impulsivity, who were more prone to imbalance manifestations and more strongly influenced by external factors, were more vulnerable to the risk of developing alcohol and food addictions, addictions to a healthy lifestyle, smoking or interpersonal relationships.

Reported Cases of Alcohol Consumption and Poisoning for the Years 2015 to 2022 in Hail, Saudi Arabia.

This study aimed to determine the pattern of alcohol consumption and its poisoning among the Saudi population in the city of Hail, KSA. Data from a retrospective cohort were collected qualitatively at King Khalid Hospital (KKH) and Hail General Hospital (HGH), covering 550 participants from 2015 to 2022. Two groups were formed comprising patients admitted to the emergency room (ER) and community members; their ages ranged from 19 to 75 years. Group 1 contained 400 participants, of which 250 were patients (244 males, six females) who came to the (ER) with a suspected alcohol overdose or poisoning, and 150 were patients (128 males and 22 females) who were discharged from the (ER) with minimal complaints because of their drinking. Group 2 comprised 150 participants (128 males, 22 females) who were community members, who were surveyed using a questionnaire or interview. In Group 1, 30% of patients reported an altered state of consciousness as a major complaint, 28.8% of patients exhibited abnormal liver function tests (LFTs), 27% had abnormal renal function tests (RFT) with decreased glomerular filtration rates (GFR) and elevated levels of urea and creatinine or low levels of electrolytes or calcium, and 35.6% patients showed elevated levels of pancreatic enzymes. One death was reported due to high alcohol consumption. In Group 2, the community participants reported that they started drinking alcohol due to the influence of other people (29%), stress (11%), depression (10.8%), curiosity (4.4%), and boredom (4%). In addition, 77% of participants were frequent alcohol drinkers and 20% consumed it daily. Further, 68.7% claimed to drink alcohol for more than one hour at a time, while 83.3% experienced blackouts and 70% had problems related to their liver. Moreover, 72.7% of the participants ended up in the hospital and 34.6% suffered from multiple chronic diseases. It is concluded that social influences and stress contributed to the initiation of alcohol use. Despite data gaps, the findings of this study provide a practical understanding of alcohol consumption among the Saudi population and guidance for policymakers.

Risk factors for natural menopause before the age of 45: evidence from two British population-based birth cohort studies

BackgroundMenopause that occurs before the age of 45 and is not medically induced (referred to here as ‘early natural menopause’) affects around one in 10 women and has serious health consequences. These consequences include increased risk of all-cause mortality, cardiovascular disease, osteoporosis, and type 2 diabetes.MethodsWe investigate risk factors for the onset of natural menopause before the age of 45 in two population-based prospective cohort studies in Britain: the 1958 cohort following 8959 women and the 1970 cohort following 8655 women. These studies follow women from birth to adulthood, and we use harmonized data on birth and early life characteristics, reproductive health, health behaviour, and socioeconomic characteristics for 6805 women who were pre-menopausal, peri-menopausal or had undergone natural menopause. Of these 6805 women, 3614 participated in the 1958 cohort (of which 368 had early menopause) and 3191 participated in the 1970 cohort (of which 206 had early menopause). Taking a life course approach, we focus on three distinct life stages - birth/early life, childhood, and early adulthood - to understand when risk factors are most harmful. Respecting the temporal sequence of exposures, we use a series of multivariable logistic regression models to estimate associations between early menopause and each potential risk factor adjusted for confounders.ResultsWe find that early menopause is influenced by circumstances at birth. Women born in lower social class families, whose mother smoked during the pregnancy or who were breastfed 1 month or less were more likely to undergo early menopause. Early menopause is also associated with poorer cognitive ability and smoking in childhood. Adult health behaviour also matters. Smoking is positively correlated with early menopause, while regular exercise and moderate frequency of alcohol drinking in women’s early thirties are associated with reduced risk of early menopause. The occurrence of gynaecological problems by women’s early thirties is also linked to early menopause.ConclusionsWe demonstrate that characteristics at different periods of life are associated with early menopause. Some of these associations relate to modifiable behaviours and thus the risks of early menopause and the adverse health outcomes associated with it may be preventable.

Complications and risk factors after digital subtraction angiography: 1-year single-center study.

Digital subtraction angiography (DSA) is an imaging technique used to diagnose and confirm abnormal lesions of cerebral blood vessels in various situations. Several complications such as cerebral infarction, contrast-induced allergy, and angio-site hematoma or infection can occur after DSA. We investigated complication rates and risk factors related to DSA. All patients who underwent DSA at Incheon St. Mary's Hospital from January to December 2021 were included. Those who underwent emergency DSA due to stroke or who underwent endovascular surgery within 1 week after DSA were excluded. Complications that occurred within 1 week after DSA were included in the study and was classified into three categories (neurologic complications, contrast-induced allergy, and wound complications). The mean age was 57.7±13.2 years old and the female was dominant at 63.9%. The overall complication rate was 5% (n=20/407). Regarding neurologic complications, the presence of malignancy (p<0.01), and a longer procedure time (>15 minutes, p=0.04) were statistically significant factors. Contrast-induced allergy did not show any statistically significant difference in any parameter. The wound complication rate was higher in men (p=0.02), trans-femoral approach (p=0.02), frequent alcohol drinkers (p=0.04), those taking anticoagulants (p=0.03), and longer procedure time (>15 minutes, p<0.01). DSA is an invasive diagnostic modality and can cause several complications. Patients with cancer should be more careful about the occurrence of cerebral infarction, and men taking anticoagulants or drinking frequently should be more careful about the occurrence of angio-site hematomas.

The impact of prolonged exposure to air pollution on the incidence of chronic non-communicable disease based on a cohort in Tianjin

Evidence on the associations of prolonged ambient pollutants exposure with chronic non-communicable diseases among middle-aged and elderly residents is still limited. This prospective cohort study intends to investigate the long-term effects of ambient pollution on hypertension and diabetes incidence among relatively older residents in China. Individual particulate matter exposure levels were estimated by satellite-based model. Individual gaseous pollutants exposure levels were estimated by Inverse Distance Weighted model. A Cox regression model was employed to assess the risks of hypertension and diabetes morbidity linked to air pollutants exposures. The cross-product term of ambient pollutants exposure and covariates was further added into the regression model to test whether covariates would modify these air pollution-morbidity associations. During the period from 2014 to 2018, a total of 97,982 subjects completed follow-up. 12,371 incidents of hypertension and 2034 of diabetes occurred. In the multi-covariates model, the hazard ratios (HR) and 95% confidence interval (CI) were 1.49 (1.45–1.52), 1.28 (1.26–1.30), 1.17 (1.15–1.18), 1.21 (1.17–1.25) and 1.33 (1.31–1.35) for hypertension morbidity per 10 μg/m3 increment in PM1, PM2.5, PM10, NO2 and SO2, respectively. For diabetes onsets, the HR (95% CI) were 1.17 (1.11–1.23), 1.09 (1.04–1.13), 1.06 (1.02–1.09), 1.02 (0.95–1.10), and 1.24 (1.19–1.29), respectively. In addition, for hypertension analyses, the effect estimates were more pronounced in the participants with age <60 years old, BMI ≥24 kg/m2, and frequent alcohol drinking. These findings provided the evidence on elevated risks of morbidity of hypertension and diabetes associated with prolonged ambient pollutants exposure at relatively high levels.

개인별 목표 설정과 스마트폰 기반 영양상담이 과체중 및 비만 시내버스 운전자의 식습관 개선과 체중감소에 미치는 효과

Background/Objectives: This study investigates the impact of personalized goal setting and smartphone-based nutrition counseling on dietary habit improvement and weight loss among city bus drivers with overweight and obesity.&#x0D; Methods: The intervention program was conducted for 52 city bus drivers with overweight and obesity who agreed to participate in the program. Before the program, three specific action goals for behavior modification, including two dietary goals and one activity goal, were individually established, and a goal checklist and a smartwatch were provided to monitor goal attainment. Smartphone-based nutrition counseling using Kakao Talk messenger by a dietitian was conducted for six weeks. Each participant sent the checklist and the smartwatch record to the dietitian once a week, and the dietitian provided feedback through messages. Health habits, dietary habits, food consumption, and anthropometry were measured before and after the intervention.&#x0D; Results: After the intervention, the frequency of alcohol drinking significantly decreased, physical activity increased, and perceived health status improved; the consumption frequencies of multi-grain rice and milk/dairy products significantly increased; and the consumption frequencies of instant noodles, snacks, late-night meals, sweet- and salty-foods decreased significantly compared to those before the program. In addition, after six weeks, the average weight, body mass index, body fat mass, percent body fat, and waist circumference significantly decreased. Most participants (94.2%) were satisfied with nutrition counseling, and 88.5% and 75.0% of participants answered that the intervention program was helpful in improving their dietary habits and in managing their body weight, respectively.&#x0D; Conclusion/Implications: Despite the short intervention period of six weeks, positive changes in health habits, dietary habits, and weight loss were observed. Personalized goal setting and smartphone-based nutrition counseling is considered a good model for dietary habit improvement and weight loss for time-limited workers, such as bus drivers, who have difficulty in making time for nutrition intervention.

Skipping Breakfast and Incidence of Frequent Alcohol Drinking in University Students in Japan: A Retrospective Cohort Study.

Frequency of alcohol drinking is a potential predictor of binge drinking of alcohol, a serious social problem for university students. Although previous studies have identified skipping breakfast as a predictor of various health-compromising behaviors and cardiometabolic diseases, few studies have assessed the association between skipping breakfast and the incidence of frequent alcohol drinking. This retrospective cohort study included 17,380 male and 8799 female university students aged 18–22 years admitted to Osaka universities between 2004 and 2015. The association between breakfast frequency (eating every day, skipping occasionally, and skipping often/usually) and the incidence of frequent alcohol drinking, defined as drinking ≥4 days/week, was assessed using multivariable-adjusted Cox proportional hazards models. During the median observational period of 3.0 years, 878 (5.1%) men and 190 (2.2%) women engaged in frequent alcohol drinking. Skipping breakfast was significantly associated with the incidence of frequent alcohol drinking (adjusted hazard ratios [95% confidence interval] of eating every day, skipping occasionally, and skipping often/usually: 1.00 [reference], 1.02 [0.84–1.25], and 1.48 [1.17–1.88] in men; 1.00 [reference], 1.60 [1.03–2.49], and 3.14 [1.88–5.24] in women, respectively). University students who skipped breakfast were at a higher risk of frequent alcohol drinking than those who ate breakfast every day.

Parenting styles in relation to childhood obesity, smoking and drinking: A gene-environment interaction study.

The present study assessed gene-environment interactions linking maternal parenting styles to childhood obesity and alcohol and tobacco use. Data were retrieved from the first wave of the German Twin Family Panel. Participants comprised three birth cohorts, aged 5, 11 and 17 years, with approximately 500 pairs of same-sex monozygotic twins and 500 pairs of same-sex dizygotic twins per cohort. Self-reported parenting styles were measured in five dimensions: emotional warmth, psychological control, negative communication, monitoring and inconsistent parenting. Outcome variables included children's body mass index z-score (BMIz) and smoking and alcohol drinking frequency. Gene-environment interaction models were used to assess how parenting styles might moderate genetic and environmental influences on BMIz and smoking and drinking behaviours. A positive interaction of genetic effects with psychological control was found for BMIz at age 5 years, indicating that genetic influences on BMIz increased with psychological control. No interaction effect was found for BMIz at ages 11 and 17 years. Regarding adolescent smoking, positive interaction between genetic effects and negative communication was found, indicating that genetic influences on smoking increased with negative communication. There was no significant moderating effect of parenting styles on adolescent drinking. The present study found preliminary evidence indicating that parenting styles moderated genetic and environmental impacts on body weight status and smoking. Moderation effects of parenting on BMIz were observed only at a very young age. The moderating effects of parenting influenced adolescent smoking but not drinking.

Parental Effects on Obesity, Smoking, and Drinking in Children and Adolescents: A Twin Study

PurposeParenting style has been associated with children’s weight-related outcomes and health behaviors, but this relationship may be confounded by genetic influences. Using a twin design to better control for genetics and shared home environments, this study aimed to estimate the longitudinal parental effects on obesity, smoking, and drinking in children and adolescents. MethodsData were retrieved from the first two waves of the German Twin Family Panel. A total of 631 pairs of same-sex monozygotic twins were analyzed, including three birth cohorts aged 5, 11, and 17 years. Self-reported parenting styles were measured in five dimensions: emotional warmth, psychological control, negative communication, monitoring, and inconsistent parenting. Outcome variables included children’s body mass index z-score (BMIz) and smoking and alcohol drinking frequency. The differencing method was used to examine the relationship between within-monozygotic pair differences in parenting styles and health outcomes, controlling for differences at baseline, genetic influences, and other shared characteristics between twins. ResultsControlling for genetics, shared environmental effects, and body weight status at baseline, the twin who received harsher parenting in communication had lower BMI than their cotwin. Subgroup analyses found that negative communication had a stronger impact on the youngest cohort and female twins. Paternal parenting differentially relates to child weight compared to maternal parenting. No concurrent and long-lasting effects of paternal parenting on smoking and drinking were found. DiscussionThe twin study design is a unique epidemiological tool to measure the contribution of genetics as opposed to the environment, to a given health trait. This study found that negative communication was associated with lower BMI in German twin families. However, it failed to identify strong evidence for the causal link between other parenting dimensions and child’s weight status and alcohol and tobacco use. More twin studies with objective measurements are warranted to understand the critical role of parenting, especially family communication, in predicting children’s BMIs and their health behaviors across cultures.

Alcohol use trajectories among U.S. adults during the first 42weeks of the COVID-19 pandemic.

BackgroundThis study characterized the prevalence, drinking patterns, and sociodemographic characteristics of U.S. adult subpopulations with distinct drinking trajectories during the COVID‐19 pandemic's first 42 weeks.MethodsAdult respondents (n = 8130) in a nationally representative prospective longitudinal study completed 21 biweekly web surveys (March 2020 to January 2021). Past‐week alcohol drinking frequency (drinking days [range: 0 to 7]) and intensity (binge drinking on usual past‐week drinking day [yes/no]) were assessed at each timepoint. Growth mixture models identified multiple subpopulations with homogenous drinking trajectories based on mean drinking days or binge drinking proportional probabilities across time.ResultsFour drinking frequency trajectories were identified: Minimal/stable (72.8% [95% CI = 71.8 to 73.8]) with <1 mean past‐week drinking days throughout; Moderate/late decreasing (6.7% [95% CI = 6.2 to 7.3) with 3.13 mean March drinking days and reductions during summer, reaching 2.12 days by January 2021; Moderate/early increasing (12.9% [95% CI = 12.2 to 13.6) with 2.13 mean March drinking days that increased in April and then plateaued, ending with 3.20 mean days in January 2021; and Near daily/early increasing (7.6% [95% CI = 7.0 to 8.2]) with 5.58 mean March drinking days that continued increasing without returning to baseline. Four drinking intensity trajectories were identified: Minimal/stable (85.8% [95% CI = 85.0% to 86.5%]) with <0.01 binge drinking probabilities throughout; Low‐to‐moderate/fluctuating (7.4% [95% CI = 6.8% to 8%]) with varying binge probabilities across timepoints (range:0.12 to 0.26); Moderate/mid increasing (4.2% [95% CI = 3.7% to 4.6%]) with 0.39 April binge drinking probability rising to 0.65 during August–September without returning to baseline; High/early increasing trajectory (2.7% [95% CI = 2.3% to 3%]) with 0.84 binge drinking probability rising to 0.96 by June without returning to baseline. Males, Whites, middle‐aged/older adults, college degree recipients, those consistently working, and those above the poverty limit were overrepresented in various increasing (vs. minimal/stable) frequency trajectories. Males, Whites, nonmarried, those without college degree, 18 to 39‐year‐olds, and middle aged were overrepresented in increasing (vs. minimal/stable) intensity trajectories.ConclusionsSeveral distinct U.S. adult sociodemographic subpopulations appear to have acquired new drinking patterns during the pandemic's first 42 weeks. Frequent alcohol use assessment in the COVID‐19 era could improve personalized medicine and population health efforts to reduce drinking.

The factors affecting alcohol drinking frequency during the COVID-19 pandemic

The factors affecting alcohol drinking frequency during the COVID-19 pandemic

Binge drinking among adults, by select characteristics and state — United States, 2018

This article describes the prevalence, frequency, and intensity of binge alcohol drinking among US adults. Prevalence was highest among adults aged 25-34 years old, men, non-Hispanic whites, those with higher income levels, and those with a college degree. This report highlights the potential demographic shifts that may be reflected in the increasing practice of liver transplantation for acute alcohol-related hepatitis. This article describes the prevalence, frequency, and intensity of binge alcohol drinking among US adults. Prevalence was highest among adults aged 25-34 years old, men, non-Hispanic whites, those with higher income levels, and those with a college degree. This report highlights the potential demographic shifts that may be reflected in the increasing practice of liver transplantation for acute alcohol-related hepatitis. Excessive alcohol use* is associated with disease, injury, and poor pregnancy outcomes and is responsible for approximately 95 000 deaths in the United States each year.1Esser MB Sherk A Liu Y et al.Deaths and years of potential life lost from excessive alcohol use—United States, 2011–2015.MMWR Morb Mortal Wkly Rep. 2020; 69 (doi:10.15585/mmwr.mm6939a6. PMID:33001874.): 1428-1433Crossref PubMed Scopus (46) Google Scholar Binge drinking (five or more drinks on at least one occasion for men or four or more drinks for women) is the most common and costly pattern of excessive alcohol use.2Sacks JJ Gonzales KR Bouchery EE Tomedi LE Brewer RD. 2010 national and state costs of excessive alcohol consumption.Am J Prev Med. 2015; 49 (doi:10.1016/j.amepre.2015.05.031. PMID:26477807.): e73-e79Abstract Full Text Full Text PDF PubMed Scopus (689) Google Scholar CDC analyzed data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) to estimate past 30-day binge drinking prevalence, frequency, and intensity (number of drinks per occasion), overall and by select characteristics and state. The overall unadjusted prevalence of binge drinking during the past 30 days was 16.6%, representing an estimated 38.5 million U.S. adults aged ≥18 years; prevalence was highest (26.0%) among those aged 25–34 years. The age-standardized binge drinking prevalence was higher among men (22.5%) than among women (12.6%), increased with income, and was highest among non-Hispanic White adults and adults in the Midwest Census region. State-level age-standardized binge drinking prevalence ranged from 10.5% (Utah) to 25.8% (Wisconsin). Among adults who reported binge drinking, 25.0% did so at least weekly, on average, and 25.0% consumed at least eight drinks on an occasion. To reduce binge drinking, the Community Preventive Services Task Force recommends increasing alcohol taxes and implementing strategies that strengthen regulations to reduce alcohol availability.† The U.S. Preventive Services Task Force recommends clinicians screen adults for alcohol misuse in primary care settings and provide counseling as needed.§ BRFSS is an ongoing, state-based, random-digit–dialed, landline and cellular telephone survey of the U.S. noninstitutionalized adult population that collects health-related data nationwide.¶ In 2018, the median survey response rate**** for all states and the District of Columbia was 49.9% (range = 38.8%–67.2%).†† CDC analyzed data from 398 485 respondents aged ≥18 years in the 2018 BRFSS to estimate past 30-day binge drinking prevalence, frequency, and intensity. Binge drinking prevalence and frequency were assessed with the question, “Considering all types of alcoholic beverages, how many times during the past 30 days did you have 5 (4 for women) or more drinks on an occasion?”§§ Intensity was assessed with the question, “During the past 30 days, what is the largest number of drinks you had on any occasion?”.3Esser MB Kanny D Brewer RD Naimi TS. Binge drinking intensity: a comparison of two measures.Am J Prev Med. 2012; 42 (doi:10.1016/j.amepre.2012.03.001. PMID:22608381.): 625-629Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar Unadjusted and age-standardized (to the 2000 U.S. standard population) binge drinking prevalence and 95% confidence intervals (CIs) were estimated overall. Age-standardized prevalence was also estimated by respondents’ sociodemographic characteristics (except prevalence by age group), including sex, race/ethnicity, income, marital status, veteran status, education, region, county urbanization level,¶¶ and state. State-level prevalence estimates and 95% CIs were grouped into tertiles to identify geographic patterns. Because of the highly right-skewed distribution of the data, similar measures of binge drinking frequency and intensity among adults reporting binge drinking were estimated with medians and variances derived using Taylor series linearization. The means and 75th and 90th percentiles for frequency and intensity were also calculated to further characterize the distributions of these measures. Statistically significant differences between medians were defined as p < .05 using pairwise tests and nonoverlapping CIs. All analyses were performed using SAS-callable SUDAAN (version 11.0.3; RTI International), and sampling weights were applied to account for complex sampling design, including nonresponse bias and noncoverage errors, and to improve representation of the adult U.S. population in different states. In 2018, the overall nationwide unadjusted binge drinking prevalence among U.S. adults was 16.6% (95% CI = 16.3%–16.8%), representing an estimated 38.5 million adults (Table 1); prevalence was highest among adults aged 25–34 years (26.0%). Age-standardized binge drinking prevalence was 17.4% (95% CI = 17.2%–17.7%) and varied by sociodemographic group and by state (range = 10.5% [Utah] to 25.8% [Wisconsin]) (Figure 1) (Supplementary Table, https://stacks.cdc.gov/view/cdc/110373). Binge drinking prevalence was significantly higher among men (22.5%) than among women (12.6%) and was highest among non-Hispanic White adults (19.7%), those with annual household incomes ≥$75 000 (21.4%), those who were never married (18.5%) or were divorced/separated/widowed (19.4%), and veterans (20.9%). Binge drinking prevalence was significantly higher among adults with a college degree (18.9%) than among adults with less than a high school diploma (14.9%). States with higher binge drinking prevalences clustered in the Midwest and Northeast.TABLE 1Prevalence of binge drinking among adults aged ≥18 years, by selected characteristics — Behavioral Risk Factor Surveillance System, United States,aThe study sample included 398 485 adult respondents aged ≥18 years from all 50 states and the District of Columbia with complete information on age, sex, and binge drinking. Weighted numbers were derived through application of survey weights. 2018CharacteristicWeighted no.bCategories in subgroups might not sum to total because of missing responses for some variables. of adults reporting binge drinking,cBinge drinking was defined as consuming five or more drinks (men) or four or more drinks (women) on at least one occasion during the past 30 days. x 1000Binge drinkingcBinge drinking was defined as consuming five or more drinks (men) or four or more drinks (women) on at least one occasion during the past 30 days. prevalence,dPrevalence estimates for all characteristics except age group were age-standardized to the 2000 U.S. standard population. % (95% CI)Overall, unadjusted38 544`6.6 (16.3–16.8)Overall, age–adjustedNA17.4 (17.2–17.7)Age group, yrs18–24701024.0 (23.1–24.9)25–3410 59526.0 (25.3–26.7)35–44757920.4 (19.7–21.0)45–6410 87114.3 (13.9–14.7)≥6524895.0 (4.8–5.3)SexMen24 60322.5 (22.0–22.9)Women13 94112.6 (12.2–12.9)Race/EthnicityWhite, non-Hispanic25 65419.7 (19.4–20.0)Black, non-Hispanic357013.4 (12.7–14.2)Hispanic648316.3 (15.5–17.2)AI/AN, non-Hispanic33616.1 (14.0–18.6)A/PI, non-Hispanic12819.7 (8.7–10.8)Other, non-Hispanic73816.9 (15.6–18.2)Annual household income<$25 000707214.6 (14.1–15.2)$25 000–$49 999754517.8 (17.1–18.4)$50 000–$74 999551919.0 (18.3–19.8)≥$75 00014 76121.4 (20.9–21.9)Marital statusMarriedeMarried includes unmarried cohabitating couples.19 31816.6 (16.2–17.0)Divorced/Separated/Widowed582019.4 (18.2–20.7)Never married13 24318.5 (17.9–19.1)Veteran statusVeteran401520.9 (19.8–21.9)Nonveteran34 49417.1 (16.8–17.4)Education levelLess than high school graduate411614.9 (14.0–15.8)High school graduate or equivalent10 36317.2 (16.7–17.8)Some college12 86218.4 (17.9–18.9)College graduate11 15418.9 (18.4–19.3)U.S. Census regionfRegions were based on U.S. Census Bureau definitions.https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdfNortheast674217.9 (17.3–18.5)Midwest934020.0 (19.6–20.5)South13 68816.3 (15.8–16.8)West877516.6 (16.1–17.1)County urban-rural statusgCounties were classified using the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties.https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdfLarge central metro12 29817.7 (17.1–18.2)Large fringe metro945717.3 (16.8–17.9)Medium metro797917.5 (17.0–18.0)Small metro351917.7 (17.1–18.4)Micropolitan308316.7 (16.1–17.4)Noncore221016.5 (15.7–17.3)Abbreviations: A/PI = Asian/Pacific Islander; AI/AN = American Indian/Alaska Native; CI = confidence interval; NA = not applicable.a The study sample included 398 485 adult respondents aged ≥18 years from all 50 states and the District of Columbia with complete information on age, sex, and binge drinking. Weighted numbers were derived through application of survey weights.b Categories in subgroups might not sum to total because of missing responses for some variables.c Binge drinking was defined as consuming five or more drinks (men) or four or more drinks (women) on at least one occasion during the past 30 days.d Prevalence estimates for all characteristics except age group were age-standardized to the 2000 U.S. standard population.e Married includes unmarried cohabitating couples.f Regions were based on U.S. Census Bureau definitions.https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdfg Counties were classified using the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties.https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdf Open table in a new tab Abbreviations: A/PI = Asian/Pacific Islander; AI/AN = American Indian/Alaska Native; CI = confidence interval; NA = not applicable. Among adults who reported binge drinking, the median frequency was 1.7 (mean = 4.6) binge drinking occasions during the past 30 days, and the median intensity was 5.5 (mean = 7.2) drinks on an occasion. (Table 2). The upper frequency quartile was >4.0 (95% CI = 3.9–4.1) binge drinking occasions in the past 30 days and the upper intensity quartile was >7.7 (95% CI = 7.6–7.8) drinks on an occasion. Median binge drinking frequency and intensity were significantly higher among men (1.9 occasions and 5.9 drinks, respectively) than among women (1.4 occasions and 4.5 drinks, respectively), and decreased with education level. Median binge drinking intensity was highest among adults aged 18–24 years and decreased with age. Median binge drinking frequency among states ranged from 1.5 occasions (eight states) to 2.1 occasions (Mississippi) in the past 30 days; median binge-drinking intensity on an occasion ranged from 5.2 drinks (New Jersey, District of Columbia, and Connecticut) to 6.4 drinks (West Virginia).TABLE 2Binge drinking frequency and intensity among adults aged ≥18 years who binge drank in the past 30 days, by selected characteristics and state — Behavioral Risk Factor Surveillance System, United States,aThe study sample included adult respondents aged ≥18 years from all 50 states and the District of Columbia with complete information on age, sex, and binge drinking and who reported binge drinking. 2018CharacteristicAdults who binge drank in the past 30 daysBinge drinkingbBinge drinking was defined as consuming five or more drinks (men) or four or more drinks (women) on at least one occasion during the past 30 days. frequencycNumber of binge drinking occasions in the past 30 days among adults who reported binge drinking. (n = 54 045)Binge drinkingbBinge drinking was defined as consuming five or more drinks (men) or four or more drinks (women) on at least one occasion during the past 30 days. intensitydLargest number of drinks consumed on any occasion in the past 30 days among adults who reported binge drinking. (n = 50 527)No. of occasionseUnadjusted weighted medians and percentiles were derived from SAS-callable SUDAAN “proc descript”; variance estimates were derived using the Taylor series linearization method. (95% CI)No. of drinkseUnadjusted weighted medians and percentiles were derived from SAS-callable SUDAAN “proc descript”; variance estimates were derived using the Taylor series linearization method. (95% CI)Median75th percentile90th percentileMedian75th percentile90th percentileOverall, unadjusted1.7 (1.7–1.7)4.0 (3.9–4.1)9.5 (9.4–9.7)5.5 (5.5–5.5)7.7 (7.6–7.8)11.5 (11.4–11.6)Age group, yrs18–241.7 (1.6–1.8)4.1 (3.9–4.4)9.1 (7.7–9.4)5.9 (5.8–6.0)9.2 (8.9–9.3)13.6 (12.0–14.3)25–341.6 (1.5–1.7)3.8 (3.6–3.9)9.1 (7.8–9.4)5.7 (5.7–5.8)8.9 (8.2–9.1)11.9 (11.7–12.3)35–441.7 (1.6–1.8)3.9 (3.8–4.1)9.7 (9.4–11.1)5.5 (5.4–5.6)7.6 (7.4–7.8)11.4 (11.2–11.6)45–641.8 (1.7–1.9)4.3 (4.0–4.4)10.0 (9.7–11.9)5.3 (5.2–5.3)7.0 (6.7–7.2)11.1 (9.9–11.3)≥651.8 (1.6–1.9)4.4 (4.1–4.7)14.2 (9.9–18.5)5.0 (4.9–5.0)5.8 (5.7–5.8)7.9 (7.4– 9.1)SexMen1.9 (1.8–1.9)4.6 (4.4–4.7)11.4 (10.0–12.0)5.9 (5.9–6.0)9.3 (9.2–9.4)12.7 (12.0–13.8)Women1.4 (1.3–1.5)3.3 (3.2–3.4)6.8 (6.2–7.3)4.5 (4.5–4.5)5.6 (5.5–5.7)7.9 (7.7– 8.6)Education levelLess than high school graduate2.3 (2.0–2.6)4.9 (4.5–5.8)19.1 (13.6–24.9)5.8 (5.7–5.9)9.7 (9.3–10.0)14.8 (14.1–17.2)High school graduate or equivalent1.9 (1.8–2.0)4.6 (4.4–4.8)11.6 (10.0–13.9)5.7 (5.6–5.7)9.0 (8.4–9.2)11.9 (11.8–12.9)Some college1.7 (1.6–1.8)4.0 (3.9–4.2)9.5 (9.2–9.7)5.5 (5.5–5.6)7.7 (7.5–7.8)11.4 (11.2–11.5)College graduate1.4 (1.3–1.4)3.3 (3.1–3.4)6.9 (6.2–7.3)5.2 (5.2–5.3)6.8 (6.6–6.9)9.6 (9.5– 9.7)Race/EthnicityWhite, non-Hispanic1.7 (1.7–1.8)4.1 (4.0–4.2)9.7 (9.5–9.8)5.5 (5.5–5.6)7.7 (7.6–7.8)11.5 (11.4–11.6)Black, non-Hispanic1.8 (1.7–2.0)4.2 (3.8–4.6)9.7 (8.4–12.3)5.1 (5.0–5.3)6.3 (6.0–6.7)9.7 (9.3–11.1)Hispanic1.6 (1.5–1.7)3.7 (3.5–3.9)7.7 (7.1–9.3)5.7 (5.6–5.8)8.9 (7.8–9.2)11.8 (11.5–12.0)AI/AN, non-Hispanic2.2 (1.8–2.9)5.5 (4.4–7.2)18.8 (14.1–25.0)5.7 (5.4–6.0)9.1 (7.4–9.7)13.0 (11.5–14.7)A/PI, non-Hispanic1.4 (1.2–1.6)3.3 (2.7–3.9)7.2 (5.1–9.4)5.3 (5.1–5.5)6.8 (6.2–7.4)9.9 (9.4–11.3)Other, non-Hispanic1.9 (1.7–2.2)4.7 (3.9–5.7)11.4 (9.5–14.4)5.7 (5.5–5.8)8.3 (7.7–9.3)12.7 (11.5–14.7)Annual household income<$25 0001.9 (1.8–2.0)4.5 (4.2–4.7)14.2 (11.4–14.8)5.5 (5.5–5.6)8.0 (7.7–9.0)11.8 (11.6–12.0)$25 000–$49 9991.7 (1.6–1.8)4.0 (3.8–4.3)9.7 (9.3–10.0)5.5 (5.5–5.6)7.8 (7.5–8.1)11.6 (11.4–11.9)$50 000–$74 9991.8 (1.7–1.9)4.2 (3.9–4.5)9.5 (9.1–9.9)5.6 (5.6–5.7)8.0 (7.8–9.0)11.7 (11.5–11.9)≥$75 0001.6 (1.5–1.7)3.8 (3.7–3.9)9.1 (7.8–9.3)5.5 (5.4–5.5)7.5 (7.3–7.6)11.2 (11.1–11.4)Marital statusMarriedfMarried includes unmarried cohabitating couples.1.6 (1.5–1.6)3.7 (3.6–3.8)9.2 (8.0–9.4)5.4 (5.3–5.4)7.2 (7.1–7.3)11.0 (10.0–11.2)Divorced/Separated/Widowed1.9 (1.8–2.0)4.8 (4.5–5.2)18.2 (14.5–19.9)5.4 (5.3–5.5)7.6 (7.3–7.8)11.6 (11.4–11.9)Never married1.8 (1.7–1.9)4.2 (4.0–4.4)9.3 (9.0–9.5)5.8 (5.7–5.9)9.1 (8.8–9.2)12.0 (11.8–12.7)Veteran statusVeteran1.9 (1.7–2.0)4.6 (4.2–4.9)14.3 (10.9–15.4)5.7 (5.6–5.8)7.9 (7.6–9.0)11.7 (11.4–11.9)Nonveteran1.7 (1.6–1.7)4.0 (3.9–4.1)9.4 (9.2–9.6)5.5 (5.4–5.5)7.7 (7.6–7.8)11.5 (11.4–11.6)County urban-rural statusgCounties were classified using the 2013 National Center for Health Statistics Urban–Rural Classification Scheme for Counties.https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdfLarge central metro1.6 (1.6–1.7)3.9 (3.7–4.0)9.2 (8.0–9.6)5.5 (5.4–5.5)7.5 (7.3–7.7)11.3 (11.0–11.5)Large fringe metro1.6 (1.5–1.7)3.8 (3.6–4.0)9.0 (7.7–9.4)5.4 (5.4–5.5)7.5 (7.3–7.7)11.3 (11.1–11.5)Medium metro1.7 (1.7–1.8)4.2 (4.0–4.4)9.7 (9.4–10.0)5.5 (5.5–5.6)7.7 (7.5–7.9)11.5 (11.3–11.7)Small metro1.9 (1.8–2.0)4.4 (4.1–4.6)9.9 (9.4–11.9)5.6 (5.6–5.7)9.0 (7.9–9.2)11.8 (11.5–12.0)Micropolitan1.8 (1.7–1.9)4.5 (4.2–4.8)11.5 (9.8–14.1)5.6 (5.5–5.7)8.3 (7.8–9.1)11.9 (11.7–14.1)Noncore1.9 (1.8–2.1)4.5 (4.1–4.8)12.0 (10.0–14.3)5.7 (5.6–5.8)9.1 (8.3–9.3)12.1 (11.8–14.3)U.S. Census regionhRegions were based on U.S. Census Bureau definitions.https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdfNortheast1.6 (1.5–1.7)3.7 (3.6–3.9)7.7 (7.3–9.1)5.4 (5.3–5.4)7.3 (7.1–7.5)11.0 (9.9–11.3)Midwest1.8 (1.7–1.8)4.2 (4.0–4.4)9.7 (9.5–10.0)5.6 (5.5–5.7)8.0 (7.9–8.7)11.8 (11.6–11.9)South1.8 (1.7–1.8)4.3 (4.1–4.6)10.1 (9.7–11.8)5.5 (5.5–5.6)7.7 (7.5–7.9)11.5 (11.4–11.7)West1.6 (1.5–1.7)3.8 (3.6–3.9)9.1 (7.8–9.4)5.5 (5.5–5.6)7.7 (7.5–7.8)11.4 (11.2–11.6)StateAlabama1.7 (1.5–1.9)4.0 (3.4–4.6)8.4 (6.7–9.7)5.5 (5.3–5.6)7.2 (6.5–8.0)10.8 (9.8–11.9)Alaska1.9 (1.4–2.7)4.4 (3.7–5.1)9.6 (5.8–14.1)5.4 (5.2–5.6)7.1 (6.0–7.6)9.6 (7.9–11.4)Arizona1.5 (1.3–1.7)3.8 (3.2–4.3)9.2 (7.2–9.8)5.5 (5.4–5.7)7.9 (7.3–9.1)11.3 (9.9–11.8)Arkansas1.9 (1.6–2.3)4.5 (3.8–5.5)11.2 (9.3–14.6)5.6 (5.4–5.8)9.2 (7.4–9.7)11.8 (11.3–14.0)California1.6 (1.4–1.7)3.7 (3.4–3.9)7.9 (7.2–9.4)5.5 (5.4–5.6)7.7 (7.4–8.2)11.5 (11.2–11.8)Colorado1.6 (1.4–1.7)3.7 (3.4–4.0)9.1 (7.3–9.6)5.6 (5.5–5.7)7.6 (7.3–7.8)11.4 (9.9–11.8)Connecticut1.6 (1.4–1.7)3.5 (3.1–3.8)7.8 (5.9–9.5)5.2 (5.1–5.3)6.5 (6.0–6.9)9.3 (8.0– 9.8)Delaware1.7 (1.5–2.0)4.4 (3.8–5.2)11.3 (8.4–14.6)5.3 (5.0–5.5)7.3 (6.6–7.8)11.3 (9.6–11.9)District of Columbia1.5 (1.3–1.7)3.4 (2.9–4.0)6.9 (5.5–7.9)5.2 (5.0–5.4)6.7 (6.0–7.2)9.4 (8.1– 9.8)Florida1.8 (1.6–2.0)4.9 (4.1–5.8)14.2 (9.7–19.7)5.5 (5.3–5.6)7.3 (6.9–7.7)11.1 (9.7–11.7)Georgia1.6 (1.5–1.8)4.1 (3.7–4.5)9.7 (8.3–13.9)5.3 (5.1–5.5)7.5 (7.1–7.9)11.5 (11.0–11.8)Hawaii1.9 (1.7–2.2)4.3 (3.9–4.7)11.1 (8.8–14.3)5.7 (5.5–5.8)9.2 (7.9–9.6)13.3 (11.5–14.8)Idaho2.0 (1.7–2.5)4.5 (3.9–5.3)10.5 (8.0–17.9)5.8 (5.6–6.1)9.1 (7.7–9.7)13.1 (11.4–14.4)Illinois1.9 (1.7–2.1)4.4 (3.9–4.8)9.7 (7.9–11.9)5.7 (5.5–5.8)9.1 (7.8–9.5)14.0 (11.7–14.6)Indiana1.7 (1.5–1.9)4.2 (3.7–4.8)9.9 (9.2–14.5)5.6 (5.4–5.8)9.1 (7.8–9.6)14.1 (11.7–15.5)Iowa1.9 (1.8–2.0)4.3 (4.0–4.5)9.5 (9.1–9.9)5.8 (5.7–5.9)9.2 (8.8–9.4)11.9 (11.7–12.7)Kansas1.6 (1.4–1.7)3.8 (3.5–4.1)8.6 (7.1–9.8)5.6 (5.5–5.7)7.8 (7.5–9.0)11.5 (11.1–11.9)Kentucky1.9 (1.7–2.4)5.0 (4.2–6.9)10.2 (9.6–14.4)5.7 (5.5–5.9)8.8 (7.5–9.4)13.7 (11.4–14.8)Louisiana2.0 (1.7–2.4)4.7 (4.2–5.5)9.9 (9.1–14.1)5.5 (5.3–5.6)7.4 (6.7–8.0)11.3 (9.8–11.9)Maine1.8 (1.6–2.0)4.1 (3.6–4.7)9.3 (7.3–11.4)5.4 (5.2–5.5)7.1 (6.5–7.8)11.2 (9.6–11.6)Maryland1.6 (1.4–1.8)3.7 (3.4–4.0)7.9 (6.7–9.7)5.3 (5.2–5.5)7.0 (6.6–7.3)10.0 (9.6–11.5)Massachusetts1.5 (1.4–1.7)3.6 (3.2–3.9)7.0 (5.4–7.9)5.4 (5.2–5.5)7.0 (6.5–7.6)11.2 (9.8–11.6)Michigan1.8 (1.6–1.9)4.3 (3.9–4.7)10.0 (9.4–14.0)5.6 (5.4–5.7)7.9 (7.6–9.0)11.7 (11.2–14.0)Minnesota1.5 (1.4–1.6)3.7 (3.5–3.9)9.1 (7.4–9.4)5.5 (5.4–5.6)7.7 (7.5–7.9)11.3 (11.0–11.6)Mississippi2.1 (1.8–2.7)5.4 (4.1–8.8)14.8 (10.0–22.1)5.7 (5.5–5.9)9.4 (7.8–10.3)14.6 (11.3–17.4)Missouri1.8 (1.6–2.2)4.8 (4.1–6.1)14.0 (9.5–14.9)5.5 (5.3–5.7)7.7 (7.1–9.0)11.7 (11.2–12.5)Montana1.6 (1.4–1.8)4.0 (3.5–4.5)9.1 (6.9–14.3)5.4 (5.3–5.6)7.3 (6.8–7.8)10.4 (9.6–11.5)Nebraska1.7 (1.6–1.8)3.9 (3.6–4.3)9.1 (7.5–9.7)5.6 (5.5–5.7)8.0 (7.6–9.1)11.4 (11.1–11.7)Nevada1.6 (1.3–1.9)3.7 (2.9–4.5)7.4 (5.6–9.3)5.5 (5.2–5.7)7.8 (6.7–9.3)11.2 (9.8–14.1)New Hampshire1.5 (1.3–1.8)4.0 (3.4–4.7)9.9 (7.7–13.2)5.5 (5.3–5.7)7.6 (6.9–8.3)11.2 (9.5–11.7)New Jersey—iEstimates are unreliable if the relative standard error is >0.3.3.5 (2.9–3.9)7.2 (4.7–9.6)5.2 (4.9–5.6)7.3 (6.3–9.1)9.7 (9.4– 9.9)New Mexico1.8 (1.5–2.1)4.5 (4.0–5.2)11.4 (9.5–17.8)5.5 (5.3–5.7)7.9 (7.2–9.4)13.1 (11.5–18.3)New York1.6 (1.5–1.7)3.7 (3.4–4.0)7.7 (6.9–9.2)5.4 (5.3–5.5)7.1 (6.8–7.4)11.0 (9.8–11.4)North Carolina1.8 (1.5–2.2)4.6 (3.9–5.5)10.7 (7.7–14.6)5.4 (5.3–5.6)7.0 (6.4–7.7)10.5 (9.7–11.3)North Dakota1.8 (1.5–2.0)4.2 (3.7–4.7)8.2 (6.9–9.9)5.9 (5.7–6.3)9.4 (9.1–9.7)14.1 (11.7–14.6)Ohio1.9 (1.7–2.2)4.4 (3.9–4.9)12.0 (9.5–18.6)5.7 (5.5–5.8)8.2 (7.7–9.2)11.8 (11.3–13.6)Oklahoma1.5 (1.2–1.7)3.8 (3.0–4.5)9.3 (7.3–16.0)5.6 (5.4–5.8)7.9 (7.2–9.3)11.5 (9.9–12.0)Oregon1.7 (1.5–1.9)4.1 (3.6–4.5)9.6 (8.3–13.2)5.5 (5.4–5.6)7.4 (6.9–7.8)9.9 (9.6–11.4)Pennsylvania1.8 (1.5–2.0)3.9 (3.6–4.4)7.9 (7.0–10.1)5.5 (5.3–5.6)7.9 (7.4–9.1)11.5 (10.0–12.7)Rhode Island1.6 (1.4–1.8)3.8 (3.1–4.4)8.5 (6.5–9.9)5.4 (5.1–5.6)7.2 (6.7–7.7)9.7 (9.2–10.5)South Carolina1.9 (1.7–2.2)4.6 (4.1–5.2)11.4 (9.5–14.9)5.7 (5.5–5.8)8.4 (7.7–9.3)11.7 (11.2–12.0)South Dakota1.7 (1.4–2.1)4.0 (3.5–4.6)7.8 (5.9–9.3)5.7 (5.5–5.9)7.9 (7.4–9.2)11.6 (9.9–12.8)Tennessee1.7 (1.4–2.1)4.4 (3.6–5.2)9.2 (6.8–12.2)5.5 (5.3–5.7)7.6 (6.9–9.1)11.8 (11.2–14.4)Texas1.8 (1.5–2.0)4.1 (3.6–4.9)9.9 (7.6–14.0)5.6 (5.4–5.8)9.0 (7.6–9.5)11.7 (11.2–14.2)Utah1.7 (1.6–1.9)4.4 (4.0–4.8)10.2 (8.9–14.4)5.6 (5.4–5.7)7.8 (7.4–8.7)11.4 (10.3–14.1)Vermont1.9 (1.6–2.3)4.3 (3.7–5.1)9.5 (8.1–12.3)5.5 (5.2–5.7)7.5 (7.0–8.0)10.6 (9.8–11.6)Virginia1.6 (1.5–1.8)3.7 (3.4–4.0)9.8 (9.1–14.0)5.5 (5.3–5.6)7.2 (6.8–7.7)11.3 (10.0–11.6)Washington1.5 (1.4–1.7)3.6 (3.3–3.9)9.2 (7.0–9.8)5.3 (5.1–5.4)6.8 (6.3–7.2)9.9 (9.5–11.3)West Virginia1.8 (1.5–2.4)5.1 (4.3–7.1)15.2 (11.5–20.8)6.4 (5.9–7.2)9.8 (9.2–11.3)14.6 (12.0–17.5)Wisconsin1.7 (1.5–1.9)3.8 (3.4–4.3)9.1 (6.6–9.7)5.5 (5.4–5.7)7.5 (7.0–7.9)10.8 (9.8–11.4)Wyoming1.5 (1.3–1.7)4.1 (3.5–4.6)10.4 (8.3–14.3)5.5 (5.4–5.7)7.5 (6.8–8.8)11.6 (11.0–12.5)Abbreviations: A/PI, Asian/Pacific Islander; AI/AN, American Indian/Alaska Native; CI, confidence interval.a The study sample included adult respondents aged ≥18 years from all 50 states and the District of Columbia with complete information on age, sex, and binge drinking and who reported binge drinking.b Binge drinking was defined as consuming five or more drinks (men) or four or more drinks (women) on at least one occasion during the past 30 days.c Number of binge drinking occasions in the past 30 days among adults who reported binge drinking.d Largest number of drinks consumed on any occasion in the past 30 days among adults who reported binge drinking.e Unadjusted weighted medians and percentiles were derived from SAS-callable SUDAAN “proc descript”; variance estimates were derived using the Taylor series linearization method.f Married includes unmarried cohabitating couples.g Counties were classified using the 2013 National Center for Health Statistics Urban–Rural Classification Scheme for Counties.https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdfh Regions were based on U.S. Census Bureau definitions.https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdfi Estimates are unreliable if the relative standard error is >0.3. Open table in a new tab Abbreviations: A/PI, Asian/Pacific Islander; AI/AN, American Indian/Alaska Native; CI, confidence interval. During 2018, one in six U.S. adults reported binge drinking during the past 30 days, increasing their risk for many preventable adverse health outcomes. Among those who binge drank, one half did so at least twice per month; one half of men consumed at least six drinks and one half of women consumed at least five drinks on a binge occasion. These median values are lower than the mean values for binge drinking frequency and intensity, but better represent how often adults who binge drink typically do so and how many drinks they usually consume. The higher values for the 90th percentiles for frequency (9.5 occasions in the past 30 days) and intensity (11.5 drinks on an occasion) indicate that a small proportion of adults binge drink very frequently, consume large quantities of alcohol, or both, which is consistent with previous findings.4Esser MB Sacks JJ Sherk A et al.Distribution of drinks consumed by U.S. adults by average daily alcohol consumption: a comparison of 2 nationwide surveys.Am J Prev Med. 2020; 59 (doi:10.1016/j.amepre.2020.04.018. PMID:32747177.): 669-677Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar Binge drinking prevalence decreased from 18.9% in 2011 to 18.0% in 2017.5Kanny D Naimi TS Liu Y Brewer RD. Trends in total binge drinks per adult who reported binge drinking—United States, 2011–2017.MMWR Morb Mortal Wkly Rep. 2020; 69 (doi:10.15585/mmwr.mm6902a2. PMID:31945030.): 30-34Crossref PubMed Scopus (23) Google Scholar This report found binge drinking prevalence was 17.4% in 2018, indicating that binge drinking remained common. Alcohol consumption patterns might have since changed, including during the COVID-19 pandemic. Collectively, all three measures (prevalence, frequency, and intensity) address a complex pattern of binge drinking. For example, lower education and income levels were associated with lower binge drinking prevalence, but among adults who reported binge drinking, those with less than a high school diploma reported higher frequency and intensity than did college graduates. Similarly, adults in the lowest income level binge drank more frequently than did adults in the highest income level. The finding that the prevalence of binge drinking was lower in the most rural counties than in the most urban counties is consistent with earlier reports.6Matthews KA Croft JB Liu Y et al.Health-related behaviors by urban-rural county classification—United States, 2013.MMWR Surveill Summ. 2017; 66 (doi:10.15585/mmwr.ss6605a1. PMID:28151923.): 1-8Crossref PubMed Scopus (173) Google Scholar However, adults in the most rural counties who binge drank did so more frequently and at higher intensity than did adults in the most urban counties. The prevalence of binge drinking in Mississippi and in West Virginia was lower than in the United States overall, but Mississippi had the highest median frequency and West Virginia had the highest median intensity of binge drinking among all states. SummaryWhat is already known about this topic?Excessive alcohol use has contributed to declines in life expectancy. Binge drinking is a common and costly pattern of excessive alcohol use.What is added by this report?During 2018, one in six U.S. adults reported binge drinking during the past 30 days. Among those who binge drank, 25% did so at least weekly, on average, and 25% consumed at least eight drinks during a binge occasion. Some sociodemographic groups and states with low binge drinking prevalence reported large quantities of alcohol consumed during binge occasions.What are the implications for public health practice?An effective population health approach including regulating alcohol sales, increasing alcohol taxes, and alcohol screening and brief counseling by clinicians can help reduce binge drinking. Excessive alcohol use has contributed to declines in life expectancy. Binge drinking is a common and costly pattern of excessive alcohol use. During 2018, one in six U.S. adults reported binge drinking during the past 30 days. Among those who binge drank, 25% did so at least weekly, on average, and 25% consumed at least eight drinks during a binge occasion. Some sociodemographic groups and states with low binge drinking prevalence reported large quantities of alcohol consumed during binge occasions. An effective population health approach including regulating alcohol sales, increasing alcohol taxes, and alcohol screening and brief counseling by clinicians can help reduce binge drinking. Excessive alcohol use is associated with increasing mortality from alcoholic liver disease, which has contributed to recently observed declines in U.S. life expectancy, notably among men, young and middle-aged adults, and persons with less than a high school education and limited income living in rural areas.7Woolf SH Schoomaker H. Life expectancy and mortality rates in the United States, 1959–2017.JAMA. 2019; 322 (doi:10.1001/jama.2019.16932. PMID:31769830.): 1996-2016Crossref PubMed Scopus (409) Google Scholar The results of this study highlight the importance of reducing binge drinking, particularly among groups who are disproportionately affected. The findings in this report are subject to at least three limitations. First, the BRFSS response rate indicates the potential for selection bias to the extent that survey respondents differ from nonrespondents. Second, responses are self-reported and subject to recall, social desirability, and nonresponse biases, which could vary across states and groups, and lead to underestimates of binge drinking.8Stockwell T Donath S Cooper-Stanbury M Chikritzhs T Catalano P Mateo C. Under-reporting of alcohol consumption in household surveys: a comparison of quantity-frequency, graduated-frequency and recent recall.Addiction. 2004; 99 (doi:10.1111/j.1360-0443.2004.00815.x. PMID:15265099.): 1024-1033Crossref PubMed Scopus (369) Google Scholar A study comparing BRFSS estimates to alcohol sales data found that although they were consistently correlated, survey data substantially underestimated consumption.9Nelson DE Naimi TS Brewer RD Roeber J. US state alcohol sales compared to survey data, 1993–2006.Addiction. 2010; 105 (doi:10.1111/j.1360-0443.2010.03007.x. PMID:20626370.): 1589-1596Crossref PubMed Scopus (76) Google Scholar Finally, binge drinking intensity based on the largest number of drinks reported on any occasion in the past 30 days might overestimate intensity. A previous analysis found that among demographic groups, this measure was 0.1–1.2 drinks higher than the reported number of drinks consumed during the most recent binge, but the two measures were strongly correlated.3Esser MB Kanny D Brewer RD Naimi TS. Binge drinking intensity: a comparison of two measures.Am J Prev Med. 2012; 42 (doi:10.1016/j.amepre.2012.03.001. PMID:22608381.): 625-629Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar However, they were not correlated among adults without a high school diploma; in 2018, intensity by education level was highest among this group. A population health approach has been shown to reduce excessive drinking, including binge drinking. The Community Preventive Services Task Force recommends the following strategies to reduce excessive drinking: increasing alcohol taxes, limiting hours and days of alcohol sales, and regulating alcohol outlet density. Fewer than one half of adults who report binge drinking to a health care provider during a medical checkup are advised to reduce their drinking.10McKnight-Eily LR Okoro CA Turay K Acero C Hungerford D. Screening for alcohol use and brief counseling of adults—13 states and the District of Columbia, 2017.MMWR Morb Mortal Wkly Rep. 2020; 69 (doi:10.15585/mmwr.mm6910a3. PMID:32163383.): 265-270Crossref PubMed Google Scholar Clinicians should follow the U.S. Preventive Services Task Force recommendation to screen all adults for alcohol misuse and provide brief intervention and referral to treatment as needed. All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed. * Excessive alcohol use includes binge drinking, heavy drinking (i.e., 15 or more drinks per week for men; eight or more drinks per week for women), and any drinking by pregnant women or persons aged <21 years. https://www.cdc.gov/alcohol/fact-sheets/alcohol-use.htm † https://www.thecommunityguide.org/topic/excessive-alcohol-consumption § https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/unhealthy-alcohol-use-in-adolescents-and-adults-screening-and-behavioral-counseling-intervention ¶ https://www.cdc.gov/brfss/annual_data/2018/pdf/overview-2018-508.pdf ** Response rates for BRFSS are calculated using standards set by the American Association for Public Opinion Research. https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx †† https://www.cdc.gov/brfss/annual_data/2018/pdf/2018-sdqr-508.pdf §§ https://www.cdc.gov/brfss/questionnaires/pdf-ques/2018_BRFSS_English_Questionnaire.pdf ¶¶ https://www.cdc.gov/nchs/data_access/urban_rural.htm

Gastrointestinal Symptoms and Irritable Bowel Syndrome Are Associated With Female Sex and Smoking in the General Population and With Unemployment in Men.

Background: The influence of daily life exposure on the gastrointestinal tract is not fully understood. This study aimed to examine associations between functional gastrointestinal symptoms and sociodemographic status and lifestyle habits in the general population.Methods: The Malmö Offspring Study (MOS) included 2,648 participants from the general population who had answered a questionnaire about sociodemographic status, lifestyle habits, medical health, and self-reported irritable bowel syndrome (IBS). The visual analog scale for IBS (VAS-IBS) was completed to assess gastrointestinal symptoms the past 2 weeks. Subjects with organic gastrointestinal diseases were excluded. Presence of self-reported IBS and gastrointestinal symptoms the past 2 weeks were used as dependent variables to study the associations with age, sex, body mass index, education, occupation, marital status, smoking, snuff using, alcohol drinking frequency, alcohol amount per drinking occasion, physical activity at work, and physical activity during leisure time, using logistic regression and generalized linear model.Results: Self-reported IBS was associated with gastrointestinal symptoms the past 2 weeks (p < 0.001). There was an association between IBS and female sex (p < 0.001), former smoking (p < 0.001), present smoking (p < 0.001), and an inverse association with drinking 3–4 standard glasses per occasion (p = 0.038). Gastrointestinal symptoms were associated with age 50–59 years (p = 0.009), ≥60 years (p = 0.004), female sex (p < 0.001), studying (p = 0.036), unemployment (p = 0.009), former smoking (p = 0.001), and present smoking (p = 0.012). In men, IBS was associated with middle-age and both IBS and gastrointestinal symptoms were associated with unemployment (p < 0.001 and p = 0.001, respectively). In women, IBS was associated with present smoking (p = 0.022), and gastrointestinal symptoms were associated with former smoking and inversely associated with higher age (p = 0.006) and intermediate physical activity at work (p = 0.008). No associations were found with BMI, education, marital status, or snuff using.Conclusion: Self-reported IBS in the general population shows strongest association with female sex and smoking, whereas gastrointestinal symptoms also are associated with unemployment and inversely associated with higher age. In men, both IBS and gastrointestinal symptoms are associated with unemployment. In women, both IBS and gastrointestinal symptoms are associated with smoking, whereas symptoms are inversely associated with higher age and intermediate physical activity.

Traumatic events during childhood and its risks to substance use in adulthood: an observational and genome-wide by environment interaction study in UK Biobank

We aimed to explore the underlying genetic mechanisms of traumatic events during childhood affecting the risks of adult substance use in present study. Using UK Biobank cohort, linear regression model was first applied to assess the relationships between cigarette smoking and alcohol drinking in adults with traumatic events during childhood, including felt hated by family member (41,648–111,465), felt loved (46,394–124,481) and sexually molested (47,598–127,766). Using traumatic events as exposure variables, genome-wide by environment interaction study was then performed by PLINK 2.0 to identify cigarette smoking and alcohol drinking associated genes interacting with traumatic events during childhood. We found that the frequency of cigarette smoking was significantly associated with felt hated by family member (coefficient = 0.42, P < 1.0 × 10–9), felt loved (coefficient = −0.31, P < 1.0 × 10–9) and sexually molested (coefficient = 0.46, P < 1.0 × 10–9). We also observed weaker associations of alcohol drinking with felt hated by family member (coefficient = 0.08, P = 3.10 × 10–6) and felt loved (coefficient = −0.06, P = 3.15 × 10–7). GWEIS identified multiple candidate loci interacting with traumatic events, such as CTNNA3 (rs189142060, P = 4.23 × 10–8) between felt hated by family member and the frequency of cigarette smoking, GABRG3 (rs117020886, P = 2.77 × 10–8) between felt hated by family member and the frequency of alcohol drinking. Our results suggested the significant impact of traumatic events during childhood on the risk of cigarette smoking and alcohol drinking.

Longitudinal study of the relationship between coffee consumption and type 2 diabetes in Chinese adult residents: Data from China Health and Nutrition Survey.

BackgroundIncreasing coffee intake was inversely associated with risk of type 2 diabetes in Western countries. However, in China where coffee consumption and diabetes population has been growing fast in recent years, studies on the impact of coffee intakes on the onset of type 2 diabetes are lacking. This study attempts to determine the associations between coffee consumption and type 2 diabetes in Chinese adults.MethodsThis longitudinal study analyzed 10447 adults who had participated in at least two rounds of the China Health and Nutrition Survey (CHNS), which is a survey database of multistage, random cluster process during 1993–2011. Coffee consumption and type 2 diabetes incidence were measured in the survey. Body mass index (BMI), age, sex, place of residence, waves, education level, smoking, drinking alcohol and tea drinking frequency were adjusted as covariate. We used longitudinal fixed effects regression models to assess changes within person.ResultsAfter adjusting confounding factors, lower risk of diabetes is observed among Chinese adults who drink coffee occasionally (Adjusted Odds Ratio (AOR)  = 0.13, 95% CI = 0.05, 0.34) and drink almost every day (AOR = 0.61, 95% CI = 0.45, 0.83), compared with those who do not or hardly drink. In the subgroup analysis, among women aged 45–59 who drink coffee one to three times a week (AOR = 0.21, 95% CI = 0.08, 0.52) and men over 60 who drink coffee almost every day (AOR = 0.19, 95% CI = 0.07, 0.53), protective effects were found. For young men aged 19–29, drinking coffee almost every day showed a risk effect (AOR = 20.21, 95% CI = 5.96–68.57).ConclusionsCoffee drinking habit is an independent protective factor for adult on type 2 diabetes in China. And it varies among people with different ages and genders. The rapid growth of coffee consumption in China in recent years may help reduce the risk of type 2 diabetes, but at the same time, the risk of type 2 diabetes in adolescents needs attention.