Paper
24 March 2023 Alcohol intake and depression in women aged ≥21 years: NHANES 2013–2016
Ziang Chen
Author Affiliations +
Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126113X (2023) https://doi.org/10.1117/12.2669975
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom
Abstract
Alcohol abuse and depression have been commonly linked together, and women have a higher risk of depression. The association, however, between depression and alcohol intake remains unclear among legal-drinking aged women. The purpose of this study was to evaluate the relationship between alcohol intake and depression. U.S. National Health and Nutrition Examination Survey (NHANES) (2013–2016) was the source of data in this research. We compared the alcohol intake among female participants grouped by depression score based on Patient Health Questionnaire-9 (PHQ-9). Female participants were categorized into four groups by alcohol intake(0, < 1, 1- < 8, and ≥8 drinks per week). After the hypothesis test used to prove the association, we built the logistic regression model to evaluate this association, with adjusting of the covariate that are clinically or statistically significant to the regression model. Our regression analysis indicated that depression was associated with alcohol intake among female participants. Inside the drinking group, heavy drinking had the highest odds ratio of depression compared to non-drinking participants.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ziang Chen "Alcohol intake and depression in women aged ≥21 years: NHANES 2013–2016", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126113X (24 March 2023); https://doi.org/10.1117/12.2669975
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KEYWORDS
Statistical analysis

Databases

Analytical research

Diseases and disorders

Statistical modeling

Analytics

Biological samples

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