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Alcohol Use and Depression Risk

Elizabeth Keane, Joey Lam, Janna Nixon, Angela Schwab

Andrews University

FDNT 560-999

Dr. Jean Cadet

November 17, 2024

Objectives

Research question: Is increased alcohol use related to higher prevalence of depression?

Ho: Alcohol use does not have an effect on depression screening risk.

Ha: Alcohol use has an effect on depression screening risk.

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Objectives

Research question: Is increased alcohol use related to higher prevalence of depression?

Ho: Alcohol use does not have an effect on depression screening risk.

Ha: Alcohol use has an effect on depression screening risk.

Background and Rationale

The World Health Organization (WHO, 2023) estimates that 3.8 percent of the global

population and 5 percent of adults have depression, which involves depressed mood or extended

periods where there is a loss of pleasure or interest in activities. According to results from the

2015–2020 National Survey on Drug Use and Health on U.S. individuals aged ≥12 years, 9.2

percent of Americans experienced depression in the year 2020, up from 7.3 percent in the year

2015. The highest prevalence of depression was among young adults aged 18-25 years at 17.2

percent, followed by adolescents aged 12-17 years at 16.9 percent (Goodwin et al., 2022).

Increasing rates of depression occur on a global scale as well, with an estimated 50 percent

increase in prevalence between 1990 and 2017 (Visontay et al., 2023). Evidence indicates that

depression is linked with diminished quality of life along with elevated risk for

non-communicable diseases and all-cause mortality (Machado et al., 2018). With the

burdensome impact of depression and the concerning trend of its increasing prevalence, there is a

need for more research on contributing and preventive factors in order to address depression in

public health.

Evidence also indicates that Alcohol Use Disorder (AUD) is the most common

co-occuring disorder among individuals with major depressive disorder such that individuals

with AUD are 2.3 times more likely to have major depressive disorder in the last year (McHugh

& Weiss, 2019). Risky drinking is defined as “more than four standard drinks per day and 14

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drinks total per week for men and more than three drinks per day and seven drinks in total per

week for women” (Nunes, 2023). A secondary analysis of the National Longitudinal Survey of

Youth 1979 cohort revealed surprising findings of a J-shape relationship between drinking levels

and risk of depression (Visontay et al., 2023). Light to moderate drinkers were found to have the

lowest risk for depression, while abstainers have a higher risk, and risky drinkers have the

highest risk for depression (Visontay et al, 2022). This J-shape relationship between depression

and AUD has been replicated in many studies, and is therefore the expected result in this analysis

of data collected by the National Health and Nutrition Examination Survey (NHANES).

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Methodology

a) Research Design

This study will adopt a mixed research approach to answer the above research questions.

While the first method will use the survey technique to measure the co-relations of major

variables, the second mechanism will employ semi-structured interviews to identify and

understand the existential arrangement of participants' lives. The use of statistical trends and the

personal accounts of other students makes the research results provide a broad perception of a

given topic.

b) Sample

As study participants, purposive sampling will be used to sample individuals. The study

will target 100 participants who meet the inclusion criteria: participants usually are people

between the ages of 18 and 50 who speak English and have backgrounds related to the research.

Patients with aphasia or other cognitive abnormalities that could make them incapable of giving

informed consent or otherwise unable to participate in the study entirely will be excluded. The

recruitment process will involve using social networks and enrollment in community-based

organizations. However, before participating in the study, participants will have to fill out the

pre-study survey to ensure they meet the inclusion criteria. So, while retaining the element of

chance and randomness that is needed in order to achieve a certain level of generalizability, a

combination of survey sampling (strict/no/accidental) and interview sampling (convenience) will

be utilized (ALQ_L, 2021).

c) Measurement/Instrumentation

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The two main variables of concern are psychological health indexed by the dependent

variable and social determinants to which the independent variable belongs. Well-being data will

be collected using standard self-complete measures like the General Health Questionnaire

(GHQ-12) and an original demographic survey to elicit exposure information. The GHQ-12 is

proper based on the reliability and validity indices: Cronbach's alpha = 0.89. Quantitative data

will be collected through questionnaires, while questionnaire data will be quantitative because

they are structured and measure response strength (ALQ_L, 2021).

d) Detailed Study Procedures

Recruitment of participants will be done over a three-month timeline. As part of the

survey, participants will respond to questionnaires on the Internet, and interview participants will

meet the researcher face-to-face using secure video conferencing. The steps taken to protect the

participants' identity involve removing any identifiable information from the databases, storing

records electronically in safe locations, and shredding or erasing any identifiable information

after six months of the study. To ensure anonymity, participants will be assigned unique

identification numbers.

The timeline for participation includes:

● Week 1-4: Recruitment and screening.

● Week 5-8: Survey dissemination and response collection.

● Week 9-12: Conducting interviews and transcribing data.

All measures align with ethical standards, minimizing participant risks and maintaining

voluntary, informed consent.

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e) Internal Validity

To reduce internal validity threats in the study, it will be possible to blind some of the

data analysts, and they were not to know the identity of the participants. Bias is controlled in the

following ways: The questions to be asked in the survey are pre-designed, and interview

procedures are uniform across surveyors. External validity concerns, like sample

representativeness, will be mitigated by subject recruitment from a wide area with further

diversification by geographic area and demographic characteristics.

f) Data Analysis

Data described in the quantitative form will be tabulated, and means, standard deviations

and other tests of significance will be used, as well as regression analysis. The analysis will be

done with the help of SPSS software. For qualitative data collection, interview data will be

analyzed using thematic analysis, where the interview data will be coded to develop themes or

patterns. Such a dual approach is used in order to enrich the study results with statistical and

narrative data at the same time.

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Data Analysis

Descriptive Results

This is where Elizabeth’s descriptive analysis of the demographic data will go.

Inferential Results

Angela and Joey, I used the binary logistic test with the level of significance set at 0.05 (for

your reference, this is unit 7 in biostatistics; Dr. Kijai’s video on this topic is called “On binary

logistic regression analysis,” and it’s the fourth link from the top of the unit 7 Learning Hub

page). The binary logistic test is the appropriate choice for our data because the dependent

variable (depression risk) is categorical and binary (depressed or not). I re-defined depression as

depressed (1-3 in the codebook) or not depressed (0 in the codebook). I re-defined alcohol use as

non-drinker (0 in the codebook), light drinker (6-10 in the codebook), and moderate/heavy

drinker (1-5 in the codebook). Based on what’s in tables 1 and 2, Level of alcohol use is

non-drinker, Level of alcohol use (1) is light drinker, and Level of alcohol use (2) is

moderate/heavy drinker. Redefining the original alcohol and depression variables allowed me to

create new variables based on those categories, and I used those new variables (along with the

gender data) when I ran the binary logistic tests. The results show that not drinking alcohol and

being a moderate/heavy drinker ARE both significantly correlated with depression when we

don’t control for gender (table 2). However, when we do control for gender (table 3), the results

change: gender IS significantly correlated with depression, but alcohol use is NO LONGER

significantly correlated with depression. If I’m understanding this correctly, I think that means

that gender is a greater predictor of depression risk than alcohol use. I was unable to get SPSS to

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show me each gender individually, even after trying a few different things; I can ask Dr. Cadet

about this after class on Monday. Please text me if you have any questions!

Table 1, Descriptive Statistics

Variables n M SD Skewness

Depressed or not 5518 .33 .47 .70

Gender 11933 1.53 .50 .02

Level of alcohol use 4917 1.20 .71 .04

Valid N (listwise) 4917

Table 2, Binary Logistic Regression Model without Gender

b SE Wald df p Exp(B)

Level of alcohol use 6.42 2 .04

Level of alcohol use(1)

.09 .09 1.07 1 .30 1.10

Level of alcohol use(2)

.17 .07 6.42 1 .01 1.18

Constant -.76 .0507 226.94 1 <.001 .47

Note: R2=0.002, p<.001

Table 3, Binary Logistic Regression Model with Gender

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b SE Wald df p Exp(B)

Gender .30 .06 23.53 1 <.001 1.35

Level of alcohol use 3.01 2 .22

Level of alcohol use(1) .06 .09 .47 1 .50 1.06

Level of alcohol use(2) .12 .07 3.00 1 .08 1.12

Constant -1.19 .10 132.57 1 <.001 .30

Note: R2=0.008, p<.001

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References

Ballester, L., Alayo, I., Vilagut, G., Almenara, J., Cebrià, A. I., Echeburúa, E., … &

UNIVERSAL Study Group. (2021). Validation of an online version of the alcohol use

disorders identification test (AUDIT) for alcohol screening in Spanish university

students. International journal of environmental research and public health, 18(10),

5213. https://www.mdpi.com/1660-4601/18/10/5213

Berner, D., & Amrhein, V. (2022). Why and how we should join the shift from significance

testing to estimation. Journal of evolutionary biology, 35(6), 777-787.

https://academic.oup.com/jeb/article-abstract/35/6/777/7317859

Centers for Disease Control and Prevention. (2024, September). Alcohol Use (ALQ_L).

National Health and Nutrition Examination Survey August 2021-August 2023

Questionnaire Data.

https://wwwn.cdc.gov/Nchs/Nhanes/2021-2022/ALQ_L.htm#SEQN

Centers for Disease Control and Prevention. (2024, September). Mental Health – Depression

Screener (DPQ_L). Centers for Disease Control and Prevention.

https://wwwn.cdc.gov/Nchs/Nhanes/2021-2022/DPQ_L.ht

Goodwin, R. D., Dierker, L. C., Wu, M., Galea, S., Hoven, C. W., & Weinberger, A. H. (2022).

Trends in U.S. Depression Prevalence From 2015 to 2020: The Widening Treatment Gap.

American Journal of Preventive Medicine, 63(5), 726–733.

https://doi.org/10.1016/j.amepre.2022.05.014

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Machado, M. O., Veronese, N., Sanches, M., Stubbs, B., Koyanagi, A., Thompson, T., Tzoulaki,

I., Solmi, M., Vancampfort, D., Schuch, F. B., Maes, M., Fava, G. A., Ioannidis, J. P. A.,

& Carvalho, A. F. (2018). The association of depression and all-cause and cause-specific

mortality: an umbrella review of systematic reviews and meta-analyses. BMC Medicine,

16(1). https://doi.org/10.1186/s12916-018-1101-z

McHugh, R. K., & Weiss, R. D. (2019). Alcohol Use Disorder and Depressive Disorders.

Alcohol research : current reviews, 40(1), arcr.v40.1.01.

https://doi.org/10.35946/arcr.v40.1.01

Nunes, E. V. (2023). Alcohol and the Etiology of Depression. American Journal of Psychiatry,

180(3), 179–181. https://doi.org/10.1176/appi.ajp.20230004

Visontay, R., Mewton, L., Slade, T., Aris, I. M., & Sunderland, M. (2023). Moderate Alcohol

Consumption and Depression: A Marginal Structural Model Approach Promoting

Causal Inference. American Journal of Psychiatry, 180(3), 209–217.

https://doi.org/10.1176/appi.ajp.22010043

Visontay, R., Sunderland, M., Slade, T. et al. (2022). Are there non-linear relationships between

alcohol consumption and long-term health?: a systematic review of observational studies

employing approaches to improve causal inference. BMC Med Res Methodol 22, 16.

https://doi.org/10.1186/s12874-021-01486-5

World Health Organization. (2023). Depressive disorder (depression). World Health

Organization; World Health Organization.

https://www.who.int/news-room/fact-sheets/detail/depression

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Appendix

Codebook

Variable/Label SPSS Variable Name

Variable Type

Code & Variable Description

Demographics

Gender of the participant.

RIAGENDR Dichotomous 1 = Male 2 = Female . = Missing

Age in years of the participant at the time of screening. Individuals 80 and over are top-coded at 80 years of age.

RIDAGEYR Continuous 0 to 79 = range of values 80 = 80 years of age and over . = Missing

Recode of reported race and Hispanic origin information, with Non-Hispanic Asian Category

RIDRETH3 Categorical 1 = Mexican American 2 = Other Hispanic 3 = Non-Hispanic White 4 = Non-Hispanic Black 6 = Non-Hispanic Asian 7 = Other Race – Including Multi-Racial . = Missing

What is the highest grade or level of school {you have/SP has} completed or the highest degree {you have/s/he has} received?

DMDEDUC2 Categorical 1 = Less than 9th grade 2 = 9-11th grade (Includes 12th grade with no diploma) 3 = High school graduate/GED or equivalent 4 = Some college or AA degree 5 = College graduate or above 7 = Refused 9 = Don’t know . = Missing

Study Variables

[Over the last 2 weeks, how often have you been

DPQ020 Categorical 0 = Not at all 1 = Several days 2 = More than half the days 3 = Nearly every day

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bothered by the following problems:] feeling down, depressed, or hopeless?

During the past 12 months, about how often did you drink any type of alcoholic beverage? PROBE: In other words, how many days per week, per month, or per year did you drink?

ALQ121 Categorical 0 = Never in the last year 1= Every day 2= Nearly every day 3 = 3 to 4 times a week 4 = 2 times a week 5 = Once a week 6 = 2 to 3 times a month 7 = Once a month 8 = 7 to 11 times in the last year 9 = 3 to 6 times in the last year 10= 1 to 2 times in the last year