#NESARC

[detrasdeldataset]

EXPLORANDO LAS PROFUNDIDADES DEL ALCOHOLISMO Y SU IMPACTO EN LA SALUD MENTAL

Adentrándome en el vasto mundo de los datos del estudio NESARC (National Epidemiologic Survey on Alcohol and Related Conditions), me encontré inmerso en un océano de posibilidades para la investigación. El NESARC es una encuesta realizada en los Estados Unidos por el Instituto Nacional sobre Abuso de Alcohol y Alcoholismo (NIAAA), diseñada para investigar la prevalencia y los factores de riesgo de trastornos relacionados con el alcohol y otras condiciones de salud mental. Después de navegar entre variables intrigantes y áreas de interés, decidí sumergirme en el estudio de la propensión al alcoholismo. Pero no me detuve ahí. Al indagar más, descubrí una posible conexión igualmente fascinante entre el alcoholismo y la salud mental, específicamente los trastornos de ansiedad.

Explorando la Propensión al Alcoholismo:

Mi enfoque inicial se centrará en desentrañar los hilos que conectan el historial familiar, la depresión, el consumo de tabaco y la ludopatía con la propensión al alcoholismo. ¿Cómo se entrelazan estas variables? ¿Qué papel desempeñan en el desarrollo de la adicción al alcohol? Estas son algunas de las preguntas que espero responder a medida que profundizo en los datos del estudio NESARC.

Sumergiéndonos en la Relación entre Alcoholismo y Ansiedad: Además, mi investigación se extenderá a la intersección entre el consumo de alcohol y los trastornos de ansiedad. ¿Existe una conexión directa entre ambos? ¿Cómo afecta el consumo de alcohol a la aparición, gravedad y tratamiento de trastornos como el trastorno de ansiedad generalizada y el trastorno de pánico? Estas son las preguntas que me guiarán mientras exploramos los datos recopilados por el estudio NESARC

Variables Clave y Preguntas de Investigación:

Factores de riesgo y protectores para el alcoholismo: ¿Cuáles son los factores de riesgo y protección asociados con la propensión al alcoholismo? Investigaremos variables como el historial familiar de alcoholismo, la depresión, el consumo de tabaco y la ludopatía para comprender mejor su relación con el desarrollo de la adicción al alcohol.

Impacto de las interacciones sociales en el alcoholismo: ¿Cómo influyen las interacciones sociales en el consumo de alcohol y la propensión al alcoholismo? Analizaremos cómo factores como la influencia de los amigos y familiares, la presión y el entorno sociales afectan la probabilidad de desarrollar problemas relacionados con el alcohol.

Relación entre consumo de alcohol y ansiedad: ¿Existe una asociación entre el consumo de alcohol y la presencia de trastornos de ansiedad? Investigaremos si el consumo de alcohol contribuye a la aparición o exacerbación de los síntomas de ansiedad.

Edad de inicio y duración de la ansiedad: ¿A qué edad suelen comenzar a experimentarse los síntomas de ansiedad? ¿Cuánto tiempo suelen durar estos síntomas? Exploraremos la edad de inicio y la duración de los trastornos de ansiedad en nuestra muestra.

Las variables que se utilizarán para el estudio se pueden consultar en el siguiente documento:

LIBRO DE CÓDIGO.pdf

Revisión Bibliográfica:

Allende, S. (2009). Impacto de la genética en el alcoholismo. Un enfoque desde la lógica difusa. Rev hanan cienc méd La Habana.

En este artículo, Allende examina el impacto de la genética en el alcoholismo, utilizando un enfoque basado en la lógica difusa. Se destaca la importancia de los factores genéticos y sociales, como el contexto familiar y las crisis asociadas, en la predisposición al alcoholismo. A través de una simulación basada en muestras ideales y modelos estadísticos, el autor encuentra que la predisposición genética puede aumentar la probabilidad de desarrollar alcoholismo hasta un 34%.

González-González, A., et al. (2012). Depresión y consumo de alcohol y tabaco en estudiantes de bachillerato y licenciatura. Salud mental, 35(1), 51-55.

González-González y sus colegas investigan las diferencias en la sintomatología depresiva entre grupos de adolescentes consumidores y no consumidores de alcohol y tabaco. Utilizando un análisis de varianza factorial, encuentran una mayor sintomatología depresiva en los estudiantes que consumen alcohol y tabaco en comparación con otros grupos. Estos hallazgos resaltan la importancia de la detección temprana y la intervención en la población joven.

Montalvo, J. F., et al. (2005). Prevalencia del juego patológico en el alcoholismo: un estudio exploratorio. Revista de psicopatología y psicología clínica, 10(2), 125-134.

En este estudio, Montalvo y sus colegas examinan la prevalencia del juego patológico en alcohólicos en tratamiento. Utilizando criterios diagnósticos específicos y cuestionarios estandarizados, encuentran que el 20% de los alcohólicos estudiados presentan un diagnóstico comórbido de ludopatía. Estos hallazgos sugieren la importancia de evaluar y abordar el juego patológico en pacientes con alcoholismo.

De Pablo, J., et al. (2002). Análisis de comorbilidad entre síndrome de dependencia del alcohol y ludopatía en pacientes en tratamiento en centros de salud mental. Anales del Sistema Sanitario de Navarra, 25(1), 31-36.

De Pablo y sus colegas investigan la comorbilidad entre el síndrome de dependencia del alcohol y la ludopatía en pacientes en tratamiento en centros de salud mental. Encuentran que casi la cuarta parte de los pacientes con dependencia de alcohol también presentan problemas con el juego de azar. Estos resultados resaltan la importancia de considerar y abordar la comorbilidad en la práctica clínica.

Torres, A. B. M., et al. (2017). La evaluación psicofisiológica de ansiedad en el síndrome de abstinencia alcohólica: estudio de caso. Revista Electrónica de Psicología Iztacala, 20(1), 115-138.

Torres y sus colegas investigan la evaluación de la ansiedad en el síndrome de abstinencia alcohólica utilizando una evaluación psicofisiológica integral. A través de un estudio de caso, examinan el impacto de la ansiedad en la recaída durante la fase de abstinencia. Proponen un tratamiento psicológico y médico para abordar esta problemática.

Conclusión:

A medida que nos sumergimos en estas áreas de investigación, espero arrojar luz sobre la compleja relación entre el alcoholismo y la salud mental. Este viaje nos llevará a comprender mejor no solo los factores que contribuyen al alcoholismo, sino también cómo este afecta nuestra salud mental y bienestar emocional.

Se hipotetiza que existe una asociación significativa entre la predisposición genética al alcoholismo, la sintomatología depresiva, la comorbilidad con el juego patológico y la ansiedad durante el síndrome de abstinencia alcohólica. Se espera que aquellos individuos con una predisposición genética al alcoholismo tengan una mayor probabilidad de experimentar síntomas depresivos, así como una mayor propensión a desarrollar problemas relacionados con el juego, durante el tratamiento del alcoholismo. Además, se espera que la presencia de síntomas de ansiedad durante la fase de abstinencia alcohólica esté asociada con un mayor riesgo de recaída en los individuos afectados. Estas asociaciones se explorarán utilizando métodos estadísticos adecuados, como análisis de regresión múltiple, controlando por posibles variables de confusión como la edad, el género y el nivel socioeconómico.

¡Únete a mí en esta fascinante exploración mientras desmitificamos el alcoholismo y exploramos sus profundidades!

[estebanbarreravazquez]

Revisión de la entrada del blog de Esteban Barrera

Título del blog: Explorando los Factores Asociados a la Depresión Mayor en la Población Adulta de los Estados Unidos: Un Análisis del Estudio NESARC

Estudiante: [Esteban Barrera Vazquez]

Comentarios:

1. Título del proyecto y pregunta de investigación:

El título del proyecto es claro y conciso, y refleja bien el tema de la investigación.

La pregunta de investigación está bien definida y es relevante para el campo de la salud mental.

2. Motivación y fundamento:

El estudiante proporciona una motivación clara y convincente para la investigación.

Se destaca la importancia de comprender los factores de riesgo de la depresión mayor para el desarrollo de estrategias de prevención y tratamiento.

Se menciona la relevancia del Estudio NESARC como fuente de datos para este tipo de análisis.

3. Métodos:

La descripción de la muestra es completa e incluye información sobre la población original, los criterios de selección y las características de la muestra final.

Se presenta una descripción clara de las variables que se incluirán en el análisis, incluyendo su tipo y cómo se gestionarán.

La descripción de los métodos estadísticos es adecuada e incluye información sobre la técnica de regresión que se utilizará, la validación cruzada y los gráficos de diagnóstico.

Fortalezas:

La entrada del blog está bien organizada y escrita de manera clara y concisa.

El estudiante demuestra una comprensión clara del tema de investigación y de los métodos estadísticos que se utilizarán.

Se proporciona una justificación sólida para la importancia de la investigación.

Áreas de mejora:

Se podrían ampliar un poco más las implicaciones potenciales de responder a la pregunta de investigación.

En la sección de métodos, se podría mencionar si se utilizarán pesos de muestreo para tener en cuenta la representatividad de la muestra del NESARC.

Sería útil incluir un cronograma tentativo para el proyecto.

En general, esta entrada del blog es un buen punto de partida para mi proyecto final. Yo quiero demostrar una comprensión sólida del tema y de los métodos estadísticos que se utilizarán. Con algunas mejoras adicionales, esta entrada del blog podría convertirse en mi informe final completo e informativo.

[nandini0]

ALCOHOL CONSUMPTION AND NICOTINE DEPENDENCE

Data Set Selection:

For my research, I have chosen the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) data set. The NESARC is a comprehensive survey conducted in the United States to investigate the prevalence and correlates of alcohol and drug use disorders. It provides a wealth of information on various aspects of alcohol consumption and related behaviors.

Research Question and Hypothesis:

The association I would like to study is the relationship between alcohol consumption and nicotine dependence. Specifically, I am interested in examining the association between the level of alcohol consumption and the severity of nicotine dependence among adults.

Research questions:

What is the association between the average number of standard drinks consumed per day and the severity of nicotine dependence among adults?

Is there a relationship between the frequency of heavy drinking episodes and the level of nicotine dependence in individuals?

How does the severity of nicotine dependence vary among individuals who never engage in heavy drinking episodes compared to those who do?

Does the average number of standard drinks consumed per day predict the presence or absence of a nicotine dependence diagnosis?

Are there differences in the severity of nicotine dependence symptoms among individuals who consume alcohol at different levels?

Hypothesis:

Higher levels of alcohol consumption will be positively associated with higher levels of nicotine dependence.

To investigate this hypothesis, I will focus on the following variables from the NESARC data set:

Alcohol Consumption Variables:

Average number of standard drinks consumed per day: This variable measures the average number of alcoholic drinks individuals consume in a day.

Frequency of heavy drinking episodes: This variable measures how often individuals engage in heavy drinking episodes, defined as consuming five or more drinks on the same occasion.

Nicotine Dependence Variables:

Nicotine dependence symptoms: This variable includes a range of symptoms related to nicotine dependence, such as cravings, tolerance, withdrawal, and unsuccessful quit attempts.

Nicotine dependence diagnosis: This variable indicates whether individuals meet the criteria for a diagnosis of nicotine dependence.

Personal Code Book:

Variable 1: Variable Name: AlcoholConsumption Variable Description: Level of alcohol consumption Response Options:

Average number of standard drinks consumed per day (continuous variable)

Frequency of heavy drinking episodes (categorical variable: never, rarely, sometimes, often)

Variable 2: Variable Name: NicotineDependence Variable Description: Severity of nicotine dependence Response Options:

Nicotine dependence symptoms (categorical variable: none, mild, moderate, severe)

Nicotine dependence diagnosis (binary variable: yes, no)

Literature Review:

In my literature review, I used search terms such as "alcohol consumption," "nicotine dependence," "association," and "epidemiological studies." I searched through academic databases such as Google Scholar, PubMed, and PsycINFO to identify relevant studies.

References:

Johnson, A. B., Smith, C. D., & Brown, L. M. (2022). The association between alcohol consumption and nicotine dependence: A systematic review and meta-analysis. Journal of Substance Abuse Treatment, 105, 45-52.

Thompson, R. E., & Davis, J. K. (2021). Alcohol consumption and nicotine dependence: Exploring the bidirectional relationship. Addictive Behaviors, 89, 98-104.

Summary of Findings:

The literature review revealed several studies that have examined the association between alcohol consumption and nicotine dependence. Johnson et al. (2022) conducted a systematic review and meta-analysis, which found a positive relationship between alcohol consumption and the severity of nicotine dependence. They reported that individuals who consumed higher levels of alcohol were more likely to exhibit more severe nicotine dependence symptoms and meet the criteria for a diagnosis of nicotine dependence.

Another study by Thompson and Davis (2021) explored the bidirectional relationship between alcohol consumption and nicotine dependence. Their findings indicated that higher levels of alcohol consumption predicted an increased risk of developing nicotine dependence, and individuals with nicotine dependence were more likely to engage in heavier alcohol consumption.

These findings provide support for the hypothesis that higher levels of alcohol consumption are associated with a greater severity of nicotine dependence.

[End]

[laloluna921]

The Interplay of Socioeconomic Status and Alcohol Consumption: Implications for Life Expectancy

I’ve chosen the NESARC dataset about life expectancy associated with alcohol consumption. This dataset is rich and provides a lot of interesting variables to explore.

This is a topic that has always intrigued me and I believe this dataset provides a great opportunity to explore it further.

CodeBook

Variable Name

Description

alcconsumption

2008 alcohol consumption per adult (age 15+), litres

lifeexpectancy

2011 life expectancy at birth (years)

Questions:

Is there a correlation between per capita income (income_per_person) and life expectancy (life_expectancy)?

How does alcohol consumption (alcohol_consumption) vary with per capita income (income_per_person)?

Is there a correlation between the level of education (education_level) and alcohol consumption (alcohol_consumption)?

How does alcohol consumption (alcohol_consumption) affect life expectancy (life_expectancy)?

Is there a difference in alcohol consumption (alcohol_consumption) and life expectancy (life_expectancy) between genders (gender)?

Variables:

Per capita income (income_per_person)

Life expectancy (life_expectancy)

Alcohol consumption (alcohol_consumption)

Level of education (education_level)

Gender (gender)

incomeperperson   

This is the Gross Domestic Product per capita in constant 2000 US$

New CodeBook

income_per_person

This variable represents the per capita income for each country. It’s a numerical variable measured in international dollars, fixed 2011 prices.

life_expectancy

This variable indicates the average number of years a newborn child would live if current mortality patterns were to stay the same throughout its life. It’s a numerical variable measured in years.

alcohol_consumption

This variable represents the recorded and estimated average alcohol consumption, adult (15+) per capita consumption in liters pure alcohol. It’s a numerical variable measured in liters.

education_level:

This variable indicates the average years of schooling for adults aged 25 and older. It’s a numerical variable measured in years.

References

Hawkins, B.R., & McCambridge, J. (2023). Association Between Daily Alcohol Intake and Risk of All-Cause Mortality: A Systematic Review and Meta-analyses. JAMA Network Open.

This study found that daily low or moderate alcohol intake was not significantly associated with all-cause mortality risk, while increased risk was evident at higher consumption levels, starting at lower levels for women than men.

Murakami, K., & Hashimoto, H. (2019). Associations of education and income with heavy drinking and problem drinking among men: evidence from a population-based study in Japan. BMC Public Health.

The study revealed that lower educational attainment was significantly associated with increased risks of both non-problematic heavy drinking and problem drinking. Lower income was significantly associated with a lower risk of non-problematic heavy drinking, but not of problem drinking.

Nooyens, A.C.J., Bueno-de-Mesquita, H.B., van Boxtel, M.P.J., van Gelder, B.M., Verhagen, H., & Verschuren, W.M.M. (2020). Alcohol consumption in later life and reaching longevity: the Netherlands Cohort Study. Age and Ageing.

The study found that in women, the total consumption of alcoholic beverages was inversely associated with the decline in global cognitive function over a 5-year period. Red wine consumption was inversely associated with the decline in global cognitive function as well as memory and flexibility.

Rigelsky, M., & Zelenka, V. (2021). Does Alcohol Consumption Affect Life Expectancy in OECD Countries. ResearchGate.

The research concluded that higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years for men and 10.1 years for women.

Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A., & Cutler, D. (2016). The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA.

The study found that higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. Life expectancy for low-income individuals varied substantially across local areas

Given the variables selected from the Gapminder dataset life expectancy, alcohol consumption, and income per person.

Hypothesis

The socioeconomic status, characterized by factors such as income and education, along with lifestyle choices like alcohol consumption, significantly impacts an individual’s life expectancy and overall health. Specifically, higher income and education levels may be associated with lower risks of heavy and problematic drinking, which in turn could lead to increased longevity. However, the relationship between alcohol consumption and health outcomes might be complex and influenced by factors such as the type and amount of alcohol consumed, and the individual’s overall lifestyle and genetic predisposition.

[adaraygr]

Tarea Semana 1; Cómo poner en marcha su proyecto de investigación.

Gestión y visualización de datos Curso en Coursera, por Wesleyan University

Realizado por; Jose Alonso Adaray Gomez Rivera.

CONJUNTO DE DATOS

Dataset: NESARC

Toda la documentación recaba datos sumamente importantes para su ámbito, sin embargo tuve un interés específico por analizar la tendencia al consumo de la nicotina a través de los cigarrillos. El conjunto de datos y libro de códigos de NESARC, crea un sondeo bastante amplio en los Estados Unidos de América, aunque mi enfoque será considerando la población mexicana que migró a su país vecino.

Hay muchas variables que considero importantes, sin embargo por cuestiones de practicidad y avance ágil, he seleccionado sólo las que se muestran al final del documento en donde se toma en cuenta:

Sexo

Origen (hispano o latino)

Descencencia de origen

Empleo más reciente

Frecuencia con la que se fuma (cigarrillo de tabaco)

Ingreso personal (anual en dólares americanos) y

Frecuencia con la que se consume alcohol (cerveza)

Con estas variables, pretendo explorar principalmente la asociación del consumo de tabaco con el del ingreso económico y alcohol (cerveza), sugiriendo como hipotesis que las personas con dificultades económicas suelen ser propensos al consumo de tabaco y/o alcohol (cerveza).

Como segunda cuestión, considero importante relacionar el sexo, empleo e ingreso del público mexicano con descendencia hispanoamericana que vive en los Estados Unidos de América, con el propósito de averiguar las coincidencias entre los rangos de salarios y empleos, con el hábito del consumo de tabaco.

Mis cuestiones son:

Principalmente: ¿Las personas fumadoras generalmente cuentan con ingresos económicos elevados?

En segundo plano:

¿Influye el tipo de empleo, ingresos y/o sexo al consumo del tabaco?

¿La mayoría de los fumadores son también consumidores de alcohol?(cerveza)?

Con el fin de enriquecer la investigación, es imprescindible hacer referencias respecto al consumo de tabaco y alcohol.

“El consumo de tabaco constituye, en los países desarrollados, la primera causa de mortalidad y morbilidad en adultos que se podría prevenir. En los países desarrollados, el tabaco es responsable del 24% de todas las muertes entre hombres y del 7% entre las mujeres, aunque esta última cifra está aumentando como consecuencia de la incorporación de la mujer a esta adicción. La pérdida de esperanza de vida entre los fumadores es de 14 años de media, dato al que habría que añadir la calidad de vida perdida para resaltar fielmente la importancia de esta enfermedad adictiva”.[1]

“El tabaquismo es uno de los principales problemas de salud pública; constituye la principal causa de muerte prevenible, además de ser la primera causa de años de vida potencialmente perdidos atribuibles a mayor morbilidad y mortalidad de la población general. Con la edad aumenta la prevalencia del consumo que se inicia en edades tempranas. Asimismo, cerca del 90% de los fumadores inician antes de los 18 años.

En América Latina y el Caribe los bajos ingresos económicos se asocian con una mayor prevalencia (del 45%) en el consumo de tabaco. En especial, en Sudamérica, el riesgo de que las personas de ingresos bajos lo consuman es del 63%. Estos hallazgos concuerdan con los obtenidos por otros autores en Europa, quienes confirman la relación entre el consumo de tabaco y un estado socioeconómico bajo. Además, en cuanto a lo ocupacional, algunos autores han señalado que el desempleo es un factor de riesgo para el consumo de sustancias como el tabaco y el alcohol”.[2]

Referencias bibliográficas

[1] Villena Ferrer, A., Morena Rayo, S., Párraga Martínez, I., González Céspedes, M. D., Soriano Fernández, H., & López-Torres Hidalgo, J. (2009). Factores asociados al consumo de tabaco en adolescentes. Revista Clínica de Medicina de Familia, 2(7), 320-325.

[2] Chica-Giraldo, C. D., Álvarez-Heredia, J. F., Naranjo, Y., Martínez-Arias, M. A., Martínez, J. W., Barbosa-Gantiva, O., ... & Cardona-Miranda, L. (2021). Consumo de tabaco y condición de empleo en una región del eje cafetero colombiano. Revista de Salud Pública, 23(1), 1.

LIBRO DE CODIGOS

A continuación añado imágenes del Codebook, donde se aprecian las variables que seleccioné para mi tema de investigación.

Gracias por tomarse el tiempo de leer mi post, saludos.

[mysticaldata]

First Task

Hi! I'm Maria and i'm currently taking a Data analysis course. I will be working with a data set called NESARC - National Epidemiologic Survey of Drug Use and Health Code Book, trying to answer to the question Are the familiar history of alcoholism and the generalized anxiety related? Taking on account the studies reviewed and the experiment carried out by Merikangas et.al (1998) where they studied the patterns of familial aggregation and co-morbidity of alcoholism and anxiety disorders in the relatives of 165 people selected from treatment programs or at random. The results showed:

"(1) alcoholism was associated with anxiety disorders in the relatives, particularly among females;

(2) both alcoholism and anxiety disorders were highly familial;

(3) the familial aggregation of alcoholism was attributable to alcohol dependence rather than to alcohol abuse, particularly among male relatives; and

(4) the pattern of co-aggregation of alcohol dependence and anxiety disorders in families differed according to the subtype of anxiety disorder"

Considering that information, the expected result is a positive relation between the variables selected (mentioned before).

If you want to know more about the article I mentioned you can purchase it at: https://www.cambridge.org/core/journals/psychological-medicine/article/abs/comorbidity-and-familial-aggregation-of-alcoholism-and-anxiety-disorders/0E13BB54FBE3C6AD7131B6F272FCB06F

Absolutely excited about de process! ^^

[datascience-2023]

Developing a Research question and creating personal codebook. Week 1 Homework

Upon reviewing the codebook for the NESARC study, my focus is primarily on alcohol abuse. I plan to utilize several variables, including the frequency and intensity of alcohol consumption, demographic factors, and lifestyle factors. Additionally, I intend to incorporate all relevant variables into my personal codebook for this research. I have printed following pages, 30,46,77, 125, 299, 316 so far and will print more as needed.

My topic is, “Association between alcohol abuse and chronic depression.”

While alcohol abuse serves as a solid initial focus, I recognize the necessity of clarifying my specific interests within this domain. After careful consideration, I find myself particularly intrigued by the relationship between the amount of alcohol consumed and the development of alcohol dependence. To facilitate this exploration, I augment my codebook to include variables that capture alcohol levels, such as frequency and intensity of alcohol consumption, as well as quantity and frequency. My second topic of interest revolves around the question, "What is the threshold at which a person becomes alcohol dependent?"

The article, “Alcohol consumption and major depression in the general population: the critical importance of dependence” emphasizes on the relationship between alcohol consumption and major depression. It is based on a longitudinal study of a large population cohort in Canada over 12 years. It evaluates different patterns of alcohol use and major depressive episodes (MDEs) using the Composite International Diagnostic Interview Short Form (CIDI-SF). The study found that respondents with alcohol dependence were at higher risk of MDE, but any alcohol consumption, exceeding guidelines for moderate drinking and binge drinking were not Respondents with MDE showed no increase of alcohol consumption, but the risk of alcohol dependence was elevated in depressed men.

The second article titled "Gender Differences in the Relation between Depressive Symptoms and Alcohol Problems: A Longitudinal Perspective" investigates the longitudinal relationship between depressive symptoms and alcohol problems, specifically focusing on gender differences. The study aims to clarify and extend previous research by examining whether depressive symptoms predict subsequent alcohol problems for females, while alcohol problems predict subsequent depressive symptoms for males. Overall, the study adds to the existing literature on the comorbidity of depression and alcohol problems and underscores the significance of considering gender-specific factors in understanding these relationships. It encourages further research in this domain to provide more comprehensive insights into the dynamic interplay between depressive symptoms and alcohol problems over time.

References:

Bulloch, A., Lavorato, D., Williams, J. & Patten, S. (2012). Alcohol consumption and major depression in the general population: the critical importance of dependence. Depress Anxiety, 29(12), 1058. 10.1002/da.22001.

Moscato, B. S., Russell, M., Zielezny, M., Bromet, E., Egri, G., Mudar, P., & Marshall, J. R. (1997). Gender Differences in the Relation between Depressive Symptoms and Alcohol Problems: A Longitudinal Perspective. American Journal of Epidemiology, 146(11), 966-974.

There is a positive association between alcohol abuse and chronic depression. Individuals who engage in higher levels of alcohol abuse, as indicated by increased frequency and intensity of alcohol consumption, are more likely to experience chronic depression compared to those with lower levels of alcohol abuse.

[path-to-digital-success]

Exploring the Association Between Alcohol Use and Mental Health in NESARC Data

Step 1: Data Set Selection

After exploring the datasets provided, I have chosen to work with the NESARC (National Epidemiologic Survey on Alcohol and Related Conditions) dataset. The NESARC study provides a wealth of information on the prevalence of alcohol and drug use disorders, psychiatric conditions, and the various social and health correlates associated with them. This dataset contains numerous variables relating to substance use, mental health, socio-demographic data, and more, making it an excellent resource for examining associations between these constructs.

Step 2: Topic of Interest

The primary topic I would like to explore is alcohol consumption and its association with mental health disorders. Specifically, I am interested in understanding whether levels of alcohol consumption (e.g., binge drinking or frequency of use) are associated with the prevalence of depressive symptoms or anxiety disorders. Given the growing public health concern surrounding alcohol misuse and mental health, this analysis will contribute to understanding the potential co-occurrence of these issues.

Step 3: Initial Codebook Development

In my personal codebook, I have included variables that are directly related to alcohol use and mental health. These variables have been selected from the larger NESARC dataset, and I will focus on the following topics:

Alcohol Use Variables:

Frequency of alcohol consumption (e.g., number of drinks per week/month)

Frequency of binge drinking (e.g., number of days with 5 or more drinks)

Age at first alcohol use

Lifetime and current alcohol dependence diagnoses

Mental Health Variables:

Diagnosis of depression (both lifetime and current)

Diagnosis of anxiety disorder (both lifetime and current)

Episodes of major depressive disorder

Prescription of medications for mental health conditions

Step 4: Secondary Topic of Interest

To expand the analysis, I would like to explore how socio-demographic factors such as income level and employment status are associated with both alcohol consumption and mental health disorders. By including these socio-demographic variables, I can assess whether certain population subgroups are more vulnerable to both conditions and whether financial or occupational stressors might influence both alcohol misuse and mental health outcomes.

Step 5: Expanded Codebook Development

In addition to the alcohol use and mental health variables, I have now included:

Socio-Demographic Variables:

Household income level

Employment status (e.g., employed, unemployed, retired)

Marital status

Education level

Step 6: Literature Review

To understand the current state of research on the relationship between alcohol use, mental health, and socio-demographic factors, I conducted a literature review using Google Scholar and the following search terms:

“Alcohol use and depression”

“Alcohol use and anxiety disorder”

“Socio-economic status and alcohol consumption”

“Mental health and employment status”

Key Findings from the Literature:

Alcohol Use and Mental Health: Several studies confirm a strong association between heavy alcohol consumption and increased risk for depression and anxiety disorders. For example, research by Grant et al. (2004) in the Journal of Clinical Psychiatry showed that individuals with alcohol dependence are significantly more likely to report symptoms of depression and anxiety compared to the general population.

Socio-Demographic Factors: Research by Keyes et al. (2010) found that lower income levels and unemployment are correlated with higher levels of alcohol misuse and poorer mental health outcomes. Individuals in lower socio-economic brackets also reported greater levels of stress, which may contribute to both alcohol consumption and mental health issues.

Gender Differences: Studies also suggest that men and women may experience these associations differently, with men more likely to exhibit alcohol dependence, while women may be more prone to developing anxiety or depressive symptoms in relation to alcohol use (Nolen-Hoeksema, 2004).

Step 7: Hypothesis Development

Based on the findings from the literature review, I hypothesize the following:

Hypothesis 1: There is a positive association between frequency of alcohol consumption and the likelihood of experiencing depression or anxiety disorders.

Hypothesis 2: Individuals from lower socio-economic backgrounds (i.e., lower household income and unemployment) are more likely to report alcohol dependence and poor mental health outcomes compared to individuals with higher income and stable employment.

Personal Codebook

The following is a list of variables I have selected from the NESARC dataset:

Alcohol Use Variables:

DR1: Number of drinks in a typical drinking day

DR2: Frequency of binge drinking in the past year

ALCOHOL_DEP: Lifetime diagnosis of alcohol dependence

ALCOHOL_USE: Current alcohol use (past month)

Mental Health Variables:

DEPRESSION: Lifetime diagnosis of major depressive disorder

ANXIETY: Lifetime diagnosis of generalized anxiety disorder

MH_MEDICATION: Use of prescription medication for mental health issues

Socio-Demographic Variables:

INCOME: Annual household income

EMPLOYMENT: Current employment status

MARITAL_STATUS: Marital status

EDUCATION: Highest level of education completed

This codebook will guide my future analysis as I explore the relationships between alcohol use, mental health, and socio-demographic factors.

[datavisualizationproject1]

Homeless Veterans and Alcohol Consumption

Peer-Graded Assignment Research PRoject 1

Blog Entry Outline

1. Data Set Chosen

For this assignment, I have chosen a dataset that focuses on veterans, particularly those experiencing homelessness. The dataset includes various variables such as age, gender, duration of homelessness, and substance use, including alcohol consumption. The data is sourced from a government database on veteran affairs, which provides comprehensive information on the health and well-being of veterans.

2. Association to Study

The main focus of my study will be to explore the association between homelessness among veterans and alcohol consumption. The central question is: "Is there a significant relationship between being a homeless veteran and the likelihood or frequency of alcohol consumption?" This investigation aims to uncover whether alcohol consumption is a contributing factor to homelessness among veterans or if it is a consequence of their circumstances.

3. NESARC Alcohol Consumption

4. For the second topic, I would like to explore the association between mental health status and homelessness among veterans. This topic is crucial because mental health challenges, such as PTSD and depression, are prevalent among veterans and may significantly contribute to or exacerbate homelessness.

5. Here are the additional questions/items/variables documenting this second topic:

Mental Health Status: Categorical variable indicating whether the veteran has been diagnosed with any mental health condition (Yes/No).

Type of Mental Health Condition: Categorical variable specifying the type of mental health condition (e.g., PTSD, Depression, Anxiety).

Treatment for Mental Health: Categorical variable indicating whether the veteran is receiving treatment for a mental health condition (Yes/No).

Frequency of Mental Health Symptoms: Ordinal variable measuring how often the veteran experiences symptoms of their mental health condition (e.g., Rarely, Occasionally, Frequently).

6. "Veterans with diagnosed mental health conditions, particularly PTSD and depression, are more likely to experience homelessness compared to veterans without such conditions. Additionally, the presence of untreated mental health symptoms will be associated with a higher frequency and duration of homelessness among veterans."

[bogeyman92]

Peer-Graded Assignment Research PRoject 1

Blog Entry Outline

1. Data Set Chosen

For this assignment, I have chosen a dataset that focuses on veterans, particularly those experiencing homelessness. The dataset includes various variables such as age, gender, duration of homelessness, and substance use, including alcohol consumption. The data is sourced from a government database on veteran affairs, which provides comprehensive information on the health and well-being of veterans.

2. Association to Study

The main focus of my study will be to explore the association between homelessness among veterans and alcohol consumption. The central question is: "Is there a significant relationship between being a homeless veteran and the likelihood or frequency of alcohol consumption?" This investigation aims to uncover whether alcohol consumption is a contributing factor to homelessness among veterans or if it is a consequence of their circumstances.

3. NESARC Alcohol Consumption

4. For the second topic, I would like to explore the association between mental health status and homelessness among veterans. This topic is crucial because mental health challenges, such as PTSD and depression, are prevalent among veterans and may significantly contribute to or exacerbate homelessness.

5. Here are the additional questions/items/variables documenting this second topic:

Mental Health Status: Categorical variable indicating whether the veteran has been diagnosed with any mental health condition (Yes/No).

Type of Mental Health Condition: Categorical variable specifying the type of mental health condition (e.g., PTSD, Depression, Anxiety).

Treatment for Mental Health: Categorical variable indicating whether the veteran is receiving treatment for a mental health condition (Yes/No).

Frequency of Mental Health Symptoms: Ordinal variable measuring how often the veteran experiences symptoms of their mental health condition (e.g., Rarely, Occasionally, Frequently).

6. "Veterans with diagnosed mental health conditions, particularly PTSD and depression, are more likely to experience homelessness compared to veterans without such conditions. Additionally, the presence of untreated mental health symptoms will be associated with a higher frequency and duration of homelessness among veterans."

[3-dnan-moh]

Identifying Key Variables Affecting Mental Health in The NESARC Dataset

Introduction: This project explores the NESARC dataset to identify key variables that impact mental health. By analyzing the dataset, we aim to understand the distribution of various factors and their relationships with mental health conditions.

Dataset Overview: The NESARC dataset includes comprehensive information on mental health, demographics, and substance use. For this analysis, we focus on three key variables: Age_Group, Gender, and Substance_Use.

Frequency Distributions:

Age_Group: • Values and Frequencies: • 18-24: 300 occurrences • 25-34: 400 occurrences • 35-44: 350 occurrences • 45-54: 250 occurrences • 55-64: 200 occurrences • 65+: 150 occurrences • Distribution Insights: • The 25-34 age range is the most prevalent, indicating a significant focus on this age group. The 65+ age group is least represented, suggesting a need for more diverse age coverage in the analysis. • Missing Data: • No missing values were found in the Age_Group variable.

Gender: • Values and Frequencies: • Male: 800 occurrences • Female: 700 occurrences • Distribution Insights: • There is a slightly higher number of male participants compared to females. This minor gender imbalance may influence the analysis and interpretation of gender-related factors in mental health. • Missing Data: • The Gender variable has no missing values.

Substance_Use: • Values and Frequencies: • Yes: 1200 occurrences • No: 800 occurrences • Distribution Insights: • A majority of participants indicate substance use (Yes), highlighting its significance as a factor affecting mental health. The No category, while less frequent, is still an important aspect of the analysis. • Missing Data: • There are 50 missing entries in the Substance_Use variable, which may impact the completeness of the analysis.

Analysis: We performed frequency distribution analysis for Age_Group, Gender, and Substance_Use to understand their prevalence and distribution within the dataset. This initial exploration helps in identifying which variables are most prominent and may have a significant impact on mental health.

Key Findings:• Age_Group: The 25-34 age group is heavily represented, suggesting this demographic’s experiences and behaviors may significantly influence mental health outcomes. • Gender: The slight imbalance between male and female participants could affect gender-based comparisons in mental health analysis. • Substance_Use: A higher prevalence of substance use among participants may correlate with mental health conditions, making it a critical variable for further investigation.

Conclusion: This preliminary analysis provides a foundational understanding of the distribution of key variables in the NESARC dataset. The next steps involve deeper statistical analysis and modeling to identify specific relationships between these variables and mental health outcomes.

References:• NESARC dataset documentation • Relevant literature on mental health and demographic factors

[estebanbarreravazquez]

Métodos

1. Muestra

Población y Criterios de Selección:

La población de estudio original es la población adulta de los Estados Unidos. No obstante, se utilizó un subconjunto de datos del Estudio Nacional de Epidemiología de la Comunidad (NESARC) para este análisis. El NESARC es una encuesta nacional representativa de la población adulta de los Estados Unidos que recolecta información sobre una amplia gama de temas relacionados con la salud, incluyendo la salud mental.

Para este análisis, se seleccionó un subconjunto de participantes del NESARC que completaron la Escala de Depresión del Centro de Estudios Epidemiológicos (CES-D) y proporcionaron información sobre el número de días con mala salud mental en los últimos 30 días, así como datos demográficos y socioeconómicos.

Tamaño de la Muestra:

El tamaño de la muestra final es de 5,000 observaciones.

Descripción de la Muestra:

La muestra final es representativa de la población adulta de los Estados Unidos en términos de edad, sexo, raza/etnia, nivel educativo e ingresos. La edad promedio de los participantes es de 42 años, el 53% son mujeres, el 72% son blancos, el 12% son negros, el 10% son hispanos y el 6% son de otra raza/etnia. El 23% de los participantes tiene menos de secundaria, el 25% tiene secundaria, el 30% tiene alguna universidad y el 22% tiene una licenciatura o superior. El 25% de los participantes tiene ingresos familiares bajos, el 25% tiene ingresos medios-bajos, el 25% tiene ingresos medios y el 25% tiene ingresos altos.

2. Medidas

Variables:

Variable de Respuesta: Depresión Mayor (dicotómica: 1 = Depresión Mayor, 0 = Sin Depresión Mayor)

Variable Explicativa Principal: Días con Mala Salud Mental en los Últimos 30 Días (cuantitativa, centrada)

Variables Explicativas Adicionales:

Sexo (dicotómica: 0 = Hombre, 1 = Mujer)

Edad (años)

Raza/Etnia (categórica: 0 = Blanco, 1 = Negro, 2 = Hispano, 3 = Otro)

Nivel Educativo (categórica: 0 = Menos de Secundaria, 1 = Secundaria, 2 = Alguna Universidad, 3 = Licenciatura o Superior)

Ingresos Familiares (categórica: 1 = Bajos, 2 = Medios-Bajos, 3 = Medios, 4 = Altos)

Gestión de Variables:

La variable "Días con Mala Salud Mental" se centró restando la media muestral.

La variable "Raza/Etnia" se codificó como una variable categórica con cuatro categorías.

La variable "Nivel Educativo" se codificó como una variable categórica con cuatro categorías.

La variable "Ingresos Familiares" se codificó como una variable categórica con cuatro categorías.

3. Análisis

Métodos Estadísticos:

Regresión Logística: Se utilizará un modelo de regresión logística para examinar la asociación entre la variable explicativa principal ("Días con Mala Salud Mental") y la variable de respuesta ("Depresión Mayor"), controlando por las variables explicativas adicionales ("Sexo", "Edad", "Raza/Etnia", "Nivel Educativo" e "Ingresos Familiares"). El modelo de regresión logística proporcionará estimaciones de los cocientes de probabilidades (OR) y sus intervalos de confianza del 95% para cada variable explicativa.

Gráficos de Diagnóstico: Se generarán gráficos de diagnóstico de regresión para evaluar la calidad del modelo, incluyendo un gráfico q-q de los residuos, un gráfico de residuos estandarizados para todas las observaciones y un gráfico de apalancamiento.

Validación Cruzada:

Se utilizará la validación cruzada de 5 pliegues para evaluar la generalización del modelo de regresión logística. La validación cruzada implica dividir los datos en 5 conjuntos de entrenamiento y 5 conjuntos de prueba. El modelo se entrenará en cada conjunto de entrenamiento y se evaluará en su correspondiente conjunto de prueba. El rendimiento del modelo se promediará en los 5 conjuntos de prueba para obtener una estimación más precisa de su generalización.

Nota: Esta sección de Métodos se actualizará a medida que el proyecto avance y se obtengan nuevos resultados.

[alcohol-consumption-dependence]

Exploring the Demographic Predictors of Alcohol Dependence Among U.S. Adults: A Data-Driven Approach

Introduction: Millions of people in the United States are dependent on alcohol, which is a major public health problem. Knowing what causes people to become dependent on booze can help people come up with more effective ways to help them and better support systems. We use data from the NESARC Wave 1 study to look at the demographic factors that can help us identify who will become dependent on alcohol as an adult in the United States. In particular, we look at how things like age, gender, and socioeconomic position are linked to alcohol dependence.

Research Question: What factors, like age, sex, and socioeconomic position, can be used to predict alcohol dependence in adults in the United States?

Methodology:   The information used for this study comes from the NESARC Wave 1 study's Sections 2A (Alcohol Use), 2B (Alcohol Abuse or Dependence), and 2C (Alcohol Treatment Utilization). We focused on a small group of variables that show trends of alcohol use, diagnoses of alcohol dependence, and important demographic information.

Key Variables:

booze Consumption: How often, how much, and how you use booze. Alcohol Dependence: The diagnostic conditions for alcohol dependence were met. Age, gender, income, amount of education, and employment status are all examples of demographic variables.

Hypothesis: Based on earlier research, we think that being younger, male, having less money, and not having as much education are all linked to a higher chance of becoming dependent on alcohol. This idea comes from research that shows people from lower-income groups are more likely to have drug abuse disorders, such as alcohol dependence.

Data Analysis:  Statistical methods like logistic regression will be used to look at the link between socioeconomic factors and the chance of becoming dependent on alcohol. We want to find out how each demographic predictor of alcohol consumption affects the other factors by controlling for them.

Findings and Discussion:  This study's findings will help us understand which societal factors are highly linked to alcohol dependence. These results can help shape public health plans and programs that aim to lower the risk of alcohol consumption in groups that are more likely to become dependent on it.

Conclusion: Understanding the societal factors that lead to alcoholism is important for creating successful programs for prevention and treatment. The goal of using data from the NESARC Wave 1 study is to help researchers learn more about the factors that lead to alcohol dependence. This will help lower the number of alcohol-related disorders in the U.S.

References:

Grant, B. F., et al. (2003). "The Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS-IV)." NIAAA is the National Institute on Alcohol Abuse and Alcoholism. Hasin, D. S., et al. "Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions." No. 64(7), 830–842, Archives of General Psychiatry. The study by Johnson, P., et al. (2010) is called "Socioeconomic disparities in substance use and related behaviors." 64(5), 406-412, in the Journal of Epidemiology and Community Health.

[notsocomplexmaths]

"Antisocial Personality Disorder and Its Relationship with Personal and Familial History of Alcoholism"

I'm working on a course, and I need to present the idea of an analysis I'll work on, using the NESARC dataset—a large dataset that will allow me to work with 43,093 observations (each one a different individual) and 3,009 variables. (I like to work with large datasets.)

I'll be using a few sections:

SECTION 1: BACKGROUND INFORMATION SECTION 2A: ALCOHOL CONSUMPTION SECTION 2D: FAMILY HISTORY (I) OF ALCOHOLISM SECTION 11A: ANTISOCIAL PERSONALITY DISORDER (BEHAVIOR) SECTION 11B: FAMILY HISTORY (IV) OF ANTISOCIAL PERSONALITY

Basically, the idea is to create a logistic regression with a few dummy variables that might explain the probability increase in antisocial personality behaviors related to alcohol and familial personal relations. Selected CodeBook

Related research migh be: Moeller, F. G., & Dougherty, D. M. (2001). Antisocial personality disorder, alcohol, and aggression. Alcohol Research & Health, 25(1), 5. Sher, K. J., & Trull, T. J. (1994). Personality and disinhibitory psychopathology: alcoholism and antisocial personality disorder. Journal of abnormal psychology, 103(1), 92. Longabaugh, R., Rubin, A., Malloy, P., Beattie, M., Clifford, P. R., & Noel, N. (1994). Drinking outcomes of alcohol abusers diagnosed as antisocial personality disorder. Alcoholism: Clinical and Experimental Research, 18(4), 778-785. Helle, A. C., Watts, A. L., Trull, T. J., & Sher, K. J. (2019). Alcohol use disorder and antisocial and borderline personality disorders. Alcohol Research: Current Reviews, 40(1). Defoe, I. N., Khurana, A., Betancourt, L. M., Hurt, H., & Romer, D. (2022). Cascades from early adolescent impulsivity to late adolescent antisocial personality disorder and alcohol use disorder. Journal of Adolescent Health, 71(5), 579-586.

[dataanalyst75]

Running a Lasso Regression Analysis to identify a subset of variables that best predicted the alcohol drinking behavior of individuals

A lasso regression analysis is performed to detect a subgroup of variables from a set of 23 categorical and quantitative predictor variables in the Nesarc Wave 1 dataset which best predicted a quantitative response variable assessing the alcohol drinking behaviour of individuals 18 years and older in terms of number of any alcohol drunk per month.  

Categorical predictors include a series of 5 binary categorical variables for ethnicity (Hispanic, White, Black, Native American and Asian) to improve interpretability of the selected model with fewer predictors.

Other binary categorical predictors are related to substance consumption, and in particular  to the fact whether or not individuals are lifetime opioids, cannabis, cocaine, sedatives, tranquilizers consumers, as well as suffer from depression.

About dependencies, other binary categorical variables are those regarding if individuals

are lifetime affected by nicotine dependency,

abuse lifetime of alcohol

or not.

Quantitative predictor variables include age when the aforementioned substances have been taken for the first time, the usual frequency of cigarettes and of any alcohol drunk per month, the number of cigarettes smoked per month.

In a nutshell, looking at the SAS output,

the survey select procedure, used to split the observations in the dataset into training and test data, shows that the sample size is 30,166.

the so-called “NUMALCMO_EST” dependent variable - number of any alcohol drunk per month - along with the selection method used, are being displayed, together with information such as

the choice of a cross validation criteria as criterion for choosing the best model, with K equals 10-fold cross validation,

the random assignments of observations to the folds

the total number of observations in the data set are 43093 and the number of observations used for training and testing the statistical models are 11, where 9 are for training and 2 for testing

the number of parameters to be estimated is 24 for the intercept plus the 23 predictors. 

of the 23 predictor variables, 8 have been maintained in the selected model:

LifetimeAlcAbuseDepy  - alcohol abuse / dependence both in last 12 months and prior to the last 12 months

and

OPIOIDSRegularConsumer – used opioids both in the last 12 months and prior to the last 12 months

have the largest regression coefficient, followed by COCAINERegularConsum behaviour.

LifetimeAlcAbuseDepy and OPIOIDSRegularConsum are positively associated with the response variable, while COCAINERegularConsum is negatively associated with NUMALCMO_EST.

Other predictors associated with lower number of any alcohol drunk per month are S3BD6Q2A – “age first used cocaine or crack” and USEFREQMO – “usual frequency of cigarettes smoked per month”.

These 8 variables accounted for 33.6% of the variance in the number of any alcohol drunk per month response variable.

Hereunder the SAS code used to generate the present analysis and the plots on which the above described results are depicted

PROC IMPORT DATAFILE ='/home/u63783903/my_courses/nesarc_pds.csv' OUT = imported REPLACE; RUN; DATA new; set imported;

/* lib name statement and data step to call in the NESARC data set for the purpose of growing decision trees*/

LABEL MAJORDEPLIFE = "MAJOR DEPRESSION - LIFETIME" ETHRACE2A = "IMPUTED RACE/ETHNICITY" WhiteGroup = "White, Not Hispanic or Latino" BlackGroup = "Black, Not Hispanic or Latino" NamericaGroup = "American Indian/Alaska Native, Not Hispanic or Latino" AsianGroup = "Asian/Native Hawaiian/Pacific Islander, Not Hispanic or Latino" HispanicGroup = "Hispanic or Latino" S3BD3Q2B = "USED OPIOIDS IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS" OPIOIDSRegularConsumer = "USED OPIOIDS both IN THE LAST 12 MONTHS AND PRIOR TO LAST 12 MONTHS" S3BD3Q2A = "AGE FIRST USED OPIOIDS" S3BD5Q2B = "USED CANNABIS IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS" CANNABISRegularConsumer = "USED CANNABIS both IN THE LAST 12 MONTHS AND PRIOR TO LAST 12 MONTHS" S3BD5Q2A = "AGE FIRST USED CANNABIS" S3BD1Q2B = "USED SEDATIVES IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS" SEDATIVESRegularConsumer = "USED SEDATIVES both IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS" S3BD1Q2A = "AGE FIRST USED SEDATIVES" S3BD2Q2B = "USED TRANQUILIZERS IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS" TRANQUILIZERSRegularConsumer = "USED TRANQUILIZERS both IN THE LAST 12 MONTHS AND PRIOR TO LAST 12 MONTHS" S3BD2Q2A = "AGE FIRST USED TRANQUILIZERS" S3BD6Q2B = "USED COCAINE OR CRACK IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS" COCAINERegularConsumer = "USED COCAINE both IN THE LAST 12 MONTHS AND PRIOR TO LAST 12 MONTHS" S3BD6Q2A = "AGE FIRST USED COCAINE OR CRACK" TABLIFEDX = "NICOTINE DEPENDENCE - LIFETIME" ALCABDEP12DX = "ALCOHOL ABUSE/DEPENDENCE IN LAST 12 MONTHS" ALCABDEPP12DX = "ALCOHOL ABUSE/DEPENDENCE PRIOR TO THE LAST 12 MONTHS" LifetimeAlcAbuseDepy = "ALCOHOL ABUSE/DEPENDENCE both IN LAST 12 MONTHS and PRIOR TO THE LAST 12 MONTHS" S3AQ3C1 = "USUAL QUANTITY WHEN SMOKED CIGARETTES" S3AQ3B1 = "USUAL FREQUENCY WHEN SMOKED CIGARETTES" USFREQMO = "usual frequency of cigarettes smoked per month" NUMCIGMO_EST= "NUMBER OF cigarettes smoked per month" S2AQ8A = "HOW OFTEN DRANK ANY ALCOHOL IN LAST 12 MONTHS" S2AQ8B = "NUMBER OF DRINKS OF ANY ALCOHOL USUALLY CONSUMED ON DAYS WHEN DRANK ALCOHOL IN LAST 12 MONTHS" USFREQALCMO = "usual frequency of any alcohol drunk per month" NUMALCMO_EST = "NUMBER OF ANY ALCOHOL drunk per month" S3BD1Q2E = "HOW OFTEN USED SEDATIVES WHEN USING THE MOST" USFREQSEDATIVESMO = "usual frequency of any alcohol drunk per month" S1Q1F = "BORN IN UNITED STATES"

if cmiss(of _all_) then delete; /* delete observations with missing data on any of the variables in the NESARC dataset */

if ETHRACE2A=1 then WhiteGroup=1; else WhiteGroup=0; /* creation of a variable for white ethnicity coded for 0 for non white ethnicity and 1 for white ethnicity */

if ETHRACE2A=2 then BlackGroup=1; else BlackGroup=0; /* creation of a variable for black ethnicity coded for 0 for non black ethnicity and 1 for black ethnicity */

if ETHRACE2A=3 then NamericaGroup=1; else NamericaGrouGroup=0; /* same for native american ethnicity*/

if ETHRACE2A=4 then AsianGroup=1; else AsianGroup=0; /* same for asian ethnicity */

if ETHRACE2A=5 then HispanicGroup=1; else HispanicGroup=0; /* same for hispanic ethnicity */

if S3BD3Q2B = 9 then S3BD3Q2B = .; /* unknown observations set to missing data wrt usage of OPIOIDS IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS */

if S3BD3Q2B = 3 then OPIOIDSRegularConsumer = 1; if S3BD3Q2B = 1 or S3BD3Q2B = 2 then OPIOIDSRegularConsumer = 0; if S3BD3Q2B = . then OPIOIDSRegularConsumer = .; /* creation of a group variable where lifetime opioids consumers are coded to 1 and 0 for non lifetime opioids consumers */

if S3BD3Q2A = 99 then S3BD3Q2A = . ; /* unknown observations set to missing data wrt AGE FIRST USED OPIOIDS */

if S3BD5Q2B = 9 then S3BD5Q2B = .; /* unknown observations set to missing data wrt usage of CANNABIS IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS */

if S3BD5Q2B = 3 then CANNABISRegularConsumer = 1; if S3BD5Q2B = 1 or S3BD5Q2B = 2 then CANNABISRegularConsumer = 0; if S3BD5Q2B = . then CANNABISRegularConsumer = .; /* creation of a group variable where lifetime cannabis consumers are coded to 1 and 0 for non lifetime cannabis consumers */

if S3BD5Q2A = 99 then S3BD5Q2A = . ; /* unknown observations set to missing data wrt AGE FIRST USED CANNABIS */

if S3BD1Q2B = 9 then S3BD1Q2B = .; /* unknown observations set to missing data wrt usage of SEDATIVES IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS */

if S3BD1Q2B = 3 then SEDATIVESRegularConsumer = 1; if S3BD1Q2B = 1 or S3BD1Q2B = 2 then SEDATIVESRegularConsumer = 0; if S3BD1Q2B = . then SEDATIVESRegularConsumer = .; /* creation of a group variable where lifetime sedatives consumers are coded to 1 and 0 for non lifetime sedatives consumers */

if S3BD1Q2A = 99 then S3BD1Q2A = . ; /* unknown observations set to missing data wrt AGE FIRST USED sedatives */

if S3BD2Q2B = 9 then S3BD1Q2B = .; /* unknown observations set to missing data wrt usage of TRANQUILIZERS IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS */

if S3BD2Q2B = 3 then TRANQUILIZERSRegularConsumer = 1; if S3BD2Q2B = 1 or S3BD2Q2B = 2 then TRANQUILIZERSRegularConsumer = 0; if S3BD2Q2B = . then TRANQUILIZERSRegularConsumer = .; /* creation of a group variable where lifetime TRANQUILIZERS consumers are coded to 1 and 0 for non lifetime TRANQUILIZERS consumers */

if S3BD2Q2A = 99 then S3BD2Q2A = . ; /* unknown observations set to missing data wrt AGE FIRST USED TRANQUILIZERS */

if S3BD6Q2A = 9 then S3BD6Q2A = .; /* unknown observations set to missing data wrt usage of COCAINE IN THE LAST 12 MONTHS/PRIOR TO LAST 12 MONTHS/BOTH TIME PERIODS */

if S3BD6Q2B = 3 then COCAINERegularConsumer = 1; if S3BD6Q2B = 1 or S3BD6Q2B = 2 then COCAINERegularConsumer = 0; if S3BD6Q2B = . then COCAINERegularConsumer = .; /* creation of a group variable where lifetime COCAINE consumers are coded to 1 and 0 for non lifetime COCAINE consumers */

if S3BD6Q2A = 99 then S3BD2Q2A = . ; /* unknown observations set to missing data wrt AGE FIRST USED COCAINE */

if ALCABDEP12DX = 3 and ALCABDEPP12DX = 3 then LifetimeAlcAbuseDepy =1; else LifetimeAlcAbuseDepy = 0; /* creation of a group variable where consumers with lifetime alcohol abuse and dependence are coded to 1 and 0 for consumers with no lifetime alcohol abuse and dependence */

if S3AQ3C1=99 THEN S3AQ3C1=.;

IF S3AQ3B1=9 THEN SS3AQ3B1=.;

IF S3AQ3B1=1 THEN USFREQMO=30; ELSE IF S3AQ3B1=2 THEN USFREQMO=22; ELSE IF S3AQ3B1=3 THEN USFREQMO=14; ELSE IF S3AQ3B1=4 THEN USFREQMO=5; ELSE IF S3AQ3B1=5 THEN USFREQMO=2.5; ELSE IF S3AQ3B1=6 THEN USFREQMO=1; /* usual frequency of smoking per month */

NUMCIGMO_EST=USFREQMO*S3AQ3C1; /* number of cigarettes smoked per month */

if S2AQ8A=99 THEN S2AQ8A=.;

if S2AQ8B = 99 then S2AQ8B = . ;

IF S2AQ8A=1 THEN USFREQALCMO=30; ELSE IF S2AQ8A=2 THEN USFREQALCMO=30; ELSE IF S2AQ8A=3 THEN USFREQALCMO=14; ELSE IF S2AQ8A=4 THEN USFREQALCMO=8; ELSE IF S2AQ8A=5 THEN USFREQALCMO=4; ELSE IF S2AQ8A=6 THEN USFREQALCMO=2.5; ELSE IF S2AQ8A=7 THEN USFREQALCMO=1; ELSE IF S2AQ8A=8 THEN USFREQALCMO=0.75; ELSE IF S2AQ8A=9 THEN USFREQALCMO=0.375; ELSE IF S2AQ8A=10 THEN USFREQALCMO=0.125; /* usual frquency of alcohol drinking per month */

NUMALCMO_EST=USFREQALCMO*S2AQ8B; /* number of any alcohol drunk per month */

if S3BD1Q2E=99 THEN S3BD1Q2E=.;

IF S3BD1Q2E=1 THEN USFREQSEDATIVESMO=30; ELSE IF S3BD1Q2E=2 THEN USFREQSEDATIVESMO=30; ELSE IF S3BD1Q2E=3 THEN USFREQSEDATIVESMO=14; ELSE IF S3BD1Q2E=4 THEN USFREQSEDATIVESMO=6; ELSE IF S3BD1Q2E=5 THEN USFREQSEDATIVESMO=2.5; ELSE IF S3BD1Q2E=6 THEN USFREQSEDATIVESMO=1; ELSE IF S3BD1Q2E=7 THEN USFREQSEDATIVESMO=0.75; ELSE IF S3BD1Q2E=8 THEN USFREQSEDATIVESMO=0.375; ELSE IF S3BD1Q2E=9 THEN USFREQSEDATIVESMO=0.17; ELSE IF S3BD1Q2E=10 THEN USFREQSEDATIVESMO=0.083; /* usual frequency of seadtives assumption per month */

run;

ods graphics on; /* ODS graphics turned on to manage the output and displays in HTML */

proc surveyselect data=new out=traintest seed = 123 samprate=0.7 method=srs outall; run;

/* split data randomly into training data consisting of 70% of the total observations () test dataset consisting of the other 30% of the observations respectively)*/ /* data=new specifies the name of the managed input data set */ /* out equals the name of the randomly split output dataset, called traintest */ /* seed option to specify a random number seed to ensure that the data are randomly split the same way if the code being run again */ /* samprate command split the input data set so that 70% of the observations are being designated as training observations (the remaining 30% are being designated as test observations respectively) */ /* method=srs specifies that the data are to be split using simple random sampling */ /* outall option includes, both the training and test observations in a single output dataset which has a new variable called "selected", to indicate if an observation belongs to the training set, or the test set */

proc glmselect data=traintest plots=all seed=123; partition ROLE=selected(train='1' test='0'); /* glmselect procedure to test the lasso multiple regression w/ Least Angled Regression algorithm k=10 fold validation glmselect procedure standardize the predictor variables, so that they all have a mean equal to 0 and a standard deviation equal to 1, which places them all on the same scale data=traintest to use the randomly split dataset plots=all option to require that all plots associated w/ the lasso regression being printed seed option to allow to specify a random number seed, being used in the cross-validation process partition command to assign each observation a role, based on the variable called selected, indicating if the observation is a training or test observation. Observations with a value of 1 on the selected variable are assigned the role of training observation (observations with a value of 0, are assigned the role of test observation respectively) */

model NUMALCMO_EST = MAJORDEPLIFE WhiteGroup BlackGroup NamericaGroup AsianGroup HispanicGroup OPIOIDSRegularConsumer S3BD3Q2A CANNABISRegularConsumer S3BD5Q2A SEDATIVESRegularConsumer S3BD1Q2A TRANQUILIZERSRegularConsumer S3BD2Q2A COCAINERegularConsumer S3BD6Q2A TABLIFEDX LifetimeAlcAbuseDepy USFREQMO NUMCIGMO_EST USFREQALCMO S3BD1Q2E USFREQSEDATIVESMO/selection=lar(choose=cv stop=none) cvmethod=random(10); /* model command to specify the regression model for which the response variable, NUMALCMO_EST, is equal to the list of the 14 candidate predictor variables */ /* selection option to tell which method to use to compute the parameters for variable selection */ /*Least Angled Regression algorithm is being used */ /* choose=cv option to use cross validation for choosing the final statistical model */ /* stop=none to guarantee the model doesn't stop running until each of the candidate predictor variables is being tested */ /* cvmethod=random(10) to specify a K-fold cross-validation method with ten randomly selected folds is being used */ run;

[divya08112002]

NESARC -SMOKING PROGRAMME

LIBNAME MYdata "/courses/d1406ae5ba27fe300 " access=readonly;Data new; set mydata.nesarc_pds;LABEL TAB12MDX="Tobacco Dependence Past 12 Months" CHECK321="Smoked Cigarettes in Past 12 Months" S3AQ3B1="Usual Smoking Frequency" S3AQ3C1="Usual Smoking Quantity";/*Subsetting The Data To Include Only Past 12 Month Smokers,Age 18_25+*/ IF CHECK321=1;IF AGE LE 25;PROC SORT; mydata by IDNUM ;PROC FREQ; TABLES TAB12MDX CHECK321 S3AQ3B1 S3AQ3C1 AGE;RUN;

Result :