#nesarc

Dataset

For this project, I selected the NESARC Wave 1 dataset, a nationally representative survey focused on substance use and mental health issues among U.S. adults.

Research Topic

I'm interested in exploring whether the age someone starts smoking impacts their likelihood of developing nicotine dependence.

Selected Variables

S3AQ3B1 – Age respondent first smoked a cigarette

S3AQ3C1 – Frequency of smoking in the past 12 months

S3BQ1A5 – Diagnosed with nicotine dependence (DSM-IV criteria)

Research Questions

Does starting to smoke at a younger age increase the risk of developing nicotine dependence?

At what age does the risk become significantly higher?

Summary

This study looks into the potential link between early smoking behavior and later addiction. By focusing on the age of smoking initiation and diagnosis of nicotine dependence, the goal is to highlight how early interventions could change addiction outcomes in adulthood.

Literature Review

Prior research supports a strong connection between early smoking and later dependence:

Kandel & Chen (2000) showed that the earlier individuals start smoking, the greater their risk of developing heavy smoking habits and nicotine dependence.

Breslau et al. (1998) emphasized that younger initiation age predicts more difficulty quitting and higher addiction severity.

References

Kandel, D. B., & Chen, K. (2000). Extending adolescent smoking patterns into adulthood: The link between early onset and later nicotine dependence. American Journal of Public Health, 90(5), 692–695.

Breslau, N., Peterson, E. L., & Schultz, L. R. (1998). Smoking cessation in young adults: Age at initiation of cigarette smoking and other suspected influences. Preventive Medicine, 27(2), 217–224.

Hypothesis

"Individuals who begin smoking earlier are more likely to develop nicotine dependence compared to those who start later."

Dataset Selection

For this data analysis project, we chose the U.S. National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

NESARC is ideal as a representative sample of the U.S. adult population and provides detailed data on mental health and substance use, allowing for robust testing of comorbidity hypotheses.

Association of Interest

The focus of my research is to explore the association between Generalized Anxiety Disorder (GAD) and Nicotine Dependence.

Topic 1: Nicotine Dependence (TABLIFEDX) Topic 2: Generalized Anxiety Disorder (GENAXLIFE)

Research Question and Hypothesis

Research Question: Are individuals with a lifetime reported history of Generalized Anxiety Disorder (GAD) (GENAXLIFE) more likely to have a lifetime diagnosis of Nicotine Dependence (TABLIFEDX) than those without a history of GAD?

Clear Hypothesis:

The presence of a lifetime diagnosis of Generalized Anxiety Disorder (GENAXLIFE = Yes) will be significantly associated with an Odds Ratio greater than 1.0 for a lifetime diagnosis of Nicotine Dependence (TABLIFEDX = Yes) in the NESARC population.

Literature Review

Search Terms Used

NESARC comorbidity anxiety nicotine dependence

Generalized Anxiety Disorder Nicotine Dependence association epidemiology

Summary of Findings (Patterns and Variables)

The epidemiological literature demonstrates a strong comorbidity, indicating that:

Pattern: Individuals with anxiety disorders exhibit significantly higher rates of nicotine dependence. The self-medication mechanism is a central hypothesis, where nicotine is used to modulate or reduce the tension and hyperexcitability characteristic of GAD.

Controlling for Variables: Researchers in the fields of public health and psychiatry consistently controlled for variables such as age and sex, as they are known to influence the prevalence of both disorders.

Key Reference (Example)

James M. Zech, Tapan A. Patel, Jesse R. Cougle, et al. (2025). Prevalence and Correlates of Alcohol, Cannabis, and Tobacco Use Disorder in DSM-5 Generalized Anxiety Disorder: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions-III.

This recent NESARC-III research validates the focus on the high correlation between Tobacco Use Disorder (Dependência de Nicotina) and Generalized Anxiety Disorder, strongly supporting the proposed hypothesis involving the variables GENAXLIFE and TABLIFEDX.

I chose to work with the NESARC dataset (National Epidemiologic Survey on Alcohol and Related Conditions) because it contains rich data on substance use, mental health, and personal experiences across different age groups. I selected this dataset because I want to explore how drug use among young people may be linked to behavioral problems.

How I Chose My Topic: As someone passionate about public health, I’ve been noticing a worrying trend, many young people today are getting involved with drugs at an early age. This is not just about addiction—I’m also concerned about how drug use may lead to fights, school issues, and problems at home. That’s why I decided to explore the connection between youth drug use and behavioral issues.

Research Question

Is there a relationship between drug use and behavior problems among young people?

Topics I Chose to Study

Main Topic (Independent Variable): Drug use among youth

Second Topic (Dependent Variable): Behavioral problems (e.g., fighting, skipping school, disobeying parents)

My Personal Codebook: I went through the NESARC codebook and selected only the pages related to my two topics. These pages form my personal codebook.

 Codebook Pages Printed:

Section 1 (Age/Demographics): Pages 1–29

Section 3C (Drug Use & Impact): Pages 125–144

Section 11A (Behavioral Outcomes): Pages 419–436

Variables Selected

S3CQ12A1 (Section 3C)

S3CQ12A4 (Section 3C)

S11AQ1A1 (Section 11A)

S11AQ1A2 (Section 11A)

S11AQ1A4 (Section 11A)

AGE (Section 1)

Literature Review

To support my research question, I reviewed two academic articles related to youth drug use and behavior problems.

1. Disruptive Behavior Disorders and Substance Use in Adolescents

🔗 Clark & Winters, 2000 – Taylor & Francis This article explains that many teens who use drugs also show disruptive behaviors like fighting or skipping school. These behaviors may appear before or along with drug use, and they make addiction harder to treat. The authors recommend therapy and early interventions.

2. Substance Abuse in Young People

🔗 McArdle & McKenzie, 2011 – Cambridge.org This article looks at how substance use in teens often leads to behavior and mental health problems. It also talks about how many prevention programs fail due to poor implementation and follow-up. It recommends stronger, evidence-based approaches for helping young people.

Search Terms Used:

"youth drug use and behavior"

"substance abuse and behavior problems in adolescents"

"adolescent drug use and school trouble"

Hypothesis

Just ran a Pearson correlation between total personal income (S1Q10A) and number of children under 18 in the household (CHLD0_17) using the NESARC dataset. The result?

r = -0.016 p = 0.0009

💥 Yes, it's statistically significant. 😅 But practically? Not so much.

This teeny-tiny negative correlation means higher income is slightly linked to fewer kids—but the effect is so small it’s basically a shrug from the universe. 🤷‍♂️📉

In short: Money ≠ more (or fewer) children — at least not in any meaningful way.

in case you want to try out the code:

import pandas as pd import numpy as np import seaborn as sns import scipy.stats import matplotlib.pyplot as plt

Load the data

data = pd.read_csv('nesarc.csv', low_memory=False)

Convert to numeric properly

data['CHLD0_17'] = pd.to_numeric(data['CHLD0_17'], errors='coerce') data['S1Q10A'] = pd.to_numeric(data['S1Q10A'], errors='coerce')

Drop rows with NA in either of the two columns

data_clean = data[['S1Q10A', 'CHLD0_17']].dropna()

Scatterplot

sns.regplot(x="S1Q10A", y="CHLD0_17", fit_reg=True, data=data_clean) plt.xlabel('Total Personal Income (USD)') plt.ylabel('Number of Children Under 18 in Household') plt.title('Scatterplot: Income vs. Number of Children') plt.show()

Pearson correlation

Using data from the NESARC survey, we dug into how your relationship status might influence how much you drink. The stats? Unfiltered and telling.

📊 Key Insight:

There’s a significant association between marital status and alcohol consumption (Chi² = 392.18, p < .001).

Married individuals top the charts across all drinking levels.

Divorced/Separated drink moderately — but steadily.

Never Married lean toward lighter drinking, but mid-range isn't rare.

Widowed appear quietly in higher consumption brackets. (Coping, perhaps?)

⚖️ Pairwise Comparisons:

We broke it down further with post-hoc testing:

Married vs Never Married → Big difference (p < .001)

Married vs Divorced/Cohabiting → Still distinct (p < .01)

Never Married vs Separated → Subtle, yet significant (p ≈ .015)

Your marital status might be quietly shaping your drinking habits — whether you're toasting to love, numbing the breakup, or just navigating life solo. In case you want to try the code yourself ````````````````````````

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns import matplotlib.pyplot as plt

Load the dataset

data = pd.read_csv('nesarc.csv', low_memory=False)

Convert variables to numeric

data['S2AQ8A'] = pd.to_numeric(data['S2AQ8A'], errors='coerce') # Alcohol frequency data['MARITAL'] = pd.to_numeric(data['MARITAL'], errors='coerce') # Marital status

Subset data (assuming sub1 already exists — else, create one)

sub1 = data.copy() sub2 = sub1.copy()

Replace invalid or missing codes with NaN

sub2['S2AQ8A'].replace([99, np.nan], np.nan, inplace=True) sub2['MARITAL'].replace(9, np.nan, inplace=True)

Filter only valid values

sub2 = sub2[(sub2['S2AQ8A'].between(1, 10)) & (sub2['MARITAL'].between(1, 6))]

Create a label-friendly MARITALSTATUS column (you can use actual labels instead of scores)

marital_map = { 1: 'Married', 2: 'Cohabiting', 3: 'Widowed', 4: 'Divorced', 5: 'Separated', 6: 'Never Married' } sub2['MARITALSTATUS'] = sub2['MARITAL'].map(marital_map)

-------------------------

🔍 Main Chi-Square Test

-------------------------

ct_main = pd.crosstab(sub2['S2AQ8A'], sub2['MARITALSTATUS']) print("Main Contingency Table:\n", ct_main)

print("\nColumn Percentages:\n", ct_main / ct_main.sum(axis=0))

chi2, p, dof, expected = stats.chi2_contingency(ct_main) print("\nChi-square test results:") print(f"Chi2 Value: {chi2:.2f}") print(f"p-value: {p:.4f}") print(f"Degrees of Freedom: {dof}") print("Expected Counts:\n", pd.DataFrame(expected, index=ct_main.index, columns=ct_main.columns))

-------------------------

📊 Visualization

-------------------------

plt.figure(figsize=(12, 6)) sns.countplot(data=sub2, x='MARITALSTATUS', hue='S2AQ8A', palette='Set2') plt.title("Drinking Frequency by Marital Status") plt.xlabel("Marital Status") plt.ylabel("Count of Respondents") plt.legend(title="Drinking Frequency Code", bbox_to_anchor=(1.05, 1), loc='upper left') plt.tight_layout() plt.show()

-------------------------

🔁 Optional: Pairwise Comparisons

-------------------------

def chi_square_pairwise(df, group_col, compare_col, groups): sub_df = df[df[group_col].isin(groups)] ct = pd.crosstab(sub_df[compare_col], sub_df[group_col]) chi2, p, dof, expected = stats.chi2_contingency(ct) print(f"\nChi-square for {groups[0]} vs {groups[1]}") print(f"Chi2: {chi2:.2f}, p-value: {p:.4f}, DOF: {dof}") print("Contingency Table:\n", ct)

Run pairwise tests

pairwise_groups = ('Married', 'Never Married'), ('Married', 'Divorced'), ('Married', 'Cohabiting'), ('Never Married', 'Separated')

for g1, g2 in pairwise_groups: chi_square_pairwise(sub2, 'MARITALSTATUS', 'S2AQ8A', [g1, g2])

Using data from the NESARC survey, we dug into how your relationship status might influence how much you drink. The stats? Unfiltered and telling.

📊 Key Insight:

There’s a significant association between marital status and alcohol consumption (Chi² = 392.18, p < .001).

Married individuals top the charts across all drinking levels.

Divorced/Separated drink moderately — but steadily.

Never Married lean toward lighter drinking, but mid-range isn't rare.

Widowed appear quietly in higher consumption brackets. (Coping, perhaps?)

⚖️ Pairwise Comparisons:

We broke it down further with post-hoc testing:

Married vs Never Married → Big difference (p < .001)

Married vs Divorced/Cohabiting → Still distinct (p < .01)

Never Married vs Separated → Subtle, yet significant (p ≈ .015)

Your marital status might be quietly shaping your drinking habits — whether you're toasting to love, numbing the breakup, or just navigating life solo. In case you want to try the code yourself ````````````````````````

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns import matplotlib.pyplot as plt

Load the dataset

data = pd.read_csv('nesarc.csv', low_memory=False)

Convert variables to numeric

data['S2AQ8A'] = pd.to_numeric(data['S2AQ8A'], errors='coerce') # Alcohol frequency data['MARITAL'] = pd.to_numeric(data['MARITAL'], errors='coerce') # Marital status

Subset data (assuming sub1 already exists — else, create one)

sub1 = data.copy() sub2 = sub1.copy()

Replace invalid or missing codes with NaN

sub2['S2AQ8A'].replace([99, np.nan], np.nan, inplace=True) sub2['MARITAL'].replace(9, np.nan, inplace=True)

Filter only valid values

sub2 = sub2[(sub2['S2AQ8A'].between(1, 10)) & (sub2['MARITAL'].between(1, 6))]

Create a label-friendly MARITALSTATUS column (you can use actual labels instead of scores)

marital_map = { 1: 'Married', 2: 'Cohabiting', 3: 'Widowed', 4: 'Divorced', 5: 'Separated', 6: 'Never Married' } sub2['MARITALSTATUS'] = sub2['MARITAL'].map(marital_map)

-------------------------

🔍 Main Chi-Square Test

-------------------------

ct_main = pd.crosstab(sub2['S2AQ8A'], sub2['MARITALSTATUS']) print("Main Contingency Table:\n", ct_main)

print("\nColumn Percentages:\n", ct_main / ct_main.sum(axis=0))

chi2, p, dof, expected = stats.chi2_contingency(ct_main) print("\nChi-square test results:") print(f"Chi2 Value: {chi2:.2f}") print(f"p-value: {p:.4f}") print(f"Degrees of Freedom: {dof}") print("Expected Counts:\n", pd.DataFrame(expected, index=ct_main.index, columns=ct_main.columns))

-------------------------

📊 Visualization

-------------------------

plt.figure(figsize=(12, 6)) sns.countplot(data=sub2, x='MARITALSTATUS', hue='S2AQ8A', palette='Set2') plt.title("Drinking Frequency by Marital Status") plt.xlabel("Marital Status") plt.ylabel("Count of Respondents") plt.legend(title="Drinking Frequency Code", bbox_to_anchor=(1.05, 1), loc='upper left') plt.tight_layout() plt.show()

-------------------------

🔁 Optional: Pairwise Comparisons

-------------------------

def chi_square_pairwise(df, group_col, compare_col, groups): sub_df = df[df[group_col].isin(groups)] ct = pd.crosstab(sub_df[compare_col], sub_df[group_col]) chi2, p, dof, expected = stats.chi2_contingency(ct) print(f"\nChi-square for {groups[0]} vs {groups[1]}") print(f"Chi2: {chi2:.2f}, p-value: {p:.4f}, DOF: {dof}") print("Contingency Table:\n", ct)

Run pairwise tests

pairwise_groups = ('Married', 'Never Married'), ('Married', 'Divorced'), ('Married', 'Cohabiting'), ('Never Married', 'Separated')

for g1, g2 in pairwise_groups: chi_square_pairwise(sub2, 'MARITALSTATUS', 'S2AQ8A', [g1, g2])

````````````````````````

Introducción a árbol de decisiones

Para esta actividad, se utilizó la base de datos del NESARC con la siguiente pregunta de investigación: ¿Qué efecto tiene la edad de inicio del consumo de alcohol en la situación laboral en la vida adulta?

La variable explicativa es la edad de inicio del consumo de alcohol (S2BQ2D, cuantitativa)

La variable de respuesta es el tipo de ocupación (S1Q9C, categórica)

Bibliotecas utilizadas

Instrucciones y variables

Arbol de decisiones

Conclusiones

El árbol de decisión ha encontrado que la categoría dominante es "Nunca ha trabajó".

Las ocupaciones gubernamentales, de negocio propio o empresariales aparecen en nodos más mezclados con mayor diversidad (gini alto).

Regresión logística

Introducción

Para esta actividad, se utilizó la base de datos del NESARC con la siguiente pregunta de investigación: ¿Qué efecto tiene la edad de inicio del consumo de alcohol en la situación laboral en la vida adulta?

La variable explicativa fue la edad de inicio del consumo de alcohol (S2BQ2D, cuantitativa) y se categorizó (Edad_cat, categórica), con los siguientes valores:

0: Preadolescentes (11 - 13 años)

1: Adolescentes tempranos (14 -16 años)

2: Adolescentes tardios (17 - 19 años)

La variable de respuesta fue si la persona trabaja en la actualidad (S1Q9C, categórica) y se transformó en una variable nominal (S1Q9C_cat2, nominal), con los siguientes valores:

0: No trabaja actualmente

1: Trabaja actualmente (incluye empleo gubernamental, no gubernamental y otras categorías)

Se incluyeron las siguientes covariables:

Sexo (SEX)

Consumo de drogas (DGSTATUS)

Grado de estudios categorizada (S1Q6A_Group)

Además, se exploraron otras variables, como la ascendencia y la región de residencia, pero no fueron significativas en el análisis bivariado.

Librerías y variables utilizadas

Regresión Logística entre la edad de consumo de alcohol y el trabajo actual

Regresión Logística entre la edad de consumo de alcohol y el trabajo actual y agregando variables para observar confusión.

Regresión Logística entre la edad de consumo de alcohol y el trabajo actual y covariables significativas.

Conclusión

El modelo de regresión logística evalúa el efecto de la edad de inicio del consumo de alcohol en la probabilidad de trabajar en la adultez, controlando por consumo de drogas y nivel educativo.

Los resultados indican lo siguiente:

1. Edad de inicio del consumo de alcohol (Edad_cat)

OR = 1.69 (IC 95%: 1.21 - 2.37, p = 0.002)

Esto sugiere que a medida que la edad de inicio del consumo de alcohol sea mayor, la probabilidad de estar empleado también aumenta en un 69% en comparación con el grupo de referencia, controlando por las demás variables.

2. Consumo de drogas (DGSTATUS)

OR = 0.75 (IC 95%: 0.58 - 0.97, p = 0.023)

Indica que el consumo de drogas reduce la probabilidad de estar empleado en un 25%, en comparación con aquellos que no consumen drogas.

Nivel educativo (S1Q6A_Group)

OR = 2.02 (IC 95%: 1.51 - 2.69, p < 0.001)

Un mayor nivel educativo más alto tiene la probabilidad de estar empleado en la adultez en comparación con el grupo de  referencia.

Comprobación de hipótesis

Los resultados indican que la edad de inicio del consumo de alcohol influye en la situación laboral en la adultez. Específicamente, comenzar a beber alcohol a una edad más tardía se asocia con una mayor probabilidad de estar empleado en comparación con aquellos que inician a una edad más temprana.

Además, factores como el consumo de drogas y el nivel educativo también desempeñan un papel importante: mientras que el consumo de drogas reduce las probabilidades de empleo, un mayor nivel educativo las incrementa.