[This project utilized R programming code and excel. The code script and data are available upon request.]

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Introduction

In this report, all references to addiction pertain specifically to substance addiction. Substance use alters the brain’s normal function, compelling individuals to repeatedly use the substance or engage in behaviors they know may be harmful. Substance addiction is a significant concern, with 21 million Americans addicted to at least one substance, and approximately 25% of those who use illicit drugs developing an addiction, according to the Addiction Center. Common substances of abuse include alcohol, marijuana, opioids, nicotine, cocaine, and methamphetamine.

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This report concentrates on nicotine (in the form of cigarettes) and alcohol. This study aims to address two main questions: How does addiction vary across different demographics? And, does the willingness to reduce or cease substance use contribute to successfully overcoming addiction? The data utilized for this analysis is sourced from the 2016 National Survey on Drug Use and Health (NSDUH), conducted by the Substance Abuse and Mental Health Services Administration (SAMHSA).

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Demographics of Survey Respondents

Prior to performing any analysis, it is essential to first understand the demographics of the survey's respondents.

More than half of the respondents are 30 years or older, accounting for 56.76%, while about 75% are 24 years or older. Only around 16% are 21 years old or younger, with a notable decline in the percentage of respondents for each year younger than 21.

Gender distribution among respondents is nearly even, ensuring a representative sample. There are only 586 more female respondents than male, which translates to about a 1% difference given the large sample size. In terms of racial composition, the majority of respondents are white (69.3%), followed by Hispanic (13.3%) and Black or African American (9.7%) respondents. Native Hawaiian and other Pacific Islander respondents constitute a mere 0.4% of the sample.

Lastly, the overall health of respondents is predominantly rated as positive, with 66.3% considering their health to be very good or excellent and only 7.7% rating their health as fair or poor. These demographics provide a comprehensive overview of the study's sample population.

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Addictions of Survey Respondents

When survey respondents were asked if they agreed that they sometimes have strong and uncontrollable cigarette cravings, only 22% indicated that the statement was very or extremely true, while 46% reported that it was not at all true for them.

Regarding alcohol use, an overwhelming majority (94%) of respondents stated that they neither wanted to nor attempted to cut down or completely eliminate alcohol consumption. Conversely, over two-thirds of respondents (67%) reported that they had either cut down on or stopped drinking alcohol at least once in the past 12 months.

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These responses provide important insights into the addictive behaviors and tendencies related to cigarette and alcohol use among the survey's respondents.

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Statistical Analysis of Respondents' Cravings

In this section of the report, I delve into the relationships between various demographic factors and cigarette cravings among survey respondents. Using statistical tests such as the chi-squared test of independence and the Analysis of Variance (ANOVA) test, I explore how gender, age, income, and overall health correlate with the intensity of cigarette cravings. By analyzing these factors, I aim to uncover patterns and insights that may inform our understanding of addictive behaviors and contribute to more effective intervention strategies. The following analyses highlight key findings from my statistical evaluations.

The chi-squared test of independence returned a low p-value, suggesting a significant relationship between gender and cigarette cravings. According to the accompanying chart, females are more likely to experience strong and uncontrollable cigarette cravings than males, with 5% more females reporting these cravings compared to their male counterparts.

Additionally, the chi-squared test of independence indicated a significant relationship between age and cigarette cravings. The chart reveals that younger individuals are less likely to report cigarette cravings compared to older individuals. Specifically, the "Not at all true" category shows that younger respondents are more likely to report not having strong and uncontrollable cravings. Notably, people aged 50 or older are the most likely to report experiencing some level of cravings, while individuals between 26 and 34 years old reported having the strongest cravings overall.

The Analysis of Variance (ANOVA) test also returned a low p-value, indicating that income levels vary significantly based on the degree of cigarette cravings. The chart illustrates that the mean income for individuals with more intense cravings is lower than that of those with fewer cravings. This finding raises an interesting question: does being of lower income lead individuals to develop cigarette cravings, or do cigarette cravings contribute to a lower income?

Finally, the chi-squared test of independence suggested a significant relationship between overall health and cigarette cravings. The chart demonstrates that individuals with poorer health conditions are more likely to experience cigarette cravings. This finding aligns with my expectations, as smoking is widely recognized to deteriorate an individual's health.

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These statistical analyses provide important insights into the factors associated with cigarette cravings among different demographics, including gender, age, income, and overall health.

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What Helps People Overcome Alcohol Addiction

To determine whether a person’s willingness to reduce or stop being addicted significantly predicts their ability to overcome addiction, I conducted a binomial logistic regression analysis focusing on alcohol use. The analysis included variables such as willingness to cut down or stop drinking alcohol, gender, race (White, Asian, more than one race), and income, as shown in the table below. The p-values for these variables were extremely low, indicating a significant relationship between them and the ability to cut down or stop drinking alcohol. Therefore, I concentrated on these specific variables for the analysis.

According to the regression results:

• A person who attempted to cut down or stop drinking alcohol in the past 12 months is 1900% more likely to succeed in reducing or quitting alcohol, making this a strong predictor of whether someone will stop drinking alcohol.
• Males are 110% more likely than females to cut down or stop drinking alcohol, suggesting that females are less likely to quit.
• Asians are 29% less likely than Hispanics to cut down or stop drinking alcohol.
• Individuals identifying with more than one race are 18% less likely than Hispanics to cut down or stop drinking alcohol.
• Whites are 38% less likely than Hispanics to cut down or stop drinking alcohol.
• For each increase in income, individuals are 1% less likely to cut down or stop drinking alcohol. This is a marginal difference, indicating that income does not have a substantial effect on the ability to reduce or stop drinking alcohol.

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Conclusion

The report uncovers many important insights into nicotine and alcohol addictions using various statistical methods. This analysis reveals that younger individuals generally experience fewer cravings, with those aged 26 to 34 reporting the most intense cravings. The report suggests a relationship between lower income and stronger cigarette cravings, prompting further consideration of the underlying reasons for this connection.

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Regarding alcohol use, the report identifies that a willingness to cut down or stop drinking is a strong indicator of whether someone will eventually succeed in reducing or quitting alcohol consumption. This is particularly true for men, who are more likely than women to accomplish this task. Moreover, the analysis reveals that different ethnic and racial groups exhibit varying likelihoods of cutting down on alcohol, with Asians, individuals identifying with more than one race, and Whites being less likely than Hispanics to reduce their alcohol consumption.

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Overall, these findings provide a comprehensive understanding of how demographic factors such as gender, age, income, and race can influence addictive behaviors. This knowledge can inform targeted intervention strategies to better address addiction issues across diverse populations.