#data_analysis

[mariammahfouz]

Investigating the Relationship Between Smoking Frequency and Nicotine Dependence, and the Role of Socioeconomic Status

Blog Entry: Investigating the Relationship Between Smoking Frequency and Nicotine Dependence, and the Role of Socioeconomic Status

Dataset Selection

After reviewing the available codebooks, I selected the NESARC dataset (National Epidemiologic Survey on Alcohol and Related Conditions). It is an extensive and nationally representative dataset that provides in-depth information on substance use, addiction behaviors, and a broad range of demographic and socioeconomic variables. It is ideally suited for studying smoking behaviors and their psychosocial correlates.

Primary Topic and Research Question

Primary Topic: The core of my investigation is to explore how regular smoking behavior is associated with nicotine dependence.

Research Question: Is there an association between smoking frequency/intensity and nicotine dependence among adults in the U.S., and does socioeconomic status influence this relationship?

Personal Codebook Certainly! Here is Step 3: Personal Codebook in plain English text (without the table) that you can copy and paste:

Personal Codebook

S3AQ3B1: Number of days smoked in the past 30 days – represents smoking frequency.

S3AQ3C1: Average number of cigarettes smoked per day – represents smoking intensity.

S3BQ1A5: Age when regular smoking began – captures when smoking behavior was established.

S3AQ8A: Difficulty quitting smoking – indicates behavioral signs of nicotine dependence.

TAB12MDX: Nicotine dependence diagnosis (DSM-IV criteria) – clinical measure of addiction.

EDUCA: Education level – socioeconomic status indicator.

INCOME: Income level – socioeconomic status indicator.

EMPSTATY: Employment status – socioeconomic status indicator.

Secondary Topic

In addition to the direct relationship between smoking behavior and dependence, I am also interested in how socioeconomic status (SES) may impact this relationship. Prior research has shown that factors such as education, income, and employment status can significantly influence health behaviors, including addiction risk.

Literature Review

I conducted a literature search using Google Scholar with key phrases like:

“nicotine dependence and smoking behavior”

“socioeconomic status and addiction”

Key References:

Lopez-Quintero, C., Pérez de los Cobos, J., Hasin, D. S., Okuda, M., Wang, S., Grant, B. F., & Blanco, C. (2011). Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: Results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug and Alcohol Dependence, 115(1–2), 120–130.

Summary: This study utilized NESARC data to demonstrate that individuals who smoke more frequently or start at an earlier age are significantly more likely to become dependent. The risk increases with the intensity and regularity of use.

Hiscock, R., Bauld, L., Amos, A., Fidler, J. A., & Munafò, M. (2012). Socioeconomic status and smoking: A review. Annals of the New York Academy of Sciences, 1248(1), 107–123.

Summary: This review emphasizes that lower socioeconomic groups are more likely to smoke heavily and experience greater difficulty quitting. It suggests that SES is a strong moderator in the addiction pathway.

Terms and Variables Clarified in Literature:

Nicotine Dependence: Often measured by the presence of withdrawal symptoms, craving, and inability to quit despite attempts.

Smoking Frequency/Intensity: Typically refers to the number of days smoked and cigarettes per day.

Socioeconomic Status (SES): Measured using indicators like education, income, and employment. Lower SES is linked to higher health risks and substance use.

Hypothesis Development

Hypothesis: Greater smoking frequency and intensity are associated with higher likelihood of nicotine dependence. Additionally, individuals with lower levels of education, income, and employment are more likely to develop dependence even with similar smoking levels, indicating that socioeconomic status moderates the smoking-dependence relationship.

Summary of Association to Be Studied

This study will focus on the association between smoking behavior (frequency, intensity, and age of onset) and nicotine dependence, as diagnosed by DSM-IV criteria. It will further explore how socioeconomic status variables (education, income, employment) influence this relationship—whether by increasing vulnerability to addiction or by limiting access to cessation resources.

Conclusion

This investigation is grounded in a rich dataset (NESARC), backed by established literature, and guided by a clear hypothesis. It aims to contribute to understanding how behavioral and structural factors combine to affect addiction risk, particularly in vulnerable populations.

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[mariammahfouz]

Exploring the Relationship Between Regular Smoking and Nicotine Dependence, and the Role of Socioeconomic Status

Blog Entry: Exploring the Relationship Between Regular Smoking and Nicotine Dependence, and the Role of Socioeconomic Status

Dataset Selection

I selected the NESARC dataset (National Epidemiologic Survey on Alcohol and Related Conditions) due to its comprehensive data on mental health, addiction behaviors, and socioeconomic factors.

Primary Topic

The focus of my exploration is the relationship between regular smoking levels and nicotine dependence. I've noticed that some smokers develop addiction early on, while others seem to resist it for years. This pattern inspired my research question.

Personal Codebook – Selected Variables:

S3AQ3B1: Number of days smoked in the past 30 days.

S3AQ3C1: Average number of cigarettes per day.

S3BQ1A5: Age when regular smoking began.

S3AQ8A: Difficulty quitting smoking.

TAB12MDX: Nicotine dependence diagnosis (DSM-IV).

Secondary Topic: Socioeconomic Influence

I decided to include a secondary focus: how socioeconomic status affects nicotine dependence. Prior studies suggest that individuals with lower income or education levels are more prone to smoking and addiction.

Selected Socioeconomic Variables:

EDUCA: Education level.

INCOME: Income level.

EMPSTATY: Employment status.

Literature Review

I searched Google Scholar using terms like “nicotine dependence and smoking frequency” and “socioeconomic status and smoking behavior”.

Relevant studies include:

Lopez-Quintero et al. (2011) found that more frequent cigarette use increases the likelihood of dependence.

Hiscock et al. (2012) showed that people from lower SES groups tend to smoke more and are more likely to be dependent.

Research Hypothesis

Based on this review, my hypothesis is: The more regularly an individual smokes, the higher the likelihood of nicotine dependence, and this relationship may be influenced by socioeconomic factors such as education and income.

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Pandas for Data Science

Pandas are powerful libraries in python for data manipulation and analysis, providing data structure and functionality for efficient operations.it is well suited for working with tabular data such as spreadsheets or database. Also, Pandas help in Data Cleaning, Data manipulation, pre-processing, sorting etc. from the Data Frame. It is built on top of NumPy library and the data is used for Data Visualization in Seaborn, Matplotlib, Plotty etc.

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[phonemantra-blog]

The update is available for Windows and macOS In 2020, scientists decided to simply rework the alphanumeric characters they used to represent genes, rather than try to figure out the Excel function that interpreted their data as dates and automatically reformatted it. Recently, a member of the Excel team announced that the company is releasing an update for Windows and macOS. Excel's automatic conversions are designed to make entering certain types of frequently entered data, such as numbers and dates, easier and faster. But for scientists, this feature can corrupt input data, as a 2016 study found. [caption id="attachment_72975" align="aligncenter" width="780"] Microsoft Fixes Excel Function[/caption] Microsoft Fixes Excel Function That Was Breaking Scientific Data Microsoft detailed the update on its blog, adding an option in Settings to “Convert continuous letters and numbers to a date.” The update builds on the automatic data conversion settings the company added last year, which included Excel's ability to let you load a file without automatic conversion so you can be sure the feature won't mess anything up. Microsoft's blog has caveats - for example, Excel avoids conversion by storing data as text, which means the data may not work for further calculations. There is also a known issue where conversions cannot be disabled when running macros.

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What's data analysis?

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. It's like sifting through a pile of sand to find hidden treasures. By analyzing data, we can uncover patterns, trends, and relationships that would be difficult or impossible to see with the naked eye. This information can then be used to make better decisions, solve problems, and improve efficiency.

* https://en.wikipedia.org/wiki/Data_analysis

[omarelfarouk90]

The significance of alcohol consumption and average income on suicide rate

I have performed some data analysis on the data gathered by the gap minder foundation in Stockholm to help in the UN development . i have used the same gathered data to analyze the effect of alcohol consumption and average income on the suicide rate per 100 people, the analysis was conducted using the OLS regression technique from the python stats model.

the input of average income was divided into two categories, greater than or equal $2000 per month and less than $2000 USD per month

the input of the alcohol consumption was divided into two categories according to their consumption per liter

category 0  below 3L per month

category 1 from 3L to 9L consumption per month

the code was as following

Created on Sat Aug  8 10:27:52 2020

@author: omar.elfarouk """

import numpy import pandas as pd import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi from pandas import DataFrame as df

data = pd.read_csv('gapminder.csv', low_memory=False) df = pd.DataFrame(data) #setting variables you will be working with to numeric df = df.replace(r'\s+', 0, regex=True) #Replace empty strings with zero

#subset data to income per person , alcohol consumption ,suiside rate , and employment sub1=data sub1 = sub1.replace(r'\s+', 0, regex=True) #Replace empty strings with zero #SETTING MISSING DATA

# Creating a secondary variable multiplying income by alcohol consumption by employment rate

#sub1['suicideper100th']=sub1['suicideper100th'].replace(0, numpy.nan)

sub1['suicideper100th']= pd.to_numeric(sub1['suicideper100th'])

#sub1['Income']= pd.to_numeric(sub1['Income']) ct1 = sub1.groupby('suicideper100th').size() print (ct1)

# using ols function for calculating the F-statistic and associated p value model1 = smf.ols(formula='suicideper100th ~ C(Income)', data=sub1).fit() results1 = model1 print (results1.summary())

sub2 = sub1[['suicideper100th', 'Income']].dropna()

print ('means for income by suicide status') m1= sub2.groupby('Income').mean() print (m1)

print ('standard deviations for income suiside status') sd1 = sub2.groupby('Income').std() print (sd1) #i will call it sub3 sub3 = sub1[['suicideper100th', 'Alcoholuse']].dropna()

model2 = smf.ols(formula='suicideper100th ~ C(Alcoholuse)', data=sub3).fit() print (model2.summary())

print ('means for alcohol use by suicide status') m2= sub3.groupby('Alcoholuse').mean() print (m2)

print ('standard deviations for alcohol use by suicide') sd2 = sub3.groupby('Alcoholuse').std() print (sd2) #tuckey honesty test comparision for post hoc test mc1 = multi.MultiComparison(sub3['suicideper100th'], sub3['Alcoholuse']) res1 = mc1.tukeyhsd() print(res1.summary())

the null hypothesis indicates that there is no difference in the level of consumption of alcohol on the suicide rate and also there is no difference in the income level on the suicide rate.

the alternative hypothesis is that there is a significance difference on the alcohol consumption and the average income on the suicide rate.

the results are displayed as following

OLS Regression Results                             ============================================================================== Dep. Variable:        suicideper100th   R-squared:                       0.013 Model:                            OLS   Adj. R-squared:                  0.009 Method:                 Least Squares   F-statistic:                     2.875 Date:                Sun, 09 Aug 2020   Prob (F-statistic):             0.0914 Time:                        02:48:14   Log-Likelihood:                -703.84 No. Observations:                 213   AIC:                             1412. Df Residuals:                     211   BIC:                             1418. Df Model:                           1                                         Covariance Type:            nonrobust      

the low value of F- statistics and P value being greater that 0.025 indicates that we have failed to reject the null hypothesis and we accept the fact that there is no significant difference on the effect of annual income value on the suicide rate

 OLS Regression Results                             ============================================================================== Dep. Variable:        suicideper100th   R-squared:                       0.006 Model:                            OLS   Adj. R-squared:                 -0.004 Method:                 Least Squares   F-statistic:                    0.5930 Date:                Sun, 09 Aug 2020   Prob (F-statistic):              0.554 Time:                        02:48:14   Log-Likelihood:                -704.69 No. Observations:                 213   AIC:                             1415. Df Residuals:                     210   BIC:                             1425. Df Model:                           2                                         Covariance Type:            nonrobust                                         ======================================================================================                         coef    std err          t      P>|t|      [0.025      0.975] -------------------------------------------------------------------------------------- Intercept              7.7779      0.942      8.254      0.000       5.920       9.635 C(Alcoholuse)[T.1]     0.9818      1.204      0.815      0.416      -1.392       3.355 C(Alcoholuse)[T.2]     1.2756      1.190      1.072      0.285      -1.071       3.622

the low value of F- statistics and P value being greater that 0.025 indicates that we have failed to reject the null hypothesis and we accept the fact that there is no significant difference on the effect of alcohol consumption level on the suicide rate.

another analysis have been conducted,which is called the post hoc test, it is used to analyze the difference between the groups of categorical level without increasing the type 1 error in an accumulative manner. we use  the Tuckey honesty test for post hoc comparison. and it agrees with the fact that there is no difference between the alcohol usage levels on the suicide rate .

means for alcohol use by suicide status            suicideper100th Alcoholuse                 0                  7.777891 1                  8.759692 2                  9.053453 standard deviations for alcohol use by suicide            suicideper100th Alcoholuse                 0                  6.086994 1                  5.809631 2                  7.663338 Multiple Comparison of Means - Tukey HSD, FWER=0.05 =================================================== group1 group2 meandiff p-adj   lower  upper  reject ---------------------------------------------------     0      1   0.9818  0.678 -1.8605 3.8241  False     0      2   1.2756 0.5313 -1.5338 4.0849  False     1      2   0.2938    0.9 -2.1713 2.7588  False ---------------------------------------------------

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