Assessing the impact of the WHO Framework Convention on Tobacco Control on Prevalence of Tobacco Use and Cardiovascular Disease Mortality.

Data Source

Download raw data from open access database:

• WHO Mortality Database
• WHO The Global Health Observatory
• Signature and Ratification of FCTC from UN

Installation

Download and install Git Download and install Python

conda create -n [ENV_NAME] python=3.10 
conda activate [ENV_NAME]  
cd [CLONED_DIRECTORY]
pip install -r requirements.txt

• Buildup the src path

conda install conda-build
conda develop src
cd src

Usage

Cleaning process

• Cleans the mortality dataframe by removing specified columns and filtering rows with missing values

from utility import select_df

Preprocess the WHO CVD mortality data

• Prepares and filters cardiovascular disease data
• Groups data into specific age categories

from utility import preprocess_cvd, create_age_grouping

Preprocess the Prevalence of Tobacco Use data

• Formats the tobacco data for merging

from utility import tobacco_layout_modified

Merge CVD df and tobacco df

• Merges CVD and tobacco data by country

from utility import tobacco_layout_modified

Determine countries who signed the WHO FCTC treaty

WHOFCTC_parties_date.py

Data visualization

Preprocess

preprocess_analysis.py

create figures

plot.py

Example 1 of multi-subplot line chart

df = pd.read_excel('~/test_file/19_ratified_country.xlsx')
plot_line_chart(df,column1='Male_Total_Percentage_of_Cause_Specific_Deaths_Out_Of_Total_Deaths' ,
                    column2='Female_Total_Percentage_of_Cause_Specific_Deaths_Out_Of_Total_Deaths',
                    save_path=[CLONED_DIRECTORY])

Example 2 of line chart between CVD Mortality and Prevalence of Tobacco Use in both Males and Females in Netherland

df = pd.read_excel('~/test_file/19_ratified_country.xlsx')
select_country = ['Netherlands']
male_tobacco = 'Male_Estimate_of_Current_Tobacco_Use_Prevalence_age_standardized_rate'
male_cvd = 'Male_Total_Percentage_of_Cause_Specific_Deaths_Out_Of_Total_Deaths'
female_tobacco = 'Female_Estimate_of_Current_Tobacco_Use_Prevalence_age_standardized_rate'
female_cvd = 'Female_Total_Percentage_of_Cause_Specific_Deaths_Out_Of_Total_Deaths'
save_path = "[CLONED_DIRECTORY]/[FIGURE_NAME].png"

relationship_cvd_tobacco(df, select_country=select_country,
                         variable_1=male_tobacco,
                         variable_2=male_cvd,
                         variable_3=female_tobacco,
                         variable_4=female_cvd, save_path=[CLONED_DIRECTORY]
                         )

Dashboard

Demo

Please download the dashboard.pbix file and open it in Power BI.

Contact

Wei Jan, Chang