Prevalence of current tobacco use, females (% of female adults)

The prevalence of current tobacco use among females is a significant public health indicator, reflecting the percentage of adult women who actively use tobacco products. This metric is vital for understanding broader trends in health behaviors, the effectiveness of tobacco control policies, and the overall health landscape of populations. Tobacco use remains a leading cause of preventable diseases and premature deaths worldwide, highlighting the necessity of monitoring its prevalence among different demographics, including women.

In 2020, the global prevalence of current tobacco use among females stood at 8.66%. This figure is notable as it has shown a downward trend from 17.84% in the year 2000, which signifies progress in public health initiatives aimed at reducing smoking rates among women. Despite this overall decline, the median value across reported regions was recorded at 6.4%, indicating significant variance in tobacco use among different countries.

The data reveals stark disparities, especially when examining the top five areas with the highest prevalence rates of tobacco use among females. Nauru leads with an alarming rate of 49.1%, followed by Serbia at 39.1%, Bulgaria at 37.1%, Croatia at 36.1%, and France at 31.9%. These figures underscore the cultural, social, and economic factors that drive tobacco usage in these regions. For instance, in countries like Nauru, where tobacco products may be more accessible and socio-cultural norms do not stigmatize smoking, higher prevalence rates can be expected. Additionally, these regions may also lack stringent tobacco control policies, contributing to elevated tobacco use rates among women.

Conversely, looking at the bottom five areas reveals an entirely different narrative. In North Korea, the prevalence is recorded at 0.0%, followed by Azerbaijan at 0.1%, Eritrea at 0.2%, Ghana at 0.3%, and Egypt at 0.4%. These low figures might reflect a combination of effective anti-tobacco campaigns, limited availability of tobacco products, and socio-political factors that discourage tobacco use. However, they may also raise concerns about underreporting or inadequate data collection in these regions, which can produce misleading statistics about tobacco use.

The importance of monitoring the prevalence of tobacco use among females cannot be overstated. It serves as a key indicator related to several other health metrics, including lung cancer rates, heart disease, and overall life expectancy. Moreover, understanding female tobacco use is critical for designing gender-sensitive public health interventions. For example, health campaigns aiming to reduce tobacco use among women may need to consider gender-specific factors such as social norms, women's roles within families, and access to cessation resources.

Several factors contribute to female tobacco use prevalence. Socioeconomic status, education levels, cultural attitudes towards smoking, and access to tobacco products play significant roles. Women in low-income communities may experience higher stress levels, leading to increased smoking rates as a coping mechanism. Additionally, advertising and marketing strategies that target women can significantly influence their smoking behavior. Tobacco companies often promote products in ways that appeal to women, perpetuating myths about the benefits of smoking, such as weight control or social acceptance.

To combat the persistent issue of tobacco use among women, particularly in regions with high prevalence rates, a range of strategies can be employed. Legislative measures, such as implementing stricter regulations on tobacco advertising and increasing taxes on tobacco products, have proven effective in reducing smoking prevalence. Public health campaigns tailored to women, focusing on the health risks of smoking and providing support for cessation, are also vital. These campaigns could highlight the unique consequences of tobacco use for women's health, including reproductive health impacts and increased risk of heart disease.

Access to cessation support services is another critical factor in reducing female tobacco use. Programs providing counseling, nicotine replacement therapies, and peer support can empower women to quit smoking. It is also necessary to engage with community leaders and influencers to change cultural perceptions of tobacco use and promote non-smoking norms.

Despite the efforts, there are inherent flaws in the approach to tackling female tobacco use. One of the biggest challenges is the stigma associated with smoking among women. In some cultures, while male smoking may be normalized, female smokers may still face societal judgments and discrimination. This can deter women from seeking help or participating in cessation programs. Additionally, the continuous evolution of tobacco products, including e-cigarettes and vaping, presents new challenges, as these alternatives are often perceived as safer by younger women. This perception can lead to an increase in usage rates, complicating existing public health efforts.

In conclusion, the prevalence of tobacco use among females is a crucial indicator with significant implications for public health policy and societal well-being. The historical decline in smoking prevalence among women is encouraging, yet the substantial variation across different regions highlights the need for targeted interventions. Understanding the factors influencing tobacco use and implementing comprehensive strategies can help achieve further reductions, ultimately leading to healthier populations and improved quality of life for women globally.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SH.PRV.SMOK.FE'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SH.PRV.SMOK.FE'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))