Prevalence of current tobacco use (% of adults)

The prevalence of current tobacco use among adults is a critical public health indicator that reflects smoking habits and broader societal attitudes towards tobacco consumption. As of 2020, the global prevalence stood at approximately 22.95%, indicating a significant decline from the 34.14% recorded in 2000. This decline highlights ongoing public health efforts, regulatory measures, and a growing awareness of the health risks associated with tobacco use.

Understanding the implications of tobacco use prevalence is vital for developing effective health policies. High prevalence rates are often associated with increased rates of diseases such as lung cancer, cardiovascular diseases, and respiratory conditions. These health issues create a burden on healthcare systems, detracting from economic productivity and quality of life. In regions with elevated tobacco use, governments face greater healthcare costs, making it imperative to address smoking habits through comprehensive public health strategies.

The importance of tracking tobacco use extends to its relationship with other indicators such as overall health outcomes, healthcare expenditure, and social determinants of health. For example, areas with high tobacco prevalence often exhibit higher rates of preventable diseases, leading to increased mortality rates and a lower life expectancy. Countries investing in cessation programs, education, and preventive healthcare tend to see a decrease in tobacco consumption, correlating with improved health outcomes and reduced healthcare spending.

Several factors influence the prevalence of tobacco use, including socio-economic status, cultural perceptions of smoking, and the availability of smoking cessation programs. In high-prevalence regions, social acceptance of smoking may contribute to sustained use. Conversely, countries with active campaigns against smoking and robust healthcare systems often report lower prevalence rates. Economic factors also play a critical role; higher taxes on tobacco products, for instance, can effectively deter use by making smoking less affordable.

Strategies to reduce tobacco use typically encompass a combination of legislation, education, and community support. Legislation may include imposing higher taxes, implementing smoke-free laws, and placing strict regulations on tobacco advertising. Public health campaigns aimed at educating the population about the dangers of smoking and providing information on cessation resources can also facilitate change. Community-based interventions, such as support groups and counseling services, are instrumental in helping individuals quit smoking, particularly in communities where tobacco use is culturally entrenched.

Despite the positive trends, challenges in reducing tobacco prevalence persist. While many areas have implemented successful reduction strategies, disparities in prevalence still exist globally. For instance, the top five areas with the highest prevalence rates in 2020 include Nauru (48.5%), Myanmar (44.1%), Kiribati (40.6%), Serbia (39.8%), and Papua New Guinea (39.3%). These figures starkly contrast with the bottom five areas, including Ghana (3.5%), Nigeria (3.7%), Panama (5.0%), Ethiopia (5.1%), and Turkmenistan (5.5%). Such discrepancies can be attributed to varying levels of public health initiatives, economic conditions, and cultural attitudes towards smoking.

In assessing these values, Nauru's staggering prevalence at 48.5% raises concerns about the effectiveness of existing tobacco control measures in place. In contrast, countries like Ghana and Nigeria, with prevalence rates under 4%, showcase the potential for successful prevention strategies. These extreme differences in tobacco use rates emphasize the need for tailored approaches, taking into account local contexts and challenges.

Looking at the global values over the last two decades offers insight into trends in tobacco use. The data shows a consistent decline from 34.14% in 2000 to 22.95% in 2020, indicating that public health efforts aimed at reducing tobacco use are making an impact. However, the reductions are gradual, suggesting the need for intensified efforts in certain regions, particularly those with high prevalence.

Strategies to bridge the gap between high and low prevalence areas could include sharing best practices, leveraging successful case studies as roadmaps for regions struggling with high tobacco use rates. International collaborations and initiatives play a significant role in addressing these disparities, allowing for the exchange of knowledge and resources to combat tobacco addiction globally.

In summary, the prevalence of current tobacco use among adults is a significant indicator of public health. While there has been a notable global decline, the stark differences between high and low prevalence regions illustrate the need for ongoing efforts. Understanding the multidimensional factors influencing tobacco use, alongside the adoption of effective strategies, is essential to reducing prevalence further. As we move forward, focusing on tailored interventions addressing the unique cultural, economic, and health contexts of each area will be crucial in the continued fight against tobacco use.

                    
# 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'

# 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'

# 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))