Prevalence of current tobacco use, males (% of male adults)

The prevalence of current tobacco use among males is a crucial public health indicator, representing the percentage of adult males who actively use tobacco products. This indicator sheds light on the smoking habits of male adults, an essential component in understanding health trends and crafting effective public health policies.

The importance of tracking this indicator lies not only in its direct impact on health outcomes but also in its broader implications. High rates of tobacco use are associated with increased rates of respiratory diseases, cancers, and cardiovascular disorders. Additionally, tobacco use can strain healthcare systems and contribute to economic burdens, emphasizing the need for effective public health interventions. The data from 2020 reveal a median prevalence of 28.4% among male adults, indicating that over a quarter of men globally engage in tobacco use.

When examining the data further, the disparity between regions is striking. The top five areas, including Indonesia (71.4%), Myanmar (68.5%), and Timor-Leste (67.6%), feature staggering rates of tobacco use. Such elevated statistics can often be attributed to cultural norms, economic conditions, and insufficient regulatory measures concerning tobacco products. In contrast, the bottom five areas—Ghana (6.6%), Nigeria (6.9%), and Panama (7.7%)—exhibit significantly lower prevalence rates, which may correlate with more successful public health campaigns, stronger regulatory environments, or cultural attitudes that discourage tobacco use.

The global data trend from 2000 to 2020 reveals a downward trajectory in overall tobacco use rates among males, decreasing from 50.48% in 2000 to 37.29% in 2020. While this 13% decline over two decades signifies progress, the figures still represent a substantial number of males engaging with tobacco. The decline can be attributed to several factors, including heightened awareness of health issues associated with tobacco, improved access to cessation programs, and stronger legislation against tobacco advertising. However, despite these positive trends, the percentage of tobacco users remains alarmingly high in many regions, suggesting that while steps have been taken, significant work remains to be done.

Various factors influence the prevalence of tobacco use, including socioeconomic status, education, and accessibility to tobacco products. In countries with lower average incomes, the sale and consumption of tobacco may be more rampant due to fewer restrictions and lower prices. Furthermore, targeted marketing of tobacco products towards vulnerable populations can exacerbate the issue, particularly in developing nations where regulatory oversight is weaker.

The relationship between tobacco use and other public health indicators is also critical. For example, regions with high tobacco use often coincide with elevated rates of non-communicable diseases (NCDs), such as heart disease and diabetes. Tackling tobacco use is thus a vital aspect of broader health initiatives aimed at reducing the prevalence of NCDs. Additionally, the prevalence of tobacco use intersects with mental health indicators, as studies have shown a higher incidence of tobacco use among individuals dealing with mental health issues.

Addressing tobacco use effectively requires the deployment of comprehensive strategies. Public health campaigns aimed at increasing awareness of the dangers associated with tobacco are crucial. Additionally, implementing and enforcing strong regulatory measures—such as banning smoking in public places, increasing taxation on tobacco products, and limiting advertising—have proven effective in reducing tobacco prevalence. Countries like Australia and the UK have spearheaded such initiatives, resulting in notable declines in smoking rates.

Moreover, providing resources for tobacco cessation—including counseling, support groups, and nicotine replacement therapies—can aid those wishing to quit. Successful cessation programs often incorporate community-based approaches that engage local leaders and organizations to reach individuals in culturally sensitive ways.

However, the fight against tobacco use is not without its flaws. Despite advancements in public health advocacy and awareness, some regions remain resistant to anti-tobacco reforms due to cultural acceptance or strong lobbying from tobacco companies. These companies frequently employ tactics to undermine evidence supporting the health risks of tobacco or to promote their products as less harmful alternatives. Furthermore, the economic benefits derived from the tobacco industry present challenges, particularly in regions where reliance on tobacco farming serves as a significant source of income.

In conclusion, monitoring the prevalence of current tobacco use among males is essential for shaping effective public health policies aimed at reducing tobacco-related diseases and improving overall health outcomes. While encouraging declines in tobacco use rates highlight progress, the persistence of high prevalence in certain regions underscores the ongoing challenges. A multifaceted approach incorporating awareness, regulation, and cessation support is vital in combating this public health crisis. Stakeholders must acknowledge the complexities involved, addressing not only health but also cultural and economic factors to foster a healthier future where tobacco use is minimized.

                    
# 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.MA'

# 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.MA'

# 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))