Probability of dying among adolescents ages 15-19 years (per 1,000)

The probability of dying among adolescents aged 15-19 years, expressed per 1,000 individuals, serves as a vital indicator of health system effectiveness and societal well-being. This demographic is at a critical juncture in their lives, experiencing rapid physical, emotional, and social development. The latest median value for this indicator in 2022 stands at 3.5 deaths per 1,000 adolescents, reflecting both progress in global health and ongoing challenges that many regions continue to face.

In understanding the importance of this statistic, it is essential to recognize that it provides insights into various underlying health issues. For instance, high adolescent mortality rates often indicate a lack of access to quality healthcare, insufficient nutritional resources, and prevalent social issues such as violence, addiction, or mental health problems. Lower rates, on the other hand, are typically found in countries with robust healthcare systems, effective education programs, and supportive social conditions. The interplay between this indicator and others—such as life expectancy, maternal health, and economic conditions—highlights the multifaceted nature of health and development, as improvements in one area can lead to progress in others.

When analyzing the top five areas with the highest adolescent death probabilities in 2022, we see alarming figures: Somalia leads with 17.7 deaths per 1,000 adolescents, followed by Congo-Kinshasa at 16.4, Sierra Leone at 16.2, South Sudan at 15.5, and Timor-Leste at 15.0. These numbers suggest extreme challenges facing these nations, such as ongoing conflicts, infrastructural breakdowns, and pervasive poverty. For instance, Somalia has struggled with decades of civil war, which has severely hindered healthcare access and education. In these regions, factors like infectious diseases, malnutrition, and limited access to reproductive health services significantly contribute to the high mortality rates.

Conversely, the bottom five areas—Cyprus, San Marino, Denmark, Ireland, and Spain—show a stark contrast with probabilities ranging from 0.8 to 0.9 deaths per 1,000 adolescents. This reflects the effectiveness of health and social systems in these countries. High standards of living, comprehensive healthcare coverage, and extensive educational and mental health resources contribute to these low figures, illustrating the benefit of governmental and societal investment in health infrastructure and adolescent support services.

Examining world values from 1990 to 2022, there has been a gradual decrease in the global average for adolescent mortality rates. The rate has declined from 7.8 in 1990 to 4.5 in 2022. This steady decline demonstrates advancements in healthcare technologies, increased awareness of youth health issues, and improved global health initiatives. However, translating this data into actionable insights is crucial. The ongoing challenges faced by certain regions point to inequities that persist within global health progress, suggesting that while the world average is improving, significant disparities remain.

Several factors influence adolescent mortality. Socioeconomic status remains paramount, with poorer regions often lacking the necessary healthcare resources to ensure young people can receive timely medical attention or preventative care. Cultural attitudes toward healthcare can also play a role; in some areas, issues like teenage pregnancy or mental health stigma prevent adolescents from seeking help. Additionally, environmental factors such as pollution and availability of clean water can affect health outcomes. Education about health, particularly sexual and reproductive health, is another critical determinant in reducing mortality among adolescents.

To address these disparities and improve the probability of dying among adolescents, governments and organizations must implement comprehensive strategies. Initiatives should focus on enhancing healthcare access, specifically targeting rural and underserved populations. Education plays a pivotal role—both through school curricula that incorporate health education and community outreach programs designed to inform adolescents about health risks and available resources.

Moreover, cross-sector partnerships can significantly bolster these efforts. Collaborations among governments, NGOs, and private sector entities can lead to innovative solutions that address the root causes of adolescent mortality. For example, mobile health clinics can enhance access in remote areas, while digital platforms can provide vital health education and resources to young people.

Nevertheless, flaws in implementation persist. For instance, underfunding of health initiatives, inconsistent political will, and the challenge of measuring program effectiveness can hinder progress. Furthermore, cultural barriers can complicate outreach efforts, necessitating tailored approaches to engage communities meaningfully. The focus should not only be on reducing numbers but ensuring that the solutions are equitable and sustainable, targeting the most vulnerable populations.

In conclusion, the probability of dying among adolescents aged 15-19 years is not just a statistic; it reveals deeper narratives about health, education, and societal priorities. As we strive for a healthier future, prioritizing the well-being of adolescents must remain at the forefront of public health agendas globally. By addressing the disparities and implementing targeted strategies, there is hope for continued improvement in this critical area of adolescent health.

                    
# 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.DYN.1519'

# 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.DYN.1519'

# 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))