Probability of dying among adolescents ages 10-14 years (per 1,000)

The probability of dying among adolescents aged 10-14 years is an essential health indicator that reflects the broader realities regarding the health and safety of a society’s youth. This metric, measured in deaths per 1,000 adolescents, serves as a critical tool in understanding the risk factors that contribute to adolescent mortality and serves as a necessary guide for public health initiatives.

As of 2022, the global median value for this indicator stands at 1.6 deaths per 1,000 adolescents. This figure provides a baseline against which various regions and countries can be compared. By examining the disparities between different regions, policymakers can better allocate resources and tailor interventions to address specific health issues. For instance, the top areas for adolescent mortality are concerningly high: Niger at 11.9, Central African Republic at 10.2, Chad at 10.0, Sierra Leone at 9.6, and Somalia at 8.8. These figures starkly highlight how far many adolescents in these regions are from the global median.

In stark contrast, the lowest figures recorded are found in high-income nations such as Luxembourg, San Marino, Denmark, Iceland, and Ireland, all hovering around 0.2-0.3 deaths per 1,000. This pronounced disparity suggests an urgent need for international attention and resources focused on high-mortality regions, aiming to understand and mitigate the numerous factors contributing to these premature deaths.

The importance of this indicator extends beyond mere numbers; it encapsulates issues such as access to healthcare, education, nutrition, and social stability. Adolescents are at a critical juncture in their development, one where health services and educational opportunities can significantly dictate future outcomes. A high probability of dying in this age group may correlate with other negative health indicators, such as maternal health rates or prevalence of infectious diseases, emphasizing the interconnected nature of public health data.

Several factors influence the probability of dying among adolescents. Socioeconomic conditions play a significant role; areas with high poverty rates often experience limited access to healthcare services, inadequate nutrition, and overall poor living conditions. Additionally, educational opportunities can greatly affect health outcomes. For instance, adolescents in highly educated environments tend to have better knowledge about health issues and improved healthcare access. Cultural norms and values also affect behaviors that can either mitigate or exacerbate health risks, such as attitudes towards vaccination or sexual health education.

Infrastructural issues, such as the availability of clean water and sanitation, particularly in rural or underdeveloped areas, also directly impact the health of adolescents. Furthermore, emerging global challenges like climate change, conflict, and the COVID-19 pandemic can create new vulnerabilities for this demographic. For example, disruptions in healthcare access or education not only increase immediate risks but can have long-term repercussions on the overall health and wellbeing of these young individuals.

To address the high rate of mortality among adolescents, a multi-faceted approach is necessary. Governments and NGOs must implement strategies focusing on improving healthcare systems, particularly in underserved areas. This includes increasing access to vaccinations, establishing youth-friendly health services, and providing education on health matters. Health education should be tailored to adolescents' unique needs and delivered in ways that resonate with them, promoting healthy behaviors and choices.

Developing community-based programs that engage families and educate them about nutrition, healthcare access, and the importance of education can empower communities to take charge of their health outcomes. Additionally, incorporating mental health services into adolescent health programs is essential, as mental health challenges are significant contributors to mortality rates.

Despite the advancements made globally in reducing adolescent mortality rates—from a world average of 4.8 in 1990 to 2.6 in 2022—the statistics reveal the difficulties stubbornly present in various regions, particularly in low-income countries. The world values over the years show a positive trend in overall decline in this indicator, hinting at possible improvements in healthcare and overall societal wellbeing. Nonetheless, the marked differences between regions underscore that significant progress still needs to be achieved, especially in areas with the highest mortality rates.

Challenges persist in interpreting and addressing the probability of dying among adolescents, particularly in the face of complex factors influencing health outcomes. For example, data collection can be inconsistent, especially in conflict zones or impoverished regions, leading to underreported numbers. Understanding the socio-cultural dynamics that accompany these statistics can also be tricky, making solutions less straightforward.

In conclusion, the probability of dying among adolescents aged 10-14 years is a vital indicator of societal health that requires urgent global attention. While median values show gradual improvement, the reality faced by adolescents in high-mortality regions must drive international collaboration and focused interventions. Addressing the multifaceted factors that influence these outcomes can ultimately save young lives and promote a healthier future generation.

                    
# 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.1014'

# 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.1014'

# 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))