Probability of dying among children ages 5-9 years (per 1,000)

The indicator of the probability of dying among children ages 5-9 years (per 1,000) offers crucial insights into the health and well-being of younger populations around the globe. This statistic provides a concise measurement of child mortality within a specific age group, reflecting the effectiveness of healthcare systems, social conditions, and economic factors affecting families. By analyzing this indicator, policymakers, health organizations, and international agencies can identify areas with critical needs and design strategies to reduce child mortality.

The importance of this indicator cannot be overstated. Childhood is a critical stage of development where health directly influences later life outcomes. High mortality rates during these formative years often indicate underlying issues, such as inadequate healthcare access, malnutrition, or unsafe living environments. Hence, understanding and addressing factors that drive the probability of dying among children aged 5-9 is essential for improving public health outcomes and achieving goals set by international frameworks such as the Sustainable Development Goals (SDGs).

This indicator is intricately related to other health and social indicators. For instance, it correlates with maternal mortality rates, nutritional status, access to clean water, and sanitation facilities. In areas where children's mortality rates are high, one often finds higher levels of poverty, limited educational opportunities, and insufficient healthcare services. Therefore, the probability of dying can serve as a barometer for broader social and economic challenges faced by communities and nations.

Several factors can significantly impact the probability of death among children in this age group. Health determinants such as lack of vaccinations and preventable diseases, which disproportionately affect low-income countries, play a critical role. Environmental factors such as pollution and unsafe housing conditions additionally contribute to health risks. Social factors like gender inequality and lack of education for mothers can exacerbate instances of child mortality, making it important to address these areas comprehensively.

To mitigate child mortality and improve health outcomes, several strategies and solutions can be employed. Enhancing healthcare access is foundational; ensuring that families have access to quality medical services and preventative care is crucial. Implementing community health programs that focus on education and awareness about nutrition, hygiene, and child care practices can empower families to take charge of their health. Additionally, ensuring that children receive vaccinations against communicable diseases can significantly lower mortality rates.

Furthermore, investments in education, especially for women, can lead to improved health outcomes for children. Educated mothers are more likely to make informed health choices, seek medical help when necessary, and provide better nutrition, which collectively reduces child mortality rates. Addressing broader socio-economic factors is also vital; reducing poverty, improving infrastructure, and ensuring economic opportunities can enable families to invest in their children’s health and well-being.

Despite the advancements made in reducing child mortality rates globally, flaws still exist in the data collection and analysis of this indicator. In many regions, especially rural or conflict-affected areas, reliable data is scarce or outdated, leading to a skewed understanding of the situation. Moreover, cultural factors sometimes distort the perceived value of certain indicators, as societal attitudes towards health can vary dramatically between different contexts. Unequal distribution of resources and capabilities also plays a significant role in the effectiveness of interventions, sometimes leaving the most vulnerable populations without adequate support.

Looking specifically at the global child mortality data, the latest figures for 2022 indicate a median value of 1.3 deaths per 1,000 children aged 5-9 years. This relatively low median value reflects the overall progress made since the early 1990s, where global values hovered around 9.6, indicating a significant decline over time. However, this average masks significant disparities between different regions. The top five areas with the highest child mortality rates are concerning: Niger at 19.7, Sierra Leone at 14.9, Somalia at 14.8, South Sudan at 13.8, and the Central African Republic at 12.8. These figures starkly contrast with the lowest rates found in Luxembourg (0.1), Iceland (0.2), San Marino (0.2), Andorra (0.3), and Austria (0.3).

Countries with high mortality rates are often grappling with multifaceted challenges, including geopolitical instability, economic crises, and inadequate health infrastructures. Conversely, nations with low child mortality rates typically benefit from robust healthcare systems, strong administrative structures, and higher levels of education and economic stability. This disparity highlights the ongoing need for targeted interventions in high-mortality regions while drawing lessons and best practices from low-mortality countries.

In conclusion, the probability of dying among children ages 5-9 years serves as a potent indicator of global health and development. As we continue to strive for better health outcomes, understanding and addressing the myriad factors influencing this statistic is paramount. Collaborative efforts to enhance healthcare access, reduce socioeconomic disparities, and empower communities will ultimately foster a healthier, more resilient future for children worldwide.

                    
# 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.0509'

# 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.0509'

# 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))