Mortality rate, under-5 (per 1,000 live births)

The under-5 mortality rate, measured in deaths per 1,000 live births, serves as a crucial indicator of child health and reflects the effectiveness of health care systems, the socio-economic status of communities, and the overall quality of life in a given region. This metric captures the risk of death that children face before reaching their fifth birthday, making it an essential measurement for policymakers, healthcare providers, and researchers aiming to improve child health globally.

Understanding this indicator is vital because it encompasses various factors that affect children, including nutrition, maternal health, disease burden, education, and accessibility of medical care. High mortality rates among children under five can signal underlying issues such as inadequate healthcare infrastructure, poor maternal health, malnutrition, or widespread poverty. It can also highlight disparities within and between countries, showcasing how socio-economic factors and regional disparities affect children's health.

The importance of the under-5 mortality rate transcends individual health, as it is also interrelated with numerous other indicators. For instance, it is closely linked to maternal mortality rates, as healthier mothers contribute to better birth outcomes. Access to clean water and sanitation significantly impacts childhood mortality rates, as waterborne diseases are a leading cause of deaths in young children. Furthermore, educational attainment and economic conditions within a community can lead to better health awareness, promoting preventive healthcare practices, and consequently reducing mortality rates.

Several factors determine the under-5 mortality rate, including healthcare system access, economic stability, education levels, and cultural beliefs. Within this context, poverty stands out as a primary driver; families with limited financial resources often cannot access quality healthcare or proper nutrition, increasing their children's susceptibility to life-threatening diseases. Additionally, regions that experience healthcare worker shortages, especially in rural or underfunded areas, can lead to inadequate maternal and child health services, driving up mortality statistics.

Moreover, infectious diseases remain a significant contributor to under-5 mortality, particularly in developing countries. Conditions such as pneumonia, diarrhea, and malaria are prevalent in regions with limited healthcare access and can fatally impact vulnerable children. Efforts to improve vaccination coverage and promote public health messages about hygiene and nutrition play a critical role in reducing mortality rates.

To combat high under-5 mortality rates, various strategies and solutions have been deployed globally. Enhancing healthcare delivery through community health programs that educate families about nutrition, hygiene, and preventive care has proven effective. Integrated approaches that focus on prenatal care, skilled birth attendance, and postnatal support are essential in reducing both maternal and child mortality. Additionally, initiatives to improve access to clean water and sanitation significantly contribute to lowering disease rates, subsequently reducing mortality.

Global efforts, exemplified by the United Nations Sustainable Development Goals, aim to end preventable deaths of newborns and children under five years of age by 2030. Governments and organizations are called to prioritize investments in health systems, focused particularly on vulnerable populations and at-risk regions, to realign historical disparities.

Despite these strategies, challenges and flaws remain in adequately addressing the under-5 mortality rate. There are often gaps in data collection and reporting, especially in low-resource settings. These gaps can hinder understanding the true scale of under-5 mortality and the effectiveness of interventions deployed. Additionally, socio-political factors such as conflicts and economic instability can disrupt health services and deter progress made in child health, leading to stagnant or rising mortality rates in affected regions.

In 2022, the global under-5 mortality rate reported a figure of 37.1 deaths per 1,000 live births, evidencing ongoing challenges. This statistic reflects a gradual improvement compared to earlier decades, yet a significant burden remains. The median value in 2022 was reported at 15.05, indicating that some regions have made substantial progress, while others continue to struggle.

When looking at the top five areas with the highest under-5 mortality rates—Niger (117.3), Nigeria (107.2), Somalia (106.1), Chad (102.9), and Sierra Leone (100.8)—the disparity becomes alarmingly clear. These countries face extreme public health challenges, including limited access to healthcare facilities, frequent conflicts, and high levels of poverty. This exacerbates child vulnerabilities to diseases and limits access to necessary medical interventions.

Conversely, regions like San Marino (1.5), Estonia (1.9), Norway (2.2), Singapore (2.2), and Finland (2.3) display markedly lower mortality rates, a testament to the robust health systems, strong economic conditions, and comprehensive social support structures present in these societies. These nations benefit from advanced healthcare infrastructure, increased public health funding, and widespread education regarding health practices, showcasing the drastic impacts of national policies on child mortality.

In summary, the under-5 mortality rate is a pivotal indicator reflecting a society’s health and socio-economic status. While substantial progress has been made in reducing child mortality rates globally, significant work remains, particularly in the nations facing the highest burdens. Targeted strategies focused on enhancing healthcare access, improving economic conditions, and addressing educational disparities offer viable pathways to ensure that every child has the opportunity to reach their fifth birthday and beyond.

                    
# 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.MORT'

# 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.MORT'

# 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))