Mortality rate, infant (per 1,000 live births)

The infant mortality rate (IMR), indicated as the number of infant deaths per 1,000 live births, serves as a crucial marker of a nation's health and well-being. As a primary indicator of child health, it reflects the overall socioeconomic conditions, healthcare systems, and level of development within a country. The IMR provides insight not only into the immediate health of infants but also into the broader context of maternal health, nutrition, and access to medical care.

According to the latest data from 2022, the median global infant mortality rate stands at 12.8 per 1,000 live births. While this figure indicates progress from previous decades, it is crucial to recognize that the rates vary dramatically across different regions, illuminating stark disparities in health outcomes. For instance, countries such as Sierra Leone, Central African Republic, and Nigeria report rates exceeding 60 deaths per 1,000 live births, signifying a critical public health challenge. Sierra Leone holds the highest rate at 76.0, a tragedy reflecting the nation's ongoing struggles with poverty, inadequate healthcare infrastructure, and persistent socioeconomic challenges.

In contrast, nations like San Marino, Estonia, and Japan showcase exemplary health standards with rates below 2 per 1,000 live births. San Marino, with an impressive rate of 1.3, highlights the impact of robust healthcare systems, effective public health policies, and socio-economic stability in ensuring healthier outcomes for infants. The disparities between these top and bottom regions underscore the complexities surrounding infant mortality and the varied factors contributing to it.

The importance of monitoring infant mortality rates cannot be overstated, as they offer vital insights into a country’s healthcare practices, maternal health, and economic conditions. Countries with high infant mortality rates often also face challenges such as limited maternal healthcare access, high rates of poverty, inadequate nutrition, and insufficient immunization coverage. This correlation reinforces the importance of addressing maternal health to improve infant survival rates.

Several key factors influence the infant mortality rate, most notably maternal health, education, income level, and access to healthcare services. Mothers who lack access to education or healthcare are more likely to give birth to infants with health complications, contributing to higher mortality rates. Socioeconomic factors play a significant role as well; families in poverty-stricken regions may face malnutrition, limited medical access, and unsafe living conditions, all of which adversely affect infant health.

Besides these socio-economic determinants, environmental factors are also crucial. Clean water and sanitation are fundamental for reducing infections and diseases that could lead to infant mortality. In many high-rate regions, inadequate sanitation, polluted water sources, and poor healthcare infrastructure exacerbate the IMR challenges. Strategies to improve these conditions could include enhancing healthcare accessibility, providing education on maternal health, and ensuring clean water supply.

Successful strategies to reduce infant mortality rates often incorporate a multifaceted approach that includes health interventions, educational programs, and international support. For instance, implementing widespread vaccination programs can dramatically decrease the incidence of diseases that contribute to infant mortality. Similarly, investing in maternal health care, including prenatal and postnatal care, can ensure that mothers and infants receive adequate health support during critical times.

International organizations such as the World Health Organization (WHO) and UNICEF play an essential role in tracking and developing strategies to combat high infant mortality rates. Their efforts in establishing health policies and providing resources to underprivileged countries have led to remarkable improvements in child health in several areas. Moreover, collaborations between governments, NGOs, and local communities are crucial for creating sustainable health systems capable of reducing the IMR.

Despite these promising strategies, several flaws must be acknowledged in the efforts to combat high infant mortality rates. One significant issue is the lack of consistent data and tracking mechanisms in lower-income countries, which complicates the understanding of true infant mortality statistics. Additionally, political instability and conflicts can disrupt healthcare services, further aggravating the situation. Ensuring that health policies are inclusive and equitable remains a significant hurdle in effectively addressing the underlying challenges that contribute to high infant mortality.

From a global perspective, the world has made significant strides in reducing the infant mortality rate, with a noticeable drop from 63.7 deaths per 1,000 live births in 1990 to 27.9 in 2022. However, the journey is far from over, as evidenced by the current disparities. Continued efforts are critical for countries with the highest rates, as addressing these challenges will require sustained commitment to improving healthcare infrastructure, women's access to education and health services, and strong community engagement to create a healthier 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 = 'SP.DYN.IMRT.IN'

# 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 <- 'SP.DYN.IMRT.IN'

# 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))