Immunization, DPT (% of children ages 12-23 months)

Immunization against diphtheria, pertussis, and tetanus (DPT) for children aged 12-23 months is a critical public health indicator that illustrates the effectiveness of immunization programs and the overall health infrastructure in a country. The percentage of immunized children in this age group provides insights not just into the immediate health prospects of these children, but also the long-term health trends within a population. A high immunization rate correlates with lower rates of morbidity and mortality from these preventable diseases.

The importance of DPT immunization cannot be overstated. Diphtheria, pertussis, and tetanus can lead to severe complications, including neurological damage and death, especially in young children. Therefore, maintaining high immunization levels is crucial. For developing countries, achieving high vaccination rates is a fundamental step toward improving child health and fulfilling global health initiatives, such as the World Health Organization's (WHO) vaccination targets.

The relationship between DPT immunization rates and other health indicators is significant. For instance, countries with high DPT coverage often exhibit improved child survival rates, lower rates of hospital admissions for preventable illnesses, and increased life expectancy. Additionally, high vaccination coverage can relieve healthcare systems from the burden of managing outbreaks of vaccine-preventable diseases, leading to more resources available for other health challenges.

Several factors influence DPT immunization rates. Access to healthcare services, socioeconomic conditions, cultural beliefs regarding vaccinations, and public health policies all play pivotal roles. In areas where healthcare infrastructure is robust, and there is government support for immunization programs, vaccination rates tend to be higher. Conversely, in regions suffering from conflict, poor healthcare access, and distrust in medical systems, such as North Korea, Papua New Guinea, and Somalia—with some of the lowest immunization rates in the world—the percentages can plummet to alarming lows. For instance, in 2023, North Korea reported a mere 16% coverage, while Somalia and Yemen only achieved 42% and 46%, respectively.

Strategies to enhance DPT immunization coverage can include increased outreach efforts in underserved areas, community education campaigns emphasizing the safety and efficacy of vaccines, and collaboration with local leaders to build trust. Furthermore, improving logistical aspects of vaccine delivery—ensuring that vaccines are available in health facilities and transport mechanisms are in place—can significantly boost rates. International organizations can also provide support through funding and training programs to equip healthcare workers with the tools needed to effectively administer vaccines.

Despite the strides made in immunization programs globally, flaws still exist. Data show fluctuations in immunization rates over time; for example, between 2021 and 2023, there was a decrease from 81.89% to 84.05%. Disruptions due to the COVID-19 pandemic posed significant challenges to immunization programs in many countries. Regular tracking and reporting of immunization data are essential to identify trends and to implement timely corrective actions when coverage decreases.

Focusing on the latest data, the median DPT coverage in 2023 stands at 91.0%. The top five areas achieving impressive coverage levels, all at 99%, include Bahrain, Bhutan, Brunei, Costa Rica, and Cuba. These countries effectively demonstrate that with adequate healthcare infrastructure, consistent public health messaging, and community engagement, high vaccination rates are achievable. On the other hand, the bottom five areas highlight the challenges in regions where healthcare is inadequate, social instability is prevalent, and distrust in medical interventions exists. The stark contrast between the top and bottom areas sends a message: the global community must work together to address and rectify disparities in healthcare access.

As we look to the future, continued investment in healthcare infrastructure, public health education, and community engagement will be essential for improving DPT immunization rates worldwide. By understanding the key factors at play and implementing comprehensive strategies, we can help ensure a healthier future for children everywhere, protecting them against diphtheria, pertussis, and tetanus.

                    
# 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.IMM.IDPT'

# 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.IMM.IDPT'

# 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))