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

The immunization rate for measles among children aged 12 to 23 months is a critical public health indicator reflecting the overall health of populations. This measure indicates the percentage of children who received one dose of a measles-containing vaccine by their first birthday. Measles is a highly contagious viral disease, and its vaccination is vital in preventing outbreaks and protecting community health.

Understanding the importance of high immunization rates cannot be overstated. A high percentage of immunization not only protects individual children from severe complications related to measles, such as pneumonia and encephalitis, but it also contributes to herd immunity. Herd immunity occurs when a sufficient proportion of a population is immunized, thereby reducing the overall spread of the disease. This protective barrier is particularly crucial for those who cannot be vaccinated, such as infants too young for vaccination or individuals with certain medical conditions. Moreover, measles vaccination is linked to wider public health successes, often viewed as a surrogate for the effectiveness of a country’s broader vaccination programs.

Immunization rates for measles correlate with various other social and health indicators. For instance, areas with a high rate of measles vaccination often exhibit better overall health care systems, robust infrastructure, and effective public health campaigns. Conversely, low immunization rates may tie to higher rates of infectious diseases, infant mortality, and other health-related issues. The median value for measles vaccination in 2023 stands at 90.0%, indicating a generally positive trend in global immunization efforts. However, this figure also highlights the work that still lies ahead, especially in lower-performing regions.

The top five areas with the highest immunization rates are Algeria, Andorra, Bahrain, Bhutan, and Cuba, all reporting a commendable 99.0%. This achievement can be attributed to comprehensive public health strategies, effective outreach programs, and consistent government support for vaccination initiatives. These countries have made significant investments in healthcare infrastructure, emphasizing preventive healthcare and community engagement. The successes in these areas serve as models for other nations aiming to improve their immunization rates.

On the other end of the spectrum, the lowest performers include Montenegro at 24.0%, North Korea at 28.0%, Central African Republic at 41.0%, Yemen at 45.0%, and Somalia at 46.0%. These figures reflect the challenges faced by these regions, including political instability, limited healthcare access, misinformation regarding vaccinations, and economic barriers that hinder vaccine distribution. The stark contrast between the top and bottom performers underscores the inequality in global health and the urgent need for targeted interventions to enhance vaccination coverage in underserved populations.

Several key factors impact measles immunization rates. Health workforce availability, vaccine accessibility, and education level concerning vaccine efficacy and safety all play pivotal roles. Countries with robust healthcare systems and extensive outreach programs typically showcase higher immunization rates. Public health messaging that addresses common misconceptions about vaccines can significantly influence a community’s willingness to participate in vaccination programs. Moreover, logistical hurdles, such as supply chain issues related to vaccine distribution, can also impede the successful implementation of immunization campaigns, particularly in rural or conflict-affected areas.

Strategies to improve measles vaccination rates can be multifaceted. Engaging communities through education and awareness campaigns is vital. Such efforts need to dispel myths about vaccinations and highlight their importance. Collaboration with local leaders and health workers can foster trust and encourage more families to vaccinate their children. Additionally, simplifying access to vaccines through mobile clinics or community-based health systems can mitigate barriers. Strengthening healthcare infrastructure to ensure that vaccines are available at the point of care is crucial for enhancing coverage. Alongside these strategies, governments must work to eliminate financial barriers, such as ensuring that vaccines are free of charge, which can be a major deterrent for low-income families.

Despite the ability of vaccination programs to save lives, flaws persist in global immunization efforts. Stagnation or decline in vaccination rates, as evidenced by the decline from 86.36% in 2018 to 83.13% in 2023, raises alarm bells. The COVID-19 pandemic disrupted routine immunization services, causing widespread delays and interruptions in vaccination campaigns. Addressing these declines will require innovative approaches to ensure that immunization remains a high priority. The fragile state of healthcare systems in low-income countries further complicates efforts to maintain robust vaccination rates. Thus, addressing systemic issues within healthcare delivery is just as important as enhancing vaccination programs. This holistic approach must also include strengthening emergency response mechanisms to ensure that vaccination campaigns can adapt to future challenges.

In conclusion, measles immunization rates for children aged 12 to 23 months serve as an important metric for public health. As the world strives to improve these rates, lessons must be learned from both achieving and failing countries. Collaborating on global health initiatives, strengthening health systems, eliminating barriers to access, and enhancing community outreach can drive progress. The ongoing commitment to vaccination as a critical public health intervention is essential, not only to combat measles but also to uphold the health of generations to come.

                    
# 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.MEAS'

# 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.MEAS'

# 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))