Exclusive breastfeeding (% of children under 6 months)

Exclusive breastfeeding, defined as the practice of feeding infants solely with breast milk for the first six months of life, is a crucial indicator of maternal and child health. This practice not only provides essential nutrients to young infants but also fosters the vital mother-infant bond. The proportion of children under six months who are exclusively breastfed serves as an essential public health indicator reflecting both maternal health practices and the effectiveness of health policies aimed at improving child nutrition.

The importance of exclusive breastfeeding cannot be overstated. It offers a unique blend of nutrients, antibodies, and other bioactive molecules that significantly enhance infants' immune systems and protect against infections, reducing infant mortality rates. Furthermore, studies have shown that infants who are exclusively breastfed are less likely to become overweight or obese later in life, as breastfeeding promotes healthy growth patterns. This practice also contributes to the mothers’ health, helping to lower the risk of breast and ovarian cancer, and allows for postpartum weight loss through calorie expenditure related to milk production.

The relation of exclusive breastfeeding to other health indicators is profound. It often correlates with nutritional status, rates of childhood diseases, and overall child health outcomes. For instance, areas with higher rates of exclusive breastfeeding tend to report lower incidences of diarrheal diseases and respiratory infections. Moreover, exclusive breastfeeding is interconnected with maternal education levels, access to healthcare, and socio-economic status. Mothers who are educated and empowered are more likely to have access to resources and support systems that promote breastfeeding practices, leading to higher exclusive breastfeeding rates.

Several factors affect the prevalence of exclusive breastfeeding. Cultural beliefs, societal practices, and economic conditions can create barriers for mothers looking to breastfeed. In some communities, there exists a stigma against breastfeeding, leading to social pressure to use formula instead. In addition, inadequate maternity leave policies and lack of breastfeeding support in the workplace can deter mothers from maintaining exclusive breastfeeding. Access to healthcare services, including prenatal education and postnatal support, also significantly influences a mother’s ability to initiate and continue exclusive breastfeeding. Furthermore, the marketing of formula milk and baby foods can undermine breastfeeding practices by creating misconceptions about artificial feeding.

Strategies to promote and sustain exclusive breastfeeding include implementing educational campaigns that inform expecting and new mothers about the benefits of breastfeeding. These campaigns can help to dispel myths surrounding breastfeeding and encourage mothers to prioritize it as the primary feeding method for their infants. Breastfeeding-friendly policies, such as paid maternity leave and workplace accommodations, are essential to support mothers in balancing their professional lives with their breastfeeding goals. Additionally, healthcare providers play a crucial role in fostering breastfeeding practices by providing guidance and support during prenatal and postnatal visits. Programs that encourage peer support among mothers can also create a community of encouragement and shared experiences, further promoting exclusive breastfeeding.

While there are various strategies in place, flaws still exist in the overall efforts to promote exclusive breastfeeding. These flaws may include inadequate funding for breastfeeding programs, insufficient training for healthcare professionals, and a lack of comprehensive public policy support. There can also be a disconnect between guidelines provided by health organizations and the practical realities faced by mothers. As a result, there is often a gap in the successful implementation of breastfeeding promotion initiatives.

Examining the latest data from 2020 reveals significant variation in exclusive breastfeeding rates across different regions. The global median value for exclusive breastfeeding among children under six months was 56.61%. This figure represents a notable improvement since the year 2000 when the corresponding value was just 38.42%. The continuing upward trend is encouraging but highlights that there is still much work to be done. Among countries, Rwanda leads with an impressive 80.9% of infants exclusively breastfed, a remarkable achievement that can be attributed to widespread public health campaigns and community support systems aimed at encouraging breastfeeding. Following Rwanda are Timor-Leste (64.97%), Mongolia (58.0%), Liberia (55.23%), and Gambia (53.63%), all exhibiting better-than-average rates of exclusive breastfeeding.

Conversely, certain regions face significant challenges in achieving higher exclusive breastfeeding rates. The Palestinian Territories report the lowest rate at 38.9%, indicating potential barriers related to social, economic, and healthcare access. The Gambia, Liberia, Mongolia, and Timor-Leste, although not at the bottom, still represent areas where exclusive breastfeeding rates could be much improved. These regions may benefit from targeted initiatives aimed at increasing awareness, improving maternal support, and addressing cultural myths surrounding breastfeeding.

In conclusion, exclusive breastfeeding is a critical metric in evaluating child health and maternal support systems globally. While substantial progress has been made since the beginning of the century, ongoing efforts are necessary to build upon these gains. By addressing the barriers that impede breastfeeding practices, leveraging community support, and enhancing maternal education and workplace accommodations, the global community can move closer to achieving optimal health outcomes for infants and mothers alike.

                    
# 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.STA.BFED.ZS'

# 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.STA.BFED.ZS'

# 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))