Prevalence of wasting, weight for height (% of children under 5)

The prevalence of wasting among children under five, measured as a percentage of those children whose weight for height is significantly below the median accepted standard, serves as a critical indicator of nutritional status and health outcomes. Wasting reflects acute malnutrition, often resulting from inadequate nutrient intake, illness, or both, and can lead to serious health complications and increased mortality. Understanding this indicator provides insights into broader issues of food security, public health, and socioeconomic conditions.

This indicator holds immense importance, especially in developing countries where the prevalence of malnutrition can be alarmingly high. Children who are wasted not only face immediate health risks but are also at higher risk of long-term developmental impairments, including stunted growth and cognitive delays. The consequences of wasting extend beyond the affected child, influencing family dynamics, societal productivity, and national healthcare burdens. Consequently, monitoring the prevalence of wasting is vital for governments and organizations aiming to improve child health outcomes and reduce poverty.

Wasting is closely related to other health indicators, such as stunting and underweight. While wasting focuses on weight relative to height at a specific point in time, stunting measures height relative to age and indicates chronic malnutrition. Both indicators are interconnected; poor dietary practices, frequent illness, and lack of healthcare can lead to both stunting and wasting. In some contexts, a community may face high rates of stunting while simultaneously grappling with acute cases of wasting, complicating intervention strategies.

Several factors influence the prevalence of wasting. First and foremost is food insecurity, which can stem from several sources, including economic instability, poor agricultural practices, or disruptions caused by conflict or natural disasters. When families lack consistent access to sufficient and nutritious food, children are at greater risk of experiencing malnutrition. Health factors are also critical; repeated infections, inadequate healthcare access, and lack of maternal education about nutrition can exacerbate the situation. Sociocultural practices around breastfeeding and weaning also play a pivotal role; improper feeding practices during a child’s early years can drastically affect their nutritional status.

Strategies to combat wasting must be multifaceted. Programs focusing on improving food security, healthcare access, and education about nutrition are essential. Implementing community health programs that include monitoring and support for at-risk children can significantly reduce the prevalence of wasting. Moreover, programs aimed at improving maternal and child health, such as prenatal care and nutritional counseling, can lay a strong foundation for preventing malnutrition. Food assistance programs, including supplemental feeding for vulnerable populations and interventions during crises, can help mitigate acute cases of wasting.

While there are many approaches to combating wasting, flaws often exist in their implementation. For instance, interventions may not be uniformly distributed throughout a region, leading to disparities in access and effectiveness. In some cases, funding for nutrition programs can be insufficient or mismanaged, leading to ineffective initiatives. Additionally, a lack of coordination among various stakeholders—including governments, NGOs, and communities—can hinder progress. Ensuring that strategies are culturally appropriate and considerate of local contexts is also crucial for success.

Focusing on the specific data related to the prevalence of wasting in 2023, the median value stands at a notably low 2.3%. This figure signals a decline compared to historical data, evidencing some progress in addressing the issue of acute malnutrition globally. For example, the historical data from 2000 through 2022 shows a consistent trend of decreasing percentages, moving from 8.73% in 2000 to the current 2.3%. This decline reflects improvements in nutrition, healthcare access, and possibly economic stability in various regions.

Interestingly, the data for 2023 shows that Jordan, which registers a prevalence of 2.3%, forms both the top and bottom of the list, indicating a unique situation where no other region was reported. This scenario suggests that within Jordan, there may be effective interventions that have led to reduced prevalence of wasting, yet it also raises questions about whether this low rate is indicative of broader challenges within the country or simply a reflection of data collection methods. Such a phenomenon, where a single area is both at the top and bottom, could highlight the importance of tailored approaches to nutrition, considering the specific socio-economic and geographic contexts of each region.

The global decline in wasting prevalence, coupled with the promising data from 2023, suggests that through concerted efforts, it is possible to combat malnutrition effectively. However, sustained commitment to addressing underlying factors is essential to maintain progress and further reduce the incidence of wasting, especially as new challenges arise, such as climate change and global conflicts that threaten food security. Recognizing waste as a complex issue with multiple interacting factors can help policymakers create comprehensive strategies that not only address immediate nutritional needs but also fortify communities against future crises.

                    
# 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.WAST.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.WAST.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))